diff --git a/CMakeLists.txt b/CMakeLists.txt index a1b946d..74602a2 100755 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,9 +1,19 @@ cmake_minimum_required(VERSION 3.10) -project(BaiPiaoOCR) +project(RapidOCR) + + +# Disable in-source builds to prevent source tree corruption. +if(" ${CMAKE_SOURCE_DIR}" STREQUAL " ${CMAKE_BINARY_DIR}") + message(FATAL_ERROR " +FATAL: In-source builds are not allowed. + You should create a separate directory for build files. +") +endif() + include_directories(${CMAKE_CURRENT_SOURCE_DIR}/cpp/BaiPiaoOcrOnnx/include) include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include/onnxruntime/) -include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include/) +#include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include/) @@ -18,15 +28,17 @@ if(WIN32) endif() elseif(UNIX) - include_directories(/usr/include/opencv4/) + + # set（OpenCV_DIR /path/to/opencv/build） FIND_PACKAGE(OpenCV REQUIRED) if(OPENCV_FOUND) - message("OPenCV FOUND") - set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenCV_C_FLAGS}") - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenCV_CXX_FLAGS}") - set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenCV_EXE_LINKER_FLAGS}") - message(${CMAKE_EXE_LINKER_FLAGS}) + message("OpenCV FOUND") + include_directories(${OpenCV_INCLUDE_DIRS}) + link_directories(${OpenCV_LIBRARY_DIRS}) + link_libraries(${OpenCV_LIBS}) + endif() + endif() # openmp @@ -44,17 +56,17 @@ aux_source_directory(cpp/BaiPiaoOcrOnnx/src/ SRC_MAIN) list(REMOVE_ITEM SRC_MAIN "main.cpp") set(OCR_SRC api4cpp/baipiaoocr_api.cpp ${SRC_MAIN} ) -add_library(bpocrapi SHARED ${OCR_SRC}) +add_library(rapidocrapi SHARED ${OCR_SRC}) -target_compile_definitions(bpocrapi PUBLIC -D_BPOCR_LIB) +target_compile_definitions(rapidocrapi PUBLIC -D_BPOCR_LIB) # test program set(BPOCRTESTSRC api4cpp/bpocrapitest.cpp) -add_executable(bpocrtest ${BPOCRTESTSRC}) +add_executable(rapidocrtest ${BPOCRTESTSRC}) #opencv -set(OPENCV_LIB opencv_core opencv_imgproc opencv_imgcodecs) +#set(OPENCV_LIB opencv_core opencv_imgproc opencv_imgcodecs) if(WIN32) @@ -64,11 +76,11 @@ target_link_libraries(bpocrtest bpocrapi) elseif(UNIX) message(STATUS ${CMAKE_CURRENT_SOURCE_DIR}/lib/linux) -target_link_directories(bpocrapi PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/lib/linux ) -target_link_libraries(bpocrapi onnxruntime ${OPENCV_LIB}) +target_link_directories(rapidocrapi PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/lib/linux ) +target_link_libraries(rapidocrapi onnxruntime ${OPENCV_LIB}) -target_link_directories(bpocrtest PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/lib/linux) -target_link_libraries(bpocrtest onnxruntime ${OPENCV_LIB} bpocrapi gomp) +target_link_directories(rapidocrtest PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/lib/linux) +target_link_libraries(rapidocrtest onnxruntime ${OPENCV_LIB} rapidocrapi gomp) endif() diff --git a/android/BaiPiaoOcrAndroidOnnx/OcrLibrary/build.gradle b/android/BaiPiaoOcrAndroidOnnx/OcrLibrary/build.gradle old mode 100644 new mode 100755 diff --git a/android/BaiPiaoOcrAndroidOnnx/OcrLibrary/src/main/cpp/src/OcrLite.cpp b/android/BaiPiaoOcrAndroidOnnx/OcrLibrary/src/main/cpp/src/OcrLite.cpp old mode 100644 new mode 100755 diff --git a/android/BaiPiaoOcrAndroidOnnx/OcrLibrary/src/main/cpp/src/main.cpp b/android/BaiPiaoOcrAndroidOnnx/OcrLibrary/src/main/cpp/src/main.cpp old mode 100644 new mode 100755 diff --git a/android/BaiPiaoOcrAndroidOnnx/OcrLibrary/src/main/java/com/benjaminwan/ocrlibrary/OcrEngine.kt b/android/BaiPiaoOcrAndroidOnnx/OcrLibrary/src/main/java/com/benjaminwan/ocrlibrary/OcrEngine.kt old mode 100644 new mode 100755 diff --git a/android/BaiPiaoOcrAndroidOnnx/README.md b/android/BaiPiaoOcrAndroidOnnx/README.md old mode 100644 new mode 100755 diff --git a/android/BaiPiaoOcrAndroidOnnx/app/build.gradle b/android/BaiPiaoOcrAndroidOnnx/app/build.gradle old mode 100644 new mode 100755 diff --git a/android/BaiPiaoOcrAndroidOnnx/app/src/main/java/com/benjaminwan/ocr/onnx/CameraActivity.kt b/android/BaiPiaoOcrAndroidOnnx/app/src/main/java/com/benjaminwan/ocr/onnx/CameraActivity.kt old mode 100644 new mode 100755 diff --git a/android/BaiPiaoOcrAndroidOnnx/app/src/main/java/com/benjaminwan/ocr/onnx/GalleryActivity.kt b/android/BaiPiaoOcrAndroidOnnx/app/src/main/java/com/benjaminwan/ocr/onnx/GalleryActivity.kt old mode 100644 new mode 100755 diff --git a/android/BaiPiaoOcrAndroidOnnx/app/src/main/java/com/benjaminwan/ocr/onnx/IdCardFrontActivity.kt b/android/BaiPiaoOcrAndroidOnnx/app/src/main/java/com/benjaminwan/ocr/onnx/IdCardFrontActivity.kt old mode 100644 new mode 100755 diff --git a/android/BaiPiaoOcrAndroidOnnx/app/src/main/res/layout/activity_camera.xml b/android/BaiPiaoOcrAndroidOnnx/app/src/main/res/layout/activity_camera.xml old mode 100644 new mode 100755 diff --git a/android/BaiPiaoOcrAndroidOnnx/app/src/main/res/layout/activity_gallery.xml b/android/BaiPiaoOcrAndroidOnnx/app/src/main/res/layout/activity_gallery.xml old mode 100644 new mode 100755 diff --git a/android/BaiPiaoOcrAndroidOnnx/app/src/main/res/layout/activity_idcard_front.xml b/android/BaiPiaoOcrAndroidOnnx/app/src/main/res/layout/activity_idcard_front.xml old mode 100644 new mode 100755 diff --git a/android/BaiPiaoOcrAndroidOnnx/app/src/main/res/values/strings.xml b/android/BaiPiaoOcrAndroidOnnx/app/src/main/res/values/strings.xml old mode 100644 new mode 100755 diff --git a/api4cpp/bpocrapitest.cpp b/api4cpp/bpocrapitest.cpp index acd8e2c..e42ecfe 100755 --- a/api4cpp/bpocrapitest.cpp +++ b/api4cpp/bpocrapitest.cpp @@ -18,9 +18,9 @@ const char* DEFAULT_MODEL_DIR = "E:\\bai-piao-ocr\\cpp\\BaiPiaoOcrOnnx\\models\ const char* DEFAULT_TEST_IMG = "E:\\bai-piao-ocr\\cpp\\BaiPiaoOcrOnnx\\images\\1.jpg"; #else -const char * DEFAULT_MODEL_DIR ="/data/workprj/BaiPiaoOCR/models"; +const char * DEFAULT_MODEL_DIR ="/data/workprj/RapidOCR/models/"; -const char * DEFAULT_TEST_IMG ="/data/workprj/BaiPiaoOCR/images/1.jpg"; +const char * DEFAULT_TEST_IMG ="/data/workprj/RapidOCR/images/1.jpg"; #endif int main(int argc, char * argv[]) { diff --git a/cpp/BaiPiaoOcrOnnx/README.md b/cpp/BaiPiaoOcrOnnx/README.md old mode 100644 new mode 100755 diff --git a/cpp/BaiPiaoOcrOnnx/include/AngleNet.h b/cpp/BaiPiaoOcrOnnx/include/AngleNet.h old mode 100644 new mode 100755 diff --git a/cpp/BaiPiaoOcrOnnx/include/CrnnNet.h b/cpp/BaiPiaoOcrOnnx/include/CrnnNet.h old mode 100644 new mode 100755 diff --git a/cpp/BaiPiaoOcrOnnx/include/DbNet.h b/cpp/BaiPiaoOcrOnnx/include/DbNet.h old mode 100644 new mode 100755 diff --git a/cpp/BaiPiaoOcrOnnx/include/OcrUtils.h b/cpp/BaiPiaoOcrOnnx/include/OcrUtils.h old mode 100644 new mode 100755 diff --git a/cpp/BaiPiaoOcrOnnx/include/version.h b/cpp/BaiPiaoOcrOnnx/include/version.h old mode 100644 new mode 100755 diff --git a/images/1.jpg b/images/1.jpg old mode 100644 new mode 100755 diff --git a/images/clear.cmd b/images/clear.cmd old mode 100644 new mode 100755 diff --git a/images/clear.sh b/images/clear.sh old mode 100644 new mode 100755 diff --git a/images/long1.jpg b/images/long1.jpg old mode 100644 new mode 100755 diff --git a/images/readme.md b/images/readme.md old mode 100644 new mode 100755 diff --git a/include/onnxruntime/cpu_provider_factory.h b/include/onnxruntime/cpu_provider_factory.h old mode 100644 new mode 100755 diff --git a/include/onnxruntime/cuda_provider_factory.h b/include/onnxruntime/cuda_provider_factory.h old mode 100644 new mode 100755 diff --git a/include/onnxruntime/onnxruntime_c_api.h b/include/onnxruntime/onnxruntime_c_api.h old mode 100644 new mode 100755 diff --git a/include/onnxruntime/onnxruntime_cxx_api.h b/include/onnxruntime/onnxruntime_cxx_api.h old mode 100644 new mode 100755 diff --git a/include/onnxruntime/onnxruntime_cxx_inline.h b/include/onnxruntime/onnxruntime_cxx_inline.h old mode 100644 new mode 100755 diff --git a/include/onnxruntime/onnxruntime_session_options_config_keys.h b/include/onnxruntime/onnxruntime_session_options_config_keys.h old mode 100644 new mode 100755 diff --git a/include/opencv/cv.h b/include/opencv/cv.h deleted file mode 100755 index 19a74e2..0000000 --- a/include/opencv/cv.h +++ /dev/null @@ -1,73 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_OLD_CV_H -#define OPENCV_OLD_CV_H - -#if defined(_MSC_VER) - #define CV_DO_PRAGMA(x) __pragma(x) - #define __CVSTR2__(x) #x - #define __CVSTR1__(x) __CVSTR2__(x) - #define __CVMSVCLOC__ __FILE__ "("__CVSTR1__(__LINE__)") : " - #define CV_MSG_PRAGMA(_msg) CV_DO_PRAGMA(message (__CVMSVCLOC__ _msg)) -#elif defined(__GNUC__) - #define CV_DO_PRAGMA(x) _Pragma (#x) - #define CV_MSG_PRAGMA(_msg) CV_DO_PRAGMA(message (_msg)) -#else - #define CV_DO_PRAGMA(x) - #define CV_MSG_PRAGMA(_msg) -#endif -#define CV_WARNING(x) CV_MSG_PRAGMA("Warning: " #x) - -//CV_WARNING("This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module") - -#include "opencv2/core/core_c.h" -#include "opencv2/imgproc/imgproc_c.h" -#include "opencv2/photo/photo_c.h" -#include "opencv2/video/tracking_c.h" -#include "opencv2/objdetect/objdetect_c.h" - -#if !defined(CV_IMPL) -#define CV_IMPL extern "C" -#endif //CV_IMPL - -#endif // __OPENCV_OLD_CV_H_ diff --git a/include/opencv/cv.hpp b/include/opencv/cv.hpp deleted file mode 100755 index 8673956..0000000 --- a/include/opencv/cv.hpp +++ /dev/null @@ -1,60 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_OLD_CV_HPP -#define OPENCV_OLD_CV_HPP - -//#if defined(__GNUC__) -//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" -//#endif - -#include "cv.h" -#include "opencv2/core.hpp" -#include "opencv2/imgproc.hpp" -#include "opencv2/photo.hpp" -#include "opencv2/video.hpp" -#include "opencv2/highgui.hpp" -#include "opencv2/features2d.hpp" -#include "opencv2/calib3d.hpp" -#include "opencv2/objdetect.hpp" - -#endif diff --git a/include/opencv/cvaux.h b/include/opencv/cvaux.h deleted file mode 100755 index c0367cc..0000000 --- a/include/opencv/cvaux.h +++ /dev/null @@ -1,57 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// Intel License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000, Intel Corporation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of Intel Corporation may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_OLD_AUX_H -#define OPENCV_OLD_AUX_H - -//#if defined(__GNUC__) -//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" -//#endif - -#include "opencv2/core/core_c.h" -#include "opencv2/imgproc/imgproc_c.h" -#include "opencv2/photo/photo_c.h" -#include "opencv2/video/tracking_c.h" -#include "opencv2/objdetect/objdetect_c.h" - -#endif - -/* End of file. */ diff --git a/include/opencv/cvaux.hpp b/include/opencv/cvaux.hpp deleted file mode 100755 index 4888eef..0000000 --- a/include/opencv/cvaux.hpp +++ /dev/null @@ -1,52 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// Intel License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000, Intel Corporation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of Intel Corporation may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_OLD_AUX_HPP -#define OPENCV_OLD_AUX_HPP - -//#if defined(__GNUC__) -//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" -//#endif - -#include "cvaux.h" -#include "opencv2/core/utility.hpp" - -#endif diff --git a/include/opencv/cvwimage.h b/include/opencv/cvwimage.h deleted file mode 100755 index ec0ab14..0000000 --- a/include/opencv/cvwimage.h +++ /dev/null @@ -1,46 +0,0 @@ -/////////////////////////////////////////////////////////////////////////////// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to -// this license. If you do not agree to this license, do not download, -// install, copy or use the software. -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2008, Google, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of Intel Corporation or contributors may not be used to endorse -// or promote products derived from this software without specific -// prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" -// and any express or implied warranties, including, but not limited to, the -// implied warranties of merchantability and fitness for a particular purpose -// are disclaimed. In no event shall the Intel Corporation or contributors be -// liable for any direct, indirect, incidental, special, exemplary, or -// consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. - - -#ifndef OPENCV_OLD_WIMAGE_HPP -#define OPENCV_OLD_WIMAGE_HPP - -#include "opencv2/core/wimage.hpp" - -#endif diff --git a/include/opencv/cxcore.h b/include/opencv/cxcore.h deleted file mode 100755 index dc070c7..0000000 --- a/include/opencv/cxcore.h +++ /dev/null @@ -1,52 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_OLD_CXCORE_H -#define OPENCV_OLD_CXCORE_H - -//#if defined(__GNUC__) -//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" -//#endif - -#include "opencv2/core/core_c.h" - -#endif diff --git a/include/opencv/cxcore.hpp b/include/opencv/cxcore.hpp deleted file mode 100755 index c371677..0000000 --- a/include/opencv/cxcore.hpp +++ /dev/null @@ -1,53 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_OLD_CXCORE_HPP -#define OPENCV_OLD_CXCORE_HPP - -//#if defined(__GNUC__) -//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module" -//#endif - -#include "cxcore.h" -#include "opencv2/core.hpp" - -#endif diff --git a/include/opencv/cxeigen.hpp b/include/opencv/cxeigen.hpp deleted file mode 100755 index 1d3df91..0000000 --- a/include/opencv/cxeigen.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_OLD_EIGEN_HPP -#define OPENCV_OLD_EIGEN_HPP - -#include "opencv2/core/eigen.hpp" - -#endif diff --git a/include/opencv/cxmisc.h b/include/opencv/cxmisc.h deleted file mode 100755 index 9b9bc82..0000000 --- a/include/opencv/cxmisc.h +++ /dev/null @@ -1,8 +0,0 @@ -#ifndef OPENCV_OLD_CXMISC_H -#define OPENCV_OLD_CXMISC_H - -#ifdef __cplusplus -# include "opencv2/core/utility.hpp" -#endif - -#endif diff --git a/include/opencv/highgui.h b/include/opencv/highgui.h deleted file mode 100755 index 69b394e..0000000 --- a/include/opencv/highgui.h +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// Intel License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000, Intel Corporation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of Intel Corporation may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_OLD_HIGHGUI_H -#define OPENCV_OLD_HIGHGUI_H - -#include "opencv2/core/core_c.h" -#include "opencv2/highgui/highgui_c.h" - -#endif diff --git a/include/opencv/ml.h b/include/opencv/ml.h deleted file mode 100755 index 0c376ba..0000000 --- a/include/opencv/ml.h +++ /dev/null @@ -1,47 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// Intel License Agreement -// -// Copyright (C) 2000, Intel Corporation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of Intel Corporation may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_OLD_ML_H -#define OPENCV_OLD_ML_H - -#include "opencv2/core/core_c.h" -#include "opencv2/ml.hpp" - -#endif diff --git a/include/opencv2/calib3d.hpp b/include/opencv2/calib3d.hpp deleted file mode 100755 index b48c928..0000000 --- a/include/opencv2/calib3d.hpp +++ /dev/null @@ -1,3256 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CALIB3D_HPP -#define OPENCV_CALIB3D_HPP - -#include "opencv2/core.hpp" -#include "opencv2/features2d.hpp" -#include "opencv2/core/affine.hpp" - -/** - @defgroup calib3d Camera Calibration and 3D Reconstruction - -The functions in this section use a so-called pinhole camera model. The view of a scene -is obtained by projecting a scene's 3D point \f$P_w\f$ into the image plane using a perspective -transformation which forms the corresponding pixel \f$p\f$. Both \f$P_w\f$ and \f$p\f$ are -represented in homogeneous coordinates, i.e. as 3D and 2D homogeneous vector respectively. You will -find a brief introduction to projective geometry, homogeneous vectors and homogeneous -transformations at the end of this section's introduction. For more succinct notation, we often drop -the 'homogeneous' and say vector instead of homogeneous vector. - -The distortion-free projective transformation given by a pinhole camera model is shown below. - -\f[s \; p = A \begin{bmatrix} R|t \end{bmatrix} P_w,\f] - -where \f$P_w\f$ is a 3D point expressed with respect to the world coordinate system, -\f$p\f$ is a 2D pixel in the image plane, \f$A\f$ is the camera intrinsic matrix, -\f$R\f$ and \f$t\f$ are the rotation and translation that describe the change of coordinates from -world to camera coordinate systems (or camera frame) and \f$s\f$ is the projective transformation's -arbitrary scaling and not part of the camera model. - -The camera intrinsic matrix \f$A\f$ (notation used as in @cite Zhang2000 and also generally notated -as \f$K\f$) projects 3D points given in the camera coordinate system to 2D pixel coordinates, i.e. - -\f[p = A P_c.\f] - -The camera intrinsic matrix \f$A\f$ is composed of the focal lengths \f$f_x\f$ and \f$f_y\f$, which are -expressed in pixel units, and the principal point \f$(c_x, c_y)\f$, that is usually close to the -image center: - -\f[A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1},\f] - -and thus - -\f[s \vecthree{u}{v}{1} = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1} \vecthree{X_c}{Y_c}{Z_c}.\f] - -The matrix of intrinsic parameters does not depend on the scene viewed. So, once estimated, it can -be re-used as long as the focal length is fixed (in case of a zoom lens). Thus, if an image from the -camera is scaled by a factor, all of these parameters need to be scaled (multiplied/divided, -respectively) by the same factor. - -The joint rotation-translation matrix \f$[R|t]\f$ is the matrix product of a projective -transformation and a homogeneous transformation. The 3-by-4 projective transformation maps 3D points -represented in camera coordinates to 2D points in the image plane and represented in normalized -camera coordinates \f$x' = X_c / Z_c\f$ and \f$y' = Y_c / Z_c\f$: - -\f[Z_c \begin{bmatrix} -x' \\ -y' \\ -1 -\end{bmatrix} = \begin{bmatrix} -1 & 0 & 0 & 0 \\ -0 & 1 & 0 & 0 \\ -0 & 0 & 1 & 0 -\end{bmatrix} -\begin{bmatrix} -X_c \\ -Y_c \\ -Z_c \\ -1 -\end{bmatrix}.\f] - -The homogeneous transformation is encoded by the extrinsic parameters \f$R\f$ and \f$t\f$ and -represents the change of basis from world coordinate system \f$w\f$ to the camera coordinate sytem -\f$c\f$. Thus, given the representation of the point \f$P\f$ in world coordinates, \f$P_w\f$, we -obtain \f$P\f$'s representation in the camera coordinate system, \f$P_c\f$, by - -\f[P_c = \begin{bmatrix} -R & t \\ -0 & 1 -\end{bmatrix} P_w,\f] - -This homogeneous transformation is composed out of \f$R\f$, a 3-by-3 rotation matrix, and \f$t\f$, a -3-by-1 translation vector: - -\f[\begin{bmatrix} -R & t \\ -0 & 1 -\end{bmatrix} = \begin{bmatrix} -r_{11} & r_{12} & r_{13} & t_x \\ -r_{21} & r_{22} & r_{23} & t_y \\ -r_{31} & r_{32} & r_{33} & t_z \\ -0 & 0 & 0 & 1 -\end{bmatrix}, -\f] - -and therefore - -\f[\begin{bmatrix} -X_c \\ -Y_c \\ -Z_c \\ -1 -\end{bmatrix} = \begin{bmatrix} -r_{11} & r_{12} & r_{13} & t_x \\ -r_{21} & r_{22} & r_{23} & t_y \\ -r_{31} & r_{32} & r_{33} & t_z \\ -0 & 0 & 0 & 1 -\end{bmatrix} -\begin{bmatrix} -X_w \\ -Y_w \\ -Z_w \\ -1 -\end{bmatrix}.\f] - -Combining the projective transformation and the homogeneous transformation, we obtain the projective -transformation that maps 3D points in world coordinates into 2D points in the image plane and in -normalized camera coordinates: - -\f[Z_c \begin{bmatrix} -x' \\ -y' \\ -1 -\end{bmatrix} = \begin{bmatrix} R|t \end{bmatrix} \begin{bmatrix} -X_w \\ -Y_w \\ -Z_w \\ -1 -\end{bmatrix} = \begin{bmatrix} -r_{11} & r_{12} & r_{13} & t_x \\ -r_{21} & r_{22} & r_{23} & t_y \\ -r_{31} & r_{32} & r_{33} & t_z -\end{bmatrix} -\begin{bmatrix} -X_w \\ -Y_w \\ -Z_w \\ -1 -\end{bmatrix},\f] - -with \f$x' = X_c / Z_c\f$ and \f$y' = Y_c / Z_c\f$. Putting the equations for instrincs and extrinsics together, we can write out -\f$s \; p = A \begin{bmatrix} R|t \end{bmatrix} P_w\f$ as - -\f[s \vecthree{u}{v}{1} = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1} -\begin{bmatrix} -r_{11} & r_{12} & r_{13} & t_x \\ -r_{21} & r_{22} & r_{23} & t_y \\ -r_{31} & r_{32} & r_{33} & t_z -\end{bmatrix} -\begin{bmatrix} -X_w \\ -Y_w \\ -Z_w \\ -1 -\end{bmatrix}.\f] - -If \f$Z_c \ne 0\f$, the transformation above is equivalent to the following, - -\f[\begin{bmatrix} -u \\ -v -\end{bmatrix} = \begin{bmatrix} -f_x X_c/Z_c + c_x \\ -f_y Y_c/Z_c + c_y -\end{bmatrix}\f] - -with - -\f[\vecthree{X_c}{Y_c}{Z_c} = \begin{bmatrix} -R|t -\end{bmatrix} \begin{bmatrix} -X_w \\ -Y_w \\ -Z_w \\ -1 -\end{bmatrix}.\f] - -The following figure illustrates the pinhole camera model. - -![Pinhole camera model](pics/pinhole_camera_model.png) - -Real lenses usually have some distortion, mostly radial distortion, and slight tangential distortion. -So, the above model is extended as: - -\f[\begin{bmatrix} -u \\ -v -\end{bmatrix} = \begin{bmatrix} -f_x x'' + c_x \\ -f_y y'' + c_y -\end{bmatrix}\f] - -where - -\f[\begin{bmatrix} -x'' \\ -y'' -\end{bmatrix} = \begin{bmatrix} -x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2 p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4 \\ -y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ -\end{bmatrix}\f] - -with - -\f[r^2 = x'^2 + y'^2\f] - -and - -\f[\begin{bmatrix} -x'\\ -y' -\end{bmatrix} = \begin{bmatrix} -X_c/Z_c \\ -Y_c/Z_c -\end{bmatrix},\f] - -if \f$Z_c \ne 0\f$. - -The distortion parameters are the radial coefficients \f$k_1\f$, \f$k_2\f$, \f$k_3\f$, \f$k_4\f$, \f$k_5\f$, and \f$k_6\f$ -,\f$p_1\f$ and \f$p_2\f$ are the tangential distortion coefficients, and \f$s_1\f$, \f$s_2\f$, \f$s_3\f$, and \f$s_4\f$, -are the thin prism distortion coefficients. Higher-order coefficients are not considered in OpenCV. - -The next figures show two common types of radial distortion: barrel distortion -(\f$ 1 + k_1 r^2 + k_2 r^4 + k_3 r^6 \f$ monotonically decreasing) -and pincushion distortion (\f$ 1 + k_1 r^2 + k_2 r^4 + k_3 r^6 \f$ monotonically increasing). -Radial distortion is always monotonic for real lenses, -and if the estimator produces a non-monotonic result, -this should be considered a calibration failure. -More generally, radial distortion must be monotonic and the distortion function must be bijective. -A failed estimation result may look deceptively good near the image center -but will work poorly in e.g. AR/SFM applications. -The optimization method used in OpenCV camera calibration does not include these constraints as -the framework does not support the required integer programming and polynomial inequalities. -See [issue #15992](https://github.com/opencv/opencv/issues/15992) for additional information. - -![](pics/distortion_examples.png) -![](pics/distortion_examples2.png) - -In some cases, the image sensor may be tilted in order to focus an oblique plane in front of the -camera (Scheimpflug principle). This can be useful for particle image velocimetry (PIV) or -triangulation with a laser fan. The tilt causes a perspective distortion of \f$x''\f$ and -\f$y''\f$. This distortion can be modeled in the following way, see e.g. @cite Louhichi07. - -\f[\begin{bmatrix} -u \\ -v -\end{bmatrix} = \begin{bmatrix} -f_x x''' + c_x \\ -f_y y''' + c_y -\end{bmatrix},\f] - -where - -\f[s\vecthree{x'''}{y'''}{1} = -\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}(\tau_x, \tau_y)} -{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} -{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\f] - -and the matrix \f$R(\tau_x, \tau_y)\f$ is defined by two rotations with angular parameter -\f$\tau_x\f$ and \f$\tau_y\f$, respectively, - -\f[ -R(\tau_x, \tau_y) = -\vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)} -\vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} = -\vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)} -{0}{\cos(\tau_x)}{\sin(\tau_x)} -{\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}. -\f] - -In the functions below the coefficients are passed or returned as - -\f[(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f] - -vector. That is, if the vector contains four elements, it means that \f$k_3=0\f$ . The distortion -coefficients do not depend on the scene viewed. Thus, they also belong to the intrinsic camera -parameters. And they remain the same regardless of the captured image resolution. If, for example, a -camera has been calibrated on images of 320 x 240 resolution, absolutely the same distortion -coefficients can be used for 640 x 480 images from the same camera while \f$f_x\f$, \f$f_y\f$, -\f$c_x\f$, and \f$c_y\f$ need to be scaled appropriately. - -The functions below use the above model to do the following: - -- Project 3D points to the image plane given intrinsic and extrinsic parameters. -- Compute extrinsic parameters given intrinsic parameters, a few 3D points, and their -projections. -- Estimate intrinsic and extrinsic camera parameters from several views of a known calibration -pattern (every view is described by several 3D-2D point correspondences). -- Estimate the relative position and orientation of the stereo camera "heads" and compute the -*rectification* transformation that makes the camera optical axes parallel. - - Homogeneous Coordinates
-Homogeneous Coordinates are a system of coordinates that are used in projective geometry. Their use -allows to represent points at infinity by finite coordinates and simplifies formulas when compared -to the cartesian counterparts, e.g. they have the advantage that affine transformations can be -expressed as linear homogeneous transformation. - -One obtains the homogeneous vector \f$P_h\f$ by appending a 1 along an n-dimensional cartesian -vector \f$P\f$ e.g. for a 3D cartesian vector the mapping \f$P \rightarrow P_h\f$ is: - -\f[\begin{bmatrix} -X \\ -Y \\ -Z -\end{bmatrix} \rightarrow \begin{bmatrix} -X \\ -Y \\ -Z \\ -1 -\end{bmatrix}.\f] - -For the inverse mapping \f$P_h \rightarrow P\f$, one divides all elements of the homogeneous vector -by its last element, e.g. for a 3D homogeneous vector one gets its 2D cartesian counterpart by: - -\f[\begin{bmatrix} -X \\ -Y \\ -W -\end{bmatrix} \rightarrow \begin{bmatrix} -X / W \\ -Y / W -\end{bmatrix},\f] - -if \f$W \ne 0\f$. - -Due to this mapping, all multiples \f$k P_h\f$, for \f$k \ne 0\f$, of a homogeneous point represent -the same point \f$P_h\f$. An intuitive understanding of this property is that under a projective -transformation, all multiples of \f$P_h\f$ are mapped to the same point. This is the physical -observation one does for pinhole cameras, as all points along a ray through the camera's pinhole are -projected to the same image point, e.g. all points along the red ray in the image of the pinhole -camera model above would be mapped to the same image coordinate. This property is also the source -for the scale ambiguity s in the equation of the pinhole camera model. - -As mentioned, by using homogeneous coordinates we can express any change of basis parameterized by -\f$R\f$ and \f$t\f$ as a linear transformation, e.g. for the change of basis from coordinate system -0 to coordinate system 1 becomes: - -\f[P_1 = R P_0 + t \rightarrow P_{h_1} = \begin{bmatrix} -R & t \\ -0 & 1 -\end{bmatrix} P_{h_0}.\f] - -@note - - Many functions in this module take a camera intrinsic matrix as an input parameter. Although all - functions assume the same structure of this parameter, they may name it differently. The - parameter's description, however, will be clear in that a camera intrinsic matrix with the structure - shown above is required. - - A calibration sample for 3 cameras in a horizontal position can be found at - opencv_source_code/samples/cpp/3calibration.cpp - - A calibration sample based on a sequence of images can be found at - opencv_source_code/samples/cpp/calibration.cpp - - A calibration sample in order to do 3D reconstruction can be found at - opencv_source_code/samples/cpp/build3dmodel.cpp - - A calibration example on stereo calibration can be found at - opencv_source_code/samples/cpp/stereo_calib.cpp - - A calibration example on stereo matching can be found at - opencv_source_code/samples/cpp/stereo_match.cpp - - (Python) A camera calibration sample can be found at - opencv_source_code/samples/python/calibrate.py - - @{ - @defgroup calib3d_fisheye Fisheye camera model - - Definitions: Let P be a point in 3D of coordinates X in the world reference frame (stored in the - matrix X) The coordinate vector of P in the camera reference frame is: - - \f[Xc = R X + T\f] - - where R is the rotation matrix corresponding to the rotation vector om: R = rodrigues(om); call x, y - and z the 3 coordinates of Xc: - - \f[x = Xc_1 \\ y = Xc_2 \\ z = Xc_3\f] - - The pinhole projection coordinates of P is [a; b] where - - \f[a = x / z \ and \ b = y / z \\ r^2 = a^2 + b^2 \\ \theta = atan(r)\f] - - Fisheye distortion: - - \f[\theta_d = \theta (1 + k_1 \theta^2 + k_2 \theta^4 + k_3 \theta^6 + k_4 \theta^8)\f] - - The distorted point coordinates are [x'; y'] where - - \f[x' = (\theta_d / r) a \\ y' = (\theta_d / r) b \f] - - Finally, conversion into pixel coordinates: The final pixel coordinates vector [u; v] where: - - \f[u = f_x (x' + \alpha y') + c_x \\ - v = f_y y' + c_y\f] - - @defgroup calib3d_c C API - - @} - */ - -namespace cv -{ - -//! @addtogroup calib3d -//! @{ - -//! type of the robust estimation algorithm -enum { LMEDS = 4, //!< least-median of squares algorithm - RANSAC = 8, //!< RANSAC algorithm - RHO = 16 //!< RHO algorithm - }; - -enum SolvePnPMethod { - SOLVEPNP_ITERATIVE = 0, - SOLVEPNP_EPNP = 1, //!< EPnP: Efficient Perspective-n-Point Camera Pose Estimation @cite lepetit2009epnp - SOLVEPNP_P3P = 2, //!< Complete Solution Classification for the Perspective-Three-Point Problem @cite gao2003complete - SOLVEPNP_DLS = 3, //!< **Broken implementation. Using this flag will fallback to EPnP.** \n - //!< A Direct Least-Squares (DLS) Method for PnP @cite hesch2011direct - SOLVEPNP_UPNP = 4, //!< **Broken implementation. Using this flag will fallback to EPnP.** \n - //!< Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation @cite penate2013exhaustive - SOLVEPNP_AP3P = 5, //!< An Efficient Algebraic Solution to the Perspective-Three-Point Problem @cite Ke17 - SOLVEPNP_IPPE = 6, //!< Infinitesimal Plane-Based Pose Estimation @cite Collins14 \n - //!< Object points must be coplanar. - SOLVEPNP_IPPE_SQUARE = 7, //!< Infinitesimal Plane-Based Pose Estimation @cite Collins14 \n - //!< This is a special case suitable for marker pose estimation.\n - //!< 4 coplanar object points must be defined in the following order: - //!< - point 0: [-squareLength / 2, squareLength / 2, 0] - //!< - point 1: [ squareLength / 2, squareLength / 2, 0] - //!< - point 2: [ squareLength / 2, -squareLength / 2, 0] - //!< - point 3: [-squareLength / 2, -squareLength / 2, 0] - SOLVEPNP_SQPNP = 8, //!< SQPnP: A Consistently Fast and Globally OptimalSolution to the Perspective-n-Point Problem @cite Terzakis20 -#ifndef CV_DOXYGEN - SOLVEPNP_MAX_COUNT //!< Used for count -#endif -}; - -enum { CALIB_CB_ADAPTIVE_THRESH = 1, - CALIB_CB_NORMALIZE_IMAGE = 2, - CALIB_CB_FILTER_QUADS = 4, - CALIB_CB_FAST_CHECK = 8 - }; - -enum { CALIB_CB_SYMMETRIC_GRID = 1, - CALIB_CB_ASYMMETRIC_GRID = 2, - CALIB_CB_CLUSTERING = 4 - }; - -enum { CALIB_USE_INTRINSIC_GUESS = 0x00001, - CALIB_FIX_ASPECT_RATIO = 0x00002, - CALIB_FIX_PRINCIPAL_POINT = 0x00004, - CALIB_ZERO_TANGENT_DIST = 0x00008, - CALIB_FIX_FOCAL_LENGTH = 0x00010, - CALIB_FIX_K1 = 0x00020, - CALIB_FIX_K2 = 0x00040, - CALIB_FIX_K3 = 0x00080, - CALIB_FIX_K4 = 0x00800, - CALIB_FIX_K5 = 0x01000, - CALIB_FIX_K6 = 0x02000, - CALIB_RATIONAL_MODEL = 0x04000, - CALIB_THIN_PRISM_MODEL = 0x08000, - CALIB_FIX_S1_S2_S3_S4 = 0x10000, - CALIB_TILTED_MODEL = 0x40000, - CALIB_FIX_TAUX_TAUY = 0x80000, - CALIB_USE_QR = 0x100000, //!< use QR instead of SVD decomposition for solving. Faster but potentially less precise - CALIB_FIX_TANGENT_DIST = 0x200000, - // only for stereo - CALIB_FIX_INTRINSIC = 0x00100, - CALIB_SAME_FOCAL_LENGTH = 0x00200, - // for stereo rectification - CALIB_ZERO_DISPARITY = 0x00400, - CALIB_USE_LU = (1 << 17), //!< use LU instead of SVD decomposition for solving. much faster but potentially less precise - CALIB_USE_EXTRINSIC_GUESS = (1 << 22) //!< for stereoCalibrate - }; - -//! the algorithm for finding fundamental matrix -enum { FM_7POINT = 1, //!< 7-point algorithm - FM_8POINT = 2, //!< 8-point algorithm - FM_LMEDS = 4, //!< least-median algorithm. 7-point algorithm is used. - FM_RANSAC = 8 //!< RANSAC algorithm. It needs at least 15 points. 7-point algorithm is used. - }; - -enum HandEyeCalibrationMethod -{ - CALIB_HAND_EYE_TSAI = 0, //!< A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/Eye Calibration @cite Tsai89 - CALIB_HAND_EYE_PARK = 1, //!< Robot Sensor Calibration: Solving AX = XB on the Euclidean Group @cite Park94 - CALIB_HAND_EYE_HORAUD = 2, //!< Hand-eye Calibration @cite Horaud95 - CALIB_HAND_EYE_ANDREFF = 3, //!< On-line Hand-Eye Calibration @cite Andreff99 - CALIB_HAND_EYE_DANIILIDIS = 4 //!< Hand-Eye Calibration Using Dual Quaternions @cite Daniilidis98 -}; - - -/** @brief Converts a rotation matrix to a rotation vector or vice versa. - -@param src Input rotation vector (3x1 or 1x3) or rotation matrix (3x3). -@param dst Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively. -@param jacobian Optional output Jacobian matrix, 3x9 or 9x3, which is a matrix of partial -derivatives of the output array components with respect to the input array components. - -\f[\begin{array}{l} \theta \leftarrow norm(r) \\ r \leftarrow r/ \theta \\ R = \cos(\theta) I + (1- \cos{\theta} ) r r^T + \sin(\theta) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} \end{array}\f] - -Inverse transformation can be also done easily, since - -\f[\sin ( \theta ) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} = \frac{R - R^T}{2}\f] - -A rotation vector is a convenient and most compact representation of a rotation matrix (since any -rotation matrix has just 3 degrees of freedom). The representation is used in the global 3D geometry -optimization procedures like @ref calibrateCamera, @ref stereoCalibrate, or @ref solvePnP . - -@note More information about the computation of the derivative of a 3D rotation matrix with respect to its exponential coordinate -can be found in: - - A Compact Formula for the Derivative of a 3-D Rotation in Exponential Coordinates, Guillermo Gallego, Anthony J. Yezzi @cite Gallego2014ACF - -@note Useful information on SE(3) and Lie Groups can be found in: - - A tutorial on SE(3) transformation parameterizations and on-manifold optimization, Jose-Luis Blanco @cite blanco2010tutorial - - Lie Groups for 2D and 3D Transformation, Ethan Eade @cite Eade17 - - A micro Lie theory for state estimation in robotics, Joan Solà, Jérémie Deray, Dinesh Atchuthan @cite Sol2018AML - */ -CV_EXPORTS_W void Rodrigues( InputArray src, OutputArray dst, OutputArray jacobian = noArray() ); - -/** @example samples/cpp/tutorial_code/features2D/Homography/pose_from_homography.cpp -An example program about pose estimation from coplanar points - -Check @ref tutorial_homography "the corresponding tutorial" for more details -*/ - -/** @brief Finds a perspective transformation between two planes. - -@param srcPoints Coordinates of the points in the original plane, a matrix of the type CV_32FC2 -or vector\ . -@param dstPoints Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or -a vector\ . -@param method Method used to compute a homography matrix. The following methods are possible: -- **0** - a regular method using all the points, i.e., the least squares method -- @ref RANSAC - RANSAC-based robust method -- @ref LMEDS - Least-Median robust method -- @ref RHO - PROSAC-based robust method -@param ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier -(used in the RANSAC and RHO methods only). That is, if -\f[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} * \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\f] -then the point \f$i\f$ is considered as an outlier. If srcPoints and dstPoints are measured in pixels, -it usually makes sense to set this parameter somewhere in the range of 1 to 10. -@param mask Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the input -mask values are ignored. -@param maxIters The maximum number of RANSAC iterations. -@param confidence Confidence level, between 0 and 1. - -The function finds and returns the perspective transformation \f$H\f$ between the source and the -destination planes: - -\f[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\f] - -so that the back-projection error - -\f[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\f] - -is minimized. If the parameter method is set to the default value 0, the function uses all the point -pairs to compute an initial homography estimate with a simple least-squares scheme. - -However, if not all of the point pairs ( \f$srcPoints_i\f$, \f$dstPoints_i\f$ ) fit the rigid perspective -transformation (that is, there are some outliers), this initial estimate will be poor. In this case, -you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different -random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix -using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the -computed homography (which is the number of inliers for RANSAC or the least median re-projection error for -LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and -the mask of inliers/outliers. - -Regardless of the method, robust or not, the computed homography matrix is refined further (using -inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the -re-projection error even more. - -The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to -distinguish inliers from outliers. The method LMeDS does not need any threshold but it works -correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the -noise is rather small, use the default method (method=0). - -The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is -determined up to a scale. Thus, it is normalized so that \f$h_{33}=1\f$. Note that whenever an \f$H\f$ matrix -cannot be estimated, an empty one will be returned. - -@sa -getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, -perspectiveTransform - */ -CV_EXPORTS_W Mat findHomography( InputArray srcPoints, InputArray dstPoints, - int method = 0, double ransacReprojThreshold = 3, - OutputArray mask=noArray(), const int maxIters = 2000, - const double confidence = 0.995); - -/** @overload */ -CV_EXPORTS Mat findHomography( InputArray srcPoints, InputArray dstPoints, - OutputArray mask, int method = 0, double ransacReprojThreshold = 3 ); - -/** @brief Computes an RQ decomposition of 3x3 matrices. - -@param src 3x3 input matrix. -@param mtxR Output 3x3 upper-triangular matrix. -@param mtxQ Output 3x3 orthogonal matrix. -@param Qx Optional output 3x3 rotation matrix around x-axis. -@param Qy Optional output 3x3 rotation matrix around y-axis. -@param Qz Optional output 3x3 rotation matrix around z-axis. - -The function computes a RQ decomposition using the given rotations. This function is used in -decomposeProjectionMatrix to decompose the left 3x3 submatrix of a projection matrix into a camera -and a rotation matrix. - -It optionally returns three rotation matrices, one for each axis, and the three Euler angles in -degrees (as the return value) that could be used in OpenGL. Note, there is always more than one -sequence of rotations about the three principal axes that results in the same orientation of an -object, e.g. see @cite Slabaugh . Returned tree rotation matrices and corresponding three Euler angles -are only one of the possible solutions. - */ -CV_EXPORTS_W Vec3d RQDecomp3x3( InputArray src, OutputArray mtxR, OutputArray mtxQ, - OutputArray Qx = noArray(), - OutputArray Qy = noArray(), - OutputArray Qz = noArray()); - -/** @brief Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix. - -@param projMatrix 3x4 input projection matrix P. -@param cameraMatrix Output 3x3 camera intrinsic matrix \f$\cameramatrix{A}\f$. -@param rotMatrix Output 3x3 external rotation matrix R. -@param transVect Output 4x1 translation vector T. -@param rotMatrixX Optional 3x3 rotation matrix around x-axis. -@param rotMatrixY Optional 3x3 rotation matrix around y-axis. -@param rotMatrixZ Optional 3x3 rotation matrix around z-axis. -@param eulerAngles Optional three-element vector containing three Euler angles of rotation in -degrees. - -The function computes a decomposition of a projection matrix into a calibration and a rotation -matrix and the position of a camera. - -It optionally returns three rotation matrices, one for each axis, and three Euler angles that could -be used in OpenGL. Note, there is always more than one sequence of rotations about the three -principal axes that results in the same orientation of an object, e.g. see @cite Slabaugh . Returned -tree rotation matrices and corresponding three Euler angles are only one of the possible solutions. - -The function is based on RQDecomp3x3 . - */ -CV_EXPORTS_W void decomposeProjectionMatrix( InputArray projMatrix, OutputArray cameraMatrix, - OutputArray rotMatrix, OutputArray transVect, - OutputArray rotMatrixX = noArray(), - OutputArray rotMatrixY = noArray(), - OutputArray rotMatrixZ = noArray(), - OutputArray eulerAngles =noArray() ); - -/** @brief Computes partial derivatives of the matrix product for each multiplied matrix. - -@param A First multiplied matrix. -@param B Second multiplied matrix. -@param dABdA First output derivative matrix d(A\*B)/dA of size -\f$\texttt{A.rows*B.cols} \times {A.rows*A.cols}\f$ . -@param dABdB Second output derivative matrix d(A\*B)/dB of size -\f$\texttt{A.rows*B.cols} \times {B.rows*B.cols}\f$ . - -The function computes partial derivatives of the elements of the matrix product \f$A*B\f$ with regard to -the elements of each of the two input matrices. The function is used to compute the Jacobian -matrices in stereoCalibrate but can also be used in any other similar optimization function. - */ -CV_EXPORTS_W void matMulDeriv( InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB ); - -/** @brief Combines two rotation-and-shift transformations. - -@param rvec1 First rotation vector. -@param tvec1 First translation vector. -@param rvec2 Second rotation vector. -@param tvec2 Second translation vector. -@param rvec3 Output rotation vector of the superposition. -@param tvec3 Output translation vector of the superposition. -@param dr3dr1 Optional output derivative of rvec3 with regard to rvec1 -@param dr3dt1 Optional output derivative of rvec3 with regard to tvec1 -@param dr3dr2 Optional output derivative of rvec3 with regard to rvec2 -@param dr3dt2 Optional output derivative of rvec3 with regard to tvec2 -@param dt3dr1 Optional output derivative of tvec3 with regard to rvec1 -@param dt3dt1 Optional output derivative of tvec3 with regard to tvec1 -@param dt3dr2 Optional output derivative of tvec3 with regard to rvec2 -@param dt3dt2 Optional output derivative of tvec3 with regard to tvec2 - -The functions compute: - -\f[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\f] - -where \f$\mathrm{rodrigues}\f$ denotes a rotation vector to a rotation matrix transformation, and -\f$\mathrm{rodrigues}^{-1}\f$ denotes the inverse transformation. See Rodrigues for details. - -Also, the functions can compute the derivatives of the output vectors with regards to the input -vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in -your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a -function that contains a matrix multiplication. - */ -CV_EXPORTS_W void composeRT( InputArray rvec1, InputArray tvec1, - InputArray rvec2, InputArray tvec2, - OutputArray rvec3, OutputArray tvec3, - OutputArray dr3dr1 = noArray(), OutputArray dr3dt1 = noArray(), - OutputArray dr3dr2 = noArray(), OutputArray dr3dt2 = noArray(), - OutputArray dt3dr1 = noArray(), OutputArray dt3dt1 = noArray(), - OutputArray dt3dr2 = noArray(), OutputArray dt3dt2 = noArray() ); - -/** @brief Projects 3D points to an image plane. - -@param objectPoints Array of object points expressed wrt. the world coordinate frame. A 3xN/Nx3 -1-channel or 1xN/Nx1 3-channel (or vector\ ), where N is the number of points in the view. -@param rvec The rotation vector (@ref Rodrigues) that, together with tvec, performs a change of -basis from world to camera coordinate system, see @ref calibrateCamera for details. -@param tvec The translation vector, see parameter description above. -@param cameraMatrix Camera intrinsic matrix \f$\cameramatrix{A}\f$ . -@param distCoeffs Input vector of distortion coefficients -\f$\distcoeffs\f$ . If the vector is empty, the zero distortion coefficients are assumed. -@param imagePoints Output array of image points, 1xN/Nx1 2-channel, or -vector\ . -@param jacobian Optional output 2Nx(10+\) jacobian matrix of derivatives of image -points with respect to components of the rotation vector, translation vector, focal lengths, -coordinates of the principal point and the distortion coefficients. In the old interface different -components of the jacobian are returned via different output parameters. -@param aspectRatio Optional "fixed aspect ratio" parameter. If the parameter is not 0, the -function assumes that the aspect ratio (\f$f_x / f_y\f$) is fixed and correspondingly adjusts the -jacobian matrix. - -The function computes the 2D projections of 3D points to the image plane, given intrinsic and -extrinsic camera parameters. Optionally, the function computes Jacobians -matrices of partial -derivatives of image points coordinates (as functions of all the input parameters) with respect to -the particular parameters, intrinsic and/or extrinsic. The Jacobians are used during the global -optimization in @ref calibrateCamera, @ref solvePnP, and @ref stereoCalibrate. The function itself -can also be used to compute a re-projection error, given the current intrinsic and extrinsic -parameters. - -@note By setting rvec = tvec = \f$[0, 0, 0]\f$, or by setting cameraMatrix to a 3x3 identity matrix, -or by passing zero distortion coefficients, one can get various useful partial cases of the -function. This means, one can compute the distorted coordinates for a sparse set of points or apply -a perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup. - */ -CV_EXPORTS_W void projectPoints( InputArray objectPoints, - InputArray rvec, InputArray tvec, - InputArray cameraMatrix, InputArray distCoeffs, - OutputArray imagePoints, - OutputArray jacobian = noArray(), - double aspectRatio = 0 ); - -/** @example samples/cpp/tutorial_code/features2D/Homography/homography_from_camera_displacement.cpp -An example program about homography from the camera displacement - -Check @ref tutorial_homography "the corresponding tutorial" for more details -*/ - -/** @brief Finds an object pose from 3D-2D point correspondences. -This function returns the rotation and the translation vectors that transform a 3D point expressed in the object -coordinate frame to the camera coordinate frame, using different methods: -- P3P methods (@ref SOLVEPNP_P3P, @ref SOLVEPNP_AP3P): need 4 input points to return a unique solution. -- @ref SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. -- @ref SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. -Number of input points must be 4. Object points must be defined in the following order: - - point 0: [-squareLength / 2, squareLength / 2, 0] - - point 1: [ squareLength / 2, squareLength / 2, 0] - - point 2: [ squareLength / 2, -squareLength / 2, 0] - - point 3: [-squareLength / 2, -squareLength / 2, 0] -- for all the other flags, number of input points must be >= 4 and object points can be in any configuration. - -@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or -1xN/Nx1 3-channel, where N is the number of points. vector\ can be also passed here. -@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, -where N is the number of points. vector\ can be also passed here. -@param cameraMatrix Input camera intrinsic matrix \f$\cameramatrix{A}\f$ . -@param distCoeffs Input vector of distortion coefficients -\f$\distcoeffs\f$. If the vector is NULL/empty, the zero distortion coefficients are -assumed. -@param rvec Output rotation vector (see @ref Rodrigues ) that, together with tvec, brings points from -the model coordinate system to the camera coordinate system. -@param tvec Output translation vector. -@param useExtrinsicGuess Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses -the provided rvec and tvec values as initial approximations of the rotation and translation -vectors, respectively, and further optimizes them. -@param flags Method for solving a PnP problem: -- @ref SOLVEPNP_ITERATIVE Iterative method is based on a Levenberg-Marquardt optimization. In -this case the function finds such a pose that minimizes reprojection error, that is the sum -of squared distances between the observed projections imagePoints and the projected (using -@ref projectPoints ) objectPoints . -- @ref SOLVEPNP_P3P Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang -"Complete Solution Classification for the Perspective-Three-Point Problem" (@cite gao2003complete). -In this case the function requires exactly four object and image points. -- @ref SOLVEPNP_AP3P Method is based on the paper of T. Ke, S. Roumeliotis -"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17). -In this case the function requires exactly four object and image points. -- @ref SOLVEPNP_EPNP Method has been introduced by F. Moreno-Noguer, V. Lepetit and P. Fua in the -paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" (@cite lepetit2009epnp). -- @ref SOLVEPNP_DLS **Broken implementation. Using this flag will fallback to EPnP.** \n -Method is based on the paper of J. Hesch and S. Roumeliotis. -"A Direct Least-Squares (DLS) Method for PnP" (@cite hesch2011direct). -- @ref SOLVEPNP_UPNP **Broken implementation. Using this flag will fallback to EPnP.** \n -Method is based on the paper of A. Penate-Sanchez, J. Andrade-Cetto, -F. Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length -Estimation" (@cite penate2013exhaustive). In this case the function also estimates the parameters \f$f_x\f$ and \f$f_y\f$ -assuming that both have the same value. Then the cameraMatrix is updated with the estimated -focal length. -- @ref SOLVEPNP_IPPE Method is based on the paper of T. Collins and A. Bartoli. -"Infinitesimal Plane-Based Pose Estimation" (@cite Collins14). This method requires coplanar object points. -- @ref SOLVEPNP_IPPE_SQUARE Method is based on the paper of Toby Collins and Adrien Bartoli. -"Infinitesimal Plane-Based Pose Estimation" (@cite Collins14). This method is suitable for marker pose estimation. -It requires 4 coplanar object points defined in the following order: - - point 0: [-squareLength / 2, squareLength / 2, 0] - - point 1: [ squareLength / 2, squareLength / 2, 0] - - point 2: [ squareLength / 2, -squareLength / 2, 0] - - point 3: [-squareLength / 2, -squareLength / 2, 0] -- @ref SOLVEPNP_SQPNP Method is based on the paper "A Consistently Fast and Globally Optimal Solution to the -Perspective-n-Point Problem" by G. Terzakis and M.Lourakis (@cite Terzakis20). It requires 3 or more points. - - -The function estimates the object pose given a set of object points, their corresponding image -projections, as well as the camera intrinsic matrix and the distortion coefficients, see the figure below -(more precisely, the X-axis of the camera frame is pointing to the right, the Y-axis downward -and the Z-axis forward). - -![](pnp.jpg) - -Points expressed in the world frame \f$ \bf{X}_w \f$ are projected into the image plane \f$ \left[ u, v \right] \f$ -using the perspective projection model \f$ \Pi \f$ and the camera intrinsic parameters matrix \f$ \bf{A} \f$: - -\f[ - \begin{align*} - \begin{bmatrix} - u \\ - v \\ - 1 - \end{bmatrix} &= - \bf{A} \hspace{0.1em} \Pi \hspace{0.2em} ^{c}\bf{T}_w - \begin{bmatrix} - X_{w} \\ - Y_{w} \\ - Z_{w} \\ - 1 - \end{bmatrix} \\ - \begin{bmatrix} - u \\ - v \\ - 1 - \end{bmatrix} &= - \begin{bmatrix} - f_x & 0 & c_x \\ - 0 & f_y & c_y \\ - 0 & 0 & 1 - \end{bmatrix} - \begin{bmatrix} - 1 & 0 & 0 & 0 \\ - 0 & 1 & 0 & 0 \\ - 0 & 0 & 1 & 0 - \end{bmatrix} - \begin{bmatrix} - r_{11} & r_{12} & r_{13} & t_x \\ - r_{21} & r_{22} & r_{23} & t_y \\ - r_{31} & r_{32} & r_{33} & t_z \\ - 0 & 0 & 0 & 1 - \end{bmatrix} - \begin{bmatrix} - X_{w} \\ - Y_{w} \\ - Z_{w} \\ - 1 - \end{bmatrix} - \end{align*} -\f] - -The estimated pose is thus the rotation (`rvec`) and the translation (`tvec`) vectors that allow transforming -a 3D point expressed in the world frame into the camera frame: - -\f[ - \begin{align*} - \begin{bmatrix} - X_c \\ - Y_c \\ - Z_c \\ - 1 - \end{bmatrix} &= - \hspace{0.2em} ^{c}\bf{T}_w - \begin{bmatrix} - X_{w} \\ - Y_{w} \\ - Z_{w} \\ - 1 - \end{bmatrix} \\ - \begin{bmatrix} - X_c \\ - Y_c \\ - Z_c \\ - 1 - \end{bmatrix} &= - \begin{bmatrix} - r_{11} & r_{12} & r_{13} & t_x \\ - r_{21} & r_{22} & r_{23} & t_y \\ - r_{31} & r_{32} & r_{33} & t_z \\ - 0 & 0 & 0 & 1 - \end{bmatrix} - \begin{bmatrix} - X_{w} \\ - Y_{w} \\ - Z_{w} \\ - 1 - \end{bmatrix} - \end{align*} -\f] - -@note - - An example of how to use solvePnP for planar augmented reality can be found at - opencv_source_code/samples/python/plane_ar.py - - If you are using Python: - - Numpy array slices won't work as input because solvePnP requires contiguous - arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of - modules/calib3d/src/solvepnp.cpp version 2.4.9) - - The P3P algorithm requires image points to be in an array of shape (N,1,2) due - to its calling of cv::undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) - which requires 2-channel information. - - Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of - it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = - np.ascontiguousarray(D[:,:2]).reshape((N,1,2)) - - The methods @ref SOLVEPNP_DLS and @ref SOLVEPNP_UPNP cannot be used as the current implementations are - unstable and sometimes give completely wrong results. If you pass one of these two - flags, @ref SOLVEPNP_EPNP method will be used instead. - - The minimum number of points is 4 in the general case. In the case of @ref SOLVEPNP_P3P and @ref SOLVEPNP_AP3P - methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions - of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error). - - With @ref SOLVEPNP_ITERATIVE method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points - are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the - global solution to converge. - - With @ref SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar. - - With @ref SOLVEPNP_IPPE_SQUARE this is a special case suitable for marker pose estimation. - Number of input points must be 4. Object points must be defined in the following order: - - point 0: [-squareLength / 2, squareLength / 2, 0] - - point 1: [ squareLength / 2, squareLength / 2, 0] - - point 2: [ squareLength / 2, -squareLength / 2, 0] - - point 3: [-squareLength / 2, -squareLength / 2, 0] - - With @ref SOLVEPNP_SQPNP input points must be >= 3 - */ -CV_EXPORTS_W bool solvePnP( InputArray objectPoints, InputArray imagePoints, - InputArray cameraMatrix, InputArray distCoeffs, - OutputArray rvec, OutputArray tvec, - bool useExtrinsicGuess = false, int flags = SOLVEPNP_ITERATIVE ); - -/** @brief Finds an object pose from 3D-2D point correspondences using the RANSAC scheme. - -@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or -1xN/Nx1 3-channel, where N is the number of points. vector\ can be also passed here. -@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, -where N is the number of points. vector\ can be also passed here. -@param cameraMatrix Input camera intrinsic matrix \f$\cameramatrix{A}\f$ . -@param distCoeffs Input vector of distortion coefficients -\f$\distcoeffs\f$. If the vector is NULL/empty, the zero distortion coefficients are -assumed. -@param rvec Output rotation vector (see @ref Rodrigues ) that, together with tvec, brings points from -the model coordinate system to the camera coordinate system. -@param tvec Output translation vector. -@param useExtrinsicGuess Parameter used for @ref SOLVEPNP_ITERATIVE. If true (1), the function uses -the provided rvec and tvec values as initial approximations of the rotation and translation -vectors, respectively, and further optimizes them. -@param iterationsCount Number of iterations. -@param reprojectionError Inlier threshold value used by the RANSAC procedure. The parameter value -is the maximum allowed distance between the observed and computed point projections to consider it -an inlier. -@param confidence The probability that the algorithm produces a useful result. -@param inliers Output vector that contains indices of inliers in objectPoints and imagePoints . -@param flags Method for solving a PnP problem (see @ref solvePnP ). - -The function estimates an object pose given a set of object points, their corresponding image -projections, as well as the camera intrinsic matrix and the distortion coefficients. This function finds such -a pose that minimizes reprojection error, that is, the sum of squared distances between the observed -projections imagePoints and the projected (using @ref projectPoints ) objectPoints. The use of RANSAC -makes the function resistant to outliers. - -@note - - An example of how to use solvePNPRansac for object detection can be found at - opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/ - - The default method used to estimate the camera pose for the Minimal Sample Sets step - is #SOLVEPNP_EPNP. Exceptions are: - - if you choose #SOLVEPNP_P3P or #SOLVEPNP_AP3P, these methods will be used. - - if the number of input points is equal to 4, #SOLVEPNP_P3P is used. - - The method used to estimate the camera pose using all the inliers is defined by the - flags parameters unless it is equal to #SOLVEPNP_P3P or #SOLVEPNP_AP3P. In this case, - the method #SOLVEPNP_EPNP will be used instead. - */ -CV_EXPORTS_W bool solvePnPRansac( InputArray objectPoints, InputArray imagePoints, - InputArray cameraMatrix, InputArray distCoeffs, - OutputArray rvec, OutputArray tvec, - bool useExtrinsicGuess = false, int iterationsCount = 100, - float reprojectionError = 8.0, double confidence = 0.99, - OutputArray inliers = noArray(), int flags = SOLVEPNP_ITERATIVE ); - -/** @brief Finds an object pose from 3 3D-2D point correspondences. - -@param objectPoints Array of object points in the object coordinate space, 3x3 1-channel or -1x3/3x1 3-channel. vector\ can be also passed here. -@param imagePoints Array of corresponding image points, 3x2 1-channel or 1x3/3x1 2-channel. - vector\ can be also passed here. -@param cameraMatrix Input camera intrinsic matrix \f$\cameramatrix{A}\f$ . -@param distCoeffs Input vector of distortion coefficients -\f$\distcoeffs\f$. If the vector is NULL/empty, the zero distortion coefficients are -assumed. -@param rvecs Output rotation vectors (see @ref Rodrigues ) that, together with tvecs, brings points from -the model coordinate system to the camera coordinate system. A P3P problem has up to 4 solutions. -@param tvecs Output translation vectors. -@param flags Method for solving a P3P problem: -- @ref SOLVEPNP_P3P Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang -"Complete Solution Classification for the Perspective-Three-Point Problem" (@cite gao2003complete). -- @ref SOLVEPNP_AP3P Method is based on the paper of T. Ke and S. Roumeliotis. -"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17). - -The function estimates the object pose given 3 object points, their corresponding image -projections, as well as the camera intrinsic matrix and the distortion coefficients. - -@note -The solutions are sorted by reprojection errors (lowest to highest). - */ -CV_EXPORTS_W int solveP3P( InputArray objectPoints, InputArray imagePoints, - InputArray cameraMatrix, InputArray distCoeffs, - OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, - int flags ); - -/** @brief Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame -to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution. - -@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, -where N is the number of points. vector\ can also be passed here. -@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, -where N is the number of points. vector\ can also be passed here. -@param cameraMatrix Input camera intrinsic matrix \f$\cameramatrix{A}\f$ . -@param distCoeffs Input vector of distortion coefficients -\f$\distcoeffs\f$. If the vector is NULL/empty, the zero distortion coefficients are -assumed. -@param rvec Input/Output rotation vector (see @ref Rodrigues ) that, together with tvec, brings points from -the model coordinate system to the camera coordinate system. Input values are used as an initial solution. -@param tvec Input/Output translation vector. Input values are used as an initial solution. -@param criteria Criteria when to stop the Levenberg-Marquard iterative algorithm. - -The function refines the object pose given at least 3 object points, their corresponding image -projections, an initial solution for the rotation and translation vector, -as well as the camera intrinsic matrix and the distortion coefficients. -The function minimizes the projection error with respect to the rotation and the translation vectors, according -to a Levenberg-Marquardt iterative minimization @cite Madsen04 @cite Eade13 process. - */ -CV_EXPORTS_W void solvePnPRefineLM( InputArray objectPoints, InputArray imagePoints, - InputArray cameraMatrix, InputArray distCoeffs, - InputOutputArray rvec, InputOutputArray tvec, - TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 20, FLT_EPSILON)); - -/** @brief Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frame -to the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution. - -@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, -where N is the number of points. vector\ can also be passed here. -@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, -where N is the number of points. vector\ can also be passed here. -@param cameraMatrix Input camera intrinsic matrix \f$\cameramatrix{A}\f$ . -@param distCoeffs Input vector of distortion coefficients -\f$\distcoeffs\f$. If the vector is NULL/empty, the zero distortion coefficients are -assumed. -@param rvec Input/Output rotation vector (see @ref Rodrigues ) that, together with tvec, brings points from -the model coordinate system to the camera coordinate system. Input values are used as an initial solution. -@param tvec Input/Output translation vector. Input values are used as an initial solution. -@param criteria Criteria when to stop the Levenberg-Marquard iterative algorithm. -@param VVSlambda Gain for the virtual visual servoing control law, equivalent to the \f$\alpha\f$ -gain in the Damped Gauss-Newton formulation. - -The function refines the object pose given at least 3 object points, their corresponding image -projections, an initial solution for the rotation and translation vector, -as well as the camera intrinsic matrix and the distortion coefficients. -The function minimizes the projection error with respect to the rotation and the translation vectors, using a -virtual visual servoing (VVS) @cite Chaumette06 @cite Marchand16 scheme. - */ -CV_EXPORTS_W void solvePnPRefineVVS( InputArray objectPoints, InputArray imagePoints, - InputArray cameraMatrix, InputArray distCoeffs, - InputOutputArray rvec, InputOutputArray tvec, - TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 20, FLT_EPSILON), - double VVSlambda = 1); - -/** @brief Finds an object pose from 3D-2D point correspondences. -This function returns a list of all the possible solutions (a solution is a -couple), depending on the number of input points and the chosen method: -- P3P methods (@ref SOLVEPNP_P3P, @ref SOLVEPNP_AP3P): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points. -- @ref SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. Returns 2 solutions. -- @ref SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. -Number of input points must be 4 and 2 solutions are returned. Object points must be defined in the following order: - - point 0: [-squareLength / 2, squareLength / 2, 0] - - point 1: [ squareLength / 2, squareLength / 2, 0] - - point 2: [ squareLength / 2, -squareLength / 2, 0] - - point 3: [-squareLength / 2, -squareLength / 2, 0] -- for all the other flags, number of input points must be >= 4 and object points can be in any configuration. -Only 1 solution is returned. - -@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or -1xN/Nx1 3-channel, where N is the number of points. vector\ can be also passed here. -@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, -where N is the number of points. vector\ can be also passed here. -@param cameraMatrix Input camera intrinsic matrix \f$\cameramatrix{A}\f$ . -@param distCoeffs Input vector of distortion coefficients -\f$\distcoeffs\f$. If the vector is NULL/empty, the zero distortion coefficients are -assumed. -@param rvecs Vector of output rotation vectors (see @ref Rodrigues ) that, together with tvecs, brings points from -the model coordinate system to the camera coordinate system. -@param tvecs Vector of output translation vectors. -@param useExtrinsicGuess Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses -the provided rvec and tvec values as initial approximations of the rotation and translation -vectors, respectively, and further optimizes them. -@param flags Method for solving a PnP problem: -- @ref SOLVEPNP_ITERATIVE Iterative method is based on a Levenberg-Marquardt optimization. In -this case the function finds such a pose that minimizes reprojection error, that is the sum -of squared distances between the observed projections imagePoints and the projected (using -projectPoints ) objectPoints . -- @ref SOLVEPNP_P3P Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang -"Complete Solution Classification for the Perspective-Three-Point Problem" (@cite gao2003complete). -In this case the function requires exactly four object and image points. -- @ref SOLVEPNP_AP3P Method is based on the paper of T. Ke, S. Roumeliotis -"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17). -In this case the function requires exactly four object and image points. -- @ref SOLVEPNP_EPNP Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the -paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" (@cite lepetit2009epnp). -- @ref SOLVEPNP_DLS **Broken implementation. Using this flag will fallback to EPnP.** \n -Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis. -"A Direct Least-Squares (DLS) Method for PnP" (@cite hesch2011direct). -- @ref SOLVEPNP_UPNP **Broken implementation. Using this flag will fallback to EPnP.** \n -Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto, -F.Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length -Estimation" (@cite penate2013exhaustive). In this case the function also estimates the parameters \f$f_x\f$ and \f$f_y\f$ -assuming that both have the same value. Then the cameraMatrix is updated with the estimated -focal length. -- @ref SOLVEPNP_IPPE Method is based on the paper of T. Collins and A. Bartoli. -"Infinitesimal Plane-Based Pose Estimation" (@cite Collins14). This method requires coplanar object points. -- @ref SOLVEPNP_IPPE_SQUARE Method is based on the paper of Toby Collins and Adrien Bartoli. -"Infinitesimal Plane-Based Pose Estimation" (@cite Collins14). This method is suitable for marker pose estimation. -It requires 4 coplanar object points defined in the following order: - - point 0: [-squareLength / 2, squareLength / 2, 0] - - point 1: [ squareLength / 2, squareLength / 2, 0] - - point 2: [ squareLength / 2, -squareLength / 2, 0] - - point 3: [-squareLength / 2, -squareLength / 2, 0] -@param rvec Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is @ref SOLVEPNP_ITERATIVE -and useExtrinsicGuess is set to true. -@param tvec Translation vector used to initialize an iterative PnP refinement algorithm, when flag is @ref SOLVEPNP_ITERATIVE -and useExtrinsicGuess is set to true. -@param reprojectionError Optional vector of reprojection error, that is the RMS error -(\f$ \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \f$) between the input image points -and the 3D object points projected with the estimated pose. - -The function estimates the object pose given a set of object points, their corresponding image -projections, as well as the camera intrinsic matrix and the distortion coefficients, see the figure below -(more precisely, the X-axis of the camera frame is pointing to the right, the Y-axis downward -and the Z-axis forward). - -![](pnp.jpg) - -Points expressed in the world frame \f$ \bf{X}_w \f$ are projected into the image plane \f$ \left[ u, v \right] \f$ -using the perspective projection model \f$ \Pi \f$ and the camera intrinsic parameters matrix \f$ \bf{A} \f$: - -\f[ - \begin{align*} - \begin{bmatrix} - u \\ - v \\ - 1 - \end{bmatrix} &= - \bf{A} \hspace{0.1em} \Pi \hspace{0.2em} ^{c}\bf{T}_w - \begin{bmatrix} - X_{w} \\ - Y_{w} \\ - Z_{w} \\ - 1 - \end{bmatrix} \\ - \begin{bmatrix} - u \\ - v \\ - 1 - \end{bmatrix} &= - \begin{bmatrix} - f_x & 0 & c_x \\ - 0 & f_y & c_y \\ - 0 & 0 & 1 - \end{bmatrix} - \begin{bmatrix} - 1 & 0 & 0 & 0 \\ - 0 & 1 & 0 & 0 \\ - 0 & 0 & 1 & 0 - \end{bmatrix} - \begin{bmatrix} - r_{11} & r_{12} & r_{13} & t_x \\ - r_{21} & r_{22} & r_{23} & t_y \\ - r_{31} & r_{32} & r_{33} & t_z \\ - 0 & 0 & 0 & 1 - \end{bmatrix} - \begin{bmatrix} - X_{w} \\ - Y_{w} \\ - Z_{w} \\ - 1 - \end{bmatrix} - \end{align*} -\f] - -The estimated pose is thus the rotation (`rvec`) and the translation (`tvec`) vectors that allow transforming -a 3D point expressed in the world frame into the camera frame: - -\f[ - \begin{align*} - \begin{bmatrix} - X_c \\ - Y_c \\ - Z_c \\ - 1 - \end{bmatrix} &= - \hspace{0.2em} ^{c}\bf{T}_w - \begin{bmatrix} - X_{w} \\ - Y_{w} \\ - Z_{w} \\ - 1 - \end{bmatrix} \\ - \begin{bmatrix} - X_c \\ - Y_c \\ - Z_c \\ - 1 - \end{bmatrix} &= - \begin{bmatrix} - r_{11} & r_{12} & r_{13} & t_x \\ - r_{21} & r_{22} & r_{23} & t_y \\ - r_{31} & r_{32} & r_{33} & t_z \\ - 0 & 0 & 0 & 1 - \end{bmatrix} - \begin{bmatrix} - X_{w} \\ - Y_{w} \\ - Z_{w} \\ - 1 - \end{bmatrix} - \end{align*} -\f] - -@note - - An example of how to use solvePnP for planar augmented reality can be found at - opencv_source_code/samples/python/plane_ar.py - - If you are using Python: - - Numpy array slices won't work as input because solvePnP requires contiguous - arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of - modules/calib3d/src/solvepnp.cpp version 2.4.9) - - The P3P algorithm requires image points to be in an array of shape (N,1,2) due - to its calling of cv::undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) - which requires 2-channel information. - - Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of - it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = - np.ascontiguousarray(D[:,:2]).reshape((N,1,2)) - - The methods @ref SOLVEPNP_DLS and @ref SOLVEPNP_UPNP cannot be used as the current implementations are - unstable and sometimes give completely wrong results. If you pass one of these two - flags, @ref SOLVEPNP_EPNP method will be used instead. - - The minimum number of points is 4 in the general case. In the case of @ref SOLVEPNP_P3P and @ref SOLVEPNP_AP3P - methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions - of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error). - - With @ref SOLVEPNP_ITERATIVE method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points - are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the - global solution to converge. - - With @ref SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar. - - With @ref SOLVEPNP_IPPE_SQUARE this is a special case suitable for marker pose estimation. - Number of input points must be 4. Object points must be defined in the following order: - - point 0: [-squareLength / 2, squareLength / 2, 0] - - point 1: [ squareLength / 2, squareLength / 2, 0] - - point 2: [ squareLength / 2, -squareLength / 2, 0] - - point 3: [-squareLength / 2, -squareLength / 2, 0] - */ -CV_EXPORTS_W int solvePnPGeneric( InputArray objectPoints, InputArray imagePoints, - InputArray cameraMatrix, InputArray distCoeffs, - OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, - bool useExtrinsicGuess = false, SolvePnPMethod flags = SOLVEPNP_ITERATIVE, - InputArray rvec = noArray(), InputArray tvec = noArray(), - OutputArray reprojectionError = noArray() ); - -/** @brief Finds an initial camera intrinsic matrix from 3D-2D point correspondences. - -@param objectPoints Vector of vectors of the calibration pattern points in the calibration pattern -coordinate space. In the old interface all the per-view vectors are concatenated. See -calibrateCamera for details. -@param imagePoints Vector of vectors of the projections of the calibration pattern points. In the -old interface all the per-view vectors are concatenated. -@param imageSize Image size in pixels used to initialize the principal point. -@param aspectRatio If it is zero or negative, both \f$f_x\f$ and \f$f_y\f$ are estimated independently. -Otherwise, \f$f_x = f_y * \texttt{aspectRatio}\f$ . - -The function estimates and returns an initial camera intrinsic matrix for the camera calibration process. -Currently, the function only supports planar calibration patterns, which are patterns where each -object point has z-coordinate =0. - */ -CV_EXPORTS_W Mat initCameraMatrix2D( InputArrayOfArrays objectPoints, - InputArrayOfArrays imagePoints, - Size imageSize, double aspectRatio = 1.0 ); - -/** @brief Finds the positions of internal corners of the chessboard. - -@param image Source chessboard view. It must be an 8-bit grayscale or color image. -@param patternSize Number of inner corners per a chessboard row and column -( patternSize = cvSize(points_per_row,points_per_colum) = cvSize(columns,rows) ). -@param corners Output array of detected corners. -@param flags Various operation flags that can be zero or a combination of the following values: -- @ref CALIB_CB_ADAPTIVE_THRESH Use adaptive thresholding to convert the image to black -and white, rather than a fixed threshold level (computed from the average image brightness). -- @ref CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with equalizeHist before -applying fixed or adaptive thresholding. -- @ref CALIB_CB_FILTER_QUADS Use additional criteria (like contour area, perimeter, -square-like shape) to filter out false quads extracted at the contour retrieval stage. -- @ref CALIB_CB_FAST_CHECK Run a fast check on the image that looks for chessboard corners, -and shortcut the call if none is found. This can drastically speed up the call in the -degenerate condition when no chessboard is observed. - -The function attempts to determine whether the input image is a view of the chessboard pattern and -locate the internal chessboard corners. The function returns a non-zero value if all of the corners -are found and they are placed in a certain order (row by row, left to right in every row). -Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, -a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black -squares touch each other. The detected coordinates are approximate, and to determine their positions -more accurately, the function calls cornerSubPix. You also may use the function cornerSubPix with -different parameters if returned coordinates are not accurate enough. - -Sample usage of detecting and drawing chessboard corners: : -@code - Size patternsize(8,6); //interior number of corners - Mat gray = ....; //source image - vector corners; //this will be filled by the detected corners - - //CALIB_CB_FAST_CHECK saves a lot of time on images - //that do not contain any chessboard corners - bool patternfound = findChessboardCorners(gray, patternsize, corners, - CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE - + CALIB_CB_FAST_CHECK); - - if(patternfound) - cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1), - TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1)); - - drawChessboardCorners(img, patternsize, Mat(corners), patternfound); -@endcode -@note The function requires white space (like a square-thick border, the wider the better) around -the board to make the detection more robust in various environments. Otherwise, if there is no -border and the background is dark, the outer black squares cannot be segmented properly and so the -square grouping and ordering algorithm fails. - */ -CV_EXPORTS_W bool findChessboardCorners( InputArray image, Size patternSize, OutputArray corners, - int flags = CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE ); - -//! finds subpixel-accurate positions of the chessboard corners -CV_EXPORTS_W bool find4QuadCornerSubpix( InputArray img, InputOutputArray corners, Size region_size ); - -/** @brief Renders the detected chessboard corners. - -@param image Destination image. It must be an 8-bit color image. -@param patternSize Number of inner corners per a chessboard row and column -(patternSize = cv::Size(points_per_row,points_per_column)). -@param corners Array of detected corners, the output of findChessboardCorners. -@param patternWasFound Parameter indicating whether the complete board was found or not. The -return value of findChessboardCorners should be passed here. - -The function draws individual chessboard corners detected either as red circles if the board was not -found, or as colored corners connected with lines if the board was found. - */ -CV_EXPORTS_W void drawChessboardCorners( InputOutputArray image, Size patternSize, - InputArray corners, bool patternWasFound ); - -/** @brief Draw axes of the world/object coordinate system from pose estimation. @sa solvePnP - -@param image Input/output image. It must have 1 or 3 channels. The number of channels is not altered. -@param cameraMatrix Input 3x3 floating-point matrix of camera intrinsic parameters. -\f$\cameramatrix{A}\f$ -@param distCoeffs Input vector of distortion coefficients -\f$\distcoeffs\f$. If the vector is empty, the zero distortion coefficients are assumed. -@param rvec Rotation vector (see @ref Rodrigues ) that, together with tvec, brings points from -the model coordinate system to the camera coordinate system. -@param tvec Translation vector. -@param length Length of the painted axes in the same unit than tvec (usually in meters). -@param thickness Line thickness of the painted axes. - -This function draws the axes of the world/object coordinate system w.r.t. to the camera frame. -OX is drawn in red, OY in green and OZ in blue. - */ -CV_EXPORTS_W void drawFrameAxes(InputOutputArray image, InputArray cameraMatrix, InputArray distCoeffs, - InputArray rvec, InputArray tvec, float length, int thickness=3); - -struct CV_EXPORTS_W_SIMPLE CirclesGridFinderParameters -{ - CV_WRAP CirclesGridFinderParameters(); - CV_PROP_RW cv::Size2f densityNeighborhoodSize; - CV_PROP_RW float minDensity; - CV_PROP_RW int kmeansAttempts; - CV_PROP_RW int minDistanceToAddKeypoint; - CV_PROP_RW int keypointScale; - CV_PROP_RW float minGraphConfidence; - CV_PROP_RW float vertexGain; - CV_PROP_RW float vertexPenalty; - CV_PROP_RW float existingVertexGain; - CV_PROP_RW float edgeGain; - CV_PROP_RW float edgePenalty; - CV_PROP_RW float convexHullFactor; - CV_PROP_RW float minRNGEdgeSwitchDist; - - enum GridType - { - SYMMETRIC_GRID, ASYMMETRIC_GRID - }; - GridType gridType; -}; - -struct CV_EXPORTS_W_SIMPLE CirclesGridFinderParameters2 : public CirclesGridFinderParameters -{ - CV_WRAP CirclesGridFinderParameters2(); - - CV_PROP_RW float squareSize; //!< Distance between two adjacent points. Used by CALIB_CB_CLUSTERING. - CV_PROP_RW float maxRectifiedDistance; //!< Max deviation from prediction. Used by CALIB_CB_CLUSTERING. -}; - -/** @brief Finds centers in the grid of circles. - -@param image grid view of input circles; it must be an 8-bit grayscale or color image. -@param patternSize number of circles per row and column -( patternSize = Size(points_per_row, points_per_colum) ). -@param centers output array of detected centers. -@param flags various operation flags that can be one of the following values: -- @ref CALIB_CB_SYMMETRIC_GRID uses symmetric pattern of circles. -- @ref CALIB_CB_ASYMMETRIC_GRID uses asymmetric pattern of circles. -- @ref CALIB_CB_CLUSTERING uses a special algorithm for grid detection. It is more robust to -perspective distortions but much more sensitive to background clutter. -@param blobDetector feature detector that finds blobs like dark circles on light background. - If `blobDetector` is NULL then `image` represents Point2f array of candidates. -@param parameters struct for finding circles in a grid pattern. - -The function attempts to determine whether the input image contains a grid of circles. If it is, the -function locates centers of the circles. The function returns a non-zero value if all of the centers -have been found and they have been placed in a certain order (row by row, left to right in every -row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. - -Sample usage of detecting and drawing the centers of circles: : -@code - Size patternsize(7,7); //number of centers - Mat gray = ...; //source image - vector centers; //this will be filled by the detected centers - - bool patternfound = findCirclesGrid(gray, patternsize, centers); - - drawChessboardCorners(img, patternsize, Mat(centers), patternfound); -@endcode -@note The function requires white space (like a square-thick border, the wider the better) around -the board to make the detection more robust in various environments. - */ -CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize, - OutputArray centers, int flags, - const Ptr &blobDetector, - CirclesGridFinderParameters parameters); - -/** @overload */ -CV_EXPORTS_W bool findCirclesGrid2( InputArray image, Size patternSize, - OutputArray centers, int flags, - const Ptr &blobDetector, - CirclesGridFinderParameters2 parameters); - -/** @overload */ -CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize, - OutputArray centers, int flags = CALIB_CB_SYMMETRIC_GRID, - const Ptr &blobDetector = SimpleBlobDetector::create()); - -/** @brief Finds the camera intrinsic and extrinsic parameters from several views of a calibration -pattern. - -@param objectPoints In the new interface it is a vector of vectors of calibration pattern points in -the calibration pattern coordinate space (e.g. std::vector>). The outer -vector contains as many elements as the number of pattern views. If the same calibration pattern -is shown in each view and it is fully visible, all the vectors will be the same. Although, it is -possible to use partially occluded patterns or even different patterns in different views. Then, -the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's -XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig. -In the old interface all the vectors of object points from different views are concatenated -together. -@param imagePoints In the new interface it is a vector of vectors of the projections of calibration -pattern points (e.g. std::vector>). imagePoints.size() and -objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal, -respectively. In the old interface all the vectors of object points from different views are -concatenated together. -@param imageSize Size of the image used only to initialize the camera intrinsic matrix. -@param cameraMatrix Input/output 3x3 floating-point camera intrinsic matrix -\f$\cameramatrix{A}\f$ . If @ref CALIB_USE_INTRINSIC_GUESS -and/or @ref CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be -initialized before calling the function. -@param distCoeffs Input/output vector of distortion coefficients -\f$\distcoeffs\f$. -@param rvecs Output vector of rotation vectors (@ref Rodrigues ) estimated for each pattern view -(e.g. std::vector>). That is, each i-th rotation vector together with the corresponding -i-th translation vector (see the next output parameter description) brings the calibration pattern -from the object coordinate space (in which object points are specified) to the camera coordinate -space. In more technical terms, the tuple of the i-th rotation and translation vector performs -a change of basis from object coordinate space to camera coordinate space. Due to its duality, this -tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate -space. -@param tvecs Output vector of translation vectors estimated for each pattern view, see parameter -describtion above. -@param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic -parameters. Order of deviations values: -\f$(f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, - s_4, \tau_x, \tau_y)\f$ If one of parameters is not estimated, it's deviation is equals to zero. -@param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic -parameters. Order of deviations values: \f$(R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\f$ where M is -the number of pattern views. \f$R_i, T_i\f$ are concatenated 1x3 vectors. - @param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. -@param flags Different flags that may be zero or a combination of the following values: -- @ref CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of -fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image -center ( imageSize is used), and focal distances are computed in a least-squares fashion. -Note, that if intrinsic parameters are known, there is no need to use this function just to -estimate extrinsic parameters. Use solvePnP instead. -- @ref CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global -optimization. It stays at the center or at a different location specified when - @ref CALIB_USE_INTRINSIC_GUESS is set too. -- @ref CALIB_FIX_ASPECT_RATIO The functions consider only fy as a free parameter. The -ratio fx/fy stays the same as in the input cameraMatrix . When - @ref CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are -ignored, only their ratio is computed and used further. -- @ref CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \f$(p_1, p_2)\f$ are set -to zeros and stay zero. -- @ref CALIB_FIX_K1,..., @ref CALIB_FIX_K6 The corresponding radial distortion -coefficient is not changed during the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is -set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. -- @ref CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the -backward compatibility, this extra flag should be explicitly specified to make the -calibration function use the rational model and return 8 coefficients. If the flag is not -set, the function computes and returns only 5 distortion coefficients. -- @ref CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the -backward compatibility, this extra flag should be explicitly specified to make the -calibration function use the thin prism model and return 12 coefficients. If the flag is not -set, the function computes and returns only 5 distortion coefficients. -- @ref CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during -the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the -supplied distCoeffs matrix is used. Otherwise, it is set to 0. -- @ref CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the -backward compatibility, this extra flag should be explicitly specified to make the -calibration function use the tilted sensor model and return 14 coefficients. If the flag is not -set, the function computes and returns only 5 distortion coefficients. -- @ref CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during -the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the -supplied distCoeffs matrix is used. Otherwise, it is set to 0. -@param criteria Termination criteria for the iterative optimization algorithm. - -@return the overall RMS re-projection error. - -The function estimates the intrinsic camera parameters and extrinsic parameters for each of the -views. The algorithm is based on @cite Zhang2000 and @cite BouguetMCT . The coordinates of 3D object -points and their corresponding 2D projections in each view must be specified. That may be achieved -by using an object with known geometry and easily detectable feature points. Such an object is -called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as -a calibration rig (see @ref findChessboardCorners). Currently, initialization of intrinsic -parameters (when @ref CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration -patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also -be used as long as initial cameraMatrix is provided. - -The algorithm performs the following steps: - -- Compute the initial intrinsic parameters (the option only available for planar calibration - patterns) or read them from the input parameters. The distortion coefficients are all set to - zeros initially unless some of CALIB_FIX_K? are specified. - -- Estimate the initial camera pose as if the intrinsic parameters have been already known. This is - done using solvePnP . - -- Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, - that is, the total sum of squared distances between the observed feature points imagePoints and - the projected (using the current estimates for camera parameters and the poses) object points - objectPoints. See projectPoints for details. - -@note - If you use a non-square (i.e. non-N-by-N) grid and @ref findChessboardCorners for calibration, - and @ref calibrateCamera returns bad values (zero distortion coefficients, \f$c_x\f$ and - \f$c_y\f$ very far from the image center, and/or large differences between \f$f_x\f$ and - \f$f_y\f$ (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols) - instead of using patternSize=cvSize(cols,rows) in @ref findChessboardCorners. - -@sa - findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort - */ -CV_EXPORTS_AS(calibrateCameraExtended) double calibrateCamera( InputArrayOfArrays objectPoints, - InputArrayOfArrays imagePoints, Size imageSize, - InputOutputArray cameraMatrix, InputOutputArray distCoeffs, - OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, - OutputArray stdDeviationsIntrinsics, - OutputArray stdDeviationsExtrinsics, - OutputArray perViewErrors, - int flags = 0, TermCriteria criteria = TermCriteria( - TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ); - -/** @overload double calibrateCamera( InputArrayOfArrays objectPoints, - InputArrayOfArrays imagePoints, Size imageSize, - InputOutputArray cameraMatrix, InputOutputArray distCoeffs, - OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, - OutputArray stdDeviations, OutputArray perViewErrors, - int flags = 0, TermCriteria criteria = TermCriteria( - TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ) - */ -CV_EXPORTS_W double calibrateCamera( InputArrayOfArrays objectPoints, - InputArrayOfArrays imagePoints, Size imageSize, - InputOutputArray cameraMatrix, InputOutputArray distCoeffs, - OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, - int flags = 0, TermCriteria criteria = TermCriteria( - TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) ); - -/** @brief Computes useful camera characteristics from the camera intrinsic matrix. - -@param cameraMatrix Input camera intrinsic matrix that can be estimated by calibrateCamera or -stereoCalibrate . -@param imageSize Input image size in pixels. -@param apertureWidth Physical width in mm of the sensor. -@param apertureHeight Physical height in mm of the sensor. -@param fovx Output field of view in degrees along the horizontal sensor axis. -@param fovy Output field of view in degrees along the vertical sensor axis. -@param focalLength Focal length of the lens in mm. -@param principalPoint Principal point in mm. -@param aspectRatio \f$f_y/f_x\f$ - -The function computes various useful camera characteristics from the previously estimated camera -matrix. - -@note - Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for - the chessboard pitch (it can thus be any value). - */ -CV_EXPORTS_W void calibrationMatrixValues( InputArray cameraMatrix, Size imageSize, - double apertureWidth, double apertureHeight, - CV_OUT double& fovx, CV_OUT double& fovy, - CV_OUT double& focalLength, CV_OUT Point2d& principalPoint, - CV_OUT double& aspectRatio ); - -/** @brief Calibrates a stereo camera set up. This function finds the intrinsic parameters -for each of the two cameras and the extrinsic parameters between the two cameras. - -@param objectPoints Vector of vectors of the calibration pattern points. The same structure as -in @ref calibrateCamera. For each pattern view, both cameras need to see the same object -points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be -equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to -be equal for each i. -@param imagePoints1 Vector of vectors of the projections of the calibration pattern points, -observed by the first camera. The same structure as in @ref calibrateCamera. -@param imagePoints2 Vector of vectors of the projections of the calibration pattern points, -observed by the second camera. The same structure as in @ref calibrateCamera. -@param cameraMatrix1 Input/output camera intrinsic matrix for the first camera, the same as in -@ref calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below. -@param distCoeffs1 Input/output vector of distortion coefficients, the same as in -@ref calibrateCamera. -@param cameraMatrix2 Input/output second camera intrinsic matrix for the second camera. See description for -cameraMatrix1. -@param distCoeffs2 Input/output lens distortion coefficients for the second camera. See -description for distCoeffs1. -@param imageSize Size of the image used only to initialize the camera intrinsic matrices. -@param R Output rotation matrix. Together with the translation vector T, this matrix brings -points given in the first camera's coordinate system to points in the second camera's -coordinate system. In more technical terms, the tuple of R and T performs a change of basis -from the first camera's coordinate system to the second camera's coordinate system. Due to its -duality, this tuple is equivalent to the position of the first camera with respect to the -second camera coordinate system. -@param T Output translation vector, see description above. -@param E Output essential matrix. -@param F Output fundamental matrix. -@param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. -@param flags Different flags that may be zero or a combination of the following values: -- @ref CALIB_FIX_INTRINSIC Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F -matrices are estimated. -- @ref CALIB_USE_INTRINSIC_GUESS Optimize some or all of the intrinsic parameters -according to the specified flags. Initial values are provided by the user. -- @ref CALIB_USE_EXTRINSIC_GUESS R and T contain valid initial values that are optimized further. -Otherwise R and T are initialized to the median value of the pattern views (each dimension separately). -- @ref CALIB_FIX_PRINCIPAL_POINT Fix the principal points during the optimization. -- @ref CALIB_FIX_FOCAL_LENGTH Fix \f$f^{(j)}_x\f$ and \f$f^{(j)}_y\f$ . -- @ref CALIB_FIX_ASPECT_RATIO Optimize \f$f^{(j)}_y\f$ . Fix the ratio \f$f^{(j)}_x/f^{(j)}_y\f$ -. -- @ref CALIB_SAME_FOCAL_LENGTH Enforce \f$f^{(0)}_x=f^{(1)}_x\f$ and \f$f^{(0)}_y=f^{(1)}_y\f$ . -- @ref CALIB_ZERO_TANGENT_DIST Set tangential distortion coefficients for each camera to -zeros and fix there. -- @ref CALIB_FIX_K1,..., @ref CALIB_FIX_K6 Do not change the corresponding radial -distortion coefficient during the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, -the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. -- @ref CALIB_RATIONAL_MODEL Enable coefficients k4, k5, and k6. To provide the backward -compatibility, this extra flag should be explicitly specified to make the calibration -function use the rational model and return 8 coefficients. If the flag is not set, the -function computes and returns only 5 distortion coefficients. -- @ref CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the -backward compatibility, this extra flag should be explicitly specified to make the -calibration function use the thin prism model and return 12 coefficients. If the flag is not -set, the function computes and returns only 5 distortion coefficients. -- @ref CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during -the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the -supplied distCoeffs matrix is used. Otherwise, it is set to 0. -- @ref CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the -backward compatibility, this extra flag should be explicitly specified to make the -calibration function use the tilted sensor model and return 14 coefficients. If the flag is not -set, the function computes and returns only 5 distortion coefficients. -- @ref CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during -the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the -supplied distCoeffs matrix is used. Otherwise, it is set to 0. -@param criteria Termination criteria for the iterative optimization algorithm. - -The function estimates the transformation between two cameras making a stereo pair. If one computes -the poses of an object relative to the first camera and to the second camera, -( \f$R_1\f$,\f$T_1\f$ ) and (\f$R_2\f$,\f$T_2\f$), respectively, for a stereo camera where the -relative position and orientation between the two cameras are fixed, then those poses definitely -relate to each other. This means, if the relative position and orientation (\f$R\f$,\f$T\f$) of the -two cameras is known, it is possible to compute (\f$R_2\f$,\f$T_2\f$) when (\f$R_1\f$,\f$T_1\f$) is -given. This is what the described function does. It computes (\f$R\f$,\f$T\f$) such that: - -\f[R_2=R R_1\f] -\f[T_2=R T_1 + T.\f] - -Therefore, one can compute the coordinate representation of a 3D point for the second camera's -coordinate system when given the point's coordinate representation in the first camera's coordinate -system: - -\f[\begin{bmatrix} -X_2 \\ -Y_2 \\ -Z_2 \\ -1 -\end{bmatrix} = \begin{bmatrix} -R & T \\ -0 & 1 -\end{bmatrix} \begin{bmatrix} -X_1 \\ -Y_1 \\ -Z_1 \\ -1 -\end{bmatrix}.\f] - - -Optionally, it computes the essential matrix E: - -\f[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} R\f] - -where \f$T_i\f$ are components of the translation vector \f$T\f$ : \f$T=[T_0, T_1, T_2]^T\f$ . -And the function can also compute the fundamental matrix F: - -\f[F = cameraMatrix2^{-T}\cdot E \cdot cameraMatrix1^{-1}\f] - -Besides the stereo-related information, the function can also perform a full calibration of each of -the two cameras. However, due to the high dimensionality of the parameter space and noise in the -input data, the function can diverge from the correct solution. If the intrinsic parameters can be -estimated with high accuracy for each of the cameras individually (for example, using -calibrateCamera ), you are recommended to do so and then pass @ref CALIB_FIX_INTRINSIC flag to the -function along with the computed intrinsic parameters. Otherwise, if all the parameters are -estimated at once, it makes sense to restrict some parameters, for example, pass - @ref CALIB_SAME_FOCAL_LENGTH and @ref CALIB_ZERO_TANGENT_DIST flags, which is usually a -reasonable assumption. - -Similarly to calibrateCamera, the function minimizes the total re-projection error for all the -points in all the available views from both cameras. The function returns the final value of the -re-projection error. - */ -CV_EXPORTS_AS(stereoCalibrateExtended) double stereoCalibrate( InputArrayOfArrays objectPoints, - InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, - InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1, - InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2, - Size imageSize, InputOutputArray R,InputOutputArray T, OutputArray E, OutputArray F, - OutputArray perViewErrors, int flags = CALIB_FIX_INTRINSIC, - TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) ); - -/// @overload -CV_EXPORTS_W double stereoCalibrate( InputArrayOfArrays objectPoints, - InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, - InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1, - InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2, - Size imageSize, OutputArray R,OutputArray T, OutputArray E, OutputArray F, - int flags = CALIB_FIX_INTRINSIC, - TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) ); - -/** @brief Computes rectification transforms for each head of a calibrated stereo camera. - -@param cameraMatrix1 First camera intrinsic matrix. -@param distCoeffs1 First camera distortion parameters. -@param cameraMatrix2 Second camera intrinsic matrix. -@param distCoeffs2 Second camera distortion parameters. -@param imageSize Size of the image used for stereo calibration. -@param R Rotation matrix from the coordinate system of the first camera to the second camera, -see @ref stereoCalibrate. -@param T Translation vector from the coordinate system of the first camera to the second camera, -see @ref stereoCalibrate. -@param R1 Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix -brings points given in the unrectified first camera's coordinate system to points in the rectified -first camera's coordinate system. In more technical terms, it performs a change of basis from the -unrectified first camera's coordinate system to the rectified first camera's coordinate system. -@param R2 Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix -brings points given in the unrectified second camera's coordinate system to points in the rectified -second camera's coordinate system. In more technical terms, it performs a change of basis from the -unrectified second camera's coordinate system to the rectified second camera's coordinate system. -@param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first -camera, i.e. it projects points given in the rectified first camera coordinate system into the -rectified first camera's image. -@param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second -camera, i.e. it projects points given in the rectified first camera coordinate system into the -rectified second camera's image. -@param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see @ref reprojectImageTo3D). -@param flags Operation flags that may be zero or @ref CALIB_ZERO_DISPARITY . If the flag is set, -the function makes the principal points of each camera have the same pixel coordinates in the -rectified views. And if the flag is not set, the function may still shift the images in the -horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the -useful image area. -@param alpha Free scaling parameter. If it is -1 or absent, the function performs the default -scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified -images are zoomed and shifted so that only valid pixels are visible (no black areas after -rectification). alpha=1 means that the rectified image is decimated and shifted so that all the -pixels from the original images from the cameras are retained in the rectified images (no source -image pixels are lost). Any intermediate value yields an intermediate result between -those two extreme cases. -@param newImageSize New image resolution after rectification. The same size should be passed to -initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) -is passed (default), it is set to the original imageSize . Setting it to a larger value can help you -preserve details in the original image, especially when there is a big radial distortion. -@param validPixROI1 Optional output rectangles inside the rectified images where all the pixels -are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller -(see the picture below). -@param validPixROI2 Optional output rectangles inside the rectified images where all the pixels -are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller -(see the picture below). - -The function computes the rotation matrices for each camera that (virtually) make both camera image -planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies -the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate -as input. As output, it provides two rotation matrices and also two projection matrices in the new -coordinates. The function distinguishes the following two cases: - -- **Horizontal stereo**: the first and the second camera views are shifted relative to each other - mainly along the x-axis (with possible small vertical shift). In the rectified images, the - corresponding epipolar lines in the left and right cameras are horizontal and have the same - y-coordinate. P1 and P2 look like: - - \f[\texttt{P1} = \begin{bmatrix} - f & 0 & cx_1 & 0 \\ - 0 & f & cy & 0 \\ - 0 & 0 & 1 & 0 - \end{bmatrix}\f] - - \f[\texttt{P2} = \begin{bmatrix} - f & 0 & cx_2 & T_x*f \\ - 0 & f & cy & 0 \\ - 0 & 0 & 1 & 0 - \end{bmatrix} ,\f] - - where \f$T_x\f$ is a horizontal shift between the cameras and \f$cx_1=cx_2\f$ if - @ref CALIB_ZERO_DISPARITY is set. - -- **Vertical stereo**: the first and the second camera views are shifted relative to each other - mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar - lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like: - - \f[\texttt{P1} = \begin{bmatrix} - f & 0 & cx & 0 \\ - 0 & f & cy_1 & 0 \\ - 0 & 0 & 1 & 0 - \end{bmatrix}\f] - - \f[\texttt{P2} = \begin{bmatrix} - f & 0 & cx & 0 \\ - 0 & f & cy_2 & T_y*f \\ - 0 & 0 & 1 & 0 - \end{bmatrix},\f] - - where \f$T_y\f$ is a vertical shift between the cameras and \f$cy_1=cy_2\f$ if - @ref CALIB_ZERO_DISPARITY is set. - -As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera -matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to -initialize the rectification map for each camera. - -See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through -the corresponding image regions. This means that the images are well rectified, which is what most -stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that -their interiors are all valid pixels. - -![image](pics/stereo_undistort.jpg) - */ -CV_EXPORTS_W void stereoRectify( InputArray cameraMatrix1, InputArray distCoeffs1, - InputArray cameraMatrix2, InputArray distCoeffs2, - Size imageSize, InputArray R, InputArray T, - OutputArray R1, OutputArray R2, - OutputArray P1, OutputArray P2, - OutputArray Q, int flags = CALIB_ZERO_DISPARITY, - double alpha = -1, Size newImageSize = Size(), - CV_OUT Rect* validPixROI1 = 0, CV_OUT Rect* validPixROI2 = 0 ); - -/** @brief Computes a rectification transform for an uncalibrated stereo camera. - -@param points1 Array of feature points in the first image. -@param points2 The corresponding points in the second image. The same formats as in -findFundamentalMat are supported. -@param F Input fundamental matrix. It can be computed from the same set of point pairs using -findFundamentalMat . -@param imgSize Size of the image. -@param H1 Output rectification homography matrix for the first image. -@param H2 Output rectification homography matrix for the second image. -@param threshold Optional threshold used to filter out the outliers. If the parameter is greater -than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points -for which \f$|\texttt{points2[i]}^T*\texttt{F}*\texttt{points1[i]}|>\texttt{threshold}\f$ ) are -rejected prior to computing the homographies. Otherwise, all the points are considered inliers. - -The function computes the rectification transformations without knowing intrinsic parameters of the -cameras and their relative position in the space, which explains the suffix "uncalibrated". Another -related difference from stereoRectify is that the function outputs not the rectification -transformations in the object (3D) space, but the planar perspective transformations encoded by the -homography matrices H1 and H2 . The function implements the algorithm @cite Hartley99 . - -@note - While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily - depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion, - it would be better to correct it before computing the fundamental matrix and calling this - function. For example, distortion coefficients can be estimated for each head of stereo camera - separately by using calibrateCamera . Then, the images can be corrected using undistort , or - just the point coordinates can be corrected with undistortPoints . - */ -CV_EXPORTS_W bool stereoRectifyUncalibrated( InputArray points1, InputArray points2, - InputArray F, Size imgSize, - OutputArray H1, OutputArray H2, - double threshold = 5 ); - -//! computes the rectification transformations for 3-head camera, where all the heads are on the same line. -CV_EXPORTS_W float rectify3Collinear( InputArray cameraMatrix1, InputArray distCoeffs1, - InputArray cameraMatrix2, InputArray distCoeffs2, - InputArray cameraMatrix3, InputArray distCoeffs3, - InputArrayOfArrays imgpt1, InputArrayOfArrays imgpt3, - Size imageSize, InputArray R12, InputArray T12, - InputArray R13, InputArray T13, - OutputArray R1, OutputArray R2, OutputArray R3, - OutputArray P1, OutputArray P2, OutputArray P3, - OutputArray Q, double alpha, Size newImgSize, - CV_OUT Rect* roi1, CV_OUT Rect* roi2, int flags ); - -/** @brief Returns the new camera intrinsic matrix based on the free scaling parameter. - -@param cameraMatrix Input camera intrinsic matrix. -@param distCoeffs Input vector of distortion coefficients -\f$\distcoeffs\f$. If the vector is NULL/empty, the zero distortion coefficients are -assumed. -@param imageSize Original image size. -@param alpha Free scaling parameter between 0 (when all the pixels in the undistorted image are -valid) and 1 (when all the source image pixels are retained in the undistorted image). See -stereoRectify for details. -@param newImgSize Image size after rectification. By default, it is set to imageSize . -@param validPixROI Optional output rectangle that outlines all-good-pixels region in the -undistorted image. See roi1, roi2 description in stereoRectify . -@param centerPrincipalPoint Optional flag that indicates whether in the new camera intrinsic matrix the -principal point should be at the image center or not. By default, the principal point is chosen to -best fit a subset of the source image (determined by alpha) to the corrected image. -@return new_camera_matrix Output new camera intrinsic matrix. - -The function computes and returns the optimal new camera intrinsic matrix based on the free scaling parameter. -By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original -image pixels if there is valuable information in the corners alpha=1 , or get something in between. -When alpha\>0 , the undistorted result is likely to have some black pixels corresponding to -"virtual" pixels outside of the captured distorted image. The original camera intrinsic matrix, distortion -coefficients, the computed new camera intrinsic matrix, and newImageSize should be passed to -initUndistortRectifyMap to produce the maps for remap . - */ -CV_EXPORTS_W Mat getOptimalNewCameraMatrix( InputArray cameraMatrix, InputArray distCoeffs, - Size imageSize, double alpha, Size newImgSize = Size(), - CV_OUT Rect* validPixROI = 0, - bool centerPrincipalPoint = false); - -/** @brief Computes Hand-Eye calibration: \f$_{}^{g}\textrm{T}_c\f$ - -@param[in] R_gripper2base Rotation part extracted from the homogeneous matrix that transforms a point -expressed in the gripper frame to the robot base frame (\f$_{}^{b}\textrm{T}_g\f$). -This is a vector (`vector`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors, -for all the transformations from gripper frame to robot base frame. -@param[in] t_gripper2base Translation part extracted from the homogeneous matrix that transforms a point -expressed in the gripper frame to the robot base frame (\f$_{}^{b}\textrm{T}_g\f$). -This is a vector (`vector`) that contains the `(3x1)` translation vectors for all the transformations -from gripper frame to robot base frame. -@param[in] R_target2cam Rotation part extracted from the homogeneous matrix that transforms a point -expressed in the target frame to the camera frame (\f$_{}^{c}\textrm{T}_t\f$). -This is a vector (`vector`) that contains the rotation, `(3x3)` rotation matrices or `(3x1)` rotation vectors, -for all the transformations from calibration target frame to camera frame. -@param[in] t_target2cam Rotation part extracted from the homogeneous matrix that transforms a point -expressed in the target frame to the camera frame (\f$_{}^{c}\textrm{T}_t\f$). -This is a vector (`vector`) that contains the `(3x1)` translation vectors for all the transformations -from calibration target frame to camera frame. -@param[out] R_cam2gripper Estimated `(3x3)` rotation part extracted from the homogeneous matrix that transforms a point -expressed in the camera frame to the gripper frame (\f$_{}^{g}\textrm{T}_c\f$). -@param[out] t_cam2gripper Estimated `(3x1)` translation part extracted from the homogeneous matrix that transforms a point -expressed in the camera frame to the gripper frame (\f$_{}^{g}\textrm{T}_c\f$). -@param[in] method One of the implemented Hand-Eye calibration method, see cv::HandEyeCalibrationMethod - -The function performs the Hand-Eye calibration using various methods. One approach consists in estimating the -rotation then the translation (separable solutions) and the following methods are implemented: - - R. Tsai, R. Lenz A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/EyeCalibration \cite Tsai89 - - F. Park, B. Martin Robot Sensor Calibration: Solving AX = XB on the Euclidean Group \cite Park94 - - R. Horaud, F. Dornaika Hand-Eye Calibration \cite Horaud95 - -Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), -with the following implemented method: - - N. Andreff, R. Horaud, B. Espiau On-line Hand-Eye Calibration \cite Andreff99 - - K. Daniilidis Hand-Eye Calibration Using Dual Quaternions \cite Daniilidis98 - -The following picture describes the Hand-Eye calibration problem where the transformation between a camera ("eye") -mounted on a robot gripper ("hand") has to be estimated. - -![](pics/hand-eye_figure.png) - -The calibration procedure is the following: - - a static calibration pattern is used to estimate the transformation between the target frame - and the camera frame - - the robot gripper is moved in order to acquire several poses - - for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for - instance the robot kinematics -\f[ - \begin{bmatrix} - X_b\\ - Y_b\\ - Z_b\\ - 1 - \end{bmatrix} - = - \begin{bmatrix} - _{}^{b}\textrm{R}_g & _{}^{b}\textrm{t}_g \\ - 0_{1 \times 3} & 1 - \end{bmatrix} - \begin{bmatrix} - X_g\\ - Y_g\\ - Z_g\\ - 1 - \end{bmatrix} -\f] - - for each pose, the homogeneous transformation between the calibration target frame and the camera frame is recorded using - for instance a pose estimation method (PnP) from 2D-3D point correspondences -\f[ - \begin{bmatrix} - X_c\\ - Y_c\\ - Z_c\\ - 1 - \end{bmatrix} - = - \begin{bmatrix} - _{}^{c}\textrm{R}_t & _{}^{c}\textrm{t}_t \\ - 0_{1 \times 3} & 1 - \end{bmatrix} - \begin{bmatrix} - X_t\\ - Y_t\\ - Z_t\\ - 1 - \end{bmatrix} -\f] - -The Hand-Eye calibration procedure returns the following homogeneous transformation -\f[ - \begin{bmatrix} - X_g\\ - Y_g\\ - Z_g\\ - 1 - \end{bmatrix} - = - \begin{bmatrix} - _{}^{g}\textrm{R}_c & _{}^{g}\textrm{t}_c \\ - 0_{1 \times 3} & 1 - \end{bmatrix} - \begin{bmatrix} - X_c\\ - Y_c\\ - Z_c\\ - 1 - \end{bmatrix} -\f] - -This problem is also known as solving the \f$\mathbf{A}\mathbf{X}=\mathbf{X}\mathbf{B}\f$ equation: -\f[ - \begin{align*} - ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= - \hspace{0.1em} ^{b}{\textrm{T}_g}^{(2)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ - - (^{b}{\textrm{T}_g}^{(2)})^{-1} \hspace{0.2em} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c &= - \hspace{0.1em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\ - - \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ - \end{align*} -\f] - -\note -Additional information can be found on this [website](http://campar.in.tum.de/Chair/HandEyeCalibration). -\note -A minimum of 2 motions with non parallel rotation axes are necessary to determine the hand-eye transformation. -So at least 3 different poses are required, but it is strongly recommended to use many more poses. - - */ -CV_EXPORTS_W void calibrateHandEye( InputArrayOfArrays R_gripper2base, InputArrayOfArrays t_gripper2base, - InputArrayOfArrays R_target2cam, InputArrayOfArrays t_target2cam, - OutputArray R_cam2gripper, OutputArray t_cam2gripper, - HandEyeCalibrationMethod method=CALIB_HAND_EYE_TSAI ); - -/** @brief Converts points from Euclidean to homogeneous space. - -@param src Input vector of N-dimensional points. -@param dst Output vector of N+1-dimensional points. - -The function converts points from Euclidean to homogeneous space by appending 1's to the tuple of -point coordinates. That is, each point (x1, x2, ..., xn) is converted to (x1, x2, ..., xn, 1). - */ -CV_EXPORTS_W void convertPointsToHomogeneous( InputArray src, OutputArray dst ); - -/** @brief Converts points from homogeneous to Euclidean space. - -@param src Input vector of N-dimensional points. -@param dst Output vector of N-1-dimensional points. - -The function converts points homogeneous to Euclidean space using perspective projection. That is, -each point (x1, x2, ... x(n-1), xn) is converted to (x1/xn, x2/xn, ..., x(n-1)/xn). When xn=0, the -output point coordinates will be (0,0,0,...). - */ -CV_EXPORTS_W void convertPointsFromHomogeneous( InputArray src, OutputArray dst ); - -/** @brief Converts points to/from homogeneous coordinates. - -@param src Input array or vector of 2D, 3D, or 4D points. -@param dst Output vector of 2D, 3D, or 4D points. - -The function converts 2D or 3D points from/to homogeneous coordinates by calling either -convertPointsToHomogeneous or convertPointsFromHomogeneous. - -@note The function is obsolete. Use one of the previous two functions instead. - */ -CV_EXPORTS void convertPointsHomogeneous( InputArray src, OutputArray dst ); - -/** @brief Calculates a fundamental matrix from the corresponding points in two images. - -@param points1 Array of N points from the first image. The point coordinates should be -floating-point (single or double precision). -@param points2 Array of the second image points of the same size and format as points1 . -@param method Method for computing a fundamental matrix. -- @ref FM_7POINT for a 7-point algorithm. \f$N = 7\f$ -- @ref FM_8POINT for an 8-point algorithm. \f$N \ge 8\f$ -- @ref FM_RANSAC for the RANSAC algorithm. \f$N \ge 8\f$ -- @ref FM_LMEDS for the LMedS algorithm. \f$N \ge 8\f$ -@param ransacReprojThreshold Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar -line in pixels, beyond which the point is considered an outlier and is not used for computing the -final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the -point localization, image resolution, and the image noise. -@param confidence Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level -of confidence (probability) that the estimated matrix is correct. -@param[out] mask optional output mask -@param maxIters The maximum number of robust method iterations. - -The epipolar geometry is described by the following equation: - -\f[[p_2; 1]^T F [p_1; 1] = 0\f] - -where \f$F\f$ is a fundamental matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the -second images, respectively. - -The function calculates the fundamental matrix using one of four methods listed above and returns -the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point -algorithm, the function may return up to 3 solutions ( \f$9 \times 3\f$ matrix that stores all 3 -matrices sequentially). - -The calculated fundamental matrix may be passed further to computeCorrespondEpilines that finds the -epipolar lines corresponding to the specified points. It can also be passed to -stereoRectifyUncalibrated to compute the rectification transformation. : -@code - // Example. Estimation of fundamental matrix using the RANSAC algorithm - int point_count = 100; - vector points1(point_count); - vector points2(point_count); - - // initialize the points here ... - for( int i = 0; i < point_count; i++ ) - { - points1[i] = ...; - points2[i] = ...; - } - - Mat fundamental_matrix = - findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99); -@endcode - */ -CV_EXPORTS_W Mat findFundamentalMat( InputArray points1, InputArray points2, - int method, double ransacReprojThreshold, double confidence, - int maxIters, OutputArray mask = noArray() ); - -/** @overload */ -CV_EXPORTS_W Mat findFundamentalMat( InputArray points1, InputArray points2, - int method = FM_RANSAC, - double ransacReprojThreshold = 3., double confidence = 0.99, - OutputArray mask = noArray() ); - -/** @overload */ -CV_EXPORTS Mat findFundamentalMat( InputArray points1, InputArray points2, - OutputArray mask, int method = FM_RANSAC, - double ransacReprojThreshold = 3., double confidence = 0.99 ); - -/** @brief Calculates an essential matrix from the corresponding points in two images. - -@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should -be floating-point (single or double precision). -@param points2 Array of the second image points of the same size and format as points1 . -@param cameraMatrix Camera intrinsic matrix \f$\cameramatrix{A}\f$ . -Note that this function assumes that points1 and points2 are feature points from cameras with the -same camera intrinsic matrix. If this assumption does not hold for your use case, use -`undistortPoints()` with `P = cv::NoArray()` for both cameras to transform image points -to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When -passing these coordinates, pass the identity matrix for this parameter. -@param method Method for computing an essential matrix. -- @ref RANSAC for the RANSAC algorithm. -- @ref LMEDS for the LMedS algorithm. -@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of -confidence (probability) that the estimated matrix is correct. -@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar -line in pixels, beyond which the point is considered an outlier and is not used for computing the -final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the -point localization, image resolution, and the image noise. -@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1 -for the other points. The array is computed only in the RANSAC and LMedS methods. - -This function estimates essential matrix based on the five-point algorithm solver in @cite Nister03 . -@cite SteweniusCFS is also a related. The epipolar geometry is described by the following equation: - -\f[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\f] - -where \f$E\f$ is an essential matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the -second images, respectively. The result of this function may be passed further to -decomposeEssentialMat or recoverPose to recover the relative pose between cameras. - */ -CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2, - InputArray cameraMatrix, int method = RANSAC, - double prob = 0.999, double threshold = 1.0, - OutputArray mask = noArray() ); - -/** @overload -@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should -be floating-point (single or double precision). -@param points2 Array of the second image points of the same size and format as points1 . -@param focal focal length of the camera. Note that this function assumes that points1 and points2 -are feature points from cameras with same focal length and principal point. -@param pp principal point of the camera. -@param method Method for computing a fundamental matrix. -- @ref RANSAC for the RANSAC algorithm. -- @ref LMEDS for the LMedS algorithm. -@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar -line in pixels, beyond which the point is considered an outlier and is not used for computing the -final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the -point localization, image resolution, and the image noise. -@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of -confidence (probability) that the estimated matrix is correct. -@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1 -for the other points. The array is computed only in the RANSAC and LMedS methods. - -This function differs from the one above that it computes camera intrinsic matrix from focal length and -principal point: - -\f[A = -\begin{bmatrix} -f & 0 & x_{pp} \\ -0 & f & y_{pp} \\ -0 & 0 & 1 -\end{bmatrix}\f] - */ -CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2, - double focal = 1.0, Point2d pp = Point2d(0, 0), - int method = RANSAC, double prob = 0.999, - double threshold = 1.0, OutputArray mask = noArray() ); - -/** @brief Decompose an essential matrix to possible rotations and translation. - -@param E The input essential matrix. -@param R1 One possible rotation matrix. -@param R2 Another possible rotation matrix. -@param t One possible translation. - -This function decomposes the essential matrix E using svd decomposition @cite HartleyZ00. In -general, four possible poses exist for the decomposition of E. They are \f$[R_1, t]\f$, -\f$[R_1, -t]\f$, \f$[R_2, t]\f$, \f$[R_2, -t]\f$. - -If E gives the epipolar constraint \f$[p_2; 1]^T A^{-T} E A^{-1} [p_1; 1] = 0\f$ between the image -points \f$p_1\f$ in the first image and \f$p_2\f$ in second image, then any of the tuples -\f$[R_1, t]\f$, \f$[R_1, -t]\f$, \f$[R_2, t]\f$, \f$[R_2, -t]\f$ is a change of basis from the first -camera's coordinate system to the second camera's coordinate system. However, by decomposing E, one -can only get the direction of the translation. For this reason, the translation t is returned with -unit length. - */ -CV_EXPORTS_W void decomposeEssentialMat( InputArray E, OutputArray R1, OutputArray R2, OutputArray t ); - -/** @brief Recovers the relative camera rotation and the translation from an estimated essential -matrix and the corresponding points in two images, using cheirality check. Returns the number of -inliers that pass the check. - -@param E The input essential matrix. -@param points1 Array of N 2D points from the first image. The point coordinates should be -floating-point (single or double precision). -@param points2 Array of the second image points of the same size and format as points1 . -@param cameraMatrix Camera intrinsic matrix \f$\cameramatrix{A}\f$ . -Note that this function assumes that points1 and points2 are feature points from cameras with the -same camera intrinsic matrix. -@param R Output rotation matrix. Together with the translation vector, this matrix makes up a tuple -that performs a change of basis from the first camera's coordinate system to the second camera's -coordinate system. Note that, in general, t can not be used for this tuple, see the parameter -described below. -@param t Output translation vector. This vector is obtained by @ref decomposeEssentialMat and -therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit -length. -@param mask Input/output mask for inliers in points1 and points2. If it is not empty, then it marks -inliers in points1 and points2 for then given essential matrix E. Only these inliers will be used to -recover pose. In the output mask only inliers which pass the cheirality check. - -This function decomposes an essential matrix using @ref decomposeEssentialMat and then verifies -possible pose hypotheses by doing cheirality check. The cheirality check means that the -triangulated 3D points should have positive depth. Some details can be found in @cite Nister03. - -This function can be used to process the output E and mask from @ref findEssentialMat. In this -scenario, points1 and points2 are the same input for findEssentialMat.: -@code - // Example. Estimation of fundamental matrix using the RANSAC algorithm - int point_count = 100; - vector points1(point_count); - vector points2(point_count); - - // initialize the points here ... - for( int i = 0; i < point_count; i++ ) - { - points1[i] = ...; - points2[i] = ...; - } - - // cametra matrix with both focal lengths = 1, and principal point = (0, 0) - Mat cameraMatrix = Mat::eye(3, 3, CV_64F); - - Mat E, R, t, mask; - - E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask); - recoverPose(E, points1, points2, cameraMatrix, R, t, mask); -@endcode - */ -CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, - InputArray cameraMatrix, OutputArray R, OutputArray t, - InputOutputArray mask = noArray() ); - -/** @overload -@param E The input essential matrix. -@param points1 Array of N 2D points from the first image. The point coordinates should be -floating-point (single or double precision). -@param points2 Array of the second image points of the same size and format as points1 . -@param R Output rotation matrix. Together with the translation vector, this matrix makes up a tuple -that performs a change of basis from the first camera's coordinate system to the second camera's -coordinate system. Note that, in general, t can not be used for this tuple, see the parameter -description below. -@param t Output translation vector. This vector is obtained by @ref decomposeEssentialMat and -therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit -length. -@param focal Focal length of the camera. Note that this function assumes that points1 and points2 -are feature points from cameras with same focal length and principal point. -@param pp principal point of the camera. -@param mask Input/output mask for inliers in points1 and points2. If it is not empty, then it marks -inliers in points1 and points2 for then given essential matrix E. Only these inliers will be used to -recover pose. In the output mask only inliers which pass the cheirality check. - -This function differs from the one above that it computes camera intrinsic matrix from focal length and -principal point: - -\f[A = -\begin{bmatrix} -f & 0 & x_{pp} \\ -0 & f & y_{pp} \\ -0 & 0 & 1 -\end{bmatrix}\f] - */ -CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, - OutputArray R, OutputArray t, - double focal = 1.0, Point2d pp = Point2d(0, 0), - InputOutputArray mask = noArray() ); - -/** @overload -@param E The input essential matrix. -@param points1 Array of N 2D points from the first image. The point coordinates should be -floating-point (single or double precision). -@param points2 Array of the second image points of the same size and format as points1. -@param cameraMatrix Camera intrinsic matrix \f$\cameramatrix{A}\f$ . -Note that this function assumes that points1 and points2 are feature points from cameras with the -same camera intrinsic matrix. -@param R Output rotation matrix. Together with the translation vector, this matrix makes up a tuple -that performs a change of basis from the first camera's coordinate system to the second camera's -coordinate system. Note that, in general, t can not be used for this tuple, see the parameter -description below. -@param t Output translation vector. This vector is obtained by @ref decomposeEssentialMat and -therefore is only known up to scale, i.e. t is the direction of the translation vector and has unit -length. -@param distanceThresh threshold distance which is used to filter out far away points (i.e. infinite -points). -@param mask Input/output mask for inliers in points1 and points2. If it is not empty, then it marks -inliers in points1 and points2 for then given essential matrix E. Only these inliers will be used to -recover pose. In the output mask only inliers which pass the cheirality check. -@param triangulatedPoints 3D points which were reconstructed by triangulation. - -This function differs from the one above that it outputs the triangulated 3D point that are used for -the cheirality check. - */ -CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2, - InputArray cameraMatrix, OutputArray R, OutputArray t, double distanceThresh, InputOutputArray mask = noArray(), - OutputArray triangulatedPoints = noArray()); - -/** @brief For points in an image of a stereo pair, computes the corresponding epilines in the other image. - -@param points Input points. \f$N \times 1\f$ or \f$1 \times N\f$ matrix of type CV_32FC2 or -vector\ . -@param whichImage Index of the image (1 or 2) that contains the points . -@param F Fundamental matrix that can be estimated using findFundamentalMat or stereoRectify . -@param lines Output vector of the epipolar lines corresponding to the points in the other image. -Each line \f$ax + by + c=0\f$ is encoded by 3 numbers \f$(a, b, c)\f$ . - -For every point in one of the two images of a stereo pair, the function finds the equation of the -corresponding epipolar line in the other image. - -From the fundamental matrix definition (see findFundamentalMat ), line \f$l^{(2)}_i\f$ in the second -image for the point \f$p^{(1)}_i\f$ in the first image (when whichImage=1 ) is computed as: - -\f[l^{(2)}_i = F p^{(1)}_i\f] - -And vice versa, when whichImage=2, \f$l^{(1)}_i\f$ is computed from \f$p^{(2)}_i\f$ as: - -\f[l^{(1)}_i = F^T p^{(2)}_i\f] - -Line coefficients are defined up to a scale. They are normalized so that \f$a_i^2+b_i^2=1\f$ . - */ -CV_EXPORTS_W void computeCorrespondEpilines( InputArray points, int whichImage, - InputArray F, OutputArray lines ); - -/** @brief This function reconstructs 3-dimensional points (in homogeneous coordinates) by using -their observations with a stereo camera. - -@param projMatr1 3x4 projection matrix of the first camera, i.e. this matrix projects 3D points -given in the world's coordinate system into the first image. -@param projMatr2 3x4 projection matrix of the second camera, i.e. this matrix projects 3D points -given in the world's coordinate system into the second image. -@param projPoints1 2xN array of feature points in the first image. In the case of the c++ version, -it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1. -@param projPoints2 2xN array of corresponding points in the second image. In the case of the c++ -version, it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1. -@param points4D 4xN array of reconstructed points in homogeneous coordinates. These points are -returned in the world's coordinate system. - -@note - Keep in mind that all input data should be of float type in order for this function to work. - -@note - If the projection matrices from @ref stereoRectify are used, then the returned points are - represented in the first camera's rectified coordinate system. - -@sa - reprojectImageTo3D - */ -CV_EXPORTS_W void triangulatePoints( InputArray projMatr1, InputArray projMatr2, - InputArray projPoints1, InputArray projPoints2, - OutputArray points4D ); - -/** @brief Refines coordinates of corresponding points. - -@param F 3x3 fundamental matrix. -@param points1 1xN array containing the first set of points. -@param points2 1xN array containing the second set of points. -@param newPoints1 The optimized points1. -@param newPoints2 The optimized points2. - -The function implements the Optimal Triangulation Method (see Multiple View Geometry for details). -For each given point correspondence points1[i] \<-\> points2[i], and a fundamental matrix F, it -computes the corrected correspondences newPoints1[i] \<-\> newPoints2[i] that minimize the geometric -error \f$d(points1[i], newPoints1[i])^2 + d(points2[i],newPoints2[i])^2\f$ (where \f$d(a,b)\f$ is the -geometric distance between points \f$a\f$ and \f$b\f$ ) subject to the epipolar constraint -\f$newPoints2^T * F * newPoints1 = 0\f$ . - */ -CV_EXPORTS_W void correctMatches( InputArray F, InputArray points1, InputArray points2, - OutputArray newPoints1, OutputArray newPoints2 ); - -/** @brief Filters off small noise blobs (speckles) in the disparity map - -@param img The input 16-bit signed disparity image -@param newVal The disparity value used to paint-off the speckles -@param maxSpeckleSize The maximum speckle size to consider it a speckle. Larger blobs are not -affected by the algorithm -@param maxDiff Maximum difference between neighbor disparity pixels to put them into the same -blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point -disparity map, where disparity values are multiplied by 16, this scale factor should be taken into -account when specifying this parameter value. -@param buf The optional temporary buffer to avoid memory allocation within the function. - */ -CV_EXPORTS_W void filterSpeckles( InputOutputArray img, double newVal, - int maxSpeckleSize, double maxDiff, - InputOutputArray buf = noArray() ); - -//! computes valid disparity ROI from the valid ROIs of the rectified images (that are returned by cv::stereoRectify()) -CV_EXPORTS_W Rect getValidDisparityROI( Rect roi1, Rect roi2, - int minDisparity, int numberOfDisparities, - int blockSize ); - -//! validates disparity using the left-right check. The matrix "cost" should be computed by the stereo correspondence algorithm -CV_EXPORTS_W void validateDisparity( InputOutputArray disparity, InputArray cost, - int minDisparity, int numberOfDisparities, - int disp12MaxDisp = 1 ); - -/** @brief Reprojects a disparity image to 3D space. - -@param disparity Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit -floating-point disparity image. The values of 8-bit / 16-bit signed formats are assumed to have no -fractional bits. If the disparity is 16-bit signed format, as computed by @ref StereoBM or -@ref StereoSGBM and maybe other algorithms, it should be divided by 16 (and scaled to float) before -being used here. -@param _3dImage Output 3-channel floating-point image of the same size as disparity. Each element of -_3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity map. If one -uses Q obtained by @ref stereoRectify, then the returned points are represented in the first -camera's rectified coordinate system. -@param Q \f$4 \times 4\f$ perspective transformation matrix that can be obtained with -@ref stereoRectify. -@param handleMissingValues Indicates, whether the function should handle missing values (i.e. -points where the disparity was not computed). If handleMissingValues=true, then pixels with the -minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed -to 3D points with a very large Z value (currently set to 10000). -@param ddepth The optional output array depth. If it is -1, the output image will have CV_32F -depth. ddepth can also be set to CV_16S, CV_32S or CV_32F. - -The function transforms a single-channel disparity map to a 3-channel image representing a 3D -surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it -computes: - -\f[\begin{bmatrix} -X \\ -Y \\ -Z \\ -W -\end{bmatrix} = Q \begin{bmatrix} -x \\ -y \\ -\texttt{disparity} (x,y) \\ -z -\end{bmatrix}.\f] - -@sa - To reproject a sparse set of points {(x,y,d),...} to 3D space, use perspectiveTransform. - */ -CV_EXPORTS_W void reprojectImageTo3D( InputArray disparity, - OutputArray _3dImage, InputArray Q, - bool handleMissingValues = false, - int ddepth = -1 ); - -/** @brief Calculates the Sampson Distance between two points. - -The function cv::sampsonDistance calculates and returns the first order approximation of the geometric error as: -\f[ -sd( \texttt{pt1} , \texttt{pt2} )= -\frac{(\texttt{pt2}^t \cdot \texttt{F} \cdot \texttt{pt1})^2} -{((\texttt{F} \cdot \texttt{pt1})(0))^2 + -((\texttt{F} \cdot \texttt{pt1})(1))^2 + -((\texttt{F}^t \cdot \texttt{pt2})(0))^2 + -((\texttt{F}^t \cdot \texttt{pt2})(1))^2} -\f] -The fundamental matrix may be calculated using the cv::findFundamentalMat function. See @cite HartleyZ00 11.4.3 for details. -@param pt1 first homogeneous 2d point -@param pt2 second homogeneous 2d point -@param F fundamental matrix -@return The computed Sampson distance. -*/ -CV_EXPORTS_W double sampsonDistance(InputArray pt1, InputArray pt2, InputArray F); - -/** @brief Computes an optimal affine transformation between two 3D point sets. - -It computes -\f[ -\begin{bmatrix} -x\\ -y\\ -z\\ -\end{bmatrix} -= -\begin{bmatrix} -a_{11} & a_{12} & a_{13}\\ -a_{21} & a_{22} & a_{23}\\ -a_{31} & a_{32} & a_{33}\\ -\end{bmatrix} -\begin{bmatrix} -X\\ -Y\\ -Z\\ -\end{bmatrix} -+ -\begin{bmatrix} -b_1\\ -b_2\\ -b_3\\ -\end{bmatrix} -\f] - -@param src First input 3D point set containing \f$(X,Y,Z)\f$. -@param dst Second input 3D point set containing \f$(x,y,z)\f$. -@param out Output 3D affine transformation matrix \f$3 \times 4\f$ of the form -\f[ -\begin{bmatrix} -a_{11} & a_{12} & a_{13} & b_1\\ -a_{21} & a_{22} & a_{23} & b_2\\ -a_{31} & a_{32} & a_{33} & b_3\\ -\end{bmatrix} -\f] -@param inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). -@param ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as -an inlier. -@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything -between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation -significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. - -The function estimates an optimal 3D affine transformation between two 3D point sets using the -RANSAC algorithm. - */ -CV_EXPORTS_W int estimateAffine3D(InputArray src, InputArray dst, - OutputArray out, OutputArray inliers, - double ransacThreshold = 3, double confidence = 0.99); - -/** @brief Computes an optimal affine transformation between two 2D point sets. - -It computes -\f[ -\begin{bmatrix} -x\\ -y\\ -\end{bmatrix} -= -\begin{bmatrix} -a_{11} & a_{12}\\ -a_{21} & a_{22}\\ -\end{bmatrix} -\begin{bmatrix} -X\\ -Y\\ -\end{bmatrix} -+ -\begin{bmatrix} -b_1\\ -b_2\\ -\end{bmatrix} -\f] - -@param from First input 2D point set containing \f$(X,Y)\f$. -@param to Second input 2D point set containing \f$(x,y)\f$. -@param inliers Output vector indicating which points are inliers (1-inlier, 0-outlier). -@param method Robust method used to compute transformation. The following methods are possible: -- @ref RANSAC - RANSAC-based robust method -- @ref LMEDS - Least-Median robust method -RANSAC is the default method. -@param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider -a point as an inlier. Applies only to RANSAC. -@param maxIters The maximum number of robust method iterations. -@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything -between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation -significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. -@param refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). -Passing 0 will disable refining, so the output matrix will be output of robust method. - -@return Output 2D affine transformation matrix \f$2 \times 3\f$ or empty matrix if transformation -could not be estimated. The returned matrix has the following form: -\f[ -\begin{bmatrix} -a_{11} & a_{12} & b_1\\ -a_{21} & a_{22} & b_2\\ -\end{bmatrix} -\f] - -The function estimates an optimal 2D affine transformation between two 2D point sets using the -selected robust algorithm. - -The computed transformation is then refined further (using only inliers) with the -Levenberg-Marquardt method to reduce the re-projection error even more. - -@note -The RANSAC method can handle practically any ratio of outliers but needs a threshold to -distinguish inliers from outliers. The method LMeDS does not need any threshold but it works -correctly only when there are more than 50% of inliers. - -@sa estimateAffinePartial2D, getAffineTransform -*/ -CV_EXPORTS_W cv::Mat estimateAffine2D(InputArray from, InputArray to, OutputArray inliers = noArray(), - int method = RANSAC, double ransacReprojThreshold = 3, - size_t maxIters = 2000, double confidence = 0.99, - size_t refineIters = 10); - -/** @brief Computes an optimal limited affine transformation with 4 degrees of freedom between -two 2D point sets. - -@param from First input 2D point set. -@param to Second input 2D point set. -@param inliers Output vector indicating which points are inliers. -@param method Robust method used to compute transformation. The following methods are possible: -- @ref RANSAC - RANSAC-based robust method -- @ref LMEDS - Least-Median robust method -RANSAC is the default method. -@param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider -a point as an inlier. Applies only to RANSAC. -@param maxIters The maximum number of robust method iterations. -@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything -between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation -significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation. -@param refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt). -Passing 0 will disable refining, so the output matrix will be output of robust method. - -@return Output 2D affine transformation (4 degrees of freedom) matrix \f$2 \times 3\f$ or -empty matrix if transformation could not be estimated. - -The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to -combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust -estimation. - -The computed transformation is then refined further (using only inliers) with the -Levenberg-Marquardt method to reduce the re-projection error even more. - -Estimated transformation matrix is: -\f[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ - \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y -\end{bmatrix} \f] -Where \f$ \theta \f$ is the rotation angle, \f$ s \f$ the scaling factor and \f$ t_x, t_y \f$ are -translations in \f$ x, y \f$ axes respectively. - -@note -The RANSAC method can handle practically any ratio of outliers but need a threshold to -distinguish inliers from outliers. The method LMeDS does not need any threshold but it works -correctly only when there are more than 50% of inliers. - -@sa estimateAffine2D, getAffineTransform -*/ -CV_EXPORTS_W cv::Mat estimateAffinePartial2D(InputArray from, InputArray to, OutputArray inliers = noArray(), - int method = RANSAC, double ransacReprojThreshold = 3, - size_t maxIters = 2000, double confidence = 0.99, - size_t refineIters = 10); - -/** @example samples/cpp/tutorial_code/features2D/Homography/decompose_homography.cpp -An example program with homography decomposition. - -Check @ref tutorial_homography "the corresponding tutorial" for more details. -*/ - -/** @brief Decompose a homography matrix to rotation(s), translation(s) and plane normal(s). - -@param H The input homography matrix between two images. -@param K The input camera intrinsic matrix. -@param rotations Array of rotation matrices. -@param translations Array of translation matrices. -@param normals Array of plane normal matrices. - -This function extracts relative camera motion between two views of a planar object and returns up to -four mathematical solution tuples of rotation, translation, and plane normal. The decomposition of -the homography matrix H is described in detail in @cite Malis. - -If the homography H, induced by the plane, gives the constraint -\f[s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\f] on the source image points -\f$p_i\f$ and the destination image points \f$p'_i\f$, then the tuple of rotations[k] and -translations[k] is a change of basis from the source camera's coordinate system to the destination -camera's coordinate system. However, by decomposing H, one can only get the translation normalized -by the (typically unknown) depth of the scene, i.e. its direction but with normalized length. - -If point correspondences are available, at least two solutions may further be invalidated, by -applying positive depth constraint, i.e. all points must be in front of the camera. - */ -CV_EXPORTS_W int decomposeHomographyMat(InputArray H, - InputArray K, - OutputArrayOfArrays rotations, - OutputArrayOfArrays translations, - OutputArrayOfArrays normals); - -/** @brief Filters homography decompositions based on additional information. - -@param rotations Vector of rotation matrices. -@param normals Vector of plane normal matrices. -@param beforePoints Vector of (rectified) visible reference points before the homography is applied -@param afterPoints Vector of (rectified) visible reference points after the homography is applied -@param possibleSolutions Vector of int indices representing the viable solution set after filtering -@param pointsMask optional Mat/Vector of 8u type representing the mask for the inliers as given by the findHomography function - -This function is intended to filter the output of the decomposeHomographyMat based on additional -information as described in @cite Malis . The summary of the method: the decomposeHomographyMat function -returns 2 unique solutions and their "opposites" for a total of 4 solutions. If we have access to the -sets of points visible in the camera frame before and after the homography transformation is applied, -we can determine which are the true potential solutions and which are the opposites by verifying which -homographies are consistent with all visible reference points being in front of the camera. The inputs -are left unchanged; the filtered solution set is returned as indices into the existing one. - -*/ -CV_EXPORTS_W void filterHomographyDecompByVisibleRefpoints(InputArrayOfArrays rotations, - InputArrayOfArrays normals, - InputArray beforePoints, - InputArray afterPoints, - OutputArray possibleSolutions, - InputArray pointsMask = noArray()); - -/** @brief The base class for stereo correspondence algorithms. - */ -class CV_EXPORTS_W StereoMatcher : public Algorithm -{ -public: - enum { DISP_SHIFT = 4, - DISP_SCALE = (1 << DISP_SHIFT) - }; - - /** @brief Computes disparity map for the specified stereo pair - - @param left Left 8-bit single-channel image. - @param right Right image of the same size and the same type as the left one. - @param disparity Output disparity map. It has the same size as the input images. Some algorithms, - like StereoBM or StereoSGBM compute 16-bit fixed-point disparity map (where each disparity value - has 4 fractional bits), whereas other algorithms output 32-bit floating-point disparity map. - */ - CV_WRAP virtual void compute( InputArray left, InputArray right, - OutputArray disparity ) = 0; - - CV_WRAP virtual int getMinDisparity() const = 0; - CV_WRAP virtual void setMinDisparity(int minDisparity) = 0; - - CV_WRAP virtual int getNumDisparities() const = 0; - CV_WRAP virtual void setNumDisparities(int numDisparities) = 0; - - CV_WRAP virtual int getBlockSize() const = 0; - CV_WRAP virtual void setBlockSize(int blockSize) = 0; - - CV_WRAP virtual int getSpeckleWindowSize() const = 0; - CV_WRAP virtual void setSpeckleWindowSize(int speckleWindowSize) = 0; - - CV_WRAP virtual int getSpeckleRange() const = 0; - CV_WRAP virtual void setSpeckleRange(int speckleRange) = 0; - - CV_WRAP virtual int getDisp12MaxDiff() const = 0; - CV_WRAP virtual void setDisp12MaxDiff(int disp12MaxDiff) = 0; -}; - - -/** @brief Class for computing stereo correspondence using the block matching algorithm, introduced and -contributed to OpenCV by K. Konolige. - */ -class CV_EXPORTS_W StereoBM : public StereoMatcher -{ -public: - enum { PREFILTER_NORMALIZED_RESPONSE = 0, - PREFILTER_XSOBEL = 1 - }; - - CV_WRAP virtual int getPreFilterType() const = 0; - CV_WRAP virtual void setPreFilterType(int preFilterType) = 0; - - CV_WRAP virtual int getPreFilterSize() const = 0; - CV_WRAP virtual void setPreFilterSize(int preFilterSize) = 0; - - CV_WRAP virtual int getPreFilterCap() const = 0; - CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0; - - CV_WRAP virtual int getTextureThreshold() const = 0; - CV_WRAP virtual void setTextureThreshold(int textureThreshold) = 0; - - CV_WRAP virtual int getUniquenessRatio() const = 0; - CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0; - - CV_WRAP virtual int getSmallerBlockSize() const = 0; - CV_WRAP virtual void setSmallerBlockSize(int blockSize) = 0; - - CV_WRAP virtual Rect getROI1() const = 0; - CV_WRAP virtual void setROI1(Rect roi1) = 0; - - CV_WRAP virtual Rect getROI2() const = 0; - CV_WRAP virtual void setROI2(Rect roi2) = 0; - - /** @brief Creates StereoBM object - - @param numDisparities the disparity search range. For each pixel algorithm will find the best - disparity from 0 (default minimum disparity) to numDisparities. The search range can then be - shifted by changing the minimum disparity. - @param blockSize the linear size of the blocks compared by the algorithm. The size should be odd - (as the block is centered at the current pixel). Larger block size implies smoother, though less - accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher - chance for algorithm to find a wrong correspondence. - - The function create StereoBM object. You can then call StereoBM::compute() to compute disparity for - a specific stereo pair. - */ - CV_WRAP static Ptr create(int numDisparities = 0, int blockSize = 21); -}; - -/** @brief The class implements the modified H. Hirschmuller algorithm @cite HH08 that differs from the original -one as follows: - -- By default, the algorithm is single-pass, which means that you consider only 5 directions -instead of 8. Set mode=StereoSGBM::MODE_HH in createStereoSGBM to run the full variant of the -algorithm but beware that it may consume a lot of memory. -- The algorithm matches blocks, not individual pixels. Though, setting blockSize=1 reduces the -blocks to single pixels. -- Mutual information cost function is not implemented. Instead, a simpler Birchfield-Tomasi -sub-pixel metric from @cite BT98 is used. Though, the color images are supported as well. -- Some pre- and post- processing steps from K. Konolige algorithm StereoBM are included, for -example: pre-filtering (StereoBM::PREFILTER_XSOBEL type) and post-filtering (uniqueness -check, quadratic interpolation and speckle filtering). - -@note - - (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found - at opencv_source_code/samples/python/stereo_match.py - */ -class CV_EXPORTS_W StereoSGBM : public StereoMatcher -{ -public: - enum - { - MODE_SGBM = 0, - MODE_HH = 1, - MODE_SGBM_3WAY = 2, - MODE_HH4 = 3 - }; - - CV_WRAP virtual int getPreFilterCap() const = 0; - CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0; - - CV_WRAP virtual int getUniquenessRatio() const = 0; - CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0; - - CV_WRAP virtual int getP1() const = 0; - CV_WRAP virtual void setP1(int P1) = 0; - - CV_WRAP virtual int getP2() const = 0; - CV_WRAP virtual void setP2(int P2) = 0; - - CV_WRAP virtual int getMode() const = 0; - CV_WRAP virtual void setMode(int mode) = 0; - - /** @brief Creates StereoSGBM object - - @param minDisparity Minimum possible disparity value. Normally, it is zero but sometimes - rectification algorithms can shift images, so this parameter needs to be adjusted accordingly. - @param numDisparities Maximum disparity minus minimum disparity. The value is always greater than - zero. In the current implementation, this parameter must be divisible by 16. - @param blockSize Matched block size. It must be an odd number \>=1 . Normally, it should be - somewhere in the 3..11 range. - @param P1 The first parameter controlling the disparity smoothness. See below. - @param P2 The second parameter controlling the disparity smoothness. The larger the values are, - the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1 - between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor - pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good - P1 and P2 values are shown (like 8\*number_of_image_channels\*blockSize\*blockSize and - 32\*number_of_image_channels\*blockSize\*blockSize , respectively). - @param disp12MaxDiff Maximum allowed difference (in integer pixel units) in the left-right - disparity check. Set it to a non-positive value to disable the check. - @param preFilterCap Truncation value for the prefiltered image pixels. The algorithm first - computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval. - The result values are passed to the Birchfield-Tomasi pixel cost function. - @param uniquenessRatio Margin in percentage by which the best (minimum) computed cost function - value should "win" the second best value to consider the found match correct. Normally, a value - within the 5-15 range is good enough. - @param speckleWindowSize Maximum size of smooth disparity regions to consider their noise speckles - and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the - 50-200 range. - @param speckleRange Maximum disparity variation within each connected component. If you do speckle - filtering, set the parameter to a positive value, it will be implicitly multiplied by 16. - Normally, 1 or 2 is good enough. - @param mode Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming - algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and - huge for HD-size pictures. By default, it is set to false . - - The first constructor initializes StereoSGBM with all the default parameters. So, you only have to - set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter - to a custom value. - */ - CV_WRAP static Ptr create(int minDisparity = 0, int numDisparities = 16, int blockSize = 3, - int P1 = 0, int P2 = 0, int disp12MaxDiff = 0, - int preFilterCap = 0, int uniquenessRatio = 0, - int speckleWindowSize = 0, int speckleRange = 0, - int mode = StereoSGBM::MODE_SGBM); -}; - -//! @} calib3d - -/** @brief The methods in this namespace use a so-called fisheye camera model. - @ingroup calib3d_fisheye -*/ -namespace fisheye -{ -//! @addtogroup calib3d_fisheye -//! @{ - - enum{ - CALIB_USE_INTRINSIC_GUESS = 1 << 0, - CALIB_RECOMPUTE_EXTRINSIC = 1 << 1, - CALIB_CHECK_COND = 1 << 2, - CALIB_FIX_SKEW = 1 << 3, - CALIB_FIX_K1 = 1 << 4, - CALIB_FIX_K2 = 1 << 5, - CALIB_FIX_K3 = 1 << 6, - CALIB_FIX_K4 = 1 << 7, - CALIB_FIX_INTRINSIC = 1 << 8, - CALIB_FIX_PRINCIPAL_POINT = 1 << 9, - CALIB_ZERO_DISPARITY = 1 << 10 - }; - - /** @brief Projects points using fisheye model - - @param objectPoints Array of object points, 1xN/Nx1 3-channel (or vector\ ), where N is - the number of points in the view. - @param imagePoints Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or - vector\. - @param affine - @param K Camera intrinsic matrix \f$cameramatrix{K}\f$. - @param D Input vector of distortion coefficients \f$\distcoeffsfisheye\f$. - @param alpha The skew coefficient. - @param jacobian Optional output 2Nx15 jacobian matrix of derivatives of image points with respect - to components of the focal lengths, coordinates of the principal point, distortion coefficients, - rotation vector, translation vector, and the skew. In the old interface different components of - the jacobian are returned via different output parameters. - - The function computes projections of 3D points to the image plane given intrinsic and extrinsic - camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of - image points coordinates (as functions of all the input parameters) with respect to the particular - parameters, intrinsic and/or extrinsic. - */ - CV_EXPORTS void projectPoints(InputArray objectPoints, OutputArray imagePoints, const Affine3d& affine, - InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray()); - - /** @overload */ - CV_EXPORTS_W void projectPoints(InputArray objectPoints, OutputArray imagePoints, InputArray rvec, InputArray tvec, - InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray()); - - /** @brief Distorts 2D points using fisheye model. - - @param undistorted Array of object points, 1xN/Nx1 2-channel (or vector\ ), where N is - the number of points in the view. - @param K Camera intrinsic matrix \f$cameramatrix{K}\f$. - @param D Input vector of distortion coefficients \f$\distcoeffsfisheye\f$. - @param alpha The skew coefficient. - @param distorted Output array of image points, 1xN/Nx1 2-channel, or vector\ . - - Note that the function assumes the camera intrinsic matrix of the undistorted points to be identity. - This means if you want to transform back points undistorted with undistortPoints() you have to - multiply them with \f$P^{-1}\f$. - */ - CV_EXPORTS_W void distortPoints(InputArray undistorted, OutputArray distorted, InputArray K, InputArray D, double alpha = 0); - - /** @brief Undistorts 2D points using fisheye model - - @param distorted Array of object points, 1xN/Nx1 2-channel (or vector\ ), where N is the - number of points in the view. - @param K Camera intrinsic matrix \f$cameramatrix{K}\f$. - @param D Input vector of distortion coefficients \f$\distcoeffsfisheye\f$. - @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 - 1-channel or 1x1 3-channel - @param P New camera intrinsic matrix (3x3) or new projection matrix (3x4) - @param undistorted Output array of image points, 1xN/Nx1 2-channel, or vector\ . - */ - CV_EXPORTS_W void undistortPoints(InputArray distorted, OutputArray undistorted, - InputArray K, InputArray D, InputArray R = noArray(), InputArray P = noArray()); - - /** @brief Computes undistortion and rectification maps for image transform by cv::remap(). If D is empty zero - distortion is used, if R or P is empty identity matrixes are used. - - @param K Camera intrinsic matrix \f$cameramatrix{K}\f$. - @param D Input vector of distortion coefficients \f$\distcoeffsfisheye\f$. - @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 - 1-channel or 1x1 3-channel - @param P New camera intrinsic matrix (3x3) or new projection matrix (3x4) - @param size Undistorted image size. - @param m1type Type of the first output map that can be CV_32FC1 or CV_16SC2 . See convertMaps() - for details. - @param map1 The first output map. - @param map2 The second output map. - */ - CV_EXPORTS_W void initUndistortRectifyMap(InputArray K, InputArray D, InputArray R, InputArray P, - const cv::Size& size, int m1type, OutputArray map1, OutputArray map2); - - /** @brief Transforms an image to compensate for fisheye lens distortion. - - @param distorted image with fisheye lens distortion. - @param undistorted Output image with compensated fisheye lens distortion. - @param K Camera intrinsic matrix \f$cameramatrix{K}\f$. - @param D Input vector of distortion coefficients \f$\distcoeffsfisheye\f$. - @param Knew Camera intrinsic matrix of the distorted image. By default, it is the identity matrix but you - may additionally scale and shift the result by using a different matrix. - @param new_size the new size - - The function transforms an image to compensate radial and tangential lens distortion. - - The function is simply a combination of fisheye::initUndistortRectifyMap (with unity R ) and remap - (with bilinear interpolation). See the former function for details of the transformation being - performed. - - See below the results of undistortImage. - - a\) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3, - k_4, k_5, k_6) of distortion were optimized under calibration) - - b\) result of fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2, - k_3, k_4) of fisheye distortion were optimized under calibration) - - c\) original image was captured with fisheye lens - - Pictures a) and b) almost the same. But if we consider points of image located far from the center - of image, we can notice that on image a) these points are distorted. - - ![image](pics/fisheye_undistorted.jpg) - */ - CV_EXPORTS_W void undistortImage(InputArray distorted, OutputArray undistorted, - InputArray K, InputArray D, InputArray Knew = cv::noArray(), const Size& new_size = Size()); - - /** @brief Estimates new camera intrinsic matrix for undistortion or rectification. - - @param K Camera intrinsic matrix \f$cameramatrix{K}\f$. - @param image_size Size of the image - @param D Input vector of distortion coefficients \f$\distcoeffsfisheye\f$. - @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 - 1-channel or 1x1 3-channel - @param P New camera intrinsic matrix (3x3) or new projection matrix (3x4) - @param balance Sets the new focal length in range between the min focal length and the max focal - length. Balance is in range of [0, 1]. - @param new_size the new size - @param fov_scale Divisor for new focal length. - */ - CV_EXPORTS_W void estimateNewCameraMatrixForUndistortRectify(InputArray K, InputArray D, const Size &image_size, InputArray R, - OutputArray P, double balance = 0.0, const Size& new_size = Size(), double fov_scale = 1.0); - - /** @brief Performs camera calibaration - - @param objectPoints vector of vectors of calibration pattern points in the calibration pattern - coordinate space. - @param imagePoints vector of vectors of the projections of calibration pattern points. - imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to - objectPoints[i].size() for each i. - @param image_size Size of the image used only to initialize the camera intrinsic matrix. - @param K Output 3x3 floating-point camera intrinsic matrix - \f$\cameramatrix{A}\f$ . If - @ref fisheye::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be - initialized before calling the function. - @param D Output vector of distortion coefficients \f$\distcoeffsfisheye\f$. - @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view. - That is, each k-th rotation vector together with the corresponding k-th translation vector (see - the next output parameter description) brings the calibration pattern from the model coordinate - space (in which object points are specified) to the world coordinate space, that is, a real - position of the calibration pattern in the k-th pattern view (k=0.. *M* -1). - @param tvecs Output vector of translation vectors estimated for each pattern view. - @param flags Different flags that may be zero or a combination of the following values: - - @ref fisheye::CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of - fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image - center ( imageSize is used), and focal distances are computed in a least-squares fashion. - - @ref fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration - of intrinsic optimization. - - @ref fisheye::CALIB_CHECK_COND The functions will check validity of condition number. - - @ref fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. - - @ref fisheye::CALIB_FIX_K1,..., @ref fisheye::CALIB_FIX_K4 Selected distortion coefficients - are set to zeros and stay zero. - - @ref fisheye::CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global -optimization. It stays at the center or at a different location specified when @ref fisheye::CALIB_USE_INTRINSIC_GUESS is set too. - @param criteria Termination criteria for the iterative optimization algorithm. - */ - CV_EXPORTS_W double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, const Size& image_size, - InputOutputArray K, InputOutputArray D, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0, - TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)); - - /** @brief Stereo rectification for fisheye camera model - - @param K1 First camera intrinsic matrix. - @param D1 First camera distortion parameters. - @param K2 Second camera intrinsic matrix. - @param D2 Second camera distortion parameters. - @param imageSize Size of the image used for stereo calibration. - @param R Rotation matrix between the coordinate systems of the first and the second - cameras. - @param tvec Translation vector between coordinate systems of the cameras. - @param R1 Output 3x3 rectification transform (rotation matrix) for the first camera. - @param R2 Output 3x3 rectification transform (rotation matrix) for the second camera. - @param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first - camera. - @param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second - camera. - @param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ). - @param flags Operation flags that may be zero or @ref fisheye::CALIB_ZERO_DISPARITY . If the flag is set, - the function makes the principal points of each camera have the same pixel coordinates in the - rectified views. And if the flag is not set, the function may still shift the images in the - horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the - useful image area. - @param newImageSize New image resolution after rectification. The same size should be passed to - initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) - is passed (default), it is set to the original imageSize . Setting it to larger value can help you - preserve details in the original image, especially when there is a big radial distortion. - @param balance Sets the new focal length in range between the min focal length and the max focal - length. Balance is in range of [0, 1]. - @param fov_scale Divisor for new focal length. - */ - CV_EXPORTS_W void stereoRectify(InputArray K1, InputArray D1, InputArray K2, InputArray D2, const Size &imageSize, InputArray R, InputArray tvec, - OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2, OutputArray Q, int flags, const Size &newImageSize = Size(), - double balance = 0.0, double fov_scale = 1.0); - - /** @brief Performs stereo calibration - - @param objectPoints Vector of vectors of the calibration pattern points. - @param imagePoints1 Vector of vectors of the projections of the calibration pattern points, - observed by the first camera. - @param imagePoints2 Vector of vectors of the projections of the calibration pattern points, - observed by the second camera. - @param K1 Input/output first camera intrinsic matrix: - \f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If - any of @ref fisheye::CALIB_USE_INTRINSIC_GUESS , @ref fisheye::CALIB_FIX_INTRINSIC are specified, - some or all of the matrix components must be initialized. - @param D1 Input/output vector of distortion coefficients \f$\distcoeffsfisheye\f$ of 4 elements. - @param K2 Input/output second camera intrinsic matrix. The parameter is similar to K1 . - @param D2 Input/output lens distortion coefficients for the second camera. The parameter is - similar to D1 . - @param imageSize Size of the image used only to initialize camera intrinsic matrix. - @param R Output rotation matrix between the 1st and the 2nd camera coordinate systems. - @param T Output translation vector between the coordinate systems of the cameras. - @param flags Different flags that may be zero or a combination of the following values: - - @ref fisheye::CALIB_FIX_INTRINSIC Fix K1, K2? and D1, D2? so that only R, T matrices - are estimated. - - @ref fisheye::CALIB_USE_INTRINSIC_GUESS K1, K2 contains valid initial values of - fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image - center (imageSize is used), and focal distances are computed in a least-squares fashion. - - @ref fisheye::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration - of intrinsic optimization. - - @ref fisheye::CALIB_CHECK_COND The functions will check validity of condition number. - - @ref fisheye::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero. - - @ref fisheye::CALIB_FIX_K1,..., @ref fisheye::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay - zero. - @param criteria Termination criteria for the iterative optimization algorithm. - */ - CV_EXPORTS_W double stereoCalibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, - InputOutputArray K1, InputOutputArray D1, InputOutputArray K2, InputOutputArray D2, Size imageSize, - OutputArray R, OutputArray T, int flags = fisheye::CALIB_FIX_INTRINSIC, - TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)); - -//! @} calib3d_fisheye -} // end namespace fisheye - -} //end namespace cv - -#ifndef DISABLE_OPENCV_24_COMPATIBILITY -#include "opencv2/calib3d/calib3d_c.h" -#endif - -#endif diff --git a/include/opencv2/calib3d/calib3d.hpp b/include/opencv2/calib3d/calib3d.hpp deleted file mode 100755 index b3da45e..0000000 --- a/include/opencv2/calib3d/calib3d.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/calib3d.hpp" diff --git a/include/opencv2/calib3d/calib3d_c.h b/include/opencv2/calib3d/calib3d_c.h deleted file mode 100755 index 8ec6390..0000000 --- a/include/opencv2/calib3d/calib3d_c.h +++ /dev/null @@ -1,427 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CALIB3D_C_H -#define OPENCV_CALIB3D_C_H - -#include "opencv2/core/core_c.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @addtogroup calib3d_c - @{ - */ - -/****************************************************************************************\ -* Camera Calibration, Pose Estimation and Stereo * -\****************************************************************************************/ - -typedef struct CvPOSITObject CvPOSITObject; - -/* Allocates and initializes CvPOSITObject structure before doing cvPOSIT */ -CVAPI(CvPOSITObject*) cvCreatePOSITObject( CvPoint3D32f* points, int point_count ); - - -/* Runs POSIT (POSe from ITeration) algorithm for determining 3d position of - an object given its model and projection in a weak-perspective case */ -CVAPI(void) cvPOSIT( CvPOSITObject* posit_object, CvPoint2D32f* image_points, - double focal_length, CvTermCriteria criteria, - float* rotation_matrix, float* translation_vector); - -/* Releases CvPOSITObject structure */ -CVAPI(void) cvReleasePOSITObject( CvPOSITObject** posit_object ); - -/* updates the number of RANSAC iterations */ -CVAPI(int) cvRANSACUpdateNumIters( double p, double err_prob, - int model_points, int max_iters ); - -CVAPI(void) cvConvertPointsHomogeneous( const CvMat* src, CvMat* dst ); - -/* Calculates fundamental matrix given a set of corresponding points */ -#define CV_FM_7POINT 1 -#define CV_FM_8POINT 2 - -#define CV_LMEDS 4 -#define CV_RANSAC 8 - -#define CV_FM_LMEDS_ONLY CV_LMEDS -#define CV_FM_RANSAC_ONLY CV_RANSAC -#define CV_FM_LMEDS CV_LMEDS -#define CV_FM_RANSAC CV_RANSAC - -enum -{ - CV_ITERATIVE = 0, - CV_EPNP = 1, // F.Moreno-Noguer, V.Lepetit and P.Fua "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" - CV_P3P = 2, // X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang; "Complete Solution Classification for the Perspective-Three-Point Problem" - CV_DLS = 3 // Joel A. Hesch and Stergios I. Roumeliotis. "A Direct Least-Squares (DLS) Method for PnP" -}; - -CVAPI(int) cvFindFundamentalMat( const CvMat* points1, const CvMat* points2, - CvMat* fundamental_matrix, - int method CV_DEFAULT(CV_FM_RANSAC), - double param1 CV_DEFAULT(3.), double param2 CV_DEFAULT(0.99), - CvMat* status CV_DEFAULT(NULL) ); - -/* For each input point on one of images - computes parameters of the corresponding - epipolar line on the other image */ -CVAPI(void) cvComputeCorrespondEpilines( const CvMat* points, - int which_image, - const CvMat* fundamental_matrix, - CvMat* correspondent_lines ); - -/* Triangulation functions */ - -CVAPI(void) cvTriangulatePoints(CvMat* projMatr1, CvMat* projMatr2, - CvMat* projPoints1, CvMat* projPoints2, - CvMat* points4D); - -CVAPI(void) cvCorrectMatches(CvMat* F, CvMat* points1, CvMat* points2, - CvMat* new_points1, CvMat* new_points2); - - -/* Computes the optimal new camera matrix according to the free scaling parameter alpha: - alpha=0 - only valid pixels will be retained in the undistorted image - alpha=1 - all the source image pixels will be retained in the undistorted image -*/ -CVAPI(void) cvGetOptimalNewCameraMatrix( const CvMat* camera_matrix, - const CvMat* dist_coeffs, - CvSize image_size, double alpha, - CvMat* new_camera_matrix, - CvSize new_imag_size CV_DEFAULT(cvSize(0,0)), - CvRect* valid_pixel_ROI CV_DEFAULT(0), - int center_principal_point CV_DEFAULT(0)); - -/* Converts rotation vector to rotation matrix or vice versa */ -CVAPI(int) cvRodrigues2( const CvMat* src, CvMat* dst, - CvMat* jacobian CV_DEFAULT(0) ); - -/* Finds perspective transformation between the object plane and image (view) plane */ -CVAPI(int) cvFindHomography( const CvMat* src_points, - const CvMat* dst_points, - CvMat* homography, - int method CV_DEFAULT(0), - double ransacReprojThreshold CV_DEFAULT(3), - CvMat* mask CV_DEFAULT(0), - int maxIters CV_DEFAULT(2000), - double confidence CV_DEFAULT(0.995)); - -/* Computes RQ decomposition for 3x3 matrices */ -CVAPI(void) cvRQDecomp3x3( const CvMat *matrixM, CvMat *matrixR, CvMat *matrixQ, - CvMat *matrixQx CV_DEFAULT(NULL), - CvMat *matrixQy CV_DEFAULT(NULL), - CvMat *matrixQz CV_DEFAULT(NULL), - CvPoint3D64f *eulerAngles CV_DEFAULT(NULL)); - -/* Computes projection matrix decomposition */ -CVAPI(void) cvDecomposeProjectionMatrix( const CvMat *projMatr, CvMat *calibMatr, - CvMat *rotMatr, CvMat *posVect, - CvMat *rotMatrX CV_DEFAULT(NULL), - CvMat *rotMatrY CV_DEFAULT(NULL), - CvMat *rotMatrZ CV_DEFAULT(NULL), - CvPoint3D64f *eulerAngles CV_DEFAULT(NULL)); - -/* Computes d(AB)/dA and d(AB)/dB */ -CVAPI(void) cvCalcMatMulDeriv( const CvMat* A, const CvMat* B, CvMat* dABdA, CvMat* dABdB ); - -/* Computes r3 = rodrigues(rodrigues(r2)*rodrigues(r1)), - t3 = rodrigues(r2)*t1 + t2 and the respective derivatives */ -CVAPI(void) cvComposeRT( const CvMat* _rvec1, const CvMat* _tvec1, - const CvMat* _rvec2, const CvMat* _tvec2, - CvMat* _rvec3, CvMat* _tvec3, - CvMat* dr3dr1 CV_DEFAULT(0), CvMat* dr3dt1 CV_DEFAULT(0), - CvMat* dr3dr2 CV_DEFAULT(0), CvMat* dr3dt2 CV_DEFAULT(0), - CvMat* dt3dr1 CV_DEFAULT(0), CvMat* dt3dt1 CV_DEFAULT(0), - CvMat* dt3dr2 CV_DEFAULT(0), CvMat* dt3dt2 CV_DEFAULT(0) ); - -/* Projects object points to the view plane using - the specified extrinsic and intrinsic camera parameters */ -CVAPI(void) cvProjectPoints2( const CvMat* object_points, const CvMat* rotation_vector, - const CvMat* translation_vector, const CvMat* camera_matrix, - const CvMat* distortion_coeffs, CvMat* image_points, - CvMat* dpdrot CV_DEFAULT(NULL), CvMat* dpdt CV_DEFAULT(NULL), - CvMat* dpdf CV_DEFAULT(NULL), CvMat* dpdc CV_DEFAULT(NULL), - CvMat* dpddist CV_DEFAULT(NULL), - double aspect_ratio CV_DEFAULT(0)); - -/* Finds extrinsic camera parameters from - a few known corresponding point pairs and intrinsic parameters */ -CVAPI(void) cvFindExtrinsicCameraParams2( const CvMat* object_points, - const CvMat* image_points, - const CvMat* camera_matrix, - const CvMat* distortion_coeffs, - CvMat* rotation_vector, - CvMat* translation_vector, - int use_extrinsic_guess CV_DEFAULT(0) ); - -/* Computes initial estimate of the intrinsic camera parameters - in case of planar calibration target (e.g. chessboard) */ -CVAPI(void) cvInitIntrinsicParams2D( const CvMat* object_points, - const CvMat* image_points, - const CvMat* npoints, CvSize image_size, - CvMat* camera_matrix, - double aspect_ratio CV_DEFAULT(1.) ); - -#define CV_CALIB_CB_ADAPTIVE_THRESH 1 -#define CV_CALIB_CB_NORMALIZE_IMAGE 2 -#define CV_CALIB_CB_FILTER_QUADS 4 -#define CV_CALIB_CB_FAST_CHECK 8 - -// Performs a fast check if a chessboard is in the input image. This is a workaround to -// a problem of cvFindChessboardCorners being slow on images with no chessboard -// - src: input image -// - size: chessboard size -// Returns 1 if a chessboard can be in this image and findChessboardCorners should be called, -// 0 if there is no chessboard, -1 in case of error -CVAPI(int) cvCheckChessboard(IplImage* src, CvSize size); - - /* Detects corners on a chessboard calibration pattern */ -CVAPI(int) cvFindChessboardCorners( const void* image, CvSize pattern_size, - CvPoint2D32f* corners, - int* corner_count CV_DEFAULT(NULL), - int flags CV_DEFAULT(CV_CALIB_CB_ADAPTIVE_THRESH+CV_CALIB_CB_NORMALIZE_IMAGE) ); - -/* Draws individual chessboard corners or the whole chessboard detected */ -CVAPI(void) cvDrawChessboardCorners( CvArr* image, CvSize pattern_size, - CvPoint2D32f* corners, - int count, int pattern_was_found ); - -#define CV_CALIB_USE_INTRINSIC_GUESS 1 -#define CV_CALIB_FIX_ASPECT_RATIO 2 -#define CV_CALIB_FIX_PRINCIPAL_POINT 4 -#define CV_CALIB_ZERO_TANGENT_DIST 8 -#define CV_CALIB_FIX_FOCAL_LENGTH 16 -#define CV_CALIB_FIX_K1 32 -#define CV_CALIB_FIX_K2 64 -#define CV_CALIB_FIX_K3 128 -#define CV_CALIB_FIX_K4 2048 -#define CV_CALIB_FIX_K5 4096 -#define CV_CALIB_FIX_K6 8192 -#define CV_CALIB_RATIONAL_MODEL 16384 -#define CV_CALIB_THIN_PRISM_MODEL 32768 -#define CV_CALIB_FIX_S1_S2_S3_S4 65536 -#define CV_CALIB_TILTED_MODEL 262144 -#define CV_CALIB_FIX_TAUX_TAUY 524288 -#define CV_CALIB_FIX_TANGENT_DIST 2097152 - -#define CV_CALIB_NINTRINSIC 18 - -/* Finds intrinsic and extrinsic camera parameters - from a few views of known calibration pattern */ -CVAPI(double) cvCalibrateCamera2( const CvMat* object_points, - const CvMat* image_points, - const CvMat* point_counts, - CvSize image_size, - CvMat* camera_matrix, - CvMat* distortion_coeffs, - CvMat* rotation_vectors CV_DEFAULT(NULL), - CvMat* translation_vectors CV_DEFAULT(NULL), - int flags CV_DEFAULT(0), - CvTermCriteria term_crit CV_DEFAULT(cvTermCriteria( - CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,DBL_EPSILON)) ); - -/* Computes various useful characteristics of the camera from the data computed by - cvCalibrateCamera2 */ -CVAPI(void) cvCalibrationMatrixValues( const CvMat *camera_matrix, - CvSize image_size, - double aperture_width CV_DEFAULT(0), - double aperture_height CV_DEFAULT(0), - double *fovx CV_DEFAULT(NULL), - double *fovy CV_DEFAULT(NULL), - double *focal_length CV_DEFAULT(NULL), - CvPoint2D64f *principal_point CV_DEFAULT(NULL), - double *pixel_aspect_ratio CV_DEFAULT(NULL)); - -#define CV_CALIB_FIX_INTRINSIC 256 -#define CV_CALIB_SAME_FOCAL_LENGTH 512 - -/* Computes the transformation from one camera coordinate system to another one - from a few correspondent views of the same calibration target. Optionally, calibrates - both cameras */ -CVAPI(double) cvStereoCalibrate( const CvMat* object_points, const CvMat* image_points1, - const CvMat* image_points2, const CvMat* npoints, - CvMat* camera_matrix1, CvMat* dist_coeffs1, - CvMat* camera_matrix2, CvMat* dist_coeffs2, - CvSize image_size, CvMat* R, CvMat* T, - CvMat* E CV_DEFAULT(0), CvMat* F CV_DEFAULT(0), - int flags CV_DEFAULT(CV_CALIB_FIX_INTRINSIC), - CvTermCriteria term_crit CV_DEFAULT(cvTermCriteria( - CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,1e-6)) ); - -#define CV_CALIB_ZERO_DISPARITY 1024 - -/* Computes 3D rotations (+ optional shift) for each camera coordinate system to make both - views parallel (=> to make all the epipolar lines horizontal or vertical) */ -CVAPI(void) cvStereoRectify( const CvMat* camera_matrix1, const CvMat* camera_matrix2, - const CvMat* dist_coeffs1, const CvMat* dist_coeffs2, - CvSize image_size, const CvMat* R, const CvMat* T, - CvMat* R1, CvMat* R2, CvMat* P1, CvMat* P2, - CvMat* Q CV_DEFAULT(0), - int flags CV_DEFAULT(CV_CALIB_ZERO_DISPARITY), - double alpha CV_DEFAULT(-1), - CvSize new_image_size CV_DEFAULT(cvSize(0,0)), - CvRect* valid_pix_ROI1 CV_DEFAULT(0), - CvRect* valid_pix_ROI2 CV_DEFAULT(0)); - -/* Computes rectification transformations for uncalibrated pair of images using a set - of point correspondences */ -CVAPI(int) cvStereoRectifyUncalibrated( const CvMat* points1, const CvMat* points2, - const CvMat* F, CvSize img_size, - CvMat* H1, CvMat* H2, - double threshold CV_DEFAULT(5)); - - - -/* stereo correspondence parameters and functions */ - -#define CV_STEREO_BM_NORMALIZED_RESPONSE 0 -#define CV_STEREO_BM_XSOBEL 1 - -/* Block matching algorithm structure */ -typedef struct CvStereoBMState -{ - // pre-filtering (normalization of input images) - int preFilterType; // =CV_STEREO_BM_NORMALIZED_RESPONSE now - int preFilterSize; // averaging window size: ~5x5..21x21 - int preFilterCap; // the output of pre-filtering is clipped by [-preFilterCap,preFilterCap] - - // correspondence using Sum of Absolute Difference (SAD) - int SADWindowSize; // ~5x5..21x21 - int minDisparity; // minimum disparity (can be negative) - int numberOfDisparities; // maximum disparity - minimum disparity (> 0) - - // post-filtering - int textureThreshold; // the disparity is only computed for pixels - // with textured enough neighborhood - int uniquenessRatio; // accept the computed disparity d* only if - // SAD(d) >= SAD(d*)*(1 + uniquenessRatio/100.) - // for any d != d*+/-1 within the search range. - int speckleWindowSize; // disparity variation window - int speckleRange; // acceptable range of variation in window - - int trySmallerWindows; // if 1, the results may be more accurate, - // at the expense of slower processing - CvRect roi1, roi2; - int disp12MaxDiff; - - // temporary buffers - CvMat* preFilteredImg0; - CvMat* preFilteredImg1; - CvMat* slidingSumBuf; - CvMat* cost; - CvMat* disp; -} CvStereoBMState; - -#define CV_STEREO_BM_BASIC 0 -#define CV_STEREO_BM_FISH_EYE 1 -#define CV_STEREO_BM_NARROW 2 - -CVAPI(CvStereoBMState*) cvCreateStereoBMState(int preset CV_DEFAULT(CV_STEREO_BM_BASIC), - int numberOfDisparities CV_DEFAULT(0)); - -CVAPI(void) cvReleaseStereoBMState( CvStereoBMState** state ); - -CVAPI(void) cvFindStereoCorrespondenceBM( const CvArr* left, const CvArr* right, - CvArr* disparity, CvStereoBMState* state ); - -CVAPI(CvRect) cvGetValidDisparityROI( CvRect roi1, CvRect roi2, int minDisparity, - int numberOfDisparities, int SADWindowSize ); - -CVAPI(void) cvValidateDisparity( CvArr* disparity, const CvArr* cost, - int minDisparity, int numberOfDisparities, - int disp12MaxDiff CV_DEFAULT(1) ); - -/* Reprojects the computed disparity image to the 3D space using the specified 4x4 matrix */ -CVAPI(void) cvReprojectImageTo3D( const CvArr* disparityImage, - CvArr* _3dImage, const CvMat* Q, - int handleMissingValues CV_DEFAULT(0) ); - -/** @} calib3d_c */ - -#ifdef __cplusplus -} // extern "C" - -////////////////////////////////////////////////////////////////////////////////////////// -class CV_EXPORTS CvLevMarq -{ -public: - CvLevMarq(); - CvLevMarq( int nparams, int nerrs, CvTermCriteria criteria= - cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON), - bool completeSymmFlag=false ); - ~CvLevMarq(); - void init( int nparams, int nerrs, CvTermCriteria criteria= - cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON), - bool completeSymmFlag=false ); - bool update( const CvMat*& param, CvMat*& J, CvMat*& err ); - bool updateAlt( const CvMat*& param, CvMat*& JtJ, CvMat*& JtErr, double*& errNorm ); - - void clear(); - void step(); - enum { DONE=0, STARTED=1, CALC_J=2, CHECK_ERR=3 }; - - cv::Ptr mask; - cv::Ptr prevParam; - cv::Ptr param; - cv::Ptr J; - cv::Ptr err; - cv::Ptr JtJ; - cv::Ptr JtJN; - cv::Ptr JtErr; - cv::Ptr JtJV; - cv::Ptr JtJW; - double prevErrNorm, errNorm; - int lambdaLg10; - CvTermCriteria criteria; - int state; - int iters; - bool completeSymmFlag; - int solveMethod; -}; - -#endif - -#endif /* OPENCV_CALIB3D_C_H */ diff --git a/include/opencv2/core.hpp b/include/opencv2/core.hpp deleted file mode 100755 index fc2432d..0000000 --- a/include/opencv2/core.hpp +++ /dev/null @@ -1,3299 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2015, Intel Corporation, all rights reserved. -// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. -// Copyright (C) 2015, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_HPP -#define OPENCV_CORE_HPP - -#ifndef __cplusplus -# error core.hpp header must be compiled as C++ -#endif - -#include "opencv2/core/cvdef.h" -#include "opencv2/core/version.hpp" -#include "opencv2/core/base.hpp" -#include "opencv2/core/cvstd.hpp" -#include "opencv2/core/traits.hpp" -#include "opencv2/core/matx.hpp" -#include "opencv2/core/types.hpp" -#include "opencv2/core/mat.hpp" -#include "opencv2/core/persistence.hpp" - -/** -@defgroup core Core functionality -@{ - @defgroup core_basic Basic structures - @defgroup core_c C structures and operations - @{ - @defgroup core_c_glue Connections with C++ - @} - @defgroup core_array Operations on arrays - @defgroup core_async Asynchronous API - @defgroup core_xml XML/YAML Persistence - @defgroup core_cluster Clustering - @defgroup core_utils Utility and system functions and macros - @{ - @defgroup core_logging Logging facilities - @defgroup core_utils_sse SSE utilities - @defgroup core_utils_neon NEON utilities - @defgroup core_utils_vsx VSX utilities - @defgroup core_utils_softfloat Softfloat support - @defgroup core_utils_samples Utility functions for OpenCV samples - @} - @defgroup core_opengl OpenGL interoperability - @defgroup core_ipp Intel IPP Asynchronous C/C++ Converters - @defgroup core_optim Optimization Algorithms - @defgroup core_directx DirectX interoperability - @defgroup core_eigen Eigen support - @defgroup core_opencl OpenCL support - @defgroup core_va_intel Intel VA-API/OpenCL (CL-VA) interoperability - @defgroup core_hal Hardware Acceleration Layer - @{ - @defgroup core_hal_functions Functions - @defgroup core_hal_interface Interface - @defgroup core_hal_intrin Universal intrinsics - @{ - @defgroup core_hal_intrin_impl Private implementation helpers - @} - @} -@} - */ - -namespace cv { - -//! @addtogroup core_utils -//! @{ - -/*! @brief Class passed to an error. - -This class encapsulates all or almost all necessary -information about the error happened in the program. The exception is -usually constructed and thrown implicitly via CV_Error and CV_Error_ macros. -@see error - */ -class CV_EXPORTS Exception : public std::exception -{ -public: - /*! - Default constructor - */ - Exception(); - /*! - Full constructor. Normally the constructor is not called explicitly. - Instead, the macros CV_Error(), CV_Error_() and CV_Assert() are used. - */ - Exception(int _code, const String& _err, const String& _func, const String& _file, int _line); - virtual ~Exception() throw(); - - /*! - \return the error description and the context as a text string. - */ - virtual const char *what() const throw() CV_OVERRIDE; - void formatMessage(); - - String msg; ///< the formatted error message - - int code; ///< error code @see CVStatus - String err; ///< error description - String func; ///< function name. Available only when the compiler supports getting it - String file; ///< source file name where the error has occurred - int line; ///< line number in the source file where the error has occurred -}; - -/*! @brief Signals an error and raises the exception. - -By default the function prints information about the error to stderr, -then it either stops if cv::setBreakOnError() had been called before or raises the exception. -It is possible to alternate error processing by using #redirectError(). -@param exc the exception raisen. -@deprecated drop this version - */ -CV_EXPORTS void error( const Exception& exc ); - -enum SortFlags { SORT_EVERY_ROW = 0, //!< each matrix row is sorted independently - SORT_EVERY_COLUMN = 1, //!< each matrix column is sorted - //!< independently; this flag and the previous one are - //!< mutually exclusive. - SORT_ASCENDING = 0, //!< each matrix row is sorted in the ascending - //!< order. - SORT_DESCENDING = 16 //!< each matrix row is sorted in the - //!< descending order; this flag and the previous one are also - //!< mutually exclusive. - }; - -//! @} core_utils - -//! @addtogroup core -//! @{ - -//! Covariation flags -enum CovarFlags { - /** The output covariance matrix is calculated as: - \f[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...],\f] - The covariance matrix will be nsamples x nsamples. Such an unusual covariance matrix is used - for fast PCA of a set of very large vectors (see, for example, the EigenFaces technique for - face recognition). Eigenvalues of this "scrambled" matrix match the eigenvalues of the true - covariance matrix. The "true" eigenvectors can be easily calculated from the eigenvectors of - the "scrambled" covariance matrix. */ - COVAR_SCRAMBLED = 0, - /**The output covariance matrix is calculated as: - \f[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...] \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T,\f] - covar will be a square matrix of the same size as the total number of elements in each input - vector. One and only one of #COVAR_SCRAMBLED and #COVAR_NORMAL must be specified.*/ - COVAR_NORMAL = 1, - /** If the flag is specified, the function does not calculate mean from - the input vectors but, instead, uses the passed mean vector. This is useful if mean has been - pre-calculated or known in advance, or if the covariance matrix is calculated by parts. In - this case, mean is not a mean vector of the input sub-set of vectors but rather the mean - vector of the whole set.*/ - COVAR_USE_AVG = 2, - /** If the flag is specified, the covariance matrix is scaled. In the - "normal" mode, scale is 1./nsamples . In the "scrambled" mode, scale is the reciprocal of the - total number of elements in each input vector. By default (if the flag is not specified), the - covariance matrix is not scaled ( scale=1 ).*/ - COVAR_SCALE = 4, - /** If the flag is - specified, all the input vectors are stored as rows of the samples matrix. mean should be a - single-row vector in this case.*/ - COVAR_ROWS = 8, - /** If the flag is - specified, all the input vectors are stored as columns of the samples matrix. mean should be a - single-column vector in this case.*/ - COVAR_COLS = 16 -}; - -//! @addtogroup core_cluster -//! @{ - -//! k-Means flags -enum KmeansFlags { - /** Select random initial centers in each attempt.*/ - KMEANS_RANDOM_CENTERS = 0, - /** Use kmeans++ center initialization by Arthur and Vassilvitskii [Arthur2007].*/ - KMEANS_PP_CENTERS = 2, - /** During the first (and possibly the only) attempt, use the - user-supplied labels instead of computing them from the initial centers. For the second and - further attempts, use the random or semi-random centers. Use one of KMEANS_\*_CENTERS flag - to specify the exact method.*/ - KMEANS_USE_INITIAL_LABELS = 1 -}; - -//! @} core_cluster - -//! type of line -enum LineTypes { - FILLED = -1, - LINE_4 = 4, //!< 4-connected line - LINE_8 = 8, //!< 8-connected line - LINE_AA = 16 //!< antialiased line -}; - -//! Only a subset of Hershey fonts are supported -enum HersheyFonts { - FONT_HERSHEY_SIMPLEX = 0, //!< normal size sans-serif font - FONT_HERSHEY_PLAIN = 1, //!< small size sans-serif font - FONT_HERSHEY_DUPLEX = 2, //!< normal size sans-serif font (more complex than FONT_HERSHEY_SIMPLEX) - FONT_HERSHEY_COMPLEX = 3, //!< normal size serif font - FONT_HERSHEY_TRIPLEX = 4, //!< normal size serif font (more complex than FONT_HERSHEY_COMPLEX) - FONT_HERSHEY_COMPLEX_SMALL = 5, //!< smaller version of FONT_HERSHEY_COMPLEX - FONT_HERSHEY_SCRIPT_SIMPLEX = 6, //!< hand-writing style font - FONT_HERSHEY_SCRIPT_COMPLEX = 7, //!< more complex variant of FONT_HERSHEY_SCRIPT_SIMPLEX - FONT_ITALIC = 16 //!< flag for italic font -}; - -//! @addtogroup core_array -//! @{ - -enum ReduceTypes { REDUCE_SUM = 0, //!< the output is the sum of all rows/columns of the matrix. - REDUCE_AVG = 1, //!< the output is the mean vector of all rows/columns of the matrix. - REDUCE_MAX = 2, //!< the output is the maximum (column/row-wise) of all rows/columns of the matrix. - REDUCE_MIN = 3 //!< the output is the minimum (column/row-wise) of all rows/columns of the matrix. - }; - -//! @} core_array - -/** @brief Swaps two matrices -*/ -CV_EXPORTS void swap(Mat& a, Mat& b); -/** @overload */ -CV_EXPORTS void swap( UMat& a, UMat& b ); - -//! @} core - -//! @addtogroup core_array -//! @{ - -/** @brief Computes the source location of an extrapolated pixel. - -The function computes and returns the coordinate of a donor pixel corresponding to the specified -extrapolated pixel when using the specified extrapolation border mode. For example, if you use -cv::BORDER_WRAP mode in the horizontal direction, cv::BORDER_REFLECT_101 in the vertical direction and -want to compute value of the "virtual" pixel Point(-5, 100) in a floating-point image img , it -looks like: -@code{.cpp} - float val = img.at(borderInterpolate(100, img.rows, cv::BORDER_REFLECT_101), - borderInterpolate(-5, img.cols, cv::BORDER_WRAP)); -@endcode -Normally, the function is not called directly. It is used inside filtering functions and also in -copyMakeBorder. -@param p 0-based coordinate of the extrapolated pixel along one of the axes, likely \<0 or \>= len -@param len Length of the array along the corresponding axis. -@param borderType Border type, one of the #BorderTypes, except for #BORDER_TRANSPARENT and -#BORDER_ISOLATED . When borderType==#BORDER_CONSTANT , the function always returns -1, regardless -of p and len. - -@sa copyMakeBorder -*/ -CV_EXPORTS_W int borderInterpolate(int p, int len, int borderType); - -/** @example samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp -An example using copyMakeBorder function. -Check @ref tutorial_copyMakeBorder "the corresponding tutorial" for more details -*/ - -/** @brief Forms a border around an image. - -The function copies the source image into the middle of the destination image. The areas to the -left, to the right, above and below the copied source image will be filled with extrapolated -pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but -what other more complex functions, including your own, may do to simplify image boundary handling. - -The function supports the mode when src is already in the middle of dst . In this case, the -function does not copy src itself but simply constructs the border, for example: - -@code{.cpp} - // let border be the same in all directions - int border=2; - // constructs a larger image to fit both the image and the border - Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth()); - // select the middle part of it w/o copying data - Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows)); - // convert image from RGB to grayscale - cvtColor(rgb, gray, COLOR_RGB2GRAY); - // form a border in-place - copyMakeBorder(gray, gray_buf, border, border, - border, border, BORDER_REPLICATE); - // now do some custom filtering ... - ... -@endcode -@note When the source image is a part (ROI) of a bigger image, the function will try to use the -pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as -if src was not a ROI, use borderType | #BORDER_ISOLATED. - -@param src Source image. -@param dst Destination image of the same type as src and the size Size(src.cols+left+right, -src.rows+top+bottom) . -@param top the top pixels -@param bottom the bottom pixels -@param left the left pixels -@param right Parameter specifying how many pixels in each direction from the source image rectangle -to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs -to be built. -@param borderType Border type. See borderInterpolate for details. -@param value Border value if borderType==BORDER_CONSTANT . - -@sa borderInterpolate -*/ -CV_EXPORTS_W void copyMakeBorder(InputArray src, OutputArray dst, - int top, int bottom, int left, int right, - int borderType, const Scalar& value = Scalar() ); - -/** @brief Calculates the per-element sum of two arrays or an array and a scalar. - -The function add calculates: -- Sum of two arrays when both input arrays have the same size and the same number of channels: -\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f] -- Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of -elements as `src1.channels()`: -\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f] -- Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of -elements as `src2.channels()`: -\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} + \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f] -where `I` is a multi-dimensional index of array elements. In case of multi-channel arrays, each -channel is processed independently. - -The first function in the list above can be replaced with matrix expressions: -@code{.cpp} - dst = src1 + src2; - dst += src1; // equivalent to add(dst, src1, dst); -@endcode -The input arrays and the output array can all have the same or different depths. For example, you -can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit -floating-point array. Depth of the output array is determined by the dtype parameter. In the second -and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can -be set to the default -1. In this case, the output array will have the same depth as the input -array, be it src1, src2 or both. -@note Saturation is not applied when the output array has the depth CV_32S. You may even get -result of an incorrect sign in the case of overflow. -@param src1 first input array or a scalar. -@param src2 second input array or a scalar. -@param dst output array that has the same size and number of channels as the input array(s); the -depth is defined by dtype or src1/src2. -@param mask optional operation mask - 8-bit single channel array, that specifies elements of the -output array to be changed. -@param dtype optional depth of the output array (see the discussion below). -@sa subtract, addWeighted, scaleAdd, Mat::convertTo -*/ -CV_EXPORTS_W void add(InputArray src1, InputArray src2, OutputArray dst, - InputArray mask = noArray(), int dtype = -1); - -/** @brief Calculates the per-element difference between two arrays or array and a scalar. - -The function subtract calculates: -- Difference between two arrays, when both input arrays have the same size and the same number of -channels: - \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f] -- Difference between an array and a scalar, when src2 is constructed from Scalar or has the same -number of elements as `src1.channels()`: - \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f] -- Difference between a scalar and an array, when src1 is constructed from Scalar or has the same -number of elements as `src2.channels()`: - \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} - \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f] -- The reverse difference between a scalar and an array in the case of `SubRS`: - \f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src2} - \texttt{src1}(I) ) \quad \texttt{if mask}(I) \ne0\f] -where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each -channel is processed independently. - -The first function in the list above can be replaced with matrix expressions: -@code{.cpp} - dst = src1 - src2; - dst -= src1; // equivalent to subtract(dst, src1, dst); -@endcode -The input arrays and the output array can all have the same or different depths. For example, you -can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of -the output array is determined by dtype parameter. In the second and third cases above, as well as -in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this -case the output array will have the same depth as the input array, be it src1, src2 or both. -@note Saturation is not applied when the output array has the depth CV_32S. You may even get -result of an incorrect sign in the case of overflow. -@param src1 first input array or a scalar. -@param src2 second input array or a scalar. -@param dst output array of the same size and the same number of channels as the input array. -@param mask optional operation mask; this is an 8-bit single channel array that specifies elements -of the output array to be changed. -@param dtype optional depth of the output array -@sa add, addWeighted, scaleAdd, Mat::convertTo - */ -CV_EXPORTS_W void subtract(InputArray src1, InputArray src2, OutputArray dst, - InputArray mask = noArray(), int dtype = -1); - - -/** @brief Calculates the per-element scaled product of two arrays. - -The function multiply calculates the per-element product of two arrays: - -\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{scale} \cdot \texttt{src1} (I) \cdot \texttt{src2} (I))\f] - -There is also a @ref MatrixExpressions -friendly variant of the first function. See Mat::mul . - -For a not-per-element matrix product, see gemm . - -@note Saturation is not applied when the output array has the depth -CV_32S. You may even get result of an incorrect sign in the case of -overflow. -@param src1 first input array. -@param src2 second input array of the same size and the same type as src1. -@param dst output array of the same size and type as src1. -@param scale optional scale factor. -@param dtype optional depth of the output array -@sa add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare, -Mat::convertTo -*/ -CV_EXPORTS_W void multiply(InputArray src1, InputArray src2, - OutputArray dst, double scale = 1, int dtype = -1); - -/** @brief Performs per-element division of two arrays or a scalar by an array. - -The function cv::divide divides one array by another: -\f[\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}\f] -or a scalar by an array when there is no src1 : -\f[\texttt{dst(I) = saturate(scale/src2(I))}\f] - -When src2(I) is zero, dst(I) will also be zero. Different channels of -multi-channel arrays are processed independently. - -@note Saturation is not applied when the output array has the depth CV_32S. You may even get -result of an incorrect sign in the case of overflow. -@param src1 first input array. -@param src2 second input array of the same size and type as src1. -@param scale scalar factor. -@param dst output array of the same size and type as src2. -@param dtype optional depth of the output array; if -1, dst will have depth src2.depth(), but in -case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth(). -@sa multiply, add, subtract -*/ -CV_EXPORTS_W void divide(InputArray src1, InputArray src2, OutputArray dst, - double scale = 1, int dtype = -1); - -/** @overload */ -CV_EXPORTS_W void divide(double scale, InputArray src2, - OutputArray dst, int dtype = -1); - -/** @brief Calculates the sum of a scaled array and another array. - -The function scaleAdd is one of the classical primitive linear algebra operations, known as DAXPY -or SAXPY in [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms). It calculates -the sum of a scaled array and another array: -\f[\texttt{dst} (I)= \texttt{scale} \cdot \texttt{src1} (I) + \texttt{src2} (I)\f] -The function can also be emulated with a matrix expression, for example: -@code{.cpp} - Mat A(3, 3, CV_64F); - ... - A.row(0) = A.row(1)*2 + A.row(2); -@endcode -@param src1 first input array. -@param alpha scale factor for the first array. -@param src2 second input array of the same size and type as src1. -@param dst output array of the same size and type as src1. -@sa add, addWeighted, subtract, Mat::dot, Mat::convertTo -*/ -CV_EXPORTS_W void scaleAdd(InputArray src1, double alpha, InputArray src2, OutputArray dst); - -/** @example samples/cpp/tutorial_code/HighGUI/AddingImagesTrackbar.cpp -Check @ref tutorial_trackbar "the corresponding tutorial" for more details -*/ - -/** @brief Calculates the weighted sum of two arrays. - -The function addWeighted calculates the weighted sum of two arrays as follows: -\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} + \texttt{src2} (I)* \texttt{beta} + \texttt{gamma} )\f] -where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each -channel is processed independently. -The function can be replaced with a matrix expression: -@code{.cpp} - dst = src1*alpha + src2*beta + gamma; -@endcode -@note Saturation is not applied when the output array has the depth CV_32S. You may even get -result of an incorrect sign in the case of overflow. -@param src1 first input array. -@param alpha weight of the first array elements. -@param src2 second input array of the same size and channel number as src1. -@param beta weight of the second array elements. -@param gamma scalar added to each sum. -@param dst output array that has the same size and number of channels as the input arrays. -@param dtype optional depth of the output array; when both input arrays have the same depth, dtype -can be set to -1, which will be equivalent to src1.depth(). -@sa add, subtract, scaleAdd, Mat::convertTo -*/ -CV_EXPORTS_W void addWeighted(InputArray src1, double alpha, InputArray src2, - double beta, double gamma, OutputArray dst, int dtype = -1); - -/** @brief Scales, calculates absolute values, and converts the result to 8-bit. - -On each element of the input array, the function convertScaleAbs -performs three operations sequentially: scaling, taking an absolute -value, conversion to an unsigned 8-bit type: -\f[\texttt{dst} (I)= \texttt{saturate\_cast} (| \texttt{src} (I)* \texttt{alpha} + \texttt{beta} |)\f] -In case of multi-channel arrays, the function processes each channel -independently. When the output is not 8-bit, the operation can be -emulated by calling the Mat::convertTo method (or by using matrix -expressions) and then by calculating an absolute value of the result. -For example: -@code{.cpp} - Mat_ A(30,30); - randu(A, Scalar(-100), Scalar(100)); - Mat_ B = A*5 + 3; - B = abs(B); - // Mat_ B = abs(A*5+3) will also do the job, - // but it will allocate a temporary matrix -@endcode -@param src input array. -@param dst output array. -@param alpha optional scale factor. -@param beta optional delta added to the scaled values. -@sa Mat::convertTo, cv::abs(const Mat&) -*/ -CV_EXPORTS_W void convertScaleAbs(InputArray src, OutputArray dst, - double alpha = 1, double beta = 0); - -/** @brief Converts an array to half precision floating number. - -This function converts FP32 (single precision floating point) from/to FP16 (half precision floating point). CV_16S format is used to represent FP16 data. -There are two use modes (src -> dst): CV_32F -> CV_16S and CV_16S -> CV_32F. The input array has to have type of CV_32F or -CV_16S to represent the bit depth. If the input array is neither of them, the function will raise an error. -The format of half precision floating point is defined in IEEE 754-2008. - -@param src input array. -@param dst output array. -*/ -CV_EXPORTS_W void convertFp16(InputArray src, OutputArray dst); - -/** @brief Performs a look-up table transform of an array. - -The function LUT fills the output array with values from the look-up table. Indices of the entries -are taken from the input array. That is, the function processes each element of src as follows: -\f[\texttt{dst} (I) \leftarrow \texttt{lut(src(I) + d)}\f] -where -\f[d = \fork{0}{if \(\texttt{src}\) has depth \(\texttt{CV_8U}\)}{128}{if \(\texttt{src}\) has depth \(\texttt{CV_8S}\)}\f] -@param src input array of 8-bit elements. -@param lut look-up table of 256 elements; in case of multi-channel input array, the table should -either have a single channel (in this case the same table is used for all channels) or the same -number of channels as in the input array. -@param dst output array of the same size and number of channels as src, and the same depth as lut. -@sa convertScaleAbs, Mat::convertTo -*/ -CV_EXPORTS_W void LUT(InputArray src, InputArray lut, OutputArray dst); - -/** @brief Calculates the sum of array elements. - -The function cv::sum calculates and returns the sum of array elements, -independently for each channel. -@param src input array that must have from 1 to 4 channels. -@sa countNonZero, mean, meanStdDev, norm, minMaxLoc, reduce -*/ -CV_EXPORTS_AS(sumElems) Scalar sum(InputArray src); - -/** @brief Counts non-zero array elements. - -The function returns the number of non-zero elements in src : -\f[\sum _{I: \; \texttt{src} (I) \ne0 } 1\f] -@param src single-channel array. -@sa mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix -*/ -CV_EXPORTS_W int countNonZero( InputArray src ); - -/** @brief Returns the list of locations of non-zero pixels - -Given a binary matrix (likely returned from an operation such -as threshold(), compare(), >, ==, etc, return all of -the non-zero indices as a cv::Mat or std::vector (x,y) -For example: -@code{.cpp} - cv::Mat binaryImage; // input, binary image - cv::Mat locations; // output, locations of non-zero pixels - cv::findNonZero(binaryImage, locations); - - // access pixel coordinates - Point pnt = locations.at(i); -@endcode -or -@code{.cpp} - cv::Mat binaryImage; // input, binary image - vector locations; // output, locations of non-zero pixels - cv::findNonZero(binaryImage, locations); - - // access pixel coordinates - Point pnt = locations[i]; -@endcode -@param src single-channel array (type CV_8UC1) -@param idx the output array, type of cv::Mat or std::vector, corresponding to non-zero indices in the input -*/ -CV_EXPORTS_W void findNonZero( InputArray src, OutputArray idx ); - -/** @brief Calculates an average (mean) of array elements. - -The function cv::mean calculates the mean value M of array elements, -independently for each channel, and return it: -\f[\begin{array}{l} N = \sum _{I: \; \texttt{mask} (I) \ne 0} 1 \\ M_c = \left ( \sum _{I: \; \texttt{mask} (I) \ne 0}{ \texttt{mtx} (I)_c} \right )/N \end{array}\f] -When all the mask elements are 0's, the function returns Scalar::all(0) -@param src input array that should have from 1 to 4 channels so that the result can be stored in -Scalar_ . -@param mask optional operation mask. -@sa countNonZero, meanStdDev, norm, minMaxLoc -*/ -CV_EXPORTS_W Scalar mean(InputArray src, InputArray mask = noArray()); - -/** Calculates a mean and standard deviation of array elements. - -The function cv::meanStdDev calculates the mean and the standard deviation M -of array elements independently for each channel and returns it via the -output parameters: -\f[\begin{array}{l} N = \sum _{I, \texttt{mask} (I) \ne 0} 1 \\ \texttt{mean} _c = \frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \texttt{src} (I)_c}{N} \\ \texttt{stddev} _c = \sqrt{\frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \left ( \texttt{src} (I)_c - \texttt{mean} _c \right )^2}{N}} \end{array}\f] -When all the mask elements are 0's, the function returns -mean=stddev=Scalar::all(0). -@note The calculated standard deviation is only the diagonal of the -complete normalized covariance matrix. If the full matrix is needed, you -can reshape the multi-channel array M x N to the single-channel array -M\*N x mtx.channels() (only possible when the matrix is continuous) and -then pass the matrix to calcCovarMatrix . -@param src input array that should have from 1 to 4 channels so that the results can be stored in -Scalar_ 's. -@param mean output parameter: calculated mean value. -@param stddev output parameter: calculated standard deviation. -@param mask optional operation mask. -@sa countNonZero, mean, norm, minMaxLoc, calcCovarMatrix -*/ -CV_EXPORTS_W void meanStdDev(InputArray src, OutputArray mean, OutputArray stddev, - InputArray mask=noArray()); - -/** @brief Calculates the absolute norm of an array. - -This version of #norm calculates the absolute norm of src1. The type of norm to calculate is specified using #NormTypes. - -As example for one array consider the function \f$r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\f$. -The \f$ L_{1}, L_{2} \f$ and \f$ L_{\infty} \f$ norm for the sample value \f$r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\f$ -is calculated as follows -\f{align*} - \| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\ - \| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\ - \| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2 -\f} -and for \f$r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\f$ the calculation is -\f{align*} - \| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\ - \| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\ - \| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5. -\f} -The following graphic shows all values for the three norm functions \f$\| r(x) \|_{L_1}, \| r(x) \|_{L_2}\f$ and \f$\| r(x) \|_{L_\infty}\f$. -It is notable that the \f$ L_{1} \f$ norm forms the upper and the \f$ L_{\infty} \f$ norm forms the lower border for the example function \f$ r(x) \f$. -![Graphs for the different norm functions from the above example](pics/NormTypes_OneArray_1-2-INF.png) - -When the mask parameter is specified and it is not empty, the norm is - -If normType is not specified, #NORM_L2 is used. -calculated only over the region specified by the mask. - -Multi-channel input arrays are treated as single-channel arrays, that is, -the results for all channels are combined. - -Hamming norms can only be calculated with CV_8U depth arrays. - -@param src1 first input array. -@param normType type of the norm (see #NormTypes). -@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type. -*/ -CV_EXPORTS_W double norm(InputArray src1, int normType = NORM_L2, InputArray mask = noArray()); - -/** @brief Calculates an absolute difference norm or a relative difference norm. - -This version of cv::norm calculates the absolute difference norm -or the relative difference norm of arrays src1 and src2. -The type of norm to calculate is specified using #NormTypes. - -@param src1 first input array. -@param src2 second input array of the same size and the same type as src1. -@param normType type of the norm (see #NormTypes). -@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type. -*/ -CV_EXPORTS_W double norm(InputArray src1, InputArray src2, - int normType = NORM_L2, InputArray mask = noArray()); -/** @overload -@param src first input array. -@param normType type of the norm (see #NormTypes). -*/ -CV_EXPORTS double norm( const SparseMat& src, int normType ); - -/** @brief Computes the Peak Signal-to-Noise Ratio (PSNR) image quality metric. - -This function calculates the Peak Signal-to-Noise Ratio (PSNR) image quality metric in decibels (dB), between two input arrays src1 and src2. Arrays must have depth CV_8U. - -The PSNR is calculated as follows: - -\f[ -\texttt{PSNR} = 10 \cdot \log_{10}{\left( \frac{R^2}{MSE} \right) } -\f] - -where R is the maximum integer value of depth CV_8U (255) and MSE is the mean squared error between the two arrays. - -@param src1 first input array. -@param src2 second input array of the same size as src1. - - */ -CV_EXPORTS_W double PSNR(InputArray src1, InputArray src2); - -/** @brief naive nearest neighbor finder - -see http://en.wikipedia.org/wiki/Nearest_neighbor_search -@todo document - */ -CV_EXPORTS_W void batchDistance(InputArray src1, InputArray src2, - OutputArray dist, int dtype, OutputArray nidx, - int normType = NORM_L2, int K = 0, - InputArray mask = noArray(), int update = 0, - bool crosscheck = false); - -/** @brief Normalizes the norm or value range of an array. - -The function cv::normalize normalizes scale and shift the input array elements so that -\f[\| \texttt{dst} \| _{L_p}= \texttt{alpha}\f] -(where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that -\f[\min _I \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I \texttt{dst} (I)= \texttt{beta}\f] - -when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be -normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this -sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or -min-max but modify the whole array, you can use norm and Mat::convertTo. - -In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, -the range transformation for sparse matrices is not allowed since it can shift the zero level. - -Possible usage with some positive example data: -@code{.cpp} - vector positiveData = { 2.0, 8.0, 10.0 }; - vector normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax; - - // Norm to probability (total count) - // sum(numbers) = 20.0 - // 2.0 0.1 (2.0/20.0) - // 8.0 0.4 (8.0/20.0) - // 10.0 0.5 (10.0/20.0) - normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1); - - // Norm to unit vector: ||positiveData|| = 1.0 - // 2.0 0.15 - // 8.0 0.62 - // 10.0 0.77 - normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2); - - // Norm to max element - // 2.0 0.2 (2.0/10.0) - // 8.0 0.8 (8.0/10.0) - // 10.0 1.0 (10.0/10.0) - normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF); - - // Norm to range [0.0;1.0] - // 2.0 0.0 (shift to left border) - // 8.0 0.75 (6.0/8.0) - // 10.0 1.0 (shift to right border) - normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX); -@endcode - -@param src input array. -@param dst output array of the same size as src . -@param alpha norm value to normalize to or the lower range boundary in case of the range -normalization. -@param beta upper range boundary in case of the range normalization; it is not used for the norm -normalization. -@param norm_type normalization type (see cv::NormTypes). -@param dtype when negative, the output array has the same type as src; otherwise, it has the same -number of channels as src and the depth =CV_MAT_DEPTH(dtype). -@param mask optional operation mask. -@sa norm, Mat::convertTo, SparseMat::convertTo -*/ -CV_EXPORTS_W void normalize( InputArray src, InputOutputArray dst, double alpha = 1, double beta = 0, - int norm_type = NORM_L2, int dtype = -1, InputArray mask = noArray()); - -/** @overload -@param src input array. -@param dst output array of the same size as src . -@param alpha norm value to normalize to or the lower range boundary in case of the range -normalization. -@param normType normalization type (see cv::NormTypes). -*/ -CV_EXPORTS void normalize( const SparseMat& src, SparseMat& dst, double alpha, int normType ); - -/** @brief Finds the global minimum and maximum in an array. - -The function cv::minMaxLoc finds the minimum and maximum element values and their positions. The -extremums are searched across the whole array or, if mask is not an empty array, in the specified -array region. - -The function do not work with multi-channel arrays. If you need to find minimum or maximum -elements across all the channels, use Mat::reshape first to reinterpret the array as -single-channel. Or you may extract the particular channel using either extractImageCOI , or -mixChannels , or split . -@param src input single-channel array. -@param minVal pointer to the returned minimum value; NULL is used if not required. -@param maxVal pointer to the returned maximum value; NULL is used if not required. -@param minLoc pointer to the returned minimum location (in 2D case); NULL is used if not required. -@param maxLoc pointer to the returned maximum location (in 2D case); NULL is used if not required. -@param mask optional mask used to select a sub-array. -@sa max, min, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape -*/ -CV_EXPORTS_W void minMaxLoc(InputArray src, CV_OUT double* minVal, - CV_OUT double* maxVal = 0, CV_OUT Point* minLoc = 0, - CV_OUT Point* maxLoc = 0, InputArray mask = noArray()); - - -/** @brief Finds the global minimum and maximum in an array - -The function cv::minMaxIdx finds the minimum and maximum element values and their positions. The -extremums are searched across the whole array or, if mask is not an empty array, in the specified -array region. The function does not work with multi-channel arrays. If you need to find minimum or -maximum elements across all the channels, use Mat::reshape first to reinterpret the array as -single-channel. Or you may extract the particular channel using either extractImageCOI , or -mixChannels , or split . In case of a sparse matrix, the minimum is found among non-zero elements -only. -@note When minIdx is not NULL, it must have at least 2 elements (as well as maxIdx), even if src is -a single-row or single-column matrix. In OpenCV (following MATLAB) each array has at least 2 -dimensions, i.e. single-column matrix is Mx1 matrix (and therefore minIdx/maxIdx will be -(i1,0)/(i2,0)) and single-row matrix is 1xN matrix (and therefore minIdx/maxIdx will be -(0,j1)/(0,j2)). -@param src input single-channel array. -@param minVal pointer to the returned minimum value; NULL is used if not required. -@param maxVal pointer to the returned maximum value; NULL is used if not required. -@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required; -Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element -in each dimension are stored there sequentially. -@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required. -@param mask specified array region -*/ -CV_EXPORTS void minMaxIdx(InputArray src, double* minVal, double* maxVal = 0, - int* minIdx = 0, int* maxIdx = 0, InputArray mask = noArray()); - -/** @overload -@param a input single-channel array. -@param minVal pointer to the returned minimum value; NULL is used if not required. -@param maxVal pointer to the returned maximum value; NULL is used if not required. -@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required; -Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element -in each dimension are stored there sequentially. -@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required. -*/ -CV_EXPORTS void minMaxLoc(const SparseMat& a, double* minVal, - double* maxVal, int* minIdx = 0, int* maxIdx = 0); - -/** @brief Reduces a matrix to a vector. - -The function #reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of -1D vectors and performing the specified operation on the vectors until a single row/column is -obtained. For example, the function can be used to compute horizontal and vertical projections of a -raster image. In case of #REDUCE_MAX and #REDUCE_MIN , the output image should have the same type as the source one. -In case of #REDUCE_SUM and #REDUCE_AVG , the output may have a larger element bit-depth to preserve accuracy. -And multi-channel arrays are also supported in these two reduction modes. - -The following code demonstrates its usage for a single channel matrix. -@snippet snippets/core_reduce.cpp example - -And the following code demonstrates its usage for a two-channel matrix. -@snippet snippets/core_reduce.cpp example2 - -@param src input 2D matrix. -@param dst output vector. Its size and type is defined by dim and dtype parameters. -@param dim dimension index along which the matrix is reduced. 0 means that the matrix is reduced to -a single row. 1 means that the matrix is reduced to a single column. -@param rtype reduction operation that could be one of #ReduceTypes -@param dtype when negative, the output vector will have the same type as the input matrix, -otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()). -@sa repeat -*/ -CV_EXPORTS_W void reduce(InputArray src, OutputArray dst, int dim, int rtype, int dtype = -1); - -/** @brief Creates one multi-channel array out of several single-channel ones. - -The function cv::merge merges several arrays to make a single multi-channel array. That is, each -element of the output array will be a concatenation of the elements of the input arrays, where -elements of i-th input array are treated as mv[i].channels()-element vectors. - -The function cv::split does the reverse operation. If you need to shuffle channels in some other -advanced way, use cv::mixChannels. - -The following example shows how to merge 3 single channel matrices into a single 3-channel matrix. -@snippet snippets/core_merge.cpp example - -@param mv input array of matrices to be merged; all the matrices in mv must have the same -size and the same depth. -@param count number of input matrices when mv is a plain C array; it must be greater than zero. -@param dst output array of the same size and the same depth as mv[0]; The number of channels will -be equal to the parameter count. -@sa mixChannels, split, Mat::reshape -*/ -CV_EXPORTS void merge(const Mat* mv, size_t count, OutputArray dst); - -/** @overload -@param mv input vector of matrices to be merged; all the matrices in mv must have the same -size and the same depth. -@param dst output array of the same size and the same depth as mv[0]; The number of channels will -be the total number of channels in the matrix array. - */ -CV_EXPORTS_W void merge(InputArrayOfArrays mv, OutputArray dst); - -/** @brief Divides a multi-channel array into several single-channel arrays. - -The function cv::split splits a multi-channel array into separate single-channel arrays: -\f[\texttt{mv} [c](I) = \texttt{src} (I)_c\f] -If you need to extract a single channel or do some other sophisticated channel permutation, use -mixChannels . - -The following example demonstrates how to split a 3-channel matrix into 3 single channel matrices. -@snippet snippets/core_split.cpp example - -@param src input multi-channel array. -@param mvbegin output array; the number of arrays must match src.channels(); the arrays themselves are -reallocated, if needed. -@sa merge, mixChannels, cvtColor -*/ -CV_EXPORTS void split(const Mat& src, Mat* mvbegin); - -/** @overload -@param m input multi-channel array. -@param mv output vector of arrays; the arrays themselves are reallocated, if needed. -*/ -CV_EXPORTS_W void split(InputArray m, OutputArrayOfArrays mv); - -/** @brief Copies specified channels from input arrays to the specified channels of -output arrays. - -The function cv::mixChannels provides an advanced mechanism for shuffling image channels. - -cv::split,cv::merge,cv::extractChannel,cv::insertChannel and some forms of cv::cvtColor are partial cases of cv::mixChannels. - -In the example below, the code splits a 4-channel BGRA image into a 3-channel BGR (with B and R -channels swapped) and a separate alpha-channel image: -@code{.cpp} - Mat bgra( 100, 100, CV_8UC4, Scalar(255,0,0,255) ); - Mat bgr( bgra.rows, bgra.cols, CV_8UC3 ); - Mat alpha( bgra.rows, bgra.cols, CV_8UC1 ); - - // forming an array of matrices is a quite efficient operation, - // because the matrix data is not copied, only the headers - Mat out[] = { bgr, alpha }; - // bgra[0] -> bgr[2], bgra[1] -> bgr[1], - // bgra[2] -> bgr[0], bgra[3] -> alpha[0] - int from_to[] = { 0,2, 1,1, 2,0, 3,3 }; - mixChannels( &bgra, 1, out, 2, from_to, 4 ); -@endcode -@note Unlike many other new-style C++ functions in OpenCV (see the introduction section and -Mat::create ), cv::mixChannels requires the output arrays to be pre-allocated before calling the -function. -@param src input array or vector of matrices; all of the matrices must have the same size and the -same depth. -@param nsrcs number of matrices in `src`. -@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and -depth must be the same as in `src[0]`. -@param ndsts number of matrices in `dst`. -@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is -a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in -dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to -src[0].channels()-1, the second input image channels are indexed from src[0].channels() to -src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image -channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is -filled with zero . -@param npairs number of index pairs in `fromTo`. -@sa split, merge, extractChannel, insertChannel, cvtColor -*/ -CV_EXPORTS void mixChannels(const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts, - const int* fromTo, size_t npairs); - -/** @overload -@param src input array or vector of matrices; all of the matrices must have the same size and the -same depth. -@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and -depth must be the same as in src[0]. -@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is -a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in -dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to -src[0].channels()-1, the second input image channels are indexed from src[0].channels() to -src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image -channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is -filled with zero . -@param npairs number of index pairs in fromTo. -*/ -CV_EXPORTS void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst, - const int* fromTo, size_t npairs); - -/** @overload -@param src input array or vector of matrices; all of the matrices must have the same size and the -same depth. -@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and -depth must be the same as in src[0]. -@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is -a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in -dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to -src[0].channels()-1, the second input image channels are indexed from src[0].channels() to -src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image -channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is -filled with zero . -*/ -CV_EXPORTS_W void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst, - const std::vector& fromTo); - -/** @brief Extracts a single channel from src (coi is 0-based index) -@param src input array -@param dst output array -@param coi index of channel to extract -@sa mixChannels, split -*/ -CV_EXPORTS_W void extractChannel(InputArray src, OutputArray dst, int coi); - -/** @brief Inserts a single channel to dst (coi is 0-based index) -@param src input array -@param dst output array -@param coi index of channel for insertion -@sa mixChannels, merge -*/ -CV_EXPORTS_W void insertChannel(InputArray src, InputOutputArray dst, int coi); - -/** @brief Flips a 2D array around vertical, horizontal, or both axes. - -The function cv::flip flips the array in one of three different ways (row -and column indices are 0-based): -\f[\texttt{dst} _{ij} = -\left\{ -\begin{array}{l l} -\texttt{src} _{\texttt{src.rows}-i-1,j} & if\; \texttt{flipCode} = 0 \\ -\texttt{src} _{i, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} > 0 \\ -\texttt{src} _{ \texttt{src.rows} -i-1, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} < 0 \\ -\end{array} -\right.\f] -The example scenarios of using the function are the following: -* Vertical flipping of the image (flipCode == 0) to switch between - top-left and bottom-left image origin. This is a typical operation - in video processing on Microsoft Windows\* OS. -* Horizontal flipping of the image with the subsequent horizontal - shift and absolute difference calculation to check for a - vertical-axis symmetry (flipCode \> 0). -* Simultaneous horizontal and vertical flipping of the image with - the subsequent shift and absolute difference calculation to check - for a central symmetry (flipCode \< 0). -* Reversing the order of point arrays (flipCode \> 0 or - flipCode == 0). -@param src input array. -@param dst output array of the same size and type as src. -@param flipCode a flag to specify how to flip the array; 0 means -flipping around the x-axis and positive value (for example, 1) means -flipping around y-axis. Negative value (for example, -1) means flipping -around both axes. -@sa transpose , repeat , completeSymm -*/ -CV_EXPORTS_W void flip(InputArray src, OutputArray dst, int flipCode); - -enum RotateFlags { - ROTATE_90_CLOCKWISE = 0, //! A = (cv::Mat_(3, 2) << 1, 4, - 2, 5, - 3, 6); - cv::Mat_ B = (cv::Mat_(3, 2) << 7, 10, - 8, 11, - 9, 12); - - cv::Mat C; - cv::hconcat(A, B, C); - //C: - //[1, 4, 7, 10; - // 2, 5, 8, 11; - // 3, 6, 9, 12] - @endcode - @param src1 first input array to be considered for horizontal concatenation. - @param src2 second input array to be considered for horizontal concatenation. - @param dst output array. It has the same number of rows and depth as the src1 and src2, and the sum of cols of the src1 and src2. - */ -CV_EXPORTS void hconcat(InputArray src1, InputArray src2, OutputArray dst); -/** @overload - @code{.cpp} - std::vector matrices = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)), - cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)), - cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),}; - - cv::Mat out; - cv::hconcat( matrices, out ); - //out: - //[1, 2, 3; - // 1, 2, 3; - // 1, 2, 3; - // 1, 2, 3] - @endcode - @param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth. - @param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src. -same depth. - */ -CV_EXPORTS_W void hconcat(InputArrayOfArrays src, OutputArray dst); - -/** @brief Applies vertical concatenation to given matrices. - -The function vertically concatenates two or more cv::Mat matrices (with the same number of cols). -@code{.cpp} - cv::Mat matArray[] = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), - cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), - cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; - - cv::Mat out; - cv::vconcat( matArray, 3, out ); - //out: - //[1, 1, 1, 1; - // 2, 2, 2, 2; - // 3, 3, 3, 3] -@endcode -@param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth. -@param nsrc number of matrices in src. -@param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src. -@sa cv::hconcat(const Mat*, size_t, OutputArray), @sa cv::hconcat(InputArrayOfArrays, OutputArray) and @sa cv::hconcat(InputArray, InputArray, OutputArray) -*/ -CV_EXPORTS void vconcat(const Mat* src, size_t nsrc, OutputArray dst); -/** @overload - @code{.cpp} - cv::Mat_ A = (cv::Mat_(3, 2) << 1, 7, - 2, 8, - 3, 9); - cv::Mat_ B = (cv::Mat_(3, 2) << 4, 10, - 5, 11, - 6, 12); - - cv::Mat C; - cv::vconcat(A, B, C); - //C: - //[1, 7; - // 2, 8; - // 3, 9; - // 4, 10; - // 5, 11; - // 6, 12] - @endcode - @param src1 first input array to be considered for vertical concatenation. - @param src2 second input array to be considered for vertical concatenation. - @param dst output array. It has the same number of cols and depth as the src1 and src2, and the sum of rows of the src1 and src2. - */ -CV_EXPORTS void vconcat(InputArray src1, InputArray src2, OutputArray dst); -/** @overload - @code{.cpp} - std::vector matrices = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), - cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), - cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),}; - - cv::Mat out; - cv::vconcat( matrices, out ); - //out: - //[1, 1, 1, 1; - // 2, 2, 2, 2; - // 3, 3, 3, 3] - @endcode - @param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth - @param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src. -same depth. - */ -CV_EXPORTS_W void vconcat(InputArrayOfArrays src, OutputArray dst); - -/** @brief computes bitwise conjunction of the two arrays (dst = src1 & src2) -Calculates the per-element bit-wise conjunction of two arrays or an -array and a scalar. - -The function cv::bitwise_and calculates the per-element bit-wise logical conjunction for: -* Two arrays when src1 and src2 have the same size: - \f[\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] -* An array and a scalar when src2 is constructed from Scalar or has - the same number of elements as `src1.channels()`: - \f[\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] -* A scalar and an array when src1 is constructed from Scalar or has - the same number of elements as `src2.channels()`: - \f[\texttt{dst} (I) = \texttt{src1} \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] -In case of floating-point arrays, their machine-specific bit -representations (usually IEEE754-compliant) are used for the operation. -In case of multi-channel arrays, each channel is processed -independently. In the second and third cases above, the scalar is first -converted to the array type. -@param src1 first input array or a scalar. -@param src2 second input array or a scalar. -@param dst output array that has the same size and type as the input -arrays. -@param mask optional operation mask, 8-bit single channel array, that -specifies elements of the output array to be changed. -*/ -CV_EXPORTS_W void bitwise_and(InputArray src1, InputArray src2, - OutputArray dst, InputArray mask = noArray()); - -/** @brief Calculates the per-element bit-wise disjunction of two arrays or an -array and a scalar. - -The function cv::bitwise_or calculates the per-element bit-wise logical disjunction for: -* Two arrays when src1 and src2 have the same size: - \f[\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] -* An array and a scalar when src2 is constructed from Scalar or has - the same number of elements as `src1.channels()`: - \f[\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] -* A scalar and an array when src1 is constructed from Scalar or has - the same number of elements as `src2.channels()`: - \f[\texttt{dst} (I) = \texttt{src1} \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] -In case of floating-point arrays, their machine-specific bit -representations (usually IEEE754-compliant) are used for the operation. -In case of multi-channel arrays, each channel is processed -independently. In the second and third cases above, the scalar is first -converted to the array type. -@param src1 first input array or a scalar. -@param src2 second input array or a scalar. -@param dst output array that has the same size and type as the input -arrays. -@param mask optional operation mask, 8-bit single channel array, that -specifies elements of the output array to be changed. -*/ -CV_EXPORTS_W void bitwise_or(InputArray src1, InputArray src2, - OutputArray dst, InputArray mask = noArray()); - -/** @brief Calculates the per-element bit-wise "exclusive or" operation on two -arrays or an array and a scalar. - -The function cv::bitwise_xor calculates the per-element bit-wise logical "exclusive-or" -operation for: -* Two arrays when src1 and src2 have the same size: - \f[\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] -* An array and a scalar when src2 is constructed from Scalar or has - the same number of elements as `src1.channels()`: - \f[\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} \quad \texttt{if mask} (I) \ne0\f] -* A scalar and an array when src1 is constructed from Scalar or has - the same number of elements as `src2.channels()`: - \f[\texttt{dst} (I) = \texttt{src1} \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f] -In case of floating-point arrays, their machine-specific bit -representations (usually IEEE754-compliant) are used for the operation. -In case of multi-channel arrays, each channel is processed -independently. In the 2nd and 3rd cases above, the scalar is first -converted to the array type. -@param src1 first input array or a scalar. -@param src2 second input array or a scalar. -@param dst output array that has the same size and type as the input -arrays. -@param mask optional operation mask, 8-bit single channel array, that -specifies elements of the output array to be changed. -*/ -CV_EXPORTS_W void bitwise_xor(InputArray src1, InputArray src2, - OutputArray dst, InputArray mask = noArray()); - -/** @brief Inverts every bit of an array. - -The function cv::bitwise_not calculates per-element bit-wise inversion of the input -array: -\f[\texttt{dst} (I) = \neg \texttt{src} (I)\f] -In case of a floating-point input array, its machine-specific bit -representation (usually IEEE754-compliant) is used for the operation. In -case of multi-channel arrays, each channel is processed independently. -@param src input array. -@param dst output array that has the same size and type as the input -array. -@param mask optional operation mask, 8-bit single channel array, that -specifies elements of the output array to be changed. -*/ -CV_EXPORTS_W void bitwise_not(InputArray src, OutputArray dst, - InputArray mask = noArray()); - -/** @brief Calculates the per-element absolute difference between two arrays or between an array and a scalar. - -The function cv::absdiff calculates: -* Absolute difference between two arrays when they have the same - size and type: - \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2}(I)|)\f] -* Absolute difference between an array and a scalar when the second - array is constructed from Scalar or has as many elements as the - number of channels in `src1`: - \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2} |)\f] -* Absolute difference between a scalar and an array when the first - array is constructed from Scalar or has as many elements as the - number of channels in `src2`: - \f[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1} - \texttt{src2}(I) |)\f] - where I is a multi-dimensional index of array elements. In case of - multi-channel arrays, each channel is processed independently. -@note Saturation is not applied when the arrays have the depth CV_32S. -You may even get a negative value in the case of overflow. -@param src1 first input array or a scalar. -@param src2 second input array or a scalar. -@param dst output array that has the same size and type as input arrays. -@sa cv::abs(const Mat&) -*/ -CV_EXPORTS_W void absdiff(InputArray src1, InputArray src2, OutputArray dst); - -/** @brief Checks if array elements lie between the elements of two other arrays. - -The function checks the range as follows: -- For every element of a single-channel input array: - \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0\f] -- For two-channel arrays: - \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0 \land \texttt{lowerb} (I)_1 \leq \texttt{src} (I)_1 \leq \texttt{upperb} (I)_1\f] -- and so forth. - -That is, dst (I) is set to 255 (all 1 -bits) if src (I) is within the -specified 1D, 2D, 3D, ... box and 0 otherwise. - -When the lower and/or upper boundary parameters are scalars, the indexes -(I) at lowerb and upperb in the above formulas should be omitted. -@param src first input array. -@param lowerb inclusive lower boundary array or a scalar. -@param upperb inclusive upper boundary array or a scalar. -@param dst output array of the same size as src and CV_8U type. -*/ -CV_EXPORTS_W void inRange(InputArray src, InputArray lowerb, - InputArray upperb, OutputArray dst); - -/** @brief Performs the per-element comparison of two arrays or an array and scalar value. - -The function compares: -* Elements of two arrays when src1 and src2 have the same size: - \f[\texttt{dst} (I) = \texttt{src1} (I) \,\texttt{cmpop}\, \texttt{src2} (I)\f] -* Elements of src1 with a scalar src2 when src2 is constructed from - Scalar or has a single element: - \f[\texttt{dst} (I) = \texttt{src1}(I) \,\texttt{cmpop}\, \texttt{src2}\f] -* src1 with elements of src2 when src1 is constructed from Scalar or - has a single element: - \f[\texttt{dst} (I) = \texttt{src1} \,\texttt{cmpop}\, \texttt{src2} (I)\f] -When the comparison result is true, the corresponding element of output -array is set to 255. The comparison operations can be replaced with the -equivalent matrix expressions: -@code{.cpp} - Mat dst1 = src1 >= src2; - Mat dst2 = src1 < 8; - ... -@endcode -@param src1 first input array or a scalar; when it is an array, it must have a single channel. -@param src2 second input array or a scalar; when it is an array, it must have a single channel. -@param dst output array of type ref CV_8U that has the same size and the same number of channels as - the input arrays. -@param cmpop a flag, that specifies correspondence between the arrays (cv::CmpTypes) -@sa checkRange, min, max, threshold -*/ -CV_EXPORTS_W void compare(InputArray src1, InputArray src2, OutputArray dst, int cmpop); - -/** @brief Calculates per-element minimum of two arrays or an array and a scalar. - -The function cv::min calculates the per-element minimum of two arrays: -\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{src2} (I))\f] -or array and a scalar: -\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{value} )\f] -@param src1 first input array. -@param src2 second input array of the same size and type as src1. -@param dst output array of the same size and type as src1. -@sa max, compare, inRange, minMaxLoc -*/ -CV_EXPORTS_W void min(InputArray src1, InputArray src2, OutputArray dst); -/** @overload -needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) -*/ -CV_EXPORTS void min(const Mat& src1, const Mat& src2, Mat& dst); -/** @overload -needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) -*/ -CV_EXPORTS void min(const UMat& src1, const UMat& src2, UMat& dst); - -/** @brief Calculates per-element maximum of two arrays or an array and a scalar. - -The function cv::max calculates the per-element maximum of two arrays: -\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))\f] -or array and a scalar: -\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{value} )\f] -@param src1 first input array. -@param src2 second input array of the same size and type as src1 . -@param dst output array of the same size and type as src1. -@sa min, compare, inRange, minMaxLoc, @ref MatrixExpressions -*/ -CV_EXPORTS_W void max(InputArray src1, InputArray src2, OutputArray dst); -/** @overload -needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) -*/ -CV_EXPORTS void max(const Mat& src1, const Mat& src2, Mat& dst); -/** @overload -needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare) -*/ -CV_EXPORTS void max(const UMat& src1, const UMat& src2, UMat& dst); - -/** @brief Calculates a square root of array elements. - -The function cv::sqrt calculates a square root of each input array element. -In case of multi-channel arrays, each channel is processed -independently. The accuracy is approximately the same as of the built-in -std::sqrt . -@param src input floating-point array. -@param dst output array of the same size and type as src. -*/ -CV_EXPORTS_W void sqrt(InputArray src, OutputArray dst); - -/** @brief Raises every array element to a power. - -The function cv::pow raises every element of the input array to power : -\f[\texttt{dst} (I) = \fork{\texttt{src}(I)^{power}}{if \(\texttt{power}\) is integer}{|\texttt{src}(I)|^{power}}{otherwise}\f] - -So, for a non-integer power exponent, the absolute values of input array -elements are used. However, it is possible to get true values for -negative values using some extra operations. In the example below, -computing the 5th root of array src shows: -@code{.cpp} - Mat mask = src < 0; - pow(src, 1./5, dst); - subtract(Scalar::all(0), dst, dst, mask); -@endcode -For some values of power, such as integer values, 0.5 and -0.5, -specialized faster algorithms are used. - -Special values (NaN, Inf) are not handled. -@param src input array. -@param power exponent of power. -@param dst output array of the same size and type as src. -@sa sqrt, exp, log, cartToPolar, polarToCart -*/ -CV_EXPORTS_W void pow(InputArray src, double power, OutputArray dst); - -/** @brief Calculates the exponent of every array element. - -The function cv::exp calculates the exponent of every element of the input -array: -\f[\texttt{dst} [I] = e^{ src(I) }\f] - -The maximum relative error is about 7e-6 for single-precision input and -less than 1e-10 for double-precision input. Currently, the function -converts denormalized values to zeros on output. Special values (NaN, -Inf) are not handled. -@param src input array. -@param dst output array of the same size and type as src. -@sa log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude -*/ -CV_EXPORTS_W void exp(InputArray src, OutputArray dst); - -/** @brief Calculates the natural logarithm of every array element. - -The function cv::log calculates the natural logarithm of every element of the input array: -\f[\texttt{dst} (I) = \log (\texttt{src}(I)) \f] - -Output on zero, negative and special (NaN, Inf) values is undefined. - -@param src input array. -@param dst output array of the same size and type as src . -@sa exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude -*/ -CV_EXPORTS_W void log(InputArray src, OutputArray dst); - -/** @brief Calculates x and y coordinates of 2D vectors from their magnitude and angle. - -The function cv::polarToCart calculates the Cartesian coordinates of each 2D -vector represented by the corresponding elements of magnitude and angle: -\f[\begin{array}{l} \texttt{x} (I) = \texttt{magnitude} (I) \cos ( \texttt{angle} (I)) \\ \texttt{y} (I) = \texttt{magnitude} (I) \sin ( \texttt{angle} (I)) \\ \end{array}\f] - -The relative accuracy of the estimated coordinates is about 1e-6. -@param magnitude input floating-point array of magnitudes of 2D vectors; -it can be an empty matrix (=Mat()), in this case, the function assumes -that all the magnitudes are =1; if it is not empty, it must have the -same size and type as angle. -@param angle input floating-point array of angles of 2D vectors. -@param x output array of x-coordinates of 2D vectors; it has the same -size and type as angle. -@param y output array of y-coordinates of 2D vectors; it has the same -size and type as angle. -@param angleInDegrees when true, the input angles are measured in -degrees, otherwise, they are measured in radians. -@sa cartToPolar, magnitude, phase, exp, log, pow, sqrt -*/ -CV_EXPORTS_W void polarToCart(InputArray magnitude, InputArray angle, - OutputArray x, OutputArray y, bool angleInDegrees = false); - -/** @brief Calculates the magnitude and angle of 2D vectors. - -The function cv::cartToPolar calculates either the magnitude, angle, or both -for every 2D vector (x(I),y(I)): -\f[\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}\f] - -The angles are calculated with accuracy about 0.3 degrees. For the point -(0,0), the angle is set to 0. -@param x array of x-coordinates; this must be a single-precision or -double-precision floating-point array. -@param y array of y-coordinates, that must have the same size and same type as x. -@param magnitude output array of magnitudes of the same size and type as x. -@param angle output array of angles that has the same size and type as -x; the angles are measured in radians (from 0 to 2\*Pi) or in degrees (0 to 360 degrees). -@param angleInDegrees a flag, indicating whether the angles are measured -in radians (which is by default), or in degrees. -@sa Sobel, Scharr -*/ -CV_EXPORTS_W void cartToPolar(InputArray x, InputArray y, - OutputArray magnitude, OutputArray angle, - bool angleInDegrees = false); - -/** @brief Calculates the rotation angle of 2D vectors. - -The function cv::phase calculates the rotation angle of each 2D vector that -is formed from the corresponding elements of x and y : -\f[\texttt{angle} (I) = \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))\f] - -The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 , -the corresponding angle(I) is set to 0. -@param x input floating-point array of x-coordinates of 2D vectors. -@param y input array of y-coordinates of 2D vectors; it must have the -same size and the same type as x. -@param angle output array of vector angles; it has the same size and -same type as x . -@param angleInDegrees when true, the function calculates the angle in -degrees, otherwise, they are measured in radians. -*/ -CV_EXPORTS_W void phase(InputArray x, InputArray y, OutputArray angle, - bool angleInDegrees = false); - -/** @brief Calculates the magnitude of 2D vectors. - -The function cv::magnitude calculates the magnitude of 2D vectors formed -from the corresponding elements of x and y arrays: -\f[\texttt{dst} (I) = \sqrt{\texttt{x}(I)^2 + \texttt{y}(I)^2}\f] -@param x floating-point array of x-coordinates of the vectors. -@param y floating-point array of y-coordinates of the vectors; it must -have the same size as x. -@param magnitude output array of the same size and type as x. -@sa cartToPolar, polarToCart, phase, sqrt -*/ -CV_EXPORTS_W void magnitude(InputArray x, InputArray y, OutputArray magnitude); - -/** @brief Checks every element of an input array for invalid values. - -The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal \> --DBL_MAX and maxVal \< DBL_MAX, the function also checks that each value is between minVal and -maxVal. In case of multi-channel arrays, each channel is processed independently. If some values -are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the -function either returns false (when quiet=true) or throws an exception. -@param a input array. -@param quiet a flag, indicating whether the functions quietly return false when the array elements -are out of range or they throw an exception. -@param pos optional output parameter, when not NULL, must be a pointer to array of src.dims -elements. -@param minVal inclusive lower boundary of valid values range. -@param maxVal exclusive upper boundary of valid values range. -*/ -CV_EXPORTS_W bool checkRange(InputArray a, bool quiet = true, CV_OUT Point* pos = 0, - double minVal = -DBL_MAX, double maxVal = DBL_MAX); - -/** @brief converts NaNs to the given number -@param a input/output matrix (CV_32F type). -@param val value to convert the NaNs -*/ -CV_EXPORTS_W void patchNaNs(InputOutputArray a, double val = 0); - -/** @brief Performs generalized matrix multiplication. - -The function cv::gemm performs generalized matrix multiplication similar to the -gemm functions in BLAS level 3. For example, -`gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)` -corresponds to -\f[\texttt{dst} = \texttt{alpha} \cdot \texttt{src1} ^T \cdot \texttt{src2} + \texttt{beta} \cdot \texttt{src3} ^T\f] - -In case of complex (two-channel) data, performed a complex matrix -multiplication. - -The function can be replaced with a matrix expression. For example, the -above call can be replaced with: -@code{.cpp} - dst = alpha*src1.t()*src2 + beta*src3.t(); -@endcode -@param src1 first multiplied input matrix that could be real(CV_32FC1, -CV_64FC1) or complex(CV_32FC2, CV_64FC2). -@param src2 second multiplied input matrix of the same type as src1. -@param alpha weight of the matrix product. -@param src3 third optional delta matrix added to the matrix product; it -should have the same type as src1 and src2. -@param beta weight of src3. -@param dst output matrix; it has the proper size and the same type as -input matrices. -@param flags operation flags (cv::GemmFlags) -@sa mulTransposed , transform -*/ -CV_EXPORTS_W void gemm(InputArray src1, InputArray src2, double alpha, - InputArray src3, double beta, OutputArray dst, int flags = 0); - -/** @brief Calculates the product of a matrix and its transposition. - -The function cv::mulTransposed calculates the product of src and its -transposition: -\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )\f] -if aTa=true , and -\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T\f] -otherwise. The function is used to calculate the covariance matrix. With -zero delta, it can be used as a faster substitute for general matrix -product A\*B when B=A' -@param src input single-channel matrix. Note that unlike gemm, the -function can multiply not only floating-point matrices. -@param dst output square matrix. -@param aTa Flag specifying the multiplication ordering. See the -description below. -@param delta Optional delta matrix subtracted from src before the -multiplication. When the matrix is empty ( delta=noArray() ), it is -assumed to be zero, that is, nothing is subtracted. If it has the same -size as src , it is simply subtracted. Otherwise, it is "repeated" (see -repeat ) to cover the full src and then subtracted. Type of the delta -matrix, when it is not empty, must be the same as the type of created -output matrix. See the dtype parameter description below. -@param scale Optional scale factor for the matrix product. -@param dtype Optional type of the output matrix. When it is negative, -the output matrix will have the same type as src . Otherwise, it will be -type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F . -@sa calcCovarMatrix, gemm, repeat, reduce -*/ -CV_EXPORTS_W void mulTransposed( InputArray src, OutputArray dst, bool aTa, - InputArray delta = noArray(), - double scale = 1, int dtype = -1 ); - -/** @brief Transposes a matrix. - -The function cv::transpose transposes the matrix src : -\f[\texttt{dst} (i,j) = \texttt{src} (j,i)\f] -@note No complex conjugation is done in case of a complex matrix. It -should be done separately if needed. -@param src input array. -@param dst output array of the same type as src. -*/ -CV_EXPORTS_W void transpose(InputArray src, OutputArray dst); - -/** @brief Performs the matrix transformation of every array element. - -The function cv::transform performs the matrix transformation of every -element of the array src and stores the results in dst : -\f[\texttt{dst} (I) = \texttt{m} \cdot \texttt{src} (I)\f] -(when m.cols=src.channels() ), or -\f[\texttt{dst} (I) = \texttt{m} \cdot [ \texttt{src} (I); 1]\f] -(when m.cols=src.channels()+1 ) - -Every element of the N -channel array src is interpreted as N -element -vector that is transformed using the M x N or M x (N+1) matrix m to -M-element vector - the corresponding element of the output array dst . - -The function may be used for geometrical transformation of -N -dimensional points, arbitrary linear color space transformation (such -as various kinds of RGB to YUV transforms), shuffling the image -channels, and so forth. -@param src input array that must have as many channels (1 to 4) as -m.cols or m.cols-1. -@param dst output array of the same size and depth as src; it has as -many channels as m.rows. -@param m transformation 2x2 or 2x3 floating-point matrix. -@sa perspectiveTransform, getAffineTransform, estimateAffine2D, warpAffine, warpPerspective -*/ -CV_EXPORTS_W void transform(InputArray src, OutputArray dst, InputArray m ); - -/** @brief Performs the perspective matrix transformation of vectors. - -The function cv::perspectiveTransform transforms every element of src by -treating it as a 2D or 3D vector, in the following way: -\f[(x, y, z) \rightarrow (x'/w, y'/w, z'/w)\f] -where -\f[(x', y', z', w') = \texttt{mat} \cdot \begin{bmatrix} x & y & z & 1 \end{bmatrix}\f] -and -\f[w = \fork{w'}{if \(w' \ne 0\)}{\infty}{otherwise}\f] - -Here a 3D vector transformation is shown. In case of a 2D vector -transformation, the z component is omitted. - -@note The function transforms a sparse set of 2D or 3D vectors. If you -want to transform an image using perspective transformation, use -warpPerspective . If you have an inverse problem, that is, you want to -compute the most probable perspective transformation out of several -pairs of corresponding points, you can use getPerspectiveTransform or -findHomography . -@param src input two-channel or three-channel floating-point array; each -element is a 2D/3D vector to be transformed. -@param dst output array of the same size and type as src. -@param m 3x3 or 4x4 floating-point transformation matrix. -@sa transform, warpPerspective, getPerspectiveTransform, findHomography -*/ -CV_EXPORTS_W void perspectiveTransform(InputArray src, OutputArray dst, InputArray m ); - -/** @brief Copies the lower or the upper half of a square matrix to its another half. - -The function cv::completeSymm copies the lower or the upper half of a square matrix to -its another half. The matrix diagonal remains unchanged: - - \f$\texttt{m}_{ij}=\texttt{m}_{ji}\f$ for \f$i > j\f$ if - lowerToUpper=false - - \f$\texttt{m}_{ij}=\texttt{m}_{ji}\f$ for \f$i < j\f$ if - lowerToUpper=true - -@param m input-output floating-point square matrix. -@param lowerToUpper operation flag; if true, the lower half is copied to -the upper half. Otherwise, the upper half is copied to the lower half. -@sa flip, transpose -*/ -CV_EXPORTS_W void completeSymm(InputOutputArray m, bool lowerToUpper = false); - -/** @brief Initializes a scaled identity matrix. - -The function cv::setIdentity initializes a scaled identity matrix: -\f[\texttt{mtx} (i,j)= \fork{\texttt{value}}{ if \(i=j\)}{0}{otherwise}\f] - -The function can also be emulated using the matrix initializers and the -matrix expressions: -@code - Mat A = Mat::eye(4, 3, CV_32F)*5; - // A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]] -@endcode -@param mtx matrix to initialize (not necessarily square). -@param s value to assign to diagonal elements. -@sa Mat::zeros, Mat::ones, Mat::setTo, Mat::operator= -*/ -CV_EXPORTS_W void setIdentity(InputOutputArray mtx, const Scalar& s = Scalar(1)); - -/** @brief Returns the determinant of a square floating-point matrix. - -The function cv::determinant calculates and returns the determinant of the -specified matrix. For small matrices ( mtx.cols=mtx.rows\<=3 ), the -direct method is used. For larger matrices, the function uses LU -factorization with partial pivoting. - -For symmetric positively-determined matrices, it is also possible to use -eigen decomposition to calculate the determinant. -@param mtx input matrix that must have CV_32FC1 or CV_64FC1 type and -square size. -@sa trace, invert, solve, eigen, @ref MatrixExpressions -*/ -CV_EXPORTS_W double determinant(InputArray mtx); - -/** @brief Returns the trace of a matrix. - -The function cv::trace returns the sum of the diagonal elements of the -matrix mtx . -\f[\mathrm{tr} ( \texttt{mtx} ) = \sum _i \texttt{mtx} (i,i)\f] -@param mtx input matrix. -*/ -CV_EXPORTS_W Scalar trace(InputArray mtx); - -/** @brief Finds the inverse or pseudo-inverse of a matrix. - -The function cv::invert inverts the matrix src and stores the result in dst -. When the matrix src is singular or non-square, the function calculates -the pseudo-inverse matrix (the dst matrix) so that norm(src\*dst - I) is -minimal, where I is an identity matrix. - -In case of the #DECOMP_LU method, the function returns non-zero value if -the inverse has been successfully calculated and 0 if src is singular. - -In case of the #DECOMP_SVD method, the function returns the inverse -condition number of src (the ratio of the smallest singular value to the -largest singular value) and 0 if src is singular. The SVD method -calculates a pseudo-inverse matrix if src is singular. - -Similarly to #DECOMP_LU, the method #DECOMP_CHOLESKY works only with -non-singular square matrices that should also be symmetrical and -positively defined. In this case, the function stores the inverted -matrix in dst and returns non-zero. Otherwise, it returns 0. - -@param src input floating-point M x N matrix. -@param dst output matrix of N x M size and the same type as src. -@param flags inversion method (cv::DecompTypes) -@sa solve, SVD -*/ -CV_EXPORTS_W double invert(InputArray src, OutputArray dst, int flags = DECOMP_LU); - -/** @brief Solves one or more linear systems or least-squares problems. - -The function cv::solve solves a linear system or least-squares problem (the -latter is possible with SVD or QR methods, or by specifying the flag -#DECOMP_NORMAL ): -\f[\texttt{dst} = \arg \min _X \| \texttt{src1} \cdot \texttt{X} - \texttt{src2} \|\f] - -If #DECOMP_LU or #DECOMP_CHOLESKY method is used, the function returns 1 -if src1 (or \f$\texttt{src1}^T\texttt{src1}\f$ ) is non-singular. Otherwise, -it returns 0. In the latter case, dst is not valid. Other methods find a -pseudo-solution in case of a singular left-hand side part. - -@note If you want to find a unity-norm solution of an under-defined -singular system \f$\texttt{src1}\cdot\texttt{dst}=0\f$ , the function solve -will not do the work. Use SVD::solveZ instead. - -@param src1 input matrix on the left-hand side of the system. -@param src2 input matrix on the right-hand side of the system. -@param dst output solution. -@param flags solution (matrix inversion) method (#DecompTypes) -@sa invert, SVD, eigen -*/ -CV_EXPORTS_W bool solve(InputArray src1, InputArray src2, - OutputArray dst, int flags = DECOMP_LU); - -/** @brief Sorts each row or each column of a matrix. - -The function cv::sort sorts each matrix row or each matrix column in -ascending or descending order. So you should pass two operation flags to -get desired behaviour. If you want to sort matrix rows or columns -lexicographically, you can use STL std::sort generic function with the -proper comparison predicate. - -@param src input single-channel array. -@param dst output array of the same size and type as src. -@param flags operation flags, a combination of #SortFlags -@sa sortIdx, randShuffle -*/ -CV_EXPORTS_W void sort(InputArray src, OutputArray dst, int flags); - -/** @brief Sorts each row or each column of a matrix. - -The function cv::sortIdx sorts each matrix row or each matrix column in the -ascending or descending order. So you should pass two operation flags to -get desired behaviour. Instead of reordering the elements themselves, it -stores the indices of sorted elements in the output array. For example: -@code - Mat A = Mat::eye(3,3,CV_32F), B; - sortIdx(A, B, SORT_EVERY_ROW + SORT_ASCENDING); - // B will probably contain - // (because of equal elements in A some permutations are possible): - // [[1, 2, 0], [0, 2, 1], [0, 1, 2]] -@endcode -@param src input single-channel array. -@param dst output integer array of the same size as src. -@param flags operation flags that could be a combination of cv::SortFlags -@sa sort, randShuffle -*/ -CV_EXPORTS_W void sortIdx(InputArray src, OutputArray dst, int flags); - -/** @brief Finds the real roots of a cubic equation. - -The function solveCubic finds the real roots of a cubic equation: -- if coeffs is a 4-element vector: -\f[\texttt{coeffs} [0] x^3 + \texttt{coeffs} [1] x^2 + \texttt{coeffs} [2] x + \texttt{coeffs} [3] = 0\f] -- if coeffs is a 3-element vector: -\f[x^3 + \texttt{coeffs} [0] x^2 + \texttt{coeffs} [1] x + \texttt{coeffs} [2] = 0\f] - -The roots are stored in the roots array. -@param coeffs equation coefficients, an array of 3 or 4 elements. -@param roots output array of real roots that has 1 or 3 elements. -@return number of real roots. It can be 0, 1 or 2. -*/ -CV_EXPORTS_W int solveCubic(InputArray coeffs, OutputArray roots); - -/** @brief Finds the real or complex roots of a polynomial equation. - -The function cv::solvePoly finds real and complex roots of a polynomial equation: -\f[\texttt{coeffs} [n] x^{n} + \texttt{coeffs} [n-1] x^{n-1} + ... + \texttt{coeffs} [1] x + \texttt{coeffs} [0] = 0\f] -@param coeffs array of polynomial coefficients. -@param roots output (complex) array of roots. -@param maxIters maximum number of iterations the algorithm does. -*/ -CV_EXPORTS_W double solvePoly(InputArray coeffs, OutputArray roots, int maxIters = 300); - -/** @brief Calculates eigenvalues and eigenvectors of a symmetric matrix. - -The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric -matrix src: -@code - src*eigenvectors.row(i).t() = eigenvalues.at(i)*eigenvectors.row(i).t() -@endcode - -@note Use cv::eigenNonSymmetric for calculation of real eigenvalues and eigenvectors of non-symmetric matrix. - -@param src input matrix that must have CV_32FC1 or CV_64FC1 type, square size and be symmetrical -(src ^T^ == src). -@param eigenvalues output vector of eigenvalues of the same type as src; the eigenvalues are stored -in the descending order. -@param eigenvectors output matrix of eigenvectors; it has the same size and type as src; the -eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding -eigenvalues. -@sa eigenNonSymmetric, completeSymm , PCA -*/ -CV_EXPORTS_W bool eigen(InputArray src, OutputArray eigenvalues, - OutputArray eigenvectors = noArray()); - -/** @brief Calculates eigenvalues and eigenvectors of a non-symmetric matrix (real eigenvalues only). - -@note Assumes real eigenvalues. - -The function calculates eigenvalues and eigenvectors (optional) of the square matrix src: -@code - src*eigenvectors.row(i).t() = eigenvalues.at(i)*eigenvectors.row(i).t() -@endcode - -@param src input matrix (CV_32FC1 or CV_64FC1 type). -@param eigenvalues output vector of eigenvalues (type is the same type as src). -@param eigenvectors output matrix of eigenvectors (type is the same type as src). The eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding eigenvalues. -@sa eigen -*/ -CV_EXPORTS_W void eigenNonSymmetric(InputArray src, OutputArray eigenvalues, - OutputArray eigenvectors); - -/** @brief Calculates the covariance matrix of a set of vectors. - -The function cv::calcCovarMatrix calculates the covariance matrix and, optionally, the mean vector of -the set of input vectors. -@param samples samples stored as separate matrices -@param nsamples number of samples -@param covar output covariance matrix of the type ctype and square size. -@param mean input or output (depending on the flags) array as the average value of the input vectors. -@param flags operation flags as a combination of #CovarFlags -@param ctype type of the matrixl; it equals 'CV_64F' by default. -@sa PCA, mulTransposed, Mahalanobis -@todo InputArrayOfArrays -*/ -CV_EXPORTS void calcCovarMatrix( const Mat* samples, int nsamples, Mat& covar, Mat& mean, - int flags, int ctype = CV_64F); - -/** @overload -@note use #COVAR_ROWS or #COVAR_COLS flag -@param samples samples stored as rows/columns of a single matrix. -@param covar output covariance matrix of the type ctype and square size. -@param mean input or output (depending on the flags) array as the average value of the input vectors. -@param flags operation flags as a combination of #CovarFlags -@param ctype type of the matrixl; it equals 'CV_64F' by default. -*/ -CV_EXPORTS_W void calcCovarMatrix( InputArray samples, OutputArray covar, - InputOutputArray mean, int flags, int ctype = CV_64F); - -/** wrap PCA::operator() */ -CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean, - OutputArray eigenvectors, int maxComponents = 0); - -/** wrap PCA::operator() and add eigenvalues output parameter */ -CV_EXPORTS_AS(PCACompute2) void PCACompute(InputArray data, InputOutputArray mean, - OutputArray eigenvectors, OutputArray eigenvalues, - int maxComponents = 0); - -/** wrap PCA::operator() */ -CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean, - OutputArray eigenvectors, double retainedVariance); - -/** wrap PCA::operator() and add eigenvalues output parameter */ -CV_EXPORTS_AS(PCACompute2) void PCACompute(InputArray data, InputOutputArray mean, - OutputArray eigenvectors, OutputArray eigenvalues, - double retainedVariance); - -/** wrap PCA::project */ -CV_EXPORTS_W void PCAProject(InputArray data, InputArray mean, - InputArray eigenvectors, OutputArray result); - -/** wrap PCA::backProject */ -CV_EXPORTS_W void PCABackProject(InputArray data, InputArray mean, - InputArray eigenvectors, OutputArray result); - -/** wrap SVD::compute */ -CV_EXPORTS_W void SVDecomp( InputArray src, OutputArray w, OutputArray u, OutputArray vt, int flags = 0 ); - -/** wrap SVD::backSubst */ -CV_EXPORTS_W void SVBackSubst( InputArray w, InputArray u, InputArray vt, - InputArray rhs, OutputArray dst ); - -/** @brief Calculates the Mahalanobis distance between two vectors. - -The function cv::Mahalanobis calculates and returns the weighted distance between two vectors: -\f[d( \texttt{vec1} , \texttt{vec2} )= \sqrt{\sum_{i,j}{\texttt{icovar(i,j)}\cdot(\texttt{vec1}(I)-\texttt{vec2}(I))\cdot(\texttt{vec1(j)}-\texttt{vec2(j)})} }\f] -The covariance matrix may be calculated using the #calcCovarMatrix function and then inverted using -the invert function (preferably using the #DECOMP_SVD method, as the most accurate). -@param v1 first 1D input vector. -@param v2 second 1D input vector. -@param icovar inverse covariance matrix. -*/ -CV_EXPORTS_W double Mahalanobis(InputArray v1, InputArray v2, InputArray icovar); - -/** @brief Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array. - -The function cv::dft performs one of the following: -- Forward the Fourier transform of a 1D vector of N elements: - \f[Y = F^{(N)} \cdot X,\f] - where \f$F^{(N)}_{jk}=\exp(-2\pi i j k/N)\f$ and \f$i=\sqrt{-1}\f$ -- Inverse the Fourier transform of a 1D vector of N elements: - \f[\begin{array}{l} X'= \left (F^{(N)} \right )^{-1} \cdot Y = \left (F^{(N)} \right )^* \cdot y \\ X = (1/N) \cdot X, \end{array}\f] - where \f$F^*=\left(\textrm{Re}(F^{(N)})-\textrm{Im}(F^{(N)})\right)^T\f$ -- Forward the 2D Fourier transform of a M x N matrix: - \f[Y = F^{(M)} \cdot X \cdot F^{(N)}\f] -- Inverse the 2D Fourier transform of a M x N matrix: - \f[\begin{array}{l} X'= \left (F^{(M)} \right )^* \cdot Y \cdot \left (F^{(N)} \right )^* \\ X = \frac{1}{M \cdot N} \cdot X' \end{array}\f] - -In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input -spectrum of the inverse Fourier transform can be represented in a packed format called *CCS* -(complex-conjugate-symmetrical). It was borrowed from IPL (Intel\* Image Processing Library). Here -is how 2D *CCS* spectrum looks: -\f[\begin{bmatrix} Re Y_{0,0} & Re Y_{0,1} & Im Y_{0,1} & Re Y_{0,2} & Im Y_{0,2} & \cdots & Re Y_{0,N/2-1} & Im Y_{0,N/2-1} & Re Y_{0,N/2} \\ Re Y_{1,0} & Re Y_{1,1} & Im Y_{1,1} & Re Y_{1,2} & Im Y_{1,2} & \cdots & Re Y_{1,N/2-1} & Im Y_{1,N/2-1} & Re Y_{1,N/2} \\ Im Y_{1,0} & Re Y_{2,1} & Im Y_{2,1} & Re Y_{2,2} & Im Y_{2,2} & \cdots & Re Y_{2,N/2-1} & Im Y_{2,N/2-1} & Im Y_{1,N/2} \\ \hdotsfor{9} \\ Re Y_{M/2-1,0} & Re Y_{M-3,1} & Im Y_{M-3,1} & \hdotsfor{3} & Re Y_{M-3,N/2-1} & Im Y_{M-3,N/2-1}& Re Y_{M/2-1,N/2} \\ Im Y_{M/2-1,0} & Re Y_{M-2,1} & Im Y_{M-2,1} & \hdotsfor{3} & Re Y_{M-2,N/2-1} & Im Y_{M-2,N/2-1}& Im Y_{M/2-1,N/2} \\ Re Y_{M/2,0} & Re Y_{M-1,1} & Im Y_{M-1,1} & \hdotsfor{3} & Re Y_{M-1,N/2-1} & Im Y_{M-1,N/2-1}& Re Y_{M/2,N/2} \end{bmatrix}\f] - -In case of 1D transform of a real vector, the output looks like the first row of the matrix above. - -So, the function chooses an operation mode depending on the flags and size of the input array: -- If #DFT_ROWS is set or the input array has a single row or single column, the function - performs a 1D forward or inverse transform of each row of a matrix when #DFT_ROWS is set. - Otherwise, it performs a 2D transform. -- If the input array is real and #DFT_INVERSE is not set, the function performs a forward 1D or - 2D transform: - - When #DFT_COMPLEX_OUTPUT is set, the output is a complex matrix of the same size as - input. - - When #DFT_COMPLEX_OUTPUT is not set, the output is a real matrix of the same size as - input. In case of 2D transform, it uses the packed format as shown above. In case of a - single 1D transform, it looks like the first row of the matrix above. In case of - multiple 1D transforms (when using the #DFT_ROWS flag), each row of the output matrix - looks like the first row of the matrix above. -- If the input array is complex and either #DFT_INVERSE or #DFT_REAL_OUTPUT are not set, the - output is a complex array of the same size as input. The function performs a forward or - inverse 1D or 2D transform of the whole input array or each row of the input array - independently, depending on the flags DFT_INVERSE and DFT_ROWS. -- When #DFT_INVERSE is set and the input array is real, or it is complex but #DFT_REAL_OUTPUT - is set, the output is a real array of the same size as input. The function performs a 1D or 2D - inverse transformation of the whole input array or each individual row, depending on the flags - #DFT_INVERSE and #DFT_ROWS. - -If #DFT_SCALE is set, the scaling is done after the transformation. - -Unlike dct , the function supports arrays of arbitrary size. But only those arrays are processed -efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the -current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize -method. - -The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays: -@code - void convolveDFT(InputArray A, InputArray B, OutputArray C) - { - // reallocate the output array if needed - C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type()); - Size dftSize; - // calculate the size of DFT transform - dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1); - dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1); - - // allocate temporary buffers and initialize them with 0's - Mat tempA(dftSize, A.type(), Scalar::all(0)); - Mat tempB(dftSize, B.type(), Scalar::all(0)); - - // copy A and B to the top-left corners of tempA and tempB, respectively - Mat roiA(tempA, Rect(0,0,A.cols,A.rows)); - A.copyTo(roiA); - Mat roiB(tempB, Rect(0,0,B.cols,B.rows)); - B.copyTo(roiB); - - // now transform the padded A & B in-place; - // use "nonzeroRows" hint for faster processing - dft(tempA, tempA, 0, A.rows); - dft(tempB, tempB, 0, B.rows); - - // multiply the spectrums; - // the function handles packed spectrum representations well - mulSpectrums(tempA, tempB, tempA); - - // transform the product back from the frequency domain. - // Even though all the result rows will be non-zero, - // you need only the first C.rows of them, and thus you - // pass nonzeroRows == C.rows - dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows); - - // now copy the result back to C. - tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C); - - // all the temporary buffers will be deallocated automatically - } -@endcode -To optimize this sample, consider the following approaches: -- Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to - the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole - tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols) - rightmost columns of the matrices. -- This DFT-based convolution does not have to be applied to the whole big arrays, especially if B - is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts. - To do this, you need to split the output array C into multiple tiles. For each tile, estimate - which parts of A and B are required to calculate convolution in this tile. If the tiles in C are - too small, the speed will decrease a lot because of repeated work. In the ultimate case, when - each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution - algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and - there is also a slowdown because of bad cache locality. So, there is an optimal tile size - somewhere in the middle. -- If different tiles in C can be calculated in parallel and, thus, the convolution is done by - parts, the loop can be threaded. - -All of the above improvements have been implemented in #matchTemplate and #filter2D . Therefore, by -using them, you can get the performance even better than with the above theoretically optimal -implementation. Though, those two functions actually calculate cross-correlation, not convolution, -so you need to "flip" the second convolution operand B vertically and horizontally using flip . -@note -- An example using the discrete fourier transform can be found at - opencv_source_code/samples/cpp/dft.cpp -- (Python) An example using the dft functionality to perform Wiener deconvolution can be found - at opencv_source/samples/python/deconvolution.py -- (Python) An example rearranging the quadrants of a Fourier image can be found at - opencv_source/samples/python/dft.py -@param src input array that could be real or complex. -@param dst output array whose size and type depends on the flags . -@param flags transformation flags, representing a combination of the #DftFlags -@param nonzeroRows when the parameter is not zero, the function assumes that only the first -nonzeroRows rows of the input array (#DFT_INVERSE is not set) or only the first nonzeroRows of the -output array (#DFT_INVERSE is set) contain non-zeros, thus, the function can handle the rest of the -rows more efficiently and save some time; this technique is very useful for calculating array -cross-correlation or convolution using DFT. -@sa dct , getOptimalDFTSize , mulSpectrums, filter2D , matchTemplate , flip , cartToPolar , -magnitude , phase -*/ -CV_EXPORTS_W void dft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0); - -/** @brief Calculates the inverse Discrete Fourier Transform of a 1D or 2D array. - -idft(src, dst, flags) is equivalent to dft(src, dst, flags | #DFT_INVERSE) . -@note None of dft and idft scales the result by default. So, you should pass #DFT_SCALE to one of -dft or idft explicitly to make these transforms mutually inverse. -@sa dft, dct, idct, mulSpectrums, getOptimalDFTSize -@param src input floating-point real or complex array. -@param dst output array whose size and type depend on the flags. -@param flags operation flags (see dft and #DftFlags). -@param nonzeroRows number of dst rows to process; the rest of the rows have undefined content (see -the convolution sample in dft description. -*/ -CV_EXPORTS_W void idft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0); - -/** @brief Performs a forward or inverse discrete Cosine transform of 1D or 2D array. - -The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D -floating-point array: -- Forward Cosine transform of a 1D vector of N elements: - \f[Y = C^{(N)} \cdot X\f] - where - \f[C^{(N)}_{jk}= \sqrt{\alpha_j/N} \cos \left ( \frac{\pi(2k+1)j}{2N} \right )\f] - and - \f$\alpha_0=1\f$, \f$\alpha_j=2\f$ for *j \> 0*. -- Inverse Cosine transform of a 1D vector of N elements: - \f[X = \left (C^{(N)} \right )^{-1} \cdot Y = \left (C^{(N)} \right )^T \cdot Y\f] - (since \f$C^{(N)}\f$ is an orthogonal matrix, \f$C^{(N)} \cdot \left(C^{(N)}\right)^T = I\f$ ) -- Forward 2D Cosine transform of M x N matrix: - \f[Y = C^{(N)} \cdot X \cdot \left (C^{(N)} \right )^T\f] -- Inverse 2D Cosine transform of M x N matrix: - \f[X = \left (C^{(N)} \right )^T \cdot X \cdot C^{(N)}\f] - -The function chooses the mode of operation by looking at the flags and size of the input array: -- If (flags & #DCT_INVERSE) == 0 , the function does a forward 1D or 2D transform. Otherwise, it - is an inverse 1D or 2D transform. -- If (flags & #DCT_ROWS) != 0 , the function performs a 1D transform of each row. -- If the array is a single column or a single row, the function performs a 1D transform. -- If none of the above is true, the function performs a 2D transform. - -@note Currently dct supports even-size arrays (2, 4, 6 ...). For data analysis and approximation, you -can pad the array when necessary. -Also, the function performance depends very much, and not monotonically, on the array size (see -getOptimalDFTSize ). In the current implementation DCT of a vector of size N is calculated via DFT -of a vector of size N/2 . Thus, the optimal DCT size N1 \>= N can be calculated as: -@code - size_t getOptimalDCTSize(size_t N) { return 2*getOptimalDFTSize((N+1)/2); } - N1 = getOptimalDCTSize(N); -@endcode -@param src input floating-point array. -@param dst output array of the same size and type as src . -@param flags transformation flags as a combination of cv::DftFlags (DCT_*) -@sa dft , getOptimalDFTSize , idct -*/ -CV_EXPORTS_W void dct(InputArray src, OutputArray dst, int flags = 0); - -/** @brief Calculates the inverse Discrete Cosine Transform of a 1D or 2D array. - -idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE). -@param src input floating-point single-channel array. -@param dst output array of the same size and type as src. -@param flags operation flags. -@sa dct, dft, idft, getOptimalDFTSize -*/ -CV_EXPORTS_W void idct(InputArray src, OutputArray dst, int flags = 0); - -/** @brief Performs the per-element multiplication of two Fourier spectrums. - -The function cv::mulSpectrums performs the per-element multiplication of the two CCS-packed or complex -matrices that are results of a real or complex Fourier transform. - -The function, together with dft and idft , may be used to calculate convolution (pass conjB=false ) -or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are -simply multiplied (per element) with an optional conjugation of the second-array elements. When the -arrays are real, they are assumed to be CCS-packed (see dft for details). -@param a first input array. -@param b second input array of the same size and type as src1 . -@param c output array of the same size and type as src1 . -@param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that -each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value. -@param conjB optional flag that conjugates the second input array before the multiplication (true) -or not (false). -*/ -CV_EXPORTS_W void mulSpectrums(InputArray a, InputArray b, OutputArray c, - int flags, bool conjB = false); - -/** @brief Returns the optimal DFT size for a given vector size. - -DFT performance is not a monotonic function of a vector size. Therefore, when you calculate -convolution of two arrays or perform the spectral analysis of an array, it usually makes sense to -pad the input data with zeros to get a bit larger array that can be transformed much faster than the -original one. Arrays whose size is a power-of-two (2, 4, 8, 16, 32, ...) are the fastest to process. -Though, the arrays whose size is a product of 2's, 3's, and 5's (for example, 300 = 5\*5\*3\*2\*2) -are also processed quite efficiently. - -The function cv::getOptimalDFTSize returns the minimum number N that is greater than or equal to vecsize -so that the DFT of a vector of size N can be processed efficiently. In the current implementation N -= 2 ^p^ \* 3 ^q^ \* 5 ^r^ for some integer p, q, r. - -The function returns a negative number if vecsize is too large (very close to INT_MAX ). - -While the function cannot be used directly to estimate the optimal vector size for DCT transform -(since the current DCT implementation supports only even-size vectors), it can be easily processed -as getOptimalDFTSize((vecsize+1)/2)\*2. -@param vecsize vector size. -@sa dft , dct , idft , idct , mulSpectrums -*/ -CV_EXPORTS_W int getOptimalDFTSize(int vecsize); - -/** @brief Returns the default random number generator. - -The function cv::theRNG returns the default random number generator. For each thread, there is a -separate random number generator, so you can use the function safely in multi-thread environments. -If you just need to get a single random number using this generator or initialize an array, you can -use randu or randn instead. But if you are going to generate many random numbers inside a loop, it -is much faster to use this function to retrieve the generator and then use RNG::operator _Tp() . -@sa RNG, randu, randn -*/ -CV_EXPORTS RNG& theRNG(); - -/** @brief Sets state of default random number generator. - -The function cv::setRNGSeed sets state of default random number generator to custom value. -@param seed new state for default random number generator -@sa RNG, randu, randn -*/ -CV_EXPORTS_W void setRNGSeed(int seed); - -/** @brief Generates a single uniformly-distributed random number or an array of random numbers. - -Non-template variant of the function fills the matrix dst with uniformly-distributed -random numbers from the specified range: -\f[\texttt{low} _c \leq \texttt{dst} (I)_c < \texttt{high} _c\f] -@param dst output array of random numbers; the array must be pre-allocated. -@param low inclusive lower boundary of the generated random numbers. -@param high exclusive upper boundary of the generated random numbers. -@sa RNG, randn, theRNG -*/ -CV_EXPORTS_W void randu(InputOutputArray dst, InputArray low, InputArray high); - -/** @brief Fills the array with normally distributed random numbers. - -The function cv::randn fills the matrix dst with normally distributed random numbers with the specified -mean vector and the standard deviation matrix. The generated random numbers are clipped to fit the -value range of the output array data type. -@param dst output array of random numbers; the array must be pre-allocated and have 1 to 4 channels. -@param mean mean value (expectation) of the generated random numbers. -@param stddev standard deviation of the generated random numbers; it can be either a vector (in -which case a diagonal standard deviation matrix is assumed) or a square matrix. -@sa RNG, randu -*/ -CV_EXPORTS_W void randn(InputOutputArray dst, InputArray mean, InputArray stddev); - -/** @brief Shuffles the array elements randomly. - -The function cv::randShuffle shuffles the specified 1D array by randomly choosing pairs of elements and -swapping them. The number of such swap operations will be dst.rows\*dst.cols\*iterFactor . -@param dst input/output numerical 1D array. -@param iterFactor scale factor that determines the number of random swap operations (see the details -below). -@param rng optional random number generator used for shuffling; if it is zero, theRNG () is used -instead. -@sa RNG, sort -*/ -CV_EXPORTS_W void randShuffle(InputOutputArray dst, double iterFactor = 1., RNG* rng = 0); - -/** @brief Principal Component Analysis - -The class is used to calculate a special basis for a set of vectors. The -basis will consist of eigenvectors of the covariance matrix calculated -from the input set of vectors. The class %PCA can also transform -vectors to/from the new coordinate space defined by the basis. Usually, -in this new coordinate system, each vector from the original set (and -any linear combination of such vectors) can be quite accurately -approximated by taking its first few components, corresponding to the -eigenvectors of the largest eigenvalues of the covariance matrix. -Geometrically it means that you calculate a projection of the vector to -a subspace formed by a few eigenvectors corresponding to the dominant -eigenvalues of the covariance matrix. And usually such a projection is -very close to the original vector. So, you can represent the original -vector from a high-dimensional space with a much shorter vector -consisting of the projected vector's coordinates in the subspace. Such a -transformation is also known as Karhunen-Loeve Transform, or KLT. -See http://en.wikipedia.org/wiki/Principal_component_analysis - -The sample below is the function that takes two matrices. The first -function stores a set of vectors (a row per vector) that is used to -calculate PCA. The second function stores another "test" set of vectors -(a row per vector). First, these vectors are compressed with PCA, then -reconstructed back, and then the reconstruction error norm is computed -and printed for each vector. : - -@code{.cpp} -using namespace cv; - -PCA compressPCA(const Mat& pcaset, int maxComponents, - const Mat& testset, Mat& compressed) -{ - PCA pca(pcaset, // pass the data - Mat(), // we do not have a pre-computed mean vector, - // so let the PCA engine to compute it - PCA::DATA_AS_ROW, // indicate that the vectors - // are stored as matrix rows - // (use PCA::DATA_AS_COL if the vectors are - // the matrix columns) - maxComponents // specify, how many principal components to retain - ); - // if there is no test data, just return the computed basis, ready-to-use - if( !testset.data ) - return pca; - CV_Assert( testset.cols == pcaset.cols ); - - compressed.create(testset.rows, maxComponents, testset.type()); - - Mat reconstructed; - for( int i = 0; i < testset.rows; i++ ) - { - Mat vec = testset.row(i), coeffs = compressed.row(i), reconstructed; - // compress the vector, the result will be stored - // in the i-th row of the output matrix - pca.project(vec, coeffs); - // and then reconstruct it - pca.backProject(coeffs, reconstructed); - // and measure the error - printf("%d. diff = %g\n", i, norm(vec, reconstructed, NORM_L2)); - } - return pca; -} -@endcode -@sa calcCovarMatrix, mulTransposed, SVD, dft, dct -*/ -class CV_EXPORTS PCA -{ -public: - enum Flags { DATA_AS_ROW = 0, //!< indicates that the input samples are stored as matrix rows - DATA_AS_COL = 1, //!< indicates that the input samples are stored as matrix columns - USE_AVG = 2 //! - }; - - /** @brief default constructor - - The default constructor initializes an empty %PCA structure. The other - constructors initialize the structure and call PCA::operator()(). - */ - PCA(); - - /** @overload - @param data input samples stored as matrix rows or matrix columns. - @param mean optional mean value; if the matrix is empty (@c noArray()), - the mean is computed from the data. - @param flags operation flags; currently the parameter is only used to - specify the data layout (PCA::Flags) - @param maxComponents maximum number of components that %PCA should - retain; by default, all the components are retained. - */ - PCA(InputArray data, InputArray mean, int flags, int maxComponents = 0); - - /** @overload - @param data input samples stored as matrix rows or matrix columns. - @param mean optional mean value; if the matrix is empty (noArray()), - the mean is computed from the data. - @param flags operation flags; currently the parameter is only used to - specify the data layout (PCA::Flags) - @param retainedVariance Percentage of variance that PCA should retain. - Using this parameter will let the PCA decided how many components to - retain but it will always keep at least 2. - */ - PCA(InputArray data, InputArray mean, int flags, double retainedVariance); - - /** @brief performs %PCA - - The operator performs %PCA of the supplied dataset. It is safe to reuse - the same PCA structure for multiple datasets. That is, if the structure - has been previously used with another dataset, the existing internal - data is reclaimed and the new @ref eigenvalues, @ref eigenvectors and @ref - mean are allocated and computed. - - The computed @ref eigenvalues are sorted from the largest to the smallest and - the corresponding @ref eigenvectors are stored as eigenvectors rows. - - @param data input samples stored as the matrix rows or as the matrix - columns. - @param mean optional mean value; if the matrix is empty (noArray()), - the mean is computed from the data. - @param flags operation flags; currently the parameter is only used to - specify the data layout. (Flags) - @param maxComponents maximum number of components that PCA should - retain; by default, all the components are retained. - */ - PCA& operator()(InputArray data, InputArray mean, int flags, int maxComponents = 0); - - /** @overload - @param data input samples stored as the matrix rows or as the matrix - columns. - @param mean optional mean value; if the matrix is empty (noArray()), - the mean is computed from the data. - @param flags operation flags; currently the parameter is only used to - specify the data layout. (PCA::Flags) - @param retainedVariance Percentage of variance that %PCA should retain. - Using this parameter will let the %PCA decided how many components to - retain but it will always keep at least 2. - */ - PCA& operator()(InputArray data, InputArray mean, int flags, double retainedVariance); - - /** @brief Projects vector(s) to the principal component subspace. - - The methods project one or more vectors to the principal component - subspace, where each vector projection is represented by coefficients in - the principal component basis. The first form of the method returns the - matrix that the second form writes to the result. So the first form can - be used as a part of expression while the second form can be more - efficient in a processing loop. - @param vec input vector(s); must have the same dimensionality and the - same layout as the input data used at %PCA phase, that is, if - DATA_AS_ROW are specified, then `vec.cols==data.cols` - (vector dimensionality) and `vec.rows` is the number of vectors to - project, and the same is true for the PCA::DATA_AS_COL case. - */ - Mat project(InputArray vec) const; - - /** @overload - @param vec input vector(s); must have the same dimensionality and the - same layout as the input data used at PCA phase, that is, if - DATA_AS_ROW are specified, then `vec.cols==data.cols` - (vector dimensionality) and `vec.rows` is the number of vectors to - project, and the same is true for the PCA::DATA_AS_COL case. - @param result output vectors; in case of PCA::DATA_AS_COL, the - output matrix has as many columns as the number of input vectors, this - means that `result.cols==vec.cols` and the number of rows match the - number of principal components (for example, `maxComponents` parameter - passed to the constructor). - */ - void project(InputArray vec, OutputArray result) const; - - /** @brief Reconstructs vectors from their PC projections. - - The methods are inverse operations to PCA::project. They take PC - coordinates of projected vectors and reconstruct the original vectors. - Unless all the principal components have been retained, the - reconstructed vectors are different from the originals. But typically, - the difference is small if the number of components is large enough (but - still much smaller than the original vector dimensionality). As a - result, PCA is used. - @param vec coordinates of the vectors in the principal component - subspace, the layout and size are the same as of PCA::project output - vectors. - */ - Mat backProject(InputArray vec) const; - - /** @overload - @param vec coordinates of the vectors in the principal component - subspace, the layout and size are the same as of PCA::project output - vectors. - @param result reconstructed vectors; the layout and size are the same as - of PCA::project input vectors. - */ - void backProject(InputArray vec, OutputArray result) const; - - /** @brief write PCA objects - - Writes @ref eigenvalues @ref eigenvectors and @ref mean to specified FileStorage - */ - void write(FileStorage& fs) const; - - /** @brief load PCA objects - - Loads @ref eigenvalues @ref eigenvectors and @ref mean from specified FileNode - */ - void read(const FileNode& fn); - - Mat eigenvectors; //!< eigenvectors of the covariation matrix - Mat eigenvalues; //!< eigenvalues of the covariation matrix - Mat mean; //!< mean value subtracted before the projection and added after the back projection -}; - -/** @example samples/cpp/pca.cpp -An example using %PCA for dimensionality reduction while maintaining an amount of variance -*/ - -/** @example samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp -Check @ref tutorial_introduction_to_pca "the corresponding tutorial" for more details -*/ - -/** -@brief Linear Discriminant Analysis -@todo document this class -*/ -class CV_EXPORTS LDA -{ -public: - /** @brief constructor - Initializes a LDA with num_components (default 0). - */ - explicit LDA(int num_components = 0); - - /** Initializes and performs a Discriminant Analysis with Fisher's - Optimization Criterion on given data in src and corresponding labels - in labels. If 0 (or less) number of components are given, they are - automatically determined for given data in computation. - */ - LDA(InputArrayOfArrays src, InputArray labels, int num_components = 0); - - /** Serializes this object to a given filename. - */ - void save(const String& filename) const; - - /** Deserializes this object from a given filename. - */ - void load(const String& filename); - - /** Serializes this object to a given cv::FileStorage. - */ - void save(FileStorage& fs) const; - - /** Deserializes this object from a given cv::FileStorage. - */ - void load(const FileStorage& node); - - /** destructor - */ - ~LDA(); - - /** Compute the discriminants for data in src (row aligned) and labels. - */ - void compute(InputArrayOfArrays src, InputArray labels); - - /** Projects samples into the LDA subspace. - src may be one or more row aligned samples. - */ - Mat project(InputArray src); - - /** Reconstructs projections from the LDA subspace. - src may be one or more row aligned projections. - */ - Mat reconstruct(InputArray src); - - /** Returns the eigenvectors of this LDA. - */ - Mat eigenvectors() const { return _eigenvectors; } - - /** Returns the eigenvalues of this LDA. - */ - Mat eigenvalues() const { return _eigenvalues; } - - static Mat subspaceProject(InputArray W, InputArray mean, InputArray src); - static Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src); - -protected: - bool _dataAsRow; // unused, but needed for 3.0 ABI compatibility. - int _num_components; - Mat _eigenvectors; - Mat _eigenvalues; - void lda(InputArrayOfArrays src, InputArray labels); -}; - -/** @brief Singular Value Decomposition - -Class for computing Singular Value Decomposition of a floating-point -matrix. The Singular Value Decomposition is used to solve least-square -problems, under-determined linear systems, invert matrices, compute -condition numbers, and so on. - -If you want to compute a condition number of a matrix or an absolute value of -its determinant, you do not need `u` and `vt`. You can pass -flags=SVD::NO_UV|... . Another flag SVD::FULL_UV indicates that full-size u -and vt must be computed, which is not necessary most of the time. - -@sa invert, solve, eigen, determinant -*/ -class CV_EXPORTS SVD -{ -public: - enum Flags { - /** allow the algorithm to modify the decomposed matrix; it can save space and speed up - processing. currently ignored. */ - MODIFY_A = 1, - /** indicates that only a vector of singular values `w` is to be processed, while u and vt - will be set to empty matrices */ - NO_UV = 2, - /** when the matrix is not square, by default the algorithm produces u and vt matrices of - sufficiently large size for the further A reconstruction; if, however, FULL_UV flag is - specified, u and vt will be full-size square orthogonal matrices.*/ - FULL_UV = 4 - }; - - /** @brief the default constructor - - initializes an empty SVD structure - */ - SVD(); - - /** @overload - initializes an empty SVD structure and then calls SVD::operator() - @param src decomposed matrix. The depth has to be CV_32F or CV_64F. - @param flags operation flags (SVD::Flags) - */ - SVD( InputArray src, int flags = 0 ); - - /** @brief the operator that performs SVD. The previously allocated u, w and vt are released. - - The operator performs the singular value decomposition of the supplied - matrix. The u,`vt` , and the vector of singular values w are stored in - the structure. The same SVD structure can be reused many times with - different matrices. Each time, if needed, the previous u,`vt` , and w - are reclaimed and the new matrices are created, which is all handled by - Mat::create. - @param src decomposed matrix. The depth has to be CV_32F or CV_64F. - @param flags operation flags (SVD::Flags) - */ - SVD& operator ()( InputArray src, int flags = 0 ); - - /** @brief decomposes matrix and stores the results to user-provided matrices - - The methods/functions perform SVD of matrix. Unlike SVD::SVD constructor - and SVD::operator(), they store the results to the user-provided - matrices: - - @code{.cpp} - Mat A, w, u, vt; - SVD::compute(A, w, u, vt); - @endcode - - @param src decomposed matrix. The depth has to be CV_32F or CV_64F. - @param w calculated singular values - @param u calculated left singular vectors - @param vt transposed matrix of right singular vectors - @param flags operation flags - see SVD::Flags. - */ - static void compute( InputArray src, OutputArray w, - OutputArray u, OutputArray vt, int flags = 0 ); - - /** @overload - computes singular values of a matrix - @param src decomposed matrix. The depth has to be CV_32F or CV_64F. - @param w calculated singular values - @param flags operation flags - see SVD::Flags. - */ - static void compute( InputArray src, OutputArray w, int flags = 0 ); - - /** @brief performs back substitution - */ - static void backSubst( InputArray w, InputArray u, - InputArray vt, InputArray rhs, - OutputArray dst ); - - /** @brief solves an under-determined singular linear system - - The method finds a unit-length solution x of a singular linear system - A\*x = 0. Depending on the rank of A, there can be no solutions, a - single solution or an infinite number of solutions. In general, the - algorithm solves the following problem: - \f[dst = \arg \min _{x: \| x \| =1} \| src \cdot x \|\f] - @param src left-hand-side matrix. - @param dst found solution. - */ - static void solveZ( InputArray src, OutputArray dst ); - - /** @brief performs a singular value back substitution. - - The method calculates a back substitution for the specified right-hand - side: - - \f[\texttt{x} = \texttt{vt} ^T \cdot diag( \texttt{w} )^{-1} \cdot \texttt{u} ^T \cdot \texttt{rhs} \sim \texttt{A} ^{-1} \cdot \texttt{rhs}\f] - - Using this technique you can either get a very accurate solution of the - convenient linear system, or the best (in the least-squares terms) - pseudo-solution of an overdetermined linear system. - - @param rhs right-hand side of a linear system (u\*w\*v')\*dst = rhs to - be solved, where A has been previously decomposed. - - @param dst found solution of the system. - - @note Explicit SVD with the further back substitution only makes sense - if you need to solve many linear systems with the same left-hand side - (for example, src ). If all you need is to solve a single system - (possibly with multiple rhs immediately available), simply call solve - add pass #DECOMP_SVD there. It does absolutely the same thing. - */ - void backSubst( InputArray rhs, OutputArray dst ) const; - - /** @todo document */ - template static - void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt ); - - /** @todo document */ - template static - void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w ); - - /** @todo document */ - template static - void backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, Matx<_Tp, n, nb>& dst ); - - Mat u, w, vt; -}; - -/** @brief Random Number Generator - -Random number generator. It encapsulates the state (currently, a 64-bit -integer) and has methods to return scalar random values and to fill -arrays with random values. Currently it supports uniform and Gaussian -(normal) distributions. The generator uses Multiply-With-Carry -algorithm, introduced by G. Marsaglia ( - ). -Gaussian-distribution random numbers are generated using the Ziggurat -algorithm ( ), -introduced by G. Marsaglia and W. W. Tsang. -*/ -class CV_EXPORTS RNG -{ -public: - enum { UNIFORM = 0, - NORMAL = 1 - }; - - /** @brief constructor - - These are the RNG constructors. The first form sets the state to some - pre-defined value, equal to 2\*\*32-1 in the current implementation. The - second form sets the state to the specified value. If you passed state=0 - , the constructor uses the above default value instead to avoid the - singular random number sequence, consisting of all zeros. - */ - RNG(); - /** @overload - @param state 64-bit value used to initialize the RNG. - */ - RNG(uint64 state); - /**The method updates the state using the MWC algorithm and returns the - next 32-bit random number.*/ - unsigned next(); - - /**Each of the methods updates the state using the MWC algorithm and - returns the next random number of the specified type. In case of integer - types, the returned number is from the available value range for the - specified type. In case of floating-point types, the returned value is - from [0,1) range. - */ - operator uchar(); - /** @overload */ - operator schar(); - /** @overload */ - operator ushort(); - /** @overload */ - operator short(); - /** @overload */ - operator unsigned(); - /** @overload */ - operator int(); - /** @overload */ - operator float(); - /** @overload */ - operator double(); - - /** @brief returns a random integer sampled uniformly from [0, N). - - The methods transform the state using the MWC algorithm and return the - next random number. The first form is equivalent to RNG::next . The - second form returns the random number modulo N , which means that the - result is in the range [0, N) . - */ - unsigned operator ()(); - /** @overload - @param N upper non-inclusive boundary of the returned random number. - */ - unsigned operator ()(unsigned N); - - /** @brief returns uniformly distributed integer random number from [a,b) range - - The methods transform the state using the MWC algorithm and return the - next uniformly-distributed random number of the specified type, deduced - from the input parameter type, from the range [a, b) . There is a nuance - illustrated by the following sample: - - @code{.cpp} - RNG rng; - - // always produces 0 - double a = rng.uniform(0, 1); - - // produces double from [0, 1) - double a1 = rng.uniform((double)0, (double)1); - - // produces float from [0, 1) - float b = rng.uniform(0.f, 1.f); - - // produces double from [0, 1) - double c = rng.uniform(0., 1.); - - // may cause compiler error because of ambiguity: - // RNG::uniform(0, (int)0.999999)? or RNG::uniform((double)0, 0.99999)? - double d = rng.uniform(0, 0.999999); - @endcode - - The compiler does not take into account the type of the variable to - which you assign the result of RNG::uniform . The only thing that - matters to the compiler is the type of a and b parameters. So, if you - want a floating-point random number, but the range boundaries are - integer numbers, either put dots in the end, if they are constants, or - use explicit type cast operators, as in the a1 initialization above. - @param a lower inclusive boundary of the returned random number. - @param b upper non-inclusive boundary of the returned random number. - */ - int uniform(int a, int b); - /** @overload */ - float uniform(float a, float b); - /** @overload */ - double uniform(double a, double b); - - /** @brief Fills arrays with random numbers. - - @param mat 2D or N-dimensional matrix; currently matrices with more than - 4 channels are not supported by the methods, use Mat::reshape as a - possible workaround. - @param distType distribution type, RNG::UNIFORM or RNG::NORMAL. - @param a first distribution parameter; in case of the uniform - distribution, this is an inclusive lower boundary, in case of the normal - distribution, this is a mean value. - @param b second distribution parameter; in case of the uniform - distribution, this is a non-inclusive upper boundary, in case of the - normal distribution, this is a standard deviation (diagonal of the - standard deviation matrix or the full standard deviation matrix). - @param saturateRange pre-saturation flag; for uniform distribution only; - if true, the method will first convert a and b to the acceptable value - range (according to the mat datatype) and then will generate uniformly - distributed random numbers within the range [saturate(a), saturate(b)), - if saturateRange=false, the method will generate uniformly distributed - random numbers in the original range [a, b) and then will saturate them, - it means, for example, that - theRNG().fill(mat_8u, RNG::UNIFORM, -DBL_MAX, DBL_MAX) will likely - produce array mostly filled with 0's and 255's, since the range (0, 255) - is significantly smaller than [-DBL_MAX, DBL_MAX). - - Each of the methods fills the matrix with the random values from the - specified distribution. As the new numbers are generated, the RNG state - is updated accordingly. In case of multiple-channel images, every - channel is filled independently, which means that RNG cannot generate - samples from the multi-dimensional Gaussian distribution with - non-diagonal covariance matrix directly. To do that, the method - generates samples from multi-dimensional standard Gaussian distribution - with zero mean and identity covariation matrix, and then transforms them - using transform to get samples from the specified Gaussian distribution. - */ - void fill( InputOutputArray mat, int distType, InputArray a, InputArray b, bool saturateRange = false ); - - /** @brief Returns the next random number sampled from the Gaussian distribution - @param sigma standard deviation of the distribution. - - The method transforms the state using the MWC algorithm and returns the - next random number from the Gaussian distribution N(0,sigma) . That is, - the mean value of the returned random numbers is zero and the standard - deviation is the specified sigma . - */ - double gaussian(double sigma); - - uint64 state; - - bool operator ==(const RNG& other) const; -}; - -/** @brief Mersenne Twister random number generator - -Inspired by http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.c -@todo document -*/ -class CV_EXPORTS RNG_MT19937 -{ -public: - RNG_MT19937(); - RNG_MT19937(unsigned s); - void seed(unsigned s); - - unsigned next(); - - operator int(); - operator unsigned(); - operator float(); - operator double(); - - unsigned operator ()(unsigned N); - unsigned operator ()(); - - /** @brief returns uniformly distributed integer random number from [a,b) range*/ - int uniform(int a, int b); - /** @brief returns uniformly distributed floating-point random number from [a,b) range*/ - float uniform(float a, float b); - /** @brief returns uniformly distributed double-precision floating-point random number from [a,b) range*/ - double uniform(double a, double b); - -private: - enum PeriodParameters {N = 624, M = 397}; - unsigned state[N]; - int mti; -}; - -//! @} core_array - -//! @addtogroup core_cluster -//! @{ - -/** @example samples/cpp/kmeans.cpp -An example on K-means clustering -*/ - -/** @brief Finds centers of clusters and groups input samples around the clusters. - -The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters -and groups the input samples around the clusters. As an output, \f$\texttt{bestLabels}_i\f$ contains a -0-based cluster index for the sample stored in the \f$i^{th}\f$ row of the samples matrix. - -@note -- (Python) An example on K-means clustering can be found at - opencv_source_code/samples/python/kmeans.py -@param data Data for clustering. An array of N-Dimensional points with float coordinates is needed. -Examples of this array can be: -- Mat points(count, 2, CV_32F); -- Mat points(count, 1, CV_32FC2); -- Mat points(1, count, CV_32FC2); -- std::vector\ points(sampleCount); -@param K Number of clusters to split the set by. -@param bestLabels Input/output integer array that stores the cluster indices for every sample. -@param criteria The algorithm termination criteria, that is, the maximum number of iterations and/or -the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster -centers moves by less than criteria.epsilon on some iteration, the algorithm stops. -@param attempts Flag to specify the number of times the algorithm is executed using different -initial labellings. The algorithm returns the labels that yield the best compactness (see the last -function parameter). -@param flags Flag that can take values of cv::KmeansFlags -@param centers Output matrix of the cluster centers, one row per each cluster center. -@return The function returns the compactness measure that is computed as -\f[\sum _i \| \texttt{samples} _i - \texttt{centers} _{ \texttt{labels} _i} \| ^2\f] -after every attempt. The best (minimum) value is chosen and the corresponding labels and the -compactness value are returned by the function. Basically, you can use only the core of the -function, set the number of attempts to 1, initialize labels each time using a custom algorithm, -pass them with the ( flags = #KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best -(most-compact) clustering. -*/ -CV_EXPORTS_W double kmeans( InputArray data, int K, InputOutputArray bestLabels, - TermCriteria criteria, int attempts, - int flags, OutputArray centers = noArray() ); - -//! @} core_cluster - -//! @addtogroup core_basic -//! @{ - -/////////////////////////////// Formatted output of cv::Mat /////////////////////////// - -/** @todo document */ -class CV_EXPORTS Formatted -{ -public: - virtual const char* next() = 0; - virtual void reset() = 0; - virtual ~Formatted(); -}; - -/** @todo document */ -class CV_EXPORTS Formatter -{ -public: - enum { FMT_DEFAULT = 0, - FMT_MATLAB = 1, - FMT_CSV = 2, - FMT_PYTHON = 3, - FMT_NUMPY = 4, - FMT_C = 5 - }; - - virtual ~Formatter(); - - virtual Ptr format(const Mat& mtx) const = 0; - - virtual void set32fPrecision(int p = 8) = 0; - virtual void set64fPrecision(int p = 16) = 0; - virtual void setMultiline(bool ml = true) = 0; - - static Ptr get(int fmt = FMT_DEFAULT); - -}; - -static inline -String& operator << (String& out, Ptr fmtd) -{ - fmtd->reset(); - for(const char* str = fmtd->next(); str; str = fmtd->next()) - out += cv::String(str); - return out; -} - -static inline -String& operator << (String& out, const Mat& mtx) -{ - return out << Formatter::get()->format(mtx); -} - -//////////////////////////////////////// Algorithm //////////////////////////////////// - -class CV_EXPORTS Algorithm; - -template struct ParamType {}; - - -/** @brief This is a base class for all more or less complex algorithms in OpenCV - -especially for classes of algorithms, for which there can be multiple implementations. The examples -are stereo correspondence (for which there are algorithms like block matching, semi-global block -matching, graph-cut etc.), background subtraction (which can be done using mixture-of-gaussians -models, codebook-based algorithm etc.), optical flow (block matching, Lucas-Kanade, Horn-Schunck -etc.). - -Here is example of SimpleBlobDetector use in your application via Algorithm interface: -@snippet snippets/core_various.cpp Algorithm -*/ -class CV_EXPORTS_W Algorithm -{ -public: - Algorithm(); - virtual ~Algorithm(); - - /** @brief Clears the algorithm state - */ - CV_WRAP virtual void clear() {} - - /** @brief Stores algorithm parameters in a file storage - */ - virtual void write(FileStorage& fs) const { CV_UNUSED(fs); } - - /** @brief simplified API for language bindings - * @overload - */ - CV_WRAP void write(const Ptr& fs, const String& name = String()) const; - - /** @brief Reads algorithm parameters from a file storage - */ - CV_WRAP virtual void read(const FileNode& fn) { CV_UNUSED(fn); } - - /** @brief Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read - */ - CV_WRAP virtual bool empty() const { return false; } - - /** @brief Reads algorithm from the file node - - This is static template method of Algorithm. It's usage is following (in the case of SVM): - @code - cv::FileStorage fsRead("example.xml", FileStorage::READ); - Ptr svm = Algorithm::read(fsRead.root()); - @endcode - In order to make this method work, the derived class must overwrite Algorithm::read(const - FileNode& fn) and also have static create() method without parameters - (or with all the optional parameters) - */ - template static Ptr<_Tp> read(const FileNode& fn) - { - Ptr<_Tp> obj = _Tp::create(); - obj->read(fn); - return !obj->empty() ? obj : Ptr<_Tp>(); - } - - /** @brief Loads algorithm from the file - - @param filename Name of the file to read. - @param objname The optional name of the node to read (if empty, the first top-level node will be used) - - This is static template method of Algorithm. It's usage is following (in the case of SVM): - @code - Ptr svm = Algorithm::load("my_svm_model.xml"); - @endcode - In order to make this method work, the derived class must overwrite Algorithm::read(const - FileNode& fn). - */ - template static Ptr<_Tp> load(const String& filename, const String& objname=String()) - { - FileStorage fs(filename, FileStorage::READ); - CV_Assert(fs.isOpened()); - FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname]; - if (fn.empty()) return Ptr<_Tp>(); - Ptr<_Tp> obj = _Tp::create(); - obj->read(fn); - return !obj->empty() ? obj : Ptr<_Tp>(); - } - - /** @brief Loads algorithm from a String - - @param strModel The string variable containing the model you want to load. - @param objname The optional name of the node to read (if empty, the first top-level node will be used) - - This is static template method of Algorithm. It's usage is following (in the case of SVM): - @code - Ptr svm = Algorithm::loadFromString(myStringModel); - @endcode - */ - template static Ptr<_Tp> loadFromString(const String& strModel, const String& objname=String()) - { - FileStorage fs(strModel, FileStorage::READ + FileStorage::MEMORY); - FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname]; - Ptr<_Tp> obj = _Tp::create(); - obj->read(fn); - return !obj->empty() ? obj : Ptr<_Tp>(); - } - - /** Saves the algorithm to a file. - In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */ - CV_WRAP virtual void save(const String& filename) const; - - /** Returns the algorithm string identifier. - This string is used as top level xml/yml node tag when the object is saved to a file or string. */ - CV_WRAP virtual String getDefaultName() const; - -protected: - void writeFormat(FileStorage& fs) const; -}; - -struct Param { - enum { INT=0, BOOLEAN=1, REAL=2, STRING=3, MAT=4, MAT_VECTOR=5, ALGORITHM=6, FLOAT=7, - UNSIGNED_INT=8, UINT64=9, UCHAR=11, SCALAR=12 }; -}; - - - -template<> struct ParamType -{ - typedef bool const_param_type; - typedef bool member_type; - - enum { type = Param::BOOLEAN }; -}; - -template<> struct ParamType -{ - typedef int const_param_type; - typedef int member_type; - - enum { type = Param::INT }; -}; - -template<> struct ParamType -{ - typedef double const_param_type; - typedef double member_type; - - enum { type = Param::REAL }; -}; - -template<> struct ParamType -{ - typedef const String& const_param_type; - typedef String member_type; - - enum { type = Param::STRING }; -}; - -template<> struct ParamType -{ - typedef const Mat& const_param_type; - typedef Mat member_type; - - enum { type = Param::MAT }; -}; - -template<> struct ParamType > -{ - typedef const std::vector& const_param_type; - typedef std::vector member_type; - - enum { type = Param::MAT_VECTOR }; -}; - -template<> struct ParamType -{ - typedef const Ptr& const_param_type; - typedef Ptr member_type; - - enum { type = Param::ALGORITHM }; -}; - -template<> struct ParamType -{ - typedef float const_param_type; - typedef float member_type; - - enum { type = Param::FLOAT }; -}; - -template<> struct ParamType -{ - typedef unsigned const_param_type; - typedef unsigned member_type; - - enum { type = Param::UNSIGNED_INT }; -}; - -template<> struct ParamType -{ - typedef uint64 const_param_type; - typedef uint64 member_type; - - enum { type = Param::UINT64 }; -}; - -template<> struct ParamType -{ - typedef uchar const_param_type; - typedef uchar member_type; - - enum { type = Param::UCHAR }; -}; - -template<> struct ParamType -{ - typedef const Scalar& const_param_type; - typedef Scalar member_type; - - enum { type = Param::SCALAR }; -}; - -//! @} core_basic - -} //namespace cv - -#include "opencv2/core/operations.hpp" -#include "opencv2/core/cvstd.inl.hpp" -#include "opencv2/core/utility.hpp" -#include "opencv2/core/optim.hpp" -#include "opencv2/core/ovx.hpp" - -#endif /*OPENCV_CORE_HPP*/ diff --git a/include/opencv2/core/affine.hpp b/include/opencv2/core/affine.hpp deleted file mode 100755 index 7e2ed30..0000000 --- a/include/opencv2/core/affine.hpp +++ /dev/null @@ -1,678 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_AFFINE3_HPP -#define OPENCV_CORE_AFFINE3_HPP - -#ifdef __cplusplus - -#include - -namespace cv -{ - -//! @addtogroup core -//! @{ - - /** @brief Affine transform - * - * It represents a 4x4 homogeneous transformation matrix \f$T\f$ - * - * \f[T = - * \begin{bmatrix} - * R & t\\ - * 0 & 1\\ - * \end{bmatrix} - * \f] - * - * where \f$R\f$ is a 3x3 rotation matrix and \f$t\f$ is a 3x1 translation vector. - * - * You can specify \f$R\f$ either by a 3x3 rotation matrix or by a 3x1 rotation vector, - * which is converted to a 3x3 rotation matrix by the Rodrigues formula. - * - * To construct a matrix \f$T\f$ representing first rotation around the axis \f$r\f$ with rotation - * angle \f$|r|\f$ in radian (right hand rule) and then translation by the vector \f$t\f$, you can use - * - * @code - * cv::Vec3f r, t; - * cv::Affine3f T(r, t); - * @endcode - * - * If you already have the rotation matrix \f$R\f$, then you can use - * - * @code - * cv::Matx33f R; - * cv::Affine3f T(R, t); - * @endcode - * - * To extract the rotation matrix \f$R\f$ from \f$T\f$, use - * - * @code - * cv::Matx33f R = T.rotation(); - * @endcode - * - * To extract the translation vector \f$t\f$ from \f$T\f$, use - * - * @code - * cv::Vec3f t = T.translation(); - * @endcode - * - * To extract the rotation vector \f$r\f$ from \f$T\f$, use - * - * @code - * cv::Vec3f r = T.rvec(); - * @endcode - * - * Note that since the mapping from rotation vectors to rotation matrices - * is many to one. The returned rotation vector is not necessarily the one - * you used before to set the matrix. - * - * If you have two transformations \f$T = T_1 * T_2\f$, use - * - * @code - * cv::Affine3f T, T1, T2; - * T = T2.concatenate(T1); - * @endcode - * - * To get the inverse transform of \f$T\f$, use - * - * @code - * cv::Affine3f T, T_inv; - * T_inv = T.inv(); - * @endcode - * - */ - template - class Affine3 - { - public: - typedef T float_type; - typedef Matx Mat3; - typedef Matx Mat4; - typedef Vec Vec3; - - //! Default constructor. It represents a 4x4 identity matrix. - Affine3(); - - //! Augmented affine matrix - Affine3(const Mat4& affine); - - /** - * The resulting 4x4 matrix is - * - * \f[ - * \begin{bmatrix} - * R & t\\ - * 0 & 1\\ - * \end{bmatrix} - * \f] - * - * @param R 3x3 rotation matrix. - * @param t 3x1 translation vector. - */ - Affine3(const Mat3& R, const Vec3& t = Vec3::all(0)); - - /** - * Rodrigues vector. - * - * The last row of the current matrix is set to [0,0,0,1]. - * - * @param rvec 3x1 rotation vector. Its direction indicates the rotation axis and its length - * indicates the rotation angle in radian (using right hand rule). - * @param t 3x1 translation vector. - */ - Affine3(const Vec3& rvec, const Vec3& t = Vec3::all(0)); - - /** - * Combines all constructors above. Supports 4x4, 3x4, 3x3, 1x3, 3x1 sizes of data matrix. - * - * The last row of the current matrix is set to [0,0,0,1] when data is not 4x4. - * - * @param data 1-channel matrix. - * when it is 4x4, it is copied to the current matrix and t is not used. - * When it is 3x4, it is copied to the upper part 3x4 of the current matrix and t is not used. - * When it is 3x3, it is copied to the upper left 3x3 part of the current matrix. - * When it is 3x1 or 1x3, it is treated as a rotation vector and the Rodrigues formula is used - * to compute a 3x3 rotation matrix. - * @param t 3x1 translation vector. It is used only when data is neither 4x4 nor 3x4. - */ - explicit Affine3(const Mat& data, const Vec3& t = Vec3::all(0)); - - //! From 16-element array - explicit Affine3(const float_type* vals); - - //! Create an 4x4 identity transform - static Affine3 Identity(); - - /** - * Rotation matrix. - * - * Copy the rotation matrix to the upper left 3x3 part of the current matrix. - * The remaining elements of the current matrix are not changed. - * - * @param R 3x3 rotation matrix. - * - */ - void rotation(const Mat3& R); - - /** - * Rodrigues vector. - * - * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected. - * - * @param rvec 3x1 rotation vector. The direction indicates the rotation axis and - * its length indicates the rotation angle in radian (using the right thumb convention). - */ - void rotation(const Vec3& rvec); - - /** - * Combines rotation methods above. Supports 3x3, 1x3, 3x1 sizes of data matrix. - * - * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected. - * - * @param data 1-channel matrix. - * When it is a 3x3 matrix, it sets the upper left 3x3 part of the current matrix. - * When it is a 1x3 or 3x1 matrix, it is used as a rotation vector. The Rodrigues formula - * is used to compute the rotation matrix and sets the upper left 3x3 part of the current matrix. - */ - void rotation(const Mat& data); - - /** - * Copy the 3x3 matrix L to the upper left part of the current matrix - * - * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected. - * - * @param L 3x3 matrix. - */ - void linear(const Mat3& L); - - /** - * Copy t to the first three elements of the last column of the current matrix - * - * It sets the upper right 3x1 part of the matrix. The remaining part is unaffected. - * - * @param t 3x1 translation vector. - */ - void translation(const Vec3& t); - - //! @return the upper left 3x3 part - Mat3 rotation() const; - - //! @return the upper left 3x3 part - Mat3 linear() const; - - //! @return the upper right 3x1 part - Vec3 translation() const; - - //! Rodrigues vector. - //! @return a vector representing the upper left 3x3 rotation matrix of the current matrix. - //! @warning Since the mapping between rotation vectors and rotation matrices is many to one, - //! this function returns only one rotation vector that represents the current rotation matrix, - //! which is not necessarily the same one set by `rotation(const Vec3& rvec)`. - Vec3 rvec() const; - - //! @return the inverse of the current matrix. - Affine3 inv(int method = cv::DECOMP_SVD) const; - - //! a.rotate(R) is equivalent to Affine(R, 0) * a; - Affine3 rotate(const Mat3& R) const; - - //! a.rotate(rvec) is equivalent to Affine(rvec, 0) * a; - Affine3 rotate(const Vec3& rvec) const; - - //! a.translate(t) is equivalent to Affine(E, t) * a, where E is an identity matrix - Affine3 translate(const Vec3& t) const; - - //! a.concatenate(affine) is equivalent to affine * a; - Affine3 concatenate(const Affine3& affine) const; - - template operator Affine3() const; - - template Affine3 cast() const; - - Mat4 matrix; - -#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H - Affine3(const Eigen::Transform& affine); - Affine3(const Eigen::Transform& affine); - operator Eigen::Transform() const; - operator Eigen::Transform() const; -#endif - }; - - template static - Affine3 operator*(const Affine3& affine1, const Affine3& affine2); - - //! V is a 3-element vector with member fields x, y and z - template static - V operator*(const Affine3& affine, const V& vector); - - typedef Affine3 Affine3f; - typedef Affine3 Affine3d; - - static Vec3f operator*(const Affine3f& affine, const Vec3f& vector); - static Vec3d operator*(const Affine3d& affine, const Vec3d& vector); - - template class DataType< Affine3<_Tp> > - { - public: - typedef Affine3<_Tp> value_type; - typedef Affine3::work_type> work_type; - typedef _Tp channel_type; - - enum { generic_type = 0, - channels = 16, - fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED - ,depth = DataType::depth - ,type = CV_MAKETYPE(depth, channels) -#endif - }; - - typedef Vec vec_type; - }; - - namespace traits { - template - struct Depth< Affine3<_Tp> > { enum { value = Depth<_Tp>::value }; }; - template - struct Type< Affine3<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 16) }; }; - } // namespace - -//! @} core - -} - -//! @cond IGNORED - -/////////////////////////////////////////////////////////////////////////////////// -// Implementation - -template inline -cv::Affine3::Affine3() - : matrix(Mat4::eye()) -{} - -template inline -cv::Affine3::Affine3(const Mat4& affine) - : matrix(affine) -{} - -template inline -cv::Affine3::Affine3(const Mat3& R, const Vec3& t) -{ - rotation(R); - translation(t); - matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; - matrix.val[15] = 1; -} - -template inline -cv::Affine3::Affine3(const Vec3& _rvec, const Vec3& t) -{ - rotation(_rvec); - translation(t); - matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; - matrix.val[15] = 1; -} - -template inline -cv::Affine3::Affine3(const cv::Mat& data, const Vec3& t) -{ - CV_Assert(data.type() == cv::traits::Type::value); - CV_Assert(data.channels() == 1); - - if (data.cols == 4 && data.rows == 4) - { - data.copyTo(matrix); - return; - } - else if (data.cols == 4 && data.rows == 3) - { - rotation(data(Rect(0, 0, 3, 3))); - translation(data(Rect(3, 0, 1, 3))); - } - else - { - rotation(data); - translation(t); - } - - matrix.val[12] = matrix.val[13] = matrix.val[14] = 0; - matrix.val[15] = 1; -} - -template inline -cv::Affine3::Affine3(const float_type* vals) : matrix(vals) -{} - -template inline -cv::Affine3 cv::Affine3::Identity() -{ - return Affine3(cv::Affine3::Mat4::eye()); -} - -template inline -void cv::Affine3::rotation(const Mat3& R) -{ - linear(R); -} - -template inline -void cv::Affine3::rotation(const Vec3& _rvec) -{ - double theta = norm(_rvec); - - if (theta < DBL_EPSILON) - rotation(Mat3::eye()); - else - { - double c = std::cos(theta); - double s = std::sin(theta); - double c1 = 1. - c; - double itheta = (theta != 0) ? 1./theta : 0.; - - Point3_ r = _rvec*itheta; - - Mat3 rrt( r.x*r.x, r.x*r.y, r.x*r.z, r.x*r.y, r.y*r.y, r.y*r.z, r.x*r.z, r.y*r.z, r.z*r.z ); - Mat3 r_x( 0, -r.z, r.y, r.z, 0, -r.x, -r.y, r.x, 0 ); - - // R = cos(theta)*I + (1 - cos(theta))*r*rT + sin(theta)*[r_x] - // where [r_x] is [0 -rz ry; rz 0 -rx; -ry rx 0] - Mat3 R = c*Mat3::eye() + c1*rrt + s*r_x; - - rotation(R); - } -} - -//Combines rotation methods above. Supports 3x3, 1x3, 3x1 sizes of data matrix; -template inline -void cv::Affine3::rotation(const cv::Mat& data) -{ - CV_Assert(data.type() == cv::traits::Type::value); - CV_Assert(data.channels() == 1); - - if (data.cols == 3 && data.rows == 3) - { - Mat3 R; - data.copyTo(R); - rotation(R); - } - else if ((data.cols == 3 && data.rows == 1) || (data.cols == 1 && data.rows == 3)) - { - Vec3 _rvec; - data.reshape(1, 3).copyTo(_rvec); - rotation(_rvec); - } - else - CV_Error(Error::StsError, "Input matrix can only be 3x3, 1x3 or 3x1"); -} - -template inline -void cv::Affine3::linear(const Mat3& L) -{ - matrix.val[0] = L.val[0]; matrix.val[1] = L.val[1]; matrix.val[ 2] = L.val[2]; - matrix.val[4] = L.val[3]; matrix.val[5] = L.val[4]; matrix.val[ 6] = L.val[5]; - matrix.val[8] = L.val[6]; matrix.val[9] = L.val[7]; matrix.val[10] = L.val[8]; -} - -template inline -void cv::Affine3::translation(const Vec3& t) -{ - matrix.val[3] = t[0]; matrix.val[7] = t[1]; matrix.val[11] = t[2]; -} - -template inline -typename cv::Affine3::Mat3 cv::Affine3::rotation() const -{ - return linear(); -} - -template inline -typename cv::Affine3::Mat3 cv::Affine3::linear() const -{ - typename cv::Affine3::Mat3 R; - R.val[0] = matrix.val[0]; R.val[1] = matrix.val[1]; R.val[2] = matrix.val[ 2]; - R.val[3] = matrix.val[4]; R.val[4] = matrix.val[5]; R.val[5] = matrix.val[ 6]; - R.val[6] = matrix.val[8]; R.val[7] = matrix.val[9]; R.val[8] = matrix.val[10]; - return R; -} - -template inline -typename cv::Affine3::Vec3 cv::Affine3::translation() const -{ - return Vec3(matrix.val[3], matrix.val[7], matrix.val[11]); -} - -template inline -typename cv::Affine3::Vec3 cv::Affine3::rvec() const -{ - cv::Vec3d w; - cv::Matx33d u, vt, R = rotation(); - cv::SVD::compute(R, w, u, vt, cv::SVD::FULL_UV + cv::SVD::MODIFY_A); - R = u * vt; - - double rx = R.val[7] - R.val[5]; - double ry = R.val[2] - R.val[6]; - double rz = R.val[3] - R.val[1]; - - double s = std::sqrt((rx*rx + ry*ry + rz*rz)*0.25); - double c = (R.val[0] + R.val[4] + R.val[8] - 1) * 0.5; - c = c > 1.0 ? 1.0 : c < -1.0 ? -1.0 : c; - double theta = acos(c); - - if( s < 1e-5 ) - { - if( c > 0 ) - rx = ry = rz = 0; - else - { - double t; - t = (R.val[0] + 1) * 0.5; - rx = std::sqrt(std::max(t, 0.0)); - t = (R.val[4] + 1) * 0.5; - ry = std::sqrt(std::max(t, 0.0)) * (R.val[1] < 0 ? -1.0 : 1.0); - t = (R.val[8] + 1) * 0.5; - rz = std::sqrt(std::max(t, 0.0)) * (R.val[2] < 0 ? -1.0 : 1.0); - - if( fabs(rx) < fabs(ry) && fabs(rx) < fabs(rz) && (R.val[5] > 0) != (ry*rz > 0) ) - rz = -rz; - theta /= std::sqrt(rx*rx + ry*ry + rz*rz); - rx *= theta; - ry *= theta; - rz *= theta; - } - } - else - { - double vth = 1/(2*s); - vth *= theta; - rx *= vth; ry *= vth; rz *= vth; - } - - return cv::Vec3d(rx, ry, rz); -} - -template inline -cv::Affine3 cv::Affine3::inv(int method) const -{ - return matrix.inv(method); -} - -template inline -cv::Affine3 cv::Affine3::rotate(const Mat3& R) const -{ - Mat3 Lc = linear(); - Vec3 tc = translation(); - Mat4 result; - result.val[12] = result.val[13] = result.val[14] = 0; - result.val[15] = 1; - - for(int j = 0; j < 3; ++j) - { - for(int i = 0; i < 3; ++i) - { - float_type value = 0; - for(int k = 0; k < 3; ++k) - value += R(j, k) * Lc(k, i); - result(j, i) = value; - } - - result(j, 3) = R.row(j).dot(tc.t()); - } - return result; -} - -template inline -cv::Affine3 cv::Affine3::rotate(const Vec3& _rvec) const -{ - return rotate(Affine3f(_rvec).rotation()); -} - -template inline -cv::Affine3 cv::Affine3::translate(const Vec3& t) const -{ - Mat4 m = matrix; - m.val[ 3] += t[0]; - m.val[ 7] += t[1]; - m.val[11] += t[2]; - return m; -} - -template inline -cv::Affine3 cv::Affine3::concatenate(const Affine3& affine) const -{ - return (*this).rotate(affine.rotation()).translate(affine.translation()); -} - -template template inline -cv::Affine3::operator Affine3() const -{ - return Affine3(matrix); -} - -template template inline -cv::Affine3 cv::Affine3::cast() const -{ - return Affine3(matrix); -} - -template inline -cv::Affine3 cv::operator*(const cv::Affine3& affine1, const cv::Affine3& affine2) -{ - return affine2.concatenate(affine1); -} - -template inline -V cv::operator*(const cv::Affine3& affine, const V& v) -{ - const typename Affine3::Mat4& m = affine.matrix; - - V r; - r.x = m.val[0] * v.x + m.val[1] * v.y + m.val[ 2] * v.z + m.val[ 3]; - r.y = m.val[4] * v.x + m.val[5] * v.y + m.val[ 6] * v.z + m.val[ 7]; - r.z = m.val[8] * v.x + m.val[9] * v.y + m.val[10] * v.z + m.val[11]; - return r; -} - -static inline -cv::Vec3f cv::operator*(const cv::Affine3f& affine, const cv::Vec3f& v) -{ - const cv::Matx44f& m = affine.matrix; - cv::Vec3f r; - r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3]; - r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7]; - r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11]; - return r; -} - -static inline -cv::Vec3d cv::operator*(const cv::Affine3d& affine, const cv::Vec3d& v) -{ - const cv::Matx44d& m = affine.matrix; - cv::Vec3d r; - r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3]; - r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7]; - r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11]; - return r; -} - - - -#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H - -template inline -cv::Affine3::Affine3(const Eigen::Transform& affine) -{ - cv::Mat(4, 4, cv::traits::Type::value, affine.matrix().data()).copyTo(matrix); -} - -template inline -cv::Affine3::Affine3(const Eigen::Transform& affine) -{ - Eigen::Transform a = affine; - cv::Mat(4, 4, cv::traits::Type::value, a.matrix().data()).copyTo(matrix); -} - -template inline -cv::Affine3::operator Eigen::Transform() const -{ - Eigen::Transform r; - cv::Mat hdr(4, 4, cv::traits::Type::value, r.matrix().data()); - cv::Mat(matrix, false).copyTo(hdr); - return r; -} - -template inline -cv::Affine3::operator Eigen::Transform() const -{ - return this->operator Eigen::Transform(); -} - -#endif /* defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H */ - -//! @endcond - -#endif /* __cplusplus */ - -#endif /* OPENCV_CORE_AFFINE3_HPP */ diff --git a/include/opencv2/core/async.hpp b/include/opencv2/core/async.hpp deleted file mode 100755 index 54560c7..0000000 --- a/include/opencv2/core/async.hpp +++ /dev/null @@ -1,105 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_CORE_ASYNC_HPP -#define OPENCV_CORE_ASYNC_HPP - -#include - -#ifdef CV_CXX11 -//#include -#include -#endif - -namespace cv { - -/** @addtogroup core_async - -@{ -*/ - - -/** @brief Returns result of asynchronous operations - -Object has attached asynchronous state. -Assignment operator doesn't clone asynchronous state (it is shared between all instances). - -Result can be fetched via get() method only once. - -*/ -class CV_EXPORTS_W AsyncArray -{ -public: - ~AsyncArray() CV_NOEXCEPT; - CV_WRAP AsyncArray() CV_NOEXCEPT; - AsyncArray(const AsyncArray& o) CV_NOEXCEPT; - AsyncArray& operator=(const AsyncArray& o) CV_NOEXCEPT; - CV_WRAP void release() CV_NOEXCEPT; - - /** Fetch the result. - @param[out] dst destination array - - Waits for result until container has valid result. - Throws exception if exception was stored as a result. - - Throws exception on invalid container state. - - @note Result or stored exception can be fetched only once. - */ - CV_WRAP void get(OutputArray dst) const; - - /** Retrieving the result with timeout - @param[out] dst destination array - @param[in] timeoutNs timeout in nanoseconds, -1 for infinite wait - - @returns true if result is ready, false if the timeout has expired - - @note Result or stored exception can be fetched only once. - */ - bool get(OutputArray dst, int64 timeoutNs) const; - - CV_WRAP inline - bool get(OutputArray dst, double timeoutNs) const { return get(dst, (int64)timeoutNs); } - - bool wait_for(int64 timeoutNs) const; - - CV_WRAP inline - bool wait_for(double timeoutNs) const { return wait_for((int64)timeoutNs); } - - CV_WRAP bool valid() const CV_NOEXCEPT; - -#ifdef CV_CXX11 - inline AsyncArray(AsyncArray&& o) { p = o.p; o.p = NULL; } - inline AsyncArray& operator=(AsyncArray&& o) CV_NOEXCEPT { std::swap(p, o.p); return *this; } - - template - inline bool get(OutputArray dst, const std::chrono::duration<_Rep, _Period>& timeout) - { - return get(dst, (int64)(std::chrono::nanoseconds(timeout).count())); - } - - template - inline bool wait_for(const std::chrono::duration<_Rep, _Period>& timeout) - { - return wait_for((int64)(std::chrono::nanoseconds(timeout).count())); - } - -#if 0 - std::future getFutureMat() const; - std::future getFutureUMat() const; -#endif -#endif - - - // PImpl - struct Impl; friend struct Impl; - inline void* _getImpl() const CV_NOEXCEPT { return p; } -protected: - Impl* p; -}; - - -//! @} -} // namespace -#endif // OPENCV_CORE_ASYNC_HPP diff --git a/include/opencv2/core/base.hpp b/include/opencv2/core/base.hpp deleted file mode 100755 index 546140e..0000000 --- a/include/opencv2/core/base.hpp +++ /dev/null @@ -1,707 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2014, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_BASE_HPP -#define OPENCV_CORE_BASE_HPP - -#ifndef __cplusplus -# error base.hpp header must be compiled as C++ -#endif - -#include "opencv2/opencv_modules.hpp" - -#include -#include - -#include "opencv2/core/cvdef.h" -#include "opencv2/core/cvstd.hpp" - -namespace cv -{ - -//! @addtogroup core_utils -//! @{ - -namespace Error { -//! error codes -enum Code { - StsOk= 0, //!< everything is ok - StsBackTrace= -1, //!< pseudo error for back trace - StsError= -2, //!< unknown /unspecified error - StsInternal= -3, //!< internal error (bad state) - StsNoMem= -4, //!< insufficient memory - StsBadArg= -5, //!< function arg/param is bad - StsBadFunc= -6, //!< unsupported function - StsNoConv= -7, //!< iteration didn't converge - StsAutoTrace= -8, //!< tracing - HeaderIsNull= -9, //!< image header is NULL - BadImageSize= -10, //!< image size is invalid - BadOffset= -11, //!< offset is invalid - BadDataPtr= -12, //!< - BadStep= -13, //!< image step is wrong, this may happen for a non-continuous matrix. - BadModelOrChSeq= -14, //!< - BadNumChannels= -15, //!< bad number of channels, for example, some functions accept only single channel matrices. - BadNumChannel1U= -16, //!< - BadDepth= -17, //!< input image depth is not supported by the function - BadAlphaChannel= -18, //!< - BadOrder= -19, //!< number of dimensions is out of range - BadOrigin= -20, //!< incorrect input origin - BadAlign= -21, //!< incorrect input align - BadCallBack= -22, //!< - BadTileSize= -23, //!< - BadCOI= -24, //!< input COI is not supported - BadROISize= -25, //!< incorrect input roi - MaskIsTiled= -26, //!< - StsNullPtr= -27, //!< null pointer - StsVecLengthErr= -28, //!< incorrect vector length - StsFilterStructContentErr= -29, //!< incorrect filter structure content - StsKernelStructContentErr= -30, //!< incorrect transform kernel content - StsFilterOffsetErr= -31, //!< incorrect filter offset value - StsBadSize= -201, //!< the input/output structure size is incorrect - StsDivByZero= -202, //!< division by zero - StsInplaceNotSupported= -203, //!< in-place operation is not supported - StsObjectNotFound= -204, //!< request can't be completed - StsUnmatchedFormats= -205, //!< formats of input/output arrays differ - StsBadFlag= -206, //!< flag is wrong or not supported - StsBadPoint= -207, //!< bad CvPoint - StsBadMask= -208, //!< bad format of mask (neither 8uC1 nor 8sC1) - StsUnmatchedSizes= -209, //!< sizes of input/output structures do not match - StsUnsupportedFormat= -210, //!< the data format/type is not supported by the function - StsOutOfRange= -211, //!< some of parameters are out of range - StsParseError= -212, //!< invalid syntax/structure of the parsed file - StsNotImplemented= -213, //!< the requested function/feature is not implemented - StsBadMemBlock= -214, //!< an allocated block has been corrupted - StsAssert= -215, //!< assertion failed - GpuNotSupported= -216, //!< no CUDA support - GpuApiCallError= -217, //!< GPU API call error - OpenGlNotSupported= -218, //!< no OpenGL support - OpenGlApiCallError= -219, //!< OpenGL API call error - OpenCLApiCallError= -220, //!< OpenCL API call error - OpenCLDoubleNotSupported= -221, - OpenCLInitError= -222, //!< OpenCL initialization error - OpenCLNoAMDBlasFft= -223 -}; -} //Error - -//! @} core_utils - -//! @addtogroup core_array -//! @{ - -//! matrix decomposition types -enum DecompTypes { - /** Gaussian elimination with the optimal pivot element chosen. */ - DECOMP_LU = 0, - /** singular value decomposition (SVD) method; the system can be over-defined and/or the matrix - src1 can be singular */ - DECOMP_SVD = 1, - /** eigenvalue decomposition; the matrix src1 must be symmetrical */ - DECOMP_EIG = 2, - /** Cholesky \f$LL^T\f$ factorization; the matrix src1 must be symmetrical and positively - defined */ - DECOMP_CHOLESKY = 3, - /** QR factorization; the system can be over-defined and/or the matrix src1 can be singular */ - DECOMP_QR = 4, - /** while all the previous flags are mutually exclusive, this flag can be used together with - any of the previous; it means that the normal equations - \f$\texttt{src1}^T\cdot\texttt{src1}\cdot\texttt{dst}=\texttt{src1}^T\texttt{src2}\f$ are - solved instead of the original system - \f$\texttt{src1}\cdot\texttt{dst}=\texttt{src2}\f$ */ - DECOMP_NORMAL = 16 -}; - -/** norm types - -src1 and src2 denote input arrays. -*/ - -enum NormTypes { - /** - \f[ - norm = \forkthree - {\|\texttt{src1}\|_{L_{\infty}} = \max _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) } - {\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} = \max _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) } - {\frac{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} }{\|\texttt{src2}\|_{L_{\infty}} }}{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_INF}\) } - \f] - */ - NORM_INF = 1, - /** - \f[ - norm = \forkthree - {\| \texttt{src1} \| _{L_1} = \sum _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\)} - { \| \texttt{src1} - \texttt{src2} \| _{L_1} = \sum _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\) } - { \frac{\|\texttt{src1}-\texttt{src2}\|_{L_1} }{\|\texttt{src2}\|_{L_1}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L1}\) } - \f]*/ - NORM_L1 = 2, - /** - \f[ - norm = \forkthree - { \| \texttt{src1} \| _{L_2} = \sqrt{\sum_I \texttt{src1}(I)^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) } - { \| \texttt{src1} - \texttt{src2} \| _{L_2} = \sqrt{\sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) } - { \frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L2}\) } - \f] - */ - NORM_L2 = 4, - /** - \f[ - norm = \forkthree - { \| \texttt{src1} \| _{L_2} ^{2} = \sum_I \texttt{src1}(I)^2} {if \(\texttt{normType} = \texttt{NORM_L2SQR}\)} - { \| \texttt{src1} - \texttt{src2} \| _{L_2} ^{2} = \sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2 }{if \(\texttt{normType} = \texttt{NORM_L2SQR}\) } - { \left(\frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}}\right)^2 }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L2SQR}\) } - \f] - */ - NORM_L2SQR = 5, - /** - In the case of one input array, calculates the Hamming distance of the array from zero, - In the case of two input arrays, calculates the Hamming distance between the arrays. - */ - NORM_HAMMING = 6, - /** - Similar to NORM_HAMMING, but in the calculation, each two bits of the input sequence will - be added and treated as a single bit to be used in the same calculation as NORM_HAMMING. - */ - NORM_HAMMING2 = 7, - NORM_TYPE_MASK = 7, //!< bit-mask which can be used to separate norm type from norm flags - NORM_RELATIVE = 8, //!< flag - NORM_MINMAX = 32 //!< flag - }; - -//! comparison types -enum CmpTypes { CMP_EQ = 0, //!< src1 is equal to src2. - CMP_GT = 1, //!< src1 is greater than src2. - CMP_GE = 2, //!< src1 is greater than or equal to src2. - CMP_LT = 3, //!< src1 is less than src2. - CMP_LE = 4, //!< src1 is less than or equal to src2. - CMP_NE = 5 //!< src1 is unequal to src2. - }; - -//! generalized matrix multiplication flags -enum GemmFlags { GEMM_1_T = 1, //!< transposes src1 - GEMM_2_T = 2, //!< transposes src2 - GEMM_3_T = 4 //!< transposes src3 - }; - -enum DftFlags { - /** performs an inverse 1D or 2D transform instead of the default forward - transform. */ - DFT_INVERSE = 1, - /** scales the result: divide it by the number of array elements. Normally, it is - combined with DFT_INVERSE. */ - DFT_SCALE = 2, - /** performs a forward or inverse transform of every individual row of the input - matrix; this flag enables you to transform multiple vectors simultaneously and can be used to - decrease the overhead (which is sometimes several times larger than the processing itself) to - perform 3D and higher-dimensional transformations and so forth.*/ - DFT_ROWS = 4, - /** performs a forward transformation of 1D or 2D real array; the result, - though being a complex array, has complex-conjugate symmetry (*CCS*, see the function - description below for details), and such an array can be packed into a real array of the same - size as input, which is the fastest option and which is what the function does by default; - however, you may wish to get a full complex array (for simpler spectrum analysis, and so on) - - pass the flag to enable the function to produce a full-size complex output array. */ - DFT_COMPLEX_OUTPUT = 16, - /** performs an inverse transformation of a 1D or 2D complex array; the - result is normally a complex array of the same size, however, if the input array has - conjugate-complex symmetry (for example, it is a result of forward transformation with - DFT_COMPLEX_OUTPUT flag), the output is a real array; while the function itself does not - check whether the input is symmetrical or not, you can pass the flag and then the function - will assume the symmetry and produce the real output array (note that when the input is packed - into a real array and inverse transformation is executed, the function treats the input as a - packed complex-conjugate symmetrical array, and the output will also be a real array). */ - DFT_REAL_OUTPUT = 32, - /** specifies that input is complex input. If this flag is set, the input must have 2 channels. - On the other hand, for backwards compatibility reason, if input has 2 channels, input is - already considered complex. */ - DFT_COMPLEX_INPUT = 64, - /** performs an inverse 1D or 2D transform instead of the default forward transform. */ - DCT_INVERSE = DFT_INVERSE, - /** performs a forward or inverse transform of every individual row of the input - matrix. This flag enables you to transform multiple vectors simultaneously and can be used to - decrease the overhead (which is sometimes several times larger than the processing itself) to - perform 3D and higher-dimensional transforms and so forth.*/ - DCT_ROWS = DFT_ROWS -}; - -//! Various border types, image boundaries are denoted with `|` -//! @see borderInterpolate, copyMakeBorder -enum BorderTypes { - BORDER_CONSTANT = 0, //!< `iiiiii|abcdefgh|iiiiiii` with some specified `i` - BORDER_REPLICATE = 1, //!< `aaaaaa|abcdefgh|hhhhhhh` - BORDER_REFLECT = 2, //!< `fedcba|abcdefgh|hgfedcb` - BORDER_WRAP = 3, //!< `cdefgh|abcdefgh|abcdefg` - BORDER_REFLECT_101 = 4, //!< `gfedcb|abcdefgh|gfedcba` - BORDER_TRANSPARENT = 5, //!< `uvwxyz|abcdefgh|ijklmno` - - BORDER_REFLECT101 = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101 - BORDER_DEFAULT = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101 - BORDER_ISOLATED = 16 //!< do not look outside of ROI -}; - -//! @} core_array - -//! @addtogroup core_utils -//! @{ - -/*! @brief Signals an error and raises the exception. - -By default the function prints information about the error to stderr, -then it either stops if setBreakOnError() had been called before or raises the exception. -It is possible to alternate error processing by using redirectError(). -@param _code - error code (Error::Code) -@param _err - error description -@param _func - function name. Available only when the compiler supports getting it -@param _file - source file name where the error has occurred -@param _line - line number in the source file where the error has occurred -@see CV_Error, CV_Error_, CV_Assert, CV_DbgAssert - */ -CV_EXPORTS void error(int _code, const String& _err, const char* _func, const char* _file, int _line); - -#ifdef __GNUC__ -# if defined __clang__ || defined __APPLE__ -# pragma GCC diagnostic push -# pragma GCC diagnostic ignored "-Winvalid-noreturn" -# endif -#endif - -/** same as cv::error, but does not return */ -CV_INLINE CV_NORETURN void errorNoReturn(int _code, const String& _err, const char* _func, const char* _file, int _line) -{ - error(_code, _err, _func, _file, _line); -#ifdef __GNUC__ -# if !defined __clang__ && !defined __APPLE__ - // this suppresses this warning: "noreturn" function does return [enabled by default] - __builtin_trap(); - // or use infinite loop: for (;;) {} -# endif -#endif -} -#ifdef __GNUC__ -# if defined __clang__ || defined __APPLE__ -# pragma GCC diagnostic pop -# endif -#endif - -#ifdef CV_STATIC_ANALYSIS - -// In practice, some macro are not processed correctly (noreturn is not detected). -// We need to use simplified definition for them. -#define CV_Error(code, msg) do { (void)(code); (void)(msg); abort(); } while (0) -#define CV_Error_(code, args) do { (void)(code); (void)(cv::format args); abort(); } while (0) -#define CV_Assert( expr ) do { if (!(expr)) abort(); } while (0) -#define CV_ErrorNoReturn CV_Error -#define CV_ErrorNoReturn_ CV_Error_ - -#else // CV_STATIC_ANALYSIS - -/** @brief Call the error handler. - -Currently, the error handler prints the error code and the error message to the standard -error stream `stderr`. In the Debug configuration, it then provokes memory access violation, so that -the execution stack and all the parameters can be analyzed by the debugger. In the Release -configuration, the exception is thrown. - -@param code one of Error::Code -@param msg error message -*/ -#define CV_Error( code, msg ) cv::error( code, msg, CV_Func, __FILE__, __LINE__ ) - -/** @brief Call the error handler. - -This macro can be used to construct an error message on-fly to include some dynamic information, -for example: -@code - // note the extra parentheses around the formatted text message - CV_Error_(Error::StsOutOfRange, - ("the value at (%d, %d)=%g is out of range", badPt.x, badPt.y, badValue)); -@endcode -@param code one of Error::Code -@param args printf-like formatted error message in parentheses -*/ -#define CV_Error_( code, args ) cv::error( code, cv::format args, CV_Func, __FILE__, __LINE__ ) - -/** @brief Checks a condition at runtime and throws exception if it fails - -The macros CV_Assert (and CV_DbgAssert(expr)) evaluate the specified expression. If it is 0, the macros -raise an error (see cv::error). The macro CV_Assert checks the condition in both Debug and Release -configurations while CV_DbgAssert is only retained in the Debug configuration. -*/ -#define CV_Assert( expr ) do { if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0) - -//! @cond IGNORED -#define CV__ErrorNoReturn( code, msg ) cv::errorNoReturn( code, msg, CV_Func, __FILE__, __LINE__ ) -#define CV__ErrorNoReturn_( code, args ) cv::errorNoReturn( code, cv::format args, CV_Func, __FILE__, __LINE__ ) -#ifdef __OPENCV_BUILD -#undef CV_Error -#define CV_Error CV__ErrorNoReturn -#undef CV_Error_ -#define CV_Error_ CV__ErrorNoReturn_ -#undef CV_Assert -#define CV_Assert( expr ) do { if(!!(expr)) ; else cv::errorNoReturn( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0) -#else -// backward compatibility -#define CV_ErrorNoReturn CV__ErrorNoReturn -#define CV_ErrorNoReturn_ CV__ErrorNoReturn_ -#endif -//! @endcond - -#endif // CV_STATIC_ANALYSIS - -//! @cond IGNORED - -#if defined OPENCV_FORCE_MULTIARG_ASSERT_CHECK && defined CV_STATIC_ANALYSIS -#warning "OPENCV_FORCE_MULTIARG_ASSERT_CHECK can't be used with CV_STATIC_ANALYSIS" -#undef OPENCV_FORCE_MULTIARG_ASSERT_CHECK -#endif - -#ifdef OPENCV_FORCE_MULTIARG_ASSERT_CHECK -#define CV_Assert_1( expr ) do { if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0) -#else -#define CV_Assert_1 CV_Assert -#endif -#define CV_Assert_2( expr1, expr2 ) CV_Assert_1(expr1); CV_Assert_1(expr2) -#define CV_Assert_3( expr1, expr2, expr3 ) CV_Assert_2(expr1, expr2); CV_Assert_1(expr3) -#define CV_Assert_4( expr1, expr2, expr3, expr4 ) CV_Assert_3(expr1, expr2, expr3); CV_Assert_1(expr4) -#define CV_Assert_5( expr1, expr2, expr3, expr4, expr5 ) CV_Assert_4(expr1, expr2, expr3, expr4); CV_Assert_1(expr5) -#define CV_Assert_6( expr1, expr2, expr3, expr4, expr5, expr6 ) CV_Assert_5(expr1, expr2, expr3, expr4, expr5); CV_Assert_1(expr6) -#define CV_Assert_7( expr1, expr2, expr3, expr4, expr5, expr6, expr7 ) CV_Assert_6(expr1, expr2, expr3, expr4, expr5, expr6 ); CV_Assert_1(expr7) -#define CV_Assert_8( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8 ) CV_Assert_7(expr1, expr2, expr3, expr4, expr5, expr6, expr7 ); CV_Assert_1(expr8) -#define CV_Assert_9( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9 ) CV_Assert_8(expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8 ); CV_Assert_1(expr9) -#define CV_Assert_10( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9, expr10 ) CV_Assert_9(expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9 ); CV_Assert_1(expr10) - -#define CV_Assert_N(...) do { __CV_CAT(CV_Assert_, __CV_VA_NUM_ARGS(__VA_ARGS__)) (__VA_ARGS__); } while(0) - -#ifdef OPENCV_FORCE_MULTIARG_ASSERT_CHECK -#undef CV_Assert -#define CV_Assert CV_Assert_N -#endif -//! @endcond - -#if defined _DEBUG || defined CV_STATIC_ANALYSIS -# define CV_DbgAssert(expr) CV_Assert(expr) -#else -/** replaced with CV_Assert(expr) in Debug configuration */ -# define CV_DbgAssert(expr) -#endif - -/* - * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor - * bit count of A exclusive XOR'ed with B - */ -struct CV_EXPORTS Hamming -{ - enum { normType = NORM_HAMMING }; - typedef unsigned char ValueType; - typedef int ResultType; - - /** this will count the bits in a ^ b - */ - ResultType operator()( const unsigned char* a, const unsigned char* b, int size ) const; -}; - -typedef Hamming HammingLUT; - -/////////////////////////////////// inline norms //////////////////////////////////// - -template inline _Tp cv_abs(_Tp x) { return std::abs(x); } -inline int cv_abs(uchar x) { return x; } -inline int cv_abs(schar x) { return std::abs(x); } -inline int cv_abs(ushort x) { return x; } -inline int cv_abs(short x) { return std::abs(x); } - -template static inline -_AccTp normL2Sqr(const _Tp* a, int n) -{ - _AccTp s = 0; - int i=0; -#if CV_ENABLE_UNROLLED - for( ; i <= n - 4; i += 4 ) - { - _AccTp v0 = a[i], v1 = a[i+1], v2 = a[i+2], v3 = a[i+3]; - s += v0*v0 + v1*v1 + v2*v2 + v3*v3; - } -#endif - for( ; i < n; i++ ) - { - _AccTp v = a[i]; - s += v*v; - } - return s; -} - -template static inline -_AccTp normL1(const _Tp* a, int n) -{ - _AccTp s = 0; - int i = 0; -#if CV_ENABLE_UNROLLED - for(; i <= n - 4; i += 4 ) - { - s += (_AccTp)cv_abs(a[i]) + (_AccTp)cv_abs(a[i+1]) + - (_AccTp)cv_abs(a[i+2]) + (_AccTp)cv_abs(a[i+3]); - } -#endif - for( ; i < n; i++ ) - s += cv_abs(a[i]); - return s; -} - -template static inline -_AccTp normInf(const _Tp* a, int n) -{ - _AccTp s = 0; - for( int i = 0; i < n; i++ ) - s = std::max(s, (_AccTp)cv_abs(a[i])); - return s; -} - -template static inline -_AccTp normL2Sqr(const _Tp* a, const _Tp* b, int n) -{ - _AccTp s = 0; - int i= 0; -#if CV_ENABLE_UNROLLED - for(; i <= n - 4; i += 4 ) - { - _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]); - s += v0*v0 + v1*v1 + v2*v2 + v3*v3; - } -#endif - for( ; i < n; i++ ) - { - _AccTp v = _AccTp(a[i] - b[i]); - s += v*v; - } - return s; -} - -static inline float normL2Sqr(const float* a, const float* b, int n) -{ - float s = 0.f; - for( int i = 0; i < n; i++ ) - { - float v = a[i] - b[i]; - s += v*v; - } - return s; -} - -template static inline -_AccTp normL1(const _Tp* a, const _Tp* b, int n) -{ - _AccTp s = 0; - int i= 0; -#if CV_ENABLE_UNROLLED - for(; i <= n - 4; i += 4 ) - { - _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]); - s += std::abs(v0) + std::abs(v1) + std::abs(v2) + std::abs(v3); - } -#endif - for( ; i < n; i++ ) - { - _AccTp v = _AccTp(a[i] - b[i]); - s += std::abs(v); - } - return s; -} - -inline float normL1(const float* a, const float* b, int n) -{ - float s = 0.f; - for( int i = 0; i < n; i++ ) - { - s += std::abs(a[i] - b[i]); - } - return s; -} - -inline int normL1(const uchar* a, const uchar* b, int n) -{ - int s = 0; - for( int i = 0; i < n; i++ ) - { - s += std::abs(a[i] - b[i]); - } - return s; -} - -template static inline -_AccTp normInf(const _Tp* a, const _Tp* b, int n) -{ - _AccTp s = 0; - for( int i = 0; i < n; i++ ) - { - _AccTp v0 = a[i] - b[i]; - s = std::max(s, std::abs(v0)); - } - return s; -} - -/** @brief Computes the cube root of an argument. - - The function cubeRoot computes \f$\sqrt[3]{\texttt{val}}\f$. Negative arguments are handled correctly. - NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for - single-precision data. - @param val A function argument. - */ -CV_EXPORTS_W float cubeRoot(float val); - -/** @brief Calculates the angle of a 2D vector in degrees. - - The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured - in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees. - @param x x-coordinate of the vector. - @param y y-coordinate of the vector. - */ -CV_EXPORTS_W float fastAtan2(float y, float x); - -/** proxy for hal::LU */ -CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n); -/** proxy for hal::LU */ -CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n); -/** proxy for hal::Cholesky */ -CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n); -/** proxy for hal::Cholesky */ -CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n); - -////////////////// forward declarations for important OpenCV types ////////////////// - -//! @cond IGNORED - -template class Vec; -template class Matx; - -template class Complex; -template class Point_; -template class Point3_; -template class Size_; -template class Rect_; -template class Scalar_; - -class CV_EXPORTS RotatedRect; -class CV_EXPORTS Range; -class CV_EXPORTS TermCriteria; -class CV_EXPORTS KeyPoint; -class CV_EXPORTS DMatch; -class CV_EXPORTS RNG; - -class CV_EXPORTS Mat; -class CV_EXPORTS MatExpr; - -class CV_EXPORTS UMat; - -class CV_EXPORTS SparseMat; -typedef Mat MatND; - -template class Mat_; -template class SparseMat_; - -class CV_EXPORTS MatConstIterator; -class CV_EXPORTS SparseMatIterator; -class CV_EXPORTS SparseMatConstIterator; -template class MatIterator_; -template class MatConstIterator_; -template class SparseMatIterator_; -template class SparseMatConstIterator_; - -namespace ogl -{ - class CV_EXPORTS Buffer; - class CV_EXPORTS Texture2D; - class CV_EXPORTS Arrays; -} - -namespace cuda -{ - class CV_EXPORTS GpuMat; - class CV_EXPORTS HostMem; - class CV_EXPORTS Stream; - class CV_EXPORTS Event; -} - -namespace cudev -{ - template class GpuMat_; -} - -namespace ipp -{ -#if OPENCV_ABI_COMPATIBILITY > 300 -CV_EXPORTS unsigned long long getIppFeatures(); -#else -CV_EXPORTS int getIppFeatures(); -#endif -CV_EXPORTS void setIppStatus(int status, const char * const funcname = NULL, const char * const filename = NULL, - int line = 0); -CV_EXPORTS int getIppStatus(); -CV_EXPORTS String getIppErrorLocation(); -CV_EXPORTS_W bool useIPP(); -CV_EXPORTS_W void setUseIPP(bool flag); -CV_EXPORTS_W String getIppVersion(); - -// IPP Not-Exact mode. This function may force use of IPP then both IPP and OpenCV provide proper results -// but have internal accuracy differences which have too much direct or indirect impact on accuracy tests. -CV_EXPORTS_W bool useIPP_NotExact(); -CV_EXPORTS_W void setUseIPP_NotExact(bool flag); -#if OPENCV_ABI_COMPATIBILITY < 400 -CV_EXPORTS_W bool useIPP_NE(); -CV_EXPORTS_W void setUseIPP_NE(bool flag); -#endif - -} // ipp - -//! @endcond - -//! @} core_utils - - - - -} // cv - -#include "opencv2/core/neon_utils.hpp" -#include "opencv2/core/vsx_utils.hpp" -#include "opencv2/core/check.hpp" - -#endif //OPENCV_CORE_BASE_HPP diff --git a/include/opencv2/core/bindings_utils.hpp b/include/opencv2/core/bindings_utils.hpp deleted file mode 100755 index 92f27f0..0000000 --- a/include/opencv2/core/bindings_utils.hpp +++ /dev/null @@ -1,93 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_CORE_BINDINGS_UTILS_HPP -#define OPENCV_CORE_BINDINGS_UTILS_HPP - -#include -#include - -namespace cv { namespace utils { -//! @addtogroup core_utils -//! @{ - -CV_EXPORTS_W String dumpInputArray(InputArray argument); - -CV_EXPORTS_W String dumpInputArrayOfArrays(InputArrayOfArrays argument); - -CV_EXPORTS_W String dumpInputOutputArray(InputOutputArray argument); - -CV_EXPORTS_W String dumpInputOutputArrayOfArrays(InputOutputArrayOfArrays argument); - -CV_WRAP static inline -String dumpBool(bool argument) -{ - return (argument) ? String("Bool: True") : String("Bool: False"); -} - -CV_WRAP static inline -String dumpInt(int argument) -{ - return cv::format("Int: %d", argument); -} - -CV_WRAP static inline -String dumpSizeT(size_t argument) -{ - std::ostringstream oss("size_t: ", std::ios::ate); - oss << argument; - return oss.str(); -} - -CV_WRAP static inline -String dumpFloat(float argument) -{ - return cv::format("Float: %.2f", argument); -} - -CV_WRAP static inline -String dumpDouble(double argument) -{ - return cv::format("Double: %.2f", argument); -} - -CV_WRAP static inline -String dumpCString(const char* argument) -{ - return cv::format("String: %s", argument); -} - -CV_WRAP static inline -String dumpString(const String& argument) -{ - return cv::format("String: %s", argument.c_str()); -} - -CV_WRAP static inline -AsyncArray testAsyncArray(InputArray argument) -{ - AsyncPromise p; - p.setValue(argument); - return p.getArrayResult(); -} - -CV_WRAP static inline -AsyncArray testAsyncException() -{ - AsyncPromise p; - try - { - CV_Error(Error::StsOk, "Test: Generated async error"); - } - catch (const cv::Exception& e) - { - p.setException(e); - } - return p.getArrayResult(); -} - -//! @} -}} // namespace - -#endif // OPENCV_CORE_BINDINGS_UTILS_HPP diff --git a/include/opencv2/core/bufferpool.hpp b/include/opencv2/core/bufferpool.hpp deleted file mode 100755 index 4698e5d..0000000 --- a/include/opencv2/core/bufferpool.hpp +++ /dev/null @@ -1,40 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. -// -// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved. - -#ifndef OPENCV_CORE_BUFFER_POOL_HPP -#define OPENCV_CORE_BUFFER_POOL_HPP - -#ifdef _MSC_VER -#pragma warning(push) -#pragma warning(disable: 4265) -#endif - -namespace cv -{ - -//! @addtogroup core -//! @{ - -class BufferPoolController -{ -protected: - ~BufferPoolController() { } -public: - virtual size_t getReservedSize() const = 0; - virtual size_t getMaxReservedSize() const = 0; - virtual void setMaxReservedSize(size_t size) = 0; - virtual void freeAllReservedBuffers() = 0; -}; - -//! @} - -} - -#ifdef _MSC_VER -#pragma warning(pop) -#endif - -#endif // OPENCV_CORE_BUFFER_POOL_HPP diff --git a/include/opencv2/core/check.hpp b/include/opencv2/core/check.hpp deleted file mode 100755 index d975223..0000000 --- a/include/opencv2/core/check.hpp +++ /dev/null @@ -1,160 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_CORE_CHECK_HPP -#define OPENCV_CORE_CHECK_HPP - -#include - -namespace cv { - -/** Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or "" */ -CV_EXPORTS const char* depthToString(int depth); - -/** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or "" */ -CV_EXPORTS const String typeToString(int type); - - -//! @cond IGNORED -namespace detail { - -/** Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or NULL */ -CV_EXPORTS const char* depthToString_(int depth); - -/** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or cv::String() */ -CV_EXPORTS const cv::String typeToString_(int type); - -enum TestOp { - TEST_CUSTOM = 0, - TEST_EQ = 1, - TEST_NE = 2, - TEST_LE = 3, - TEST_LT = 4, - TEST_GE = 5, - TEST_GT = 6, - CV__LAST_TEST_OP -}; - -struct CheckContext { - const char* func; - const char* file; - int line; - enum TestOp testOp; - const char* message; - const char* p1_str; - const char* p2_str; -}; - -#ifndef CV__CHECK_FILENAME -# define CV__CHECK_FILENAME __FILE__ -#endif - -#ifndef CV__CHECK_FUNCTION -# if defined _MSC_VER -# define CV__CHECK_FUNCTION __FUNCSIG__ -# elif defined __GNUC__ -# define CV__CHECK_FUNCTION __PRETTY_FUNCTION__ -# else -# define CV__CHECK_FUNCTION "" -# endif -#endif - -#define CV__CHECK_LOCATION_VARNAME(id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_check_, id), __LINE__) -#define CV__DEFINE_CHECK_CONTEXT(id, message, testOp, p1_str, p2_str) \ - static const cv::detail::CheckContext CV__CHECK_LOCATION_VARNAME(id) = \ - { CV__CHECK_FUNCTION, CV__CHECK_FILENAME, __LINE__, testOp, "" message, "" p1_str, "" p2_str } - -CV_EXPORTS void CV_NORETURN check_failed_auto(const int v1, const int v2, const CheckContext& ctx); -CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v1, const size_t v2, const CheckContext& ctx); -CV_EXPORTS void CV_NORETURN check_failed_auto(const float v1, const float v2, const CheckContext& ctx); -CV_EXPORTS void CV_NORETURN check_failed_auto(const double v1, const double v2, const CheckContext& ctx); -CV_EXPORTS void CV_NORETURN check_failed_auto(const Size_ v1, const Size_ v2, const CheckContext& ctx); -CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v1, const int v2, const CheckContext& ctx); -CV_EXPORTS void CV_NORETURN check_failed_MatType(const int v1, const int v2, const CheckContext& ctx); -CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v1, const int v2, const CheckContext& ctx); - -CV_EXPORTS void CV_NORETURN check_failed_auto(const int v, const CheckContext& ctx); -CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v, const CheckContext& ctx); -CV_EXPORTS void CV_NORETURN check_failed_auto(const float v, const CheckContext& ctx); -CV_EXPORTS void CV_NORETURN check_failed_auto(const double v, const CheckContext& ctx); -CV_EXPORTS void CV_NORETURN check_failed_auto(const Size_ v, const CheckContext& ctx); -CV_EXPORTS void CV_NORETURN check_failed_auto(const std::string& v1, const CheckContext& ctx); -CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v, const CheckContext& ctx); -CV_EXPORTS void CV_NORETURN check_failed_MatType(const int v, const CheckContext& ctx); -CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v, const CheckContext& ctx); - - -#define CV__TEST_EQ(v1, v2) ((v1) == (v2)) -#define CV__TEST_NE(v1, v2) ((v1) != (v2)) -#define CV__TEST_LE(v1, v2) ((v1) <= (v2)) -#define CV__TEST_LT(v1, v2) ((v1) < (v2)) -#define CV__TEST_GE(v1, v2) ((v1) >= (v2)) -#define CV__TEST_GT(v1, v2) ((v1) > (v2)) - -#define CV__CHECK(id, op, type, v1, v2, v1_str, v2_str, msg_str) do { \ - if(CV__TEST_##op((v1), (v2))) ; else { \ - CV__DEFINE_CHECK_CONTEXT(id, msg_str, cv::detail::TEST_ ## op, v1_str, v2_str); \ - cv::detail::check_failed_ ## type((v1), (v2), CV__CHECK_LOCATION_VARNAME(id)); \ - } \ -} while (0) - -#define CV__CHECK_CUSTOM_TEST(id, type, v, test_expr, v_str, test_expr_str, msg_str) do { \ - if(!!(test_expr)) ; else { \ - CV__DEFINE_CHECK_CONTEXT(id, msg_str, cv::detail::TEST_CUSTOM, v_str, test_expr_str); \ - cv::detail::check_failed_ ## type((v), CV__CHECK_LOCATION_VARNAME(id)); \ - } \ -} while (0) - -} // namespace -//! @endcond - - -/// Supported values of these types: int, float, double -#define CV_CheckEQ(v1, v2, msg) CV__CHECK(_, EQ, auto, v1, v2, #v1, #v2, msg) -#define CV_CheckNE(v1, v2, msg) CV__CHECK(_, NE, auto, v1, v2, #v1, #v2, msg) -#define CV_CheckLE(v1, v2, msg) CV__CHECK(_, LE, auto, v1, v2, #v1, #v2, msg) -#define CV_CheckLT(v1, v2, msg) CV__CHECK(_, LT, auto, v1, v2, #v1, #v2, msg) -#define CV_CheckGE(v1, v2, msg) CV__CHECK(_, GE, auto, v1, v2, #v1, #v2, msg) -#define CV_CheckGT(v1, v2, msg) CV__CHECK(_, GT, auto, v1, v2, #v1, #v2, msg) - -/// Check with additional "decoding" of type values in error message -#define CV_CheckTypeEQ(t1, t2, msg) CV__CHECK(_, EQ, MatType, t1, t2, #t1, #t2, msg) -/// Check with additional "decoding" of depth values in error message -#define CV_CheckDepthEQ(d1, d2, msg) CV__CHECK(_, EQ, MatDepth, d1, d2, #d1, #d2, msg) - -#define CV_CheckChannelsEQ(c1, c2, msg) CV__CHECK(_, EQ, MatChannels, c1, c2, #c1, #c2, msg) - -/// Example: type == CV_8UC1 || type == CV_8UC3 -#define CV_CheckType(t, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, MatType, t, (test_expr), #t, #test_expr, msg) - -/// Example: depth == CV_32F || depth == CV_64F -#define CV_CheckDepth(t, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, MatDepth, t, (test_expr), #t, #test_expr, msg) - -/// Example: v == A || v == B -#define CV_Check(v, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg) - -/// Some complex conditions: CV_Check(src2, src2.empty() || (src2.type() == src1.type() && src2.size() == src1.size()), "src2 should have same size/type as src1") -// TODO define pretty-printers - -#ifndef NDEBUG -#define CV_DbgCheck(v, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg) -#define CV_DbgCheckEQ(v1, v2, msg) CV__CHECK(_, EQ, auto, v1, v2, #v1, #v2, msg) -#define CV_DbgCheckNE(v1, v2, msg) CV__CHECK(_, NE, auto, v1, v2, #v1, #v2, msg) -#define CV_DbgCheckLE(v1, v2, msg) CV__CHECK(_, LE, auto, v1, v2, #v1, #v2, msg) -#define CV_DbgCheckLT(v1, v2, msg) CV__CHECK(_, LT, auto, v1, v2, #v1, #v2, msg) -#define CV_DbgCheckGE(v1, v2, msg) CV__CHECK(_, GE, auto, v1, v2, #v1, #v2, msg) -#define CV_DbgCheckGT(v1, v2, msg) CV__CHECK(_, GT, auto, v1, v2, #v1, #v2, msg) -#else -#define CV_DbgCheck(v, test_expr, msg) do { } while (0) -#define CV_DbgCheckEQ(v1, v2, msg) do { } while (0) -#define CV_DbgCheckNE(v1, v2, msg) do { } while (0) -#define CV_DbgCheckLE(v1, v2, msg) do { } while (0) -#define CV_DbgCheckLT(v1, v2, msg) do { } while (0) -#define CV_DbgCheckGE(v1, v2, msg) do { } while (0) -#define CV_DbgCheckGT(v1, v2, msg) do { } while (0) -#endif - -} // namespace - -#endif // OPENCV_CORE_CHECK_HPP diff --git a/include/opencv2/core/core.hpp b/include/opencv2/core/core.hpp deleted file mode 100755 index 4389183..0000000 --- a/include/opencv2/core/core.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/core.hpp" diff --git a/include/opencv2/core/core_c.h b/include/opencv2/core/core_c.h deleted file mode 100755 index 95a98cf..0000000 --- a/include/opencv2/core/core_c.h +++ /dev/null @@ -1,3175 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - - -#ifndef OPENCV_CORE_C_H -#define OPENCV_CORE_C_H - -#include "opencv2/core/types_c.h" - -#ifdef __cplusplus -# ifdef _MSC_VER -/* disable warning C4190: 'function' has C-linkage specified, but returns UDT 'typename' - which is incompatible with C - - It is OK to disable it because we only extend few plain structures with - C++ constructors for simpler interoperability with C++ API of the library -*/ -# pragma warning(disable:4190) -# elif defined __clang__ && __clang_major__ >= 3 -# pragma GCC diagnostic ignored "-Wreturn-type-c-linkage" -# endif -#endif - -#ifdef __cplusplus -extern "C" { -#endif - -/** @addtogroup core_c - @{ -*/ - -/****************************************************************************************\ -* Array allocation, deallocation, initialization and access to elements * -\****************************************************************************************/ - -/** `malloc` wrapper. - If there is no enough memory, the function - (as well as other OpenCV functions that call cvAlloc) - raises an error. */ -CVAPI(void*) cvAlloc( size_t size ); - -/** `free` wrapper. - Here and further all the memory releasing functions - (that all call cvFree) take double pointer in order to - to clear pointer to the data after releasing it. - Passing pointer to NULL pointer is Ok: nothing happens in this case -*/ -CVAPI(void) cvFree_( void* ptr ); -#define cvFree(ptr) (cvFree_(*(ptr)), *(ptr)=0) - -/** @brief Creates an image header but does not allocate the image data. - -@param size Image width and height -@param depth Image depth (see cvCreateImage ) -@param channels Number of channels (see cvCreateImage ) - */ -CVAPI(IplImage*) cvCreateImageHeader( CvSize size, int depth, int channels ); - -/** @brief Initializes an image header that was previously allocated. - -The returned IplImage\* points to the initialized header. -@param image Image header to initialize -@param size Image width and height -@param depth Image depth (see cvCreateImage ) -@param channels Number of channels (see cvCreateImage ) -@param origin Top-left IPL_ORIGIN_TL or bottom-left IPL_ORIGIN_BL -@param align Alignment for image rows, typically 4 or 8 bytes - */ -CVAPI(IplImage*) cvInitImageHeader( IplImage* image, CvSize size, int depth, - int channels, int origin CV_DEFAULT(0), - int align CV_DEFAULT(4)); - -/** @brief Creates an image header and allocates the image data. - -This function call is equivalent to the following code: -@code - header = cvCreateImageHeader(size, depth, channels); - cvCreateData(header); -@endcode -@param size Image width and height -@param depth Bit depth of image elements. See IplImage for valid depths. -@param channels Number of channels per pixel. See IplImage for details. This function only creates -images with interleaved channels. - */ -CVAPI(IplImage*) cvCreateImage( CvSize size, int depth, int channels ); - -/** @brief Deallocates an image header. - -This call is an analogue of : -@code - if(image ) - { - iplDeallocate(*image, IPL_IMAGE_HEADER | IPL_IMAGE_ROI); - *image = 0; - } -@endcode -but it does not use IPL functions by default (see the CV_TURN_ON_IPL_COMPATIBILITY macro). -@param image Double pointer to the image header - */ -CVAPI(void) cvReleaseImageHeader( IplImage** image ); - -/** @brief Deallocates the image header and the image data. - -This call is a shortened form of : -@code - if(*image ) - { - cvReleaseData(*image); - cvReleaseImageHeader(image); - } -@endcode -@param image Double pointer to the image header -*/ -CVAPI(void) cvReleaseImage( IplImage** image ); - -/** Creates a copy of IPL image (widthStep may differ) */ -CVAPI(IplImage*) cvCloneImage( const IplImage* image ); - -/** @brief Sets the channel of interest in an IplImage. - -If the ROI is set to NULL and the coi is *not* 0, the ROI is allocated. Most OpenCV functions do -*not* support the COI setting, so to process an individual image/matrix channel one may copy (via -cvCopy or cvSplit) the channel to a separate image/matrix, process it and then copy the result -back (via cvCopy or cvMerge) if needed. -@param image A pointer to the image header -@param coi The channel of interest. 0 - all channels are selected, 1 - first channel is selected, -etc. Note that the channel indices become 1-based. - */ -CVAPI(void) cvSetImageCOI( IplImage* image, int coi ); - -/** @brief Returns the index of the channel of interest. - -Returns the channel of interest of in an IplImage. Returned values correspond to the coi in -cvSetImageCOI. -@param image A pointer to the image header - */ -CVAPI(int) cvGetImageCOI( const IplImage* image ); - -/** @brief Sets an image Region Of Interest (ROI) for a given rectangle. - -If the original image ROI was NULL and the rect is not the whole image, the ROI structure is -allocated. - -Most OpenCV functions support the use of ROI and treat the image rectangle as a separate image. For -example, all of the pixel coordinates are counted from the top-left (or bottom-left) corner of the -ROI, not the original image. -@param image A pointer to the image header -@param rect The ROI rectangle - */ -CVAPI(void) cvSetImageROI( IplImage* image, CvRect rect ); - -/** @brief Resets the image ROI to include the entire image and releases the ROI structure. - -This produces a similar result to the following, but in addition it releases the ROI structure. : -@code - cvSetImageROI(image, cvRect(0, 0, image->width, image->height )); - cvSetImageCOI(image, 0); -@endcode -@param image A pointer to the image header - */ -CVAPI(void) cvResetImageROI( IplImage* image ); - -/** @brief Returns the image ROI. - -If there is no ROI set, cvRect(0,0,image-\>width,image-\>height) is returned. -@param image A pointer to the image header - */ -CVAPI(CvRect) cvGetImageROI( const IplImage* image ); - -/** @brief Creates a matrix header but does not allocate the matrix data. - -The function allocates a new matrix header and returns a pointer to it. The matrix data can then be -allocated using cvCreateData or set explicitly to user-allocated data via cvSetData. -@param rows Number of rows in the matrix -@param cols Number of columns in the matrix -@param type Type of the matrix elements, see cvCreateMat - */ -CVAPI(CvMat*) cvCreateMatHeader( int rows, int cols, int type ); - -#define CV_AUTOSTEP 0x7fffffff - -/** @brief Initializes a pre-allocated matrix header. - -This function is often used to process raw data with OpenCV matrix functions. For example, the -following code computes the matrix product of two matrices, stored as ordinary arrays: -@code - double a[] = { 1, 2, 3, 4, - 5, 6, 7, 8, - 9, 10, 11, 12 }; - - double b[] = { 1, 5, 9, - 2, 6, 10, - 3, 7, 11, - 4, 8, 12 }; - - double c[9]; - CvMat Ma, Mb, Mc ; - - cvInitMatHeader(&Ma, 3, 4, CV_64FC1, a); - cvInitMatHeader(&Mb, 4, 3, CV_64FC1, b); - cvInitMatHeader(&Mc, 3, 3, CV_64FC1, c); - - cvMatMulAdd(&Ma, &Mb, 0, &Mc); - // the c array now contains the product of a (3x4) and b (4x3) -@endcode -@param mat A pointer to the matrix header to be initialized -@param rows Number of rows in the matrix -@param cols Number of columns in the matrix -@param type Type of the matrix elements, see cvCreateMat . -@param data Optional: data pointer assigned to the matrix header -@param step Optional: full row width in bytes of the assigned data. By default, the minimal -possible step is used which assumes there are no gaps between subsequent rows of the matrix. - */ -CVAPI(CvMat*) cvInitMatHeader( CvMat* mat, int rows, int cols, - int type, void* data CV_DEFAULT(NULL), - int step CV_DEFAULT(CV_AUTOSTEP) ); - -/** @brief Creates a matrix header and allocates the matrix data. - -The function call is equivalent to the following code: -@code - CvMat* mat = cvCreateMatHeader(rows, cols, type); - cvCreateData(mat); -@endcode -@param rows Number of rows in the matrix -@param cols Number of columns in the matrix -@param type The type of the matrix elements in the form -CV_\\C\ , where S=signed, U=unsigned, F=float. For -example, CV _ 8UC1 means the elements are 8-bit unsigned and the there is 1 channel, and CV _ -32SC2 means the elements are 32-bit signed and there are 2 channels. - */ -CVAPI(CvMat*) cvCreateMat( int rows, int cols, int type ); - -/** @brief Deallocates a matrix. - -The function decrements the matrix data reference counter and deallocates matrix header. If the data -reference counter is 0, it also deallocates the data. : -@code - if(*mat ) - cvDecRefData(*mat); - cvFree((void**)mat); -@endcode -@param mat Double pointer to the matrix - */ -CVAPI(void) cvReleaseMat( CvMat** mat ); - -/** @brief Decrements an array data reference counter. - -The function decrements the data reference counter in a CvMat or CvMatND if the reference counter - -pointer is not NULL. If the counter reaches zero, the data is deallocated. In the current -implementation the reference counter is not NULL only if the data was allocated using the -cvCreateData function. The counter will be NULL in other cases such as: external data was assigned -to the header using cvSetData, header is part of a larger matrix or image, or the header was -converted from an image or n-dimensional matrix header. -@param arr Pointer to an array header - */ -CV_INLINE void cvDecRefData( CvArr* arr ) -{ - if( CV_IS_MAT( arr )) - { - CvMat* mat = (CvMat*)arr; - mat->data.ptr = NULL; - if( mat->refcount != NULL && --*mat->refcount == 0 ) - cvFree( &mat->refcount ); - mat->refcount = NULL; - } - else if( CV_IS_MATND( arr )) - { - CvMatND* mat = (CvMatND*)arr; - mat->data.ptr = NULL; - if( mat->refcount != NULL && --*mat->refcount == 0 ) - cvFree( &mat->refcount ); - mat->refcount = NULL; - } -} - -/** @brief Increments array data reference counter. - -The function increments CvMat or CvMatND data reference counter and returns the new counter value if -the reference counter pointer is not NULL, otherwise it returns zero. -@param arr Array header - */ -CV_INLINE int cvIncRefData( CvArr* arr ) -{ - int refcount = 0; - if( CV_IS_MAT( arr )) - { - CvMat* mat = (CvMat*)arr; - if( mat->refcount != NULL ) - refcount = ++*mat->refcount; - } - else if( CV_IS_MATND( arr )) - { - CvMatND* mat = (CvMatND*)arr; - if( mat->refcount != NULL ) - refcount = ++*mat->refcount; - } - return refcount; -} - - -/** Creates an exact copy of the input matrix (except, may be, step value) */ -CVAPI(CvMat*) cvCloneMat( const CvMat* mat ); - - -/** @brief Returns matrix header corresponding to the rectangular sub-array of input image or matrix. - -The function returns header, corresponding to a specified rectangle of the input array. In other - -words, it allows the user to treat a rectangular part of input array as a stand-alone array. ROI is -taken into account by the function so the sub-array of ROI is actually extracted. -@param arr Input array -@param submat Pointer to the resultant sub-array header -@param rect Zero-based coordinates of the rectangle of interest - */ -CVAPI(CvMat*) cvGetSubRect( const CvArr* arr, CvMat* submat, CvRect rect ); -#define cvGetSubArr cvGetSubRect - -/** @brief Returns array row or row span. - -The function returns the header, corresponding to a specified row/row span of the input array. -cvGetRow(arr, submat, row) is a shortcut for cvGetRows(arr, submat, row, row+1). -@param arr Input array -@param submat Pointer to the resulting sub-array header -@param start_row Zero-based index of the starting row (inclusive) of the span -@param end_row Zero-based index of the ending row (exclusive) of the span -@param delta_row Index step in the row span. That is, the function extracts every delta_row -th -row from start_row and up to (but not including) end_row . - */ -CVAPI(CvMat*) cvGetRows( const CvArr* arr, CvMat* submat, - int start_row, int end_row, - int delta_row CV_DEFAULT(1)); - -/** @overload -@param arr Input array -@param submat Pointer to the resulting sub-array header -@param row Zero-based index of the selected row -*/ -CV_INLINE CvMat* cvGetRow( const CvArr* arr, CvMat* submat, int row ) -{ - return cvGetRows( arr, submat, row, row + 1, 1 ); -} - - -/** @brief Returns one of more array columns. - -The function returns the header, corresponding to a specified column span of the input array. That - -is, no data is copied. Therefore, any modifications of the submatrix will affect the original array. -If you need to copy the columns, use cvCloneMat. cvGetCol(arr, submat, col) is a shortcut for -cvGetCols(arr, submat, col, col+1). -@param arr Input array -@param submat Pointer to the resulting sub-array header -@param start_col Zero-based index of the starting column (inclusive) of the span -@param end_col Zero-based index of the ending column (exclusive) of the span - */ -CVAPI(CvMat*) cvGetCols( const CvArr* arr, CvMat* submat, - int start_col, int end_col ); - -/** @overload -@param arr Input array -@param submat Pointer to the resulting sub-array header -@param col Zero-based index of the selected column -*/ -CV_INLINE CvMat* cvGetCol( const CvArr* arr, CvMat* submat, int col ) -{ - return cvGetCols( arr, submat, col, col + 1 ); -} - -/** @brief Returns one of array diagonals. - -The function returns the header, corresponding to a specified diagonal of the input array. -@param arr Input array -@param submat Pointer to the resulting sub-array header -@param diag Index of the array diagonal. Zero value corresponds to the main diagonal, -1 -corresponds to the diagonal above the main, 1 corresponds to the diagonal below the main, and so -forth. - */ -CVAPI(CvMat*) cvGetDiag( const CvArr* arr, CvMat* submat, - int diag CV_DEFAULT(0)); - -/** low-level scalar <-> raw data conversion functions */ -CVAPI(void) cvScalarToRawData( const CvScalar* scalar, void* data, int type, - int extend_to_12 CV_DEFAULT(0) ); - -CVAPI(void) cvRawDataToScalar( const void* data, int type, CvScalar* scalar ); - -/** @brief Creates a new matrix header but does not allocate the matrix data. - -The function allocates a header for a multi-dimensional dense array. The array data can further be -allocated using cvCreateData or set explicitly to user-allocated data via cvSetData. -@param dims Number of array dimensions -@param sizes Array of dimension sizes -@param type Type of array elements, see cvCreateMat - */ -CVAPI(CvMatND*) cvCreateMatNDHeader( int dims, const int* sizes, int type ); - -/** @brief Creates the header and allocates the data for a multi-dimensional dense array. - -This function call is equivalent to the following code: -@code - CvMatND* mat = cvCreateMatNDHeader(dims, sizes, type); - cvCreateData(mat); -@endcode -@param dims Number of array dimensions. This must not exceed CV_MAX_DIM (32 by default, but can be -changed at build time). -@param sizes Array of dimension sizes. -@param type Type of array elements, see cvCreateMat . - */ -CVAPI(CvMatND*) cvCreateMatND( int dims, const int* sizes, int type ); - -/** @brief Initializes a pre-allocated multi-dimensional array header. - -@param mat A pointer to the array header to be initialized -@param dims The number of array dimensions -@param sizes An array of dimension sizes -@param type Type of array elements, see cvCreateMat -@param data Optional data pointer assigned to the matrix header - */ -CVAPI(CvMatND*) cvInitMatNDHeader( CvMatND* mat, int dims, const int* sizes, - int type, void* data CV_DEFAULT(NULL) ); - -/** @brief Deallocates a multi-dimensional array. - -The function decrements the array data reference counter and releases the array header. If the -reference counter reaches 0, it also deallocates the data. : -@code - if(*mat ) - cvDecRefData(*mat); - cvFree((void**)mat); -@endcode -@param mat Double pointer to the array - */ -CV_INLINE void cvReleaseMatND( CvMatND** mat ) -{ - cvReleaseMat( (CvMat**)mat ); -} - -/** Creates a copy of CvMatND (except, may be, steps) */ -CVAPI(CvMatND*) cvCloneMatND( const CvMatND* mat ); - -/** @brief Creates sparse array. - -The function allocates a multi-dimensional sparse array. Initially the array contain no elements, -that is PtrND and other related functions will return 0 for every index. -@param dims Number of array dimensions. In contrast to the dense matrix, the number of dimensions is -practically unlimited (up to \f$2^{16}\f$ ). -@param sizes Array of dimension sizes -@param type Type of array elements. The same as for CvMat - */ -CVAPI(CvSparseMat*) cvCreateSparseMat( int dims, const int* sizes, int type ); - -/** @brief Deallocates sparse array. - -The function releases the sparse array and clears the array pointer upon exit. -@param mat Double pointer to the array - */ -CVAPI(void) cvReleaseSparseMat( CvSparseMat** mat ); - -/** Creates a copy of CvSparseMat (except, may be, zero items) */ -CVAPI(CvSparseMat*) cvCloneSparseMat( const CvSparseMat* mat ); - -/** @brief Initializes sparse array elements iterator. - -The function initializes iterator of sparse array elements and returns pointer to the first element, -or NULL if the array is empty. -@param mat Input array -@param mat_iterator Initialized iterator - */ -CVAPI(CvSparseNode*) cvInitSparseMatIterator( const CvSparseMat* mat, - CvSparseMatIterator* mat_iterator ); - -/** @brief Returns the next sparse matrix element - -The function moves iterator to the next sparse matrix element and returns pointer to it. In the -current version there is no any particular order of the elements, because they are stored in the -hash table. The sample below demonstrates how to iterate through the sparse matrix: -@code - // print all the non-zero sparse matrix elements and compute their sum - double sum = 0; - int i, dims = cvGetDims(sparsemat); - CvSparseMatIterator it; - CvSparseNode* node = cvInitSparseMatIterator(sparsemat, &it); - - for(; node != 0; node = cvGetNextSparseNode(&it)) - { - int* idx = CV_NODE_IDX(array, node); - float val = *(float*)CV_NODE_VAL(array, node); - printf("M"); - for(i = 0; i < dims; i++ ) - printf("[%d]", idx[i]); - printf("=%g\n", val); - - sum += val; - } - - printf("nTotal sum = %g\n", sum); -@endcode -@param mat_iterator Sparse array iterator - */ -CV_INLINE CvSparseNode* cvGetNextSparseNode( CvSparseMatIterator* mat_iterator ) -{ - if( mat_iterator->node->next ) - return mat_iterator->node = mat_iterator->node->next; - else - { - int idx; - for( idx = ++mat_iterator->curidx; idx < mat_iterator->mat->hashsize; idx++ ) - { - CvSparseNode* node = (CvSparseNode*)mat_iterator->mat->hashtable[idx]; - if( node ) - { - mat_iterator->curidx = idx; - return mat_iterator->node = node; - } - } - return NULL; - } -} - - -#define CV_MAX_ARR 10 - -/** matrix iterator: used for n-ary operations on dense arrays */ -typedef struct CvNArrayIterator -{ - int count; /**< number of arrays */ - int dims; /**< number of dimensions to iterate */ - CvSize size; /**< maximal common linear size: { width = size, height = 1 } */ - uchar* ptr[CV_MAX_ARR]; /**< pointers to the array slices */ - int stack[CV_MAX_DIM]; /**< for internal use */ - CvMatND* hdr[CV_MAX_ARR]; /**< pointers to the headers of the - matrices that are processed */ -} -CvNArrayIterator; - -#define CV_NO_DEPTH_CHECK 1 -#define CV_NO_CN_CHECK 2 -#define CV_NO_SIZE_CHECK 4 - -/** initializes iterator that traverses through several arrays simultaneously - (the function together with cvNextArraySlice is used for - N-ari element-wise operations) */ -CVAPI(int) cvInitNArrayIterator( int count, CvArr** arrs, - const CvArr* mask, CvMatND* stubs, - CvNArrayIterator* array_iterator, - int flags CV_DEFAULT(0) ); - -/** returns zero value if iteration is finished, non-zero (slice length) otherwise */ -CVAPI(int) cvNextNArraySlice( CvNArrayIterator* array_iterator ); - - -/** @brief Returns type of array elements. - -The function returns type of the array elements. In the case of IplImage the type is converted to -CvMat-like representation. For example, if the image has been created as: -@code - IplImage* img = cvCreateImage(cvSize(640, 480), IPL_DEPTH_8U, 3); -@endcode -The code cvGetElemType(img) will return CV_8UC3. -@param arr Input array - */ -CVAPI(int) cvGetElemType( const CvArr* arr ); - -/** @brief Return number of array dimensions - -The function returns the array dimensionality and the array of dimension sizes. In the case of -IplImage or CvMat it always returns 2 regardless of number of image/matrix rows. For example, the -following code calculates total number of array elements: -@code - int sizes[CV_MAX_DIM]; - int i, total = 1; - int dims = cvGetDims(arr, size); - for(i = 0; i < dims; i++ ) - total *= sizes[i]; -@endcode -@param arr Input array -@param sizes Optional output vector of the array dimension sizes. For 2d arrays the number of rows -(height) goes first, number of columns (width) next. - */ -CVAPI(int) cvGetDims( const CvArr* arr, int* sizes CV_DEFAULT(NULL) ); - - -/** @brief Returns array size along the specified dimension. - -@param arr Input array -@param index Zero-based dimension index (for matrices 0 means number of rows, 1 means number of -columns; for images 0 means height, 1 means width) - */ -CVAPI(int) cvGetDimSize( const CvArr* arr, int index ); - - -/** @brief Return pointer to a particular array element. - -The functions return a pointer to a specific array element. Number of array dimension should match -to the number of indices passed to the function except for cvPtr1D function that can be used for -sequential access to 1D, 2D or nD dense arrays. - -The functions can be used for sparse arrays as well - if the requested node does not exist they -create it and set it to zero. - -All these as well as other functions accessing array elements ( cvGetND , cvGetRealND , cvSet -, cvSetND , cvSetRealND ) raise an error in case if the element index is out of range. -@param arr Input array -@param idx0 The first zero-based component of the element index -@param type Optional output parameter: type of matrix elements - */ -CVAPI(uchar*) cvPtr1D( const CvArr* arr, int idx0, int* type CV_DEFAULT(NULL)); -/** @overload */ -CVAPI(uchar*) cvPtr2D( const CvArr* arr, int idx0, int idx1, int* type CV_DEFAULT(NULL) ); -/** @overload */ -CVAPI(uchar*) cvPtr3D( const CvArr* arr, int idx0, int idx1, int idx2, - int* type CV_DEFAULT(NULL)); -/** @overload -@param arr Input array -@param idx Array of the element indices -@param type Optional output parameter: type of matrix elements -@param create_node Optional input parameter for sparse matrices. Non-zero value of the parameter -means that the requested element is created if it does not exist already. -@param precalc_hashval Optional input parameter for sparse matrices. If the pointer is not NULL, -the function does not recalculate the node hash value, but takes it from the specified location. -It is useful for speeding up pair-wise operations (TODO: provide an example) -*/ -CVAPI(uchar*) cvPtrND( const CvArr* arr, const int* idx, int* type CV_DEFAULT(NULL), - int create_node CV_DEFAULT(1), - unsigned* precalc_hashval CV_DEFAULT(NULL)); - -/** @brief Return a specific array element. - -The functions return a specific array element. In the case of a sparse array the functions return 0 -if the requested node does not exist (no new node is created by the functions). -@param arr Input array -@param idx0 The first zero-based component of the element index - */ -CVAPI(CvScalar) cvGet1D( const CvArr* arr, int idx0 ); -/** @overload */ -CVAPI(CvScalar) cvGet2D( const CvArr* arr, int idx0, int idx1 ); -/** @overload */ -CVAPI(CvScalar) cvGet3D( const CvArr* arr, int idx0, int idx1, int idx2 ); -/** @overload -@param arr Input array -@param idx Array of the element indices -*/ -CVAPI(CvScalar) cvGetND( const CvArr* arr, const int* idx ); - -/** @brief Return a specific element of single-channel 1D, 2D, 3D or nD array. - -Returns a specific element of a single-channel array. If the array has multiple channels, a runtime -error is raised. Note that Get?D functions can be used safely for both single-channel and -multiple-channel arrays though they are a bit slower. - -In the case of a sparse array the functions return 0 if the requested node does not exist (no new -node is created by the functions). -@param arr Input array. Must have a single channel. -@param idx0 The first zero-based component of the element index - */ -CVAPI(double) cvGetReal1D( const CvArr* arr, int idx0 ); -/** @overload */ -CVAPI(double) cvGetReal2D( const CvArr* arr, int idx0, int idx1 ); -/** @overload */ -CVAPI(double) cvGetReal3D( const CvArr* arr, int idx0, int idx1, int idx2 ); -/** @overload -@param arr Input array. Must have a single channel. -@param idx Array of the element indices -*/ -CVAPI(double) cvGetRealND( const CvArr* arr, const int* idx ); - -/** @brief Change the particular array element. - -The functions assign the new value to a particular array element. In the case of a sparse array the -functions create the node if it does not exist yet. -@param arr Input array -@param idx0 The first zero-based component of the element index -@param value The assigned value - */ -CVAPI(void) cvSet1D( CvArr* arr, int idx0, CvScalar value ); -/** @overload */ -CVAPI(void) cvSet2D( CvArr* arr, int idx0, int idx1, CvScalar value ); -/** @overload */ -CVAPI(void) cvSet3D( CvArr* arr, int idx0, int idx1, int idx2, CvScalar value ); -/** @overload -@param arr Input array -@param idx Array of the element indices -@param value The assigned value -*/ -CVAPI(void) cvSetND( CvArr* arr, const int* idx, CvScalar value ); - -/** @brief Change a specific array element. - -The functions assign a new value to a specific element of a single-channel array. If the array has -multiple channels, a runtime error is raised. Note that the Set\*D function can be used safely for -both single-channel and multiple-channel arrays, though they are a bit slower. - -In the case of a sparse array the functions create the node if it does not yet exist. -@param arr Input array -@param idx0 The first zero-based component of the element index -@param value The assigned value - */ -CVAPI(void) cvSetReal1D( CvArr* arr, int idx0, double value ); -/** @overload */ -CVAPI(void) cvSetReal2D( CvArr* arr, int idx0, int idx1, double value ); -/** @overload */ -CVAPI(void) cvSetReal3D( CvArr* arr, int idx0, - int idx1, int idx2, double value ); -/** @overload -@param arr Input array -@param idx Array of the element indices -@param value The assigned value -*/ -CVAPI(void) cvSetRealND( CvArr* arr, const int* idx, double value ); - -/** clears element of ND dense array, - in case of sparse arrays it deletes the specified node */ -CVAPI(void) cvClearND( CvArr* arr, const int* idx ); - -/** @brief Returns matrix header for arbitrary array. - -The function returns a matrix header for the input array that can be a matrix - CvMat, an image - -IplImage, or a multi-dimensional dense array - CvMatND (the third option is allowed only if -allowND != 0) . In the case of matrix the function simply returns the input pointer. In the case of -IplImage\* or CvMatND it initializes the header structure with parameters of the current image ROI -and returns &header. Because COI is not supported by CvMat, it is returned separately. - -The function provides an easy way to handle both types of arrays - IplImage and CvMat using the same -code. Input array must have non-zero data pointer, otherwise the function will report an error. - -@note If the input array is IplImage with planar data layout and COI set, the function returns the -pointer to the selected plane and COI == 0. This feature allows user to process IplImage structures -with planar data layout, even though OpenCV does not support such images. -@param arr Input array -@param header Pointer to CvMat structure used as a temporary buffer -@param coi Optional output parameter for storing COI -@param allowND If non-zero, the function accepts multi-dimensional dense arrays (CvMatND\*) and -returns 2D matrix (if CvMatND has two dimensions) or 1D matrix (when CvMatND has 1 dimension or -more than 2 dimensions). The CvMatND array must be continuous. -@sa cvGetImage, cvarrToMat. - */ -CVAPI(CvMat*) cvGetMat( const CvArr* arr, CvMat* header, - int* coi CV_DEFAULT(NULL), - int allowND CV_DEFAULT(0)); - -/** @brief Returns image header for arbitrary array. - -The function returns the image header for the input array that can be a matrix (CvMat) or image -(IplImage). In the case of an image the function simply returns the input pointer. In the case of -CvMat it initializes an image_header structure with the parameters of the input matrix. Note that -if we transform IplImage to CvMat using cvGetMat and then transform CvMat back to IplImage using -this function, we will get different headers if the ROI is set in the original image. -@param arr Input array -@param image_header Pointer to IplImage structure used as a temporary buffer - */ -CVAPI(IplImage*) cvGetImage( const CvArr* arr, IplImage* image_header ); - - -/** @brief Changes the shape of a multi-dimensional array without copying the data. - -The function is an advanced version of cvReshape that can work with multi-dimensional arrays as -well (though it can work with ordinary images and matrices) and change the number of dimensions. - -Below are the two samples from the cvReshape description rewritten using cvReshapeMatND: -@code - IplImage* color_img = cvCreateImage(cvSize(320,240), IPL_DEPTH_8U, 3); - IplImage gray_img_hdr, *gray_img; - gray_img = (IplImage*)cvReshapeMatND(color_img, sizeof(gray_img_hdr), &gray_img_hdr, 1, 0, 0); - ... - int size[] = { 2, 2, 2 }; - CvMatND* mat = cvCreateMatND(3, size, CV_32F); - CvMat row_header, *row; - row = (CvMat*)cvReshapeMatND(mat, sizeof(row_header), &row_header, 0, 1, 0); -@endcode -In C, the header file for this function includes a convenient macro cvReshapeND that does away with -the sizeof_header parameter. So, the lines containing the call to cvReshapeMatND in the examples -may be replaced as follow: -@code - gray_img = (IplImage*)cvReshapeND(color_img, &gray_img_hdr, 1, 0, 0); - ... - row = (CvMat*)cvReshapeND(mat, &row_header, 0, 1, 0); -@endcode -@param arr Input array -@param sizeof_header Size of output header to distinguish between IplImage, CvMat and CvMatND -output headers -@param header Output header to be filled -@param new_cn New number of channels. new_cn = 0 means that the number of channels remains -unchanged. -@param new_dims New number of dimensions. new_dims = 0 means that the number of dimensions -remains the same. -@param new_sizes Array of new dimension sizes. Only new_dims-1 values are used, because the -total number of elements must remain the same. Thus, if new_dims = 1, new_sizes array is not -used. - */ -CVAPI(CvArr*) cvReshapeMatND( const CvArr* arr, - int sizeof_header, CvArr* header, - int new_cn, int new_dims, int* new_sizes ); - -#define cvReshapeND( arr, header, new_cn, new_dims, new_sizes ) \ - cvReshapeMatND( (arr), sizeof(*(header)), (header), \ - (new_cn), (new_dims), (new_sizes)) - -/** @brief Changes shape of matrix/image without copying data. - -The function initializes the CvMat header so that it points to the same data as the original array -but has a different shape - different number of channels, different number of rows, or both. - -The following example code creates one image buffer and two image headers, the first is for a -320x240x3 image and the second is for a 960x240x1 image: -@code - IplImage* color_img = cvCreateImage(cvSize(320,240), IPL_DEPTH_8U, 3); - CvMat gray_mat_hdr; - IplImage gray_img_hdr, *gray_img; - cvReshape(color_img, &gray_mat_hdr, 1); - gray_img = cvGetImage(&gray_mat_hdr, &gray_img_hdr); -@endcode -And the next example converts a 3x3 matrix to a single 1x9 vector: -@code - CvMat* mat = cvCreateMat(3, 3, CV_32F); - CvMat row_header, *row; - row = cvReshape(mat, &row_header, 0, 1); -@endcode -@param arr Input array -@param header Output header to be filled -@param new_cn New number of channels. 'new_cn = 0' means that the number of channels remains -unchanged. -@param new_rows New number of rows. 'new_rows = 0' means that the number of rows remains -unchanged unless it needs to be changed according to new_cn value. -*/ -CVAPI(CvMat*) cvReshape( const CvArr* arr, CvMat* header, - int new_cn, int new_rows CV_DEFAULT(0) ); - -/** Repeats source 2d array several times in both horizontal and - vertical direction to fill destination array */ -CVAPI(void) cvRepeat( const CvArr* src, CvArr* dst ); - -/** @brief Allocates array data - -The function allocates image, matrix or multi-dimensional dense array data. Note that in the case of -matrix types OpenCV allocation functions are used. In the case of IplImage they are used unless -CV_TURN_ON_IPL_COMPATIBILITY() has been called before. In the latter case IPL functions are used -to allocate the data. -@param arr Array header - */ -CVAPI(void) cvCreateData( CvArr* arr ); - -/** @brief Releases array data. - -The function releases the array data. In the case of CvMat or CvMatND it simply calls -cvDecRefData(), that is the function can not deallocate external data. See also the note to -cvCreateData . -@param arr Array header - */ -CVAPI(void) cvReleaseData( CvArr* arr ); - -/** @brief Assigns user data to the array header. - -The function assigns user data to the array header. Header should be initialized before using -cvCreateMatHeader, cvCreateImageHeader, cvCreateMatNDHeader, cvInitMatHeader, -cvInitImageHeader or cvInitMatNDHeader. -@param arr Array header -@param data User data -@param step Full row length in bytes - */ -CVAPI(void) cvSetData( CvArr* arr, void* data, int step ); - -/** @brief Retrieves low-level information about the array. - -The function fills output variables with low-level information about the array data. All output - -parameters are optional, so some of the pointers may be set to NULL. If the array is IplImage with -ROI set, the parameters of ROI are returned. - -The following example shows how to get access to array elements. It computes absolute values of the -array elements : -@code - float* data; - int step; - CvSize size; - - cvGetRawData(array, (uchar**)&data, &step, &size); - step /= sizeof(data[0]); - - for(int y = 0; y < size.height; y++, data += step ) - for(int x = 0; x < size.width; x++ ) - data[x] = (float)fabs(data[x]); -@endcode -@param arr Array header -@param data Output pointer to the whole image origin or ROI origin if ROI is set -@param step Output full row length in bytes -@param roi_size Output ROI size - */ -CVAPI(void) cvGetRawData( const CvArr* arr, uchar** data, - int* step CV_DEFAULT(NULL), - CvSize* roi_size CV_DEFAULT(NULL)); - -/** @brief Returns size of matrix or image ROI. - -The function returns number of rows (CvSize::height) and number of columns (CvSize::width) of the -input matrix or image. In the case of image the size of ROI is returned. -@param arr array header - */ -CVAPI(CvSize) cvGetSize( const CvArr* arr ); - -/** @brief Copies one array to another. - -The function copies selected elements from an input array to an output array: - -\f[\texttt{dst} (I)= \texttt{src} (I) \quad \text{if} \quad \texttt{mask} (I) \ne 0.\f] - -If any of the passed arrays is of IplImage type, then its ROI and COI fields are used. Both arrays -must have the same type, the same number of dimensions, and the same size. The function can also -copy sparse arrays (mask is not supported in this case). -@param src The source array -@param dst The destination array -@param mask Operation mask, 8-bit single channel array; specifies elements of the destination array -to be changed - */ -CVAPI(void) cvCopy( const CvArr* src, CvArr* dst, - const CvArr* mask CV_DEFAULT(NULL) ); - -/** @brief Sets every element of an array to a given value. - -The function copies the scalar value to every selected element of the destination array: -\f[\texttt{arr} (I)= \texttt{value} \quad \text{if} \quad \texttt{mask} (I) \ne 0\f] -If array arr is of IplImage type, then is ROI used, but COI must not be set. -@param arr The destination array -@param value Fill value -@param mask Operation mask, 8-bit single channel array; specifies elements of the destination -array to be changed - */ -CVAPI(void) cvSet( CvArr* arr, CvScalar value, - const CvArr* mask CV_DEFAULT(NULL) ); - -/** @brief Clears the array. - -The function clears the array. In the case of dense arrays (CvMat, CvMatND or IplImage), -cvZero(array) is equivalent to cvSet(array,cvScalarAll(0),0). In the case of sparse arrays all the -elements are removed. -@param arr Array to be cleared - */ -CVAPI(void) cvSetZero( CvArr* arr ); -#define cvZero cvSetZero - - -/** Splits a multi-channel array into the set of single-channel arrays or - extracts particular [color] plane */ -CVAPI(void) cvSplit( const CvArr* src, CvArr* dst0, CvArr* dst1, - CvArr* dst2, CvArr* dst3 ); - -/** Merges a set of single-channel arrays into the single multi-channel array - or inserts one particular [color] plane to the array */ -CVAPI(void) cvMerge( const CvArr* src0, const CvArr* src1, - const CvArr* src2, const CvArr* src3, - CvArr* dst ); - -/** Copies several channels from input arrays to - certain channels of output arrays */ -CVAPI(void) cvMixChannels( const CvArr** src, int src_count, - CvArr** dst, int dst_count, - const int* from_to, int pair_count ); - -/** @brief Converts one array to another with optional linear transformation. - -The function has several different purposes, and thus has several different names. It copies one -array to another with optional scaling, which is performed first, and/or optional type conversion, -performed after: - -\f[\texttt{dst} (I) = \texttt{scale} \texttt{src} (I) + ( \texttt{shift} _0, \texttt{shift} _1,...)\f] - -All the channels of multi-channel arrays are processed independently. - -The type of conversion is done with rounding and saturation, that is if the result of scaling + -conversion can not be represented exactly by a value of the destination array element type, it is -set to the nearest representable value on the real axis. -@param src Source array -@param dst Destination array -@param scale Scale factor -@param shift Value added to the scaled source array elements - */ -CVAPI(void) cvConvertScale( const CvArr* src, CvArr* dst, - double scale CV_DEFAULT(1), - double shift CV_DEFAULT(0) ); -#define cvCvtScale cvConvertScale -#define cvScale cvConvertScale -#define cvConvert( src, dst ) cvConvertScale( (src), (dst), 1, 0 ) - - -/** Performs linear transformation on every source array element, - stores absolute value of the result: - dst(x,y,c) = abs(scale*src(x,y,c)+shift). - destination array must have 8u type. - In other cases one may use cvConvertScale + cvAbsDiffS */ -CVAPI(void) cvConvertScaleAbs( const CvArr* src, CvArr* dst, - double scale CV_DEFAULT(1), - double shift CV_DEFAULT(0) ); -#define cvCvtScaleAbs cvConvertScaleAbs - - -/** checks termination criteria validity and - sets eps to default_eps (if it is not set), - max_iter to default_max_iters (if it is not set) -*/ -CVAPI(CvTermCriteria) cvCheckTermCriteria( CvTermCriteria criteria, - double default_eps, - int default_max_iters ); - -/****************************************************************************************\ -* Arithmetic, logic and comparison operations * -\****************************************************************************************/ - -/** dst(mask) = src1(mask) + src2(mask) */ -CVAPI(void) cvAdd( const CvArr* src1, const CvArr* src2, CvArr* dst, - const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(mask) = src(mask) + value */ -CVAPI(void) cvAddS( const CvArr* src, CvScalar value, CvArr* dst, - const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(mask) = src1(mask) - src2(mask) */ -CVAPI(void) cvSub( const CvArr* src1, const CvArr* src2, CvArr* dst, - const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(mask) = src(mask) - value = src(mask) + (-value) */ -CV_INLINE void cvSubS( const CvArr* src, CvScalar value, CvArr* dst, - const CvArr* mask CV_DEFAULT(NULL)) -{ - cvAddS( src, cvScalar( -value.val[0], -value.val[1], -value.val[2], -value.val[3]), - dst, mask ); -} - -/** dst(mask) = value - src(mask) */ -CVAPI(void) cvSubRS( const CvArr* src, CvScalar value, CvArr* dst, - const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(idx) = src1(idx) * src2(idx) * scale - (scaled element-wise multiplication of 2 arrays) */ -CVAPI(void) cvMul( const CvArr* src1, const CvArr* src2, - CvArr* dst, double scale CV_DEFAULT(1) ); - -/** element-wise division/inversion with scaling: - dst(idx) = src1(idx) * scale / src2(idx) - or dst(idx) = scale / src2(idx) if src1 == 0 */ -CVAPI(void) cvDiv( const CvArr* src1, const CvArr* src2, - CvArr* dst, double scale CV_DEFAULT(1)); - -/** dst = src1 * scale + src2 */ -CVAPI(void) cvScaleAdd( const CvArr* src1, CvScalar scale, - const CvArr* src2, CvArr* dst ); -#define cvAXPY( A, real_scalar, B, C ) cvScaleAdd(A, cvRealScalar(real_scalar), B, C) - -/** dst = src1 * alpha + src2 * beta + gamma */ -CVAPI(void) cvAddWeighted( const CvArr* src1, double alpha, - const CvArr* src2, double beta, - double gamma, CvArr* dst ); - -/** @brief Calculates the dot product of two arrays in Euclidean metrics. - -The function calculates and returns the Euclidean dot product of two arrays. - -\f[src1 \bullet src2 = \sum _I ( \texttt{src1} (I) \texttt{src2} (I))\f] - -In the case of multiple channel arrays, the results for all channels are accumulated. In particular, -cvDotProduct(a,a) where a is a complex vector, will return \f$||\texttt{a}||^2\f$. The function can -process multi-dimensional arrays, row by row, layer by layer, and so on. -@param src1 The first source array -@param src2 The second source array - */ -CVAPI(double) cvDotProduct( const CvArr* src1, const CvArr* src2 ); - -/** dst(idx) = src1(idx) & src2(idx) */ -CVAPI(void) cvAnd( const CvArr* src1, const CvArr* src2, - CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(idx) = src(idx) & value */ -CVAPI(void) cvAndS( const CvArr* src, CvScalar value, - CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(idx) = src1(idx) | src2(idx) */ -CVAPI(void) cvOr( const CvArr* src1, const CvArr* src2, - CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(idx) = src(idx) | value */ -CVAPI(void) cvOrS( const CvArr* src, CvScalar value, - CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(idx) = src1(idx) ^ src2(idx) */ -CVAPI(void) cvXor( const CvArr* src1, const CvArr* src2, - CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(idx) = src(idx) ^ value */ -CVAPI(void) cvXorS( const CvArr* src, CvScalar value, - CvArr* dst, const CvArr* mask CV_DEFAULT(NULL)); - -/** dst(idx) = ~src(idx) */ -CVAPI(void) cvNot( const CvArr* src, CvArr* dst ); - -/** dst(idx) = lower(idx) <= src(idx) < upper(idx) */ -CVAPI(void) cvInRange( const CvArr* src, const CvArr* lower, - const CvArr* upper, CvArr* dst ); - -/** dst(idx) = lower <= src(idx) < upper */ -CVAPI(void) cvInRangeS( const CvArr* src, CvScalar lower, - CvScalar upper, CvArr* dst ); - -#define CV_CMP_EQ 0 -#define CV_CMP_GT 1 -#define CV_CMP_GE 2 -#define CV_CMP_LT 3 -#define CV_CMP_LE 4 -#define CV_CMP_NE 5 - -/** The comparison operation support single-channel arrays only. - Destination image should be 8uC1 or 8sC1 */ - -/** dst(idx) = src1(idx) _cmp_op_ src2(idx) */ -CVAPI(void) cvCmp( const CvArr* src1, const CvArr* src2, CvArr* dst, int cmp_op ); - -/** dst(idx) = src1(idx) _cmp_op_ value */ -CVAPI(void) cvCmpS( const CvArr* src, double value, CvArr* dst, int cmp_op ); - -/** dst(idx) = min(src1(idx),src2(idx)) */ -CVAPI(void) cvMin( const CvArr* src1, const CvArr* src2, CvArr* dst ); - -/** dst(idx) = max(src1(idx),src2(idx)) */ -CVAPI(void) cvMax( const CvArr* src1, const CvArr* src2, CvArr* dst ); - -/** dst(idx) = min(src(idx),value) */ -CVAPI(void) cvMinS( const CvArr* src, double value, CvArr* dst ); - -/** dst(idx) = max(src(idx),value) */ -CVAPI(void) cvMaxS( const CvArr* src, double value, CvArr* dst ); - -/** dst(x,y,c) = abs(src1(x,y,c) - src2(x,y,c)) */ -CVAPI(void) cvAbsDiff( const CvArr* src1, const CvArr* src2, CvArr* dst ); - -/** dst(x,y,c) = abs(src(x,y,c) - value(c)) */ -CVAPI(void) cvAbsDiffS( const CvArr* src, CvArr* dst, CvScalar value ); -#define cvAbs( src, dst ) cvAbsDiffS( (src), (dst), cvScalarAll(0)) - -/****************************************************************************************\ -* Math operations * -\****************************************************************************************/ - -/** Does cartesian->polar coordinates conversion. - Either of output components (magnitude or angle) is optional */ -CVAPI(void) cvCartToPolar( const CvArr* x, const CvArr* y, - CvArr* magnitude, CvArr* angle CV_DEFAULT(NULL), - int angle_in_degrees CV_DEFAULT(0)); - -/** Does polar->cartesian coordinates conversion. - Either of output components (magnitude or angle) is optional. - If magnitude is missing it is assumed to be all 1's */ -CVAPI(void) cvPolarToCart( const CvArr* magnitude, const CvArr* angle, - CvArr* x, CvArr* y, - int angle_in_degrees CV_DEFAULT(0)); - -/** Does powering: dst(idx) = src(idx)^power */ -CVAPI(void) cvPow( const CvArr* src, CvArr* dst, double power ); - -/** Does exponention: dst(idx) = exp(src(idx)). - Overflow is not handled yet. Underflow is handled. - Maximal relative error is ~7e-6 for single-precision input */ -CVAPI(void) cvExp( const CvArr* src, CvArr* dst ); - -/** Calculates natural logarithms: dst(idx) = log(abs(src(idx))). - Logarithm of 0 gives large negative number(~-700) - Maximal relative error is ~3e-7 for single-precision output -*/ -CVAPI(void) cvLog( const CvArr* src, CvArr* dst ); - -/** Fast arctangent calculation */ -CVAPI(float) cvFastArctan( float y, float x ); - -/** Fast cubic root calculation */ -CVAPI(float) cvCbrt( float value ); - -#define CV_CHECK_RANGE 1 -#define CV_CHECK_QUIET 2 -/** Checks array values for NaNs, Infs or simply for too large numbers - (if CV_CHECK_RANGE is set). If CV_CHECK_QUIET is set, - no runtime errors is raised (function returns zero value in case of "bad" values). - Otherwise cvError is called */ -CVAPI(int) cvCheckArr( const CvArr* arr, int flags CV_DEFAULT(0), - double min_val CV_DEFAULT(0), double max_val CV_DEFAULT(0)); -#define cvCheckArray cvCheckArr - -#define CV_RAND_UNI 0 -#define CV_RAND_NORMAL 1 - -/** @brief Fills an array with random numbers and updates the RNG state. - -The function fills the destination array with uniformly or normally distributed random numbers. -@param rng CvRNG state initialized by cvRNG -@param arr The destination array -@param dist_type Distribution type -> - **CV_RAND_UNI** uniform distribution -> - **CV_RAND_NORMAL** normal or Gaussian distribution -@param param1 The first parameter of the distribution. In the case of a uniform distribution it is -the inclusive lower boundary of the random numbers range. In the case of a normal distribution it -is the mean value of the random numbers. -@param param2 The second parameter of the distribution. In the case of a uniform distribution it -is the exclusive upper boundary of the random numbers range. In the case of a normal distribution -it is the standard deviation of the random numbers. -@sa randu, randn, RNG::fill. - */ -CVAPI(void) cvRandArr( CvRNG* rng, CvArr* arr, int dist_type, - CvScalar param1, CvScalar param2 ); - -CVAPI(void) cvRandShuffle( CvArr* mat, CvRNG* rng, - double iter_factor CV_DEFAULT(1.)); - -#define CV_SORT_EVERY_ROW 0 -#define CV_SORT_EVERY_COLUMN 1 -#define CV_SORT_ASCENDING 0 -#define CV_SORT_DESCENDING 16 - -CVAPI(void) cvSort( const CvArr* src, CvArr* dst CV_DEFAULT(NULL), - CvArr* idxmat CV_DEFAULT(NULL), - int flags CV_DEFAULT(0)); - -/** Finds real roots of a cubic equation */ -CVAPI(int) cvSolveCubic( const CvMat* coeffs, CvMat* roots ); - -/** Finds all real and complex roots of a polynomial equation */ -CVAPI(void) cvSolvePoly(const CvMat* coeffs, CvMat *roots2, - int maxiter CV_DEFAULT(20), int fig CV_DEFAULT(100)); - -/****************************************************************************************\ -* Matrix operations * -\****************************************************************************************/ - -/** @brief Calculates the cross product of two 3D vectors. - -The function calculates the cross product of two 3D vectors: -\f[\texttt{dst} = \texttt{src1} \times \texttt{src2}\f] -or: -\f[\begin{array}{l} \texttt{dst} _1 = \texttt{src1} _2 \texttt{src2} _3 - \texttt{src1} _3 \texttt{src2} _2 \\ \texttt{dst} _2 = \texttt{src1} _3 \texttt{src2} _1 - \texttt{src1} _1 \texttt{src2} _3 \\ \texttt{dst} _3 = \texttt{src1} _1 \texttt{src2} _2 - \texttt{src1} _2 \texttt{src2} _1 \end{array}\f] -@param src1 The first source vector -@param src2 The second source vector -@param dst The destination vector - */ -CVAPI(void) cvCrossProduct( const CvArr* src1, const CvArr* src2, CvArr* dst ); - -/** Matrix transform: dst = A*B + C, C is optional */ -#define cvMatMulAdd( src1, src2, src3, dst ) cvGEMM( (src1), (src2), 1., (src3), 1., (dst), 0 ) -#define cvMatMul( src1, src2, dst ) cvMatMulAdd( (src1), (src2), NULL, (dst)) - -#define CV_GEMM_A_T 1 -#define CV_GEMM_B_T 2 -#define CV_GEMM_C_T 4 -/** Extended matrix transform: - dst = alpha*op(A)*op(B) + beta*op(C), where op(X) is X or X^T */ -CVAPI(void) cvGEMM( const CvArr* src1, const CvArr* src2, double alpha, - const CvArr* src3, double beta, CvArr* dst, - int tABC CV_DEFAULT(0)); -#define cvMatMulAddEx cvGEMM - -/** Transforms each element of source array and stores - resultant vectors in destination array */ -CVAPI(void) cvTransform( const CvArr* src, CvArr* dst, - const CvMat* transmat, - const CvMat* shiftvec CV_DEFAULT(NULL)); -#define cvMatMulAddS cvTransform - -/** Does perspective transform on every element of input array */ -CVAPI(void) cvPerspectiveTransform( const CvArr* src, CvArr* dst, - const CvMat* mat ); - -/** Calculates (A-delta)*(A-delta)^T (order=0) or (A-delta)^T*(A-delta) (order=1) */ -CVAPI(void) cvMulTransposed( const CvArr* src, CvArr* dst, int order, - const CvArr* delta CV_DEFAULT(NULL), - double scale CV_DEFAULT(1.) ); - -/** Transposes matrix. Square matrices can be transposed in-place */ -CVAPI(void) cvTranspose( const CvArr* src, CvArr* dst ); -#define cvT cvTranspose - -/** Completes the symmetric matrix from the lower (LtoR=0) or from the upper (LtoR!=0) part */ -CVAPI(void) cvCompleteSymm( CvMat* matrix, int LtoR CV_DEFAULT(0) ); - -/** Mirror array data around horizontal (flip=0), - vertical (flip=1) or both(flip=-1) axises: - cvFlip(src) flips images vertically and sequences horizontally (inplace) */ -CVAPI(void) cvFlip( const CvArr* src, CvArr* dst CV_DEFAULT(NULL), - int flip_mode CV_DEFAULT(0)); -#define cvMirror cvFlip - - -#define CV_SVD_MODIFY_A 1 -#define CV_SVD_U_T 2 -#define CV_SVD_V_T 4 - -/** Performs Singular Value Decomposition of a matrix */ -CVAPI(void) cvSVD( CvArr* A, CvArr* W, CvArr* U CV_DEFAULT(NULL), - CvArr* V CV_DEFAULT(NULL), int flags CV_DEFAULT(0)); - -/** Performs Singular Value Back Substitution (solves A*X = B): - flags must be the same as in cvSVD */ -CVAPI(void) cvSVBkSb( const CvArr* W, const CvArr* U, - const CvArr* V, const CvArr* B, - CvArr* X, int flags ); - -#define CV_LU 0 -#define CV_SVD 1 -#define CV_SVD_SYM 2 -#define CV_CHOLESKY 3 -#define CV_QR 4 -#define CV_NORMAL 16 - -/** Inverts matrix */ -CVAPI(double) cvInvert( const CvArr* src, CvArr* dst, - int method CV_DEFAULT(CV_LU)); -#define cvInv cvInvert - -/** Solves linear system (src1)*(dst) = (src2) - (returns 0 if src1 is a singular and CV_LU method is used) */ -CVAPI(int) cvSolve( const CvArr* src1, const CvArr* src2, CvArr* dst, - int method CV_DEFAULT(CV_LU)); - -/** Calculates determinant of input matrix */ -CVAPI(double) cvDet( const CvArr* mat ); - -/** Calculates trace of the matrix (sum of elements on the main diagonal) */ -CVAPI(CvScalar) cvTrace( const CvArr* mat ); - -/** Finds eigen values and vectors of a symmetric matrix */ -CVAPI(void) cvEigenVV( CvArr* mat, CvArr* evects, CvArr* evals, - double eps CV_DEFAULT(0), - int lowindex CV_DEFAULT(-1), - int highindex CV_DEFAULT(-1)); - -///* Finds selected eigen values and vectors of a symmetric matrix */ -//CVAPI(void) cvSelectedEigenVV( CvArr* mat, CvArr* evects, CvArr* evals, -// int lowindex, int highindex ); - -/** Makes an identity matrix (mat_ij = i == j) */ -CVAPI(void) cvSetIdentity( CvArr* mat, CvScalar value CV_DEFAULT(cvRealScalar(1)) ); - -/** Fills matrix with given range of numbers */ -CVAPI(CvArr*) cvRange( CvArr* mat, double start, double end ); - -/** @anchor core_c_CovarFlags -@name Flags for cvCalcCovarMatrix -@see cvCalcCovarMatrix - @{ -*/ - -/** flag for cvCalcCovarMatrix, transpose([v1-avg, v2-avg,...]) * [v1-avg,v2-avg,...] */ -#define CV_COVAR_SCRAMBLED 0 - -/** flag for cvCalcCovarMatrix, [v1-avg, v2-avg,...] * transpose([v1-avg,v2-avg,...]) */ -#define CV_COVAR_NORMAL 1 - -/** flag for cvCalcCovarMatrix, do not calc average (i.e. mean vector) - use the input vector instead - (useful for calculating covariance matrix by parts) */ -#define CV_COVAR_USE_AVG 2 - -/** flag for cvCalcCovarMatrix, scale the covariance matrix coefficients by number of the vectors */ -#define CV_COVAR_SCALE 4 - -/** flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its rows */ -#define CV_COVAR_ROWS 8 - -/** flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its columns */ -#define CV_COVAR_COLS 16 - -/** @} */ - -/** Calculates covariation matrix for a set of vectors -@see @ref core_c_CovarFlags "flags" -*/ -CVAPI(void) cvCalcCovarMatrix( const CvArr** vects, int count, - CvArr* cov_mat, CvArr* avg, int flags ); - -#define CV_PCA_DATA_AS_ROW 0 -#define CV_PCA_DATA_AS_COL 1 -#define CV_PCA_USE_AVG 2 -CVAPI(void) cvCalcPCA( const CvArr* data, CvArr* mean, - CvArr* eigenvals, CvArr* eigenvects, int flags ); - -CVAPI(void) cvProjectPCA( const CvArr* data, const CvArr* mean, - const CvArr* eigenvects, CvArr* result ); - -CVAPI(void) cvBackProjectPCA( const CvArr* proj, const CvArr* mean, - const CvArr* eigenvects, CvArr* result ); - -/** Calculates Mahalanobis(weighted) distance */ -CVAPI(double) cvMahalanobis( const CvArr* vec1, const CvArr* vec2, const CvArr* mat ); -#define cvMahalonobis cvMahalanobis - -/****************************************************************************************\ -* Array Statistics * -\****************************************************************************************/ - -/** Finds sum of array elements */ -CVAPI(CvScalar) cvSum( const CvArr* arr ); - -/** Calculates number of non-zero pixels */ -CVAPI(int) cvCountNonZero( const CvArr* arr ); - -/** Calculates mean value of array elements */ -CVAPI(CvScalar) cvAvg( const CvArr* arr, const CvArr* mask CV_DEFAULT(NULL) ); - -/** Calculates mean and standard deviation of pixel values */ -CVAPI(void) cvAvgSdv( const CvArr* arr, CvScalar* mean, CvScalar* std_dev, - const CvArr* mask CV_DEFAULT(NULL) ); - -/** Finds global minimum, maximum and their positions */ -CVAPI(void) cvMinMaxLoc( const CvArr* arr, double* min_val, double* max_val, - CvPoint* min_loc CV_DEFAULT(NULL), - CvPoint* max_loc CV_DEFAULT(NULL), - const CvArr* mask CV_DEFAULT(NULL) ); - -/** @anchor core_c_NormFlags - @name Flags for cvNorm and cvNormalize - @{ -*/ -#define CV_C 1 -#define CV_L1 2 -#define CV_L2 4 -#define CV_NORM_MASK 7 -#define CV_RELATIVE 8 -#define CV_DIFF 16 -#define CV_MINMAX 32 - -#define CV_DIFF_C (CV_DIFF | CV_C) -#define CV_DIFF_L1 (CV_DIFF | CV_L1) -#define CV_DIFF_L2 (CV_DIFF | CV_L2) -#define CV_RELATIVE_C (CV_RELATIVE | CV_C) -#define CV_RELATIVE_L1 (CV_RELATIVE | CV_L1) -#define CV_RELATIVE_L2 (CV_RELATIVE | CV_L2) -/** @} */ - -/** Finds norm, difference norm or relative difference norm for an array (or two arrays) -@see ref core_c_NormFlags "flags" -*/ -CVAPI(double) cvNorm( const CvArr* arr1, const CvArr* arr2 CV_DEFAULT(NULL), - int norm_type CV_DEFAULT(CV_L2), - const CvArr* mask CV_DEFAULT(NULL) ); - -/** @see ref core_c_NormFlags "flags" */ -CVAPI(void) cvNormalize( const CvArr* src, CvArr* dst, - double a CV_DEFAULT(1.), double b CV_DEFAULT(0.), - int norm_type CV_DEFAULT(CV_L2), - const CvArr* mask CV_DEFAULT(NULL) ); - -/** @anchor core_c_ReduceFlags - @name Flags for cvReduce - @{ -*/ -#define CV_REDUCE_SUM 0 -#define CV_REDUCE_AVG 1 -#define CV_REDUCE_MAX 2 -#define CV_REDUCE_MIN 3 -/** @} */ - -/** @see @ref core_c_ReduceFlags "flags" */ -CVAPI(void) cvReduce( const CvArr* src, CvArr* dst, int dim CV_DEFAULT(-1), - int op CV_DEFAULT(CV_REDUCE_SUM) ); - -/****************************************************************************************\ -* Discrete Linear Transforms and Related Functions * -\****************************************************************************************/ - -/** @anchor core_c_DftFlags - @name Flags for cvDFT, cvDCT and cvMulSpectrums - @{ - */ -#define CV_DXT_FORWARD 0 -#define CV_DXT_INVERSE 1 -#define CV_DXT_SCALE 2 /**< divide result by size of array */ -#define CV_DXT_INV_SCALE (CV_DXT_INVERSE + CV_DXT_SCALE) -#define CV_DXT_INVERSE_SCALE CV_DXT_INV_SCALE -#define CV_DXT_ROWS 4 /**< transform each row individually */ -#define CV_DXT_MUL_CONJ 8 /**< conjugate the second argument of cvMulSpectrums */ -/** @} */ - -/** Discrete Fourier Transform: - complex->complex, - real->ccs (forward), - ccs->real (inverse) -@see core_c_DftFlags "flags" -*/ -CVAPI(void) cvDFT( const CvArr* src, CvArr* dst, int flags, - int nonzero_rows CV_DEFAULT(0) ); -#define cvFFT cvDFT - -/** Multiply results of DFTs: DFT(X)*DFT(Y) or DFT(X)*conj(DFT(Y)) -@see core_c_DftFlags "flags" -*/ -CVAPI(void) cvMulSpectrums( const CvArr* src1, const CvArr* src2, - CvArr* dst, int flags ); - -/** Finds optimal DFT vector size >= size0 */ -CVAPI(int) cvGetOptimalDFTSize( int size0 ); - -/** Discrete Cosine Transform -@see core_c_DftFlags "flags" -*/ -CVAPI(void) cvDCT( const CvArr* src, CvArr* dst, int flags ); - -/****************************************************************************************\ -* Dynamic data structures * -\****************************************************************************************/ - -/** Calculates length of sequence slice (with support of negative indices). */ -CVAPI(int) cvSliceLength( CvSlice slice, const CvSeq* seq ); - - -/** Creates new memory storage. - block_size == 0 means that default, - somewhat optimal size, is used (currently, it is 64K) */ -CVAPI(CvMemStorage*) cvCreateMemStorage( int block_size CV_DEFAULT(0)); - - -/** Creates a memory storage that will borrow memory blocks from parent storage */ -CVAPI(CvMemStorage*) cvCreateChildMemStorage( CvMemStorage* parent ); - - -/** Releases memory storage. All the children of a parent must be released before - the parent. A child storage returns all the blocks to parent when it is released */ -CVAPI(void) cvReleaseMemStorage( CvMemStorage** storage ); - - -/** Clears memory storage. This is the only way(!!!) (besides cvRestoreMemStoragePos) - to reuse memory allocated for the storage - cvClearSeq,cvClearSet ... - do not free any memory. - A child storage returns all the blocks to the parent when it is cleared */ -CVAPI(void) cvClearMemStorage( CvMemStorage* storage ); - -/** Remember a storage "free memory" position */ -CVAPI(void) cvSaveMemStoragePos( const CvMemStorage* storage, CvMemStoragePos* pos ); - -/** Restore a storage "free memory" position */ -CVAPI(void) cvRestoreMemStoragePos( CvMemStorage* storage, CvMemStoragePos* pos ); - -/** Allocates continuous buffer of the specified size in the storage */ -CVAPI(void*) cvMemStorageAlloc( CvMemStorage* storage, size_t size ); - -/** Allocates string in memory storage */ -CVAPI(CvString) cvMemStorageAllocString( CvMemStorage* storage, const char* ptr, - int len CV_DEFAULT(-1) ); - -/** Creates new empty sequence that will reside in the specified storage */ -CVAPI(CvSeq*) cvCreateSeq( int seq_flags, size_t header_size, - size_t elem_size, CvMemStorage* storage ); - -/** Changes default size (granularity) of sequence blocks. - The default size is ~1Kbyte */ -CVAPI(void) cvSetSeqBlockSize( CvSeq* seq, int delta_elems ); - - -/** Adds new element to the end of sequence. Returns pointer to the element */ -CVAPI(schar*) cvSeqPush( CvSeq* seq, const void* element CV_DEFAULT(NULL)); - - -/** Adds new element to the beginning of sequence. Returns pointer to it */ -CVAPI(schar*) cvSeqPushFront( CvSeq* seq, const void* element CV_DEFAULT(NULL)); - - -/** Removes the last element from sequence and optionally saves it */ -CVAPI(void) cvSeqPop( CvSeq* seq, void* element CV_DEFAULT(NULL)); - - -/** Removes the first element from sequence and optioanally saves it */ -CVAPI(void) cvSeqPopFront( CvSeq* seq, void* element CV_DEFAULT(NULL)); - - -#define CV_FRONT 1 -#define CV_BACK 0 -/** Adds several new elements to the end of sequence */ -CVAPI(void) cvSeqPushMulti( CvSeq* seq, const void* elements, - int count, int in_front CV_DEFAULT(0) ); - -/** Removes several elements from the end of sequence and optionally saves them */ -CVAPI(void) cvSeqPopMulti( CvSeq* seq, void* elements, - int count, int in_front CV_DEFAULT(0) ); - -/** Inserts a new element in the middle of sequence. - cvSeqInsert(seq,0,elem) == cvSeqPushFront(seq,elem) */ -CVAPI(schar*) cvSeqInsert( CvSeq* seq, int before_index, - const void* element CV_DEFAULT(NULL)); - -/** Removes specified sequence element */ -CVAPI(void) cvSeqRemove( CvSeq* seq, int index ); - - -/** Removes all the elements from the sequence. The freed memory - can be reused later only by the same sequence unless cvClearMemStorage - or cvRestoreMemStoragePos is called */ -CVAPI(void) cvClearSeq( CvSeq* seq ); - - -/** Retrieves pointer to specified sequence element. - Negative indices are supported and mean counting from the end - (e.g -1 means the last sequence element) */ -CVAPI(schar*) cvGetSeqElem( const CvSeq* seq, int index ); - -/** Calculates index of the specified sequence element. - Returns -1 if element does not belong to the sequence */ -CVAPI(int) cvSeqElemIdx( const CvSeq* seq, const void* element, - CvSeqBlock** block CV_DEFAULT(NULL) ); - -/** Initializes sequence writer. The new elements will be added to the end of sequence */ -CVAPI(void) cvStartAppendToSeq( CvSeq* seq, CvSeqWriter* writer ); - - -/** Combination of cvCreateSeq and cvStartAppendToSeq */ -CVAPI(void) cvStartWriteSeq( int seq_flags, int header_size, - int elem_size, CvMemStorage* storage, - CvSeqWriter* writer ); - -/** Closes sequence writer, updates sequence header and returns pointer - to the resultant sequence - (which may be useful if the sequence was created using cvStartWriteSeq)) -*/ -CVAPI(CvSeq*) cvEndWriteSeq( CvSeqWriter* writer ); - - -/** Updates sequence header. May be useful to get access to some of previously - written elements via cvGetSeqElem or sequence reader */ -CVAPI(void) cvFlushSeqWriter( CvSeqWriter* writer ); - - -/** Initializes sequence reader. - The sequence can be read in forward or backward direction */ -CVAPI(void) cvStartReadSeq( const CvSeq* seq, CvSeqReader* reader, - int reverse CV_DEFAULT(0) ); - - -/** Returns current sequence reader position (currently observed sequence element) */ -CVAPI(int) cvGetSeqReaderPos( CvSeqReader* reader ); - - -/** Changes sequence reader position. It may seek to an absolute or - to relative to the current position */ -CVAPI(void) cvSetSeqReaderPos( CvSeqReader* reader, int index, - int is_relative CV_DEFAULT(0)); - -/** Copies sequence content to a continuous piece of memory */ -CVAPI(void*) cvCvtSeqToArray( const CvSeq* seq, void* elements, - CvSlice slice CV_DEFAULT(CV_WHOLE_SEQ) ); - -/** Creates sequence header for array. - After that all the operations on sequences that do not alter the content - can be applied to the resultant sequence */ -CVAPI(CvSeq*) cvMakeSeqHeaderForArray( int seq_type, int header_size, - int elem_size, void* elements, int total, - CvSeq* seq, CvSeqBlock* block ); - -/** Extracts sequence slice (with or without copying sequence elements) */ -CVAPI(CvSeq*) cvSeqSlice( const CvSeq* seq, CvSlice slice, - CvMemStorage* storage CV_DEFAULT(NULL), - int copy_data CV_DEFAULT(0)); - -CV_INLINE CvSeq* cvCloneSeq( const CvSeq* seq, CvMemStorage* storage CV_DEFAULT(NULL)) -{ - return cvSeqSlice( seq, CV_WHOLE_SEQ, storage, 1 ); -} - -/** Removes sequence slice */ -CVAPI(void) cvSeqRemoveSlice( CvSeq* seq, CvSlice slice ); - -/** Inserts a sequence or array into another sequence */ -CVAPI(void) cvSeqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr ); - -/** a < b ? -1 : a > b ? 1 : 0 */ -typedef int (CV_CDECL* CvCmpFunc)(const void* a, const void* b, void* userdata ); - -/** Sorts sequence in-place given element comparison function */ -CVAPI(void) cvSeqSort( CvSeq* seq, CvCmpFunc func, void* userdata CV_DEFAULT(NULL) ); - -/** Finds element in a [sorted] sequence */ -CVAPI(schar*) cvSeqSearch( CvSeq* seq, const void* elem, CvCmpFunc func, - int is_sorted, int* elem_idx, - void* userdata CV_DEFAULT(NULL) ); - -/** Reverses order of sequence elements in-place */ -CVAPI(void) cvSeqInvert( CvSeq* seq ); - -/** Splits sequence into one or more equivalence classes using the specified criteria */ -CVAPI(int) cvSeqPartition( const CvSeq* seq, CvMemStorage* storage, - CvSeq** labels, CvCmpFunc is_equal, void* userdata ); - -/************ Internal sequence functions ************/ -CVAPI(void) cvChangeSeqBlock( void* reader, int direction ); -CVAPI(void) cvCreateSeqBlock( CvSeqWriter* writer ); - - -/** Creates a new set */ -CVAPI(CvSet*) cvCreateSet( int set_flags, int header_size, - int elem_size, CvMemStorage* storage ); - -/** Adds new element to the set and returns pointer to it */ -CVAPI(int) cvSetAdd( CvSet* set_header, CvSetElem* elem CV_DEFAULT(NULL), - CvSetElem** inserted_elem CV_DEFAULT(NULL) ); - -/** Fast variant of cvSetAdd */ -CV_INLINE CvSetElem* cvSetNew( CvSet* set_header ) -{ - CvSetElem* elem = set_header->free_elems; - if( elem ) - { - set_header->free_elems = elem->next_free; - elem->flags = elem->flags & CV_SET_ELEM_IDX_MASK; - set_header->active_count++; - } - else - cvSetAdd( set_header, NULL, &elem ); - return elem; -} - -/** Removes set element given its pointer */ -CV_INLINE void cvSetRemoveByPtr( CvSet* set_header, void* elem ) -{ - CvSetElem* _elem = (CvSetElem*)elem; - assert( _elem->flags >= 0 /*&& (elem->flags & CV_SET_ELEM_IDX_MASK) < set_header->total*/ ); - _elem->next_free = set_header->free_elems; - _elem->flags = (_elem->flags & CV_SET_ELEM_IDX_MASK) | CV_SET_ELEM_FREE_FLAG; - set_header->free_elems = _elem; - set_header->active_count--; -} - -/** Removes element from the set by its index */ -CVAPI(void) cvSetRemove( CvSet* set_header, int index ); - -/** Returns a set element by index. If the element doesn't belong to the set, - NULL is returned */ -CV_INLINE CvSetElem* cvGetSetElem( const CvSet* set_header, int idx ) -{ - CvSetElem* elem = (CvSetElem*)(void *)cvGetSeqElem( (CvSeq*)set_header, idx ); - return elem && CV_IS_SET_ELEM( elem ) ? elem : 0; -} - -/** Removes all the elements from the set */ -CVAPI(void) cvClearSet( CvSet* set_header ); - -/** Creates new graph */ -CVAPI(CvGraph*) cvCreateGraph( int graph_flags, int header_size, - int vtx_size, int edge_size, - CvMemStorage* storage ); - -/** Adds new vertex to the graph */ -CVAPI(int) cvGraphAddVtx( CvGraph* graph, const CvGraphVtx* vtx CV_DEFAULT(NULL), - CvGraphVtx** inserted_vtx CV_DEFAULT(NULL) ); - - -/** Removes vertex from the graph together with all incident edges */ -CVAPI(int) cvGraphRemoveVtx( CvGraph* graph, int index ); -CVAPI(int) cvGraphRemoveVtxByPtr( CvGraph* graph, CvGraphVtx* vtx ); - - -/** Link two vertices specified by indices or pointers if they - are not connected or return pointer to already existing edge - connecting the vertices. - Functions return 1 if a new edge was created, 0 otherwise */ -CVAPI(int) cvGraphAddEdge( CvGraph* graph, - int start_idx, int end_idx, - const CvGraphEdge* edge CV_DEFAULT(NULL), - CvGraphEdge** inserted_edge CV_DEFAULT(NULL) ); - -CVAPI(int) cvGraphAddEdgeByPtr( CvGraph* graph, - CvGraphVtx* start_vtx, CvGraphVtx* end_vtx, - const CvGraphEdge* edge CV_DEFAULT(NULL), - CvGraphEdge** inserted_edge CV_DEFAULT(NULL) ); - -/** Remove edge connecting two vertices */ -CVAPI(void) cvGraphRemoveEdge( CvGraph* graph, int start_idx, int end_idx ); -CVAPI(void) cvGraphRemoveEdgeByPtr( CvGraph* graph, CvGraphVtx* start_vtx, - CvGraphVtx* end_vtx ); - -/** Find edge connecting two vertices */ -CVAPI(CvGraphEdge*) cvFindGraphEdge( const CvGraph* graph, int start_idx, int end_idx ); -CVAPI(CvGraphEdge*) cvFindGraphEdgeByPtr( const CvGraph* graph, - const CvGraphVtx* start_vtx, - const CvGraphVtx* end_vtx ); -#define cvGraphFindEdge cvFindGraphEdge -#define cvGraphFindEdgeByPtr cvFindGraphEdgeByPtr - -/** Remove all vertices and edges from the graph */ -CVAPI(void) cvClearGraph( CvGraph* graph ); - - -/** Count number of edges incident to the vertex */ -CVAPI(int) cvGraphVtxDegree( const CvGraph* graph, int vtx_idx ); -CVAPI(int) cvGraphVtxDegreeByPtr( const CvGraph* graph, const CvGraphVtx* vtx ); - - -/** Retrieves graph vertex by given index */ -#define cvGetGraphVtx( graph, idx ) (CvGraphVtx*)cvGetSetElem((CvSet*)(graph), (idx)) - -/** Retrieves index of a graph vertex given its pointer */ -#define cvGraphVtxIdx( graph, vtx ) ((vtx)->flags & CV_SET_ELEM_IDX_MASK) - -/** Retrieves index of a graph edge given its pointer */ -#define cvGraphEdgeIdx( graph, edge ) ((edge)->flags & CV_SET_ELEM_IDX_MASK) - -#define cvGraphGetVtxCount( graph ) ((graph)->active_count) -#define cvGraphGetEdgeCount( graph ) ((graph)->edges->active_count) - -#define CV_GRAPH_VERTEX 1 -#define CV_GRAPH_TREE_EDGE 2 -#define CV_GRAPH_BACK_EDGE 4 -#define CV_GRAPH_FORWARD_EDGE 8 -#define CV_GRAPH_CROSS_EDGE 16 -#define CV_GRAPH_ANY_EDGE 30 -#define CV_GRAPH_NEW_TREE 32 -#define CV_GRAPH_BACKTRACKING 64 -#define CV_GRAPH_OVER -1 - -#define CV_GRAPH_ALL_ITEMS -1 - -/** flags for graph vertices and edges */ -#define CV_GRAPH_ITEM_VISITED_FLAG (1 << 30) -#define CV_IS_GRAPH_VERTEX_VISITED(vtx) \ - (((CvGraphVtx*)(vtx))->flags & CV_GRAPH_ITEM_VISITED_FLAG) -#define CV_IS_GRAPH_EDGE_VISITED(edge) \ - (((CvGraphEdge*)(edge))->flags & CV_GRAPH_ITEM_VISITED_FLAG) -#define CV_GRAPH_SEARCH_TREE_NODE_FLAG (1 << 29) -#define CV_GRAPH_FORWARD_EDGE_FLAG (1 << 28) - -typedef struct CvGraphScanner -{ - CvGraphVtx* vtx; /* current graph vertex (or current edge origin) */ - CvGraphVtx* dst; /* current graph edge destination vertex */ - CvGraphEdge* edge; /* current edge */ - - CvGraph* graph; /* the graph */ - CvSeq* stack; /* the graph vertex stack */ - int index; /* the lower bound of certainly visited vertices */ - int mask; /* event mask */ -} -CvGraphScanner; - -/** Creates new graph scanner. */ -CVAPI(CvGraphScanner*) cvCreateGraphScanner( CvGraph* graph, - CvGraphVtx* vtx CV_DEFAULT(NULL), - int mask CV_DEFAULT(CV_GRAPH_ALL_ITEMS)); - -/** Releases graph scanner. */ -CVAPI(void) cvReleaseGraphScanner( CvGraphScanner** scanner ); - -/** Get next graph element */ -CVAPI(int) cvNextGraphItem( CvGraphScanner* scanner ); - -/** Creates a copy of graph */ -CVAPI(CvGraph*) cvCloneGraph( const CvGraph* graph, CvMemStorage* storage ); - - -/** Does look-up transformation. Elements of the source array - (that should be 8uC1 or 8sC1) are used as indexes in lutarr 256-element table */ -CVAPI(void) cvLUT( const CvArr* src, CvArr* dst, const CvArr* lut ); - - -/******************* Iteration through the sequence tree *****************/ -typedef struct CvTreeNodeIterator -{ - const void* node; - int level; - int max_level; -} -CvTreeNodeIterator; - -CVAPI(void) cvInitTreeNodeIterator( CvTreeNodeIterator* tree_iterator, - const void* first, int max_level ); -CVAPI(void*) cvNextTreeNode( CvTreeNodeIterator* tree_iterator ); -CVAPI(void*) cvPrevTreeNode( CvTreeNodeIterator* tree_iterator ); - -/** Inserts sequence into tree with specified "parent" sequence. - If parent is equal to frame (e.g. the most external contour), - then added contour will have null pointer to parent. */ -CVAPI(void) cvInsertNodeIntoTree( void* node, void* parent, void* frame ); - -/** Removes contour from tree (together with the contour children). */ -CVAPI(void) cvRemoveNodeFromTree( void* node, void* frame ); - -/** Gathers pointers to all the sequences, - accessible from the `first`, to the single sequence */ -CVAPI(CvSeq*) cvTreeToNodeSeq( const void* first, int header_size, - CvMemStorage* storage ); - -/** The function implements the K-means algorithm for clustering an array of sample - vectors in a specified number of classes */ -#define CV_KMEANS_USE_INITIAL_LABELS 1 -CVAPI(int) cvKMeans2( const CvArr* samples, int cluster_count, CvArr* labels, - CvTermCriteria termcrit, int attempts CV_DEFAULT(1), - CvRNG* rng CV_DEFAULT(0), int flags CV_DEFAULT(0), - CvArr* _centers CV_DEFAULT(0), double* compactness CV_DEFAULT(0) ); - -/****************************************************************************************\ -* System functions * -\****************************************************************************************/ - -/** Loads optimized functions from IPP, MKL etc. or switches back to pure C code */ -CVAPI(int) cvUseOptimized( int on_off ); - -typedef IplImage* (CV_STDCALL* Cv_iplCreateImageHeader) - (int,int,int,char*,char*,int,int,int,int,int, - IplROI*,IplImage*,void*,IplTileInfo*); -typedef void (CV_STDCALL* Cv_iplAllocateImageData)(IplImage*,int,int); -typedef void (CV_STDCALL* Cv_iplDeallocate)(IplImage*,int); -typedef IplROI* (CV_STDCALL* Cv_iplCreateROI)(int,int,int,int,int); -typedef IplImage* (CV_STDCALL* Cv_iplCloneImage)(const IplImage*); - -/** @brief Makes OpenCV use IPL functions for allocating IplImage and IplROI structures. - -Normally, the function is not called directly. Instead, a simple macro -CV_TURN_ON_IPL_COMPATIBILITY() is used that calls cvSetIPLAllocators and passes there pointers -to IPL allocation functions. : -@code - ... - CV_TURN_ON_IPL_COMPATIBILITY() - ... -@endcode -@param create_header pointer to a function, creating IPL image header. -@param allocate_data pointer to a function, allocating IPL image data. -@param deallocate pointer to a function, deallocating IPL image. -@param create_roi pointer to a function, creating IPL image ROI (i.e. Region of Interest). -@param clone_image pointer to a function, cloning an IPL image. - */ -CVAPI(void) cvSetIPLAllocators( Cv_iplCreateImageHeader create_header, - Cv_iplAllocateImageData allocate_data, - Cv_iplDeallocate deallocate, - Cv_iplCreateROI create_roi, - Cv_iplCloneImage clone_image ); - -#define CV_TURN_ON_IPL_COMPATIBILITY() \ - cvSetIPLAllocators( iplCreateImageHeader, iplAllocateImage, \ - iplDeallocate, iplCreateROI, iplCloneImage ) - -/****************************************************************************************\ -* Data Persistence * -\****************************************************************************************/ - -/********************************** High-level functions ********************************/ - -/** @brief Opens file storage for reading or writing data. - -The function opens file storage for reading or writing data. In the latter case, a new file is -created or an existing file is rewritten. The type of the read or written file is determined by the -filename extension: .xml for XML, .yml or .yaml for YAML and .json for JSON. - -At the same time, it also supports adding parameters like "example.xml?base64". - -The function returns a pointer to the CvFileStorage structure. -If the file cannot be opened then the function returns NULL. -@param filename Name of the file associated with the storage -@param memstorage Memory storage used for temporary data and for -: storing dynamic structures, such as CvSeq or CvGraph . If it is NULL, a temporary memory - storage is created and used. -@param flags Can be one of the following: -> - **CV_STORAGE_READ** the storage is open for reading -> - **CV_STORAGE_WRITE** the storage is open for writing - (use **CV_STORAGE_WRITE | CV_STORAGE_WRITE_BASE64** to write rawdata in Base64) -@param encoding - */ -CVAPI(CvFileStorage*) cvOpenFileStorage( const char* filename, CvMemStorage* memstorage, - int flags, const char* encoding CV_DEFAULT(NULL) ); - -/** @brief Releases file storage. - -The function closes the file associated with the storage and releases all the temporary structures. -It must be called after all I/O operations with the storage are finished. -@param fs Double pointer to the released file storage - */ -CVAPI(void) cvReleaseFileStorage( CvFileStorage** fs ); - -/** returns attribute value or 0 (NULL) if there is no such attribute */ -CVAPI(const char*) cvAttrValue( const CvAttrList* attr, const char* attr_name ); - -/** @brief Starts writing a new structure. - -The function starts writing a compound structure (collection) that can be a sequence or a map. After -all the structure fields, which can be scalars or structures, are written, cvEndWriteStruct should -be called. The function can be used to group some objects or to implement the write function for a -some user object (see CvTypeInfo). -@param fs File storage -@param name Name of the written structure. The structure can be accessed by this name when the -storage is read. -@param struct_flags A combination one of the following values: -- **CV_NODE_SEQ** the written structure is a sequence (see discussion of CvFileStorage ), - that is, its elements do not have a name. -- **CV_NODE_MAP** the written structure is a map (see discussion of CvFileStorage ), that - is, all its elements have names. -One and only one of the two above flags must be specified -- **CV_NODE_FLOW** the optional flag that makes sense only for YAML streams. It means that - the structure is written as a flow (not as a block), which is more compact. It is - recommended to use this flag for structures or arrays whose elements are all scalars. -@param type_name Optional parameter - the object type name. In - case of XML it is written as a type_id attribute of the structure opening tag. In the case of - YAML it is written after a colon following the structure name (see the example in - CvFileStorage description). In case of JSON it is written as a name/value pair. - Mainly it is used with user objects. When the storage is read, the - encoded type name is used to determine the object type (see CvTypeInfo and cvFindType ). -@param attributes This parameter is not used in the current implementation - */ -CVAPI(void) cvStartWriteStruct( CvFileStorage* fs, const char* name, - int struct_flags, const char* type_name CV_DEFAULT(NULL), - CvAttrList attributes CV_DEFAULT(cvAttrList())); - -/** @brief Finishes writing to a file node collection. -@param fs File storage -@sa cvStartWriteStruct. - */ -CVAPI(void) cvEndWriteStruct( CvFileStorage* fs ); - -/** @brief Writes an integer value. - -The function writes a single integer value (with or without a name) to the file storage. -@param fs File storage -@param name Name of the written value. Should be NULL if and only if the parent structure is a -sequence. -@param value The written value - */ -CVAPI(void) cvWriteInt( CvFileStorage* fs, const char* name, int value ); - -/** @brief Writes a floating-point value. - -The function writes a single floating-point value (with or without a name) to file storage. Special -values are encoded as follows: NaN (Not A Number) as .NaN, infinity as +.Inf or -.Inf. - -The following example shows how to use the low-level writing functions to store custom structures, -such as termination criteria, without registering a new type. : -@code - void write_termcriteria( CvFileStorage* fs, const char* struct_name, - CvTermCriteria* termcrit ) - { - cvStartWriteStruct( fs, struct_name, CV_NODE_MAP, NULL, cvAttrList(0,0)); - cvWriteComment( fs, "termination criteria", 1 ); // just a description - if( termcrit->type & CV_TERMCRIT_ITER ) - cvWriteInteger( fs, "max_iterations", termcrit->max_iter ); - if( termcrit->type & CV_TERMCRIT_EPS ) - cvWriteReal( fs, "accuracy", termcrit->epsilon ); - cvEndWriteStruct( fs ); - } -@endcode -@param fs File storage -@param name Name of the written value. Should be NULL if and only if the parent structure is a -sequence. -@param value The written value -*/ -CVAPI(void) cvWriteReal( CvFileStorage* fs, const char* name, double value ); - -/** @brief Writes a text string. - -The function writes a text string to file storage. -@param fs File storage -@param name Name of the written string . Should be NULL if and only if the parent structure is a -sequence. -@param str The written text string -@param quote If non-zero, the written string is put in quotes, regardless of whether they are -required. Otherwise, if the flag is zero, quotes are used only when they are required (e.g. when -the string starts with a digit or contains spaces). - */ -CVAPI(void) cvWriteString( CvFileStorage* fs, const char* name, - const char* str, int quote CV_DEFAULT(0) ); - -/** @brief Writes a comment. - -The function writes a comment into file storage. The comments are skipped when the storage is read. -@param fs File storage -@param comment The written comment, single-line or multi-line -@param eol_comment If non-zero, the function tries to put the comment at the end of current line. -If the flag is zero, if the comment is multi-line, or if it does not fit at the end of the current -line, the comment starts a new line. - */ -CVAPI(void) cvWriteComment( CvFileStorage* fs, const char* comment, - int eol_comment ); - -/** @brief Writes an object to file storage. - -The function writes an object to file storage. First, the appropriate type info is found using -cvTypeOf. Then, the write method associated with the type info is called. - -Attributes are used to customize the writing procedure. The standard types support the following -attributes (all the dt attributes have the same format as in cvWriteRawData): - --# CvSeq - - **header_dt** description of user fields of the sequence header that follow CvSeq, or - CvChain (if the sequence is a Freeman chain) or CvContour (if the sequence is a contour or - point sequence) - - **dt** description of the sequence elements. - - **recursive** if the attribute is present and is not equal to "0" or "false", the whole - tree of sequences (contours) is stored. --# CvGraph - - **header_dt** description of user fields of the graph header that follows CvGraph; - - **vertex_dt** description of user fields of graph vertices - - **edge_dt** description of user fields of graph edges (note that the edge weight is - always written, so there is no need to specify it explicitly) - -Below is the code that creates the YAML file shown in the CvFileStorage description: -@code - #include "cxcore.h" - - int main( int argc, char** argv ) - { - CvMat* mat = cvCreateMat( 3, 3, CV_32F ); - CvFileStorage* fs = cvOpenFileStorage( "example.yml", 0, CV_STORAGE_WRITE ); - - cvSetIdentity( mat ); - cvWrite( fs, "A", mat, cvAttrList(0,0) ); - - cvReleaseFileStorage( &fs ); - cvReleaseMat( &mat ); - return 0; - } -@endcode -@param fs File storage -@param name Name of the written object. Should be NULL if and only if the parent structure is a -sequence. -@param ptr Pointer to the object -@param attributes The attributes of the object. They are specific for each particular type (see -the discussion below). - */ -CVAPI(void) cvWrite( CvFileStorage* fs, const char* name, const void* ptr, - CvAttrList attributes CV_DEFAULT(cvAttrList())); - -/** @brief Starts the next stream. - -The function finishes the currently written stream and starts the next stream. In the case of XML -the file with multiple streams looks like this: -@code{.xml} - - - - - - - ... -@endcode -The YAML file will look like this: -@code{.yaml} - %YAML 1.0 - # stream #1 data - ... - --- - # stream #2 data -@endcode -This is useful for concatenating files or for resuming the writing process. -@param fs File storage - */ -CVAPI(void) cvStartNextStream( CvFileStorage* fs ); - -/** @brief Writes multiple numbers. - -The function writes an array, whose elements consist of single or multiple numbers. The function -call can be replaced with a loop containing a few cvWriteInt and cvWriteReal calls, but a single -call is more efficient. Note that because none of the elements have a name, they should be written -to a sequence rather than a map. -@param fs File storage -@param src Pointer to the written array -@param len Number of the array elements to write -@param dt Specification of each array element, see @ref format_spec "format specification" - */ -CVAPI(void) cvWriteRawData( CvFileStorage* fs, const void* src, - int len, const char* dt ); - -/** @brief Writes multiple numbers in Base64. - -If either CV_STORAGE_WRITE_BASE64 or cv::FileStorage::WRITE_BASE64 is used, -this function will be the same as cvWriteRawData. If neither, the main -difference is that it outputs a sequence in Base64 encoding rather than -in plain text. - -This function can only be used to write a sequence with a type "binary". - -@param fs File storage -@param src Pointer to the written array -@param len Number of the array elements to write -@param dt Specification of each array element, see @ref format_spec "format specification" -*/ -CVAPI(void) cvWriteRawDataBase64( CvFileStorage* fs, const void* src, - int len, const char* dt ); - -/** @brief Returns a unique pointer for a given name. - -The function returns a unique pointer for each particular file node name. This pointer can be then -passed to the cvGetFileNode function that is faster than cvGetFileNodeByName because it compares -text strings by comparing pointers rather than the strings' content. - -Consider the following example where an array of points is encoded as a sequence of 2-entry maps: -@code - points: - - { x: 10, y: 10 } - - { x: 20, y: 20 } - - { x: 30, y: 30 } - # ... -@endcode -Then, it is possible to get hashed "x" and "y" pointers to speed up decoding of the points. : -@code - #include "cxcore.h" - - int main( int argc, char** argv ) - { - CvFileStorage* fs = cvOpenFileStorage( "points.yml", 0, CV_STORAGE_READ ); - CvStringHashNode* x_key = cvGetHashedNode( fs, "x", -1, 1 ); - CvStringHashNode* y_key = cvGetHashedNode( fs, "y", -1, 1 ); - CvFileNode* points = cvGetFileNodeByName( fs, 0, "points" ); - - if( CV_NODE_IS_SEQ(points->tag) ) - { - CvSeq* seq = points->data.seq; - int i, total = seq->total; - CvSeqReader reader; - cvStartReadSeq( seq, &reader, 0 ); - for( i = 0; i < total; i++ ) - { - CvFileNode* pt = (CvFileNode*)reader.ptr; - #if 1 // faster variant - CvFileNode* xnode = cvGetFileNode( fs, pt, x_key, 0 ); - CvFileNode* ynode = cvGetFileNode( fs, pt, y_key, 0 ); - assert( xnode && CV_NODE_IS_INT(xnode->tag) && - ynode && CV_NODE_IS_INT(ynode->tag)); - int x = xnode->data.i; // or x = cvReadInt( xnode, 0 ); - int y = ynode->data.i; // or y = cvReadInt( ynode, 0 ); - #elif 1 // slower variant; does not use x_key & y_key - CvFileNode* xnode = cvGetFileNodeByName( fs, pt, "x" ); - CvFileNode* ynode = cvGetFileNodeByName( fs, pt, "y" ); - assert( xnode && CV_NODE_IS_INT(xnode->tag) && - ynode && CV_NODE_IS_INT(ynode->tag)); - int x = xnode->data.i; // or x = cvReadInt( xnode, 0 ); - int y = ynode->data.i; // or y = cvReadInt( ynode, 0 ); - #else // the slowest yet the easiest to use variant - int x = cvReadIntByName( fs, pt, "x", 0 ); - int y = cvReadIntByName( fs, pt, "y", 0 ); - #endif - CV_NEXT_SEQ_ELEM( seq->elem_size, reader ); - printf(" - } - } - cvReleaseFileStorage( &fs ); - return 0; - } -@endcode -Please note that whatever method of accessing a map you are using, it is still much slower than -using plain sequences; for example, in the above example, it is more efficient to encode the points -as pairs of integers in a single numeric sequence. -@param fs File storage -@param name Literal node name -@param len Length of the name (if it is known apriori), or -1 if it needs to be calculated -@param create_missing Flag that specifies, whether an absent key should be added into the hash table -*/ -CVAPI(CvStringHashNode*) cvGetHashedKey( CvFileStorage* fs, const char* name, - int len CV_DEFAULT(-1), - int create_missing CV_DEFAULT(0)); - -/** @brief Retrieves one of the top-level nodes of the file storage. - -The function returns one of the top-level file nodes. The top-level nodes do not have a name, they -correspond to the streams that are stored one after another in the file storage. If the index is out -of range, the function returns a NULL pointer, so all the top-level nodes can be iterated by -subsequent calls to the function with stream_index=0,1,..., until the NULL pointer is returned. -This function can be used as a base for recursive traversal of the file storage. -@param fs File storage -@param stream_index Zero-based index of the stream. See cvStartNextStream . In most cases, -there is only one stream in the file; however, there can be several. - */ -CVAPI(CvFileNode*) cvGetRootFileNode( const CvFileStorage* fs, - int stream_index CV_DEFAULT(0) ); - -/** @brief Finds a node in a map or file storage. - -The function finds a file node. It is a faster version of cvGetFileNodeByName (see -cvGetHashedKey discussion). Also, the function can insert a new node, if it is not in the map yet. -@param fs File storage -@param map The parent map. If it is NULL, the function searches a top-level node. If both map and -key are NULLs, the function returns the root file node - a map that contains top-level nodes. -@param key Unique pointer to the node name, retrieved with cvGetHashedKey -@param create_missing Flag that specifies whether an absent node should be added to the map - */ -CVAPI(CvFileNode*) cvGetFileNode( CvFileStorage* fs, CvFileNode* map, - const CvStringHashNode* key, - int create_missing CV_DEFAULT(0) ); - -/** @brief Finds a node in a map or file storage. - -The function finds a file node by name. The node is searched either in map or, if the pointer is -NULL, among the top-level file storage nodes. Using this function for maps and cvGetSeqElem (or -sequence reader) for sequences, it is possible to navigate through the file storage. To speed up -multiple queries for a certain key (e.g., in the case of an array of structures) one may use a -combination of cvGetHashedKey and cvGetFileNode. -@param fs File storage -@param map The parent map. If it is NULL, the function searches in all the top-level nodes -(streams), starting with the first one. -@param name The file node name - */ -CVAPI(CvFileNode*) cvGetFileNodeByName( const CvFileStorage* fs, - const CvFileNode* map, - const char* name ); - -/** @brief Retrieves an integer value from a file node. - -The function returns an integer that is represented by the file node. If the file node is NULL, the -default_value is returned (thus, it is convenient to call the function right after cvGetFileNode -without checking for a NULL pointer). If the file node has type CV_NODE_INT, then node-\>data.i is -returned. If the file node has type CV_NODE_REAL, then node-\>data.f is converted to an integer -and returned. Otherwise the error is reported. -@param node File node -@param default_value The value that is returned if node is NULL - */ -CV_INLINE int cvReadInt( const CvFileNode* node, int default_value CV_DEFAULT(0) ) -{ - return !node ? default_value : - CV_NODE_IS_INT(node->tag) ? node->data.i : - CV_NODE_IS_REAL(node->tag) ? cvRound(node->data.f) : 0x7fffffff; -} - -/** @brief Finds a file node and returns its value. - -The function is a simple superposition of cvGetFileNodeByName and cvReadInt. -@param fs File storage -@param map The parent map. If it is NULL, the function searches a top-level node. -@param name The node name -@param default_value The value that is returned if the file node is not found - */ -CV_INLINE int cvReadIntByName( const CvFileStorage* fs, const CvFileNode* map, - const char* name, int default_value CV_DEFAULT(0) ) -{ - return cvReadInt( cvGetFileNodeByName( fs, map, name ), default_value ); -} - -/** @brief Retrieves a floating-point value from a file node. - -The function returns a floating-point value that is represented by the file node. If the file node -is NULL, the default_value is returned (thus, it is convenient to call the function right after -cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_REAL , -then node-\>data.f is returned. If the file node has type CV_NODE_INT , then node-:math:\>data.f -is converted to floating-point and returned. Otherwise the result is not determined. -@param node File node -@param default_value The value that is returned if node is NULL - */ -CV_INLINE double cvReadReal( const CvFileNode* node, double default_value CV_DEFAULT(0.) ) -{ - return !node ? default_value : - CV_NODE_IS_INT(node->tag) ? (double)node->data.i : - CV_NODE_IS_REAL(node->tag) ? node->data.f : 1e300; -} - -/** @brief Finds a file node and returns its value. - -The function is a simple superposition of cvGetFileNodeByName and cvReadReal . -@param fs File storage -@param map The parent map. If it is NULL, the function searches a top-level node. -@param name The node name -@param default_value The value that is returned if the file node is not found - */ -CV_INLINE double cvReadRealByName( const CvFileStorage* fs, const CvFileNode* map, - const char* name, double default_value CV_DEFAULT(0.) ) -{ - return cvReadReal( cvGetFileNodeByName( fs, map, name ), default_value ); -} - -/** @brief Retrieves a text string from a file node. - -The function returns a text string that is represented by the file node. If the file node is NULL, -the default_value is returned (thus, it is convenient to call the function right after -cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_STR , then -node-:math:\>data.str.ptr is returned. Otherwise the result is not determined. -@param node File node -@param default_value The value that is returned if node is NULL - */ -CV_INLINE const char* cvReadString( const CvFileNode* node, - const char* default_value CV_DEFAULT(NULL) ) -{ - return !node ? default_value : CV_NODE_IS_STRING(node->tag) ? node->data.str.ptr : 0; -} - -/** @brief Finds a file node by its name and returns its value. - -The function is a simple superposition of cvGetFileNodeByName and cvReadString . -@param fs File storage -@param map The parent map. If it is NULL, the function searches a top-level node. -@param name The node name -@param default_value The value that is returned if the file node is not found - */ -CV_INLINE const char* cvReadStringByName( const CvFileStorage* fs, const CvFileNode* map, - const char* name, const char* default_value CV_DEFAULT(NULL) ) -{ - return cvReadString( cvGetFileNodeByName( fs, map, name ), default_value ); -} - - -/** @brief Decodes an object and returns a pointer to it. - -The function decodes a user object (creates an object in a native representation from the file -storage subtree) and returns it. The object to be decoded must be an instance of a registered type -that supports the read method (see CvTypeInfo). The type of the object is determined by the type -name that is encoded in the file. If the object is a dynamic structure, it is created either in -memory storage and passed to cvOpenFileStorage or, if a NULL pointer was passed, in temporary -memory storage, which is released when cvReleaseFileStorage is called. Otherwise, if the object is -not a dynamic structure, it is created in a heap and should be released with a specialized function -or by using the generic cvRelease. -@param fs File storage -@param node The root object node -@param attributes Unused parameter - */ -CVAPI(void*) cvRead( CvFileStorage* fs, CvFileNode* node, - CvAttrList* attributes CV_DEFAULT(NULL)); - -/** @brief Finds an object by name and decodes it. - -The function is a simple superposition of cvGetFileNodeByName and cvRead. -@param fs File storage -@param map The parent map. If it is NULL, the function searches a top-level node. -@param name The node name -@param attributes Unused parameter - */ -CV_INLINE void* cvReadByName( CvFileStorage* fs, const CvFileNode* map, - const char* name, CvAttrList* attributes CV_DEFAULT(NULL) ) -{ - return cvRead( fs, cvGetFileNodeByName( fs, map, name ), attributes ); -} - - -/** @brief Initializes the file node sequence reader. - -The function initializes the sequence reader to read data from a file node. The initialized reader -can be then passed to cvReadRawDataSlice. -@param fs File storage -@param src The file node (a sequence) to read numbers from -@param reader Pointer to the sequence reader - */ -CVAPI(void) cvStartReadRawData( const CvFileStorage* fs, const CvFileNode* src, - CvSeqReader* reader ); - -/** @brief Initializes file node sequence reader. - -The function reads one or more elements from the file node, representing a sequence, to a -user-specified array. The total number of read sequence elements is a product of total and the -number of components in each array element. For example, if dt=2if, the function will read total\*3 -sequence elements. As with any sequence, some parts of the file node sequence can be skipped or read -repeatedly by repositioning the reader using cvSetSeqReaderPos. -@param fs File storage -@param reader The sequence reader. Initialize it with cvStartReadRawData . -@param count The number of elements to read -@param dst Pointer to the destination array -@param dt Specification of each array element. It has the same format as in cvWriteRawData . - */ -CVAPI(void) cvReadRawDataSlice( const CvFileStorage* fs, CvSeqReader* reader, - int count, void* dst, const char* dt ); - -/** @brief Reads multiple numbers. - -The function reads elements from a file node that represents a sequence of scalars. -@param fs File storage -@param src The file node (a sequence) to read numbers from -@param dst Pointer to the destination array -@param dt Specification of each array element. It has the same format as in cvWriteRawData . - */ -CVAPI(void) cvReadRawData( const CvFileStorage* fs, const CvFileNode* src, - void* dst, const char* dt ); - -/** @brief Writes a file node to another file storage. - -The function writes a copy of a file node to file storage. Possible applications of the function are -merging several file storages into one and conversion between XML, YAML and JSON formats. -@param fs Destination file storage -@param new_node_name New name of the file node in the destination file storage. To keep the -existing name, use cvcvGetFileNodeName -@param node The written node -@param embed If the written node is a collection and this parameter is not zero, no extra level of -hierarchy is created. Instead, all the elements of node are written into the currently written -structure. Of course, map elements can only be embedded into another map, and sequence elements -can only be embedded into another sequence. - */ -CVAPI(void) cvWriteFileNode( CvFileStorage* fs, const char* new_node_name, - const CvFileNode* node, int embed ); - -/** @brief Returns the name of a file node. - -The function returns the name of a file node or NULL, if the file node does not have a name or if -node is NULL. -@param node File node - */ -CVAPI(const char*) cvGetFileNodeName( const CvFileNode* node ); - -/*********************************** Adding own types ***********************************/ - -/** @brief Registers a new type. - -The function registers a new type, which is described by info . The function creates a copy of the -structure, so the user should delete it after calling the function. -@param info Type info structure - */ -CVAPI(void) cvRegisterType( const CvTypeInfo* info ); - -/** @brief Unregisters the type. - -The function unregisters a type with a specified name. If the name is unknown, it is possible to -locate the type info by an instance of the type using cvTypeOf or by iterating the type list, -starting from cvFirstType, and then calling cvUnregisterType(info-\>typeName). -@param type_name Name of an unregistered type - */ -CVAPI(void) cvUnregisterType( const char* type_name ); - -/** @brief Returns the beginning of a type list. - -The function returns the first type in the list of registered types. Navigation through the list can -be done via the prev and next fields of the CvTypeInfo structure. - */ -CVAPI(CvTypeInfo*) cvFirstType(void); - -/** @brief Finds a type by its name. - -The function finds a registered type by its name. It returns NULL if there is no type with the -specified name. -@param type_name Type name - */ -CVAPI(CvTypeInfo*) cvFindType( const char* type_name ); - -/** @brief Returns the type of an object. - -The function finds the type of a given object. It iterates through the list of registered types and -calls the is_instance function/method for every type info structure with that object until one of -them returns non-zero or until the whole list has been traversed. In the latter case, the function -returns NULL. -@param struct_ptr The object pointer - */ -CVAPI(CvTypeInfo*) cvTypeOf( const void* struct_ptr ); - -/** @brief Releases an object. - -The function finds the type of a given object and calls release with the double pointer. -@param struct_ptr Double pointer to the object - */ -CVAPI(void) cvRelease( void** struct_ptr ); - -/** @brief Makes a clone of an object. - -The function finds the type of a given object and calls clone with the passed object. Of course, if -you know the object type, for example, struct_ptr is CvMat\*, it is faster to call the specific -function, like cvCloneMat. -@param struct_ptr The object to clone - */ -CVAPI(void*) cvClone( const void* struct_ptr ); - -/** @brief Saves an object to a file. - -The function saves an object to a file. It provides a simple interface to cvWrite . -@param filename File name -@param struct_ptr Object to save -@param name Optional object name. If it is NULL, the name will be formed from filename . -@param comment Optional comment to put in the beginning of the file -@param attributes Optional attributes passed to cvWrite - */ -CVAPI(void) cvSave( const char* filename, const void* struct_ptr, - const char* name CV_DEFAULT(NULL), - const char* comment CV_DEFAULT(NULL), - CvAttrList attributes CV_DEFAULT(cvAttrList())); - -/** @brief Loads an object from a file. - -The function loads an object from a file. It basically reads the specified file, find the first -top-level node and calls cvRead for that node. If the file node does not have type information or -the type information can not be found by the type name, the function returns NULL. After the object -is loaded, the file storage is closed and all the temporary buffers are deleted. Thus, to load a -dynamic structure, such as a sequence, contour, or graph, one should pass a valid memory storage -destination to the function. -@param filename File name -@param memstorage Memory storage for dynamic structures, such as CvSeq or CvGraph . It is not used -for matrices or images. -@param name Optional object name. If it is NULL, the first top-level object in the storage will be -loaded. -@param real_name Optional output parameter that will contain the name of the loaded object -(useful if name=NULL ) - */ -CVAPI(void*) cvLoad( const char* filename, - CvMemStorage* memstorage CV_DEFAULT(NULL), - const char* name CV_DEFAULT(NULL), - const char** real_name CV_DEFAULT(NULL) ); - -/*********************************** Measuring Execution Time ***************************/ - -/** helper functions for RNG initialization and accurate time measurement: - uses internal clock counter on x86 */ -CVAPI(int64) cvGetTickCount( void ); -CVAPI(double) cvGetTickFrequency( void ); - -/*********************************** CPU capabilities ***********************************/ - -CVAPI(int) cvCheckHardwareSupport(int feature); - -/*********************************** Multi-Threading ************************************/ - -/** retrieve/set the number of threads used in OpenMP implementations */ -CVAPI(int) cvGetNumThreads( void ); -CVAPI(void) cvSetNumThreads( int threads CV_DEFAULT(0) ); -/** get index of the thread being executed */ -CVAPI(int) cvGetThreadNum( void ); - - -/********************************** Error Handling **************************************/ - -/** Get current OpenCV error status */ -CVAPI(int) cvGetErrStatus( void ); - -/** Sets error status silently */ -CVAPI(void) cvSetErrStatus( int status ); - -#define CV_ErrModeLeaf 0 /* Print error and exit program */ -#define CV_ErrModeParent 1 /* Print error and continue */ -#define CV_ErrModeSilent 2 /* Don't print and continue */ - -/** Retrieves current error processing mode */ -CVAPI(int) cvGetErrMode( void ); - -/** Sets error processing mode, returns previously used mode */ -CVAPI(int) cvSetErrMode( int mode ); - -/** Sets error status and performs some additional actions (displaying message box, - writing message to stderr, terminating application etc.) - depending on the current error mode */ -CVAPI(void) cvError( int status, const char* func_name, - const char* err_msg, const char* file_name, int line ); - -/** Retrieves textual description of the error given its code */ -CVAPI(const char*) cvErrorStr( int status ); - -/** Retrieves detailed information about the last error occurred */ -CVAPI(int) cvGetErrInfo( const char** errcode_desc, const char** description, - const char** filename, int* line ); - -/** Maps IPP error codes to the counterparts from OpenCV */ -CVAPI(int) cvErrorFromIppStatus( int ipp_status ); - -typedef int (CV_CDECL *CvErrorCallback)( int status, const char* func_name, - const char* err_msg, const char* file_name, int line, void* userdata ); - -/** Assigns a new error-handling function */ -CVAPI(CvErrorCallback) cvRedirectError( CvErrorCallback error_handler, - void* userdata CV_DEFAULT(NULL), - void** prev_userdata CV_DEFAULT(NULL) ); - -/** Output nothing */ -CVAPI(int) cvNulDevReport( int status, const char* func_name, const char* err_msg, - const char* file_name, int line, void* userdata ); - -/** Output to console(fprintf(stderr,...)) */ -CVAPI(int) cvStdErrReport( int status, const char* func_name, const char* err_msg, - const char* file_name, int line, void* userdata ); - -/** Output to MessageBox(WIN32) */ -CVAPI(int) cvGuiBoxReport( int status, const char* func_name, const char* err_msg, - const char* file_name, int line, void* userdata ); - -#define OPENCV_ERROR(status,func,context) \ -cvError((status),(func),(context),__FILE__,__LINE__) - -#define OPENCV_ASSERT(expr,func,context) \ -{if (! (expr)) \ -{OPENCV_ERROR(CV_StsInternal,(func),(context));}} - -#define OPENCV_CALL( Func ) \ -{ \ -Func; \ -} - - -/** CV_FUNCNAME macro defines icvFuncName constant which is used by CV_ERROR macro */ -#ifdef CV_NO_FUNC_NAMES -#define CV_FUNCNAME( Name ) -#define cvFuncName "" -#else -#define CV_FUNCNAME( Name ) \ -static char cvFuncName[] = Name -#endif - - -/** - CV_ERROR macro unconditionally raises error with passed code and message. - After raising error, control will be transferred to the exit label. - */ -#define CV_ERROR( Code, Msg ) \ -{ \ - cvError( (Code), cvFuncName, Msg, __FILE__, __LINE__ ); \ - __CV_EXIT__; \ -} - -/** - CV_CHECK macro checks error status after CV (or IPL) - function call. If error detected, control will be transferred to the exit - label. - */ -#define CV_CHECK() \ -{ \ - if( cvGetErrStatus() < 0 ) \ - CV_ERROR( CV_StsBackTrace, "Inner function failed." ); \ -} - - -/** - CV_CALL macro calls CV (or IPL) function, checks error status and - signals a error if the function failed. Useful in "parent node" - error processing mode - */ -#define CV_CALL( Func ) \ -{ \ - Func; \ - CV_CHECK(); \ -} - - -/** Runtime assertion macro */ -#define CV_ASSERT( Condition ) \ -{ \ - if( !(Condition) ) \ - CV_ERROR( CV_StsInternal, "Assertion: " #Condition " failed" ); \ -} - -#define __CV_BEGIN__ { -#define __CV_END__ goto exit; exit: ; } -#define __CV_EXIT__ goto exit - -/** @} core_c */ - -#ifdef __cplusplus -} // extern "C" -#endif - -#ifdef __cplusplus - -//! @addtogroup core_c_glue -//! @{ - -//! class for automatic module/RTTI data registration/unregistration -struct CV_EXPORTS CvType -{ - CvType( const char* type_name, - CvIsInstanceFunc is_instance, CvReleaseFunc release=0, - CvReadFunc read=0, CvWriteFunc write=0, CvCloneFunc clone=0 ); - ~CvType(); - CvTypeInfo* info; - - static CvTypeInfo* first; - static CvTypeInfo* last; -}; - -//! @} - -#include "opencv2/core/utility.hpp" - -namespace cv -{ - -//! @addtogroup core_c_glue -//! @{ - -/////////////////////////////////////////// glue /////////////////////////////////////////// - -//! converts array (CvMat or IplImage) to cv::Mat -CV_EXPORTS Mat cvarrToMat(const CvArr* arr, bool copyData=false, - bool allowND=true, int coiMode=0, - AutoBuffer* buf=0); - -static inline Mat cvarrToMatND(const CvArr* arr, bool copyData=false, int coiMode=0) -{ - return cvarrToMat(arr, copyData, true, coiMode); -} - - -//! extracts Channel of Interest from CvMat or IplImage and makes cv::Mat out of it. -CV_EXPORTS void extractImageCOI(const CvArr* arr, OutputArray coiimg, int coi=-1); -//! inserts single-channel cv::Mat into a multi-channel CvMat or IplImage -CV_EXPORTS void insertImageCOI(InputArray coiimg, CvArr* arr, int coi=-1); - - - -////// specialized implementations of DefaultDeleter::operator() for classic OpenCV types ////// - -template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMat* obj) const; -template<> CV_EXPORTS void DefaultDeleter::operator ()(IplImage* obj) const; -template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMatND* obj) const; -template<> CV_EXPORTS void DefaultDeleter::operator ()(CvSparseMat* obj) const; -template<> CV_EXPORTS void DefaultDeleter::operator ()(CvMemStorage* obj) const; - -////////////// convenient wrappers for operating old-style dynamic structures ////////////// - -template class SeqIterator; - -typedef Ptr MemStorage; - -/*! - Template Sequence Class derived from CvSeq - - The class provides more convenient access to sequence elements, - STL-style operations and iterators. - - \note The class is targeted for simple data types, - i.e. no constructors or destructors - are called for the sequence elements. -*/ -template class Seq -{ -public: - typedef SeqIterator<_Tp> iterator; - typedef SeqIterator<_Tp> const_iterator; - - //! the default constructor - Seq(); - //! the constructor for wrapping CvSeq structure. The real element type in CvSeq should match _Tp. - Seq(const CvSeq* seq); - //! creates the empty sequence that resides in the specified storage - Seq(MemStorage& storage, int headerSize = sizeof(CvSeq)); - //! returns read-write reference to the specified element - _Tp& operator [](int idx); - //! returns read-only reference to the specified element - const _Tp& operator[](int idx) const; - //! returns iterator pointing to the beginning of the sequence - SeqIterator<_Tp> begin() const; - //! returns iterator pointing to the element following the last sequence element - SeqIterator<_Tp> end() const; - //! returns the number of elements in the sequence - size_t size() const; - //! returns the type of sequence elements (CV_8UC1 ... CV_64FC(CV_CN_MAX) ...) - int type() const; - //! returns the depth of sequence elements (CV_8U ... CV_64F) - int depth() const; - //! returns the number of channels in each sequence element - int channels() const; - //! returns the size of each sequence element - size_t elemSize() const; - //! returns index of the specified sequence element - size_t index(const _Tp& elem) const; - //! appends the specified element to the end of the sequence - void push_back(const _Tp& elem); - //! appends the specified element to the front of the sequence - void push_front(const _Tp& elem); - //! appends zero or more elements to the end of the sequence - void push_back(const _Tp* elems, size_t count); - //! appends zero or more elements to the front of the sequence - void push_front(const _Tp* elems, size_t count); - //! inserts the specified element to the specified position - void insert(int idx, const _Tp& elem); - //! inserts zero or more elements to the specified position - void insert(int idx, const _Tp* elems, size_t count); - //! removes element at the specified position - void remove(int idx); - //! removes the specified subsequence - void remove(const Range& r); - - //! returns reference to the first sequence element - _Tp& front(); - //! returns read-only reference to the first sequence element - const _Tp& front() const; - //! returns reference to the last sequence element - _Tp& back(); - //! returns read-only reference to the last sequence element - const _Tp& back() const; - //! returns true iff the sequence contains no elements - bool empty() const; - - //! removes all the elements from the sequence - void clear(); - //! removes the first element from the sequence - void pop_front(); - //! removes the last element from the sequence - void pop_back(); - //! removes zero or more elements from the beginning of the sequence - void pop_front(_Tp* elems, size_t count); - //! removes zero or more elements from the end of the sequence - void pop_back(_Tp* elems, size_t count); - - //! copies the whole sequence or the sequence slice to the specified vector - void copyTo(std::vector<_Tp>& vec, const Range& range=Range::all()) const; - //! returns the vector containing all the sequence elements - operator std::vector<_Tp>() const; - - CvSeq* seq; -}; - - -/*! - STL-style Sequence Iterator inherited from the CvSeqReader structure -*/ -template class SeqIterator : public CvSeqReader -{ -public: - //! the default constructor - SeqIterator(); - //! the constructor setting the iterator to the beginning or to the end of the sequence - SeqIterator(const Seq<_Tp>& seq, bool seekEnd=false); - //! positions the iterator within the sequence - void seek(size_t pos); - //! reports the current iterator position - size_t tell() const; - //! returns reference to the current sequence element - _Tp& operator *(); - //! returns read-only reference to the current sequence element - const _Tp& operator *() const; - //! moves iterator to the next sequence element - SeqIterator& operator ++(); - //! moves iterator to the next sequence element - SeqIterator operator ++(int) const; - //! moves iterator to the previous sequence element - SeqIterator& operator --(); - //! moves iterator to the previous sequence element - SeqIterator operator --(int) const; - - //! moves iterator forward by the specified offset (possibly negative) - SeqIterator& operator +=(int); - //! moves iterator backward by the specified offset (possibly negative) - SeqIterator& operator -=(int); - - // this is index of the current element module seq->total*2 - // (to distinguish between 0 and seq->total) - int index; -}; - - - -// bridge C++ => C Seq API -CV_EXPORTS schar* seqPush( CvSeq* seq, const void* element=0); -CV_EXPORTS schar* seqPushFront( CvSeq* seq, const void* element=0); -CV_EXPORTS void seqPop( CvSeq* seq, void* element=0); -CV_EXPORTS void seqPopFront( CvSeq* seq, void* element=0); -CV_EXPORTS void seqPopMulti( CvSeq* seq, void* elements, - int count, int in_front=0 ); -CV_EXPORTS void seqRemove( CvSeq* seq, int index ); -CV_EXPORTS void clearSeq( CvSeq* seq ); -CV_EXPORTS schar* getSeqElem( const CvSeq* seq, int index ); -CV_EXPORTS void seqRemoveSlice( CvSeq* seq, CvSlice slice ); -CV_EXPORTS void seqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr ); - -template inline Seq<_Tp>::Seq() : seq(0) {} -template inline Seq<_Tp>::Seq( const CvSeq* _seq ) : seq((CvSeq*)_seq) -{ - CV_Assert(!_seq || _seq->elem_size == sizeof(_Tp)); -} - -template inline Seq<_Tp>::Seq( MemStorage& storage, - int headerSize ) -{ - CV_Assert(headerSize >= (int)sizeof(CvSeq)); - seq = cvCreateSeq(DataType<_Tp>::type, headerSize, sizeof(_Tp), storage); -} - -template inline _Tp& Seq<_Tp>::operator [](int idx) -{ return *(_Tp*)getSeqElem(seq, idx); } - -template inline const _Tp& Seq<_Tp>::operator [](int idx) const -{ return *(_Tp*)getSeqElem(seq, idx); } - -template inline SeqIterator<_Tp> Seq<_Tp>::begin() const -{ return SeqIterator<_Tp>(*this); } - -template inline SeqIterator<_Tp> Seq<_Tp>::end() const -{ return SeqIterator<_Tp>(*this, true); } - -template inline size_t Seq<_Tp>::size() const -{ return seq ? seq->total : 0; } - -template inline int Seq<_Tp>::type() const -{ return seq ? CV_MAT_TYPE(seq->flags) : 0; } - -template inline int Seq<_Tp>::depth() const -{ return seq ? CV_MAT_DEPTH(seq->flags) : 0; } - -template inline int Seq<_Tp>::channels() const -{ return seq ? CV_MAT_CN(seq->flags) : 0; } - -template inline size_t Seq<_Tp>::elemSize() const -{ return seq ? seq->elem_size : 0; } - -template inline size_t Seq<_Tp>::index(const _Tp& elem) const -{ return cvSeqElemIdx(seq, &elem); } - -template inline void Seq<_Tp>::push_back(const _Tp& elem) -{ cvSeqPush(seq, &elem); } - -template inline void Seq<_Tp>::push_front(const _Tp& elem) -{ cvSeqPushFront(seq, &elem); } - -template inline void Seq<_Tp>::push_back(const _Tp* elem, size_t count) -{ cvSeqPushMulti(seq, elem, (int)count, 0); } - -template inline void Seq<_Tp>::push_front(const _Tp* elem, size_t count) -{ cvSeqPushMulti(seq, elem, (int)count, 1); } - -template inline _Tp& Seq<_Tp>::back() -{ return *(_Tp*)getSeqElem(seq, -1); } - -template inline const _Tp& Seq<_Tp>::back() const -{ return *(const _Tp*)getSeqElem(seq, -1); } - -template inline _Tp& Seq<_Tp>::front() -{ return *(_Tp*)getSeqElem(seq, 0); } - -template inline const _Tp& Seq<_Tp>::front() const -{ return *(const _Tp*)getSeqElem(seq, 0); } - -template inline bool Seq<_Tp>::empty() const -{ return !seq || seq->total == 0; } - -template inline void Seq<_Tp>::clear() -{ if(seq) clearSeq(seq); } - -template inline void Seq<_Tp>::pop_back() -{ seqPop(seq); } - -template inline void Seq<_Tp>::pop_front() -{ seqPopFront(seq); } - -template inline void Seq<_Tp>::pop_back(_Tp* elem, size_t count) -{ seqPopMulti(seq, elem, (int)count, 0); } - -template inline void Seq<_Tp>::pop_front(_Tp* elem, size_t count) -{ seqPopMulti(seq, elem, (int)count, 1); } - -template inline void Seq<_Tp>::insert(int idx, const _Tp& elem) -{ seqInsert(seq, idx, &elem); } - -template inline void Seq<_Tp>::insert(int idx, const _Tp* elems, size_t count) -{ - CvMat m = cvMat(1, count, DataType<_Tp>::type, elems); - seqInsertSlice(seq, idx, &m); -} - -template inline void Seq<_Tp>::remove(int idx) -{ seqRemove(seq, idx); } - -template inline void Seq<_Tp>::remove(const Range& r) -{ seqRemoveSlice(seq, cvSlice(r.start, r.end)); } - -template inline void Seq<_Tp>::copyTo(std::vector<_Tp>& vec, const Range& range) const -{ - size_t len = !seq ? 0 : range == Range::all() ? seq->total : range.end - range.start; - vec.resize(len); - if( seq && len ) - cvCvtSeqToArray(seq, &vec[0], cvSlice(range)); -} - -template inline Seq<_Tp>::operator std::vector<_Tp>() const -{ - std::vector<_Tp> vec; - copyTo(vec); - return vec; -} - -template inline SeqIterator<_Tp>::SeqIterator() -{ memset(this, 0, sizeof(*this)); } - -template inline SeqIterator<_Tp>::SeqIterator(const Seq<_Tp>& _seq, bool seekEnd) -{ - cvStartReadSeq(_seq.seq, this); - index = seekEnd ? _seq.seq->total : 0; -} - -template inline void SeqIterator<_Tp>::seek(size_t pos) -{ - cvSetSeqReaderPos(this, (int)pos, false); - index = pos; -} - -template inline size_t SeqIterator<_Tp>::tell() const -{ return index; } - -template inline _Tp& SeqIterator<_Tp>::operator *() -{ return *(_Tp*)ptr; } - -template inline const _Tp& SeqIterator<_Tp>::operator *() const -{ return *(const _Tp*)ptr; } - -template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator ++() -{ - CV_NEXT_SEQ_ELEM(sizeof(_Tp), *this); - if( ++index >= seq->total*2 ) - index = 0; - return *this; -} - -template inline SeqIterator<_Tp> SeqIterator<_Tp>::operator ++(int) const -{ - SeqIterator<_Tp> it = *this; - ++*this; - return it; -} - -template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator --() -{ - CV_PREV_SEQ_ELEM(sizeof(_Tp), *this); - if( --index < 0 ) - index = seq->total*2-1; - return *this; -} - -template inline SeqIterator<_Tp> SeqIterator<_Tp>::operator --(int) const -{ - SeqIterator<_Tp> it = *this; - --*this; - return it; -} - -template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator +=(int delta) -{ - cvSetSeqReaderPos(this, delta, 1); - index += delta; - int n = seq->total*2; - if( index < 0 ) - index += n; - if( index >= n ) - index -= n; - return *this; -} - -template inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator -=(int delta) -{ - return (*this += -delta); -} - -template inline ptrdiff_t operator - (const SeqIterator<_Tp>& a, - const SeqIterator<_Tp>& b) -{ - ptrdiff_t delta = a.index - b.index, n = a.seq->total; - if( delta > n || delta < -n ) - delta += delta < 0 ? n : -n; - return delta; -} - -template inline bool operator == (const SeqIterator<_Tp>& a, - const SeqIterator<_Tp>& b) -{ - return a.seq == b.seq && a.index == b.index; -} - -template inline bool operator != (const SeqIterator<_Tp>& a, - const SeqIterator<_Tp>& b) -{ - return !(a == b); -} - -//! @} - -} // cv - -#endif - -#endif diff --git a/include/opencv2/core/cuda.hpp b/include/opencv2/core/cuda.hpp deleted file mode 100755 index 7d7bb62..0000000 --- a/include/opencv2/core/cuda.hpp +++ /dev/null @@ -1,1049 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_CUDA_HPP -#define OPENCV_CORE_CUDA_HPP - -#ifndef __cplusplus -# error cuda.hpp header must be compiled as C++ -#endif - -#include "opencv2/core.hpp" -#include "opencv2/core/cuda_types.hpp" - -/** - @defgroup cuda CUDA-accelerated Computer Vision - @{ - @defgroup cudacore Core part - @{ - @defgroup cudacore_init Initialization and Information - @defgroup cudacore_struct Data Structures - @} - @} - */ - -namespace cv { namespace cuda { - -//! @addtogroup cudacore_struct -//! @{ - -//=================================================================================== -// GpuMat -//=================================================================================== - -/** @brief Base storage class for GPU memory with reference counting. - -Its interface matches the Mat interface with the following limitations: - -- no arbitrary dimensions support (only 2D) -- no functions that return references to their data (because references on GPU are not valid for - CPU) -- no expression templates technique support - -Beware that the latter limitation may lead to overloaded matrix operators that cause memory -allocations. The GpuMat class is convertible to cuda::PtrStepSz and cuda::PtrStep so it can be -passed directly to the kernel. - -@note In contrast with Mat, in most cases GpuMat::isContinuous() == false . This means that rows are -aligned to a size depending on the hardware. Single-row GpuMat is always a continuous matrix. - -@note You are not recommended to leave static or global GpuMat variables allocated, that is, to rely -on its destructor. The destruction order of such variables and CUDA context is undefined. GPU memory -release function returns error if the CUDA context has been destroyed before. - -Some member functions are described as a "Blocking Call" while some are described as a -"Non-Blocking Call". Blocking functions are synchronous to host. It is guaranteed that the GPU -operation is finished when the function returns. However, non-blocking functions are asynchronous to -host. Those functions may return even if the GPU operation is not finished. - -Compared to their blocking counterpart, non-blocking functions accept Stream as an additional -argument. If a non-default stream is passed, the GPU operation may overlap with operations in other -streams. - -@sa Mat - */ -class CV_EXPORTS GpuMat -{ -public: - class CV_EXPORTS Allocator - { - public: - virtual ~Allocator() {} - - // allocator must fill data, step and refcount fields - virtual bool allocate(GpuMat* mat, int rows, int cols, size_t elemSize) = 0; - virtual void free(GpuMat* mat) = 0; - }; - - //! default allocator - static Allocator* defaultAllocator(); - static void setDefaultAllocator(Allocator* allocator); - - //! default constructor - explicit GpuMat(Allocator* allocator = defaultAllocator()); - - //! constructs GpuMat of the specified size and type - GpuMat(int rows, int cols, int type, Allocator* allocator = defaultAllocator()); - GpuMat(Size size, int type, Allocator* allocator = defaultAllocator()); - - //! constructs GpuMat and fills it with the specified value _s - GpuMat(int rows, int cols, int type, Scalar s, Allocator* allocator = defaultAllocator()); - GpuMat(Size size, int type, Scalar s, Allocator* allocator = defaultAllocator()); - - //! copy constructor - GpuMat(const GpuMat& m); - - //! constructor for GpuMat headers pointing to user-allocated data - GpuMat(int rows, int cols, int type, void* data, size_t step = Mat::AUTO_STEP); - GpuMat(Size size, int type, void* data, size_t step = Mat::AUTO_STEP); - - //! creates a GpuMat header for a part of the bigger matrix - GpuMat(const GpuMat& m, Range rowRange, Range colRange); - GpuMat(const GpuMat& m, Rect roi); - - //! builds GpuMat from host memory (Blocking call) - explicit GpuMat(InputArray arr, Allocator* allocator = defaultAllocator()); - - //! destructor - calls release() - ~GpuMat(); - - //! assignment operators - GpuMat& operator =(const GpuMat& m); - - //! allocates new GpuMat data unless the GpuMat already has specified size and type - void create(int rows, int cols, int type); - void create(Size size, int type); - - //! decreases reference counter, deallocate the data when reference counter reaches 0 - void release(); - - //! swaps with other smart pointer - void swap(GpuMat& mat); - - /** @brief Performs data upload to GpuMat (Blocking call) - - This function copies data from host memory to device memory. As being a blocking call, it is - guaranteed that the copy operation is finished when this function returns. - */ - void upload(InputArray arr); - - /** @brief Performs data upload to GpuMat (Non-Blocking call) - - This function copies data from host memory to device memory. As being a non-blocking call, this - function may return even if the copy operation is not finished. - - The copy operation may be overlapped with operations in other non-default streams if \p stream is - not the default stream and \p dst is HostMem allocated with HostMem::PAGE_LOCKED option. - */ - void upload(InputArray arr, Stream& stream); - - /** @brief Performs data download from GpuMat (Blocking call) - - This function copies data from device memory to host memory. As being a blocking call, it is - guaranteed that the copy operation is finished when this function returns. - */ - void download(OutputArray dst) const; - - /** @brief Performs data download from GpuMat (Non-Blocking call) - - This function copies data from device memory to host memory. As being a non-blocking call, this - function may return even if the copy operation is not finished. - - The copy operation may be overlapped with operations in other non-default streams if \p stream is - not the default stream and \p dst is HostMem allocated with HostMem::PAGE_LOCKED option. - */ - void download(OutputArray dst, Stream& stream) const; - - //! returns deep copy of the GpuMat, i.e. the data is copied - GpuMat clone() const; - - //! copies the GpuMat content to device memory (Blocking call) - void copyTo(OutputArray dst) const; - - //! copies the GpuMat content to device memory (Non-Blocking call) - void copyTo(OutputArray dst, Stream& stream) const; - - //! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Blocking call) - void copyTo(OutputArray dst, InputArray mask) const; - - //! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Non-Blocking call) - void copyTo(OutputArray dst, InputArray mask, Stream& stream) const; - - //! sets some of the GpuMat elements to s (Blocking call) - GpuMat& setTo(Scalar s); - - //! sets some of the GpuMat elements to s (Non-Blocking call) - GpuMat& setTo(Scalar s, Stream& stream); - - //! sets some of the GpuMat elements to s, according to the mask (Blocking call) - GpuMat& setTo(Scalar s, InputArray mask); - - //! sets some of the GpuMat elements to s, according to the mask (Non-Blocking call) - GpuMat& setTo(Scalar s, InputArray mask, Stream& stream); - - //! converts GpuMat to another datatype (Blocking call) - void convertTo(OutputArray dst, int rtype) const; - - //! converts GpuMat to another datatype (Non-Blocking call) - void convertTo(OutputArray dst, int rtype, Stream& stream) const; - - //! converts GpuMat to another datatype with scaling (Blocking call) - void convertTo(OutputArray dst, int rtype, double alpha, double beta = 0.0) const; - - //! converts GpuMat to another datatype with scaling (Non-Blocking call) - void convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const; - - //! converts GpuMat to another datatype with scaling (Non-Blocking call) - void convertTo(OutputArray dst, int rtype, double alpha, double beta, Stream& stream) const; - - void assignTo(GpuMat& m, int type=-1) const; - - //! returns pointer to y-th row - uchar* ptr(int y = 0); - const uchar* ptr(int y = 0) const; - - //! template version of the above method - template _Tp* ptr(int y = 0); - template const _Tp* ptr(int y = 0) const; - - template operator PtrStepSz<_Tp>() const; - template operator PtrStep<_Tp>() const; - - //! returns a new GpuMat header for the specified row - GpuMat row(int y) const; - - //! returns a new GpuMat header for the specified column - GpuMat col(int x) const; - - //! ... for the specified row span - GpuMat rowRange(int startrow, int endrow) const; - GpuMat rowRange(Range r) const; - - //! ... for the specified column span - GpuMat colRange(int startcol, int endcol) const; - GpuMat colRange(Range r) const; - - //! extracts a rectangular sub-GpuMat (this is a generalized form of row, rowRange etc.) - GpuMat operator ()(Range rowRange, Range colRange) const; - GpuMat operator ()(Rect roi) const; - - //! creates alternative GpuMat header for the same data, with different - //! number of channels and/or different number of rows - GpuMat reshape(int cn, int rows = 0) const; - - //! locates GpuMat header within a parent GpuMat - void locateROI(Size& wholeSize, Point& ofs) const; - - //! moves/resizes the current GpuMat ROI inside the parent GpuMat - GpuMat& adjustROI(int dtop, int dbottom, int dleft, int dright); - - //! returns true iff the GpuMat data is continuous - //! (i.e. when there are no gaps between successive rows) - bool isContinuous() const; - - //! returns element size in bytes - size_t elemSize() const; - - //! returns the size of element channel in bytes - size_t elemSize1() const; - - //! returns element type - int type() const; - - //! returns element type - int depth() const; - - //! returns number of channels - int channels() const; - - //! returns step/elemSize1() - size_t step1() const; - - //! returns GpuMat size : width == number of columns, height == number of rows - Size size() const; - - //! returns true if GpuMat data is NULL - bool empty() const; - - //! internal use method: updates the continuity flag - void updateContinuityFlag(); - - /*! includes several bit-fields: - - the magic signature - - continuity flag - - depth - - number of channels - */ - int flags; - - //! the number of rows and columns - int rows, cols; - - //! a distance between successive rows in bytes; includes the gap if any - size_t step; - - //! pointer to the data - uchar* data; - - //! pointer to the reference counter; - //! when GpuMat points to user-allocated data, the pointer is NULL - int* refcount; - - //! helper fields used in locateROI and adjustROI - uchar* datastart; - const uchar* dataend; - - //! allocator - Allocator* allocator; -}; - -/** @brief Creates a continuous matrix. - -@param rows Row count. -@param cols Column count. -@param type Type of the matrix. -@param arr Destination matrix. This parameter changes only if it has a proper type and area ( -\f$\texttt{rows} \times \texttt{cols}\f$ ). - -Matrix is called continuous if its elements are stored continuously, that is, without gaps at the -end of each row. - */ -CV_EXPORTS void createContinuous(int rows, int cols, int type, OutputArray arr); - -/** @brief Ensures that the size of a matrix is big enough and the matrix has a proper type. - -@param rows Minimum desired number of rows. -@param cols Minimum desired number of columns. -@param type Desired matrix type. -@param arr Destination matrix. - -The function does not reallocate memory if the matrix has proper attributes already. - */ -CV_EXPORTS void ensureSizeIsEnough(int rows, int cols, int type, OutputArray arr); - -/** @brief BufferPool for use with CUDA streams - -BufferPool utilizes Stream's allocator to create new buffers for GpuMat's. It is -only useful when enabled with #setBufferPoolUsage. - -@code - setBufferPoolUsage(true); -@endcode - -@note #setBufferPoolUsage must be called \em before any Stream declaration. - -Users may specify custom allocator for Stream and may implement their own stream based -functions utilizing the same underlying GPU memory management. - -If custom allocator is not specified, BufferPool utilizes StackAllocator by -default. StackAllocator allocates a chunk of GPU device memory beforehand, -and when GpuMat is declared later on, it is given the pre-allocated memory. -This kind of strategy reduces the number of calls for memory allocating APIs -such as cudaMalloc or cudaMallocPitch. - -Below is an example that utilizes BufferPool with StackAllocator: - -@code - #include - - using namespace cv; - using namespace cv::cuda - - int main() - { - setBufferPoolUsage(true); // Tell OpenCV that we are going to utilize BufferPool - setBufferPoolConfig(getDevice(), 1024 * 1024 * 64, 2); // Allocate 64 MB, 2 stacks (default is 10 MB, 5 stacks) - - Stream stream1, stream2; // Each stream uses 1 stack - BufferPool pool1(stream1), pool2(stream2); - - GpuMat d_src1 = pool1.getBuffer(4096, 4096, CV_8UC1); // 16MB - GpuMat d_dst1 = pool1.getBuffer(4096, 4096, CV_8UC3); // 48MB, pool1 is now full - - GpuMat d_src2 = pool2.getBuffer(1024, 1024, CV_8UC1); // 1MB - GpuMat d_dst2 = pool2.getBuffer(1024, 1024, CV_8UC3); // 3MB - - cvtColor(d_src1, d_dst1, CV_GRAY2BGR, 0, stream1); - cvtColor(d_src2, d_dst2, CV_GRAY2BGR, 0, stream2); - } -@endcode - -If we allocate another GpuMat on pool1 in the above example, it will be carried out by -the DefaultAllocator since the stack for pool1 is full. - -@code - GpuMat d_add1 = pool1.getBuffer(1024, 1024, CV_8UC1); // Stack for pool1 is full, memory is allocated with DefaultAllocator -@endcode - -If a third stream is declared in the above example, allocating with #getBuffer -within that stream will also be carried out by the DefaultAllocator because we've run out of -stacks. - -@code - Stream stream3; // Only 2 stacks were allocated, we've run out of stacks - BufferPool pool3(stream3); - GpuMat d_src3 = pool3.getBuffer(1024, 1024, CV_8UC1); // Memory is allocated with DefaultAllocator -@endcode - -@warning When utilizing StackAllocator, deallocation order is important. - -Just like a stack, deallocation must be done in LIFO order. Below is an example of -erroneous usage that violates LIFO rule. If OpenCV is compiled in Debug mode, this -sample code will emit CV_Assert error. - -@code - int main() - { - setBufferPoolUsage(true); // Tell OpenCV that we are going to utilize BufferPool - Stream stream; // A default size (10 MB) stack is allocated to this stream - BufferPool pool(stream); - - GpuMat mat1 = pool.getBuffer(1024, 1024, CV_8UC1); // Allocate mat1 (1MB) - GpuMat mat2 = pool.getBuffer(1024, 1024, CV_8UC1); // Allocate mat2 (1MB) - - mat1.release(); // erroneous usage : mat2 must be deallocated before mat1 - } -@endcode - -Since C++ local variables are destroyed in the reverse order of construction, -the code sample below satisfies the LIFO rule. Local GpuMat's are deallocated -and the corresponding memory is automatically returned to the pool for later usage. - -@code - int main() - { - setBufferPoolUsage(true); // Tell OpenCV that we are going to utilize BufferPool - setBufferPoolConfig(getDevice(), 1024 * 1024 * 64, 2); // Allocate 64 MB, 2 stacks (default is 10 MB, 5 stacks) - - Stream stream1, stream2; // Each stream uses 1 stack - BufferPool pool1(stream1), pool2(stream2); - - for (int i = 0; i < 10; i++) - { - GpuMat d_src1 = pool1.getBuffer(4096, 4096, CV_8UC1); // 16MB - GpuMat d_dst1 = pool1.getBuffer(4096, 4096, CV_8UC3); // 48MB, pool1 is now full - - GpuMat d_src2 = pool2.getBuffer(1024, 1024, CV_8UC1); // 1MB - GpuMat d_dst2 = pool2.getBuffer(1024, 1024, CV_8UC3); // 3MB - - d_src1.setTo(Scalar(i), stream1); - d_src2.setTo(Scalar(i), stream2); - - cvtColor(d_src1, d_dst1, CV_GRAY2BGR, 0, stream1); - cvtColor(d_src2, d_dst2, CV_GRAY2BGR, 0, stream2); - // The order of destruction of the local variables is: - // d_dst2 => d_src2 => d_dst1 => d_src1 - // LIFO rule is satisfied, this code runs without error - } - } -@endcode - */ -class CV_EXPORTS BufferPool -{ -public: - - //! Gets the BufferPool for the given stream. - explicit BufferPool(Stream& stream); - - //! Allocates a new GpuMat of given size and type. - GpuMat getBuffer(int rows, int cols, int type); - - //! Allocates a new GpuMat of given size and type. - GpuMat getBuffer(Size size, int type) { return getBuffer(size.height, size.width, type); } - - //! Returns the allocator associated with the stream. - Ptr getAllocator() const { return allocator_; } - -private: - Ptr allocator_; -}; - -//! BufferPool management (must be called before Stream creation) -CV_EXPORTS void setBufferPoolUsage(bool on); -CV_EXPORTS void setBufferPoolConfig(int deviceId, size_t stackSize, int stackCount); - -//=================================================================================== -// HostMem -//=================================================================================== - -/** @brief Class with reference counting wrapping special memory type allocation functions from CUDA. - -Its interface is also Mat-like but with additional memory type parameters. - -- **PAGE_LOCKED** sets a page locked memory type used commonly for fast and asynchronous - uploading/downloading data from/to GPU. -- **SHARED** specifies a zero copy memory allocation that enables mapping the host memory to GPU - address space, if supported. -- **WRITE_COMBINED** sets the write combined buffer that is not cached by CPU. Such buffers are - used to supply GPU with data when GPU only reads it. The advantage is a better CPU cache - utilization. - -@note Allocation size of such memory types is usually limited. For more details, see *CUDA 2.2 -Pinned Memory APIs* document or *CUDA C Programming Guide*. - */ -class CV_EXPORTS HostMem -{ -public: - enum AllocType { PAGE_LOCKED = 1, SHARED = 2, WRITE_COMBINED = 4 }; - - static MatAllocator* getAllocator(AllocType alloc_type = PAGE_LOCKED); - - explicit HostMem(AllocType alloc_type = PAGE_LOCKED); - - HostMem(const HostMem& m); - - HostMem(int rows, int cols, int type, AllocType alloc_type = PAGE_LOCKED); - HostMem(Size size, int type, AllocType alloc_type = PAGE_LOCKED); - - //! creates from host memory with coping data - explicit HostMem(InputArray arr, AllocType alloc_type = PAGE_LOCKED); - - ~HostMem(); - - HostMem& operator =(const HostMem& m); - - //! swaps with other smart pointer - void swap(HostMem& b); - - //! returns deep copy of the matrix, i.e. the data is copied - HostMem clone() const; - - //! allocates new matrix data unless the matrix already has specified size and type. - void create(int rows, int cols, int type); - void create(Size size, int type); - - //! creates alternative HostMem header for the same data, with different - //! number of channels and/or different number of rows - HostMem reshape(int cn, int rows = 0) const; - - //! decrements reference counter and released memory if needed. - void release(); - - //! returns matrix header with disabled reference counting for HostMem data. - Mat createMatHeader() const; - - /** @brief Maps CPU memory to GPU address space and creates the cuda::GpuMat header without reference counting - for it. - - This can be done only if memory was allocated with the SHARED flag and if it is supported by the - hardware. Laptops often share video and CPU memory, so address spaces can be mapped, which - eliminates an extra copy. - */ - GpuMat createGpuMatHeader() const; - - // Please see cv::Mat for descriptions - bool isContinuous() const; - size_t elemSize() const; - size_t elemSize1() const; - int type() const; - int depth() const; - int channels() const; - size_t step1() const; - Size size() const; - bool empty() const; - - // Please see cv::Mat for descriptions - int flags; - int rows, cols; - size_t step; - - uchar* data; - int* refcount; - - uchar* datastart; - const uchar* dataend; - - AllocType alloc_type; -}; - -/** @brief Page-locks the memory of matrix and maps it for the device(s). - -@param m Input matrix. - */ -CV_EXPORTS void registerPageLocked(Mat& m); - -/** @brief Unmaps the memory of matrix and makes it pageable again. - -@param m Input matrix. - */ -CV_EXPORTS void unregisterPageLocked(Mat& m); - -//=================================================================================== -// Stream -//=================================================================================== - -/** @brief This class encapsulates a queue of asynchronous calls. - -@note Currently, you may face problems if an operation is enqueued twice with different data. Some -functions use the constant GPU memory, and next call may update the memory before the previous one -has been finished. But calling different operations asynchronously is safe because each operation -has its own constant buffer. Memory copy/upload/download/set operations to the buffers you hold are -also safe. - -@note The Stream class is not thread-safe. Please use different Stream objects for different CPU threads. - -@code -void thread1() -{ - cv::cuda::Stream stream1; - cv::cuda::func1(..., stream1); -} - -void thread2() -{ - cv::cuda::Stream stream2; - cv::cuda::func2(..., stream2); -} -@endcode - -@note By default all CUDA routines are launched in Stream::Null() object, if the stream is not specified by user. -In multi-threading environment the stream objects must be passed explicitly (see previous note). - */ -class CV_EXPORTS Stream -{ - typedef void (Stream::*bool_type)() const; - void this_type_does_not_support_comparisons() const {} - -public: - typedef void (*StreamCallback)(int status, void* userData); - - //! creates a new asynchronous stream - Stream(); - - //! creates a new asynchronous stream with custom allocator - Stream(const Ptr& allocator); - - /** @brief Returns true if the current stream queue is finished. Otherwise, it returns false. - */ - bool queryIfComplete() const; - - /** @brief Blocks the current CPU thread until all operations in the stream are complete. - */ - void waitForCompletion(); - - /** @brief Makes a compute stream wait on an event. - */ - void waitEvent(const Event& event); - - /** @brief Adds a callback to be called on the host after all currently enqueued items in the stream have - completed. - - @note Callbacks must not make any CUDA API calls. Callbacks must not perform any synchronization - that may depend on outstanding device work or other callbacks that are not mandated to run earlier. - Callbacks without a mandated order (in independent streams) execute in undefined order and may be - serialized. - */ - void enqueueHostCallback(StreamCallback callback, void* userData); - - //! return Stream object for default CUDA stream - static Stream& Null(); - - //! returns true if stream object is not default (!= 0) - operator bool_type() const; - - class Impl; - -private: - Ptr impl_; - Stream(const Ptr& impl); - - friend struct StreamAccessor; - friend class BufferPool; - friend class DefaultDeviceInitializer; -}; - -class CV_EXPORTS Event -{ -public: - enum CreateFlags - { - DEFAULT = 0x00, /**< Default event flag */ - BLOCKING_SYNC = 0x01, /**< Event uses blocking synchronization */ - DISABLE_TIMING = 0x02, /**< Event will not record timing data */ - INTERPROCESS = 0x04 /**< Event is suitable for interprocess use. DisableTiming must be set */ - }; - - explicit Event(CreateFlags flags = DEFAULT); - - //! records an event - void record(Stream& stream = Stream::Null()); - - //! queries an event's status - bool queryIfComplete() const; - - //! waits for an event to complete - void waitForCompletion(); - - //! computes the elapsed time between events - static float elapsedTime(const Event& start, const Event& end); - - class Impl; - -private: - Ptr impl_; - Event(const Ptr& impl); - - friend struct EventAccessor; -}; - -//! @} cudacore_struct - -//=================================================================================== -// Initialization & Info -//=================================================================================== - -//! @addtogroup cudacore_init -//! @{ - -/** @brief Returns the number of installed CUDA-enabled devices. - -Use this function before any other CUDA functions calls. If OpenCV is compiled without CUDA support, -this function returns 0. If the CUDA driver is not installed, or is incompatible, this function -returns -1. - */ -CV_EXPORTS int getCudaEnabledDeviceCount(); - -/** @brief Sets a device and initializes it for the current thread. - -@param device System index of a CUDA device starting with 0. - -If the call of this function is omitted, a default device is initialized at the fist CUDA usage. - */ -CV_EXPORTS void setDevice(int device); - -/** @brief Returns the current device index set by cuda::setDevice or initialized by default. - */ -CV_EXPORTS int getDevice(); - -/** @brief Explicitly destroys and cleans up all resources associated with the current device in the current -process. - -Any subsequent API call to this device will reinitialize the device. - */ -CV_EXPORTS void resetDevice(); - -/** @brief Enumeration providing CUDA computing features. - */ -enum FeatureSet -{ - FEATURE_SET_COMPUTE_10 = 10, - FEATURE_SET_COMPUTE_11 = 11, - FEATURE_SET_COMPUTE_12 = 12, - FEATURE_SET_COMPUTE_13 = 13, - FEATURE_SET_COMPUTE_20 = 20, - FEATURE_SET_COMPUTE_21 = 21, - FEATURE_SET_COMPUTE_30 = 30, - FEATURE_SET_COMPUTE_32 = 32, - FEATURE_SET_COMPUTE_35 = 35, - FEATURE_SET_COMPUTE_50 = 50, - - GLOBAL_ATOMICS = FEATURE_SET_COMPUTE_11, - SHARED_ATOMICS = FEATURE_SET_COMPUTE_12, - NATIVE_DOUBLE = FEATURE_SET_COMPUTE_13, - WARP_SHUFFLE_FUNCTIONS = FEATURE_SET_COMPUTE_30, - DYNAMIC_PARALLELISM = FEATURE_SET_COMPUTE_35 -}; - -//! checks whether current device supports the given feature -CV_EXPORTS bool deviceSupports(FeatureSet feature_set); - -/** @brief Class providing a set of static methods to check what NVIDIA\* card architecture the CUDA module was -built for. - -According to the CUDA C Programming Guide Version 3.2: "PTX code produced for some specific compute -capability can always be compiled to binary code of greater or equal compute capability". - */ -class CV_EXPORTS TargetArchs -{ -public: - /** @brief The following method checks whether the module was built with the support of the given feature: - - @param feature_set Features to be checked. See :ocvcuda::FeatureSet. - */ - static bool builtWith(FeatureSet feature_set); - - /** @brief There is a set of methods to check whether the module contains intermediate (PTX) or binary CUDA - code for the given architecture(s): - - @param major Major compute capability version. - @param minor Minor compute capability version. - */ - static bool has(int major, int minor); - static bool hasPtx(int major, int minor); - static bool hasBin(int major, int minor); - - static bool hasEqualOrLessPtx(int major, int minor); - static bool hasEqualOrGreater(int major, int minor); - static bool hasEqualOrGreaterPtx(int major, int minor); - static bool hasEqualOrGreaterBin(int major, int minor); -}; - -/** @brief Class providing functionality for querying the specified GPU properties. - */ -class CV_EXPORTS DeviceInfo -{ -public: - //! creates DeviceInfo object for the current GPU - DeviceInfo(); - - /** @brief The constructors. - - @param device_id System index of the CUDA device starting with 0. - - Constructs the DeviceInfo object for the specified device. If device_id parameter is missed, it - constructs an object for the current device. - */ - DeviceInfo(int device_id); - - /** @brief Returns system index of the CUDA device starting with 0. - */ - int deviceID() const; - - //! ASCII string identifying device - const char* name() const; - - //! global memory available on device in bytes - size_t totalGlobalMem() const; - - //! shared memory available per block in bytes - size_t sharedMemPerBlock() const; - - //! 32-bit registers available per block - int regsPerBlock() const; - - //! warp size in threads - int warpSize() const; - - //! maximum pitch in bytes allowed by memory copies - size_t memPitch() const; - - //! maximum number of threads per block - int maxThreadsPerBlock() const; - - //! maximum size of each dimension of a block - Vec3i maxThreadsDim() const; - - //! maximum size of each dimension of a grid - Vec3i maxGridSize() const; - - //! clock frequency in kilohertz - int clockRate() const; - - //! constant memory available on device in bytes - size_t totalConstMem() const; - - //! major compute capability - int majorVersion() const; - - //! minor compute capability - int minorVersion() const; - - //! alignment requirement for textures - size_t textureAlignment() const; - - //! pitch alignment requirement for texture references bound to pitched memory - size_t texturePitchAlignment() const; - - //! number of multiprocessors on device - int multiProcessorCount() const; - - //! specified whether there is a run time limit on kernels - bool kernelExecTimeoutEnabled() const; - - //! device is integrated as opposed to discrete - bool integrated() const; - - //! device can map host memory with cudaHostAlloc/cudaHostGetDevicePointer - bool canMapHostMemory() const; - - enum ComputeMode - { - ComputeModeDefault, /**< default compute mode (Multiple threads can use cudaSetDevice with this device) */ - ComputeModeExclusive, /**< compute-exclusive-thread mode (Only one thread in one process will be able to use cudaSetDevice with this device) */ - ComputeModeProhibited, /**< compute-prohibited mode (No threads can use cudaSetDevice with this device) */ - ComputeModeExclusiveProcess /**< compute-exclusive-process mode (Many threads in one process will be able to use cudaSetDevice with this device) */ - }; - - //! compute mode - ComputeMode computeMode() const; - - //! maximum 1D texture size - int maxTexture1D() const; - - //! maximum 1D mipmapped texture size - int maxTexture1DMipmap() const; - - //! maximum size for 1D textures bound to linear memory - int maxTexture1DLinear() const; - - //! maximum 2D texture dimensions - Vec2i maxTexture2D() const; - - //! maximum 2D mipmapped texture dimensions - Vec2i maxTexture2DMipmap() const; - - //! maximum dimensions (width, height, pitch) for 2D textures bound to pitched memory - Vec3i maxTexture2DLinear() const; - - //! maximum 2D texture dimensions if texture gather operations have to be performed - Vec2i maxTexture2DGather() const; - - //! maximum 3D texture dimensions - Vec3i maxTexture3D() const; - - //! maximum Cubemap texture dimensions - int maxTextureCubemap() const; - - //! maximum 1D layered texture dimensions - Vec2i maxTexture1DLayered() const; - - //! maximum 2D layered texture dimensions - Vec3i maxTexture2DLayered() const; - - //! maximum Cubemap layered texture dimensions - Vec2i maxTextureCubemapLayered() const; - - //! maximum 1D surface size - int maxSurface1D() const; - - //! maximum 2D surface dimensions - Vec2i maxSurface2D() const; - - //! maximum 3D surface dimensions - Vec3i maxSurface3D() const; - - //! maximum 1D layered surface dimensions - Vec2i maxSurface1DLayered() const; - - //! maximum 2D layered surface dimensions - Vec3i maxSurface2DLayered() const; - - //! maximum Cubemap surface dimensions - int maxSurfaceCubemap() const; - - //! maximum Cubemap layered surface dimensions - Vec2i maxSurfaceCubemapLayered() const; - - //! alignment requirements for surfaces - size_t surfaceAlignment() const; - - //! device can possibly execute multiple kernels concurrently - bool concurrentKernels() const; - - //! device has ECC support enabled - bool ECCEnabled() const; - - //! PCI bus ID of the device - int pciBusID() const; - - //! PCI device ID of the device - int pciDeviceID() const; - - //! PCI domain ID of the device - int pciDomainID() const; - - //! true if device is a Tesla device using TCC driver, false otherwise - bool tccDriver() const; - - //! number of asynchronous engines - int asyncEngineCount() const; - - //! device shares a unified address space with the host - bool unifiedAddressing() const; - - //! peak memory clock frequency in kilohertz - int memoryClockRate() const; - - //! global memory bus width in bits - int memoryBusWidth() const; - - //! size of L2 cache in bytes - int l2CacheSize() const; - - //! maximum resident threads per multiprocessor - int maxThreadsPerMultiProcessor() const; - - //! gets free and total device memory - void queryMemory(size_t& totalMemory, size_t& freeMemory) const; - size_t freeMemory() const; - size_t totalMemory() const; - - /** @brief Provides information on CUDA feature support. - - @param feature_set Features to be checked. See cuda::FeatureSet. - - This function returns true if the device has the specified CUDA feature. Otherwise, it returns false - */ - bool supports(FeatureSet feature_set) const; - - /** @brief Checks the CUDA module and device compatibility. - - This function returns true if the CUDA module can be run on the specified device. Otherwise, it - returns false . - */ - bool isCompatible() const; - -private: - int device_id_; -}; - -CV_EXPORTS void printCudaDeviceInfo(int device); -CV_EXPORTS void printShortCudaDeviceInfo(int device); - -/** @brief Converts an array to half precision floating number. - -@param _src input array. -@param _dst output array. -@param stream Stream for the asynchronous version. -@sa convertFp16 -*/ -CV_EXPORTS void convertFp16(InputArray _src, OutputArray _dst, Stream& stream = Stream::Null()); - -//! @} cudacore_init - -}} // namespace cv { namespace cuda { - - -#include "opencv2/core/cuda.inl.hpp" - -#endif /* OPENCV_CORE_CUDA_HPP */ diff --git a/include/opencv2/core/cuda.inl.hpp b/include/opencv2/core/cuda.inl.hpp deleted file mode 100755 index 35ae2e4..0000000 --- a/include/opencv2/core/cuda.inl.hpp +++ /dev/null @@ -1,631 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_CUDAINL_HPP -#define OPENCV_CORE_CUDAINL_HPP - -#include "opencv2/core/cuda.hpp" - -//! @cond IGNORED - -namespace cv { namespace cuda { - -//=================================================================================== -// GpuMat -//=================================================================================== - -inline -GpuMat::GpuMat(Allocator* allocator_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) -{} - -inline -GpuMat::GpuMat(int rows_, int cols_, int type_, Allocator* allocator_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) -{ - if (rows_ > 0 && cols_ > 0) - create(rows_, cols_, type_); -} - -inline -GpuMat::GpuMat(Size size_, int type_, Allocator* allocator_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) -{ - if (size_.height > 0 && size_.width > 0) - create(size_.height, size_.width, type_); -} - -inline -GpuMat::GpuMat(int rows_, int cols_, int type_, Scalar s_, Allocator* allocator_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) -{ - if (rows_ > 0 && cols_ > 0) - { - create(rows_, cols_, type_); - setTo(s_); - } -} - -inline -GpuMat::GpuMat(Size size_, int type_, Scalar s_, Allocator* allocator_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) -{ - if (size_.height > 0 && size_.width > 0) - { - create(size_.height, size_.width, type_); - setTo(s_); - } -} - -inline -GpuMat::GpuMat(const GpuMat& m) - : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), allocator(m.allocator) -{ - if (refcount) - CV_XADD(refcount, 1); -} - -inline -GpuMat::GpuMat(InputArray arr, Allocator* allocator_) : - flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_) -{ - upload(arr); -} - -inline -GpuMat::~GpuMat() -{ - release(); -} - -inline -GpuMat& GpuMat::operator =(const GpuMat& m) -{ - if (this != &m) - { - GpuMat temp(m); - swap(temp); - } - - return *this; -} - -inline -void GpuMat::create(Size size_, int type_) -{ - create(size_.height, size_.width, type_); -} - -inline -void GpuMat::swap(GpuMat& b) -{ - std::swap(flags, b.flags); - std::swap(rows, b.rows); - std::swap(cols, b.cols); - std::swap(step, b.step); - std::swap(data, b.data); - std::swap(datastart, b.datastart); - std::swap(dataend, b.dataend); - std::swap(refcount, b.refcount); - std::swap(allocator, b.allocator); -} - -inline -GpuMat GpuMat::clone() const -{ - GpuMat m; - copyTo(m); - return m; -} - -inline -void GpuMat::copyTo(OutputArray dst, InputArray mask) const -{ - copyTo(dst, mask, Stream::Null()); -} - -inline -GpuMat& GpuMat::setTo(Scalar s) -{ - return setTo(s, Stream::Null()); -} - -inline -GpuMat& GpuMat::setTo(Scalar s, InputArray mask) -{ - return setTo(s, mask, Stream::Null()); -} - -inline -void GpuMat::convertTo(OutputArray dst, int rtype) const -{ - convertTo(dst, rtype, Stream::Null()); -} - -inline -void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, double beta) const -{ - convertTo(dst, rtype, alpha, beta, Stream::Null()); -} - -inline -void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const -{ - convertTo(dst, rtype, alpha, 0.0, stream); -} - -inline -void GpuMat::assignTo(GpuMat& m, int _type) const -{ - if (_type < 0) - m = *this; - else - convertTo(m, _type); -} - -inline -uchar* GpuMat::ptr(int y) -{ - CV_DbgAssert( (unsigned)y < (unsigned)rows ); - return data + step * y; -} - -inline -const uchar* GpuMat::ptr(int y) const -{ - CV_DbgAssert( (unsigned)y < (unsigned)rows ); - return data + step * y; -} - -template inline -_Tp* GpuMat::ptr(int y) -{ - return (_Tp*)ptr(y); -} - -template inline -const _Tp* GpuMat::ptr(int y) const -{ - return (const _Tp*)ptr(y); -} - -template inline -GpuMat::operator PtrStepSz() const -{ - return PtrStepSz(rows, cols, (T*)data, step); -} - -template inline -GpuMat::operator PtrStep() const -{ - return PtrStep((T*)data, step); -} - -inline -GpuMat GpuMat::row(int y) const -{ - return GpuMat(*this, Range(y, y+1), Range::all()); -} - -inline -GpuMat GpuMat::col(int x) const -{ - return GpuMat(*this, Range::all(), Range(x, x+1)); -} - -inline -GpuMat GpuMat::rowRange(int startrow, int endrow) const -{ - return GpuMat(*this, Range(startrow, endrow), Range::all()); -} - -inline -GpuMat GpuMat::rowRange(Range r) const -{ - return GpuMat(*this, r, Range::all()); -} - -inline -GpuMat GpuMat::colRange(int startcol, int endcol) const -{ - return GpuMat(*this, Range::all(), Range(startcol, endcol)); -} - -inline -GpuMat GpuMat::colRange(Range r) const -{ - return GpuMat(*this, Range::all(), r); -} - -inline -GpuMat GpuMat::operator ()(Range rowRange_, Range colRange_) const -{ - return GpuMat(*this, rowRange_, colRange_); -} - -inline -GpuMat GpuMat::operator ()(Rect roi) const -{ - return GpuMat(*this, roi); -} - -inline -bool GpuMat::isContinuous() const -{ - return (flags & Mat::CONTINUOUS_FLAG) != 0; -} - -inline -size_t GpuMat::elemSize() const -{ - return CV_ELEM_SIZE(flags); -} - -inline -size_t GpuMat::elemSize1() const -{ - return CV_ELEM_SIZE1(flags); -} - -inline -int GpuMat::type() const -{ - return CV_MAT_TYPE(flags); -} - -inline -int GpuMat::depth() const -{ - return CV_MAT_DEPTH(flags); -} - -inline -int GpuMat::channels() const -{ - return CV_MAT_CN(flags); -} - -inline -size_t GpuMat::step1() const -{ - return step / elemSize1(); -} - -inline -Size GpuMat::size() const -{ - return Size(cols, rows); -} - -inline -bool GpuMat::empty() const -{ - return data == 0; -} - -static inline -GpuMat createContinuous(int rows, int cols, int type) -{ - GpuMat m; - createContinuous(rows, cols, type, m); - return m; -} - -static inline -void createContinuous(Size size, int type, OutputArray arr) -{ - createContinuous(size.height, size.width, type, arr); -} - -static inline -GpuMat createContinuous(Size size, int type) -{ - GpuMat m; - createContinuous(size, type, m); - return m; -} - -static inline -void ensureSizeIsEnough(Size size, int type, OutputArray arr) -{ - ensureSizeIsEnough(size.height, size.width, type, arr); -} - -static inline -void swap(GpuMat& a, GpuMat& b) -{ - a.swap(b); -} - -//=================================================================================== -// HostMem -//=================================================================================== - -inline -HostMem::HostMem(AllocType alloc_type_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) -{ -} - -inline -HostMem::HostMem(const HostMem& m) - : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), alloc_type(m.alloc_type) -{ - if( refcount ) - CV_XADD(refcount, 1); -} - -inline -HostMem::HostMem(int rows_, int cols_, int type_, AllocType alloc_type_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) -{ - if (rows_ > 0 && cols_ > 0) - create(rows_, cols_, type_); -} - -inline -HostMem::HostMem(Size size_, int type_, AllocType alloc_type_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) -{ - if (size_.height > 0 && size_.width > 0) - create(size_.height, size_.width, type_); -} - -inline -HostMem::HostMem(InputArray arr, AllocType alloc_type_) - : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_) -{ - arr.getMat().copyTo(*this); -} - -inline -HostMem::~HostMem() -{ - release(); -} - -inline -HostMem& HostMem::operator =(const HostMem& m) -{ - if (this != &m) - { - HostMem temp(m); - swap(temp); - } - - return *this; -} - -inline -void HostMem::swap(HostMem& b) -{ - std::swap(flags, b.flags); - std::swap(rows, b.rows); - std::swap(cols, b.cols); - std::swap(step, b.step); - std::swap(data, b.data); - std::swap(datastart, b.datastart); - std::swap(dataend, b.dataend); - std::swap(refcount, b.refcount); - std::swap(alloc_type, b.alloc_type); -} - -inline -HostMem HostMem::clone() const -{ - HostMem m(size(), type(), alloc_type); - createMatHeader().copyTo(m); - return m; -} - -inline -void HostMem::create(Size size_, int type_) -{ - create(size_.height, size_.width, type_); -} - -inline -Mat HostMem::createMatHeader() const -{ - return Mat(size(), type(), data, step); -} - -inline -bool HostMem::isContinuous() const -{ - return (flags & Mat::CONTINUOUS_FLAG) != 0; -} - -inline -size_t HostMem::elemSize() const -{ - return CV_ELEM_SIZE(flags); -} - -inline -size_t HostMem::elemSize1() const -{ - return CV_ELEM_SIZE1(flags); -} - -inline -int HostMem::type() const -{ - return CV_MAT_TYPE(flags); -} - -inline -int HostMem::depth() const -{ - return CV_MAT_DEPTH(flags); -} - -inline -int HostMem::channels() const -{ - return CV_MAT_CN(flags); -} - -inline -size_t HostMem::step1() const -{ - return step / elemSize1(); -} - -inline -Size HostMem::size() const -{ - return Size(cols, rows); -} - -inline -bool HostMem::empty() const -{ - return data == 0; -} - -static inline -void swap(HostMem& a, HostMem& b) -{ - a.swap(b); -} - -//=================================================================================== -// Stream -//=================================================================================== - -inline -Stream::Stream(const Ptr& impl) - : impl_(impl) -{ -} - -//=================================================================================== -// Event -//=================================================================================== - -inline -Event::Event(const Ptr& impl) - : impl_(impl) -{ -} - -//=================================================================================== -// Initialization & Info -//=================================================================================== - -inline -bool TargetArchs::has(int major, int minor) -{ - return hasPtx(major, minor) || hasBin(major, minor); -} - -inline -bool TargetArchs::hasEqualOrGreater(int major, int minor) -{ - return hasEqualOrGreaterPtx(major, minor) || hasEqualOrGreaterBin(major, minor); -} - -inline -DeviceInfo::DeviceInfo() -{ - device_id_ = getDevice(); -} - -inline -DeviceInfo::DeviceInfo(int device_id) -{ - CV_Assert( device_id >= 0 && device_id < getCudaEnabledDeviceCount() ); - device_id_ = device_id; -} - -inline -int DeviceInfo::deviceID() const -{ - return device_id_; -} - -inline -size_t DeviceInfo::freeMemory() const -{ - size_t _totalMemory = 0, _freeMemory = 0; - queryMemory(_totalMemory, _freeMemory); - return _freeMemory; -} - -inline -size_t DeviceInfo::totalMemory() const -{ - size_t _totalMemory = 0, _freeMemory = 0; - queryMemory(_totalMemory, _freeMemory); - return _totalMemory; -} - -inline -bool DeviceInfo::supports(FeatureSet feature_set) const -{ - int version = majorVersion() * 10 + minorVersion(); - return version >= feature_set; -} - - -}} // namespace cv { namespace cuda { - -//=================================================================================== -// Mat -//=================================================================================== - -namespace cv { - -inline -Mat::Mat(const cuda::GpuMat& m) - : flags(0), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows) -{ - m.download(*this); -} - -} - -//! @endcond - -#endif // OPENCV_CORE_CUDAINL_HPP diff --git a/include/opencv2/core/cuda/block.hpp b/include/opencv2/core/cuda/block.hpp deleted file mode 100755 index c277f0e..0000000 --- a/include/opencv2/core/cuda/block.hpp +++ /dev/null @@ -1,211 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_DEVICE_BLOCK_HPP -#define OPENCV_CUDA_DEVICE_BLOCK_HPP - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - struct Block - { - static __device__ __forceinline__ unsigned int id() - { - return blockIdx.x; - } - - static __device__ __forceinline__ unsigned int stride() - { - return blockDim.x * blockDim.y * blockDim.z; - } - - static __device__ __forceinline__ void sync() - { - __syncthreads(); - } - - static __device__ __forceinline__ int flattenedThreadId() - { - return threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x; - } - - template - static __device__ __forceinline__ void fill(It beg, It end, const T& value) - { - int STRIDE = stride(); - It t = beg + flattenedThreadId(); - - for(; t < end; t += STRIDE) - *t = value; - } - - template - static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value) - { - int STRIDE = stride(); - int tid = flattenedThreadId(); - value += tid; - - for(OutIt t = beg + tid; t < end; t += STRIDE, value += STRIDE) - *t = value; - } - - template - static __device__ __forceinline__ void copy(InIt beg, InIt end, OutIt out) - { - int STRIDE = stride(); - InIt t = beg + flattenedThreadId(); - OutIt o = out + (t - beg); - - for(; t < end; t += STRIDE, o += STRIDE) - *o = *t; - } - - template - static __device__ __forceinline__ void transform(InIt beg, InIt end, OutIt out, UnOp op) - { - int STRIDE = stride(); - InIt t = beg + flattenedThreadId(); - OutIt o = out + (t - beg); - - for(; t < end; t += STRIDE, o += STRIDE) - *o = op(*t); - } - - template - static __device__ __forceinline__ void transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op) - { - int STRIDE = stride(); - InIt1 t1 = beg1 + flattenedThreadId(); - InIt2 t2 = beg2 + flattenedThreadId(); - OutIt o = out + (t1 - beg1); - - for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, o += STRIDE) - *o = op(*t1, *t2); - } - - template - static __device__ __forceinline__ void reduce(volatile T* buffer, BinOp op) - { - int tid = flattenedThreadId(); - T val = buffer[tid]; - - if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); } - if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); } - if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); } - if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); } - - if (tid < 32) - { - if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); } - if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); } - if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); } - if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); } - if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); } - if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); } - } - } - - template - static __device__ __forceinline__ T reduce(volatile T* buffer, T init, BinOp op) - { - int tid = flattenedThreadId(); - T val = buffer[tid] = init; - __syncthreads(); - - if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); } - if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); } - if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); } - if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); } - - if (tid < 32) - { - if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); } - if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); } - if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); } - if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); } - if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); } - if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); } - } - __syncthreads(); - return buffer[0]; - } - - template - static __device__ __forceinline__ void reduce_n(T* data, unsigned int n, BinOp op) - { - int ftid = flattenedThreadId(); - int sft = stride(); - - if (sft < n) - { - for (unsigned int i = sft + ftid; i < n; i += sft) - data[ftid] = op(data[ftid], data[i]); - - __syncthreads(); - - n = sft; - } - - while (n > 1) - { - unsigned int half = n/2; - - if (ftid < half) - data[ftid] = op(data[ftid], data[n - ftid - 1]); - - __syncthreads(); - - n = n - half; - } - } - }; -}}} - -//! @endcond - -#endif /* OPENCV_CUDA_DEVICE_BLOCK_HPP */ diff --git a/include/opencv2/core/cuda/border_interpolate.hpp b/include/opencv2/core/cuda/border_interpolate.hpp deleted file mode 100755 index 874f705..0000000 --- a/include/opencv2/core/cuda/border_interpolate.hpp +++ /dev/null @@ -1,722 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_BORDER_INTERPOLATE_HPP -#define OPENCV_CUDA_BORDER_INTERPOLATE_HPP - -#include "saturate_cast.hpp" -#include "vec_traits.hpp" -#include "vec_math.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - ////////////////////////////////////////////////////////////// - // BrdConstant - - template struct BrdRowConstant - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdRowConstant(int width_, const D& val_ = VecTraits::all(0)) : width(width_), val(val_) {} - - template __device__ __forceinline__ D at_low(int x, const T* data) const - { - return x >= 0 ? saturate_cast(data[x]) : val; - } - - template __device__ __forceinline__ D at_high(int x, const T* data) const - { - return x < width ? saturate_cast(data[x]) : val; - } - - template __device__ __forceinline__ D at(int x, const T* data) const - { - return (x >= 0 && x < width) ? saturate_cast(data[x]) : val; - } - - int width; - D val; - }; - - template struct BrdColConstant - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdColConstant(int height_, const D& val_ = VecTraits::all(0)) : height(height_), val(val_) {} - - template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const - { - return y >= 0 ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; - } - - template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const - { - return y < height ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; - } - - template __device__ __forceinline__ D at(int y, const T* data, size_t step) const - { - return (y >= 0 && y < height) ? saturate_cast(*(const T*)((const char*)data + y * step)) : val; - } - - int height; - D val; - }; - - template struct BrdConstant - { - typedef D result_type; - - __host__ __device__ __forceinline__ BrdConstant(int height_, int width_, const D& val_ = VecTraits::all(0)) : height(height_), width(width_), val(val_) - { - } - - template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const - { - return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(((const T*)((const uchar*)data + y * step))[x]) : val; - } - - template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const - { - return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(src(y, x)) : val; - } - - int height; - int width; - D val; - }; - - ////////////////////////////////////////////////////////////// - // BrdReplicate - - template struct BrdRowReplicate - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdRowReplicate(int width) : last_col(width - 1) {} - template __host__ __device__ __forceinline__ BrdRowReplicate(int width, U) : last_col(width - 1) {} - - __device__ __forceinline__ int idx_col_low(int x) const - { - return ::max(x, 0); - } - - __device__ __forceinline__ int idx_col_high(int x) const - { - return ::min(x, last_col); - } - - __device__ __forceinline__ int idx_col(int x) const - { - return idx_col_low(idx_col_high(x)); - } - - template __device__ __forceinline__ D at_low(int x, const T* data) const - { - return saturate_cast(data[idx_col_low(x)]); - } - - template __device__ __forceinline__ D at_high(int x, const T* data) const - { - return saturate_cast(data[idx_col_high(x)]); - } - - template __device__ __forceinline__ D at(int x, const T* data) const - { - return saturate_cast(data[idx_col(x)]); - } - - int last_col; - }; - - template struct BrdColReplicate - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdColReplicate(int height) : last_row(height - 1) {} - template __host__ __device__ __forceinline__ BrdColReplicate(int height, U) : last_row(height - 1) {} - - __device__ __forceinline__ int idx_row_low(int y) const - { - return ::max(y, 0); - } - - __device__ __forceinline__ int idx_row_high(int y) const - { - return ::min(y, last_row); - } - - __device__ __forceinline__ int idx_row(int y) const - { - return idx_row_low(idx_row_high(y)); - } - - template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const - { - return saturate_cast(*(const T*)((const char*)data + idx_row_low(y) * step)); - } - - template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const - { - return saturate_cast(*(const T*)((const char*)data + idx_row_high(y) * step)); - } - - template __device__ __forceinline__ D at(int y, const T* data, size_t step) const - { - return saturate_cast(*(const T*)((const char*)data + idx_row(y) * step)); - } - - int last_row; - }; - - template struct BrdReplicate - { - typedef D result_type; - - __host__ __device__ __forceinline__ BrdReplicate(int height, int width) : last_row(height - 1), last_col(width - 1) {} - template __host__ __device__ __forceinline__ BrdReplicate(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} - - __device__ __forceinline__ int idx_row_low(int y) const - { - return ::max(y, 0); - } - - __device__ __forceinline__ int idx_row_high(int y) const - { - return ::min(y, last_row); - } - - __device__ __forceinline__ int idx_row(int y) const - { - return idx_row_low(idx_row_high(y)); - } - - __device__ __forceinline__ int idx_col_low(int x) const - { - return ::max(x, 0); - } - - __device__ __forceinline__ int idx_col_high(int x) const - { - return ::min(x, last_col); - } - - __device__ __forceinline__ int idx_col(int x) const - { - return idx_col_low(idx_col_high(x)); - } - - template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const - { - return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); - } - - template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const - { - return saturate_cast(src(idx_row(y), idx_col(x))); - } - - int last_row; - int last_col; - }; - - ////////////////////////////////////////////////////////////// - // BrdReflect101 - - template struct BrdRowReflect101 - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdRowReflect101(int width) : last_col(width - 1) {} - template __host__ __device__ __forceinline__ BrdRowReflect101(int width, U) : last_col(width - 1) {} - - __device__ __forceinline__ int idx_col_low(int x) const - { - return ::abs(x) % (last_col + 1); - } - - __device__ __forceinline__ int idx_col_high(int x) const - { - return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1); - } - - __device__ __forceinline__ int idx_col(int x) const - { - return idx_col_low(idx_col_high(x)); - } - - template __device__ __forceinline__ D at_low(int x, const T* data) const - { - return saturate_cast(data[idx_col_low(x)]); - } - - template __device__ __forceinline__ D at_high(int x, const T* data) const - { - return saturate_cast(data[idx_col_high(x)]); - } - - template __device__ __forceinline__ D at(int x, const T* data) const - { - return saturate_cast(data[idx_col(x)]); - } - - int last_col; - }; - - template struct BrdColReflect101 - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdColReflect101(int height) : last_row(height - 1) {} - template __host__ __device__ __forceinline__ BrdColReflect101(int height, U) : last_row(height - 1) {} - - __device__ __forceinline__ int idx_row_low(int y) const - { - return ::abs(y) % (last_row + 1); - } - - __device__ __forceinline__ int idx_row_high(int y) const - { - return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1); - } - - __device__ __forceinline__ int idx_row(int y) const - { - return idx_row_low(idx_row_high(y)); - } - - template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); - } - - template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); - } - - template __device__ __forceinline__ D at(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); - } - - int last_row; - }; - - template struct BrdReflect101 - { - typedef D result_type; - - __host__ __device__ __forceinline__ BrdReflect101(int height, int width) : last_row(height - 1), last_col(width - 1) {} - template __host__ __device__ __forceinline__ BrdReflect101(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} - - __device__ __forceinline__ int idx_row_low(int y) const - { - return ::abs(y) % (last_row + 1); - } - - __device__ __forceinline__ int idx_row_high(int y) const - { - return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1); - } - - __device__ __forceinline__ int idx_row(int y) const - { - return idx_row_low(idx_row_high(y)); - } - - __device__ __forceinline__ int idx_col_low(int x) const - { - return ::abs(x) % (last_col + 1); - } - - __device__ __forceinline__ int idx_col_high(int x) const - { - return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1); - } - - __device__ __forceinline__ int idx_col(int x) const - { - return idx_col_low(idx_col_high(x)); - } - - template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const - { - return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); - } - - template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const - { - return saturate_cast(src(idx_row(y), idx_col(x))); - } - - int last_row; - int last_col; - }; - - ////////////////////////////////////////////////////////////// - // BrdReflect - - template struct BrdRowReflect - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdRowReflect(int width) : last_col(width - 1) {} - template __host__ __device__ __forceinline__ BrdRowReflect(int width, U) : last_col(width - 1) {} - - __device__ __forceinline__ int idx_col_low(int x) const - { - return (::abs(x) - (x < 0)) % (last_col + 1); - } - - __device__ __forceinline__ int idx_col_high(int x) const - { - return ::abs(last_col - ::abs(last_col - x) + (x > last_col)) % (last_col + 1); - } - - __device__ __forceinline__ int idx_col(int x) const - { - return idx_col_high(::abs(x) - (x < 0)); - } - - template __device__ __forceinline__ D at_low(int x, const T* data) const - { - return saturate_cast(data[idx_col_low(x)]); - } - - template __device__ __forceinline__ D at_high(int x, const T* data) const - { - return saturate_cast(data[idx_col_high(x)]); - } - - template __device__ __forceinline__ D at(int x, const T* data) const - { - return saturate_cast(data[idx_col(x)]); - } - - int last_col; - }; - - template struct BrdColReflect - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdColReflect(int height) : last_row(height - 1) {} - template __host__ __device__ __forceinline__ BrdColReflect(int height, U) : last_row(height - 1) {} - - __device__ __forceinline__ int idx_row_low(int y) const - { - return (::abs(y) - (y < 0)) % (last_row + 1); - } - - __device__ __forceinline__ int idx_row_high(int y) const - { - return ::abs(last_row - ::abs(last_row - y) + (y > last_row)) % (last_row + 1); - } - - __device__ __forceinline__ int idx_row(int y) const - { - return idx_row_high(::abs(y) - (y < 0)); - } - - template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); - } - - template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); - } - - template __device__ __forceinline__ D at(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); - } - - int last_row; - }; - - template struct BrdReflect - { - typedef D result_type; - - __host__ __device__ __forceinline__ BrdReflect(int height, int width) : last_row(height - 1), last_col(width - 1) {} - template __host__ __device__ __forceinline__ BrdReflect(int height, int width, U) : last_row(height - 1), last_col(width - 1) {} - - __device__ __forceinline__ int idx_row_low(int y) const - { - return (::abs(y) - (y < 0)) % (last_row + 1); - } - - __device__ __forceinline__ int idx_row_high(int y) const - { - return /*::abs*/(last_row - ::abs(last_row - y) + (y > last_row)) /*% (last_row + 1)*/; - } - - __device__ __forceinline__ int idx_row(int y) const - { - return idx_row_low(idx_row_high(y)); - } - - __device__ __forceinline__ int idx_col_low(int x) const - { - return (::abs(x) - (x < 0)) % (last_col + 1); - } - - __device__ __forceinline__ int idx_col_high(int x) const - { - return (last_col - ::abs(last_col - x) + (x > last_col)); - } - - __device__ __forceinline__ int idx_col(int x) const - { - return idx_col_low(idx_col_high(x)); - } - - template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const - { - return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); - } - - template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const - { - return saturate_cast(src(idx_row(y), idx_col(x))); - } - - int last_row; - int last_col; - }; - - ////////////////////////////////////////////////////////////// - // BrdWrap - - template struct BrdRowWrap - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdRowWrap(int width_) : width(width_) {} - template __host__ __device__ __forceinline__ BrdRowWrap(int width_, U) : width(width_) {} - - __device__ __forceinline__ int idx_col_low(int x) const - { - return (x >= 0) * x + (x < 0) * (x - ((x - width + 1) / width) * width); - } - - __device__ __forceinline__ int idx_col_high(int x) const - { - return (x < width) * x + (x >= width) * (x % width); - } - - __device__ __forceinline__ int idx_col(int x) const - { - return idx_col_high(idx_col_low(x)); - } - - template __device__ __forceinline__ D at_low(int x, const T* data) const - { - return saturate_cast(data[idx_col_low(x)]); - } - - template __device__ __forceinline__ D at_high(int x, const T* data) const - { - return saturate_cast(data[idx_col_high(x)]); - } - - template __device__ __forceinline__ D at(int x, const T* data) const - { - return saturate_cast(data[idx_col(x)]); - } - - int width; - }; - - template struct BrdColWrap - { - typedef D result_type; - - explicit __host__ __device__ __forceinline__ BrdColWrap(int height_) : height(height_) {} - template __host__ __device__ __forceinline__ BrdColWrap(int height_, U) : height(height_) {} - - __device__ __forceinline__ int idx_row_low(int y) const - { - return (y >= 0) * y + (y < 0) * (y - ((y - height + 1) / height) * height); - } - - __device__ __forceinline__ int idx_row_high(int y) const - { - return (y < height) * y + (y >= height) * (y % height); - } - - __device__ __forceinline__ int idx_row(int y) const - { - return idx_row_high(idx_row_low(y)); - } - - template __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row_low(y) * step)); - } - - template __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row_high(y) * step)); - } - - template __device__ __forceinline__ D at(int y, const T* data, size_t step) const - { - return saturate_cast(*(const D*)((const char*)data + idx_row(y) * step)); - } - - int height; - }; - - template struct BrdWrap - { - typedef D result_type; - - __host__ __device__ __forceinline__ BrdWrap(int height_, int width_) : - height(height_), width(width_) - { - } - template - __host__ __device__ __forceinline__ BrdWrap(int height_, int width_, U) : - height(height_), width(width_) - { - } - - __device__ __forceinline__ int idx_row_low(int y) const - { - return (y >= 0) ? y : (y - ((y - height + 1) / height) * height); - } - - __device__ __forceinline__ int idx_row_high(int y) const - { - return (y < height) ? y : (y % height); - } - - __device__ __forceinline__ int idx_row(int y) const - { - return idx_row_high(idx_row_low(y)); - } - - __device__ __forceinline__ int idx_col_low(int x) const - { - return (x >= 0) ? x : (x - ((x - width + 1) / width) * width); - } - - __device__ __forceinline__ int idx_col_high(int x) const - { - return (x < width) ? x : (x % width); - } - - __device__ __forceinline__ int idx_col(int x) const - { - return idx_col_high(idx_col_low(x)); - } - - template __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const - { - return saturate_cast(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]); - } - - template __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const - { - return saturate_cast(src(idx_row(y), idx_col(x))); - } - - int height; - int width; - }; - - ////////////////////////////////////////////////////////////// - // BorderReader - - template struct BorderReader - { - typedef typename B::result_type elem_type; - typedef typename Ptr2D::index_type index_type; - - __host__ __device__ __forceinline__ BorderReader(const Ptr2D& ptr_, const B& b_) : ptr(ptr_), b(b_) {} - - __device__ __forceinline__ elem_type operator ()(index_type y, index_type x) const - { - return b.at(y, x, ptr); - } - - Ptr2D ptr; - B b; - }; - - // under win32 there is some bug with templated types that passed as kernel parameters - // with this specialization all works fine - template struct BorderReader< Ptr2D, BrdConstant > - { - typedef typename BrdConstant::result_type elem_type; - typedef typename Ptr2D::index_type index_type; - - __host__ __device__ __forceinline__ BorderReader(const Ptr2D& src_, const BrdConstant& b) : - src(src_), height(b.height), width(b.width), val(b.val) - { - } - - __device__ __forceinline__ D operator ()(index_type y, index_type x) const - { - return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast(src(y, x)) : val; - } - - Ptr2D src; - int height; - int width; - D val; - }; -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // OPENCV_CUDA_BORDER_INTERPOLATE_HPP diff --git a/include/opencv2/core/cuda/color.hpp b/include/opencv2/core/cuda/color.hpp deleted file mode 100755 index dcce280..0000000 --- a/include/opencv2/core/cuda/color.hpp +++ /dev/null @@ -1,309 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_COLOR_HPP -#define OPENCV_CUDA_COLOR_HPP - -#include "detail/color_detail.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - // All OPENCV_CUDA_IMPLEMENT_*_TRAITS(ColorSpace1_to_ColorSpace2, ...) macros implements - // template class ColorSpace1_to_ColorSpace2_traits - // { - // typedef ... functor_type; - // static __host__ __device__ functor_type create_functor(); - // }; - - OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgb, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_bgra, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgba, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_bgr, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgb, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgba, 4, 4, 2) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr555, 3, 0, 5) - OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr565, 3, 0, 6) - OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr555, 3, 2, 5) - OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr565, 3, 2, 6) - OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr555, 4, 0, 5) - OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr565, 4, 0, 6) - OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr555, 4, 2, 5) - OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr565, 4, 2, 6) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgb, 3, 2, 5) - OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgb, 3, 2, 6) - OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgr, 3, 0, 5) - OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgr, 3, 0, 6) - OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgba, 4, 2, 5) - OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgba, 4, 2, 6) - OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgra, 4, 0, 5) - OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgra, 4, 0, 6) - - #undef OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgr, 3) - OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgra, 4) - - #undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr555, 5) - OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr565, 6) - - #undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr555_to_gray, 5) - OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr565_to_gray, 6) - - #undef OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgb_to_gray, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgr_to_gray, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgba_to_gray, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgra_to_gray, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv4, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv4, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv4, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv4, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS - - OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgb, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgba, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgb, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgba, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgr, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgra, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgr, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgra, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb4, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb4, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb4, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb4, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS - - OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgb, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgba, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgb, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgba, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgr, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgra, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgr, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgra, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz4, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz4, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz4, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz4, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS - - OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgb, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgb, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgba, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgba, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgr, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgr, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgra, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgra, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv4, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv4, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv4, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv4, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS - - OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgb, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgba, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgb, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgba, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgr, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgra, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgr, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgra, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls4, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls4, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls4, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls4, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS - - OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgb, 3, 3, 2) - OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgba, 3, 4, 2) - OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgb, 4, 3, 2) - OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgba, 4, 4, 2) - OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgr, 3, 3, 0) - OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgra, 3, 4, 0) - OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgr, 4, 3, 0) - OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgra, 4, 4, 0) - - #undef OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab, 3, 3, true, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab, 4, 3, true, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab4, 3, 4, true, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab4, 4, 4, true, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab, 3, 3, true, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab, 4, 3, true, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab4, 3, 4, true, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab4, 4, 4, true, 0) - - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab, 3, 3, false, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab, 4, 3, false, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab4, 3, 4, false, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab4, 4, 4, false, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab, 3, 3, false, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab, 4, 3, false, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab4, 3, 4, false, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab4, 4, 4, false, 0) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS - - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgb, 3, 3, true, 2) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgb, 4, 3, true, 2) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgba, 3, 4, true, 2) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgba, 4, 4, true, 2) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgr, 3, 3, true, 0) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgr, 4, 3, true, 0) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgra, 3, 4, true, 0) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgra, 4, 4, true, 0) - - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgb, 3, 3, false, 2) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgb, 4, 3, false, 2) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgba, 3, 4, false, 2) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgba, 4, 4, false, 2) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgr, 3, 3, false, 0) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgr, 4, 3, false, 0) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgra, 3, 4, false, 0) - OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgra, 4, 4, false, 0) - - #undef OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS - - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv, 3, 3, true, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv, 4, 3, true, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv4, 3, 4, true, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv4, 4, 4, true, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv, 3, 3, true, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv, 4, 3, true, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv4, 3, 4, true, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv4, 4, 4, true, 0) - - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv, 3, 3, false, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv, 4, 3, false, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv4, 3, 4, false, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv4, 4, 4, false, 2) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv, 3, 3, false, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv, 4, 3, false, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv4, 3, 4, false, 0) - OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv4, 4, 4, false, 0) - - #undef OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS - - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgb, 3, 3, true, 2) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgb, 4, 3, true, 2) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgba, 3, 4, true, 2) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgba, 4, 4, true, 2) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgr, 3, 3, true, 0) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgr, 4, 3, true, 0) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgra, 3, 4, true, 0) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgra, 4, 4, true, 0) - - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgb, 3, 3, false, 2) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgb, 4, 3, false, 2) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgba, 3, 4, false, 2) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgba, 4, 4, false, 2) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgr, 3, 3, false, 0) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgr, 4, 3, false, 0) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgra, 3, 4, false, 0) - OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgra, 4, 4, false, 0) - - #undef OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // OPENCV_CUDA_COLOR_HPP diff --git a/include/opencv2/core/cuda/common.hpp b/include/opencv2/core/cuda/common.hpp deleted file mode 100755 index 80b2ff0..0000000 --- a/include/opencv2/core/cuda/common.hpp +++ /dev/null @@ -1,123 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_COMMON_HPP -#define OPENCV_CUDA_COMMON_HPP - -#include -#include "opencv2/core/cuda_types.hpp" -#include "opencv2/core/cvdef.h" -#include "opencv2/core/base.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -#ifndef CV_PI_F - #ifndef CV_PI - #define CV_PI_F 3.14159265f - #else - #define CV_PI_F ((float)CV_PI) - #endif -#endif - -namespace cv { namespace cuda { - static inline void checkCudaError(cudaError_t err, const char* file, const int line, const char* func) - { - if (cudaSuccess != err) - cv::error(cv::Error::GpuApiCallError, cudaGetErrorString(err), func, file, line); - } -}} - -#ifndef cudaSafeCall - #define cudaSafeCall(expr) cv::cuda::checkCudaError(expr, __FILE__, __LINE__, CV_Func) -#endif - -namespace cv { namespace cuda -{ - template static inline bool isAligned(const T* ptr, size_t size) - { - return reinterpret_cast(ptr) % size == 0; - } - - static inline bool isAligned(size_t step, size_t size) - { - return step % size == 0; - } -}} - -namespace cv { namespace cuda -{ - namespace device - { - __host__ __device__ __forceinline__ int divUp(int total, int grain) - { - return (total + grain - 1) / grain; - } - - template inline void bindTexture(const textureReference* tex, const PtrStepSz& img) - { - cudaChannelFormatDesc desc = cudaCreateChannelDesc(); - cudaSafeCall( cudaBindTexture2D(0, tex, img.ptr(), &desc, img.cols, img.rows, img.step) ); - } - - template inline void createTextureObjectPitch2D(cudaTextureObject_t* tex, PtrStepSz& img, const cudaTextureDesc& texDesc) - { - cudaResourceDesc resDesc; - memset(&resDesc, 0, sizeof(resDesc)); - resDesc.resType = cudaResourceTypePitch2D; - resDesc.res.pitch2D.devPtr = static_cast(img.ptr()); - resDesc.res.pitch2D.height = img.rows; - resDesc.res.pitch2D.width = img.cols; - resDesc.res.pitch2D.pitchInBytes = img.step; - resDesc.res.pitch2D.desc = cudaCreateChannelDesc(); - - cudaSafeCall( cudaCreateTextureObject(tex, &resDesc, &texDesc, NULL) ); - } - } -}} - -//! @endcond - -#endif // OPENCV_CUDA_COMMON_HPP diff --git a/include/opencv2/core/cuda/datamov_utils.hpp b/include/opencv2/core/cuda/datamov_utils.hpp deleted file mode 100755 index 6820d0f..0000000 --- a/include/opencv2/core/cuda/datamov_utils.hpp +++ /dev/null @@ -1,113 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_DATAMOV_UTILS_HPP -#define OPENCV_CUDA_DATAMOV_UTILS_HPP - -#include "common.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 200 - - // for Fermi memory space is detected automatically - template struct ForceGlob - { - __device__ __forceinline__ static void Load(const T* ptr, int offset, T& val) { val = ptr[offset]; } - }; - - #else // __CUDA_ARCH__ >= 200 - - #if defined(_WIN64) || defined(__LP64__) - // 64-bit register modifier for inlined asm - #define OPENCV_CUDA_ASM_PTR "l" - #else - // 32-bit register modifier for inlined asm - #define OPENCV_CUDA_ASM_PTR "r" - #endif - - template struct ForceGlob; - - #define OPENCV_CUDA_DEFINE_FORCE_GLOB(base_type, ptx_type, reg_mod) \ - template <> struct ForceGlob \ - { \ - __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \ - { \ - asm("ld.global."#ptx_type" %0, [%1];" : "="#reg_mod(val) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \ - } \ - }; - - #define OPENCV_CUDA_DEFINE_FORCE_GLOB_B(base_type, ptx_type) \ - template <> struct ForceGlob \ - { \ - __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \ - { \ - asm("ld.global."#ptx_type" %0, [%1];" : "=r"(*reinterpret_cast(&val)) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \ - } \ - }; - - OPENCV_CUDA_DEFINE_FORCE_GLOB_B(uchar, u8) - OPENCV_CUDA_DEFINE_FORCE_GLOB_B(schar, s8) - OPENCV_CUDA_DEFINE_FORCE_GLOB_B(char, b8) - OPENCV_CUDA_DEFINE_FORCE_GLOB (ushort, u16, h) - OPENCV_CUDA_DEFINE_FORCE_GLOB (short, s16, h) - OPENCV_CUDA_DEFINE_FORCE_GLOB (uint, u32, r) - OPENCV_CUDA_DEFINE_FORCE_GLOB (int, s32, r) - OPENCV_CUDA_DEFINE_FORCE_GLOB (float, f32, f) - OPENCV_CUDA_DEFINE_FORCE_GLOB (double, f64, d) - - #undef OPENCV_CUDA_DEFINE_FORCE_GLOB - #undef OPENCV_CUDA_DEFINE_FORCE_GLOB_B - #undef OPENCV_CUDA_ASM_PTR - - #endif // __CUDA_ARCH__ >= 200 -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // OPENCV_CUDA_DATAMOV_UTILS_HPP diff --git a/include/opencv2/core/cuda/detail/color_detail.hpp b/include/opencv2/core/cuda/detail/color_detail.hpp deleted file mode 100755 index bfb4055..0000000 --- a/include/opencv2/core/cuda/detail/color_detail.hpp +++ /dev/null @@ -1,1980 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_COLOR_DETAIL_HPP -#define OPENCV_CUDA_COLOR_DETAIL_HPP - -#include "../common.hpp" -#include "../vec_traits.hpp" -#include "../saturate_cast.hpp" -#include "../limits.hpp" -#include "../functional.hpp" - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - #ifndef CV_DESCALE - #define CV_DESCALE(x, n) (((x) + (1 << ((n)-1))) >> (n)) - #endif - - namespace color_detail - { - template struct ColorChannel - { - typedef float worktype_f; - static __device__ __forceinline__ T max() { return numeric_limits::max(); } - static __device__ __forceinline__ T half() { return (T)(max()/2 + 1); } - }; - - template<> struct ColorChannel - { - typedef float worktype_f; - static __device__ __forceinline__ float max() { return 1.f; } - static __device__ __forceinline__ float half() { return 0.5f; } - }; - - template static __device__ __forceinline__ void setAlpha(typename TypeVec::vec_type& vec, T val) - { - } - - template static __device__ __forceinline__ void setAlpha(typename TypeVec::vec_type& vec, T val) - { - vec.w = val; - } - - template static __device__ __forceinline__ T getAlpha(const typename TypeVec::vec_type& vec) - { - return ColorChannel::max(); - } - - template static __device__ __forceinline__ T getAlpha(const typename TypeVec::vec_type& vec) - { - return vec.w; - } - - enum - { - yuv_shift = 14, - xyz_shift = 12, - R2Y = 4899, - G2Y = 9617, - B2Y = 1868, - BLOCK_SIZE = 256 - }; - } - -////////////////// Various 3/4-channel to 3/4-channel RGB transformations ///////////////// - - namespace color_detail - { - template struct RGB2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - dst.x = (&src.x)[bidx]; - dst.y = src.y; - dst.z = (&src.x)[bidx^2]; - setAlpha(dst, getAlpha(src)); - - return dst; - } - - __host__ __device__ __forceinline__ RGB2RGB() {} - __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {} - }; - - template <> struct RGB2RGB : unary_function - { - __device__ uint operator()(uint src) const - { - uint dst = 0; - - dst |= (0xffu & (src >> 16)); - dst |= (0xffu & (src >> 8)) << 8; - dst |= (0xffu & (src)) << 16; - dst |= (0xffu & (src >> 24)) << 24; - - return dst; - } - - __host__ __device__ __forceinline__ RGB2RGB() {} - __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -/////////// Transforming 16-bit (565 or 555) RGB to/from 24/32-bit (888[8]) RGB ////////// - - namespace color_detail - { - template struct RGB2RGB5x5Converter; - template struct RGB2RGB5x5Converter<6, bidx> - { - static __device__ __forceinline__ ushort cvt(const uchar3& src) - { - return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~3) << 3) | (((&src.x)[bidx^2] & ~7) << 8)); - } - - static __device__ __forceinline__ ushort cvt(uint src) - { - uint b = 0xffu & (src >> (bidx * 8)); - uint g = 0xffu & (src >> 8); - uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); - return (ushort)((b >> 3) | ((g & ~3) << 3) | ((r & ~7) << 8)); - } - }; - - template struct RGB2RGB5x5Converter<5, bidx> - { - static __device__ __forceinline__ ushort cvt(const uchar3& src) - { - return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~7) << 2) | (((&src.x)[bidx^2] & ~7) << 7)); - } - - static __device__ __forceinline__ ushort cvt(uint src) - { - uint b = 0xffu & (src >> (bidx * 8)); - uint g = 0xffu & (src >> 8); - uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); - uint a = 0xffu & (src >> 24); - return (ushort)((b >> 3) | ((g & ~7) << 2) | ((r & ~7) << 7) | (a * 0x8000)); - } - }; - - template struct RGB2RGB5x5; - - template struct RGB2RGB5x5<3, bidx,green_bits> : unary_function - { - __device__ __forceinline__ ushort operator()(const uchar3& src) const - { - return RGB2RGB5x5Converter::cvt(src); - } - - __host__ __device__ __forceinline__ RGB2RGB5x5() {} - __host__ __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5&) {} - }; - - template struct RGB2RGB5x5<4, bidx,green_bits> : unary_function - { - __device__ __forceinline__ ushort operator()(uint src) const - { - return RGB2RGB5x5Converter::cvt(src); - } - - __host__ __device__ __forceinline__ RGB2RGB5x5() {} - __host__ __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(name, scn, bidx, green_bits) \ - struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2RGB5x5 functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - template struct RGB5x52RGBConverter; - - template struct RGB5x52RGBConverter<5, bidx> - { - static __device__ __forceinline__ void cvt(uint src, uchar3& dst) - { - (&dst.x)[bidx] = src << 3; - dst.y = (src >> 2) & ~7; - (&dst.x)[bidx ^ 2] = (src >> 7) & ~7; - } - - static __device__ __forceinline__ void cvt(uint src, uint& dst) - { - dst = 0; - - dst |= (0xffu & (src << 3)) << (bidx * 8); - dst |= (0xffu & ((src >> 2) & ~7)) << 8; - dst |= (0xffu & ((src >> 7) & ~7)) << ((bidx ^ 2) * 8); - dst |= ((src & 0x8000) * 0xffu) << 24; - } - }; - - template struct RGB5x52RGBConverter<6, bidx> - { - static __device__ __forceinline__ void cvt(uint src, uchar3& dst) - { - (&dst.x)[bidx] = src << 3; - dst.y = (src >> 3) & ~3; - (&dst.x)[bidx ^ 2] = (src >> 8) & ~7; - } - - static __device__ __forceinline__ void cvt(uint src, uint& dst) - { - dst = 0xffu << 24; - - dst |= (0xffu & (src << 3)) << (bidx * 8); - dst |= (0xffu &((src >> 3) & ~3)) << 8; - dst |= (0xffu & ((src >> 8) & ~7)) << ((bidx ^ 2) * 8); - } - }; - - template struct RGB5x52RGB; - - template struct RGB5x52RGB<3, bidx, green_bits> : unary_function - { - __device__ __forceinline__ uchar3 operator()(ushort src) const - { - uchar3 dst; - RGB5x52RGBConverter::cvt(src, dst); - return dst; - } - __host__ __device__ __forceinline__ RGB5x52RGB() {} - __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {} - - }; - - template struct RGB5x52RGB<4, bidx, green_bits> : unary_function - { - __device__ __forceinline__ uint operator()(ushort src) const - { - uint dst; - RGB5x52RGBConverter::cvt(src, dst); - return dst; - } - __host__ __device__ __forceinline__ RGB5x52RGB() {} - __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(name, dcn, bidx, green_bits) \ - struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB5x52RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -///////////////////////////////// Grayscale to Color //////////////////////////////// - - namespace color_detail - { - template struct Gray2RGB : unary_function::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator()(T src) const - { - typename TypeVec::vec_type dst; - - dst.z = dst.y = dst.x = src; - setAlpha(dst, ColorChannel::max()); - - return dst; - } - __host__ __device__ __forceinline__ Gray2RGB() {} - __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {} - }; - - template <> struct Gray2RGB : unary_function - { - __device__ __forceinline__ uint operator()(uint src) const - { - uint dst = 0xffu << 24; - - dst |= src; - dst |= src << 8; - dst |= src << 16; - - return dst; - } - __host__ __device__ __forceinline__ Gray2RGB() {} - __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(name, dcn) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::Gray2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - template struct Gray2RGB5x5Converter; - template<> struct Gray2RGB5x5Converter<6> - { - static __device__ __forceinline__ ushort cvt(uint t) - { - return (ushort)((t >> 3) | ((t & ~3) << 3) | ((t & ~7) << 8)); - } - }; - - template<> struct Gray2RGB5x5Converter<5> - { - static __device__ __forceinline__ ushort cvt(uint t) - { - t >>= 3; - return (ushort)(t | (t << 5) | (t << 10)); - } - }; - - template struct Gray2RGB5x5 : unary_function - { - __device__ __forceinline__ ushort operator()(uint src) const - { - return Gray2RGB5x5Converter::cvt(src); - } - - __host__ __device__ __forceinline__ Gray2RGB5x5() {} - __host__ __device__ __forceinline__ Gray2RGB5x5(const Gray2RGB5x5&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(name, green_bits) \ - struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::Gray2RGB5x5 functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -///////////////////////////////// Color to Grayscale //////////////////////////////// - - namespace color_detail - { - template struct RGB5x52GrayConverter; - template <> struct RGB5x52GrayConverter<6> - { - static __device__ __forceinline__ uchar cvt(uint t) - { - return (uchar)CV_DESCALE(((t << 3) & 0xf8) * B2Y + ((t >> 3) & 0xfc) * G2Y + ((t >> 8) & 0xf8) * R2Y, yuv_shift); - } - }; - - template <> struct RGB5x52GrayConverter<5> - { - static __device__ __forceinline__ uchar cvt(uint t) - { - return (uchar)CV_DESCALE(((t << 3) & 0xf8) * B2Y + ((t >> 2) & 0xf8) * G2Y + ((t >> 7) & 0xf8) * R2Y, yuv_shift); - } - }; - - template struct RGB5x52Gray : unary_function - { - __device__ __forceinline__ uchar operator()(uint src) const - { - return RGB5x52GrayConverter::cvt(src); - } - __host__ __device__ __forceinline__ RGB5x52Gray() {} - __host__ __device__ __forceinline__ RGB5x52Gray(const RGB5x52Gray&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(name, green_bits) \ - struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB5x52Gray functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - template static __device__ __forceinline__ T RGB2GrayConvert(const T* src) - { - return (T)CV_DESCALE((unsigned)(src[bidx] * B2Y + src[1] * G2Y + src[bidx^2] * R2Y), yuv_shift); - } - - template static __device__ __forceinline__ uchar RGB2GrayConvert(uint src) - { - uint b = 0xffu & (src >> (bidx * 8)); - uint g = 0xffu & (src >> 8); - uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); - return CV_DESCALE((uint)(b * B2Y + g * G2Y + r * R2Y), yuv_shift); - } - - template static __device__ __forceinline__ float RGB2GrayConvert(const float* src) - { - return src[bidx] * 0.114f + src[1] * 0.587f + src[bidx^2] * 0.299f; - } - - template struct RGB2Gray : unary_function::vec_type, T> - { - __device__ __forceinline__ T operator()(const typename TypeVec::vec_type& src) const - { - return RGB2GrayConvert(&src.x); - } - __host__ __device__ __forceinline__ RGB2Gray() {} - __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {} - }; - - template struct RGB2Gray : unary_function - { - __device__ __forceinline__ uchar operator()(uint src) const - { - return RGB2GrayConvert(src); - } - __host__ __device__ __forceinline__ RGB2Gray() {} - __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(name, scn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2Gray functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -///////////////////////////////////// RGB <-> YUV ////////////////////////////////////// - - namespace color_detail - { - __constant__ float c_RGB2YUVCoeffs_f[5] = { 0.114f, 0.587f, 0.299f, 0.492f, 0.877f }; - __constant__ int c_RGB2YUVCoeffs_i[5] = { B2Y, G2Y, R2Y, 8061, 14369 }; - - template static __device__ void RGB2YUVConvert(const T* src, D& dst) - { - const int delta = ColorChannel::half() * (1 << yuv_shift); - - const int Y = CV_DESCALE(src[0] * c_RGB2YUVCoeffs_i[bidx^2] + src[1] * c_RGB2YUVCoeffs_i[1] + src[2] * c_RGB2YUVCoeffs_i[bidx], yuv_shift); - const int Cr = CV_DESCALE((src[bidx^2] - Y) * c_RGB2YUVCoeffs_i[3] + delta, yuv_shift); - const int Cb = CV_DESCALE((src[bidx] - Y) * c_RGB2YUVCoeffs_i[4] + delta, yuv_shift); - - dst.x = saturate_cast(Y); - dst.y = saturate_cast(Cr); - dst.z = saturate_cast(Cb); - } - - template static __device__ __forceinline__ void RGB2YUVConvert(const float* src, D& dst) - { - dst.x = src[0] * c_RGB2YUVCoeffs_f[bidx^2] + src[1] * c_RGB2YUVCoeffs_f[1] + src[2] * c_RGB2YUVCoeffs_f[bidx]; - dst.y = (src[bidx^2] - dst.x) * c_RGB2YUVCoeffs_f[3] + ColorChannel::half(); - dst.z = (src[bidx] - dst.x) * c_RGB2YUVCoeffs_f[4] + ColorChannel::half(); - } - - template struct RGB2YUV - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - RGB2YUVConvert(&src.x, dst); - return dst; - } - __host__ __device__ __forceinline__ RGB2YUV() {} - __host__ __device__ __forceinline__ RGB2YUV(const RGB2YUV&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2YUV functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - __constant__ float c_YUV2RGBCoeffs_f[5] = { 2.032f, -0.395f, -0.581f, 1.140f }; - __constant__ int c_YUV2RGBCoeffs_i[5] = { 33292, -6472, -9519, 18678 }; - - template static __device__ void YUV2RGBConvert(const T& src, D* dst) - { - const int b = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift); - - const int g = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[2] - + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift); - - const int r = src.x + CV_DESCALE((src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift); - - dst[bidx] = saturate_cast(b); - dst[1] = saturate_cast(g); - dst[bidx^2] = saturate_cast(r); - } - - template static __device__ uint YUV2RGBConvert(uint src) - { - const int x = 0xff & (src); - const int y = 0xff & (src >> 8); - const int z = 0xff & (src >> 16); - - const int b = x + CV_DESCALE((z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift); - - const int g = x + CV_DESCALE((z - ColorChannel::half()) * c_YUV2RGBCoeffs_i[2] - + (y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift); - - const int r = x + CV_DESCALE((y - ColorChannel::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift); - - uint dst = 0xffu << 24; - - dst |= saturate_cast(b) << (bidx * 8); - dst |= saturate_cast(g) << 8; - dst |= saturate_cast(r) << ((bidx ^ 2) * 8); - - return dst; - } - - template static __device__ __forceinline__ void YUV2RGBConvert(const T& src, float* dst) - { - dst[bidx] = src.x + (src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_f[3]; - - dst[1] = src.x + (src.z - ColorChannel::half()) * c_YUV2RGBCoeffs_f[2] - + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_f[1]; - - dst[bidx^2] = src.x + (src.y - ColorChannel::half()) * c_YUV2RGBCoeffs_f[0]; - } - - template struct YUV2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - YUV2RGBConvert(src, &dst.x); - setAlpha(dst, ColorChannel::max()); - - return dst; - } - __host__ __device__ __forceinline__ YUV2RGB() {} - __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {} - }; - - template struct YUV2RGB : unary_function - { - __device__ __forceinline__ uint operator ()(uint src) const - { - return YUV2RGBConvert(src); - } - __host__ __device__ __forceinline__ YUV2RGB() {} - __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::YUV2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -///////////////////////////////////// RGB <-> YCrCb ////////////////////////////////////// - - namespace color_detail - { - __constant__ float c_RGB2YCrCbCoeffs_f[5] = {0.299f, 0.587f, 0.114f, 0.713f, 0.564f}; - __constant__ int c_RGB2YCrCbCoeffs_i[5] = {R2Y, G2Y, B2Y, 11682, 9241}; - - template static __device__ void RGB2YCrCbConvert(const T* src, D& dst) - { - const int delta = ColorChannel::half() * (1 << yuv_shift); - - const int Y = CV_DESCALE(src[0] * c_RGB2YCrCbCoeffs_i[bidx^2] + src[1] * c_RGB2YCrCbCoeffs_i[1] + src[2] * c_RGB2YCrCbCoeffs_i[bidx], yuv_shift); - const int Cr = CV_DESCALE((src[bidx^2] - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift); - const int Cb = CV_DESCALE((src[bidx] - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift); - - dst.x = saturate_cast(Y); - dst.y = saturate_cast(Cr); - dst.z = saturate_cast(Cb); - } - - template static __device__ uint RGB2YCrCbConvert(uint src) - { - const int delta = ColorChannel::half() * (1 << yuv_shift); - - const int Y = CV_DESCALE((0xffu & src) * c_RGB2YCrCbCoeffs_i[bidx^2] + (0xffu & (src >> 8)) * c_RGB2YCrCbCoeffs_i[1] + (0xffu & (src >> 16)) * c_RGB2YCrCbCoeffs_i[bidx], yuv_shift); - const int Cr = CV_DESCALE(((0xffu & (src >> ((bidx ^ 2) * 8))) - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift); - const int Cb = CV_DESCALE(((0xffu & (src >> (bidx * 8))) - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift); - - uint dst = 0; - - dst |= saturate_cast(Y); - dst |= saturate_cast(Cr) << 8; - dst |= saturate_cast(Cb) << 16; - - return dst; - } - - template static __device__ __forceinline__ void RGB2YCrCbConvert(const float* src, D& dst) - { - dst.x = src[0] * c_RGB2YCrCbCoeffs_f[bidx^2] + src[1] * c_RGB2YCrCbCoeffs_f[1] + src[2] * c_RGB2YCrCbCoeffs_f[bidx]; - dst.y = (src[bidx^2] - dst.x) * c_RGB2YCrCbCoeffs_f[3] + ColorChannel::half(); - dst.z = (src[bidx] - dst.x) * c_RGB2YCrCbCoeffs_f[4] + ColorChannel::half(); - } - - template struct RGB2YCrCb - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - RGB2YCrCbConvert(&src.x, dst); - return dst; - } - __host__ __device__ __forceinline__ RGB2YCrCb() {} - __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {} - }; - - template struct RGB2YCrCb : unary_function - { - __device__ __forceinline__ uint operator ()(uint src) const - { - return RGB2YCrCbConvert(src); - } - - __host__ __device__ __forceinline__ RGB2YCrCb() {} - __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2YCrCb functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - __constant__ float c_YCrCb2RGBCoeffs_f[5] = {1.403f, -0.714f, -0.344f, 1.773f}; - __constant__ int c_YCrCb2RGBCoeffs_i[5] = {22987, -11698, -5636, 29049}; - - template static __device__ void YCrCb2RGBConvert(const T& src, D* dst) - { - const int b = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift); - const int g = src.x + CV_DESCALE((src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[2] + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift); - const int r = src.x + CV_DESCALE((src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift); - - dst[bidx] = saturate_cast(b); - dst[1] = saturate_cast(g); - dst[bidx^2] = saturate_cast(r); - } - - template static __device__ uint YCrCb2RGBConvert(uint src) - { - const int x = 0xff & (src); - const int y = 0xff & (src >> 8); - const int z = 0xff & (src >> 16); - - const int b = x + CV_DESCALE((z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift); - const int g = x + CV_DESCALE((z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[2] + (y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift); - const int r = x + CV_DESCALE((y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift); - - uint dst = 0xffu << 24; - - dst |= saturate_cast(b) << (bidx * 8); - dst |= saturate_cast(g) << 8; - dst |= saturate_cast(r) << ((bidx ^ 2) * 8); - - return dst; - } - - template __device__ __forceinline__ void YCrCb2RGBConvert(const T& src, float* dst) - { - dst[bidx] = src.x + (src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[3]; - dst[1] = src.x + (src.z - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[2] + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[1]; - dst[bidx^2] = src.x + (src.y - ColorChannel::half()) * c_YCrCb2RGBCoeffs_f[0]; - } - - template struct YCrCb2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - YCrCb2RGBConvert(src, &dst.x); - setAlpha(dst, ColorChannel::max()); - - return dst; - } - __host__ __device__ __forceinline__ YCrCb2RGB() {} - __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {} - }; - - template struct YCrCb2RGB : unary_function - { - __device__ __forceinline__ uint operator ()(uint src) const - { - return YCrCb2RGBConvert(src); - } - __host__ __device__ __forceinline__ YCrCb2RGB() {} - __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::YCrCb2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -////////////////////////////////////// RGB <-> XYZ /////////////////////////////////////// - - namespace color_detail - { - __constant__ float c_RGB2XYZ_D65f[9] = { 0.412453f, 0.357580f, 0.180423f, 0.212671f, 0.715160f, 0.072169f, 0.019334f, 0.119193f, 0.950227f }; - __constant__ int c_RGB2XYZ_D65i[9] = { 1689, 1465, 739, 871, 2929, 296, 79, 488, 3892 }; - - template static __device__ __forceinline__ void RGB2XYZConvert(const T* src, D& dst) - { - dst.z = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[6] + src[1] * c_RGB2XYZ_D65i[7] + src[bidx] * c_RGB2XYZ_D65i[8], xyz_shift)); - dst.x = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[0] + src[1] * c_RGB2XYZ_D65i[1] + src[bidx] * c_RGB2XYZ_D65i[2], xyz_shift)); - dst.y = saturate_cast(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[3] + src[1] * c_RGB2XYZ_D65i[4] + src[bidx] * c_RGB2XYZ_D65i[5], xyz_shift)); - } - - template static __device__ __forceinline__ uint RGB2XYZConvert(uint src) - { - const uint b = 0xffu & (src >> (bidx * 8)); - const uint g = 0xffu & (src >> 8); - const uint r = 0xffu & (src >> ((bidx ^ 2) * 8)); - - const uint x = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[0] + g * c_RGB2XYZ_D65i[1] + b * c_RGB2XYZ_D65i[2], xyz_shift)); - const uint y = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[3] + g * c_RGB2XYZ_D65i[4] + b * c_RGB2XYZ_D65i[5], xyz_shift)); - const uint z = saturate_cast(CV_DESCALE(r * c_RGB2XYZ_D65i[6] + g * c_RGB2XYZ_D65i[7] + b * c_RGB2XYZ_D65i[8], xyz_shift)); - - uint dst = 0; - - dst |= x; - dst |= y << 8; - dst |= z << 16; - - return dst; - } - - template static __device__ __forceinline__ void RGB2XYZConvert(const float* src, D& dst) - { - dst.x = src[bidx^2] * c_RGB2XYZ_D65f[0] + src[1] * c_RGB2XYZ_D65f[1] + src[bidx] * c_RGB2XYZ_D65f[2]; - dst.y = src[bidx^2] * c_RGB2XYZ_D65f[3] + src[1] * c_RGB2XYZ_D65f[4] + src[bidx] * c_RGB2XYZ_D65f[5]; - dst.z = src[bidx^2] * c_RGB2XYZ_D65f[6] + src[1] * c_RGB2XYZ_D65f[7] + src[bidx] * c_RGB2XYZ_D65f[8]; - } - - template struct RGB2XYZ - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - RGB2XYZConvert(&src.x, dst); - - return dst; - } - __host__ __device__ __forceinline__ RGB2XYZ() {} - __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {} - }; - - template struct RGB2XYZ : unary_function - { - __device__ __forceinline__ uint operator()(uint src) const - { - return RGB2XYZConvert(src); - } - __host__ __device__ __forceinline__ RGB2XYZ() {} - __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2XYZ functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - __constant__ float c_XYZ2sRGB_D65f[9] = { 3.240479f, -1.53715f, -0.498535f, -0.969256f, 1.875991f, 0.041556f, 0.055648f, -0.204043f, 1.057311f }; - __constant__ int c_XYZ2sRGB_D65i[9] = { 13273, -6296, -2042, -3970, 7684, 170, 228, -836, 4331 }; - - template static __device__ __forceinline__ void XYZ2RGBConvert(const T& src, D* dst) - { - dst[bidx^2] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[0] + src.y * c_XYZ2sRGB_D65i[1] + src.z * c_XYZ2sRGB_D65i[2], xyz_shift)); - dst[1] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[3] + src.y * c_XYZ2sRGB_D65i[4] + src.z * c_XYZ2sRGB_D65i[5], xyz_shift)); - dst[bidx] = saturate_cast(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[6] + src.y * c_XYZ2sRGB_D65i[7] + src.z * c_XYZ2sRGB_D65i[8], xyz_shift)); - } - - template static __device__ __forceinline__ uint XYZ2RGBConvert(uint src) - { - const int x = 0xff & src; - const int y = 0xff & (src >> 8); - const int z = 0xff & (src >> 16); - - const uint r = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[0] + y * c_XYZ2sRGB_D65i[1] + z * c_XYZ2sRGB_D65i[2], xyz_shift)); - const uint g = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[3] + y * c_XYZ2sRGB_D65i[4] + z * c_XYZ2sRGB_D65i[5], xyz_shift)); - const uint b = saturate_cast(CV_DESCALE(x * c_XYZ2sRGB_D65i[6] + y * c_XYZ2sRGB_D65i[7] + z * c_XYZ2sRGB_D65i[8], xyz_shift)); - - uint dst = 0xffu << 24; - - dst |= b << (bidx * 8); - dst |= g << 8; - dst |= r << ((bidx ^ 2) * 8); - - return dst; - } - - template static __device__ __forceinline__ void XYZ2RGBConvert(const T& src, float* dst) - { - dst[bidx^2] = src.x * c_XYZ2sRGB_D65f[0] + src.y * c_XYZ2sRGB_D65f[1] + src.z * c_XYZ2sRGB_D65f[2]; - dst[1] = src.x * c_XYZ2sRGB_D65f[3] + src.y * c_XYZ2sRGB_D65f[4] + src.z * c_XYZ2sRGB_D65f[5]; - dst[bidx] = src.x * c_XYZ2sRGB_D65f[6] + src.y * c_XYZ2sRGB_D65f[7] + src.z * c_XYZ2sRGB_D65f[8]; - } - - template struct XYZ2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - XYZ2RGBConvert(src, &dst.x); - setAlpha(dst, ColorChannel::max()); - - return dst; - } - __host__ __device__ __forceinline__ XYZ2RGB() {} - __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {} - }; - - template struct XYZ2RGB : unary_function - { - __device__ __forceinline__ uint operator()(uint src) const - { - return XYZ2RGBConvert(src); - } - __host__ __device__ __forceinline__ XYZ2RGB() {} - __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::XYZ2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -////////////////////////////////////// RGB <-> HSV /////////////////////////////////////// - - namespace color_detail - { - __constant__ int c_HsvDivTable [256] = {0, 1044480, 522240, 348160, 261120, 208896, 174080, 149211, 130560, 116053, 104448, 94953, 87040, 80345, 74606, 69632, 65280, 61440, 58027, 54973, 52224, 49737, 47476, 45412, 43520, 41779, 40172, 38684, 37303, 36017, 34816, 33693, 32640, 31651, 30720, 29842, 29013, 28229, 27486, 26782, 26112, 25475, 24869, 24290, 23738, 23211, 22706, 22223, 21760, 21316, 20890, 20480, 20086, 19707, 19342, 18991, 18651, 18324, 18008, 17703, 17408, 17123, 16846, 16579, 16320, 16069, 15825, 15589, 15360, 15137, 14921, 14711, 14507, 14308, 14115, 13926, 13743, 13565, 13391, 13221, 13056, 12895, 12738, 12584, 12434, 12288, 12145, 12006, 11869, 11736, 11605, 11478, 11353, 11231, 11111, 10995, 10880, 10768, 10658, 10550, 10445, 10341, 10240, 10141, 10043, 9947, 9854, 9761, 9671, 9582, 9495, 9410, 9326, 9243, 9162, 9082, 9004, 8927, 8852, 8777, 8704, 8632, 8561, 8492, 8423, 8356, 8290, 8224, 8160, 8097, 8034, 7973, 7913, 7853, 7795, 7737, 7680, 7624, 7569, 7514, 7461, 7408, 7355, 7304, 7253, 7203, 7154, 7105, 7057, 7010, 6963, 6917, 6872, 6827, 6782, 6739, 6695, 6653, 6611, 6569, 6528, 6487, 6447, 6408, 6369, 6330, 6292, 6254, 6217, 6180, 6144, 6108, 6073, 6037, 6003, 5968, 5935, 5901, 5868, 5835, 5803, 5771, 5739, 5708, 5677, 5646, 5615, 5585, 5556, 5526, 5497, 5468, 5440, 5412, 5384, 5356, 5329, 5302, 5275, 5249, 5222, 5196, 5171, 5145, 5120, 5095, 5070, 5046, 5022, 4998, 4974, 4950, 4927, 4904, 4881, 4858, 4836, 4813, 4791, 4769, 4748, 4726, 4705, 4684, 4663, 4642, 4622, 4601, 4581, 4561, 4541, 4522, 4502, 4483, 4464, 4445, 4426, 4407, 4389, 4370, 4352, 4334, 4316, 4298, 4281, 4263, 4246, 4229, 4212, 4195, 4178, 4161, 4145, 4128, 4112, 4096}; - __constant__ int c_HsvDivTable180[256] = {0, 122880, 61440, 40960, 30720, 24576, 20480, 17554, 15360, 13653, 12288, 11171, 10240, 9452, 8777, 8192, 7680, 7228, 6827, 6467, 6144, 5851, 5585, 5343, 5120, 4915, 4726, 4551, 4389, 4237, 4096, 3964, 3840, 3724, 3614, 3511, 3413, 3321, 3234, 3151, 3072, 2997, 2926, 2858, 2793, 2731, 2671, 2614, 2560, 2508, 2458, 2409, 2363, 2318, 2276, 2234, 2194, 2156, 2119, 2083, 2048, 2014, 1982, 1950, 1920, 1890, 1862, 1834, 1807, 1781, 1755, 1731, 1707, 1683, 1661, 1638, 1617, 1596, 1575, 1555, 1536, 1517, 1499, 1480, 1463, 1446, 1429, 1412, 1396, 1381, 1365, 1350, 1336, 1321, 1307, 1293, 1280, 1267, 1254, 1241, 1229, 1217, 1205, 1193, 1182, 1170, 1159, 1148, 1138, 1127, 1117, 1107, 1097, 1087, 1078, 1069, 1059, 1050, 1041, 1033, 1024, 1016, 1007, 999, 991, 983, 975, 968, 960, 953, 945, 938, 931, 924, 917, 910, 904, 897, 890, 884, 878, 871, 865, 859, 853, 847, 842, 836, 830, 825, 819, 814, 808, 803, 798, 793, 788, 783, 778, 773, 768, 763, 759, 754, 749, 745, 740, 736, 731, 727, 723, 719, 714, 710, 706, 702, 698, 694, 690, 686, 683, 679, 675, 671, 668, 664, 661, 657, 654, 650, 647, 643, 640, 637, 633, 630, 627, 624, 621, 617, 614, 611, 608, 605, 602, 599, 597, 594, 591, 588, 585, 582, 580, 577, 574, 572, 569, 566, 564, 561, 559, 556, 554, 551, 549, 546, 544, 541, 539, 537, 534, 532, 530, 527, 525, 523, 521, 518, 516, 514, 512, 510, 508, 506, 504, 502, 500, 497, 495, 493, 492, 490, 488, 486, 484, 482}; - __constant__ int c_HsvDivTable256[256] = {0, 174763, 87381, 58254, 43691, 34953, 29127, 24966, 21845, 19418, 17476, 15888, 14564, 13443, 12483, 11651, 10923, 10280, 9709, 9198, 8738, 8322, 7944, 7598, 7282, 6991, 6722, 6473, 6242, 6026, 5825, 5638, 5461, 5296, 5140, 4993, 4855, 4723, 4599, 4481, 4369, 4263, 4161, 4064, 3972, 3884, 3799, 3718, 3641, 3567, 3495, 3427, 3361, 3297, 3236, 3178, 3121, 3066, 3013, 2962, 2913, 2865, 2819, 2774, 2731, 2689, 2648, 2608, 2570, 2533, 2497, 2461, 2427, 2394, 2362, 2330, 2300, 2270, 2241, 2212, 2185, 2158, 2131, 2106, 2081, 2056, 2032, 2009, 1986, 1964, 1942, 1920, 1900, 1879, 1859, 1840, 1820, 1802, 1783, 1765, 1748, 1730, 1713, 1697, 1680, 1664, 1649, 1633, 1618, 1603, 1589, 1574, 1560, 1547, 1533, 1520, 1507, 1494, 1481, 1469, 1456, 1444, 1432, 1421, 1409, 1398, 1387, 1376, 1365, 1355, 1344, 1334, 1324, 1314, 1304, 1295, 1285, 1276, 1266, 1257, 1248, 1239, 1231, 1222, 1214, 1205, 1197, 1189, 1181, 1173, 1165, 1157, 1150, 1142, 1135, 1128, 1120, 1113, 1106, 1099, 1092, 1085, 1079, 1072, 1066, 1059, 1053, 1046, 1040, 1034, 1028, 1022, 1016, 1010, 1004, 999, 993, 987, 982, 976, 971, 966, 960, 955, 950, 945, 940, 935, 930, 925, 920, 915, 910, 906, 901, 896, 892, 887, 883, 878, 874, 869, 865, 861, 857, 853, 848, 844, 840, 836, 832, 828, 824, 820, 817, 813, 809, 805, 802, 798, 794, 791, 787, 784, 780, 777, 773, 770, 767, 763, 760, 757, 753, 750, 747, 744, 741, 737, 734, 731, 728, 725, 722, 719, 716, 713, 710, 708, 705, 702, 699, 696, 694, 691, 688, 685}; - - template static __device__ void RGB2HSVConvert(const uchar* src, D& dst) - { - const int hsv_shift = 12; - const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256; - - int b = src[bidx], g = src[1], r = src[bidx^2]; - int h, s, v = b; - int vmin = b, diff; - int vr, vg; - - v = ::max(v, g); - v = ::max(v, r); - vmin = ::min(vmin, g); - vmin = ::min(vmin, r); - - diff = v - vmin; - vr = (v == r) * -1; - vg = (v == g) * -1; - - s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift; - h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff)))); - h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift; - h += (h < 0) * hr; - - dst.x = saturate_cast(h); - dst.y = (uchar)s; - dst.z = (uchar)v; - } - - template static __device__ uint RGB2HSVConvert(uint src) - { - const int hsv_shift = 12; - const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256; - - const int b = 0xff & (src >> (bidx * 8)); - const int g = 0xff & (src >> 8); - const int r = 0xff & (src >> ((bidx ^ 2) * 8)); - - int h, s, v = b; - int vmin = b, diff; - int vr, vg; - - v = ::max(v, g); - v = ::max(v, r); - vmin = ::min(vmin, g); - vmin = ::min(vmin, r); - - diff = v - vmin; - vr = (v == r) * -1; - vg = (v == g) * -1; - - s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift; - h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff)))); - h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift; - h += (h < 0) * hr; - - uint dst = 0; - - dst |= saturate_cast(h); - dst |= (0xffu & s) << 8; - dst |= (0xffu & v) << 16; - - return dst; - } - - template static __device__ void RGB2HSVConvert(const float* src, D& dst) - { - const float hscale = hr * (1.f / 360.f); - - float b = src[bidx], g = src[1], r = src[bidx^2]; - float h, s, v; - - float vmin, diff; - - v = vmin = r; - v = fmax(v, g); - v = fmax(v, b); - vmin = fmin(vmin, g); - vmin = fmin(vmin, b); - - diff = v - vmin; - s = diff / (float)(::fabs(v) + numeric_limits::epsilon()); - diff = (float)(60. / (diff + numeric_limits::epsilon())); - - h = (v == r) * (g - b) * diff; - h += (v != r && v == g) * ((b - r) * diff + 120.f); - h += (v != r && v != g) * ((r - g) * diff + 240.f); - h += (h < 0) * 360.f; - - dst.x = h * hscale; - dst.y = s; - dst.z = v; - } - - template struct RGB2HSV - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - RGB2HSVConvert(&src.x, dst); - - return dst; - } - __host__ __device__ __forceinline__ RGB2HSV() {} - __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {} - }; - - template struct RGB2HSV : unary_function - { - __device__ __forceinline__ uint operator()(uint src) const - { - return RGB2HSVConvert(src); - } - __host__ __device__ __forceinline__ RGB2HSV() {} - __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template struct name ## _full_traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template <> struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template <> struct name ## _full_traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2HSV functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - __constant__ int c_HsvSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} }; - - template static __device__ void HSV2RGBConvert(const T& src, float* dst) - { - const float hscale = 6.f / hr; - - float h = src.x, s = src.y, v = src.z; - float b = v, g = v, r = v; - - if (s != 0) - { - h *= hscale; - - if( h < 0 ) - do h += 6; while( h < 0 ); - else if( h >= 6 ) - do h -= 6; while( h >= 6 ); - - int sector = __float2int_rd(h); - h -= sector; - - if ( (unsigned)sector >= 6u ) - { - sector = 0; - h = 0.f; - } - - float tab[4]; - tab[0] = v; - tab[1] = v * (1.f - s); - tab[2] = v * (1.f - s * h); - tab[3] = v * (1.f - s * (1.f - h)); - - b = tab[c_HsvSectorData[sector][0]]; - g = tab[c_HsvSectorData[sector][1]]; - r = tab[c_HsvSectorData[sector][2]]; - } - - dst[bidx] = b; - dst[1] = g; - dst[bidx^2] = r; - } - - template static __device__ void HSV2RGBConvert(const T& src, uchar* dst) - { - float3 buf; - - buf.x = src.x; - buf.y = src.y * (1.f / 255.f); - buf.z = src.z * (1.f / 255.f); - - HSV2RGBConvert(buf, &buf.x); - - dst[0] = saturate_cast(buf.x * 255.f); - dst[1] = saturate_cast(buf.y * 255.f); - dst[2] = saturate_cast(buf.z * 255.f); - } - - template static __device__ uint HSV2RGBConvert(uint src) - { - float3 buf; - - buf.x = src & 0xff; - buf.y = ((src >> 8) & 0xff) * (1.f/255.f); - buf.z = ((src >> 16) & 0xff) * (1.f/255.f); - - HSV2RGBConvert(buf, &buf.x); - - uint dst = 0xffu << 24; - - dst |= saturate_cast(buf.x * 255.f); - dst |= saturate_cast(buf.y * 255.f) << 8; - dst |= saturate_cast(buf.z * 255.f) << 16; - - return dst; - } - - template struct HSV2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - HSV2RGBConvert(src, &dst.x); - setAlpha(dst, ColorChannel::max()); - - return dst; - } - __host__ __device__ __forceinline__ HSV2RGB() {} - __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {} - }; - - template struct HSV2RGB : unary_function - { - __device__ __forceinline__ uint operator()(uint src) const - { - return HSV2RGBConvert(src); - } - __host__ __device__ __forceinline__ HSV2RGB() {} - __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template struct name ## _full_traits \ - { \ - typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template <> struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template <> struct name ## _full_traits \ - { \ - typedef ::cv::cuda::device::color_detail::HSV2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -/////////////////////////////////////// RGB <-> HLS //////////////////////////////////////// - - namespace color_detail - { - template static __device__ void RGB2HLSConvert(const float* src, D& dst) - { - const float hscale = hr * (1.f / 360.f); - - float b = src[bidx], g = src[1], r = src[bidx^2]; - float h = 0.f, s = 0.f, l; - float vmin, vmax, diff; - - vmax = vmin = r; - vmax = fmax(vmax, g); - vmax = fmax(vmax, b); - vmin = fmin(vmin, g); - vmin = fmin(vmin, b); - - diff = vmax - vmin; - l = (vmax + vmin) * 0.5f; - - if (diff > numeric_limits::epsilon()) - { - s = (l < 0.5f) * diff / (vmax + vmin); - s += (l >= 0.5f) * diff / (2.0f - vmax - vmin); - - diff = 60.f / diff; - - h = (vmax == r) * (g - b) * diff; - h += (vmax != r && vmax == g) * ((b - r) * diff + 120.f); - h += (vmax != r && vmax != g) * ((r - g) * diff + 240.f); - h += (h < 0.f) * 360.f; - } - - dst.x = h * hscale; - dst.y = l; - dst.z = s; - } - - template static __device__ void RGB2HLSConvert(const uchar* src, D& dst) - { - float3 buf; - - buf.x = src[0] * (1.f / 255.f); - buf.y = src[1] * (1.f / 255.f); - buf.z = src[2] * (1.f / 255.f); - - RGB2HLSConvert(&buf.x, buf); - - dst.x = saturate_cast(buf.x); - dst.y = saturate_cast(buf.y*255.f); - dst.z = saturate_cast(buf.z*255.f); - } - - template static __device__ uint RGB2HLSConvert(uint src) - { - float3 buf; - - buf.x = (0xff & src) * (1.f / 255.f); - buf.y = (0xff & (src >> 8)) * (1.f / 255.f); - buf.z = (0xff & (src >> 16)) * (1.f / 255.f); - - RGB2HLSConvert(&buf.x, buf); - - uint dst = 0xffu << 24; - - dst |= saturate_cast(buf.x); - dst |= saturate_cast(buf.y * 255.f) << 8; - dst |= saturate_cast(buf.z * 255.f) << 16; - - return dst; - } - - template struct RGB2HLS - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - RGB2HLSConvert(&src.x, dst); - - return dst; - } - __host__ __device__ __forceinline__ RGB2HLS() {} - __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {} - }; - - template struct RGB2HLS : unary_function - { - __device__ __forceinline__ uint operator()(uint src) const - { - return RGB2HLSConvert(src); - } - __host__ __device__ __forceinline__ RGB2HLS() {} - __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template struct name ## _full_traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template <> struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template <> struct name ## _full_traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2HLS functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - __constant__ int c_HlsSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} }; - - template static __device__ void HLS2RGBConvert(const T& src, float* dst) - { - const float hscale = 6.0f / hr; - - float h = src.x, l = src.y, s = src.z; - float b = l, g = l, r = l; - - if (s != 0) - { - float p2 = (l <= 0.5f) * l * (1 + s); - p2 += (l > 0.5f) * (l + s - l * s); - float p1 = 2 * l - p2; - - h *= hscale; - - if( h < 0 ) - do h += 6; while( h < 0 ); - else if( h >= 6 ) - do h -= 6; while( h >= 6 ); - - int sector; - sector = __float2int_rd(h); - - h -= sector; - - float tab[4]; - tab[0] = p2; - tab[1] = p1; - tab[2] = p1 + (p2 - p1) * (1 - h); - tab[3] = p1 + (p2 - p1) * h; - - b = tab[c_HlsSectorData[sector][0]]; - g = tab[c_HlsSectorData[sector][1]]; - r = tab[c_HlsSectorData[sector][2]]; - } - - dst[bidx] = b; - dst[1] = g; - dst[bidx^2] = r; - } - - template static __device__ void HLS2RGBConvert(const T& src, uchar* dst) - { - float3 buf; - - buf.x = src.x; - buf.y = src.y * (1.f / 255.f); - buf.z = src.z * (1.f / 255.f); - - HLS2RGBConvert(buf, &buf.x); - - dst[0] = saturate_cast(buf.x * 255.f); - dst[1] = saturate_cast(buf.y * 255.f); - dst[2] = saturate_cast(buf.z * 255.f); - } - - template static __device__ uint HLS2RGBConvert(uint src) - { - float3 buf; - - buf.x = 0xff & src; - buf.y = (0xff & (src >> 8)) * (1.f / 255.f); - buf.z = (0xff & (src >> 16)) * (1.f / 255.f); - - HLS2RGBConvert(buf, &buf.x); - - uint dst = 0xffu << 24; - - dst |= saturate_cast(buf.x * 255.f); - dst |= saturate_cast(buf.y * 255.f) << 8; - dst |= saturate_cast(buf.z * 255.f) << 16; - - return dst; - } - - template struct HLS2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - HLS2RGBConvert(src, &dst.x); - setAlpha(dst, ColorChannel::max()); - - return dst; - } - __host__ __device__ __forceinline__ HLS2RGB() {} - __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {} - }; - - template struct HLS2RGB : unary_function - { - __device__ __forceinline__ uint operator()(uint src) const - { - return HLS2RGBConvert(src); - } - __host__ __device__ __forceinline__ HLS2RGB() {} - __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(name, scn, dcn, bidx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template struct name ## _full_traits \ - { \ - typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template <> struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; \ - template <> struct name ## _full_traits \ - { \ - typedef ::cv::cuda::device::color_detail::HLS2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -///////////////////////////////////// RGB <-> Lab ///////////////////////////////////// - - namespace color_detail - { - enum - { - LAB_CBRT_TAB_SIZE = 1024, - GAMMA_TAB_SIZE = 1024, - lab_shift = xyz_shift, - gamma_shift = 3, - lab_shift2 = (lab_shift + gamma_shift), - LAB_CBRT_TAB_SIZE_B = (256 * 3 / 2 * (1 << gamma_shift)) - }; - - __constant__ ushort c_sRGBGammaTab_b[] = {0,1,1,2,2,3,4,4,5,6,6,7,8,8,9,10,11,11,12,13,14,15,16,17,19,20,21,22,24,25,26,28,29,31,33,34,36,38,40,41,43,45,47,49,51,54,56,58,60,63,65,68,70,73,75,78,81,83,86,89,92,95,98,101,105,108,111,115,118,121,125,129,132,136,140,144,147,151,155,160,164,168,172,176,181,185,190,194,199,204,209,213,218,223,228,233,239,244,249,255,260,265,271,277,282,288,294,300,306,312,318,324,331,337,343,350,356,363,370,376,383,390,397,404,411,418,426,433,440,448,455,463,471,478,486,494,502,510,518,527,535,543,552,560,569,578,586,595,604,613,622,631,641,650,659,669,678,688,698,707,717,727,737,747,757,768,778,788,799,809,820,831,842,852,863,875,886,897,908,920,931,943,954,966,978,990,1002,1014,1026,1038,1050,1063,1075,1088,1101,1113,1126,1139,1152,1165,1178,1192,1205,1218,1232,1245,1259,1273,1287,1301,1315,1329,1343,1357,1372,1386,1401,1415,1430,1445,1460,1475,1490,1505,1521,1536,1551,1567,1583,1598,1614,1630,1646,1662,1678,1695,1711,1728,1744,1761,1778,1794,1811,1828,1846,1863,1880,1897,1915,1933,1950,1968,1986,2004,2022,2040}; - - __device__ __forceinline__ int LabCbrt_b(int i) - { - float x = i * (1.f / (255.f * (1 << gamma_shift))); - return (1 << lab_shift2) * (x < 0.008856f ? x * 7.787f + 0.13793103448275862f : ::cbrtf(x)); - } - - template - __device__ __forceinline__ void RGB2LabConvert_b(const T& src, D& dst) - { - const int Lscale = (116 * 255 + 50) / 100; - const int Lshift = -((16 * 255 * (1 << lab_shift2) + 50) / 100); - - int B = blueIdx == 0 ? src.x : src.z; - int G = src.y; - int R = blueIdx == 0 ? src.z : src.x; - - if (srgb) - { - B = c_sRGBGammaTab_b[B]; - G = c_sRGBGammaTab_b[G]; - R = c_sRGBGammaTab_b[R]; - } - else - { - B <<= 3; - G <<= 3; - R <<= 3; - } - - int fX = LabCbrt_b(CV_DESCALE(B * 778 + G * 1541 + R * 1777, lab_shift)); - int fY = LabCbrt_b(CV_DESCALE(B * 296 + G * 2929 + R * 871, lab_shift)); - int fZ = LabCbrt_b(CV_DESCALE(B * 3575 + G * 448 + R * 73, lab_shift)); - - int L = CV_DESCALE(Lscale * fY + Lshift, lab_shift2); - int a = CV_DESCALE(500 * (fX - fY) + 128 * (1 << lab_shift2), lab_shift2); - int b = CV_DESCALE(200 * (fY - fZ) + 128 * (1 << lab_shift2), lab_shift2); - - dst.x = saturate_cast(L); - dst.y = saturate_cast(a); - dst.z = saturate_cast(b); - } - - __device__ __forceinline__ float splineInterpolate(float x, const float* tab, int n) - { - int ix = ::min(::max(int(x), 0), n-1); - x -= ix; - tab += ix * 4; - return ((tab[3] * x + tab[2]) * x + tab[1]) * x + tab[0]; - } - - __constant__ float c_sRGBGammaTab[] = 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- - template - __device__ __forceinline__ void RGB2LabConvert_f(const T& src, D& dst) - { - const float _1_3 = 1.0f / 3.0f; - const float _a = 16.0f / 116.0f; - - float B = blueIdx == 0 ? src.x : src.z; - float G = src.y; - float R = blueIdx == 0 ? src.z : src.x; - - if (srgb) - { - B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); - G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); - R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); - } - - float X = B * 0.189828f + G * 0.376219f + R * 0.433953f; - float Y = B * 0.072169f + G * 0.715160f + R * 0.212671f; - float Z = B * 0.872766f + G * 0.109477f + R * 0.017758f; - - float FX = X > 0.008856f ? ::powf(X, _1_3) : (7.787f * X + _a); - float FY = Y > 0.008856f ? ::powf(Y, _1_3) : (7.787f * Y + _a); - float FZ = Z > 0.008856f ? ::powf(Z, _1_3) : (7.787f * Z + _a); - - float L = Y > 0.008856f ? (116.f * FY - 16.f) : (903.3f * Y); - float a = 500.f * (FX - FY); - float b = 200.f * (FY - FZ); - - dst.x = L; - dst.y = a; - dst.z = b; - } - - template struct RGB2Lab; - template - struct RGB2Lab - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - RGB2LabConvert_b(src, dst); - - return dst; - } - __host__ __device__ __forceinline__ RGB2Lab() {} - __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {} - }; - template - struct RGB2Lab - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - RGB2LabConvert_f(src, dst); - - return dst; - } - __host__ __device__ __forceinline__ RGB2Lab() {} - __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(name, scn, dcn, srgb, blueIdx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2Lab functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - __constant__ float c_sRGBInvGammaTab[] = 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- - template - __device__ __forceinline__ void Lab2RGBConvert_f(const T& src, D& dst) - { - const float lThresh = 0.008856f * 903.3f; - const float fThresh = 7.787f * 0.008856f + 16.0f / 116.0f; - - float Y, fy; - - if (src.x <= lThresh) - { - Y = src.x / 903.3f; - fy = 7.787f * Y + 16.0f / 116.0f; - } - else - { - fy = (src.x + 16.0f) / 116.0f; - Y = fy * fy * fy; - } - - float X = src.y / 500.0f + fy; - float Z = fy - src.z / 200.0f; - - if (X <= fThresh) - X = (X - 16.0f / 116.0f) / 7.787f; - else - X = X * X * X; - - if (Z <= fThresh) - Z = (Z - 16.0f / 116.0f) / 7.787f; - else - Z = Z * Z * Z; - - float B = 0.052891f * X - 0.204043f * Y + 1.151152f * Z; - float G = -0.921235f * X + 1.875991f * Y + 0.045244f * Z; - float R = 3.079933f * X - 1.537150f * Y - 0.542782f * Z; - - if (srgb) - { - B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); - G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); - R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); - } - - dst.x = blueIdx == 0 ? B : R; - dst.y = G; - dst.z = blueIdx == 0 ? R : B; - setAlpha(dst, ColorChannel::max()); - } - - template - __device__ __forceinline__ void Lab2RGBConvert_b(const T& src, D& dst) - { - float3 srcf, dstf; - - srcf.x = src.x * (100.f / 255.f); - srcf.y = src.y - 128; - srcf.z = src.z - 128; - - Lab2RGBConvert_f(srcf, dstf); - - dst.x = saturate_cast(dstf.x * 255.f); - dst.y = saturate_cast(dstf.y * 255.f); - dst.z = saturate_cast(dstf.z * 255.f); - setAlpha(dst, ColorChannel::max()); - } - - template struct Lab2RGB; - template - struct Lab2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - Lab2RGBConvert_b(src, dst); - - return dst; - } - __host__ __device__ __forceinline__ Lab2RGB() {} - __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {} - }; - template - struct Lab2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - Lab2RGBConvert_f(src, dst); - - return dst; - } - __host__ __device__ __forceinline__ Lab2RGB() {} - __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::Lab2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - -///////////////////////////////////// RGB <-> Luv ///////////////////////////////////// - - namespace color_detail - { - __constant__ float c_LabCbrtTab[] = 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- - template - __device__ __forceinline__ void RGB2LuvConvert_f(const T& src, D& dst) - { - const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3); - const float _un = 13 * (4 * 0.950456f * _d); - const float _vn = 13 * (9 * _d); - - float B = blueIdx == 0 ? src.x : src.z; - float G = src.y; - float R = blueIdx == 0 ? src.z : src.x; - - if (srgb) - { - B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); - G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); - R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE); - } - - float X = R * 0.412453f + G * 0.357580f + B * 0.180423f; - float Y = R * 0.212671f + G * 0.715160f + B * 0.072169f; - float Z = R * 0.019334f + G * 0.119193f + B * 0.950227f; - - float L = splineInterpolate(Y * (LAB_CBRT_TAB_SIZE / 1.5f), c_LabCbrtTab, LAB_CBRT_TAB_SIZE); - L = 116.f * L - 16.f; - - const float d = (4 * 13) / ::fmaxf(X + 15 * Y + 3 * Z, numeric_limits::epsilon()); - float u = L * (X * d - _un); - float v = L * ((9 * 0.25f) * Y * d - _vn); - - dst.x = L; - dst.y = u; - dst.z = v; - } - - template - __device__ __forceinline__ void RGB2LuvConvert_b(const T& src, D& dst) - { - float3 srcf, dstf; - - srcf.x = src.x * (1.f / 255.f); - srcf.y = src.y * (1.f / 255.f); - srcf.z = src.z * (1.f / 255.f); - - RGB2LuvConvert_f(srcf, dstf); - - dst.x = saturate_cast(dstf.x * 2.55f); - dst.y = saturate_cast(dstf.y * 0.72033898305084743f + 96.525423728813564f); - dst.z = saturate_cast(dstf.z * 0.9732824427480916f + 136.259541984732824f); - } - - template struct RGB2Luv; - template - struct RGB2Luv - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - RGB2LuvConvert_b(src, dst); - - return dst; - } - __host__ __device__ __forceinline__ RGB2Luv() {} - __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {} - }; - template - struct RGB2Luv - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - RGB2LuvConvert_f(src, dst); - - return dst; - } - __host__ __device__ __forceinline__ RGB2Luv() {} - __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(name, scn, dcn, srgb, blueIdx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::RGB2Luv functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - namespace color_detail - { - template - __device__ __forceinline__ void Luv2RGBConvert_f(const T& src, D& dst) - { - const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3); - const float _un = 4 * 0.950456f * _d; - const float _vn = 9 * _d; - - float L = src.x; - float u = src.y; - float v = src.z; - - float Y = (L + 16.f) * (1.f / 116.f); - Y = Y * Y * Y; - - float d = (1.f / 13.f) / L; - u = u * d + _un; - v = v * d + _vn; - - float iv = 1.f / v; - float X = 2.25f * u * Y * iv; - float Z = (12 - 3 * u - 20 * v) * Y * 0.25f * iv; - - float B = 0.055648f * X - 0.204043f * Y + 1.057311f * Z; - float G = -0.969256f * X + 1.875991f * Y + 0.041556f * Z; - float R = 3.240479f * X - 1.537150f * Y - 0.498535f * Z; - - if (srgb) - { - B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); - G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); - R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE); - } - - dst.x = blueIdx == 0 ? B : R; - dst.y = G; - dst.z = blueIdx == 0 ? R : B; - setAlpha(dst, ColorChannel::max()); - } - - template - __device__ __forceinline__ void Luv2RGBConvert_b(const T& src, D& dst) - { - float3 srcf, dstf; - - srcf.x = src.x * (100.f / 255.f); - srcf.y = src.y * 1.388235294117647f - 134.f; - srcf.z = src.z * 1.027450980392157f - 140.f; - - Luv2RGBConvert_f(srcf, dstf); - - dst.x = saturate_cast(dstf.x * 255.f); - dst.y = saturate_cast(dstf.y * 255.f); - dst.z = saturate_cast(dstf.z * 255.f); - setAlpha(dst, ColorChannel::max()); - } - - template struct Luv2RGB; - template - struct Luv2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - Luv2RGBConvert_b(src, dst); - - return dst; - } - __host__ __device__ __forceinline__ Luv2RGB() {} - __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {} - }; - template - struct Luv2RGB - : unary_function::vec_type, typename TypeVec::vec_type> - { - __device__ __forceinline__ typename TypeVec::vec_type operator ()(const typename TypeVec::vec_type& src) const - { - typename TypeVec::vec_type dst; - - Luv2RGBConvert_f(src, dst); - - return dst; - } - __host__ __device__ __forceinline__ Luv2RGB() {} - __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {} - }; - } - -#define OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \ - template struct name ## _traits \ - { \ - typedef ::cv::cuda::device::color_detail::Luv2RGB functor_type; \ - static __host__ __device__ __forceinline__ functor_type create_functor() \ - { \ - return functor_type(); \ - } \ - }; - - #undef CV_DESCALE - -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // OPENCV_CUDA_COLOR_DETAIL_HPP diff --git a/include/opencv2/core/cuda/detail/reduce.hpp b/include/opencv2/core/cuda/detail/reduce.hpp deleted file mode 100755 index 8af20b0..0000000 --- a/include/opencv2/core/cuda/detail/reduce.hpp +++ /dev/null @@ -1,365 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_REDUCE_DETAIL_HPP -#define OPENCV_CUDA_REDUCE_DETAIL_HPP - -#include -#include "../warp.hpp" -#include "../warp_shuffle.hpp" - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - namespace reduce_detail - { - template struct GetType; - template struct GetType - { - typedef T type; - }; - template struct GetType - { - typedef T type; - }; - template struct GetType - { - typedef T type; - }; - - template - struct For - { - template - static __device__ void loadToSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid) - { - thrust::get(smem)[tid] = thrust::get(val); - - For::loadToSmem(smem, val, tid); - } - template - static __device__ void loadFromSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid) - { - thrust::get(val) = thrust::get(smem)[tid]; - - For::loadFromSmem(smem, val, tid); - } - - template - static __device__ void merge(const PointerTuple& smem, const ValTuple& val, unsigned int tid, unsigned int delta, const OpTuple& op) - { - typename GetType::type>::type reg = thrust::get(smem)[tid + delta]; - thrust::get(smem)[tid] = thrust::get(val) = thrust::get(op)(thrust::get(val), reg); - - For::merge(smem, val, tid, delta, op); - } - template - static __device__ void mergeShfl(const ValTuple& val, unsigned int delta, unsigned int width, const OpTuple& op) - { - typename GetType::type>::type reg = shfl_down(thrust::get(val), delta, width); - thrust::get(val) = thrust::get(op)(thrust::get(val), reg); - - For::mergeShfl(val, delta, width, op); - } - }; - template - struct For - { - template - static __device__ void loadToSmem(const PointerTuple&, const ValTuple&, unsigned int) - { - } - template - static __device__ void loadFromSmem(const PointerTuple&, const ValTuple&, unsigned int) - { - } - - template - static __device__ void merge(const PointerTuple&, const ValTuple&, unsigned int, unsigned int, const OpTuple&) - { - } - template - static __device__ void mergeShfl(const ValTuple&, unsigned int, unsigned int, const OpTuple&) - { - } - }; - - template - __device__ __forceinline__ void loadToSmem(volatile T* smem, T& val, unsigned int tid) - { - smem[tid] = val; - } - template - __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& val, unsigned int tid) - { - val = smem[tid]; - } - template - __device__ __forceinline__ void loadToSmem(const thrust::tuple& smem, - const thrust::tuple& val, - unsigned int tid) - { - For<0, thrust::tuple_size >::value>::loadToSmem(smem, val, tid); - } - template - __device__ __forceinline__ void loadFromSmem(const thrust::tuple& smem, - const thrust::tuple& val, - unsigned int tid) - { - For<0, thrust::tuple_size >::value>::loadFromSmem(smem, val, tid); - } - - template - __device__ __forceinline__ void merge(volatile T* smem, T& val, unsigned int tid, unsigned int delta, const Op& op) - { - T reg = smem[tid + delta]; - smem[tid] = val = op(val, reg); - } - template - __device__ __forceinline__ void mergeShfl(T& val, unsigned int delta, unsigned int width, const Op& op) - { - T reg = shfl_down(val, delta, width); - val = op(val, reg); - } - template - __device__ __forceinline__ void merge(const thrust::tuple& smem, - const thrust::tuple& val, - unsigned int tid, - unsigned int delta, - const thrust::tuple& op) - { - For<0, thrust::tuple_size >::value>::merge(smem, val, tid, delta, op); - } - template - __device__ __forceinline__ void mergeShfl(const thrust::tuple& val, - unsigned int delta, - unsigned int width, - const thrust::tuple& op) - { - For<0, thrust::tuple_size >::value>::mergeShfl(val, delta, width, op); - } - - template struct Generic - { - template - static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) - { - loadToSmem(smem, val, tid); - if (N >= 32) - __syncthreads(); - - if (N >= 2048) - { - if (tid < 1024) - merge(smem, val, tid, 1024, op); - - __syncthreads(); - } - if (N >= 1024) - { - if (tid < 512) - merge(smem, val, tid, 512, op); - - __syncthreads(); - } - if (N >= 512) - { - if (tid < 256) - merge(smem, val, tid, 256, op); - - __syncthreads(); - } - if (N >= 256) - { - if (tid < 128) - merge(smem, val, tid, 128, op); - - __syncthreads(); - } - if (N >= 128) - { - if (tid < 64) - merge(smem, val, tid, 64, op); - - __syncthreads(); - } - if (N >= 64) - { - if (tid < 32) - merge(smem, val, tid, 32, op); - } - - if (tid < 16) - { - merge(smem, val, tid, 16, op); - merge(smem, val, tid, 8, op); - merge(smem, val, tid, 4, op); - merge(smem, val, tid, 2, op); - merge(smem, val, tid, 1, op); - } - } - }; - - template - struct Unroll - { - static __device__ void loopShfl(Reference val, Op op, unsigned int N) - { - mergeShfl(val, I, N, op); - Unroll::loopShfl(val, op, N); - } - static __device__ void loop(Pointer smem, Reference val, unsigned int tid, Op op) - { - merge(smem, val, tid, I, op); - Unroll::loop(smem, val, tid, op); - } - }; - template - struct Unroll<0, Pointer, Reference, Op> - { - static __device__ void loopShfl(Reference, Op, unsigned int) - { - } - static __device__ void loop(Pointer, Reference, unsigned int, Op) - { - } - }; - - template struct WarpOptimized - { - template - static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - CV_UNUSED(smem); - CV_UNUSED(tid); - - Unroll::loopShfl(val, op, N); - #else - loadToSmem(smem, val, tid); - - if (tid < N / 2) - Unroll::loop(smem, val, tid, op); - #endif - } - }; - - template struct GenericOptimized32 - { - enum { M = N / 32 }; - - template - static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op) - { - const unsigned int laneId = Warp::laneId(); - - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - Unroll<16, Pointer, Reference, Op>::loopShfl(val, op, warpSize); - - if (laneId == 0) - loadToSmem(smem, val, tid / 32); - #else - loadToSmem(smem, val, tid); - - if (laneId < 16) - Unroll<16, Pointer, Reference, Op>::loop(smem, val, tid, op); - - __syncthreads(); - - if (laneId == 0) - loadToSmem(smem, val, tid / 32); - #endif - - __syncthreads(); - - loadFromSmem(smem, val, tid); - - if (tid < 32) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - Unroll::loopShfl(val, op, M); - #else - Unroll::loop(smem, val, tid, op); - #endif - } - } - }; - - template struct StaticIf; - template struct StaticIf - { - typedef T1 type; - }; - template struct StaticIf - { - typedef T2 type; - }; - - template struct IsPowerOf2 - { - enum { value = ((N != 0) && !(N & (N - 1))) }; - }; - - template struct Dispatcher - { - typedef typename StaticIf< - (N <= 32) && IsPowerOf2::value, - WarpOptimized, - typename StaticIf< - (N <= 1024) && IsPowerOf2::value, - GenericOptimized32, - Generic - >::type - >::type reductor; - }; - } -}}} - -//! @endcond - -#endif // OPENCV_CUDA_REDUCE_DETAIL_HPP diff --git a/include/opencv2/core/cuda/detail/reduce_key_val.hpp b/include/opencv2/core/cuda/detail/reduce_key_val.hpp deleted file mode 100755 index df37c17..0000000 --- a/include/opencv2/core/cuda/detail/reduce_key_val.hpp +++ /dev/null @@ -1,502 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP -#define OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP - -#include -#include "../warp.hpp" -#include "../warp_shuffle.hpp" - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - namespace reduce_key_val_detail - { - template struct GetType; - template struct GetType - { - typedef T type; - }; - template struct GetType - { - typedef T type; - }; - template struct GetType - { - typedef T type; - }; - - template - struct For - { - template - static __device__ void loadToSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid) - { - thrust::get(smem)[tid] = thrust::get(data); - - For::loadToSmem(smem, data, tid); - } - template - static __device__ void loadFromSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid) - { - thrust::get(data) = thrust::get(smem)[tid]; - - For::loadFromSmem(smem, data, tid); - } - - template - static __device__ void copyShfl(const ReferenceTuple& val, unsigned int delta, int width) - { - thrust::get(val) = shfl_down(thrust::get(val), delta, width); - - For::copyShfl(val, delta, width); - } - template - static __device__ void copy(const PointerTuple& svals, const ReferenceTuple& val, unsigned int tid, unsigned int delta) - { - thrust::get(svals)[tid] = thrust::get(val) = thrust::get(svals)[tid + delta]; - - For::copy(svals, val, tid, delta); - } - - template - static __device__ void mergeShfl(const KeyReferenceTuple& key, const ValReferenceTuple& val, const CmpTuple& cmp, unsigned int delta, int width) - { - typename GetType::type>::type reg = shfl_down(thrust::get(key), delta, width); - - if (thrust::get(cmp)(reg, thrust::get(key))) - { - thrust::get(key) = reg; - thrust::get(val) = shfl_down(thrust::get(val), delta, width); - } - - For::mergeShfl(key, val, cmp, delta, width); - } - template - static __device__ void merge(const KeyPointerTuple& skeys, const KeyReferenceTuple& key, - const ValPointerTuple& svals, const ValReferenceTuple& val, - const CmpTuple& cmp, - unsigned int tid, unsigned int delta) - { - typename GetType::type>::type reg = thrust::get(skeys)[tid + delta]; - - if (thrust::get(cmp)(reg, thrust::get(key))) - { - thrust::get(skeys)[tid] = thrust::get(key) = reg; - thrust::get(svals)[tid] = thrust::get(val) = thrust::get(svals)[tid + delta]; - } - - For::merge(skeys, key, svals, val, cmp, tid, delta); - } - }; - template - struct For - { - template - static __device__ void loadToSmem(const PointerTuple&, const ReferenceTuple&, unsigned int) - { - } - template - static __device__ void loadFromSmem(const PointerTuple&, const ReferenceTuple&, unsigned int) - { - } - - template - static __device__ void copyShfl(const ReferenceTuple&, unsigned int, int) - { - } - template - static __device__ void copy(const PointerTuple&, const ReferenceTuple&, unsigned int, unsigned int) - { - } - - template - static __device__ void mergeShfl(const KeyReferenceTuple&, const ValReferenceTuple&, const CmpTuple&, unsigned int, int) - { - } - template - static __device__ void merge(const KeyPointerTuple&, const KeyReferenceTuple&, - const ValPointerTuple&, const ValReferenceTuple&, - const CmpTuple&, - unsigned int, unsigned int) - { - } - }; - - ////////////////////////////////////////////////////// - // loadToSmem - - template - __device__ __forceinline__ void loadToSmem(volatile T* smem, T& data, unsigned int tid) - { - smem[tid] = data; - } - template - __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& data, unsigned int tid) - { - data = smem[tid]; - } - template - __device__ __forceinline__ void loadToSmem(const thrust::tuple& smem, - const thrust::tuple& data, - unsigned int tid) - { - For<0, thrust::tuple_size >::value>::loadToSmem(smem, data, tid); - } - template - __device__ __forceinline__ void loadFromSmem(const thrust::tuple& smem, - const thrust::tuple& data, - unsigned int tid) - { - For<0, thrust::tuple_size >::value>::loadFromSmem(smem, data, tid); - } - - ////////////////////////////////////////////////////// - // copyVals - - template - __device__ __forceinline__ void copyValsShfl(V& val, unsigned int delta, int width) - { - val = shfl_down(val, delta, width); - } - template - __device__ __forceinline__ void copyVals(volatile V* svals, V& val, unsigned int tid, unsigned int delta) - { - svals[tid] = val = svals[tid + delta]; - } - template - __device__ __forceinline__ void copyValsShfl(const thrust::tuple& val, - unsigned int delta, - int width) - { - For<0, thrust::tuple_size >::value>::copyShfl(val, delta, width); - } - template - __device__ __forceinline__ void copyVals(const thrust::tuple& svals, - const thrust::tuple& val, - unsigned int tid, unsigned int delta) - { - For<0, thrust::tuple_size >::value>::copy(svals, val, tid, delta); - } - - ////////////////////////////////////////////////////// - // merge - - template - __device__ __forceinline__ void mergeShfl(K& key, V& val, const Cmp& cmp, unsigned int delta, int width) - { - K reg = shfl_down(key, delta, width); - - if (cmp(reg, key)) - { - key = reg; - copyValsShfl(val, delta, width); - } - } - template - __device__ __forceinline__ void merge(volatile K* skeys, K& key, volatile V* svals, V& val, const Cmp& cmp, unsigned int tid, unsigned int delta) - { - K reg = skeys[tid + delta]; - - if (cmp(reg, key)) - { - skeys[tid] = key = reg; - copyVals(svals, val, tid, delta); - } - } - template - __device__ __forceinline__ void mergeShfl(K& key, - const thrust::tuple& val, - const Cmp& cmp, - unsigned int delta, int width) - { - K reg = shfl_down(key, delta, width); - - if (cmp(reg, key)) - { - key = reg; - copyValsShfl(val, delta, width); - } - } - template - __device__ __forceinline__ void merge(volatile K* skeys, K& key, - const thrust::tuple& svals, - const thrust::tuple& val, - const Cmp& cmp, unsigned int tid, unsigned int delta) - { - K reg = skeys[tid + delta]; - - if (cmp(reg, key)) - { - skeys[tid] = key = reg; - copyVals(svals, val, tid, delta); - } - } - template - __device__ __forceinline__ void mergeShfl(const thrust::tuple& key, - const thrust::tuple& val, - const thrust::tuple& cmp, - unsigned int delta, int width) - { - For<0, thrust::tuple_size >::value>::mergeShfl(key, val, cmp, delta, width); - } - template - __device__ __forceinline__ void merge(const thrust::tuple& skeys, - const thrust::tuple& key, - const thrust::tuple& svals, - const thrust::tuple& val, - const thrust::tuple& cmp, - unsigned int tid, unsigned int delta) - { - For<0, thrust::tuple_size >::value>::merge(skeys, key, svals, val, cmp, tid, delta); - } - - ////////////////////////////////////////////////////// - // Generic - - template struct Generic - { - template - static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) - { - loadToSmem(skeys, key, tid); - loadValsToSmem(svals, val, tid); - if (N >= 32) - __syncthreads(); - - if (N >= 2048) - { - if (tid < 1024) - merge(skeys, key, svals, val, cmp, tid, 1024); - - __syncthreads(); - } - if (N >= 1024) - { - if (tid < 512) - merge(skeys, key, svals, val, cmp, tid, 512); - - __syncthreads(); - } - if (N >= 512) - { - if (tid < 256) - merge(skeys, key, svals, val, cmp, tid, 256); - - __syncthreads(); - } - if (N >= 256) - { - if (tid < 128) - merge(skeys, key, svals, val, cmp, tid, 128); - - __syncthreads(); - } - if (N >= 128) - { - if (tid < 64) - merge(skeys, key, svals, val, cmp, tid, 64); - - __syncthreads(); - } - if (N >= 64) - { - if (tid < 32) - merge(skeys, key, svals, val, cmp, tid, 32); - } - - if (tid < 16) - { - merge(skeys, key, svals, val, cmp, tid, 16); - merge(skeys, key, svals, val, cmp, tid, 8); - merge(skeys, key, svals, val, cmp, tid, 4); - merge(skeys, key, svals, val, cmp, tid, 2); - merge(skeys, key, svals, val, cmp, tid, 1); - } - } - }; - - template - struct Unroll - { - static __device__ void loopShfl(KR key, VR val, Cmp cmp, unsigned int N) - { - mergeShfl(key, val, cmp, I, N); - Unroll::loopShfl(key, val, cmp, N); - } - static __device__ void loop(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) - { - merge(skeys, key, svals, val, cmp, tid, I); - Unroll::loop(skeys, key, svals, val, tid, cmp); - } - }; - template - struct Unroll<0, KP, KR, VP, VR, Cmp> - { - static __device__ void loopShfl(KR, VR, Cmp, unsigned int) - { - } - static __device__ void loop(KP, KR, VP, VR, unsigned int, Cmp) - { - } - }; - - template struct WarpOptimized - { - template - static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) - { - #if 0 // __CUDA_ARCH__ >= 300 - CV_UNUSED(skeys); - CV_UNUSED(svals); - CV_UNUSED(tid); - - Unroll::loopShfl(key, val, cmp, N); - #else - loadToSmem(skeys, key, tid); - loadToSmem(svals, val, tid); - - if (tid < N / 2) - Unroll::loop(skeys, key, svals, val, tid, cmp); - #endif - } - }; - - template struct GenericOptimized32 - { - enum { M = N / 32 }; - - template - static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp) - { - const unsigned int laneId = Warp::laneId(); - - #if 0 // __CUDA_ARCH__ >= 300 - Unroll<16, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, warpSize); - - if (laneId == 0) - { - loadToSmem(skeys, key, tid / 32); - loadToSmem(svals, val, tid / 32); - } - #else - loadToSmem(skeys, key, tid); - loadToSmem(svals, val, tid); - - if (laneId < 16) - Unroll<16, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp); - - __syncthreads(); - - if (laneId == 0) - { - loadToSmem(skeys, key, tid / 32); - loadToSmem(svals, val, tid / 32); - } - #endif - - __syncthreads(); - - loadFromSmem(skeys, key, tid); - - if (tid < 32) - { - #if 0 // __CUDA_ARCH__ >= 300 - loadFromSmem(svals, val, tid); - - Unroll::loopShfl(key, val, cmp, M); - #else - Unroll::loop(skeys, key, svals, val, tid, cmp); - #endif - } - } - }; - - template struct StaticIf; - template struct StaticIf - { - typedef T1 type; - }; - template struct StaticIf - { - typedef T2 type; - }; - - template struct IsPowerOf2 - { - enum { value = ((N != 0) && !(N & (N - 1))) }; - }; - - template struct Dispatcher - { - typedef typename StaticIf< - (N <= 32) && IsPowerOf2::value, - WarpOptimized, - typename StaticIf< - (N <= 1024) && IsPowerOf2::value, - GenericOptimized32, - Generic - >::type - >::type reductor; - }; - } -}}} - -//! @endcond - -#endif // OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP diff --git a/include/opencv2/core/cuda/detail/transform_detail.hpp b/include/opencv2/core/cuda/detail/transform_detail.hpp deleted file mode 100755 index 1919848..0000000 --- a/include/opencv2/core/cuda/detail/transform_detail.hpp +++ /dev/null @@ -1,392 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_TRANSFORM_DETAIL_HPP -#define OPENCV_CUDA_TRANSFORM_DETAIL_HPP - -#include "../common.hpp" -#include "../vec_traits.hpp" -#include "../functional.hpp" - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - namespace transform_detail - { - //! Read Write Traits - - template struct UnaryReadWriteTraits - { - typedef typename TypeVec::vec_type read_type; - typedef typename TypeVec::vec_type write_type; - }; - - template struct BinaryReadWriteTraits - { - typedef typename TypeVec::vec_type read_type1; - typedef typename TypeVec::vec_type read_type2; - typedef typename TypeVec::vec_type write_type; - }; - - //! Transform kernels - - template struct OpUnroller; - template <> struct OpUnroller<1> - { - template - static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.x = op(src.x); - } - - template - static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.x = op(src1.x, src2.x); - } - }; - template <> struct OpUnroller<2> - { - template - static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.x = op(src.x); - if (mask(y, x_shifted + 1)) - dst.y = op(src.y); - } - - template - static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.x = op(src1.x, src2.x); - if (mask(y, x_shifted + 1)) - dst.y = op(src1.y, src2.y); - } - }; - template <> struct OpUnroller<3> - { - template - static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.x = op(src.x); - if (mask(y, x_shifted + 1)) - dst.y = op(src.y); - if (mask(y, x_shifted + 2)) - dst.z = op(src.z); - } - - template - static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.x = op(src1.x, src2.x); - if (mask(y, x_shifted + 1)) - dst.y = op(src1.y, src2.y); - if (mask(y, x_shifted + 2)) - dst.z = op(src1.z, src2.z); - } - }; - template <> struct OpUnroller<4> - { - template - static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.x = op(src.x); - if (mask(y, x_shifted + 1)) - dst.y = op(src.y); - if (mask(y, x_shifted + 2)) - dst.z = op(src.z); - if (mask(y, x_shifted + 3)) - dst.w = op(src.w); - } - - template - static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.x = op(src1.x, src2.x); - if (mask(y, x_shifted + 1)) - dst.y = op(src1.y, src2.y); - if (mask(y, x_shifted + 2)) - dst.z = op(src1.z, src2.z); - if (mask(y, x_shifted + 3)) - dst.w = op(src1.w, src2.w); - } - }; - template <> struct OpUnroller<8> - { - template - static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.a0 = op(src.a0); - if (mask(y, x_shifted + 1)) - dst.a1 = op(src.a1); - if (mask(y, x_shifted + 2)) - dst.a2 = op(src.a2); - if (mask(y, x_shifted + 3)) - dst.a3 = op(src.a3); - if (mask(y, x_shifted + 4)) - dst.a4 = op(src.a4); - if (mask(y, x_shifted + 5)) - dst.a5 = op(src.a5); - if (mask(y, x_shifted + 6)) - dst.a6 = op(src.a6); - if (mask(y, x_shifted + 7)) - dst.a7 = op(src.a7); - } - - template - static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y) - { - if (mask(y, x_shifted)) - dst.a0 = op(src1.a0, src2.a0); - if (mask(y, x_shifted + 1)) - dst.a1 = op(src1.a1, src2.a1); - if (mask(y, x_shifted + 2)) - dst.a2 = op(src1.a2, src2.a2); - if (mask(y, x_shifted + 3)) - dst.a3 = op(src1.a3, src2.a3); - if (mask(y, x_shifted + 4)) - dst.a4 = op(src1.a4, src2.a4); - if (mask(y, x_shifted + 5)) - dst.a5 = op(src1.a5, src2.a5); - if (mask(y, x_shifted + 6)) - dst.a6 = op(src1.a6, src2.a6); - if (mask(y, x_shifted + 7)) - dst.a7 = op(src1.a7, src2.a7); - } - }; - - template - static __global__ void transformSmart(const PtrStepSz src_, PtrStep dst_, const Mask mask, const UnOp op) - { - typedef TransformFunctorTraits ft; - typedef typename UnaryReadWriteTraits::read_type read_type; - typedef typename UnaryReadWriteTraits::write_type write_type; - - const int x = threadIdx.x + blockIdx.x * blockDim.x; - const int y = threadIdx.y + blockIdx.y * blockDim.y; - const int x_shifted = x * ft::smart_shift; - - if (y < src_.rows) - { - const T* src = src_.ptr(y); - D* dst = dst_.ptr(y); - - if (x_shifted + ft::smart_shift - 1 < src_.cols) - { - const read_type src_n_el = ((const read_type*)src)[x]; - OpUnroller::unroll(src_n_el, ((write_type*)dst)[x], mask, op, x_shifted, y); - } - else - { - for (int real_x = x_shifted; real_x < src_.cols; ++real_x) - { - if (mask(y, real_x)) - dst[real_x] = op(src[real_x]); - } - } - } - } - - template - __global__ static void transformSimple(const PtrStepSz src, PtrStep dst, const Mask mask, const UnOp op) - { - const int x = blockDim.x * blockIdx.x + threadIdx.x; - const int y = blockDim.y * blockIdx.y + threadIdx.y; - - if (x < src.cols && y < src.rows && mask(y, x)) - { - dst.ptr(y)[x] = op(src.ptr(y)[x]); - } - } - - template - static __global__ void transformSmart(const PtrStepSz src1_, const PtrStep src2_, PtrStep dst_, - const Mask mask, const BinOp op) - { - typedef TransformFunctorTraits ft; - typedef typename BinaryReadWriteTraits::read_type1 read_type1; - typedef typename BinaryReadWriteTraits::read_type2 read_type2; - typedef typename BinaryReadWriteTraits::write_type write_type; - - const int x = threadIdx.x + blockIdx.x * blockDim.x; - const int y = threadIdx.y + blockIdx.y * blockDim.y; - const int x_shifted = x * ft::smart_shift; - - if (y < src1_.rows) - { - const T1* src1 = src1_.ptr(y); - const T2* src2 = src2_.ptr(y); - D* dst = dst_.ptr(y); - - if (x_shifted + ft::smart_shift - 1 < src1_.cols) - { - const read_type1 src1_n_el = ((const read_type1*)src1)[x]; - const read_type2 src2_n_el = ((const read_type2*)src2)[x]; - - OpUnroller::unroll(src1_n_el, src2_n_el, ((write_type*)dst)[x], mask, op, x_shifted, y); - } - else - { - for (int real_x = x_shifted; real_x < src1_.cols; ++real_x) - { - if (mask(y, real_x)) - dst[real_x] = op(src1[real_x], src2[real_x]); - } - } - } - } - - template - static __global__ void transformSimple(const PtrStepSz src1, const PtrStep src2, PtrStep dst, - const Mask mask, const BinOp op) - { - const int x = blockDim.x * blockIdx.x + threadIdx.x; - const int y = blockDim.y * blockIdx.y + threadIdx.y; - - if (x < src1.cols && y < src1.rows && mask(y, x)) - { - const T1 src1_data = src1.ptr(y)[x]; - const T2 src2_data = src2.ptr(y)[x]; - dst.ptr(y)[x] = op(src1_data, src2_data); - } - } - - template struct TransformDispatcher; - template<> struct TransformDispatcher - { - template - static void call(PtrStepSz src, PtrStepSz dst, UnOp op, Mask mask, cudaStream_t stream) - { - typedef TransformFunctorTraits ft; - - const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1); - const dim3 grid(divUp(src.cols, threads.x), divUp(src.rows, threads.y), 1); - - transformSimple<<>>(src, dst, mask, op); - cudaSafeCall( cudaGetLastError() ); - - if (stream == 0) - cudaSafeCall( cudaDeviceSynchronize() ); - } - - template - static void call(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, Mask mask, cudaStream_t stream) - { - typedef TransformFunctorTraits ft; - - const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1); - const dim3 grid(divUp(src1.cols, threads.x), divUp(src1.rows, threads.y), 1); - - transformSimple<<>>(src1, src2, dst, mask, op); - cudaSafeCall( cudaGetLastError() ); - - if (stream == 0) - cudaSafeCall( cudaDeviceSynchronize() ); - } - }; - template<> struct TransformDispatcher - { - template - static void call(PtrStepSz src, PtrStepSz dst, UnOp op, Mask mask, cudaStream_t stream) - { - typedef TransformFunctorTraits ft; - - CV_StaticAssert(ft::smart_shift != 1, ""); - - if (!isAligned(src.data, ft::smart_shift * sizeof(T)) || !isAligned(src.step, ft::smart_shift * sizeof(T)) || - !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D))) - { - TransformDispatcher::call(src, dst, op, mask, stream); - return; - } - - const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1); - const dim3 grid(divUp(src.cols, threads.x * ft::smart_shift), divUp(src.rows, threads.y), 1); - - transformSmart<<>>(src, dst, mask, op); - cudaSafeCall( cudaGetLastError() ); - - if (stream == 0) - cudaSafeCall( cudaDeviceSynchronize() ); - } - - template - static void call(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, Mask mask, cudaStream_t stream) - { - typedef TransformFunctorTraits ft; - - CV_StaticAssert(ft::smart_shift != 1, ""); - - if (!isAligned(src1.data, ft::smart_shift * sizeof(T1)) || !isAligned(src1.step, ft::smart_shift * sizeof(T1)) || - !isAligned(src2.data, ft::smart_shift * sizeof(T2)) || !isAligned(src2.step, ft::smart_shift * sizeof(T2)) || - !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D))) - { - TransformDispatcher::call(src1, src2, dst, op, mask, stream); - return; - } - - const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1); - const dim3 grid(divUp(src1.cols, threads.x * ft::smart_shift), divUp(src1.rows, threads.y), 1); - - transformSmart<<>>(src1, src2, dst, mask, op); - cudaSafeCall( cudaGetLastError() ); - - if (stream == 0) - cudaSafeCall( cudaDeviceSynchronize() ); - } - }; - } // namespace transform_detail -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // OPENCV_CUDA_TRANSFORM_DETAIL_HPP diff --git a/include/opencv2/core/cuda/detail/type_traits_detail.hpp b/include/opencv2/core/cuda/detail/type_traits_detail.hpp deleted file mode 100755 index a78bd2c..0000000 --- a/include/opencv2/core/cuda/detail/type_traits_detail.hpp +++ /dev/null @@ -1,191 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP -#define OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP - -#include "../common.hpp" -#include "../vec_traits.hpp" - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - namespace type_traits_detail - { - template struct Select { typedef T1 type; }; - template struct Select { typedef T2 type; }; - - template struct IsSignedIntergral { enum {value = 0}; }; - template <> struct IsSignedIntergral { enum {value = 1}; }; - template <> struct IsSignedIntergral { enum {value = 1}; }; - template <> struct IsSignedIntergral { enum {value = 1}; }; - template <> struct IsSignedIntergral { enum {value = 1}; }; - template <> struct IsSignedIntergral { enum {value = 1}; }; - template <> struct IsSignedIntergral { enum {value = 1}; }; - - template struct IsUnsignedIntegral { enum {value = 0}; }; - template <> struct IsUnsignedIntegral { enum {value = 1}; }; - template <> struct IsUnsignedIntegral { enum {value = 1}; }; - template <> struct IsUnsignedIntegral { enum {value = 1}; }; - template <> struct IsUnsignedIntegral { enum {value = 1}; }; - template <> struct IsUnsignedIntegral { enum {value = 1}; }; - template <> struct IsUnsignedIntegral { enum {value = 1}; }; - - template struct IsIntegral { enum {value = IsSignedIntergral::value || IsUnsignedIntegral::value}; }; - template <> struct IsIntegral { enum {value = 1}; }; - template <> struct IsIntegral { enum {value = 1}; }; - - template struct IsFloat { enum {value = 0}; }; - template <> struct IsFloat { enum {value = 1}; }; - template <> struct IsFloat { enum {value = 1}; }; - - template struct IsVec { enum {value = 0}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - template <> struct IsVec { enum {value = 1}; }; - - template struct AddParameterType { typedef const U& type; }; - template struct AddParameterType { typedef U& type; }; - template <> struct AddParameterType { typedef void type; }; - - template struct ReferenceTraits - { - enum { value = false }; - typedef U type; - }; - template struct ReferenceTraits - { - enum { value = true }; - typedef U type; - }; - - template struct PointerTraits - { - enum { value = false }; - typedef void type; - }; - template struct PointerTraits - { - enum { value = true }; - typedef U type; - }; - template struct PointerTraits - { - enum { value = true }; - typedef U type; - }; - - template struct UnConst - { - typedef U type; - enum { value = 0 }; - }; - template struct UnConst - { - typedef U type; - enum { value = 1 }; - }; - template struct UnConst - { - typedef U& type; - enum { value = 1 }; - }; - - template struct UnVolatile - { - typedef U type; - enum { value = 0 }; - }; - template struct UnVolatile - { - typedef U type; - enum { value = 1 }; - }; - template struct UnVolatile - { - typedef U& type; - enum { value = 1 }; - }; - } // namespace type_traits_detail -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP diff --git a/include/opencv2/core/cuda/detail/vec_distance_detail.hpp b/include/opencv2/core/cuda/detail/vec_distance_detail.hpp deleted file mode 100755 index 8283a99..0000000 --- a/include/opencv2/core/cuda/detail/vec_distance_detail.hpp +++ /dev/null @@ -1,121 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP -#define OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP - -#include "../datamov_utils.hpp" - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - namespace vec_distance_detail - { - template struct UnrollVecDiffCached - { - template - static __device__ void calcCheck(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int ind) - { - if (ind < len) - { - T1 val1 = *vecCached++; - - T2 val2; - ForceGlob::Load(vecGlob, ind, val2); - - dist.reduceIter(val1, val2); - - UnrollVecDiffCached::calcCheck(vecCached, vecGlob, len, dist, ind + THREAD_DIM); - } - } - - template - static __device__ void calcWithoutCheck(const T1* vecCached, const T2* vecGlob, Dist& dist) - { - T1 val1 = *vecCached++; - - T2 val2; - ForceGlob::Load(vecGlob, 0, val2); - vecGlob += THREAD_DIM; - - dist.reduceIter(val1, val2); - - UnrollVecDiffCached::calcWithoutCheck(vecCached, vecGlob, dist); - } - }; - template struct UnrollVecDiffCached - { - template - static __device__ __forceinline__ void calcCheck(const T1*, const T2*, int, Dist&, int) - { - } - - template - static __device__ __forceinline__ void calcWithoutCheck(const T1*, const T2*, Dist&) - { - } - }; - - template struct VecDiffCachedCalculator; - template struct VecDiffCachedCalculator - { - template - static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid) - { - UnrollVecDiffCached::calcCheck(vecCached, vecGlob, len, dist, tid); - } - }; - template struct VecDiffCachedCalculator - { - template - static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid) - { - UnrollVecDiffCached::calcWithoutCheck(vecCached, vecGlob + tid, dist); - } - }; - } // namespace vec_distance_detail -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP diff --git a/include/opencv2/core/cuda/dynamic_smem.hpp b/include/opencv2/core/cuda/dynamic_smem.hpp deleted file mode 100755 index 42570c6..0000000 --- a/include/opencv2/core/cuda/dynamic_smem.hpp +++ /dev/null @@ -1,88 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_DYNAMIC_SMEM_HPP -#define OPENCV_CUDA_DYNAMIC_SMEM_HPP - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template struct DynamicSharedMem - { - __device__ __forceinline__ operator T*() - { - extern __shared__ int __smem[]; - return (T*)__smem; - } - - __device__ __forceinline__ operator const T*() const - { - extern __shared__ int __smem[]; - return (T*)__smem; - } - }; - - // specialize for double to avoid unaligned memory access compile errors - template<> struct DynamicSharedMem - { - __device__ __forceinline__ operator double*() - { - extern __shared__ double __smem_d[]; - return (double*)__smem_d; - } - - __device__ __forceinline__ operator const double*() const - { - extern __shared__ double __smem_d[]; - return (double*)__smem_d; - } - }; -}}} - -//! @endcond - -#endif // OPENCV_CUDA_DYNAMIC_SMEM_HPP diff --git a/include/opencv2/core/cuda/emulation.hpp b/include/opencv2/core/cuda/emulation.hpp deleted file mode 100755 index 17dc117..0000000 --- a/include/opencv2/core/cuda/emulation.hpp +++ /dev/null @@ -1,269 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_EMULATION_HPP_ -#define OPENCV_CUDA_EMULATION_HPP_ - -#include "common.hpp" -#include "warp_reduce.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - struct Emulation - { - - static __device__ __forceinline__ int syncthreadsOr(int pred) - { -#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 200) - // just campilation stab - return 0; -#else - return __syncthreads_or(pred); -#endif - } - - template - static __forceinline__ __device__ int Ballot(int predicate) - { -#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 200) - return __ballot(predicate); -#else - __shared__ volatile int cta_buffer[CTA_SIZE]; - - int tid = threadIdx.x; - cta_buffer[tid] = predicate ? (1 << (tid & 31)) : 0; - return warp_reduce(cta_buffer); -#endif - } - - struct smem - { - enum { TAG_MASK = (1U << ( (sizeof(unsigned int) << 3) - 5U)) - 1U }; - - template - static __device__ __forceinline__ T atomicInc(T* address, T val) - { -#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) - T count; - unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U); - do - { - count = *address & TAG_MASK; - count = tag | (count + 1); - *address = count; - } while (*address != count); - - return (count & TAG_MASK) - 1; -#else - return ::atomicInc(address, val); -#endif - } - - template - static __device__ __forceinline__ T atomicAdd(T* address, T val) - { -#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) - T count; - unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U); - do - { - count = *address & TAG_MASK; - count = tag | (count + val); - *address = count; - } while (*address != count); - - return (count & TAG_MASK) - val; -#else - return ::atomicAdd(address, val); -#endif - } - - template - static __device__ __forceinline__ T atomicMin(T* address, T val) - { -#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120) - T count = ::min(*address, val); - do - { - *address = count; - } while (*address > count); - - return count; -#else - return ::atomicMin(address, val); -#endif - } - }; // struct cmem - - struct glob - { - static __device__ __forceinline__ int atomicAdd(int* address, int val) - { - return ::atomicAdd(address, val); - } - static __device__ __forceinline__ unsigned int atomicAdd(unsigned int* address, unsigned int val) - { - return ::atomicAdd(address, val); - } - static __device__ __forceinline__ float atomicAdd(float* address, float val) - { - #if __CUDA_ARCH__ >= 200 - return ::atomicAdd(address, val); - #else - int* address_as_i = (int*) address; - int old = *address_as_i, assumed; - do { - assumed = old; - old = ::atomicCAS(address_as_i, assumed, - __float_as_int(val + __int_as_float(assumed))); - } while (assumed != old); - return __int_as_float(old); - #endif - } - static __device__ __forceinline__ double atomicAdd(double* address, double val) - { - #if __CUDA_ARCH__ >= 130 - unsigned long long int* address_as_ull = (unsigned long long int*) address; - unsigned long long int old = *address_as_ull, assumed; - do { - assumed = old; - old = ::atomicCAS(address_as_ull, assumed, - __double_as_longlong(val + __longlong_as_double(assumed))); - } while (assumed != old); - return __longlong_as_double(old); - #else - CV_UNUSED(address); - CV_UNUSED(val); - return 0.0; - #endif - } - - static __device__ __forceinline__ int atomicMin(int* address, int val) - { - return ::atomicMin(address, val); - } - static __device__ __forceinline__ float atomicMin(float* address, float val) - { - #if __CUDA_ARCH__ >= 120 - int* address_as_i = (int*) address; - int old = *address_as_i, assumed; - do { - assumed = old; - old = ::atomicCAS(address_as_i, assumed, - __float_as_int(::fminf(val, __int_as_float(assumed)))); - } while (assumed != old); - return __int_as_float(old); - #else - CV_UNUSED(address); - CV_UNUSED(val); - return 0.0f; - #endif - } - static __device__ __forceinline__ double atomicMin(double* address, double val) - { - #if __CUDA_ARCH__ >= 130 - unsigned long long int* address_as_ull = (unsigned long long int*) address; - unsigned long long int old = *address_as_ull, assumed; - do { - assumed = old; - old = ::atomicCAS(address_as_ull, assumed, - __double_as_longlong(::fmin(val, __longlong_as_double(assumed)))); - } while (assumed != old); - return __longlong_as_double(old); - #else - CV_UNUSED(address); - CV_UNUSED(val); - return 0.0; - #endif - } - - static __device__ __forceinline__ int atomicMax(int* address, int val) - { - return ::atomicMax(address, val); - } - static __device__ __forceinline__ float atomicMax(float* address, float val) - { - #if __CUDA_ARCH__ >= 120 - int* address_as_i = (int*) address; - int old = *address_as_i, assumed; - do { - assumed = old; - old = ::atomicCAS(address_as_i, assumed, - __float_as_int(::fmaxf(val, __int_as_float(assumed)))); - } while (assumed != old); - return __int_as_float(old); - #else - CV_UNUSED(address); - CV_UNUSED(val); - return 0.0f; - #endif - } - static __device__ __forceinline__ double atomicMax(double* address, double val) - { - #if __CUDA_ARCH__ >= 130 - unsigned long long int* address_as_ull = (unsigned long long int*) address; - unsigned long long int old = *address_as_ull, assumed; - do { - assumed = old; - old = ::atomicCAS(address_as_ull, assumed, - __double_as_longlong(::fmax(val, __longlong_as_double(assumed)))); - } while (assumed != old); - return __longlong_as_double(old); - #else - CV_UNUSED(address); - CV_UNUSED(val); - return 0.0; - #endif - } - }; - }; //struct Emulation -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif /* OPENCV_CUDA_EMULATION_HPP_ */ diff --git a/include/opencv2/core/cuda/filters.hpp b/include/opencv2/core/cuda/filters.hpp deleted file mode 100755 index bb94212..0000000 --- a/include/opencv2/core/cuda/filters.hpp +++ /dev/null @@ -1,286 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_FILTERS_HPP -#define OPENCV_CUDA_FILTERS_HPP - -#include "saturate_cast.hpp" -#include "vec_traits.hpp" -#include "vec_math.hpp" -#include "type_traits.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template struct PointFilter - { - typedef typename Ptr2D::elem_type elem_type; - typedef float index_type; - - explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) - : src(src_) - { - CV_UNUSED(fx); - CV_UNUSED(fy); - } - - __device__ __forceinline__ elem_type operator ()(float y, float x) const - { - return src(__float2int_rz(y), __float2int_rz(x)); - } - - Ptr2D src; - }; - - template struct LinearFilter - { - typedef typename Ptr2D::elem_type elem_type; - typedef float index_type; - - explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) - : src(src_) - { - CV_UNUSED(fx); - CV_UNUSED(fy); - } - __device__ __forceinline__ elem_type operator ()(float y, float x) const - { - typedef typename TypeVec::cn>::vec_type work_type; - - work_type out = VecTraits::all(0); - - const int x1 = __float2int_rd(x); - const int y1 = __float2int_rd(y); - const int x2 = x1 + 1; - const int y2 = y1 + 1; - - elem_type src_reg = src(y1, x1); - out = out + src_reg * ((x2 - x) * (y2 - y)); - - src_reg = src(y1, x2); - out = out + src_reg * ((x - x1) * (y2 - y)); - - src_reg = src(y2, x1); - out = out + src_reg * ((x2 - x) * (y - y1)); - - src_reg = src(y2, x2); - out = out + src_reg * ((x - x1) * (y - y1)); - - return saturate_cast(out); - } - - Ptr2D src; - }; - - template struct CubicFilter - { - typedef typename Ptr2D::elem_type elem_type; - typedef float index_type; - typedef typename TypeVec::cn>::vec_type work_type; - - explicit __host__ __device__ __forceinline__ CubicFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) - : src(src_) - { - CV_UNUSED(fx); - CV_UNUSED(fy); - } - - static __device__ __forceinline__ float bicubicCoeff(float x_) - { - float x = fabsf(x_); - if (x <= 1.0f) - { - return x * x * (1.5f * x - 2.5f) + 1.0f; - } - else if (x < 2.0f) - { - return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f; - } - else - { - return 0.0f; - } - } - - __device__ elem_type operator ()(float y, float x) const - { - const float xmin = ::ceilf(x - 2.0f); - const float xmax = ::floorf(x + 2.0f); - - const float ymin = ::ceilf(y - 2.0f); - const float ymax = ::floorf(y + 2.0f); - - work_type sum = VecTraits::all(0); - float wsum = 0.0f; - - for (float cy = ymin; cy <= ymax; cy += 1.0f) - { - for (float cx = xmin; cx <= xmax; cx += 1.0f) - { - const float w = bicubicCoeff(x - cx) * bicubicCoeff(y - cy); - sum = sum + w * src(__float2int_rd(cy), __float2int_rd(cx)); - wsum += w; - } - } - - work_type res = (!wsum)? VecTraits::all(0) : sum / wsum; - - return saturate_cast(res); - } - - Ptr2D src; - }; - // for integer scaling - template struct IntegerAreaFilter - { - typedef typename Ptr2D::elem_type elem_type; - typedef float index_type; - - explicit __host__ __device__ __forceinline__ IntegerAreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_) - : src(src_), scale_x(scale_x_), scale_y(scale_y_), scale(1.f / (scale_x * scale_y)) {} - - __device__ __forceinline__ elem_type operator ()(float y, float x) const - { - float fsx1 = x * scale_x; - float fsx2 = fsx1 + scale_x; - - int sx1 = __float2int_ru(fsx1); - int sx2 = __float2int_rd(fsx2); - - float fsy1 = y * scale_y; - float fsy2 = fsy1 + scale_y; - - int sy1 = __float2int_ru(fsy1); - int sy2 = __float2int_rd(fsy2); - - typedef typename TypeVec::cn>::vec_type work_type; - work_type out = VecTraits::all(0.f); - - for(int dy = sy1; dy < sy2; ++dy) - for(int dx = sx1; dx < sx2; ++dx) - { - out = out + src(dy, dx) * scale; - } - - return saturate_cast(out); - } - - Ptr2D src; - float scale_x, scale_y ,scale; - }; - - template struct AreaFilter - { - typedef typename Ptr2D::elem_type elem_type; - typedef float index_type; - - explicit __host__ __device__ __forceinline__ AreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_) - : src(src_), scale_x(scale_x_), scale_y(scale_y_){} - - __device__ __forceinline__ elem_type operator ()(float y, float x) const - { - float fsx1 = x * scale_x; - float fsx2 = fsx1 + scale_x; - - int sx1 = __float2int_ru(fsx1); - int sx2 = __float2int_rd(fsx2); - - float fsy1 = y * scale_y; - float fsy2 = fsy1 + scale_y; - - int sy1 = __float2int_ru(fsy1); - int sy2 = __float2int_rd(fsy2); - - float scale = 1.f / (fminf(scale_x, src.width - fsx1) * fminf(scale_y, src.height - fsy1)); - - typedef typename TypeVec::cn>::vec_type work_type; - work_type out = VecTraits::all(0.f); - - for (int dy = sy1; dy < sy2; ++dy) - { - for (int dx = sx1; dx < sx2; ++dx) - out = out + src(dy, dx) * scale; - - if (sx1 > fsx1) - out = out + src(dy, (sx1 -1) ) * ((sx1 - fsx1) * scale); - - if (sx2 < fsx2) - out = out + src(dy, sx2) * ((fsx2 -sx2) * scale); - } - - if (sy1 > fsy1) - for (int dx = sx1; dx < sx2; ++dx) - out = out + src( (sy1 - 1) , dx) * ((sy1 -fsy1) * scale); - - if (sy2 < fsy2) - for (int dx = sx1; dx < sx2; ++dx) - out = out + src(sy2, dx) * ((fsy2 -sy2) * scale); - - if ((sy1 > fsy1) && (sx1 > fsx1)) - out = out + src( (sy1 - 1) , (sx1 - 1)) * ((sy1 -fsy1) * (sx1 -fsx1) * scale); - - if ((sy1 > fsy1) && (sx2 < fsx2)) - out = out + src( (sy1 - 1) , sx2) * ((sy1 -fsy1) * (fsx2 -sx2) * scale); - - if ((sy2 < fsy2) && (sx2 < fsx2)) - out = out + src(sy2, sx2) * ((fsy2 -sy2) * (fsx2 -sx2) * scale); - - if ((sy2 < fsy2) && (sx1 > fsx1)) - out = out + src(sy2, (sx1 - 1)) * ((fsy2 -sy2) * (sx1 -fsx1) * scale); - - return saturate_cast(out); - } - - Ptr2D src; - float scale_x, scale_y; - int width, haight; - }; -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // OPENCV_CUDA_FILTERS_HPP diff --git a/include/opencv2/core/cuda/funcattrib.hpp b/include/opencv2/core/cuda/funcattrib.hpp deleted file mode 100755 index f582080..0000000 --- a/include/opencv2/core/cuda/funcattrib.hpp +++ /dev/null @@ -1,79 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP -#define OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP - -#include - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template - void printFuncAttrib(Func& func) - { - - cudaFuncAttributes attrs; - cudaFuncGetAttributes(&attrs, func); - - printf("=== Function stats ===\n"); - printf("Name: \n"); - printf("sharedSizeBytes = %d\n", attrs.sharedSizeBytes); - printf("constSizeBytes = %d\n", attrs.constSizeBytes); - printf("localSizeBytes = %d\n", attrs.localSizeBytes); - printf("maxThreadsPerBlock = %d\n", attrs.maxThreadsPerBlock); - printf("numRegs = %d\n", attrs.numRegs); - printf("ptxVersion = %d\n", attrs.ptxVersion); - printf("binaryVersion = %d\n", attrs.binaryVersion); - printf("\n"); - fflush(stdout); - } -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif /* OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP */ diff --git a/include/opencv2/core/cuda/functional.hpp b/include/opencv2/core/cuda/functional.hpp deleted file mode 100755 index 3b531a1..0000000 --- a/include/opencv2/core/cuda/functional.hpp +++ /dev/null @@ -1,810 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_FUNCTIONAL_HPP -#define OPENCV_CUDA_FUNCTIONAL_HPP - -#include -#include "saturate_cast.hpp" -#include "vec_traits.hpp" -#include "type_traits.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - // Function Objects -#ifdef CV_CXX11 - template struct unary_function - { - typedef Argument argument_type; - typedef Result result_type; - }; - template struct binary_function - { - typedef Argument1 first_argument_type; - typedef Argument2 second_argument_type; - typedef Result result_type; - }; -#else - template struct unary_function : public std::unary_function {}; - template struct binary_function : public std::binary_function {}; -#endif - - // Arithmetic Operations - template struct plus : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a + b; - } - __host__ __device__ __forceinline__ plus() {} - __host__ __device__ __forceinline__ plus(const plus&) {} - }; - - template struct minus : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a - b; - } - __host__ __device__ __forceinline__ minus() {} - __host__ __device__ __forceinline__ minus(const minus&) {} - }; - - template struct multiplies : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a * b; - } - __host__ __device__ __forceinline__ multiplies() {} - __host__ __device__ __forceinline__ multiplies(const multiplies&) {} - }; - - template struct divides : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a / b; - } - __host__ __device__ __forceinline__ divides() {} - __host__ __device__ __forceinline__ divides(const divides&) {} - }; - - template struct modulus : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a % b; - } - __host__ __device__ __forceinline__ modulus() {} - __host__ __device__ __forceinline__ modulus(const modulus&) {} - }; - - template struct negate : unary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a) const - { - return -a; - } - __host__ __device__ __forceinline__ negate() {} - __host__ __device__ __forceinline__ negate(const negate&) {} - }; - - // Comparison Operations - template struct equal_to : binary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a == b; - } - __host__ __device__ __forceinline__ equal_to() {} - __host__ __device__ __forceinline__ equal_to(const equal_to&) {} - }; - - template struct not_equal_to : binary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a != b; - } - __host__ __device__ __forceinline__ not_equal_to() {} - __host__ __device__ __forceinline__ not_equal_to(const not_equal_to&) {} - }; - - template struct greater : binary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a > b; - } - __host__ __device__ __forceinline__ greater() {} - __host__ __device__ __forceinline__ greater(const greater&) {} - }; - - template struct less : binary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a < b; - } - __host__ __device__ __forceinline__ less() {} - __host__ __device__ __forceinline__ less(const less&) {} - }; - - template struct greater_equal : binary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a >= b; - } - __host__ __device__ __forceinline__ greater_equal() {} - __host__ __device__ __forceinline__ greater_equal(const greater_equal&) {} - }; - - template struct less_equal : binary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a <= b; - } - __host__ __device__ __forceinline__ less_equal() {} - __host__ __device__ __forceinline__ less_equal(const less_equal&) {} - }; - - // Logical Operations - template struct logical_and : binary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a && b; - } - __host__ __device__ __forceinline__ logical_and() {} - __host__ __device__ __forceinline__ logical_and(const logical_and&) {} - }; - - template struct logical_or : binary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a || b; - } - __host__ __device__ __forceinline__ logical_or() {} - __host__ __device__ __forceinline__ logical_or(const logical_or&) {} - }; - - template struct logical_not : unary_function - { - __device__ __forceinline__ bool operator ()(typename TypeTraits::ParameterType a) const - { - return !a; - } - __host__ __device__ __forceinline__ logical_not() {} - __host__ __device__ __forceinline__ logical_not(const logical_not&) {} - }; - - // Bitwise Operations - template struct bit_and : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a & b; - } - __host__ __device__ __forceinline__ bit_and() {} - __host__ __device__ __forceinline__ bit_and(const bit_and&) {} - }; - - template struct bit_or : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a | b; - } - __host__ __device__ __forceinline__ bit_or() {} - __host__ __device__ __forceinline__ bit_or(const bit_or&) {} - }; - - template struct bit_xor : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType a, - typename TypeTraits::ParameterType b) const - { - return a ^ b; - } - __host__ __device__ __forceinline__ bit_xor() {} - __host__ __device__ __forceinline__ bit_xor(const bit_xor&) {} - }; - - template struct bit_not : unary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType v) const - { - return ~v; - } - __host__ __device__ __forceinline__ bit_not() {} - __host__ __device__ __forceinline__ bit_not(const bit_not&) {} - }; - - // Generalized Identity Operations - template struct identity : unary_function - { - __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType x) const - { - return x; - } - __host__ __device__ __forceinline__ identity() {} - __host__ __device__ __forceinline__ identity(const identity&) {} - }; - - template struct project1st : binary_function - { - __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const - { - return lhs; - } - __host__ __device__ __forceinline__ project1st() {} - __host__ __device__ __forceinline__ project1st(const project1st&) {} - }; - - template struct project2nd : binary_function - { - __device__ __forceinline__ typename TypeTraits::ParameterType operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const - { - return rhs; - } - __host__ __device__ __forceinline__ project2nd() {} - __host__ __device__ __forceinline__ project2nd(const project2nd&) {} - }; - - // Min/Max Operations - -#define OPENCV_CUDA_IMPLEMENT_MINMAX(name, type, op) \ - template <> struct name : binary_function \ - { \ - __device__ __forceinline__ type operator()(type lhs, type rhs) const {return op(lhs, rhs);} \ - __host__ __device__ __forceinline__ name() {}\ - __host__ __device__ __forceinline__ name(const name&) {}\ - }; - - template struct maximum : binary_function - { - __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const - { - return max(lhs, rhs); - } - __host__ __device__ __forceinline__ maximum() {} - __host__ __device__ __forceinline__ maximum(const maximum&) {} - }; - - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uchar, ::max) - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, schar, ::max) - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, char, ::max) - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, ushort, ::max) - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, short, ::max) - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, int, ::max) - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uint, ::max) - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, float, ::fmax) - OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, double, ::fmax) - - template struct minimum : binary_function - { - __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType lhs, typename TypeTraits::ParameterType rhs) const - { - return min(lhs, rhs); - } - __host__ __device__ __forceinline__ minimum() {} - __host__ __device__ __forceinline__ minimum(const minimum&) {} - }; - - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uchar, ::min) - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, schar, ::min) - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, char, ::min) - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, ushort, ::min) - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, short, ::min) - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, int, ::min) - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uint, ::min) - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, float, ::fmin) - OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, double, ::fmin) - -#undef OPENCV_CUDA_IMPLEMENT_MINMAX - - // Math functions - - template struct abs_func : unary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType x) const - { - return abs(x); - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ unsigned char operator ()(unsigned char x) const - { - return x; - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ signed char operator ()(signed char x) const - { - return ::abs((int)x); - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ char operator ()(char x) const - { - return ::abs((int)x); - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ unsigned short operator ()(unsigned short x) const - { - return x; - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ short operator ()(short x) const - { - return ::abs((int)x); - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ unsigned int operator ()(unsigned int x) const - { - return x; - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ int operator ()(int x) const - { - return ::abs(x); - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ float operator ()(float x) const - { - return ::fabsf(x); - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - template <> struct abs_func : unary_function - { - __device__ __forceinline__ double operator ()(double x) const - { - return ::fabs(x); - } - - __host__ __device__ __forceinline__ abs_func() {} - __host__ __device__ __forceinline__ abs_func(const abs_func&) {} - }; - -#define OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(name, func) \ - template struct name ## _func : unary_function \ - { \ - __device__ __forceinline__ float operator ()(typename TypeTraits::ParameterType v) const \ - { \ - return func ## f(v); \ - } \ - __host__ __device__ __forceinline__ name ## _func() {} \ - __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ - }; \ - template <> struct name ## _func : unary_function \ - { \ - __device__ __forceinline__ double operator ()(double v) const \ - { \ - return func(v); \ - } \ - __host__ __device__ __forceinline__ name ## _func() {} \ - __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ - }; - -#define OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(name, func) \ - template struct name ## _func : binary_function \ - { \ - __device__ __forceinline__ float operator ()(typename TypeTraits::ParameterType v1, typename TypeTraits::ParameterType v2) const \ - { \ - return func ## f(v1, v2); \ - } \ - __host__ __device__ __forceinline__ name ## _func() {} \ - __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ - }; \ - template <> struct name ## _func : binary_function \ - { \ - __device__ __forceinline__ double operator ()(double v1, double v2) const \ - { \ - return func(v1, v2); \ - } \ - __host__ __device__ __forceinline__ name ## _func() {} \ - __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \ - }; - - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sqrt, ::sqrt) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp, ::exp) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp2, ::exp2) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp10, ::exp10) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log, ::log) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log2, ::log2) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log10, ::log10) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sin, ::sin) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cos, ::cos) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tan, ::tan) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asin, ::asin) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acos, ::acos) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atan, ::atan) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sinh, ::sinh) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cosh, ::cosh) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tanh, ::tanh) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asinh, ::asinh) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acosh, ::acosh) - OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atanh, ::atanh) - - OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(hypot, ::hypot) - OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(atan2, ::atan2) - OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(pow, ::pow) - - #undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR - #undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR_NO_DOUBLE - #undef OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR - - template struct hypot_sqr_func : binary_function - { - __device__ __forceinline__ T operator ()(typename TypeTraits::ParameterType src1, typename TypeTraits::ParameterType src2) const - { - return src1 * src1 + src2 * src2; - } - __host__ __device__ __forceinline__ hypot_sqr_func() {} - __host__ __device__ __forceinline__ hypot_sqr_func(const hypot_sqr_func&) {} - }; - - // Saturate Cast Functor - template struct saturate_cast_func : unary_function - { - __device__ __forceinline__ D operator ()(typename TypeTraits::ParameterType v) const - { - return saturate_cast(v); - } - __host__ __device__ __forceinline__ saturate_cast_func() {} - __host__ __device__ __forceinline__ saturate_cast_func(const saturate_cast_func&) {} - }; - - // Threshold Functors - template struct thresh_binary_func : unary_function - { - __host__ __device__ __forceinline__ thresh_binary_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {} - - __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const - { - return (src > thresh) * maxVal; - } - - __host__ __device__ __forceinline__ thresh_binary_func() {} - __host__ __device__ __forceinline__ thresh_binary_func(const thresh_binary_func& other) - : thresh(other.thresh), maxVal(other.maxVal) {} - - T thresh; - T maxVal; - }; - - template struct thresh_binary_inv_func : unary_function - { - __host__ __device__ __forceinline__ thresh_binary_inv_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {} - - __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const - { - return (src <= thresh) * maxVal; - } - - __host__ __device__ __forceinline__ thresh_binary_inv_func() {} - __host__ __device__ __forceinline__ thresh_binary_inv_func(const thresh_binary_inv_func& other) - : thresh(other.thresh), maxVal(other.maxVal) {} - - T thresh; - T maxVal; - }; - - template struct thresh_trunc_func : unary_function - { - explicit __host__ __device__ __forceinline__ thresh_trunc_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);} - - __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const - { - return minimum()(src, thresh); - } - - __host__ __device__ __forceinline__ thresh_trunc_func() {} - __host__ __device__ __forceinline__ thresh_trunc_func(const thresh_trunc_func& other) - : thresh(other.thresh) {} - - T thresh; - }; - - template struct thresh_to_zero_func : unary_function - { - explicit __host__ __device__ __forceinline__ thresh_to_zero_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);} - - __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const - { - return (src > thresh) * src; - } - - __host__ __device__ __forceinline__ thresh_to_zero_func() {} - __host__ __device__ __forceinline__ thresh_to_zero_func(const thresh_to_zero_func& other) - : thresh(other.thresh) {} - - T thresh; - }; - - template struct thresh_to_zero_inv_func : unary_function - { - explicit __host__ __device__ __forceinline__ thresh_to_zero_inv_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);} - - __device__ __forceinline__ T operator()(typename TypeTraits::ParameterType src) const - { - return (src <= thresh) * src; - } - - __host__ __device__ __forceinline__ thresh_to_zero_inv_func() {} - __host__ __device__ __forceinline__ thresh_to_zero_inv_func(const thresh_to_zero_inv_func& other) - : thresh(other.thresh) {} - - T thresh; - }; - - // Function Object Adaptors - template struct unary_negate : unary_function - { - explicit __host__ __device__ __forceinline__ unary_negate(const Predicate& p) : pred(p) {} - - __device__ __forceinline__ bool operator()(typename TypeTraits::ParameterType x) const - { - return !pred(x); - } - - __host__ __device__ __forceinline__ unary_negate() {} - __host__ __device__ __forceinline__ unary_negate(const unary_negate& other) : pred(other.pred) {} - - Predicate pred; - }; - - template __host__ __device__ __forceinline__ unary_negate not1(const Predicate& pred) - { - return unary_negate(pred); - } - - template struct binary_negate : binary_function - { - explicit __host__ __device__ __forceinline__ binary_negate(const Predicate& p) : pred(p) {} - - __device__ __forceinline__ bool operator()(typename TypeTraits::ParameterType x, - typename TypeTraits::ParameterType y) const - { - return !pred(x,y); - } - - __host__ __device__ __forceinline__ binary_negate() {} - __host__ __device__ __forceinline__ binary_negate(const binary_negate& other) : pred(other.pred) {} - - Predicate pred; - }; - - template __host__ __device__ __forceinline__ binary_negate not2(const BinaryPredicate& pred) - { - return binary_negate(pred); - } - - template struct binder1st : unary_function - { - __host__ __device__ __forceinline__ binder1st(const Op& op_, const typename Op::first_argument_type& arg1_) : op(op_), arg1(arg1_) {} - - __device__ __forceinline__ typename Op::result_type operator ()(typename TypeTraits::ParameterType a) const - { - return op(arg1, a); - } - - __host__ __device__ __forceinline__ binder1st() {} - __host__ __device__ __forceinline__ binder1st(const binder1st& other) : op(other.op), arg1(other.arg1) {} - - Op op; - typename Op::first_argument_type arg1; - }; - - template __host__ __device__ __forceinline__ binder1st bind1st(const Op& op, const T& x) - { - return binder1st(op, typename Op::first_argument_type(x)); - } - - template struct binder2nd : unary_function - { - __host__ __device__ __forceinline__ binder2nd(const Op& op_, const typename Op::second_argument_type& arg2_) : op(op_), arg2(arg2_) {} - - __forceinline__ __device__ typename Op::result_type operator ()(typename TypeTraits::ParameterType a) const - { - return op(a, arg2); - } - - __host__ __device__ __forceinline__ binder2nd() {} - __host__ __device__ __forceinline__ binder2nd(const binder2nd& other) : op(other.op), arg2(other.arg2) {} - - Op op; - typename Op::second_argument_type arg2; - }; - - template __host__ __device__ __forceinline__ binder2nd bind2nd(const Op& op, const T& x) - { - return binder2nd(op, typename Op::second_argument_type(x)); - } - - // Functor Traits - template struct IsUnaryFunction - { - typedef char Yes; - struct No {Yes a[2];}; - - template static Yes check(unary_function); - static No check(...); - - static F makeF(); - - enum { value = (sizeof(check(makeF())) == sizeof(Yes)) }; - }; - - template struct IsBinaryFunction - { - typedef char Yes; - struct No {Yes a[2];}; - - template static Yes check(binary_function); - static No check(...); - - static F makeF(); - - enum { value = (sizeof(check(makeF())) == sizeof(Yes)) }; - }; - - namespace functional_detail - { - template struct UnOpShift { enum { shift = 1 }; }; - template struct UnOpShift { enum { shift = 4 }; }; - template struct UnOpShift { enum { shift = 2 }; }; - - template struct DefaultUnaryShift - { - enum { shift = UnOpShift::shift }; - }; - - template struct BinOpShift { enum { shift = 1 }; }; - template struct BinOpShift { enum { shift = 4 }; }; - template struct BinOpShift { enum { shift = 2 }; }; - - template struct DefaultBinaryShift - { - enum { shift = BinOpShift::shift }; - }; - - template ::value> struct ShiftDispatcher; - template struct ShiftDispatcher - { - enum { shift = DefaultUnaryShift::shift }; - }; - template struct ShiftDispatcher - { - enum { shift = DefaultBinaryShift::shift }; - }; - } - - template struct DefaultTransformShift - { - enum { shift = functional_detail::ShiftDispatcher::shift }; - }; - - template struct DefaultTransformFunctorTraits - { - enum { simple_block_dim_x = 16 }; - enum { simple_block_dim_y = 16 }; - - enum { smart_block_dim_x = 16 }; - enum { smart_block_dim_y = 16 }; - enum { smart_shift = DefaultTransformShift::shift }; - }; - - template struct TransformFunctorTraits : DefaultTransformFunctorTraits {}; - -#define OPENCV_CUDA_TRANSFORM_FUNCTOR_TRAITS(type) \ - template <> struct TransformFunctorTraits< type > : DefaultTransformFunctorTraits< type > -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // OPENCV_CUDA_FUNCTIONAL_HPP diff --git a/include/opencv2/core/cuda/limits.hpp b/include/opencv2/core/cuda/limits.hpp deleted file mode 100755 index 7e15ed6..0000000 --- a/include/opencv2/core/cuda/limits.hpp +++ /dev/null @@ -1,128 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_LIMITS_HPP -#define OPENCV_CUDA_LIMITS_HPP - -#include -#include -#include "common.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ -template struct numeric_limits; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static bool min() { return false; } - __device__ __forceinline__ static bool max() { return true; } - static const bool is_signed = false; -}; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static signed char min() { return SCHAR_MIN; } - __device__ __forceinline__ static signed char max() { return SCHAR_MAX; } - static const bool is_signed = true; -}; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static unsigned char min() { return 0; } - __device__ __forceinline__ static unsigned char max() { return UCHAR_MAX; } - static const bool is_signed = false; -}; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static short min() { return SHRT_MIN; } - __device__ __forceinline__ static short max() { return SHRT_MAX; } - static const bool is_signed = true; -}; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static unsigned short min() { return 0; } - __device__ __forceinline__ static unsigned short max() { return USHRT_MAX; } - static const bool is_signed = false; -}; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static int min() { return INT_MIN; } - __device__ __forceinline__ static int max() { return INT_MAX; } - static const bool is_signed = true; -}; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static unsigned int min() { return 0; } - __device__ __forceinline__ static unsigned int max() { return UINT_MAX; } - static const bool is_signed = false; -}; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static float min() { return FLT_MIN; } - __device__ __forceinline__ static float max() { return FLT_MAX; } - __device__ __forceinline__ static float epsilon() { return FLT_EPSILON; } - static const bool is_signed = true; -}; - -template <> struct numeric_limits -{ - __device__ __forceinline__ static double min() { return DBL_MIN; } - __device__ __forceinline__ static double max() { return DBL_MAX; } - __device__ __forceinline__ static double epsilon() { return DBL_EPSILON; } - static const bool is_signed = true; -}; -}}} // namespace cv { namespace cuda { namespace cudev { - -//! @endcond - -#endif // OPENCV_CUDA_LIMITS_HPP diff --git a/include/opencv2/core/cuda/reduce.hpp b/include/opencv2/core/cuda/reduce.hpp deleted file mode 100755 index 5de3650..0000000 --- a/include/opencv2/core/cuda/reduce.hpp +++ /dev/null @@ -1,209 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_REDUCE_HPP -#define OPENCV_CUDA_REDUCE_HPP - -#ifndef THRUST_DEBUG // eliminate -Wundef warning -#define THRUST_DEBUG 0 -#endif - -#include -#include "detail/reduce.hpp" -#include "detail/reduce_key_val.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template - __device__ __forceinline__ void reduce(volatile T* smem, T& val, unsigned int tid, const Op& op) - { - reduce_detail::Dispatcher::reductor::template reduce(smem, val, tid, op); - } - template - __device__ __forceinline__ void reduce(const thrust::tuple& smem, - const thrust::tuple& val, - unsigned int tid, - const thrust::tuple& op) - { - reduce_detail::Dispatcher::reductor::template reduce< - const thrust::tuple&, - const thrust::tuple&, - const thrust::tuple&>(smem, val, tid, op); - } - - template - __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key, volatile V* svals, V& val, unsigned int tid, const Cmp& cmp) - { - reduce_key_val_detail::Dispatcher::reductor::template reduce(skeys, key, svals, val, tid, cmp); - } - template - __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key, - const thrust::tuple& svals, - const thrust::tuple& val, - unsigned int tid, const Cmp& cmp) - { - reduce_key_val_detail::Dispatcher::reductor::template reduce&, - const thrust::tuple&, - const Cmp&>(skeys, key, svals, val, tid, cmp); - } - template - __device__ __forceinline__ void reduceKeyVal(const thrust::tuple& skeys, - const thrust::tuple& key, - const thrust::tuple& svals, - const thrust::tuple& val, - unsigned int tid, - const thrust::tuple& cmp) - { - reduce_key_val_detail::Dispatcher::reductor::template reduce< - const thrust::tuple&, - const thrust::tuple&, - const thrust::tuple&, - const thrust::tuple&, - const thrust::tuple& - >(skeys, key, svals, val, tid, cmp); - } - - // smem_tuple - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0) - { - return thrust::make_tuple((volatile T0*) t0); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1, T2* t2) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8); - } - - template - __device__ __forceinline__ - thrust::tuple - smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8, T9* t9) - { - return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8, (volatile T9*) t9); - } -}}} - -//! @endcond - -#endif // OPENCV_CUDA_REDUCE_HPP diff --git a/include/opencv2/core/cuda/saturate_cast.hpp b/include/opencv2/core/cuda/saturate_cast.hpp deleted file mode 100755 index c3a3d1c..0000000 --- a/include/opencv2/core/cuda/saturate_cast.hpp +++ /dev/null @@ -1,292 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_SATURATE_CAST_HPP -#define OPENCV_CUDA_SATURATE_CAST_HPP - -#include "common.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template __device__ __forceinline__ _Tp saturate_cast(uchar v) { return _Tp(v); } - template __device__ __forceinline__ _Tp saturate_cast(schar v) { return _Tp(v); } - template __device__ __forceinline__ _Tp saturate_cast(ushort v) { return _Tp(v); } - template __device__ __forceinline__ _Tp saturate_cast(short v) { return _Tp(v); } - template __device__ __forceinline__ _Tp saturate_cast(uint v) { return _Tp(v); } - template __device__ __forceinline__ _Tp saturate_cast(int v) { return _Tp(v); } - template __device__ __forceinline__ _Tp saturate_cast(float v) { return _Tp(v); } - template __device__ __forceinline__ _Tp saturate_cast(double v) { return _Tp(v); } - - template<> __device__ __forceinline__ uchar saturate_cast(schar v) - { - uint res = 0; - int vi = v; - asm("cvt.sat.u8.s8 %0, %1;" : "=r"(res) : "r"(vi)); - return res; - } - template<> __device__ __forceinline__ uchar saturate_cast(short v) - { - uint res = 0; - asm("cvt.sat.u8.s16 %0, %1;" : "=r"(res) : "h"(v)); - return res; - } - template<> __device__ __forceinline__ uchar saturate_cast(ushort v) - { - uint res = 0; - asm("cvt.sat.u8.u16 %0, %1;" : "=r"(res) : "h"(v)); - return res; - } - template<> __device__ __forceinline__ uchar saturate_cast(int v) - { - uint res = 0; - asm("cvt.sat.u8.s32 %0, %1;" : "=r"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ uchar saturate_cast(uint v) - { - uint res = 0; - asm("cvt.sat.u8.u32 %0, %1;" : "=r"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ uchar saturate_cast(float v) - { - uint res = 0; - asm("cvt.rni.sat.u8.f32 %0, %1;" : "=r"(res) : "f"(v)); - return res; - } - template<> __device__ __forceinline__ uchar saturate_cast(double v) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 - uint res = 0; - asm("cvt.rni.sat.u8.f64 %0, %1;" : "=r"(res) : "d"(v)); - return res; - #else - return saturate_cast((float)v); - #endif - } - - template<> __device__ __forceinline__ schar saturate_cast(uchar v) - { - uint res = 0; - uint vi = v; - asm("cvt.sat.s8.u8 %0, %1;" : "=r"(res) : "r"(vi)); - return res; - } - template<> __device__ __forceinline__ schar saturate_cast(short v) - { - uint res = 0; - asm("cvt.sat.s8.s16 %0, %1;" : "=r"(res) : "h"(v)); - return res; - } - template<> __device__ __forceinline__ schar saturate_cast(ushort v) - { - uint res = 0; - asm("cvt.sat.s8.u16 %0, %1;" : "=r"(res) : "h"(v)); - return res; - } - template<> __device__ __forceinline__ schar saturate_cast(int v) - { - uint res = 0; - asm("cvt.sat.s8.s32 %0, %1;" : "=r"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ schar saturate_cast(uint v) - { - uint res = 0; - asm("cvt.sat.s8.u32 %0, %1;" : "=r"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ schar saturate_cast(float v) - { - uint res = 0; - asm("cvt.rni.sat.s8.f32 %0, %1;" : "=r"(res) : "f"(v)); - return res; - } - template<> __device__ __forceinline__ schar saturate_cast(double v) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 - uint res = 0; - asm("cvt.rni.sat.s8.f64 %0, %1;" : "=r"(res) : "d"(v)); - return res; - #else - return saturate_cast((float)v); - #endif - } - - template<> __device__ __forceinline__ ushort saturate_cast(schar v) - { - ushort res = 0; - int vi = v; - asm("cvt.sat.u16.s8 %0, %1;" : "=h"(res) : "r"(vi)); - return res; - } - template<> __device__ __forceinline__ ushort saturate_cast(short v) - { - ushort res = 0; - asm("cvt.sat.u16.s16 %0, %1;" : "=h"(res) : "h"(v)); - return res; - } - template<> __device__ __forceinline__ ushort saturate_cast(int v) - { - ushort res = 0; - asm("cvt.sat.u16.s32 %0, %1;" : "=h"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ ushort saturate_cast(uint v) - { - ushort res = 0; - asm("cvt.sat.u16.u32 %0, %1;" : "=h"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ ushort saturate_cast(float v) - { - ushort res = 0; - asm("cvt.rni.sat.u16.f32 %0, %1;" : "=h"(res) : "f"(v)); - return res; - } - template<> __device__ __forceinline__ ushort saturate_cast(double v) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 - ushort res = 0; - asm("cvt.rni.sat.u16.f64 %0, %1;" : "=h"(res) : "d"(v)); - return res; - #else - return saturate_cast((float)v); - #endif - } - - template<> __device__ __forceinline__ short saturate_cast(ushort v) - { - short res = 0; - asm("cvt.sat.s16.u16 %0, %1;" : "=h"(res) : "h"(v)); - return res; - } - template<> __device__ __forceinline__ short saturate_cast(int v) - { - short res = 0; - asm("cvt.sat.s16.s32 %0, %1;" : "=h"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ short saturate_cast(uint v) - { - short res = 0; - asm("cvt.sat.s16.u32 %0, %1;" : "=h"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ short saturate_cast(float v) - { - short res = 0; - asm("cvt.rni.sat.s16.f32 %0, %1;" : "=h"(res) : "f"(v)); - return res; - } - template<> __device__ __forceinline__ short saturate_cast(double v) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 - short res = 0; - asm("cvt.rni.sat.s16.f64 %0, %1;" : "=h"(res) : "d"(v)); - return res; - #else - return saturate_cast((float)v); - #endif - } - - template<> __device__ __forceinline__ int saturate_cast(uint v) - { - int res = 0; - asm("cvt.sat.s32.u32 %0, %1;" : "=r"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ int saturate_cast(float v) - { - return __float2int_rn(v); - } - template<> __device__ __forceinline__ int saturate_cast(double v) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 - return __double2int_rn(v); - #else - return saturate_cast((float)v); - #endif - } - - template<> __device__ __forceinline__ uint saturate_cast(schar v) - { - uint res = 0; - int vi = v; - asm("cvt.sat.u32.s8 %0, %1;" : "=r"(res) : "r"(vi)); - return res; - } - template<> __device__ __forceinline__ uint saturate_cast(short v) - { - uint res = 0; - asm("cvt.sat.u32.s16 %0, %1;" : "=r"(res) : "h"(v)); - return res; - } - template<> __device__ __forceinline__ uint saturate_cast(int v) - { - uint res = 0; - asm("cvt.sat.u32.s32 %0, %1;" : "=r"(res) : "r"(v)); - return res; - } - template<> __device__ __forceinline__ uint saturate_cast(float v) - { - return __float2uint_rn(v); - } - template<> __device__ __forceinline__ uint saturate_cast(double v) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130 - return __double2uint_rn(v); - #else - return saturate_cast((float)v); - #endif - } -}}} - -//! @endcond - -#endif /* OPENCV_CUDA_SATURATE_CAST_HPP */ diff --git a/include/opencv2/core/cuda/scan.hpp b/include/opencv2/core/cuda/scan.hpp deleted file mode 100755 index e128fb0..0000000 --- a/include/opencv2/core/cuda/scan.hpp +++ /dev/null @@ -1,258 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_SCAN_HPP -#define OPENCV_CUDA_SCAN_HPP - -#include "opencv2/core/cuda/common.hpp" -#include "opencv2/core/cuda/utility.hpp" -#include "opencv2/core/cuda/warp.hpp" -#include "opencv2/core/cuda/warp_shuffle.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - enum ScanKind { EXCLUSIVE = 0, INCLUSIVE = 1 }; - - template struct WarpScan - { - __device__ __forceinline__ WarpScan() {} - __device__ __forceinline__ WarpScan(const WarpScan& other) { CV_UNUSED(other); } - - __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx) - { - const unsigned int lane = idx & 31; - F op; - - if ( lane >= 1) ptr [idx ] = op(ptr [idx - 1], ptr [idx]); - if ( lane >= 2) ptr [idx ] = op(ptr [idx - 2], ptr [idx]); - if ( lane >= 4) ptr [idx ] = op(ptr [idx - 4], ptr [idx]); - if ( lane >= 8) ptr [idx ] = op(ptr [idx - 8], ptr [idx]); - if ( lane >= 16) ptr [idx ] = op(ptr [idx - 16], ptr [idx]); - - if( Kind == INCLUSIVE ) - return ptr [idx]; - else - return (lane > 0) ? ptr [idx - 1] : 0; - } - - __device__ __forceinline__ unsigned int index(const unsigned int tid) - { - return tid; - } - - __device__ __forceinline__ void init(volatile T *ptr){} - - static const int warp_offset = 0; - - typedef WarpScan merge; - }; - - template struct WarpScanNoComp - { - __device__ __forceinline__ WarpScanNoComp() {} - __device__ __forceinline__ WarpScanNoComp(const WarpScanNoComp& other) { CV_UNUSED(other); } - - __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx) - { - const unsigned int lane = threadIdx.x & 31; - F op; - - ptr [idx ] = op(ptr [idx - 1], ptr [idx]); - ptr [idx ] = op(ptr [idx - 2], ptr [idx]); - ptr [idx ] = op(ptr [idx - 4], ptr [idx]); - ptr [idx ] = op(ptr [idx - 8], ptr [idx]); - ptr [idx ] = op(ptr [idx - 16], ptr [idx]); - - if( Kind == INCLUSIVE ) - return ptr [idx]; - else - return (lane > 0) ? ptr [idx - 1] : 0; - } - - __device__ __forceinline__ unsigned int index(const unsigned int tid) - { - return (tid >> warp_log) * warp_smem_stride + 16 + (tid & warp_mask); - } - - __device__ __forceinline__ void init(volatile T *ptr) - { - ptr[threadIdx.x] = 0; - } - - static const int warp_smem_stride = 32 + 16 + 1; - static const int warp_offset = 16; - static const int warp_log = 5; - static const int warp_mask = 31; - - typedef WarpScanNoComp merge; - }; - - template struct BlockScan - { - __device__ __forceinline__ BlockScan() {} - __device__ __forceinline__ BlockScan(const BlockScan& other) { CV_UNUSED(other); } - - __device__ __forceinline__ T operator()(volatile T *ptr) - { - const unsigned int tid = threadIdx.x; - const unsigned int lane = tid & warp_mask; - const unsigned int warp = tid >> warp_log; - - Sc scan; - typename Sc::merge merge_scan; - const unsigned int idx = scan.index(tid); - - T val = scan(ptr, idx); - __syncthreads (); - - if( warp == 0) - scan.init(ptr); - __syncthreads (); - - if( lane == 31 ) - ptr [scan.warp_offset + warp ] = (Kind == INCLUSIVE) ? val : ptr [idx]; - __syncthreads (); - - if( warp == 0 ) - merge_scan(ptr, idx); - __syncthreads(); - - if ( warp > 0) - val = ptr [scan.warp_offset + warp - 1] + val; - __syncthreads (); - - ptr[idx] = val; - __syncthreads (); - - return val ; - } - - static const int warp_log = 5; - static const int warp_mask = 31; - }; - - template - __device__ T warpScanInclusive(T idata, volatile T* s_Data, unsigned int tid) - { - #if __CUDA_ARCH__ >= 300 - const unsigned int laneId = cv::cuda::device::Warp::laneId(); - - // scan on shuffl functions - #pragma unroll - for (int i = 1; i <= (OPENCV_CUDA_WARP_SIZE / 2); i *= 2) - { - const T n = cv::cuda::device::shfl_up(idata, i); - if (laneId >= i) - idata += n; - } - - return idata; - #else - unsigned int pos = 2 * tid - (tid & (OPENCV_CUDA_WARP_SIZE - 1)); - s_Data[pos] = 0; - pos += OPENCV_CUDA_WARP_SIZE; - s_Data[pos] = idata; - - s_Data[pos] += s_Data[pos - 1]; - s_Data[pos] += s_Data[pos - 2]; - s_Data[pos] += s_Data[pos - 4]; - s_Data[pos] += s_Data[pos - 8]; - s_Data[pos] += s_Data[pos - 16]; - - return s_Data[pos]; - #endif - } - - template - __device__ __forceinline__ T warpScanExclusive(T idata, volatile T* s_Data, unsigned int tid) - { - return warpScanInclusive(idata, s_Data, tid) - idata; - } - - template - __device__ T blockScanInclusive(T idata, volatile T* s_Data, unsigned int tid) - { - if (tiNumScanThreads > OPENCV_CUDA_WARP_SIZE) - { - //Bottom-level inclusive warp scan - T warpResult = warpScanInclusive(idata, s_Data, tid); - - //Save top elements of each warp for exclusive warp scan - //sync to wait for warp scans to complete (because s_Data is being overwritten) - __syncthreads(); - if ((tid & (OPENCV_CUDA_WARP_SIZE - 1)) == (OPENCV_CUDA_WARP_SIZE - 1)) - { - s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE] = warpResult; - } - - //wait for warp scans to complete - __syncthreads(); - - if (tid < (tiNumScanThreads / OPENCV_CUDA_WARP_SIZE) ) - { - //grab top warp elements - T val = s_Data[tid]; - //calculate exclusive scan and write back to shared memory - s_Data[tid] = warpScanExclusive(val, s_Data, tid); - } - - //return updated warp scans with exclusive scan results - __syncthreads(); - - return warpResult + s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE]; - } - else - { - return warpScanInclusive(idata, s_Data, tid); - } - } -}}} - -//! @endcond - -#endif // OPENCV_CUDA_SCAN_HPP diff --git a/include/opencv2/core/cuda/simd_functions.hpp b/include/opencv2/core/cuda/simd_functions.hpp deleted file mode 100755 index 3d8c2e0..0000000 --- a/include/opencv2/core/cuda/simd_functions.hpp +++ /dev/null @@ -1,869 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -/* - * Copyright (c) 2013 NVIDIA Corporation. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions are met: - * - * Redistributions of source code must retain the above copyright notice, - * this list of conditions and the following disclaimer. - * - * Redistributions in binary form must reproduce the above copyright notice, - * this list of conditions and the following disclaimer in the documentation - * and/or other materials provided with the distribution. - * - * Neither the name of NVIDIA Corporation nor the names of its contributors - * may be used to endorse or promote products derived from this software - * without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" - * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE - * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE - * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR - * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF - * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS - * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN - * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) - * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE - * POSSIBILITY OF SUCH DAMAGE. - */ - -#ifndef OPENCV_CUDA_SIMD_FUNCTIONS_HPP -#define OPENCV_CUDA_SIMD_FUNCTIONS_HPP - -#include "common.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - // 2 - - static __device__ __forceinline__ unsigned int vadd2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vadd2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vadd.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vadd.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s; - s = a ^ b; // sum bits - r = a + b; // actual sum - s = s ^ r; // determine carry-ins for each bit position - s = s & 0x00010000; // carry-in to high word (= carry-out from low word) - r = r - s; // subtract out carry-out from low word - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsub2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vsub2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vsub.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vsub.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s; - s = a ^ b; // sum bits - r = a - b; // actual sum - s = s ^ r; // determine carry-ins for each bit position - s = s & 0x00010000; // borrow to high word - r = r + s; // compensate for borrow from low word - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vabsdiff2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vabsdiff2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vabsdiff.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vabsdiff.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s, t, u, v; - s = a & 0x0000ffff; // extract low halfword - r = b & 0x0000ffff; // extract low halfword - u = ::max(r, s); // maximum of low halfwords - v = ::min(r, s); // minimum of low halfwords - s = a & 0xffff0000; // extract high halfword - r = b & 0xffff0000; // extract high halfword - t = ::max(r, s); // maximum of high halfwords - s = ::min(r, s); // minimum of high halfwords - r = u | t; // maximum of both halfwords - s = v | s; // minimum of both halfwords - r = r - s; // |a - b| = max(a,b) - min(a,b); - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vavg2(unsigned int a, unsigned int b) - { - unsigned int r, s; - - // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==> - // (a + b) / 2 = (a & b) + ((a ^ b) >> 1) - s = a ^ b; - r = a & b; - s = s & 0xfffefffe; // ensure shift doesn't cross halfword boundaries - s = s >> 1; - s = r + s; - - return s; - } - - static __device__ __forceinline__ unsigned int vavrg2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vavrg2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==> - // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1) - unsigned int s; - s = a ^ b; - r = a | b; - s = s & 0xfffefffe; // ensure shift doesn't cross half-word boundaries - s = s >> 1; - r = r - s; - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vseteq2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset2.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - // inspired by Alan Mycroft's null-byte detection algorithm: - // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) - unsigned int c; - r = a ^ b; // 0x0000 if a == b - c = r | 0x80008000; // set msbs, to catch carry out - r = r ^ c; // extract msbs, msb = 1 if r < 0x8000 - c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 - c = r & ~c; // msb = 1, if r was 0x0000 - r = c >> 15; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmpeq2(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vseteq2(a, b); - c = r << 16; // convert bool - r = c - r; // into mask - #else - // inspired by Alan Mycroft's null-byte detection algorithm: - // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) - r = a ^ b; // 0x0000 if a == b - c = r | 0x80008000; // set msbs, to catch carry out - r = r ^ c; // extract msbs, msb = 1 if r < 0x8000 - c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 - c = r & ~c; // msb = 1, if r was 0x0000 - r = c >> 15; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetge2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset2.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int c; - asm("not.b32 %0, %0;" : "+r"(b)); - c = vavrg2(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 - c = c & 0x80008000; // msb = carry-outs - r = c >> 15; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmpge2(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetge2(a, b); - c = r << 16; // convert bool - r = c - r; // into mask - #else - asm("not.b32 %0, %0;" : "+r"(b)); - c = vavrg2(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 - c = c & 0x80008000; // msb = carry-outs - r = c >> 15; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetgt2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset2.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int c; - asm("not.b32 %0, %0;" : "+r"(b)); - c = vavg2(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] - c = c & 0x80008000; // msbs = carry-outs - r = c >> 15; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmpgt2(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetgt2(a, b); - c = r << 16; // convert bool - r = c - r; // into mask - #else - asm("not.b32 %0, %0;" : "+r"(b)); - c = vavg2(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] - c = c & 0x80008000; // msbs = carry-outs - r = c >> 15; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetle2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset2.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int c; - asm("not.b32 %0, %0;" : "+r"(a)); - c = vavrg2(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 - c = c & 0x80008000; // msb = carry-outs - r = c >> 15; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmple2(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetle2(a, b); - c = r << 16; // convert bool - r = c - r; // into mask - #else - asm("not.b32 %0, %0;" : "+r"(a)); - c = vavrg2(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 - c = c & 0x80008000; // msb = carry-outs - r = c >> 15; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetlt2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset2.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int c; - asm("not.b32 %0, %0;" : "+r"(a)); - c = vavg2(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] - c = c & 0x80008000; // msb = carry-outs - r = c >> 15; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmplt2(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetlt2(a, b); - c = r << 16; // convert bool - r = c - r; // into mask - #else - asm("not.b32 %0, %0;" : "+r"(a)); - c = vavg2(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] - c = c & 0x80008000; // msb = carry-outs - r = c >> 15; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetne2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm ("vset2.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - // inspired by Alan Mycroft's null-byte detection algorithm: - // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) - unsigned int c; - r = a ^ b; // 0x0000 if a == b - c = r | 0x80008000; // set msbs, to catch carry out - c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 - c = r | c; // msb = 1, if r was not 0x0000 - c = c & 0x80008000; // extract msbs - r = c >> 15; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmpne2(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetne2(a, b); - c = r << 16; // convert bool - r = c - r; // into mask - #else - // inspired by Alan Mycroft's null-byte detection algorithm: - // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) - r = a ^ b; // 0x0000 if a == b - c = r | 0x80008000; // set msbs, to catch carry out - c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000 - c = r | c; // msb = 1, if r was not 0x0000 - c = c & 0x80008000; // extract msbs - r = c >> 15; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vmax2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vmax2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vmax.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vmax.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s, t, u; - r = a & 0x0000ffff; // extract low halfword - s = b & 0x0000ffff; // extract low halfword - t = ::max(r, s); // maximum of low halfwords - r = a & 0xffff0000; // extract high halfword - s = b & 0xffff0000; // extract high halfword - u = ::max(r, s); // maximum of high halfwords - r = t | u; // combine halfword maximums - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vmin2(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vmin2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vmin.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vmin.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s, t, u; - r = a & 0x0000ffff; // extract low halfword - s = b & 0x0000ffff; // extract low halfword - t = ::min(r, s); // minimum of low halfwords - r = a & 0xffff0000; // extract high halfword - s = b & 0xffff0000; // extract high halfword - u = ::min(r, s); // minimum of high halfwords - r = t | u; // combine halfword minimums - #endif - - return r; - } - - // 4 - - static __device__ __forceinline__ unsigned int vadd4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vadd4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vadd.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vadd.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vadd.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vadd.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s, t; - s = a ^ b; // sum bits - r = a & 0x7f7f7f7f; // clear msbs - t = b & 0x7f7f7f7f; // clear msbs - s = s & 0x80808080; // msb sum bits - r = r + t; // add without msbs, record carry-out in msbs - r = r ^ s; // sum of msb sum and carry-in bits, w/o carry-out - #endif /* __CUDA_ARCH__ >= 300 */ - - return r; - } - - static __device__ __forceinline__ unsigned int vsub4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vsub4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vsub.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vsub.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vsub.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vsub.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s, t; - s = a ^ ~b; // inverted sum bits - r = a | 0x80808080; // set msbs - t = b & 0x7f7f7f7f; // clear msbs - s = s & 0x80808080; // inverted msb sum bits - r = r - t; // subtract w/o msbs, record inverted borrows in msb - r = r ^ s; // combine inverted msb sum bits and borrows - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vavg4(unsigned int a, unsigned int b) - { - unsigned int r, s; - - // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==> - // (a + b) / 2 = (a & b) + ((a ^ b) >> 1) - s = a ^ b; - r = a & b; - s = s & 0xfefefefe; // ensure following shift doesn't cross byte boundaries - s = s >> 1; - s = r + s; - - return s; - } - - static __device__ __forceinline__ unsigned int vavrg4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vavrg4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==> - // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1) - unsigned int c; - c = a ^ b; - r = a | b; - c = c & 0xfefefefe; // ensure following shift doesn't cross byte boundaries - c = c >> 1; - r = r - c; - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vseteq4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset4.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - // inspired by Alan Mycroft's null-byte detection algorithm: - // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) - unsigned int c; - r = a ^ b; // 0x00 if a == b - c = r | 0x80808080; // set msbs, to catch carry out - r = r ^ c; // extract msbs, msb = 1 if r < 0x80 - c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 - c = r & ~c; // msb = 1, if r was 0x00 - r = c >> 7; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmpeq4(unsigned int a, unsigned int b) - { - unsigned int r, t; - - #if __CUDA_ARCH__ >= 300 - r = vseteq4(a, b); - t = r << 8; // convert bool - r = t - r; // to mask - #else - // inspired by Alan Mycroft's null-byte detection algorithm: - // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) - t = a ^ b; // 0x00 if a == b - r = t | 0x80808080; // set msbs, to catch carry out - t = t ^ r; // extract msbs, msb = 1 if t < 0x80 - r = r - 0x01010101; // msb = 0, if t was 0x00 or 0x80 - r = t & ~r; // msb = 1, if t was 0x00 - t = r >> 7; // build mask - t = r - t; // from - r = t | r; // msbs - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetle4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset4.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int c; - asm("not.b32 %0, %0;" : "+r"(a)); - c = vavrg4(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 - c = c & 0x80808080; // msb = carry-outs - r = c >> 7; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmple4(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetle4(a, b); - c = r << 8; // convert bool - r = c - r; // to mask - #else - asm("not.b32 %0, %0;" : "+r"(a)); - c = vavrg4(a, b); // (b + ~a + 1) / 2 = (b - a) / 2 - c = c & 0x80808080; // msbs = carry-outs - r = c >> 7; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetlt4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset4.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int c; - asm("not.b32 %0, %0;" : "+r"(a)); - c = vavg4(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] - c = c & 0x80808080; // msb = carry-outs - r = c >> 7; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmplt4(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetlt4(a, b); - c = r << 8; // convert bool - r = c - r; // to mask - #else - asm("not.b32 %0, %0;" : "+r"(a)); - c = vavg4(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down] - c = c & 0x80808080; // msbs = carry-outs - r = c >> 7; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetge4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset4.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int c; - asm("not.b32 %0, %0;" : "+r"(b)); - c = vavrg4(a, b); // (a + ~b + 1) / 2 = (a - b) / 2 - c = c & 0x80808080; // msb = carry-outs - r = c >> 7; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmpge4(unsigned int a, unsigned int b) - { - unsigned int r, s; - - #if __CUDA_ARCH__ >= 300 - r = vsetge4(a, b); - s = r << 8; // convert bool - r = s - r; // to mask - #else - asm ("not.b32 %0,%0;" : "+r"(b)); - r = vavrg4 (a, b); // (a + ~b + 1) / 2 = (a - b) / 2 - r = r & 0x80808080; // msb = carry-outs - s = r >> 7; // build mask - s = r - s; // from - r = s | r; // msbs - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetgt4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset4.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int c; - asm("not.b32 %0, %0;" : "+r"(b)); - c = vavg4(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] - c = c & 0x80808080; // msb = carry-outs - r = c >> 7; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmpgt4(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetgt4(a, b); - c = r << 8; // convert bool - r = c - r; // to mask - #else - asm("not.b32 %0, %0;" : "+r"(b)); - c = vavg4(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down] - c = c & 0x80808080; // msb = carry-outs - r = c >> 7; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vsetne4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vset4.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - // inspired by Alan Mycroft's null-byte detection algorithm: - // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) - unsigned int c; - r = a ^ b; // 0x00 if a == b - c = r | 0x80808080; // set msbs, to catch carry out - c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 - c = r | c; // msb = 1, if r was not 0x00 - c = c & 0x80808080; // extract msbs - r = c >> 7; // convert to bool - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vcmpne4(unsigned int a, unsigned int b) - { - unsigned int r, c; - - #if __CUDA_ARCH__ >= 300 - r = vsetne4(a, b); - c = r << 8; // convert bool - r = c - r; // to mask - #else - // inspired by Alan Mycroft's null-byte detection algorithm: - // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080)) - r = a ^ b; // 0x00 if a == b - c = r | 0x80808080; // set msbs, to catch carry out - c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80 - c = r | c; // msb = 1, if r was not 0x00 - c = c & 0x80808080; // extract msbs - r = c >> 7; // convert - r = c - r; // msbs to - r = c | r; // mask - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vabsdiff4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vabsdiff4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vabsdiff.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vabsdiff.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vabsdiff.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vabsdiff.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s; - s = vcmpge4(a, b); // mask = 0xff if a >= b - r = a ^ b; // - s = (r & s) ^ b; // select a when a >= b, else select b => max(a,b) - r = s ^ r; // select a when b >= a, else select b => min(a,b) - r = s - r; // |a - b| = max(a,b) - min(a,b); - #endif - - return r; - } - - static __device__ __forceinline__ unsigned int vmax4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vmax4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vmax.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vmax.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vmax.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vmax.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s; - s = vcmpge4(a, b); // mask = 0xff if a >= b - r = a & s; // select a when b >= a - s = b & ~s; // select b when b < a - r = r | s; // combine byte selections - #endif - - return r; // byte-wise unsigned maximum - } - - static __device__ __forceinline__ unsigned int vmin4(unsigned int a, unsigned int b) - { - unsigned int r = 0; - - #if __CUDA_ARCH__ >= 300 - asm("vmin4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #elif __CUDA_ARCH__ >= 200 - asm("vmin.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vmin.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vmin.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - asm("vmin.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r)); - #else - unsigned int s; - s = vcmpge4(b, a); // mask = 0xff if a >= b - r = a & s; // select a when b >= a - s = b & ~s; // select b when b < a - r = r | s; // combine byte selections - #endif - - return r; - } -}}} - -//! @endcond - -#endif // OPENCV_CUDA_SIMD_FUNCTIONS_HPP diff --git a/include/opencv2/core/cuda/transform.hpp b/include/opencv2/core/cuda/transform.hpp deleted file mode 100755 index 42aa6ea..0000000 --- a/include/opencv2/core/cuda/transform.hpp +++ /dev/null @@ -1,75 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_TRANSFORM_HPP -#define OPENCV_CUDA_TRANSFORM_HPP - -#include "common.hpp" -#include "utility.hpp" -#include "detail/transform_detail.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template - static inline void transform(PtrStepSz src, PtrStepSz dst, UnOp op, const Mask& mask, cudaStream_t stream) - { - typedef TransformFunctorTraits ft; - transform_detail::TransformDispatcher::cn == 1 && VecTraits::cn == 1 && ft::smart_shift != 1>::call(src, dst, op, mask, stream); - } - - template - static inline void transform(PtrStepSz src1, PtrStepSz src2, PtrStepSz dst, BinOp op, const Mask& mask, cudaStream_t stream) - { - typedef TransformFunctorTraits ft; - transform_detail::TransformDispatcher::cn == 1 && VecTraits::cn == 1 && VecTraits::cn == 1 && ft::smart_shift != 1>::call(src1, src2, dst, op, mask, stream); - } -}}} - -//! @endcond - -#endif // OPENCV_CUDA_TRANSFORM_HPP diff --git a/include/opencv2/core/cuda/type_traits.hpp b/include/opencv2/core/cuda/type_traits.hpp deleted file mode 100755 index 8b7a3fd..0000000 --- a/include/opencv2/core/cuda/type_traits.hpp +++ /dev/null @@ -1,90 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_TYPE_TRAITS_HPP -#define OPENCV_CUDA_TYPE_TRAITS_HPP - -#include "detail/type_traits_detail.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template struct IsSimpleParameter - { - enum {value = type_traits_detail::IsIntegral::value || type_traits_detail::IsFloat::value || - type_traits_detail::PointerTraits::type>::value}; - }; - - template struct TypeTraits - { - typedef typename type_traits_detail::UnConst::type NonConstType; - typedef typename type_traits_detail::UnVolatile::type NonVolatileType; - typedef typename type_traits_detail::UnVolatile::type>::type UnqualifiedType; - typedef typename type_traits_detail::PointerTraits::type PointeeType; - typedef typename type_traits_detail::ReferenceTraits::type ReferredType; - - enum { isConst = type_traits_detail::UnConst::value }; - enum { isVolatile = type_traits_detail::UnVolatile::value }; - - enum { isReference = type_traits_detail::ReferenceTraits::value }; - enum { isPointer = type_traits_detail::PointerTraits::type>::value }; - - enum { isUnsignedInt = type_traits_detail::IsUnsignedIntegral::value }; - enum { isSignedInt = type_traits_detail::IsSignedIntergral::value }; - enum { isIntegral = type_traits_detail::IsIntegral::value }; - enum { isFloat = type_traits_detail::IsFloat::value }; - enum { isArith = isIntegral || isFloat }; - enum { isVec = type_traits_detail::IsVec::value }; - - typedef typename type_traits_detail::Select::value, - T, typename type_traits_detail::AddParameterType::type>::type ParameterType; - }; -}}} - -//! @endcond - -#endif // OPENCV_CUDA_TYPE_TRAITS_HPP diff --git a/include/opencv2/core/cuda/utility.hpp b/include/opencv2/core/cuda/utility.hpp deleted file mode 100755 index 7f5db48..0000000 --- a/include/opencv2/core/cuda/utility.hpp +++ /dev/null @@ -1,230 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_UTILITY_HPP -#define OPENCV_CUDA_UTILITY_HPP - -#include "saturate_cast.hpp" -#include "datamov_utils.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - struct CV_EXPORTS ThrustAllocator - { - typedef uchar value_type; - virtual ~ThrustAllocator(); - virtual __device__ __host__ uchar* allocate(size_t numBytes) = 0; - virtual __device__ __host__ void deallocate(uchar* ptr, size_t numBytes) = 0; - static ThrustAllocator& getAllocator(); - static void setAllocator(ThrustAllocator* allocator); - }; - #define OPENCV_CUDA_LOG_WARP_SIZE (5) - #define OPENCV_CUDA_WARP_SIZE (1 << OPENCV_CUDA_LOG_WARP_SIZE) - #define OPENCV_CUDA_LOG_MEM_BANKS ((__CUDA_ARCH__ >= 200) ? 5 : 4) // 32 banks on fermi, 16 on tesla - #define OPENCV_CUDA_MEM_BANKS (1 << OPENCV_CUDA_LOG_MEM_BANKS) - - /////////////////////////////////////////////////////////////////////////////// - // swap - - template void __device__ __host__ __forceinline__ swap(T& a, T& b) - { - const T temp = a; - a = b; - b = temp; - } - - /////////////////////////////////////////////////////////////////////////////// - // Mask Reader - - struct SingleMask - { - explicit __host__ __device__ __forceinline__ SingleMask(PtrStepb mask_) : mask(mask_) {} - __host__ __device__ __forceinline__ SingleMask(const SingleMask& mask_): mask(mask_.mask){} - - __device__ __forceinline__ bool operator()(int y, int x) const - { - return mask.ptr(y)[x] != 0; - } - - PtrStepb mask; - }; - - struct SingleMaskChannels - { - __host__ __device__ __forceinline__ SingleMaskChannels(PtrStepb mask_, int channels_) - : mask(mask_), channels(channels_) {} - __host__ __device__ __forceinline__ SingleMaskChannels(const SingleMaskChannels& mask_) - :mask(mask_.mask), channels(mask_.channels){} - - __device__ __forceinline__ bool operator()(int y, int x) const - { - return mask.ptr(y)[x / channels] != 0; - } - - PtrStepb mask; - int channels; - }; - - struct MaskCollection - { - explicit __host__ __device__ __forceinline__ MaskCollection(PtrStepb* maskCollection_) - : maskCollection(maskCollection_) {} - - __device__ __forceinline__ MaskCollection(const MaskCollection& masks_) - : maskCollection(masks_.maskCollection), curMask(masks_.curMask){} - - __device__ __forceinline__ void next() - { - curMask = *maskCollection++; - } - __device__ __forceinline__ void setMask(int z) - { - curMask = maskCollection[z]; - } - - __device__ __forceinline__ bool operator()(int y, int x) const - { - uchar val; - return curMask.data == 0 || (ForceGlob::Load(curMask.ptr(y), x, val), (val != 0)); - } - - const PtrStepb* maskCollection; - PtrStepb curMask; - }; - - struct WithOutMask - { - __host__ __device__ __forceinline__ WithOutMask(){} - __host__ __device__ __forceinline__ WithOutMask(const WithOutMask&){} - - __device__ __forceinline__ void next() const - { - } - __device__ __forceinline__ void setMask(int) const - { - } - - __device__ __forceinline__ bool operator()(int, int) const - { - return true; - } - - __device__ __forceinline__ bool operator()(int, int, int) const - { - return true; - } - - static __device__ __forceinline__ bool check(int, int) - { - return true; - } - - static __device__ __forceinline__ bool check(int, int, int) - { - return true; - } - }; - - /////////////////////////////////////////////////////////////////////////////// - // Solve linear system - - // solve 2x2 linear system Ax=b - template __device__ __forceinline__ bool solve2x2(const T A[2][2], const T b[2], T x[2]) - { - T det = A[0][0] * A[1][1] - A[1][0] * A[0][1]; - - if (det != 0) - { - double invdet = 1.0 / det; - - x[0] = saturate_cast(invdet * (b[0] * A[1][1] - b[1] * A[0][1])); - - x[1] = saturate_cast(invdet * (A[0][0] * b[1] - A[1][0] * b[0])); - - return true; - } - - return false; - } - - // solve 3x3 linear system Ax=b - template __device__ __forceinline__ bool solve3x3(const T A[3][3], const T b[3], T x[3]) - { - T det = A[0][0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) - - A[0][1] * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) - + A[0][2] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]); - - if (det != 0) - { - double invdet = 1.0 / det; - - x[0] = saturate_cast(invdet * - (b[0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) - - A[0][1] * (b[1] * A[2][2] - A[1][2] * b[2] ) + - A[0][2] * (b[1] * A[2][1] - A[1][1] * b[2] ))); - - x[1] = saturate_cast(invdet * - (A[0][0] * (b[1] * A[2][2] - A[1][2] * b[2] ) - - b[0] * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) + - A[0][2] * (A[1][0] * b[2] - b[1] * A[2][0]))); - - x[2] = saturate_cast(invdet * - (A[0][0] * (A[1][1] * b[2] - b[1] * A[2][1]) - - A[0][1] * (A[1][0] * b[2] - b[1] * A[2][0]) + - b[0] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]))); - - return true; - } - - return false; - } -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // OPENCV_CUDA_UTILITY_HPP diff --git a/include/opencv2/core/cuda/vec_distance.hpp b/include/opencv2/core/cuda/vec_distance.hpp deleted file mode 100755 index ef6e510..0000000 --- a/include/opencv2/core/cuda/vec_distance.hpp +++ /dev/null @@ -1,232 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_VEC_DISTANCE_HPP -#define OPENCV_CUDA_VEC_DISTANCE_HPP - -#include "reduce.hpp" -#include "functional.hpp" -#include "detail/vec_distance_detail.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template struct L1Dist - { - typedef int value_type; - typedef int result_type; - - __device__ __forceinline__ L1Dist() : mySum(0) {} - - __device__ __forceinline__ void reduceIter(int val1, int val2) - { - mySum = __sad(val1, val2, mySum); - } - - template __device__ __forceinline__ void reduceAll(int* smem, int tid) - { - reduce(smem, mySum, tid, plus()); - } - - __device__ __forceinline__ operator int() const - { - return mySum; - } - - int mySum; - }; - template <> struct L1Dist - { - typedef float value_type; - typedef float result_type; - - __device__ __forceinline__ L1Dist() : mySum(0.0f) {} - - __device__ __forceinline__ void reduceIter(float val1, float val2) - { - mySum += ::fabs(val1 - val2); - } - - template __device__ __forceinline__ void reduceAll(float* smem, int tid) - { - reduce(smem, mySum, tid, plus()); - } - - __device__ __forceinline__ operator float() const - { - return mySum; - } - - float mySum; - }; - - struct L2Dist - { - typedef float value_type; - typedef float result_type; - - __device__ __forceinline__ L2Dist() : mySum(0.0f) {} - - __device__ __forceinline__ void reduceIter(float val1, float val2) - { - float reg = val1 - val2; - mySum += reg * reg; - } - - template __device__ __forceinline__ void reduceAll(float* smem, int tid) - { - reduce(smem, mySum, tid, plus()); - } - - __device__ __forceinline__ operator float() const - { - return sqrtf(mySum); - } - - float mySum; - }; - - struct HammingDist - { - typedef int value_type; - typedef int result_type; - - __device__ __forceinline__ HammingDist() : mySum(0) {} - - __device__ __forceinline__ void reduceIter(int val1, int val2) - { - mySum += __popc(val1 ^ val2); - } - - template __device__ __forceinline__ void reduceAll(int* smem, int tid) - { - reduce(smem, mySum, tid, plus()); - } - - __device__ __forceinline__ operator int() const - { - return mySum; - } - - int mySum; - }; - - // calc distance between two vectors in global memory - template - __device__ void calcVecDiffGlobal(const T1* vec1, const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) - { - for (int i = tid; i < len; i += THREAD_DIM) - { - T1 val1; - ForceGlob::Load(vec1, i, val1); - - T2 val2; - ForceGlob::Load(vec2, i, val2); - - dist.reduceIter(val1, val2); - } - - dist.reduceAll(smem, tid); - } - - // calc distance between two vectors, first vector is cached in register or shared memory, second vector is in global memory - template - __device__ __forceinline__ void calcVecDiffCached(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, typename Dist::result_type* smem, int tid) - { - vec_distance_detail::VecDiffCachedCalculator::calc(vecCached, vecGlob, len, dist, tid); - - dist.reduceAll(smem, tid); - } - - // calc distance between two vectors in global memory - template struct VecDiffGlobal - { - explicit __device__ __forceinline__ VecDiffGlobal(const T1* vec1_, int = 0, void* = 0, int = 0, int = 0) - { - vec1 = vec1_; - } - - template - __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const - { - calcVecDiffGlobal(vec1, vec2, len, dist, smem, tid); - } - - const T1* vec1; - }; - - // calc distance between two vectors, first vector is cached in register memory, second vector is in global memory - template struct VecDiffCachedRegister - { - template __device__ __forceinline__ VecDiffCachedRegister(const T1* vec1, int len, U* smem, int glob_tid, int tid) - { - if (glob_tid < len) - smem[glob_tid] = vec1[glob_tid]; - __syncthreads(); - - U* vec1ValsPtr = vec1Vals; - - #pragma unroll - for (int i = tid; i < MAX_LEN; i += THREAD_DIM) - *vec1ValsPtr++ = smem[i]; - - __syncthreads(); - } - - template - __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const - { - calcVecDiffCached(vec1Vals, vec2, len, dist, smem, tid); - } - - U vec1Vals[MAX_LEN / THREAD_DIM]; - }; -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // OPENCV_CUDA_VEC_DISTANCE_HPP diff --git a/include/opencv2/core/cuda/vec_math.hpp b/include/opencv2/core/cuda/vec_math.hpp deleted file mode 100755 index 9085b92..0000000 --- a/include/opencv2/core/cuda/vec_math.hpp +++ /dev/null @@ -1,930 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_VECMATH_HPP -#define OPENCV_CUDA_VECMATH_HPP - -#include "vec_traits.hpp" -#include "saturate_cast.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - -// saturate_cast - -namespace vec_math_detail -{ - template struct SatCastHelper; - template struct SatCastHelper<1, VecD> - { - template static __device__ __forceinline__ VecD cast(const VecS& v) - { - typedef typename VecTraits::elem_type D; - return VecTraits::make(saturate_cast(v.x)); - } - }; - template struct SatCastHelper<2, VecD> - { - template static __device__ __forceinline__ VecD cast(const VecS& v) - { - typedef typename VecTraits::elem_type D; - return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y)); - } - }; - template struct SatCastHelper<3, VecD> - { - template static __device__ __forceinline__ VecD cast(const VecS& v) - { - typedef typename VecTraits::elem_type D; - return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y), saturate_cast(v.z)); - } - }; - template struct SatCastHelper<4, VecD> - { - template static __device__ __forceinline__ VecD cast(const VecS& v) - { - typedef typename VecTraits::elem_type D; - return VecTraits::make(saturate_cast(v.x), saturate_cast(v.y), saturate_cast(v.z), saturate_cast(v.w)); - } - }; - - template static __device__ __forceinline__ VecD saturate_cast_helper(const VecS& v) - { - return SatCastHelper::cn, VecD>::cast(v); - } -} - -template static __device__ __forceinline__ T saturate_cast(const uchar1& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const char1& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const ushort1& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const short1& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const uint1& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const int1& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const float1& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const double1& v) {return vec_math_detail::saturate_cast_helper(v);} - -template static __device__ __forceinline__ T saturate_cast(const uchar2& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const char2& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const ushort2& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const short2& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const uint2& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const int2& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const float2& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const double2& v) {return vec_math_detail::saturate_cast_helper(v);} - -template static __device__ __forceinline__ T saturate_cast(const uchar3& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const char3& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const ushort3& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const short3& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const uint3& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const int3& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const float3& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const double3& v) {return vec_math_detail::saturate_cast_helper(v);} - -template static __device__ __forceinline__ T saturate_cast(const uchar4& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const char4& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const ushort4& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const short4& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const uint4& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const int4& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const float4& v) {return vec_math_detail::saturate_cast_helper(v);} -template static __device__ __forceinline__ T saturate_cast(const double4& v) {return vec_math_detail::saturate_cast_helper(v);} - -// unary operators - -#define CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(op, input_type, output_type) \ - __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a) \ - { \ - return VecTraits::make(op (a.x)); \ - } \ - __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a) \ - { \ - return VecTraits::make(op (a.x), op (a.y)); \ - } \ - __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a) \ - { \ - return VecTraits::make(op (a.x), op (a.y), op (a.z)); \ - } \ - __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a) \ - { \ - return VecTraits::make(op (a.x), op (a.y), op (a.z), op (a.w)); \ - } - -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, char, char) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, short, short) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, int, int) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, char, char) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, ushort, ushort) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, short, short) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, int, int) -CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uint, uint) - -#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_OP - -// unary functions - -#define CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(func_name, func, input_type, output_type) \ - __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a) \ - { \ - return VecTraits::make(func (a.x)); \ - } \ - __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a) \ - { \ - return VecTraits::make(func (a.x), func (a.y)); \ - } \ - __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a) \ - { \ - return VecTraits::make(func (a.x), func (a.y), func (a.z)); \ - } \ - __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a) \ - { \ - return VecTraits::make(func (a.x), func (a.y), func (a.z), func (a.w)); \ - } - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, char, char) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, ushort, ushort) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, short, short) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, int, int) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uint, uint) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabsf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabs, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrt, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::exp, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::log, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sin, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cos, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tan, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asin, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acos, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atan, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinh, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::cosh, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanh, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinh, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acosh, double, double) - -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, char, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, short, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, int, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, float, float) -CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanh, double, double) - -#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC - -// binary operators (vec & vec) - -#define CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(op, input_type, output_type) \ - __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, const input_type ## 1 & b) \ - { \ - return VecTraits::make(a.x op b.x); \ - } \ - __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, const input_type ## 2 & b) \ - { \ - return VecTraits::make(a.x op b.x, a.y op b.y); \ - } \ - __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, const input_type ## 3 & b) \ - { \ - return VecTraits::make(a.x op b.x, a.y op b.y, a.z op b.z); \ - } \ - __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, const input_type ## 4 & b) \ - { \ - return VecTraits::make(a.x op b.x, a.y op b.y, a.z op b.z, a.w op b.w); \ - } - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uchar, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, char, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, ushort, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, short, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uint, uint) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, float, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, double, double) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uchar, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, char, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, ushort, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, short, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uint, uint) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, float, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, double, double) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uchar, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, char, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, ushort, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, short, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uint, uint) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, float, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, double, double) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uchar, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, char, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, ushort, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, short, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uint, uint) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, float, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, double, double) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, char, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, ushort, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, short, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, int, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uint, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, float, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, double, uchar) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, char, char) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, ushort, ushort) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, short, short) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uint, uint) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, char, char) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, ushort, ushort) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, short, short) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uint, uint) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, char, char) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, ushort, ushort) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, short, short) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uint, uint) - -#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_OP - -// binary operators (vec & scalar) - -#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(op, input_type, scalar_type, output_type) \ - __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, scalar_type s) \ - { \ - return VecTraits::make(a.x op s); \ - } \ - __device__ __forceinline__ output_type ## 1 operator op(scalar_type s, const input_type ## 1 & b) \ - { \ - return VecTraits::make(s op b.x); \ - } \ - __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, scalar_type s) \ - { \ - return VecTraits::make(a.x op s, a.y op s); \ - } \ - __device__ __forceinline__ output_type ## 2 operator op(scalar_type s, const input_type ## 2 & b) \ - { \ - return VecTraits::make(s op b.x, s op b.y); \ - } \ - __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, scalar_type s) \ - { \ - return VecTraits::make(a.x op s, a.y op s, a.z op s); \ - } \ - __device__ __forceinline__ output_type ## 3 operator op(scalar_type s, const input_type ## 3 & b) \ - { \ - return VecTraits::make(s op b.x, s op b.y, s op b.z); \ - } \ - __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, scalar_type s) \ - { \ - return VecTraits::make(a.x op s, a.y op s, a.z op s, a.w op s); \ - } \ - __device__ __forceinline__ output_type ## 4 operator op(scalar_type s, const input_type ## 4 & b) \ - { \ - return VecTraits::make(s op b.x, s op b.y, s op b.z, s op b.w); \ - } - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, uint, uint) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, double, double, double) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, uint, uint) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, double, double, double) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, uint, uint) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, double, double, double) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, uint, uint) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, double, double, double) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, char, char, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, ushort, ushort, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, short, short, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, int, int, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uint, uint, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, float, float, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, double, double, uchar) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, char, char, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, ushort, ushort, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, short, short, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, int, int, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uint, uint, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, float, float, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, double, double, uchar) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, char, char, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, ushort, ushort, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, short, short, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, int, int, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uint, uint, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, float, float, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, double, double, uchar) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, char, char, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, ushort, ushort, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, short, short, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, int, int, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uint, uint, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, float, float, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, double, double, uchar) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, char, char, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, ushort, ushort, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, short, short, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, int, int, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uint, uint, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, float, float, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, double, double, uchar) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, char, char, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, ushort, ushort, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, short, short, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, int, int, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uint, uint, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, float, float, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, double, double, uchar) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, char, char, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, ushort, ushort, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, short, short, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, int, int, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uint, uint, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, float, float, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, double, double, uchar) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, char, char, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, ushort, ushort, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, short, short, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, int, int, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uint, uint, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, float, float, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, double, double, uchar) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, char, char, char) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, ushort, ushort, ushort) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, short, short, short) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uint, uint, uint) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, char, char, char) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, ushort, ushort, ushort) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, short, short, short) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uint, uint, uint) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, char, char, char) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, ushort, ushort, ushort) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, short, short, short) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uint, uint, uint) - -#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP - -// binary function (vec & vec) - -#define CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(func_name, func, input_type, output_type) \ - __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, const input_type ## 1 & b) \ - { \ - return VecTraits::make(func (a.x, b.x)); \ - } \ - __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, const input_type ## 2 & b) \ - { \ - return VecTraits::make(func (a.x, b.x), func (a.y, b.y)); \ - } \ - __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, const input_type ## 3 & b) \ - { \ - return VecTraits::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z)); \ - } \ - __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, const input_type ## 4 & b) \ - { \ - return VecTraits::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z), func (a.w, b.w)); \ - } - -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, char, char) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, ushort, ushort) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, short, short) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uint, uint) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmaxf, float, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmax, double, double) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uchar, uchar) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, char, char) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, ushort, ushort) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, short, short) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uint, uint) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, int, int) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fminf, float, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fmin, double, double) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, char, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, short, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uint, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, int, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, float, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypot, double, double) - -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uchar, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, char, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, ushort, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, short, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uint, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, int, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, float, float) -CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2, double, double) - -#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC - -// binary function (vec & scalar) - -#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(func_name, func, input_type, scalar_type, output_type) \ - __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, scalar_type s) \ - { \ - return VecTraits::make(func ((output_type) a.x, (output_type) s)); \ - } \ - __device__ __forceinline__ output_type ## 1 func_name(scalar_type s, const input_type ## 1 & b) \ - { \ - return VecTraits::make(func ((output_type) s, (output_type) b.x)); \ - } \ - __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, scalar_type s) \ - { \ - return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s)); \ - } \ - __device__ __forceinline__ output_type ## 2 func_name(scalar_type s, const input_type ## 2 & b) \ - { \ - return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y)); \ - } \ - __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, scalar_type s) \ - { \ - return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s)); \ - } \ - __device__ __forceinline__ output_type ## 3 func_name(scalar_type s, const input_type ## 3 & b) \ - { \ - return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z)); \ - } \ - __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, scalar_type s) \ - { \ - return VecTraits::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s), func ((output_type) a.w, (output_type) s)); \ - } \ - __device__ __forceinline__ output_type ## 4 func_name(scalar_type s, const input_type ## 4 & b) \ - { \ - return VecTraits::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z), func ((output_type) s, (output_type) b.w)); \ - } - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uchar, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uchar, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, char, char, char) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, char, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, char, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, ushort, ushort, ushort) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, ushort, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, ushort, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, short, short, short) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, short, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, short, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uint, uint, uint) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uint, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uint, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, int, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, int, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, float, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, float, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, double, double, double) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uchar, uchar, uchar) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uchar, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uchar, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, char, char, char) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, char, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, char, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, ushort, ushort, ushort) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, ushort, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, ushort, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, short, short, short) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, short, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, short, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uint, uint, uint) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uint, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uint, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, int, int, int) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, int, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, int, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, float, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, float, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, double, double, double) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uchar, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uchar, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, char, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, char, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, ushort, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, ushort, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, short, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, short, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uint, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uint, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, int, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, int, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, float, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, float, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, double, double, double) - -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uchar, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uchar, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, char, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, char, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, ushort, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, ushort, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, short, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, short, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uint, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uint, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, int, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, int, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, float, float, float) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, float, double, double) -CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, double, double, double) - -#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC - -}}} // namespace cv { namespace cuda { namespace device - -//! @endcond - -#endif // OPENCV_CUDA_VECMATH_HPP diff --git a/include/opencv2/core/cuda/vec_traits.hpp b/include/opencv2/core/cuda/vec_traits.hpp deleted file mode 100755 index b5ff281..0000000 --- a/include/opencv2/core/cuda/vec_traits.hpp +++ /dev/null @@ -1,288 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_VEC_TRAITS_HPP -#define OPENCV_CUDA_VEC_TRAITS_HPP - -#include "common.hpp" - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template struct TypeVec; - - struct __align__(8) uchar8 - { - uchar a0, a1, a2, a3, a4, a5, a6, a7; - }; - static __host__ __device__ __forceinline__ uchar8 make_uchar8(uchar a0, uchar a1, uchar a2, uchar a3, uchar a4, uchar a5, uchar a6, uchar a7) - { - uchar8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; - return val; - } - struct __align__(8) char8 - { - schar a0, a1, a2, a3, a4, a5, a6, a7; - }; - static __host__ __device__ __forceinline__ char8 make_char8(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7) - { - char8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; - return val; - } - struct __align__(16) ushort8 - { - ushort a0, a1, a2, a3, a4, a5, a6, a7; - }; - static __host__ __device__ __forceinline__ ushort8 make_ushort8(ushort a0, ushort a1, ushort a2, ushort a3, ushort a4, ushort a5, ushort a6, ushort a7) - { - ushort8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; - return val; - } - struct __align__(16) short8 - { - short a0, a1, a2, a3, a4, a5, a6, a7; - }; - static __host__ __device__ __forceinline__ short8 make_short8(short a0, short a1, short a2, short a3, short a4, short a5, short a6, short a7) - { - short8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; - return val; - } - struct __align__(32) uint8 - { - uint a0, a1, a2, a3, a4, a5, a6, a7; - }; - static __host__ __device__ __forceinline__ uint8 make_uint8(uint a0, uint a1, uint a2, uint a3, uint a4, uint a5, uint a6, uint a7) - { - uint8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; - return val; - } - struct __align__(32) int8 - { - int a0, a1, a2, a3, a4, a5, a6, a7; - }; - static __host__ __device__ __forceinline__ int8 make_int8(int a0, int a1, int a2, int a3, int a4, int a5, int a6, int a7) - { - int8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; - return val; - } - struct __align__(32) float8 - { - float a0, a1, a2, a3, a4, a5, a6, a7; - }; - static __host__ __device__ __forceinline__ float8 make_float8(float a0, float a1, float a2, float a3, float a4, float a5, float a6, float a7) - { - float8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; - return val; - } - struct double8 - { - double a0, a1, a2, a3, a4, a5, a6, a7; - }; - static __host__ __device__ __forceinline__ double8 make_double8(double a0, double a1, double a2, double a3, double a4, double a5, double a6, double a7) - { - double8 val = {a0, a1, a2, a3, a4, a5, a6, a7}; - return val; - } - -#define OPENCV_CUDA_IMPLEMENT_TYPE_VEC(type) \ - template<> struct TypeVec { typedef type vec_type; }; \ - template<> struct TypeVec { typedef type ## 1 vec_type; }; \ - template<> struct TypeVec { typedef type ## 2 vec_type; }; \ - template<> struct TypeVec { typedef type ## 2 vec_type; }; \ - template<> struct TypeVec { typedef type ## 3 vec_type; }; \ - template<> struct TypeVec { typedef type ## 3 vec_type; }; \ - template<> struct TypeVec { typedef type ## 4 vec_type; }; \ - template<> struct TypeVec { typedef type ## 4 vec_type; }; \ - template<> struct TypeVec { typedef type ## 8 vec_type; }; \ - template<> struct TypeVec { typedef type ## 8 vec_type; }; - - OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uchar) - OPENCV_CUDA_IMPLEMENT_TYPE_VEC(char) - OPENCV_CUDA_IMPLEMENT_TYPE_VEC(ushort) - OPENCV_CUDA_IMPLEMENT_TYPE_VEC(short) - OPENCV_CUDA_IMPLEMENT_TYPE_VEC(int) - OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uint) - OPENCV_CUDA_IMPLEMENT_TYPE_VEC(float) - OPENCV_CUDA_IMPLEMENT_TYPE_VEC(double) - - #undef OPENCV_CUDA_IMPLEMENT_TYPE_VEC - - template<> struct TypeVec { typedef schar vec_type; }; - template<> struct TypeVec { typedef char2 vec_type; }; - template<> struct TypeVec { typedef char3 vec_type; }; - template<> struct TypeVec { typedef char4 vec_type; }; - template<> struct TypeVec { typedef char8 vec_type; }; - - template<> struct TypeVec { typedef uchar vec_type; }; - template<> struct TypeVec { typedef uchar2 vec_type; }; - template<> struct TypeVec { typedef uchar3 vec_type; }; - template<> struct TypeVec { typedef uchar4 vec_type; }; - template<> struct TypeVec { typedef uchar8 vec_type; }; - - template struct VecTraits; - -#define OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(type) \ - template<> struct VecTraits \ - { \ - typedef type elem_type; \ - enum {cn=1}; \ - static __device__ __host__ __forceinline__ type all(type v) {return v;} \ - static __device__ __host__ __forceinline__ type make(type x) {return x;} \ - static __device__ __host__ __forceinline__ type make(const type* v) {return *v;} \ - }; \ - template<> struct VecTraits \ - { \ - typedef type elem_type; \ - enum {cn=1}; \ - static __device__ __host__ __forceinline__ type ## 1 all(type v) {return make_ ## type ## 1(v);} \ - static __device__ __host__ __forceinline__ type ## 1 make(type x) {return make_ ## type ## 1(x);} \ - static __device__ __host__ __forceinline__ type ## 1 make(const type* v) {return make_ ## type ## 1(*v);} \ - }; \ - template<> struct VecTraits \ - { \ - typedef type elem_type; \ - enum {cn=2}; \ - static __device__ __host__ __forceinline__ type ## 2 all(type v) {return make_ ## type ## 2(v, v);} \ - static __device__ __host__ __forceinline__ type ## 2 make(type x, type y) {return make_ ## type ## 2(x, y);} \ - static __device__ __host__ __forceinline__ type ## 2 make(const type* v) {return make_ ## type ## 2(v[0], v[1]);} \ - }; \ - template<> struct VecTraits \ - { \ - typedef type elem_type; \ - enum {cn=3}; \ - static __device__ __host__ __forceinline__ type ## 3 all(type v) {return make_ ## type ## 3(v, v, v);} \ - static __device__ __host__ __forceinline__ type ## 3 make(type x, type y, type z) {return make_ ## type ## 3(x, y, z);} \ - static __device__ __host__ __forceinline__ type ## 3 make(const type* v) {return make_ ## type ## 3(v[0], v[1], v[2]);} \ - }; \ - template<> struct VecTraits \ - { \ - typedef type elem_type; \ - enum {cn=4}; \ - static __device__ __host__ __forceinline__ type ## 4 all(type v) {return make_ ## type ## 4(v, v, v, v);} \ - static __device__ __host__ __forceinline__ type ## 4 make(type x, type y, type z, type w) {return make_ ## type ## 4(x, y, z, w);} \ - static __device__ __host__ __forceinline__ type ## 4 make(const type* v) {return make_ ## type ## 4(v[0], v[1], v[2], v[3]);} \ - }; \ - template<> struct VecTraits \ - { \ - typedef type elem_type; \ - enum {cn=8}; \ - static __device__ __host__ __forceinline__ type ## 8 all(type v) {return make_ ## type ## 8(v, v, v, v, v, v, v, v);} \ - static __device__ __host__ __forceinline__ type ## 8 make(type a0, type a1, type a2, type a3, type a4, type a5, type a6, type a7) {return make_ ## type ## 8(a0, a1, a2, a3, a4, a5, a6, a7);} \ - static __device__ __host__ __forceinline__ type ## 8 make(const type* v) {return make_ ## type ## 8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);} \ - }; - - OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uchar) - OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(ushort) - OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(short) - OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(int) - OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uint) - OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(float) - OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(double) - - #undef OPENCV_CUDA_IMPLEMENT_VEC_TRAITS - - template<> struct VecTraits - { - typedef char elem_type; - enum {cn=1}; - static __device__ __host__ __forceinline__ char all(char v) {return v;} - static __device__ __host__ __forceinline__ char make(char x) {return x;} - static __device__ __host__ __forceinline__ char make(const char* x) {return *x;} - }; - template<> struct VecTraits - { - typedef schar elem_type; - enum {cn=1}; - static __device__ __host__ __forceinline__ schar all(schar v) {return v;} - static __device__ __host__ __forceinline__ schar make(schar x) {return x;} - static __device__ __host__ __forceinline__ schar make(const schar* x) {return *x;} - }; - template<> struct VecTraits - { - typedef schar elem_type; - enum {cn=1}; - static __device__ __host__ __forceinline__ char1 all(schar v) {return make_char1(v);} - static __device__ __host__ __forceinline__ char1 make(schar x) {return make_char1(x);} - static __device__ __host__ __forceinline__ char1 make(const schar* v) {return make_char1(v[0]);} - }; - template<> struct VecTraits - { - typedef schar elem_type; - enum {cn=2}; - static __device__ __host__ __forceinline__ char2 all(schar v) {return make_char2(v, v);} - static __device__ __host__ __forceinline__ char2 make(schar x, schar y) {return make_char2(x, y);} - static __device__ __host__ __forceinline__ char2 make(const schar* v) {return make_char2(v[0], v[1]);} - }; - template<> struct VecTraits - { - typedef schar elem_type; - enum {cn=3}; - static __device__ __host__ __forceinline__ char3 all(schar v) {return make_char3(v, v, v);} - static __device__ __host__ __forceinline__ char3 make(schar x, schar y, schar z) {return make_char3(x, y, z);} - static __device__ __host__ __forceinline__ char3 make(const schar* v) {return make_char3(v[0], v[1], v[2]);} - }; - template<> struct VecTraits - { - typedef schar elem_type; - enum {cn=4}; - static __device__ __host__ __forceinline__ char4 all(schar v) {return make_char4(v, v, v, v);} - static __device__ __host__ __forceinline__ char4 make(schar x, schar y, schar z, schar w) {return make_char4(x, y, z, w);} - static __device__ __host__ __forceinline__ char4 make(const schar* v) {return make_char4(v[0], v[1], v[2], v[3]);} - }; - template<> struct VecTraits - { - typedef schar elem_type; - enum {cn=8}; - static __device__ __host__ __forceinline__ char8 all(schar v) {return make_char8(v, v, v, v, v, v, v, v);} - static __device__ __host__ __forceinline__ char8 make(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7) {return make_char8(a0, a1, a2, a3, a4, a5, a6, a7);} - static __device__ __host__ __forceinline__ char8 make(const schar* v) {return make_char8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);} - }; -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif // OPENCV_CUDA_VEC_TRAITS_HPP diff --git a/include/opencv2/core/cuda/warp.hpp b/include/opencv2/core/cuda/warp.hpp deleted file mode 100755 index 8af7e6a..0000000 --- a/include/opencv2/core/cuda/warp.hpp +++ /dev/null @@ -1,139 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_DEVICE_WARP_HPP -#define OPENCV_CUDA_DEVICE_WARP_HPP - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - struct Warp - { - enum - { - LOG_WARP_SIZE = 5, - WARP_SIZE = 1 << LOG_WARP_SIZE, - STRIDE = WARP_SIZE - }; - - /** \brief Returns the warp lane ID of the calling thread. */ - static __device__ __forceinline__ unsigned int laneId() - { - unsigned int ret; - asm("mov.u32 %0, %%laneid;" : "=r"(ret) ); - return ret; - } - - template - static __device__ __forceinline__ void fill(It beg, It end, const T& value) - { - for(It t = beg + laneId(); t < end; t += STRIDE) - *t = value; - } - - template - static __device__ __forceinline__ OutIt copy(InIt beg, InIt end, OutIt out) - { - for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE) - *out = *t; - return out; - } - - template - static __device__ __forceinline__ OutIt transform(InIt beg, InIt end, OutIt out, UnOp op) - { - for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE) - *out = op(*t); - return out; - } - - template - static __device__ __forceinline__ OutIt transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op) - { - unsigned int lane = laneId(); - - InIt1 t1 = beg1 + lane; - InIt2 t2 = beg2 + lane; - for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, out += STRIDE) - *out = op(*t1, *t2); - return out; - } - - template - static __device__ __forceinline__ T reduce(volatile T *ptr, BinOp op) - { - const unsigned int lane = laneId(); - - if (lane < 16) - { - T partial = ptr[lane]; - - ptr[lane] = partial = op(partial, ptr[lane + 16]); - ptr[lane] = partial = op(partial, ptr[lane + 8]); - ptr[lane] = partial = op(partial, ptr[lane + 4]); - ptr[lane] = partial = op(partial, ptr[lane + 2]); - ptr[lane] = partial = op(partial, ptr[lane + 1]); - } - - return *ptr; - } - - template - static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value) - { - unsigned int lane = laneId(); - value += lane; - - for(OutIt t = beg + lane; t < end; t += STRIDE, value += STRIDE) - *t = value; - } - }; -}}} // namespace cv { namespace cuda { namespace cudev - -//! @endcond - -#endif /* OPENCV_CUDA_DEVICE_WARP_HPP */ diff --git a/include/opencv2/core/cuda/warp_reduce.hpp b/include/opencv2/core/cuda/warp_reduce.hpp deleted file mode 100755 index 530303d..0000000 --- a/include/opencv2/core/cuda/warp_reduce.hpp +++ /dev/null @@ -1,76 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_WARP_REDUCE_HPP__ -#define OPENCV_CUDA_WARP_REDUCE_HPP__ - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ - template - __device__ __forceinline__ T warp_reduce(volatile T *ptr , const unsigned int tid = threadIdx.x) - { - const unsigned int lane = tid & 31; // index of thread in warp (0..31) - - if (lane < 16) - { - T partial = ptr[tid]; - - ptr[tid] = partial = partial + ptr[tid + 16]; - ptr[tid] = partial = partial + ptr[tid + 8]; - ptr[tid] = partial = partial + ptr[tid + 4]; - ptr[tid] = partial = partial + ptr[tid + 2]; - ptr[tid] = partial = partial + ptr[tid + 1]; - } - - return ptr[tid - lane]; - } -}}} // namespace cv { namespace cuda { namespace cudev { - -//! @endcond - -#endif /* OPENCV_CUDA_WARP_REDUCE_HPP__ */ diff --git a/include/opencv2/core/cuda/warp_shuffle.hpp b/include/opencv2/core/cuda/warp_shuffle.hpp deleted file mode 100755 index 0da54ae..0000000 --- a/include/opencv2/core/cuda/warp_shuffle.hpp +++ /dev/null @@ -1,162 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CUDA_WARP_SHUFFLE_HPP -#define OPENCV_CUDA_WARP_SHUFFLE_HPP - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -namespace cv { namespace cuda { namespace device -{ -#if __CUDACC_VER_MAJOR__ >= 9 -# define __shfl(x, y, z) __shfl_sync(0xFFFFFFFFU, x, y, z) -# define __shfl_up(x, y, z) __shfl_up_sync(0xFFFFFFFFU, x, y, z) -# define __shfl_down(x, y, z) __shfl_down_sync(0xFFFFFFFFU, x, y, z) -#endif - template - __device__ __forceinline__ T shfl(T val, int srcLane, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - return __shfl(val, srcLane, width); - #else - return T(); - #endif - } - __device__ __forceinline__ unsigned int shfl(unsigned int val, int srcLane, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - return (unsigned int) __shfl((int) val, srcLane, width); - #else - return 0; - #endif - } - __device__ __forceinline__ double shfl(double val, int srcLane, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - int lo = __double2loint(val); - int hi = __double2hiint(val); - - lo = __shfl(lo, srcLane, width); - hi = __shfl(hi, srcLane, width); - - return __hiloint2double(hi, lo); - #else - return 0.0; - #endif - } - - template - __device__ __forceinline__ T shfl_down(T val, unsigned int delta, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - return __shfl_down(val, delta, width); - #else - return T(); - #endif - } - __device__ __forceinline__ unsigned int shfl_down(unsigned int val, unsigned int delta, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - return (unsigned int) __shfl_down((int) val, delta, width); - #else - return 0; - #endif - } - __device__ __forceinline__ double shfl_down(double val, unsigned int delta, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - int lo = __double2loint(val); - int hi = __double2hiint(val); - - lo = __shfl_down(lo, delta, width); - hi = __shfl_down(hi, delta, width); - - return __hiloint2double(hi, lo); - #else - return 0.0; - #endif - } - - template - __device__ __forceinline__ T shfl_up(T val, unsigned int delta, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - return __shfl_up(val, delta, width); - #else - return T(); - #endif - } - __device__ __forceinline__ unsigned int shfl_up(unsigned int val, unsigned int delta, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - return (unsigned int) __shfl_up((int) val, delta, width); - #else - return 0; - #endif - } - __device__ __forceinline__ double shfl_up(double val, unsigned int delta, int width = warpSize) - { - #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300 - int lo = __double2loint(val); - int hi = __double2hiint(val); - - lo = __shfl_up(lo, delta, width); - hi = __shfl_up(hi, delta, width); - - return __hiloint2double(hi, lo); - #else - return 0.0; - #endif - } -}}} - -# undef __shfl -# undef __shfl_up -# undef __shfl_down - -//! @endcond - -#endif // OPENCV_CUDA_WARP_SHUFFLE_HPP diff --git a/include/opencv2/core/cuda_stream_accessor.hpp b/include/opencv2/core/cuda_stream_accessor.hpp deleted file mode 100755 index deaf356..0000000 --- a/include/opencv2/core/cuda_stream_accessor.hpp +++ /dev/null @@ -1,86 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP -#define OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP - -#ifndef __cplusplus -# error cuda_stream_accessor.hpp header must be compiled as C++ -#endif - -/** @file cuda_stream_accessor.hpp - * This is only header file that depends on CUDA Runtime API. All other headers are independent. - */ - -#include -#include "opencv2/core/cuda.hpp" - -namespace cv -{ - namespace cuda - { - -//! @addtogroup cudacore_struct -//! @{ - - /** @brief Class that enables getting cudaStream_t from cuda::Stream - */ - struct StreamAccessor - { - CV_EXPORTS static cudaStream_t getStream(const Stream& stream); - CV_EXPORTS static Stream wrapStream(cudaStream_t stream); - }; - - /** @brief Class that enables getting cudaEvent_t from cuda::Event - */ - struct EventAccessor - { - CV_EXPORTS static cudaEvent_t getEvent(const Event& event); - CV_EXPORTS static Event wrapEvent(cudaEvent_t event); - }; - -//! @} - - } -} - -#endif /* OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP */ diff --git a/include/opencv2/core/cuda_types.hpp b/include/opencv2/core/cuda_types.hpp deleted file mode 100755 index b33f061..0000000 --- a/include/opencv2/core/cuda_types.hpp +++ /dev/null @@ -1,144 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_CUDA_TYPES_HPP -#define OPENCV_CORE_CUDA_TYPES_HPP - -#ifndef __cplusplus -# error cuda_types.hpp header must be compiled as C++ -#endif - -#if defined(__OPENCV_BUILD) && defined(__clang__) -#pragma clang diagnostic ignored "-Winconsistent-missing-override" -#endif -#if defined(__OPENCV_BUILD) && defined(__GNUC__) && __GNUC__ >= 5 -#pragma GCC diagnostic ignored "-Wsuggest-override" -#endif - -/** @file - * @deprecated Use @ref cudev instead. - */ - -//! @cond IGNORED - -#ifdef __CUDACC__ - #define __CV_CUDA_HOST_DEVICE__ __host__ __device__ __forceinline__ -#else - #define __CV_CUDA_HOST_DEVICE__ -#endif - -namespace cv -{ - namespace cuda - { - - // Simple lightweight structures that encapsulates information about an image on device. - // It is intended to pass to nvcc-compiled code. GpuMat depends on headers that nvcc can't compile - - template struct DevPtr - { - typedef T elem_type; - typedef int index_type; - - enum { elem_size = sizeof(elem_type) }; - - T* data; - - __CV_CUDA_HOST_DEVICE__ DevPtr() : data(0) {} - __CV_CUDA_HOST_DEVICE__ DevPtr(T* data_) : data(data_) {} - - __CV_CUDA_HOST_DEVICE__ size_t elemSize() const { return elem_size; } - __CV_CUDA_HOST_DEVICE__ operator T*() { return data; } - __CV_CUDA_HOST_DEVICE__ operator const T*() const { return data; } - }; - - template struct PtrSz : public DevPtr - { - __CV_CUDA_HOST_DEVICE__ PtrSz() : size(0) {} - __CV_CUDA_HOST_DEVICE__ PtrSz(T* data_, size_t size_) : DevPtr(data_), size(size_) {} - - size_t size; - }; - - template struct PtrStep : public DevPtr - { - __CV_CUDA_HOST_DEVICE__ PtrStep() : step(0) {} - __CV_CUDA_HOST_DEVICE__ PtrStep(T* data_, size_t step_) : DevPtr(data_), step(step_) {} - - size_t step; - - __CV_CUDA_HOST_DEVICE__ T* ptr(int y = 0) { return ( T*)( ( char*)(((DevPtr*)this)->data) + y * step); } - __CV_CUDA_HOST_DEVICE__ const T* ptr(int y = 0) const { return (const T*)( (const char*)(((DevPtr*)this)->data) + y * step); } - - __CV_CUDA_HOST_DEVICE__ T& operator ()(int y, int x) { return ptr(y)[x]; } - __CV_CUDA_HOST_DEVICE__ const T& operator ()(int y, int x) const { return ptr(y)[x]; } - }; - - template struct PtrStepSz : public PtrStep - { - __CV_CUDA_HOST_DEVICE__ PtrStepSz() : cols(0), rows(0) {} - __CV_CUDA_HOST_DEVICE__ PtrStepSz(int rows_, int cols_, T* data_, size_t step_) - : PtrStep(data_, step_), cols(cols_), rows(rows_) {} - - template - explicit PtrStepSz(const PtrStepSz& d) : PtrStep((T*)d.data, d.step), cols(d.cols), rows(d.rows){} - - int cols; - int rows; - }; - - typedef PtrStepSz PtrStepSzb; - typedef PtrStepSz PtrStepSzus; - typedef PtrStepSz PtrStepSzf; - typedef PtrStepSz PtrStepSzi; - - typedef PtrStep PtrStepb; - typedef PtrStep PtrStepus; - typedef PtrStep PtrStepf; - typedef PtrStep PtrStepi; - - } -} - -//! @endcond - -#endif /* OPENCV_CORE_CUDA_TYPES_HPP */ diff --git a/include/opencv2/core/cv_cpu_dispatch.h b/include/opencv2/core/cv_cpu_dispatch.h deleted file mode 100755 index 540fbb6..0000000 --- a/include/opencv2/core/cv_cpu_dispatch.h +++ /dev/null @@ -1,350 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#if defined __OPENCV_BUILD \ - -#include "cv_cpu_config.h" -#include "cv_cpu_helper.h" - -#ifdef CV_CPU_DISPATCH_MODE -#define CV_CPU_OPTIMIZATION_NAMESPACE __CV_CAT(opt_, CV_CPU_DISPATCH_MODE) -#define CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN namespace __CV_CAT(opt_, CV_CPU_DISPATCH_MODE) { -#define CV_CPU_OPTIMIZATION_NAMESPACE_END } -#else -#define CV_CPU_OPTIMIZATION_NAMESPACE cpu_baseline -#define CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN namespace cpu_baseline { -#define CV_CPU_OPTIMIZATION_NAMESPACE_END } -#define CV_CPU_BASELINE_MODE 1 -#endif - - -#define __CV_CPU_DISPATCH_CHAIN_END(fn, args, mode, ...) /* done */ -#define __CV_CPU_DISPATCH(fn, args, mode, ...) __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) -#define __CV_CPU_DISPATCH_EXPAND(fn, args, ...) __CV_EXPAND(__CV_CPU_DISPATCH(fn, args, __VA_ARGS__)) -#define CV_CPU_DISPATCH(fn, args, ...) __CV_CPU_DISPATCH_EXPAND(fn, args, __VA_ARGS__, END) // expand macros - - -#if defined CV_ENABLE_INTRINSICS \ - && !defined CV_DISABLE_OPTIMIZATION \ - && !defined __CUDACC__ /* do not include SSE/AVX/NEON headers for NVCC compiler */ \ - -#ifdef CV_CPU_COMPILE_SSE2 -# include -# define CV_MMX 1 -# define CV_SSE 1 -# define CV_SSE2 1 -#endif -#ifdef CV_CPU_COMPILE_SSE3 -# include -# define CV_SSE3 1 -#endif -#ifdef CV_CPU_COMPILE_SSSE3 -# include -# define CV_SSSE3 1 -#endif -#ifdef CV_CPU_COMPILE_SSE4_1 -# include -# define CV_SSE4_1 1 -#endif -#ifdef CV_CPU_COMPILE_SSE4_2 -# include -# define CV_SSE4_2 1 -#endif -#ifdef CV_CPU_COMPILE_POPCNT -# ifdef _MSC_VER -# include -# if defined(_M_X64) -# define CV_POPCNT_U64 _mm_popcnt_u64 -# endif -# define CV_POPCNT_U32 _mm_popcnt_u32 -# else -# include -# if defined(__x86_64__) -# define CV_POPCNT_U64 __builtin_popcountll -# endif -# define CV_POPCNT_U32 __builtin_popcount -# endif -# define CV_POPCNT 1 -#endif -#ifdef CV_CPU_COMPILE_AVX -# include -# define CV_AVX 1 -#endif -#ifdef CV_CPU_COMPILE_FP16 -# if defined(__arm__) || defined(__aarch64__) || defined(_M_ARM) || defined(_M_ARM64) -# include -# else -# include -# endif -# define CV_FP16 1 -#endif -#ifdef CV_CPU_COMPILE_AVX2 -# include -# define CV_AVX2 1 -#endif -#ifdef CV_CPU_COMPILE_AVX_512F -# include -# define CV_AVX_512F 1 -#endif -#ifdef CV_CPU_COMPILE_AVX512_COMMON -# define CV_AVX512_COMMON 1 -# define CV_AVX_512CD 1 -#endif -#ifdef CV_CPU_COMPILE_AVX512_KNL -# define CV_AVX512_KNL 1 -# define CV_AVX_512ER 1 -# define CV_AVX_512PF 1 -#endif -#ifdef CV_CPU_COMPILE_AVX512_KNM -# define CV_AVX512_KNM 1 -# define CV_AVX_5124FMAPS 1 -# define CV_AVX_5124VNNIW 1 -# define CV_AVX_512VPOPCNTDQ 1 -#endif -#ifdef CV_CPU_COMPILE_AVX512_SKX -# define CV_AVX512_SKX 1 -# define CV_AVX_512VL 1 -# define CV_AVX_512BW 1 -# define CV_AVX_512DQ 1 -#endif -#ifdef CV_CPU_COMPILE_AVX512_CNL -# define CV_AVX512_CNL 1 -# define CV_AVX_512IFMA 1 -# define CV_AVX_512VBMI 1 -#endif -#ifdef CV_CPU_COMPILE_AVX512_CLX -# define CV_AVX512_CLX 1 -# define CV_AVX_512VNNI 1 -#endif -#ifdef CV_CPU_COMPILE_AVX512_ICL -# define CV_AVX512_ICL 1 -# undef CV_AVX_512IFMA -# define CV_AVX_512IFMA 1 -# undef CV_AVX_512VBMI -# define CV_AVX_512VBMI 1 -# undef CV_AVX_512VNNI -# define CV_AVX_512VNNI 1 -# define CV_AVX_512VBMI2 1 -# define CV_AVX_512BITALG 1 -# define CV_AVX_512VPOPCNTDQ 1 -#endif -#ifdef CV_CPU_COMPILE_FMA3 -# define CV_FMA3 1 -#endif - -#if defined _WIN32 && (defined(_M_ARM) || defined(_M_ARM64)) && (defined(CV_CPU_COMPILE_NEON) || !defined(_MSC_VER)) -# include -# include -# define CV_NEON 1 -#elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__)) -# include -# define CV_NEON 1 -#endif - -#if defined(__ARM_NEON__) || defined(__aarch64__) -# include -#endif - -#ifdef CV_CPU_COMPILE_VSX -# include -# undef vector -# undef pixel -# undef bool -# define CV_VSX 1 -#endif - -#ifdef CV_CPU_COMPILE_VSX3 -# define CV_VSX3 1 -#endif - -#ifdef CV_CPU_COMPILE_MSA -# include "hal/msa_macros.h" -# define CV_MSA 1 -#endif - -#ifdef __EMSCRIPTEN__ -# define CV_WASM_SIMD 1 -# include -#endif - -#endif // CV_ENABLE_INTRINSICS && !CV_DISABLE_OPTIMIZATION && !__CUDACC__ - -#if defined CV_CPU_COMPILE_AVX && !defined CV_CPU_BASELINE_COMPILE_AVX -struct VZeroUpperGuard { -#ifdef __GNUC__ - __attribute__((always_inline)) -#endif - inline VZeroUpperGuard() { _mm256_zeroupper(); } -#ifdef __GNUC__ - __attribute__((always_inline)) -#endif - inline ~VZeroUpperGuard() { _mm256_zeroupper(); } -}; -#define __CV_AVX_GUARD VZeroUpperGuard __vzeroupper_guard; CV_UNUSED(__vzeroupper_guard); -#endif - -#ifdef __CV_AVX_GUARD -#define CV_AVX_GUARD __CV_AVX_GUARD -#else -#define CV_AVX_GUARD -#endif - -#endif // __OPENCV_BUILD - - - -#if !defined __OPENCV_BUILD /* Compatibility code */ \ - && !defined __CUDACC__ /* do not include SSE/AVX/NEON headers for NVCC compiler */ -#if defined __SSE2__ || defined _M_X64 || (defined _M_IX86_FP && _M_IX86_FP >= 2) -# include -# define CV_MMX 1 -# define CV_SSE 1 -# define CV_SSE2 1 -#elif defined _WIN32 && (defined(_M_ARM) || defined(_M_ARM64)) && (defined(CV_CPU_COMPILE_NEON) || !defined(_MSC_VER)) -# include -# include -# define CV_NEON 1 -#elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__)) -# include -# define CV_NEON 1 -#elif defined(__VSX__) && defined(__PPC64__) && defined(__LITTLE_ENDIAN__) -# include -# undef vector -# undef pixel -# undef bool -# define CV_VSX 1 -#endif - -#ifdef __F16C__ -# include -# define CV_FP16 1 -#endif - -#endif // !__OPENCV_BUILD && !__CUDACC (Compatibility code) - - - -#ifndef CV_MMX -# define CV_MMX 0 -#endif -#ifndef CV_SSE -# define CV_SSE 0 -#endif -#ifndef CV_SSE2 -# define CV_SSE2 0 -#endif -#ifndef CV_SSE3 -# define CV_SSE3 0 -#endif -#ifndef CV_SSSE3 -# define CV_SSSE3 0 -#endif -#ifndef CV_SSE4_1 -# define CV_SSE4_1 0 -#endif -#ifndef CV_SSE4_2 -# define CV_SSE4_2 0 -#endif -#ifndef CV_POPCNT -# define CV_POPCNT 0 -#endif -#ifndef CV_AVX -# define CV_AVX 0 -#endif -#ifndef CV_FP16 -# define CV_FP16 0 -#endif -#ifndef CV_AVX2 -# define CV_AVX2 0 -#endif -#ifndef CV_FMA3 -# define CV_FMA3 0 -#endif -#ifndef CV_AVX_512F -# define CV_AVX_512F 0 -#endif -#ifndef CV_AVX_512BW -# define CV_AVX_512BW 0 -#endif -#ifndef CV_AVX_512CD -# define CV_AVX_512CD 0 -#endif -#ifndef CV_AVX_512DQ -# define CV_AVX_512DQ 0 -#endif -#ifndef CV_AVX_512ER -# define CV_AVX_512ER 0 -#endif -#ifndef CV_AVX_512IFMA -# define CV_AVX_512IFMA 0 -#endif -#define CV_AVX_512IFMA512 CV_AVX_512IFMA // deprecated -#ifndef CV_AVX_512PF -# define CV_AVX_512PF 0 -#endif -#ifndef CV_AVX_512VBMI -# define CV_AVX_512VBMI 0 -#endif -#ifndef CV_AVX_512VL -# define CV_AVX_512VL 0 -#endif -#ifndef CV_AVX_5124FMAPS -# define CV_AVX_5124FMAPS 0 -#endif -#ifndef CV_AVX_5124VNNIW -# define CV_AVX_5124VNNIW 0 -#endif -#ifndef CV_AVX_512VPOPCNTDQ -# define CV_AVX_512VPOPCNTDQ 0 -#endif -#ifndef CV_AVX_512VNNI -# define CV_AVX_512VNNI 0 -#endif -#ifndef CV_AVX_512VBMI2 -# define CV_AVX_512VBMI2 0 -#endif -#ifndef CV_AVX_512BITALG -# define CV_AVX_512BITALG 0 -#endif -#ifndef CV_AVX512_COMMON -# define CV_AVX512_COMMON 0 -#endif -#ifndef CV_AVX512_KNL -# define CV_AVX512_KNL 0 -#endif -#ifndef CV_AVX512_KNM -# define CV_AVX512_KNM 0 -#endif -#ifndef CV_AVX512_SKX -# define CV_AVX512_SKX 0 -#endif -#ifndef CV_AVX512_CNL -# define CV_AVX512_CNL 0 -#endif -#ifndef CV_AVX512_CLX -# define CV_AVX512_CLX 0 -#endif -#ifndef CV_AVX512_ICL -# define CV_AVX512_ICL 0 -#endif - -#ifndef CV_NEON -# define CV_NEON 0 -#endif - -#ifndef CV_VSX -# define CV_VSX 0 -#endif - -#ifndef CV_VSX3 -# define CV_VSX3 0 -#endif - -#ifndef CV_MSA -# define CV_MSA 0 -#endif - -#ifndef CV_WASM_SIMD -# define CV_WASM_SIMD 0 -#endif diff --git a/include/opencv2/core/cv_cpu_helper.h b/include/opencv2/core/cv_cpu_helper.h deleted file mode 100755 index aaa89ed..0000000 --- a/include/opencv2/core/cv_cpu_helper.h +++ /dev/null @@ -1,487 +0,0 @@ -// AUTOGENERATED, DO NOT EDIT - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE -# define CV_TRY_SSE 1 -# define CV_CPU_FORCE_SSE 1 -# define CV_CPU_HAS_SUPPORT_SSE 1 -# define CV_CPU_CALL_SSE(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_SSE_(fn, args) return (opt_SSE::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE -# define CV_TRY_SSE 1 -# define CV_CPU_FORCE_SSE 0 -# define CV_CPU_HAS_SUPPORT_SSE (cv::checkHardwareSupport(CV_CPU_SSE)) -# define CV_CPU_CALL_SSE(fn, args) if (CV_CPU_HAS_SUPPORT_SSE) return (opt_SSE::fn args) -# define CV_CPU_CALL_SSE_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE) return (opt_SSE::fn args) -#else -# define CV_TRY_SSE 0 -# define CV_CPU_FORCE_SSE 0 -# define CV_CPU_HAS_SUPPORT_SSE 0 -# define CV_CPU_CALL_SSE(fn, args) -# define CV_CPU_CALL_SSE_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_SSE(fn, args, mode, ...) CV_CPU_CALL_SSE(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE2 -# define CV_TRY_SSE2 1 -# define CV_CPU_FORCE_SSE2 1 -# define CV_CPU_HAS_SUPPORT_SSE2 1 -# define CV_CPU_CALL_SSE2(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_SSE2_(fn, args) return (opt_SSE2::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE2 -# define CV_TRY_SSE2 1 -# define CV_CPU_FORCE_SSE2 0 -# define CV_CPU_HAS_SUPPORT_SSE2 (cv::checkHardwareSupport(CV_CPU_SSE2)) -# define CV_CPU_CALL_SSE2(fn, args) if (CV_CPU_HAS_SUPPORT_SSE2) return (opt_SSE2::fn args) -# define CV_CPU_CALL_SSE2_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE2) return (opt_SSE2::fn args) -#else -# define CV_TRY_SSE2 0 -# define CV_CPU_FORCE_SSE2 0 -# define CV_CPU_HAS_SUPPORT_SSE2 0 -# define CV_CPU_CALL_SSE2(fn, args) -# define CV_CPU_CALL_SSE2_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_SSE2(fn, args, mode, ...) CV_CPU_CALL_SSE2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE3 -# define CV_TRY_SSE3 1 -# define CV_CPU_FORCE_SSE3 1 -# define CV_CPU_HAS_SUPPORT_SSE3 1 -# define CV_CPU_CALL_SSE3(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_SSE3_(fn, args) return (opt_SSE3::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE3 -# define CV_TRY_SSE3 1 -# define CV_CPU_FORCE_SSE3 0 -# define CV_CPU_HAS_SUPPORT_SSE3 (cv::checkHardwareSupport(CV_CPU_SSE3)) -# define CV_CPU_CALL_SSE3(fn, args) if (CV_CPU_HAS_SUPPORT_SSE3) return (opt_SSE3::fn args) -# define CV_CPU_CALL_SSE3_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE3) return (opt_SSE3::fn args) -#else -# define CV_TRY_SSE3 0 -# define CV_CPU_FORCE_SSE3 0 -# define CV_CPU_HAS_SUPPORT_SSE3 0 -# define CV_CPU_CALL_SSE3(fn, args) -# define CV_CPU_CALL_SSE3_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_SSE3(fn, args, mode, ...) CV_CPU_CALL_SSE3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSSE3 -# define CV_TRY_SSSE3 1 -# define CV_CPU_FORCE_SSSE3 1 -# define CV_CPU_HAS_SUPPORT_SSSE3 1 -# define CV_CPU_CALL_SSSE3(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_SSSE3_(fn, args) return (opt_SSSE3::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSSE3 -# define CV_TRY_SSSE3 1 -# define CV_CPU_FORCE_SSSE3 0 -# define CV_CPU_HAS_SUPPORT_SSSE3 (cv::checkHardwareSupport(CV_CPU_SSSE3)) -# define CV_CPU_CALL_SSSE3(fn, args) if (CV_CPU_HAS_SUPPORT_SSSE3) return (opt_SSSE3::fn args) -# define CV_CPU_CALL_SSSE3_(fn, args) if (CV_CPU_HAS_SUPPORT_SSSE3) return (opt_SSSE3::fn args) -#else -# define CV_TRY_SSSE3 0 -# define CV_CPU_FORCE_SSSE3 0 -# define CV_CPU_HAS_SUPPORT_SSSE3 0 -# define CV_CPU_CALL_SSSE3(fn, args) -# define CV_CPU_CALL_SSSE3_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_SSSE3(fn, args, mode, ...) CV_CPU_CALL_SSSE3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE4_1 -# define CV_TRY_SSE4_1 1 -# define CV_CPU_FORCE_SSE4_1 1 -# define CV_CPU_HAS_SUPPORT_SSE4_1 1 -# define CV_CPU_CALL_SSE4_1(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_SSE4_1_(fn, args) return (opt_SSE4_1::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE4_1 -# define CV_TRY_SSE4_1 1 -# define CV_CPU_FORCE_SSE4_1 0 -# define CV_CPU_HAS_SUPPORT_SSE4_1 (cv::checkHardwareSupport(CV_CPU_SSE4_1)) -# define CV_CPU_CALL_SSE4_1(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_1) return (opt_SSE4_1::fn args) -# define CV_CPU_CALL_SSE4_1_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_1) return (opt_SSE4_1::fn args) -#else -# define CV_TRY_SSE4_1 0 -# define CV_CPU_FORCE_SSE4_1 0 -# define CV_CPU_HAS_SUPPORT_SSE4_1 0 -# define CV_CPU_CALL_SSE4_1(fn, args) -# define CV_CPU_CALL_SSE4_1_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_SSE4_1(fn, args, mode, ...) CV_CPU_CALL_SSE4_1(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE4_2 -# define CV_TRY_SSE4_2 1 -# define CV_CPU_FORCE_SSE4_2 1 -# define CV_CPU_HAS_SUPPORT_SSE4_2 1 -# define CV_CPU_CALL_SSE4_2(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_SSE4_2_(fn, args) return (opt_SSE4_2::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE4_2 -# define CV_TRY_SSE4_2 1 -# define CV_CPU_FORCE_SSE4_2 0 -# define CV_CPU_HAS_SUPPORT_SSE4_2 (cv::checkHardwareSupport(CV_CPU_SSE4_2)) -# define CV_CPU_CALL_SSE4_2(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_2) return (opt_SSE4_2::fn args) -# define CV_CPU_CALL_SSE4_2_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_2) return (opt_SSE4_2::fn args) -#else -# define CV_TRY_SSE4_2 0 -# define CV_CPU_FORCE_SSE4_2 0 -# define CV_CPU_HAS_SUPPORT_SSE4_2 0 -# define CV_CPU_CALL_SSE4_2(fn, args) -# define CV_CPU_CALL_SSE4_2_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_SSE4_2(fn, args, mode, ...) CV_CPU_CALL_SSE4_2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_POPCNT -# define CV_TRY_POPCNT 1 -# define CV_CPU_FORCE_POPCNT 1 -# define CV_CPU_HAS_SUPPORT_POPCNT 1 -# define CV_CPU_CALL_POPCNT(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_POPCNT_(fn, args) return (opt_POPCNT::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_POPCNT -# define CV_TRY_POPCNT 1 -# define CV_CPU_FORCE_POPCNT 0 -# define CV_CPU_HAS_SUPPORT_POPCNT (cv::checkHardwareSupport(CV_CPU_POPCNT)) -# define CV_CPU_CALL_POPCNT(fn, args) if (CV_CPU_HAS_SUPPORT_POPCNT) return (opt_POPCNT::fn args) -# define CV_CPU_CALL_POPCNT_(fn, args) if (CV_CPU_HAS_SUPPORT_POPCNT) return (opt_POPCNT::fn args) -#else -# define CV_TRY_POPCNT 0 -# define CV_CPU_FORCE_POPCNT 0 -# define CV_CPU_HAS_SUPPORT_POPCNT 0 -# define CV_CPU_CALL_POPCNT(fn, args) -# define CV_CPU_CALL_POPCNT_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_POPCNT(fn, args, mode, ...) CV_CPU_CALL_POPCNT(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX -# define CV_TRY_AVX 1 -# define CV_CPU_FORCE_AVX 1 -# define CV_CPU_HAS_SUPPORT_AVX 1 -# define CV_CPU_CALL_AVX(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_AVX_(fn, args) return (opt_AVX::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX -# define CV_TRY_AVX 1 -# define CV_CPU_FORCE_AVX 0 -# define CV_CPU_HAS_SUPPORT_AVX (cv::checkHardwareSupport(CV_CPU_AVX)) -# define CV_CPU_CALL_AVX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX) return (opt_AVX::fn args) -# define CV_CPU_CALL_AVX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX) return (opt_AVX::fn args) -#else -# define CV_TRY_AVX 0 -# define CV_CPU_FORCE_AVX 0 -# define CV_CPU_HAS_SUPPORT_AVX 0 -# define CV_CPU_CALL_AVX(fn, args) -# define CV_CPU_CALL_AVX_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_AVX(fn, args, mode, ...) CV_CPU_CALL_AVX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_FP16 -# define CV_TRY_FP16 1 -# define CV_CPU_FORCE_FP16 1 -# define CV_CPU_HAS_SUPPORT_FP16 1 -# define CV_CPU_CALL_FP16(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_FP16_(fn, args) return (opt_FP16::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_FP16 -# define CV_TRY_FP16 1 -# define CV_CPU_FORCE_FP16 0 -# define CV_CPU_HAS_SUPPORT_FP16 (cv::checkHardwareSupport(CV_CPU_FP16)) -# define CV_CPU_CALL_FP16(fn, args) if (CV_CPU_HAS_SUPPORT_FP16) return (opt_FP16::fn args) -# define CV_CPU_CALL_FP16_(fn, args) if (CV_CPU_HAS_SUPPORT_FP16) return (opt_FP16::fn args) -#else -# define CV_TRY_FP16 0 -# define CV_CPU_FORCE_FP16 0 -# define CV_CPU_HAS_SUPPORT_FP16 0 -# define CV_CPU_CALL_FP16(fn, args) -# define CV_CPU_CALL_FP16_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_FP16(fn, args, mode, ...) CV_CPU_CALL_FP16(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX2 -# define CV_TRY_AVX2 1 -# define CV_CPU_FORCE_AVX2 1 -# define CV_CPU_HAS_SUPPORT_AVX2 1 -# define CV_CPU_CALL_AVX2(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_AVX2_(fn, args) return (opt_AVX2::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX2 -# define CV_TRY_AVX2 1 -# define CV_CPU_FORCE_AVX2 0 -# define CV_CPU_HAS_SUPPORT_AVX2 (cv::checkHardwareSupport(CV_CPU_AVX2)) -# define CV_CPU_CALL_AVX2(fn, args) if (CV_CPU_HAS_SUPPORT_AVX2) return (opt_AVX2::fn args) -# define CV_CPU_CALL_AVX2_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX2) return (opt_AVX2::fn args) -#else -# define CV_TRY_AVX2 0 -# define CV_CPU_FORCE_AVX2 0 -# define CV_CPU_HAS_SUPPORT_AVX2 0 -# define CV_CPU_CALL_AVX2(fn, args) -# define CV_CPU_CALL_AVX2_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_AVX2(fn, args, mode, ...) CV_CPU_CALL_AVX2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_FMA3 -# define CV_TRY_FMA3 1 -# define CV_CPU_FORCE_FMA3 1 -# define CV_CPU_HAS_SUPPORT_FMA3 1 -# define CV_CPU_CALL_FMA3(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_FMA3_(fn, args) return (opt_FMA3::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_FMA3 -# define CV_TRY_FMA3 1 -# define CV_CPU_FORCE_FMA3 0 -# define CV_CPU_HAS_SUPPORT_FMA3 (cv::checkHardwareSupport(CV_CPU_FMA3)) -# define CV_CPU_CALL_FMA3(fn, args) if (CV_CPU_HAS_SUPPORT_FMA3) return (opt_FMA3::fn args) -# define CV_CPU_CALL_FMA3_(fn, args) if (CV_CPU_HAS_SUPPORT_FMA3) return (opt_FMA3::fn args) -#else -# define CV_TRY_FMA3 0 -# define CV_CPU_FORCE_FMA3 0 -# define CV_CPU_HAS_SUPPORT_FMA3 0 -# define CV_CPU_CALL_FMA3(fn, args) -# define CV_CPU_CALL_FMA3_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_FMA3(fn, args, mode, ...) CV_CPU_CALL_FMA3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX_512F -# define CV_TRY_AVX_512F 1 -# define CV_CPU_FORCE_AVX_512F 1 -# define CV_CPU_HAS_SUPPORT_AVX_512F 1 -# define CV_CPU_CALL_AVX_512F(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_AVX_512F_(fn, args) return (opt_AVX_512F::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX_512F -# define CV_TRY_AVX_512F 1 -# define CV_CPU_FORCE_AVX_512F 0 -# define CV_CPU_HAS_SUPPORT_AVX_512F (cv::checkHardwareSupport(CV_CPU_AVX_512F)) -# define CV_CPU_CALL_AVX_512F(fn, args) if (CV_CPU_HAS_SUPPORT_AVX_512F) return (opt_AVX_512F::fn args) -# define CV_CPU_CALL_AVX_512F_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX_512F) return (opt_AVX_512F::fn args) -#else -# define CV_TRY_AVX_512F 0 -# define CV_CPU_FORCE_AVX_512F 0 -# define CV_CPU_HAS_SUPPORT_AVX_512F 0 -# define CV_CPU_CALL_AVX_512F(fn, args) -# define CV_CPU_CALL_AVX_512F_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_AVX_512F(fn, args, mode, ...) CV_CPU_CALL_AVX_512F(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_COMMON -# define CV_TRY_AVX512_COMMON 1 -# define CV_CPU_FORCE_AVX512_COMMON 1 -# define CV_CPU_HAS_SUPPORT_AVX512_COMMON 1 -# define CV_CPU_CALL_AVX512_COMMON(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_AVX512_COMMON_(fn, args) return (opt_AVX512_COMMON::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_COMMON -# define CV_TRY_AVX512_COMMON 1 -# define CV_CPU_FORCE_AVX512_COMMON 0 -# define CV_CPU_HAS_SUPPORT_AVX512_COMMON (cv::checkHardwareSupport(CV_CPU_AVX512_COMMON)) -# define CV_CPU_CALL_AVX512_COMMON(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_COMMON) return (opt_AVX512_COMMON::fn args) -# define CV_CPU_CALL_AVX512_COMMON_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_COMMON) return (opt_AVX512_COMMON::fn args) -#else -# define CV_TRY_AVX512_COMMON 0 -# define CV_CPU_FORCE_AVX512_COMMON 0 -# define CV_CPU_HAS_SUPPORT_AVX512_COMMON 0 -# define CV_CPU_CALL_AVX512_COMMON(fn, args) -# define CV_CPU_CALL_AVX512_COMMON_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_AVX512_COMMON(fn, args, mode, ...) CV_CPU_CALL_AVX512_COMMON(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_KNL -# define CV_TRY_AVX512_KNL 1 -# define CV_CPU_FORCE_AVX512_KNL 1 -# define CV_CPU_HAS_SUPPORT_AVX512_KNL 1 -# define CV_CPU_CALL_AVX512_KNL(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_AVX512_KNL_(fn, args) return (opt_AVX512_KNL::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_KNL -# define CV_TRY_AVX512_KNL 1 -# define CV_CPU_FORCE_AVX512_KNL 0 -# define CV_CPU_HAS_SUPPORT_AVX512_KNL (cv::checkHardwareSupport(CV_CPU_AVX512_KNL)) -# define CV_CPU_CALL_AVX512_KNL(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_KNL) return (opt_AVX512_KNL::fn args) -# define CV_CPU_CALL_AVX512_KNL_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_KNL) return (opt_AVX512_KNL::fn args) -#else -# define CV_TRY_AVX512_KNL 0 -# define CV_CPU_FORCE_AVX512_KNL 0 -# define CV_CPU_HAS_SUPPORT_AVX512_KNL 0 -# define CV_CPU_CALL_AVX512_KNL(fn, args) -# define CV_CPU_CALL_AVX512_KNL_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_AVX512_KNL(fn, args, mode, ...) CV_CPU_CALL_AVX512_KNL(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_KNM -# define CV_TRY_AVX512_KNM 1 -# define CV_CPU_FORCE_AVX512_KNM 1 -# define CV_CPU_HAS_SUPPORT_AVX512_KNM 1 -# define CV_CPU_CALL_AVX512_KNM(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_AVX512_KNM_(fn, args) return (opt_AVX512_KNM::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_KNM -# define CV_TRY_AVX512_KNM 1 -# define CV_CPU_FORCE_AVX512_KNM 0 -# define CV_CPU_HAS_SUPPORT_AVX512_KNM (cv::checkHardwareSupport(CV_CPU_AVX512_KNM)) -# define CV_CPU_CALL_AVX512_KNM(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_KNM) return (opt_AVX512_KNM::fn args) -# define CV_CPU_CALL_AVX512_KNM_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_KNM) return (opt_AVX512_KNM::fn args) -#else -# define CV_TRY_AVX512_KNM 0 -# define CV_CPU_FORCE_AVX512_KNM 0 -# define CV_CPU_HAS_SUPPORT_AVX512_KNM 0 -# define CV_CPU_CALL_AVX512_KNM(fn, args) -# define CV_CPU_CALL_AVX512_KNM_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_AVX512_KNM(fn, args, mode, ...) CV_CPU_CALL_AVX512_KNM(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_SKX -# define CV_TRY_AVX512_SKX 1 -# define CV_CPU_FORCE_AVX512_SKX 1 -# define CV_CPU_HAS_SUPPORT_AVX512_SKX 1 -# define CV_CPU_CALL_AVX512_SKX(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_AVX512_SKX_(fn, args) return (opt_AVX512_SKX::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_SKX -# define CV_TRY_AVX512_SKX 1 -# define CV_CPU_FORCE_AVX512_SKX 0 -# define CV_CPU_HAS_SUPPORT_AVX512_SKX (cv::checkHardwareSupport(CV_CPU_AVX512_SKX)) -# define CV_CPU_CALL_AVX512_SKX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_SKX) return (opt_AVX512_SKX::fn args) -# define CV_CPU_CALL_AVX512_SKX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_SKX) return (opt_AVX512_SKX::fn args) -#else -# define CV_TRY_AVX512_SKX 0 -# define CV_CPU_FORCE_AVX512_SKX 0 -# define CV_CPU_HAS_SUPPORT_AVX512_SKX 0 -# define CV_CPU_CALL_AVX512_SKX(fn, args) -# define CV_CPU_CALL_AVX512_SKX_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_AVX512_SKX(fn, args, mode, ...) CV_CPU_CALL_AVX512_SKX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_CNL -# define CV_TRY_AVX512_CNL 1 -# define CV_CPU_FORCE_AVX512_CNL 1 -# define CV_CPU_HAS_SUPPORT_AVX512_CNL 1 -# define CV_CPU_CALL_AVX512_CNL(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_AVX512_CNL_(fn, args) return (opt_AVX512_CNL::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_CNL -# define CV_TRY_AVX512_CNL 1 -# define CV_CPU_FORCE_AVX512_CNL 0 -# define CV_CPU_HAS_SUPPORT_AVX512_CNL (cv::checkHardwareSupport(CV_CPU_AVX512_CNL)) -# define CV_CPU_CALL_AVX512_CNL(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_CNL) return (opt_AVX512_CNL::fn args) -# define CV_CPU_CALL_AVX512_CNL_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_CNL) return (opt_AVX512_CNL::fn args) -#else -# define CV_TRY_AVX512_CNL 0 -# define CV_CPU_FORCE_AVX512_CNL 0 -# define CV_CPU_HAS_SUPPORT_AVX512_CNL 0 -# define CV_CPU_CALL_AVX512_CNL(fn, args) -# define CV_CPU_CALL_AVX512_CNL_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_AVX512_CNL(fn, args, mode, ...) CV_CPU_CALL_AVX512_CNL(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_CLX -# define CV_TRY_AVX512_CLX 1 -# define CV_CPU_FORCE_AVX512_CLX 1 -# define CV_CPU_HAS_SUPPORT_AVX512_CLX 1 -# define CV_CPU_CALL_AVX512_CLX(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_AVX512_CLX_(fn, args) return (opt_AVX512_CLX::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_CLX -# define CV_TRY_AVX512_CLX 1 -# define CV_CPU_FORCE_AVX512_CLX 0 -# define CV_CPU_HAS_SUPPORT_AVX512_CLX (cv::checkHardwareSupport(CV_CPU_AVX512_CLX)) -# define CV_CPU_CALL_AVX512_CLX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_CLX) return (opt_AVX512_CLX::fn args) -# define CV_CPU_CALL_AVX512_CLX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_CLX) return (opt_AVX512_CLX::fn args) -#else -# define CV_TRY_AVX512_CLX 0 -# define CV_CPU_FORCE_AVX512_CLX 0 -# define CV_CPU_HAS_SUPPORT_AVX512_CLX 0 -# define CV_CPU_CALL_AVX512_CLX(fn, args) -# define CV_CPU_CALL_AVX512_CLX_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_AVX512_CLX(fn, args, mode, ...) CV_CPU_CALL_AVX512_CLX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_ICL -# define CV_TRY_AVX512_ICL 1 -# define CV_CPU_FORCE_AVX512_ICL 1 -# define CV_CPU_HAS_SUPPORT_AVX512_ICL 1 -# define CV_CPU_CALL_AVX512_ICL(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_AVX512_ICL_(fn, args) return (opt_AVX512_ICL::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_ICL -# define CV_TRY_AVX512_ICL 1 -# define CV_CPU_FORCE_AVX512_ICL 0 -# define CV_CPU_HAS_SUPPORT_AVX512_ICL (cv::checkHardwareSupport(CV_CPU_AVX512_ICL)) -# define CV_CPU_CALL_AVX512_ICL(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_ICL) return (opt_AVX512_ICL::fn args) -# define CV_CPU_CALL_AVX512_ICL_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_ICL) return (opt_AVX512_ICL::fn args) -#else -# define CV_TRY_AVX512_ICL 0 -# define CV_CPU_FORCE_AVX512_ICL 0 -# define CV_CPU_HAS_SUPPORT_AVX512_ICL 0 -# define CV_CPU_CALL_AVX512_ICL(fn, args) -# define CV_CPU_CALL_AVX512_ICL_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_AVX512_ICL(fn, args, mode, ...) CV_CPU_CALL_AVX512_ICL(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_NEON -# define CV_TRY_NEON 1 -# define CV_CPU_FORCE_NEON 1 -# define CV_CPU_HAS_SUPPORT_NEON 1 -# define CV_CPU_CALL_NEON(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_NEON_(fn, args) return (opt_NEON::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_NEON -# define CV_TRY_NEON 1 -# define CV_CPU_FORCE_NEON 0 -# define CV_CPU_HAS_SUPPORT_NEON (cv::checkHardwareSupport(CV_CPU_NEON)) -# define CV_CPU_CALL_NEON(fn, args) if (CV_CPU_HAS_SUPPORT_NEON) return (opt_NEON::fn args) -# define CV_CPU_CALL_NEON_(fn, args) if (CV_CPU_HAS_SUPPORT_NEON) return (opt_NEON::fn args) -#else -# define CV_TRY_NEON 0 -# define CV_CPU_FORCE_NEON 0 -# define CV_CPU_HAS_SUPPORT_NEON 0 -# define CV_CPU_CALL_NEON(fn, args) -# define CV_CPU_CALL_NEON_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_NEON(fn, args, mode, ...) CV_CPU_CALL_NEON(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_MSA -# define CV_TRY_MSA 1 -# define CV_CPU_FORCE_MSA 1 -# define CV_CPU_HAS_SUPPORT_MSA 1 -# define CV_CPU_CALL_MSA(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_MSA_(fn, args) return (opt_MSA::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_MSA -# define CV_TRY_MSA 1 -# define CV_CPU_FORCE_MSA 0 -# define CV_CPU_HAS_SUPPORT_MSA (cv::checkHardwareSupport(CV_CPU_MSA)) -# define CV_CPU_CALL_MSA(fn, args) if (CV_CPU_HAS_SUPPORT_MSA) return (opt_MSA::fn args) -# define CV_CPU_CALL_MSA_(fn, args) if (CV_CPU_HAS_SUPPORT_MSA) return (opt_MSA::fn args) -#else -# define CV_TRY_MSA 0 -# define CV_CPU_FORCE_MSA 0 -# define CV_CPU_HAS_SUPPORT_MSA 0 -# define CV_CPU_CALL_MSA(fn, args) -# define CV_CPU_CALL_MSA_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_MSA(fn, args, mode, ...) CV_CPU_CALL_MSA(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_VSX -# define CV_TRY_VSX 1 -# define CV_CPU_FORCE_VSX 1 -# define CV_CPU_HAS_SUPPORT_VSX 1 -# define CV_CPU_CALL_VSX(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_VSX_(fn, args) return (opt_VSX::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_VSX -# define CV_TRY_VSX 1 -# define CV_CPU_FORCE_VSX 0 -# define CV_CPU_HAS_SUPPORT_VSX (cv::checkHardwareSupport(CV_CPU_VSX)) -# define CV_CPU_CALL_VSX(fn, args) if (CV_CPU_HAS_SUPPORT_VSX) return (opt_VSX::fn args) -# define CV_CPU_CALL_VSX_(fn, args) if (CV_CPU_HAS_SUPPORT_VSX) return (opt_VSX::fn args) -#else -# define CV_TRY_VSX 0 -# define CV_CPU_FORCE_VSX 0 -# define CV_CPU_HAS_SUPPORT_VSX 0 -# define CV_CPU_CALL_VSX(fn, args) -# define CV_CPU_CALL_VSX_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_VSX(fn, args, mode, ...) CV_CPU_CALL_VSX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_VSX3 -# define CV_TRY_VSX3 1 -# define CV_CPU_FORCE_VSX3 1 -# define CV_CPU_HAS_SUPPORT_VSX3 1 -# define CV_CPU_CALL_VSX3(fn, args) return (cpu_baseline::fn args) -# define CV_CPU_CALL_VSX3_(fn, args) return (opt_VSX3::fn args) -#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_VSX3 -# define CV_TRY_VSX3 1 -# define CV_CPU_FORCE_VSX3 0 -# define CV_CPU_HAS_SUPPORT_VSX3 (cv::checkHardwareSupport(CV_CPU_VSX3)) -# define CV_CPU_CALL_VSX3(fn, args) if (CV_CPU_HAS_SUPPORT_VSX3) return (opt_VSX3::fn args) -# define CV_CPU_CALL_VSX3_(fn, args) if (CV_CPU_HAS_SUPPORT_VSX3) return (opt_VSX3::fn args) -#else -# define CV_TRY_VSX3 0 -# define CV_CPU_FORCE_VSX3 0 -# define CV_CPU_HAS_SUPPORT_VSX3 0 -# define CV_CPU_CALL_VSX3(fn, args) -# define CV_CPU_CALL_VSX3_(fn, args) -#endif -#define __CV_CPU_DISPATCH_CHAIN_VSX3(fn, args, mode, ...) CV_CPU_CALL_VSX3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) - -#define CV_CPU_CALL_BASELINE(fn, args) return (cpu_baseline::fn args) -#define __CV_CPU_DISPATCH_CHAIN_BASELINE(fn, args, mode, ...) CV_CPU_CALL_BASELINE(fn, args) /* last in sequence */ diff --git a/include/opencv2/core/cvdef.h b/include/opencv2/core/cvdef.h deleted file mode 100755 index 6488b8b..0000000 --- a/include/opencv2/core/cvdef.h +++ /dev/null @@ -1,845 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_CVDEF_H -#define OPENCV_CORE_CVDEF_H - -//! @addtogroup core_utils -//! @{ - -#ifdef OPENCV_INCLUDE_PORT_FILE // User-provided header file with custom platform configuration -#include OPENCV_INCLUDE_PORT_FILE -#endif - -#if !defined CV_DOXYGEN && !defined CV_IGNORE_DEBUG_BUILD_GUARD -#if (defined(_MSC_VER) && (defined(DEBUG) || defined(_DEBUG))) || \ - (defined(_GLIBCXX_DEBUG) || defined(_GLIBCXX_DEBUG_PEDANTIC)) -// Guard to prevent using of binary incompatible binaries / runtimes -// https://github.com/opencv/opencv/pull/9161 -#define CV__DEBUG_NS_BEGIN namespace debug_build_guard { -#define CV__DEBUG_NS_END } -namespace cv { namespace debug_build_guard { } using namespace debug_build_guard; } -#endif -#endif - -#ifndef CV__DEBUG_NS_BEGIN -#define CV__DEBUG_NS_BEGIN -#define CV__DEBUG_NS_END -#endif - - -#ifdef __OPENCV_BUILD -#include "cvconfig.h" -#endif - -#ifndef __CV_EXPAND -#define __CV_EXPAND(x) x -#endif - -#ifndef __CV_CAT -#define __CV_CAT__(x, y) x ## y -#define __CV_CAT_(x, y) __CV_CAT__(x, y) -#define __CV_CAT(x, y) __CV_CAT_(x, y) -#endif - -#define __CV_VA_NUM_ARGS_HELPER(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, N, ...) N -#define __CV_VA_NUM_ARGS(...) __CV_VA_NUM_ARGS_HELPER(__VA_ARGS__, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0) - -#ifdef CV_Func -// keep current value (through OpenCV port file) -#elif defined __GNUC__ || (defined (__cpluscplus) && (__cpluscplus >= 201103)) -#define CV_Func __func__ -#elif defined __clang__ && (__clang_minor__ * 100 + __clang_major__ >= 305) -#define CV_Func __func__ -#elif defined(__STDC_VERSION__) && (__STDC_VERSION >= 199901) -#define CV_Func __func__ -#elif defined _MSC_VER -#define CV_Func __FUNCTION__ -#elif defined(__INTEL_COMPILER) && (_INTEL_COMPILER >= 600) -#define CV_Func __FUNCTION__ -#elif defined __IBMCPP__ && __IBMCPP__ >=500 -#define CV_Func __FUNCTION__ -#elif defined __BORLAND__ && (__BORLANDC__ >= 0x550) -#define CV_Func __FUNC__ -#else -#define CV_Func "" -#endif - -//! @cond IGNORED - -//////////////// static assert ///////////////// -#define CVAUX_CONCAT_EXP(a, b) a##b -#define CVAUX_CONCAT(a, b) CVAUX_CONCAT_EXP(a,b) - -#if defined(__clang__) -# ifndef __has_extension -# define __has_extension __has_feature /* compatibility, for older versions of clang */ -# endif -# if __has_extension(cxx_static_assert) -# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) -# elif __has_extension(c_static_assert) -# define CV_StaticAssert(condition, reason) _Static_assert((condition), reason " " #condition) -# endif -#elif defined(__GNUC__) -# if (defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L) -# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) -# endif -#elif defined(_MSC_VER) -# if _MSC_VER >= 1600 /* MSVC 10 */ -# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition) -# endif -#endif -#ifndef CV_StaticAssert -# if !defined(__clang__) && defined(__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 302) -# define CV_StaticAssert(condition, reason) ({ extern int __attribute__((error("CV_StaticAssert: " reason " " #condition))) CV_StaticAssert(); ((condition) ? 0 : CV_StaticAssert()); }) -# else -namespace cv { - template struct CV_StaticAssert_failed; - template <> struct CV_StaticAssert_failed { enum { val = 1 }; }; - template struct CV_StaticAssert_test {}; -} -# define CV_StaticAssert(condition, reason)\ - typedef cv::CV_StaticAssert_test< sizeof(cv::CV_StaticAssert_failed< static_cast(condition) >) > CVAUX_CONCAT(CV_StaticAssert_failed_at_, __LINE__) -# endif -#endif - -// Suppress warning "-Wdeprecated-declarations" / C4996 -#if defined(_MSC_VER) - #define CV_DO_PRAGMA(x) __pragma(x) -#elif defined(__GNUC__) - #define CV_DO_PRAGMA(x) _Pragma (#x) -#else - #define CV_DO_PRAGMA(x) -#endif - -#ifdef _MSC_VER -#define CV_SUPPRESS_DEPRECATED_START \ - CV_DO_PRAGMA(warning(push)) \ - CV_DO_PRAGMA(warning(disable: 4996)) -#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(warning(pop)) -#elif defined (__clang__) || ((__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 405)) -#define CV_SUPPRESS_DEPRECATED_START \ - CV_DO_PRAGMA(GCC diagnostic push) \ - CV_DO_PRAGMA(GCC diagnostic ignored "-Wdeprecated-declarations") -#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(GCC diagnostic pop) -#else -#define CV_SUPPRESS_DEPRECATED_START -#define CV_SUPPRESS_DEPRECATED_END -#endif - -#define CV_UNUSED(name) (void)name - -#if defined __GNUC__ && !defined __EXCEPTIONS -#define CV_TRY -#define CV_CATCH(A, B) for (A B; false; ) -#define CV_CATCH_ALL if (false) -#define CV_THROW(A) abort() -#define CV_RETHROW() abort() -#else -#define CV_TRY try -#define CV_CATCH(A, B) catch(const A & B) -#define CV_CATCH_ALL catch(...) -#define CV_THROW(A) throw A -#define CV_RETHROW() throw -#endif - -//! @endcond - -// undef problematic defines sometimes defined by system headers (windows.h in particular) -#undef small -#undef min -#undef max -#undef abs -#undef Complex - -#if defined __cplusplus -#include -#else -#include -#endif - -#include "opencv2/core/hal/interface.h" - -#if defined __ICL -# define CV_ICC __ICL -#elif defined __ICC -# define CV_ICC __ICC -#elif defined __ECL -# define CV_ICC __ECL -#elif defined __ECC -# define CV_ICC __ECC -#elif defined __INTEL_COMPILER -# define CV_ICC __INTEL_COMPILER -#endif - -#ifndef CV_INLINE -# if defined __cplusplus -# define CV_INLINE static inline -# elif defined _MSC_VER -# define CV_INLINE __inline -# else -# define CV_INLINE static -# endif -#endif - -#ifndef CV_ALWAYS_INLINE -#if defined(__GNUC__) && (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 1)) -#define CV_ALWAYS_INLINE inline __attribute__((always_inline)) -#elif defined(_MSC_VER) -#define CV_ALWAYS_INLINE __forceinline -#else -#define CV_ALWAYS_INLINE inline -#endif -#endif - -#if defined CV_DISABLE_OPTIMIZATION || (defined CV_ICC && !defined CV_ENABLE_UNROLLED) -# define CV_ENABLE_UNROLLED 0 -#else -# define CV_ENABLE_UNROLLED 1 -#endif - -#ifdef __GNUC__ -# define CV_DECL_ALIGNED(x) __attribute__ ((aligned (x))) -#elif defined _MSC_VER -# define CV_DECL_ALIGNED(x) __declspec(align(x)) -#else -# define CV_DECL_ALIGNED(x) -#endif - -/* CPU features and intrinsics support */ -#define CV_CPU_NONE 0 -#define CV_CPU_MMX 1 -#define CV_CPU_SSE 2 -#define CV_CPU_SSE2 3 -#define CV_CPU_SSE3 4 -#define CV_CPU_SSSE3 5 -#define CV_CPU_SSE4_1 6 -#define CV_CPU_SSE4_2 7 -#define CV_CPU_POPCNT 8 -#define CV_CPU_FP16 9 -#define CV_CPU_AVX 10 -#define CV_CPU_AVX2 11 -#define CV_CPU_FMA3 12 - -#define CV_CPU_AVX_512F 13 -#define CV_CPU_AVX_512BW 14 -#define CV_CPU_AVX_512CD 15 -#define CV_CPU_AVX_512DQ 16 -#define CV_CPU_AVX_512ER 17 -#define CV_CPU_AVX_512IFMA512 18 // deprecated -#define CV_CPU_AVX_512IFMA 18 -#define CV_CPU_AVX_512PF 19 -#define CV_CPU_AVX_512VBMI 20 -#define CV_CPU_AVX_512VL 21 -#define CV_CPU_AVX_512VBMI2 22 -#define CV_CPU_AVX_512VNNI 23 -#define CV_CPU_AVX_512BITALG 24 -#define CV_CPU_AVX_512VPOPCNTDQ 25 -#define CV_CPU_AVX_5124VNNIW 26 -#define CV_CPU_AVX_5124FMAPS 27 - -#define CV_CPU_NEON 100 - -#define CV_CPU_MSA 150 - -#define CV_CPU_VSX 200 -#define CV_CPU_VSX3 201 - -// CPU features groups -#define CV_CPU_AVX512_SKX 256 -#define CV_CPU_AVX512_COMMON 257 -#define CV_CPU_AVX512_KNL 258 -#define CV_CPU_AVX512_KNM 259 -#define CV_CPU_AVX512_CNL 260 -#define CV_CPU_AVX512_CLX 261 -#define CV_CPU_AVX512_ICL 262 - -// when adding to this list remember to update the following enum -#define CV_HARDWARE_MAX_FEATURE 512 - -/** @brief Available CPU features. -*/ -enum CpuFeatures { - CPU_MMX = 1, - CPU_SSE = 2, - CPU_SSE2 = 3, - CPU_SSE3 = 4, - CPU_SSSE3 = 5, - CPU_SSE4_1 = 6, - CPU_SSE4_2 = 7, - CPU_POPCNT = 8, - CPU_FP16 = 9, - CPU_AVX = 10, - CPU_AVX2 = 11, - CPU_FMA3 = 12, - - CPU_AVX_512F = 13, - CPU_AVX_512BW = 14, - CPU_AVX_512CD = 15, - CPU_AVX_512DQ = 16, - CPU_AVX_512ER = 17, - CPU_AVX_512IFMA512 = 18, // deprecated - CPU_AVX_512IFMA = 18, - CPU_AVX_512PF = 19, - CPU_AVX_512VBMI = 20, - CPU_AVX_512VL = 21, - CPU_AVX_512VBMI2 = 22, - CPU_AVX_512VNNI = 23, - CPU_AVX_512BITALG = 24, - CPU_AVX_512VPOPCNTDQ= 25, - CPU_AVX_5124VNNIW = 26, - CPU_AVX_5124FMAPS = 27, - - CPU_NEON = 100, - - CPU_MSA = 150, - - CPU_VSX = 200, - CPU_VSX3 = 201, - - CPU_AVX512_SKX = 256, //!< Skylake-X with AVX-512F/CD/BW/DQ/VL - CPU_AVX512_COMMON = 257, //!< Common instructions AVX-512F/CD for all CPUs that support AVX-512 - CPU_AVX512_KNL = 258, //!< Knights Landing with AVX-512F/CD/ER/PF - CPU_AVX512_KNM = 259, //!< Knights Mill with AVX-512F/CD/ER/PF/4FMAPS/4VNNIW/VPOPCNTDQ - CPU_AVX512_CNL = 260, //!< Cannon Lake with AVX-512F/CD/BW/DQ/VL/IFMA/VBMI - CPU_AVX512_CLX = 261, //!< Cascade Lake with AVX-512F/CD/BW/DQ/VL/VNNI - CPU_AVX512_ICL = 262, //!< Ice Lake with AVX-512F/CD/BW/DQ/VL/IFMA/VBMI/VNNI/VBMI2/BITALG/VPOPCNTDQ - - CPU_MAX_FEATURE = 512 // see CV_HARDWARE_MAX_FEATURE -}; - - -#include "cv_cpu_dispatch.h" - -#if !defined(CV_STRONG_ALIGNMENT) && defined(__arm__) && !(defined(__aarch64__) || defined(_M_ARM64)) -// int*, int64* should be propertly aligned pointers on ARMv7 -#define CV_STRONG_ALIGNMENT 1 -#endif -#if !defined(CV_STRONG_ALIGNMENT) -#define CV_STRONG_ALIGNMENT 0 -#endif - -/* fundamental constants */ -#define CV_PI 3.1415926535897932384626433832795 -#define CV_2PI 6.283185307179586476925286766559 -#define CV_LOG2 0.69314718055994530941723212145818 - -#if defined __ARM_FP16_FORMAT_IEEE \ - && !defined __CUDACC__ -# define CV_FP16_TYPE 1 -#else -# define CV_FP16_TYPE 0 -#endif - -typedef union Cv16suf -{ - short i; - ushort u; -#if CV_FP16_TYPE - __fp16 h; -#endif -} -Cv16suf; - -typedef union Cv32suf -{ - int i; - unsigned u; - float f; -} -Cv32suf; - -typedef union Cv64suf -{ - int64 i; - uint64 u; - double f; -} -Cv64suf; - -#define OPENCV_ABI_COMPATIBILITY 300 - -#ifdef __OPENCV_BUILD -# define DISABLE_OPENCV_24_COMPATIBILITY -# define OPENCV_DISABLE_DEPRECATED_COMPATIBILITY -#endif - -#ifndef CV_EXPORTS -# if (defined _WIN32 || defined WINCE || defined __CYGWIN__) && defined(CVAPI_EXPORTS) -# define CV_EXPORTS __declspec(dllexport) -# elif defined __GNUC__ && __GNUC__ >= 4 && (defined(CVAPI_EXPORTS) || defined(__APPLE__)) -# define CV_EXPORTS __attribute__ ((visibility ("default"))) -# endif -#endif - -#ifndef CV_EXPORTS -# define CV_EXPORTS -#endif - -#ifdef _MSC_VER -# define CV_EXPORTS_TEMPLATE -#else -# define CV_EXPORTS_TEMPLATE CV_EXPORTS -#endif - -#ifndef CV_DEPRECATED -# if defined(__GNUC__) -# define CV_DEPRECATED __attribute__ ((deprecated)) -# elif defined(_MSC_VER) -# define CV_DEPRECATED __declspec(deprecated) -# else -# define CV_DEPRECATED -# endif -#endif - -#ifndef CV_DEPRECATED_EXTERNAL -# if defined(__OPENCV_BUILD) -# define CV_DEPRECATED_EXTERNAL /* nothing */ -# else -# define CV_DEPRECATED_EXTERNAL CV_DEPRECATED -# endif -#endif - - -#ifndef CV_EXTERN_C -# ifdef __cplusplus -# define CV_EXTERN_C extern "C" -# else -# define CV_EXTERN_C -# endif -#endif - -/* special informative macros for wrapper generators */ -#define CV_EXPORTS_W CV_EXPORTS -#define CV_EXPORTS_W_SIMPLE CV_EXPORTS -#define CV_EXPORTS_AS(synonym) CV_EXPORTS -#define CV_EXPORTS_W_MAP CV_EXPORTS -#define CV_IN_OUT -#define CV_OUT -#define CV_PROP -#define CV_PROP_RW -#define CV_WRAP -#define CV_WRAP_AS(synonym) - -/****************************************************************************************\ -* Matrix type (Mat) * -\****************************************************************************************/ - -#define CV_MAT_CN_MASK ((CV_CN_MAX - 1) << CV_CN_SHIFT) -#define CV_MAT_CN(flags) ((((flags) & CV_MAT_CN_MASK) >> CV_CN_SHIFT) + 1) -#define CV_MAT_TYPE_MASK (CV_DEPTH_MAX*CV_CN_MAX - 1) -#define CV_MAT_TYPE(flags) ((flags) & CV_MAT_TYPE_MASK) -#define CV_MAT_CONT_FLAG_SHIFT 14 -#define CV_MAT_CONT_FLAG (1 << CV_MAT_CONT_FLAG_SHIFT) -#define CV_IS_MAT_CONT(flags) ((flags) & CV_MAT_CONT_FLAG) -#define CV_IS_CONT_MAT CV_IS_MAT_CONT -#define CV_SUBMAT_FLAG_SHIFT 15 -#define CV_SUBMAT_FLAG (1 << CV_SUBMAT_FLAG_SHIFT) -#define CV_IS_SUBMAT(flags) ((flags) & CV_MAT_SUBMAT_FLAG) - -/** Size of each channel item, - 0x8442211 = 1000 0100 0100 0010 0010 0001 0001 ~ array of sizeof(arr_type_elem) */ -#define CV_ELEM_SIZE1(type) \ - ((((sizeof(size_t)<<28)|0x8442211) >> CV_MAT_DEPTH(type)*4) & 15) - -/** 0x3a50 = 11 10 10 01 01 00 00 ~ array of log2(sizeof(arr_type_elem)) */ -#define CV_ELEM_SIZE(type) \ - (CV_MAT_CN(type) << ((((sizeof(size_t)/4+1)*16384|0x3a50) >> CV_MAT_DEPTH(type)*2) & 3)) - -#ifndef MIN -# define MIN(a,b) ((a) > (b) ? (b) : (a)) -#endif - -#ifndef MAX -# define MAX(a,b) ((a) < (b) ? (b) : (a)) -#endif - -/****************************************************************************************\ -* static analysys * -\****************************************************************************************/ - -// In practice, some macro are not processed correctly (noreturn is not detected). -// We need to use simplified definition for them. -#ifndef CV_STATIC_ANALYSIS -# if defined(__KLOCWORK__) || defined(__clang_analyzer__) || defined(__COVERITY__) -# define CV_STATIC_ANALYSIS 1 -# endif -#else -# if defined(CV_STATIC_ANALYSIS) && !(__CV_CAT(1, CV_STATIC_ANALYSIS) == 1) // defined and not empty -# if 0 == CV_STATIC_ANALYSIS -# undef CV_STATIC_ANALYSIS -# endif -# endif -#endif - -/****************************************************************************************\ -* Thread sanitizer * -\****************************************************************************************/ -#ifndef CV_THREAD_SANITIZER -# if defined(__has_feature) -# if __has_feature(thread_sanitizer) -# define CV_THREAD_SANITIZER -# endif -# endif -#endif - -/****************************************************************************************\ -* exchange-add operation for atomic operations on reference counters * -\****************************************************************************************/ - -#ifdef CV_XADD - // allow to use user-defined macro -#elif defined __GNUC__ || defined __clang__ -# if defined __clang__ && __clang_major__ >= 3 && !defined __ANDROID__ && !defined __EMSCRIPTEN__ && !defined(__CUDACC__) && !defined __INTEL_COMPILER -# ifdef __ATOMIC_ACQ_REL -# define CV_XADD(addr, delta) __c11_atomic_fetch_add((_Atomic(int)*)(addr), delta, __ATOMIC_ACQ_REL) -# else -# define CV_XADD(addr, delta) __atomic_fetch_add((_Atomic(int)*)(addr), delta, 4) -# endif -# else -# if defined __ATOMIC_ACQ_REL && !defined __clang__ - // version for gcc >= 4.7 -# define CV_XADD(addr, delta) (int)__atomic_fetch_add((unsigned*)(addr), (unsigned)(delta), __ATOMIC_ACQ_REL) -# else -# define CV_XADD(addr, delta) (int)__sync_fetch_and_add((unsigned*)(addr), (unsigned)(delta)) -# endif -# endif -#elif defined _MSC_VER && !defined RC_INVOKED -# include -# define CV_XADD(addr, delta) (int)_InterlockedExchangeAdd((long volatile*)addr, delta) -#else - #ifdef OPENCV_FORCE_UNSAFE_XADD - CV_INLINE CV_XADD(int* addr, int delta) { int tmp = *addr; *addr += delta; return tmp; } - #else - #error "OpenCV: can't define safe CV_XADD macro for current platform (unsupported). Define CV_XADD macro through custom port header (see OPENCV_INCLUDE_PORT_FILE)" - #endif -#endif - - -/****************************************************************************************\ -* CV_NORETURN attribute * -\****************************************************************************************/ - -#ifndef CV_NORETURN -# if defined(__GNUC__) -# define CV_NORETURN __attribute__((__noreturn__)) -# elif defined(_MSC_VER) && (_MSC_VER >= 1300) -# define CV_NORETURN __declspec(noreturn) -# else -# define CV_NORETURN /* nothing by default */ -# endif -#endif - - -/****************************************************************************************\ -* CV_NODISCARD attribute * -* encourages the compiler to issue a warning if the return value is discarded (C++17) * -\****************************************************************************************/ -#ifndef CV_NODISCARD -# if defined(__GNUC__) -# define CV_NODISCARD __attribute__((__warn_unused_result__)) // at least available with GCC 3.4 -# elif defined(__clang__) && defined(__has_attribute) -# if __has_attribute(__warn_unused_result__) -# define CV_NODISCARD __attribute__((__warn_unused_result__)) -# endif -# endif -#endif -#ifndef CV_NODISCARD -# define CV_NODISCARD /* nothing by default */ -#endif - - -/****************************************************************************************\ -* C++ 11 * -\****************************************************************************************/ -#ifndef CV_CXX11 -# if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1800) -# define CV_CXX11 1 -# endif -#else -# if CV_CXX11 == 0 -# undef CV_CXX11 -# endif -#endif - - -/****************************************************************************************\ -* C++ Move semantics * -\****************************************************************************************/ - -#ifndef CV_CXX_MOVE_SEMANTICS -# if __cplusplus >= 201103L || defined(__GXX_EXPERIMENTAL_CXX0X__) || (defined(_MSC_VER) && _MSC_VER >= 1600) -# define CV_CXX_MOVE_SEMANTICS 1 -# elif defined(__clang) -# if __has_feature(cxx_rvalue_references) -# define CV_CXX_MOVE_SEMANTICS 1 -# endif -# endif -#else -# if CV_CXX_MOVE_SEMANTICS == 0 -# undef CV_CXX_MOVE_SEMANTICS -# endif -#endif - -#ifdef CV_CXX_MOVE_SEMANTICS -#define CV_CXX_MOVE(x) std::move(x) -#else -#define CV_CXX_MOVE(x) (x) -#endif - - -/****************************************************************************************\ -* C++11 std::array * -\****************************************************************************************/ - -#ifndef CV_CXX_STD_ARRAY -# if __cplusplus >= 201103L || (defined(__cplusplus) && defined(_MSC_VER) && _MSC_VER >= 1900/*MSVS 2015*/) -# define CV_CXX_STD_ARRAY 1 -# include -# endif -#else -# if CV_CXX_STD_ARRAY == 0 -# undef CV_CXX_STD_ARRAY -# endif -#endif - - -/****************************************************************************************\ -* C++11 override / final * -\****************************************************************************************/ - -#ifndef CV_OVERRIDE -# ifdef CV_CXX11 -# define CV_OVERRIDE override -# endif -#endif -#ifndef CV_OVERRIDE -# define CV_OVERRIDE -#endif - -#ifndef CV_FINAL -# ifdef CV_CXX11 -# define CV_FINAL final -# endif -#endif -#ifndef CV_FINAL -# define CV_FINAL -#endif - -/****************************************************************************************\ -* C++11 noexcept * -\****************************************************************************************/ - -#ifndef CV_NOEXCEPT -# if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1900/*MSVS 2015*/) -# define CV_NOEXCEPT noexcept -# endif -#endif -#ifndef CV_NOEXCEPT -# define CV_NOEXCEPT -#endif - - - -// Integer types portatibility -#ifdef OPENCV_STDINT_HEADER -#include OPENCV_STDINT_HEADER -#elif defined(__cplusplus) -#if defined(_MSC_VER) && _MSC_VER < 1600 /* MSVS 2010 */ -namespace cv { -typedef signed char int8_t; -typedef unsigned char uint8_t; -typedef signed short int16_t; -typedef unsigned short uint16_t; -typedef signed int int32_t; -typedef unsigned int uint32_t; -typedef signed __int64 int64_t; -typedef unsigned __int64 uint64_t; -} -#elif defined(_MSC_VER) || __cplusplus >= 201103L -#include -namespace cv { -using std::int8_t; -using std::uint8_t; -using std::int16_t; -using std::uint16_t; -using std::int32_t; -using std::uint32_t; -using std::int64_t; -using std::uint64_t; -} -#else -#include -namespace cv { -typedef ::int8_t int8_t; -typedef ::uint8_t uint8_t; -typedef ::int16_t int16_t; -typedef ::uint16_t uint16_t; -typedef ::int32_t int32_t; -typedef ::uint32_t uint32_t; -typedef ::int64_t int64_t; -typedef ::uint64_t uint64_t; -} -#endif -#else // pure C -#include -#endif - -#ifdef __cplusplus -namespace cv -{ - -class float16_t -{ -public: -#if CV_FP16_TYPE - - float16_t() {} - explicit float16_t(float x) { h = (__fp16)x; } - operator float() const { return (float)h; } - static float16_t fromBits(ushort w) - { - Cv16suf u; - u.u = w; - float16_t result; - result.h = u.h; - return result; - } - static float16_t zero() - { - float16_t result; - result.h = (__fp16)0; - return result; - } - ushort bits() const - { - Cv16suf u; - u.h = h; - return u.u; - } -protected: - __fp16 h; - -#else - float16_t() {} - explicit float16_t(float x) - { - #if CV_FP16 - __m128 v = _mm_load_ss(&x); - w = (ushort)_mm_cvtsi128_si32(_mm_cvtps_ph(v, 0)); - #else - Cv32suf in; - in.f = x; - unsigned sign = in.u & 0x80000000; - in.u ^= sign; - - if( in.u >= 0x47800000 ) - w = (ushort)(in.u > 0x7f800000 ? 0x7e00 : 0x7c00); - else - { - if (in.u < 0x38800000) - { - in.f += 0.5f; - w = (ushort)(in.u - 0x3f000000); - } - else - { - unsigned t = in.u + 0xc8000fff; - w = (ushort)((t + ((in.u >> 13) & 1)) >> 13); - } - } - - w = (ushort)(w | (sign >> 16)); - #endif - } - - operator float() const - { - #if CV_FP16 - float f; - _mm_store_ss(&f, _mm_cvtph_ps(_mm_cvtsi32_si128(w))); - return f; - #else - Cv32suf out; - - unsigned t = ((w & 0x7fff) << 13) + 0x38000000; - unsigned sign = (w & 0x8000) << 16; - unsigned e = w & 0x7c00; - - out.u = t + (1 << 23); - out.u = (e >= 0x7c00 ? t + 0x38000000 : - e == 0 ? (out.f -= 6.103515625e-05f, out.u) : t) | sign; - return out.f; - #endif - } - - static float16_t fromBits(ushort b) - { - float16_t result; - result.w = b; - return result; - } - static float16_t zero() - { - float16_t result; - result.w = (ushort)0; - return result; - } - ushort bits() const { return w; } -protected: - ushort w; - -#endif -}; - -} -#endif - -//! @} - -#ifndef __cplusplus -#include "opencv2/core/fast_math.hpp" // define cvRound(double) -#endif - -#endif // OPENCV_CORE_CVDEF_H diff --git a/include/opencv2/core/cvstd.hpp b/include/opencv2/core/cvstd.hpp deleted file mode 100755 index fbf6d31..0000000 --- a/include/opencv2/core/cvstd.hpp +++ /dev/null @@ -1,1074 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_CVSTD_HPP -#define OPENCV_CORE_CVSTD_HPP - -#ifndef __cplusplus -# error cvstd.hpp header must be compiled as C++ -#endif - -#include "opencv2/core/cvdef.h" -#include -#include -#include - -#include - -// import useful primitives from stl -# include -# include -# include //for abs(int) -# include - -namespace cv -{ - static inline uchar abs(uchar a) { return a; } - static inline ushort abs(ushort a) { return a; } - static inline unsigned abs(unsigned a) { return a; } - static inline uint64 abs(uint64 a) { return a; } - - using std::min; - using std::max; - using std::abs; - using std::swap; - using std::sqrt; - using std::exp; - using std::pow; - using std::log; -} - -namespace cv { - -//! @addtogroup core_utils -//! @{ - -//////////////////////////// memory management functions //////////////////////////// - -/** @brief Allocates an aligned memory buffer. - -The function allocates the buffer of the specified size and returns it. When the buffer size is 16 -bytes or more, the returned buffer is aligned to 16 bytes. -@param bufSize Allocated buffer size. - */ -CV_EXPORTS void* fastMalloc(size_t bufSize); - -/** @brief Deallocates a memory buffer. - -The function deallocates the buffer allocated with fastMalloc . If NULL pointer is passed, the -function does nothing. C version of the function clears the pointer *pptr* to avoid problems with -double memory deallocation. -@param ptr Pointer to the allocated buffer. - */ -CV_EXPORTS void fastFree(void* ptr); - -/*! - The STL-compliant memory Allocator based on cv::fastMalloc() and cv::fastFree() -*/ -template class Allocator -{ -public: - typedef _Tp value_type; - typedef value_type* pointer; - typedef const value_type* const_pointer; - typedef value_type& reference; - typedef const value_type& const_reference; - typedef size_t size_type; - typedef ptrdiff_t difference_type; - template class rebind { typedef Allocator other; }; - - explicit Allocator() {} - ~Allocator() {} - explicit Allocator(Allocator const&) {} - template - explicit Allocator(Allocator const&) {} - - // address - pointer address(reference r) { return &r; } - const_pointer address(const_reference r) { return &r; } - - pointer allocate(size_type count, const void* =0) { return reinterpret_cast(fastMalloc(count * sizeof (_Tp))); } - void deallocate(pointer p, size_type) { fastFree(p); } - - void construct(pointer p, const _Tp& v) { new(static_cast(p)) _Tp(v); } - void destroy(pointer p) { p->~_Tp(); } - - size_type max_size() const { return cv::max(static_cast<_Tp>(-1)/sizeof(_Tp), 1); } -}; - -//! @} core_utils - -//! @cond IGNORED - -namespace detail -{ - -// Metafunction to avoid taking a reference to void. -template -struct RefOrVoid { typedef T& type; }; - -template<> -struct RefOrVoid{ typedef void type; }; - -template<> -struct RefOrVoid{ typedef const void type; }; - -template<> -struct RefOrVoid{ typedef volatile void type; }; - -template<> -struct RefOrVoid{ typedef const volatile void type; }; - -// This class would be private to Ptr, if it didn't have to be a non-template. -struct PtrOwner; - -} - -template -struct DefaultDeleter -{ - void operator () (Y* p) const; -}; - -//! @endcond - -//! @addtogroup core_basic -//! @{ - -/** @brief Template class for smart pointers with shared ownership - -A Ptr\ pretends to be a pointer to an object of type T. Unlike an ordinary pointer, however, the -object will be automatically cleaned up once all Ptr instances pointing to it are destroyed. - -Ptr is similar to boost::shared_ptr that is part of the Boost library -() and std::shared_ptr from -the [C++11](http://en.wikipedia.org/wiki/C++11) standard. - -This class provides the following advantages: -- Default constructor, copy constructor, and assignment operator for an arbitrary C++ class or C - structure. For some objects, like files, windows, mutexes, sockets, and others, a copy - constructor or an assignment operator are difficult to define. For some other objects, like - complex classifiers in OpenCV, copy constructors are absent and not easy to implement. Finally, - some of complex OpenCV and your own data structures may be written in C. However, copy - constructors and default constructors can simplify programming a lot. Besides, they are often - required (for example, by STL containers). By using a Ptr to such an object instead of the - object itself, you automatically get all of the necessary constructors and the assignment - operator. -- *O(1)* complexity of the above-mentioned operations. While some structures, like std::vector, - provide a copy constructor and an assignment operator, the operations may take a considerable - amount of time if the data structures are large. But if the structures are put into a Ptr, the - overhead is small and independent of the data size. -- Automatic and customizable cleanup, even for C structures. See the example below with FILE\*. -- Heterogeneous collections of objects. The standard STL and most other C++ and OpenCV containers - can store only objects of the same type and the same size. The classical solution to store - objects of different types in the same container is to store pointers to the base class (Base\*) - instead but then you lose the automatic memory management. Again, by using Ptr\ instead - of raw pointers, you can solve the problem. - -A Ptr is said to *own* a pointer - that is, for each Ptr there is a pointer that will be deleted -once all Ptr instances that own it are destroyed. The owned pointer may be null, in which case -nothing is deleted. Each Ptr also *stores* a pointer. The stored pointer is the pointer the Ptr -pretends to be; that is, the one you get when you use Ptr::get or the conversion to T\*. It's -usually the same as the owned pointer, but if you use casts or the general shared-ownership -constructor, the two may diverge: the Ptr will still own the original pointer, but will itself point -to something else. - -The owned pointer is treated as a black box. The only thing Ptr needs to know about it is how to -delete it. This knowledge is encapsulated in the *deleter* - an auxiliary object that is associated -with the owned pointer and shared between all Ptr instances that own it. The default deleter is an -instance of DefaultDeleter, which uses the standard C++ delete operator; as such it will work with -any pointer allocated with the standard new operator. - -However, if the pointer must be deleted in a different way, you must specify a custom deleter upon -Ptr construction. A deleter is simply a callable object that accepts the pointer as its sole -argument. For example, if you want to wrap FILE, you may do so as follows: -@code - Ptr f(fopen("myfile.txt", "w"), fclose); - if(!f) throw ...; - fprintf(f, ....); - ... - // the file will be closed automatically by f's destructor. -@endcode -Alternatively, if you want all pointers of a particular type to be deleted the same way, you can -specialize DefaultDeleter::operator() for that type, like this: -@code - namespace cv { - template<> void DefaultDeleter::operator ()(FILE * obj) const - { - fclose(obj); - } - } -@endcode -For convenience, the following types from the OpenCV C API already have such a specialization that -calls the appropriate release function: -- CvCapture -- CvFileStorage -- CvHaarClassifierCascade -- CvMat -- CvMatND -- CvMemStorage -- CvSparseMat -- CvVideoWriter -- IplImage -@note The shared ownership mechanism is implemented with reference counting. As such, cyclic -ownership (e.g. when object a contains a Ptr to object b, which contains a Ptr to object a) will -lead to all involved objects never being cleaned up. Avoid such situations. -@note It is safe to concurrently read (but not write) a Ptr instance from multiple threads and -therefore it is normally safe to use it in multi-threaded applications. The same is true for Mat and -other C++ OpenCV classes that use internal reference counts. -*/ -template -struct Ptr -{ - /** Generic programming support. */ - typedef T element_type; - - /** The default constructor creates a null Ptr - one that owns and stores a null pointer. - */ - Ptr(); - - /** - If p is null, these are equivalent to the default constructor. - Otherwise, these constructors assume ownership of p - that is, the created Ptr owns and stores p - and assumes it is the sole owner of it. Don't use them if p is already owned by another Ptr, or - else p will get deleted twice. - With the first constructor, DefaultDeleter\() becomes the associated deleter (so p will - eventually be deleted with the standard delete operator). Y must be a complete type at the point - of invocation. - With the second constructor, d becomes the associated deleter. - Y\* must be convertible to T\*. - @param p Pointer to own. - @note It is often easier to use makePtr instead. - */ - template -#ifdef DISABLE_OPENCV_24_COMPATIBILITY - explicit -#endif - Ptr(Y* p); - - /** @overload - @param d Deleter to use for the owned pointer. - @param p Pointer to own. - */ - template - Ptr(Y* p, D d); - - /** - These constructors create a Ptr that shares ownership with another Ptr - that is, own the same - pointer as o. - With the first two, the same pointer is stored, as well; for the second, Y\* must be convertible - to T\*. - With the third, p is stored, and Y may be any type. This constructor allows to have completely - unrelated owned and stored pointers, and should be used with care to avoid confusion. A relatively - benign use is to create a non-owning Ptr, like this: - @code - ptr = Ptr(Ptr(), dont_delete_me); // owns nothing; will not delete the pointer. - @endcode - @param o Ptr to share ownership with. - */ - Ptr(const Ptr& o); - - /** @overload - @param o Ptr to share ownership with. - */ - template - Ptr(const Ptr& o); - - /** @overload - @param o Ptr to share ownership with. - @param p Pointer to store. - */ - template - Ptr(const Ptr& o, T* p); - - /** The destructor is equivalent to calling Ptr::release. */ - ~Ptr(); - - /** - Assignment replaces the current Ptr instance with one that owns and stores same pointers as o and - then destroys the old instance. - @param o Ptr to share ownership with. - */ - Ptr& operator = (const Ptr& o); - - /** @overload */ - template - Ptr& operator = (const Ptr& o); - - /** If no other Ptr instance owns the owned pointer, deletes it with the associated deleter. Then sets - both the owned and the stored pointers to NULL. - */ - void release(); - - /** - `ptr.reset(...)` is equivalent to `ptr = Ptr(...)`. - @param p Pointer to own. - */ - template - void reset(Y* p); - - /** @overload - @param d Deleter to use for the owned pointer. - @param p Pointer to own. - */ - template - void reset(Y* p, D d); - - /** - Swaps the owned and stored pointers (and deleters, if any) of this and o. - @param o Ptr to swap with. - */ - void swap(Ptr& o); - - /** Returns the stored pointer. */ - T* get() const; - - /** Ordinary pointer emulation. */ - typename detail::RefOrVoid::type operator * () const; - - /** Ordinary pointer emulation. */ - T* operator -> () const; - - /** Equivalent to get(). */ - operator T* () const; - - /** ptr.empty() is equivalent to `!ptr.get()`. */ - bool empty() const; - - /** Returns a Ptr that owns the same pointer as this, and stores the same - pointer as this, except converted via static_cast to Y*. - */ - template - Ptr staticCast() const; - - /** Ditto for const_cast. */ - template - Ptr constCast() const; - - /** Ditto for dynamic_cast. */ - template - Ptr dynamicCast() const; - -#ifdef CV_CXX_MOVE_SEMANTICS - Ptr(Ptr&& o); - Ptr& operator = (Ptr&& o); -#endif - -private: - detail::PtrOwner* owner; - T* stored; - - template - friend struct Ptr; // have to do this for the cross-type copy constructor -}; - -/** Equivalent to ptr1.swap(ptr2). Provided to help write generic algorithms. */ -template -void swap(Ptr& ptr1, Ptr& ptr2); - -/** Return whether ptr1.get() and ptr2.get() are equal and not equal, respectively. */ -template -bool operator == (const Ptr& ptr1, const Ptr& ptr2); -template -bool operator != (const Ptr& ptr1, const Ptr& ptr2); - -/** `makePtr(...)` is equivalent to `Ptr(new T(...))`. It is shorter than the latter, and it's -marginally safer than using a constructor or Ptr::reset, since it ensures that the owned pointer -is new and thus not owned by any other Ptr instance. -Unfortunately, perfect forwarding is impossible to implement in C++03, and so makePtr is limited -to constructors of T that have up to 10 arguments, none of which are non-const references. - */ -template -Ptr makePtr(); -/** @overload */ -template -Ptr makePtr(const A1& a1); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9); -/** @overload */ -template -Ptr makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10); - -//////////////////////////////// string class //////////////////////////////// - -class CV_EXPORTS FileNode; //for string constructor from FileNode - -class CV_EXPORTS String -{ -public: - typedef char value_type; - typedef char& reference; - typedef const char& const_reference; - typedef char* pointer; - typedef const char* const_pointer; - typedef ptrdiff_t difference_type; - typedef size_t size_type; - typedef char* iterator; - typedef const char* const_iterator; - - static const size_t npos = size_t(-1); - - String(); - String(const String& str); - String(const String& str, size_t pos, size_t len = npos); - String(const char* s); - String(const char* s, size_t n); - String(size_t n, char c); - String(const char* first, const char* last); - template String(Iterator first, Iterator last); - explicit String(const FileNode& fn); - ~String(); - - String& operator=(const String& str); - String& operator=(const char* s); - String& operator=(char c); - - String& operator+=(const String& str); - String& operator+=(const char* s); - String& operator+=(char c); - - size_t size() const; - size_t length() const; - - char operator[](size_t idx) const; - char operator[](int idx) const; - - const char* begin() const; - const char* end() const; - - const char* c_str() const; - - bool empty() const; - void clear(); - - int compare(const char* s) const; - int compare(const String& str) const; - - void swap(String& str); - String substr(size_t pos = 0, size_t len = npos) const; - - size_t find(const char* s, size_t pos, size_t n) const; - size_t find(char c, size_t pos = 0) const; - size_t find(const String& str, size_t pos = 0) const; - size_t find(const char* s, size_t pos = 0) const; - - size_t rfind(const char* s, size_t pos, size_t n) const; - size_t rfind(char c, size_t pos = npos) const; - size_t rfind(const String& str, size_t pos = npos) const; - size_t rfind(const char* s, size_t pos = npos) const; - - size_t find_first_of(const char* s, size_t pos, size_t n) const; - size_t find_first_of(char c, size_t pos = 0) const; - size_t find_first_of(const String& str, size_t pos = 0) const; - size_t find_first_of(const char* s, size_t pos = 0) const; - - size_t find_last_of(const char* s, size_t pos, size_t n) const; - size_t find_last_of(char c, size_t pos = npos) const; - size_t find_last_of(const String& str, size_t pos = npos) const; - size_t find_last_of(const char* s, size_t pos = npos) const; - - friend String operator+ (const String& lhs, const String& rhs); - friend String operator+ (const String& lhs, const char* rhs); - friend String operator+ (const char* lhs, const String& rhs); - friend String operator+ (const String& lhs, char rhs); - friend String operator+ (char lhs, const String& rhs); - - String toLowerCase() const; - - String(const std::string& str); - String(const std::string& str, size_t pos, size_t len = npos); - String& operator=(const std::string& str); - String& operator+=(const std::string& str); - operator std::string() const; - - friend String operator+ (const String& lhs, const std::string& rhs); - friend String operator+ (const std::string& lhs, const String& rhs); - -private: - char* cstr_; - size_t len_; - - char* allocate(size_t len); // len without trailing 0 - void deallocate(); - - String(int); // disabled and invalid. Catch invalid usages like, commandLineParser.has(0) problem -}; - -//! @} core_basic - -////////////////////////// cv::String implementation ///////////////////////// - -//! @cond IGNORED - -inline -String::String() - : cstr_(0), len_(0) -{} - -inline -String::String(const String& str) - : cstr_(str.cstr_), len_(str.len_) -{ - if (cstr_) - CV_XADD(((int*)cstr_)-1, 1); -} - -inline -String::String(const String& str, size_t pos, size_t len) - : cstr_(0), len_(0) -{ - pos = min(pos, str.len_); - len = min(str.len_ - pos, len); - if (!len) return; - if (len == str.len_) - { - CV_XADD(((int*)str.cstr_)-1, 1); - cstr_ = str.cstr_; - len_ = str.len_; - return; - } - memcpy(allocate(len), str.cstr_ + pos, len); -} - -inline -String::String(const char* s) - : cstr_(0), len_(0) -{ - if (!s) return; - size_t len = strlen(s); - if (!len) return; - memcpy(allocate(len), s, len); -} - -inline -String::String(const char* s, size_t n) - : cstr_(0), len_(0) -{ - if (!n) return; - if (!s) return; - memcpy(allocate(n), s, n); -} - -inline -String::String(size_t n, char c) - : cstr_(0), len_(0) -{ - if (!n) return; - memset(allocate(n), c, n); -} - -inline -String::String(const char* first, const char* last) - : cstr_(0), len_(0) -{ - size_t len = (size_t)(last - first); - if (!len) return; - memcpy(allocate(len), first, len); -} - -template inline -String::String(Iterator first, Iterator last) - : cstr_(0), len_(0) -{ - size_t len = (size_t)(last - first); - if (!len) return; - char* str = allocate(len); - while (first != last) - { - *str++ = *first; - ++first; - } -} - -inline -String::~String() -{ - deallocate(); -} - -inline -String& String::operator=(const String& str) -{ - if (&str == this) return *this; - - deallocate(); - if (str.cstr_) CV_XADD(((int*)str.cstr_)-1, 1); - cstr_ = str.cstr_; - len_ = str.len_; - return *this; -} - -inline -String& String::operator=(const char* s) -{ - deallocate(); - if (!s) return *this; - size_t len = strlen(s); - if (len) memcpy(allocate(len), s, len); - return *this; -} - -inline -String& String::operator=(char c) -{ - deallocate(); - allocate(1)[0] = c; - return *this; -} - -inline -String& String::operator+=(const String& str) -{ - *this = *this + str; - return *this; -} - -inline -String& String::operator+=(const char* s) -{ - *this = *this + s; - return *this; -} - -inline -String& String::operator+=(char c) -{ - *this = *this + c; - return *this; -} - -inline -size_t String::size() const -{ - return len_; -} - -inline -size_t String::length() const -{ - return len_; -} - -inline -char String::operator[](size_t idx) const -{ - return cstr_[idx]; -} - -inline -char String::operator[](int idx) const -{ - return cstr_[idx]; -} - -inline -const char* String::begin() const -{ - return cstr_; -} - -inline -const char* String::end() const -{ - return len_ ? cstr_ + len_ : NULL; -} - -inline -bool String::empty() const -{ - return len_ == 0; -} - -inline -const char* String::c_str() const -{ - return cstr_ ? cstr_ : ""; -} - -inline -void String::swap(String& str) -{ - cv::swap(cstr_, str.cstr_); - cv::swap(len_, str.len_); -} - -inline -void String::clear() -{ - deallocate(); -} - -inline -int String::compare(const char* s) const -{ - if (cstr_ == s) return 0; - return strcmp(c_str(), s); -} - -inline -int String::compare(const String& str) const -{ - if (cstr_ == str.cstr_) return 0; - return strcmp(c_str(), str.c_str()); -} - -inline -String String::substr(size_t pos, size_t len) const -{ - return String(*this, pos, len); -} - -inline -size_t String::find(const char* s, size_t pos, size_t n) const -{ - if (n == 0 || pos + n > len_) return npos; - const char* lmax = cstr_ + len_ - n; - for (const char* i = cstr_ + pos; i <= lmax; ++i) - { - size_t j = 0; - while (j < n && s[j] == i[j]) ++j; - if (j == n) return (size_t)(i - cstr_); - } - return npos; -} - -inline -size_t String::find(char c, size_t pos) const -{ - return find(&c, pos, 1); -} - -inline -size_t String::find(const String& str, size_t pos) const -{ - return find(str.c_str(), pos, str.len_); -} - -inline -size_t String::find(const char* s, size_t pos) const -{ - if (pos >= len_ || !s[0]) return npos; - const char* lmax = cstr_ + len_; - for (const char* i = cstr_ + pos; i < lmax; ++i) - { - size_t j = 0; - while (s[j] && s[j] == i[j]) - { if(i + j >= lmax) return npos; - ++j; - } - if (!s[j]) return (size_t)(i - cstr_); - } - return npos; -} - -inline -size_t String::rfind(const char* s, size_t pos, size_t n) const -{ - if (n > len_) return npos; - if (pos > len_ - n) pos = len_ - n; - for (const char* i = cstr_ + pos; i >= cstr_; --i) - { - size_t j = 0; - while (j < n && s[j] == i[j]) ++j; - if (j == n) return (size_t)(i - cstr_); - } - return npos; -} - -inline -size_t String::rfind(char c, size_t pos) const -{ - return rfind(&c, pos, 1); -} - -inline -size_t String::rfind(const String& str, size_t pos) const -{ - return rfind(str.c_str(), pos, str.len_); -} - -inline -size_t String::rfind(const char* s, size_t pos) const -{ - return rfind(s, pos, strlen(s)); -} - -inline -size_t String::find_first_of(const char* s, size_t pos, size_t n) const -{ - if (n == 0 || pos + n > len_) return npos; - const char* lmax = cstr_ + len_; - for (const char* i = cstr_ + pos; i < lmax; ++i) - { - for (size_t j = 0; j < n; ++j) - if (s[j] == *i) - return (size_t)(i - cstr_); - } - return npos; -} - -inline -size_t String::find_first_of(char c, size_t pos) const -{ - return find_first_of(&c, pos, 1); -} - -inline -size_t String::find_first_of(const String& str, size_t pos) const -{ - return find_first_of(str.c_str(), pos, str.len_); -} - -inline -size_t String::find_first_of(const char* s, size_t pos) const -{ - if (len_ == 0) return npos; - if (pos >= len_ || !s[0]) return npos; - const char* lmax = cstr_ + len_; - for (const char* i = cstr_ + pos; i < lmax; ++i) - { - for (size_t j = 0; s[j]; ++j) - if (s[j] == *i) - return (size_t)(i - cstr_); - } - return npos; -} - -inline -size_t String::find_last_of(const char* s, size_t pos, size_t n) const -{ - if (len_ == 0) return npos; - if (pos >= len_) pos = len_ - 1; - for (const char* i = cstr_ + pos; i >= cstr_; --i) - { - for (size_t j = 0; j < n; ++j) - if (s[j] == *i) - return (size_t)(i - cstr_); - } - return npos; -} - -inline -size_t String::find_last_of(char c, size_t pos) const -{ - return find_last_of(&c, pos, 1); -} - -inline -size_t String::find_last_of(const String& str, size_t pos) const -{ - return find_last_of(str.c_str(), pos, str.len_); -} - -inline -size_t String::find_last_of(const char* s, size_t pos) const -{ - if (len_ == 0) return npos; - if (pos >= len_) pos = len_ - 1; - for (const char* i = cstr_ + pos; i >= cstr_; --i) - { - for (size_t j = 0; s[j]; ++j) - if (s[j] == *i) - return (size_t)(i - cstr_); - } - return npos; -} - -inline -String String::toLowerCase() const -{ - if (!cstr_) - return String(); - String res(cstr_, len_); - for (size_t i = 0; i < len_; ++i) - res.cstr_[i] = (char) ::tolower(cstr_[i]); - - return res; -} - -//! @endcond - -// ************************* cv::String non-member functions ************************* - -//! @relates cv::String -//! @{ - -inline -String operator + (const String& lhs, const String& rhs) -{ - String s; - s.allocate(lhs.len_ + rhs.len_); - if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_); - if (rhs.len_) memcpy(s.cstr_ + lhs.len_, rhs.cstr_, rhs.len_); - return s; -} - -inline -String operator + (const String& lhs, const char* rhs) -{ - String s; - size_t rhslen = strlen(rhs); - s.allocate(lhs.len_ + rhslen); - if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_); - if (rhslen) memcpy(s.cstr_ + lhs.len_, rhs, rhslen); - return s; -} - -inline -String operator + (const char* lhs, const String& rhs) -{ - String s; - size_t lhslen = strlen(lhs); - s.allocate(lhslen + rhs.len_); - if (lhslen) memcpy(s.cstr_, lhs, lhslen); - if (rhs.len_) memcpy(s.cstr_ + lhslen, rhs.cstr_, rhs.len_); - return s; -} - -inline -String operator + (const String& lhs, char rhs) -{ - String s; - s.allocate(lhs.len_ + 1); - if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_); - s.cstr_[lhs.len_] = rhs; - return s; -} - -inline -String operator + (char lhs, const String& rhs) -{ - String s; - s.allocate(rhs.len_ + 1); - s.cstr_[0] = lhs; - if (rhs.len_) memcpy(s.cstr_ + 1, rhs.cstr_, rhs.len_); - return s; -} - -static inline bool operator== (const String& lhs, const String& rhs) { return 0 == lhs.compare(rhs); } -static inline bool operator== (const char* lhs, const String& rhs) { return 0 == rhs.compare(lhs); } -static inline bool operator== (const String& lhs, const char* rhs) { return 0 == lhs.compare(rhs); } -static inline bool operator!= (const String& lhs, const String& rhs) { return 0 != lhs.compare(rhs); } -static inline bool operator!= (const char* lhs, const String& rhs) { return 0 != rhs.compare(lhs); } -static inline bool operator!= (const String& lhs, const char* rhs) { return 0 != lhs.compare(rhs); } -static inline bool operator< (const String& lhs, const String& rhs) { return lhs.compare(rhs) < 0; } -static inline bool operator< (const char* lhs, const String& rhs) { return rhs.compare(lhs) > 0; } -static inline bool operator< (const String& lhs, const char* rhs) { return lhs.compare(rhs) < 0; } -static inline bool operator<= (const String& lhs, const String& rhs) { return lhs.compare(rhs) <= 0; } -static inline bool operator<= (const char* lhs, const String& rhs) { return rhs.compare(lhs) >= 0; } -static inline bool operator<= (const String& lhs, const char* rhs) { return lhs.compare(rhs) <= 0; } -static inline bool operator> (const String& lhs, const String& rhs) { return lhs.compare(rhs) > 0; } -static inline bool operator> (const char* lhs, const String& rhs) { return rhs.compare(lhs) < 0; } -static inline bool operator> (const String& lhs, const char* rhs) { return lhs.compare(rhs) > 0; } -static inline bool operator>= (const String& lhs, const String& rhs) { return lhs.compare(rhs) >= 0; } -static inline bool operator>= (const char* lhs, const String& rhs) { return rhs.compare(lhs) <= 0; } -static inline bool operator>= (const String& lhs, const char* rhs) { return lhs.compare(rhs) >= 0; } - - -#ifndef OPENCV_DISABLE_STRING_LOWER_UPPER_CONVERSIONS - -//! @cond IGNORED -namespace details { -// std::tolower is int->int -static inline char char_tolower(char ch) -{ - return (char)std::tolower((int)ch); -} -// std::toupper is int->int -static inline char char_toupper(char ch) -{ - return (char)std::toupper((int)ch); -} -} // namespace details -//! @endcond - -static inline std::string toLowerCase(const std::string& str) -{ - std::string result(str); - std::transform(result.begin(), result.end(), result.begin(), details::char_tolower); - return result; -} - -static inline std::string toUpperCase(const std::string& str) -{ - std::string result(str); - std::transform(result.begin(), result.end(), result.begin(), details::char_toupper); - return result; -} - -#endif // OPENCV_DISABLE_STRING_LOWER_UPPER_CONVERSIONS - -//! @} relates cv::String - -} // cv - -namespace std -{ - static inline void swap(cv::String& a, cv::String& b) { a.swap(b); } -} - -#include "opencv2/core/ptr.inl.hpp" - -#endif //OPENCV_CORE_CVSTD_HPP diff --git a/include/opencv2/core/cvstd.inl.hpp b/include/opencv2/core/cvstd.inl.hpp deleted file mode 100755 index 36c83e2..0000000 --- a/include/opencv2/core/cvstd.inl.hpp +++ /dev/null @@ -1,286 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_CVSTDINL_HPP -#define OPENCV_CORE_CVSTDINL_HPP - -#include -#include -#include - -//! @cond IGNORED - -#ifdef _MSC_VER -#pragma warning( push ) -#pragma warning( disable: 4127 ) -#endif - -namespace cv -{ - -template class DataType< std::complex<_Tp> > -{ -public: - typedef std::complex<_Tp> value_type; - typedef value_type work_type; - typedef _Tp channel_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = 2, - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) }; - - typedef Vec vec_type; -}; - -inline -String::String(const std::string& str) - : cstr_(0), len_(0) -{ - size_t len = str.size(); - if (len) memcpy(allocate(len), str.c_str(), len); -} - -inline -String::String(const std::string& str, size_t pos, size_t len) - : cstr_(0), len_(0) -{ - size_t strlen = str.size(); - pos = min(pos, strlen); - len = min(strlen - pos, len); - if (!len) return; - memcpy(allocate(len), str.c_str() + pos, len); -} - -inline -String& String::operator = (const std::string& str) -{ - deallocate(); - size_t len = str.size(); - if (len) memcpy(allocate(len), str.c_str(), len); - return *this; -} - -inline -String& String::operator += (const std::string& str) -{ - *this = *this + str; - return *this; -} - -inline -String::operator std::string() const -{ - return std::string(cstr_, len_); -} - -inline -String operator + (const String& lhs, const std::string& rhs) -{ - String s; - size_t rhslen = rhs.size(); - s.allocate(lhs.len_ + rhslen); - if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_); - if (rhslen) memcpy(s.cstr_ + lhs.len_, rhs.c_str(), rhslen); - return s; -} - -inline -String operator + (const std::string& lhs, const String& rhs) -{ - String s; - size_t lhslen = lhs.size(); - s.allocate(lhslen + rhs.len_); - if (lhslen) memcpy(s.cstr_, lhs.c_str(), lhslen); - if (rhs.len_) memcpy(s.cstr_ + lhslen, rhs.cstr_, rhs.len_); - return s; -} - -inline -FileNode::operator std::string() const -{ - String value; - read(*this, value, value); - return value; -} - -template<> inline -void operator >> (const FileNode& n, std::string& value) -{ - read(n, value, std::string()); -} - -template<> inline -FileStorage& operator << (FileStorage& fs, const std::string& value) -{ - return fs << cv::String(value); -} - -static inline -std::ostream& operator << (std::ostream& os, const String& str) -{ - return os << str.c_str(); -} - -static inline -std::ostream& operator << (std::ostream& out, Ptr fmtd) -{ - fmtd->reset(); - for(const char* str = fmtd->next(); str; str = fmtd->next()) - out << str; - return out; -} - -static inline -std::ostream& operator << (std::ostream& out, const Mat& mtx) -{ - return out << Formatter::get()->format(mtx); -} - -static inline -std::ostream& operator << (std::ostream& out, const UMat& m) -{ - return out << m.getMat(ACCESS_READ); -} - -template static inline -std::ostream& operator << (std::ostream& out, const Complex<_Tp>& c) -{ - return out << "(" << c.re << "," << c.im << ")"; -} - -template static inline -std::ostream& operator << (std::ostream& out, const std::vector >& vec) -{ - return out << Formatter::get()->format(Mat(vec)); -} - - -template static inline -std::ostream& operator << (std::ostream& out, const std::vector >& vec) -{ - return out << Formatter::get()->format(Mat(vec)); -} - - -template static inline -std::ostream& operator << (std::ostream& out, const Matx<_Tp, m, n>& matx) -{ - return out << Formatter::get()->format(Mat(matx)); -} - -template static inline -std::ostream& operator << (std::ostream& out, const Point_<_Tp>& p) -{ - out << "[" << p.x << ", " << p.y << "]"; - return out; -} - -template static inline -std::ostream& operator << (std::ostream& out, const Point3_<_Tp>& p) -{ - out << "[" << p.x << ", " << p.y << ", " << p.z << "]"; - return out; -} - -template static inline -std::ostream& operator << (std::ostream& out, const Vec<_Tp, n>& vec) -{ - out << "["; - if (cv::traits::Depth<_Tp>::value <= CV_32S) - { - for (int i = 0; i < n - 1; ++i) { - out << (int)vec[i] << ", "; - } - out << (int)vec[n-1] << "]"; - } - else - { - for (int i = 0; i < n - 1; ++i) { - out << vec[i] << ", "; - } - out << vec[n-1] << "]"; - } - - return out; -} - -template static inline -std::ostream& operator << (std::ostream& out, const Size_<_Tp>& size) -{ - return out << "[" << size.width << " x " << size.height << "]"; -} - -template static inline -std::ostream& operator << (std::ostream& out, const Rect_<_Tp>& rect) -{ - return out << "[" << rect.width << " x " << rect.height << " from (" << rect.x << ", " << rect.y << ")]"; -} - -static inline std::ostream& operator << (std::ostream& out, const MatSize& msize) -{ - int i, dims = msize.dims(); - for( i = 0; i < dims; i++ ) - { - out << msize[i]; - if( i < dims-1 ) - out << " x "; - } - return out; -} - -static inline std::ostream &operator<< (std::ostream &s, cv::Range &r) -{ - return s << "[" << r.start << " : " << r.end << ")"; -} - -} // cv - -#ifdef _MSC_VER -#pragma warning( pop ) -#endif - -//! @endcond - -#endif // OPENCV_CORE_CVSTDINL_HPP diff --git a/include/opencv2/core/detail/async_promise.hpp b/include/opencv2/core/detail/async_promise.hpp deleted file mode 100755 index 6eb3fb5..0000000 --- a/include/opencv2/core/detail/async_promise.hpp +++ /dev/null @@ -1,71 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_CORE_ASYNC_PROMISE_HPP -#define OPENCV_CORE_ASYNC_PROMISE_HPP - -#include "../async.hpp" - -#include "exception_ptr.hpp" - -namespace cv { - -/** @addtogroup core_async -@{ -*/ - - -/** @brief Provides result of asynchronous operations - -*/ -class CV_EXPORTS AsyncPromise -{ -public: - ~AsyncPromise() CV_NOEXCEPT; - AsyncPromise() CV_NOEXCEPT; - explicit AsyncPromise(const AsyncPromise& o) CV_NOEXCEPT; - AsyncPromise& operator=(const AsyncPromise& o) CV_NOEXCEPT; - void release() CV_NOEXCEPT; - - /** Returns associated AsyncArray - @note Can be called once - */ - AsyncArray getArrayResult(); - - /** Stores asynchronous result. - @param[in] value result - */ - void setValue(InputArray value); - - // TODO "move" setters - -#if CV__EXCEPTION_PTR - /** Stores exception. - @param[in] exception exception to be raised in AsyncArray - */ - void setException(std::exception_ptr exception); -#endif - - /** Stores exception. - @param[in] exception exception to be raised in AsyncArray - */ - void setException(const cv::Exception& exception); - -#ifdef CV_CXX11 - explicit AsyncPromise(AsyncPromise&& o) { p = o.p; o.p = NULL; } - AsyncPromise& operator=(AsyncPromise&& o) CV_NOEXCEPT { std::swap(p, o.p); return *this; } -#endif - - - // PImpl - typedef struct AsyncArray::Impl Impl; friend struct AsyncArray::Impl; - inline void* _getImpl() const CV_NOEXCEPT { return p; } -protected: - Impl* p; -}; - - -//! @} -} // namespace -#endif // OPENCV_CORE_ASYNC_PROMISE_HPP diff --git a/include/opencv2/core/detail/exception_ptr.hpp b/include/opencv2/core/detail/exception_ptr.hpp deleted file mode 100755 index d98ffc4..0000000 --- a/include/opencv2/core/detail/exception_ptr.hpp +++ /dev/null @@ -1,27 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_CORE_DETAILS_EXCEPTION_PTR_H -#define OPENCV_CORE_DETAILS_EXCEPTION_PTR_H - -#ifndef CV__EXCEPTION_PTR -# if defined(__ANDROID__) && defined(ATOMIC_INT_LOCK_FREE) && ATOMIC_INT_LOCK_FREE < 2 -# define CV__EXCEPTION_PTR 0 // Not supported, details: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=58938 -# elif defined(CV_CXX11) -# define CV__EXCEPTION_PTR 1 -# elif defined(_MSC_VER) -# define CV__EXCEPTION_PTR (_MSC_VER >= 1600) -# elif defined(__clang__) -# define CV__EXCEPTION_PTR 0 // C++11 only (see above) -# elif defined(__GNUC__) && defined(__GXX_EXPERIMENTAL_CXX0X__) -# define CV__EXCEPTION_PTR (__GXX_EXPERIMENTAL_CXX0X__ > 0) -# endif -#endif -#ifndef CV__EXCEPTION_PTR -# define CV__EXCEPTION_PTR 0 -#elif CV__EXCEPTION_PTR -# include // std::exception_ptr -#endif - -#endif // OPENCV_CORE_DETAILS_EXCEPTION_PTR_H diff --git a/include/opencv2/core/directx.hpp b/include/opencv2/core/directx.hpp deleted file mode 100755 index 056a85a..0000000 --- a/include/opencv2/core/directx.hpp +++ /dev/null @@ -1,184 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors as is and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the copyright holders or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_DIRECTX_HPP -#define OPENCV_CORE_DIRECTX_HPP - -#include "mat.hpp" -#include "ocl.hpp" - -#if !defined(__d3d11_h__) -struct ID3D11Device; -struct ID3D11Texture2D; -#endif - -#if !defined(__d3d10_h__) -struct ID3D10Device; -struct ID3D10Texture2D; -#endif - -#if !defined(_D3D9_H_) -struct IDirect3DDevice9; -struct IDirect3DDevice9Ex; -struct IDirect3DSurface9; -#endif - - -namespace cv { namespace directx { - -namespace ocl { -using namespace cv::ocl; - -//! @addtogroup core_directx -// This section describes OpenCL and DirectX interoperability. -// -// To enable DirectX support, configure OpenCV using CMake with WITH_DIRECTX=ON . Note, DirectX is -// supported only on Windows. -// -// To use OpenCL functionality you should first initialize OpenCL context from DirectX resource. -// -//! @{ - -// TODO static functions in the Context class -//! @brief Creates OpenCL context from D3D11 device -// -//! @param pD3D11Device - pointer to D3D11 device -//! @return Returns reference to OpenCL Context -CV_EXPORTS Context& initializeContextFromD3D11Device(ID3D11Device* pD3D11Device); - -//! @brief Creates OpenCL context from D3D10 device -// -//! @param pD3D10Device - pointer to D3D10 device -//! @return Returns reference to OpenCL Context -CV_EXPORTS Context& initializeContextFromD3D10Device(ID3D10Device* pD3D10Device); - -//! @brief Creates OpenCL context from Direct3DDevice9Ex device -// -//! @param pDirect3DDevice9Ex - pointer to Direct3DDevice9Ex device -//! @return Returns reference to OpenCL Context -CV_EXPORTS Context& initializeContextFromDirect3DDevice9Ex(IDirect3DDevice9Ex* pDirect3DDevice9Ex); - -//! @brief Creates OpenCL context from Direct3DDevice9 device -// -//! @param pDirect3DDevice9 - pointer to Direct3Device9 device -//! @return Returns reference to OpenCL Context -CV_EXPORTS Context& initializeContextFromDirect3DDevice9(IDirect3DDevice9* pDirect3DDevice9); - -//! @} - -} // namespace cv::directx::ocl - -//! @addtogroup core_directx -//! @{ - -//! @brief Converts InputArray to ID3D11Texture2D. If destination texture format is DXGI_FORMAT_NV12 then -//! input UMat expected to be in BGR format and data will be downsampled and color-converted to NV12. -// -//! @note Note: Destination texture must be allocated by application. Function does memory copy from src to -//! pD3D11Texture2D -// -//! @param src - source InputArray -//! @param pD3D11Texture2D - destination D3D11 texture -CV_EXPORTS void convertToD3D11Texture2D(InputArray src, ID3D11Texture2D* pD3D11Texture2D); - -//! @brief Converts ID3D11Texture2D to OutputArray. If input texture format is DXGI_FORMAT_NV12 then -//! data will be upsampled and color-converted to BGR format. -// -//! @note Note: Destination matrix will be re-allocated if it has not enough memory to match texture size. -//! function does memory copy from pD3D11Texture2D to dst -// -//! @param pD3D11Texture2D - source D3D11 texture -//! @param dst - destination OutputArray -CV_EXPORTS void convertFromD3D11Texture2D(ID3D11Texture2D* pD3D11Texture2D, OutputArray dst); - -//! @brief Converts InputArray to ID3D10Texture2D -// -//! @note Note: function does memory copy from src to -//! pD3D10Texture2D -// -//! @param src - source InputArray -//! @param pD3D10Texture2D - destination D3D10 texture -CV_EXPORTS void convertToD3D10Texture2D(InputArray src, ID3D10Texture2D* pD3D10Texture2D); - -//! @brief Converts ID3D10Texture2D to OutputArray -// -//! @note Note: function does memory copy from pD3D10Texture2D -//! to dst -// -//! @param pD3D10Texture2D - source D3D10 texture -//! @param dst - destination OutputArray -CV_EXPORTS void convertFromD3D10Texture2D(ID3D10Texture2D* pD3D10Texture2D, OutputArray dst); - -//! @brief Converts InputArray to IDirect3DSurface9 -// -//! @note Note: function does memory copy from src to -//! pDirect3DSurface9 -// -//! @param src - source InputArray -//! @param pDirect3DSurface9 - destination D3D10 texture -//! @param surfaceSharedHandle - shared handle -CV_EXPORTS void convertToDirect3DSurface9(InputArray src, IDirect3DSurface9* pDirect3DSurface9, void* surfaceSharedHandle = NULL); - -//! @brief Converts IDirect3DSurface9 to OutputArray -// -//! @note Note: function does memory copy from pDirect3DSurface9 -//! to dst -// -//! @param pDirect3DSurface9 - source D3D10 texture -//! @param dst - destination OutputArray -//! @param surfaceSharedHandle - shared handle -CV_EXPORTS void convertFromDirect3DSurface9(IDirect3DSurface9* pDirect3DSurface9, OutputArray dst, void* surfaceSharedHandle = NULL); - -//! @brief Get OpenCV type from DirectX type -//! @param iDXGI_FORMAT - enum DXGI_FORMAT for D3D10/D3D11 -//! @return OpenCV type or -1 if there is no equivalent -CV_EXPORTS int getTypeFromDXGI_FORMAT(const int iDXGI_FORMAT); // enum DXGI_FORMAT for D3D10/D3D11 - -//! @brief Get OpenCV type from DirectX type -//! @param iD3DFORMAT - enum D3DTYPE for D3D9 -//! @return OpenCV type or -1 if there is no equivalent -CV_EXPORTS int getTypeFromD3DFORMAT(const int iD3DFORMAT); // enum D3DTYPE for D3D9 - -//! @} - -} } // namespace cv::directx - -#endif // OPENCV_CORE_DIRECTX_HPP diff --git a/include/opencv2/core/eigen.hpp b/include/opencv2/core/eigen.hpp deleted file mode 100755 index 51f4147..0000000 --- a/include/opencv2/core/eigen.hpp +++ /dev/null @@ -1,402 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - - -#ifndef OPENCV_CORE_EIGEN_HPP -#define OPENCV_CORE_EIGEN_HPP - -#ifndef EIGEN_WORLD_VERSION -#error "Wrong usage of OpenCV's Eigen utility header. Include Eigen's headers first. See https://github.com/opencv/opencv/issues/17366" -#endif - -#include "opencv2/core.hpp" - -#if defined _MSC_VER && _MSC_VER >= 1200 -#define NOMINMAX // fix https://github.com/opencv/opencv/issues/17548 -#pragma warning( disable: 4714 ) //__forceinline is not inlined -#pragma warning( disable: 4127 ) //conditional expression is constant -#pragma warning( disable: 4244 ) //conversion from '__int64' to 'int', possible loss of data -#endif - -#if !defined(OPENCV_DISABLE_EIGEN_TENSOR_SUPPORT) -#if EIGEN_WORLD_VERSION == 3 && EIGEN_MAJOR_VERSION >= 3 \ - && defined(CV_CXX11) && defined(CV_CXX_STD_ARRAY) -#include -#define OPENCV_EIGEN_TENSOR_SUPPORT 1 -#endif // EIGEN_WORLD_VERSION == 3 && EIGEN_MAJOR_VERSION >= 3 -#endif // !defined(OPENCV_DISABLE_EIGEN_TENSOR_SUPPORT) - -namespace cv -{ - -/** @addtogroup core_eigen -These functions are provided for OpenCV-Eigen interoperability. They convert `Mat` -objects to corresponding `Eigen::Matrix` objects and vice-versa. Consult the [Eigen -documentation](https://eigen.tuxfamily.org/dox/group__TutorialMatrixClass.html) for -information about the `Matrix` template type. - -@note Using these functions requires the `Eigen/Dense` or similar header to be -included before this header. -*/ -//! @{ - -#if defined(OPENCV_EIGEN_TENSOR_SUPPORT) || defined(CV_DOXYGEN) -/** @brief Converts an Eigen::Tensor to a cv::Mat. - -The method converts an Eigen::Tensor with shape (H x W x C) to a cv::Mat where: - H = number of rows - W = number of columns - C = number of channels - -Usage: -\code -Eigen::Tensor a_tensor(...); -// populate tensor with values -Mat a_mat; -eigen2cv(a_tensor, a_mat); -\endcode -*/ -template static inline -void eigen2cv( const Eigen::Tensor<_Tp, 3, _layout> &src, OutputArray dst ) -{ - if( !(_layout & Eigen::RowMajorBit) ) - { - const std::array shuffle{2, 1, 0}; - Eigen::Tensor<_Tp, 3, !_layout> row_major_tensor = src.swap_layout().shuffle(shuffle); - Mat _src(src.dimension(0), src.dimension(1), CV_MAKETYPE(DataType<_Tp>::type, src.dimension(2)), row_major_tensor.data()); - _src.copyTo(dst); - } - else - { - Mat _src(src.dimension(0), src.dimension(1), CV_MAKETYPE(DataType<_Tp>::type, src.dimension(2)), (void *)src.data()); - _src.copyTo(dst); - } -} - -/** @brief Converts a cv::Mat to an Eigen::Tensor. - -The method converts a cv::Mat to an Eigen Tensor with shape (H x W x C) where: - H = number of rows - W = number of columns - C = number of channels - -Usage: -\code -Mat a_mat(...); -// populate Mat with values -Eigen::Tensor a_tensor(...); -cv2eigen(a_mat, a_tensor); -\endcode -*/ -template static inline -void cv2eigen( const Mat &src, Eigen::Tensor<_Tp, 3, _layout> &dst ) -{ - if( !(_layout & Eigen::RowMajorBit) ) - { - Eigen::Tensor<_Tp, 3, !_layout> row_major_tensor(src.rows, src.cols, src.channels()); - Mat _dst(src.rows, src.cols, CV_MAKETYPE(DataType<_Tp>::type, src.channels()), row_major_tensor.data()); - if (src.type() == _dst.type()) - src.copyTo(_dst); - else - src.convertTo(_dst, _dst.type()); - const std::array shuffle{2, 1, 0}; - dst = row_major_tensor.swap_layout().shuffle(shuffle); - } - else - { - dst.resize(src.rows, src.cols, src.channels()); - Mat _dst(src.rows, src.cols, CV_MAKETYPE(DataType<_Tp>::type, src.channels()), dst.data()); - if (src.type() == _dst.type()) - src.copyTo(_dst); - else - src.convertTo(_dst, _dst.type()); - } -} - -/** @brief Maps cv::Mat data to an Eigen::TensorMap. - -The method wraps an existing Mat data array with an Eigen TensorMap of shape (H x W x C) where: - H = number of rows - W = number of columns - C = number of channels - -Explicit instantiation of the return type is required. - -@note Caller should be aware of the lifetime of the cv::Mat instance and take appropriate safety measures. -The cv::Mat instance will retain ownership of the data and the Eigen::TensorMap will lose access when the cv::Mat data is deallocated. - -The example below initializes a cv::Mat and produces an Eigen::TensorMap: -\code -float arr[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}; -Mat a_mat(2, 2, CV_32FC3, arr); -Eigen::TensorMap> a_tensormap = cv2eigen_tensormap(a_mat); -\endcode -*/ -template static inline -Eigen::TensorMap> cv2eigen_tensormap(InputArray src) -{ - Mat mat = src.getMat(); - CV_CheckTypeEQ(mat.type(), CV_MAKETYPE(traits::Type<_Tp>::value, mat.channels()), ""); - return Eigen::TensorMap>((_Tp *)mat.data, mat.rows, mat.cols, mat.channels()); -} -#endif // OPENCV_EIGEN_TENSOR_SUPPORT - -template static inline -void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src, OutputArray dst ) -{ - if( !(src.Flags & Eigen::RowMajorBit) ) - { - Mat _src(src.cols(), src.rows(), traits::Type<_Tp>::value, - (void*)src.data(), src.outerStride()*sizeof(_Tp)); - transpose(_src, dst); - } - else - { - Mat _src(src.rows(), src.cols(), traits::Type<_Tp>::value, - (void*)src.data(), src.outerStride()*sizeof(_Tp)); - _src.copyTo(dst); - } -} - -// Matx case -template static inline -void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src, - Matx<_Tp, _rows, _cols>& dst ) -{ - if( !(src.Flags & Eigen::RowMajorBit) ) - { - dst = Matx<_Tp, _cols, _rows>(static_cast(src.data())).t(); - } - else - { - dst = Matx<_Tp, _rows, _cols>(static_cast(src.data())); - } -} - -template static inline -void cv2eigen( const Mat& src, - Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst ) -{ - CV_DbgAssert(src.rows == _rows && src.cols == _cols); - if( !(dst.Flags & Eigen::RowMajorBit) ) - { - const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value, - dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); - if( src.type() == _dst.type() ) - transpose(src, _dst); - else if( src.cols == src.rows ) - { - src.convertTo(_dst, _dst.type()); - transpose(_dst, _dst); - } - else - Mat(src.t()).convertTo(_dst, _dst.type()); - } - else - { - const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value, - dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); - src.convertTo(_dst, _dst.type()); - } -} - -// Matx case -template static inline -void cv2eigen( const Matx<_Tp, _rows, _cols>& src, - Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst ) -{ - if( !(dst.Flags & Eigen::RowMajorBit) ) - { - const Mat _dst(_cols, _rows, traits::Type<_Tp>::value, - dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); - transpose(src, _dst); - } - else - { - const Mat _dst(_rows, _cols, traits::Type<_Tp>::value, - dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); - Mat(src).copyTo(_dst); - } -} - -template static inline -void cv2eigen( const Mat& src, - Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst ) -{ - dst.resize(src.rows, src.cols); - if( !(dst.Flags & Eigen::RowMajorBit) ) - { - const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value, - dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); - if( src.type() == _dst.type() ) - transpose(src, _dst); - else if( src.cols == src.rows ) - { - src.convertTo(_dst, _dst.type()); - transpose(_dst, _dst); - } - else - Mat(src.t()).convertTo(_dst, _dst.type()); - } - else - { - const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value, - dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); - src.convertTo(_dst, _dst.type()); - } -} - -// Matx case -template static inline -void cv2eigen( const Matx<_Tp, _rows, _cols>& src, - Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst ) -{ - dst.resize(_rows, _cols); - if( !(dst.Flags & Eigen::RowMajorBit) ) - { - const Mat _dst(_cols, _rows, traits::Type<_Tp>::value, - dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); - transpose(src, _dst); - } - else - { - const Mat _dst(_rows, _cols, traits::Type<_Tp>::value, - dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); - Mat(src).copyTo(_dst); - } -} - -template static inline -void cv2eigen( const Mat& src, - Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst ) -{ - CV_Assert(src.cols == 1); - dst.resize(src.rows); - - if( !(dst.Flags & Eigen::RowMajorBit) ) - { - const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value, - dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); - if( src.type() == _dst.type() ) - transpose(src, _dst); - else - Mat(src.t()).convertTo(_dst, _dst.type()); - } - else - { - const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value, - dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); - src.convertTo(_dst, _dst.type()); - } -} - -// Matx case -template static inline -void cv2eigen( const Matx<_Tp, _rows, 1>& src, - Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst ) -{ - dst.resize(_rows); - - if( !(dst.Flags & Eigen::RowMajorBit) ) - { - const Mat _dst(1, _rows, traits::Type<_Tp>::value, - dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); - transpose(src, _dst); - } - else - { - const Mat _dst(_rows, 1, traits::Type<_Tp>::value, - dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); - src.copyTo(_dst); - } -} - - -template static inline -void cv2eigen( const Mat& src, - Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst ) -{ - CV_Assert(src.rows == 1); - dst.resize(src.cols); - if( !(dst.Flags & Eigen::RowMajorBit) ) - { - const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value, - dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); - if( src.type() == _dst.type() ) - transpose(src, _dst); - else - Mat(src.t()).convertTo(_dst, _dst.type()); - } - else - { - const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value, - dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); - src.convertTo(_dst, _dst.type()); - } -} - -//Matx -template static inline -void cv2eigen( const Matx<_Tp, 1, _cols>& src, - Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst ) -{ - dst.resize(_cols); - if( !(dst.Flags & Eigen::RowMajorBit) ) - { - const Mat _dst(_cols, 1, traits::Type<_Tp>::value, - dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); - transpose(src, _dst); - } - else - { - const Mat _dst(1, _cols, traits::Type<_Tp>::value, - dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp))); - Mat(src).copyTo(_dst); - } -} - -//! @} - -} // cv - -#endif diff --git a/include/opencv2/core/fast_math.hpp b/include/opencv2/core/fast_math.hpp deleted file mode 100755 index 0f53cf5..0000000 --- a/include/opencv2/core/fast_math.hpp +++ /dev/null @@ -1,408 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_FAST_MATH_HPP -#define OPENCV_CORE_FAST_MATH_HPP - -#include "opencv2/core/cvdef.h" - -//! @addtogroup core_utils -//! @{ - -/****************************************************************************************\ -* fast math * -\****************************************************************************************/ - -#ifdef __cplusplus -# include -#else -# ifdef __BORLANDC__ -# include -# else -# include -# endif -#endif - -#if defined(__CUDACC__) - // nothing, intrinsics/asm code is not supported -#else - #if ((defined _MSC_VER && defined _M_X64) \ - || (defined __GNUC__ && defined __x86_64__ && defined __SSE2__)) \ - && !defined(OPENCV_SKIP_INCLUDE_EMMINTRIN_H) - #include - #endif - - #if defined __PPC64__ && defined __GNUC__ && defined _ARCH_PWR8 \ - && !defined(OPENCV_SKIP_INCLUDE_ALTIVEC_H) - #include - #endif - - #if defined(CV_INLINE_ROUND_FLT) - // user-specified version - // CV_INLINE_ROUND_DBL should be defined too - #elif defined __GNUC__ && defined __arm__ && (defined __ARM_PCS_VFP || defined __ARM_VFPV3__ || defined __ARM_NEON__) && !defined __SOFTFP__ - // 1. general scheme - #define ARM_ROUND(_value, _asm_string) \ - int res; \ - float temp; \ - CV_UNUSED(temp); \ - __asm__(_asm_string : [res] "=r" (res), [temp] "=w" (temp) : [value] "w" (_value)); \ - return res - // 2. version for double - #ifdef __clang__ - #define CV_INLINE_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %[value] \n vmov %[res], %[temp]") - #else - #define CV_INLINE_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %P[value] \n vmov %[res], %[temp]") - #endif - // 3. version for float - #define CV_INLINE_ROUND_FLT(value) ARM_ROUND(value, "vcvtr.s32.f32 %[temp], %[value]\n vmov %[res], %[temp]") - #elif defined __PPC64__ && defined __GNUC__ && defined _ARCH_PWR8 - // P8 and newer machines can convert fp32/64 to int quickly. - #define CV_INLINE_ROUND_DBL(value) \ - int out; \ - double temp; \ - __asm__( "fctiw %[temp],%[in]\n\tmfvsrwz %[out],%[temp]\n\t" : [out] "=r" (out), [temp] "=d" (temp) : [in] "d" ((double)(value)) : ); \ - return out; - - // FP32 also works with FP64 routine above - #define CV_INLINE_ROUND_FLT(value) CV_INLINE_ROUND_DBL(value) - #endif - - #ifdef CV_INLINE_ISINF_FLT - // user-specified version - // CV_INLINE_ISINF_DBL should be defined too - #elif defined __PPC64__ && defined _ARCH_PWR9 && defined(scalar_test_data_class) - #define CV_INLINE_ISINF_DBL(value) return scalar_test_data_class(value, 0x30); - #define CV_INLINE_ISINF_FLT(value) CV_INLINE_ISINF_DBL(value) - #endif - - #ifdef CV_INLINE_ISNAN_FLT - // user-specified version - // CV_INLINE_ISNAN_DBL should be defined too - #elif defined __PPC64__ && defined _ARCH_PWR9 && defined(scalar_test_data_class) - #define CV_INLINE_ISNAN_DBL(value) return scalar_test_data_class(value, 0x40); - #define CV_INLINE_ISNAN_FLT(value) CV_INLINE_ISNAN_DBL(value) - #endif - - #if !defined(OPENCV_USE_FASTMATH_BUILTINS) \ - && ( \ - defined(__x86_64__) || defined(__i686__) \ - || defined(__arm__) \ - || defined(__PPC64__) \ - ) - /* Let builtin C math functions when available. Dedicated hardware is available to - round and convert FP values. */ - #define OPENCV_USE_FASTMATH_BUILTINS 1 - #endif - - /* Enable builtin math functions if possible, desired, and available. - Note, not all math functions inline equally. E.g lrint will not inline - without the -fno-math-errno option. */ - #if defined(CV_ICC) - // nothing - #elif defined(OPENCV_USE_FASTMATH_BUILTINS) && OPENCV_USE_FASTMATH_BUILTINS - #if defined(__clang__) - #define CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS - #if !defined(CV_INLINE_ISNAN_DBL) && __has_builtin(__builtin_isnan) - #define CV_INLINE_ISNAN_DBL(value) return __builtin_isnan(value); - #endif - #if !defined(CV_INLINE_ISNAN_FLT) && __has_builtin(__builtin_isnan) - #define CV_INLINE_ISNAN_FLT(value) return __builtin_isnan(value); - #endif - #if !defined(CV_INLINE_ISINF_DBL) && __has_builtin(__builtin_isinf) - #define CV_INLINE_ISINF_DBL(value) return __builtin_isinf(value); - #endif - #if !defined(CV_INLINE_ISINF_FLT) && __has_builtin(__builtin_isinf) - #define CV_INLINE_ISINF_FLT(value) return __builtin_isinf(value); - #endif - #elif defined(__GNUC__) - #define CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS - #if !defined(CV_INLINE_ISNAN_DBL) - #define CV_INLINE_ISNAN_DBL(value) return __builtin_isnan(value); - #endif - #if !defined(CV_INLINE_ISNAN_FLT) - #define CV_INLINE_ISNAN_FLT(value) return __builtin_isnanf(value); - #endif - #if !defined(CV_INLINE_ISINF_DBL) - #define CV_INLINE_ISINF_DBL(value) return __builtin_isinf(value); - #endif - #if !defined(CV_INLINE_ISINF_FLT) - #define CV_INLINE_ISINF_FLT(value) return __builtin_isinff(value); - #endif - #elif defined(_MSC_VER) - #if !defined(CV_INLINE_ISNAN_DBL) - #define CV_INLINE_ISNAN_DBL(value) return isnan(value); - #endif - #if !defined(CV_INLINE_ISNAN_FLT) - #define CV_INLINE_ISNAN_FLT(value) return isnan(value); - #endif - #if !defined(CV_INLINE_ISINF_DBL) - #define CV_INLINE_ISINF_DBL(value) return isinf(value); - #endif - #if !defined(CV_INLINE_ISINF_FLT) - #define CV_INLINE_ISINF_FLT(value) return isinf(value); - #endif - #endif - #endif - -#endif // defined(__CUDACC__) - -/** @brief Rounds floating-point number to the nearest integer - - @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the - result is not defined. - */ -CV_INLINE int -cvRound( double value ) -{ -#if defined CV_INLINE_ROUND_DBL - CV_INLINE_ROUND_DBL(value); -#elif ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \ - && defined __SSE2__ && !defined __APPLE__) || CV_SSE2) \ - && !defined(__CUDACC__) - __m128d t = _mm_set_sd( value ); - return _mm_cvtsd_si32(t); -#elif defined _MSC_VER && defined _M_IX86 - int t; - __asm - { - fld value; - fistp t; - } - return t; -#elif defined CV_ICC || defined __GNUC__ - return (int)(lrint(value)); -#else - /* it's ok if round does not comply with IEEE754 standard; - the tests should allow +/-1 difference when the tested functions use round */ - return (int)(value + (value >= 0 ? 0.5 : -0.5)); -#endif -} - - -/** @brief Rounds floating-point number to the nearest integer not larger than the original. - - The function computes an integer i such that: - \f[i \le \texttt{value} < i+1\f] - @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the - result is not defined. - */ -CV_INLINE int cvFloor( double value ) -{ -#if (defined CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS || defined CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS) \ - && ( \ - defined(__PPC64__) \ - ) - return __builtin_floor(value); -#else - int i = (int)value; - return i - (i > value); -#endif -} - -/** @brief Rounds floating-point number to the nearest integer not smaller than the original. - - The function computes an integer i such that: - \f[i \le \texttt{value} < i+1\f] - @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the - result is not defined. - */ -CV_INLINE int cvCeil( double value ) -{ -#if (defined CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS || defined CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS) \ - && ( \ - defined(__PPC64__) \ - ) - return __builtin_ceil(value); -#else - int i = (int)value; - return i + (i < value); -#endif -} - -/** @brief Determines if the argument is Not A Number. - - @param value The input floating-point value - - The function returns 1 if the argument is Not A Number (as defined by IEEE754 standard), 0 - otherwise. */ -CV_INLINE int cvIsNaN( double value ) -{ -#if defined CV_INLINE_ISNAN_DBL - CV_INLINE_ISNAN_DBL(value); -#else - Cv64suf ieee754; - ieee754.f = value; - return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) + - ((unsigned)ieee754.u != 0) > 0x7ff00000; -#endif -} - -/** @brief Determines if the argument is Infinity. - - @param value The input floating-point value - - The function returns 1 if the argument is a plus or minus infinity (as defined by IEEE754 standard) - and 0 otherwise. */ -CV_INLINE int cvIsInf( double value ) -{ -#if defined CV_INLINE_ISINF_DBL - CV_INLINE_ISINF_DBL(value); -#elif defined(__x86_64__) || defined(_M_X64) || defined(__aarch64__) || defined(_M_ARM64) || defined(__PPC64__) - Cv64suf ieee754; - ieee754.f = value; - return (ieee754.u & 0x7fffffff00000000) == - 0x7ff0000000000000; -#else - Cv64suf ieee754; - ieee754.f = value; - return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) == 0x7ff00000 && - (unsigned)ieee754.u == 0; -#endif -} - -#ifdef __cplusplus - -/** @overload */ -CV_INLINE int cvRound(float value) -{ -#if defined CV_INLINE_ROUND_FLT - CV_INLINE_ROUND_FLT(value); -#elif ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \ - && defined __SSE2__ && !defined __APPLE__) || CV_SSE2) \ - && !defined(__CUDACC__) - __m128 t = _mm_set_ss( value ); - return _mm_cvtss_si32(t); -#elif defined _MSC_VER && defined _M_IX86 - int t; - __asm - { - fld value; - fistp t; - } - return t; -#elif defined CV_ICC || defined __GNUC__ - return (int)(lrintf(value)); -#else - /* it's ok if round does not comply with IEEE754 standard; - the tests should allow +/-1 difference when the tested functions use round */ - return (int)(value + (value >= 0 ? 0.5f : -0.5f)); -#endif -} - -/** @overload */ -CV_INLINE int cvRound( int value ) -{ - return value; -} - -/** @overload */ -CV_INLINE int cvFloor( float value ) -{ -#if (defined CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS || defined CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS) \ - && ( \ - defined(__PPC64__) \ - ) - return __builtin_floorf(value); -#else - int i = (int)value; - return i - (i > value); -#endif -} - -/** @overload */ -CV_INLINE int cvFloor( int value ) -{ - return value; -} - -/** @overload */ -CV_INLINE int cvCeil( float value ) -{ -#if (defined CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS || defined CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS) \ - && ( \ - defined(__PPC64__) \ - ) - return __builtin_ceilf(value); -#else - int i = (int)value; - return i + (i < value); -#endif -} - -/** @overload */ -CV_INLINE int cvCeil( int value ) -{ - return value; -} - -/** @overload */ -CV_INLINE int cvIsNaN( float value ) -{ -#if defined CV_INLINE_ISNAN_FLT - CV_INLINE_ISNAN_FLT(value); -#else - Cv32suf ieee754; - ieee754.f = value; - return (ieee754.u & 0x7fffffff) > 0x7f800000; -#endif -} - -/** @overload */ -CV_INLINE int cvIsInf( float value ) -{ -#if defined CV_INLINE_ISINF_FLT - CV_INLINE_ISINF_FLT(value); -#else - Cv32suf ieee754; - ieee754.f = value; - return (ieee754.u & 0x7fffffff) == 0x7f800000; -#endif -} - -#endif // __cplusplus - -//! @} core_utils - -#endif diff --git a/include/opencv2/core/hal/hal.hpp b/include/opencv2/core/hal/hal.hpp deleted file mode 100755 index 68900ec..0000000 --- a/include/opencv2/core/hal/hal.hpp +++ /dev/null @@ -1,250 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_HAL_HPP -#define OPENCV_HAL_HPP - -#include "opencv2/core/cvdef.h" -#include "opencv2/core/cvstd.hpp" -#include "opencv2/core/hal/interface.h" - -namespace cv { namespace hal { - -//! @addtogroup core_hal_functions -//! @{ - -CV_EXPORTS int normHamming(const uchar* a, int n); -CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n); - -CV_EXPORTS int normHamming(const uchar* a, int n, int cellSize); -CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n, int cellSize); - -CV_EXPORTS int LU32f(float* A, size_t astep, int m, float* b, size_t bstep, int n); -CV_EXPORTS int LU64f(double* A, size_t astep, int m, double* b, size_t bstep, int n); -CV_EXPORTS bool Cholesky32f(float* A, size_t astep, int m, float* b, size_t bstep, int n); -CV_EXPORTS bool Cholesky64f(double* A, size_t astep, int m, double* b, size_t bstep, int n); -CV_EXPORTS void SVD32f(float* At, size_t astep, float* W, float* U, size_t ustep, float* Vt, size_t vstep, int m, int n, int flags); -CV_EXPORTS void SVD64f(double* At, size_t astep, double* W, double* U, size_t ustep, double* Vt, size_t vstep, int m, int n, int flags); -CV_EXPORTS int QR32f(float* A, size_t astep, int m, int n, int k, float* b, size_t bstep, float* hFactors); -CV_EXPORTS int QR64f(double* A, size_t astep, int m, int n, int k, double* b, size_t bstep, double* hFactors); - -CV_EXPORTS void gemm32f(const float* src1, size_t src1_step, const float* src2, size_t src2_step, - float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step, - int m_a, int n_a, int n_d, int flags); -CV_EXPORTS void gemm64f(const double* src1, size_t src1_step, const double* src2, size_t src2_step, - double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step, - int m_a, int n_a, int n_d, int flags); -CV_EXPORTS void gemm32fc(const float* src1, size_t src1_step, const float* src2, size_t src2_step, - float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step, - int m_a, int n_a, int n_d, int flags); -CV_EXPORTS void gemm64fc(const double* src1, size_t src1_step, const double* src2, size_t src2_step, - double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step, - int m_a, int n_a, int n_d, int flags); - -CV_EXPORTS int normL1_(const uchar* a, const uchar* b, int n); -CV_EXPORTS float normL1_(const float* a, const float* b, int n); -CV_EXPORTS float normL2Sqr_(const float* a, const float* b, int n); - -CV_EXPORTS void exp32f(const float* src, float* dst, int n); -CV_EXPORTS void exp64f(const double* src, double* dst, int n); -CV_EXPORTS void log32f(const float* src, float* dst, int n); -CV_EXPORTS void log64f(const double* src, double* dst, int n); - -CV_EXPORTS void fastAtan32f(const float* y, const float* x, float* dst, int n, bool angleInDegrees); -CV_EXPORTS void fastAtan64f(const double* y, const double* x, double* dst, int n, bool angleInDegrees); -CV_EXPORTS void magnitude32f(const float* x, const float* y, float* dst, int n); -CV_EXPORTS void magnitude64f(const double* x, const double* y, double* dst, int n); -CV_EXPORTS void sqrt32f(const float* src, float* dst, int len); -CV_EXPORTS void sqrt64f(const double* src, double* dst, int len); -CV_EXPORTS void invSqrt32f(const float* src, float* dst, int len); -CV_EXPORTS void invSqrt64f(const double* src, double* dst, int len); - -CV_EXPORTS void split8u(const uchar* src, uchar** dst, int len, int cn ); -CV_EXPORTS void split16u(const ushort* src, ushort** dst, int len, int cn ); -CV_EXPORTS void split32s(const int* src, int** dst, int len, int cn ); -CV_EXPORTS void split64s(const int64* src, int64** dst, int len, int cn ); - -CV_EXPORTS void merge8u(const uchar** src, uchar* dst, int len, int cn ); -CV_EXPORTS void merge16u(const ushort** src, ushort* dst, int len, int cn ); -CV_EXPORTS void merge32s(const int** src, int* dst, int len, int cn ); -CV_EXPORTS void merge64s(const int64** src, int64* dst, int len, int cn ); - -CV_EXPORTS void add8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void add8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void add16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void add16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void add32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void add32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void add64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); - -CV_EXPORTS void sub8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void sub8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void sub16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void sub16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void sub32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void sub32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void sub64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); - -CV_EXPORTS void max8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void max8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void max16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void max16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void max32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void max32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void max64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); - -CV_EXPORTS void min8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void min8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void min16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void min16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void min32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void min32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void min64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); - -CV_EXPORTS void absdiff8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void absdiff8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void absdiff16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void absdiff16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void absdiff32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void absdiff32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void absdiff64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* ); - -CV_EXPORTS void and8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void or8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void xor8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); -CV_EXPORTS void not8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* ); - -CV_EXPORTS void cmp8u(const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); -CV_EXPORTS void cmp8s(const schar* src1, size_t step1, const schar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); -CV_EXPORTS void cmp16u(const ushort* src1, size_t step1, const ushort* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); -CV_EXPORTS void cmp16s(const short* src1, size_t step1, const short* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); -CV_EXPORTS void cmp32s(const int* src1, size_t step1, const int* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); -CV_EXPORTS void cmp32f(const float* src1, size_t step1, const float* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); -CV_EXPORTS void cmp64f(const double* src1, size_t step1, const double* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop); - -CV_EXPORTS void mul8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void mul8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void mul16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void mul16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void mul32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void mul32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void mul64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); - -CV_EXPORTS void div8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void div8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void div16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void div16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void div32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void div32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void div64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); - -CV_EXPORTS void recip8u( const uchar *, size_t, const uchar * src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void recip8s( const schar *, size_t, const schar * src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void recip16u( const ushort *, size_t, const ushort * src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void recip16s( const short *, size_t, const short * src2, size_t step2, short* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void recip32s( const int *, size_t, const int * src2, size_t step2, int* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void recip32f( const float *, size_t, const float * src2, size_t step2, float* dst, size_t step, int width, int height, void* scale); -CV_EXPORTS void recip64f( const double *, size_t, const double * src2, size_t step2, double* dst, size_t step, int width, int height, void* scale); - -CV_EXPORTS void addWeighted8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _scalars ); -CV_EXPORTS void addWeighted8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scalars ); -CV_EXPORTS void addWeighted16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scalars ); -CV_EXPORTS void addWeighted16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scalars ); -CV_EXPORTS void addWeighted32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scalars ); -CV_EXPORTS void addWeighted32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scalars ); -CV_EXPORTS void addWeighted64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scalars ); - -struct CV_EXPORTS DFT1D -{ - static Ptr create(int len, int count, int depth, int flags, bool * useBuffer = 0); - virtual void apply(const uchar *src, uchar *dst) = 0; - virtual ~DFT1D() {} -}; - -struct CV_EXPORTS DFT2D -{ - static Ptr create(int width, int height, int depth, - int src_channels, int dst_channels, - int flags, int nonzero_rows = 0); - virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0; - virtual ~DFT2D() {} -}; - -struct CV_EXPORTS DCT2D -{ - static Ptr create(int width, int height, int depth, int flags); - virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0; - virtual ~DCT2D() {} -}; - -//! @} core_hal - -//============================================================================= -// for binary compatibility with 3.0 - -//! @cond IGNORED - -CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n); -CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n); -CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n); -CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n); - -CV_EXPORTS void exp(const float* src, float* dst, int n); -CV_EXPORTS void exp(const double* src, double* dst, int n); -CV_EXPORTS void log(const float* src, float* dst, int n); -CV_EXPORTS void log(const double* src, double* dst, int n); - -CV_EXPORTS void fastAtan2(const float* y, const float* x, float* dst, int n, bool angleInDegrees); -CV_EXPORTS void magnitude(const float* x, const float* y, float* dst, int n); -CV_EXPORTS void magnitude(const double* x, const double* y, double* dst, int n); -CV_EXPORTS void sqrt(const float* src, float* dst, int len); -CV_EXPORTS void sqrt(const double* src, double* dst, int len); -CV_EXPORTS void invSqrt(const float* src, float* dst, int len); -CV_EXPORTS void invSqrt(const double* src, double* dst, int len); - -//! @endcond - -}} //cv::hal - -#endif //OPENCV_HAL_HPP diff --git a/include/opencv2/core/hal/interface.h b/include/opencv2/core/hal/interface.h deleted file mode 100755 index 8f64025..0000000 --- a/include/opencv2/core/hal/interface.h +++ /dev/null @@ -1,182 +0,0 @@ -#ifndef OPENCV_CORE_HAL_INTERFACE_H -#define OPENCV_CORE_HAL_INTERFACE_H - -//! @addtogroup core_hal_interface -//! @{ - -//! @name Return codes -//! @{ -#define CV_HAL_ERROR_OK 0 -#define CV_HAL_ERROR_NOT_IMPLEMENTED 1 -#define CV_HAL_ERROR_UNKNOWN -1 -//! @} - -#ifdef __cplusplus -#include -#else -#include -#include -#endif - -//! @name Data types -//! primitive types -//! - schar - signed 1 byte integer -//! - uchar - unsigned 1 byte integer -//! - short - signed 2 byte integer -//! - ushort - unsigned 2 byte integer -//! - int - signed 4 byte integer -//! - uint - unsigned 4 byte integer -//! - int64 - signed 8 byte integer -//! - uint64 - unsigned 8 byte integer -//! @{ -#if !defined _MSC_VER && !defined __BORLANDC__ -# if defined __cplusplus && __cplusplus >= 201103L && !defined __APPLE__ -# include -# ifdef __NEWLIB__ - typedef unsigned int uint; -# else - typedef std::uint32_t uint; -# endif -# else -# include - typedef uint32_t uint; -# endif -#else - typedef unsigned uint; -#endif - -typedef signed char schar; - -#ifndef __IPL_H__ - typedef unsigned char uchar; - typedef unsigned short ushort; -#endif - -#if defined _MSC_VER || defined __BORLANDC__ - typedef __int64 int64; - typedef unsigned __int64 uint64; -# define CV_BIG_INT(n) n##I64 -# define CV_BIG_UINT(n) n##UI64 -#else - typedef int64_t int64; - typedef uint64_t uint64; -# define CV_BIG_INT(n) n##LL -# define CV_BIG_UINT(n) n##ULL -#endif - -#define CV_CN_MAX 512 -#define CV_CN_SHIFT 3 -#define CV_DEPTH_MAX (1 << CV_CN_SHIFT) - -#define CV_8U 0 -#define CV_8S 1 -#define CV_16U 2 -#define CV_16S 3 -#define CV_32S 4 -#define CV_32F 5 -#define CV_64F 6 -#define CV_USRTYPE1 7 - -#define CV_MAT_DEPTH_MASK (CV_DEPTH_MAX - 1) -#define CV_MAT_DEPTH(flags) ((flags) & CV_MAT_DEPTH_MASK) - -#define CV_MAKETYPE(depth,cn) (CV_MAT_DEPTH(depth) + (((cn)-1) << CV_CN_SHIFT)) -#define CV_MAKE_TYPE CV_MAKETYPE - -#define CV_8UC1 CV_MAKETYPE(CV_8U,1) -#define CV_8UC2 CV_MAKETYPE(CV_8U,2) -#define CV_8UC3 CV_MAKETYPE(CV_8U,3) -#define CV_8UC4 CV_MAKETYPE(CV_8U,4) -#define CV_8UC(n) CV_MAKETYPE(CV_8U,(n)) - -#define CV_8SC1 CV_MAKETYPE(CV_8S,1) -#define CV_8SC2 CV_MAKETYPE(CV_8S,2) -#define CV_8SC3 CV_MAKETYPE(CV_8S,3) -#define CV_8SC4 CV_MAKETYPE(CV_8S,4) -#define CV_8SC(n) CV_MAKETYPE(CV_8S,(n)) - -#define CV_16UC1 CV_MAKETYPE(CV_16U,1) -#define CV_16UC2 CV_MAKETYPE(CV_16U,2) -#define CV_16UC3 CV_MAKETYPE(CV_16U,3) -#define CV_16UC4 CV_MAKETYPE(CV_16U,4) -#define CV_16UC(n) CV_MAKETYPE(CV_16U,(n)) - -#define CV_16SC1 CV_MAKETYPE(CV_16S,1) -#define CV_16SC2 CV_MAKETYPE(CV_16S,2) -#define CV_16SC3 CV_MAKETYPE(CV_16S,3) -#define CV_16SC4 CV_MAKETYPE(CV_16S,4) -#define CV_16SC(n) CV_MAKETYPE(CV_16S,(n)) - -#define CV_32SC1 CV_MAKETYPE(CV_32S,1) -#define CV_32SC2 CV_MAKETYPE(CV_32S,2) -#define CV_32SC3 CV_MAKETYPE(CV_32S,3) -#define CV_32SC4 CV_MAKETYPE(CV_32S,4) -#define CV_32SC(n) CV_MAKETYPE(CV_32S,(n)) - -#define CV_32FC1 CV_MAKETYPE(CV_32F,1) -#define CV_32FC2 CV_MAKETYPE(CV_32F,2) -#define CV_32FC3 CV_MAKETYPE(CV_32F,3) -#define CV_32FC4 CV_MAKETYPE(CV_32F,4) -#define CV_32FC(n) CV_MAKETYPE(CV_32F,(n)) - -#define CV_64FC1 CV_MAKETYPE(CV_64F,1) -#define CV_64FC2 CV_MAKETYPE(CV_64F,2) -#define CV_64FC3 CV_MAKETYPE(CV_64F,3) -#define CV_64FC4 CV_MAKETYPE(CV_64F,4) -#define CV_64FC(n) CV_MAKETYPE(CV_64F,(n)) -//! @} - -//! @name Comparison operation -//! @sa cv::CmpTypes -//! @{ -#define CV_HAL_CMP_EQ 0 -#define CV_HAL_CMP_GT 1 -#define CV_HAL_CMP_GE 2 -#define CV_HAL_CMP_LT 3 -#define CV_HAL_CMP_LE 4 -#define CV_HAL_CMP_NE 5 -//! @} - -//! @name Border processing modes -//! @sa cv::BorderTypes -//! @{ -#define CV_HAL_BORDER_CONSTANT 0 -#define CV_HAL_BORDER_REPLICATE 1 -#define CV_HAL_BORDER_REFLECT 2 -#define CV_HAL_BORDER_WRAP 3 -#define CV_HAL_BORDER_REFLECT_101 4 -#define CV_HAL_BORDER_TRANSPARENT 5 -#define CV_HAL_BORDER_ISOLATED 16 -//! @} - -//! @name DFT flags -//! @{ -#define CV_HAL_DFT_INVERSE 1 -#define CV_HAL_DFT_SCALE 2 -#define CV_HAL_DFT_ROWS 4 -#define CV_HAL_DFT_COMPLEX_OUTPUT 16 -#define CV_HAL_DFT_REAL_OUTPUT 32 -#define CV_HAL_DFT_TWO_STAGE 64 -#define CV_HAL_DFT_STAGE_COLS 128 -#define CV_HAL_DFT_IS_CONTINUOUS 512 -#define CV_HAL_DFT_IS_INPLACE 1024 -//! @} - -//! @name SVD flags -//! @{ -#define CV_HAL_SVD_NO_UV 1 -#define CV_HAL_SVD_SHORT_UV 2 -#define CV_HAL_SVD_MODIFY_A 4 -#define CV_HAL_SVD_FULL_UV 8 -//! @} - -//! @name Gemm flags -//! @{ -#define CV_HAL_GEMM_1_T 1 -#define CV_HAL_GEMM_2_T 2 -#define CV_HAL_GEMM_3_T 4 -//! @} - -//! @} - -#endif diff --git a/include/opencv2/core/hal/intrin.hpp b/include/opencv2/core/hal/intrin.hpp deleted file mode 100755 index 52f6b5d..0000000 --- a/include/opencv2/core/hal/intrin.hpp +++ /dev/null @@ -1,520 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_HAL_INTRIN_HPP -#define OPENCV_HAL_INTRIN_HPP - -#include -#include -#include -#include "opencv2/core/cvdef.h" - -#define OPENCV_HAL_ADD(a, b) ((a) + (b)) -#define OPENCV_HAL_AND(a, b) ((a) & (b)) -#define OPENCV_HAL_NOP(a) (a) -#define OPENCV_HAL_1ST(a, b) (a) - -namespace { -inline unsigned int trailingZeros32(unsigned int value) { -#if defined(_MSC_VER) -#if (_MSC_VER < 1700) || defined(_M_ARM) || defined(_M_ARM64) - unsigned long index = 0; - _BitScanForward(&index, value); - return (unsigned int)index; -#elif defined(__clang__) - // clang-cl doesn't export _tzcnt_u32 for non BMI systems - return value ? __builtin_ctz(value) : 32; -#else - return _tzcnt_u32(value); -#endif -#elif defined(__GNUC__) || defined(__GNUG__) - return __builtin_ctz(value); -#elif defined(__ICC) || defined(__INTEL_COMPILER) - return _bit_scan_forward(value); -#elif defined(__clang__) - return llvm.cttz.i32(value, true); -#else - static const int MultiplyDeBruijnBitPosition[32] = { - 0, 1, 28, 2, 29, 14, 24, 3, 30, 22, 20, 15, 25, 17, 4, 8, - 31, 27, 13, 23, 21, 19, 16, 7, 26, 12, 18, 6, 11, 5, 10, 9 }; - return MultiplyDeBruijnBitPosition[((uint32_t)((value & -value) * 0x077CB531U)) >> 27]; -#endif -} -} - -// unlike HAL API, which is in cv::hal, -// we put intrinsics into cv namespace to make its -// access from within opencv code more accessible -namespace cv { - -namespace hal { - -enum StoreMode -{ - STORE_UNALIGNED = 0, - STORE_ALIGNED = 1, - STORE_ALIGNED_NOCACHE = 2 -}; - -} - -// TODO FIXIT: Don't use "God" traits. Split on separate cases. -template struct V_TypeTraits -{ -}; - -#define CV_INTRIN_DEF_TYPE_TRAITS(type, int_type_, uint_type_, abs_type_, w_type_, q_type_, sum_type_, nlanes128_) \ - template<> struct V_TypeTraits \ - { \ - typedef type value_type; \ - typedef int_type_ int_type; \ - typedef abs_type_ abs_type; \ - typedef uint_type_ uint_type; \ - typedef w_type_ w_type; \ - typedef q_type_ q_type; \ - typedef sum_type_ sum_type; \ - enum { nlanes128 = nlanes128_ }; \ - \ - static inline int_type reinterpret_int(type x) \ - { \ - union { type l; int_type i; } v; \ - v.l = x; \ - return v.i; \ - } \ - \ - static inline type reinterpret_from_int(int_type x) \ - { \ - union { type l; int_type i; } v; \ - v.i = x; \ - return v.l; \ - } \ - } - -#define CV_INTRIN_DEF_TYPE_TRAITS_NO_Q_TYPE(type, int_type_, uint_type_, abs_type_, w_type_, sum_type_, nlanes128_) \ - template<> struct V_TypeTraits \ - { \ - typedef type value_type; \ - typedef int_type_ int_type; \ - typedef abs_type_ abs_type; \ - typedef uint_type_ uint_type; \ - typedef w_type_ w_type; \ - typedef sum_type_ sum_type; \ - enum { nlanes128 = nlanes128_ }; \ - \ - static inline int_type reinterpret_int(type x) \ - { \ - union { type l; int_type i; } v; \ - v.l = x; \ - return v.i; \ - } \ - \ - static inline type reinterpret_from_int(int_type x) \ - { \ - union { type l; int_type i; } v; \ - v.i = x; \ - return v.l; \ - } \ - } - -CV_INTRIN_DEF_TYPE_TRAITS(uchar, schar, uchar, uchar, ushort, unsigned, unsigned, 16); -CV_INTRIN_DEF_TYPE_TRAITS(schar, schar, uchar, uchar, short, int, int, 16); -CV_INTRIN_DEF_TYPE_TRAITS(ushort, short, ushort, ushort, unsigned, uint64, unsigned, 8); -CV_INTRIN_DEF_TYPE_TRAITS(short, short, ushort, ushort, int, int64, int, 8); -CV_INTRIN_DEF_TYPE_TRAITS_NO_Q_TYPE(unsigned, int, unsigned, unsigned, uint64, unsigned, 4); -CV_INTRIN_DEF_TYPE_TRAITS_NO_Q_TYPE(int, int, unsigned, unsigned, int64, int, 4); -CV_INTRIN_DEF_TYPE_TRAITS_NO_Q_TYPE(float, int, unsigned, float, double, float, 4); -CV_INTRIN_DEF_TYPE_TRAITS_NO_Q_TYPE(uint64, int64, uint64, uint64, void, uint64, 2); -CV_INTRIN_DEF_TYPE_TRAITS_NO_Q_TYPE(int64, int64, uint64, uint64, void, int64, 2); -CV_INTRIN_DEF_TYPE_TRAITS_NO_Q_TYPE(double, int64, uint64, double, void, double, 2); - -#ifndef CV_DOXYGEN - -#ifndef CV_CPU_OPTIMIZATION_HAL_NAMESPACE -#ifdef CV_FORCE_SIMD128_CPP - #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE hal_EMULATOR_CPP - #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace hal_EMULATOR_CPP { - #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END } -#elif defined(CV_CPU_DISPATCH_MODE) - #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE __CV_CAT(hal_, CV_CPU_DISPATCH_MODE) - #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace __CV_CAT(hal_, CV_CPU_DISPATCH_MODE) { - #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END } -#else - #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE hal_baseline - #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace hal_baseline { - #define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END } -#endif -#endif // CV_CPU_OPTIMIZATION_HAL_NAMESPACE - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END -using namespace CV_CPU_OPTIMIZATION_HAL_NAMESPACE; -#endif -} - -#ifdef CV_DOXYGEN -# undef CV_AVX2 -# undef CV_SSE2 -# undef CV_NEON -# undef CV_VSX -# undef CV_FP16 -# undef CV_MSA -#endif - -#if (CV_SSE2 || CV_NEON || CV_VSX || CV_MSA || CV_WASM_SIMD) && !defined(CV_FORCE_SIMD128_CPP) -#define CV__SIMD_FORWARD 128 -#include "opencv2/core/hal/intrin_forward.hpp" -#endif - -#if CV_SSE2 && !defined(CV_FORCE_SIMD128_CPP) - -#include "opencv2/core/hal/intrin_sse_em.hpp" -#include "opencv2/core/hal/intrin_sse.hpp" - -#elif CV_NEON && !defined(CV_FORCE_SIMD128_CPP) - -#include "opencv2/core/hal/intrin_neon.hpp" - -#elif CV_VSX && !defined(CV_FORCE_SIMD128_CPP) - -#include "opencv2/core/hal/intrin_vsx.hpp" - -#elif CV_MSA && !defined(CV_FORCE_SIMD128_CPP) - -#include "opencv2/core/hal/intrin_msa.hpp" - -#elif CV_WASM_SIMD && !defined(CV_FORCE_SIMD128_CPP) -#include "opencv2/core/hal/intrin_wasm.hpp" - -#else - -#include "opencv2/core/hal/intrin_cpp.hpp" - -#endif - -// AVX2 can be used together with SSE2, so -// we define those two sets of intrinsics at once. -// Most of the intrinsics do not conflict (the proper overloaded variant is -// resolved by the argument types, e.g. v_float32x4 ~ SSE2, v_float32x8 ~ AVX2), -// but some of AVX2 intrinsics get v256_ prefix instead of v_, e.g. v256_load() vs v_load(). -// Correspondingly, the wide intrinsics (which are mapped to the "widest" -// available instruction set) will get vx_ prefix -// (and will be mapped to v256_ counterparts) (e.g. vx_load() => v256_load()) -#if CV_AVX2 - -#define CV__SIMD_FORWARD 256 -#include "opencv2/core/hal/intrin_forward.hpp" -#include "opencv2/core/hal/intrin_avx.hpp" - -#endif - -// AVX512 can be used together with SSE2 and AVX2, so -// we define those sets of intrinsics at once. -// For some of AVX512 intrinsics get v512_ prefix instead of v_, e.g. v512_load() vs v_load(). -// Wide intrinsics will be mapped to v512_ counterparts in this case(e.g. vx_load() => v512_load()) -#if CV_AVX512_SKX - -#define CV__SIMD_FORWARD 512 -#include "opencv2/core/hal/intrin_forward.hpp" -#include "opencv2/core/hal/intrin_avx512.hpp" - -#endif - -//! @cond IGNORED - -namespace cv { - -#ifndef CV_DOXYGEN -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN -#endif - -#ifndef CV_SIMD128 -#define CV_SIMD128 0 -#endif - -#ifndef CV_SIMD128_CPP -#define CV_SIMD128_CPP 0 -#endif - -#ifndef CV_SIMD128_64F -#define CV_SIMD128_64F 0 -#endif - -#ifndef CV_SIMD256 -#define CV_SIMD256 0 -#endif - -#ifndef CV_SIMD256_64F -#define CV_SIMD256_64F 0 -#endif - -#ifndef CV_SIMD512 -#define CV_SIMD512 0 -#endif - -#ifndef CV_SIMD512_64F -#define CV_SIMD512_64F 0 -#endif - -#ifndef CV_SIMD128_FP16 -#define CV_SIMD128_FP16 0 -#endif - -#ifndef CV_SIMD256_FP16 -#define CV_SIMD256_FP16 0 -#endif - -#ifndef CV_SIMD512_FP16 -#define CV_SIMD512_FP16 0 -#endif - -//================================================================================================== - -#define CV_INTRIN_DEFINE_WIDE_INTRIN(typ, vtyp, short_typ, prefix, loadsfx) \ - inline vtyp vx_setall_##short_typ(typ v) { return prefix##_setall_##short_typ(v); } \ - inline vtyp vx_setzero_##short_typ() { return prefix##_setzero_##short_typ(); } \ - inline vtyp vx_##loadsfx(const typ* ptr) { return prefix##_##loadsfx(ptr); } \ - inline vtyp vx_##loadsfx##_aligned(const typ* ptr) { return prefix##_##loadsfx##_aligned(ptr); } \ - inline vtyp vx_##loadsfx##_low(const typ* ptr) { return prefix##_##loadsfx##_low(ptr); } \ - inline vtyp vx_##loadsfx##_halves(const typ* ptr0, const typ* ptr1) { return prefix##_##loadsfx##_halves(ptr0, ptr1); } \ - inline void vx_store(typ* ptr, const vtyp& v) { return v_store(ptr, v); } \ - inline void vx_store_aligned(typ* ptr, const vtyp& v) { return v_store_aligned(ptr, v); } \ - inline vtyp vx_lut(const typ* ptr, const int* idx) { return prefix##_lut(ptr, idx); } \ - inline vtyp vx_lut_pairs(const typ* ptr, const int* idx) { return prefix##_lut_pairs(ptr, idx); } - -#define CV_INTRIN_DEFINE_WIDE_LUT_QUAD(typ, vtyp, prefix) \ - inline vtyp vx_lut_quads(const typ* ptr, const int* idx) { return prefix##_lut_quads(ptr, idx); } - -#define CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(typ, wtyp, prefix) \ - inline wtyp vx_load_expand(const typ* ptr) { return prefix##_load_expand(ptr); } - -#define CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND_Q(typ, qtyp, prefix) \ - inline qtyp vx_load_expand_q(const typ* ptr) { return prefix##_load_expand_q(ptr); } - -#define CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(typ, vtyp, short_typ, wtyp, qtyp, prefix, loadsfx) \ - CV_INTRIN_DEFINE_WIDE_INTRIN(typ, vtyp, short_typ, prefix, loadsfx) \ - CV_INTRIN_DEFINE_WIDE_LUT_QUAD(typ, vtyp, prefix) \ - CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(typ, wtyp, prefix) \ - CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND_Q(typ, qtyp, prefix) - -#define CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(prefix) \ - CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(uchar, v_uint8, u8, v_uint16, v_uint32, prefix, load) \ - CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(schar, v_int8, s8, v_int16, v_int32, prefix, load) \ - CV_INTRIN_DEFINE_WIDE_INTRIN(ushort, v_uint16, u16, prefix, load) \ - CV_INTRIN_DEFINE_WIDE_LUT_QUAD(ushort, v_uint16, prefix) \ - CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(ushort, v_uint32, prefix) \ - CV_INTRIN_DEFINE_WIDE_INTRIN(short, v_int16, s16, prefix, load) \ - CV_INTRIN_DEFINE_WIDE_LUT_QUAD(short, v_int16, prefix) \ - CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(short, v_int32, prefix) \ - CV_INTRIN_DEFINE_WIDE_INTRIN(int, v_int32, s32, prefix, load) \ - CV_INTRIN_DEFINE_WIDE_LUT_QUAD(int, v_int32, prefix) \ - CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(int, v_int64, prefix) \ - CV_INTRIN_DEFINE_WIDE_INTRIN(unsigned, v_uint32, u32, prefix, load) \ - CV_INTRIN_DEFINE_WIDE_LUT_QUAD(unsigned, v_uint32, prefix) \ - CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(unsigned, v_uint64, prefix) \ - CV_INTRIN_DEFINE_WIDE_INTRIN(float, v_float32, f32, prefix, load) \ - CV_INTRIN_DEFINE_WIDE_LUT_QUAD(float, v_float32, prefix) \ - CV_INTRIN_DEFINE_WIDE_INTRIN(int64, v_int64, s64, prefix, load) \ - CV_INTRIN_DEFINE_WIDE_INTRIN(uint64, v_uint64, u64, prefix, load) \ - CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(float16_t, v_float32, prefix) - -template struct V_RegTraits -{ -}; - -#define CV_DEF_REG_TRAITS(prefix, _reg, lane_type, suffix, _u_reg, _w_reg, _q_reg, _int_reg, _round_reg) \ - template<> struct V_RegTraits<_reg> \ - { \ - typedef _reg reg; \ - typedef _u_reg u_reg; \ - typedef _w_reg w_reg; \ - typedef _q_reg q_reg; \ - typedef _int_reg int_reg; \ - typedef _round_reg round_reg; \ - } - -#if CV_SIMD128 || CV_SIMD128_CPP - CV_DEF_REG_TRAITS(v, v_uint8x16, uchar, u8, v_uint8x16, v_uint16x8, v_uint32x4, v_int8x16, void); - CV_DEF_REG_TRAITS(v, v_int8x16, schar, s8, v_uint8x16, v_int16x8, v_int32x4, v_int8x16, void); - CV_DEF_REG_TRAITS(v, v_uint16x8, ushort, u16, v_uint16x8, v_uint32x4, v_uint64x2, v_int16x8, void); - CV_DEF_REG_TRAITS(v, v_int16x8, short, s16, v_uint16x8, v_int32x4, v_int64x2, v_int16x8, void); - CV_DEF_REG_TRAITS(v, v_uint32x4, unsigned, u32, v_uint32x4, v_uint64x2, void, v_int32x4, void); - CV_DEF_REG_TRAITS(v, v_int32x4, int, s32, v_uint32x4, v_int64x2, void, v_int32x4, void); -#if CV_SIMD128_64F || CV_SIMD128_CPP - CV_DEF_REG_TRAITS(v, v_float32x4, float, f32, v_float32x4, v_float64x2, void, v_int32x4, v_int32x4); -#else - CV_DEF_REG_TRAITS(v, v_float32x4, float, f32, v_float32x4, void, void, v_int32x4, v_int32x4); -#endif - CV_DEF_REG_TRAITS(v, v_uint64x2, uint64, u64, v_uint64x2, void, void, v_int64x2, void); - CV_DEF_REG_TRAITS(v, v_int64x2, int64, s64, v_uint64x2, void, void, v_int64x2, void); -#if CV_SIMD128_64F - CV_DEF_REG_TRAITS(v, v_float64x2, double, f64, v_float64x2, void, void, v_int64x2, v_int32x4); -#endif -#endif - -#if CV_SIMD256 - CV_DEF_REG_TRAITS(v256, v_uint8x32, uchar, u8, v_uint8x32, v_uint16x16, v_uint32x8, v_int8x32, void); - CV_DEF_REG_TRAITS(v256, v_int8x32, schar, s8, v_uint8x32, v_int16x16, v_int32x8, v_int8x32, void); - CV_DEF_REG_TRAITS(v256, v_uint16x16, ushort, u16, v_uint16x16, v_uint32x8, v_uint64x4, v_int16x16, void); - CV_DEF_REG_TRAITS(v256, v_int16x16, short, s16, v_uint16x16, v_int32x8, v_int64x4, v_int16x16, void); - CV_DEF_REG_TRAITS(v256, v_uint32x8, unsigned, u32, v_uint32x8, v_uint64x4, void, v_int32x8, void); - CV_DEF_REG_TRAITS(v256, v_int32x8, int, s32, v_uint32x8, v_int64x4, void, v_int32x8, void); - CV_DEF_REG_TRAITS(v256, v_float32x8, float, f32, v_float32x8, v_float64x4, void, v_int32x8, v_int32x8); - CV_DEF_REG_TRAITS(v256, v_uint64x4, uint64, u64, v_uint64x4, void, void, v_int64x4, void); - CV_DEF_REG_TRAITS(v256, v_int64x4, int64, s64, v_uint64x4, void, void, v_int64x4, void); - CV_DEF_REG_TRAITS(v256, v_float64x4, double, f64, v_float64x4, void, void, v_int64x4, v_int32x8); -#endif - -#if CV_SIMD512 - CV_DEF_REG_TRAITS(v512, v_uint8x64, uchar, u8, v_uint8x64, v_uint16x32, v_uint32x16, v_int8x64, void); - CV_DEF_REG_TRAITS(v512, v_int8x64, schar, s8, v_uint8x64, v_int16x32, v_int32x16, v_int8x64, void); - CV_DEF_REG_TRAITS(v512, v_uint16x32, ushort, u16, v_uint16x32, v_uint32x16, v_uint64x8, v_int16x32, void); - CV_DEF_REG_TRAITS(v512, v_int16x32, short, s16, v_uint16x32, v_int32x16, v_int64x8, v_int16x32, void); - CV_DEF_REG_TRAITS(v512, v_uint32x16, unsigned, u32, v_uint32x16, v_uint64x8, void, v_int32x16, void); - CV_DEF_REG_TRAITS(v512, v_int32x16, int, s32, v_uint32x16, v_int64x8, void, v_int32x16, void); - CV_DEF_REG_TRAITS(v512, v_float32x16, float, f32, v_float32x16, v_float64x8, void, v_int32x16, v_int32x16); - CV_DEF_REG_TRAITS(v512, v_uint64x8, uint64, u64, v_uint64x8, void, void, v_int64x8, void); - CV_DEF_REG_TRAITS(v512, v_int64x8, int64, s64, v_uint64x8, void, void, v_int64x8, void); - CV_DEF_REG_TRAITS(v512, v_float64x8, double, f64, v_float64x8, void, void, v_int64x8, v_int32x16); -#endif - -#if CV_SIMD512 && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 512) -#define CV__SIMD_NAMESPACE simd512 -namespace CV__SIMD_NAMESPACE { - #define CV_SIMD 1 - #define CV_SIMD_64F CV_SIMD512_64F - #define CV_SIMD_FP16 CV_SIMD512_FP16 - #define CV_SIMD_WIDTH 64 - typedef v_uint8x64 v_uint8; - typedef v_int8x64 v_int8; - typedef v_uint16x32 v_uint16; - typedef v_int16x32 v_int16; - typedef v_uint32x16 v_uint32; - typedef v_int32x16 v_int32; - typedef v_uint64x8 v_uint64; - typedef v_int64x8 v_int64; - typedef v_float32x16 v_float32; - CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(v512) -#if CV_SIMD512_64F - typedef v_float64x8 v_float64; - CV_INTRIN_DEFINE_WIDE_INTRIN(double, v_float64, f64, v512, load) -#endif - inline void vx_cleanup() { v512_cleanup(); } -} // namespace -using namespace CV__SIMD_NAMESPACE; -#elif CV_SIMD256 && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 256) -#define CV__SIMD_NAMESPACE simd256 -namespace CV__SIMD_NAMESPACE { - #define CV_SIMD 1 - #define CV_SIMD_64F CV_SIMD256_64F - #define CV_SIMD_FP16 CV_SIMD256_FP16 - #define CV_SIMD_WIDTH 32 - typedef v_uint8x32 v_uint8; - typedef v_int8x32 v_int8; - typedef v_uint16x16 v_uint16; - typedef v_int16x16 v_int16; - typedef v_uint32x8 v_uint32; - typedef v_int32x8 v_int32; - typedef v_uint64x4 v_uint64; - typedef v_int64x4 v_int64; - typedef v_float32x8 v_float32; - CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(v256) - #if CV_SIMD256_64F - typedef v_float64x4 v_float64; - CV_INTRIN_DEFINE_WIDE_INTRIN(double, v_float64, f64, v256, load) - #endif - inline void vx_cleanup() { v256_cleanup(); } -} // namespace -using namespace CV__SIMD_NAMESPACE; -#elif (CV_SIMD128 || CV_SIMD128_CPP) && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 128) -#if defined CV_SIMD128_CPP -#define CV__SIMD_NAMESPACE simd128_cpp -#else -#define CV__SIMD_NAMESPACE simd128 -#endif -namespace CV__SIMD_NAMESPACE { - #define CV_SIMD CV_SIMD128 - #define CV_SIMD_64F CV_SIMD128_64F - #define CV_SIMD_WIDTH 16 - typedef v_uint8x16 v_uint8; - typedef v_int8x16 v_int8; - typedef v_uint16x8 v_uint16; - typedef v_int16x8 v_int16; - typedef v_uint32x4 v_uint32; - typedef v_int32x4 v_int32; - typedef v_uint64x2 v_uint64; - typedef v_int64x2 v_int64; - typedef v_float32x4 v_float32; - CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(v) - #if CV_SIMD128_64F - typedef v_float64x2 v_float64; - CV_INTRIN_DEFINE_WIDE_INTRIN(double, v_float64, f64, v, load) - #endif - inline void vx_cleanup() { v_cleanup(); } -} // namespace -using namespace CV__SIMD_NAMESPACE; -#endif - -#ifndef CV_SIMD_64F -#define CV_SIMD_64F 0 -#endif - -#ifndef CV_SIMD_FP16 -#define CV_SIMD_FP16 0 //!< Defined to 1 on native support of operations with float16x8_t / float16x16_t (SIMD256) types -#endif - -#ifndef CV_SIMD -#define CV_SIMD 0 -#endif - -#include "simd_utils.impl.hpp" - -#ifndef CV_DOXYGEN -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END -#endif - -} // cv:: - -//! @endcond - -#endif diff --git a/include/opencv2/core/hal/intrin_avx.hpp b/include/opencv2/core/hal/intrin_avx.hpp deleted file mode 100755 index 54e8927..0000000 --- a/include/opencv2/core/hal/intrin_avx.hpp +++ /dev/null @@ -1,3165 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html - -#ifndef OPENCV_HAL_INTRIN_AVX_HPP -#define OPENCV_HAL_INTRIN_AVX_HPP - -#define CV_SIMD256 1 -#define CV_SIMD256_64F 1 -#define CV_SIMD256_FP16 0 // no native operations with FP16 type. Only load/store from float32x8 are available (if CV_FP16 == 1) - -namespace cv -{ - -//! @cond IGNORED - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN - -///////// Utils //////////// - -inline __m256i _v256_combine(const __m128i& lo, const __m128i& hi) -{ return _mm256_inserti128_si256(_mm256_castsi128_si256(lo), hi, 1); } - -inline __m256 _v256_combine(const __m128& lo, const __m128& hi) -{ return _mm256_insertf128_ps(_mm256_castps128_ps256(lo), hi, 1); } - -inline __m256d _v256_combine(const __m128d& lo, const __m128d& hi) -{ return _mm256_insertf128_pd(_mm256_castpd128_pd256(lo), hi, 1); } - -inline int _v_cvtsi256_si32(const __m256i& a) -{ return _mm_cvtsi128_si32(_mm256_castsi256_si128(a)); } - -inline __m256i _v256_shuffle_odd_64(const __m256i& v) -{ return _mm256_permute4x64_epi64(v, _MM_SHUFFLE(3, 1, 2, 0)); } - -inline __m256d _v256_shuffle_odd_64(const __m256d& v) -{ return _mm256_permute4x64_pd(v, _MM_SHUFFLE(3, 1, 2, 0)); } - -template -inline __m256i _v256_permute2x128(const __m256i& a, const __m256i& b) -{ return _mm256_permute2x128_si256(a, b, imm); } - -template -inline __m256 _v256_permute2x128(const __m256& a, const __m256& b) -{ return _mm256_permute2f128_ps(a, b, imm); } - -template -inline __m256d _v256_permute2x128(const __m256d& a, const __m256d& b) -{ return _mm256_permute2f128_pd(a, b, imm); } - -template -inline _Tpvec v256_permute2x128(const _Tpvec& a, const _Tpvec& b) -{ return _Tpvec(_v256_permute2x128(a.val, b.val)); } - -template -inline __m256i _v256_permute4x64(const __m256i& a) -{ return _mm256_permute4x64_epi64(a, imm); } - -template -inline __m256d _v256_permute4x64(const __m256d& a) -{ return _mm256_permute4x64_pd(a, imm); } - -template -inline _Tpvec v256_permute4x64(const _Tpvec& a) -{ return _Tpvec(_v256_permute4x64(a.val)); } - -inline __m128i _v256_extract_high(const __m256i& v) -{ return _mm256_extracti128_si256(v, 1); } - -inline __m128 _v256_extract_high(const __m256& v) -{ return _mm256_extractf128_ps(v, 1); } - -inline __m128d _v256_extract_high(const __m256d& v) -{ return _mm256_extractf128_pd(v, 1); } - -inline __m128i _v256_extract_low(const __m256i& v) -{ return _mm256_castsi256_si128(v); } - -inline __m128 _v256_extract_low(const __m256& v) -{ return _mm256_castps256_ps128(v); } - -inline __m128d _v256_extract_low(const __m256d& v) -{ return _mm256_castpd256_pd128(v); } - -inline __m256i _v256_packs_epu32(const __m256i& a, const __m256i& b) -{ - const __m256i m = _mm256_set1_epi32(65535); - __m256i am = _mm256_min_epu32(a, m); - __m256i bm = _mm256_min_epu32(b, m); - return _mm256_packus_epi32(am, bm); -} - -template -inline int _v256_extract_epi8(const __m256i& a) -{ -#if defined(CV__SIMD_HAVE_mm256_extract_epi8) || (CV_AVX2 && (!defined(_MSC_VER) || _MSC_VER >= 1910/*MSVS 2017*/)) - return _mm256_extract_epi8(a, i); -#else - __m128i b = _mm256_extractf128_si256(a, ((i) >> 4)); - return _mm_extract_epi8(b, i & 15); // SSE4.1 -#endif -} - -template -inline int _v256_extract_epi16(const __m256i& a) -{ -#if defined(CV__SIMD_HAVE_mm256_extract_epi8) || (CV_AVX2 && (!defined(_MSC_VER) || _MSC_VER >= 1910/*MSVS 2017*/)) - return _mm256_extract_epi16(a, i); -#else - __m128i b = _mm256_extractf128_si256(a, ((i) >> 3)); - return _mm_extract_epi16(b, i & 7); // SSE2 -#endif -} - -template -inline int _v256_extract_epi32(const __m256i& a) -{ -#if defined(CV__SIMD_HAVE_mm256_extract_epi8) || (CV_AVX2 && (!defined(_MSC_VER) || _MSC_VER >= 1910/*MSVS 2017*/)) - return _mm256_extract_epi32(a, i); -#else - __m128i b = _mm256_extractf128_si256(a, ((i) >> 2)); - return _mm_extract_epi32(b, i & 3); // SSE4.1 -#endif -} - -template -inline int64 _v256_extract_epi64(const __m256i& a) -{ -#if defined(CV__SIMD_HAVE_mm256_extract_epi8) || (CV_AVX2 && (!defined(_MSC_VER) || _MSC_VER >= 1910/*MSVS 2017*/)) - return _mm256_extract_epi64(a, i); -#else - __m128i b = _mm256_extractf128_si256(a, ((i) >> 1)); - return _mm_extract_epi64(b, i & 1); // SSE4.1 -#endif -} - -///////// Types //////////// - -struct v_uint8x32 -{ - typedef uchar lane_type; - enum { nlanes = 32 }; - __m256i val; - - explicit v_uint8x32(__m256i v) : val(v) {} - v_uint8x32(uchar v0, uchar v1, uchar v2, uchar v3, - uchar v4, uchar v5, uchar v6, uchar v7, - uchar v8, uchar v9, uchar v10, uchar v11, - uchar v12, uchar v13, uchar v14, uchar v15, - uchar v16, uchar v17, uchar v18, uchar v19, - uchar v20, uchar v21, uchar v22, uchar v23, - uchar v24, uchar v25, uchar v26, uchar v27, - uchar v28, uchar v29, uchar v30, uchar v31) - { - val = _mm256_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3, - (char)v4, (char)v5, (char)v6 , (char)v7, (char)v8, (char)v9, - (char)v10, (char)v11, (char)v12, (char)v13, (char)v14, (char)v15, - (char)v16, (char)v17, (char)v18, (char)v19, (char)v20, (char)v21, - (char)v22, (char)v23, (char)v24, (char)v25, (char)v26, (char)v27, - (char)v28, (char)v29, (char)v30, (char)v31); - } - /* coverity[uninit_ctor]: suppress warning */ - v_uint8x32() {} - - uchar get0() const { return (uchar)_v_cvtsi256_si32(val); } -}; - -struct v_int8x32 -{ - typedef schar lane_type; - enum { nlanes = 32 }; - __m256i val; - - explicit v_int8x32(__m256i v) : val(v) {} - v_int8x32(schar v0, schar v1, schar v2, schar v3, - schar v4, schar v5, schar v6, schar v7, - schar v8, schar v9, schar v10, schar v11, - schar v12, schar v13, schar v14, schar v15, - schar v16, schar v17, schar v18, schar v19, - schar v20, schar v21, schar v22, schar v23, - schar v24, schar v25, schar v26, schar v27, - schar v28, schar v29, schar v30, schar v31) - { - val = _mm256_setr_epi8(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, - v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, - v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31); - } - /* coverity[uninit_ctor]: suppress warning */ - v_int8x32() {} - - schar get0() const { return (schar)_v_cvtsi256_si32(val); } -}; - -struct v_uint16x16 -{ - typedef ushort lane_type; - enum { nlanes = 16 }; - __m256i val; - - explicit v_uint16x16(__m256i v) : val(v) {} - v_uint16x16(ushort v0, ushort v1, ushort v2, ushort v3, - ushort v4, ushort v5, ushort v6, ushort v7, - ushort v8, ushort v9, ushort v10, ushort v11, - ushort v12, ushort v13, ushort v14, ushort v15) - { - val = _mm256_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3, - (short)v4, (short)v5, (short)v6, (short)v7, (short)v8, (short)v9, - (short)v10, (short)v11, (short)v12, (short)v13, (short)v14, (short)v15); - } - /* coverity[uninit_ctor]: suppress warning */ - v_uint16x16() {} - - ushort get0() const { return (ushort)_v_cvtsi256_si32(val); } -}; - -struct v_int16x16 -{ - typedef short lane_type; - enum { nlanes = 16 }; - __m256i val; - - explicit v_int16x16(__m256i v) : val(v) {} - v_int16x16(short v0, short v1, short v2, short v3, - short v4, short v5, short v6, short v7, - short v8, short v9, short v10, short v11, - short v12, short v13, short v14, short v15) - { - val = _mm256_setr_epi16(v0, v1, v2, v3, v4, v5, v6, v7, - v8, v9, v10, v11, v12, v13, v14, v15); - } - /* coverity[uninit_ctor]: suppress warning */ - v_int16x16() {} - - short get0() const { return (short)_v_cvtsi256_si32(val); } -}; - -struct v_uint32x8 -{ - typedef unsigned lane_type; - enum { nlanes = 8 }; - __m256i val; - - explicit v_uint32x8(__m256i v) : val(v) {} - v_uint32x8(unsigned v0, unsigned v1, unsigned v2, unsigned v3, - unsigned v4, unsigned v5, unsigned v6, unsigned v7) - { - val = _mm256_setr_epi32((unsigned)v0, (unsigned)v1, (unsigned)v2, - (unsigned)v3, (unsigned)v4, (unsigned)v5, (unsigned)v6, (unsigned)v7); - } - /* coverity[uninit_ctor]: suppress warning */ - v_uint32x8() {} - - unsigned get0() const { return (unsigned)_v_cvtsi256_si32(val); } -}; - -struct v_int32x8 -{ - typedef int lane_type; - enum { nlanes = 8 }; - __m256i val; - - explicit v_int32x8(__m256i v) : val(v) {} - v_int32x8(int v0, int v1, int v2, int v3, - int v4, int v5, int v6, int v7) - { - val = _mm256_setr_epi32(v0, v1, v2, v3, v4, v5, v6, v7); - } - /* coverity[uninit_ctor]: suppress warning */ - v_int32x8() {} - - int get0() const { return _v_cvtsi256_si32(val); } -}; - -struct v_float32x8 -{ - typedef float lane_type; - enum { nlanes = 8 }; - __m256 val; - - explicit v_float32x8(__m256 v) : val(v) {} - v_float32x8(float v0, float v1, float v2, float v3, - float v4, float v5, float v6, float v7) - { - val = _mm256_setr_ps(v0, v1, v2, v3, v4, v5, v6, v7); - } - /* coverity[uninit_ctor]: suppress warning */ - v_float32x8() {} - - float get0() const { return _mm_cvtss_f32(_mm256_castps256_ps128(val)); } -}; - -struct v_uint64x4 -{ - typedef uint64 lane_type; - enum { nlanes = 4 }; - __m256i val; - - explicit v_uint64x4(__m256i v) : val(v) {} - v_uint64x4(uint64 v0, uint64 v1, uint64 v2, uint64 v3) - { val = _mm256_setr_epi64x((int64)v0, (int64)v1, (int64)v2, (int64)v3); } - /* coverity[uninit_ctor]: suppress warning */ - v_uint64x4() {} - - uint64 get0() const - { - #if defined __x86_64__ || defined _M_X64 - return (uint64)_mm_cvtsi128_si64(_mm256_castsi256_si128(val)); - #else - int a = _mm_cvtsi128_si32(_mm256_castsi256_si128(val)); - int b = _mm_cvtsi128_si32(_mm256_castsi256_si128(_mm256_srli_epi64(val, 32))); - return (unsigned)a | ((uint64)(unsigned)b << 32); - #endif - } -}; - -struct v_int64x4 -{ - typedef int64 lane_type; - enum { nlanes = 4 }; - __m256i val; - - explicit v_int64x4(__m256i v) : val(v) {} - v_int64x4(int64 v0, int64 v1, int64 v2, int64 v3) - { val = _mm256_setr_epi64x(v0, v1, v2, v3); } - /* coverity[uninit_ctor]: suppress warning */ - v_int64x4() {} - - int64 get0() const - { - #if defined __x86_64__ || defined _M_X64 - return (int64)_mm_cvtsi128_si64(_mm256_castsi256_si128(val)); - #else - int a = _mm_cvtsi128_si32(_mm256_castsi256_si128(val)); - int b = _mm_cvtsi128_si32(_mm256_castsi256_si128(_mm256_srli_epi64(val, 32))); - return (int64)((unsigned)a | ((uint64)(unsigned)b << 32)); - #endif - } -}; - -struct v_float64x4 -{ - typedef double lane_type; - enum { nlanes = 4 }; - __m256d val; - - explicit v_float64x4(__m256d v) : val(v) {} - v_float64x4(double v0, double v1, double v2, double v3) - { val = _mm256_setr_pd(v0, v1, v2, v3); } - /* coverity[uninit_ctor]: suppress warning */ - v_float64x4() {} - - double get0() const { return _mm_cvtsd_f64(_mm256_castpd256_pd128(val)); } -}; - -//////////////// Load and store operations /////////////// - -#define OPENCV_HAL_IMPL_AVX_LOADSTORE(_Tpvec, _Tp) \ - inline _Tpvec v256_load(const _Tp* ptr) \ - { return _Tpvec(_mm256_loadu_si256((const __m256i*)ptr)); } \ - inline _Tpvec v256_load_aligned(const _Tp* ptr) \ - { return _Tpvec(_mm256_load_si256((const __m256i*)ptr)); } \ - inline _Tpvec v256_load_low(const _Tp* ptr) \ - { \ - __m128i v128 = _mm_loadu_si128((const __m128i*)ptr); \ - return _Tpvec(_mm256_castsi128_si256(v128)); \ - } \ - inline _Tpvec v256_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ - { \ - __m128i vlo = _mm_loadu_si128((const __m128i*)ptr0); \ - __m128i vhi = _mm_loadu_si128((const __m128i*)ptr1); \ - return _Tpvec(_v256_combine(vlo, vhi)); \ - } \ - inline void v_store(_Tp* ptr, const _Tpvec& a) \ - { _mm256_storeu_si256((__m256i*)ptr, a.val); } \ - inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ - { _mm256_store_si256((__m256i*)ptr, a.val); } \ - inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ - { _mm256_stream_si256((__m256i*)ptr, a.val); } \ - inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ - { \ - if( mode == hal::STORE_UNALIGNED ) \ - _mm256_storeu_si256((__m256i*)ptr, a.val); \ - else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ - _mm256_stream_si256((__m256i*)ptr, a.val); \ - else \ - _mm256_store_si256((__m256i*)ptr, a.val); \ - } \ - inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ - { _mm_storeu_si128((__m128i*)ptr, _v256_extract_low(a.val)); } \ - inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ - { _mm_storeu_si128((__m128i*)ptr, _v256_extract_high(a.val)); } - -OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint8x32, uchar) -OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int8x32, schar) -OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint16x16, ushort) -OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int16x16, short) -OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint32x8, unsigned) -OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int32x8, int) -OPENCV_HAL_IMPL_AVX_LOADSTORE(v_uint64x4, uint64) -OPENCV_HAL_IMPL_AVX_LOADSTORE(v_int64x4, int64) - -#define OPENCV_HAL_IMPL_AVX_LOADSTORE_FLT(_Tpvec, _Tp, suffix, halfreg) \ - inline _Tpvec v256_load(const _Tp* ptr) \ - { return _Tpvec(_mm256_loadu_##suffix(ptr)); } \ - inline _Tpvec v256_load_aligned(const _Tp* ptr) \ - { return _Tpvec(_mm256_load_##suffix(ptr)); } \ - inline _Tpvec v256_load_low(const _Tp* ptr) \ - { \ - return _Tpvec(_mm256_cast##suffix##128_##suffix##256 \ - (_mm_loadu_##suffix(ptr))); \ - } \ - inline _Tpvec v256_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ - { \ - halfreg vlo = _mm_loadu_##suffix(ptr0); \ - halfreg vhi = _mm_loadu_##suffix(ptr1); \ - return _Tpvec(_v256_combine(vlo, vhi)); \ - } \ - inline void v_store(_Tp* ptr, const _Tpvec& a) \ - { _mm256_storeu_##suffix(ptr, a.val); } \ - inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ - { _mm256_store_##suffix(ptr, a.val); } \ - inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ - { _mm256_stream_##suffix(ptr, a.val); } \ - inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ - { \ - if( mode == hal::STORE_UNALIGNED ) \ - _mm256_storeu_##suffix(ptr, a.val); \ - else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ - _mm256_stream_##suffix(ptr, a.val); \ - else \ - _mm256_store_##suffix(ptr, a.val); \ - } \ - inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ - { _mm_storeu_##suffix(ptr, _v256_extract_low(a.val)); } \ - inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ - { _mm_storeu_##suffix(ptr, _v256_extract_high(a.val)); } - -OPENCV_HAL_IMPL_AVX_LOADSTORE_FLT(v_float32x8, float, ps, __m128) -OPENCV_HAL_IMPL_AVX_LOADSTORE_FLT(v_float64x4, double, pd, __m128d) - -#define OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, _Tpvecf, suffix, cast) \ - inline _Tpvec v_reinterpret_as_##suffix(const _Tpvecf& a) \ - { return _Tpvec(cast(a.val)); } - -#define OPENCV_HAL_IMPL_AVX_INIT(_Tpvec, _Tp, suffix, ssuffix, ctype_s) \ - inline _Tpvec v256_setzero_##suffix() \ - { return _Tpvec(_mm256_setzero_si256()); } \ - inline _Tpvec v256_setall_##suffix(_Tp v) \ - { return _Tpvec(_mm256_set1_##ssuffix((ctype_s)v)); } \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint8x32, suffix, OPENCV_HAL_NOP) \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int8x32, suffix, OPENCV_HAL_NOP) \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint16x16, suffix, OPENCV_HAL_NOP) \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int16x16, suffix, OPENCV_HAL_NOP) \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint32x8, suffix, OPENCV_HAL_NOP) \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int32x8, suffix, OPENCV_HAL_NOP) \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint64x4, suffix, OPENCV_HAL_NOP) \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int64x4, suffix, OPENCV_HAL_NOP) \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_float32x8, suffix, _mm256_castps_si256) \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_float64x4, suffix, _mm256_castpd_si256) - -OPENCV_HAL_IMPL_AVX_INIT(v_uint8x32, uchar, u8, epi8, char) -OPENCV_HAL_IMPL_AVX_INIT(v_int8x32, schar, s8, epi8, char) -OPENCV_HAL_IMPL_AVX_INIT(v_uint16x16, ushort, u16, epi16, short) -OPENCV_HAL_IMPL_AVX_INIT(v_int16x16, short, s16, epi16, short) -OPENCV_HAL_IMPL_AVX_INIT(v_uint32x8, unsigned, u32, epi32, int) -OPENCV_HAL_IMPL_AVX_INIT(v_int32x8, int, s32, epi32, int) -OPENCV_HAL_IMPL_AVX_INIT(v_uint64x4, uint64, u64, epi64x, int64) -OPENCV_HAL_IMPL_AVX_INIT(v_int64x4, int64, s64, epi64x, int64) - -#define OPENCV_HAL_IMPL_AVX_INIT_FLT(_Tpvec, _Tp, suffix, zsuffix, cast) \ - inline _Tpvec v256_setzero_##suffix() \ - { return _Tpvec(_mm256_setzero_##zsuffix()); } \ - inline _Tpvec v256_setall_##suffix(_Tp v) \ - { return _Tpvec(_mm256_set1_##zsuffix(v)); } \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint8x32, suffix, cast) \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int8x32, suffix, cast) \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint16x16, suffix, cast) \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int16x16, suffix, cast) \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint32x8, suffix, cast) \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int32x8, suffix, cast) \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_uint64x4, suffix, cast) \ - OPENCV_HAL_IMPL_AVX_CAST(_Tpvec, v_int64x4, suffix, cast) - -OPENCV_HAL_IMPL_AVX_INIT_FLT(v_float32x8, float, f32, ps, _mm256_castsi256_ps) -OPENCV_HAL_IMPL_AVX_INIT_FLT(v_float64x4, double, f64, pd, _mm256_castsi256_pd) - -inline v_float32x8 v_reinterpret_as_f32(const v_float32x8& a) -{ return a; } -inline v_float32x8 v_reinterpret_as_f32(const v_float64x4& a) -{ return v_float32x8(_mm256_castpd_ps(a.val)); } - -inline v_float64x4 v_reinterpret_as_f64(const v_float64x4& a) -{ return a; } -inline v_float64x4 v_reinterpret_as_f64(const v_float32x8& a) -{ return v_float64x4(_mm256_castps_pd(a.val)); } - -/* Recombine */ -/*#define OPENCV_HAL_IMPL_AVX_COMBINE(_Tpvec, perm) \ - inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \ - { return _Tpvec(perm(a.val, b.val, 0x20)); } \ - inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \ - { return _Tpvec(perm(a.val, b.val, 0x31)); } \ - inline void v_recombine(const _Tpvec& a, const _Tpvec& b, \ - _Tpvec& c, _Tpvec& d) \ - { c = v_combine_low(a, b); d = v_combine_high(a, b); } - -#define OPENCV_HAL_IMPL_AVX_UNPACKS(_Tpvec, suffix) \ - OPENCV_HAL_IMPL_AVX_COMBINE(_Tpvec, _mm256_permute2x128_si256) \ - inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, \ - _Tpvec& b0, _Tpvec& b1) \ - { \ - __m256i v0 = _v256_shuffle_odd_64(a0.val); \ - __m256i v1 = _v256_shuffle_odd_64(a1.val); \ - b0.val = _mm256_unpacklo_##suffix(v0, v1); \ - b1.val = _mm256_unpackhi_##suffix(v0, v1); \ - } - -OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint8x32, epi8) -OPENCV_HAL_IMPL_AVX_UNPACKS(v_int8x32, epi8) -OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint16x16, epi16) -OPENCV_HAL_IMPL_AVX_UNPACKS(v_int16x16, epi16) -OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint32x8, epi32) -OPENCV_HAL_IMPL_AVX_UNPACKS(v_int32x8, epi32) -OPENCV_HAL_IMPL_AVX_UNPACKS(v_uint64x4, epi64) -OPENCV_HAL_IMPL_AVX_UNPACKS(v_int64x4, epi64) -OPENCV_HAL_IMPL_AVX_COMBINE(v_float32x8, _mm256_permute2f128_ps) -OPENCV_HAL_IMPL_AVX_COMBINE(v_float64x4, _mm256_permute2f128_pd) - -inline void v_zip(const v_float32x8& a0, const v_float32x8& a1, v_float32x8& b0, v_float32x8& b1) -{ - __m256 v0 = _mm256_unpacklo_ps(a0.val, a1.val); - __m256 v1 = _mm256_unpackhi_ps(a0.val, a1.val); - v_recombine(v_float32x8(v0), v_float32x8(v1), b0, b1); -} - -inline void v_zip(const v_float64x4& a0, const v_float64x4& a1, v_float64x4& b0, v_float64x4& b1) -{ - __m256d v0 = _v_shuffle_odd_64(a0.val); - __m256d v1 = _v_shuffle_odd_64(a1.val); - b0.val = _mm256_unpacklo_pd(v0, v1); - b1.val = _mm256_unpackhi_pd(v0, v1); -}*/ - -//////////////// Variant Value reordering /////////////// - -// unpacks -#define OPENCV_HAL_IMPL_AVX_UNPACK(_Tpvec, suffix) \ - inline _Tpvec v256_unpacklo(const _Tpvec& a, const _Tpvec& b) \ - { return _Tpvec(_mm256_unpacklo_##suffix(a.val, b.val)); } \ - inline _Tpvec v256_unpackhi(const _Tpvec& a, const _Tpvec& b) \ - { return _Tpvec(_mm256_unpackhi_##suffix(a.val, b.val)); } - -OPENCV_HAL_IMPL_AVX_UNPACK(v_uint8x32, epi8) -OPENCV_HAL_IMPL_AVX_UNPACK(v_int8x32, epi8) -OPENCV_HAL_IMPL_AVX_UNPACK(v_uint16x16, epi16) -OPENCV_HAL_IMPL_AVX_UNPACK(v_int16x16, epi16) -OPENCV_HAL_IMPL_AVX_UNPACK(v_uint32x8, epi32) -OPENCV_HAL_IMPL_AVX_UNPACK(v_int32x8, epi32) -OPENCV_HAL_IMPL_AVX_UNPACK(v_uint64x4, epi64) -OPENCV_HAL_IMPL_AVX_UNPACK(v_int64x4, epi64) -OPENCV_HAL_IMPL_AVX_UNPACK(v_float32x8, ps) -OPENCV_HAL_IMPL_AVX_UNPACK(v_float64x4, pd) - -// blend -#define OPENCV_HAL_IMPL_AVX_BLEND(_Tpvec, suffix) \ - template \ - inline _Tpvec v256_blend(const _Tpvec& a, const _Tpvec& b) \ - { return _Tpvec(_mm256_blend_##suffix(a.val, b.val, m)); } - -OPENCV_HAL_IMPL_AVX_BLEND(v_uint16x16, epi16) -OPENCV_HAL_IMPL_AVX_BLEND(v_int16x16, epi16) -OPENCV_HAL_IMPL_AVX_BLEND(v_uint32x8, epi32) -OPENCV_HAL_IMPL_AVX_BLEND(v_int32x8, epi32) -OPENCV_HAL_IMPL_AVX_BLEND(v_float32x8, ps) -OPENCV_HAL_IMPL_AVX_BLEND(v_float64x4, pd) - -template -inline v_uint64x4 v256_blend(const v_uint64x4& a, const v_uint64x4& b) -{ - enum {M0 = m}; - enum {M1 = (M0 | (M0 << 2)) & 0x33}; - enum {M2 = (M1 | (M1 << 1)) & 0x55}; - enum {MM = M2 | (M2 << 1)}; - return v_uint64x4(_mm256_blend_epi32(a.val, b.val, MM)); -} -template -inline v_int64x4 v256_blend(const v_int64x4& a, const v_int64x4& b) -{ return v_int64x4(v256_blend(v_uint64x4(a.val), v_uint64x4(b.val)).val); } - -// shuffle -// todo: emulate 64bit -#define OPENCV_HAL_IMPL_AVX_SHUFFLE(_Tpvec, intrin) \ - template \ - inline _Tpvec v256_shuffle(const _Tpvec& a) \ - { return _Tpvec(_mm256_##intrin(a.val, m)); } - -OPENCV_HAL_IMPL_AVX_SHUFFLE(v_uint32x8, shuffle_epi32) -OPENCV_HAL_IMPL_AVX_SHUFFLE(v_int32x8, shuffle_epi32) -OPENCV_HAL_IMPL_AVX_SHUFFLE(v_float32x8, permute_ps) -OPENCV_HAL_IMPL_AVX_SHUFFLE(v_float64x4, permute_pd) - -template -inline void v256_zip(const _Tpvec& a, const _Tpvec& b, _Tpvec& ab0, _Tpvec& ab1) -{ - ab0 = v256_unpacklo(a, b); - ab1 = v256_unpackhi(a, b); -} - -template -inline _Tpvec v256_combine_diagonal(const _Tpvec& a, const _Tpvec& b) -{ return _Tpvec(_mm256_blend_epi32(a.val, b.val, 0xf0)); } - -inline v_float32x8 v256_combine_diagonal(const v_float32x8& a, const v_float32x8& b) -{ return v256_blend<0xf0>(a, b); } - -inline v_float64x4 v256_combine_diagonal(const v_float64x4& a, const v_float64x4& b) -{ return v256_blend<0xc>(a, b); } - -template -inline _Tpvec v256_alignr_128(const _Tpvec& a, const _Tpvec& b) -{ return v256_permute2x128<0x21>(a, b); } - -template -inline _Tpvec v256_alignr_64(const _Tpvec& a, const _Tpvec& b) -{ return _Tpvec(_mm256_alignr_epi8(a.val, b.val, 8)); } -inline v_float64x4 v256_alignr_64(const v_float64x4& a, const v_float64x4& b) -{ return v_float64x4(_mm256_shuffle_pd(b.val, a.val, _MM_SHUFFLE(0, 0, 1, 1))); } -// todo: emulate float32 - -template -inline _Tpvec v256_swap_halves(const _Tpvec& a) -{ return v256_permute2x128<1>(a, a); } - -template -inline _Tpvec v256_reverse_64(const _Tpvec& a) -{ return v256_permute4x64<_MM_SHUFFLE(0, 1, 2, 3)>(a); } - -// ZIP -#define OPENCV_HAL_IMPL_AVX_ZIP(_Tpvec) \ - inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \ - { return v256_permute2x128<0x20>(a, b); } \ - inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \ - { return v256_permute2x128<0x31>(a, b); } \ - inline void v_recombine(const _Tpvec& a, const _Tpvec& b, \ - _Tpvec& c, _Tpvec& d) \ - { \ - _Tpvec a1b0 = v256_alignr_128(a, b); \ - c = v256_combine_diagonal(a, a1b0); \ - d = v256_combine_diagonal(a1b0, b); \ - } \ - inline void v_zip(const _Tpvec& a, const _Tpvec& b, \ - _Tpvec& ab0, _Tpvec& ab1) \ - { \ - _Tpvec ab0ab2, ab1ab3; \ - v256_zip(a, b, ab0ab2, ab1ab3); \ - v_recombine(ab0ab2, ab1ab3, ab0, ab1); \ - } - -OPENCV_HAL_IMPL_AVX_ZIP(v_uint8x32) -OPENCV_HAL_IMPL_AVX_ZIP(v_int8x32) -OPENCV_HAL_IMPL_AVX_ZIP(v_uint16x16) -OPENCV_HAL_IMPL_AVX_ZIP(v_int16x16) -OPENCV_HAL_IMPL_AVX_ZIP(v_uint32x8) -OPENCV_HAL_IMPL_AVX_ZIP(v_int32x8) -OPENCV_HAL_IMPL_AVX_ZIP(v_uint64x4) -OPENCV_HAL_IMPL_AVX_ZIP(v_int64x4) -OPENCV_HAL_IMPL_AVX_ZIP(v_float32x8) -OPENCV_HAL_IMPL_AVX_ZIP(v_float64x4) - -////////// Arithmetic, bitwise and comparison operations ///////// - -/* Element-wise binary and unary operations */ - -/** Arithmetics **/ -#define OPENCV_HAL_IMPL_AVX_BIN_OP(bin_op, _Tpvec, intrin) \ - inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ - { return _Tpvec(intrin(a.val, b.val)); } \ - inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ - { a.val = intrin(a.val, b.val); return a; } - -OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint8x32, _mm256_adds_epu8) -OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint8x32, _mm256_subs_epu8) -OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int8x32, _mm256_adds_epi8) -OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int8x32, _mm256_subs_epi8) -OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint16x16, _mm256_adds_epu16) -OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint16x16, _mm256_subs_epu16) -OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int16x16, _mm256_adds_epi16) -OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int16x16, _mm256_subs_epi16) -OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint32x8, _mm256_add_epi32) -OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint32x8, _mm256_sub_epi32) -OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_uint32x8, _mm256_mullo_epi32) -OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int32x8, _mm256_add_epi32) -OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int32x8, _mm256_sub_epi32) -OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_int32x8, _mm256_mullo_epi32) -OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_uint64x4, _mm256_add_epi64) -OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_uint64x4, _mm256_sub_epi64) -OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_int64x4, _mm256_add_epi64) -OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_int64x4, _mm256_sub_epi64) - -OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_float32x8, _mm256_add_ps) -OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_float32x8, _mm256_sub_ps) -OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_float32x8, _mm256_mul_ps) -OPENCV_HAL_IMPL_AVX_BIN_OP(/, v_float32x8, _mm256_div_ps) -OPENCV_HAL_IMPL_AVX_BIN_OP(+, v_float64x4, _mm256_add_pd) -OPENCV_HAL_IMPL_AVX_BIN_OP(-, v_float64x4, _mm256_sub_pd) -OPENCV_HAL_IMPL_AVX_BIN_OP(*, v_float64x4, _mm256_mul_pd) -OPENCV_HAL_IMPL_AVX_BIN_OP(/, v_float64x4, _mm256_div_pd) - -// saturating multiply 8-bit, 16-bit -inline v_uint8x32 operator * (const v_uint8x32& a, const v_uint8x32& b) -{ - v_uint16x16 c, d; - v_mul_expand(a, b, c, d); - return v_pack(c, d); -} -inline v_int8x32 operator * (const v_int8x32& a, const v_int8x32& b) -{ - v_int16x16 c, d; - v_mul_expand(a, b, c, d); - return v_pack(c, d); -} -inline v_uint16x16 operator * (const v_uint16x16& a, const v_uint16x16& b) -{ - __m256i pl = _mm256_mullo_epi16(a.val, b.val); - __m256i ph = _mm256_mulhi_epu16(a.val, b.val); - __m256i p0 = _mm256_unpacklo_epi16(pl, ph); - __m256i p1 = _mm256_unpackhi_epi16(pl, ph); - return v_uint16x16(_v256_packs_epu32(p0, p1)); -} -inline v_int16x16 operator * (const v_int16x16& a, const v_int16x16& b) -{ - __m256i pl = _mm256_mullo_epi16(a.val, b.val); - __m256i ph = _mm256_mulhi_epi16(a.val, b.val); - __m256i p0 = _mm256_unpacklo_epi16(pl, ph); - __m256i p1 = _mm256_unpackhi_epi16(pl, ph); - return v_int16x16(_mm256_packs_epi32(p0, p1)); -} -inline v_uint8x32& operator *= (v_uint8x32& a, const v_uint8x32& b) -{ a = a * b; return a; } -inline v_int8x32& operator *= (v_int8x32& a, const v_int8x32& b) -{ a = a * b; return a; } -inline v_uint16x16& operator *= (v_uint16x16& a, const v_uint16x16& b) -{ a = a * b; return a; } -inline v_int16x16& operator *= (v_int16x16& a, const v_int16x16& b) -{ a = a * b; return a; } - -/** Non-saturating arithmetics **/ -#define OPENCV_HAL_IMPL_AVX_BIN_FUNC(func, _Tpvec, intrin) \ - inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ - { return _Tpvec(intrin(a.val, b.val)); } - -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_uint8x32, _mm256_add_epi8) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_int8x32, _mm256_add_epi8) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_uint16x16, _mm256_add_epi16) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_add_wrap, v_int16x16, _mm256_add_epi16) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_uint8x32, _mm256_sub_epi8) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_int8x32, _mm256_sub_epi8) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_uint16x16, _mm256_sub_epi16) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_sub_wrap, v_int16x16, _mm256_sub_epi16) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_mul_wrap, v_uint16x16, _mm256_mullo_epi16) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_mul_wrap, v_int16x16, _mm256_mullo_epi16) - -inline v_uint8x32 v_mul_wrap(const v_uint8x32& a, const v_uint8x32& b) -{ - __m256i ad = _mm256_srai_epi16(a.val, 8); - __m256i bd = _mm256_srai_epi16(b.val, 8); - __m256i p0 = _mm256_mullo_epi16(a.val, b.val); // even - __m256i p1 = _mm256_slli_epi16(_mm256_mullo_epi16(ad, bd), 8); // odd - - const __m256i b01 = _mm256_set1_epi32(0xFF00FF00); - return v_uint8x32(_mm256_blendv_epi8(p0, p1, b01)); -} -inline v_int8x32 v_mul_wrap(const v_int8x32& a, const v_int8x32& b) -{ - return v_reinterpret_as_s8(v_mul_wrap(v_reinterpret_as_u8(a), v_reinterpret_as_u8(b))); -} - -// Multiply and expand -inline void v_mul_expand(const v_uint8x32& a, const v_uint8x32& b, - v_uint16x16& c, v_uint16x16& d) -{ - v_uint16x16 a0, a1, b0, b1; - v_expand(a, a0, a1); - v_expand(b, b0, b1); - c = v_mul_wrap(a0, b0); - d = v_mul_wrap(a1, b1); -} - -inline void v_mul_expand(const v_int8x32& a, const v_int8x32& b, - v_int16x16& c, v_int16x16& d) -{ - v_int16x16 a0, a1, b0, b1; - v_expand(a, a0, a1); - v_expand(b, b0, b1); - c = v_mul_wrap(a0, b0); - d = v_mul_wrap(a1, b1); -} - -inline void v_mul_expand(const v_int16x16& a, const v_int16x16& b, - v_int32x8& c, v_int32x8& d) -{ - v_int16x16 vhi = v_int16x16(_mm256_mulhi_epi16(a.val, b.val)); - - v_int16x16 v0, v1; - v_zip(v_mul_wrap(a, b), vhi, v0, v1); - - c = v_reinterpret_as_s32(v0); - d = v_reinterpret_as_s32(v1); -} - -inline void v_mul_expand(const v_uint16x16& a, const v_uint16x16& b, - v_uint32x8& c, v_uint32x8& d) -{ - v_uint16x16 vhi = v_uint16x16(_mm256_mulhi_epu16(a.val, b.val)); - - v_uint16x16 v0, v1; - v_zip(v_mul_wrap(a, b), vhi, v0, v1); - - c = v_reinterpret_as_u32(v0); - d = v_reinterpret_as_u32(v1); -} - -inline void v_mul_expand(const v_uint32x8& a, const v_uint32x8& b, - v_uint64x4& c, v_uint64x4& d) -{ - __m256i v0 = _mm256_mul_epu32(a.val, b.val); - __m256i v1 = _mm256_mul_epu32(_mm256_srli_epi64(a.val, 32), _mm256_srli_epi64(b.val, 32)); - v_zip(v_uint64x4(v0), v_uint64x4(v1), c, d); -} - -inline v_int16x16 v_mul_hi(const v_int16x16& a, const v_int16x16& b) { return v_int16x16(_mm256_mulhi_epi16(a.val, b.val)); } -inline v_uint16x16 v_mul_hi(const v_uint16x16& a, const v_uint16x16& b) { return v_uint16x16(_mm256_mulhi_epu16(a.val, b.val)); } - -/** Bitwise shifts **/ -#define OPENCV_HAL_IMPL_AVX_SHIFT_OP(_Tpuvec, _Tpsvec, suffix, srai) \ - inline _Tpuvec operator << (const _Tpuvec& a, int imm) \ - { return _Tpuvec(_mm256_slli_##suffix(a.val, imm)); } \ - inline _Tpsvec operator << (const _Tpsvec& a, int imm) \ - { return _Tpsvec(_mm256_slli_##suffix(a.val, imm)); } \ - inline _Tpuvec operator >> (const _Tpuvec& a, int imm) \ - { return _Tpuvec(_mm256_srli_##suffix(a.val, imm)); } \ - inline _Tpsvec operator >> (const _Tpsvec& a, int imm) \ - { return _Tpsvec(srai(a.val, imm)); } \ - template \ - inline _Tpuvec v_shl(const _Tpuvec& a) \ - { return _Tpuvec(_mm256_slli_##suffix(a.val, imm)); } \ - template \ - inline _Tpsvec v_shl(const _Tpsvec& a) \ - { return _Tpsvec(_mm256_slli_##suffix(a.val, imm)); } \ - template \ - inline _Tpuvec v_shr(const _Tpuvec& a) \ - { return _Tpuvec(_mm256_srli_##suffix(a.val, imm)); } \ - template \ - inline _Tpsvec v_shr(const _Tpsvec& a) \ - { return _Tpsvec(srai(a.val, imm)); } - -OPENCV_HAL_IMPL_AVX_SHIFT_OP(v_uint16x16, v_int16x16, epi16, _mm256_srai_epi16) -OPENCV_HAL_IMPL_AVX_SHIFT_OP(v_uint32x8, v_int32x8, epi32, _mm256_srai_epi32) - -inline __m256i _mm256_srai_epi64xx(const __m256i a, int imm) -{ - __m256i d = _mm256_set1_epi64x((int64)1 << 63); - __m256i r = _mm256_srli_epi64(_mm256_add_epi64(a, d), imm); - return _mm256_sub_epi64(r, _mm256_srli_epi64(d, imm)); -} -OPENCV_HAL_IMPL_AVX_SHIFT_OP(v_uint64x4, v_int64x4, epi64, _mm256_srai_epi64xx) - - -/** Bitwise logic **/ -#define OPENCV_HAL_IMPL_AVX_LOGIC_OP(_Tpvec, suffix, not_const) \ - OPENCV_HAL_IMPL_AVX_BIN_OP(&, _Tpvec, _mm256_and_##suffix) \ - OPENCV_HAL_IMPL_AVX_BIN_OP(|, _Tpvec, _mm256_or_##suffix) \ - OPENCV_HAL_IMPL_AVX_BIN_OP(^, _Tpvec, _mm256_xor_##suffix) \ - inline _Tpvec operator ~ (const _Tpvec& a) \ - { return _Tpvec(_mm256_xor_##suffix(a.val, not_const)); } - -OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint8x32, si256, _mm256_set1_epi32(-1)) -OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int8x32, si256, _mm256_set1_epi32(-1)) -OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint16x16, si256, _mm256_set1_epi32(-1)) -OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int16x16, si256, _mm256_set1_epi32(-1)) -OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint32x8, si256, _mm256_set1_epi32(-1)) -OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int32x8, si256, _mm256_set1_epi32(-1)) -OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_uint64x4, si256, _mm256_set1_epi64x(-1)) -OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_int64x4, si256, _mm256_set1_epi64x(-1)) -OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_float32x8, ps, _mm256_castsi256_ps(_mm256_set1_epi32(-1))) -OPENCV_HAL_IMPL_AVX_LOGIC_OP(v_float64x4, pd, _mm256_castsi256_pd(_mm256_set1_epi32(-1))) - -/** Select **/ -#define OPENCV_HAL_IMPL_AVX_SELECT(_Tpvec, suffix) \ - inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ - { return _Tpvec(_mm256_blendv_##suffix(b.val, a.val, mask.val)); } - -OPENCV_HAL_IMPL_AVX_SELECT(v_uint8x32, epi8) -OPENCV_HAL_IMPL_AVX_SELECT(v_int8x32, epi8) -OPENCV_HAL_IMPL_AVX_SELECT(v_uint16x16, epi8) -OPENCV_HAL_IMPL_AVX_SELECT(v_int16x16, epi8) -OPENCV_HAL_IMPL_AVX_SELECT(v_uint32x8, epi8) -OPENCV_HAL_IMPL_AVX_SELECT(v_int32x8, epi8) -OPENCV_HAL_IMPL_AVX_SELECT(v_float32x8, ps) -OPENCV_HAL_IMPL_AVX_SELECT(v_float64x4, pd) - -/** Comparison **/ -#define OPENCV_HAL_IMPL_AVX_CMP_OP_OV(_Tpvec) \ - inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ - { return ~(a == b); } \ - inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ - { return b > a; } \ - inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ - { return ~(a < b); } \ - inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ - { return b >= a; } - -#define OPENCV_HAL_IMPL_AVX_CMP_OP_INT(_Tpuvec, _Tpsvec, suffix, sbit) \ - inline _Tpuvec operator == (const _Tpuvec& a, const _Tpuvec& b) \ - { return _Tpuvec(_mm256_cmpeq_##suffix(a.val, b.val)); } \ - inline _Tpuvec operator > (const _Tpuvec& a, const _Tpuvec& b) \ - { \ - __m256i smask = _mm256_set1_##suffix(sbit); \ - return _Tpuvec(_mm256_cmpgt_##suffix( \ - _mm256_xor_si256(a.val, smask), \ - _mm256_xor_si256(b.val, smask))); \ - } \ - inline _Tpsvec operator == (const _Tpsvec& a, const _Tpsvec& b) \ - { return _Tpsvec(_mm256_cmpeq_##suffix(a.val, b.val)); } \ - inline _Tpsvec operator > (const _Tpsvec& a, const _Tpsvec& b) \ - { return _Tpsvec(_mm256_cmpgt_##suffix(a.val, b.val)); } \ - OPENCV_HAL_IMPL_AVX_CMP_OP_OV(_Tpuvec) \ - OPENCV_HAL_IMPL_AVX_CMP_OP_OV(_Tpsvec) - -OPENCV_HAL_IMPL_AVX_CMP_OP_INT(v_uint8x32, v_int8x32, epi8, (char)-128) -OPENCV_HAL_IMPL_AVX_CMP_OP_INT(v_uint16x16, v_int16x16, epi16, (short)-32768) -OPENCV_HAL_IMPL_AVX_CMP_OP_INT(v_uint32x8, v_int32x8, epi32, (int)0x80000000) - -#define OPENCV_HAL_IMPL_AVX_CMP_OP_64BIT(_Tpvec) \ - inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ - { return _Tpvec(_mm256_cmpeq_epi64(a.val, b.val)); } \ - inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ - { return ~(a == b); } - -OPENCV_HAL_IMPL_AVX_CMP_OP_64BIT(v_uint64x4) -OPENCV_HAL_IMPL_AVX_CMP_OP_64BIT(v_int64x4) - -#define OPENCV_HAL_IMPL_AVX_CMP_FLT(bin_op, imm8, _Tpvec, suffix) \ - inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ - { return _Tpvec(_mm256_cmp_##suffix(a.val, b.val, imm8)); } - -#define OPENCV_HAL_IMPL_AVX_CMP_OP_FLT(_Tpvec, suffix) \ - OPENCV_HAL_IMPL_AVX_CMP_FLT(==, _CMP_EQ_OQ, _Tpvec, suffix) \ - OPENCV_HAL_IMPL_AVX_CMP_FLT(!=, _CMP_NEQ_OQ, _Tpvec, suffix) \ - OPENCV_HAL_IMPL_AVX_CMP_FLT(<, _CMP_LT_OQ, _Tpvec, suffix) \ - OPENCV_HAL_IMPL_AVX_CMP_FLT(>, _CMP_GT_OQ, _Tpvec, suffix) \ - OPENCV_HAL_IMPL_AVX_CMP_FLT(<=, _CMP_LE_OQ, _Tpvec, suffix) \ - OPENCV_HAL_IMPL_AVX_CMP_FLT(>=, _CMP_GE_OQ, _Tpvec, suffix) - -OPENCV_HAL_IMPL_AVX_CMP_OP_FLT(v_float32x8, ps) -OPENCV_HAL_IMPL_AVX_CMP_OP_FLT(v_float64x4, pd) - -inline v_float32x8 v_not_nan(const v_float32x8& a) -{ return v_float32x8(_mm256_cmp_ps(a.val, a.val, _CMP_ORD_Q)); } -inline v_float64x4 v_not_nan(const v_float64x4& a) -{ return v_float64x4(_mm256_cmp_pd(a.val, a.val, _CMP_ORD_Q)); } - -/** min/max **/ -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_uint8x32, _mm256_min_epu8) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_uint8x32, _mm256_max_epu8) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_int8x32, _mm256_min_epi8) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_int8x32, _mm256_max_epi8) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_uint16x16, _mm256_min_epu16) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_uint16x16, _mm256_max_epu16) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_int16x16, _mm256_min_epi16) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_int16x16, _mm256_max_epi16) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_uint32x8, _mm256_min_epu32) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_uint32x8, _mm256_max_epu32) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_int32x8, _mm256_min_epi32) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_int32x8, _mm256_max_epi32) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_float32x8, _mm256_min_ps) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_float32x8, _mm256_max_ps) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_min, v_float64x4, _mm256_min_pd) -OPENCV_HAL_IMPL_AVX_BIN_FUNC(v_max, v_float64x4, _mm256_max_pd) - -/** Rotate **/ -template -inline v_uint8x32 v_rotate_left(const v_uint8x32& a, const v_uint8x32& b) -{ - enum {IMM_R = (16 - imm) & 0xFF}; - enum {IMM_R2 = (32 - imm) & 0xFF}; - - if (imm == 0) return a; - if (imm == 32) return b; - if (imm > 32) return v_uint8x32(); - - __m256i swap = _mm256_permute2x128_si256(a.val, b.val, 0x03); - if (imm == 16) return v_uint8x32(swap); - if (imm < 16) return v_uint8x32(_mm256_alignr_epi8(a.val, swap, IMM_R)); - return v_uint8x32(_mm256_alignr_epi8(swap, b.val, IMM_R2)); // imm < 32 -} - -template -inline v_uint8x32 v_rotate_right(const v_uint8x32& a, const v_uint8x32& b) -{ - enum {IMM_L = (imm - 16) & 0xFF}; - - if (imm == 0) return a; - if (imm == 32) return b; - if (imm > 32) return v_uint8x32(); - - __m256i swap = _mm256_permute2x128_si256(a.val, b.val, 0x21); - if (imm == 16) return v_uint8x32(swap); - if (imm < 16) return v_uint8x32(_mm256_alignr_epi8(swap, a.val, imm)); - return v_uint8x32(_mm256_alignr_epi8(b.val, swap, IMM_L)); -} - -template -inline v_uint8x32 v_rotate_left(const v_uint8x32& a) -{ - enum {IMM_L = (imm - 16) & 0xFF}; - enum {IMM_R = (16 - imm) & 0xFF}; - - if (imm == 0) return a; - if (imm > 32) return v_uint8x32(); - - // ESAC control[3] ? [127:0] = 0 - __m256i swapz = _mm256_permute2x128_si256(a.val, a.val, _MM_SHUFFLE(0, 0, 2, 0)); - if (imm == 16) return v_uint8x32(swapz); - if (imm < 16) return v_uint8x32(_mm256_alignr_epi8(a.val, swapz, IMM_R)); - return v_uint8x32(_mm256_slli_si256(swapz, IMM_L)); -} - -template -inline v_uint8x32 v_rotate_right(const v_uint8x32& a) -{ - enum {IMM_L = (imm - 16) & 0xFF}; - - if (imm == 0) return a; - if (imm > 32) return v_uint8x32(); - - // ESAC control[3] ? [127:0] = 0 - __m256i swapz = _mm256_permute2x128_si256(a.val, a.val, _MM_SHUFFLE(2, 0, 0, 1)); - if (imm == 16) return v_uint8x32(swapz); - if (imm < 16) return v_uint8x32(_mm256_alignr_epi8(swapz, a.val, imm)); - return v_uint8x32(_mm256_srli_si256(swapz, IMM_L)); -} - -#define OPENCV_HAL_IMPL_AVX_ROTATE_CAST(intrin, _Tpvec, cast) \ - template \ - inline _Tpvec intrin(const _Tpvec& a, const _Tpvec& b) \ - { \ - enum {IMMxW = imm * sizeof(typename _Tpvec::lane_type)}; \ - v_uint8x32 ret = intrin(v_reinterpret_as_u8(a), \ - v_reinterpret_as_u8(b)); \ - return _Tpvec(cast(ret.val)); \ - } \ - template \ - inline _Tpvec intrin(const _Tpvec& a) \ - { \ - enum {IMMxW = imm * sizeof(typename _Tpvec::lane_type)}; \ - v_uint8x32 ret = intrin(v_reinterpret_as_u8(a)); \ - return _Tpvec(cast(ret.val)); \ - } - -#define OPENCV_HAL_IMPL_AVX_ROTATE(_Tpvec) \ - OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_left, _Tpvec, OPENCV_HAL_NOP) \ - OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_right, _Tpvec, OPENCV_HAL_NOP) - -OPENCV_HAL_IMPL_AVX_ROTATE(v_int8x32) -OPENCV_HAL_IMPL_AVX_ROTATE(v_uint16x16) -OPENCV_HAL_IMPL_AVX_ROTATE(v_int16x16) -OPENCV_HAL_IMPL_AVX_ROTATE(v_uint32x8) -OPENCV_HAL_IMPL_AVX_ROTATE(v_int32x8) -OPENCV_HAL_IMPL_AVX_ROTATE(v_uint64x4) -OPENCV_HAL_IMPL_AVX_ROTATE(v_int64x4) - -OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_left, v_float32x8, _mm256_castsi256_ps) -OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_right, v_float32x8, _mm256_castsi256_ps) -OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_left, v_float64x4, _mm256_castsi256_pd) -OPENCV_HAL_IMPL_AVX_ROTATE_CAST(v_rotate_right, v_float64x4, _mm256_castsi256_pd) - -/** Reverse **/ -inline v_uint8x32 v_reverse(const v_uint8x32 &a) -{ - static const __m256i perm = _mm256_setr_epi8( - 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, - 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0); - __m256i vec = _mm256_shuffle_epi8(a.val, perm); - return v_uint8x32(_mm256_permute2x128_si256(vec, vec, 1)); -} - -inline v_int8x32 v_reverse(const v_int8x32 &a) -{ return v_reinterpret_as_s8(v_reverse(v_reinterpret_as_u8(a))); } - -inline v_uint16x16 v_reverse(const v_uint16x16 &a) -{ - static const __m256i perm = _mm256_setr_epi8( - 14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1, - 14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1); - __m256i vec = _mm256_shuffle_epi8(a.val, perm); - return v_uint16x16(_mm256_permute2x128_si256(vec, vec, 1)); -} - -inline v_int16x16 v_reverse(const v_int16x16 &a) -{ return v_reinterpret_as_s16(v_reverse(v_reinterpret_as_u16(a))); } - -inline v_uint32x8 v_reverse(const v_uint32x8 &a) -{ - static const __m256i perm = _mm256_setr_epi32(7, 6, 5, 4, 3, 2, 1, 0); - return v_uint32x8(_mm256_permutevar8x32_epi32(a.val, perm)); -} - -inline v_int32x8 v_reverse(const v_int32x8 &a) -{ return v_reinterpret_as_s32(v_reverse(v_reinterpret_as_u32(a))); } - -inline v_float32x8 v_reverse(const v_float32x8 &a) -{ return v_reinterpret_as_f32(v_reverse(v_reinterpret_as_u32(a))); } - -inline v_uint64x4 v_reverse(const v_uint64x4 &a) -{ - return v_uint64x4(_mm256_permute4x64_epi64(a.val, _MM_SHUFFLE(0, 1, 2, 3))); -} - -inline v_int64x4 v_reverse(const v_int64x4 &a) -{ return v_reinterpret_as_s64(v_reverse(v_reinterpret_as_u64(a))); } - -inline v_float64x4 v_reverse(const v_float64x4 &a) -{ return v_reinterpret_as_f64(v_reverse(v_reinterpret_as_u64(a))); } - -////////// Reduce and mask ///////// - -/** Reduce **/ -inline unsigned v_reduce_sum(const v_uint8x32& a) -{ - __m256i half = _mm256_sad_epu8(a.val, _mm256_setzero_si256()); - __m128i quarter = _mm_add_epi32(_v256_extract_low(half), _v256_extract_high(half)); - return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(quarter, _mm_unpackhi_epi64(quarter, quarter))); -} -inline int v_reduce_sum(const v_int8x32& a) -{ - __m256i half = _mm256_sad_epu8(_mm256_xor_si256(a.val, _mm256_set1_epi8((schar)-128)), _mm256_setzero_si256()); - __m128i quarter = _mm_add_epi32(_v256_extract_low(half), _v256_extract_high(half)); - return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(quarter, _mm_unpackhi_epi64(quarter, quarter))) - 4096; -} -#define OPENCV_HAL_IMPL_AVX_REDUCE_32(_Tpvec, sctype, func, intrin) \ - inline sctype v_reduce_##func(const _Tpvec& a) \ - { \ - __m128i val = intrin(_v256_extract_low(a.val), _v256_extract_high(a.val)); \ - val = intrin(val, _mm_srli_si128(val,8)); \ - val = intrin(val, _mm_srli_si128(val,4)); \ - val = intrin(val, _mm_srli_si128(val,2)); \ - val = intrin(val, _mm_srli_si128(val,1)); \ - return (sctype)_mm_cvtsi128_si32(val); \ - } - -OPENCV_HAL_IMPL_AVX_REDUCE_32(v_uint8x32, uchar, min, _mm_min_epu8) -OPENCV_HAL_IMPL_AVX_REDUCE_32(v_int8x32, schar, min, _mm_min_epi8) -OPENCV_HAL_IMPL_AVX_REDUCE_32(v_uint8x32, uchar, max, _mm_max_epu8) -OPENCV_HAL_IMPL_AVX_REDUCE_32(v_int8x32, schar, max, _mm_max_epi8) - -#define OPENCV_HAL_IMPL_AVX_REDUCE_16(_Tpvec, sctype, func, intrin) \ - inline sctype v_reduce_##func(const _Tpvec& a) \ - { \ - __m128i v0 = _v256_extract_low(a.val); \ - __m128i v1 = _v256_extract_high(a.val); \ - v0 = intrin(v0, v1); \ - v0 = intrin(v0, _mm_srli_si128(v0, 8)); \ - v0 = intrin(v0, _mm_srli_si128(v0, 4)); \ - v0 = intrin(v0, _mm_srli_si128(v0, 2)); \ - return (sctype) _mm_cvtsi128_si32(v0); \ - } - -OPENCV_HAL_IMPL_AVX_REDUCE_16(v_uint16x16, ushort, min, _mm_min_epu16) -OPENCV_HAL_IMPL_AVX_REDUCE_16(v_int16x16, short, min, _mm_min_epi16) -OPENCV_HAL_IMPL_AVX_REDUCE_16(v_uint16x16, ushort, max, _mm_max_epu16) -OPENCV_HAL_IMPL_AVX_REDUCE_16(v_int16x16, short, max, _mm_max_epi16) - -#define OPENCV_HAL_IMPL_AVX_REDUCE_8(_Tpvec, sctype, func, intrin) \ - inline sctype v_reduce_##func(const _Tpvec& a) \ - { \ - __m128i v0 = _v256_extract_low(a.val); \ - __m128i v1 = _v256_extract_high(a.val); \ - v0 = intrin(v0, v1); \ - v0 = intrin(v0, _mm_srli_si128(v0, 8)); \ - v0 = intrin(v0, _mm_srli_si128(v0, 4)); \ - return (sctype) _mm_cvtsi128_si32(v0); \ - } - -OPENCV_HAL_IMPL_AVX_REDUCE_8(v_uint32x8, unsigned, min, _mm_min_epu32) -OPENCV_HAL_IMPL_AVX_REDUCE_8(v_int32x8, int, min, _mm_min_epi32) -OPENCV_HAL_IMPL_AVX_REDUCE_8(v_uint32x8, unsigned, max, _mm_max_epu32) -OPENCV_HAL_IMPL_AVX_REDUCE_8(v_int32x8, int, max, _mm_max_epi32) - -#define OPENCV_HAL_IMPL_AVX_REDUCE_FLT(func, intrin) \ - inline float v_reduce_##func(const v_float32x8& a) \ - { \ - __m128 v0 = _v256_extract_low(a.val); \ - __m128 v1 = _v256_extract_high(a.val); \ - v0 = intrin(v0, v1); \ - v0 = intrin(v0, _mm_permute_ps(v0, _MM_SHUFFLE(0, 0, 3, 2))); \ - v0 = intrin(v0, _mm_permute_ps(v0, _MM_SHUFFLE(0, 0, 0, 1))); \ - return _mm_cvtss_f32(v0); \ - } - -OPENCV_HAL_IMPL_AVX_REDUCE_FLT(min, _mm_min_ps) -OPENCV_HAL_IMPL_AVX_REDUCE_FLT(max, _mm_max_ps) - -inline int v_reduce_sum(const v_int32x8& a) -{ - __m256i s0 = _mm256_hadd_epi32(a.val, a.val); - s0 = _mm256_hadd_epi32(s0, s0); - - __m128i s1 = _v256_extract_high(s0); - s1 = _mm_add_epi32(_v256_extract_low(s0), s1); - - return _mm_cvtsi128_si32(s1); -} - -inline unsigned v_reduce_sum(const v_uint32x8& a) -{ return v_reduce_sum(v_reinterpret_as_s32(a)); } - -inline int v_reduce_sum(const v_int16x16& a) -{ return v_reduce_sum(v_expand_low(a) + v_expand_high(a)); } -inline unsigned v_reduce_sum(const v_uint16x16& a) -{ return v_reduce_sum(v_expand_low(a) + v_expand_high(a)); } - -inline float v_reduce_sum(const v_float32x8& a) -{ - __m256 s0 = _mm256_hadd_ps(a.val, a.val); - s0 = _mm256_hadd_ps(s0, s0); - - __m128 s1 = _v256_extract_high(s0); - s1 = _mm_add_ps(_v256_extract_low(s0), s1); - - return _mm_cvtss_f32(s1); -} - -inline uint64 v_reduce_sum(const v_uint64x4& a) -{ - uint64 CV_DECL_ALIGNED(32) idx[2]; - _mm_store_si128((__m128i*)idx, _mm_add_epi64(_v256_extract_low(a.val), _v256_extract_high(a.val))); - return idx[0] + idx[1]; -} -inline int64 v_reduce_sum(const v_int64x4& a) -{ - int64 CV_DECL_ALIGNED(32) idx[2]; - _mm_store_si128((__m128i*)idx, _mm_add_epi64(_v256_extract_low(a.val), _v256_extract_high(a.val))); - return idx[0] + idx[1]; -} -inline double v_reduce_sum(const v_float64x4& a) -{ - __m256d s0 = _mm256_hadd_pd(a.val, a.val); - return _mm_cvtsd_f64(_mm_add_pd(_v256_extract_low(s0), _v256_extract_high(s0))); -} - -inline v_float32x8 v_reduce_sum4(const v_float32x8& a, const v_float32x8& b, - const v_float32x8& c, const v_float32x8& d) -{ - __m256 ab = _mm256_hadd_ps(a.val, b.val); - __m256 cd = _mm256_hadd_ps(c.val, d.val); - return v_float32x8(_mm256_hadd_ps(ab, cd)); -} - -inline unsigned v_reduce_sad(const v_uint8x32& a, const v_uint8x32& b) -{ - __m256i half = _mm256_sad_epu8(a.val, b.val); - __m128i quarter = _mm_add_epi32(_v256_extract_low(half), _v256_extract_high(half)); - return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(quarter, _mm_unpackhi_epi64(quarter, quarter))); -} -inline unsigned v_reduce_sad(const v_int8x32& a, const v_int8x32& b) -{ - __m256i half = _mm256_set1_epi8(0x7f); - half = _mm256_sad_epu8(_mm256_add_epi8(a.val, half), _mm256_add_epi8(b.val, half)); - __m128i quarter = _mm_add_epi32(_v256_extract_low(half), _v256_extract_high(half)); - return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(quarter, _mm_unpackhi_epi64(quarter, quarter))); -} -inline unsigned v_reduce_sad(const v_uint16x16& a, const v_uint16x16& b) -{ - v_uint32x8 l, h; - v_expand(v_add_wrap(a - b, b - a), l, h); - return v_reduce_sum(l + h); -} -inline unsigned v_reduce_sad(const v_int16x16& a, const v_int16x16& b) -{ - v_uint32x8 l, h; - v_expand(v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))), l, h); - return v_reduce_sum(l + h); -} -inline unsigned v_reduce_sad(const v_uint32x8& a, const v_uint32x8& b) -{ - return v_reduce_sum(v_max(a, b) - v_min(a, b)); -} -inline unsigned v_reduce_sad(const v_int32x8& a, const v_int32x8& b) -{ - v_int32x8 m = a < b; - return v_reduce_sum(v_reinterpret_as_u32(((a - b) ^ m) - m)); -} -inline float v_reduce_sad(const v_float32x8& a, const v_float32x8& b) -{ - return v_reduce_sum((a - b) & v_float32x8(_mm256_castsi256_ps(_mm256_set1_epi32(0x7fffffff)))); -} - -/** Popcount **/ -inline v_uint8x32 v_popcount(const v_uint8x32& a) -{ - __m256i _popcnt_table = _mm256_setr_epi8(0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, - 0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4); - __m256i _popcnt_mask = _mm256_set1_epi8(0x0F); - return v_uint8x32(_mm256_add_epi8(_mm256_shuffle_epi8(_popcnt_table, _mm256_and_si256( a.val , _popcnt_mask)), - _mm256_shuffle_epi8(_popcnt_table, _mm256_and_si256(_mm256_srli_epi16(a.val, 4), _popcnt_mask)))); -} -inline v_uint16x16 v_popcount(const v_uint16x16& a) -{ - v_uint8x32 p = v_popcount(v_reinterpret_as_u8(a)); - p += v_rotate_right<1>(p); - return v_reinterpret_as_u16(p) & v256_setall_u16(0x00ff); -} -inline v_uint32x8 v_popcount(const v_uint32x8& a) -{ - v_uint8x32 p = v_popcount(v_reinterpret_as_u8(a)); - p += v_rotate_right<1>(p); - p += v_rotate_right<2>(p); - return v_reinterpret_as_u32(p) & v256_setall_u32(0x000000ff); -} -inline v_uint64x4 v_popcount(const v_uint64x4& a) -{ - return v_uint64x4(_mm256_sad_epu8(v_popcount(v_reinterpret_as_u8(a)).val, _mm256_setzero_si256())); -} -inline v_uint8x32 v_popcount(const v_int8x32& a) -{ return v_popcount(v_reinterpret_as_u8(a)); } -inline v_uint16x16 v_popcount(const v_int16x16& a) -{ return v_popcount(v_reinterpret_as_u16(a)); } -inline v_uint32x8 v_popcount(const v_int32x8& a) -{ return v_popcount(v_reinterpret_as_u32(a)); } -inline v_uint64x4 v_popcount(const v_int64x4& a) -{ return v_popcount(v_reinterpret_as_u64(a)); } - -/** Mask **/ -inline int v_signmask(const v_int8x32& a) -{ return _mm256_movemask_epi8(a.val); } -inline int v_signmask(const v_uint8x32& a) -{ return v_signmask(v_reinterpret_as_s8(a)); } - -inline int v_signmask(const v_int16x16& a) -{ return v_signmask(v_pack(a, a)) & 0xFFFF; } -inline int v_signmask(const v_uint16x16& a) -{ return v_signmask(v_reinterpret_as_s16(a)); } - -inline int v_signmask(const v_float32x8& a) -{ return _mm256_movemask_ps(a.val); } -inline int v_signmask(const v_float64x4& a) -{ return _mm256_movemask_pd(a.val); } - -inline int v_signmask(const v_int32x8& a) -{ return v_signmask(v_reinterpret_as_f32(a)); } -inline int v_signmask(const v_uint32x8& a) -{ return v_signmask(v_reinterpret_as_f32(a)); } - -inline int v_signmask(const v_int64x4& a) -{ return v_signmask(v_reinterpret_as_f64(a)); } -inline int v_signmask(const v_uint64x4& a) -{ return v_signmask(v_reinterpret_as_f64(a)); } - -inline int v_scan_forward(const v_int8x32& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))); } -inline int v_scan_forward(const v_uint8x32& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))); } -inline int v_scan_forward(const v_int16x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 2; } -inline int v_scan_forward(const v_uint16x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 2; } -inline int v_scan_forward(const v_int32x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; } -inline int v_scan_forward(const v_uint32x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; } -inline int v_scan_forward(const v_float32x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; } -inline int v_scan_forward(const v_int64x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; } -inline int v_scan_forward(const v_uint64x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; } -inline int v_scan_forward(const v_float64x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; } - -/** Checks **/ -#define OPENCV_HAL_IMPL_AVX_CHECK(_Tpvec, allmask) \ - inline bool v_check_all(const _Tpvec& a) { return v_signmask(a) == allmask; } \ - inline bool v_check_any(const _Tpvec& a) { return v_signmask(a) != 0; } -OPENCV_HAL_IMPL_AVX_CHECK(v_uint8x32, -1) -OPENCV_HAL_IMPL_AVX_CHECK(v_int8x32, -1) -OPENCV_HAL_IMPL_AVX_CHECK(v_uint32x8, 255) -OPENCV_HAL_IMPL_AVX_CHECK(v_int32x8, 255) -OPENCV_HAL_IMPL_AVX_CHECK(v_uint64x4, 15) -OPENCV_HAL_IMPL_AVX_CHECK(v_int64x4, 15) -OPENCV_HAL_IMPL_AVX_CHECK(v_float32x8, 255) -OPENCV_HAL_IMPL_AVX_CHECK(v_float64x4, 15) - -#define OPENCV_HAL_IMPL_AVX_CHECK_SHORT(_Tpvec) \ - inline bool v_check_all(const _Tpvec& a) { return (v_signmask(v_reinterpret_as_s8(a)) & 0xaaaaaaaa) == 0xaaaaaaaa; } \ - inline bool v_check_any(const _Tpvec& a) { return (v_signmask(v_reinterpret_as_s8(a)) & 0xaaaaaaaa) != 0; } -OPENCV_HAL_IMPL_AVX_CHECK_SHORT(v_uint16x16) -OPENCV_HAL_IMPL_AVX_CHECK_SHORT(v_int16x16) - -////////// Other math ///////// - -/** Some frequent operations **/ -#define OPENCV_HAL_IMPL_AVX_MULADD(_Tpvec, suffix) \ - inline _Tpvec v_fma(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ - { return _Tpvec(_mm256_fmadd_##suffix(a.val, b.val, c.val)); } \ - inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ - { return _Tpvec(_mm256_fmadd_##suffix(a.val, b.val, c.val)); } \ - inline _Tpvec v_sqrt(const _Tpvec& x) \ - { return _Tpvec(_mm256_sqrt_##suffix(x.val)); } \ - inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \ - { return v_fma(a, a, b * b); } \ - inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \ - { return v_sqrt(v_fma(a, a, b*b)); } - -OPENCV_HAL_IMPL_AVX_MULADD(v_float32x8, ps) -OPENCV_HAL_IMPL_AVX_MULADD(v_float64x4, pd) - -inline v_int32x8 v_fma(const v_int32x8& a, const v_int32x8& b, const v_int32x8& c) -{ - return a * b + c; -} - -inline v_int32x8 v_muladd(const v_int32x8& a, const v_int32x8& b, const v_int32x8& c) -{ - return v_fma(a, b, c); -} - -inline v_float32x8 v_invsqrt(const v_float32x8& x) -{ - v_float32x8 half = x * v256_setall_f32(0.5); - v_float32x8 t = v_float32x8(_mm256_rsqrt_ps(x.val)); - // todo: _mm256_fnmsub_ps - t *= v256_setall_f32(1.5) - ((t * t) * half); - return t; -} - -inline v_float64x4 v_invsqrt(const v_float64x4& x) -{ - return v256_setall_f64(1.) / v_sqrt(x); -} - -/** Absolute values **/ -#define OPENCV_HAL_IMPL_AVX_ABS(_Tpvec, suffix) \ - inline v_u##_Tpvec v_abs(const v_##_Tpvec& x) \ - { return v_u##_Tpvec(_mm256_abs_##suffix(x.val)); } - -OPENCV_HAL_IMPL_AVX_ABS(int8x32, epi8) -OPENCV_HAL_IMPL_AVX_ABS(int16x16, epi16) -OPENCV_HAL_IMPL_AVX_ABS(int32x8, epi32) - -inline v_float32x8 v_abs(const v_float32x8& x) -{ return x & v_float32x8(_mm256_castsi256_ps(_mm256_set1_epi32(0x7fffffff))); } -inline v_float64x4 v_abs(const v_float64x4& x) -{ return x & v_float64x4(_mm256_castsi256_pd(_mm256_srli_epi64(_mm256_set1_epi64x(-1), 1))); } - -/** Absolute difference **/ -inline v_uint8x32 v_absdiff(const v_uint8x32& a, const v_uint8x32& b) -{ return v_add_wrap(a - b, b - a); } -inline v_uint16x16 v_absdiff(const v_uint16x16& a, const v_uint16x16& b) -{ return v_add_wrap(a - b, b - a); } -inline v_uint32x8 v_absdiff(const v_uint32x8& a, const v_uint32x8& b) -{ return v_max(a, b) - v_min(a, b); } - -inline v_uint8x32 v_absdiff(const v_int8x32& a, const v_int8x32& b) -{ - v_int8x32 d = v_sub_wrap(a, b); - v_int8x32 m = a < b; - return v_reinterpret_as_u8(v_sub_wrap(d ^ m, m)); -} - -inline v_uint16x16 v_absdiff(const v_int16x16& a, const v_int16x16& b) -{ return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); } - -inline v_uint32x8 v_absdiff(const v_int32x8& a, const v_int32x8& b) -{ - v_int32x8 d = a - b; - v_int32x8 m = a < b; - return v_reinterpret_as_u32((d ^ m) - m); -} - -inline v_float32x8 v_absdiff(const v_float32x8& a, const v_float32x8& b) -{ return v_abs(a - b); } - -inline v_float64x4 v_absdiff(const v_float64x4& a, const v_float64x4& b) -{ return v_abs(a - b); } - -/** Saturating absolute difference **/ -inline v_int8x32 v_absdiffs(const v_int8x32& a, const v_int8x32& b) -{ - v_int8x32 d = a - b; - v_int8x32 m = a < b; - return (d ^ m) - m; -} -inline v_int16x16 v_absdiffs(const v_int16x16& a, const v_int16x16& b) -{ return v_max(a, b) - v_min(a, b); } - -////////// Conversions ///////// - -/** Rounding **/ -inline v_int32x8 v_round(const v_float32x8& a) -{ return v_int32x8(_mm256_cvtps_epi32(a.val)); } - -inline v_int32x8 v_round(const v_float64x4& a) -{ return v_int32x8(_mm256_castsi128_si256(_mm256_cvtpd_epi32(a.val))); } - -inline v_int32x8 v_round(const v_float64x4& a, const v_float64x4& b) -{ - __m128i ai = _mm256_cvtpd_epi32(a.val), bi = _mm256_cvtpd_epi32(b.val); - return v_int32x8(_v256_combine(ai, bi)); -} - -inline v_int32x8 v_trunc(const v_float32x8& a) -{ return v_int32x8(_mm256_cvttps_epi32(a.val)); } - -inline v_int32x8 v_trunc(const v_float64x4& a) -{ return v_int32x8(_mm256_castsi128_si256(_mm256_cvttpd_epi32(a.val))); } - -inline v_int32x8 v_floor(const v_float32x8& a) -{ return v_int32x8(_mm256_cvttps_epi32(_mm256_floor_ps(a.val))); } - -inline v_int32x8 v_floor(const v_float64x4& a) -{ return v_trunc(v_float64x4(_mm256_floor_pd(a.val))); } - -inline v_int32x8 v_ceil(const v_float32x8& a) -{ return v_int32x8(_mm256_cvttps_epi32(_mm256_ceil_ps(a.val))); } - -inline v_int32x8 v_ceil(const v_float64x4& a) -{ return v_trunc(v_float64x4(_mm256_ceil_pd(a.val))); } - -/** To float **/ -inline v_float32x8 v_cvt_f32(const v_int32x8& a) -{ return v_float32x8(_mm256_cvtepi32_ps(a.val)); } - -inline v_float32x8 v_cvt_f32(const v_float64x4& a) -{ return v_float32x8(_mm256_castps128_ps256(_mm256_cvtpd_ps(a.val))); } - -inline v_float32x8 v_cvt_f32(const v_float64x4& a, const v_float64x4& b) -{ - __m128 af = _mm256_cvtpd_ps(a.val), bf = _mm256_cvtpd_ps(b.val); - return v_float32x8(_v256_combine(af, bf)); -} - -inline v_float64x4 v_cvt_f64(const v_int32x8& a) -{ return v_float64x4(_mm256_cvtepi32_pd(_v256_extract_low(a.val))); } - -inline v_float64x4 v_cvt_f64_high(const v_int32x8& a) -{ return v_float64x4(_mm256_cvtepi32_pd(_v256_extract_high(a.val))); } - -inline v_float64x4 v_cvt_f64(const v_float32x8& a) -{ return v_float64x4(_mm256_cvtps_pd(_v256_extract_low(a.val))); } - -inline v_float64x4 v_cvt_f64_high(const v_float32x8& a) -{ return v_float64x4(_mm256_cvtps_pd(_v256_extract_high(a.val))); } - -// from (Mysticial and wim) https://stackoverflow.com/q/41144668 -inline v_float64x4 v_cvt_f64(const v_int64x4& v) -{ - // constants encoded as floating-point - __m256i magic_i_lo = _mm256_set1_epi64x(0x4330000000000000); // 2^52 - __m256i magic_i_hi32 = _mm256_set1_epi64x(0x4530000080000000); // 2^84 + 2^63 - __m256i magic_i_all = _mm256_set1_epi64x(0x4530000080100000); // 2^84 + 2^63 + 2^52 - __m256d magic_d_all = _mm256_castsi256_pd(magic_i_all); - - // Blend the 32 lowest significant bits of v with magic_int_lo - __m256i v_lo = _mm256_blend_epi32(magic_i_lo, v.val, 0x55); - // Extract the 32 most significant bits of v - __m256i v_hi = _mm256_srli_epi64(v.val, 32); - // Flip the msb of v_hi and blend with 0x45300000 - v_hi = _mm256_xor_si256(v_hi, magic_i_hi32); - // Compute in double precision - __m256d v_hi_dbl = _mm256_sub_pd(_mm256_castsi256_pd(v_hi), magic_d_all); - // (v_hi - magic_d_all) + v_lo Do not assume associativity of floating point addition - __m256d result = _mm256_add_pd(v_hi_dbl, _mm256_castsi256_pd(v_lo)); - return v_float64x4(result); -} - -////////////// Lookup table access //////////////////// - -inline v_int8x32 v256_lut(const schar* tab, const int* idx) -{ - return v_int8x32(_mm256_setr_epi8(tab[idx[ 0]], tab[idx[ 1]], tab[idx[ 2]], tab[idx[ 3]], tab[idx[ 4]], tab[idx[ 5]], tab[idx[ 6]], tab[idx[ 7]], - tab[idx[ 8]], tab[idx[ 9]], tab[idx[10]], tab[idx[11]], tab[idx[12]], tab[idx[13]], tab[idx[14]], tab[idx[15]], - tab[idx[16]], tab[idx[17]], tab[idx[18]], tab[idx[19]], tab[idx[20]], tab[idx[21]], tab[idx[22]], tab[idx[23]], - tab[idx[24]], tab[idx[25]], tab[idx[26]], tab[idx[27]], tab[idx[28]], tab[idx[29]], tab[idx[30]], tab[idx[31]])); -} -inline v_int8x32 v256_lut_pairs(const schar* tab, const int* idx) -{ - return v_int8x32(_mm256_setr_epi16(*(const short*)(tab + idx[ 0]), *(const short*)(tab + idx[ 1]), *(const short*)(tab + idx[ 2]), *(const short*)(tab + idx[ 3]), - *(const short*)(tab + idx[ 4]), *(const short*)(tab + idx[ 5]), *(const short*)(tab + idx[ 6]), *(const short*)(tab + idx[ 7]), - *(const short*)(tab + idx[ 8]), *(const short*)(tab + idx[ 9]), *(const short*)(tab + idx[10]), *(const short*)(tab + idx[11]), - *(const short*)(tab + idx[12]), *(const short*)(tab + idx[13]), *(const short*)(tab + idx[14]), *(const short*)(tab + idx[15]))); -} -inline v_int8x32 v256_lut_quads(const schar* tab, const int* idx) -{ - return v_int8x32(_mm256_i32gather_epi32((const int*)tab, _mm256_loadu_si256((const __m256i*)idx), 1)); -} -inline v_uint8x32 v256_lut(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v256_lut((const schar *)tab, idx)); } -inline v_uint8x32 v256_lut_pairs(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v256_lut_pairs((const schar *)tab, idx)); } -inline v_uint8x32 v256_lut_quads(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v256_lut_quads((const schar *)tab, idx)); } - -inline v_int16x16 v256_lut(const short* tab, const int* idx) -{ - return v_int16x16(_mm256_setr_epi16(tab[idx[0]], tab[idx[1]], tab[idx[ 2]], tab[idx[ 3]], tab[idx[ 4]], tab[idx[ 5]], tab[idx[ 6]], tab[idx[ 7]], - tab[idx[8]], tab[idx[9]], tab[idx[10]], tab[idx[11]], tab[idx[12]], tab[idx[13]], tab[idx[14]], tab[idx[15]])); -} -inline v_int16x16 v256_lut_pairs(const short* tab, const int* idx) -{ - return v_int16x16(_mm256_i32gather_epi32((const int*)tab, _mm256_loadu_si256((const __m256i*)idx), 2)); -} -inline v_int16x16 v256_lut_quads(const short* tab, const int* idx) -{ -#if defined(__GNUC__) - return v_int16x16(_mm256_i32gather_epi64((const long long int*)tab, _mm_loadu_si128((const __m128i*)idx), 2));//Looks like intrinsic has wrong definition -#else - return v_int16x16(_mm256_i32gather_epi64((const int64*)tab, _mm_loadu_si128((const __m128i*)idx), 2)); -#endif -} -inline v_uint16x16 v256_lut(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v256_lut((const short *)tab, idx)); } -inline v_uint16x16 v256_lut_pairs(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v256_lut_pairs((const short *)tab, idx)); } -inline v_uint16x16 v256_lut_quads(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v256_lut_quads((const short *)tab, idx)); } - -inline v_int32x8 v256_lut(const int* tab, const int* idx) -{ - return v_int32x8(_mm256_i32gather_epi32(tab, _mm256_loadu_si256((const __m256i*)idx), 4)); -} -inline v_int32x8 v256_lut_pairs(const int* tab, const int* idx) -{ -#if defined(__GNUC__) - return v_int32x8(_mm256_i32gather_epi64((const long long int*)tab, _mm_loadu_si128((const __m128i*)idx), 4)); -#else - return v_int32x8(_mm256_i32gather_epi64((const int64*)tab, _mm_loadu_si128((const __m128i*)idx), 4)); -#endif -} -inline v_int32x8 v256_lut_quads(const int* tab, const int* idx) -{ - return v_int32x8(_v256_combine(_mm_loadu_si128((const __m128i*)(tab + idx[0])), _mm_loadu_si128((const __m128i*)(tab + idx[1])))); -} -inline v_uint32x8 v256_lut(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v256_lut((const int *)tab, idx)); } -inline v_uint32x8 v256_lut_pairs(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v256_lut_pairs((const int *)tab, idx)); } -inline v_uint32x8 v256_lut_quads(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v256_lut_quads((const int *)tab, idx)); } - -inline v_int64x4 v256_lut(const int64* tab, const int* idx) -{ -#if defined(__GNUC__) - return v_int64x4(_mm256_i32gather_epi64((const long long int*)tab, _mm_loadu_si128((const __m128i*)idx), 8)); -#else - return v_int64x4(_mm256_i32gather_epi64(tab, _mm_loadu_si128((const __m128i*)idx), 8)); -#endif -} -inline v_int64x4 v256_lut_pairs(const int64* tab, const int* idx) -{ - return v_int64x4(_v256_combine(_mm_loadu_si128((const __m128i*)(tab + idx[0])), _mm_loadu_si128((const __m128i*)(tab + idx[1])))); -} -inline v_uint64x4 v256_lut(const uint64* tab, const int* idx) { return v_reinterpret_as_u64(v256_lut((const int64 *)tab, idx)); } -inline v_uint64x4 v256_lut_pairs(const uint64* tab, const int* idx) { return v_reinterpret_as_u64(v256_lut_pairs((const int64 *)tab, idx)); } - -inline v_float32x8 v256_lut(const float* tab, const int* idx) -{ - return v_float32x8(_mm256_i32gather_ps(tab, _mm256_loadu_si256((const __m256i*)idx), 4)); -} -inline v_float32x8 v256_lut_pairs(const float* tab, const int* idx) { return v_reinterpret_as_f32(v256_lut_pairs((const int *)tab, idx)); } -inline v_float32x8 v256_lut_quads(const float* tab, const int* idx) { return v_reinterpret_as_f32(v256_lut_quads((const int *)tab, idx)); } - -inline v_float64x4 v256_lut(const double* tab, const int* idx) -{ - return v_float64x4(_mm256_i32gather_pd(tab, _mm_loadu_si128((const __m128i*)idx), 8)); -} -inline v_float64x4 v256_lut_pairs(const double* tab, const int* idx) { return v_float64x4(_v256_combine(_mm_loadu_pd(tab + idx[0]), _mm_loadu_pd(tab + idx[1]))); } - -inline v_int32x8 v_lut(const int* tab, const v_int32x8& idxvec) -{ - return v_int32x8(_mm256_i32gather_epi32(tab, idxvec.val, 4)); -} - -inline v_uint32x8 v_lut(const unsigned* tab, const v_int32x8& idxvec) -{ - return v_reinterpret_as_u32(v_lut((const int *)tab, idxvec)); -} - -inline v_float32x8 v_lut(const float* tab, const v_int32x8& idxvec) -{ - return v_float32x8(_mm256_i32gather_ps(tab, idxvec.val, 4)); -} - -inline v_float64x4 v_lut(const double* tab, const v_int32x8& idxvec) -{ - return v_float64x4(_mm256_i32gather_pd(tab, _mm256_castsi256_si128(idxvec.val), 8)); -} - -inline void v_lut_deinterleave(const float* tab, const v_int32x8& idxvec, v_float32x8& x, v_float32x8& y) -{ - int CV_DECL_ALIGNED(32) idx[8]; - v_store_aligned(idx, idxvec); - __m128 z = _mm_setzero_ps(); - __m128 xy01, xy45, xy23, xy67; - xy01 = _mm_loadl_pi(z, (const __m64*)(tab + idx[0])); - xy01 = _mm_loadh_pi(xy01, (const __m64*)(tab + idx[1])); - xy45 = _mm_loadl_pi(z, (const __m64*)(tab + idx[4])); - xy45 = _mm_loadh_pi(xy45, (const __m64*)(tab + idx[5])); - __m256 xy0145 = _v256_combine(xy01, xy45); - xy23 = _mm_loadl_pi(z, (const __m64*)(tab + idx[2])); - xy23 = _mm_loadh_pi(xy23, (const __m64*)(tab + idx[3])); - xy67 = _mm_loadl_pi(z, (const __m64*)(tab + idx[6])); - xy67 = _mm_loadh_pi(xy67, (const __m64*)(tab + idx[7])); - __m256 xy2367 = _v256_combine(xy23, xy67); - - __m256 xxyy0145 = _mm256_unpacklo_ps(xy0145, xy2367); - __m256 xxyy2367 = _mm256_unpackhi_ps(xy0145, xy2367); - - x = v_float32x8(_mm256_unpacklo_ps(xxyy0145, xxyy2367)); - y = v_float32x8(_mm256_unpackhi_ps(xxyy0145, xxyy2367)); -} - -inline void v_lut_deinterleave(const double* tab, const v_int32x8& idxvec, v_float64x4& x, v_float64x4& y) -{ - int CV_DECL_ALIGNED(32) idx[4]; - v_store_low(idx, idxvec); - __m128d xy0 = _mm_loadu_pd(tab + idx[0]); - __m128d xy2 = _mm_loadu_pd(tab + idx[2]); - __m128d xy1 = _mm_loadu_pd(tab + idx[1]); - __m128d xy3 = _mm_loadu_pd(tab + idx[3]); - __m256d xy02 = _v256_combine(xy0, xy2); - __m256d xy13 = _v256_combine(xy1, xy3); - - x = v_float64x4(_mm256_unpacklo_pd(xy02, xy13)); - y = v_float64x4(_mm256_unpackhi_pd(xy02, xy13)); -} - -inline v_int8x32 v_interleave_pairs(const v_int8x32& vec) -{ - return v_int8x32(_mm256_shuffle_epi8(vec.val, _mm256_set_epi64x(0x0f0d0e0c0b090a08, 0x0705060403010200, 0x0f0d0e0c0b090a08, 0x0705060403010200))); -} -inline v_uint8x32 v_interleave_pairs(const v_uint8x32& vec) { return v_reinterpret_as_u8(v_interleave_pairs(v_reinterpret_as_s8(vec))); } -inline v_int8x32 v_interleave_quads(const v_int8x32& vec) -{ - return v_int8x32(_mm256_shuffle_epi8(vec.val, _mm256_set_epi64x(0x0f0b0e0a0d090c08, 0x0703060205010400, 0x0f0b0e0a0d090c08, 0x0703060205010400))); -} -inline v_uint8x32 v_interleave_quads(const v_uint8x32& vec) { return v_reinterpret_as_u8(v_interleave_quads(v_reinterpret_as_s8(vec))); } - -inline v_int16x16 v_interleave_pairs(const v_int16x16& vec) -{ - return v_int16x16(_mm256_shuffle_epi8(vec.val, _mm256_set_epi64x(0x0f0e0b0a0d0c0908, 0x0706030205040100, 0x0f0e0b0a0d0c0908, 0x0706030205040100))); -} -inline v_uint16x16 v_interleave_pairs(const v_uint16x16& vec) { return v_reinterpret_as_u16(v_interleave_pairs(v_reinterpret_as_s16(vec))); } -inline v_int16x16 v_interleave_quads(const v_int16x16& vec) -{ - return v_int16x16(_mm256_shuffle_epi8(vec.val, _mm256_set_epi64x(0x0f0e07060d0c0504, 0x0b0a030209080100, 0x0f0e07060d0c0504, 0x0b0a030209080100))); -} -inline v_uint16x16 v_interleave_quads(const v_uint16x16& vec) { return v_reinterpret_as_u16(v_interleave_quads(v_reinterpret_as_s16(vec))); } - -inline v_int32x8 v_interleave_pairs(const v_int32x8& vec) -{ - return v_int32x8(_mm256_shuffle_epi32(vec.val, _MM_SHUFFLE(3, 1, 2, 0))); -} -inline v_uint32x8 v_interleave_pairs(const v_uint32x8& vec) { return v_reinterpret_as_u32(v_interleave_pairs(v_reinterpret_as_s32(vec))); } -inline v_float32x8 v_interleave_pairs(const v_float32x8& vec) { return v_reinterpret_as_f32(v_interleave_pairs(v_reinterpret_as_s32(vec))); } - -inline v_int8x32 v_pack_triplets(const v_int8x32& vec) -{ - return v_int8x32(_mm256_permutevar8x32_epi32(_mm256_shuffle_epi8(vec.val, _mm256_broadcastsi128_si256(_mm_set_epi64x(0xffffff0f0e0d0c0a, 0x0908060504020100))), - _mm256_set_epi64x(0x0000000700000007, 0x0000000600000005, 0x0000000400000002, 0x0000000100000000))); -} -inline v_uint8x32 v_pack_triplets(const v_uint8x32& vec) { return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec))); } - -inline v_int16x16 v_pack_triplets(const v_int16x16& vec) -{ - return v_int16x16(_mm256_permutevar8x32_epi32(_mm256_shuffle_epi8(vec.val, _mm256_broadcastsi128_si256(_mm_set_epi64x(0xffff0f0e0d0c0b0a, 0x0908050403020100))), - _mm256_set_epi64x(0x0000000700000007, 0x0000000600000005, 0x0000000400000002, 0x0000000100000000))); -} -inline v_uint16x16 v_pack_triplets(const v_uint16x16& vec) { return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec))); } - -inline v_int32x8 v_pack_triplets(const v_int32x8& vec) -{ - return v_int32x8(_mm256_permutevar8x32_epi32(vec.val, _mm256_set_epi64x(0x0000000700000007, 0x0000000600000005, 0x0000000400000002, 0x0000000100000000))); -} -inline v_uint32x8 v_pack_triplets(const v_uint32x8& vec) { return v_reinterpret_as_u32(v_pack_triplets(v_reinterpret_as_s32(vec))); } -inline v_float32x8 v_pack_triplets(const v_float32x8& vec) -{ - return v_float32x8(_mm256_permutevar8x32_ps(vec.val, _mm256_set_epi64x(0x0000000700000007, 0x0000000600000005, 0x0000000400000002, 0x0000000100000000))); -} - -////////// Matrix operations ///////// - -//////// Dot Product //////// - -// 16 >> 32 -inline v_int32x8 v_dotprod(const v_int16x16& a, const v_int16x16& b) -{ return v_int32x8(_mm256_madd_epi16(a.val, b.val)); } -inline v_int32x8 v_dotprod(const v_int16x16& a, const v_int16x16& b, const v_int32x8& c) -{ return v_dotprod(a, b) + c; } - -// 32 >> 64 -inline v_int64x4 v_dotprod(const v_int32x8& a, const v_int32x8& b) -{ - __m256i even = _mm256_mul_epi32(a.val, b.val); - __m256i odd = _mm256_mul_epi32(_mm256_srli_epi64(a.val, 32), _mm256_srli_epi64(b.val, 32)); - return v_int64x4(_mm256_add_epi64(even, odd)); -} -inline v_int64x4 v_dotprod(const v_int32x8& a, const v_int32x8& b, const v_int64x4& c) -{ return v_dotprod(a, b) + c; } - -// 8 >> 32 -inline v_uint32x8 v_dotprod_expand(const v_uint8x32& a, const v_uint8x32& b) -{ - __m256i even_m = _mm256_set1_epi32(0xFF00FF00); - __m256i even_a = _mm256_blendv_epi8(a.val, _mm256_setzero_si256(), even_m); - __m256i odd_a = _mm256_srli_epi16(a.val, 8); - - __m256i even_b = _mm256_blendv_epi8(b.val, _mm256_setzero_si256(), even_m); - __m256i odd_b = _mm256_srli_epi16(b.val, 8); - - __m256i prod0 = _mm256_madd_epi16(even_a, even_b); - __m256i prod1 = _mm256_madd_epi16(odd_a, odd_b); - return v_uint32x8(_mm256_add_epi32(prod0, prod1)); -} -inline v_uint32x8 v_dotprod_expand(const v_uint8x32& a, const v_uint8x32& b, const v_uint32x8& c) -{ return v_dotprod_expand(a, b) + c; } - -inline v_int32x8 v_dotprod_expand(const v_int8x32& a, const v_int8x32& b) -{ - __m256i even_a = _mm256_srai_epi16(_mm256_bslli_epi128(a.val, 1), 8); - __m256i odd_a = _mm256_srai_epi16(a.val, 8); - - __m256i even_b = _mm256_srai_epi16(_mm256_bslli_epi128(b.val, 1), 8); - __m256i odd_b = _mm256_srai_epi16(b.val, 8); - - __m256i prod0 = _mm256_madd_epi16(even_a, even_b); - __m256i prod1 = _mm256_madd_epi16(odd_a, odd_b); - return v_int32x8(_mm256_add_epi32(prod0, prod1)); -} -inline v_int32x8 v_dotprod_expand(const v_int8x32& a, const v_int8x32& b, const v_int32x8& c) -{ return v_dotprod_expand(a, b) + c; } - -// 16 >> 64 -inline v_uint64x4 v_dotprod_expand(const v_uint16x16& a, const v_uint16x16& b) -{ - __m256i mullo = _mm256_mullo_epi16(a.val, b.val); - __m256i mulhi = _mm256_mulhi_epu16(a.val, b.val); - __m256i mul0 = _mm256_unpacklo_epi16(mullo, mulhi); - __m256i mul1 = _mm256_unpackhi_epi16(mullo, mulhi); - - __m256i p02 = _mm256_blend_epi32(mul0, _mm256_setzero_si256(), 0xAA); - __m256i p13 = _mm256_srli_epi64(mul0, 32); - __m256i p46 = _mm256_blend_epi32(mul1, _mm256_setzero_si256(), 0xAA); - __m256i p57 = _mm256_srli_epi64(mul1, 32); - - __m256i p15_ = _mm256_add_epi64(p02, p13); - __m256i p9d_ = _mm256_add_epi64(p46, p57); - - return v_uint64x4(_mm256_add_epi64( - _mm256_unpacklo_epi64(p15_, p9d_), - _mm256_unpackhi_epi64(p15_, p9d_) - )); -} -inline v_uint64x4 v_dotprod_expand(const v_uint16x16& a, const v_uint16x16& b, const v_uint64x4& c) -{ return v_dotprod_expand(a, b) + c; } - -inline v_int64x4 v_dotprod_expand(const v_int16x16& a, const v_int16x16& b) -{ - __m256i prod = _mm256_madd_epi16(a.val, b.val); - __m256i sign = _mm256_srai_epi32(prod, 31); - - __m256i lo = _mm256_unpacklo_epi32(prod, sign); - __m256i hi = _mm256_unpackhi_epi32(prod, sign); - - return v_int64x4(_mm256_add_epi64( - _mm256_unpacklo_epi64(lo, hi), - _mm256_unpackhi_epi64(lo, hi) - )); -} -inline v_int64x4 v_dotprod_expand(const v_int16x16& a, const v_int16x16& b, const v_int64x4& c) -{ return v_dotprod_expand(a, b) + c; } - -// 32 >> 64f -inline v_float64x4 v_dotprod_expand(const v_int32x8& a, const v_int32x8& b) -{ return v_cvt_f64(v_dotprod(a, b)); } -inline v_float64x4 v_dotprod_expand(const v_int32x8& a, const v_int32x8& b, const v_float64x4& c) -{ return v_dotprod_expand(a, b) + c; } - -//////// Fast Dot Product //////// - -// 16 >> 32 -inline v_int32x8 v_dotprod_fast(const v_int16x16& a, const v_int16x16& b) -{ return v_dotprod(a, b); } -inline v_int32x8 v_dotprod_fast(const v_int16x16& a, const v_int16x16& b, const v_int32x8& c) -{ return v_dotprod(a, b, c); } - -// 32 >> 64 -inline v_int64x4 v_dotprod_fast(const v_int32x8& a, const v_int32x8& b) -{ return v_dotprod(a, b); } -inline v_int64x4 v_dotprod_fast(const v_int32x8& a, const v_int32x8& b, const v_int64x4& c) -{ return v_dotprod(a, b, c); } - -// 8 >> 32 -inline v_uint32x8 v_dotprod_expand_fast(const v_uint8x32& a, const v_uint8x32& b) -{ return v_dotprod_expand(a, b); } -inline v_uint32x8 v_dotprod_expand_fast(const v_uint8x32& a, const v_uint8x32& b, const v_uint32x8& c) -{ return v_dotprod_expand(a, b, c); } - -inline v_int32x8 v_dotprod_expand_fast(const v_int8x32& a, const v_int8x32& b) -{ return v_dotprod_expand(a, b); } -inline v_int32x8 v_dotprod_expand_fast(const v_int8x32& a, const v_int8x32& b, const v_int32x8& c) -{ return v_dotprod_expand(a, b, c); } - -// 16 >> 64 -inline v_uint64x4 v_dotprod_expand_fast(const v_uint16x16& a, const v_uint16x16& b) -{ - __m256i mullo = _mm256_mullo_epi16(a.val, b.val); - __m256i mulhi = _mm256_mulhi_epu16(a.val, b.val); - __m256i mul0 = _mm256_unpacklo_epi16(mullo, mulhi); - __m256i mul1 = _mm256_unpackhi_epi16(mullo, mulhi); - - __m256i p02 = _mm256_blend_epi32(mul0, _mm256_setzero_si256(), 0xAA); - __m256i p13 = _mm256_srli_epi64(mul0, 32); - __m256i p46 = _mm256_blend_epi32(mul1, _mm256_setzero_si256(), 0xAA); - __m256i p57 = _mm256_srli_epi64(mul1, 32); - - __m256i p15_ = _mm256_add_epi64(p02, p13); - __m256i p9d_ = _mm256_add_epi64(p46, p57); - - return v_uint64x4(_mm256_add_epi64(p15_, p9d_)); -} -inline v_uint64x4 v_dotprod_expand_fast(const v_uint16x16& a, const v_uint16x16& b, const v_uint64x4& c) -{ return v_dotprod_expand_fast(a, b) + c; } - -inline v_int64x4 v_dotprod_expand_fast(const v_int16x16& a, const v_int16x16& b) -{ - __m256i prod = _mm256_madd_epi16(a.val, b.val); - __m256i sign = _mm256_srai_epi32(prod, 31); - __m256i lo = _mm256_unpacklo_epi32(prod, sign); - __m256i hi = _mm256_unpackhi_epi32(prod, sign); - return v_int64x4(_mm256_add_epi64(lo, hi)); -} -inline v_int64x4 v_dotprod_expand_fast(const v_int16x16& a, const v_int16x16& b, const v_int64x4& c) -{ return v_dotprod_expand_fast(a, b) + c; } - -// 32 >> 64f -inline v_float64x4 v_dotprod_expand_fast(const v_int32x8& a, const v_int32x8& b) -{ return v_dotprod_expand(a, b); } -inline v_float64x4 v_dotprod_expand_fast(const v_int32x8& a, const v_int32x8& b, const v_float64x4& c) -{ return v_dotprod_expand(a, b, c); } - -#define OPENCV_HAL_AVX_SPLAT2_PS(a, im) \ - v_float32x8(_mm256_permute_ps(a.val, _MM_SHUFFLE(im, im, im, im))) - -inline v_float32x8 v_matmul(const v_float32x8& v, const v_float32x8& m0, - const v_float32x8& m1, const v_float32x8& m2, - const v_float32x8& m3) -{ - v_float32x8 v04 = OPENCV_HAL_AVX_SPLAT2_PS(v, 0); - v_float32x8 v15 = OPENCV_HAL_AVX_SPLAT2_PS(v, 1); - v_float32x8 v26 = OPENCV_HAL_AVX_SPLAT2_PS(v, 2); - v_float32x8 v37 = OPENCV_HAL_AVX_SPLAT2_PS(v, 3); - return v_fma(v04, m0, v_fma(v15, m1, v_fma(v26, m2, v37 * m3))); -} - -inline v_float32x8 v_matmuladd(const v_float32x8& v, const v_float32x8& m0, - const v_float32x8& m1, const v_float32x8& m2, - const v_float32x8& a) -{ - v_float32x8 v04 = OPENCV_HAL_AVX_SPLAT2_PS(v, 0); - v_float32x8 v15 = OPENCV_HAL_AVX_SPLAT2_PS(v, 1); - v_float32x8 v26 = OPENCV_HAL_AVX_SPLAT2_PS(v, 2); - return v_fma(v04, m0, v_fma(v15, m1, v_fma(v26, m2, a))); -} - -#define OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(_Tpvec, suffix, cast_from, cast_to) \ - inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \ - const _Tpvec& a2, const _Tpvec& a3, \ - _Tpvec& b0, _Tpvec& b1, _Tpvec& b2, _Tpvec& b3) \ - { \ - __m256i t0 = cast_from(_mm256_unpacklo_##suffix(a0.val, a1.val)); \ - __m256i t1 = cast_from(_mm256_unpacklo_##suffix(a2.val, a3.val)); \ - __m256i t2 = cast_from(_mm256_unpackhi_##suffix(a0.val, a1.val)); \ - __m256i t3 = cast_from(_mm256_unpackhi_##suffix(a2.val, a3.val)); \ - b0.val = cast_to(_mm256_unpacklo_epi64(t0, t1)); \ - b1.val = cast_to(_mm256_unpackhi_epi64(t0, t1)); \ - b2.val = cast_to(_mm256_unpacklo_epi64(t2, t3)); \ - b3.val = cast_to(_mm256_unpackhi_epi64(t2, t3)); \ - } - -OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(v_uint32x8, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(v_int32x8, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_AVX_TRANSPOSE4x4(v_float32x8, ps, _mm256_castps_si256, _mm256_castsi256_ps) - -//////////////// Value reordering /////////////// - -/* Expand */ -#define OPENCV_HAL_IMPL_AVX_EXPAND(_Tpvec, _Tpwvec, _Tp, intrin) \ - inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ - { \ - b0.val = intrin(_v256_extract_low(a.val)); \ - b1.val = intrin(_v256_extract_high(a.val)); \ - } \ - inline _Tpwvec v_expand_low(const _Tpvec& a) \ - { return _Tpwvec(intrin(_v256_extract_low(a.val))); } \ - inline _Tpwvec v_expand_high(const _Tpvec& a) \ - { return _Tpwvec(intrin(_v256_extract_high(a.val))); } \ - inline _Tpwvec v256_load_expand(const _Tp* ptr) \ - { \ - __m128i a = _mm_loadu_si128((const __m128i*)ptr); \ - return _Tpwvec(intrin(a)); \ - } - -OPENCV_HAL_IMPL_AVX_EXPAND(v_uint8x32, v_uint16x16, uchar, _mm256_cvtepu8_epi16) -OPENCV_HAL_IMPL_AVX_EXPAND(v_int8x32, v_int16x16, schar, _mm256_cvtepi8_epi16) -OPENCV_HAL_IMPL_AVX_EXPAND(v_uint16x16, v_uint32x8, ushort, _mm256_cvtepu16_epi32) -OPENCV_HAL_IMPL_AVX_EXPAND(v_int16x16, v_int32x8, short, _mm256_cvtepi16_epi32) -OPENCV_HAL_IMPL_AVX_EXPAND(v_uint32x8, v_uint64x4, unsigned, _mm256_cvtepu32_epi64) -OPENCV_HAL_IMPL_AVX_EXPAND(v_int32x8, v_int64x4, int, _mm256_cvtepi32_epi64) - -#define OPENCV_HAL_IMPL_AVX_EXPAND_Q(_Tpvec, _Tp, intrin) \ - inline _Tpvec v256_load_expand_q(const _Tp* ptr) \ - { \ - __m128i a = _mm_loadl_epi64((const __m128i*)ptr); \ - return _Tpvec(intrin(a)); \ - } - -OPENCV_HAL_IMPL_AVX_EXPAND_Q(v_uint32x8, uchar, _mm256_cvtepu8_epi32) -OPENCV_HAL_IMPL_AVX_EXPAND_Q(v_int32x8, schar, _mm256_cvtepi8_epi32) - -/* pack */ -// 16 -inline v_int8x32 v_pack(const v_int16x16& a, const v_int16x16& b) -{ return v_int8x32(_v256_shuffle_odd_64(_mm256_packs_epi16(a.val, b.val))); } - -inline v_uint8x32 v_pack(const v_uint16x16& a, const v_uint16x16& b) -{ - __m256i t = _mm256_set1_epi16(255); - __m256i a1 = _mm256_min_epu16(a.val, t); - __m256i b1 = _mm256_min_epu16(b.val, t); - return v_uint8x32(_v256_shuffle_odd_64(_mm256_packus_epi16(a1, b1))); -} - -inline v_uint8x32 v_pack_u(const v_int16x16& a, const v_int16x16& b) -{ - return v_uint8x32(_v256_shuffle_odd_64(_mm256_packus_epi16(a.val, b.val))); -} - -inline void v_pack_store(schar* ptr, const v_int16x16& a) -{ v_store_low(ptr, v_pack(a, a)); } - -inline void v_pack_store(uchar* ptr, const v_uint16x16& a) -{ - const __m256i m = _mm256_set1_epi16(255); - __m256i am = _mm256_min_epu16(a.val, m); - am = _v256_shuffle_odd_64(_mm256_packus_epi16(am, am)); - v_store_low(ptr, v_uint8x32(am)); -} - -inline void v_pack_u_store(uchar* ptr, const v_int16x16& a) -{ v_store_low(ptr, v_pack_u(a, a)); } - -template inline -v_uint8x32 v_rshr_pack(const v_uint16x16& a, const v_uint16x16& b) -{ - // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. - v_uint16x16 delta = v256_setall_u16((short)(1 << (n-1))); - return v_pack_u(v_reinterpret_as_s16((a + delta) >> n), - v_reinterpret_as_s16((b + delta) >> n)); -} - -template inline -void v_rshr_pack_store(uchar* ptr, const v_uint16x16& a) -{ - v_uint16x16 delta = v256_setall_u16((short)(1 << (n-1))); - v_pack_u_store(ptr, v_reinterpret_as_s16((a + delta) >> n)); -} - -template inline -v_uint8x32 v_rshr_pack_u(const v_int16x16& a, const v_int16x16& b) -{ - v_int16x16 delta = v256_setall_s16((short)(1 << (n-1))); - return v_pack_u((a + delta) >> n, (b + delta) >> n); -} - -template inline -void v_rshr_pack_u_store(uchar* ptr, const v_int16x16& a) -{ - v_int16x16 delta = v256_setall_s16((short)(1 << (n-1))); - v_pack_u_store(ptr, (a + delta) >> n); -} - -template inline -v_int8x32 v_rshr_pack(const v_int16x16& a, const v_int16x16& b) -{ - v_int16x16 delta = v256_setall_s16((short)(1 << (n-1))); - return v_pack((a + delta) >> n, (b + delta) >> n); -} - -template inline -void v_rshr_pack_store(schar* ptr, const v_int16x16& a) -{ - v_int16x16 delta = v256_setall_s16((short)(1 << (n-1))); - v_pack_store(ptr, (a + delta) >> n); -} - -// 32 -inline v_int16x16 v_pack(const v_int32x8& a, const v_int32x8& b) -{ return v_int16x16(_v256_shuffle_odd_64(_mm256_packs_epi32(a.val, b.val))); } - -inline v_uint16x16 v_pack(const v_uint32x8& a, const v_uint32x8& b) -{ return v_uint16x16(_v256_shuffle_odd_64(_v256_packs_epu32(a.val, b.val))); } - -inline v_uint16x16 v_pack_u(const v_int32x8& a, const v_int32x8& b) -{ return v_uint16x16(_v256_shuffle_odd_64(_mm256_packus_epi32(a.val, b.val))); } - -inline void v_pack_store(short* ptr, const v_int32x8& a) -{ v_store_low(ptr, v_pack(a, a)); } - -inline void v_pack_store(ushort* ptr, const v_uint32x8& a) -{ - const __m256i m = _mm256_set1_epi32(65535); - __m256i am = _mm256_min_epu32(a.val, m); - am = _v256_shuffle_odd_64(_mm256_packus_epi32(am, am)); - v_store_low(ptr, v_uint16x16(am)); -} - -inline void v_pack_u_store(ushort* ptr, const v_int32x8& a) -{ v_store_low(ptr, v_pack_u(a, a)); } - - -template inline -v_uint16x16 v_rshr_pack(const v_uint32x8& a, const v_uint32x8& b) -{ - // we assume that n > 0, and so the shifted 32-bit values can be treated as signed numbers. - v_uint32x8 delta = v256_setall_u32(1 << (n-1)); - return v_pack_u(v_reinterpret_as_s32((a + delta) >> n), - v_reinterpret_as_s32((b + delta) >> n)); -} - -template inline -void v_rshr_pack_store(ushort* ptr, const v_uint32x8& a) -{ - v_uint32x8 delta = v256_setall_u32(1 << (n-1)); - v_pack_u_store(ptr, v_reinterpret_as_s32((a + delta) >> n)); -} - -template inline -v_uint16x16 v_rshr_pack_u(const v_int32x8& a, const v_int32x8& b) -{ - v_int32x8 delta = v256_setall_s32(1 << (n-1)); - return v_pack_u((a + delta) >> n, (b + delta) >> n); -} - -template inline -void v_rshr_pack_u_store(ushort* ptr, const v_int32x8& a) -{ - v_int32x8 delta = v256_setall_s32(1 << (n-1)); - v_pack_u_store(ptr, (a + delta) >> n); -} - -template inline -v_int16x16 v_rshr_pack(const v_int32x8& a, const v_int32x8& b) -{ - v_int32x8 delta = v256_setall_s32(1 << (n-1)); - return v_pack((a + delta) >> n, (b + delta) >> n); -} - -template inline -void v_rshr_pack_store(short* ptr, const v_int32x8& a) -{ - v_int32x8 delta = v256_setall_s32(1 << (n-1)); - v_pack_store(ptr, (a + delta) >> n); -} - -// 64 -// Non-saturating pack -inline v_uint32x8 v_pack(const v_uint64x4& a, const v_uint64x4& b) -{ - __m256i a0 = _mm256_shuffle_epi32(a.val, _MM_SHUFFLE(0, 0, 2, 0)); - __m256i b0 = _mm256_shuffle_epi32(b.val, _MM_SHUFFLE(0, 0, 2, 0)); - __m256i ab = _mm256_unpacklo_epi64(a0, b0); // a0, a1, b0, b1, a2, a3, b2, b3 - return v_uint32x8(_v256_shuffle_odd_64(ab)); -} - -inline v_int32x8 v_pack(const v_int64x4& a, const v_int64x4& b) -{ return v_reinterpret_as_s32(v_pack(v_reinterpret_as_u64(a), v_reinterpret_as_u64(b))); } - -inline void v_pack_store(unsigned* ptr, const v_uint64x4& a) -{ - __m256i a0 = _mm256_shuffle_epi32(a.val, _MM_SHUFFLE(0, 0, 2, 0)); - v_store_low(ptr, v_uint32x8(_v256_shuffle_odd_64(a0))); -} - -inline void v_pack_store(int* ptr, const v_int64x4& b) -{ v_pack_store((unsigned*)ptr, v_reinterpret_as_u64(b)); } - -template inline -v_uint32x8 v_rshr_pack(const v_uint64x4& a, const v_uint64x4& b) -{ - v_uint64x4 delta = v256_setall_u64((uint64)1 << (n-1)); - return v_pack((a + delta) >> n, (b + delta) >> n); -} - -template inline -void v_rshr_pack_store(unsigned* ptr, const v_uint64x4& a) -{ - v_uint64x4 delta = v256_setall_u64((uint64)1 << (n-1)); - v_pack_store(ptr, (a + delta) >> n); -} - -template inline -v_int32x8 v_rshr_pack(const v_int64x4& a, const v_int64x4& b) -{ - v_int64x4 delta = v256_setall_s64((int64)1 << (n-1)); - return v_pack((a + delta) >> n, (b + delta) >> n); -} - -template inline -void v_rshr_pack_store(int* ptr, const v_int64x4& a) -{ - v_int64x4 delta = v256_setall_s64((int64)1 << (n-1)); - v_pack_store(ptr, (a + delta) >> n); -} - -// pack boolean -inline v_uint8x32 v_pack_b(const v_uint16x16& a, const v_uint16x16& b) -{ - __m256i ab = _mm256_packs_epi16(a.val, b.val); - return v_uint8x32(_v256_shuffle_odd_64(ab)); -} - -inline v_uint8x32 v_pack_b(const v_uint32x8& a, const v_uint32x8& b, - const v_uint32x8& c, const v_uint32x8& d) -{ - __m256i ab = _mm256_packs_epi32(a.val, b.val); - __m256i cd = _mm256_packs_epi32(c.val, d.val); - - __m256i abcd = _v256_shuffle_odd_64(_mm256_packs_epi16(ab, cd)); - return v_uint8x32(_mm256_shuffle_epi32(abcd, _MM_SHUFFLE(3, 1, 2, 0))); -} - -inline v_uint8x32 v_pack_b(const v_uint64x4& a, const v_uint64x4& b, const v_uint64x4& c, - const v_uint64x4& d, const v_uint64x4& e, const v_uint64x4& f, - const v_uint64x4& g, const v_uint64x4& h) -{ - __m256i ab = _mm256_packs_epi32(a.val, b.val); - __m256i cd = _mm256_packs_epi32(c.val, d.val); - __m256i ef = _mm256_packs_epi32(e.val, f.val); - __m256i gh = _mm256_packs_epi32(g.val, h.val); - - __m256i abcd = _mm256_packs_epi32(ab, cd); - __m256i efgh = _mm256_packs_epi32(ef, gh); - __m256i pkall = _v256_shuffle_odd_64(_mm256_packs_epi16(abcd, efgh)); - - __m256i rev = _mm256_alignr_epi8(pkall, pkall, 8); - return v_uint8x32(_mm256_unpacklo_epi16(pkall, rev)); -} - -/* Recombine */ -// its up there with load and store operations - -/* Extract */ -#define OPENCV_HAL_IMPL_AVX_EXTRACT(_Tpvec) \ - template \ - inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) \ - { return v_rotate_right(a, b); } - -OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint8x32) -OPENCV_HAL_IMPL_AVX_EXTRACT(v_int8x32) -OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint16x16) -OPENCV_HAL_IMPL_AVX_EXTRACT(v_int16x16) -OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint32x8) -OPENCV_HAL_IMPL_AVX_EXTRACT(v_int32x8) -OPENCV_HAL_IMPL_AVX_EXTRACT(v_uint64x4) -OPENCV_HAL_IMPL_AVX_EXTRACT(v_int64x4) -OPENCV_HAL_IMPL_AVX_EXTRACT(v_float32x8) -OPENCV_HAL_IMPL_AVX_EXTRACT(v_float64x4) - -template -inline uchar v_extract_n(v_uint8x32 a) -{ - return (uchar)_v256_extract_epi8(a.val); -} - -template -inline schar v_extract_n(v_int8x32 a) -{ - return (schar)v_extract_n(v_reinterpret_as_u8(a)); -} - -template -inline ushort v_extract_n(v_uint16x16 a) -{ - return (ushort)_v256_extract_epi16(a.val); -} - -template -inline short v_extract_n(v_int16x16 a) -{ - return (short)v_extract_n(v_reinterpret_as_u16(a)); -} - -template -inline uint v_extract_n(v_uint32x8 a) -{ - return (uint)_v256_extract_epi32(a.val); -} - -template -inline int v_extract_n(v_int32x8 a) -{ - return (int)v_extract_n(v_reinterpret_as_u32(a)); -} - -template -inline uint64 v_extract_n(v_uint64x4 a) -{ - return (uint64)_v256_extract_epi64(a.val); -} - -template -inline int64 v_extract_n(v_int64x4 v) -{ - return (int64)v_extract_n(v_reinterpret_as_u64(v)); -} - -template -inline float v_extract_n(v_float32x8 v) -{ - union { uint iv; float fv; } d; - d.iv = v_extract_n(v_reinterpret_as_u32(v)); - return d.fv; -} - -template -inline double v_extract_n(v_float64x4 v) -{ - union { uint64 iv; double dv; } d; - d.iv = v_extract_n(v_reinterpret_as_u64(v)); - return d.dv; -} - -template -inline v_uint32x8 v_broadcast_element(v_uint32x8 a) -{ - static const __m256i perm = _mm256_set1_epi32((char)i); - return v_uint32x8(_mm256_permutevar8x32_epi32(a.val, perm)); -} - -template -inline v_int32x8 v_broadcast_element(const v_int32x8 &a) -{ return v_reinterpret_as_s32(v_broadcast_element(v_reinterpret_as_u32(a))); } - -template -inline v_float32x8 v_broadcast_element(const v_float32x8 &a) -{ return v_reinterpret_as_f32(v_broadcast_element(v_reinterpret_as_u32(a))); } - - -///////////////////// load deinterleave ///////////////////////////// - -inline void v_load_deinterleave( const uchar* ptr, v_uint8x32& a, v_uint8x32& b ) -{ - __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr); - __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); - - const __m256i sh = _mm256_setr_epi8(0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15, - 0, 2, 4, 6, 8, 10, 12, 14, 1, 3, 5, 7, 9, 11, 13, 15); - __m256i p0 = _mm256_shuffle_epi8(ab0, sh); - __m256i p1 = _mm256_shuffle_epi8(ab1, sh); - __m256i pl = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); - __m256i ph = _mm256_permute2x128_si256(p0, p1, 1 + 3*16); - __m256i a0 = _mm256_unpacklo_epi64(pl, ph); - __m256i b0 = _mm256_unpackhi_epi64(pl, ph); - a = v_uint8x32(a0); - b = v_uint8x32(b0); -} - -inline void v_load_deinterleave( const ushort* ptr, v_uint16x16& a, v_uint16x16& b ) -{ - __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr); - __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); - - const __m256i sh = _mm256_setr_epi8(0, 1, 4, 5, 8, 9, 12, 13, 2, 3, 6, 7, 10, 11, 14, 15, - 0, 1, 4, 5, 8, 9, 12, 13, 2, 3, 6, 7, 10, 11, 14, 15); - __m256i p0 = _mm256_shuffle_epi8(ab0, sh); - __m256i p1 = _mm256_shuffle_epi8(ab1, sh); - __m256i pl = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); - __m256i ph = _mm256_permute2x128_si256(p0, p1, 1 + 3*16); - __m256i a0 = _mm256_unpacklo_epi64(pl, ph); - __m256i b0 = _mm256_unpackhi_epi64(pl, ph); - a = v_uint16x16(a0); - b = v_uint16x16(b0); -} - -inline void v_load_deinterleave( const unsigned* ptr, v_uint32x8& a, v_uint32x8& b ) -{ - __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr); - __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); - - const int sh = 0+2*4+1*16+3*64; - __m256i p0 = _mm256_shuffle_epi32(ab0, sh); - __m256i p1 = _mm256_shuffle_epi32(ab1, sh); - __m256i pl = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); - __m256i ph = _mm256_permute2x128_si256(p0, p1, 1 + 3*16); - __m256i a0 = _mm256_unpacklo_epi64(pl, ph); - __m256i b0 = _mm256_unpackhi_epi64(pl, ph); - a = v_uint32x8(a0); - b = v_uint32x8(b0); -} - -inline void v_load_deinterleave( const uint64* ptr, v_uint64x4& a, v_uint64x4& b ) -{ - __m256i ab0 = _mm256_loadu_si256((const __m256i*)ptr); - __m256i ab1 = _mm256_loadu_si256((const __m256i*)(ptr + 4)); - - __m256i pl = _mm256_permute2x128_si256(ab0, ab1, 0 + 2*16); - __m256i ph = _mm256_permute2x128_si256(ab0, ab1, 1 + 3*16); - __m256i a0 = _mm256_unpacklo_epi64(pl, ph); - __m256i b0 = _mm256_unpackhi_epi64(pl, ph); - a = v_uint64x4(a0); - b = v_uint64x4(b0); -} - -inline void v_load_deinterleave( const uchar* ptr, v_uint8x32& a, v_uint8x32& b, v_uint8x32& c ) -{ - __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); - __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); - __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 64)); - - __m256i s02_low = _mm256_permute2x128_si256(bgr0, bgr2, 0 + 2*16); - __m256i s02_high = _mm256_permute2x128_si256(bgr0, bgr2, 1 + 3*16); - - const __m256i m0 = _mm256_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, - 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0); - const __m256i m1 = _mm256_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, - -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1); - - __m256i b0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_low, s02_high, m0), bgr1, m1); - __m256i g0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_high, s02_low, m1), bgr1, m0); - __m256i r0 = _mm256_blendv_epi8(_mm256_blendv_epi8(bgr1, s02_low, m0), s02_high, m1); - - const __m256i - sh_b = _mm256_setr_epi8(0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, 1, 4, 7, 10, 13, - 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, 1, 4, 7, 10, 13), - sh_g = _mm256_setr_epi8(1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, - 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14), - sh_r = _mm256_setr_epi8(2, 5, 8, 11, 14, 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, - 2, 5, 8, 11, 14, 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15); - b0 = _mm256_shuffle_epi8(b0, sh_b); - g0 = _mm256_shuffle_epi8(g0, sh_g); - r0 = _mm256_shuffle_epi8(r0, sh_r); - - a = v_uint8x32(b0); - b = v_uint8x32(g0); - c = v_uint8x32(r0); -} - -inline void v_load_deinterleave( const ushort* ptr, v_uint16x16& a, v_uint16x16& b, v_uint16x16& c ) -{ - __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); - __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); - __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); - - __m256i s02_low = _mm256_permute2x128_si256(bgr0, bgr2, 0 + 2*16); - __m256i s02_high = _mm256_permute2x128_si256(bgr0, bgr2, 1 + 3*16); - - const __m256i m0 = _mm256_setr_epi8(0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, - 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0); - const __m256i m1 = _mm256_setr_epi8(0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, - -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0); - __m256i b0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_low, s02_high, m0), bgr1, m1); - __m256i g0 = _mm256_blendv_epi8(_mm256_blendv_epi8(bgr1, s02_low, m0), s02_high, m1); - __m256i r0 = _mm256_blendv_epi8(_mm256_blendv_epi8(s02_high, s02_low, m1), bgr1, m0); - const __m256i sh_b = _mm256_setr_epi8(0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11, - 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11); - const __m256i sh_g = _mm256_setr_epi8(2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1, 6, 7, 12, 13, - 2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1, 6, 7, 12, 13); - const __m256i sh_r = _mm256_setr_epi8(4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, - 4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15); - b0 = _mm256_shuffle_epi8(b0, sh_b); - g0 = _mm256_shuffle_epi8(g0, sh_g); - r0 = _mm256_shuffle_epi8(r0, sh_r); - - a = v_uint16x16(b0); - b = v_uint16x16(g0); - c = v_uint16x16(r0); -} - -inline void v_load_deinterleave( const unsigned* ptr, v_uint32x8& a, v_uint32x8& b, v_uint32x8& c ) -{ - __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); - __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); - __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); - - __m256i s02_low = _mm256_permute2x128_si256(bgr0, bgr2, 0 + 2*16); - __m256i s02_high = _mm256_permute2x128_si256(bgr0, bgr2, 1 + 3*16); - - __m256i b0 = _mm256_blend_epi32(_mm256_blend_epi32(s02_low, s02_high, 0x24), bgr1, 0x92); - __m256i g0 = _mm256_blend_epi32(_mm256_blend_epi32(s02_high, s02_low, 0x92), bgr1, 0x24); - __m256i r0 = _mm256_blend_epi32(_mm256_blend_epi32(bgr1, s02_low, 0x24), s02_high, 0x92); - - b0 = _mm256_shuffle_epi32(b0, 0x6c); - g0 = _mm256_shuffle_epi32(g0, 0xb1); - r0 = _mm256_shuffle_epi32(r0, 0xc6); - - a = v_uint32x8(b0); - b = v_uint32x8(g0); - c = v_uint32x8(r0); -} - -inline void v_load_deinterleave( const uint64* ptr, v_uint64x4& a, v_uint64x4& b, v_uint64x4& c ) -{ - __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); - __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 4)); - __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); - - __m256i s01 = _mm256_blend_epi32(bgr0, bgr1, 0xf0); - __m256i s12 = _mm256_blend_epi32(bgr1, bgr2, 0xf0); - __m256i s20r = _mm256_permute4x64_epi64(_mm256_blend_epi32(bgr2, bgr0, 0xf0), 0x1b); - __m256i b0 = _mm256_unpacklo_epi64(s01, s20r); - __m256i g0 = _mm256_alignr_epi8(s12, s01, 8); - __m256i r0 = _mm256_unpackhi_epi64(s20r, s12); - - a = v_uint64x4(b0); - b = v_uint64x4(g0); - c = v_uint64x4(r0); -} - -inline void v_load_deinterleave( const uchar* ptr, v_uint8x32& a, v_uint8x32& b, v_uint8x32& c, v_uint8x32& d ) -{ - __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); - __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); - __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 64)); - __m256i bgr3 = _mm256_loadu_si256((const __m256i*)(ptr + 96)); - const __m256i sh = _mm256_setr_epi8(0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15, - 0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15); - - __m256i p0 = _mm256_shuffle_epi8(bgr0, sh); - __m256i p1 = _mm256_shuffle_epi8(bgr1, sh); - __m256i p2 = _mm256_shuffle_epi8(bgr2, sh); - __m256i p3 = _mm256_shuffle_epi8(bgr3, sh); - - __m256i p01l = _mm256_unpacklo_epi32(p0, p1); - __m256i p01h = _mm256_unpackhi_epi32(p0, p1); - __m256i p23l = _mm256_unpacklo_epi32(p2, p3); - __m256i p23h = _mm256_unpackhi_epi32(p2, p3); - - __m256i pll = _mm256_permute2x128_si256(p01l, p23l, 0 + 2*16); - __m256i plh = _mm256_permute2x128_si256(p01l, p23l, 1 + 3*16); - __m256i phl = _mm256_permute2x128_si256(p01h, p23h, 0 + 2*16); - __m256i phh = _mm256_permute2x128_si256(p01h, p23h, 1 + 3*16); - - __m256i b0 = _mm256_unpacklo_epi32(pll, plh); - __m256i g0 = _mm256_unpackhi_epi32(pll, plh); - __m256i r0 = _mm256_unpacklo_epi32(phl, phh); - __m256i a0 = _mm256_unpackhi_epi32(phl, phh); - - a = v_uint8x32(b0); - b = v_uint8x32(g0); - c = v_uint8x32(r0); - d = v_uint8x32(a0); -} - -inline void v_load_deinterleave( const ushort* ptr, v_uint16x16& a, v_uint16x16& b, v_uint16x16& c, v_uint16x16& d ) -{ - __m256i bgr0 = _mm256_loadu_si256((const __m256i*)ptr); - __m256i bgr1 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); - __m256i bgr2 = _mm256_loadu_si256((const __m256i*)(ptr + 32)); - __m256i bgr3 = _mm256_loadu_si256((const __m256i*)(ptr + 48)); - const __m256i sh = _mm256_setr_epi8(0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15, - 0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15); - __m256i p0 = _mm256_shuffle_epi8(bgr0, sh); - __m256i p1 = _mm256_shuffle_epi8(bgr1, sh); - __m256i p2 = _mm256_shuffle_epi8(bgr2, sh); - __m256i p3 = _mm256_shuffle_epi8(bgr3, sh); - - __m256i p01l = _mm256_unpacklo_epi32(p0, p1); - __m256i p01h = _mm256_unpackhi_epi32(p0, p1); - __m256i p23l = _mm256_unpacklo_epi32(p2, p3); - __m256i p23h = _mm256_unpackhi_epi32(p2, p3); - - __m256i pll = _mm256_permute2x128_si256(p01l, p23l, 0 + 2*16); - __m256i plh = _mm256_permute2x128_si256(p01l, p23l, 1 + 3*16); - __m256i phl = _mm256_permute2x128_si256(p01h, p23h, 0 + 2*16); - __m256i phh = _mm256_permute2x128_si256(p01h, p23h, 1 + 3*16); - - __m256i b0 = _mm256_unpacklo_epi32(pll, plh); - __m256i g0 = _mm256_unpackhi_epi32(pll, plh); - __m256i r0 = _mm256_unpacklo_epi32(phl, phh); - __m256i a0 = _mm256_unpackhi_epi32(phl, phh); - - a = v_uint16x16(b0); - b = v_uint16x16(g0); - c = v_uint16x16(r0); - d = v_uint16x16(a0); -} - -inline void v_load_deinterleave( const unsigned* ptr, v_uint32x8& a, v_uint32x8& b, v_uint32x8& c, v_uint32x8& d ) -{ - __m256i p0 = _mm256_loadu_si256((const __m256i*)ptr); - __m256i p1 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); - __m256i p2 = _mm256_loadu_si256((const __m256i*)(ptr + 16)); - __m256i p3 = _mm256_loadu_si256((const __m256i*)(ptr + 24)); - - __m256i p01l = _mm256_unpacklo_epi32(p0, p1); - __m256i p01h = _mm256_unpackhi_epi32(p0, p1); - __m256i p23l = _mm256_unpacklo_epi32(p2, p3); - __m256i p23h = _mm256_unpackhi_epi32(p2, p3); - - __m256i pll = _mm256_permute2x128_si256(p01l, p23l, 0 + 2*16); - __m256i plh = _mm256_permute2x128_si256(p01l, p23l, 1 + 3*16); - __m256i phl = _mm256_permute2x128_si256(p01h, p23h, 0 + 2*16); - __m256i phh = _mm256_permute2x128_si256(p01h, p23h, 1 + 3*16); - - __m256i b0 = _mm256_unpacklo_epi32(pll, plh); - __m256i g0 = _mm256_unpackhi_epi32(pll, plh); - __m256i r0 = _mm256_unpacklo_epi32(phl, phh); - __m256i a0 = _mm256_unpackhi_epi32(phl, phh); - - a = v_uint32x8(b0); - b = v_uint32x8(g0); - c = v_uint32x8(r0); - d = v_uint32x8(a0); -} - -inline void v_load_deinterleave( const uint64* ptr, v_uint64x4& a, v_uint64x4& b, v_uint64x4& c, v_uint64x4& d ) -{ - __m256i bgra0 = _mm256_loadu_si256((const __m256i*)ptr); - __m256i bgra1 = _mm256_loadu_si256((const __m256i*)(ptr + 4)); - __m256i bgra2 = _mm256_loadu_si256((const __m256i*)(ptr + 8)); - __m256i bgra3 = _mm256_loadu_si256((const __m256i*)(ptr + 12)); - - __m256i l02 = _mm256_permute2x128_si256(bgra0, bgra2, 0 + 2*16); - __m256i h02 = _mm256_permute2x128_si256(bgra0, bgra2, 1 + 3*16); - __m256i l13 = _mm256_permute2x128_si256(bgra1, bgra3, 0 + 2*16); - __m256i h13 = _mm256_permute2x128_si256(bgra1, bgra3, 1 + 3*16); - - __m256i b0 = _mm256_unpacklo_epi64(l02, l13); - __m256i g0 = _mm256_unpackhi_epi64(l02, l13); - __m256i r0 = _mm256_unpacklo_epi64(h02, h13); - __m256i a0 = _mm256_unpackhi_epi64(h02, h13); - - a = v_uint64x4(b0); - b = v_uint64x4(g0); - c = v_uint64x4(r0); - d = v_uint64x4(a0); -} - -///////////////////////////// store interleave ///////////////////////////////////// - -inline void v_store_interleave( uchar* ptr, const v_uint8x32& x, const v_uint8x32& y, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - __m256i xy_l = _mm256_unpacklo_epi8(x.val, y.val); - __m256i xy_h = _mm256_unpackhi_epi8(x.val, y.val); - - __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16); - __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm256_stream_si256((__m256i*)ptr, xy0); - _mm256_stream_si256((__m256i*)(ptr + 32), xy1); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm256_store_si256((__m256i*)ptr, xy0); - _mm256_store_si256((__m256i*)(ptr + 32), xy1); - } - else - { - _mm256_storeu_si256((__m256i*)ptr, xy0); - _mm256_storeu_si256((__m256i*)(ptr + 32), xy1); - } -} - -inline void v_store_interleave( ushort* ptr, const v_uint16x16& x, const v_uint16x16& y, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - __m256i xy_l = _mm256_unpacklo_epi16(x.val, y.val); - __m256i xy_h = _mm256_unpackhi_epi16(x.val, y.val); - - __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16); - __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm256_stream_si256((__m256i*)ptr, xy0); - _mm256_stream_si256((__m256i*)(ptr + 16), xy1); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm256_store_si256((__m256i*)ptr, xy0); - _mm256_store_si256((__m256i*)(ptr + 16), xy1); - } - else - { - _mm256_storeu_si256((__m256i*)ptr, xy0); - _mm256_storeu_si256((__m256i*)(ptr + 16), xy1); - } -} - -inline void v_store_interleave( unsigned* ptr, const v_uint32x8& x, const v_uint32x8& y, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - __m256i xy_l = _mm256_unpacklo_epi32(x.val, y.val); - __m256i xy_h = _mm256_unpackhi_epi32(x.val, y.val); - - __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16); - __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm256_stream_si256((__m256i*)ptr, xy0); - _mm256_stream_si256((__m256i*)(ptr + 8), xy1); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm256_store_si256((__m256i*)ptr, xy0); - _mm256_store_si256((__m256i*)(ptr + 8), xy1); - } - else - { - _mm256_storeu_si256((__m256i*)ptr, xy0); - _mm256_storeu_si256((__m256i*)(ptr + 8), xy1); - } -} - -inline void v_store_interleave( uint64* ptr, const v_uint64x4& x, const v_uint64x4& y, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - __m256i xy_l = _mm256_unpacklo_epi64(x.val, y.val); - __m256i xy_h = _mm256_unpackhi_epi64(x.val, y.val); - - __m256i xy0 = _mm256_permute2x128_si256(xy_l, xy_h, 0 + 2*16); - __m256i xy1 = _mm256_permute2x128_si256(xy_l, xy_h, 1 + 3*16); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm256_stream_si256((__m256i*)ptr, xy0); - _mm256_stream_si256((__m256i*)(ptr + 4), xy1); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm256_store_si256((__m256i*)ptr, xy0); - _mm256_store_si256((__m256i*)(ptr + 4), xy1); - } - else - { - _mm256_storeu_si256((__m256i*)ptr, xy0); - _mm256_storeu_si256((__m256i*)(ptr + 4), xy1); - } -} - -inline void v_store_interleave( uchar* ptr, const v_uint8x32& a, const v_uint8x32& b, const v_uint8x32& c, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - const __m256i sh_b = _mm256_setr_epi8( - 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, 5, - 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, 5); - const __m256i sh_g = _mm256_setr_epi8( - 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, - 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10); - const __m256i sh_r = _mm256_setr_epi8( - 10, 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, - 10, 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15); - - __m256i b0 = _mm256_shuffle_epi8(a.val, sh_b); - __m256i g0 = _mm256_shuffle_epi8(b.val, sh_g); - __m256i r0 = _mm256_shuffle_epi8(c.val, sh_r); - - const __m256i m0 = _mm256_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, - 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0); - const __m256i m1 = _mm256_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, - 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0); - - __m256i p0 = _mm256_blendv_epi8(_mm256_blendv_epi8(b0, g0, m0), r0, m1); - __m256i p1 = _mm256_blendv_epi8(_mm256_blendv_epi8(g0, r0, m0), b0, m1); - __m256i p2 = _mm256_blendv_epi8(_mm256_blendv_epi8(r0, b0, m0), g0, m1); - - __m256i bgr0 = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); - __m256i bgr1 = _mm256_permute2x128_si256(p2, p0, 0 + 3*16); - __m256i bgr2 = _mm256_permute2x128_si256(p1, p2, 1 + 3*16); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm256_stream_si256((__m256i*)ptr, bgr0); - _mm256_stream_si256((__m256i*)(ptr + 32), bgr1); - _mm256_stream_si256((__m256i*)(ptr + 64), bgr2); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm256_store_si256((__m256i*)ptr, bgr0); - _mm256_store_si256((__m256i*)(ptr + 32), bgr1); - _mm256_store_si256((__m256i*)(ptr + 64), bgr2); - } - else - { - _mm256_storeu_si256((__m256i*)ptr, bgr0); - _mm256_storeu_si256((__m256i*)(ptr + 32), bgr1); - _mm256_storeu_si256((__m256i*)(ptr + 64), bgr2); - } -} - -inline void v_store_interleave( ushort* ptr, const v_uint16x16& a, const v_uint16x16& b, const v_uint16x16& c, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - const __m256i sh_b = _mm256_setr_epi8( - 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11, - 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11); - const __m256i sh_g = _mm256_setr_epi8( - 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, - 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5); - const __m256i sh_r = _mm256_setr_epi8( - 4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, - 4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15); - - __m256i b0 = _mm256_shuffle_epi8(a.val, sh_b); - __m256i g0 = _mm256_shuffle_epi8(b.val, sh_g); - __m256i r0 = _mm256_shuffle_epi8(c.val, sh_r); - - const __m256i m0 = _mm256_setr_epi8(0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, - 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0); - const __m256i m1 = _mm256_setr_epi8(0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, - -1, -1, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, -1, -1, 0, 0); - - __m256i p0 = _mm256_blendv_epi8(_mm256_blendv_epi8(b0, g0, m0), r0, m1); - __m256i p1 = _mm256_blendv_epi8(_mm256_blendv_epi8(g0, r0, m0), b0, m1); - __m256i p2 = _mm256_blendv_epi8(_mm256_blendv_epi8(r0, b0, m0), g0, m1); - - __m256i bgr0 = _mm256_permute2x128_si256(p0, p2, 0 + 2*16); - //__m256i bgr1 = p1; - __m256i bgr2 = _mm256_permute2x128_si256(p0, p2, 1 + 3*16); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm256_stream_si256((__m256i*)ptr, bgr0); - _mm256_stream_si256((__m256i*)(ptr + 16), p1); - _mm256_stream_si256((__m256i*)(ptr + 32), bgr2); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm256_store_si256((__m256i*)ptr, bgr0); - _mm256_store_si256((__m256i*)(ptr + 16), p1); - _mm256_store_si256((__m256i*)(ptr + 32), bgr2); - } - else - { - _mm256_storeu_si256((__m256i*)ptr, bgr0); - _mm256_storeu_si256((__m256i*)(ptr + 16), p1); - _mm256_storeu_si256((__m256i*)(ptr + 32), bgr2); - } -} - -inline void v_store_interleave( unsigned* ptr, const v_uint32x8& a, const v_uint32x8& b, const v_uint32x8& c, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - __m256i b0 = _mm256_shuffle_epi32(a.val, 0x6c); - __m256i g0 = _mm256_shuffle_epi32(b.val, 0xb1); - __m256i r0 = _mm256_shuffle_epi32(c.val, 0xc6); - - __m256i p0 = _mm256_blend_epi32(_mm256_blend_epi32(b0, g0, 0x92), r0, 0x24); - __m256i p1 = _mm256_blend_epi32(_mm256_blend_epi32(g0, r0, 0x92), b0, 0x24); - __m256i p2 = _mm256_blend_epi32(_mm256_blend_epi32(r0, b0, 0x92), g0, 0x24); - - __m256i bgr0 = _mm256_permute2x128_si256(p0, p1, 0 + 2*16); - //__m256i bgr1 = p2; - __m256i bgr2 = _mm256_permute2x128_si256(p0, p1, 1 + 3*16); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm256_stream_si256((__m256i*)ptr, bgr0); - _mm256_stream_si256((__m256i*)(ptr + 8), p2); - _mm256_stream_si256((__m256i*)(ptr + 16), bgr2); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm256_store_si256((__m256i*)ptr, bgr0); - _mm256_store_si256((__m256i*)(ptr + 8), p2); - _mm256_store_si256((__m256i*)(ptr + 16), bgr2); - } - else - { - _mm256_storeu_si256((__m256i*)ptr, bgr0); - _mm256_storeu_si256((__m256i*)(ptr + 8), p2); - _mm256_storeu_si256((__m256i*)(ptr + 16), bgr2); - } -} - -inline void v_store_interleave( uint64* ptr, const v_uint64x4& a, const v_uint64x4& b, const v_uint64x4& c, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - __m256i s01 = _mm256_unpacklo_epi64(a.val, b.val); - __m256i s12 = _mm256_unpackhi_epi64(b.val, c.val); - __m256i s20 = _mm256_blend_epi32(c.val, a.val, 0xcc); - - __m256i bgr0 = _mm256_permute2x128_si256(s01, s20, 0 + 2*16); - __m256i bgr1 = _mm256_blend_epi32(s01, s12, 0x0f); - __m256i bgr2 = _mm256_permute2x128_si256(s20, s12, 1 + 3*16); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm256_stream_si256((__m256i*)ptr, bgr0); - _mm256_stream_si256((__m256i*)(ptr + 4), bgr1); - _mm256_stream_si256((__m256i*)(ptr + 8), bgr2); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm256_store_si256((__m256i*)ptr, bgr0); - _mm256_store_si256((__m256i*)(ptr + 4), bgr1); - _mm256_store_si256((__m256i*)(ptr + 8), bgr2); - } - else - { - _mm256_storeu_si256((__m256i*)ptr, bgr0); - _mm256_storeu_si256((__m256i*)(ptr + 4), bgr1); - _mm256_storeu_si256((__m256i*)(ptr + 8), bgr2); - } -} - -inline void v_store_interleave( uchar* ptr, const v_uint8x32& a, const v_uint8x32& b, - const v_uint8x32& c, const v_uint8x32& d, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - __m256i bg0 = _mm256_unpacklo_epi8(a.val, b.val); - __m256i bg1 = _mm256_unpackhi_epi8(a.val, b.val); - __m256i ra0 = _mm256_unpacklo_epi8(c.val, d.val); - __m256i ra1 = _mm256_unpackhi_epi8(c.val, d.val); - - __m256i bgra0_ = _mm256_unpacklo_epi16(bg0, ra0); - __m256i bgra1_ = _mm256_unpackhi_epi16(bg0, ra0); - __m256i bgra2_ = _mm256_unpacklo_epi16(bg1, ra1); - __m256i bgra3_ = _mm256_unpackhi_epi16(bg1, ra1); - - __m256i bgra0 = _mm256_permute2x128_si256(bgra0_, bgra1_, 0 + 2*16); - __m256i bgra2 = _mm256_permute2x128_si256(bgra0_, bgra1_, 1 + 3*16); - __m256i bgra1 = _mm256_permute2x128_si256(bgra2_, bgra3_, 0 + 2*16); - __m256i bgra3 = _mm256_permute2x128_si256(bgra2_, bgra3_, 1 + 3*16); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm256_stream_si256((__m256i*)ptr, bgra0); - _mm256_stream_si256((__m256i*)(ptr + 32), bgra1); - _mm256_stream_si256((__m256i*)(ptr + 64), bgra2); - _mm256_stream_si256((__m256i*)(ptr + 96), bgra3); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm256_store_si256((__m256i*)ptr, bgra0); - _mm256_store_si256((__m256i*)(ptr + 32), bgra1); - _mm256_store_si256((__m256i*)(ptr + 64), bgra2); - _mm256_store_si256((__m256i*)(ptr + 96), bgra3); - } - else - { - _mm256_storeu_si256((__m256i*)ptr, bgra0); - _mm256_storeu_si256((__m256i*)(ptr + 32), bgra1); - _mm256_storeu_si256((__m256i*)(ptr + 64), bgra2); - _mm256_storeu_si256((__m256i*)(ptr + 96), bgra3); - } -} - -inline void v_store_interleave( ushort* ptr, const v_uint16x16& a, const v_uint16x16& b, - const v_uint16x16& c, const v_uint16x16& d, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - __m256i bg0 = _mm256_unpacklo_epi16(a.val, b.val); - __m256i bg1 = _mm256_unpackhi_epi16(a.val, b.val); - __m256i ra0 = _mm256_unpacklo_epi16(c.val, d.val); - __m256i ra1 = _mm256_unpackhi_epi16(c.val, d.val); - - __m256i bgra0_ = _mm256_unpacklo_epi32(bg0, ra0); - __m256i bgra1_ = _mm256_unpackhi_epi32(bg0, ra0); - __m256i bgra2_ = _mm256_unpacklo_epi32(bg1, ra1); - __m256i bgra3_ = _mm256_unpackhi_epi32(bg1, ra1); - - __m256i bgra0 = _mm256_permute2x128_si256(bgra0_, bgra1_, 0 + 2*16); - __m256i bgra2 = _mm256_permute2x128_si256(bgra0_, bgra1_, 1 + 3*16); - __m256i bgra1 = _mm256_permute2x128_si256(bgra2_, bgra3_, 0 + 2*16); - __m256i bgra3 = _mm256_permute2x128_si256(bgra2_, bgra3_, 1 + 3*16); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm256_stream_si256((__m256i*)ptr, bgra0); - _mm256_stream_si256((__m256i*)(ptr + 16), bgra1); - _mm256_stream_si256((__m256i*)(ptr + 32), bgra2); - _mm256_stream_si256((__m256i*)(ptr + 48), bgra3); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm256_store_si256((__m256i*)ptr, bgra0); - _mm256_store_si256((__m256i*)(ptr + 16), bgra1); - _mm256_store_si256((__m256i*)(ptr + 32), bgra2); - _mm256_store_si256((__m256i*)(ptr + 48), bgra3); - } - else - { - _mm256_storeu_si256((__m256i*)ptr, bgra0); - _mm256_storeu_si256((__m256i*)(ptr + 16), bgra1); - _mm256_storeu_si256((__m256i*)(ptr + 32), bgra2); - _mm256_storeu_si256((__m256i*)(ptr + 48), bgra3); - } -} - -inline void v_store_interleave( unsigned* ptr, const v_uint32x8& a, const v_uint32x8& b, - const v_uint32x8& c, const v_uint32x8& d, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - __m256i bg0 = _mm256_unpacklo_epi32(a.val, b.val); - __m256i bg1 = _mm256_unpackhi_epi32(a.val, b.val); - __m256i ra0 = _mm256_unpacklo_epi32(c.val, d.val); - __m256i ra1 = _mm256_unpackhi_epi32(c.val, d.val); - - __m256i bgra0_ = _mm256_unpacklo_epi64(bg0, ra0); - __m256i bgra1_ = _mm256_unpackhi_epi64(bg0, ra0); - __m256i bgra2_ = _mm256_unpacklo_epi64(bg1, ra1); - __m256i bgra3_ = _mm256_unpackhi_epi64(bg1, ra1); - - __m256i bgra0 = _mm256_permute2x128_si256(bgra0_, bgra1_, 0 + 2*16); - __m256i bgra2 = _mm256_permute2x128_si256(bgra0_, bgra1_, 1 + 3*16); - __m256i bgra1 = _mm256_permute2x128_si256(bgra2_, bgra3_, 0 + 2*16); - __m256i bgra3 = _mm256_permute2x128_si256(bgra2_, bgra3_, 1 + 3*16); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm256_stream_si256((__m256i*)ptr, bgra0); - _mm256_stream_si256((__m256i*)(ptr + 8), bgra1); - _mm256_stream_si256((__m256i*)(ptr + 16), bgra2); - _mm256_stream_si256((__m256i*)(ptr + 24), bgra3); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm256_store_si256((__m256i*)ptr, bgra0); - _mm256_store_si256((__m256i*)(ptr + 8), bgra1); - _mm256_store_si256((__m256i*)(ptr + 16), bgra2); - _mm256_store_si256((__m256i*)(ptr + 24), bgra3); - } - else - { - _mm256_storeu_si256((__m256i*)ptr, bgra0); - _mm256_storeu_si256((__m256i*)(ptr + 8), bgra1); - _mm256_storeu_si256((__m256i*)(ptr + 16), bgra2); - _mm256_storeu_si256((__m256i*)(ptr + 24), bgra3); - } -} - -inline void v_store_interleave( uint64* ptr, const v_uint64x4& a, const v_uint64x4& b, - const v_uint64x4& c, const v_uint64x4& d, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - __m256i bg0 = _mm256_unpacklo_epi64(a.val, b.val); - __m256i bg1 = _mm256_unpackhi_epi64(a.val, b.val); - __m256i ra0 = _mm256_unpacklo_epi64(c.val, d.val); - __m256i ra1 = _mm256_unpackhi_epi64(c.val, d.val); - - __m256i bgra0 = _mm256_permute2x128_si256(bg0, ra0, 0 + 2*16); - __m256i bgra1 = _mm256_permute2x128_si256(bg1, ra1, 0 + 2*16); - __m256i bgra2 = _mm256_permute2x128_si256(bg0, ra0, 1 + 3*16); - __m256i bgra3 = _mm256_permute2x128_si256(bg1, ra1, 1 + 3*16); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm256_stream_si256((__m256i*)ptr, bgra0); - _mm256_stream_si256((__m256i*)(ptr + 4), bgra1); - _mm256_stream_si256((__m256i*)(ptr + 8), bgra2); - _mm256_stream_si256((__m256i*)(ptr + 12), bgra3); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm256_store_si256((__m256i*)ptr, bgra0); - _mm256_store_si256((__m256i*)(ptr + 4), bgra1); - _mm256_store_si256((__m256i*)(ptr + 8), bgra2); - _mm256_store_si256((__m256i*)(ptr + 12), bgra3); - } - else - { - _mm256_storeu_si256((__m256i*)ptr, bgra0); - _mm256_storeu_si256((__m256i*)(ptr + 4), bgra1); - _mm256_storeu_si256((__m256i*)(ptr + 8), bgra2); - _mm256_storeu_si256((__m256i*)(ptr + 12), bgra3); - } -} - -#define OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(_Tpvec0, _Tp0, suffix0, _Tpvec1, _Tp1, suffix1) \ -inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0 ) \ -{ \ - _Tpvec1 a1, b1; \ - v_load_deinterleave((const _Tp1*)ptr, a1, b1); \ - a0 = v_reinterpret_as_##suffix0(a1); \ - b0 = v_reinterpret_as_##suffix0(b1); \ -} \ -inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0 ) \ -{ \ - _Tpvec1 a1, b1, c1; \ - v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1); \ - a0 = v_reinterpret_as_##suffix0(a1); \ - b0 = v_reinterpret_as_##suffix0(b1); \ - c0 = v_reinterpret_as_##suffix0(c1); \ -} \ -inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0, _Tpvec0& d0 ) \ -{ \ - _Tpvec1 a1, b1, c1, d1; \ - v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1, d1); \ - a0 = v_reinterpret_as_##suffix0(a1); \ - b0 = v_reinterpret_as_##suffix0(b1); \ - c0 = v_reinterpret_as_##suffix0(c1); \ - d0 = v_reinterpret_as_##suffix0(d1); \ -} \ -inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ - hal::StoreMode mode=hal::STORE_UNALIGNED ) \ -{ \ - _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ - _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ - v_store_interleave((_Tp1*)ptr, a1, b1, mode); \ -} \ -inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, const _Tpvec0& c0, \ - hal::StoreMode mode=hal::STORE_UNALIGNED ) \ -{ \ - _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ - _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ - _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ - v_store_interleave((_Tp1*)ptr, a1, b1, c1, mode); \ -} \ -inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ - const _Tpvec0& c0, const _Tpvec0& d0, \ - hal::StoreMode mode=hal::STORE_UNALIGNED ) \ -{ \ - _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ - _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ - _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ - _Tpvec1 d1 = v_reinterpret_as_##suffix1(d0); \ - v_store_interleave((_Tp1*)ptr, a1, b1, c1, d1, mode); \ -} - -OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int8x32, schar, s8, v_uint8x32, uchar, u8) -OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int16x16, short, s16, v_uint16x16, ushort, u16) -OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int32x8, int, s32, v_uint32x8, unsigned, u32) -OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_float32x8, float, f32, v_uint32x8, unsigned, u32) -OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_int64x4, int64, s64, v_uint64x4, uint64, u64) -OPENCV_HAL_IMPL_AVX_LOADSTORE_INTERLEAVE(v_float64x4, double, f64, v_uint64x4, uint64, u64) - -// -// FP16 -// - -inline v_float32x8 v256_load_expand(const float16_t* ptr) -{ -#if CV_FP16 - return v_float32x8(_mm256_cvtph_ps(_mm_loadu_si128((const __m128i*)ptr))); -#else - float CV_DECL_ALIGNED(32) buf[8]; - for (int i = 0; i < 8; i++) - buf[i] = (float)ptr[i]; - return v256_load_aligned(buf); -#endif -} - -inline void v_pack_store(float16_t* ptr, const v_float32x8& a) -{ -#if CV_FP16 - __m128i ah = _mm256_cvtps_ph(a.val, 0); - _mm_storeu_si128((__m128i*)ptr, ah); -#else - float CV_DECL_ALIGNED(32) buf[8]; - v_store_aligned(buf, a); - for (int i = 0; i < 8; i++) - ptr[i] = float16_t(buf[i]); -#endif -} - -// -// end of FP16 -// - -inline void v256_cleanup() { _mm256_zeroall(); } - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END - -//! @endcond - -} // cv:: - -#endif // OPENCV_HAL_INTRIN_AVX_HPP diff --git a/include/opencv2/core/hal/intrin_avx512.hpp b/include/opencv2/core/hal/intrin_avx512.hpp deleted file mode 100755 index 75a3bd4..0000000 --- a/include/opencv2/core/hal/intrin_avx512.hpp +++ /dev/null @@ -1,3078 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html - -#ifndef OPENCV_HAL_INTRIN_AVX512_HPP -#define OPENCV_HAL_INTRIN_AVX512_HPP - -#if defined(_MSC_VER) && (_MSC_VER < 1920/*MSVS2019*/) -# pragma warning(disable:4146) // unary minus operator applied to unsigned type, result still unsigned -# pragma warning(disable:4309) // 'argument': truncation of constant value -# pragma warning(disable:4310) // cast truncates constant value -#endif - -#define CVT_ROUND_MODES_IMPLEMENTED 0 - -#define CV_SIMD512 1 -#define CV_SIMD512_64F 1 -#define CV_SIMD512_FP16 0 // no native operations with FP16 type. Only load/store from float32x8 are available (if CV_FP16 == 1) - -#define _v512_set_epu64(a7, a6, a5, a4, a3, a2, a1, a0) _mm512_set_epi64((int64)(a7),(int64)(a6),(int64)(a5),(int64)(a4),(int64)(a3),(int64)(a2),(int64)(a1),(int64)(a0)) -#define _v512_set_epu32(a15, a14, a13, a12, a11, a10, a9, a8, a7, a6, a5, a4, a3, a2, a1, a0) \ - _mm512_set_epi64(((int64)(a15)<<32)|(int64)(a14), ((int64)(a13)<<32)|(int64)(a12), ((int64)(a11)<<32)|(int64)(a10), ((int64)( a9)<<32)|(int64)( a8), \ - ((int64)( a7)<<32)|(int64)( a6), ((int64)( a5)<<32)|(int64)( a4), ((int64)( a3)<<32)|(int64)( a2), ((int64)( a1)<<32)|(int64)( a0)) -#define _v512_set_epu16(a31, a30, a29, a28, a27, a26, a25, a24, a23, a22, a21, a20, a19, a18, a17, a16, \ - a15, a14, a13, a12, a11, a10, a9, a8, a7, a6, a5, a4, a3, a2, a1, a0) \ - _v512_set_epu32(((unsigned)(a31)<<16)|(unsigned)(a30), ((unsigned)(a29)<<16)|(unsigned)(a28), ((unsigned)(a27)<<16)|(unsigned)(a26), ((unsigned)(a25)<<16)|(unsigned)(a24), \ - ((unsigned)(a23)<<16)|(unsigned)(a22), ((unsigned)(a21)<<16)|(unsigned)(a20), ((unsigned)(a19)<<16)|(unsigned)(a18), ((unsigned)(a17)<<16)|(unsigned)(a16), \ - ((unsigned)(a15)<<16)|(unsigned)(a14), ((unsigned)(a13)<<16)|(unsigned)(a12), ((unsigned)(a11)<<16)|(unsigned)(a10), ((unsigned)( a9)<<16)|(unsigned)( a8), \ - ((unsigned)( a7)<<16)|(unsigned)( a6), ((unsigned)( a5)<<16)|(unsigned)( a4), ((unsigned)( a3)<<16)|(unsigned)( a2), ((unsigned)( a1)<<16)|(unsigned)( a0)) -#define _v512_set_epu8(a63, a62, a61, a60, a59, a58, a57, a56, a55, a54, a53, a52, a51, a50, a49, a48, \ - a47, a46, a45, a44, a43, a42, a41, a40, a39, a38, a37, a36, a35, a34, a33, a32, \ - a31, a30, a29, a28, a27, a26, a25, a24, a23, a22, a21, a20, a19, a18, a17, a16, \ - a15, a14, a13, a12, a11, a10, a9, a8, a7, a6, a5, a4, a3, a2, a1, a0) \ - _v512_set_epu32(((unsigned)(a63)<<24)|((unsigned)(a62)<<16)|((unsigned)(a61)<<8)|(unsigned)(a60),((unsigned)(a59)<<24)|((unsigned)(a58)<<16)|((unsigned)(a57)<<8)|(unsigned)(a56), \ - ((unsigned)(a55)<<24)|((unsigned)(a54)<<16)|((unsigned)(a53)<<8)|(unsigned)(a52),((unsigned)(a51)<<24)|((unsigned)(a50)<<16)|((unsigned)(a49)<<8)|(unsigned)(a48), \ - ((unsigned)(a47)<<24)|((unsigned)(a46)<<16)|((unsigned)(a45)<<8)|(unsigned)(a44),((unsigned)(a43)<<24)|((unsigned)(a42)<<16)|((unsigned)(a41)<<8)|(unsigned)(a40), \ - ((unsigned)(a39)<<24)|((unsigned)(a38)<<16)|((unsigned)(a37)<<8)|(unsigned)(a36),((unsigned)(a35)<<24)|((unsigned)(a34)<<16)|((unsigned)(a33)<<8)|(unsigned)(a32), \ - ((unsigned)(a31)<<24)|((unsigned)(a30)<<16)|((unsigned)(a29)<<8)|(unsigned)(a28),((unsigned)(a27)<<24)|((unsigned)(a26)<<16)|((unsigned)(a25)<<8)|(unsigned)(a24), \ - ((unsigned)(a23)<<24)|((unsigned)(a22)<<16)|((unsigned)(a21)<<8)|(unsigned)(a20),((unsigned)(a19)<<24)|((unsigned)(a18)<<16)|((unsigned)(a17)<<8)|(unsigned)(a16), \ - ((unsigned)(a15)<<24)|((unsigned)(a14)<<16)|((unsigned)(a13)<<8)|(unsigned)(a12),((unsigned)(a11)<<24)|((unsigned)(a10)<<16)|((unsigned)( a9)<<8)|(unsigned)( a8), \ - ((unsigned)( a7)<<24)|((unsigned)( a6)<<16)|((unsigned)( a5)<<8)|(unsigned)( a4),((unsigned)( a3)<<24)|((unsigned)( a2)<<16)|((unsigned)( a1)<<8)|(unsigned)( a0)) -#define _v512_set_epi8(a63, a62, a61, a60, a59, a58, a57, a56, a55, a54, a53, a52, a51, a50, a49, a48, \ - a47, a46, a45, a44, a43, a42, a41, a40, a39, a38, a37, a36, a35, a34, a33, a32, \ - a31, a30, a29, a28, a27, a26, a25, a24, a23, a22, a21, a20, a19, a18, a17, a16, \ - a15, a14, a13, a12, a11, a10, a9, a8, a7, a6, a5, a4, a3, a2, a1, a0) \ - _v512_set_epu8((uchar)(a63), (uchar)(a62), (uchar)(a61), (uchar)(a60), (uchar)(a59), (uchar)(a58), (uchar)(a57), (uchar)(a56), \ - (uchar)(a55), (uchar)(a54), (uchar)(a53), (uchar)(a52), (uchar)(a51), (uchar)(a50), (uchar)(a49), (uchar)(a48), \ - (uchar)(a47), (uchar)(a46), (uchar)(a45), (uchar)(a44), (uchar)(a43), (uchar)(a42), (uchar)(a41), (uchar)(a40), \ - (uchar)(a39), (uchar)(a38), (uchar)(a37), (uchar)(a36), (uchar)(a35), (uchar)(a34), (uchar)(a33), (uchar)(a32), \ - (uchar)(a31), (uchar)(a30), (uchar)(a29), (uchar)(a28), (uchar)(a27), (uchar)(a26), (uchar)(a25), (uchar)(a24), \ - (uchar)(a23), (uchar)(a22), (uchar)(a21), (uchar)(a20), (uchar)(a19), (uchar)(a18), (uchar)(a17), (uchar)(a16), \ - (uchar)(a15), (uchar)(a14), (uchar)(a13), (uchar)(a12), (uchar)(a11), (uchar)(a10), (uchar)( a9), (uchar)( a8), \ - (uchar)( a7), (uchar)( a6), (uchar)( a5), (uchar)( a4), (uchar)( a3), (uchar)( a2), (uchar)( a1), (uchar)( a0)) - -#ifndef _mm512_cvtpd_pslo -#ifdef _mm512_zextsi256_si512 -#define _mm512_cvtpd_pslo(a) _mm512_zextps256_ps512(_mm512_cvtpd_ps(a)) -#else -//if preferred way to extend with zeros is unavailable -#define _mm512_cvtpd_pslo(a) _mm512_castps256_ps512(_mm512_cvtpd_ps(a)) -#endif -#endif -///////// Utils //////////// - -namespace -{ - -inline __m512i _v512_combine(const __m256i& lo, const __m256i& hi) -{ return _mm512_inserti32x8(_mm512_castsi256_si512(lo), hi, 1); } - -inline __m512 _v512_combine(const __m256& lo, const __m256& hi) -{ return _mm512_insertf32x8(_mm512_castps256_ps512(lo), hi, 1); } - -inline __m512d _v512_combine(const __m256d& lo, const __m256d& hi) -{ return _mm512_insertf64x4(_mm512_castpd256_pd512(lo), hi, 1); } - -inline int _v_cvtsi512_si32(const __m512i& a) -{ return _mm_cvtsi128_si32(_mm512_castsi512_si128(a)); } - -inline __m256i _v512_extract_high(const __m512i& v) -{ return _mm512_extracti32x8_epi32(v, 1); } - -inline __m256 _v512_extract_high(const __m512& v) -{ return _mm512_extractf32x8_ps(v, 1); } - -inline __m256d _v512_extract_high(const __m512d& v) -{ return _mm512_extractf64x4_pd(v, 1); } - -inline __m256i _v512_extract_low(const __m512i& v) -{ return _mm512_castsi512_si256(v); } - -inline __m256 _v512_extract_low(const __m512& v) -{ return _mm512_castps512_ps256(v); } - -inline __m256d _v512_extract_low(const __m512d& v) -{ return _mm512_castpd512_pd256(v); } - -inline __m512i _v512_insert(const __m512i& a, const __m256i& b) -{ return _mm512_inserti32x8(a, b, 0); } - -inline __m512 _v512_insert(const __m512& a, const __m256& b) -{ return _mm512_insertf32x8(a, b, 0); } - -inline __m512d _v512_insert(const __m512d& a, const __m256d& b) -{ return _mm512_insertf64x4(a, b, 0); } - -} - -namespace cv -{ - -//! @cond IGNORED - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN - -///////// Types //////////// - -struct v_uint8x64 -{ - typedef uchar lane_type; - enum { nlanes = 64 }; - __m512i val; - - explicit v_uint8x64(__m512i v) : val(v) {} - v_uint8x64(uchar v0, uchar v1, uchar v2, uchar v3, - uchar v4, uchar v5, uchar v6, uchar v7, - uchar v8, uchar v9, uchar v10, uchar v11, - uchar v12, uchar v13, uchar v14, uchar v15, - uchar v16, uchar v17, uchar v18, uchar v19, - uchar v20, uchar v21, uchar v22, uchar v23, - uchar v24, uchar v25, uchar v26, uchar v27, - uchar v28, uchar v29, uchar v30, uchar v31, - uchar v32, uchar v33, uchar v34, uchar v35, - uchar v36, uchar v37, uchar v38, uchar v39, - uchar v40, uchar v41, uchar v42, uchar v43, - uchar v44, uchar v45, uchar v46, uchar v47, - uchar v48, uchar v49, uchar v50, uchar v51, - uchar v52, uchar v53, uchar v54, uchar v55, - uchar v56, uchar v57, uchar v58, uchar v59, - uchar v60, uchar v61, uchar v62, uchar v63) - { - val = _v512_set_epu8(v63, v62, v61, v60, v59, v58, v57, v56, v55, v54, v53, v52, v51, v50, v49, v48, - v47, v46, v45, v44, v43, v42, v41, v40, v39, v38, v37, v36, v35, v34, v33, v32, - v31, v30, v29, v28, v27, v26, v25, v24, v23, v22, v21, v20, v19, v18, v17, v16, - v15, v14, v13, v12, v11, v10, v9, v8, v7, v6, v5, v4, v3, v2, v1, v0); - } - v_uint8x64() {} - - static inline v_uint8x64 zero() { return v_uint8x64(_mm512_setzero_si512()); } - - uchar get0() const { return (uchar)_v_cvtsi512_si32(val); } -}; - -struct v_int8x64 -{ - typedef schar lane_type; - enum { nlanes = 64 }; - __m512i val; - - explicit v_int8x64(__m512i v) : val(v) {} - v_int8x64(schar v0, schar v1, schar v2, schar v3, - schar v4, schar v5, schar v6, schar v7, - schar v8, schar v9, schar v10, schar v11, - schar v12, schar v13, schar v14, schar v15, - schar v16, schar v17, schar v18, schar v19, - schar v20, schar v21, schar v22, schar v23, - schar v24, schar v25, schar v26, schar v27, - schar v28, schar v29, schar v30, schar v31, - schar v32, schar v33, schar v34, schar v35, - schar v36, schar v37, schar v38, schar v39, - schar v40, schar v41, schar v42, schar v43, - schar v44, schar v45, schar v46, schar v47, - schar v48, schar v49, schar v50, schar v51, - schar v52, schar v53, schar v54, schar v55, - schar v56, schar v57, schar v58, schar v59, - schar v60, schar v61, schar v62, schar v63) - { - val = _v512_set_epi8(v63, v62, v61, v60, v59, v58, v57, v56, v55, v54, v53, v52, v51, v50, v49, v48, - v47, v46, v45, v44, v43, v42, v41, v40, v39, v38, v37, v36, v35, v34, v33, v32, - v31, v30, v29, v28, v27, v26, v25, v24, v23, v22, v21, v20, v19, v18, v17, v16, - v15, v14, v13, v12, v11, v10, v9, v8, v7, v6, v5, v4, v3, v2, v1, v0); - } - v_int8x64() {} - - static inline v_int8x64 zero() { return v_int8x64(_mm512_setzero_si512()); } - - schar get0() const { return (schar)_v_cvtsi512_si32(val); } -}; - -struct v_uint16x32 -{ - typedef ushort lane_type; - enum { nlanes = 32 }; - __m512i val; - - explicit v_uint16x32(__m512i v) : val(v) {} - v_uint16x32(ushort v0, ushort v1, ushort v2, ushort v3, - ushort v4, ushort v5, ushort v6, ushort v7, - ushort v8, ushort v9, ushort v10, ushort v11, - ushort v12, ushort v13, ushort v14, ushort v15, - ushort v16, ushort v17, ushort v18, ushort v19, - ushort v20, ushort v21, ushort v22, ushort v23, - ushort v24, ushort v25, ushort v26, ushort v27, - ushort v28, ushort v29, ushort v30, ushort v31) - { - val = _v512_set_epu16(v31, v30, v29, v28, v27, v26, v25, v24, v23, v22, v21, v20, v19, v18, v17, v16, - v15, v14, v13, v12, v11, v10, v9, v8, v7, v6, v5, v4, v3, v2, v1, v0); - } - v_uint16x32() {} - - static inline v_uint16x32 zero() { return v_uint16x32(_mm512_setzero_si512()); } - - ushort get0() const { return (ushort)_v_cvtsi512_si32(val); } -}; - -struct v_int16x32 -{ - typedef short lane_type; - enum { nlanes = 32 }; - __m512i val; - - explicit v_int16x32(__m512i v) : val(v) {} - v_int16x32(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7, - short v8, short v9, short v10, short v11, short v12, short v13, short v14, short v15, - short v16, short v17, short v18, short v19, short v20, short v21, short v22, short v23, - short v24, short v25, short v26, short v27, short v28, short v29, short v30, short v31) - { - val = _v512_set_epu16((ushort)v31, (ushort)v30, (ushort)v29, (ushort)v28, (ushort)v27, (ushort)v26, (ushort)v25, (ushort)v24, - (ushort)v23, (ushort)v22, (ushort)v21, (ushort)v20, (ushort)v19, (ushort)v18, (ushort)v17, (ushort)v16, - (ushort)v15, (ushort)v14, (ushort)v13, (ushort)v12, (ushort)v11, (ushort)v10, (ushort)v9 , (ushort)v8, - (ushort)v7 , (ushort)v6 , (ushort)v5 , (ushort)v4 , (ushort)v3 , (ushort)v2 , (ushort)v1 , (ushort)v0); - } - v_int16x32() {} - - static inline v_int16x32 zero() { return v_int16x32(_mm512_setzero_si512()); } - - short get0() const { return (short)_v_cvtsi512_si32(val); } -}; - -struct v_uint32x16 -{ - typedef unsigned lane_type; - enum { nlanes = 16 }; - __m512i val; - - explicit v_uint32x16(__m512i v) : val(v) {} - v_uint32x16(unsigned v0, unsigned v1, unsigned v2, unsigned v3, - unsigned v4, unsigned v5, unsigned v6, unsigned v7, - unsigned v8, unsigned v9, unsigned v10, unsigned v11, - unsigned v12, unsigned v13, unsigned v14, unsigned v15) - { - val = _mm512_setr_epi32((int)v0, (int)v1, (int)v2, (int)v3, (int)v4, (int)v5, (int)v6, (int)v7, - (int)v8, (int)v9, (int)v10, (int)v11, (int)v12, (int)v13, (int)v14, (int)v15); - } - v_uint32x16() {} - - static inline v_uint32x16 zero() { return v_uint32x16(_mm512_setzero_si512()); } - - unsigned get0() const { return (unsigned)_v_cvtsi512_si32(val); } -}; - -struct v_int32x16 -{ - typedef int lane_type; - enum { nlanes = 16 }; - __m512i val; - - explicit v_int32x16(__m512i v) : val(v) {} - v_int32x16(int v0, int v1, int v2, int v3, int v4, int v5, int v6, int v7, - int v8, int v9, int v10, int v11, int v12, int v13, int v14, int v15) - { - val = _mm512_setr_epi32(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15); - } - v_int32x16() {} - - static inline v_int32x16 zero() { return v_int32x16(_mm512_setzero_si512()); } - - int get0() const { return _v_cvtsi512_si32(val); } -}; - -struct v_float32x16 -{ - typedef float lane_type; - enum { nlanes = 16 }; - __m512 val; - - explicit v_float32x16(__m512 v) : val(v) {} - v_float32x16(float v0, float v1, float v2, float v3, float v4, float v5, float v6, float v7, - float v8, float v9, float v10, float v11, float v12, float v13, float v14, float v15) - { - val = _mm512_setr_ps(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15); - } - v_float32x16() {} - - static inline v_float32x16 zero() { return v_float32x16(_mm512_setzero_ps()); } - - float get0() const { return _mm_cvtss_f32(_mm512_castps512_ps128(val)); } -}; - -struct v_uint64x8 -{ - typedef uint64 lane_type; - enum { nlanes = 8 }; - __m512i val; - - explicit v_uint64x8(__m512i v) : val(v) {} - v_uint64x8(uint64 v0, uint64 v1, uint64 v2, uint64 v3, uint64 v4, uint64 v5, uint64 v6, uint64 v7) - { val = _mm512_setr_epi64((int64)v0, (int64)v1, (int64)v2, (int64)v3, (int64)v4, (int64)v5, (int64)v6, (int64)v7); } - v_uint64x8() {} - - static inline v_uint64x8 zero() { return v_uint64x8(_mm512_setzero_si512()); } - - uint64 get0() const - { - #if defined __x86_64__ || defined _M_X64 - return (uint64)_mm_cvtsi128_si64(_mm512_castsi512_si128(val)); - #else - int a = _mm_cvtsi128_si32(_mm512_castsi512_si128(val)); - int b = _mm_cvtsi128_si32(_mm512_castsi512_si128(_mm512_srli_epi64(val, 32))); - return (unsigned)a | ((uint64)(unsigned)b << 32); - #endif - } -}; - -struct v_int64x8 -{ - typedef int64 lane_type; - enum { nlanes = 8 }; - __m512i val; - - explicit v_int64x8(__m512i v) : val(v) {} - v_int64x8(int64 v0, int64 v1, int64 v2, int64 v3, int64 v4, int64 v5, int64 v6, int64 v7) - { val = _mm512_setr_epi64(v0, v1, v2, v3, v4, v5, v6, v7); } - v_int64x8() {} - - static inline v_int64x8 zero() { return v_int64x8(_mm512_setzero_si512()); } - - int64 get0() const - { - #if defined __x86_64__ || defined _M_X64 - return (int64)_mm_cvtsi128_si64(_mm512_castsi512_si128(val)); - #else - int a = _mm_cvtsi128_si32(_mm512_castsi512_si128(val)); - int b = _mm_cvtsi128_si32(_mm512_castsi512_si128(_mm512_srli_epi64(val, 32))); - return (int64)((unsigned)a | ((uint64)(unsigned)b << 32)); - #endif - } -}; - -struct v_float64x8 -{ - typedef double lane_type; - enum { nlanes = 8 }; - __m512d val; - - explicit v_float64x8(__m512d v) : val(v) {} - v_float64x8(double v0, double v1, double v2, double v3, double v4, double v5, double v6, double v7) - { val = _mm512_setr_pd(v0, v1, v2, v3, v4, v5, v6, v7); } - v_float64x8() {} - - static inline v_float64x8 zero() { return v_float64x8(_mm512_setzero_pd()); } - - double get0() const { return _mm_cvtsd_f64(_mm512_castpd512_pd128(val)); } -}; - -//////////////// Load and store operations /////////////// - -#define OPENCV_HAL_IMPL_AVX512_LOADSTORE(_Tpvec, _Tp) \ - inline _Tpvec v512_load(const _Tp* ptr) \ - { return _Tpvec(_mm512_loadu_si512((const __m512i*)ptr)); } \ - inline _Tpvec v512_load_aligned(const _Tp* ptr) \ - { return _Tpvec(_mm512_load_si512((const __m512i*)ptr)); } \ - inline _Tpvec v512_load_low(const _Tp* ptr) \ - { \ - __m256i v256 = _mm256_loadu_si256((const __m256i*)ptr); \ - return _Tpvec(_mm512_castsi256_si512(v256)); \ - } \ - inline _Tpvec v512_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ - { \ - __m256i vlo = _mm256_loadu_si256((const __m256i*)ptr0); \ - __m256i vhi = _mm256_loadu_si256((const __m256i*)ptr1); \ - return _Tpvec(_v512_combine(vlo, vhi)); \ - } \ - inline void v_store(_Tp* ptr, const _Tpvec& a) \ - { _mm512_storeu_si512((__m512i*)ptr, a.val); } \ - inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ - { _mm512_store_si512((__m512i*)ptr, a.val); } \ - inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ - { _mm512_stream_si512((__m512i*)ptr, a.val); } \ - inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ - { \ - if( mode == hal::STORE_UNALIGNED ) \ - _mm512_storeu_si512((__m512i*)ptr, a.val); \ - else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ - _mm512_stream_si512((__m512i*)ptr, a.val); \ - else \ - _mm512_store_si512((__m512i*)ptr, a.val); \ - } \ - inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ - { _mm256_storeu_si256((__m256i*)ptr, _v512_extract_low(a.val)); } \ - inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ - { _mm256_storeu_si256((__m256i*)ptr, _v512_extract_high(a.val)); } - -OPENCV_HAL_IMPL_AVX512_LOADSTORE(v_uint8x64, uchar) -OPENCV_HAL_IMPL_AVX512_LOADSTORE(v_int8x64, schar) -OPENCV_HAL_IMPL_AVX512_LOADSTORE(v_uint16x32, ushort) -OPENCV_HAL_IMPL_AVX512_LOADSTORE(v_int16x32, short) -OPENCV_HAL_IMPL_AVX512_LOADSTORE(v_uint32x16, unsigned) -OPENCV_HAL_IMPL_AVX512_LOADSTORE(v_int32x16, int) -OPENCV_HAL_IMPL_AVX512_LOADSTORE(v_uint64x8, uint64) -OPENCV_HAL_IMPL_AVX512_LOADSTORE(v_int64x8, int64) - -#define OPENCV_HAL_IMPL_AVX512_LOADSTORE_FLT(_Tpvec, _Tp, suffix, halfreg) \ - inline _Tpvec v512_load(const _Tp* ptr) \ - { return _Tpvec(_mm512_loadu_##suffix(ptr)); } \ - inline _Tpvec v512_load_aligned(const _Tp* ptr) \ - { return _Tpvec(_mm512_load_##suffix(ptr)); } \ - inline _Tpvec v512_load_low(const _Tp* ptr) \ - { \ - return _Tpvec(_mm512_cast##suffix##256_##suffix##512 \ - (_mm256_loadu_##suffix(ptr))); \ - } \ - inline _Tpvec v512_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ - { \ - halfreg vlo = _mm256_loadu_##suffix(ptr0); \ - halfreg vhi = _mm256_loadu_##suffix(ptr1); \ - return _Tpvec(_v512_combine(vlo, vhi)); \ - } \ - inline void v_store(_Tp* ptr, const _Tpvec& a) \ - { _mm512_storeu_##suffix(ptr, a.val); } \ - inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ - { _mm512_store_##suffix(ptr, a.val); } \ - inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ - { _mm512_stream_##suffix(ptr, a.val); } \ - inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ - { \ - if( mode == hal::STORE_UNALIGNED ) \ - _mm512_storeu_##suffix(ptr, a.val); \ - else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ - _mm512_stream_##suffix(ptr, a.val); \ - else \ - _mm512_store_##suffix(ptr, a.val); \ - } \ - inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ - { _mm256_storeu_##suffix(ptr, _v512_extract_low(a.val)); } \ - inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ - { _mm256_storeu_##suffix(ptr, _v512_extract_high(a.val)); } - -OPENCV_HAL_IMPL_AVX512_LOADSTORE_FLT(v_float32x16, float, ps, __m256) -OPENCV_HAL_IMPL_AVX512_LOADSTORE_FLT(v_float64x8, double, pd, __m256d) - -#define OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, _Tpvecf, suffix, cast) \ - inline _Tpvec v_reinterpret_as_##suffix(const _Tpvecf& a) \ - { return _Tpvec(cast(a.val)); } - -#define OPENCV_HAL_IMPL_AVX512_INIT(_Tpvec, _Tp, suffix, ssuffix, ctype_s) \ - inline _Tpvec v512_setzero_##suffix() \ - { return _Tpvec(_mm512_setzero_si512()); } \ - inline _Tpvec v512_setall_##suffix(_Tp v) \ - { return _Tpvec(_mm512_set1_##ssuffix((ctype_s)v)); } \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_uint8x64, suffix, OPENCV_HAL_NOP) \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_int8x64, suffix, OPENCV_HAL_NOP) \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_uint16x32, suffix, OPENCV_HAL_NOP) \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_int16x32, suffix, OPENCV_HAL_NOP) \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_uint32x16, suffix, OPENCV_HAL_NOP) \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_int32x16, suffix, OPENCV_HAL_NOP) \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_uint64x8, suffix, OPENCV_HAL_NOP) \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_int64x8, suffix, OPENCV_HAL_NOP) \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_float32x16, suffix, _mm512_castps_si512) \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_float64x8, suffix, _mm512_castpd_si512) - -OPENCV_HAL_IMPL_AVX512_INIT(v_uint8x64, uchar, u8, epi8, char) -OPENCV_HAL_IMPL_AVX512_INIT(v_int8x64, schar, s8, epi8, char) -OPENCV_HAL_IMPL_AVX512_INIT(v_uint16x32, ushort, u16, epi16, short) -OPENCV_HAL_IMPL_AVX512_INIT(v_int16x32, short, s16, epi16, short) -OPENCV_HAL_IMPL_AVX512_INIT(v_uint32x16, unsigned, u32, epi32, int) -OPENCV_HAL_IMPL_AVX512_INIT(v_int32x16, int, s32, epi32, int) -OPENCV_HAL_IMPL_AVX512_INIT(v_uint64x8, uint64, u64, epi64, int64) -OPENCV_HAL_IMPL_AVX512_INIT(v_int64x8, int64, s64, epi64, int64) - -#define OPENCV_HAL_IMPL_AVX512_INIT_FLT(_Tpvec, _Tp, suffix, zsuffix, cast) \ - inline _Tpvec v512_setzero_##suffix() \ - { return _Tpvec(_mm512_setzero_##zsuffix()); } \ - inline _Tpvec v512_setall_##suffix(_Tp v) \ - { return _Tpvec(_mm512_set1_##zsuffix(v)); } \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_uint8x64, suffix, cast) \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_int8x64, suffix, cast) \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_uint16x32, suffix, cast) \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_int16x32, suffix, cast) \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_uint32x16, suffix, cast) \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_int32x16, suffix, cast) \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_uint64x8, suffix, cast) \ - OPENCV_HAL_IMPL_AVX512_CAST(_Tpvec, v_int64x8, suffix, cast) - -OPENCV_HAL_IMPL_AVX512_INIT_FLT(v_float32x16, float, f32, ps, _mm512_castsi512_ps) -OPENCV_HAL_IMPL_AVX512_INIT_FLT(v_float64x8, double, f64, pd, _mm512_castsi512_pd) - -inline v_float32x16 v_reinterpret_as_f32(const v_float32x16& a) -{ return a; } -inline v_float32x16 v_reinterpret_as_f32(const v_float64x8& a) -{ return v_float32x16(_mm512_castpd_ps(a.val)); } - -inline v_float64x8 v_reinterpret_as_f64(const v_float64x8& a) -{ return a; } -inline v_float64x8 v_reinterpret_as_f64(const v_float32x16& a) -{ return v_float64x8(_mm512_castps_pd(a.val)); } - -// FP16 -inline v_float32x16 v512_load_expand(const float16_t* ptr) -{ - return v_float32x16(_mm512_cvtph_ps(_mm256_loadu_si256((const __m256i*)ptr))); -} - -inline void v_pack_store(float16_t* ptr, const v_float32x16& a) -{ - __m256i ah = _mm512_cvtps_ph(a.val, 0); - _mm256_storeu_si256((__m256i*)ptr, ah); -} - -/* Recombine & ZIP */ -inline void v_zip(const v_int8x64& a, const v_int8x64& b, v_int8x64& ab0, v_int8x64& ab1) -{ -#if CV_AVX_512VBMI - __m512i mask0 = _v512_set_epu8( 95, 31, 94, 30, 93, 29, 92, 28, 91, 27, 90, 26, 89, 25, 88, 24, - 87, 23, 86, 22, 85, 21, 84, 20, 83, 19, 82, 18, 81, 17, 80, 16, - 79, 15, 78, 14, 77, 13, 76, 12, 75, 11, 74, 10, 73, 9, 72, 8, - 71, 7, 70, 6, 69, 5, 68, 4, 67, 3, 66, 2, 65, 1, 64, 0); - ab0 = v_int8x64(_mm512_permutex2var_epi8(a.val, mask0, b.val)); - __m512i mask1 = _v512_set_epu8(127, 63, 126, 62, 125, 61, 124, 60, 123, 59, 122, 58, 121, 57, 120, 56, - 119, 55, 118, 54, 117, 53, 116, 52, 115, 51, 114, 50, 113, 49, 112, 48, - 111, 47, 110, 46, 109, 45, 108, 44, 107, 43, 106, 42, 105, 41, 104, 40, - 103, 39, 102, 38, 101, 37, 100, 36, 99, 35, 98, 34, 97, 33, 96, 32); - ab1 = v_int8x64(_mm512_permutex2var_epi8(a.val, mask1, b.val)); -#else - __m512i low = _mm512_unpacklo_epi8(a.val, b.val); - __m512i high = _mm512_unpackhi_epi8(a.val, b.val); - ab0 = v_int8x64(_mm512_permutex2var_epi64(low, _v512_set_epu64(11, 10, 3, 2, 9, 8, 1, 0), high)); - ab1 = v_int8x64(_mm512_permutex2var_epi64(low, _v512_set_epu64(15, 14, 7, 6, 13, 12, 5, 4), high)); -#endif -} -inline void v_zip(const v_int16x32& a, const v_int16x32& b, v_int16x32& ab0, v_int16x32& ab1) -{ - __m512i mask0 = _v512_set_epu16(47, 15, 46, 14, 45, 13, 44, 12, 43, 11, 42, 10, 41, 9, 40, 8, - 39, 7, 38, 6, 37, 5, 36, 4, 35, 3, 34, 2, 33, 1, 32, 0); - ab0 = v_int16x32(_mm512_permutex2var_epi16(a.val, mask0, b.val)); - __m512i mask1 = _v512_set_epu16(63, 31, 62, 30, 61, 29, 60, 28, 59, 27, 58, 26, 57, 25, 56, 24, - 55, 23, 54, 22, 53, 21, 52, 20, 51, 19, 50, 18, 49, 17, 48, 16); - ab1 = v_int16x32(_mm512_permutex2var_epi16(a.val, mask1, b.val)); -} -inline void v_zip(const v_int32x16& a, const v_int32x16& b, v_int32x16& ab0, v_int32x16& ab1) -{ - __m512i mask0 = _v512_set_epu32(23, 7, 22, 6, 21, 5, 20, 4, 19, 3, 18, 2, 17, 1, 16, 0); - ab0 = v_int32x16(_mm512_permutex2var_epi32(a.val, mask0, b.val)); - __m512i mask1 = _v512_set_epu32(31, 15, 30, 14, 29, 13, 28, 12, 27, 11, 26, 10, 25, 9, 24, 8); - ab1 = v_int32x16(_mm512_permutex2var_epi32(a.val, mask1, b.val)); -} -inline void v_zip(const v_int64x8& a, const v_int64x8& b, v_int64x8& ab0, v_int64x8& ab1) -{ - __m512i mask0 = _v512_set_epu64(11, 3, 10, 2, 9, 1, 8, 0); - ab0 = v_int64x8(_mm512_permutex2var_epi64(a.val, mask0, b.val)); - __m512i mask1 = _v512_set_epu64(15, 7, 14, 6, 13, 5, 12, 4); - ab1 = v_int64x8(_mm512_permutex2var_epi64(a.val, mask1, b.val)); -} - -inline void v_zip(const v_uint8x64& a, const v_uint8x64& b, v_uint8x64& ab0, v_uint8x64& ab1) -{ - v_int8x64 i0, i1; - v_zip(v_reinterpret_as_s8(a), v_reinterpret_as_s8(b), i0, i1); - ab0 = v_reinterpret_as_u8(i0); - ab1 = v_reinterpret_as_u8(i1); -} -inline void v_zip(const v_uint16x32& a, const v_uint16x32& b, v_uint16x32& ab0, v_uint16x32& ab1) -{ - v_int16x32 i0, i1; - v_zip(v_reinterpret_as_s16(a), v_reinterpret_as_s16(b), i0, i1); - ab0 = v_reinterpret_as_u16(i0); - ab1 = v_reinterpret_as_u16(i1); -} -inline void v_zip(const v_uint32x16& a, const v_uint32x16& b, v_uint32x16& ab0, v_uint32x16& ab1) -{ - v_int32x16 i0, i1; - v_zip(v_reinterpret_as_s32(a), v_reinterpret_as_s32(b), i0, i1); - ab0 = v_reinterpret_as_u32(i0); - ab1 = v_reinterpret_as_u32(i1); -} -inline void v_zip(const v_uint64x8& a, const v_uint64x8& b, v_uint64x8& ab0, v_uint64x8& ab1) -{ - v_int64x8 i0, i1; - v_zip(v_reinterpret_as_s64(a), v_reinterpret_as_s64(b), i0, i1); - ab0 = v_reinterpret_as_u64(i0); - ab1 = v_reinterpret_as_u64(i1); -} -inline void v_zip(const v_float32x16& a, const v_float32x16& b, v_float32x16& ab0, v_float32x16& ab1) -{ - v_int32x16 i0, i1; - v_zip(v_reinterpret_as_s32(a), v_reinterpret_as_s32(b), i0, i1); - ab0 = v_reinterpret_as_f32(i0); - ab1 = v_reinterpret_as_f32(i1); -} -inline void v_zip(const v_float64x8& a, const v_float64x8& b, v_float64x8& ab0, v_float64x8& ab1) -{ - v_int64x8 i0, i1; - v_zip(v_reinterpret_as_s64(a), v_reinterpret_as_s64(b), i0, i1); - ab0 = v_reinterpret_as_f64(i0); - ab1 = v_reinterpret_as_f64(i1); -} - -#define OPENCV_HAL_IMPL_AVX512_COMBINE(_Tpvec, suffix) \ - inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \ - { return _Tpvec(_v512_combine(_v512_extract_low(a.val), _v512_extract_low(b.val))); } \ - inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \ - { return _Tpvec(_v512_insert(b.val, _v512_extract_high(a.val))); } \ - inline void v_recombine(const _Tpvec& a, const _Tpvec& b, \ - _Tpvec& c, _Tpvec& d) \ - { \ - c.val = _v512_combine(_v512_extract_low(a.val),_v512_extract_low(b.val)); \ - d.val = _v512_insert(b.val,_v512_extract_high(a.val)); \ - } - - -OPENCV_HAL_IMPL_AVX512_COMBINE(v_uint8x64, epi8) -OPENCV_HAL_IMPL_AVX512_COMBINE(v_int8x64, epi8) -OPENCV_HAL_IMPL_AVX512_COMBINE(v_uint16x32, epi16) -OPENCV_HAL_IMPL_AVX512_COMBINE(v_int16x32, epi16) -OPENCV_HAL_IMPL_AVX512_COMBINE(v_uint32x16, epi32) -OPENCV_HAL_IMPL_AVX512_COMBINE(v_int32x16, epi32) -OPENCV_HAL_IMPL_AVX512_COMBINE(v_uint64x8, epi64) -OPENCV_HAL_IMPL_AVX512_COMBINE(v_int64x8, epi64) -OPENCV_HAL_IMPL_AVX512_COMBINE(v_float32x16, ps) -OPENCV_HAL_IMPL_AVX512_COMBINE(v_float64x8, pd) - -////////// Arithmetic, bitwise and comparison operations ///////// - -/* Element-wise binary and unary operations */ - -/** Non-saturating arithmetics **/ -#define OPENCV_HAL_IMPL_AVX512_BIN_FUNC(func, _Tpvec, intrin) \ - inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ - { return _Tpvec(intrin(a.val, b.val)); } - -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_add_wrap, v_uint8x64, _mm512_add_epi8) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_add_wrap, v_int8x64, _mm512_add_epi8) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_add_wrap, v_uint16x32, _mm512_add_epi16) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_add_wrap, v_int16x32, _mm512_add_epi16) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_sub_wrap, v_uint8x64, _mm512_sub_epi8) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_sub_wrap, v_int8x64, _mm512_sub_epi8) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_sub_wrap, v_uint16x32, _mm512_sub_epi16) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_sub_wrap, v_int16x32, _mm512_sub_epi16) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_mul_wrap, v_uint16x32, _mm512_mullo_epi16) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_mul_wrap, v_int16x32, _mm512_mullo_epi16) - -inline v_uint8x64 v_mul_wrap(const v_uint8x64& a, const v_uint8x64& b) -{ - __m512i ad = _mm512_srai_epi16(a.val, 8); - __m512i bd = _mm512_srai_epi16(b.val, 8); - __m512i p0 = _mm512_mullo_epi16(a.val, b.val); // even - __m512i p1 = _mm512_slli_epi16(_mm512_mullo_epi16(ad, bd), 8); // odd - return v_uint8x64(_mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, p0, p1)); -} -inline v_int8x64 v_mul_wrap(const v_int8x64& a, const v_int8x64& b) -{ - return v_reinterpret_as_s8(v_mul_wrap(v_reinterpret_as_u8(a), v_reinterpret_as_u8(b))); -} - -#define OPENCV_HAL_IMPL_AVX512_BIN_OP(bin_op, _Tpvec, intrin) \ - inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ - { return _Tpvec(intrin(a.val, b.val)); } \ - inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ - { a.val = intrin(a.val, b.val); return a; } - -OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_uint32x16, _mm512_add_epi32) -OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_uint32x16, _mm512_sub_epi32) -OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_int32x16, _mm512_add_epi32) -OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_int32x16, _mm512_sub_epi32) -OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_uint64x8, _mm512_add_epi64) -OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_uint64x8, _mm512_sub_epi64) -OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_int64x8, _mm512_add_epi64) -OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_int64x8, _mm512_sub_epi64) - -OPENCV_HAL_IMPL_AVX512_BIN_OP(*, v_uint32x16, _mm512_mullo_epi32) -OPENCV_HAL_IMPL_AVX512_BIN_OP(*, v_int32x16, _mm512_mullo_epi32) -OPENCV_HAL_IMPL_AVX512_BIN_OP(*, v_uint64x8, _mm512_mullo_epi64) -OPENCV_HAL_IMPL_AVX512_BIN_OP(*, v_int64x8, _mm512_mullo_epi64) - -/** Saturating arithmetics **/ -OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_uint8x64, _mm512_adds_epu8) -OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_uint8x64, _mm512_subs_epu8) -OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_int8x64, _mm512_adds_epi8) -OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_int8x64, _mm512_subs_epi8) -OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_uint16x32, _mm512_adds_epu16) -OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_uint16x32, _mm512_subs_epu16) -OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_int16x32, _mm512_adds_epi16) -OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_int16x32, _mm512_subs_epi16) - -OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_float32x16, _mm512_add_ps) -OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_float32x16, _mm512_sub_ps) -OPENCV_HAL_IMPL_AVX512_BIN_OP(*, v_float32x16, _mm512_mul_ps) -OPENCV_HAL_IMPL_AVX512_BIN_OP(/, v_float32x16, _mm512_div_ps) -OPENCV_HAL_IMPL_AVX512_BIN_OP(+, v_float64x8, _mm512_add_pd) -OPENCV_HAL_IMPL_AVX512_BIN_OP(-, v_float64x8, _mm512_sub_pd) -OPENCV_HAL_IMPL_AVX512_BIN_OP(*, v_float64x8, _mm512_mul_pd) -OPENCV_HAL_IMPL_AVX512_BIN_OP(/, v_float64x8, _mm512_div_pd) - -// saturating multiply -inline v_uint8x64 operator * (const v_uint8x64& a, const v_uint8x64& b) -{ - v_uint16x32 c, d; - v_mul_expand(a, b, c, d); - return v_pack(c, d); -} -inline v_int8x64 operator * (const v_int8x64& a, const v_int8x64& b) -{ - v_int16x32 c, d; - v_mul_expand(a, b, c, d); - return v_pack(c, d); -} -inline v_uint16x32 operator * (const v_uint16x32& a, const v_uint16x32& b) -{ - __m512i pl = _mm512_mullo_epi16(a.val, b.val); - __m512i ph = _mm512_mulhi_epu16(a.val, b.val); - __m512i p0 = _mm512_unpacklo_epi16(pl, ph); - __m512i p1 = _mm512_unpackhi_epi16(pl, ph); - - const __m512i m = _mm512_set1_epi32(65535); - return v_uint16x32(_mm512_packus_epi32(_mm512_min_epu32(p0, m), _mm512_min_epu32(p1, m))); -} -inline v_int16x32 operator * (const v_int16x32& a, const v_int16x32& b) -{ - __m512i pl = _mm512_mullo_epi16(a.val, b.val); - __m512i ph = _mm512_mulhi_epi16(a.val, b.val); - __m512i p0 = _mm512_unpacklo_epi16(pl, ph); - __m512i p1 = _mm512_unpackhi_epi16(pl, ph); - return v_int16x32(_mm512_packs_epi32(p0, p1)); -} - -inline v_uint8x64& operator *= (v_uint8x64& a, const v_uint8x64& b) -{ a = a * b; return a; } -inline v_int8x64& operator *= (v_int8x64& a, const v_int8x64& b) -{ a = a * b; return a; } -inline v_uint16x32& operator *= (v_uint16x32& a, const v_uint16x32& b) -{ a = a * b; return a; } -inline v_int16x32& operator *= (v_int16x32& a, const v_int16x32& b) -{ a = a * b; return a; } - -inline v_int16x32 v_mul_hi(const v_int16x32& a, const v_int16x32& b) { return v_int16x32(_mm512_mulhi_epi16(a.val, b.val)); } -inline v_uint16x32 v_mul_hi(const v_uint16x32& a, const v_uint16x32& b) { return v_uint16x32(_mm512_mulhi_epu16(a.val, b.val)); } - -// Multiply and expand -inline void v_mul_expand(const v_uint8x64& a, const v_uint8x64& b, - v_uint16x32& c, v_uint16x32& d) -{ - v_uint16x32 a0, a1, b0, b1; - v_expand(a, a0, a1); - v_expand(b, b0, b1); - c = v_mul_wrap(a0, b0); - d = v_mul_wrap(a1, b1); -} - -inline void v_mul_expand(const v_int8x64& a, const v_int8x64& b, - v_int16x32& c, v_int16x32& d) -{ - v_int16x32 a0, a1, b0, b1; - v_expand(a, a0, a1); - v_expand(b, b0, b1); - c = v_mul_wrap(a0, b0); - d = v_mul_wrap(a1, b1); -} - -inline void v_mul_expand(const v_int16x32& a, const v_int16x32& b, - v_int32x16& c, v_int32x16& d) -{ - v_int16x32 v0, v1; - v_zip(v_mul_wrap(a, b), v_mul_hi(a, b), v0, v1); - - c = v_reinterpret_as_s32(v0); - d = v_reinterpret_as_s32(v1); -} - -inline void v_mul_expand(const v_uint16x32& a, const v_uint16x32& b, - v_uint32x16& c, v_uint32x16& d) -{ - v_uint16x32 v0, v1; - v_zip(v_mul_wrap(a, b), v_mul_hi(a, b), v0, v1); - - c = v_reinterpret_as_u32(v0); - d = v_reinterpret_as_u32(v1); -} - -inline void v_mul_expand(const v_uint32x16& a, const v_uint32x16& b, - v_uint64x8& c, v_uint64x8& d) -{ - v_zip(v_uint64x8(_mm512_mul_epu32(a.val, b.val)), - v_uint64x8(_mm512_mul_epu32(_mm512_srli_epi64(a.val, 32), _mm512_srli_epi64(b.val, 32))), c, d); -} - -inline void v_mul_expand(const v_int32x16& a, const v_int32x16& b, - v_int64x8& c, v_int64x8& d) -{ - v_zip(v_int64x8(_mm512_mul_epi32(a.val, b.val)), - v_int64x8(_mm512_mul_epi32(_mm512_srli_epi64(a.val, 32), _mm512_srli_epi64(b.val, 32))), c, d); -} - -/** Bitwise shifts **/ -#define OPENCV_HAL_IMPL_AVX512_SHIFT_OP(_Tpuvec, _Tpsvec, suffix) \ - inline _Tpuvec operator << (const _Tpuvec& a, int imm) \ - { return _Tpuvec(_mm512_slli_##suffix(a.val, imm)); } \ - inline _Tpsvec operator << (const _Tpsvec& a, int imm) \ - { return _Tpsvec(_mm512_slli_##suffix(a.val, imm)); } \ - inline _Tpuvec operator >> (const _Tpuvec& a, int imm) \ - { return _Tpuvec(_mm512_srli_##suffix(a.val, imm)); } \ - inline _Tpsvec operator >> (const _Tpsvec& a, int imm) \ - { return _Tpsvec(_mm512_srai_##suffix(a.val, imm)); } \ - template \ - inline _Tpuvec v_shl(const _Tpuvec& a) \ - { return _Tpuvec(_mm512_slli_##suffix(a.val, imm)); } \ - template \ - inline _Tpsvec v_shl(const _Tpsvec& a) \ - { return _Tpsvec(_mm512_slli_##suffix(a.val, imm)); } \ - template \ - inline _Tpuvec v_shr(const _Tpuvec& a) \ - { return _Tpuvec(_mm512_srli_##suffix(a.val, imm)); } \ - template \ - inline _Tpsvec v_shr(const _Tpsvec& a) \ - { return _Tpsvec(_mm512_srai_##suffix(a.val, imm)); } - -OPENCV_HAL_IMPL_AVX512_SHIFT_OP(v_uint16x32, v_int16x32, epi16) -OPENCV_HAL_IMPL_AVX512_SHIFT_OP(v_uint32x16, v_int32x16, epi32) -OPENCV_HAL_IMPL_AVX512_SHIFT_OP(v_uint64x8, v_int64x8, epi64) - - -/** Bitwise logic **/ -#define OPENCV_HAL_IMPL_AVX512_LOGIC_OP(_Tpvec, suffix, not_const) \ - OPENCV_HAL_IMPL_AVX512_BIN_OP(&, _Tpvec, _mm512_and_##suffix) \ - OPENCV_HAL_IMPL_AVX512_BIN_OP(|, _Tpvec, _mm512_or_##suffix) \ - OPENCV_HAL_IMPL_AVX512_BIN_OP(^, _Tpvec, _mm512_xor_##suffix) \ - inline _Tpvec operator ~ (const _Tpvec& a) \ - { return _Tpvec(_mm512_xor_##suffix(a.val, not_const)); } - -OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_uint8x64, si512, _mm512_set1_epi32(-1)) -OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_int8x64, si512, _mm512_set1_epi32(-1)) -OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_uint16x32, si512, _mm512_set1_epi32(-1)) -OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_int16x32, si512, _mm512_set1_epi32(-1)) -OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_uint32x16, si512, _mm512_set1_epi32(-1)) -OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_int32x16, si512, _mm512_set1_epi32(-1)) -OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_uint64x8, si512, _mm512_set1_epi64(-1)) -OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_int64x8, si512, _mm512_set1_epi64(-1)) -OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_float32x16, ps, _mm512_castsi512_ps(_mm512_set1_epi32(-1))) -OPENCV_HAL_IMPL_AVX512_LOGIC_OP(v_float64x8, pd, _mm512_castsi512_pd(_mm512_set1_epi32(-1))) - -/** Select **/ -#define OPENCV_HAL_IMPL_AVX512_SELECT(_Tpvec, suffix, zsuf) \ - inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ - { return _Tpvec(_mm512_mask_blend_##suffix(_mm512_cmp_##suffix##_mask(mask.val, _mm512_setzero_##zsuf(), _MM_CMPINT_EQ), a.val, b.val)); } - -OPENCV_HAL_IMPL_AVX512_SELECT(v_uint8x64, epi8, si512) -OPENCV_HAL_IMPL_AVX512_SELECT(v_int8x64, epi8, si512) -OPENCV_HAL_IMPL_AVX512_SELECT(v_uint16x32, epi16, si512) -OPENCV_HAL_IMPL_AVX512_SELECT(v_int16x32, epi16, si512) -OPENCV_HAL_IMPL_AVX512_SELECT(v_uint32x16, epi32, si512) -OPENCV_HAL_IMPL_AVX512_SELECT(v_int32x16, epi32, si512) -OPENCV_HAL_IMPL_AVX512_SELECT(v_uint64x8, epi64, si512) -OPENCV_HAL_IMPL_AVX512_SELECT(v_int64x8, epi64, si512) -OPENCV_HAL_IMPL_AVX512_SELECT(v_float32x16, ps, ps) -OPENCV_HAL_IMPL_AVX512_SELECT(v_float64x8, pd, pd) - -/** Comparison **/ -#define OPENCV_HAL_IMPL_AVX512_CMP_INT(bin_op, imm8, _Tpvec, sufcmp, sufset, tval) \ - inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ - { return _Tpvec(_mm512_maskz_set1_##sufset(_mm512_cmp_##sufcmp##_mask(a.val, b.val, imm8), tval)); } - -#define OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(_Tpvec, sufcmp, sufset, tval) \ - OPENCV_HAL_IMPL_AVX512_CMP_INT(==, _MM_CMPINT_EQ, _Tpvec, sufcmp, sufset, tval) \ - OPENCV_HAL_IMPL_AVX512_CMP_INT(!=, _MM_CMPINT_NE, _Tpvec, sufcmp, sufset, tval) \ - OPENCV_HAL_IMPL_AVX512_CMP_INT(<, _MM_CMPINT_LT, _Tpvec, sufcmp, sufset, tval) \ - OPENCV_HAL_IMPL_AVX512_CMP_INT(>, _MM_CMPINT_NLE, _Tpvec, sufcmp, sufset, tval) \ - OPENCV_HAL_IMPL_AVX512_CMP_INT(<=, _MM_CMPINT_LE, _Tpvec, sufcmp, sufset, tval) \ - OPENCV_HAL_IMPL_AVX512_CMP_INT(>=, _MM_CMPINT_NLT, _Tpvec, sufcmp, sufset, tval) - -OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(v_uint8x64, epu8, epi8, (char)-1) -OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(v_int8x64, epi8, epi8, (char)-1) -OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(v_uint16x32, epu16, epi16, (short)-1) -OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(v_int16x32, epi16, epi16, (short)-1) -OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(v_uint32x16, epu32, epi32, (int)-1) -OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(v_int32x16, epi32, epi32, (int)-1) -OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(v_uint64x8, epu64, epi64, (int64)-1) -OPENCV_HAL_IMPL_AVX512_CMP_OP_INT(v_int64x8, epi64, epi64, (int64)-1) - -#define OPENCV_HAL_IMPL_AVX512_CMP_FLT(bin_op, imm8, _Tpvec, sufcmp, sufset, tval) \ - inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ - { return _Tpvec(_mm512_castsi512_##sufcmp(_mm512_maskz_set1_##sufset(_mm512_cmp_##sufcmp##_mask(a.val, b.val, imm8), tval))); } - -#define OPENCV_HAL_IMPL_AVX512_CMP_OP_FLT(_Tpvec, sufcmp, sufset, tval) \ - OPENCV_HAL_IMPL_AVX512_CMP_FLT(==, _CMP_EQ_OQ, _Tpvec, sufcmp, sufset, tval) \ - OPENCV_HAL_IMPL_AVX512_CMP_FLT(!=, _CMP_NEQ_OQ, _Tpvec, sufcmp, sufset, tval) \ - OPENCV_HAL_IMPL_AVX512_CMP_FLT(<, _CMP_LT_OQ, _Tpvec, sufcmp, sufset, tval) \ - OPENCV_HAL_IMPL_AVX512_CMP_FLT(>, _CMP_GT_OQ, _Tpvec, sufcmp, sufset, tval) \ - OPENCV_HAL_IMPL_AVX512_CMP_FLT(<=, _CMP_LE_OQ, _Tpvec, sufcmp, sufset, tval) \ - OPENCV_HAL_IMPL_AVX512_CMP_FLT(>=, _CMP_GE_OQ, _Tpvec, sufcmp, sufset, tval) - -OPENCV_HAL_IMPL_AVX512_CMP_OP_FLT(v_float32x16, ps, epi32, (int)-1) -OPENCV_HAL_IMPL_AVX512_CMP_OP_FLT(v_float64x8, pd, epi64, (int64)-1) - -inline v_float32x16 v_not_nan(const v_float32x16& a) -{ return v_float32x16(_mm512_castsi512_ps(_mm512_maskz_set1_epi32(_mm512_cmp_ps_mask(a.val, a.val, _CMP_ORD_Q), (int)-1))); } -inline v_float64x8 v_not_nan(const v_float64x8& a) -{ return v_float64x8(_mm512_castsi512_pd(_mm512_maskz_set1_epi64(_mm512_cmp_pd_mask(a.val, a.val, _CMP_ORD_Q), (int64)-1))); } - -/** min/max **/ -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_uint8x64, _mm512_min_epu8) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_uint8x64, _mm512_max_epu8) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_int8x64, _mm512_min_epi8) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_int8x64, _mm512_max_epi8) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_uint16x32, _mm512_min_epu16) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_uint16x32, _mm512_max_epu16) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_int16x32, _mm512_min_epi16) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_int16x32, _mm512_max_epi16) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_uint32x16, _mm512_min_epu32) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_uint32x16, _mm512_max_epu32) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_int32x16, _mm512_min_epi32) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_int32x16, _mm512_max_epi32) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_uint64x8, _mm512_min_epu64) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_uint64x8, _mm512_max_epu64) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_int64x8, _mm512_min_epi64) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_int64x8, _mm512_max_epi64) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_float32x16, _mm512_min_ps) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_float32x16, _mm512_max_ps) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_min, v_float64x8, _mm512_min_pd) -OPENCV_HAL_IMPL_AVX512_BIN_FUNC(v_max, v_float64x8, _mm512_max_pd) - -/** Rotate **/ -namespace { - template - struct _v_rotate_right { static inline v_int8x64 eval(const v_int8x64&, const v_int8x64&) { return v_int8x64(); }}; - template - struct _v_rotate_right { static inline v_int8x64 eval(const v_int8x64& a, const v_int8x64& b) - { - return v_int8x64(_mm512_or_si512(_mm512_srli_epi32(_mm512_alignr_epi32(b.val, a.val, imm32 ), imm4 *8), - _mm512_slli_epi32(_mm512_alignr_epi32(b.val, a.val, imm32 + 1), (4-imm4)*8))); - }}; - template - struct _v_rotate_right { static inline v_int8x64 eval(const v_int8x64& a, const v_int8x64& b) - { - return v_int8x64(_mm512_or_si512(_mm512_srli_epi32(_mm512_alignr_epi32(b.val, a.val, 15), imm4 *8), - _mm512_slli_epi32( b.val, (4-imm4)*8))); - }}; - template - struct _v_rotate_right { static inline v_int8x64 eval(const v_int8x64&, const v_int8x64& b) - { - return v_int8x64(_mm512_or_si512(_mm512_srli_epi32(_mm512_alignr_epi32(_mm512_setzero_si512(), b.val, imm32 - 16), imm4 *8), - _mm512_slli_epi32(_mm512_alignr_epi32(_mm512_setzero_si512(), b.val, imm32 - 15), (4-imm4)*8))); - }}; - template - struct _v_rotate_right { static inline v_int8x64 eval(const v_int8x64&, const v_int8x64& b) - { return v_int8x64(_mm512_srli_epi32(_mm512_alignr_epi32(_mm512_setzero_si512(), b.val, 15), imm4*8)); }}; - template - struct _v_rotate_right { static inline v_int8x64 eval(const v_int8x64& a, const v_int8x64& b) - { return v_int8x64(_mm512_alignr_epi32(b.val, a.val, imm32)); }}; - template<> - struct _v_rotate_right { static inline v_int8x64 eval(const v_int8x64& a, const v_int8x64&) { return a; }}; - template - struct _v_rotate_right { static inline v_int8x64 eval(const v_int8x64&, const v_int8x64& b) - { return v_int8x64(_mm512_alignr_epi32(_mm512_setzero_si512(), b.val, imm32 - 16)); }}; - template<> - struct _v_rotate_right { static inline v_int8x64 eval(const v_int8x64&, const v_int8x64& b) { return b; }}; - template<> - struct _v_rotate_right { static inline v_int8x64 eval(const v_int8x64&, const v_int8x64&) { return v_int8x64(); }}; -} -template inline v_int8x64 v_rotate_right(const v_int8x64& a, const v_int8x64& b) -{ - return imm >= 128 ? v_int8x64() : -#if CV_AVX_512VBMI - v_int8x64(_mm512_permutex2var_epi8(a.val, - _v512_set_epu8(0x3f + imm, 0x3e + imm, 0x3d + imm, 0x3c + imm, 0x3b + imm, 0x3a + imm, 0x39 + imm, 0x38 + imm, - 0x37 + imm, 0x36 + imm, 0x35 + imm, 0x34 + imm, 0x33 + imm, 0x32 + imm, 0x31 + imm, 0x30 + imm, - 0x2f + imm, 0x2e + imm, 0x2d + imm, 0x2c + imm, 0x2b + imm, 0x2a + imm, 0x29 + imm, 0x28 + imm, - 0x27 + imm, 0x26 + imm, 0x25 + imm, 0x24 + imm, 0x23 + imm, 0x22 + imm, 0x21 + imm, 0x20 + imm, - 0x1f + imm, 0x1e + imm, 0x1d + imm, 0x1c + imm, 0x1b + imm, 0x1a + imm, 0x19 + imm, 0x18 + imm, - 0x17 + imm, 0x16 + imm, 0x15 + imm, 0x14 + imm, 0x13 + imm, 0x12 + imm, 0x11 + imm, 0x10 + imm, - 0x0f + imm, 0x0e + imm, 0x0d + imm, 0x0c + imm, 0x0b + imm, 0x0a + imm, 0x09 + imm, 0x08 + imm, - 0x07 + imm, 0x06 + imm, 0x05 + imm, 0x04 + imm, 0x03 + imm, 0x02 + imm, 0x01 + imm, 0x00 + imm), b.val)); -#else - _v_rotate_right 15), imm/4>::eval(a, b); -#endif -} -template -inline v_int8x64 v_rotate_left(const v_int8x64& a, const v_int8x64& b) -{ - if (imm == 0) return a; - if (imm == 64) return b; - if (imm >= 128) return v_int8x64(); -#if CV_AVX_512VBMI - return v_int8x64(_mm512_permutex2var_epi8(b.val, - _v512_set_epi8(0x7f - imm,0x7e - imm,0x7d - imm,0x7c - imm,0x7b - imm,0x7a - imm,0x79 - imm,0x78 - imm, - 0x77 - imm,0x76 - imm,0x75 - imm,0x74 - imm,0x73 - imm,0x72 - imm,0x71 - imm,0x70 - imm, - 0x6f - imm,0x6e - imm,0x6d - imm,0x6c - imm,0x6b - imm,0x6a - imm,0x69 - imm,0x68 - imm, - 0x67 - imm,0x66 - imm,0x65 - imm,0x64 - imm,0x63 - imm,0x62 - imm,0x61 - imm,0x60 - imm, - 0x5f - imm,0x5e - imm,0x5d - imm,0x5c - imm,0x5b - imm,0x5a - imm,0x59 - imm,0x58 - imm, - 0x57 - imm,0x56 - imm,0x55 - imm,0x54 - imm,0x53 - imm,0x52 - imm,0x51 - imm,0x50 - imm, - 0x4f - imm,0x4e - imm,0x4d - imm,0x4c - imm,0x4b - imm,0x4a - imm,0x49 - imm,0x48 - imm, - 0x47 - imm,0x46 - imm,0x45 - imm,0x44 - imm,0x43 - imm,0x42 - imm,0x41 - imm,0x40 - imm), a.val)); -#else - return imm < 64 ? v_rotate_right<64 - imm>(b, a) : v_rotate_right<128 - imm>(v512_setzero_s8(), b); -#endif -} -template -inline v_int8x64 v_rotate_right(const v_int8x64& a) -{ - if (imm == 0) return a; - if (imm >= 64) return v_int8x64(); -#if CV_AVX_512VBMI - return v_int8x64(_mm512_maskz_permutexvar_epi8(0xFFFFFFFFFFFFFFFF >> imm, - _v512_set_epu8(0x3f + imm,0x3e + imm,0x3d + imm,0x3c + imm,0x3b + imm,0x3a + imm,0x39 + imm,0x38 + imm, - 0x37 + imm,0x36 + imm,0x35 + imm,0x34 + imm,0x33 + imm,0x32 + imm,0x31 + imm,0x30 + imm, - 0x2f + imm,0x2e + imm,0x2d + imm,0x2c + imm,0x2b + imm,0x2a + imm,0x29 + imm,0x28 + imm, - 0x27 + imm,0x26 + imm,0x25 + imm,0x24 + imm,0x23 + imm,0x22 + imm,0x21 + imm,0x20 + imm, - 0x1f + imm,0x1e + imm,0x1d + imm,0x1c + imm,0x1b + imm,0x1a + imm,0x19 + imm,0x18 + imm, - 0x17 + imm,0x16 + imm,0x15 + imm,0x14 + imm,0x13 + imm,0x12 + imm,0x11 + imm,0x10 + imm, - 0x0f + imm,0x0e + imm,0x0d + imm,0x0c + imm,0x0b + imm,0x0a + imm,0x09 + imm,0x08 + imm, - 0x07 + imm,0x06 + imm,0x05 + imm,0x04 + imm,0x03 + imm,0x02 + imm,0x01 + imm,0x00 + imm), a.val)); -#else - return v_rotate_right(a, v512_setzero_s8()); -#endif -} -template -inline v_int8x64 v_rotate_left(const v_int8x64& a) -{ - if (imm == 0) return a; - if (imm >= 64) return v_int8x64(); -#if CV_AVX_512VBMI - return v_int8x64(_mm512_maskz_permutexvar_epi8(0xFFFFFFFFFFFFFFFF << imm, - _v512_set_epi8(0x3f - imm,0x3e - imm,0x3d - imm,0x3c - imm,0x3b - imm,0x3a - imm,0x39 - imm,0x38 - imm, - 0x37 - imm,0x36 - imm,0x35 - imm,0x34 - imm,0x33 - imm,0x32 - imm,0x31 - imm,0x30 - imm, - 0x2f - imm,0x2e - imm,0x2d - imm,0x2c - imm,0x2b - imm,0x2a - imm,0x29 - imm,0x28 - imm, - 0x27 - imm,0x26 - imm,0x25 - imm,0x24 - imm,0x23 - imm,0x22 - imm,0x21 - imm,0x20 - imm, - 0x1f - imm,0x1e - imm,0x1d - imm,0x1c - imm,0x1b - imm,0x1a - imm,0x19 - imm,0x18 - imm, - 0x17 - imm,0x16 - imm,0x15 - imm,0x14 - imm,0x13 - imm,0x12 - imm,0x11 - imm,0x10 - imm, - 0x0f - imm,0x0e - imm,0x0d - imm,0x0c - imm,0x0b - imm,0x0a - imm,0x09 - imm,0x08 - imm, - 0x07 - imm,0x06 - imm,0x05 - imm,0x04 - imm,0x03 - imm,0x02 - imm,0x01 - imm,0x00 - imm), a.val)); -#else - return v_rotate_right<64 - imm>(v512_setzero_s8(), a); -#endif -} - -#define OPENCV_HAL_IMPL_AVX512_ROTATE_PM(_Tpvec, suffix) \ -template inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b) \ -{ return v_reinterpret_as_##suffix(v_rotate_left(v_reinterpret_as_s8(a), v_reinterpret_as_s8(b))); } \ -template inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b) \ -{ return v_reinterpret_as_##suffix(v_rotate_right(v_reinterpret_as_s8(a), v_reinterpret_as_s8(b))); } \ -template inline _Tpvec v_rotate_left(const _Tpvec& a) \ -{ return v_reinterpret_as_##suffix(v_rotate_left(v_reinterpret_as_s8(a))); } \ -template inline _Tpvec v_rotate_right(const _Tpvec& a) \ -{ return v_reinterpret_as_##suffix(v_rotate_right(v_reinterpret_as_s8(a))); } - -#define OPENCV_HAL_IMPL_AVX512_ROTATE_EC(_Tpvec, suffix) \ -template \ -inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b) \ -{ \ - enum { SHIFT2 = (_Tpvec::nlanes - imm) }; \ - enum { MASK = ((1 << _Tpvec::nlanes) - 1) }; \ - if (imm == 0) return a; \ - if (imm == _Tpvec::nlanes) return b; \ - if (imm >= 2*_Tpvec::nlanes) return _Tpvec::zero(); \ - return _Tpvec(_mm512_mask_expand_##suffix(_mm512_maskz_compress_##suffix((MASK << SHIFT2)&MASK, b.val), (MASK << (imm))&MASK, a.val)); \ -} \ -template \ -inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b) \ -{ \ - enum { SHIFT2 = (_Tpvec::nlanes - imm) }; \ - enum { MASK = ((1 << _Tpvec::nlanes) - 1) }; \ - if (imm == 0) return a; \ - if (imm == _Tpvec::nlanes) return b; \ - if (imm >= 2*_Tpvec::nlanes) return _Tpvec::zero(); \ - return _Tpvec(_mm512_mask_expand_##suffix(_mm512_maskz_compress_##suffix((MASK << (imm))&MASK, a.val), (MASK << SHIFT2)&MASK, b.val)); \ -} \ -template \ -inline _Tpvec v_rotate_left(const _Tpvec& a) \ -{ \ - if (imm == 0) return a; \ - if (imm >= _Tpvec::nlanes) return _Tpvec::zero(); \ - return _Tpvec(_mm512_maskz_expand_##suffix((1 << _Tpvec::nlanes) - (1 << (imm)), a.val)); \ -} \ -template \ -inline _Tpvec v_rotate_right(const _Tpvec& a) \ -{ \ - if (imm == 0) return a; \ - if (imm >= _Tpvec::nlanes) return _Tpvec::zero(); \ - return _Tpvec(_mm512_maskz_compress_##suffix((1 << _Tpvec::nlanes) - (1 << (imm)), a.val)); \ -} - -OPENCV_HAL_IMPL_AVX512_ROTATE_PM(v_uint8x64, u8) -OPENCV_HAL_IMPL_AVX512_ROTATE_PM(v_uint16x32, u16) -OPENCV_HAL_IMPL_AVX512_ROTATE_PM(v_int16x32, s16) -OPENCV_HAL_IMPL_AVX512_ROTATE_EC(v_uint32x16, epi32) -OPENCV_HAL_IMPL_AVX512_ROTATE_EC(v_int32x16, epi32) -OPENCV_HAL_IMPL_AVX512_ROTATE_EC(v_uint64x8, epi64) -OPENCV_HAL_IMPL_AVX512_ROTATE_EC(v_int64x8, epi64) -OPENCV_HAL_IMPL_AVX512_ROTATE_EC(v_float32x16, ps) -OPENCV_HAL_IMPL_AVX512_ROTATE_EC(v_float64x8, pd) - -/** Reverse **/ -inline v_uint8x64 v_reverse(const v_uint8x64 &a) -{ -#if CV_AVX_512VBMI - static const __m512i perm = _mm512_set_epi32( - 0x00010203, 0x04050607, 0x08090a0b, 0x0c0d0e0f, - 0x10111213, 0x14151617, 0x18191a1b, 0x1c1d1e1f, - 0x20212223, 0x24252627, 0x28292a2b, 0x2c2d2e2f, - 0x30313233, 0x34353637, 0x38393a3b, 0x3c3d3e3f); - return v_uint8x64(_mm512_permutexvar_epi8(perm, a.val)); -#else - static const __m512i shuf = _mm512_set_epi32( - 0x00010203, 0x04050607, 0x08090a0b, 0x0c0d0e0f, - 0x00010203, 0x04050607, 0x08090a0b, 0x0c0d0e0f, - 0x00010203, 0x04050607, 0x08090a0b, 0x0c0d0e0f, - 0x00010203, 0x04050607, 0x08090a0b, 0x0c0d0e0f); - static const __m512i perm = _mm512_set_epi64(1, 0, 3, 2, 5, 4, 7, 6); - __m512i vec = _mm512_shuffle_epi8(a.val, shuf); - return v_uint8x64(_mm512_permutexvar_epi64(perm, vec)); -#endif -} - -inline v_int8x64 v_reverse(const v_int8x64 &a) -{ return v_reinterpret_as_s8(v_reverse(v_reinterpret_as_u8(a))); } - -inline v_uint16x32 v_reverse(const v_uint16x32 &a) -{ -#if CV_AVX_512VBMI - static const __m512i perm = _mm512_set_epi32( - 0x00000001, 0x00020003, 0x00040005, 0x00060007, - 0x00080009, 0x000a000b, 0x000c000d, 0x000e000f, - 0x00100011, 0x00120013, 0x00140015, 0x00160017, - 0x00180019, 0x001a001b, 0x001c001d, 0x001e001f); - return v_uint16x32(_mm512_permutexvar_epi16(perm, a.val)); -#else - static const __m512i shuf = _mm512_set_epi32( - 0x01000302, 0x05040706, 0x09080b0a, 0x0d0c0f0e, - 0x01000302, 0x05040706, 0x09080b0a, 0x0d0c0f0e, - 0x01000302, 0x05040706, 0x09080b0a, 0x0d0c0f0e, - 0x01000302, 0x05040706, 0x09080b0a, 0x0d0c0f0e); - static const __m512i perm = _mm512_set_epi64(1, 0, 3, 2, 5, 4, 7, 6); - __m512i vec = _mm512_shuffle_epi8(a.val, shuf); - return v_uint16x32(_mm512_permutexvar_epi64(perm, vec)); -#endif -} - -inline v_int16x32 v_reverse(const v_int16x32 &a) -{ return v_reinterpret_as_s16(v_reverse(v_reinterpret_as_u16(a))); } - -inline v_uint32x16 v_reverse(const v_uint32x16 &a) -{ - static const __m512i perm = _mm512_set_epi32(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15); - return v_uint32x16(_mm512_permutexvar_epi32(perm, a.val)); -} - -inline v_int32x16 v_reverse(const v_int32x16 &a) -{ return v_reinterpret_as_s32(v_reverse(v_reinterpret_as_u32(a))); } - -inline v_float32x16 v_reverse(const v_float32x16 &a) -{ return v_reinterpret_as_f32(v_reverse(v_reinterpret_as_u32(a))); } - -inline v_uint64x8 v_reverse(const v_uint64x8 &a) -{ - static const __m512i perm = _mm512_set_epi64(0, 1, 2, 3, 4, 5, 6, 7); - return v_uint64x8(_mm512_permutexvar_epi64(perm, a.val)); -} - -inline v_int64x8 v_reverse(const v_int64x8 &a) -{ return v_reinterpret_as_s64(v_reverse(v_reinterpret_as_u64(a))); } - -inline v_float64x8 v_reverse(const v_float64x8 &a) -{ return v_reinterpret_as_f64(v_reverse(v_reinterpret_as_u64(a))); } - -////////// Reduce ///////// - -/** Reduce **/ -#define OPENCV_HAL_IMPL_AVX512_REDUCE_ADD64(a, b) a + b -#define OPENCV_HAL_IMPL_AVX512_REDUCE_8(sctype, func, _Tpvec, ifunc, scop) \ - inline sctype v_reduce_##func(const _Tpvec& a) \ - { __m256i half = _mm256_##ifunc(_v512_extract_low(a.val), _v512_extract_high(a.val)); \ - sctype CV_DECL_ALIGNED(64) idx[2]; \ - _mm_store_si128((__m128i*)idx, _mm_##ifunc(_mm256_castsi256_si128(half), _mm256_extracti128_si256(half, 1))); \ - return scop(idx[0], idx[1]); } -OPENCV_HAL_IMPL_AVX512_REDUCE_8(uint64, min, v_uint64x8, min_epu64, min) -OPENCV_HAL_IMPL_AVX512_REDUCE_8(uint64, max, v_uint64x8, max_epu64, max) -OPENCV_HAL_IMPL_AVX512_REDUCE_8(uint64, sum, v_uint64x8, add_epi64, OPENCV_HAL_IMPL_AVX512_REDUCE_ADD64) -OPENCV_HAL_IMPL_AVX512_REDUCE_8(int64, min, v_int64x8, min_epi64, min) -OPENCV_HAL_IMPL_AVX512_REDUCE_8(int64, max, v_int64x8, max_epi64, max) -OPENCV_HAL_IMPL_AVX512_REDUCE_8(int64, sum, v_int64x8, add_epi64, OPENCV_HAL_IMPL_AVX512_REDUCE_ADD64) - -#define OPENCV_HAL_IMPL_AVX512_REDUCE_8F(func, ifunc, scop) \ - inline double v_reduce_##func(const v_float64x8& a) \ - { __m256d half = _mm256_##ifunc(_v512_extract_low(a.val), _v512_extract_high(a.val)); \ - double CV_DECL_ALIGNED(64) idx[2]; \ - _mm_store_pd(idx, _mm_##ifunc(_mm256_castpd256_pd128(half), _mm256_extractf128_pd(half, 1))); \ - return scop(idx[0], idx[1]); } -OPENCV_HAL_IMPL_AVX512_REDUCE_8F(min, min_pd, min) -OPENCV_HAL_IMPL_AVX512_REDUCE_8F(max, max_pd, max) -OPENCV_HAL_IMPL_AVX512_REDUCE_8F(sum, add_pd, OPENCV_HAL_IMPL_AVX512_REDUCE_ADD64) - -#define OPENCV_HAL_IMPL_AVX512_REDUCE_16(sctype, func, _Tpvec, ifunc) \ - inline sctype v_reduce_##func(const _Tpvec& a) \ - { __m256i half = _mm256_##ifunc(_v512_extract_low(a.val), _v512_extract_high(a.val)); \ - __m128i quarter = _mm_##ifunc(_mm256_castsi256_si128(half), _mm256_extracti128_si256(half, 1)); \ - quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 8)); \ - quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 4)); \ - return (sctype)_mm_cvtsi128_si32(quarter); } -OPENCV_HAL_IMPL_AVX512_REDUCE_16(uint, min, v_uint32x16, min_epu32) -OPENCV_HAL_IMPL_AVX512_REDUCE_16(uint, max, v_uint32x16, max_epu32) -OPENCV_HAL_IMPL_AVX512_REDUCE_16(int, min, v_int32x16, min_epi32) -OPENCV_HAL_IMPL_AVX512_REDUCE_16(int, max, v_int32x16, max_epi32) - -#define OPENCV_HAL_IMPL_AVX512_REDUCE_16F(func, ifunc) \ - inline float v_reduce_##func(const v_float32x16& a) \ - { __m256 half = _mm256_##ifunc(_v512_extract_low(a.val), _v512_extract_high(a.val)); \ - __m128 quarter = _mm_##ifunc(_mm256_castps256_ps128(half), _mm256_extractf128_ps(half, 1)); \ - quarter = _mm_##ifunc(quarter, _mm_permute_ps(quarter, _MM_SHUFFLE(0, 0, 3, 2))); \ - quarter = _mm_##ifunc(quarter, _mm_permute_ps(quarter, _MM_SHUFFLE(0, 0, 0, 1))); \ - return _mm_cvtss_f32(quarter); } -OPENCV_HAL_IMPL_AVX512_REDUCE_16F(min, min_ps) -OPENCV_HAL_IMPL_AVX512_REDUCE_16F(max, max_ps) - -inline float v_reduce_sum(const v_float32x16& a) -{ - __m256 half = _mm256_add_ps(_v512_extract_low(a.val), _v512_extract_high(a.val)); - __m128 quarter = _mm_add_ps(_mm256_castps256_ps128(half), _mm256_extractf128_ps(half, 1)); - quarter = _mm_hadd_ps(quarter, quarter); - return _mm_cvtss_f32(_mm_hadd_ps(quarter, quarter)); -} -inline int v_reduce_sum(const v_int32x16& a) -{ - __m256i half = _mm256_add_epi32(_v512_extract_low(a.val), _v512_extract_high(a.val)); - __m128i quarter = _mm_add_epi32(_mm256_castsi256_si128(half), _mm256_extracti128_si256(half, 1)); - quarter = _mm_hadd_epi32(quarter, quarter); - return _mm_cvtsi128_si32(_mm_hadd_epi32(quarter, quarter)); -} -inline uint v_reduce_sum(const v_uint32x16& a) -{ return (uint)v_reduce_sum(v_reinterpret_as_s32(a)); } - -#define OPENCV_HAL_IMPL_AVX512_REDUCE_32(sctype, func, _Tpvec, ifunc) \ - inline sctype v_reduce_##func(const _Tpvec& a) \ - { __m256i half = _mm256_##ifunc(_v512_extract_low(a.val), _v512_extract_high(a.val)); \ - __m128i quarter = _mm_##ifunc(_mm256_castsi256_si128(half), _mm256_extracti128_si256(half, 1)); \ - quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 8)); \ - quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 4)); \ - quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 2)); \ - return (sctype)_mm_cvtsi128_si32(quarter); } -OPENCV_HAL_IMPL_AVX512_REDUCE_32(ushort, min, v_uint16x32, min_epu16) -OPENCV_HAL_IMPL_AVX512_REDUCE_32(ushort, max, v_uint16x32, max_epu16) -OPENCV_HAL_IMPL_AVX512_REDUCE_32(short, min, v_int16x32, min_epi16) -OPENCV_HAL_IMPL_AVX512_REDUCE_32(short, max, v_int16x32, max_epi16) - -inline int v_reduce_sum(const v_int16x32& a) -{ return v_reduce_sum(v_expand_low(a) + v_expand_high(a)); } -inline uint v_reduce_sum(const v_uint16x32& a) -{ return v_reduce_sum(v_expand_low(a) + v_expand_high(a)); } - -#define OPENCV_HAL_IMPL_AVX512_REDUCE_64(sctype, func, _Tpvec, ifunc) \ - inline sctype v_reduce_##func(const _Tpvec& a) \ - { __m256i half = _mm256_##ifunc(_v512_extract_low(a.val), _v512_extract_high(a.val)); \ - __m128i quarter = _mm_##ifunc(_mm256_castsi256_si128(half), _mm256_extracti128_si256(half, 1)); \ - quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 8)); \ - quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 4)); \ - quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 2)); \ - quarter = _mm_##ifunc(quarter, _mm_srli_si128(quarter, 1)); \ - return (sctype)_mm_cvtsi128_si32(quarter); } -OPENCV_HAL_IMPL_AVX512_REDUCE_64(uchar, min, v_uint8x64, min_epu8) -OPENCV_HAL_IMPL_AVX512_REDUCE_64(uchar, max, v_uint8x64, max_epu8) -OPENCV_HAL_IMPL_AVX512_REDUCE_64(schar, min, v_int8x64, min_epi8) -OPENCV_HAL_IMPL_AVX512_REDUCE_64(schar, max, v_int8x64, max_epi8) - -#define OPENCV_HAL_IMPL_AVX512_REDUCE_64_SUM(sctype, _Tpvec, suffix) \ - inline sctype v_reduce_sum(const _Tpvec& a) \ - { __m512i a16 = _mm512_add_epi16(_mm512_cvt##suffix##_epi16(_v512_extract_low(a.val)), \ - _mm512_cvt##suffix##_epi16(_v512_extract_high(a.val))); \ - a16 = _mm512_cvtepi16_epi32(_mm256_add_epi16(_v512_extract_low(a16), _v512_extract_high(a16))); \ - __m256i a8 = _mm256_add_epi32(_v512_extract_low(a16), _v512_extract_high(a16)); \ - __m128i a4 = _mm_add_epi32(_mm256_castsi256_si128(a8), _mm256_extracti128_si256(a8, 1)); \ - a4 = _mm_hadd_epi32(a4, a4); \ - return (sctype)_mm_cvtsi128_si32(_mm_hadd_epi32(a4, a4)); } -OPENCV_HAL_IMPL_AVX512_REDUCE_64_SUM(uint, v_uint8x64, epu8) -OPENCV_HAL_IMPL_AVX512_REDUCE_64_SUM(int, v_int8x64, epi8) - -inline v_float32x16 v_reduce_sum4(const v_float32x16& a, const v_float32x16& b, - const v_float32x16& c, const v_float32x16& d) -{ - __m256 abl = _mm256_hadd_ps(_v512_extract_low(a.val), _v512_extract_low(b.val)); - __m256 abh = _mm256_hadd_ps(_v512_extract_high(a.val), _v512_extract_high(b.val)); - __m256 cdl = _mm256_hadd_ps(_v512_extract_low(c.val), _v512_extract_low(d.val)); - __m256 cdh = _mm256_hadd_ps(_v512_extract_high(c.val), _v512_extract_high(d.val)); - return v_float32x16(_v512_combine(_mm256_hadd_ps(abl, cdl), _mm256_hadd_ps(abh, cdh))); -} - -inline unsigned v_reduce_sad(const v_uint8x64& a, const v_uint8x64& b) -{ - __m512i val = _mm512_sad_epu8(a.val, b.val); - __m256i half = _mm256_add_epi32(_v512_extract_low(val), _v512_extract_high(val)); - __m128i quarter = _mm_add_epi32(_mm256_castsi256_si128(half), _mm256_extracti128_si256(half, 1)); - return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(quarter, _mm_unpackhi_epi64(quarter, quarter))); -} -inline unsigned v_reduce_sad(const v_int8x64& a, const v_int8x64& b) -{ - __m512i val = _mm512_set1_epi8(-128); - val = _mm512_sad_epu8(_mm512_add_epi8(a.val, val), _mm512_add_epi8(b.val, val)); - __m256i half = _mm256_add_epi32(_v512_extract_low(val), _v512_extract_high(val)); - __m128i quarter = _mm_add_epi32(_mm256_castsi256_si128(half), _mm256_extracti128_si256(half, 1)); - return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(quarter, _mm_unpackhi_epi64(quarter, quarter))); -} -inline unsigned v_reduce_sad(const v_uint16x32& a, const v_uint16x32& b) -{ return v_reduce_sum(v_add_wrap(a - b, b - a)); } -inline unsigned v_reduce_sad(const v_int16x32& a, const v_int16x32& b) -{ return v_reduce_sum(v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b)))); } -inline unsigned v_reduce_sad(const v_uint32x16& a, const v_uint32x16& b) -{ return v_reduce_sum(v_max(a, b) - v_min(a, b)); } -inline unsigned v_reduce_sad(const v_int32x16& a, const v_int32x16& b) -{ return v_reduce_sum(v_reinterpret_as_u32(v_max(a, b) - v_min(a, b))); } -inline float v_reduce_sad(const v_float32x16& a, const v_float32x16& b) -{ return v_reduce_sum((a - b) & v_float32x16(_mm512_castsi512_ps(_mm512_set1_epi32(0x7fffffff)))); } -inline double v_reduce_sad(const v_float64x8& a, const v_float64x8& b) -{ return v_reduce_sum((a - b) & v_float64x8(_mm512_castsi512_pd(_mm512_set1_epi64(0x7fffffffffffffff)))); } - -/** Popcount **/ -inline v_uint8x64 v_popcount(const v_int8x64& a) -{ -#if CV_AVX_512BITALG - return v_uint8x64(_mm512_popcnt_epi8(a.val)); -#elif CV_AVX_512VBMI - __m512i _popcnt_table0 = _v512_set_epu8(7, 6, 6, 5, 6, 5, 5, 4, 6, 5, 5, 4, 5, 4, 4, 3, - 5, 4, 4, 3, 4, 3, 3, 2, 4, 3, 3, 2, 3, 2, 2, 1, - 5, 4, 4, 3, 4, 3, 3, 2, 4, 3, 3, 2, 3, 2, 2, 1, - 4, 3, 3, 2, 3, 2, 2, 1, 3, 2, 2, 1, 2, 1, 1, 0); - __m512i _popcnt_table1 = _v512_set_epu8(7, 6, 6, 5, 6, 5, 5, 4, 6, 5, 5, 4, 5, 4, 4, 3, - 6, 5, 5, 4, 5, 4, 4, 3, 5, 4, 4, 3, 4, 3, 3, 2, - 6, 5, 5, 4, 5, 4, 4, 3, 5, 4, 4, 3, 4, 3, 3, 2, - 5, 4, 4, 3, 4, 3, 3, 2, 4, 3, 3, 2, 3, 2, 2, 1); - return v_uint8x64(_mm512_sub_epi8(_mm512_permutex2var_epi8(_popcnt_table0, a.val, _popcnt_table1), _mm512_movm_epi8(_mm512_movepi8_mask(a.val)))); -#else - __m512i _popcnt_table = _mm512_set4_epi32(0x04030302, 0x03020201, 0x03020201, 0x02010100); - __m512i _popcnt_mask = _mm512_set1_epi8(0x0F); - - return v_uint8x64(_mm512_add_epi8(_mm512_shuffle_epi8(_popcnt_table, _mm512_and_si512( a.val, _popcnt_mask)), - _mm512_shuffle_epi8(_popcnt_table, _mm512_and_si512(_mm512_srli_epi16(a.val, 4), _popcnt_mask)))); -#endif -} -inline v_uint16x32 v_popcount(const v_int16x32& a) -{ -#if CV_AVX_512BITALG - return v_uint16x32(_mm512_popcnt_epi16(a.val)); -#elif CV_AVX_512VPOPCNTDQ - __m512i zero = _mm512_setzero_si512(); - return v_uint16x32(_mm512_packs_epi32(_mm512_popcnt_epi32(_mm512_unpacklo_epi16(a.val, zero)), - _mm512_popcnt_epi32(_mm512_unpackhi_epi16(a.val, zero)))); -#else - v_uint8x64 p = v_popcount(v_reinterpret_as_s8(a)); - p += v_rotate_right<1>(p); - return v_reinterpret_as_u16(p) & v512_setall_u16(0x00ff); -#endif -} -inline v_uint32x16 v_popcount(const v_int32x16& a) -{ -#if CV_AVX_512VPOPCNTDQ - return v_uint32x16(_mm512_popcnt_epi32(a.val)); -#else - v_uint8x64 p = v_popcount(v_reinterpret_as_s8(a)); - p += v_rotate_right<1>(p); - p += v_rotate_right<2>(p); - return v_reinterpret_as_u32(p) & v512_setall_u32(0x000000ff); -#endif -} -inline v_uint64x8 v_popcount(const v_int64x8& a) -{ -#if CV_AVX_512VPOPCNTDQ - return v_uint64x8(_mm512_popcnt_epi64(a.val)); -#else - return v_uint64x8(_mm512_sad_epu8(v_popcount(v_reinterpret_as_s8(a)).val, _mm512_setzero_si512())); -#endif -} - - -inline v_uint8x64 v_popcount(const v_uint8x64& a) { return v_popcount(v_reinterpret_as_s8 (a)); } -inline v_uint16x32 v_popcount(const v_uint16x32& a) { return v_popcount(v_reinterpret_as_s16(a)); } -inline v_uint32x16 v_popcount(const v_uint32x16& a) { return v_popcount(v_reinterpret_as_s32(a)); } -inline v_uint64x8 v_popcount(const v_uint64x8& a) { return v_popcount(v_reinterpret_as_s64(a)); } - - -////////// Other math ///////// - -/** Some frequent operations **/ -#define OPENCV_HAL_IMPL_AVX512_MULADD(_Tpvec, suffix) \ - inline _Tpvec v_fma(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ - { return _Tpvec(_mm512_fmadd_##suffix(a.val, b.val, c.val)); } \ - inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ - { return _Tpvec(_mm512_fmadd_##suffix(a.val, b.val, c.val)); } \ - inline _Tpvec v_sqrt(const _Tpvec& x) \ - { return _Tpvec(_mm512_sqrt_##suffix(x.val)); } \ - inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \ - { return v_fma(a, a, b * b); } \ - inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \ - { return v_sqrt(v_fma(a, a, b * b)); } - -OPENCV_HAL_IMPL_AVX512_MULADD(v_float32x16, ps) -OPENCV_HAL_IMPL_AVX512_MULADD(v_float64x8, pd) - -inline v_int32x16 v_fma(const v_int32x16& a, const v_int32x16& b, const v_int32x16& c) -{ return a * b + c; } -inline v_int32x16 v_muladd(const v_int32x16& a, const v_int32x16& b, const v_int32x16& c) -{ return v_fma(a, b, c); } - -inline v_float32x16 v_invsqrt(const v_float32x16& x) -{ -#if CV_AVX_512ER - return v_float32x16(_mm512_rsqrt28_ps(x.val)); -#else - v_float32x16 half = x * v512_setall_f32(0.5); - v_float32x16 t = v_float32x16(_mm512_rsqrt14_ps(x.val)); - t *= v512_setall_f32(1.5) - ((t * t) * half); - return t; -#endif -} - -inline v_float64x8 v_invsqrt(const v_float64x8& x) -{ -#if CV_AVX_512ER - return v_float64x8(_mm512_rsqrt28_pd(x.val)); -#else - return v512_setall_f64(1.) / v_sqrt(x); -// v_float64x8 half = x * v512_setall_f64(0.5); -// v_float64x8 t = v_float64x8(_mm512_rsqrt14_pd(x.val)); -// t *= v512_setall_f64(1.5) - ((t * t) * half); -// t *= v512_setall_f64(1.5) - ((t * t) * half); -// return t; -#endif -} - -/** Absolute values **/ -#define OPENCV_HAL_IMPL_AVX512_ABS(_Tpvec, _Tpuvec, suffix) \ - inline _Tpuvec v_abs(const _Tpvec& x) \ - { return _Tpuvec(_mm512_abs_##suffix(x.val)); } - -OPENCV_HAL_IMPL_AVX512_ABS(v_int8x64, v_uint8x64, epi8) -OPENCV_HAL_IMPL_AVX512_ABS(v_int16x32, v_uint16x32, epi16) -OPENCV_HAL_IMPL_AVX512_ABS(v_int32x16, v_uint32x16, epi32) -OPENCV_HAL_IMPL_AVX512_ABS(v_int64x8, v_uint64x8, epi64) - -inline v_float32x16 v_abs(const v_float32x16& x) -{ -#ifdef _mm512_abs_pd - return v_float32x16(_mm512_abs_ps(x.val)); -#else - return v_float32x16(_mm512_castsi512_ps(_mm512_and_si512(_mm512_castps_si512(x.val), - _v512_set_epu64(0x7FFFFFFF7FFFFFFF, 0x7FFFFFFF7FFFFFFF, 0x7FFFFFFF7FFFFFFF, 0x7FFFFFFF7FFFFFFF, - 0x7FFFFFFF7FFFFFFF, 0x7FFFFFFF7FFFFFFF, 0x7FFFFFFF7FFFFFFF, 0x7FFFFFFF7FFFFFFF)))); -#endif -} - -inline v_float64x8 v_abs(const v_float64x8& x) -{ -#ifdef _mm512_abs_pd - #if defined __GNUC__ && (__GNUC__ < 7 || (__GNUC__ == 7 && __GNUC_MINOR__ <= 3) || (__GNUC__ == 8 && __GNUC_MINOR__ <= 2)) - // Workaround for https://gcc.gnu.org/bugzilla/show_bug.cgi?id=87476 - return v_float64x8(_mm512_abs_pd(_mm512_castpd_ps(x.val))); - #else - return v_float64x8(_mm512_abs_pd(x.val)); - #endif -#else - return v_float64x8(_mm512_castsi512_pd(_mm512_and_si512(_mm512_castpd_si512(x.val), - _v512_set_epu64(0x7FFFFFFFFFFFFFFF, 0x7FFFFFFFFFFFFFFF, 0x7FFFFFFFFFFFFFFF, 0x7FFFFFFFFFFFFFFF, - 0x7FFFFFFFFFFFFFFF, 0x7FFFFFFFFFFFFFFF, 0x7FFFFFFFFFFFFFFF, 0x7FFFFFFFFFFFFFFF)))); -#endif -} - -/** Absolute difference **/ -inline v_uint8x64 v_absdiff(const v_uint8x64& a, const v_uint8x64& b) -{ return v_add_wrap(a - b, b - a); } -inline v_uint16x32 v_absdiff(const v_uint16x32& a, const v_uint16x32& b) -{ return v_add_wrap(a - b, b - a); } -inline v_uint32x16 v_absdiff(const v_uint32x16& a, const v_uint32x16& b) -{ return v_max(a, b) - v_min(a, b); } - -inline v_uint8x64 v_absdiff(const v_int8x64& a, const v_int8x64& b) -{ - v_int8x64 d = v_sub_wrap(a, b); - v_int8x64 m = a < b; - return v_reinterpret_as_u8(v_sub_wrap(d ^ m, m)); -} - -inline v_uint16x32 v_absdiff(const v_int16x32& a, const v_int16x32& b) -{ return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); } - -inline v_uint32x16 v_absdiff(const v_int32x16& a, const v_int32x16& b) -{ - v_int32x16 d = a - b; - v_int32x16 m = a < b; - return v_reinterpret_as_u32((d ^ m) - m); -} - -inline v_float32x16 v_absdiff(const v_float32x16& a, const v_float32x16& b) -{ return v_abs(a - b); } - -inline v_float64x8 v_absdiff(const v_float64x8& a, const v_float64x8& b) -{ return v_abs(a - b); } - -/** Saturating absolute difference **/ -inline v_int8x64 v_absdiffs(const v_int8x64& a, const v_int8x64& b) -{ - v_int8x64 d = a - b; - v_int8x64 m = a < b; - return (d ^ m) - m; -} -inline v_int16x32 v_absdiffs(const v_int16x32& a, const v_int16x32& b) -{ return v_max(a, b) - v_min(a, b); } - -////////// Conversions ///////// - -/** Rounding **/ -inline v_int32x16 v_round(const v_float32x16& a) -{ return v_int32x16(_mm512_cvtps_epi32(a.val)); } - -inline v_int32x16 v_round(const v_float64x8& a) -{ return v_int32x16(_mm512_castsi256_si512(_mm512_cvtpd_epi32(a.val))); } - -inline v_int32x16 v_round(const v_float64x8& a, const v_float64x8& b) -{ return v_int32x16(_v512_combine(_mm512_cvtpd_epi32(a.val), _mm512_cvtpd_epi32(b.val))); } - -inline v_int32x16 v_trunc(const v_float32x16& a) -{ return v_int32x16(_mm512_cvttps_epi32(a.val)); } - -inline v_int32x16 v_trunc(const v_float64x8& a) -{ return v_int32x16(_mm512_castsi256_si512(_mm512_cvttpd_epi32(a.val))); } - -#if CVT_ROUND_MODES_IMPLEMENTED -inline v_int32x16 v_floor(const v_float32x16& a) -{ return v_int32x16(_mm512_cvt_roundps_epi32(a.val, _MM_FROUND_TO_NEG_INF | _MM_FROUND_NO_EXC)); } - -inline v_int32x16 v_floor(const v_float64x8& a) -{ return v_int32x16(_mm512_castsi256_si512(_mm512_cvt_roundpd_epi32(a.val, _MM_FROUND_TO_NEG_INF | _MM_FROUND_NO_EXC))); } - -inline v_int32x16 v_ceil(const v_float32x16& a) -{ return v_int32x16(_mm512_cvt_roundps_epi32(a.val, _MM_FROUND_TO_POS_INF | _MM_FROUND_NO_EXC)); } - -inline v_int32x16 v_ceil(const v_float64x8& a) -{ return v_int32x16(_mm512_castsi256_si512(_mm512_cvt_roundpd_epi32(a.val, _MM_FROUND_TO_POS_INF | _MM_FROUND_NO_EXC))); } -#else -inline v_int32x16 v_floor(const v_float32x16& a) -{ return v_int32x16(_mm512_cvtps_epi32(_mm512_roundscale_ps(a.val, 1))); } - -inline v_int32x16 v_floor(const v_float64x8& a) -{ return v_int32x16(_mm512_castsi256_si512(_mm512_cvtpd_epi32(_mm512_roundscale_pd(a.val, 1)))); } - -inline v_int32x16 v_ceil(const v_float32x16& a) -{ return v_int32x16(_mm512_cvtps_epi32(_mm512_roundscale_ps(a.val, 2))); } - -inline v_int32x16 v_ceil(const v_float64x8& a) -{ return v_int32x16(_mm512_castsi256_si512(_mm512_cvtpd_epi32(_mm512_roundscale_pd(a.val, 2)))); } -#endif - -/** To float **/ -inline v_float32x16 v_cvt_f32(const v_int32x16& a) -{ return v_float32x16(_mm512_cvtepi32_ps(a.val)); } - -inline v_float32x16 v_cvt_f32(const v_float64x8& a) -{ return v_float32x16(_mm512_cvtpd_pslo(a.val)); } - -inline v_float32x16 v_cvt_f32(const v_float64x8& a, const v_float64x8& b) -{ return v_float32x16(_v512_combine(_mm512_cvtpd_ps(a.val), _mm512_cvtpd_ps(b.val))); } - -inline v_float64x8 v_cvt_f64(const v_int32x16& a) -{ return v_float64x8(_mm512_cvtepi32_pd(_v512_extract_low(a.val))); } - -inline v_float64x8 v_cvt_f64_high(const v_int32x16& a) -{ return v_float64x8(_mm512_cvtepi32_pd(_v512_extract_high(a.val))); } - -inline v_float64x8 v_cvt_f64(const v_float32x16& a) -{ return v_float64x8(_mm512_cvtps_pd(_v512_extract_low(a.val))); } - -inline v_float64x8 v_cvt_f64_high(const v_float32x16& a) -{ return v_float64x8(_mm512_cvtps_pd(_v512_extract_high(a.val))); } - -// from (Mysticial and wim) https://stackoverflow.com/q/41144668 -inline v_float64x8 v_cvt_f64(const v_int64x8& v) -{ -#if CV_AVX_512DQ - return v_float64x8(_mm512_cvtepi64_pd(v.val)); -#else - // constants encoded as floating-point - __m512i magic_i_lo = _mm512_set1_epi64(0x4330000000000000); // 2^52 - __m512i magic_i_hi32 = _mm512_set1_epi64(0x4530000080000000); // 2^84 + 2^63 - __m512i magic_i_all = _mm512_set1_epi64(0x4530000080100000); // 2^84 + 2^63 + 2^52 - __m512d magic_d_all = _mm512_castsi512_pd(magic_i_all); - - // Blend the 32 lowest significant bits of v with magic_int_lo - __m512i v_lo = _mm512_mask_blend_epi32(0x5555, magic_i_lo, v.val); - // Extract the 32 most significant bits of v - __m512i v_hi = _mm512_srli_epi64(v.val, 32); - // Flip the msb of v_hi and blend with 0x45300000 - v_hi = _mm512_xor_si512(v_hi, magic_i_hi32); - // Compute in double precision - __m512d v_hi_dbl = _mm512_sub_pd(_mm512_castsi512_pd(v_hi), magic_d_all); - // (v_hi - magic_d_all) + v_lo Do not assume associativity of floating point addition - __m512d result = _mm512_add_pd(v_hi_dbl, _mm512_castsi512_pd(v_lo)); - return v_float64x8(result); -#endif -} - -////////////// Lookup table access //////////////////// - -inline v_int8x64 v512_lut(const schar* tab, const int* idx) -{ - __m128i p0 = _mm512_cvtepi32_epi8(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx ), (const int *)tab, 1)); - __m128i p1 = _mm512_cvtepi32_epi8(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx + 1), (const int *)tab, 1)); - __m128i p2 = _mm512_cvtepi32_epi8(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx + 2), (const int *)tab, 1)); - __m128i p3 = _mm512_cvtepi32_epi8(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx + 3), (const int *)tab, 1)); - return v_int8x64(_mm512_inserti32x4(_mm512_inserti32x4(_mm512_inserti32x4(_mm512_castsi128_si512(p0), p1, 1), p2, 2), p3, 3)); -} -inline v_int8x64 v512_lut_pairs(const schar* tab, const int* idx) -{ - __m256i p0 = _mm512_cvtepi32_epi16(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx ), (const int *)tab, 1)); - __m256i p1 = _mm512_cvtepi32_epi16(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx + 1), (const int *)tab, 1)); - return v_int8x64(_v512_combine(p0, p1)); -} -inline v_int8x64 v512_lut_quads(const schar* tab, const int* idx) -{ - return v_int8x64(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx), (const int *)tab, 1)); -} -inline v_uint8x64 v512_lut(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v512_lut((const schar *)tab, idx)); } -inline v_uint8x64 v512_lut_pairs(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v512_lut_pairs((const schar *)tab, idx)); } -inline v_uint8x64 v512_lut_quads(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v512_lut_quads((const schar *)tab, idx)); } - -inline v_int16x32 v512_lut(const short* tab, const int* idx) -{ - __m256i p0 = _mm512_cvtepi32_epi16(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx ), (const int *)tab, 2)); - __m256i p1 = _mm512_cvtepi32_epi16(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx + 1), (const int *)tab, 2)); - return v_int16x32(_v512_combine(p0, p1)); -} -inline v_int16x32 v512_lut_pairs(const short* tab, const int* idx) -{ - return v_int16x32(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx), (const int *)tab, 2)); -} -inline v_int16x32 v512_lut_quads(const short* tab, const int* idx) -{ -#if defined(__GNUC__) - return v_int16x32(_mm512_i32gather_epi64(_mm256_loadu_si256((const __m256i*)idx), (const long long int*)tab, 2)); -#else - return v_int16x32(_mm512_i32gather_epi64(_mm256_loadu_si256((const __m256i*)idx), (const int64*)tab, 2)); -#endif -} -inline v_uint16x32 v512_lut(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v512_lut((const short *)tab, idx)); } -inline v_uint16x32 v512_lut_pairs(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v512_lut_pairs((const short *)tab, idx)); } -inline v_uint16x32 v512_lut_quads(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v512_lut_quads((const short *)tab, idx)); } - -inline v_int32x16 v512_lut(const int* tab, const int* idx) -{ - return v_int32x16(_mm512_i32gather_epi32(_mm512_loadu_si512((const __m512i*)idx), tab, 4)); -} -inline v_int32x16 v512_lut_pairs(const int* tab, const int* idx) -{ -#if defined(__GNUC__) - return v_int32x16(_mm512_i32gather_epi64(_mm256_loadu_si256((const __m256i*)idx), (const long long int*)tab, 4)); -#else - return v_int32x16(_mm512_i32gather_epi64(_mm256_loadu_si256((const __m256i*)idx), (const int64*)tab, 4)); -#endif -} -inline v_int32x16 v512_lut_quads(const int* tab, const int* idx) -{ - return v_int32x16(_mm512_inserti32x4(_mm512_inserti32x4(_mm512_inserti32x4(_mm512_castsi128_si512( - _mm_loadu_si128((const __m128i*)(tab + idx[0]))), - _mm_loadu_si128((const __m128i*)(tab + idx[1])), 1), - _mm_loadu_si128((const __m128i*)(tab + idx[2])), 2), - _mm_loadu_si128((const __m128i*)(tab + idx[3])), 3)); -} -inline v_uint32x16 v512_lut(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v512_lut((const int *)tab, idx)); } -inline v_uint32x16 v512_lut_pairs(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v512_lut_pairs((const int *)tab, idx)); } -inline v_uint32x16 v512_lut_quads(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v512_lut_quads((const int *)tab, idx)); } - -inline v_int64x8 v512_lut(const int64* tab, const int* idx) -{ -#if defined(__GNUC__) - return v_int64x8(_mm512_i32gather_epi64(_mm256_loadu_si256((const __m256i*)idx), (const long long int*)tab, 8)); -#else - return v_int64x8(_mm512_i32gather_epi64(_mm256_loadu_si256((const __m256i*)idx), tab , 8)); -#endif -} -inline v_int64x8 v512_lut_pairs(const int64* tab, const int* idx) -{ - return v_int64x8(_mm512_inserti32x4(_mm512_inserti32x4(_mm512_inserti32x4(_mm512_castsi128_si512( - _mm_loadu_si128((const __m128i*)(tab + idx[0]))), - _mm_loadu_si128((const __m128i*)(tab + idx[1])), 1), - _mm_loadu_si128((const __m128i*)(tab + idx[2])), 2), - _mm_loadu_si128((const __m128i*)(tab + idx[3])), 3)); -} -inline v_uint64x8 v512_lut(const uint64* tab, const int* idx) { return v_reinterpret_as_u64(v512_lut((const int64 *)tab, idx)); } -inline v_uint64x8 v512_lut_pairs(const uint64* tab, const int* idx) { return v_reinterpret_as_u64(v512_lut_pairs((const int64 *)tab, idx)); } - -inline v_float32x16 v512_lut(const float* tab, const int* idx) -{ - return v_float32x16(_mm512_i32gather_ps(_mm512_loadu_si512((const __m512i*)idx), tab, 4)); -} -inline v_float32x16 v512_lut_pairs(const float* tab, const int* idx) { return v_reinterpret_as_f32(v512_lut_pairs((const int *)tab, idx)); } -inline v_float32x16 v512_lut_quads(const float* tab, const int* idx) { return v_reinterpret_as_f32(v512_lut_quads((const int *)tab, idx)); } - -inline v_float64x8 v512_lut(const double* tab, const int* idx) -{ - return v_float64x8(_mm512_i32gather_pd(_mm256_loadu_si256((const __m256i*)idx), tab, 8)); -} -inline v_float64x8 v512_lut_pairs(const double* tab, const int* idx) -{ - return v_float64x8(_mm512_insertf64x2(_mm512_insertf64x2(_mm512_insertf64x2(_mm512_castpd128_pd512( - _mm_loadu_pd(tab + idx[0])), - _mm_loadu_pd(tab + idx[1]), 1), - _mm_loadu_pd(tab + idx[2]), 2), - _mm_loadu_pd(tab + idx[3]), 3)); -} - -inline v_int32x16 v_lut(const int* tab, const v_int32x16& idxvec) -{ - return v_int32x16(_mm512_i32gather_epi32(idxvec.val, tab, 4)); -} - -inline v_uint32x16 v_lut(const unsigned* tab, const v_int32x16& idxvec) -{ - return v_reinterpret_as_u32(v_lut((const int *)tab, idxvec)); -} - -inline v_float32x16 v_lut(const float* tab, const v_int32x16& idxvec) -{ - return v_float32x16(_mm512_i32gather_ps(idxvec.val, tab, 4)); -} - -inline v_float64x8 v_lut(const double* tab, const v_int32x16& idxvec) -{ - return v_float64x8(_mm512_i32gather_pd(_v512_extract_low(idxvec.val), tab, 8)); -} - -inline void v_lut_deinterleave(const float* tab, const v_int32x16& idxvec, v_float32x16& x, v_float32x16& y) -{ - x.val = _mm512_i32gather_ps(idxvec.val, tab, 4); - y.val = _mm512_i32gather_ps(idxvec.val, &tab[1], 4); -} - -inline void v_lut_deinterleave(const double* tab, const v_int32x16& idxvec, v_float64x8& x, v_float64x8& y) -{ - x.val = _mm512_i32gather_pd(_v512_extract_low(idxvec.val), tab, 8); - y.val = _mm512_i32gather_pd(_v512_extract_low(idxvec.val), &tab[1], 8); -} - -inline v_int8x64 v_interleave_pairs(const v_int8x64& vec) -{ - return v_int8x64(_mm512_shuffle_epi8(vec.val, _mm512_set4_epi32(0x0f0d0e0c, 0x0b090a08, 0x07050604, 0x03010200))); -} -inline v_uint8x64 v_interleave_pairs(const v_uint8x64& vec) { return v_reinterpret_as_u8(v_interleave_pairs(v_reinterpret_as_s8(vec))); } -inline v_int8x64 v_interleave_quads(const v_int8x64& vec) -{ - return v_int8x64(_mm512_shuffle_epi8(vec.val, _mm512_set4_epi32(0x0f0b0e0a, 0x0d090c08, 0x07030602, 0x05010400))); -} -inline v_uint8x64 v_interleave_quads(const v_uint8x64& vec) { return v_reinterpret_as_u8(v_interleave_quads(v_reinterpret_as_s8(vec))); } - -inline v_int16x32 v_interleave_pairs(const v_int16x32& vec) -{ - return v_int16x32(_mm512_shuffle_epi8(vec.val, _mm512_set4_epi32(0x0f0e0b0a, 0x0d0c0908, 0x07060302, 0x05040100))); -} -inline v_uint16x32 v_interleave_pairs(const v_uint16x32& vec) { return v_reinterpret_as_u16(v_interleave_pairs(v_reinterpret_as_s16(vec))); } -inline v_int16x32 v_interleave_quads(const v_int16x32& vec) -{ - return v_int16x32(_mm512_shuffle_epi8(vec.val, _mm512_set4_epi32(0x0f0e0706, 0x0d0c0504, 0x0b0a0302, 0x09080100))); -} -inline v_uint16x32 v_interleave_quads(const v_uint16x32& vec) { return v_reinterpret_as_u16(v_interleave_quads(v_reinterpret_as_s16(vec))); } - -inline v_int32x16 v_interleave_pairs(const v_int32x16& vec) -{ - return v_int32x16(_mm512_shuffle_epi32(vec.val, _MM_PERM_ACBD)); -} -inline v_uint32x16 v_interleave_pairs(const v_uint32x16& vec) { return v_reinterpret_as_u32(v_interleave_pairs(v_reinterpret_as_s32(vec))); } -inline v_float32x16 v_interleave_pairs(const v_float32x16& vec) { return v_reinterpret_as_f32(v_interleave_pairs(v_reinterpret_as_s32(vec))); } - -inline v_int8x64 v_pack_triplets(const v_int8x64& vec) -{ - return v_int8x64(_mm512_permutexvar_epi32(_v512_set_epu64(0x0000000f0000000f, 0x0000000f0000000f, 0x0000000e0000000d, 0x0000000c0000000a, - 0x0000000900000008, 0x0000000600000005, 0x0000000400000002, 0x0000000100000000), - _mm512_shuffle_epi8(vec.val, _mm512_set4_epi32(0xffffff0f, 0x0e0d0c0a, 0x09080605, 0x04020100)))); -} -inline v_uint8x64 v_pack_triplets(const v_uint8x64& vec) { return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec))); } - -inline v_int16x32 v_pack_triplets(const v_int16x32& vec) -{ - return v_int16x32(_mm512_permutexvar_epi16(_v512_set_epu64(0x001f001f001f001f, 0x001f001f001f001f, 0x001e001d001c001a, 0x0019001800160015, - 0x0014001200110010, 0x000e000d000c000a, 0x0009000800060005, 0x0004000200010000), vec.val)); -} -inline v_uint16x32 v_pack_triplets(const v_uint16x32& vec) { return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec))); } - -inline v_int32x16 v_pack_triplets(const v_int32x16& vec) -{ - return v_int32x16(_mm512_permutexvar_epi32(_v512_set_epu64(0x0000000f0000000f, 0x0000000f0000000f, 0x0000000e0000000d, 0x0000000c0000000a, - 0x0000000900000008, 0x0000000600000005, 0x0000000400000002, 0x0000000100000000), vec.val)); -} -inline v_uint32x16 v_pack_triplets(const v_uint32x16& vec) { return v_reinterpret_as_u32(v_pack_triplets(v_reinterpret_as_s32(vec))); } -inline v_float32x16 v_pack_triplets(const v_float32x16& vec) -{ - return v_float32x16(_mm512_permutexvar_ps(_v512_set_epu64(0x0000000f0000000f, 0x0000000f0000000f, 0x0000000e0000000d, 0x0000000c0000000a, - 0x0000000900000008, 0x0000000600000005, 0x0000000400000002, 0x0000000100000000), vec.val)); -} - -////////// Matrix operations ///////// - -//////// Dot Product //////// - -// 16 >> 32 -inline v_int32x16 v_dotprod(const v_int16x32& a, const v_int16x32& b) -{ return v_int32x16(_mm512_madd_epi16(a.val, b.val)); } -inline v_int32x16 v_dotprod(const v_int16x32& a, const v_int16x32& b, const v_int32x16& c) -{ return v_dotprod(a, b) + c; } - -// 32 >> 64 -inline v_int64x8 v_dotprod(const v_int32x16& a, const v_int32x16& b) -{ - __m512i even = _mm512_mul_epi32(a.val, b.val); - __m512i odd = _mm512_mul_epi32(_mm512_srli_epi64(a.val, 32), _mm512_srli_epi64(b.val, 32)); - return v_int64x8(_mm512_add_epi64(even, odd)); -} -inline v_int64x8 v_dotprod(const v_int32x16& a, const v_int32x16& b, const v_int64x8& c) -{ return v_dotprod(a, b) + c; } - -// 8 >> 32 -inline v_uint32x16 v_dotprod_expand(const v_uint8x64& a, const v_uint8x64& b) -{ - __m512i even_a = _mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, a.val, _mm512_setzero_si512()); - __m512i odd_a = _mm512_srli_epi16(a.val, 8); - - __m512i even_b = _mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, b.val, _mm512_setzero_si512()); - __m512i odd_b = _mm512_srli_epi16(b.val, 8); - - __m512i prod0 = _mm512_madd_epi16(even_a, even_b); - __m512i prod1 = _mm512_madd_epi16(odd_a, odd_b); - return v_uint32x16(_mm512_add_epi32(prod0, prod1)); -} -inline v_uint32x16 v_dotprod_expand(const v_uint8x64& a, const v_uint8x64& b, const v_uint32x16& c) -{ return v_dotprod_expand(a, b) + c; } - -inline v_int32x16 v_dotprod_expand(const v_int8x64& a, const v_int8x64& b) -{ - __m512i even_a = _mm512_srai_epi16(_mm512_bslli_epi128(a.val, 1), 8); - __m512i odd_a = _mm512_srai_epi16(a.val, 8); - - __m512i even_b = _mm512_srai_epi16(_mm512_bslli_epi128(b.val, 1), 8); - __m512i odd_b = _mm512_srai_epi16(b.val, 8); - - __m512i prod0 = _mm512_madd_epi16(even_a, even_b); - __m512i prod1 = _mm512_madd_epi16(odd_a, odd_b); - return v_int32x16(_mm512_add_epi32(prod0, prod1)); -} -inline v_int32x16 v_dotprod_expand(const v_int8x64& a, const v_int8x64& b, const v_int32x16& c) -{ return v_dotprod_expand(a, b) + c; } - -// 16 >> 64 -inline v_uint64x8 v_dotprod_expand(const v_uint16x32& a, const v_uint16x32& b) -{ - __m512i mullo = _mm512_mullo_epi16(a.val, b.val); - __m512i mulhi = _mm512_mulhi_epu16(a.val, b.val); - __m512i mul0 = _mm512_unpacklo_epi16(mullo, mulhi); - __m512i mul1 = _mm512_unpackhi_epi16(mullo, mulhi); - - __m512i p02 = _mm512_mask_blend_epi32(0xAAAA, mul0, _mm512_setzero_si512()); - __m512i p13 = _mm512_srli_epi64(mul0, 32); - __m512i p46 = _mm512_mask_blend_epi32(0xAAAA, mul1, _mm512_setzero_si512()); - __m512i p57 = _mm512_srli_epi64(mul1, 32); - - __m512i p15_ = _mm512_add_epi64(p02, p13); - __m512i p9d_ = _mm512_add_epi64(p46, p57); - - return v_uint64x8(_mm512_add_epi64( - _mm512_unpacklo_epi64(p15_, p9d_), - _mm512_unpackhi_epi64(p15_, p9d_) - )); -} -inline v_uint64x8 v_dotprod_expand(const v_uint16x32& a, const v_uint16x32& b, const v_uint64x8& c) -{ return v_dotprod_expand(a, b) + c; } - -inline v_int64x8 v_dotprod_expand(const v_int16x32& a, const v_int16x32& b) -{ - __m512i prod = _mm512_madd_epi16(a.val, b.val); - __m512i even = _mm512_srai_epi64(_mm512_bslli_epi128(prod, 4), 32); - __m512i odd = _mm512_srai_epi64(prod, 32); - return v_int64x8(_mm512_add_epi64(even, odd)); -} -inline v_int64x8 v_dotprod_expand(const v_int16x32& a, const v_int16x32& b, const v_int64x8& c) -{ return v_dotprod_expand(a, b) + c; } - -// 32 >> 64f -inline v_float64x8 v_dotprod_expand(const v_int32x16& a, const v_int32x16& b) -{ return v_cvt_f64(v_dotprod(a, b)); } -inline v_float64x8 v_dotprod_expand(const v_int32x16& a, const v_int32x16& b, const v_float64x8& c) -{ return v_dotprod_expand(a, b) + c; } - -//////// Fast Dot Product //////// - -// 16 >> 32 -inline v_int32x16 v_dotprod_fast(const v_int16x32& a, const v_int16x32& b) -{ return v_dotprod(a, b); } -inline v_int32x16 v_dotprod_fast(const v_int16x32& a, const v_int16x32& b, const v_int32x16& c) -{ return v_dotprod(a, b, c); } - -// 32 >> 64 -inline v_int64x8 v_dotprod_fast(const v_int32x16& a, const v_int32x16& b) -{ return v_dotprod(a, b); } -inline v_int64x8 v_dotprod_fast(const v_int32x16& a, const v_int32x16& b, const v_int64x8& c) -{ return v_dotprod(a, b, c); } - -// 8 >> 32 -inline v_uint32x16 v_dotprod_expand_fast(const v_uint8x64& a, const v_uint8x64& b) -{ return v_dotprod_expand(a, b); } -inline v_uint32x16 v_dotprod_expand_fast(const v_uint8x64& a, const v_uint8x64& b, const v_uint32x16& c) -{ return v_dotprod_expand(a, b, c); } - -inline v_int32x16 v_dotprod_expand_fast(const v_int8x64& a, const v_int8x64& b) -{ return v_dotprod_expand(a, b); } -inline v_int32x16 v_dotprod_expand_fast(const v_int8x64& a, const v_int8x64& b, const v_int32x16& c) -{ return v_dotprod_expand(a, b, c); } - -// 16 >> 64 -inline v_uint64x8 v_dotprod_expand_fast(const v_uint16x32& a, const v_uint16x32& b) -{ - __m512i mullo = _mm512_mullo_epi16(a.val, b.val); - __m512i mulhi = _mm512_mulhi_epu16(a.val, b.val); - __m512i mul0 = _mm512_unpacklo_epi16(mullo, mulhi); - __m512i mul1 = _mm512_unpackhi_epi16(mullo, mulhi); - - __m512i p02 = _mm512_mask_blend_epi32(0xAAAA, mul0, _mm512_setzero_si512()); - __m512i p13 = _mm512_srli_epi64(mul0, 32); - __m512i p46 = _mm512_mask_blend_epi32(0xAAAA, mul1, _mm512_setzero_si512()); - __m512i p57 = _mm512_srli_epi64(mul1, 32); - - __m512i p15_ = _mm512_add_epi64(p02, p13); - __m512i p9d_ = _mm512_add_epi64(p46, p57); - return v_uint64x8(_mm512_add_epi64(p15_, p9d_)); -} -inline v_uint64x8 v_dotprod_expand_fast(const v_uint16x32& a, const v_uint16x32& b, const v_uint64x8& c) -{ return v_dotprod_expand_fast(a, b) + c; } - -inline v_int64x8 v_dotprod_expand_fast(const v_int16x32& a, const v_int16x32& b) -{ return v_dotprod_expand(a, b); } -inline v_int64x8 v_dotprod_expand_fast(const v_int16x32& a, const v_int16x32& b, const v_int64x8& c) -{ return v_dotprod_expand(a, b, c); } - -// 32 >> 64f -inline v_float64x8 v_dotprod_expand_fast(const v_int32x16& a, const v_int32x16& b) -{ return v_dotprod_expand(a, b); } -inline v_float64x8 v_dotprod_expand_fast(const v_int32x16& a, const v_int32x16& b, const v_float64x8& c) -{ return v_dotprod_expand(a, b) + c; } - - -#define OPENCV_HAL_AVX512_SPLAT2_PS(a, im) \ - v_float32x16(_mm512_permute_ps(a.val, _MM_SHUFFLE(im, im, im, im))) - -inline v_float32x16 v_matmul(const v_float32x16& v, - const v_float32x16& m0, const v_float32x16& m1, - const v_float32x16& m2, const v_float32x16& m3) -{ - v_float32x16 v04 = OPENCV_HAL_AVX512_SPLAT2_PS(v, 0); - v_float32x16 v15 = OPENCV_HAL_AVX512_SPLAT2_PS(v, 1); - v_float32x16 v26 = OPENCV_HAL_AVX512_SPLAT2_PS(v, 2); - v_float32x16 v37 = OPENCV_HAL_AVX512_SPLAT2_PS(v, 3); - return v_fma(v04, m0, v_fma(v15, m1, v_fma(v26, m2, v37 * m3))); -} - -inline v_float32x16 v_matmuladd(const v_float32x16& v, - const v_float32x16& m0, const v_float32x16& m1, - const v_float32x16& m2, const v_float32x16& a) -{ - v_float32x16 v04 = OPENCV_HAL_AVX512_SPLAT2_PS(v, 0); - v_float32x16 v15 = OPENCV_HAL_AVX512_SPLAT2_PS(v, 1); - v_float32x16 v26 = OPENCV_HAL_AVX512_SPLAT2_PS(v, 2); - return v_fma(v04, m0, v_fma(v15, m1, v_fma(v26, m2, a))); -} - -#define OPENCV_HAL_IMPL_AVX512_TRANSPOSE4x4(_Tpvec, suffix, cast_from, cast_to) \ - inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \ - const _Tpvec& a2, const _Tpvec& a3, \ - _Tpvec& b0, _Tpvec& b1, _Tpvec& b2, _Tpvec& b3) \ - { \ - __m512i t0 = cast_from(_mm512_unpacklo_##suffix(a0.val, a1.val)); \ - __m512i t1 = cast_from(_mm512_unpacklo_##suffix(a2.val, a3.val)); \ - __m512i t2 = cast_from(_mm512_unpackhi_##suffix(a0.val, a1.val)); \ - __m512i t3 = cast_from(_mm512_unpackhi_##suffix(a2.val, a3.val)); \ - b0.val = cast_to(_mm512_unpacklo_epi64(t0, t1)); \ - b1.val = cast_to(_mm512_unpackhi_epi64(t0, t1)); \ - b2.val = cast_to(_mm512_unpacklo_epi64(t2, t3)); \ - b3.val = cast_to(_mm512_unpackhi_epi64(t2, t3)); \ - } - -OPENCV_HAL_IMPL_AVX512_TRANSPOSE4x4(v_uint32x16, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_AVX512_TRANSPOSE4x4(v_int32x16, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_AVX512_TRANSPOSE4x4(v_float32x16, ps, _mm512_castps_si512, _mm512_castsi512_ps) - -//////////////// Value reordering /////////////// - -/* Expand */ -#define OPENCV_HAL_IMPL_AVX512_EXPAND(_Tpvec, _Tpwvec, _Tp, intrin) \ - inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ - { \ - b0.val = intrin(_v512_extract_low(a.val)); \ - b1.val = intrin(_v512_extract_high(a.val)); \ - } \ - inline _Tpwvec v_expand_low(const _Tpvec& a) \ - { return _Tpwvec(intrin(_v512_extract_low(a.val))); } \ - inline _Tpwvec v_expand_high(const _Tpvec& a) \ - { return _Tpwvec(intrin(_v512_extract_high(a.val))); } \ - inline _Tpwvec v512_load_expand(const _Tp* ptr) \ - { \ - __m256i a = _mm256_loadu_si256((const __m256i*)ptr); \ - return _Tpwvec(intrin(a)); \ - } - -OPENCV_HAL_IMPL_AVX512_EXPAND(v_uint8x64, v_uint16x32, uchar, _mm512_cvtepu8_epi16) -OPENCV_HAL_IMPL_AVX512_EXPAND(v_int8x64, v_int16x32, schar, _mm512_cvtepi8_epi16) -OPENCV_HAL_IMPL_AVX512_EXPAND(v_uint16x32, v_uint32x16, ushort, _mm512_cvtepu16_epi32) -OPENCV_HAL_IMPL_AVX512_EXPAND(v_int16x32, v_int32x16, short, _mm512_cvtepi16_epi32) -OPENCV_HAL_IMPL_AVX512_EXPAND(v_uint32x16, v_uint64x8, unsigned, _mm512_cvtepu32_epi64) -OPENCV_HAL_IMPL_AVX512_EXPAND(v_int32x16, v_int64x8, int, _mm512_cvtepi32_epi64) - -#define OPENCV_HAL_IMPL_AVX512_EXPAND_Q(_Tpvec, _Tp, intrin) \ - inline _Tpvec v512_load_expand_q(const _Tp* ptr) \ - { \ - __m128i a = _mm_loadu_si128((const __m128i*)ptr); \ - return _Tpvec(intrin(a)); \ - } - -OPENCV_HAL_IMPL_AVX512_EXPAND_Q(v_uint32x16, uchar, _mm512_cvtepu8_epi32) -OPENCV_HAL_IMPL_AVX512_EXPAND_Q(v_int32x16, schar, _mm512_cvtepi8_epi32) - -/* pack */ -// 16 -inline v_int8x64 v_pack(const v_int16x32& a, const v_int16x32& b) -{ return v_int8x64(_mm512_permutexvar_epi64(_v512_set_epu64(7, 5, 3, 1, 6, 4, 2, 0), _mm512_packs_epi16(a.val, b.val))); } - -inline v_uint8x64 v_pack(const v_uint16x32& a, const v_uint16x32& b) -{ - const __m512i t = _mm512_set1_epi16(255); - return v_uint8x64(_v512_combine(_mm512_cvtepi16_epi8(_mm512_min_epu16(a.val, t)), _mm512_cvtepi16_epi8(_mm512_min_epu16(b.val, t)))); -} - -inline v_uint8x64 v_pack_u(const v_int16x32& a, const v_int16x32& b) -{ - return v_uint8x64(_mm512_permutexvar_epi64(_v512_set_epu64(7, 5, 3, 1, 6, 4, 2, 0), _mm512_packus_epi16(a.val, b.val))); -} - -inline void v_pack_store(schar* ptr, const v_int16x32& a) -{ v_store_low(ptr, v_pack(a, a)); } - -inline void v_pack_store(uchar* ptr, const v_uint16x32& a) -{ - const __m512i m = _mm512_set1_epi16(255); - _mm256_storeu_si256((__m256i*)ptr, _mm512_cvtepi16_epi8(_mm512_min_epu16(a.val, m))); -} - -inline void v_pack_u_store(uchar* ptr, const v_int16x32& a) -{ v_store_low(ptr, v_pack_u(a, a)); } - -template inline -v_uint8x64 v_rshr_pack(const v_uint16x32& a, const v_uint16x32& b) -{ - // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. - v_uint16x32 delta = v512_setall_u16((short)(1 << (n-1))); - return v_pack_u(v_reinterpret_as_s16((a + delta) >> n), - v_reinterpret_as_s16((b + delta) >> n)); -} - -template inline -void v_rshr_pack_store(uchar* ptr, const v_uint16x32& a) -{ - v_uint16x32 delta = v512_setall_u16((short)(1 << (n-1))); - v_pack_u_store(ptr, v_reinterpret_as_s16((a + delta) >> n)); -} - -template inline -v_uint8x64 v_rshr_pack_u(const v_int16x32& a, const v_int16x32& b) -{ - v_int16x32 delta = v512_setall_s16((short)(1 << (n-1))); - return v_pack_u((a + delta) >> n, (b + delta) >> n); -} - -template inline -void v_rshr_pack_u_store(uchar* ptr, const v_int16x32& a) -{ - v_int16x32 delta = v512_setall_s16((short)(1 << (n-1))); - v_pack_u_store(ptr, (a + delta) >> n); -} - -template inline -v_int8x64 v_rshr_pack(const v_int16x32& a, const v_int16x32& b) -{ - v_int16x32 delta = v512_setall_s16((short)(1 << (n-1))); - return v_pack((a + delta) >> n, (b + delta) >> n); -} - -template inline -void v_rshr_pack_store(schar* ptr, const v_int16x32& a) -{ - v_int16x32 delta = v512_setall_s16((short)(1 << (n-1))); - v_pack_store(ptr, (a + delta) >> n); -} - -// 32 -inline v_int16x32 v_pack(const v_int32x16& a, const v_int32x16& b) -{ return v_int16x32(_mm512_permutexvar_epi64(_v512_set_epu64(7, 5, 3, 1, 6, 4, 2, 0), _mm512_packs_epi32(a.val, b.val))); } - -inline v_uint16x32 v_pack(const v_uint32x16& a, const v_uint32x16& b) -{ - const __m512i m = _mm512_set1_epi32(65535); - return v_uint16x32(_v512_combine(_mm512_cvtepi32_epi16(_mm512_min_epu32(a.val, m)), _mm512_cvtepi32_epi16(_mm512_min_epu32(b.val, m)))); -} - -inline v_uint16x32 v_pack_u(const v_int32x16& a, const v_int32x16& b) -{ return v_uint16x32(_mm512_permutexvar_epi64(_v512_set_epu64(7, 5, 3, 1, 6, 4, 2, 0), _mm512_packus_epi32(a.val, b.val))); } - -inline void v_pack_store(short* ptr, const v_int32x16& a) -{ v_store_low(ptr, v_pack(a, a)); } - -inline void v_pack_store(ushort* ptr, const v_uint32x16& a) -{ - const __m512i m = _mm512_set1_epi32(65535); - _mm256_storeu_si256((__m256i*)ptr, _mm512_cvtepi32_epi16(_mm512_min_epu32(a.val, m))); -} - -inline void v_pack_u_store(ushort* ptr, const v_int32x16& a) -{ v_store_low(ptr, v_pack_u(a, a)); } - - -template inline -v_uint16x32 v_rshr_pack(const v_uint32x16& a, const v_uint32x16& b) -{ - v_uint32x16 delta = v512_setall_u32(1 << (n-1)); - return v_pack_u(v_reinterpret_as_s32((a + delta) >> n), - v_reinterpret_as_s32((b + delta) >> n)); -} - -template inline -void v_rshr_pack_store(ushort* ptr, const v_uint32x16& a) -{ - v_uint32x16 delta = v512_setall_u32(1 << (n-1)); - v_pack_u_store(ptr, v_reinterpret_as_s32((a + delta) >> n)); -} - -template inline -v_uint16x32 v_rshr_pack_u(const v_int32x16& a, const v_int32x16& b) -{ - v_int32x16 delta = v512_setall_s32(1 << (n-1)); - return v_pack_u((a + delta) >> n, (b + delta) >> n); -} - -template inline -void v_rshr_pack_u_store(ushort* ptr, const v_int32x16& a) -{ - v_int32x16 delta = v512_setall_s32(1 << (n-1)); - v_pack_u_store(ptr, (a + delta) >> n); -} - -template inline -v_int16x32 v_rshr_pack(const v_int32x16& a, const v_int32x16& b) -{ - v_int32x16 delta = v512_setall_s32(1 << (n-1)); - return v_pack((a + delta) >> n, (b + delta) >> n); -} - -template inline -void v_rshr_pack_store(short* ptr, const v_int32x16& a) -{ - v_int32x16 delta = v512_setall_s32(1 << (n-1)); - v_pack_store(ptr, (a + delta) >> n); -} - -// 64 -// Non-saturating pack -inline v_uint32x16 v_pack(const v_uint64x8& a, const v_uint64x8& b) -{ return v_uint32x16(_v512_combine(_mm512_cvtepi64_epi32(a.val), _mm512_cvtepi64_epi32(b.val))); } - -inline v_int32x16 v_pack(const v_int64x8& a, const v_int64x8& b) -{ return v_reinterpret_as_s32(v_pack(v_reinterpret_as_u64(a), v_reinterpret_as_u64(b))); } - -inline void v_pack_store(unsigned* ptr, const v_uint64x8& a) -{ _mm256_storeu_si256((__m256i*)ptr, _mm512_cvtepi64_epi32(a.val)); } - -inline void v_pack_store(int* ptr, const v_int64x8& b) -{ v_pack_store((unsigned*)ptr, v_reinterpret_as_u64(b)); } - -template inline -v_uint32x16 v_rshr_pack(const v_uint64x8& a, const v_uint64x8& b) -{ - v_uint64x8 delta = v512_setall_u64((uint64)1 << (n-1)); - return v_pack((a + delta) >> n, (b + delta) >> n); -} - -template inline -void v_rshr_pack_store(unsigned* ptr, const v_uint64x8& a) -{ - v_uint64x8 delta = v512_setall_u64((uint64)1 << (n-1)); - v_pack_store(ptr, (a + delta) >> n); -} - -template inline -v_int32x16 v_rshr_pack(const v_int64x8& a, const v_int64x8& b) -{ - v_int64x8 delta = v512_setall_s64((int64)1 << (n-1)); - return v_pack((a + delta) >> n, (b + delta) >> n); -} - -template inline -void v_rshr_pack_store(int* ptr, const v_int64x8& a) -{ - v_int64x8 delta = v512_setall_s64((int64)1 << (n-1)); - v_pack_store(ptr, (a + delta) >> n); -} - -// pack boolean -inline v_uint8x64 v_pack_b(const v_uint16x32& a, const v_uint16x32& b) -{ return v_uint8x64(_mm512_permutexvar_epi64(_v512_set_epu64(7, 5, 3, 1, 6, 4, 2, 0), _mm512_packs_epi16(a.val, b.val))); } - -inline v_uint8x64 v_pack_b(const v_uint32x16& a, const v_uint32x16& b, - const v_uint32x16& c, const v_uint32x16& d) -{ - __m512i ab = _mm512_packs_epi32(a.val, b.val); - __m512i cd = _mm512_packs_epi32(c.val, d.val); - - return v_uint8x64(_mm512_permutexvar_epi32(_v512_set_epu32(15, 11, 7, 3, 14, 10, 6, 2, 13, 9, 5, 1, 12, 8, 4, 0), _mm512_packs_epi16(ab, cd))); -} - -inline v_uint8x64 v_pack_b(const v_uint64x8& a, const v_uint64x8& b, const v_uint64x8& c, - const v_uint64x8& d, const v_uint64x8& e, const v_uint64x8& f, - const v_uint64x8& g, const v_uint64x8& h) -{ - __m512i ab = _mm512_packs_epi32(a.val, b.val); - __m512i cd = _mm512_packs_epi32(c.val, d.val); - __m512i ef = _mm512_packs_epi32(e.val, f.val); - __m512i gh = _mm512_packs_epi32(g.val, h.val); - - __m512i abcd = _mm512_packs_epi32(ab, cd); - __m512i efgh = _mm512_packs_epi32(ef, gh); - - return v_uint8x64(_mm512_permutexvar_epi16(_v512_set_epu16(31, 23, 15, 7, 30, 22, 14, 6, 29, 21, 13, 5, 28, 20, 12, 4, - 27, 19, 11, 3, 26, 18, 10, 2, 25, 17, 9, 1, 24, 16, 8, 0), _mm512_packs_epi16(abcd, efgh))); -} - -/* Recombine */ -// its up there with load and store operations - -/* Extract */ -#define OPENCV_HAL_IMPL_AVX512_EXTRACT(_Tpvec) \ - template \ - inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) \ - { return v_rotate_right(a, b); } - -OPENCV_HAL_IMPL_AVX512_EXTRACT(v_uint8x64) -OPENCV_HAL_IMPL_AVX512_EXTRACT(v_int8x64) -OPENCV_HAL_IMPL_AVX512_EXTRACT(v_uint16x32) -OPENCV_HAL_IMPL_AVX512_EXTRACT(v_int16x32) -OPENCV_HAL_IMPL_AVX512_EXTRACT(v_uint32x16) -OPENCV_HAL_IMPL_AVX512_EXTRACT(v_int32x16) -OPENCV_HAL_IMPL_AVX512_EXTRACT(v_uint64x8) -OPENCV_HAL_IMPL_AVX512_EXTRACT(v_int64x8) -OPENCV_HAL_IMPL_AVX512_EXTRACT(v_float32x16) -OPENCV_HAL_IMPL_AVX512_EXTRACT(v_float64x8) - -#define OPENCV_HAL_IMPL_AVX512_EXTRACT_N(_Tpvec, _Tp) \ -template inline _Tp v_extract_n(_Tpvec v) { return v_rotate_right(v).get0(); } - -OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_uint8x64, uchar) -OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_int8x64, schar) -OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_uint16x32, ushort) -OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_int16x32, short) -OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_uint32x16, uint) -OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_int32x16, int) -OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_uint64x8, uint64) -OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_int64x8, int64) -OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_float32x16, float) -OPENCV_HAL_IMPL_AVX512_EXTRACT_N(v_float64x8, double) - -template -inline v_uint32x16 v_broadcast_element(v_uint32x16 a) -{ - static const __m512i perm = _mm512_set1_epi32((char)i); - return v_uint32x16(_mm512_permutexvar_epi32(perm, a.val)); -} - -template -inline v_int32x16 v_broadcast_element(const v_int32x16 &a) -{ return v_reinterpret_as_s32(v_broadcast_element(v_reinterpret_as_u32(a))); } - -template -inline v_float32x16 v_broadcast_element(const v_float32x16 &a) -{ return v_reinterpret_as_f32(v_broadcast_element(v_reinterpret_as_u32(a))); } - - -///////////////////// load deinterleave ///////////////////////////// - -inline void v_load_deinterleave( const uchar* ptr, v_uint8x64& a, v_uint8x64& b ) -{ - __m512i ab0 = _mm512_loadu_si512((const __m512i*)ptr); - __m512i ab1 = _mm512_loadu_si512((const __m512i*)(ptr + 64)); -#if CV_AVX_512VBMI - __m512i mask0 = _v512_set_epu8(126, 124, 122, 120, 118, 116, 114, 112, 110, 108, 106, 104, 102, 100, 98, 96, - 94, 92, 90, 88, 86, 84, 82, 80, 78, 76, 74, 72, 70, 68, 66, 64, - 62, 60, 58, 56, 54, 52, 50, 48, 46, 44, 42, 40, 38, 36, 34, 32, - 30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10, 8, 6, 4, 2, 0); - __m512i mask1 = _v512_set_epu8(127, 125, 123, 121, 119, 117, 115, 113, 111, 109, 107, 105, 103, 101, 99, 97, - 95, 93, 91, 89, 87, 85, 83, 81, 79, 77, 75, 73, 71, 69, 67, 65, - 63, 61, 59, 57, 55, 53, 51, 49, 47, 45, 43, 41, 39, 37, 35, 33, - 31, 29, 27, 25, 23, 21, 19, 17, 15, 13, 11, 9, 7, 5, 3, 1); - a = v_uint8x64(_mm512_permutex2var_epi8(ab0, mask0, ab1)); - b = v_uint8x64(_mm512_permutex2var_epi8(ab0, mask1, ab1)); -#else - __m512i mask0 = _mm512_set4_epi32(0x0f0d0b09, 0x07050301, 0x0e0c0a08, 0x06040200); - __m512i a0b0 = _mm512_shuffle_epi8(ab0, mask0); - __m512i a1b1 = _mm512_shuffle_epi8(ab1, mask0); - __m512i mask1 = _v512_set_epu64(14, 12, 10, 8, 6, 4, 2, 0); - __m512i mask2 = _v512_set_epu64(15, 13, 11, 9, 7, 5, 3, 1); - a = v_uint8x64(_mm512_permutex2var_epi64(a0b0, mask1, a1b1)); - b = v_uint8x64(_mm512_permutex2var_epi64(a0b0, mask2, a1b1)); -#endif -} - -inline void v_load_deinterleave( const ushort* ptr, v_uint16x32& a, v_uint16x32& b ) -{ - __m512i ab0 = _mm512_loadu_si512((const __m512i*)ptr); - __m512i ab1 = _mm512_loadu_si512((const __m512i*)(ptr + 32)); - __m512i mask0 = _v512_set_epu16(62, 60, 58, 56, 54, 52, 50, 48, 46, 44, 42, 40, 38, 36, 34, 32, - 30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10, 8, 6, 4, 2, 0); - __m512i mask1 = _v512_set_epu16(63, 61, 59, 57, 55, 53, 51, 49, 47, 45, 43, 41, 39, 37, 35, 33, - 31, 29, 27, 25, 23, 21, 19, 17, 15, 13, 11, 9, 7, 5, 3, 1); - a = v_uint16x32(_mm512_permutex2var_epi16(ab0, mask0, ab1)); - b = v_uint16x32(_mm512_permutex2var_epi16(ab0, mask1, ab1)); -} - -inline void v_load_deinterleave( const unsigned* ptr, v_uint32x16& a, v_uint32x16& b ) -{ - __m512i ab0 = _mm512_loadu_si512((const __m512i*)ptr); - __m512i ab1 = _mm512_loadu_si512((const __m512i*)(ptr + 16)); - __m512i mask0 = _v512_set_epu32(30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10, 8, 6, 4, 2, 0); - __m512i mask1 = _v512_set_epu32(31, 29, 27, 25, 23, 21, 19, 17, 15, 13, 11, 9, 7, 5, 3, 1); - a = v_uint32x16(_mm512_permutex2var_epi32(ab0, mask0, ab1)); - b = v_uint32x16(_mm512_permutex2var_epi32(ab0, mask1, ab1)); -} - -inline void v_load_deinterleave( const uint64* ptr, v_uint64x8& a, v_uint64x8& b ) -{ - __m512i ab0 = _mm512_loadu_si512((const __m512i*)ptr); - __m512i ab1 = _mm512_loadu_si512((const __m512i*)(ptr + 8)); - __m512i mask0 = _v512_set_epu64(14, 12, 10, 8, 6, 4, 2, 0); - __m512i mask1 = _v512_set_epu64(15, 13, 11, 9, 7, 5, 3, 1); - a = v_uint64x8(_mm512_permutex2var_epi64(ab0, mask0, ab1)); - b = v_uint64x8(_mm512_permutex2var_epi64(ab0, mask1, ab1)); -} - -inline void v_load_deinterleave( const uchar* ptr, v_uint8x64& a, v_uint8x64& b, v_uint8x64& c ) -{ - __m512i bgr0 = _mm512_loadu_si512((const __m512i*)ptr); - __m512i bgr1 = _mm512_loadu_si512((const __m512i*)(ptr + 64)); - __m512i bgr2 = _mm512_loadu_si512((const __m512i*)(ptr + 128)); - -#if CV_AVX_512VBMI2 - __m512i mask0 = _v512_set_epu8(126, 123, 120, 117, 114, 111, 108, 105, 102, 99, 96, 93, 90, 87, 84, 81, - 78, 75, 72, 69, 66, 63, 60, 57, 54, 51, 48, 45, 42, 39, 36, 33, - 30, 27, 24, 21, 18, 15, 12, 9, 6, 3, 0, 62, 59, 56, 53, 50, - 47, 44, 41, 38, 35, 32, 29, 26, 23, 20, 17, 14, 11, 8, 5, 2); - __m512i r0b01 = _mm512_permutex2var_epi8(bgr0, mask0, bgr1); - __m512i b1g12 = _mm512_permutex2var_epi8(bgr1, mask0, bgr2); - __m512i r12b2 = _mm512_permutex2var_epi8(bgr1, - _v512_set_epu8(125, 122, 119, 116, 113, 110, 107, 104, 101, 98, 95, 92, 89, 86, 83, 80, - 77, 74, 71, 68, 65, 127, 124, 121, 118, 115, 112, 109, 106, 103, 100, 97, - 94, 91, 88, 85, 82, 79, 76, 73, 70, 67, 64, 61, 58, 55, 52, 49, - 46, 43, 40, 37, 34, 31, 28, 25, 22, 19, 16, 13, 10, 7, 4, 1), bgr2); - a = v_uint8x64(_mm512_mask_compress_epi8(r12b2, 0xffffffffffe00000, r0b01)); - b = v_uint8x64(_mm512_mask_compress_epi8(b1g12, 0x2492492492492492, bgr0)); - c = v_uint8x64(_mm512_mask_expand_epi8(r0b01, 0xffffffffffe00000, r12b2)); -#elif CV_AVX_512VBMI - __m512i b0g0b1 = _mm512_mask_blend_epi8(0xb6db6db6db6db6db, bgr1, bgr0); - __m512i g1r1g2 = _mm512_mask_blend_epi8(0xb6db6db6db6db6db, bgr2, bgr1); - __m512i r2b2r0 = _mm512_mask_blend_epi8(0xb6db6db6db6db6db, bgr0, bgr2); - a = v_uint8x64(_mm512_permutex2var_epi8(b0g0b1, _v512_set_epu8(125, 122, 119, 116, 113, 110, 107, 104, 101, 98, 95, 92, 89, 86, 83, 80, - 77, 74, 71, 68, 65, 63, 61, 60, 58, 57, 55, 54, 52, 51, 49, 48, - 46, 45, 43, 42, 40, 39, 37, 36, 34, 33, 31, 30, 28, 27, 25, 24, - 23, 21, 20, 18, 17, 15, 14, 12, 11, 9, 8, 6, 5, 3, 2, 0), bgr2)); - b = v_uint8x64(_mm512_permutex2var_epi8(g1r1g2, _v512_set_epu8( 63, 61, 60, 58, 57, 55, 54, 52, 51, 49, 48, 46, 45, 43, 42, 40, - 39, 37, 36, 34, 33, 31, 30, 28, 27, 25, 24, 23, 21, 20, 18, 17, - 15, 14, 12, 11, 9, 8, 6, 5, 3, 2, 0, 126, 123, 120, 117, 114, - 111, 108, 105, 102, 99, 96, 93, 90, 87, 84, 81, 78, 75, 72, 69, 66), bgr0)); - c = v_uint8x64(_mm512_permutex2var_epi8(r2b2r0, _v512_set_epu8( 63, 60, 57, 54, 51, 48, 45, 42, 39, 36, 33, 30, 27, 24, 21, 18, - 15, 12, 9, 6, 3, 0, 125, 122, 119, 116, 113, 110, 107, 104, 101, 98, - 95, 92, 89, 86, 83, 80, 77, 74, 71, 68, 65, 62, 59, 56, 53, 50, - 47, 44, 41, 38, 35, 32, 29, 26, 23, 20, 17, 14, 11, 8, 5, 2), bgr1)); -#else - __m512i mask0 = _v512_set_epu16(61, 58, 55, 52, 49, 46, 43, 40, 37, 34, 63, 60, 57, 54, 51, 48, - 45, 42, 39, 36, 33, 30, 27, 24, 21, 18, 15, 12, 9, 6, 3, 0); - __m512i b01g1 = _mm512_permutex2var_epi16(bgr0, mask0, bgr1); - __m512i r12b2 = _mm512_permutex2var_epi16(bgr1, mask0, bgr2); - __m512i g20r0 = _mm512_permutex2var_epi16(bgr2, mask0, bgr0); - - __m512i b0g0 = _mm512_mask_blend_epi32(0xf800, b01g1, r12b2); - __m512i r0b1 = _mm512_permutex2var_epi16(bgr1, _v512_set_epu16(42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 29, 26, 23, 20, 17, - 14, 11, 8, 5, 2, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43), g20r0); - __m512i g1r1 = _mm512_alignr_epi32(r12b2, g20r0, 11); - a = v_uint8x64(_mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, b0g0, r0b1)); - c = v_uint8x64(_mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, r0b1, g1r1)); - b = v_uint8x64(_mm512_shuffle_epi8(_mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, g1r1, b0g0), _mm512_set4_epi32(0x0e0f0c0d, 0x0a0b0809, 0x06070405, 0x02030001))); -#endif -} - -inline void v_load_deinterleave( const ushort* ptr, v_uint16x32& a, v_uint16x32& b, v_uint16x32& c ) -{ - __m512i bgr0 = _mm512_loadu_si512((const __m512i*)ptr); - __m512i bgr1 = _mm512_loadu_si512((const __m512i*)(ptr + 32)); - __m512i bgr2 = _mm512_loadu_si512((const __m512i*)(ptr + 64)); - - __m512i mask0 = _v512_set_epu16(61, 58, 55, 52, 49, 46, 43, 40, 37, 34, 63, 60, 57, 54, 51, 48, - 45, 42, 39, 36, 33, 30, 27, 24, 21, 18, 15, 12, 9, 6, 3, 0); - __m512i b01g1 = _mm512_permutex2var_epi16(bgr0, mask0, bgr1); - __m512i r12b2 = _mm512_permutex2var_epi16(bgr1, mask0, bgr2); - __m512i g20r0 = _mm512_permutex2var_epi16(bgr2, mask0, bgr0); - - a = v_uint16x32(_mm512_mask_blend_epi32(0xf800, b01g1, r12b2)); - b = v_uint16x32(_mm512_permutex2var_epi16(bgr1, _v512_set_epu16(42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 29, 26, 23, 20, 17, - 14, 11, 8, 5, 2, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43), g20r0)); - c = v_uint16x32(_mm512_alignr_epi32(r12b2, g20r0, 11)); -} - -inline void v_load_deinterleave( const unsigned* ptr, v_uint32x16& a, v_uint32x16& b, v_uint32x16& c ) -{ - __m512i bgr0 = _mm512_loadu_si512((const __m512i*)ptr); - __m512i bgr1 = _mm512_loadu_si512((const __m512i*)(ptr + 16)); - __m512i bgr2 = _mm512_loadu_si512((const __m512i*)(ptr + 32)); - - __m512i mask0 = _v512_set_epu32(29, 26, 23, 20, 17, 30, 27, 24, 21, 18, 15, 12, 9, 6, 3, 0); - __m512i b01r1 = _mm512_permutex2var_epi32(bgr0, mask0, bgr1); - __m512i g12b2 = _mm512_permutex2var_epi32(bgr1, mask0, bgr2); - __m512i r20g0 = _mm512_permutex2var_epi32(bgr2, mask0, bgr0); - - a = v_uint32x16(_mm512_mask_blend_epi32(0xf800, b01r1, g12b2)); - b = v_uint32x16(_mm512_alignr_epi32(g12b2, r20g0, 11)); - c = v_uint32x16(_mm512_permutex2var_epi32(bgr1, _v512_set_epu32(21, 20, 19, 18, 17, 16, 13, 10, 7, 4, 1, 26, 25, 24, 23, 22), r20g0)); -} - -inline void v_load_deinterleave( const uint64* ptr, v_uint64x8& a, v_uint64x8& b, v_uint64x8& c ) -{ - __m512i bgr0 = _mm512_loadu_si512((const __m512i*)ptr); - __m512i bgr1 = _mm512_loadu_si512((const __m512i*)(ptr + 8)); - __m512i bgr2 = _mm512_loadu_si512((const __m512i*)(ptr + 16)); - - __m512i mask0 = _v512_set_epu64(13, 10, 15, 12, 9, 6, 3, 0); - __m512i b01g1 = _mm512_permutex2var_epi64(bgr0, mask0, bgr1); - __m512i r12b2 = _mm512_permutex2var_epi64(bgr1, mask0, bgr2); - __m512i g20r0 = _mm512_permutex2var_epi64(bgr2, mask0, bgr0); - - a = v_uint64x8(_mm512_mask_blend_epi64(0xc0, b01g1, r12b2)); - c = v_uint64x8(_mm512_alignr_epi64(r12b2, g20r0, 6)); - b = v_uint64x8(_mm512_permutex2var_epi64(bgr1, _v512_set_epu64(10, 9, 8, 5, 2, 13, 12, 11), g20r0)); -} - -inline void v_load_deinterleave( const uchar* ptr, v_uint8x64& a, v_uint8x64& b, v_uint8x64& c, v_uint8x64& d ) -{ - __m512i bgra0 = _mm512_loadu_si512((const __m512i*)ptr); - __m512i bgra1 = _mm512_loadu_si512((const __m512i*)(ptr + 64)); - __m512i bgra2 = _mm512_loadu_si512((const __m512i*)(ptr + 128)); - __m512i bgra3 = _mm512_loadu_si512((const __m512i*)(ptr + 192)); - -#if CV_AVX_512VBMI - __m512i mask0 = _v512_set_epu8(126, 124, 122, 120, 118, 116, 114, 112, 110, 108, 106, 104, 102, 100, 98, 96, - 94, 92, 90, 88, 86, 84, 82, 80, 78, 76, 74, 72, 70, 68, 66, 64, - 62, 60, 58, 56, 54, 52, 50, 48, 46, 44, 42, 40, 38, 36, 34, 32, - 30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10, 8, 6, 4, 2, 0); - __m512i mask1 = _v512_set_epu8(127, 125, 123, 121, 119, 117, 115, 113, 111, 109, 107, 105, 103, 101, 99, 97, - 95, 93, 91, 89, 87, 85, 83, 81, 79, 77, 75, 73, 71, 69, 67, 65, - 63, 61, 59, 57, 55, 53, 51, 49, 47, 45, 43, 41, 39, 37, 35, 33, - 31, 29, 27, 25, 23, 21, 19, 17, 15, 13, 11, 9, 7, 5, 3, 1); - - __m512i br01 = _mm512_permutex2var_epi8(bgra0, mask0, bgra1); - __m512i ga01 = _mm512_permutex2var_epi8(bgra0, mask1, bgra1); - __m512i br23 = _mm512_permutex2var_epi8(bgra2, mask0, bgra3); - __m512i ga23 = _mm512_permutex2var_epi8(bgra2, mask1, bgra3); - - a = v_uint8x64(_mm512_permutex2var_epi8(br01, mask0, br23)); - c = v_uint8x64(_mm512_permutex2var_epi8(br01, mask1, br23)); - b = v_uint8x64(_mm512_permutex2var_epi8(ga01, mask0, ga23)); - d = v_uint8x64(_mm512_permutex2var_epi8(ga01, mask1, ga23)); -#else - __m512i mask = _mm512_set4_epi32(0x0f0b0703, 0x0e0a0602, 0x0d090501, 0x0c080400); - __m512i b0g0r0a0 = _mm512_shuffle_epi8(bgra0, mask); - __m512i b1g1r1a1 = _mm512_shuffle_epi8(bgra1, mask); - __m512i b2g2r2a2 = _mm512_shuffle_epi8(bgra2, mask); - __m512i b3g3r3a3 = _mm512_shuffle_epi8(bgra3, mask); - - __m512i mask0 = _v512_set_epu32(30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10, 8, 6, 4, 2, 0); - __m512i mask1 = _v512_set_epu32(31, 29, 27, 25, 23, 21, 19, 17, 15, 13, 11, 9, 7, 5, 3, 1); - - __m512i br01 = _mm512_permutex2var_epi32(b0g0r0a0, mask0, b1g1r1a1); - __m512i ga01 = _mm512_permutex2var_epi32(b0g0r0a0, mask1, b1g1r1a1); - __m512i br23 = _mm512_permutex2var_epi32(b2g2r2a2, mask0, b3g3r3a3); - __m512i ga23 = _mm512_permutex2var_epi32(b2g2r2a2, mask1, b3g3r3a3); - - a = v_uint8x64(_mm512_permutex2var_epi32(br01, mask0, br23)); - c = v_uint8x64(_mm512_permutex2var_epi32(br01, mask1, br23)); - b = v_uint8x64(_mm512_permutex2var_epi32(ga01, mask0, ga23)); - d = v_uint8x64(_mm512_permutex2var_epi32(ga01, mask1, ga23)); -#endif -} - -inline void v_load_deinterleave( const ushort* ptr, v_uint16x32& a, v_uint16x32& b, v_uint16x32& c, v_uint16x32& d ) -{ - __m512i bgra0 = _mm512_loadu_si512((const __m512i*)ptr); - __m512i bgra1 = _mm512_loadu_si512((const __m512i*)(ptr + 32)); - __m512i bgra2 = _mm512_loadu_si512((const __m512i*)(ptr + 64)); - __m512i bgra3 = _mm512_loadu_si512((const __m512i*)(ptr + 96)); - - __m512i mask0 = _v512_set_epu16(62, 60, 58, 56, 54, 52, 50, 48, 46, 44, 42, 40, 38, 36, 34, 32, - 30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10, 8, 6, 4, 2, 0); - __m512i mask1 = _v512_set_epu16(63, 61, 59, 57, 55, 53, 51, 49, 47, 45, 43, 41, 39, 37, 35, 33, - 31, 29, 27, 25, 23, 21, 19, 17, 15, 13, 11, 9, 7, 5, 3, 1); - - __m512i br01 = _mm512_permutex2var_epi16(bgra0, mask0, bgra1); - __m512i ga01 = _mm512_permutex2var_epi16(bgra0, mask1, bgra1); - __m512i br23 = _mm512_permutex2var_epi16(bgra2, mask0, bgra3); - __m512i ga23 = _mm512_permutex2var_epi16(bgra2, mask1, bgra3); - - a = v_uint16x32(_mm512_permutex2var_epi16(br01, mask0, br23)); - c = v_uint16x32(_mm512_permutex2var_epi16(br01, mask1, br23)); - b = v_uint16x32(_mm512_permutex2var_epi16(ga01, mask0, ga23)); - d = v_uint16x32(_mm512_permutex2var_epi16(ga01, mask1, ga23)); -} - -inline void v_load_deinterleave( const unsigned* ptr, v_uint32x16& a, v_uint32x16& b, v_uint32x16& c, v_uint32x16& d ) -{ - __m512i bgra0 = _mm512_loadu_si512((const __m512i*)ptr); - __m512i bgra1 = _mm512_loadu_si512((const __m512i*)(ptr + 16)); - __m512i bgra2 = _mm512_loadu_si512((const __m512i*)(ptr + 32)); - __m512i bgra3 = _mm512_loadu_si512((const __m512i*)(ptr + 48)); - - __m512i mask0 = _v512_set_epu32(30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10, 8, 6, 4, 2, 0); - __m512i mask1 = _v512_set_epu32(31, 29, 27, 25, 23, 21, 19, 17, 15, 13, 11, 9, 7, 5, 3, 1); - - __m512i br01 = _mm512_permutex2var_epi32(bgra0, mask0, bgra1); - __m512i ga01 = _mm512_permutex2var_epi32(bgra0, mask1, bgra1); - __m512i br23 = _mm512_permutex2var_epi32(bgra2, mask0, bgra3); - __m512i ga23 = _mm512_permutex2var_epi32(bgra2, mask1, bgra3); - - a = v_uint32x16(_mm512_permutex2var_epi32(br01, mask0, br23)); - c = v_uint32x16(_mm512_permutex2var_epi32(br01, mask1, br23)); - b = v_uint32x16(_mm512_permutex2var_epi32(ga01, mask0, ga23)); - d = v_uint32x16(_mm512_permutex2var_epi32(ga01, mask1, ga23)); -} - -inline void v_load_deinterleave( const uint64* ptr, v_uint64x8& a, v_uint64x8& b, v_uint64x8& c, v_uint64x8& d ) -{ - __m512i bgra0 = _mm512_loadu_si512((const __m512i*)ptr); - __m512i bgra1 = _mm512_loadu_si512((const __m512i*)(ptr + 8)); - __m512i bgra2 = _mm512_loadu_si512((const __m512i*)(ptr + 16)); - __m512i bgra3 = _mm512_loadu_si512((const __m512i*)(ptr + 24)); - - __m512i mask0 = _v512_set_epu64(14, 12, 10, 8, 6, 4, 2, 0); - __m512i mask1 = _v512_set_epu64(15, 13, 11, 9, 7, 5, 3, 1); - - __m512i br01 = _mm512_permutex2var_epi64(bgra0, mask0, bgra1); - __m512i ga01 = _mm512_permutex2var_epi64(bgra0, mask1, bgra1); - __m512i br23 = _mm512_permutex2var_epi64(bgra2, mask0, bgra3); - __m512i ga23 = _mm512_permutex2var_epi64(bgra2, mask1, bgra3); - - a = v_uint64x8(_mm512_permutex2var_epi64(br01, mask0, br23)); - c = v_uint64x8(_mm512_permutex2var_epi64(br01, mask1, br23)); - b = v_uint64x8(_mm512_permutex2var_epi64(ga01, mask0, ga23)); - d = v_uint64x8(_mm512_permutex2var_epi64(ga01, mask1, ga23)); -} - -///////////////////////////// store interleave ///////////////////////////////////// - -inline void v_store_interleave( uchar* ptr, const v_uint8x64& x, const v_uint8x64& y, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - v_uint8x64 low, high; - v_zip(x, y, low, high); - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm512_stream_si512((__m512i*)ptr, low.val); - _mm512_stream_si512((__m512i*)(ptr + 64), high.val); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm512_store_si512((__m512i*)ptr, low.val); - _mm512_store_si512((__m512i*)(ptr + 64), high.val); - } - else - { - _mm512_storeu_si512((__m512i*)ptr, low.val); - _mm512_storeu_si512((__m512i*)(ptr + 64), high.val); - } -} - -inline void v_store_interleave( ushort* ptr, const v_uint16x32& x, const v_uint16x32& y, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - v_uint16x32 low, high; - v_zip(x, y, low, high); - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm512_stream_si512((__m512i*)ptr, low.val); - _mm512_stream_si512((__m512i*)(ptr + 32), high.val); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm512_store_si512((__m512i*)ptr, low.val); - _mm512_store_si512((__m512i*)(ptr + 32), high.val); - } - else - { - _mm512_storeu_si512((__m512i*)ptr, low.val); - _mm512_storeu_si512((__m512i*)(ptr + 32), high.val); - } -} - -inline void v_store_interleave( unsigned* ptr, const v_uint32x16& x, const v_uint32x16& y, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - v_uint32x16 low, high; - v_zip(x, y, low, high); - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm512_stream_si512((__m512i*)ptr, low.val); - _mm512_stream_si512((__m512i*)(ptr + 16), high.val); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm512_store_si512((__m512i*)ptr, low.val); - _mm512_store_si512((__m512i*)(ptr + 16), high.val); - } - else - { - _mm512_storeu_si512((__m512i*)ptr, low.val); - _mm512_storeu_si512((__m512i*)(ptr + 16), high.val); - } -} - -inline void v_store_interleave( uint64* ptr, const v_uint64x8& x, const v_uint64x8& y, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - v_uint64x8 low, high; - v_zip(x, y, low, high); - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm512_stream_si512((__m512i*)ptr, low.val); - _mm512_stream_si512((__m512i*)(ptr + 8), high.val); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm512_store_si512((__m512i*)ptr, low.val); - _mm512_store_si512((__m512i*)(ptr + 8), high.val); - } - else - { - _mm512_storeu_si512((__m512i*)ptr, low.val); - _mm512_storeu_si512((__m512i*)(ptr + 8), high.val); - } -} - -inline void v_store_interleave( uchar* ptr, const v_uint8x64& a, const v_uint8x64& b, const v_uint8x64& c, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ -#if CV_AVX_512VBMI - __m512i mask0 = _v512_set_epu8(127, 84, 20, 126, 83, 19, 125, 82, 18, 124, 81, 17, 123, 80, 16, 122, - 79, 15, 121, 78, 14, 120, 77, 13, 119, 76, 12, 118, 75, 11, 117, 74, - 10, 116, 73, 9, 115, 72, 8, 114, 71, 7, 113, 70, 6, 112, 69, 5, - 111, 68, 4, 110, 67, 3, 109, 66, 2, 108, 65, 1, 107, 64, 0, 106); - __m512i mask1 = _v512_set_epu8( 21, 42, 105, 20, 41, 104, 19, 40, 103, 18, 39, 102, 17, 38, 101, 16, - 37, 100, 15, 36, 99, 14, 35, 98, 13, 34, 97, 12, 33, 96, 11, 32, - 95, 10, 31, 94, 9, 30, 93, 8, 29, 92, 7, 28, 91, 6, 27, 90, - 5, 26, 89, 4, 25, 88, 3, 24, 87, 2, 23, 86, 1, 22, 85, 0); - __m512i mask2 = _v512_set_epu8(106, 127, 63, 105, 126, 62, 104, 125, 61, 103, 124, 60, 102, 123, 59, 101, - 122, 58, 100, 121, 57, 99, 120, 56, 98, 119, 55, 97, 118, 54, 96, 117, - 53, 95, 116, 52, 94, 115, 51, 93, 114, 50, 92, 113, 49, 91, 112, 48, - 90, 111, 47, 89, 110, 46, 88, 109, 45, 87, 108, 44, 86, 107, 43, 85); - __m512i r2g0r0 = _mm512_permutex2var_epi8(b.val, mask0, c.val); - __m512i b0r1b1 = _mm512_permutex2var_epi8(a.val, mask1, c.val); - __m512i g1b2g2 = _mm512_permutex2var_epi8(a.val, mask2, b.val); - - __m512i bgr0 = _mm512_mask_blend_epi8(0x9249249249249249, r2g0r0, b0r1b1); - __m512i bgr1 = _mm512_mask_blend_epi8(0x9249249249249249, b0r1b1, g1b2g2); - __m512i bgr2 = _mm512_mask_blend_epi8(0x9249249249249249, g1b2g2, r2g0r0); -#else - __m512i g1g0 = _mm512_shuffle_epi8(b.val, _mm512_set4_epi32(0x0e0f0c0d, 0x0a0b0809, 0x06070405, 0x02030001)); - __m512i b0g0 = _mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, a.val, g1g0); - __m512i r0b1 = _mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, c.val, a.val); - __m512i g1r1 = _mm512_mask_blend_epi8(0xAAAAAAAAAAAAAAAA, g1g0, c.val); - - __m512i mask0 = _v512_set_epu16(42, 10, 31, 41, 9, 30, 40, 8, 29, 39, 7, 28, 38, 6, 27, 37, - 5, 26, 36, 4, 25, 35, 3, 24, 34, 2, 23, 33, 1, 22, 32, 0); - __m512i mask1 = _v512_set_epu16(21, 52, 41, 20, 51, 40, 19, 50, 39, 18, 49, 38, 17, 48, 37, 16, - 47, 36, 15, 46, 35, 14, 45, 34, 13, 44, 33, 12, 43, 32, 11, 42); - __m512i mask2 = _v512_set_epu16(63, 31, 20, 62, 30, 19, 61, 29, 18, 60, 28, 17, 59, 27, 16, 58, - 26, 15, 57, 25, 14, 56, 24, 13, 55, 23, 12, 54, 22, 11, 53, 21); - __m512i b0g0b2 = _mm512_permutex2var_epi16(b0g0, mask0, r0b1); - __m512i r1b1r0 = _mm512_permutex2var_epi16(b0g0, mask1, g1r1); - __m512i g2r2g1 = _mm512_permutex2var_epi16(r0b1, mask2, g1r1); - - __m512i bgr0 = _mm512_mask_blend_epi16(0x24924924, b0g0b2, r1b1r0); - __m512i bgr1 = _mm512_mask_blend_epi16(0x24924924, r1b1r0, g2r2g1); - __m512i bgr2 = _mm512_mask_blend_epi16(0x24924924, g2r2g1, b0g0b2); -#endif - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm512_stream_si512((__m512i*)ptr, bgr0); - _mm512_stream_si512((__m512i*)(ptr + 64), bgr1); - _mm512_stream_si512((__m512i*)(ptr + 128), bgr2); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm512_store_si512((__m512i*)ptr, bgr0); - _mm512_store_si512((__m512i*)(ptr + 64), bgr1); - _mm512_store_si512((__m512i*)(ptr + 128), bgr2); - } - else - { - _mm512_storeu_si512((__m512i*)ptr, bgr0); - _mm512_storeu_si512((__m512i*)(ptr + 64), bgr1); - _mm512_storeu_si512((__m512i*)(ptr + 128), bgr2); - } -} - -inline void v_store_interleave( ushort* ptr, const v_uint16x32& a, const v_uint16x32& b, const v_uint16x32& c, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - __m512i mask0 = _v512_set_epu16(42, 10, 31, 41, 9, 30, 40, 8, 29, 39, 7, 28, 38, 6, 27, 37, - 5, 26, 36, 4, 25, 35, 3, 24, 34, 2, 23, 33, 1, 22, 32, 0); - __m512i mask1 = _v512_set_epu16(21, 52, 41, 20, 51, 40, 19, 50, 39, 18, 49, 38, 17, 48, 37, 16, - 47, 36, 15, 46, 35, 14, 45, 34, 13, 44, 33, 12, 43, 32, 11, 42); - __m512i mask2 = _v512_set_epu16(63, 31, 20, 62, 30, 19, 61, 29, 18, 60, 28, 17, 59, 27, 16, 58, - 26, 15, 57, 25, 14, 56, 24, 13, 55, 23, 12, 54, 22, 11, 53, 21); - __m512i b0g0b2 = _mm512_permutex2var_epi16(a.val, mask0, b.val); - __m512i r1b1r0 = _mm512_permutex2var_epi16(a.val, mask1, c.val); - __m512i g2r2g1 = _mm512_permutex2var_epi16(b.val, mask2, c.val); - - __m512i bgr0 = _mm512_mask_blend_epi16(0x24924924, b0g0b2, r1b1r0); - __m512i bgr1 = _mm512_mask_blend_epi16(0x24924924, r1b1r0, g2r2g1); - __m512i bgr2 = _mm512_mask_blend_epi16(0x24924924, g2r2g1, b0g0b2); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm512_stream_si512((__m512i*)ptr, bgr0); - _mm512_stream_si512((__m512i*)(ptr + 32), bgr1); - _mm512_stream_si512((__m512i*)(ptr + 64), bgr2); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm512_store_si512((__m512i*)ptr, bgr0); - _mm512_store_si512((__m512i*)(ptr + 32), bgr1); - _mm512_store_si512((__m512i*)(ptr + 64), bgr2); - } - else - { - _mm512_storeu_si512((__m512i*)ptr, bgr0); - _mm512_storeu_si512((__m512i*)(ptr + 32), bgr1); - _mm512_storeu_si512((__m512i*)(ptr + 64), bgr2); - } -} - -inline void v_store_interleave( unsigned* ptr, const v_uint32x16& a, const v_uint32x16& b, const v_uint32x16& c, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - __m512i mask0 = _v512_set_epu32(26, 31, 15, 25, 30, 14, 24, 29, 13, 23, 28, 12, 22, 27, 11, 21); - __m512i mask1 = _v512_set_epu32(31, 10, 25, 30, 9, 24, 29, 8, 23, 28, 7, 22, 27, 6, 21, 26); - __m512i g1b2g2 = _mm512_permutex2var_epi32(a.val, mask0, b.val); - __m512i r2r1b1 = _mm512_permutex2var_epi32(a.val, mask1, c.val); - - __m512i bgr0 = _mm512_mask_expand_epi32(_mm512_mask_expand_epi32(_mm512_maskz_expand_epi32(0x9249, a.val), 0x2492, b.val), 0x4924, c.val); - __m512i bgr1 = _mm512_mask_blend_epi32(0x9249, r2r1b1, g1b2g2); - __m512i bgr2 = _mm512_mask_blend_epi32(0x9249, g1b2g2, r2r1b1); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm512_stream_si512((__m512i*)ptr, bgr0); - _mm512_stream_si512((__m512i*)(ptr + 16), bgr1); - _mm512_stream_si512((__m512i*)(ptr + 32), bgr2); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm512_store_si512((__m512i*)ptr, bgr0); - _mm512_store_si512((__m512i*)(ptr + 16), bgr1); - _mm512_store_si512((__m512i*)(ptr + 32), bgr2); - } - else - { - _mm512_storeu_si512((__m512i*)ptr, bgr0); - _mm512_storeu_si512((__m512i*)(ptr + 16), bgr1); - _mm512_storeu_si512((__m512i*)(ptr + 32), bgr2); - } -} - -inline void v_store_interleave( uint64* ptr, const v_uint64x8& a, const v_uint64x8& b, const v_uint64x8& c, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - __m512i mask0 = _v512_set_epu64( 5, 12, 7, 4, 11, 6, 3, 10); - __m512i mask1 = _v512_set_epu64(15, 7, 4, 14, 6, 3, 13, 5); - __m512i r1b1b2 = _mm512_permutex2var_epi64(a.val, mask0, c.val); - __m512i g2r2g1 = _mm512_permutex2var_epi64(b.val, mask1, c.val); - - __m512i bgr0 = _mm512_mask_expand_epi64(_mm512_mask_expand_epi64(_mm512_maskz_expand_epi64(0x49, a.val), 0x92, b.val), 0x24, c.val); - __m512i bgr1 = _mm512_mask_blend_epi64(0xdb, g2r2g1, r1b1b2); - __m512i bgr2 = _mm512_mask_blend_epi64(0xdb, r1b1b2, g2r2g1); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm512_stream_si512((__m512i*)ptr, bgr0); - _mm512_stream_si512((__m512i*)(ptr + 8), bgr1); - _mm512_stream_si512((__m512i*)(ptr + 16), bgr2); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm512_store_si512((__m512i*)ptr, bgr0); - _mm512_store_si512((__m512i*)(ptr + 8), bgr1); - _mm512_store_si512((__m512i*)(ptr + 16), bgr2); - } - else - { - _mm512_storeu_si512((__m512i*)ptr, bgr0); - _mm512_storeu_si512((__m512i*)(ptr + 8), bgr1); - _mm512_storeu_si512((__m512i*)(ptr + 16), bgr2); - } -} - -inline void v_store_interleave( uchar* ptr, const v_uint8x64& a, const v_uint8x64& b, - const v_uint8x64& c, const v_uint8x64& d, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - v_uint8x64 br01, br23, ga01, ga23; - v_zip(a, c, br01, br23); - v_zip(b, d, ga01, ga23); - v_uint8x64 bgra0, bgra1, bgra2, bgra3; - v_zip(br01, ga01, bgra0, bgra1); - v_zip(br23, ga23, bgra2, bgra3); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm512_stream_si512((__m512i*)ptr, bgra0.val); - _mm512_stream_si512((__m512i*)(ptr + 64), bgra1.val); - _mm512_stream_si512((__m512i*)(ptr + 128), bgra2.val); - _mm512_stream_si512((__m512i*)(ptr + 192), bgra3.val); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm512_store_si512((__m512i*)ptr, bgra0.val); - _mm512_store_si512((__m512i*)(ptr + 64), bgra1.val); - _mm512_store_si512((__m512i*)(ptr + 128), bgra2.val); - _mm512_store_si512((__m512i*)(ptr + 192), bgra3.val); - } - else - { - _mm512_storeu_si512((__m512i*)ptr, bgra0.val); - _mm512_storeu_si512((__m512i*)(ptr + 64), bgra1.val); - _mm512_storeu_si512((__m512i*)(ptr + 128), bgra2.val); - _mm512_storeu_si512((__m512i*)(ptr + 192), bgra3.val); - } -} - -inline void v_store_interleave( ushort* ptr, const v_uint16x32& a, const v_uint16x32& b, - const v_uint16x32& c, const v_uint16x32& d, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - v_uint16x32 br01, br23, ga01, ga23; - v_zip(a, c, br01, br23); - v_zip(b, d, ga01, ga23); - v_uint16x32 bgra0, bgra1, bgra2, bgra3; - v_zip(br01, ga01, bgra0, bgra1); - v_zip(br23, ga23, bgra2, bgra3); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm512_stream_si512((__m512i*)ptr, bgra0.val); - _mm512_stream_si512((__m512i*)(ptr + 32), bgra1.val); - _mm512_stream_si512((__m512i*)(ptr + 64), bgra2.val); - _mm512_stream_si512((__m512i*)(ptr + 96), bgra3.val); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm512_store_si512((__m512i*)ptr, bgra0.val); - _mm512_store_si512((__m512i*)(ptr + 32), bgra1.val); - _mm512_store_si512((__m512i*)(ptr + 64), bgra2.val); - _mm512_store_si512((__m512i*)(ptr + 96), bgra3.val); - } - else - { - _mm512_storeu_si512((__m512i*)ptr, bgra0.val); - _mm512_storeu_si512((__m512i*)(ptr + 32), bgra1.val); - _mm512_storeu_si512((__m512i*)(ptr + 64), bgra2.val); - _mm512_storeu_si512((__m512i*)(ptr + 96), bgra3.val); - } -} - -inline void v_store_interleave( unsigned* ptr, const v_uint32x16& a, const v_uint32x16& b, - const v_uint32x16& c, const v_uint32x16& d, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - v_uint32x16 br01, br23, ga01, ga23; - v_zip(a, c, br01, br23); - v_zip(b, d, ga01, ga23); - v_uint32x16 bgra0, bgra1, bgra2, bgra3; - v_zip(br01, ga01, bgra0, bgra1); - v_zip(br23, ga23, bgra2, bgra3); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm512_stream_si512((__m512i*)ptr, bgra0.val); - _mm512_stream_si512((__m512i*)(ptr + 16), bgra1.val); - _mm512_stream_si512((__m512i*)(ptr + 32), bgra2.val); - _mm512_stream_si512((__m512i*)(ptr + 48), bgra3.val); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm512_store_si512((__m512i*)ptr, bgra0.val); - _mm512_store_si512((__m512i*)(ptr + 16), bgra1.val); - _mm512_store_si512((__m512i*)(ptr + 32), bgra2.val); - _mm512_store_si512((__m512i*)(ptr + 48), bgra3.val); - } - else - { - _mm512_storeu_si512((__m512i*)ptr, bgra0.val); - _mm512_storeu_si512((__m512i*)(ptr + 16), bgra1.val); - _mm512_storeu_si512((__m512i*)(ptr + 32), bgra2.val); - _mm512_storeu_si512((__m512i*)(ptr + 48), bgra3.val); - } -} - -inline void v_store_interleave( uint64* ptr, const v_uint64x8& a, const v_uint64x8& b, - const v_uint64x8& c, const v_uint64x8& d, - hal::StoreMode mode=hal::STORE_UNALIGNED ) -{ - v_uint64x8 br01, br23, ga01, ga23; - v_zip(a, c, br01, br23); - v_zip(b, d, ga01, ga23); - v_uint64x8 bgra0, bgra1, bgra2, bgra3; - v_zip(br01, ga01, bgra0, bgra1); - v_zip(br23, ga23, bgra2, bgra3); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm512_stream_si512((__m512i*)ptr, bgra0.val); - _mm512_stream_si512((__m512i*)(ptr + 8), bgra1.val); - _mm512_stream_si512((__m512i*)(ptr + 16), bgra2.val); - _mm512_stream_si512((__m512i*)(ptr + 24), bgra3.val); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm512_store_si512((__m512i*)ptr, bgra0.val); - _mm512_store_si512((__m512i*)(ptr + 8), bgra1.val); - _mm512_store_si512((__m512i*)(ptr + 16), bgra2.val); - _mm512_store_si512((__m512i*)(ptr + 24), bgra3.val); - } - else - { - _mm512_storeu_si512((__m512i*)ptr, bgra0.val); - _mm512_storeu_si512((__m512i*)(ptr + 8), bgra1.val); - _mm512_storeu_si512((__m512i*)(ptr + 16), bgra2.val); - _mm512_storeu_si512((__m512i*)(ptr + 24), bgra3.val); - } -} - -#define OPENCV_HAL_IMPL_AVX512_LOADSTORE_INTERLEAVE(_Tpvec0, _Tp0, suffix0, _Tpvec1, _Tp1, suffix1) \ -inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0 ) \ -{ \ - _Tpvec1 a1, b1; \ - v_load_deinterleave((const _Tp1*)ptr, a1, b1); \ - a0 = v_reinterpret_as_##suffix0(a1); \ - b0 = v_reinterpret_as_##suffix0(b1); \ -} \ -inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0 ) \ -{ \ - _Tpvec1 a1, b1, c1; \ - v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1); \ - a0 = v_reinterpret_as_##suffix0(a1); \ - b0 = v_reinterpret_as_##suffix0(b1); \ - c0 = v_reinterpret_as_##suffix0(c1); \ -} \ -inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0, _Tpvec0& d0 ) \ -{ \ - _Tpvec1 a1, b1, c1, d1; \ - v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1, d1); \ - a0 = v_reinterpret_as_##suffix0(a1); \ - b0 = v_reinterpret_as_##suffix0(b1); \ - c0 = v_reinterpret_as_##suffix0(c1); \ - d0 = v_reinterpret_as_##suffix0(d1); \ -} \ -inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ - hal::StoreMode mode=hal::STORE_UNALIGNED ) \ -{ \ - _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ - _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ - v_store_interleave((_Tp1*)ptr, a1, b1, mode); \ -} \ -inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, const _Tpvec0& c0, \ - hal::StoreMode mode=hal::STORE_UNALIGNED ) \ -{ \ - _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ - _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ - _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ - v_store_interleave((_Tp1*)ptr, a1, b1, c1, mode); \ -} \ -inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ - const _Tpvec0& c0, const _Tpvec0& d0, \ - hal::StoreMode mode=hal::STORE_UNALIGNED ) \ -{ \ - _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ - _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ - _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ - _Tpvec1 d1 = v_reinterpret_as_##suffix1(d0); \ - v_store_interleave((_Tp1*)ptr, a1, b1, c1, d1, mode); \ -} - -OPENCV_HAL_IMPL_AVX512_LOADSTORE_INTERLEAVE(v_int8x64, schar, s8, v_uint8x64, uchar, u8) -OPENCV_HAL_IMPL_AVX512_LOADSTORE_INTERLEAVE(v_int16x32, short, s16, v_uint16x32, ushort, u16) -OPENCV_HAL_IMPL_AVX512_LOADSTORE_INTERLEAVE(v_int32x16, int, s32, v_uint32x16, unsigned, u32) -OPENCV_HAL_IMPL_AVX512_LOADSTORE_INTERLEAVE(v_float32x16, float, f32, v_uint32x16, unsigned, u32) -OPENCV_HAL_IMPL_AVX512_LOADSTORE_INTERLEAVE(v_int64x8, int64, s64, v_uint64x8, uint64, u64) -OPENCV_HAL_IMPL_AVX512_LOADSTORE_INTERLEAVE(v_float64x8, double, f64, v_uint64x8, uint64, u64) - -////////// Mask and checks ///////// - -/** Mask **/ -inline int64 v_signmask(const v_int8x64& a) { return (int64)_mm512_movepi8_mask(a.val); } -inline int v_signmask(const v_int16x32& a) { return (int)_mm512_cmp_epi16_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_LT); } -inline int v_signmask(const v_int32x16& a) { return (int)_mm512_cmp_epi32_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_LT); } -inline int v_signmask(const v_int64x8& a) { return (int)_mm512_cmp_epi64_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_LT); } - -inline int64 v_signmask(const v_uint8x64& a) { return v_signmask(v_reinterpret_as_s8(a)); } -inline int v_signmask(const v_uint16x32& a) { return v_signmask(v_reinterpret_as_s16(a)); } -inline int v_signmask(const v_uint32x16& a) { return v_signmask(v_reinterpret_as_s32(a)); } -inline int v_signmask(const v_uint64x8& a) { return v_signmask(v_reinterpret_as_s64(a)); } -inline int v_signmask(const v_float32x16& a) { return v_signmask(v_reinterpret_as_s32(a)); } -inline int v_signmask(const v_float64x8& a) { return v_signmask(v_reinterpret_as_s64(a)); } - -/** Checks **/ -inline bool v_check_all(const v_int8x64& a) { return !(bool)_mm512_cmp_epi8_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_NLT); } -inline bool v_check_any(const v_int8x64& a) { return (bool)_mm512_movepi8_mask(a.val); } -inline bool v_check_all(const v_int16x32& a) { return !(bool)_mm512_cmp_epi16_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_NLT); } -inline bool v_check_any(const v_int16x32& a) { return (bool)_mm512_cmp_epi16_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_LT); } -inline bool v_check_all(const v_int32x16& a) { return !(bool)_mm512_cmp_epi32_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_NLT); } -inline bool v_check_any(const v_int32x16& a) { return (bool)_mm512_cmp_epi32_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_LT); } -inline bool v_check_all(const v_int64x8& a) { return !(bool)_mm512_cmp_epi64_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_NLT); } -inline bool v_check_any(const v_int64x8& a) { return (bool)_mm512_cmp_epi64_mask(a.val, _mm512_setzero_si512(), _MM_CMPINT_LT); } - -inline bool v_check_all(const v_float32x16& a) { return v_check_all(v_reinterpret_as_s32(a)); } -inline bool v_check_any(const v_float32x16& a) { return v_check_any(v_reinterpret_as_s32(a)); } -inline bool v_check_all(const v_float64x8& a) { return v_check_all(v_reinterpret_as_s64(a)); } -inline bool v_check_any(const v_float64x8& a) { return v_check_any(v_reinterpret_as_s64(a)); } -inline bool v_check_all(const v_uint8x64& a) { return v_check_all(v_reinterpret_as_s8(a)); } -inline bool v_check_all(const v_uint16x32& a) { return v_check_all(v_reinterpret_as_s16(a)); } -inline bool v_check_all(const v_uint32x16& a) { return v_check_all(v_reinterpret_as_s32(a)); } -inline bool v_check_all(const v_uint64x8& a) { return v_check_all(v_reinterpret_as_s64(a)); } -inline bool v_check_any(const v_uint8x64& a) { return v_check_any(v_reinterpret_as_s8(a)); } -inline bool v_check_any(const v_uint16x32& a) { return v_check_any(v_reinterpret_as_s16(a)); } -inline bool v_check_any(const v_uint32x16& a) { return v_check_any(v_reinterpret_as_s32(a)); } -inline bool v_check_any(const v_uint64x8& a) { return v_check_any(v_reinterpret_as_s64(a)); } - -inline int v_scan_forward(const v_int8x64& a) -{ - int64 mask = _mm512_movepi8_mask(a.val); - int mask32 = (int)mask; - return mask != 0 ? mask32 != 0 ? trailingZeros32(mask32) : 32 + trailingZeros32((int)(mask >> 32)) : 0; -} -inline int v_scan_forward(const v_uint8x64& a) { return v_scan_forward(v_reinterpret_as_s8(a)); } -inline int v_scan_forward(const v_int16x32& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s16(a))); } -inline int v_scan_forward(const v_uint16x32& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s16(a))); } -inline int v_scan_forward(const v_int32x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s16(a))) / 2; } -inline int v_scan_forward(const v_uint32x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s16(a))) / 2; } -inline int v_scan_forward(const v_float32x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s16(a))) / 2; } -inline int v_scan_forward(const v_int64x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s16(a))) / 4; } -inline int v_scan_forward(const v_uint64x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s16(a))) / 4; } -inline int v_scan_forward(const v_float64x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s16(a))) / 4; } - -inline void v512_cleanup() { _mm256_zeroall(); } - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END - -//! @endcond - -} // cv:: - -#endif // OPENCV_HAL_INTRIN_AVX_HPP diff --git a/include/opencv2/core/hal/intrin_cpp.hpp b/include/opencv2/core/hal/intrin_cpp.hpp deleted file mode 100755 index 859bfd7..0000000 --- a/include/opencv2/core/hal/intrin_cpp.hpp +++ /dev/null @@ -1,2781 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_HAL_INTRIN_CPP_HPP -#define OPENCV_HAL_INTRIN_CPP_HPP - -#include -#include -#include -#include "opencv2/core/saturate.hpp" - -//! @cond IGNORED -#define CV_SIMD128_CPP 1 -#if defined(CV_FORCE_SIMD128_CPP) || defined(CV_DOXYGEN) -#define CV_SIMD128 1 -#define CV_SIMD128_64F 1 -#endif -//! @endcond - -namespace cv -{ - -#ifndef CV_DOXYGEN -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN -#endif - -/** @addtogroup core_hal_intrin - -"Universal intrinsics" is a types and functions set intended to simplify vectorization of code on -different platforms. Currently there are two supported SIMD extensions: __SSE/SSE2__ on x86 -architectures and __NEON__ on ARM architectures, both allow working with 128 bit registers -containing packed values of different types. In case when there is no SIMD extension available -during compilation, fallback C++ implementation of intrinsics will be chosen and code will work as -expected although it could be slower. - -### Types - -There are several types representing 128-bit register as a vector of packed values, each type is -implemented as a structure based on a one SIMD register. - -- cv::v_uint8x16 and cv::v_int8x16: sixteen 8-bit integer values (unsigned/signed) - char -- cv::v_uint16x8 and cv::v_int16x8: eight 16-bit integer values (unsigned/signed) - short -- cv::v_uint32x4 and cv::v_int32x4: four 32-bit integer values (unsigned/signed) - int -- cv::v_uint64x2 and cv::v_int64x2: two 64-bit integer values (unsigned/signed) - int64 -- cv::v_float32x4: four 32-bit floating point values (signed) - float -- cv::v_float64x2: two 64-bit floating point values (signed) - double - -@note -cv::v_float64x2 is not implemented in NEON variant, if you want to use this type, don't forget to -check the CV_SIMD128_64F preprocessor definition: -@code -#if CV_SIMD128_64F -//... -#endif -@endcode - -### Load and store operations - -These operations allow to set contents of the register explicitly or by loading it from some memory -block and to save contents of the register to memory block. - -- Constructors: -@ref v_reg::v_reg(const _Tp *ptr) "from memory", -@ref v_reg::v_reg(_Tp s0, _Tp s1) "from two values", ... -- Other create methods: -@ref v_setall_s8, @ref v_setall_u8, ..., -@ref v_setzero_u8, @ref v_setzero_s8, ... -- Memory operations: -@ref v_load, @ref v_load_aligned, @ref v_load_low, @ref v_load_halves, -@ref v_store, @ref v_store_aligned, -@ref v_store_high, @ref v_store_low - -### Value reordering - -These operations allow to reorder or recombine elements in one or multiple vectors. - -- Interleave, deinterleave (2, 3 and 4 channels): @ref v_load_deinterleave, @ref v_store_interleave -- Expand: @ref v_load_expand, @ref v_load_expand_q, @ref v_expand, @ref v_expand_low, @ref v_expand_high -- Pack: @ref v_pack, @ref v_pack_u, @ref v_pack_b, @ref v_rshr_pack, @ref v_rshr_pack_u, -@ref v_pack_store, @ref v_pack_u_store, @ref v_rshr_pack_store, @ref v_rshr_pack_u_store -- Recombine: @ref v_zip, @ref v_recombine, @ref v_combine_low, @ref v_combine_high -- Reverse: @ref v_reverse -- Extract: @ref v_extract - - -### Arithmetic, bitwise and comparison operations - -Element-wise binary and unary operations. - -- Arithmetics: -@ref operator +(const v_reg &a, const v_reg &b) "+", -@ref operator -(const v_reg &a, const v_reg &b) "-", -@ref operator *(const v_reg &a, const v_reg &b) "*", -@ref operator /(const v_reg &a, const v_reg &b) "/", -@ref v_mul_expand - -- Non-saturating arithmetics: @ref v_add_wrap, @ref v_sub_wrap - -- Bitwise shifts: -@ref operator <<(const v_reg &a, int s) "<<", -@ref operator >>(const v_reg &a, int s) ">>", -@ref v_shl, @ref v_shr - -- Bitwise logic: -@ref operator &(const v_reg &a, const v_reg &b) "&", -@ref operator |(const v_reg &a, const v_reg &b) "|", -@ref operator ^(const v_reg &a, const v_reg &b) "^", -@ref operator ~(const v_reg &a) "~" - -- Comparison: -@ref operator >(const v_reg &a, const v_reg &b) ">", -@ref operator >=(const v_reg &a, const v_reg &b) ">=", -@ref operator <(const v_reg &a, const v_reg &b) "<", -@ref operator <=(const v_reg &a, const v_reg &b) "<=", -@ref operator==(const v_reg &a, const v_reg &b) "==", -@ref operator !=(const v_reg &a, const v_reg &b) "!=" - -- min/max: @ref v_min, @ref v_max - -### Reduce and mask - -Most of these operations return only one value. - -- Reduce: @ref v_reduce_min, @ref v_reduce_max, @ref v_reduce_sum, @ref v_popcount -- Mask: @ref v_signmask, @ref v_check_all, @ref v_check_any, @ref v_select - -### Other math - -- Some frequent operations: @ref v_sqrt, @ref v_invsqrt, @ref v_magnitude, @ref v_sqr_magnitude -- Absolute values: @ref v_abs, @ref v_absdiff, @ref v_absdiffs - -### Conversions - -Different type conversions and casts: - -- Rounding: @ref v_round, @ref v_floor, @ref v_ceil, @ref v_trunc, -- To float: @ref v_cvt_f32, @ref v_cvt_f64 -- Reinterpret: @ref v_reinterpret_as_u8, @ref v_reinterpret_as_s8, ... - -### Matrix operations - -In these operations vectors represent matrix rows/columns: @ref v_dotprod, @ref v_dotprod_fast, -@ref v_dotprod_expand, @ref v_dotprod_expand_fast, @ref v_matmul, @ref v_transpose4x4 - -### Usability - -Most operations are implemented only for some subset of the available types, following matrices -shows the applicability of different operations to the types. - -Regular integers: - -| Operations\\Types | uint 8x16 | int 8x16 | uint 16x8 | int 16x8 | uint 32x4 | int 32x4 | -|-------------------|:-:|:-:|:-:|:-:|:-:|:-:| -|load, store | x | x | x | x | x | x | -|interleave | x | x | x | x | x | x | -|expand | x | x | x | x | x | x | -|expand_low | x | x | x | x | x | x | -|expand_high | x | x | x | x | x | x | -|expand_q | x | x | | | | | -|add, sub | x | x | x | x | x | x | -|add_wrap, sub_wrap | x | x | x | x | | | -|mul_wrap | x | x | x | x | | | -|mul | x | x | x | x | x | x | -|mul_expand | x | x | x | x | x | | -|compare | x | x | x | x | x | x | -|shift | | | x | x | x | x | -|dotprod | | | | x | | x | -|dotprod_fast | | | | x | | x | -|dotprod_expand | x | x | x | x | | x | -|dotprod_expand_fast| x | x | x | x | | x | -|logical | x | x | x | x | x | x | -|min, max | x | x | x | x | x | x | -|absdiff | x | x | x | x | x | x | -|absdiffs | | x | | x | | | -|reduce | x | x | x | x | x | x | -|mask | x | x | x | x | x | x | -|pack | x | x | x | x | x | x | -|pack_u | x | | x | | | | -|pack_b | x | | | | | | -|unpack | x | x | x | x | x | x | -|extract | x | x | x | x | x | x | -|rotate (lanes) | x | x | x | x | x | x | -|cvt_flt32 | | | | | | x | -|cvt_flt64 | | | | | | x | -|transpose4x4 | | | | | x | x | -|reverse | x | x | x | x | x | x | -|extract_n | x | x | x | x | x | x | -|broadcast_element | | | | | x | x | - -Big integers: - -| Operations\\Types | uint 64x2 | int 64x2 | -|-------------------|:-:|:-:| -|load, store | x | x | -|add, sub | x | x | -|shift | x | x | -|logical | x | x | -|reverse | x | x | -|extract | x | x | -|rotate (lanes) | x | x | -|cvt_flt64 | | x | -|extract_n | x | x | - -Floating point: - -| Operations\\Types | float 32x4 | float 64x2 | -|-------------------|:-:|:-:| -|load, store | x | x | -|interleave | x | | -|add, sub | x | x | -|mul | x | x | -|div | x | x | -|compare | x | x | -|min, max | x | x | -|absdiff | x | x | -|reduce | x | | -|mask | x | x | -|unpack | x | x | -|cvt_flt32 | | x | -|cvt_flt64 | x | | -|sqrt, abs | x | x | -|float math | x | x | -|transpose4x4 | x | | -|extract | x | x | -|rotate (lanes) | x | x | -|reverse | x | x | -|extract_n | x | x | -|broadcast_element | x | | - - @{ */ - -template struct v_reg -{ -//! @cond IGNORED - typedef _Tp lane_type; - enum { nlanes = n }; -// !@endcond - - /** @brief Constructor - - Initializes register with data from memory - @param ptr pointer to memory block with data for register */ - explicit v_reg(const _Tp* ptr) { for( int i = 0; i < n; i++ ) s[i] = ptr[i]; } - - /** @brief Constructor - - Initializes register with two 64-bit values */ - v_reg(_Tp s0, _Tp s1) { s[0] = s0; s[1] = s1; } - - /** @brief Constructor - - Initializes register with four 32-bit values */ - v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3) { s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; } - - /** @brief Constructor - - Initializes register with eight 16-bit values */ - v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3, - _Tp s4, _Tp s5, _Tp s6, _Tp s7) - { - s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; - s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7; - } - - /** @brief Constructor - - Initializes register with sixteen 8-bit values */ - v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3, - _Tp s4, _Tp s5, _Tp s6, _Tp s7, - _Tp s8, _Tp s9, _Tp s10, _Tp s11, - _Tp s12, _Tp s13, _Tp s14, _Tp s15) - { - s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; - s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7; - s[8] = s8; s[9] = s9; s[10] = s10; s[11] = s11; - s[12] = s12; s[13] = s13; s[14] = s14; s[15] = s15; - } - - /** @brief Default constructor - - Does not initialize anything*/ - v_reg() {} - - /** @brief Copy constructor */ - v_reg(const v_reg<_Tp, n> & r) - { - for( int i = 0; i < n; i++ ) - s[i] = r.s[i]; - } - /** @brief Access first value - - Returns value of the first lane according to register type, for example: - @code{.cpp} - v_int32x4 r(1, 2, 3, 4); - int v = r.get0(); // returns 1 - v_uint64x2 r(1, 2); - uint64_t v = r.get0(); // returns 1 - @endcode - */ - _Tp get0() const { return s[0]; } - -//! @cond IGNORED - _Tp get(const int i) const { return s[i]; } - v_reg<_Tp, n> high() const - { - v_reg<_Tp, n> c; - int i; - for( i = 0; i < n/2; i++ ) - { - c.s[i] = s[i+(n/2)]; - c.s[i+(n/2)] = 0; - } - return c; - } - - static v_reg<_Tp, n> zero() - { - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = (_Tp)0; - return c; - } - - static v_reg<_Tp, n> all(_Tp s) - { - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = s; - return c; - } - - template v_reg<_Tp2, n2> reinterpret_as() const - { - size_t bytes = std::min(sizeof(_Tp2)*n2, sizeof(_Tp)*n); - v_reg<_Tp2, n2> c; - std::memcpy(&c.s[0], &s[0], bytes); - return c; - } - - v_reg& operator=(const v_reg<_Tp, n> & r) - { - for( int i = 0; i < n; i++ ) - s[i] = r.s[i]; - return *this; - } - - _Tp s[n]; -//! @endcond -}; - -/** @brief Sixteen 8-bit unsigned integer values */ -typedef v_reg v_uint8x16; -/** @brief Sixteen 8-bit signed integer values */ -typedef v_reg v_int8x16; -/** @brief Eight 16-bit unsigned integer values */ -typedef v_reg v_uint16x8; -/** @brief Eight 16-bit signed integer values */ -typedef v_reg v_int16x8; -/** @brief Four 32-bit unsigned integer values */ -typedef v_reg v_uint32x4; -/** @brief Four 32-bit signed integer values */ -typedef v_reg v_int32x4; -/** @brief Four 32-bit floating point values (single precision) */ -typedef v_reg v_float32x4; -/** @brief Two 64-bit floating point values (double precision) */ -typedef v_reg v_float64x2; -/** @brief Two 64-bit unsigned integer values */ -typedef v_reg v_uint64x2; -/** @brief Two 64-bit signed integer values */ -typedef v_reg v_int64x2; - -/** @brief Add values - -For all types. */ -template CV_INLINE v_reg<_Tp, n> operator+(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b); -template CV_INLINE v_reg<_Tp, n>& operator+=(v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b); - -/** @brief Subtract values - -For all types. */ -template CV_INLINE v_reg<_Tp, n> operator-(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b); -template CV_INLINE v_reg<_Tp, n>& operator-=(v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b); - -/** @brief Multiply values - -For 16- and 32-bit integer types and floating types. */ -template CV_INLINE v_reg<_Tp, n> operator*(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b); -template CV_INLINE v_reg<_Tp, n>& operator*=(v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b); - -/** @brief Divide values - -For floating types only. */ -template CV_INLINE v_reg<_Tp, n> operator/(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b); -template CV_INLINE v_reg<_Tp, n>& operator/=(v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b); - - -/** @brief Bitwise AND - -Only for integer types. */ -template CV_INLINE v_reg<_Tp, n> operator&(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b); -template CV_INLINE v_reg<_Tp, n>& operator&=(v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b); - -/** @brief Bitwise OR - -Only for integer types. */ -template CV_INLINE v_reg<_Tp, n> operator|(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b); -template CV_INLINE v_reg<_Tp, n>& operator|=(v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b); - -/** @brief Bitwise XOR - -Only for integer types.*/ -template CV_INLINE v_reg<_Tp, n> operator^(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b); -template CV_INLINE v_reg<_Tp, n>& operator^=(v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b); - -/** @brief Bitwise NOT - -Only for integer types.*/ -template CV_INLINE v_reg<_Tp, n> operator~(const v_reg<_Tp, n>& a); - - -#ifndef CV_DOXYGEN - -#define CV__HAL_INTRIN_EXPAND_WITH_INTEGER_TYPES(macro_name, ...) \ -__CV_EXPAND(macro_name(uchar, __VA_ARGS__)) \ -__CV_EXPAND(macro_name(schar, __VA_ARGS__)) \ -__CV_EXPAND(macro_name(ushort, __VA_ARGS__)) \ -__CV_EXPAND(macro_name(short, __VA_ARGS__)) \ -__CV_EXPAND(macro_name(unsigned, __VA_ARGS__)) \ -__CV_EXPAND(macro_name(int, __VA_ARGS__)) \ -__CV_EXPAND(macro_name(uint64, __VA_ARGS__)) \ -__CV_EXPAND(macro_name(int64, __VA_ARGS__)) \ - -#define CV__HAL_INTRIN_EXPAND_WITH_FP_TYPES(macro_name, ...) \ -__CV_EXPAND(macro_name(float, __VA_ARGS__)) \ -__CV_EXPAND(macro_name(double, __VA_ARGS__)) \ - -#define CV__HAL_INTRIN_EXPAND_WITH_ALL_TYPES(macro_name, ...) \ -CV__HAL_INTRIN_EXPAND_WITH_INTEGER_TYPES(macro_name, __VA_ARGS__) \ -CV__HAL_INTRIN_EXPAND_WITH_FP_TYPES(macro_name, __VA_ARGS__) \ - -#define CV__HAL_INTRIN_IMPL_BIN_OP_(_Tp, bin_op) \ -template inline \ -v_reg<_Tp, n> operator bin_op (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - v_reg<_Tp, n> c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \ - return c; \ -} \ -template inline \ -v_reg<_Tp, n>& operator bin_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - for( int i = 0; i < n; i++ ) \ - a.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \ - return a; \ -} - -#define CV__HAL_INTRIN_IMPL_BIN_OP(bin_op) CV__HAL_INTRIN_EXPAND_WITH_ALL_TYPES(CV__HAL_INTRIN_IMPL_BIN_OP_, bin_op) - -CV__HAL_INTRIN_IMPL_BIN_OP(+) -CV__HAL_INTRIN_IMPL_BIN_OP(-) -CV__HAL_INTRIN_IMPL_BIN_OP(*) -CV__HAL_INTRIN_EXPAND_WITH_FP_TYPES(CV__HAL_INTRIN_IMPL_BIN_OP_, /) - -#define CV__HAL_INTRIN_IMPL_BIT_OP_(_Tp, bit_op) \ -template CV_INLINE \ -v_reg<_Tp, n> operator bit_op (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - v_reg<_Tp, n> c; \ - typedef typename V_TypeTraits<_Tp>::int_type itype; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \ - V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \ - return c; \ -} \ -template CV_INLINE \ -v_reg<_Tp, n>& operator bit_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - typedef typename V_TypeTraits<_Tp>::int_type itype; \ - for( int i = 0; i < n; i++ ) \ - a.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \ - V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \ - return a; \ -} - -#define CV__HAL_INTRIN_IMPL_BIT_OP(bit_op) \ -CV__HAL_INTRIN_EXPAND_WITH_INTEGER_TYPES(CV__HAL_INTRIN_IMPL_BIT_OP_, bit_op) \ -CV__HAL_INTRIN_EXPAND_WITH_FP_TYPES(CV__HAL_INTRIN_IMPL_BIT_OP_, bit_op) /* TODO: FIXIT remove this after masks refactoring */ - - -CV__HAL_INTRIN_IMPL_BIT_OP(&) -CV__HAL_INTRIN_IMPL_BIT_OP(|) -CV__HAL_INTRIN_IMPL_BIT_OP(^) - -#define CV__HAL_INTRIN_IMPL_BITWISE_NOT_(_Tp, dummy) \ -template CV_INLINE \ -v_reg<_Tp, n> operator ~ (const v_reg<_Tp, n>& a) \ -{ \ - v_reg<_Tp, n> c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int(~V_TypeTraits<_Tp>::reinterpret_int(a.s[i])); \ - return c; \ -} \ - -CV__HAL_INTRIN_EXPAND_WITH_INTEGER_TYPES(CV__HAL_INTRIN_IMPL_BITWISE_NOT_, ~) - -#endif // !CV_DOXYGEN - - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_MATH_FUNC(func, cfunc, _Tp2) \ -template inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a) \ -{ \ - v_reg<_Tp2, n> c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = cfunc(a.s[i]); \ - return c; \ -} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_MATH_FUNC_FLOAT(func, cfunc) \ -inline v_reg func(const v_reg& a) \ -{ \ - v_reg c; \ - for( int i = 0; i < 4; i++ ) \ - c.s[i] = cfunc(a.s[i]); \ - return c; \ -} \ -inline v_reg func(const v_reg& a) \ -{ \ - v_reg c; \ - for( int i = 0; i < 2; i++ ) \ - { \ - c.s[i] = cfunc(a.s[i]); \ - c.s[i + 2] = 0; \ - } \ - return c; \ -} - -/** @brief Square root of elements - -Only for floating point types.*/ -OPENCV_HAL_IMPL_MATH_FUNC(v_sqrt, std::sqrt, _Tp) - -//! @cond IGNORED -OPENCV_HAL_IMPL_MATH_FUNC(v_sin, std::sin, _Tp) -OPENCV_HAL_IMPL_MATH_FUNC(v_cos, std::cos, _Tp) -OPENCV_HAL_IMPL_MATH_FUNC(v_exp, std::exp, _Tp) -OPENCV_HAL_IMPL_MATH_FUNC(v_log, std::log, _Tp) -//! @endcond - -/** @brief Absolute value of elements - -Only for floating point types.*/ -OPENCV_HAL_IMPL_MATH_FUNC(v_abs, (typename V_TypeTraits<_Tp>::abs_type)std::abs, - typename V_TypeTraits<_Tp>::abs_type) - -/** @brief Round elements - -Only for floating point types.*/ -OPENCV_HAL_IMPL_MATH_FUNC_FLOAT(v_round, cvRound) - -/** @brief Floor elements - -Only for floating point types.*/ -OPENCV_HAL_IMPL_MATH_FUNC_FLOAT(v_floor, cvFloor) - -/** @brief Ceil elements - -Only for floating point types.*/ -OPENCV_HAL_IMPL_MATH_FUNC_FLOAT(v_ceil, cvCeil) - -/** @brief Truncate elements - -Only for floating point types.*/ -OPENCV_HAL_IMPL_MATH_FUNC_FLOAT(v_trunc, int) - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_MINMAX_FUNC(func, cfunc) \ -template inline v_reg<_Tp, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - v_reg<_Tp, n> c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = cfunc(a.s[i], b.s[i]); \ - return c; \ -} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(func, cfunc) \ -template inline _Tp func(const v_reg<_Tp, n>& a) \ -{ \ - _Tp c = a.s[0]; \ - for( int i = 1; i < n; i++ ) \ - c = cfunc(c, a.s[i]); \ - return c; \ -} - -/** @brief Choose min values for each pair - -Scheme: -@code -{A1 A2 ...} -{B1 B2 ...} --------------- -{min(A1,B1) min(A2,B2) ...} -@endcode -For all types except 64-bit integer. */ -OPENCV_HAL_IMPL_MINMAX_FUNC(v_min, std::min) - -/** @brief Choose max values for each pair - -Scheme: -@code -{A1 A2 ...} -{B1 B2 ...} --------------- -{max(A1,B1) max(A2,B2) ...} -@endcode -For all types except 64-bit integer. */ -OPENCV_HAL_IMPL_MINMAX_FUNC(v_max, std::max) - -/** @brief Find one min value - -Scheme: -@code -{A1 A2 A3 ...} => min(A1,A2,A3,...) -@endcode -For all types except 64-bit integer and 64-bit floating point types. */ -OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_min, std::min) - -/** @brief Find one max value - -Scheme: -@code -{A1 A2 A3 ...} => max(A1,A2,A3,...) -@endcode -For all types except 64-bit integer and 64-bit floating point types. */ -OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_max, std::max) - -static const unsigned char popCountTable[] = -{ - 0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, - 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, - 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, - 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, - 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, - 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8, -}; -/** @brief Count the 1 bits in the vector lanes and return result as corresponding unsigned type - -Scheme: -@code -{A1 A2 A3 ...} => {popcount(A1), popcount(A2), popcount(A3), ...} -@endcode -For all integer types. */ -template -inline v_reg::abs_type, n> v_popcount(const v_reg<_Tp, n>& a) -{ - v_reg::abs_type, n> b = v_reg::abs_type, n>::zero(); - for (int i = 0; i < n*(int)sizeof(_Tp); i++) - b.s[i/sizeof(_Tp)] += popCountTable[v_reinterpret_as_u8(a).s[i]]; - return b; -} - - -//! @cond IGNORED -template -inline void v_minmax( const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - v_reg<_Tp, n>& minval, v_reg<_Tp, n>& maxval ) -{ - for( int i = 0; i < n; i++ ) - { - minval.s[i] = std::min(a.s[i], b.s[i]); - maxval.s[i] = std::max(a.s[i], b.s[i]); - } -} -//! @endcond - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_CMP_OP(cmp_op) \ -template \ -inline v_reg<_Tp, n> operator cmp_op(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - typedef typename V_TypeTraits<_Tp>::int_type itype; \ - v_reg<_Tp, n> c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)-(int)(a.s[i] cmp_op b.s[i])); \ - return c; \ -} - -/** @brief Less-than comparison - -For all types except 64-bit integer values. */ -OPENCV_HAL_IMPL_CMP_OP(<) - -/** @brief Greater-than comparison - -For all types except 64-bit integer values. */ -OPENCV_HAL_IMPL_CMP_OP(>) - -/** @brief Less-than or equal comparison - -For all types except 64-bit integer values. */ -OPENCV_HAL_IMPL_CMP_OP(<=) - -/** @brief Greater-than or equal comparison - -For all types except 64-bit integer values. */ -OPENCV_HAL_IMPL_CMP_OP(>=) - -/** @brief Equal comparison - -For all types except 64-bit integer values. */ -OPENCV_HAL_IMPL_CMP_OP(==) - -/** @brief Not equal comparison - -For all types except 64-bit integer values. */ -OPENCV_HAL_IMPL_CMP_OP(!=) - -template -inline v_reg v_not_nan(const v_reg& a) -{ - typedef typename V_TypeTraits::int_type itype; - v_reg c; - for (int i = 0; i < n; i++) - c.s[i] = V_TypeTraits::reinterpret_from_int((itype)-(int)(a.s[i] == a.s[i])); - return c; -} -template -inline v_reg v_not_nan(const v_reg& a) -{ - typedef typename V_TypeTraits::int_type itype; - v_reg c; - for (int i = 0; i < n; i++) - c.s[i] = V_TypeTraits::reinterpret_from_int((itype)-(int)(a.s[i] == a.s[i])); - return c; -} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_ARITHM_OP(func, bin_op, cast_op, _Tp2) \ -template \ -inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - typedef _Tp2 rtype; \ - v_reg c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = cast_op(a.s[i] bin_op b.s[i]); \ - return c; \ -} - -/** @brief Add values without saturation - -For 8- and 16-bit integer values. */ -OPENCV_HAL_IMPL_ARITHM_OP(v_add_wrap, +, (_Tp), _Tp) - -/** @brief Subtract values without saturation - -For 8- and 16-bit integer values. */ -OPENCV_HAL_IMPL_ARITHM_OP(v_sub_wrap, -, (_Tp), _Tp) - -/** @brief Multiply values without saturation - -For 8- and 16-bit integer values. */ -OPENCV_HAL_IMPL_ARITHM_OP(v_mul_wrap, *, (_Tp), _Tp) - -//! @cond IGNORED -template inline T _absdiff(T a, T b) -{ - return a > b ? a - b : b - a; -} -//! @endcond - -/** @brief Absolute difference - -Returns \f$ |a - b| \f$ converted to corresponding unsigned type. -Example: -@code{.cpp} -v_int32x4 a, b; // {1, 2, 3, 4} and {4, 3, 2, 1} -v_uint32x4 c = v_absdiff(a, b); // result is {3, 1, 1, 3} -@endcode -For 8-, 16-, 32-bit integer source types. */ -template -inline v_reg::abs_type, n> v_absdiff(const v_reg<_Tp, n>& a, const v_reg<_Tp, n> & b) -{ - typedef typename V_TypeTraits<_Tp>::abs_type rtype; - v_reg c; - const rtype mask = (rtype)(std::numeric_limits<_Tp>::is_signed ? (1 << (sizeof(rtype)*8 - 1)) : 0); - for( int i = 0; i < n; i++ ) - { - rtype ua = a.s[i] ^ mask; - rtype ub = b.s[i] ^ mask; - c.s[i] = _absdiff(ua, ub); - } - return c; -} - -/** @overload - -For 32-bit floating point values */ -inline v_float32x4 v_absdiff(const v_float32x4& a, const v_float32x4& b) -{ - v_float32x4 c; - for( int i = 0; i < c.nlanes; i++ ) - c.s[i] = _absdiff(a.s[i], b.s[i]); - return c; -} - -/** @overload - -For 64-bit floating point values */ -inline v_float64x2 v_absdiff(const v_float64x2& a, const v_float64x2& b) -{ - v_float64x2 c; - for( int i = 0; i < c.nlanes; i++ ) - c.s[i] = _absdiff(a.s[i], b.s[i]); - return c; -} - -/** @brief Saturating absolute difference - -Returns \f$ saturate(|a - b|) \f$ . -For 8-, 16-bit signed integer source types. */ -template -inline v_reg<_Tp, n> v_absdiffs(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++) - c.s[i] = saturate_cast<_Tp>(std::abs(a.s[i] - b.s[i])); - return c; -} - -/** @brief Inversed square root - -Returns \f$ 1/sqrt(a) \f$ -For floating point types only. */ -template -inline v_reg<_Tp, n> v_invsqrt(const v_reg<_Tp, n>& a) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = 1.f/std::sqrt(a.s[i]); - return c; -} - -/** @brief Magnitude - -Returns \f$ sqrt(a^2 + b^2) \f$ -For floating point types only. */ -template -inline v_reg<_Tp, n> v_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = std::sqrt(a.s[i]*a.s[i] + b.s[i]*b.s[i]); - return c; -} - -/** @brief Square of the magnitude - -Returns \f$ a^2 + b^2 \f$ -For floating point types only. */ -template -inline v_reg<_Tp, n> v_sqr_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = a.s[i]*a.s[i] + b.s[i]*b.s[i]; - return c; -} - -/** @brief Multiply and add - - Returns \f$ a*b + c \f$ - For floating point types and signed 32bit int only. */ -template -inline v_reg<_Tp, n> v_fma(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - const v_reg<_Tp, n>& c) -{ - v_reg<_Tp, n> d; - for( int i = 0; i < n; i++ ) - d.s[i] = a.s[i]*b.s[i] + c.s[i]; - return d; -} - -/** @brief A synonym for v_fma */ -template -inline v_reg<_Tp, n> v_muladd(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - const v_reg<_Tp, n>& c) -{ - return v_fma(a, b, c); -} - -/** @brief Dot product of elements - -Multiply values in two registers and sum adjacent result pairs. - -Scheme: -@code - {A1 A2 ...} // 16-bit -x {B1 B2 ...} // 16-bit -------------- -{A1B1+A2B2 ...} // 32-bit - -@endcode -*/ -template inline v_reg::w_type, n/2> -v_dotprod(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - typedef typename V_TypeTraits<_Tp>::w_type w_type; - v_reg c; - for( int i = 0; i < (n/2); i++ ) - c.s[i] = (w_type)a.s[i*2]*b.s[i*2] + (w_type)a.s[i*2+1]*b.s[i*2+1]; - return c; -} - -/** @brief Dot product of elements - -Same as cv::v_dotprod, but add a third element to the sum of adjacent pairs. -Scheme: -@code - {A1 A2 ...} // 16-bit -x {B1 B2 ...} // 16-bit -------------- - {A1B1+A2B2+C1 ...} // 32-bit -@endcode -*/ -template inline v_reg::w_type, n/2> -v_dotprod(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - const v_reg::w_type, n / 2>& c) -{ - typedef typename V_TypeTraits<_Tp>::w_type w_type; - v_reg s; - for( int i = 0; i < (n/2); i++ ) - s.s[i] = (w_type)a.s[i*2]*b.s[i*2] + (w_type)a.s[i*2+1]*b.s[i*2+1] + c.s[i]; - return s; -} - -/** @brief Fast Dot product of elements - -Same as cv::v_dotprod, but it may perform unorder sum between result pairs in some platforms, -this intrinsic can be used if the sum among all lanes is only matters -and also it should be yielding better performance on the affected platforms. - -*/ -template inline v_reg::w_type, n/2> -v_dotprod_fast(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ return v_dotprod(a, b); } - -/** @brief Fast Dot product of elements - -Same as cv::v_dotprod_fast, but add a third element to the sum of adjacent pairs. -*/ -template inline v_reg::w_type, n/2> -v_dotprod_fast(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - const v_reg::w_type, n / 2>& c) -{ return v_dotprod(a, b, c); } - -/** @brief Dot product of elements and expand - -Multiply values in two registers and expand the sum of adjacent result pairs. - -Scheme: -@code - {A1 A2 A3 A4 ...} // 8-bit -x {B1 B2 B3 B4 ...} // 8-bit -------------- - {A1B1+A2B2+A3B3+A4B4 ...} // 32-bit - -@endcode -*/ -template inline v_reg::q_type, n/4> -v_dotprod_expand(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - typedef typename V_TypeTraits<_Tp>::q_type q_type; - v_reg s; - for( int i = 0; i < (n/4); i++ ) - s.s[i] = (q_type)a.s[i*4 ]*b.s[i*4 ] + (q_type)a.s[i*4 + 1]*b.s[i*4 + 1] + - (q_type)a.s[i*4 + 2]*b.s[i*4 + 2] + (q_type)a.s[i*4 + 3]*b.s[i*4 + 3]; - return s; -} - -/** @brief Dot product of elements - -Same as cv::v_dotprod_expand, but add a third element to the sum of adjacent pairs. -Scheme: -@code - {A1 A2 A3 A4 ...} // 8-bit -x {B1 B2 B3 B4 ...} // 8-bit -------------- - {A1B1+A2B2+A3B3+A4B4+C1 ...} // 32-bit -@endcode -*/ -template inline v_reg::q_type, n/4> -v_dotprod_expand(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - const v_reg::q_type, n / 4>& c) -{ - typedef typename V_TypeTraits<_Tp>::q_type q_type; - v_reg s; - for( int i = 0; i < (n/4); i++ ) - s.s[i] = (q_type)a.s[i*4 ]*b.s[i*4 ] + (q_type)a.s[i*4 + 1]*b.s[i*4 + 1] + - (q_type)a.s[i*4 + 2]*b.s[i*4 + 2] + (q_type)a.s[i*4 + 3]*b.s[i*4 + 3] + c.s[i]; - return s; -} - -/** @brief Fast Dot product of elements and expand - -Multiply values in two registers and expand the sum of adjacent result pairs. - -Same as cv::v_dotprod_expand, but it may perform unorder sum between result pairs in some platforms, -this intrinsic can be used if the sum among all lanes is only matters -and also it should be yielding better performance on the affected platforms. - -*/ -template inline v_reg::q_type, n/4> -v_dotprod_expand_fast(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ return v_dotprod_expand(a, b); } - -/** @brief Fast Dot product of elements - -Same as cv::v_dotprod_expand_fast, but add a third element to the sum of adjacent pairs. -*/ -template inline v_reg::q_type, n/4> -v_dotprod_expand_fast(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - const v_reg::q_type, n / 4>& c) -{ return v_dotprod_expand(a, b, c); } - -/** @brief Multiply and expand - -Multiply values two registers and store results in two registers with wider pack type. -Scheme: -@code - {A B C D} // 32-bit -x {E F G H} // 32-bit ---------------- -{AE BF} // 64-bit - {CG DH} // 64-bit -@endcode -Example: -@code{.cpp} -v_uint32x4 a, b; // {1,2,3,4} and {2,2,2,2} -v_uint64x2 c, d; // results -v_mul_expand(a, b, c, d); // c, d = {2,4}, {6, 8} -@endcode -Implemented only for 16- and unsigned 32-bit source types (v_int16x8, v_uint16x8, v_uint32x4). -*/ -template inline void v_mul_expand(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - v_reg::w_type, n/2>& c, - v_reg::w_type, n/2>& d) -{ - typedef typename V_TypeTraits<_Tp>::w_type w_type; - for( int i = 0; i < (n/2); i++ ) - { - c.s[i] = (w_type)a.s[i]*b.s[i]; - d.s[i] = (w_type)a.s[i+(n/2)]*b.s[i+(n/2)]; - } -} - -/** @brief Multiply and extract high part - -Multiply values two registers and store high part of the results. -Implemented only for 16-bit source types (v_int16x8, v_uint16x8). Returns \f$ a*b >> 16 \f$ -*/ -template inline v_reg<_Tp, n> v_mul_hi(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - typedef typename V_TypeTraits<_Tp>::w_type w_type; - v_reg<_Tp, n> c; - for (int i = 0; i < n; i++) - c.s[i] = (_Tp)(((w_type)a.s[i] * b.s[i]) >> sizeof(_Tp)*8); - return c; -} - -//! @cond IGNORED -template inline void v_hsum(const v_reg<_Tp, n>& a, - v_reg::w_type, n/2>& c) -{ - typedef typename V_TypeTraits<_Tp>::w_type w_type; - for( int i = 0; i < (n/2); i++ ) - { - c.s[i] = (w_type)a.s[i*2] + a.s[i*2+1]; - } -} -//! @endcond - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_SHIFT_OP(shift_op) \ -template inline v_reg<_Tp, n> operator shift_op(const v_reg<_Tp, n>& a, int imm) \ -{ \ - v_reg<_Tp, n> c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = (_Tp)(a.s[i] shift_op imm); \ - return c; \ -} - -/** @brief Bitwise shift left - -For 16-, 32- and 64-bit integer values. */ -OPENCV_HAL_IMPL_SHIFT_OP(<< ) - -/** @brief Bitwise shift right - -For 16-, 32- and 64-bit integer values. */ -OPENCV_HAL_IMPL_SHIFT_OP(>> ) - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(suffix,opA,opB) \ -template inline v_reg<_Tp, n> v_rotate_##suffix(const v_reg<_Tp, n>& a) \ -{ \ - v_reg<_Tp, n> b; \ - for (int i = 0; i < n; i++) \ - { \ - int sIndex = i opA imm; \ - if (0 <= sIndex && sIndex < n) \ - { \ - b.s[i] = a.s[sIndex]; \ - } \ - else \ - { \ - b.s[i] = 0; \ - } \ - } \ - return b; \ -} \ -template inline v_reg<_Tp, n> v_rotate_##suffix(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - v_reg<_Tp, n> c; \ - for (int i = 0; i < n; i++) \ - { \ - int aIndex = i opA imm; \ - int bIndex = i opA imm opB n; \ - if (0 <= bIndex && bIndex < n) \ - { \ - c.s[i] = b.s[bIndex]; \ - } \ - else if (0 <= aIndex && aIndex < n) \ - { \ - c.s[i] = a.s[aIndex]; \ - } \ - else \ - { \ - c.s[i] = 0; \ - } \ - } \ - return c; \ -} - -/** @brief Element shift left among vector - -For all type */ -OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(left, -, +) - -/** @brief Element shift right among vector - -For all type */ -OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(right, +, -) - -/** @brief Sum packed values - -Scheme: -@code -{A1 A2 A3 ...} => sum{A1,A2,A3,...} -@endcode -*/ -template inline typename V_TypeTraits<_Tp>::sum_type v_reduce_sum(const v_reg<_Tp, n>& a) -{ - typename V_TypeTraits<_Tp>::sum_type c = a.s[0]; - for( int i = 1; i < n; i++ ) - c += a.s[i]; - return c; -} - -/** @brief Sums all elements of each input vector, returns the vector of sums - - Scheme: - @code - result[0] = a[0] + a[1] + a[2] + a[3] - result[1] = b[0] + b[1] + b[2] + b[3] - result[2] = c[0] + c[1] + c[2] + c[3] - result[3] = d[0] + d[1] + d[2] + d[3] - @endcode -*/ -inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, - const v_float32x4& c, const v_float32x4& d) -{ - v_float32x4 r; - r.s[0] = a.s[0] + a.s[1] + a.s[2] + a.s[3]; - r.s[1] = b.s[0] + b.s[1] + b.s[2] + b.s[3]; - r.s[2] = c.s[0] + c.s[1] + c.s[2] + c.s[3]; - r.s[3] = d.s[0] + d.s[1] + d.s[2] + d.s[3]; - return r; -} - -/** @brief Sum absolute differences of values - -Scheme: -@code -{A1 A2 A3 ...} {B1 B2 B3 ...} => sum{ABS(A1-B1),abs(A2-B2),abs(A3-B3),...} -@endcode -For all types except 64-bit types.*/ -template inline typename V_TypeTraits< typename V_TypeTraits<_Tp>::abs_type >::sum_type v_reduce_sad(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - typename V_TypeTraits< typename V_TypeTraits<_Tp>::abs_type >::sum_type c = _absdiff(a.s[0], b.s[0]); - for (int i = 1; i < n; i++) - c += _absdiff(a.s[i], b.s[i]); - return c; -} - -/** @brief Get negative values mask -@deprecated v_signmask depends on a lane count heavily and therefore isn't universal enough - -Returned value is a bit mask with bits set to 1 on places corresponding to negative packed values indexes. -Example: -@code{.cpp} -v_int32x4 r; // set to {-1, -1, 1, 1} -int mask = v_signmask(r); // mask = 3 <== 00000000 00000000 00000000 00000011 -@endcode -*/ -template inline int v_signmask(const v_reg<_Tp, n>& a) -{ - int mask = 0; - for( int i = 0; i < n; i++ ) - mask |= (V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0) << i; - return mask; -} - -/** @brief Get first negative lane index - -Returned value is an index of first negative lane (undefined for input of all positive values) -Example: -@code{.cpp} -v_int32x4 r; // set to {0, 0, -1, -1} -int idx = v_heading_zeros(r); // idx = 2 -@endcode -*/ -template inline int v_scan_forward(const v_reg<_Tp, n>& a) -{ - for (int i = 0; i < n; i++) - if(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0) - return i; - return 0; -} - -/** @brief Check if all packed values are less than zero - -Unsigned values will be casted to signed: `uchar 254 => char -2`. -*/ -template inline bool v_check_all(const v_reg<_Tp, n>& a) -{ - for( int i = 0; i < n; i++ ) - if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) >= 0 ) - return false; - return true; -} - -/** @brief Check if any of packed values is less than zero - -Unsigned values will be casted to signed: `uchar 254 => char -2`. -*/ -template inline bool v_check_any(const v_reg<_Tp, n>& a) -{ - for( int i = 0; i < n; i++ ) - if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0 ) - return true; - return false; -} - -/** @brief Per-element select (blend operation) - -Return value will be built by combining values _a_ and _b_ using the following scheme: - result[i] = mask[i] ? a[i] : b[i]; - -@note: _mask_ element values are restricted to these values: -- 0: select element from _b_ -- 0xff/0xffff/etc: select element from _a_ -(fully compatible with bitwise-based operator) -*/ -template inline v_reg<_Tp, n> v_select(const v_reg<_Tp, n>& mask, - const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - typedef V_TypeTraits<_Tp> Traits; - typedef typename Traits::int_type int_type; - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - { - int_type m = Traits::reinterpret_int(mask.s[i]); - CV_DbgAssert(m == 0 || m == (~(int_type)0)); // restrict mask values: 0 or 0xff/0xffff/etc - c.s[i] = m ? a.s[i] : b.s[i]; - } - return c; -} - -/** @brief Expand values to the wider pack type - -Copy contents of register to two registers with 2x wider pack type. -Scheme: -@code - int32x4 int64x2 int64x2 -{A B C D} ==> {A B} , {C D} -@endcode */ -template inline void v_expand(const v_reg<_Tp, n>& a, - v_reg::w_type, n/2>& b0, - v_reg::w_type, n/2>& b1) -{ - for( int i = 0; i < (n/2); i++ ) - { - b0.s[i] = a.s[i]; - b1.s[i] = a.s[i+(n/2)]; - } -} - -/** @brief Expand lower values to the wider pack type - -Same as cv::v_expand, but return lower half of the vector. - -Scheme: -@code - int32x4 int64x2 -{A B C D} ==> {A B} -@endcode */ -template -inline v_reg::w_type, n/2> -v_expand_low(const v_reg<_Tp, n>& a) -{ - v_reg::w_type, n/2> b; - for( int i = 0; i < (n/2); i++ ) - b.s[i] = a.s[i]; - return b; -} - -/** @brief Expand higher values to the wider pack type - -Same as cv::v_expand_low, but expand higher half of the vector instead. - -Scheme: -@code - int32x4 int64x2 -{A B C D} ==> {C D} -@endcode */ -template -inline v_reg::w_type, n/2> -v_expand_high(const v_reg<_Tp, n>& a) -{ - v_reg::w_type, n/2> b; - for( int i = 0; i < (n/2); i++ ) - b.s[i] = a.s[i+(n/2)]; - return b; -} - -//! @cond IGNORED -template inline v_reg::int_type, n> - v_reinterpret_as_int(const v_reg<_Tp, n>& a) -{ - v_reg::int_type, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = V_TypeTraits<_Tp>::reinterpret_int(a.s[i]); - return c; -} - -template inline v_reg::uint_type, n> - v_reinterpret_as_uint(const v_reg<_Tp, n>& a) -{ - v_reg::uint_type, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = V_TypeTraits<_Tp>::reinterpret_uint(a.s[i]); - return c; -} -//! @endcond - -/** @brief Interleave two vectors - -Scheme: -@code - {A1 A2 A3 A4} - {B1 B2 B3 B4} ---------------- - {A1 B1 A2 B2} and {A3 B3 A4 B4} -@endcode -For all types except 64-bit. -*/ -template inline void v_zip( const v_reg<_Tp, n>& a0, const v_reg<_Tp, n>& a1, - v_reg<_Tp, n>& b0, v_reg<_Tp, n>& b1 ) -{ - int i; - for( i = 0; i < n/2; i++ ) - { - b0.s[i*2] = a0.s[i]; - b0.s[i*2+1] = a1.s[i]; - } - for( ; i < n; i++ ) - { - b1.s[i*2-n] = a0.s[i]; - b1.s[i*2-n+1] = a1.s[i]; - } -} - -/** @brief Load register contents from memory - -@param ptr pointer to memory block with data -@return register object - -@note Returned type will be detected from passed pointer type, for example uchar ==> cv::v_uint8x16, int ==> cv::v_int32x4, etc. - -@note Alignment requirement: -if CV_STRONG_ALIGNMENT=1 then passed pointer must be aligned (`sizeof(lane type)` should be enough). -Do not cast pointer types without runtime check for pointer alignment (like `uchar*` => `int*`). - */ -template -inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load(const _Tp* ptr) -{ -#if CV_STRONG_ALIGNMENT - CV_Assert(isAligned(ptr)); -#endif - return v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128>(ptr); -} - -/** @brief Load register contents from memory (aligned) - -similar to cv::v_load, but source memory block should be aligned (to 16-byte boundary in case of SIMD128, 32-byte - SIMD256, etc) - */ -template -inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load_aligned(const _Tp* ptr) -{ - CV_Assert(isAligned::nlanes128>)>(ptr)); - return v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128>(ptr); -} - -/** @brief Load 64-bits of data to lower part (high part is undefined). - -@param ptr memory block containing data for first half (0..n/2) - -@code{.cpp} -int lo[2] = { 1, 2 }; -v_int32x4 r = v_load_low(lo); -@endcode - */ -template -inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load_low(const _Tp* ptr) -{ -#if CV_STRONG_ALIGNMENT - CV_Assert(isAligned(ptr)); -#endif - v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> c; - for( int i = 0; i < c.nlanes/2; i++ ) - { - c.s[i] = ptr[i]; - } - return c; -} - -/** @brief Load register contents from two memory blocks - -@param loptr memory block containing data for first half (0..n/2) -@param hiptr memory block containing data for second half (n/2..n) - -@code{.cpp} -int lo[2] = { 1, 2 }, hi[2] = { 3, 4 }; -v_int32x4 r = v_load_halves(lo, hi); -@endcode - */ -template -inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load_halves(const _Tp* loptr, const _Tp* hiptr) -{ -#if CV_STRONG_ALIGNMENT - CV_Assert(isAligned(loptr)); - CV_Assert(isAligned(hiptr)); -#endif - v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> c; - for( int i = 0; i < c.nlanes/2; i++ ) - { - c.s[i] = loptr[i]; - c.s[i+c.nlanes/2] = hiptr[i]; - } - return c; -} - -/** @brief Load register contents from memory with double expand - -Same as cv::v_load, but result pack type will be 2x wider than memory type. - -@code{.cpp} -short buf[4] = {1, 2, 3, 4}; // type is int16 -v_int32x4 r = v_load_expand(buf); // r = {1, 2, 3, 4} - type is int32 -@endcode -For 8-, 16-, 32-bit integer source types. */ -template -inline v_reg::w_type, V_TypeTraits<_Tp>::nlanes128 / 2> -v_load_expand(const _Tp* ptr) -{ -#if CV_STRONG_ALIGNMENT - CV_Assert(isAligned(ptr)); -#endif - typedef typename V_TypeTraits<_Tp>::w_type w_type; - v_reg::nlanes128> c; - for( int i = 0; i < c.nlanes; i++ ) - { - c.s[i] = ptr[i]; - } - return c; -} - -/** @brief Load register contents from memory with quad expand - -Same as cv::v_load_expand, but result type is 4 times wider than source. -@code{.cpp} -char buf[4] = {1, 2, 3, 4}; // type is int8 -v_int32x4 r = v_load_q(buf); // r = {1, 2, 3, 4} - type is int32 -@endcode -For 8-bit integer source types. */ -template -inline v_reg::q_type, V_TypeTraits<_Tp>::nlanes128 / 4> -v_load_expand_q(const _Tp* ptr) -{ -#if CV_STRONG_ALIGNMENT - CV_Assert(isAligned(ptr)); -#endif - typedef typename V_TypeTraits<_Tp>::q_type q_type; - v_reg::nlanes128> c; - for( int i = 0; i < c.nlanes; i++ ) - { - c.s[i] = ptr[i]; - } - return c; -} - -/** @brief Load and deinterleave (2 channels) - -Load data from memory deinterleave and store to 2 registers. -Scheme: -@code -{A1 B1 A2 B2 ...} ==> {A1 A2 ...}, {B1 B2 ...} -@endcode -For all types except 64-bit. */ -template inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, - v_reg<_Tp, n>& b) -{ -#if CV_STRONG_ALIGNMENT - CV_Assert(isAligned(ptr)); -#endif - int i, i2; - for( i = i2 = 0; i < n; i++, i2 += 2 ) - { - a.s[i] = ptr[i2]; - b.s[i] = ptr[i2+1]; - } -} - -/** @brief Load and deinterleave (3 channels) - -Load data from memory deinterleave and store to 3 registers. -Scheme: -@code -{A1 B1 C1 A2 B2 C2 ...} ==> {A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...} -@endcode -For all types except 64-bit. */ -template inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, - v_reg<_Tp, n>& b, v_reg<_Tp, n>& c) -{ -#if CV_STRONG_ALIGNMENT - CV_Assert(isAligned(ptr)); -#endif - int i, i3; - for( i = i3 = 0; i < n; i++, i3 += 3 ) - { - a.s[i] = ptr[i3]; - b.s[i] = ptr[i3+1]; - c.s[i] = ptr[i3+2]; - } -} - -/** @brief Load and deinterleave (4 channels) - -Load data from memory deinterleave and store to 4 registers. -Scheme: -@code -{A1 B1 C1 D1 A2 B2 C2 D2 ...} ==> {A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}, {D1 D2 ...} -@endcode -For all types except 64-bit. */ -template -inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, - v_reg<_Tp, n>& b, v_reg<_Tp, n>& c, - v_reg<_Tp, n>& d) -{ -#if CV_STRONG_ALIGNMENT - CV_Assert(isAligned(ptr)); -#endif - int i, i4; - for( i = i4 = 0; i < n; i++, i4 += 4 ) - { - a.s[i] = ptr[i4]; - b.s[i] = ptr[i4+1]; - c.s[i] = ptr[i4+2]; - d.s[i] = ptr[i4+3]; - } -} - -/** @brief Interleave and store (2 channels) - -Interleave and store data from 2 registers to memory. -Scheme: -@code -{A1 A2 ...}, {B1 B2 ...} ==> {A1 B1 A2 B2 ...} -@endcode -For all types except 64-bit. */ -template -inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, - const v_reg<_Tp, n>& b, - hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) -{ -#if CV_STRONG_ALIGNMENT - CV_Assert(isAligned(ptr)); -#endif - int i, i2; - for( i = i2 = 0; i < n; i++, i2 += 2 ) - { - ptr[i2] = a.s[i]; - ptr[i2+1] = b.s[i]; - } -} - -/** @brief Interleave and store (3 channels) - -Interleave and store data from 3 registers to memory. -Scheme: -@code -{A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...} ==> {A1 B1 C1 A2 B2 C2 ...} -@endcode -For all types except 64-bit. */ -template -inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, - const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c, - hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) -{ -#if CV_STRONG_ALIGNMENT - CV_Assert(isAligned(ptr)); -#endif - int i, i3; - for( i = i3 = 0; i < n; i++, i3 += 3 ) - { - ptr[i3] = a.s[i]; - ptr[i3+1] = b.s[i]; - ptr[i3+2] = c.s[i]; - } -} - -/** @brief Interleave and store (4 channels) - -Interleave and store data from 4 registers to memory. -Scheme: -@code -{A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}, {D1 D2 ...} ==> {A1 B1 C1 D1 A2 B2 C2 D2 ...} -@endcode -For all types except 64-bit. */ -template inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, - const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c, - const v_reg<_Tp, n>& d, - hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) -{ -#if CV_STRONG_ALIGNMENT - CV_Assert(isAligned(ptr)); -#endif - int i, i4; - for( i = i4 = 0; i < n; i++, i4 += 4 ) - { - ptr[i4] = a.s[i]; - ptr[i4+1] = b.s[i]; - ptr[i4+2] = c.s[i]; - ptr[i4+3] = d.s[i]; - } -} - -/** @brief Store data to memory - -Store register contents to memory. -Scheme: -@code - REG {A B C D} ==> MEM {A B C D} -@endcode -Pointer can be unaligned. */ -template -inline void v_store(_Tp* ptr, const v_reg<_Tp, n>& a) -{ -#if CV_STRONG_ALIGNMENT - CV_Assert(isAligned(ptr)); -#endif - for( int i = 0; i < n; i++ ) - ptr[i] = a.s[i]; -} - -template -inline void v_store(_Tp* ptr, const v_reg<_Tp, n>& a, hal::StoreMode /*mode*/) -{ -#if CV_STRONG_ALIGNMENT - CV_Assert(isAligned(ptr)); -#endif - v_store(ptr, a); -} - -/** @brief Store data to memory (lower half) - -Store lower half of register contents to memory. -Scheme: -@code - REG {A B C D} ==> MEM {A B} -@endcode */ -template -inline void v_store_low(_Tp* ptr, const v_reg<_Tp, n>& a) -{ -#if CV_STRONG_ALIGNMENT - CV_Assert(isAligned(ptr)); -#endif - for( int i = 0; i < (n/2); i++ ) - ptr[i] = a.s[i]; -} - -/** @brief Store data to memory (higher half) - -Store higher half of register contents to memory. -Scheme: -@code - REG {A B C D} ==> MEM {C D} -@endcode */ -template -inline void v_store_high(_Tp* ptr, const v_reg<_Tp, n>& a) -{ -#if CV_STRONG_ALIGNMENT - CV_Assert(isAligned(ptr)); -#endif - for( int i = 0; i < (n/2); i++ ) - ptr[i] = a.s[i+(n/2)]; -} - -/** @brief Store data to memory (aligned) - -Store register contents to memory. -Scheme: -@code - REG {A B C D} ==> MEM {A B C D} -@endcode -Pointer __should__ be aligned by 16-byte boundary. */ -template -inline void v_store_aligned(_Tp* ptr, const v_reg<_Tp, n>& a) -{ - CV_Assert(isAligned)>(ptr)); - v_store(ptr, a); -} - -template -inline void v_store_aligned_nocache(_Tp* ptr, const v_reg<_Tp, n>& a) -{ - CV_Assert(isAligned)>(ptr)); - v_store(ptr, a); -} - -template -inline void v_store_aligned(_Tp* ptr, const v_reg<_Tp, n>& a, hal::StoreMode /*mode*/) -{ - CV_Assert(isAligned)>(ptr)); - v_store(ptr, a); -} - -/** @brief Combine vector from first elements of two vectors - -Scheme: -@code - {A1 A2 A3 A4} - {B1 B2 B3 B4} ---------------- - {A1 A2 B1 B2} -@endcode -For all types except 64-bit. */ -template -inline v_reg<_Tp, n> v_combine_low(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < (n/2); i++ ) - { - c.s[i] = a.s[i]; - c.s[i+(n/2)] = b.s[i]; - } - return c; -} - -/** @brief Combine vector from last elements of two vectors - -Scheme: -@code - {A1 A2 A3 A4} - {B1 B2 B3 B4} ---------------- - {A3 A4 B3 B4} -@endcode -For all types except 64-bit. */ -template -inline v_reg<_Tp, n> v_combine_high(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < (n/2); i++ ) - { - c.s[i] = a.s[i+(n/2)]; - c.s[i+(n/2)] = b.s[i+(n/2)]; - } - return c; -} - -/** @brief Combine two vectors from lower and higher parts of two other vectors - -@code{.cpp} -low = cv::v_combine_low(a, b); -high = cv::v_combine_high(a, b); -@endcode */ -template -inline void v_recombine(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - v_reg<_Tp, n>& low, v_reg<_Tp, n>& high) -{ - for( int i = 0; i < (n/2); i++ ) - { - low.s[i] = a.s[i]; - low.s[i+(n/2)] = b.s[i]; - high.s[i] = a.s[i+(n/2)]; - high.s[i+(n/2)] = b.s[i+(n/2)]; - } -} - -/** @brief Vector reverse order - -Reverse the order of the vector -Scheme: -@code - REG {A1 ... An} ==> REG {An ... A1} -@endcode -For all types. */ -template -inline v_reg<_Tp, n> v_reverse(const v_reg<_Tp, n>& a) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = a.s[n-i-1]; - return c; -} - -/** @brief Vector extract - -Scheme: -@code - {A1 A2 A3 A4} - {B1 B2 B3 B4} -======================== -shift = 1 {A2 A3 A4 B1} -shift = 2 {A3 A4 B1 B2} -shift = 3 {A4 B1 B2 B3} -@endcode -Restriction: 0 <= shift < nlanes - -Usage: -@code -v_int32x4 a, b, c; -c = v_extract<2>(a, b); -@endcode -For all types. */ -template -inline v_reg<_Tp, n> v_extract(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - v_reg<_Tp, n> r; - const int shift = n - s; - int i = 0; - for (; i < shift; ++i) - r.s[i] = a.s[i+s]; - for (; i < n; ++i) - r.s[i] = b.s[i-shift]; - return r; -} - -/** @brief Vector extract - -Scheme: -Return the s-th element of v. -Restriction: 0 <= s < nlanes - -Usage: -@code -v_int32x4 a; -int r; -r = v_extract_n<2>(a); -@endcode -For all types. */ -template -inline _Tp v_extract_n(const v_reg<_Tp, n>& v) -{ - CV_DbgAssert(s >= 0 && s < n); - return v.s[s]; -} - -/** @brief Broadcast i-th element of vector - -Scheme: -@code -{ v[0] v[1] v[2] ... v[SZ] } => { v[i], v[i], v[i] ... v[i] } -@endcode -Restriction: 0 <= i < nlanes -Supported types: 32-bit integers and floats (s32/u32/f32) - */ -template -inline v_reg<_Tp, n> v_broadcast_element(const v_reg<_Tp, n>& a) -{ - CV_DbgAssert(i >= 0 && i < n); - return v_reg<_Tp, n>::all(a.s[i]); -} - -/** @brief Round - -Rounds each value. Input type is float vector ==> output type is int vector.*/ -template inline v_reg v_round(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = cvRound(a.s[i]); - return c; -} - -/** @overload */ -template inline v_reg v_round(const v_reg& a, const v_reg& b) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = cvRound(a.s[i]); - c.s[i+n] = cvRound(b.s[i]); - } - return c; -} - -/** @brief Floor - -Floor each value. Input type is float vector ==> output type is int vector.*/ -template inline v_reg v_floor(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = cvFloor(a.s[i]); - return c; -} - -/** @brief Ceil - -Ceil each value. Input type is float vector ==> output type is int vector.*/ -template inline v_reg v_ceil(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = cvCeil(a.s[i]); - return c; -} - -/** @brief Trunc - -Truncate each value. Input type is float vector ==> output type is int vector.*/ -template inline v_reg v_trunc(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = (int)(a.s[i]); - return c; -} - -/** @overload */ -template inline v_reg v_round(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = cvRound(a.s[i]); - c.s[i+n] = 0; - } - return c; -} - -/** @overload */ -template inline v_reg v_floor(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = cvFloor(a.s[i]); - c.s[i+n] = 0; - } - return c; -} - -/** @overload */ -template inline v_reg v_ceil(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = cvCeil(a.s[i]); - c.s[i+n] = 0; - } - return c; -} - -/** @overload */ -template inline v_reg v_trunc(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = cvCeil(a.s[i]); - c.s[i+n] = 0; - } - return c; -} - -/** @brief Convert to float - -Supported input type is cv::v_int32x4. */ -template inline v_reg v_cvt_f32(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = (float)a.s[i]; - return c; -} - -template inline v_reg v_cvt_f32(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = (float)a.s[i]; - c.s[i+n] = 0; - } - return c; -} - -template inline v_reg v_cvt_f32(const v_reg& a, const v_reg& b) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = (float)a.s[i]; - c.s[i+n] = (float)b.s[i]; - } - return c; -} - -/** @brief Convert to double - -Supported input type is cv::v_int32x4. */ -CV_INLINE v_reg v_cvt_f64(const v_reg& a) -{ - enum { n = 2 }; - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = (double)a.s[i]; - return c; -} - -/** @brief Convert to double high part of vector - -Supported input type is cv::v_int32x4. */ -CV_INLINE v_reg v_cvt_f64_high(const v_reg& a) -{ - enum { n = 2 }; - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = (double)a.s[i + 2]; - return c; -} - -/** @brief Convert to double - -Supported input type is cv::v_float32x4. */ -CV_INLINE v_reg v_cvt_f64(const v_reg& a) -{ - enum { n = 2 }; - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = (double)a.s[i]; - return c; -} - -/** @brief Convert to double high part of vector - -Supported input type is cv::v_float32x4. */ -CV_INLINE v_reg v_cvt_f64_high(const v_reg& a) -{ - enum { n = 2 }; - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = (double)a.s[i + 2]; - return c; -} - -/** @brief Convert to double - -Supported input type is cv::v_int64x2. */ -CV_INLINE v_reg v_cvt_f64(const v_reg& a) -{ - enum { n = 2 }; - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = (double)a.s[i]; - return c; -} - -/** @brief Convert to double high part of vector - -Supported input type is cv::v_int64x2. */ -CV_INLINE v_reg v_cvt_f64_high(const v_reg& a) -{ - enum { n = 2 }; - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = (double)a.s[i]; - return c; -} - - -template inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_lut(const _Tp* tab, const int* idx) -{ - v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> c; - for (int i = 0; i < V_TypeTraits<_Tp>::nlanes128; i++) - c.s[i] = tab[idx[i]]; - return c; -} -template inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_lut_pairs(const _Tp* tab, const int* idx) -{ - v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> c; - for (int i = 0; i < V_TypeTraits<_Tp>::nlanes128; i++) - c.s[i] = tab[idx[i / 2] + i % 2]; - return c; -} -template inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_lut_quads(const _Tp* tab, const int* idx) -{ - v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> c; - for (int i = 0; i < V_TypeTraits<_Tp>::nlanes128; i++) - c.s[i] = tab[idx[i / 4] + i % 4]; - return c; -} - -template inline v_reg v_lut(const int* tab, const v_reg& idx) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = tab[idx.s[i]]; - return c; -} - -template inline v_reg v_lut(const unsigned* tab, const v_reg& idx) -{ - v_reg c; - for (int i = 0; i < n; i++) - c.s[i] = tab[idx.s[i]]; - return c; -} - -template inline v_reg v_lut(const float* tab, const v_reg& idx) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = tab[idx.s[i]]; - return c; -} - -template inline v_reg v_lut(const double* tab, const v_reg& idx) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = tab[idx.s[i]]; - return c; -} - - -inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec) -{ - return v_lut(tab, idxvec.s); -} - -inline v_uint32x4 v_lut(const unsigned* tab, const v_int32x4& idxvec) -{ - return v_lut(tab, idxvec.s); -} - -inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec) -{ - return v_lut(tab, idxvec.s); -} - -inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec) -{ - return v_lut(tab, idxvec.s); -} - - -template inline void v_lut_deinterleave(const float* tab, const v_reg& idx, - v_reg& x, v_reg& y) -{ - for( int i = 0; i < n; i++ ) - { - int j = idx.s[i]; - x.s[i] = tab[j]; - y.s[i] = tab[j+1]; - } -} - -template inline void v_lut_deinterleave(const double* tab, const v_reg& idx, - v_reg& x, v_reg& y) -{ - for( int i = 0; i < n; i++ ) - { - int j = idx.s[i]; - x.s[i] = tab[j]; - y.s[i] = tab[j+1]; - } -} - -template inline v_reg<_Tp, n> v_interleave_pairs(const v_reg<_Tp, n>& vec) -{ - v_reg<_Tp, n> c; - for (int i = 0; i < n/4; i++) - { - c.s[4*i ] = vec.s[4*i ]; - c.s[4*i+1] = vec.s[4*i+2]; - c.s[4*i+2] = vec.s[4*i+1]; - c.s[4*i+3] = vec.s[4*i+3]; - } - return c; -} - -template inline v_reg<_Tp, n> v_interleave_quads(const v_reg<_Tp, n>& vec) -{ - v_reg<_Tp, n> c; - for (int i = 0; i < n/8; i++) - { - c.s[8*i ] = vec.s[8*i ]; - c.s[8*i+1] = vec.s[8*i+4]; - c.s[8*i+2] = vec.s[8*i+1]; - c.s[8*i+3] = vec.s[8*i+5]; - c.s[8*i+4] = vec.s[8*i+2]; - c.s[8*i+5] = vec.s[8*i+6]; - c.s[8*i+6] = vec.s[8*i+3]; - c.s[8*i+7] = vec.s[8*i+7]; - } - return c; -} - -template inline v_reg<_Tp, n> v_pack_triplets(const v_reg<_Tp, n>& vec) -{ - v_reg<_Tp, n> c; - for (int i = 0; i < n/4; i++) - { - c.s[3*i ] = vec.s[4*i ]; - c.s[3*i+1] = vec.s[4*i+1]; - c.s[3*i+2] = vec.s[4*i+2]; - } - return c; -} - -/** @brief Transpose 4x4 matrix - -Scheme: -@code -a0 {A1 A2 A3 A4} -a1 {B1 B2 B3 B4} -a2 {C1 C2 C3 C4} -a3 {D1 D2 D3 D4} -=============== -b0 {A1 B1 C1 D1} -b1 {A2 B2 C2 D2} -b2 {A3 B3 C3 D3} -b3 {A4 B4 C4 D4} -@endcode -*/ -template -inline void v_transpose4x4( v_reg<_Tp, 4>& a0, const v_reg<_Tp, 4>& a1, - const v_reg<_Tp, 4>& a2, const v_reg<_Tp, 4>& a3, - v_reg<_Tp, 4>& b0, v_reg<_Tp, 4>& b1, - v_reg<_Tp, 4>& b2, v_reg<_Tp, 4>& b3 ) -{ - b0 = v_reg<_Tp, 4>(a0.s[0], a1.s[0], a2.s[0], a3.s[0]); - b1 = v_reg<_Tp, 4>(a0.s[1], a1.s[1], a2.s[1], a3.s[1]); - b2 = v_reg<_Tp, 4>(a0.s[2], a1.s[2], a2.s[2], a3.s[2]); - b3 = v_reg<_Tp, 4>(a0.s[3], a1.s[3], a2.s[3], a3.s[3]); -} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_INIT_ZERO(_Tpvec, _Tp, suffix) \ -inline _Tpvec v_setzero_##suffix() { return _Tpvec::zero(); } - -//! @name Init with zero -//! @{ -//! @brief Create new vector with zero elements -OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint8x16, uchar, u8) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_int8x16, schar, s8) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint16x8, ushort, u16) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_int16x8, short, s16) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_int32x4, int, s32) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_float32x4, float, f32) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_float64x2, double, f64) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint64x2, uint64, u64) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_int64x2, int64, s64) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_INIT_VAL(_Tpvec, _Tp, suffix) \ -inline _Tpvec v_setall_##suffix(_Tp val) { return _Tpvec::all(val); } - -//! @name Init with value -//! @{ -//! @brief Create new vector with elements set to a specific value -OPENCV_HAL_IMPL_C_INIT_VAL(v_uint8x16, uchar, u8) -OPENCV_HAL_IMPL_C_INIT_VAL(v_int8x16, schar, s8) -OPENCV_HAL_IMPL_C_INIT_VAL(v_uint16x8, ushort, u16) -OPENCV_HAL_IMPL_C_INIT_VAL(v_int16x8, short, s16) -OPENCV_HAL_IMPL_C_INIT_VAL(v_uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_C_INIT_VAL(v_int32x4, int, s32) -OPENCV_HAL_IMPL_C_INIT_VAL(v_float32x4, float, f32) -OPENCV_HAL_IMPL_C_INIT_VAL(v_float64x2, double, f64) -OPENCV_HAL_IMPL_C_INIT_VAL(v_uint64x2, uint64, u64) -OPENCV_HAL_IMPL_C_INIT_VAL(v_int64x2, int64, s64) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_REINTERPRET(_Tpvec, _Tp, suffix) \ -template inline _Tpvec \ - v_reinterpret_as_##suffix(const v_reg<_Tp0, n0>& a) \ -{ return a.template reinterpret_as<_Tp, _Tpvec::nlanes>(); } - -//! @name Reinterpret -//! @{ -//! @brief Convert vector to different type without modifying underlying data. -OPENCV_HAL_IMPL_C_REINTERPRET(v_uint8x16, uchar, u8) -OPENCV_HAL_IMPL_C_REINTERPRET(v_int8x16, schar, s8) -OPENCV_HAL_IMPL_C_REINTERPRET(v_uint16x8, ushort, u16) -OPENCV_HAL_IMPL_C_REINTERPRET(v_int16x8, short, s16) -OPENCV_HAL_IMPL_C_REINTERPRET(v_uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_C_REINTERPRET(v_int32x4, int, s32) -OPENCV_HAL_IMPL_C_REINTERPRET(v_float32x4, float, f32) -OPENCV_HAL_IMPL_C_REINTERPRET(v_float64x2, double, f64) -OPENCV_HAL_IMPL_C_REINTERPRET(v_uint64x2, uint64, u64) -OPENCV_HAL_IMPL_C_REINTERPRET(v_int64x2, int64, s64) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_SHIFTL(_Tpvec, _Tp) \ -template inline _Tpvec v_shl(const _Tpvec& a) \ -{ return a << n; } - -//! @name Left shift -//! @{ -//! @brief Shift left -OPENCV_HAL_IMPL_C_SHIFTL(v_uint16x8, ushort) -OPENCV_HAL_IMPL_C_SHIFTL(v_int16x8, short) -OPENCV_HAL_IMPL_C_SHIFTL(v_uint32x4, unsigned) -OPENCV_HAL_IMPL_C_SHIFTL(v_int32x4, int) -OPENCV_HAL_IMPL_C_SHIFTL(v_uint64x2, uint64) -OPENCV_HAL_IMPL_C_SHIFTL(v_int64x2, int64) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_SHIFTR(_Tpvec, _Tp) \ -template inline _Tpvec v_shr(const _Tpvec& a) \ -{ return a >> n; } - -//! @name Right shift -//! @{ -//! @brief Shift right -OPENCV_HAL_IMPL_C_SHIFTR(v_uint16x8, ushort) -OPENCV_HAL_IMPL_C_SHIFTR(v_int16x8, short) -OPENCV_HAL_IMPL_C_SHIFTR(v_uint32x4, unsigned) -OPENCV_HAL_IMPL_C_SHIFTR(v_int32x4, int) -OPENCV_HAL_IMPL_C_SHIFTR(v_uint64x2, uint64) -OPENCV_HAL_IMPL_C_SHIFTR(v_int64x2, int64) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_RSHIFTR(_Tpvec, _Tp) \ -template inline _Tpvec v_rshr(const _Tpvec& a) \ -{ \ - _Tpvec c; \ - for( int i = 0; i < _Tpvec::nlanes; i++ ) \ - c.s[i] = (_Tp)((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ - return c; \ -} - -//! @name Rounding shift -//! @{ -//! @brief Rounding shift right -OPENCV_HAL_IMPL_C_RSHIFTR(v_uint16x8, ushort) -OPENCV_HAL_IMPL_C_RSHIFTR(v_int16x8, short) -OPENCV_HAL_IMPL_C_RSHIFTR(v_uint32x4, unsigned) -OPENCV_HAL_IMPL_C_RSHIFTR(v_int32x4, int) -OPENCV_HAL_IMPL_C_RSHIFTR(v_uint64x2, uint64) -OPENCV_HAL_IMPL_C_RSHIFTR(v_int64x2, int64) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_PACK(_Tpvec, _Tpnvec, _Tpn, pack_suffix, cast) \ -inline _Tpnvec v_##pack_suffix(const _Tpvec& a, const _Tpvec& b) \ -{ \ - _Tpnvec c; \ - for( int i = 0; i < _Tpvec::nlanes; i++ ) \ - { \ - c.s[i] = cast<_Tpn>(a.s[i]); \ - c.s[i+_Tpvec::nlanes] = cast<_Tpn>(b.s[i]); \ - } \ - return c; \ -} - -//! @name Pack -//! @{ -//! @brief Pack values from two vectors to one -//! -//! Return vector type have twice more elements than input vector types. Variant with _u_ suffix also -//! converts to corresponding unsigned type. -//! -//! - pack: for 16-, 32- and 64-bit integer input types -//! - pack_u: for 16- and 32-bit signed integer input types -//! -//! @note All variants except 64-bit use saturation. -OPENCV_HAL_IMPL_C_PACK(v_uint16x8, v_uint8x16, uchar, pack, saturate_cast) -OPENCV_HAL_IMPL_C_PACK(v_int16x8, v_int8x16, schar, pack, saturate_cast) -OPENCV_HAL_IMPL_C_PACK(v_uint32x4, v_uint16x8, ushort, pack, saturate_cast) -OPENCV_HAL_IMPL_C_PACK(v_int32x4, v_int16x8, short, pack, saturate_cast) -OPENCV_HAL_IMPL_C_PACK(v_uint64x2, v_uint32x4, unsigned, pack, static_cast) -OPENCV_HAL_IMPL_C_PACK(v_int64x2, v_int32x4, int, pack, static_cast) -OPENCV_HAL_IMPL_C_PACK(v_int16x8, v_uint8x16, uchar, pack_u, saturate_cast) -OPENCV_HAL_IMPL_C_PACK(v_int32x4, v_uint16x8, ushort, pack_u, saturate_cast) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_RSHR_PACK(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \ -template inline _Tpnvec v_rshr_##pack_suffix(const _Tpvec& a, const _Tpvec& b) \ -{ \ - _Tpnvec c; \ - for( int i = 0; i < _Tpvec::nlanes; i++ ) \ - { \ - c.s[i] = cast<_Tpn>((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ - c.s[i+_Tpvec::nlanes] = cast<_Tpn>((b.s[i] + ((_Tp)1 << (n - 1))) >> n); \ - } \ - return c; \ -} - -//! @name Pack with rounding shift -//! @{ -//! @brief Pack values from two vectors to one with rounding shift -//! -//! Values from the input vectors will be shifted right by _n_ bits with rounding, converted to narrower -//! type and returned in the result vector. Variant with _u_ suffix converts to unsigned type. -//! -//! - pack: for 16-, 32- and 64-bit integer input types -//! - pack_u: for 16- and 32-bit signed integer input types -//! -//! @note All variants except 64-bit use saturation. -OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_int16x8, short, v_int8x16, schar, pack, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_int32x4, int, v_int16x8, short, pack, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_int64x2, int64, v_int32x4, int, pack, static_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_PACK_STORE(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \ -inline void v_##pack_suffix##_store(_Tpn* ptr, const _Tpvec& a) \ -{ \ - for( int i = 0; i < _Tpvec::nlanes; i++ ) \ - ptr[i] = cast<_Tpn>(a.s[i]); \ -} - -//! @name Pack and store -//! @{ -//! @brief Store values from the input vector into memory with pack -//! -//! Values will be stored into memory with conversion to narrower type. -//! Variant with _u_ suffix converts to corresponding unsigned type. -//! -//! - pack: for 16-, 32- and 64-bit integer input types -//! - pack_u: for 16- and 32-bit signed integer input types -//! -//! @note All variants except 64-bit use saturation. -OPENCV_HAL_IMPL_C_PACK_STORE(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast) -OPENCV_HAL_IMPL_C_PACK_STORE(v_int16x8, short, v_int8x16, schar, pack, saturate_cast) -OPENCV_HAL_IMPL_C_PACK_STORE(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast) -OPENCV_HAL_IMPL_C_PACK_STORE(v_int32x4, int, v_int16x8, short, pack, saturate_cast) -OPENCV_HAL_IMPL_C_PACK_STORE(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast) -OPENCV_HAL_IMPL_C_PACK_STORE(v_int64x2, int64, v_int32x4, int, pack, static_cast) -OPENCV_HAL_IMPL_C_PACK_STORE(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast) -OPENCV_HAL_IMPL_C_PACK_STORE(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast) -//! @} - -//! @brief Helper macro -//! @ingroup core_hal_intrin_impl -#define OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \ -template inline void v_rshr_##pack_suffix##_store(_Tpn* ptr, const _Tpvec& a) \ -{ \ - for( int i = 0; i < _Tpvec::nlanes; i++ ) \ - ptr[i] = cast<_Tpn>((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ -} - -//! @name Pack and store with rounding shift -//! @{ -//! @brief Store values from the input vector into memory with pack -//! -//! Values will be shifted _n_ bits right with rounding, converted to narrower type and stored into -//! memory. Variant with _u_ suffix converts to unsigned type. -//! -//! - pack: for 16-, 32- and 64-bit integer input types -//! - pack_u: for 16- and 32-bit signed integer input types -//! -//! @note All variants except 64-bit use saturation. -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int16x8, short, v_int8x16, schar, pack, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int32x4, int, v_int16x8, short, pack, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int64x2, int64, v_int32x4, int, pack, static_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast) -//! @} - -//! @cond IGNORED -template -inline void _pack_b(_Tpm* mptr, const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - for (int i = 0; i < n; ++i) - { - mptr[i] = (_Tpm)a.s[i]; - mptr[i + n] = (_Tpm)b.s[i]; - } -} -//! @endcond - -//! @name Pack boolean values -//! @{ -//! @brief Pack boolean values from multiple vectors to one unsigned 8-bit integer vector -//! -//! @note Must provide valid boolean values to guarantee same result for all architectures. - -/** @brief -//! For 16-bit boolean values - -Scheme: -@code -a {0xFFFF 0 0 0xFFFF 0 0xFFFF 0xFFFF 0} -b {0xFFFF 0 0xFFFF 0 0 0xFFFF 0 0xFFFF} -=============== -{ - 0xFF 0 0 0xFF 0 0xFF 0xFF 0 - 0xFF 0 0xFF 0 0 0xFF 0 0xFF -} -@endcode */ - -inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) -{ - v_uint8x16 mask; - _pack_b(mask.s, a, b); - return mask; -} - -/** @overload -For 32-bit boolean values - -Scheme: -@code -a {0xFFFF.. 0 0 0xFFFF..} -b {0 0xFFFF.. 0xFFFF.. 0} -c {0xFFFF.. 0 0xFFFF.. 0} -d {0 0xFFFF.. 0 0xFFFF..} -=============== -{ - 0xFF 0 0 0xFF 0 0xFF 0xFF 0 - 0xFF 0 0xFF 0 0 0xFF 0 0xFF -} -@endcode */ - -inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, - const v_uint32x4& c, const v_uint32x4& d) -{ - v_uint8x16 mask; - _pack_b(mask.s, a, b); - _pack_b(mask.s + 8, c, d); - return mask; -} - -/** @overload -For 64-bit boolean values - -Scheme: -@code -a {0xFFFF.. 0} -b {0 0xFFFF..} -c {0xFFFF.. 0} -d {0 0xFFFF..} - -e {0xFFFF.. 0} -f {0xFFFF.. 0} -g {0 0xFFFF..} -h {0 0xFFFF..} -=============== -{ - 0xFF 0 0 0xFF 0xFF 0 0 0xFF - 0xFF 0 0xFF 0 0 0xFF 0 0xFF -} -@endcode */ -inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, - const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, - const v_uint64x2& g, const v_uint64x2& h) -{ - v_uint8x16 mask; - _pack_b(mask.s, a, b); - _pack_b(mask.s + 4, c, d); - _pack_b(mask.s + 8, e, f); - _pack_b(mask.s + 12, g, h); - return mask; -} -//! @} - -/** @brief Matrix multiplication - -Scheme: -@code -{A0 A1 A2 A3} |V0| -{B0 B1 B2 B3} |V1| -{C0 C1 C2 C3} |V2| -{D0 D1 D2 D3} x |V3| -==================== -{R0 R1 R2 R3}, where: -R0 = A0V0 + A1V1 + A2V2 + A3V3, -R1 = B0V0 + B1V1 + B2V2 + B3V3 -... -@endcode -*/ -inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, - const v_float32x4& m1, const v_float32x4& m2, - const v_float32x4& m3) -{ - return v_float32x4(v.s[0]*m0.s[0] + v.s[1]*m1.s[0] + v.s[2]*m2.s[0] + v.s[3]*m3.s[0], - v.s[0]*m0.s[1] + v.s[1]*m1.s[1] + v.s[2]*m2.s[1] + v.s[3]*m3.s[1], - v.s[0]*m0.s[2] + v.s[1]*m1.s[2] + v.s[2]*m2.s[2] + v.s[3]*m3.s[2], - v.s[0]*m0.s[3] + v.s[1]*m1.s[3] + v.s[2]*m2.s[3] + v.s[3]*m3.s[3]); -} - -/** @brief Matrix multiplication and add - -Scheme: -@code -{A0 A1 A2 } |V0| |D0| -{B0 B1 B2 } |V1| |D1| -{C0 C1 C2 } x |V2| + |D2| -==================== -{R0 R1 R2 R3}, where: -R0 = A0V0 + A1V1 + A2V2 + D0, -R1 = B0V0 + B1V1 + B2V2 + D1 -... -@endcode -*/ -inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, - const v_float32x4& m1, const v_float32x4& m2, - const v_float32x4& m3) -{ - return v_float32x4(v.s[0]*m0.s[0] + v.s[1]*m1.s[0] + v.s[2]*m2.s[0] + m3.s[0], - v.s[0]*m0.s[1] + v.s[1]*m1.s[1] + v.s[2]*m2.s[1] + m3.s[1], - v.s[0]*m0.s[2] + v.s[1]*m1.s[2] + v.s[2]*m2.s[2] + m3.s[2], - v.s[0]*m0.s[3] + v.s[1]*m1.s[3] + v.s[2]*m2.s[3] + m3.s[3]); -} - - -inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b) -{ return v_fma(v_cvt_f64(a), v_cvt_f64(b), v_cvt_f64_high(a) * v_cvt_f64_high(b)); } -inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c) -{ return v_fma(v_cvt_f64(a), v_cvt_f64(b), v_fma(v_cvt_f64_high(a), v_cvt_f64_high(b), c)); } - -inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b) -{ return v_dotprod_expand(a, b); } -inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c) -{ return v_dotprod_expand(a, b, c); } - -////// FP16 support /////// - -inline v_reg::nlanes128> -v_load_expand(const float16_t* ptr) -{ - v_reg::nlanes128> v; - for( int i = 0; i < v.nlanes; i++ ) - { - v.s[i] = ptr[i]; - } - return v; -} - -inline void -v_pack_store(float16_t* ptr, const v_reg::nlanes128>& v) -{ - for( int i = 0; i < v.nlanes; i++ ) - { - ptr[i] = float16_t(v.s[i]); - } -} - -inline void v_cleanup() {} - -//! @} - -#ifndef CV_DOXYGEN -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END -#endif -} - -#endif diff --git a/include/opencv2/core/hal/intrin_forward.hpp b/include/opencv2/core/hal/intrin_forward.hpp deleted file mode 100755 index 979f15a..0000000 --- a/include/opencv2/core/hal/intrin_forward.hpp +++ /dev/null @@ -1,191 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html - -#ifndef CV__SIMD_FORWARD -#error "Need to pre-define forward width" -#endif - -namespace cv -{ - -//! @cond IGNORED - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN - -/** Types **/ -#if CV__SIMD_FORWARD == 1024 -// [todo] 1024 -#error "1024-long ops not implemented yet" -#elif CV__SIMD_FORWARD == 512 -// 512 -#define __CV_VX(fun) v512_##fun -#define __CV_V_UINT8 v_uint8x64 -#define __CV_V_INT8 v_int8x64 -#define __CV_V_UINT16 v_uint16x32 -#define __CV_V_INT16 v_int16x32 -#define __CV_V_UINT32 v_uint32x16 -#define __CV_V_INT32 v_int32x16 -#define __CV_V_UINT64 v_uint64x8 -#define __CV_V_INT64 v_int64x8 -#define __CV_V_FLOAT32 v_float32x16 -#define __CV_V_FLOAT64 v_float64x8 -struct v_uint8x64; -struct v_int8x64; -struct v_uint16x32; -struct v_int16x32; -struct v_uint32x16; -struct v_int32x16; -struct v_uint64x8; -struct v_int64x8; -struct v_float32x16; -struct v_float64x8; -#elif CV__SIMD_FORWARD == 256 -// 256 -#define __CV_VX(fun) v256_##fun -#define __CV_V_UINT8 v_uint8x32 -#define __CV_V_INT8 v_int8x32 -#define __CV_V_UINT16 v_uint16x16 -#define __CV_V_INT16 v_int16x16 -#define __CV_V_UINT32 v_uint32x8 -#define __CV_V_INT32 v_int32x8 -#define __CV_V_UINT64 v_uint64x4 -#define __CV_V_INT64 v_int64x4 -#define __CV_V_FLOAT32 v_float32x8 -#define __CV_V_FLOAT64 v_float64x4 -struct v_uint8x32; -struct v_int8x32; -struct v_uint16x16; -struct v_int16x16; -struct v_uint32x8; -struct v_int32x8; -struct v_uint64x4; -struct v_int64x4; -struct v_float32x8; -struct v_float64x4; -#else -// 128 -#define __CV_VX(fun) v_##fun -#define __CV_V_UINT8 v_uint8x16 -#define __CV_V_INT8 v_int8x16 -#define __CV_V_UINT16 v_uint16x8 -#define __CV_V_INT16 v_int16x8 -#define __CV_V_UINT32 v_uint32x4 -#define __CV_V_INT32 v_int32x4 -#define __CV_V_UINT64 v_uint64x2 -#define __CV_V_INT64 v_int64x2 -#define __CV_V_FLOAT32 v_float32x4 -#define __CV_V_FLOAT64 v_float64x2 -struct v_uint8x16; -struct v_int8x16; -struct v_uint16x8; -struct v_int16x8; -struct v_uint32x4; -struct v_int32x4; -struct v_uint64x2; -struct v_int64x2; -struct v_float32x4; -struct v_float64x2; -#endif - -/** Value reordering **/ - -// Expansion -void v_expand(const __CV_V_UINT8&, __CV_V_UINT16&, __CV_V_UINT16&); -void v_expand(const __CV_V_INT8&, __CV_V_INT16&, __CV_V_INT16&); -void v_expand(const __CV_V_UINT16&, __CV_V_UINT32&, __CV_V_UINT32&); -void v_expand(const __CV_V_INT16&, __CV_V_INT32&, __CV_V_INT32&); -void v_expand(const __CV_V_UINT32&, __CV_V_UINT64&, __CV_V_UINT64&); -void v_expand(const __CV_V_INT32&, __CV_V_INT64&, __CV_V_INT64&); -// Low Expansion -__CV_V_UINT16 v_expand_low(const __CV_V_UINT8&); -__CV_V_INT16 v_expand_low(const __CV_V_INT8&); -__CV_V_UINT32 v_expand_low(const __CV_V_UINT16&); -__CV_V_INT32 v_expand_low(const __CV_V_INT16&); -__CV_V_UINT64 v_expand_low(const __CV_V_UINT32&); -__CV_V_INT64 v_expand_low(const __CV_V_INT32&); -// High Expansion -__CV_V_UINT16 v_expand_high(const __CV_V_UINT8&); -__CV_V_INT16 v_expand_high(const __CV_V_INT8&); -__CV_V_UINT32 v_expand_high(const __CV_V_UINT16&); -__CV_V_INT32 v_expand_high(const __CV_V_INT16&); -__CV_V_UINT64 v_expand_high(const __CV_V_UINT32&); -__CV_V_INT64 v_expand_high(const __CV_V_INT32&); -// Load & Low Expansion -__CV_V_UINT16 __CV_VX(load_expand)(const uchar*); -__CV_V_INT16 __CV_VX(load_expand)(const schar*); -__CV_V_UINT32 __CV_VX(load_expand)(const ushort*); -__CV_V_INT32 __CV_VX(load_expand)(const short*); -__CV_V_UINT64 __CV_VX(load_expand)(const uint*); -__CV_V_INT64 __CV_VX(load_expand)(const int*); -// Load lower 8-bit and expand into 32-bit -__CV_V_UINT32 __CV_VX(load_expand_q)(const uchar*); -__CV_V_INT32 __CV_VX(load_expand_q)(const schar*); - -// Saturating Pack -__CV_V_UINT8 v_pack(const __CV_V_UINT16&, const __CV_V_UINT16&); -__CV_V_INT8 v_pack(const __CV_V_INT16&, const __CV_V_INT16&); -__CV_V_UINT16 v_pack(const __CV_V_UINT32&, const __CV_V_UINT32&); -__CV_V_INT16 v_pack(const __CV_V_INT32&, const __CV_V_INT32&); -// Non-saturating Pack -__CV_V_UINT32 v_pack(const __CV_V_UINT64&, const __CV_V_UINT64&); -__CV_V_INT32 v_pack(const __CV_V_INT64&, const __CV_V_INT64&); -// Pack signed integers with unsigned saturation -__CV_V_UINT8 v_pack_u(const __CV_V_INT16&, const __CV_V_INT16&); -__CV_V_UINT16 v_pack_u(const __CV_V_INT32&, const __CV_V_INT32&); - -/** Arithmetic, bitwise and comparison operations **/ - -// Non-saturating multiply -#if CV_VSX -template -Tvec v_mul_wrap(const Tvec& a, const Tvec& b); -#else -__CV_V_UINT8 v_mul_wrap(const __CV_V_UINT8&, const __CV_V_UINT8&); -__CV_V_INT8 v_mul_wrap(const __CV_V_INT8&, const __CV_V_INT8&); -__CV_V_UINT16 v_mul_wrap(const __CV_V_UINT16&, const __CV_V_UINT16&); -__CV_V_INT16 v_mul_wrap(const __CV_V_INT16&, const __CV_V_INT16&); -#endif - -// Multiply and expand -#if CV_VSX -template -void v_mul_expand(const Tvec& a, const Tvec& b, Twvec& c, Twvec& d); -#else -void v_mul_expand(const __CV_V_UINT8&, const __CV_V_UINT8&, __CV_V_UINT16&, __CV_V_UINT16&); -void v_mul_expand(const __CV_V_INT8&, const __CV_V_INT8&, __CV_V_INT16&, __CV_V_INT16&); -void v_mul_expand(const __CV_V_UINT16&, const __CV_V_UINT16&, __CV_V_UINT32&, __CV_V_UINT32&); -void v_mul_expand(const __CV_V_INT16&, const __CV_V_INT16&, __CV_V_INT32&, __CV_V_INT32&); -void v_mul_expand(const __CV_V_UINT32&, const __CV_V_UINT32&, __CV_V_UINT64&, __CV_V_UINT64&); -void v_mul_expand(const __CV_V_INT32&, const __CV_V_INT32&, __CV_V_INT64&, __CV_V_INT64&); -#endif - -// Conversions -__CV_V_FLOAT32 v_cvt_f32(const __CV_V_INT32& a); -__CV_V_FLOAT32 v_cvt_f32(const __CV_V_FLOAT64& a); -__CV_V_FLOAT32 v_cvt_f32(const __CV_V_FLOAT64& a, const __CV_V_FLOAT64& b); -__CV_V_FLOAT64 v_cvt_f64(const __CV_V_INT32& a); -__CV_V_FLOAT64 v_cvt_f64_high(const __CV_V_INT32& a); -__CV_V_FLOAT64 v_cvt_f64(const __CV_V_FLOAT32& a); -__CV_V_FLOAT64 v_cvt_f64_high(const __CV_V_FLOAT32& a); -__CV_V_FLOAT64 v_cvt_f64(const __CV_V_INT64& a); - -/** Cleanup **/ -#undef CV__SIMD_FORWARD -#undef __CV_VX -#undef __CV_V_UINT8 -#undef __CV_V_INT8 -#undef __CV_V_UINT16 -#undef __CV_V_INT16 -#undef __CV_V_UINT32 -#undef __CV_V_INT32 -#undef __CV_V_UINT64 -#undef __CV_V_INT64 -#undef __CV_V_FLOAT32 -#undef __CV_V_FLOAT64 - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END - -//! @endcond - -} // cv:: \ No newline at end of file diff --git a/include/opencv2/core/hal/intrin_msa.hpp b/include/opencv2/core/hal/intrin_msa.hpp deleted file mode 100755 index a1fbb09..0000000 --- a/include/opencv2/core/hal/intrin_msa.hpp +++ /dev/null @@ -1,1887 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_HAL_INTRIN_MSA_HPP -#define OPENCV_HAL_INTRIN_MSA_HPP - -#include -#include "opencv2/core/utility.hpp" - -namespace cv -{ - -//! @cond IGNORED -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN - -#define CV_SIMD128 1 - -//MSA implements 128-bit wide vector registers shared with the 64-bit wide floating-point unit registers. -//MSA and FPU can not be both present, unless the FPU has 64-bit floating-point registers. -#define CV_SIMD128_64F 1 - -struct v_uint8x16 -{ - typedef uchar lane_type; - enum { nlanes = 16 }; - - v_uint8x16() {} - explicit v_uint8x16(v16u8 v) : val(v) {} - v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, - uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) - { - uchar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; - val = msa_ld1q_u8(v); - } - - uchar get0() const - { - return msa_getq_lane_u8(val, 0); - } - - v16u8 val; -}; - -struct v_int8x16 -{ - typedef schar lane_type; - enum { nlanes = 16 }; - - v_int8x16() {} - explicit v_int8x16(v16i8 v) : val(v) {} - v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, - schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) - { - schar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; - val = msa_ld1q_s8(v); - } - - schar get0() const - { - return msa_getq_lane_s8(val, 0); - } - - v16i8 val; -}; - -struct v_uint16x8 -{ - typedef ushort lane_type; - enum { nlanes = 8 }; - - v_uint16x8() {} - explicit v_uint16x8(v8u16 v) : val(v) {} - v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) - { - ushort v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; - val = msa_ld1q_u16(v); - } - - ushort get0() const - { - return msa_getq_lane_u16(val, 0); - } - - v8u16 val; -}; - -struct v_int16x8 -{ - typedef short lane_type; - enum { nlanes = 8 }; - - v_int16x8() {} - explicit v_int16x8(v8i16 v) : val(v) {} - v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) - { - short v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; - val = msa_ld1q_s16(v); - } - - short get0() const - { - return msa_getq_lane_s16(val, 0); - } - - v8i16 val; -}; - -struct v_uint32x4 -{ - typedef unsigned int lane_type; - enum { nlanes = 4 }; - - v_uint32x4() {} - explicit v_uint32x4(v4u32 v) : val(v) {} - v_uint32x4(unsigned int v0, unsigned int v1, unsigned int v2, unsigned int v3) - { - unsigned int v[] = {v0, v1, v2, v3}; - val = msa_ld1q_u32(v); - } - - unsigned int get0() const - { - return msa_getq_lane_u32(val, 0); - } - - v4u32 val; -}; - -struct v_int32x4 -{ - typedef int lane_type; - enum { nlanes = 4 }; - - v_int32x4() {} - explicit v_int32x4(v4i32 v) : val(v) {} - v_int32x4(int v0, int v1, int v2, int v3) - { - int v[] = {v0, v1, v2, v3}; - val = msa_ld1q_s32(v); - } - - int get0() const - { - return msa_getq_lane_s32(val, 0); - } - - v4i32 val; -}; - -struct v_float32x4 -{ - typedef float lane_type; - enum { nlanes = 4 }; - - v_float32x4() {} - explicit v_float32x4(v4f32 v) : val(v) {} - v_float32x4(float v0, float v1, float v2, float v3) - { - float v[] = {v0, v1, v2, v3}; - val = msa_ld1q_f32(v); - } - - float get0() const - { - return msa_getq_lane_f32(val, 0); - } - - v4f32 val; -}; - -struct v_uint64x2 -{ - typedef uint64 lane_type; - enum { nlanes = 2 }; - - v_uint64x2() {} - explicit v_uint64x2(v2u64 v) : val(v) {} - v_uint64x2(uint64 v0, uint64 v1) - { - uint64 v[] = {v0, v1}; - val = msa_ld1q_u64(v); - } - - uint64 get0() const - { - return msa_getq_lane_u64(val, 0); - } - - v2u64 val; -}; - -struct v_int64x2 -{ - typedef int64 lane_type; - enum { nlanes = 2 }; - - v_int64x2() {} - explicit v_int64x2(v2i64 v) : val(v) {} - v_int64x2(int64 v0, int64 v1) - { - int64 v[] = {v0, v1}; - val = msa_ld1q_s64(v); - } - - int64 get0() const - { - return msa_getq_lane_s64(val, 0); - } - - v2i64 val; -}; - -struct v_float64x2 -{ - typedef double lane_type; - enum { nlanes = 2 }; - - v_float64x2() {} - explicit v_float64x2(v2f64 v) : val(v) {} - v_float64x2(double v0, double v1) - { - double v[] = {v0, v1}; - val = msa_ld1q_f64(v); - } - - double get0() const - { - return msa_getq_lane_f64(val, 0); - } - - v2f64 val; -}; - -#define OPENCV_HAL_IMPL_MSA_INIT(_Tpv, _Tp, suffix) \ -inline v_##_Tpv v_setzero_##suffix() { return v_##_Tpv(msa_dupq_n_##suffix((_Tp)0)); } \ -inline v_##_Tpv v_setall_##suffix(_Tp v) { return v_##_Tpv(msa_dupq_n_##suffix(v)); } \ -inline v_uint8x16 v_reinterpret_as_u8(const v_##_Tpv& v) { return v_uint8x16(MSA_TPV_REINTERPRET(v16u8, v.val)); } \ -inline v_int8x16 v_reinterpret_as_s8(const v_##_Tpv& v) { return v_int8x16(MSA_TPV_REINTERPRET(v16i8, v.val)); } \ -inline v_uint16x8 v_reinterpret_as_u16(const v_##_Tpv& v) { return v_uint16x8(MSA_TPV_REINTERPRET(v8u16, v.val)); } \ -inline v_int16x8 v_reinterpret_as_s16(const v_##_Tpv& v) { return v_int16x8(MSA_TPV_REINTERPRET(v8i16, v.val)); } \ -inline v_uint32x4 v_reinterpret_as_u32(const v_##_Tpv& v) { return v_uint32x4(MSA_TPV_REINTERPRET(v4u32, v.val)); } \ -inline v_int32x4 v_reinterpret_as_s32(const v_##_Tpv& v) { return v_int32x4(MSA_TPV_REINTERPRET(v4i32, v.val)); } \ -inline v_uint64x2 v_reinterpret_as_u64(const v_##_Tpv& v) { return v_uint64x2(MSA_TPV_REINTERPRET(v2u64, v.val)); } \ -inline v_int64x2 v_reinterpret_as_s64(const v_##_Tpv& v) { return v_int64x2(MSA_TPV_REINTERPRET(v2i64, v.val)); } \ -inline v_float32x4 v_reinterpret_as_f32(const v_##_Tpv& v) { return v_float32x4(MSA_TPV_REINTERPRET(v4f32, v.val)); } \ -inline v_float64x2 v_reinterpret_as_f64(const v_##_Tpv& v) { return v_float64x2(MSA_TPV_REINTERPRET(v2f64, v.val)); } - -OPENCV_HAL_IMPL_MSA_INIT(uint8x16, uchar, u8) -OPENCV_HAL_IMPL_MSA_INIT(int8x16, schar, s8) -OPENCV_HAL_IMPL_MSA_INIT(uint16x8, ushort, u16) -OPENCV_HAL_IMPL_MSA_INIT(int16x8, short, s16) -OPENCV_HAL_IMPL_MSA_INIT(uint32x4, unsigned int, u32) -OPENCV_HAL_IMPL_MSA_INIT(int32x4, int, s32) -OPENCV_HAL_IMPL_MSA_INIT(uint64x2, uint64, u64) -OPENCV_HAL_IMPL_MSA_INIT(int64x2, int64, s64) -OPENCV_HAL_IMPL_MSA_INIT(float32x4, float, f32) -OPENCV_HAL_IMPL_MSA_INIT(float64x2, double, f64) - -#define OPENCV_HAL_IMPL_MSA_PACK(_Tpvec, _Tpwvec, pack, mov, rshr) \ -inline _Tpvec v_##pack(const _Tpwvec& a, const _Tpwvec& b) \ -{ \ - return _Tpvec(mov(a.val, b.val)); \ -} \ -template inline \ -_Tpvec v_rshr_##pack(const _Tpwvec& a, const _Tpwvec& b) \ -{ \ - return _Tpvec(rshr(a.val, b.val, n)); \ -} - -OPENCV_HAL_IMPL_MSA_PACK(v_uint8x16, v_uint16x8, pack, msa_qpack_u16, msa_qrpackr_u16) -OPENCV_HAL_IMPL_MSA_PACK(v_int8x16, v_int16x8, pack, msa_qpack_s16, msa_qrpackr_s16) -OPENCV_HAL_IMPL_MSA_PACK(v_uint16x8, v_uint32x4, pack, msa_qpack_u32, msa_qrpackr_u32) -OPENCV_HAL_IMPL_MSA_PACK(v_int16x8, v_int32x4, pack, msa_qpack_s32, msa_qrpackr_s32) -OPENCV_HAL_IMPL_MSA_PACK(v_uint32x4, v_uint64x2, pack, msa_pack_u64, msa_rpackr_u64) -OPENCV_HAL_IMPL_MSA_PACK(v_int32x4, v_int64x2, pack, msa_pack_s64, msa_rpackr_s64) -OPENCV_HAL_IMPL_MSA_PACK(v_uint8x16, v_int16x8, pack_u, msa_qpacku_s16, msa_qrpackru_s16) -OPENCV_HAL_IMPL_MSA_PACK(v_uint16x8, v_int32x4, pack_u, msa_qpacku_s32, msa_qrpackru_s32) - -#define OPENCV_HAL_IMPL_MSA_PACK_STORE(_Tpvec, _Tp, hreg, suffix, _Tpwvec, pack, mov, rshr) \ -inline void v_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ -{ \ - hreg a1 = mov(a.val); \ - msa_st1_##suffix(ptr, a1); \ -} \ -template inline \ -void v_rshr_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ -{ \ - hreg a1 = rshr(a.val, n); \ - msa_st1_##suffix(ptr, a1); \ -} - -OPENCV_HAL_IMPL_MSA_PACK_STORE(v_uint8x16, uchar, v8u8, u8, v_uint16x8, pack, msa_qmovn_u16, msa_qrshrn_n_u16) -OPENCV_HAL_IMPL_MSA_PACK_STORE(v_int8x16, schar, v8i8, s8, v_int16x8, pack, msa_qmovn_s16, msa_qrshrn_n_s16) -OPENCV_HAL_IMPL_MSA_PACK_STORE(v_uint16x8, ushort, v4u16, u16, v_uint32x4, pack, msa_qmovn_u32, msa_qrshrn_n_u32) -OPENCV_HAL_IMPL_MSA_PACK_STORE(v_int16x8, short, v4i16, s16, v_int32x4, pack, msa_qmovn_s32, msa_qrshrn_n_s32) -OPENCV_HAL_IMPL_MSA_PACK_STORE(v_uint32x4, unsigned, v2u32, u32, v_uint64x2, pack, msa_movn_u64, msa_rshrn_n_u64) -OPENCV_HAL_IMPL_MSA_PACK_STORE(v_int32x4, int, v2i32, s32, v_int64x2, pack, msa_movn_s64, msa_rshrn_n_s64) -OPENCV_HAL_IMPL_MSA_PACK_STORE(v_uint8x16, uchar, v8u8, u8, v_int16x8, pack_u, msa_qmovun_s16, msa_qrshrun_n_s16) -OPENCV_HAL_IMPL_MSA_PACK_STORE(v_uint16x8, ushort, v4u16, u16, v_int32x4, pack_u, msa_qmovun_s32, msa_qrshrun_n_s32) - -// pack boolean -inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) -{ - return v_uint8x16(msa_pack_u16(a.val, b.val)); -} - -inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, - const v_uint32x4& c, const v_uint32x4& d) -{ - return v_uint8x16(msa_pack_u16(msa_pack_u32(a.val, b.val), msa_pack_u32(c.val, d.val))); -} - -inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, - const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, - const v_uint64x2& g, const v_uint64x2& h) -{ - v8u16 abcd = msa_pack_u32(msa_pack_u64(a.val, b.val), msa_pack_u64(c.val, d.val)); - v8u16 efgh = msa_pack_u32(msa_pack_u64(e.val, f.val), msa_pack_u64(g.val, h.val)); - return v_uint8x16(msa_pack_u16(abcd, efgh)); -} - -inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, - const v_float32x4& m1, const v_float32x4& m2, - const v_float32x4& m3) -{ - v4f32 v0 = v.val; - v4f32 res = msa_mulq_lane_f32(m0.val, v0, 0); - res = msa_mlaq_lane_f32(res, m1.val, v0, 1); - res = msa_mlaq_lane_f32(res, m2.val, v0, 2); - res = msa_mlaq_lane_f32(res, m3.val, v0, 3); - return v_float32x4(res); -} - -inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, - const v_float32x4& m1, const v_float32x4& m2, - const v_float32x4& a) -{ - v4f32 v0 = v.val; - v4f32 res = msa_mulq_lane_f32(m0.val, v0, 0); - res = msa_mlaq_lane_f32(res, m1.val, v0, 1); - res = msa_mlaq_lane_f32(res, m2.val, v0, 2); - res = msa_addq_f32(res, a.val); - return v_float32x4(res); -} - -#define OPENCV_HAL_IMPL_MSA_BIN_OP(bin_op, _Tpvec, intrin) \ -inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(intrin(a.val, b.val)); \ -} \ -inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ -{ \ - a.val = intrin(a.val, b.val); \ - return a; \ -} - -OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_uint8x16, msa_qaddq_u8) -OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_uint8x16, msa_qsubq_u8) -OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_int8x16, msa_qaddq_s8) -OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_int8x16, msa_qsubq_s8) -OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_uint16x8, msa_qaddq_u16) -OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_uint16x8, msa_qsubq_u16) -OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_int16x8, msa_qaddq_s16) -OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_int16x8, msa_qsubq_s16) -OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_int32x4, msa_addq_s32) -OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_int32x4, msa_subq_s32) -OPENCV_HAL_IMPL_MSA_BIN_OP(*, v_int32x4, msa_mulq_s32) -OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_uint32x4, msa_addq_u32) -OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_uint32x4, msa_subq_u32) -OPENCV_HAL_IMPL_MSA_BIN_OP(*, v_uint32x4, msa_mulq_u32) -OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_float32x4, msa_addq_f32) -OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_float32x4, msa_subq_f32) -OPENCV_HAL_IMPL_MSA_BIN_OP(*, v_float32x4, msa_mulq_f32) -OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_int64x2, msa_addq_s64) -OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_int64x2, msa_subq_s64) -OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_uint64x2, msa_addq_u64) -OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_uint64x2, msa_subq_u64) -OPENCV_HAL_IMPL_MSA_BIN_OP(/, v_float32x4, msa_divq_f32) -OPENCV_HAL_IMPL_MSA_BIN_OP(+, v_float64x2, msa_addq_f64) -OPENCV_HAL_IMPL_MSA_BIN_OP(-, v_float64x2, msa_subq_f64) -OPENCV_HAL_IMPL_MSA_BIN_OP(*, v_float64x2, msa_mulq_f64) -OPENCV_HAL_IMPL_MSA_BIN_OP(/, v_float64x2, msa_divq_f64) - -// saturating multiply 8-bit, 16-bit -#define OPENCV_HAL_IMPL_MSA_MUL_SAT(_Tpvec, _Tpwvec) \ -inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b) \ -{ \ - _Tpwvec c, d; \ - v_mul_expand(a, b, c, d); \ - return v_pack(c, d); \ -} \ -inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b) \ -{a = a * b; return a; } - -OPENCV_HAL_IMPL_MSA_MUL_SAT(v_int8x16, v_int16x8) -OPENCV_HAL_IMPL_MSA_MUL_SAT(v_uint8x16, v_uint16x8) -OPENCV_HAL_IMPL_MSA_MUL_SAT(v_int16x8, v_int32x4) -OPENCV_HAL_IMPL_MSA_MUL_SAT(v_uint16x8, v_uint32x4) - -// Multiply and expand -inline void v_mul_expand(const v_int8x16& a, const v_int8x16& b, - v_int16x8& c, v_int16x8& d) -{ - v16i8 a_lo, a_hi, b_lo, b_hi; - - ILVRL_B2_SB(a.val, msa_dupq_n_s8(0), a_lo, a_hi); - ILVRL_B2_SB(b.val, msa_dupq_n_s8(0), b_lo, b_hi); - c.val = msa_mulq_s16(msa_paddlq_s8(a_lo), msa_paddlq_s8(b_lo)); - d.val = msa_mulq_s16(msa_paddlq_s8(a_hi), msa_paddlq_s8(b_hi)); -} - -inline void v_mul_expand(const v_uint8x16& a, const v_uint8x16& b, - v_uint16x8& c, v_uint16x8& d) -{ - v16u8 a_lo, a_hi, b_lo, b_hi; - - ILVRL_B2_UB(a.val, msa_dupq_n_u8(0), a_lo, a_hi); - ILVRL_B2_UB(b.val, msa_dupq_n_u8(0), b_lo, b_hi); - c.val = msa_mulq_u16(msa_paddlq_u8(a_lo), msa_paddlq_u8(b_lo)); - d.val = msa_mulq_u16(msa_paddlq_u8(a_hi), msa_paddlq_u8(b_hi)); -} - -inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b, - v_int32x4& c, v_int32x4& d) -{ - v8i16 a_lo, a_hi, b_lo, b_hi; - - ILVRL_H2_SH(a.val, msa_dupq_n_s16(0), a_lo, a_hi); - ILVRL_H2_SH(b.val, msa_dupq_n_s16(0), b_lo, b_hi); - c.val = msa_mulq_s32(msa_paddlq_s16(a_lo), msa_paddlq_s16(b_lo)); - d.val = msa_mulq_s32(msa_paddlq_s16(a_hi), msa_paddlq_s16(b_hi)); -} - -inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b, - v_uint32x4& c, v_uint32x4& d) -{ - v8u16 a_lo, a_hi, b_lo, b_hi; - - ILVRL_H2_UH(a.val, msa_dupq_n_u16(0), a_lo, a_hi); - ILVRL_H2_UH(b.val, msa_dupq_n_u16(0), b_lo, b_hi); - c.val = msa_mulq_u32(msa_paddlq_u16(a_lo), msa_paddlq_u16(b_lo)); - d.val = msa_mulq_u32(msa_paddlq_u16(a_hi), msa_paddlq_u16(b_hi)); -} - -inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, - v_uint64x2& c, v_uint64x2& d) -{ - v4u32 a_lo, a_hi, b_lo, b_hi; - - ILVRL_W2_UW(a.val, msa_dupq_n_u32(0), a_lo, a_hi); - ILVRL_W2_UW(b.val, msa_dupq_n_u32(0), b_lo, b_hi); - c.val = msa_mulq_u64(msa_paddlq_u32(a_lo), msa_paddlq_u32(b_lo)); - d.val = msa_mulq_u64(msa_paddlq_u32(a_hi), msa_paddlq_u32(b_hi)); -} - -inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b) -{ - v8i16 a_lo, a_hi, b_lo, b_hi; - - ILVRL_H2_SH(a.val, msa_dupq_n_s16(0), a_lo, a_hi); - ILVRL_H2_SH(b.val, msa_dupq_n_s16(0), b_lo, b_hi); - - return v_int16x8(msa_packr_s32(msa_mulq_s32(msa_paddlq_s16(a_lo), msa_paddlq_s16(b_lo)), - msa_mulq_s32(msa_paddlq_s16(a_hi), msa_paddlq_s16(b_hi)), 16)); -} - -inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b) -{ - v8u16 a_lo, a_hi, b_lo, b_hi; - - ILVRL_H2_UH(a.val, msa_dupq_n_u16(0), a_lo, a_hi); - ILVRL_H2_UH(b.val, msa_dupq_n_u16(0), b_lo, b_hi); - - return v_uint16x8(msa_packr_u32(msa_mulq_u32(msa_paddlq_u16(a_lo), msa_paddlq_u16(b_lo)), - msa_mulq_u32(msa_paddlq_u16(a_hi), msa_paddlq_u16(b_hi)), 16)); -} - -//////// Dot Product //////// - -// 16 >> 32 -inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) -{ return v_int32x4(msa_dotp_s_w(a.val, b.val)); } -inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) -{ return v_int32x4(msa_dpadd_s_w(c.val , a.val, b.val)); } - -// 32 >> 64 -inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b) -{ return v_int64x2(msa_dotp_s_d(a.val, b.val)); } -inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c) -{ return v_int64x2(msa_dpadd_s_d(c.val , a.val, b.val)); } - -// 8 >> 32 -inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b) -{ - v8u16 even_a = msa_shrq_n_u16(msa_shlq_n_u16(MSA_TPV_REINTERPRET(v8u16, a.val), 8), 8); - v8u16 odd_a = msa_shrq_n_u16(MSA_TPV_REINTERPRET(v8u16, a.val), 8); - v8u16 even_b = msa_shrq_n_u16(msa_shlq_n_u16(MSA_TPV_REINTERPRET(v8u16, b.val), 8), 8); - v8u16 odd_b = msa_shrq_n_u16(MSA_TPV_REINTERPRET(v8u16, b.val), 8); - v4u32 prod = msa_dotp_u_w(even_a, even_b); - return v_uint32x4(msa_dpadd_u_w(prod, odd_a, odd_b)); -} -inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c) -{ - v8u16 even_a = msa_shrq_n_u16(msa_shlq_n_u16(MSA_TPV_REINTERPRET(v8u16, a.val), 8), 8); - v8u16 odd_a = msa_shrq_n_u16(MSA_TPV_REINTERPRET(v8u16, a.val), 8); - v8u16 even_b = msa_shrq_n_u16(msa_shlq_n_u16(MSA_TPV_REINTERPRET(v8u16, b.val), 8), 8); - v8u16 odd_b = msa_shrq_n_u16(MSA_TPV_REINTERPRET(v8u16, b.val), 8); - v4u32 prod = msa_dpadd_u_w(c.val, even_a, even_b); - return v_uint32x4(msa_dpadd_u_w(prod, odd_a, odd_b)); -} - -inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b) -{ - v8i16 prod = msa_dotp_s_h(a.val, b.val); - return v_int32x4(msa_hadd_s32(prod, prod)); -} -inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b, - const v_int32x4& c) -{ return v_dotprod_expand(a, b) + c; } - -// 16 >> 64 -inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b) -{ - v4u32 even_a = msa_shrq_n_u32(msa_shlq_n_u32(MSA_TPV_REINTERPRET(v4u32, a.val), 16), 16); - v4u32 odd_a = msa_shrq_n_u32(MSA_TPV_REINTERPRET(v4u32, a.val), 16); - v4u32 even_b = msa_shrq_n_u32(msa_shlq_n_u32(MSA_TPV_REINTERPRET(v4u32, b.val), 16), 16); - v4u32 odd_b = msa_shrq_n_u32(MSA_TPV_REINTERPRET(v4u32, b.val), 16); - v2u64 prod = msa_dotp_u_d(even_a, even_b); - return v_uint64x2(msa_dpadd_u_d(prod, odd_a, odd_b)); -} -inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b, - const v_uint64x2& c) -{ - v4u32 even_a = msa_shrq_n_u32(msa_shlq_n_u32(MSA_TPV_REINTERPRET(v4u32, a.val), 16), 16); - v4u32 odd_a = msa_shrq_n_u32(MSA_TPV_REINTERPRET(v4u32, a.val), 16); - v4u32 even_b = msa_shrq_n_u32(msa_shlq_n_u32(MSA_TPV_REINTERPRET(v4u32, b.val), 16), 16); - v4u32 odd_b = msa_shrq_n_u32(MSA_TPV_REINTERPRET(v4u32, b.val), 16); - v2u64 prod = msa_dpadd_u_d(c.val, even_a, even_b); - return v_uint64x2(msa_dpadd_u_d(prod, odd_a, odd_b)); -} - -inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b) -{ - v4i32 prod = msa_dotp_s_w(a.val, b.val); - return v_int64x2(msa_hadd_s64(prod, prod)); -} -inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c) -{ return v_dotprod_expand(a, b) + c; } - -// 32 >> 64f -inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b) -{ return v_cvt_f64(v_dotprod(a, b)); } -inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c) -{ return v_dotprod_expand(a, b) + c; } - - -//////// Fast Dot Product //////// - -// 16 >> 32 -inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b) -{ return v_dotprod(a, b); } -inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) -{ return v_dotprod(a, b, c); } - -// 32 >> 64 -inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b) -{ return v_dotprod(a, b); } -inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c) -{ return v_dotprod(a, b, c); } - -// 8 >> 32 -inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b) -{ return v_dotprod_expand(a, b); } -inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c) -{ return v_dotprod_expand(a, b, c); } -inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b) -{ return v_dotprod_expand(a, b); } -inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c) -{ return v_dotprod_expand(a, b, c); } - -// 16 >> 64 -inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b) -{ return v_dotprod_expand(a, b); } -inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c) -{ return v_dotprod_expand(a, b, c); } -inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b) -{ return v_dotprod_expand(a, b); } -inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c) -{ return v_dotprod_expand(a, b, c); } - -// 32 >> 64f -inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b) -{ return v_dotprod_expand(a, b); } -inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c) -{ return v_dotprod_expand(a, b, c); } - -#define OPENCV_HAL_IMPL_MSA_LOGIC_OP(_Tpvec, _Tpv, suffix) \ -OPENCV_HAL_IMPL_MSA_BIN_OP(&, _Tpvec, msa_andq_##suffix) \ -OPENCV_HAL_IMPL_MSA_BIN_OP(|, _Tpvec, msa_orrq_##suffix) \ -OPENCV_HAL_IMPL_MSA_BIN_OP(^, _Tpvec, msa_eorq_##suffix) \ -inline _Tpvec operator ~ (const _Tpvec& a) \ -{ \ - return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_mvnq_u8(MSA_TPV_REINTERPRET(v16u8, a.val)))); \ -} - -OPENCV_HAL_IMPL_MSA_LOGIC_OP(v_uint8x16, v16u8, u8) -OPENCV_HAL_IMPL_MSA_LOGIC_OP(v_int8x16, v16i8, s8) -OPENCV_HAL_IMPL_MSA_LOGIC_OP(v_uint16x8, v8u16, u16) -OPENCV_HAL_IMPL_MSA_LOGIC_OP(v_int16x8, v8i16, s16) -OPENCV_HAL_IMPL_MSA_LOGIC_OP(v_uint32x4, v4u32, u32) -OPENCV_HAL_IMPL_MSA_LOGIC_OP(v_int32x4, v4i32, s32) -OPENCV_HAL_IMPL_MSA_LOGIC_OP(v_uint64x2, v2u64, u64) -OPENCV_HAL_IMPL_MSA_LOGIC_OP(v_int64x2, v2i64, s64) - -#define OPENCV_HAL_IMPL_MSA_FLT_BIT_OP(bin_op, intrin) \ -inline v_float32x4 operator bin_op (const v_float32x4& a, const v_float32x4& b) \ -{ \ - return v_float32x4(MSA_TPV_REINTERPRET(v4f32, intrin(MSA_TPV_REINTERPRET(v4i32, a.val), MSA_TPV_REINTERPRET(v4i32, b.val)))); \ -} \ -inline v_float32x4& operator bin_op##= (v_float32x4& a, const v_float32x4& b) \ -{ \ - a.val = MSA_TPV_REINTERPRET(v4f32, intrin(MSA_TPV_REINTERPRET(v4i32, a.val), MSA_TPV_REINTERPRET(v4i32, b.val))); \ - return a; \ -} - -OPENCV_HAL_IMPL_MSA_FLT_BIT_OP(&, msa_andq_s32) -OPENCV_HAL_IMPL_MSA_FLT_BIT_OP(|, msa_orrq_s32) -OPENCV_HAL_IMPL_MSA_FLT_BIT_OP(^, msa_eorq_s32) - -inline v_float32x4 operator ~ (const v_float32x4& a) -{ - return v_float32x4(MSA_TPV_REINTERPRET(v4f32, msa_mvnq_s32(MSA_TPV_REINTERPRET(v4i32, a.val)))); -} - -/* v_abs */ -#define OPENCV_HAL_IMPL_MSA_ABS(_Tpuvec, _Tpsvec, usuffix, ssuffix) \ -inline _Tpuvec v_abs(const _Tpsvec& a) \ -{ \ - return v_reinterpret_as_##usuffix(_Tpsvec(msa_absq_##ssuffix(a.val))); \ -} - -OPENCV_HAL_IMPL_MSA_ABS(v_uint8x16, v_int8x16, u8, s8) -OPENCV_HAL_IMPL_MSA_ABS(v_uint16x8, v_int16x8, u16, s16) -OPENCV_HAL_IMPL_MSA_ABS(v_uint32x4, v_int32x4, u32, s32) - -/* v_abs(float), v_sqrt, v_invsqrt */ -#define OPENCV_HAL_IMPL_MSA_BASIC_FUNC(_Tpvec, func, intrin) \ -inline _Tpvec func(const _Tpvec& a) \ -{ \ - return _Tpvec(intrin(a.val)); \ -} - -OPENCV_HAL_IMPL_MSA_BASIC_FUNC(v_float32x4, v_abs, msa_absq_f32) -OPENCV_HAL_IMPL_MSA_BASIC_FUNC(v_float64x2, v_abs, msa_absq_f64) -OPENCV_HAL_IMPL_MSA_BASIC_FUNC(v_float32x4, v_sqrt, msa_sqrtq_f32) -OPENCV_HAL_IMPL_MSA_BASIC_FUNC(v_float32x4, v_invsqrt, msa_rsqrtq_f32) -OPENCV_HAL_IMPL_MSA_BASIC_FUNC(v_float64x2, v_sqrt, msa_sqrtq_f64) -OPENCV_HAL_IMPL_MSA_BASIC_FUNC(v_float64x2, v_invsqrt, msa_rsqrtq_f64) - -#define OPENCV_HAL_IMPL_MSA_DBL_BIT_OP(bin_op, intrin) \ -inline v_float64x2 operator bin_op (const v_float64x2& a, const v_float64x2& b) \ -{ \ - return v_float64x2(MSA_TPV_REINTERPRET(v2f64, intrin(MSA_TPV_REINTERPRET(v2i64, a.val), MSA_TPV_REINTERPRET(v2i64, b.val)))); \ -} \ -inline v_float64x2& operator bin_op##= (v_float64x2& a, const v_float64x2& b) \ -{ \ - a.val = MSA_TPV_REINTERPRET(v2f64, intrin(MSA_TPV_REINTERPRET(v2i64, a.val), MSA_TPV_REINTERPRET(v2i64, b.val))); \ - return a; \ -} - -OPENCV_HAL_IMPL_MSA_DBL_BIT_OP(&, msa_andq_s64) -OPENCV_HAL_IMPL_MSA_DBL_BIT_OP(|, msa_orrq_s64) -OPENCV_HAL_IMPL_MSA_DBL_BIT_OP(^, msa_eorq_s64) - -inline v_float64x2 operator ~ (const v_float64x2& a) -{ - return v_float64x2(MSA_TPV_REINTERPRET(v2f64, msa_mvnq_s32(MSA_TPV_REINTERPRET(v4i32, a.val)))); -} - -// TODO: exp, log, sin, cos - -#define OPENCV_HAL_IMPL_MSA_BIN_FUNC(_Tpvec, func, intrin) \ -inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(intrin(a.val, b.val)); \ -} - -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint8x16, v_min, msa_minq_u8) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint8x16, v_max, msa_maxq_u8) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int8x16, v_min, msa_minq_s8) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int8x16, v_max, msa_maxq_s8) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint16x8, v_min, msa_minq_u16) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint16x8, v_max, msa_maxq_u16) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int16x8, v_min, msa_minq_s16) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int16x8, v_max, msa_maxq_s16) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint32x4, v_min, msa_minq_u32) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint32x4, v_max, msa_maxq_u32) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int32x4, v_min, msa_minq_s32) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int32x4, v_max, msa_maxq_s32) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_float32x4, v_min, msa_minq_f32) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_float32x4, v_max, msa_maxq_f32) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_float64x2, v_min, msa_minq_f64) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_float64x2, v_max, msa_maxq_f64) - -#define OPENCV_HAL_IMPL_MSA_INT_CMP_OP(_Tpvec, _Tpv, suffix, not_suffix) \ -inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_ceqq_##suffix(a.val, b.val))); } \ -inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_mvnq_##not_suffix(msa_ceqq_##suffix(a.val, b.val)))); } \ -inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_cltq_##suffix(a.val, b.val))); } \ -inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_cgtq_##suffix(a.val, b.val))); } \ -inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_cleq_##suffix(a.val, b.val))); } \ -inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_cgeq_##suffix(a.val, b.val))); } - -OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_uint8x16, v16u8, u8, u8) -OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_int8x16, v16i8, s8, u8) -OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_uint16x8, v8u16, u16, u16) -OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_int16x8, v8i16, s16, u16) -OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_uint32x4, v4u32, u32, u32) -OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_int32x4, v4i32, s32, u32) -OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_float32x4, v4f32, f32, u32) -OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_uint64x2, v2u64, u64, u64) -OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_int64x2, v2i64, s64, u64) -OPENCV_HAL_IMPL_MSA_INT_CMP_OP(v_float64x2, v2f64, f64, u64) - -inline v_float32x4 v_not_nan(const v_float32x4& a) -{ return v_float32x4(MSA_TPV_REINTERPRET(v4f32, msa_ceqq_f32(a.val, a.val))); } -inline v_float64x2 v_not_nan(const v_float64x2& a) -{ return v_float64x2(MSA_TPV_REINTERPRET(v2f64, msa_ceqq_f64(a.val, a.val))); } - -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint8x16, v_add_wrap, msa_addq_u8) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int8x16, v_add_wrap, msa_addq_s8) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint16x8, v_add_wrap, msa_addq_u16) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int16x8, v_add_wrap, msa_addq_s16) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint8x16, v_sub_wrap, msa_subq_u8) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int8x16, v_sub_wrap, msa_subq_s8) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint16x8, v_sub_wrap, msa_subq_u16) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int16x8, v_sub_wrap, msa_subq_s16) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint8x16, v_mul_wrap, msa_mulq_u8) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int8x16, v_mul_wrap, msa_mulq_s8) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint16x8, v_mul_wrap, msa_mulq_u16) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int16x8, v_mul_wrap, msa_mulq_s16) - -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint8x16, v_absdiff, msa_abdq_u8) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint16x8, v_absdiff, msa_abdq_u16) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_uint32x4, v_absdiff, msa_abdq_u32) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_float32x4, v_absdiff, msa_abdq_f32) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_float64x2, v_absdiff, msa_abdq_f64) - -/** Saturating absolute difference **/ -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int8x16, v_absdiffs, msa_qabdq_s8) -OPENCV_HAL_IMPL_MSA_BIN_FUNC(v_int16x8, v_absdiffs, msa_qabdq_s16) - -#define OPENCV_HAL_IMPL_MSA_BIN_FUNC2(_Tpvec, _Tpvec2, _Tpv, func, intrin) \ -inline _Tpvec2 func(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec2(MSA_TPV_REINTERPRET(_Tpv, intrin(a.val, b.val))); \ -} - -OPENCV_HAL_IMPL_MSA_BIN_FUNC2(v_int8x16, v_uint8x16, v16u8, v_absdiff, msa_abdq_s8) -OPENCV_HAL_IMPL_MSA_BIN_FUNC2(v_int16x8, v_uint16x8, v8u16, v_absdiff, msa_abdq_s16) -OPENCV_HAL_IMPL_MSA_BIN_FUNC2(v_int32x4, v_uint32x4, v4u32, v_absdiff, msa_abdq_s32) - -/* v_magnitude, v_sqr_magnitude, v_fma, v_muladd */ -inline v_float32x4 v_magnitude(const v_float32x4& a, const v_float32x4& b) -{ - v_float32x4 x(msa_mlaq_f32(msa_mulq_f32(a.val, a.val), b.val, b.val)); - return v_sqrt(x); -} - -inline v_float32x4 v_sqr_magnitude(const v_float32x4& a, const v_float32x4& b) -{ - return v_float32x4(msa_mlaq_f32(msa_mulq_f32(a.val, a.val), b.val, b.val)); -} - -inline v_float32x4 v_fma(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c) -{ - return v_float32x4(msa_mlaq_f32(c.val, a.val, b.val)); -} - -inline v_int32x4 v_fma(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) -{ - return v_int32x4(msa_mlaq_s32(c.val, a.val, b.val)); -} - -inline v_float32x4 v_muladd(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c) -{ - return v_fma(a, b, c); -} - -inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) -{ - return v_fma(a, b, c); -} - -inline v_float64x2 v_magnitude(const v_float64x2& a, const v_float64x2& b) -{ - v_float64x2 x(msa_mlaq_f64(msa_mulq_f64(a.val, a.val), b.val, b.val)); - return v_sqrt(x); -} - -inline v_float64x2 v_sqr_magnitude(const v_float64x2& a, const v_float64x2& b) -{ - return v_float64x2(msa_mlaq_f64(msa_mulq_f64(a.val, a.val), b.val, b.val)); -} - -inline v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c) -{ - return v_float64x2(msa_mlaq_f64(c.val, a.val, b.val)); -} - -inline v_float64x2 v_muladd(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c) -{ - return v_fma(a, b, c); -} - -// trade efficiency for convenience -#define OPENCV_HAL_IMPL_MSA_SHIFT_OP(_Tpvec, suffix, _Tps, ssuffix) \ -inline _Tpvec operator << (const _Tpvec& a, int n) \ -{ return _Tpvec(msa_shlq_##suffix(a.val, msa_dupq_n_##ssuffix((_Tps)n))); } \ -inline _Tpvec operator >> (const _Tpvec& a, int n) \ -{ return _Tpvec(msa_shrq_##suffix(a.val, msa_dupq_n_##ssuffix((_Tps)n))); } \ -template inline _Tpvec v_shl(const _Tpvec& a) \ -{ return _Tpvec(msa_shlq_n_##suffix(a.val, n)); } \ -template inline _Tpvec v_shr(const _Tpvec& a) \ -{ return _Tpvec(msa_shrq_n_##suffix(a.val, n)); } \ -template inline _Tpvec v_rshr(const _Tpvec& a) \ -{ return _Tpvec(msa_rshrq_n_##suffix(a.val, n)); } - -OPENCV_HAL_IMPL_MSA_SHIFT_OP(v_uint8x16, u8, schar, s8) -OPENCV_HAL_IMPL_MSA_SHIFT_OP(v_int8x16, s8, schar, s8) -OPENCV_HAL_IMPL_MSA_SHIFT_OP(v_uint16x8, u16, short, s16) -OPENCV_HAL_IMPL_MSA_SHIFT_OP(v_int16x8, s16, short, s16) -OPENCV_HAL_IMPL_MSA_SHIFT_OP(v_uint32x4, u32, int, s32) -OPENCV_HAL_IMPL_MSA_SHIFT_OP(v_int32x4, s32, int, s32) -OPENCV_HAL_IMPL_MSA_SHIFT_OP(v_uint64x2, u64, int64, s64) -OPENCV_HAL_IMPL_MSA_SHIFT_OP(v_int64x2, s64, int64, s64) - -/* v_rotate_right, v_rotate_left */ -#define OPENCV_HAL_IMPL_MSA_ROTATE_OP(_Tpvec, _Tpv, _Tpvs, suffix) \ -template inline _Tpvec v_rotate_right(const _Tpvec& a) \ -{ \ - return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_extq_##suffix(MSA_TPV_REINTERPRET(_Tpvs, a.val), msa_dupq_n_##suffix(0), n))); \ -} \ -template inline _Tpvec v_rotate_left(const _Tpvec& a) \ -{ \ - return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_extq_##suffix(msa_dupq_n_##suffix(0), MSA_TPV_REINTERPRET(_Tpvs, a.val), _Tpvec::nlanes - n))); \ -} \ -template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a) \ -{ \ - return a; \ -} \ -template inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_extq_##suffix(MSA_TPV_REINTERPRET(_Tpvs, a.val), MSA_TPV_REINTERPRET(_Tpvs, b.val), n))); \ -} \ -template inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_extq_##suffix(MSA_TPV_REINTERPRET(_Tpvs, b.val), MSA_TPV_REINTERPRET(_Tpvs, a.val), _Tpvec::nlanes - n))); \ -} \ -template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a, const _Tpvec& b) \ -{ \ - CV_UNUSED(b); \ - return a; \ -} - -OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_uint8x16, v16u8, v16i8, s8) -OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_int8x16, v16i8, v16i8, s8) -OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_uint16x8, v8u16, v8i16, s16) -OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_int16x8, v8i16, v8i16, s16) -OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_uint32x4, v4u32, v4i32, s32) -OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_int32x4, v4i32, v4i32, s32) -OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_float32x4, v4f32, v4i32, s32) -OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_uint64x2, v2u64, v2i64, s64) -OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_int64x2, v2i64, v2i64, s64) -OPENCV_HAL_IMPL_MSA_ROTATE_OP(v_float64x2, v2f64, v2i64, s64) - -#define OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(_Tpvec, _Tp, suffix) \ -inline _Tpvec v_load(const _Tp* ptr) \ -{ return _Tpvec(msa_ld1q_##suffix(ptr)); } \ -inline _Tpvec v_load_aligned(const _Tp* ptr) \ -{ return _Tpvec(msa_ld1q_##suffix(ptr)); } \ -inline _Tpvec v_load_low(const _Tp* ptr) \ -{ return _Tpvec(msa_combine_##suffix(msa_ld1_##suffix(ptr), msa_dup_n_##suffix((_Tp)0))); } \ -inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ -{ return _Tpvec(msa_combine_##suffix(msa_ld1_##suffix(ptr0), msa_ld1_##suffix(ptr1))); } \ -inline void v_store(_Tp* ptr, const _Tpvec& a) \ -{ msa_st1q_##suffix(ptr, a.val); } \ -inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ -{ msa_st1q_##suffix(ptr, a.val); } \ -inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ -{ msa_st1q_##suffix(ptr, a.val); } \ -inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode /*mode*/) \ -{ msa_st1q_##suffix(ptr, a.val); } \ -inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ -{ \ - int n = _Tpvec::nlanes; \ - for( int i = 0; i < (n/2); i++ ) \ - ptr[i] = a.val[i]; \ -} \ -inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ -{ \ - int n = _Tpvec::nlanes; \ - for( int i = 0; i < (n/2); i++ ) \ - ptr[i] = a.val[i+(n/2)]; \ -} - -OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_uint8x16, uchar, u8) -OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_int8x16, schar, s8) -OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_uint16x8, ushort, u16) -OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_int16x8, short, s16) -OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_int32x4, int, s32) -OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_uint64x2, uint64, u64) -OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_int64x2, int64, s64) -OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_float32x4, float, f32) -OPENCV_HAL_IMPL_MSA_LOADSTORE_OP(v_float64x2, double, f64) - - -/** Reverse **/ -inline v_uint8x16 v_reverse(const v_uint8x16 &a) -{ - v_uint8x16 c = v_uint8x16((v16u8)__builtin_msa_vshf_b((v16i8)((v2i64){0x08090A0B0C0D0E0F, 0x0001020304050607}), msa_dupq_n_s8(0), (v16i8)a.val)); - return c; -} - -inline v_int8x16 v_reverse(const v_int8x16 &a) -{ return v_reinterpret_as_s8(v_reverse(v_reinterpret_as_u8(a))); } - -inline v_uint16x8 v_reverse(const v_uint16x8 &a) -{ - v_uint16x8 c = v_uint16x8((v8u16)__builtin_msa_vshf_h((v8i16)((v2i64){0x0004000500060007, 0x0000000100020003}), msa_dupq_n_s16(0), (v8i16)a.val)); - return c; -} - -inline v_int16x8 v_reverse(const v_int16x8 &a) -{ return v_reinterpret_as_s16(v_reverse(v_reinterpret_as_u16(a))); } - -inline v_uint32x4 v_reverse(const v_uint32x4 &a) -{ - v_uint32x4 c; - c.val[0] = a.val[3]; - c.val[1] = a.val[2]; - c.val[2] = a.val[1]; - c.val[3] = a.val[0]; - return c; -} - -inline v_int32x4 v_reverse(const v_int32x4 &a) -{ return v_reinterpret_as_s32(v_reverse(v_reinterpret_as_u32(a))); } - -inline v_float32x4 v_reverse(const v_float32x4 &a) -{ return v_reinterpret_as_f32(v_reverse(v_reinterpret_as_u32(a))); } - -inline v_uint64x2 v_reverse(const v_uint64x2 &a) -{ - v_uint64x2 c; - c.val[0] = a.val[1]; - c.val[1] = a.val[0]; - return c; -} - -inline v_int64x2 v_reverse(const v_int64x2 &a) -{ return v_reinterpret_as_s64(v_reverse(v_reinterpret_as_u64(a))); } - -inline v_float64x2 v_reverse(const v_float64x2 &a) -{ return v_reinterpret_as_f64(v_reverse(v_reinterpret_as_u64(a))); } - - -#define OPENCV_HAL_IMPL_MSA_REDUCE_OP_8U(func, cfunc) \ -inline unsigned short v_reduce_##func(const v_uint16x8& a) \ -{ \ - v8u16 a_lo, a_hi; \ - ILVRL_H2_UH(a.val, msa_dupq_n_u16(0), a_lo, a_hi); \ - v4u32 b = msa_##func##q_u32(msa_paddlq_u16(a_lo), msa_paddlq_u16(a_hi)); \ - v4u32 b_lo, b_hi; \ - ILVRL_W2_UW(b, msa_dupq_n_u32(0), b_lo, b_hi); \ - v2u64 c = msa_##func##q_u64(msa_paddlq_u32(b_lo), msa_paddlq_u32(b_hi)); \ - return (unsigned short)cfunc(c[0], c[1]); \ -} - -OPENCV_HAL_IMPL_MSA_REDUCE_OP_8U(max, std::max) -OPENCV_HAL_IMPL_MSA_REDUCE_OP_8U(min, std::min) - -#define OPENCV_HAL_IMPL_MSA_REDUCE_OP_8S(func, cfunc) \ -inline short v_reduce_##func(const v_int16x8& a) \ -{ \ - v8i16 a_lo, a_hi; \ - ILVRL_H2_SH(a.val, msa_dupq_n_s16(0), a_lo, a_hi); \ - v4i32 b = msa_##func##q_s32(msa_paddlq_s16(a_lo), msa_paddlq_s16(a_hi)); \ - v4i32 b_lo, b_hi; \ - ILVRL_W2_SW(b, msa_dupq_n_s32(0), b_lo, b_hi); \ - v2i64 c = msa_##func##q_s64(msa_paddlq_s32(b_lo), msa_paddlq_s32(b_hi)); \ - return (short)cfunc(c[0], c[1]); \ -} - -OPENCV_HAL_IMPL_MSA_REDUCE_OP_8S(max, std::max) -OPENCV_HAL_IMPL_MSA_REDUCE_OP_8S(min, std::min) - -#define OPENCV_HAL_IMPL_MSA_REDUCE_OP_4(_Tpvec, scalartype, func, cfunc) \ -inline scalartype v_reduce_##func(const _Tpvec& a) \ -{ \ - return (scalartype)cfunc(cfunc(a.val[0], a.val[1]), cfunc(a.val[2], a.val[3])); \ -} - -OPENCV_HAL_IMPL_MSA_REDUCE_OP_4(v_uint32x4, unsigned, max, std::max) -OPENCV_HAL_IMPL_MSA_REDUCE_OP_4(v_uint32x4, unsigned, min, std::min) -OPENCV_HAL_IMPL_MSA_REDUCE_OP_4(v_int32x4, int, max, std::max) -OPENCV_HAL_IMPL_MSA_REDUCE_OP_4(v_int32x4, int, min, std::min) -OPENCV_HAL_IMPL_MSA_REDUCE_OP_4(v_float32x4, float, max, std::max) -OPENCV_HAL_IMPL_MSA_REDUCE_OP_4(v_float32x4, float, min, std::min) - - -#define OPENCV_HAL_IMPL_MSA_REDUCE_OP_16(_Tpvec, scalartype, _Tpvec2, func) \ -inline scalartype v_reduce_##func(const _Tpvec& a) \ -{ \ - _Tpvec2 a1, a2; \ - v_expand(a, a1, a2); \ - return (scalartype)v_reduce_##func(v_##func(a1, a2)); \ -} - -OPENCV_HAL_IMPL_MSA_REDUCE_OP_16(v_uint8x16, uchar, v_uint16x8, min) -OPENCV_HAL_IMPL_MSA_REDUCE_OP_16(v_uint8x16, uchar, v_uint16x8, max) -OPENCV_HAL_IMPL_MSA_REDUCE_OP_16(v_int8x16, char, v_int16x8, min) -OPENCV_HAL_IMPL_MSA_REDUCE_OP_16(v_int8x16, char, v_int16x8, max) - - - -#define OPENCV_HAL_IMPL_MSA_REDUCE_SUM(_Tpvec, scalartype, suffix) \ -inline scalartype v_reduce_sum(const _Tpvec& a) \ -{ \ - return (scalartype)msa_sum_##suffix(a.val); \ -} - -OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_uint8x16, unsigned char, u8) -OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_int8x16, char, s8) -OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_uint16x8, unsigned short, u16) -OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_int16x8, short, s16) -OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_int32x4, int, s32) -OPENCV_HAL_IMPL_MSA_REDUCE_SUM(v_float32x4, float, f32) - -inline uint64 v_reduce_sum(const v_uint64x2& a) -{ return (uint64)(msa_getq_lane_u64(a.val, 0) + msa_getq_lane_u64(a.val, 1)); } -inline int64 v_reduce_sum(const v_int64x2& a) -{ return (int64)(msa_getq_lane_s64(a.val, 0) + msa_getq_lane_s64(a.val, 1)); } -inline double v_reduce_sum(const v_float64x2& a) -{ - return msa_getq_lane_f64(a.val, 0) + msa_getq_lane_f64(a.val, 1); -} - -/* v_reduce_sum4, v_reduce_sad */ -inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, - const v_float32x4& c, const v_float32x4& d) -{ - v4f32 u0 = msa_addq_f32(MSA_TPV_REINTERPRET(v4f32, msa_ilvevq_s32(MSA_TPV_REINTERPRET(v4i32, b.val), MSA_TPV_REINTERPRET(v4i32, a.val))), - MSA_TPV_REINTERPRET(v4f32, msa_ilvodq_s32(MSA_TPV_REINTERPRET(v4i32, b.val), MSA_TPV_REINTERPRET(v4i32, a.val)))); // a0+a1 b0+b1 a2+a3 b2+b3 - v4f32 u1 = msa_addq_f32(MSA_TPV_REINTERPRET(v4f32, msa_ilvevq_s32(MSA_TPV_REINTERPRET(v4i32, d.val), MSA_TPV_REINTERPRET(v4i32, c.val))), - MSA_TPV_REINTERPRET(v4f32, msa_ilvodq_s32(MSA_TPV_REINTERPRET(v4i32, d.val), MSA_TPV_REINTERPRET(v4i32, c.val)))); // c0+c1 d0+d1 c2+c3 d2+d3 - - return v_float32x4(msa_addq_f32(MSA_TPV_REINTERPRET(v4f32, msa_ilvrq_s64(MSA_TPV_REINTERPRET(v2i64, u1), MSA_TPV_REINTERPRET(v2i64, u0))), - MSA_TPV_REINTERPRET(v4f32, msa_ilvlq_s64(MSA_TPV_REINTERPRET(v2i64, u1), MSA_TPV_REINTERPRET(v2i64, u0))))); -} - -inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b) -{ - v16u8 t0 = msa_abdq_u8(a.val, b.val); - v8u16 t1 = msa_paddlq_u8(t0); - v4u32 t2 = msa_paddlq_u16(t1); - return msa_sum_u32(t2); -} -inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b) -{ - v16u8 t0 = MSA_TPV_REINTERPRET(v16u8, msa_abdq_s8(a.val, b.val)); - v8u16 t1 = msa_paddlq_u8(t0); - v4u32 t2 = msa_paddlq_u16(t1); - return msa_sum_u32(t2); -} -inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b) -{ - v8u16 t0 = msa_abdq_u16(a.val, b.val); - v4u32 t1 = msa_paddlq_u16(t0); - return msa_sum_u32(t1); -} -inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b) -{ - v8u16 t0 = MSA_TPV_REINTERPRET(v8u16, msa_abdq_s16(a.val, b.val)); - v4u32 t1 = msa_paddlq_u16(t0); - return msa_sum_u32(t1); -} -inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b) -{ - v4u32 t0 = msa_abdq_u32(a.val, b.val); - return msa_sum_u32(t0); -} -inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b) -{ - v4u32 t0 = MSA_TPV_REINTERPRET(v4u32, msa_abdq_s32(a.val, b.val)); - return msa_sum_u32(t0); -} -inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b) -{ - v4f32 t0 = msa_abdq_f32(a.val, b.val); - return msa_sum_f32(t0); -} - -/* v_popcount */ -#define OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE8(_Tpvec) \ -inline v_uint8x16 v_popcount(const _Tpvec& a) \ -{ \ - v16u8 t = MSA_TPV_REINTERPRET(v16u8, msa_cntq_s8(MSA_TPV_REINTERPRET(v16i8, a.val))); \ - return v_uint8x16(t); \ -} -OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE8(v_uint8x16) -OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE8(v_int8x16) - -#define OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE16(_Tpvec) \ -inline v_uint16x8 v_popcount(const _Tpvec& a) \ -{ \ - v8u16 t = MSA_TPV_REINTERPRET(v8u16, msa_cntq_s16(MSA_TPV_REINTERPRET(v8i16, a.val))); \ - return v_uint16x8(t); \ -} -OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE16(v_uint16x8) -OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE16(v_int16x8) - -#define OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE32(_Tpvec) \ -inline v_uint32x4 v_popcount(const _Tpvec& a) \ -{ \ - v4u32 t = MSA_TPV_REINTERPRET(v4u32, msa_cntq_s32(MSA_TPV_REINTERPRET(v4i32, a.val))); \ - return v_uint32x4(t); \ -} -OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE32(v_uint32x4) -OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE32(v_int32x4) - -#define OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE64(_Tpvec) \ -inline v_uint64x2 v_popcount(const _Tpvec& a) \ -{ \ - v2u64 t = MSA_TPV_REINTERPRET(v2u64, msa_cntq_s64(MSA_TPV_REINTERPRET(v2i64, a.val))); \ - return v_uint64x2(t); \ -} -OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE64(v_uint64x2) -OPENCV_HAL_IMPL_MSA_POPCOUNT_SIZE64(v_int64x2) - -inline int v_signmask(const v_uint8x16& a) -{ - v8i8 m0 = msa_create_s8(CV_BIG_UINT(0x0706050403020100)); - v16u8 v0 = msa_shlq_u8(msa_shrq_n_u8(a.val, 7), msa_combine_s8(m0, m0)); - v8u16 v1 = msa_paddlq_u8(v0); - v4u32 v2 = msa_paddlq_u16(v1); - v2u64 v3 = msa_paddlq_u32(v2); - return (int)msa_getq_lane_u64(v3, 0) + ((int)msa_getq_lane_u64(v3, 1) << 8); -} -inline int v_signmask(const v_int8x16& a) -{ return v_signmask(v_reinterpret_as_u8(a)); } - -inline int v_signmask(const v_uint16x8& a) -{ - v4i16 m0 = msa_create_s16(CV_BIG_UINT(0x0003000200010000)); - v8u16 v0 = msa_shlq_u16(msa_shrq_n_u16(a.val, 15), msa_combine_s16(m0, m0)); - v4u32 v1 = msa_paddlq_u16(v0); - v2u64 v2 = msa_paddlq_u32(v1); - return (int)msa_getq_lane_u64(v2, 0) + ((int)msa_getq_lane_u64(v2, 1) << 4); -} -inline int v_signmask(const v_int16x8& a) -{ return v_signmask(v_reinterpret_as_u16(a)); } - -inline int v_signmask(const v_uint32x4& a) -{ - v2i32 m0 = msa_create_s32(CV_BIG_UINT(0x0000000100000000)); - v4u32 v0 = msa_shlq_u32(msa_shrq_n_u32(a.val, 31), msa_combine_s32(m0, m0)); - v2u64 v1 = msa_paddlq_u32(v0); - return (int)msa_getq_lane_u64(v1, 0) + ((int)msa_getq_lane_u64(v1, 1) << 2); -} -inline int v_signmask(const v_int32x4& a) -{ return v_signmask(v_reinterpret_as_u32(a)); } -inline int v_signmask(const v_float32x4& a) -{ return v_signmask(v_reinterpret_as_u32(a)); } - -inline int v_signmask(const v_uint64x2& a) -{ - v2u64 v0 = msa_shrq_n_u64(a.val, 63); - return (int)msa_getq_lane_u64(v0, 0) + ((int)msa_getq_lane_u64(v0, 1) << 1); -} -inline int v_signmask(const v_int64x2& a) -{ return v_signmask(v_reinterpret_as_u64(a)); } -inline int v_signmask(const v_float64x2& a) -{ return v_signmask(v_reinterpret_as_u64(a)); } - -inline int v_scan_forward(const v_int8x16& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_uint8x16& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_int16x8& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_uint16x8& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_int32x4& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_uint32x4& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_float32x4& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_int64x2& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_uint64x2& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_float64x2& a) { return trailingZeros32(v_signmask(a)); } - -#define OPENCV_HAL_IMPL_MSA_CHECK_ALLANY(_Tpvec, _Tpvec2, suffix, shift) \ -inline bool v_check_all(const v_##_Tpvec& a) \ -{ \ - _Tpvec2 v0 = msa_shrq_n_##suffix(msa_mvnq_##suffix(a.val), shift); \ - v2u64 v1 = MSA_TPV_REINTERPRET(v2u64, v0); \ - return (msa_getq_lane_u64(v1, 0) | msa_getq_lane_u64(v1, 1)) == 0; \ -} \ -inline bool v_check_any(const v_##_Tpvec& a) \ -{ \ - _Tpvec2 v0 = msa_shrq_n_##suffix(a.val, shift); \ - v2u64 v1 = MSA_TPV_REINTERPRET(v2u64, v0); \ - return (msa_getq_lane_u64(v1, 0) | msa_getq_lane_u64(v1, 1)) != 0; \ -} - -OPENCV_HAL_IMPL_MSA_CHECK_ALLANY(uint8x16, v16u8, u8, 7) -OPENCV_HAL_IMPL_MSA_CHECK_ALLANY(uint16x8, v8u16, u16, 15) -OPENCV_HAL_IMPL_MSA_CHECK_ALLANY(uint32x4, v4u32, u32, 31) -OPENCV_HAL_IMPL_MSA_CHECK_ALLANY(uint64x2, v2u64, u64, 63) - -inline bool v_check_all(const v_int8x16& a) -{ return v_check_all(v_reinterpret_as_u8(a)); } -inline bool v_check_all(const v_int16x8& a) -{ return v_check_all(v_reinterpret_as_u16(a)); } -inline bool v_check_all(const v_int32x4& a) -{ return v_check_all(v_reinterpret_as_u32(a)); } -inline bool v_check_all(const v_float32x4& a) -{ return v_check_all(v_reinterpret_as_u32(a)); } - -inline bool v_check_any(const v_int8x16& a) -{ return v_check_any(v_reinterpret_as_u8(a)); } -inline bool v_check_any(const v_int16x8& a) -{ return v_check_any(v_reinterpret_as_u16(a)); } -inline bool v_check_any(const v_int32x4& a) -{ return v_check_any(v_reinterpret_as_u32(a)); } -inline bool v_check_any(const v_float32x4& a) -{ return v_check_any(v_reinterpret_as_u32(a)); } - -inline bool v_check_all(const v_int64x2& a) -{ return v_check_all(v_reinterpret_as_u64(a)); } -inline bool v_check_all(const v_float64x2& a) -{ return v_check_all(v_reinterpret_as_u64(a)); } -inline bool v_check_any(const v_int64x2& a) -{ return v_check_any(v_reinterpret_as_u64(a)); } -inline bool v_check_any(const v_float64x2& a) -{ return v_check_any(v_reinterpret_as_u64(a)); } - -/* v_select */ -#define OPENCV_HAL_IMPL_MSA_SELECT(_Tpvec, _Tpv, _Tpvu) \ -inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_bslq_u8(MSA_TPV_REINTERPRET(_Tpvu, mask.val), \ - MSA_TPV_REINTERPRET(_Tpvu, b.val), MSA_TPV_REINTERPRET(_Tpvu, a.val)))); \ -} - -OPENCV_HAL_IMPL_MSA_SELECT(v_uint8x16, v16u8, v16u8) -OPENCV_HAL_IMPL_MSA_SELECT(v_int8x16, v16i8, v16u8) -OPENCV_HAL_IMPL_MSA_SELECT(v_uint16x8, v8u16, v16u8) -OPENCV_HAL_IMPL_MSA_SELECT(v_int16x8, v8i16, v16u8) -OPENCV_HAL_IMPL_MSA_SELECT(v_uint32x4, v4u32, v16u8) -OPENCV_HAL_IMPL_MSA_SELECT(v_int32x4, v4i32, v16u8) -OPENCV_HAL_IMPL_MSA_SELECT(v_float32x4, v4f32, v16u8) -OPENCV_HAL_IMPL_MSA_SELECT(v_float64x2, v2f64, v16u8) - -#define OPENCV_HAL_IMPL_MSA_EXPAND(_Tpvec, _Tpwvec, _Tp, suffix, ssuffix, _Tpv, _Tpvs) \ -inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ -{ \ - _Tpv a_lo = MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a.val), msa_dupq_n_##ssuffix(0))); \ - _Tpv a_hi = MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a.val), msa_dupq_n_##ssuffix(0))); \ - b0.val = msa_paddlq_##suffix(a_lo); \ - b1.val = msa_paddlq_##suffix(a_hi); \ -} \ -inline _Tpwvec v_expand_low(const _Tpvec& a) \ -{ \ - _Tpv a_lo = MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a.val), msa_dupq_n_##ssuffix(0))); \ - return _Tpwvec(msa_paddlq_##suffix(a_lo)); \ -} \ -inline _Tpwvec v_expand_high(const _Tpvec& a) \ -{ \ - _Tpv a_hi = MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a.val), msa_dupq_n_##ssuffix(0))); \ - return _Tpwvec(msa_paddlq_##suffix(a_hi)); \ -} \ -inline _Tpwvec v_load_expand(const _Tp* ptr) \ -{ \ - return _Tpwvec(msa_movl_##suffix(msa_ld1_##suffix(ptr))); \ -} - -OPENCV_HAL_IMPL_MSA_EXPAND(v_uint8x16, v_uint16x8, uchar, u8, s8, v16u8, v16i8) -OPENCV_HAL_IMPL_MSA_EXPAND(v_int8x16, v_int16x8, schar, s8, s8, v16i8, v16i8) -OPENCV_HAL_IMPL_MSA_EXPAND(v_uint16x8, v_uint32x4, ushort, u16, s16, v8u16, v8i16) -OPENCV_HAL_IMPL_MSA_EXPAND(v_int16x8, v_int32x4, short, s16, s16, v8i16, v8i16) -OPENCV_HAL_IMPL_MSA_EXPAND(v_uint32x4, v_uint64x2, uint, u32, s32, v4u32, v4i32) -OPENCV_HAL_IMPL_MSA_EXPAND(v_int32x4, v_int64x2, int, s32, s32, v4i32, v4i32) - -inline v_uint32x4 v_load_expand_q(const uchar* ptr) -{ - return v_uint32x4((v4u32){ptr[0], ptr[1], ptr[2], ptr[3]}); -} - -inline v_int32x4 v_load_expand_q(const schar* ptr) -{ - return v_int32x4((v4i32){ptr[0], ptr[1], ptr[2], ptr[3]}); -} - -/* v_zip, v_combine_low, v_combine_high, v_recombine */ -#define OPENCV_HAL_IMPL_MSA_UNPACKS(_Tpvec, _Tpv, _Tpvs, ssuffix) \ -inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1) \ -{ \ - b0.val = MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a1.val), MSA_TPV_REINTERPRET(_Tpvs, a0.val))); \ - b1.val = MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a1.val), MSA_TPV_REINTERPRET(_Tpvs, a0.val))); \ -} \ -inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_s64(MSA_TPV_REINTERPRET(v2i64, b.val), MSA_TPV_REINTERPRET(v2i64, a.val)))); \ -} \ -inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_s64(MSA_TPV_REINTERPRET(v2i64, b.val), MSA_TPV_REINTERPRET(v2i64, a.val)))); \ -} \ -inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d) \ -{ \ - c.val = MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_s64(MSA_TPV_REINTERPRET(v2i64, b.val), MSA_TPV_REINTERPRET(v2i64, a.val))); \ - d.val = MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_s64(MSA_TPV_REINTERPRET(v2i64, b.val), MSA_TPV_REINTERPRET(v2i64, a.val))); \ -} - -OPENCV_HAL_IMPL_MSA_UNPACKS(v_uint8x16, v16u8, v16i8, s8) -OPENCV_HAL_IMPL_MSA_UNPACKS(v_int8x16, v16i8, v16i8, s8) -OPENCV_HAL_IMPL_MSA_UNPACKS(v_uint16x8, v8u16, v8i16, s16) -OPENCV_HAL_IMPL_MSA_UNPACKS(v_int16x8, v8i16, v8i16, s16) -OPENCV_HAL_IMPL_MSA_UNPACKS(v_uint32x4, v4u32, v4i32, s32) -OPENCV_HAL_IMPL_MSA_UNPACKS(v_int32x4, v4i32, v4i32, s32) -OPENCV_HAL_IMPL_MSA_UNPACKS(v_float32x4, v4f32, v4i32, s32) -OPENCV_HAL_IMPL_MSA_UNPACKS(v_float64x2, v2f64, v2i64, s64) - -/* v_extract */ -#define OPENCV_HAL_IMPL_MSA_EXTRACT(_Tpvec, _Tpv, _Tpvs, suffix) \ -template \ -inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(MSA_TPV_REINTERPRET(_Tpv, msa_extq_##suffix(MSA_TPV_REINTERPRET(_Tpvs, a.val), MSA_TPV_REINTERPRET(_Tpvs, b.val), s))); \ -} - -OPENCV_HAL_IMPL_MSA_EXTRACT(v_uint8x16, v16u8, v16i8, s8) -OPENCV_HAL_IMPL_MSA_EXTRACT(v_int8x16, v16i8, v16i8, s8) -OPENCV_HAL_IMPL_MSA_EXTRACT(v_uint16x8, v8u16, v8i16, s16) -OPENCV_HAL_IMPL_MSA_EXTRACT(v_int16x8, v8i16, v8i16, s16) -OPENCV_HAL_IMPL_MSA_EXTRACT(v_uint32x4, v4u32, v4i32, s32) -OPENCV_HAL_IMPL_MSA_EXTRACT(v_int32x4, v4i32, v4i32, s32) -OPENCV_HAL_IMPL_MSA_EXTRACT(v_uint64x2, v2u64, v2i64, s64) -OPENCV_HAL_IMPL_MSA_EXTRACT(v_int64x2, v2i64, v2i64, s64) -OPENCV_HAL_IMPL_MSA_EXTRACT(v_float32x4, v4f32, v4i32, s32) -OPENCV_HAL_IMPL_MSA_EXTRACT(v_float64x2, v2f64, v2i64, s64) - -/* v_round, v_floor, v_ceil, v_trunc */ -inline v_int32x4 v_round(const v_float32x4& a) -{ - return v_int32x4(msa_cvttintq_s32_f32(a.val)); -} - -inline v_int32x4 v_floor(const v_float32x4& a) -{ - v4i32 a1 = msa_cvttintq_s32_f32(a.val); - return v_int32x4(msa_addq_s32(a1, MSA_TPV_REINTERPRET(v4i32, msa_cgtq_f32(msa_cvtfintq_f32_s32(a1), a.val)))); -} - -inline v_int32x4 v_ceil(const v_float32x4& a) -{ - v4i32 a1 = msa_cvttintq_s32_f32(a.val); - return v_int32x4(msa_subq_s32(a1, MSA_TPV_REINTERPRET(v4i32, msa_cgtq_f32(a.val, msa_cvtfintq_f32_s32(a1))))); -} - -inline v_int32x4 v_trunc(const v_float32x4& a) -{ - return v_int32x4(msa_cvttruncq_s32_f32(a.val)); -} - -inline v_int32x4 v_round(const v_float64x2& a) -{ - return v_int32x4(msa_pack_s64(msa_cvttintq_s64_f64(a.val), msa_dupq_n_s64(0))); -} - -inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b) -{ - return v_int32x4(msa_pack_s64(msa_cvttintq_s64_f64(a.val), msa_cvttintq_s64_f64(b.val))); -} - -inline v_int32x4 v_floor(const v_float64x2& a) -{ - v2f64 a1 = msa_cvtrintq_f64(a.val); - return v_int32x4(msa_pack_s64(msa_addq_s64(msa_cvttruncq_s64_f64(a1), MSA_TPV_REINTERPRET(v2i64, msa_cgtq_f64(a1, a.val))), msa_dupq_n_s64(0))); -} - -inline v_int32x4 v_ceil(const v_float64x2& a) -{ - v2f64 a1 = msa_cvtrintq_f64(a.val); - return v_int32x4(msa_pack_s64(msa_subq_s64(msa_cvttruncq_s64_f64(a1), MSA_TPV_REINTERPRET(v2i64, msa_cgtq_f64(a.val, a1))), msa_dupq_n_s64(0))); -} - -inline v_int32x4 v_trunc(const v_float64x2& a) -{ - return v_int32x4(msa_pack_s64(msa_cvttruncq_s64_f64(a.val), msa_dupq_n_s64(0))); -} - -#define OPENCV_HAL_IMPL_MSA_TRANSPOSE4x4(_Tpvec, _Tpv, _Tpvs, ssuffix) \ -inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \ - const _Tpvec& a2, const _Tpvec& a3, \ - _Tpvec& b0, _Tpvec& b1, \ - _Tpvec& b2, _Tpvec& b3) \ -{ \ - _Tpv t00 = MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a1.val), MSA_TPV_REINTERPRET(_Tpvs, a0.val))); \ - _Tpv t01 = MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a1.val), MSA_TPV_REINTERPRET(_Tpvs, a0.val))); \ - _Tpv t10 = MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a3.val), MSA_TPV_REINTERPRET(_Tpvs, a2.val))); \ - _Tpv t11 = MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_##ssuffix(MSA_TPV_REINTERPRET(_Tpvs, a3.val), MSA_TPV_REINTERPRET(_Tpvs, a2.val))); \ - b0.val = MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_s64(MSA_TPV_REINTERPRET(v2i64, t10), MSA_TPV_REINTERPRET(v2i64, t00))); \ - b1.val = MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_s64(MSA_TPV_REINTERPRET(v2i64, t10), MSA_TPV_REINTERPRET(v2i64, t00))); \ - b2.val = MSA_TPV_REINTERPRET(_Tpv, msa_ilvrq_s64(MSA_TPV_REINTERPRET(v2i64, t11), MSA_TPV_REINTERPRET(v2i64, t01))); \ - b3.val = MSA_TPV_REINTERPRET(_Tpv, msa_ilvlq_s64(MSA_TPV_REINTERPRET(v2i64, t11), MSA_TPV_REINTERPRET(v2i64, t01))); \ -} - -OPENCV_HAL_IMPL_MSA_TRANSPOSE4x4(v_uint32x4, v4u32, v4i32, s32) -OPENCV_HAL_IMPL_MSA_TRANSPOSE4x4(v_int32x4, v4i32, v4i32, s32) -OPENCV_HAL_IMPL_MSA_TRANSPOSE4x4(v_float32x4, v4f32, v4i32, s32) - -#define OPENCV_HAL_IMPL_MSA_INTERLEAVED(_Tpvec, _Tp, suffix) \ -inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b) \ -{ \ - msa_ld2q_##suffix(ptr, &a.val, &b.val); \ -} \ -inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, v_##_Tpvec& c) \ -{ \ - msa_ld3q_##suffix(ptr, &a.val, &b.val, &c.val); \ -} \ -inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, \ - v_##_Tpvec& c, v_##_Tpvec& d) \ -{ \ - msa_ld4q_##suffix(ptr, &a.val, &b.val, &c.val, &d.val); \ -} \ -inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \ - hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ -{ \ - msa_st2q_##suffix(ptr, a.val, b.val); \ -} \ -inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \ - const v_##_Tpvec& c, hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ -{ \ - msa_st3q_##suffix(ptr, a.val, b.val, c.val); \ -} \ -inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \ - const v_##_Tpvec& c, const v_##_Tpvec& d, \ - hal::StoreMode /*mode*/=hal::STORE_UNALIGNED ) \ -{ \ - msa_st4q_##suffix(ptr, a.val, b.val, c.val, d.val); \ -} - -OPENCV_HAL_IMPL_MSA_INTERLEAVED(uint8x16, uchar, u8) -OPENCV_HAL_IMPL_MSA_INTERLEAVED(int8x16, schar, s8) -OPENCV_HAL_IMPL_MSA_INTERLEAVED(uint16x8, ushort, u16) -OPENCV_HAL_IMPL_MSA_INTERLEAVED(int16x8, short, s16) -OPENCV_HAL_IMPL_MSA_INTERLEAVED(uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_MSA_INTERLEAVED(int32x4, int, s32) -OPENCV_HAL_IMPL_MSA_INTERLEAVED(float32x4, float, f32) -OPENCV_HAL_IMPL_MSA_INTERLEAVED(uint64x2, uint64, u64) -OPENCV_HAL_IMPL_MSA_INTERLEAVED(int64x2, int64, s64) -OPENCV_HAL_IMPL_MSA_INTERLEAVED(float64x2, double, f64) - -/* v_cvt_f32, v_cvt_f64, v_cvt_f64_high */ -inline v_float32x4 v_cvt_f32(const v_int32x4& a) -{ - return v_float32x4(msa_cvtfintq_f32_s32(a.val)); -} - -inline v_float32x4 v_cvt_f32(const v_float64x2& a) -{ - return v_float32x4(msa_cvtfq_f32_f64(a.val, msa_dupq_n_f64(0.0f))); -} - -inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b) -{ - return v_float32x4(msa_cvtfq_f32_f64(a.val, b.val)); -} - -inline v_float64x2 v_cvt_f64(const v_int32x4& a) -{ - return v_float64x2(msa_cvtflq_f64_f32(msa_cvtfintq_f32_s32(a.val))); -} - -inline v_float64x2 v_cvt_f64_high(const v_int32x4& a) -{ - return v_float64x2(msa_cvtfhq_f64_f32(msa_cvtfintq_f32_s32(a.val))); -} - -inline v_float64x2 v_cvt_f64(const v_float32x4& a) -{ - return v_float64x2(msa_cvtflq_f64_f32(a.val)); -} - -inline v_float64x2 v_cvt_f64_high(const v_float32x4& a) -{ - return v_float64x2(msa_cvtfhq_f64_f32(a.val)); -} - -inline v_float64x2 v_cvt_f64(const v_int64x2& a) -{ - return v_float64x2(msa_cvtfintq_f64_s64(a.val)); -} - -////////////// Lookup table access //////////////////// -inline v_int8x16 v_lut(const schar* tab, const int* idx) -{ - schar CV_DECL_ALIGNED(32) elems[16] = - { - tab[idx[ 0]], - tab[idx[ 1]], - tab[idx[ 2]], - tab[idx[ 3]], - tab[idx[ 4]], - tab[idx[ 5]], - tab[idx[ 6]], - tab[idx[ 7]], - tab[idx[ 8]], - tab[idx[ 9]], - tab[idx[10]], - tab[idx[11]], - tab[idx[12]], - tab[idx[13]], - tab[idx[14]], - tab[idx[15]] - }; - return v_int8x16(msa_ld1q_s8(elems)); -} -inline v_int8x16 v_lut_pairs(const schar* tab, const int* idx) -{ - schar CV_DECL_ALIGNED(32) elems[16] = - { - tab[idx[0]], - tab[idx[0] + 1], - tab[idx[1]], - tab[idx[1] + 1], - tab[idx[2]], - tab[idx[2] + 1], - tab[idx[3]], - tab[idx[3] + 1], - tab[idx[4]], - tab[idx[4] + 1], - tab[idx[5]], - tab[idx[5] + 1], - tab[idx[6]], - tab[idx[6] + 1], - tab[idx[7]], - tab[idx[7] + 1] - }; - return v_int8x16(msa_ld1q_s8(elems)); -} -inline v_int8x16 v_lut_quads(const schar* tab, const int* idx) -{ - schar CV_DECL_ALIGNED(32) elems[16] = - { - tab[idx[0]], - tab[idx[0] + 1], - tab[idx[0] + 2], - tab[idx[0] + 3], - tab[idx[1]], - tab[idx[1] + 1], - tab[idx[1] + 2], - tab[idx[1] + 3], - tab[idx[2]], - tab[idx[2] + 1], - tab[idx[2] + 2], - tab[idx[2] + 3], - tab[idx[3]], - tab[idx[3] + 1], - tab[idx[3] + 2], - tab[idx[3] + 3] - }; - return v_int8x16(msa_ld1q_s8(elems)); -} -inline v_uint8x16 v_lut(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut((schar*)tab, idx)); } -inline v_uint8x16 v_lut_pairs(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_pairs((schar*)tab, idx)); } -inline v_uint8x16 v_lut_quads(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_quads((schar*)tab, idx)); } - - -inline v_int16x8 v_lut(const short* tab, const int* idx) -{ - short CV_DECL_ALIGNED(32) elems[8] = - { - tab[idx[0]], - tab[idx[1]], - tab[idx[2]], - tab[idx[3]], - tab[idx[4]], - tab[idx[5]], - tab[idx[6]], - tab[idx[7]] - }; - return v_int16x8(msa_ld1q_s16(elems)); -} -inline v_int16x8 v_lut_pairs(const short* tab, const int* idx) -{ - short CV_DECL_ALIGNED(32) elems[8] = - { - tab[idx[0]], - tab[idx[0] + 1], - tab[idx[1]], - tab[idx[1] + 1], - tab[idx[2]], - tab[idx[2] + 1], - tab[idx[3]], - tab[idx[3] + 1] - }; - return v_int16x8(msa_ld1q_s16(elems)); -} -inline v_int16x8 v_lut_quads(const short* tab, const int* idx) -{ - return v_int16x8(msa_combine_s16(msa_ld1_s16(tab + idx[0]), msa_ld1_s16(tab + idx[1]))); -} -inline v_uint16x8 v_lut(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut((short*)tab, idx)); } -inline v_uint16x8 v_lut_pairs(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_pairs((short*)tab, idx)); } -inline v_uint16x8 v_lut_quads(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_quads((short*)tab, idx)); } - -inline v_int32x4 v_lut(const int* tab, const int* idx) -{ - int CV_DECL_ALIGNED(32) elems[4] = - { - tab[idx[0]], - tab[idx[1]], - tab[idx[2]], - tab[idx[3]] - }; - return v_int32x4(msa_ld1q_s32(elems)); -} -inline v_int32x4 v_lut_pairs(const int* tab, const int* idx) -{ - return v_int32x4(msa_combine_s32(msa_ld1_s32(tab + idx[0]), msa_ld1_s32(tab + idx[1]))); -} -inline v_int32x4 v_lut_quads(const int* tab, const int* idx) -{ - return v_int32x4(msa_ld1q_s32(tab + idx[0])); -} -inline v_uint32x4 v_lut(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut((int*)tab, idx)); } -inline v_uint32x4 v_lut_pairs(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_pairs((int*)tab, idx)); } -inline v_uint32x4 v_lut_quads(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_quads((int*)tab, idx)); } - -inline v_int64x2 v_lut(const int64_t* tab, const int* idx) -{ - return v_int64x2(msa_combine_s64(msa_create_s64(tab[idx[0]]), msa_create_s64(tab[idx[1]]))); -} -inline v_int64x2 v_lut_pairs(const int64_t* tab, const int* idx) -{ - return v_int64x2(msa_ld1q_s64(tab + idx[0])); -} -inline v_uint64x2 v_lut(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut((const int64_t *)tab, idx)); } -inline v_uint64x2 v_lut_pairs(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut_pairs((const int64_t *)tab, idx)); } - -inline v_float32x4 v_lut(const float* tab, const int* idx) -{ - float CV_DECL_ALIGNED(32) elems[4] = - { - tab[idx[0]], - tab[idx[1]], - tab[idx[2]], - tab[idx[3]] - }; - return v_float32x4(msa_ld1q_f32(elems)); -} -inline v_float32x4 v_lut_pairs(const float* tab, const int* idx) -{ - uint64 CV_DECL_ALIGNED(32) elems[2] = - { - *(uint64*)(tab + idx[0]), - *(uint64*)(tab + idx[1]) - }; - return v_float32x4(MSA_TPV_REINTERPRET(v4f32, msa_ld1q_u64(elems))); -} -inline v_float32x4 v_lut_quads(const float* tab, const int* idx) -{ - return v_float32x4(msa_ld1q_f32(tab + idx[0])); -} - -inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec) -{ - int CV_DECL_ALIGNED(32) idx[4]; - v_store_aligned(idx, idxvec); - - return v_int32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); -} - -inline v_uint32x4 v_lut(const unsigned* tab, const v_int32x4& idxvec) -{ - unsigned CV_DECL_ALIGNED(32) elems[4] = - { - tab[msa_getq_lane_s32(idxvec.val, 0)], - tab[msa_getq_lane_s32(idxvec.val, 1)], - tab[msa_getq_lane_s32(idxvec.val, 2)], - tab[msa_getq_lane_s32(idxvec.val, 3)] - }; - return v_uint32x4(msa_ld1q_u32(elems)); -} - -inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec) -{ - int CV_DECL_ALIGNED(32) idx[4]; - v_store_aligned(idx, idxvec); - - return v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); -} - -inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y) -{ - int CV_DECL_ALIGNED(32) idx[4]; - v_store_aligned(idx, idxvec); - - v4f32 xy02 = msa_combine_f32(msa_ld1_f32(tab + idx[0]), msa_ld1_f32(tab + idx[2])); - v4f32 xy13 = msa_combine_f32(msa_ld1_f32(tab + idx[1]), msa_ld1_f32(tab + idx[3])); - x = v_float32x4(MSA_TPV_REINTERPRET(v4f32, msa_ilvevq_s32(MSA_TPV_REINTERPRET(v4i32, xy13), MSA_TPV_REINTERPRET(v4i32, xy02)))); - y = v_float32x4(MSA_TPV_REINTERPRET(v4f32, msa_ilvodq_s32(MSA_TPV_REINTERPRET(v4i32, xy13), MSA_TPV_REINTERPRET(v4i32, xy02)))); -} - -inline v_int8x16 v_interleave_pairs(const v_int8x16& vec) -{ - v_int8x16 c = v_int8x16(__builtin_msa_vshf_b((v16i8)((v2i64){0x0705060403010200, 0x0F0D0E0C0B090A08}), msa_dupq_n_s8(0), vec.val)); - return c; -} -inline v_uint8x16 v_interleave_pairs(const v_uint8x16& vec) -{ return v_reinterpret_as_u8(v_interleave_pairs(v_reinterpret_as_s8(vec))); } -inline v_int8x16 v_interleave_quads(const v_int8x16& vec) -{ - v_int8x16 c = v_int8x16(__builtin_msa_vshf_b((v16i8)((v2i64){0x0703060205010400, 0x0F0B0E0A0D090C08}), msa_dupq_n_s8(0), vec.val)); - return c; -} -inline v_uint8x16 v_interleave_quads(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_interleave_quads(v_reinterpret_as_s8(vec))); } - -inline v_int16x8 v_interleave_pairs(const v_int16x8& vec) -{ - v_int16x8 c = v_int16x8(__builtin_msa_vshf_h((v8i16)((v2i64){0x0003000100020000, 0x0007000500060004}), msa_dupq_n_s16(0), vec.val)); - return c; -} - -inline v_uint16x8 v_interleave_pairs(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_pairs(v_reinterpret_as_s16(vec))); } - -inline v_int16x8 v_interleave_quads(const v_int16x8& vec) -{ - v_int16x8 c = v_int16x8(__builtin_msa_vshf_h((v8i16)((v2i64){0x0005000100040000, 0x0007000300060002}), msa_dupq_n_s16(0), vec.val)); - return c; -} - -inline v_uint16x8 v_interleave_quads(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_quads(v_reinterpret_as_s16(vec))); } - -inline v_int32x4 v_interleave_pairs(const v_int32x4& vec) -{ - v_int32x4 c; - c.val[0] = vec.val[0]; - c.val[1] = vec.val[2]; - c.val[2] = vec.val[1]; - c.val[3] = vec.val[3]; - return c; -} - -inline v_uint32x4 v_interleave_pairs(const v_uint32x4& vec) { return v_reinterpret_as_u32(v_interleave_pairs(v_reinterpret_as_s32(vec))); } -inline v_float32x4 v_interleave_pairs(const v_float32x4& vec) { return v_reinterpret_as_f32(v_interleave_pairs(v_reinterpret_as_s32(vec))); } - -inline v_int8x16 v_pack_triplets(const v_int8x16& vec) -{ - v_int8x16 c = v_int8x16(__builtin_msa_vshf_b((v16i8)((v2i64){0x0908060504020100, 0x131211100E0D0C0A}), msa_dupq_n_s8(0), vec.val)); - return c; -} - -inline v_uint8x16 v_pack_triplets(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec))); } - -inline v_int16x8 v_pack_triplets(const v_int16x8& vec) -{ - v_int16x8 c = v_int16x8(__builtin_msa_vshf_h((v8i16)((v2i64){0x0004000200010000, 0x0009000800060005}), msa_dupq_n_s16(0), vec.val)); - return c; -} - -inline v_uint16x8 v_pack_triplets(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec))); } -inline v_int32x4 v_pack_triplets(const v_int32x4& vec) { return vec; } -inline v_uint32x4 v_pack_triplets(const v_uint32x4& vec) { return vec; } -inline v_float32x4 v_pack_triplets(const v_float32x4& vec) { return vec; } - -inline v_float64x2 v_lut(const double* tab, const int* idx) -{ - double CV_DECL_ALIGNED(32) elems[2] = - { - tab[idx[0]], - tab[idx[1]] - }; - return v_float64x2(msa_ld1q_f64(elems)); -} - -inline v_float64x2 v_lut_pairs(const double* tab, const int* idx) -{ - return v_float64x2(msa_ld1q_f64(tab + idx[0])); -} - -inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec) -{ - int CV_DECL_ALIGNED(32) idx[4]; - v_store_aligned(idx, idxvec); - - return v_float64x2(tab[idx[0]], tab[idx[1]]); -} - -inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y) -{ - int CV_DECL_ALIGNED(32) idx[4]; - v_store_aligned(idx, idxvec); - - v2f64 xy0 = msa_ld1q_f64(tab + idx[0]); - v2f64 xy1 = msa_ld1q_f64(tab + idx[1]); - x = v_float64x2(MSA_TPV_REINTERPRET(v2f64, msa_ilvevq_s64(MSA_TPV_REINTERPRET(v2i64, xy1), MSA_TPV_REINTERPRET(v2i64, xy0)))); - y = v_float64x2(MSA_TPV_REINTERPRET(v2f64, msa_ilvodq_s64(MSA_TPV_REINTERPRET(v2i64, xy1), MSA_TPV_REINTERPRET(v2i64, xy0)))); -} - -template -inline typename _Tp::lane_type v_extract_n(const _Tp& a) -{ - return v_rotate_right(a).get0(); -} - -template -inline v_uint32x4 v_broadcast_element(const v_uint32x4& a) -{ - return v_setall_u32(v_extract_n(a)); -} -template -inline v_int32x4 v_broadcast_element(const v_int32x4& a) -{ - return v_setall_s32(v_extract_n(a)); -} -template -inline v_float32x4 v_broadcast_element(const v_float32x4& a) -{ - return v_setall_f32(v_extract_n(a)); -} - -////// FP16 support /////// -#if CV_FP16 -inline v_float32x4 v_load_expand(const float16_t* ptr) -{ -#ifndef msa_ld1_f16 - v4f16 v = (v4f16)msa_ld1_s16((const short*)ptr); -#else - v4f16 v = msa_ld1_f16((const __fp16*)ptr); -#endif - return v_float32x4(msa_cvt_f32_f16(v)); -} - -inline void v_pack_store(float16_t* ptr, const v_float32x4& v) -{ - v4f16 hv = msa_cvt_f16_f32(v.val); - -#ifndef msa_st1_f16 - msa_st1_s16((short*)ptr, (int16x4_t)hv); -#else - msa_st1_f16((__fp16*)ptr, hv); -#endif -} -#else -inline v_float32x4 v_load_expand(const float16_t* ptr) -{ - float buf[4]; - for( int i = 0; i < 4; i++ ) - buf[i] = (float)ptr[i]; - return v_load(buf); -} - -inline void v_pack_store(float16_t* ptr, const v_float32x4& v) -{ - float buf[4]; - v_store(buf, v); - for( int i = 0; i < 4; i++ ) - ptr[i] = (float16_t)buf[i]; -} -#endif - -inline void v_cleanup() {} - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END - -//! @endcond - -} - -#endif diff --git a/include/opencv2/core/hal/intrin_neon.hpp b/include/opencv2/core/hal/intrin_neon.hpp deleted file mode 100755 index 280691b..0000000 --- a/include/opencv2/core/hal/intrin_neon.hpp +++ /dev/null @@ -1,2346 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_HAL_INTRIN_NEON_HPP -#define OPENCV_HAL_INTRIN_NEON_HPP - -#include -#include "opencv2/core/utility.hpp" - -namespace cv -{ - -//! @cond IGNORED - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN - -#define CV_SIMD128 1 -#if defined(__aarch64__) || defined(_M_ARM64) -#define CV_SIMD128_64F 1 -#else -#define CV_SIMD128_64F 0 -#endif - -// TODO -#define CV_NEON_DOT 0 - -//////////// Utils //////////// - -#if CV_SIMD128_64F -#define OPENCV_HAL_IMPL_NEON_UNZIP(_Tpv, _Tpvx2, suffix) \ - inline void _v128_unzip(const _Tpv& a, const _Tpv& b, _Tpv& c, _Tpv& d) \ - { c = vuzp1q_##suffix(a, b); d = vuzp2q_##suffix(a, b); } -#define OPENCV_HAL_IMPL_NEON_UNZIP_L(_Tpv, _Tpvx2, suffix) \ - inline void _v128_unzip(const _Tpv&a, const _Tpv&b, _Tpv& c, _Tpv& d) \ - { c = vuzp1_##suffix(a, b); d = vuzp2_##suffix(a, b); } -#else -#define OPENCV_HAL_IMPL_NEON_UNZIP(_Tpv, _Tpvx2, suffix) \ - inline void _v128_unzip(const _Tpv& a, const _Tpv& b, _Tpv& c, _Tpv& d) \ - { _Tpvx2 ab = vuzpq_##suffix(a, b); c = ab.val[0]; d = ab.val[1]; } -#define OPENCV_HAL_IMPL_NEON_UNZIP_L(_Tpv, _Tpvx2, suffix) \ - inline void _v128_unzip(const _Tpv& a, const _Tpv& b, _Tpv& c, _Tpv& d) \ - { _Tpvx2 ab = vuzp_##suffix(a, b); c = ab.val[0]; d = ab.val[1]; } -#endif - -#if CV_SIMD128_64F -#define OPENCV_HAL_IMPL_NEON_REINTERPRET(_Tpv, suffix) \ - template static inline \ - _Tpv vreinterpretq_##suffix##_f64(T a) { return (_Tpv) a; } \ - template static inline \ - float64x2_t vreinterpretq_f64_##suffix(T a) { return (float64x2_t) a; } -#else -#define OPENCV_HAL_IMPL_NEON_REINTERPRET(_Tpv, suffix) -#endif - -#define OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX(_Tpv, _Tpvl, suffix) \ - OPENCV_HAL_IMPL_NEON_UNZIP(_Tpv##_t, _Tpv##x2_t, suffix) \ - OPENCV_HAL_IMPL_NEON_UNZIP_L(_Tpvl##_t, _Tpvl##x2_t, suffix) \ - OPENCV_HAL_IMPL_NEON_REINTERPRET(_Tpv##_t, suffix) - -#define OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX_I64(_Tpv, _Tpvl, suffix) \ - OPENCV_HAL_IMPL_NEON_REINTERPRET(_Tpv##_t, suffix) - -#define OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX_F64(_Tpv, _Tpvl, suffix) \ - OPENCV_HAL_IMPL_NEON_UNZIP(_Tpv##_t, _Tpv##x2_t, suffix) - -OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX(uint8x16, uint8x8, u8) -OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX(int8x16, int8x8, s8) -OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX(uint16x8, uint16x4, u16) -OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX(int16x8, int16x4, s16) -OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX(uint32x4, uint32x2, u32) -OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX(int32x4, int32x2, s32) -OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX(float32x4, float32x2, f32) -OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX_I64(uint64x2, uint64x1, u64) -OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX_I64(int64x2, int64x1, s64) -#if CV_SIMD128_64F -OPENCV_HAL_IMPL_NEON_UTILS_SUFFIX_F64(float64x2, float64x1,f64) -#endif - -//////////// Types //////////// - -struct v_uint8x16 -{ - typedef uchar lane_type; - enum { nlanes = 16 }; - - v_uint8x16() {} - explicit v_uint8x16(uint8x16_t v) : val(v) {} - v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, - uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) - { - uchar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; - val = vld1q_u8(v); - } - uchar get0() const - { - return vgetq_lane_u8(val, 0); - } - - uint8x16_t val; -}; - -struct v_int8x16 -{ - typedef schar lane_type; - enum { nlanes = 16 }; - - v_int8x16() {} - explicit v_int8x16(int8x16_t v) : val(v) {} - v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, - schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) - { - schar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; - val = vld1q_s8(v); - } - schar get0() const - { - return vgetq_lane_s8(val, 0); - } - - int8x16_t val; -}; - -struct v_uint16x8 -{ - typedef ushort lane_type; - enum { nlanes = 8 }; - - v_uint16x8() {} - explicit v_uint16x8(uint16x8_t v) : val(v) {} - v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) - { - ushort v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; - val = vld1q_u16(v); - } - ushort get0() const - { - return vgetq_lane_u16(val, 0); - } - - uint16x8_t val; -}; - -struct v_int16x8 -{ - typedef short lane_type; - enum { nlanes = 8 }; - - v_int16x8() {} - explicit v_int16x8(int16x8_t v) : val(v) {} - v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) - { - short v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; - val = vld1q_s16(v); - } - short get0() const - { - return vgetq_lane_s16(val, 0); - } - - int16x8_t val; -}; - -struct v_uint32x4 -{ - typedef unsigned lane_type; - enum { nlanes = 4 }; - - v_uint32x4() {} - explicit v_uint32x4(uint32x4_t v) : val(v) {} - v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) - { - unsigned v[] = {v0, v1, v2, v3}; - val = vld1q_u32(v); - } - unsigned get0() const - { - return vgetq_lane_u32(val, 0); - } - - uint32x4_t val; -}; - -struct v_int32x4 -{ - typedef int lane_type; - enum { nlanes = 4 }; - - v_int32x4() {} - explicit v_int32x4(int32x4_t v) : val(v) {} - v_int32x4(int v0, int v1, int v2, int v3) - { - int v[] = {v0, v1, v2, v3}; - val = vld1q_s32(v); - } - int get0() const - { - return vgetq_lane_s32(val, 0); - } - int32x4_t val; -}; - -struct v_float32x4 -{ - typedef float lane_type; - enum { nlanes = 4 }; - - v_float32x4() {} - explicit v_float32x4(float32x4_t v) : val(v) {} - v_float32x4(float v0, float v1, float v2, float v3) - { - float v[] = {v0, v1, v2, v3}; - val = vld1q_f32(v); - } - float get0() const - { - return vgetq_lane_f32(val, 0); - } - float32x4_t val; -}; - -struct v_uint64x2 -{ - typedef uint64 lane_type; - enum { nlanes = 2 }; - - v_uint64x2() {} - explicit v_uint64x2(uint64x2_t v) : val(v) {} - v_uint64x2(uint64 v0, uint64 v1) - { - uint64 v[] = {v0, v1}; - val = vld1q_u64(v); - } - uint64 get0() const - { - return vgetq_lane_u64(val, 0); - } - uint64x2_t val; -}; - -struct v_int64x2 -{ - typedef int64 lane_type; - enum { nlanes = 2 }; - - v_int64x2() {} - explicit v_int64x2(int64x2_t v) : val(v) {} - v_int64x2(int64 v0, int64 v1) - { - int64 v[] = {v0, v1}; - val = vld1q_s64(v); - } - int64 get0() const - { - return vgetq_lane_s64(val, 0); - } - int64x2_t val; -}; - -#if CV_SIMD128_64F -struct v_float64x2 -{ - typedef double lane_type; - enum { nlanes = 2 }; - - v_float64x2() {} - explicit v_float64x2(float64x2_t v) : val(v) {} - v_float64x2(double v0, double v1) - { - double v[] = {v0, v1}; - val = vld1q_f64(v); - } - double get0() const - { - return vgetq_lane_f64(val, 0); - } - float64x2_t val; -}; -#endif - -#define OPENCV_HAL_IMPL_NEON_INIT(_Tpv, _Tp, suffix) \ -inline v_##_Tpv v_setzero_##suffix() { return v_##_Tpv(vdupq_n_##suffix((_Tp)0)); } \ -inline v_##_Tpv v_setall_##suffix(_Tp v) { return v_##_Tpv(vdupq_n_##suffix(v)); } \ -inline _Tpv##_t vreinterpretq_##suffix##_##suffix(_Tpv##_t v) { return v; } \ -inline v_uint8x16 v_reinterpret_as_u8(const v_##_Tpv& v) { return v_uint8x16(vreinterpretq_u8_##suffix(v.val)); } \ -inline v_int8x16 v_reinterpret_as_s8(const v_##_Tpv& v) { return v_int8x16(vreinterpretq_s8_##suffix(v.val)); } \ -inline v_uint16x8 v_reinterpret_as_u16(const v_##_Tpv& v) { return v_uint16x8(vreinterpretq_u16_##suffix(v.val)); } \ -inline v_int16x8 v_reinterpret_as_s16(const v_##_Tpv& v) { return v_int16x8(vreinterpretq_s16_##suffix(v.val)); } \ -inline v_uint32x4 v_reinterpret_as_u32(const v_##_Tpv& v) { return v_uint32x4(vreinterpretq_u32_##suffix(v.val)); } \ -inline v_int32x4 v_reinterpret_as_s32(const v_##_Tpv& v) { return v_int32x4(vreinterpretq_s32_##suffix(v.val)); } \ -inline v_uint64x2 v_reinterpret_as_u64(const v_##_Tpv& v) { return v_uint64x2(vreinterpretq_u64_##suffix(v.val)); } \ -inline v_int64x2 v_reinterpret_as_s64(const v_##_Tpv& v) { return v_int64x2(vreinterpretq_s64_##suffix(v.val)); } \ -inline v_float32x4 v_reinterpret_as_f32(const v_##_Tpv& v) { return v_float32x4(vreinterpretq_f32_##suffix(v.val)); } - -OPENCV_HAL_IMPL_NEON_INIT(uint8x16, uchar, u8) -OPENCV_HAL_IMPL_NEON_INIT(int8x16, schar, s8) -OPENCV_HAL_IMPL_NEON_INIT(uint16x8, ushort, u16) -OPENCV_HAL_IMPL_NEON_INIT(int16x8, short, s16) -OPENCV_HAL_IMPL_NEON_INIT(uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_NEON_INIT(int32x4, int, s32) -OPENCV_HAL_IMPL_NEON_INIT(uint64x2, uint64, u64) -OPENCV_HAL_IMPL_NEON_INIT(int64x2, int64, s64) -OPENCV_HAL_IMPL_NEON_INIT(float32x4, float, f32) -#if CV_SIMD128_64F -#define OPENCV_HAL_IMPL_NEON_INIT_64(_Tpv, suffix) \ -inline v_float64x2 v_reinterpret_as_f64(const v_##_Tpv& v) { return v_float64x2(vreinterpretq_f64_##suffix(v.val)); } -OPENCV_HAL_IMPL_NEON_INIT(float64x2, double, f64) -OPENCV_HAL_IMPL_NEON_INIT_64(uint8x16, u8) -OPENCV_HAL_IMPL_NEON_INIT_64(int8x16, s8) -OPENCV_HAL_IMPL_NEON_INIT_64(uint16x8, u16) -OPENCV_HAL_IMPL_NEON_INIT_64(int16x8, s16) -OPENCV_HAL_IMPL_NEON_INIT_64(uint32x4, u32) -OPENCV_HAL_IMPL_NEON_INIT_64(int32x4, s32) -OPENCV_HAL_IMPL_NEON_INIT_64(uint64x2, u64) -OPENCV_HAL_IMPL_NEON_INIT_64(int64x2, s64) -OPENCV_HAL_IMPL_NEON_INIT_64(float32x4, f32) -OPENCV_HAL_IMPL_NEON_INIT_64(float64x2, f64) -#endif - -#define OPENCV_HAL_IMPL_NEON_PACK(_Tpvec, _Tp, hreg, suffix, _Tpwvec, pack, mov, rshr) \ -inline _Tpvec v_##pack(const _Tpwvec& a, const _Tpwvec& b) \ -{ \ - hreg a1 = mov(a.val), b1 = mov(b.val); \ - return _Tpvec(vcombine_##suffix(a1, b1)); \ -} \ -inline void v_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ -{ \ - hreg a1 = mov(a.val); \ - vst1_##suffix(ptr, a1); \ -} \ -template inline \ -_Tpvec v_rshr_##pack(const _Tpwvec& a, const _Tpwvec& b) \ -{ \ - hreg a1 = rshr(a.val, n); \ - hreg b1 = rshr(b.val, n); \ - return _Tpvec(vcombine_##suffix(a1, b1)); \ -} \ -template inline \ -void v_rshr_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ -{ \ - hreg a1 = rshr(a.val, n); \ - vst1_##suffix(ptr, a1); \ -} - -OPENCV_HAL_IMPL_NEON_PACK(v_uint8x16, uchar, uint8x8_t, u8, v_uint16x8, pack, vqmovn_u16, vqrshrn_n_u16) -OPENCV_HAL_IMPL_NEON_PACK(v_int8x16, schar, int8x8_t, s8, v_int16x8, pack, vqmovn_s16, vqrshrn_n_s16) -OPENCV_HAL_IMPL_NEON_PACK(v_uint16x8, ushort, uint16x4_t, u16, v_uint32x4, pack, vqmovn_u32, vqrshrn_n_u32) -OPENCV_HAL_IMPL_NEON_PACK(v_int16x8, short, int16x4_t, s16, v_int32x4, pack, vqmovn_s32, vqrshrn_n_s32) -OPENCV_HAL_IMPL_NEON_PACK(v_uint32x4, unsigned, uint32x2_t, u32, v_uint64x2, pack, vmovn_u64, vrshrn_n_u64) -OPENCV_HAL_IMPL_NEON_PACK(v_int32x4, int, int32x2_t, s32, v_int64x2, pack, vmovn_s64, vrshrn_n_s64) - -OPENCV_HAL_IMPL_NEON_PACK(v_uint8x16, uchar, uint8x8_t, u8, v_int16x8, pack_u, vqmovun_s16, vqrshrun_n_s16) -OPENCV_HAL_IMPL_NEON_PACK(v_uint16x8, ushort, uint16x4_t, u16, v_int32x4, pack_u, vqmovun_s32, vqrshrun_n_s32) - -// pack boolean -inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) -{ - uint8x16_t ab = vcombine_u8(vmovn_u16(a.val), vmovn_u16(b.val)); - return v_uint8x16(ab); -} - -inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, - const v_uint32x4& c, const v_uint32x4& d) -{ - uint16x8_t nab = vcombine_u16(vmovn_u32(a.val), vmovn_u32(b.val)); - uint16x8_t ncd = vcombine_u16(vmovn_u32(c.val), vmovn_u32(d.val)); - return v_uint8x16(vcombine_u8(vmovn_u16(nab), vmovn_u16(ncd))); -} - -inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, - const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, - const v_uint64x2& g, const v_uint64x2& h) -{ - uint32x4_t ab = vcombine_u32(vmovn_u64(a.val), vmovn_u64(b.val)); - uint32x4_t cd = vcombine_u32(vmovn_u64(c.val), vmovn_u64(d.val)); - uint32x4_t ef = vcombine_u32(vmovn_u64(e.val), vmovn_u64(f.val)); - uint32x4_t gh = vcombine_u32(vmovn_u64(g.val), vmovn_u64(h.val)); - - uint16x8_t abcd = vcombine_u16(vmovn_u32(ab), vmovn_u32(cd)); - uint16x8_t efgh = vcombine_u16(vmovn_u32(ef), vmovn_u32(gh)); - return v_uint8x16(vcombine_u8(vmovn_u16(abcd), vmovn_u16(efgh))); -} - -inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, - const v_float32x4& m1, const v_float32x4& m2, - const v_float32x4& m3) -{ - float32x2_t vl = vget_low_f32(v.val), vh = vget_high_f32(v.val); - float32x4_t res = vmulq_lane_f32(m0.val, vl, 0); - res = vmlaq_lane_f32(res, m1.val, vl, 1); - res = vmlaq_lane_f32(res, m2.val, vh, 0); - res = vmlaq_lane_f32(res, m3.val, vh, 1); - return v_float32x4(res); -} - -inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, - const v_float32x4& m1, const v_float32x4& m2, - const v_float32x4& a) -{ - float32x2_t vl = vget_low_f32(v.val), vh = vget_high_f32(v.val); - float32x4_t res = vmulq_lane_f32(m0.val, vl, 0); - res = vmlaq_lane_f32(res, m1.val, vl, 1); - res = vmlaq_lane_f32(res, m2.val, vh, 0); - res = vaddq_f32(res, a.val); - return v_float32x4(res); -} - -#define OPENCV_HAL_IMPL_NEON_BIN_OP(bin_op, _Tpvec, intrin) \ -inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(intrin(a.val, b.val)); \ -} \ -inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ -{ \ - a.val = intrin(a.val, b.val); \ - return a; \ -} - -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint8x16, vqaddq_u8) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint8x16, vqsubq_u8) -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int8x16, vqaddq_s8) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int8x16, vqsubq_s8) -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint16x8, vqaddq_u16) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint16x8, vqsubq_u16) -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int16x8, vqaddq_s16) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int16x8, vqsubq_s16) -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int32x4, vaddq_s32) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int32x4, vsubq_s32) -OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_int32x4, vmulq_s32) -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint32x4, vaddq_u32) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint32x4, vsubq_u32) -OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_uint32x4, vmulq_u32) -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_float32x4, vaddq_f32) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_float32x4, vsubq_f32) -OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_float32x4, vmulq_f32) -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int64x2, vaddq_s64) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int64x2, vsubq_s64) -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint64x2, vaddq_u64) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint64x2, vsubq_u64) -#if CV_SIMD128_64F -OPENCV_HAL_IMPL_NEON_BIN_OP(/, v_float32x4, vdivq_f32) -OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_float64x2, vaddq_f64) -OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_float64x2, vsubq_f64) -OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_float64x2, vmulq_f64) -OPENCV_HAL_IMPL_NEON_BIN_OP(/, v_float64x2, vdivq_f64) -#else -inline v_float32x4 operator / (const v_float32x4& a, const v_float32x4& b) -{ - float32x4_t reciprocal = vrecpeq_f32(b.val); - reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); - reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); - return v_float32x4(vmulq_f32(a.val, reciprocal)); -} -inline v_float32x4& operator /= (v_float32x4& a, const v_float32x4& b) -{ - float32x4_t reciprocal = vrecpeq_f32(b.val); - reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); - reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal); - a.val = vmulq_f32(a.val, reciprocal); - return a; -} -#endif - -// saturating multiply 8-bit, 16-bit -#define OPENCV_HAL_IMPL_NEON_MUL_SAT(_Tpvec, _Tpwvec) \ - inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b) \ - { \ - _Tpwvec c, d; \ - v_mul_expand(a, b, c, d); \ - return v_pack(c, d); \ - } \ - inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b) \ - { a = a * b; return a; } - -OPENCV_HAL_IMPL_NEON_MUL_SAT(v_int8x16, v_int16x8) -OPENCV_HAL_IMPL_NEON_MUL_SAT(v_uint8x16, v_uint16x8) -OPENCV_HAL_IMPL_NEON_MUL_SAT(v_int16x8, v_int32x4) -OPENCV_HAL_IMPL_NEON_MUL_SAT(v_uint16x8, v_uint32x4) - -// Multiply and expand -inline void v_mul_expand(const v_int8x16& a, const v_int8x16& b, - v_int16x8& c, v_int16x8& d) -{ - c.val = vmull_s8(vget_low_s8(a.val), vget_low_s8(b.val)); - d.val = vmull_s8(vget_high_s8(a.val), vget_high_s8(b.val)); -} - -inline void v_mul_expand(const v_uint8x16& a, const v_uint8x16& b, - v_uint16x8& c, v_uint16x8& d) -{ - c.val = vmull_u8(vget_low_u8(a.val), vget_low_u8(b.val)); - d.val = vmull_u8(vget_high_u8(a.val), vget_high_u8(b.val)); -} - -inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b, - v_int32x4& c, v_int32x4& d) -{ - c.val = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val)); - d.val = vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)); -} - -inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b, - v_uint32x4& c, v_uint32x4& d) -{ - c.val = vmull_u16(vget_low_u16(a.val), vget_low_u16(b.val)); - d.val = vmull_u16(vget_high_u16(a.val), vget_high_u16(b.val)); -} - -inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, - v_uint64x2& c, v_uint64x2& d) -{ - c.val = vmull_u32(vget_low_u32(a.val), vget_low_u32(b.val)); - d.val = vmull_u32(vget_high_u32(a.val), vget_high_u32(b.val)); -} - -inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b) -{ - return v_int16x8(vcombine_s16( - vshrn_n_s32(vmull_s16( vget_low_s16(a.val), vget_low_s16(b.val)), 16), - vshrn_n_s32(vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)), 16) - )); -} -inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b) -{ - return v_uint16x8(vcombine_u16( - vshrn_n_u32(vmull_u16( vget_low_u16(a.val), vget_low_u16(b.val)), 16), - vshrn_n_u32(vmull_u16(vget_high_u16(a.val), vget_high_u16(b.val)), 16) - )); -} - -//////// Dot Product //////// - -// 16 >> 32 -inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) -{ - int16x8_t uzp1, uzp2; - _v128_unzip(a.val, b.val, uzp1, uzp2); - int16x4_t a0 = vget_low_s16(uzp1); - int16x4_t b0 = vget_high_s16(uzp1); - int16x4_t a1 = vget_low_s16(uzp2); - int16x4_t b1 = vget_high_s16(uzp2); - int32x4_t p = vmull_s16(a0, b0); - return v_int32x4(vmlal_s16(p, a1, b1)); -} -inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) -{ - int16x8_t uzp1, uzp2; - _v128_unzip(a.val, b.val, uzp1, uzp2); - int16x4_t a0 = vget_low_s16(uzp1); - int16x4_t b0 = vget_high_s16(uzp1); - int16x4_t a1 = vget_low_s16(uzp2); - int16x4_t b1 = vget_high_s16(uzp2); - int32x4_t p = vmlal_s16(c.val, a0, b0); - return v_int32x4(vmlal_s16(p, a1, b1)); -} - -// 32 >> 64 -inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b) -{ - int32x4_t uzp1, uzp2; - _v128_unzip(a.val, b.val, uzp1, uzp2); - int32x2_t a0 = vget_low_s32(uzp1); - int32x2_t b0 = vget_high_s32(uzp1); - int32x2_t a1 = vget_low_s32(uzp2); - int32x2_t b1 = vget_high_s32(uzp2); - int64x2_t p = vmull_s32(a0, b0); - return v_int64x2(vmlal_s32(p, a1, b1)); -} -inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c) -{ - int32x4_t uzp1, uzp2; - _v128_unzip(a.val, b.val, uzp1, uzp2); - int32x2_t a0 = vget_low_s32(uzp1); - int32x2_t b0 = vget_high_s32(uzp1); - int32x2_t a1 = vget_low_s32(uzp2); - int32x2_t b1 = vget_high_s32(uzp2); - int64x2_t p = vmlal_s32(c.val, a0, b0); - return v_int64x2(vmlal_s32(p, a1, b1)); -} - -// 8 >> 32 -inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b) -{ -#if CV_NEON_DOT - return v_uint32x4(vdotq_u32(vdupq_n_u32(0), a.val, b.val)); -#else - const uint8x16_t zero = vreinterpretq_u8_u32(vdupq_n_u32(0)); - const uint8x16_t mask = vreinterpretq_u8_u32(vdupq_n_u32(0x00FF00FF)); - const uint16x8_t zero32 = vreinterpretq_u16_u32(vdupq_n_u32(0)); - const uint16x8_t mask32 = vreinterpretq_u16_u32(vdupq_n_u32(0x0000FFFF)); - - uint16x8_t even = vmulq_u16(vreinterpretq_u16_u8(vbslq_u8(mask, a.val, zero)), - vreinterpretq_u16_u8(vbslq_u8(mask, b.val, zero))); - uint16x8_t odd = vmulq_u16(vshrq_n_u16(vreinterpretq_u16_u8(a.val), 8), - vshrq_n_u16(vreinterpretq_u16_u8(b.val), 8)); - - uint32x4_t s0 = vaddq_u32(vreinterpretq_u32_u16(vbslq_u16(mask32, even, zero32)), - vreinterpretq_u32_u16(vbslq_u16(mask32, odd, zero32))); - uint32x4_t s1 = vaddq_u32(vshrq_n_u32(vreinterpretq_u32_u16(even), 16), - vshrq_n_u32(vreinterpretq_u32_u16(odd), 16)); - return v_uint32x4(vaddq_u32(s0, s1)); -#endif -} -inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b, - const v_uint32x4& c) -{ -#if CV_NEON_DOT - return v_uint32x4(vdotq_u32(c.val, a.val, b.val)); -#else - return v_dotprod_expand(a, b) + c; -#endif -} - -inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b) -{ -#if CV_NEON_DOT - return v_int32x4(vdotq_s32(vdupq_n_s32(0), a.val, b.val)); -#else - int16x8_t p0 = vmull_s8(vget_low_s8(a.val), vget_low_s8(b.val)); - int16x8_t p1 = vmull_s8(vget_high_s8(a.val), vget_high_s8(b.val)); - int16x8_t uzp1, uzp2; - _v128_unzip(p0, p1, uzp1, uzp2); - int16x8_t sum = vaddq_s16(uzp1, uzp2); - int16x4_t uzpl1, uzpl2; - _v128_unzip(vget_low_s16(sum), vget_high_s16(sum), uzpl1, uzpl2); - return v_int32x4(vaddl_s16(uzpl1, uzpl2)); -#endif -} -inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b, - const v_int32x4& c) -{ -#if CV_NEON_DOT - return v_int32x4(vdotq_s32(c.val, a.val, b.val)); -#else - return v_dotprod_expand(a, b) + c; -#endif -} - -// 16 >> 64 -inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b) -{ - const uint16x8_t zero = vreinterpretq_u16_u32(vdupq_n_u32(0)); - const uint16x8_t mask = vreinterpretq_u16_u32(vdupq_n_u32(0x0000FFFF)); - - uint32x4_t even = vmulq_u32(vreinterpretq_u32_u16(vbslq_u16(mask, a.val, zero)), - vreinterpretq_u32_u16(vbslq_u16(mask, b.val, zero))); - uint32x4_t odd = vmulq_u32(vshrq_n_u32(vreinterpretq_u32_u16(a.val), 16), - vshrq_n_u32(vreinterpretq_u32_u16(b.val), 16)); - uint32x4_t uzp1, uzp2; - _v128_unzip(even, odd, uzp1, uzp2); - uint64x2_t s0 = vaddl_u32(vget_low_u32(uzp1), vget_high_u32(uzp1)); - uint64x2_t s1 = vaddl_u32(vget_low_u32(uzp2), vget_high_u32(uzp2)); - return v_uint64x2(vaddq_u64(s0, s1)); -} -inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c) -{ return v_dotprod_expand(a, b) + c; } - -inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b) -{ - int32x4_t p0 = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val)); - int32x4_t p1 = vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val)); - - int32x4_t uzp1, uzp2; - _v128_unzip(p0, p1, uzp1, uzp2); - int32x4_t sum = vaddq_s32(uzp1, uzp2); - - int32x2_t uzpl1, uzpl2; - _v128_unzip(vget_low_s32(sum), vget_high_s32(sum), uzpl1, uzpl2); - return v_int64x2(vaddl_s32(uzpl1, uzpl2)); -} -inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b, - const v_int64x2& c) -{ return v_dotprod_expand(a, b) + c; } - -// 32 >> 64f -#if CV_SIMD128_64F -inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b) -{ return v_cvt_f64(v_dotprod(a, b)); } -inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b, - const v_float64x2& c) -{ return v_dotprod_expand(a, b) + c; } -#endif - -//////// Fast Dot Product //////// - -// 16 >> 32 -inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b) -{ - int16x4_t a0 = vget_low_s16(a.val); - int16x4_t a1 = vget_high_s16(a.val); - int16x4_t b0 = vget_low_s16(b.val); - int16x4_t b1 = vget_high_s16(b.val); - int32x4_t p = vmull_s16(a0, b0); - return v_int32x4(vmlal_s16(p, a1, b1)); -} -inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) -{ - int16x4_t a0 = vget_low_s16(a.val); - int16x4_t a1 = vget_high_s16(a.val); - int16x4_t b0 = vget_low_s16(b.val); - int16x4_t b1 = vget_high_s16(b.val); - int32x4_t p = vmlal_s16(c.val, a0, b0); - return v_int32x4(vmlal_s16(p, a1, b1)); -} - -// 32 >> 64 -inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b) -{ - int32x2_t a0 = vget_low_s32(a.val); - int32x2_t a1 = vget_high_s32(a.val); - int32x2_t b0 = vget_low_s32(b.val); - int32x2_t b1 = vget_high_s32(b.val); - int64x2_t p = vmull_s32(a0, b0); - return v_int64x2(vmlal_s32(p, a1, b1)); -} -inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c) -{ - int32x2_t a0 = vget_low_s32(a.val); - int32x2_t a1 = vget_high_s32(a.val); - int32x2_t b0 = vget_low_s32(b.val); - int32x2_t b1 = vget_high_s32(b.val); - int64x2_t p = vmlal_s32(c.val, a0, b0); - return v_int64x2(vmlal_s32(p, a1, b1)); -} - -// 8 >> 32 -inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b) -{ -#if CV_NEON_DOT - return v_uint32x4(vdotq_u32(vdupq_n_u32(0), a.val, b.val)); -#else - uint16x8_t p0 = vmull_u8(vget_low_u8(a.val), vget_low_u8(b.val)); - uint16x8_t p1 = vmull_u8(vget_high_u8(a.val), vget_high_u8(b.val)); - uint32x4_t s0 = vaddl_u16(vget_low_u16(p0), vget_low_u16(p1)); - uint32x4_t s1 = vaddl_u16(vget_high_u16(p0), vget_high_u16(p1)); - return v_uint32x4(vaddq_u32(s0, s1)); -#endif -} -inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c) -{ -#if CV_NEON_DOT - return v_uint32x4(vdotq_u32(c.val, a.val, b.val)); -#else - return v_dotprod_expand_fast(a, b) + c; -#endif -} - -inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b) -{ -#if CV_NEON_DOT - return v_int32x4(vdotq_s32(vdupq_n_s32(0), a.val, b.val)); -#else - int16x8_t prod = vmull_s8(vget_low_s8(a.val), vget_low_s8(b.val)); - prod = vmlal_s8(prod, vget_high_s8(a.val), vget_high_s8(b.val)); - return v_int32x4(vaddl_s16(vget_low_s16(prod), vget_high_s16(prod))); -#endif -} -inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c) -{ -#if CV_NEON_DOT - return v_int32x4(vdotq_s32(c.val, a.val, b.val)); -#else - return v_dotprod_expand_fast(a, b) + c; -#endif -} - -// 16 >> 64 -inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b) -{ - uint32x4_t p0 = vmull_u16(vget_low_u16(a.val), vget_low_u16(b.val)); - uint32x4_t p1 = vmull_u16(vget_high_u16(a.val), vget_high_u16(b.val)); - uint64x2_t s0 = vaddl_u32(vget_low_u32(p0), vget_high_u32(p0)); - uint64x2_t s1 = vaddl_u32(vget_low_u32(p1), vget_high_u32(p1)); - return v_uint64x2(vaddq_u64(s0, s1)); -} -inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c) -{ return v_dotprod_expand_fast(a, b) + c; } - -inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b) -{ - int32x4_t prod = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val)); - prod = vmlal_s16(prod, vget_high_s16(a.val), vget_high_s16(b.val)); - return v_int64x2(vaddl_s32(vget_low_s32(prod), vget_high_s32(prod))); -} -inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c) -{ return v_dotprod_expand_fast(a, b) + c; } - -// 32 >> 64f -#if CV_SIMD128_64F -inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b) -{ return v_cvt_f64(v_dotprod_fast(a, b)); } -inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c) -{ return v_dotprod_expand_fast(a, b) + c; } -#endif - - -#define OPENCV_HAL_IMPL_NEON_LOGIC_OP(_Tpvec, suffix) \ - OPENCV_HAL_IMPL_NEON_BIN_OP(&, _Tpvec, vandq_##suffix) \ - OPENCV_HAL_IMPL_NEON_BIN_OP(|, _Tpvec, vorrq_##suffix) \ - OPENCV_HAL_IMPL_NEON_BIN_OP(^, _Tpvec, veorq_##suffix) \ - inline _Tpvec operator ~ (const _Tpvec& a) \ - { \ - return _Tpvec(vreinterpretq_##suffix##_u8(vmvnq_u8(vreinterpretq_u8_##suffix(a.val)))); \ - } - -OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint8x16, u8) -OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int8x16, s8) -OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint16x8, u16) -OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int16x8, s16) -OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint32x4, u32) -OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int32x4, s32) -OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint64x2, u64) -OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int64x2, s64) - -#define OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(bin_op, intrin) \ -inline v_float32x4 operator bin_op (const v_float32x4& a, const v_float32x4& b) \ -{ \ - return v_float32x4(vreinterpretq_f32_s32(intrin(vreinterpretq_s32_f32(a.val), vreinterpretq_s32_f32(b.val)))); \ -} \ -inline v_float32x4& operator bin_op##= (v_float32x4& a, const v_float32x4& b) \ -{ \ - a.val = vreinterpretq_f32_s32(intrin(vreinterpretq_s32_f32(a.val), vreinterpretq_s32_f32(b.val))); \ - return a; \ -} - -OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(&, vandq_s32) -OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(|, vorrq_s32) -OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(^, veorq_s32) - -inline v_float32x4 operator ~ (const v_float32x4& a) -{ - return v_float32x4(vreinterpretq_f32_s32(vmvnq_s32(vreinterpretq_s32_f32(a.val)))); -} - -#if CV_SIMD128_64F -inline v_float32x4 v_sqrt(const v_float32x4& x) -{ - return v_float32x4(vsqrtq_f32(x.val)); -} - -inline v_float32x4 v_invsqrt(const v_float32x4& x) -{ - v_float32x4 one = v_setall_f32(1.0f); - return one / v_sqrt(x); -} -#else -inline v_float32x4 v_sqrt(const v_float32x4& x) -{ - float32x4_t x1 = vmaxq_f32(x.val, vdupq_n_f32(FLT_MIN)); - float32x4_t e = vrsqrteq_f32(x1); - e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x1, e), e), e); - e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x1, e), e), e); - return v_float32x4(vmulq_f32(x.val, e)); -} - -inline v_float32x4 v_invsqrt(const v_float32x4& x) -{ - float32x4_t e = vrsqrteq_f32(x.val); - e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x.val, e), e), e); - e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x.val, e), e), e); - return v_float32x4(e); -} -#endif - -#define OPENCV_HAL_IMPL_NEON_ABS(_Tpuvec, _Tpsvec, usuffix, ssuffix) \ -inline _Tpuvec v_abs(const _Tpsvec& a) { return v_reinterpret_as_##usuffix(_Tpsvec(vabsq_##ssuffix(a.val))); } - -OPENCV_HAL_IMPL_NEON_ABS(v_uint8x16, v_int8x16, u8, s8) -OPENCV_HAL_IMPL_NEON_ABS(v_uint16x8, v_int16x8, u16, s16) -OPENCV_HAL_IMPL_NEON_ABS(v_uint32x4, v_int32x4, u32, s32) - -inline v_float32x4 v_abs(v_float32x4 x) -{ return v_float32x4(vabsq_f32(x.val)); } - -#if CV_SIMD128_64F -#define OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(bin_op, intrin) \ -inline v_float64x2 operator bin_op (const v_float64x2& a, const v_float64x2& b) \ -{ \ - return v_float64x2(vreinterpretq_f64_s64(intrin(vreinterpretq_s64_f64(a.val), vreinterpretq_s64_f64(b.val)))); \ -} \ -inline v_float64x2& operator bin_op##= (v_float64x2& a, const v_float64x2& b) \ -{ \ - a.val = vreinterpretq_f64_s64(intrin(vreinterpretq_s64_f64(a.val), vreinterpretq_s64_f64(b.val))); \ - return a; \ -} - -OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(&, vandq_s64) -OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(|, vorrq_s64) -OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(^, veorq_s64) - -inline v_float64x2 operator ~ (const v_float64x2& a) -{ - return v_float64x2(vreinterpretq_f64_s32(vmvnq_s32(vreinterpretq_s32_f64(a.val)))); -} - -inline v_float64x2 v_sqrt(const v_float64x2& x) -{ - return v_float64x2(vsqrtq_f64(x.val)); -} - -inline v_float64x2 v_invsqrt(const v_float64x2& x) -{ - v_float64x2 one = v_setall_f64(1.0f); - return one / v_sqrt(x); -} - -inline v_float64x2 v_abs(v_float64x2 x) -{ return v_float64x2(vabsq_f64(x.val)); } -#endif - -// TODO: exp, log, sin, cos - -#define OPENCV_HAL_IMPL_NEON_BIN_FUNC(_Tpvec, func, intrin) \ -inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(intrin(a.val, b.val)); \ -} - -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_min, vminq_u8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_max, vmaxq_u8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_min, vminq_s8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_max, vmaxq_s8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_min, vminq_u16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_max, vmaxq_u16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_min, vminq_s16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_max, vmaxq_s16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_min, vminq_u32) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_max, vmaxq_u32) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int32x4, v_min, vminq_s32) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int32x4, v_max, vmaxq_s32) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_min, vminq_f32) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_max, vmaxq_f32) -#if CV_SIMD128_64F -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_min, vminq_f64) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_max, vmaxq_f64) -#endif - -#if CV_SIMD128_64F -inline int64x2_t vmvnq_s64(int64x2_t a) -{ - int64x2_t vx = vreinterpretq_s64_u32(vdupq_n_u32(0xFFFFFFFF)); - return veorq_s64(a, vx); -} -inline uint64x2_t vmvnq_u64(uint64x2_t a) -{ - uint64x2_t vx = vreinterpretq_u64_u32(vdupq_n_u32(0xFFFFFFFF)); - return veorq_u64(a, vx); -} -#endif -#define OPENCV_HAL_IMPL_NEON_INT_CMP_OP(_Tpvec, cast, suffix, not_suffix) \ -inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(cast(vceqq_##suffix(a.val, b.val))); } \ -inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(cast(vmvnq_##not_suffix(vceqq_##suffix(a.val, b.val)))); } \ -inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(cast(vcltq_##suffix(a.val, b.val))); } \ -inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(cast(vcgtq_##suffix(a.val, b.val))); } \ -inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(cast(vcleq_##suffix(a.val, b.val))); } \ -inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(cast(vcgeq_##suffix(a.val, b.val))); } - -OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint8x16, OPENCV_HAL_NOP, u8, u8) -OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int8x16, vreinterpretq_s8_u8, s8, u8) -OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint16x8, OPENCV_HAL_NOP, u16, u16) -OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int16x8, vreinterpretq_s16_u16, s16, u16) -OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint32x4, OPENCV_HAL_NOP, u32, u32) -OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int32x4, vreinterpretq_s32_u32, s32, u32) -OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_float32x4, vreinterpretq_f32_u32, f32, u32) -#if CV_SIMD128_64F -OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint64x2, OPENCV_HAL_NOP, u64, u64) -OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int64x2, vreinterpretq_s64_u64, s64, u64) -OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_float64x2, vreinterpretq_f64_u64, f64, u64) -#endif - -inline v_float32x4 v_not_nan(const v_float32x4& a) -{ return v_float32x4(vreinterpretq_f32_u32(vceqq_f32(a.val, a.val))); } -#if CV_SIMD128_64F -inline v_float64x2 v_not_nan(const v_float64x2& a) -{ return v_float64x2(vreinterpretq_f64_u64(vceqq_f64(a.val, a.val))); } -#endif - -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_add_wrap, vaddq_u8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_add_wrap, vaddq_s8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_add_wrap, vaddq_u16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_add_wrap, vaddq_s16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_sub_wrap, vsubq_u8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_sub_wrap, vsubq_s8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_sub_wrap, vsubq_u16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_sub_wrap, vsubq_s16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_mul_wrap, vmulq_u8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_mul_wrap, vmulq_s8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_mul_wrap, vmulq_u16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_mul_wrap, vmulq_s16) - -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_absdiff, vabdq_u8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_absdiff, vabdq_u16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_absdiff, vabdq_u32) -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_absdiff, vabdq_f32) -#if CV_SIMD128_64F -OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_absdiff, vabdq_f64) -#endif - -/** Saturating absolute difference **/ -inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b) -{ return v_int8x16(vqabsq_s8(vqsubq_s8(a.val, b.val))); } -inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b) -{ return v_int16x8(vqabsq_s16(vqsubq_s16(a.val, b.val))); } - -#define OPENCV_HAL_IMPL_NEON_BIN_FUNC2(_Tpvec, _Tpvec2, cast, func, intrin) \ -inline _Tpvec2 func(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec2(cast(intrin(a.val, b.val))); \ -} - -OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int8x16, v_uint8x16, vreinterpretq_u8_s8, v_absdiff, vabdq_s8) -OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int16x8, v_uint16x8, vreinterpretq_u16_s16, v_absdiff, vabdq_s16) -OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int32x4, v_uint32x4, vreinterpretq_u32_s32, v_absdiff, vabdq_s32) - -inline v_float32x4 v_magnitude(const v_float32x4& a, const v_float32x4& b) -{ - v_float32x4 x(vmlaq_f32(vmulq_f32(a.val, a.val), b.val, b.val)); - return v_sqrt(x); -} - -inline v_float32x4 v_sqr_magnitude(const v_float32x4& a, const v_float32x4& b) -{ - return v_float32x4(vmlaq_f32(vmulq_f32(a.val, a.val), b.val, b.val)); -} - -inline v_float32x4 v_fma(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c) -{ -#if CV_SIMD128_64F - // ARMv8, which adds support for 64-bit floating-point (so CV_SIMD128_64F is defined), - // also adds FMA support both for single- and double-precision floating-point vectors - return v_float32x4(vfmaq_f32(c.val, a.val, b.val)); -#else - return v_float32x4(vmlaq_f32(c.val, a.val, b.val)); -#endif -} - -inline v_int32x4 v_fma(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) -{ - return v_int32x4(vmlaq_s32(c.val, a.val, b.val)); -} - -inline v_float32x4 v_muladd(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c) -{ - return v_fma(a, b, c); -} - -inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) -{ - return v_fma(a, b, c); -} - -#if CV_SIMD128_64F -inline v_float64x2 v_magnitude(const v_float64x2& a, const v_float64x2& b) -{ - v_float64x2 x(vaddq_f64(vmulq_f64(a.val, a.val), vmulq_f64(b.val, b.val))); - return v_sqrt(x); -} - -inline v_float64x2 v_sqr_magnitude(const v_float64x2& a, const v_float64x2& b) -{ - return v_float64x2(vaddq_f64(vmulq_f64(a.val, a.val), vmulq_f64(b.val, b.val))); -} - -inline v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c) -{ - return v_float64x2(vfmaq_f64(c.val, a.val, b.val)); -} - -inline v_float64x2 v_muladd(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c) -{ - return v_fma(a, b, c); -} -#endif - -// trade efficiency for convenience -#define OPENCV_HAL_IMPL_NEON_SHIFT_OP(_Tpvec, suffix, _Tps, ssuffix) \ -inline _Tpvec operator << (const _Tpvec& a, int n) \ -{ return _Tpvec(vshlq_##suffix(a.val, vdupq_n_##ssuffix((_Tps)n))); } \ -inline _Tpvec operator >> (const _Tpvec& a, int n) \ -{ return _Tpvec(vshlq_##suffix(a.val, vdupq_n_##ssuffix((_Tps)-n))); } \ -template inline _Tpvec v_shl(const _Tpvec& a) \ -{ return _Tpvec(vshlq_n_##suffix(a.val, n)); } \ -template inline _Tpvec v_shr(const _Tpvec& a) \ -{ return _Tpvec(vshrq_n_##suffix(a.val, n)); } \ -template inline _Tpvec v_rshr(const _Tpvec& a) \ -{ return _Tpvec(vrshrq_n_##suffix(a.val, n)); } - -OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint8x16, u8, schar, s8) -OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int8x16, s8, schar, s8) -OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint16x8, u16, short, s16) -OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int16x8, s16, short, s16) -OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint32x4, u32, int, s32) -OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int32x4, s32, int, s32) -OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint64x2, u64, int64, s64) -OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int64x2, s64, int64, s64) - -#define OPENCV_HAL_IMPL_NEON_ROTATE_OP(_Tpvec, suffix) \ -template inline _Tpvec v_rotate_right(const _Tpvec& a) \ -{ return _Tpvec(vextq_##suffix(a.val, vdupq_n_##suffix(0), n)); } \ -template inline _Tpvec v_rotate_left(const _Tpvec& a) \ -{ return _Tpvec(vextq_##suffix(vdupq_n_##suffix(0), a.val, _Tpvec::nlanes - n)); } \ -template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a) \ -{ return a; } \ -template inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(vextq_##suffix(a.val, b.val, n)); } \ -template inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(vextq_##suffix(b.val, a.val, _Tpvec::nlanes - n)); } \ -template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a, const _Tpvec& b) \ -{ CV_UNUSED(b); return a; } - -OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint8x16, u8) -OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int8x16, s8) -OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint16x8, u16) -OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int16x8, s16) -OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint32x4, u32) -OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int32x4, s32) -OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_float32x4, f32) -OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint64x2, u64) -OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int64x2, s64) -#if CV_SIMD128_64F -OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_float64x2, f64) -#endif - -#if defined(__clang__) && defined(__aarch64__) -// avoid LD2 instruction. details: https://github.com/opencv/opencv/issues/14863 -#define OPENCV_HAL_IMPL_NEON_LOAD_LOW_OP(_Tpvec, _Tp, suffix) \ -inline _Tpvec v_load_low(const _Tp* ptr) \ -{ \ -typedef uint64 CV_DECL_ALIGNED(1) unaligned_uint64; \ -uint64 v = *(unaligned_uint64*)ptr; \ -return _Tpvec(v_reinterpret_as_##suffix(v_uint64x2(v, (uint64)123456))); \ -} -#else -#define OPENCV_HAL_IMPL_NEON_LOAD_LOW_OP(_Tpvec, _Tp, suffix) \ -inline _Tpvec v_load_low(const _Tp* ptr) \ -{ return _Tpvec(vcombine_##suffix(vld1_##suffix(ptr), vdup_n_##suffix((_Tp)0))); } -#endif - -#define OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(_Tpvec, _Tp, suffix) \ -inline _Tpvec v_load(const _Tp* ptr) \ -{ return _Tpvec(vld1q_##suffix(ptr)); } \ -inline _Tpvec v_load_aligned(const _Tp* ptr) \ -{ return _Tpvec(vld1q_##suffix(ptr)); } \ -OPENCV_HAL_IMPL_NEON_LOAD_LOW_OP(_Tpvec, _Tp, suffix) \ -inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ -{ return _Tpvec(vcombine_##suffix(vld1_##suffix(ptr0), vld1_##suffix(ptr1))); } \ -inline void v_store(_Tp* ptr, const _Tpvec& a) \ -{ vst1q_##suffix(ptr, a.val); } \ -inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ -{ vst1q_##suffix(ptr, a.val); } \ -inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ -{ vst1q_##suffix(ptr, a.val); } \ -inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode /*mode*/) \ -{ vst1q_##suffix(ptr, a.val); } \ -inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ -{ vst1_##suffix(ptr, vget_low_##suffix(a.val)); } \ -inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ -{ vst1_##suffix(ptr, vget_high_##suffix(a.val)); } - -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint8x16, uchar, u8) -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int8x16, schar, s8) -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint16x8, ushort, u16) -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int16x8, short, s16) -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int32x4, int, s32) -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint64x2, uint64, u64) -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int64x2, int64, s64) -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_float32x4, float, f32) -#if CV_SIMD128_64F -OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_float64x2, double, f64) -#endif - -inline unsigned v_reduce_sum(const v_uint8x16& a) -{ - uint32x4_t t0 = vpaddlq_u16(vpaddlq_u8(a.val)); - uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); - return vget_lane_u32(vpadd_u32(t1, t1), 0); -} -inline int v_reduce_sum(const v_int8x16& a) -{ - int32x4_t t0 = vpaddlq_s16(vpaddlq_s8(a.val)); - int32x2_t t1 = vpadd_s32(vget_low_s32(t0), vget_high_s32(t0)); - return vget_lane_s32(vpadd_s32(t1, t1), 0); -} -inline unsigned v_reduce_sum(const v_uint16x8& a) -{ - uint32x4_t t0 = vpaddlq_u16(a.val); - uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); - return vget_lane_u32(vpadd_u32(t1, t1), 0); -} -inline int v_reduce_sum(const v_int16x8& a) -{ - int32x4_t t0 = vpaddlq_s16(a.val); - int32x2_t t1 = vpadd_s32(vget_low_s32(t0), vget_high_s32(t0)); - return vget_lane_s32(vpadd_s32(t1, t1), 0); -} - -#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_16(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \ -inline scalartype v_reduce_##func(const _Tpvec& a) \ -{ \ - _Tpnvec##_t a0 = vp##vectorfunc##_##suffix(vget_low_##suffix(a.val), vget_high_##suffix(a.val)); \ - a0 = vp##vectorfunc##_##suffix(a0, a0); \ - a0 = vp##vectorfunc##_##suffix(a0, a0); \ - return (scalartype)vget_lane_##suffix(vp##vectorfunc##_##suffix(a0, a0),0); \ -} - -OPENCV_HAL_IMPL_NEON_REDUCE_OP_16(v_uint8x16, uint8x8, uchar, max, max, u8) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_16(v_uint8x16, uint8x8, uchar, min, min, u8) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_16(v_int8x16, int8x8, schar, max, max, s8) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_16(v_int8x16, int8x8, schar, min, min, s8) - -#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \ -inline scalartype v_reduce_##func(const _Tpvec& a) \ -{ \ - _Tpnvec##_t a0 = vp##vectorfunc##_##suffix(vget_low_##suffix(a.val), vget_high_##suffix(a.val)); \ - a0 = vp##vectorfunc##_##suffix(a0, a0); \ - return (scalartype)vget_lane_##suffix(vp##vectorfunc##_##suffix(a0, a0),0); \ -} - -OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, ushort, max, max, u16) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, ushort, min, min, u16) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, max, max, s16) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, min, min, s16) - -#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \ -inline scalartype v_reduce_##func(const _Tpvec& a) \ -{ \ - _Tpnvec##_t a0 = vp##vectorfunc##_##suffix(vget_low_##suffix(a.val), vget_high_##suffix(a.val)); \ - return (scalartype)vget_lane_##suffix(vp##vectorfunc##_##suffix(a0, vget_high_##suffix(a.val)),0); \ -} - -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, sum, add, u32) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, max, max, u32) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, min, min, u32) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, sum, add, s32) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, max, max, s32) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, min, min, s32) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, sum, add, f32) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, max, max, f32) -OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, min, min, f32) - -inline uint64 v_reduce_sum(const v_uint64x2& a) -{ return vget_lane_u64(vadd_u64(vget_low_u64(a.val), vget_high_u64(a.val)),0); } -inline int64 v_reduce_sum(const v_int64x2& a) -{ return vget_lane_s64(vadd_s64(vget_low_s64(a.val), vget_high_s64(a.val)),0); } -#if CV_SIMD128_64F -inline double v_reduce_sum(const v_float64x2& a) -{ - return vgetq_lane_f64(a.val, 0) + vgetq_lane_f64(a.val, 1); -} -#endif - -inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, - const v_float32x4& c, const v_float32x4& d) -{ - float32x4x2_t ab = vtrnq_f32(a.val, b.val); - float32x4x2_t cd = vtrnq_f32(c.val, d.val); - - float32x4_t u0 = vaddq_f32(ab.val[0], ab.val[1]); // a0+a1 b0+b1 a2+a3 b2+b3 - float32x4_t u1 = vaddq_f32(cd.val[0], cd.val[1]); // c0+c1 d0+d1 c2+c3 d2+d3 - - float32x4_t v0 = vcombine_f32(vget_low_f32(u0), vget_low_f32(u1)); - float32x4_t v1 = vcombine_f32(vget_high_f32(u0), vget_high_f32(u1)); - - return v_float32x4(vaddq_f32(v0, v1)); -} - -inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b) -{ - uint32x4_t t0 = vpaddlq_u16(vpaddlq_u8(vabdq_u8(a.val, b.val))); - uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); - return vget_lane_u32(vpadd_u32(t1, t1), 0); -} -inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b) -{ - uint32x4_t t0 = vpaddlq_u16(vpaddlq_u8(vreinterpretq_u8_s8(vabdq_s8(a.val, b.val)))); - uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); - return vget_lane_u32(vpadd_u32(t1, t1), 0); -} -inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b) -{ - uint32x4_t t0 = vpaddlq_u16(vabdq_u16(a.val, b.val)); - uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); - return vget_lane_u32(vpadd_u32(t1, t1), 0); -} -inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b) -{ - uint32x4_t t0 = vpaddlq_u16(vreinterpretq_u16_s16(vabdq_s16(a.val, b.val))); - uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); - return vget_lane_u32(vpadd_u32(t1, t1), 0); -} -inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b) -{ - uint32x4_t t0 = vabdq_u32(a.val, b.val); - uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); - return vget_lane_u32(vpadd_u32(t1, t1), 0); -} -inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b) -{ - uint32x4_t t0 = vreinterpretq_u32_s32(vabdq_s32(a.val, b.val)); - uint32x2_t t1 = vpadd_u32(vget_low_u32(t0), vget_high_u32(t0)); - return vget_lane_u32(vpadd_u32(t1, t1), 0); -} -inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b) -{ - float32x4_t t0 = vabdq_f32(a.val, b.val); - float32x2_t t1 = vpadd_f32(vget_low_f32(t0), vget_high_f32(t0)); - return vget_lane_f32(vpadd_f32(t1, t1), 0); -} - -inline v_uint8x16 v_popcount(const v_uint8x16& a) -{ return v_uint8x16(vcntq_u8(a.val)); } -inline v_uint8x16 v_popcount(const v_int8x16& a) -{ return v_uint8x16(vcntq_u8(vreinterpretq_u8_s8(a.val))); } -inline v_uint16x8 v_popcount(const v_uint16x8& a) -{ return v_uint16x8(vpaddlq_u8(vcntq_u8(vreinterpretq_u8_u16(a.val)))); } -inline v_uint16x8 v_popcount(const v_int16x8& a) -{ return v_uint16x8(vpaddlq_u8(vcntq_u8(vreinterpretq_u8_s16(a.val)))); } -inline v_uint32x4 v_popcount(const v_uint32x4& a) -{ return v_uint32x4(vpaddlq_u16(vpaddlq_u8(vcntq_u8(vreinterpretq_u8_u32(a.val))))); } -inline v_uint32x4 v_popcount(const v_int32x4& a) -{ return v_uint32x4(vpaddlq_u16(vpaddlq_u8(vcntq_u8(vreinterpretq_u8_s32(a.val))))); } -inline v_uint64x2 v_popcount(const v_uint64x2& a) -{ return v_uint64x2(vpaddlq_u32(vpaddlq_u16(vpaddlq_u8(vcntq_u8(vreinterpretq_u8_u64(a.val)))))); } -inline v_uint64x2 v_popcount(const v_int64x2& a) -{ return v_uint64x2(vpaddlq_u32(vpaddlq_u16(vpaddlq_u8(vcntq_u8(vreinterpretq_u8_s64(a.val)))))); } - -inline int v_signmask(const v_uint8x16& a) -{ - int8x8_t m0 = vcreate_s8(CV_BIG_UINT(0x0706050403020100)); - uint8x16_t v0 = vshlq_u8(vshrq_n_u8(a.val, 7), vcombine_s8(m0, m0)); - uint64x2_t v1 = vpaddlq_u32(vpaddlq_u16(vpaddlq_u8(v0))); - return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 8); -} -inline int v_signmask(const v_int8x16& a) -{ return v_signmask(v_reinterpret_as_u8(a)); } - -inline int v_signmask(const v_uint16x8& a) -{ - int16x4_t m0 = vcreate_s16(CV_BIG_UINT(0x0003000200010000)); - uint16x8_t v0 = vshlq_u16(vshrq_n_u16(a.val, 15), vcombine_s16(m0, m0)); - uint64x2_t v1 = vpaddlq_u32(vpaddlq_u16(v0)); - return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 4); -} -inline int v_signmask(const v_int16x8& a) -{ return v_signmask(v_reinterpret_as_u16(a)); } - -inline int v_signmask(const v_uint32x4& a) -{ - int32x2_t m0 = vcreate_s32(CV_BIG_UINT(0x0000000100000000)); - uint32x4_t v0 = vshlq_u32(vshrq_n_u32(a.val, 31), vcombine_s32(m0, m0)); - uint64x2_t v1 = vpaddlq_u32(v0); - return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 2); -} -inline int v_signmask(const v_int32x4& a) -{ return v_signmask(v_reinterpret_as_u32(a)); } -inline int v_signmask(const v_float32x4& a) -{ return v_signmask(v_reinterpret_as_u32(a)); } -inline int v_signmask(const v_uint64x2& a) -{ - int64x1_t m0 = vdup_n_s64(0); - uint64x2_t v0 = vshlq_u64(vshrq_n_u64(a.val, 63), vcombine_s64(m0, m0)); - return (int)vgetq_lane_u64(v0, 0) + ((int)vgetq_lane_u64(v0, 1) << 1); -} -inline int v_signmask(const v_int64x2& a) -{ return v_signmask(v_reinterpret_as_u64(a)); } -#if CV_SIMD128_64F -inline int v_signmask(const v_float64x2& a) -{ return v_signmask(v_reinterpret_as_u64(a)); } -#endif - -inline int v_scan_forward(const v_int8x16& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_uint8x16& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_int16x8& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_uint16x8& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_int32x4& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_uint32x4& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_float32x4& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_int64x2& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_uint64x2& a) { return trailingZeros32(v_signmask(a)); } -#if CV_SIMD128_64F -inline int v_scan_forward(const v_float64x2& a) { return trailingZeros32(v_signmask(a)); } -#endif - -#define OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(_Tpvec, suffix, shift) \ -inline bool v_check_all(const v_##_Tpvec& a) \ -{ \ - _Tpvec##_t v0 = vshrq_n_##suffix(vmvnq_##suffix(a.val), shift); \ - uint64x2_t v1 = vreinterpretq_u64_##suffix(v0); \ - return (vgetq_lane_u64(v1, 0) | vgetq_lane_u64(v1, 1)) == 0; \ -} \ -inline bool v_check_any(const v_##_Tpvec& a) \ -{ \ - _Tpvec##_t v0 = vshrq_n_##suffix(a.val, shift); \ - uint64x2_t v1 = vreinterpretq_u64_##suffix(v0); \ - return (vgetq_lane_u64(v1, 0) | vgetq_lane_u64(v1, 1)) != 0; \ -} - -OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint8x16, u8, 7) -OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint16x8, u16, 15) -OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint32x4, u32, 31) - -inline bool v_check_all(const v_uint64x2& a) -{ - uint64x2_t v0 = vshrq_n_u64(a.val, 63); - return (vgetq_lane_u64(v0, 0) & vgetq_lane_u64(v0, 1)) == 1; -} -inline bool v_check_any(const v_uint64x2& a) -{ - uint64x2_t v0 = vshrq_n_u64(a.val, 63); - return (vgetq_lane_u64(v0, 0) | vgetq_lane_u64(v0, 1)) != 0; -} - -inline bool v_check_all(const v_int8x16& a) -{ return v_check_all(v_reinterpret_as_u8(a)); } -inline bool v_check_all(const v_int16x8& a) -{ return v_check_all(v_reinterpret_as_u16(a)); } -inline bool v_check_all(const v_int32x4& a) -{ return v_check_all(v_reinterpret_as_u32(a)); } -inline bool v_check_all(const v_float32x4& a) -{ return v_check_all(v_reinterpret_as_u32(a)); } - -inline bool v_check_any(const v_int8x16& a) -{ return v_check_any(v_reinterpret_as_u8(a)); } -inline bool v_check_any(const v_int16x8& a) -{ return v_check_any(v_reinterpret_as_u16(a)); } -inline bool v_check_any(const v_int32x4& a) -{ return v_check_any(v_reinterpret_as_u32(a)); } -inline bool v_check_any(const v_float32x4& a) -{ return v_check_any(v_reinterpret_as_u32(a)); } - -inline bool v_check_all(const v_int64x2& a) -{ return v_check_all(v_reinterpret_as_u64(a)); } -inline bool v_check_any(const v_int64x2& a) -{ return v_check_any(v_reinterpret_as_u64(a)); } -#if CV_SIMD128_64F -inline bool v_check_all(const v_float64x2& a) -{ return v_check_all(v_reinterpret_as_u64(a)); } -inline bool v_check_any(const v_float64x2& a) -{ return v_check_any(v_reinterpret_as_u64(a)); } -#endif - -#define OPENCV_HAL_IMPL_NEON_SELECT(_Tpvec, suffix, usuffix) \ -inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(vbslq_##suffix(vreinterpretq_##usuffix##_##suffix(mask.val), a.val, b.val)); \ -} - -OPENCV_HAL_IMPL_NEON_SELECT(v_uint8x16, u8, u8) -OPENCV_HAL_IMPL_NEON_SELECT(v_int8x16, s8, u8) -OPENCV_HAL_IMPL_NEON_SELECT(v_uint16x8, u16, u16) -OPENCV_HAL_IMPL_NEON_SELECT(v_int16x8, s16, u16) -OPENCV_HAL_IMPL_NEON_SELECT(v_uint32x4, u32, u32) -OPENCV_HAL_IMPL_NEON_SELECT(v_int32x4, s32, u32) -OPENCV_HAL_IMPL_NEON_SELECT(v_float32x4, f32, u32) -#if CV_SIMD128_64F -OPENCV_HAL_IMPL_NEON_SELECT(v_float64x2, f64, u64) -#endif - -#define OPENCV_HAL_IMPL_NEON_EXPAND(_Tpvec, _Tpwvec, _Tp, suffix) \ -inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ -{ \ - b0.val = vmovl_##suffix(vget_low_##suffix(a.val)); \ - b1.val = vmovl_##suffix(vget_high_##suffix(a.val)); \ -} \ -inline _Tpwvec v_expand_low(const _Tpvec& a) \ -{ \ - return _Tpwvec(vmovl_##suffix(vget_low_##suffix(a.val))); \ -} \ -inline _Tpwvec v_expand_high(const _Tpvec& a) \ -{ \ - return _Tpwvec(vmovl_##suffix(vget_high_##suffix(a.val))); \ -} \ -inline _Tpwvec v_load_expand(const _Tp* ptr) \ -{ \ - return _Tpwvec(vmovl_##suffix(vld1_##suffix(ptr))); \ -} - -OPENCV_HAL_IMPL_NEON_EXPAND(v_uint8x16, v_uint16x8, uchar, u8) -OPENCV_HAL_IMPL_NEON_EXPAND(v_int8x16, v_int16x8, schar, s8) -OPENCV_HAL_IMPL_NEON_EXPAND(v_uint16x8, v_uint32x4, ushort, u16) -OPENCV_HAL_IMPL_NEON_EXPAND(v_int16x8, v_int32x4, short, s16) -OPENCV_HAL_IMPL_NEON_EXPAND(v_uint32x4, v_uint64x2, uint, u32) -OPENCV_HAL_IMPL_NEON_EXPAND(v_int32x4, v_int64x2, int, s32) - -inline v_uint32x4 v_load_expand_q(const uchar* ptr) -{ - typedef unsigned int CV_DECL_ALIGNED(1) unaligned_uint; - uint8x8_t v0 = vcreate_u8(*(unaligned_uint*)ptr); - uint16x4_t v1 = vget_low_u16(vmovl_u8(v0)); - return v_uint32x4(vmovl_u16(v1)); -} - -inline v_int32x4 v_load_expand_q(const schar* ptr) -{ - typedef unsigned int CV_DECL_ALIGNED(1) unaligned_uint; - int8x8_t v0 = vcreate_s8(*(unaligned_uint*)ptr); - int16x4_t v1 = vget_low_s16(vmovl_s8(v0)); - return v_int32x4(vmovl_s16(v1)); -} - -#if defined(__aarch64__) || defined(_M_ARM64) -#define OPENCV_HAL_IMPL_NEON_UNPACKS(_Tpvec, suffix) \ -inline void v_zip(const v_##_Tpvec& a0, const v_##_Tpvec& a1, v_##_Tpvec& b0, v_##_Tpvec& b1) \ -{ \ - b0.val = vzip1q_##suffix(a0.val, a1.val); \ - b1.val = vzip2q_##suffix(a0.val, a1.val); \ -} \ -inline v_##_Tpvec v_combine_low(const v_##_Tpvec& a, const v_##_Tpvec& b) \ -{ \ - return v_##_Tpvec(vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val))); \ -} \ -inline v_##_Tpvec v_combine_high(const v_##_Tpvec& a, const v_##_Tpvec& b) \ -{ \ - return v_##_Tpvec(vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val))); \ -} \ -inline void v_recombine(const v_##_Tpvec& a, const v_##_Tpvec& b, v_##_Tpvec& c, v_##_Tpvec& d) \ -{ \ - c.val = vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val)); \ - d.val = vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val)); \ -} -#else -#define OPENCV_HAL_IMPL_NEON_UNPACKS(_Tpvec, suffix) \ -inline void v_zip(const v_##_Tpvec& a0, const v_##_Tpvec& a1, v_##_Tpvec& b0, v_##_Tpvec& b1) \ -{ \ - _Tpvec##x2_t p = vzipq_##suffix(a0.val, a1.val); \ - b0.val = p.val[0]; \ - b1.val = p.val[1]; \ -} \ -inline v_##_Tpvec v_combine_low(const v_##_Tpvec& a, const v_##_Tpvec& b) \ -{ \ - return v_##_Tpvec(vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val))); \ -} \ -inline v_##_Tpvec v_combine_high(const v_##_Tpvec& a, const v_##_Tpvec& b) \ -{ \ - return v_##_Tpvec(vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val))); \ -} \ -inline void v_recombine(const v_##_Tpvec& a, const v_##_Tpvec& b, v_##_Tpvec& c, v_##_Tpvec& d) \ -{ \ - c.val = vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val)); \ - d.val = vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val)); \ -} -#endif - -OPENCV_HAL_IMPL_NEON_UNPACKS(uint8x16, u8) -OPENCV_HAL_IMPL_NEON_UNPACKS(int8x16, s8) -OPENCV_HAL_IMPL_NEON_UNPACKS(uint16x8, u16) -OPENCV_HAL_IMPL_NEON_UNPACKS(int16x8, s16) -OPENCV_HAL_IMPL_NEON_UNPACKS(uint32x4, u32) -OPENCV_HAL_IMPL_NEON_UNPACKS(int32x4, s32) -OPENCV_HAL_IMPL_NEON_UNPACKS(float32x4, f32) -#if CV_SIMD128_64F -OPENCV_HAL_IMPL_NEON_UNPACKS(float64x2, f64) -#endif - -inline v_uint8x16 v_reverse(const v_uint8x16 &a) -{ - uint8x16_t vec = vrev64q_u8(a.val); - return v_uint8x16(vextq_u8(vec, vec, 8)); -} - -inline v_int8x16 v_reverse(const v_int8x16 &a) -{ return v_reinterpret_as_s8(v_reverse(v_reinterpret_as_u8(a))); } - -inline v_uint16x8 v_reverse(const v_uint16x8 &a) -{ - uint16x8_t vec = vrev64q_u16(a.val); - return v_uint16x8(vextq_u16(vec, vec, 4)); -} - -inline v_int16x8 v_reverse(const v_int16x8 &a) -{ return v_reinterpret_as_s16(v_reverse(v_reinterpret_as_u16(a))); } - -inline v_uint32x4 v_reverse(const v_uint32x4 &a) -{ - uint32x4_t vec = vrev64q_u32(a.val); - return v_uint32x4(vextq_u32(vec, vec, 2)); -} - -inline v_int32x4 v_reverse(const v_int32x4 &a) -{ return v_reinterpret_as_s32(v_reverse(v_reinterpret_as_u32(a))); } - -inline v_float32x4 v_reverse(const v_float32x4 &a) -{ return v_reinterpret_as_f32(v_reverse(v_reinterpret_as_u32(a))); } - -inline v_uint64x2 v_reverse(const v_uint64x2 &a) -{ - uint64x2_t vec = a.val; - uint64x1_t vec_lo = vget_low_u64(vec); - uint64x1_t vec_hi = vget_high_u64(vec); - return v_uint64x2(vcombine_u64(vec_hi, vec_lo)); -} - -inline v_int64x2 v_reverse(const v_int64x2 &a) -{ return v_reinterpret_as_s64(v_reverse(v_reinterpret_as_u64(a))); } - -#if CV_SIMD128_64F -inline v_float64x2 v_reverse(const v_float64x2 &a) -{ return v_reinterpret_as_f64(v_reverse(v_reinterpret_as_u64(a))); } -#endif - -#define OPENCV_HAL_IMPL_NEON_EXTRACT(_Tpvec, suffix) \ -template \ -inline v_##_Tpvec v_extract(const v_##_Tpvec& a, const v_##_Tpvec& b) \ -{ \ - return v_##_Tpvec(vextq_##suffix(a.val, b.val, s)); \ -} - -OPENCV_HAL_IMPL_NEON_EXTRACT(uint8x16, u8) -OPENCV_HAL_IMPL_NEON_EXTRACT(int8x16, s8) -OPENCV_HAL_IMPL_NEON_EXTRACT(uint16x8, u16) -OPENCV_HAL_IMPL_NEON_EXTRACT(int16x8, s16) -OPENCV_HAL_IMPL_NEON_EXTRACT(uint32x4, u32) -OPENCV_HAL_IMPL_NEON_EXTRACT(int32x4, s32) -OPENCV_HAL_IMPL_NEON_EXTRACT(uint64x2, u64) -OPENCV_HAL_IMPL_NEON_EXTRACT(int64x2, s64) -OPENCV_HAL_IMPL_NEON_EXTRACT(float32x4, f32) -#if CV_SIMD128_64F -OPENCV_HAL_IMPL_NEON_EXTRACT(float64x2, f64) -#endif - -#define OPENCV_HAL_IMPL_NEON_EXTRACT_N(_Tpvec, _Tp, suffix) \ -template inline _Tp v_extract_n(_Tpvec v) { return vgetq_lane_##suffix(v.val, i); } - -OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_uint8x16, uchar, u8) -OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_int8x16, schar, s8) -OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_uint16x8, ushort, u16) -OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_int16x8, short, s16) -OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_uint32x4, uint, u32) -OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_int32x4, int, s32) -OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_uint64x2, uint64, u64) -OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_int64x2, int64, s64) -OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_float32x4, float, f32) -#if CV_SIMD128_64F -OPENCV_HAL_IMPL_NEON_EXTRACT_N(v_float64x2, double, f64) -#endif - -#define OPENCV_HAL_IMPL_NEON_BROADCAST(_Tpvec, _Tp, suffix) \ -template inline _Tpvec v_broadcast_element(_Tpvec v) { _Tp t = v_extract_n(v); return v_setall_##suffix(t); } - -OPENCV_HAL_IMPL_NEON_BROADCAST(v_uint8x16, uchar, u8) -OPENCV_HAL_IMPL_NEON_BROADCAST(v_int8x16, schar, s8) -OPENCV_HAL_IMPL_NEON_BROADCAST(v_uint16x8, ushort, u16) -OPENCV_HAL_IMPL_NEON_BROADCAST(v_int16x8, short, s16) -OPENCV_HAL_IMPL_NEON_BROADCAST(v_uint32x4, uint, u32) -OPENCV_HAL_IMPL_NEON_BROADCAST(v_int32x4, int, s32) -OPENCV_HAL_IMPL_NEON_BROADCAST(v_uint64x2, uint64, u64) -OPENCV_HAL_IMPL_NEON_BROADCAST(v_int64x2, int64, s64) -OPENCV_HAL_IMPL_NEON_BROADCAST(v_float32x4, float, f32) -#if CV_SIMD128_64F -OPENCV_HAL_IMPL_NEON_BROADCAST(v_float64x2, double, f64) -#endif - -#if CV_SIMD128_64F -inline v_int32x4 v_round(const v_float32x4& a) -{ - float32x4_t a_ = a.val; - int32x4_t result; - __asm__ ("fcvtns %0.4s, %1.4s" - : "=w"(result) - : "w"(a_) - : /* No clobbers */); - return v_int32x4(result); -} -#else -inline v_int32x4 v_round(const v_float32x4& a) -{ - static const int32x4_t v_sign = vdupq_n_s32(1 << 31), - v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f)); - - int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(a.val))); - return v_int32x4(vcvtq_s32_f32(vaddq_f32(a.val, vreinterpretq_f32_s32(v_addition)))); -} -#endif -inline v_int32x4 v_floor(const v_float32x4& a) -{ - int32x4_t a1 = vcvtq_s32_f32(a.val); - uint32x4_t mask = vcgtq_f32(vcvtq_f32_s32(a1), a.val); - return v_int32x4(vaddq_s32(a1, vreinterpretq_s32_u32(mask))); -} - -inline v_int32x4 v_ceil(const v_float32x4& a) -{ - int32x4_t a1 = vcvtq_s32_f32(a.val); - uint32x4_t mask = vcgtq_f32(a.val, vcvtq_f32_s32(a1)); - return v_int32x4(vsubq_s32(a1, vreinterpretq_s32_u32(mask))); -} - -inline v_int32x4 v_trunc(const v_float32x4& a) -{ return v_int32x4(vcvtq_s32_f32(a.val)); } - -#if CV_SIMD128_64F -inline v_int32x4 v_round(const v_float64x2& a) -{ - static const int32x2_t zero = vdup_n_s32(0); - return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), zero)); -} - -inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b) -{ - return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), vmovn_s64(vcvtaq_s64_f64(b.val)))); -} - -inline v_int32x4 v_floor(const v_float64x2& a) -{ - static const int32x2_t zero = vdup_n_s32(0); - int64x2_t a1 = vcvtq_s64_f64(a.val); - uint64x2_t mask = vcgtq_f64(vcvtq_f64_s64(a1), a.val); - a1 = vaddq_s64(a1, vreinterpretq_s64_u64(mask)); - return v_int32x4(vcombine_s32(vmovn_s64(a1), zero)); -} - -inline v_int32x4 v_ceil(const v_float64x2& a) -{ - static const int32x2_t zero = vdup_n_s32(0); - int64x2_t a1 = vcvtq_s64_f64(a.val); - uint64x2_t mask = vcgtq_f64(a.val, vcvtq_f64_s64(a1)); - a1 = vsubq_s64(a1, vreinterpretq_s64_u64(mask)); - return v_int32x4(vcombine_s32(vmovn_s64(a1), zero)); -} - -inline v_int32x4 v_trunc(const v_float64x2& a) -{ - static const int32x2_t zero = vdup_n_s32(0); - return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), zero)); -} -#endif - -#define OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(_Tpvec, suffix) \ -inline void v_transpose4x4(const v_##_Tpvec& a0, const v_##_Tpvec& a1, \ - const v_##_Tpvec& a2, const v_##_Tpvec& a3, \ - v_##_Tpvec& b0, v_##_Tpvec& b1, \ - v_##_Tpvec& b2, v_##_Tpvec& b3) \ -{ \ - /* m00 m01 m02 m03 */ \ - /* m10 m11 m12 m13 */ \ - /* m20 m21 m22 m23 */ \ - /* m30 m31 m32 m33 */ \ - _Tpvec##x2_t t0 = vtrnq_##suffix(a0.val, a1.val); \ - _Tpvec##x2_t t1 = vtrnq_##suffix(a2.val, a3.val); \ - /* m00 m10 m02 m12 */ \ - /* m01 m11 m03 m13 */ \ - /* m20 m30 m22 m32 */ \ - /* m21 m31 m23 m33 */ \ - b0.val = vcombine_##suffix(vget_low_##suffix(t0.val[0]), vget_low_##suffix(t1.val[0])); \ - b1.val = vcombine_##suffix(vget_low_##suffix(t0.val[1]), vget_low_##suffix(t1.val[1])); \ - b2.val = vcombine_##suffix(vget_high_##suffix(t0.val[0]), vget_high_##suffix(t1.val[0])); \ - b3.val = vcombine_##suffix(vget_high_##suffix(t0.val[1]), vget_high_##suffix(t1.val[1])); \ -} - -OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(uint32x4, u32) -OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(int32x4, s32) -OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(float32x4, f32) - -#define OPENCV_HAL_IMPL_NEON_INTERLEAVED(_Tpvec, _Tp, suffix) \ -inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b) \ -{ \ - _Tpvec##x2_t v = vld2q_##suffix(ptr); \ - a.val = v.val[0]; \ - b.val = v.val[1]; \ -} \ -inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, v_##_Tpvec& c) \ -{ \ - _Tpvec##x3_t v = vld3q_##suffix(ptr); \ - a.val = v.val[0]; \ - b.val = v.val[1]; \ - c.val = v.val[2]; \ -} \ -inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, \ - v_##_Tpvec& c, v_##_Tpvec& d) \ -{ \ - _Tpvec##x4_t v = vld4q_##suffix(ptr); \ - a.val = v.val[0]; \ - b.val = v.val[1]; \ - c.val = v.val[2]; \ - d.val = v.val[3]; \ -} \ -inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \ - hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ -{ \ - _Tpvec##x2_t v; \ - v.val[0] = a.val; \ - v.val[1] = b.val; \ - vst2q_##suffix(ptr, v); \ -} \ -inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \ - const v_##_Tpvec& c, hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ -{ \ - _Tpvec##x3_t v; \ - v.val[0] = a.val; \ - v.val[1] = b.val; \ - v.val[2] = c.val; \ - vst3q_##suffix(ptr, v); \ -} \ -inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \ - const v_##_Tpvec& c, const v_##_Tpvec& d, \ - hal::StoreMode /*mode*/=hal::STORE_UNALIGNED ) \ -{ \ - _Tpvec##x4_t v; \ - v.val[0] = a.val; \ - v.val[1] = b.val; \ - v.val[2] = c.val; \ - v.val[3] = d.val; \ - vst4q_##suffix(ptr, v); \ -} - -#define OPENCV_HAL_IMPL_NEON_INTERLEAVED_INT64(tp, suffix) \ -inline void v_load_deinterleave( const tp* ptr, v_##tp##x2& a, v_##tp##x2& b ) \ -{ \ - tp##x1_t a0 = vld1_##suffix(ptr); \ - tp##x1_t b0 = vld1_##suffix(ptr + 1); \ - tp##x1_t a1 = vld1_##suffix(ptr + 2); \ - tp##x1_t b1 = vld1_##suffix(ptr + 3); \ - a = v_##tp##x2(vcombine_##suffix(a0, a1)); \ - b = v_##tp##x2(vcombine_##suffix(b0, b1)); \ -} \ - \ -inline void v_load_deinterleave( const tp* ptr, v_##tp##x2& a, \ - v_##tp##x2& b, v_##tp##x2& c ) \ -{ \ - tp##x1_t a0 = vld1_##suffix(ptr); \ - tp##x1_t b0 = vld1_##suffix(ptr + 1); \ - tp##x1_t c0 = vld1_##suffix(ptr + 2); \ - tp##x1_t a1 = vld1_##suffix(ptr + 3); \ - tp##x1_t b1 = vld1_##suffix(ptr + 4); \ - tp##x1_t c1 = vld1_##suffix(ptr + 5); \ - a = v_##tp##x2(vcombine_##suffix(a0, a1)); \ - b = v_##tp##x2(vcombine_##suffix(b0, b1)); \ - c = v_##tp##x2(vcombine_##suffix(c0, c1)); \ -} \ - \ -inline void v_load_deinterleave( const tp* ptr, v_##tp##x2& a, v_##tp##x2& b, \ - v_##tp##x2& c, v_##tp##x2& d ) \ -{ \ - tp##x1_t a0 = vld1_##suffix(ptr); \ - tp##x1_t b0 = vld1_##suffix(ptr + 1); \ - tp##x1_t c0 = vld1_##suffix(ptr + 2); \ - tp##x1_t d0 = vld1_##suffix(ptr + 3); \ - tp##x1_t a1 = vld1_##suffix(ptr + 4); \ - tp##x1_t b1 = vld1_##suffix(ptr + 5); \ - tp##x1_t c1 = vld1_##suffix(ptr + 6); \ - tp##x1_t d1 = vld1_##suffix(ptr + 7); \ - a = v_##tp##x2(vcombine_##suffix(a0, a1)); \ - b = v_##tp##x2(vcombine_##suffix(b0, b1)); \ - c = v_##tp##x2(vcombine_##suffix(c0, c1)); \ - d = v_##tp##x2(vcombine_##suffix(d0, d1)); \ -} \ - \ -inline void v_store_interleave( tp* ptr, const v_##tp##x2& a, const v_##tp##x2& b, \ - hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ -{ \ - vst1_##suffix(ptr, vget_low_##suffix(a.val)); \ - vst1_##suffix(ptr + 1, vget_low_##suffix(b.val)); \ - vst1_##suffix(ptr + 2, vget_high_##suffix(a.val)); \ - vst1_##suffix(ptr + 3, vget_high_##suffix(b.val)); \ -} \ - \ -inline void v_store_interleave( tp* ptr, const v_##tp##x2& a, \ - const v_##tp##x2& b, const v_##tp##x2& c, \ - hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ -{ \ - vst1_##suffix(ptr, vget_low_##suffix(a.val)); \ - vst1_##suffix(ptr + 1, vget_low_##suffix(b.val)); \ - vst1_##suffix(ptr + 2, vget_low_##suffix(c.val)); \ - vst1_##suffix(ptr + 3, vget_high_##suffix(a.val)); \ - vst1_##suffix(ptr + 4, vget_high_##suffix(b.val)); \ - vst1_##suffix(ptr + 5, vget_high_##suffix(c.val)); \ -} \ - \ -inline void v_store_interleave( tp* ptr, const v_##tp##x2& a, const v_##tp##x2& b, \ - const v_##tp##x2& c, const v_##tp##x2& d, \ - hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ -{ \ - vst1_##suffix(ptr, vget_low_##suffix(a.val)); \ - vst1_##suffix(ptr + 1, vget_low_##suffix(b.val)); \ - vst1_##suffix(ptr + 2, vget_low_##suffix(c.val)); \ - vst1_##suffix(ptr + 3, vget_low_##suffix(d.val)); \ - vst1_##suffix(ptr + 4, vget_high_##suffix(a.val)); \ - vst1_##suffix(ptr + 5, vget_high_##suffix(b.val)); \ - vst1_##suffix(ptr + 6, vget_high_##suffix(c.val)); \ - vst1_##suffix(ptr + 7, vget_high_##suffix(d.val)); \ -} - -OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint8x16, uchar, u8) -OPENCV_HAL_IMPL_NEON_INTERLEAVED(int8x16, schar, s8) -OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint16x8, ushort, u16) -OPENCV_HAL_IMPL_NEON_INTERLEAVED(int16x8, short, s16) -OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_NEON_INTERLEAVED(int32x4, int, s32) -OPENCV_HAL_IMPL_NEON_INTERLEAVED(float32x4, float, f32) -#if CV_SIMD128_64F -OPENCV_HAL_IMPL_NEON_INTERLEAVED(float64x2, double, f64) -#endif - -OPENCV_HAL_IMPL_NEON_INTERLEAVED_INT64(int64, s64) -OPENCV_HAL_IMPL_NEON_INTERLEAVED_INT64(uint64, u64) - -inline v_float32x4 v_cvt_f32(const v_int32x4& a) -{ - return v_float32x4(vcvtq_f32_s32(a.val)); -} - -#if CV_SIMD128_64F -inline v_float32x4 v_cvt_f32(const v_float64x2& a) -{ - float32x2_t zero = vdup_n_f32(0.0f); - return v_float32x4(vcombine_f32(vcvt_f32_f64(a.val), zero)); -} - -inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b) -{ - return v_float32x4(vcombine_f32(vcvt_f32_f64(a.val), vcvt_f32_f64(b.val))); -} - -inline v_float64x2 v_cvt_f64(const v_int32x4& a) -{ - return v_float64x2(vcvt_f64_f32(vcvt_f32_s32(vget_low_s32(a.val)))); -} - -inline v_float64x2 v_cvt_f64_high(const v_int32x4& a) -{ - return v_float64x2(vcvt_f64_f32(vcvt_f32_s32(vget_high_s32(a.val)))); -} - -inline v_float64x2 v_cvt_f64(const v_float32x4& a) -{ - return v_float64x2(vcvt_f64_f32(vget_low_f32(a.val))); -} - -inline v_float64x2 v_cvt_f64_high(const v_float32x4& a) -{ - return v_float64x2(vcvt_f64_f32(vget_high_f32(a.val))); -} - -inline v_float64x2 v_cvt_f64(const v_int64x2& a) -{ return v_float64x2(vcvtq_f64_s64(a.val)); } - -#endif - -////////////// Lookup table access //////////////////// - -inline v_int8x16 v_lut(const schar* tab, const int* idx) -{ - schar CV_DECL_ALIGNED(32) elems[16] = - { - tab[idx[ 0]], - tab[idx[ 1]], - tab[idx[ 2]], - tab[idx[ 3]], - tab[idx[ 4]], - tab[idx[ 5]], - tab[idx[ 6]], - tab[idx[ 7]], - tab[idx[ 8]], - tab[idx[ 9]], - tab[idx[10]], - tab[idx[11]], - tab[idx[12]], - tab[idx[13]], - tab[idx[14]], - tab[idx[15]] - }; - return v_int8x16(vld1q_s8(elems)); -} -inline v_int8x16 v_lut_pairs(const schar* tab, const int* idx) -{ - schar CV_DECL_ALIGNED(32) elems[16] = - { - tab[idx[0]], - tab[idx[0] + 1], - tab[idx[1]], - tab[idx[1] + 1], - tab[idx[2]], - tab[idx[2] + 1], - tab[idx[3]], - tab[idx[3] + 1], - tab[idx[4]], - tab[idx[4] + 1], - tab[idx[5]], - tab[idx[5] + 1], - tab[idx[6]], - tab[idx[6] + 1], - tab[idx[7]], - tab[idx[7] + 1] - }; - return v_int8x16(vld1q_s8(elems)); -} -inline v_int8x16 v_lut_quads(const schar* tab, const int* idx) -{ - schar CV_DECL_ALIGNED(32) elems[16] = - { - tab[idx[0]], - tab[idx[0] + 1], - tab[idx[0] + 2], - tab[idx[0] + 3], - tab[idx[1]], - tab[idx[1] + 1], - tab[idx[1] + 2], - tab[idx[1] + 3], - tab[idx[2]], - tab[idx[2] + 1], - tab[idx[2] + 2], - tab[idx[2] + 3], - tab[idx[3]], - tab[idx[3] + 1], - tab[idx[3] + 2], - tab[idx[3] + 3] - }; - return v_int8x16(vld1q_s8(elems)); -} -inline v_uint8x16 v_lut(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut((schar*)tab, idx)); } -inline v_uint8x16 v_lut_pairs(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_pairs((schar*)tab, idx)); } -inline v_uint8x16 v_lut_quads(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_quads((schar*)tab, idx)); } - -inline v_int16x8 v_lut(const short* tab, const int* idx) -{ - short CV_DECL_ALIGNED(32) elems[8] = - { - tab[idx[0]], - tab[idx[1]], - tab[idx[2]], - tab[idx[3]], - tab[idx[4]], - tab[idx[5]], - tab[idx[6]], - tab[idx[7]] - }; - return v_int16x8(vld1q_s16(elems)); -} -inline v_int16x8 v_lut_pairs(const short* tab, const int* idx) -{ - short CV_DECL_ALIGNED(32) elems[8] = - { - tab[idx[0]], - tab[idx[0] + 1], - tab[idx[1]], - tab[idx[1] + 1], - tab[idx[2]], - tab[idx[2] + 1], - tab[idx[3]], - tab[idx[3] + 1] - }; - return v_int16x8(vld1q_s16(elems)); -} -inline v_int16x8 v_lut_quads(const short* tab, const int* idx) -{ - return v_int16x8(vcombine_s16(vld1_s16(tab + idx[0]), vld1_s16(tab + idx[1]))); -} -inline v_uint16x8 v_lut(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut((short*)tab, idx)); } -inline v_uint16x8 v_lut_pairs(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_pairs((short*)tab, idx)); } -inline v_uint16x8 v_lut_quads(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_quads((short*)tab, idx)); } - -inline v_int32x4 v_lut(const int* tab, const int* idx) -{ - int CV_DECL_ALIGNED(32) elems[4] = - { - tab[idx[0]], - tab[idx[1]], - tab[idx[2]], - tab[idx[3]] - }; - return v_int32x4(vld1q_s32(elems)); -} -inline v_int32x4 v_lut_pairs(const int* tab, const int* idx) -{ - return v_int32x4(vcombine_s32(vld1_s32(tab + idx[0]), vld1_s32(tab + idx[1]))); -} -inline v_int32x4 v_lut_quads(const int* tab, const int* idx) -{ - return v_int32x4(vld1q_s32(tab + idx[0])); -} -inline v_uint32x4 v_lut(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut((int*)tab, idx)); } -inline v_uint32x4 v_lut_pairs(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_pairs((int*)tab, idx)); } -inline v_uint32x4 v_lut_quads(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_quads((int*)tab, idx)); } - -inline v_int64x2 v_lut(const int64_t* tab, const int* idx) -{ - return v_int64x2(vcombine_s64(vcreate_s64(tab[idx[0]]), vcreate_s64(tab[idx[1]]))); -} -inline v_int64x2 v_lut_pairs(const int64_t* tab, const int* idx) -{ - return v_int64x2(vld1q_s64(tab + idx[0])); -} -inline v_uint64x2 v_lut(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut((const int64_t *)tab, idx)); } -inline v_uint64x2 v_lut_pairs(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut_pairs((const int64_t *)tab, idx)); } - -inline v_float32x4 v_lut(const float* tab, const int* idx) -{ - float CV_DECL_ALIGNED(32) elems[4] = - { - tab[idx[0]], - tab[idx[1]], - tab[idx[2]], - tab[idx[3]] - }; - return v_float32x4(vld1q_f32(elems)); -} -inline v_float32x4 v_lut_pairs(const float* tab, const int* idx) -{ - typedef uint64 CV_DECL_ALIGNED(1) unaligned_uint64; - - uint64 CV_DECL_ALIGNED(32) elems[2] = - { - *(unaligned_uint64*)(tab + idx[0]), - *(unaligned_uint64*)(tab + idx[1]) - }; - return v_float32x4(vreinterpretq_f32_u64(vld1q_u64(elems))); -} -inline v_float32x4 v_lut_quads(const float* tab, const int* idx) -{ - return v_float32x4(vld1q_f32(tab + idx[0])); -} - -inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec) -{ - int CV_DECL_ALIGNED(32) elems[4] = - { - tab[vgetq_lane_s32(idxvec.val, 0)], - tab[vgetq_lane_s32(idxvec.val, 1)], - tab[vgetq_lane_s32(idxvec.val, 2)], - tab[vgetq_lane_s32(idxvec.val, 3)] - }; - return v_int32x4(vld1q_s32(elems)); -} - -inline v_uint32x4 v_lut(const unsigned* tab, const v_int32x4& idxvec) -{ - unsigned CV_DECL_ALIGNED(32) elems[4] = - { - tab[vgetq_lane_s32(idxvec.val, 0)], - tab[vgetq_lane_s32(idxvec.val, 1)], - tab[vgetq_lane_s32(idxvec.val, 2)], - tab[vgetq_lane_s32(idxvec.val, 3)] - }; - return v_uint32x4(vld1q_u32(elems)); -} - -inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec) -{ - float CV_DECL_ALIGNED(32) elems[4] = - { - tab[vgetq_lane_s32(idxvec.val, 0)], - tab[vgetq_lane_s32(idxvec.val, 1)], - tab[vgetq_lane_s32(idxvec.val, 2)], - tab[vgetq_lane_s32(idxvec.val, 3)] - }; - return v_float32x4(vld1q_f32(elems)); -} - -inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y) -{ - /*int CV_DECL_ALIGNED(32) idx[4]; - v_store(idx, idxvec); - - float32x4_t xy02 = vcombine_f32(vld1_f32(tab + idx[0]), vld1_f32(tab + idx[2])); - float32x4_t xy13 = vcombine_f32(vld1_f32(tab + idx[1]), vld1_f32(tab + idx[3])); - - float32x4x2_t xxyy = vuzpq_f32(xy02, xy13); - x = v_float32x4(xxyy.val[0]); - y = v_float32x4(xxyy.val[1]);*/ - int CV_DECL_ALIGNED(32) idx[4]; - v_store_aligned(idx, idxvec); - - x = v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); - y = v_float32x4(tab[idx[0]+1], tab[idx[1]+1], tab[idx[2]+1], tab[idx[3]+1]); -} - -inline v_int8x16 v_interleave_pairs(const v_int8x16& vec) -{ - return v_int8x16(vcombine_s8(vtbl1_s8(vget_low_s8(vec.val), vcreate_s8(0x0705060403010200)), vtbl1_s8(vget_high_s8(vec.val), vcreate_s8(0x0705060403010200)))); -} -inline v_uint8x16 v_interleave_pairs(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_interleave_pairs(v_reinterpret_as_s8(vec))); } -inline v_int8x16 v_interleave_quads(const v_int8x16& vec) -{ - return v_int8x16(vcombine_s8(vtbl1_s8(vget_low_s8(vec.val), vcreate_s8(0x0703060205010400)), vtbl1_s8(vget_high_s8(vec.val), vcreate_s8(0x0703060205010400)))); -} -inline v_uint8x16 v_interleave_quads(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_interleave_quads(v_reinterpret_as_s8(vec))); } - -inline v_int16x8 v_interleave_pairs(const v_int16x8& vec) -{ - return v_int16x8(vreinterpretq_s16_s8(vcombine_s8(vtbl1_s8(vget_low_s8(vreinterpretq_s8_s16(vec.val)), vcreate_s8(0x0706030205040100)), vtbl1_s8(vget_high_s8(vreinterpretq_s8_s16(vec.val)), vcreate_s8(0x0706030205040100))))); -} -inline v_uint16x8 v_interleave_pairs(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_pairs(v_reinterpret_as_s16(vec))); } -inline v_int16x8 v_interleave_quads(const v_int16x8& vec) -{ - int16x4x2_t res = vzip_s16(vget_low_s16(vec.val), vget_high_s16(vec.val)); - return v_int16x8(vcombine_s16(res.val[0], res.val[1])); -} -inline v_uint16x8 v_interleave_quads(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_quads(v_reinterpret_as_s16(vec))); } - -inline v_int32x4 v_interleave_pairs(const v_int32x4& vec) -{ - int32x2x2_t res = vzip_s32(vget_low_s32(vec.val), vget_high_s32(vec.val)); - return v_int32x4(vcombine_s32(res.val[0], res.val[1])); -} -inline v_uint32x4 v_interleave_pairs(const v_uint32x4& vec) { return v_reinterpret_as_u32(v_interleave_pairs(v_reinterpret_as_s32(vec))); } -inline v_float32x4 v_interleave_pairs(const v_float32x4& vec) { return v_reinterpret_as_f32(v_interleave_pairs(v_reinterpret_as_s32(vec))); } - -inline v_int8x16 v_pack_triplets(const v_int8x16& vec) -{ - return v_int8x16(vextq_s8(vcombine_s8(vtbl1_s8(vget_low_s8(vec.val), vcreate_s8(0x0605040201000000)), vtbl1_s8(vget_high_s8(vec.val), vcreate_s8(0x0807060504020100))), vdupq_n_s8(0), 2)); -} -inline v_uint8x16 v_pack_triplets(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec))); } - -inline v_int16x8 v_pack_triplets(const v_int16x8& vec) -{ - return v_int16x8(vreinterpretq_s16_s8(vextq_s8(vcombine_s8(vtbl1_s8(vget_low_s8(vreinterpretq_s8_s16(vec.val)), vcreate_s8(0x0504030201000000)), vget_high_s8(vreinterpretq_s8_s16(vec.val))), vdupq_n_s8(0), 2))); -} -inline v_uint16x8 v_pack_triplets(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec))); } - -inline v_int32x4 v_pack_triplets(const v_int32x4& vec) { return vec; } -inline v_uint32x4 v_pack_triplets(const v_uint32x4& vec) { return vec; } -inline v_float32x4 v_pack_triplets(const v_float32x4& vec) { return vec; } - -#if CV_SIMD128_64F -inline v_float64x2 v_lut(const double* tab, const int* idx) -{ - double CV_DECL_ALIGNED(32) elems[2] = - { - tab[idx[0]], - tab[idx[1]] - }; - return v_float64x2(vld1q_f64(elems)); -} - -inline v_float64x2 v_lut_pairs(const double* tab, const int* idx) -{ - return v_float64x2(vld1q_f64(tab + idx[0])); -} - -inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec) -{ - double CV_DECL_ALIGNED(32) elems[2] = - { - tab[vgetq_lane_s32(idxvec.val, 0)], - tab[vgetq_lane_s32(idxvec.val, 1)], - }; - return v_float64x2(vld1q_f64(elems)); -} - -inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y) -{ - int CV_DECL_ALIGNED(32) idx[4]; - v_store_aligned(idx, idxvec); - - x = v_float64x2(tab[idx[0]], tab[idx[1]]); - y = v_float64x2(tab[idx[0]+1], tab[idx[1]+1]); -} -#endif - -////// FP16 support /////// -#if CV_FP16 -inline v_float32x4 v_load_expand(const float16_t* ptr) -{ - float16x4_t v = - #ifndef vld1_f16 // APPLE compiler defines vld1_f16 as macro - (float16x4_t)vld1_s16((const short*)ptr); - #else - vld1_f16((const __fp16*)ptr); - #endif - return v_float32x4(vcvt_f32_f16(v)); -} - -inline void v_pack_store(float16_t* ptr, const v_float32x4& v) -{ - float16x4_t hv = vcvt_f16_f32(v.val); - - #ifndef vst1_f16 // APPLE compiler defines vst1_f16 as macro - vst1_s16((short*)ptr, (int16x4_t)hv); - #else - vst1_f16((__fp16*)ptr, hv); - #endif -} -#else -inline v_float32x4 v_load_expand(const float16_t* ptr) -{ - const int N = 4; - float buf[N]; - for( int i = 0; i < N; i++ ) buf[i] = (float)ptr[i]; - return v_load(buf); -} - -inline void v_pack_store(float16_t* ptr, const v_float32x4& v) -{ - const int N = 4; - float buf[N]; - v_store(buf, v); - for( int i = 0; i < N; i++ ) ptr[i] = float16_t(buf[i]); -} -#endif - -inline void v_cleanup() {} - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END - -//! @endcond - -} - -#endif diff --git a/include/opencv2/core/hal/intrin_sse.hpp b/include/opencv2/core/hal/intrin_sse.hpp deleted file mode 100755 index f4b43a2..0000000 --- a/include/opencv2/core/hal/intrin_sse.hpp +++ /dev/null @@ -1,3455 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_HAL_SSE_HPP -#define OPENCV_HAL_SSE_HPP - -#include -#include "opencv2/core/utility.hpp" - -#define CV_SIMD128 1 -#define CV_SIMD128_64F 1 -#define CV_SIMD128_FP16 0 // no native operations with FP16 type. - -namespace cv -{ - -//! @cond IGNORED - -// -// Compilation troubleshooting: -// - MSVC: error C2719: 'a': formal parameter with requested alignment of 16 won't be aligned -// Replace parameter declaration to const reference: -// -v_int32x4 a -// +const v_int32x4& a -// - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN - -///////// Types //////////// - -struct v_uint8x16 -{ - typedef uchar lane_type; - typedef __m128i vector_type; - enum { nlanes = 16 }; - - /* coverity[uninit_ctor]: suppress warning */ - v_uint8x16() {} - explicit v_uint8x16(__m128i v) : val(v) {} - v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, - uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) - { - val = _mm_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3, - (char)v4, (char)v5, (char)v6, (char)v7, - (char)v8, (char)v9, (char)v10, (char)v11, - (char)v12, (char)v13, (char)v14, (char)v15); - } - - uchar get0() const - { - return (uchar)_mm_cvtsi128_si32(val); - } - - __m128i val; -}; - -struct v_int8x16 -{ - typedef schar lane_type; - typedef __m128i vector_type; - enum { nlanes = 16 }; - - /* coverity[uninit_ctor]: suppress warning */ - v_int8x16() {} - explicit v_int8x16(__m128i v) : val(v) {} - v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, - schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) - { - val = _mm_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3, - (char)v4, (char)v5, (char)v6, (char)v7, - (char)v8, (char)v9, (char)v10, (char)v11, - (char)v12, (char)v13, (char)v14, (char)v15); - } - - schar get0() const - { - return (schar)_mm_cvtsi128_si32(val); - } - - __m128i val; -}; - -struct v_uint16x8 -{ - typedef ushort lane_type; - typedef __m128i vector_type; - enum { nlanes = 8 }; - - /* coverity[uninit_ctor]: suppress warning */ - v_uint16x8() {} - explicit v_uint16x8(__m128i v) : val(v) {} - v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) - { - val = _mm_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3, - (short)v4, (short)v5, (short)v6, (short)v7); - } - - ushort get0() const - { - return (ushort)_mm_cvtsi128_si32(val); - } - - __m128i val; -}; - -struct v_int16x8 -{ - typedef short lane_type; - typedef __m128i vector_type; - enum { nlanes = 8 }; - - /* coverity[uninit_ctor]: suppress warning */ - v_int16x8() {} - explicit v_int16x8(__m128i v) : val(v) {} - v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) - { - val = _mm_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3, - (short)v4, (short)v5, (short)v6, (short)v7); - } - - short get0() const - { - return (short)_mm_cvtsi128_si32(val); - } - - __m128i val; -}; - -struct v_uint32x4 -{ - typedef unsigned lane_type; - typedef __m128i vector_type; - enum { nlanes = 4 }; - - /* coverity[uninit_ctor]: suppress warning */ - v_uint32x4() {} - explicit v_uint32x4(__m128i v) : val(v) {} - v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) - { - val = _mm_setr_epi32((int)v0, (int)v1, (int)v2, (int)v3); - } - - unsigned get0() const - { - return (unsigned)_mm_cvtsi128_si32(val); - } - - __m128i val; -}; - -struct v_int32x4 -{ - typedef int lane_type; - typedef __m128i vector_type; - enum { nlanes = 4 }; - - /* coverity[uninit_ctor]: suppress warning */ - v_int32x4() {} - explicit v_int32x4(__m128i v) : val(v) {} - v_int32x4(int v0, int v1, int v2, int v3) - { - val = _mm_setr_epi32(v0, v1, v2, v3); - } - - int get0() const - { - return _mm_cvtsi128_si32(val); - } - - __m128i val; -}; - -struct v_float32x4 -{ - typedef float lane_type; - typedef __m128 vector_type; - enum { nlanes = 4 }; - - /* coverity[uninit_ctor]: suppress warning */ - v_float32x4() {} - explicit v_float32x4(__m128 v) : val(v) {} - v_float32x4(float v0, float v1, float v2, float v3) - { - val = _mm_setr_ps(v0, v1, v2, v3); - } - - float get0() const - { - return _mm_cvtss_f32(val); - } - - __m128 val; -}; - -struct v_uint64x2 -{ - typedef uint64 lane_type; - typedef __m128i vector_type; - enum { nlanes = 2 }; - - /* coverity[uninit_ctor]: suppress warning */ - v_uint64x2() {} - explicit v_uint64x2(__m128i v) : val(v) {} - v_uint64x2(uint64 v0, uint64 v1) - { - val = _mm_setr_epi32((int)v0, (int)(v0 >> 32), (int)v1, (int)(v1 >> 32)); - } - - uint64 get0() const - { - #if !defined(__x86_64__) && !defined(_M_X64) - int a = _mm_cvtsi128_si32(val); - int b = _mm_cvtsi128_si32(_mm_srli_epi64(val, 32)); - return (unsigned)a | ((uint64)(unsigned)b << 32); - #else - return (uint64)_mm_cvtsi128_si64(val); - #endif - } - - __m128i val; -}; - -struct v_int64x2 -{ - typedef int64 lane_type; - typedef __m128i vector_type; - enum { nlanes = 2 }; - - /* coverity[uninit_ctor]: suppress warning */ - v_int64x2() {} - explicit v_int64x2(__m128i v) : val(v) {} - v_int64x2(int64 v0, int64 v1) - { - val = _mm_setr_epi32((int)v0, (int)(v0 >> 32), (int)v1, (int)(v1 >> 32)); - } - - int64 get0() const - { - #if !defined(__x86_64__) && !defined(_M_X64) - int a = _mm_cvtsi128_si32(val); - int b = _mm_cvtsi128_si32(_mm_srli_epi64(val, 32)); - return (int64)((unsigned)a | ((uint64)(unsigned)b << 32)); - #else - return _mm_cvtsi128_si64(val); - #endif - } - - __m128i val; -}; - -struct v_float64x2 -{ - typedef double lane_type; - typedef __m128d vector_type; - enum { nlanes = 2 }; - - /* coverity[uninit_ctor]: suppress warning */ - v_float64x2() {} - explicit v_float64x2(__m128d v) : val(v) {} - v_float64x2(double v0, double v1) - { - val = _mm_setr_pd(v0, v1); - } - - double get0() const - { - return _mm_cvtsd_f64(val); - } - - __m128d val; -}; - -namespace hal_sse_internal -{ - template - to_sse_type v_sse_reinterpret_as(const from_sse_type& val); - -#define OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(to_sse_type, from_sse_type, sse_cast_intrin) \ - template<> inline \ - to_sse_type v_sse_reinterpret_as(const from_sse_type& a) \ - { return sse_cast_intrin(a); } - - OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128i, OPENCV_HAL_NOP) - OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128, _mm_castps_si128) - OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128d, _mm_castpd_si128) - OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128i, _mm_castsi128_ps) - OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128, OPENCV_HAL_NOP) - OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128d, _mm_castpd_ps) - OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128i, _mm_castsi128_pd) - OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128, _mm_castps_pd) - OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128d, OPENCV_HAL_NOP) -} - -#define OPENCV_HAL_IMPL_SSE_INITVEC(_Tpvec, _Tp, suffix, zsuffix, ssuffix, _Tps, cast) \ -inline _Tpvec v_setzero_##suffix() { return _Tpvec(_mm_setzero_##zsuffix()); } \ -inline _Tpvec v_setall_##suffix(_Tp v) { return _Tpvec(_mm_set1_##ssuffix((_Tps)v)); } \ -template inline _Tpvec v_reinterpret_as_##suffix(const _Tpvec0& a) \ -{ return _Tpvec(cast(a.val)); } - -OPENCV_HAL_IMPL_SSE_INITVEC(v_uint8x16, uchar, u8, si128, epi8, schar, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_INITVEC(v_int8x16, schar, s8, si128, epi8, schar, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_INITVEC(v_uint16x8, ushort, u16, si128, epi16, short, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_INITVEC(v_int16x8, short, s16, si128, epi16, short, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_INITVEC(v_uint32x4, unsigned, u32, si128, epi32, int, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_INITVEC(v_int32x4, int, s32, si128, epi32, int, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_INITVEC(v_float32x4, float, f32, ps, ps, float, _mm_castsi128_ps) -OPENCV_HAL_IMPL_SSE_INITVEC(v_float64x2, double, f64, pd, pd, double, _mm_castsi128_pd) - -inline v_uint64x2 v_setzero_u64() { return v_uint64x2(_mm_setzero_si128()); } -inline v_int64x2 v_setzero_s64() { return v_int64x2(_mm_setzero_si128()); } -inline v_uint64x2 v_setall_u64(uint64 val) { return v_uint64x2(val, val); } -inline v_int64x2 v_setall_s64(int64 val) { return v_int64x2(val, val); } - -template inline -v_uint64x2 v_reinterpret_as_u64(const _Tpvec& a) { return v_uint64x2(a.val); } -template inline -v_int64x2 v_reinterpret_as_s64(const _Tpvec& a) { return v_int64x2(a.val); } -inline v_float32x4 v_reinterpret_as_f32(const v_uint64x2& a) -{ return v_float32x4(_mm_castsi128_ps(a.val)); } -inline v_float32x4 v_reinterpret_as_f32(const v_int64x2& a) -{ return v_float32x4(_mm_castsi128_ps(a.val)); } -inline v_float64x2 v_reinterpret_as_f64(const v_uint64x2& a) -{ return v_float64x2(_mm_castsi128_pd(a.val)); } -inline v_float64x2 v_reinterpret_as_f64(const v_int64x2& a) -{ return v_float64x2(_mm_castsi128_pd(a.val)); } - -#define OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(_Tpvec, suffix) \ -inline _Tpvec v_reinterpret_as_##suffix(const v_float32x4& a) \ -{ return _Tpvec(_mm_castps_si128(a.val)); } \ -inline _Tpvec v_reinterpret_as_##suffix(const v_float64x2& a) \ -{ return _Tpvec(_mm_castpd_si128(a.val)); } - -OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint8x16, u8) -OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int8x16, s8) -OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint16x8, u16) -OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int16x8, s16) -OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint32x4, u32) -OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int32x4, s32) -OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint64x2, u64) -OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int64x2, s64) - -inline v_float32x4 v_reinterpret_as_f32(const v_float32x4& a) {return a; } -inline v_float64x2 v_reinterpret_as_f64(const v_float64x2& a) {return a; } -inline v_float32x4 v_reinterpret_as_f32(const v_float64x2& a) {return v_float32x4(_mm_castpd_ps(a.val)); } -inline v_float64x2 v_reinterpret_as_f64(const v_float32x4& a) {return v_float64x2(_mm_castps_pd(a.val)); } - -//////////////// PACK /////////////// -inline v_uint8x16 v_pack(const v_uint16x8& a, const v_uint16x8& b) -{ - __m128i delta = _mm_set1_epi16(255); - return v_uint8x16(_mm_packus_epi16(_mm_subs_epu16(a.val, _mm_subs_epu16(a.val, delta)), - _mm_subs_epu16(b.val, _mm_subs_epu16(b.val, delta)))); -} - -inline void v_pack_store(uchar* ptr, const v_uint16x8& a) -{ - __m128i delta = _mm_set1_epi16(255); - __m128i a1 = _mm_subs_epu16(a.val, _mm_subs_epu16(a.val, delta)); - _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); -} - -inline v_uint8x16 v_pack_u(const v_int16x8& a, const v_int16x8& b) -{ return v_uint8x16(_mm_packus_epi16(a.val, b.val)); } - -inline void v_pack_u_store(uchar* ptr, const v_int16x8& a) -{ _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a.val, a.val)); } - -template inline -v_uint8x16 v_rshr_pack(const v_uint16x8& a, const v_uint16x8& b) -{ - // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. - __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); - return v_uint8x16(_mm_packus_epi16(_mm_srli_epi16(_mm_adds_epu16(a.val, delta), n), - _mm_srli_epi16(_mm_adds_epu16(b.val, delta), n))); -} - -template inline -void v_rshr_pack_store(uchar* ptr, const v_uint16x8& a) -{ - __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); - __m128i a1 = _mm_srli_epi16(_mm_adds_epu16(a.val, delta), n); - _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); -} - -template inline -v_uint8x16 v_rshr_pack_u(const v_int16x8& a, const v_int16x8& b) -{ - __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); - return v_uint8x16(_mm_packus_epi16(_mm_srai_epi16(_mm_adds_epi16(a.val, delta), n), - _mm_srai_epi16(_mm_adds_epi16(b.val, delta), n))); -} - -template inline -void v_rshr_pack_u_store(uchar* ptr, const v_int16x8& a) -{ - __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); - __m128i a1 = _mm_srai_epi16(_mm_adds_epi16(a.val, delta), n); - _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1)); -} - -inline v_int8x16 v_pack(const v_int16x8& a, const v_int16x8& b) -{ return v_int8x16(_mm_packs_epi16(a.val, b.val)); } - -inline void v_pack_store(schar* ptr, const v_int16x8& a) -{ _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi16(a.val, a.val)); } - -template inline -v_int8x16 v_rshr_pack(const v_int16x8& a, const v_int16x8& b) -{ - // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. - __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); - return v_int8x16(_mm_packs_epi16(_mm_srai_epi16(_mm_adds_epi16(a.val, delta), n), - _mm_srai_epi16(_mm_adds_epi16(b.val, delta), n))); -} -template inline -void v_rshr_pack_store(schar* ptr, const v_int16x8& a) -{ - // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers. - __m128i delta = _mm_set1_epi16((short)(1 << (n-1))); - __m128i a1 = _mm_srai_epi16(_mm_adds_epi16(a.val, delta), n); - _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi16(a1, a1)); -} - - -// byte-wise "mask ? a : b" -inline __m128i v_select_si128(__m128i mask, __m128i a, __m128i b) -{ -#if CV_SSE4_1 - return _mm_blendv_epi8(b, a, mask); -#else - return _mm_xor_si128(b, _mm_and_si128(_mm_xor_si128(a, b), mask)); -#endif -} - -inline v_uint16x8 v_pack(const v_uint32x4& a, const v_uint32x4& b) -{ return v_uint16x8(_v128_packs_epu32(a.val, b.val)); } - -inline void v_pack_store(ushort* ptr, const v_uint32x4& a) -{ - __m128i z = _mm_setzero_si128(), maxval32 = _mm_set1_epi32(65535), delta32 = _mm_set1_epi32(32768); - __m128i a1 = _mm_sub_epi32(v_select_si128(_mm_cmpgt_epi32(z, a.val), maxval32, a.val), delta32); - __m128i r = _mm_packs_epi32(a1, a1); - _mm_storel_epi64((__m128i*)ptr, _mm_sub_epi16(r, _mm_set1_epi16(-32768))); -} - -template inline -v_uint16x8 v_rshr_pack(const v_uint32x4& a, const v_uint32x4& b) -{ - __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); - __m128i a1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(a.val, delta), n), delta32); - __m128i b1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(b.val, delta), n), delta32); - return v_uint16x8(_mm_sub_epi16(_mm_packs_epi32(a1, b1), _mm_set1_epi16(-32768))); -} - -template inline -void v_rshr_pack_store(ushort* ptr, const v_uint32x4& a) -{ - __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); - __m128i a1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(a.val, delta), n), delta32); - __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); - _mm_storel_epi64((__m128i*)ptr, a2); -} - -inline v_uint16x8 v_pack_u(const v_int32x4& a, const v_int32x4& b) -{ -#if CV_SSE4_1 - return v_uint16x8(_mm_packus_epi32(a.val, b.val)); -#else - __m128i delta32 = _mm_set1_epi32(32768); - - // preliminary saturate negative values to zero - __m128i a1 = _mm_and_si128(a.val, _mm_cmpgt_epi32(a.val, _mm_set1_epi32(0))); - __m128i b1 = _mm_and_si128(b.val, _mm_cmpgt_epi32(b.val, _mm_set1_epi32(0))); - - __m128i r = _mm_packs_epi32(_mm_sub_epi32(a1, delta32), _mm_sub_epi32(b1, delta32)); - return v_uint16x8(_mm_sub_epi16(r, _mm_set1_epi16(-32768))); -#endif -} - -inline void v_pack_u_store(ushort* ptr, const v_int32x4& a) -{ -#if CV_SSE4_1 - _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi32(a.val, a.val)); -#else - __m128i delta32 = _mm_set1_epi32(32768); - __m128i a1 = _mm_sub_epi32(a.val, delta32); - __m128i r = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); - _mm_storel_epi64((__m128i*)ptr, r); -#endif -} - -template inline -v_uint16x8 v_rshr_pack_u(const v_int32x4& a, const v_int32x4& b) -{ -#if CV_SSE4_1 - __m128i delta = _mm_set1_epi32(1 << (n - 1)); - return v_uint16x8(_mm_packus_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), - _mm_srai_epi32(_mm_add_epi32(b.val, delta), n))); -#else - __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); - __m128i a1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), delta32); - __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); - __m128i b1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(b.val, delta), n), delta32); - __m128i b2 = _mm_sub_epi16(_mm_packs_epi32(b1, b1), _mm_set1_epi16(-32768)); - return v_uint16x8(_mm_unpacklo_epi64(a2, b2)); -#endif -} - -template inline -void v_rshr_pack_u_store(ushort* ptr, const v_int32x4& a) -{ -#if CV_SSE4_1 - __m128i delta = _mm_set1_epi32(1 << (n - 1)); - __m128i a1 = _mm_srai_epi32(_mm_add_epi32(a.val, delta), n); - _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi32(a1, a1)); -#else - __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768); - __m128i a1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), delta32); - __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768)); - _mm_storel_epi64((__m128i*)ptr, a2); -#endif -} - -inline v_int16x8 v_pack(const v_int32x4& a, const v_int32x4& b) -{ return v_int16x8(_mm_packs_epi32(a.val, b.val)); } - -inline void v_pack_store(short* ptr, const v_int32x4& a) -{ - _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi32(a.val, a.val)); -} - -template inline -v_int16x8 v_rshr_pack(const v_int32x4& a, const v_int32x4& b) -{ - __m128i delta = _mm_set1_epi32(1 << (n-1)); - return v_int16x8(_mm_packs_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), - _mm_srai_epi32(_mm_add_epi32(b.val, delta), n))); -} - -template inline -void v_rshr_pack_store(short* ptr, const v_int32x4& a) -{ - __m128i delta = _mm_set1_epi32(1 << (n-1)); - __m128i a1 = _mm_srai_epi32(_mm_add_epi32(a.val, delta), n); - _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi32(a1, a1)); -} - - -// [a0 0 | b0 0] [a1 0 | b1 0] -inline v_uint32x4 v_pack(const v_uint64x2& a, const v_uint64x2& b) -{ - __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); // a0 a1 0 0 - __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); // b0 b1 0 0 - return v_uint32x4(_mm_unpacklo_epi32(v0, v1)); -} - -inline void v_pack_store(unsigned* ptr, const v_uint64x2& a) -{ - __m128i a1 = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 2, 2, 0)); - _mm_storel_epi64((__m128i*)ptr, a1); -} - -// [a0 0 | b0 0] [a1 0 | b1 0] -inline v_int32x4 v_pack(const v_int64x2& a, const v_int64x2& b) -{ - __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); // a0 a1 0 0 - __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); // b0 b1 0 0 - return v_int32x4(_mm_unpacklo_epi32(v0, v1)); -} - -inline void v_pack_store(int* ptr, const v_int64x2& a) -{ - __m128i a1 = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 2, 2, 0)); - _mm_storel_epi64((__m128i*)ptr, a1); -} - -template inline -v_uint32x4 v_rshr_pack(const v_uint64x2& a, const v_uint64x2& b) -{ - uint64 delta = (uint64)1 << (n-1); - v_uint64x2 delta2(delta, delta); - __m128i a1 = _mm_srli_epi64(_mm_add_epi64(a.val, delta2.val), n); - __m128i b1 = _mm_srli_epi64(_mm_add_epi64(b.val, delta2.val), n); - __m128i v0 = _mm_unpacklo_epi32(a1, b1); // a0 a1 0 0 - __m128i v1 = _mm_unpackhi_epi32(a1, b1); // b0 b1 0 0 - return v_uint32x4(_mm_unpacklo_epi32(v0, v1)); -} - -template inline -void v_rshr_pack_store(unsigned* ptr, const v_uint64x2& a) -{ - uint64 delta = (uint64)1 << (n-1); - v_uint64x2 delta2(delta, delta); - __m128i a1 = _mm_srli_epi64(_mm_add_epi64(a.val, delta2.val), n); - __m128i a2 = _mm_shuffle_epi32(a1, _MM_SHUFFLE(0, 2, 2, 0)); - _mm_storel_epi64((__m128i*)ptr, a2); -} - -inline __m128i v_sign_epi64(__m128i a) -{ - return _mm_shuffle_epi32(_mm_srai_epi32(a, 31), _MM_SHUFFLE(3, 3, 1, 1)); // x m0 | x m1 -} - -inline __m128i v_srai_epi64(__m128i a, int imm) -{ - __m128i smask = v_sign_epi64(a); - return _mm_xor_si128(_mm_srli_epi64(_mm_xor_si128(a, smask), imm), smask); -} - -template inline -v_int32x4 v_rshr_pack(const v_int64x2& a, const v_int64x2& b) -{ - int64 delta = (int64)1 << (n-1); - v_int64x2 delta2(delta, delta); - __m128i a1 = v_srai_epi64(_mm_add_epi64(a.val, delta2.val), n); - __m128i b1 = v_srai_epi64(_mm_add_epi64(b.val, delta2.val), n); - __m128i v0 = _mm_unpacklo_epi32(a1, b1); // a0 a1 0 0 - __m128i v1 = _mm_unpackhi_epi32(a1, b1); // b0 b1 0 0 - return v_int32x4(_mm_unpacklo_epi32(v0, v1)); -} - -template inline -void v_rshr_pack_store(int* ptr, const v_int64x2& a) -{ - int64 delta = (int64)1 << (n-1); - v_int64x2 delta2(delta, delta); - __m128i a1 = v_srai_epi64(_mm_add_epi64(a.val, delta2.val), n); - __m128i a2 = _mm_shuffle_epi32(a1, _MM_SHUFFLE(0, 2, 2, 0)); - _mm_storel_epi64((__m128i*)ptr, a2); -} - -// pack boolean -inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) -{ - __m128i ab = _mm_packs_epi16(a.val, b.val); - return v_uint8x16(ab); -} - -inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, - const v_uint32x4& c, const v_uint32x4& d) -{ - __m128i ab = _mm_packs_epi32(a.val, b.val); - __m128i cd = _mm_packs_epi32(c.val, d.val); - return v_uint8x16(_mm_packs_epi16(ab, cd)); -} - -inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, - const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, - const v_uint64x2& g, const v_uint64x2& h) -{ - __m128i ab = _mm_packs_epi32(a.val, b.val); - __m128i cd = _mm_packs_epi32(c.val, d.val); - __m128i ef = _mm_packs_epi32(e.val, f.val); - __m128i gh = _mm_packs_epi32(g.val, h.val); - - __m128i abcd = _mm_packs_epi32(ab, cd); - __m128i efgh = _mm_packs_epi32(ef, gh); - return v_uint8x16(_mm_packs_epi16(abcd, efgh)); -} - -inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, - const v_float32x4& m1, const v_float32x4& m2, - const v_float32x4& m3) -{ - __m128 v0 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(0, 0, 0, 0)), m0.val); - __m128 v1 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(1, 1, 1, 1)), m1.val); - __m128 v2 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(2, 2, 2, 2)), m2.val); - __m128 v3 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(3, 3, 3, 3)), m3.val); - - return v_float32x4(_mm_add_ps(_mm_add_ps(v0, v1), _mm_add_ps(v2, v3))); -} - -inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, - const v_float32x4& m1, const v_float32x4& m2, - const v_float32x4& a) -{ - __m128 v0 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(0, 0, 0, 0)), m0.val); - __m128 v1 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(1, 1, 1, 1)), m1.val); - __m128 v2 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(2, 2, 2, 2)), m2.val); - - return v_float32x4(_mm_add_ps(_mm_add_ps(v0, v1), _mm_add_ps(v2, a.val))); -} - -#define OPENCV_HAL_IMPL_SSE_BIN_OP(bin_op, _Tpvec, intrin) \ - inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ - { \ - return _Tpvec(intrin(a.val, b.val)); \ - } \ - inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ - { \ - a.val = intrin(a.val, b.val); \ - return a; \ - } - -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint8x16, _mm_adds_epu8) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint8x16, _mm_subs_epu8) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int8x16, _mm_adds_epi8) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int8x16, _mm_subs_epi8) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint16x8, _mm_adds_epu16) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint16x8, _mm_subs_epu16) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int16x8, _mm_adds_epi16) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int16x8, _mm_subs_epi16) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint32x4, _mm_add_epi32) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint32x4, _mm_sub_epi32) -OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_uint32x4, _v128_mullo_epi32) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int32x4, _mm_add_epi32) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int32x4, _mm_sub_epi32) -OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_int32x4, _v128_mullo_epi32) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_float32x4, _mm_add_ps) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_float32x4, _mm_sub_ps) -OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_float32x4, _mm_mul_ps) -OPENCV_HAL_IMPL_SSE_BIN_OP(/, v_float32x4, _mm_div_ps) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_float64x2, _mm_add_pd) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_float64x2, _mm_sub_pd) -OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_float64x2, _mm_mul_pd) -OPENCV_HAL_IMPL_SSE_BIN_OP(/, v_float64x2, _mm_div_pd) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint64x2, _mm_add_epi64) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint64x2, _mm_sub_epi64) -OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int64x2, _mm_add_epi64) -OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int64x2, _mm_sub_epi64) - -// saturating multiply 8-bit, 16-bit -#define OPENCV_HAL_IMPL_SSE_MUL_SAT(_Tpvec, _Tpwvec) \ - inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b) \ - { \ - _Tpwvec c, d; \ - v_mul_expand(a, b, c, d); \ - return v_pack(c, d); \ - } \ - inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b) \ - { a = a * b; return a; } - -OPENCV_HAL_IMPL_SSE_MUL_SAT(v_uint8x16, v_uint16x8) -OPENCV_HAL_IMPL_SSE_MUL_SAT(v_int8x16, v_int16x8) -OPENCV_HAL_IMPL_SSE_MUL_SAT(v_uint16x8, v_uint32x4) -OPENCV_HAL_IMPL_SSE_MUL_SAT(v_int16x8, v_int32x4) - -// Multiply and expand -inline void v_mul_expand(const v_uint8x16& a, const v_uint8x16& b, - v_uint16x8& c, v_uint16x8& d) -{ - v_uint16x8 a0, a1, b0, b1; - v_expand(a, a0, a1); - v_expand(b, b0, b1); - c = v_mul_wrap(a0, b0); - d = v_mul_wrap(a1, b1); -} - -inline void v_mul_expand(const v_int8x16& a, const v_int8x16& b, - v_int16x8& c, v_int16x8& d) -{ - v_int16x8 a0, a1, b0, b1; - v_expand(a, a0, a1); - v_expand(b, b0, b1); - c = v_mul_wrap(a0, b0); - d = v_mul_wrap(a1, b1); -} - -inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b, - v_int32x4& c, v_int32x4& d) -{ - __m128i v0 = _mm_mullo_epi16(a.val, b.val); - __m128i v1 = _mm_mulhi_epi16(a.val, b.val); - c.val = _mm_unpacklo_epi16(v0, v1); - d.val = _mm_unpackhi_epi16(v0, v1); -} - -inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b, - v_uint32x4& c, v_uint32x4& d) -{ - __m128i v0 = _mm_mullo_epi16(a.val, b.val); - __m128i v1 = _mm_mulhi_epu16(a.val, b.val); - c.val = _mm_unpacklo_epi16(v0, v1); - d.val = _mm_unpackhi_epi16(v0, v1); -} - -inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, - v_uint64x2& c, v_uint64x2& d) -{ - __m128i c0 = _mm_mul_epu32(a.val, b.val); - __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32)); - c.val = _mm_unpacklo_epi64(c0, c1); - d.val = _mm_unpackhi_epi64(c0, c1); -} - -inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b) { return v_int16x8(_mm_mulhi_epi16(a.val, b.val)); } -inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b) { return v_uint16x8(_mm_mulhi_epu16(a.val, b.val)); } - -//////// Dot Product //////// - -// 16 >> 32 -inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) -{ return v_int32x4(_mm_madd_epi16(a.val, b.val)); } -inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) -{ return v_dotprod(a, b) + c; } - -// 32 >> 64 -inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b) -{ -#if CV_SSE4_1 - __m128i even = _mm_mul_epi32(a.val, b.val); - __m128i odd = _mm_mul_epi32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32)); - return v_int64x2(_mm_add_epi64(even, odd)); -#else - __m128i even_u = _mm_mul_epu32(a.val, b.val); - __m128i odd_u = _mm_mul_epu32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32)); - // convert unsigned to signed high multiplication (from: Agner Fog(veclib) and H S Warren: Hacker's delight, 2003, p. 132) - __m128i a_sign = _mm_srai_epi32(a.val, 31); - __m128i b_sign = _mm_srai_epi32(b.val, 31); - // |x * sign of x - __m128i axb = _mm_and_si128(a.val, b_sign); - __m128i bxa = _mm_and_si128(b.val, a_sign); - // sum of sign corrections - __m128i ssum = _mm_add_epi32(bxa, axb); - __m128i even_ssum = _mm_slli_epi64(ssum, 32); - __m128i odd_ssum = _mm_and_si128(ssum, _mm_set_epi32(-1, 0, -1, 0)); - // convert to signed and prod - return v_int64x2(_mm_add_epi64(_mm_sub_epi64(even_u, even_ssum), _mm_sub_epi64(odd_u, odd_ssum))); -#endif -} -inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c) -{ return v_dotprod(a, b) + c; } - -// 8 >> 32 -inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b) -{ - __m128i a0 = _mm_srli_epi16(_mm_slli_si128(a.val, 1), 8); // even - __m128i a1 = _mm_srli_epi16(a.val, 8); // odd - __m128i b0 = _mm_srli_epi16(_mm_slli_si128(b.val, 1), 8); - __m128i b1 = _mm_srli_epi16(b.val, 8); - __m128i p0 = _mm_madd_epi16(a0, b0); - __m128i p1 = _mm_madd_epi16(a1, b1); - return v_uint32x4(_mm_add_epi32(p0, p1)); -} -inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c) -{ return v_dotprod_expand(a, b) + c; } - -inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b) -{ - __m128i a0 = _mm_srai_epi16(_mm_slli_si128(a.val, 1), 8); // even - __m128i a1 = _mm_srai_epi16(a.val, 8); // odd - __m128i b0 = _mm_srai_epi16(_mm_slli_si128(b.val, 1), 8); - __m128i b1 = _mm_srai_epi16(b.val, 8); - __m128i p0 = _mm_madd_epi16(a0, b0); - __m128i p1 = _mm_madd_epi16(a1, b1); - return v_int32x4(_mm_add_epi32(p0, p1)); -} -inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c) -{ return v_dotprod_expand(a, b) + c; } - -// 16 >> 64 -inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b) -{ - v_uint32x4 c, d; - v_mul_expand(a, b, c, d); - - v_uint64x2 c0, c1, d0, d1; - v_expand(c, c0, c1); - v_expand(d, d0, d1); - - c0 += c1; d0 += d1; - return v_uint64x2(_mm_add_epi64( - _mm_unpacklo_epi64(c0.val, d0.val), - _mm_unpackhi_epi64(c0.val, d0.val) - )); -} -inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c) -{ return v_dotprod_expand(a, b) + c; } - -inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b) -{ - v_int32x4 prod = v_dotprod(a, b); - v_int64x2 c, d; - v_expand(prod, c, d); - return v_int64x2(_mm_add_epi64( - _mm_unpacklo_epi64(c.val, d.val), - _mm_unpackhi_epi64(c.val, d.val) - )); -} -inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c) -{ return v_dotprod_expand(a, b) + c; } - -// 32 >> 64f -inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b) -{ -#if CV_SSE4_1 - return v_cvt_f64(v_dotprod(a, b)); -#else - v_float64x2 c = v_cvt_f64(a) * v_cvt_f64(b); - v_float64x2 d = v_cvt_f64_high(a) * v_cvt_f64_high(b); - - return v_float64x2(_mm_add_pd( - _mm_unpacklo_pd(c.val, d.val), - _mm_unpackhi_pd(c.val, d.val) - )); -#endif -} -inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c) -{ return v_dotprod_expand(a, b) + c; } - -//////// Fast Dot Product //////// - -// 16 >> 32 -inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b) -{ return v_dotprod(a, b); } -inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) -{ return v_dotprod(a, b) + c; } - -// 32 >> 64 -inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b) -{ return v_dotprod(a, b); } -inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c) -{ return v_dotprod_fast(a, b) + c; } - -// 8 >> 32 -inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b) -{ - __m128i a0 = v_expand_low(a).val; - __m128i a1 = v_expand_high(a).val; - __m128i b0 = v_expand_low(b).val; - __m128i b1 = v_expand_high(b).val; - __m128i p0 = _mm_madd_epi16(a0, b0); - __m128i p1 = _mm_madd_epi16(a1, b1); - return v_uint32x4(_mm_add_epi32(p0, p1)); -} -inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c) -{ return v_dotprod_expand_fast(a, b) + c; } - -inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b) -{ -#if CV_SSE4_1 - __m128i a0 = _mm_cvtepi8_epi16(a.val); - __m128i a1 = v_expand_high(a).val; - __m128i b0 = _mm_cvtepi8_epi16(b.val); - __m128i b1 = v_expand_high(b).val; - __m128i p0 = _mm_madd_epi16(a0, b0); - __m128i p1 = _mm_madd_epi16(a1, b1); - return v_int32x4(_mm_add_epi32(p0, p1)); -#else - return v_dotprod_expand(a, b); -#endif -} -inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c) -{ return v_dotprod_expand_fast(a, b) + c; } - -// 16 >> 64 -inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b) -{ - v_uint32x4 c, d; - v_mul_expand(a, b, c, d); - - v_uint64x2 c0, c1, d0, d1; - v_expand(c, c0, c1); - v_expand(d, d0, d1); - - c0 += c1; d0 += d1; - return c0 + d0; -} -inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c) -{ return v_dotprod_expand_fast(a, b) + c; } - -inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b) -{ - v_int32x4 prod = v_dotprod(a, b); - v_int64x2 c, d; - v_expand(prod, c, d); - return c + d; -} -inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c) -{ return v_dotprod_expand_fast(a, b) + c; } - -// 32 >> 64f -v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c); -inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b) -{ return v_fma(v_cvt_f64(a), v_cvt_f64(b), v_cvt_f64_high(a) * v_cvt_f64_high(b)); } -inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c) -{ return v_fma(v_cvt_f64(a), v_cvt_f64(b), v_fma(v_cvt_f64_high(a), v_cvt_f64_high(b), c)); } - -#define OPENCV_HAL_IMPL_SSE_LOGIC_OP(_Tpvec, suffix, not_const) \ - OPENCV_HAL_IMPL_SSE_BIN_OP(&, _Tpvec, _mm_and_##suffix) \ - OPENCV_HAL_IMPL_SSE_BIN_OP(|, _Tpvec, _mm_or_##suffix) \ - OPENCV_HAL_IMPL_SSE_BIN_OP(^, _Tpvec, _mm_xor_##suffix) \ - inline _Tpvec operator ~ (const _Tpvec& a) \ - { \ - return _Tpvec(_mm_xor_##suffix(a.val, not_const)); \ - } - -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint8x16, si128, _mm_set1_epi32(-1)) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int8x16, si128, _mm_set1_epi32(-1)) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint16x8, si128, _mm_set1_epi32(-1)) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int16x8, si128, _mm_set1_epi32(-1)) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint32x4, si128, _mm_set1_epi32(-1)) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int32x4, si128, _mm_set1_epi32(-1)) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint64x2, si128, _mm_set1_epi32(-1)) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int64x2, si128, _mm_set1_epi32(-1)) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_float32x4, ps, _mm_castsi128_ps(_mm_set1_epi32(-1))) -OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_float64x2, pd, _mm_castsi128_pd(_mm_set1_epi32(-1))) - -inline v_float32x4 v_sqrt(const v_float32x4& x) -{ return v_float32x4(_mm_sqrt_ps(x.val)); } - -inline v_float32x4 v_invsqrt(const v_float32x4& x) -{ - const __m128 _0_5 = _mm_set1_ps(0.5f), _1_5 = _mm_set1_ps(1.5f); - __m128 t = x.val; - __m128 h = _mm_mul_ps(t, _0_5); - t = _mm_rsqrt_ps(t); - t = _mm_mul_ps(t, _mm_sub_ps(_1_5, _mm_mul_ps(_mm_mul_ps(t, t), h))); - return v_float32x4(t); -} - -inline v_float64x2 v_sqrt(const v_float64x2& x) -{ return v_float64x2(_mm_sqrt_pd(x.val)); } - -inline v_float64x2 v_invsqrt(const v_float64x2& x) -{ - const __m128d v_1 = _mm_set1_pd(1.); - return v_float64x2(_mm_div_pd(v_1, _mm_sqrt_pd(x.val))); -} - -#define OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(_Tpuvec, _Tpsvec, func, suffix, subWidth) \ -inline _Tpuvec v_abs(const _Tpsvec& x) \ -{ return _Tpuvec(_mm_##func##_ep##suffix(x.val, _mm_sub_ep##subWidth(_mm_setzero_si128(), x.val))); } - -OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(v_uint8x16, v_int8x16, min, u8, i8) -OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(v_uint16x8, v_int16x8, max, i16, i16) -inline v_uint32x4 v_abs(const v_int32x4& x) -{ - __m128i s = _mm_srli_epi32(x.val, 31); - __m128i f = _mm_srai_epi32(x.val, 31); - return v_uint32x4(_mm_add_epi32(_mm_xor_si128(x.val, f), s)); -} -inline v_float32x4 v_abs(const v_float32x4& x) -{ return v_float32x4(_mm_and_ps(x.val, _mm_castsi128_ps(_mm_set1_epi32(0x7fffffff)))); } -inline v_float64x2 v_abs(const v_float64x2& x) -{ - return v_float64x2(_mm_and_pd(x.val, - _mm_castsi128_pd(_mm_srli_epi64(_mm_set1_epi32(-1), 1)))); -} - -// TODO: exp, log, sin, cos - -#define OPENCV_HAL_IMPL_SSE_BIN_FUNC(_Tpvec, func, intrin) \ -inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(intrin(a.val, b.val)); \ -} - -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_min, _mm_min_epu8) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_max, _mm_max_epu8) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_min, _mm_min_epi16) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_max, _mm_max_epi16) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float32x4, v_min, _mm_min_ps) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float32x4, v_max, _mm_max_ps) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float64x2, v_min, _mm_min_pd) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float64x2, v_max, _mm_max_pd) - -inline v_int8x16 v_min(const v_int8x16& a, const v_int8x16& b) -{ -#if CV_SSE4_1 - return v_int8x16(_mm_min_epi8(a.val, b.val)); -#else - __m128i delta = _mm_set1_epi8((char)-128); - return v_int8x16(_mm_xor_si128(delta, _mm_min_epu8(_mm_xor_si128(a.val, delta), - _mm_xor_si128(b.val, delta)))); -#endif -} -inline v_int8x16 v_max(const v_int8x16& a, const v_int8x16& b) -{ -#if CV_SSE4_1 - return v_int8x16(_mm_max_epi8(a.val, b.val)); -#else - __m128i delta = _mm_set1_epi8((char)-128); - return v_int8x16(_mm_xor_si128(delta, _mm_max_epu8(_mm_xor_si128(a.val, delta), - _mm_xor_si128(b.val, delta)))); -#endif -} -inline v_uint16x8 v_min(const v_uint16x8& a, const v_uint16x8& b) -{ -#if CV_SSE4_1 - return v_uint16x8(_mm_min_epu16(a.val, b.val)); -#else - return v_uint16x8(_mm_subs_epu16(a.val, _mm_subs_epu16(a.val, b.val))); -#endif -} -inline v_uint16x8 v_max(const v_uint16x8& a, const v_uint16x8& b) -{ -#if CV_SSE4_1 - return v_uint16x8(_mm_max_epu16(a.val, b.val)); -#else - return v_uint16x8(_mm_adds_epu16(_mm_subs_epu16(a.val, b.val), b.val)); -#endif -} -inline v_uint32x4 v_min(const v_uint32x4& a, const v_uint32x4& b) -{ -#if CV_SSE4_1 - return v_uint32x4(_mm_min_epu32(a.val, b.val)); -#else - __m128i delta = _mm_set1_epi32((int)0x80000000); - __m128i mask = _mm_cmpgt_epi32(_mm_xor_si128(a.val, delta), _mm_xor_si128(b.val, delta)); - return v_uint32x4(v_select_si128(mask, b.val, a.val)); -#endif -} -inline v_uint32x4 v_max(const v_uint32x4& a, const v_uint32x4& b) -{ -#if CV_SSE4_1 - return v_uint32x4(_mm_max_epu32(a.val, b.val)); -#else - __m128i delta = _mm_set1_epi32((int)0x80000000); - __m128i mask = _mm_cmpgt_epi32(_mm_xor_si128(a.val, delta), _mm_xor_si128(b.val, delta)); - return v_uint32x4(v_select_si128(mask, a.val, b.val)); -#endif -} -inline v_int32x4 v_min(const v_int32x4& a, const v_int32x4& b) -{ -#if CV_SSE4_1 - return v_int32x4(_mm_min_epi32(a.val, b.val)); -#else - return v_int32x4(v_select_si128(_mm_cmpgt_epi32(a.val, b.val), b.val, a.val)); -#endif -} -inline v_int32x4 v_max(const v_int32x4& a, const v_int32x4& b) -{ -#if CV_SSE4_1 - return v_int32x4(_mm_max_epi32(a.val, b.val)); -#else - return v_int32x4(v_select_si128(_mm_cmpgt_epi32(a.val, b.val), a.val, b.val)); -#endif -} - -#define OPENCV_HAL_IMPL_SSE_INT_CMP_OP(_Tpuvec, _Tpsvec, suffix, sbit) \ -inline _Tpuvec operator == (const _Tpuvec& a, const _Tpuvec& b) \ -{ return _Tpuvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ -inline _Tpuvec operator != (const _Tpuvec& a, const _Tpuvec& b) \ -{ \ - __m128i not_mask = _mm_set1_epi32(-1); \ - return _Tpuvec(_mm_xor_si128(_mm_cmpeq_##suffix(a.val, b.val), not_mask)); \ -} \ -inline _Tpsvec operator == (const _Tpsvec& a, const _Tpsvec& b) \ -{ return _Tpsvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ -inline _Tpsvec operator != (const _Tpsvec& a, const _Tpsvec& b) \ -{ \ - __m128i not_mask = _mm_set1_epi32(-1); \ - return _Tpsvec(_mm_xor_si128(_mm_cmpeq_##suffix(a.val, b.val), not_mask)); \ -} \ -inline _Tpuvec operator < (const _Tpuvec& a, const _Tpuvec& b) \ -{ \ - __m128i smask = _mm_set1_##suffix(sbit); \ - return _Tpuvec(_mm_cmpgt_##suffix(_mm_xor_si128(b.val, smask), _mm_xor_si128(a.val, smask))); \ -} \ -inline _Tpuvec operator > (const _Tpuvec& a, const _Tpuvec& b) \ -{ \ - __m128i smask = _mm_set1_##suffix(sbit); \ - return _Tpuvec(_mm_cmpgt_##suffix(_mm_xor_si128(a.val, smask), _mm_xor_si128(b.val, smask))); \ -} \ -inline _Tpuvec operator <= (const _Tpuvec& a, const _Tpuvec& b) \ -{ \ - __m128i smask = _mm_set1_##suffix(sbit); \ - __m128i not_mask = _mm_set1_epi32(-1); \ - __m128i res = _mm_cmpgt_##suffix(_mm_xor_si128(a.val, smask), _mm_xor_si128(b.val, smask)); \ - return _Tpuvec(_mm_xor_si128(res, not_mask)); \ -} \ -inline _Tpuvec operator >= (const _Tpuvec& a, const _Tpuvec& b) \ -{ \ - __m128i smask = _mm_set1_##suffix(sbit); \ - __m128i not_mask = _mm_set1_epi32(-1); \ - __m128i res = _mm_cmpgt_##suffix(_mm_xor_si128(b.val, smask), _mm_xor_si128(a.val, smask)); \ - return _Tpuvec(_mm_xor_si128(res, not_mask)); \ -} \ -inline _Tpsvec operator < (const _Tpsvec& a, const _Tpsvec& b) \ -{ \ - return _Tpsvec(_mm_cmpgt_##suffix(b.val, a.val)); \ -} \ -inline _Tpsvec operator > (const _Tpsvec& a, const _Tpsvec& b) \ -{ \ - return _Tpsvec(_mm_cmpgt_##suffix(a.val, b.val)); \ -} \ -inline _Tpsvec operator <= (const _Tpsvec& a, const _Tpsvec& b) \ -{ \ - __m128i not_mask = _mm_set1_epi32(-1); \ - return _Tpsvec(_mm_xor_si128(_mm_cmpgt_##suffix(a.val, b.val), not_mask)); \ -} \ -inline _Tpsvec operator >= (const _Tpsvec& a, const _Tpsvec& b) \ -{ \ - __m128i not_mask = _mm_set1_epi32(-1); \ - return _Tpsvec(_mm_xor_si128(_mm_cmpgt_##suffix(b.val, a.val), not_mask)); \ -} - -OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint8x16, v_int8x16, epi8, (char)-128) -OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint16x8, v_int16x8, epi16, (short)-32768) -OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint32x4, v_int32x4, epi32, (int)0x80000000) - -#define OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(_Tpvec, suffix) \ -inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(_mm_cmpeq_##suffix(a.val, b.val)); } \ -inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(_mm_cmpneq_##suffix(a.val, b.val)); } \ -inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(_mm_cmplt_##suffix(a.val, b.val)); } \ -inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(_mm_cmpgt_##suffix(a.val, b.val)); } \ -inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(_mm_cmple_##suffix(a.val, b.val)); } \ -inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(_mm_cmpge_##suffix(a.val, b.val)); } - -OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(v_float32x4, ps) -OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(v_float64x2, pd) - -#if CV_SSE4_1 -#define OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(_Tpvec) \ -inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(_mm_cmpeq_epi64(a.val, b.val)); } \ -inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ -{ return ~(a == b); } -#else -#define OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(_Tpvec) \ -inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ -{ __m128i cmp = _mm_cmpeq_epi32(a.val, b.val); \ - return _Tpvec(_mm_and_si128(cmp, _mm_shuffle_epi32(cmp, _MM_SHUFFLE(2, 3, 0, 1)))); } \ -inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ -{ return ~(a == b); } -#endif - -OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(v_uint64x2) -OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(v_int64x2) - -inline v_float32x4 v_not_nan(const v_float32x4& a) -{ return v_float32x4(_mm_cmpord_ps(a.val, a.val)); } -inline v_float64x2 v_not_nan(const v_float64x2& a) -{ return v_float64x2(_mm_cmpord_pd(a.val, a.val)); } - -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_add_wrap, _mm_add_epi8) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int8x16, v_add_wrap, _mm_add_epi8) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_add_wrap, _mm_add_epi16) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_add_wrap, _mm_add_epi16) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_sub_wrap, _mm_sub_epi8) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int8x16, v_sub_wrap, _mm_sub_epi8) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_sub_wrap, _mm_sub_epi16) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_sub_wrap, _mm_sub_epi16) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_mul_wrap, _mm_mullo_epi16) -OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_mul_wrap, _mm_mullo_epi16) - -inline v_uint8x16 v_mul_wrap(const v_uint8x16& a, const v_uint8x16& b) -{ - __m128i ad = _mm_srai_epi16(a.val, 8); - __m128i bd = _mm_srai_epi16(b.val, 8); - __m128i p0 = _mm_mullo_epi16(a.val, b.val); // even - __m128i p1 = _mm_slli_epi16(_mm_mullo_epi16(ad, bd), 8); // odd - const __m128i b01 = _mm_set1_epi32(0xFF00FF00); - return v_uint8x16(_v128_blendv_epi8(p0, p1, b01)); -} -inline v_int8x16 v_mul_wrap(const v_int8x16& a, const v_int8x16& b) -{ - return v_reinterpret_as_s8(v_mul_wrap(v_reinterpret_as_u8(a), v_reinterpret_as_u8(b))); -} - -/** Absolute difference **/ - -inline v_uint8x16 v_absdiff(const v_uint8x16& a, const v_uint8x16& b) -{ return v_add_wrap(a - b, b - a); } -inline v_uint16x8 v_absdiff(const v_uint16x8& a, const v_uint16x8& b) -{ return v_add_wrap(a - b, b - a); } -inline v_uint32x4 v_absdiff(const v_uint32x4& a, const v_uint32x4& b) -{ return v_max(a, b) - v_min(a, b); } - -inline v_uint8x16 v_absdiff(const v_int8x16& a, const v_int8x16& b) -{ - v_int8x16 d = v_sub_wrap(a, b); - v_int8x16 m = a < b; - return v_reinterpret_as_u8(v_sub_wrap(d ^ m, m)); -} -inline v_uint16x8 v_absdiff(const v_int16x8& a, const v_int16x8& b) -{ - return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); -} -inline v_uint32x4 v_absdiff(const v_int32x4& a, const v_int32x4& b) -{ - v_int32x4 d = a - b; - v_int32x4 m = a < b; - return v_reinterpret_as_u32((d ^ m) - m); -} - -/** Saturating absolute difference **/ -inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b) -{ - v_int8x16 d = a - b; - v_int8x16 m = a < b; - return (d ^ m) - m; - } -inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b) -{ return v_max(a, b) - v_min(a, b); } - - -inline v_int32x4 v_fma(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) -{ - return a * b + c; -} - -inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) -{ - return v_fma(a, b, c); -} - -inline v_float32x4 v_fma(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c) -{ -#if CV_FMA3 - return v_float32x4(_mm_fmadd_ps(a.val, b.val, c.val)); -#else - return v_float32x4(_mm_add_ps(_mm_mul_ps(a.val, b.val), c.val)); -#endif -} - -inline v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c) -{ -#if CV_FMA3 - return v_float64x2(_mm_fmadd_pd(a.val, b.val, c.val)); -#else - return v_float64x2(_mm_add_pd(_mm_mul_pd(a.val, b.val), c.val)); -#endif -} - -#define OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(_Tpvec, _Tp, _Tpreg, suffix, absmask_vec) \ -inline _Tpvec v_absdiff(const _Tpvec& a, const _Tpvec& b) \ -{ \ - _Tpreg absmask = _mm_castsi128_##suffix(absmask_vec); \ - return _Tpvec(_mm_and_##suffix(_mm_sub_##suffix(a.val, b.val), absmask)); \ -} \ -inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \ -{ \ - _Tpvec res = v_fma(a, a, b*b); \ - return _Tpvec(_mm_sqrt_##suffix(res.val)); \ -} \ -inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return v_fma(a, a, b*b); \ -} \ -inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ -{ \ - return v_fma(a, b, c); \ -} - -OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(v_float32x4, float, __m128, ps, _mm_set1_epi32((int)0x7fffffff)) -OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(v_float64x2, double, __m128d, pd, _mm_srli_epi64(_mm_set1_epi32(-1), 1)) - -#define OPENCV_HAL_IMPL_SSE_SHIFT_OP(_Tpuvec, _Tpsvec, suffix, srai) \ -inline _Tpuvec operator << (const _Tpuvec& a, int imm) \ -{ \ - return _Tpuvec(_mm_slli_##suffix(a.val, imm)); \ -} \ -inline _Tpsvec operator << (const _Tpsvec& a, int imm) \ -{ \ - return _Tpsvec(_mm_slli_##suffix(a.val, imm)); \ -} \ -inline _Tpuvec operator >> (const _Tpuvec& a, int imm) \ -{ \ - return _Tpuvec(_mm_srli_##suffix(a.val, imm)); \ -} \ -inline _Tpsvec operator >> (const _Tpsvec& a, int imm) \ -{ \ - return _Tpsvec(srai(a.val, imm)); \ -} \ -template \ -inline _Tpuvec v_shl(const _Tpuvec& a) \ -{ \ - return _Tpuvec(_mm_slli_##suffix(a.val, imm)); \ -} \ -template \ -inline _Tpsvec v_shl(const _Tpsvec& a) \ -{ \ - return _Tpsvec(_mm_slli_##suffix(a.val, imm)); \ -} \ -template \ -inline _Tpuvec v_shr(const _Tpuvec& a) \ -{ \ - return _Tpuvec(_mm_srli_##suffix(a.val, imm)); \ -} \ -template \ -inline _Tpsvec v_shr(const _Tpsvec& a) \ -{ \ - return _Tpsvec(srai(a.val, imm)); \ -} - -OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint16x8, v_int16x8, epi16, _mm_srai_epi16) -OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint32x4, v_int32x4, epi32, _mm_srai_epi32) -OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint64x2, v_int64x2, epi64, v_srai_epi64) - -namespace hal_sse_internal -{ - template 16)), - bool is_first = (imm == 0), - bool is_half = (imm == 8), - bool is_second = (imm == 16), - bool is_other = (((imm > 0) && (imm < 8)) || ((imm > 8) && (imm < 16)))> - class v_sse_palignr_u8_class; - - template - class v_sse_palignr_u8_class; - - template - class v_sse_palignr_u8_class - { - public: - inline __m128i operator()(const __m128i& a, const __m128i&) const - { - return a; - } - }; - - template - class v_sse_palignr_u8_class - { - public: - inline __m128i operator()(const __m128i& a, const __m128i& b) const - { - return _mm_unpacklo_epi64(_mm_unpackhi_epi64(a, a), b); - } - }; - - template - class v_sse_palignr_u8_class - { - public: - inline __m128i operator()(const __m128i&, const __m128i& b) const - { - return b; - } - }; - - template - class v_sse_palignr_u8_class - { -#if CV_SSSE3 - public: - inline __m128i operator()(const __m128i& a, const __m128i& b) const - { - return _mm_alignr_epi8(b, a, imm); - } -#else - public: - inline __m128i operator()(const __m128i& a, const __m128i& b) const - { - enum { imm2 = (sizeof(__m128i) - imm) }; - return _mm_or_si128(_mm_srli_si128(a, imm), _mm_slli_si128(b, imm2)); - } -#endif - }; - - template - inline __m128i v_sse_palignr_u8(const __m128i& a, const __m128i& b) - { - CV_StaticAssert((imm >= 0) && (imm <= 16), "Invalid imm for v_sse_palignr_u8."); - return v_sse_palignr_u8_class()(a, b); - } -} - -template -inline _Tpvec v_rotate_right(const _Tpvec &a) -{ - using namespace hal_sse_internal; - enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) }; - return _Tpvec(v_sse_reinterpret_as( - _mm_srli_si128( - v_sse_reinterpret_as<__m128i>(a.val), imm2))); -} - -template -inline _Tpvec v_rotate_left(const _Tpvec &a) -{ - using namespace hal_sse_internal; - enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) }; - return _Tpvec(v_sse_reinterpret_as( - _mm_slli_si128( - v_sse_reinterpret_as<__m128i>(a.val), imm2))); -} - -template -inline _Tpvec v_rotate_right(const _Tpvec &a, const _Tpvec &b) -{ - using namespace hal_sse_internal; - enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) }; - return _Tpvec(v_sse_reinterpret_as( - v_sse_palignr_u8( - v_sse_reinterpret_as<__m128i>(a.val), - v_sse_reinterpret_as<__m128i>(b.val)))); -} - -template -inline _Tpvec v_rotate_left(const _Tpvec &a, const _Tpvec &b) -{ - using namespace hal_sse_internal; - enum { imm2 = ((_Tpvec::nlanes - imm) * sizeof(typename _Tpvec::lane_type)) }; - return _Tpvec(v_sse_reinterpret_as( - v_sse_palignr_u8( - v_sse_reinterpret_as<__m128i>(b.val), - v_sse_reinterpret_as<__m128i>(a.val)))); -} - -#define OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(_Tpvec, _Tp) \ -inline _Tpvec v_load(const _Tp* ptr) \ -{ return _Tpvec(_mm_loadu_si128((const __m128i*)ptr)); } \ -inline _Tpvec v_load_aligned(const _Tp* ptr) \ -{ return _Tpvec(_mm_load_si128((const __m128i*)ptr)); } \ -inline _Tpvec v_load_low(const _Tp* ptr) \ -{ return _Tpvec(_mm_loadl_epi64((const __m128i*)ptr)); } \ -inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ -{ \ - return _Tpvec(_mm_unpacklo_epi64(_mm_loadl_epi64((const __m128i*)ptr0), \ - _mm_loadl_epi64((const __m128i*)ptr1))); \ -} \ -inline void v_store(_Tp* ptr, const _Tpvec& a) \ -{ _mm_storeu_si128((__m128i*)ptr, a.val); } \ -inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ -{ _mm_store_si128((__m128i*)ptr, a.val); } \ -inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ -{ _mm_stream_si128((__m128i*)ptr, a.val); } \ -inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ -{ \ - if( mode == hal::STORE_UNALIGNED ) \ - _mm_storeu_si128((__m128i*)ptr, a.val); \ - else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ - _mm_stream_si128((__m128i*)ptr, a.val); \ - else \ - _mm_store_si128((__m128i*)ptr, a.val); \ -} \ -inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ -{ _mm_storel_epi64((__m128i*)ptr, a.val); } \ -inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ -{ _mm_storel_epi64((__m128i*)ptr, _mm_unpackhi_epi64(a.val, a.val)); } - -OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint8x16, uchar) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int8x16, schar) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint16x8, ushort) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int16x8, short) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint32x4, unsigned) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int32x4, int) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint64x2, uint64) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int64x2, int64) - -#define OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(_Tpvec, _Tp, suffix) \ -inline _Tpvec v_load(const _Tp* ptr) \ -{ return _Tpvec(_mm_loadu_##suffix(ptr)); } \ -inline _Tpvec v_load_aligned(const _Tp* ptr) \ -{ return _Tpvec(_mm_load_##suffix(ptr)); } \ -inline _Tpvec v_load_low(const _Tp* ptr) \ -{ return _Tpvec(_mm_castsi128_##suffix(_mm_loadl_epi64((const __m128i*)ptr))); } \ -inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ -{ \ - return _Tpvec(_mm_castsi128_##suffix( \ - _mm_unpacklo_epi64(_mm_loadl_epi64((const __m128i*)ptr0), \ - _mm_loadl_epi64((const __m128i*)ptr1)))); \ -} \ -inline void v_store(_Tp* ptr, const _Tpvec& a) \ -{ _mm_storeu_##suffix(ptr, a.val); } \ -inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ -{ _mm_store_##suffix(ptr, a.val); } \ -inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ -{ _mm_stream_##suffix(ptr, a.val); } \ -inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ -{ \ - if( mode == hal::STORE_UNALIGNED ) \ - _mm_storeu_##suffix(ptr, a.val); \ - else if( mode == hal::STORE_ALIGNED_NOCACHE ) \ - _mm_stream_##suffix(ptr, a.val); \ - else \ - _mm_store_##suffix(ptr, a.val); \ -} \ -inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ -{ _mm_storel_epi64((__m128i*)ptr, _mm_cast##suffix##_si128(a.val)); } \ -inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ -{ \ - __m128i a1 = _mm_cast##suffix##_si128(a.val); \ - _mm_storel_epi64((__m128i*)ptr, _mm_unpackhi_epi64(a1, a1)); \ -} - -OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float32x4, float, ps) -OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float64x2, double, pd) - -inline unsigned v_reduce_sum(const v_uint8x16& a) -{ - __m128i half = _mm_sad_epu8(a.val, _mm_setzero_si128()); - return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(half, _mm_unpackhi_epi64(half, half))); -} -inline int v_reduce_sum(const v_int8x16& a) -{ - __m128i half = _mm_set1_epi8((schar)-128); - half = _mm_sad_epu8(_mm_xor_si128(a.val, half), _mm_setzero_si128()); - return _mm_cvtsi128_si32(_mm_add_epi32(half, _mm_unpackhi_epi64(half, half))) - 2048; -} -#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_16(func) \ -inline schar v_reduce_##func(const v_int8x16& a) \ -{ \ - __m128i val = a.val; \ - __m128i smask = _mm_set1_epi8((schar)-128); \ - val = _mm_xor_si128(val, smask); \ - val = _mm_##func##_epu8(val, _mm_srli_si128(val,8)); \ - val = _mm_##func##_epu8(val, _mm_srli_si128(val,4)); \ - val = _mm_##func##_epu8(val, _mm_srli_si128(val,2)); \ - val = _mm_##func##_epu8(val, _mm_srli_si128(val,1)); \ - return (schar)_mm_cvtsi128_si32(val) ^ (schar)-128; \ -} \ -inline uchar v_reduce_##func(const v_uint8x16& a) \ -{ \ - __m128i val = a.val; \ - val = _mm_##func##_epu8(val, _mm_srli_si128(val,8)); \ - val = _mm_##func##_epu8(val, _mm_srli_si128(val,4)); \ - val = _mm_##func##_epu8(val, _mm_srli_si128(val,2)); \ - val = _mm_##func##_epu8(val, _mm_srli_si128(val,1)); \ - return (uchar)_mm_cvtsi128_si32(val); \ -} -OPENCV_HAL_IMPL_SSE_REDUCE_OP_16(max) -OPENCV_HAL_IMPL_SSE_REDUCE_OP_16(min) - -#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(_Tpvec, scalartype, func, suffix, sbit) \ -inline scalartype v_reduce_##func(const v_##_Tpvec& a) \ -{ \ - __m128i val = a.val; \ - val = _mm_##func##_##suffix(val, _mm_srli_si128(val,8)); \ - val = _mm_##func##_##suffix(val, _mm_srli_si128(val,4)); \ - val = _mm_##func##_##suffix(val, _mm_srli_si128(val,2)); \ - return (scalartype)_mm_cvtsi128_si32(val); \ -} \ -inline unsigned scalartype v_reduce_##func(const v_u##_Tpvec& a) \ -{ \ - __m128i val = a.val; \ - __m128i smask = _mm_set1_epi16(sbit); \ - val = _mm_xor_si128(val, smask); \ - val = _mm_##func##_##suffix(val, _mm_srli_si128(val,8)); \ - val = _mm_##func##_##suffix(val, _mm_srli_si128(val,4)); \ - val = _mm_##func##_##suffix(val, _mm_srli_si128(val,2)); \ - return (unsigned scalartype)(_mm_cvtsi128_si32(val) ^ sbit); \ -} -OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(int16x8, short, max, epi16, (short)-32768) -OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(int16x8, short, min, epi16, (short)-32768) - -#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(_Tpvec, scalartype, regtype, suffix, cast_from, cast_to, extract) \ -inline scalartype v_reduce_sum(const _Tpvec& a) \ -{ \ - regtype val = a.val; \ - val = _mm_add_##suffix(val, cast_to(_mm_srli_si128(cast_from(val), 8))); \ - val = _mm_add_##suffix(val, cast_to(_mm_srli_si128(cast_from(val), 4))); \ - return (scalartype)_mm_cvt##extract(val); \ -} - -#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(_Tpvec, scalartype, func, scalar_func) \ -inline scalartype v_reduce_##func(const _Tpvec& a) \ -{ \ - scalartype CV_DECL_ALIGNED(16) buf[4]; \ - v_store_aligned(buf, a); \ - scalartype s0 = scalar_func(buf[0], buf[1]); \ - scalartype s1 = scalar_func(buf[2], buf[3]); \ - return scalar_func(s0, s1); \ -} - -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_uint32x4, unsigned, __m128i, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP, si128_si32) -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_int32x4, int, __m128i, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP, si128_si32) -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_float32x4, float, __m128, ps, _mm_castps_si128, _mm_castsi128_ps, ss_f32) - -inline int v_reduce_sum(const v_int16x8& a) -{ return v_reduce_sum(v_expand_low(a) + v_expand_high(a)); } -inline unsigned v_reduce_sum(const v_uint16x8& a) -{ return v_reduce_sum(v_expand_low(a) + v_expand_high(a)); } - -inline uint64 v_reduce_sum(const v_uint64x2& a) -{ - uint64 CV_DECL_ALIGNED(32) idx[2]; - v_store_aligned(idx, a); - return idx[0] + idx[1]; -} -inline int64 v_reduce_sum(const v_int64x2& a) -{ - int64 CV_DECL_ALIGNED(32) idx[2]; - v_store_aligned(idx, a); - return idx[0] + idx[1]; -} -inline double v_reduce_sum(const v_float64x2& a) -{ - double CV_DECL_ALIGNED(32) idx[2]; - v_store_aligned(idx, a); - return idx[0] + idx[1]; -} - -inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, - const v_float32x4& c, const v_float32x4& d) -{ -#if CV_SSE3 - __m128 ab = _mm_hadd_ps(a.val, b.val); - __m128 cd = _mm_hadd_ps(c.val, d.val); - return v_float32x4(_mm_hadd_ps(ab, cd)); -#else - __m128 ac = _mm_add_ps(_mm_unpacklo_ps(a.val, c.val), _mm_unpackhi_ps(a.val, c.val)); - __m128 bd = _mm_add_ps(_mm_unpacklo_ps(b.val, d.val), _mm_unpackhi_ps(b.val, d.val)); - return v_float32x4(_mm_add_ps(_mm_unpacklo_ps(ac, bd), _mm_unpackhi_ps(ac, bd))); -#endif -} - -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, max, std::max) -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, min, std::min) -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, max, std::max) -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, min, std::min) -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, max, std::max) -OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, min, std::min) - -inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b) -{ - __m128i half = _mm_sad_epu8(a.val, b.val); - return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(half, _mm_unpackhi_epi64(half, half))); -} -inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b) -{ - __m128i half = _mm_set1_epi8(0x7f); - half = _mm_sad_epu8(_mm_add_epi8(a.val, half), _mm_add_epi8(b.val, half)); - return (unsigned)_mm_cvtsi128_si32(_mm_add_epi32(half, _mm_unpackhi_epi64(half, half))); -} -inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b) -{ - v_uint32x4 l, h; - v_expand(v_absdiff(a, b), l, h); - return v_reduce_sum(l + h); -} -inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b) -{ - v_uint32x4 l, h; - v_expand(v_absdiff(a, b), l, h); - return v_reduce_sum(l + h); -} -inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b) -{ - return v_reduce_sum(v_absdiff(a, b)); -} -inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b) -{ - return v_reduce_sum(v_absdiff(a, b)); -} -inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b) -{ - return v_reduce_sum(v_absdiff(a, b)); -} - -inline v_uint8x16 v_popcount(const v_uint8x16& a) -{ - __m128i m1 = _mm_set1_epi32(0x55555555); - __m128i m2 = _mm_set1_epi32(0x33333333); - __m128i m4 = _mm_set1_epi32(0x0f0f0f0f); - __m128i p = a.val; - p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 1), m1), _mm_and_si128(p, m1)); - p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 2), m2), _mm_and_si128(p, m2)); - p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 4), m4), _mm_and_si128(p, m4)); - return v_uint8x16(p); -} -inline v_uint16x8 v_popcount(const v_uint16x8& a) -{ - v_uint8x16 p = v_popcount(v_reinterpret_as_u8(a)); - p += v_rotate_right<1>(p); - return v_reinterpret_as_u16(p) & v_setall_u16(0x00ff); -} -inline v_uint32x4 v_popcount(const v_uint32x4& a) -{ - v_uint8x16 p = v_popcount(v_reinterpret_as_u8(a)); - p += v_rotate_right<1>(p); - p += v_rotate_right<2>(p); - return v_reinterpret_as_u32(p) & v_setall_u32(0x000000ff); -} -inline v_uint64x2 v_popcount(const v_uint64x2& a) -{ - return v_uint64x2(_mm_sad_epu8(v_popcount(v_reinterpret_as_u8(a)).val, _mm_setzero_si128())); -} -inline v_uint8x16 v_popcount(const v_int8x16& a) -{ return v_popcount(v_reinterpret_as_u8(a)); } -inline v_uint16x8 v_popcount(const v_int16x8& a) -{ return v_popcount(v_reinterpret_as_u16(a)); } -inline v_uint32x4 v_popcount(const v_int32x4& a) -{ return v_popcount(v_reinterpret_as_u32(a)); } -inline v_uint64x2 v_popcount(const v_int64x2& a) -{ return v_popcount(v_reinterpret_as_u64(a)); } - -#define OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(_Tpvec, suffix, cast_op, allmask) \ -inline int v_signmask(const _Tpvec& a) { return _mm_movemask_##suffix(cast_op(a.val)); } \ -inline bool v_check_all(const _Tpvec& a) { return _mm_movemask_##suffix(cast_op(a.val)) == allmask; } \ -inline bool v_check_any(const _Tpvec& a) { return _mm_movemask_##suffix(cast_op(a.val)) != 0; } -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint8x16, epi8, OPENCV_HAL_NOP, 65535) -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int8x16, epi8, OPENCV_HAL_NOP, 65535) -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint32x4, ps, _mm_castsi128_ps, 15) -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int32x4, ps, _mm_castsi128_ps, 15) -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint64x2, pd, _mm_castsi128_pd, 3) -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int64x2, pd, _mm_castsi128_pd, 3) -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_float32x4, ps, OPENCV_HAL_NOP, 15) -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_float64x2, pd, OPENCV_HAL_NOP, 3) - -#define OPENCV_HAL_IMPL_SSE_CHECK_SIGNS_SHORT(_Tpvec) \ -inline int v_signmask(const _Tpvec& a) { return _mm_movemask_epi8(_mm_packs_epi16(a.val, a.val)) & 255; } \ -inline bool v_check_all(const _Tpvec& a) { return (_mm_movemask_epi8(a.val) & 0xaaaa) == 0xaaaa; } \ -inline bool v_check_any(const _Tpvec& a) { return (_mm_movemask_epi8(a.val) & 0xaaaa) != 0; } -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS_SHORT(v_uint16x8) -OPENCV_HAL_IMPL_SSE_CHECK_SIGNS_SHORT(v_int16x8) - -inline int v_scan_forward(const v_int8x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))); } -inline int v_scan_forward(const v_uint8x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))); } -inline int v_scan_forward(const v_int16x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 2; } -inline int v_scan_forward(const v_uint16x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 2; } -inline int v_scan_forward(const v_int32x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; } -inline int v_scan_forward(const v_uint32x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; } -inline int v_scan_forward(const v_float32x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; } -inline int v_scan_forward(const v_int64x2& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; } -inline int v_scan_forward(const v_uint64x2& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; } -inline int v_scan_forward(const v_float64x2& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; } - -#if CV_SSE4_1 -#define OPENCV_HAL_IMPL_SSE_SELECT(_Tpvec, cast_ret, cast, suffix) \ -inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(cast_ret(_mm_blendv_##suffix(cast(b.val), cast(a.val), cast(mask.val)))); \ -} - -OPENCV_HAL_IMPL_SSE_SELECT(v_uint8x16, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8) -OPENCV_HAL_IMPL_SSE_SELECT(v_int8x16, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8) -OPENCV_HAL_IMPL_SSE_SELECT(v_uint16x8, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8) -OPENCV_HAL_IMPL_SSE_SELECT(v_int16x8, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8) -OPENCV_HAL_IMPL_SSE_SELECT(v_uint32x4, _mm_castps_si128, _mm_castsi128_ps, ps) -OPENCV_HAL_IMPL_SSE_SELECT(v_int32x4, _mm_castps_si128, _mm_castsi128_ps, ps) -// OPENCV_HAL_IMPL_SSE_SELECT(v_uint64x2, TBD, TBD, pd) -// OPENCV_HAL_IMPL_SSE_SELECT(v_int64x2, TBD, TBD, ps) -OPENCV_HAL_IMPL_SSE_SELECT(v_float32x4, OPENCV_HAL_NOP, OPENCV_HAL_NOP, ps) -OPENCV_HAL_IMPL_SSE_SELECT(v_float64x2, OPENCV_HAL_NOP, OPENCV_HAL_NOP, pd) - -#else // CV_SSE4_1 - -#define OPENCV_HAL_IMPL_SSE_SELECT(_Tpvec, suffix) \ -inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(_mm_xor_##suffix(b.val, _mm_and_##suffix(_mm_xor_##suffix(b.val, a.val), mask.val))); \ -} - -OPENCV_HAL_IMPL_SSE_SELECT(v_uint8x16, si128) -OPENCV_HAL_IMPL_SSE_SELECT(v_int8x16, si128) -OPENCV_HAL_IMPL_SSE_SELECT(v_uint16x8, si128) -OPENCV_HAL_IMPL_SSE_SELECT(v_int16x8, si128) -OPENCV_HAL_IMPL_SSE_SELECT(v_uint32x4, si128) -OPENCV_HAL_IMPL_SSE_SELECT(v_int32x4, si128) -// OPENCV_HAL_IMPL_SSE_SELECT(v_uint64x2, si128) -// OPENCV_HAL_IMPL_SSE_SELECT(v_int64x2, si128) -OPENCV_HAL_IMPL_SSE_SELECT(v_float32x4, ps) -OPENCV_HAL_IMPL_SSE_SELECT(v_float64x2, pd) -#endif - -/* Expand */ -#define OPENCV_HAL_IMPL_SSE_EXPAND(_Tpvec, _Tpwvec, _Tp, intrin) \ - inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ - { \ - b0.val = intrin(a.val); \ - b1.val = __CV_CAT(intrin, _high)(a.val); \ - } \ - inline _Tpwvec v_expand_low(const _Tpvec& a) \ - { return _Tpwvec(intrin(a.val)); } \ - inline _Tpwvec v_expand_high(const _Tpvec& a) \ - { return _Tpwvec(__CV_CAT(intrin, _high)(a.val)); } \ - inline _Tpwvec v_load_expand(const _Tp* ptr) \ - { \ - __m128i a = _mm_loadl_epi64((const __m128i*)ptr); \ - return _Tpwvec(intrin(a)); \ - } - -OPENCV_HAL_IMPL_SSE_EXPAND(v_uint8x16, v_uint16x8, uchar, _v128_cvtepu8_epi16) -OPENCV_HAL_IMPL_SSE_EXPAND(v_int8x16, v_int16x8, schar, _v128_cvtepi8_epi16) -OPENCV_HAL_IMPL_SSE_EXPAND(v_uint16x8, v_uint32x4, ushort, _v128_cvtepu16_epi32) -OPENCV_HAL_IMPL_SSE_EXPAND(v_int16x8, v_int32x4, short, _v128_cvtepi16_epi32) -OPENCV_HAL_IMPL_SSE_EXPAND(v_uint32x4, v_uint64x2, unsigned, _v128_cvtepu32_epi64) -OPENCV_HAL_IMPL_SSE_EXPAND(v_int32x4, v_int64x2, int, _v128_cvtepi32_epi64) - -#define OPENCV_HAL_IMPL_SSE_EXPAND_Q(_Tpvec, _Tp, intrin) \ - inline _Tpvec v_load_expand_q(const _Tp* ptr) \ - { \ - __m128i a = _mm_cvtsi32_si128(*(const int*)ptr); \ - return _Tpvec(intrin(a)); \ - } - -OPENCV_HAL_IMPL_SSE_EXPAND_Q(v_uint32x4, uchar, _v128_cvtepu8_epi32) -OPENCV_HAL_IMPL_SSE_EXPAND_Q(v_int32x4, schar, _v128_cvtepi8_epi32) - -#define OPENCV_HAL_IMPL_SSE_UNPACKS(_Tpvec, suffix, cast_from, cast_to) \ -inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1) \ -{ \ - b0.val = _mm_unpacklo_##suffix(a0.val, a1.val); \ - b1.val = _mm_unpackhi_##suffix(a0.val, a1.val); \ -} \ -inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \ -{ \ - __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ - return _Tpvec(cast_to(_mm_unpacklo_epi64(a1, b1))); \ -} \ -inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \ -{ \ - __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ - return _Tpvec(cast_to(_mm_unpackhi_epi64(a1, b1))); \ -} \ -inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d) \ -{ \ - __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \ - c.val = cast_to(_mm_unpacklo_epi64(a1, b1)); \ - d.val = cast_to(_mm_unpackhi_epi64(a1, b1)); \ -} - -OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_UNPACKS(v_int8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint16x8, epi16, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_UNPACKS(v_int16x8, epi16, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_UNPACKS(v_int32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_UNPACKS(v_float32x4, ps, _mm_castps_si128, _mm_castsi128_ps) -OPENCV_HAL_IMPL_SSE_UNPACKS(v_float64x2, pd, _mm_castpd_si128, _mm_castsi128_pd) - -inline v_uint8x16 v_reverse(const v_uint8x16 &a) -{ -#if CV_SSSE3 - static const __m128i perm = _mm_setr_epi8(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0); - return v_uint8x16(_mm_shuffle_epi8(a.val, perm)); -#else - uchar CV_DECL_ALIGNED(32) d[16]; - v_store_aligned(d, a); - return v_uint8x16(d[15], d[14], d[13], d[12], d[11], d[10], d[9], d[8], d[7], d[6], d[5], d[4], d[3], d[2], d[1], d[0]); -#endif -} - -inline v_int8x16 v_reverse(const v_int8x16 &a) -{ return v_reinterpret_as_s8(v_reverse(v_reinterpret_as_u8(a))); } - -inline v_uint16x8 v_reverse(const v_uint16x8 &a) -{ -#if CV_SSSE3 - static const __m128i perm = _mm_setr_epi8(14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1); - return v_uint16x8(_mm_shuffle_epi8(a.val, perm)); -#else - __m128i r = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 1, 2, 3)); - r = _mm_shufflelo_epi16(r, _MM_SHUFFLE(2, 3, 0, 1)); - r = _mm_shufflehi_epi16(r, _MM_SHUFFLE(2, 3, 0, 1)); - return v_uint16x8(r); -#endif -} - -inline v_int16x8 v_reverse(const v_int16x8 &a) -{ return v_reinterpret_as_s16(v_reverse(v_reinterpret_as_u16(a))); } - -inline v_uint32x4 v_reverse(const v_uint32x4 &a) -{ - return v_uint32x4(_mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 1, 2, 3))); -} - -inline v_int32x4 v_reverse(const v_int32x4 &a) -{ return v_reinterpret_as_s32(v_reverse(v_reinterpret_as_u32(a))); } - -inline v_float32x4 v_reverse(const v_float32x4 &a) -{ return v_reinterpret_as_f32(v_reverse(v_reinterpret_as_u32(a))); } - -inline v_uint64x2 v_reverse(const v_uint64x2 &a) -{ - return v_uint64x2(_mm_shuffle_epi32(a.val, _MM_SHUFFLE(1, 0, 3, 2))); -} - -inline v_int64x2 v_reverse(const v_int64x2 &a) -{ return v_reinterpret_as_s64(v_reverse(v_reinterpret_as_u64(a))); } - -inline v_float64x2 v_reverse(const v_float64x2 &a) -{ return v_reinterpret_as_f64(v_reverse(v_reinterpret_as_u64(a))); } - -template -inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) -{ - return v_rotate_right(a, b); -} - -inline v_int32x4 v_round(const v_float32x4& a) -{ return v_int32x4(_mm_cvtps_epi32(a.val)); } - -inline v_int32x4 v_floor(const v_float32x4& a) -{ - __m128i a1 = _mm_cvtps_epi32(a.val); - __m128i mask = _mm_castps_si128(_mm_cmpgt_ps(_mm_cvtepi32_ps(a1), a.val)); - return v_int32x4(_mm_add_epi32(a1, mask)); -} - -inline v_int32x4 v_ceil(const v_float32x4& a) -{ - __m128i a1 = _mm_cvtps_epi32(a.val); - __m128i mask = _mm_castps_si128(_mm_cmpgt_ps(a.val, _mm_cvtepi32_ps(a1))); - return v_int32x4(_mm_sub_epi32(a1, mask)); -} - -inline v_int32x4 v_trunc(const v_float32x4& a) -{ return v_int32x4(_mm_cvttps_epi32(a.val)); } - -inline v_int32x4 v_round(const v_float64x2& a) -{ return v_int32x4(_mm_cvtpd_epi32(a.val)); } - -inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b) -{ - __m128i ai = _mm_cvtpd_epi32(a.val), bi = _mm_cvtpd_epi32(b.val); - return v_int32x4(_mm_unpacklo_epi64(ai, bi)); -} - -inline v_int32x4 v_floor(const v_float64x2& a) -{ - __m128i a1 = _mm_cvtpd_epi32(a.val); - __m128i mask = _mm_castpd_si128(_mm_cmpgt_pd(_mm_cvtepi32_pd(a1), a.val)); - mask = _mm_srli_si128(_mm_slli_si128(mask, 4), 8); // m0 m0 m1 m1 => m0 m1 0 0 - return v_int32x4(_mm_add_epi32(a1, mask)); -} - -inline v_int32x4 v_ceil(const v_float64x2& a) -{ - __m128i a1 = _mm_cvtpd_epi32(a.val); - __m128i mask = _mm_castpd_si128(_mm_cmpgt_pd(a.val, _mm_cvtepi32_pd(a1))); - mask = _mm_srli_si128(_mm_slli_si128(mask, 4), 8); // m0 m0 m1 m1 => m0 m1 0 0 - return v_int32x4(_mm_sub_epi32(a1, mask)); -} - -inline v_int32x4 v_trunc(const v_float64x2& a) -{ return v_int32x4(_mm_cvttpd_epi32(a.val)); } - -#define OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(_Tpvec, suffix, cast_from, cast_to) \ -inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \ - const _Tpvec& a2, const _Tpvec& a3, \ - _Tpvec& b0, _Tpvec& b1, \ - _Tpvec& b2, _Tpvec& b3) \ -{ \ - __m128i t0 = cast_from(_mm_unpacklo_##suffix(a0.val, a1.val)); \ - __m128i t1 = cast_from(_mm_unpacklo_##suffix(a2.val, a3.val)); \ - __m128i t2 = cast_from(_mm_unpackhi_##suffix(a0.val, a1.val)); \ - __m128i t3 = cast_from(_mm_unpackhi_##suffix(a2.val, a3.val)); \ -\ - b0.val = cast_to(_mm_unpacklo_epi64(t0, t1)); \ - b1.val = cast_to(_mm_unpackhi_epi64(t0, t1)); \ - b2.val = cast_to(_mm_unpacklo_epi64(t2, t3)); \ - b3.val = cast_to(_mm_unpackhi_epi64(t2, t3)); \ -} - -OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_uint32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_int32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP) -OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_float32x4, ps, _mm_castps_si128, _mm_castsi128_ps) - -// load deinterleave -inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b) -{ - __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); - __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 16)); - - __m128i t10 = _mm_unpacklo_epi8(t00, t01); - __m128i t11 = _mm_unpackhi_epi8(t00, t01); - - __m128i t20 = _mm_unpacklo_epi8(t10, t11); - __m128i t21 = _mm_unpackhi_epi8(t10, t11); - - __m128i t30 = _mm_unpacklo_epi8(t20, t21); - __m128i t31 = _mm_unpackhi_epi8(t20, t21); - - a.val = _mm_unpacklo_epi8(t30, t31); - b.val = _mm_unpackhi_epi8(t30, t31); -} - -inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c) -{ -#if CV_SSE4_1 - const __m128i m0 = _mm_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0); - const __m128i m1 = _mm_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0); - __m128i s0 = _mm_loadu_si128((const __m128i*)ptr); - __m128i s1 = _mm_loadu_si128((const __m128i*)(ptr + 16)); - __m128i s2 = _mm_loadu_si128((const __m128i*)(ptr + 32)); - __m128i a0 = _mm_blendv_epi8(_mm_blendv_epi8(s0, s1, m0), s2, m1); - __m128i b0 = _mm_blendv_epi8(_mm_blendv_epi8(s1, s2, m0), s0, m1); - __m128i c0 = _mm_blendv_epi8(_mm_blendv_epi8(s2, s0, m0), s1, m1); - const __m128i sh_b = _mm_setr_epi8(0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14, 1, 4, 7, 10, 13); - const __m128i sh_g = _mm_setr_epi8(1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15, 2, 5, 8, 11, 14); - const __m128i sh_r = _mm_setr_epi8(2, 5, 8, 11, 14, 1, 4, 7, 10, 13, 0, 3, 6, 9, 12, 15); - a0 = _mm_shuffle_epi8(a0, sh_b); - b0 = _mm_shuffle_epi8(b0, sh_g); - c0 = _mm_shuffle_epi8(c0, sh_r); - a.val = a0; - b.val = b0; - c.val = c0; -#elif CV_SSSE3 - const __m128i m0 = _mm_setr_epi8(0, 3, 6, 9, 12, 15, 1, 4, 7, 10, 13, 2, 5, 8, 11, 14); - const __m128i m1 = _mm_alignr_epi8(m0, m0, 11); - const __m128i m2 = _mm_alignr_epi8(m0, m0, 6); - - __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); - __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 16)); - __m128i t2 = _mm_loadu_si128((const __m128i*)(ptr + 32)); - - __m128i s0 = _mm_shuffle_epi8(t0, m0); - __m128i s1 = _mm_shuffle_epi8(t1, m1); - __m128i s2 = _mm_shuffle_epi8(t2, m2); - - t0 = _mm_alignr_epi8(s1, _mm_slli_si128(s0, 10), 5); - a.val = _mm_alignr_epi8(s2, t0, 5); - - t1 = _mm_alignr_epi8(_mm_srli_si128(s1, 5), _mm_slli_si128(s0, 5), 6); - b.val = _mm_alignr_epi8(_mm_srli_si128(s2, 5), t1, 5); - - t2 = _mm_alignr_epi8(_mm_srli_si128(s2, 10), s1, 11); - c.val = _mm_alignr_epi8(t2, s0, 11); -#else - __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); - __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 16)); - __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 32)); - - __m128i t10 = _mm_unpacklo_epi8(t00, _mm_unpackhi_epi64(t01, t01)); - __m128i t11 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t00, t00), t02); - __m128i t12 = _mm_unpacklo_epi8(t01, _mm_unpackhi_epi64(t02, t02)); - - __m128i t20 = _mm_unpacklo_epi8(t10, _mm_unpackhi_epi64(t11, t11)); - __m128i t21 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t10, t10), t12); - __m128i t22 = _mm_unpacklo_epi8(t11, _mm_unpackhi_epi64(t12, t12)); - - __m128i t30 = _mm_unpacklo_epi8(t20, _mm_unpackhi_epi64(t21, t21)); - __m128i t31 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t20, t20), t22); - __m128i t32 = _mm_unpacklo_epi8(t21, _mm_unpackhi_epi64(t22, t22)); - - a.val = _mm_unpacklo_epi8(t30, _mm_unpackhi_epi64(t31, t31)); - b.val = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t30, t30), t32); - c.val = _mm_unpacklo_epi8(t31, _mm_unpackhi_epi64(t32, t32)); -#endif -} - -inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c, v_uint8x16& d) -{ - __m128i u0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 c0 d0 a1 b1 c1 d1 ... - __m128i u1 = _mm_loadu_si128((const __m128i*)(ptr + 16)); // a4 b4 c4 d4 ... - __m128i u2 = _mm_loadu_si128((const __m128i*)(ptr + 32)); // a8 b8 c8 d8 ... - __m128i u3 = _mm_loadu_si128((const __m128i*)(ptr + 48)); // a12 b12 c12 d12 ... - - __m128i v0 = _mm_unpacklo_epi8(u0, u2); // a0 a8 b0 b8 ... - __m128i v1 = _mm_unpackhi_epi8(u0, u2); // a2 a10 b2 b10 ... - __m128i v2 = _mm_unpacklo_epi8(u1, u3); // a4 a12 b4 b12 ... - __m128i v3 = _mm_unpackhi_epi8(u1, u3); // a6 a14 b6 b14 ... - - u0 = _mm_unpacklo_epi8(v0, v2); // a0 a4 a8 a12 ... - u1 = _mm_unpacklo_epi8(v1, v3); // a2 a6 a10 a14 ... - u2 = _mm_unpackhi_epi8(v0, v2); // a1 a5 a9 a13 ... - u3 = _mm_unpackhi_epi8(v1, v3); // a3 a7 a11 a15 ... - - v0 = _mm_unpacklo_epi8(u0, u1); // a0 a2 a4 a6 ... - v1 = _mm_unpacklo_epi8(u2, u3); // a1 a3 a5 a7 ... - v2 = _mm_unpackhi_epi8(u0, u1); // c0 c2 c4 c6 ... - v3 = _mm_unpackhi_epi8(u2, u3); // c1 c3 c5 c7 ... - - a.val = _mm_unpacklo_epi8(v0, v1); - b.val = _mm_unpackhi_epi8(v0, v1); - c.val = _mm_unpacklo_epi8(v2, v3); - d.val = _mm_unpackhi_epi8(v2, v3); -} - -inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b) -{ - __m128i v0 = _mm_loadu_si128((__m128i*)(ptr)); // a0 b0 a1 b1 a2 b2 a3 b3 - __m128i v1 = _mm_loadu_si128((__m128i*)(ptr + 8)); // a4 b4 a5 b5 a6 b6 a7 b7 - - __m128i v2 = _mm_unpacklo_epi16(v0, v1); // a0 a4 b0 b4 a1 a5 b1 b5 - __m128i v3 = _mm_unpackhi_epi16(v0, v1); // a2 a6 b2 b6 a3 a7 b3 b7 - __m128i v4 = _mm_unpacklo_epi16(v2, v3); // a0 a2 a4 a6 b0 b2 b4 b6 - __m128i v5 = _mm_unpackhi_epi16(v2, v3); // a1 a3 a5 a7 b1 b3 b5 b7 - - a.val = _mm_unpacklo_epi16(v4, v5); // a0 a1 a2 a3 a4 a5 a6 a7 - b.val = _mm_unpackhi_epi16(v4, v5); // b0 b1 ab b3 b4 b5 b6 b7 -} - -inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c) -{ -#if CV_SSE4_1 - __m128i v0 = _mm_loadu_si128((__m128i*)(ptr)); - __m128i v1 = _mm_loadu_si128((__m128i*)(ptr + 8)); - __m128i v2 = _mm_loadu_si128((__m128i*)(ptr + 16)); - __m128i a0 = _mm_blend_epi16(_mm_blend_epi16(v0, v1, 0x92), v2, 0x24); - __m128i b0 = _mm_blend_epi16(_mm_blend_epi16(v2, v0, 0x92), v1, 0x24); - __m128i c0 = _mm_blend_epi16(_mm_blend_epi16(v1, v2, 0x92), v0, 0x24); - - const __m128i sh_a = _mm_setr_epi8(0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11); - const __m128i sh_b = _mm_setr_epi8(2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1, 6, 7, 12, 13); - const __m128i sh_c = _mm_setr_epi8(4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15); - a0 = _mm_shuffle_epi8(a0, sh_a); - b0 = _mm_shuffle_epi8(b0, sh_b); - c0 = _mm_shuffle_epi8(c0, sh_c); - - a.val = a0; - b.val = b0; - c.val = c0; -#else - __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); - __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 8)); - __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 16)); - - __m128i t10 = _mm_unpacklo_epi16(t00, _mm_unpackhi_epi64(t01, t01)); - __m128i t11 = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t00, t00), t02); - __m128i t12 = _mm_unpacklo_epi16(t01, _mm_unpackhi_epi64(t02, t02)); - - __m128i t20 = _mm_unpacklo_epi16(t10, _mm_unpackhi_epi64(t11, t11)); - __m128i t21 = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t10, t10), t12); - __m128i t22 = _mm_unpacklo_epi16(t11, _mm_unpackhi_epi64(t12, t12)); - - a.val = _mm_unpacklo_epi16(t20, _mm_unpackhi_epi64(t21, t21)); - b.val = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t20, t20), t22); - c.val = _mm_unpacklo_epi16(t21, _mm_unpackhi_epi64(t22, t22)); -#endif -} - -inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c, v_uint16x8& d) -{ - __m128i u0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 c0 d0 a1 b1 c1 d1 - __m128i u1 = _mm_loadu_si128((const __m128i*)(ptr + 8)); // a2 b2 c2 d2 ... - __m128i u2 = _mm_loadu_si128((const __m128i*)(ptr + 16)); // a4 b4 c4 d4 ... - __m128i u3 = _mm_loadu_si128((const __m128i*)(ptr + 24)); // a6 b6 c6 d6 ... - - __m128i v0 = _mm_unpacklo_epi16(u0, u2); // a0 a4 b0 b4 ... - __m128i v1 = _mm_unpackhi_epi16(u0, u2); // a1 a5 b1 b5 ... - __m128i v2 = _mm_unpacklo_epi16(u1, u3); // a2 a6 b2 b6 ... - __m128i v3 = _mm_unpackhi_epi16(u1, u3); // a3 a7 b3 b7 ... - - u0 = _mm_unpacklo_epi16(v0, v2); // a0 a2 a4 a6 ... - u1 = _mm_unpacklo_epi16(v1, v3); // a1 a3 a5 a7 ... - u2 = _mm_unpackhi_epi16(v0, v2); // c0 c2 c4 c6 ... - u3 = _mm_unpackhi_epi16(v1, v3); // c1 c3 c5 c7 ... - - a.val = _mm_unpacklo_epi16(u0, u1); - b.val = _mm_unpackhi_epi16(u0, u1); - c.val = _mm_unpacklo_epi16(u2, u3); - d.val = _mm_unpackhi_epi16(u2, u3); -} - -inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b) -{ - __m128i v0 = _mm_loadu_si128((__m128i*)(ptr)); // a0 b0 a1 b1 - __m128i v1 = _mm_loadu_si128((__m128i*)(ptr + 4)); // a2 b2 a3 b3 - - __m128i v2 = _mm_unpacklo_epi32(v0, v1); // a0 a2 b0 b2 - __m128i v3 = _mm_unpackhi_epi32(v0, v1); // a1 a3 b1 b3 - - a.val = _mm_unpacklo_epi32(v2, v3); // a0 a1 a2 a3 - b.val = _mm_unpackhi_epi32(v2, v3); // b0 b1 ab b3 -} - -inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c) -{ - __m128i t00 = _mm_loadu_si128((const __m128i*)ptr); - __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 4)); - __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 8)); - - __m128i t10 = _mm_unpacklo_epi32(t00, _mm_unpackhi_epi64(t01, t01)); - __m128i t11 = _mm_unpacklo_epi32(_mm_unpackhi_epi64(t00, t00), t02); - __m128i t12 = _mm_unpacklo_epi32(t01, _mm_unpackhi_epi64(t02, t02)); - - a.val = _mm_unpacklo_epi32(t10, _mm_unpackhi_epi64(t11, t11)); - b.val = _mm_unpacklo_epi32(_mm_unpackhi_epi64(t10, t10), t12); - c.val = _mm_unpacklo_epi32(t11, _mm_unpackhi_epi64(t12, t12)); -} - -inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c, v_uint32x4& d) -{ - v_uint32x4 s0(_mm_loadu_si128((const __m128i*)ptr)); // a0 b0 c0 d0 - v_uint32x4 s1(_mm_loadu_si128((const __m128i*)(ptr + 4))); // a1 b1 c1 d1 - v_uint32x4 s2(_mm_loadu_si128((const __m128i*)(ptr + 8))); // a2 b2 c2 d2 - v_uint32x4 s3(_mm_loadu_si128((const __m128i*)(ptr + 12))); // a3 b3 c3 d3 - - v_transpose4x4(s0, s1, s2, s3, a, b, c, d); -} - -inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b) -{ - __m128 u0 = _mm_loadu_ps(ptr); // a0 b0 a1 b1 - __m128 u1 = _mm_loadu_ps((ptr + 4)); // a2 b2 a3 b3 - - a.val = _mm_shuffle_ps(u0, u1, _MM_SHUFFLE(2, 0, 2, 0)); // a0 a1 a2 a3 - b.val = _mm_shuffle_ps(u0, u1, _MM_SHUFFLE(3, 1, 3, 1)); // b0 b1 ab b3 -} - -inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b, v_float32x4& c) -{ - __m128 t0 = _mm_loadu_ps(ptr + 0); - __m128 t1 = _mm_loadu_ps(ptr + 4); - __m128 t2 = _mm_loadu_ps(ptr + 8); - - __m128 at12 = _mm_shuffle_ps(t1, t2, _MM_SHUFFLE(0, 1, 0, 2)); - a.val = _mm_shuffle_ps(t0, at12, _MM_SHUFFLE(2, 0, 3, 0)); - - __m128 bt01 = _mm_shuffle_ps(t0, t1, _MM_SHUFFLE(0, 0, 0, 1)); - __m128 bt12 = _mm_shuffle_ps(t1, t2, _MM_SHUFFLE(0, 2, 0, 3)); - b.val = _mm_shuffle_ps(bt01, bt12, _MM_SHUFFLE(2, 0, 2, 0)); - - __m128 ct01 = _mm_shuffle_ps(t0, t1, _MM_SHUFFLE(0, 1, 0, 2)); - c.val = _mm_shuffle_ps(ct01, t2, _MM_SHUFFLE(3, 0, 2, 0)); -} - -inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b, v_float32x4& c, v_float32x4& d) -{ - __m128 t0 = _mm_loadu_ps(ptr + 0); - __m128 t1 = _mm_loadu_ps(ptr + 4); - __m128 t2 = _mm_loadu_ps(ptr + 8); - __m128 t3 = _mm_loadu_ps(ptr + 12); - __m128 t02lo = _mm_unpacklo_ps(t0, t2); - __m128 t13lo = _mm_unpacklo_ps(t1, t3); - __m128 t02hi = _mm_unpackhi_ps(t0, t2); - __m128 t13hi = _mm_unpackhi_ps(t1, t3); - a.val = _mm_unpacklo_ps(t02lo, t13lo); - b.val = _mm_unpackhi_ps(t02lo, t13lo); - c.val = _mm_unpacklo_ps(t02hi, t13hi); - d.val = _mm_unpackhi_ps(t02hi, t13hi); -} - -inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, v_uint64x2& b) -{ - __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); - __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 2)); - - a = v_uint64x2(_mm_unpacklo_epi64(t0, t1)); - b = v_uint64x2(_mm_unpackhi_epi64(t0, t1)); -} - -inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, v_uint64x2& b, v_uint64x2& c) -{ - __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); // a0, b0 - __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 2)); // c0, a1 - __m128i t2 = _mm_loadu_si128((const __m128i*)(ptr + 4)); // b1, c1 - - t1 = _mm_shuffle_epi32(t1, 0x4e); // a1, c0 - - a = v_uint64x2(_mm_unpacklo_epi64(t0, t1)); - b = v_uint64x2(_mm_unpacklo_epi64(_mm_unpackhi_epi64(t0, t0), t2)); - c = v_uint64x2(_mm_unpackhi_epi64(t1, t2)); -} - -inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, - v_uint64x2& b, v_uint64x2& c, v_uint64x2& d) -{ - __m128i t0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 - __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 2)); // c0 d0 - __m128i t2 = _mm_loadu_si128((const __m128i*)(ptr + 4)); // a1 b1 - __m128i t3 = _mm_loadu_si128((const __m128i*)(ptr + 6)); // c1 d1 - - a = v_uint64x2(_mm_unpacklo_epi64(t0, t2)); - b = v_uint64x2(_mm_unpackhi_epi64(t0, t2)); - c = v_uint64x2(_mm_unpacklo_epi64(t1, t3)); - d = v_uint64x2(_mm_unpackhi_epi64(t1, t3)); -} - -// store interleave - -inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, - hal::StoreMode mode = hal::STORE_UNALIGNED) -{ - __m128i v0 = _mm_unpacklo_epi8(a.val, b.val); - __m128i v1 = _mm_unpackhi_epi8(a.val, b.val); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm_stream_si128((__m128i*)(ptr), v0); - _mm_stream_si128((__m128i*)(ptr + 16), v1); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm_store_si128((__m128i*)(ptr), v0); - _mm_store_si128((__m128i*)(ptr + 16), v1); - } - else - { - _mm_storeu_si128((__m128i*)(ptr), v0); - _mm_storeu_si128((__m128i*)(ptr + 16), v1); - } -} - -inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, - const v_uint8x16& c, hal::StoreMode mode = hal::STORE_UNALIGNED) -{ -#if CV_SSE4_1 - const __m128i sh_a = _mm_setr_epi8(0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10, 5); - const __m128i sh_b = _mm_setr_epi8(5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15, 10); - const __m128i sh_c = _mm_setr_epi8(10, 5, 0, 11, 6, 1, 12, 7, 2, 13, 8, 3, 14, 9, 4, 15); - __m128i a0 = _mm_shuffle_epi8(a.val, sh_a); - __m128i b0 = _mm_shuffle_epi8(b.val, sh_b); - __m128i c0 = _mm_shuffle_epi8(c.val, sh_c); - - const __m128i m0 = _mm_setr_epi8(0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0); - const __m128i m1 = _mm_setr_epi8(0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0); - __m128i v0 = _mm_blendv_epi8(_mm_blendv_epi8(a0, b0, m1), c0, m0); - __m128i v1 = _mm_blendv_epi8(_mm_blendv_epi8(b0, c0, m1), a0, m0); - __m128i v2 = _mm_blendv_epi8(_mm_blendv_epi8(c0, a0, m1), b0, m0); -#elif CV_SSSE3 - const __m128i m0 = _mm_setr_epi8(0, 6, 11, 1, 7, 12, 2, 8, 13, 3, 9, 14, 4, 10, 15, 5); - const __m128i m1 = _mm_setr_epi8(5, 11, 0, 6, 12, 1, 7, 13, 2, 8, 14, 3, 9, 15, 4, 10); - const __m128i m2 = _mm_setr_epi8(10, 0, 5, 11, 1, 6, 12, 2, 7, 13, 3, 8, 14, 4, 9, 15); - - __m128i t0 = _mm_alignr_epi8(b.val, _mm_slli_si128(a.val, 10), 5); - t0 = _mm_alignr_epi8(c.val, t0, 5); - __m128i v0 = _mm_shuffle_epi8(t0, m0); - - __m128i t1 = _mm_alignr_epi8(_mm_srli_si128(b.val, 5), _mm_slli_si128(a.val, 5), 6); - t1 = _mm_alignr_epi8(_mm_srli_si128(c.val, 5), t1, 5); - __m128i v1 = _mm_shuffle_epi8(t1, m1); - - __m128i t2 = _mm_alignr_epi8(_mm_srli_si128(c.val, 10), b.val, 11); - t2 = _mm_alignr_epi8(t2, a.val, 11); - __m128i v2 = _mm_shuffle_epi8(t2, m2); -#else - __m128i z = _mm_setzero_si128(); - __m128i ab0 = _mm_unpacklo_epi8(a.val, b.val); - __m128i ab1 = _mm_unpackhi_epi8(a.val, b.val); - __m128i c0 = _mm_unpacklo_epi8(c.val, z); - __m128i c1 = _mm_unpackhi_epi8(c.val, z); - - __m128i p00 = _mm_unpacklo_epi16(ab0, c0); - __m128i p01 = _mm_unpackhi_epi16(ab0, c0); - __m128i p02 = _mm_unpacklo_epi16(ab1, c1); - __m128i p03 = _mm_unpackhi_epi16(ab1, c1); - - __m128i p10 = _mm_unpacklo_epi32(p00, p01); - __m128i p11 = _mm_unpackhi_epi32(p00, p01); - __m128i p12 = _mm_unpacklo_epi32(p02, p03); - __m128i p13 = _mm_unpackhi_epi32(p02, p03); - - __m128i p20 = _mm_unpacklo_epi64(p10, p11); - __m128i p21 = _mm_unpackhi_epi64(p10, p11); - __m128i p22 = _mm_unpacklo_epi64(p12, p13); - __m128i p23 = _mm_unpackhi_epi64(p12, p13); - - p20 = _mm_slli_si128(p20, 1); - p22 = _mm_slli_si128(p22, 1); - - __m128i p30 = _mm_slli_epi64(_mm_unpacklo_epi32(p20, p21), 8); - __m128i p31 = _mm_srli_epi64(_mm_unpackhi_epi32(p20, p21), 8); - __m128i p32 = _mm_slli_epi64(_mm_unpacklo_epi32(p22, p23), 8); - __m128i p33 = _mm_srli_epi64(_mm_unpackhi_epi32(p22, p23), 8); - - __m128i p40 = _mm_unpacklo_epi64(p30, p31); - __m128i p41 = _mm_unpackhi_epi64(p30, p31); - __m128i p42 = _mm_unpacklo_epi64(p32, p33); - __m128i p43 = _mm_unpackhi_epi64(p32, p33); - - __m128i v0 = _mm_or_si128(_mm_srli_si128(p40, 2), _mm_slli_si128(p41, 10)); - __m128i v1 = _mm_or_si128(_mm_srli_si128(p41, 6), _mm_slli_si128(p42, 6)); - __m128i v2 = _mm_or_si128(_mm_srli_si128(p42, 10), _mm_slli_si128(p43, 2)); -#endif - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm_stream_si128((__m128i*)(ptr), v0); - _mm_stream_si128((__m128i*)(ptr + 16), v1); - _mm_stream_si128((__m128i*)(ptr + 32), v2); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm_store_si128((__m128i*)(ptr), v0); - _mm_store_si128((__m128i*)(ptr + 16), v1); - _mm_store_si128((__m128i*)(ptr + 32), v2); - } - else - { - _mm_storeu_si128((__m128i*)(ptr), v0); - _mm_storeu_si128((__m128i*)(ptr + 16), v1); - _mm_storeu_si128((__m128i*)(ptr + 32), v2); - } -} - -inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, - const v_uint8x16& c, const v_uint8x16& d, - hal::StoreMode mode = hal::STORE_UNALIGNED) -{ - // a0 a1 a2 a3 .... - // b0 b1 b2 b3 .... - // c0 c1 c2 c3 .... - // d0 d1 d2 d3 .... - __m128i u0 = _mm_unpacklo_epi8(a.val, c.val); // a0 c0 a1 c1 ... - __m128i u1 = _mm_unpackhi_epi8(a.val, c.val); // a8 c8 a9 c9 ... - __m128i u2 = _mm_unpacklo_epi8(b.val, d.val); // b0 d0 b1 d1 ... - __m128i u3 = _mm_unpackhi_epi8(b.val, d.val); // b8 d8 b9 d9 ... - - __m128i v0 = _mm_unpacklo_epi8(u0, u2); // a0 b0 c0 d0 ... - __m128i v1 = _mm_unpackhi_epi8(u0, u2); // a4 b4 c4 d4 ... - __m128i v2 = _mm_unpacklo_epi8(u1, u3); // a8 b8 c8 d8 ... - __m128i v3 = _mm_unpackhi_epi8(u1, u3); // a12 b12 c12 d12 ... - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm_stream_si128((__m128i*)(ptr), v0); - _mm_stream_si128((__m128i*)(ptr + 16), v1); - _mm_stream_si128((__m128i*)(ptr + 32), v2); - _mm_stream_si128((__m128i*)(ptr + 48), v3); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm_store_si128((__m128i*)(ptr), v0); - _mm_store_si128((__m128i*)(ptr + 16), v1); - _mm_store_si128((__m128i*)(ptr + 32), v2); - _mm_store_si128((__m128i*)(ptr + 48), v3); - } - else - { - _mm_storeu_si128((__m128i*)(ptr), v0); - _mm_storeu_si128((__m128i*)(ptr + 16), v1); - _mm_storeu_si128((__m128i*)(ptr + 32), v2); - _mm_storeu_si128((__m128i*)(ptr + 48), v3); - } -} - -inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, const v_uint16x8& b, - hal::StoreMode mode = hal::STORE_UNALIGNED) -{ - __m128i v0 = _mm_unpacklo_epi16(a.val, b.val); - __m128i v1 = _mm_unpackhi_epi16(a.val, b.val); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm_stream_si128((__m128i*)(ptr), v0); - _mm_stream_si128((__m128i*)(ptr + 8), v1); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm_store_si128((__m128i*)(ptr), v0); - _mm_store_si128((__m128i*)(ptr + 8), v1); - } - else - { - _mm_storeu_si128((__m128i*)(ptr), v0); - _mm_storeu_si128((__m128i*)(ptr + 8), v1); - } -} - -inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, - const v_uint16x8& b, const v_uint16x8& c, - hal::StoreMode mode = hal::STORE_UNALIGNED) -{ -#if CV_SSE4_1 - const __m128i sh_a = _mm_setr_epi8(0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11); - const __m128i sh_b = _mm_setr_epi8(10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5); - const __m128i sh_c = _mm_setr_epi8(4, 5, 10, 11, 0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15); - __m128i a0 = _mm_shuffle_epi8(a.val, sh_a); - __m128i b0 = _mm_shuffle_epi8(b.val, sh_b); - __m128i c0 = _mm_shuffle_epi8(c.val, sh_c); - - __m128i v0 = _mm_blend_epi16(_mm_blend_epi16(a0, b0, 0x92), c0, 0x24); - __m128i v1 = _mm_blend_epi16(_mm_blend_epi16(c0, a0, 0x92), b0, 0x24); - __m128i v2 = _mm_blend_epi16(_mm_blend_epi16(b0, c0, 0x92), a0, 0x24); -#else - __m128i z = _mm_setzero_si128(); - __m128i ab0 = _mm_unpacklo_epi16(a.val, b.val); - __m128i ab1 = _mm_unpackhi_epi16(a.val, b.val); - __m128i c0 = _mm_unpacklo_epi16(c.val, z); - __m128i c1 = _mm_unpackhi_epi16(c.val, z); - - __m128i p10 = _mm_unpacklo_epi32(ab0, c0); - __m128i p11 = _mm_unpackhi_epi32(ab0, c0); - __m128i p12 = _mm_unpacklo_epi32(ab1, c1); - __m128i p13 = _mm_unpackhi_epi32(ab1, c1); - - __m128i p20 = _mm_unpacklo_epi64(p10, p11); - __m128i p21 = _mm_unpackhi_epi64(p10, p11); - __m128i p22 = _mm_unpacklo_epi64(p12, p13); - __m128i p23 = _mm_unpackhi_epi64(p12, p13); - - p20 = _mm_slli_si128(p20, 2); - p22 = _mm_slli_si128(p22, 2); - - __m128i p30 = _mm_unpacklo_epi64(p20, p21); - __m128i p31 = _mm_unpackhi_epi64(p20, p21); - __m128i p32 = _mm_unpacklo_epi64(p22, p23); - __m128i p33 = _mm_unpackhi_epi64(p22, p23); - - __m128i v0 = _mm_or_si128(_mm_srli_si128(p30, 2), _mm_slli_si128(p31, 10)); - __m128i v1 = _mm_or_si128(_mm_srli_si128(p31, 6), _mm_slli_si128(p32, 6)); - __m128i v2 = _mm_or_si128(_mm_srli_si128(p32, 10), _mm_slli_si128(p33, 2)); -#endif - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm_stream_si128((__m128i*)(ptr), v0); - _mm_stream_si128((__m128i*)(ptr + 8), v1); - _mm_stream_si128((__m128i*)(ptr + 16), v2); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm_store_si128((__m128i*)(ptr), v0); - _mm_store_si128((__m128i*)(ptr + 8), v1); - _mm_store_si128((__m128i*)(ptr + 16), v2); - } - else - { - _mm_storeu_si128((__m128i*)(ptr), v0); - _mm_storeu_si128((__m128i*)(ptr + 8), v1); - _mm_storeu_si128((__m128i*)(ptr + 16), v2); - } -} - -inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, const v_uint16x8& b, - const v_uint16x8& c, const v_uint16x8& d, - hal::StoreMode mode = hal::STORE_UNALIGNED) -{ - // a0 a1 a2 a3 .... - // b0 b1 b2 b3 .... - // c0 c1 c2 c3 .... - // d0 d1 d2 d3 .... - __m128i u0 = _mm_unpacklo_epi16(a.val, c.val); // a0 c0 a1 c1 ... - __m128i u1 = _mm_unpackhi_epi16(a.val, c.val); // a4 c4 a5 c5 ... - __m128i u2 = _mm_unpacklo_epi16(b.val, d.val); // b0 d0 b1 d1 ... - __m128i u3 = _mm_unpackhi_epi16(b.val, d.val); // b4 d4 b5 d5 ... - - __m128i v0 = _mm_unpacklo_epi16(u0, u2); // a0 b0 c0 d0 ... - __m128i v1 = _mm_unpackhi_epi16(u0, u2); // a2 b2 c2 d2 ... - __m128i v2 = _mm_unpacklo_epi16(u1, u3); // a4 b4 c4 d4 ... - __m128i v3 = _mm_unpackhi_epi16(u1, u3); // a6 b6 c6 d6 ... - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm_stream_si128((__m128i*)(ptr), v0); - _mm_stream_si128((__m128i*)(ptr + 8), v1); - _mm_stream_si128((__m128i*)(ptr + 16), v2); - _mm_stream_si128((__m128i*)(ptr + 24), v3); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm_store_si128((__m128i*)(ptr), v0); - _mm_store_si128((__m128i*)(ptr + 8), v1); - _mm_store_si128((__m128i*)(ptr + 16), v2); - _mm_store_si128((__m128i*)(ptr + 24), v3); - } - else - { - _mm_storeu_si128((__m128i*)(ptr), v0); - _mm_storeu_si128((__m128i*)(ptr + 8), v1); - _mm_storeu_si128((__m128i*)(ptr + 16), v2); - _mm_storeu_si128((__m128i*)(ptr + 24), v3); - } -} - -inline void v_store_interleave( unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, - hal::StoreMode mode = hal::STORE_UNALIGNED) -{ - __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); - __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm_stream_si128((__m128i*)(ptr), v0); - _mm_stream_si128((__m128i*)(ptr + 4), v1); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm_store_si128((__m128i*)(ptr), v0); - _mm_store_si128((__m128i*)(ptr + 4), v1); - } - else - { - _mm_storeu_si128((__m128i*)(ptr), v0); - _mm_storeu_si128((__m128i*)(ptr + 4), v1); - } -} - -inline void v_store_interleave( unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, - const v_uint32x4& c, hal::StoreMode mode = hal::STORE_UNALIGNED) -{ - v_uint32x4 z = v_setzero_u32(), u0, u1, u2, u3; - v_transpose4x4(a, b, c, z, u0, u1, u2, u3); - - __m128i v0 = _mm_or_si128(u0.val, _mm_slli_si128(u1.val, 12)); - __m128i v1 = _mm_or_si128(_mm_srli_si128(u1.val, 4), _mm_slli_si128(u2.val, 8)); - __m128i v2 = _mm_or_si128(_mm_srli_si128(u2.val, 8), _mm_slli_si128(u3.val, 4)); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm_stream_si128((__m128i*)(ptr), v0); - _mm_stream_si128((__m128i*)(ptr + 4), v1); - _mm_stream_si128((__m128i*)(ptr + 8), v2); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm_store_si128((__m128i*)(ptr), v0); - _mm_store_si128((__m128i*)(ptr + 4), v1); - _mm_store_si128((__m128i*)(ptr + 8), v2); - } - else - { - _mm_storeu_si128((__m128i*)(ptr), v0); - _mm_storeu_si128((__m128i*)(ptr + 4), v1); - _mm_storeu_si128((__m128i*)(ptr + 8), v2); - } -} - -inline void v_store_interleave(unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, - const v_uint32x4& c, const v_uint32x4& d, - hal::StoreMode mode = hal::STORE_UNALIGNED) -{ - v_uint32x4 v0, v1, v2, v3; - v_transpose4x4(a, b, c, d, v0, v1, v2, v3); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm_stream_si128((__m128i*)(ptr), v0.val); - _mm_stream_si128((__m128i*)(ptr + 4), v1.val); - _mm_stream_si128((__m128i*)(ptr + 8), v2.val); - _mm_stream_si128((__m128i*)(ptr + 12), v3.val); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm_store_si128((__m128i*)(ptr), v0.val); - _mm_store_si128((__m128i*)(ptr + 4), v1.val); - _mm_store_si128((__m128i*)(ptr + 8), v2.val); - _mm_store_si128((__m128i*)(ptr + 12), v3.val); - } - else - { - _mm_storeu_si128((__m128i*)(ptr), v0.val); - _mm_storeu_si128((__m128i*)(ptr + 4), v1.val); - _mm_storeu_si128((__m128i*)(ptr + 8), v2.val); - _mm_storeu_si128((__m128i*)(ptr + 12), v3.val); - } -} - -// 2-channel, float only -inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b, - hal::StoreMode mode = hal::STORE_UNALIGNED) -{ - __m128 v0 = _mm_unpacklo_ps(a.val, b.val); // a0 b0 a1 b1 - __m128 v1 = _mm_unpackhi_ps(a.val, b.val); // a2 b2 a3 b3 - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm_stream_ps(ptr, v0); - _mm_stream_ps(ptr + 4, v1); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm_store_ps(ptr, v0); - _mm_store_ps(ptr + 4, v1); - } - else - { - _mm_storeu_ps(ptr, v0); - _mm_storeu_ps(ptr + 4, v1); - } -} - -inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b, - const v_float32x4& c, hal::StoreMode mode = hal::STORE_UNALIGNED) -{ - __m128 u0 = _mm_shuffle_ps(a.val, b.val, _MM_SHUFFLE(0, 0, 0, 0)); - __m128 u1 = _mm_shuffle_ps(c.val, a.val, _MM_SHUFFLE(1, 1, 0, 0)); - __m128 v0 = _mm_shuffle_ps(u0, u1, _MM_SHUFFLE(2, 0, 2, 0)); - __m128 u2 = _mm_shuffle_ps(b.val, c.val, _MM_SHUFFLE(1, 1, 1, 1)); - __m128 u3 = _mm_shuffle_ps(a.val, b.val, _MM_SHUFFLE(2, 2, 2, 2)); - __m128 v1 = _mm_shuffle_ps(u2, u3, _MM_SHUFFLE(2, 0, 2, 0)); - __m128 u4 = _mm_shuffle_ps(c.val, a.val, _MM_SHUFFLE(3, 3, 2, 2)); - __m128 u5 = _mm_shuffle_ps(b.val, c.val, _MM_SHUFFLE(3, 3, 3, 3)); - __m128 v2 = _mm_shuffle_ps(u4, u5, _MM_SHUFFLE(2, 0, 2, 0)); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm_stream_ps(ptr, v0); - _mm_stream_ps(ptr + 4, v1); - _mm_stream_ps(ptr + 8, v2); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm_store_ps(ptr, v0); - _mm_store_ps(ptr + 4, v1); - _mm_store_ps(ptr + 8, v2); - } - else - { - _mm_storeu_ps(ptr, v0); - _mm_storeu_ps(ptr + 4, v1); - _mm_storeu_ps(ptr + 8, v2); - } -} - -inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b, - const v_float32x4& c, const v_float32x4& d, - hal::StoreMode mode = hal::STORE_UNALIGNED) -{ - __m128 u0 = _mm_unpacklo_ps(a.val, c.val); - __m128 u1 = _mm_unpacklo_ps(b.val, d.val); - __m128 u2 = _mm_unpackhi_ps(a.val, c.val); - __m128 u3 = _mm_unpackhi_ps(b.val, d.val); - __m128 v0 = _mm_unpacklo_ps(u0, u1); - __m128 v2 = _mm_unpacklo_ps(u2, u3); - __m128 v1 = _mm_unpackhi_ps(u0, u1); - __m128 v3 = _mm_unpackhi_ps(u2, u3); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm_stream_ps(ptr, v0); - _mm_stream_ps(ptr + 4, v1); - _mm_stream_ps(ptr + 8, v2); - _mm_stream_ps(ptr + 12, v3); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm_store_ps(ptr, v0); - _mm_store_ps(ptr + 4, v1); - _mm_store_ps(ptr + 8, v2); - _mm_store_ps(ptr + 12, v3); - } - else - { - _mm_storeu_ps(ptr, v0); - _mm_storeu_ps(ptr + 4, v1); - _mm_storeu_ps(ptr + 8, v2); - _mm_storeu_ps(ptr + 12, v3); - } -} - -inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b, - hal::StoreMode mode = hal::STORE_UNALIGNED) -{ - __m128i v0 = _mm_unpacklo_epi64(a.val, b.val); - __m128i v1 = _mm_unpackhi_epi64(a.val, b.val); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm_stream_si128((__m128i*)(ptr), v0); - _mm_stream_si128((__m128i*)(ptr + 2), v1); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm_store_si128((__m128i*)(ptr), v0); - _mm_store_si128((__m128i*)(ptr + 2), v1); - } - else - { - _mm_storeu_si128((__m128i*)(ptr), v0); - _mm_storeu_si128((__m128i*)(ptr + 2), v1); - } -} - -inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b, - const v_uint64x2& c, hal::StoreMode mode = hal::STORE_UNALIGNED) -{ - __m128i v0 = _mm_unpacklo_epi64(a.val, b.val); - __m128i v1 = _mm_unpacklo_epi64(c.val, _mm_unpackhi_epi64(a.val, a.val)); - __m128i v2 = _mm_unpackhi_epi64(b.val, c.val); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm_stream_si128((__m128i*)(ptr), v0); - _mm_stream_si128((__m128i*)(ptr + 2), v1); - _mm_stream_si128((__m128i*)(ptr + 4), v2); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm_store_si128((__m128i*)(ptr), v0); - _mm_store_si128((__m128i*)(ptr + 2), v1); - _mm_store_si128((__m128i*)(ptr + 4), v2); - } - else - { - _mm_storeu_si128((__m128i*)(ptr), v0); - _mm_storeu_si128((__m128i*)(ptr + 2), v1); - _mm_storeu_si128((__m128i*)(ptr + 4), v2); - } -} - -inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b, - const v_uint64x2& c, const v_uint64x2& d, - hal::StoreMode mode = hal::STORE_UNALIGNED) -{ - __m128i v0 = _mm_unpacklo_epi64(a.val, b.val); - __m128i v1 = _mm_unpacklo_epi64(c.val, d.val); - __m128i v2 = _mm_unpackhi_epi64(a.val, b.val); - __m128i v3 = _mm_unpackhi_epi64(c.val, d.val); - - if( mode == hal::STORE_ALIGNED_NOCACHE ) - { - _mm_stream_si128((__m128i*)(ptr), v0); - _mm_stream_si128((__m128i*)(ptr + 2), v1); - _mm_stream_si128((__m128i*)(ptr + 4), v2); - _mm_stream_si128((__m128i*)(ptr + 6), v3); - } - else if( mode == hal::STORE_ALIGNED ) - { - _mm_store_si128((__m128i*)(ptr), v0); - _mm_store_si128((__m128i*)(ptr + 2), v1); - _mm_store_si128((__m128i*)(ptr + 4), v2); - _mm_store_si128((__m128i*)(ptr + 6), v3); - } - else - { - _mm_storeu_si128((__m128i*)(ptr), v0); - _mm_storeu_si128((__m128i*)(ptr + 2), v1); - _mm_storeu_si128((__m128i*)(ptr + 4), v2); - _mm_storeu_si128((__m128i*)(ptr + 6), v3); - } -} - -#define OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(_Tpvec0, _Tp0, suffix0, _Tpvec1, _Tp1, suffix1) \ -inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0 ) \ -{ \ - _Tpvec1 a1, b1; \ - v_load_deinterleave((const _Tp1*)ptr, a1, b1); \ - a0 = v_reinterpret_as_##suffix0(a1); \ - b0 = v_reinterpret_as_##suffix0(b1); \ -} \ -inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0 ) \ -{ \ - _Tpvec1 a1, b1, c1; \ - v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1); \ - a0 = v_reinterpret_as_##suffix0(a1); \ - b0 = v_reinterpret_as_##suffix0(b1); \ - c0 = v_reinterpret_as_##suffix0(c1); \ -} \ -inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0, _Tpvec0& d0 ) \ -{ \ - _Tpvec1 a1, b1, c1, d1; \ - v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1, d1); \ - a0 = v_reinterpret_as_##suffix0(a1); \ - b0 = v_reinterpret_as_##suffix0(b1); \ - c0 = v_reinterpret_as_##suffix0(c1); \ - d0 = v_reinterpret_as_##suffix0(d1); \ -} \ -inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ - hal::StoreMode mode = hal::STORE_UNALIGNED ) \ -{ \ - _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ - _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ - v_store_interleave((_Tp1*)ptr, a1, b1, mode); \ -} \ -inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ - const _Tpvec0& c0, hal::StoreMode mode = hal::STORE_UNALIGNED ) \ -{ \ - _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ - _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ - _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ - v_store_interleave((_Tp1*)ptr, a1, b1, c1, mode); \ -} \ -inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ - const _Tpvec0& c0, const _Tpvec0& d0, \ - hal::StoreMode mode = hal::STORE_UNALIGNED ) \ -{ \ - _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ - _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ - _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ - _Tpvec1 d1 = v_reinterpret_as_##suffix1(d0); \ - v_store_interleave((_Tp1*)ptr, a1, b1, c1, d1, mode); \ -} - -OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int8x16, schar, s8, v_uint8x16, uchar, u8) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int16x8, short, s16, v_uint16x8, ushort, u16) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int32x4, int, s32, v_uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int64x2, int64, s64, v_uint64x2, uint64, u64) -OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_float64x2, double, f64, v_uint64x2, uint64, u64) - -inline v_float32x4 v_cvt_f32(const v_int32x4& a) -{ - return v_float32x4(_mm_cvtepi32_ps(a.val)); -} - -inline v_float32x4 v_cvt_f32(const v_float64x2& a) -{ - return v_float32x4(_mm_cvtpd_ps(a.val)); -} - -inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b) -{ - return v_float32x4(_mm_movelh_ps(_mm_cvtpd_ps(a.val), _mm_cvtpd_ps(b.val))); -} - -inline v_float64x2 v_cvt_f64(const v_int32x4& a) -{ - return v_float64x2(_mm_cvtepi32_pd(a.val)); -} - -inline v_float64x2 v_cvt_f64_high(const v_int32x4& a) -{ - return v_float64x2(_mm_cvtepi32_pd(_mm_srli_si128(a.val,8))); -} - -inline v_float64x2 v_cvt_f64(const v_float32x4& a) -{ - return v_float64x2(_mm_cvtps_pd(a.val)); -} - -inline v_float64x2 v_cvt_f64_high(const v_float32x4& a) -{ - return v_float64x2(_mm_cvtps_pd(_mm_movehl_ps(a.val, a.val))); -} - -// from (Mysticial and wim) https://stackoverflow.com/q/41144668 -inline v_float64x2 v_cvt_f64(const v_int64x2& v) -{ - // constants encoded as floating-point - __m128i magic_i_hi32 = _mm_set1_epi64x(0x4530000080000000); // 2^84 + 2^63 - __m128i magic_i_all = _mm_set1_epi64x(0x4530000080100000); // 2^84 + 2^63 + 2^52 - __m128d magic_d_all = _mm_castsi128_pd(magic_i_all); - // Blend the 32 lowest significant bits of v with magic_int_lo -#if CV_SSE4_1 - __m128i magic_i_lo = _mm_set1_epi64x(0x4330000000000000); // 2^52 - __m128i v_lo = _mm_blend_epi16(v.val, magic_i_lo, 0xcc); -#else - __m128i magic_i_lo = _mm_set1_epi32(0x43300000); // 2^52 - __m128i v_lo = _mm_unpacklo_epi32(_mm_shuffle_epi32(v.val, _MM_SHUFFLE(0, 0, 2, 0)), magic_i_lo); -#endif - // Extract the 32 most significant bits of v - __m128i v_hi = _mm_srli_epi64(v.val, 32); - // Flip the msb of v_hi and blend with 0x45300000 - v_hi = _mm_xor_si128(v_hi, magic_i_hi32); - // Compute in double precision - __m128d v_hi_dbl = _mm_sub_pd(_mm_castsi128_pd(v_hi), magic_d_all); - // (v_hi - magic_d_all) + v_lo Do not assume associativity of floating point addition - __m128d result = _mm_add_pd(v_hi_dbl, _mm_castsi128_pd(v_lo)); - return v_float64x2(result); -} - -////////////// Lookup table access //////////////////// - -inline v_int8x16 v_lut(const schar* tab, const int* idx) -{ -#if defined(_MSC_VER) - return v_int8x16(_mm_setr_epi8(tab[idx[0]], tab[idx[1]], tab[idx[ 2]], tab[idx[ 3]], tab[idx[ 4]], tab[idx[ 5]], tab[idx[ 6]], tab[idx[ 7]], - tab[idx[8]], tab[idx[9]], tab[idx[10]], tab[idx[11]], tab[idx[12]], tab[idx[13]], tab[idx[14]], tab[idx[15]])); -#else - return v_int8x16(_mm_setr_epi64( - _mm_setr_pi8(tab[idx[0]], tab[idx[1]], tab[idx[ 2]], tab[idx[ 3]], tab[idx[ 4]], tab[idx[ 5]], tab[idx[ 6]], tab[idx[ 7]]), - _mm_setr_pi8(tab[idx[8]], tab[idx[9]], tab[idx[10]], tab[idx[11]], tab[idx[12]], tab[idx[13]], tab[idx[14]], tab[idx[15]]) - )); -#endif -} -inline v_int8x16 v_lut_pairs(const schar* tab, const int* idx) -{ -#if defined(_MSC_VER) - return v_int8x16(_mm_setr_epi16(*(const short*)(tab + idx[0]), *(const short*)(tab + idx[1]), *(const short*)(tab + idx[2]), *(const short*)(tab + idx[3]), - *(const short*)(tab + idx[4]), *(const short*)(tab + idx[5]), *(const short*)(tab + idx[6]), *(const short*)(tab + idx[7]))); -#else - return v_int8x16(_mm_setr_epi64( - _mm_setr_pi16(*(const short*)(tab + idx[0]), *(const short*)(tab + idx[1]), *(const short*)(tab + idx[2]), *(const short*)(tab + idx[3])), - _mm_setr_pi16(*(const short*)(tab + idx[4]), *(const short*)(tab + idx[5]), *(const short*)(tab + idx[6]), *(const short*)(tab + idx[7])) - )); -#endif -} -inline v_int8x16 v_lut_quads(const schar* tab, const int* idx) -{ -#if defined(_MSC_VER) - return v_int8x16(_mm_setr_epi32(*(const int*)(tab + idx[0]), *(const int*)(tab + idx[1]), - *(const int*)(tab + idx[2]), *(const int*)(tab + idx[3]))); -#else - return v_int8x16(_mm_setr_epi64( - _mm_setr_pi32(*(const int*)(tab + idx[0]), *(const int*)(tab + idx[1])), - _mm_setr_pi32(*(const int*)(tab + idx[2]), *(const int*)(tab + idx[3])) - )); -#endif -} -inline v_uint8x16 v_lut(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut((const schar *)tab, idx)); } -inline v_uint8x16 v_lut_pairs(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_pairs((const schar *)tab, idx)); } -inline v_uint8x16 v_lut_quads(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_quads((const schar *)tab, idx)); } - -inline v_int16x8 v_lut(const short* tab, const int* idx) -{ -#if defined(_MSC_VER) - return v_int16x8(_mm_setr_epi16(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]], - tab[idx[4]], tab[idx[5]], tab[idx[6]], tab[idx[7]])); -#else - return v_int16x8(_mm_setr_epi64( - _mm_setr_pi16(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]), - _mm_setr_pi16(tab[idx[4]], tab[idx[5]], tab[idx[6]], tab[idx[7]]) - )); -#endif -} -inline v_int16x8 v_lut_pairs(const short* tab, const int* idx) -{ -#if defined(_MSC_VER) - return v_int16x8(_mm_setr_epi32(*(const int*)(tab + idx[0]), *(const int*)(tab + idx[1]), - *(const int*)(tab + idx[2]), *(const int*)(tab + idx[3]))); -#else - return v_int16x8(_mm_setr_epi64( - _mm_setr_pi32(*(const int*)(tab + idx[0]), *(const int*)(tab + idx[1])), - _mm_setr_pi32(*(const int*)(tab + idx[2]), *(const int*)(tab + idx[3])) - )); -#endif -} -inline v_int16x8 v_lut_quads(const short* tab, const int* idx) -{ - return v_int16x8(_mm_set_epi64x(*(const int64_t*)(tab + idx[1]), *(const int64_t*)(tab + idx[0]))); -} -inline v_uint16x8 v_lut(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut((const short *)tab, idx)); } -inline v_uint16x8 v_lut_pairs(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_pairs((const short *)tab, idx)); } -inline v_uint16x8 v_lut_quads(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_quads((const short *)tab, idx)); } - -inline v_int32x4 v_lut(const int* tab, const int* idx) -{ -#if defined(_MSC_VER) - return v_int32x4(_mm_setr_epi32(tab[idx[0]], tab[idx[1]], - tab[idx[2]], tab[idx[3]])); -#else - return v_int32x4(_mm_setr_epi64( - _mm_setr_pi32(tab[idx[0]], tab[idx[1]]), - _mm_setr_pi32(tab[idx[2]], tab[idx[3]]) - )); -#endif -} -inline v_int32x4 v_lut_pairs(const int* tab, const int* idx) -{ - return v_int32x4(_mm_set_epi64x(*(const int64_t*)(tab + idx[1]), *(const int64_t*)(tab + idx[0]))); -} -inline v_int32x4 v_lut_quads(const int* tab, const int* idx) -{ - return v_int32x4(_mm_loadu_si128((const __m128i*)(tab + idx[0]))); -} -inline v_uint32x4 v_lut(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut((const int *)tab, idx)); } -inline v_uint32x4 v_lut_pairs(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_pairs((const int *)tab, idx)); } -inline v_uint32x4 v_lut_quads(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_quads((const int *)tab, idx)); } - -inline v_int64x2 v_lut(const int64_t* tab, const int* idx) -{ - return v_int64x2(_mm_set_epi64x(tab[idx[1]], tab[idx[0]])); -} -inline v_int64x2 v_lut_pairs(const int64_t* tab, const int* idx) -{ - return v_int64x2(_mm_loadu_si128((const __m128i*)(tab + idx[0]))); -} -inline v_uint64x2 v_lut(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut((const int64_t *)tab, idx)); } -inline v_uint64x2 v_lut_pairs(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut_pairs((const int64_t *)tab, idx)); } - -inline v_float32x4 v_lut(const float* tab, const int* idx) -{ - return v_float32x4(_mm_setr_ps(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]])); -} -inline v_float32x4 v_lut_pairs(const float* tab, const int* idx) { return v_reinterpret_as_f32(v_lut_pairs((const int *)tab, idx)); } -inline v_float32x4 v_lut_quads(const float* tab, const int* idx) { return v_reinterpret_as_f32(v_lut_quads((const int *)tab, idx)); } - -inline v_float64x2 v_lut(const double* tab, const int* idx) -{ - return v_float64x2(_mm_setr_pd(tab[idx[0]], tab[idx[1]])); -} -inline v_float64x2 v_lut_pairs(const double* tab, const int* idx) { return v_float64x2(_mm_castsi128_pd(_mm_loadu_si128((const __m128i*)(tab + idx[0])))); } - -inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec) -{ - int CV_DECL_ALIGNED(32) idx[4]; - v_store_aligned(idx, idxvec); - return v_int32x4(_mm_setr_epi32(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]])); -} - -inline v_uint32x4 v_lut(const unsigned* tab, const v_int32x4& idxvec) -{ - return v_reinterpret_as_u32(v_lut((const int *)tab, idxvec)); -} - -inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec) -{ - int CV_DECL_ALIGNED(32) idx[4]; - v_store_aligned(idx, idxvec); - return v_float32x4(_mm_setr_ps(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]])); -} - -inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec) -{ - int idx[2]; - v_store_low(idx, idxvec); - return v_float64x2(_mm_setr_pd(tab[idx[0]], tab[idx[1]])); -} - -// loads pairs from the table and deinterleaves them, e.g. returns: -// x = (tab[idxvec[0], tab[idxvec[1]], tab[idxvec[2]], tab[idxvec[3]]), -// y = (tab[idxvec[0]+1], tab[idxvec[1]+1], tab[idxvec[2]+1], tab[idxvec[3]+1]) -// note that the indices are float's indices, not the float-pair indices. -// in theory, this function can be used to implement bilinear interpolation, -// when idxvec are the offsets within the image. -inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y) -{ - int CV_DECL_ALIGNED(32) idx[4]; - v_store_aligned(idx, idxvec); - __m128 z = _mm_setzero_ps(); - __m128 xy01 = _mm_loadl_pi(z, (__m64*)(tab + idx[0])); - __m128 xy23 = _mm_loadl_pi(z, (__m64*)(tab + idx[2])); - xy01 = _mm_loadh_pi(xy01, (__m64*)(tab + idx[1])); - xy23 = _mm_loadh_pi(xy23, (__m64*)(tab + idx[3])); - __m128 xxyy02 = _mm_unpacklo_ps(xy01, xy23); - __m128 xxyy13 = _mm_unpackhi_ps(xy01, xy23); - x = v_float32x4(_mm_unpacklo_ps(xxyy02, xxyy13)); - y = v_float32x4(_mm_unpackhi_ps(xxyy02, xxyy13)); -} - -inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y) -{ - int idx[2]; - v_store_low(idx, idxvec); - __m128d xy0 = _mm_loadu_pd(tab + idx[0]); - __m128d xy1 = _mm_loadu_pd(tab + idx[1]); - x = v_float64x2(_mm_unpacklo_pd(xy0, xy1)); - y = v_float64x2(_mm_unpackhi_pd(xy0, xy1)); -} - -inline v_int8x16 v_interleave_pairs(const v_int8x16& vec) -{ -#if CV_SSSE3 - return v_int8x16(_mm_shuffle_epi8(vec.val, _mm_set_epi64x(0x0f0d0e0c0b090a08, 0x0705060403010200))); -#else - __m128i a = _mm_shufflelo_epi16(vec.val, _MM_SHUFFLE(3, 1, 2, 0)); - a = _mm_shufflehi_epi16(a, _MM_SHUFFLE(3, 1, 2, 0)); - a = _mm_shuffle_epi32(a, _MM_SHUFFLE(3, 1, 2, 0)); - return v_int8x16(_mm_unpacklo_epi8(a, _mm_unpackhi_epi64(a, a))); -#endif -} -inline v_uint8x16 v_interleave_pairs(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_interleave_pairs(v_reinterpret_as_s8(vec))); } -inline v_int8x16 v_interleave_quads(const v_int8x16& vec) -{ -#if CV_SSSE3 - return v_int8x16(_mm_shuffle_epi8(vec.val, _mm_set_epi64x(0x0f0b0e0a0d090c08, 0x0703060205010400))); -#else - __m128i a = _mm_shuffle_epi32(vec.val, _MM_SHUFFLE(3, 1, 2, 0)); - return v_int8x16(_mm_unpacklo_epi8(a, _mm_unpackhi_epi64(a, a))); -#endif -} -inline v_uint8x16 v_interleave_quads(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_interleave_quads(v_reinterpret_as_s8(vec))); } - -inline v_int16x8 v_interleave_pairs(const v_int16x8& vec) -{ -#if CV_SSSE3 - return v_int16x8(_mm_shuffle_epi8(vec.val, _mm_set_epi64x(0x0f0e0b0a0d0c0908, 0x0706030205040100))); -#else - __m128i a = _mm_shufflelo_epi16(vec.val, _MM_SHUFFLE(3, 1, 2, 0)); - return v_int16x8(_mm_shufflehi_epi16(a, _MM_SHUFFLE(3, 1, 2, 0))); -#endif -} -inline v_uint16x8 v_interleave_pairs(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_pairs(v_reinterpret_as_s16(vec))); } -inline v_int16x8 v_interleave_quads(const v_int16x8& vec) -{ -#if CV_SSSE3 - return v_int16x8(_mm_shuffle_epi8(vec.val, _mm_set_epi64x(0x0f0e07060d0c0504, 0x0b0a030209080100))); -#else - return v_int16x8(_mm_unpacklo_epi16(vec.val, _mm_unpackhi_epi64(vec.val, vec.val))); -#endif -} -inline v_uint16x8 v_interleave_quads(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_quads(v_reinterpret_as_s16(vec))); } - -inline v_int32x4 v_interleave_pairs(const v_int32x4& vec) -{ - return v_int32x4(_mm_shuffle_epi32(vec.val, _MM_SHUFFLE(3, 1, 2, 0))); -} -inline v_uint32x4 v_interleave_pairs(const v_uint32x4& vec) { return v_reinterpret_as_u32(v_interleave_pairs(v_reinterpret_as_s32(vec))); } -inline v_float32x4 v_interleave_pairs(const v_float32x4& vec) { return v_reinterpret_as_f32(v_interleave_pairs(v_reinterpret_as_s32(vec))); } - -inline v_int8x16 v_pack_triplets(const v_int8x16& vec) -{ -#if CV_SSSE3 - return v_int8x16(_mm_shuffle_epi8(vec.val, _mm_set_epi64x(0xffffff0f0e0d0c0a, 0x0908060504020100))); -#else - __m128i mask = _mm_set1_epi64x(0x00000000FFFFFFFF); - __m128i a = _mm_srli_si128(_mm_or_si128(_mm_andnot_si128(mask, vec.val), _mm_and_si128(mask, _mm_sll_epi32(vec.val, _mm_set_epi64x(0, 8)))), 1); - return v_int8x16(_mm_srli_si128(_mm_shufflelo_epi16(a, _MM_SHUFFLE(2, 1, 0, 3)), 2)); -#endif -} -inline v_uint8x16 v_pack_triplets(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec))); } - -inline v_int16x8 v_pack_triplets(const v_int16x8& vec) -{ -#if CV_SSSE3 - return v_int16x8(_mm_shuffle_epi8(vec.val, _mm_set_epi64x(0xffff0f0e0d0c0b0a, 0x0908050403020100))); -#else - return v_int16x8(_mm_srli_si128(_mm_shufflelo_epi16(vec.val, _MM_SHUFFLE(2, 1, 0, 3)), 2)); -#endif -} -inline v_uint16x8 v_pack_triplets(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec))); } - -inline v_int32x4 v_pack_triplets(const v_int32x4& vec) { return vec; } -inline v_uint32x4 v_pack_triplets(const v_uint32x4& vec) { return vec; } -inline v_float32x4 v_pack_triplets(const v_float32x4& vec) { return vec; } - -template -inline uchar v_extract_n(const v_uint8x16& v) -{ -#if CV_SSE4_1 - return (uchar)_mm_extract_epi8(v.val, i); -#else - return v_rotate_right(v).get0(); -#endif -} - -template -inline schar v_extract_n(const v_int8x16& v) -{ - return (schar)v_extract_n(v_reinterpret_as_u8(v)); -} - -template -inline ushort v_extract_n(const v_uint16x8& v) -{ - return (ushort)_mm_extract_epi16(v.val, i); -} - -template -inline short v_extract_n(const v_int16x8& v) -{ - return (short)v_extract_n(v_reinterpret_as_u16(v)); -} - -template -inline uint v_extract_n(const v_uint32x4& v) -{ -#if CV_SSE4_1 - return (uint)_mm_extract_epi32(v.val, i); -#else - return v_rotate_right(v).get0(); -#endif -} - -template -inline int v_extract_n(const v_int32x4& v) -{ - return (int)v_extract_n(v_reinterpret_as_u32(v)); -} - -template -inline uint64 v_extract_n(const v_uint64x2& v) -{ -#ifdef CV__SIMD_NATIVE_mm_extract_epi64 - return (uint64)_v128_extract_epi64(v.val); -#else - return v_rotate_right(v).get0(); -#endif -} - -template -inline int64 v_extract_n(const v_int64x2& v) -{ - return (int64)v_extract_n(v_reinterpret_as_u64(v)); -} - -template -inline float v_extract_n(const v_float32x4& v) -{ - union { uint iv; float fv; } d; - d.iv = v_extract_n(v_reinterpret_as_u32(v)); - return d.fv; -} - -template -inline double v_extract_n(const v_float64x2& v) -{ - union { uint64 iv; double dv; } d; - d.iv = v_extract_n(v_reinterpret_as_u64(v)); - return d.dv; -} - -template -inline v_int32x4 v_broadcast_element(const v_int32x4& v) -{ - return v_int32x4(_mm_shuffle_epi32(v.val, _MM_SHUFFLE(i,i,i,i))); -} - -template -inline v_uint32x4 v_broadcast_element(const v_uint32x4& v) -{ - return v_uint32x4(_mm_shuffle_epi32(v.val, _MM_SHUFFLE(i,i,i,i))); -} - -template -inline v_float32x4 v_broadcast_element(const v_float32x4& v) -{ - return v_float32x4(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE((char)i,(char)i,(char)i,(char)i))); -} - -////////////// FP16 support /////////////////////////// - -inline v_float32x4 v_load_expand(const float16_t* ptr) -{ -#if CV_FP16 - return v_float32x4(_mm_cvtph_ps(_mm_loadu_si128((const __m128i*)ptr))); -#else - const __m128i z = _mm_setzero_si128(), delta = _mm_set1_epi32(0x38000000); - const __m128i signmask = _mm_set1_epi32(0x80000000), maxexp = _mm_set1_epi32(0x7c000000); - const __m128 deltaf = _mm_castsi128_ps(_mm_set1_epi32(0x38800000)); - __m128i bits = _mm_unpacklo_epi16(z, _mm_loadl_epi64((const __m128i*)ptr)); // h << 16 - __m128i e = _mm_and_si128(bits, maxexp), sign = _mm_and_si128(bits, signmask); - __m128i t = _mm_add_epi32(_mm_srli_epi32(_mm_xor_si128(bits, sign), 3), delta); // ((h & 0x7fff) << 13) + delta - __m128i zt = _mm_castps_si128(_mm_sub_ps(_mm_castsi128_ps(_mm_add_epi32(t, _mm_set1_epi32(1 << 23))), deltaf)); - - t = _mm_add_epi32(t, _mm_and_si128(delta, _mm_cmpeq_epi32(maxexp, e))); - __m128i zmask = _mm_cmpeq_epi32(e, z); - __m128i ft = v_select_si128(zmask, zt, t); - return v_float32x4(_mm_castsi128_ps(_mm_or_si128(ft, sign))); -#endif -} - -inline void v_pack_store(float16_t* ptr, const v_float32x4& v) -{ -#if CV_FP16 - __m128i fp16_value = _mm_cvtps_ph(v.val, 0); - _mm_storel_epi64((__m128i*)ptr, fp16_value); -#else - const __m128i signmask = _mm_set1_epi32(0x80000000); - const __m128i rval = _mm_set1_epi32(0x3f000000); - - __m128i t = _mm_castps_si128(v.val); - __m128i sign = _mm_srai_epi32(_mm_and_si128(t, signmask), 16); - t = _mm_andnot_si128(signmask, t); - - __m128i finitemask = _mm_cmpgt_epi32(_mm_set1_epi32(0x47800000), t); - __m128i isnan = _mm_cmpgt_epi32(t, _mm_set1_epi32(0x7f800000)); - __m128i naninf = v_select_si128(isnan, _mm_set1_epi32(0x7e00), _mm_set1_epi32(0x7c00)); - __m128i tinymask = _mm_cmpgt_epi32(_mm_set1_epi32(0x38800000), t); - __m128i tt = _mm_castps_si128(_mm_add_ps(_mm_castsi128_ps(t), _mm_castsi128_ps(rval))); - tt = _mm_sub_epi32(tt, rval); - __m128i odd = _mm_and_si128(_mm_srli_epi32(t, 13), _mm_set1_epi32(1)); - __m128i nt = _mm_add_epi32(t, _mm_set1_epi32(0xc8000fff)); - nt = _mm_srli_epi32(_mm_add_epi32(nt, odd), 13); - t = v_select_si128(tinymask, tt, nt); - t = v_select_si128(finitemask, t, naninf); - t = _mm_or_si128(t, sign); - t = _mm_packs_epi32(t, t); - _mm_storel_epi64((__m128i*)ptr, t); -#endif -} - -inline void v_cleanup() {} - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END - -//! @endcond - -} - -#endif diff --git a/include/opencv2/core/hal/intrin_sse_em.hpp b/include/opencv2/core/hal/intrin_sse_em.hpp deleted file mode 100755 index 6fb0881..0000000 --- a/include/opencv2/core/hal/intrin_sse_em.hpp +++ /dev/null @@ -1,180 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html - -#ifndef OPENCV_HAL_INTRIN_SSE_EM_HPP -#define OPENCV_HAL_INTRIN_SSE_EM_HPP - -namespace cv -{ - -//! @cond IGNORED - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN - -#define OPENCV_HAL_SSE_WRAP_1(fun, tp) \ - inline tp _v128_##fun(const tp& a) \ - { return _mm_##fun(a); } - -#define OPENCV_HAL_SSE_WRAP_2(fun, tp) \ - inline tp _v128_##fun(const tp& a, const tp& b) \ - { return _mm_##fun(a, b); } - -#define OPENCV_HAL_SSE_WRAP_3(fun, tp) \ - inline tp _v128_##fun(const tp& a, const tp& b, const tp& c) \ - { return _mm_##fun(a, b, c); } - -///////////////////////////// XOP ///////////////////////////// - -// [todo] define CV_XOP -#if 1 // CV_XOP -inline __m128i _v128_comgt_epu32(const __m128i& a, const __m128i& b) -{ - const __m128i delta = _mm_set1_epi32((int)0x80000000); - return _mm_cmpgt_epi32(_mm_xor_si128(a, delta), _mm_xor_si128(b, delta)); -} -// wrapping XOP -#else -OPENCV_HAL_SSE_WRAP_2(_v128_comgt_epu32, __m128i) -#endif // !CV_XOP - -///////////////////////////// SSE4.1 ///////////////////////////// - -#if !CV_SSE4_1 - -/** Swizzle **/ -inline __m128i _v128_blendv_epi8(const __m128i& a, const __m128i& b, const __m128i& mask) -{ return _mm_xor_si128(a, _mm_and_si128(_mm_xor_si128(b, a), mask)); } - -/** Convert **/ -// 8 >> 16 -inline __m128i _v128_cvtepu8_epi16(const __m128i& a) -{ - const __m128i z = _mm_setzero_si128(); - return _mm_unpacklo_epi8(a, z); -} -inline __m128i _v128_cvtepi8_epi16(const __m128i& a) -{ return _mm_srai_epi16(_mm_unpacklo_epi8(a, a), 8); } -// 8 >> 32 -inline __m128i _v128_cvtepu8_epi32(const __m128i& a) -{ - const __m128i z = _mm_setzero_si128(); - return _mm_unpacklo_epi16(_mm_unpacklo_epi8(a, z), z); -} -inline __m128i _v128_cvtepi8_epi32(const __m128i& a) -{ - __m128i r = _mm_unpacklo_epi8(a, a); - r = _mm_unpacklo_epi8(r, r); - return _mm_srai_epi32(r, 24); -} -// 16 >> 32 -inline __m128i _v128_cvtepu16_epi32(const __m128i& a) -{ - const __m128i z = _mm_setzero_si128(); - return _mm_unpacklo_epi16(a, z); -} -inline __m128i _v128_cvtepi16_epi32(const __m128i& a) -{ return _mm_srai_epi32(_mm_unpacklo_epi16(a, a), 16); } -// 32 >> 64 -inline __m128i _v128_cvtepu32_epi64(const __m128i& a) -{ - const __m128i z = _mm_setzero_si128(); - return _mm_unpacklo_epi32(a, z); -} -inline __m128i _v128_cvtepi32_epi64(const __m128i& a) -{ return _mm_unpacklo_epi32(a, _mm_srai_epi32(a, 31)); } - -/** Arithmetic **/ -inline __m128i _v128_mullo_epi32(const __m128i& a, const __m128i& b) -{ - __m128i c0 = _mm_mul_epu32(a, b); - __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a, 32), _mm_srli_epi64(b, 32)); - __m128i d0 = _mm_unpacklo_epi32(c0, c1); - __m128i d1 = _mm_unpackhi_epi32(c0, c1); - return _mm_unpacklo_epi64(d0, d1); -} - -/** Math **/ -inline __m128i _v128_min_epu32(const __m128i& a, const __m128i& b) -{ return _v128_blendv_epi8(a, b, _v128_comgt_epu32(a, b)); } - -// wrapping SSE4.1 -#else -OPENCV_HAL_SSE_WRAP_1(cvtepu8_epi16, __m128i) -OPENCV_HAL_SSE_WRAP_1(cvtepi8_epi16, __m128i) -OPENCV_HAL_SSE_WRAP_1(cvtepu8_epi32, __m128i) -OPENCV_HAL_SSE_WRAP_1(cvtepi8_epi32, __m128i) -OPENCV_HAL_SSE_WRAP_1(cvtepu16_epi32, __m128i) -OPENCV_HAL_SSE_WRAP_1(cvtepi16_epi32, __m128i) -OPENCV_HAL_SSE_WRAP_1(cvtepu32_epi64, __m128i) -OPENCV_HAL_SSE_WRAP_1(cvtepi32_epi64, __m128i) -OPENCV_HAL_SSE_WRAP_2(min_epu32, __m128i) -OPENCV_HAL_SSE_WRAP_2(mullo_epi32, __m128i) -OPENCV_HAL_SSE_WRAP_3(blendv_epi8, __m128i) -#endif // !CV_SSE4_1 - -///////////////////////////// Revolutionary ///////////////////////////// - -/** Convert **/ -// 16 << 8 -inline __m128i _v128_cvtepu8_epi16_high(const __m128i& a) -{ - const __m128i z = _mm_setzero_si128(); - return _mm_unpackhi_epi8(a, z); -} -inline __m128i _v128_cvtepi8_epi16_high(const __m128i& a) -{ return _mm_srai_epi16(_mm_unpackhi_epi8(a, a), 8); } -// 32 << 16 -inline __m128i _v128_cvtepu16_epi32_high(const __m128i& a) -{ - const __m128i z = _mm_setzero_si128(); - return _mm_unpackhi_epi16(a, z); -} -inline __m128i _v128_cvtepi16_epi32_high(const __m128i& a) -{ return _mm_srai_epi32(_mm_unpackhi_epi16(a, a), 16); } -// 64 << 32 -inline __m128i _v128_cvtepu32_epi64_high(const __m128i& a) -{ - const __m128i z = _mm_setzero_si128(); - return _mm_unpackhi_epi32(a, z); -} -inline __m128i _v128_cvtepi32_epi64_high(const __m128i& a) -{ return _mm_unpackhi_epi32(a, _mm_srai_epi32(a, 31)); } - -/** Miscellaneous **/ -inline __m128i _v128_packs_epu32(const __m128i& a, const __m128i& b) -{ - const __m128i m = _mm_set1_epi32(65535); - __m128i am = _v128_min_epu32(a, m); - __m128i bm = _v128_min_epu32(b, m); -#if CV_SSE4_1 - return _mm_packus_epi32(am, bm); -#else - const __m128i d = _mm_set1_epi32(32768), nd = _mm_set1_epi16(-32768); - am = _mm_sub_epi32(am, d); - bm = _mm_sub_epi32(bm, d); - am = _mm_packs_epi32(am, bm); - return _mm_sub_epi16(am, nd); -#endif -} - -template -inline int64 _v128_extract_epi64(const __m128i& a) -{ -#if defined(CV__SIMD_HAVE_mm_extract_epi64) || (CV_SSE4_1 && (defined(__x86_64__)/*GCC*/ || defined(_M_X64)/*MSVC*/)) -#define CV__SIMD_NATIVE_mm_extract_epi64 1 - return _mm_extract_epi64(a, i); -#else - CV_DECL_ALIGNED(16) int64 tmp[2]; - _mm_store_si128((__m128i*)tmp, a); - return tmp[i]; -#endif -} - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END - -//! @endcond - -} // cv:: - -#endif // OPENCV_HAL_INTRIN_SSE_EM_HPP diff --git a/include/opencv2/core/hal/intrin_vsx.hpp b/include/opencv2/core/hal/intrin_vsx.hpp deleted file mode 100755 index b198643..0000000 --- a/include/opencv2/core/hal/intrin_vsx.hpp +++ /dev/null @@ -1,1608 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html - -#ifndef OPENCV_HAL_VSX_HPP -#define OPENCV_HAL_VSX_HPP - -#include -#include "opencv2/core/utility.hpp" - -#define CV_SIMD128 1 -#define CV_SIMD128_64F 1 - -namespace cv -{ - -//! @cond IGNORED - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN - -///////// Types //////////// - -struct v_uint8x16 -{ - typedef uchar lane_type; - enum { nlanes = 16 }; - vec_uchar16 val; - - explicit v_uint8x16(const vec_uchar16& v) : val(v) - {} - v_uint8x16() - {} - v_uint8x16(vec_bchar16 v) : val(vec_uchar16_c(v)) - {} - v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, - uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) - : val(vec_uchar16_set(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15)) - {} - - static inline v_uint8x16 zero() { return v_uint8x16(vec_uchar16_z); } - - uchar get0() const - { return vec_extract(val, 0); } -}; - -struct v_int8x16 -{ - typedef schar lane_type; - enum { nlanes = 16 }; - vec_char16 val; - - explicit v_int8x16(const vec_char16& v) : val(v) - {} - v_int8x16() - {} - v_int8x16(vec_bchar16 v) : val(vec_char16_c(v)) - {} - v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, - schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) - : val(vec_char16_set(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15)) - {} - - static inline v_int8x16 zero() { return v_int8x16(vec_char16_z); } - - schar get0() const - { return vec_extract(val, 0); } -}; - -struct v_uint16x8 -{ - typedef ushort lane_type; - enum { nlanes = 8 }; - vec_ushort8 val; - - explicit v_uint16x8(const vec_ushort8& v) : val(v) - {} - v_uint16x8() - {} - v_uint16x8(vec_bshort8 v) : val(vec_ushort8_c(v)) - {} - v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) - : val(vec_ushort8_set(v0, v1, v2, v3, v4, v5, v6, v7)) - {} - - static inline v_uint16x8 zero() { return v_uint16x8(vec_ushort8_z); } - - ushort get0() const - { return vec_extract(val, 0); } -}; - -struct v_int16x8 -{ - typedef short lane_type; - enum { nlanes = 8 }; - vec_short8 val; - - explicit v_int16x8(const vec_short8& v) : val(v) - {} - v_int16x8() - {} - v_int16x8(vec_bshort8 v) : val(vec_short8_c(v)) - {} - v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) - : val(vec_short8_set(v0, v1, v2, v3, v4, v5, v6, v7)) - {} - - static inline v_int16x8 zero() { return v_int16x8(vec_short8_z); } - - short get0() const - { return vec_extract(val, 0); } -}; - -struct v_uint32x4 -{ - typedef unsigned lane_type; - enum { nlanes = 4 }; - vec_uint4 val; - - explicit v_uint32x4(const vec_uint4& v) : val(v) - {} - v_uint32x4() - {} - v_uint32x4(vec_bint4 v) : val(vec_uint4_c(v)) - {} - v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) : val(vec_uint4_set(v0, v1, v2, v3)) - {} - - static inline v_uint32x4 zero() { return v_uint32x4(vec_uint4_z); } - - uint get0() const - { return vec_extract(val, 0); } -}; - -struct v_int32x4 -{ - typedef int lane_type; - enum { nlanes = 4 }; - vec_int4 val; - - explicit v_int32x4(const vec_int4& v) : val(v) - {} - v_int32x4() - {} - v_int32x4(vec_bint4 v) : val(vec_int4_c(v)) - {} - v_int32x4(int v0, int v1, int v2, int v3) : val(vec_int4_set(v0, v1, v2, v3)) - {} - - static inline v_int32x4 zero() { return v_int32x4(vec_int4_z); } - - int get0() const - { return vec_extract(val, 0); } -}; - -struct v_float32x4 -{ - typedef float lane_type; - enum { nlanes = 4 }; - vec_float4 val; - - explicit v_float32x4(const vec_float4& v) : val(v) - {} - v_float32x4() - {} - v_float32x4(vec_bint4 v) : val(vec_float4_c(v)) - {} - v_float32x4(float v0, float v1, float v2, float v3) : val(vec_float4_set(v0, v1, v2, v3)) - {} - - static inline v_float32x4 zero() { return v_float32x4(vec_float4_z); } - - float get0() const - { return vec_extract(val, 0); } -}; - -struct v_uint64x2 -{ - typedef uint64 lane_type; - enum { nlanes = 2 }; - vec_udword2 val; - - explicit v_uint64x2(const vec_udword2& v) : val(v) - {} - v_uint64x2() - {} - v_uint64x2(vec_bdword2 v) : val(vec_udword2_c(v)) - {} - v_uint64x2(uint64 v0, uint64 v1) : val(vec_udword2_set(v0, v1)) - {} - - static inline v_uint64x2 zero() { return v_uint64x2(vec_udword2_z); } - - uint64 get0() const - { return vec_extract(val, 0); } -}; - -struct v_int64x2 -{ - typedef int64 lane_type; - enum { nlanes = 2 }; - vec_dword2 val; - - explicit v_int64x2(const vec_dword2& v) : val(v) - {} - v_int64x2() - {} - v_int64x2(vec_bdword2 v) : val(vec_dword2_c(v)) - {} - v_int64x2(int64 v0, int64 v1) : val(vec_dword2_set(v0, v1)) - {} - - static inline v_int64x2 zero() { return v_int64x2(vec_dword2_z); } - - int64 get0() const - { return vec_extract(val, 0); } -}; - -struct v_float64x2 -{ - typedef double lane_type; - enum { nlanes = 2 }; - vec_double2 val; - - explicit v_float64x2(const vec_double2& v) : val(v) - {} - v_float64x2() - {} - v_float64x2(vec_bdword2 v) : val(vec_double2_c(v)) - {} - v_float64x2(double v0, double v1) : val(vec_double2_set(v0, v1)) - {} - - static inline v_float64x2 zero() { return v_float64x2(vec_double2_z); } - - double get0() const - { return vec_extract(val, 0); } -}; - -#define OPENCV_HAL_IMPL_VSX_EXTRACT_N(_Tpvec, _Tp) \ -template inline _Tp v_extract_n(VSX_UNUSED(_Tpvec v)) { return vec_extract(v.val, i); } - -OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_uint8x16, uchar) -OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_int8x16, schar) -OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_uint16x8, ushort) -OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_int16x8, short) -OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_uint32x4, uint) -OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_int32x4, int) -OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_uint64x2, uint64) -OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_int64x2, int64) -OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_float32x4, float) -OPENCV_HAL_IMPL_VSX_EXTRACT_N(v_float64x2, double) - -//////////////// Load and store operations /////////////// - -/* - * clang-5 aborted during parse "vec_xxx_c" only if it's - * inside a function template which is defined by preprocessor macro. - * - * if vec_xxx_c defined as C++ cast, clang-5 will pass it -*/ -#define OPENCV_HAL_IMPL_VSX_INITVEC(_Tpvec, _Tp, suffix, cast) \ -inline _Tpvec v_setzero_##suffix() { return _Tpvec(vec_splats((_Tp)0)); } \ -inline _Tpvec v_setall_##suffix(_Tp v) { return _Tpvec(vec_splats((_Tp)v));} \ -template inline _Tpvec v_reinterpret_as_##suffix(const _Tpvec0 &a) \ -{ return _Tpvec((cast)a.val); } - -OPENCV_HAL_IMPL_VSX_INITVEC(v_uint8x16, uchar, u8, vec_uchar16) -OPENCV_HAL_IMPL_VSX_INITVEC(v_int8x16, schar, s8, vec_char16) -OPENCV_HAL_IMPL_VSX_INITVEC(v_uint16x8, ushort, u16, vec_ushort8) -OPENCV_HAL_IMPL_VSX_INITVEC(v_int16x8, short, s16, vec_short8) -OPENCV_HAL_IMPL_VSX_INITVEC(v_uint32x4, uint, u32, vec_uint4) -OPENCV_HAL_IMPL_VSX_INITVEC(v_int32x4, int, s32, vec_int4) -OPENCV_HAL_IMPL_VSX_INITVEC(v_uint64x2, uint64, u64, vec_udword2) -OPENCV_HAL_IMPL_VSX_INITVEC(v_int64x2, int64, s64, vec_dword2) -OPENCV_HAL_IMPL_VSX_INITVEC(v_float32x4, float, f32, vec_float4) -OPENCV_HAL_IMPL_VSX_INITVEC(v_float64x2, double, f64, vec_double2) - -#define OPENCV_HAL_IMPL_VSX_LOADSTORE_C(_Tpvec, _Tp, ld, ld_a, st, st_a) \ -inline _Tpvec v_load(const _Tp* ptr) \ -{ return _Tpvec(ld(0, ptr)); } \ -inline _Tpvec v_load_aligned(VSX_UNUSED(const _Tp* ptr)) \ -{ return _Tpvec(ld_a(0, ptr)); } \ -inline _Tpvec v_load_low(const _Tp* ptr) \ -{ return _Tpvec(vec_ld_l8(ptr)); } \ -inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ -{ return _Tpvec(vec_mergesqh(vec_ld_l8(ptr0), vec_ld_l8(ptr1))); } \ -inline void v_store(_Tp* ptr, const _Tpvec& a) \ -{ st(a.val, 0, ptr); } \ -inline void v_store_aligned(VSX_UNUSED(_Tp* ptr), const _Tpvec& a) \ -{ st_a(a.val, 0, ptr); } \ -inline void v_store_aligned_nocache(VSX_UNUSED(_Tp* ptr), const _Tpvec& a) \ -{ st_a(a.val, 0, ptr); } \ -inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode mode) \ -{ if(mode == hal::STORE_UNALIGNED) st(a.val, 0, ptr); else st_a(a.val, 0, ptr); } \ -inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ -{ vec_st_l8(a.val, ptr); } \ -inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ -{ vec_st_h8(a.val, ptr); } - -// working around gcc bug for aligned ld/st -// if runtime check for vec_ld/st fail we failback to unaligned ld/st -// https://github.com/opencv/opencv/issues/13211 -#ifdef CV_COMPILER_VSX_BROKEN_ALIGNED - #define OPENCV_HAL_IMPL_VSX_LOADSTORE(_Tpvec, _Tp) \ - OPENCV_HAL_IMPL_VSX_LOADSTORE_C(_Tpvec, _Tp, vsx_ld, vsx_ld, vsx_st, vsx_st) -#else - #define OPENCV_HAL_IMPL_VSX_LOADSTORE(_Tpvec, _Tp) \ - OPENCV_HAL_IMPL_VSX_LOADSTORE_C(_Tpvec, _Tp, vsx_ld, vec_ld, vsx_st, vec_st) -#endif - -OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint8x16, uchar) -OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int8x16, schar) -OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint16x8, ushort) -OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int16x8, short) -OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint32x4, uint) -OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int32x4, int) -OPENCV_HAL_IMPL_VSX_LOADSTORE(v_float32x4, float) - -OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_float64x2, double, vsx_ld, vsx_ld, vsx_st, vsx_st) -OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_uint64x2, uint64, vsx_ld2, vsx_ld2, vsx_st2, vsx_st2) -OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_int64x2, int64, vsx_ld2, vsx_ld2, vsx_st2, vsx_st2) - -//////////////// Value reordering /////////////// - -/* de&interleave */ -#define OPENCV_HAL_IMPL_VSX_INTERLEAVE(_Tp, _Tpvec) \ -inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, _Tpvec& b) \ -{ vec_ld_deinterleave(ptr, a.val, b.val);} \ -inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, \ - _Tpvec& b, _Tpvec& c) \ -{ vec_ld_deinterleave(ptr, a.val, b.val, c.val); } \ -inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, _Tpvec& b, \ - _Tpvec& c, _Tpvec& d) \ -{ vec_ld_deinterleave(ptr, a.val, b.val, c.val, d.val); } \ -inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, const _Tpvec& b, \ - hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ -{ vec_st_interleave(a.val, b.val, ptr); } \ -inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, \ - const _Tpvec& b, const _Tpvec& c, \ - hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ -{ vec_st_interleave(a.val, b.val, c.val, ptr); } \ -inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, const _Tpvec& b, \ - const _Tpvec& c, const _Tpvec& d, \ - hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) \ -{ vec_st_interleave(a.val, b.val, c.val, d.val, ptr); } - -OPENCV_HAL_IMPL_VSX_INTERLEAVE(uchar, v_uint8x16) -OPENCV_HAL_IMPL_VSX_INTERLEAVE(schar, v_int8x16) -OPENCV_HAL_IMPL_VSX_INTERLEAVE(ushort, v_uint16x8) -OPENCV_HAL_IMPL_VSX_INTERLEAVE(short, v_int16x8) -OPENCV_HAL_IMPL_VSX_INTERLEAVE(uint, v_uint32x4) -OPENCV_HAL_IMPL_VSX_INTERLEAVE(int, v_int32x4) -OPENCV_HAL_IMPL_VSX_INTERLEAVE(float, v_float32x4) -OPENCV_HAL_IMPL_VSX_INTERLEAVE(double, v_float64x2) -OPENCV_HAL_IMPL_VSX_INTERLEAVE(int64, v_int64x2) -OPENCV_HAL_IMPL_VSX_INTERLEAVE(uint64, v_uint64x2) - -/* Expand */ -#define OPENCV_HAL_IMPL_VSX_EXPAND(_Tpvec, _Tpwvec, _Tp, fl, fh) \ -inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ -{ \ - b0.val = fh(a.val); \ - b1.val = fl(a.val); \ -} \ -inline _Tpwvec v_expand_low(const _Tpvec& a) \ -{ return _Tpwvec(fh(a.val)); } \ -inline _Tpwvec v_expand_high(const _Tpvec& a) \ -{ return _Tpwvec(fl(a.val)); } \ -inline _Tpwvec v_load_expand(const _Tp* ptr) \ -{ return _Tpwvec(fh(vec_ld_l8(ptr))); } - -OPENCV_HAL_IMPL_VSX_EXPAND(v_uint8x16, v_uint16x8, uchar, vec_unpacklu, vec_unpackhu) -OPENCV_HAL_IMPL_VSX_EXPAND(v_int8x16, v_int16x8, schar, vec_unpackl, vec_unpackh) -OPENCV_HAL_IMPL_VSX_EXPAND(v_uint16x8, v_uint32x4, ushort, vec_unpacklu, vec_unpackhu) -OPENCV_HAL_IMPL_VSX_EXPAND(v_int16x8, v_int32x4, short, vec_unpackl, vec_unpackh) -OPENCV_HAL_IMPL_VSX_EXPAND(v_uint32x4, v_uint64x2, uint, vec_unpacklu, vec_unpackhu) -OPENCV_HAL_IMPL_VSX_EXPAND(v_int32x4, v_int64x2, int, vec_unpackl, vec_unpackh) - -/* Load and zero expand a 4 byte value into the second dword, first is don't care. */ -#if !defined(CV_COMPILER_VSX_BROKEN_ASM) - #define _LXSIWZX(out, ptr, T) __asm__ ("lxsiwzx %x0, 0, %1\r\n" : "=wa"(out) : "r" (ptr) : "memory"); -#else - /* This is compiler-agnostic, but will introduce an unneeded splat on the critical path. */ - #define _LXSIWZX(out, ptr, T) out = (T)vec_udword2_sp(*(uint32_t*)(ptr)); -#endif - -inline v_uint32x4 v_load_expand_q(const uchar* ptr) -{ - // Zero-extend the extra 24B instead of unpacking. Usually faster in small kernel - // Likewise note, value is zero extended and upper 4 bytes are zero'ed. - vec_uchar16 pmu = {8, 12, 12, 12, 9, 12, 12, 12, 10, 12, 12, 12, 11, 12, 12, 12}; - vec_uchar16 out; - - _LXSIWZX(out, ptr, vec_uchar16); - out = vec_perm(out, out, pmu); - return v_uint32x4((vec_uint4)out); -} - -inline v_int32x4 v_load_expand_q(const schar* ptr) -{ - vec_char16 out; - vec_short8 outs; - vec_int4 outw; - - _LXSIWZX(out, ptr, vec_char16); - outs = vec_unpackl(out); - outw = vec_unpackh(outs); - return v_int32x4(outw); -} - -/* pack */ -#define OPENCV_HAL_IMPL_VSX_PACK(_Tpvec, _Tp, _Tpwvec, _Tpvn, _Tpdel, sfnc, pkfnc, addfnc, pack) \ -inline _Tpvec v_##pack(const _Tpwvec& a, const _Tpwvec& b) \ -{ \ - return _Tpvec(pkfnc(a.val, b.val)); \ -} \ -inline void v_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ -{ \ - vec_st_l8(pkfnc(a.val, a.val), ptr); \ -} \ -template \ -inline _Tpvec v_rshr_##pack(const _Tpwvec& a, const _Tpwvec& b) \ -{ \ - const __vector _Tpvn vn = vec_splats((_Tpvn)n); \ - const __vector _Tpdel delta = vec_splats((_Tpdel)((_Tpdel)1 << (n-1))); \ - return _Tpvec(pkfnc(sfnc(addfnc(a.val, delta), vn), sfnc(addfnc(b.val, delta), vn))); \ -} \ -template \ -inline void v_rshr_##pack##_store(_Tp* ptr, const _Tpwvec& a) \ -{ \ - const __vector _Tpvn vn = vec_splats((_Tpvn)n); \ - const __vector _Tpdel delta = vec_splats((_Tpdel)((_Tpdel)1 << (n-1))); \ - vec_st_l8(pkfnc(sfnc(addfnc(a.val, delta), vn), delta), ptr); \ -} - -OPENCV_HAL_IMPL_VSX_PACK(v_uint8x16, uchar, v_uint16x8, unsigned short, unsigned short, - vec_sr, vec_packs, vec_adds, pack) -OPENCV_HAL_IMPL_VSX_PACK(v_int8x16, schar, v_int16x8, unsigned short, short, - vec_sra, vec_packs, vec_adds, pack) - -OPENCV_HAL_IMPL_VSX_PACK(v_uint16x8, ushort, v_uint32x4, unsigned int, unsigned int, - vec_sr, vec_packs, vec_add, pack) -OPENCV_HAL_IMPL_VSX_PACK(v_int16x8, short, v_int32x4, unsigned int, int, - vec_sra, vec_packs, vec_add, pack) - -OPENCV_HAL_IMPL_VSX_PACK(v_uint32x4, uint, v_uint64x2, unsigned long long, unsigned long long, - vec_sr, vec_pack, vec_add, pack) -OPENCV_HAL_IMPL_VSX_PACK(v_int32x4, int, v_int64x2, unsigned long long, long long, - vec_sra, vec_pack, vec_add, pack) - -OPENCV_HAL_IMPL_VSX_PACK(v_uint8x16, uchar, v_int16x8, unsigned short, short, - vec_sra, vec_packsu, vec_adds, pack_u) -OPENCV_HAL_IMPL_VSX_PACK(v_uint16x8, ushort, v_int32x4, unsigned int, int, - vec_sra, vec_packsu, vec_add, pack_u) -// Following variant is not implemented on other platforms: -//OPENCV_HAL_IMPL_VSX_PACK(v_uint32x4, uint, v_int64x2, unsigned long long, long long, -// vec_sra, vec_packsu, vec_add, pack_u) - -// pack boolean -inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) -{ - vec_uchar16 ab = vec_pack(a.val, b.val); - return v_uint8x16(ab); -} - -inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, - const v_uint32x4& c, const v_uint32x4& d) -{ - vec_ushort8 ab = vec_pack(a.val, b.val); - vec_ushort8 cd = vec_pack(c.val, d.val); - return v_uint8x16(vec_pack(ab, cd)); -} - -inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, - const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, - const v_uint64x2& g, const v_uint64x2& h) -{ - vec_uint4 ab = vec_pack(a.val, b.val); - vec_uint4 cd = vec_pack(c.val, d.val); - vec_uint4 ef = vec_pack(e.val, f.val); - vec_uint4 gh = vec_pack(g.val, h.val); - - vec_ushort8 abcd = vec_pack(ab, cd); - vec_ushort8 efgh = vec_pack(ef, gh); - return v_uint8x16(vec_pack(abcd, efgh)); -} - -/* Recombine */ -template -inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1) -{ - b0.val = vec_mergeh(a0.val, a1.val); - b1.val = vec_mergel(a0.val, a1.val); -} - -template -inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) -{ return _Tpvec(vec_mergesql(a.val, b.val)); } - -template -inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) -{ return _Tpvec(vec_mergesqh(a.val, b.val)); } - -template -inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d) -{ - c.val = vec_mergesqh(a.val, b.val); - d.val = vec_mergesql(a.val, b.val); -} - -////////// Arithmetic, bitwise and comparison operations ///////// - -/* Element-wise binary and unary operations */ -/** Arithmetics **/ -#define OPENCV_HAL_IMPL_VSX_BIN_OP(bin_op, _Tpvec, intrin) \ -inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(intrin(a.val, b.val)); } \ -inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ -{ a.val = intrin(a.val, b.val); return a; } - -OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint8x16, vec_adds) -OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint8x16, vec_subs) -OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int8x16, vec_adds) -OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int8x16, vec_subs) -OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint16x8, vec_adds) -OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint16x8, vec_subs) -OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int16x8, vec_adds) -OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int16x8, vec_subs) -OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint32x4, vec_add) -OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint32x4, vec_sub) -OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_uint32x4, vec_mul) -OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int32x4, vec_add) -OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int32x4, vec_sub) -OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_int32x4, vec_mul) -OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_float32x4, vec_add) -OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_float32x4, vec_sub) -OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_float32x4, vec_mul) -OPENCV_HAL_IMPL_VSX_BIN_OP(/, v_float32x4, vec_div) -OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_float64x2, vec_add) -OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_float64x2, vec_sub) -OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_float64x2, vec_mul) -OPENCV_HAL_IMPL_VSX_BIN_OP(/, v_float64x2, vec_div) -OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint64x2, vec_add) -OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint64x2, vec_sub) -OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int64x2, vec_add) -OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int64x2, vec_sub) - -// saturating multiply -#define OPENCV_HAL_IMPL_VSX_MUL_SAT(_Tpvec, _Tpwvec) \ - inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b) \ - { \ - _Tpwvec c, d; \ - v_mul_expand(a, b, c, d); \ - return v_pack(c, d); \ - } \ - inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b) \ - { a = a * b; return a; } - -OPENCV_HAL_IMPL_VSX_MUL_SAT(v_int8x16, v_int16x8) -OPENCV_HAL_IMPL_VSX_MUL_SAT(v_uint8x16, v_uint16x8) -OPENCV_HAL_IMPL_VSX_MUL_SAT(v_int16x8, v_int32x4) -OPENCV_HAL_IMPL_VSX_MUL_SAT(v_uint16x8, v_uint32x4) - -template -inline void v_mul_expand(const Tvec& a, const Tvec& b, Twvec& c, Twvec& d) -{ - Twvec p0 = Twvec(vec_mule(a.val, b.val)); - Twvec p1 = Twvec(vec_mulo(a.val, b.val)); - v_zip(p0, p1, c, d); -} - -inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b) -{ - vec_int4 p0 = vec_mule(a.val, b.val); - vec_int4 p1 = vec_mulo(a.val, b.val); - static const vec_uchar16 perm = {2, 3, 18, 19, 6, 7, 22, 23, 10, 11, 26, 27, 14, 15, 30, 31}; - return v_int16x8(vec_perm(vec_short8_c(p0), vec_short8_c(p1), perm)); -} -inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b) -{ - vec_uint4 p0 = vec_mule(a.val, b.val); - vec_uint4 p1 = vec_mulo(a.val, b.val); - static const vec_uchar16 perm = {2, 3, 18, 19, 6, 7, 22, 23, 10, 11, 26, 27, 14, 15, 30, 31}; - return v_uint16x8(vec_perm(vec_ushort8_c(p0), vec_ushort8_c(p1), perm)); -} - -/** Non-saturating arithmetics **/ -#define OPENCV_HAL_IMPL_VSX_BIN_FUNC(func, intrin) \ -template \ -inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(intrin(a.val, b.val)); } - -OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_add_wrap, vec_add) -OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_sub_wrap, vec_sub) -OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_mul_wrap, vec_mul) - -/** Bitwise shifts **/ -#define OPENCV_HAL_IMPL_VSX_SHIFT_OP(_Tpvec, shr, splfunc) \ -inline _Tpvec operator << (const _Tpvec& a, int imm) \ -{ return _Tpvec(vec_sl(a.val, splfunc(imm))); } \ -inline _Tpvec operator >> (const _Tpvec& a, int imm) \ -{ return _Tpvec(shr(a.val, splfunc(imm))); } \ -template inline _Tpvec v_shl(const _Tpvec& a) \ -{ return _Tpvec(vec_sl(a.val, splfunc(imm))); } \ -template inline _Tpvec v_shr(const _Tpvec& a) \ -{ return _Tpvec(shr(a.val, splfunc(imm))); } - -OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint8x16, vec_sr, vec_uchar16_sp) -OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint16x8, vec_sr, vec_ushort8_sp) -OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint32x4, vec_sr, vec_uint4_sp) -OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint64x2, vec_sr, vec_udword2_sp) -// algebraic right shift -OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int8x16, vec_sra, vec_uchar16_sp) -OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int16x8, vec_sra, vec_ushort8_sp) -OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int32x4, vec_sra, vec_uint4_sp) -OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int64x2, vec_sra, vec_udword2_sp) - -/** Bitwise logic **/ -#define OPENCV_HAL_IMPL_VSX_LOGIC_OP(_Tpvec) \ -OPENCV_HAL_IMPL_VSX_BIN_OP(&, _Tpvec, vec_and) \ -OPENCV_HAL_IMPL_VSX_BIN_OP(|, _Tpvec, vec_or) \ -OPENCV_HAL_IMPL_VSX_BIN_OP(^, _Tpvec, vec_xor) \ -inline _Tpvec operator ~ (const _Tpvec& a) \ -{ return _Tpvec(vec_not(a.val)); } - -OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint8x16) -OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int8x16) -OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint16x8) -OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int16x8) -OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint32x4) -OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int32x4) -OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint64x2) -OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int64x2) -OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_float32x4) -OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_float64x2) - -/** Bitwise select **/ -#define OPENCV_HAL_IMPL_VSX_SELECT(_Tpvec, cast) \ -inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(vec_sel(b.val, a.val, cast(mask.val))); } - -OPENCV_HAL_IMPL_VSX_SELECT(v_uint8x16, vec_bchar16_c) -OPENCV_HAL_IMPL_VSX_SELECT(v_int8x16, vec_bchar16_c) -OPENCV_HAL_IMPL_VSX_SELECT(v_uint16x8, vec_bshort8_c) -OPENCV_HAL_IMPL_VSX_SELECT(v_int16x8, vec_bshort8_c) -OPENCV_HAL_IMPL_VSX_SELECT(v_uint32x4, vec_bint4_c) -OPENCV_HAL_IMPL_VSX_SELECT(v_int32x4, vec_bint4_c) -OPENCV_HAL_IMPL_VSX_SELECT(v_float32x4, vec_bint4_c) -OPENCV_HAL_IMPL_VSX_SELECT(v_float64x2, vec_bdword2_c) - -/** Comparison **/ -#define OPENCV_HAL_IMPL_VSX_INT_CMP_OP(_Tpvec) \ -inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(vec_cmpeq(a.val, b.val)); } \ -inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(vec_cmpne(a.val, b.val)); } \ -inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(vec_cmplt(a.val, b.val)); } \ -inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(vec_cmpgt(a.val, b.val)); } \ -inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(vec_cmple(a.val, b.val)); } \ -inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(vec_cmpge(a.val, b.val)); } - -OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint8x16) -OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int8x16) -OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint16x8) -OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int16x8) -OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint32x4) -OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int32x4) -OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_float32x4) -OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_float64x2) -OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint64x2) -OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int64x2) - -inline v_float32x4 v_not_nan(const v_float32x4& a) -{ return v_float32x4(vec_cmpeq(a.val, a.val)); } -inline v_float64x2 v_not_nan(const v_float64x2& a) -{ return v_float64x2(vec_cmpeq(a.val, a.val)); } - -/** min/max **/ -OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_min, vec_min) -OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_max, vec_max) - -/** Rotate **/ -#define OPENCV_IMPL_VSX_ROTATE(_Tpvec, suffix, shf, cast) \ -template \ -inline _Tpvec v_rotate_##suffix(const _Tpvec& a) \ -{ \ - const int wd = imm * sizeof(typename _Tpvec::lane_type); \ - if (wd > 15) \ - return _Tpvec::zero(); \ - return _Tpvec((cast)shf(vec_uchar16_c(a.val), vec_uchar16_sp(wd << 3))); \ -} - -#define OPENCV_IMPL_VSX_ROTATE_LR(_Tpvec, cast) \ -OPENCV_IMPL_VSX_ROTATE(_Tpvec, left, vec_slo, cast) \ -OPENCV_IMPL_VSX_ROTATE(_Tpvec, right, vec_sro, cast) - -OPENCV_IMPL_VSX_ROTATE_LR(v_uint8x16, vec_uchar16) -OPENCV_IMPL_VSX_ROTATE_LR(v_int8x16, vec_char16) -OPENCV_IMPL_VSX_ROTATE_LR(v_uint16x8, vec_ushort8) -OPENCV_IMPL_VSX_ROTATE_LR(v_int16x8, vec_short8) -OPENCV_IMPL_VSX_ROTATE_LR(v_uint32x4, vec_uint4) -OPENCV_IMPL_VSX_ROTATE_LR(v_int32x4, vec_int4) -OPENCV_IMPL_VSX_ROTATE_LR(v_float32x4, vec_float4) -OPENCV_IMPL_VSX_ROTATE_LR(v_uint64x2, vec_udword2) -OPENCV_IMPL_VSX_ROTATE_LR(v_int64x2, vec_dword2) -OPENCV_IMPL_VSX_ROTATE_LR(v_float64x2, vec_double2) - -template -inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b) -{ - enum { CV_SHIFT = 16 - imm * (sizeof(typename _Tpvec::lane_type)) }; - if (CV_SHIFT == 16) - return a; -#ifdef __IBMCPP__ - return _Tpvec(vec_sld(b.val, a.val, CV_SHIFT & 15)); -#else - return _Tpvec(vec_sld(b.val, a.val, CV_SHIFT)); -#endif -} - -template -inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b) -{ - enum { CV_SHIFT = imm * (sizeof(typename _Tpvec::lane_type)) }; - if (CV_SHIFT == 16) - return b; - return _Tpvec(vec_sld(a.val, b.val, CV_SHIFT)); -} - -#define OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, suffix, rg1, rg2) \ -template \ -inline _Tpvec v_rotate_##suffix(const _Tpvec& a, const _Tpvec& b) \ -{ \ - if (imm == 1) \ - return _Tpvec(vec_permi(rg1.val, rg2.val, 2)); \ - return imm ? b : a; \ -} - -#define OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(_Tpvec) \ -OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, left, b, a) \ -OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, right, a, b) - -OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_float64x2) -OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_uint64x2) -OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_int64x2) - -/* Reverse */ -inline v_uint8x16 v_reverse(const v_uint8x16 &a) -{ - static const vec_uchar16 perm = {15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0}; - vec_uchar16 vec = (vec_uchar16)a.val; - return v_uint8x16(vec_perm(vec, vec, perm)); -} - -inline v_int8x16 v_reverse(const v_int8x16 &a) -{ return v_reinterpret_as_s8(v_reverse(v_reinterpret_as_u8(a))); } - -inline v_uint16x8 v_reverse(const v_uint16x8 &a) -{ - static const vec_uchar16 perm = {14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1}; - vec_uchar16 vec = (vec_uchar16)a.val; - return v_reinterpret_as_u16(v_uint8x16(vec_perm(vec, vec, perm))); -} - -inline v_int16x8 v_reverse(const v_int16x8 &a) -{ return v_reinterpret_as_s16(v_reverse(v_reinterpret_as_u16(a))); } - -inline v_uint32x4 v_reverse(const v_uint32x4 &a) -{ - static const vec_uchar16 perm = {12, 13, 14, 15, 8, 9, 10, 11, 4, 5, 6, 7, 0, 1, 2, 3}; - vec_uchar16 vec = (vec_uchar16)a.val; - return v_reinterpret_as_u32(v_uint8x16(vec_perm(vec, vec, perm))); -} - -inline v_int32x4 v_reverse(const v_int32x4 &a) -{ return v_reinterpret_as_s32(v_reverse(v_reinterpret_as_u32(a))); } - -inline v_float32x4 v_reverse(const v_float32x4 &a) -{ return v_reinterpret_as_f32(v_reverse(v_reinterpret_as_u32(a))); } - -inline v_uint64x2 v_reverse(const v_uint64x2 &a) -{ - static const vec_uchar16 perm = {8, 9, 10, 11, 12, 13, 14, 15, 0, 1, 2, 3, 4, 5, 6, 7}; - vec_uchar16 vec = (vec_uchar16)a.val; - return v_reinterpret_as_u64(v_uint8x16(vec_perm(vec, vec, perm))); -} - -inline v_int64x2 v_reverse(const v_int64x2 &a) -{ return v_reinterpret_as_s64(v_reverse(v_reinterpret_as_u64(a))); } - -inline v_float64x2 v_reverse(const v_float64x2 &a) -{ return v_reinterpret_as_f64(v_reverse(v_reinterpret_as_u64(a))); } - -/* Extract */ -template -inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) -{ return v_rotate_right(a, b); } - -////////// Reduce and mask ///////// - -/** Reduce **/ -inline uint v_reduce_sum(const v_uint8x16& a) -{ - const vec_uint4 zero4 = vec_uint4_z; - vec_uint4 sum4 = vec_sum4s(a.val, zero4); - return (uint)vec_extract(vec_sums(vec_int4_c(sum4), vec_int4_c(zero4)), 3); -} -inline int v_reduce_sum(const v_int8x16& a) -{ - const vec_int4 zero4 = vec_int4_z; - vec_int4 sum4 = vec_sum4s(a.val, zero4); - return (int)vec_extract(vec_sums(sum4, zero4), 3); -} -inline int v_reduce_sum(const v_int16x8& a) -{ - const vec_int4 zero = vec_int4_z; - return saturate_cast(vec_extract(vec_sums(vec_sum4s(a.val, zero), zero), 3)); -} -inline uint v_reduce_sum(const v_uint16x8& a) -{ - const vec_int4 v4 = vec_int4_c(vec_unpackhu(vec_adds(a.val, vec_sld(a.val, a.val, 8)))); - return saturate_cast(vec_extract(vec_sums(v4, vec_int4_z), 3)); -} - -#define OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(_Tpvec, _Tpvec2, scalartype, suffix, func) \ -inline scalartype v_reduce_##suffix(const _Tpvec& a) \ -{ \ - const _Tpvec2 rs = func(a.val, vec_sld(a.val, a.val, 8)); \ - return vec_extract(func(rs, vec_sld(rs, rs, 4)), 0); \ -} -OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, sum, vec_add) -OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, max, vec_max) -OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, min, vec_min) -OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, sum, vec_add) -OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, max, vec_max) -OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, min, vec_min) -OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, sum, vec_add) -OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, max, vec_max) -OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, min, vec_min) - -inline uint64 v_reduce_sum(const v_uint64x2& a) -{ - return vec_extract(vec_add(a.val, vec_permi(a.val, a.val, 3)), 0); -} -inline int64 v_reduce_sum(const v_int64x2& a) -{ - return vec_extract(vec_add(a.val, vec_permi(a.val, a.val, 3)), 0); -} -inline double v_reduce_sum(const v_float64x2& a) -{ - return vec_extract(vec_add(a.val, vec_permi(a.val, a.val, 3)), 0); -} - -#define OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(_Tpvec, _Tpvec2, scalartype, suffix, func) \ -inline scalartype v_reduce_##suffix(const _Tpvec& a) \ -{ \ - _Tpvec2 rs = func(a.val, vec_sld(a.val, a.val, 8)); \ - rs = func(rs, vec_sld(rs, rs, 4)); \ - return vec_extract(func(rs, vec_sld(rs, rs, 2)), 0); \ -} -OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_uint16x8, vec_ushort8, ushort, max, vec_max) -OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_uint16x8, vec_ushort8, ushort, min, vec_min) -OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_int16x8, vec_short8, short, max, vec_max) -OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_int16x8, vec_short8, short, min, vec_min) - -#define OPENCV_HAL_IMPL_VSX_REDUCE_OP_16(_Tpvec, _Tpvec2, scalartype, suffix, func) \ -inline scalartype v_reduce_##suffix(const _Tpvec& a) \ -{ \ - _Tpvec2 rs = func(a.val, vec_sld(a.val, a.val, 8)); \ - rs = func(rs, vec_sld(rs, rs, 4)); \ - rs = func(rs, vec_sld(rs, rs, 2)); \ - return vec_extract(func(rs, vec_sld(rs, rs, 1)), 0); \ -} -OPENCV_HAL_IMPL_VSX_REDUCE_OP_16(v_uint8x16, vec_uchar16, uchar, max, vec_max) -OPENCV_HAL_IMPL_VSX_REDUCE_OP_16(v_uint8x16, vec_uchar16, uchar, min, vec_min) -OPENCV_HAL_IMPL_VSX_REDUCE_OP_16(v_int8x16, vec_char16, schar, max, vec_max) -OPENCV_HAL_IMPL_VSX_REDUCE_OP_16(v_int8x16, vec_char16, schar, min, vec_min) - -inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, - const v_float32x4& c, const v_float32x4& d) -{ - vec_float4 ac = vec_add(vec_mergel(a.val, c.val), vec_mergeh(a.val, c.val)); - ac = vec_add(ac, vec_sld(ac, ac, 8)); - - vec_float4 bd = vec_add(vec_mergel(b.val, d.val), vec_mergeh(b.val, d.val)); - bd = vec_add(bd, vec_sld(bd, bd, 8)); - return v_float32x4(vec_mergeh(ac, bd)); -} - -inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b) -{ - const vec_uint4 zero4 = vec_uint4_z; - vec_uint4 sum4 = vec_sum4s(vec_absd(a.val, b.val), zero4); - return (unsigned)vec_extract(vec_sums(vec_int4_c(sum4), vec_int4_c(zero4)), 3); -} -inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b) -{ - const vec_int4 zero4 = vec_int4_z; - vec_char16 ad = vec_abss(vec_subs(a.val, b.val)); - vec_int4 sum4 = vec_sum4s(ad, zero4); - return (unsigned)vec_extract(vec_sums(sum4, zero4), 3); -} -inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b) -{ - vec_ushort8 ad = vec_absd(a.val, b.val); - VSX_UNUSED(vec_int4) sum = vec_sums(vec_int4_c(vec_unpackhu(ad)) + vec_int4_c(vec_unpacklu(ad)), vec_int4_z); - return (unsigned)vec_extract(sum, 3); -} -inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b) -{ - const vec_int4 zero4 = vec_int4_z; - vec_short8 ad = vec_abss(vec_subs(a.val, b.val)); - vec_int4 sum4 = vec_sum4s(ad, zero4); - return (unsigned)vec_extract(vec_sums(sum4, zero4), 3); -} -inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b) -{ - const vec_uint4 ad = vec_absd(a.val, b.val); - const vec_uint4 rd = vec_add(ad, vec_sld(ad, ad, 8)); - return vec_extract(vec_add(rd, vec_sld(rd, rd, 4)), 0); -} -inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b) -{ - vec_int4 ad = vec_abss(vec_sub(a.val, b.val)); - return (unsigned)vec_extract(vec_sums(ad, vec_int4_z), 3); -} -inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b) -{ - const vec_float4 ad = vec_abs(vec_sub(a.val, b.val)); - const vec_float4 rd = vec_add(ad, vec_sld(ad, ad, 8)); - return vec_extract(vec_add(rd, vec_sld(rd, rd, 4)), 0); -} - -/** Popcount **/ -inline v_uint8x16 v_popcount(const v_uint8x16& a) -{ return v_uint8x16(vec_popcntu(a.val)); } -inline v_uint8x16 v_popcount(const v_int8x16& a) -{ return v_uint8x16(vec_popcntu(a.val)); } -inline v_uint16x8 v_popcount(const v_uint16x8& a) -{ return v_uint16x8(vec_popcntu(a.val)); } -inline v_uint16x8 v_popcount(const v_int16x8& a) -{ return v_uint16x8(vec_popcntu(a.val)); } -inline v_uint32x4 v_popcount(const v_uint32x4& a) -{ return v_uint32x4(vec_popcntu(a.val)); } -inline v_uint32x4 v_popcount(const v_int32x4& a) -{ return v_uint32x4(vec_popcntu(a.val)); } -inline v_uint64x2 v_popcount(const v_uint64x2& a) -{ return v_uint64x2(vec_popcntu(a.val)); } -inline v_uint64x2 v_popcount(const v_int64x2& a) -{ return v_uint64x2(vec_popcntu(a.val)); } - -/** Mask **/ -inline int v_signmask(const v_uint8x16& a) -{ - static const vec_uchar16 qperm = {120, 112, 104, 96, 88, 80, 72, 64, 56, 48, 40, 32, 24, 16, 8, 0}; - return vec_extract((vec_int4)vec_vbpermq(v_reinterpret_as_u8(a).val, qperm), 2); -} -inline int v_signmask(const v_int8x16& a) -{ return v_signmask(v_reinterpret_as_u8(a)); } - -inline int v_signmask(const v_int16x8& a) -{ - static const vec_uchar16 qperm = {112, 96, 80, 64, 48, 32, 16, 0, 128, 128, 128, 128, 128, 128, 128, 128}; - return vec_extract((vec_int4)vec_vbpermq(v_reinterpret_as_u8(a).val, qperm), 2); -} -inline int v_signmask(const v_uint16x8& a) -{ return v_signmask(v_reinterpret_as_s16(a)); } - -inline int v_signmask(const v_int32x4& a) -{ - static const vec_uchar16 qperm = {96, 64, 32, 0, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128}; - return vec_extract((vec_int4)vec_vbpermq(v_reinterpret_as_u8(a).val, qperm), 2); -} -inline int v_signmask(const v_uint32x4& a) -{ return v_signmask(v_reinterpret_as_s32(a)); } -inline int v_signmask(const v_float32x4& a) -{ return v_signmask(v_reinterpret_as_s32(a)); } - -inline int v_signmask(const v_int64x2& a) -{ - VSX_UNUSED(const vec_dword2) sv = vec_sr(a.val, vec_udword2_sp(63)); - return (int)vec_extract(sv, 0) | (int)vec_extract(sv, 1) << 1; -} -inline int v_signmask(const v_uint64x2& a) -{ return v_signmask(v_reinterpret_as_s64(a)); } -inline int v_signmask(const v_float64x2& a) -{ return v_signmask(v_reinterpret_as_s64(a)); } - -inline int v_scan_forward(const v_int8x16& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_uint8x16& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_int16x8& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_uint16x8& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_int32x4& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_uint32x4& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_float32x4& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_int64x2& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_uint64x2& a) { return trailingZeros32(v_signmask(a)); } -inline int v_scan_forward(const v_float64x2& a) { return trailingZeros32(v_signmask(a)); } - -template -inline bool v_check_all(const _Tpvec& a) -{ return vec_all_lt(a.val, _Tpvec::zero().val); } -inline bool v_check_all(const v_uint8x16& a) -{ return v_check_all(v_reinterpret_as_s8(a)); } -inline bool v_check_all(const v_uint16x8& a) -{ return v_check_all(v_reinterpret_as_s16(a)); } -inline bool v_check_all(const v_uint32x4& a) -{ return v_check_all(v_reinterpret_as_s32(a)); } -inline bool v_check_all(const v_uint64x2& a) -{ return v_check_all(v_reinterpret_as_s64(a)); } -inline bool v_check_all(const v_float32x4& a) -{ return v_check_all(v_reinterpret_as_s32(a)); } -inline bool v_check_all(const v_float64x2& a) -{ return v_check_all(v_reinterpret_as_s64(a)); } - -template -inline bool v_check_any(const _Tpvec& a) -{ return vec_any_lt(a.val, _Tpvec::zero().val); } -inline bool v_check_any(const v_uint8x16& a) -{ return v_check_any(v_reinterpret_as_s8(a)); } -inline bool v_check_any(const v_uint16x8& a) -{ return v_check_any(v_reinterpret_as_s16(a)); } -inline bool v_check_any(const v_uint32x4& a) -{ return v_check_any(v_reinterpret_as_s32(a)); } -inline bool v_check_any(const v_uint64x2& a) -{ return v_check_any(v_reinterpret_as_s64(a)); } -inline bool v_check_any(const v_float32x4& a) -{ return v_check_any(v_reinterpret_as_s32(a)); } -inline bool v_check_any(const v_float64x2& a) -{ return v_check_any(v_reinterpret_as_s64(a)); } - -////////// Other math ///////// - -/** Some frequent operations **/ -inline v_float32x4 v_sqrt(const v_float32x4& x) -{ return v_float32x4(vec_sqrt(x.val)); } -inline v_float64x2 v_sqrt(const v_float64x2& x) -{ return v_float64x2(vec_sqrt(x.val)); } - -inline v_float32x4 v_invsqrt(const v_float32x4& x) -{ return v_float32x4(vec_rsqrt(x.val)); } -inline v_float64x2 v_invsqrt(const v_float64x2& x) -{ return v_float64x2(vec_rsqrt(x.val)); } - -#define OPENCV_HAL_IMPL_VSX_MULADD(_Tpvec) \ -inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(vec_sqrt(vec_madd(a.val, a.val, vec_mul(b.val, b.val)))); } \ -inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(vec_madd(a.val, a.val, vec_mul(b.val, b.val))); } \ -inline _Tpvec v_fma(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ -{ return _Tpvec(vec_madd(a.val, b.val, c.val)); } \ -inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ -{ return _Tpvec(vec_madd(a.val, b.val, c.val)); } - -OPENCV_HAL_IMPL_VSX_MULADD(v_float32x4) -OPENCV_HAL_IMPL_VSX_MULADD(v_float64x2) - -inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) -{ return a * b + c; } - -// TODO: exp, log, sin, cos - -/** Absolute values **/ -inline v_uint8x16 v_abs(const v_int8x16& x) -{ return v_uint8x16(vec_uchar16_c(vec_abs(x.val))); } - -inline v_uint16x8 v_abs(const v_int16x8& x) -{ return v_uint16x8(vec_ushort8_c(vec_abs(x.val))); } - -inline v_uint32x4 v_abs(const v_int32x4& x) -{ return v_uint32x4(vec_uint4_c(vec_abs(x.val))); } - -inline v_float32x4 v_abs(const v_float32x4& x) -{ return v_float32x4(vec_abs(x.val)); } - -inline v_float64x2 v_abs(const v_float64x2& x) -{ return v_float64x2(vec_abs(x.val)); } - -/** Absolute difference **/ -// unsigned -OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_absdiff, vec_absd) - -inline v_uint8x16 v_absdiff(const v_int8x16& a, const v_int8x16& b) -{ return v_reinterpret_as_u8(v_sub_wrap(v_max(a, b), v_min(a, b))); } -inline v_uint16x8 v_absdiff(const v_int16x8& a, const v_int16x8& b) -{ return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); } -inline v_uint32x4 v_absdiff(const v_int32x4& a, const v_int32x4& b) -{ return v_reinterpret_as_u32(v_max(a, b) - v_min(a, b)); } - -inline v_float32x4 v_absdiff(const v_float32x4& a, const v_float32x4& b) -{ return v_abs(a - b); } -inline v_float64x2 v_absdiff(const v_float64x2& a, const v_float64x2& b) -{ return v_abs(a - b); } - -/** Absolute difference for signed integers **/ -inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b) -{ return v_int8x16(vec_abss(vec_subs(a.val, b.val))); } -inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b) -{ return v_int16x8(vec_abss(vec_subs(a.val, b.val))); } - -////////// Conversions ///////// - -/** Rounding **/ -inline v_int32x4 v_round(const v_float32x4& a) -{ return v_int32x4(vec_cts(vec_rint(a.val))); } - -inline v_int32x4 v_round(const v_float64x2& a) -{ return v_int32x4(vec_mergesqo(vec_ctso(vec_rint(a.val)), vec_int4_z)); } - -inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b) -{ return v_int32x4(vec_mergesqo(vec_ctso(vec_rint(a.val)), vec_ctso(vec_rint(b.val)))); } - -inline v_int32x4 v_floor(const v_float32x4& a) -{ return v_int32x4(vec_cts(vec_floor(a.val))); } - -inline v_int32x4 v_floor(const v_float64x2& a) -{ return v_int32x4(vec_mergesqo(vec_ctso(vec_floor(a.val)), vec_int4_z)); } - -inline v_int32x4 v_ceil(const v_float32x4& a) -{ return v_int32x4(vec_cts(vec_ceil(a.val))); } - -inline v_int32x4 v_ceil(const v_float64x2& a) -{ return v_int32x4(vec_mergesqo(vec_ctso(vec_ceil(a.val)), vec_int4_z)); } - -inline v_int32x4 v_trunc(const v_float32x4& a) -{ return v_int32x4(vec_cts(a.val)); } - -inline v_int32x4 v_trunc(const v_float64x2& a) -{ return v_int32x4(vec_mergesqo(vec_ctso(a.val), vec_int4_z)); } - -/** To float **/ -inline v_float32x4 v_cvt_f32(const v_int32x4& a) -{ return v_float32x4(vec_ctf(a.val)); } - -inline v_float32x4 v_cvt_f32(const v_float64x2& a) -{ return v_float32x4(vec_mergesqo(vec_cvfo(a.val), vec_float4_z)); } - -inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b) -{ return v_float32x4(vec_mergesqo(vec_cvfo(a.val), vec_cvfo(b.val))); } - -inline v_float64x2 v_cvt_f64(const v_int32x4& a) -{ return v_float64x2(vec_ctdo(vec_mergeh(a.val, a.val))); } - -inline v_float64x2 v_cvt_f64_high(const v_int32x4& a) -{ return v_float64x2(vec_ctdo(vec_mergel(a.val, a.val))); } - -inline v_float64x2 v_cvt_f64(const v_float32x4& a) -{ return v_float64x2(vec_cvfo(vec_mergeh(a.val, a.val))); } - -inline v_float64x2 v_cvt_f64_high(const v_float32x4& a) -{ return v_float64x2(vec_cvfo(vec_mergel(a.val, a.val))); } - -inline v_float64x2 v_cvt_f64(const v_int64x2& a) -{ return v_float64x2(vec_ctd(a.val)); } - -////////////// Lookup table access //////////////////// - -inline v_int8x16 v_lut(const schar* tab, const int* idx) -{ - return v_int8x16(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]], tab[idx[4]], tab[idx[5]], tab[idx[6]], tab[idx[7]], - tab[idx[8]], tab[idx[9]], tab[idx[10]], tab[idx[11]], tab[idx[12]], tab[idx[13]], tab[idx[14]], tab[idx[15]]); -} -inline v_int8x16 v_lut_pairs(const schar* tab, const int* idx) -{ - return v_reinterpret_as_s8(v_int16x8(*(const short*)(tab+idx[0]), *(const short*)(tab+idx[1]), *(const short*)(tab+idx[2]), *(const short*)(tab+idx[3]), - *(const short*)(tab+idx[4]), *(const short*)(tab+idx[5]), *(const short*)(tab+idx[6]), *(const short*)(tab+idx[7]))); -} -inline v_int8x16 v_lut_quads(const schar* tab, const int* idx) -{ - return v_reinterpret_as_s8(v_int32x4(*(const int*)(tab+idx[0]), *(const int*)(tab+idx[1]), *(const int*)(tab+idx[2]), *(const int*)(tab+idx[3]))); -} -inline v_uint8x16 v_lut(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut((const schar*)tab, idx)); } -inline v_uint8x16 v_lut_pairs(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_pairs((const schar*)tab, idx)); } -inline v_uint8x16 v_lut_quads(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_quads((const schar*)tab, idx)); } - -inline v_int16x8 v_lut(const short* tab, const int* idx) -{ - return v_int16x8(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]], tab[idx[4]], tab[idx[5]], tab[idx[6]], tab[idx[7]]); -} -inline v_int16x8 v_lut_pairs(const short* tab, const int* idx) -{ - return v_reinterpret_as_s16(v_int32x4(*(const int*)(tab + idx[0]), *(const int*)(tab + idx[1]), *(const int*)(tab + idx[2]), *(const int*)(tab + idx[3]))); -} -inline v_int16x8 v_lut_quads(const short* tab, const int* idx) -{ - return v_reinterpret_as_s16(v_int64x2(*(const int64*)(tab + idx[0]), *(const int64*)(tab + idx[1]))); -} -inline v_uint16x8 v_lut(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut((const short*)tab, idx)); } -inline v_uint16x8 v_lut_pairs(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_pairs((const short*)tab, idx)); } -inline v_uint16x8 v_lut_quads(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_quads((const short*)tab, idx)); } - -inline v_int32x4 v_lut(const int* tab, const int* idx) -{ - return v_int32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); -} -inline v_int32x4 v_lut_pairs(const int* tab, const int* idx) -{ - return v_reinterpret_as_s32(v_int64x2(*(const int64*)(tab + idx[0]), *(const int64*)(tab + idx[1]))); -} -inline v_int32x4 v_lut_quads(const int* tab, const int* idx) -{ - return v_int32x4(vsx_ld(0, tab + idx[0])); -} -inline v_uint32x4 v_lut(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut((const int*)tab, idx)); } -inline v_uint32x4 v_lut_pairs(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_pairs((const int*)tab, idx)); } -inline v_uint32x4 v_lut_quads(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_quads((const int*)tab, idx)); } - -inline v_int64x2 v_lut(const int64_t* tab, const int* idx) -{ - return v_int64x2(tab[idx[0]], tab[idx[1]]); -} -inline v_int64x2 v_lut_pairs(const int64_t* tab, const int* idx) -{ - return v_int64x2(vsx_ld2(0, tab + idx[0])); -} -inline v_uint64x2 v_lut(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut((const int64_t *)tab, idx)); } -inline v_uint64x2 v_lut_pairs(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut_pairs((const int64_t *)tab, idx)); } - -inline v_float32x4 v_lut(const float* tab, const int* idx) -{ - return v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); -} -inline v_float32x4 v_lut_pairs(const float* tab, const int* idx) { return v_reinterpret_as_f32(v_lut_pairs((const int*)tab, idx)); } -inline v_float32x4 v_lut_quads(const float* tab, const int* idx) { return v_load(tab + *idx); } - -inline v_float64x2 v_lut(const double* tab, const int* idx) -{ - return v_float64x2(tab[idx[0]], tab[idx[1]]); -} -inline v_float64x2 v_lut_pairs(const double* tab, const int* idx) { return v_load(tab + *idx); } - -inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec) -{ - const int idx[4] = { - vec_extract(idxvec.val, 0), - vec_extract(idxvec.val, 1), - vec_extract(idxvec.val, 2), - vec_extract(idxvec.val, 3) - }; - return v_int32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); -} - -inline v_uint32x4 v_lut(const unsigned* tab, const v_int32x4& idxvec) -{ - const int idx[4] = { - vec_extract(idxvec.val, 0), - vec_extract(idxvec.val, 1), - vec_extract(idxvec.val, 2), - vec_extract(idxvec.val, 3) - }; - return v_uint32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); -} - -inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec) -{ - const int idx[4] = { - vec_extract(idxvec.val, 0), - vec_extract(idxvec.val, 1), - vec_extract(idxvec.val, 2), - vec_extract(idxvec.val, 3) - }; - return v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); -} - -inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec) -{ - const int idx[2] = { - vec_extract(idxvec.val, 0), - vec_extract(idxvec.val, 1) - }; - return v_float64x2(tab[idx[0]], tab[idx[1]]); -} - -inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y) -{ - vec_float4 xy0 = vec_ld_l8(tab + vec_extract(idxvec.val, 0)); - vec_float4 xy1 = vec_ld_l8(tab + vec_extract(idxvec.val, 1)); - vec_float4 xy2 = vec_ld_l8(tab + vec_extract(idxvec.val, 2)); - vec_float4 xy3 = vec_ld_l8(tab + vec_extract(idxvec.val, 3)); - vec_float4 xy02 = vec_mergeh(xy0, xy2); // x0, x2, y0, y2 - vec_float4 xy13 = vec_mergeh(xy1, xy3); // x1, x3, y1, y3 - x.val = vec_mergeh(xy02, xy13); - y.val = vec_mergel(xy02, xy13); -} -inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y) -{ - vec_double2 xy0 = vsx_ld(vec_extract(idxvec.val, 0), tab); - vec_double2 xy1 = vsx_ld(vec_extract(idxvec.val, 1), tab); - x.val = vec_mergeh(xy0, xy1); - y.val = vec_mergel(xy0, xy1); -} - -inline v_int8x16 v_interleave_pairs(const v_int8x16& vec) -{ - static const vec_uchar16 perm = {0, 2, 1, 3, 4, 6, 5, 7, 8, 10, 9, 11, 12, 14, 13, 15}; - return v_int8x16(vec_perm(vec.val, vec.val, perm)); -} -inline v_uint8x16 v_interleave_pairs(const v_uint8x16& vec) -{ return v_reinterpret_as_u8(v_interleave_pairs(v_reinterpret_as_s8(vec))); } - -inline v_int8x16 v_interleave_quads(const v_int8x16& vec) -{ - static const vec_uchar16 perm = {0, 4, 1, 5, 2, 6, 3, 7, 8, 12, 9, 13, 10, 14, 11, 15}; - return v_int8x16(vec_perm(vec.val, vec.val, perm)); -} -inline v_uint8x16 v_interleave_quads(const v_uint8x16& vec) -{ return v_reinterpret_as_u8(v_interleave_quads(v_reinterpret_as_s8(vec))); } - -inline v_int16x8 v_interleave_pairs(const v_int16x8& vec) -{ - static const vec_uchar16 perm = {0,1, 4,5, 2,3, 6,7, 8,9, 12,13, 10,11, 14,15}; - return v_int16x8(vec_perm(vec.val, vec.val, perm)); -} -inline v_uint16x8 v_interleave_pairs(const v_uint16x8& vec) -{ return v_reinterpret_as_u16(v_interleave_pairs(v_reinterpret_as_s16(vec))); } - -inline v_int16x8 v_interleave_quads(const v_int16x8& vec) -{ - static const vec_uchar16 perm = {0,1, 8,9, 2,3, 10,11, 4,5, 12,13, 6,7, 14,15}; - return v_int16x8(vec_perm(vec.val, vec.val, perm)); -} -inline v_uint16x8 v_interleave_quads(const v_uint16x8& vec) -{ return v_reinterpret_as_u16(v_interleave_quads(v_reinterpret_as_s16(vec))); } - -inline v_int32x4 v_interleave_pairs(const v_int32x4& vec) -{ - static const vec_uchar16 perm = {0,1,2,3, 8,9,10,11, 4,5,6,7, 12,13,14,15}; - return v_int32x4(vec_perm(vec.val, vec.val, perm)); -} -inline v_uint32x4 v_interleave_pairs(const v_uint32x4& vec) -{ return v_reinterpret_as_u32(v_interleave_pairs(v_reinterpret_as_s32(vec))); } -inline v_float32x4 v_interleave_pairs(const v_float32x4& vec) -{ return v_reinterpret_as_f32(v_interleave_pairs(v_reinterpret_as_s32(vec))); } - -inline v_int8x16 v_pack_triplets(const v_int8x16& vec) -{ - static const vec_uchar16 perm = {0, 1, 2, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 15, 15, 15}; - return v_int8x16(vec_perm(vec.val, vec.val, perm)); -} -inline v_uint8x16 v_pack_triplets(const v_uint8x16& vec) -{ return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec))); } - -inline v_int16x8 v_pack_triplets(const v_int16x8& vec) -{ - static const vec_uchar16 perm = {0,1, 2,3, 4,5, 8,9, 10,11, 12,13, 14,15, 14,15}; - return v_int16x8(vec_perm(vec.val, vec.val, perm)); -} -inline v_uint16x8 v_pack_triplets(const v_uint16x8& vec) -{ return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec))); } - -inline v_int32x4 v_pack_triplets(const v_int32x4& vec) -{ return vec; } -inline v_uint32x4 v_pack_triplets(const v_uint32x4& vec) -{ return vec; } -inline v_float32x4 v_pack_triplets(const v_float32x4& vec) -{ return vec; } - -/////// FP16 support //////// - -inline v_float32x4 v_load_expand(const float16_t* ptr) -{ - vec_ushort8 vf16 = vec_ld_l8((const ushort*)ptr); -#if CV_VSX3 && defined(vec_extract_fp_from_shorth) - return v_float32x4(vec_extract_fp_from_shorth(vf16)); -#elif CV_VSX3 && !defined(CV_COMPILER_VSX_BROKEN_ASM) - vec_float4 vf32; - __asm__ __volatile__ ("xvcvhpsp %x0,%x1" : "=wa" (vf32) : "wa" (vec_mergeh(vf16, vf16))); - return v_float32x4(vf32); -#else - const vec_int4 z = vec_int4_z, delta = vec_int4_sp(0x38000000); - const vec_int4 signmask = vec_int4_sp(0x80000000); - const vec_int4 maxexp = vec_int4_sp(0x7c000000); - const vec_float4 deltaf = vec_float4_c(vec_int4_sp(0x38800000)); - - vec_int4 bits = vec_int4_c(vec_mergeh(vec_short8_c(z), vec_short8_c(vf16))); - vec_int4 e = vec_and(bits, maxexp), sign = vec_and(bits, signmask); - vec_int4 t = vec_add(vec_sr(vec_xor(bits, sign), vec_uint4_sp(3)), delta); // ((h & 0x7fff) << 13) + delta - vec_int4 zt = vec_int4_c(vec_sub(vec_float4_c(vec_add(t, vec_int4_sp(1 << 23))), deltaf)); - - t = vec_add(t, vec_and(delta, vec_cmpeq(maxexp, e))); - vec_bint4 zmask = vec_cmpeq(e, z); - vec_int4 ft = vec_sel(t, zt, zmask); - return v_float32x4(vec_float4_c(vec_or(ft, sign))); -#endif -} - -inline void v_pack_store(float16_t* ptr, const v_float32x4& v) -{ -// fixme: Is there any builtin op or intrinsic that cover "xvcvsphp"? -#if CV_VSX3 && !defined(CV_COMPILER_VSX_BROKEN_ASM) - vec_ushort8 vf16; - __asm__ __volatile__ ("xvcvsphp %x0,%x1" : "=wa" (vf16) : "wa" (v.val)); - vec_st_l8(vec_mergesqe(vf16, vf16), ptr); -#else - const vec_int4 signmask = vec_int4_sp(0x80000000); - const vec_int4 rval = vec_int4_sp(0x3f000000); - - vec_int4 t = vec_int4_c(v.val); - vec_int4 sign = vec_sra(vec_and(t, signmask), vec_uint4_sp(16)); - t = vec_and(vec_nor(signmask, signmask), t); - - vec_bint4 finitemask = vec_cmpgt(vec_int4_sp(0x47800000), t); - vec_bint4 isnan = vec_cmpgt(t, vec_int4_sp(0x7f800000)); - vec_int4 naninf = vec_sel(vec_int4_sp(0x7c00), vec_int4_sp(0x7e00), isnan); - vec_bint4 tinymask = vec_cmpgt(vec_int4_sp(0x38800000), t); - vec_int4 tt = vec_int4_c(vec_add(vec_float4_c(t), vec_float4_c(rval))); - tt = vec_sub(tt, rval); - vec_int4 odd = vec_and(vec_sr(t, vec_uint4_sp(13)), vec_int4_sp(1)); - vec_int4 nt = vec_add(t, vec_int4_sp(0xc8000fff)); - nt = vec_sr(vec_add(nt, odd), vec_uint4_sp(13)); - t = vec_sel(nt, tt, tinymask); - t = vec_sel(naninf, t, finitemask); - t = vec_or(t, sign); - vec_st_l8(vec_packs(t, t), ptr); -#endif -} - -inline void v_cleanup() {} - - -/** Reinterpret **/ -/** its up there with load and store operations **/ - -////////// Matrix operations ///////// - -//////// Dot Product //////// -// 16 >> 32 -inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) -{ return v_int32x4(vec_msum(a.val, b.val, vec_int4_z)); } -inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) -{ return v_int32x4(vec_msum(a.val, b.val, c.val)); } - -// 32 >> 64 -inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b) -{ - vec_dword2 even = vec_mule(a.val, b.val); - vec_dword2 odd = vec_mulo(a.val, b.val); - return v_int64x2(vec_add(even, odd)); -} -inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c) -{ return v_dotprod(a, b) + c; } - -// 8 >> 32 -inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c) -{ return v_uint32x4(vec_msum(a.val, b.val, c.val)); } -inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b) -{ return v_uint32x4(vec_msum(a.val, b.val, vec_uint4_z)); } - -inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b) -{ - const vec_ushort8 eight = vec_ushort8_sp(8); - vec_short8 a0 = vec_sra((vec_short8)vec_sld(a.val, a.val, 1), eight); // even - vec_short8 a1 = vec_sra((vec_short8)a.val, eight); // odd - vec_short8 b0 = vec_sra((vec_short8)vec_sld(b.val, b.val, 1), eight); - vec_short8 b1 = vec_sra((vec_short8)b.val, eight); - return v_int32x4(vec_msum(a0, b0, vec_msum(a1, b1, vec_int4_z))); -} - -inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c) -{ - const vec_ushort8 eight = vec_ushort8_sp(8); - vec_short8 a0 = vec_sra((vec_short8)vec_sld(a.val, a.val, 1), eight); // even - vec_short8 a1 = vec_sra((vec_short8)a.val, eight); // odd - vec_short8 b0 = vec_sra((vec_short8)vec_sld(b.val, b.val, 1), eight); - vec_short8 b1 = vec_sra((vec_short8)b.val, eight); - return v_int32x4(vec_msum(a0, b0, vec_msum(a1, b1, c.val))); -} - -// 16 >> 64 -inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b) -{ - const vec_uint4 zero = vec_uint4_z; - vec_uint4 even = vec_mule(a.val, b.val); - vec_uint4 odd = vec_mulo(a.val, b.val); - vec_udword2 e0 = (vec_udword2)vec_mergee(even, zero); - vec_udword2 e1 = (vec_udword2)vec_mergeo(even, zero); - vec_udword2 o0 = (vec_udword2)vec_mergee(odd, zero); - vec_udword2 o1 = (vec_udword2)vec_mergeo(odd, zero); - vec_udword2 s0 = vec_add(e0, o0); - vec_udword2 s1 = vec_add(e1, o1); - return v_uint64x2(vec_add(s0, s1)); -} -inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c) -{ return v_dotprod_expand(a, b) + c; } - -inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b) -{ - v_int32x4 prod = v_dotprod(a, b); - v_int64x2 c, d; - v_expand(prod, c, d); - return v_int64x2(vec_add(vec_mergeh(c.val, d.val), vec_mergel(c.val, d.val))); -} -inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c) -{ return v_dotprod_expand(a, b) + c; } - -// 32 >> 64f -inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b) -{ return v_cvt_f64(v_dotprod(a, b)); } -inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c) -{ return v_dotprod_expand(a, b) + c; } - -//////// Fast Dot Product //////// - -// 16 >> 32 -inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b) -{ return v_dotprod(a, b); } -inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) -{ return v_int32x4(vec_msum(a.val, b.val, vec_int4_z)) + c; } -// 32 >> 64 -inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b) -{ return v_dotprod(a, b); } -inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c) -{ return v_dotprod(a, b, c); } - -// 8 >> 32 -inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b) -{ return v_dotprod_expand(a, b); } -inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c) -{ return v_uint32x4(vec_msum(a.val, b.val, vec_uint4_z)) + c; } - -inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b) -{ - vec_short8 a0 = vec_unpackh(a.val); - vec_short8 a1 = vec_unpackl(a.val); - vec_short8 b0 = vec_unpackh(b.val); - vec_short8 b1 = vec_unpackl(b.val); - return v_int32x4(vec_msum(a0, b0, vec_msum(a1, b1, vec_int4_z))); -} -inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c) -{ return v_dotprod_expand_fast(a, b) + c; } - -// 16 >> 64 -inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b) -{ return v_dotprod_expand(a, b); } -inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c) -{ return v_dotprod_expand(a, b, c); } - -inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b) -{ - v_int32x4 prod = v_dotprod(a, b); - v_int64x2 c, d; - v_expand(prod, c, d); - return c + d; -} -inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c) -{ return v_dotprod_expand_fast(a, b) + c; } - -// 32 >> 64f -inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b) -{ return v_dotprod_expand(a, b); } -inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c) -{ return v_dotprod_expand(a, b, c); } - -inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, - const v_float32x4& m1, const v_float32x4& m2, - const v_float32x4& m3) -{ - const vec_float4 v0 = vec_splat(v.val, 0); - const vec_float4 v1 = vec_splat(v.val, 1); - const vec_float4 v2 = vec_splat(v.val, 2); - VSX_UNUSED(const vec_float4) v3 = vec_splat(v.val, 3); - return v_float32x4(vec_madd(v0, m0.val, vec_madd(v1, m1.val, vec_madd(v2, m2.val, vec_mul(v3, m3.val))))); -} - -inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, - const v_float32x4& m1, const v_float32x4& m2, - const v_float32x4& a) -{ - const vec_float4 v0 = vec_splat(v.val, 0); - const vec_float4 v1 = vec_splat(v.val, 1); - const vec_float4 v2 = vec_splat(v.val, 2); - return v_float32x4(vec_madd(v0, m0.val, vec_madd(v1, m1.val, vec_madd(v2, m2.val, a.val)))); -} - -#define OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(_Tpvec, _Tpvec2) \ -inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \ - const _Tpvec& a2, const _Tpvec& a3, \ - _Tpvec& b0, _Tpvec& b1, _Tpvec& b2, _Tpvec& b3) \ -{ \ - _Tpvec2 a02 = vec_mergeh(a0.val, a2.val); \ - _Tpvec2 a13 = vec_mergeh(a1.val, a3.val); \ - b0.val = vec_mergeh(a02, a13); \ - b1.val = vec_mergel(a02, a13); \ - a02 = vec_mergel(a0.val, a2.val); \ - a13 = vec_mergel(a1.val, a3.val); \ - b2.val = vec_mergeh(a02, a13); \ - b3.val = vec_mergel(a02, a13); \ -} -OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_uint32x4, vec_uint4) -OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_int32x4, vec_int4) -OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_float32x4, vec_float4) - -template -inline Tvec v_broadcast_element(const Tvec& v) -{ return Tvec(vec_splat(v.val, i)); } - - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END - -//! @endcond - -} - -#endif // OPENCV_HAL_VSX_HPP diff --git a/include/opencv2/core/hal/intrin_wasm.hpp b/include/opencv2/core/hal/intrin_wasm.hpp deleted file mode 100755 index 22c4a34..0000000 --- a/include/opencv2/core/hal/intrin_wasm.hpp +++ /dev/null @@ -1,4083 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_HAL_INTRIN_WASM_HPP -#define OPENCV_HAL_INTRIN_WASM_HPP - -#include -#include -#include -#include "opencv2/core/saturate.hpp" - -#define CV_SIMD128 1 -#define CV_SIMD128_64F 0 // Now all implementation of f64 use fallback, so disable it. -#define CV_SIMD128_FP16 0 - -namespace cv -{ - -//! @cond IGNORED - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN - -#if (__EMSCRIPTEN_major__ * 1000000 + __EMSCRIPTEN_minor__ * 1000 + __EMSCRIPTEN_tiny__) < (1038046) -// handle renames: https://github.com/emscripten-core/emscripten/pull/9440 (https://github.com/emscripten-core/emscripten/commit/755d5b46cb84d0aa120c10981b11d05646c29673) -#define wasm_i32x4_trunc_saturate_f32x4 wasm_trunc_saturate_i32x4_f32x4 -#define wasm_u32x4_trunc_saturate_f32x4 wasm_trunc_saturate_u32x4_f32x4 -#define wasm_i64x2_trunc_saturate_f64x2 wasm_trunc_saturate_i64x2_f64x2 -#define wasm_u64x2_trunc_saturate_f64x2 wasm_trunc_saturate_u64x2_f64x2 -#define wasm_f32x4_convert_i32x4 wasm_convert_f32x4_i32x4 -#define wasm_f32x4_convert_u32x4 wasm_convert_f32x4_u32x4 -#define wasm_f64x2_convert_i64x2 wasm_convert_f64x2_i64x2 -#define wasm_f64x2_convert_u64x2 wasm_convert_f64x2_u64x2 -#endif // COMPATIBILITY: <1.38.46 - -///////// Types /////////// - -struct v_uint8x16 -{ - typedef uchar lane_type; - typedef v128_t vector_type; - enum { nlanes = 16 }; - - v_uint8x16() {} - explicit v_uint8x16(v128_t v) : val(v) {} - v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7, - uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15) - { - uchar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; - val = wasm_v128_load(v); - } - - uchar get0() const - { - return (uchar)wasm_i8x16_extract_lane(val, 0); - } - - v128_t val; -}; - -struct v_int8x16 -{ - typedef schar lane_type; - typedef v128_t vector_type; - enum { nlanes = 16 }; - - v_int8x16() {} - explicit v_int8x16(v128_t v) : val(v) {} - v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7, - schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15) - { - schar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15}; - val = wasm_v128_load(v); - } - - schar get0() const - { - return wasm_i8x16_extract_lane(val, 0); - } - - v128_t val; -}; - -struct v_uint16x8 -{ - typedef ushort lane_type; - typedef v128_t vector_type; - enum { nlanes = 8 }; - - v_uint16x8() {} - explicit v_uint16x8(v128_t v) : val(v) {} - v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7) - { - ushort v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; - val = wasm_v128_load(v); - } - - ushort get0() const - { - return (ushort)wasm_i16x8_extract_lane(val, 0); // wasm_u16x8_extract_lane() unimplemented yet - } - - v128_t val; -}; - -struct v_int16x8 -{ - typedef short lane_type; - typedef v128_t vector_type; - enum { nlanes = 8 }; - - v_int16x8() {} - explicit v_int16x8(v128_t v) : val(v) {} - v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7) - { - short v[] = {v0, v1, v2, v3, v4, v5, v6, v7}; - val = wasm_v128_load(v); - } - - short get0() const - { - return wasm_i16x8_extract_lane(val, 0); - } - - v128_t val; -}; - -struct v_uint32x4 -{ - typedef unsigned lane_type; - typedef v128_t vector_type; - enum { nlanes = 4 }; - - v_uint32x4() {} - explicit v_uint32x4(v128_t v) : val(v) {} - v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) - { - unsigned v[] = {v0, v1, v2, v3}; - val = wasm_v128_load(v); - } - - unsigned get0() const - { - return (unsigned)wasm_i32x4_extract_lane(val, 0); - } - - v128_t val; -}; - -struct v_int32x4 -{ - typedef int lane_type; - typedef v128_t vector_type; - enum { nlanes = 4 }; - - v_int32x4() {} - explicit v_int32x4(v128_t v) : val(v) {} - v_int32x4(int v0, int v1, int v2, int v3) - { - int v[] = {v0, v1, v2, v3}; - val = wasm_v128_load(v); - } - - int get0() const - { - return wasm_i32x4_extract_lane(val, 0); - } - - v128_t val; -}; - -struct v_float32x4 -{ - typedef float lane_type; - typedef v128_t vector_type; - enum { nlanes = 4 }; - - v_float32x4() {} - explicit v_float32x4(v128_t v) : val(v) {} - v_float32x4(float v0, float v1, float v2, float v3) - { - float v[] = {v0, v1, v2, v3}; - val = wasm_v128_load(v); - } - - float get0() const - { - return wasm_f32x4_extract_lane(val, 0); - } - - v128_t val; -}; - -struct v_uint64x2 -{ - typedef uint64 lane_type; - typedef v128_t vector_type; - enum { nlanes = 2 }; - - v_uint64x2() {} - explicit v_uint64x2(v128_t v) : val(v) {} - v_uint64x2(uint64 v0, uint64 v1) - { - uint64 v[] = {v0, v1}; - val = wasm_v128_load(v); - } - - uint64 get0() const - { - return (uint64)wasm_i64x2_extract_lane(val, 0); - } - - v128_t val; -}; - -struct v_int64x2 -{ - typedef int64 lane_type; - typedef v128_t vector_type; - enum { nlanes = 2 }; - - v_int64x2() {} - explicit v_int64x2(v128_t v) : val(v) {} - v_int64x2(int64 v0, int64 v1) - { - int64 v[] = {v0, v1}; - val = wasm_v128_load(v); - } - - int64 get0() const - { - return wasm_i64x2_extract_lane(val, 0); - } - - v128_t val; -}; - -struct v_float64x2 -{ - typedef double lane_type; - typedef v128_t vector_type; - enum { nlanes = 2 }; - - v_float64x2() {} - explicit v_float64x2(v128_t v) : val(v) {} - v_float64x2(double v0, double v1) - { - double v[] = {v0, v1}; - val = wasm_v128_load(v); - } - - double get0() const - { - return wasm_f64x2_extract_lane(val, 0); - } - - v128_t val; -}; - -namespace fallback -{ - -template struct v_reg -{ - typedef _Tp lane_type; - enum { nlanes = n }; - - explicit v_reg(const _Tp* ptr) { for( int i = 0; i < n; i++ ) s[i] = ptr[i]; } - - v_reg(_Tp s0, _Tp s1) { s[0] = s0; s[1] = s1; } - - v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3) { s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; } - - v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3, - _Tp s4, _Tp s5, _Tp s6, _Tp s7) - { - s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; - s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7; - } - - v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3, - _Tp s4, _Tp s5, _Tp s6, _Tp s7, - _Tp s8, _Tp s9, _Tp s10, _Tp s11, - _Tp s12, _Tp s13, _Tp s14, _Tp s15) - { - s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; - s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7; - s[8] = s8; s[9] = s9; s[10] = s10; s[11] = s11; - s[12] = s12; s[13] = s13; s[14] = s14; s[15] = s15; - } - - v_reg() {} - - v_reg(const v_reg<_Tp, n> & r) - { - for( int i = 0; i < n; i++ ) - s[i] = r.s[i]; - } - - _Tp get0() const { return s[0]; } - - _Tp get(const int i) const { return s[i]; } - v_reg<_Tp, n> high() const - { - v_reg<_Tp, n> c; - int i; - for( i = 0; i < n/2; i++ ) - { - c.s[i] = s[i+(n/2)]; - c.s[i+(n/2)] = 0; - } - return c; - } - - static v_reg<_Tp, n> zero() - { - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = (_Tp)0; - return c; - } - - static v_reg<_Tp, n> all(_Tp s) - { - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = s; - return c; - } - - template v_reg<_Tp2, n2> reinterpret_as() const - { - size_t bytes = std::min(sizeof(_Tp2)*n2, sizeof(_Tp)*n); - v_reg<_Tp2, n2> c; - std::memcpy(&c.s[0], &s[0], bytes); - return c; - } - - v_reg(const cv::v_uint8x16& v) { wasm_v128_store(&s, v.val); } - v_reg(const cv::v_int8x16& v) { wasm_v128_store(&s, v.val); } - v_reg(const cv::v_uint16x8& v) { wasm_v128_store(&s, v.val); } - v_reg(const cv::v_int16x8& v) { wasm_v128_store(&s, v.val); } - v_reg(const cv::v_uint32x4& v) { wasm_v128_store(&s, v.val); } - v_reg(const cv::v_int32x4& v) { wasm_v128_store(&s, v.val); } - v_reg(const cv::v_float32x4& v) { wasm_v128_store(&s, v.val); } - v_reg(const cv::v_float64x2& v) { wasm_v128_store(&s, v.val); } - v_reg(const cv::v_uint64x2& v) { wasm_v128_store(&s, v.val); } - v_reg(const cv::v_int64x2& v) { wasm_v128_store(&s, v.val); } - - operator cv::v_uint8x16() const { return cv::v_uint8x16(wasm_v128_load(&s)); } - operator cv::v_int8x16() const { return cv::v_int8x16(wasm_v128_load(&s)); } - operator cv::v_uint16x8() const { return cv::v_uint16x8(wasm_v128_load(&s)); } - operator cv::v_int16x8() const { return cv::v_int16x8(wasm_v128_load(&s)); } - operator cv::v_uint32x4() const { return cv::v_uint32x4(wasm_v128_load(&s)); } - operator cv::v_int32x4() const { return cv::v_int32x4(wasm_v128_load(&s)); } - operator cv::v_float32x4() const { return cv::v_float32x4(wasm_v128_load(&s)); } - operator cv::v_float64x2() const { return cv::v_float64x2(wasm_v128_load(&s)); } - operator cv::v_uint64x2() const { return cv::v_uint64x2(wasm_v128_load(&s)); } - operator cv::v_int64x2() const { return cv::v_int64x2(wasm_v128_load(&s)); } - - _Tp s[n]; -}; - -typedef v_reg v_uint8x16; -typedef v_reg v_int8x16; -typedef v_reg v_uint16x8; -typedef v_reg v_int16x8; -typedef v_reg v_uint32x4; -typedef v_reg v_int32x4; -typedef v_reg v_float32x4; -typedef v_reg v_float64x2; -typedef v_reg v_uint64x2; -typedef v_reg v_int64x2; - -#define OPENCV_HAL_IMPL_BIN_OP(bin_op) \ -template inline v_reg<_Tp, n> \ - operator bin_op (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - v_reg<_Tp, n> c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \ - return c; \ -} \ -template inline v_reg<_Tp, n>& \ - operator bin_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - for( int i = 0; i < n; i++ ) \ - a.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \ - return a; \ -} - -OPENCV_HAL_IMPL_BIN_OP(+) -OPENCV_HAL_IMPL_BIN_OP(-) -OPENCV_HAL_IMPL_BIN_OP(*) -OPENCV_HAL_IMPL_BIN_OP(/) - -#define OPENCV_HAL_IMPL_BIT_OP(bit_op) \ -template inline v_reg<_Tp, n> operator bit_op \ - (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - v_reg<_Tp, n> c; \ - typedef typename V_TypeTraits<_Tp>::int_type itype; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \ - V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \ - return c; \ -} \ -template inline v_reg<_Tp, n>& operator \ - bit_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - typedef typename V_TypeTraits<_Tp>::int_type itype; \ - for( int i = 0; i < n; i++ ) \ - a.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \ - V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \ - return a; \ -} - -OPENCV_HAL_IMPL_BIT_OP(&) -OPENCV_HAL_IMPL_BIT_OP(|) -OPENCV_HAL_IMPL_BIT_OP(^) - -template inline v_reg<_Tp, n> operator ~ (const v_reg<_Tp, n>& a) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int(~V_TypeTraits<_Tp>::reinterpret_int(a.s[i])); - } - return c; -} - -#define OPENCV_HAL_IMPL_MATH_FUNC(func, cfunc, _Tp2) \ -template inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a) \ -{ \ - v_reg<_Tp2, n> c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = cfunc(a.s[i]); \ - return c; \ -} - -OPENCV_HAL_IMPL_MATH_FUNC(v_sqrt, std::sqrt, _Tp) -OPENCV_HAL_IMPL_MATH_FUNC(v_sin, std::sin, _Tp) -OPENCV_HAL_IMPL_MATH_FUNC(v_cos, std::cos, _Tp) -OPENCV_HAL_IMPL_MATH_FUNC(v_exp, std::exp, _Tp) -OPENCV_HAL_IMPL_MATH_FUNC(v_log, std::log, _Tp) -OPENCV_HAL_IMPL_MATH_FUNC(v_abs, (typename V_TypeTraits<_Tp>::abs_type)std::abs, - typename V_TypeTraits<_Tp>::abs_type) -OPENCV_HAL_IMPL_MATH_FUNC(v_round, cvRound, int) -OPENCV_HAL_IMPL_MATH_FUNC(v_floor, cvFloor, int) -OPENCV_HAL_IMPL_MATH_FUNC(v_ceil, cvCeil, int) -OPENCV_HAL_IMPL_MATH_FUNC(v_trunc, int, int) - -#define OPENCV_HAL_IMPL_MINMAX_FUNC(func, cfunc) \ -template inline v_reg<_Tp, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - v_reg<_Tp, n> c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = cfunc(a.s[i], b.s[i]); \ - return c; \ -} - -#define OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(func, cfunc) \ -template inline _Tp func(const v_reg<_Tp, n>& a) \ -{ \ - _Tp c = a.s[0]; \ - for( int i = 1; i < n; i++ ) \ - c = cfunc(c, a.s[i]); \ - return c; \ -} - -OPENCV_HAL_IMPL_MINMAX_FUNC(v_min, std::min) -OPENCV_HAL_IMPL_MINMAX_FUNC(v_max, std::max) -OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_min, std::min) -OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_max, std::max) - -static const unsigned char popCountTable[] = -{ - 0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, - 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, - 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, - 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, - 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, - 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8, -}; - -template -inline v_reg::abs_type, n> v_popcount(const v_reg<_Tp, n>& a) -{ - v_reg::abs_type, n> b = v_reg::abs_type, n>::zero(); - for (int i = 0; i < (int)(n*sizeof(_Tp)); i++) - b.s[i/sizeof(_Tp)] += popCountTable[v_reinterpret_as_u8(a).s[i]]; - return b; -} - -template -inline void v_minmax( const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - v_reg<_Tp, n>& minval, v_reg<_Tp, n>& maxval ) -{ - for( int i = 0; i < n; i++ ) - { - minval.s[i] = std::min(a.s[i], b.s[i]); - maxval.s[i] = std::max(a.s[i], b.s[i]); - } -} - -#define OPENCV_HAL_IMPL_CMP_OP(cmp_op) \ -template \ -inline v_reg<_Tp, n> operator cmp_op(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - typedef typename V_TypeTraits<_Tp>::int_type itype; \ - v_reg<_Tp, n> c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)-(int)(a.s[i] cmp_op b.s[i])); \ - return c; \ -} - -OPENCV_HAL_IMPL_CMP_OP(<) -OPENCV_HAL_IMPL_CMP_OP(>) -OPENCV_HAL_IMPL_CMP_OP(<=) -OPENCV_HAL_IMPL_CMP_OP(>=) -OPENCV_HAL_IMPL_CMP_OP(==) -OPENCV_HAL_IMPL_CMP_OP(!=) - -template -inline v_reg v_not_nan(const v_reg& a) -{ - typedef typename V_TypeTraits::int_type itype; - v_reg c; - for (int i = 0; i < n; i++) - c.s[i] = V_TypeTraits::reinterpret_from_int((itype)-(int)(a.s[i] == a.s[i])); - return c; -} -template -inline v_reg v_not_nan(const v_reg& a) -{ - typedef typename V_TypeTraits::int_type itype; - v_reg c; - for (int i = 0; i < n; i++) - c.s[i] = V_TypeTraits::reinterpret_from_int((itype)-(int)(a.s[i] == a.s[i])); - return c; -} - -#define OPENCV_HAL_IMPL_ARITHM_OP(func, bin_op, cast_op, _Tp2) \ -template \ -inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - typedef _Tp2 rtype; \ - v_reg c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = cast_op(a.s[i] bin_op b.s[i]); \ - return c; \ -} - -OPENCV_HAL_IMPL_ARITHM_OP(v_add_wrap, +, (_Tp), _Tp) -OPENCV_HAL_IMPL_ARITHM_OP(v_sub_wrap, -, (_Tp), _Tp) -OPENCV_HAL_IMPL_ARITHM_OP(v_mul_wrap, *, (_Tp), _Tp) - -template inline T _absdiff(T a, T b) -{ - return a > b ? a - b : b - a; -} - -template -inline v_reg::abs_type, n> v_absdiff(const v_reg<_Tp, n>& a, const v_reg<_Tp, n> & b) -{ - typedef typename V_TypeTraits<_Tp>::abs_type rtype; - v_reg c; - const rtype mask = (rtype)(std::numeric_limits<_Tp>::is_signed ? (1 << (sizeof(rtype)*8 - 1)) : 0); - for( int i = 0; i < n; i++ ) - { - rtype ua = a.s[i] ^ mask; - rtype ub = b.s[i] ^ mask; - c.s[i] = _absdiff(ua, ub); - } - return c; -} - -inline v_float32x4 v_absdiff(const v_float32x4& a, const v_float32x4& b) -{ - v_float32x4 c; - for( int i = 0; i < c.nlanes; i++ ) - c.s[i] = _absdiff(a.s[i], b.s[i]); - return c; -} - -inline v_float64x2 v_absdiff(const v_float64x2& a, const v_float64x2& b) -{ - v_float64x2 c; - for( int i = 0; i < c.nlanes; i++ ) - c.s[i] = _absdiff(a.s[i], b.s[i]); - return c; -} - -template -inline v_reg<_Tp, n> v_absdiffs(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++) - c.s[i] = saturate_cast<_Tp>(std::abs(a.s[i] - b.s[i])); - return c; -} - -template -inline v_reg<_Tp, n> v_invsqrt(const v_reg<_Tp, n>& a) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = 1.f/std::sqrt(a.s[i]); - return c; -} - -template -inline v_reg<_Tp, n> v_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = std::sqrt(a.s[i]*a.s[i] + b.s[i]*b.s[i]); - return c; -} - -template -inline v_reg<_Tp, n> v_sqr_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = a.s[i]*a.s[i] + b.s[i]*b.s[i]; - return c; -} - -template -inline v_reg<_Tp, n> v_fma(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - const v_reg<_Tp, n>& c) -{ - v_reg<_Tp, n> d; - for( int i = 0; i < n; i++ ) - d.s[i] = a.s[i]*b.s[i] + c.s[i]; - return d; -} - -template -inline v_reg<_Tp, n> v_muladd(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - const v_reg<_Tp, n>& c) -{ - return v_fma(a, b, c); -} - -template inline v_reg::w_type, n/2> - v_dotprod(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - typedef typename V_TypeTraits<_Tp>::w_type w_type; - v_reg c; - for( int i = 0; i < (n/2); i++ ) - c.s[i] = (w_type)a.s[i*2]*b.s[i*2] + (w_type)a.s[i*2+1]*b.s[i*2+1]; - return c; -} - -template inline v_reg::w_type, n/2> - v_dotprod(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, const v_reg::w_type, n / 2>& c) -{ - typedef typename V_TypeTraits<_Tp>::w_type w_type; - v_reg s; - for( int i = 0; i < (n/2); i++ ) - s.s[i] = (w_type)a.s[i*2]*b.s[i*2] + (w_type)a.s[i*2+1]*b.s[i*2+1] + c.s[i]; - return s; -} - -template inline v_reg::q_type, n/4> - v_dotprod_expand(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - typedef typename V_TypeTraits<_Tp>::q_type q_type; - v_reg s; - for( int i = 0; i < (n/4); i++ ) - s.s[i] = (q_type)a.s[i*4 ]*b.s[i*4 ] + (q_type)a.s[i*4 + 1]*b.s[i*4 + 1] + - (q_type)a.s[i*4 + 2]*b.s[i*4 + 2] + (q_type)a.s[i*4 + 3]*b.s[i*4 + 3]; - return s; -} - -template inline v_reg::q_type, n/4> - v_dotprod_expand(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - const v_reg::q_type, n / 4>& c) -{ - typedef typename V_TypeTraits<_Tp>::q_type q_type; - v_reg s; - for( int i = 0; i < (n/4); i++ ) - s.s[i] = (q_type)a.s[i*4 ]*b.s[i*4 ] + (q_type)a.s[i*4 + 1]*b.s[i*4 + 1] + - (q_type)a.s[i*4 + 2]*b.s[i*4 + 2] + (q_type)a.s[i*4 + 3]*b.s[i*4 + 3] + c.s[i]; - return s; -} - -template inline void v_mul_expand(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - v_reg::w_type, n/2>& c, - v_reg::w_type, n/2>& d) -{ - typedef typename V_TypeTraits<_Tp>::w_type w_type; - for( int i = 0; i < (n/2); i++ ) - { - c.s[i] = (w_type)a.s[i]*b.s[i]; - d.s[i] = (w_type)a.s[i+(n/2)]*b.s[i+(n/2)]; - } -} - -template inline v_reg<_Tp, n> v_mul_hi(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - typedef typename V_TypeTraits<_Tp>::w_type w_type; - v_reg<_Tp, n> c; - for (int i = 0; i < n; i++) - c.s[i] = (_Tp)(((w_type)a.s[i] * b.s[i]) >> sizeof(_Tp)*8); - return c; -} - -template inline void v_hsum(const v_reg<_Tp, n>& a, - v_reg::w_type, n/2>& c) -{ - typedef typename V_TypeTraits<_Tp>::w_type w_type; - for( int i = 0; i < (n/2); i++ ) - { - c.s[i] = (w_type)a.s[i*2] + a.s[i*2+1]; - } -} - -#define OPENCV_HAL_IMPL_SHIFT_OP(shift_op) \ -template inline v_reg<_Tp, n> operator shift_op(const v_reg<_Tp, n>& a, int imm) \ -{ \ - v_reg<_Tp, n> c; \ - for( int i = 0; i < n; i++ ) \ - c.s[i] = (_Tp)(a.s[i] shift_op imm); \ - return c; \ -} - -OPENCV_HAL_IMPL_SHIFT_OP(<< ) -OPENCV_HAL_IMPL_SHIFT_OP(>> ) - -#define OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(suffix,opA,opB) \ -template inline v_reg<_Tp, n> v_rotate_##suffix(const v_reg<_Tp, n>& a) \ -{ \ - v_reg<_Tp, n> b; \ - for (int i = 0; i < n; i++) \ - { \ - int sIndex = i opA imm; \ - if (0 <= sIndex && sIndex < n) \ - { \ - b.s[i] = a.s[sIndex]; \ - } \ - else \ - { \ - b.s[i] = 0; \ - } \ - } \ - return b; \ -} \ -template inline v_reg<_Tp, n> v_rotate_##suffix(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \ -{ \ - v_reg<_Tp, n> c; \ - for (int i = 0; i < n; i++) \ - { \ - int aIndex = i opA imm; \ - int bIndex = i opA imm opB n; \ - if (0 <= bIndex && bIndex < n) \ - { \ - c.s[i] = b.s[bIndex]; \ - } \ - else if (0 <= aIndex && aIndex < n) \ - { \ - c.s[i] = a.s[aIndex]; \ - } \ - else \ - { \ - c.s[i] = 0; \ - } \ - } \ - return c; \ -} - -OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(left, -, +) -OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(right, +, -) - -template inline typename V_TypeTraits<_Tp>::sum_type v_reduce_sum(const v_reg<_Tp, n>& a) -{ - typename V_TypeTraits<_Tp>::sum_type c = a.s[0]; - for( int i = 1; i < n; i++ ) - c += a.s[i]; - return c; -} - -inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, - const v_float32x4& c, const v_float32x4& d) -{ - v_float32x4 r; - r.s[0] = a.s[0] + a.s[1] + a.s[2] + a.s[3]; - r.s[1] = b.s[0] + b.s[1] + b.s[2] + b.s[3]; - r.s[2] = c.s[0] + c.s[1] + c.s[2] + c.s[3]; - r.s[3] = d.s[0] + d.s[1] + d.s[2] + d.s[3]; - return r; -} - -template inline typename V_TypeTraits< typename V_TypeTraits<_Tp>::abs_type >::sum_type v_reduce_sad(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - typename V_TypeTraits< typename V_TypeTraits<_Tp>::abs_type >::sum_type c = _absdiff(a.s[0], b.s[0]); - for (int i = 1; i < n; i++) - c += _absdiff(a.s[i], b.s[i]); - return c; -} - -template inline int v_signmask(const v_reg<_Tp, n>& a) -{ - int mask = 0; - for( int i = 0; i < n; i++ ) - mask |= (V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0) << i; - return mask; -} - -template inline bool v_check_all(const v_reg<_Tp, n>& a) -{ - for( int i = 0; i < n; i++ ) - if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) >= 0 ) - return false; - return true; -} - -template inline bool v_check_any(const v_reg<_Tp, n>& a) -{ - for( int i = 0; i < n; i++ ) - if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0 ) - return true; - return false; -} - -template inline v_reg<_Tp, n> v_select(const v_reg<_Tp, n>& mask, - const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - typedef V_TypeTraits<_Tp> Traits; - typedef typename Traits::int_type int_type; - v_reg<_Tp, n> c; - for( int i = 0; i < n; i++ ) - { - int_type m = Traits::reinterpret_int(mask.s[i]); - CV_DbgAssert(m == 0 || m == (~(int_type)0)); // restrict mask values: 0 or 0xff/0xffff/etc - c.s[i] = m ? a.s[i] : b.s[i]; - } - return c; -} - -template inline void v_expand(const v_reg<_Tp, n>& a, - v_reg::w_type, n/2>& b0, - v_reg::w_type, n/2>& b1) -{ - for( int i = 0; i < (n/2); i++ ) - { - b0.s[i] = a.s[i]; - b1.s[i] = a.s[i+(n/2)]; - } -} - -template -inline v_reg::w_type, n/2> -v_expand_low(const v_reg<_Tp, n>& a) -{ - v_reg::w_type, n/2> b; - for( int i = 0; i < (n/2); i++ ) - b.s[i] = a.s[i]; - return b; -} - -template -inline v_reg::w_type, n/2> -v_expand_high(const v_reg<_Tp, n>& a) -{ - v_reg::w_type, n/2> b; - for( int i = 0; i < (n/2); i++ ) - b.s[i] = a.s[i+(n/2)]; - return b; -} - -template inline v_reg::int_type, n> - v_reinterpret_as_int(const v_reg<_Tp, n>& a) -{ - v_reg::int_type, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = V_TypeTraits<_Tp>::reinterpret_int(a.s[i]); - return c; -} - -template inline v_reg::uint_type, n> - v_reinterpret_as_uint(const v_reg<_Tp, n>& a) -{ - v_reg::uint_type, n> c; - for( int i = 0; i < n; i++ ) - c.s[i] = V_TypeTraits<_Tp>::reinterpret_uint(a.s[i]); - return c; -} - -template inline void v_zip( const v_reg<_Tp, n>& a0, const v_reg<_Tp, n>& a1, - v_reg<_Tp, n>& b0, v_reg<_Tp, n>& b1 ) -{ - int i; - for( i = 0; i < n/2; i++ ) - { - b0.s[i*2] = a0.s[i]; - b0.s[i*2+1] = a1.s[i]; - } - for( ; i < n; i++ ) - { - b1.s[i*2-n] = a0.s[i]; - b1.s[i*2-n+1] = a1.s[i]; - } -} - -template -inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load(const _Tp* ptr) -{ - return v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128>(ptr); -} - -template -inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load_aligned(const _Tp* ptr) -{ - return v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128>(ptr); -} - -template -inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load_low(const _Tp* ptr) -{ - v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> c; - for( int i = 0; i < c.nlanes/2; i++ ) - { - c.s[i] = ptr[i]; - } - return c; -} - -template -inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_load_halves(const _Tp* loptr, const _Tp* hiptr) -{ - v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> c; - for( int i = 0; i < c.nlanes/2; i++ ) - { - c.s[i] = loptr[i]; - c.s[i+c.nlanes/2] = hiptr[i]; - } - return c; -} - -template -inline v_reg::w_type, V_TypeTraits<_Tp>::nlanes128 / 2> -v_load_expand(const _Tp* ptr) -{ - typedef typename V_TypeTraits<_Tp>::w_type w_type; - v_reg::nlanes128> c; - for( int i = 0; i < c.nlanes; i++ ) - { - c.s[i] = ptr[i]; - } - return c; -} - -template -inline v_reg::q_type, V_TypeTraits<_Tp>::nlanes128 / 4> -v_load_expand_q(const _Tp* ptr) -{ - typedef typename V_TypeTraits<_Tp>::q_type q_type; - v_reg::nlanes128> c; - for( int i = 0; i < c.nlanes; i++ ) - { - c.s[i] = ptr[i]; - } - return c; -} - -template inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, - v_reg<_Tp, n>& b) -{ - int i, i2; - for( i = i2 = 0; i < n; i++, i2 += 2 ) - { - a.s[i] = ptr[i2]; - b.s[i] = ptr[i2+1]; - } -} - -template inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, - v_reg<_Tp, n>& b, v_reg<_Tp, n>& c) -{ - int i, i3; - for( i = i3 = 0; i < n; i++, i3 += 3 ) - { - a.s[i] = ptr[i3]; - b.s[i] = ptr[i3+1]; - c.s[i] = ptr[i3+2]; - } -} - -template -inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a, - v_reg<_Tp, n>& b, v_reg<_Tp, n>& c, - v_reg<_Tp, n>& d) -{ - int i, i4; - for( i = i4 = 0; i < n; i++, i4 += 4 ) - { - a.s[i] = ptr[i4]; - b.s[i] = ptr[i4+1]; - c.s[i] = ptr[i4+2]; - d.s[i] = ptr[i4+3]; - } -} - -template -inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, - const v_reg<_Tp, n>& b, - hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) -{ - int i, i2; - for( i = i2 = 0; i < n; i++, i2 += 2 ) - { - ptr[i2] = a.s[i]; - ptr[i2+1] = b.s[i]; - } -} - -template -inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, - const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c, - hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) -{ - int i, i3; - for( i = i3 = 0; i < n; i++, i3 += 3 ) - { - ptr[i3] = a.s[i]; - ptr[i3+1] = b.s[i]; - ptr[i3+2] = c.s[i]; - } -} - -template inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a, - const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c, - const v_reg<_Tp, n>& d, - hal::StoreMode /*mode*/=hal::STORE_UNALIGNED) -{ - int i, i4; - for( i = i4 = 0; i < n; i++, i4 += 4 ) - { - ptr[i4] = a.s[i]; - ptr[i4+1] = b.s[i]; - ptr[i4+2] = c.s[i]; - ptr[i4+3] = d.s[i]; - } -} - -template -inline void v_store(_Tp* ptr, const v_reg<_Tp, n>& a, hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED) -{ - for( int i = 0; i < n; i++ ) - ptr[i] = a.s[i]; -} - -template -inline void v_store_low(_Tp* ptr, const v_reg<_Tp, n>& a) -{ - for( int i = 0; i < (n/2); i++ ) - ptr[i] = a.s[i]; -} - -template -inline void v_store_high(_Tp* ptr, const v_reg<_Tp, n>& a) -{ - for( int i = 0; i < (n/2); i++ ) - ptr[i] = a.s[i+(n/2)]; -} - -template -inline void v_store_aligned(_Tp* ptr, const v_reg<_Tp, n>& a) -{ - for( int i = 0; i < n; i++ ) - ptr[i] = a.s[i]; -} - -template -inline void v_store_aligned_nocache(_Tp* ptr, const v_reg<_Tp, n>& a) -{ - for( int i = 0; i < n; i++ ) - ptr[i] = a.s[i]; -} - -template -inline void v_store_aligned(_Tp* ptr, const v_reg<_Tp, n>& a, hal::StoreMode /*mode*/) -{ - for( int i = 0; i < n; i++ ) - ptr[i] = a.s[i]; -} - -template -inline v_reg<_Tp, n> v_combine_low(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < (n/2); i++ ) - { - c.s[i] = a.s[i]; - c.s[i+(n/2)] = b.s[i]; - } - return c; -} - -template -inline v_reg<_Tp, n> v_combine_high(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - v_reg<_Tp, n> c; - for( int i = 0; i < (n/2); i++ ) - { - c.s[i] = a.s[i+(n/2)]; - c.s[i+(n/2)] = b.s[i+(n/2)]; - } - return c; -} - -template -inline void v_recombine(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, - v_reg<_Tp, n>& low, v_reg<_Tp, n>& high) -{ - for( int i = 0; i < (n/2); i++ ) - { - low.s[i] = a.s[i]; - low.s[i+(n/2)] = b.s[i]; - high.s[i] = a.s[i+(n/2)]; - high.s[i+(n/2)] = b.s[i+(n/2)]; - } -} - -template -inline v_reg<_Tp, n> v_extract(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - v_reg<_Tp, n> r; - const int shift = n - s; - int i = 0; - for (; i < shift; ++i) - r.s[i] = a.s[i+s]; - for (; i < n; ++i) - r.s[i] = b.s[i-shift]; - return r; -} - -template inline v_reg v_round(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = cvRound(a.s[i]); - return c; -} - -template inline v_reg v_round(const v_reg& a, const v_reg& b) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = cvRound(a.s[i]); - c.s[i+n] = cvRound(b.s[i]); - } - return c; -} - -template inline v_reg v_floor(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = cvFloor(a.s[i]); - return c; -} - -template inline v_reg v_ceil(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = cvCeil(a.s[i]); - return c; -} - -template inline v_reg v_trunc(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = (int)(a.s[i]); - return c; -} - -template inline v_reg v_round(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = cvRound(a.s[i]); - c.s[i+n] = 0; - } - return c; -} - -template inline v_reg v_floor(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = cvFloor(a.s[i]); - c.s[i+n] = 0; - } - return c; -} - -template inline v_reg v_ceil(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = cvCeil(a.s[i]); - c.s[i+n] = 0; - } - return c; -} - -template inline v_reg v_trunc(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = (int)(a.s[i]); - c.s[i+n] = 0; - } - return c; -} - -template inline v_reg v_cvt_f32(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = (float)a.s[i]; - return c; -} - -template inline v_reg v_cvt_f32(const v_reg& a) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = (float)a.s[i]; - c.s[i+n] = 0; - } - return c; -} - -template inline v_reg v_cvt_f32(const v_reg& a, const v_reg& b) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - { - c.s[i] = (float)a.s[i]; - c.s[i+n] = (float)b.s[i]; - } - return c; -} - -inline v_float64x2 v_cvt_f64(const v_int32x4& a) -{ - v_float64x2 c; - for( int i = 0; i < 2; i++ ) - c.s[i] = (double)a.s[i]; - return c; -} - -inline v_float64x2 v_cvt_f64_high(const v_int32x4& a) -{ - v_float64x2 c; - for( int i = 0; i < 2; i++ ) - c.s[i] = (double)a.s[i+2]; - return c; -} - -inline v_float64x2 v_cvt_f64(const v_float32x4& a) -{ - v_float64x2 c; - for( int i = 0; i < 2; i++ ) - c.s[i] = (double)a.s[i]; - return c; -} - -inline v_float64x2 v_cvt_f64_high(const v_float32x4& a) -{ - v_float64x2 c; - for( int i = 0; i < 2; i++ ) - c.s[i] = (double)a.s[i+2]; - return c; -} - -inline v_float64x2 v_cvt_f64(const v_int64x2& a) -{ - v_float64x2 c; - for( int i = 0; i < 2; i++ ) - c.s[i] = (double)a.s[i]; - return c; -} - -template inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_lut(const _Tp* tab, const int* idx) -{ - v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> c; - for (int i = 0; i < V_TypeTraits<_Tp>::nlanes128; i++) - c.s[i] = tab[idx[i]]; - return c; -} -template inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_lut_pairs(const _Tp* tab, const int* idx) -{ - v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> c; - for (int i = 0; i < V_TypeTraits<_Tp>::nlanes128; i++) - c.s[i] = tab[idx[i / 2] + i % 2]; - return c; -} -template inline v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> v_lut_quads(const _Tp* tab, const int* idx) -{ - v_reg<_Tp, V_TypeTraits<_Tp>::nlanes128> c; - for (int i = 0; i < V_TypeTraits<_Tp>::nlanes128; i++) - c.s[i] = tab[idx[i / 4] + i % 4]; - return c; -} - -template inline v_reg v_lut(const int* tab, const v_reg& idx) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = tab[idx.s[i]]; - return c; -} - -template inline v_reg v_lut(const unsigned* tab, const v_reg& idx) -{ - v_reg c; - for (int i = 0; i < n; i++) - c.s[i] = tab[idx.s[i]]; - return c; -} - -template inline v_reg v_lut(const float* tab, const v_reg& idx) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = tab[idx.s[i]]; - return c; -} - -template inline v_reg v_lut(const double* tab, const v_reg& idx) -{ - v_reg c; - for( int i = 0; i < n; i++ ) - c.s[i] = tab[idx.s[i]]; - return c; -} - -template inline void v_lut_deinterleave(const float* tab, const v_reg& idx, - v_reg& x, v_reg& y) -{ - for( int i = 0; i < n; i++ ) - { - int j = idx.s[i]; - x.s[i] = tab[j]; - y.s[i] = tab[j+1]; - } -} - -template inline void v_lut_deinterleave(const double* tab, const v_reg& idx, - v_reg& x, v_reg& y) -{ - for( int i = 0; i < n; i++ ) - { - int j = idx.s[i]; - x.s[i] = tab[j]; - y.s[i] = tab[j+1]; - } -} - -template inline v_reg<_Tp, n> v_interleave_pairs(const v_reg<_Tp, n>& vec) -{ - v_reg<_Tp, n> c; - for (int i = 0; i < n/4; i++) - { - c.s[4*i ] = vec.s[4*i ]; - c.s[4*i+1] = vec.s[4*i+2]; - c.s[4*i+2] = vec.s[4*i+1]; - c.s[4*i+3] = vec.s[4*i+3]; - } - return c; -} - -template inline v_reg<_Tp, n> v_interleave_quads(const v_reg<_Tp, n>& vec) -{ - v_reg<_Tp, n> c; - for (int i = 0; i < n/8; i++) - { - c.s[8*i ] = vec.s[8*i ]; - c.s[8*i+1] = vec.s[8*i+4]; - c.s[8*i+2] = vec.s[8*i+1]; - c.s[8*i+3] = vec.s[8*i+5]; - c.s[8*i+4] = vec.s[8*i+2]; - c.s[8*i+5] = vec.s[8*i+6]; - c.s[8*i+6] = vec.s[8*i+3]; - c.s[8*i+7] = vec.s[8*i+7]; - } - return c; -} - -template inline v_reg<_Tp, n> v_pack_triplets(const v_reg<_Tp, n>& vec) -{ - v_reg<_Tp, n> c; - for (int i = 0; i < n/4; i++) - { - c.s[3*i ] = vec.s[4*i ]; - c.s[3*i+1] = vec.s[4*i+1]; - c.s[3*i+2] = vec.s[4*i+2]; - } - return c; -} - -template -inline void v_transpose4x4( v_reg<_Tp, 4>& a0, const v_reg<_Tp, 4>& a1, - const v_reg<_Tp, 4>& a2, const v_reg<_Tp, 4>& a3, - v_reg<_Tp, 4>& b0, v_reg<_Tp, 4>& b1, - v_reg<_Tp, 4>& b2, v_reg<_Tp, 4>& b3 ) -{ - b0 = v_reg<_Tp, 4>(a0.s[0], a1.s[0], a2.s[0], a3.s[0]); - b1 = v_reg<_Tp, 4>(a0.s[1], a1.s[1], a2.s[1], a3.s[1]); - b2 = v_reg<_Tp, 4>(a0.s[2], a1.s[2], a2.s[2], a3.s[2]); - b3 = v_reg<_Tp, 4>(a0.s[3], a1.s[3], a2.s[3], a3.s[3]); -} - -#define OPENCV_HAL_IMPL_C_INIT_ZERO(_Tpvec, _Tp, suffix) \ -inline _Tpvec v_setzero_##suffix() { return _Tpvec::zero(); } - -OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint8x16, uchar, u8) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_int8x16, schar, s8) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint16x8, ushort, u16) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_int16x8, short, s16) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_int32x4, int, s32) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_float32x4, float, f32) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_float64x2, double, f64) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint64x2, uint64, u64) -OPENCV_HAL_IMPL_C_INIT_ZERO(v_int64x2, int64, s64) - -#define OPENCV_HAL_IMPL_C_INIT_VAL(_Tpvec, _Tp, suffix) \ -inline _Tpvec v_setall_##suffix(_Tp val) { return _Tpvec::all(val); } - -OPENCV_HAL_IMPL_C_INIT_VAL(v_int8x16, schar, s8) -OPENCV_HAL_IMPL_C_INIT_VAL(v_uint16x8, ushort, u16) -OPENCV_HAL_IMPL_C_INIT_VAL(v_int16x8, short, s16) -OPENCV_HAL_IMPL_C_INIT_VAL(v_uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_C_INIT_VAL(v_int32x4, int, s32) -OPENCV_HAL_IMPL_C_INIT_VAL(v_float32x4, float, f32) -OPENCV_HAL_IMPL_C_INIT_VAL(v_float64x2, double, f64) -OPENCV_HAL_IMPL_C_INIT_VAL(v_uint64x2, uint64, u64) -OPENCV_HAL_IMPL_C_INIT_VAL(v_int64x2, int64, s64) - -#define OPENCV_HAL_IMPL_C_REINTERPRET(_Tpvec, _Tp, suffix) \ -template inline _Tpvec \ - v_reinterpret_as_##suffix(const v_reg<_Tp0, n0>& a) \ -{ return a.template reinterpret_as<_Tp, _Tpvec::nlanes>(); } - -OPENCV_HAL_IMPL_C_REINTERPRET(v_uint8x16, uchar, u8) -OPENCV_HAL_IMPL_C_REINTERPRET(v_int8x16, schar, s8) -OPENCV_HAL_IMPL_C_REINTERPRET(v_uint16x8, ushort, u16) -OPENCV_HAL_IMPL_C_REINTERPRET(v_int16x8, short, s16) -OPENCV_HAL_IMPL_C_REINTERPRET(v_uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_C_REINTERPRET(v_int32x4, int, s32) -OPENCV_HAL_IMPL_C_REINTERPRET(v_float32x4, float, f32) -OPENCV_HAL_IMPL_C_REINTERPRET(v_float64x2, double, f64) -OPENCV_HAL_IMPL_C_REINTERPRET(v_uint64x2, uint64, u64) -OPENCV_HAL_IMPL_C_REINTERPRET(v_int64x2, int64, s64) - -#define OPENCV_HAL_IMPL_C_SHIFTL(_Tpvec, _Tp) \ -template inline _Tpvec v_shl(const _Tpvec& a) \ -{ return a << n; } - -OPENCV_HAL_IMPL_C_SHIFTL(v_uint16x8, ushort) -OPENCV_HAL_IMPL_C_SHIFTL(v_int16x8, short) -OPENCV_HAL_IMPL_C_SHIFTL(v_uint32x4, unsigned) -OPENCV_HAL_IMPL_C_SHIFTL(v_int32x4, int) -OPENCV_HAL_IMPL_C_SHIFTL(v_uint64x2, uint64) -OPENCV_HAL_IMPL_C_SHIFTL(v_int64x2, int64) - -#define OPENCV_HAL_IMPL_C_SHIFTR(_Tpvec, _Tp) \ -template inline _Tpvec v_shr(const _Tpvec& a) \ -{ return a >> n; } - -OPENCV_HAL_IMPL_C_SHIFTR(v_uint16x8, ushort) -OPENCV_HAL_IMPL_C_SHIFTR(v_int16x8, short) -OPENCV_HAL_IMPL_C_SHIFTR(v_uint32x4, unsigned) -OPENCV_HAL_IMPL_C_SHIFTR(v_int32x4, int) -OPENCV_HAL_IMPL_C_SHIFTR(v_uint64x2, uint64) -OPENCV_HAL_IMPL_C_SHIFTR(v_int64x2, int64) - -#define OPENCV_HAL_IMPL_C_RSHIFTR(_Tpvec, _Tp) \ -template inline _Tpvec v_rshr(const _Tpvec& a) \ -{ \ - _Tpvec c; \ - for( int i = 0; i < _Tpvec::nlanes; i++ ) \ - c.s[i] = (_Tp)((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ - return c; \ -} - -OPENCV_HAL_IMPL_C_RSHIFTR(v_uint16x8, ushort) -OPENCV_HAL_IMPL_C_RSHIFTR(v_int16x8, short) -OPENCV_HAL_IMPL_C_RSHIFTR(v_uint32x4, unsigned) -OPENCV_HAL_IMPL_C_RSHIFTR(v_int32x4, int) -OPENCV_HAL_IMPL_C_RSHIFTR(v_uint64x2, uint64) -OPENCV_HAL_IMPL_C_RSHIFTR(v_int64x2, int64) - -#define OPENCV_HAL_IMPL_C_PACK(_Tpvec, _Tpnvec, _Tpn, pack_suffix, cast) \ -inline _Tpnvec v_##pack_suffix(const _Tpvec& a, const _Tpvec& b) \ -{ \ - _Tpnvec c; \ - for( int i = 0; i < _Tpvec::nlanes; i++ ) \ - { \ - c.s[i] = cast<_Tpn>(a.s[i]); \ - c.s[i+_Tpvec::nlanes] = cast<_Tpn>(b.s[i]); \ - } \ - return c; \ -} - -OPENCV_HAL_IMPL_C_PACK(v_uint16x8, v_uint8x16, uchar, pack, saturate_cast) -OPENCV_HAL_IMPL_C_PACK(v_int16x8, v_int8x16, schar, pack, saturate_cast) -OPENCV_HAL_IMPL_C_PACK(v_uint32x4, v_uint16x8, ushort, pack, saturate_cast) -OPENCV_HAL_IMPL_C_PACK(v_int32x4, v_int16x8, short, pack, saturate_cast) -OPENCV_HAL_IMPL_C_PACK(v_uint64x2, v_uint32x4, unsigned, pack, static_cast) -OPENCV_HAL_IMPL_C_PACK(v_int64x2, v_int32x4, int, pack, static_cast) -OPENCV_HAL_IMPL_C_PACK(v_int16x8, v_uint8x16, uchar, pack_u, saturate_cast) -OPENCV_HAL_IMPL_C_PACK(v_int32x4, v_uint16x8, ushort, pack_u, saturate_cast) - -#define OPENCV_HAL_IMPL_C_RSHR_PACK(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \ -template inline _Tpnvec v_rshr_##pack_suffix(const _Tpvec& a, const _Tpvec& b) \ -{ \ - _Tpnvec c; \ - for( int i = 0; i < _Tpvec::nlanes; i++ ) \ - { \ - c.s[i] = cast<_Tpn>((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ - c.s[i+_Tpvec::nlanes] = cast<_Tpn>((b.s[i] + ((_Tp)1 << (n - 1))) >> n); \ - } \ - return c; \ -} - -OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_int16x8, short, v_int8x16, schar, pack, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_int32x4, int, v_int16x8, short, pack, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_int64x2, int64, v_int32x4, int, pack, static_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast) - -#define OPENCV_HAL_IMPL_C_PACK_STORE(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \ -inline void v_##pack_suffix##_store(_Tpn* ptr, const _Tpvec& a) \ -{ \ - for( int i = 0; i < _Tpvec::nlanes; i++ ) \ - ptr[i] = cast<_Tpn>(a.s[i]); \ -} - -OPENCV_HAL_IMPL_C_PACK_STORE(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast) -OPENCV_HAL_IMPL_C_PACK_STORE(v_int16x8, short, v_int8x16, schar, pack, saturate_cast) -OPENCV_HAL_IMPL_C_PACK_STORE(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast) -OPENCV_HAL_IMPL_C_PACK_STORE(v_int32x4, int, v_int16x8, short, pack, saturate_cast) -OPENCV_HAL_IMPL_C_PACK_STORE(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast) -OPENCV_HAL_IMPL_C_PACK_STORE(v_int64x2, int64, v_int32x4, int, pack, static_cast) -OPENCV_HAL_IMPL_C_PACK_STORE(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast) -OPENCV_HAL_IMPL_C_PACK_STORE(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast) - -#define OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \ -template inline void v_rshr_##pack_suffix##_store(_Tpn* ptr, const _Tpvec& a) \ -{ \ - for( int i = 0; i < _Tpvec::nlanes; i++ ) \ - ptr[i] = cast<_Tpn>((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \ -} - -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int16x8, short, v_int8x16, schar, pack, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int32x4, int, v_int16x8, short, pack, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int64x2, int64, v_int32x4, int, pack, static_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast) -OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast) - -template -inline void _pack_b(_Tpm* mptr, const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) -{ - for (int i = 0; i < n; ++i) - { - mptr[i] = (_Tpm)a.s[i]; - mptr[i + n] = (_Tpm)b.s[i]; - } -} - -inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) -{ - v_uint8x16 mask; - _pack_b(mask.s, a, b); - return mask; -} - -inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, - const v_uint32x4& c, const v_uint32x4& d) -{ - v_uint8x16 mask; - _pack_b(mask.s, a, b); - _pack_b(mask.s + 8, c, d); - return mask; -} - -inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, - const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, - const v_uint64x2& g, const v_uint64x2& h) -{ - v_uint8x16 mask; - _pack_b(mask.s, a, b); - _pack_b(mask.s + 4, c, d); - _pack_b(mask.s + 8, e, f); - _pack_b(mask.s + 12, g, h); - return mask; -} - -inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, - const v_float32x4& m1, const v_float32x4& m2, - const v_float32x4& m3) -{ - return v_float32x4(v.s[0]*m0.s[0] + v.s[1]*m1.s[0] + v.s[2]*m2.s[0] + v.s[3]*m3.s[0], - v.s[0]*m0.s[1] + v.s[1]*m1.s[1] + v.s[2]*m2.s[1] + v.s[3]*m3.s[1], - v.s[0]*m0.s[2] + v.s[1]*m1.s[2] + v.s[2]*m2.s[2] + v.s[3]*m3.s[2], - v.s[0]*m0.s[3] + v.s[1]*m1.s[3] + v.s[2]*m2.s[3] + v.s[3]*m3.s[3]); -} - -inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, - const v_float32x4& m1, const v_float32x4& m2, - const v_float32x4& m3) -{ - return v_float32x4(v.s[0]*m0.s[0] + v.s[1]*m1.s[0] + v.s[2]*m2.s[0] + m3.s[0], - v.s[0]*m0.s[1] + v.s[1]*m1.s[1] + v.s[2]*m2.s[1] + m3.s[1], - v.s[0]*m0.s[2] + v.s[1]*m1.s[2] + v.s[2]*m2.s[2] + m3.s[2], - v.s[0]*m0.s[3] + v.s[1]*m1.s[3] + v.s[2]*m2.s[3] + m3.s[3]); -} - -inline v_reg::nlanes128> -v_load_expand(const float16_t* ptr) -{ - v_reg::nlanes128> v; - for( int i = 0; i < v.nlanes; i++ ) - { - v.s[i] = ptr[i]; - } - return v; -} - -inline void -v_pack_store(float16_t* ptr, const v_reg::nlanes128>& v) -{ - for( int i = 0; i < v.nlanes; i++ ) - { - ptr[i] = float16_t(v.s[i]); - } -} - -inline void v_cleanup() {} -} // namespace fallback - -static v128_t wasm_unpacklo_i8x16(v128_t a, v128_t b) { - return wasm_v8x16_shuffle(a, b, 0,16,1,17,2,18,3,19,4,20,5,21,6,22,7,23); -} - -static v128_t wasm_unpacklo_i16x8(v128_t a, v128_t b) { - return wasm_v8x16_shuffle(a, b, 0,1,16,17,2,3,18,19,4,5,20,21,6,7,22,23); -} - -static v128_t wasm_unpacklo_i32x4(v128_t a, v128_t b) { - return wasm_v8x16_shuffle(a, b, 0,1,2,3,16,17,18,19,4,5,6,7,20,21,22,23); -} - -static v128_t wasm_unpacklo_i64x2(v128_t a, v128_t b) { - return wasm_v8x16_shuffle(a, b, 0,1,2,3,4,5,6,7,16,17,18,19,20,21,22,23); -} - -static v128_t wasm_unpackhi_i8x16(v128_t a, v128_t b) { - return wasm_v8x16_shuffle(a, b, 8,24,9,25,10,26,11,27,12,28,13,29,14,30,15,31); -} - -static v128_t wasm_unpackhi_i16x8(v128_t a, v128_t b) { - return wasm_v8x16_shuffle(a, b, 8,9,24,25,10,11,26,27,12,13,28,29,14,15,30,31); -} - -static v128_t wasm_unpackhi_i32x4(v128_t a, v128_t b) { - return wasm_v8x16_shuffle(a, b, 8,9,10,11,24,25,26,27,12,13,14,15,28,29,30,31); -} - -static v128_t wasm_unpackhi_i64x2(v128_t a, v128_t b) { - return wasm_v8x16_shuffle(a, b, 8,9,10,11,12,13,14,15,24,25,26,27,28,29,30,31); -} - -/** Convert **/ -// 8 >> 16 -inline v128_t v128_cvtu8x16_i16x8(const v128_t& a) -{ - const v128_t z = wasm_i8x16_splat(0); - return wasm_unpacklo_i8x16(a, z); -} -inline v128_t v128_cvti8x16_i16x8(const v128_t& a) -{ return wasm_i16x8_shr(wasm_unpacklo_i8x16(a, a), 8); } -// 8 >> 32 -inline v128_t v128_cvtu8x16_i32x4(const v128_t& a) -{ - const v128_t z = wasm_i8x16_splat(0); - return wasm_unpacklo_i16x8(wasm_unpacklo_i8x16(a, z), z); -} -inline v128_t v128_cvti8x16_i32x4(const v128_t& a) -{ - v128_t r = wasm_unpacklo_i8x16(a, a); - r = wasm_unpacklo_i8x16(r, r); - return wasm_i32x4_shr(r, 24); -} -// 16 >> 32 -inline v128_t v128_cvtu16x8_i32x4(const v128_t& a) -{ - const v128_t z = wasm_i8x16_splat(0); - return wasm_unpacklo_i16x8(a, z); -} -inline v128_t v128_cvti16x8_i32x4(const v128_t& a) -{ return wasm_i32x4_shr(wasm_unpacklo_i16x8(a, a), 16); } -// 32 >> 64 -inline v128_t v128_cvtu32x4_i64x2(const v128_t& a) -{ - const v128_t z = wasm_i8x16_splat(0); - return wasm_unpacklo_i32x4(a, z); -} -inline v128_t v128_cvti32x4_i64x2(const v128_t& a) -{ return wasm_unpacklo_i32x4(a, wasm_i32x4_shr(a, 31)); } - -// 16 << 8 -inline v128_t v128_cvtu8x16_i16x8_high(const v128_t& a) -{ - const v128_t z = wasm_i8x16_splat(0); - return wasm_unpackhi_i8x16(a, z); -} -inline v128_t v128_cvti8x16_i16x8_high(const v128_t& a) -{ return wasm_i16x8_shr(wasm_unpackhi_i8x16(a, a), 8); } -// 32 << 16 -inline v128_t v128_cvtu16x8_i32x4_high(const v128_t& a) -{ - const v128_t z = wasm_i8x16_splat(0); - return wasm_unpackhi_i16x8(a, z); -} -inline v128_t v128_cvti16x8_i32x4_high(const v128_t& a) -{ return wasm_i32x4_shr(wasm_unpackhi_i16x8(a, a), 16); } -// 64 << 32 -inline v128_t v128_cvtu32x4_i64x2_high(const v128_t& a) -{ - const v128_t z = wasm_i8x16_splat(0); - return wasm_unpackhi_i32x4(a, z); -} -inline v128_t v128_cvti32x4_i64x2_high(const v128_t& a) -{ return wasm_unpackhi_i32x4(a, wasm_i32x4_shr(a, 31)); } - -#define OPENCV_HAL_IMPL_WASM_INITVEC(_Tpvec, _Tp, suffix, zsuffix, _Tps) \ -inline _Tpvec v_setzero_##suffix() { return _Tpvec(wasm_##zsuffix##_splat((_Tps)0)); } \ -inline _Tpvec v_setall_##suffix(_Tp v) { return _Tpvec(wasm_##zsuffix##_splat((_Tps)v)); } \ -template inline _Tpvec v_reinterpret_as_##suffix(const _Tpvec0& a) \ -{ return _Tpvec(a.val); } - -OPENCV_HAL_IMPL_WASM_INITVEC(v_uint8x16, uchar, u8, i8x16, schar) -OPENCV_HAL_IMPL_WASM_INITVEC(v_int8x16, schar, s8, i8x16, schar) -OPENCV_HAL_IMPL_WASM_INITVEC(v_uint16x8, ushort, u16, i16x8, short) -OPENCV_HAL_IMPL_WASM_INITVEC(v_int16x8, short, s16, i16x8, short) -OPENCV_HAL_IMPL_WASM_INITVEC(v_uint32x4, unsigned, u32, i32x4, int) -OPENCV_HAL_IMPL_WASM_INITVEC(v_int32x4, int, s32, i32x4, int) -OPENCV_HAL_IMPL_WASM_INITVEC(v_float32x4, float, f32, f32x4, float) -OPENCV_HAL_IMPL_WASM_INITVEC(v_uint64x2, uint64, u64, i64x2, int64) -OPENCV_HAL_IMPL_WASM_INITVEC(v_int64x2, int64, s64, i64x2, int64) -OPENCV_HAL_IMPL_WASM_INITVEC(v_float64x2, double, f64, f64x2, double) - -//////////////// PACK /////////////// -inline v_uint8x16 v_pack(const v_uint16x8& a, const v_uint16x8& b) -{ - v128_t maxval = wasm_i16x8_splat(255); - v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_u16x8_gt(a.val, maxval)); - v128_t b1 = wasm_v128_bitselect(maxval, b.val, wasm_u16x8_gt(b.val, maxval)); - return v_uint8x16(wasm_v8x16_shuffle(a1, b1, 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30)); -} -inline v_int8x16 v_pack(const v_int16x8& a, const v_int16x8& b) -{ - v128_t maxval = wasm_i16x8_splat(127); - v128_t minval = wasm_i16x8_splat(-128); - v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_i16x8_gt(a.val, maxval)); - v128_t b1 = wasm_v128_bitselect(maxval, b.val, wasm_i16x8_gt(b.val, maxval)); - v128_t a2 = wasm_v128_bitselect(minval, a1, wasm_i16x8_lt(a1, minval)); - v128_t b2 = wasm_v128_bitselect(minval, b1, wasm_i16x8_lt(b1, minval)); - return v_int8x16(wasm_v8x16_shuffle(a2, b2, 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30)); -} -inline v_uint16x8 v_pack(const v_uint32x4& a, const v_uint32x4& b) -{ - v128_t maxval = wasm_i32x4_splat(65535); - v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_u32x4_gt(a.val, maxval)); - v128_t b1 = wasm_v128_bitselect(maxval, b.val, wasm_u32x4_gt(b.val, maxval)); - return v_uint16x8(wasm_v8x16_shuffle(a1, b1, 0,1,4,5,8,9,12,13,16,17,20,21,24,25,28,29)); -} -inline v_int16x8 v_pack(const v_int32x4& a, const v_int32x4& b) -{ - v128_t maxval = wasm_i32x4_splat(32767); - v128_t minval = wasm_i32x4_splat(-32768); - v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_i32x4_gt(a.val, maxval)); - v128_t b1 = wasm_v128_bitselect(maxval, b.val, wasm_i32x4_gt(b.val, maxval)); - v128_t a2 = wasm_v128_bitselect(minval, a1, wasm_i32x4_lt(a1, minval)); - v128_t b2 = wasm_v128_bitselect(minval, b1, wasm_i32x4_lt(b1, minval)); - return v_int16x8(wasm_v8x16_shuffle(a2, b2, 0,1,4,5,8,9,12,13,16,17,20,21,24,25,28,29)); -} -inline v_uint32x4 v_pack(const v_uint64x2& a, const v_uint64x2& b) -{ - return v_uint32x4(wasm_v8x16_shuffle(a.val, b.val, 0,1,2,3,8,9,10,11,16,17,18,19,24,25,26,27)); -} -inline v_int32x4 v_pack(const v_int64x2& a, const v_int64x2& b) -{ - return v_int32x4(wasm_v8x16_shuffle(a.val, b.val, 0,1,2,3,8,9,10,11,16,17,18,19,24,25,26,27)); -} -inline v_uint8x16 v_pack_u(const v_int16x8& a, const v_int16x8& b) -{ - v128_t maxval = wasm_i16x8_splat(255); - v128_t minval = wasm_i16x8_splat(0); - v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_i16x8_gt(a.val, maxval)); - v128_t b1 = wasm_v128_bitselect(maxval, b.val, wasm_i16x8_gt(b.val, maxval)); - v128_t a2 = wasm_v128_bitselect(minval, a1, wasm_i16x8_lt(a1, minval)); - v128_t b2 = wasm_v128_bitselect(minval, b1, wasm_i16x8_lt(b1, minval)); - return v_uint8x16(wasm_v8x16_shuffle(a2, b2, 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30)); -} -inline v_uint16x8 v_pack_u(const v_int32x4& a, const v_int32x4& b) -{ - v128_t maxval = wasm_i32x4_splat(65535); - v128_t minval = wasm_i32x4_splat(0); - v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_i32x4_gt(a.val, maxval)); - v128_t b1 = wasm_v128_bitselect(maxval, b.val, wasm_i32x4_gt(b.val, maxval)); - v128_t a2 = wasm_v128_bitselect(minval, a1, wasm_i32x4_lt(a1, minval)); - v128_t b2 = wasm_v128_bitselect(minval, b1, wasm_i32x4_lt(b1, minval)); - return v_uint16x8(wasm_v8x16_shuffle(a2, b2, 0,1,4,5,8,9,12,13,16,17,20,21,24,25,28,29)); -} - -template -inline v_uint8x16 v_rshr_pack(const v_uint16x8& a, const v_uint16x8& b) -{ - v128_t delta = wasm_i16x8_splat(((short)1 << (n-1))); - v128_t a1 = wasm_u16x8_shr(wasm_i16x8_add(a.val, delta), n); - v128_t b1 = wasm_u16x8_shr(wasm_i16x8_add(b.val, delta), n); - v128_t maxval = wasm_i16x8_splat(255); - v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_u16x8_gt(a1, maxval)); - v128_t b2 = wasm_v128_bitselect(maxval, b1, wasm_u16x8_gt(b1, maxval)); - return v_uint8x16(wasm_v8x16_shuffle(a2, b2, 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30)); -} -template -inline v_int8x16 v_rshr_pack(const v_int16x8& a, const v_int16x8& b) -{ - v128_t delta = wasm_i16x8_splat(((short)1 << (n-1))); - v128_t a1 = wasm_i16x8_shr(wasm_i16x8_add(a.val, delta), n); - v128_t b1 = wasm_i16x8_shr(wasm_i16x8_add(b.val, delta), n); - v128_t maxval = wasm_i16x8_splat(127); - v128_t minval = wasm_i16x8_splat(-128); - v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_i16x8_gt(a1, maxval)); - v128_t b2 = wasm_v128_bitselect(maxval, b1, wasm_i16x8_gt(b1, maxval)); - v128_t a3 = wasm_v128_bitselect(minval, a2, wasm_i16x8_lt(a1, minval)); - v128_t b3 = wasm_v128_bitselect(minval, b2, wasm_i16x8_lt(b1, minval)); - return v_int8x16(wasm_v8x16_shuffle(a3, b3, 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30)); -} -template -inline v_uint16x8 v_rshr_pack(const v_uint32x4& a, const v_uint32x4& b) -{ - v128_t delta = wasm_i32x4_splat(((int)1 << (n-1))); - v128_t a1 = wasm_u32x4_shr(wasm_i32x4_add(a.val, delta), n); - v128_t b1 = wasm_u32x4_shr(wasm_i32x4_add(b.val, delta), n); - v128_t maxval = wasm_i32x4_splat(65535); - v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_u32x4_gt(a1, maxval)); - v128_t b2 = wasm_v128_bitselect(maxval, b1, wasm_u32x4_gt(b1, maxval)); - return v_uint16x8(wasm_v8x16_shuffle(a2, b2, 0,1,4,5,8,9,12,13,16,17,20,21,24,25,28,29)); -} -template -inline v_int16x8 v_rshr_pack(const v_int32x4& a, const v_int32x4& b) -{ - v128_t delta = wasm_i32x4_splat(((int)1 << (n-1))); - v128_t a1 = wasm_i32x4_shr(wasm_i32x4_add(a.val, delta), n); - v128_t b1 = wasm_i32x4_shr(wasm_i32x4_add(b.val, delta), n); - v128_t maxval = wasm_i32x4_splat(32767); - v128_t minval = wasm_i16x8_splat(-32768); - v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_i32x4_gt(a1, maxval)); - v128_t b2 = wasm_v128_bitselect(maxval, b1, wasm_i32x4_gt(b1, maxval)); - v128_t a3 = wasm_v128_bitselect(minval, a2, wasm_i32x4_lt(a1, minval)); - v128_t b3 = wasm_v128_bitselect(minval, b2, wasm_i32x4_lt(b1, minval)); - return v_int16x8(wasm_v8x16_shuffle(a3, b3, 0,1,4,5,8,9,12,13,16,17,20,21,24,25,28,29)); -} -template -inline v_uint32x4 v_rshr_pack(const v_uint64x2& a, const v_uint64x2& b) -{ - v128_t delta = wasm_i64x2_splat(((int64)1 << (n-1))); - v128_t a1 = wasm_u64x2_shr(wasm_i64x2_add(a.val, delta), n); - v128_t b1 = wasm_u64x2_shr(wasm_i64x2_add(b.val, delta), n); - return v_uint32x4(wasm_v8x16_shuffle(a1, b1, 0,1,2,3,8,9,10,11,16,17,18,19,24,25,26,27)); -} -template -inline v_int32x4 v_rshr_pack(const v_int64x2& a, const v_int64x2& b) -{ - v128_t delta = wasm_i64x2_splat(((int64)1 << (n-1))); - v128_t a1 = wasm_i64x2_shr(wasm_i64x2_add(a.val, delta), n); - v128_t b1 = wasm_i64x2_shr(wasm_i64x2_add(b.val, delta), n); - return v_int32x4(wasm_v8x16_shuffle(a1, b1, 0,1,2,3,8,9,10,11,16,17,18,19,24,25,26,27)); -} -template -inline v_uint8x16 v_rshr_pack_u(const v_int16x8& a, const v_int16x8& b) -{ - v128_t delta = wasm_i16x8_splat(((short)1 << (n-1))); - v128_t a1 = wasm_i16x8_shr(wasm_i16x8_add(a.val, delta), n); - v128_t b1 = wasm_i16x8_shr(wasm_i16x8_add(b.val, delta), n); - v128_t maxval = wasm_i16x8_splat(255); - v128_t minval = wasm_i16x8_splat(0); - v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_i16x8_gt(a1, maxval)); - v128_t b2 = wasm_v128_bitselect(maxval, b1, wasm_i16x8_gt(b1, maxval)); - v128_t a3 = wasm_v128_bitselect(minval, a2, wasm_i16x8_lt(a1, minval)); - v128_t b3 = wasm_v128_bitselect(minval, b2, wasm_i16x8_lt(b1, minval)); - return v_uint8x16(wasm_v8x16_shuffle(a3, b3, 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30)); -} -template -inline v_uint16x8 v_rshr_pack_u(const v_int32x4& a, const v_int32x4& b) -{ - v128_t delta = wasm_i32x4_splat(((int)1 << (n-1))); - v128_t a1 = wasm_i32x4_shr(wasm_i32x4_add(a.val, delta), n); - v128_t b1 = wasm_i32x4_shr(wasm_i32x4_add(b.val, delta), n); - v128_t maxval = wasm_i32x4_splat(65535); - v128_t minval = wasm_i16x8_splat(0); - v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_i32x4_gt(a1, maxval)); - v128_t b2 = wasm_v128_bitselect(maxval, b1, wasm_i32x4_gt(b1, maxval)); - v128_t a3 = wasm_v128_bitselect(minval, a2, wasm_i32x4_lt(a1, minval)); - v128_t b3 = wasm_v128_bitselect(minval, b2, wasm_i32x4_lt(b1, minval)); - return v_uint16x8(wasm_v8x16_shuffle(a3, b3, 0,1,4,5,8,9,12,13,16,17,20,21,24,25,28,29)); -} - -inline void v_pack_store(uchar* ptr, const v_uint16x8& a) -{ - v128_t maxval = wasm_i16x8_splat(255); - v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_u16x8_gt(a.val, maxval)); - v128_t r = wasm_v8x16_shuffle(a1, a1, 0,2,4,6,8,10,12,14,0,2,4,6,8,10,12,14); - uchar t_ptr[16]; - wasm_v128_store(t_ptr, r); - for (int i=0; i<8; ++i) { - ptr[i] = t_ptr[i]; - } -} -inline void v_pack_store(schar* ptr, const v_int16x8& a) -{ - v128_t maxval = wasm_i16x8_splat(127); - v128_t minval = wasm_i16x8_splat(-128); - v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_i16x8_gt(a.val, maxval)); - v128_t a2 = wasm_v128_bitselect(minval, a1, wasm_i16x8_lt(a1, minval)); - v128_t r = wasm_v8x16_shuffle(a2, a2, 0,2,4,6,8,10,12,14,0,2,4,6,8,10,12,14); - schar t_ptr[16]; - wasm_v128_store(t_ptr, r); - for (int i=0; i<8; ++i) { - ptr[i] = t_ptr[i]; - } -} -inline void v_pack_store(ushort* ptr, const v_uint32x4& a) -{ - v128_t maxval = wasm_i32x4_splat(65535); - v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_u32x4_gt(a.val, maxval)); - v128_t r = wasm_v8x16_shuffle(a1, a1, 0,1,4,5,8,9,12,13,0,1,4,5,8,9,12,13); - ushort t_ptr[8]; - wasm_v128_store(t_ptr, r); - for (int i=0; i<4; ++i) { - ptr[i] = t_ptr[i]; - } -} -inline void v_pack_store(short* ptr, const v_int32x4& a) -{ - v128_t maxval = wasm_i32x4_splat(32767); - v128_t minval = wasm_i32x4_splat(-32768); - v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_i32x4_gt(a.val, maxval)); - v128_t a2 = wasm_v128_bitselect(minval, a1, wasm_i32x4_lt(a1, minval)); - v128_t r = wasm_v8x16_shuffle(a2, a2, 0,1,4,5,8,9,12,13,0,1,4,5,8,9,12,13); - short t_ptr[8]; - wasm_v128_store(t_ptr, r); - for (int i=0; i<4; ++i) { - ptr[i] = t_ptr[i]; - } -} -inline void v_pack_store(unsigned* ptr, const v_uint64x2& a) -{ - v128_t r = wasm_v8x16_shuffle(a.val, a.val, 0,1,2,3,8,9,10,11,0,1,2,3,8,9,10,11); - unsigned t_ptr[4]; - wasm_v128_store(t_ptr, r); - for (int i=0; i<2; ++i) { - ptr[i] = t_ptr[i]; - } -} -inline void v_pack_store(int* ptr, const v_int64x2& a) -{ - v128_t r = wasm_v8x16_shuffle(a.val, a.val, 0,1,2,3,8,9,10,11,0,1,2,3,8,9,10,11); - int t_ptr[4]; - wasm_v128_store(t_ptr, r); - for (int i=0; i<2; ++i) { - ptr[i] = t_ptr[i]; - } -} -inline void v_pack_u_store(uchar* ptr, const v_int16x8& a) -{ - v128_t maxval = wasm_i16x8_splat(255); - v128_t minval = wasm_i16x8_splat(0); - v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_i16x8_gt(a.val, maxval)); - v128_t a2 = wasm_v128_bitselect(minval, a1, wasm_i16x8_lt(a1, minval)); - v128_t r = wasm_v8x16_shuffle(a2, a2, 0,2,4,6,8,10,12,14,0,2,4,6,8,10,12,14); - uchar t_ptr[16]; - wasm_v128_store(t_ptr, r); - for (int i=0; i<8; ++i) { - ptr[i] = t_ptr[i]; - } -} -inline void v_pack_u_store(ushort* ptr, const v_int32x4& a) -{ - v128_t maxval = wasm_i32x4_splat(65535); - v128_t minval = wasm_i32x4_splat(0); - v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_i32x4_gt(a.val, maxval)); - v128_t a2 = wasm_v128_bitselect(minval, a1, wasm_i32x4_lt(a1, minval)); - v128_t r = wasm_v8x16_shuffle(a2, a2, 0,1,4,5,8,9,12,13,0,1,4,5,8,9,12,13); - ushort t_ptr[8]; - wasm_v128_store(t_ptr, r); - for (int i=0; i<4; ++i) { - ptr[i] = t_ptr[i]; - } -} - -template -inline void v_rshr_pack_store(uchar* ptr, const v_uint16x8& a) -{ - v128_t delta = wasm_i16x8_splat((short)(1 << (n-1))); - v128_t a1 = wasm_u16x8_shr(wasm_i16x8_add(a.val, delta), n); - v128_t maxval = wasm_i16x8_splat(255); - v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_u16x8_gt(a1, maxval)); - v128_t r = wasm_v8x16_shuffle(a2, a2, 0,2,4,6,8,10,12,14,0,2,4,6,8,10,12,14); - uchar t_ptr[16]; - wasm_v128_store(t_ptr, r); - for (int i=0; i<8; ++i) { - ptr[i] = t_ptr[i]; - } -} -template -inline void v_rshr_pack_store(schar* ptr, const v_int16x8& a) -{ - v128_t delta = wasm_i16x8_splat(((short)1 << (n-1))); - v128_t a1 = wasm_i16x8_shr(wasm_i16x8_add(a.val, delta), n); - v128_t maxval = wasm_i16x8_splat(127); - v128_t minval = wasm_i16x8_splat(-128); - v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_i16x8_gt(a1, maxval)); - v128_t a3 = wasm_v128_bitselect(minval, a2, wasm_i16x8_lt(a1, minval)); - v128_t r = wasm_v8x16_shuffle(a3, a3, 0,2,4,6,8,10,12,14,0,2,4,6,8,10,12,14); - schar t_ptr[16]; - wasm_v128_store(t_ptr, r); - for (int i=0; i<8; ++i) { - ptr[i] = t_ptr[i]; - } -} -template -inline void v_rshr_pack_store(ushort* ptr, const v_uint32x4& a) -{ - v128_t delta = wasm_i32x4_splat(((int)1 << (n-1))); - v128_t a1 = wasm_u32x4_shr(wasm_i32x4_add(a.val, delta), n); - v128_t maxval = wasm_i32x4_splat(65535); - v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_u32x4_gt(a1, maxval)); - v128_t r = wasm_v8x16_shuffle(a2, a2, 0,1,4,5,8,9,12,13,0,1,4,5,8,9,12,13); - ushort t_ptr[8]; - wasm_v128_store(t_ptr, r); - for (int i=0; i<4; ++i) { - ptr[i] = t_ptr[i]; - } -} -template -inline void v_rshr_pack_store(short* ptr, const v_int32x4& a) -{ - v128_t delta = wasm_i32x4_splat(((int)1 << (n-1))); - v128_t a1 = wasm_i32x4_shr(wasm_i32x4_add(a.val, delta), n); - v128_t maxval = wasm_i32x4_splat(32767); - v128_t minval = wasm_i32x4_splat(-32768); - v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_i32x4_gt(a1, maxval)); - v128_t a3 = wasm_v128_bitselect(minval, a2, wasm_i32x4_lt(a1, minval)); - v128_t r = wasm_v8x16_shuffle(a3, a3, 0,1,4,5,8,9,12,13,0,1,4,5,8,9,12,13); - short t_ptr[8]; - wasm_v128_store(t_ptr, r); - for (int i=0; i<4; ++i) { - ptr[i] = t_ptr[i]; - } -} -template -inline void v_rshr_pack_store(unsigned* ptr, const v_uint64x2& a) -{ - v128_t delta = wasm_i64x2_splat(((int64)1 << (n-1))); - v128_t a1 = wasm_u64x2_shr(wasm_i64x2_add(a.val, delta), n); - v128_t r = wasm_v8x16_shuffle(a1, a1, 0,1,2,3,8,9,10,11,0,1,2,3,8,9,10,11); - unsigned t_ptr[4]; - wasm_v128_store(t_ptr, r); - for (int i=0; i<2; ++i) { - ptr[i] = t_ptr[i]; - } -} -template -inline void v_rshr_pack_store(int* ptr, const v_int64x2& a) -{ - v128_t delta = wasm_i64x2_splat(((int64)1 << (n-1))); - v128_t a1 = wasm_i64x2_shr(wasm_i64x2_add(a.val, delta), n); - v128_t r = wasm_v8x16_shuffle(a1, a1, 0,1,2,3,8,9,10,11,0,1,2,3,8,9,10,11); - int t_ptr[4]; - wasm_v128_store(t_ptr, r); - for (int i=0; i<2; ++i) { - ptr[i] = t_ptr[i]; - } -} -template -inline void v_rshr_pack_u_store(uchar* ptr, const v_int16x8& a) -{ - v128_t delta = wasm_i16x8_splat(((short)1 << (n-1))); - v128_t a1 = wasm_i16x8_shr(wasm_i16x8_add(a.val, delta), n); - v128_t maxval = wasm_i16x8_splat(255); - v128_t minval = wasm_i16x8_splat(0); - v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_i16x8_gt(a1, maxval)); - v128_t a3 = wasm_v128_bitselect(minval, a2, wasm_i16x8_lt(a1, minval)); - v128_t r = wasm_v8x16_shuffle(a3, a3, 0,2,4,6,8,10,12,14,0,2,4,6,8,10,12,14); - uchar t_ptr[16]; - wasm_v128_store(t_ptr, r); - for (int i=0; i<8; ++i) { - ptr[i] = t_ptr[i]; - } -} -template -inline void v_rshr_pack_u_store(ushort* ptr, const v_int32x4& a) -{ - v128_t delta = wasm_i32x4_splat(((int)1 << (n-1))); - v128_t a1 = wasm_i32x4_shr(wasm_i32x4_add(a.val, delta), n); - v128_t maxval = wasm_i32x4_splat(65535); - v128_t minval = wasm_i32x4_splat(0); - v128_t a2 = wasm_v128_bitselect(maxval, a1, wasm_i32x4_gt(a1, maxval)); - v128_t a3 = wasm_v128_bitselect(minval, a2, wasm_i32x4_lt(a1, minval)); - v128_t r = wasm_v8x16_shuffle(a3, a3, 0,1,4,5,8,9,12,13,0,1,4,5,8,9,12,13); - ushort t_ptr[8]; - wasm_v128_store(t_ptr, r); - for (int i=0; i<4; ++i) { - ptr[i] = t_ptr[i]; - } -} - -inline v_uint8x16 v_pack_b(const v_uint16x8& a, const v_uint16x8& b) -{ - v128_t maxval = wasm_i16x8_splat(255); - v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_u16x8_gt(a.val, maxval)); - v128_t b1 = wasm_v128_bitselect(maxval, b.val, wasm_u16x8_gt(b.val, maxval)); - return v_uint8x16(wasm_v8x16_shuffle(a1, b1, 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30)); -} - -inline v_uint8x16 v_pack_b(const v_uint32x4& a, const v_uint32x4& b, - const v_uint32x4& c, const v_uint32x4& d) -{ - v128_t maxval = wasm_i32x4_splat(255); - v128_t a1 = wasm_v128_bitselect(maxval, a.val, wasm_u32x4_gt(a.val, maxval)); - v128_t b1 = wasm_v128_bitselect(maxval, b.val, wasm_u32x4_gt(b.val, maxval)); - v128_t c1 = wasm_v128_bitselect(maxval, c.val, wasm_u32x4_gt(c.val, maxval)); - v128_t d1 = wasm_v128_bitselect(maxval, d.val, wasm_u32x4_gt(d.val, maxval)); - v128_t ab = wasm_v8x16_shuffle(a1, b1, 0,4,8,12,16,20,24,28,0,4,8,12,16,20,24,28); - v128_t cd = wasm_v8x16_shuffle(c1, d1, 0,4,8,12,16,20,24,28,0,4,8,12,16,20,24,28); - return v_uint8x16(wasm_v8x16_shuffle(ab, cd, 0,1,2,3,4,5,6,7,16,17,18,19,20,21,22,23)); -} - -inline v_uint8x16 v_pack_b(const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c, - const v_uint64x2& d, const v_uint64x2& e, const v_uint64x2& f, - const v_uint64x2& g, const v_uint64x2& h) -{ - v128_t maxval = wasm_i32x4_splat(255); - v128_t a1 = wasm_v128_bitselect(maxval, a.val, ((__u64x2)(a.val) > (__u64x2)maxval)); - v128_t b1 = wasm_v128_bitselect(maxval, b.val, ((__u64x2)(b.val) > (__u64x2)maxval)); - v128_t c1 = wasm_v128_bitselect(maxval, c.val, ((__u64x2)(c.val) > (__u64x2)maxval)); - v128_t d1 = wasm_v128_bitselect(maxval, d.val, ((__u64x2)(d.val) > (__u64x2)maxval)); - v128_t e1 = wasm_v128_bitselect(maxval, e.val, ((__u64x2)(e.val) > (__u64x2)maxval)); - v128_t f1 = wasm_v128_bitselect(maxval, f.val, ((__u64x2)(f.val) > (__u64x2)maxval)); - v128_t g1 = wasm_v128_bitselect(maxval, g.val, ((__u64x2)(g.val) > (__u64x2)maxval)); - v128_t h1 = wasm_v128_bitselect(maxval, h.val, ((__u64x2)(h.val) > (__u64x2)maxval)); - v128_t ab = wasm_v8x16_shuffle(a1, b1, 0,8,16,24,0,8,16,24,0,8,16,24,0,8,16,24); - v128_t cd = wasm_v8x16_shuffle(c1, d1, 0,8,16,24,0,8,16,24,0,8,16,24,0,8,16,24); - v128_t ef = wasm_v8x16_shuffle(e1, f1, 0,8,16,24,0,8,16,24,0,8,16,24,0,8,16,24); - v128_t gh = wasm_v8x16_shuffle(g1, h1, 0,8,16,24,0,8,16,24,0,8,16,24,0,8,16,24); - v128_t abcd = wasm_v8x16_shuffle(ab, cd, 0,1,2,3,16,17,18,19,0,1,2,3,16,17,18,19); - v128_t efgh = wasm_v8x16_shuffle(ef, gh, 0,1,2,3,16,17,18,19,0,1,2,3,16,17,18,19); - return v_uint8x16(wasm_v8x16_shuffle(abcd, efgh, 0,1,2,3,4,5,6,7,16,17,18,19,20,21,22,23)); -} - -inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0, - const v_float32x4& m1, const v_float32x4& m2, - const v_float32x4& m3) -{ - v128_t v0 = wasm_f32x4_splat(wasm_f32x4_extract_lane(v.val, 0)); - v128_t v1 = wasm_f32x4_splat(wasm_f32x4_extract_lane(v.val, 1)); - v128_t v2 = wasm_f32x4_splat(wasm_f32x4_extract_lane(v.val, 2)); - v128_t v3 = wasm_f32x4_splat(wasm_f32x4_extract_lane(v.val, 3)); - v0 = wasm_f32x4_mul(v0, m0.val); - v1 = wasm_f32x4_mul(v1, m1.val); - v2 = wasm_f32x4_mul(v2, m2.val); - v3 = wasm_f32x4_mul(v3, m3.val); - - return v_float32x4(wasm_f32x4_add(wasm_f32x4_add(v0, v1), wasm_f32x4_add(v2, v3))); -} - -inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0, - const v_float32x4& m1, const v_float32x4& m2, - const v_float32x4& a) -{ - v128_t v0 = wasm_f32x4_splat(wasm_f32x4_extract_lane(v.val, 0)); - v128_t v1 = wasm_f32x4_splat(wasm_f32x4_extract_lane(v.val, 1)); - v128_t v2 = wasm_f32x4_splat(wasm_f32x4_extract_lane(v.val, 2)); - v0 = wasm_f32x4_mul(v0, m0.val); - v1 = wasm_f32x4_mul(v1, m1.val); - v2 = wasm_f32x4_mul(v2, m2.val); - - return v_float32x4(wasm_f32x4_add(wasm_f32x4_add(v0, v1), wasm_f32x4_add(v2, a.val))); -} - -#define OPENCV_HAL_IMPL_WASM_BIN_OP(bin_op, _Tpvec, intrin) \ -inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(intrin(a.val, b.val)); \ -} \ -inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \ -{ \ - a.val = intrin(a.val, b.val); \ - return a; \ -} - -OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_uint8x16, wasm_u8x16_add_saturate) -OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_uint8x16, wasm_u8x16_sub_saturate) -OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_int8x16, wasm_i8x16_add_saturate) -OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_int8x16, wasm_i8x16_sub_saturate) -OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_uint16x8, wasm_u16x8_add_saturate) -OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_uint16x8, wasm_u16x8_sub_saturate) -OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_int16x8, wasm_i16x8_add_saturate) -OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_int16x8, wasm_i16x8_sub_saturate) -OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_uint32x4, wasm_i32x4_add) -OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_uint32x4, wasm_i32x4_sub) -OPENCV_HAL_IMPL_WASM_BIN_OP(*, v_uint32x4, wasm_i32x4_mul) -OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_int32x4, wasm_i32x4_add) -OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_int32x4, wasm_i32x4_sub) -OPENCV_HAL_IMPL_WASM_BIN_OP(*, v_int32x4, wasm_i32x4_mul) -OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_float32x4, wasm_f32x4_add) -OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_float32x4, wasm_f32x4_sub) -OPENCV_HAL_IMPL_WASM_BIN_OP(*, v_float32x4, wasm_f32x4_mul) -OPENCV_HAL_IMPL_WASM_BIN_OP(/, v_float32x4, wasm_f32x4_div) -OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_uint64x2, wasm_i64x2_add) -OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_uint64x2, wasm_i64x2_sub) -OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_int64x2, wasm_i64x2_add) -OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_int64x2, wasm_i64x2_sub) -OPENCV_HAL_IMPL_WASM_BIN_OP(+, v_float64x2, wasm_f64x2_add) -OPENCV_HAL_IMPL_WASM_BIN_OP(-, v_float64x2, wasm_f64x2_sub) -OPENCV_HAL_IMPL_WASM_BIN_OP(*, v_float64x2, wasm_f64x2_mul) -OPENCV_HAL_IMPL_WASM_BIN_OP(/, v_float64x2, wasm_f64x2_div) - -// saturating multiply 8-bit, 16-bit -#define OPENCV_HAL_IMPL_WASM_MUL_SAT(_Tpvec, _Tpwvec) \ -inline _Tpvec operator * (const _Tpvec& a, const _Tpvec& b) \ -{ \ - _Tpwvec c, d; \ - v_mul_expand(a, b, c, d); \ - return v_pack(c, d); \ -} \ -inline _Tpvec& operator *= (_Tpvec& a, const _Tpvec& b) \ -{ a = a * b; return a; } - -OPENCV_HAL_IMPL_WASM_MUL_SAT(v_uint8x16, v_uint16x8) -OPENCV_HAL_IMPL_WASM_MUL_SAT(v_int8x16, v_int16x8) -OPENCV_HAL_IMPL_WASM_MUL_SAT(v_uint16x8, v_uint32x4) -OPENCV_HAL_IMPL_WASM_MUL_SAT(v_int16x8, v_int32x4) - -// Multiply and expand -inline void v_mul_expand(const v_uint8x16& a, const v_uint8x16& b, - v_uint16x8& c, v_uint16x8& d) -{ - v_uint16x8 a0, a1, b0, b1; - v_expand(a, a0, a1); - v_expand(b, b0, b1); - c = v_mul_wrap(a0, b0); - d = v_mul_wrap(a1, b1); -} - -inline void v_mul_expand(const v_int8x16& a, const v_int8x16& b, - v_int16x8& c, v_int16x8& d) -{ - v_int16x8 a0, a1, b0, b1; - v_expand(a, a0, a1); - v_expand(b, b0, b1); - c = v_mul_wrap(a0, b0); - d = v_mul_wrap(a1, b1); -} - -inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b, - v_int32x4& c, v_int32x4& d) -{ - v_int32x4 a0, a1, b0, b1; - v_expand(a, a0, a1); - v_expand(b, b0, b1); - c.val = wasm_i32x4_mul(a0.val, b0.val); - d.val = wasm_i32x4_mul(a1.val, b1.val); -} - -inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b, - v_uint32x4& c, v_uint32x4& d) -{ - v_uint32x4 a0, a1, b0, b1; - v_expand(a, a0, a1); - v_expand(b, b0, b1); - c.val = wasm_i32x4_mul(a0.val, b0.val); - d.val = wasm_i32x4_mul(a1.val, b1.val); -} - -inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, - v_uint64x2& c, v_uint64x2& d) -{ - v_uint64x2 a0, a1, b0, b1; - v_expand(a, a0, a1); - v_expand(b, b0, b1); - c.val = ((__u64x2)(a0.val) * (__u64x2)(b0.val)); - d.val = ((__u64x2)(a1.val) * (__u64x2)(b1.val)); -} - -inline v_int16x8 v_mul_hi(const v_int16x8& a, const v_int16x8& b) -{ - v_int32x4 a0, a1, b0, b1; - v_expand(a, a0, a1); - v_expand(b, b0, b1); - v128_t c = wasm_i32x4_mul(a0.val, b0.val); - v128_t d = wasm_i32x4_mul(a1.val, b1.val); - return v_int16x8(wasm_v8x16_shuffle(c, d, 2,3,6,7,10,11,14,15,18,19,22,23,26,27,30,31)); -} -inline v_uint16x8 v_mul_hi(const v_uint16x8& a, const v_uint16x8& b) -{ - v_uint32x4 a0, a1, b0, b1; - v_expand(a, a0, a1); - v_expand(b, b0, b1); - v128_t c = wasm_i32x4_mul(a0.val, b0.val); - v128_t d = wasm_i32x4_mul(a1.val, b1.val); - return v_uint16x8(wasm_v8x16_shuffle(c, d, 2,3,6,7,10,11,14,15,18,19,22,23,26,27,30,31)); -} - -//////// Dot Product //////// - -inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b) -{ - v128_t a0 = wasm_i32x4_shr(wasm_i32x4_shl(a.val, 16), 16); - v128_t a1 = wasm_i32x4_shr(a.val, 16); - v128_t b0 = wasm_i32x4_shr(wasm_i32x4_shl(b.val, 16), 16); - v128_t b1 = wasm_i32x4_shr(b.val, 16); - v128_t c = wasm_i32x4_mul(a0, b0); - v128_t d = wasm_i32x4_mul(a1, b1); - return v_int32x4(wasm_i32x4_add(c, d)); -} - -inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) -{ return v_dotprod(a, b) + c; } - -inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b) -{ - v128_t a0 = wasm_i64x2_shr(wasm_i64x2_shl(a.val, 32), 32); - v128_t a1 = wasm_i64x2_shr(a.val, 32); - v128_t b0 = wasm_i64x2_shr(wasm_i64x2_shl(b.val, 32), 32); - v128_t b1 = wasm_i64x2_shr(b.val, 32); - v128_t c = (v128_t)((__i64x2)a0 * (__i64x2)b0); - v128_t d = (v128_t)((__i64x2)a1 * (__i64x2)b1); - return v_int64x2(wasm_i64x2_add(c, d)); -} -inline v_int64x2 v_dotprod(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c) -{ - return v_dotprod(a, b) + c; -} - -// 8 >> 32 -inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b) -{ - v128_t a0 = wasm_u16x8_shr(wasm_i16x8_shl(a.val, 8), 8); - v128_t a1 = wasm_u16x8_shr(a.val, 8); - v128_t b0 = wasm_u16x8_shr(wasm_i16x8_shl(b.val, 8), 8); - v128_t b1 = wasm_u16x8_shr(b.val, 8); - return v_uint32x4(( - v_dotprod(v_int16x8(a0), v_int16x8(b0)) + - v_dotprod(v_int16x8(a1), v_int16x8(b1))).val - ); -} -inline v_uint32x4 v_dotprod_expand(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c) -{ return v_dotprod_expand(a, b) + c; } - -inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b) -{ - v128_t a0 = wasm_i16x8_shr(wasm_i16x8_shl(a.val, 8), 8); - v128_t a1 = wasm_i16x8_shr(a.val, 8); - v128_t b0 = wasm_i16x8_shr(wasm_i16x8_shl(b.val, 8), 8); - v128_t b1 = wasm_i16x8_shr(b.val, 8); - return v_int32x4( - v_dotprod(v_int16x8(a0), v_int16x8(b0)) + - v_dotprod(v_int16x8(a1), v_int16x8(b1)) - ); -} -inline v_int32x4 v_dotprod_expand(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c) -{ return v_dotprod_expand(a, b) + c; } - -// 16 >> 64 -inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b) -{ - v128_t a0 = wasm_u32x4_shr(wasm_i32x4_shl(a.val, 16), 16); - v128_t a1 = wasm_u32x4_shr(a.val, 16); - v128_t b0 = wasm_u32x4_shr(wasm_i32x4_shl(b.val, 16), 16); - v128_t b1 = wasm_u32x4_shr(b.val, 16); - return v_uint64x2(( - v_dotprod(v_int32x4(a0), v_int32x4(b0)) + - v_dotprod(v_int32x4(a1), v_int32x4(b1))).val - ); -} -inline v_uint64x2 v_dotprod_expand(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c) -{ return v_dotprod_expand(a, b) + c; } - -inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b) -{ - v128_t a0 = wasm_i32x4_shr(wasm_i32x4_shl(a.val, 16), 16); - v128_t a1 = wasm_i32x4_shr(a.val, 16); - v128_t b0 = wasm_i32x4_shr(wasm_i32x4_shl(b.val, 16), 16); - v128_t b1 = wasm_i32x4_shr(b.val, 16); - return v_int64x2(( - v_dotprod(v_int32x4(a0), v_int32x4(b0)) + - v_dotprod(v_int32x4(a1), v_int32x4(b1))) - ); -} - -inline v_int64x2 v_dotprod_expand(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c) -{ return v_dotprod_expand(a, b) + c; } - -// 32 >> 64f -inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b) -{ return v_cvt_f64(v_dotprod(a, b)); } -inline v_float64x2 v_dotprod_expand(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c) -{ return v_dotprod_expand(a, b) + c; } - -//////// Fast Dot Product //////// - -// 16 >> 32 -inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b) -{ return v_dotprod(a, b); } -inline v_int32x4 v_dotprod_fast(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c) -{ return v_dotprod(a, b, c); } - -// 32 >> 64 -inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b) -{ return v_dotprod(a, b); } -inline v_int64x2 v_dotprod_fast(const v_int32x4& a, const v_int32x4& b, const v_int64x2& c) -{ return v_dotprod(a, b, c); } - -// 8 >> 32 -inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b) -{ return v_dotprod_expand(a, b); } -inline v_uint32x4 v_dotprod_expand_fast(const v_uint8x16& a, const v_uint8x16& b, const v_uint32x4& c) -{ return v_dotprod_expand(a, b, c); } -inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b) -{ return v_dotprod_expand(a, b); } -inline v_int32x4 v_dotprod_expand_fast(const v_int8x16& a, const v_int8x16& b, const v_int32x4& c) -{ return v_dotprod_expand(a, b, c); } - -// 16 >> 64 -inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b) -{ return v_dotprod_expand(a, b); } -inline v_uint64x2 v_dotprod_expand_fast(const v_uint16x8& a, const v_uint16x8& b, const v_uint64x2& c) -{ return v_dotprod_expand(a, b, c); } -inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b) -{ return v_dotprod_expand(a, b); } -inline v_int64x2 v_dotprod_expand_fast(const v_int16x8& a, const v_int16x8& b, const v_int64x2& c) -{ return v_dotprod_expand(a, b, c); } - -// 32 >> 64f -inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b) -{ return v_dotprod_expand(a, b); } -inline v_float64x2 v_dotprod_expand_fast(const v_int32x4& a, const v_int32x4& b, const v_float64x2& c) -{ return v_dotprod_expand(a, b, c); } - -#define OPENCV_HAL_IMPL_WASM_LOGIC_OP(_Tpvec) \ -OPENCV_HAL_IMPL_WASM_BIN_OP(&, _Tpvec, wasm_v128_and) \ -OPENCV_HAL_IMPL_WASM_BIN_OP(|, _Tpvec, wasm_v128_or) \ -OPENCV_HAL_IMPL_WASM_BIN_OP(^, _Tpvec, wasm_v128_xor) \ -inline _Tpvec operator ~ (const _Tpvec& a) \ -{ \ - return _Tpvec(wasm_v128_not(a.val)); \ -} - -OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_uint8x16) -OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_int8x16) -OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_uint16x8) -OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_int16x8) -OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_uint32x4) -OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_int32x4) -OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_uint64x2) -OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_int64x2) -OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_float32x4) -OPENCV_HAL_IMPL_WASM_LOGIC_OP(v_float64x2) - -inline v_float32x4 v_sqrt(const v_float32x4& x) -{ - return v_float32x4(wasm_f32x4_sqrt(x.val)); -} - -inline v_float32x4 v_invsqrt(const v_float32x4& x) -{ - const v128_t _1_0 = wasm_f32x4_splat(1.0); - return v_float32x4(wasm_f32x4_div(_1_0, wasm_f32x4_sqrt(x.val))); -} - -inline v_float64x2 v_sqrt(const v_float64x2& x) -{ - return v_float64x2(wasm_f64x2_sqrt(x.val)); -} - -inline v_float64x2 v_invsqrt(const v_float64x2& x) -{ - const v128_t _1_0 = wasm_f64x2_splat(1.0); - return v_float64x2(wasm_f64x2_div(_1_0, wasm_f64x2_sqrt(x.val))); -} - -#define OPENCV_HAL_IMPL_WASM_ABS_INT_FUNC(_Tpuvec, _Tpsvec, suffix, zsuffix, shiftWidth) \ -inline _Tpuvec v_abs(const _Tpsvec& x) \ -{ \ - v128_t s = wasm_##suffix##_shr(x.val, shiftWidth); \ - v128_t f = wasm_##zsuffix##_shr(x.val, shiftWidth); \ - return _Tpuvec(wasm_##zsuffix##_add(wasm_v128_xor(x.val, f), s)); \ -} - -OPENCV_HAL_IMPL_WASM_ABS_INT_FUNC(v_uint8x16, v_int8x16, u8x16, i8x16, 7) -OPENCV_HAL_IMPL_WASM_ABS_INT_FUNC(v_uint16x8, v_int16x8, u16x8, i16x8, 15) -OPENCV_HAL_IMPL_WASM_ABS_INT_FUNC(v_uint32x4, v_int32x4, u32x4, i32x4, 31) - -inline v_float32x4 v_abs(const v_float32x4& x) -{ return v_float32x4(wasm_f32x4_abs(x.val)); } -inline v_float64x2 v_abs(const v_float64x2& x) -{ - return v_float64x2(wasm_f64x2_abs(x.val)); -} - -// TODO: exp, log, sin, cos - -#define OPENCV_HAL_IMPL_WASM_BIN_FUNC(_Tpvec, func, intrin) \ -inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(intrin(a.val, b.val)); \ -} - -#define OPENCV_HAL_IMPL_WASM_BIN_FUNC_FALLBACK(_Tpvec, func, intrin) \ -inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \ -{ \ - fallback::_Tpvec a_(a); \ - fallback::_Tpvec b_(b); \ - return _Tpvec(fallback::func(a_, b_)); \ -} - -OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_float32x4, v_min, wasm_f32x4_min) -OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_float32x4, v_max, wasm_f32x4_max) -OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_float64x2, v_min, wasm_f64x2_min) -OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_float64x2, v_max, wasm_f64x2_max) - -#define OPENCV_HAL_IMPL_WASM_MINMAX_S_INIT_FUNC(_Tpvec, suffix) \ -inline _Tpvec v_min(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(wasm_v128_bitselect(b.val, a.val, wasm_##suffix##_gt(a.val, b.val))); \ -} \ -inline _Tpvec v_max(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(wasm_v128_bitselect(a.val, b.val, wasm_##suffix##_gt(a.val, b.val))); \ -} - -OPENCV_HAL_IMPL_WASM_MINMAX_S_INIT_FUNC(v_int8x16, i8x16) -OPENCV_HAL_IMPL_WASM_MINMAX_S_INIT_FUNC(v_int16x8, i16x8) -OPENCV_HAL_IMPL_WASM_MINMAX_S_INIT_FUNC(v_int32x4, i32x4) - -#define OPENCV_HAL_IMPL_WASM_MINMAX_U_INIT_FUNC(_Tpvec, suffix, deltaNum) \ -inline _Tpvec v_min(const _Tpvec& a, const _Tpvec& b) \ -{ \ - v128_t delta = wasm_##suffix##_splat(deltaNum); \ - v128_t mask = wasm_##suffix##_gt(wasm_v128_xor(a.val, delta), wasm_v128_xor(b.val, delta)); \ - return _Tpvec(wasm_v128_bitselect(b.val, a.val, mask)); \ -} \ -inline _Tpvec v_max(const _Tpvec& a, const _Tpvec& b) \ -{ \ - v128_t delta = wasm_##suffix##_splat(deltaNum); \ - v128_t mask = wasm_##suffix##_gt(wasm_v128_xor(a.val, delta), wasm_v128_xor(b.val, delta)); \ - return _Tpvec(wasm_v128_bitselect(a.val, b.val, mask)); \ -} - -OPENCV_HAL_IMPL_WASM_MINMAX_U_INIT_FUNC(v_uint8x16, i8x16, (schar)0x80) -OPENCV_HAL_IMPL_WASM_MINMAX_U_INIT_FUNC(v_uint16x8, i16x8, (short)0x8000) -OPENCV_HAL_IMPL_WASM_MINMAX_U_INIT_FUNC(v_uint32x4, i32x4, (int)0x80000000) - -#define OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(_Tpvec, suffix, esuffix) \ -inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(wasm_##esuffix##_eq(a.val, b.val)); } \ -inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(wasm_##esuffix##_ne(a.val, b.val)); } \ -inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(wasm_##suffix##_lt(a.val, b.val)); } \ -inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(wasm_##suffix##_gt(a.val, b.val)); } \ -inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(wasm_##suffix##_le(a.val, b.val)); } \ -inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \ -{ return _Tpvec(wasm_##suffix##_ge(a.val, b.val)); } - -OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(v_uint8x16, u8x16, i8x16) -OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(v_int8x16, i8x16, i8x16) -OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(v_uint16x8, u16x8, i16x8) -OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(v_int16x8, i16x8, i16x8) -OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(v_uint32x4, u32x4, i32x4) -OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(v_int32x4, i32x4, i32x4) -OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(v_float32x4, f32x4, f32x4) -OPENCV_HAL_IMPL_WASM_INIT_CMP_OP(v_float64x2, f64x2, f64x2) - -#define OPENCV_HAL_IMPL_WASM_64BIT_CMP_OP(_Tpvec, cast) \ -inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \ -{ return cast(v_reinterpret_as_f64(a) == v_reinterpret_as_f64(b)); } \ -inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \ -{ return cast(v_reinterpret_as_f64(a) != v_reinterpret_as_f64(b)); } - -OPENCV_HAL_IMPL_WASM_64BIT_CMP_OP(v_uint64x2, v_reinterpret_as_u64) -OPENCV_HAL_IMPL_WASM_64BIT_CMP_OP(v_int64x2, v_reinterpret_as_s64) - -inline v_float32x4 v_not_nan(const v_float32x4& a) -{ - v128_t z = wasm_i32x4_splat(0x7fffffff); - v128_t t = wasm_i32x4_splat(0x7f800000); - return v_float32x4(wasm_u32x4_lt(wasm_v128_and(a.val, z), t)); -} -inline v_float64x2 v_not_nan(const v_float64x2& a) -{ - v128_t z = wasm_i64x2_splat(0x7fffffffffffffff); - v128_t t = wasm_i64x2_splat(0x7ff0000000000000); - return v_float64x2((__u64x2)(wasm_v128_and(a.val, z)) < (__u64x2)t); -} - -OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_uint8x16, v_add_wrap, wasm_i8x16_add) -OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_int8x16, v_add_wrap, wasm_i8x16_add) -OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_uint16x8, v_add_wrap, wasm_i16x8_add) -OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_int16x8, v_add_wrap, wasm_i16x8_add) -OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_uint8x16, v_sub_wrap, wasm_i8x16_sub) -OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_int8x16, v_sub_wrap, wasm_i8x16_sub) -OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_uint16x8, v_sub_wrap, wasm_i16x8_sub) -OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_int16x8, v_sub_wrap, wasm_i16x8_sub) -#if (__EMSCRIPTEN_major__ * 1000000 + __EMSCRIPTEN_minor__ * 1000 + __EMSCRIPTEN_tiny__) >= (2000000) -// details: https://github.com/opencv/opencv/issues/18097 ( https://github.com/emscripten-core/emscripten/issues/12018 ) -OPENCV_HAL_IMPL_WASM_BIN_FUNC_FALLBACK(v_uint8x16, v_mul_wrap, wasm_i8x16_mul) -OPENCV_HAL_IMPL_WASM_BIN_FUNC_FALLBACK(v_int8x16, v_mul_wrap, wasm_i8x16_mul) -#else -OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_uint8x16, v_mul_wrap, wasm_i8x16_mul) -OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_int8x16, v_mul_wrap, wasm_i8x16_mul) -#endif -OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_uint16x8, v_mul_wrap, wasm_i16x8_mul) -OPENCV_HAL_IMPL_WASM_BIN_FUNC(v_int16x8, v_mul_wrap, wasm_i16x8_mul) - - -/** Absolute difference **/ - -inline v_uint8x16 v_absdiff(const v_uint8x16& a, const v_uint8x16& b) -{ return v_add_wrap(a - b, b - a); } -inline v_uint16x8 v_absdiff(const v_uint16x8& a, const v_uint16x8& b) -{ return v_add_wrap(a - b, b - a); } -inline v_uint32x4 v_absdiff(const v_uint32x4& a, const v_uint32x4& b) -{ return v_max(a, b) - v_min(a, b); } - -inline v_uint8x16 v_absdiff(const v_int8x16& a, const v_int8x16& b) -{ - v_int8x16 d = v_sub_wrap(a, b); - v_int8x16 m = a < b; - return v_reinterpret_as_u8(v_sub_wrap(d ^ m, m)); -} -inline v_uint16x8 v_absdiff(const v_int16x8& a, const v_int16x8& b) -{ - return v_reinterpret_as_u16(v_sub_wrap(v_max(a, b), v_min(a, b))); -} -inline v_uint32x4 v_absdiff(const v_int32x4& a, const v_int32x4& b) -{ - v_int32x4 d = a - b; - v_int32x4 m = a < b; - return v_reinterpret_as_u32((d ^ m) - m); -} - -/** Saturating absolute difference **/ -inline v_int8x16 v_absdiffs(const v_int8x16& a, const v_int8x16& b) -{ - v_int8x16 d = a - b; - v_int8x16 m = a < b; - return (d ^ m) - m; - } -inline v_int16x8 v_absdiffs(const v_int16x8& a, const v_int16x8& b) -{ return v_max(a, b) - v_min(a, b); } - - -inline v_int32x4 v_fma(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) -{ - return a * b + c; -} - -inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c) -{ - return v_fma(a, b, c); -} - -inline v_float32x4 v_fma(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c) -{ - return a * b + c; -} - -inline v_float64x2 v_fma(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c) -{ - return a * b + c; -} - -inline v_float32x4 v_absdiff(const v_float32x4& a, const v_float32x4& b) -{ - v128_t absmask_vec = wasm_i32x4_splat(0x7fffffff); - return v_float32x4(wasm_v128_and(wasm_f32x4_sub(a.val, b.val), absmask_vec)); -} -inline v_float64x2 v_absdiff(const v_float64x2& a, const v_float64x2& b) -{ - v128_t absmask_vec = wasm_u64x2_shr(wasm_i32x4_splat(-1), 1); - return v_float64x2(wasm_v128_and(wasm_f64x2_sub(a.val, b.val), absmask_vec)); -} - -#define OPENCV_HAL_IMPL_WASM_MISC_FLT_OP(_Tpvec, suffix) \ -inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \ -{ \ - v128_t a_Square = wasm_##suffix##_mul(a.val, a.val); \ - v128_t b_Square = wasm_##suffix##_mul(b.val, b.val); \ - return _Tpvec(wasm_##suffix##_sqrt(wasm_##suffix##_add(a_Square, b_Square))); \ -} \ -inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \ -{ \ - v128_t a_Square = wasm_##suffix##_mul(a.val, a.val); \ - v128_t b_Square = wasm_##suffix##_mul(b.val, b.val); \ - return _Tpvec(wasm_##suffix##_add(a_Square, b_Square)); \ -} \ -inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \ -{ \ - return _Tpvec(wasm_##suffix##_add(wasm_##suffix##_mul(a.val, b.val), c.val)); \ -} - -OPENCV_HAL_IMPL_WASM_MISC_FLT_OP(v_float32x4, f32x4) -OPENCV_HAL_IMPL_WASM_MISC_FLT_OP(v_float64x2, f64x2) - -#define OPENCV_HAL_IMPL_WASM_SHIFT_OP(_Tpuvec, _Tpsvec, suffix, ssuffix) \ -inline _Tpuvec operator << (const _Tpuvec& a, int imm) \ -{ \ - return _Tpuvec(wasm_##suffix##_shl(a.val, imm)); \ -} \ -inline _Tpsvec operator << (const _Tpsvec& a, int imm) \ -{ \ - return _Tpsvec(wasm_##suffix##_shl(a.val, imm)); \ -} \ -inline _Tpuvec operator >> (const _Tpuvec& a, int imm) \ -{ \ - return _Tpuvec(wasm_##ssuffix##_shr(a.val, imm)); \ -} \ -inline _Tpsvec operator >> (const _Tpsvec& a, int imm) \ -{ \ - return _Tpsvec(wasm_##suffix##_shr(a.val, imm)); \ -} \ -template \ -inline _Tpuvec v_shl(const _Tpuvec& a) \ -{ \ - return _Tpuvec(wasm_##suffix##_shl(a.val, imm)); \ -} \ -template \ -inline _Tpsvec v_shl(const _Tpsvec& a) \ -{ \ - return _Tpsvec(wasm_##suffix##_shl(a.val, imm)); \ -} \ -template \ -inline _Tpuvec v_shr(const _Tpuvec& a) \ -{ \ - return _Tpuvec(wasm_##ssuffix##_shr(a.val, imm)); \ -} \ -template \ -inline _Tpsvec v_shr(const _Tpsvec& a) \ -{ \ - return _Tpsvec(wasm_##suffix##_shr(a.val, imm)); \ -} - -OPENCV_HAL_IMPL_WASM_SHIFT_OP(v_uint8x16, v_int8x16, i8x16, u8x16) -OPENCV_HAL_IMPL_WASM_SHIFT_OP(v_uint16x8, v_int16x8, i16x8, u16x8) -OPENCV_HAL_IMPL_WASM_SHIFT_OP(v_uint32x4, v_int32x4, i32x4, u32x4) -OPENCV_HAL_IMPL_WASM_SHIFT_OP(v_uint64x2, v_int64x2, i64x2, u64x2) - -namespace hal_wasm_internal -{ - template 16)), - bool is_first = (imm == 0), - bool is_second = (imm == 16), - bool is_other = (((imm > 0) && (imm < 16)))> - class v_wasm_palignr_u8_class; - - template - class v_wasm_palignr_u8_class; - - template - class v_wasm_palignr_u8_class - { - public: - inline v128_t operator()(const v128_t& a, const v128_t&) const - { - return a; - } - }; - - template - class v_wasm_palignr_u8_class - { - public: - inline v128_t operator()(const v128_t&, const v128_t& b) const - { - return b; - } - }; - - template - class v_wasm_palignr_u8_class - { - public: - inline v128_t operator()(const v128_t& a, const v128_t& b) const - { - enum { imm2 = (sizeof(v128_t) - imm) }; - return wasm_v8x16_shuffle(a, b, - imm, imm+1, imm+2, imm+3, - imm+4, imm+5, imm+6, imm+7, - imm+8, imm+9, imm+10, imm+11, - imm+12, imm+13, imm+14, imm+15); - } - }; - - template - inline v128_t v_wasm_palignr_u8(const v128_t& a, const v128_t& b) - { - CV_StaticAssert((imm >= 0) && (imm <= 16), "Invalid imm for v_wasm_palignr_u8."); - return v_wasm_palignr_u8_class()(a, b); - } -} - -template -inline _Tpvec v_rotate_right(const _Tpvec &a) -{ - using namespace hal_wasm_internal; - enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) }; - v128_t z = wasm_i8x16_splat(0); - return _Tpvec(v_wasm_palignr_u8(a.val, z)); -} - -template -inline _Tpvec v_rotate_left(const _Tpvec &a) -{ - using namespace hal_wasm_internal; - enum { imm2 = ((_Tpvec::nlanes - imm) * sizeof(typename _Tpvec::lane_type)) }; - v128_t z = wasm_i8x16_splat(0); - return _Tpvec(v_wasm_palignr_u8(z, a.val)); -} - -template -inline _Tpvec v_rotate_right(const _Tpvec &a, const _Tpvec &b) -{ - using namespace hal_wasm_internal; - enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) }; - return _Tpvec(v_wasm_palignr_u8(a.val, b.val)); -} - -template -inline _Tpvec v_rotate_left(const _Tpvec &a, const _Tpvec &b) -{ - using namespace hal_wasm_internal; - enum { imm2 = ((_Tpvec::nlanes - imm) * sizeof(typename _Tpvec::lane_type)) }; - return _Tpvec(v_wasm_palignr_u8(b.val, a.val)); -} - -#define OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(_Tpvec, _Tp) \ -inline _Tpvec v_load(const _Tp* ptr) \ -{ return _Tpvec(wasm_v128_load(ptr)); } \ -inline _Tpvec v_load_aligned(const _Tp* ptr) \ -{ return _Tpvec(wasm_v128_load(ptr)); } \ -inline _Tpvec v_load_low(const _Tp* ptr) \ -{ \ - _Tp tmp[_Tpvec::nlanes] = {0}; \ - for (int i=0; i<_Tpvec::nlanes/2; ++i) { \ - tmp[i] = ptr[i]; \ - } \ - return _Tpvec(wasm_v128_load(tmp)); \ -} \ -inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \ -{ \ - _Tp tmp[_Tpvec::nlanes]; \ - for (int i=0; i<_Tpvec::nlanes/2; ++i) { \ - tmp[i] = ptr0[i]; \ - tmp[i+_Tpvec::nlanes/2] = ptr1[i]; \ - } \ - return _Tpvec(wasm_v128_load(tmp)); \ -} \ -inline void v_store(_Tp* ptr, const _Tpvec& a) \ -{ wasm_v128_store(ptr, a.val); } \ -inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \ -{ wasm_v128_store(ptr, a.val); } \ -inline void v_store_aligned_nocache(_Tp* ptr, const _Tpvec& a) \ -{ wasm_v128_store(ptr, a.val); } \ -inline void v_store(_Tp* ptr, const _Tpvec& a, hal::StoreMode /*mode*/) \ -{ \ - wasm_v128_store(ptr, a.val); \ -} \ -inline void v_store_low(_Tp* ptr, const _Tpvec& a) \ -{ \ - fallback::_Tpvec a_(a); \ - fallback::v_store_low(ptr, a_); \ -} \ -inline void v_store_high(_Tp* ptr, const _Tpvec& a) \ -{ \ - fallback::_Tpvec a_(a); \ - fallback::v_store_high(ptr, a_); \ -} - -OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_uint8x16, uchar) -OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_int8x16, schar) -OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_uint16x8, ushort) -OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_int16x8, short) -OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_uint32x4, unsigned) -OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_int32x4, int) -OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_uint64x2, uint64) -OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_int64x2, int64) -OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_float32x4, float) -OPENCV_HAL_IMPL_WASM_LOADSTORE_INT_OP(v_float64x2, double) - - -/** Reverse **/ -inline v_uint8x16 v_reverse(const v_uint8x16 &a) -{ return v_uint8x16(wasm_v8x16_shuffle(a.val, a.val, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0)); } - -inline v_int8x16 v_reverse(const v_int8x16 &a) -{ return v_reinterpret_as_s8(v_reverse(v_reinterpret_as_u8(a))); } - -inline v_uint16x8 v_reverse(const v_uint16x8 &a) -{ return v_uint16x8(wasm_v8x16_shuffle(a.val, a.val, 14, 15, 12, 13, 10, 11, 8, 9, 6, 7, 4, 5, 2, 3, 0, 1)); } - -inline v_int16x8 v_reverse(const v_int16x8 &a) -{ return v_reinterpret_as_s16(v_reverse(v_reinterpret_as_u16(a))); } - -inline v_uint32x4 v_reverse(const v_uint32x4 &a) -{ return v_uint32x4(wasm_v8x16_shuffle(a.val, a.val, 12, 13, 14, 15, 8, 9, 10, 11, 4, 5, 6, 7, 0, 1, 2, 3)); } - -inline v_int32x4 v_reverse(const v_int32x4 &a) -{ return v_reinterpret_as_s32(v_reverse(v_reinterpret_as_u32(a))); } - -inline v_float32x4 v_reverse(const v_float32x4 &a) -{ return v_reinterpret_as_f32(v_reverse(v_reinterpret_as_u32(a))); } - -inline v_uint64x2 v_reverse(const v_uint64x2 &a) -{ return v_uint64x2(wasm_v8x16_shuffle(a.val, a.val, 8, 9, 10, 11, 12, 13, 14, 15, 0, 1, 2, 3, 4, 5, 6, 7)); } - -inline v_int64x2 v_reverse(const v_int64x2 &a) -{ return v_reinterpret_as_s64(v_reverse(v_reinterpret_as_u64(a))); } - -inline v_float64x2 v_reverse(const v_float64x2 &a) -{ return v_reinterpret_as_f64(v_reverse(v_reinterpret_as_u64(a))); } - - -#define OPENCV_HAL_IMPL_WASM_REDUCE_OP_4_SUM(_Tpvec, scalartype, regtype, suffix, esuffix) \ -inline scalartype v_reduce_sum(const _Tpvec& a) \ -{ \ - regtype val = a.val; \ - val = wasm_##suffix##_add(val, wasm_v8x16_shuffle(val, val, 8,9,10,11,12,13,14,15,0,1,2,3,4,5,6,7)); \ - val = wasm_##suffix##_add(val, wasm_v8x16_shuffle(val, val, 4,5,6,7,8,9,10,11,12,13,14,15,0,1,2,3)); \ - return (scalartype)wasm_##esuffix##_extract_lane(val, 0); \ -} - -OPENCV_HAL_IMPL_WASM_REDUCE_OP_4_SUM(v_uint32x4, unsigned, v128_t, i32x4, i32x4) -OPENCV_HAL_IMPL_WASM_REDUCE_OP_4_SUM(v_int32x4, int, v128_t, i32x4, i32x4) -OPENCV_HAL_IMPL_WASM_REDUCE_OP_4_SUM(v_float32x4, float, v128_t, f32x4, f32x4) - -// To do: Optimize v_reduce_sum with wasm intrin. -// Now use fallback implementation as there is no widening op in wasm intrin. - -#define OPENCV_HAL_IMPL_FALLBACK_REDUCE_OP_SUM(_Tpvec, scalartype) \ -inline scalartype v_reduce_sum(const _Tpvec& a) \ -{ \ - fallback::_Tpvec a_(a); \ - return fallback::v_reduce_sum(a_); \ -} - -OPENCV_HAL_IMPL_FALLBACK_REDUCE_OP_SUM(v_uint8x16, unsigned) -OPENCV_HAL_IMPL_FALLBACK_REDUCE_OP_SUM(v_int8x16, int) -OPENCV_HAL_IMPL_FALLBACK_REDUCE_OP_SUM(v_uint16x8, unsigned) -OPENCV_HAL_IMPL_FALLBACK_REDUCE_OP_SUM(v_int16x8, int) - - -#define OPENCV_HAL_IMPL_WASM_REDUCE_OP_2_SUM(_Tpvec, scalartype, regtype, suffix, esuffix) \ -inline scalartype v_reduce_sum(const _Tpvec& a) \ -{ \ - regtype val = a.val; \ - val = wasm_##suffix##_add(val, wasm_v8x16_shuffle(val, val, 8,9,10,11,12,13,14,15,0,1,2,3,4,5,6,7)); \ - return (scalartype)wasm_##esuffix##_extract_lane(val, 0); \ -} -OPENCV_HAL_IMPL_WASM_REDUCE_OP_2_SUM(v_uint64x2, uint64, v128_t, i64x2, i64x2) -OPENCV_HAL_IMPL_WASM_REDUCE_OP_2_SUM(v_int64x2, int64, v128_t, i64x2, i64x2) -OPENCV_HAL_IMPL_WASM_REDUCE_OP_2_SUM(v_float64x2, double, v128_t, f64x2,f64x2) - -inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b, - const v_float32x4& c, const v_float32x4& d) -{ - v128_t ac = wasm_f32x4_add(wasm_unpacklo_i32x4(a.val, c.val), wasm_unpackhi_i32x4(a.val, c.val)); - v128_t bd = wasm_f32x4_add(wasm_unpacklo_i32x4(b.val, d.val), wasm_unpackhi_i32x4(b.val, d.val)); - return v_float32x4(wasm_f32x4_add(wasm_unpacklo_i32x4(ac, bd), wasm_unpackhi_i32x4(ac, bd))); -} - -#define OPENCV_HAL_IMPL_WASM_REDUCE_OP(_Tpvec, scalartype, func, scalar_func) \ -inline scalartype v_reduce_##func(const _Tpvec& a) \ -{ \ - scalartype buf[_Tpvec::nlanes]; \ - v_store(buf, a); \ - scalartype tmp = buf[0]; \ - for (int i=1; i<_Tpvec::nlanes; ++i) { \ - tmp = scalar_func(tmp, buf[i]); \ - } \ - return tmp; \ -} - -OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_uint8x16, uchar, max, std::max) -OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_uint8x16, uchar, min, std::min) -OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_int8x16, schar, max, std::max) -OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_int8x16, schar, min, std::min) -OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_uint16x8, ushort, max, std::max) -OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_uint16x8, ushort, min, std::min) -OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_int16x8, short, max, std::max) -OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_int16x8, short, min, std::min) -OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_uint32x4, unsigned, max, std::max) -OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_uint32x4, unsigned, min, std::min) -OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_int32x4, int, max, std::max) -OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_int32x4, int, min, std::min) -OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_float32x4, float, max, std::max) -OPENCV_HAL_IMPL_WASM_REDUCE_OP(v_float32x4, float, min, std::min) - -inline unsigned v_reduce_sad(const v_uint8x16& a, const v_uint8x16& b) -{ - v_uint16x8 l16, h16; - v_uint32x4 l16_l32, l16_h32, h16_l32, h16_h32; - v_expand(v_absdiff(a, b), l16, h16); - v_expand(l16, l16_l32, l16_h32); - v_expand(h16, h16_l32, h16_h32); - return v_reduce_sum(l16_l32+l16_h32+h16_l32+h16_h32); -} -inline unsigned v_reduce_sad(const v_int8x16& a, const v_int8x16& b) -{ - v_uint16x8 l16, h16; - v_uint32x4 l16_l32, l16_h32, h16_l32, h16_h32; - v_expand(v_absdiff(a, b), l16, h16); - v_expand(l16, l16_l32, l16_h32); - v_expand(h16, h16_l32, h16_h32); - return v_reduce_sum(l16_l32+l16_h32+h16_l32+h16_h32); -} -inline unsigned v_reduce_sad(const v_uint16x8& a, const v_uint16x8& b) -{ - v_uint32x4 l, h; - v_expand(v_absdiff(a, b), l, h); - return v_reduce_sum(l + h); -} -inline unsigned v_reduce_sad(const v_int16x8& a, const v_int16x8& b) -{ - v_uint32x4 l, h; - v_expand(v_absdiff(a, b), l, h); - return v_reduce_sum(l + h); -} -inline unsigned v_reduce_sad(const v_uint32x4& a, const v_uint32x4& b) -{ - return v_reduce_sum(v_absdiff(a, b)); -} -inline unsigned v_reduce_sad(const v_int32x4& a, const v_int32x4& b) -{ - return v_reduce_sum(v_absdiff(a, b)); -} -inline float v_reduce_sad(const v_float32x4& a, const v_float32x4& b) -{ - return v_reduce_sum(v_absdiff(a, b)); -} - -inline v_uint8x16 v_popcount(const v_uint8x16& a) -{ - v128_t m1 = wasm_i32x4_splat(0x55555555); - v128_t m2 = wasm_i32x4_splat(0x33333333); - v128_t m4 = wasm_i32x4_splat(0x0f0f0f0f); - v128_t p = a.val; - p = wasm_i32x4_add(wasm_v128_and(wasm_u32x4_shr(p, 1), m1), wasm_v128_and(p, m1)); - p = wasm_i32x4_add(wasm_v128_and(wasm_u32x4_shr(p, 2), m2), wasm_v128_and(p, m2)); - p = wasm_i32x4_add(wasm_v128_and(wasm_u32x4_shr(p, 4), m4), wasm_v128_and(p, m4)); - return v_uint8x16(p); -} -inline v_uint16x8 v_popcount(const v_uint16x8& a) -{ - v_uint8x16 p = v_popcount(v_reinterpret_as_u8(a)); - p += v_rotate_right<1>(p); - return v_reinterpret_as_u16(p) & v_setall_u16(0x00ff); -} -inline v_uint32x4 v_popcount(const v_uint32x4& a) -{ - v_uint8x16 p = v_popcount(v_reinterpret_as_u8(a)); - p += v_rotate_right<1>(p); - p += v_rotate_right<2>(p); - return v_reinterpret_as_u32(p) & v_setall_u32(0x000000ff); -} -inline v_uint64x2 v_popcount(const v_uint64x2& a) -{ - fallback::v_uint64x2 a_(a); - return fallback::v_popcount(a_); -} -inline v_uint8x16 v_popcount(const v_int8x16& a) -{ return v_popcount(v_reinterpret_as_u8(a)); } -inline v_uint16x8 v_popcount(const v_int16x8& a) -{ return v_popcount(v_reinterpret_as_u16(a)); } -inline v_uint32x4 v_popcount(const v_int32x4& a) -{ return v_popcount(v_reinterpret_as_u32(a)); } -inline v_uint64x2 v_popcount(const v_int64x2& a) -{ return v_popcount(v_reinterpret_as_u64(a)); } - -#define OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(_Tpvec, suffix, scalarType) \ -inline int v_signmask(const _Tpvec& a) \ -{ \ - fallback::_Tpvec a_(a); \ - return fallback::v_signmask(a_); \ -} \ -inline bool v_check_all(const _Tpvec& a) \ -{ return wasm_i8x16_all_true(wasm_##suffix##_lt(a.val, wasm_##suffix##_splat(0))); } \ -inline bool v_check_any(const _Tpvec& a) \ -{ return wasm_i8x16_any_true(wasm_##suffix##_lt(a.val, wasm_##suffix##_splat(0)));; } - -OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(v_uint8x16, i8x16, schar) -OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(v_int8x16, i8x16, schar) -OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(v_uint16x8, i16x8, short) -OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(v_int16x8, i16x8, short) -OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(v_uint32x4, i32x4, int) -OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(v_int32x4, i32x4, int) -OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(v_float32x4, i32x4, float) -OPENCV_HAL_IMPL_WASM_CHECK_SIGNS(v_float64x2, f64x2, double) - -#define OPENCV_HAL_IMPL_WASM_CHECK_ALL_ANY(_Tpvec, suffix, esuffix) \ -inline bool v_check_all(const _Tpvec& a) \ -{ \ - v128_t masked = v_reinterpret_as_##esuffix(a).val; \ - masked = wasm_i32x4_replace_lane(masked, 0, 0xffffffff); \ - masked = wasm_i32x4_replace_lane(masked, 2, 0xffffffff); \ - return wasm_i8x16_all_true(wasm_##suffix##_lt(masked, wasm_##suffix##_splat(0))); \ -} \ -inline bool v_check_any(const _Tpvec& a) \ -{ \ - v128_t masked = v_reinterpret_as_##esuffix(a).val; \ - masked = wasm_i32x4_replace_lane(masked, 0, 0x0); \ - masked = wasm_i32x4_replace_lane(masked, 2, 0x0); \ - return wasm_i8x16_any_true(wasm_##suffix##_lt(masked, wasm_##suffix##_splat(0))); \ -} \ - -OPENCV_HAL_IMPL_WASM_CHECK_ALL_ANY(v_int64x2, i32x4, s32) -OPENCV_HAL_IMPL_WASM_CHECK_ALL_ANY(v_uint64x2, i32x4, u32) - - -inline int v_scan_forward(const v_int8x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))); } -inline int v_scan_forward(const v_uint8x16& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))); } -inline int v_scan_forward(const v_int16x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 2; } -inline int v_scan_forward(const v_uint16x8& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 2; } -inline int v_scan_forward(const v_int32x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; } -inline int v_scan_forward(const v_uint32x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; } -inline int v_scan_forward(const v_float32x4& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 4; } -inline int v_scan_forward(const v_int64x2& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; } -inline int v_scan_forward(const v_uint64x2& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; } -inline int v_scan_forward(const v_float64x2& a) { return trailingZeros32(v_signmask(v_reinterpret_as_s8(a))) / 8; } - -#define OPENCV_HAL_IMPL_WASM_SELECT(_Tpvec) \ -inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(wasm_v128_bitselect(a.val, b.val, mask.val)); \ -} - -OPENCV_HAL_IMPL_WASM_SELECT(v_uint8x16) -OPENCV_HAL_IMPL_WASM_SELECT(v_int8x16) -OPENCV_HAL_IMPL_WASM_SELECT(v_uint16x8) -OPENCV_HAL_IMPL_WASM_SELECT(v_int16x8) -OPENCV_HAL_IMPL_WASM_SELECT(v_uint32x4) -OPENCV_HAL_IMPL_WASM_SELECT(v_int32x4) -OPENCV_HAL_IMPL_WASM_SELECT(v_uint64x2) -OPENCV_HAL_IMPL_WASM_SELECT(v_int64x2) -OPENCV_HAL_IMPL_WASM_SELECT(v_float32x4) -OPENCV_HAL_IMPL_WASM_SELECT(v_float64x2) - -#define OPENCV_HAL_IMPL_WASM_EXPAND(_Tpvec, _Tpwvec, _Tp, intrin) \ -inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \ -{ \ - b0.val = intrin(a.val); \ - b1.val = __CV_CAT(intrin, _high)(a.val); \ -} \ -inline _Tpwvec v_expand_low(const _Tpvec& a) \ -{ return _Tpwvec(intrin(a.val)); } \ -inline _Tpwvec v_expand_high(const _Tpvec& a) \ -{ return _Tpwvec(__CV_CAT(intrin, _high)(a.val)); } \ -inline _Tpwvec v_load_expand(const _Tp* ptr) \ -{ \ - v128_t a = wasm_v128_load(ptr); \ - return _Tpwvec(intrin(a)); \ -} - -OPENCV_HAL_IMPL_WASM_EXPAND(v_uint8x16, v_uint16x8, uchar, v128_cvtu8x16_i16x8) -OPENCV_HAL_IMPL_WASM_EXPAND(v_int8x16, v_int16x8, schar, v128_cvti8x16_i16x8) -OPENCV_HAL_IMPL_WASM_EXPAND(v_uint16x8, v_uint32x4, ushort, v128_cvtu16x8_i32x4) -OPENCV_HAL_IMPL_WASM_EXPAND(v_int16x8, v_int32x4, short, v128_cvti16x8_i32x4) -OPENCV_HAL_IMPL_WASM_EXPAND(v_uint32x4, v_uint64x2, unsigned, v128_cvtu32x4_i64x2) -OPENCV_HAL_IMPL_WASM_EXPAND(v_int32x4, v_int64x2, int, v128_cvti32x4_i64x2) - -#define OPENCV_HAL_IMPL_WASM_EXPAND_Q(_Tpvec, _Tp, intrin) \ -inline _Tpvec v_load_expand_q(const _Tp* ptr) \ -{ \ - v128_t a = wasm_v128_load(ptr); \ - return _Tpvec(intrin(a)); \ -} - -OPENCV_HAL_IMPL_WASM_EXPAND_Q(v_uint32x4, uchar, v128_cvtu8x16_i32x4) -OPENCV_HAL_IMPL_WASM_EXPAND_Q(v_int32x4, schar, v128_cvti8x16_i32x4) - -#define OPENCV_HAL_IMPL_WASM_UNPACKS(_Tpvec, suffix) \ -inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1) \ -{ \ - b0.val = wasm_unpacklo_##suffix(a0.val, a1.val); \ - b1.val = wasm_unpackhi_##suffix(a0.val, a1.val); \ -} \ -inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(wasm_unpacklo_i64x2(a.val, b.val)); \ -} \ -inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \ -{ \ - return _Tpvec(wasm_unpackhi_i64x2(a.val, b.val)); \ -} \ -inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d) \ -{ \ - c.val = wasm_unpacklo_i64x2(a.val, b.val); \ - d.val = wasm_unpackhi_i64x2(a.val, b.val); \ -} - -OPENCV_HAL_IMPL_WASM_UNPACKS(v_uint8x16, i8x16) -OPENCV_HAL_IMPL_WASM_UNPACKS(v_int8x16, i8x16) -OPENCV_HAL_IMPL_WASM_UNPACKS(v_uint16x8, i16x8) -OPENCV_HAL_IMPL_WASM_UNPACKS(v_int16x8, i16x8) -OPENCV_HAL_IMPL_WASM_UNPACKS(v_uint32x4, i32x4) -OPENCV_HAL_IMPL_WASM_UNPACKS(v_int32x4, i32x4) -OPENCV_HAL_IMPL_WASM_UNPACKS(v_float32x4, i32x4) -OPENCV_HAL_IMPL_WASM_UNPACKS(v_float64x2, i64x2) - -template -inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b) -{ - return v_rotate_right(a, b); -} - -inline v_int32x4 v_round(const v_float32x4& a) -{ - v128_t h = wasm_f32x4_splat(0.5); - return v_int32x4(wasm_i32x4_trunc_saturate_f32x4(wasm_f32x4_add(a.val, h))); -} - -inline v_int32x4 v_floor(const v_float32x4& a) -{ - v128_t a1 = wasm_i32x4_trunc_saturate_f32x4(a.val); - v128_t mask = wasm_f32x4_lt(a.val, wasm_f32x4_convert_i32x4(a1)); - return v_int32x4(wasm_i32x4_add(a1, mask)); -} - -inline v_int32x4 v_ceil(const v_float32x4& a) -{ - v128_t a1 = wasm_i32x4_trunc_saturate_f32x4(a.val); - v128_t mask = wasm_f32x4_gt(a.val, wasm_f32x4_convert_i32x4(a1)); - return v_int32x4(wasm_i32x4_sub(a1, mask)); -} - -inline v_int32x4 v_trunc(const v_float32x4& a) -{ return v_int32x4(wasm_i32x4_trunc_saturate_f32x4(a.val)); } - -#define OPENCV_HAL_IMPL_WASM_MATH_FUNC(func, cfunc, _Tpvec, _Tpnvec, _Tp, _Tpn) \ -inline _Tpnvec func(const _Tpvec& a) \ -{ \ - fallback::_Tpvec a_(a); \ - return fallback::func(a_); \ -} - -OPENCV_HAL_IMPL_WASM_MATH_FUNC(v_round, cvRound, v_float64x2, v_int32x4, double, int) -OPENCV_HAL_IMPL_WASM_MATH_FUNC(v_floor, cvFloor, v_float64x2, v_int32x4, double, int) -OPENCV_HAL_IMPL_WASM_MATH_FUNC(v_ceil, cvCeil, v_float64x2, v_int32x4, double, int) -OPENCV_HAL_IMPL_WASM_MATH_FUNC(v_trunc, int, v_float64x2, v_int32x4, double, int) - -inline v_int32x4 v_round(const v_float64x2& a, const v_float64x2& b) -{ - fallback::v_float64x2 a_(a), b_(b); - return fallback::v_round(a_, b_); -} - -#define OPENCV_HAL_IMPL_WASM_TRANSPOSE4x4(_Tpvec, suffix) \ -inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \ - const _Tpvec& a2, const _Tpvec& a3, \ - _Tpvec& b0, _Tpvec& b1, \ - _Tpvec& b2, _Tpvec& b3) \ -{ \ - v128_t t0 = wasm_unpacklo_##suffix(a0.val, a1.val); \ - v128_t t1 = wasm_unpacklo_##suffix(a2.val, a3.val); \ - v128_t t2 = wasm_unpackhi_##suffix(a0.val, a1.val); \ - v128_t t3 = wasm_unpackhi_##suffix(a2.val, a3.val); \ -\ - b0.val = wasm_unpacklo_i64x2(t0, t1); \ - b1.val = wasm_unpackhi_i64x2(t0, t1); \ - b2.val = wasm_unpacklo_i64x2(t2, t3); \ - b3.val = wasm_unpackhi_i64x2(t2, t3); \ -} - -OPENCV_HAL_IMPL_WASM_TRANSPOSE4x4(v_uint32x4, i32x4) -OPENCV_HAL_IMPL_WASM_TRANSPOSE4x4(v_int32x4, i32x4) -OPENCV_HAL_IMPL_WASM_TRANSPOSE4x4(v_float32x4, i32x4) - -// load deinterleave -inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b) -{ - v128_t t00 = wasm_v128_load(ptr); - v128_t t01 = wasm_v128_load(ptr + 16); - - a.val = wasm_v8x16_shuffle(t00, t01, 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30); - b.val = wasm_v8x16_shuffle(t00, t01, 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31); -} - -inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c) -{ - v128_t t00 = wasm_v128_load(ptr); - v128_t t01 = wasm_v128_load(ptr + 16); - v128_t t02 = wasm_v128_load(ptr + 32); - - v128_t t10 = wasm_v8x16_shuffle(t00, t01, 0,3,6,9,12,15,18,21,24,27,30,1,2,4,5,7); - v128_t t11 = wasm_v8x16_shuffle(t00, t01, 1,4,7,10,13,16,19,22,25,28,31,0,2,3,5,6); - v128_t t12 = wasm_v8x16_shuffle(t00, t01, 2,5,8,11,14,17,20,23,26,29,0,1,3,4,6,7); - - a.val = wasm_v8x16_shuffle(t10, t02, 0,1,2,3,4,5,6,7,8,9,10,17,20,23,26,29); - b.val = wasm_v8x16_shuffle(t11, t02, 0,1,2,3,4,5,6,7,8,9,10,18,21,24,27,30); - c.val = wasm_v8x16_shuffle(t12, t02, 0,1,2,3,4,5,6,7,8,9,16,19,22,25,28,31); -} - -inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c, v_uint8x16& d) -{ - v128_t u0 = wasm_v128_load(ptr); // a0 b0 c0 d0 a1 b1 c1 d1 ... - v128_t u1 = wasm_v128_load(ptr + 16); // a4 b4 c4 d4 ... - v128_t u2 = wasm_v128_load(ptr + 32); // a8 b8 c8 d8 ... - v128_t u3 = wasm_v128_load(ptr + 48); // a12 b12 c12 d12 ... - - v128_t v0 = wasm_v8x16_shuffle(u0, u1, 0,4,8,12,16,20,24,28,1,5,9,13,17,21,25,29); - v128_t v1 = wasm_v8x16_shuffle(u2, u3, 0,4,8,12,16,20,24,28,1,5,9,13,17,21,25,29); - v128_t v2 = wasm_v8x16_shuffle(u0, u1, 2,6,10,14,18,22,26,30,3,7,11,15,19,23,27,31); - v128_t v3 = wasm_v8x16_shuffle(u2, u3, 2,6,10,14,18,22,26,30,3,7,11,15,19,23,27,31); - - a.val = wasm_v8x16_shuffle(v0, v1, 0,1,2,3,4,5,6,7,16,17,18,19,20,21,22,23); - b.val = wasm_v8x16_shuffle(v0, v1, 8,9,10,11,12,13,14,15,24,25,26,27,28,29,30,31); - c.val = wasm_v8x16_shuffle(v2, v3, 0,1,2,3,4,5,6,7,16,17,18,19,20,21,22,23); - d.val = wasm_v8x16_shuffle(v2, v3, 8,9,10,11,12,13,14,15,24,25,26,27,28,29,30,31); -} - -inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b) -{ - v128_t v0 = wasm_v128_load(ptr); // a0 b0 a1 b1 a2 b2 a3 b3 - v128_t v1 = wasm_v128_load(ptr + 8); // a4 b4 a5 b5 a6 b6 a7 b7 - - a.val = wasm_v8x16_shuffle(v0, v1, 0,1,4,5,8,9,12,13,16,17,20,21,24,25,28,29); // a0 a1 a2 a3 a4 a5 a6 a7 - b.val = wasm_v8x16_shuffle(v0, v1, 2,3,6,7,10,11,14,15,18,19,22,23,26,27,30,31); // b0 b1 ab b3 b4 b5 b6 b7 -} - -inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c) -{ - v128_t t00 = wasm_v128_load(ptr); // a0 b0 c0 a1 b1 c1 a2 b2 - v128_t t01 = wasm_v128_load(ptr + 8); // c2 a3 b3 c3 a4 b4 c4 a5 - v128_t t02 = wasm_v128_load(ptr + 16); // b5 c5 a6 b6 c6 a7 b7 c7 - - v128_t t10 = wasm_v8x16_shuffle(t00, t01, 0,1,6,7,12,13,18,19,24,25,30,31,2,3,4,5); - v128_t t11 = wasm_v8x16_shuffle(t00, t01, 2,3,8,9,14,15,20,21,26,27,0,1,4,5,6,7); - v128_t t12 = wasm_v8x16_shuffle(t00, t01, 4,5,10,11,16,17,22,23,28,29,0,1,2,3,6,7); - - a.val = wasm_v8x16_shuffle(t10, t02, 0,1,2,3,4,5,6,7,8,9,10,11,20,21,26,27); - b.val = wasm_v8x16_shuffle(t11, t02, 0,1,2,3,4,5,6,7,8,9,16,17,22,23,28,29); - c.val = wasm_v8x16_shuffle(t12, t02, 0,1,2,3,4,5,6,7,8,9,18,19,24,25,30,31); -} - -inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c, v_uint16x8& d) -{ - v128_t u0 = wasm_v128_load(ptr); // a0 b0 c0 d0 a1 b1 c1 d1 - v128_t u1 = wasm_v128_load(ptr + 8); // a2 b2 c2 d2 ... - v128_t u2 = wasm_v128_load(ptr + 16); // a4 b4 c4 d4 ... - v128_t u3 = wasm_v128_load(ptr + 24); // a6 b6 c6 d6 ... - - v128_t v0 = wasm_v8x16_shuffle(u0, u1, 0,1,8,9,16,17,24,25,2,3,10,11,18,19,26,27); // a0 a1 a2 a3 b0 b1 b2 b3 - v128_t v1 = wasm_v8x16_shuffle(u2, u3, 0,1,8,9,16,17,24,25,2,3,10,11,18,19,26,27); // a4 a5 a6 a7 b4 b5 b6 b7 - v128_t v2 = wasm_v8x16_shuffle(u0, u1, 4,5,12,13,20,21,28,29,6,7,14,15,22,23,30,31); // c0 c1 c2 c3 d0 d1 d2 d3 - v128_t v3 = wasm_v8x16_shuffle(u2, u3, 4,5,12,13,20,21,28,29,6,7,14,15,22,23,30,31); // c4 c5 c6 c7 d4 d5 d6 d7 - - a.val = wasm_v8x16_shuffle(v0, v1, 0,1,2,3,4,5,6,7,16,17,18,19,20,21,22,23); - b.val = wasm_v8x16_shuffle(v0, v1, 8,9,10,11,12,13,14,15,24,25,26,27,28,29,30,31); - c.val = wasm_v8x16_shuffle(v2, v3, 0,1,2,3,4,5,6,7,16,17,18,19,20,21,22,23); - d.val = wasm_v8x16_shuffle(v2, v3, 8,9,10,11,12,13,14,15,24,25,26,27,28,29,30,31); -} - -inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b) -{ - v128_t v0 = wasm_v128_load(ptr); // a0 b0 a1 b1 - v128_t v1 = wasm_v128_load(ptr + 4); // a2 b2 a3 b3 - - a.val = wasm_v8x16_shuffle(v0, v1, 0,1,2,3,8,9,10,11,16,17,18,19,24,25,26,27); // a0 a1 a2 a3 - b.val = wasm_v8x16_shuffle(v0, v1, 4,5,6,7,12,13,14,15,20,21,22,23,28,29,30,31); // b0 b1 b2 b3 -} - -inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c) -{ - v128_t t00 = wasm_v128_load(ptr); // a0 b0 c0 a1 - v128_t t01 = wasm_v128_load(ptr + 4); // b2 c2 a3 b3 - v128_t t02 = wasm_v128_load(ptr + 8); // c3 a4 b4 c4 - - v128_t t10 = wasm_v8x16_shuffle(t00, t01, 0,1,2,3,12,13,14,15,24,25,26,27,4,5,6,7); - v128_t t11 = wasm_v8x16_shuffle(t00, t01, 4,5,6,7,16,17,18,19,28,29,30,31,0,1,2,3); - v128_t t12 = wasm_v8x16_shuffle(t00, t01, 8,9,10,11,20,21,22,23,0,1,2,3,4,5,6,7); - - a.val = wasm_v8x16_shuffle(t10, t02, 0,1,2,3,4,5,6,7,8,9,10,11,20,21,22,23); - b.val = wasm_v8x16_shuffle(t11, t02, 0,1,2,3,4,5,6,7,8,9,10,11,24,25,26,27); - c.val = wasm_v8x16_shuffle(t12, t02, 0,1,2,3,4,5,6,7,16,17,18,19,28,29,30,31); -} - -inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c, v_uint32x4& d) -{ - v_uint32x4 s0(wasm_v128_load(ptr)); // a0 b0 c0 d0 - v_uint32x4 s1(wasm_v128_load(ptr + 4)); // a1 b1 c1 d1 - v_uint32x4 s2(wasm_v128_load(ptr + 8)); // a2 b2 c2 d2 - v_uint32x4 s3(wasm_v128_load(ptr + 12)); // a3 b3 c3 d3 - - v_transpose4x4(s0, s1, s2, s3, a, b, c, d); -} - -inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b) -{ - v128_t v0 = wasm_v128_load(ptr); // a0 b0 a1 b1 - v128_t v1 = wasm_v128_load((ptr + 4)); // a2 b2 a3 b3 - - a.val = wasm_v8x16_shuffle(v0, v1, 0,1,2,3,8,9,10,11,16,17,18,19,24,25,26,27); // a0 a1 a2 a3 - b.val = wasm_v8x16_shuffle(v0, v1, 4,5,6,7,12,13,14,15,20,21,22,23,28,29,30,31); // b0 b1 b2 b3 -} - -inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b, v_float32x4& c) -{ - v128_t t00 = wasm_v128_load(ptr); // a0 b0 c0 a1 - v128_t t01 = wasm_v128_load(ptr + 4); // b2 c2 a3 b3 - v128_t t02 = wasm_v128_load(ptr + 8); // c3 a4 b4 c4 - - v128_t t10 = wasm_v8x16_shuffle(t00, t01, 0,1,2,3,12,13,14,15,24,25,26,27,4,5,6,7); - v128_t t11 = wasm_v8x16_shuffle(t00, t01, 4,5,6,7,16,17,18,19,28,29,30,31,0,1,2,3); - v128_t t12 = wasm_v8x16_shuffle(t00, t01, 8,9,10,11,20,21,22,23,0,1,2,3,4,5,6,7); - - a.val = wasm_v8x16_shuffle(t10, t02, 0,1,2,3,4,5,6,7,8,9,10,11,20,21,22,23); - b.val = wasm_v8x16_shuffle(t11, t02, 0,1,2,3,4,5,6,7,8,9,10,11,24,25,26,27); - c.val = wasm_v8x16_shuffle(t12, t02, 0,1,2,3,4,5,6,7,16,17,18,19,28,29,30,31); -} - -inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b, v_float32x4& c, v_float32x4& d) -{ - v_float32x4 s0(wasm_v128_load(ptr)); // a0 b0 c0 d0 - v_float32x4 s1(wasm_v128_load(ptr + 4)); // a1 b1 c1 d1 - v_float32x4 s2(wasm_v128_load(ptr + 8)); // a2 b2 c2 d2 - v_float32x4 s3(wasm_v128_load(ptr + 12)); // a3 b3 c3 d3 - - v_transpose4x4(s0, s1, s2, s3, a, b, c, d); -} - -inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, v_uint64x2& b) -{ - v128_t t0 = wasm_v128_load(ptr); // a0 b0 - v128_t t1 = wasm_v128_load(ptr + 2); // a1 b1 - - a.val = wasm_unpacklo_i64x2(t0, t1); - b.val = wasm_unpackhi_i64x2(t0, t1); -} - -inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, v_uint64x2& b, v_uint64x2& c) -{ - v128_t t0 = wasm_v128_load(ptr); // a0, b0 - v128_t t1 = wasm_v128_load(ptr + 2); // c0, a1 - v128_t t2 = wasm_v128_load(ptr + 4); // b1, c1 - - a.val = wasm_v8x16_shuffle(t0, t1, 0,1,2,3,4,5,6,7,24,25,26,27,28,29,30,31); - b.val = wasm_v8x16_shuffle(t0, t2, 8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23); - c.val = wasm_v8x16_shuffle(t1, t2, 0,1,2,3,4,5,6,7,24,25,26,27,28,29,30,31); -} - -inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, - v_uint64x2& b, v_uint64x2& c, v_uint64x2& d) -{ - v128_t t0 = wasm_v128_load(ptr); // a0 b0 - v128_t t1 = wasm_v128_load(ptr + 2); // c0 d0 - v128_t t2 = wasm_v128_load(ptr + 4); // a1 b1 - v128_t t3 = wasm_v128_load(ptr + 6); // c1 d1 - - a.val = wasm_unpacklo_i64x2(t0, t2); - b.val = wasm_unpackhi_i64x2(t0, t2); - c.val = wasm_unpacklo_i64x2(t1, t3); - d.val = wasm_unpackhi_i64x2(t1, t3); -} - -// store interleave - -inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, - hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED) -{ - v128_t v0 = wasm_unpacklo_i8x16(a.val, b.val); - v128_t v1 = wasm_unpackhi_i8x16(a.val, b.val); - - wasm_v128_store(ptr, v0); - wasm_v128_store(ptr + 16, v1); -} - -inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, - const v_uint8x16& c, hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED) -{ - v128_t t00 = wasm_v8x16_shuffle(a.val, b.val, 0,16,0,1,17,0,2,18,0,3,19,0,4,20,0,5); - v128_t t01 = wasm_v8x16_shuffle(a.val, b.val, 21,0,6,22,0,7,23,0,8,24,0,9,25,0,10,26); - v128_t t02 = wasm_v8x16_shuffle(a.val, b.val, 0,11,27,0,12,28,0,13,29,0,14,30,0,15,31,0); - - v128_t t10 = wasm_v8x16_shuffle(t00, c.val, 0,1,16,3,4,17,6,7,18,9,10,19,12,13,20,15); - v128_t t11 = wasm_v8x16_shuffle(t01, c.val, 0,21,2,3,22,5,6,23,8,9,24,11,12,25,14,15); - v128_t t12 = wasm_v8x16_shuffle(t02, c.val, 26,1,2,27,4,5,28,7,8,29,10,11,30,13,14,31); - - wasm_v128_store(ptr, t10); - wasm_v128_store(ptr + 16, t11); - wasm_v128_store(ptr + 32, t12); -} - -inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b, - const v_uint8x16& c, const v_uint8x16& d, - hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED) -{ - // a0 a1 a2 a3 .... - // b0 b1 b2 b3 .... - // c0 c1 c2 c3 .... - // d0 d1 d2 d3 .... - v128_t u0 = wasm_unpacklo_i8x16(a.val, c.val); // a0 c0 a1 c1 ... - v128_t u1 = wasm_unpackhi_i8x16(a.val, c.val); // a8 c8 a9 c9 ... - v128_t u2 = wasm_unpacklo_i8x16(b.val, d.val); // b0 d0 b1 d1 ... - v128_t u3 = wasm_unpackhi_i8x16(b.val, d.val); // b8 d8 b9 d9 ... - - v128_t v0 = wasm_unpacklo_i8x16(u0, u2); // a0 b0 c0 d0 ... - v128_t v1 = wasm_unpackhi_i8x16(u0, u2); // a4 b4 c4 d4 ... - v128_t v2 = wasm_unpacklo_i8x16(u1, u3); // a8 b8 c8 d8 ... - v128_t v3 = wasm_unpackhi_i8x16(u1, u3); // a12 b12 c12 d12 ... - - wasm_v128_store(ptr, v0); - wasm_v128_store(ptr + 16, v1); - wasm_v128_store(ptr + 32, v2); - wasm_v128_store(ptr + 48, v3); -} - -inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, const v_uint16x8& b, - hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED) -{ - v128_t v0 = wasm_unpacklo_i16x8(a.val, b.val); - v128_t v1 = wasm_unpackhi_i16x8(a.val, b.val); - - wasm_v128_store(ptr, v0); - wasm_v128_store(ptr + 8, v1); -} - -inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, - const v_uint16x8& b, const v_uint16x8& c, - hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED) -{ - v128_t t00 = wasm_v8x16_shuffle(a.val, b.val, 0,1,16,17,0,0,2,3,18,19,0,0,4,5,20,21); - v128_t t01 = wasm_v8x16_shuffle(a.val, b.val, 0,0,6,7,22,23,0,0,8,9,24,25,0,0,10,11); - v128_t t02 = wasm_v8x16_shuffle(a.val, b.val, 26,27,0,0,12,13,28,29,0,0,14,15,30,31,0,0); - - v128_t t10 = wasm_v8x16_shuffle(t00, c.val, 0,1,2,3,16,17,6,7,8,9,18,19,12,13,14,15); - v128_t t11 = wasm_v8x16_shuffle(t01, c.val, 20,21,2,3,4,5,22,23,8,9,10,11,24,25,14,15); - v128_t t12 = wasm_v8x16_shuffle(t02, c.val, 0,1,26,27,4,5,6,7,28,29,10,11,12,13,30,31); - - wasm_v128_store(ptr, t10); - wasm_v128_store(ptr + 8, t11); - wasm_v128_store(ptr + 16, t12); -} - -inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, const v_uint16x8& b, - const v_uint16x8& c, const v_uint16x8& d, - hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED) -{ - // a0 a1 a2 a3 .... - // b0 b1 b2 b3 .... - // c0 c1 c2 c3 .... - // d0 d1 d2 d3 .... - v128_t u0 = wasm_unpacklo_i16x8(a.val, c.val); // a0 c0 a1 c1 ... - v128_t u1 = wasm_unpackhi_i16x8(a.val, c.val); // a4 c4 a5 c5 ... - v128_t u2 = wasm_unpacklo_i16x8(b.val, d.val); // b0 d0 b1 d1 ... - v128_t u3 = wasm_unpackhi_i16x8(b.val, d.val); // b4 d4 b5 d5 ... - - v128_t v0 = wasm_unpacklo_i16x8(u0, u2); // a0 b0 c0 d0 ... - v128_t v1 = wasm_unpackhi_i16x8(u0, u2); // a2 b2 c2 d2 ... - v128_t v2 = wasm_unpacklo_i16x8(u1, u3); // a4 b4 c4 d4 ... - v128_t v3 = wasm_unpackhi_i16x8(u1, u3); // a6 b6 c6 d6 ... - - wasm_v128_store(ptr, v0); - wasm_v128_store(ptr + 8, v1); - wasm_v128_store(ptr + 16, v2); - wasm_v128_store(ptr + 24, v3); -} - -inline void v_store_interleave( unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, - hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED) -{ - v128_t v0 = wasm_unpacklo_i32x4(a.val, b.val); - v128_t v1 = wasm_unpackhi_i32x4(a.val, b.val); - - wasm_v128_store(ptr, v0); - wasm_v128_store(ptr + 4, v1); -} - -inline void v_store_interleave( unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, - const v_uint32x4& c, hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED) -{ - v128_t t00 = wasm_v8x16_shuffle(a.val, b.val, 0,1,2,3,16,17,18,19,0,0,0,0,4,5,6,7); - v128_t t01 = wasm_v8x16_shuffle(a.val, b.val, 20,21,22,23,0,0,0,0,8,9,10,11,24,25,26,27); - v128_t t02 = wasm_v8x16_shuffle(a.val, b.val, 0,0,0,0,12,13,14,15,28,29,30,31,0,0,0,0); - - v128_t t10 = wasm_v8x16_shuffle(t00, c.val, 0,1,2,3,4,5,6,7,16,17,18,19,12,13,14,15); - v128_t t11 = wasm_v8x16_shuffle(t01, c.val, 0,1,2,3,20,21,22,23,8,9,10,11,12,13,14,15); - v128_t t12 = wasm_v8x16_shuffle(t02, c.val, 24,25,26,27,4,5,6,7,8,9,10,11,28,29,30,31); - - wasm_v128_store(ptr, t10); - wasm_v128_store(ptr + 4, t11); - wasm_v128_store(ptr + 8, t12); -} - -inline void v_store_interleave(unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b, - const v_uint32x4& c, const v_uint32x4& d, - hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED) -{ - v_uint32x4 v0, v1, v2, v3; - v_transpose4x4(a, b, c, d, v0, v1, v2, v3); - - wasm_v128_store(ptr, v0.val); - wasm_v128_store(ptr + 4, v1.val); - wasm_v128_store(ptr + 8, v2.val); - wasm_v128_store(ptr + 12, v3.val); -} - -// 2-channel, float only -inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b, - hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED) -{ - v128_t v0 = wasm_unpacklo_i32x4(a.val, b.val); - v128_t v1 = wasm_unpackhi_i32x4(a.val, b.val); - - wasm_v128_store(ptr, v0); - wasm_v128_store(ptr + 4, v1); -} - -inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b, - const v_float32x4& c, hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED) -{ - v128_t t00 = wasm_v8x16_shuffle(a.val, b.val, 0,1,2,3,16,17,18,19,0,0,0,0,4,5,6,7); - v128_t t01 = wasm_v8x16_shuffle(a.val, b.val, 20,21,22,23,0,0,0,0,8,9,10,11,24,25,26,27); - v128_t t02 = wasm_v8x16_shuffle(a.val, b.val, 0,0,0,0,12,13,14,15,28,29,30,31,0,0,0,0); - - v128_t t10 = wasm_v8x16_shuffle(t00, c.val, 0,1,2,3,4,5,6,7,16,17,18,19,12,13,14,15); - v128_t t11 = wasm_v8x16_shuffle(t01, c.val, 0,1,2,3,20,21,22,23,8,9,10,11,12,13,14,15); - v128_t t12 = wasm_v8x16_shuffle(t02, c.val, 24,25,26,27,4,5,6,7,8,9,10,11,28,29,30,31); - - wasm_v128_store(ptr, t10); - wasm_v128_store(ptr + 4, t11); - wasm_v128_store(ptr + 8, t12); -} - -inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b, - const v_float32x4& c, const v_float32x4& d, - hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED) -{ - v_float32x4 v0, v1, v2, v3; - v_transpose4x4(a, b, c, d, v0, v1, v2, v3); - - wasm_v128_store(ptr, v0.val); - wasm_v128_store(ptr + 4, v1.val); - wasm_v128_store(ptr + 8, v2.val); - wasm_v128_store(ptr + 12, v3.val); -} - -inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b, - hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED) -{ - v128_t v0 = wasm_unpacklo_i64x2(a.val, b.val); - v128_t v1 = wasm_unpackhi_i64x2(a.val, b.val); - - wasm_v128_store(ptr, v0); - wasm_v128_store(ptr + 2, v1); -} - -inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b, - const v_uint64x2& c, hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED) -{ - v128_t v0 = wasm_v8x16_shuffle(a.val, b.val, 0,1,2,3,4,5,6,7,16,17,18,19,20,21,22,23); - v128_t v1 = wasm_v8x16_shuffle(a.val, c.val, 16,17,18,19,20,21,22,23,8,9,10,11,12,13,14,15); - v128_t v2 = wasm_v8x16_shuffle(b.val, c.val, 8,9,10,11,12,13,14,15,24,25,26,27,28,29,30,31); - - wasm_v128_store(ptr, v0); - wasm_v128_store(ptr + 2, v1); - wasm_v128_store(ptr + 4, v2); -} - -inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b, - const v_uint64x2& c, const v_uint64x2& d, - hal::StoreMode /*mode*/ = hal::STORE_UNALIGNED) -{ - v128_t v0 = wasm_unpacklo_i64x2(a.val, b.val); - v128_t v1 = wasm_unpacklo_i64x2(c.val, d.val); - v128_t v2 = wasm_unpackhi_i64x2(a.val, b.val); - v128_t v3 = wasm_unpackhi_i64x2(c.val, d.val); - - wasm_v128_store(ptr, v0); - wasm_v128_store(ptr + 2, v1); - wasm_v128_store(ptr + 4, v2); - wasm_v128_store(ptr + 6, v3); -} - -#define OPENCV_HAL_IMPL_WASM_LOADSTORE_INTERLEAVE(_Tpvec0, _Tp0, suffix0, _Tpvec1, _Tp1, suffix1) \ -inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0 ) \ -{ \ - _Tpvec1 a1, b1; \ - v_load_deinterleave((const _Tp1*)ptr, a1, b1); \ - a0 = v_reinterpret_as_##suffix0(a1); \ - b0 = v_reinterpret_as_##suffix0(b1); \ -} \ -inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0 ) \ -{ \ - _Tpvec1 a1, b1, c1; \ - v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1); \ - a0 = v_reinterpret_as_##suffix0(a1); \ - b0 = v_reinterpret_as_##suffix0(b1); \ - c0 = v_reinterpret_as_##suffix0(c1); \ -} \ -inline void v_load_deinterleave( const _Tp0* ptr, _Tpvec0& a0, _Tpvec0& b0, _Tpvec0& c0, _Tpvec0& d0 ) \ -{ \ - _Tpvec1 a1, b1, c1, d1; \ - v_load_deinterleave((const _Tp1*)ptr, a1, b1, c1, d1); \ - a0 = v_reinterpret_as_##suffix0(a1); \ - b0 = v_reinterpret_as_##suffix0(b1); \ - c0 = v_reinterpret_as_##suffix0(c1); \ - d0 = v_reinterpret_as_##suffix0(d1); \ -} \ -inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ - hal::StoreMode mode = hal::STORE_UNALIGNED ) \ -{ \ - _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ - _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ - v_store_interleave((_Tp1*)ptr, a1, b1, mode); \ -} \ -inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ - const _Tpvec0& c0, hal::StoreMode mode = hal::STORE_UNALIGNED ) \ -{ \ - _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ - _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ - _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ - v_store_interleave((_Tp1*)ptr, a1, b1, c1, mode); \ -} \ -inline void v_store_interleave( _Tp0* ptr, const _Tpvec0& a0, const _Tpvec0& b0, \ - const _Tpvec0& c0, const _Tpvec0& d0, \ - hal::StoreMode mode = hal::STORE_UNALIGNED ) \ -{ \ - _Tpvec1 a1 = v_reinterpret_as_##suffix1(a0); \ - _Tpvec1 b1 = v_reinterpret_as_##suffix1(b0); \ - _Tpvec1 c1 = v_reinterpret_as_##suffix1(c0); \ - _Tpvec1 d1 = v_reinterpret_as_##suffix1(d0); \ - v_store_interleave((_Tp1*)ptr, a1, b1, c1, d1, mode); \ -} - -OPENCV_HAL_IMPL_WASM_LOADSTORE_INTERLEAVE(v_int8x16, schar, s8, v_uint8x16, uchar, u8) -OPENCV_HAL_IMPL_WASM_LOADSTORE_INTERLEAVE(v_int16x8, short, s16, v_uint16x8, ushort, u16) -OPENCV_HAL_IMPL_WASM_LOADSTORE_INTERLEAVE(v_int32x4, int, s32, v_uint32x4, unsigned, u32) -OPENCV_HAL_IMPL_WASM_LOADSTORE_INTERLEAVE(v_int64x2, int64, s64, v_uint64x2, uint64, u64) -OPENCV_HAL_IMPL_WASM_LOADSTORE_INTERLEAVE(v_float64x2, double, f64, v_uint64x2, uint64, u64) - -inline v_float32x4 v_cvt_f32(const v_int32x4& a) -{ - return v_float32x4(wasm_f32x4_convert_i32x4(a.val)); -} - -inline v_float32x4 v_cvt_f32(const v_float64x2& a) -{ - fallback::v_float64x2 a_(a); - return fallback::v_cvt_f32(a_); -} - -inline v_float32x4 v_cvt_f32(const v_float64x2& a, const v_float64x2& b) -{ - fallback::v_float64x2 a_(a), b_(b); - return fallback::v_cvt_f32(a_, b_); -} - -inline v_float64x2 v_cvt_f64(const v_int32x4& a) -{ -#ifdef __wasm_unimplemented_simd128__ - v128_t p = v128_cvti32x4_i64x2(a.val); - return v_float64x2(wasm_f64x2_convert_i64x2(p)); -#else - fallback::v_int32x4 a_(a); - return fallback::v_cvt_f64(a_); -#endif -} - -inline v_float64x2 v_cvt_f64_high(const v_int32x4& a) -{ -#ifdef __wasm_unimplemented_simd128__ - v128_t p = v128_cvti32x4_i64x2_high(a.val); - return v_float64x2(wasm_f64x2_convert_i64x2(p)); -#else - fallback::v_int32x4 a_(a); - return fallback::v_cvt_f64_high(a_); -#endif -} - -inline v_float64x2 v_cvt_f64(const v_float32x4& a) -{ - fallback::v_float32x4 a_(a); - return fallback::v_cvt_f64(a_); -} - -inline v_float64x2 v_cvt_f64_high(const v_float32x4& a) -{ - fallback::v_float32x4 a_(a); - return fallback::v_cvt_f64_high(a_); -} - -inline v_float64x2 v_cvt_f64(const v_int64x2& a) -{ -#ifdef __wasm_unimplemented_simd128__ - return v_float64x2(wasm_f64x2_convert_i64x2(a.val)); -#else - fallback::v_int64x2 a_(a); - return fallback::v_cvt_f64(a_); -#endif -} - -////////////// Lookup table access //////////////////// - -inline v_int8x16 v_lut(const schar* tab, const int* idx) -{ - return v_int8x16(tab[idx[0]], tab[idx[1]], tab[idx[ 2]], tab[idx[ 3]], tab[idx[ 4]], tab[idx[ 5]], tab[idx[ 6]], tab[idx[ 7]], - tab[idx[8]], tab[idx[9]], tab[idx[10]], tab[idx[11]], tab[idx[12]], tab[idx[13]], tab[idx[14]], tab[idx[15]]); -} -inline v_int8x16 v_lut_pairs(const schar* tab, const int* idx) -{ - return v_int8x16(tab[idx[0]], tab[idx[0]+1], tab[idx[1]], tab[idx[1]+1], tab[idx[2]], tab[idx[2]+1], tab[idx[3]], tab[idx[3]+1], - tab[idx[4]], tab[idx[4]+1], tab[idx[5]], tab[idx[5]+1], tab[idx[6]], tab[idx[6]+1], tab[idx[7]], tab[idx[7]+1]); -} -inline v_int8x16 v_lut_quads(const schar* tab, const int* idx) -{ - return v_int8x16(tab[idx[0]], tab[idx[0]+1], tab[idx[0]+2], tab[idx[0]+3], tab[idx[1]], tab[idx[1]+1], tab[idx[1]+2], tab[idx[1]+3], - tab[idx[2]], tab[idx[2]+1], tab[idx[2]+2], tab[idx[2]+3], tab[idx[3]], tab[idx[3]+1], tab[idx[3]+2], tab[idx[3]+3]); -} -inline v_uint8x16 v_lut(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut((const schar *)tab, idx)); } -inline v_uint8x16 v_lut_pairs(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_pairs((const schar *)tab, idx)); } -inline v_uint8x16 v_lut_quads(const uchar* tab, const int* idx) { return v_reinterpret_as_u8(v_lut_quads((const schar *)tab, idx)); } - -inline v_int16x8 v_lut(const short* tab, const int* idx) -{ - return v_int16x8(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]], - tab[idx[4]], tab[idx[5]], tab[idx[6]], tab[idx[7]]); -} -inline v_int16x8 v_lut_pairs(const short* tab, const int* idx) -{ - return v_int16x8(tab[idx[0]], tab[idx[0]+1], tab[idx[1]], tab[idx[1]+1], - tab[idx[2]], tab[idx[2]+1], tab[idx[3]], tab[idx[3]+1]); -} -inline v_int16x8 v_lut_quads(const short* tab, const int* idx) -{ - return v_int16x8(tab[idx[0]], tab[idx[0]+1], tab[idx[0]+2], tab[idx[0]+3], - tab[idx[1]], tab[idx[1]+1], tab[idx[1]+2], tab[idx[1]+3]); -} -inline v_uint16x8 v_lut(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut((const short *)tab, idx)); } -inline v_uint16x8 v_lut_pairs(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_pairs((const short *)tab, idx)); } -inline v_uint16x8 v_lut_quads(const ushort* tab, const int* idx) { return v_reinterpret_as_u16(v_lut_quads((const short *)tab, idx)); } - -inline v_int32x4 v_lut(const int* tab, const int* idx) -{ - return v_int32x4(tab[idx[0]], tab[idx[1]], - tab[idx[2]], tab[idx[3]]); -} -inline v_int32x4 v_lut_pairs(const int* tab, const int* idx) -{ - return v_int32x4(tab[idx[0]], tab[idx[0]+1], - tab[idx[1]], tab[idx[1]+1]); -} -inline v_int32x4 v_lut_quads(const int* tab, const int* idx) -{ - return v_int32x4(wasm_v128_load(tab + idx[0])); -} -inline v_uint32x4 v_lut(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut((const int *)tab, idx)); } -inline v_uint32x4 v_lut_pairs(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_pairs((const int *)tab, idx)); } -inline v_uint32x4 v_lut_quads(const unsigned* tab, const int* idx) { return v_reinterpret_as_u32(v_lut_quads((const int *)tab, idx)); } - -inline v_int64x2 v_lut(const int64_t* tab, const int* idx) -{ - return v_int64x2(tab[idx[0]], tab[idx[1]]); -} -inline v_int64x2 v_lut_pairs(const int64_t* tab, const int* idx) -{ - return v_int64x2(wasm_v128_load(tab + idx[0])); -} -inline v_uint64x2 v_lut(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut((const int64_t *)tab, idx)); } -inline v_uint64x2 v_lut_pairs(const uint64_t* tab, const int* idx) { return v_reinterpret_as_u64(v_lut_pairs((const int64_t *)tab, idx)); } - -inline v_float32x4 v_lut(const float* tab, const int* idx) -{ - return v_float32x4(tab[idx[0]], tab[idx[1]], tab[idx[2]], tab[idx[3]]); -} -inline v_float32x4 v_lut_pairs(const float* tab, const int* idx) { return v_reinterpret_as_f32(v_lut_pairs((const int *)tab, idx)); } -inline v_float32x4 v_lut_quads(const float* tab, const int* idx) { return v_reinterpret_as_f32(v_lut_quads((const int *)tab, idx)); } - -inline v_float64x2 v_lut(const double* tab, const int* idx) -{ - return v_float64x2(tab[idx[0]], tab[idx[1]]); -} -inline v_float64x2 v_lut_pairs(const double* tab, const int* idx) -{ - return v_float64x2(wasm_v128_load(tab + idx[0])); -} - -inline v_int32x4 v_lut(const int* tab, const v_int32x4& idxvec) -{ - return v_int32x4(tab[wasm_i32x4_extract_lane(idxvec.val, 0)], - tab[wasm_i32x4_extract_lane(idxvec.val, 1)], - tab[wasm_i32x4_extract_lane(idxvec.val, 2)], - tab[wasm_i32x4_extract_lane(idxvec.val, 3)]); -} - -inline v_uint32x4 v_lut(const unsigned* tab, const v_int32x4& idxvec) -{ - return v_reinterpret_as_u32(v_lut((const int *)tab, idxvec)); -} - -inline v_float32x4 v_lut(const float* tab, const v_int32x4& idxvec) -{ - return v_float32x4(tab[wasm_i32x4_extract_lane(idxvec.val, 0)], - tab[wasm_i32x4_extract_lane(idxvec.val, 1)], - tab[wasm_i32x4_extract_lane(idxvec.val, 2)], - tab[wasm_i32x4_extract_lane(idxvec.val, 3)]); -} - -inline v_float64x2 v_lut(const double* tab, const v_int32x4& idxvec) -{ - return v_float64x2(tab[wasm_i32x4_extract_lane(idxvec.val, 0)], - tab[wasm_i32x4_extract_lane(idxvec.val, 1)]); -} - -// loads pairs from the table and deinterleaves them, e.g. returns: -// x = (tab[idxvec[0], tab[idxvec[1]], tab[idxvec[2]], tab[idxvec[3]]), -// y = (tab[idxvec[0]+1], tab[idxvec[1]+1], tab[idxvec[2]+1], tab[idxvec[3]+1]) -// note that the indices are float's indices, not the float-pair indices. -// in theory, this function can be used to implement bilinear interpolation, -// when idxvec are the offsets within the image. -inline void v_lut_deinterleave(const float* tab, const v_int32x4& idxvec, v_float32x4& x, v_float32x4& y) -{ - x = v_float32x4(tab[wasm_i32x4_extract_lane(idxvec.val, 0)], - tab[wasm_i32x4_extract_lane(idxvec.val, 1)], - tab[wasm_i32x4_extract_lane(idxvec.val, 2)], - tab[wasm_i32x4_extract_lane(idxvec.val, 3)]); - y = v_float32x4(tab[wasm_i32x4_extract_lane(idxvec.val, 0)+1], - tab[wasm_i32x4_extract_lane(idxvec.val, 1)+1], - tab[wasm_i32x4_extract_lane(idxvec.val, 2)+1], - tab[wasm_i32x4_extract_lane(idxvec.val, 3)+1]); -} - -inline void v_lut_deinterleave(const double* tab, const v_int32x4& idxvec, v_float64x2& x, v_float64x2& y) -{ - v128_t xy0 = wasm_v128_load(tab + wasm_i32x4_extract_lane(idxvec.val, 0)); - v128_t xy1 = wasm_v128_load(tab + wasm_i32x4_extract_lane(idxvec.val, 1)); - x.val = wasm_unpacklo_i64x2(xy0, xy1); - y.val = wasm_unpacklo_i64x2(xy0, xy1); -} - -inline v_int8x16 v_interleave_pairs(const v_int8x16& vec) -{ - return v_int8x16(wasm_v8x16_shuffle(vec.val, vec.val, 0,2,1,3,4,6,5,7,8,10,9,11,12,14,13,15)); -} -inline v_uint8x16 v_interleave_pairs(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_interleave_pairs(v_reinterpret_as_s8(vec))); } -inline v_int8x16 v_interleave_quads(const v_int8x16& vec) -{ - return v_int8x16(wasm_v8x16_shuffle(vec.val, vec.val, 0,4,1,5,2,6,3,7,8,12,9,13,10,14,11,15)); -} -inline v_uint8x16 v_interleave_quads(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_interleave_quads(v_reinterpret_as_s8(vec))); } - -inline v_int16x8 v_interleave_pairs(const v_int16x8& vec) -{ - return v_int16x8(wasm_v8x16_shuffle(vec.val, vec.val, 0,1,4,5,2,3,6,7,8,9,12,13,10,11,14,15)); -} -inline v_uint16x8 v_interleave_pairs(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_pairs(v_reinterpret_as_s16(vec))); } -inline v_int16x8 v_interleave_quads(const v_int16x8& vec) -{ - return v_int16x8(wasm_v8x16_shuffle(vec.val, vec.val, 0,1,8,9,2,3,10,11,4,5,12,13,6,7,14,15)); -} -inline v_uint16x8 v_interleave_quads(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_interleave_quads(v_reinterpret_as_s16(vec))); } - -inline v_int32x4 v_interleave_pairs(const v_int32x4& vec) -{ - return v_int32x4(wasm_v8x16_shuffle(vec.val, vec.val, 0,1,2,3,8,9,10,11,4,5,6,7,12,13,14,15)); -} -inline v_uint32x4 v_interleave_pairs(const v_uint32x4& vec) { return v_reinterpret_as_u32(v_interleave_pairs(v_reinterpret_as_s32(vec))); } -inline v_float32x4 v_interleave_pairs(const v_float32x4& vec) -{ - return v_float32x4(wasm_v8x16_shuffle(vec.val, vec.val, 0,1,2,3,8,9,10,11,4,5,6,7,12,13,14,15)); -} - -inline v_int8x16 v_pack_triplets(const v_int8x16& vec) -{ - return v_int8x16(wasm_v8x16_shuffle(vec.val, vec.val, 0,1,2,4,5,6,8,9,10,12,13,14,16,16,16,16)); -} -inline v_uint8x16 v_pack_triplets(const v_uint8x16& vec) { return v_reinterpret_as_u8(v_pack_triplets(v_reinterpret_as_s8(vec))); } - -inline v_int16x8 v_pack_triplets(const v_int16x8& vec) -{ - return v_int16x8(wasm_v8x16_shuffle(vec.val, vec.val, 0,1,2,3,4,5,8,9,10,11,12,13,14,15,6,7)); -} -inline v_uint16x8 v_pack_triplets(const v_uint16x8& vec) { return v_reinterpret_as_u16(v_pack_triplets(v_reinterpret_as_s16(vec))); } - -inline v_int32x4 v_pack_triplets(const v_int32x4& vec) { return vec; } -inline v_uint32x4 v_pack_triplets(const v_uint32x4& vec) { return vec; } -inline v_float32x4 v_pack_triplets(const v_float32x4& vec) { return vec; } - -template -inline typename _Tp::lane_type v_extract_n(const _Tp& a) -{ - return v_rotate_right(a).get0(); -} - -template -inline v_uint32x4 v_broadcast_element(const v_uint32x4& a) -{ - return v_setall_u32(v_extract_n(a)); -} -template -inline v_int32x4 v_broadcast_element(const v_int32x4& a) -{ - return v_setall_s32(v_extract_n(a)); -} -template -inline v_float32x4 v_broadcast_element(const v_float32x4& a) -{ - return v_setall_f32(v_extract_n(a)); -} - - -////////////// FP16 support /////////////////////////// - -inline v_float32x4 v_load_expand(const float16_t* ptr) -{ - return fallback::v_load_expand(ptr); -} - -inline void v_pack_store(float16_t* ptr, const v_float32x4& v) -{ - fallback::v_float32x4 v_(v); - fallback::v_pack_store(ptr, v_); -} - -inline void v_cleanup() {} - -CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END - -//! @endcond - -} - -#endif diff --git a/include/opencv2/core/hal/msa_macros.h b/include/opencv2/core/hal/msa_macros.h deleted file mode 100755 index bd6ddb1..0000000 --- a/include/opencv2/core/hal/msa_macros.h +++ /dev/null @@ -1,1558 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_CORE_HAL_MSA_MACROS_H -#define OPENCV_CORE_HAL_MSA_MACROS_H - -#ifdef __mips_msa -#include "msa.h" -#include - -#ifdef __cplusplus -extern "C" { -#endif - -/* Define 64 bits vector types */ -typedef signed char v8i8 __attribute__ ((vector_size(8), aligned(8))); -typedef unsigned char v8u8 __attribute__ ((vector_size(8), aligned(8))); -typedef short v4i16 __attribute__ ((vector_size(8), aligned(8))); -typedef unsigned short v4u16 __attribute__ ((vector_size(8), aligned(8))); -typedef int v2i32 __attribute__ ((vector_size(8), aligned(8))); -typedef unsigned int v2u32 __attribute__ ((vector_size(8), aligned(8))); -typedef long long v1i64 __attribute__ ((vector_size(8), aligned(8))); -typedef unsigned long long v1u64 __attribute__ ((vector_size(8), aligned(8))); -typedef float v2f32 __attribute__ ((vector_size(8), aligned(8))); -typedef double v1f64 __attribute__ ((vector_size(8), aligned(8))); - - -/* Load values from the given memory a 64-bit vector. */ -#define msa_ld1_s8(__a) (*((v8i8*)(__a))) -#define msa_ld1_s16(__a) (*((v4i16*)(__a))) -#define msa_ld1_s32(__a) (*((v2i32*)(__a))) -#define msa_ld1_s64(__a) (*((v1i64*)(__a))) -#define msa_ld1_u8(__a) (*((v8u8*)(__a))) -#define msa_ld1_u16(__a) (*((v4u16*)(__a))) -#define msa_ld1_u32(__a) (*((v2u32*)(__a))) -#define msa_ld1_u64(__a) (*((v1u64*)(__a))) -#define msa_ld1_f32(__a) (*((v2f32*)(__a))) -#define msa_ld1_f64(__a) (*((v1f64*)(__a))) - -/* Load values from the given memory address to a 128-bit vector */ -#define msa_ld1q_s8(__a) ((v16i8)__builtin_msa_ld_b(__a, 0)) -#define msa_ld1q_s16(__a) ((v8i16)__builtin_msa_ld_h(__a, 0)) -#define msa_ld1q_s32(__a) ((v4i32)__builtin_msa_ld_w(__a, 0)) -#define msa_ld1q_s64(__a) ((v2i64)__builtin_msa_ld_d(__a, 0)) -#define msa_ld1q_u8(__a) ((v16u8)__builtin_msa_ld_b(__a, 0)) -#define msa_ld1q_u16(__a) ((v8u16)__builtin_msa_ld_h(__a, 0)) -#define msa_ld1q_u32(__a) ((v4u32)__builtin_msa_ld_w(__a, 0)) -#define msa_ld1q_u64(__a) ((v2u64)__builtin_msa_ld_d(__a, 0)) -#define msa_ld1q_f32(__a) ((v4f32)__builtin_msa_ld_w(__a, 0)) -#define msa_ld1q_f64(__a) ((v2f64)__builtin_msa_ld_d(__a, 0)) - -/* Store 64bits vector elements values to the given memory address. */ -#define msa_st1_s8(__a, __b) (*((v8i8*)(__a)) = __b) -#define msa_st1_s16(__a, __b) (*((v4i16*)(__a)) = __b) -#define msa_st1_s32(__a, __b) (*((v2i32*)(__a)) = __b) -#define msa_st1_s64(__a, __b) (*((v1i64*)(__a)) = __b) -#define msa_st1_u8(__a, __b) (*((v8u8*)(__a)) = __b) -#define msa_st1_u16(__a, __b) (*((v4u16*)(__a)) = __b) -#define msa_st1_u32(__a, __b) (*((v2u32*)(__a)) = __b) -#define msa_st1_u64(__a, __b) (*((v1u64*)(__a)) = __b) -#define msa_st1_f32(__a, __b) (*((v2f32*)(__a)) = __b) -#define msa_st1_f64(__a, __b) (*((v1f64*)(__a)) = __b) - -/* Store the values of elements in the 128 bits vector __a to the given memory address __a. */ -#define msa_st1q_s8(__a, __b) (__builtin_msa_st_b((v16i8)(__b), __a, 0)) -#define msa_st1q_s16(__a, __b) (__builtin_msa_st_h((v8i16)(__b), __a, 0)) -#define msa_st1q_s32(__a, __b) (__builtin_msa_st_w((v4i32)(__b), __a, 0)) -#define msa_st1q_s64(__a, __b) (__builtin_msa_st_d((v2i64)(__b), __a, 0)) -#define msa_st1q_u8(__a, __b) (__builtin_msa_st_b((v16i8)(__b), __a, 0)) -#define msa_st1q_u16(__a, __b) (__builtin_msa_st_h((v8i16)(__b), __a, 0)) -#define msa_st1q_u32(__a, __b) (__builtin_msa_st_w((v4i32)(__b), __a, 0)) -#define msa_st1q_u64(__a, __b) (__builtin_msa_st_d((v2i64)(__b), __a, 0)) -#define msa_st1q_f32(__a, __b) (__builtin_msa_st_w((v4i32)(__b), __a, 0)) -#define msa_st1q_f64(__a, __b) (__builtin_msa_st_d((v2i64)(__b), __a, 0)) - -/* Store the value of the element with the index __c in vector __a to the given memory address __a. */ -#define msa_st1_lane_s8(__a, __b, __c) (*((int8_t*)(__a)) = __b[__c]) -#define msa_st1_lane_s16(__a, __b, __c) (*((int16_t*)(__a)) = __b[__c]) -#define msa_st1_lane_s32(__a, __b, __c) (*((int32_t*)(__a)) = __b[__c]) -#define msa_st1_lane_s64(__a, __b, __c) (*((int64_t*)(__a)) = __b[__c]) -#define msa_st1_lane_u8(__a, __b, __c) (*((uint8_t*)(__a)) = __b[__c]) -#define msa_st1_lane_u16(__a, __b, __c) (*((uint16_t*)(__a)) = __b[__c]) -#define msa_st1_lane_u32(__a, __b, __c) (*((uint32_t*)(__a)) = __b[__c]) -#define msa_st1_lane_u64(__a, __b, __c) (*((uint64_t*)(__a)) = __b[__c]) -#define msa_st1_lane_f32(__a, __b, __c) (*((float*)(__a)) = __b[__c]) -#define msa_st1_lane_f64(__a, __b, __c) (*((double*)(__a)) = __b[__c]) -#define msa_st1q_lane_s8(__a, __b, __c) (*((int8_t*)(__a)) = (int8_t)__builtin_msa_copy_s_b(__b, __c)) -#define msa_st1q_lane_s16(__a, __b, __c) (*((int16_t*)(__a)) = (int16_t)__builtin_msa_copy_s_h(__b, __c)) -#define msa_st1q_lane_s32(__a, __b, __c) (*((int32_t*)(__a)) = __builtin_msa_copy_s_w(__b, __c)) -#define msa_st1q_lane_s64(__a, __b, __c) (*((int64_t*)(__a)) = __builtin_msa_copy_s_d(__b, __c)) -#define msa_st1q_lane_u8(__a, __b, __c) (*((uint8_t*)(__a)) = (uint8_t)__builtin_msa_copy_u_b((v16i8)(__b), __c)) -#define msa_st1q_lane_u16(__a, __b, __c) (*((uint16_t*)(__a)) = (uint16_t)__builtin_msa_copy_u_h((v8i16)(__b), __c)) -#define msa_st1q_lane_u32(__a, __b, __c) (*((uint32_t*)(__a)) = __builtin_msa_copy_u_w((v4i32)(__b), __c)) -#define msa_st1q_lane_u64(__a, __b, __c) (*((uint64_t*)(__a)) = __builtin_msa_copy_u_d((v2i64)(__b), __c)) -#define msa_st1q_lane_f32(__a, __b, __c) (*((float*)(__a)) = __b[__c]) -#define msa_st1q_lane_f64(__a, __b, __c) (*((double*)(__a)) = __b[__c]) - -/* Duplicate elements for 64-bit doubleword vectors */ -#define msa_dup_n_s8(__a) ((v8i8)__builtin_msa_copy_s_d((v2i64)__builtin_msa_fill_b((int32_t)(__a)), 0)) -#define msa_dup_n_s16(__a) ((v4i16)__builtin_msa_copy_s_d((v2i64)__builtin_msa_fill_h((int32_t)(__a)), 0)) -#define msa_dup_n_s32(__a) ((v2i32){__a, __a}) -#define msa_dup_n_s64(__a) ((v1i64){__a}) -#define msa_dup_n_u8(__a) ((v8u8)__builtin_msa_copy_u_d((v2i64)__builtin_msa_fill_b((int32_t)(__a)), 0)) -#define msa_dup_n_u16(__a) ((v4u16)__builtin_msa_copy_u_d((v2i64)__builtin_msa_fill_h((int32_t)(__a)), 0)) -#define msa_dup_n_u32(__a) ((v2u32){__a, __a}) -#define msa_dup_n_u64(__a) ((v1u64){__a}) -#define msa_dup_n_f32(__a) ((v2f32){__a, __a}) -#define msa_dup_n_f64(__a) ((v1f64){__a}) - -/* Duplicate elements for 128-bit quadword vectors */ -#define msa_dupq_n_s8(__a) (__builtin_msa_fill_b((int32_t)(__a))) -#define msa_dupq_n_s16(__a) (__builtin_msa_fill_h((int32_t)(__a))) -#define msa_dupq_n_s32(__a) (__builtin_msa_fill_w((int32_t)(__a))) -#define msa_dupq_n_s64(__a) (__builtin_msa_fill_d((int64_t)(__a))) -#define msa_dupq_n_u8(__a) ((v16u8)__builtin_msa_fill_b((int32_t)(__a))) -#define msa_dupq_n_u16(__a) ((v8u16)__builtin_msa_fill_h((int32_t)(__a))) -#define msa_dupq_n_u32(__a) ((v4u32)__builtin_msa_fill_w((int32_t)(__a))) -#define msa_dupq_n_u64(__a) ((v2u64)__builtin_msa_fill_d((int64_t)(__a))) -#define msa_dupq_n_f32(__a) ((v4f32){__a, __a, __a, __a}) -#define msa_dupq_n_f64(__a) ((v2f64){__a, __a}) -#define msa_dupq_lane_s8(__a, __b) (__builtin_msa_splat_b(__a, __b)) -#define msa_dupq_lane_s16(__a, __b) (__builtin_msa_splat_h(__a, __b)) -#define msa_dupq_lane_s32(__a, __b) (__builtin_msa_splat_w(__a, __b)) -#define msa_dupq_lane_s64(__a, __b) (__builtin_msa_splat_d(__a, __b)) -#define msa_dupq_lane_u8(__a, __b) ((v16u8)__builtin_msa_splat_b((v16i8)(__a), __b)) -#define msa_dupq_lane_u16(__a, __b) ((v8u16)__builtin_msa_splat_h((v8i16)(__a), __b)) -#define msa_dupq_lane_u32(__a, __b) ((v4u32)__builtin_msa_splat_w((v4i32)(__a), __b)) -#define msa_dupq_lane_u64(__a, __b) ((v2u64)__builtin_msa_splat_d((v2i64)(__a), __b)) - -/* Create a 64 bits vector */ -#define msa_create_s8(__a) ((v8i8)((uint64_t)(__a))) -#define msa_create_s16(__a) ((v4i16)((uint64_t)(__a))) -#define msa_create_s32(__a) ((v2i32)((uint64_t)(__a))) -#define msa_create_s64(__a) ((v1i64)((uint64_t)(__a))) -#define msa_create_u8(__a) ((v8u8)((uint64_t)(__a))) -#define msa_create_u16(__a) ((v4u16)((uint64_t)(__a))) -#define msa_create_u32(__a) ((v2u32)((uint64_t)(__a))) -#define msa_create_u64(__a) ((v1u64)((uint64_t)(__a))) -#define msa_create_f32(__a) ((v2f32)((uint64_t)(__a))) -#define msa_create_f64(__a) ((v1f64)((uint64_t)(__a))) - -/* Sign extends or zero extends each element in a 64 bits vector to twice its original length, and places the results in a 128 bits vector. */ -/*Transform v8i8 to v8i16*/ -#define msa_movl_s8(__a) \ -((v8i16){(__a)[0], (__a)[1], (__a)[2], (__a)[3], \ - (__a)[4], (__a)[5], (__a)[6], (__a)[7]}) - -/*Transform v8u8 to v8u16*/ -#define msa_movl_u8(__a) \ -((v8u16){(__a)[0], (__a)[1], (__a)[2], (__a)[3], \ - (__a)[4], (__a)[5], (__a)[6], (__a)[7]}) - -/*Transform v4i16 to v8i16*/ -#define msa_movl_s16(__a) ((v4i32){(__a)[0], (__a)[1], (__a)[2], (__a)[3]}) - -/*Transform v2i32 to v4i32*/ -#define msa_movl_s32(__a) ((v2i64){(__a)[0], (__a)[1]}) - -/*Transform v4u16 to v8u16*/ -#define msa_movl_u16(__a) ((v4u32){(__a)[0], (__a)[1], (__a)[2], (__a)[3]}) - -/*Transform v2u32 to v4u32*/ -#define msa_movl_u32(__a) ((v2u64){(__a)[0], (__a)[1]}) - -/* Copies the least significant half of each element of a 128 bits vector into the corresponding elements of a 64 bits vector. */ -#define msa_movn_s16(__a) \ -({ \ - v16i8 __d = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)(__a)); \ - (v8i8)__builtin_msa_copy_s_d((v2i64)__d, 0); \ -}) - -#define msa_movn_s32(__a) \ -({ \ - v8i16 __d = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)(__a)); \ - (v4i16)__builtin_msa_copy_s_d((v2i64)__d, 0); \ -}) - -#define msa_movn_s64(__a) \ -({ \ - v4i32 __d = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)(__a)); \ - (v2i32)__builtin_msa_copy_s_d((v2i64)__d, 0); \ -}) - -#define msa_movn_u16(__a) \ -({ \ - v16i8 __d = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)(__a)); \ - (v8u8)__builtin_msa_copy_u_d((v2i64)__d, 0); \ -}) - -#define msa_movn_u32(__a) \ -({ \ - v8i16 __d = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)(__a)); \ - (v4u16)__builtin_msa_copy_u_d((v2i64)__d, 0); \ -}) - -#define msa_movn_u64(__a) \ -({ \ - v4i32 __d = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)(__a)); \ - (v2u32)__builtin_msa_copy_u_d((v2i64)__d, 0); \ -}) - -/* qmovn */ -#define msa_qmovn_s16(__a) \ -({ \ - v16i8 __d = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__builtin_msa_sat_s_h((v8i16)(__a), 7)); \ - (v8i8)__builtin_msa_copy_s_d((v2i64)__d, 0); \ -}) - -#define msa_qmovn_s32(__a) \ -({ \ - v8i16 __d = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__builtin_msa_sat_s_w((v4i32)(__a), 15)); \ - (v4i16)__builtin_msa_copy_s_d((v2i64)__d, 0); \ -}) - -#define msa_qmovn_s64(__a) \ -({ \ - v4i32 __d = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__builtin_msa_sat_s_d((v2i64)(__a), 31)); \ - (v2i32)__builtin_msa_copy_s_d((v2i64)__d, 0); \ -}) - -#define msa_qmovn_u16(__a) \ -({ \ - v16i8 __d = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__builtin_msa_sat_u_h((v8u16)(__a), 7)); \ - (v8u8)__builtin_msa_copy_u_d((v2i64)__d, 0); \ -}) - -#define msa_qmovn_u32(__a) \ -({ \ - v8i16 __d = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__builtin_msa_sat_u_w((v4u32)(__a), 15)); \ - (v4u16)__builtin_msa_copy_u_d((v2i64)__d, 0); \ -}) - -#define msa_qmovn_u64(__a) \ -({ \ - v4i32 __d = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__builtin_msa_sat_u_d((v2u64)(__a), 31)); \ - (v2u32)__builtin_msa_copy_u_d((v2i64)__d, 0); \ -}) - -/* qmovun */ -#define msa_qmovun_s16(__a) \ -({ \ - v8i16 __d = __builtin_msa_max_s_h(__builtin_msa_fill_h(0), (v8i16)(__a)); \ - v16i8 __e = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__builtin_msa_sat_u_h((v8u16)__d, 7)); \ - (v8u8)__builtin_msa_copy_u_d((v2i64)__e, 0); \ -}) - -#define msa_qmovun_s32(__a) \ -({ \ - v4i32 __d = __builtin_msa_max_s_w(__builtin_msa_fill_w(0), (v4i32)(__a)); \ - v8i16 __e = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__builtin_msa_sat_u_w((v4u32)__d, 15)); \ - (v4u16)__builtin_msa_copy_u_d((v2i64)__e, 0); \ -}) - -#define msa_qmovun_s64(__a) \ -({ \ - v2i64 __d = __builtin_msa_max_s_d(__builtin_msa_fill_d(0), (v2i64)(__a)); \ - v4i32 __e = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__builtin_msa_sat_u_d((v2u64)__d, 31)); \ - (v2u32)__builtin_msa_copy_u_d((v2i64)__e, 0); \ -}) - -/* Right shift elements in a 128 bits vector by an immediate value, and places the results in a 64 bits vector. */ -#define msa_shrn_n_s16(__a, __b) \ -({ \ - v16i8 __d = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__builtin_msa_srai_h((v8i16)(__a), (int)(__b))); \ - (v8i8)__builtin_msa_copy_s_d((v2i64)__d, 0); \ -}) - -#define msa_shrn_n_s32(__a, __b) \ -({ \ - v8i16 __d = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__builtin_msa_srai_w((v4i32)(__a), (int)(__b))); \ - (v4i16)__builtin_msa_copy_s_d((v2i64)__d, 0); \ -}) - -#define msa_shrn_n_s64(__a, __b) \ -({ \ - v4i32 __d = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__builtin_msa_srai_d((v2i64)(__a), (int)(__b))); \ - (v2i32)__builtin_msa_copy_s_d((v2i64)__d, 0); \ -}) - -#define msa_shrn_n_u16(__a, __b) \ -({ \ - v16i8 __d = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__builtin_msa_srli_h((v8i16)(__a), (int)(__b))); \ - (v8u8)__builtin_msa_copy_u_d((v2i64)__d, 0); \ -}) - -#define msa_shrn_n_u32(__a, __b) \ -({ \ - v8i16 __d = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__builtin_msa_srli_w((v4i32)(__a), (int)(__b))); \ - (v4u16)__builtin_msa_copy_u_d((v2i64)__d, 0); \ -}) - -#define msa_shrn_n_u64(__a, __b) \ -({ \ - v4i32 __d = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__builtin_msa_srli_d((v2i64)(__a), (int)(__b))); \ - (v2u32)__builtin_msa_copy_u_d((v2i64)__d, 0); \ -}) - -/* Right shift elements in a 128 bits vector by an immediate value, and places the results in a 64 bits vector. */ -#define msa_rshrn_n_s16(__a, __b) \ -({ \ - v16i8 __d = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__builtin_msa_srari_h((v8i16)(__a), (int)__b)); \ - (v8i8)__builtin_msa_copy_s_d((v2i64)__d, 0); \ -}) - -#define msa_rshrn_n_s32(__a, __b) \ -({ \ - v8i16 __d = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__builtin_msa_srari_w((v4i32)(__a), (int)__b)); \ - (v4i16)__builtin_msa_copy_s_d((v2i64)__d, 0); \ -}) - -#define msa_rshrn_n_s64(__a, __b) \ -({ \ - v4i32 __d = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__builtin_msa_srari_d((v2i64)(__a), (int)__b)); \ - (v2i32)__builtin_msa_copy_s_d((v2i64)__d, 0); \ -}) - -#define msa_rshrn_n_u16(__a, __b) \ -({ \ - v16i8 __d = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__builtin_msa_srlri_h((v8i16)(__a), (int)__b)); \ - (v8u8)__builtin_msa_copy_u_d((v2i64)__d, 0); \ -}) - -#define msa_rshrn_n_u32(__a, __b) \ -({ \ - v8i16 __d = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__builtin_msa_srlri_w((v4i32)(__a), (int)__b)); \ - (v4u16)__builtin_msa_copy_u_d((v2i64)__d, 0); \ -}) - -#define msa_rshrn_n_u64(__a, __b) \ -({ \ - v4i32 __d = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__builtin_msa_srlri_d((v2i64)(__a), (int)__b)); \ - (v2u32)__builtin_msa_copy_u_d((v2i64)__d, 0); \ -}) - -/* Right shift elements in a 128 bits vector by an immediate value, saturate the results and them in a 64 bits vector. */ -#define msa_qrshrn_n_s16(__a, __b) \ -({ \ - v8i16 __d = __builtin_msa_sat_s_h(__builtin_msa_srari_h((v8i16)(__a), (int)(__b)), 7); \ - v16i8 __e = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__d); \ - (v8i8)__builtin_msa_copy_s_d((v2i64)__e, 0); \ -}) - -#define msa_qrshrn_n_s32(__a, __b) \ -({ \ - v4i32 __d = __builtin_msa_sat_s_w(__builtin_msa_srari_w((v4i32)(__a), (int)(__b)), 15); \ - v8i16 __e = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__d); \ - (v4i16)__builtin_msa_copy_s_d((v2i64)__e, 0); \ -}) - -#define msa_qrshrn_n_s64(__a, __b) \ -({ \ - v2i64 __d = __builtin_msa_sat_s_d(__builtin_msa_srari_d((v2i64)(__a), (int)(__b)), 31); \ - v4i32 __e = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__d); \ - (v2i32)__builtin_msa_copy_s_d((v2i64)__e, 0); \ -}) - -#define msa_qrshrn_n_u16(__a, __b) \ -({ \ - v8u16 __d = __builtin_msa_sat_u_h((v8u16)__builtin_msa_srlri_h((v8i16)(__a), (int)(__b)), 7); \ - v16i8 __e = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__d); \ - (v8u8)__builtin_msa_copy_u_d((v2i64)__e, 0); \ -}) - -#define msa_qrshrn_n_u32(__a, __b) \ -({ \ - v4u32 __d = __builtin_msa_sat_u_w((v4u32)__builtin_msa_srlri_w((v4i32)(__a), (int)(__b)), 15); \ - v8i16 __e = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__d); \ - (v4u16)__builtin_msa_copy_u_d((v2i64)__e, 0); \ -}) - -#define msa_qrshrn_n_u64(__a, __b) \ -({ \ - v2u64 __d = __builtin_msa_sat_u_d((v2u64)__builtin_msa_srlri_d((v2i64)(__a), (int)(__b)), 31); \ - v4i32 __e = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__d); \ - (v2u32)__builtin_msa_copy_u_d((v2i64)__e, 0); \ -}) - -/* Right shift elements in a 128 bits vector by an immediate value, saturate the results and them in a 64 bits vector. - Input is signed and output is unsigned. */ -#define msa_qrshrun_n_s16(__a, __b) \ -({ \ - v8i16 __d = __builtin_msa_srlri_h(__builtin_msa_max_s_h(__builtin_msa_fill_h(0), (v8i16)(__a)), (int)(__b)); \ - v16i8 __e = __builtin_msa_pckev_b(__builtin_msa_fill_b(0), (v16i8)__builtin_msa_sat_u_h((v8u16)__d, 7)); \ - (v8u8)__builtin_msa_copy_u_d((v2i64)__e, 0); \ -}) - -#define msa_qrshrun_n_s32(__a, __b) \ -({ \ - v4i32 __d = __builtin_msa_srlri_w(__builtin_msa_max_s_w(__builtin_msa_fill_w(0), (v4i32)(__a)), (int)(__b)); \ - v8i16 __e = __builtin_msa_pckev_h(__builtin_msa_fill_h(0), (v8i16)__builtin_msa_sat_u_w((v4u32)__d, 15)); \ - (v4u16)__builtin_msa_copy_u_d((v2i64)__e, 0); \ -}) - -#define msa_qrshrun_n_s64(__a, __b) \ -({ \ - v2i64 __d = __builtin_msa_srlri_d(__builtin_msa_max_s_d(__builtin_msa_fill_d(0), (v2i64)(__a)), (int)(__b)); \ - v4i32 __e = __builtin_msa_pckev_w(__builtin_msa_fill_w(0), (v4i32)__builtin_msa_sat_u_d((v2u64)__d, 31)); \ - (v2u32)__builtin_msa_copy_u_d((v2i64)__e, 0); \ -}) - -/* pack */ -#define msa_pack_s16(__a, __b) (__builtin_msa_pckev_b((v16i8)(__b), (v16i8)(__a))) -#define msa_pack_s32(__a, __b) (__builtin_msa_pckev_h((v8i16)(__b), (v8i16)(__a))) -#define msa_pack_s64(__a, __b) (__builtin_msa_pckev_w((v4i32)(__b), (v4i32)(__a))) -#define msa_pack_u16(__a, __b) ((v16u8)__builtin_msa_pckev_b((v16i8)(__b), (v16i8)(__a))) -#define msa_pack_u32(__a, __b) ((v8u16)__builtin_msa_pckev_h((v8i16)(__b), (v8i16)(__a))) -#define msa_pack_u64(__a, __b) ((v4u32)__builtin_msa_pckev_w((v4i32)(__b), (v4i32)(__a))) - -/* qpack */ -#define msa_qpack_s16(__a, __b) \ -(__builtin_msa_pckev_b((v16i8)__builtin_msa_sat_s_h((v8i16)(__b), 7), (v16i8)__builtin_msa_sat_s_h((v8i16)(__a), 7))) -#define msa_qpack_s32(__a, __b) \ -(__builtin_msa_pckev_h((v8i16)__builtin_msa_sat_s_w((v4i32)(__b), 15), (v8i16)__builtin_msa_sat_s_w((v4i32)(__a), 15))) -#define msa_qpack_s64(__a, __b) \ -(__builtin_msa_pckev_w((v4i32)__builtin_msa_sat_s_d((v2i64)(__b), 31), (v4i32)__builtin_msa_sat_s_d((v2i64)(__a), 31))) -#define msa_qpack_u16(__a, __b) \ -((v16u8)__builtin_msa_pckev_b((v16i8)__builtin_msa_sat_u_h((v8u16)(__b), 7), (v16i8)__builtin_msa_sat_u_h((v8u16)(__a), 7))) -#define msa_qpack_u32(__a, __b) \ -((v8u16)__builtin_msa_pckev_h((v8i16)__builtin_msa_sat_u_w((v4u32)(__b), 15), (v8i16)__builtin_msa_sat_u_w((v4u32)(__a), 15))) -#define msa_qpack_u64(__a, __b) \ -((v4u32)__builtin_msa_pckev_w((v4i32)__builtin_msa_sat_u_d((v2u64)(__b), 31), (v4i32)__builtin_msa_sat_u_d((v2u64)(__a), 31))) - -/* qpacku */ -#define msa_qpacku_s16(__a, __b) \ -((v16u8)__builtin_msa_pckev_b((v16i8)__builtin_msa_sat_u_h((v8u16)(__builtin_msa_max_s_h(__builtin_msa_fill_h(0), (v8i16)(__b))), 7), \ - (v16i8)__builtin_msa_sat_u_h((v8u16)(__builtin_msa_max_s_h(__builtin_msa_fill_h(0), (v8i16)(__a))), 7))) -#define msa_qpacku_s32(__a, __b) \ -((v8u16)__builtin_msa_pckev_h((v8i16)__builtin_msa_sat_u_w((v4u32)(__builtin_msa_max_s_w(__builtin_msa_fill_w(0), (v4i32)(__b))), 15), \ - (v8i16)__builtin_msa_sat_u_w((v4u32)(__builtin_msa_max_s_w(__builtin_msa_fill_w(0), (v4i32)(__a))), 15))) -#define msa_qpacku_s64(__a, __b) \ -((v4u32)__builtin_msa_pckev_w((v4i32)__builtin_msa_sat_u_d((v2u64)(__builtin_msa_max_s_d(__builtin_msa_fill_d(0), (v2i64)(__b))), 31), \ - (v4i32)__builtin_msa_sat_u_d((v2u64)(__builtin_msa_max_s_d(__builtin_msa_fill_d(0), (v2i64)(__a))), 31))) - -/* packr */ -#define msa_packr_s16(__a, __b, __c) \ -(__builtin_msa_pckev_b((v16i8)__builtin_msa_srai_h((v8i16)(__b), (int)(__c)), (v16i8)__builtin_msa_srai_h((v8i16)(__a), (int)(__c)))) -#define msa_packr_s32(__a, __b, __c) \ -(__builtin_msa_pckev_h((v8i16)__builtin_msa_srai_w((v4i32)(__b), (int)(__c)), (v8i16)__builtin_msa_srai_w((v4i32)(__a), (int)(__c)))) -#define msa_packr_s64(__a, __b, __c) \ -(__builtin_msa_pckev_w((v4i32)__builtin_msa_srai_d((v2i64)(__b), (int)(__c)), (v4i32)__builtin_msa_srai_d((v2i64)(__a), (int)(__c)))) -#define msa_packr_u16(__a, __b, __c) \ -((v16u8)__builtin_msa_pckev_b((v16i8)__builtin_msa_srli_h((v8i16)(__b), (int)(__c)), (v16i8)__builtin_msa_srli_h((v8i16)(__a), (int)(__c)))) -#define msa_packr_u32(__a, __b, __c) \ -((v8u16)__builtin_msa_pckev_h((v8i16)__builtin_msa_srli_w((v4i32)(__b), (int)(__c)), (v8i16)__builtin_msa_srli_w((v4i32)(__a), (int)(__c)))) -#define msa_packr_u64(__a, __b, __c) \ -((v4u32)__builtin_msa_pckev_w((v4i32)__builtin_msa_srli_d((v2i64)(__b), (int)(__c)), (v4i32)__builtin_msa_srli_d((v2i64)(__a), (int)(__c)))) - -/* rpackr */ -#define msa_rpackr_s16(__a, __b, __c) \ -(__builtin_msa_pckev_b((v16i8)__builtin_msa_srari_h((v8i16)(__b), (int)(__c)), (v16i8)__builtin_msa_srari_h((v8i16)(__a), (int)(__c)))) -#define msa_rpackr_s32(__a, __b, __c) \ -(__builtin_msa_pckev_h((v8i16)__builtin_msa_srari_w((v4i32)(__b), (int)(__c)), (v8i16)__builtin_msa_srari_w((v4i32)(__a), (int)(__c)))) -#define msa_rpackr_s64(__a, __b, __c) \ -(__builtin_msa_pckev_w((v4i32)__builtin_msa_srari_d((v2i64)(__b), (int)(__c)), (v4i32)__builtin_msa_srari_d((v2i64)(__a), (int)(__c)))) -#define msa_rpackr_u16(__a, __b, __c) \ -((v16u8)__builtin_msa_pckev_b((v16i8)__builtin_msa_srlri_h((v8i16)(__b), (int)(__c)), (v16i8)__builtin_msa_srlri_h((v8i16)(__a), (int)(__c)))) -#define msa_rpackr_u32(__a, __b, __c) \ -((v8u16)__builtin_msa_pckev_h((v8i16)__builtin_msa_srlri_w((v4i32)(__b), (int)(__c)), (v8i16)__builtin_msa_srlri_w((v4i32)(__a), (int)(__c)))) -#define msa_rpackr_u64(__a, __b, __c) \ -((v4u32)__builtin_msa_pckev_w((v4i32)__builtin_msa_srlri_d((v2i64)(__b), (int)(__c)), (v4i32)__builtin_msa_srlri_d((v2i64)(__a), (int)(__c)))) - -/* qrpackr */ -#define msa_qrpackr_s16(__a, __b, __c) \ -(__builtin_msa_pckev_b((v16i8)__builtin_msa_sat_s_h(__builtin_msa_srari_h((v8i16)(__b), (int)(__c)), 7), \ - (v16i8)__builtin_msa_sat_s_h(__builtin_msa_srari_h((v8i16)(__a), (int)(__c)), 7))) -#define msa_qrpackr_s32(__a, __b, __c) \ -(__builtin_msa_pckev_h((v8i16)__builtin_msa_sat_s_w(__builtin_msa_srari_w((v4i32)(__b), (int)(__c)), 15), \ - (v8i16)__builtin_msa_sat_s_w(__builtin_msa_srari_w((v4i32)(__a), (int)(__c)), 15))) -#define msa_qrpackr_s64(__a, __b, __c) \ -(__builtin_msa_pckev_w((v4i32)__builtin_msa_sat_s_d(__builtin_msa_srari_d((v2i64)(__b), (int)(__c)), 31), \ - (v4i32)__builtin_msa_sat_s_d(__builtin_msa_srari_d((v2i64)(__a), (int)(__c)), 31))) -#define msa_qrpackr_u16(__a, __b, __c) \ -((v16u8)__builtin_msa_pckev_b((v16i8)__builtin_msa_sat_u_h((v8u16)__builtin_msa_srlri_h((v8i16)(__b), (int)(__c)), 7), \ - (v16i8)__builtin_msa_sat_u_h((v8u16)__builtin_msa_srlri_h((v8i16)(__a), (int)(__c)), 7))) -#define msa_qrpackr_u32(__a, __b, __c) \ -((v8u16)__builtin_msa_pckev_h((v8i16)__builtin_msa_sat_u_w((v4u32)__builtin_msa_srlri_w((v4i32)(__b), (int)(__c)), 15), \ - (v8i16)__builtin_msa_sat_u_w((v4u32)__builtin_msa_srlri_w((v4i32)(__a), (int)(__c)), 15))) -#define msa_qrpackr_u64(__a, __b, __c) \ -((v4u32)__builtin_msa_pckev_w((v4i32)__builtin_msa_sat_u_d((v2u64)__builtin_msa_srlri_d((v2i64)(__b), (int)(__c)), 31), \ - (v4i32)__builtin_msa_sat_u_d((v2u64)__builtin_msa_srlri_d((v2i64)(__a), (int)(__c)), 31))) - -/* qrpackru */ -#define msa_qrpackru_s16(__a, __b, __c) \ -({ \ - v8i16 __d = __builtin_msa_srlri_h(__builtin_msa_max_s_h(__builtin_msa_fill_h(0), (v8i16)(__a)), (int)(__c)); \ - v8i16 __e = __builtin_msa_srlri_h(__builtin_msa_max_s_h(__builtin_msa_fill_h(0), (v8i16)(__b)), (int)(__c)); \ - (v16u8)__builtin_msa_pckev_b((v16i8)__builtin_msa_sat_u_h((v8u16)__e, 7), (v16i8)__builtin_msa_sat_u_h((v8u16)__d, 7)); \ -}) - -#define msa_qrpackru_s32(__a, __b, __c) \ -({ \ - v4i32 __d = __builtin_msa_srlri_w(__builtin_msa_max_s_w(__builtin_msa_fill_w(0), (v4i32)(__a)), (int)(__c)); \ - v4i32 __e = __builtin_msa_srlri_w(__builtin_msa_max_s_w(__builtin_msa_fill_w(0), (v4i32)(__b)), (int)(__c)); \ - (v8u16)__builtin_msa_pckev_h((v8i16)__builtin_msa_sat_u_w((v4u32)__e, 15), (v8i16)__builtin_msa_sat_u_w((v4u32)__d, 15)); \ -}) - -#define msa_qrpackru_s64(__a, __b, __c) \ -({ \ - v2i64 __d = __builtin_msa_srlri_d(__builtin_msa_max_s_d(__builtin_msa_fill_d(0), (v2i64)(__a)), (int)(__c)); \ - v2i64 __e = __builtin_msa_srlri_d(__builtin_msa_max_s_d(__builtin_msa_fill_d(0), (v2i64)(__b)), (int)(__c)); \ - (v4u32)__builtin_msa_pckev_w((v4i32)__builtin_msa_sat_u_d((v2u64)__e, 31), (v4i32)__builtin_msa_sat_u_d((v2u64)__d, 31)); \ -}) - -/* Minimum values between corresponding elements in the two vectors are written to the returned vector. */ -#define msa_minq_s8(__a, __b) (__builtin_msa_min_s_b(__a, __b)) -#define msa_minq_s16(__a, __b) (__builtin_msa_min_s_h(__a, __b)) -#define msa_minq_s32(__a, __b) (__builtin_msa_min_s_w(__a, __b)) -#define msa_minq_s64(__a, __b) (__builtin_msa_min_s_d(__a, __b)) -#define msa_minq_u8(__a, __b) ((v16u8)__builtin_msa_min_u_b(__a, __b)) -#define msa_minq_u16(__a, __b) ((v8u16)__builtin_msa_min_u_h(__a, __b)) -#define msa_minq_u32(__a, __b) ((v4u32)__builtin_msa_min_u_w(__a, __b)) -#define msa_minq_u64(__a, __b) ((v2u64)__builtin_msa_min_u_d(__a, __b)) -#define msa_minq_f32(__a, __b) (__builtin_msa_fmin_w(__a, __b)) -#define msa_minq_f64(__a, __b) (__builtin_msa_fmin_d(__a, __b)) - -/* Maximum values between corresponding elements in the two vectors are written to the returned vector. */ -#define msa_maxq_s8(__a, __b) (__builtin_msa_max_s_b(__a, __b)) -#define msa_maxq_s16(__a, __b) (__builtin_msa_max_s_h(__a, __b)) -#define msa_maxq_s32(__a, __b) (__builtin_msa_max_s_w(__a, __b)) -#define msa_maxq_s64(__a, __b) (__builtin_msa_max_s_d(__a, __b)) -#define msa_maxq_u8(__a, __b) ((v16u8)__builtin_msa_max_u_b(__a, __b)) -#define msa_maxq_u16(__a, __b) ((v8u16)__builtin_msa_max_u_h(__a, __b)) -#define msa_maxq_u32(__a, __b) ((v4u32)__builtin_msa_max_u_w(__a, __b)) -#define msa_maxq_u64(__a, __b) ((v2u64)__builtin_msa_max_u_d(__a, __b)) -#define msa_maxq_f32(__a, __b) (__builtin_msa_fmax_w(__a, __b)) -#define msa_maxq_f64(__a, __b) (__builtin_msa_fmax_d(__a, __b)) - -/* Vector type reinterpretion */ -#define MSA_TPV_REINTERPRET(_Tpv, Vec) ((_Tpv)(Vec)) - -/* Add the odd elements in vector __a with the even elements in vector __b to double width elements in the returned vector. */ -/* v8i16 msa_hadd_s16 ((v16i8)__a, (v16i8)__b) */ -#define msa_hadd_s16(__a, __b) (__builtin_msa_hadd_s_h((v16i8)(__a), (v16i8)(__b))) -/* v4i32 msa_hadd_s32 ((v8i16)__a, (v8i16)__b) */ -#define msa_hadd_s32(__a, __b) (__builtin_msa_hadd_s_w((v8i16)(__a), (v8i16)(__b))) -/* v2i64 msa_hadd_s64 ((v4i32)__a, (v4i32)__b) */ -#define msa_hadd_s64(__a, __b) (__builtin_msa_hadd_s_d((v4i32)(__a), (v4i32)(__b))) - -/* Copy even elements in __a to the left half and even elements in __b to the right half and return the result vector. */ -#define msa_pckev_s8(__a, __b) (__builtin_msa_pckev_b((v16i8)(__a), (v16i8)(__b))) -#define msa_pckev_s16(__a, __b) (__builtin_msa_pckev_h((v8i16)(__a), (v8i16)(__b))) -#define msa_pckev_s32(__a, __b) (__builtin_msa_pckev_w((v4i32)(__a), (v4i32)(__b))) -#define msa_pckev_s64(__a, __b) (__builtin_msa_pckev_d((v2i64)(__a), (v2i64)(__b))) - -/* Copy even elements in __a to the left half and even elements in __b to the right half and return the result vector. */ -#define msa_pckod_s8(__a, __b) (__builtin_msa_pckod_b((v16i8)(__a), (v16i8)(__b))) -#define msa_pckod_s16(__a, __b) (__builtin_msa_pckod_h((v8i16)(__a), (v8i16)(__b))) -#define msa_pckod_s32(__a, __b) (__builtin_msa_pckod_w((v4i32)(__a), (v4i32)(__b))) -#define msa_pckod_s64(__a, __b) (__builtin_msa_pckod_d((v2i64)(__a), (v2i64)(__b))) - -#ifdef _MIPSEB -#define LANE_IMM0_1(x) (0b1 - ((x) & 0b1)) -#define LANE_IMM0_3(x) (0b11 - ((x) & 0b11)) -#define LANE_IMM0_7(x) (0b111 - ((x) & 0b111)) -#define LANE_IMM0_15(x) (0b1111 - ((x) & 0b1111)) -#else -#define LANE_IMM0_1(x) ((x) & 0b1) -#define LANE_IMM0_3(x) ((x) & 0b11) -#define LANE_IMM0_7(x) ((x) & 0b111) -#define LANE_IMM0_15(x) ((x) & 0b1111) -#endif - -#define msa_get_lane_u8(__a, __b) ((uint8_t)(__a)[LANE_IMM0_7(__b)]) -#define msa_get_lane_s8(__a, __b) ((int8_t)(__a)[LANE_IMM0_7(__b)]) -#define msa_get_lane_u16(__a, __b) ((uint16_t)(__a)[LANE_IMM0_3(__b)]) -#define msa_get_lane_s16(__a, __b) ((int16_t)(__a)[LANE_IMM0_3(__b)]) -#define msa_get_lane_u32(__a, __b) ((uint32_t)(__a)[LANE_IMM0_1(__b)]) -#define msa_get_lane_s32(__a, __b) ((int32_t)(__a)[LANE_IMM0_1(__b)]) -#define msa_get_lane_f32(__a, __b) ((float)(__a)[LANE_IMM0_3(__b)]) -#define msa_get_lane_s64(__a, __b) ((int64_t)(__a)[LANE_IMM0_1(__b)]) -#define msa_get_lane_u64(__a, __b) ((uint64_t)(__a)[LANE_IMM0_1(__b)]) -#define msa_get_lane_f64(__a, __b) ((double)(__a)[LANE_IMM0_1(__b)]) -#define msa_getq_lane_u8(__a, imm0_15) ((uint8_t)__builtin_msa_copy_u_b((v16i8)(__a), imm0_15)) -#define msa_getq_lane_s8(__a, imm0_15) ((int8_t)__builtin_msa_copy_s_b(__a, imm0_15)) -#define msa_getq_lane_u16(__a, imm0_7) ((uint16_t)__builtin_msa_copy_u_h((v8i16)(__a), imm0_7)) -#define msa_getq_lane_s16(__a, imm0_7) ((int16_t)__builtin_msa_copy_s_h(__a, imm0_7)) -#define msa_getq_lane_u32(__a, imm0_3) __builtin_msa_copy_u_w((v4i32)(__a), imm0_3) -#define msa_getq_lane_s32 __builtin_msa_copy_s_w -#define msa_getq_lane_f32(__a, __b) ((float)(__a)[LANE_IMM0_3(__b)]) -#define msa_getq_lane_f64(__a, __b) ((double)(__a)[LANE_IMM0_1(__b)]) -#if (__mips == 64) -#define msa_getq_lane_u64(__a, imm0_1) __builtin_msa_copy_u_d((v2i64)(__a), imm0_1) -#define msa_getq_lane_s64 __builtin_msa_copy_s_d -#else -#define msa_getq_lane_u64(__a, imm0_1) ((uint64_t)(__a)[LANE_IMM0_1(imm0_1)]) -#define msa_getq_lane_s64(__a, imm0_1) ((int64_t)(__a)[LANE_IMM0_1(imm0_1)]) -#endif - -/* combine */ -#if (__mips == 64) -#define __COMBINE_64_64(__TYPE, a, b) ((__TYPE)((v2u64){((v1u64)(a))[0], ((v1u64)(b))[0]})) -#else -#define __COMBINE_64_64(__TYPE, a, b) ((__TYPE)((v4u32){((v2u32)(a))[0], ((v2u32)(a))[1], \ - ((v2u32)(b))[0], ((v2u32)(b))[1]})) -#endif - -/* v16i8 msa_combine_s8 (v8i8 __a, v8i8 __b) */ -#define msa_combine_s8(__a, __b) __COMBINE_64_64(v16i8, __a, __b) - -/* v8i16 msa_combine_s16(v4i16 __a, v4i16 __b) */ -#define msa_combine_s16(__a, __b) __COMBINE_64_64(v8i16, __a, __b) - -/* v4i32 msa_combine_s32(v2i32 __a, v2i32 __b) */ -#define msa_combine_s32(__a, __b) __COMBINE_64_64(v4i32, __a, __b) - -/* v2i64 msa_combine_s64(v1i64 __a, v1i64 __b) */ -#define msa_combine_s64(__a, __b) __COMBINE_64_64(v2i64, __a, __b) - -/* v4f32 msa_combine_f32(v2f32 __a, v2f32 __b) */ -#define msa_combine_f32(__a, __b) __COMBINE_64_64(v4f32, __a, __b) - -/* v16u8 msa_combine_u8(v8u8 __a, v8u8 __b) */ -#define msa_combine_u8(__a, __b) __COMBINE_64_64(v16u8, __a, __b) - -/* v8u16 msa_combine_u16(v4u16 __a, v4u16 __b) */ -#define msa_combine_u16(__a, __b) __COMBINE_64_64(v8u16, __a, __b) - -/* v4u32 msa_combine_u32(v2u32 __a, v2u32 __b) */ -#define msa_combine_u32(__a, __b) __COMBINE_64_64(v4u32, __a, __b) - -/* v2u64 msa_combine_u64(v1u64 __a, v1u64 __b) */ -#define msa_combine_u64(__a, __b) __COMBINE_64_64(v2u64, __a, __b) - -/* v2f64 msa_combine_f64(v1f64 __a, v1f64 __b) */ -#define msa_combine_f64(__a, __b) __COMBINE_64_64(v2f64, __a, __b) - -/* get_low, get_high */ -#if (__mips == 64) -#define __GET_LOW(__TYPE, a) ((__TYPE)((v1u64)(__builtin_msa_copy_u_d((v2i64)(a), 0)))) -#define __GET_HIGH(__TYPE, a) ((__TYPE)((v1u64)(__builtin_msa_copy_u_d((v2i64)(a), 1)))) -#else -#define __GET_LOW(__TYPE, a) ((__TYPE)(((v2u64)(a))[0])) -#define __GET_HIGH(__TYPE, a) ((__TYPE)(((v2u64)(a))[1])) -#endif - -/* v8i8 msa_get_low_s8(v16i8 __a) */ -#define msa_get_low_s8(__a) __GET_LOW(v8i8, __a) - -/* v4i16 msa_get_low_s16(v8i16 __a) */ -#define msa_get_low_s16(__a) __GET_LOW(v4i16, __a) - -/* v2i32 msa_get_low_s32(v4i32 __a) */ -#define msa_get_low_s32(__a) __GET_LOW(v2i32, __a) - -/* v1i64 msa_get_low_s64(v2i64 __a) */ -#define msa_get_low_s64(__a) __GET_LOW(v1i64, __a) - -/* v8u8 msa_get_low_u8(v16u8 __a) */ -#define msa_get_low_u8(__a) __GET_LOW(v8u8, __a) - -/* v4u16 msa_get_low_u16(v8u16 __a) */ -#define msa_get_low_u16(__a) __GET_LOW(v4u16, __a) - -/* v2u32 msa_get_low_u32(v4u32 __a) */ -#define msa_get_low_u32(__a) __GET_LOW(v2u32, __a) - -/* v1u64 msa_get_low_u64(v2u64 __a) */ -#define msa_get_low_u64(__a) __GET_LOW(v1u64, __a) - -/* v2f32 msa_get_low_f32(v4f32 __a) */ -#define msa_get_low_f32(__a) __GET_LOW(v2f32, __a) - -/* v1f64 msa_get_low_f64(v2f64 __a) */ -#define msa_get_low_f64(__a) __GET_LOW(v1f64, __a) - -/* v8i8 msa_get_high_s8(v16i8 __a) */ -#define msa_get_high_s8(__a) __GET_HIGH(v8i8, __a) - -/* v4i16 msa_get_high_s16(v8i16 __a) */ -#define msa_get_high_s16(__a) __GET_HIGH(v4i16, __a) - -/* v2i32 msa_get_high_s32(v4i32 __a) */ -#define msa_get_high_s32(__a) __GET_HIGH(v2i32, __a) - -/* v1i64 msa_get_high_s64(v2i64 __a) */ -#define msa_get_high_s64(__a) __GET_HIGH(v1i64, __a) - -/* v8u8 msa_get_high_u8(v16u8 __a) */ -#define msa_get_high_u8(__a) __GET_HIGH(v8u8, __a) - -/* v4u16 msa_get_high_u16(v8u16 __a) */ -#define msa_get_high_u16(__a) __GET_HIGH(v4u16, __a) - -/* v2u32 msa_get_high_u32(v4u32 __a) */ -#define msa_get_high_u32(__a) __GET_HIGH(v2u32, __a) - -/* v1u64 msa_get_high_u64(v2u64 __a) */ -#define msa_get_high_u64(__a) __GET_HIGH(v1u64, __a) - -/* v2f32 msa_get_high_f32(v4f32 __a) */ -#define msa_get_high_f32(__a) __GET_HIGH(v2f32, __a) - -/* v1f64 msa_get_high_f64(v2f64 __a) */ -#define msa_get_high_f64(__a) __GET_HIGH(v1f64, __a) - -/* ri = ai * b[lane] */ -/* v4f32 msa_mulq_lane_f32(v4f32 __a, v4f32 __b, const int __lane) */ -#define msa_mulq_lane_f32(__a, __b, __lane) ((__a) * msa_getq_lane_f32(__b, __lane)) - -/* ri = ai + bi * c[lane] */ -/* v4f32 msa_mlaq_lane_f32(v4f32 __a, v4f32 __b, v4f32 __c, const int __lane) */ -#define msa_mlaq_lane_f32(__a, __b, __c, __lane) ((__a) + ((__b) * msa_getq_lane_f32(__c, __lane))) - -/* uint16_t msa_sum_u16(v8u16 __a)*/ -#define msa_sum_u16(__a) \ -({ \ - v4u32 _b; \ - v2u64 _c; \ - _b = __builtin_msa_hadd_u_w(__a, __a); \ - _c = __builtin_msa_hadd_u_d(_b, _b); \ - (uint16_t)(_c[0] + _c[1]); \ -}) - -/* int16_t msa_sum_s16(v8i16 __a) */ -#define msa_sum_s16(__a) \ -({ \ - v4i32 _b; \ - v2i64 _c; \ - _b = __builtin_msa_hadd_s_w(__a, __a); \ - _c = __builtin_msa_hadd_s_d(_b, _b); \ - (int16_t)(_c[0] + _c[1]); \ -}) - - -/* uint32_t msa_sum_u32(v4u32 __a)*/ -#define msa_sum_u32(__a) \ -({ \ - v2u64 _b; \ - _b = __builtin_msa_hadd_u_d(__a, __a); \ - (uint32_t)(_b[0] + _b[1]); \ -}) - -/* int32_t msa_sum_s32(v4i32 __a)*/ -#define msa_sum_s32(__a) \ -({ \ - v2i64 _b; \ - _b = __builtin_msa_hadd_s_d(__a, __a); \ - (int32_t)(_b[0] + _b[1]); \ -}) - -/* uint8_t msa_sum_u8(v16u8 __a)*/ -#define msa_sum_u8(__a) \ -({ \ - v8u16 _b16; \ - v4u32 _c32; \ - _b16 = __builtin_msa_hadd_u_h(__a, __a); \ - _c32 = __builtin_msa_hadd_u_w(_b16, _b16); \ - (uint8_t)msa_sum_u32(_c32); \ -}) - -/* int8_t msa_sum_s8(v16s8 __a)*/ -#define msa_sum_s8(__a) \ -({ \ - v8i16 _b16; \ - v4i32 _c32; \ - _b16 = __builtin_msa_hadd_s_h(__a, __a); \ - _c32 = __builtin_msa_hadd_s_w(_b16, _b16); \ - (int8_t)msa_sum_s32(_c32); \ -}) - -/* float msa_sum_f32(v4f32 __a)*/ -#define msa_sum_f32(__a) ((__a)[0] + (__a)[1] + (__a)[2] + (__a)[3]) - -/* v8u16 msa_paddlq_u8(v16u8 __a) */ -#define msa_paddlq_u8(__a) (__builtin_msa_hadd_u_h(__a, __a)) - -/* v8i16 msa_paddlq_s8(v16i8 __a) */ -#define msa_paddlq_s8(__a) (__builtin_msa_hadd_s_h(__a, __a)) - -/* v4u32 msa_paddlq_u16 (v8u16 __a)*/ -#define msa_paddlq_u16(__a) (__builtin_msa_hadd_u_w(__a, __a)) - -/* v4i32 msa_paddlq_s16 (v8i16 __a)*/ -#define msa_paddlq_s16(__a) (__builtin_msa_hadd_s_w(__a, __a)) - -/* v2u64 msa_paddlq_u32(v4u32 __a) */ -#define msa_paddlq_u32(__a) (__builtin_msa_hadd_u_d(__a, __a)) - -/* v2i64 msa_paddlq_s32(v4i32 __a) */ -#define msa_paddlq_s32(__a) (__builtin_msa_hadd_s_d(__a, __a)) - -#define V8U8_2_V8U16(x) {(uint16_t)x[0], (uint16_t)x[1], (uint16_t)x[2], (uint16_t)x[3], \ - (uint16_t)x[4], (uint16_t)x[5], (uint16_t)x[6], (uint16_t)x[7]} -#define V8U8_2_V8I16(x) {(int16_t)x[0], (int16_t)x[1], (int16_t)x[2], (int16_t)x[3], \ - (int16_t)x[4], (int16_t)x[5], (int16_t)x[6], (int16_t)x[7]} -#define V8I8_2_V8I16(x) {(int16_t)x[0], (int16_t)x[1], (int16_t)x[2], (int16_t)x[3], \ - (int16_t)x[4], (int16_t)x[5], (int16_t)x[6], (int16_t)x[7]} -#define V4U16_2_V4U32(x) {(uint32_t)x[0], (uint32_t)x[1], (uint32_t)x[2], (uint32_t)x[3]} -#define V4U16_2_V4I32(x) {(int32_t)x[0], (int32_t)x[1], (int32_t)x[2], (int32_t)x[3]} -#define V4I16_2_V4I32(x) {(int32_t)x[0], (int32_t)x[1], (int32_t)x[2], (int32_t)x[3]} -#define V2U32_2_V2U64(x) {(uint64_t)x[0], (uint64_t)x[1]} -#define V2U32_2_V2I64(x) {(int64_t)x[0], (int64_t)x[1]} - -/* v8u16 msa_mull_u8(v8u8 __a, v8u8 __b) */ -#define msa_mull_u8(__a, __b) ((v8u16)__builtin_msa_mulv_h((v8i16)V8U8_2_V8I16(__a), (v8i16)V8U8_2_V8I16(__b))) - -/* v8i16 msa_mull_s8(v8i8 __a, v8i8 __b)*/ -#define msa_mull_s8(__a, __b) (__builtin_msa_mulv_h((v8i16)V8I8_2_V8I16(__a), (v8i16)V8I8_2_V8I16(__b))) - -/* v4u32 msa_mull_u16(v4u16 __a, v4u16 __b) */ -#define msa_mull_u16(__a, __b) ((v4u32)__builtin_msa_mulv_w((v4i32)V4U16_2_V4I32(__a), (v4i32)V4U16_2_V4I32(__b))) - -/* v4i32 msa_mull_s16(v4i16 __a, v4i16 __b) */ -#define msa_mull_s16(__a, __b) (__builtin_msa_mulv_w((v4i32)V4I16_2_V4I32(__a), (v4i32)V4I16_2_V4I32(__b))) - -/* v2u64 msa_mull_u32(v2u32 __a, v2u32 __b) */ -#define msa_mull_u32(__a, __b) ((v2u64)__builtin_msa_mulv_d((v2i64)V2U32_2_V2I64(__a), (v2i64)V2U32_2_V2I64(__b))) - -/* bitwise and: __builtin_msa_and_v */ -#define msa_andq_u8(__a, __b) ((v16u8)__builtin_msa_and_v((v16u8)(__a), (v16u8)(__b))) -#define msa_andq_s8(__a, __b) ((v16i8)__builtin_msa_and_v((v16u8)(__a), (v16u8)(__b))) -#define msa_andq_u16(__a, __b) ((v8u16)__builtin_msa_and_v((v16u8)(__a), (v16u8)(__b))) -#define msa_andq_s16(__a, __b) ((v8i16)__builtin_msa_and_v((v16u8)(__a), (v16u8)(__b))) -#define msa_andq_u32(__a, __b) ((v4u32)__builtin_msa_and_v((v16u8)(__a), (v16u8)(__b))) -#define msa_andq_s32(__a, __b) ((v4i32)__builtin_msa_and_v((v16u8)(__a), (v16u8)(__b))) -#define msa_andq_u64(__a, __b) ((v2u64)__builtin_msa_and_v((v16u8)(__a), (v16u8)(__b))) -#define msa_andq_s64(__a, __b) ((v2i64)__builtin_msa_and_v((v16u8)(__a), (v16u8)(__b))) - -/* bitwise or: __builtin_msa_or_v */ -#define msa_orrq_u8(__a, __b) ((v16u8)__builtin_msa_or_v((v16u8)(__a), (v16u8)(__b))) -#define msa_orrq_s8(__a, __b) ((v16i8)__builtin_msa_or_v((v16u8)(__a), (v16u8)(__b))) -#define msa_orrq_u16(__a, __b) ((v8u16)__builtin_msa_or_v((v16u8)(__a), (v16u8)(__b))) -#define msa_orrq_s16(__a, __b) ((v8i16)__builtin_msa_or_v((v16u8)(__a), (v16u8)(__b))) -#define msa_orrq_u32(__a, __b) ((v4u32)__builtin_msa_or_v((v16u8)(__a), (v16u8)(__b))) -#define msa_orrq_s32(__a, __b) ((v4i32)__builtin_msa_or_v((v16u8)(__a), (v16u8)(__b))) -#define msa_orrq_u64(__a, __b) ((v2u64)__builtin_msa_or_v((v16u8)(__a), (v16u8)(__b))) -#define msa_orrq_s64(__a, __b) ((v2i64)__builtin_msa_or_v((v16u8)(__a), (v16u8)(__b))) - -/* bitwise xor: __builtin_msa_xor_v */ -#define msa_eorq_u8(__a, __b) ((v16u8)__builtin_msa_xor_v((v16u8)(__a), (v16u8)(__b))) -#define msa_eorq_s8(__a, __b) ((v16i8)__builtin_msa_xor_v((v16u8)(__a), (v16u8)(__b))) -#define msa_eorq_u16(__a, __b) ((v8u16)__builtin_msa_xor_v((v16u8)(__a), (v16u8)(__b))) -#define msa_eorq_s16(__a, __b) ((v8i16)__builtin_msa_xor_v((v16u8)(__a), (v16u8)(__b))) -#define msa_eorq_u32(__a, __b) ((v4u32)__builtin_msa_xor_v((v16u8)(__a), (v16u8)(__b))) -#define msa_eorq_s32(__a, __b) ((v4i32)__builtin_msa_xor_v((v16u8)(__a), (v16u8)(__b))) -#define msa_eorq_u64(__a, __b) ((v2u64)__builtin_msa_xor_v((v16u8)(__a), (v16u8)(__b))) -#define msa_eorq_s64(__a, __b) ((v2i64)__builtin_msa_xor_v((v16u8)(__a), (v16u8)(__b))) - -/* bitwise not: v16u8 __builtin_msa_xori_b (v16u8, 0xff) */ -#define msa_mvnq_u8(__a) ((v16u8)__builtin_msa_xori_b((v16u8)(__a), 0xFF)) -#define msa_mvnq_s8(__a) ((v16i8)__builtin_msa_xori_b((v16u8)(__a), 0xFF)) -#define msa_mvnq_u16(__a) ((v8u16)__builtin_msa_xori_b((v16u8)(__a), 0xFF)) -#define msa_mvnq_s16(__a) ((v8i16)__builtin_msa_xori_b((v16u8)(__a), 0xFF)) -#define msa_mvnq_u32(__a) ((v4u32)__builtin_msa_xori_b((v16u8)(__a), 0xFF)) -#define msa_mvnq_s32(__a) ((v4i32)__builtin_msa_xori_b((v16u8)(__a), 0xFF)) -#define msa_mvnq_u64(__a) ((v2u64)__builtin_msa_xori_b((v16u8)(__a), 0xFF)) -#define msa_mvnq_s64(__a) ((v2i64)__builtin_msa_xori_b((v16u8)(__a), 0xFF)) - -/* compare equal: ceq -> ri = ai == bi ? 1...1:0...0 */ -#define msa_ceqq_u8(__a, __b) ((v16u8)__builtin_msa_ceq_b((v16i8)(__a), (v16i8)(__b))) -#define msa_ceqq_s8(__a, __b) ((v16u8)__builtin_msa_ceq_b((v16i8)(__a), (v16i8)(__b))) -#define msa_ceqq_u16(__a, __b) ((v8u16)__builtin_msa_ceq_h((v8i16)(__a), (v8i16)(__b))) -#define msa_ceqq_s16(__a, __b) ((v8u16)__builtin_msa_ceq_h((v8i16)(__a), (v8i16)(__b))) -#define msa_ceqq_u32(__a, __b) ((v4u32)__builtin_msa_ceq_w((v4i32)(__a), (v4i32)(__b))) -#define msa_ceqq_s32(__a, __b) ((v4u32)__builtin_msa_ceq_w((v4i32)(__a), (v4i32)(__b))) -#define msa_ceqq_f32(__a, __b) ((v4u32)__builtin_msa_fceq_w((v4f32)(__a), (v4f32)(__b))) -#define msa_ceqq_u64(__a, __b) ((v2u64)__builtin_msa_ceq_d((v2i64)(__a), (v2i64)(__b))) -#define msa_ceqq_s64(__a, __b) ((v2u64)__builtin_msa_ceq_d((v2i64)(__a), (v2i64)(__b))) -#define msa_ceqq_f64(__a, __b) ((v2u64)__builtin_msa_fceq_d((v2f64)(__a), (v2f64)(__b))) - -/* Compare less-than: clt -> ri = ai < bi ? 1...1:0...0 */ -#define msa_cltq_u8(__a, __b) ((v16u8)__builtin_msa_clt_u_b((v16u8)(__a), (v16u8)(__b))) -#define msa_cltq_s8(__a, __b) ((v16u8)__builtin_msa_clt_s_b((v16i8)(__a), (v16i8)(__b))) -#define msa_cltq_u16(__a, __b) ((v8u16)__builtin_msa_clt_u_h((v8u16)(__a), (v8u16)(__b))) -#define msa_cltq_s16(__a, __b) ((v8u16)__builtin_msa_clt_s_h((v8i16)(__a), (v8i16)(__b))) -#define msa_cltq_u32(__a, __b) ((v4u32)__builtin_msa_clt_u_w((v4u32)(__a), (v4u32)(__b))) -#define msa_cltq_s32(__a, __b) ((v4u32)__builtin_msa_clt_s_w((v4i32)(__a), (v4i32)(__b))) -#define msa_cltq_f32(__a, __b) ((v4u32)__builtin_msa_fclt_w((v4f32)(__a), (v4f32)(__b))) -#define msa_cltq_u64(__a, __b) ((v2u64)__builtin_msa_clt_u_d((v2u64)(__a), (v2u64)(__b))) -#define msa_cltq_s64(__a, __b) ((v2u64)__builtin_msa_clt_s_d((v2i64)(__a), (v2i64)(__b))) -#define msa_cltq_f64(__a, __b) ((v2u64)__builtin_msa_fclt_d((v2f64)(__a), (v2f64)(__b))) - -/* compare greater-than: cgt -> ri = ai > bi ? 1...1:0...0 */ -#define msa_cgtq_u8(__a, __b) ((v16u8)__builtin_msa_clt_u_b((v16u8)(__b), (v16u8)(__a))) -#define msa_cgtq_s8(__a, __b) ((v16u8)__builtin_msa_clt_s_b((v16i8)(__b), (v16i8)(__a))) -#define msa_cgtq_u16(__a, __b) ((v8u16)__builtin_msa_clt_u_h((v8u16)(__b), (v8u16)(__a))) -#define msa_cgtq_s16(__a, __b) ((v8u16)__builtin_msa_clt_s_h((v8i16)(__b), (v8i16)(__a))) -#define msa_cgtq_u32(__a, __b) ((v4u32)__builtin_msa_clt_u_w((v4u32)(__b), (v4u32)(__a))) -#define msa_cgtq_s32(__a, __b) ((v4u32)__builtin_msa_clt_s_w((v4i32)(__b), (v4i32)(__a))) -#define msa_cgtq_f32(__a, __b) ((v4u32)__builtin_msa_fclt_w((v4f32)(__b), (v4f32)(__a))) -#define msa_cgtq_u64(__a, __b) ((v2u64)__builtin_msa_clt_u_d((v2u64)(__b), (v2u64)(__a))) -#define msa_cgtq_s64(__a, __b) ((v2u64)__builtin_msa_clt_s_d((v2i64)(__b), (v2i64)(__a))) -#define msa_cgtq_f64(__a, __b) ((v2u64)__builtin_msa_fclt_d((v2f64)(__b), (v2f64)(__a))) - -/* compare less-equal: cle -> ri = ai <= bi ? 1...1:0...0 */ -#define msa_cleq_u8(__a, __b) ((v16u8)__builtin_msa_cle_u_b((v16u8)(__a), (v16u8)(__b))) -#define msa_cleq_s8(__a, __b) ((v16u8)__builtin_msa_cle_s_b((v16i8)(__a), (v16i8)(__b))) -#define msa_cleq_u16(__a, __b) ((v8u16)__builtin_msa_cle_u_h((v8u16)(__a), (v8u16)(__b))) -#define msa_cleq_s16(__a, __b) ((v8u16)__builtin_msa_cle_s_h((v8i16)(__a), (v8i16)(__b))) -#define msa_cleq_u32(__a, __b) ((v4u32)__builtin_msa_cle_u_w((v4u32)(__a), (v4u32)(__b))) -#define msa_cleq_s32(__a, __b) ((v4u32)__builtin_msa_cle_s_w((v4i32)(__a), (v4i32)(__b))) -#define msa_cleq_f32(__a, __b) ((v4u32)__builtin_msa_fcle_w((v4f32)(__a), (v4f32)(__b))) -#define msa_cleq_u64(__a, __b) ((v2u64)__builtin_msa_cle_u_d((v2u64)(__a), (v2u64)(__b))) -#define msa_cleq_s64(__a, __b) ((v2u64)__builtin_msa_cle_s_d((v2i64)(__a), (v2i64)(__b))) -#define msa_cleq_f64(__a, __b) ((v2u64)__builtin_msa_fcle_d((v2f64)(__a), (v2f64)(__b))) - -/* compare greater-equal: cge -> ri = ai >= bi ? 1...1:0...0 */ -#define msa_cgeq_u8(__a, __b) ((v16u8)__builtin_msa_cle_u_b((v16u8)(__b), (v16u8)(__a))) -#define msa_cgeq_s8(__a, __b) ((v16u8)__builtin_msa_cle_s_b((v16i8)(__b), (v16i8)(__a))) -#define msa_cgeq_u16(__a, __b) ((v8u16)__builtin_msa_cle_u_h((v8u16)(__b), (v8u16)(__a))) -#define msa_cgeq_s16(__a, __b) ((v8u16)__builtin_msa_cle_s_h((v8i16)(__b), (v8i16)(__a))) -#define msa_cgeq_u32(__a, __b) ((v4u32)__builtin_msa_cle_u_w((v4u32)(__b), (v4u32)(__a))) -#define msa_cgeq_s32(__a, __b) ((v4u32)__builtin_msa_cle_s_w((v4i32)(__b), (v4i32)(__a))) -#define msa_cgeq_f32(__a, __b) ((v4u32)__builtin_msa_fcle_w((v4f32)(__b), (v4f32)(__a))) -#define msa_cgeq_u64(__a, __b) ((v2u64)__builtin_msa_cle_u_d((v2u64)(__b), (v2u64)(__a))) -#define msa_cgeq_s64(__a, __b) ((v2u64)__builtin_msa_cle_s_d((v2i64)(__b), (v2i64)(__a))) -#define msa_cgeq_f64(__a, __b) ((v2u64)__builtin_msa_fcle_d((v2f64)(__b), (v2f64)(__a))) - -/* Shift Left Logical: shl -> ri = ai << bi; */ -#define msa_shlq_u8(__a, __b) ((v16u8)__builtin_msa_sll_b((v16i8)(__a), (v16i8)(__b))) -#define msa_shlq_s8(__a, __b) ((v16i8)__builtin_msa_sll_b((v16i8)(__a), (v16i8)(__b))) -#define msa_shlq_u16(__a, __b) ((v8u16)__builtin_msa_sll_h((v8i16)(__a), (v8i16)(__b))) -#define msa_shlq_s16(__a, __b) ((v8i16)__builtin_msa_sll_h((v8i16)(__a), (v8i16)(__b))) -#define msa_shlq_u32(__a, __b) ((v4u32)__builtin_msa_sll_w((v4i32)(__a), (v4i32)(__b))) -#define msa_shlq_s32(__a, __b) ((v4i32)__builtin_msa_sll_w((v4i32)(__a), (v4i32)(__b))) -#define msa_shlq_u64(__a, __b) ((v2u64)__builtin_msa_sll_d((v2i64)(__a), (v2i64)(__b))) -#define msa_shlq_s64(__a, __b) ((v2i64)__builtin_msa_sll_d((v2i64)(__a), (v2i64)(__b))) - -/* Immediate Shift Left Logical: shl -> ri = ai << imm; */ -#define msa_shlq_n_u8(__a, __imm) ((v16u8)__builtin_msa_slli_b((v16i8)(__a), __imm)) -#define msa_shlq_n_s8(__a, __imm) ((v16i8)__builtin_msa_slli_b((v16i8)(__a), __imm)) -#define msa_shlq_n_u16(__a, __imm) ((v8u16)__builtin_msa_slli_h((v8i16)(__a), __imm)) -#define msa_shlq_n_s16(__a, __imm) ((v8i16)__builtin_msa_slli_h((v8i16)(__a), __imm)) -#define msa_shlq_n_u32(__a, __imm) ((v4u32)__builtin_msa_slli_w((v4i32)(__a), __imm)) -#define msa_shlq_n_s32(__a, __imm) ((v4i32)__builtin_msa_slli_w((v4i32)(__a), __imm)) -#define msa_shlq_n_u64(__a, __imm) ((v2u64)__builtin_msa_slli_d((v2i64)(__a), __imm)) -#define msa_shlq_n_s64(__a, __imm) ((v2i64)__builtin_msa_slli_d((v2i64)(__a), __imm)) - -/* shift right: shrq -> ri = ai >> bi; */ -#define msa_shrq_u8(__a, __b) ((v16u8)__builtin_msa_srl_b((v16i8)(__a), (v16i8)(__b))) -#define msa_shrq_s8(__a, __b) ((v16i8)__builtin_msa_sra_b((v16i8)(__a), (v16i8)(__b))) -#define msa_shrq_u16(__a, __b) ((v8u16)__builtin_msa_srl_h((v8i16)(__a), (v8i16)(__b))) -#define msa_shrq_s16(__a, __b) ((v8i16)__builtin_msa_sra_h((v8i16)(__a), (v8i16)(__b))) -#define msa_shrq_u32(__a, __b) ((v4u32)__builtin_msa_srl_w((v4i32)(__a), (v4i32)(__b))) -#define msa_shrq_s32(__a, __b) ((v4i32)__builtin_msa_sra_w((v4i32)(__a), (v4i32)(__b))) -#define msa_shrq_u64(__a, __b) ((v2u64)__builtin_msa_srl_d((v2i64)(__a), (v2i64)(__b))) -#define msa_shrq_s64(__a, __b) ((v2i64)__builtin_msa_sra_d((v2i64)(__a), (v2i64)(__b))) - -/* Immediate Shift Right: shr -> ri = ai >> imm; */ -#define msa_shrq_n_u8(__a, __imm) ((v16u8)__builtin_msa_srli_b((v16i8)(__a), __imm)) -#define msa_shrq_n_s8(__a, __imm) ((v16i8)__builtin_msa_srai_b((v16i8)(__a), __imm)) -#define msa_shrq_n_u16(__a, __imm) ((v8u16)__builtin_msa_srli_h((v8i16)(__a), __imm)) -#define msa_shrq_n_s16(__a, __imm) ((v8i16)__builtin_msa_srai_h((v8i16)(__a), __imm)) -#define msa_shrq_n_u32(__a, __imm) ((v4u32)__builtin_msa_srli_w((v4i32)(__a), __imm)) -#define msa_shrq_n_s32(__a, __imm) ((v4i32)__builtin_msa_srai_w((v4i32)(__a), __imm)) -#define msa_shrq_n_u64(__a, __imm) ((v2u64)__builtin_msa_srli_d((v2i64)(__a), __imm)) -#define msa_shrq_n_s64(__a, __imm) ((v2i64)__builtin_msa_srai_d((v2i64)(__a), __imm)) - -/* Immediate Shift Right Rounded: shr -> ri = ai >> (rounded)imm; */ -#define msa_rshrq_n_u8(__a, __imm) ((v16u8)__builtin_msa_srlri_b((v16i8)(__a), __imm)) -#define msa_rshrq_n_s8(__a, __imm) ((v16i8)__builtin_msa_srari_b((v16i8)(__a), __imm)) -#define msa_rshrq_n_u16(__a, __imm) ((v8u16)__builtin_msa_srlri_h((v8i16)(__a), __imm)) -#define msa_rshrq_n_s16(__a, __imm) ((v8i16)__builtin_msa_srari_h((v8i16)(__a), __imm)) -#define msa_rshrq_n_u32(__a, __imm) ((v4u32)__builtin_msa_srlri_w((v4i32)(__a), __imm)) -#define msa_rshrq_n_s32(__a, __imm) ((v4i32)__builtin_msa_srari_w((v4i32)(__a), __imm)) -#define msa_rshrq_n_u64(__a, __imm) ((v2u64)__builtin_msa_srlri_d((v2i64)(__a), __imm)) -#define msa_rshrq_n_s64(__a, __imm) ((v2i64)__builtin_msa_srari_d((v2i64)(__a), __imm)) - -/* Vector saturating rounding shift left, qrshl -> ri = ai << bi; */ -#define msa_qrshrq_s32(a, b) ((v4i32)__msa_srar_w((v4i32)(a), (v4i32)(b))) - -/* Rename the msa builtin func to unify the name style for intrin_msa.hpp */ -#define msa_qaddq_u8 __builtin_msa_adds_u_b -#define msa_qaddq_s8 __builtin_msa_adds_s_b -#define msa_qaddq_u16 __builtin_msa_adds_u_h -#define msa_qaddq_s16 __builtin_msa_adds_s_h -#define msa_qaddq_u32 __builtin_msa_adds_u_w -#define msa_qaddq_s32 __builtin_msa_adds_s_w -#define msa_qaddq_u64 __builtin_msa_adds_u_d -#define msa_qaddq_s64 __builtin_msa_adds_s_d -#define msa_addq_u8(a, b) ((v16u8)__builtin_msa_addv_b((v16i8)(a), (v16i8)(b))) -#define msa_addq_s8 __builtin_msa_addv_b -#define msa_addq_u16(a, b) ((v8u16)__builtin_msa_addv_h((v8i16)(a), (v8i16)(b))) -#define msa_addq_s16 __builtin_msa_addv_h -#define msa_addq_u32(a, b) ((v4u32)__builtin_msa_addv_w((v4i32)(a), (v4i32)(b))) -#define msa_addq_s32 __builtin_msa_addv_w -#define msa_addq_f32 __builtin_msa_fadd_w -#define msa_addq_u64(a, b) ((v2u64)__builtin_msa_addv_d((v2i64)(a), (v2i64)(b))) -#define msa_addq_s64 __builtin_msa_addv_d -#define msa_addq_f64 __builtin_msa_fadd_d -#define msa_qsubq_u8 __builtin_msa_subs_u_b -#define msa_qsubq_s8 __builtin_msa_subs_s_b -#define msa_qsubq_u16 __builtin_msa_subs_u_h -#define msa_qsubq_s16 __builtin_msa_subs_s_h -#define msa_subq_u8(a, b) ((v16u8)__builtin_msa_subv_b((v16i8)(a), (v16i8)(b))) -#define msa_subq_s8 __builtin_msa_subv_b -#define msa_subq_u16(a, b) ((v8u16)__builtin_msa_subv_h((v8i16)(a), (v8i16)(b))) -#define msa_subq_s16 __builtin_msa_subv_h -#define msa_subq_u32(a, b) ((v4u32)__builtin_msa_subv_w((v4i32)(a), (v4i32)(b))) -#define msa_subq_s32 __builtin_msa_subv_w -#define msa_subq_f32 __builtin_msa_fsub_w -#define msa_subq_u64(a, b) ((v2u64)__builtin_msa_subv_d((v2i64)(a), (v2i64)(b))) -#define msa_subq_s64 __builtin_msa_subv_d -#define msa_subq_f64 __builtin_msa_fsub_d -#define msa_mulq_u8(a, b) ((v16u8)__builtin_msa_mulv_b((v16i8)(a), (v16i8)(b))) -#define msa_mulq_s8(a, b) ((v16i8)__builtin_msa_mulv_b((v16i8)(a), (v16i8)(b))) -#define msa_mulq_u16(a, b) ((v8u16)__builtin_msa_mulv_h((v8i16)(a), (v8i16)(b))) -#define msa_mulq_s16(a, b) ((v8i16)__builtin_msa_mulv_h((v8i16)(a), (v8i16)(b))) -#define msa_mulq_u32(a, b) ((v4u32)__builtin_msa_mulv_w((v4i32)(a), (v4i32)(b))) -#define msa_mulq_s32(a, b) ((v4i32)__builtin_msa_mulv_w((v4i32)(a), (v4i32)(b))) -#define msa_mulq_u64(a, b) ((v2u64)__builtin_msa_mulv_d((v2i64)(a), (v2i64)(b))) -#define msa_mulq_s64(a, b) ((v2i64)__builtin_msa_mulv_d((v2i64)(a), (v2i64)(b))) -#define msa_mulq_f32 __builtin_msa_fmul_w -#define msa_mulq_f64 __builtin_msa_fmul_d -#define msa_divq_f32 __builtin_msa_fdiv_w -#define msa_divq_f64 __builtin_msa_fdiv_d -#define msa_dotp_s_h __builtin_msa_dotp_s_h -#define msa_dotp_s_w __builtin_msa_dotp_s_w -#define msa_dotp_s_d __builtin_msa_dotp_s_d -#define msa_dotp_u_h __builtin_msa_dotp_u_h -#define msa_dotp_u_w __builtin_msa_dotp_u_w -#define msa_dotp_u_d __builtin_msa_dotp_u_d -#define msa_dpadd_s_h __builtin_msa_dpadd_s_h -#define msa_dpadd_s_w __builtin_msa_dpadd_s_w -#define msa_dpadd_s_d __builtin_msa_dpadd_s_d -#define msa_dpadd_u_h __builtin_msa_dpadd_u_h -#define msa_dpadd_u_w __builtin_msa_dpadd_u_w -#define msa_dpadd_u_d __builtin_msa_dpadd_u_d - -#define ILVRL_B2(RTYPE, in0, in1, low, hi) do { \ - low = (RTYPE)__builtin_msa_ilvr_b((v16i8)(in0), (v16i8)(in1)); \ - hi = (RTYPE)__builtin_msa_ilvl_b((v16i8)(in0), (v16i8)(in1)); \ - } while (0) -#define ILVRL_B2_UB(...) ILVRL_B2(v16u8, __VA_ARGS__) -#define ILVRL_B2_SB(...) ILVRL_B2(v16i8, __VA_ARGS__) -#define ILVRL_B2_UH(...) ILVRL_B2(v8u16, __VA_ARGS__) -#define ILVRL_B2_SH(...) ILVRL_B2(v8i16, __VA_ARGS__) -#define ILVRL_B2_SW(...) ILVRL_B2(v4i32, __VA_ARGS__) - -#define ILVRL_H2(RTYPE, in0, in1, low, hi) do { \ - low = (RTYPE)__builtin_msa_ilvr_h((v8i16)(in0), (v8i16)(in1)); \ - hi = (RTYPE)__builtin_msa_ilvl_h((v8i16)(in0), (v8i16)(in1)); \ - } while (0) -#define ILVRL_H2_UB(...) ILVRL_H2(v16u8, __VA_ARGS__) -#define ILVRL_H2_SB(...) ILVRL_H2(v16i8, __VA_ARGS__) -#define ILVRL_H2_UH(...) ILVRL_H2(v8u16, __VA_ARGS__) -#define ILVRL_H2_SH(...) ILVRL_H2(v8i16, __VA_ARGS__) -#define ILVRL_H2_SW(...) ILVRL_H2(v4i32, __VA_ARGS__) -#define ILVRL_H2_UW(...) ILVRL_H2(v4u32, __VA_ARGS__) - -#define ILVRL_W2(RTYPE, in0, in1, low, hi) do { \ - low = (RTYPE)__builtin_msa_ilvr_w((v4i32)(in0), (v4i32)(in1)); \ - hi = (RTYPE)__builtin_msa_ilvl_w((v4i32)(in0), (v4i32)(in1)); \ - } while (0) -#define ILVRL_W2_UB(...) ILVRL_W2(v16u8, __VA_ARGS__) -#define ILVRL_W2_SH(...) ILVRL_W2(v8i16, __VA_ARGS__) -#define ILVRL_W2_SW(...) ILVRL_W2(v4i32, __VA_ARGS__) -#define ILVRL_W2_UW(...) ILVRL_W2(v4u32, __VA_ARGS__) - -/* absq, qabsq (r = |a|;) */ -#define msa_absq_s8(a) __builtin_msa_add_a_b(a, __builtin_msa_fill_b(0)) -#define msa_absq_s16(a) __builtin_msa_add_a_h(a, __builtin_msa_fill_h(0)) -#define msa_absq_s32(a) __builtin_msa_add_a_w(a, __builtin_msa_fill_w(0)) -#define msa_absq_s64(a) __builtin_msa_add_a_d(a, __builtin_msa_fill_d(0)) -#define msa_absq_f32(a) ((v4f32)__builtin_msa_bclri_w((v4u32)(a), 31)) -#define msa_absq_f64(a) ((v2f64)__builtin_msa_bclri_d((v2u64)(a), 63)) -#define msa_qabsq_s8(a) __builtin_msa_adds_a_b(a, __builtin_msa_fill_b(0)) -#define msa_qabsq_s16(a) __builtin_msa_adds_a_h(a, __builtin_msa_fill_h(0)) -#define msa_qabsq_s32(a) __builtin_msa_adds_a_w(a, __builtin_msa_fill_w(0)) -#define msa_qabsq_s64(a) __builtin_msa_adds_a_d(a, __builtin_msa_fill_d(0)) - -/* abdq, qabdq (r = |a - b|;) */ -#define msa_abdq_u8 __builtin_msa_asub_u_b -#define msa_abdq_s8 __builtin_msa_asub_s_b -#define msa_abdq_u16 __builtin_msa_asub_u_h -#define msa_abdq_s16 __builtin_msa_asub_s_h -#define msa_abdq_u32 __builtin_msa_asub_u_w -#define msa_abdq_s32 __builtin_msa_asub_s_w -#define msa_abdq_u64 __builtin_msa_asub_u_d -#define msa_abdq_s64 __builtin_msa_asub_s_d -#define msa_abdq_f32(a, b) msa_absq_f32(__builtin_msa_fsub_w(a, b)) -#define msa_abdq_f64(a, b) msa_absq_f64(__builtin_msa_fsub_d(a, b)) -#define msa_qabdq_s8(a, b) msa_qabsq_s8(__builtin_msa_subs_s_b(a, b)) -#define msa_qabdq_s16(a, b) msa_qabsq_s16(__builtin_msa_subs_s_h(a, b)) -#define msa_qabdq_s32(a, b) msa_qabsq_s32(__builtin_msa_subs_s_w(a, b)) -#define msa_qabdq_s64(a, b) msa_qabsq_s64(__builtin_msa_subs_s_d(a, b)) - -/* sqrtq, rsqrtq */ -#define msa_sqrtq_f32 __builtin_msa_fsqrt_w -#define msa_sqrtq_f64 __builtin_msa_fsqrt_d -#define msa_rsqrtq_f32 __builtin_msa_frsqrt_w -#define msa_rsqrtq_f64 __builtin_msa_frsqrt_d - - -/* mlaq: r = a + b * c; */ -__extension__ extern __inline v4i32 -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) -msa_mlaq_s32(v4i32 __a, v4i32 __b, v4i32 __c) -{ - __asm__ volatile("maddv.w %w[__a], %w[__b], %w[__c]\n" - // Outputs - : [__a] "+f"(__a) - // Inputs - : [__b] "f"(__b), [__c] "f"(__c)); - return __a; -} - -__extension__ extern __inline v2i64 -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) -msa_mlaq_s64(v2i64 __a, v2i64 __b, v2i64 __c) -{ - __asm__ volatile("maddv.d %w[__a], %w[__b], %w[__c]\n" - // Outputs - : [__a] "+f"(__a) - // Inputs - : [__b] "f"(__b), [__c] "f"(__c)); - return __a; -} - -__extension__ extern __inline v4f32 -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) -msa_mlaq_f32(v4f32 __a, v4f32 __b, v4f32 __c) -{ - __asm__ volatile("fmadd.w %w[__a], %w[__b], %w[__c]\n" - // Outputs - : [__a] "+f"(__a) - // Inputs - : [__b] "f"(__b), [__c] "f"(__c)); - return __a; -} - -__extension__ extern __inline v2f64 -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) -msa_mlaq_f64(v2f64 __a, v2f64 __b, v2f64 __c) -{ - __asm__ volatile("fmadd.d %w[__a], %w[__b], %w[__c]\n" - // Outputs - : [__a] "+f"(__a) - // Inputs - : [__b] "f"(__b), [__c] "f"(__c)); - return __a; -} - -/* cntq */ -#define msa_cntq_s8 __builtin_msa_pcnt_b -#define msa_cntq_s16 __builtin_msa_pcnt_h -#define msa_cntq_s32 __builtin_msa_pcnt_w -#define msa_cntq_s64 __builtin_msa_pcnt_d - -/* bslq (a: mask; r = b(if a == 0); r = c(if a == 1);) */ -#define msa_bslq_u8 __builtin_msa_bsel_v - -/* ilvrq, ilvlq (For EL only, ilvrq: b0, a0, b1, a1; ilvlq: b2, a2, b3, a3;) */ -#define msa_ilvrq_s8 __builtin_msa_ilvr_b -#define msa_ilvrq_s16 __builtin_msa_ilvr_h -#define msa_ilvrq_s32 __builtin_msa_ilvr_w -#define msa_ilvrq_s64 __builtin_msa_ilvr_d -#define msa_ilvlq_s8 __builtin_msa_ilvl_b -#define msa_ilvlq_s16 __builtin_msa_ilvl_h -#define msa_ilvlq_s32 __builtin_msa_ilvl_w -#define msa_ilvlq_s64 __builtin_msa_ilvl_d - -/* ilvevq, ilvodq (ilvevq: b0, a0, b2, a2; ilvodq: b1, a1, b3, a3; ) */ -#define msa_ilvevq_s8 __builtin_msa_ilvev_b -#define msa_ilvevq_s16 __builtin_msa_ilvev_h -#define msa_ilvevq_s32 __builtin_msa_ilvev_w -#define msa_ilvevq_s64 __builtin_msa_ilvev_d -#define msa_ilvodq_s8 __builtin_msa_ilvod_b -#define msa_ilvodq_s16 __builtin_msa_ilvod_h -#define msa_ilvodq_s32 __builtin_msa_ilvod_w -#define msa_ilvodq_s64 __builtin_msa_ilvod_d - -/* extq (r = (a || b); a concatenation b and get elements from index c) */ -#ifdef _MIPSEB -#define msa_extq_s8(a, b, c) \ -(__builtin_msa_vshf_b(__builtin_msa_subv_b((v16i8)((v2i64){0x1716151413121110, 0x1F1E1D1C1B1A1918}), __builtin_msa_fill_b(c)), a, b)) -#define msa_extq_s16(a, b, c) \ -(__builtin_msa_vshf_h(__builtin_msa_subv_h((v8i16)((v2i64){0x000B000A00090008, 0x000F000E000D000C}), __builtin_msa_fill_h(c)), a, b)) -#define msa_extq_s32(a, b, c) \ -(__builtin_msa_vshf_w(__builtin_msa_subv_w((v4i32)((v2i64){0x0000000500000004, 0x0000000700000006}), __builtin_msa_fill_w(c)), a, b)) -#define msa_extq_s64(a, b, c) \ -(__builtin_msa_vshf_d(__builtin_msa_subv_d((v2i64){0x0000000000000002, 0x0000000000000003}, __builtin_msa_fill_d(c)), a, b)) -#else -#define msa_extq_s8(a, b, c) \ -(__builtin_msa_vshf_b(__builtin_msa_addv_b((v16i8)((v2i64){0x0706050403020100, 0x0F0E0D0C0B0A0908}), __builtin_msa_fill_b(c)), b, a)) -#define msa_extq_s16(a, b, c) \ -(__builtin_msa_vshf_h(__builtin_msa_addv_h((v8i16)((v2i64){0x0003000200010000, 0x0007000600050004}), __builtin_msa_fill_h(c)), b, a)) -#define msa_extq_s32(a, b, c) \ -(__builtin_msa_vshf_w(__builtin_msa_addv_w((v4i32)((v2i64){0x0000000100000000, 0x0000000300000002}), __builtin_msa_fill_w(c)), b, a)) -#define msa_extq_s64(a, b, c) \ -(__builtin_msa_vshf_d(__builtin_msa_addv_d((v2i64){0x0000000000000000, 0x0000000000000001}, __builtin_msa_fill_d(c)), b, a)) -#endif /* _MIPSEB */ - -/* cvttruncq, cvttintq, cvtrintq */ -#define msa_cvttruncq_u32_f32 __builtin_msa_ftrunc_u_w -#define msa_cvttruncq_s32_f32 __builtin_msa_ftrunc_s_w -#define msa_cvttruncq_u64_f64 __builtin_msa_ftrunc_u_d -#define msa_cvttruncq_s64_f64 __builtin_msa_ftrunc_s_d -#define msa_cvttintq_u32_f32 __builtin_msa_ftint_u_w -#define msa_cvttintq_s32_f32 __builtin_msa_ftint_s_w -#define msa_cvttintq_u64_f64 __builtin_msa_ftint_u_d -#define msa_cvttintq_s64_f64 __builtin_msa_ftint_s_d -#define msa_cvtrintq_f32 __builtin_msa_frint_w -#define msa_cvtrintq_f64 __builtin_msa_frint_d - -/* cvtfintq, cvtfq */ -#define msa_cvtfintq_f32_u32 __builtin_msa_ffint_u_w -#define msa_cvtfintq_f32_s32 __builtin_msa_ffint_s_w -#define msa_cvtfintq_f64_u64 __builtin_msa_ffint_u_d -#define msa_cvtfintq_f64_s64 __builtin_msa_ffint_s_d -#define msa_cvtfq_f32_f64 __builtin_msa_fexdo_w -#define msa_cvtflq_f64_f32 __builtin_msa_fexupr_d -#define msa_cvtfhq_f64_f32 __builtin_msa_fexupl_d - -#define msa_addl_u8(a, b) ((v8u16)__builtin_msa_addv_h((v8i16)V8U8_2_V8I16(a), (v8i16)V8U8_2_V8I16(b))) -#define msa_addl_s8(a, b) (__builtin_msa_addv_h((v8i16)V8I8_2_V8I16(a), (v8i16)V8I8_2_V8I16(b))) -#define msa_addl_u16(a, b) ((v4u32)__builtin_msa_addv_w((v4i32)V4U16_2_V4I32(a), (v4i32)V4U16_2_V4I32(b))) -#define msa_addl_s16(a, b) (__builtin_msa_addv_w((v4i32)V4I16_2_V4I32(a), (v4i32)V4I16_2_V4I32(b))) -#define msa_subl_s16(a, b) (__builtin_msa_subv_w((v4i32)V4I16_2_V4I32(a), (v4i32)V4I16_2_V4I32(b))) -#define msa_recpeq_f32 __builtin_msa_frcp_w -#define msa_recpsq_f32(a, b) (__builtin_msa_fsub_w(msa_dupq_n_f32(2.0f), __builtin_msa_fmul_w(a, b))) - -#define MSA_INTERLEAVED_IMPL_LOAD2_STORE2(_Tp, _Tpv, _Tpvs, suffix, df, nlanes) \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_ld2q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b) \ -{ \ - _Tpv v0 = msa_ld1q_##suffix(ptr); \ - _Tpv v1 = msa_ld1q_##suffix(ptr + nlanes); \ - *a = (_Tpv)__builtin_msa_pckev_##df((_Tpvs)v1, (_Tpvs)v0); \ - *b = (_Tpv)__builtin_msa_pckod_##df((_Tpvs)v1, (_Tpvs)v0); \ -} \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_st2q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b) \ -{ \ - msa_st1q_##suffix(ptr, (_Tpv)__builtin_msa_ilvr_##df((_Tpvs)b, (_Tpvs)a)); \ - msa_st1q_##suffix(ptr + nlanes, (_Tpv)__builtin_msa_ilvl_##df((_Tpvs)b, (_Tpvs)a)); \ -} - -MSA_INTERLEAVED_IMPL_LOAD2_STORE2(uint8_t, v16u8, v16i8, u8, b, 16) -MSA_INTERLEAVED_IMPL_LOAD2_STORE2(int8_t, v16i8, v16i8, s8, b, 16) -MSA_INTERLEAVED_IMPL_LOAD2_STORE2(uint16_t, v8u16, v8i16, u16, h, 8) -MSA_INTERLEAVED_IMPL_LOAD2_STORE2(int16_t, v8i16, v8i16, s16, h, 8) -MSA_INTERLEAVED_IMPL_LOAD2_STORE2(uint32_t, v4u32, v4i32, u32, w, 4) -MSA_INTERLEAVED_IMPL_LOAD2_STORE2(int32_t, v4i32, v4i32, s32, w, 4) -MSA_INTERLEAVED_IMPL_LOAD2_STORE2(float, v4f32, v4i32, f32, w, 4) -MSA_INTERLEAVED_IMPL_LOAD2_STORE2(uint64_t, v2u64, v2i64, u64, d, 2) -MSA_INTERLEAVED_IMPL_LOAD2_STORE2(int64_t, v2i64, v2i64, s64, d, 2) -MSA_INTERLEAVED_IMPL_LOAD2_STORE2(double, v2f64, v2i64, f64, d, 2) - -#ifdef _MIPSEB -#define MSA_INTERLEAVED_IMPL_LOAD3_8(_Tp, _Tpv, _Tpvs, suffix) \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_ld3q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b, _Tpv* c) \ -{ \ - _Tpv v0 = msa_ld1q_##suffix(ptr); \ - _Tpv v1 = msa_ld1q_##suffix(ptr + 16); \ - _Tpv v2 = msa_ld1q_##suffix(ptr + 32); \ - _Tpvs v3 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0704011F1F1F1F1F, 0x1F1C191613100D0A}), (_Tpvs)v0, (_Tpvs)v1); \ - *a = (_Tpv)__builtin_msa_vshf_b((_Tpvs)((v2i64){0x1716150E0B080502, 0x1F1E1D1C1B1A1918}), v3, (_Tpvs)v2); \ - v3 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0603001F1F1F1F1F, 0x1E1B1815120F0C09}), (_Tpvs)v0, (_Tpvs)v1); \ - *b = (_Tpv)__builtin_msa_vshf_b((_Tpvs)((v2i64){0x1716150D0A070401, 0x1F1E1D1C1B1A1918}), v3, (_Tpvs)v2); \ - v3 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x05021F1F1F1F1F1F, 0x1D1A1714110E0B08}), (_Tpvs)v0, (_Tpvs)v1); \ - *c = (_Tpv)__builtin_msa_vshf_b((_Tpvs)((v2i64){0x17160F0C09060300, 0x1F1E1D1C1B1A1918}), v3, (_Tpvs)v2); \ -} -#else -#define MSA_INTERLEAVED_IMPL_LOAD3_8(_Tp, _Tpv, _Tpvs, suffix) \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_ld3q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b, _Tpv* c) \ -{ \ - _Tpv v0 = msa_ld1q_##suffix(ptr); \ - _Tpv v1 = msa_ld1q_##suffix(ptr + 16); \ - _Tpv v2 = msa_ld1q_##suffix(ptr + 32); \ - _Tpvs v3 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x15120F0C09060300, 0x00000000001E1B18}), (_Tpvs)v1, (_Tpvs)v0); \ - *a = (_Tpv)__builtin_msa_vshf_b((_Tpvs)((v2i64){0x0706050403020100, 0x1D1A1714110A0908}), (_Tpvs)v2, v3); \ - v3 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x1613100D0A070401, 0x00000000001F1C19}), (_Tpvs)v1, (_Tpvs)v0); \ - *b = (_Tpv)__builtin_msa_vshf_b((_Tpvs)((v2i64){0x0706050403020100, 0x1E1B1815120A0908}), (_Tpvs)v2, v3); \ - v3 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x1714110E0B080502, 0x0000000000001D1A}), (_Tpvs)v1, (_Tpvs)v0); \ - *c = (_Tpv)__builtin_msa_vshf_b((_Tpvs)((v2i64){0x0706050403020100, 0x1F1C191613100908}), (_Tpvs)v2, v3); \ -} -#endif - -MSA_INTERLEAVED_IMPL_LOAD3_8(uint8_t, v16u8, v16i8, u8) -MSA_INTERLEAVED_IMPL_LOAD3_8(int8_t, v16i8, v16i8, s8) - -#ifdef _MIPSEB -#define MSA_INTERLEAVED_IMPL_LOAD3_16(_Tp, _Tpv, _Tpvs, suffix) \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_ld3q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b, _Tpv* c) \ -{ \ - _Tpv v0 = msa_ld1q_##suffix(ptr); \ - _Tpv v1 = msa_ld1q_##suffix(ptr + 8); \ - _Tpv v2 = msa_ld1q_##suffix(ptr + 16); \ - _Tpvs v3 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x00030000000F000F, 0x000F000C00090006}), (_Tpvs)v1, (_Tpvs)v0); \ - *a = (_Tpv)__builtin_msa_vshf_h((_Tpvs)((v2i64){0x000B000A00050002, 0x000F000E000D000C}), (_Tpvs)v2, v3); \ - v3 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x0002000F000F000F, 0x000E000B00080005}), (_Tpvs)v1, (_Tpvs)v0); \ - *b = (_Tpv)__builtin_msa_vshf_h((_Tpvs)((v2i64){0x000B000700040001, 0x000F000E000D000C}), (_Tpvs)v2, v3); \ - v3 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x0001000F000F000F, 0x000D000A00070004}), (_Tpvs)v1, (_Tpvs)v0); \ - *c = (_Tpv)__builtin_msa_vshf_h((_Tpvs)((v2i64){0x000B000600030000, 0x000F000E000D000C}), (_Tpvs)v2, v3); \ -} -#else -#define MSA_INTERLEAVED_IMPL_LOAD3_16(_Tp, _Tpv, _Tpvs, suffix) \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_ld3q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b, _Tpv* c) \ -{ \ - _Tpv v0 = msa_ld1q_##suffix(ptr); \ - _Tpv v1 = msa_ld1q_##suffix(ptr + 8); \ - _Tpv v2 = msa_ld1q_##suffix(ptr + 16); \ - _Tpvs v3 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x0009000600030000, 0x00000000000F000C}), (_Tpvs)v1, (_Tpvs)v0); \ - *a = (_Tpv)__builtin_msa_vshf_h((_Tpvs)((v2i64){0x0003000200010000, 0x000D000A00050004}), (_Tpvs)v2, v3); \ - v3 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x000A000700040001, 0x000000000000000D}), (_Tpvs)v1, (_Tpvs)v0); \ - *b = (_Tpv)__builtin_msa_vshf_h((_Tpvs)((v2i64){0x0003000200010000, 0x000E000B00080004}), (_Tpvs)v2, v3); \ - v3 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x000B000800050002, 0x000000000000000E}), (_Tpvs)v1, (_Tpvs)v0); \ - *c = (_Tpv)__builtin_msa_vshf_h((_Tpvs)((v2i64){0x0003000200010000, 0x000F000C00090004}), (_Tpvs)v2, v3); \ -} -#endif - -MSA_INTERLEAVED_IMPL_LOAD3_16(uint16_t, v8u16, v8i16, u16) -MSA_INTERLEAVED_IMPL_LOAD3_16(int16_t, v8i16, v8i16, s16) - -#define MSA_INTERLEAVED_IMPL_LOAD3_32(_Tp, _Tpv, _Tpvs, suffix) \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_ld3q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b, _Tpv* c) \ -{ \ - _Tpv v00 = msa_ld1q_##suffix(ptr); \ - _Tpv v01 = msa_ld1q_##suffix(ptr + 4); \ - _Tpv v02 = msa_ld1q_##suffix(ptr + 8); \ - _Tpvs v10 = __builtin_msa_ilvr_w((_Tpvs)__builtin_msa_ilvl_d((v2i64)v01, (v2i64)v01), (_Tpvs)v00); \ - _Tpvs v11 = __builtin_msa_ilvr_w((_Tpvs)v02, (_Tpvs)__builtin_msa_ilvl_d((v2i64)v00, (v2i64)v00)); \ - _Tpvs v12 = __builtin_msa_ilvr_w((_Tpvs)__builtin_msa_ilvl_d((v2i64)v02, (v2i64)v02), (_Tpvs)v01); \ - *a = (_Tpv)__builtin_msa_ilvr_w((_Tpvs)__builtin_msa_ilvl_d((v2i64)v11, (v2i64)v11), v10); \ - *b = (_Tpv)__builtin_msa_ilvr_w(v12, (_Tpvs)__builtin_msa_ilvl_d((v2i64)v10, (v2i64)v10)); \ - *c = (_Tpv)__builtin_msa_ilvr_w((_Tpvs)__builtin_msa_ilvl_d((v2i64)v12, (v2i64)v12), v11); \ -} - -MSA_INTERLEAVED_IMPL_LOAD3_32(uint32_t, v4u32, v4i32, u32) -MSA_INTERLEAVED_IMPL_LOAD3_32(int32_t, v4i32, v4i32, s32) -MSA_INTERLEAVED_IMPL_LOAD3_32(float, v4f32, v4i32, f32) - -#define MSA_INTERLEAVED_IMPL_LOAD3_64(_Tp, _Tpv, suffix) \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_ld3q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b, _Tpv* c) \ -{ \ - *((_Tp*)a) = *ptr; *((_Tp*)b) = *(ptr + 1); *((_Tp*)c) = *(ptr + 2); \ - *((_Tp*)a + 1) = *(ptr + 3); *((_Tp*)b + 1) = *(ptr + 4); *((_Tp*)c + 1) = *(ptr + 5); \ -} - -MSA_INTERLEAVED_IMPL_LOAD3_64(uint64_t, v2u64, u64) -MSA_INTERLEAVED_IMPL_LOAD3_64(int64_t, v2i64, s64) -MSA_INTERLEAVED_IMPL_LOAD3_64(double, v2f64, f64) - -#ifdef _MIPSEB -#define MSA_INTERLEAVED_IMPL_STORE3_8(_Tp, _Tpv, _Tpvs, suffix) \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_st3q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c) \ -{ \ - _Tpvs v0 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0F0E0D0C0B1F1F1F, 0x1F1E1D1C1B1A1F1F}), (_Tpvs)b, (_Tpvs)a); \ - _Tpvs v1 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0D1C140C1B130B1A, 0x1F170F1E160E1D15}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr, (_Tpv)v1); \ - v0 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0A09080706051F1F, 0x19181716151F1F1F}), (_Tpvs)b, (_Tpvs)a); \ - v1 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x1D14071C13061B12, 0x170A1F16091E1508}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr + 16, (_Tpv)v1); \ - v0 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x04030201001F1F1F, 0x14131211101F1F1F}), (_Tpvs)b, (_Tpvs)a); \ - v1 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x15021C14011B1300, 0x051F17041E16031D}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr + 32, (_Tpv)v1); \ -} -#else -#define MSA_INTERLEAVED_IMPL_STORE3_8(_Tp, _Tpv, _Tpvs, suffix) \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_st3q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c) \ -{ \ - _Tpvs v0 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0000050403020100, 0x0000001413121110}), (_Tpvs)b, (_Tpvs)a); \ - _Tpvs v1 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0A02110901100800, 0x05140C04130B0312}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr, (_Tpv)v1); \ - v0 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0000000A09080706, 0x00001A1918171615}), (_Tpvs)b, (_Tpvs)a); \ - v1 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x170A011609001508, 0x0D04190C03180B02}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr + 16, (_Tpv)v1); \ - v0 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x0000000F0E0D0C0B, 0x0000001F1E1D1C1B}), (_Tpvs)b, (_Tpvs)a); \ - v1 = __builtin_msa_vshf_b((_Tpvs)((v2i64){0x021C09011B08001A, 0x1F0C041E0B031D0A}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr + 32, (_Tpv)v1); \ -} -#endif - -MSA_INTERLEAVED_IMPL_STORE3_8(uint8_t, v16u8, v16i8, u8) -MSA_INTERLEAVED_IMPL_STORE3_8(int8_t, v16i8, v16i8, s8) - -#ifdef _MIPSEB -#define MSA_INTERLEAVED_IMPL_STORE3_16(_Tp, _Tpv, _Tpvs, suffix) \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_st3q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c) \ -{ \ - _Tpvs v0 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x000700060005000F, 0x000F000E000D000F}), (_Tpvs)b, (_Tpvs)a); \ - _Tpvs v1 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x000A0006000D0009, 0x000F000B0007000E}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr, (_Tpv)v1); \ - v0 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x00040003000F000F, 0x000C000B000A000F}), (_Tpvs)b, (_Tpvs)a); \ - v1 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x000E000A0003000D, 0x0005000F000B0004}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr + 8, (_Tpv)v1); \ - v0 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x000200010000000F, 0x00090008000F000F}), (_Tpvs)b, (_Tpvs)a); \ - v1 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x0001000E00090000, 0x000B0002000F000A}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr + 16, (_Tpv)v1); \ -} -#else -#define MSA_INTERLEAVED_IMPL_STORE3_16(_Tp, _Tpv, _Tpvs, suffix) \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_st3q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c) \ -{ \ - _Tpvs v0 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x0000000200010000, 0x0000000A00090008}), (_Tpvs)b, (_Tpvs)a); \ - _Tpvs v1 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x0001000800040000, 0x0006000200090005}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr, (_Tpv)v1); \ - v0 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x0000000500040003, 0x00000000000C000B}), (_Tpvs)b, (_Tpvs)a); \ - v1 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x000B00040000000A, 0x0002000C00050001}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr + 8, (_Tpv)v1); \ - v0 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x0000000000070006, 0x0000000F000E000D}), (_Tpvs)b, (_Tpvs)a); \ - v1 = __builtin_msa_vshf_h((_Tpvs)((v2i64){0x00050000000D0004, 0x000F00060001000E}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr + 16, (_Tpv)v1); \ -} -#endif - -MSA_INTERLEAVED_IMPL_STORE3_16(uint16_t, v8u16, v8i16, u16) -MSA_INTERLEAVED_IMPL_STORE3_16(int16_t, v8i16, v8i16, s16) - -#ifdef _MIPSEB -#define MSA_INTERLEAVED_IMPL_STORE3_32(_Tp, _Tpv, _Tpvs, suffix) \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_st3q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c) \ -{ \ - _Tpvs v0 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000300000007, 0x0000000700000006}), (_Tpvs)b, (_Tpvs)a); \ - _Tpvs v1 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000300000006, 0x0000000700000005}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr, (_Tpv)v1); \ - v0 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000200000001, 0x0000000500000007}), (_Tpvs)b, (_Tpvs)a); \ - v1 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000700000004, 0x0000000500000002}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr + 4, (_Tpv)v1); \ - v0 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000000000007, 0x0000000400000007}), (_Tpvs)b, (_Tpvs)a); \ - v1 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000500000000, 0x0000000100000007}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr + 8, (_Tpv)v1); \ -} -#else -#define MSA_INTERLEAVED_IMPL_STORE3_32(_Tp, _Tpv, _Tpvs, suffix) \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_st3q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c) \ -{ \ - _Tpvs v0 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000100000000, 0x0000000000000004}), (_Tpvs)b, (_Tpvs)a); \ - _Tpvs v1 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000200000000, 0x0000000100000004}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr, (_Tpv)v1); \ - v0 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000000000002, 0x0000000600000005}), (_Tpvs)b, (_Tpvs)a); \ - v1 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000500000002, 0x0000000300000000}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr + 4, (_Tpv)v1); \ - v0 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000000000003, 0x0000000000000007}), (_Tpvs)b, (_Tpvs)a); \ - v1 = __builtin_msa_vshf_w((_Tpvs)((v2i64){0x0000000000000006, 0x0000000700000002}), (_Tpvs)c, (_Tpvs)v0); \ - msa_st1q_##suffix(ptr + 8, (_Tpv)v1); \ -} -#endif - -MSA_INTERLEAVED_IMPL_STORE3_32(uint32_t, v4u32, v4i32, u32) -MSA_INTERLEAVED_IMPL_STORE3_32(int32_t, v4i32, v4i32, s32) -MSA_INTERLEAVED_IMPL_STORE3_32(float, v4f32, v4i32, f32) - -#define MSA_INTERLEAVED_IMPL_STORE3_64(_Tp, _Tpv, suffix) \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_st3q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c) \ -{ \ - *ptr = a[0]; *(ptr + 1) = b[0]; *(ptr + 2) = c[0]; \ - *(ptr + 3) = a[1]; *(ptr + 4) = b[1]; *(ptr + 5) = c[1]; \ -} - -MSA_INTERLEAVED_IMPL_STORE3_64(uint64_t, v2u64, u64) -MSA_INTERLEAVED_IMPL_STORE3_64(int64_t, v2i64, s64) -MSA_INTERLEAVED_IMPL_STORE3_64(double, v2f64, f64) - -#define MSA_INTERLEAVED_IMPL_LOAD4_STORE4(_Tp, _Tpv, _Tpvs, suffix, df, nlanes) \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_ld4q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b, _Tpv* c, _Tpv* d) \ -{ \ - _Tpv v0 = msa_ld1q_##suffix(ptr); \ - _Tpv v1 = msa_ld1q_##suffix(ptr + nlanes); \ - _Tpv v2 = msa_ld1q_##suffix(ptr + nlanes * 2); \ - _Tpv v3 = msa_ld1q_##suffix(ptr + nlanes * 3); \ - _Tpvs t0 = __builtin_msa_pckev_##df((_Tpvs)v1, (_Tpvs)v0); \ - _Tpvs t1 = __builtin_msa_pckev_##df((_Tpvs)v3, (_Tpvs)v2); \ - _Tpvs t2 = __builtin_msa_pckod_##df((_Tpvs)v1, (_Tpvs)v0); \ - _Tpvs t3 = __builtin_msa_pckod_##df((_Tpvs)v3, (_Tpvs)v2); \ - *a = (_Tpv)__builtin_msa_pckev_##df(t1, t0); \ - *b = (_Tpv)__builtin_msa_pckev_##df(t3, t2); \ - *c = (_Tpv)__builtin_msa_pckod_##df(t1, t0); \ - *d = (_Tpv)__builtin_msa_pckod_##df(t3, t2); \ -} \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_st4q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c, const _Tpv d) \ -{ \ - _Tpvs v0 = __builtin_msa_ilvr_##df((_Tpvs)c, (_Tpvs)a); \ - _Tpvs v1 = __builtin_msa_ilvr_##df((_Tpvs)d, (_Tpvs)b); \ - _Tpvs v2 = __builtin_msa_ilvl_##df((_Tpvs)c, (_Tpvs)a); \ - _Tpvs v3 = __builtin_msa_ilvl_##df((_Tpvs)d, (_Tpvs)b); \ - msa_st1q_##suffix(ptr, (_Tpv)__builtin_msa_ilvr_##df(v1, v0)); \ - msa_st1q_##suffix(ptr + nlanes, (_Tpv)__builtin_msa_ilvl_##df(v1, v0)); \ - msa_st1q_##suffix(ptr + 2 * nlanes, (_Tpv)__builtin_msa_ilvr_##df(v3, v2)); \ - msa_st1q_##suffix(ptr + 3 * nlanes, (_Tpv)__builtin_msa_ilvl_##df(v3, v2)); \ -} - -MSA_INTERLEAVED_IMPL_LOAD4_STORE4(uint8_t, v16u8, v16i8, u8, b, 16) -MSA_INTERLEAVED_IMPL_LOAD4_STORE4(int8_t, v16i8, v16i8, s8, b, 16) -MSA_INTERLEAVED_IMPL_LOAD4_STORE4(uint16_t, v8u16, v8i16, u16, h, 8) -MSA_INTERLEAVED_IMPL_LOAD4_STORE4(int16_t, v8i16, v8i16, s16, h, 8) -MSA_INTERLEAVED_IMPL_LOAD4_STORE4(uint32_t, v4u32, v4i32, u32, w, 4) -MSA_INTERLEAVED_IMPL_LOAD4_STORE4(int32_t, v4i32, v4i32, s32, w, 4) -MSA_INTERLEAVED_IMPL_LOAD4_STORE4(float, v4f32, v4i32, f32, w, 4) - -#define MSA_INTERLEAVED_IMPL_LOAD4_STORE4_64(_Tp, _Tpv, _Tpvs, suffix) \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_ld4q_##suffix(const _Tp* ptr, _Tpv* a, _Tpv* b, _Tpv* c, _Tpv* d) \ -{ \ - _Tpv v0 = msa_ld1q_##suffix(ptr); \ - _Tpv v1 = msa_ld1q_##suffix(ptr + 2); \ - _Tpv v2 = msa_ld1q_##suffix(ptr + 4); \ - _Tpv v3 = msa_ld1q_##suffix(ptr + 6); \ - *a = (_Tpv)__builtin_msa_ilvr_d((_Tpvs)v2, (_Tpvs)v0); \ - *b = (_Tpv)__builtin_msa_ilvl_d((_Tpvs)v2, (_Tpvs)v0); \ - *c = (_Tpv)__builtin_msa_ilvr_d((_Tpvs)v3, (_Tpvs)v1); \ - *d = (_Tpv)__builtin_msa_ilvl_d((_Tpvs)v3, (_Tpvs)v1); \ -} \ -__extension__ extern __inline void \ -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) \ -msa_st4q_##suffix(_Tp* ptr, const _Tpv a, const _Tpv b, const _Tpv c, const _Tpv d) \ -{ \ - msa_st1q_##suffix(ptr, (_Tpv)__builtin_msa_ilvr_d((_Tpvs)b, (_Tpvs)a)); \ - msa_st1q_##suffix(ptr + 2, (_Tpv)__builtin_msa_ilvr_d((_Tpvs)d, (_Tpvs)c)); \ - msa_st1q_##suffix(ptr + 4, (_Tpv)__builtin_msa_ilvl_d((_Tpvs)b, (_Tpvs)a)); \ - msa_st1q_##suffix(ptr + 6, (_Tpv)__builtin_msa_ilvl_d((_Tpvs)d, (_Tpvs)c)); \ -} - -MSA_INTERLEAVED_IMPL_LOAD4_STORE4_64(uint64_t, v2u64, v2i64, u64) -MSA_INTERLEAVED_IMPL_LOAD4_STORE4_64(int64_t, v2i64, v2i64, s64) -MSA_INTERLEAVED_IMPL_LOAD4_STORE4_64(double, v2f64, v2i64, f64) - -__extension__ extern __inline v8i16 -__attribute__ ((__always_inline__, __gnu_inline__, __artificial__)) -msa_qdmulhq_n_s16(v8i16 a, int16_t b) -{ - v8i16 a_lo, a_hi; - ILVRL_H2_SH(a, msa_dupq_n_s16(0), a_lo, a_hi); - return msa_packr_s32(msa_shlq_n_s32(msa_mulq_s32(msa_paddlq_s16(a_lo), msa_dupq_n_s32(b)), 1), - msa_shlq_n_s32(msa_mulq_s32(msa_paddlq_s16(a_hi), msa_dupq_n_s32(b)), 1), 16); -} - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif /*__mips_msa*/ -#endif /* OPENCV_CORE_MSA_MACROS_H */ diff --git a/include/opencv2/core/hal/simd_utils.impl.hpp b/include/opencv2/core/hal/simd_utils.impl.hpp deleted file mode 100755 index fff8f94..0000000 --- a/include/opencv2/core/hal/simd_utils.impl.hpp +++ /dev/null @@ -1,146 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html - -// This header is not standalone. Don't include directly, use "intrin.hpp" instead. -#ifdef OPENCV_HAL_INTRIN_HPP // defined in intrin.hpp - - -#if CV_SIMD128 || CV_SIMD128_CPP - -template struct Type2Vec128_Traits; -#define CV_INTRIN_DEF_TYPE2VEC128_TRAITS(type_, vec_type_) \ - template<> struct Type2Vec128_Traits \ - { \ - typedef vec_type_ vec_type; \ - } - -CV_INTRIN_DEF_TYPE2VEC128_TRAITS(uchar, v_uint8x16); -CV_INTRIN_DEF_TYPE2VEC128_TRAITS(schar, v_int8x16); -CV_INTRIN_DEF_TYPE2VEC128_TRAITS(ushort, v_uint16x8); -CV_INTRIN_DEF_TYPE2VEC128_TRAITS(short, v_int16x8); -CV_INTRIN_DEF_TYPE2VEC128_TRAITS(unsigned, v_uint32x4); -CV_INTRIN_DEF_TYPE2VEC128_TRAITS(int, v_int32x4); -CV_INTRIN_DEF_TYPE2VEC128_TRAITS(float, v_float32x4); -CV_INTRIN_DEF_TYPE2VEC128_TRAITS(uint64, v_uint64x2); -CV_INTRIN_DEF_TYPE2VEC128_TRAITS(int64, v_int64x2); -#if CV_SIMD128_64F -CV_INTRIN_DEF_TYPE2VEC128_TRAITS(double, v_float64x2); -#endif - -template static inline -typename Type2Vec128_Traits<_T>::vec_type v_setall(const _T& a); - -template<> inline Type2Vec128_Traits< uchar>::vec_type v_setall< uchar>(const uchar& a) { return v_setall_u8(a); } -template<> inline Type2Vec128_Traits< schar>::vec_type v_setall< schar>(const schar& a) { return v_setall_s8(a); } -template<> inline Type2Vec128_Traits::vec_type v_setall(const ushort& a) { return v_setall_u16(a); } -template<> inline Type2Vec128_Traits< short>::vec_type v_setall< short>(const short& a) { return v_setall_s16(a); } -template<> inline Type2Vec128_Traits< uint>::vec_type v_setall< uint>(const uint& a) { return v_setall_u32(a); } -template<> inline Type2Vec128_Traits< int>::vec_type v_setall< int>(const int& a) { return v_setall_s32(a); } -template<> inline Type2Vec128_Traits::vec_type v_setall(const uint64& a) { return v_setall_u64(a); } -template<> inline Type2Vec128_Traits< int64>::vec_type v_setall< int64>(const int64& a) { return v_setall_s64(a); } -template<> inline Type2Vec128_Traits< float>::vec_type v_setall< float>(const float& a) { return v_setall_f32(a); } -#if CV_SIMD128_64F -template<> inline Type2Vec128_Traits::vec_type v_setall(const double& a) { return v_setall_f64(a); } -#endif - -#endif // SIMD128 - - -#if CV_SIMD256 - -template struct Type2Vec256_Traits; -#define CV_INTRIN_DEF_TYPE2VEC256_TRAITS(type_, vec_type_) \ - template<> struct Type2Vec256_Traits \ - { \ - typedef vec_type_ vec_type; \ - } - -CV_INTRIN_DEF_TYPE2VEC256_TRAITS(uchar, v_uint8x32); -CV_INTRIN_DEF_TYPE2VEC256_TRAITS(schar, v_int8x32); -CV_INTRIN_DEF_TYPE2VEC256_TRAITS(ushort, v_uint16x16); -CV_INTRIN_DEF_TYPE2VEC256_TRAITS(short, v_int16x16); -CV_INTRIN_DEF_TYPE2VEC256_TRAITS(unsigned, v_uint32x8); -CV_INTRIN_DEF_TYPE2VEC256_TRAITS(int, v_int32x8); -CV_INTRIN_DEF_TYPE2VEC256_TRAITS(float, v_float32x8); -CV_INTRIN_DEF_TYPE2VEC256_TRAITS(uint64, v_uint64x4); -CV_INTRIN_DEF_TYPE2VEC256_TRAITS(int64, v_int64x4); -#if CV_SIMD256_64F -CV_INTRIN_DEF_TYPE2VEC256_TRAITS(double, v_float64x4); -#endif - -template static inline -typename Type2Vec256_Traits<_T>::vec_type v256_setall(const _T& a); - -template<> inline Type2Vec256_Traits< uchar>::vec_type v256_setall< uchar>(const uchar& a) { return v256_setall_u8(a); } -template<> inline Type2Vec256_Traits< schar>::vec_type v256_setall< schar>(const schar& a) { return v256_setall_s8(a); } -template<> inline Type2Vec256_Traits::vec_type v256_setall(const ushort& a) { return v256_setall_u16(a); } -template<> inline Type2Vec256_Traits< short>::vec_type v256_setall< short>(const short& a) { return v256_setall_s16(a); } -template<> inline Type2Vec256_Traits< uint>::vec_type v256_setall< uint>(const uint& a) { return v256_setall_u32(a); } -template<> inline Type2Vec256_Traits< int>::vec_type v256_setall< int>(const int& a) { return v256_setall_s32(a); } -template<> inline Type2Vec256_Traits::vec_type v256_setall(const uint64& a) { return v256_setall_u64(a); } -template<> inline Type2Vec256_Traits< int64>::vec_type v256_setall< int64>(const int64& a) { return v256_setall_s64(a); } -template<> inline Type2Vec256_Traits< float>::vec_type v256_setall< float>(const float& a) { return v256_setall_f32(a); } -#if CV_SIMD256_64F -template<> inline Type2Vec256_Traits::vec_type v256_setall(const double& a) { return v256_setall_f64(a); } -#endif - -#endif // SIMD256 - - -#if CV_SIMD512 - -template struct Type2Vec512_Traits; -#define CV_INTRIN_DEF_TYPE2VEC512_TRAITS(type_, vec_type_) \ - template<> struct Type2Vec512_Traits \ - { \ - typedef vec_type_ vec_type; \ - } - -CV_INTRIN_DEF_TYPE2VEC512_TRAITS(uchar, v_uint8x64); -CV_INTRIN_DEF_TYPE2VEC512_TRAITS(schar, v_int8x64); -CV_INTRIN_DEF_TYPE2VEC512_TRAITS(ushort, v_uint16x32); -CV_INTRIN_DEF_TYPE2VEC512_TRAITS(short, v_int16x32); -CV_INTRIN_DEF_TYPE2VEC512_TRAITS(unsigned, v_uint32x16); -CV_INTRIN_DEF_TYPE2VEC512_TRAITS(int, v_int32x16); -CV_INTRIN_DEF_TYPE2VEC512_TRAITS(float, v_float32x16); -CV_INTRIN_DEF_TYPE2VEC512_TRAITS(uint64, v_uint64x8); -CV_INTRIN_DEF_TYPE2VEC512_TRAITS(int64, v_int64x8); -#if CV_SIMD512_64F -CV_INTRIN_DEF_TYPE2VEC512_TRAITS(double, v_float64x8); -#endif - -template static inline -typename Type2Vec512_Traits<_T>::vec_type v512_setall(const _T& a); - -template<> inline Type2Vec512_Traits< uchar>::vec_type v512_setall< uchar>(const uchar& a) { return v512_setall_u8(a); } -template<> inline Type2Vec512_Traits< schar>::vec_type v512_setall< schar>(const schar& a) { return v512_setall_s8(a); } -template<> inline Type2Vec512_Traits::vec_type v512_setall(const ushort& a) { return v512_setall_u16(a); } -template<> inline Type2Vec512_Traits< short>::vec_type v512_setall< short>(const short& a) { return v512_setall_s16(a); } -template<> inline Type2Vec512_Traits< uint>::vec_type v512_setall< uint>(const uint& a) { return v512_setall_u32(a); } -template<> inline Type2Vec512_Traits< int>::vec_type v512_setall< int>(const int& a) { return v512_setall_s32(a); } -template<> inline Type2Vec512_Traits::vec_type v512_setall(const uint64& a) { return v512_setall_u64(a); } -template<> inline Type2Vec512_Traits< int64>::vec_type v512_setall< int64>(const int64& a) { return v512_setall_s64(a); } -template<> inline Type2Vec512_Traits< float>::vec_type v512_setall< float>(const float& a) { return v512_setall_f32(a); } -#if CV_SIMD512_64F -template<> inline Type2Vec512_Traits::vec_type v512_setall(const double& a) { return v512_setall_f64(a); } -#endif - -#endif // SIMD512 - - -#if CV_SIMD_WIDTH == 16 -template static inline -typename Type2Vec128_Traits<_T>::vec_type vx_setall(const _T& a) { return v_setall(a); } -#elif CV_SIMD_WIDTH == 32 -template static inline -typename Type2Vec256_Traits<_T>::vec_type vx_setall(const _T& a) { return v256_setall(a); } -#elif CV_SIMD_WIDTH == 64 -template static inline -typename Type2Vec512_Traits<_T>::vec_type vx_setall(const _T& a) { return v512_setall(a); } -#else -#error "Build configuration error, unsupported CV_SIMD_WIDTH" -#endif - - -#endif // OPENCV_HAL_INTRIN_HPP diff --git a/include/opencv2/core/mat.hpp b/include/opencv2/core/mat.hpp deleted file mode 100755 index 98f451c..0000000 --- a/include/opencv2/core/mat.hpp +++ /dev/null @@ -1,3762 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_MAT_HPP -#define OPENCV_CORE_MAT_HPP - -#ifndef __cplusplus -# error mat.hpp header must be compiled as C++ -#endif - -#include "opencv2/core/matx.hpp" -#include "opencv2/core/types.hpp" - -#include "opencv2/core/bufferpool.hpp" - -#ifdef CV_CXX11 -#include -#endif - -namespace cv -{ - -//! @addtogroup core_basic -//! @{ - -enum { ACCESS_READ=1<<24, ACCESS_WRITE=1<<25, - ACCESS_RW=3<<24, ACCESS_MASK=ACCESS_RW, ACCESS_FAST=1<<26 }; - -CV__DEBUG_NS_BEGIN - -class CV_EXPORTS _OutputArray; - -//////////////////////// Input/Output Array Arguments ///////////////////////////////// - -/** @brief This is the proxy class for passing read-only input arrays into OpenCV functions. - -It is defined as: -@code - typedef const _InputArray& InputArray; -@endcode -where _InputArray is a class that can be constructed from `Mat`, `Mat_`, `Matx`, -`std::vector`, `std::vector >`, `std::vector`, `std::vector >`, -`UMat`, `std::vector` or `double`. It can also be constructed from a matrix expression. - -Since this is mostly implementation-level class, and its interface may change in future versions, we -do not describe it in details. There are a few key things, though, that should be kept in mind: - -- When you see in the reference manual or in OpenCV source code a function that takes - InputArray, it means that you can actually pass `Mat`, `Matx`, `vector` etc. (see above the - complete list). -- Optional input arguments: If some of the input arrays may be empty, pass cv::noArray() (or - simply cv::Mat() as you probably did before). -- The class is designed solely for passing parameters. That is, normally you *should not* - declare class members, local and global variables of this type. -- If you want to design your own function or a class method that can operate of arrays of - multiple types, you can use InputArray (or OutputArray) for the respective parameters. Inside - a function you should use _InputArray::getMat() method to construct a matrix header for the - array (without copying data). _InputArray::kind() can be used to distinguish Mat from - `vector<>` etc., but normally it is not needed. - -Here is how you can use a function that takes InputArray : -@code - std::vector vec; - // points or a circle - for( int i = 0; i < 30; i++ ) - vec.push_back(Point2f((float)(100 + 30*cos(i*CV_PI*2/5)), - (float)(100 - 30*sin(i*CV_PI*2/5)))); - cv::transform(vec, vec, cv::Matx23f(0.707, -0.707, 10, 0.707, 0.707, 20)); -@endcode -That is, we form an STL vector containing points, and apply in-place affine transformation to the -vector using the 2x3 matrix created inline as `Matx` instance. - -Here is how such a function can be implemented (for simplicity, we implement a very specific case of -it, according to the assertion statement inside) : -@code - void myAffineTransform(InputArray _src, OutputArray _dst, InputArray _m) - { - // get Mat headers for input arrays. This is O(1) operation, - // unless _src and/or _m are matrix expressions. - Mat src = _src.getMat(), m = _m.getMat(); - CV_Assert( src.type() == CV_32FC2 && m.type() == CV_32F && m.size() == Size(3, 2) ); - - // [re]create the output array so that it has the proper size and type. - // In case of Mat it calls Mat::create, in case of STL vector it calls vector::resize. - _dst.create(src.size(), src.type()); - Mat dst = _dst.getMat(); - - for( int i = 0; i < src.rows; i++ ) - for( int j = 0; j < src.cols; j++ ) - { - Point2f pt = src.at(i, j); - dst.at(i, j) = Point2f(m.at(0, 0)*pt.x + - m.at(0, 1)*pt.y + - m.at(0, 2), - m.at(1, 0)*pt.x + - m.at(1, 1)*pt.y + - m.at(1, 2)); - } - } -@endcode -There is another related type, InputArrayOfArrays, which is currently defined as a synonym for -InputArray: -@code - typedef InputArray InputArrayOfArrays; -@endcode -It denotes function arguments that are either vectors of vectors or vectors of matrices. A separate -synonym is needed to generate Python/Java etc. wrappers properly. At the function implementation -level their use is similar, but _InputArray::getMat(idx) should be used to get header for the -idx-th component of the outer vector and _InputArray::size().area() should be used to find the -number of components (vectors/matrices) of the outer vector. - -In general, type support is limited to cv::Mat types. Other types are forbidden. -But in some cases we need to support passing of custom non-general Mat types, like arrays of cv::KeyPoint, cv::DMatch, etc. -This data is not intended to be interpreted as an image data, or processed somehow like regular cv::Mat. -To pass such custom type use rawIn() / rawOut() / rawInOut() wrappers. -Custom type is wrapped as Mat-compatible `CV_8UC` values (N = sizeof(T), N <= CV_CN_MAX). - */ -class CV_EXPORTS _InputArray -{ -public: - enum { - KIND_SHIFT = 16, - FIXED_TYPE = 0x8000 << KIND_SHIFT, - FIXED_SIZE = 0x4000 << KIND_SHIFT, - KIND_MASK = 31 << KIND_SHIFT, - - NONE = 0 << KIND_SHIFT, - MAT = 1 << KIND_SHIFT, - MATX = 2 << KIND_SHIFT, - STD_VECTOR = 3 << KIND_SHIFT, - STD_VECTOR_VECTOR = 4 << KIND_SHIFT, - STD_VECTOR_MAT = 5 << KIND_SHIFT, - EXPR = 6 << KIND_SHIFT, //!< removed - OPENGL_BUFFER = 7 << KIND_SHIFT, - CUDA_HOST_MEM = 8 << KIND_SHIFT, - CUDA_GPU_MAT = 9 << KIND_SHIFT, - UMAT =10 << KIND_SHIFT, - STD_VECTOR_UMAT =11 << KIND_SHIFT, - STD_BOOL_VECTOR =12 << KIND_SHIFT, - STD_VECTOR_CUDA_GPU_MAT = 13 << KIND_SHIFT, - STD_ARRAY =14 << KIND_SHIFT, - STD_ARRAY_MAT =15 << KIND_SHIFT - }; - - _InputArray(); - _InputArray(int _flags, void* _obj); - _InputArray(const Mat& m); - _InputArray(const MatExpr& expr); - _InputArray(const std::vector& vec); - template _InputArray(const Mat_<_Tp>& m); - template _InputArray(const std::vector<_Tp>& vec); - _InputArray(const std::vector& vec); - template _InputArray(const std::vector >& vec); - _InputArray(const std::vector >&); - template _InputArray(const std::vector >& vec); - template _InputArray(const _Tp* vec, int n); - template _InputArray(const Matx<_Tp, m, n>& matx); - _InputArray(const double& val); - _InputArray(const cuda::GpuMat& d_mat); - _InputArray(const std::vector& d_mat_array); - _InputArray(const ogl::Buffer& buf); - _InputArray(const cuda::HostMem& cuda_mem); - template _InputArray(const cudev::GpuMat_<_Tp>& m); - _InputArray(const UMat& um); - _InputArray(const std::vector& umv); - -#ifdef CV_CXX_STD_ARRAY - template _InputArray(const std::array<_Tp, _Nm>& arr); - template _InputArray(const std::array& arr); -#endif - - template static _InputArray rawIn(const std::vector<_Tp>& vec); -#ifdef CV_CXX_STD_ARRAY - template static _InputArray rawIn(const std::array<_Tp, _Nm>& arr); -#endif - - Mat getMat(int idx=-1) const; - Mat getMat_(int idx=-1) const; - UMat getUMat(int idx=-1) const; - void getMatVector(std::vector& mv) const; - void getUMatVector(std::vector& umv) const; - void getGpuMatVector(std::vector& gpumv) const; - cuda::GpuMat getGpuMat() const; - ogl::Buffer getOGlBuffer() const; - - int getFlags() const; - void* getObj() const; - Size getSz() const; - - int kind() const; - int dims(int i=-1) const; - int cols(int i=-1) const; - int rows(int i=-1) const; - Size size(int i=-1) const; - int sizend(int* sz, int i=-1) const; - bool sameSize(const _InputArray& arr) const; - size_t total(int i=-1) const; - int type(int i=-1) const; - int depth(int i=-1) const; - int channels(int i=-1) const; - bool isContinuous(int i=-1) const; - bool isSubmatrix(int i=-1) const; - bool empty() const; - void copyTo(const _OutputArray& arr) const; - void copyTo(const _OutputArray& arr, const _InputArray & mask) const; - size_t offset(int i=-1) const; - size_t step(int i=-1) const; - bool isMat() const; - bool isUMat() const; - bool isMatVector() const; - bool isUMatVector() const; - bool isMatx() const; - bool isVector() const; - bool isGpuMat() const; - bool isGpuMatVector() const; - ~_InputArray(); - -protected: - int flags; - void* obj; - Size sz; - - void init(int _flags, const void* _obj); - void init(int _flags, const void* _obj, Size _sz); -}; - - -/** @brief This type is very similar to InputArray except that it is used for input/output and output function -parameters. - -Just like with InputArray, OpenCV users should not care about OutputArray, they just pass `Mat`, -`vector` etc. to the functions. The same limitation as for `InputArray`: *Do not explicitly -create OutputArray instances* applies here too. - -If you want to make your function polymorphic (i.e. accept different arrays as output parameters), -it is also not very difficult. Take the sample above as the reference. Note that -_OutputArray::create() needs to be called before _OutputArray::getMat(). This way you guarantee -that the output array is properly allocated. - -Optional output parameters. If you do not need certain output array to be computed and returned to -you, pass cv::noArray(), just like you would in the case of optional input array. At the -implementation level, use _OutputArray::needed() to check if certain output array needs to be -computed or not. - -There are several synonyms for OutputArray that are used to assist automatic Python/Java/... wrapper -generators: -@code - typedef OutputArray OutputArrayOfArrays; - typedef OutputArray InputOutputArray; - typedef OutputArray InputOutputArrayOfArrays; -@endcode - */ -class CV_EXPORTS _OutputArray : public _InputArray -{ -public: - enum - { - DEPTH_MASK_8U = 1 << CV_8U, - DEPTH_MASK_8S = 1 << CV_8S, - DEPTH_MASK_16U = 1 << CV_16U, - DEPTH_MASK_16S = 1 << CV_16S, - DEPTH_MASK_32S = 1 << CV_32S, - DEPTH_MASK_32F = 1 << CV_32F, - DEPTH_MASK_64F = 1 << CV_64F, - DEPTH_MASK_ALL = (DEPTH_MASK_64F<<1)-1, - DEPTH_MASK_ALL_BUT_8S = DEPTH_MASK_ALL & ~DEPTH_MASK_8S, - DEPTH_MASK_FLT = DEPTH_MASK_32F + DEPTH_MASK_64F - }; - - _OutputArray(); - _OutputArray(int _flags, void* _obj); - _OutputArray(Mat& m); - _OutputArray(std::vector& vec); - _OutputArray(cuda::GpuMat& d_mat); - _OutputArray(std::vector& d_mat); - _OutputArray(ogl::Buffer& buf); - _OutputArray(cuda::HostMem& cuda_mem); - template _OutputArray(cudev::GpuMat_<_Tp>& m); - template _OutputArray(std::vector<_Tp>& vec); - _OutputArray(std::vector& vec); - template _OutputArray(std::vector >& vec); - _OutputArray(std::vector >&); - template _OutputArray(std::vector >& vec); - template _OutputArray(Mat_<_Tp>& m); - template _OutputArray(_Tp* vec, int n); - template _OutputArray(Matx<_Tp, m, n>& matx); - _OutputArray(UMat& m); - _OutputArray(std::vector& vec); - - _OutputArray(const Mat& m); - _OutputArray(const std::vector& vec); - _OutputArray(const cuda::GpuMat& d_mat); - _OutputArray(const std::vector& d_mat); - _OutputArray(const ogl::Buffer& buf); - _OutputArray(const cuda::HostMem& cuda_mem); - template _OutputArray(const cudev::GpuMat_<_Tp>& m); - template _OutputArray(const std::vector<_Tp>& vec); - template _OutputArray(const std::vector >& vec); - template _OutputArray(const std::vector >& vec); - template _OutputArray(const Mat_<_Tp>& m); - template _OutputArray(const _Tp* vec, int n); - template _OutputArray(const Matx<_Tp, m, n>& matx); - _OutputArray(const UMat& m); - _OutputArray(const std::vector& vec); - -#ifdef CV_CXX_STD_ARRAY - template _OutputArray(std::array<_Tp, _Nm>& arr); - template _OutputArray(const std::array<_Tp, _Nm>& arr); - template _OutputArray(std::array& arr); - template _OutputArray(const std::array& arr); -#endif - - template static _OutputArray rawOut(std::vector<_Tp>& vec); -#ifdef CV_CXX_STD_ARRAY - template static _OutputArray rawOut(std::array<_Tp, _Nm>& arr); -#endif - - bool fixedSize() const; - bool fixedType() const; - bool needed() const; - Mat& getMatRef(int i=-1) const; - UMat& getUMatRef(int i=-1) const; - cuda::GpuMat& getGpuMatRef() const; - std::vector& getGpuMatVecRef() const; - ogl::Buffer& getOGlBufferRef() const; - cuda::HostMem& getHostMemRef() const; - void create(Size sz, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; - void create(int rows, int cols, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; - void create(int dims, const int* size, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const; - void createSameSize(const _InputArray& arr, int mtype) const; - void release() const; - void clear() const; - void setTo(const _InputArray& value, const _InputArray & mask = _InputArray()) const; - - void assign(const UMat& u) const; - void assign(const Mat& m) const; - - void assign(const std::vector& v) const; - void assign(const std::vector& v) const; - - void move(UMat& u) const; - void move(Mat& m) const; -}; - - -class CV_EXPORTS _InputOutputArray : public _OutputArray -{ -public: - _InputOutputArray(); - _InputOutputArray(int _flags, void* _obj); - _InputOutputArray(Mat& m); - _InputOutputArray(std::vector& vec); - _InputOutputArray(cuda::GpuMat& d_mat); - _InputOutputArray(ogl::Buffer& buf); - _InputOutputArray(cuda::HostMem& cuda_mem); - template _InputOutputArray(cudev::GpuMat_<_Tp>& m); - template _InputOutputArray(std::vector<_Tp>& vec); - _InputOutputArray(std::vector& vec); - template _InputOutputArray(std::vector >& vec); - template _InputOutputArray(std::vector >& vec); - template _InputOutputArray(Mat_<_Tp>& m); - template _InputOutputArray(_Tp* vec, int n); - template _InputOutputArray(Matx<_Tp, m, n>& matx); - _InputOutputArray(UMat& m); - _InputOutputArray(std::vector& vec); - - _InputOutputArray(const Mat& m); - _InputOutputArray(const std::vector& vec); - _InputOutputArray(const cuda::GpuMat& d_mat); - _InputOutputArray(const std::vector& d_mat); - _InputOutputArray(const ogl::Buffer& buf); - _InputOutputArray(const cuda::HostMem& cuda_mem); - template _InputOutputArray(const cudev::GpuMat_<_Tp>& m); - template _InputOutputArray(const std::vector<_Tp>& vec); - template _InputOutputArray(const std::vector >& vec); - template _InputOutputArray(const std::vector >& vec); - template _InputOutputArray(const Mat_<_Tp>& m); - template _InputOutputArray(const _Tp* vec, int n); - template _InputOutputArray(const Matx<_Tp, m, n>& matx); - _InputOutputArray(const UMat& m); - _InputOutputArray(const std::vector& vec); - -#ifdef CV_CXX_STD_ARRAY - template _InputOutputArray(std::array<_Tp, _Nm>& arr); - template _InputOutputArray(const std::array<_Tp, _Nm>& arr); - template _InputOutputArray(std::array& arr); - template _InputOutputArray(const std::array& arr); -#endif - - template static _InputOutputArray rawInOut(std::vector<_Tp>& vec); -#ifdef CV_CXX_STD_ARRAY - template _InputOutputArray rawInOut(std::array<_Tp, _Nm>& arr); -#endif - -}; - -/** Helper to wrap custom types. @see InputArray */ -template static inline _InputArray rawIn(_Tp& v); -/** Helper to wrap custom types. @see InputArray */ -template static inline _OutputArray rawOut(_Tp& v); -/** Helper to wrap custom types. @see InputArray */ -template static inline _InputOutputArray rawInOut(_Tp& v); - -CV__DEBUG_NS_END - -typedef const _InputArray& InputArray; -typedef InputArray InputArrayOfArrays; -typedef const _OutputArray& OutputArray; -typedef OutputArray OutputArrayOfArrays; -typedef const _InputOutputArray& InputOutputArray; -typedef InputOutputArray InputOutputArrayOfArrays; - -CV_EXPORTS InputOutputArray noArray(); - -/////////////////////////////////// MatAllocator ////////////////////////////////////// - -//! Usage flags for allocator -enum UMatUsageFlags -{ - USAGE_DEFAULT = 0, - - // buffer allocation policy is platform and usage specific - USAGE_ALLOCATE_HOST_MEMORY = 1 << 0, - USAGE_ALLOCATE_DEVICE_MEMORY = 1 << 1, - USAGE_ALLOCATE_SHARED_MEMORY = 1 << 2, // It is not equal to: USAGE_ALLOCATE_HOST_MEMORY | USAGE_ALLOCATE_DEVICE_MEMORY - - __UMAT_USAGE_FLAGS_32BIT = 0x7fffffff // Binary compatibility hint -}; - -struct CV_EXPORTS UMatData; - -/** @brief Custom array allocator -*/ -class CV_EXPORTS MatAllocator -{ -public: - MatAllocator() {} - virtual ~MatAllocator() {} - - // let's comment it off for now to detect and fix all the uses of allocator - //virtual void allocate(int dims, const int* sizes, int type, int*& refcount, - // uchar*& datastart, uchar*& data, size_t* step) = 0; - //virtual void deallocate(int* refcount, uchar* datastart, uchar* data) = 0; - virtual UMatData* allocate(int dims, const int* sizes, int type, - void* data, size_t* step, int flags, UMatUsageFlags usageFlags) const = 0; - virtual bool allocate(UMatData* data, int accessflags, UMatUsageFlags usageFlags) const = 0; - virtual void deallocate(UMatData* data) const = 0; - virtual void map(UMatData* data, int accessflags) const; - virtual void unmap(UMatData* data) const; - virtual void download(UMatData* data, void* dst, int dims, const size_t sz[], - const size_t srcofs[], const size_t srcstep[], - const size_t dststep[]) const; - virtual void upload(UMatData* data, const void* src, int dims, const size_t sz[], - const size_t dstofs[], const size_t dststep[], - const size_t srcstep[]) const; - virtual void copy(UMatData* srcdata, UMatData* dstdata, int dims, const size_t sz[], - const size_t srcofs[], const size_t srcstep[], - const size_t dstofs[], const size_t dststep[], bool sync) const; - - // default implementation returns DummyBufferPoolController - virtual BufferPoolController* getBufferPoolController(const char* id = NULL) const; -}; - - -//////////////////////////////// MatCommaInitializer ////////////////////////////////// - -/** @brief Comma-separated Matrix Initializer - - The class instances are usually not created explicitly. - Instead, they are created on "matrix << firstValue" operator. - - The sample below initializes 2x2 rotation matrix: - - \code - double angle = 30, a = cos(angle*CV_PI/180), b = sin(angle*CV_PI/180); - Mat R = (Mat_(2,2) << a, -b, b, a); - \endcode -*/ -template class MatCommaInitializer_ -{ -public: - //! the constructor, created by "matrix << firstValue" operator, where matrix is cv::Mat - MatCommaInitializer_(Mat_<_Tp>* _m); - //! the operator that takes the next value and put it to the matrix - template MatCommaInitializer_<_Tp>& operator , (T2 v); - //! another form of conversion operator - operator Mat_<_Tp>() const; -protected: - MatIterator_<_Tp> it; -}; - - -/////////////////////////////////////// Mat /////////////////////////////////////////// - -// note that umatdata might be allocated together -// with the matrix data, not as a separate object. -// therefore, it does not have constructor or destructor; -// it should be explicitly initialized using init(). -struct CV_EXPORTS UMatData -{ - enum { COPY_ON_MAP=1, HOST_COPY_OBSOLETE=2, - DEVICE_COPY_OBSOLETE=4, TEMP_UMAT=8, TEMP_COPIED_UMAT=24, - USER_ALLOCATED=32, DEVICE_MEM_MAPPED=64, - ASYNC_CLEANUP=128 - }; - UMatData(const MatAllocator* allocator); - ~UMatData(); - - // provide atomic access to the structure - void lock(); - void unlock(); - - bool hostCopyObsolete() const; - bool deviceCopyObsolete() const; - bool deviceMemMapped() const; - bool copyOnMap() const; - bool tempUMat() const; - bool tempCopiedUMat() const; - void markHostCopyObsolete(bool flag); - void markDeviceCopyObsolete(bool flag); - void markDeviceMemMapped(bool flag); - - const MatAllocator* prevAllocator; - const MatAllocator* currAllocator; - int urefcount; - int refcount; - uchar* data; - uchar* origdata; - size_t size; - - int flags; - void* handle; - void* userdata; - int allocatorFlags_; - int mapcount; - UMatData* originalUMatData; -}; - - -struct CV_EXPORTS MatSize -{ - explicit MatSize(int* _p); - int dims() const; - Size operator()() const; - const int& operator[](int i) const; - int& operator[](int i); - operator const int*() const; // TODO OpenCV 4.0: drop this - bool operator == (const MatSize& sz) const; - bool operator != (const MatSize& sz) const; - - int* p; -}; - -struct CV_EXPORTS MatStep -{ - MatStep(); - explicit MatStep(size_t s); - const size_t& operator[](int i) const; - size_t& operator[](int i); - operator size_t() const; - MatStep& operator = (size_t s); - - size_t* p; - size_t buf[2]; -protected: - MatStep& operator = (const MatStep&); -}; - -/** @example samples/cpp/cout_mat.cpp -An example demonstrating the serial out capabilities of cv::Mat -*/ - - /** @brief n-dimensional dense array class \anchor CVMat_Details - -The class Mat represents an n-dimensional dense numerical single-channel or multi-channel array. It -can be used to store real or complex-valued vectors and matrices, grayscale or color images, voxel -volumes, vector fields, point clouds, tensors, histograms (though, very high-dimensional histograms -may be better stored in a SparseMat ). The data layout of the array `M` is defined by the array -`M.step[]`, so that the address of element \f$(i_0,...,i_{M.dims-1})\f$, where \f$0\leq i_k= M.step[i+1]` (in fact, `M.step[i] >= M.step[i+1]*M.size[i+1]` ). This means -that 2-dimensional matrices are stored row-by-row, 3-dimensional matrices are stored plane-by-plane, -and so on. M.step[M.dims-1] is minimal and always equal to the element size M.elemSize() . - -So, the data layout in Mat is fully compatible with CvMat, IplImage, and CvMatND types from OpenCV -1.x. It is also compatible with the majority of dense array types from the standard toolkits and -SDKs, such as Numpy (ndarray), Win32 (independent device bitmaps), and others, that is, with any -array that uses *steps* (or *strides*) to compute the position of a pixel. Due to this -compatibility, it is possible to make a Mat header for user-allocated data and process it in-place -using OpenCV functions. - -There are many different ways to create a Mat object. The most popular options are listed below: - -- Use the create(nrows, ncols, type) method or the similar Mat(nrows, ncols, type[, fillValue]) -constructor. A new array of the specified size and type is allocated. type has the same meaning as -in the cvCreateMat method. For example, CV_8UC1 means a 8-bit single-channel array, CV_32FC2 -means a 2-channel (complex) floating-point array, and so on. -@code - // make a 7x7 complex matrix filled with 1+3j. - Mat M(7,7,CV_32FC2,Scalar(1,3)); - // and now turn M to a 100x60 15-channel 8-bit matrix. - // The old content will be deallocated - M.create(100,60,CV_8UC(15)); -@endcode -As noted in the introduction to this chapter, create() allocates only a new array when the shape -or type of the current array are different from the specified ones. - -- Create a multi-dimensional array: -@code - // create a 100x100x100 8-bit array - int sz[] = {100, 100, 100}; - Mat bigCube(3, sz, CV_8U, Scalar::all(0)); -@endcode -It passes the number of dimensions =1 to the Mat constructor but the created array will be -2-dimensional with the number of columns set to 1. So, Mat::dims is always \>= 2 (can also be 0 -when the array is empty). - -- Use a copy constructor or assignment operator where there can be an array or expression on the -right side (see below). As noted in the introduction, the array assignment is an O(1) operation -because it only copies the header and increases the reference counter. The Mat::clone() method can -be used to get a full (deep) copy of the array when you need it. - -- Construct a header for a part of another array. It can be a single row, single column, several -rows, several columns, rectangular region in the array (called a *minor* in algebra) or a -diagonal. Such operations are also O(1) because the new header references the same data. You can -actually modify a part of the array using this feature, for example: -@code - // add the 5-th row, multiplied by 3 to the 3rd row - M.row(3) = M.row(3) + M.row(5)*3; - // now copy the 7-th column to the 1-st column - // M.col(1) = M.col(7); // this will not work - Mat M1 = M.col(1); - M.col(7).copyTo(M1); - // create a new 320x240 image - Mat img(Size(320,240),CV_8UC3); - // select a ROI - Mat roi(img, Rect(10,10,100,100)); - // fill the ROI with (0,255,0) (which is green in RGB space); - // the original 320x240 image will be modified - roi = Scalar(0,255,0); -@endcode -Due to the additional datastart and dataend members, it is possible to compute a relative -sub-array position in the main *container* array using locateROI(): -@code - Mat A = Mat::eye(10, 10, CV_32S); - // extracts A columns, 1 (inclusive) to 3 (exclusive). - Mat B = A(Range::all(), Range(1, 3)); - // extracts B rows, 5 (inclusive) to 9 (exclusive). - // that is, C \~ A(Range(5, 9), Range(1, 3)) - Mat C = B(Range(5, 9), Range::all()); - Size size; Point ofs; - C.locateROI(size, ofs); - // size will be (width=10,height=10) and the ofs will be (x=1, y=5) -@endcode -As in case of whole matrices, if you need a deep copy, use the `clone()` method of the extracted -sub-matrices. - -- Make a header for user-allocated data. It can be useful to do the following: - -# Process "foreign" data using OpenCV (for example, when you implement a DirectShow\* filter or - a processing module for gstreamer, and so on). For example: - @code - Mat process_video_frame(const unsigned char* pixels, - int width, int height, int step) - { - // wrap input buffer - Mat img(height, width, CV_8UC3, (unsigned char*)pixels, step); - - Mat result; - GaussianBlur(img, result, Size(7, 7), 1.5, 1.5); - - return result; - } - @endcode - -# Quickly initialize small matrices and/or get a super-fast element access. - @code - double m[3][3] = {{a, b, c}, {d, e, f}, {g, h, i}}; - Mat M = Mat(3, 3, CV_64F, m).inv(); - @endcode - . - Partial yet very common cases of this *user-allocated data* case are conversions from CvMat and - IplImage to Mat. For this purpose, there is function cv::cvarrToMat taking pointers to CvMat or - IplImage and the optional flag indicating whether to copy the data or not. - @snippet samples/cpp/image.cpp iplimage - -- Use MATLAB-style array initializers, zeros(), ones(), eye(), for example: -@code - // create a double-precision identity matrix and add it to M. - M += Mat::eye(M.rows, M.cols, CV_64F); -@endcode - -- Use a comma-separated initializer: -@code - // create a 3x3 double-precision identity matrix - Mat M = (Mat_(3,3) << 1, 0, 0, 0, 1, 0, 0, 0, 1); -@endcode -With this approach, you first call a constructor of the Mat class with the proper parameters, and -then you just put `<< operator` followed by comma-separated values that can be constants, -variables, expressions, and so on. Also, note the extra parentheses required to avoid compilation -errors. - -Once the array is created, it is automatically managed via a reference-counting mechanism. If the -array header is built on top of user-allocated data, you should handle the data by yourself. The -array data is deallocated when no one points to it. If you want to release the data pointed by a -array header before the array destructor is called, use Mat::release(). - -The next important thing to learn about the array class is element access. This manual already -described how to compute an address of each array element. Normally, you are not required to use the -formula directly in the code. If you know the array element type (which can be retrieved using the -method Mat::type() ), you can access the element \f$M_{ij}\f$ of a 2-dimensional array as: -@code - M.at(i,j) += 1.f; -@endcode -assuming that `M` is a double-precision floating-point array. There are several variants of the method -at for a different number of dimensions. - -If you need to process a whole row of a 2D array, the most efficient way is to get the pointer to -the row first, and then just use the plain C operator [] : -@code - // compute sum of positive matrix elements - // (assuming that M is a double-precision matrix) - double sum=0; - for(int i = 0; i < M.rows; i++) - { - const double* Mi = M.ptr(i); - for(int j = 0; j < M.cols; j++) - sum += std::max(Mi[j], 0.); - } -@endcode -Some operations, like the one above, do not actually depend on the array shape. They just process -elements of an array one by one (or elements from multiple arrays that have the same coordinates, -for example, array addition). Such operations are called *element-wise*. It makes sense to check -whether all the input/output arrays are continuous, namely, have no gaps at the end of each row. If -yes, process them as a long single row: -@code - // compute the sum of positive matrix elements, optimized variant - double sum=0; - int cols = M.cols, rows = M.rows; - if(M.isContinuous()) - { - cols *= rows; - rows = 1; - } - for(int i = 0; i < rows; i++) - { - const double* Mi = M.ptr(i); - for(int j = 0; j < cols; j++) - sum += std::max(Mi[j], 0.); - } -@endcode -In case of the continuous matrix, the outer loop body is executed just once. So, the overhead is -smaller, which is especially noticeable in case of small matrices. - -Finally, there are STL-style iterators that are smart enough to skip gaps between successive rows: -@code - // compute sum of positive matrix elements, iterator-based variant - double sum=0; - MatConstIterator_ it = M.begin(), it_end = M.end(); - for(; it != it_end; ++it) - sum += std::max(*it, 0.); -@endcode -The matrix iterators are random-access iterators, so they can be passed to any STL algorithm, -including std::sort(). - -@note Matrix Expressions and arithmetic see MatExpr -*/ -class CV_EXPORTS Mat -{ -public: - /** - These are various constructors that form a matrix. As noted in the AutomaticAllocation, often - the default constructor is enough, and the proper matrix will be allocated by an OpenCV function. - The constructed matrix can further be assigned to another matrix or matrix expression or can be - allocated with Mat::create . In the former case, the old content is de-referenced. - */ - Mat(); - - /** @overload - @param rows Number of rows in a 2D array. - @param cols Number of columns in a 2D array. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - */ - Mat(int rows, int cols, int type); - - /** @overload - @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the - number of columns go in the reverse order. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - */ - Mat(Size size, int type); - - /** @overload - @param rows Number of rows in a 2D array. - @param cols Number of columns in a 2D array. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - @param s An optional value to initialize each matrix element with. To set all the matrix elements to - the particular value after the construction, use the assignment operator - Mat::operator=(const Scalar& value) . - */ - Mat(int rows, int cols, int type, const Scalar& s); - - /** @overload - @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the - number of columns go in the reverse order. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - @param s An optional value to initialize each matrix element with. To set all the matrix elements to - the particular value after the construction, use the assignment operator - Mat::operator=(const Scalar& value) . - */ - Mat(Size size, int type, const Scalar& s); - - /** @overload - @param ndims Array dimensionality. - @param sizes Array of integers specifying an n-dimensional array shape. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - */ - Mat(int ndims, const int* sizes, int type); - - /** @overload - @param sizes Array of integers specifying an n-dimensional array shape. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - */ - Mat(const std::vector& sizes, int type); - - /** @overload - @param ndims Array dimensionality. - @param sizes Array of integers specifying an n-dimensional array shape. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - @param s An optional value to initialize each matrix element with. To set all the matrix elements to - the particular value after the construction, use the assignment operator - Mat::operator=(const Scalar& value) . - */ - Mat(int ndims, const int* sizes, int type, const Scalar& s); - - /** @overload - @param sizes Array of integers specifying an n-dimensional array shape. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - @param s An optional value to initialize each matrix element with. To set all the matrix elements to - the particular value after the construction, use the assignment operator - Mat::operator=(const Scalar& value) . - */ - Mat(const std::vector& sizes, int type, const Scalar& s); - - - /** @overload - @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied - by these constructors. Instead, the header pointing to m data or its sub-array is constructed and - associated with it. The reference counter, if any, is incremented. So, when you modify the matrix - formed using such a constructor, you also modify the corresponding elements of m . If you want to - have an independent copy of the sub-array, use Mat::clone() . - */ - Mat(const Mat& m); - - /** @overload - @param rows Number of rows in a 2D array. - @param cols Number of columns in a 2D array. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - @param data Pointer to the user data. Matrix constructors that take data and step parameters do not - allocate matrix data. Instead, they just initialize the matrix header that points to the specified - data, which means that no data is copied. This operation is very efficient and can be used to - process external data using OpenCV functions. The external data is not automatically deallocated, so - you should take care of it. - @param step Number of bytes each matrix row occupies. The value should include the padding bytes at - the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed - and the actual step is calculated as cols*elemSize(). See Mat::elemSize. - */ - Mat(int rows, int cols, int type, void* data, size_t step=AUTO_STEP); - - /** @overload - @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the - number of columns go in the reverse order. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - @param data Pointer to the user data. Matrix constructors that take data and step parameters do not - allocate matrix data. Instead, they just initialize the matrix header that points to the specified - data, which means that no data is copied. This operation is very efficient and can be used to - process external data using OpenCV functions. The external data is not automatically deallocated, so - you should take care of it. - @param step Number of bytes each matrix row occupies. The value should include the padding bytes at - the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed - and the actual step is calculated as cols*elemSize(). See Mat::elemSize. - */ - Mat(Size size, int type, void* data, size_t step=AUTO_STEP); - - /** @overload - @param ndims Array dimensionality. - @param sizes Array of integers specifying an n-dimensional array shape. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - @param data Pointer to the user data. Matrix constructors that take data and step parameters do not - allocate matrix data. Instead, they just initialize the matrix header that points to the specified - data, which means that no data is copied. This operation is very efficient and can be used to - process external data using OpenCV functions. The external data is not automatically deallocated, so - you should take care of it. - @param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always - set to the element size). If not specified, the matrix is assumed to be continuous. - */ - Mat(int ndims, const int* sizes, int type, void* data, const size_t* steps=0); - - /** @overload - @param sizes Array of integers specifying an n-dimensional array shape. - @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or - CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices. - @param data Pointer to the user data. Matrix constructors that take data and step parameters do not - allocate matrix data. Instead, they just initialize the matrix header that points to the specified - data, which means that no data is copied. This operation is very efficient and can be used to - process external data using OpenCV functions. The external data is not automatically deallocated, so - you should take care of it. - @param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always - set to the element size). If not specified, the matrix is assumed to be continuous. - */ - Mat(const std::vector& sizes, int type, void* data, const size_t* steps=0); - - /** @overload - @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied - by these constructors. Instead, the header pointing to m data or its sub-array is constructed and - associated with it. The reference counter, if any, is incremented. So, when you modify the matrix - formed using such a constructor, you also modify the corresponding elements of m . If you want to - have an independent copy of the sub-array, use Mat::clone() . - @param rowRange Range of the m rows to take. As usual, the range start is inclusive and the range - end is exclusive. Use Range::all() to take all the rows. - @param colRange Range of the m columns to take. Use Range::all() to take all the columns. - */ - Mat(const Mat& m, const Range& rowRange, const Range& colRange=Range::all()); - - /** @overload - @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied - by these constructors. Instead, the header pointing to m data or its sub-array is constructed and - associated with it. The reference counter, if any, is incremented. So, when you modify the matrix - formed using such a constructor, you also modify the corresponding elements of m . If you want to - have an independent copy of the sub-array, use Mat::clone() . - @param roi Region of interest. - */ - Mat(const Mat& m, const Rect& roi); - - /** @overload - @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied - by these constructors. Instead, the header pointing to m data or its sub-array is constructed and - associated with it. The reference counter, if any, is incremented. So, when you modify the matrix - formed using such a constructor, you also modify the corresponding elements of m . If you want to - have an independent copy of the sub-array, use Mat::clone() . - @param ranges Array of selected ranges of m along each dimensionality. - */ - Mat(const Mat& m, const Range* ranges); - - /** @overload - @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied - by these constructors. Instead, the header pointing to m data or its sub-array is constructed and - associated with it. The reference counter, if any, is incremented. So, when you modify the matrix - formed using such a constructor, you also modify the corresponding elements of m . If you want to - have an independent copy of the sub-array, use Mat::clone() . - @param ranges Array of selected ranges of m along each dimensionality. - */ - Mat(const Mat& m, const std::vector& ranges); - - /** @overload - @param vec STL vector whose elements form the matrix. The matrix has a single column and the number - of rows equal to the number of vector elements. Type of the matrix matches the type of vector - elements. The constructor can handle arbitrary types, for which there is a properly declared - DataType . This means that the vector elements must be primitive numbers or uni-type numerical - tuples of numbers. Mixed-type structures are not supported. The corresponding constructor is - explicit. Since STL vectors are not automatically converted to Mat instances, you should write - Mat(vec) explicitly. Unless you copy the data into the matrix ( copyData=true ), no new elements - will be added to the vector because it can potentially yield vector data reallocation, and, thus, - the matrix data pointer will be invalid. - @param copyData Flag to specify whether the underlying data of the STL vector should be copied - to (true) or shared with (false) the newly constructed matrix. When the data is copied, the - allocated buffer is managed using Mat reference counting mechanism. While the data is shared, - the reference counter is NULL, and you should not deallocate the data until the matrix is not - destructed. - */ - template explicit Mat(const std::vector<_Tp>& vec, bool copyData=false); - -#ifdef CV_CXX11 - /** @overload - */ - template::value>::type> - explicit Mat(const std::initializer_list<_Tp> list); - - /** @overload - */ - template explicit Mat(const std::initializer_list sizes, const std::initializer_list<_Tp> list); -#endif - -#ifdef CV_CXX_STD_ARRAY - /** @overload - */ - template explicit Mat(const std::array<_Tp, _Nm>& arr, bool copyData=false); -#endif - - /** @overload - */ - template explicit Mat(const Vec<_Tp, n>& vec, bool copyData=true); - - /** @overload - */ - template explicit Mat(const Matx<_Tp, m, n>& mtx, bool copyData=true); - - /** @overload - */ - template explicit Mat(const Point_<_Tp>& pt, bool copyData=true); - - /** @overload - */ - template explicit Mat(const Point3_<_Tp>& pt, bool copyData=true); - - /** @overload - */ - template explicit Mat(const MatCommaInitializer_<_Tp>& commaInitializer); - - //! download data from GpuMat - explicit Mat(const cuda::GpuMat& m); - - //! destructor - calls release() - ~Mat(); - - /** @brief assignment operators - - These are available assignment operators. Since they all are very different, make sure to read the - operator parameters description. - @param m Assigned, right-hand-side matrix. Matrix assignment is an O(1) operation. This means that - no data is copied but the data is shared and the reference counter, if any, is incremented. Before - assigning new data, the old data is de-referenced via Mat::release . - */ - Mat& operator = (const Mat& m); - - /** @overload - @param expr Assigned matrix expression object. As opposite to the first form of the assignment - operation, the second form can reuse already allocated matrix if it has the right size and type to - fit the matrix expression result. It is automatically handled by the real function that the matrix - expressions is expanded to. For example, C=A+B is expanded to add(A, B, C), and add takes care of - automatic C reallocation. - */ - Mat& operator = (const MatExpr& expr); - - //! retrieve UMat from Mat - UMat getUMat(int accessFlags, UMatUsageFlags usageFlags = USAGE_DEFAULT) const; - - /** @brief Creates a matrix header for the specified matrix row. - - The method makes a new header for the specified matrix row and returns it. This is an O(1) - operation, regardless of the matrix size. The underlying data of the new matrix is shared with the - original matrix. Here is the example of one of the classical basic matrix processing operations, - axpy, used by LU and many other algorithms: - @code - inline void matrix_axpy(Mat& A, int i, int j, double alpha) - { - A.row(i) += A.row(j)*alpha; - } - @endcode - @note In the current implementation, the following code does not work as expected: - @code - Mat A; - ... - A.row(i) = A.row(j); // will not work - @endcode - This happens because A.row(i) forms a temporary header that is further assigned to another header. - Remember that each of these operations is O(1), that is, no data is copied. Thus, the above - assignment is not true if you may have expected the j-th row to be copied to the i-th row. To - achieve that, you should either turn this simple assignment into an expression or use the - Mat::copyTo method: - @code - Mat A; - ... - // works, but looks a bit obscure. - A.row(i) = A.row(j) + 0; - // this is a bit longer, but the recommended method. - A.row(j).copyTo(A.row(i)); - @endcode - @param y A 0-based row index. - */ - Mat row(int y) const; - - /** @brief Creates a matrix header for the specified matrix column. - - The method makes a new header for the specified matrix column and returns it. This is an O(1) - operation, regardless of the matrix size. The underlying data of the new matrix is shared with the - original matrix. See also the Mat::row description. - @param x A 0-based column index. - */ - Mat col(int x) const; - - /** @brief Creates a matrix header for the specified row span. - - The method makes a new header for the specified row span of the matrix. Similarly to Mat::row and - Mat::col , this is an O(1) operation. - @param startrow An inclusive 0-based start index of the row span. - @param endrow An exclusive 0-based ending index of the row span. - */ - Mat rowRange(int startrow, int endrow) const; - - /** @overload - @param r Range structure containing both the start and the end indices. - */ - Mat rowRange(const Range& r) const; - - /** @brief Creates a matrix header for the specified column span. - - The method makes a new header for the specified column span of the matrix. Similarly to Mat::row and - Mat::col , this is an O(1) operation. - @param startcol An inclusive 0-based start index of the column span. - @param endcol An exclusive 0-based ending index of the column span. - */ - Mat colRange(int startcol, int endcol) const; - - /** @overload - @param r Range structure containing both the start and the end indices. - */ - Mat colRange(const Range& r) const; - - /** @brief Extracts a diagonal from a matrix - - The method makes a new header for the specified matrix diagonal. The new matrix is represented as a - single-column matrix. Similarly to Mat::row and Mat::col, this is an O(1) operation. - @param d index of the diagonal, with the following values: - - `d=0` is the main diagonal. - - `d<0` is a diagonal from the lower half. For example, d=-1 means the diagonal is set - immediately below the main one. - - `d>0` is a diagonal from the upper half. For example, d=1 means the diagonal is set - immediately above the main one. - For example: - @code - Mat m = (Mat_(3,3) << - 1,2,3, - 4,5,6, - 7,8,9); - Mat d0 = m.diag(0); - Mat d1 = m.diag(1); - Mat d_1 = m.diag(-1); - @endcode - The resulting matrices are - @code - d0 = - [1; - 5; - 9] - d1 = - [2; - 6] - d_1 = - [4; - 8] - @endcode - */ - Mat diag(int d=0) const; - - /** @brief creates a diagonal matrix - - The method creates a square diagonal matrix from specified main diagonal. - @param d One-dimensional matrix that represents the main diagonal. - */ - static Mat diag(const Mat& d); - - /** @brief Creates a full copy of the array and the underlying data. - - The method creates a full copy of the array. The original step[] is not taken into account. So, the - array copy is a continuous array occupying total()*elemSize() bytes. - */ - Mat clone() const CV_NODISCARD; - - /** @brief Copies the matrix to another one. - - The method copies the matrix data to another matrix. Before copying the data, the method invokes : - @code - m.create(this->size(), this->type()); - @endcode - so that the destination matrix is reallocated if needed. While m.copyTo(m); works flawlessly, the - function does not handle the case of a partial overlap between the source and the destination - matrices. - - When the operation mask is specified, if the Mat::create call shown above reallocates the matrix, - the newly allocated matrix is initialized with all zeros before copying the data. - @param m Destination matrix. If it does not have a proper size or type before the operation, it is - reallocated. - */ - void copyTo( OutputArray m ) const; - - /** @overload - @param m Destination matrix. If it does not have a proper size or type before the operation, it is - reallocated. - @param mask Operation mask of the same size as \*this. Its non-zero elements indicate which matrix - elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels. - */ - void copyTo( OutputArray m, InputArray mask ) const; - - /** @brief Converts an array to another data type with optional scaling. - - The method converts source pixel values to the target data type. saturate_cast\<\> is applied at - the end to avoid possible overflows: - - \f[m(x,y) = saturate \_ cast( \alpha (*this)(x,y) + \beta )\f] - @param m output matrix; if it does not have a proper size or type before the operation, it is - reallocated. - @param rtype desired output matrix type or, rather, the depth since the number of channels are the - same as the input has; if rtype is negative, the output matrix will have the same type as the input. - @param alpha optional scale factor. - @param beta optional delta added to the scaled values. - */ - void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const; - - /** @brief Provides a functional form of convertTo. - - This is an internally used method called by the @ref MatrixExpressions engine. - @param m Destination array. - @param type Desired destination array depth (or -1 if it should be the same as the source type). - */ - void assignTo( Mat& m, int type=-1 ) const; - - /** @brief Sets all or some of the array elements to the specified value. - @param s Assigned scalar converted to the actual array type. - */ - Mat& operator = (const Scalar& s); - - /** @brief Sets all or some of the array elements to the specified value. - - This is an advanced variant of the Mat::operator=(const Scalar& s) operator. - @param value Assigned scalar converted to the actual array type. - @param mask Operation mask of the same size as \*this. Its non-zero elements indicate which matrix - elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels - */ - Mat& setTo(InputArray value, InputArray mask=noArray()); - - /** @brief Changes the shape and/or the number of channels of a 2D matrix without copying the data. - - The method makes a new matrix header for \*this elements. The new matrix may have a different size - and/or different number of channels. Any combination is possible if: - - No extra elements are included into the new matrix and no elements are excluded. Consequently, - the product rows\*cols\*channels() must stay the same after the transformation. - - No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of - rows, or the operation changes the indices of elements row in some other way, the matrix must be - continuous. See Mat::isContinuous . - - For example, if there is a set of 3D points stored as an STL vector, and you want to represent the - points as a 3xN matrix, do the following: - @code - std::vector vec; - ... - Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation - reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel. - // Also, an O(1) operation - t(); // finally, transpose the Nx3 matrix. - // This involves copying all the elements - @endcode - @param cn New number of channels. If the parameter is 0, the number of channels remains the same. - @param rows New number of rows. If the parameter is 0, the number of rows remains the same. - */ - Mat reshape(int cn, int rows=0) const; - - /** @overload */ - Mat reshape(int cn, int newndims, const int* newsz) const; - - /** @overload */ - Mat reshape(int cn, const std::vector& newshape) const; - - /** @brief Transposes a matrix. - - The method performs matrix transposition by means of matrix expressions. It does not perform the - actual transposition but returns a temporary matrix transposition object that can be further used as - a part of more complex matrix expressions or can be assigned to a matrix: - @code - Mat A1 = A + Mat::eye(A.size(), A.type())*lambda; - Mat C = A1.t()*A1; // compute (A + lambda*I)^t * (A + lamda*I) - @endcode - */ - MatExpr t() const; - - /** @brief Inverses a matrix. - - The method performs a matrix inversion by means of matrix expressions. This means that a temporary - matrix inversion object is returned by the method and can be used further as a part of more complex - matrix expressions or can be assigned to a matrix. - @param method Matrix inversion method. One of cv::DecompTypes - */ - MatExpr inv(int method=DECOMP_LU) const; - - /** @brief Performs an element-wise multiplication or division of the two matrices. - - The method returns a temporary object encoding per-element array multiplication, with optional - scale. Note that this is not a matrix multiplication that corresponds to a simpler "\*" operator. - - Example: - @code - Mat C = A.mul(5/B); // equivalent to divide(A, B, C, 5) - @endcode - @param m Another array of the same type and the same size as \*this, or a matrix expression. - @param scale Optional scale factor. - */ - MatExpr mul(InputArray m, double scale=1) const; - - /** @brief Computes a cross-product of two 3-element vectors. - - The method computes a cross-product of two 3-element vectors. The vectors must be 3-element - floating-point vectors of the same shape and size. The result is another 3-element vector of the - same shape and type as operands. - @param m Another cross-product operand. - */ - Mat cross(InputArray m) const; - - /** @brief Computes a dot-product of two vectors. - - The method computes a dot-product of two matrices. If the matrices are not single-column or - single-row vectors, the top-to-bottom left-to-right scan ordering is used to treat them as 1D - vectors. The vectors must have the same size and type. If the matrices have more than one channel, - the dot products from all the channels are summed together. - @param m another dot-product operand. - */ - double dot(InputArray m) const; - - /** @brief Returns a zero array of the specified size and type. - - The method returns a Matlab-style zero array initializer. It can be used to quickly form a constant - array as a function parameter, part of a matrix expression, or as a matrix initializer: - @code - Mat A; - A = Mat::zeros(3, 3, CV_32F); - @endcode - In the example above, a new matrix is allocated only if A is not a 3x3 floating-point matrix. - Otherwise, the existing matrix A is filled with zeros. - @param rows Number of rows. - @param cols Number of columns. - @param type Created matrix type. - */ - static MatExpr zeros(int rows, int cols, int type); - - /** @overload - @param size Alternative to the matrix size specification Size(cols, rows) . - @param type Created matrix type. - */ - static MatExpr zeros(Size size, int type); - - /** @overload - @param ndims Array dimensionality. - @param sz Array of integers specifying the array shape. - @param type Created matrix type. - */ - static MatExpr zeros(int ndims, const int* sz, int type); - - /** @brief Returns an array of all 1's of the specified size and type. - - The method returns a Matlab-style 1's array initializer, similarly to Mat::zeros. Note that using - this method you can initialize an array with an arbitrary value, using the following Matlab idiom: - @code - Mat A = Mat::ones(100, 100, CV_8U)*3; // make 100x100 matrix filled with 3. - @endcode - The above operation does not form a 100x100 matrix of 1's and then multiply it by 3. Instead, it - just remembers the scale factor (3 in this case) and use it when actually invoking the matrix - initializer. - @note In case of multi-channels type, only the first channel will be initialized with 1's, the - others will be set to 0's. - @param rows Number of rows. - @param cols Number of columns. - @param type Created matrix type. - */ - static MatExpr ones(int rows, int cols, int type); - - /** @overload - @param size Alternative to the matrix size specification Size(cols, rows) . - @param type Created matrix type. - */ - static MatExpr ones(Size size, int type); - - /** @overload - @param ndims Array dimensionality. - @param sz Array of integers specifying the array shape. - @param type Created matrix type. - */ - static MatExpr ones(int ndims, const int* sz, int type); - - /** @brief Returns an identity matrix of the specified size and type. - - The method returns a Matlab-style identity matrix initializer, similarly to Mat::zeros. Similarly to - Mat::ones, you can use a scale operation to create a scaled identity matrix efficiently: - @code - // make a 4x4 diagonal matrix with 0.1's on the diagonal. - Mat A = Mat::eye(4, 4, CV_32F)*0.1; - @endcode - @note In case of multi-channels type, identity matrix will be initialized only for the first channel, - the others will be set to 0's - @param rows Number of rows. - @param cols Number of columns. - @param type Created matrix type. - */ - static MatExpr eye(int rows, int cols, int type); - - /** @overload - @param size Alternative matrix size specification as Size(cols, rows) . - @param type Created matrix type. - */ - static MatExpr eye(Size size, int type); - - /** @brief Allocates new array data if needed. - - This is one of the key Mat methods. Most new-style OpenCV functions and methods that produce arrays - call this method for each output array. The method uses the following algorithm: - - -# If the current array shape and the type match the new ones, return immediately. Otherwise, - de-reference the previous data by calling Mat::release. - -# Initialize the new header. - -# Allocate the new data of total()\*elemSize() bytes. - -# Allocate the new, associated with the data, reference counter and set it to 1. - - Such a scheme makes the memory management robust and efficient at the same time and helps avoid - extra typing for you. This means that usually there is no need to explicitly allocate output arrays. - That is, instead of writing: - @code - Mat color; - ... - Mat gray(color.rows, color.cols, color.depth()); - cvtColor(color, gray, COLOR_BGR2GRAY); - @endcode - you can simply write: - @code - Mat color; - ... - Mat gray; - cvtColor(color, gray, COLOR_BGR2GRAY); - @endcode - because cvtColor, as well as the most of OpenCV functions, calls Mat::create() for the output array - internally. - @param rows New number of rows. - @param cols New number of columns. - @param type New matrix type. - */ - void create(int rows, int cols, int type); - - /** @overload - @param size Alternative new matrix size specification: Size(cols, rows) - @param type New matrix type. - */ - void create(Size size, int type); - - /** @overload - @param ndims New array dimensionality. - @param sizes Array of integers specifying a new array shape. - @param type New matrix type. - */ - void create(int ndims, const int* sizes, int type); - - /** @overload - @param sizes Array of integers specifying a new array shape. - @param type New matrix type. - */ - void create(const std::vector& sizes, int type); - - /** @brief Increments the reference counter. - - The method increments the reference counter associated with the matrix data. If the matrix header - points to an external data set (see Mat::Mat ), the reference counter is NULL, and the method has no - effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It - is called implicitly by the matrix assignment operator. The reference counter increment is an atomic - operation on the platforms that support it. Thus, it is safe to operate on the same matrices - asynchronously in different threads. - */ - void addref(); - - /** @brief Decrements the reference counter and deallocates the matrix if needed. - - The method decrements the reference counter associated with the matrix data. When the reference - counter reaches 0, the matrix data is deallocated and the data and the reference counter pointers - are set to NULL's. If the matrix header points to an external data set (see Mat::Mat ), the - reference counter is NULL, and the method has no effect in this case. - - This method can be called manually to force the matrix data deallocation. But since this method is - automatically called in the destructor, or by any other method that changes the data pointer, it is - usually not needed. The reference counter decrement and check for 0 is an atomic operation on the - platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in - different threads. - */ - void release(); - - //! internal use function, consider to use 'release' method instead; deallocates the matrix data - void deallocate(); - //! internal use function; properly re-allocates _size, _step arrays - void copySize(const Mat& m); - - /** @brief Reserves space for the certain number of rows. - - The method reserves space for sz rows. If the matrix already has enough space to store sz rows, - nothing happens. If the matrix is reallocated, the first Mat::rows rows are preserved. The method - emulates the corresponding method of the STL vector class. - @param sz Number of rows. - */ - void reserve(size_t sz); - - /** @brief Reserves space for the certain number of bytes. - - The method reserves space for sz bytes. If the matrix already has enough space to store sz bytes, - nothing happens. If matrix has to be reallocated its previous content could be lost. - @param sz Number of bytes. - */ - void reserveBuffer(size_t sz); - - /** @brief Changes the number of matrix rows. - - The methods change the number of matrix rows. If the matrix is reallocated, the first - min(Mat::rows, sz) rows are preserved. The methods emulate the corresponding methods of the STL - vector class. - @param sz New number of rows. - */ - void resize(size_t sz); - - /** @overload - @param sz New number of rows. - @param s Value assigned to the newly added elements. - */ - void resize(size_t sz, const Scalar& s); - - //! internal function - void push_back_(const void* elem); - - /** @brief Adds elements to the bottom of the matrix. - - The methods add one or more elements to the bottom of the matrix. They emulate the corresponding - method of the STL vector class. When elem is Mat , its type and the number of columns must be the - same as in the container matrix. - @param elem Added element(s). - */ - template void push_back(const _Tp& elem); - - /** @overload - @param elem Added element(s). - */ - template void push_back(const Mat_<_Tp>& elem); - - /** @overload - @param elem Added element(s). - */ - template void push_back(const std::vector<_Tp>& elem); - - /** @overload - @param m Added line(s). - */ - void push_back(const Mat& m); - - /** @brief Removes elements from the bottom of the matrix. - - The method removes one or more rows from the bottom of the matrix. - @param nelems Number of removed rows. If it is greater than the total number of rows, an exception - is thrown. - */ - void pop_back(size_t nelems=1); - - /** @brief Locates the matrix header within a parent matrix. - - After you extracted a submatrix from a matrix using Mat::row, Mat::col, Mat::rowRange, - Mat::colRange, and others, the resultant submatrix points just to the part of the original big - matrix. However, each submatrix contains information (represented by datastart and dataend - fields) that helps reconstruct the original matrix size and the position of the extracted - submatrix within the original matrix. The method locateROI does exactly that. - @param wholeSize Output parameter that contains the size of the whole matrix containing *this* - as a part. - @param ofs Output parameter that contains an offset of *this* inside the whole matrix. - */ - void locateROI( Size& wholeSize, Point& ofs ) const; - - /** @brief Adjusts a submatrix size and position within the parent matrix. - - The method is complimentary to Mat::locateROI . The typical use of these functions is to determine - the submatrix position within the parent matrix and then shift the position somehow. Typically, it - can be required for filtering operations when pixels outside of the ROI should be taken into - account. When all the method parameters are positive, the ROI needs to grow in all directions by the - specified amount, for example: - @code - A.adjustROI(2, 2, 2, 2); - @endcode - In this example, the matrix size is increased by 4 elements in each direction. The matrix is shifted - by 2 elements to the left and 2 elements up, which brings in all the necessary pixels for the - filtering with the 5x5 kernel. - - adjustROI forces the adjusted ROI to be inside of the parent matrix that is boundaries of the - adjusted ROI are constrained by boundaries of the parent matrix. For example, if the submatrix A is - located in the first row of a parent matrix and you called A.adjustROI(2, 2, 2, 2) then A will not - be increased in the upward direction. - - The function is used internally by the OpenCV filtering functions, like filter2D , morphological - operations, and so on. - @param dtop Shift of the top submatrix boundary upwards. - @param dbottom Shift of the bottom submatrix boundary downwards. - @param dleft Shift of the left submatrix boundary to the left. - @param dright Shift of the right submatrix boundary to the right. - @sa copyMakeBorder - */ - Mat& adjustROI( int dtop, int dbottom, int dleft, int dright ); - - /** @brief Extracts a rectangular submatrix. - - The operators make a new header for the specified sub-array of \*this . They are the most - generalized forms of Mat::row, Mat::col, Mat::rowRange, and Mat::colRange . For example, - `A(Range(0, 10), Range::all())` is equivalent to `A.rowRange(0, 10)`. Similarly to all of the above, - the operators are O(1) operations, that is, no matrix data is copied. - @param rowRange Start and end row of the extracted submatrix. The upper boundary is not included. To - select all the rows, use Range::all(). - @param colRange Start and end column of the extracted submatrix. The upper boundary is not included. - To select all the columns, use Range::all(). - */ - Mat operator()( Range rowRange, Range colRange ) const; - - /** @overload - @param roi Extracted submatrix specified as a rectangle. - */ - Mat operator()( const Rect& roi ) const; - - /** @overload - @param ranges Array of selected ranges along each array dimension. - */ - Mat operator()( const Range* ranges ) const; - - /** @overload - @param ranges Array of selected ranges along each array dimension. - */ - Mat operator()(const std::vector& ranges) const; - - // //! converts header to CvMat; no data is copied - // operator CvMat() const; - // //! converts header to CvMatND; no data is copied - // operator CvMatND() const; - // //! converts header to IplImage; no data is copied - // operator IplImage() const; - - template operator std::vector<_Tp>() const; - template operator Vec<_Tp, n>() const; - template operator Matx<_Tp, m, n>() const; - -#ifdef CV_CXX_STD_ARRAY - template operator std::array<_Tp, _Nm>() const; -#endif - - /** @brief Reports whether the matrix is continuous or not. - - The method returns true if the matrix elements are stored continuously without gaps at the end of - each row. Otherwise, it returns false. Obviously, 1x1 or 1xN matrices are always continuous. - Matrices created with Mat::create are always continuous. But if you extract a part of the matrix - using Mat::col, Mat::diag, and so on, or constructed a matrix header for externally allocated data, - such matrices may no longer have this property. - - The continuity flag is stored as a bit in the Mat::flags field and is computed automatically when - you construct a matrix header. Thus, the continuity check is a very fast operation, though - theoretically it could be done as follows: - @code - // alternative implementation of Mat::isContinuous() - bool myCheckMatContinuity(const Mat& m) - { - //return (m.flags & Mat::CONTINUOUS_FLAG) != 0; - return m.rows == 1 || m.step == m.cols*m.elemSize(); - } - @endcode - The method is used in quite a few of OpenCV functions. The point is that element-wise operations - (such as arithmetic and logical operations, math functions, alpha blending, color space - transformations, and others) do not depend on the image geometry. Thus, if all the input and output - arrays are continuous, the functions can process them as very long single-row vectors. The example - below illustrates how an alpha-blending function can be implemented: - @code - template - void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst) - { - const float alpha_scale = (float)std::numeric_limits::max(), - inv_scale = 1.f/alpha_scale; - - CV_Assert( src1.type() == src2.type() && - src1.type() == CV_MAKETYPE(traits::Depth::value, 4) && - src1.size() == src2.size()); - Size size = src1.size(); - dst.create(size, src1.type()); - - // here is the idiom: check the arrays for continuity and, - // if this is the case, - // treat the arrays as 1D vectors - if( src1.isContinuous() && src2.isContinuous() && dst.isContinuous() ) - { - size.width *= size.height; - size.height = 1; - } - size.width *= 4; - - for( int i = 0; i < size.height; i++ ) - { - // when the arrays are continuous, - // the outer loop is executed only once - const T* ptr1 = src1.ptr(i); - const T* ptr2 = src2.ptr(i); - T* dptr = dst.ptr(i); - - for( int j = 0; j < size.width; j += 4 ) - { - float alpha = ptr1[j+3]*inv_scale, beta = ptr2[j+3]*inv_scale; - dptr[j] = saturate_cast(ptr1[j]*alpha + ptr2[j]*beta); - dptr[j+1] = saturate_cast(ptr1[j+1]*alpha + ptr2[j+1]*beta); - dptr[j+2] = saturate_cast(ptr1[j+2]*alpha + ptr2[j+2]*beta); - dptr[j+3] = saturate_cast((1 - (1-alpha)*(1-beta))*alpha_scale); - } - } - } - @endcode - This approach, while being very simple, can boost the performance of a simple element-operation by - 10-20 percents, especially if the image is rather small and the operation is quite simple. - - Another OpenCV idiom in this function, a call of Mat::create for the destination array, that - allocates the destination array unless it already has the proper size and type. And while the newly - allocated arrays are always continuous, you still need to check the destination array because - Mat::create does not always allocate a new matrix. - */ - bool isContinuous() const; - - //! returns true if the matrix is a submatrix of another matrix - bool isSubmatrix() const; - - /** @brief Returns the matrix element size in bytes. - - The method returns the matrix element size in bytes. For example, if the matrix type is CV_16SC3 , - the method returns 3\*sizeof(short) or 6. - */ - size_t elemSize() const; - - /** @brief Returns the size of each matrix element channel in bytes. - - The method returns the matrix element channel size in bytes, that is, it ignores the number of - channels. For example, if the matrix type is CV_16SC3 , the method returns sizeof(short) or 2. - */ - size_t elemSize1() const; - - /** @brief Returns the type of a matrix element. - - The method returns a matrix element type. This is an identifier compatible with the CvMat type - system, like CV_16SC3 or 16-bit signed 3-channel array, and so on. - */ - int type() const; - - /** @brief Returns the depth of a matrix element. - - The method returns the identifier of the matrix element depth (the type of each individual channel). - For example, for a 16-bit signed element array, the method returns CV_16S . A complete list of - matrix types contains the following values: - - CV_8U - 8-bit unsigned integers ( 0..255 ) - - CV_8S - 8-bit signed integers ( -128..127 ) - - CV_16U - 16-bit unsigned integers ( 0..65535 ) - - CV_16S - 16-bit signed integers ( -32768..32767 ) - - CV_32S - 32-bit signed integers ( -2147483648..2147483647 ) - - CV_32F - 32-bit floating-point numbers ( -FLT_MAX..FLT_MAX, INF, NAN ) - - CV_64F - 64-bit floating-point numbers ( -DBL_MAX..DBL_MAX, INF, NAN ) - */ - int depth() const; - - /** @brief Returns the number of matrix channels. - - The method returns the number of matrix channels. - */ - int channels() const; - - /** @brief Returns a normalized step. - - The method returns a matrix step divided by Mat::elemSize1() . It can be useful to quickly access an - arbitrary matrix element. - */ - size_t step1(int i=0) const; - - /** @brief Returns true if the array has no elements. - - The method returns true if Mat::total() is 0 or if Mat::data is NULL. Because of pop_back() and - resize() methods `M.total() == 0` does not imply that `M.data == NULL`. - */ - bool empty() const; - - /** @brief Returns the total number of array elements. - - The method returns the number of array elements (a number of pixels if the array represents an - image). - */ - size_t total() const; - - /** @brief Returns the total number of array elements. - - The method returns the number of elements within a certain sub-array slice with startDim <= dim < endDim - */ - size_t total(int startDim, int endDim=INT_MAX) const; - - /** - * @param elemChannels Number of channels or number of columns the matrix should have. - * For a 2-D matrix, when the matrix has only 1 column, then it should have - * elemChannels channels; When the matrix has only 1 channel, - * then it should have elemChannels columns. - * For a 3-D matrix, it should have only one channel. Furthermore, - * if the number of planes is not one, then the number of rows - * within every plane has to be 1; if the number of rows within - * every plane is not 1, then the number of planes has to be 1. - * @param depth The depth the matrix should have. Set it to -1 when any depth is fine. - * @param requireContinuous Set it to true to require the matrix to be continuous - * @return -1 if the requirement is not satisfied. - * Otherwise, it returns the number of elements in the matrix. Note - * that an element may have multiple channels. - * - * The following code demonstrates its usage for a 2-d matrix: - * @snippet snippets/core_mat_checkVector.cpp example-2d - * - * The following code demonstrates its usage for a 3-d matrix: - * @snippet snippets/core_mat_checkVector.cpp example-3d - */ - int checkVector(int elemChannels, int depth=-1, bool requireContinuous=true) const; - - /** @brief Returns a pointer to the specified matrix row. - - The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in - Mat::isContinuous to know how to use these methods. - @param i0 A 0-based row index. - */ - uchar* ptr(int i0=0); - /** @overload */ - const uchar* ptr(int i0=0) const; - - /** @overload - @param row Index along the dimension 0 - @param col Index along the dimension 1 - */ - uchar* ptr(int row, int col); - /** @overload - @param row Index along the dimension 0 - @param col Index along the dimension 1 - */ - const uchar* ptr(int row, int col) const; - - /** @overload */ - uchar* ptr(int i0, int i1, int i2); - /** @overload */ - const uchar* ptr(int i0, int i1, int i2) const; - - /** @overload */ - uchar* ptr(const int* idx); - /** @overload */ - const uchar* ptr(const int* idx) const; - /** @overload */ - template uchar* ptr(const Vec& idx); - /** @overload */ - template const uchar* ptr(const Vec& idx) const; - - /** @overload */ - template _Tp* ptr(int i0=0); - /** @overload */ - template const _Tp* ptr(int i0=0) const; - /** @overload - @param row Index along the dimension 0 - @param col Index along the dimension 1 - */ - template _Tp* ptr(int row, int col); - /** @overload - @param row Index along the dimension 0 - @param col Index along the dimension 1 - */ - template const _Tp* ptr(int row, int col) const; - /** @overload */ - template _Tp* ptr(int i0, int i1, int i2); - /** @overload */ - template const _Tp* ptr(int i0, int i1, int i2) const; - /** @overload */ - template _Tp* ptr(const int* idx); - /** @overload */ - template const _Tp* ptr(const int* idx) const; - /** @overload */ - template _Tp* ptr(const Vec& idx); - /** @overload */ - template const _Tp* ptr(const Vec& idx) const; - - /** @brief Returns a reference to the specified array element. - - The template methods return a reference to the specified array element. For the sake of higher - performance, the index range checks are only performed in the Debug configuration. - - Note that the variants with a single index (i) can be used to access elements of single-row or - single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and - B is an M x 1 integer matrix, you can simply write `A.at(k+4)` and `B.at(2*i+1)` - instead of `A.at(0,k+4)` and `B.at(2*i+1,0)`, respectively. - - The example below initializes a Hilbert matrix: - @code - Mat H(100, 100, CV_64F); - for(int i = 0; i < H.rows; i++) - for(int j = 0; j < H.cols; j++) - H.at(i,j)=1./(i+j+1); - @endcode - - Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends - on the image from which you are trying to retrieve the data. The table below gives a better insight in this: - - If matrix is of type `CV_8U` then use `Mat.at(y,x)`. - - If matrix is of type `CV_8S` then use `Mat.at(y,x)`. - - If matrix is of type `CV_16U` then use `Mat.at(y,x)`. - - If matrix is of type `CV_16S` then use `Mat.at(y,x)`. - - If matrix is of type `CV_32S` then use `Mat.at(y,x)`. - - If matrix is of type `CV_32F` then use `Mat.at(y,x)`. - - If matrix is of type `CV_64F` then use `Mat.at(y,x)`. - - @param i0 Index along the dimension 0 - */ - template _Tp& at(int i0=0); - /** @overload - @param i0 Index along the dimension 0 - */ - template const _Tp& at(int i0=0) const; - /** @overload - @param row Index along the dimension 0 - @param col Index along the dimension 1 - */ - template _Tp& at(int row, int col); - /** @overload - @param row Index along the dimension 0 - @param col Index along the dimension 1 - */ - template const _Tp& at(int row, int col) const; - - /** @overload - @param i0 Index along the dimension 0 - @param i1 Index along the dimension 1 - @param i2 Index along the dimension 2 - */ - template _Tp& at(int i0, int i1, int i2); - /** @overload - @param i0 Index along the dimension 0 - @param i1 Index along the dimension 1 - @param i2 Index along the dimension 2 - */ - template const _Tp& at(int i0, int i1, int i2) const; - - /** @overload - @param idx Array of Mat::dims indices. - */ - template _Tp& at(const int* idx); - /** @overload - @param idx Array of Mat::dims indices. - */ - template const _Tp& at(const int* idx) const; - - /** @overload */ - template _Tp& at(const Vec& idx); - /** @overload */ - template const _Tp& at(const Vec& idx) const; - - /** @overload - special versions for 2D arrays (especially convenient for referencing image pixels) - @param pt Element position specified as Point(j,i) . - */ - template _Tp& at(Point pt); - /** @overload - special versions for 2D arrays (especially convenient for referencing image pixels) - @param pt Element position specified as Point(j,i) . - */ - template const _Tp& at(Point pt) const; - - /** @brief Returns the matrix iterator and sets it to the first matrix element. - - The methods return the matrix read-only or read-write iterators. The use of matrix iterators is very - similar to the use of bi-directional STL iterators. In the example below, the alpha blending - function is rewritten using the matrix iterators: - @code - template - void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst) - { - typedef Vec VT; - - const float alpha_scale = (float)std::numeric_limits::max(), - inv_scale = 1.f/alpha_scale; - - CV_Assert( src1.type() == src2.type() && - src1.type() == traits::Type::value && - src1.size() == src2.size()); - Size size = src1.size(); - dst.create(size, src1.type()); - - MatConstIterator_ it1 = src1.begin(), it1_end = src1.end(); - MatConstIterator_ it2 = src2.begin(); - MatIterator_ dst_it = dst.begin(); - - for( ; it1 != it1_end; ++it1, ++it2, ++dst_it ) - { - VT pix1 = *it1, pix2 = *it2; - float alpha = pix1[3]*inv_scale, beta = pix2[3]*inv_scale; - *dst_it = VT(saturate_cast(pix1[0]*alpha + pix2[0]*beta), - saturate_cast(pix1[1]*alpha + pix2[1]*beta), - saturate_cast(pix1[2]*alpha + pix2[2]*beta), - saturate_cast((1 - (1-alpha)*(1-beta))*alpha_scale)); - } - } - @endcode - */ - template MatIterator_<_Tp> begin(); - template MatConstIterator_<_Tp> begin() const; - - /** @brief Returns the matrix iterator and sets it to the after-last matrix element. - - The methods return the matrix read-only or read-write iterators, set to the point following the last - matrix element. - */ - template MatIterator_<_Tp> end(); - template MatConstIterator_<_Tp> end() const; - - /** @brief Runs the given functor over all matrix elements in parallel. - - The operation passed as argument has to be a function pointer, a function object or a lambda(C++11). - - Example 1. All of the operations below put 0xFF the first channel of all matrix elements: - @code - Mat image(1920, 1080, CV_8UC3); - typedef cv::Point3_ Pixel; - - // first. raw pointer access. - for (int r = 0; r < image.rows; ++r) { - Pixel* ptr = image.ptr(r, 0); - const Pixel* ptr_end = ptr + image.cols; - for (; ptr != ptr_end; ++ptr) { - ptr->x = 255; - } - } - - // Using MatIterator. (Simple but there are a Iterator's overhead) - for (Pixel &p : cv::Mat_(image)) { - p.x = 255; - } - - // Parallel execution with function object. - struct Operator { - void operator ()(Pixel &pixel, const int * position) { - pixel.x = 255; - } - }; - image.forEach(Operator()); - - // Parallel execution using C++11 lambda. - image.forEach([](Pixel &p, const int * position) -> void { - p.x = 255; - }); - @endcode - Example 2. Using the pixel's position: - @code - // Creating 3D matrix (255 x 255 x 255) typed uint8_t - // and initialize all elements by the value which equals elements position. - // i.e. pixels (x,y,z) = (1,2,3) is (b,g,r) = (1,2,3). - - int sizes[] = { 255, 255, 255 }; - typedef cv::Point3_ Pixel; - - Mat_ image = Mat::zeros(3, sizes, CV_8UC3); - - image.forEach([&](Pixel& pixel, const int position[]) -> void { - pixel.x = position[0]; - pixel.y = position[1]; - pixel.z = position[2]; - }); - @endcode - */ - template void forEach(const Functor& operation); - /** @overload */ - template void forEach(const Functor& operation) const; - -#ifdef CV_CXX_MOVE_SEMANTICS - Mat(Mat&& m); - Mat& operator = (Mat&& m); -#endif - - enum { MAGIC_VAL = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG }; - enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 }; - - /*! includes several bit-fields: - - the magic signature - - continuity flag - - depth - - number of channels - */ - int flags; - //! the matrix dimensionality, >= 2 - int dims; - //! the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions - int rows, cols; - //! pointer to the data - uchar* data; - - //! helper fields used in locateROI and adjustROI - const uchar* datastart; - const uchar* dataend; - const uchar* datalimit; - - //! custom allocator - MatAllocator* allocator; - //! and the standard allocator - static MatAllocator* getStdAllocator(); - static MatAllocator* getDefaultAllocator(); - static void setDefaultAllocator(MatAllocator* allocator); - - //! internal use method: updates the continuity flag - void updateContinuityFlag(); - - //! interaction with UMat - UMatData* u; - - MatSize size; - MatStep step; - -protected: - template void forEach_impl(const Functor& operation); -}; - - -///////////////////////////////// Mat_<_Tp> //////////////////////////////////// - -/** @brief Template matrix class derived from Mat - -@code{.cpp} - template class Mat_ : public Mat - { - public: - // ... some specific methods - // and - // no new extra fields - }; -@endcode -The class `Mat_<_Tp>` is a *thin* template wrapper on top of the Mat class. It does not have any -extra data fields. Nor this class nor Mat has any virtual methods. Thus, references or pointers to -these two classes can be freely but carefully converted one to another. For example: -@code{.cpp} - // create a 100x100 8-bit matrix - Mat M(100,100,CV_8U); - // this will be compiled fine. no any data conversion will be done. - Mat_& M1 = (Mat_&)M; - // the program is likely to crash at the statement below - M1(99,99) = 1.f; -@endcode -While Mat is sufficient in most cases, Mat_ can be more convenient if you use a lot of element -access operations and if you know matrix type at the compilation time. Note that -`Mat::at(int y,int x)` and `Mat_::operator()(int y,int x)` do absolutely the same -and run at the same speed, but the latter is certainly shorter: -@code{.cpp} - Mat_ M(20,20); - for(int i = 0; i < M.rows; i++) - for(int j = 0; j < M.cols; j++) - M(i,j) = 1./(i+j+1); - Mat E, V; - eigen(M,E,V); - cout << E.at(0,0)/E.at(M.rows-1,0); -@endcode -To use Mat_ for multi-channel images/matrices, pass Vec as a Mat_ parameter: -@code{.cpp} - // allocate a 320x240 color image and fill it with green (in RGB space) - Mat_ img(240, 320, Vec3b(0,255,0)); - // now draw a diagonal white line - for(int i = 0; i < 100; i++) - img(i,i)=Vec3b(255,255,255); - // and now scramble the 2nd (red) channel of each pixel - for(int i = 0; i < img.rows; i++) - for(int j = 0; j < img.cols; j++) - img(i,j)[2] ^= (uchar)(i ^ j); -@endcode -Mat_ is fully compatible with C++11 range-based for loop. For example such loop -can be used to safely apply look-up table: -@code{.cpp} -void applyTable(Mat_& I, const uchar* const table) -{ - for(auto& pixel : I) - { - pixel = table[pixel]; - } -} -@endcode - */ -template class Mat_ : public Mat -{ -public: - typedef _Tp value_type; - typedef typename DataType<_Tp>::channel_type channel_type; - typedef MatIterator_<_Tp> iterator; - typedef MatConstIterator_<_Tp> const_iterator; - - //! default constructor - Mat_(); - //! equivalent to Mat(_rows, _cols, DataType<_Tp>::type) - Mat_(int _rows, int _cols); - //! constructor that sets each matrix element to specified value - Mat_(int _rows, int _cols, const _Tp& value); - //! equivalent to Mat(_size, DataType<_Tp>::type) - explicit Mat_(Size _size); - //! constructor that sets each matrix element to specified value - Mat_(Size _size, const _Tp& value); - //! n-dim array constructor - Mat_(int _ndims, const int* _sizes); - //! n-dim array constructor that sets each matrix element to specified value - Mat_(int _ndims, const int* _sizes, const _Tp& value); - //! copy/conversion constructor. If m is of different type, it's converted - Mat_(const Mat& m); - //! copy constructor - Mat_(const Mat_& m); - //! constructs a matrix on top of user-allocated data. step is in bytes(!!!), regardless of the type - Mat_(int _rows, int _cols, _Tp* _data, size_t _step=AUTO_STEP); - //! constructs n-dim matrix on top of user-allocated data. steps are in bytes(!!!), regardless of the type - Mat_(int _ndims, const int* _sizes, _Tp* _data, const size_t* _steps=0); - //! selects a submatrix - Mat_(const Mat_& m, const Range& rowRange, const Range& colRange=Range::all()); - //! selects a submatrix - Mat_(const Mat_& m, const Rect& roi); - //! selects a submatrix, n-dim version - Mat_(const Mat_& m, const Range* ranges); - //! selects a submatrix, n-dim version - Mat_(const Mat_& m, const std::vector& ranges); - //! from a matrix expression - explicit Mat_(const MatExpr& e); - //! makes a matrix out of Vec, std::vector, Point_ or Point3_. The matrix will have a single column - explicit Mat_(const std::vector<_Tp>& vec, bool copyData=false); - template explicit Mat_(const Vec::channel_type, n>& vec, bool copyData=true); - template explicit Mat_(const Matx::channel_type, m, n>& mtx, bool copyData=true); - explicit Mat_(const Point_::channel_type>& pt, bool copyData=true); - explicit Mat_(const Point3_::channel_type>& pt, bool copyData=true); - explicit Mat_(const MatCommaInitializer_<_Tp>& commaInitializer); - -#ifdef CV_CXX11 - Mat_(std::initializer_list<_Tp> values); - explicit Mat_(const std::initializer_list sizes, const std::initializer_list<_Tp> values); -#endif - -#ifdef CV_CXX_STD_ARRAY - template explicit Mat_(const std::array<_Tp, _Nm>& arr, bool copyData=false); -#endif - - Mat_& operator = (const Mat& m); - Mat_& operator = (const Mat_& m); - //! set all the elements to s. - Mat_& operator = (const _Tp& s); - //! assign a matrix expression - Mat_& operator = (const MatExpr& e); - - //! iterators; they are smart enough to skip gaps in the end of rows - iterator begin(); - iterator end(); - const_iterator begin() const; - const_iterator end() const; - - //! template methods for for operation over all matrix elements. - // the operations take care of skipping gaps in the end of rows (if any) - template void forEach(const Functor& operation); - template void forEach(const Functor& operation) const; - - //! equivalent to Mat::create(_rows, _cols, DataType<_Tp>::type) - void create(int _rows, int _cols); - //! equivalent to Mat::create(_size, DataType<_Tp>::type) - void create(Size _size); - //! equivalent to Mat::create(_ndims, _sizes, DatType<_Tp>::type) - void create(int _ndims, const int* _sizes); - //! equivalent to Mat::release() - void release(); - //! cross-product - Mat_ cross(const Mat_& m) const; - //! data type conversion - template operator Mat_() const; - //! overridden forms of Mat::row() etc. - Mat_ row(int y) const; - Mat_ col(int x) const; - Mat_ diag(int d=0) const; - Mat_ clone() const CV_NODISCARD; - - //! overridden forms of Mat::elemSize() etc. - size_t elemSize() const; - size_t elemSize1() const; - int type() const; - int depth() const; - int channels() const; - size_t step1(int i=0) const; - //! returns step()/sizeof(_Tp) - size_t stepT(int i=0) const; - - //! overridden forms of Mat::zeros() etc. Data type is omitted, of course - static MatExpr zeros(int rows, int cols); - static MatExpr zeros(Size size); - static MatExpr zeros(int _ndims, const int* _sizes); - static MatExpr ones(int rows, int cols); - static MatExpr ones(Size size); - static MatExpr ones(int _ndims, const int* _sizes); - static MatExpr eye(int rows, int cols); - static MatExpr eye(Size size); - - //! some more overridden methods - Mat_& adjustROI( int dtop, int dbottom, int dleft, int dright ); - Mat_ operator()( const Range& rowRange, const Range& colRange ) const; - Mat_ operator()( const Rect& roi ) const; - Mat_ operator()( const Range* ranges ) const; - Mat_ operator()(const std::vector& ranges) const; - - //! more convenient forms of row and element access operators - _Tp* operator [](int y); - const _Tp* operator [](int y) const; - - //! returns reference to the specified element - _Tp& operator ()(const int* idx); - //! returns read-only reference to the specified element - const _Tp& operator ()(const int* idx) const; - - //! returns reference to the specified element - template _Tp& operator ()(const Vec& idx); - //! returns read-only reference to the specified element - template const _Tp& operator ()(const Vec& idx) const; - - //! returns reference to the specified element (1D case) - _Tp& operator ()(int idx0); - //! returns read-only reference to the specified element (1D case) - const _Tp& operator ()(int idx0) const; - //! returns reference to the specified element (2D case) - _Tp& operator ()(int row, int col); - //! returns read-only reference to the specified element (2D case) - const _Tp& operator ()(int row, int col) const; - //! returns reference to the specified element (3D case) - _Tp& operator ()(int idx0, int idx1, int idx2); - //! returns read-only reference to the specified element (3D case) - const _Tp& operator ()(int idx0, int idx1, int idx2) const; - - _Tp& operator ()(Point pt); - const _Tp& operator ()(Point pt) const; - - //! conversion to vector. - operator std::vector<_Tp>() const; - -#ifdef CV_CXX_STD_ARRAY - //! conversion to array. - template operator std::array<_Tp, _Nm>() const; -#endif - - //! conversion to Vec - template operator Vec::channel_type, n>() const; - //! conversion to Matx - template operator Matx::channel_type, m, n>() const; - -#ifdef CV_CXX_MOVE_SEMANTICS - Mat_(Mat_&& m); - Mat_& operator = (Mat_&& m); - - Mat_(Mat&& m); - Mat_& operator = (Mat&& m); - - Mat_(MatExpr&& e); -#endif -}; - -typedef Mat_ Mat1b; -typedef Mat_ Mat2b; -typedef Mat_ Mat3b; -typedef Mat_ Mat4b; - -typedef Mat_ Mat1s; -typedef Mat_ Mat2s; -typedef Mat_ Mat3s; -typedef Mat_ Mat4s; - -typedef Mat_ Mat1w; -typedef Mat_ Mat2w; -typedef Mat_ Mat3w; -typedef Mat_ Mat4w; - -typedef Mat_ Mat1i; -typedef Mat_ Mat2i; -typedef Mat_ Mat3i; -typedef Mat_ Mat4i; - -typedef Mat_ Mat1f; -typedef Mat_ Mat2f; -typedef Mat_ Mat3f; -typedef Mat_ Mat4f; - -typedef Mat_ Mat1d; -typedef Mat_ Mat2d; -typedef Mat_ Mat3d; -typedef Mat_ Mat4d; - -/** @todo document */ -class CV_EXPORTS UMat -{ -public: - //! default constructor - UMat(UMatUsageFlags usageFlags = USAGE_DEFAULT); - //! constructs 2D matrix of the specified size and type - // (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.) - UMat(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); - UMat(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); - //! constructs 2D matrix and fills it with the specified value _s. - UMat(int rows, int cols, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); - UMat(Size size, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); - - //! constructs n-dimensional matrix - UMat(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); - UMat(int ndims, const int* sizes, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT); - - //! copy constructor - UMat(const UMat& m); - - //! creates a matrix header for a part of the bigger matrix - UMat(const UMat& m, const Range& rowRange, const Range& colRange=Range::all()); - UMat(const UMat& m, const Rect& roi); - UMat(const UMat& m, const Range* ranges); - UMat(const UMat& m, const std::vector& ranges); - - // FIXIT copyData=false is not implemented, drop this in favor of cv::Mat (OpenCV 5.0) - //! builds matrix from std::vector with or without copying the data - template explicit UMat(const std::vector<_Tp>& vec, bool copyData=false); - - //! destructor - calls release() - ~UMat(); - //! assignment operators - UMat& operator = (const UMat& m); - - Mat getMat(int flags) const; - - //! returns a new matrix header for the specified row - UMat row(int y) const; - //! returns a new matrix header for the specified column - UMat col(int x) const; - //! ... for the specified row span - UMat rowRange(int startrow, int endrow) const; - UMat rowRange(const Range& r) const; - //! ... for the specified column span - UMat colRange(int startcol, int endcol) const; - UMat colRange(const Range& r) const; - //! ... for the specified diagonal - //! (d=0 - the main diagonal, - //! >0 - a diagonal from the upper half, - //! <0 - a diagonal from the lower half) - UMat diag(int d=0) const; - //! constructs a square diagonal matrix which main diagonal is vector "d" - static UMat diag(const UMat& d); - - //! returns deep copy of the matrix, i.e. the data is copied - UMat clone() const CV_NODISCARD; - //! copies the matrix content to "m". - // It calls m.create(this->size(), this->type()). - void copyTo( OutputArray m ) const; - //! copies those matrix elements to "m" that are marked with non-zero mask elements. - void copyTo( OutputArray m, InputArray mask ) const; - //! converts matrix to another datatype with optional scaling. See cvConvertScale. - void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const; - - void assignTo( UMat& m, int type=-1 ) const; - - //! sets every matrix element to s - UMat& operator = (const Scalar& s); - //! sets some of the matrix elements to s, according to the mask - UMat& setTo(InputArray value, InputArray mask=noArray()); - //! creates alternative matrix header for the same data, with different - // number of channels and/or different number of rows. see cvReshape. - UMat reshape(int cn, int rows=0) const; - UMat reshape(int cn, int newndims, const int* newsz) const; - - //! matrix transposition by means of matrix expressions - UMat t() const; - //! matrix inversion by means of matrix expressions - UMat inv(int method=DECOMP_LU) const; - //! per-element matrix multiplication by means of matrix expressions - UMat mul(InputArray m, double scale=1) const; - - //! computes dot-product - double dot(InputArray m) const; - - //! Matlab-style matrix initialization - static UMat zeros(int rows, int cols, int type); - static UMat zeros(Size size, int type); - static UMat zeros(int ndims, const int* sz, int type); - static UMat ones(int rows, int cols, int type); - static UMat ones(Size size, int type); - static UMat ones(int ndims, const int* sz, int type); - static UMat eye(int rows, int cols, int type); - static UMat eye(Size size, int type); - - //! allocates new matrix data unless the matrix already has specified size and type. - // previous data is unreferenced if needed. - void create(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); - void create(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); - void create(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); - void create(const std::vector& sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT); - - //! increases the reference counter; use with care to avoid memleaks - void addref(); - //! decreases reference counter; - // deallocates the data when reference counter reaches 0. - void release(); - - //! deallocates the matrix data - void deallocate(); - //! internal use function; properly re-allocates _size, _step arrays - void copySize(const UMat& m); - - //! locates matrix header within a parent matrix. See below - void locateROI( Size& wholeSize, Point& ofs ) const; - //! moves/resizes the current matrix ROI inside the parent matrix. - UMat& adjustROI( int dtop, int dbottom, int dleft, int dright ); - //! extracts a rectangular sub-matrix - // (this is a generalized form of row, rowRange etc.) - UMat operator()( Range rowRange, Range colRange ) const; - UMat operator()( const Rect& roi ) const; - UMat operator()( const Range* ranges ) const; - UMat operator()(const std::vector& ranges) const; - - //! returns true iff the matrix data is continuous - // (i.e. when there are no gaps between successive rows). - // similar to CV_IS_MAT_CONT(cvmat->type) - bool isContinuous() const; - - //! returns true if the matrix is a submatrix of another matrix - bool isSubmatrix() const; - - //! returns element size in bytes, - // similar to CV_ELEM_SIZE(cvmat->type) - size_t elemSize() const; - //! returns the size of element channel in bytes. - size_t elemSize1() const; - //! returns element type, similar to CV_MAT_TYPE(cvmat->type) - int type() const; - //! returns element type, similar to CV_MAT_DEPTH(cvmat->type) - int depth() const; - //! returns element type, similar to CV_MAT_CN(cvmat->type) - int channels() const; - //! returns step/elemSize1() - size_t step1(int i=0) const; - //! returns true if matrix data is NULL - bool empty() const; - //! returns the total number of matrix elements - size_t total() const; - - //! returns N if the matrix is 1-channel (N x ptdim) or ptdim-channel (1 x N) or (N x 1); negative number otherwise - int checkVector(int elemChannels, int depth=-1, bool requireContinuous=true) const; - -#ifdef CV_CXX_MOVE_SEMANTICS - UMat(UMat&& m); - UMat& operator = (UMat&& m); -#endif - - /*! Returns the OpenCL buffer handle on which UMat operates on. - The UMat instance should be kept alive during the use of the handle to prevent the buffer to be - returned to the OpenCV buffer pool. - */ - void* handle(int accessFlags) const; - void ndoffset(size_t* ofs) const; - - enum { MAGIC_VAL = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG }; - enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 }; - - /*! includes several bit-fields: - - the magic signature - - continuity flag - - depth - - number of channels - */ - int flags; - //! the matrix dimensionality, >= 2 - int dims; - //! the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions - int rows, cols; - - //! custom allocator - MatAllocator* allocator; - UMatUsageFlags usageFlags; // usage flags for allocator - //! and the standard allocator - static MatAllocator* getStdAllocator(); - - //! internal use method: updates the continuity flag - void updateContinuityFlag(); - - // black-box container of UMat data - UMatData* u; - - // offset of the submatrix (or 0) - size_t offset; - - MatSize size; - MatStep step; - -protected: -}; - - -/////////////////////////// multi-dimensional sparse matrix ////////////////////////// - -/** @brief The class SparseMat represents multi-dimensional sparse numerical arrays. - -Such a sparse array can store elements of any type that Mat can store. *Sparse* means that only -non-zero elements are stored (though, as a result of operations on a sparse matrix, some of its -stored elements can actually become 0. It is up to you to detect such elements and delete them -using SparseMat::erase ). The non-zero elements are stored in a hash table that grows when it is -filled so that the search time is O(1) in average (regardless of whether element is there or not). -Elements can be accessed using the following methods: -- Query operations (SparseMat::ptr and the higher-level SparseMat::ref, SparseMat::value and - SparseMat::find), for example: - @code - const int dims = 5; - int size[5] = {10, 10, 10, 10, 10}; - SparseMat sparse_mat(dims, size, CV_32F); - for(int i = 0; i < 1000; i++) - { - int idx[dims]; - for(int k = 0; k < dims; k++) - idx[k] = rand() % size[k]; - sparse_mat.ref(idx) += 1.f; - } - cout << "nnz = " << sparse_mat.nzcount() << endl; - @endcode -- Sparse matrix iterators. They are similar to MatIterator but different from NAryMatIterator. - That is, the iteration loop is familiar to STL users: - @code - // prints elements of a sparse floating-point matrix - // and the sum of elements. - SparseMatConstIterator_ - it = sparse_mat.begin(), - it_end = sparse_mat.end(); - double s = 0; - int dims = sparse_mat.dims(); - for(; it != it_end; ++it) - { - // print element indices and the element value - const SparseMat::Node* n = it.node(); - printf("("); - for(int i = 0; i < dims; i++) - printf("%d%s", n->idx[i], i < dims-1 ? ", " : ")"); - printf(": %g\n", it.value()); - s += *it; - } - printf("Element sum is %g\n", s); - @endcode - If you run this loop, you will notice that elements are not enumerated in a logical order - (lexicographical, and so on). They come in the same order as they are stored in the hash table - (semi-randomly). You may collect pointers to the nodes and sort them to get the proper ordering. - Note, however, that pointers to the nodes may become invalid when you add more elements to the - matrix. This may happen due to possible buffer reallocation. -- Combination of the above 2 methods when you need to process 2 or more sparse matrices - simultaneously. For example, this is how you can compute unnormalized cross-correlation of the 2 - floating-point sparse matrices: - @code - double cross_corr(const SparseMat& a, const SparseMat& b) - { - const SparseMat *_a = &a, *_b = &b; - // if b contains less elements than a, - // it is faster to iterate through b - if(_a->nzcount() > _b->nzcount()) - std::swap(_a, _b); - SparseMatConstIterator_ it = _a->begin(), - it_end = _a->end(); - double ccorr = 0; - for(; it != it_end; ++it) - { - // take the next element from the first matrix - float avalue = *it; - const Node* anode = it.node(); - // and try to find an element with the same index in the second matrix. - // since the hash value depends only on the element index, - // reuse the hash value stored in the node - float bvalue = _b->value(anode->idx,&anode->hashval); - ccorr += avalue*bvalue; - } - return ccorr; - } - @endcode - */ -class CV_EXPORTS SparseMat -{ -public: - typedef SparseMatIterator iterator; - typedef SparseMatConstIterator const_iterator; - - enum { MAGIC_VAL=0x42FD0000, MAX_DIM=32, HASH_SCALE=0x5bd1e995, HASH_BIT=0x80000000 }; - - //! the sparse matrix header - struct CV_EXPORTS Hdr - { - Hdr(int _dims, const int* _sizes, int _type); - void clear(); - int refcount; - int dims; - int valueOffset; - size_t nodeSize; - size_t nodeCount; - size_t freeList; - std::vector pool; - std::vector hashtab; - int size[MAX_DIM]; - }; - - //! sparse matrix node - element of a hash table - struct CV_EXPORTS Node - { - //! hash value - size_t hashval; - //! index of the next node in the same hash table entry - size_t next; - //! index of the matrix element - int idx[MAX_DIM]; - }; - - /** @brief Various SparseMat constructors. - */ - SparseMat(); - - /** @overload - @param dims Array dimensionality. - @param _sizes Sparce matrix size on all dementions. - @param _type Sparse matrix data type. - */ - SparseMat(int dims, const int* _sizes, int _type); - - /** @overload - @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted - to sparse representation. - */ - SparseMat(const SparseMat& m); - - /** @overload - @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted - to sparse representation. - */ - explicit SparseMat(const Mat& m); - - //! the destructor - ~SparseMat(); - - //! assignment operator. This is O(1) operation, i.e. no data is copied - SparseMat& operator = (const SparseMat& m); - //! equivalent to the corresponding constructor - SparseMat& operator = (const Mat& m); - - //! creates full copy of the matrix - SparseMat clone() const CV_NODISCARD; - - //! copies all the data to the destination matrix. All the previous content of m is erased - void copyTo( SparseMat& m ) const; - //! converts sparse matrix to dense matrix. - void copyTo( Mat& m ) const; - //! multiplies all the matrix elements by the specified scale factor alpha and converts the results to the specified data type - void convertTo( SparseMat& m, int rtype, double alpha=1 ) const; - //! converts sparse matrix to dense n-dim matrix with optional type conversion and scaling. - /*! - @param [out] m - output matrix; if it does not have a proper size or type before the operation, - it is reallocated - @param [in] rtype - desired output matrix type or, rather, the depth since the number of channels - are the same as the input has; if rtype is negative, the output matrix will have the - same type as the input. - @param [in] alpha - optional scale factor - @param [in] beta - optional delta added to the scaled values - */ - void convertTo( Mat& m, int rtype, double alpha=1, double beta=0 ) const; - - // not used now - void assignTo( SparseMat& m, int type=-1 ) const; - - //! reallocates sparse matrix. - /*! - If the matrix already had the proper size and type, - it is simply cleared with clear(), otherwise, - the old matrix is released (using release()) and the new one is allocated. - */ - void create(int dims, const int* _sizes, int _type); - //! sets all the sparse matrix elements to 0, which means clearing the hash table. - void clear(); - //! manually increments the reference counter to the header. - void addref(); - // decrements the header reference counter. When the counter reaches 0, the header and all the underlying data are deallocated. - void release(); - - //! converts sparse matrix to the old-style representation; all the elements are copied. - //operator CvSparseMat*() const; - //! returns the size of each element in bytes (not including the overhead - the space occupied by SparseMat::Node elements) - size_t elemSize() const; - //! returns elemSize()/channels() - size_t elemSize1() const; - - //! returns type of sparse matrix elements - int type() const; - //! returns the depth of sparse matrix elements - int depth() const; - //! returns the number of channels - int channels() const; - - //! returns the array of sizes, or NULL if the matrix is not allocated - const int* size() const; - //! returns the size of i-th matrix dimension (or 0) - int size(int i) const; - //! returns the matrix dimensionality - int dims() const; - //! returns the number of non-zero elements (=the number of hash table nodes) - size_t nzcount() const; - - //! computes the element hash value (1D case) - size_t hash(int i0) const; - //! computes the element hash value (2D case) - size_t hash(int i0, int i1) const; - //! computes the element hash value (3D case) - size_t hash(int i0, int i1, int i2) const; - //! computes the element hash value (nD case) - size_t hash(const int* idx) const; - - //!@{ - /*! - specialized variants for 1D, 2D, 3D cases and the generic_type one for n-D case. - return pointer to the matrix element. - - if the element is there (it's non-zero), the pointer to it is returned - - if it's not there and createMissing=false, NULL pointer is returned - - if it's not there and createMissing=true, then the new element - is created and initialized with 0. Pointer to it is returned - - if the optional hashval pointer is not NULL, the element hash value is - not computed, but *hashval is taken instead. - */ - //! returns pointer to the specified element (1D case) - uchar* ptr(int i0, bool createMissing, size_t* hashval=0); - //! returns pointer to the specified element (2D case) - uchar* ptr(int i0, int i1, bool createMissing, size_t* hashval=0); - //! returns pointer to the specified element (3D case) - uchar* ptr(int i0, int i1, int i2, bool createMissing, size_t* hashval=0); - //! returns pointer to the specified element (nD case) - uchar* ptr(const int* idx, bool createMissing, size_t* hashval=0); - //!@} - - //!@{ - /*! - return read-write reference to the specified sparse matrix element. - - `ref<_Tp>(i0,...[,hashval])` is equivalent to `*(_Tp*)ptr(i0,...,true[,hashval])`. - The methods always return a valid reference. - If the element did not exist, it is created and initialized with 0. - */ - //! returns reference to the specified element (1D case) - template _Tp& ref(int i0, size_t* hashval=0); - //! returns reference to the specified element (2D case) - template _Tp& ref(int i0, int i1, size_t* hashval=0); - //! returns reference to the specified element (3D case) - template _Tp& ref(int i0, int i1, int i2, size_t* hashval=0); - //! returns reference to the specified element (nD case) - template _Tp& ref(const int* idx, size_t* hashval=0); - //!@} - - //!@{ - /*! - return value of the specified sparse matrix element. - - `value<_Tp>(i0,...[,hashval])` is equivalent to - @code - { const _Tp* p = find<_Tp>(i0,...[,hashval]); return p ? *p : _Tp(); } - @endcode - - That is, if the element did not exist, the methods return 0. - */ - //! returns value of the specified element (1D case) - template _Tp value(int i0, size_t* hashval=0) const; - //! returns value of the specified element (2D case) - template _Tp value(int i0, int i1, size_t* hashval=0) const; - //! returns value of the specified element (3D case) - template _Tp value(int i0, int i1, int i2, size_t* hashval=0) const; - //! returns value of the specified element (nD case) - template _Tp value(const int* idx, size_t* hashval=0) const; - //!@} - - //!@{ - /*! - Return pointer to the specified sparse matrix element if it exists - - `find<_Tp>(i0,...[,hashval])` is equivalent to `(_const Tp*)ptr(i0,...false[,hashval])`. - - If the specified element does not exist, the methods return NULL. - */ - //! returns pointer to the specified element (1D case) - template const _Tp* find(int i0, size_t* hashval=0) const; - //! returns pointer to the specified element (2D case) - template const _Tp* find(int i0, int i1, size_t* hashval=0) const; - //! returns pointer to the specified element (3D case) - template const _Tp* find(int i0, int i1, int i2, size_t* hashval=0) const; - //! returns pointer to the specified element (nD case) - template const _Tp* find(const int* idx, size_t* hashval=0) const; - //!@} - - //! erases the specified element (2D case) - void erase(int i0, int i1, size_t* hashval=0); - //! erases the specified element (3D case) - void erase(int i0, int i1, int i2, size_t* hashval=0); - //! erases the specified element (nD case) - void erase(const int* idx, size_t* hashval=0); - - //!@{ - /*! - return the sparse matrix iterator pointing to the first sparse matrix element - */ - //! returns the sparse matrix iterator at the matrix beginning - SparseMatIterator begin(); - //! returns the sparse matrix iterator at the matrix beginning - template SparseMatIterator_<_Tp> begin(); - //! returns the read-only sparse matrix iterator at the matrix beginning - SparseMatConstIterator begin() const; - //! returns the read-only sparse matrix iterator at the matrix beginning - template SparseMatConstIterator_<_Tp> begin() const; - //!@} - /*! - return the sparse matrix iterator pointing to the element following the last sparse matrix element - */ - //! returns the sparse matrix iterator at the matrix end - SparseMatIterator end(); - //! returns the read-only sparse matrix iterator at the matrix end - SparseMatConstIterator end() const; - //! returns the typed sparse matrix iterator at the matrix end - template SparseMatIterator_<_Tp> end(); - //! returns the typed read-only sparse matrix iterator at the matrix end - template SparseMatConstIterator_<_Tp> end() const; - - //! returns the value stored in the sparse martix node - template _Tp& value(Node* n); - //! returns the value stored in the sparse martix node - template const _Tp& value(const Node* n) const; - - ////////////// some internal-use methods /////////////// - Node* node(size_t nidx); - const Node* node(size_t nidx) const; - - uchar* newNode(const int* idx, size_t hashval); - void removeNode(size_t hidx, size_t nidx, size_t previdx); - void resizeHashTab(size_t newsize); - - int flags; - Hdr* hdr; -}; - - - -///////////////////////////////// SparseMat_<_Tp> //////////////////////////////////// - -/** @brief Template sparse n-dimensional array class derived from SparseMat - -SparseMat_ is a thin wrapper on top of SparseMat created in the same way as Mat_ . It simplifies -notation of some operations: -@code - int sz[] = {10, 20, 30}; - SparseMat_ M(3, sz); - ... - M.ref(1, 2, 3) = M(4, 5, 6) + M(7, 8, 9); -@endcode - */ -template class SparseMat_ : public SparseMat -{ -public: - typedef SparseMatIterator_<_Tp> iterator; - typedef SparseMatConstIterator_<_Tp> const_iterator; - - //! the default constructor - SparseMat_(); - //! the full constructor equivalent to SparseMat(dims, _sizes, DataType<_Tp>::type) - SparseMat_(int dims, const int* _sizes); - //! the copy constructor. If DataType<_Tp>.type != m.type(), the m elements are converted - SparseMat_(const SparseMat& m); - //! the copy constructor. This is O(1) operation - no data is copied - SparseMat_(const SparseMat_& m); - //! converts dense matrix to the sparse form - SparseMat_(const Mat& m); - //! converts the old-style sparse matrix to the C++ class. All the elements are copied - //SparseMat_(const CvSparseMat* m); - //! the assignment operator. If DataType<_Tp>.type != m.type(), the m elements are converted - SparseMat_& operator = (const SparseMat& m); - //! the assignment operator. This is O(1) operation - no data is copied - SparseMat_& operator = (const SparseMat_& m); - //! converts dense matrix to the sparse form - SparseMat_& operator = (const Mat& m); - - //! makes full copy of the matrix. All the elements are duplicated - SparseMat_ clone() const CV_NODISCARD; - //! equivalent to cv::SparseMat::create(dims, _sizes, DataType<_Tp>::type) - void create(int dims, const int* _sizes); - //! converts sparse matrix to the old-style CvSparseMat. All the elements are copied - //operator CvSparseMat*() const; - - //! returns type of the matrix elements - int type() const; - //! returns depth of the matrix elements - int depth() const; - //! returns the number of channels in each matrix element - int channels() const; - - //! equivalent to SparseMat::ref<_Tp>(i0, hashval) - _Tp& ref(int i0, size_t* hashval=0); - //! equivalent to SparseMat::ref<_Tp>(i0, i1, hashval) - _Tp& ref(int i0, int i1, size_t* hashval=0); - //! equivalent to SparseMat::ref<_Tp>(i0, i1, i2, hashval) - _Tp& ref(int i0, int i1, int i2, size_t* hashval=0); - //! equivalent to SparseMat::ref<_Tp>(idx, hashval) - _Tp& ref(const int* idx, size_t* hashval=0); - - //! equivalent to SparseMat::value<_Tp>(i0, hashval) - _Tp operator()(int i0, size_t* hashval=0) const; - //! equivalent to SparseMat::value<_Tp>(i0, i1, hashval) - _Tp operator()(int i0, int i1, size_t* hashval=0) const; - //! equivalent to SparseMat::value<_Tp>(i0, i1, i2, hashval) - _Tp operator()(int i0, int i1, int i2, size_t* hashval=0) const; - //! equivalent to SparseMat::value<_Tp>(idx, hashval) - _Tp operator()(const int* idx, size_t* hashval=0) const; - - //! returns sparse matrix iterator pointing to the first sparse matrix element - SparseMatIterator_<_Tp> begin(); - //! returns read-only sparse matrix iterator pointing to the first sparse matrix element - SparseMatConstIterator_<_Tp> begin() const; - //! returns sparse matrix iterator pointing to the element following the last sparse matrix element - SparseMatIterator_<_Tp> end(); - //! returns read-only sparse matrix iterator pointing to the element following the last sparse matrix element - SparseMatConstIterator_<_Tp> end() const; -}; - - - -////////////////////////////////// MatConstIterator ////////////////////////////////// - -class CV_EXPORTS MatConstIterator -{ -public: - typedef uchar* value_type; - typedef ptrdiff_t difference_type; - typedef const uchar** pointer; - typedef uchar* reference; - - typedef std::random_access_iterator_tag iterator_category; - - //! default constructor - MatConstIterator(); - //! constructor that sets the iterator to the beginning of the matrix - MatConstIterator(const Mat* _m); - //! constructor that sets the iterator to the specified element of the matrix - MatConstIterator(const Mat* _m, int _row, int _col=0); - //! constructor that sets the iterator to the specified element of the matrix - MatConstIterator(const Mat* _m, Point _pt); - //! constructor that sets the iterator to the specified element of the matrix - MatConstIterator(const Mat* _m, const int* _idx); - //! copy constructor - MatConstIterator(const MatConstIterator& it); - - //! copy operator - MatConstIterator& operator = (const MatConstIterator& it); - //! returns the current matrix element - const uchar* operator *() const; - //! returns the i-th matrix element, relative to the current - const uchar* operator [](ptrdiff_t i) const; - - //! shifts the iterator forward by the specified number of elements - MatConstIterator& operator += (ptrdiff_t ofs); - //! shifts the iterator backward by the specified number of elements - MatConstIterator& operator -= (ptrdiff_t ofs); - //! decrements the iterator - MatConstIterator& operator --(); - //! decrements the iterator - MatConstIterator operator --(int); - //! increments the iterator - MatConstIterator& operator ++(); - //! increments the iterator - MatConstIterator operator ++(int); - //! returns the current iterator position - Point pos() const; - //! returns the current iterator position - void pos(int* _idx) const; - - ptrdiff_t lpos() const; - void seek(ptrdiff_t ofs, bool relative = false); - void seek(const int* _idx, bool relative = false); - - const Mat* m; - size_t elemSize; - const uchar* ptr; - const uchar* sliceStart; - const uchar* sliceEnd; -}; - - - -////////////////////////////////// MatConstIterator_ ///////////////////////////////// - -/** @brief Matrix read-only iterator - */ -template -class MatConstIterator_ : public MatConstIterator -{ -public: - typedef _Tp value_type; - typedef ptrdiff_t difference_type; - typedef const _Tp* pointer; - typedef const _Tp& reference; - - typedef std::random_access_iterator_tag iterator_category; - - //! default constructor - MatConstIterator_(); - //! constructor that sets the iterator to the beginning of the matrix - MatConstIterator_(const Mat_<_Tp>* _m); - //! constructor that sets the iterator to the specified element of the matrix - MatConstIterator_(const Mat_<_Tp>* _m, int _row, int _col=0); - //! constructor that sets the iterator to the specified element of the matrix - MatConstIterator_(const Mat_<_Tp>* _m, Point _pt); - //! constructor that sets the iterator to the specified element of the matrix - MatConstIterator_(const Mat_<_Tp>* _m, const int* _idx); - //! copy constructor - MatConstIterator_(const MatConstIterator_& it); - - //! copy operator - MatConstIterator_& operator = (const MatConstIterator_& it); - //! returns the current matrix element - const _Tp& operator *() const; - //! returns the i-th matrix element, relative to the current - const _Tp& operator [](ptrdiff_t i) const; - - //! shifts the iterator forward by the specified number of elements - MatConstIterator_& operator += (ptrdiff_t ofs); - //! shifts the iterator backward by the specified number of elements - MatConstIterator_& operator -= (ptrdiff_t ofs); - //! decrements the iterator - MatConstIterator_& operator --(); - //! decrements the iterator - MatConstIterator_ operator --(int); - //! increments the iterator - MatConstIterator_& operator ++(); - //! increments the iterator - MatConstIterator_ operator ++(int); - //! returns the current iterator position - Point pos() const; -}; - - - -//////////////////////////////////// MatIterator_ //////////////////////////////////// - -/** @brief Matrix read-write iterator -*/ -template -class MatIterator_ : public MatConstIterator_<_Tp> -{ -public: - typedef _Tp* pointer; - typedef _Tp& reference; - - typedef std::random_access_iterator_tag iterator_category; - - //! the default constructor - MatIterator_(); - //! constructor that sets the iterator to the beginning of the matrix - MatIterator_(Mat_<_Tp>* _m); - //! constructor that sets the iterator to the specified element of the matrix - MatIterator_(Mat_<_Tp>* _m, int _row, int _col=0); - //! constructor that sets the iterator to the specified element of the matrix - MatIterator_(Mat_<_Tp>* _m, Point _pt); - //! constructor that sets the iterator to the specified element of the matrix - MatIterator_(Mat_<_Tp>* _m, const int* _idx); - //! copy constructor - MatIterator_(const MatIterator_& it); - //! copy operator - MatIterator_& operator = (const MatIterator_<_Tp>& it ); - - //! returns the current matrix element - _Tp& operator *() const; - //! returns the i-th matrix element, relative to the current - _Tp& operator [](ptrdiff_t i) const; - - //! shifts the iterator forward by the specified number of elements - MatIterator_& operator += (ptrdiff_t ofs); - //! shifts the iterator backward by the specified number of elements - MatIterator_& operator -= (ptrdiff_t ofs); - //! decrements the iterator - MatIterator_& operator --(); - //! decrements the iterator - MatIterator_ operator --(int); - //! increments the iterator - MatIterator_& operator ++(); - //! increments the iterator - MatIterator_ operator ++(int); -}; - - - -/////////////////////////////// SparseMatConstIterator /////////////////////////////// - -/** @brief Read-Only Sparse Matrix Iterator. - - Here is how to use the iterator to compute the sum of floating-point sparse matrix elements: - - \code - SparseMatConstIterator it = m.begin(), it_end = m.end(); - double s = 0; - CV_Assert( m.type() == CV_32F ); - for( ; it != it_end; ++it ) - s += it.value(); - \endcode -*/ -class CV_EXPORTS SparseMatConstIterator -{ -public: - //! the default constructor - SparseMatConstIterator(); - //! the full constructor setting the iterator to the first sparse matrix element - SparseMatConstIterator(const SparseMat* _m); - //! the copy constructor - SparseMatConstIterator(const SparseMatConstIterator& it); - - //! the assignment operator - SparseMatConstIterator& operator = (const SparseMatConstIterator& it); - - //! template method returning the current matrix element - template const _Tp& value() const; - //! returns the current node of the sparse matrix. it.node->idx is the current element index - const SparseMat::Node* node() const; - - //! moves iterator to the previous element - SparseMatConstIterator& operator --(); - //! moves iterator to the previous element - SparseMatConstIterator operator --(int); - //! moves iterator to the next element - SparseMatConstIterator& operator ++(); - //! moves iterator to the next element - SparseMatConstIterator operator ++(int); - - //! moves iterator to the element after the last element - void seekEnd(); - - const SparseMat* m; - size_t hashidx; - uchar* ptr; -}; - - - -////////////////////////////////// SparseMatIterator ///////////////////////////////// - -/** @brief Read-write Sparse Matrix Iterator - - The class is similar to cv::SparseMatConstIterator, - but can be used for in-place modification of the matrix elements. -*/ -class CV_EXPORTS SparseMatIterator : public SparseMatConstIterator -{ -public: - //! the default constructor - SparseMatIterator(); - //! the full constructor setting the iterator to the first sparse matrix element - SparseMatIterator(SparseMat* _m); - //! the full constructor setting the iterator to the specified sparse matrix element - SparseMatIterator(SparseMat* _m, const int* idx); - //! the copy constructor - SparseMatIterator(const SparseMatIterator& it); - - //! the assignment operator - SparseMatIterator& operator = (const SparseMatIterator& it); - //! returns read-write reference to the current sparse matrix element - template _Tp& value() const; - //! returns pointer to the current sparse matrix node. it.node->idx is the index of the current element (do not modify it!) - SparseMat::Node* node() const; - - //! moves iterator to the next element - SparseMatIterator& operator ++(); - //! moves iterator to the next element - SparseMatIterator operator ++(int); -}; - - - -/////////////////////////////// SparseMatConstIterator_ ////////////////////////////// - -/** @brief Template Read-Only Sparse Matrix Iterator Class. - - This is the derived from SparseMatConstIterator class that - introduces more convenient operator *() for accessing the current element. -*/ -template class SparseMatConstIterator_ : public SparseMatConstIterator -{ -public: - - typedef std::forward_iterator_tag iterator_category; - - //! the default constructor - SparseMatConstIterator_(); - //! the full constructor setting the iterator to the first sparse matrix element - SparseMatConstIterator_(const SparseMat_<_Tp>* _m); - SparseMatConstIterator_(const SparseMat* _m); - //! the copy constructor - SparseMatConstIterator_(const SparseMatConstIterator_& it); - - //! the assignment operator - SparseMatConstIterator_& operator = (const SparseMatConstIterator_& it); - //! the element access operator - const _Tp& operator *() const; - - //! moves iterator to the next element - SparseMatConstIterator_& operator ++(); - //! moves iterator to the next element - SparseMatConstIterator_ operator ++(int); -}; - - - -///////////////////////////////// SparseMatIterator_ ///////////////////////////////// - -/** @brief Template Read-Write Sparse Matrix Iterator Class. - - This is the derived from cv::SparseMatConstIterator_ class that - introduces more convenient operator *() for accessing the current element. -*/ -template class SparseMatIterator_ : public SparseMatConstIterator_<_Tp> -{ -public: - - typedef std::forward_iterator_tag iterator_category; - - //! the default constructor - SparseMatIterator_(); - //! the full constructor setting the iterator to the first sparse matrix element - SparseMatIterator_(SparseMat_<_Tp>* _m); - SparseMatIterator_(SparseMat* _m); - //! the copy constructor - SparseMatIterator_(const SparseMatIterator_& it); - - //! the assignment operator - SparseMatIterator_& operator = (const SparseMatIterator_& it); - //! returns the reference to the current element - _Tp& operator *() const; - - //! moves the iterator to the next element - SparseMatIterator_& operator ++(); - //! moves the iterator to the next element - SparseMatIterator_ operator ++(int); -}; - - - -/////////////////////////////////// NAryMatIterator ////////////////////////////////// - -/** @brief n-ary multi-dimensional array iterator. - -Use the class to implement unary, binary, and, generally, n-ary element-wise operations on -multi-dimensional arrays. Some of the arguments of an n-ary function may be continuous arrays, some -may be not. It is possible to use conventional MatIterator 's for each array but incrementing all of -the iterators after each small operations may be a big overhead. In this case consider using -NAryMatIterator to iterate through several matrices simultaneously as long as they have the same -geometry (dimensionality and all the dimension sizes are the same). On each iteration `it.planes[0]`, -`it.planes[1]`,... will be the slices of the corresponding matrices. - -The example below illustrates how you can compute a normalized and threshold 3D color histogram: -@code - void computeNormalizedColorHist(const Mat& image, Mat& hist, int N, double minProb) - { - const int histSize[] = {N, N, N}; - - // make sure that the histogram has a proper size and type - hist.create(3, histSize, CV_32F); - - // and clear it - hist = Scalar(0); - - // the loop below assumes that the image - // is a 8-bit 3-channel. check it. - CV_Assert(image.type() == CV_8UC3); - MatConstIterator_ it = image.begin(), - it_end = image.end(); - for( ; it != it_end; ++it ) - { - const Vec3b& pix = *it; - hist.at(pix[0]*N/256, pix[1]*N/256, pix[2]*N/256) += 1.f; - } - - minProb *= image.rows*image.cols; - - // initialize iterator (the style is different from STL). - // after initialization the iterator will contain - // the number of slices or planes the iterator will go through. - // it simultaneously increments iterators for several matrices - // supplied as a null terminated list of pointers - const Mat* arrays[] = {&hist, 0}; - Mat planes[1]; - NAryMatIterator itNAry(arrays, planes, 1); - double s = 0; - // iterate through the matrix. on each iteration - // itNAry.planes[i] (of type Mat) will be set to the current plane - // of the i-th n-dim matrix passed to the iterator constructor. - for(int p = 0; p < itNAry.nplanes; p++, ++itNAry) - { - threshold(itNAry.planes[0], itNAry.planes[0], minProb, 0, THRESH_TOZERO); - s += sum(itNAry.planes[0])[0]; - } - - s = 1./s; - itNAry = NAryMatIterator(arrays, planes, 1); - for(int p = 0; p < itNAry.nplanes; p++, ++itNAry) - itNAry.planes[0] *= s; - } -@endcode - */ -class CV_EXPORTS NAryMatIterator -{ -public: - //! the default constructor - NAryMatIterator(); - //! the full constructor taking arbitrary number of n-dim matrices - NAryMatIterator(const Mat** arrays, uchar** ptrs, int narrays=-1); - //! the full constructor taking arbitrary number of n-dim matrices - NAryMatIterator(const Mat** arrays, Mat* planes, int narrays=-1); - //! the separate iterator initialization method - void init(const Mat** arrays, Mat* planes, uchar** ptrs, int narrays=-1); - - //! proceeds to the next plane of every iterated matrix - NAryMatIterator& operator ++(); - //! proceeds to the next plane of every iterated matrix (postfix increment operator) - NAryMatIterator operator ++(int); - - //! the iterated arrays - const Mat** arrays; - //! the current planes - Mat* planes; - //! data pointers - uchar** ptrs; - //! the number of arrays - int narrays; - //! the number of hyper-planes that the iterator steps through - size_t nplanes; - //! the size of each segment (in elements) - size_t size; -protected: - int iterdepth; - size_t idx; -}; - - - -///////////////////////////////// Matrix Expressions ///////////////////////////////// - -class CV_EXPORTS MatOp -{ -public: - MatOp(); - virtual ~MatOp(); - - virtual bool elementWise(const MatExpr& expr) const; - virtual void assign(const MatExpr& expr, Mat& m, int type=-1) const = 0; - virtual void roi(const MatExpr& expr, const Range& rowRange, - const Range& colRange, MatExpr& res) const; - virtual void diag(const MatExpr& expr, int d, MatExpr& res) const; - virtual void augAssignAdd(const MatExpr& expr, Mat& m) const; - virtual void augAssignSubtract(const MatExpr& expr, Mat& m) const; - virtual void augAssignMultiply(const MatExpr& expr, Mat& m) const; - virtual void augAssignDivide(const MatExpr& expr, Mat& m) const; - virtual void augAssignAnd(const MatExpr& expr, Mat& m) const; - virtual void augAssignOr(const MatExpr& expr, Mat& m) const; - virtual void augAssignXor(const MatExpr& expr, Mat& m) const; - - virtual void add(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; - virtual void add(const MatExpr& expr1, const Scalar& s, MatExpr& res) const; - - virtual void subtract(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; - virtual void subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const; - - virtual void multiply(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const; - virtual void multiply(const MatExpr& expr1, double s, MatExpr& res) const; - - virtual void divide(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const; - virtual void divide(double s, const MatExpr& expr, MatExpr& res) const; - - virtual void abs(const MatExpr& expr, MatExpr& res) const; - - virtual void transpose(const MatExpr& expr, MatExpr& res) const; - virtual void matmul(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const; - virtual void invert(const MatExpr& expr, int method, MatExpr& res) const; - - virtual Size size(const MatExpr& expr) const; - virtual int type(const MatExpr& expr) const; -}; - -/** @brief Matrix expression representation -@anchor MatrixExpressions -This is a list of implemented matrix operations that can be combined in arbitrary complex -expressions (here A, B stand for matrices ( Mat ), s for a scalar ( Scalar ), alpha for a -real-valued scalar ( double )): -- Addition, subtraction, negation: `A+B`, `A-B`, `A+s`, `A-s`, `s+A`, `s-A`, `-A` -- Scaling: `A*alpha` -- Per-element multiplication and division: `A.mul(B)`, `A/B`, `alpha/A` -- Matrix multiplication: `A*B` -- Transposition: `A.t()` (means AT) -- Matrix inversion and pseudo-inversion, solving linear systems and least-squares problems: - `A.inv([method]) (~ A-1)`, `A.inv([method])*B (~ X: AX=B)` -- Comparison: `A cmpop B`, `A cmpop alpha`, `alpha cmpop A`, where *cmpop* is one of - `>`, `>=`, `==`, `!=`, `<=`, `<`. The result of comparison is an 8-bit single channel mask whose - elements are set to 255 (if the particular element or pair of elements satisfy the condition) or - 0. -- Bitwise logical operations: `A logicop B`, `A logicop s`, `s logicop A`, `~A`, where *logicop* is one of - `&`, `|`, `^`. -- Element-wise minimum and maximum: `min(A, B)`, `min(A, alpha)`, `max(A, B)`, `max(A, alpha)` -- Element-wise absolute value: `abs(A)` -- Cross-product, dot-product: `A.cross(B)`, `A.dot(B)` -- Any function of matrix or matrices and scalars that returns a matrix or a scalar, such as norm, - mean, sum, countNonZero, trace, determinant, repeat, and others. -- Matrix initializers ( Mat::eye(), Mat::zeros(), Mat::ones() ), matrix comma-separated - initializers, matrix constructors and operators that extract sub-matrices (see Mat description). -- Mat_() constructors to cast the result to the proper type. -@note Comma-separated initializers and probably some other operations may require additional -explicit Mat() or Mat_() constructor calls to resolve a possible ambiguity. - -Here are examples of matrix expressions: -@code - // compute pseudo-inverse of A, equivalent to A.inv(DECOMP_SVD) - SVD svd(A); - Mat pinvA = svd.vt.t()*Mat::diag(1./svd.w)*svd.u.t(); - - // compute the new vector of parameters in the Levenberg-Marquardt algorithm - x -= (A.t()*A + lambda*Mat::eye(A.cols,A.cols,A.type())).inv(DECOMP_CHOLESKY)*(A.t()*err); - - // sharpen image using "unsharp mask" algorithm - Mat blurred; double sigma = 1, threshold = 5, amount = 1; - GaussianBlur(img, blurred, Size(), sigma, sigma); - Mat lowContrastMask = abs(img - blurred) < threshold; - Mat sharpened = img*(1+amount) + blurred*(-amount); - img.copyTo(sharpened, lowContrastMask); -@endcode -*/ -class CV_EXPORTS MatExpr -{ -public: - MatExpr(); - explicit MatExpr(const Mat& m); - - MatExpr(const MatOp* _op, int _flags, const Mat& _a = Mat(), const Mat& _b = Mat(), - const Mat& _c = Mat(), double _alpha = 1, double _beta = 1, const Scalar& _s = Scalar()); - - operator Mat() const; - template operator Mat_<_Tp>() const; - - Size size() const; - int type() const; - - MatExpr row(int y) const; - MatExpr col(int x) const; - MatExpr diag(int d = 0) const; - MatExpr operator()( const Range& rowRange, const Range& colRange ) const; - MatExpr operator()( const Rect& roi ) const; - - MatExpr t() const; - MatExpr inv(int method = DECOMP_LU) const; - MatExpr mul(const MatExpr& e, double scale=1) const; - MatExpr mul(const Mat& m, double scale=1) const; - - Mat cross(const Mat& m) const; - double dot(const Mat& m) const; - - void swap(MatExpr& b); - - const MatOp* op; - int flags; - - Mat a, b, c; - double alpha, beta; - Scalar s; -}; - -//! @} core_basic - -//! @relates cv::MatExpr -//! @{ -CV_EXPORTS MatExpr operator + (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator + (const Mat& a, const Scalar& s); -CV_EXPORTS MatExpr operator + (const Scalar& s, const Mat& a); -CV_EXPORTS MatExpr operator + (const MatExpr& e, const Mat& m); -CV_EXPORTS MatExpr operator + (const Mat& m, const MatExpr& e); -CV_EXPORTS MatExpr operator + (const MatExpr& e, const Scalar& s); -CV_EXPORTS MatExpr operator + (const Scalar& s, const MatExpr& e); -CV_EXPORTS MatExpr operator + (const MatExpr& e1, const MatExpr& e2); -template static inline -MatExpr operator + (const Mat& a, const Matx<_Tp, m, n>& b) { return a + Mat(b); } -template static inline -MatExpr operator + (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) + b; } - -CV_EXPORTS MatExpr operator - (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator - (const Mat& a, const Scalar& s); -CV_EXPORTS MatExpr operator - (const Scalar& s, const Mat& a); -CV_EXPORTS MatExpr operator - (const MatExpr& e, const Mat& m); -CV_EXPORTS MatExpr operator - (const Mat& m, const MatExpr& e); -CV_EXPORTS MatExpr operator - (const MatExpr& e, const Scalar& s); -CV_EXPORTS MatExpr operator - (const Scalar& s, const MatExpr& e); -CV_EXPORTS MatExpr operator - (const MatExpr& e1, const MatExpr& e2); -template static inline -MatExpr operator - (const Mat& a, const Matx<_Tp, m, n>& b) { return a - Mat(b); } -template static inline -MatExpr operator - (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) - b; } - -CV_EXPORTS MatExpr operator - (const Mat& m); -CV_EXPORTS MatExpr operator - (const MatExpr& e); - -CV_EXPORTS MatExpr operator * (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator * (const Mat& a, double s); -CV_EXPORTS MatExpr operator * (double s, const Mat& a); -CV_EXPORTS MatExpr operator * (const MatExpr& e, const Mat& m); -CV_EXPORTS MatExpr operator * (const Mat& m, const MatExpr& e); -CV_EXPORTS MatExpr operator * (const MatExpr& e, double s); -CV_EXPORTS MatExpr operator * (double s, const MatExpr& e); -CV_EXPORTS MatExpr operator * (const MatExpr& e1, const MatExpr& e2); -template static inline -MatExpr operator * (const Mat& a, const Matx<_Tp, m, n>& b) { return a * Mat(b); } -template static inline -MatExpr operator * (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) * b; } - -CV_EXPORTS MatExpr operator / (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator / (const Mat& a, double s); -CV_EXPORTS MatExpr operator / (double s, const Mat& a); -CV_EXPORTS MatExpr operator / (const MatExpr& e, const Mat& m); -CV_EXPORTS MatExpr operator / (const Mat& m, const MatExpr& e); -CV_EXPORTS MatExpr operator / (const MatExpr& e, double s); -CV_EXPORTS MatExpr operator / (double s, const MatExpr& e); -CV_EXPORTS MatExpr operator / (const MatExpr& e1, const MatExpr& e2); -template static inline -MatExpr operator / (const Mat& a, const Matx<_Tp, m, n>& b) { return a / Mat(b); } -template static inline -MatExpr operator / (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) / b; } - -CV_EXPORTS MatExpr operator < (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator < (const Mat& a, double s); -CV_EXPORTS MatExpr operator < (double s, const Mat& a); -template static inline -MatExpr operator < (const Mat& a, const Matx<_Tp, m, n>& b) { return a < Mat(b); } -template static inline -MatExpr operator < (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) < b; } - -CV_EXPORTS MatExpr operator <= (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator <= (const Mat& a, double s); -CV_EXPORTS MatExpr operator <= (double s, const Mat& a); -template static inline -MatExpr operator <= (const Mat& a, const Matx<_Tp, m, n>& b) { return a <= Mat(b); } -template static inline -MatExpr operator <= (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) <= b; } - -CV_EXPORTS MatExpr operator == (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator == (const Mat& a, double s); -CV_EXPORTS MatExpr operator == (double s, const Mat& a); -template static inline -MatExpr operator == (const Mat& a, const Matx<_Tp, m, n>& b) { return a == Mat(b); } -template static inline -MatExpr operator == (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) == b; } - -CV_EXPORTS MatExpr operator != (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator != (const Mat& a, double s); -CV_EXPORTS MatExpr operator != (double s, const Mat& a); -template static inline -MatExpr operator != (const Mat& a, const Matx<_Tp, m, n>& b) { return a != Mat(b); } -template static inline -MatExpr operator != (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) != b; } - -CV_EXPORTS MatExpr operator >= (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator >= (const Mat& a, double s); -CV_EXPORTS MatExpr operator >= (double s, const Mat& a); -template static inline -MatExpr operator >= (const Mat& a, const Matx<_Tp, m, n>& b) { return a >= Mat(b); } -template static inline -MatExpr operator >= (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) >= b; } - -CV_EXPORTS MatExpr operator > (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator > (const Mat& a, double s); -CV_EXPORTS MatExpr operator > (double s, const Mat& a); -template static inline -MatExpr operator > (const Mat& a, const Matx<_Tp, m, n>& b) { return a > Mat(b); } -template static inline -MatExpr operator > (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) > b; } - -CV_EXPORTS MatExpr operator & (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator & (const Mat& a, const Scalar& s); -CV_EXPORTS MatExpr operator & (const Scalar& s, const Mat& a); -template static inline -MatExpr operator & (const Mat& a, const Matx<_Tp, m, n>& b) { return a & Mat(b); } -template static inline -MatExpr operator & (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) & b; } - -CV_EXPORTS MatExpr operator | (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator | (const Mat& a, const Scalar& s); -CV_EXPORTS MatExpr operator | (const Scalar& s, const Mat& a); -template static inline -MatExpr operator | (const Mat& a, const Matx<_Tp, m, n>& b) { return a | Mat(b); } -template static inline -MatExpr operator | (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) | b; } - -CV_EXPORTS MatExpr operator ^ (const Mat& a, const Mat& b); -CV_EXPORTS MatExpr operator ^ (const Mat& a, const Scalar& s); -CV_EXPORTS MatExpr operator ^ (const Scalar& s, const Mat& a); -template static inline -MatExpr operator ^ (const Mat& a, const Matx<_Tp, m, n>& b) { return a ^ Mat(b); } -template static inline -MatExpr operator ^ (const Matx<_Tp, m, n>& a, const Mat& b) { return Mat(a) ^ b; } - -CV_EXPORTS MatExpr operator ~(const Mat& m); - -CV_EXPORTS MatExpr min(const Mat& a, const Mat& b); -CV_EXPORTS MatExpr min(const Mat& a, double s); -CV_EXPORTS MatExpr min(double s, const Mat& a); -template static inline -MatExpr min (const Mat& a, const Matx<_Tp, m, n>& b) { return min(a, Mat(b)); } -template static inline -MatExpr min (const Matx<_Tp, m, n>& a, const Mat& b) { return min(Mat(a), b); } - -CV_EXPORTS MatExpr max(const Mat& a, const Mat& b); -CV_EXPORTS MatExpr max(const Mat& a, double s); -CV_EXPORTS MatExpr max(double s, const Mat& a); -template static inline -MatExpr max (const Mat& a, const Matx<_Tp, m, n>& b) { return max(a, Mat(b)); } -template static inline -MatExpr max (const Matx<_Tp, m, n>& a, const Mat& b) { return max(Mat(a), b); } - -/** @brief Calculates an absolute value of each matrix element. - -abs is a meta-function that is expanded to one of absdiff or convertScaleAbs forms: -- C = abs(A-B) is equivalent to `absdiff(A, B, C)` -- C = abs(A) is equivalent to `absdiff(A, Scalar::all(0), C)` -- C = `Mat_ >(abs(A*alpha + beta))` is equivalent to `convertScaleAbs(A, C, alpha, -beta)` - -The output matrix has the same size and the same type as the input one except for the last case, -where C is depth=CV_8U . -@param m matrix. -@sa @ref MatrixExpressions, absdiff, convertScaleAbs - */ -CV_EXPORTS MatExpr abs(const Mat& m); -/** @overload -@param e matrix expression. -*/ -CV_EXPORTS MatExpr abs(const MatExpr& e); -//! @} relates cv::MatExpr - -} // cv - -#include "opencv2/core/mat.inl.hpp" - -#endif // OPENCV_CORE_MAT_HPP diff --git a/include/opencv2/core/mat.inl.hpp b/include/opencv2/core/mat.inl.hpp deleted file mode 100755 index b6ffd81..0000000 --- a/include/opencv2/core/mat.inl.hpp +++ /dev/null @@ -1,3506 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_MATRIX_OPERATIONS_HPP -#define OPENCV_CORE_MATRIX_OPERATIONS_HPP - -#ifndef __cplusplus -# error mat.inl.hpp header must be compiled as C++ -#endif - -#ifdef _MSC_VER -#pragma warning( push ) -#pragma warning( disable: 4127 ) -#endif - -#if defined(CV_SKIP_DISABLE_CLANG_ENUM_WARNINGS) - // nothing -#elif defined(CV_FORCE_DISABLE_CLANG_ENUM_WARNINGS) - #define CV_DISABLE_CLANG_ENUM_WARNINGS -#elif defined(__clang__) && defined(__has_warning) - #if __has_warning("-Wdeprecated-enum-enum-conversion") && __has_warning("-Wdeprecated-anon-enum-enum-conversion") - #define CV_DISABLE_CLANG_ENUM_WARNINGS - #endif -#endif -#ifdef CV_DISABLE_CLANG_ENUM_WARNINGS -#pragma clang diagnostic push -#pragma clang diagnostic ignored "-Wdeprecated-enum-enum-conversion" -#pragma clang diagnostic ignored "-Wdeprecated-anon-enum-enum-conversion" -#endif - -namespace cv -{ -CV__DEBUG_NS_BEGIN - - -//! @cond IGNORED - -////////////////////////// Custom (raw) type wrapper ////////////////////////// - -template static inline -int rawType() -{ - CV_StaticAssert(sizeof(_Tp) <= CV_CN_MAX, "sizeof(_Tp) is too large"); - const int elemSize = sizeof(_Tp); - return (int)CV_MAKETYPE(CV_8U, elemSize); -} - -//////////////////////// Input/Output Arrays //////////////////////// - -inline void _InputArray::init(int _flags, const void* _obj) -{ flags = _flags; obj = (void*)_obj; } - -inline void _InputArray::init(int _flags, const void* _obj, Size _sz) -{ flags = _flags; obj = (void*)_obj; sz = _sz; } - -inline void* _InputArray::getObj() const { return obj; } -inline int _InputArray::getFlags() const { return flags; } -inline Size _InputArray::getSz() const { return sz; } - -inline _InputArray::_InputArray() { init(NONE, 0); } -inline _InputArray::_InputArray(int _flags, void* _obj) { init(_flags, _obj); } -inline _InputArray::_InputArray(const Mat& m) { init(MAT+ACCESS_READ, &m); } -inline _InputArray::_InputArray(const std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_READ, &vec); } -inline _InputArray::_InputArray(const UMat& m) { init(UMAT+ACCESS_READ, &m); } -inline _InputArray::_InputArray(const std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_READ, &vec); } - -template inline -_InputArray::_InputArray(const std::vector<_Tp>& vec) -{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_READ, &vec); } - -#ifdef CV_CXX_STD_ARRAY -template inline -_InputArray::_InputArray(const std::array<_Tp, _Nm>& arr) -{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_READ, arr.data(), Size(1, _Nm)); } - -template inline -_InputArray::_InputArray(const std::array& arr) -{ init(STD_ARRAY_MAT + ACCESS_READ, arr.data(), Size(1, _Nm)); } -#endif - -inline -_InputArray::_InputArray(const std::vector& vec) -{ init(FIXED_TYPE + STD_BOOL_VECTOR + traits::Type::value + ACCESS_READ, &vec); } - -template inline -_InputArray::_InputArray(const std::vector >& vec) -{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_READ, &vec); } - -inline -_InputArray::_InputArray(const std::vector >&) -{ CV_Error(Error::StsUnsupportedFormat, "std::vector > is not supported!\n"); } - -template inline -_InputArray::_InputArray(const std::vector >& vec) -{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_READ, &vec); } - -template inline -_InputArray::_InputArray(const Matx<_Tp, m, n>& mtx) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_READ, &mtx, Size(n, m)); } - -template inline -_InputArray::_InputArray(const _Tp* vec, int n) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_READ, vec, Size(n, 1)); } - -template inline -_InputArray::_InputArray(const Mat_<_Tp>& m) -{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_READ, &m); } - -inline _InputArray::_InputArray(const double& val) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + CV_64F + ACCESS_READ, &val, Size(1,1)); } - -inline _InputArray::_InputArray(const cuda::GpuMat& d_mat) -{ init(CUDA_GPU_MAT + ACCESS_READ, &d_mat); } - -inline _InputArray::_InputArray(const std::vector& d_mat) -{ init(STD_VECTOR_CUDA_GPU_MAT + ACCESS_READ, &d_mat);} - -inline _InputArray::_InputArray(const ogl::Buffer& buf) -{ init(OPENGL_BUFFER + ACCESS_READ, &buf); } - -inline _InputArray::_InputArray(const cuda::HostMem& cuda_mem) -{ init(CUDA_HOST_MEM + ACCESS_READ, &cuda_mem); } - -template inline -_InputArray _InputArray::rawIn(const std::vector<_Tp>& vec) -{ - _InputArray v; - v.flags = _InputArray::FIXED_TYPE + _InputArray::STD_VECTOR + rawType<_Tp>() + ACCESS_READ; - v.obj = (void*)&vec; - return v; -} - -#ifdef CV_CXX_STD_ARRAY -template inline -_InputArray _InputArray::rawIn(const std::array<_Tp, _Nm>& arr) -{ - _InputArray v; - v.flags = FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_READ; - v.obj = (void*)arr.data(); - v.sz = Size(1, _Nm); - return v; -} -#endif - -inline _InputArray::~_InputArray() {} - -inline Mat _InputArray::getMat(int i) const -{ - if( kind() == MAT && i < 0 ) - return *(const Mat*)obj; - return getMat_(i); -} - -inline bool _InputArray::isMat() const { return kind() == _InputArray::MAT; } -inline bool _InputArray::isUMat() const { return kind() == _InputArray::UMAT; } -inline bool _InputArray::isMatVector() const { return kind() == _InputArray::STD_VECTOR_MAT; } -inline bool _InputArray::isUMatVector() const { return kind() == _InputArray::STD_VECTOR_UMAT; } -inline bool _InputArray::isMatx() const { return kind() == _InputArray::MATX; } -inline bool _InputArray::isVector() const { return kind() == _InputArray::STD_VECTOR || - kind() == _InputArray::STD_BOOL_VECTOR || - kind() == _InputArray::STD_ARRAY; } -inline bool _InputArray::isGpuMat() const { return kind() == _InputArray::CUDA_GPU_MAT; } -inline bool _InputArray::isGpuMatVector() const { return kind() == _InputArray::STD_VECTOR_CUDA_GPU_MAT; } - -//////////////////////////////////////////////////////////////////////////////////////// - -inline _OutputArray::_OutputArray() { init(ACCESS_WRITE, 0); } -inline _OutputArray::_OutputArray(int _flags, void* _obj) { init(_flags|ACCESS_WRITE, _obj); } -inline _OutputArray::_OutputArray(Mat& m) { init(MAT+ACCESS_WRITE, &m); } -inline _OutputArray::_OutputArray(std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_WRITE, &vec); } -inline _OutputArray::_OutputArray(UMat& m) { init(UMAT+ACCESS_WRITE, &m); } -inline _OutputArray::_OutputArray(std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_WRITE, &vec); } - -template inline -_OutputArray::_OutputArray(std::vector<_Tp>& vec) -{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } - -#ifdef CV_CXX_STD_ARRAY -template inline -_OutputArray::_OutputArray(std::array<_Tp, _Nm>& arr) -{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_WRITE, arr.data(), Size(1, _Nm)); } - -template inline -_OutputArray::_OutputArray(std::array& arr) -{ init(STD_ARRAY_MAT + ACCESS_WRITE, arr.data(), Size(1, _Nm)); } -#endif - -inline -_OutputArray::_OutputArray(std::vector&) -{ CV_Error(Error::StsUnsupportedFormat, "std::vector cannot be an output array\n"); } - -template inline -_OutputArray::_OutputArray(std::vector >& vec) -{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } - -inline -_OutputArray::_OutputArray(std::vector >&) -{ CV_Error(Error::StsUnsupportedFormat, "std::vector > cannot be an output array\n"); } - -template inline -_OutputArray::_OutputArray(std::vector >& vec) -{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } - -template inline -_OutputArray::_OutputArray(Mat_<_Tp>& m) -{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &m); } - -template inline -_OutputArray::_OutputArray(Matx<_Tp, m, n>& mtx) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, &mtx, Size(n, m)); } - -template inline -_OutputArray::_OutputArray(_Tp* vec, int n) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, vec, Size(n, 1)); } - -template inline -_OutputArray::_OutputArray(const std::vector<_Tp>& vec) -{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } - -#ifdef CV_CXX_STD_ARRAY -template inline -_OutputArray::_OutputArray(const std::array<_Tp, _Nm>& arr) -{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_WRITE, arr.data(), Size(1, _Nm)); } - -template inline -_OutputArray::_OutputArray(const std::array& arr) -{ init(FIXED_SIZE + STD_ARRAY_MAT + ACCESS_WRITE, arr.data(), Size(1, _Nm)); } -#endif - -template inline -_OutputArray::_OutputArray(const std::vector >& vec) -{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } - -template inline -_OutputArray::_OutputArray(const std::vector >& vec) -{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); } - -template inline -_OutputArray::_OutputArray(const Mat_<_Tp>& m) -{ init(FIXED_TYPE + FIXED_SIZE + MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &m); } - -template inline -_OutputArray::_OutputArray(const Matx<_Tp, m, n>& mtx) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, &mtx, Size(n, m)); } - -template inline -_OutputArray::_OutputArray(const _Tp* vec, int n) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, vec, Size(n, 1)); } - -inline _OutputArray::_OutputArray(cuda::GpuMat& d_mat) -{ init(CUDA_GPU_MAT + ACCESS_WRITE, &d_mat); } - -inline _OutputArray::_OutputArray(std::vector& d_mat) -{ init(STD_VECTOR_CUDA_GPU_MAT + ACCESS_WRITE, &d_mat);} - -inline _OutputArray::_OutputArray(ogl::Buffer& buf) -{ init(OPENGL_BUFFER + ACCESS_WRITE, &buf); } - -inline _OutputArray::_OutputArray(cuda::HostMem& cuda_mem) -{ init(CUDA_HOST_MEM + ACCESS_WRITE, &cuda_mem); } - -inline _OutputArray::_OutputArray(const Mat& m) -{ init(FIXED_TYPE + FIXED_SIZE + MAT + ACCESS_WRITE, &m); } - -inline _OutputArray::_OutputArray(const std::vector& vec) -{ init(FIXED_SIZE + STD_VECTOR_MAT + ACCESS_WRITE, &vec); } - -inline _OutputArray::_OutputArray(const UMat& m) -{ init(FIXED_TYPE + FIXED_SIZE + UMAT + ACCESS_WRITE, &m); } - -inline _OutputArray::_OutputArray(const std::vector& vec) -{ init(FIXED_SIZE + STD_VECTOR_UMAT + ACCESS_WRITE, &vec); } - -inline _OutputArray::_OutputArray(const cuda::GpuMat& d_mat) -{ init(FIXED_TYPE + FIXED_SIZE + CUDA_GPU_MAT + ACCESS_WRITE, &d_mat); } - - -inline _OutputArray::_OutputArray(const ogl::Buffer& buf) -{ init(FIXED_TYPE + FIXED_SIZE + OPENGL_BUFFER + ACCESS_WRITE, &buf); } - -inline _OutputArray::_OutputArray(const cuda::HostMem& cuda_mem) -{ init(FIXED_TYPE + FIXED_SIZE + CUDA_HOST_MEM + ACCESS_WRITE, &cuda_mem); } - -template inline -_OutputArray _OutputArray::rawOut(std::vector<_Tp>& vec) -{ - _OutputArray v; - v.flags = _InputArray::FIXED_TYPE + _InputArray::STD_VECTOR + rawType<_Tp>() + ACCESS_WRITE; - v.obj = (void*)&vec; - return v; -} - -#ifdef CV_CXX_STD_ARRAY -template inline -_OutputArray _OutputArray::rawOut(std::array<_Tp, _Nm>& arr) -{ - _OutputArray v; - v.flags = FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_WRITE; - v.obj = (void*)arr.data(); - v.sz = Size(1, _Nm); - return v; -} -#endif - -/////////////////////////////////////////////////////////////////////////////////////////// - -inline _InputOutputArray::_InputOutputArray() { init(ACCESS_RW, 0); } -inline _InputOutputArray::_InputOutputArray(int _flags, void* _obj) { init(_flags|ACCESS_RW, _obj); } -inline _InputOutputArray::_InputOutputArray(Mat& m) { init(MAT+ACCESS_RW, &m); } -inline _InputOutputArray::_InputOutputArray(std::vector& vec) { init(STD_VECTOR_MAT+ACCESS_RW, &vec); } -inline _InputOutputArray::_InputOutputArray(UMat& m) { init(UMAT+ACCESS_RW, &m); } -inline _InputOutputArray::_InputOutputArray(std::vector& vec) { init(STD_VECTOR_UMAT+ACCESS_RW, &vec); } - -template inline -_InputOutputArray::_InputOutputArray(std::vector<_Tp>& vec) -{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); } - -#ifdef CV_CXX_STD_ARRAY -template inline -_InputOutputArray::_InputOutputArray(std::array<_Tp, _Nm>& arr) -{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_RW, arr.data(), Size(1, _Nm)); } - -template inline -_InputOutputArray::_InputOutputArray(std::array& arr) -{ init(STD_ARRAY_MAT + ACCESS_RW, arr.data(), Size(1, _Nm)); } -#endif - -inline _InputOutputArray::_InputOutputArray(std::vector&) -{ CV_Error(Error::StsUnsupportedFormat, "std::vector cannot be an input/output array\n"); } - -template inline -_InputOutputArray::_InputOutputArray(std::vector >& vec) -{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); } - -template inline -_InputOutputArray::_InputOutputArray(std::vector >& vec) -{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_RW, &vec); } - -template inline -_InputOutputArray::_InputOutputArray(Mat_<_Tp>& m) -{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_RW, &m); } - -template inline -_InputOutputArray::_InputOutputArray(Matx<_Tp, m, n>& mtx) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, &mtx, Size(n, m)); } - -template inline -_InputOutputArray::_InputOutputArray(_Tp* vec, int n) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, vec, Size(n, 1)); } - -template inline -_InputOutputArray::_InputOutputArray(const std::vector<_Tp>& vec) -{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); } - -#ifdef CV_CXX_STD_ARRAY -template inline -_InputOutputArray::_InputOutputArray(const std::array<_Tp, _Nm>& arr) -{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_RW, arr.data(), Size(1, _Nm)); } - -template inline -_InputOutputArray::_InputOutputArray(const std::array& arr) -{ init(FIXED_SIZE + STD_ARRAY_MAT + ACCESS_RW, arr.data(), Size(1, _Nm)); } -#endif - -template inline -_InputOutputArray::_InputOutputArray(const std::vector >& vec) -{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); } - -template inline -_InputOutputArray::_InputOutputArray(const std::vector >& vec) -{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_RW, &vec); } - -template inline -_InputOutputArray::_InputOutputArray(const Mat_<_Tp>& m) -{ init(FIXED_TYPE + FIXED_SIZE + MAT + traits::Type<_Tp>::value + ACCESS_RW, &m); } - -template inline -_InputOutputArray::_InputOutputArray(const Matx<_Tp, m, n>& mtx) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, &mtx, Size(n, m)); } - -template inline -_InputOutputArray::_InputOutputArray(const _Tp* vec, int n) -{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, vec, Size(n, 1)); } - -inline _InputOutputArray::_InputOutputArray(cuda::GpuMat& d_mat) -{ init(CUDA_GPU_MAT + ACCESS_RW, &d_mat); } - -inline _InputOutputArray::_InputOutputArray(ogl::Buffer& buf) -{ init(OPENGL_BUFFER + ACCESS_RW, &buf); } - -inline _InputOutputArray::_InputOutputArray(cuda::HostMem& cuda_mem) -{ init(CUDA_HOST_MEM + ACCESS_RW, &cuda_mem); } - -inline _InputOutputArray::_InputOutputArray(const Mat& m) -{ init(FIXED_TYPE + FIXED_SIZE + MAT + ACCESS_RW, &m); } - -inline _InputOutputArray::_InputOutputArray(const std::vector& vec) -{ init(FIXED_SIZE + STD_VECTOR_MAT + ACCESS_RW, &vec); } - -inline _InputOutputArray::_InputOutputArray(const UMat& m) -{ init(FIXED_TYPE + FIXED_SIZE + UMAT + ACCESS_RW, &m); } - -inline _InputOutputArray::_InputOutputArray(const std::vector& vec) -{ init(FIXED_SIZE + STD_VECTOR_UMAT + ACCESS_RW, &vec); } - -inline _InputOutputArray::_InputOutputArray(const cuda::GpuMat& d_mat) -{ init(FIXED_TYPE + FIXED_SIZE + CUDA_GPU_MAT + ACCESS_RW, &d_mat); } - -inline _InputOutputArray::_InputOutputArray(const std::vector& d_mat) -{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_CUDA_GPU_MAT + ACCESS_RW, &d_mat);} - -template<> inline _InputOutputArray::_InputOutputArray(std::vector& d_mat) -{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_CUDA_GPU_MAT + ACCESS_RW, &d_mat);} - -inline _InputOutputArray::_InputOutputArray(const ogl::Buffer& buf) -{ init(FIXED_TYPE + FIXED_SIZE + OPENGL_BUFFER + ACCESS_RW, &buf); } - -inline _InputOutputArray::_InputOutputArray(const cuda::HostMem& cuda_mem) -{ init(FIXED_TYPE + FIXED_SIZE + CUDA_HOST_MEM + ACCESS_RW, &cuda_mem); } - -template inline -_InputOutputArray _InputOutputArray::rawInOut(std::vector<_Tp>& vec) -{ - _InputOutputArray v; - v.flags = _InputArray::FIXED_TYPE + _InputArray::STD_VECTOR + rawType<_Tp>() + ACCESS_RW; - v.obj = (void*)&vec; - return v; -} - -#ifdef CV_CXX_STD_ARRAY -template inline -_InputOutputArray _InputOutputArray::rawInOut(std::array<_Tp, _Nm>& arr) -{ - _InputOutputArray v; - v.flags = FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_RW; - v.obj = (void*)arr.data(); - v.sz = Size(1, _Nm); - return v; -} -#endif - - -template static inline _InputArray rawIn(_Tp& v) { return _InputArray::rawIn(v); } -template static inline _OutputArray rawOut(_Tp& v) { return _OutputArray::rawOut(v); } -template static inline _InputOutputArray rawInOut(_Tp& v) { return _InputOutputArray::rawInOut(v); } - -CV__DEBUG_NS_END - -//////////////////////////////////////////// Mat ////////////////////////////////////////// - -template inline -Mat::Mat(const std::vector<_Tp>& vec, bool copyData) - : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows((int)vec.size()), - cols(1), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) -{ - if(vec.empty()) - return; - if( !copyData ) - { - step[0] = step[1] = sizeof(_Tp); - datastart = data = (uchar*)&vec[0]; - datalimit = dataend = datastart + rows * step[0]; - } - else - Mat((int)vec.size(), 1, traits::Type<_Tp>::value, (uchar*)&vec[0]).copyTo(*this); -} - -#ifdef CV_CXX11 -template inline -Mat::Mat(const std::initializer_list<_Tp> list) - : Mat() -{ - CV_Assert(list.size() != 0); - Mat((int)list.size(), 1, traits::Type<_Tp>::value, (uchar*)list.begin()).copyTo(*this); -} - -template inline -Mat::Mat(const std::initializer_list sizes, const std::initializer_list<_Tp> list) - : Mat() -{ - size_t size_total = 1; - for(auto s : sizes) - size_total *= s; - CV_Assert(list.size() != 0); - CV_Assert(size_total == list.size()); - Mat((int)sizes.size(), (int*)sizes.begin(), traits::Type<_Tp>::value, (uchar*)list.begin()).copyTo(*this); -} -#endif - -#ifdef CV_CXX_STD_ARRAY -template inline -Mat::Mat(const std::array<_Tp, _Nm>& arr, bool copyData) - : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows((int)arr.size()), - cols(1), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) -{ - if(arr.empty()) - return; - if( !copyData ) - { - step[0] = step[1] = sizeof(_Tp); - datastart = data = (uchar*)arr.data(); - datalimit = dataend = datastart + rows * step[0]; - } - else - Mat((int)arr.size(), 1, traits::Type<_Tp>::value, (uchar*)arr.data()).copyTo(*this); -} -#endif - -template inline -Mat::Mat(const Vec<_Tp, n>& vec, bool copyData) - : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(n), cols(1), data(0), - datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) -{ - if( !copyData ) - { - step[0] = step[1] = sizeof(_Tp); - datastart = data = (uchar*)vec.val; - datalimit = dataend = datastart + rows * step[0]; - } - else - Mat(n, 1, traits::Type<_Tp>::value, (void*)vec.val).copyTo(*this); -} - - -template inline -Mat::Mat(const Matx<_Tp,m,n>& M, bool copyData) - : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(m), cols(n), data(0), - datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) -{ - if( !copyData ) - { - step[0] = cols * sizeof(_Tp); - step[1] = sizeof(_Tp); - datastart = data = (uchar*)M.val; - datalimit = dataend = datastart + rows * step[0]; - } - else - Mat(m, n, traits::Type<_Tp>::value, (uchar*)M.val).copyTo(*this); -} - -template inline -Mat::Mat(const Point_<_Tp>& pt, bool copyData) - : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(2), cols(1), data(0), - datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) -{ - if( !copyData ) - { - step[0] = step[1] = sizeof(_Tp); - datastart = data = (uchar*)&pt.x; - datalimit = dataend = datastart + rows * step[0]; - } - else - { - create(2, 1, traits::Type<_Tp>::value); - ((_Tp*)data)[0] = pt.x; - ((_Tp*)data)[1] = pt.y; - } -} - -template inline -Mat::Mat(const Point3_<_Tp>& pt, bool copyData) - : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(3), cols(1), data(0), - datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0) -{ - if( !copyData ) - { - step[0] = step[1] = sizeof(_Tp); - datastart = data = (uchar*)&pt.x; - datalimit = dataend = datastart + rows * step[0]; - } - else - { - create(3, 1, traits::Type<_Tp>::value); - ((_Tp*)data)[0] = pt.x; - ((_Tp*)data)[1] = pt.y; - ((_Tp*)data)[2] = pt.z; - } -} - -template inline -Mat::Mat(const MatCommaInitializer_<_Tp>& commaInitializer) - : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(0), rows(0), cols(0), data(0), - datastart(0), dataend(0), allocator(0), u(0), size(&rows) -{ - *this = commaInitializer.operator Mat_<_Tp>(); -} - -inline -Mat Mat::row(int y) const -{ - return Mat(*this, Range(y, y + 1), Range::all()); -} - -inline -Mat Mat::col(int x) const -{ - return Mat(*this, Range::all(), Range(x, x + 1)); -} - -inline -Mat Mat::rowRange(int startrow, int endrow) const -{ - return Mat(*this, Range(startrow, endrow), Range::all()); -} - -inline -Mat Mat::rowRange(const Range& r) const -{ - return Mat(*this, r, Range::all()); -} - -inline -Mat Mat::colRange(int startcol, int endcol) const -{ - return Mat(*this, Range::all(), Range(startcol, endcol)); -} - -inline -Mat Mat::colRange(const Range& r) const -{ - return Mat(*this, Range::all(), r); -} - -inline -Mat Mat::operator()( Range _rowRange, Range _colRange ) const -{ - return Mat(*this, _rowRange, _colRange); -} - -inline -Mat Mat::operator()( const Rect& roi ) const -{ - return Mat(*this, roi); -} - -inline -Mat Mat::operator()(const Range* ranges) const -{ - return Mat(*this, ranges); -} - -inline -Mat Mat::operator()(const std::vector& ranges) const -{ - return Mat(*this, ranges); -} - -inline -bool Mat::isContinuous() const -{ - return (flags & CONTINUOUS_FLAG) != 0; -} - -inline -bool Mat::isSubmatrix() const -{ - return (flags & SUBMATRIX_FLAG) != 0; -} - -inline -size_t Mat::elemSize() const -{ - size_t res = dims > 0 ? step.p[dims - 1] : 0; - CV_DbgAssert(res != 0); - return res; -} - -inline -size_t Mat::elemSize1() const -{ - return CV_ELEM_SIZE1(flags); -} - -inline -int Mat::type() const -{ - return CV_MAT_TYPE(flags); -} - -inline -int Mat::depth() const -{ - return CV_MAT_DEPTH(flags); -} - -inline -int Mat::channels() const -{ - return CV_MAT_CN(flags); -} - -inline -uchar* Mat::ptr(int y) -{ - CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); - return data + step.p[0] * y; -} - -inline -const uchar* Mat::ptr(int y) const -{ - CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); - return data + step.p[0] * y; -} - -template inline -_Tp* Mat::ptr(int y) -{ - CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); - return (_Tp*)(data + step.p[0] * y); -} - -template inline -const _Tp* Mat::ptr(int y) const -{ - CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) ); - return (const _Tp*)(data + step.p[0] * y); -} - -inline -uchar* Mat::ptr(int i0, int i1) -{ - CV_DbgAssert(dims >= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - return data + i0 * step.p[0] + i1 * step.p[1]; -} - -inline -const uchar* Mat::ptr(int i0, int i1) const -{ - CV_DbgAssert(dims >= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - return data + i0 * step.p[0] + i1 * step.p[1]; -} - -template inline -_Tp* Mat::ptr(int i0, int i1) -{ - CV_DbgAssert(dims >= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - return (_Tp*)(data + i0 * step.p[0] + i1 * step.p[1]); -} - -template inline -const _Tp* Mat::ptr(int i0, int i1) const -{ - CV_DbgAssert(dims >= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - return (const _Tp*)(data + i0 * step.p[0] + i1 * step.p[1]); -} - -inline -uchar* Mat::ptr(int i0, int i1, int i2) -{ - CV_DbgAssert(dims >= 3); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); - return data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]; -} - -inline -const uchar* Mat::ptr(int i0, int i1, int i2) const -{ - CV_DbgAssert(dims >= 3); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); - return data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]; -} - -template inline -_Tp* Mat::ptr(int i0, int i1, int i2) -{ - CV_DbgAssert(dims >= 3); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); - return (_Tp*)(data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]); -} - -template inline -const _Tp* Mat::ptr(int i0, int i1, int i2) const -{ - CV_DbgAssert(dims >= 3); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]); - return (const _Tp*)(data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]); -} - -inline -uchar* Mat::ptr(const int* idx) -{ - int i, d = dims; - uchar* p = data; - CV_DbgAssert( d >= 1 && p ); - for( i = 0; i < d; i++ ) - { - CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); - p += idx[i] * step.p[i]; - } - return p; -} - -inline -const uchar* Mat::ptr(const int* idx) const -{ - int i, d = dims; - uchar* p = data; - CV_DbgAssert( d >= 1 && p ); - for( i = 0; i < d; i++ ) - { - CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); - p += idx[i] * step.p[i]; - } - return p; -} - -template inline -_Tp* Mat::ptr(const int* idx) -{ - int i, d = dims; - uchar* p = data; - CV_DbgAssert( d >= 1 && p ); - for( i = 0; i < d; i++ ) - { - CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); - p += idx[i] * step.p[i]; - } - return (_Tp*)p; -} - -template inline -const _Tp* Mat::ptr(const int* idx) const -{ - int i, d = dims; - uchar* p = data; - CV_DbgAssert( d >= 1 && p ); - for( i = 0; i < d; i++ ) - { - CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] ); - p += idx[i] * step.p[i]; - } - return (const _Tp*)p; -} - -template inline -_Tp& Mat::at(int i0, int i1) -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); - CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1()); - return ((_Tp*)(data + step.p[0] * i0))[i1]; -} - -template inline -const _Tp& Mat::at(int i0, int i1) const -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); - CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1()); - return ((const _Tp*)(data + step.p[0] * i0))[i1]; -} - -template inline -_Tp& Mat::at(Point pt) -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)(pt.x * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); - CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1()); - return ((_Tp*)(data + step.p[0] * pt.y))[pt.x]; -} - -template inline -const _Tp& Mat::at(Point pt) const -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)(pt.x * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels())); - CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1()); - return ((const _Tp*)(data + step.p[0] * pt.y))[pt.x]; -} - -template inline -_Tp& Mat::at(int i0) -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)(size.p[0] * size.p[1])); - CV_DbgAssert(elemSize() == sizeof(_Tp)); - if( isContinuous() || size.p[0] == 1 ) - return ((_Tp*)data)[i0]; - if( size.p[1] == 1 ) - return *(_Tp*)(data + step.p[0] * i0); - int i = i0 / cols, j = i0 - i * cols; - return ((_Tp*)(data + step.p[0] * i))[j]; -} - -template inline -const _Tp& Mat::at(int i0) const -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)(size.p[0] * size.p[1])); - CV_DbgAssert(elemSize() == sizeof(_Tp)); - if( isContinuous() || size.p[0] == 1 ) - return ((const _Tp*)data)[i0]; - if( size.p[1] == 1 ) - return *(const _Tp*)(data + step.p[0] * i0); - int i = i0 / cols, j = i0 - i * cols; - return ((const _Tp*)(data + step.p[0] * i))[j]; -} - -template inline -_Tp& Mat::at(int i0, int i1, int i2) -{ - CV_DbgAssert( elemSize() == sizeof(_Tp) ); - return *(_Tp*)ptr(i0, i1, i2); -} - -template inline -const _Tp& Mat::at(int i0, int i1, int i2) const -{ - CV_DbgAssert( elemSize() == sizeof(_Tp) ); - return *(const _Tp*)ptr(i0, i1, i2); -} - -template inline -_Tp& Mat::at(const int* idx) -{ - CV_DbgAssert( elemSize() == sizeof(_Tp) ); - return *(_Tp*)ptr(idx); -} - -template inline -const _Tp& Mat::at(const int* idx) const -{ - CV_DbgAssert( elemSize() == sizeof(_Tp) ); - return *(const _Tp*)ptr(idx); -} - -template inline -_Tp& Mat::at(const Vec& idx) -{ - CV_DbgAssert( elemSize() == sizeof(_Tp) ); - return *(_Tp*)ptr(idx.val); -} - -template inline -const _Tp& Mat::at(const Vec& idx) const -{ - CV_DbgAssert( elemSize() == sizeof(_Tp) ); - return *(const _Tp*)ptr(idx.val); -} - -template inline -MatConstIterator_<_Tp> Mat::begin() const -{ - if (empty()) - return MatConstIterator_<_Tp>(); - CV_DbgAssert( elemSize() == sizeof(_Tp) ); - return MatConstIterator_<_Tp>((const Mat_<_Tp>*)this); -} - -template inline -MatConstIterator_<_Tp> Mat::end() const -{ - if (empty()) - return MatConstIterator_<_Tp>(); - CV_DbgAssert( elemSize() == sizeof(_Tp) ); - MatConstIterator_<_Tp> it((const Mat_<_Tp>*)this); - it += total(); - return it; -} - -template inline -MatIterator_<_Tp> Mat::begin() -{ - if (empty()) - return MatIterator_<_Tp>(); - CV_DbgAssert( elemSize() == sizeof(_Tp) ); - return MatIterator_<_Tp>((Mat_<_Tp>*)this); -} - -template inline -MatIterator_<_Tp> Mat::end() -{ - if (empty()) - return MatIterator_<_Tp>(); - CV_DbgAssert( elemSize() == sizeof(_Tp) ); - MatIterator_<_Tp> it((Mat_<_Tp>*)this); - it += total(); - return it; -} - -template inline -void Mat::forEach(const Functor& operation) { - this->forEach_impl<_Tp>(operation); -} - -template inline -void Mat::forEach(const Functor& operation) const { - // call as not const - (const_cast(this))->forEach<_Tp>(operation); -} - -template inline -Mat::operator std::vector<_Tp>() const -{ - std::vector<_Tp> v; - copyTo(v); - return v; -} - -#ifdef CV_CXX_STD_ARRAY -template inline -Mat::operator std::array<_Tp, _Nm>() const -{ - std::array<_Tp, _Nm> v; - copyTo(v); - return v; -} -#endif - -template inline -Mat::operator Vec<_Tp, n>() const -{ - CV_Assert( data && dims <= 2 && (rows == 1 || cols == 1) && - rows + cols - 1 == n && channels() == 1 ); - - if( isContinuous() && type() == traits::Type<_Tp>::value ) - return Vec<_Tp, n>((_Tp*)data); - Vec<_Tp, n> v; - Mat tmp(rows, cols, traits::Type<_Tp>::value, v.val); - convertTo(tmp, tmp.type()); - return v; -} - -template inline -Mat::operator Matx<_Tp, m, n>() const -{ - CV_Assert( data && dims <= 2 && rows == m && cols == n && channels() == 1 ); - - if( isContinuous() && type() == traits::Type<_Tp>::value ) - return Matx<_Tp, m, n>((_Tp*)data); - Matx<_Tp, m, n> mtx; - Mat tmp(rows, cols, traits::Type<_Tp>::value, mtx.val); - convertTo(tmp, tmp.type()); - return mtx; -} - -template inline -void Mat::push_back(const _Tp& elem) -{ - if( !data ) - { - *this = Mat(1, 1, traits::Type<_Tp>::value, (void*)&elem).clone(); - return; - } - CV_Assert(traits::Type<_Tp>::value == type() && cols == 1 - /* && dims == 2 (cols == 1 implies dims == 2) */); - const uchar* tmp = dataend + step[0]; - if( !isSubmatrix() && isContinuous() && tmp <= datalimit ) - { - *(_Tp*)(data + (size.p[0]++) * step.p[0]) = elem; - dataend = tmp; - } - else - push_back_(&elem); -} - -template inline -void Mat::push_back(const Mat_<_Tp>& m) -{ - push_back((const Mat&)m); -} - -template<> inline -void Mat::push_back(const MatExpr& expr) -{ - push_back(static_cast(expr)); -} - - -template inline -void Mat::push_back(const std::vector<_Tp>& v) -{ - push_back(Mat(v)); -} - -#ifdef CV_CXX_MOVE_SEMANTICS - -inline -Mat::Mat(Mat&& m) - : flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), data(m.data), - datastart(m.datastart), dataend(m.dataend), datalimit(m.datalimit), allocator(m.allocator), - u(m.u), size(&rows) -{ - if (m.dims <= 2) // move new step/size info - { - step[0] = m.step[0]; - step[1] = m.step[1]; - } - else - { - CV_DbgAssert(m.step.p != m.step.buf); - step.p = m.step.p; - size.p = m.size.p; - m.step.p = m.step.buf; - m.size.p = &m.rows; - } - m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; - m.data = NULL; m.datastart = NULL; m.dataend = NULL; m.datalimit = NULL; - m.allocator = NULL; - m.u = NULL; -} - -inline -Mat& Mat::operator = (Mat&& m) -{ - if (this == &m) - return *this; - - release(); - flags = m.flags; dims = m.dims; rows = m.rows; cols = m.cols; data = m.data; - datastart = m.datastart; dataend = m.dataend; datalimit = m.datalimit; allocator = m.allocator; - u = m.u; - if (step.p != step.buf) // release self step/size - { - fastFree(step.p); - step.p = step.buf; - size.p = &rows; - } - if (m.dims <= 2) // move new step/size info - { - step[0] = m.step[0]; - step[1] = m.step[1]; - } - else - { - CV_DbgAssert(m.step.p != m.step.buf); - step.p = m.step.p; - size.p = m.size.p; - m.step.p = m.step.buf; - m.size.p = &m.rows; - } - m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; - m.data = NULL; m.datastart = NULL; m.dataend = NULL; m.datalimit = NULL; - m.allocator = NULL; - m.u = NULL; - return *this; -} - -#endif - - -///////////////////////////// MatSize //////////////////////////// - -inline -MatSize::MatSize(int* _p) - : p(_p) {} - -inline -int MatSize::dims() const -{ - return (p - 1)[0]; -} - -inline -Size MatSize::operator()() const -{ - CV_DbgAssert(dims() <= 2); - return Size(p[1], p[0]); -} - -inline -const int& MatSize::operator[](int i) const -{ - CV_DbgAssert(i < dims()); -#ifdef __OPENCV_BUILD - CV_DbgAssert(i >= 0); -#endif - return p[i]; -} - -inline -int& MatSize::operator[](int i) -{ - CV_DbgAssert(i < dims()); -#ifdef __OPENCV_BUILD - CV_DbgAssert(i >= 0); -#endif - return p[i]; -} - -inline -MatSize::operator const int*() const -{ - return p; -} - -inline -bool MatSize::operator != (const MatSize& sz) const -{ - return !(*this == sz); -} - - - -///////////////////////////// MatStep //////////////////////////// - -inline -MatStep::MatStep() -{ - p = buf; p[0] = p[1] = 0; -} - -inline -MatStep::MatStep(size_t s) -{ - p = buf; p[0] = s; p[1] = 0; -} - -inline -const size_t& MatStep::operator[](int i) const -{ - return p[i]; -} - -inline -size_t& MatStep::operator[](int i) -{ - return p[i]; -} - -inline MatStep::operator size_t() const -{ - CV_DbgAssert( p == buf ); - return buf[0]; -} - -inline MatStep& MatStep::operator = (size_t s) -{ - CV_DbgAssert( p == buf ); - buf[0] = s; - return *this; -} - - - -////////////////////////////// Mat_<_Tp> //////////////////////////// - -template inline -Mat_<_Tp>::Mat_() - : Mat() -{ - flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; -} - -template inline -Mat_<_Tp>::Mat_(int _rows, int _cols) - : Mat(_rows, _cols, traits::Type<_Tp>::value) -{ -} - -template inline -Mat_<_Tp>::Mat_(int _rows, int _cols, const _Tp& value) - : Mat(_rows, _cols, traits::Type<_Tp>::value) -{ - *this = value; -} - -template inline -Mat_<_Tp>::Mat_(Size _sz) - : Mat(_sz.height, _sz.width, traits::Type<_Tp>::value) -{} - -template inline -Mat_<_Tp>::Mat_(Size _sz, const _Tp& value) - : Mat(_sz.height, _sz.width, traits::Type<_Tp>::value) -{ - *this = value; -} - -template inline -Mat_<_Tp>::Mat_(int _dims, const int* _sz) - : Mat(_dims, _sz, traits::Type<_Tp>::value) -{} - -template inline -Mat_<_Tp>::Mat_(int _dims, const int* _sz, const _Tp& _s) - : Mat(_dims, _sz, traits::Type<_Tp>::value, Scalar(_s)) -{} - -template inline -Mat_<_Tp>::Mat_(int _dims, const int* _sz, _Tp* _data, const size_t* _steps) - : Mat(_dims, _sz, traits::Type<_Tp>::value, _data, _steps) -{} - -template inline -Mat_<_Tp>::Mat_(const Mat_<_Tp>& m, const Range* ranges) - : Mat(m, ranges) -{} - -template inline -Mat_<_Tp>::Mat_(const Mat_<_Tp>& m, const std::vector& ranges) - : Mat(m, ranges) -{} - -template inline -Mat_<_Tp>::Mat_(const Mat& m) - : Mat() -{ - flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; - *this = m; -} - -template inline -Mat_<_Tp>::Mat_(const Mat_& m) - : Mat(m) -{} - -template inline -Mat_<_Tp>::Mat_(int _rows, int _cols, _Tp* _data, size_t steps) - : Mat(_rows, _cols, traits::Type<_Tp>::value, _data, steps) -{} - -template inline -Mat_<_Tp>::Mat_(const Mat_& m, const Range& _rowRange, const Range& _colRange) - : Mat(m, _rowRange, _colRange) -{} - -template inline -Mat_<_Tp>::Mat_(const Mat_& m, const Rect& roi) - : Mat(m, roi) -{} - -template template inline -Mat_<_Tp>::Mat_(const Vec::channel_type, n>& vec, bool copyData) - : Mat(n / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&vec) -{ - CV_Assert(n%DataType<_Tp>::channels == 0); - if( copyData ) - *this = clone(); -} - -template template inline -Mat_<_Tp>::Mat_(const Matx::channel_type, m, n>& M, bool copyData) - : Mat(m, n / DataType<_Tp>::channels, traits::Type<_Tp>::value, (void*)&M) -{ - CV_Assert(n % DataType<_Tp>::channels == 0); - if( copyData ) - *this = clone(); -} - -template inline -Mat_<_Tp>::Mat_(const Point_::channel_type>& pt, bool copyData) - : Mat(2 / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&pt) -{ - CV_Assert(2 % DataType<_Tp>::channels == 0); - if( copyData ) - *this = clone(); -} - -template inline -Mat_<_Tp>::Mat_(const Point3_::channel_type>& pt, bool copyData) - : Mat(3 / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&pt) -{ - CV_Assert(3 % DataType<_Tp>::channels == 0); - if( copyData ) - *this = clone(); -} - -template inline -Mat_<_Tp>::Mat_(const MatCommaInitializer_<_Tp>& commaInitializer) - : Mat(commaInitializer) -{} - -template inline -Mat_<_Tp>::Mat_(const std::vector<_Tp>& vec, bool copyData) - : Mat(vec, copyData) -{} - -#ifdef CV_CXX11 -template inline -Mat_<_Tp>::Mat_(std::initializer_list<_Tp> list) - : Mat(list) -{} - -template inline -Mat_<_Tp>::Mat_(const std::initializer_list sizes, std::initializer_list<_Tp> list) - : Mat(sizes, list) -{} -#endif - -#ifdef CV_CXX_STD_ARRAY -template template inline -Mat_<_Tp>::Mat_(const std::array<_Tp, _Nm>& arr, bool copyData) - : Mat(arr, copyData) -{} -#endif - -template inline -Mat_<_Tp>& Mat_<_Tp>::operator = (const Mat& m) -{ - if (m.empty()) - { - release(); - return *this; - } - if( traits::Type<_Tp>::value == m.type() ) - { - Mat::operator = (m); - return *this; - } - if( traits::Depth<_Tp>::value == m.depth() ) - { - return (*this = m.reshape(DataType<_Tp>::channels, m.dims, 0)); - } - CV_Assert(DataType<_Tp>::channels == m.channels() || m.empty()); - m.convertTo(*this, type()); - return *this; -} - -template inline -Mat_<_Tp>& Mat_<_Tp>::operator = (const Mat_& m) -{ - Mat::operator=(m); - return *this; -} - -template inline -Mat_<_Tp>& Mat_<_Tp>::operator = (const _Tp& s) -{ - typedef typename DataType<_Tp>::vec_type VT; - Mat::operator=(Scalar((const VT&)s)); - return *this; -} - -template inline -void Mat_<_Tp>::create(int _rows, int _cols) -{ - Mat::create(_rows, _cols, traits::Type<_Tp>::value); -} - -template inline -void Mat_<_Tp>::create(Size _sz) -{ - Mat::create(_sz, traits::Type<_Tp>::value); -} - -template inline -void Mat_<_Tp>::create(int _dims, const int* _sz) -{ - Mat::create(_dims, _sz, traits::Type<_Tp>::value); -} - -template inline -void Mat_<_Tp>::release() -{ - Mat::release(); - flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; -} - -template inline -Mat_<_Tp> Mat_<_Tp>::cross(const Mat_& m) const -{ - return Mat_<_Tp>(Mat::cross(m)); -} - -template template inline -Mat_<_Tp>::operator Mat_() const -{ - return Mat_(static_cast(*this)); -} - -template inline -Mat_<_Tp> Mat_<_Tp>::row(int y) const -{ - return Mat_(*this, Range(y, y+1), Range::all()); -} - -template inline -Mat_<_Tp> Mat_<_Tp>::col(int x) const -{ - return Mat_(*this, Range::all(), Range(x, x+1)); -} - -template inline -Mat_<_Tp> Mat_<_Tp>::diag(int d) const -{ - return Mat_(Mat::diag(d)); -} - -template inline -Mat_<_Tp> Mat_<_Tp>::clone() const -{ - return Mat_(Mat::clone()); -} - -template inline -size_t Mat_<_Tp>::elemSize() const -{ - CV_DbgAssert( Mat::elemSize() == sizeof(_Tp) ); - return sizeof(_Tp); -} - -template inline -size_t Mat_<_Tp>::elemSize1() const -{ - CV_DbgAssert( Mat::elemSize1() == sizeof(_Tp) / DataType<_Tp>::channels ); - return sizeof(_Tp) / DataType<_Tp>::channels; -} - -template inline -int Mat_<_Tp>::type() const -{ - CV_DbgAssert( Mat::type() == traits::Type<_Tp>::value ); - return traits::Type<_Tp>::value; -} - -template inline -int Mat_<_Tp>::depth() const -{ - CV_DbgAssert( Mat::depth() == traits::Depth<_Tp>::value ); - return traits::Depth<_Tp>::value; -} - -template inline -int Mat_<_Tp>::channels() const -{ - CV_DbgAssert( Mat::channels() == DataType<_Tp>::channels ); - return DataType<_Tp>::channels; -} - -template inline -size_t Mat_<_Tp>::stepT(int i) const -{ - return step.p[i] / elemSize(); -} - -template inline -size_t Mat_<_Tp>::step1(int i) const -{ - return step.p[i] / elemSize1(); -} - -template inline -Mat_<_Tp>& Mat_<_Tp>::adjustROI( int dtop, int dbottom, int dleft, int dright ) -{ - return (Mat_<_Tp>&)(Mat::adjustROI(dtop, dbottom, dleft, dright)); -} - -template inline -Mat_<_Tp> Mat_<_Tp>::operator()( const Range& _rowRange, const Range& _colRange ) const -{ - return Mat_<_Tp>(*this, _rowRange, _colRange); -} - -template inline -Mat_<_Tp> Mat_<_Tp>::operator()( const Rect& roi ) const -{ - return Mat_<_Tp>(*this, roi); -} - -template inline -Mat_<_Tp> Mat_<_Tp>::operator()( const Range* ranges ) const -{ - return Mat_<_Tp>(*this, ranges); -} - -template inline -Mat_<_Tp> Mat_<_Tp>::operator()(const std::vector& ranges) const -{ - return Mat_<_Tp>(*this, ranges); -} - -template inline -_Tp* Mat_<_Tp>::operator [](int y) -{ - CV_DbgAssert( 0 <= y && y < size.p[0] ); - return (_Tp*)(data + y*step.p[0]); -} - -template inline -const _Tp* Mat_<_Tp>::operator [](int y) const -{ - CV_DbgAssert( 0 <= y && y < size.p[0] ); - return (const _Tp*)(data + y*step.p[0]); -} - -template inline -_Tp& Mat_<_Tp>::operator ()(int i0, int i1) -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - CV_DbgAssert(type() == traits::Type<_Tp>::value); - return ((_Tp*)(data + step.p[0] * i0))[i1]; -} - -template inline -const _Tp& Mat_<_Tp>::operator ()(int i0, int i1) const -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]); - CV_DbgAssert(type() == traits::Type<_Tp>::value); - return ((const _Tp*)(data + step.p[0] * i0))[i1]; -} - -template inline -_Tp& Mat_<_Tp>::operator ()(Point pt) -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)pt.x < (unsigned)size.p[1]); - CV_DbgAssert(type() == traits::Type<_Tp>::value); - return ((_Tp*)(data + step.p[0] * pt.y))[pt.x]; -} - -template inline -const _Tp& Mat_<_Tp>::operator ()(Point pt) const -{ - CV_DbgAssert(dims <= 2); - CV_DbgAssert(data); - CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]); - CV_DbgAssert((unsigned)pt.x < (unsigned)size.p[1]); - CV_DbgAssert(type() == traits::Type<_Tp>::value); - return ((const _Tp*)(data + step.p[0] * pt.y))[pt.x]; -} - -template inline -_Tp& Mat_<_Tp>::operator ()(const int* idx) -{ - return Mat::at<_Tp>(idx); -} - -template inline -const _Tp& Mat_<_Tp>::operator ()(const int* idx) const -{ - return Mat::at<_Tp>(idx); -} - -template template inline -_Tp& Mat_<_Tp>::operator ()(const Vec& idx) -{ - return Mat::at<_Tp>(idx); -} - -template template inline -const _Tp& Mat_<_Tp>::operator ()(const Vec& idx) const -{ - return Mat::at<_Tp>(idx); -} - -template inline -_Tp& Mat_<_Tp>::operator ()(int i0) -{ - return this->at<_Tp>(i0); -} - -template inline -const _Tp& Mat_<_Tp>::operator ()(int i0) const -{ - return this->at<_Tp>(i0); -} - -template inline -_Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) -{ - return this->at<_Tp>(i0, i1, i2); -} - -template inline -const _Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) const -{ - return this->at<_Tp>(i0, i1, i2); -} - -template inline -Mat_<_Tp>::operator std::vector<_Tp>() const -{ - std::vector<_Tp> v; - copyTo(v); - return v; -} - -#ifdef CV_CXX_STD_ARRAY -template template inline -Mat_<_Tp>::operator std::array<_Tp, _Nm>() const -{ - std::array<_Tp, _Nm> a; - copyTo(a); - return a; -} -#endif - -template template inline -Mat_<_Tp>::operator Vec::channel_type, n>() const -{ - CV_Assert(n % DataType<_Tp>::channels == 0); - -#if defined _MSC_VER - const Mat* pMat = (const Mat*)this; // workaround for MSVS <= 2012 compiler bugs (but GCC 4.6 dislikes this workaround) - return pMat->operator Vec::channel_type, n>(); -#else - return this->Mat::operator Vec::channel_type, n>(); -#endif -} - -template template inline -Mat_<_Tp>::operator Matx::channel_type, m, n>() const -{ - CV_Assert(n % DataType<_Tp>::channels == 0); - -#if defined _MSC_VER - const Mat* pMat = (const Mat*)this; // workaround for MSVS <= 2012 compiler bugs (but GCC 4.6 dislikes this workaround) - Matx::channel_type, m, n> res = pMat->operator Matx::channel_type, m, n>(); - return res; -#else - Matx::channel_type, m, n> res = this->Mat::operator Matx::channel_type, m, n>(); - return res; -#endif -} - -template inline -MatConstIterator_<_Tp> Mat_<_Tp>::begin() const -{ - return Mat::begin<_Tp>(); -} - -template inline -MatConstIterator_<_Tp> Mat_<_Tp>::end() const -{ - return Mat::end<_Tp>(); -} - -template inline -MatIterator_<_Tp> Mat_<_Tp>::begin() -{ - return Mat::begin<_Tp>(); -} - -template inline -MatIterator_<_Tp> Mat_<_Tp>::end() -{ - return Mat::end<_Tp>(); -} - -template template inline -void Mat_<_Tp>::forEach(const Functor& operation) { - Mat::forEach<_Tp, Functor>(operation); -} - -template template inline -void Mat_<_Tp>::forEach(const Functor& operation) const { - Mat::forEach<_Tp, Functor>(operation); -} - -#ifdef CV_CXX_MOVE_SEMANTICS - -template inline -Mat_<_Tp>::Mat_(Mat_&& m) - : Mat(std::move(m)) -{ -} - -template inline -Mat_<_Tp>& Mat_<_Tp>::operator = (Mat_&& m) -{ - Mat::operator = (std::move(m)); - return *this; -} - -template inline -Mat_<_Tp>::Mat_(Mat&& m) - : Mat() -{ - flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; - *this = std::move(m); -} - -template inline -Mat_<_Tp>& Mat_<_Tp>::operator = (Mat&& m) -{ - if (m.empty()) - { - release(); - return *this; - } - if( traits::Type<_Tp>::value == m.type() ) - { - Mat::operator = ((Mat&&)m); - return *this; - } - if( traits::Depth<_Tp>::value == m.depth() ) - { - Mat::operator = ((Mat&&)m.reshape(DataType<_Tp>::channels, m.dims, 0)); - return *this; - } - CV_DbgAssert(DataType<_Tp>::channels == m.channels()); - m.convertTo(*this, type()); - return *this; -} - -template inline -Mat_<_Tp>::Mat_(MatExpr&& e) - : Mat() -{ - flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value; - *this = Mat(e); -} - -#endif - -///////////////////////////// SparseMat ///////////////////////////// - -inline -SparseMat SparseMat::clone() const -{ - SparseMat temp; - this->copyTo(temp); - return temp; -} - -inline -size_t SparseMat::elemSize() const -{ - return CV_ELEM_SIZE(flags); -} - -inline -size_t SparseMat::elemSize1() const -{ - return CV_ELEM_SIZE1(flags); -} - -inline -int SparseMat::type() const -{ - return CV_MAT_TYPE(flags); -} - -inline -int SparseMat::depth() const -{ - return CV_MAT_DEPTH(flags); -} - -inline -int SparseMat::channels() const -{ - return CV_MAT_CN(flags); -} - -inline -const int* SparseMat::size() const -{ - return hdr ? hdr->size : 0; -} - -inline -int SparseMat::size(int i) const -{ - if( hdr ) - { - CV_DbgAssert((unsigned)i < (unsigned)hdr->dims); - return hdr->size[i]; - } - return 0; -} - -inline -int SparseMat::dims() const -{ - return hdr ? hdr->dims : 0; -} - -inline -size_t SparseMat::nzcount() const -{ - return hdr ? hdr->nodeCount : 0; -} - -template inline -_Tp& SparseMat::ref(int i0, size_t* hashval) -{ - return *(_Tp*)((SparseMat*)this)->ptr(i0, true, hashval); -} - -template inline -_Tp& SparseMat::ref(int i0, int i1, size_t* hashval) -{ - return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, true, hashval); -} - -template inline -_Tp& SparseMat::ref(int i0, int i1, int i2, size_t* hashval) -{ - return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, i2, true, hashval); -} - -template inline -_Tp& SparseMat::ref(const int* idx, size_t* hashval) -{ - return *(_Tp*)((SparseMat*)this)->ptr(idx, true, hashval); -} - -template inline -_Tp SparseMat::value(int i0, size_t* hashval) const -{ - const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval); - return p ? *p : _Tp(); -} - -template inline -_Tp SparseMat::value(int i0, int i1, size_t* hashval) const -{ - const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval); - return p ? *p : _Tp(); -} - -template inline -_Tp SparseMat::value(int i0, int i1, int i2, size_t* hashval) const -{ - const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, i2, false, hashval); - return p ? *p : _Tp(); -} - -template inline -_Tp SparseMat::value(const int* idx, size_t* hashval) const -{ - const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(idx, false, hashval); - return p ? *p : _Tp(); -} - -template inline -const _Tp* SparseMat::find(int i0, size_t* hashval) const -{ - return (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval); -} - -template inline -const _Tp* SparseMat::find(int i0, int i1, size_t* hashval) const -{ - return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval); -} - -template inline -const _Tp* SparseMat::find(int i0, int i1, int i2, size_t* hashval) const -{ - return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, i2, false, hashval); -} - -template inline -const _Tp* SparseMat::find(const int* idx, size_t* hashval) const -{ - return (const _Tp*)((SparseMat*)this)->ptr(idx, false, hashval); -} - -template inline -_Tp& SparseMat::value(Node* n) -{ - return *(_Tp*)((uchar*)n + hdr->valueOffset); -} - -template inline -const _Tp& SparseMat::value(const Node* n) const -{ - return *(const _Tp*)((const uchar*)n + hdr->valueOffset); -} - -inline -SparseMat::Node* SparseMat::node(size_t nidx) -{ - return (Node*)(void*)&hdr->pool[nidx]; -} - -inline -const SparseMat::Node* SparseMat::node(size_t nidx) const -{ - return (const Node*)(const void*)&hdr->pool[nidx]; -} - -inline -SparseMatIterator SparseMat::begin() -{ - return SparseMatIterator(this); -} - -inline -SparseMatConstIterator SparseMat::begin() const -{ - return SparseMatConstIterator(this); -} - -inline -SparseMatIterator SparseMat::end() -{ - SparseMatIterator it(this); - it.seekEnd(); - return it; -} - -inline -SparseMatConstIterator SparseMat::end() const -{ - SparseMatConstIterator it(this); - it.seekEnd(); - return it; -} - -template inline -SparseMatIterator_<_Tp> SparseMat::begin() -{ - return SparseMatIterator_<_Tp>(this); -} - -template inline -SparseMatConstIterator_<_Tp> SparseMat::begin() const -{ - return SparseMatConstIterator_<_Tp>(this); -} - -template inline -SparseMatIterator_<_Tp> SparseMat::end() -{ - SparseMatIterator_<_Tp> it(this); - it.seekEnd(); - return it; -} - -template inline -SparseMatConstIterator_<_Tp> SparseMat::end() const -{ - SparseMatConstIterator_<_Tp> it(this); - it.seekEnd(); - return it; -} - - - -///////////////////////////// SparseMat_ //////////////////////////// - -template inline -SparseMat_<_Tp>::SparseMat_() -{ - flags = MAGIC_VAL | traits::Type<_Tp>::value; -} - -template inline -SparseMat_<_Tp>::SparseMat_(int _dims, const int* _sizes) - : SparseMat(_dims, _sizes, traits::Type<_Tp>::value) -{} - -template inline -SparseMat_<_Tp>::SparseMat_(const SparseMat& m) -{ - if( m.type() == traits::Type<_Tp>::value ) - *this = (const SparseMat_<_Tp>&)m; - else - m.convertTo(*this, traits::Type<_Tp>::value); -} - -template inline -SparseMat_<_Tp>::SparseMat_(const SparseMat_<_Tp>& m) -{ - this->flags = m.flags; - this->hdr = m.hdr; - if( this->hdr ) - CV_XADD(&this->hdr->refcount, 1); -} - -template inline -SparseMat_<_Tp>::SparseMat_(const Mat& m) -{ - SparseMat sm(m); - *this = sm; -} - -template inline -SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const SparseMat_<_Tp>& m) -{ - if( this != &m ) - { - if( m.hdr ) CV_XADD(&m.hdr->refcount, 1); - release(); - flags = m.flags; - hdr = m.hdr; - } - return *this; -} - -template inline -SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const SparseMat& m) -{ - if( m.type() == traits::Type<_Tp>::value ) - return (*this = (const SparseMat_<_Tp>&)m); - m.convertTo(*this, traits::Type<_Tp>::value); - return *this; -} - -template inline -SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const Mat& m) -{ - return (*this = SparseMat(m)); -} - -template inline -SparseMat_<_Tp> SparseMat_<_Tp>::clone() const -{ - SparseMat_<_Tp> m; - this->copyTo(m); - return m; -} - -template inline -void SparseMat_<_Tp>::create(int _dims, const int* _sizes) -{ - SparseMat::create(_dims, _sizes, traits::Type<_Tp>::value); -} - -template inline -int SparseMat_<_Tp>::type() const -{ - return traits::Type<_Tp>::value; -} - -template inline -int SparseMat_<_Tp>::depth() const -{ - return traits::Depth<_Tp>::value; -} - -template inline -int SparseMat_<_Tp>::channels() const -{ - return DataType<_Tp>::channels; -} - -template inline -_Tp& SparseMat_<_Tp>::ref(int i0, size_t* hashval) -{ - return SparseMat::ref<_Tp>(i0, hashval); -} - -template inline -_Tp SparseMat_<_Tp>::operator()(int i0, size_t* hashval) const -{ - return SparseMat::value<_Tp>(i0, hashval); -} - -template inline -_Tp& SparseMat_<_Tp>::ref(int i0, int i1, size_t* hashval) -{ - return SparseMat::ref<_Tp>(i0, i1, hashval); -} - -template inline -_Tp SparseMat_<_Tp>::operator()(int i0, int i1, size_t* hashval) const -{ - return SparseMat::value<_Tp>(i0, i1, hashval); -} - -template inline -_Tp& SparseMat_<_Tp>::ref(int i0, int i1, int i2, size_t* hashval) -{ - return SparseMat::ref<_Tp>(i0, i1, i2, hashval); -} - -template inline -_Tp SparseMat_<_Tp>::operator()(int i0, int i1, int i2, size_t* hashval) const -{ - return SparseMat::value<_Tp>(i0, i1, i2, hashval); -} - -template inline -_Tp& SparseMat_<_Tp>::ref(const int* idx, size_t* hashval) -{ - return SparseMat::ref<_Tp>(idx, hashval); -} - -template inline -_Tp SparseMat_<_Tp>::operator()(const int* idx, size_t* hashval) const -{ - return SparseMat::value<_Tp>(idx, hashval); -} - -template inline -SparseMatIterator_<_Tp> SparseMat_<_Tp>::begin() -{ - return SparseMatIterator_<_Tp>(this); -} - -template inline -SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::begin() const -{ - return SparseMatConstIterator_<_Tp>(this); -} - -template inline -SparseMatIterator_<_Tp> SparseMat_<_Tp>::end() -{ - SparseMatIterator_<_Tp> it(this); - it.seekEnd(); - return it; -} - -template inline -SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::end() const -{ - SparseMatConstIterator_<_Tp> it(this); - it.seekEnd(); - return it; -} - - - -////////////////////////// MatConstIterator ///////////////////////// - -inline -MatConstIterator::MatConstIterator() - : m(0), elemSize(0), ptr(0), sliceStart(0), sliceEnd(0) -{} - -inline -MatConstIterator::MatConstIterator(const Mat* _m) - : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) -{ - if( m && m->isContinuous() ) - { - CV_Assert(!m->empty()); - sliceStart = m->ptr(); - sliceEnd = sliceStart + m->total()*elemSize; - } - seek((const int*)0); -} - -inline -MatConstIterator::MatConstIterator(const Mat* _m, int _row, int _col) - : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) -{ - CV_Assert(m && m->dims <= 2); - if( m->isContinuous() ) - { - CV_Assert(!m->empty()); - sliceStart = m->ptr(); - sliceEnd = sliceStart + m->total()*elemSize; - } - int idx[] = {_row, _col}; - seek(idx); -} - -inline -MatConstIterator::MatConstIterator(const Mat* _m, Point _pt) - : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0) -{ - CV_Assert(m && m->dims <= 2); - if( m->isContinuous() ) - { - CV_Assert(!m->empty()); - sliceStart = m->ptr(); - sliceEnd = sliceStart + m->total()*elemSize; - } - int idx[] = {_pt.y, _pt.x}; - seek(idx); -} - -inline -MatConstIterator::MatConstIterator(const MatConstIterator& it) - : m(it.m), elemSize(it.elemSize), ptr(it.ptr), sliceStart(it.sliceStart), sliceEnd(it.sliceEnd) -{} - -inline -MatConstIterator& MatConstIterator::operator = (const MatConstIterator& it ) -{ - m = it.m; elemSize = it.elemSize; ptr = it.ptr; - sliceStart = it.sliceStart; sliceEnd = it.sliceEnd; - return *this; -} - -inline -const uchar* MatConstIterator::operator *() const -{ - return ptr; -} - -inline MatConstIterator& MatConstIterator::operator += (ptrdiff_t ofs) -{ - if( !m || ofs == 0 ) - return *this; - ptrdiff_t ofsb = ofs*elemSize; - ptr += ofsb; - if( ptr < sliceStart || sliceEnd <= ptr ) - { - ptr -= ofsb; - seek(ofs, true); - } - return *this; -} - -inline -MatConstIterator& MatConstIterator::operator -= (ptrdiff_t ofs) -{ - return (*this += -ofs); -} - -inline -MatConstIterator& MatConstIterator::operator --() -{ - if( m && (ptr -= elemSize) < sliceStart ) - { - ptr += elemSize; - seek(-1, true); - } - return *this; -} - -inline -MatConstIterator MatConstIterator::operator --(int) -{ - MatConstIterator b = *this; - *this += -1; - return b; -} - -inline -MatConstIterator& MatConstIterator::operator ++() -{ - if( m && (ptr += elemSize) >= sliceEnd ) - { - ptr -= elemSize; - seek(1, true); - } - return *this; -} - -inline MatConstIterator MatConstIterator::operator ++(int) -{ - MatConstIterator b = *this; - *this += 1; - return b; -} - - -static inline -bool operator == (const MatConstIterator& a, const MatConstIterator& b) -{ - return a.m == b.m && a.ptr == b.ptr; -} - -static inline -bool operator != (const MatConstIterator& a, const MatConstIterator& b) -{ - return !(a == b); -} - -static inline -bool operator < (const MatConstIterator& a, const MatConstIterator& b) -{ - return a.ptr < b.ptr; -} - -static inline -bool operator > (const MatConstIterator& a, const MatConstIterator& b) -{ - return a.ptr > b.ptr; -} - -static inline -bool operator <= (const MatConstIterator& a, const MatConstIterator& b) -{ - return a.ptr <= b.ptr; -} - -static inline -bool operator >= (const MatConstIterator& a, const MatConstIterator& b) -{ - return a.ptr >= b.ptr; -} - -static inline -ptrdiff_t operator - (const MatConstIterator& b, const MatConstIterator& a) -{ - if( a.m != b.m ) - return ((size_t)(-1) >> 1); - if( a.sliceEnd == b.sliceEnd ) - return (b.ptr - a.ptr)/static_cast(b.elemSize); - - return b.lpos() - a.lpos(); -} - -static inline -MatConstIterator operator + (const MatConstIterator& a, ptrdiff_t ofs) -{ - MatConstIterator b = a; - return b += ofs; -} - -static inline -MatConstIterator operator + (ptrdiff_t ofs, const MatConstIterator& a) -{ - MatConstIterator b = a; - return b += ofs; -} - -static inline -MatConstIterator operator - (const MatConstIterator& a, ptrdiff_t ofs) -{ - MatConstIterator b = a; - return b += -ofs; -} - - -inline -const uchar* MatConstIterator::operator [](ptrdiff_t i) const -{ - return *(*this + i); -} - - - -///////////////////////// MatConstIterator_ ///////////////////////// - -template inline -MatConstIterator_<_Tp>::MatConstIterator_() -{} - -template inline -MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m) - : MatConstIterator(_m) -{} - -template inline -MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m, int _row, int _col) - : MatConstIterator(_m, _row, _col) -{} - -template inline -MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m, Point _pt) - : MatConstIterator(_m, _pt) -{} - -template inline -MatConstIterator_<_Tp>::MatConstIterator_(const MatConstIterator_& it) - : MatConstIterator(it) -{} - -template inline -MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator = (const MatConstIterator_& it ) -{ - MatConstIterator::operator = (it); - return *this; -} - -template inline -const _Tp& MatConstIterator_<_Tp>::operator *() const -{ - return *(_Tp*)(this->ptr); -} - -template inline -MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator += (ptrdiff_t ofs) -{ - MatConstIterator::operator += (ofs); - return *this; -} - -template inline -MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator -= (ptrdiff_t ofs) -{ - return (*this += -ofs); -} - -template inline -MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator --() -{ - MatConstIterator::operator --(); - return *this; -} - -template inline -MatConstIterator_<_Tp> MatConstIterator_<_Tp>::operator --(int) -{ - MatConstIterator_ b = *this; - MatConstIterator::operator --(); - return b; -} - -template inline -MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator ++() -{ - MatConstIterator::operator ++(); - return *this; -} - -template inline -MatConstIterator_<_Tp> MatConstIterator_<_Tp>::operator ++(int) -{ - MatConstIterator_ b = *this; - MatConstIterator::operator ++(); - return b; -} - - -template inline -Point MatConstIterator_<_Tp>::pos() const -{ - if( !m ) - return Point(); - CV_DbgAssert( m->dims <= 2 ); - if( m->isContinuous() ) - { - ptrdiff_t ofs = (const _Tp*)ptr - (const _Tp*)m->data; - int y = (int)(ofs / m->cols); - int x = (int)(ofs - (ptrdiff_t)y * m->cols); - return Point(x, y); - } - else - { - ptrdiff_t ofs = (uchar*)ptr - m->data; - int y = (int)(ofs / m->step); - int x = (int)((ofs - y * m->step)/sizeof(_Tp)); - return Point(x, y); - } -} - - -template static inline -bool operator == (const MatConstIterator_<_Tp>& a, const MatConstIterator_<_Tp>& b) -{ - return a.m == b.m && a.ptr == b.ptr; -} - -template static inline -bool operator != (const MatConstIterator_<_Tp>& a, const MatConstIterator_<_Tp>& b) -{ - return a.m != b.m || a.ptr != b.ptr; -} - -template static inline -MatConstIterator_<_Tp> operator + (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) -{ - MatConstIterator t = (const MatConstIterator&)a + ofs; - return (MatConstIterator_<_Tp>&)t; -} - -template static inline -MatConstIterator_<_Tp> operator + (ptrdiff_t ofs, const MatConstIterator_<_Tp>& a) -{ - MatConstIterator t = (const MatConstIterator&)a + ofs; - return (MatConstIterator_<_Tp>&)t; -} - -template static inline -MatConstIterator_<_Tp> operator - (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs) -{ - MatConstIterator t = (const MatConstIterator&)a - ofs; - return (MatConstIterator_<_Tp>&)t; -} - -template inline -const _Tp& MatConstIterator_<_Tp>::operator [](ptrdiff_t i) const -{ - return *(_Tp*)MatConstIterator::operator [](i); -} - - - -//////////////////////////// MatIterator_ /////////////////////////// - -template inline -MatIterator_<_Tp>::MatIterator_() - : MatConstIterator_<_Tp>() -{} - -template inline -MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m) - : MatConstIterator_<_Tp>(_m) -{} - -template inline -MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, int _row, int _col) - : MatConstIterator_<_Tp>(_m, _row, _col) -{} - -template inline -MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, Point _pt) - : MatConstIterator_<_Tp>(_m, _pt) -{} - -template inline -MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, const int* _idx) - : MatConstIterator_<_Tp>(_m, _idx) -{} - -template inline -MatIterator_<_Tp>::MatIterator_(const MatIterator_& it) - : MatConstIterator_<_Tp>(it) -{} - -template inline -MatIterator_<_Tp>& MatIterator_<_Tp>::operator = (const MatIterator_<_Tp>& it ) -{ - MatConstIterator::operator = (it); - return *this; -} - -template inline -_Tp& MatIterator_<_Tp>::operator *() const -{ - return *(_Tp*)(this->ptr); -} - -template inline -MatIterator_<_Tp>& MatIterator_<_Tp>::operator += (ptrdiff_t ofs) -{ - MatConstIterator::operator += (ofs); - return *this; -} - -template inline -MatIterator_<_Tp>& MatIterator_<_Tp>::operator -= (ptrdiff_t ofs) -{ - MatConstIterator::operator += (-ofs); - return *this; -} - -template inline -MatIterator_<_Tp>& MatIterator_<_Tp>::operator --() -{ - MatConstIterator::operator --(); - return *this; -} - -template inline -MatIterator_<_Tp> MatIterator_<_Tp>::operator --(int) -{ - MatIterator_ b = *this; - MatConstIterator::operator --(); - return b; -} - -template inline -MatIterator_<_Tp>& MatIterator_<_Tp>::operator ++() -{ - MatConstIterator::operator ++(); - return *this; -} - -template inline -MatIterator_<_Tp> MatIterator_<_Tp>::operator ++(int) -{ - MatIterator_ b = *this; - MatConstIterator::operator ++(); - return b; -} - -template inline -_Tp& MatIterator_<_Tp>::operator [](ptrdiff_t i) const -{ - return *(*this + i); -} - - -template static inline -bool operator == (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b) -{ - return a.m == b.m && a.ptr == b.ptr; -} - -template static inline -bool operator != (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b) -{ - return a.m != b.m || a.ptr != b.ptr; -} - -template static inline -MatIterator_<_Tp> operator + (const MatIterator_<_Tp>& a, ptrdiff_t ofs) -{ - MatConstIterator t = (const MatConstIterator&)a + ofs; - return (MatIterator_<_Tp>&)t; -} - -template static inline -MatIterator_<_Tp> operator + (ptrdiff_t ofs, const MatIterator_<_Tp>& a) -{ - MatConstIterator t = (const MatConstIterator&)a + ofs; - return (MatIterator_<_Tp>&)t; -} - -template static inline -MatIterator_<_Tp> operator - (const MatIterator_<_Tp>& a, ptrdiff_t ofs) -{ - MatConstIterator t = (const MatConstIterator&)a - ofs; - return (MatIterator_<_Tp>&)t; -} - - - -/////////////////////// SparseMatConstIterator ////////////////////// - -inline -SparseMatConstIterator::SparseMatConstIterator() - : m(0), hashidx(0), ptr(0) -{} - -inline -SparseMatConstIterator::SparseMatConstIterator(const SparseMatConstIterator& it) - : m(it.m), hashidx(it.hashidx), ptr(it.ptr) -{} - -inline SparseMatConstIterator& SparseMatConstIterator::operator = (const SparseMatConstIterator& it) -{ - if( this != &it ) - { - m = it.m; - hashidx = it.hashidx; - ptr = it.ptr; - } - return *this; -} - -template inline -const _Tp& SparseMatConstIterator::value() const -{ - return *(const _Tp*)ptr; -} - -inline -const SparseMat::Node* SparseMatConstIterator::node() const -{ - return (ptr && m && m->hdr) ? (const SparseMat::Node*)(const void*)(ptr - m->hdr->valueOffset) : 0; -} - -inline -SparseMatConstIterator SparseMatConstIterator::operator ++(int) -{ - SparseMatConstIterator it = *this; - ++*this; - return it; -} - -inline -void SparseMatConstIterator::seekEnd() -{ - if( m && m->hdr ) - { - hashidx = m->hdr->hashtab.size(); - ptr = 0; - } -} - - -static inline -bool operator == (const SparseMatConstIterator& it1, const SparseMatConstIterator& it2) -{ - return it1.m == it2.m && it1.ptr == it2.ptr; -} - -static inline -bool operator != (const SparseMatConstIterator& it1, const SparseMatConstIterator& it2) -{ - return !(it1 == it2); -} - - - -///////////////////////// SparseMatIterator ///////////////////////// - -inline -SparseMatIterator::SparseMatIterator() -{} - -inline -SparseMatIterator::SparseMatIterator(SparseMat* _m) - : SparseMatConstIterator(_m) -{} - -inline -SparseMatIterator::SparseMatIterator(const SparseMatIterator& it) - : SparseMatConstIterator(it) -{} - -inline -SparseMatIterator& SparseMatIterator::operator = (const SparseMatIterator& it) -{ - (SparseMatConstIterator&)*this = it; - return *this; -} - -template inline -_Tp& SparseMatIterator::value() const -{ - return *(_Tp*)ptr; -} - -inline -SparseMat::Node* SparseMatIterator::node() const -{ - return (SparseMat::Node*)SparseMatConstIterator::node(); -} - -inline -SparseMatIterator& SparseMatIterator::operator ++() -{ - SparseMatConstIterator::operator ++(); - return *this; -} - -inline -SparseMatIterator SparseMatIterator::operator ++(int) -{ - SparseMatIterator it = *this; - ++*this; - return it; -} - - - -////////////////////// SparseMatConstIterator_ ////////////////////// - -template inline -SparseMatConstIterator_<_Tp>::SparseMatConstIterator_() -{} - -template inline -SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMat_<_Tp>* _m) - : SparseMatConstIterator(_m) -{} - -template inline -SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMat* _m) - : SparseMatConstIterator(_m) -{ - CV_Assert( _m->type() == traits::Type<_Tp>::value ); -} - -template inline -SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMatConstIterator_<_Tp>& it) - : SparseMatConstIterator(it) -{} - -template inline -SparseMatConstIterator_<_Tp>& SparseMatConstIterator_<_Tp>::operator = (const SparseMatConstIterator_<_Tp>& it) -{ - return reinterpret_cast&> - (*reinterpret_cast(this) = - reinterpret_cast(it)); -} - -template inline -const _Tp& SparseMatConstIterator_<_Tp>::operator *() const -{ - return *(const _Tp*)this->ptr; -} - -template inline -SparseMatConstIterator_<_Tp>& SparseMatConstIterator_<_Tp>::operator ++() -{ - SparseMatConstIterator::operator ++(); - return *this; -} - -template inline -SparseMatConstIterator_<_Tp> SparseMatConstIterator_<_Tp>::operator ++(int) -{ - SparseMatConstIterator_<_Tp> it = *this; - SparseMatConstIterator::operator ++(); - return it; -} - - - -///////////////////////// SparseMatIterator_ //////////////////////// - -template inline -SparseMatIterator_<_Tp>::SparseMatIterator_() -{} - -template inline -SparseMatIterator_<_Tp>::SparseMatIterator_(SparseMat_<_Tp>* _m) - : SparseMatConstIterator_<_Tp>(_m) -{} - -template inline -SparseMatIterator_<_Tp>::SparseMatIterator_(SparseMat* _m) - : SparseMatConstIterator_<_Tp>(_m) -{} - -template inline -SparseMatIterator_<_Tp>::SparseMatIterator_(const SparseMatIterator_<_Tp>& it) - : SparseMatConstIterator_<_Tp>(it) -{} - -template inline -SparseMatIterator_<_Tp>& SparseMatIterator_<_Tp>::operator = (const SparseMatIterator_<_Tp>& it) -{ - return reinterpret_cast&> - (*reinterpret_cast(this) = - reinterpret_cast(it)); -} - -template inline -_Tp& SparseMatIterator_<_Tp>::operator *() const -{ - return *(_Tp*)this->ptr; -} - -template inline -SparseMatIterator_<_Tp>& SparseMatIterator_<_Tp>::operator ++() -{ - SparseMatConstIterator::operator ++(); - return *this; -} - -template inline -SparseMatIterator_<_Tp> SparseMatIterator_<_Tp>::operator ++(int) -{ - SparseMatIterator_<_Tp> it = *this; - SparseMatConstIterator::operator ++(); - return it; -} - - - -//////////////////////// MatCommaInitializer_ /////////////////////// - -template inline -MatCommaInitializer_<_Tp>::MatCommaInitializer_(Mat_<_Tp>* _m) - : it(_m) -{} - -template template inline -MatCommaInitializer_<_Tp>& MatCommaInitializer_<_Tp>::operator , (T2 v) -{ - CV_DbgAssert( this->it < ((const Mat_<_Tp>*)this->it.m)->end() ); - *this->it = _Tp(v); - ++this->it; - return *this; -} - -template inline -MatCommaInitializer_<_Tp>::operator Mat_<_Tp>() const -{ - CV_DbgAssert( this->it == ((const Mat_<_Tp>*)this->it.m)->end() ); - return Mat_<_Tp>(*this->it.m); -} - - -template static inline -MatCommaInitializer_<_Tp> operator << (const Mat_<_Tp>& m, T2 val) -{ - MatCommaInitializer_<_Tp> commaInitializer((Mat_<_Tp>*)&m); - return (commaInitializer, val); -} - - - -///////////////////////// Matrix Expressions //////////////////////// - -inline -Mat& Mat::operator = (const MatExpr& e) -{ - e.op->assign(e, *this); - return *this; -} - -template inline -Mat_<_Tp>::Mat_(const MatExpr& e) -{ - e.op->assign(e, *this, traits::Type<_Tp>::value); -} - -template inline -Mat_<_Tp>& Mat_<_Tp>::operator = (const MatExpr& e) -{ - e.op->assign(e, *this, traits::Type<_Tp>::value); - return *this; -} - -template inline -MatExpr Mat_<_Tp>::zeros(int rows, int cols) -{ - return Mat::zeros(rows, cols, traits::Type<_Tp>::value); -} - -template inline -MatExpr Mat_<_Tp>::zeros(Size sz) -{ - return Mat::zeros(sz, traits::Type<_Tp>::value); -} - -template inline -MatExpr Mat_<_Tp>::ones(int rows, int cols) -{ - return Mat::ones(rows, cols, traits::Type<_Tp>::value); -} - -template inline -MatExpr Mat_<_Tp>::ones(Size sz) -{ - return Mat::ones(sz, traits::Type<_Tp>::value); -} - -template inline -MatExpr Mat_<_Tp>::eye(int rows, int cols) -{ - return Mat::eye(rows, cols, traits::Type<_Tp>::value); -} - -template inline -MatExpr Mat_<_Tp>::eye(Size sz) -{ - return Mat::eye(sz, traits::Type<_Tp>::value); -} - -inline -MatExpr::MatExpr() - : op(0), flags(0), a(Mat()), b(Mat()), c(Mat()), alpha(0), beta(0), s() -{} - -inline -MatExpr::MatExpr(const MatOp* _op, int _flags, const Mat& _a, const Mat& _b, - const Mat& _c, double _alpha, double _beta, const Scalar& _s) - : op(_op), flags(_flags), a(_a), b(_b), c(_c), alpha(_alpha), beta(_beta), s(_s) -{} - -inline -MatExpr::operator Mat() const -{ - Mat m; - op->assign(*this, m); - return m; -} - -template inline -MatExpr::operator Mat_<_Tp>() const -{ - Mat_<_Tp> m; - op->assign(*this, m, traits::Type<_Tp>::value); - return m; -} - - -template static inline -MatExpr min(const Mat_<_Tp>& a, const Mat_<_Tp>& b) -{ - return cv::min((const Mat&)a, (const Mat&)b); -} - -template static inline -MatExpr min(const Mat_<_Tp>& a, double s) -{ - return cv::min((const Mat&)a, s); -} - -template static inline -MatExpr min(double s, const Mat_<_Tp>& a) -{ - return cv::min((const Mat&)a, s); -} - -template static inline -MatExpr max(const Mat_<_Tp>& a, const Mat_<_Tp>& b) -{ - return cv::max((const Mat&)a, (const Mat&)b); -} - -template static inline -MatExpr max(const Mat_<_Tp>& a, double s) -{ - return cv::max((const Mat&)a, s); -} - -template static inline -MatExpr max(double s, const Mat_<_Tp>& a) -{ - return cv::max((const Mat&)a, s); -} - -template static inline -MatExpr abs(const Mat_<_Tp>& m) -{ - return cv::abs((const Mat&)m); -} - - -static inline -Mat& operator += (Mat& a, const MatExpr& b) -{ - b.op->augAssignAdd(b, a); - return a; -} - -static inline -const Mat& operator += (const Mat& a, const MatExpr& b) -{ - b.op->augAssignAdd(b, (Mat&)a); - return a; -} - -template static inline -Mat_<_Tp>& operator += (Mat_<_Tp>& a, const MatExpr& b) -{ - b.op->augAssignAdd(b, a); - return a; -} - -template static inline -const Mat_<_Tp>& operator += (const Mat_<_Tp>& a, const MatExpr& b) -{ - b.op->augAssignAdd(b, (Mat&)a); - return a; -} - -static inline -Mat& operator -= (Mat& a, const MatExpr& b) -{ - b.op->augAssignSubtract(b, a); - return a; -} - -static inline -const Mat& operator -= (const Mat& a, const MatExpr& b) -{ - b.op->augAssignSubtract(b, (Mat&)a); - return a; -} - -template static inline -Mat_<_Tp>& operator -= (Mat_<_Tp>& a, const MatExpr& b) -{ - b.op->augAssignSubtract(b, a); - return a; -} - -template static inline -const Mat_<_Tp>& operator -= (const Mat_<_Tp>& a, const MatExpr& b) -{ - b.op->augAssignSubtract(b, (Mat&)a); - return a; -} - -static inline -Mat& operator *= (Mat& a, const MatExpr& b) -{ - b.op->augAssignMultiply(b, a); - return a; -} - -static inline -const Mat& operator *= (const Mat& a, const MatExpr& b) -{ - b.op->augAssignMultiply(b, (Mat&)a); - return a; -} - -template static inline -Mat_<_Tp>& operator *= (Mat_<_Tp>& a, const MatExpr& b) -{ - b.op->augAssignMultiply(b, a); - return a; -} - -template static inline -const Mat_<_Tp>& operator *= (const Mat_<_Tp>& a, const MatExpr& b) -{ - b.op->augAssignMultiply(b, (Mat&)a); - return a; -} - -static inline -Mat& operator /= (Mat& a, const MatExpr& b) -{ - b.op->augAssignDivide(b, a); - return a; -} - -static inline -const Mat& operator /= (const Mat& a, const MatExpr& b) -{ - b.op->augAssignDivide(b, (Mat&)a); - return a; -} - -template static inline -Mat_<_Tp>& operator /= (Mat_<_Tp>& a, const MatExpr& b) -{ - b.op->augAssignDivide(b, a); - return a; -} - -template static inline -const Mat_<_Tp>& operator /= (const Mat_<_Tp>& a, const MatExpr& b) -{ - b.op->augAssignDivide(b, (Mat&)a); - return a; -} - - -//////////////////////////////// UMat //////////////////////////////// - -template inline -UMat::UMat(const std::vector<_Tp>& vec, bool copyData) -: flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows((int)vec.size()), -cols(1), allocator(0), usageFlags(USAGE_DEFAULT), u(0), offset(0), size(&rows) -{ - if(vec.empty()) - return; - if( !copyData ) - { - // !!!TODO!!! - CV_Error(Error::StsNotImplemented, ""); - } - else - Mat((int)vec.size(), 1, traits::Type<_Tp>::value, (uchar*)&vec[0]).copyTo(*this); -} - -inline -UMat UMat::row(int y) const -{ - return UMat(*this, Range(y, y + 1), Range::all()); -} - -inline -UMat UMat::col(int x) const -{ - return UMat(*this, Range::all(), Range(x, x + 1)); -} - -inline -UMat UMat::rowRange(int startrow, int endrow) const -{ - return UMat(*this, Range(startrow, endrow), Range::all()); -} - -inline -UMat UMat::rowRange(const Range& r) const -{ - return UMat(*this, r, Range::all()); -} - -inline -UMat UMat::colRange(int startcol, int endcol) const -{ - return UMat(*this, Range::all(), Range(startcol, endcol)); -} - -inline -UMat UMat::colRange(const Range& r) const -{ - return UMat(*this, Range::all(), r); -} - -inline -UMat UMat::operator()( Range _rowRange, Range _colRange ) const -{ - return UMat(*this, _rowRange, _colRange); -} - -inline -UMat UMat::operator()( const Rect& roi ) const -{ - return UMat(*this, roi); -} - -inline -UMat UMat::operator()(const Range* ranges) const -{ - return UMat(*this, ranges); -} - -inline -UMat UMat::operator()(const std::vector& ranges) const -{ - return UMat(*this, ranges); -} - -inline -bool UMat::isContinuous() const -{ - return (flags & CONTINUOUS_FLAG) != 0; -} - -inline -bool UMat::isSubmatrix() const -{ - return (flags & SUBMATRIX_FLAG) != 0; -} - -inline -size_t UMat::elemSize() const -{ - size_t res = dims > 0 ? step.p[dims - 1] : 0; - CV_DbgAssert(res != 0); - return res; -} - -inline -size_t UMat::elemSize1() const -{ - return CV_ELEM_SIZE1(flags); -} - -inline -int UMat::type() const -{ - return CV_MAT_TYPE(flags); -} - -inline -int UMat::depth() const -{ - return CV_MAT_DEPTH(flags); -} - -inline -int UMat::channels() const -{ - return CV_MAT_CN(flags); -} - -inline -size_t UMat::step1(int i) const -{ - return step.p[i] / elemSize1(); -} - -#ifdef CV_CXX_MOVE_SEMANTICS - -inline -UMat::UMat(UMat&& m) -: flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), allocator(m.allocator), - usageFlags(m.usageFlags), u(m.u), offset(m.offset), size(&rows) -{ - if (m.dims <= 2) // move new step/size info - { - step[0] = m.step[0]; - step[1] = m.step[1]; - } - else - { - CV_DbgAssert(m.step.p != m.step.buf); - step.p = m.step.p; - size.p = m.size.p; - m.step.p = m.step.buf; - m.size.p = &m.rows; - } - m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; - m.allocator = NULL; - m.u = NULL; - m.offset = 0; -} - -inline -UMat& UMat::operator = (UMat&& m) -{ - if (this == &m) - return *this; - release(); - flags = m.flags; dims = m.dims; rows = m.rows; cols = m.cols; - allocator = m.allocator; usageFlags = m.usageFlags; - u = m.u; - offset = m.offset; - if (step.p != step.buf) // release self step/size - { - fastFree(step.p); - step.p = step.buf; - size.p = &rows; - } - if (m.dims <= 2) // move new step/size info - { - step[0] = m.step[0]; - step[1] = m.step[1]; - } - else - { - CV_DbgAssert(m.step.p != m.step.buf); - step.p = m.step.p; - size.p = m.size.p; - m.step.p = m.step.buf; - m.size.p = &m.rows; - } - m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0; - m.allocator = NULL; - m.u = NULL; - m.offset = 0; - return *this; -} - -#endif - - -inline bool UMatData::hostCopyObsolete() const { return (flags & HOST_COPY_OBSOLETE) != 0; } -inline bool UMatData::deviceCopyObsolete() const { return (flags & DEVICE_COPY_OBSOLETE) != 0; } -inline bool UMatData::deviceMemMapped() const { return (flags & DEVICE_MEM_MAPPED) != 0; } -inline bool UMatData::copyOnMap() const { return (flags & COPY_ON_MAP) != 0; } -inline bool UMatData::tempUMat() const { return (flags & TEMP_UMAT) != 0; } -inline bool UMatData::tempCopiedUMat() const { return (flags & TEMP_COPIED_UMAT) == TEMP_COPIED_UMAT; } - -inline void UMatData::markDeviceMemMapped(bool flag) -{ - if(flag) - flags |= DEVICE_MEM_MAPPED; - else - flags &= ~DEVICE_MEM_MAPPED; -} - -inline void UMatData::markHostCopyObsolete(bool flag) -{ - if(flag) - flags |= HOST_COPY_OBSOLETE; - else - flags &= ~HOST_COPY_OBSOLETE; -} -inline void UMatData::markDeviceCopyObsolete(bool flag) -{ - if(flag) - flags |= DEVICE_COPY_OBSOLETE; - else - flags &= ~DEVICE_COPY_OBSOLETE; -} - -//! @endcond - -static inline -void swap(MatExpr& a, MatExpr& b) { a.swap(b); } - -} //cv - -#ifdef _MSC_VER -#pragma warning( pop ) -#endif - -#ifdef CV_DISABLE_CLANG_ENUM_WARNINGS -#undef CV_DISABLE_CLANG_ENUM_WARNINGS -#pragma clang diagnostic pop -#endif - -#endif diff --git a/include/opencv2/core/matx.hpp b/include/opencv2/core/matx.hpp deleted file mode 100755 index 733f675..0000000 --- a/include/opencv2/core/matx.hpp +++ /dev/null @@ -1,1518 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_MATX_HPP -#define OPENCV_CORE_MATX_HPP - -#ifndef __cplusplus -# error matx.hpp header must be compiled as C++ -#endif - -#include "opencv2/core/cvdef.h" -#include "opencv2/core/base.hpp" -#include "opencv2/core/traits.hpp" -#include "opencv2/core/saturate.hpp" - -#ifdef CV_CXX11 -#include -#endif - -namespace cv -{ - -//! @addtogroup core_basic -//! @{ - -////////////////////////////// Small Matrix /////////////////////////// - -//! @cond IGNORED -// FIXIT Remove this (especially CV_EXPORTS modifier) -struct CV_EXPORTS Matx_AddOp { Matx_AddOp() {} Matx_AddOp(const Matx_AddOp&) {} }; -struct CV_EXPORTS Matx_SubOp { Matx_SubOp() {} Matx_SubOp(const Matx_SubOp&) {} }; -struct CV_EXPORTS Matx_ScaleOp { Matx_ScaleOp() {} Matx_ScaleOp(const Matx_ScaleOp&) {} }; -struct CV_EXPORTS Matx_MulOp { Matx_MulOp() {} Matx_MulOp(const Matx_MulOp&) {} }; -struct CV_EXPORTS Matx_DivOp { Matx_DivOp() {} Matx_DivOp(const Matx_DivOp&) {} }; -struct CV_EXPORTS Matx_MatMulOp { Matx_MatMulOp() {} Matx_MatMulOp(const Matx_MatMulOp&) {} }; -struct CV_EXPORTS Matx_TOp { Matx_TOp() {} Matx_TOp(const Matx_TOp&) {} }; -//! @endcond - -/** @brief Template class for small matrices whose type and size are known at compilation time - -If you need a more flexible type, use Mat . The elements of the matrix M are accessible using the -M(i,j) notation. Most of the common matrix operations (see also @ref MatrixExpressions ) are -available. To do an operation on Matx that is not implemented, you can easily convert the matrix to -Mat and backwards: -@code{.cpp} - Matx33f m(1, 2, 3, - 4, 5, 6, - 7, 8, 9); - cout << sum(Mat(m*m.t())) << endl; -@endcode -Except of the plain constructor which takes a list of elements, Matx can be initialized from a C-array: -@code{.cpp} - float values[] = { 1, 2, 3}; - Matx31f m(values); -@endcode -In case if C++11 features are available, std::initializer_list can be also used to initialize Matx: -@code{.cpp} - Matx31f m = { 1, 2, 3}; -@endcode - */ -template class Matx -{ -public: - enum { - rows = m, - cols = n, - channels = rows*cols, -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED - depth = traits::Type<_Tp>::value, - type = CV_MAKETYPE(depth, channels), -#endif - shortdim = (m < n ? m : n) - }; - - typedef _Tp value_type; - typedef Matx<_Tp, m, n> mat_type; - typedef Matx<_Tp, shortdim, 1> diag_type; - - //! default constructor - Matx(); - - explicit Matx(_Tp v0); //!< 1x1 matrix - Matx(_Tp v0, _Tp v1); //!< 1x2 or 2x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2); //!< 1x3 or 3x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 1x4, 2x2 or 4x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 1x5 or 5x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 1x6, 2x3, 3x2 or 6x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 1x7 or 7x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 1x8, 2x4, 4x2 or 8x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 1x9, 3x3 or 9x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 1x10, 2x5 or 5x2 or 10x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, - _Tp v4, _Tp v5, _Tp v6, _Tp v7, - _Tp v8, _Tp v9, _Tp v10, _Tp v11); //!< 1x12, 2x6, 3x4, 4x3, 6x2 or 12x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, - _Tp v4, _Tp v5, _Tp v6, _Tp v7, - _Tp v8, _Tp v9, _Tp v10, _Tp v11, - _Tp v12, _Tp v13); //!< 1x14, 2x7, 7x2 or 14x1 matrix - Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, - _Tp v4, _Tp v5, _Tp v6, _Tp v7, - _Tp v8, _Tp v9, _Tp v10, _Tp v11, - _Tp v12, _Tp v13, _Tp v14, _Tp v15); //!< 1x16, 4x4 or 16x1 matrix - explicit Matx(const _Tp* vals); //!< initialize from a plain array - -#ifdef CV_CXX11 - Matx(std::initializer_list<_Tp>); //!< initialize from an initializer list -#endif - - static Matx all(_Tp alpha); - static Matx zeros(); - static Matx ones(); - static Matx eye(); - static Matx diag(const diag_type& d); - /** @brief Generates uniformly distributed random numbers - @param a Range boundary. - @param b The other range boundary (boundaries don't have to be ordered, the lower boundary is inclusive, - the upper one is exclusive). - */ - static Matx randu(_Tp a, _Tp b); - /** @brief Generates normally distributed random numbers - @param a Mean value. - @param b Standard deviation. - */ - static Matx randn(_Tp a, _Tp b); - - //! dot product computed with the default precision - _Tp dot(const Matx<_Tp, m, n>& v) const; - - //! dot product computed in double-precision arithmetics - double ddot(const Matx<_Tp, m, n>& v) const; - - //! conversion to another data type - template operator Matx() const; - - //! change the matrix shape - template Matx<_Tp, m1, n1> reshape() const; - - //! extract part of the matrix - template Matx<_Tp, m1, n1> get_minor(int base_row, int base_col) const; - - //! extract the matrix row - Matx<_Tp, 1, n> row(int i) const; - - //! extract the matrix column - Matx<_Tp, m, 1> col(int i) const; - - //! extract the matrix diagonal - diag_type diag() const; - - //! transpose the matrix - Matx<_Tp, n, m> t() const; - - //! invert the matrix - Matx<_Tp, n, m> inv(int method=DECOMP_LU, bool *p_is_ok = NULL) const; - - //! solve linear system - template Matx<_Tp, n, l> solve(const Matx<_Tp, m, l>& rhs, int flags=DECOMP_LU) const; - Vec<_Tp, n> solve(const Vec<_Tp, m>& rhs, int method) const; - - //! multiply two matrices element-wise - Matx<_Tp, m, n> mul(const Matx<_Tp, m, n>& a) const; - - //! divide two matrices element-wise - Matx<_Tp, m, n> div(const Matx<_Tp, m, n>& a) const; - - //! element access - const _Tp& operator ()(int row, int col) const; - _Tp& operator ()(int row, int col); - - //! 1D element access - const _Tp& operator ()(int i) const; - _Tp& operator ()(int i); - - Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp); - Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp); - template Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp); - Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp); - Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp); - template Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp); - Matx(const Matx<_Tp, n, m>& a, Matx_TOp); - - _Tp val[m*n]; //< matrix elements -}; - -typedef Matx Matx12f; -typedef Matx Matx12d; -typedef Matx Matx13f; -typedef Matx Matx13d; -typedef Matx Matx14f; -typedef Matx Matx14d; -typedef Matx Matx16f; -typedef Matx Matx16d; - -typedef Matx Matx21f; -typedef Matx Matx21d; -typedef Matx Matx31f; -typedef Matx Matx31d; -typedef Matx Matx41f; -typedef Matx Matx41d; -typedef Matx Matx61f; -typedef Matx Matx61d; - -typedef Matx Matx22f; -typedef Matx Matx22d; -typedef Matx Matx23f; -typedef Matx Matx23d; -typedef Matx Matx32f; -typedef Matx Matx32d; - -typedef Matx Matx33f; -typedef Matx Matx33d; - -typedef Matx Matx34f; -typedef Matx Matx34d; -typedef Matx Matx43f; -typedef Matx Matx43d; - -typedef Matx Matx44f; -typedef Matx Matx44d; -typedef Matx Matx66f; -typedef Matx Matx66d; - -/*! - traits -*/ -template class DataType< Matx<_Tp, m, n> > -{ -public: - typedef Matx<_Tp, m, n> value_type; - typedef Matx::work_type, m, n> work_type; - typedef _Tp channel_type; - typedef value_type vec_type; - - enum { generic_type = 0, - channels = m * n, - fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED - ,depth = DataType::depth - ,type = CV_MAKETYPE(depth, channels) -#endif - }; -}; - -namespace traits { -template -struct Depth< Matx<_Tp, m, n> > { enum { value = Depth<_Tp>::value }; }; -template -struct Type< Matx<_Tp, m, n> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, n*m) }; }; -} // namespace - - -/** @brief Comma-separated Matrix Initializer -*/ -template class MatxCommaInitializer -{ -public: - MatxCommaInitializer(Matx<_Tp, m, n>* _mtx); - template MatxCommaInitializer<_Tp, m, n>& operator , (T2 val); - Matx<_Tp, m, n> operator *() const; - - Matx<_Tp, m, n>* dst; - int idx; -}; - -/* - Utility methods -*/ -template static double determinant(const Matx<_Tp, m, m>& a); -template static double trace(const Matx<_Tp, m, n>& a); -template static double norm(const Matx<_Tp, m, n>& M); -template static double norm(const Matx<_Tp, m, n>& M, int normType); - - - -/////////////////////// Vec (used as element of multi-channel images ///////////////////// - -/** @brief Template class for short numerical vectors, a partial case of Matx - -This template class represents short numerical vectors (of 1, 2, 3, 4 ... elements) on which you -can perform basic arithmetical operations, access individual elements using [] operator etc. The -vectors are allocated on stack, as opposite to std::valarray, std::vector, cv::Mat etc., which -elements are dynamically allocated in the heap. - -The template takes 2 parameters: -@tparam _Tp element type -@tparam cn the number of elements - -In addition to the universal notation like Vec, you can use shorter aliases -for the most popular specialized variants of Vec, e.g. Vec3f ~ Vec. - -It is possible to convert Vec\ to/from Point_, Vec\ to/from Point3_ , and Vec\ -to CvScalar or Scalar_. Use operator[] to access the elements of Vec. - -All the expected vector operations are also implemented: -- v1 = v2 + v3 -- v1 = v2 - v3 -- v1 = v2 \* scale -- v1 = scale \* v2 -- v1 = -v2 -- v1 += v2 and other augmenting operations -- v1 == v2, v1 != v2 -- norm(v1) (euclidean norm) -The Vec class is commonly used to describe pixel types of multi-channel arrays. See Mat for details. -*/ -template class Vec : public Matx<_Tp, cn, 1> -{ -public: - typedef _Tp value_type; - enum { - channels = cn, -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED - depth = Matx<_Tp, cn, 1>::depth, - type = CV_MAKETYPE(depth, channels), -#endif - _dummy_enum_finalizer = 0 - }; - - //! default constructor - Vec(); - - Vec(_Tp v0); //!< 1-element vector constructor - Vec(_Tp v0, _Tp v1); //!< 2-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2); //!< 3-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 4-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 5-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 6-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 7-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 8-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 9-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 10-element vector constructor - Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13); //!< 14-element vector constructor - explicit Vec(const _Tp* values); - -#ifdef CV_CXX11 - Vec(std::initializer_list<_Tp>); -#endif - - Vec(const Vec<_Tp, cn>& v); - - static Vec all(_Tp alpha); - - //! per-element multiplication - Vec mul(const Vec<_Tp, cn>& v) const; - - //! conjugation (makes sense for complex numbers and quaternions) - Vec conj() const; - - /*! - cross product of the two 3D vectors. - - For other dimensionalities the exception is raised - */ - Vec cross(const Vec& v) const; - //! conversion to another data type - template operator Vec() const; - - /*! element access */ - const _Tp& operator [](int i) const; - _Tp& operator[](int i); - const _Tp& operator ()(int i) const; - _Tp& operator ()(int i); - -#ifdef CV_CXX11 - Vec<_Tp, cn>& operator=(const Vec<_Tp, cn>& rhs) = default; -#endif - - Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp); - Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp); - template Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp); -}; - -/** @name Shorter aliases for the most popular specializations of Vec - @{ -*/ -typedef Vec Vec2b; -typedef Vec Vec3b; -typedef Vec Vec4b; - -typedef Vec Vec2s; -typedef Vec Vec3s; -typedef Vec Vec4s; - -typedef Vec Vec2w; -typedef Vec Vec3w; -typedef Vec Vec4w; - -typedef Vec Vec2i; -typedef Vec Vec3i; -typedef Vec Vec4i; -typedef Vec Vec6i; -typedef Vec Vec8i; - -typedef Vec Vec2f; -typedef Vec Vec3f; -typedef Vec Vec4f; -typedef Vec Vec6f; - -typedef Vec Vec2d; -typedef Vec Vec3d; -typedef Vec Vec4d; -typedef Vec Vec6d; -/** @} */ - -/*! - traits -*/ -template class DataType< Vec<_Tp, cn> > -{ -public: - typedef Vec<_Tp, cn> value_type; - typedef Vec::work_type, cn> work_type; - typedef _Tp channel_type; - typedef value_type vec_type; - - enum { generic_type = 0, - channels = cn, - fmt = DataType::fmt + ((channels - 1) << 8), -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED - depth = DataType::depth, - type = CV_MAKETYPE(depth, channels), -#endif - _dummy_enum_finalizer = 0 - }; -}; - -namespace traits { -template -struct Depth< Vec<_Tp, cn> > { enum { value = Depth<_Tp>::value }; }; -template -struct Type< Vec<_Tp, cn> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, cn) }; }; -} // namespace - - -/** @brief Comma-separated Vec Initializer -*/ -template class VecCommaInitializer : public MatxCommaInitializer<_Tp, m, 1> -{ -public: - VecCommaInitializer(Vec<_Tp, m>* _vec); - template VecCommaInitializer<_Tp, m>& operator , (T2 val); - Vec<_Tp, m> operator *() const; -}; - -template static Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v); - -//! @} core_basic - -//! @cond IGNORED - -///////////////////////////////////// helper classes ///////////////////////////////////// -namespace internal -{ - -template struct Matx_DetOp -{ - double operator ()(const Matx<_Tp, m, m>& a) const - { - Matx<_Tp, m, m> temp = a; - double p = LU(temp.val, m*sizeof(_Tp), m, 0, 0, 0); - if( p == 0 ) - return p; - for( int i = 0; i < m; i++ ) - p *= temp(i, i); - return p; - } -}; - -template struct Matx_DetOp<_Tp, 1> -{ - double operator ()(const Matx<_Tp, 1, 1>& a) const - { - return a(0,0); - } -}; - -template struct Matx_DetOp<_Tp, 2> -{ - double operator ()(const Matx<_Tp, 2, 2>& a) const - { - return a(0,0)*a(1,1) - a(0,1)*a(1,0); - } -}; - -template struct Matx_DetOp<_Tp, 3> -{ - double operator ()(const Matx<_Tp, 3, 3>& a) const - { - return a(0,0)*(a(1,1)*a(2,2) - a(2,1)*a(1,2)) - - a(0,1)*(a(1,0)*a(2,2) - a(2,0)*a(1,2)) + - a(0,2)*(a(1,0)*a(2,1) - a(2,0)*a(1,1)); - } -}; - -template Vec<_Tp, 2> inline conjugate(const Vec<_Tp, 2>& v) -{ - return Vec<_Tp, 2>(v[0], -v[1]); -} - -template Vec<_Tp, 4> inline conjugate(const Vec<_Tp, 4>& v) -{ - return Vec<_Tp, 4>(v[0], -v[1], -v[2], -v[3]); -} - -} // internal - - - -////////////////////////////////// Matx Implementation /////////////////////////////////// - -template inline -Matx<_Tp, m, n>::Matx() -{ - for(int i = 0; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0) -{ - val[0] = v0; - for(int i = 1; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1) -{ - CV_StaticAssert(channels >= 2, "Matx should have at least 2 elements."); - val[0] = v0; val[1] = v1; - for(int i = 2; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2) -{ - CV_StaticAssert(channels >= 3, "Matx should have at least 3 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; - for(int i = 3; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3) -{ - CV_StaticAssert(channels >= 4, "Matx should have at least 4 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - for(int i = 4; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4) -{ - CV_StaticAssert(channels >= 5, "Matx should have at least 5 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; val[4] = v4; - for(int i = 5; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5) -{ - CV_StaticAssert(channels >= 6, "Matx should have at least 6 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - val[4] = v4; val[5] = v5; - for(int i = 6; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6) -{ - CV_StaticAssert(channels >= 7, "Matx should have at least 7 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - val[4] = v4; val[5] = v5; val[6] = v6; - for(int i = 7; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7) -{ - CV_StaticAssert(channels >= 8, "Matx should have at least 8 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; - for(int i = 8; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8) -{ - CV_StaticAssert(channels >= 9, "Matx should have at least 9 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; - val[8] = v8; - for(int i = 9; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9) -{ - CV_StaticAssert(channels >= 10, "Matx should have at least 10 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; - val[8] = v8; val[9] = v9; - for(int i = 10; i < channels; i++) val[i] = _Tp(0); -} - - -template inline -Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11) -{ - CV_StaticAssert(channels >= 12, "Matx should have at least 12 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; - val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; - for(int i = 12; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13) -{ - CV_StaticAssert(channels >= 14, "Matx should have at least 14 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; - val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; - val[12] = v12; val[13] = v13; - for (int i = 14; i < channels; i++) val[i] = _Tp(0); -} - - -template inline -Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13, _Tp v14, _Tp v15) -{ - CV_StaticAssert(channels >= 16, "Matx should have at least 16 elements."); - val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; - val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7; - val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11; - val[12] = v12; val[13] = v13; val[14] = v14; val[15] = v15; - for(int i = 16; i < channels; i++) val[i] = _Tp(0); -} - -template inline -Matx<_Tp, m, n>::Matx(const _Tp* values) -{ - for( int i = 0; i < channels; i++ ) val[i] = values[i]; -} - -#ifdef CV_CXX11 -template inline -Matx<_Tp, m, n>::Matx(std::initializer_list<_Tp> list) -{ - CV_DbgAssert(list.size() == channels); - int i = 0; - for(const auto& elem : list) - { - val[i++] = elem; - } -} -#endif - -template inline -Matx<_Tp, m, n> Matx<_Tp, m, n>::all(_Tp alpha) -{ - Matx<_Tp, m, n> M; - for( int i = 0; i < m*n; i++ ) M.val[i] = alpha; - return M; -} - -template inline -Matx<_Tp,m,n> Matx<_Tp,m,n>::zeros() -{ - return all(0); -} - -template inline -Matx<_Tp,m,n> Matx<_Tp,m,n>::ones() -{ - return all(1); -} - -template inline -Matx<_Tp,m,n> Matx<_Tp,m,n>::eye() -{ - Matx<_Tp,m,n> M; - for(int i = 0; i < shortdim; i++) - M(i,i) = 1; - return M; -} - -template inline -_Tp Matx<_Tp, m, n>::dot(const Matx<_Tp, m, n>& M) const -{ - _Tp s = 0; - for( int i = 0; i < channels; i++ ) s += val[i]*M.val[i]; - return s; -} - -template inline -double Matx<_Tp, m, n>::ddot(const Matx<_Tp, m, n>& M) const -{ - double s = 0; - for( int i = 0; i < channels; i++ ) s += (double)val[i]*M.val[i]; - return s; -} - -template inline -Matx<_Tp,m,n> Matx<_Tp,m,n>::diag(const typename Matx<_Tp,m,n>::diag_type& d) -{ - Matx<_Tp,m,n> M; - for(int i = 0; i < shortdim; i++) - M(i,i) = d(i, 0); - return M; -} - -template template -inline Matx<_Tp, m, n>::operator Matx() const -{ - Matx M; - for( int i = 0; i < m*n; i++ ) M.val[i] = saturate_cast(val[i]); - return M; -} - -template template inline -Matx<_Tp, m1, n1> Matx<_Tp, m, n>::reshape() const -{ - CV_StaticAssert(m1*n1 == m*n, "Input and destnarion matrices must have the same number of elements"); - return (const Matx<_Tp, m1, n1>&)*this; -} - -template -template inline -Matx<_Tp, m1, n1> Matx<_Tp, m, n>::get_minor(int base_row, int base_col) const -{ - CV_DbgAssert(0 <= base_row && base_row+m1 <= m && 0 <= base_col && base_col+n1 <= n); - Matx<_Tp, m1, n1> s; - for( int di = 0; di < m1; di++ ) - for( int dj = 0; dj < n1; dj++ ) - s(di, dj) = (*this)(base_row+di, base_col+dj); - return s; -} - -template inline -Matx<_Tp, 1, n> Matx<_Tp, m, n>::row(int i) const -{ - CV_DbgAssert((unsigned)i < (unsigned)m); - return Matx<_Tp, 1, n>(&val[i*n]); -} - -template inline -Matx<_Tp, m, 1> Matx<_Tp, m, n>::col(int j) const -{ - CV_DbgAssert((unsigned)j < (unsigned)n); - Matx<_Tp, m, 1> v; - for( int i = 0; i < m; i++ ) - v.val[i] = val[i*n + j]; - return v; -} - -template inline -typename Matx<_Tp, m, n>::diag_type Matx<_Tp, m, n>::diag() const -{ - diag_type d; - for( int i = 0; i < shortdim; i++ ) - d.val[i] = val[i*n + i]; - return d; -} - -template inline -const _Tp& Matx<_Tp, m, n>::operator()(int row_idx, int col_idx) const -{ - CV_DbgAssert( (unsigned)row_idx < (unsigned)m && (unsigned)col_idx < (unsigned)n ); - return this->val[row_idx*n + col_idx]; -} - -template inline -_Tp& Matx<_Tp, m, n>::operator ()(int row_idx, int col_idx) -{ - CV_DbgAssert( (unsigned)row_idx < (unsigned)m && (unsigned)col_idx < (unsigned)n ); - return val[row_idx*n + col_idx]; -} - -template inline -const _Tp& Matx<_Tp, m, n>::operator ()(int i) const -{ - CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row"); - CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) ); - return val[i]; -} - -template inline -_Tp& Matx<_Tp, m, n>::operator ()(int i) -{ - CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row"); - CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) ); - return val[i]; -} - -template inline -Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp) -{ - for( int i = 0; i < channels; i++ ) - val[i] = saturate_cast<_Tp>(a.val[i] + b.val[i]); -} - -template inline -Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp) -{ - for( int i = 0; i < channels; i++ ) - val[i] = saturate_cast<_Tp>(a.val[i] - b.val[i]); -} - -template template inline -Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp) -{ - for( int i = 0; i < channels; i++ ) - val[i] = saturate_cast<_Tp>(a.val[i] * alpha); -} - -template inline -Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp) -{ - for( int i = 0; i < channels; i++ ) - val[i] = saturate_cast<_Tp>(a.val[i] * b.val[i]); -} - -template inline -Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp) -{ - for( int i = 0; i < channels; i++ ) - val[i] = saturate_cast<_Tp>(a.val[i] / b.val[i]); -} - -template template inline -Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp) -{ - for( int i = 0; i < m; i++ ) - for( int j = 0; j < n; j++ ) - { - _Tp s = 0; - for( int k = 0; k < l; k++ ) - s += a(i, k) * b(k, j); - val[i*n + j] = s; - } -} - -template inline -Matx<_Tp,m,n>::Matx(const Matx<_Tp, n, m>& a, Matx_TOp) -{ - for( int i = 0; i < m; i++ ) - for( int j = 0; j < n; j++ ) - val[i*n + j] = a(j, i); -} - -template inline -Matx<_Tp, m, n> Matx<_Tp, m, n>::mul(const Matx<_Tp, m, n>& a) const -{ - return Matx<_Tp, m, n>(*this, a, Matx_MulOp()); -} - -template inline -Matx<_Tp, m, n> Matx<_Tp, m, n>::div(const Matx<_Tp, m, n>& a) const -{ - return Matx<_Tp, m, n>(*this, a, Matx_DivOp()); -} - -template inline -Matx<_Tp, n, m> Matx<_Tp, m, n>::t() const -{ - return Matx<_Tp, n, m>(*this, Matx_TOp()); -} - -template inline -Vec<_Tp, n> Matx<_Tp, m, n>::solve(const Vec<_Tp, m>& rhs, int method) const -{ - Matx<_Tp, n, 1> x = solve((const Matx<_Tp, m, 1>&)(rhs), method); - return (Vec<_Tp, n>&)(x); -} - -template static inline -double determinant(const Matx<_Tp, m, m>& a) -{ - return cv::internal::Matx_DetOp<_Tp, m>()(a); -} - -template static inline -double trace(const Matx<_Tp, m, n>& a) -{ - _Tp s = 0; - for( int i = 0; i < std::min(m, n); i++ ) - s += a(i,i); - return s; -} - -template static inline -double norm(const Matx<_Tp, m, n>& M) -{ - return std::sqrt(normL2Sqr<_Tp, double>(M.val, m*n)); -} - -template static inline -double norm(const Matx<_Tp, m, n>& M, int normType) -{ - switch(normType) { - case NORM_INF: - return (double)normInf<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); - case NORM_L1: - return (double)normL1<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); - case NORM_L2SQR: - return (double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n); - default: - case NORM_L2: - return std::sqrt((double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n)); - } -} - - - -//////////////////////////////// matx comma initializer ////////////////////////////////// - -template static inline -MatxCommaInitializer<_Tp, m, n> operator << (const Matx<_Tp, m, n>& mtx, _T2 val) -{ - MatxCommaInitializer<_Tp, m, n> commaInitializer((Matx<_Tp, m, n>*)&mtx); - return (commaInitializer, val); -} - -template inline -MatxCommaInitializer<_Tp, m, n>::MatxCommaInitializer(Matx<_Tp, m, n>* _mtx) - : dst(_mtx), idx(0) -{} - -template template inline -MatxCommaInitializer<_Tp, m, n>& MatxCommaInitializer<_Tp, m, n>::operator , (_T2 value) -{ - CV_DbgAssert( idx < m*n ); - dst->val[idx++] = saturate_cast<_Tp>(value); - return *this; -} - -template inline -Matx<_Tp, m, n> MatxCommaInitializer<_Tp, m, n>::operator *() const -{ - CV_DbgAssert( idx == n*m ); - return *dst; -} - - - -/////////////////////////////////// Vec Implementation /////////////////////////////////// - -template inline -Vec<_Tp, cn>::Vec() {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0) - : Matx<_Tp, cn, 1>(v0) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1) - : Matx<_Tp, cn, 1>(v0, v1) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2) - : Matx<_Tp, cn, 1>(v0, v1, v2) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3) - : Matx<_Tp, cn, 1>(v0, v1, v2, v3) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4) - : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5) - : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6) - : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7) - : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8) - : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9) - : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9) {} - -template inline -Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13) - : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13) {} - -template inline -Vec<_Tp, cn>::Vec(const _Tp* values) - : Matx<_Tp, cn, 1>(values) {} - -#ifdef CV_CXX11 -template inline -Vec<_Tp, cn>::Vec(std::initializer_list<_Tp> list) - : Matx<_Tp, cn, 1>(list) {} -#endif - -template inline -Vec<_Tp, cn>::Vec(const Vec<_Tp, cn>& m) - : Matx<_Tp, cn, 1>(m.val) {} - -template inline -Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp op) - : Matx<_Tp, cn, 1>(a, b, op) {} - -template inline -Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp op) - : Matx<_Tp, cn, 1>(a, b, op) {} - -template template inline -Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp op) - : Matx<_Tp, cn, 1>(a, alpha, op) {} - -template inline -Vec<_Tp, cn> Vec<_Tp, cn>::all(_Tp alpha) -{ - Vec v; - for( int i = 0; i < cn; i++ ) v.val[i] = alpha; - return v; -} - -template inline -Vec<_Tp, cn> Vec<_Tp, cn>::mul(const Vec<_Tp, cn>& v) const -{ - Vec<_Tp, cn> w; - for( int i = 0; i < cn; i++ ) w.val[i] = saturate_cast<_Tp>(this->val[i]*v.val[i]); - return w; -} - -template<> inline -Vec Vec::conj() const -{ - return cv::internal::conjugate(*this); -} - -template<> inline -Vec Vec::conj() const -{ - return cv::internal::conjugate(*this); -} - -template<> inline -Vec Vec::conj() const -{ - return cv::internal::conjugate(*this); -} - -template<> inline -Vec Vec::conj() const -{ - return cv::internal::conjugate(*this); -} - -template inline -Vec<_Tp, cn> Vec<_Tp, cn>::cross(const Vec<_Tp, cn>&) const -{ - CV_StaticAssert(cn == 3, "for arbitrary-size vector there is no cross-product defined"); - return Vec<_Tp, cn>(); -} - -template<> inline -Vec Vec::cross(const Vec& v) const -{ - return Vec(this->val[1]*v.val[2] - this->val[2]*v.val[1], - this->val[2]*v.val[0] - this->val[0]*v.val[2], - this->val[0]*v.val[1] - this->val[1]*v.val[0]); -} - -template<> inline -Vec Vec::cross(const Vec& v) const -{ - return Vec(this->val[1]*v.val[2] - this->val[2]*v.val[1], - this->val[2]*v.val[0] - this->val[0]*v.val[2], - this->val[0]*v.val[1] - this->val[1]*v.val[0]); -} - -template template inline -Vec<_Tp, cn>::operator Vec() const -{ - Vec v; - for( int i = 0; i < cn; i++ ) v.val[i] = saturate_cast(this->val[i]); - return v; -} - -template inline -const _Tp& Vec<_Tp, cn>::operator [](int i) const -{ - CV_DbgAssert( (unsigned)i < (unsigned)cn ); - return this->val[i]; -} - -template inline -_Tp& Vec<_Tp, cn>::operator [](int i) -{ - CV_DbgAssert( (unsigned)i < (unsigned)cn ); - return this->val[i]; -} - -template inline -const _Tp& Vec<_Tp, cn>::operator ()(int i) const -{ - CV_DbgAssert( (unsigned)i < (unsigned)cn ); - return this->val[i]; -} - -template inline -_Tp& Vec<_Tp, cn>::operator ()(int i) -{ - CV_DbgAssert( (unsigned)i < (unsigned)cn ); - return this->val[i]; -} - -template inline -Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v) -{ - double nv = norm(v); - return v * (nv ? 1./nv : 0.); -} - - - -//////////////////////////////// vec comma initializer ////////////////////////////////// - - -template static inline -VecCommaInitializer<_Tp, cn> operator << (const Vec<_Tp, cn>& vec, _T2 val) -{ - VecCommaInitializer<_Tp, cn> commaInitializer((Vec<_Tp, cn>*)&vec); - return (commaInitializer, val); -} - -template inline -VecCommaInitializer<_Tp, cn>::VecCommaInitializer(Vec<_Tp, cn>* _vec) - : MatxCommaInitializer<_Tp, cn, 1>(_vec) -{} - -template template inline -VecCommaInitializer<_Tp, cn>& VecCommaInitializer<_Tp, cn>::operator , (_T2 value) -{ - CV_DbgAssert( this->idx < cn ); - this->dst->val[this->idx++] = saturate_cast<_Tp>(value); - return *this; -} - -template inline -Vec<_Tp, cn> VecCommaInitializer<_Tp, cn>::operator *() const -{ - CV_DbgAssert( this->idx == cn ); - return *this->dst; -} - -//! @endcond - -///////////////////////////// Matx out-of-class operators //////////////////////////////// - -//! @relates cv::Matx -//! @{ - -template static inline -Matx<_Tp1, m, n>& operator += (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b) -{ - for( int i = 0; i < m*n; i++ ) - a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]); - return a; -} - -template static inline -Matx<_Tp1, m, n>& operator -= (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b) -{ - for( int i = 0; i < m*n; i++ ) - a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]); - return a; -} - -template static inline -Matx<_Tp, m, n> operator + (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) -{ - return Matx<_Tp, m, n>(a, b, Matx_AddOp()); -} - -template static inline -Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) -{ - return Matx<_Tp, m, n>(a, b, Matx_SubOp()); -} - -template static inline -Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, int alpha) -{ - for( int i = 0; i < m*n; i++ ) - a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); - return a; -} - -template static inline -Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, float alpha) -{ - for( int i = 0; i < m*n; i++ ) - a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); - return a; -} - -template static inline -Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, double alpha) -{ - for( int i = 0; i < m*n; i++ ) - a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha); - return a; -} - -template static inline -Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, int alpha) -{ - return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, float alpha) -{ - return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, double alpha) -{ - return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Matx<_Tp, m, n> operator * (int alpha, const Matx<_Tp, m, n>& a) -{ - return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Matx<_Tp, m, n> operator * (float alpha, const Matx<_Tp, m, n>& a) -{ - return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Matx<_Tp, m, n> operator * (double alpha, const Matx<_Tp, m, n>& a) -{ - return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Matx<_Tp, m, n>& operator /= (Matx<_Tp, m, n>& a, float alpha) -{ - for( int i = 0; i < m*n; i++ ) - a.val[i] = a.val[i] / alpha; - return a; -} - -template static inline -Matx<_Tp, m, n>& operator /= (Matx<_Tp, m, n>& a, double alpha) -{ - for( int i = 0; i < m*n; i++ ) - a.val[i] = a.val[i] / alpha; - return a; -} - -template static inline -Matx<_Tp, m, n> operator / (const Matx<_Tp, m, n>& a, float alpha) -{ - return Matx<_Tp, m, n>(a, 1.f/alpha, Matx_ScaleOp()); -} - -template static inline -Matx<_Tp, m, n> operator / (const Matx<_Tp, m, n>& a, double alpha) -{ - return Matx<_Tp, m, n>(a, 1./alpha, Matx_ScaleOp()); -} - -template static inline -Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a) -{ - return Matx<_Tp, m, n>(a, -1, Matx_ScaleOp()); -} - -template static inline -Matx<_Tp, m, n> operator * (const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b) -{ - return Matx<_Tp, m, n>(a, b, Matx_MatMulOp()); -} - -template static inline -Vec<_Tp, m> operator * (const Matx<_Tp, m, n>& a, const Vec<_Tp, n>& b) -{ - Matx<_Tp, m, 1> c(a, b, Matx_MatMulOp()); - return (const Vec<_Tp, m>&)(c); -} - -template static inline -bool operator == (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) -{ - for( int i = 0; i < m*n; i++ ) - if( a.val[i] != b.val[i] ) return false; - return true; -} - -template static inline -bool operator != (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b) -{ - return !(a == b); -} - -//! @} - -////////////////////////////// Vec out-of-class operators //////////////////////////////// - -//! @relates cv::Vec -//! @{ - -template static inline -Vec<_Tp1, cn>& operator += (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b) -{ - for( int i = 0; i < cn; i++ ) - a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]); - return a; -} - -template static inline -Vec<_Tp1, cn>& operator -= (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b) -{ - for( int i = 0; i < cn; i++ ) - a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]); - return a; -} - -template static inline -Vec<_Tp, cn> operator + (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b) -{ - return Vec<_Tp, cn>(a, b, Matx_AddOp()); -} - -template static inline -Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b) -{ - return Vec<_Tp, cn>(a, b, Matx_SubOp()); -} - -template static inline -Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, int alpha) -{ - for( int i = 0; i < cn; i++ ) - a[i] = saturate_cast<_Tp>(a[i]*alpha); - return a; -} - -template static inline -Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, float alpha) -{ - for( int i = 0; i < cn; i++ ) - a[i] = saturate_cast<_Tp>(a[i]*alpha); - return a; -} - -template static inline -Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, double alpha) -{ - for( int i = 0; i < cn; i++ ) - a[i] = saturate_cast<_Tp>(a[i]*alpha); - return a; -} - -template static inline -Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, int alpha) -{ - double ialpha = 1./alpha; - for( int i = 0; i < cn; i++ ) - a[i] = saturate_cast<_Tp>(a[i]*ialpha); - return a; -} - -template static inline -Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, float alpha) -{ - float ialpha = 1.f/alpha; - for( int i = 0; i < cn; i++ ) - a[i] = saturate_cast<_Tp>(a[i]*ialpha); - return a; -} - -template static inline -Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, double alpha) -{ - double ialpha = 1./alpha; - for( int i = 0; i < cn; i++ ) - a[i] = saturate_cast<_Tp>(a[i]*ialpha); - return a; -} - -template static inline -Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, int alpha) -{ - return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator * (int alpha, const Vec<_Tp, cn>& a) -{ - return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, float alpha) -{ - return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator * (float alpha, const Vec<_Tp, cn>& a) -{ - return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, double alpha) -{ - return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator * (double alpha, const Vec<_Tp, cn>& a) -{ - return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, int alpha) -{ - return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, float alpha) -{ - return Vec<_Tp, cn>(a, 1.f/alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, double alpha) -{ - return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp()); -} - -template static inline -Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a) -{ - Vec<_Tp,cn> t; - for( int i = 0; i < cn; i++ ) t.val[i] = saturate_cast<_Tp>(-a.val[i]); - return t; -} - -template inline Vec<_Tp, 4> operator * (const Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2) -{ - return Vec<_Tp, 4>(saturate_cast<_Tp>(v1[0]*v2[0] - v1[1]*v2[1] - v1[2]*v2[2] - v1[3]*v2[3]), - saturate_cast<_Tp>(v1[0]*v2[1] + v1[1]*v2[0] + v1[2]*v2[3] - v1[3]*v2[2]), - saturate_cast<_Tp>(v1[0]*v2[2] - v1[1]*v2[3] + v1[2]*v2[0] + v1[3]*v2[1]), - saturate_cast<_Tp>(v1[0]*v2[3] + v1[1]*v2[2] - v1[2]*v2[1] + v1[3]*v2[0])); -} - -template inline Vec<_Tp, 4>& operator *= (Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2) -{ - v1 = v1 * v2; - return v1; -} - -//! @} - -} // cv - -#endif // OPENCV_CORE_MATX_HPP diff --git a/include/opencv2/core/neon_utils.hpp b/include/opencv2/core/neon_utils.hpp deleted file mode 100755 index 573ba99..0000000 --- a/include/opencv2/core/neon_utils.hpp +++ /dev/null @@ -1,128 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_HAL_NEON_UTILS_HPP -#define OPENCV_HAL_NEON_UTILS_HPP - -#include "opencv2/core/cvdef.h" - -//! @addtogroup core_utils_neon -//! @{ - -#if CV_NEON - -inline int32x2_t cv_vrnd_s32_f32(float32x2_t v) -{ - static int32x2_t v_sign = vdup_n_s32(1 << 31), - v_05 = vreinterpret_s32_f32(vdup_n_f32(0.5f)); - - int32x2_t v_addition = vorr_s32(v_05, vand_s32(v_sign, vreinterpret_s32_f32(v))); - return vcvt_s32_f32(vadd_f32(v, vreinterpret_f32_s32(v_addition))); -} - -inline int32x4_t cv_vrndq_s32_f32(float32x4_t v) -{ - static int32x4_t v_sign = vdupq_n_s32(1 << 31), - v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f)); - - int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(v))); - return vcvtq_s32_f32(vaddq_f32(v, vreinterpretq_f32_s32(v_addition))); -} - -inline uint32x2_t cv_vrnd_u32_f32(float32x2_t v) -{ - static float32x2_t v_05 = vdup_n_f32(0.5f); - return vcvt_u32_f32(vadd_f32(v, v_05)); -} - -inline uint32x4_t cv_vrndq_u32_f32(float32x4_t v) -{ - static float32x4_t v_05 = vdupq_n_f32(0.5f); - return vcvtq_u32_f32(vaddq_f32(v, v_05)); -} - -inline float32x4_t cv_vrecpq_f32(float32x4_t val) -{ - float32x4_t reciprocal = vrecpeq_f32(val); - reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal); - reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal); - return reciprocal; -} - -inline float32x2_t cv_vrecp_f32(float32x2_t val) -{ - float32x2_t reciprocal = vrecpe_f32(val); - reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal); - reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal); - return reciprocal; -} - -inline float32x4_t cv_vrsqrtq_f32(float32x4_t val) -{ - float32x4_t e = vrsqrteq_f32(val); - e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e); - e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e); - return e; -} - -inline float32x2_t cv_vrsqrt_f32(float32x2_t val) -{ - float32x2_t e = vrsqrte_f32(val); - e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e); - e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e); - return e; -} - -inline float32x4_t cv_vsqrtq_f32(float32x4_t val) -{ - return cv_vrecpq_f32(cv_vrsqrtq_f32(val)); -} - -inline float32x2_t cv_vsqrt_f32(float32x2_t val) -{ - return cv_vrecp_f32(cv_vrsqrt_f32(val)); -} - -#endif - -//! @} - -#endif // OPENCV_HAL_NEON_UTILS_HPP diff --git a/include/opencv2/core/ocl.hpp b/include/opencv2/core/ocl.hpp deleted file mode 100755 index ca46cdc..0000000 --- a/include/opencv2/core/ocl.hpp +++ /dev/null @@ -1,848 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the OpenCV Foundation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_OPENCL_HPP -#define OPENCV_OPENCL_HPP - -#include "opencv2/core.hpp" - -namespace cv { namespace ocl { - -//! @addtogroup core_opencl -//! @{ - -CV_EXPORTS_W bool haveOpenCL(); -CV_EXPORTS_W bool useOpenCL(); -CV_EXPORTS_W bool haveAmdBlas(); -CV_EXPORTS_W bool haveAmdFft(); -CV_EXPORTS_W void setUseOpenCL(bool flag); -CV_EXPORTS_W void finish(); - -CV_EXPORTS bool haveSVM(); - -class CV_EXPORTS Context; -class CV_EXPORTS_W_SIMPLE Device; -class CV_EXPORTS Kernel; -class CV_EXPORTS Program; -class CV_EXPORTS ProgramSource; -class CV_EXPORTS Queue; -class CV_EXPORTS PlatformInfo; -class CV_EXPORTS Image2D; - -class CV_EXPORTS_W_SIMPLE Device -{ -public: - CV_WRAP Device(); - explicit Device(void* d); - Device(const Device& d); - Device& operator = (const Device& d); - CV_WRAP ~Device(); - - void set(void* d); - - enum - { - TYPE_DEFAULT = (1 << 0), - TYPE_CPU = (1 << 1), - TYPE_GPU = (1 << 2), - TYPE_ACCELERATOR = (1 << 3), - TYPE_DGPU = TYPE_GPU + (1 << 16), - TYPE_IGPU = TYPE_GPU + (1 << 17), - TYPE_ALL = 0xFFFFFFFF - }; - - CV_WRAP String name() const; - CV_WRAP String extensions() const; - CV_WRAP bool isExtensionSupported(const String& extensionName) const; - CV_WRAP String version() const; - CV_WRAP String vendorName() const; - CV_WRAP String OpenCL_C_Version() const; - CV_WRAP String OpenCLVersion() const; - CV_WRAP int deviceVersionMajor() const; - CV_WRAP int deviceVersionMinor() const; - CV_WRAP String driverVersion() const; - void* ptr() const; - - CV_WRAP int type() const; - - CV_WRAP int addressBits() const; - CV_WRAP bool available() const; - CV_WRAP bool compilerAvailable() const; - CV_WRAP bool linkerAvailable() const; - - enum - { - FP_DENORM=(1 << 0), - FP_INF_NAN=(1 << 1), - FP_ROUND_TO_NEAREST=(1 << 2), - FP_ROUND_TO_ZERO=(1 << 3), - FP_ROUND_TO_INF=(1 << 4), - FP_FMA=(1 << 5), - FP_SOFT_FLOAT=(1 << 6), - FP_CORRECTLY_ROUNDED_DIVIDE_SQRT=(1 << 7) - }; - CV_WRAP int doubleFPConfig() const; - CV_WRAP int singleFPConfig() const; - CV_WRAP int halfFPConfig() const; - - CV_WRAP bool endianLittle() const; - CV_WRAP bool errorCorrectionSupport() const; - - enum - { - EXEC_KERNEL=(1 << 0), - EXEC_NATIVE_KERNEL=(1 << 1) - }; - CV_WRAP int executionCapabilities() const; - - CV_WRAP size_t globalMemCacheSize() const; - - enum - { - NO_CACHE=0, - READ_ONLY_CACHE=1, - READ_WRITE_CACHE=2 - }; - CV_WRAP int globalMemCacheType() const; - CV_WRAP int globalMemCacheLineSize() const; - CV_WRAP size_t globalMemSize() const; - - CV_WRAP size_t localMemSize() const; - enum - { - NO_LOCAL_MEM=0, - LOCAL_IS_LOCAL=1, - LOCAL_IS_GLOBAL=2 - }; - CV_WRAP int localMemType() const; - CV_WRAP bool hostUnifiedMemory() const; - - CV_WRAP bool imageSupport() const; - - CV_WRAP bool imageFromBufferSupport() const; - uint imagePitchAlignment() const; - uint imageBaseAddressAlignment() const; - - /// deprecated, use isExtensionSupported() method (probably with "cl_khr_subgroups" value) - CV_WRAP bool intelSubgroupsSupport() const; - - CV_WRAP size_t image2DMaxWidth() const; - CV_WRAP size_t image2DMaxHeight() const; - - CV_WRAP size_t image3DMaxWidth() const; - CV_WRAP size_t image3DMaxHeight() const; - CV_WRAP size_t image3DMaxDepth() const; - - CV_WRAP size_t imageMaxBufferSize() const; - CV_WRAP size_t imageMaxArraySize() const; - - enum - { - UNKNOWN_VENDOR=0, - VENDOR_AMD=1, - VENDOR_INTEL=2, - VENDOR_NVIDIA=3 - }; - CV_WRAP int vendorID() const; - // FIXIT - // dev.isAMD() doesn't work for OpenCL CPU devices from AMD OpenCL platform. - // This method should use platform name instead of vendor name. - // After fix restore code in arithm.cpp: ocl_compare() - CV_WRAP inline bool isAMD() const { return vendorID() == VENDOR_AMD; } - CV_WRAP inline bool isIntel() const { return vendorID() == VENDOR_INTEL; } - CV_WRAP inline bool isNVidia() const { return vendorID() == VENDOR_NVIDIA; } - - CV_WRAP int maxClockFrequency() const; - CV_WRAP int maxComputeUnits() const; - CV_WRAP int maxConstantArgs() const; - CV_WRAP size_t maxConstantBufferSize() const; - - CV_WRAP size_t maxMemAllocSize() const; - CV_WRAP size_t maxParameterSize() const; - - CV_WRAP int maxReadImageArgs() const; - CV_WRAP int maxWriteImageArgs() const; - CV_WRAP int maxSamplers() const; - - CV_WRAP size_t maxWorkGroupSize() const; - CV_WRAP int maxWorkItemDims() const; - void maxWorkItemSizes(size_t*) const; - - CV_WRAP int memBaseAddrAlign() const; - - CV_WRAP int nativeVectorWidthChar() const; - CV_WRAP int nativeVectorWidthShort() const; - CV_WRAP int nativeVectorWidthInt() const; - CV_WRAP int nativeVectorWidthLong() const; - CV_WRAP int nativeVectorWidthFloat() const; - CV_WRAP int nativeVectorWidthDouble() const; - CV_WRAP int nativeVectorWidthHalf() const; - - CV_WRAP int preferredVectorWidthChar() const; - CV_WRAP int preferredVectorWidthShort() const; - CV_WRAP int preferredVectorWidthInt() const; - CV_WRAP int preferredVectorWidthLong() const; - CV_WRAP int preferredVectorWidthFloat() const; - CV_WRAP int preferredVectorWidthDouble() const; - CV_WRAP int preferredVectorWidthHalf() const; - - CV_WRAP size_t printfBufferSize() const; - CV_WRAP size_t profilingTimerResolution() const; - - CV_WRAP static const Device& getDefault(); - -protected: - struct Impl; - Impl* p; -}; - - -class CV_EXPORTS Context -{ -public: - Context(); - explicit Context(int dtype); - ~Context(); - Context(const Context& c); - Context& operator = (const Context& c); - - bool create(); - bool create(int dtype); - size_t ndevices() const; - const Device& device(size_t idx) const; - Program getProg(const ProgramSource& prog, - const String& buildopt, String& errmsg); - void unloadProg(Program& prog); - - static Context& getDefault(bool initialize = true); - void* ptr() const; - - friend void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); - - bool useSVM() const; - void setUseSVM(bool enabled); - - struct Impl; - inline Impl* getImpl() const { return (Impl*)p; } -//protected: - Impl* p; -}; - -class CV_EXPORTS Platform -{ -public: - Platform(); - ~Platform(); - Platform(const Platform& p); - Platform& operator = (const Platform& p); - - void* ptr() const; - static Platform& getDefault(); - - friend void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); -protected: - struct Impl; - Impl* p; -}; - -/** @brief Attaches OpenCL context to OpenCV -@note - OpenCV will check if available OpenCL platform has platformName name, then assign context to - OpenCV and call `clRetainContext` function. The deviceID device will be used as target device and - new command queue will be created. -@param platformName name of OpenCL platform to attach, this string is used to check if platform is available to OpenCV at runtime -@param platformID ID of platform attached context was created for -@param context OpenCL context to be attached to OpenCV -@param deviceID ID of device, must be created from attached context -*/ -CV_EXPORTS void attachContext(const String& platformName, void* platformID, void* context, void* deviceID); - -/** @brief Convert OpenCL buffer to UMat -@note - OpenCL buffer (cl_mem_buffer) should contain 2D image data, compatible with OpenCV. Memory - content is not copied from `clBuffer` to UMat. Instead, buffer handle assigned to UMat and - `clRetainMemObject` is called. -@param cl_mem_buffer source clBuffer handle -@param step num of bytes in single row -@param rows number of rows -@param cols number of cols -@param type OpenCV type of image -@param dst destination UMat -*/ -CV_EXPORTS void convertFromBuffer(void* cl_mem_buffer, size_t step, int rows, int cols, int type, UMat& dst); - -/** @brief Convert OpenCL image2d_t to UMat -@note - OpenCL `image2d_t` (cl_mem_image), should be compatible with OpenCV UMat formats. Memory content - is copied from image to UMat with `clEnqueueCopyImageToBuffer` function. -@param cl_mem_image source image2d_t handle -@param dst destination UMat -*/ -CV_EXPORTS void convertFromImage(void* cl_mem_image, UMat& dst); - -// TODO Move to internal header -void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device); - -class CV_EXPORTS Queue -{ -public: - Queue(); - explicit Queue(const Context& c, const Device& d=Device()); - ~Queue(); - Queue(const Queue& q); - Queue& operator = (const Queue& q); - - bool create(const Context& c=Context(), const Device& d=Device()); - void finish(); - void* ptr() const; - static Queue& getDefault(); - - /// @brief Returns OpenCL command queue with enable profiling mode support - const Queue& getProfilingQueue() const; - - struct Impl; friend struct Impl; - inline Impl* getImpl() const { return p; } -protected: - Impl* p; -}; - - -class CV_EXPORTS KernelArg -{ -public: - enum { LOCAL=1, READ_ONLY=2, WRITE_ONLY=4, READ_WRITE=6, CONSTANT=8, PTR_ONLY = 16, NO_SIZE=256 }; - KernelArg(int _flags, UMat* _m, int wscale=1, int iwscale=1, const void* _obj=0, size_t _sz=0); - KernelArg(); - - static KernelArg Local(size_t localMemSize) - { return KernelArg(LOCAL, 0, 1, 1, 0, localMemSize); } - static KernelArg PtrWriteOnly(const UMat& m) - { return KernelArg(PTR_ONLY+WRITE_ONLY, (UMat*)&m); } - static KernelArg PtrReadOnly(const UMat& m) - { return KernelArg(PTR_ONLY+READ_ONLY, (UMat*)&m); } - static KernelArg PtrReadWrite(const UMat& m) - { return KernelArg(PTR_ONLY+READ_WRITE, (UMat*)&m); } - static KernelArg ReadWrite(const UMat& m, int wscale=1, int iwscale=1) - { return KernelArg(READ_WRITE, (UMat*)&m, wscale, iwscale); } - static KernelArg ReadWriteNoSize(const UMat& m, int wscale=1, int iwscale=1) - { return KernelArg(READ_WRITE+NO_SIZE, (UMat*)&m, wscale, iwscale); } - static KernelArg ReadOnly(const UMat& m, int wscale=1, int iwscale=1) - { return KernelArg(READ_ONLY, (UMat*)&m, wscale, iwscale); } - static KernelArg WriteOnly(const UMat& m, int wscale=1, int iwscale=1) - { return KernelArg(WRITE_ONLY, (UMat*)&m, wscale, iwscale); } - static KernelArg ReadOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1) - { return KernelArg(READ_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); } - static KernelArg WriteOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1) - { return KernelArg(WRITE_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); } - static KernelArg Constant(const Mat& m); - template static KernelArg Constant(const _Tp* arr, size_t n) - { return KernelArg(CONSTANT, 0, 1, 1, (void*)arr, n); } - - int flags; - UMat* m; - const void* obj; - size_t sz; - int wscale, iwscale; -}; - - -class CV_EXPORTS Kernel -{ -public: - Kernel(); - Kernel(const char* kname, const Program& prog); - Kernel(const char* kname, const ProgramSource& prog, - const String& buildopts = String(), String* errmsg=0); - ~Kernel(); - Kernel(const Kernel& k); - Kernel& operator = (const Kernel& k); - - bool empty() const; - bool create(const char* kname, const Program& prog); - bool create(const char* kname, const ProgramSource& prog, - const String& buildopts, String* errmsg=0); - - int set(int i, const void* value, size_t sz); - int set(int i, const Image2D& image2D); - int set(int i, const UMat& m); - int set(int i, const KernelArg& arg); - template int set(int i, const _Tp& value) - { return set(i, &value, sizeof(value)); } - - template - Kernel& args(const _Tp0& a0) - { - set(0, a0); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1) - { - int i = set(0, a0); set(i, a1); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2) - { - int i = set(0, a0); i = set(i, a1); set(i, a2); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, - const _Tp3& a3, const _Tp4& a4) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); - i = set(i, a3); set(i, a4); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, - const _Tp3& a3, const _Tp4& a4, const _Tp5& a5) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); - i = set(i, a3); i = set(i, a4); set(i, a5); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); - i = set(i, a4); i = set(i, a5); set(i, a6); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); - i = set(i, a4); i = set(i, a5); i = set(i, a6); set(i, a7); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, - const _Tp8& a8) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); - i = set(i, a5); i = set(i, a6); i = set(i, a7); set(i, a8); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, - const _Tp8& a8, const _Tp9& a9) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); - i = set(i, a6); i = set(i, a7); i = set(i, a8); set(i, a9); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, - const _Tp8& a8, const _Tp9& a9, const _Tp10& a10) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); - i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); set(i, a10); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, - const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); - i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); set(i, a11); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, - const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, - const _Tp12& a12) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); - i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); - set(i, a12); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, - const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, - const _Tp12& a12, const _Tp13& a13) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); - i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); - i = set(i, a12); set(i, a13); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, - const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, - const _Tp12& a12, const _Tp13& a13, const _Tp14& a14) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); - i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); - i = set(i, a12); i = set(i, a13); set(i, a14); return *this; - } - - template - Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3, - const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7, - const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11, - const _Tp12& a12, const _Tp13& a13, const _Tp14& a14, const _Tp15& a15) - { - int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5); - i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11); - i = set(i, a12); i = set(i, a13); i = set(i, a14); set(i, a15); return *this; - } - /** @brief Run the OpenCL kernel. - @param dims the work problem dimensions. It is the length of globalsize and localsize. It can be either 1, 2 or 3. - @param globalsize work items for each dimension. It is not the final globalsize passed to - OpenCL. Each dimension will be adjusted to the nearest integer divisible by the corresponding - value in localsize. If localsize is NULL, it will still be adjusted depending on dims. The - adjusted values are greater than or equal to the original values. - @param localsize work-group size for each dimension. - @param sync specify whether to wait for OpenCL computation to finish before return. - @param q command queue - */ - bool run(int dims, size_t globalsize[], - size_t localsize[], bool sync, const Queue& q=Queue()); - bool runTask(bool sync, const Queue& q=Queue()); - - /** @brief Similar to synchronized run() call with returning of kernel execution time - * Separate OpenCL command queue may be used (with CL_QUEUE_PROFILING_ENABLE) - * @return Execution time in nanoseconds or negative number on error - */ - int64 runProfiling(int dims, size_t globalsize[], size_t localsize[], const Queue& q=Queue()); - - size_t workGroupSize() const; - size_t preferedWorkGroupSizeMultiple() const; - bool compileWorkGroupSize(size_t wsz[]) const; - size_t localMemSize() const; - - void* ptr() const; - struct Impl; - -protected: - Impl* p; -}; - -class CV_EXPORTS Program -{ -public: - Program(); - Program(const ProgramSource& src, - const String& buildflags, String& errmsg); - Program(const Program& prog); - - Program& operator = (const Program& prog); - ~Program(); - - bool create(const ProgramSource& src, - const String& buildflags, String& errmsg); - - void* ptr() const; - - /** - * @brief Query device-specific program binary. - * - * Returns RAW OpenCL executable binary without additional attachments. - * - * @sa ProgramSource::fromBinary - * - * @param[out] binary output buffer - */ - void getBinary(std::vector& binary) const; - - struct Impl; friend struct Impl; - inline Impl* getImpl() const { return (Impl*)p; } -protected: - Impl* p; -public: -#ifndef OPENCV_REMOVE_DEPRECATED_API - // TODO Remove this - CV_DEPRECATED bool read(const String& buf, const String& buildflags); // removed, use ProgramSource instead - CV_DEPRECATED bool write(String& buf) const; // removed, use getBinary() method instead (RAW OpenCL binary) - CV_DEPRECATED const ProgramSource& source() const; // implementation removed - CV_DEPRECATED String getPrefix() const; // deprecated, implementation replaced - CV_DEPRECATED static String getPrefix(const String& buildflags); // deprecated, implementation replaced -#endif -}; - - -class CV_EXPORTS ProgramSource -{ -public: - typedef uint64 hash_t; // deprecated - - ProgramSource(); - explicit ProgramSource(const String& module, const String& name, const String& codeStr, const String& codeHash); - explicit ProgramSource(const String& prog); // deprecated - explicit ProgramSource(const char* prog); // deprecated - ~ProgramSource(); - ProgramSource(const ProgramSource& prog); - ProgramSource& operator = (const ProgramSource& prog); - - const String& source() const; // deprecated - hash_t hash() const; // deprecated - - - /** @brief Describe OpenCL program binary. - * Do not call clCreateProgramWithBinary() and/or clBuildProgram(). - * - * Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies). - * - * This kind of binary is not portable between platforms in general - it is specific to OpenCL vendor / device / driver version. - * - * @param module name of program owner module - * @param name unique name of program (module+name is used as key for OpenCL program caching) - * @param binary buffer address. See buffer lifetime requirement in description. - * @param size buffer size - * @param buildOptions additional program-related build options passed to clBuildProgram() - * @return created ProgramSource object - */ - static ProgramSource fromBinary(const String& module, const String& name, - const unsigned char* binary, const size_t size, - const cv::String& buildOptions = cv::String()); - - /** @brief Describe OpenCL program in SPIR format. - * Do not call clCreateProgramWithBinary() and/or clBuildProgram(). - * - * Supports SPIR 1.2 by default (pass '-spir-std=X.Y' in buildOptions to override this behavior) - * - * Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies). - * - * Programs in this format are portable between OpenCL implementations with 'khr_spir' extension: - * https://www.khronos.org/registry/OpenCL/sdk/2.0/docs/man/xhtml/cl_khr_spir.html - * (but they are not portable between different platforms: 32-bit / 64-bit) - * - * Note: these programs can't support vendor specific extensions, like 'cl_intel_subgroups'. - * - * @param module name of program owner module - * @param name unique name of program (module+name is used as key for OpenCL program caching) - * @param binary buffer address. See buffer lifetime requirement in description. - * @param size buffer size - * @param buildOptions additional program-related build options passed to clBuildProgram() - * (these options are added automatically: '-x spir' and '-spir-std=1.2') - * @return created ProgramSource object. - */ - static ProgramSource fromSPIR(const String& module, const String& name, - const unsigned char* binary, const size_t size, - const cv::String& buildOptions = cv::String()); - - //OpenCL 2.1+ only - //static Program fromSPIRV(const String& module, const String& name, - // const unsigned char* binary, const size_t size, - // const cv::String& buildOptions = cv::String()); - - struct Impl; friend struct Impl; - inline Impl* getImpl() const { return (Impl*)p; } -protected: - Impl* p; -}; - -class CV_EXPORTS PlatformInfo -{ -public: - PlatformInfo(); - explicit PlatformInfo(void* id); - ~PlatformInfo(); - - PlatformInfo(const PlatformInfo& i); - PlatformInfo& operator =(const PlatformInfo& i); - - String name() const; - String vendor() const; - - /// See CL_PLATFORM_VERSION - String version() const; - int versionMajor() const; - int versionMinor() const; - - int deviceNumber() const; - void getDevice(Device& device, int d) const; - -protected: - struct Impl; - Impl* p; -}; - -CV_EXPORTS const char* convertTypeStr(int sdepth, int ddepth, int cn, char* buf); -CV_EXPORTS const char* typeToStr(int t); -CV_EXPORTS const char* memopTypeToStr(int t); -CV_EXPORTS const char* vecopTypeToStr(int t); -CV_EXPORTS const char* getOpenCLErrorString(int errorCode); -CV_EXPORTS String kernelToStr(InputArray _kernel, int ddepth = -1, const char * name = NULL); -CV_EXPORTS void getPlatfomsInfo(std::vector& platform_info); - - -enum OclVectorStrategy -{ - // all matrices have its own vector width - OCL_VECTOR_OWN = 0, - // all matrices have maximal vector width among all matrices - // (useful for cases when matrices have different data types) - OCL_VECTOR_MAX = 1, - - // default strategy - OCL_VECTOR_DEFAULT = OCL_VECTOR_OWN -}; - -CV_EXPORTS int predictOptimalVectorWidth(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), - InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), - InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(), - OclVectorStrategy strat = OCL_VECTOR_DEFAULT); - -CV_EXPORTS int checkOptimalVectorWidth(const int *vectorWidths, - InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), - InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), - InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(), - OclVectorStrategy strat = OCL_VECTOR_DEFAULT); - -// with OCL_VECTOR_MAX strategy -CV_EXPORTS int predictOptimalVectorWidthMax(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(), - InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(), - InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray()); - -CV_EXPORTS void buildOptionsAddMatrixDescription(String& buildOptions, const String& name, InputArray _m); - -class CV_EXPORTS Image2D -{ -public: - Image2D(); - - /** - @param src UMat object from which to get image properties and data - @param norm flag to enable the use of normalized channel data types - @param alias flag indicating that the image should alias the src UMat. If true, changes to the - image or src will be reflected in both objects. - */ - explicit Image2D(const UMat &src, bool norm = false, bool alias = false); - Image2D(const Image2D & i); - ~Image2D(); - - Image2D & operator = (const Image2D & i); - - /** Indicates if creating an aliased image should succeed. - Depends on the underlying platform and the dimensions of the UMat. - */ - static bool canCreateAlias(const UMat &u); - - /** Indicates if the image format is supported. - */ - static bool isFormatSupported(int depth, int cn, bool norm); - - void* ptr() const; -protected: - struct Impl; - Impl* p; -}; - -class CV_EXPORTS Timer -{ -public: - Timer(const Queue& q); - ~Timer(); - void start(); - void stop(); - - uint64 durationNS() const; //< duration in nanoseconds - -protected: - struct Impl; - Impl* const p; - -private: - Timer(const Timer&); // disabled - Timer& operator=(const Timer&); // disabled -}; - -CV_EXPORTS MatAllocator* getOpenCLAllocator(); - - -#ifdef __OPENCV_BUILD -namespace internal { - -CV_EXPORTS bool isOpenCLForced(); -#define OCL_FORCE_CHECK(condition) (cv::ocl::internal::isOpenCLForced() || (condition)) - -CV_EXPORTS bool isPerformanceCheckBypassed(); -#define OCL_PERFORMANCE_CHECK(condition) (cv::ocl::internal::isPerformanceCheckBypassed() || (condition)) - -CV_EXPORTS bool isCLBuffer(UMat& u); - -} // namespace internal -#endif - -//! @} - -}} - -#endif diff --git a/include/opencv2/core/ocl_genbase.hpp b/include/opencv2/core/ocl_genbase.hpp deleted file mode 100755 index 5334cf1..0000000 --- a/include/opencv2/core/ocl_genbase.hpp +++ /dev/null @@ -1,69 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the OpenCV Foundation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_OPENCL_GENBASE_HPP -#define OPENCV_OPENCL_GENBASE_HPP - -//! @cond IGNORED - -namespace cv { -namespace ocl { - -class ProgramSource; - -namespace internal { - -struct CV_EXPORTS ProgramEntry -{ - const char* module; - const char* name; - const char* programCode; - const char* programHash; - ProgramSource* pProgramSource; - - operator ProgramSource& () const; -}; - -} } } // namespace - -//! @endcond - -#endif diff --git a/include/opencv2/core/opencl/ocl_defs.hpp b/include/opencv2/core/opencl/ocl_defs.hpp deleted file mode 100755 index 605a65f..0000000 --- a/include/opencv2/core/opencl/ocl_defs.hpp +++ /dev/null @@ -1,75 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved. -// Third party copyrights are property of their respective owners. - -#ifndef OPENCV_CORE_OPENCL_DEFS_HPP -#define OPENCV_CORE_OPENCL_DEFS_HPP - -#include "opencv2/core/utility.hpp" -#include "cvconfig.h" - -namespace cv { namespace ocl { -#ifdef HAVE_OPENCL -/// Call is similar to useOpenCL() but doesn't try to load OpenCL runtime or create OpenCL context -CV_EXPORTS bool isOpenCLActivated(); -#else -static inline bool isOpenCLActivated() { return false; } -#endif -}} // namespace - - -//#define CV_OPENCL_RUN_ASSERT - -#ifdef HAVE_OPENCL - -#ifdef CV_OPENCL_RUN_VERBOSE -#define CV_OCL_RUN_(condition, func, ...) \ - { \ - if (cv::ocl::isOpenCLActivated() && (condition) && func) \ - { \ - printf("%s: OpenCL implementation is running\n", CV_Func); \ - fflush(stdout); \ - CV_IMPL_ADD(CV_IMPL_OCL); \ - return __VA_ARGS__; \ - } \ - else \ - { \ - printf("%s: Plain implementation is running\n", CV_Func); \ - fflush(stdout); \ - } \ - } -#elif defined CV_OPENCL_RUN_ASSERT -#define CV_OCL_RUN_(condition, func, ...) \ - { \ - if (cv::ocl::isOpenCLActivated() && (condition)) \ - { \ - if(func) \ - { \ - CV_IMPL_ADD(CV_IMPL_OCL); \ - } \ - else \ - { \ - CV_Error(cv::Error::StsAssert, #func); \ - } \ - return __VA_ARGS__; \ - } \ - } -#else -#define CV_OCL_RUN_(condition, func, ...) \ - if (cv::ocl::isOpenCLActivated() && (condition) && func) \ - { \ - CV_IMPL_ADD(CV_IMPL_OCL); \ - return __VA_ARGS__; \ - } -#endif - -#else -#define CV_OCL_RUN_(condition, func, ...) -#endif - -#define CV_OCL_RUN(condition, func) CV_OCL_RUN_(condition, func) - -#endif // OPENCV_CORE_OPENCL_DEFS_HPP diff --git a/include/opencv2/core/opencl/opencl_info.hpp b/include/opencv2/core/opencl/opencl_info.hpp deleted file mode 100755 index 5e5c846..0000000 --- a/include/opencv2/core/opencl/opencl_info.hpp +++ /dev/null @@ -1,205 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#include - -#include -#include - -#ifndef DUMP_CONFIG_PROPERTY -#define DUMP_CONFIG_PROPERTY(...) -#endif - -#ifndef DUMP_MESSAGE_STDOUT -#define DUMP_MESSAGE_STDOUT(...) do { std::cout << __VA_ARGS__ << std::endl; } while (false) -#endif - -namespace cv { - -namespace { -static std::string bytesToStringRepr(size_t value) -{ - size_t b = value % 1024; - value /= 1024; - - size_t kb = value % 1024; - value /= 1024; - - size_t mb = value % 1024; - value /= 1024; - - size_t gb = value; - - std::ostringstream stream; - - if (gb > 0) - stream << gb << " GB "; - if (mb > 0) - stream << mb << " MB "; - if (kb > 0) - stream << kb << " KB "; - if (b > 0) - stream << b << " B"; - - std::string s = stream.str(); - if (s[s.size() - 1] == ' ') - s = s.substr(0, s.size() - 1); - return s; -} - -static String getDeviceTypeString(const cv::ocl::Device& device) -{ - if (device.type() == cv::ocl::Device::TYPE_CPU) { - return "CPU"; - } - - if (device.type() == cv::ocl::Device::TYPE_GPU) { - if (device.hostUnifiedMemory()) { - return "iGPU"; - } else { - return "dGPU"; - } - } - - return "unknown"; -} -} // namespace - -static void dumpOpenCLInformation() -{ - using namespace cv::ocl; - - try - { - if (!haveOpenCL() || !useOpenCL()) - { - DUMP_MESSAGE_STDOUT("OpenCL is disabled"); - DUMP_CONFIG_PROPERTY("cv_ocl", "disabled"); - return; - } - - std::vector platforms; - cv::ocl::getPlatfomsInfo(platforms); - if (platforms.empty()) - { - DUMP_MESSAGE_STDOUT("OpenCL is not available"); - DUMP_CONFIG_PROPERTY("cv_ocl", "not available"); - return; - } - - DUMP_MESSAGE_STDOUT("OpenCL Platforms: "); - for (size_t i = 0; i < platforms.size(); i++) - { - const PlatformInfo* platform = &platforms[i]; - DUMP_MESSAGE_STDOUT(" " << platform->name()); - Device current_device; - for (int j = 0; j < platform->deviceNumber(); j++) - { - platform->getDevice(current_device, j); - String deviceTypeStr = getDeviceTypeString(current_device); - DUMP_MESSAGE_STDOUT( " " << deviceTypeStr << ": " << current_device.name() << " (" << current_device.version() << ")"); - DUMP_CONFIG_PROPERTY( cv::format("cv_ocl_platform_%d_device_%d", (int)i, j ), - cv::format("(Platform=%s)(Type=%s)(Name=%s)(Version=%s)", - platform->name().c_str(), deviceTypeStr.c_str(), current_device.name().c_str(), current_device.version().c_str()) ); - } - } - const Device& device = Device::getDefault(); - if (!device.available()) - CV_Error(Error::OpenCLInitError, "OpenCL device is not available"); - - DUMP_MESSAGE_STDOUT("Current OpenCL device: "); - - String deviceTypeStr = getDeviceTypeString(device); - DUMP_MESSAGE_STDOUT(" Type = " << deviceTypeStr); - DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceType", deviceTypeStr); - - DUMP_MESSAGE_STDOUT(" Name = " << device.name()); - DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceName", device.name()); - - DUMP_MESSAGE_STDOUT(" Version = " << device.version()); - DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceVersion", device.version()); - - DUMP_MESSAGE_STDOUT(" Driver version = " << device.driverVersion()); - DUMP_CONFIG_PROPERTY("cv_ocl_current_driverVersion", device.driverVersion()); - - DUMP_MESSAGE_STDOUT(" Address bits = " << device.addressBits()); - DUMP_CONFIG_PROPERTY("cv_ocl_current_addressBits", device.addressBits()); - - DUMP_MESSAGE_STDOUT(" Compute units = " << device.maxComputeUnits()); - DUMP_CONFIG_PROPERTY("cv_ocl_current_maxComputeUnits", device.maxComputeUnits()); - - DUMP_MESSAGE_STDOUT(" Max work group size = " << device.maxWorkGroupSize()); - DUMP_CONFIG_PROPERTY("cv_ocl_current_maxWorkGroupSize", device.maxWorkGroupSize()); - - std::string localMemorySizeStr = bytesToStringRepr(device.localMemSize()); - DUMP_MESSAGE_STDOUT(" Local memory size = " << localMemorySizeStr); - DUMP_CONFIG_PROPERTY("cv_ocl_current_localMemSize", device.localMemSize()); - - std::string maxMemAllocSizeStr = bytesToStringRepr(device.maxMemAllocSize()); - DUMP_MESSAGE_STDOUT(" Max memory allocation size = " << maxMemAllocSizeStr); - DUMP_CONFIG_PROPERTY("cv_ocl_current_maxMemAllocSize", device.maxMemAllocSize()); - - const char* doubleSupportStr = device.doubleFPConfig() > 0 ? "Yes" : "No"; - DUMP_MESSAGE_STDOUT(" Double support = " << doubleSupportStr); - DUMP_CONFIG_PROPERTY("cv_ocl_current_haveDoubleSupport", device.doubleFPConfig() > 0); - - const char* isUnifiedMemoryStr = device.hostUnifiedMemory() ? "Yes" : "No"; - DUMP_MESSAGE_STDOUT(" Host unified memory = " << isUnifiedMemoryStr); - DUMP_CONFIG_PROPERTY("cv_ocl_current_hostUnifiedMemory", device.hostUnifiedMemory()); - - DUMP_MESSAGE_STDOUT(" Device extensions:"); - String extensionsStr = device.extensions(); - size_t pos = 0; - while (pos < extensionsStr.size()) - { - size_t pos2 = extensionsStr.find(' ', pos); - if (pos2 == String::npos) - pos2 = extensionsStr.size(); - if (pos2 > pos) - { - String extensionName = extensionsStr.substr(pos, pos2 - pos); - DUMP_MESSAGE_STDOUT(" " << extensionName); - } - pos = pos2 + 1; - } - DUMP_CONFIG_PROPERTY("cv_ocl_current_extensions", extensionsStr); - - const char* haveAmdBlasStr = haveAmdBlas() ? "Yes" : "No"; - DUMP_MESSAGE_STDOUT(" Has AMD Blas = " << haveAmdBlasStr); - DUMP_CONFIG_PROPERTY("cv_ocl_current_AmdBlas", haveAmdBlas()); - - const char* haveAmdFftStr = haveAmdFft() ? "Yes" : "No"; - DUMP_MESSAGE_STDOUT(" Has AMD Fft = " << haveAmdFftStr); - DUMP_CONFIG_PROPERTY("cv_ocl_current_AmdFft", haveAmdFft()); - - - DUMP_MESSAGE_STDOUT(" Preferred vector width char = " << device.preferredVectorWidthChar()); - DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthChar", device.preferredVectorWidthChar()); - - DUMP_MESSAGE_STDOUT(" Preferred vector width short = " << device.preferredVectorWidthShort()); - DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthShort", device.preferredVectorWidthShort()); - - DUMP_MESSAGE_STDOUT(" Preferred vector width int = " << device.preferredVectorWidthInt()); - DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthInt", device.preferredVectorWidthInt()); - - DUMP_MESSAGE_STDOUT(" Preferred vector width long = " << device.preferredVectorWidthLong()); - DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthLong", device.preferredVectorWidthLong()); - - DUMP_MESSAGE_STDOUT(" Preferred vector width float = " << device.preferredVectorWidthFloat()); - DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthFloat", device.preferredVectorWidthFloat()); - - DUMP_MESSAGE_STDOUT(" Preferred vector width double = " << device.preferredVectorWidthDouble()); - DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthDouble", device.preferredVectorWidthDouble()); - } - catch (...) - { - DUMP_MESSAGE_STDOUT("Exception. Can't dump OpenCL info"); - DUMP_MESSAGE_STDOUT("OpenCL device not available"); - DUMP_CONFIG_PROPERTY("cv_ocl", "not available"); - } -} -#undef DUMP_MESSAGE_STDOUT -#undef DUMP_CONFIG_PROPERTY - -} // namespace diff --git a/include/opencv2/core/opencl/opencl_svm.hpp b/include/opencv2/core/opencl/opencl_svm.hpp deleted file mode 100755 index 7453082..0000000 --- a/include/opencv2/core/opencl/opencl_svm.hpp +++ /dev/null @@ -1,81 +0,0 @@ -/* See LICENSE file in the root OpenCV directory */ - -#ifndef OPENCV_CORE_OPENCL_SVM_HPP -#define OPENCV_CORE_OPENCL_SVM_HPP - -// -// Internal usage only (binary compatibility is not guaranteed) -// -#ifndef __OPENCV_BUILD -#error Internal header file -#endif - -#if defined(HAVE_OPENCL) && defined(HAVE_OPENCL_SVM) -#include "runtime/opencl_core.hpp" -#include "runtime/opencl_svm_20.hpp" -#include "runtime/opencl_svm_hsa_extension.hpp" - -namespace cv { namespace ocl { namespace svm { - -struct SVMCapabilities -{ - enum Value - { - SVM_COARSE_GRAIN_BUFFER = (1 << 0), - SVM_FINE_GRAIN_BUFFER = (1 << 1), - SVM_FINE_GRAIN_SYSTEM = (1 << 2), - SVM_ATOMICS = (1 << 3), - }; - int value_; - - SVMCapabilities(int capabilities = 0) : value_(capabilities) { } - operator int() const { return value_; } - - inline bool isNoSVMSupport() const { return value_ == 0; } - inline bool isSupportCoarseGrainBuffer() const { return (value_ & SVM_COARSE_GRAIN_BUFFER) != 0; } - inline bool isSupportFineGrainBuffer() const { return (value_ & SVM_FINE_GRAIN_BUFFER) != 0; } - inline bool isSupportFineGrainSystem() const { return (value_ & SVM_FINE_GRAIN_SYSTEM) != 0; } - inline bool isSupportAtomics() const { return (value_ & SVM_ATOMICS) != 0; } -}; - -CV_EXPORTS const SVMCapabilities getSVMCapabilitites(const ocl::Context& context); - -struct SVMFunctions -{ - clSVMAllocAMD_fn fn_clSVMAlloc; - clSVMFreeAMD_fn fn_clSVMFree; - clSetKernelArgSVMPointerAMD_fn fn_clSetKernelArgSVMPointer; - //clSetKernelExecInfoAMD_fn fn_clSetKernelExecInfo; - //clEnqueueSVMFreeAMD_fn fn_clEnqueueSVMFree; - clEnqueueSVMMemcpyAMD_fn fn_clEnqueueSVMMemcpy; - clEnqueueSVMMemFillAMD_fn fn_clEnqueueSVMMemFill; - clEnqueueSVMMapAMD_fn fn_clEnqueueSVMMap; - clEnqueueSVMUnmapAMD_fn fn_clEnqueueSVMUnmap; - - inline SVMFunctions() - : fn_clSVMAlloc(NULL), fn_clSVMFree(NULL), - fn_clSetKernelArgSVMPointer(NULL), /*fn_clSetKernelExecInfo(NULL),*/ - /*fn_clEnqueueSVMFree(NULL),*/ fn_clEnqueueSVMMemcpy(NULL), fn_clEnqueueSVMMemFill(NULL), - fn_clEnqueueSVMMap(NULL), fn_clEnqueueSVMUnmap(NULL) - { - // nothing - } - - inline bool isValid() const - { - return fn_clSVMAlloc != NULL && fn_clSVMFree && fn_clSetKernelArgSVMPointer && - /*fn_clSetKernelExecInfo && fn_clEnqueueSVMFree &&*/ fn_clEnqueueSVMMemcpy && - fn_clEnqueueSVMMemFill && fn_clEnqueueSVMMap && fn_clEnqueueSVMUnmap; - } -}; - -// We should guarantee that SVMFunctions lifetime is not less than context's lifetime -CV_EXPORTS const SVMFunctions* getSVMFunctions(const ocl::Context& context); - -CV_EXPORTS bool useSVM(UMatUsageFlags usageFlags); - -}}} //namespace cv::ocl::svm -#endif - -#endif // OPENCV_CORE_OPENCL_SVM_HPP -/* End of file. */ diff --git a/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdblas.hpp b/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdblas.hpp deleted file mode 100755 index 65c8493..0000000 --- a/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdblas.hpp +++ /dev/null @@ -1,714 +0,0 @@ -// -// AUTOGENERATED, DO NOT EDIT -// -#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP -#error "Invalid usage" -#endif - -// generated by parser_clamdblas.py -#define clAmdBlasAddScratchImage clAmdBlasAddScratchImage_ -#define clAmdBlasCaxpy clAmdBlasCaxpy_ -#define clAmdBlasCcopy clAmdBlasCcopy_ -#define clAmdBlasCdotc clAmdBlasCdotc_ -#define clAmdBlasCdotu clAmdBlasCdotu_ -#define clAmdBlasCgbmv clAmdBlasCgbmv_ -#define clAmdBlasCgemm clAmdBlasCgemm_ -#define clAmdBlasCgemmEx clAmdBlasCgemmEx_ -#define clAmdBlasCgemv clAmdBlasCgemv_ -#define clAmdBlasCgemvEx clAmdBlasCgemvEx_ -#define clAmdBlasCgerc clAmdBlasCgerc_ -#define clAmdBlasCgeru clAmdBlasCgeru_ -#define clAmdBlasChbmv clAmdBlasChbmv_ -#define clAmdBlasChemm clAmdBlasChemm_ -#define clAmdBlasChemv clAmdBlasChemv_ -#define clAmdBlasCher clAmdBlasCher_ -#define clAmdBlasCher2 clAmdBlasCher2_ -#define clAmdBlasCher2k clAmdBlasCher2k_ -#define clAmdBlasCherk clAmdBlasCherk_ -#define clAmdBlasChpmv clAmdBlasChpmv_ -#define clAmdBlasChpr clAmdBlasChpr_ -#define clAmdBlasChpr2 clAmdBlasChpr2_ -#define clAmdBlasCrotg clAmdBlasCrotg_ -#define clAmdBlasCscal clAmdBlasCscal_ -#define clAmdBlasCsrot clAmdBlasCsrot_ -#define clAmdBlasCsscal clAmdBlasCsscal_ -#define clAmdBlasCswap clAmdBlasCswap_ -#define clAmdBlasCsymm clAmdBlasCsymm_ -#define clAmdBlasCsyr2k clAmdBlasCsyr2k_ -#define clAmdBlasCsyr2kEx clAmdBlasCsyr2kEx_ -#define clAmdBlasCsyrk clAmdBlasCsyrk_ -#define clAmdBlasCsyrkEx clAmdBlasCsyrkEx_ -#define clAmdBlasCtbmv clAmdBlasCtbmv_ -#define clAmdBlasCtbsv clAmdBlasCtbsv_ -#define clAmdBlasCtpmv clAmdBlasCtpmv_ -#define clAmdBlasCtpsv clAmdBlasCtpsv_ -#define clAmdBlasCtrmm clAmdBlasCtrmm_ -#define clAmdBlasCtrmmEx clAmdBlasCtrmmEx_ -#define clAmdBlasCtrmv clAmdBlasCtrmv_ -#define clAmdBlasCtrsm clAmdBlasCtrsm_ -#define clAmdBlasCtrsmEx clAmdBlasCtrsmEx_ -#define clAmdBlasCtrsv clAmdBlasCtrsv_ -#define clAmdBlasDasum clAmdBlasDasum_ -#define clAmdBlasDaxpy clAmdBlasDaxpy_ -#define clAmdBlasDcopy clAmdBlasDcopy_ -#define clAmdBlasDdot clAmdBlasDdot_ -#define clAmdBlasDgbmv clAmdBlasDgbmv_ -#define clAmdBlasDgemm clAmdBlasDgemm_ -#define clAmdBlasDgemmEx clAmdBlasDgemmEx_ -#define clAmdBlasDgemv clAmdBlasDgemv_ -#define clAmdBlasDgemvEx clAmdBlasDgemvEx_ -#define clAmdBlasDger clAmdBlasDger_ -#define clAmdBlasDnrm2 clAmdBlasDnrm2_ -#define clAmdBlasDrot clAmdBlasDrot_ -#define clAmdBlasDrotg clAmdBlasDrotg_ -#define clAmdBlasDrotm clAmdBlasDrotm_ -#define clAmdBlasDrotmg clAmdBlasDrotmg_ -#define clAmdBlasDsbmv clAmdBlasDsbmv_ -#define clAmdBlasDscal clAmdBlasDscal_ -#define clAmdBlasDspmv clAmdBlasDspmv_ -#define clAmdBlasDspr clAmdBlasDspr_ -#define clAmdBlasDspr2 clAmdBlasDspr2_ -#define clAmdBlasDswap clAmdBlasDswap_ -#define clAmdBlasDsymm clAmdBlasDsymm_ -#define clAmdBlasDsymv clAmdBlasDsymv_ -#define clAmdBlasDsymvEx clAmdBlasDsymvEx_ -#define clAmdBlasDsyr clAmdBlasDsyr_ -#define clAmdBlasDsyr2 clAmdBlasDsyr2_ -#define clAmdBlasDsyr2k clAmdBlasDsyr2k_ -#define clAmdBlasDsyr2kEx clAmdBlasDsyr2kEx_ -#define clAmdBlasDsyrk clAmdBlasDsyrk_ -#define clAmdBlasDsyrkEx clAmdBlasDsyrkEx_ -#define clAmdBlasDtbmv clAmdBlasDtbmv_ -#define clAmdBlasDtbsv clAmdBlasDtbsv_ -#define clAmdBlasDtpmv clAmdBlasDtpmv_ -#define clAmdBlasDtpsv clAmdBlasDtpsv_ -#define clAmdBlasDtrmm clAmdBlasDtrmm_ -#define clAmdBlasDtrmmEx clAmdBlasDtrmmEx_ -#define clAmdBlasDtrmv clAmdBlasDtrmv_ -#define clAmdBlasDtrsm clAmdBlasDtrsm_ -#define clAmdBlasDtrsmEx clAmdBlasDtrsmEx_ -#define clAmdBlasDtrsv clAmdBlasDtrsv_ -#define clAmdBlasDzasum clAmdBlasDzasum_ -#define clAmdBlasDznrm2 clAmdBlasDznrm2_ -#define clAmdBlasGetVersion clAmdBlasGetVersion_ -#define clAmdBlasRemoveScratchImage clAmdBlasRemoveScratchImage_ -#define clAmdBlasSasum clAmdBlasSasum_ -#define clAmdBlasSaxpy clAmdBlasSaxpy_ -#define clAmdBlasScasum clAmdBlasScasum_ -#define clAmdBlasScnrm2 clAmdBlasScnrm2_ -#define clAmdBlasScopy clAmdBlasScopy_ -#define clAmdBlasSdot clAmdBlasSdot_ -#define clAmdBlasSetup clAmdBlasSetup_ -#define clAmdBlasSgbmv clAmdBlasSgbmv_ -#define clAmdBlasSgemm clAmdBlasSgemm_ -#define clAmdBlasSgemmEx clAmdBlasSgemmEx_ -#define clAmdBlasSgemv clAmdBlasSgemv_ -#define clAmdBlasSgemvEx clAmdBlasSgemvEx_ -#define clAmdBlasSger clAmdBlasSger_ -#define clAmdBlasSnrm2 clAmdBlasSnrm2_ -#define clAmdBlasSrot clAmdBlasSrot_ -#define clAmdBlasSrotg clAmdBlasSrotg_ -#define clAmdBlasSrotm clAmdBlasSrotm_ -#define clAmdBlasSrotmg clAmdBlasSrotmg_ -#define clAmdBlasSsbmv clAmdBlasSsbmv_ -#define clAmdBlasSscal clAmdBlasSscal_ -#define clAmdBlasSspmv clAmdBlasSspmv_ -#define clAmdBlasSspr clAmdBlasSspr_ -#define clAmdBlasSspr2 clAmdBlasSspr2_ -#define clAmdBlasSswap clAmdBlasSswap_ -#define clAmdBlasSsymm clAmdBlasSsymm_ -#define clAmdBlasSsymv clAmdBlasSsymv_ -#define clAmdBlasSsymvEx clAmdBlasSsymvEx_ -#define clAmdBlasSsyr clAmdBlasSsyr_ -#define clAmdBlasSsyr2 clAmdBlasSsyr2_ -#define clAmdBlasSsyr2k clAmdBlasSsyr2k_ -#define clAmdBlasSsyr2kEx clAmdBlasSsyr2kEx_ -#define clAmdBlasSsyrk clAmdBlasSsyrk_ -#define clAmdBlasSsyrkEx clAmdBlasSsyrkEx_ -#define clAmdBlasStbmv clAmdBlasStbmv_ -#define clAmdBlasStbsv clAmdBlasStbsv_ -#define clAmdBlasStpmv clAmdBlasStpmv_ -#define clAmdBlasStpsv clAmdBlasStpsv_ -#define clAmdBlasStrmm clAmdBlasStrmm_ -#define clAmdBlasStrmmEx clAmdBlasStrmmEx_ -#define clAmdBlasStrmv clAmdBlasStrmv_ -#define clAmdBlasStrsm clAmdBlasStrsm_ -#define clAmdBlasStrsmEx clAmdBlasStrsmEx_ -#define clAmdBlasStrsv clAmdBlasStrsv_ -#define clAmdBlasTeardown clAmdBlasTeardown_ -#define clAmdBlasZaxpy clAmdBlasZaxpy_ -#define clAmdBlasZcopy clAmdBlasZcopy_ -#define clAmdBlasZdotc clAmdBlasZdotc_ -#define clAmdBlasZdotu clAmdBlasZdotu_ -#define clAmdBlasZdrot clAmdBlasZdrot_ -#define clAmdBlasZdscal clAmdBlasZdscal_ -#define clAmdBlasZgbmv clAmdBlasZgbmv_ -#define clAmdBlasZgemm clAmdBlasZgemm_ -#define clAmdBlasZgemmEx clAmdBlasZgemmEx_ -#define clAmdBlasZgemv clAmdBlasZgemv_ -#define clAmdBlasZgemvEx clAmdBlasZgemvEx_ -#define clAmdBlasZgerc clAmdBlasZgerc_ -#define clAmdBlasZgeru clAmdBlasZgeru_ -#define clAmdBlasZhbmv clAmdBlasZhbmv_ -#define clAmdBlasZhemm clAmdBlasZhemm_ -#define clAmdBlasZhemv clAmdBlasZhemv_ -#define clAmdBlasZher clAmdBlasZher_ -#define clAmdBlasZher2 clAmdBlasZher2_ -#define clAmdBlasZher2k clAmdBlasZher2k_ -#define clAmdBlasZherk clAmdBlasZherk_ -#define clAmdBlasZhpmv clAmdBlasZhpmv_ -#define clAmdBlasZhpr clAmdBlasZhpr_ -#define clAmdBlasZhpr2 clAmdBlasZhpr2_ -#define clAmdBlasZrotg clAmdBlasZrotg_ -#define clAmdBlasZscal clAmdBlasZscal_ -#define clAmdBlasZswap clAmdBlasZswap_ -#define clAmdBlasZsymm clAmdBlasZsymm_ -#define clAmdBlasZsyr2k clAmdBlasZsyr2k_ -#define clAmdBlasZsyr2kEx clAmdBlasZsyr2kEx_ -#define clAmdBlasZsyrk clAmdBlasZsyrk_ -#define clAmdBlasZsyrkEx clAmdBlasZsyrkEx_ -#define clAmdBlasZtbmv clAmdBlasZtbmv_ -#define clAmdBlasZtbsv clAmdBlasZtbsv_ -#define clAmdBlasZtpmv clAmdBlasZtpmv_ -#define clAmdBlasZtpsv clAmdBlasZtpsv_ -#define clAmdBlasZtrmm clAmdBlasZtrmm_ -#define clAmdBlasZtrmmEx clAmdBlasZtrmmEx_ -#define clAmdBlasZtrmv clAmdBlasZtrmv_ -#define clAmdBlasZtrsm clAmdBlasZtrsm_ -#define clAmdBlasZtrsmEx clAmdBlasZtrsmEx_ -#define clAmdBlasZtrsv clAmdBlasZtrsv_ -#define clAmdBlasiCamax clAmdBlasiCamax_ -#define clAmdBlasiDamax clAmdBlasiDamax_ -#define clAmdBlasiSamax clAmdBlasiSamax_ -#define clAmdBlasiZamax clAmdBlasiZamax_ - -#include - -// generated by parser_clamdblas.py -#undef clAmdBlasAddScratchImage -//#define clAmdBlasAddScratchImage clAmdBlasAddScratchImage_pfn -#undef clAmdBlasCaxpy -//#define clAmdBlasCaxpy clAmdBlasCaxpy_pfn -#undef clAmdBlasCcopy -//#define clAmdBlasCcopy clAmdBlasCcopy_pfn -#undef clAmdBlasCdotc -//#define clAmdBlasCdotc clAmdBlasCdotc_pfn -#undef clAmdBlasCdotu -//#define clAmdBlasCdotu clAmdBlasCdotu_pfn -#undef clAmdBlasCgbmv -//#define clAmdBlasCgbmv clAmdBlasCgbmv_pfn -#undef clAmdBlasCgemm -//#define clAmdBlasCgemm clAmdBlasCgemm_pfn -#undef clAmdBlasCgemmEx -#define clAmdBlasCgemmEx clAmdBlasCgemmEx_pfn -#undef clAmdBlasCgemv -//#define clAmdBlasCgemv clAmdBlasCgemv_pfn -#undef clAmdBlasCgemvEx -//#define clAmdBlasCgemvEx clAmdBlasCgemvEx_pfn -#undef clAmdBlasCgerc -//#define clAmdBlasCgerc clAmdBlasCgerc_pfn -#undef clAmdBlasCgeru -//#define clAmdBlasCgeru clAmdBlasCgeru_pfn -#undef clAmdBlasChbmv -//#define clAmdBlasChbmv clAmdBlasChbmv_pfn -#undef clAmdBlasChemm -//#define clAmdBlasChemm clAmdBlasChemm_pfn -#undef clAmdBlasChemv -//#define clAmdBlasChemv clAmdBlasChemv_pfn -#undef clAmdBlasCher -//#define clAmdBlasCher clAmdBlasCher_pfn -#undef clAmdBlasCher2 -//#define clAmdBlasCher2 clAmdBlasCher2_pfn -#undef clAmdBlasCher2k -//#define clAmdBlasCher2k clAmdBlasCher2k_pfn -#undef clAmdBlasCherk -//#define clAmdBlasCherk clAmdBlasCherk_pfn -#undef clAmdBlasChpmv -//#define clAmdBlasChpmv clAmdBlasChpmv_pfn -#undef clAmdBlasChpr -//#define clAmdBlasChpr clAmdBlasChpr_pfn -#undef clAmdBlasChpr2 -//#define clAmdBlasChpr2 clAmdBlasChpr2_pfn -#undef clAmdBlasCrotg -//#define clAmdBlasCrotg clAmdBlasCrotg_pfn -#undef clAmdBlasCscal -//#define clAmdBlasCscal clAmdBlasCscal_pfn -#undef clAmdBlasCsrot -//#define clAmdBlasCsrot clAmdBlasCsrot_pfn -#undef clAmdBlasCsscal -//#define clAmdBlasCsscal clAmdBlasCsscal_pfn -#undef clAmdBlasCswap -//#define clAmdBlasCswap clAmdBlasCswap_pfn -#undef clAmdBlasCsymm -//#define clAmdBlasCsymm clAmdBlasCsymm_pfn -#undef clAmdBlasCsyr2k -//#define clAmdBlasCsyr2k clAmdBlasCsyr2k_pfn -#undef clAmdBlasCsyr2kEx -//#define clAmdBlasCsyr2kEx clAmdBlasCsyr2kEx_pfn -#undef clAmdBlasCsyrk -//#define clAmdBlasCsyrk clAmdBlasCsyrk_pfn -#undef clAmdBlasCsyrkEx -//#define clAmdBlasCsyrkEx clAmdBlasCsyrkEx_pfn -#undef clAmdBlasCtbmv -//#define clAmdBlasCtbmv clAmdBlasCtbmv_pfn -#undef clAmdBlasCtbsv -//#define clAmdBlasCtbsv clAmdBlasCtbsv_pfn -#undef clAmdBlasCtpmv -//#define clAmdBlasCtpmv clAmdBlasCtpmv_pfn -#undef clAmdBlasCtpsv -//#define clAmdBlasCtpsv clAmdBlasCtpsv_pfn -#undef clAmdBlasCtrmm -//#define clAmdBlasCtrmm clAmdBlasCtrmm_pfn -#undef clAmdBlasCtrmmEx -//#define clAmdBlasCtrmmEx clAmdBlasCtrmmEx_pfn -#undef clAmdBlasCtrmv -//#define clAmdBlasCtrmv clAmdBlasCtrmv_pfn -#undef clAmdBlasCtrsm -//#define clAmdBlasCtrsm clAmdBlasCtrsm_pfn -#undef clAmdBlasCtrsmEx -//#define clAmdBlasCtrsmEx clAmdBlasCtrsmEx_pfn -#undef clAmdBlasCtrsv -//#define clAmdBlasCtrsv clAmdBlasCtrsv_pfn -#undef clAmdBlasDasum -//#define clAmdBlasDasum clAmdBlasDasum_pfn -#undef clAmdBlasDaxpy -//#define clAmdBlasDaxpy clAmdBlasDaxpy_pfn -#undef clAmdBlasDcopy -//#define clAmdBlasDcopy clAmdBlasDcopy_pfn -#undef clAmdBlasDdot -//#define clAmdBlasDdot clAmdBlasDdot_pfn -#undef clAmdBlasDgbmv -//#define clAmdBlasDgbmv clAmdBlasDgbmv_pfn -#undef clAmdBlasDgemm -//#define clAmdBlasDgemm clAmdBlasDgemm_pfn -#undef clAmdBlasDgemmEx -#define clAmdBlasDgemmEx clAmdBlasDgemmEx_pfn -#undef clAmdBlasDgemv -//#define clAmdBlasDgemv clAmdBlasDgemv_pfn -#undef clAmdBlasDgemvEx -//#define clAmdBlasDgemvEx clAmdBlasDgemvEx_pfn -#undef clAmdBlasDger -//#define clAmdBlasDger clAmdBlasDger_pfn -#undef clAmdBlasDnrm2 -//#define clAmdBlasDnrm2 clAmdBlasDnrm2_pfn -#undef clAmdBlasDrot -//#define clAmdBlasDrot clAmdBlasDrot_pfn -#undef clAmdBlasDrotg -//#define clAmdBlasDrotg clAmdBlasDrotg_pfn -#undef clAmdBlasDrotm -//#define clAmdBlasDrotm clAmdBlasDrotm_pfn -#undef clAmdBlasDrotmg -//#define clAmdBlasDrotmg clAmdBlasDrotmg_pfn -#undef clAmdBlasDsbmv -//#define clAmdBlasDsbmv clAmdBlasDsbmv_pfn -#undef clAmdBlasDscal -//#define clAmdBlasDscal clAmdBlasDscal_pfn -#undef clAmdBlasDspmv -//#define clAmdBlasDspmv clAmdBlasDspmv_pfn -#undef clAmdBlasDspr -//#define clAmdBlasDspr clAmdBlasDspr_pfn -#undef clAmdBlasDspr2 -//#define clAmdBlasDspr2 clAmdBlasDspr2_pfn -#undef clAmdBlasDswap -//#define clAmdBlasDswap clAmdBlasDswap_pfn -#undef clAmdBlasDsymm -//#define clAmdBlasDsymm clAmdBlasDsymm_pfn -#undef clAmdBlasDsymv -//#define clAmdBlasDsymv clAmdBlasDsymv_pfn -#undef clAmdBlasDsymvEx -//#define clAmdBlasDsymvEx clAmdBlasDsymvEx_pfn -#undef clAmdBlasDsyr -//#define clAmdBlasDsyr clAmdBlasDsyr_pfn -#undef clAmdBlasDsyr2 -//#define clAmdBlasDsyr2 clAmdBlasDsyr2_pfn -#undef clAmdBlasDsyr2k -//#define clAmdBlasDsyr2k clAmdBlasDsyr2k_pfn -#undef clAmdBlasDsyr2kEx -//#define clAmdBlasDsyr2kEx clAmdBlasDsyr2kEx_pfn -#undef clAmdBlasDsyrk -//#define clAmdBlasDsyrk clAmdBlasDsyrk_pfn -#undef clAmdBlasDsyrkEx -//#define clAmdBlasDsyrkEx clAmdBlasDsyrkEx_pfn -#undef clAmdBlasDtbmv -//#define clAmdBlasDtbmv clAmdBlasDtbmv_pfn -#undef clAmdBlasDtbsv -//#define clAmdBlasDtbsv clAmdBlasDtbsv_pfn -#undef clAmdBlasDtpmv -//#define clAmdBlasDtpmv clAmdBlasDtpmv_pfn -#undef clAmdBlasDtpsv -//#define clAmdBlasDtpsv clAmdBlasDtpsv_pfn -#undef clAmdBlasDtrmm -//#define clAmdBlasDtrmm clAmdBlasDtrmm_pfn -#undef clAmdBlasDtrmmEx -//#define clAmdBlasDtrmmEx clAmdBlasDtrmmEx_pfn -#undef clAmdBlasDtrmv -//#define clAmdBlasDtrmv clAmdBlasDtrmv_pfn -#undef clAmdBlasDtrsm -//#define clAmdBlasDtrsm clAmdBlasDtrsm_pfn -#undef clAmdBlasDtrsmEx -//#define clAmdBlasDtrsmEx clAmdBlasDtrsmEx_pfn -#undef clAmdBlasDtrsv -//#define clAmdBlasDtrsv clAmdBlasDtrsv_pfn -#undef clAmdBlasDzasum -//#define clAmdBlasDzasum clAmdBlasDzasum_pfn -#undef clAmdBlasDznrm2 -//#define clAmdBlasDznrm2 clAmdBlasDznrm2_pfn -#undef clAmdBlasGetVersion -//#define clAmdBlasGetVersion clAmdBlasGetVersion_pfn -#undef clAmdBlasRemoveScratchImage -//#define clAmdBlasRemoveScratchImage clAmdBlasRemoveScratchImage_pfn -#undef clAmdBlasSasum -//#define clAmdBlasSasum clAmdBlasSasum_pfn -#undef clAmdBlasSaxpy -//#define clAmdBlasSaxpy clAmdBlasSaxpy_pfn -#undef clAmdBlasScasum -//#define clAmdBlasScasum clAmdBlasScasum_pfn -#undef clAmdBlasScnrm2 -//#define clAmdBlasScnrm2 clAmdBlasScnrm2_pfn -#undef clAmdBlasScopy -//#define clAmdBlasScopy clAmdBlasScopy_pfn -#undef clAmdBlasSdot -//#define clAmdBlasSdot clAmdBlasSdot_pfn -#undef clAmdBlasSetup -#define clAmdBlasSetup clAmdBlasSetup_pfn -#undef clAmdBlasSgbmv -//#define clAmdBlasSgbmv clAmdBlasSgbmv_pfn -#undef clAmdBlasSgemm -//#define clAmdBlasSgemm clAmdBlasSgemm_pfn -#undef clAmdBlasSgemmEx -#define clAmdBlasSgemmEx clAmdBlasSgemmEx_pfn -#undef clAmdBlasSgemv -//#define clAmdBlasSgemv clAmdBlasSgemv_pfn -#undef clAmdBlasSgemvEx -//#define clAmdBlasSgemvEx clAmdBlasSgemvEx_pfn -#undef clAmdBlasSger -//#define clAmdBlasSger clAmdBlasSger_pfn -#undef clAmdBlasSnrm2 -//#define clAmdBlasSnrm2 clAmdBlasSnrm2_pfn -#undef clAmdBlasSrot -//#define clAmdBlasSrot clAmdBlasSrot_pfn -#undef clAmdBlasSrotg -//#define clAmdBlasSrotg clAmdBlasSrotg_pfn -#undef clAmdBlasSrotm -//#define clAmdBlasSrotm clAmdBlasSrotm_pfn -#undef clAmdBlasSrotmg -//#define clAmdBlasSrotmg clAmdBlasSrotmg_pfn -#undef clAmdBlasSsbmv -//#define clAmdBlasSsbmv clAmdBlasSsbmv_pfn -#undef clAmdBlasSscal -//#define clAmdBlasSscal clAmdBlasSscal_pfn -#undef clAmdBlasSspmv -//#define clAmdBlasSspmv clAmdBlasSspmv_pfn -#undef clAmdBlasSspr -//#define clAmdBlasSspr clAmdBlasSspr_pfn -#undef clAmdBlasSspr2 -//#define clAmdBlasSspr2 clAmdBlasSspr2_pfn -#undef clAmdBlasSswap -//#define clAmdBlasSswap clAmdBlasSswap_pfn -#undef clAmdBlasSsymm -//#define clAmdBlasSsymm clAmdBlasSsymm_pfn -#undef clAmdBlasSsymv -//#define clAmdBlasSsymv clAmdBlasSsymv_pfn -#undef clAmdBlasSsymvEx -//#define clAmdBlasSsymvEx clAmdBlasSsymvEx_pfn -#undef clAmdBlasSsyr -//#define clAmdBlasSsyr clAmdBlasSsyr_pfn -#undef clAmdBlasSsyr2 -//#define clAmdBlasSsyr2 clAmdBlasSsyr2_pfn -#undef clAmdBlasSsyr2k -//#define clAmdBlasSsyr2k clAmdBlasSsyr2k_pfn -#undef clAmdBlasSsyr2kEx -//#define clAmdBlasSsyr2kEx clAmdBlasSsyr2kEx_pfn -#undef clAmdBlasSsyrk -//#define clAmdBlasSsyrk clAmdBlasSsyrk_pfn -#undef clAmdBlasSsyrkEx -//#define clAmdBlasSsyrkEx clAmdBlasSsyrkEx_pfn -#undef clAmdBlasStbmv -//#define clAmdBlasStbmv clAmdBlasStbmv_pfn -#undef clAmdBlasStbsv -//#define clAmdBlasStbsv clAmdBlasStbsv_pfn -#undef clAmdBlasStpmv -//#define clAmdBlasStpmv clAmdBlasStpmv_pfn -#undef clAmdBlasStpsv -//#define clAmdBlasStpsv clAmdBlasStpsv_pfn -#undef clAmdBlasStrmm -//#define clAmdBlasStrmm clAmdBlasStrmm_pfn -#undef clAmdBlasStrmmEx -//#define clAmdBlasStrmmEx clAmdBlasStrmmEx_pfn -#undef clAmdBlasStrmv -//#define clAmdBlasStrmv clAmdBlasStrmv_pfn -#undef clAmdBlasStrsm -//#define clAmdBlasStrsm clAmdBlasStrsm_pfn -#undef clAmdBlasStrsmEx -//#define clAmdBlasStrsmEx clAmdBlasStrsmEx_pfn -#undef clAmdBlasStrsv -//#define clAmdBlasStrsv clAmdBlasStrsv_pfn -#undef clAmdBlasTeardown -#define clAmdBlasTeardown clAmdBlasTeardown_pfn -#undef clAmdBlasZaxpy -//#define clAmdBlasZaxpy clAmdBlasZaxpy_pfn -#undef clAmdBlasZcopy -//#define clAmdBlasZcopy clAmdBlasZcopy_pfn -#undef clAmdBlasZdotc -//#define clAmdBlasZdotc clAmdBlasZdotc_pfn -#undef clAmdBlasZdotu -//#define clAmdBlasZdotu clAmdBlasZdotu_pfn -#undef clAmdBlasZdrot -//#define clAmdBlasZdrot clAmdBlasZdrot_pfn -#undef clAmdBlasZdscal -//#define clAmdBlasZdscal clAmdBlasZdscal_pfn -#undef clAmdBlasZgbmv -//#define clAmdBlasZgbmv clAmdBlasZgbmv_pfn -#undef clAmdBlasZgemm -//#define clAmdBlasZgemm clAmdBlasZgemm_pfn -#undef clAmdBlasZgemmEx -#define clAmdBlasZgemmEx clAmdBlasZgemmEx_pfn -#undef clAmdBlasZgemv -//#define clAmdBlasZgemv clAmdBlasZgemv_pfn -#undef clAmdBlasZgemvEx -//#define clAmdBlasZgemvEx clAmdBlasZgemvEx_pfn -#undef clAmdBlasZgerc -//#define clAmdBlasZgerc clAmdBlasZgerc_pfn -#undef clAmdBlasZgeru -//#define clAmdBlasZgeru clAmdBlasZgeru_pfn -#undef clAmdBlasZhbmv -//#define clAmdBlasZhbmv clAmdBlasZhbmv_pfn -#undef clAmdBlasZhemm -//#define clAmdBlasZhemm clAmdBlasZhemm_pfn -#undef clAmdBlasZhemv -//#define clAmdBlasZhemv clAmdBlasZhemv_pfn -#undef clAmdBlasZher -//#define clAmdBlasZher clAmdBlasZher_pfn -#undef clAmdBlasZher2 -//#define clAmdBlasZher2 clAmdBlasZher2_pfn -#undef clAmdBlasZher2k -//#define clAmdBlasZher2k clAmdBlasZher2k_pfn -#undef clAmdBlasZherk -//#define clAmdBlasZherk clAmdBlasZherk_pfn -#undef clAmdBlasZhpmv -//#define clAmdBlasZhpmv clAmdBlasZhpmv_pfn -#undef clAmdBlasZhpr -//#define clAmdBlasZhpr clAmdBlasZhpr_pfn -#undef clAmdBlasZhpr2 -//#define clAmdBlasZhpr2 clAmdBlasZhpr2_pfn -#undef clAmdBlasZrotg -//#define clAmdBlasZrotg clAmdBlasZrotg_pfn -#undef clAmdBlasZscal -//#define clAmdBlasZscal clAmdBlasZscal_pfn -#undef clAmdBlasZswap -//#define clAmdBlasZswap clAmdBlasZswap_pfn -#undef clAmdBlasZsymm -//#define clAmdBlasZsymm clAmdBlasZsymm_pfn -#undef clAmdBlasZsyr2k -//#define clAmdBlasZsyr2k clAmdBlasZsyr2k_pfn -#undef clAmdBlasZsyr2kEx -//#define clAmdBlasZsyr2kEx clAmdBlasZsyr2kEx_pfn -#undef clAmdBlasZsyrk -//#define clAmdBlasZsyrk clAmdBlasZsyrk_pfn -#undef clAmdBlasZsyrkEx -//#define clAmdBlasZsyrkEx clAmdBlasZsyrkEx_pfn -#undef clAmdBlasZtbmv -//#define clAmdBlasZtbmv clAmdBlasZtbmv_pfn -#undef clAmdBlasZtbsv -//#define clAmdBlasZtbsv clAmdBlasZtbsv_pfn -#undef clAmdBlasZtpmv -//#define clAmdBlasZtpmv clAmdBlasZtpmv_pfn -#undef clAmdBlasZtpsv -//#define clAmdBlasZtpsv clAmdBlasZtpsv_pfn -#undef clAmdBlasZtrmm -//#define clAmdBlasZtrmm clAmdBlasZtrmm_pfn -#undef clAmdBlasZtrmmEx -//#define clAmdBlasZtrmmEx clAmdBlasZtrmmEx_pfn -#undef clAmdBlasZtrmv -//#define clAmdBlasZtrmv clAmdBlasZtrmv_pfn -#undef clAmdBlasZtrsm -//#define clAmdBlasZtrsm clAmdBlasZtrsm_pfn -#undef clAmdBlasZtrsmEx -//#define clAmdBlasZtrsmEx clAmdBlasZtrsmEx_pfn -#undef clAmdBlasZtrsv -//#define clAmdBlasZtrsv clAmdBlasZtrsv_pfn -#undef clAmdBlasiCamax -//#define clAmdBlasiCamax clAmdBlasiCamax_pfn -#undef clAmdBlasiDamax -//#define clAmdBlasiDamax clAmdBlasiDamax_pfn -#undef clAmdBlasiSamax -//#define clAmdBlasiSamax clAmdBlasiSamax_pfn -#undef clAmdBlasiZamax -//#define clAmdBlasiZamax clAmdBlasiZamax_pfn - -// generated by parser_clamdblas.py -//extern CL_RUNTIME_EXPORT cl_ulong (*clAmdBlasAddScratchImage)(cl_context context, size_t width, size_t height, clAmdBlasStatus* status); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCaxpy)(size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCdotc)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCdotu)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, FloatComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, FloatComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, FloatComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgerc)(clAmdBlasOrder order, size_t M, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgeru)(clAmdBlasOrder order, size_t M, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChemm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChemv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, FloatComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, FloatComplex beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCher)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCher2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCher2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCherk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, float alpha, const cl_mem A, size_t offa, size_t lda, float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float2 alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChpr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChpr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCrotg)(cl_mem CA, size_t offCA, cl_mem CB, size_t offCB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCscal)(size_t N, cl_float2 alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_float C, cl_float S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsscal)(size_t N, cl_float alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, FloatComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t lda, FloatComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDaxpy)(size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDdot)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_double beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDger)(clAmdBlasOrder order, size_t M, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_double C, cl_double S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrotg)(cl_mem DA, size_t offDA, cl_mem DB, size_t offDB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrotm)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, const cl_mem DPARAM, size_t offDparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrotmg)(cl_mem DD1, size_t offDD1, cl_mem DD2, size_t offDD2, cl_mem DX1, size_t offDX1, const cl_mem DY1, size_t offDY1, cl_mem DPARAM, size_t offDparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDscal)(size_t N, cl_double alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDspmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDspr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDspr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsymv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsymvEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_double beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t lda, cl_double beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDzasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDznrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasGetVersion)(cl_uint* major, cl_uint* minor, cl_uint* patch); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasRemoveScratchImage)(cl_ulong imageID); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSaxpy)(size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasScasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasScnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasScopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSdot)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSetup)(); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_float beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSger)(clAmdBlasOrder order, size_t M, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_float C, cl_float S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrotg)(cl_mem SA, size_t offSA, cl_mem SB, size_t offSB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrotm)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, const cl_mem SPARAM, size_t offSparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrotmg)(cl_mem SD1, size_t offSD1, cl_mem SD2, size_t offSD2, cl_mem SX1, size_t offSX1, const cl_mem SY1, size_t offSY1, cl_mem SPARAM, size_t offSparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSscal)(size_t N, cl_float alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSspmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSspr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSspr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsymv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsymvEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_float beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t lda, cl_float beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -extern CL_RUNTIME_EXPORT void (*clAmdBlasTeardown)(); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZaxpy)(size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdotc)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdotu)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_double C, cl_double S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdscal)(size_t N, cl_double alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, DoubleComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, DoubleComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, DoubleComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgerc)(clAmdBlasOrder order, size_t M, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgeru)(clAmdBlasOrder order, size_t M, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhemm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhemv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, DoubleComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, DoubleComplex beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZher)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZher2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZher2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZherk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, double alpha, const cl_mem A, size_t offa, size_t lda, double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double2 alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhpr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhpr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZrotg)(cl_mem CA, size_t offCA, cl_mem CB, size_t offCB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZscal)(size_t N, cl_double2 alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, DoubleComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t lda, DoubleComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiCamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiDamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiSamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); -//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiZamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events); diff --git a/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdfft.hpp b/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdfft.hpp deleted file mode 100755 index 1457d7e..0000000 --- a/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdfft.hpp +++ /dev/null @@ -1,142 +0,0 @@ -// -// AUTOGENERATED, DO NOT EDIT -// -#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP -#error "Invalid usage" -#endif - -// generated by parser_clamdfft.py -#define clAmdFftBakePlan clAmdFftBakePlan_ -#define clAmdFftCopyPlan clAmdFftCopyPlan_ -#define clAmdFftCreateDefaultPlan clAmdFftCreateDefaultPlan_ -#define clAmdFftDestroyPlan clAmdFftDestroyPlan_ -#define clAmdFftEnqueueTransform clAmdFftEnqueueTransform_ -#define clAmdFftGetLayout clAmdFftGetLayout_ -#define clAmdFftGetPlanBatchSize clAmdFftGetPlanBatchSize_ -#define clAmdFftGetPlanContext clAmdFftGetPlanContext_ -#define clAmdFftGetPlanDim clAmdFftGetPlanDim_ -#define clAmdFftGetPlanDistance clAmdFftGetPlanDistance_ -#define clAmdFftGetPlanInStride clAmdFftGetPlanInStride_ -#define clAmdFftGetPlanLength clAmdFftGetPlanLength_ -#define clAmdFftGetPlanOutStride clAmdFftGetPlanOutStride_ -#define clAmdFftGetPlanPrecision clAmdFftGetPlanPrecision_ -#define clAmdFftGetPlanScale clAmdFftGetPlanScale_ -#define clAmdFftGetPlanTransposeResult clAmdFftGetPlanTransposeResult_ -#define clAmdFftGetResultLocation clAmdFftGetResultLocation_ -#define clAmdFftGetTmpBufSize clAmdFftGetTmpBufSize_ -#define clAmdFftGetVersion clAmdFftGetVersion_ -#define clAmdFftSetLayout clAmdFftSetLayout_ -#define clAmdFftSetPlanBatchSize clAmdFftSetPlanBatchSize_ -#define clAmdFftSetPlanDim clAmdFftSetPlanDim_ -#define clAmdFftSetPlanDistance clAmdFftSetPlanDistance_ -#define clAmdFftSetPlanInStride clAmdFftSetPlanInStride_ -#define clAmdFftSetPlanLength clAmdFftSetPlanLength_ -#define clAmdFftSetPlanOutStride clAmdFftSetPlanOutStride_ -#define clAmdFftSetPlanPrecision clAmdFftSetPlanPrecision_ -#define clAmdFftSetPlanScale clAmdFftSetPlanScale_ -#define clAmdFftSetPlanTransposeResult clAmdFftSetPlanTransposeResult_ -#define clAmdFftSetResultLocation clAmdFftSetResultLocation_ -#define clAmdFftSetup clAmdFftSetup_ -#define clAmdFftTeardown clAmdFftTeardown_ - -#include - -// generated by parser_clamdfft.py -#undef clAmdFftBakePlan -#define clAmdFftBakePlan clAmdFftBakePlan_pfn -#undef clAmdFftCopyPlan -//#define clAmdFftCopyPlan clAmdFftCopyPlan_pfn -#undef clAmdFftCreateDefaultPlan -#define clAmdFftCreateDefaultPlan clAmdFftCreateDefaultPlan_pfn -#undef clAmdFftDestroyPlan -#define clAmdFftDestroyPlan clAmdFftDestroyPlan_pfn -#undef clAmdFftEnqueueTransform -#define clAmdFftEnqueueTransform clAmdFftEnqueueTransform_pfn -#undef clAmdFftGetLayout -//#define clAmdFftGetLayout clAmdFftGetLayout_pfn -#undef clAmdFftGetPlanBatchSize -//#define clAmdFftGetPlanBatchSize clAmdFftGetPlanBatchSize_pfn -#undef clAmdFftGetPlanContext -//#define clAmdFftGetPlanContext clAmdFftGetPlanContext_pfn -#undef clAmdFftGetPlanDim -//#define clAmdFftGetPlanDim clAmdFftGetPlanDim_pfn -#undef clAmdFftGetPlanDistance -//#define clAmdFftGetPlanDistance clAmdFftGetPlanDistance_pfn -#undef clAmdFftGetPlanInStride -//#define clAmdFftGetPlanInStride clAmdFftGetPlanInStride_pfn -#undef clAmdFftGetPlanLength -//#define clAmdFftGetPlanLength clAmdFftGetPlanLength_pfn -#undef clAmdFftGetPlanOutStride -//#define clAmdFftGetPlanOutStride clAmdFftGetPlanOutStride_pfn -#undef clAmdFftGetPlanPrecision -//#define clAmdFftGetPlanPrecision clAmdFftGetPlanPrecision_pfn -#undef clAmdFftGetPlanScale -//#define clAmdFftGetPlanScale clAmdFftGetPlanScale_pfn -#undef clAmdFftGetPlanTransposeResult -//#define clAmdFftGetPlanTransposeResult clAmdFftGetPlanTransposeResult_pfn -#undef clAmdFftGetResultLocation -//#define clAmdFftGetResultLocation clAmdFftGetResultLocation_pfn -#undef clAmdFftGetTmpBufSize -#define clAmdFftGetTmpBufSize clAmdFftGetTmpBufSize_pfn -#undef clAmdFftGetVersion -#define clAmdFftGetVersion clAmdFftGetVersion_pfn -#undef clAmdFftSetLayout -#define clAmdFftSetLayout clAmdFftSetLayout_pfn -#undef clAmdFftSetPlanBatchSize -#define clAmdFftSetPlanBatchSize clAmdFftSetPlanBatchSize_pfn -#undef clAmdFftSetPlanDim -//#define clAmdFftSetPlanDim clAmdFftSetPlanDim_pfn -#undef clAmdFftSetPlanDistance -#define clAmdFftSetPlanDistance clAmdFftSetPlanDistance_pfn -#undef clAmdFftSetPlanInStride -#define clAmdFftSetPlanInStride clAmdFftSetPlanInStride_pfn -#undef clAmdFftSetPlanLength -//#define clAmdFftSetPlanLength clAmdFftSetPlanLength_pfn -#undef clAmdFftSetPlanOutStride -#define clAmdFftSetPlanOutStride clAmdFftSetPlanOutStride_pfn -#undef clAmdFftSetPlanPrecision -#define clAmdFftSetPlanPrecision clAmdFftSetPlanPrecision_pfn -#undef clAmdFftSetPlanScale -#define clAmdFftSetPlanScale clAmdFftSetPlanScale_pfn -#undef clAmdFftSetPlanTransposeResult -//#define clAmdFftSetPlanTransposeResult clAmdFftSetPlanTransposeResult_pfn -#undef clAmdFftSetResultLocation -#define clAmdFftSetResultLocation clAmdFftSetResultLocation_pfn -#undef clAmdFftSetup -#define clAmdFftSetup clAmdFftSetup_pfn -#undef clAmdFftTeardown -#define clAmdFftTeardown clAmdFftTeardown_pfn - -// generated by parser_clamdfft.py -extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftBakePlan)(clAmdFftPlanHandle plHandle, cl_uint numQueues, cl_command_queue* commQueueFFT, void (CL_CALLBACK* pfn_notify) (clAmdFftPlanHandle plHandle, void* user_data), void* user_data); -//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftCopyPlan)(clAmdFftPlanHandle* out_plHandle, cl_context new_context, clAmdFftPlanHandle in_plHandle); -extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftCreateDefaultPlan)(clAmdFftPlanHandle* plHandle, cl_context context, const clAmdFftDim dim, const size_t* clLengths); -extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftDestroyPlan)(clAmdFftPlanHandle* plHandle); -extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftEnqueueTransform)(clAmdFftPlanHandle plHandle, clAmdFftDirection dir, cl_uint numQueuesAndEvents, cl_command_queue* commQueues, cl_uint numWaitEvents, const cl_event* waitEvents, cl_event* outEvents, cl_mem* inputBuffers, cl_mem* outputBuffers, cl_mem tmpBuffer); -//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetLayout)(const clAmdFftPlanHandle plHandle, clAmdFftLayout* iLayout, clAmdFftLayout* oLayout); -//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanBatchSize)(const clAmdFftPlanHandle plHandle, size_t* batchSize); -//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanContext)(const clAmdFftPlanHandle plHandle, cl_context* context); -//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanDim)(const clAmdFftPlanHandle plHandle, clAmdFftDim* dim, cl_uint* size); -//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanDistance)(const clAmdFftPlanHandle plHandle, size_t* iDist, size_t* oDist); -//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanInStride)(const clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides); -//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanLength)(const clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clLengths); -//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanOutStride)(const clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides); -//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanPrecision)(const clAmdFftPlanHandle plHandle, clAmdFftPrecision* precision); -//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanScale)(const clAmdFftPlanHandle plHandle, clAmdFftDirection dir, cl_float* scale); -//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanTransposeResult)(const clAmdFftPlanHandle plHandle, clAmdFftResultTransposed* transposed); -//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetResultLocation)(const clAmdFftPlanHandle plHandle, clAmdFftResultLocation* placeness); -extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetTmpBufSize)(const clAmdFftPlanHandle plHandle, size_t* buffersize); -extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetVersion)(cl_uint* major, cl_uint* minor, cl_uint* patch); -extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetLayout)(clAmdFftPlanHandle plHandle, clAmdFftLayout iLayout, clAmdFftLayout oLayout); -extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanBatchSize)(clAmdFftPlanHandle plHandle, size_t batchSize); -//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanDim)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim); -extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanDistance)(clAmdFftPlanHandle plHandle, size_t iDist, size_t oDist); -extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanInStride)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides); -//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanLength)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim, const size_t* clLengths); -extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanOutStride)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides); -extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanPrecision)(clAmdFftPlanHandle plHandle, clAmdFftPrecision precision); -extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanScale)(clAmdFftPlanHandle plHandle, clAmdFftDirection dir, cl_float scale); -//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanTransposeResult)(clAmdFftPlanHandle plHandle, clAmdFftResultTransposed transposed); -extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetResultLocation)(clAmdFftPlanHandle plHandle, clAmdFftResultLocation placeness); -extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetup)(const clAmdFftSetupData* setupData); -extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftTeardown)(); diff --git a/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp b/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp deleted file mode 100755 index 28618a1..0000000 --- a/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp +++ /dev/null @@ -1,371 +0,0 @@ -// -// AUTOGENERATED, DO NOT EDIT -// -#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP -#error "Invalid usage" -#endif - -// generated by parser_cl.py -#define clBuildProgram clBuildProgram_ -#define clCompileProgram clCompileProgram_ -#define clCreateBuffer clCreateBuffer_ -#define clCreateCommandQueue clCreateCommandQueue_ -#define clCreateContext clCreateContext_ -#define clCreateContextFromType clCreateContextFromType_ -#define clCreateImage clCreateImage_ -#define clCreateImage2D clCreateImage2D_ -#define clCreateImage3D clCreateImage3D_ -#define clCreateKernel clCreateKernel_ -#define clCreateKernelsInProgram clCreateKernelsInProgram_ -#define clCreateProgramWithBinary clCreateProgramWithBinary_ -#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_ -#define clCreateProgramWithSource clCreateProgramWithSource_ -#define clCreateSampler clCreateSampler_ -#define clCreateSubBuffer clCreateSubBuffer_ -#define clCreateSubDevices clCreateSubDevices_ -#define clCreateUserEvent clCreateUserEvent_ -#define clEnqueueBarrier clEnqueueBarrier_ -#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_ -#define clEnqueueCopyBuffer clEnqueueCopyBuffer_ -#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_ -#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_ -#define clEnqueueCopyImage clEnqueueCopyImage_ -#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_ -#define clEnqueueFillBuffer clEnqueueFillBuffer_ -#define clEnqueueFillImage clEnqueueFillImage_ -#define clEnqueueMapBuffer clEnqueueMapBuffer_ -#define clEnqueueMapImage clEnqueueMapImage_ -#define clEnqueueMarker clEnqueueMarker_ -#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_ -#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_ -#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_ -#define clEnqueueNativeKernel clEnqueueNativeKernel_ -#define clEnqueueReadBuffer clEnqueueReadBuffer_ -#define clEnqueueReadBufferRect clEnqueueReadBufferRect_ -#define clEnqueueReadImage clEnqueueReadImage_ -#define clEnqueueTask clEnqueueTask_ -#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_ -#define clEnqueueWaitForEvents clEnqueueWaitForEvents_ -#define clEnqueueWriteBuffer clEnqueueWriteBuffer_ -#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_ -#define clEnqueueWriteImage clEnqueueWriteImage_ -#define clFinish clFinish_ -#define clFlush clFlush_ -#define clGetCommandQueueInfo clGetCommandQueueInfo_ -#define clGetContextInfo clGetContextInfo_ -#define clGetDeviceIDs clGetDeviceIDs_ -#define clGetDeviceInfo clGetDeviceInfo_ -#define clGetEventInfo clGetEventInfo_ -#define clGetEventProfilingInfo clGetEventProfilingInfo_ -#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_ -#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_ -#define clGetImageInfo clGetImageInfo_ -#define clGetKernelArgInfo clGetKernelArgInfo_ -#define clGetKernelInfo clGetKernelInfo_ -#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_ -#define clGetMemObjectInfo clGetMemObjectInfo_ -#define clGetPlatformIDs clGetPlatformIDs_ -#define clGetPlatformInfo clGetPlatformInfo_ -#define clGetProgramBuildInfo clGetProgramBuildInfo_ -#define clGetProgramInfo clGetProgramInfo_ -#define clGetSamplerInfo clGetSamplerInfo_ -#define clGetSupportedImageFormats clGetSupportedImageFormats_ -#define clLinkProgram clLinkProgram_ -#define clReleaseCommandQueue clReleaseCommandQueue_ -#define clReleaseContext clReleaseContext_ -#define clReleaseDevice clReleaseDevice_ -#define clReleaseEvent clReleaseEvent_ -#define clReleaseKernel clReleaseKernel_ -#define clReleaseMemObject clReleaseMemObject_ -#define clReleaseProgram clReleaseProgram_ -#define clReleaseSampler clReleaseSampler_ -#define clRetainCommandQueue clRetainCommandQueue_ -#define clRetainContext clRetainContext_ -#define clRetainDevice clRetainDevice_ -#define clRetainEvent clRetainEvent_ -#define clRetainKernel clRetainKernel_ -#define clRetainMemObject clRetainMemObject_ -#define clRetainProgram clRetainProgram_ -#define clRetainSampler clRetainSampler_ -#define clSetEventCallback clSetEventCallback_ -#define clSetKernelArg clSetKernelArg_ -#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_ -#define clSetUserEventStatus clSetUserEventStatus_ -#define clUnloadCompiler clUnloadCompiler_ -#define clUnloadPlatformCompiler clUnloadPlatformCompiler_ -#define clWaitForEvents clWaitForEvents_ - -#if defined __APPLE__ -#define CL_SILENCE_DEPRECATION -#include -#else -#include -#endif - -// generated by parser_cl.py -#undef clBuildProgram -#define clBuildProgram clBuildProgram_pfn -#undef clCompileProgram -#define clCompileProgram clCompileProgram_pfn -#undef clCreateBuffer -#define clCreateBuffer clCreateBuffer_pfn -#undef clCreateCommandQueue -#define clCreateCommandQueue clCreateCommandQueue_pfn -#undef clCreateContext -#define clCreateContext clCreateContext_pfn -#undef clCreateContextFromType -#define clCreateContextFromType clCreateContextFromType_pfn -#undef clCreateImage -#define clCreateImage clCreateImage_pfn -#undef clCreateImage2D -#define clCreateImage2D clCreateImage2D_pfn -#undef clCreateImage3D -#define clCreateImage3D clCreateImage3D_pfn -#undef clCreateKernel -#define clCreateKernel clCreateKernel_pfn -#undef clCreateKernelsInProgram -#define clCreateKernelsInProgram clCreateKernelsInProgram_pfn -#undef clCreateProgramWithBinary -#define clCreateProgramWithBinary clCreateProgramWithBinary_pfn -#undef clCreateProgramWithBuiltInKernels -#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_pfn -#undef clCreateProgramWithSource -#define clCreateProgramWithSource clCreateProgramWithSource_pfn -#undef clCreateSampler -#define clCreateSampler clCreateSampler_pfn -#undef clCreateSubBuffer -#define clCreateSubBuffer clCreateSubBuffer_pfn -#undef clCreateSubDevices -#define clCreateSubDevices clCreateSubDevices_pfn -#undef clCreateUserEvent -#define clCreateUserEvent clCreateUserEvent_pfn -#undef clEnqueueBarrier -#define clEnqueueBarrier clEnqueueBarrier_pfn -#undef clEnqueueBarrierWithWaitList -#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_pfn -#undef clEnqueueCopyBuffer -#define clEnqueueCopyBuffer clEnqueueCopyBuffer_pfn -#undef clEnqueueCopyBufferRect -#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_pfn -#undef clEnqueueCopyBufferToImage -#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_pfn -#undef clEnqueueCopyImage -#define clEnqueueCopyImage clEnqueueCopyImage_pfn -#undef clEnqueueCopyImageToBuffer -#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_pfn -#undef clEnqueueFillBuffer -#define clEnqueueFillBuffer clEnqueueFillBuffer_pfn -#undef clEnqueueFillImage -#define clEnqueueFillImage clEnqueueFillImage_pfn -#undef clEnqueueMapBuffer -#define clEnqueueMapBuffer clEnqueueMapBuffer_pfn -#undef clEnqueueMapImage -#define clEnqueueMapImage clEnqueueMapImage_pfn -#undef clEnqueueMarker -#define clEnqueueMarker clEnqueueMarker_pfn -#undef clEnqueueMarkerWithWaitList -#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_pfn -#undef clEnqueueMigrateMemObjects -#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_pfn -#undef clEnqueueNDRangeKernel -#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_pfn -#undef clEnqueueNativeKernel -#define clEnqueueNativeKernel clEnqueueNativeKernel_pfn -#undef clEnqueueReadBuffer -#define clEnqueueReadBuffer clEnqueueReadBuffer_pfn -#undef clEnqueueReadBufferRect -#define clEnqueueReadBufferRect clEnqueueReadBufferRect_pfn -#undef clEnqueueReadImage -#define clEnqueueReadImage clEnqueueReadImage_pfn -#undef clEnqueueTask -#define clEnqueueTask clEnqueueTask_pfn -#undef clEnqueueUnmapMemObject -#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_pfn -#undef clEnqueueWaitForEvents -#define clEnqueueWaitForEvents clEnqueueWaitForEvents_pfn -#undef clEnqueueWriteBuffer -#define clEnqueueWriteBuffer clEnqueueWriteBuffer_pfn -#undef clEnqueueWriteBufferRect -#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_pfn -#undef clEnqueueWriteImage -#define clEnqueueWriteImage clEnqueueWriteImage_pfn -#undef clFinish -#define clFinish clFinish_pfn -#undef clFlush -#define clFlush clFlush_pfn -#undef clGetCommandQueueInfo -#define clGetCommandQueueInfo clGetCommandQueueInfo_pfn -#undef clGetContextInfo -#define clGetContextInfo clGetContextInfo_pfn -#undef clGetDeviceIDs -#define clGetDeviceIDs clGetDeviceIDs_pfn -#undef clGetDeviceInfo -#define clGetDeviceInfo clGetDeviceInfo_pfn -#undef clGetEventInfo -#define clGetEventInfo clGetEventInfo_pfn -#undef clGetEventProfilingInfo -#define clGetEventProfilingInfo clGetEventProfilingInfo_pfn -#undef clGetExtensionFunctionAddress -#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_pfn -#undef clGetExtensionFunctionAddressForPlatform -#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_pfn -#undef clGetImageInfo -#define clGetImageInfo clGetImageInfo_pfn -#undef clGetKernelArgInfo -#define clGetKernelArgInfo clGetKernelArgInfo_pfn -#undef clGetKernelInfo -#define clGetKernelInfo clGetKernelInfo_pfn -#undef clGetKernelWorkGroupInfo -#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_pfn -#undef clGetMemObjectInfo -#define clGetMemObjectInfo clGetMemObjectInfo_pfn -#undef clGetPlatformIDs -#define clGetPlatformIDs clGetPlatformIDs_pfn -#undef clGetPlatformInfo -#define clGetPlatformInfo clGetPlatformInfo_pfn -#undef clGetProgramBuildInfo -#define clGetProgramBuildInfo clGetProgramBuildInfo_pfn -#undef clGetProgramInfo -#define clGetProgramInfo clGetProgramInfo_pfn -#undef clGetSamplerInfo -#define clGetSamplerInfo clGetSamplerInfo_pfn -#undef clGetSupportedImageFormats -#define clGetSupportedImageFormats clGetSupportedImageFormats_pfn -#undef clLinkProgram -#define clLinkProgram clLinkProgram_pfn -#undef clReleaseCommandQueue -#define clReleaseCommandQueue clReleaseCommandQueue_pfn -#undef clReleaseContext -#define clReleaseContext clReleaseContext_pfn -#undef clReleaseDevice -#define clReleaseDevice clReleaseDevice_pfn -#undef clReleaseEvent -#define clReleaseEvent clReleaseEvent_pfn -#undef clReleaseKernel -#define clReleaseKernel clReleaseKernel_pfn -#undef clReleaseMemObject -#define clReleaseMemObject clReleaseMemObject_pfn -#undef clReleaseProgram -#define clReleaseProgram clReleaseProgram_pfn -#undef clReleaseSampler -#define clReleaseSampler clReleaseSampler_pfn -#undef clRetainCommandQueue -#define clRetainCommandQueue clRetainCommandQueue_pfn -#undef clRetainContext -#define clRetainContext clRetainContext_pfn -#undef clRetainDevice -#define clRetainDevice clRetainDevice_pfn -#undef clRetainEvent -#define clRetainEvent clRetainEvent_pfn -#undef clRetainKernel -#define clRetainKernel clRetainKernel_pfn -#undef clRetainMemObject -#define clRetainMemObject clRetainMemObject_pfn -#undef clRetainProgram -#define clRetainProgram clRetainProgram_pfn -#undef clRetainSampler -#define clRetainSampler clRetainSampler_pfn -#undef clSetEventCallback -#define clSetEventCallback clSetEventCallback_pfn -#undef clSetKernelArg -#define clSetKernelArg clSetKernelArg_pfn -#undef clSetMemObjectDestructorCallback -#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_pfn -#undef clSetUserEventStatus -#define clSetUserEventStatus clSetUserEventStatus_pfn -#undef clUnloadCompiler -#define clUnloadCompiler clUnloadCompiler_pfn -#undef clUnloadPlatformCompiler -#define clUnloadPlatformCompiler clUnloadPlatformCompiler_pfn -#undef clWaitForEvents -#define clWaitForEvents clWaitForEvents_pfn - -// generated by parser_cl.py -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clBuildProgram)(cl_program, cl_uint, const cl_device_id*, const char*, void (CL_CALLBACK*) (cl_program, void*), void*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCompileProgram)(cl_program, cl_uint, const cl_device_id*, const char*, cl_uint, const cl_program*, const char**, void (CL_CALLBACK*) (cl_program, void*), void*); -extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateBuffer)(cl_context, cl_mem_flags, size_t, void*, cl_int*); -extern CL_RUNTIME_EXPORT cl_command_queue (CL_API_CALL*clCreateCommandQueue)(cl_context, cl_device_id, cl_command_queue_properties, cl_int*); -extern CL_RUNTIME_EXPORT cl_context (CL_API_CALL*clCreateContext)(const cl_context_properties*, cl_uint, const cl_device_id*, void (CL_CALLBACK*) (const char*, const void*, size_t, void*), void*, cl_int*); -extern CL_RUNTIME_EXPORT cl_context (CL_API_CALL*clCreateContextFromType)(const cl_context_properties*, cl_device_type, void (CL_CALLBACK*) (const char*, const void*, size_t, void*), void*, cl_int*); -extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage)(cl_context, cl_mem_flags, const cl_image_format*, const cl_image_desc*, void*, cl_int*); -extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage2D)(cl_context, cl_mem_flags, const cl_image_format*, size_t, size_t, size_t, void*, cl_int*); -extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage3D)(cl_context, cl_mem_flags, const cl_image_format*, size_t, size_t, size_t, size_t, size_t, void*, cl_int*); -extern CL_RUNTIME_EXPORT cl_kernel (CL_API_CALL*clCreateKernel)(cl_program, const char*, cl_int*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCreateKernelsInProgram)(cl_program, cl_uint, cl_kernel*, cl_uint*); -extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithBinary)(cl_context, cl_uint, const cl_device_id*, const size_t*, const unsigned char**, cl_int*, cl_int*); -extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithBuiltInKernels)(cl_context, cl_uint, const cl_device_id*, const char*, cl_int*); -extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithSource)(cl_context, cl_uint, const char**, const size_t*, cl_int*); -extern CL_RUNTIME_EXPORT cl_sampler (CL_API_CALL*clCreateSampler)(cl_context, cl_bool, cl_addressing_mode, cl_filter_mode, cl_int*); -extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateSubBuffer)(cl_mem, cl_mem_flags, cl_buffer_create_type, const void*, cl_int*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCreateSubDevices)(cl_device_id, const cl_device_partition_property*, cl_uint, cl_device_id*, cl_uint*); -extern CL_RUNTIME_EXPORT cl_event (CL_API_CALL*clCreateUserEvent)(cl_context, cl_int*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueBarrier)(cl_command_queue); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueBarrierWithWaitList)(cl_command_queue, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBuffer)(cl_command_queue, cl_mem, cl_mem, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBufferRect)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBufferToImage)(cl_command_queue, cl_mem, cl_mem, size_t, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyImage)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyImageToBuffer)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, size_t, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueFillBuffer)(cl_command_queue, cl_mem, const void*, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueFillImage)(cl_command_queue, cl_mem, const void*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clEnqueueMapBuffer)(cl_command_queue, cl_mem, cl_bool, cl_map_flags, size_t, size_t, cl_uint, const cl_event*, cl_event*, cl_int*); -extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clEnqueueMapImage)(cl_command_queue, cl_mem, cl_bool, cl_map_flags, const size_t*, const size_t*, size_t*, size_t*, cl_uint, const cl_event*, cl_event*, cl_int*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMarker)(cl_command_queue, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMarkerWithWaitList)(cl_command_queue, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMigrateMemObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_mem_migration_flags, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueNDRangeKernel)(cl_command_queue, cl_kernel, cl_uint, const size_t*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueNativeKernel)(cl_command_queue, void (CL_CALLBACK*) (void*), void*, size_t, cl_uint, const cl_mem*, const void**, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadBuffer)(cl_command_queue, cl_mem, cl_bool, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadBufferRect)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadImage)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueTask)(cl_command_queue, cl_kernel, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueUnmapMemObject)(cl_command_queue, cl_mem, void*, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWaitForEvents)(cl_command_queue, cl_uint, const cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteBuffer)(cl_command_queue, cl_mem, cl_bool, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteBufferRect)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteImage)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clFinish)(cl_command_queue); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clFlush)(cl_command_queue); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetCommandQueueInfo)(cl_command_queue, cl_command_queue_info, size_t, void*, size_t*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetContextInfo)(cl_context, cl_context_info, size_t, void*, size_t*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetDeviceIDs)(cl_platform_id, cl_device_type, cl_uint, cl_device_id*, cl_uint*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetDeviceInfo)(cl_device_id, cl_device_info, size_t, void*, size_t*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetEventInfo)(cl_event, cl_event_info, size_t, void*, size_t*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetEventProfilingInfo)(cl_event, cl_profiling_info, size_t, void*, size_t*); -extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clGetExtensionFunctionAddress)(const char*); -extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clGetExtensionFunctionAddressForPlatform)(cl_platform_id, const char*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetImageInfo)(cl_mem, cl_image_info, size_t, void*, size_t*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelArgInfo)(cl_kernel, cl_uint, cl_kernel_arg_info, size_t, void*, size_t*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelInfo)(cl_kernel, cl_kernel_info, size_t, void*, size_t*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelWorkGroupInfo)(cl_kernel, cl_device_id, cl_kernel_work_group_info, size_t, void*, size_t*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetMemObjectInfo)(cl_mem, cl_mem_info, size_t, void*, size_t*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetPlatformIDs)(cl_uint, cl_platform_id*, cl_uint*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetPlatformInfo)(cl_platform_id, cl_platform_info, size_t, void*, size_t*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetProgramBuildInfo)(cl_program, cl_device_id, cl_program_build_info, size_t, void*, size_t*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetProgramInfo)(cl_program, cl_program_info, size_t, void*, size_t*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetSamplerInfo)(cl_sampler, cl_sampler_info, size_t, void*, size_t*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetSupportedImageFormats)(cl_context, cl_mem_flags, cl_mem_object_type, cl_uint, cl_image_format*, cl_uint*); -extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clLinkProgram)(cl_context, cl_uint, const cl_device_id*, const char*, cl_uint, const cl_program*, void (CL_CALLBACK*) (cl_program, void*), void*, cl_int*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseCommandQueue)(cl_command_queue); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseContext)(cl_context); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseDevice)(cl_device_id); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseEvent)(cl_event); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseKernel)(cl_kernel); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseMemObject)(cl_mem); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseProgram)(cl_program); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseSampler)(cl_sampler); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainCommandQueue)(cl_command_queue); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainContext)(cl_context); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainDevice)(cl_device_id); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainEvent)(cl_event); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainKernel)(cl_kernel); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainMemObject)(cl_mem); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainProgram)(cl_program); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainSampler)(cl_sampler); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetEventCallback)(cl_event, cl_int, void (CL_CALLBACK*) (cl_event, cl_int, void*), void*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetKernelArg)(cl_kernel, cl_uint, size_t, const void*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetMemObjectDestructorCallback)(cl_mem, void (CL_CALLBACK*) (cl_mem, void*), void*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetUserEventStatus)(cl_event, cl_int); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clUnloadCompiler)(); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clUnloadPlatformCompiler)(cl_platform_id); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clWaitForEvents)(cl_uint, const cl_event*); diff --git a/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp b/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp deleted file mode 100755 index 216b22b..0000000 --- a/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp +++ /dev/null @@ -1,272 +0,0 @@ -// -// AUTOGENERATED, DO NOT EDIT -// -#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP -#error "Invalid usage" -#endif - -// generated by parser_cl.py -#undef clBuildProgram -#define clBuildProgram clBuildProgram_fn -inline cl_int clBuildProgram(cl_program p0, cl_uint p1, const cl_device_id* p2, const char* p3, void (CL_CALLBACK*p4) (cl_program, void*), void* p5) { return clBuildProgram_pfn(p0, p1, p2, p3, p4, p5); } -#undef clCompileProgram -#define clCompileProgram clCompileProgram_fn -inline cl_int clCompileProgram(cl_program p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_uint p4, const cl_program* p5, const char** p6, void (CL_CALLBACK*p7) (cl_program, void*), void* p8) { return clCompileProgram_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } -#undef clCreateBuffer -#define clCreateBuffer clCreateBuffer_fn -inline cl_mem clCreateBuffer(cl_context p0, cl_mem_flags p1, size_t p2, void* p3, cl_int* p4) { return clCreateBuffer_pfn(p0, p1, p2, p3, p4); } -#undef clCreateCommandQueue -#define clCreateCommandQueue clCreateCommandQueue_fn -inline cl_command_queue clCreateCommandQueue(cl_context p0, cl_device_id p1, cl_command_queue_properties p2, cl_int* p3) { return clCreateCommandQueue_pfn(p0, p1, p2, p3); } -#undef clCreateContext -#define clCreateContext clCreateContext_fn -inline cl_context clCreateContext(const cl_context_properties* p0, cl_uint p1, const cl_device_id* p2, void (CL_CALLBACK*p3) (const char*, const void*, size_t, void*), void* p4, cl_int* p5) { return clCreateContext_pfn(p0, p1, p2, p3, p4, p5); } -#undef clCreateContextFromType -#define clCreateContextFromType clCreateContextFromType_fn -inline cl_context clCreateContextFromType(const cl_context_properties* p0, cl_device_type p1, void (CL_CALLBACK*p2) (const char*, const void*, size_t, void*), void* p3, cl_int* p4) { return clCreateContextFromType_pfn(p0, p1, p2, p3, p4); } -#undef clCreateImage -#define clCreateImage clCreateImage_fn -inline cl_mem clCreateImage(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, const cl_image_desc* p3, void* p4, cl_int* p5) { return clCreateImage_pfn(p0, p1, p2, p3, p4, p5); } -#undef clCreateImage2D -#define clCreateImage2D clCreateImage2D_fn -inline cl_mem clCreateImage2D(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, size_t p3, size_t p4, size_t p5, void* p6, cl_int* p7) { return clCreateImage2D_pfn(p0, p1, p2, p3, p4, p5, p6, p7); } -#undef clCreateImage3D -#define clCreateImage3D clCreateImage3D_fn -inline cl_mem clCreateImage3D(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, size_t p3, size_t p4, size_t p5, size_t p6, size_t p7, void* p8, cl_int* p9) { return clCreateImage3D_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); } -#undef clCreateKernel -#define clCreateKernel clCreateKernel_fn -inline cl_kernel clCreateKernel(cl_program p0, const char* p1, cl_int* p2) { return clCreateKernel_pfn(p0, p1, p2); } -#undef clCreateKernelsInProgram -#define clCreateKernelsInProgram clCreateKernelsInProgram_fn -inline cl_int clCreateKernelsInProgram(cl_program p0, cl_uint p1, cl_kernel* p2, cl_uint* p3) { return clCreateKernelsInProgram_pfn(p0, p1, p2, p3); } -#undef clCreateProgramWithBinary -#define clCreateProgramWithBinary clCreateProgramWithBinary_fn -inline cl_program clCreateProgramWithBinary(cl_context p0, cl_uint p1, const cl_device_id* p2, const size_t* p3, const unsigned char** p4, cl_int* p5, cl_int* p6) { return clCreateProgramWithBinary_pfn(p0, p1, p2, p3, p4, p5, p6); } -#undef clCreateProgramWithBuiltInKernels -#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_fn -inline cl_program clCreateProgramWithBuiltInKernels(cl_context p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_int* p4) { return clCreateProgramWithBuiltInKernels_pfn(p0, p1, p2, p3, p4); } -#undef clCreateProgramWithSource -#define clCreateProgramWithSource clCreateProgramWithSource_fn -inline cl_program clCreateProgramWithSource(cl_context p0, cl_uint p1, const char** p2, const size_t* p3, cl_int* p4) { return clCreateProgramWithSource_pfn(p0, p1, p2, p3, p4); } -#undef clCreateSampler -#define clCreateSampler clCreateSampler_fn -inline cl_sampler clCreateSampler(cl_context p0, cl_bool p1, cl_addressing_mode p2, cl_filter_mode p3, cl_int* p4) { return clCreateSampler_pfn(p0, p1, p2, p3, p4); } -#undef clCreateSubBuffer -#define clCreateSubBuffer clCreateSubBuffer_fn -inline cl_mem clCreateSubBuffer(cl_mem p0, cl_mem_flags p1, cl_buffer_create_type p2, const void* p3, cl_int* p4) { return clCreateSubBuffer_pfn(p0, p1, p2, p3, p4); } -#undef clCreateSubDevices -#define clCreateSubDevices clCreateSubDevices_fn -inline cl_int clCreateSubDevices(cl_device_id p0, const cl_device_partition_property* p1, cl_uint p2, cl_device_id* p3, cl_uint* p4) { return clCreateSubDevices_pfn(p0, p1, p2, p3, p4); } -#undef clCreateUserEvent -#define clCreateUserEvent clCreateUserEvent_fn -inline cl_event clCreateUserEvent(cl_context p0, cl_int* p1) { return clCreateUserEvent_pfn(p0, p1); } -#undef clEnqueueBarrier -#define clEnqueueBarrier clEnqueueBarrier_fn -inline cl_int clEnqueueBarrier(cl_command_queue p0) { return clEnqueueBarrier_pfn(p0); } -#undef clEnqueueBarrierWithWaitList -#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_fn -inline cl_int clEnqueueBarrierWithWaitList(cl_command_queue p0, cl_uint p1, const cl_event* p2, cl_event* p3) { return clEnqueueBarrierWithWaitList_pfn(p0, p1, p2, p3); } -#undef clEnqueueCopyBuffer -#define clEnqueueCopyBuffer clEnqueueCopyBuffer_fn -inline cl_int clEnqueueCopyBuffer(cl_command_queue p0, cl_mem p1, cl_mem p2, size_t p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } -#undef clEnqueueCopyBufferRect -#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_fn -inline cl_int clEnqueueCopyBufferRect(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, cl_uint p10, const cl_event* p11, cl_event* p12) { return clEnqueueCopyBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12); } -#undef clEnqueueCopyBufferToImage -#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_fn -inline cl_int clEnqueueCopyBufferToImage(cl_command_queue p0, cl_mem p1, cl_mem p2, size_t p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyBufferToImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } -#undef clEnqueueCopyImage -#define clEnqueueCopyImage clEnqueueCopyImage_fn -inline cl_int clEnqueueCopyImage(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } -#undef clEnqueueCopyImageToBuffer -#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_fn -inline cl_int clEnqueueCopyImageToBuffer(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyImageToBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } -#undef clEnqueueFillBuffer -#define clEnqueueFillBuffer clEnqueueFillBuffer_fn -inline cl_int clEnqueueFillBuffer(cl_command_queue p0, cl_mem p1, const void* p2, size_t p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueFillBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } -#undef clEnqueueFillImage -#define clEnqueueFillImage clEnqueueFillImage_fn -inline cl_int clEnqueueFillImage(cl_command_queue p0, cl_mem p1, const void* p2, const size_t* p3, const size_t* p4, cl_uint p5, const cl_event* p6, cl_event* p7) { return clEnqueueFillImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7); } -#undef clEnqueueMapBuffer -#define clEnqueueMapBuffer clEnqueueMapBuffer_fn -inline void* clEnqueueMapBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, cl_map_flags p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8, cl_int* p9) { return clEnqueueMapBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); } -#undef clEnqueueMapImage -#define clEnqueueMapImage clEnqueueMapImage_fn -inline void* clEnqueueMapImage(cl_command_queue p0, cl_mem p1, cl_bool p2, cl_map_flags p3, const size_t* p4, const size_t* p5, size_t* p6, size_t* p7, cl_uint p8, const cl_event* p9, cl_event* p10, cl_int* p11) { return clEnqueueMapImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11); } -#undef clEnqueueMarker -#define clEnqueueMarker clEnqueueMarker_fn -inline cl_int clEnqueueMarker(cl_command_queue p0, cl_event* p1) { return clEnqueueMarker_pfn(p0, p1); } -#undef clEnqueueMarkerWithWaitList -#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_fn -inline cl_int clEnqueueMarkerWithWaitList(cl_command_queue p0, cl_uint p1, const cl_event* p2, cl_event* p3) { return clEnqueueMarkerWithWaitList_pfn(p0, p1, p2, p3); } -#undef clEnqueueMigrateMemObjects -#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_fn -inline cl_int clEnqueueMigrateMemObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_mem_migration_flags p3, cl_uint p4, const cl_event* p5, cl_event* p6) { return clEnqueueMigrateMemObjects_pfn(p0, p1, p2, p3, p4, p5, p6); } -#undef clEnqueueNDRangeKernel -#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_fn -inline cl_int clEnqueueNDRangeKernel(cl_command_queue p0, cl_kernel p1, cl_uint p2, const size_t* p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueNDRangeKernel_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } -#undef clEnqueueNativeKernel -#define clEnqueueNativeKernel clEnqueueNativeKernel_fn -inline cl_int clEnqueueNativeKernel(cl_command_queue p0, void (CL_CALLBACK*p1) (void*), void* p2, size_t p3, cl_uint p4, const cl_mem* p5, const void** p6, cl_uint p7, const cl_event* p8, cl_event* p9) { return clEnqueueNativeKernel_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); } -#undef clEnqueueReadBuffer -#define clEnqueueReadBuffer clEnqueueReadBuffer_fn -inline cl_int clEnqueueReadBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, size_t p3, size_t p4, void* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueReadBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } -#undef clEnqueueReadBufferRect -#define clEnqueueReadBufferRect clEnqueueReadBufferRect_fn -inline cl_int clEnqueueReadBufferRect(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, void* p10, cl_uint p11, const cl_event* p12, cl_event* p13) { return clEnqueueReadBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13); } -#undef clEnqueueReadImage -#define clEnqueueReadImage clEnqueueReadImage_fn -inline cl_int clEnqueueReadImage(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, size_t p5, size_t p6, void* p7, cl_uint p8, const cl_event* p9, cl_event* p10) { return clEnqueueReadImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10); } -#undef clEnqueueTask -#define clEnqueueTask clEnqueueTask_fn -inline cl_int clEnqueueTask(cl_command_queue p0, cl_kernel p1, cl_uint p2, const cl_event* p3, cl_event* p4) { return clEnqueueTask_pfn(p0, p1, p2, p3, p4); } -#undef clEnqueueUnmapMemObject -#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_fn -inline cl_int clEnqueueUnmapMemObject(cl_command_queue p0, cl_mem p1, void* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueUnmapMemObject_pfn(p0, p1, p2, p3, p4, p5); } -#undef clEnqueueWaitForEvents -#define clEnqueueWaitForEvents clEnqueueWaitForEvents_fn -inline cl_int clEnqueueWaitForEvents(cl_command_queue p0, cl_uint p1, const cl_event* p2) { return clEnqueueWaitForEvents_pfn(p0, p1, p2); } -#undef clEnqueueWriteBuffer -#define clEnqueueWriteBuffer clEnqueueWriteBuffer_fn -inline cl_int clEnqueueWriteBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, size_t p3, size_t p4, const void* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueWriteBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } -#undef clEnqueueWriteBufferRect -#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_fn -inline cl_int clEnqueueWriteBufferRect(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, const void* p10, cl_uint p11, const cl_event* p12, cl_event* p13) { return clEnqueueWriteBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13); } -#undef clEnqueueWriteImage -#define clEnqueueWriteImage clEnqueueWriteImage_fn -inline cl_int clEnqueueWriteImage(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, size_t p5, size_t p6, const void* p7, cl_uint p8, const cl_event* p9, cl_event* p10) { return clEnqueueWriteImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10); } -#undef clFinish -#define clFinish clFinish_fn -inline cl_int clFinish(cl_command_queue p0) { return clFinish_pfn(p0); } -#undef clFlush -#define clFlush clFlush_fn -inline cl_int clFlush(cl_command_queue p0) { return clFlush_pfn(p0); } -#undef clGetCommandQueueInfo -#define clGetCommandQueueInfo clGetCommandQueueInfo_fn -inline cl_int clGetCommandQueueInfo(cl_command_queue p0, cl_command_queue_info p1, size_t p2, void* p3, size_t* p4) { return clGetCommandQueueInfo_pfn(p0, p1, p2, p3, p4); } -#undef clGetContextInfo -#define clGetContextInfo clGetContextInfo_fn -inline cl_int clGetContextInfo(cl_context p0, cl_context_info p1, size_t p2, void* p3, size_t* p4) { return clGetContextInfo_pfn(p0, p1, p2, p3, p4); } -#undef clGetDeviceIDs -#define clGetDeviceIDs clGetDeviceIDs_fn -inline cl_int clGetDeviceIDs(cl_platform_id p0, cl_device_type p1, cl_uint p2, cl_device_id* p3, cl_uint* p4) { return clGetDeviceIDs_pfn(p0, p1, p2, p3, p4); } -#undef clGetDeviceInfo -#define clGetDeviceInfo clGetDeviceInfo_fn -inline cl_int clGetDeviceInfo(cl_device_id p0, cl_device_info p1, size_t p2, void* p3, size_t* p4) { return clGetDeviceInfo_pfn(p0, p1, p2, p3, p4); } -#undef clGetEventInfo -#define clGetEventInfo clGetEventInfo_fn -inline cl_int clGetEventInfo(cl_event p0, cl_event_info p1, size_t p2, void* p3, size_t* p4) { return clGetEventInfo_pfn(p0, p1, p2, p3, p4); } -#undef clGetEventProfilingInfo -#define clGetEventProfilingInfo clGetEventProfilingInfo_fn -inline cl_int clGetEventProfilingInfo(cl_event p0, cl_profiling_info p1, size_t p2, void* p3, size_t* p4) { return clGetEventProfilingInfo_pfn(p0, p1, p2, p3, p4); } -#undef clGetExtensionFunctionAddress -#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_fn -inline void* clGetExtensionFunctionAddress(const char* p0) { return clGetExtensionFunctionAddress_pfn(p0); } -#undef clGetExtensionFunctionAddressForPlatform -#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_fn -inline void* clGetExtensionFunctionAddressForPlatform(cl_platform_id p0, const char* p1) { return clGetExtensionFunctionAddressForPlatform_pfn(p0, p1); } -#undef clGetImageInfo -#define clGetImageInfo clGetImageInfo_fn -inline cl_int clGetImageInfo(cl_mem p0, cl_image_info p1, size_t p2, void* p3, size_t* p4) { return clGetImageInfo_pfn(p0, p1, p2, p3, p4); } -#undef clGetKernelArgInfo -#define clGetKernelArgInfo clGetKernelArgInfo_fn -inline cl_int clGetKernelArgInfo(cl_kernel p0, cl_uint p1, cl_kernel_arg_info p2, size_t p3, void* p4, size_t* p5) { return clGetKernelArgInfo_pfn(p0, p1, p2, p3, p4, p5); } -#undef clGetKernelInfo -#define clGetKernelInfo clGetKernelInfo_fn -inline cl_int clGetKernelInfo(cl_kernel p0, cl_kernel_info p1, size_t p2, void* p3, size_t* p4) { return clGetKernelInfo_pfn(p0, p1, p2, p3, p4); } -#undef clGetKernelWorkGroupInfo -#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_fn -inline cl_int clGetKernelWorkGroupInfo(cl_kernel p0, cl_device_id p1, cl_kernel_work_group_info p2, size_t p3, void* p4, size_t* p5) { return clGetKernelWorkGroupInfo_pfn(p0, p1, p2, p3, p4, p5); } -#undef clGetMemObjectInfo -#define clGetMemObjectInfo clGetMemObjectInfo_fn -inline cl_int clGetMemObjectInfo(cl_mem p0, cl_mem_info p1, size_t p2, void* p3, size_t* p4) { return clGetMemObjectInfo_pfn(p0, p1, p2, p3, p4); } -#undef clGetPlatformIDs -#define clGetPlatformIDs clGetPlatformIDs_fn -inline cl_int clGetPlatformIDs(cl_uint p0, cl_platform_id* p1, cl_uint* p2) { return clGetPlatformIDs_pfn(p0, p1, p2); } -#undef clGetPlatformInfo -#define clGetPlatformInfo clGetPlatformInfo_fn -inline cl_int clGetPlatformInfo(cl_platform_id p0, cl_platform_info p1, size_t p2, void* p3, size_t* p4) { return clGetPlatformInfo_pfn(p0, p1, p2, p3, p4); } -#undef clGetProgramBuildInfo -#define clGetProgramBuildInfo clGetProgramBuildInfo_fn -inline cl_int clGetProgramBuildInfo(cl_program p0, cl_device_id p1, cl_program_build_info p2, size_t p3, void* p4, size_t* p5) { return clGetProgramBuildInfo_pfn(p0, p1, p2, p3, p4, p5); } -#undef clGetProgramInfo -#define clGetProgramInfo clGetProgramInfo_fn -inline cl_int clGetProgramInfo(cl_program p0, cl_program_info p1, size_t p2, void* p3, size_t* p4) { return clGetProgramInfo_pfn(p0, p1, p2, p3, p4); } -#undef clGetSamplerInfo -#define clGetSamplerInfo clGetSamplerInfo_fn -inline cl_int clGetSamplerInfo(cl_sampler p0, cl_sampler_info p1, size_t p2, void* p3, size_t* p4) { return clGetSamplerInfo_pfn(p0, p1, p2, p3, p4); } -#undef clGetSupportedImageFormats -#define clGetSupportedImageFormats clGetSupportedImageFormats_fn -inline cl_int clGetSupportedImageFormats(cl_context p0, cl_mem_flags p1, cl_mem_object_type p2, cl_uint p3, cl_image_format* p4, cl_uint* p5) { return clGetSupportedImageFormats_pfn(p0, p1, p2, p3, p4, p5); } -#undef clLinkProgram -#define clLinkProgram clLinkProgram_fn -inline cl_program clLinkProgram(cl_context p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_uint p4, const cl_program* p5, void (CL_CALLBACK*p6) (cl_program, void*), void* p7, cl_int* p8) { return clLinkProgram_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); } -#undef clReleaseCommandQueue -#define clReleaseCommandQueue clReleaseCommandQueue_fn -inline cl_int clReleaseCommandQueue(cl_command_queue p0) { return clReleaseCommandQueue_pfn(p0); } -#undef clReleaseContext -#define clReleaseContext clReleaseContext_fn -inline cl_int clReleaseContext(cl_context p0) { return clReleaseContext_pfn(p0); } -#undef clReleaseDevice -#define clReleaseDevice clReleaseDevice_fn -inline cl_int clReleaseDevice(cl_device_id p0) { return clReleaseDevice_pfn(p0); } -#undef clReleaseEvent -#define clReleaseEvent clReleaseEvent_fn -inline cl_int clReleaseEvent(cl_event p0) { return clReleaseEvent_pfn(p0); } -#undef clReleaseKernel -#define clReleaseKernel clReleaseKernel_fn -inline cl_int clReleaseKernel(cl_kernel p0) { return clReleaseKernel_pfn(p0); } -#undef clReleaseMemObject -#define clReleaseMemObject clReleaseMemObject_fn -inline cl_int clReleaseMemObject(cl_mem p0) { return clReleaseMemObject_pfn(p0); } -#undef clReleaseProgram -#define clReleaseProgram clReleaseProgram_fn -inline cl_int clReleaseProgram(cl_program p0) { return clReleaseProgram_pfn(p0); } -#undef clReleaseSampler -#define clReleaseSampler clReleaseSampler_fn -inline cl_int clReleaseSampler(cl_sampler p0) { return clReleaseSampler_pfn(p0); } -#undef clRetainCommandQueue -#define clRetainCommandQueue clRetainCommandQueue_fn -inline cl_int clRetainCommandQueue(cl_command_queue p0) { return clRetainCommandQueue_pfn(p0); } -#undef clRetainContext -#define clRetainContext clRetainContext_fn -inline cl_int clRetainContext(cl_context p0) { return clRetainContext_pfn(p0); } -#undef clRetainDevice -#define clRetainDevice clRetainDevice_fn -inline cl_int clRetainDevice(cl_device_id p0) { return clRetainDevice_pfn(p0); } -#undef clRetainEvent -#define clRetainEvent clRetainEvent_fn -inline cl_int clRetainEvent(cl_event p0) { return clRetainEvent_pfn(p0); } -#undef clRetainKernel -#define clRetainKernel clRetainKernel_fn -inline cl_int clRetainKernel(cl_kernel p0) { return clRetainKernel_pfn(p0); } -#undef clRetainMemObject -#define clRetainMemObject clRetainMemObject_fn -inline cl_int clRetainMemObject(cl_mem p0) { return clRetainMemObject_pfn(p0); } -#undef clRetainProgram -#define clRetainProgram clRetainProgram_fn -inline cl_int clRetainProgram(cl_program p0) { return clRetainProgram_pfn(p0); } -#undef clRetainSampler -#define clRetainSampler clRetainSampler_fn -inline cl_int clRetainSampler(cl_sampler p0) { return clRetainSampler_pfn(p0); } -#undef clSetEventCallback -#define clSetEventCallback clSetEventCallback_fn -inline cl_int clSetEventCallback(cl_event p0, cl_int p1, void (CL_CALLBACK*p2) (cl_event, cl_int, void*), void* p3) { return clSetEventCallback_pfn(p0, p1, p2, p3); } -#undef clSetKernelArg -#define clSetKernelArg clSetKernelArg_fn -inline cl_int clSetKernelArg(cl_kernel p0, cl_uint p1, size_t p2, const void* p3) { return clSetKernelArg_pfn(p0, p1, p2, p3); } -#undef clSetMemObjectDestructorCallback -#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_fn -inline cl_int clSetMemObjectDestructorCallback(cl_mem p0, void (CL_CALLBACK*p1) (cl_mem, void*), void* p2) { return clSetMemObjectDestructorCallback_pfn(p0, p1, p2); } -#undef clSetUserEventStatus -#define clSetUserEventStatus clSetUserEventStatus_fn -inline cl_int clSetUserEventStatus(cl_event p0, cl_int p1) { return clSetUserEventStatus_pfn(p0, p1); } -#undef clUnloadCompiler -#define clUnloadCompiler clUnloadCompiler_fn -inline cl_int clUnloadCompiler() { return clUnloadCompiler_pfn(); } -#undef clUnloadPlatformCompiler -#define clUnloadPlatformCompiler clUnloadPlatformCompiler_fn -inline cl_int clUnloadPlatformCompiler(cl_platform_id p0) { return clUnloadPlatformCompiler_pfn(p0); } -#undef clWaitForEvents -#define clWaitForEvents clWaitForEvents_fn -inline cl_int clWaitForEvents(cl_uint p0, const cl_event* p1) { return clWaitForEvents_pfn(p0, p1); } diff --git a/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp b/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp deleted file mode 100755 index 0b12aed..0000000 --- a/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp +++ /dev/null @@ -1,62 +0,0 @@ -// -// AUTOGENERATED, DO NOT EDIT -// -#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP -#error "Invalid usage" -#endif - -// generated by parser_cl.py -#define clCreateFromGLBuffer clCreateFromGLBuffer_ -#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_ -#define clCreateFromGLTexture clCreateFromGLTexture_ -#define clCreateFromGLTexture2D clCreateFromGLTexture2D_ -#define clCreateFromGLTexture3D clCreateFromGLTexture3D_ -#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_ -#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_ -#define clGetGLContextInfoKHR clGetGLContextInfoKHR_ -#define clGetGLObjectInfo clGetGLObjectInfo_ -#define clGetGLTextureInfo clGetGLTextureInfo_ - -#if defined __APPLE__ -#include -#else -#include -#endif - -// generated by parser_cl.py -#undef clCreateFromGLBuffer -#define clCreateFromGLBuffer clCreateFromGLBuffer_pfn -#undef clCreateFromGLRenderbuffer -#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_pfn -#undef clCreateFromGLTexture -#define clCreateFromGLTexture clCreateFromGLTexture_pfn -#undef clCreateFromGLTexture2D -#define clCreateFromGLTexture2D clCreateFromGLTexture2D_pfn -#undef clCreateFromGLTexture3D -#define clCreateFromGLTexture3D clCreateFromGLTexture3D_pfn -#undef clEnqueueAcquireGLObjects -#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_pfn -#undef clEnqueueReleaseGLObjects -#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_pfn -#undef clGetGLContextInfoKHR -#define clGetGLContextInfoKHR clGetGLContextInfoKHR_pfn -#undef clGetGLObjectInfo -#define clGetGLObjectInfo clGetGLObjectInfo_pfn -#undef clGetGLTextureInfo -#define clGetGLTextureInfo clGetGLTextureInfo_pfn - -#ifdef cl_khr_gl_sharing - -// generated by parser_cl.py -extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLBuffer)(cl_context, cl_mem_flags, cl_GLuint, int*); -extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLRenderbuffer)(cl_context, cl_mem_flags, cl_GLuint, cl_int*); -extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*); -extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture2D)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*); -extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture3D)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueAcquireGLObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReleaseGLObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_uint, const cl_event*, cl_event*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLContextInfoKHR)(const cl_context_properties*, cl_gl_context_info, size_t, void*, size_t*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLObjectInfo)(cl_mem, cl_gl_object_type*, cl_GLuint*); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLTextureInfo)(cl_mem, cl_gl_texture_info, size_t, void*, size_t*); - -#endif // cl_khr_gl_sharing diff --git a/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp b/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp deleted file mode 100755 index 12f342b..0000000 --- a/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp +++ /dev/null @@ -1,42 +0,0 @@ -// -// AUTOGENERATED, DO NOT EDIT -// -#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP -#error "Invalid usage" -#endif - -#ifdef cl_khr_gl_sharing - -// generated by parser_cl.py -#undef clCreateFromGLBuffer -#define clCreateFromGLBuffer clCreateFromGLBuffer_fn -inline cl_mem clCreateFromGLBuffer(cl_context p0, cl_mem_flags p1, cl_GLuint p2, int* p3) { return clCreateFromGLBuffer_pfn(p0, p1, p2, p3); } -#undef clCreateFromGLRenderbuffer -#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_fn -inline cl_mem clCreateFromGLRenderbuffer(cl_context p0, cl_mem_flags p1, cl_GLuint p2, cl_int* p3) { return clCreateFromGLRenderbuffer_pfn(p0, p1, p2, p3); } -#undef clCreateFromGLTexture -#define clCreateFromGLTexture clCreateFromGLTexture_fn -inline cl_mem clCreateFromGLTexture(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture_pfn(p0, p1, p2, p3, p4, p5); } -#undef clCreateFromGLTexture2D -#define clCreateFromGLTexture2D clCreateFromGLTexture2D_fn -inline cl_mem clCreateFromGLTexture2D(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture2D_pfn(p0, p1, p2, p3, p4, p5); } -#undef clCreateFromGLTexture3D -#define clCreateFromGLTexture3D clCreateFromGLTexture3D_fn -inline cl_mem clCreateFromGLTexture3D(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture3D_pfn(p0, p1, p2, p3, p4, p5); } -#undef clEnqueueAcquireGLObjects -#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_fn -inline cl_int clEnqueueAcquireGLObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueAcquireGLObjects_pfn(p0, p1, p2, p3, p4, p5); } -#undef clEnqueueReleaseGLObjects -#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_fn -inline cl_int clEnqueueReleaseGLObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueReleaseGLObjects_pfn(p0, p1, p2, p3, p4, p5); } -#undef clGetGLContextInfoKHR -#define clGetGLContextInfoKHR clGetGLContextInfoKHR_fn -inline cl_int clGetGLContextInfoKHR(const cl_context_properties* p0, cl_gl_context_info p1, size_t p2, void* p3, size_t* p4) { return clGetGLContextInfoKHR_pfn(p0, p1, p2, p3, p4); } -#undef clGetGLObjectInfo -#define clGetGLObjectInfo clGetGLObjectInfo_fn -inline cl_int clGetGLObjectInfo(cl_mem p0, cl_gl_object_type* p1, cl_GLuint* p2) { return clGetGLObjectInfo_pfn(p0, p1, p2); } -#undef clGetGLTextureInfo -#define clGetGLTextureInfo clGetGLTextureInfo_fn -inline cl_int clGetGLTextureInfo(cl_mem p0, cl_gl_texture_info p1, size_t p2, void* p3, size_t* p4) { return clGetGLTextureInfo_pfn(p0, p1, p2, p3, p4); } - -#endif // cl_khr_gl_sharing diff --git a/include/opencv2/core/opencl/runtime/opencl_clamdblas.hpp b/include/opencv2/core/opencl/runtime/opencl_clamdblas.hpp deleted file mode 100755 index 2ad8ac0..0000000 --- a/include/opencv2/core/opencl/runtime/opencl_clamdblas.hpp +++ /dev/null @@ -1,53 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the OpenCV Foundation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP -#define OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP - -#ifdef HAVE_CLAMDBLAS - -#include "opencl_core.hpp" - -#include "autogenerated/opencl_clamdblas.hpp" - -#endif // HAVE_CLAMDBLAS - -#endif // OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP diff --git a/include/opencv2/core/opencl/runtime/opencl_clamdfft.hpp b/include/opencv2/core/opencl/runtime/opencl_clamdfft.hpp deleted file mode 100755 index a328f72..0000000 --- a/include/opencv2/core/opencl/runtime/opencl_clamdfft.hpp +++ /dev/null @@ -1,53 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the OpenCV Foundation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP -#define OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP - -#ifdef HAVE_CLAMDFFT - -#include "opencl_core.hpp" - -#include "autogenerated/opencl_clamdfft.hpp" - -#endif // HAVE_CLAMDFFT - -#endif // OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP diff --git a/include/opencv2/core/opencl/runtime/opencl_core.hpp b/include/opencv2/core/opencl/runtime/opencl_core.hpp deleted file mode 100755 index 0404b31..0000000 --- a/include/opencv2/core/opencl/runtime/opencl_core.hpp +++ /dev/null @@ -1,84 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the OpenCV Foundation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP -#define OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP - -#ifdef HAVE_OPENCL - -#ifndef CL_RUNTIME_EXPORT -#if (defined(BUILD_SHARED_LIBS) || defined(OPENCV_CORE_SHARED)) && (defined _WIN32 || defined WINCE) && \ - !(defined(__OPENCV_BUILD) && defined(OPENCV_MODULE_IS_PART_OF_WORLD)) -#define CL_RUNTIME_EXPORT __declspec(dllimport) -#else -#define CL_RUNTIME_EXPORT -#endif -#endif - -#ifdef HAVE_OPENCL_SVM -#define clSVMAlloc clSVMAlloc_ -#define clSVMFree clSVMFree_ -#define clSetKernelArgSVMPointer clSetKernelArgSVMPointer_ -#define clSetKernelExecInfo clSetKernelExecInfo_ -#define clEnqueueSVMFree clEnqueueSVMFree_ -#define clEnqueueSVMMemcpy clEnqueueSVMMemcpy_ -#define clEnqueueSVMMemFill clEnqueueSVMMemFill_ -#define clEnqueueSVMMap clEnqueueSVMMap_ -#define clEnqueueSVMUnmap clEnqueueSVMUnmap_ -#endif - -#include "autogenerated/opencl_core.hpp" - -#ifndef CL_DEVICE_DOUBLE_FP_CONFIG -#define CL_DEVICE_DOUBLE_FP_CONFIG 0x1032 -#endif - -#ifndef CL_DEVICE_HALF_FP_CONFIG -#define CL_DEVICE_HALF_FP_CONFIG 0x1033 -#endif - -#ifndef CL_VERSION_1_2 -#define CV_REQUIRE_OPENCL_1_2_ERROR CV_Error(cv::Error::OpenCLApiCallError, "OpenCV compiled without OpenCL v1.2 support, so we can't use functionality from OpenCL v1.2") -#endif - -#endif // HAVE_OPENCL - -#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP diff --git a/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp b/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp deleted file mode 100755 index 38fcae9..0000000 --- a/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp +++ /dev/null @@ -1,47 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the OpenCV Foundation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP -#define OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP - -#include "autogenerated/opencl_core_wrappers.hpp" - -#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP diff --git a/include/opencv2/core/opencl/runtime/opencl_gl.hpp b/include/opencv2/core/opencl/runtime/opencl_gl.hpp deleted file mode 100755 index 659c7d8..0000000 --- a/include/opencv2/core/opencl/runtime/opencl_gl.hpp +++ /dev/null @@ -1,53 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the OpenCV Foundation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP -#define OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP - -#if defined HAVE_OPENCL && defined HAVE_OPENGL - -#include "opencl_core.hpp" - -#include "autogenerated/opencl_gl.hpp" - -#endif // defined HAVE_OPENCL && defined HAVE_OPENGL - -#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP diff --git a/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp b/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp deleted file mode 100755 index 9700004..0000000 --- a/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp +++ /dev/null @@ -1,47 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the OpenCV Foundation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP -#define OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP - -#include "autogenerated/opencl_gl_wrappers.hpp" - -#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP diff --git a/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp b/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp deleted file mode 100755 index 9636b19..0000000 --- a/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/* See LICENSE file in the root OpenCV directory */ - -#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP -#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP - -#if defined(HAVE_OPENCL_SVM) -#include "opencl_core.hpp" - -#include "opencl_svm_definitions.hpp" - -#undef clSVMAlloc -#define clSVMAlloc clSVMAlloc_pfn -#undef clSVMFree -#define clSVMFree clSVMFree_pfn -#undef clSetKernelArgSVMPointer -#define clSetKernelArgSVMPointer clSetKernelArgSVMPointer_pfn -#undef clSetKernelExecInfo -//#define clSetKernelExecInfo clSetKernelExecInfo_pfn -#undef clEnqueueSVMFree -//#define clEnqueueSVMFree clEnqueueSVMFree_pfn -#undef clEnqueueSVMMemcpy -#define clEnqueueSVMMemcpy clEnqueueSVMMemcpy_pfn -#undef clEnqueueSVMMemFill -#define clEnqueueSVMMemFill clEnqueueSVMMemFill_pfn -#undef clEnqueueSVMMap -#define clEnqueueSVMMap clEnqueueSVMMap_pfn -#undef clEnqueueSVMUnmap -#define clEnqueueSVMUnmap clEnqueueSVMUnmap_pfn - -extern CL_RUNTIME_EXPORT void* (CL_API_CALL *clSVMAlloc)(cl_context context, cl_svm_mem_flags flags, size_t size, unsigned int alignment); -extern CL_RUNTIME_EXPORT void (CL_API_CALL *clSVMFree)(cl_context context, void* svm_pointer); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clSetKernelArgSVMPointer)(cl_kernel kernel, cl_uint arg_index, const void* arg_value); -//extern CL_RUNTIME_EXPORT void* (CL_API_CALL *clSetKernelExecInfo)(cl_kernel kernel, cl_kernel_exec_info param_name, size_t param_value_size, const void* param_value); -//extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMFree)(cl_command_queue command_queue, cl_uint num_svm_pointers, void* svm_pointers[], -// void (CL_CALLBACK *pfn_free_func)(cl_command_queue queue, cl_uint num_svm_pointers, void* svm_pointers[], void* user_data), void* user_data, -// cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMemcpy)(cl_command_queue command_queue, cl_bool blocking_copy, void* dst_ptr, const void* src_ptr, size_t size, - cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMemFill)(cl_command_queue command_queue, void* svm_ptr, const void* pattern, size_t pattern_size, size_t size, - cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMap)(cl_command_queue command_queue, cl_bool blocking_map, cl_map_flags map_flags, void* svm_ptr, size_t size, - cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); -extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMUnmap)(cl_command_queue command_queue, void* svm_ptr, - cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event); - -#endif // HAVE_OPENCL_SVM - -#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP diff --git a/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp b/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp deleted file mode 100755 index 97c927b..0000000 --- a/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp +++ /dev/null @@ -1,42 +0,0 @@ -/* See LICENSE file in the root OpenCV directory */ - -#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP -#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP - -#if defined(HAVE_OPENCL_SVM) -#if defined(CL_VERSION_2_0) - -// OpenCL 2.0 contains SVM definitions - -#else - -typedef cl_bitfield cl_device_svm_capabilities; -typedef cl_bitfield cl_svm_mem_flags; -typedef cl_uint cl_kernel_exec_info; - -// -// TODO Add real values after OpenCL 2.0 release -// - -#ifndef CL_DEVICE_SVM_CAPABILITIES -#define CL_DEVICE_SVM_CAPABILITIES 0x1053 - -#define CL_DEVICE_SVM_COARSE_GRAIN_BUFFER (1 << 0) -#define CL_DEVICE_SVM_FINE_GRAIN_BUFFER (1 << 1) -#define CL_DEVICE_SVM_FINE_GRAIN_SYSTEM (1 << 2) -#define CL_DEVICE_SVM_ATOMICS (1 << 3) -#endif - -#ifndef CL_MEM_SVM_FINE_GRAIN_BUFFER -#define CL_MEM_SVM_FINE_GRAIN_BUFFER (1 << 10) -#endif - -#ifndef CL_MEM_SVM_ATOMICS -#define CL_MEM_SVM_ATOMICS (1 << 11) -#endif - - -#endif // CL_VERSION_2_0 -#endif // HAVE_OPENCL_SVM - -#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP diff --git a/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp b/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp deleted file mode 100755 index 497bc3d..0000000 --- a/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp +++ /dev/null @@ -1,166 +0,0 @@ -/* See LICENSE file in the root OpenCV directory */ - -#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP -#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP - -#if defined(HAVE_OPENCL_SVM) -#include "opencl_core.hpp" - -#ifndef CL_DEVICE_SVM_CAPABILITIES_AMD -// -// Part of the file is an extract from the cl_ext.h file from AMD APP SDK package. -// Below is the original copyright. -// -/******************************************************************************* - * Copyright (c) 2008-2013 The Khronos Group Inc. - * - * Permission is hereby granted, free of charge, to any person obtaining a - * copy of this software and/or associated documentation files (the - * "Materials"), to deal in the Materials without restriction, including - * without limitation the rights to use, copy, modify, merge, publish, - * distribute, sublicense, and/or sell copies of the Materials, and to - * permit persons to whom the Materials are furnished to do so, subject to - * the following conditions: - * - * The above copyright notice and this permission notice shall be included - * in all copies or substantial portions of the Materials. - * - * THE MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, - * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF - * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. - * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY - * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, - * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE - * MATERIALS OR THE USE OR OTHER DEALINGS IN THE MATERIALS. - ******************************************************************************/ - -/******************************************* - * Shared Virtual Memory (SVM) extension - *******************************************/ -typedef cl_bitfield cl_device_svm_capabilities_amd; -typedef cl_bitfield cl_svm_mem_flags_amd; -typedef cl_uint cl_kernel_exec_info_amd; - -/* cl_device_info */ -#define CL_DEVICE_SVM_CAPABILITIES_AMD 0x1053 -#define CL_DEVICE_PREFERRED_PLATFORM_ATOMIC_ALIGNMENT_AMD 0x1054 - -/* cl_device_svm_capabilities_amd */ -#define CL_DEVICE_SVM_COARSE_GRAIN_BUFFER_AMD (1 << 0) -#define CL_DEVICE_SVM_FINE_GRAIN_BUFFER_AMD (1 << 1) -#define CL_DEVICE_SVM_FINE_GRAIN_SYSTEM_AMD (1 << 2) -#define CL_DEVICE_SVM_ATOMICS_AMD (1 << 3) - -/* cl_svm_mem_flags_amd */ -#define CL_MEM_SVM_FINE_GRAIN_BUFFER_AMD (1 << 10) -#define CL_MEM_SVM_ATOMICS_AMD (1 << 11) - -/* cl_mem_info */ -#define CL_MEM_USES_SVM_POINTER_AMD 0x1109 - -/* cl_kernel_exec_info_amd */ -#define CL_KERNEL_EXEC_INFO_SVM_PTRS_AMD 0x11B6 -#define CL_KERNEL_EXEC_INFO_SVM_FINE_GRAIN_SYSTEM_AMD 0x11B7 - -/* cl_command_type */ -#define CL_COMMAND_SVM_FREE_AMD 0x1209 -#define CL_COMMAND_SVM_MEMCPY_AMD 0x120A -#define CL_COMMAND_SVM_MEMFILL_AMD 0x120B -#define CL_COMMAND_SVM_MAP_AMD 0x120C -#define CL_COMMAND_SVM_UNMAP_AMD 0x120D - -typedef CL_API_ENTRY void* -(CL_API_CALL * clSVMAllocAMD_fn)( - cl_context /* context */, - cl_svm_mem_flags_amd /* flags */, - size_t /* size */, - unsigned int /* alignment */ -) CL_EXT_SUFFIX__VERSION_1_2; - -typedef CL_API_ENTRY void -(CL_API_CALL * clSVMFreeAMD_fn)( - cl_context /* context */, - void* /* svm_pointer */ -) CL_EXT_SUFFIX__VERSION_1_2; - -typedef CL_API_ENTRY cl_int -(CL_API_CALL * clEnqueueSVMFreeAMD_fn)( - cl_command_queue /* command_queue */, - cl_uint /* num_svm_pointers */, - void** /* svm_pointers */, - void (CL_CALLBACK *)( /*pfn_free_func*/ - cl_command_queue /* queue */, - cl_uint /* num_svm_pointers */, - void** /* svm_pointers */, - void* /* user_data */), - void* /* user_data */, - cl_uint /* num_events_in_wait_list */, - const cl_event* /* event_wait_list */, - cl_event* /* event */ -) CL_EXT_SUFFIX__VERSION_1_2; - -typedef CL_API_ENTRY cl_int -(CL_API_CALL * clEnqueueSVMMemcpyAMD_fn)( - cl_command_queue /* command_queue */, - cl_bool /* blocking_copy */, - void* /* dst_ptr */, - const void* /* src_ptr */, - size_t /* size */, - cl_uint /* num_events_in_wait_list */, - const cl_event* /* event_wait_list */, - cl_event* /* event */ -) CL_EXT_SUFFIX__VERSION_1_2; - -typedef CL_API_ENTRY cl_int -(CL_API_CALL * clEnqueueSVMMemFillAMD_fn)( - cl_command_queue /* command_queue */, - void* /* svm_ptr */, - const void* /* pattern */, - size_t /* pattern_size */, - size_t /* size */, - cl_uint /* num_events_in_wait_list */, - const cl_event* /* event_wait_list */, - cl_event* /* event */ -) CL_EXT_SUFFIX__VERSION_1_2; - -typedef CL_API_ENTRY cl_int -(CL_API_CALL * clEnqueueSVMMapAMD_fn)( - cl_command_queue /* command_queue */, - cl_bool /* blocking_map */, - cl_map_flags /* map_flags */, - void* /* svm_ptr */, - size_t /* size */, - cl_uint /* num_events_in_wait_list */, - const cl_event* /* event_wait_list */, - cl_event* /* event */ -) CL_EXT_SUFFIX__VERSION_1_2; - -typedef CL_API_ENTRY cl_int -(CL_API_CALL * clEnqueueSVMUnmapAMD_fn)( - cl_command_queue /* command_queue */, - void* /* svm_ptr */, - cl_uint /* num_events_in_wait_list */, - const cl_event* /* event_wait_list */, - cl_event* /* event */ -) CL_EXT_SUFFIX__VERSION_1_2; - -typedef CL_API_ENTRY cl_int -(CL_API_CALL * clSetKernelArgSVMPointerAMD_fn)( - cl_kernel /* kernel */, - cl_uint /* arg_index */, - const void * /* arg_value */ -) CL_EXT_SUFFIX__VERSION_1_2; - -typedef CL_API_ENTRY cl_int -(CL_API_CALL * clSetKernelExecInfoAMD_fn)( - cl_kernel /* kernel */, - cl_kernel_exec_info_amd /* param_name */, - size_t /* param_value_size */, - const void * /* param_value */ -) CL_EXT_SUFFIX__VERSION_1_2; - -#endif - -#endif // HAVE_OPENCL_SVM - -#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP diff --git a/include/opencv2/core/opengl.hpp b/include/opencv2/core/opengl.hpp deleted file mode 100755 index a6288be..0000000 --- a/include/opencv2/core/opengl.hpp +++ /dev/null @@ -1,725 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_OPENGL_HPP -#define OPENCV_CORE_OPENGL_HPP - -#ifndef __cplusplus -# error opengl.hpp header must be compiled as C++ -#endif - -#include "opencv2/core.hpp" -#include "ocl.hpp" - -namespace cv { namespace ogl { - -/** @addtogroup core_opengl -This section describes OpenGL interoperability. - -To enable OpenGL support, configure OpenCV using CMake with WITH_OPENGL=ON . Currently OpenGL is -supported only with WIN32, GTK and Qt backends on Windows and Linux (MacOS and Android are not -supported). For GTK backend gtkglext-1.0 library is required. - -To use OpenGL functionality you should first create OpenGL context (window or frame buffer). You can -do this with namedWindow function or with other OpenGL toolkit (GLUT, for example). -*/ -//! @{ - -/////////////////// OpenGL Objects /////////////////// - -/** @brief Smart pointer for OpenGL buffer object with reference counting. - -Buffer Objects are OpenGL objects that store an array of unformatted memory allocated by the OpenGL -context. These can be used to store vertex data, pixel data retrieved from images or the -framebuffer, and a variety of other things. - -ogl::Buffer has interface similar with Mat interface and represents 2D array memory. - -ogl::Buffer supports memory transfers between host and device and also can be mapped to CUDA memory. - */ -class CV_EXPORTS Buffer -{ -public: - /** @brief The target defines how you intend to use the buffer object. - */ - enum Target - { - ARRAY_BUFFER = 0x8892, //!< The buffer will be used as a source for vertex data - ELEMENT_ARRAY_BUFFER = 0x8893, //!< The buffer will be used for indices (in glDrawElements, for example) - PIXEL_PACK_BUFFER = 0x88EB, //!< The buffer will be used for reading from OpenGL textures - PIXEL_UNPACK_BUFFER = 0x88EC //!< The buffer will be used for writing to OpenGL textures - }; - - enum Access - { - READ_ONLY = 0x88B8, - WRITE_ONLY = 0x88B9, - READ_WRITE = 0x88BA - }; - - /** @brief The constructors. - - Creates empty ogl::Buffer object, creates ogl::Buffer object from existed buffer ( abufId - parameter), allocates memory for ogl::Buffer object or copies from host/device memory. - */ - Buffer(); - - /** @overload - @param arows Number of rows in a 2D array. - @param acols Number of columns in a 2D array. - @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. - @param abufId Buffer object name. - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - Buffer(int arows, int acols, int atype, unsigned int abufId, bool autoRelease = false); - - /** @overload - @param asize 2D array size. - @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. - @param abufId Buffer object name. - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - Buffer(Size asize, int atype, unsigned int abufId, bool autoRelease = false); - - /** @overload - @param arows Number of rows in a 2D array. - @param acols Number of columns in a 2D array. - @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. - @param target Buffer usage. See cv::ogl::Buffer::Target . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - Buffer(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); - - /** @overload - @param asize 2D array size. - @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. - @param target Buffer usage. See cv::ogl::Buffer::Target . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - Buffer(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); - - /** @overload - @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ). - @param target Buffer usage. See cv::ogl::Buffer::Target . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - explicit Buffer(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false); - - /** @brief Allocates memory for ogl::Buffer object. - - @param arows Number of rows in a 2D array. - @param acols Number of columns in a 2D array. - @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. - @param target Buffer usage. See cv::ogl::Buffer::Target . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - void create(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); - - /** @overload - @param asize 2D array size. - @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details. - @param target Buffer usage. See cv::ogl::Buffer::Target . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - void create(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false); - - /** @brief Decrements the reference counter and destroys the buffer object if needed. - - The function will call setAutoRelease(true) . - */ - void release(); - - /** @brief Sets auto release mode. - - The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was - bound to a window it could be released at any time (user can close a window). If object's destructor - is called after destruction of the context it will cause an error. Thus ogl::Buffer doesn't destroy - OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL context). - This function can force ogl::Buffer destructor to destroy OpenGL object. - @param flag Auto release mode (if true, release will be called in object's destructor). - */ - void setAutoRelease(bool flag); - - /** @brief Copies from host/device memory to OpenGL buffer. - @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ). - @param target Buffer usage. See cv::ogl::Buffer::Target . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - void copyFrom(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false); - - /** @overload */ - void copyFrom(InputArray arr, cuda::Stream& stream, Target target = ARRAY_BUFFER, bool autoRelease = false); - - /** @brief Copies from OpenGL buffer to host/device memory or another OpenGL buffer object. - - @param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , std::vector or - ogl::Buffer ). - */ - void copyTo(OutputArray arr) const; - - /** @overload */ - void copyTo(OutputArray arr, cuda::Stream& stream) const; - - /** @brief Creates a full copy of the buffer object and the underlying data. - - @param target Buffer usage for destination buffer. - @param autoRelease Auto release mode for destination buffer. - */ - Buffer clone(Target target = ARRAY_BUFFER, bool autoRelease = false) const; - - /** @brief Binds OpenGL buffer to the specified buffer binding point. - - @param target Binding point. See cv::ogl::Buffer::Target . - */ - void bind(Target target) const; - - /** @brief Unbind any buffers from the specified binding point. - - @param target Binding point. See cv::ogl::Buffer::Target . - */ - static void unbind(Target target); - - /** @brief Maps OpenGL buffer to host memory. - - mapHost maps to the client's address space the entire data store of the buffer object. The data can - then be directly read and/or written relative to the returned pointer, depending on the specified - access policy. - - A mapped data store must be unmapped with ogl::Buffer::unmapHost before its buffer object is used. - - This operation can lead to memory transfers between host and device. - - Only one buffer object can be mapped at a time. - @param access Access policy, indicating whether it will be possible to read from, write to, or both - read from and write to the buffer object's mapped data store. The symbolic constant must be - ogl::Buffer::READ_ONLY , ogl::Buffer::WRITE_ONLY or ogl::Buffer::READ_WRITE . - */ - Mat mapHost(Access access); - - /** @brief Unmaps OpenGL buffer. - */ - void unmapHost(); - - //! map to device memory (blocking) - cuda::GpuMat mapDevice(); - void unmapDevice(); - - /** @brief Maps OpenGL buffer to CUDA device memory. - - This operation doesn't copy data. Several buffer objects can be mapped to CUDA memory at a time. - - A mapped data store must be unmapped with ogl::Buffer::unmapDevice before its buffer object is used. - */ - cuda::GpuMat mapDevice(cuda::Stream& stream); - - /** @brief Unmaps OpenGL buffer. - */ - void unmapDevice(cuda::Stream& stream); - - int rows() const; - int cols() const; - Size size() const; - bool empty() const; - - int type() const; - int depth() const; - int channels() const; - int elemSize() const; - int elemSize1() const; - - //! get OpenGL opject id - unsigned int bufId() const; - - class Impl; - -private: - Ptr impl_; - int rows_; - int cols_; - int type_; -}; - -/** @brief Smart pointer for OpenGL 2D texture memory with reference counting. - */ -class CV_EXPORTS Texture2D -{ -public: - /** @brief An Image Format describes the way that the images in Textures store their data. - */ - enum Format - { - NONE = 0, - DEPTH_COMPONENT = 0x1902, //!< Depth - RGB = 0x1907, //!< Red, Green, Blue - RGBA = 0x1908 //!< Red, Green, Blue, Alpha - }; - - /** @brief The constructors. - - Creates empty ogl::Texture2D object, allocates memory for ogl::Texture2D object or copies from - host/device memory. - */ - Texture2D(); - - /** @overload */ - Texture2D(int arows, int acols, Format aformat, unsigned int atexId, bool autoRelease = false); - - /** @overload */ - Texture2D(Size asize, Format aformat, unsigned int atexId, bool autoRelease = false); - - /** @overload - @param arows Number of rows. - @param acols Number of columns. - @param aformat Image format. See cv::ogl::Texture2D::Format . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - Texture2D(int arows, int acols, Format aformat, bool autoRelease = false); - - /** @overload - @param asize 2D array size. - @param aformat Image format. See cv::ogl::Texture2D::Format . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - Texture2D(Size asize, Format aformat, bool autoRelease = false); - - /** @overload - @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ). - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - explicit Texture2D(InputArray arr, bool autoRelease = false); - - /** @brief Allocates memory for ogl::Texture2D object. - - @param arows Number of rows. - @param acols Number of columns. - @param aformat Image format. See cv::ogl::Texture2D::Format . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - void create(int arows, int acols, Format aformat, bool autoRelease = false); - /** @overload - @param asize 2D array size. - @param aformat Image format. See cv::ogl::Texture2D::Format . - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - void create(Size asize, Format aformat, bool autoRelease = false); - - /** @brief Decrements the reference counter and destroys the texture object if needed. - - The function will call setAutoRelease(true) . - */ - void release(); - - /** @brief Sets auto release mode. - - @param flag Auto release mode (if true, release will be called in object's destructor). - - The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was - bound to a window it could be released at any time (user can close a window). If object's destructor - is called after destruction of the context it will cause an error. Thus ogl::Texture2D doesn't - destroy OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL - context). This function can force ogl::Texture2D destructor to destroy OpenGL object. - */ - void setAutoRelease(bool flag); - - /** @brief Copies from host/device memory to OpenGL texture. - - @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ). - @param autoRelease Auto release mode (if true, release will be called in object's destructor). - */ - void copyFrom(InputArray arr, bool autoRelease = false); - - /** @brief Copies from OpenGL texture to host/device memory or another OpenGL texture object. - - @param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , ogl::Buffer or - ogl::Texture2D ). - @param ddepth Destination depth. - @param autoRelease Auto release mode for destination buffer (if arr is OpenGL buffer or texture). - */ - void copyTo(OutputArray arr, int ddepth = CV_32F, bool autoRelease = false) const; - - /** @brief Binds texture to current active texture unit for GL_TEXTURE_2D target. - */ - void bind() const; - - int rows() const; - int cols() const; - Size size() const; - bool empty() const; - - Format format() const; - - //! get OpenGL opject id - unsigned int texId() const; - - class Impl; - -private: - Ptr impl_; - int rows_; - int cols_; - Format format_; -}; - -/** @brief Wrapper for OpenGL Client-Side Vertex arrays. - -ogl::Arrays stores vertex data in ogl::Buffer objects. - */ -class CV_EXPORTS Arrays -{ -public: - /** @brief Default constructor - */ - Arrays(); - - /** @brief Sets an array of vertex coordinates. - @param vertex array with vertex coordinates, can be both host and device memory. - */ - void setVertexArray(InputArray vertex); - - /** @brief Resets vertex coordinates. - */ - void resetVertexArray(); - - /** @brief Sets an array of vertex colors. - @param color array with vertex colors, can be both host and device memory. - */ - void setColorArray(InputArray color); - - /** @brief Resets vertex colors. - */ - void resetColorArray(); - - /** @brief Sets an array of vertex normals. - @param normal array with vertex normals, can be both host and device memory. - */ - void setNormalArray(InputArray normal); - - /** @brief Resets vertex normals. - */ - void resetNormalArray(); - - /** @brief Sets an array of vertex texture coordinates. - @param texCoord array with vertex texture coordinates, can be both host and device memory. - */ - void setTexCoordArray(InputArray texCoord); - - /** @brief Resets vertex texture coordinates. - */ - void resetTexCoordArray(); - - /** @brief Releases all inner buffers. - */ - void release(); - - /** @brief Sets auto release mode all inner buffers. - @param flag Auto release mode. - */ - void setAutoRelease(bool flag); - - /** @brief Binds all vertex arrays. - */ - void bind() const; - - /** @brief Returns the vertex count. - */ - int size() const; - bool empty() const; - -private: - int size_; - Buffer vertex_; - Buffer color_; - Buffer normal_; - Buffer texCoord_; -}; - -/////////////////// Render Functions /////////////////// - -//! render mode -enum RenderModes { - POINTS = 0x0000, - LINES = 0x0001, - LINE_LOOP = 0x0002, - LINE_STRIP = 0x0003, - TRIANGLES = 0x0004, - TRIANGLE_STRIP = 0x0005, - TRIANGLE_FAN = 0x0006, - QUADS = 0x0007, - QUAD_STRIP = 0x0008, - POLYGON = 0x0009 -}; - -/** @brief Render OpenGL texture or primitives. -@param tex Texture to draw. -@param wndRect Region of window, where to draw a texture (normalized coordinates). -@param texRect Region of texture to draw (normalized coordinates). - */ -CV_EXPORTS void render(const Texture2D& tex, - Rect_ wndRect = Rect_(0.0, 0.0, 1.0, 1.0), - Rect_ texRect = Rect_(0.0, 0.0, 1.0, 1.0)); - -/** @overload -@param arr Array of privitives vertices. -@param mode Render mode. One of cv::ogl::RenderModes -@param color Color for all vertices. Will be used if arr doesn't contain color array. -*/ -CV_EXPORTS void render(const Arrays& arr, int mode = POINTS, Scalar color = Scalar::all(255)); - -/** @overload -@param arr Array of privitives vertices. -@param indices Array of vertices indices (host or device memory). -@param mode Render mode. One of cv::ogl::RenderModes -@param color Color for all vertices. Will be used if arr doesn't contain color array. -*/ -CV_EXPORTS void render(const Arrays& arr, InputArray indices, int mode = POINTS, Scalar color = Scalar::all(255)); - -/////////////////// CL-GL Interoperability Functions /////////////////// - -namespace ocl { -using namespace cv::ocl; - -// TODO static functions in the Context class -/** @brief Creates OpenCL context from GL. -@return Returns reference to OpenCL Context - */ -CV_EXPORTS Context& initializeContextFromGL(); - -} // namespace cv::ogl::ocl - -/** @brief Converts InputArray to Texture2D object. -@param src - source InputArray. -@param texture - destination Texture2D object. - */ -CV_EXPORTS void convertToGLTexture2D(InputArray src, Texture2D& texture); - -/** @brief Converts Texture2D object to OutputArray. -@param texture - source Texture2D object. -@param dst - destination OutputArray. - */ -CV_EXPORTS void convertFromGLTexture2D(const Texture2D& texture, OutputArray dst); - -/** @brief Maps Buffer object to process on CL side (convert to UMat). - -Function creates CL buffer from GL one, and then constructs UMat that can be used -to process buffer data with OpenCV functions. Note that in current implementation -UMat constructed this way doesn't own corresponding GL buffer object, so it is -the user responsibility to close down CL/GL buffers relationships by explicitly -calling unmapGLBuffer() function. -@param buffer - source Buffer object. -@param accessFlags - data access flags (ACCESS_READ|ACCESS_WRITE). -@return Returns UMat object - */ -CV_EXPORTS UMat mapGLBuffer(const Buffer& buffer, int accessFlags = ACCESS_READ|ACCESS_WRITE); - -/** @brief Unmaps Buffer object (releases UMat, previously mapped from Buffer). - -Function must be called explicitly by the user for each UMat previously constructed -by the call to mapGLBuffer() function. -@param u - source UMat, created by mapGLBuffer(). - */ -CV_EXPORTS void unmapGLBuffer(UMat& u); - -//! @} -}} // namespace cv::ogl - -namespace cv { namespace cuda { - -/** @brief Sets a CUDA device and initializes it for the current thread with OpenGL interoperability. - -This function should be explicitly called after OpenGL context creation and before any CUDA calls. -@param device System index of a CUDA device starting with 0. -@ingroup core_opengl - */ -CV_EXPORTS void setGlDevice(int device = 0); - -}} - -//! @cond IGNORED - -//////////////////////////////////////////////////////////////////////// -//////////////////////////////////////////////////////////////////////// -//////////////////////////////////////////////////////////////////////// - -inline -cv::ogl::Buffer::Buffer(int arows, int acols, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0) -{ - create(arows, acols, atype, target, autoRelease); -} - -inline -cv::ogl::Buffer::Buffer(Size asize, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0) -{ - create(asize, atype, target, autoRelease); -} - -inline -void cv::ogl::Buffer::create(Size asize, int atype, Target target, bool autoRelease) -{ - create(asize.height, asize.width, atype, target, autoRelease); -} - -inline -int cv::ogl::Buffer::rows() const -{ - return rows_; -} - -inline -int cv::ogl::Buffer::cols() const -{ - return cols_; -} - -inline -cv::Size cv::ogl::Buffer::size() const -{ - return Size(cols_, rows_); -} - -inline -bool cv::ogl::Buffer::empty() const -{ - return rows_ == 0 || cols_ == 0; -} - -inline -int cv::ogl::Buffer::type() const -{ - return type_; -} - -inline -int cv::ogl::Buffer::depth() const -{ - return CV_MAT_DEPTH(type_); -} - -inline -int cv::ogl::Buffer::channels() const -{ - return CV_MAT_CN(type_); -} - -inline -int cv::ogl::Buffer::elemSize() const -{ - return CV_ELEM_SIZE(type_); -} - -inline -int cv::ogl::Buffer::elemSize1() const -{ - return CV_ELEM_SIZE1(type_); -} - -/////// - -inline -cv::ogl::Texture2D::Texture2D(int arows, int acols, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE) -{ - create(arows, acols, aformat, autoRelease); -} - -inline -cv::ogl::Texture2D::Texture2D(Size asize, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE) -{ - create(asize, aformat, autoRelease); -} - -inline -void cv::ogl::Texture2D::create(Size asize, Format aformat, bool autoRelease) -{ - create(asize.height, asize.width, aformat, autoRelease); -} - -inline -int cv::ogl::Texture2D::rows() const -{ - return rows_; -} - -inline -int cv::ogl::Texture2D::cols() const -{ - return cols_; -} - -inline -cv::Size cv::ogl::Texture2D::size() const -{ - return Size(cols_, rows_); -} - -inline -bool cv::ogl::Texture2D::empty() const -{ - return rows_ == 0 || cols_ == 0; -} - -inline -cv::ogl::Texture2D::Format cv::ogl::Texture2D::format() const -{ - return format_; -} - -/////// - -inline -cv::ogl::Arrays::Arrays() : size_(0) -{ -} - -inline -int cv::ogl::Arrays::size() const -{ - return size_; -} - -inline -bool cv::ogl::Arrays::empty() const -{ - return size_ == 0; -} - -//! @endcond - -#endif /* OPENCV_CORE_OPENGL_HPP */ diff --git a/include/opencv2/core/operations.hpp b/include/opencv2/core/operations.hpp deleted file mode 100755 index ef1808a..0000000 --- a/include/opencv2/core/operations.hpp +++ /dev/null @@ -1,573 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_OPERATIONS_HPP -#define OPENCV_CORE_OPERATIONS_HPP - -#ifndef __cplusplus -# error operations.hpp header must be compiled as C++ -#endif - -#include - -//! @cond IGNORED - -namespace cv -{ - -////////////////////////////// Matx methods depending on core API ///////////////////////////// - -namespace internal -{ - -template struct Matx_FastInvOp -{ - bool operator()(const Matx<_Tp, m, n>& a, Matx<_Tp, n, m>& b, int method) const - { - return invert(a, b, method) != 0; - } -}; - -template struct Matx_FastInvOp<_Tp, m, m> -{ - bool operator()(const Matx<_Tp, m, m>& a, Matx<_Tp, m, m>& b, int method) const - { - if (method == DECOMP_LU || method == DECOMP_CHOLESKY) - { - Matx<_Tp, m, m> temp = a; - - // assume that b is all 0's on input => make it a unity matrix - for (int i = 0; i < m; i++) - b(i, i) = (_Tp)1; - - if (method == DECOMP_CHOLESKY) - return Cholesky(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m); - - return LU(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m) != 0; - } - else - { - return invert(a, b, method) != 0; - } - } -}; - -template struct Matx_FastInvOp<_Tp, 2, 2> -{ - bool operator()(const Matx<_Tp, 2, 2>& a, Matx<_Tp, 2, 2>& b, int /*method*/) const - { - _Tp d = (_Tp)determinant(a); - if (d == 0) - return false; - d = 1/d; - b(1,1) = a(0,0)*d; - b(0,0) = a(1,1)*d; - b(0,1) = -a(0,1)*d; - b(1,0) = -a(1,0)*d; - return true; - } -}; - -template struct Matx_FastInvOp<_Tp, 3, 3> -{ - bool operator()(const Matx<_Tp, 3, 3>& a, Matx<_Tp, 3, 3>& b, int /*method*/) const - { - _Tp d = (_Tp)determinant(a); - if (d == 0) - return false; - d = 1/d; - b(0,0) = (a(1,1) * a(2,2) - a(1,2) * a(2,1)) * d; - b(0,1) = (a(0,2) * a(2,1) - a(0,1) * a(2,2)) * d; - b(0,2) = (a(0,1) * a(1,2) - a(0,2) * a(1,1)) * d; - - b(1,0) = (a(1,2) * a(2,0) - a(1,0) * a(2,2)) * d; - b(1,1) = (a(0,0) * a(2,2) - a(0,2) * a(2,0)) * d; - b(1,2) = (a(0,2) * a(1,0) - a(0,0) * a(1,2)) * d; - - b(2,0) = (a(1,0) * a(2,1) - a(1,1) * a(2,0)) * d; - b(2,1) = (a(0,1) * a(2,0) - a(0,0) * a(2,1)) * d; - b(2,2) = (a(0,0) * a(1,1) - a(0,1) * a(1,0)) * d; - return true; - } -}; - - -template struct Matx_FastSolveOp -{ - bool operator()(const Matx<_Tp, m, l>& a, const Matx<_Tp, m, n>& b, - Matx<_Tp, l, n>& x, int method) const - { - return cv::solve(a, b, x, method); - } -}; - -template struct Matx_FastSolveOp<_Tp, m, m, n> -{ - bool operator()(const Matx<_Tp, m, m>& a, const Matx<_Tp, m, n>& b, - Matx<_Tp, m, n>& x, int method) const - { - if (method == DECOMP_LU || method == DECOMP_CHOLESKY) - { - Matx<_Tp, m, m> temp = a; - x = b; - if( method == DECOMP_CHOLESKY ) - return Cholesky(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n); - - return LU(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n) != 0; - } - else - { - return cv::solve(a, b, x, method); - } - } -}; - -template struct Matx_FastSolveOp<_Tp, 2, 2, 1> -{ - bool operator()(const Matx<_Tp, 2, 2>& a, const Matx<_Tp, 2, 1>& b, - Matx<_Tp, 2, 1>& x, int) const - { - _Tp d = (_Tp)determinant(a); - if (d == 0) - return false; - d = 1/d; - x(0) = (b(0)*a(1,1) - b(1)*a(0,1))*d; - x(1) = (b(1)*a(0,0) - b(0)*a(1,0))*d; - return true; - } -}; - -template struct Matx_FastSolveOp<_Tp, 3, 3, 1> -{ - bool operator()(const Matx<_Tp, 3, 3>& a, const Matx<_Tp, 3, 1>& b, - Matx<_Tp, 3, 1>& x, int) const - { - _Tp d = (_Tp)determinant(a); - if (d == 0) - return false; - d = 1/d; - x(0) = d*(b(0)*(a(1,1)*a(2,2) - a(1,2)*a(2,1)) - - a(0,1)*(b(1)*a(2,2) - a(1,2)*b(2)) + - a(0,2)*(b(1)*a(2,1) - a(1,1)*b(2))); - - x(1) = d*(a(0,0)*(b(1)*a(2,2) - a(1,2)*b(2)) - - b(0)*(a(1,0)*a(2,2) - a(1,2)*a(2,0)) + - a(0,2)*(a(1,0)*b(2) - b(1)*a(2,0))); - - x(2) = d*(a(0,0)*(a(1,1)*b(2) - b(1)*a(2,1)) - - a(0,1)*(a(1,0)*b(2) - b(1)*a(2,0)) + - b(0)*(a(1,0)*a(2,1) - a(1,1)*a(2,0))); - return true; - } -}; - -} // internal - -template inline -Matx<_Tp,m,n> Matx<_Tp,m,n>::randu(_Tp a, _Tp b) -{ - Matx<_Tp,m,n> M; - cv::randu(M, Scalar(a), Scalar(b)); - return M; -} - -template inline -Matx<_Tp,m,n> Matx<_Tp,m,n>::randn(_Tp a, _Tp b) -{ - Matx<_Tp,m,n> M; - cv::randn(M, Scalar(a), Scalar(b)); - return M; -} - -template inline -Matx<_Tp, n, m> Matx<_Tp, m, n>::inv(int method, bool *p_is_ok /*= NULL*/) const -{ - Matx<_Tp, n, m> b; - bool ok = cv::internal::Matx_FastInvOp<_Tp, m, n>()(*this, b, method); - if (p_is_ok) *p_is_ok = ok; - return ok ? b : Matx<_Tp, n, m>::zeros(); -} - -template template inline -Matx<_Tp, n, l> Matx<_Tp, m, n>::solve(const Matx<_Tp, m, l>& rhs, int method) const -{ - Matx<_Tp, n, l> x; - bool ok = cv::internal::Matx_FastSolveOp<_Tp, m, n, l>()(*this, rhs, x, method); - return ok ? x : Matx<_Tp, n, l>::zeros(); -} - - - -////////////////////////// Augmenting algebraic & logical operations ////////////////////////// - -#define CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ - static inline A& operator op (A& a, const B& b) { cvop; return a; } - -#define CV_MAT_AUG_OPERATOR(op, cvop, A, B) \ - CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ - CV_MAT_AUG_OPERATOR1(op, cvop, const A, B) - -#define CV_MAT_AUG_OPERATOR_T(op, cvop, A, B) \ - template CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \ - template CV_MAT_AUG_OPERATOR1(op, cvop, const A, B) - -#define CV_MAT_AUG_OPERATOR_TN(op, cvop, A) \ - template static inline A& operator op (A& a, const Matx<_Tp,m,n>& b) { cvop; return a; } \ - template static inline const A& operator op (const A& a, const Matx<_Tp,m,n>& b) { cvop; return a; } - -CV_MAT_AUG_OPERATOR (+=, cv::add(a, b, (const Mat&)a), Mat, Mat) -CV_MAT_AUG_OPERATOR (+=, cv::add(a, b, (const Mat&)a), Mat, Scalar) -CV_MAT_AUG_OPERATOR_T(+=, cv::add(a, b, (const Mat&)a), Mat_<_Tp>, Mat) -CV_MAT_AUG_OPERATOR_T(+=, cv::add(a, b, (const Mat&)a), Mat_<_Tp>, Scalar) -CV_MAT_AUG_OPERATOR_T(+=, cv::add(a, b, (const Mat&)a), Mat_<_Tp>, Mat_<_Tp>) -CV_MAT_AUG_OPERATOR_TN(+=, cv::add(a, Mat(b), (const Mat&)a), Mat) -CV_MAT_AUG_OPERATOR_TN(+=, cv::add(a, Mat(b), (const Mat&)a), Mat_<_Tp>) - -CV_MAT_AUG_OPERATOR (-=, cv::subtract(a, b, (const Mat&)a), Mat, Mat) -CV_MAT_AUG_OPERATOR (-=, cv::subtract(a, b, (const Mat&)a), Mat, Scalar) -CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a, b, (const Mat&)a), Mat_<_Tp>, Mat) -CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a, b, (const Mat&)a), Mat_<_Tp>, Scalar) -CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a, b, (const Mat&)a), Mat_<_Tp>, Mat_<_Tp>) -CV_MAT_AUG_OPERATOR_TN(-=, cv::subtract(a, Mat(b), (const Mat&)a), Mat) -CV_MAT_AUG_OPERATOR_TN(-=, cv::subtract(a, Mat(b), (const Mat&)a), Mat_<_Tp>) - -CV_MAT_AUG_OPERATOR (*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat, Mat) -CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat) -CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat_<_Tp>) -CV_MAT_AUG_OPERATOR (*=, a.convertTo(a, -1, b), Mat, double) -CV_MAT_AUG_OPERATOR_T(*=, a.convertTo(a, -1, b), Mat_<_Tp>, double) -CV_MAT_AUG_OPERATOR_TN(*=, cv::gemm(a, Mat(b), 1, Mat(), 0, a, 0), Mat) -CV_MAT_AUG_OPERATOR_TN(*=, cv::gemm(a, Mat(b), 1, Mat(), 0, a, 0), Mat_<_Tp>) - -CV_MAT_AUG_OPERATOR (/=, cv::divide(a, b, (const Mat&)a), Mat, Mat) -CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a, b, (const Mat&)a), Mat_<_Tp>, Mat) -CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a, b, (const Mat&)a), Mat_<_Tp>, Mat_<_Tp>) -CV_MAT_AUG_OPERATOR (/=, a.convertTo((Mat&)a, -1, 1./b), Mat, double) -CV_MAT_AUG_OPERATOR_T(/=, a.convertTo((Mat&)a, -1, 1./b), Mat_<_Tp>, double) -CV_MAT_AUG_OPERATOR_TN(/=, cv::divide(a, Mat(b), (const Mat&)a), Mat) -CV_MAT_AUG_OPERATOR_TN(/=, cv::divide(a, Mat(b), (const Mat&)a), Mat_<_Tp>) - -CV_MAT_AUG_OPERATOR (&=, cv::bitwise_and(a, b, (const Mat&)a), Mat, Mat) -CV_MAT_AUG_OPERATOR (&=, cv::bitwise_and(a, b, (const Mat&)a), Mat, Scalar) -CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a, b, (const Mat&)a), Mat_<_Tp>, Mat) -CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a, b, (const Mat&)a), Mat_<_Tp>, Scalar) -CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a, b, (const Mat&)a), Mat_<_Tp>, Mat_<_Tp>) -CV_MAT_AUG_OPERATOR_TN(&=, cv::bitwise_and(a, Mat(b), (const Mat&)a), Mat) -CV_MAT_AUG_OPERATOR_TN(&=, cv::bitwise_and(a, Mat(b), (const Mat&)a), Mat_<_Tp>) - -CV_MAT_AUG_OPERATOR (|=, cv::bitwise_or(a, b, (const Mat&)a), Mat, Mat) -CV_MAT_AUG_OPERATOR (|=, cv::bitwise_or(a, b, (const Mat&)a), Mat, Scalar) -CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a, b, (const Mat&)a), Mat_<_Tp>, Mat) -CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a, b, (const Mat&)a), Mat_<_Tp>, Scalar) -CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a, b, (const Mat&)a), Mat_<_Tp>, Mat_<_Tp>) -CV_MAT_AUG_OPERATOR_TN(|=, cv::bitwise_or(a, Mat(b), (const Mat&)a), Mat) -CV_MAT_AUG_OPERATOR_TN(|=, cv::bitwise_or(a, Mat(b), (const Mat&)a), Mat_<_Tp>) - -CV_MAT_AUG_OPERATOR (^=, cv::bitwise_xor(a, b, (const Mat&)a), Mat, Mat) -CV_MAT_AUG_OPERATOR (^=, cv::bitwise_xor(a, b, (const Mat&)a), Mat, Scalar) -CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a, b, (const Mat&)a), Mat_<_Tp>, Mat) -CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a, b, (const Mat&)a), Mat_<_Tp>, Scalar) -CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a, b, (const Mat&)a), Mat_<_Tp>, Mat_<_Tp>) -CV_MAT_AUG_OPERATOR_TN(^=, cv::bitwise_xor(a, Mat(b), (const Mat&)a), Mat) -CV_MAT_AUG_OPERATOR_TN(^=, cv::bitwise_xor(a, Mat(b), (const Mat&)a), Mat_<_Tp>) - -#undef CV_MAT_AUG_OPERATOR_TN -#undef CV_MAT_AUG_OPERATOR_T -#undef CV_MAT_AUG_OPERATOR -#undef CV_MAT_AUG_OPERATOR1 - - - -///////////////////////////////////////////// SVD ///////////////////////////////////////////// - -inline SVD::SVD() {} -inline SVD::SVD( InputArray m, int flags ) { operator ()(m, flags); } -inline void SVD::solveZ( InputArray m, OutputArray _dst ) -{ - Mat mtx = m.getMat(); - SVD svd(mtx, (mtx.rows >= mtx.cols ? 0 : SVD::FULL_UV)); - _dst.create(svd.vt.cols, 1, svd.vt.type()); - Mat dst = _dst.getMat(); - svd.vt.row(svd.vt.rows-1).reshape(1,svd.vt.cols).copyTo(dst); -} - -template inline void - SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt ) -{ - CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); - Mat _a(a, false), _u(u, false), _w(w, false), _vt(vt, false); - SVD::compute(_a, _w, _u, _vt); - CV_Assert(_w.data == (uchar*)&w.val[0] && _u.data == (uchar*)&u.val[0] && _vt.data == (uchar*)&vt.val[0]); -} - -template inline void -SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w ) -{ - CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); - Mat _a(a, false), _w(w, false); - SVD::compute(_a, _w); - CV_Assert(_w.data == (uchar*)&w.val[0]); -} - -template inline void -SVD::backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, - const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, - Matx<_Tp, n, nb>& dst ) -{ - CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector."); - Mat _u(u, false), _w(w, false), _vt(vt, false), _rhs(rhs, false), _dst(dst, false); - SVD::backSubst(_w, _u, _vt, _rhs, _dst); - CV_Assert(_dst.data == (uchar*)&dst.val[0]); -} - - - -/////////////////////////////////// Multiply-with-Carry RNG /////////////////////////////////// - -inline RNG::RNG() { state = 0xffffffff; } -inline RNG::RNG(uint64 _state) { state = _state ? _state : 0xffffffff; } - -inline RNG::operator uchar() { return (uchar)next(); } -inline RNG::operator schar() { return (schar)next(); } -inline RNG::operator ushort() { return (ushort)next(); } -inline RNG::operator short() { return (short)next(); } -inline RNG::operator int() { return (int)next(); } -inline RNG::operator unsigned() { return next(); } -inline RNG::operator float() { return next()*2.3283064365386962890625e-10f; } -inline RNG::operator double() { unsigned t = next(); return (((uint64)t << 32) | next()) * 5.4210108624275221700372640043497e-20; } - -inline unsigned RNG::operator ()(unsigned N) { return (unsigned)uniform(0,N); } -inline unsigned RNG::operator ()() { return next(); } - -inline int RNG::uniform(int a, int b) { return a == b ? a : (int)(next() % (b - a) + a); } -inline float RNG::uniform(float a, float b) { return ((float)*this)*(b - a) + a; } -inline double RNG::uniform(double a, double b) { return ((double)*this)*(b - a) + a; } - -inline bool RNG::operator ==(const RNG& other) const { return state == other.state; } - -inline unsigned RNG::next() -{ - state = (uint64)(unsigned)state* /*CV_RNG_COEFF*/ 4164903690U + (unsigned)(state >> 32); - return (unsigned)state; -} - -//! returns the next uniformly-distributed random number of the specified type -template static inline _Tp randu() -{ - return (_Tp)theRNG(); -} - -///////////////////////////////// Formatted string generation ///////////////////////////////// - -/** @brief Returns a text string formatted using the printf-like expression. - -The function acts like sprintf but forms and returns an STL string. It can be used to form an error -message in the Exception constructor. -@param fmt printf-compatible formatting specifiers. - */ -CV_EXPORTS String format( const char* fmt, ... ); - -///////////////////////////////// Formatted output of cv::Mat ///////////////////////////////// - -static inline -Ptr format(InputArray mtx, int fmt) -{ - return Formatter::get(fmt)->format(mtx.getMat()); -} - -static inline -int print(Ptr fmtd, FILE* stream = stdout) -{ - int written = 0; - fmtd->reset(); - for(const char* str = fmtd->next(); str; str = fmtd->next()) - written += fputs(str, stream); - - return written; -} - -static inline -int print(const Mat& mtx, FILE* stream = stdout) -{ - return print(Formatter::get()->format(mtx), stream); -} - -static inline -int print(const UMat& mtx, FILE* stream = stdout) -{ - return print(Formatter::get()->format(mtx.getMat(ACCESS_READ)), stream); -} - -template static inline -int print(const std::vector >& vec, FILE* stream = stdout) -{ - return print(Formatter::get()->format(Mat(vec)), stream); -} - -template static inline -int print(const std::vector >& vec, FILE* stream = stdout) -{ - return print(Formatter::get()->format(Mat(vec)), stream); -} - -template static inline -int print(const Matx<_Tp, m, n>& matx, FILE* stream = stdout) -{ - return print(Formatter::get()->format(cv::Mat(matx)), stream); -} - -//! @endcond - -/****************************************************************************************\ -* Auxiliary algorithms * -\****************************************************************************************/ - -/** @brief Splits an element set into equivalency classes. - -The generic function partition implements an \f$O(N^2)\f$ algorithm for splitting a set of \f$N\f$ elements -into one or more equivalency classes, as described in - . The function returns the number of -equivalency classes. -@param _vec Set of elements stored as a vector. -@param labels Output vector of labels. It contains as many elements as vec. Each label labels[i] is -a 0-based cluster index of `vec[i]`. -@param predicate Equivalence predicate (pointer to a boolean function of two arguments or an -instance of the class that has the method bool operator()(const _Tp& a, const _Tp& b) ). The -predicate returns true when the elements are certainly in the same class, and returns false if they -may or may not be in the same class. -@ingroup core_cluster -*/ -template int -partition( const std::vector<_Tp>& _vec, std::vector& labels, - _EqPredicate predicate=_EqPredicate()) -{ - int i, j, N = (int)_vec.size(); - const _Tp* vec = &_vec[0]; - - const int PARENT=0; - const int RANK=1; - - std::vector _nodes(N*2); - int (*nodes)[2] = (int(*)[2])&_nodes[0]; - - // The first O(N) pass: create N single-vertex trees - for(i = 0; i < N; i++) - { - nodes[i][PARENT]=-1; - nodes[i][RANK] = 0; - } - - // The main O(N^2) pass: merge connected components - for( i = 0; i < N; i++ ) - { - int root = i; - - // find root - while( nodes[root][PARENT] >= 0 ) - root = nodes[root][PARENT]; - - for( j = 0; j < N; j++ ) - { - if( i == j || !predicate(vec[i], vec[j])) - continue; - int root2 = j; - - while( nodes[root2][PARENT] >= 0 ) - root2 = nodes[root2][PARENT]; - - if( root2 != root ) - { - // unite both trees - int rank = nodes[root][RANK], rank2 = nodes[root2][RANK]; - if( rank > rank2 ) - nodes[root2][PARENT] = root; - else - { - nodes[root][PARENT] = root2; - nodes[root2][RANK] += rank == rank2; - root = root2; - } - CV_Assert( nodes[root][PARENT] < 0 ); - - int k = j, parent; - - // compress the path from node2 to root - while( (parent = nodes[k][PARENT]) >= 0 ) - { - nodes[k][PARENT] = root; - k = parent; - } - - // compress the path from node to root - k = i; - while( (parent = nodes[k][PARENT]) >= 0 ) - { - nodes[k][PARENT] = root; - k = parent; - } - } - } - } - - // Final O(N) pass: enumerate classes - labels.resize(N); - int nclasses = 0; - - for( i = 0; i < N; i++ ) - { - int root = i; - while( nodes[root][PARENT] >= 0 ) - root = nodes[root][PARENT]; - // re-use the rank as the class label - if( nodes[root][RANK] >= 0 ) - nodes[root][RANK] = ~nclasses++; - labels[i] = ~nodes[root][RANK]; - } - - return nclasses; -} - -} // cv - -#endif diff --git a/include/opencv2/core/optim.hpp b/include/opencv2/core/optim.hpp deleted file mode 100755 index 5a09400..0000000 --- a/include/opencv2/core/optim.hpp +++ /dev/null @@ -1,302 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the OpenCV Foundation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_OPTIM_HPP -#define OPENCV_OPTIM_HPP - -#include "opencv2/core.hpp" - -namespace cv -{ - -/** @addtogroup core_optim -The algorithms in this section minimize or maximize function value within specified constraints or -without any constraints. -@{ -*/ - -/** @brief Basic interface for all solvers - */ -class CV_EXPORTS MinProblemSolver : public Algorithm -{ -public: - /** @brief Represents function being optimized - */ - class CV_EXPORTS Function - { - public: - virtual ~Function() {} - virtual int getDims() const = 0; - virtual double getGradientEps() const; - virtual double calc(const double* x) const = 0; - virtual void getGradient(const double* x,double* grad); - }; - - /** @brief Getter for the optimized function. - - The optimized function is represented by Function interface, which requires derivatives to - implement the calc(double*) and getDim() methods to evaluate the function. - - @return Smart-pointer to an object that implements Function interface - it represents the - function that is being optimized. It can be empty, if no function was given so far. - */ - virtual Ptr getFunction() const = 0; - - /** @brief Setter for the optimized function. - - *It should be called at least once before the call to* minimize(), as default value is not usable. - - @param f The new function to optimize. - */ - virtual void setFunction(const Ptr& f) = 0; - - /** @brief Getter for the previously set terminal criteria for this algorithm. - - @return Deep copy of the terminal criteria used at the moment. - */ - virtual TermCriteria getTermCriteria() const = 0; - - /** @brief Set terminal criteria for solver. - - This method *is not necessary* to be called before the first call to minimize(), as the default - value is sensible. - - Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when - the function values at the vertices of simplex are within termcrit.epsilon range or simplex - becomes so small that it can enclosed in a box with termcrit.epsilon sides, whatever comes - first. - @param termcrit Terminal criteria to be used, represented as cv::TermCriteria structure. - */ - virtual void setTermCriteria(const TermCriteria& termcrit) = 0; - - /** @brief actually runs the algorithm and performs the minimization. - - The sole input parameter determines the centroid of the starting simplex (roughly, it tells - where to start), all the others (terminal criteria, initial step, function to be minimized) are - supposed to be set via the setters before the call to this method or the default values (not - always sensible) will be used. - - @param x The initial point, that will become a centroid of an initial simplex. After the algorithm - will terminate, it will be set to the point where the algorithm stops, the point of possible - minimum. - @return The value of a function at the point found. - */ - virtual double minimize(InputOutputArray x) = 0; -}; - -/** @brief This class is used to perform the non-linear non-constrained minimization of a function, - -defined on an `n`-dimensional Euclidean space, using the **Nelder-Mead method**, also known as -**downhill simplex method**. The basic idea about the method can be obtained from -. - -It should be noted, that this method, although deterministic, is rather a heuristic and therefore -may converge to a local minima, not necessary a global one. It is iterative optimization technique, -which at each step uses an information about the values of a function evaluated only at `n+1` -points, arranged as a *simplex* in `n`-dimensional space (hence the second name of the method). At -each step new point is chosen to evaluate function at, obtained value is compared with previous -ones and based on this information simplex changes it's shape , slowly moving to the local minimum. -Thus this method is using *only* function values to make decision, on contrary to, say, Nonlinear -Conjugate Gradient method (which is also implemented in optim). - -Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when the -function values at the vertices of simplex are within termcrit.epsilon range or simplex becomes so -small that it can enclosed in a box with termcrit.epsilon sides, whatever comes first, for some -defined by user positive integer termcrit.maxCount and positive non-integer termcrit.epsilon. - -@note DownhillSolver is a derivative of the abstract interface -cv::MinProblemSolver, which in turn is derived from the Algorithm interface and is used to -encapsulate the functionality, common to all non-linear optimization algorithms in the optim -module. - -@note term criteria should meet following condition: -@code - termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0 -@endcode - */ -class CV_EXPORTS DownhillSolver : public MinProblemSolver -{ -public: - /** @brief Returns the initial step that will be used in downhill simplex algorithm. - - @param step Initial step that will be used in algorithm. Note, that although corresponding setter - accepts column-vectors as well as row-vectors, this method will return a row-vector. - @see DownhillSolver::setInitStep - */ - virtual void getInitStep(OutputArray step) const=0; - - /** @brief Sets the initial step that will be used in downhill simplex algorithm. - - Step, together with initial point (given in DownhillSolver::minimize) are two `n`-dimensional - vectors that are used to determine the shape of initial simplex. Roughly said, initial point - determines the position of a simplex (it will become simplex's centroid), while step determines the - spread (size in each dimension) of a simplex. To be more precise, if \f$s,x_0\in\mathbb{R}^n\f$ are - the initial step and initial point respectively, the vertices of a simplex will be: - \f$v_0:=x_0-\frac{1}{2} s\f$ and \f$v_i:=x_0+s_i\f$ for \f$i=1,2,\dots,n\f$ where \f$s_i\f$ denotes - projections of the initial step of *n*-th coordinate (the result of projection is treated to be - vector given by \f$s_i:=e_i\cdot\left\f$, where \f$e_i\f$ form canonical basis) - - @param step Initial step that will be used in algorithm. Roughly said, it determines the spread - (size in each dimension) of an initial simplex. - */ - virtual void setInitStep(InputArray step)=0; - - /** @brief This function returns the reference to the ready-to-use DownhillSolver object. - - All the parameters are optional, so this procedure can be called even without parameters at - all. In this case, the default values will be used. As default value for terminal criteria are - the only sensible ones, MinProblemSolver::setFunction() and DownhillSolver::setInitStep() - should be called upon the obtained object, if the respective parameters were not given to - create(). Otherwise, the two ways (give parameters to createDownhillSolver() or miss them out - and call the MinProblemSolver::setFunction() and DownhillSolver::setInitStep()) are absolutely - equivalent (and will drop the same errors in the same way, should invalid input be detected). - @param f Pointer to the function that will be minimized, similarly to the one you submit via - MinProblemSolver::setFunction. - @param initStep Initial step, that will be used to construct the initial simplex, similarly to the one - you submit via MinProblemSolver::setInitStep. - @param termcrit Terminal criteria to the algorithm, similarly to the one you submit via - MinProblemSolver::setTermCriteria. - */ - static Ptr create(const Ptr& f=Ptr(), - InputArray initStep=Mat_(1,1,0.0), - TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)); -}; - -/** @brief This class is used to perform the non-linear non-constrained minimization of a function -with known gradient, - -defined on an *n*-dimensional Euclidean space, using the **Nonlinear Conjugate Gradient method**. -The implementation was done based on the beautifully clear explanatory article [An Introduction to -the Conjugate Gradient Method Without the Agonizing -Pain](http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf) by Jonathan Richard -Shewchuk. The method can be seen as an adaptation of a standard Conjugate Gradient method (see, for -example ) for numerically solving the -systems of linear equations. - -It should be noted, that this method, although deterministic, is rather a heuristic method and -therefore may converge to a local minima, not necessary a global one. What is even more disastrous, -most of its behaviour is ruled by gradient, therefore it essentially cannot distinguish between -local minima and maxima. Therefore, if it starts sufficiently near to the local maximum, it may -converge to it. Another obvious restriction is that it should be possible to compute the gradient of -a function at any point, thus it is preferable to have analytic expression for gradient and -computational burden should be born by the user. - -The latter responsibility is accomplished via the getGradient method of a -MinProblemSolver::Function interface (which represents function being optimized). This method takes -point a point in *n*-dimensional space (first argument represents the array of coordinates of that -point) and compute its gradient (it should be stored in the second argument as an array). - -@note class ConjGradSolver thus does not add any new methods to the basic MinProblemSolver interface. - -@note term criteria should meet following condition: -@code - termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0 - // or - termcrit.type == TermCriteria::MAX_ITER) && termcrit.maxCount > 0 -@endcode - */ -class CV_EXPORTS ConjGradSolver : public MinProblemSolver -{ -public: - /** @brief This function returns the reference to the ready-to-use ConjGradSolver object. - - All the parameters are optional, so this procedure can be called even without parameters at - all. In this case, the default values will be used. As default value for terminal criteria are - the only sensible ones, MinProblemSolver::setFunction() should be called upon the obtained - object, if the function was not given to create(). Otherwise, the two ways (submit it to - create() or miss it out and call the MinProblemSolver::setFunction()) are absolutely equivalent - (and will drop the same errors in the same way, should invalid input be detected). - @param f Pointer to the function that will be minimized, similarly to the one you submit via - MinProblemSolver::setFunction. - @param termcrit Terminal criteria to the algorithm, similarly to the one you submit via - MinProblemSolver::setTermCriteria. - */ - static Ptr create(const Ptr& f=Ptr(), - TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)); -}; - -//! return codes for cv::solveLP() function -enum SolveLPResult -{ - SOLVELP_UNBOUNDED = -2, //!< problem is unbounded (target function can achieve arbitrary high values) - SOLVELP_UNFEASIBLE = -1, //!< problem is unfeasible (there are no points that satisfy all the constraints imposed) - SOLVELP_SINGLE = 0, //!< there is only one maximum for target function - SOLVELP_MULTI = 1 //!< there are multiple maxima for target function - the arbitrary one is returned -}; - -/** @brief Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method). - -What we mean here by "linear programming problem" (or LP problem, for short) can be formulated as: - -\f[\mbox{Maximize } c\cdot x\\ - \mbox{Subject to:}\\ - Ax\leq b\\ - x\geq 0\f] - -Where \f$c\f$ is fixed `1`-by-`n` row-vector, \f$A\f$ is fixed `m`-by-`n` matrix, \f$b\f$ is fixed `m`-by-`1` -column vector and \f$x\f$ is an arbitrary `n`-by-`1` column vector, which satisfies the constraints. - -Simplex algorithm is one of many algorithms that are designed to handle this sort of problems -efficiently. Although it is not optimal in theoretical sense (there exist algorithms that can solve -any problem written as above in polynomial time, while simplex method degenerates to exponential -time for some special cases), it is well-studied, easy to implement and is shown to work well for -real-life purposes. - -The particular implementation is taken almost verbatim from **Introduction to Algorithms, third -edition** by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular, the -Bland's rule is used to prevent cycling. - -@param Func This row-vector corresponds to \f$c\f$ in the LP problem formulation (see above). It should -contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted, -in the latter case it is understood to correspond to \f$c^T\f$. -@param Constr `m`-by-`n+1` matrix, whose rightmost column corresponds to \f$b\f$ in formulation above -and the remaining to \f$A\f$. It should contain 32- or 64-bit floating point numbers. -@param z The solution will be returned here as a column-vector - it corresponds to \f$c\f$ in the -formulation above. It will contain 64-bit floating point numbers. -@return One of cv::SolveLPResult - */ -CV_EXPORTS_W int solveLP(const Mat& Func, const Mat& Constr, Mat& z); - -//! @} - -}// cv - -#endif diff --git a/include/opencv2/core/ovx.hpp b/include/opencv2/core/ovx.hpp deleted file mode 100755 index 8bb7d54..0000000 --- a/include/opencv2/core/ovx.hpp +++ /dev/null @@ -1,28 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -// Copyright (C) 2016, Intel Corporation, all rights reserved. -// Third party copyrights are property of their respective owners. - -// OpenVX related definitions and declarations - -#pragma once -#ifndef OPENCV_OVX_HPP -#define OPENCV_OVX_HPP - -#include "cvdef.h" - -namespace cv -{ -/// Check if use of OpenVX is possible -CV_EXPORTS_W bool haveOpenVX(); - -/// Check if use of OpenVX is enabled -CV_EXPORTS_W bool useOpenVX(); - -/// Enable/disable use of OpenVX -CV_EXPORTS_W void setUseOpenVX(bool flag); -} // namespace cv - -#endif // OPENCV_OVX_HPP diff --git a/include/opencv2/core/persistence.hpp b/include/opencv2/core/persistence.hpp deleted file mode 100755 index e3026e3..0000000 --- a/include/opencv2/core/persistence.hpp +++ /dev/null @@ -1,1383 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_PERSISTENCE_HPP -#define OPENCV_CORE_PERSISTENCE_HPP - -#ifndef CV_DOXYGEN -/// Define to support persistence legacy formats -#define CV__LEGACY_PERSISTENCE -#endif - -#ifndef __cplusplus -# error persistence.hpp header must be compiled as C++ -#endif - -//! @addtogroup core_c -//! @{ - -/** @brief "black box" representation of the file storage associated with a file on disk. - -Several functions that are described below take CvFileStorage\* as inputs and allow the user to -save or to load hierarchical collections that consist of scalar values, standard CXCore objects -(such as matrices, sequences, graphs), and user-defined objects. - -OpenCV can read and write data in XML (), YAML () or -JSON () formats. Below is an example of 3x3 floating-point identity matrix A, -stored in XML and YAML files -using CXCore functions: -XML: -@code{.xml} - - - - 3 - 3 -
f
- 1. 0. 0. 0. 1. 0. 0. 0. 1. - - -@endcode -YAML: -@code{.yaml} - %YAML:1.0 - A: !!opencv-matrix - rows: 3 - cols: 3 - dt: f - data: [ 1., 0., 0., 0., 1., 0., 0., 0., 1.] -@endcode -As it can be seen from the examples, XML uses nested tags to represent hierarchy, while YAML uses -indentation for that purpose (similar to the Python programming language). - -The same functions can read and write data in both formats; the particular format is determined by -the extension of the opened file, ".xml" for XML files, ".yml" or ".yaml" for YAML and ".json" for -JSON. - */ -typedef struct CvFileStorage CvFileStorage; -typedef struct CvFileNode CvFileNode; -typedef struct CvMat CvMat; -typedef struct CvMatND CvMatND; - -//! @} core_c - -#include "opencv2/core/types.hpp" -#include "opencv2/core/mat.hpp" - -namespace cv { - -/** @addtogroup core_xml - -XML/YAML/JSON file storages. {#xml_storage} -======================= -Writing to a file storage. --------------------------- -You can store and then restore various OpenCV data structures to/from XML (), -YAML () or JSON () formats. Also, it is possible to store -and load arbitrarily complex data structures, which include OpenCV data structures, as well as -primitive data types (integer and floating-point numbers and text strings) as their elements. - -Use the following procedure to write something to XML, YAML or JSON: --# Create new FileStorage and open it for writing. It can be done with a single call to -FileStorage::FileStorage constructor that takes a filename, or you can use the default constructor -and then call FileStorage::open. Format of the file (XML, YAML or JSON) is determined from the filename -extension (".xml", ".yml"/".yaml" and ".json", respectively) --# Write all the data you want using the streaming operator `<<`, just like in the case of STL -streams. --# Close the file using FileStorage::release. FileStorage destructor also closes the file. - -Here is an example: -@code - #include "opencv2/opencv.hpp" - #include - - using namespace cv; - - int main(int, char** argv) - { - FileStorage fs("test.yml", FileStorage::WRITE); - - fs << "frameCount" << 5; - time_t rawtime; time(&rawtime); - fs << "calibrationDate" << asctime(localtime(&rawtime)); - Mat cameraMatrix = (Mat_(3,3) << 1000, 0, 320, 0, 1000, 240, 0, 0, 1); - Mat distCoeffs = (Mat_(5,1) << 0.1, 0.01, -0.001, 0, 0); - fs << "cameraMatrix" << cameraMatrix << "distCoeffs" << distCoeffs; - fs << "features" << "["; - for( int i = 0; i < 3; i++ ) - { - int x = rand() % 640; - int y = rand() % 480; - uchar lbp = rand() % 256; - - fs << "{:" << "x" << x << "y" << y << "lbp" << "[:"; - for( int j = 0; j < 8; j++ ) - fs << ((lbp >> j) & 1); - fs << "]" << "}"; - } - fs << "]"; - fs.release(); - return 0; - } -@endcode -The sample above stores to YML an integer, a text string (calibration date), 2 matrices, and a custom -structure "feature", which includes feature coordinates and LBP (local binary pattern) value. Here -is output of the sample: -@code{.yaml} -%YAML:1.0 -frameCount: 5 -calibrationDate: "Fri Jun 17 14:09:29 2011\n" -cameraMatrix: !!opencv-matrix - rows: 3 - cols: 3 - dt: d - data: [ 1000., 0., 320., 0., 1000., 240., 0., 0., 1. ] -distCoeffs: !!opencv-matrix - rows: 5 - cols: 1 - dt: d - data: [ 1.0000000000000001e-01, 1.0000000000000000e-02, - -1.0000000000000000e-03, 0., 0. ] -features: - - { x:167, y:49, lbp:[ 1, 0, 0, 1, 1, 0, 1, 1 ] } - - { x:298, y:130, lbp:[ 0, 0, 0, 1, 0, 0, 1, 1 ] } - - { x:344, y:158, lbp:[ 1, 1, 0, 0, 0, 0, 1, 0 ] } -@endcode - -As an exercise, you can replace ".yml" with ".xml" or ".json" in the sample above and see, how the -corresponding XML file will look like. - -Several things can be noted by looking at the sample code and the output: - -- The produced YAML (and XML/JSON) consists of heterogeneous collections that can be nested. There are - 2 types of collections: named collections (mappings) and unnamed collections (sequences). In mappings - each element has a name and is accessed by name. This is similar to structures and std::map in - C/C++ and dictionaries in Python. In sequences elements do not have names, they are accessed by - indices. This is similar to arrays and std::vector in C/C++ and lists, tuples in Python. - "Heterogeneous" means that elements of each single collection can have different types. - - Top-level collection in YAML/XML/JSON is a mapping. Each matrix is stored as a mapping, and the matrix - elements are stored as a sequence. Then, there is a sequence of features, where each feature is - represented a mapping, and lbp value in a nested sequence. - -- When you write to a mapping (a structure), you write element name followed by its value. When you - write to a sequence, you simply write the elements one by one. OpenCV data structures (such as - cv::Mat) are written in absolutely the same way as simple C data structures - using `<<` - operator. - -- To write a mapping, you first write the special string `{` to the storage, then write the - elements as pairs (`fs << << `) and then write the closing - `}`. - -- To write a sequence, you first write the special string `[`, then write the elements, then - write the closing `]`. - -- In YAML/JSON (but not XML), mappings and sequences can be written in a compact Python-like inline - form. In the sample above matrix elements, as well as each feature, including its lbp value, is - stored in such inline form. To store a mapping/sequence in a compact form, put `:` after the - opening character, e.g. use `{:` instead of `{` and `[:` instead of `[`. When the - data is written to XML, those extra `:` are ignored. - -Reading data from a file storage. ---------------------------------- -To read the previously written XML, YAML or JSON file, do the following: --# Open the file storage using FileStorage::FileStorage constructor or FileStorage::open method. - In the current implementation the whole file is parsed and the whole representation of file - storage is built in memory as a hierarchy of file nodes (see FileNode) - --# Read the data you are interested in. Use FileStorage::operator [], FileNode::operator [] - and/or FileNodeIterator. - --# Close the storage using FileStorage::release. - -Here is how to read the file created by the code sample above: -@code - FileStorage fs2("test.yml", FileStorage::READ); - - // first method: use (type) operator on FileNode. - int frameCount = (int)fs2["frameCount"]; - - String date; - // second method: use FileNode::operator >> - fs2["calibrationDate"] >> date; - - Mat cameraMatrix2, distCoeffs2; - fs2["cameraMatrix"] >> cameraMatrix2; - fs2["distCoeffs"] >> distCoeffs2; - - cout << "frameCount: " << frameCount << endl - << "calibration date: " << date << endl - << "camera matrix: " << cameraMatrix2 << endl - << "distortion coeffs: " << distCoeffs2 << endl; - - FileNode features = fs2["features"]; - FileNodeIterator it = features.begin(), it_end = features.end(); - int idx = 0; - std::vector lbpval; - - // iterate through a sequence using FileNodeIterator - for( ; it != it_end; ++it, idx++ ) - { - cout << "feature #" << idx << ": "; - cout << "x=" << (int)(*it)["x"] << ", y=" << (int)(*it)["y"] << ", lbp: ("; - // you can also easily read numerical arrays using FileNode >> std::vector operator. - (*it)["lbp"] >> lbpval; - for( int i = 0; i < (int)lbpval.size(); i++ ) - cout << " " << (int)lbpval[i]; - cout << ")" << endl; - } - fs2.release(); -@endcode - -Format specification {#format_spec} --------------------- -`([count]{u|c|w|s|i|f|d})`... where the characters correspond to fundamental C++ types: -- `u` 8-bit unsigned number -- `c` 8-bit signed number -- `w` 16-bit unsigned number -- `s` 16-bit signed number -- `i` 32-bit signed number -- `f` single precision floating-point number -- `d` double precision floating-point number -- `r` pointer, 32 lower bits of which are written as a signed integer. The type can be used to - store structures with links between the elements. - -`count` is the optional counter of values of a given type. For example, `2if` means that each array -element is a structure of 2 integers, followed by a single-precision floating-point number. The -equivalent notations of the above specification are `iif`, `2i1f` and so forth. Other examples: `u` -means that the array consists of bytes, and `2d` means the array consists of pairs of doubles. - -@see @ref samples/cpp/filestorage.cpp -*/ - -//! @{ - -/** @example samples/cpp/filestorage.cpp -A complete example using the FileStorage interface -*/ - -////////////////////////// XML & YAML I/O ////////////////////////// - -class CV_EXPORTS FileNode; -class CV_EXPORTS FileNodeIterator; - -/** @brief XML/YAML/JSON file storage class that encapsulates all the information necessary for writing or -reading data to/from a file. - */ -class CV_EXPORTS_W FileStorage -{ -public: - //! file storage mode - enum Mode - { - READ = 0, //!< value, open the file for reading - WRITE = 1, //!< value, open the file for writing - APPEND = 2, //!< value, open the file for appending - MEMORY = 4, //!< flag, read data from source or write data to the internal buffer (which is - //!< returned by FileStorage::release) - FORMAT_MASK = (7<<3), //!< mask for format flags - FORMAT_AUTO = 0, //!< flag, auto format - FORMAT_XML = (1<<3), //!< flag, XML format - FORMAT_YAML = (2<<3), //!< flag, YAML format - FORMAT_JSON = (3<<3), //!< flag, JSON format - - BASE64 = 64, //!< flag, write rawdata in Base64 by default. (consider using WRITE_BASE64) - WRITE_BASE64 = BASE64 | WRITE, //!< flag, enable both WRITE and BASE64 - }; - enum - { - UNDEFINED = 0, - VALUE_EXPECTED = 1, - NAME_EXPECTED = 2, - INSIDE_MAP = 4 - }; - - /** @brief The constructors. - - The full constructor opens the file. Alternatively you can use the default constructor and then - call FileStorage::open. - */ - CV_WRAP FileStorage(); - - /** @overload - @copydoc open() - */ - CV_WRAP FileStorage(const String& filename, int flags, const String& encoding=String()); - - /** @overload */ - FileStorage(CvFileStorage* fs, bool owning=true); - - //! the destructor. calls release() - virtual ~FileStorage(); - - /** @brief Opens a file. - - See description of parameters in FileStorage::FileStorage. The method calls FileStorage::release - before opening the file. - @param filename Name of the file to open or the text string to read the data from. - Extension of the file (.xml, .yml/.yaml or .json) determines its format (XML, YAML or JSON - respectively). Also you can append .gz to work with compressed files, for example myHugeMatrix.xml.gz. If both - FileStorage::WRITE and FileStorage::MEMORY flags are specified, source is used just to specify - the output file format (e.g. mydata.xml, .yml etc.). A file name can also contain parameters. - You can use this format, "*?base64" (e.g. "file.json?base64" (case sensitive)), as an alternative to - FileStorage::BASE64 flag. - @param flags Mode of operation. One of FileStorage::Mode - @param encoding Encoding of the file. Note that UTF-16 XML encoding is not supported currently and - you should use 8-bit encoding instead of it. - */ - CV_WRAP virtual bool open(const String& filename, int flags, const String& encoding=String()); - - /** @brief Checks whether the file is opened. - - @returns true if the object is associated with the current file and false otherwise. It is a - good practice to call this method after you tried to open a file. - */ - CV_WRAP virtual bool isOpened() const; - - /** @brief Closes the file and releases all the memory buffers. - - Call this method after all I/O operations with the storage are finished. - */ - CV_WRAP virtual void release(); - - /** @brief Closes the file and releases all the memory buffers. - - Call this method after all I/O operations with the storage are finished. If the storage was - opened for writing data and FileStorage::WRITE was specified - */ - CV_WRAP virtual String releaseAndGetString(); - - /** @brief Returns the first element of the top-level mapping. - @returns The first element of the top-level mapping. - */ - CV_WRAP FileNode getFirstTopLevelNode() const; - - /** @brief Returns the top-level mapping - @param streamidx Zero-based index of the stream. In most cases there is only one stream in the file. - However, YAML supports multiple streams and so there can be several. - @returns The top-level mapping. - */ - CV_WRAP FileNode root(int streamidx=0) const; - - /** @brief Returns the specified element of the top-level mapping. - @param nodename Name of the file node. - @returns Node with the given name. - */ - FileNode operator[](const String& nodename) const; - - /** @overload */ - CV_WRAP_AS(getNode) FileNode operator[](const char* nodename) const; - - /** @brief Returns the obsolete C FileStorage structure. - @returns Pointer to the underlying C FileStorage structure - */ - CvFileStorage* operator *() { return fs.get(); } - - /** @overload */ - const CvFileStorage* operator *() const { return fs.get(); } - - /** @brief Writes multiple numbers. - - Writes one or more numbers of the specified format to the currently written structure. Usually it is - more convenient to use operator `<<` instead of this method. - @param fmt Specification of each array element, see @ref format_spec "format specification" - @param vec Pointer to the written array. - @param len Number of the uchar elements to write. - */ - void writeRaw( const String& fmt, const uchar* vec, size_t len ); - - /** @brief Writes the registered C structure (CvMat, CvMatND, CvSeq). - @param name Name of the written object. - @param obj Pointer to the object. - @see cvWrite for details. - */ - void writeObj( const String& name, const void* obj ); - - /** - * @brief Simplified writing API to use with bindings. - * @param name Name of the written object - * @param val Value of the written object - */ - CV_WRAP void write(const String& name, int val); - /// @overload - CV_WRAP void write(const String& name, double val); - /// @overload - CV_WRAP void write(const String& name, const String& val); - /// @overload - CV_WRAP void write(const String& name, InputArray val); - - /** @brief Writes a comment. - - The function writes a comment into file storage. The comments are skipped when the storage is read. - @param comment The written comment, single-line or multi-line - @param append If true, the function tries to put the comment at the end of current line. - Else if the comment is multi-line, or if it does not fit at the end of the current - line, the comment starts a new line. - */ - CV_WRAP void writeComment(const String& comment, bool append = false); - - /** @brief Starts to write a nested structure (sequence or a mapping). - @param name name of the structure (if it's a member of parent mapping, otherwise it should be empty - @param flags type of the structure (FileNode::MAP or FileNode::SEQ (both with optional FileNode::FLOW)). - @param typeName usually an empty string - */ - CV_WRAP void startWriteStruct(const String& name, int flags, const String& typeName=String()); - - /** @brief Finishes writing nested structure (should pair startWriteStruct()) - */ - CV_WRAP void endWriteStruct(); - - /** @brief Returns the normalized object name for the specified name of a file. - @param filename Name of a file - @returns The normalized object name. - */ - static String getDefaultObjectName(const String& filename); - - /** @brief Returns the current format. - * @returns The current format, see FileStorage::Mode - */ - CV_WRAP int getFormat() const; - - Ptr fs; //!< the underlying C FileStorage structure - String elname; //!< the currently written element - std::vector structs; //!< the stack of written structures - int state; //!< the writer state -}; - -template<> CV_EXPORTS void DefaultDeleter::operator ()(CvFileStorage* obj) const; - -/** @brief File Storage Node class. - -The node is used to store each and every element of the file storage opened for reading. When -XML/YAML file is read, it is first parsed and stored in the memory as a hierarchical collection of -nodes. Each node can be a "leaf" that is contain a single number or a string, or be a collection of -other nodes. There can be named collections (mappings) where each element has a name and it is -accessed by a name, and ordered collections (sequences) where elements do not have names but rather -accessed by index. Type of the file node can be determined using FileNode::type method. - -Note that file nodes are only used for navigating file storages opened for reading. When a file -storage is opened for writing, no data is stored in memory after it is written. - */ -class CV_EXPORTS_W_SIMPLE FileNode -{ -public: - //! type of the file storage node - enum Type - { - NONE = 0, //!< empty node - INT = 1, //!< an integer - REAL = 2, //!< floating-point number - FLOAT = REAL, //!< synonym or REAL - STR = 3, //!< text string in UTF-8 encoding - STRING = STR, //!< synonym for STR - REF = 4, //!< integer of size size_t. Typically used for storing complex dynamic structures where some elements reference the others - SEQ = 5, //!< sequence - MAP = 6, //!< mapping - TYPE_MASK = 7, - FLOW = 8, //!< compact representation of a sequence or mapping. Used only by YAML writer - USER = 16, //!< a registered object (e.g. a matrix) - EMPTY = 32, //!< empty structure (sequence or mapping) - NAMED = 64 //!< the node has a name (i.e. it is element of a mapping) - }; - /** @brief The constructors. - - These constructors are used to create a default file node, construct it from obsolete structures or - from the another file node. - */ - CV_WRAP FileNode(); - - /** @overload - @param fs Pointer to the obsolete file storage structure. - @param node File node to be used as initialization for the created file node. - */ - FileNode(const CvFileStorage* fs, const CvFileNode* node); - - /** @overload - @param node File node to be used as initialization for the created file node. - */ - FileNode(const FileNode& node); - - FileNode& operator=(const FileNode& node); - - /** @brief Returns element of a mapping node or a sequence node. - @param nodename Name of an element in the mapping node. - @returns Returns the element with the given identifier. - */ - FileNode operator[](const String& nodename) const; - - /** @overload - @param nodename Name of an element in the mapping node. - */ - CV_WRAP_AS(getNode) FileNode operator[](const char* nodename) const; - - /** @overload - @param i Index of an element in the sequence node. - */ - CV_WRAP_AS(at) FileNode operator[](int i) const; - - /** @brief Returns keys of a mapping node. - @returns Keys of a mapping node. - */ - CV_WRAP std::vector keys() const; - - /** @brief Returns type of the node. - @returns Type of the node. See FileNode::Type - */ - CV_WRAP int type() const; - - //! returns true if the node is empty - CV_WRAP bool empty() const; - //! returns true if the node is a "none" object - CV_WRAP bool isNone() const; - //! returns true if the node is a sequence - CV_WRAP bool isSeq() const; - //! returns true if the node is a mapping - CV_WRAP bool isMap() const; - //! returns true if the node is an integer - CV_WRAP bool isInt() const; - //! returns true if the node is a floating-point number - CV_WRAP bool isReal() const; - //! returns true if the node is a text string - CV_WRAP bool isString() const; - //! returns true if the node has a name - CV_WRAP bool isNamed() const; - //! returns the node name or an empty string if the node is nameless - CV_WRAP String name() const; - //! returns the number of elements in the node, if it is a sequence or mapping, or 1 otherwise. - CV_WRAP size_t size() const; - //! returns the node content as an integer. If the node stores floating-point number, it is rounded. - operator int() const; - //! returns the node content as float - operator float() const; - //! returns the node content as double - operator double() const; - //! returns the node content as text string - operator String() const; - operator std::string() const; - - //! returns pointer to the underlying file node - CvFileNode* operator *(); - //! returns pointer to the underlying file node - const CvFileNode* operator* () const; - - //! returns iterator pointing to the first node element - FileNodeIterator begin() const; - //! returns iterator pointing to the element following the last node element - FileNodeIterator end() const; - - /** @brief Reads node elements to the buffer with the specified format. - - Usually it is more convenient to use operator `>>` instead of this method. - @param fmt Specification of each array element. See @ref format_spec "format specification" - @param vec Pointer to the destination array. - @param len Number of bytes to read (buffer size limit). If it is greater than number of - remaining elements then all of them will be read. - */ - void readRaw( const String& fmt, uchar* vec, size_t len ) const; - - //! reads the registered object and returns pointer to it - void* readObj() const; - - //! Simplified reading API to use with bindings. - CV_WRAP double real() const; - //! Simplified reading API to use with bindings. - CV_WRAP String string() const; - //! Simplified reading API to use with bindings. - CV_WRAP Mat mat() const; - - // do not use wrapper pointer classes for better efficiency - const CvFileStorage* fs; - const CvFileNode* node; -}; - - -/** @brief used to iterate through sequences and mappings. - -A standard STL notation, with node.begin(), node.end() denoting the beginning and the end of a -sequence, stored in node. See the data reading sample in the beginning of the section. - */ -class CV_EXPORTS FileNodeIterator -{ -public: - /** @brief The constructors. - - These constructors are used to create a default iterator, set it to specific element in a file node - or construct it from another iterator. - */ - FileNodeIterator(); - - /** @overload - @param fs File storage for the iterator. - @param node File node for the iterator. - @param ofs Index of the element in the node. The created iterator will point to this element. - */ - FileNodeIterator(const CvFileStorage* fs, const CvFileNode* node, size_t ofs=0); - - /** @overload - @param it Iterator to be used as initialization for the created iterator. - */ - FileNodeIterator(const FileNodeIterator& it); - - FileNodeIterator& operator=(const FileNodeIterator& it); - - //! returns the currently observed element - FileNode operator *() const; - //! accesses the currently observed element methods - FileNode operator ->() const; - - //! moves iterator to the next node - FileNodeIterator& operator ++ (); - //! moves iterator to the next node - FileNodeIterator operator ++ (int); - //! moves iterator to the previous node - FileNodeIterator& operator -- (); - //! moves iterator to the previous node - FileNodeIterator operator -- (int); - //! moves iterator forward by the specified offset (possibly negative) - FileNodeIterator& operator += (int ofs); - //! moves iterator backward by the specified offset (possibly negative) - FileNodeIterator& operator -= (int ofs); - - /** @brief Reads node elements to the buffer with the specified format. - - Usually it is more convenient to use operator `>>` instead of this method. - @param fmt Specification of each array element. See @ref format_spec "format specification" - @param vec Pointer to the destination array. - @param len Number of bytes to read (buffer size limit). If it is greater than number of - remaining elements then all of them will be read. - - */ - FileNodeIterator& readRaw( const String& fmt, uchar* vec, - size_t len=(size_t)INT_MAX ); - - struct SeqReader - { - int header_size; - void* seq; /* sequence, beign read; CvSeq */ - void* block; /* current block; CvSeqBlock */ - schar* ptr; /* pointer to element be read next */ - schar* block_min; /* pointer to the beginning of block */ - schar* block_max; /* pointer to the end of block */ - int delta_index;/* = seq->first->start_index */ - schar* prev_elem; /* pointer to previous element */ - }; - - const CvFileStorage* fs; - const CvFileNode* container; - SeqReader reader; - size_t remaining; -}; - -//! @} core_xml - -/////////////////// XML & YAML I/O implementation ////////////////// - -//! @relates cv::FileStorage -//! @{ - -CV_EXPORTS void write( FileStorage& fs, const String& name, int value ); -CV_EXPORTS void write( FileStorage& fs, const String& name, float value ); -CV_EXPORTS void write( FileStorage& fs, const String& name, double value ); -CV_EXPORTS void write( FileStorage& fs, const String& name, const String& value ); -CV_EXPORTS void write( FileStorage& fs, const String& name, const Mat& value ); -CV_EXPORTS void write( FileStorage& fs, const String& name, const SparseMat& value ); -#ifdef CV__LEGACY_PERSISTENCE -CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector& value); -CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector& value); -#endif - -CV_EXPORTS void writeScalar( FileStorage& fs, int value ); -CV_EXPORTS void writeScalar( FileStorage& fs, float value ); -CV_EXPORTS void writeScalar( FileStorage& fs, double value ); -CV_EXPORTS void writeScalar( FileStorage& fs, const String& value ); - -//! @} - -//! @relates cv::FileNode -//! @{ - -CV_EXPORTS void read(const FileNode& node, int& value, int default_value); -CV_EXPORTS void read(const FileNode& node, float& value, float default_value); -CV_EXPORTS void read(const FileNode& node, double& value, double default_value); -CV_EXPORTS void read(const FileNode& node, String& value, const String& default_value); -CV_EXPORTS void read(const FileNode& node, std::string& value, const std::string& default_value); -CV_EXPORTS void read(const FileNode& node, Mat& mat, const Mat& default_mat = Mat() ); -CV_EXPORTS void read(const FileNode& node, SparseMat& mat, const SparseMat& default_mat = SparseMat() ); -#ifdef CV__LEGACY_PERSISTENCE -CV_EXPORTS void read(const FileNode& node, std::vector& keypoints); -CV_EXPORTS void read(const FileNode& node, std::vector& matches); -#endif -CV_EXPORTS void read(const FileNode& node, KeyPoint& value, const KeyPoint& default_value); -CV_EXPORTS void read(const FileNode& node, DMatch& value, const DMatch& default_value); - -template static inline void read(const FileNode& node, Point_<_Tp>& value, const Point_<_Tp>& default_value) -{ - std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; - value = temp.size() != 2 ? default_value : Point_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); -} - -template static inline void read(const FileNode& node, Point3_<_Tp>& value, const Point3_<_Tp>& default_value) -{ - std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; - value = temp.size() != 3 ? default_value : Point3_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), - saturate_cast<_Tp>(temp[2])); -} - -template static inline void read(const FileNode& node, Size_<_Tp>& value, const Size_<_Tp>& default_value) -{ - std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; - value = temp.size() != 2 ? default_value : Size_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); -} - -template static inline void read(const FileNode& node, Complex<_Tp>& value, const Complex<_Tp>& default_value) -{ - std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; - value = temp.size() != 2 ? default_value : Complex<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1])); -} - -template static inline void read(const FileNode& node, Rect_<_Tp>& value, const Rect_<_Tp>& default_value) -{ - std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; - value = temp.size() != 4 ? default_value : Rect_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), - saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3])); -} - -template static inline void read(const FileNode& node, Vec<_Tp, cn>& value, const Vec<_Tp, cn>& default_value) -{ - std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; - value = temp.size() != cn ? default_value : Vec<_Tp, cn>(&temp[0]); -} - -template static inline void read(const FileNode& node, Scalar_<_Tp>& value, const Scalar_<_Tp>& default_value) -{ - std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp; - value = temp.size() != 4 ? default_value : Scalar_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]), - saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3])); -} - -static inline void read(const FileNode& node, Range& value, const Range& default_value) -{ - Point2i temp(value.start, value.end); const Point2i default_temp = Point2i(default_value.start, default_value.end); - read(node, temp, default_temp); - value.start = temp.x; value.end = temp.y; -} - -//! @} - -/** @brief Writes string to a file storage. -@relates cv::FileStorage - */ -CV_EXPORTS FileStorage& operator << (FileStorage& fs, const String& str); - -//! @cond IGNORED - -namespace internal -{ - class CV_EXPORTS WriteStructContext - { - public: - WriteStructContext(FileStorage& _fs, const String& name, int flags, const String& typeName = String()); - ~WriteStructContext(); - private: - FileStorage* fs; - }; - - template class VecWriterProxy - { - public: - VecWriterProxy( FileStorage* _fs ) : fs(_fs) {} - void operator()(const std::vector<_Tp>& vec) const - { - size_t count = vec.size(); - for (size_t i = 0; i < count; i++) - write(*fs, vec[i]); - } - private: - FileStorage* fs; - }; - - template class VecWriterProxy<_Tp, 1> - { - public: - VecWriterProxy( FileStorage* _fs ) : fs(_fs) {} - void operator()(const std::vector<_Tp>& vec) const - { - int _fmt = traits::SafeFmt<_Tp>::fmt; - char fmt[] = { (char)((_fmt >> 8) + '1'), (char)_fmt, '\0' }; - fs->writeRaw(fmt, !vec.empty() ? (uchar*)&vec[0] : 0, vec.size() * sizeof(_Tp)); - } - private: - FileStorage* fs; - }; - - template class VecReaderProxy - { - public: - VecReaderProxy( FileNodeIterator* _it ) : it(_it) {} - void operator()(std::vector<_Tp>& vec, size_t count) const - { - count = std::min(count, it->remaining); - vec.resize(count); - for (size_t i = 0; i < count; i++, ++(*it)) - read(**it, vec[i], _Tp()); - } - private: - FileNodeIterator* it; - }; - - template class VecReaderProxy<_Tp, 1> - { - public: - VecReaderProxy( FileNodeIterator* _it ) : it(_it) {} - void operator()(std::vector<_Tp>& vec, size_t count) const - { - size_t remaining = it->remaining; - size_t cn = DataType<_Tp>::channels; - int _fmt = traits::SafeFmt<_Tp>::fmt; - CV_Assert((_fmt >> 8) < 9); - char fmt[] = { (char)((_fmt >> 8)+'1'), (char)_fmt, '\0' }; - CV_Assert((remaining % cn) == 0); - size_t remaining1 = remaining / cn; - count = count < remaining1 ? count : remaining1; - vec.resize(count); - it->readRaw(fmt, !vec.empty() ? (uchar*)&vec[0] : 0, count*sizeof(_Tp)); - } - private: - FileNodeIterator* it; - }; - -} // internal - -//! @endcond - -//! @relates cv::FileStorage -//! @{ - -template static inline -void write(FileStorage& fs, const _Tp& value) -{ - write(fs, String(), value); -} - -template<> inline -void write( FileStorage& fs, const int& value ) -{ - writeScalar(fs, value); -} - -template<> inline -void write( FileStorage& fs, const float& value ) -{ - writeScalar(fs, value); -} - -template<> inline -void write( FileStorage& fs, const double& value ) -{ - writeScalar(fs, value); -} - -template<> inline -void write( FileStorage& fs, const String& value ) -{ - writeScalar(fs, value); -} - -template static inline -void write(FileStorage& fs, const Point_<_Tp>& pt ) -{ - write(fs, pt.x); - write(fs, pt.y); -} - -template static inline -void write(FileStorage& fs, const Point3_<_Tp>& pt ) -{ - write(fs, pt.x); - write(fs, pt.y); - write(fs, pt.z); -} - -template static inline -void write(FileStorage& fs, const Size_<_Tp>& sz ) -{ - write(fs, sz.width); - write(fs, sz.height); -} - -template static inline -void write(FileStorage& fs, const Complex<_Tp>& c ) -{ - write(fs, c.re); - write(fs, c.im); -} - -template static inline -void write(FileStorage& fs, const Rect_<_Tp>& r ) -{ - write(fs, r.x); - write(fs, r.y); - write(fs, r.width); - write(fs, r.height); -} - -template static inline -void write(FileStorage& fs, const Vec<_Tp, cn>& v ) -{ - for(int i = 0; i < cn; i++) - write(fs, v.val[i]); -} - -template static inline -void write(FileStorage& fs, const Scalar_<_Tp>& s ) -{ - write(fs, s.val[0]); - write(fs, s.val[1]); - write(fs, s.val[2]); - write(fs, s.val[3]); -} - -static inline -void write(FileStorage& fs, const Range& r ) -{ - write(fs, r.start); - write(fs, r.end); -} - -template static inline -void write( FileStorage& fs, const std::vector<_Tp>& vec ) -{ - cv::internal::VecWriterProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> w(&fs); - w(vec); -} - -template static inline -void write(FileStorage& fs, const String& name, const Point_<_Tp>& pt ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, pt); -} - -template static inline -void write(FileStorage& fs, const String& name, const Point3_<_Tp>& pt ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, pt); -} - -template static inline -void write(FileStorage& fs, const String& name, const Size_<_Tp>& sz ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, sz); -} - -template static inline -void write(FileStorage& fs, const String& name, const Complex<_Tp>& c ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, c); -} - -template static inline -void write(FileStorage& fs, const String& name, const Rect_<_Tp>& r ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, r); -} - -template static inline -void write(FileStorage& fs, const String& name, const Vec<_Tp, cn>& v ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, v); -} - -template static inline -void write(FileStorage& fs, const String& name, const Scalar_<_Tp>& s ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, s); -} - -static inline -void write(FileStorage& fs, const String& name, const Range& r ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, r); -} - -static inline -void write(FileStorage& fs, const String& name, const KeyPoint& kpt) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, kpt.pt.x); - write(fs, kpt.pt.y); - write(fs, kpt.size); - write(fs, kpt.angle); - write(fs, kpt.response); - write(fs, kpt.octave); - write(fs, kpt.class_id); -} - -static inline -void write(FileStorage& fs, const String& name, const DMatch& m) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW); - write(fs, m.queryIdx); - write(fs, m.trainIdx); - write(fs, m.imgIdx); - write(fs, m.distance); -} - -template static inline -void write( FileStorage& fs, const String& name, const std::vector<_Tp>& vec ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+(traits::SafeFmt<_Tp>::fmt != 0 ? FileNode::FLOW : 0)); - write(fs, vec); -} - -template static inline -void write( FileStorage& fs, const String& name, const std::vector< std::vector<_Tp> >& vec ) -{ - cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ); - for(size_t i = 0; i < vec.size(); i++) - { - cv::internal::WriteStructContext ws_(fs, name, FileNode::SEQ+(traits::SafeFmt<_Tp>::fmt != 0 ? FileNode::FLOW : 0)); - write(fs, vec[i]); - } -} - -#ifdef CV__LEGACY_PERSISTENCE -// This code is not needed anymore, but it is preserved here to keep source compatibility -// Implementation is similar to templates instantiations -static inline void write(FileStorage& fs, const KeyPoint& kpt) { write(fs, String(), kpt); } -static inline void write(FileStorage& fs, const DMatch& m) { write(fs, String(), m); } -static inline void write(FileStorage& fs, const std::vector& vec) -{ - cv::internal::VecWriterProxy w(&fs); - w(vec); -} -static inline void write(FileStorage& fs, const std::vector& vec) -{ - cv::internal::VecWriterProxy w(&fs); - w(vec); - -} -#endif - -//! @} FileStorage - -//! @relates cv::FileNode -//! @{ - -static inline -void read(const FileNode& node, bool& value, bool default_value) -{ - int temp; - read(node, temp, (int)default_value); - value = temp != 0; -} - -static inline -void read(const FileNode& node, uchar& value, uchar default_value) -{ - int temp; - read(node, temp, (int)default_value); - value = saturate_cast(temp); -} - -static inline -void read(const FileNode& node, schar& value, schar default_value) -{ - int temp; - read(node, temp, (int)default_value); - value = saturate_cast(temp); -} - -static inline -void read(const FileNode& node, ushort& value, ushort default_value) -{ - int temp; - read(node, temp, (int)default_value); - value = saturate_cast(temp); -} - -static inline -void read(const FileNode& node, short& value, short default_value) -{ - int temp; - read(node, temp, (int)default_value); - value = saturate_cast(temp); -} - -template static inline -void read( FileNodeIterator& it, std::vector<_Tp>& vec, size_t maxCount = (size_t)INT_MAX ) -{ - cv::internal::VecReaderProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> r(&it); - r(vec, maxCount); -} - -template static inline -void read( const FileNode& node, std::vector<_Tp>& vec, const std::vector<_Tp>& default_value = std::vector<_Tp>() ) -{ - if(!node.node) - vec = default_value; - else - { - FileNodeIterator it = node.begin(); - read( it, vec ); - } -} - -static inline -void read( const FileNode& node, std::vector& vec, const std::vector& default_value ) -{ - if(!node.node) - vec = default_value; - else - read(node, vec); -} - -static inline -void read( const FileNode& node, std::vector& vec, const std::vector& default_value ) -{ - if(!node.node) - vec = default_value; - else - read(node, vec); -} - -//! @} FileNode - -//! @relates cv::FileStorage -//! @{ - -/** @brief Writes data to a file storage. - */ -template static inline -FileStorage& operator << (FileStorage& fs, const _Tp& value) -{ - if( !fs.isOpened() ) - return fs; - if( fs.state == FileStorage::NAME_EXPECTED + FileStorage::INSIDE_MAP ) - CV_Error( Error::StsError, "No element name has been given" ); - write( fs, fs.elname, value ); - if( fs.state & FileStorage::INSIDE_MAP ) - fs.state = FileStorage::NAME_EXPECTED + FileStorage::INSIDE_MAP; - return fs; -} - -/** @brief Writes data to a file storage. - */ -static inline -FileStorage& operator << (FileStorage& fs, const char* str) -{ - return (fs << String(str)); -} - -/** @brief Writes data to a file storage. - */ -static inline -FileStorage& operator << (FileStorage& fs, char* value) -{ - return (fs << String(value)); -} - -//! @} FileStorage - -//! @relates cv::FileNodeIterator -//! @{ - -/** @brief Reads data from a file storage. - */ -template static inline -FileNodeIterator& operator >> (FileNodeIterator& it, _Tp& value) -{ - read( *it, value, _Tp()); - return ++it; -} - -/** @brief Reads data from a file storage. - */ -template static inline -FileNodeIterator& operator >> (FileNodeIterator& it, std::vector<_Tp>& vec) -{ - cv::internal::VecReaderProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> r(&it); - r(vec, (size_t)INT_MAX); - return it; -} - -//! @} FileNodeIterator - -//! @relates cv::FileNode -//! @{ - -/** @brief Reads data from a file storage. - */ -template static inline -void operator >> (const FileNode& n, _Tp& value) -{ - read( n, value, _Tp()); -} - -/** @brief Reads data from a file storage. - */ -template static inline -void operator >> (const FileNode& n, std::vector<_Tp>& vec) -{ - FileNodeIterator it = n.begin(); - it >> vec; -} - -/** @brief Reads KeyPoint from a file storage. -*/ -//It needs special handling because it contains two types of fields, int & float. -static inline -void operator >> (const FileNode& n, KeyPoint& kpt) -{ - FileNodeIterator it = n.begin(); - it >> kpt.pt.x >> kpt.pt.y >> kpt.size >> kpt.angle >> kpt.response >> kpt.octave >> kpt.class_id; -} - -#ifdef CV__LEGACY_PERSISTENCE -static inline -void operator >> (const FileNode& n, std::vector& vec) -{ - read(n, vec); -} -static inline -void operator >> (const FileNode& n, std::vector& vec) -{ - read(n, vec); -} -#endif - -/** @brief Reads DMatch from a file storage. -*/ -//It needs special handling because it contains two types of fields, int & float. -static inline -void operator >> (const FileNode& n, DMatch& m) -{ - FileNodeIterator it = n.begin(); - it >> m.queryIdx >> m.trainIdx >> m.imgIdx >> m.distance; -} - -//! @} FileNode - -//! @relates cv::FileNodeIterator -//! @{ - -static inline -bool operator == (const FileNodeIterator& it1, const FileNodeIterator& it2) -{ - return it1.fs == it2.fs && it1.container == it2.container && - it1.reader.ptr == it2.reader.ptr && it1.remaining == it2.remaining; -} - -static inline -bool operator != (const FileNodeIterator& it1, const FileNodeIterator& it2) -{ - return !(it1 == it2); -} - -static inline -ptrdiff_t operator - (const FileNodeIterator& it1, const FileNodeIterator& it2) -{ - return it2.remaining - it1.remaining; -} - -static inline -bool operator < (const FileNodeIterator& it1, const FileNodeIterator& it2) -{ - return it1.remaining > it2.remaining; -} - -//! @} FileNodeIterator - -//! @cond IGNORED - -inline FileNode FileStorage::getFirstTopLevelNode() const { FileNode r = root(); FileNodeIterator it = r.begin(); return it != r.end() ? *it : FileNode(); } -inline FileNode::FileNode() : fs(0), node(0) {} -inline FileNode::FileNode(const CvFileStorage* _fs, const CvFileNode* _node) : fs(_fs), node(_node) {} -inline FileNode::FileNode(const FileNode& _node) : fs(_node.fs), node(_node.node) {} -inline FileNode& FileNode::operator=(const FileNode& _node) { fs = _node.fs; node = _node.node; return *this; } -inline bool FileNode::empty() const { return node == 0; } -inline bool FileNode::isNone() const { return type() == NONE; } -inline bool FileNode::isSeq() const { return type() == SEQ; } -inline bool FileNode::isMap() const { return type() == MAP; } -inline bool FileNode::isInt() const { return type() == INT; } -inline bool FileNode::isReal() const { return type() == REAL; } -inline bool FileNode::isString() const { return type() == STR; } -inline CvFileNode* FileNode::operator *() { return (CvFileNode*)node; } -inline const CvFileNode* FileNode::operator* () const { return node; } -inline FileNode::operator int() const { int value; read(*this, value, 0); return value; } -inline FileNode::operator float() const { float value; read(*this, value, 0.f); return value; } -inline FileNode::operator double() const { double value; read(*this, value, 0.); return value; } -inline FileNode::operator String() const { String value; read(*this, value, value); return value; } -inline double FileNode::real() const { return double(*this); } -inline String FileNode::string() const { return String(*this); } -inline Mat FileNode::mat() const { Mat value; read(*this, value, value); return value; } -inline FileNodeIterator FileNode::begin() const { return FileNodeIterator(fs, node); } -inline FileNodeIterator FileNode::end() const { return FileNodeIterator(fs, node, size()); } -inline void FileNode::readRaw( const String& fmt, uchar* vec, size_t len ) const { begin().readRaw( fmt, vec, len ); } -inline FileNode FileNodeIterator::operator *() const { return FileNode(fs, (const CvFileNode*)(const void*)reader.ptr); } -inline FileNode FileNodeIterator::operator ->() const { return FileNode(fs, (const CvFileNode*)(const void*)reader.ptr); } -inline String::String(const FileNode& fn): cstr_(0), len_(0) { read(fn, *this, *this); } - -//! @endcond - - -CV_EXPORTS void cvStartWriteRawData_Base64(::CvFileStorage * fs, const char* name, int len, const char* dt); - -CV_EXPORTS void cvWriteRawData_Base64(::CvFileStorage * fs, const void* _data, int len); - -CV_EXPORTS void cvEndWriteRawData_Base64(::CvFileStorage * fs); - -CV_EXPORTS void cvWriteMat_Base64(::CvFileStorage* fs, const char* name, const ::CvMat* mat); - -CV_EXPORTS void cvWriteMatND_Base64(::CvFileStorage* fs, const char* name, const ::CvMatND* mat); - -} // cv - -#endif // OPENCV_CORE_PERSISTENCE_HPP diff --git a/include/opencv2/core/saturate.hpp b/include/opencv2/core/saturate.hpp deleted file mode 100755 index 36d3121..0000000 --- a/include/opencv2/core/saturate.hpp +++ /dev/null @@ -1,163 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2014, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_SATURATE_HPP -#define OPENCV_CORE_SATURATE_HPP - -#include "opencv2/core/cvdef.h" -#include "opencv2/core/fast_math.hpp" - -namespace cv -{ - -//! @addtogroup core_utils -//! @{ - -/////////////// saturate_cast (used in image & signal processing) /////////////////// - -/** @brief Template function for accurate conversion from one primitive type to another. - - The function saturate_cast resembles the standard C++ cast operations, such as static_cast\() - and others. It perform an efficient and accurate conversion from one primitive type to another - (see the introduction chapter). saturate in the name means that when the input value v is out of the - range of the target type, the result is not formed just by taking low bits of the input, but instead - the value is clipped. For example: - @code - uchar a = saturate_cast(-100); // a = 0 (UCHAR_MIN) - short b = saturate_cast(33333.33333); // b = 32767 (SHRT_MAX) - @endcode - Such clipping is done when the target type is unsigned char , signed char , unsigned short or - signed short . For 32-bit integers, no clipping is done. - - When the parameter is a floating-point value and the target type is an integer (8-, 16- or 32-bit), - the floating-point value is first rounded to the nearest integer and then clipped if needed (when - the target type is 8- or 16-bit). - - @param v Function parameter. - @sa add, subtract, multiply, divide, Mat::convertTo - */ -template static inline _Tp saturate_cast(uchar v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(schar v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(ushort v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(short v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(unsigned v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(int v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(float v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(double v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(int64 v) { return _Tp(v); } -/** @overload */ -template static inline _Tp saturate_cast(uint64 v) { return _Tp(v); } - -template<> inline uchar saturate_cast(schar v) { return (uchar)std::max((int)v, 0); } -template<> inline uchar saturate_cast(ushort v) { return (uchar)std::min((unsigned)v, (unsigned)UCHAR_MAX); } -template<> inline uchar saturate_cast(int v) { return (uchar)((unsigned)v <= UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); } -template<> inline uchar saturate_cast(short v) { return saturate_cast((int)v); } -template<> inline uchar saturate_cast(unsigned v) { return (uchar)std::min(v, (unsigned)UCHAR_MAX); } -template<> inline uchar saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } -template<> inline uchar saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } -template<> inline uchar saturate_cast(int64 v) { return (uchar)((uint64)v <= (uint64)UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); } -template<> inline uchar saturate_cast(uint64 v) { return (uchar)std::min(v, (uint64)UCHAR_MAX); } - -template<> inline schar saturate_cast(uchar v) { return (schar)std::min((int)v, SCHAR_MAX); } -template<> inline schar saturate_cast(ushort v) { return (schar)std::min((unsigned)v, (unsigned)SCHAR_MAX); } -template<> inline schar saturate_cast(int v) { return (schar)((unsigned)(v-SCHAR_MIN) <= (unsigned)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); } -template<> inline schar saturate_cast(short v) { return saturate_cast((int)v); } -template<> inline schar saturate_cast(unsigned v) { return (schar)std::min(v, (unsigned)SCHAR_MAX); } -template<> inline schar saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } -template<> inline schar saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } -template<> inline schar saturate_cast(int64 v) { return (schar)((uint64)((int64)v-SCHAR_MIN) <= (uint64)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); } -template<> inline schar saturate_cast(uint64 v) { return (schar)std::min(v, (uint64)SCHAR_MAX); } - -template<> inline ushort saturate_cast(schar v) { return (ushort)std::max((int)v, 0); } -template<> inline ushort saturate_cast(short v) { return (ushort)std::max((int)v, 0); } -template<> inline ushort saturate_cast(int v) { return (ushort)((unsigned)v <= (unsigned)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); } -template<> inline ushort saturate_cast(unsigned v) { return (ushort)std::min(v, (unsigned)USHRT_MAX); } -template<> inline ushort saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } -template<> inline ushort saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } -template<> inline ushort saturate_cast(int64 v) { return (ushort)((uint64)v <= (uint64)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); } -template<> inline ushort saturate_cast(uint64 v) { return (ushort)std::min(v, (uint64)USHRT_MAX); } - -template<> inline short saturate_cast(ushort v) { return (short)std::min((int)v, SHRT_MAX); } -template<> inline short saturate_cast(int v) { return (short)((unsigned)(v - SHRT_MIN) <= (unsigned)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); } -template<> inline short saturate_cast(unsigned v) { return (short)std::min(v, (unsigned)SHRT_MAX); } -template<> inline short saturate_cast(float v) { int iv = cvRound(v); return saturate_cast(iv); } -template<> inline short saturate_cast(double v) { int iv = cvRound(v); return saturate_cast(iv); } -template<> inline short saturate_cast(int64 v) { return (short)((uint64)((int64)v - SHRT_MIN) <= (uint64)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); } -template<> inline short saturate_cast(uint64 v) { return (short)std::min(v, (uint64)SHRT_MAX); } - -template<> inline int saturate_cast(unsigned v) { return (int)std::min(v, (unsigned)INT_MAX); } -template<> inline int saturate_cast(int64 v) { return (int)((uint64)(v - INT_MIN) <= (uint64)UINT_MAX ? v : v > 0 ? INT_MAX : INT_MIN); } -template<> inline int saturate_cast(uint64 v) { return (int)std::min(v, (uint64)INT_MAX); } -template<> inline int saturate_cast(float v) { return cvRound(v); } -template<> inline int saturate_cast(double v) { return cvRound(v); } - -template<> inline unsigned saturate_cast(schar v) { return (unsigned)std::max(v, (schar)0); } -template<> inline unsigned saturate_cast(short v) { return (unsigned)std::max(v, (short)0); } -template<> inline unsigned saturate_cast(int v) { return (unsigned)std::max(v, (int)0); } -template<> inline unsigned saturate_cast(int64 v) { return (unsigned)((uint64)v <= (uint64)UINT_MAX ? v : v > 0 ? UINT_MAX : 0); } -template<> inline unsigned saturate_cast(uint64 v) { return (unsigned)std::min(v, (uint64)UINT_MAX); } -// we intentionally do not clip negative numbers, to make -1 become 0xffffffff etc. -template<> inline unsigned saturate_cast(float v) { return static_cast(cvRound(v)); } -template<> inline unsigned saturate_cast(double v) { return static_cast(cvRound(v)); } - -template<> inline uint64 saturate_cast(schar v) { return (uint64)std::max(v, (schar)0); } -template<> inline uint64 saturate_cast(short v) { return (uint64)std::max(v, (short)0); } -template<> inline uint64 saturate_cast(int v) { return (uint64)std::max(v, (int)0); } -template<> inline uint64 saturate_cast(int64 v) { return (uint64)std::max(v, (int64)0); } - -template<> inline int64 saturate_cast(uint64 v) { return (int64)std::min(v, (uint64)LLONG_MAX); } - -//! @} - -} // cv - -#endif // OPENCV_CORE_SATURATE_HPP diff --git a/include/opencv2/core/simd_intrinsics.hpp b/include/opencv2/core/simd_intrinsics.hpp deleted file mode 100755 index 7151d36..0000000 --- a/include/opencv2/core/simd_intrinsics.hpp +++ /dev/null @@ -1,88 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_CORE_SIMD_INTRINSICS_HPP -#define OPENCV_CORE_SIMD_INTRINSICS_HPP - -/** -Helper header to support SIMD intrinsics (universal intrinsics) in user code. -Intrinsics documentation: https://docs.opencv.org/3.4/df/d91/group__core__hal__intrin.html - - -Checks of target CPU instruction set based on compiler definitions don't work well enough. -More reliable solutions require utilization of configuration systems (like CMake). - -So, probably you need to specify your own configuration. - -You can do that via CMake in this way: - add_definitions(/DOPENCV_SIMD_CONFIG_HEADER=opencv_simd_config_custom.hpp) -or - add_definitions(/DOPENCV_SIMD_CONFIG_INCLUDE_DIR=1) - -Additionally you may need to add include directory to your files: - include_directories("${CMAKE_CURRENT_LIST_DIR}/opencv_config_${MYTARGET}") - -These files can be pre-generated for target configurations of your application -or generated by CMake on the fly (use CMAKE_BINARY_DIR for that). - -Notes: -- H/W capability checks are still responsibility of your application -- runtime dispatching is not covered by this helper header -*/ - -#ifdef __OPENCV_BUILD -#error "Use core/hal/intrin.hpp during OpenCV build" -#endif - -#ifdef OPENCV_HAL_INTRIN_HPP -#error "core/simd_intrinsics.hpp must be included before core/hal/intrin.hpp" -#endif - -#include "opencv2/core/cvdef.h" -#include "opencv2/core/version.hpp" - -#ifdef OPENCV_SIMD_CONFIG_HEADER -#include CVAUX_STR(OPENCV_SIMD_CONFIG_HEADER) -#elif defined(OPENCV_SIMD_CONFIG_INCLUDE_DIR) -#include "opencv_simd_config.hpp" // corresponding directory should be added via -I compiler parameter -#else // custom config headers - -#if (!defined(CV_AVX_512F) || !CV_AVX_512F) && (defined(__AVX512__) || defined(__AVX512F__)) -# include -# undef CV_AVX_512F -# define CV_AVX_512F 1 -# ifndef OPENCV_SIMD_DONT_ASSUME_SKX // Skylake-X with AVX-512F/CD/BW/DQ/VL -# undef CV_AVX512_SKX -# define CV_AVX512_SKX 1 -# undef CV_AVX_512CD -# define CV_AVX_512CD 1 -# undef CV_AVX_512BW -# define CV_AVX_512BW 1 -# undef CV_AVX_512DQ -# define CV_AVX_512DQ 1 -# undef CV_AVX_512VL -# define CV_AVX_512VL 1 -# endif -#endif // AVX512 - -// GCC/Clang: -mavx2 -// MSVC: /arch:AVX2 -#if defined __AVX2__ -# include -# undef CV_AVX2 -# define CV_AVX2 1 -# if defined __F16C__ -# undef CV_FP16 -# define CV_FP16 1 -# endif -#endif - -#endif - -// SSE / NEON / VSX is handled by cv_cpu_dispatch.h compatibility block -#include "cv_cpu_dispatch.h" - -#include "hal/intrin.hpp" - -#endif // OPENCV_CORE_SIMD_INTRINSICS_HPP diff --git a/include/opencv2/core/softfloat.hpp b/include/opencv2/core/softfloat.hpp deleted file mode 100755 index 485e15c..0000000 --- a/include/opencv2/core/softfloat.hpp +++ /dev/null @@ -1,514 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html - -// This file is based on files from package issued with the following license: - -/*============================================================================ - -This C header file is part of the SoftFloat IEEE Floating-Point Arithmetic -Package, Release 3c, by John R. Hauser. - -Copyright 2011, 2012, 2013, 2014, 2015, 2016, 2017 The Regents of the -University of California. All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - - 1. Redistributions of source code must retain the above copyright notice, - this list of conditions, and the following disclaimer. - - 2. Redistributions in binary form must reproduce the above copyright notice, - this list of conditions, and the following disclaimer in the documentation - and/or other materials provided with the distribution. - - 3. Neither the name of the University nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS "AS IS", AND ANY -EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, ARE -DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY -DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -=============================================================================*/ - -#pragma once -#ifndef softfloat_h -#define softfloat_h 1 - -#include "cvdef.h" - -namespace cv -{ - -/** @addtogroup core_utils_softfloat - - [SoftFloat](http://www.jhauser.us/arithmetic/SoftFloat.html) is a software implementation - of floating-point calculations according to IEEE 754 standard. - All calculations are done in integers, that's why they are machine-independent and bit-exact. - This library can be useful in accuracy-critical parts like look-up tables generation, tests, etc. - OpenCV contains a subset of SoftFloat partially rewritten to C++. - - ### Types - - There are two basic types: @ref softfloat and @ref softdouble. - These types are binary compatible with float and double types respectively - and support conversions to/from them. - Other types from original SoftFloat library like fp16 or fp128 were thrown away - as well as quiet/signaling NaN support, on-the-fly rounding mode switch - and exception flags (though exceptions can be implemented in the future). - - ### Operations - - Both types support the following: - - Construction from signed and unsigned 32-bit and 64 integers, - float/double or raw binary representation - - Conversions between each other, to float or double and to int - using @ref cvRound, @ref cvTrunc, @ref cvFloor, @ref cvCeil or a bunch of - saturate_cast functions - - Add, subtract, multiply, divide, remainder, square root, FMA with absolute precision - - Comparison operations - - Explicit sign, exponent and significand manipulation through get/set methods, - number state indicators (isInf, isNan, isSubnormal) - - Type-specific constants like eps, minimum/maximum value, best pi approximation, etc. - - min(), max(), abs(), exp(), log() and pow() functions - -*/ -//! @{ - -struct softfloat; -struct softdouble; - -struct CV_EXPORTS softfloat -{ -public: - /** @brief Default constructor */ - softfloat() { v = 0; } - /** @brief Copy constructor */ - softfloat( const softfloat& c) { v = c.v; } - /** @brief Assign constructor */ - softfloat& operator=( const softfloat& c ) - { - if(&c != this) v = c.v; - return *this; - } - /** @brief Construct from raw - - Builds new value from raw binary representation - */ - static const softfloat fromRaw( const uint32_t a ) { softfloat x; x.v = a; return x; } - - /** @brief Construct from integer */ - explicit softfloat( const uint32_t ); - explicit softfloat( const uint64_t ); - explicit softfloat( const int32_t ); - explicit softfloat( const int64_t ); - -#ifdef CV_INT32_T_IS_LONG_INT - // for platforms with int32_t = long int - explicit softfloat( const int a ) { *this = softfloat(static_cast(a)); } -#endif - - /** @brief Construct from float */ - explicit softfloat( const float a ) { Cv32suf s; s.f = a; v = s.u; } - - /** @brief Type casts */ - operator softdouble() const; - operator float() const { Cv32suf s; s.u = v; return s.f; } - - /** @brief Basic arithmetics */ - softfloat operator + (const softfloat&) const; - softfloat operator - (const softfloat&) const; - softfloat operator * (const softfloat&) const; - softfloat operator / (const softfloat&) const; - softfloat operator - () const { softfloat x; x.v = v ^ (1U << 31); return x; } - - /** @brief Remainder operator - - A quote from original SoftFloat manual: - - > The IEEE Standard remainder operation computes the value - > a - n * b, where n is the integer closest to a / b. - > If a / b is exactly halfway between two integers, n is the even integer - > closest to a / b. The IEEE Standard’s remainder operation is always exact and so requires no rounding. - > Depending on the relative magnitudes of the operands, the remainder functions - > can take considerably longer to execute than the other SoftFloat functions. - > This is an inherent characteristic of the remainder operation itself and is not a flaw - > in the SoftFloat implementation. - */ - softfloat operator % (const softfloat&) const; - - softfloat& operator += (const softfloat& a) { *this = *this + a; return *this; } - softfloat& operator -= (const softfloat& a) { *this = *this - a; return *this; } - softfloat& operator *= (const softfloat& a) { *this = *this * a; return *this; } - softfloat& operator /= (const softfloat& a) { *this = *this / a; return *this; } - softfloat& operator %= (const softfloat& a) { *this = *this % a; return *this; } - - /** @brief Comparison operations - - - Any operation with NaN produces false - + The only exception is when x is NaN: x != y for any y. - - Positive and negative zeros are equal - */ - bool operator == ( const softfloat& ) const; - bool operator != ( const softfloat& ) const; - bool operator > ( const softfloat& ) const; - bool operator >= ( const softfloat& ) const; - bool operator < ( const softfloat& ) const; - bool operator <= ( const softfloat& ) const; - - /** @brief NaN state indicator */ - inline bool isNaN() const { return (v & 0x7fffffff) > 0x7f800000; } - /** @brief Inf state indicator */ - inline bool isInf() const { return (v & 0x7fffffff) == 0x7f800000; } - /** @brief Subnormal number indicator */ - inline bool isSubnormal() const { return ((v >> 23) & 0xFF) == 0; } - - /** @brief Get sign bit */ - inline bool getSign() const { return (v >> 31) != 0; } - /** @brief Construct a copy with new sign bit */ - inline softfloat setSign(bool sign) const { softfloat x; x.v = (v & ((1U << 31) - 1)) | ((uint32_t)sign << 31); return x; } - /** @brief Get 0-based exponent */ - inline int getExp() const { return ((v >> 23) & 0xFF) - 127; } - /** @brief Construct a copy with new 0-based exponent */ - inline softfloat setExp(int e) const { softfloat x; x.v = (v & 0x807fffff) | (((e + 127) & 0xFF) << 23 ); return x; } - - /** @brief Get a fraction part - - Returns a number 1 <= x < 2 with the same significand - */ - inline softfloat getFrac() const - { - uint_fast32_t vv = (v & 0x007fffff) | (127 << 23); - return softfloat::fromRaw(vv); - } - /** @brief Construct a copy with provided significand - - Constructs a copy of a number with significand taken from parameter - */ - inline softfloat setFrac(const softfloat& s) const - { - softfloat x; - x.v = (v & 0xff800000) | (s.v & 0x007fffff); - return x; - } - - /** @brief Zero constant */ - static softfloat zero() { return softfloat::fromRaw( 0 ); } - /** @brief Positive infinity constant */ - static softfloat inf() { return softfloat::fromRaw( 0xFF << 23 ); } - /** @brief Default NaN constant */ - static softfloat nan() { return softfloat::fromRaw( 0x7fffffff ); } - /** @brief One constant */ - static softfloat one() { return softfloat::fromRaw( 127 << 23 ); } - /** @brief Smallest normalized value */ - static softfloat min() { return softfloat::fromRaw( 0x01 << 23 ); } - /** @brief Difference between 1 and next representable value */ - static softfloat eps() { return softfloat::fromRaw( (127 - 23) << 23 ); } - /** @brief Biggest finite value */ - static softfloat max() { return softfloat::fromRaw( (0xFF << 23) - 1 ); } - /** @brief Correct pi approximation */ - static softfloat pi() { return softfloat::fromRaw( 0x40490fdb ); } - - uint32_t v; -}; - -/*---------------------------------------------------------------------------- -*----------------------------------------------------------------------------*/ - -struct CV_EXPORTS softdouble -{ -public: - /** @brief Default constructor */ - softdouble() : v(0) { } - /** @brief Copy constructor */ - softdouble( const softdouble& c) { v = c.v; } - /** @brief Assign constructor */ - softdouble& operator=( const softdouble& c ) - { - if(&c != this) v = c.v; - return *this; - } - /** @brief Construct from raw - - Builds new value from raw binary representation - */ - static softdouble fromRaw( const uint64_t a ) { softdouble x; x.v = a; return x; } - - /** @brief Construct from integer */ - explicit softdouble( const uint32_t ); - explicit softdouble( const uint64_t ); - explicit softdouble( const int32_t ); - explicit softdouble( const int64_t ); - -#ifdef CV_INT32_T_IS_LONG_INT - // for platforms with int32_t = long int - explicit softdouble( const int a ) { *this = softdouble(static_cast(a)); } -#endif - - /** @brief Construct from double */ - explicit softdouble( const double a ) { Cv64suf s; s.f = a; v = s.u; } - - /** @brief Type casts */ - operator softfloat() const; - operator double() const { Cv64suf s; s.u = v; return s.f; } - - /** @brief Basic arithmetics */ - softdouble operator + (const softdouble&) const; - softdouble operator - (const softdouble&) const; - softdouble operator * (const softdouble&) const; - softdouble operator / (const softdouble&) const; - softdouble operator - () const { softdouble x; x.v = v ^ (1ULL << 63); return x; } - - /** @brief Remainder operator - - A quote from original SoftFloat manual: - - > The IEEE Standard remainder operation computes the value - > a - n * b, where n is the integer closest to a / b. - > If a / b is exactly halfway between two integers, n is the even integer - > closest to a / b. The IEEE Standard’s remainder operation is always exact and so requires no rounding. - > Depending on the relative magnitudes of the operands, the remainder functions - > can take considerably longer to execute than the other SoftFloat functions. - > This is an inherent characteristic of the remainder operation itself and is not a flaw - > in the SoftFloat implementation. - */ - softdouble operator % (const softdouble&) const; - - softdouble& operator += (const softdouble& a) { *this = *this + a; return *this; } - softdouble& operator -= (const softdouble& a) { *this = *this - a; return *this; } - softdouble& operator *= (const softdouble& a) { *this = *this * a; return *this; } - softdouble& operator /= (const softdouble& a) { *this = *this / a; return *this; } - softdouble& operator %= (const softdouble& a) { *this = *this % a; return *this; } - - /** @brief Comparison operations - - - Any operation with NaN produces false - + The only exception is when x is NaN: x != y for any y. - - Positive and negative zeros are equal - */ - bool operator == ( const softdouble& ) const; - bool operator != ( const softdouble& ) const; - bool operator > ( const softdouble& ) const; - bool operator >= ( const softdouble& ) const; - bool operator < ( const softdouble& ) const; - bool operator <= ( const softdouble& ) const; - - /** @brief NaN state indicator */ - inline bool isNaN() const { return (v & 0x7fffffffffffffff) > 0x7ff0000000000000; } - /** @brief Inf state indicator */ - inline bool isInf() const { return (v & 0x7fffffffffffffff) == 0x7ff0000000000000; } - /** @brief Subnormal number indicator */ - inline bool isSubnormal() const { return ((v >> 52) & 0x7FF) == 0; } - - /** @brief Get sign bit */ - inline bool getSign() const { return (v >> 63) != 0; } - /** @brief Construct a copy with new sign bit */ - softdouble setSign(bool sign) const { softdouble x; x.v = (v & ((1ULL << 63) - 1)) | ((uint_fast64_t)(sign) << 63); return x; } - /** @brief Get 0-based exponent */ - inline int getExp() const { return ((v >> 52) & 0x7FF) - 1023; } - /** @brief Construct a copy with new 0-based exponent */ - inline softdouble setExp(int e) const - { - softdouble x; - x.v = (v & 0x800FFFFFFFFFFFFF) | ((uint_fast64_t)((e + 1023) & 0x7FF) << 52); - return x; - } - - /** @brief Get a fraction part - - Returns a number 1 <= x < 2 with the same significand - */ - inline softdouble getFrac() const - { - uint_fast64_t vv = (v & 0x000FFFFFFFFFFFFF) | ((uint_fast64_t)(1023) << 52); - return softdouble::fromRaw(vv); - } - /** @brief Construct a copy with provided significand - - Constructs a copy of a number with significand taken from parameter - */ - inline softdouble setFrac(const softdouble& s) const - { - softdouble x; - x.v = (v & 0xFFF0000000000000) | (s.v & 0x000FFFFFFFFFFFFF); - return x; - } - - /** @brief Zero constant */ - static softdouble zero() { return softdouble::fromRaw( 0 ); } - /** @brief Positive infinity constant */ - static softdouble inf() { return softdouble::fromRaw( (uint_fast64_t)(0x7FF) << 52 ); } - /** @brief Default NaN constant */ - static softdouble nan() { return softdouble::fromRaw( CV_BIG_INT(0x7FFFFFFFFFFFFFFF) ); } - /** @brief One constant */ - static softdouble one() { return softdouble::fromRaw( (uint_fast64_t)( 1023) << 52 ); } - /** @brief Smallest normalized value */ - static softdouble min() { return softdouble::fromRaw( (uint_fast64_t)( 0x01) << 52 ); } - /** @brief Difference between 1 and next representable value */ - static softdouble eps() { return softdouble::fromRaw( (uint_fast64_t)( 1023 - 52 ) << 52 ); } - /** @brief Biggest finite value */ - static softdouble max() { return softdouble::fromRaw( ((uint_fast64_t)(0x7FF) << 52) - 1 ); } - /** @brief Correct pi approximation */ - static softdouble pi() { return softdouble::fromRaw( CV_BIG_INT(0x400921FB54442D18) ); } - - uint64_t v; -}; - -/*---------------------------------------------------------------------------- -*----------------------------------------------------------------------------*/ - -/** @brief Fused Multiplication and Addition - -Computes (a*b)+c with single rounding -*/ -CV_EXPORTS softfloat mulAdd( const softfloat& a, const softfloat& b, const softfloat & c); -CV_EXPORTS softdouble mulAdd( const softdouble& a, const softdouble& b, const softdouble& c); - -/** @brief Square root */ -CV_EXPORTS softfloat sqrt( const softfloat& a ); -CV_EXPORTS softdouble sqrt( const softdouble& a ); -} - -/*---------------------------------------------------------------------------- -| Ported from OpenCV and added for usability -*----------------------------------------------------------------------------*/ - -/** @brief Truncates number to integer with minimum magnitude */ -CV_EXPORTS int cvTrunc(const cv::softfloat& a); -CV_EXPORTS int cvTrunc(const cv::softdouble& a); - -/** @brief Rounds a number to nearest even integer */ -CV_EXPORTS int cvRound(const cv::softfloat& a); -CV_EXPORTS int cvRound(const cv::softdouble& a); - -/** @brief Rounds a number to nearest even long long integer */ -CV_EXPORTS int64_t cvRound64(const cv::softdouble& a); - -/** @brief Rounds a number down to integer */ -CV_EXPORTS int cvFloor(const cv::softfloat& a); -CV_EXPORTS int cvFloor(const cv::softdouble& a); - -/** @brief Rounds number up to integer */ -CV_EXPORTS int cvCeil(const cv::softfloat& a); -CV_EXPORTS int cvCeil(const cv::softdouble& a); - -namespace cv -{ -/** @brief Saturate casts */ -template static inline _Tp saturate_cast(softfloat a) { return _Tp(a); } -template static inline _Tp saturate_cast(softdouble a) { return _Tp(a); } - -template<> inline uchar saturate_cast(softfloat a) { return (uchar)std::max(std::min(cvRound(a), (int)UCHAR_MAX), 0); } -template<> inline uchar saturate_cast(softdouble a) { return (uchar)std::max(std::min(cvRound(a), (int)UCHAR_MAX), 0); } - -template<> inline schar saturate_cast(softfloat a) { return (schar)std::min(std::max(cvRound(a), (int)SCHAR_MIN), (int)SCHAR_MAX); } -template<> inline schar saturate_cast(softdouble a) { return (schar)std::min(std::max(cvRound(a), (int)SCHAR_MIN), (int)SCHAR_MAX); } - -template<> inline ushort saturate_cast(softfloat a) { return (ushort)std::max(std::min(cvRound(a), (int)USHRT_MAX), 0); } -template<> inline ushort saturate_cast(softdouble a) { return (ushort)std::max(std::min(cvRound(a), (int)USHRT_MAX), 0); } - -template<> inline short saturate_cast(softfloat a) { return (short)std::min(std::max(cvRound(a), (int)SHRT_MIN), (int)SHRT_MAX); } -template<> inline short saturate_cast(softdouble a) { return (short)std::min(std::max(cvRound(a), (int)SHRT_MIN), (int)SHRT_MAX); } - -template<> inline int saturate_cast(softfloat a) { return cvRound(a); } -template<> inline int saturate_cast(softdouble a) { return cvRound(a); } - -template<> inline int64_t saturate_cast(softfloat a) { return cvRound(a); } -template<> inline int64_t saturate_cast(softdouble a) { return cvRound64(a); } - -/** @brief Saturate cast to unsigned integer and unsigned long long integer -We intentionally do not clip negative numbers, to make -1 become 0xffffffff etc. -*/ -template<> inline unsigned saturate_cast(softfloat a) { return cvRound(a); } -template<> inline unsigned saturate_cast(softdouble a) { return cvRound(a); } - -template<> inline uint64_t saturate_cast(softfloat a) { return cvRound(a); } -template<> inline uint64_t saturate_cast(softdouble a) { return cvRound64(a); } - -/** @brief Min and Max functions */ -inline softfloat min(const softfloat& a, const softfloat& b) { return (a > b) ? b : a; } -inline softdouble min(const softdouble& a, const softdouble& b) { return (a > b) ? b : a; } - -inline softfloat max(const softfloat& a, const softfloat& b) { return (a > b) ? a : b; } -inline softdouble max(const softdouble& a, const softdouble& b) { return (a > b) ? a : b; } - -/** @brief Absolute value */ -inline softfloat abs( softfloat a) { softfloat x; x.v = a.v & ((1U << 31) - 1); return x; } -inline softdouble abs( softdouble a) { softdouble x; x.v = a.v & ((1ULL << 63) - 1); return x; } - -/** @brief Exponent - -Special cases: -- exp(NaN) is NaN -- exp(-Inf) == 0 -- exp(+Inf) == +Inf -*/ -CV_EXPORTS softfloat exp( const softfloat& a); -CV_EXPORTS softdouble exp( const softdouble& a); - -/** @brief Natural logarithm - -Special cases: -- log(NaN), log(x < 0) are NaN -- log(0) == -Inf -*/ -CV_EXPORTS softfloat log( const softfloat& a ); -CV_EXPORTS softdouble log( const softdouble& a ); - -/** @brief Raising to the power - -Special cases: -- x**NaN is NaN for any x -- ( |x| == 1 )**Inf is NaN -- ( |x| > 1 )**+Inf or ( |x| < 1 )**-Inf is +Inf -- ( |x| > 1 )**-Inf or ( |x| < 1 )**+Inf is 0 -- x ** 0 == 1 for any x -- x ** 1 == 1 for any x -- NaN ** y is NaN for any other y -- Inf**(y < 0) == 0 -- Inf ** y is +Inf for any other y -- (x < 0)**y is NaN for any other y if x can't be correctly rounded to integer -- 0 ** 0 == 1 -- 0 ** (y < 0) is +Inf -- 0 ** (y > 0) is 0 -*/ -CV_EXPORTS softfloat pow( const softfloat& a, const softfloat& b); -CV_EXPORTS softdouble pow( const softdouble& a, const softdouble& b); - -/** @brief Cube root - -Special cases: -- cbrt(NaN) is NaN -- cbrt(+/-Inf) is +/-Inf -*/ -CV_EXPORTS softfloat cbrt( const softfloat& a ); - -/** @brief Sine - -Special cases: -- sin(Inf) or sin(NaN) is NaN -- sin(x) == x when sin(x) is close to zero -*/ -CV_EXPORTS softdouble sin( const softdouble& a ); - -/** @brief Cosine - * -Special cases: -- cos(Inf) or cos(NaN) is NaN -- cos(x) == +/- 1 when cos(x) is close to +/- 1 -*/ -CV_EXPORTS softdouble cos( const softdouble& a ); - -//! @} core_utils_softfloat - -} // cv:: - -#endif diff --git a/include/opencv2/core/sse_utils.hpp b/include/opencv2/core/sse_utils.hpp deleted file mode 100755 index 0906583..0000000 --- a/include/opencv2/core/sse_utils.hpp +++ /dev/null @@ -1,652 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_SSE_UTILS_HPP -#define OPENCV_CORE_SSE_UTILS_HPP - -#ifndef __cplusplus -# error sse_utils.hpp header must be compiled as C++ -#endif - -#include "opencv2/core/cvdef.h" - -//! @addtogroup core_utils_sse -//! @{ - -#if CV_SSE2 - -inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) -{ - __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_g0); - __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_g0); - __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_g1); - __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_g1); - - __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk2); - __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk2); - __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk3); - __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk3); - - __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk2); - __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk2); - __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk3); - __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk3); - - __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk2); - __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk2); - __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk3); - __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk3); - - v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk2); - v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk2); - v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk3); - v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk3); -} - -inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, - __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) -{ - __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_g1); - __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_g1); - __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_b0); - __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_b0); - __m128i layer1_chunk4 = _mm_unpacklo_epi8(v_g0, v_b1); - __m128i layer1_chunk5 = _mm_unpackhi_epi8(v_g0, v_b1); - - __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk3); - __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk3); - __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk4); - __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk4); - __m128i layer2_chunk4 = _mm_unpacklo_epi8(layer1_chunk2, layer1_chunk5); - __m128i layer2_chunk5 = _mm_unpackhi_epi8(layer1_chunk2, layer1_chunk5); - - __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk3); - __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk3); - __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk4); - __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk4); - __m128i layer3_chunk4 = _mm_unpacklo_epi8(layer2_chunk2, layer2_chunk5); - __m128i layer3_chunk5 = _mm_unpackhi_epi8(layer2_chunk2, layer2_chunk5); - - __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk3); - __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk3); - __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk4); - __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk4); - __m128i layer4_chunk4 = _mm_unpacklo_epi8(layer3_chunk2, layer3_chunk5); - __m128i layer4_chunk5 = _mm_unpackhi_epi8(layer3_chunk2, layer3_chunk5); - - v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk3); - v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk3); - v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk4); - v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk4); - v_b0 = _mm_unpacklo_epi8(layer4_chunk2, layer4_chunk5); - v_b1 = _mm_unpackhi_epi8(layer4_chunk2, layer4_chunk5); -} - -inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, - __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) -{ - __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_b0); - __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_b0); - __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_b1); - __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_b1); - __m128i layer1_chunk4 = _mm_unpacklo_epi8(v_g0, v_a0); - __m128i layer1_chunk5 = _mm_unpackhi_epi8(v_g0, v_a0); - __m128i layer1_chunk6 = _mm_unpacklo_epi8(v_g1, v_a1); - __m128i layer1_chunk7 = _mm_unpackhi_epi8(v_g1, v_a1); - - __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk4); - __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk4); - __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk5); - __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk5); - __m128i layer2_chunk4 = _mm_unpacklo_epi8(layer1_chunk2, layer1_chunk6); - __m128i layer2_chunk5 = _mm_unpackhi_epi8(layer1_chunk2, layer1_chunk6); - __m128i layer2_chunk6 = _mm_unpacklo_epi8(layer1_chunk3, layer1_chunk7); - __m128i layer2_chunk7 = _mm_unpackhi_epi8(layer1_chunk3, layer1_chunk7); - - __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk4); - __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk4); - __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk5); - __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk5); - __m128i layer3_chunk4 = _mm_unpacklo_epi8(layer2_chunk2, layer2_chunk6); - __m128i layer3_chunk5 = _mm_unpackhi_epi8(layer2_chunk2, layer2_chunk6); - __m128i layer3_chunk6 = _mm_unpacklo_epi8(layer2_chunk3, layer2_chunk7); - __m128i layer3_chunk7 = _mm_unpackhi_epi8(layer2_chunk3, layer2_chunk7); - - __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk4); - __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk4); - __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk5); - __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk5); - __m128i layer4_chunk4 = _mm_unpacklo_epi8(layer3_chunk2, layer3_chunk6); - __m128i layer4_chunk5 = _mm_unpackhi_epi8(layer3_chunk2, layer3_chunk6); - __m128i layer4_chunk6 = _mm_unpacklo_epi8(layer3_chunk3, layer3_chunk7); - __m128i layer4_chunk7 = _mm_unpackhi_epi8(layer3_chunk3, layer3_chunk7); - - v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk4); - v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk4); - v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk5); - v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk5); - v_b0 = _mm_unpacklo_epi8(layer4_chunk2, layer4_chunk6); - v_b1 = _mm_unpackhi_epi8(layer4_chunk2, layer4_chunk6); - v_a0 = _mm_unpacklo_epi8(layer4_chunk3, layer4_chunk7); - v_a1 = _mm_unpackhi_epi8(layer4_chunk3, layer4_chunk7); -} - -inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) -{ - __m128i v_mask = _mm_set1_epi16(0x00ff); - - __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); - __m128i layer4_chunk2 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); - __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); - __m128i layer4_chunk3 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); - - __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); - __m128i layer3_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); - __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); - __m128i layer3_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); - - __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); - __m128i layer2_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); - __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); - __m128i layer2_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); - - __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); - __m128i layer1_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); - __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); - __m128i layer1_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); - - v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); - v_g0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); - v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); - v_g1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); -} - -inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, - __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) -{ - __m128i v_mask = _mm_set1_epi16(0x00ff); - - __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); - __m128i layer4_chunk3 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); - __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); - __m128i layer4_chunk4 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); - __m128i layer4_chunk2 = _mm_packus_epi16(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); - __m128i layer4_chunk5 = _mm_packus_epi16(_mm_srli_epi16(v_b0, 8), _mm_srli_epi16(v_b1, 8)); - - __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); - __m128i layer3_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); - __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); - __m128i layer3_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); - __m128i layer3_chunk2 = _mm_packus_epi16(_mm_and_si128(layer4_chunk4, v_mask), _mm_and_si128(layer4_chunk5, v_mask)); - __m128i layer3_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk4, 8), _mm_srli_epi16(layer4_chunk5, 8)); - - __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); - __m128i layer2_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); - __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); - __m128i layer2_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); - __m128i layer2_chunk2 = _mm_packus_epi16(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); - __m128i layer2_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk4, 8), _mm_srli_epi16(layer3_chunk5, 8)); - - __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); - __m128i layer1_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); - __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); - __m128i layer1_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); - __m128i layer1_chunk2 = _mm_packus_epi16(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); - __m128i layer1_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk4, 8), _mm_srli_epi16(layer2_chunk5, 8)); - - v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); - v_g1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); - v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); - v_b0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); - v_g0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); - v_b1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk4, 8), _mm_srli_epi16(layer1_chunk5, 8)); -} - -inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, - __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) -{ - __m128i v_mask = _mm_set1_epi16(0x00ff); - - __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); - __m128i layer4_chunk4 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8)); - __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); - __m128i layer4_chunk5 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8)); - __m128i layer4_chunk2 = _mm_packus_epi16(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); - __m128i layer4_chunk6 = _mm_packus_epi16(_mm_srli_epi16(v_b0, 8), _mm_srli_epi16(v_b1, 8)); - __m128i layer4_chunk3 = _mm_packus_epi16(_mm_and_si128(v_a0, v_mask), _mm_and_si128(v_a1, v_mask)); - __m128i layer4_chunk7 = _mm_packus_epi16(_mm_srli_epi16(v_a0, 8), _mm_srli_epi16(v_a1, 8)); - - __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask)); - __m128i layer3_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8)); - __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask)); - __m128i layer3_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8)); - __m128i layer3_chunk2 = _mm_packus_epi16(_mm_and_si128(layer4_chunk4, v_mask), _mm_and_si128(layer4_chunk5, v_mask)); - __m128i layer3_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk4, 8), _mm_srli_epi16(layer4_chunk5, 8)); - __m128i layer3_chunk3 = _mm_packus_epi16(_mm_and_si128(layer4_chunk6, v_mask), _mm_and_si128(layer4_chunk7, v_mask)); - __m128i layer3_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk6, 8), _mm_srli_epi16(layer4_chunk7, 8)); - - __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); - __m128i layer2_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8)); - __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); - __m128i layer2_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8)); - __m128i layer2_chunk2 = _mm_packus_epi16(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); - __m128i layer2_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk4, 8), _mm_srli_epi16(layer3_chunk5, 8)); - __m128i layer2_chunk3 = _mm_packus_epi16(_mm_and_si128(layer3_chunk6, v_mask), _mm_and_si128(layer3_chunk7, v_mask)); - __m128i layer2_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk6, 8), _mm_srli_epi16(layer3_chunk7, 8)); - - __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); - __m128i layer1_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8)); - __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); - __m128i layer1_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8)); - __m128i layer1_chunk2 = _mm_packus_epi16(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); - __m128i layer1_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk4, 8), _mm_srli_epi16(layer2_chunk5, 8)); - __m128i layer1_chunk3 = _mm_packus_epi16(_mm_and_si128(layer2_chunk6, v_mask), _mm_and_si128(layer2_chunk7, v_mask)); - __m128i layer1_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk6, 8), _mm_srli_epi16(layer2_chunk7, 8)); - - v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); - v_b0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8)); - v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); - v_b1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8)); - v_g0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); - v_a0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk4, 8), _mm_srli_epi16(layer1_chunk5, 8)); - v_g1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk6, v_mask), _mm_and_si128(layer1_chunk7, v_mask)); - v_a1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk6, 8), _mm_srli_epi16(layer1_chunk7, 8)); -} - -inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) -{ - __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_g0); - __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_g0); - __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_g1); - __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_g1); - - __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk2); - __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk2); - __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk3); - __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk3); - - __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk2); - __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk2); - __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk3); - __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk3); - - v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk2); - v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk2); - v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk3); - v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk3); -} - -inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, - __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) -{ - __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_g1); - __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_g1); - __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_b0); - __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_b0); - __m128i layer1_chunk4 = _mm_unpacklo_epi16(v_g0, v_b1); - __m128i layer1_chunk5 = _mm_unpackhi_epi16(v_g0, v_b1); - - __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk3); - __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk3); - __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk4); - __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk4); - __m128i layer2_chunk4 = _mm_unpacklo_epi16(layer1_chunk2, layer1_chunk5); - __m128i layer2_chunk5 = _mm_unpackhi_epi16(layer1_chunk2, layer1_chunk5); - - __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk3); - __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk3); - __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk4); - __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk4); - __m128i layer3_chunk4 = _mm_unpacklo_epi16(layer2_chunk2, layer2_chunk5); - __m128i layer3_chunk5 = _mm_unpackhi_epi16(layer2_chunk2, layer2_chunk5); - - v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk3); - v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk3); - v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk4); - v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk4); - v_b0 = _mm_unpacklo_epi16(layer3_chunk2, layer3_chunk5); - v_b1 = _mm_unpackhi_epi16(layer3_chunk2, layer3_chunk5); -} - -inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, - __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) -{ - __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_b0); - __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_b0); - __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_b1); - __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_b1); - __m128i layer1_chunk4 = _mm_unpacklo_epi16(v_g0, v_a0); - __m128i layer1_chunk5 = _mm_unpackhi_epi16(v_g0, v_a0); - __m128i layer1_chunk6 = _mm_unpacklo_epi16(v_g1, v_a1); - __m128i layer1_chunk7 = _mm_unpackhi_epi16(v_g1, v_a1); - - __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk4); - __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk4); - __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk5); - __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk5); - __m128i layer2_chunk4 = _mm_unpacklo_epi16(layer1_chunk2, layer1_chunk6); - __m128i layer2_chunk5 = _mm_unpackhi_epi16(layer1_chunk2, layer1_chunk6); - __m128i layer2_chunk6 = _mm_unpacklo_epi16(layer1_chunk3, layer1_chunk7); - __m128i layer2_chunk7 = _mm_unpackhi_epi16(layer1_chunk3, layer1_chunk7); - - __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk4); - __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk4); - __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk5); - __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk5); - __m128i layer3_chunk4 = _mm_unpacklo_epi16(layer2_chunk2, layer2_chunk6); - __m128i layer3_chunk5 = _mm_unpackhi_epi16(layer2_chunk2, layer2_chunk6); - __m128i layer3_chunk6 = _mm_unpacklo_epi16(layer2_chunk3, layer2_chunk7); - __m128i layer3_chunk7 = _mm_unpackhi_epi16(layer2_chunk3, layer2_chunk7); - - v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk4); - v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk4); - v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk5); - v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk5); - v_b0 = _mm_unpacklo_epi16(layer3_chunk2, layer3_chunk6); - v_b1 = _mm_unpackhi_epi16(layer3_chunk2, layer3_chunk6); - v_a0 = _mm_unpacklo_epi16(layer3_chunk3, layer3_chunk7); - v_a1 = _mm_unpackhi_epi16(layer3_chunk3, layer3_chunk7); -} - -#if CV_SSE4_1 - -inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1) -{ - __m128i v_mask = _mm_set1_epi32(0x0000ffff); - - __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); - __m128i layer3_chunk2 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); - __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); - __m128i layer3_chunk3 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); - - __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); - __m128i layer2_chunk2 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); - __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); - __m128i layer2_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); - - __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); - __m128i layer1_chunk2 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); - __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); - __m128i layer1_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); - - v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); - v_g0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); - v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); - v_g1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); -} - -inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, - __m128i & v_g1, __m128i & v_b0, __m128i & v_b1) -{ - __m128i v_mask = _mm_set1_epi32(0x0000ffff); - - __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); - __m128i layer3_chunk3 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); - __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); - __m128i layer3_chunk4 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); - __m128i layer3_chunk2 = _mm_packus_epi32(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); - __m128i layer3_chunk5 = _mm_packus_epi32(_mm_srli_epi32(v_b0, 16), _mm_srli_epi32(v_b1, 16)); - - __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); - __m128i layer2_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); - __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); - __m128i layer2_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); - __m128i layer2_chunk2 = _mm_packus_epi32(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); - __m128i layer2_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk4, 16), _mm_srli_epi32(layer3_chunk5, 16)); - - __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); - __m128i layer1_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); - __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); - __m128i layer1_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); - __m128i layer1_chunk2 = _mm_packus_epi32(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); - __m128i layer1_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk4, 16), _mm_srli_epi32(layer2_chunk5, 16)); - - v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); - v_g1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); - v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); - v_b0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); - v_g0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); - v_b1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk4, 16), _mm_srli_epi32(layer1_chunk5, 16)); -} - -inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1, - __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1) -{ - __m128i v_mask = _mm_set1_epi32(0x0000ffff); - - __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask)); - __m128i layer3_chunk4 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16)); - __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask)); - __m128i layer3_chunk5 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16)); - __m128i layer3_chunk2 = _mm_packus_epi32(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask)); - __m128i layer3_chunk6 = _mm_packus_epi32(_mm_srli_epi32(v_b0, 16), _mm_srli_epi32(v_b1, 16)); - __m128i layer3_chunk3 = _mm_packus_epi32(_mm_and_si128(v_a0, v_mask), _mm_and_si128(v_a1, v_mask)); - __m128i layer3_chunk7 = _mm_packus_epi32(_mm_srli_epi32(v_a0, 16), _mm_srli_epi32(v_a1, 16)); - - __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask)); - __m128i layer2_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16)); - __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask)); - __m128i layer2_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16)); - __m128i layer2_chunk2 = _mm_packus_epi32(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask)); - __m128i layer2_chunk6 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk4, 16), _mm_srli_epi32(layer3_chunk5, 16)); - __m128i layer2_chunk3 = _mm_packus_epi32(_mm_and_si128(layer3_chunk6, v_mask), _mm_and_si128(layer3_chunk7, v_mask)); - __m128i layer2_chunk7 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk6, 16), _mm_srli_epi32(layer3_chunk7, 16)); - - __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask)); - __m128i layer1_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16)); - __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask)); - __m128i layer1_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16)); - __m128i layer1_chunk2 = _mm_packus_epi32(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask)); - __m128i layer1_chunk6 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk4, 16), _mm_srli_epi32(layer2_chunk5, 16)); - __m128i layer1_chunk3 = _mm_packus_epi32(_mm_and_si128(layer2_chunk6, v_mask), _mm_and_si128(layer2_chunk7, v_mask)); - __m128i layer1_chunk7 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk6, 16), _mm_srli_epi32(layer2_chunk7, 16)); - - v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask)); - v_b0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16)); - v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask)); - v_b1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16)); - v_g0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask)); - v_a0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk4, 16), _mm_srli_epi32(layer1_chunk5, 16)); - v_g1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk6, v_mask), _mm_and_si128(layer1_chunk7, v_mask)); - v_a1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk6, 16), _mm_srli_epi32(layer1_chunk7, 16)); -} - -#endif // CV_SSE4_1 - -inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1) -{ - __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_g0); - __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_g0); - __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_g1); - __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_g1); - - __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk2); - __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk2); - __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk3); - __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk3); - - v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk2); - v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk2); - v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk3); - v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk3); -} - -inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, - __m128 & v_g1, __m128 & v_b0, __m128 & v_b1) -{ - __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_g1); - __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_g1); - __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_b0); - __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_b0); - __m128 layer1_chunk4 = _mm_unpacklo_ps(v_g0, v_b1); - __m128 layer1_chunk5 = _mm_unpackhi_ps(v_g0, v_b1); - - __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk3); - __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk3); - __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk4); - __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk4); - __m128 layer2_chunk4 = _mm_unpacklo_ps(layer1_chunk2, layer1_chunk5); - __m128 layer2_chunk5 = _mm_unpackhi_ps(layer1_chunk2, layer1_chunk5); - - v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk3); - v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk3); - v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk4); - v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk4); - v_b0 = _mm_unpacklo_ps(layer2_chunk2, layer2_chunk5); - v_b1 = _mm_unpackhi_ps(layer2_chunk2, layer2_chunk5); -} - -inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1, - __m128 & v_b0, __m128 & v_b1, __m128 & v_a0, __m128 & v_a1) -{ - __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_b0); - __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_b0); - __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_b1); - __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_b1); - __m128 layer1_chunk4 = _mm_unpacklo_ps(v_g0, v_a0); - __m128 layer1_chunk5 = _mm_unpackhi_ps(v_g0, v_a0); - __m128 layer1_chunk6 = _mm_unpacklo_ps(v_g1, v_a1); - __m128 layer1_chunk7 = _mm_unpackhi_ps(v_g1, v_a1); - - __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk4); - __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk4); - __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk5); - __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk5); - __m128 layer2_chunk4 = _mm_unpacklo_ps(layer1_chunk2, layer1_chunk6); - __m128 layer2_chunk5 = _mm_unpackhi_ps(layer1_chunk2, layer1_chunk6); - __m128 layer2_chunk6 = _mm_unpacklo_ps(layer1_chunk3, layer1_chunk7); - __m128 layer2_chunk7 = _mm_unpackhi_ps(layer1_chunk3, layer1_chunk7); - - v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk4); - v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk4); - v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk5); - v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk5); - v_b0 = _mm_unpacklo_ps(layer2_chunk2, layer2_chunk6); - v_b1 = _mm_unpackhi_ps(layer2_chunk2, layer2_chunk6); - v_a0 = _mm_unpacklo_ps(layer2_chunk3, layer2_chunk7); - v_a1 = _mm_unpackhi_ps(layer2_chunk3, layer2_chunk7); -} - -inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1) -{ - enum { mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1) }; - - __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); - __m128 layer2_chunk2 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); - __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); - __m128 layer2_chunk3 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); - - __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); - __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); - __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); - __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); - - v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); - v_g0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); - v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); - v_g1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); -} - -inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, - __m128 & v_g1, __m128 & v_b0, __m128 & v_b1) -{ - enum { mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1) }; - - __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); - __m128 layer2_chunk3 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); - __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); - __m128 layer2_chunk4 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); - __m128 layer2_chunk2 = _mm_shuffle_ps(v_b0, v_b1, mask_lo); - __m128 layer2_chunk5 = _mm_shuffle_ps(v_b0, v_b1, mask_hi); - - __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); - __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); - __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); - __m128 layer1_chunk4 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); - __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_lo); - __m128 layer1_chunk5 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_hi); - - v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); - v_g1 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); - v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); - v_b0 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); - v_g0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_lo); - v_b1 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_hi); -} - -inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1, - __m128 & v_b0, __m128 & v_b1, __m128 & v_a0, __m128 & v_a1) -{ - enum { mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1) }; - - __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo); - __m128 layer2_chunk4 = _mm_shuffle_ps(v_r0, v_r1, mask_hi); - __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo); - __m128 layer2_chunk5 = _mm_shuffle_ps(v_g0, v_g1, mask_hi); - __m128 layer2_chunk2 = _mm_shuffle_ps(v_b0, v_b1, mask_lo); - __m128 layer2_chunk6 = _mm_shuffle_ps(v_b0, v_b1, mask_hi); - __m128 layer2_chunk3 = _mm_shuffle_ps(v_a0, v_a1, mask_lo); - __m128 layer2_chunk7 = _mm_shuffle_ps(v_a0, v_a1, mask_hi); - - __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo); - __m128 layer1_chunk4 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi); - __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo); - __m128 layer1_chunk5 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi); - __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_lo); - __m128 layer1_chunk6 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_hi); - __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk6, layer2_chunk7, mask_lo); - __m128 layer1_chunk7 = _mm_shuffle_ps(layer2_chunk6, layer2_chunk7, mask_hi); - - v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo); - v_b0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi); - v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo); - v_b1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi); - v_g0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_lo); - v_a0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_hi); - v_g1 = _mm_shuffle_ps(layer1_chunk6, layer1_chunk7, mask_lo); - v_a1 = _mm_shuffle_ps(layer1_chunk6, layer1_chunk7, mask_hi); -} - -#endif // CV_SSE2 - -//! @} - -#endif //OPENCV_CORE_SSE_UTILS_HPP diff --git a/include/opencv2/core/traits.hpp b/include/opencv2/core/traits.hpp deleted file mode 100755 index 6cb10f4..0000000 --- a/include/opencv2/core/traits.hpp +++ /dev/null @@ -1,397 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_TRAITS_HPP -#define OPENCV_CORE_TRAITS_HPP - -#include "opencv2/core/cvdef.h" - -namespace cv -{ - -//#define OPENCV_TRAITS_ENABLE_DEPRECATED - -//! @addtogroup core_basic -//! @{ - -/** @brief Template "trait" class for OpenCV primitive data types. - -@note Deprecated. This is replaced by "single purpose" traits: traits::Type and traits::Depth - -A primitive OpenCV data type is one of unsigned char, bool, signed char, unsigned short, signed -short, int, float, double, or a tuple of values of one of these types, where all the values in the -tuple have the same type. Any primitive type from the list can be defined by an identifier in the -form CV_\{U|S|F}C(\), for example: uchar \~ CV_8UC1, 3-element -floating-point tuple \~ CV_32FC3, and so on. A universal OpenCV structure that is able to store a -single instance of such a primitive data type is Vec. Multiple instances of such a type can be -stored in a std::vector, Mat, Mat_, SparseMat, SparseMat_, or any other container that is able to -store Vec instances. - -The DataType class is basically used to provide a description of such primitive data types without -adding any fields or methods to the corresponding classes (and it is actually impossible to add -anything to primitive C/C++ data types). This technique is known in C++ as class traits. It is not -DataType itself that is used but its specialized versions, such as: -@code - template<> class DataType - { - typedef uchar value_type; - typedef int work_type; - typedef uchar channel_type; - enum { channel_type = CV_8U, channels = 1, fmt='u', type = CV_8U }; - }; - ... - template DataType > - { - typedef std::complex<_Tp> value_type; - typedef std::complex<_Tp> work_type; - typedef _Tp channel_type; - // DataDepth is another helper trait class - enum { depth = DataDepth<_Tp>::value, channels=2, - fmt=(channels-1)*256+DataDepth<_Tp>::fmt, - type=CV_MAKETYPE(depth, channels) }; - }; - ... -@endcode -The main purpose of this class is to convert compilation-time type information to an -OpenCV-compatible data type identifier, for example: -@code - // allocates a 30x40 floating-point matrix - Mat A(30, 40, DataType::type); - - Mat B = Mat_ >(3, 3); - // the statement below will print 6, 2 , that is depth == CV_64F, channels == 2 - cout << B.depth() << ", " << B.channels() << endl; -@endcode -So, such traits are used to tell OpenCV which data type you are working with, even if such a type is -not native to OpenCV. For example, the matrix B initialization above is compiled because OpenCV -defines the proper specialized template class DataType\ \> . This mechanism is also -useful (and used in OpenCV this way) for generic algorithms implementations. - -@note Default values were dropped to stop confusing developers about using of unsupported types (see #7599) -*/ -template class DataType -{ -public: -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED - typedef _Tp value_type; - typedef value_type work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 1, - depth = -1, - channels = 1, - fmt = 0, - type = CV_MAKETYPE(depth, channels) - }; -#endif -}; - -template<> class DataType -{ -public: - typedef bool value_type; - typedef int work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_8U, - channels = 1, - fmt = (int)'u', - type = CV_MAKETYPE(depth, channels) - }; -}; - -template<> class DataType -{ -public: - typedef uchar value_type; - typedef int work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_8U, - channels = 1, - fmt = (int)'u', - type = CV_MAKETYPE(depth, channels) - }; -}; - -template<> class DataType -{ -public: - typedef schar value_type; - typedef int work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_8S, - channels = 1, - fmt = (int)'c', - type = CV_MAKETYPE(depth, channels) - }; -}; - -template<> class DataType -{ -public: - typedef schar value_type; - typedef int work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_8S, - channels = 1, - fmt = (int)'c', - type = CV_MAKETYPE(depth, channels) - }; -}; - -template<> class DataType -{ -public: - typedef ushort value_type; - typedef int work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_16U, - channels = 1, - fmt = (int)'w', - type = CV_MAKETYPE(depth, channels) - }; -}; - -template<> class DataType -{ -public: - typedef short value_type; - typedef int work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_16S, - channels = 1, - fmt = (int)'s', - type = CV_MAKETYPE(depth, channels) - }; -}; - -template<> class DataType -{ -public: - typedef int value_type; - typedef value_type work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_32S, - channels = 1, - fmt = (int)'i', - type = CV_MAKETYPE(depth, channels) - }; -}; - -template<> class DataType -{ -public: - typedef float value_type; - typedef value_type work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_32F, - channels = 1, - fmt = (int)'f', - type = CV_MAKETYPE(depth, channels) - }; -}; - -template<> class DataType -{ -public: - typedef double value_type; - typedef value_type work_type; - typedef value_type channel_type; - typedef value_type vec_type; - enum { generic_type = 0, - depth = CV_64F, - channels = 1, - fmt = (int)'d', - type = CV_MAKETYPE(depth, channels) - }; -}; - - -/** @brief A helper class for cv::DataType - -The class is specialized for each fundamental numerical data type supported by OpenCV. It provides -DataDepth::value constant. -*/ -template class DataDepth -{ -public: - enum - { - value = DataType<_Tp>::depth, - fmt = DataType<_Tp>::fmt - }; -}; - - -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED - -template class TypeDepth -{ -#ifdef OPENCV_TRAITS_ENABLE_LEGACY_DEFAULTS - enum { depth = CV_USRTYPE1 }; - typedef void value_type; -#endif -}; - -template<> class TypeDepth -{ - enum { depth = CV_8U }; - typedef uchar value_type; -}; - -template<> class TypeDepth -{ - enum { depth = CV_8S }; - typedef schar value_type; -}; - -template<> class TypeDepth -{ - enum { depth = CV_16U }; - typedef ushort value_type; -}; - -template<> class TypeDepth -{ - enum { depth = CV_16S }; - typedef short value_type; -}; - -template<> class TypeDepth -{ - enum { depth = CV_32S }; - typedef int value_type; -}; - -template<> class TypeDepth -{ - enum { depth = CV_32F }; - typedef float value_type; -}; - -template<> class TypeDepth -{ - enum { depth = CV_64F }; - typedef double value_type; -}; - -#endif - -//! @} - -namespace traits { - -namespace internal { -#define CV_CREATE_MEMBER_CHECK(X) \ -template class CheckMember_##X { \ - struct Fallback { int X; }; \ - struct Derived : T, Fallback { }; \ - template struct Check; \ - typedef char CV_NO[1]; \ - typedef char CV_YES[2]; \ - template static CV_NO & func(Check *); \ - template static CV_YES & func(...); \ -public: \ - typedef CheckMember_##X type; \ - enum { value = sizeof(func(0)) == sizeof(CV_YES) }; \ -}; - -CV_CREATE_MEMBER_CHECK(fmt) -CV_CREATE_MEMBER_CHECK(type) - -} // namespace internal - - -template -struct Depth -{ enum { value = DataType::depth }; }; - -template -struct Type -{ enum { value = DataType::type }; }; - -/** Similar to traits::Type but has value = -1 in case of unknown type (instead of compiler error) */ -template >::value > -struct SafeType {}; - -template -struct SafeType -{ enum { value = -1 }; }; - -template -struct SafeType -{ enum { value = Type::value }; }; - - -template >::value > -struct SafeFmt {}; - -template -struct SafeFmt -{ enum { fmt = 0 }; }; - -template -struct SafeFmt -{ enum { fmt = DataType::fmt }; }; - - -} // namespace - -} // cv - -#endif // OPENCV_CORE_TRAITS_HPP diff --git a/include/opencv2/core/types.hpp b/include/opencv2/core/types.hpp deleted file mode 100755 index 4d04bef..0000000 --- a/include/opencv2/core/types.hpp +++ /dev/null @@ -1,2391 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_TYPES_HPP -#define OPENCV_CORE_TYPES_HPP - -#ifndef __cplusplus -# error types.hpp header must be compiled as C++ -#endif - -#include -#include -#include -#include - -#include "opencv2/core/cvdef.h" -#include "opencv2/core/cvstd.hpp" -#include "opencv2/core/matx.hpp" - -namespace cv -{ - -//! @addtogroup core_basic -//! @{ - -//////////////////////////////// Complex ////////////////////////////// - -/** @brief A complex number class. - - The template class is similar and compatible with std::complex, however it provides slightly - more convenient access to the real and imaginary parts using through the simple field access, as opposite - to std::complex::real() and std::complex::imag(). -*/ -template class Complex -{ -public: - - //! default constructor - Complex(); - Complex( _Tp _re, _Tp _im = 0 ); - - //! conversion to another data type - template operator Complex() const; - //! conjugation - Complex conj() const; - - _Tp re, im; //< the real and the imaginary parts -}; - -typedef Complex Complexf; -typedef Complex Complexd; - -template class DataType< Complex<_Tp> > -{ -public: - typedef Complex<_Tp> value_type; - typedef value_type work_type; - typedef _Tp channel_type; - - enum { generic_type = 0, - channels = 2, - fmt = DataType::fmt + ((channels - 1) << 8) -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED - ,depth = DataType::depth - ,type = CV_MAKETYPE(depth, channels) -#endif - }; - - typedef Vec vec_type; -}; - -namespace traits { -template -struct Depth< Complex<_Tp> > { enum { value = Depth<_Tp>::value }; }; -template -struct Type< Complex<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; }; -} // namespace - - -//////////////////////////////// Point_ //////////////////////////////// - -/** @brief Template class for 2D points specified by its coordinates `x` and `y`. - -An instance of the class is interchangeable with C structures, CvPoint and CvPoint2D32f . There is -also a cast operator to convert point coordinates to the specified type. The conversion from -floating-point coordinates to integer coordinates is done by rounding. Commonly, the conversion -uses this operation for each of the coordinates. Besides the class members listed in the -declaration above, the following operations on points are implemented: -@code - pt1 = pt2 + pt3; - pt1 = pt2 - pt3; - pt1 = pt2 * a; - pt1 = a * pt2; - pt1 = pt2 / a; - pt1 += pt2; - pt1 -= pt2; - pt1 *= a; - pt1 /= a; - double value = norm(pt); // L2 norm - pt1 == pt2; - pt1 != pt2; -@endcode -For your convenience, the following type aliases are defined: -@code - typedef Point_ Point2i; - typedef Point2i Point; - typedef Point_ Point2f; - typedef Point_ Point2d; -@endcode -Example: -@code - Point2f a(0.3f, 0.f), b(0.f, 0.4f); - Point pt = (a + b)*10.f; - cout << pt.x << ", " << pt.y << endl; -@endcode -*/ -template class Point_ -{ -public: - typedef _Tp value_type; - - //! default constructor - Point_(); - Point_(_Tp _x, _Tp _y); - Point_(const Point_& pt); - Point_(const Size_<_Tp>& sz); - Point_(const Vec<_Tp, 2>& v); - - Point_& operator = (const Point_& pt); - //! conversion to another data type - template operator Point_<_Tp2>() const; - - //! conversion to the old-style C structures - operator Vec<_Tp, 2>() const; - - //! dot product - _Tp dot(const Point_& pt) const; - //! dot product computed in double-precision arithmetics - double ddot(const Point_& pt) const; - //! cross-product - double cross(const Point_& pt) const; - //! checks whether the point is inside the specified rectangle - bool inside(const Rect_<_Tp>& r) const; - _Tp x; //!< x coordinate of the point - _Tp y; //!< y coordinate of the point -}; - -typedef Point_ Point2i; -typedef Point_ Point2l; -typedef Point_ Point2f; -typedef Point_ Point2d; -typedef Point2i Point; - -template class DataType< Point_<_Tp> > -{ -public: - typedef Point_<_Tp> value_type; - typedef Point_::work_type> work_type; - typedef _Tp channel_type; - - enum { generic_type = 0, - channels = 2, - fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED - ,depth = DataType::depth - ,type = CV_MAKETYPE(depth, channels) -#endif - }; - - typedef Vec vec_type; -}; - -namespace traits { -template -struct Depth< Point_<_Tp> > { enum { value = Depth<_Tp>::value }; }; -template -struct Type< Point_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; }; -} // namespace - - -//////////////////////////////// Point3_ //////////////////////////////// - -/** @brief Template class for 3D points specified by its coordinates `x`, `y` and `z`. - -An instance of the class is interchangeable with the C structure CvPoint2D32f . Similarly to -Point_ , the coordinates of 3D points can be converted to another type. The vector arithmetic and -comparison operations are also supported. - -The following Point3_\<\> aliases are available: -@code - typedef Point3_ Point3i; - typedef Point3_ Point3f; - typedef Point3_ Point3d; -@endcode -@see cv::Point3i, cv::Point3f and cv::Point3d -*/ -template class Point3_ -{ -public: - typedef _Tp value_type; - - //! default constructor - Point3_(); - Point3_(_Tp _x, _Tp _y, _Tp _z); - Point3_(const Point3_& pt); - explicit Point3_(const Point_<_Tp>& pt); - Point3_(const Vec<_Tp, 3>& v); - - Point3_& operator = (const Point3_& pt); - //! conversion to another data type - template operator Point3_<_Tp2>() const; - //! conversion to cv::Vec<> -#if OPENCV_ABI_COMPATIBILITY > 300 - template operator Vec<_Tp2, 3>() const; -#else - operator Vec<_Tp, 3>() const; -#endif - - //! dot product - _Tp dot(const Point3_& pt) const; - //! dot product computed in double-precision arithmetics - double ddot(const Point3_& pt) const; - //! cross product of the 2 3D points - Point3_ cross(const Point3_& pt) const; - _Tp x; //!< x coordinate of the 3D point - _Tp y; //!< y coordinate of the 3D point - _Tp z; //!< z coordinate of the 3D point -}; - -typedef Point3_ Point3i; -typedef Point3_ Point3f; -typedef Point3_ Point3d; - -template class DataType< Point3_<_Tp> > -{ -public: - typedef Point3_<_Tp> value_type; - typedef Point3_::work_type> work_type; - typedef _Tp channel_type; - - enum { generic_type = 0, - channels = 3, - fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED - ,depth = DataType::depth - ,type = CV_MAKETYPE(depth, channels) -#endif - }; - - typedef Vec vec_type; -}; - -namespace traits { -template -struct Depth< Point3_<_Tp> > { enum { value = Depth<_Tp>::value }; }; -template -struct Type< Point3_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 3) }; }; -} // namespace - -//////////////////////////////// Size_ //////////////////////////////// - -/** @brief Template class for specifying the size of an image or rectangle. - -The class includes two members called width and height. The structure can be converted to and from -the old OpenCV structures CvSize and CvSize2D32f . The same set of arithmetic and comparison -operations as for Point_ is available. - -OpenCV defines the following Size_\<\> aliases: -@code - typedef Size_ Size2i; - typedef Size2i Size; - typedef Size_ Size2f; -@endcode -*/ -template class Size_ -{ -public: - typedef _Tp value_type; - - //! default constructor - Size_(); - Size_(_Tp _width, _Tp _height); - Size_(const Size_& sz); - Size_(const Point_<_Tp>& pt); - - Size_& operator = (const Size_& sz); - //! the area (width*height) - _Tp area() const; - //! true if empty - bool empty() const; - - //! conversion of another data type. - template operator Size_<_Tp2>() const; - - _Tp width; //!< the width - _Tp height; //!< the height -}; - -typedef Size_ Size2i; -typedef Size_ Size2l; -typedef Size_ Size2f; -typedef Size_ Size2d; -typedef Size2i Size; - -template class DataType< Size_<_Tp> > -{ -public: - typedef Size_<_Tp> value_type; - typedef Size_::work_type> work_type; - typedef _Tp channel_type; - - enum { generic_type = 0, - channels = 2, - fmt = DataType::fmt + ((channels - 1) << 8) -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED - ,depth = DataType::depth - ,type = CV_MAKETYPE(depth, channels) -#endif - }; - - typedef Vec vec_type; -}; - -namespace traits { -template -struct Depth< Size_<_Tp> > { enum { value = Depth<_Tp>::value }; }; -template -struct Type< Size_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; }; -} // namespace - -//////////////////////////////// Rect_ //////////////////////////////// - -/** @brief Template class for 2D rectangles - -described by the following parameters: -- Coordinates of the top-left corner. This is a default interpretation of Rect_::x and Rect_::y - in OpenCV. Though, in your algorithms you may count x and y from the bottom-left corner. -- Rectangle width and height. - -OpenCV typically assumes that the top and left boundary of the rectangle are inclusive, while the -right and bottom boundaries are not. For example, the method Rect_::contains returns true if - -\f[x \leq pt.x < x+width, - y \leq pt.y < y+height\f] - -Virtually every loop over an image ROI in OpenCV (where ROI is specified by Rect_\ ) is -implemented as: -@code - for(int y = roi.y; y < roi.y + roi.height; y++) - for(int x = roi.x; x < roi.x + roi.width; x++) - { - // ... - } -@endcode -In addition to the class members, the following operations on rectangles are implemented: -- \f$\texttt{rect} = \texttt{rect} \pm \texttt{point}\f$ (shifting a rectangle by a certain offset) -- \f$\texttt{rect} = \texttt{rect} \pm \texttt{size}\f$ (expanding or shrinking a rectangle by a - certain amount) -- rect += point, rect -= point, rect += size, rect -= size (augmenting operations) -- rect = rect1 & rect2 (rectangle intersection) -- rect = rect1 | rect2 (minimum area rectangle containing rect1 and rect2 ) -- rect &= rect1, rect |= rect1 (and the corresponding augmenting operations) -- rect == rect1, rect != rect1 (rectangle comparison) - -This is an example how the partial ordering on rectangles can be established (rect1 \f$\subseteq\f$ -rect2): -@code - template inline bool - operator <= (const Rect_<_Tp>& r1, const Rect_<_Tp>& r2) - { - return (r1 & r2) == r1; - } -@endcode -For your convenience, the Rect_\<\> alias is available: cv::Rect -*/ -template class Rect_ -{ -public: - typedef _Tp value_type; - - //! default constructor - Rect_(); - Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height); - Rect_(const Rect_& r); - Rect_(const Point_<_Tp>& org, const Size_<_Tp>& sz); - Rect_(const Point_<_Tp>& pt1, const Point_<_Tp>& pt2); - - Rect_& operator = ( const Rect_& r ); - //! the top-left corner - Point_<_Tp> tl() const; - //! the bottom-right corner - Point_<_Tp> br() const; - - //! size (width, height) of the rectangle - Size_<_Tp> size() const; - //! area (width*height) of the rectangle - _Tp area() const; - //! true if empty - bool empty() const; - - //! conversion to another data type - template operator Rect_<_Tp2>() const; - - //! checks whether the rectangle contains the point - bool contains(const Point_<_Tp>& pt) const; - - _Tp x; //!< x coordinate of the top-left corner - _Tp y; //!< y coordinate of the top-left corner - _Tp width; //!< width of the rectangle - _Tp height; //!< height of the rectangle -}; - -typedef Rect_ Rect2i; -typedef Rect_ Rect2f; -typedef Rect_ Rect2d; -typedef Rect2i Rect; - -template class DataType< Rect_<_Tp> > -{ -public: - typedef Rect_<_Tp> value_type; - typedef Rect_::work_type> work_type; - typedef _Tp channel_type; - - enum { generic_type = 0, - channels = 4, - fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED - ,depth = DataType::depth - ,type = CV_MAKETYPE(depth, channels) -#endif - }; - - typedef Vec vec_type; -}; - -namespace traits { -template -struct Depth< Rect_<_Tp> > { enum { value = Depth<_Tp>::value }; }; -template -struct Type< Rect_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 4) }; }; -} // namespace - -///////////////////////////// RotatedRect ///////////////////////////// - -/** @brief The class represents rotated (i.e. not up-right) rectangles on a plane. - -Each rectangle is specified by the center point (mass center), length of each side (represented by -#Size2f structure) and the rotation angle in degrees. - -The sample below demonstrates how to use RotatedRect: -@snippet snippets/core_various.cpp RotatedRect_demo -![image](pics/rotatedrect.png) - -@sa CamShift, fitEllipse, minAreaRect, CvBox2D -*/ -class CV_EXPORTS RotatedRect -{ -public: - //! default constructor - RotatedRect(); - /** full constructor - @param center The rectangle mass center. - @param size Width and height of the rectangle. - @param angle The rotation angle in a clockwise direction. When the angle is 0, 90, 180, 270 etc., - the rectangle becomes an up-right rectangle. - */ - RotatedRect(const Point2f& center, const Size2f& size, float angle); - /** - Any 3 end points of the RotatedRect. They must be given in order (either clockwise or - anticlockwise). - */ - RotatedRect(const Point2f& point1, const Point2f& point2, const Point2f& point3); - - /** returns 4 vertices of the rectangle - @param pts The points array for storing rectangle vertices. The order is bottomLeft, topLeft, topRight, bottomRight. - */ - void points(Point2f pts[]) const; - //! returns the minimal up-right integer rectangle containing the rotated rectangle - Rect boundingRect() const; - //! returns the minimal (exact) floating point rectangle containing the rotated rectangle, not intended for use with images - Rect_ boundingRect2f() const; - //! returns the rectangle mass center - Point2f center; - //! returns width and height of the rectangle - Size2f size; - //! returns the rotation angle. When the angle is 0, 90, 180, 270 etc., the rectangle becomes an up-right rectangle. - float angle; -}; - -template<> class DataType< RotatedRect > -{ -public: - typedef RotatedRect value_type; - typedef value_type work_type; - typedef float channel_type; - - enum { generic_type = 0, - channels = (int)sizeof(value_type)/sizeof(channel_type), // 5 - fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED - ,depth = DataType::depth - ,type = CV_MAKETYPE(depth, channels) -#endif - }; - - typedef Vec vec_type; -}; - -namespace traits { -template<> -struct Depth< RotatedRect > { enum { value = Depth::value }; }; -template<> -struct Type< RotatedRect > { enum { value = CV_MAKETYPE(Depth::value, (int)sizeof(RotatedRect)/sizeof(float)) }; }; -} // namespace - - -//////////////////////////////// Range ///////////////////////////////// - -/** @brief Template class specifying a continuous subsequence (slice) of a sequence. - -The class is used to specify a row or a column span in a matrix ( Mat ) and for many other purposes. -Range(a,b) is basically the same as a:b in Matlab or a..b in Python. As in Python, start is an -inclusive left boundary of the range and end is an exclusive right boundary of the range. Such a -half-opened interval is usually denoted as \f$[start,end)\f$ . - -The static method Range::all() returns a special variable that means "the whole sequence" or "the -whole range", just like " : " in Matlab or " ... " in Python. All the methods and functions in -OpenCV that take Range support this special Range::all() value. But, of course, in case of your own -custom processing, you will probably have to check and handle it explicitly: -@code - void my_function(..., const Range& r, ....) - { - if(r == Range::all()) { - // process all the data - } - else { - // process [r.start, r.end) - } - } -@endcode -*/ -class CV_EXPORTS Range -{ -public: - Range(); - Range(int _start, int _end); - int size() const; - bool empty() const; - static Range all(); - - int start, end; -}; - -template<> class DataType -{ -public: - typedef Range value_type; - typedef value_type work_type; - typedef int channel_type; - - enum { generic_type = 0, - channels = 2, - fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED - ,depth = DataType::depth - ,type = CV_MAKETYPE(depth, channels) -#endif - }; - - typedef Vec vec_type; -}; - -namespace traits { -template<> -struct Depth< Range > { enum { value = Depth::value }; }; -template<> -struct Type< Range > { enum { value = CV_MAKETYPE(Depth::value, 2) }; }; -} // namespace - - -//////////////////////////////// Scalar_ /////////////////////////////// - -/** @brief Template class for a 4-element vector derived from Vec. - -Being derived from Vec\<_Tp, 4\> , Scalar\_ and Scalar can be used just as typical 4-element -vectors. In addition, they can be converted to/from CvScalar . The type Scalar is widely used in -OpenCV to pass pixel values. -*/ -template class Scalar_ : public Vec<_Tp, 4> -{ -public: - //! default constructor - Scalar_(); - Scalar_(_Tp v0, _Tp v1, _Tp v2=0, _Tp v3=0); - Scalar_(_Tp v0); - - template - Scalar_(const Vec<_Tp2, cn>& v); - - //! returns a scalar with all elements set to v0 - static Scalar_<_Tp> all(_Tp v0); - - //! conversion to another data type - template operator Scalar_() const; - - //! per-element product - Scalar_<_Tp> mul(const Scalar_<_Tp>& a, double scale=1 ) const; - - //! returns (v0, -v1, -v2, -v3) - Scalar_<_Tp> conj() const; - - //! returns true iff v1 == v2 == v3 == 0 - bool isReal() const; -}; - -typedef Scalar_ Scalar; - -template class DataType< Scalar_<_Tp> > -{ -public: - typedef Scalar_<_Tp> value_type; - typedef Scalar_::work_type> work_type; - typedef _Tp channel_type; - - enum { generic_type = 0, - channels = 4, - fmt = traits::SafeFmt::fmt + ((channels - 1) << 8) -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED - ,depth = DataType::depth - ,type = CV_MAKETYPE(depth, channels) -#endif - }; - - typedef Vec vec_type; -}; - -namespace traits { -template -struct Depth< Scalar_<_Tp> > { enum { value = Depth<_Tp>::value }; }; -template -struct Type< Scalar_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 4) }; }; -} // namespace - - -/////////////////////////////// KeyPoint //////////////////////////////// - -/** @brief Data structure for salient point detectors. - -The class instance stores a keypoint, i.e. a point feature found by one of many available keypoint -detectors, such as Harris corner detector, #FAST, %StarDetector, %SURF, %SIFT etc. - -The keypoint is characterized by the 2D position, scale (proportional to the diameter of the -neighborhood that needs to be taken into account), orientation and some other parameters. The -keypoint neighborhood is then analyzed by another algorithm that builds a descriptor (usually -represented as a feature vector). The keypoints representing the same object in different images -can then be matched using %KDTree or another method. -*/ -class CV_EXPORTS_W_SIMPLE KeyPoint -{ -public: - //! the default constructor - CV_WRAP KeyPoint(); - /** - @param _pt x & y coordinates of the keypoint - @param _size keypoint diameter - @param _angle keypoint orientation - @param _response keypoint detector response on the keypoint (that is, strength of the keypoint) - @param _octave pyramid octave in which the keypoint has been detected - @param _class_id object id - */ - KeyPoint(Point2f _pt, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1); - /** - @param x x-coordinate of the keypoint - @param y y-coordinate of the keypoint - @param _size keypoint diameter - @param _angle keypoint orientation - @param _response keypoint detector response on the keypoint (that is, strength of the keypoint) - @param _octave pyramid octave in which the keypoint has been detected - @param _class_id object id - */ - CV_WRAP KeyPoint(float x, float y, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1); - - size_t hash() const; - - /** - This method converts vector of keypoints to vector of points or the reverse, where each keypoint is - assigned the same size and the same orientation. - - @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB - @param points2f Array of (x,y) coordinates of each keypoint - @param keypointIndexes Array of indexes of keypoints to be converted to points. (Acts like a mask to - convert only specified keypoints) - */ - CV_WRAP static void convert(const std::vector& keypoints, - CV_OUT std::vector& points2f, - const std::vector& keypointIndexes=std::vector()); - /** @overload - @param points2f Array of (x,y) coordinates of each keypoint - @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB - @param size keypoint diameter - @param response keypoint detector response on the keypoint (that is, strength of the keypoint) - @param octave pyramid octave in which the keypoint has been detected - @param class_id object id - */ - CV_WRAP static void convert(const std::vector& points2f, - CV_OUT std::vector& keypoints, - float size=1, float response=1, int octave=0, int class_id=-1); - - /** - This method computes overlap for pair of keypoints. Overlap is the ratio between area of keypoint - regions' intersection and area of keypoint regions' union (considering keypoint region as circle). - If they don't overlap, we get zero. If they coincide at same location with same size, we get 1. - @param kp1 First keypoint - @param kp2 Second keypoint - */ - CV_WRAP static float overlap(const KeyPoint& kp1, const KeyPoint& kp2); - - CV_PROP_RW Point2f pt; //!< coordinates of the keypoints - CV_PROP_RW float size; //!< diameter of the meaningful keypoint neighborhood - CV_PROP_RW float angle; //!< computed orientation of the keypoint (-1 if not applicable); - //!< it's in [0,360) degrees and measured relative to - //!< image coordinate system, ie in clockwise. - CV_PROP_RW float response; //!< the response by which the most strong keypoints have been selected. Can be used for the further sorting or subsampling - CV_PROP_RW int octave; //!< octave (pyramid layer) from which the keypoint has been extracted - CV_PROP_RW int class_id; //!< object class (if the keypoints need to be clustered by an object they belong to) -}; - -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED -template<> class DataType -{ -public: - typedef KeyPoint value_type; - typedef float work_type; - typedef float channel_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 7 - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) - }; - - typedef Vec vec_type; -}; -#endif - - -//////////////////////////////// DMatch ///////////////////////////////// - -/** @brief Class for matching keypoint descriptors - -query descriptor index, train descriptor index, train image index, and distance between -descriptors. -*/ -class CV_EXPORTS_W_SIMPLE DMatch -{ -public: - CV_WRAP DMatch(); - CV_WRAP DMatch(int _queryIdx, int _trainIdx, float _distance); - CV_WRAP DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance); - - CV_PROP_RW int queryIdx; //!< query descriptor index - CV_PROP_RW int trainIdx; //!< train descriptor index - CV_PROP_RW int imgIdx; //!< train image index - - CV_PROP_RW float distance; - - // less is better - bool operator<(const DMatch &m) const; -}; - -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED -template<> class DataType -{ -public: - typedef DMatch value_type; - typedef int work_type; - typedef int channel_type; - - enum { generic_type = 0, - depth = DataType::depth, - channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 4 - fmt = DataType::fmt + ((channels - 1) << 8), - type = CV_MAKETYPE(depth, channels) - }; - - typedef Vec vec_type; -}; -#endif - - -///////////////////////////// TermCriteria ////////////////////////////// - -/** @brief The class defining termination criteria for iterative algorithms. - -You can initialize it by default constructor and then override any parameters, or the structure may -be fully initialized using the advanced variant of the constructor. -*/ -class CV_EXPORTS TermCriteria -{ -public: - /** - Criteria type, can be one of: COUNT, EPS or COUNT + EPS - */ - enum Type - { - COUNT=1, //!< the maximum number of iterations or elements to compute - MAX_ITER=COUNT, //!< ditto - EPS=2 //!< the desired accuracy or change in parameters at which the iterative algorithm stops - }; - - //! default constructor - TermCriteria(); - /** - @param type The type of termination criteria, one of TermCriteria::Type - @param maxCount The maximum number of iterations or elements to compute. - @param epsilon The desired accuracy or change in parameters at which the iterative algorithm stops. - */ - TermCriteria(int type, int maxCount, double epsilon); - - inline bool isValid() const - { - const bool isCount = (type & COUNT) && maxCount > 0; - const bool isEps = (type & EPS) && !cvIsNaN(epsilon); - return isCount || isEps; - } - - int type; //!< the type of termination criteria: COUNT, EPS or COUNT + EPS - int maxCount; //!< the maximum number of iterations/elements - double epsilon; //!< the desired accuracy -}; - - -//! @} core_basic - -///////////////////////// raster image moments ////////////////////////// - -//! @addtogroup imgproc_shape -//! @{ - -/** @brief struct returned by cv::moments - -The spatial moments \f$\texttt{Moments::m}_{ji}\f$ are computed as: - -\f[\texttt{m} _{ji}= \sum _{x,y} \left ( \texttt{array} (x,y) \cdot x^j \cdot y^i \right )\f] - -The central moments \f$\texttt{Moments::mu}_{ji}\f$ are computed as: - -\f[\texttt{mu} _{ji}= \sum _{x,y} \left ( \texttt{array} (x,y) \cdot (x - \bar{x} )^j \cdot (y - \bar{y} )^i \right )\f] - -where \f$(\bar{x}, \bar{y})\f$ is the mass center: - -\f[\bar{x} = \frac{\texttt{m}_{10}}{\texttt{m}_{00}} , \; \bar{y} = \frac{\texttt{m}_{01}}{\texttt{m}_{00}}\f] - -The normalized central moments \f$\texttt{Moments::nu}_{ij}\f$ are computed as: - -\f[\texttt{nu} _{ji}= \frac{\texttt{mu}_{ji}}{\texttt{m}_{00}^{(i+j)/2+1}} .\f] - -@note -\f$\texttt{mu}_{00}=\texttt{m}_{00}\f$, \f$\texttt{nu}_{00}=1\f$ -\f$\texttt{nu}_{10}=\texttt{mu}_{10}=\texttt{mu}_{01}=\texttt{mu}_{10}=0\f$ , hence the values are not -stored. - -The moments of a contour are defined in the same way but computed using the Green's formula (see -). So, due to a limited raster resolution, the moments -computed for a contour are slightly different from the moments computed for the same rasterized -contour. - -@note -Since the contour moments are computed using Green formula, you may get seemingly odd results for -contours with self-intersections, e.g. a zero area (m00) for butterfly-shaped contours. - */ -class CV_EXPORTS_W_MAP Moments -{ -public: - //! the default constructor - Moments(); - //! the full constructor - Moments(double m00, double m10, double m01, double m20, double m11, - double m02, double m30, double m21, double m12, double m03 ); - ////! the conversion from CvMoments - //Moments( const CvMoments& moments ); - ////! the conversion to CvMoments - //operator CvMoments() const; - - //! @name spatial moments - //! @{ - CV_PROP_RW double m00, m10, m01, m20, m11, m02, m30, m21, m12, m03; - //! @} - - //! @name central moments - //! @{ - CV_PROP_RW double mu20, mu11, mu02, mu30, mu21, mu12, mu03; - //! @} - - //! @name central normalized moments - //! @{ - CV_PROP_RW double nu20, nu11, nu02, nu30, nu21, nu12, nu03; - //! @} -}; - -template<> class DataType -{ -public: - typedef Moments value_type; - typedef double work_type; - typedef double channel_type; - - enum { generic_type = 0, - channels = (int)(sizeof(value_type)/sizeof(channel_type)), // 24 - fmt = DataType::fmt + ((channels - 1) << 8) -#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED - ,depth = DataType::depth - ,type = CV_MAKETYPE(depth, channels) -#endif - }; - - typedef Vec vec_type; -}; - -namespace traits { -template<> -struct Depth< Moments > { enum { value = Depth::value }; }; -template<> -struct Type< Moments > { enum { value = CV_MAKETYPE(Depth::value, (int)(sizeof(Moments)/sizeof(double))) }; }; -} // namespace - -//! @} imgproc_shape - -//! @cond IGNORED - -///////////////////////////////////////////////////////////////////////// -///////////////////////////// Implementation //////////////////////////// -///////////////////////////////////////////////////////////////////////// - -//////////////////////////////// Complex //////////////////////////////// - -template inline -Complex<_Tp>::Complex() - : re(0), im(0) {} - -template inline -Complex<_Tp>::Complex( _Tp _re, _Tp _im ) - : re(_re), im(_im) {} - -template template inline -Complex<_Tp>::operator Complex() const -{ - return Complex(saturate_cast(re), saturate_cast(im)); -} - -template inline -Complex<_Tp> Complex<_Tp>::conj() const -{ - return Complex<_Tp>(re, -im); -} - - -template static inline -bool operator == (const Complex<_Tp>& a, const Complex<_Tp>& b) -{ - return a.re == b.re && a.im == b.im; -} - -template static inline -bool operator != (const Complex<_Tp>& a, const Complex<_Tp>& b) -{ - return a.re != b.re || a.im != b.im; -} - -template static inline -Complex<_Tp> operator + (const Complex<_Tp>& a, const Complex<_Tp>& b) -{ - return Complex<_Tp>( a.re + b.re, a.im + b.im ); -} - -template static inline -Complex<_Tp>& operator += (Complex<_Tp>& a, const Complex<_Tp>& b) -{ - a.re += b.re; a.im += b.im; - return a; -} - -template static inline -Complex<_Tp> operator - (const Complex<_Tp>& a, const Complex<_Tp>& b) -{ - return Complex<_Tp>( a.re - b.re, a.im - b.im ); -} - -template static inline -Complex<_Tp>& operator -= (Complex<_Tp>& a, const Complex<_Tp>& b) -{ - a.re -= b.re; a.im -= b.im; - return a; -} - -template static inline -Complex<_Tp> operator - (const Complex<_Tp>& a) -{ - return Complex<_Tp>(-a.re, -a.im); -} - -template static inline -Complex<_Tp> operator * (const Complex<_Tp>& a, const Complex<_Tp>& b) -{ - return Complex<_Tp>( a.re*b.re - a.im*b.im, a.re*b.im + a.im*b.re ); -} - -template static inline -Complex<_Tp> operator * (const Complex<_Tp>& a, _Tp b) -{ - return Complex<_Tp>( a.re*b, a.im*b ); -} - -template static inline -Complex<_Tp> operator * (_Tp b, const Complex<_Tp>& a) -{ - return Complex<_Tp>( a.re*b, a.im*b ); -} - -template static inline -Complex<_Tp> operator + (const Complex<_Tp>& a, _Tp b) -{ - return Complex<_Tp>( a.re + b, a.im ); -} - -template static inline -Complex<_Tp> operator - (const Complex<_Tp>& a, _Tp b) -{ return Complex<_Tp>( a.re - b, a.im ); } - -template static inline -Complex<_Tp> operator + (_Tp b, const Complex<_Tp>& a) -{ - return Complex<_Tp>( a.re + b, a.im ); -} - -template static inline -Complex<_Tp> operator - (_Tp b, const Complex<_Tp>& a) -{ - return Complex<_Tp>( b - a.re, -a.im ); -} - -template static inline -Complex<_Tp>& operator += (Complex<_Tp>& a, _Tp b) -{ - a.re += b; return a; -} - -template static inline -Complex<_Tp>& operator -= (Complex<_Tp>& a, _Tp b) -{ - a.re -= b; return a; -} - -template static inline -Complex<_Tp>& operator *= (Complex<_Tp>& a, _Tp b) -{ - a.re *= b; a.im *= b; return a; -} - -template static inline -double abs(const Complex<_Tp>& a) -{ - return std::sqrt( (double)a.re*a.re + (double)a.im*a.im); -} - -template static inline -Complex<_Tp> operator / (const Complex<_Tp>& a, const Complex<_Tp>& b) -{ - double t = 1./((double)b.re*b.re + (double)b.im*b.im); - return Complex<_Tp>( (_Tp)((a.re*b.re + a.im*b.im)*t), - (_Tp)((-a.re*b.im + a.im*b.re)*t) ); -} - -template static inline -Complex<_Tp>& operator /= (Complex<_Tp>& a, const Complex<_Tp>& b) -{ - a = a / b; - return a; -} - -template static inline -Complex<_Tp> operator / (const Complex<_Tp>& a, _Tp b) -{ - _Tp t = (_Tp)1/b; - return Complex<_Tp>( a.re*t, a.im*t ); -} - -template static inline -Complex<_Tp> operator / (_Tp b, const Complex<_Tp>& a) -{ - return Complex<_Tp>(b)/a; -} - -template static inline -Complex<_Tp> operator /= (const Complex<_Tp>& a, _Tp b) -{ - _Tp t = (_Tp)1/b; - a.re *= t; a.im *= t; return a; -} - - - -//////////////////////////////// 2D Point /////////////////////////////// - -template inline -Point_<_Tp>::Point_() - : x(0), y(0) {} - -template inline -Point_<_Tp>::Point_(_Tp _x, _Tp _y) - : x(_x), y(_y) {} - -template inline -Point_<_Tp>::Point_(const Point_& pt) - : x(pt.x), y(pt.y) {} - -template inline -Point_<_Tp>::Point_(const Size_<_Tp>& sz) - : x(sz.width), y(sz.height) {} - -template inline -Point_<_Tp>::Point_(const Vec<_Tp,2>& v) - : x(v[0]), y(v[1]) {} - -template inline -Point_<_Tp>& Point_<_Tp>::operator = (const Point_& pt) -{ - x = pt.x; y = pt.y; - return *this; -} - -template template inline -Point_<_Tp>::operator Point_<_Tp2>() const -{ - return Point_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y)); -} - -template inline -Point_<_Tp>::operator Vec<_Tp, 2>() const -{ - return Vec<_Tp, 2>(x, y); -} - -template inline -_Tp Point_<_Tp>::dot(const Point_& pt) const -{ - return saturate_cast<_Tp>(x*pt.x + y*pt.y); -} - -template inline -double Point_<_Tp>::ddot(const Point_& pt) const -{ - return (double)x*(double)(pt.x) + (double)y*(double)(pt.y); -} - -template inline -double Point_<_Tp>::cross(const Point_& pt) const -{ - return (double)x*pt.y - (double)y*pt.x; -} - -template inline bool -Point_<_Tp>::inside( const Rect_<_Tp>& r ) const -{ - return r.contains(*this); -} - - -template static inline -Point_<_Tp>& operator += (Point_<_Tp>& a, const Point_<_Tp>& b) -{ - a.x += b.x; - a.y += b.y; - return a; -} - -template static inline -Point_<_Tp>& operator -= (Point_<_Tp>& a, const Point_<_Tp>& b) -{ - a.x -= b.x; - a.y -= b.y; - return a; -} - -template static inline -Point_<_Tp>& operator *= (Point_<_Tp>& a, int b) -{ - a.x = saturate_cast<_Tp>(a.x * b); - a.y = saturate_cast<_Tp>(a.y * b); - return a; -} - -template static inline -Point_<_Tp>& operator *= (Point_<_Tp>& a, float b) -{ - a.x = saturate_cast<_Tp>(a.x * b); - a.y = saturate_cast<_Tp>(a.y * b); - return a; -} - -template static inline -Point_<_Tp>& operator *= (Point_<_Tp>& a, double b) -{ - a.x = saturate_cast<_Tp>(a.x * b); - a.y = saturate_cast<_Tp>(a.y * b); - return a; -} - -template static inline -Point_<_Tp>& operator /= (Point_<_Tp>& a, int b) -{ - a.x = saturate_cast<_Tp>(a.x / b); - a.y = saturate_cast<_Tp>(a.y / b); - return a; -} - -template static inline -Point_<_Tp>& operator /= (Point_<_Tp>& a, float b) -{ - a.x = saturate_cast<_Tp>(a.x / b); - a.y = saturate_cast<_Tp>(a.y / b); - return a; -} - -template static inline -Point_<_Tp>& operator /= (Point_<_Tp>& a, double b) -{ - a.x = saturate_cast<_Tp>(a.x / b); - a.y = saturate_cast<_Tp>(a.y / b); - return a; -} - -template static inline -double norm(const Point_<_Tp>& pt) -{ - return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y); -} - -template static inline -bool operator == (const Point_<_Tp>& a, const Point_<_Tp>& b) -{ - return a.x == b.x && a.y == b.y; -} - -template static inline -bool operator != (const Point_<_Tp>& a, const Point_<_Tp>& b) -{ - return a.x != b.x || a.y != b.y; -} - -template static inline -Point_<_Tp> operator + (const Point_<_Tp>& a, const Point_<_Tp>& b) -{ - return Point_<_Tp>( saturate_cast<_Tp>(a.x + b.x), saturate_cast<_Tp>(a.y + b.y) ); -} - -template static inline -Point_<_Tp> operator - (const Point_<_Tp>& a, const Point_<_Tp>& b) -{ - return Point_<_Tp>( saturate_cast<_Tp>(a.x - b.x), saturate_cast<_Tp>(a.y - b.y) ); -} - -template static inline -Point_<_Tp> operator - (const Point_<_Tp>& a) -{ - return Point_<_Tp>( saturate_cast<_Tp>(-a.x), saturate_cast<_Tp>(-a.y) ); -} - -template static inline -Point_<_Tp> operator * (const Point_<_Tp>& a, int b) -{ - return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); -} - -template static inline -Point_<_Tp> operator * (int a, const Point_<_Tp>& b) -{ - return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); -} - -template static inline -Point_<_Tp> operator * (const Point_<_Tp>& a, float b) -{ - return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); -} - -template static inline -Point_<_Tp> operator * (float a, const Point_<_Tp>& b) -{ - return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); -} - -template static inline -Point_<_Tp> operator * (const Point_<_Tp>& a, double b) -{ - return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) ); -} - -template static inline -Point_<_Tp> operator * (double a, const Point_<_Tp>& b) -{ - return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) ); -} - -template static inline -Point_<_Tp> operator * (const Matx<_Tp, 2, 2>& a, const Point_<_Tp>& b) -{ - Matx<_Tp, 2, 1> tmp = a * Vec<_Tp,2>(b.x, b.y); - return Point_<_Tp>(tmp.val[0], tmp.val[1]); -} - -template static inline -Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point_<_Tp>& b) -{ - Matx<_Tp, 3, 1> tmp = a * Vec<_Tp,3>(b.x, b.y, 1); - return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]); -} - -template static inline -Point_<_Tp> operator / (const Point_<_Tp>& a, int b) -{ - Point_<_Tp> tmp(a); - tmp /= b; - return tmp; -} - -template static inline -Point_<_Tp> operator / (const Point_<_Tp>& a, float b) -{ - Point_<_Tp> tmp(a); - tmp /= b; - return tmp; -} - -template static inline -Point_<_Tp> operator / (const Point_<_Tp>& a, double b) -{ - Point_<_Tp> tmp(a); - tmp /= b; - return tmp; -} - - -template static inline _AccTp normL2Sqr(const Point_& pt); -template static inline _AccTp normL2Sqr(const Point_& pt); -template static inline _AccTp normL2Sqr(const Point_& pt); -template static inline _AccTp normL2Sqr(const Point_& pt); - -template<> inline int normL2Sqr(const Point_& pt) { return pt.dot(pt); } -template<> inline int64 normL2Sqr(const Point_& pt) { return pt.dot(pt); } -template<> inline float normL2Sqr(const Point_& pt) { return pt.dot(pt); } -template<> inline double normL2Sqr(const Point_& pt) { return pt.dot(pt); } - -template<> inline double normL2Sqr(const Point_& pt) { return pt.ddot(pt); } -template<> inline double normL2Sqr(const Point_& pt) { return pt.ddot(pt); } - - - -//////////////////////////////// 3D Point /////////////////////////////// - -template inline -Point3_<_Tp>::Point3_() - : x(0), y(0), z(0) {} - -template inline -Point3_<_Tp>::Point3_(_Tp _x, _Tp _y, _Tp _z) - : x(_x), y(_y), z(_z) {} - -template inline -Point3_<_Tp>::Point3_(const Point3_& pt) - : x(pt.x), y(pt.y), z(pt.z) {} - -template inline -Point3_<_Tp>::Point3_(const Point_<_Tp>& pt) - : x(pt.x), y(pt.y), z(_Tp()) {} - -template inline -Point3_<_Tp>::Point3_(const Vec<_Tp, 3>& v) - : x(v[0]), y(v[1]), z(v[2]) {} - -template template inline -Point3_<_Tp>::operator Point3_<_Tp2>() const -{ - return Point3_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y), saturate_cast<_Tp2>(z)); -} - -#if OPENCV_ABI_COMPATIBILITY > 300 -template template inline -Point3_<_Tp>::operator Vec<_Tp2, 3>() const -{ - return Vec<_Tp2, 3>(x, y, z); -} -#else -template inline -Point3_<_Tp>::operator Vec<_Tp, 3>() const -{ - return Vec<_Tp, 3>(x, y, z); -} -#endif - -template inline -Point3_<_Tp>& Point3_<_Tp>::operator = (const Point3_& pt) -{ - x = pt.x; y = pt.y; z = pt.z; - return *this; -} - -template inline -_Tp Point3_<_Tp>::dot(const Point3_& pt) const -{ - return saturate_cast<_Tp>(x*pt.x + y*pt.y + z*pt.z); -} - -template inline -double Point3_<_Tp>::ddot(const Point3_& pt) const -{ - return (double)x*pt.x + (double)y*pt.y + (double)z*pt.z; -} - -template inline -Point3_<_Tp> Point3_<_Tp>::cross(const Point3_<_Tp>& pt) const -{ - return Point3_<_Tp>(y*pt.z - z*pt.y, z*pt.x - x*pt.z, x*pt.y - y*pt.x); -} - - -template static inline -Point3_<_Tp>& operator += (Point3_<_Tp>& a, const Point3_<_Tp>& b) -{ - a.x += b.x; - a.y += b.y; - a.z += b.z; - return a; -} - -template static inline -Point3_<_Tp>& operator -= (Point3_<_Tp>& a, const Point3_<_Tp>& b) -{ - a.x -= b.x; - a.y -= b.y; - a.z -= b.z; - return a; -} - -template static inline -Point3_<_Tp>& operator *= (Point3_<_Tp>& a, int b) -{ - a.x = saturate_cast<_Tp>(a.x * b); - a.y = saturate_cast<_Tp>(a.y * b); - a.z = saturate_cast<_Tp>(a.z * b); - return a; -} - -template static inline -Point3_<_Tp>& operator *= (Point3_<_Tp>& a, float b) -{ - a.x = saturate_cast<_Tp>(a.x * b); - a.y = saturate_cast<_Tp>(a.y * b); - a.z = saturate_cast<_Tp>(a.z * b); - return a; -} - -template static inline -Point3_<_Tp>& operator *= (Point3_<_Tp>& a, double b) -{ - a.x = saturate_cast<_Tp>(a.x * b); - a.y = saturate_cast<_Tp>(a.y * b); - a.z = saturate_cast<_Tp>(a.z * b); - return a; -} - -template static inline -Point3_<_Tp>& operator /= (Point3_<_Tp>& a, int b) -{ - a.x = saturate_cast<_Tp>(a.x / b); - a.y = saturate_cast<_Tp>(a.y / b); - a.z = saturate_cast<_Tp>(a.z / b); - return a; -} - -template static inline -Point3_<_Tp>& operator /= (Point3_<_Tp>& a, float b) -{ - a.x = saturate_cast<_Tp>(a.x / b); - a.y = saturate_cast<_Tp>(a.y / b); - a.z = saturate_cast<_Tp>(a.z / b); - return a; -} - -template static inline -Point3_<_Tp>& operator /= (Point3_<_Tp>& a, double b) -{ - a.x = saturate_cast<_Tp>(a.x / b); - a.y = saturate_cast<_Tp>(a.y / b); - a.z = saturate_cast<_Tp>(a.z / b); - return a; -} - -template static inline -double norm(const Point3_<_Tp>& pt) -{ - return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y + (double)pt.z*pt.z); -} - -template static inline -bool operator == (const Point3_<_Tp>& a, const Point3_<_Tp>& b) -{ - return a.x == b.x && a.y == b.y && a.z == b.z; -} - -template static inline -bool operator != (const Point3_<_Tp>& a, const Point3_<_Tp>& b) -{ - return a.x != b.x || a.y != b.y || a.z != b.z; -} - -template static inline -Point3_<_Tp> operator + (const Point3_<_Tp>& a, const Point3_<_Tp>& b) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(a.x + b.x), saturate_cast<_Tp>(a.y + b.y), saturate_cast<_Tp>(a.z + b.z)); -} - -template static inline -Point3_<_Tp> operator - (const Point3_<_Tp>& a, const Point3_<_Tp>& b) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(a.x - b.x), saturate_cast<_Tp>(a.y - b.y), saturate_cast<_Tp>(a.z - b.z)); -} - -template static inline -Point3_<_Tp> operator - (const Point3_<_Tp>& a) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(-a.x), saturate_cast<_Tp>(-a.y), saturate_cast<_Tp>(-a.z) ); -} - -template static inline -Point3_<_Tp> operator * (const Point3_<_Tp>& a, int b) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b), saturate_cast<_Tp>(a.z*b) ); -} - -template static inline -Point3_<_Tp> operator * (int a, const Point3_<_Tp>& b) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); -} - -template static inline -Point3_<_Tp> operator * (const Point3_<_Tp>& a, float b) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(a.x * b), saturate_cast<_Tp>(a.y * b), saturate_cast<_Tp>(a.z * b) ); -} - -template static inline -Point3_<_Tp> operator * (float a, const Point3_<_Tp>& b) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); -} - -template static inline -Point3_<_Tp> operator * (const Point3_<_Tp>& a, double b) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(a.x * b), saturate_cast<_Tp>(a.y * b), saturate_cast<_Tp>(a.z * b) ); -} - -template static inline -Point3_<_Tp> operator * (double a, const Point3_<_Tp>& b) -{ - return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) ); -} - -template static inline -Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point3_<_Tp>& b) -{ - Matx<_Tp, 3, 1> tmp = a * Vec<_Tp,3>(b.x, b.y, b.z); - return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]); -} - -template static inline -Matx<_Tp, 4, 1> operator * (const Matx<_Tp, 4, 4>& a, const Point3_<_Tp>& b) -{ - return a * Matx<_Tp, 4, 1>(b.x, b.y, b.z, 1); -} - -template static inline -Point3_<_Tp> operator / (const Point3_<_Tp>& a, int b) -{ - Point3_<_Tp> tmp(a); - tmp /= b; - return tmp; -} - -template static inline -Point3_<_Tp> operator / (const Point3_<_Tp>& a, float b) -{ - Point3_<_Tp> tmp(a); - tmp /= b; - return tmp; -} - -template static inline -Point3_<_Tp> operator / (const Point3_<_Tp>& a, double b) -{ - Point3_<_Tp> tmp(a); - tmp /= b; - return tmp; -} - - - -////////////////////////////////// Size ///////////////////////////////// - -template inline -Size_<_Tp>::Size_() - : width(0), height(0) {} - -template inline -Size_<_Tp>::Size_(_Tp _width, _Tp _height) - : width(_width), height(_height) {} - -template inline -Size_<_Tp>::Size_(const Size_& sz) - : width(sz.width), height(sz.height) {} - -template inline -Size_<_Tp>::Size_(const Point_<_Tp>& pt) - : width(pt.x), height(pt.y) {} - -template template inline -Size_<_Tp>::operator Size_<_Tp2>() const -{ - return Size_<_Tp2>(saturate_cast<_Tp2>(width), saturate_cast<_Tp2>(height)); -} - -template inline -Size_<_Tp>& Size_<_Tp>::operator = (const Size_<_Tp>& sz) -{ - width = sz.width; height = sz.height; - return *this; -} - -template inline -_Tp Size_<_Tp>::area() const -{ - const _Tp result = width * height; - CV_DbgAssert(!std::numeric_limits<_Tp>::is_integer - || width == 0 || result / width == height); // make sure the result fits in the return value - return result; -} - -template inline -bool Size_<_Tp>::empty() const -{ - return width <= 0 || height <= 0; -} - - -template static inline -Size_<_Tp>& operator *= (Size_<_Tp>& a, _Tp b) -{ - a.width *= b; - a.height *= b; - return a; -} - -template static inline -Size_<_Tp> operator * (const Size_<_Tp>& a, _Tp b) -{ - Size_<_Tp> tmp(a); - tmp *= b; - return tmp; -} - -template static inline -Size_<_Tp>& operator /= (Size_<_Tp>& a, _Tp b) -{ - a.width /= b; - a.height /= b; - return a; -} - -template static inline -Size_<_Tp> operator / (const Size_<_Tp>& a, _Tp b) -{ - Size_<_Tp> tmp(a); - tmp /= b; - return tmp; -} - -template static inline -Size_<_Tp>& operator += (Size_<_Tp>& a, const Size_<_Tp>& b) -{ - a.width += b.width; - a.height += b.height; - return a; -} - -template static inline -Size_<_Tp> operator + (const Size_<_Tp>& a, const Size_<_Tp>& b) -{ - Size_<_Tp> tmp(a); - tmp += b; - return tmp; -} - -template static inline -Size_<_Tp>& operator -= (Size_<_Tp>& a, const Size_<_Tp>& b) -{ - a.width -= b.width; - a.height -= b.height; - return a; -} - -template static inline -Size_<_Tp> operator - (const Size_<_Tp>& a, const Size_<_Tp>& b) -{ - Size_<_Tp> tmp(a); - tmp -= b; - return tmp; -} - -template static inline -bool operator == (const Size_<_Tp>& a, const Size_<_Tp>& b) -{ - return a.width == b.width && a.height == b.height; -} - -template static inline -bool operator != (const Size_<_Tp>& a, const Size_<_Tp>& b) -{ - return !(a == b); -} - - - -////////////////////////////////// Rect ///////////////////////////////// - -template inline -Rect_<_Tp>::Rect_() - : x(0), y(0), width(0), height(0) {} - -template inline -Rect_<_Tp>::Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height) - : x(_x), y(_y), width(_width), height(_height) {} - -template inline -Rect_<_Tp>::Rect_(const Rect_<_Tp>& r) - : x(r.x), y(r.y), width(r.width), height(r.height) {} - -template inline -Rect_<_Tp>::Rect_(const Point_<_Tp>& org, const Size_<_Tp>& sz) - : x(org.x), y(org.y), width(sz.width), height(sz.height) {} - -template inline -Rect_<_Tp>::Rect_(const Point_<_Tp>& pt1, const Point_<_Tp>& pt2) -{ - x = std::min(pt1.x, pt2.x); - y = std::min(pt1.y, pt2.y); - width = std::max(pt1.x, pt2.x) - x; - height = std::max(pt1.y, pt2.y) - y; -} - -template inline -Rect_<_Tp>& Rect_<_Tp>::operator = ( const Rect_<_Tp>& r ) -{ - x = r.x; - y = r.y; - width = r.width; - height = r.height; - return *this; -} - -template inline -Point_<_Tp> Rect_<_Tp>::tl() const -{ - return Point_<_Tp>(x,y); -} - -template inline -Point_<_Tp> Rect_<_Tp>::br() const -{ - return Point_<_Tp>(x + width, y + height); -} - -template inline -Size_<_Tp> Rect_<_Tp>::size() const -{ - return Size_<_Tp>(width, height); -} - -template inline -_Tp Rect_<_Tp>::area() const -{ - const _Tp result = width * height; - CV_DbgAssert(!std::numeric_limits<_Tp>::is_integer - || width == 0 || result / width == height); // make sure the result fits in the return value - return result; -} - -template inline -bool Rect_<_Tp>::empty() const -{ - return width <= 0 || height <= 0; -} - -template template inline -Rect_<_Tp>::operator Rect_<_Tp2>() const -{ - return Rect_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y), saturate_cast<_Tp2>(width), saturate_cast<_Tp2>(height)); -} - -template inline -bool Rect_<_Tp>::contains(const Point_<_Tp>& pt) const -{ - return x <= pt.x && pt.x < x + width && y <= pt.y && pt.y < y + height; -} - - -template static inline -Rect_<_Tp>& operator += ( Rect_<_Tp>& a, const Point_<_Tp>& b ) -{ - a.x += b.x; - a.y += b.y; - return a; -} - -template static inline -Rect_<_Tp>& operator -= ( Rect_<_Tp>& a, const Point_<_Tp>& b ) -{ - a.x -= b.x; - a.y -= b.y; - return a; -} - -template static inline -Rect_<_Tp>& operator += ( Rect_<_Tp>& a, const Size_<_Tp>& b ) -{ - a.width += b.width; - a.height += b.height; - return a; -} - -template static inline -Rect_<_Tp>& operator -= ( Rect_<_Tp>& a, const Size_<_Tp>& b ) -{ - const _Tp width = a.width - b.width; - const _Tp height = a.height - b.height; - CV_DbgAssert(width >= 0 && height >= 0); - a.width = width; - a.height = height; - return a; -} - -template static inline -Rect_<_Tp>& operator &= ( Rect_<_Tp>& a, const Rect_<_Tp>& b ) -{ - _Tp x1 = std::max(a.x, b.x); - _Tp y1 = std::max(a.y, b.y); - a.width = std::min(a.x + a.width, b.x + b.width) - x1; - a.height = std::min(a.y + a.height, b.y + b.height) - y1; - a.x = x1; - a.y = y1; - if( a.width <= 0 || a.height <= 0 ) - a = Rect(); - return a; -} - -template static inline -Rect_<_Tp>& operator |= ( Rect_<_Tp>& a, const Rect_<_Tp>& b ) -{ - if (a.empty()) { - a = b; - } - else if (!b.empty()) { - _Tp x1 = std::min(a.x, b.x); - _Tp y1 = std::min(a.y, b.y); - a.width = std::max(a.x + a.width, b.x + b.width) - x1; - a.height = std::max(a.y + a.height, b.y + b.height) - y1; - a.x = x1; - a.y = y1; - } - return a; -} - -template static inline -bool operator == (const Rect_<_Tp>& a, const Rect_<_Tp>& b) -{ - return a.x == b.x && a.y == b.y && a.width == b.width && a.height == b.height; -} - -template static inline -bool operator != (const Rect_<_Tp>& a, const Rect_<_Tp>& b) -{ - return a.x != b.x || a.y != b.y || a.width != b.width || a.height != b.height; -} - -template static inline -Rect_<_Tp> operator + (const Rect_<_Tp>& a, const Point_<_Tp>& b) -{ - return Rect_<_Tp>( a.x + b.x, a.y + b.y, a.width, a.height ); -} - -template static inline -Rect_<_Tp> operator - (const Rect_<_Tp>& a, const Point_<_Tp>& b) -{ - return Rect_<_Tp>( a.x - b.x, a.y - b.y, a.width, a.height ); -} - -template static inline -Rect_<_Tp> operator + (const Rect_<_Tp>& a, const Size_<_Tp>& b) -{ - return Rect_<_Tp>( a.x, a.y, a.width + b.width, a.height + b.height ); -} - -template static inline -Rect_<_Tp> operator - (const Rect_<_Tp>& a, const Size_<_Tp>& b) -{ - const _Tp width = a.width - b.width; - const _Tp height = a.height - b.height; - CV_DbgAssert(width >= 0 && height >= 0); - return Rect_<_Tp>( a.x, a.y, width, height ); -} - -template static inline -Rect_<_Tp> operator & (const Rect_<_Tp>& a, const Rect_<_Tp>& b) -{ - Rect_<_Tp> c = a; - return c &= b; -} - -template static inline -Rect_<_Tp> operator | (const Rect_<_Tp>& a, const Rect_<_Tp>& b) -{ - Rect_<_Tp> c = a; - return c |= b; -} - -/** - * @brief measure dissimilarity between two sample sets - * - * computes the complement of the Jaccard Index as described in . - * For rectangles this reduces to computing the intersection over the union. - */ -template static inline -double jaccardDistance(const Rect_<_Tp>& a, const Rect_<_Tp>& b) { - _Tp Aa = a.area(); - _Tp Ab = b.area(); - - if ((Aa + Ab) <= std::numeric_limits<_Tp>::epsilon()) { - // jaccard_index = 1 -> distance = 0 - return 0.0; - } - - double Aab = (a & b).area(); - // distance = 1 - jaccard_index - return 1.0 - Aab / (Aa + Ab - Aab); -} - -////////////////////////////// RotatedRect ////////////////////////////// - -inline -RotatedRect::RotatedRect() - : center(), size(), angle(0) {} - -inline -RotatedRect::RotatedRect(const Point2f& _center, const Size2f& _size, float _angle) - : center(_center), size(_size), angle(_angle) {} - - - -///////////////////////////////// Range ///////////////////////////////// - -inline -Range::Range() - : start(0), end(0) {} - -inline -Range::Range(int _start, int _end) - : start(_start), end(_end) {} - -inline -int Range::size() const -{ - return end - start; -} - -inline -bool Range::empty() const -{ - return start == end; -} - -inline -Range Range::all() -{ - return Range(INT_MIN, INT_MAX); -} - - -static inline -bool operator == (const Range& r1, const Range& r2) -{ - return r1.start == r2.start && r1.end == r2.end; -} - -static inline -bool operator != (const Range& r1, const Range& r2) -{ - return !(r1 == r2); -} - -static inline -bool operator !(const Range& r) -{ - return r.start == r.end; -} - -static inline -Range operator & (const Range& r1, const Range& r2) -{ - Range r(std::max(r1.start, r2.start), std::min(r1.end, r2.end)); - r.end = std::max(r.end, r.start); - return r; -} - -static inline -Range& operator &= (Range& r1, const Range& r2) -{ - r1 = r1 & r2; - return r1; -} - -static inline -Range operator + (const Range& r1, int delta) -{ - return Range(r1.start + delta, r1.end + delta); -} - -static inline -Range operator + (int delta, const Range& r1) -{ - return Range(r1.start + delta, r1.end + delta); -} - -static inline -Range operator - (const Range& r1, int delta) -{ - return r1 + (-delta); -} - - - -///////////////////////////////// Scalar //////////////////////////////// - -template inline -Scalar_<_Tp>::Scalar_() -{ - this->val[0] = this->val[1] = this->val[2] = this->val[3] = 0; -} - -template inline -Scalar_<_Tp>::Scalar_(_Tp v0, _Tp v1, _Tp v2, _Tp v3) -{ - this->val[0] = v0; - this->val[1] = v1; - this->val[2] = v2; - this->val[3] = v3; -} - -template template inline -Scalar_<_Tp>::Scalar_(const Vec<_Tp2, cn>& v) -{ - int i; - for( i = 0; i < (cn < 4 ? cn : 4); i++ ) - this->val[i] = cv::saturate_cast<_Tp>(v.val[i]); - for( ; i < 4; i++ ) - this->val[i] = 0; -} - -template inline -Scalar_<_Tp>::Scalar_(_Tp v0) -{ - this->val[0] = v0; - this->val[1] = this->val[2] = this->val[3] = 0; -} - -template inline -Scalar_<_Tp> Scalar_<_Tp>::all(_Tp v0) -{ - return Scalar_<_Tp>(v0, v0, v0, v0); -} - - -template inline -Scalar_<_Tp> Scalar_<_Tp>::mul(const Scalar_<_Tp>& a, double scale ) const -{ - return Scalar_<_Tp>(saturate_cast<_Tp>(this->val[0] * a.val[0] * scale), - saturate_cast<_Tp>(this->val[1] * a.val[1] * scale), - saturate_cast<_Tp>(this->val[2] * a.val[2] * scale), - saturate_cast<_Tp>(this->val[3] * a.val[3] * scale)); -} - -template inline -Scalar_<_Tp> Scalar_<_Tp>::conj() const -{ - return Scalar_<_Tp>(saturate_cast<_Tp>( this->val[0]), - saturate_cast<_Tp>(-this->val[1]), - saturate_cast<_Tp>(-this->val[2]), - saturate_cast<_Tp>(-this->val[3])); -} - -template inline -bool Scalar_<_Tp>::isReal() const -{ - return this->val[1] == 0 && this->val[2] == 0 && this->val[3] == 0; -} - - -template template inline -Scalar_<_Tp>::operator Scalar_() const -{ - return Scalar_(saturate_cast(this->val[0]), - saturate_cast(this->val[1]), - saturate_cast(this->val[2]), - saturate_cast(this->val[3])); -} - - -template static inline -Scalar_<_Tp>& operator += (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) -{ - a.val[0] += b.val[0]; - a.val[1] += b.val[1]; - a.val[2] += b.val[2]; - a.val[3] += b.val[3]; - return a; -} - -template static inline -Scalar_<_Tp>& operator -= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) -{ - a.val[0] -= b.val[0]; - a.val[1] -= b.val[1]; - a.val[2] -= b.val[2]; - a.val[3] -= b.val[3]; - return a; -} - -template static inline -Scalar_<_Tp>& operator *= ( Scalar_<_Tp>& a, _Tp v ) -{ - a.val[0] *= v; - a.val[1] *= v; - a.val[2] *= v; - a.val[3] *= v; - return a; -} - -template static inline -bool operator == ( const Scalar_<_Tp>& a, const Scalar_<_Tp>& b ) -{ - return a.val[0] == b.val[0] && a.val[1] == b.val[1] && - a.val[2] == b.val[2] && a.val[3] == b.val[3]; -} - -template static inline -bool operator != ( const Scalar_<_Tp>& a, const Scalar_<_Tp>& b ) -{ - return a.val[0] != b.val[0] || a.val[1] != b.val[1] || - a.val[2] != b.val[2] || a.val[3] != b.val[3]; -} - -template static inline -Scalar_<_Tp> operator + (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) -{ - return Scalar_<_Tp>(a.val[0] + b.val[0], - a.val[1] + b.val[1], - a.val[2] + b.val[2], - a.val[3] + b.val[3]); -} - -template static inline -Scalar_<_Tp> operator - (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) -{ - return Scalar_<_Tp>(saturate_cast<_Tp>(a.val[0] - b.val[0]), - saturate_cast<_Tp>(a.val[1] - b.val[1]), - saturate_cast<_Tp>(a.val[2] - b.val[2]), - saturate_cast<_Tp>(a.val[3] - b.val[3])); -} - -template static inline -Scalar_<_Tp> operator * (const Scalar_<_Tp>& a, _Tp alpha) -{ - return Scalar_<_Tp>(a.val[0] * alpha, - a.val[1] * alpha, - a.val[2] * alpha, - a.val[3] * alpha); -} - -template static inline -Scalar_<_Tp> operator * (_Tp alpha, const Scalar_<_Tp>& a) -{ - return a*alpha; -} - -template static inline -Scalar_<_Tp> operator - (const Scalar_<_Tp>& a) -{ - return Scalar_<_Tp>(saturate_cast<_Tp>(-a.val[0]), - saturate_cast<_Tp>(-a.val[1]), - saturate_cast<_Tp>(-a.val[2]), - saturate_cast<_Tp>(-a.val[3])); -} - - -template static inline -Scalar_<_Tp> operator * (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) -{ - return Scalar_<_Tp>(saturate_cast<_Tp>(a[0]*b[0] - a[1]*b[1] - a[2]*b[2] - a[3]*b[3]), - saturate_cast<_Tp>(a[0]*b[1] + a[1]*b[0] + a[2]*b[3] - a[3]*b[2]), - saturate_cast<_Tp>(a[0]*b[2] - a[1]*b[3] + a[2]*b[0] + a[3]*b[1]), - saturate_cast<_Tp>(a[0]*b[3] + a[1]*b[2] - a[2]*b[1] + a[3]*b[0])); -} - -template static inline -Scalar_<_Tp>& operator *= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) -{ - a = a * b; - return a; -} - -template static inline -Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, _Tp alpha) -{ - return Scalar_<_Tp>(a.val[0] / alpha, - a.val[1] / alpha, - a.val[2] / alpha, - a.val[3] / alpha); -} - -template static inline -Scalar_ operator / (const Scalar_& a, float alpha) -{ - float s = 1 / alpha; - return Scalar_(a.val[0] * s, a.val[1] * s, a.val[2] * s, a.val[3] * s); -} - -template static inline -Scalar_ operator / (const Scalar_& a, double alpha) -{ - double s = 1 / alpha; - return Scalar_(a.val[0] * s, a.val[1] * s, a.val[2] * s, a.val[3] * s); -} - -template static inline -Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, _Tp alpha) -{ - a = a / alpha; - return a; -} - -template static inline -Scalar_<_Tp> operator / (_Tp a, const Scalar_<_Tp>& b) -{ - _Tp s = a / (b[0]*b[0] + b[1]*b[1] + b[2]*b[2] + b[3]*b[3]); - return b.conj() * s; -} - -template static inline -Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b) -{ - return a * ((_Tp)1 / b); -} - -template static inline -Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b) -{ - a = a / b; - return a; -} - -template static inline -Scalar operator * (const Matx<_Tp, 4, 4>& a, const Scalar& b) -{ - Matx c((Matx)a, b, Matx_MatMulOp()); - return reinterpret_cast(c); -} - -template<> inline -Scalar operator * (const Matx& a, const Scalar& b) -{ - Matx c(a, b, Matx_MatMulOp()); - return reinterpret_cast(c); -} - - - -//////////////////////////////// KeyPoint /////////////////////////////// - -inline -KeyPoint::KeyPoint() - : pt(0,0), size(0), angle(-1), response(0), octave(0), class_id(-1) {} - -inline -KeyPoint::KeyPoint(Point2f _pt, float _size, float _angle, float _response, int _octave, int _class_id) - : pt(_pt), size(_size), angle(_angle), response(_response), octave(_octave), class_id(_class_id) {} - -inline -KeyPoint::KeyPoint(float x, float y, float _size, float _angle, float _response, int _octave, int _class_id) - : pt(x, y), size(_size), angle(_angle), response(_response), octave(_octave), class_id(_class_id) {} - - - -///////////////////////////////// DMatch //////////////////////////////// - -inline -DMatch::DMatch() - : queryIdx(-1), trainIdx(-1), imgIdx(-1), distance(FLT_MAX) {} - -inline -DMatch::DMatch(int _queryIdx, int _trainIdx, float _distance) - : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(-1), distance(_distance) {} - -inline -DMatch::DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance) - : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(_imgIdx), distance(_distance) {} - -inline -bool DMatch::operator < (const DMatch &m) const -{ - return distance < m.distance; -} - - - -////////////////////////////// TermCriteria ///////////////////////////// - -inline -TermCriteria::TermCriteria() - : type(0), maxCount(0), epsilon(0) {} - -inline -TermCriteria::TermCriteria(int _type, int _maxCount, double _epsilon) - : type(_type), maxCount(_maxCount), epsilon(_epsilon) {} - -//! @endcond - -} // cv - -#endif //OPENCV_CORE_TYPES_HPP diff --git a/include/opencv2/core/types_c.h b/include/opencv2/core/types_c.h deleted file mode 100755 index eddbe7d..0000000 --- a/include/opencv2/core/types_c.h +++ /dev/null @@ -1,2141 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_TYPES_H -#define OPENCV_CORE_TYPES_H - -#ifdef CV__ENABLE_C_API_CTORS // invalid C API ctors (must be removed) -#if defined(_WIN32) && !defined(CV__SKIP_MESSAGE_MALFORMED_C_API_CTORS) -#error "C API ctors don't work on Win32: https://github.com/opencv/opencv/issues/15990" -#endif -#endif - -//#define CV__VALIDATE_UNUNITIALIZED_VARS 1 // C++11 & GCC only - -#ifdef __cplusplus - -#ifdef CV__VALIDATE_UNUNITIALIZED_VARS -#pragma GCC diagnostic ignored "-Wmissing-field-initializers" -#define CV_STRUCT_INITIALIZER {0,} -#else -#if defined(__GNUC__) && __GNUC__ == 4 // GCC 4.x warns on "= {}" initialization, fixed in GCC 5.0 -#pragma GCC diagnostic ignored "-Wmissing-field-initializers" -#endif -#define CV_STRUCT_INITIALIZER {} -#endif - -#else -#define CV_STRUCT_INITIALIZER {0} -#endif - - -#ifdef HAVE_IPL -# ifndef __IPL_H__ -# if defined _WIN32 -# include -# else -# include -# endif -# endif -#elif defined __IPL_H__ -# define HAVE_IPL -#endif - -#include "opencv2/core/cvdef.h" - -#ifndef SKIP_INCLUDES -#include -#include -#include -#include -#endif // SKIP_INCLUDES - -#if defined _WIN32 -# define CV_CDECL __cdecl -# define CV_STDCALL __stdcall -#else -# define CV_CDECL -# define CV_STDCALL -#endif - -#ifndef CV_DEFAULT -# ifdef __cplusplus -# define CV_DEFAULT(val) = val -# else -# define CV_DEFAULT(val) -# endif -#endif - -#ifndef CV_EXTERN_C_FUNCPTR -# ifdef __cplusplus -# define CV_EXTERN_C_FUNCPTR(x) extern "C" { typedef x; } -# else -# define CV_EXTERN_C_FUNCPTR(x) typedef x -# endif -#endif - -#ifndef CVAPI -# define CVAPI(rettype) CV_EXTERN_C CV_EXPORTS rettype CV_CDECL -#endif - -#ifndef CV_IMPL -# define CV_IMPL CV_EXTERN_C -#endif - -#ifdef __cplusplus -# include "opencv2/core.hpp" -#endif - -/** @addtogroup core_c - @{ -*/ - -/** @brief This is the "metatype" used *only* as a function parameter. - -It denotes that the function accepts arrays of multiple types, such as IplImage*, CvMat* or even -CvSeq* sometimes. The particular array type is determined at runtime by analyzing the first 4 -bytes of the header. In C++ interface the role of CvArr is played by InputArray and OutputArray. - */ -typedef void CvArr; - -typedef int CVStatus; - -/** @see cv::Error::Code */ -enum { - CV_StsOk= 0, /**< everything is ok */ - CV_StsBackTrace= -1, /**< pseudo error for back trace */ - CV_StsError= -2, /**< unknown /unspecified error */ - CV_StsInternal= -3, /**< internal error (bad state) */ - CV_StsNoMem= -4, /**< insufficient memory */ - CV_StsBadArg= -5, /**< function arg/param is bad */ - CV_StsBadFunc= -6, /**< unsupported function */ - CV_StsNoConv= -7, /**< iter. didn't converge */ - CV_StsAutoTrace= -8, /**< tracing */ - CV_HeaderIsNull= -9, /**< image header is NULL */ - CV_BadImageSize= -10, /**< image size is invalid */ - CV_BadOffset= -11, /**< offset is invalid */ - CV_BadDataPtr= -12, /**/ - CV_BadStep= -13, /**< image step is wrong, this may happen for a non-continuous matrix */ - CV_BadModelOrChSeq= -14, /**/ - CV_BadNumChannels= -15, /**< bad number of channels, for example, some functions accept only single channel matrices */ - CV_BadNumChannel1U= -16, /**/ - CV_BadDepth= -17, /**< input image depth is not supported by the function */ - CV_BadAlphaChannel= -18, /**/ - CV_BadOrder= -19, /**< number of dimensions is out of range */ - CV_BadOrigin= -20, /**< incorrect input origin */ - CV_BadAlign= -21, /**< incorrect input align */ - CV_BadCallBack= -22, /**/ - CV_BadTileSize= -23, /**/ - CV_BadCOI= -24, /**< input COI is not supported */ - CV_BadROISize= -25, /**< incorrect input roi */ - CV_MaskIsTiled= -26, /**/ - CV_StsNullPtr= -27, /**< null pointer */ - CV_StsVecLengthErr= -28, /**< incorrect vector length */ - CV_StsFilterStructContentErr= -29, /**< incorrect filter structure content */ - CV_StsKernelStructContentErr= -30, /**< incorrect transform kernel content */ - CV_StsFilterOffsetErr= -31, /**< incorrect filter offset value */ - CV_StsBadSize= -201, /**< the input/output structure size is incorrect */ - CV_StsDivByZero= -202, /**< division by zero */ - CV_StsInplaceNotSupported= -203, /**< in-place operation is not supported */ - CV_StsObjectNotFound= -204, /**< request can't be completed */ - CV_StsUnmatchedFormats= -205, /**< formats of input/output arrays differ */ - CV_StsBadFlag= -206, /**< flag is wrong or not supported */ - CV_StsBadPoint= -207, /**< bad CvPoint */ - CV_StsBadMask= -208, /**< bad format of mask (neither 8uC1 nor 8sC1)*/ - CV_StsUnmatchedSizes= -209, /**< sizes of input/output structures do not match */ - CV_StsUnsupportedFormat= -210, /**< the data format/type is not supported by the function*/ - CV_StsOutOfRange= -211, /**< some of parameters are out of range */ - CV_StsParseError= -212, /**< invalid syntax/structure of the parsed file */ - CV_StsNotImplemented= -213, /**< the requested function/feature is not implemented */ - CV_StsBadMemBlock= -214, /**< an allocated block has been corrupted */ - CV_StsAssert= -215, /**< assertion failed */ - CV_GpuNotSupported= -216, /**< no CUDA support */ - CV_GpuApiCallError= -217, /**< GPU API call error */ - CV_OpenGlNotSupported= -218, /**< no OpenGL support */ - CV_OpenGlApiCallError= -219, /**< OpenGL API call error */ - CV_OpenCLApiCallError= -220, /**< OpenCL API call error */ - CV_OpenCLDoubleNotSupported= -221, - CV_OpenCLInitError= -222, /**< OpenCL initialization error */ - CV_OpenCLNoAMDBlasFft= -223 -}; - -/****************************************************************************************\ -* Common macros and inline functions * -\****************************************************************************************/ - -#define CV_SWAP(a,b,t) ((t) = (a), (a) = (b), (b) = (t)) - -/** min & max without jumps */ -#define CV_IMIN(a, b) ((a) ^ (((a)^(b)) & (((a) < (b)) - 1))) - -#define CV_IMAX(a, b) ((a) ^ (((a)^(b)) & (((a) > (b)) - 1))) - -/** absolute value without jumps */ -#ifndef __cplusplus -# define CV_IABS(a) (((a) ^ ((a) < 0 ? -1 : 0)) - ((a) < 0 ? -1 : 0)) -#else -# define CV_IABS(a) abs(a) -#endif -#define CV_CMP(a,b) (((a) > (b)) - ((a) < (b))) -#define CV_SIGN(a) CV_CMP((a),0) - -#define cvInvSqrt(value) ((float)(1./sqrt(value))) -#define cvSqrt(value) ((float)sqrt(value)) - - -/*************** Random number generation *******************/ - -typedef uint64 CvRNG; - -#define CV_RNG_COEFF 4164903690U - -/** @brief Initializes a random number generator state. - -The function initializes a random number generator and returns the state. The pointer to the state -can be then passed to the cvRandInt, cvRandReal and cvRandArr functions. In the current -implementation a multiply-with-carry generator is used. -@param seed 64-bit value used to initiate a random sequence -@sa the C++ class RNG replaced CvRNG. - */ -CV_INLINE CvRNG cvRNG( int64 seed CV_DEFAULT(-1)) -{ - CvRNG rng = seed ? (uint64)seed : (uint64)(int64)-1; - return rng; -} - -/** @brief Returns a 32-bit unsigned integer and updates RNG. - -The function returns a uniformly-distributed random 32-bit unsigned integer and updates the RNG -state. It is similar to the rand() function from the C runtime library, except that OpenCV functions -always generates a 32-bit random number, regardless of the platform. -@param rng CvRNG state initialized by cvRNG. - */ -CV_INLINE unsigned cvRandInt( CvRNG* rng ) -{ - uint64 temp = *rng; - temp = (uint64)(unsigned)temp*CV_RNG_COEFF + (temp >> 32); - *rng = temp; - return (unsigned)temp; -} - -/** @brief Returns a floating-point random number and updates RNG. - -The function returns a uniformly-distributed random floating-point number between 0 and 1 (1 is not -included). -@param rng RNG state initialized by cvRNG - */ -CV_INLINE double cvRandReal( CvRNG* rng ) -{ - return cvRandInt(rng)*2.3283064365386962890625e-10 /* 2^-32 */; -} - -/****************************************************************************************\ -* Image type (IplImage) * -\****************************************************************************************/ - -#ifndef HAVE_IPL - -/* - * The following definitions (until #endif) - * is an extract from IPL headers. - * Copyright (c) 1995 Intel Corporation. - */ -#define IPL_DEPTH_SIGN 0x80000000 - -#define IPL_DEPTH_1U 1 -#define IPL_DEPTH_8U 8 -#define IPL_DEPTH_16U 16 -#define IPL_DEPTH_32F 32 - -#define IPL_DEPTH_8S (IPL_DEPTH_SIGN| 8) -#define IPL_DEPTH_16S (IPL_DEPTH_SIGN|16) -#define IPL_DEPTH_32S (IPL_DEPTH_SIGN|32) - -#define IPL_DATA_ORDER_PIXEL 0 -#define IPL_DATA_ORDER_PLANE 1 - -#define IPL_ORIGIN_TL 0 -#define IPL_ORIGIN_BL 1 - -#define IPL_ALIGN_4BYTES 4 -#define IPL_ALIGN_8BYTES 8 -#define IPL_ALIGN_16BYTES 16 -#define IPL_ALIGN_32BYTES 32 - -#define IPL_ALIGN_DWORD IPL_ALIGN_4BYTES -#define IPL_ALIGN_QWORD IPL_ALIGN_8BYTES - -#define IPL_BORDER_CONSTANT 0 -#define IPL_BORDER_REPLICATE 1 -#define IPL_BORDER_REFLECT 2 -#define IPL_BORDER_WRAP 3 - -#ifdef __cplusplus -typedef struct _IplImage IplImage; -CV_EXPORTS _IplImage cvIplImage(const cv::Mat& m); -#endif - -/** The IplImage is taken from the Intel Image Processing Library, in which the format is native. OpenCV -only supports a subset of possible IplImage formats, as outlined in the parameter list above. - -In addition to the above restrictions, OpenCV handles ROIs differently. OpenCV functions require -that the image size or ROI size of all source and destination images match exactly. On the other -hand, the Intel Image Processing Library processes the area of intersection between the source and -destination images (or ROIs), allowing them to vary independently. -*/ -typedef struct -_IplImage -{ - int nSize; /**< sizeof(IplImage) */ - int ID; /**< version (=0)*/ - int nChannels; /**< Most of OpenCV functions support 1,2,3 or 4 channels */ - int alphaChannel; /**< Ignored by OpenCV */ - int depth; /**< Pixel depth in bits: IPL_DEPTH_8U, IPL_DEPTH_8S, IPL_DEPTH_16S, - IPL_DEPTH_32S, IPL_DEPTH_32F and IPL_DEPTH_64F are supported. */ - char colorModel[4]; /**< Ignored by OpenCV */ - char channelSeq[4]; /**< ditto */ - int dataOrder; /**< 0 - interleaved color channels, 1 - separate color channels. - cvCreateImage can only create interleaved images */ - int origin; /**< 0 - top-left origin, - 1 - bottom-left origin (Windows bitmaps style). */ - int align; /**< Alignment of image rows (4 or 8). - OpenCV ignores it and uses widthStep instead. */ - int width; /**< Image width in pixels. */ - int height; /**< Image height in pixels. */ - struct _IplROI *roi; /**< Image ROI. If NULL, the whole image is selected. */ - struct _IplImage *maskROI; /**< Must be NULL. */ - void *imageId; /**< " " */ - struct _IplTileInfo *tileInfo; /**< " " */ - int imageSize; /**< Image data size in bytes - (==image->height*image->widthStep - in case of interleaved data)*/ - char *imageData; /**< Pointer to aligned image data. */ - int widthStep; /**< Size of aligned image row in bytes. */ - int BorderMode[4]; /**< Ignored by OpenCV. */ - int BorderConst[4]; /**< Ditto. */ - char *imageDataOrigin; /**< Pointer to very origin of image data - (not necessarily aligned) - - needed for correct deallocation */ - -#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) - _IplImage() {} - _IplImage(const cv::Mat& m) { *this = cvIplImage(m); } -#endif -} -IplImage; - -CV_INLINE IplImage cvIplImage() -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - IplImage self = CV_STRUCT_INITIALIZER; self.nSize = sizeof(IplImage); return self; -#else - return _IplImage(); -#endif -} - -typedef struct _IplTileInfo IplTileInfo; - -typedef struct _IplROI -{ - int coi; /**< 0 - no COI (all channels are selected), 1 - 0th channel is selected ...*/ - int xOffset; - int yOffset; - int width; - int height; -} -IplROI; - -typedef struct _IplConvKernel -{ - int nCols; - int nRows; - int anchorX; - int anchorY; - int *values; - int nShiftR; -} -IplConvKernel; - -typedef struct _IplConvKernelFP -{ - int nCols; - int nRows; - int anchorX; - int anchorY; - float *values; -} -IplConvKernelFP; - -#define IPL_IMAGE_HEADER 1 -#define IPL_IMAGE_DATA 2 -#define IPL_IMAGE_ROI 4 - -#endif/*HAVE_IPL*/ - -/** extra border mode */ -#define IPL_BORDER_REFLECT_101 4 -#define IPL_BORDER_TRANSPARENT 5 - -#define IPL_IMAGE_MAGIC_VAL ((int)sizeof(IplImage)) -#define CV_TYPE_NAME_IMAGE "opencv-image" - -#define CV_IS_IMAGE_HDR(img) \ - ((img) != NULL && ((const IplImage*)(img))->nSize == sizeof(IplImage)) - -#define CV_IS_IMAGE(img) \ - (CV_IS_IMAGE_HDR(img) && ((IplImage*)img)->imageData != NULL) - -/** for storing double-precision - floating point data in IplImage's */ -#define IPL_DEPTH_64F 64 - -/** get reference to pixel at (col,row), - for multi-channel images (col) should be multiplied by number of channels */ -#define CV_IMAGE_ELEM( image, elemtype, row, col ) \ - (((elemtype*)((image)->imageData + (image)->widthStep*(row)))[(col)]) - -/****************************************************************************************\ -* Matrix type (CvMat) * -\****************************************************************************************/ - -#define CV_AUTO_STEP 0x7fffffff -#define CV_WHOLE_ARR cvSlice( 0, 0x3fffffff ) - -#define CV_MAGIC_MASK 0xFFFF0000 -#define CV_MAT_MAGIC_VAL 0x42420000 -#define CV_TYPE_NAME_MAT "opencv-matrix" - -#ifdef __cplusplus -typedef struct CvMat CvMat; -CV_INLINE CvMat cvMat(const cv::Mat& m); -#endif - -/** Matrix elements are stored row by row. Element (i, j) (i - 0-based row index, j - 0-based column -index) of a matrix can be retrieved or modified using CV_MAT_ELEM macro: - - uchar pixval = CV_MAT_ELEM(grayimg, uchar, i, j) - CV_MAT_ELEM(cameraMatrix, float, 0, 2) = image.width*0.5f; - -To access multiple-channel matrices, you can use -CV_MAT_ELEM(matrix, type, i, j\*nchannels + channel_idx). - -@deprecated CvMat is now obsolete; consider using Mat instead. - */ -typedef struct CvMat -{ - int type; - int step; - - /* for internal use only */ - int* refcount; - int hdr_refcount; - - union - { - uchar* ptr; - short* s; - int* i; - float* fl; - double* db; - } data; - -#ifdef __cplusplus - union - { - int rows; - int height; - }; - - union - { - int cols; - int width; - }; -#else - int rows; - int cols; -#endif - -#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) - CvMat() {} - CvMat(const cv::Mat& m) { *this = cvMat(m); } -#endif -} -CvMat; - - -#define CV_IS_MAT_HDR(mat) \ - ((mat) != NULL && \ - (((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \ - ((const CvMat*)(mat))->cols > 0 && ((const CvMat*)(mat))->rows > 0) - -#define CV_IS_MAT_HDR_Z(mat) \ - ((mat) != NULL && \ - (((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \ - ((const CvMat*)(mat))->cols >= 0 && ((const CvMat*)(mat))->rows >= 0) - -#define CV_IS_MAT(mat) \ - (CV_IS_MAT_HDR(mat) && ((const CvMat*)(mat))->data.ptr != NULL) - -#define CV_IS_MASK_ARR(mat) \ - (((mat)->type & (CV_MAT_TYPE_MASK & ~CV_8SC1)) == 0) - -#define CV_ARE_TYPES_EQ(mat1, mat2) \ - ((((mat1)->type ^ (mat2)->type) & CV_MAT_TYPE_MASK) == 0) - -#define CV_ARE_CNS_EQ(mat1, mat2) \ - ((((mat1)->type ^ (mat2)->type) & CV_MAT_CN_MASK) == 0) - -#define CV_ARE_DEPTHS_EQ(mat1, mat2) \ - ((((mat1)->type ^ (mat2)->type) & CV_MAT_DEPTH_MASK) == 0) - -#define CV_ARE_SIZES_EQ(mat1, mat2) \ - ((mat1)->rows == (mat2)->rows && (mat1)->cols == (mat2)->cols) - -#define CV_IS_MAT_CONST(mat) \ - (((mat)->rows|(mat)->cols) == 1) - -#define IPL2CV_DEPTH(depth) \ - ((((CV_8U)+(CV_16U<<4)+(CV_32F<<8)+(CV_64F<<16)+(CV_8S<<20)+ \ - (CV_16S<<24)+(CV_32S<<28)) >> ((((depth) & 0xF0) >> 2) + \ - (((depth) & IPL_DEPTH_SIGN) ? 20 : 0))) & 15) - -/** Inline constructor. No data is allocated internally!!! - * (Use together with cvCreateData, or use cvCreateMat instead to - * get a matrix with allocated data): - */ -CV_INLINE CvMat cvMat( int rows, int cols, int type, void* data CV_DEFAULT(NULL)) -{ - CvMat m; - - assert( (unsigned)CV_MAT_DEPTH(type) <= CV_64F ); - type = CV_MAT_TYPE(type); - m.type = CV_MAT_MAGIC_VAL | CV_MAT_CONT_FLAG | type; - m.cols = cols; - m.rows = rows; - m.step = m.cols*CV_ELEM_SIZE(type); - m.data.ptr = (uchar*)data; - m.refcount = NULL; - m.hdr_refcount = 0; - - return m; -} - -#ifdef __cplusplus - -CV_INLINE CvMat cvMat(const cv::Mat& m) -{ - CvMat self; - CV_DbgAssert(m.dims <= 2); - self = cvMat(m.rows, m.dims == 1 ? 1 : m.cols, m.type(), m.data); - self.step = (int)m.step[0]; - self.type = (self.type & ~cv::Mat::CONTINUOUS_FLAG) | (m.flags & cv::Mat::CONTINUOUS_FLAG); - return self; -} -CV_INLINE CvMat cvMat() -{ -#if !defined(CV__ENABLE_C_API_CTORS) - CvMat self = CV_STRUCT_INITIALIZER; return self; -#else - return CvMat(); -#endif -} -CV_INLINE CvMat cvMat(const CvMat& m) -{ -#if !defined(CV__ENABLE_C_API_CTORS) - CvMat self = CV_STRUCT_INITIALIZER; memcpy(&self, &m, sizeof(self)); return self; -#else - return CvMat(m); -#endif -} - -#endif // __cplusplus - - -#define CV_MAT_ELEM_PTR_FAST( mat, row, col, pix_size ) \ - (assert( (unsigned)(row) < (unsigned)(mat).rows && \ - (unsigned)(col) < (unsigned)(mat).cols ), \ - (mat).data.ptr + (size_t)(mat).step*(row) + (pix_size)*(col)) - -#define CV_MAT_ELEM_PTR( mat, row, col ) \ - CV_MAT_ELEM_PTR_FAST( mat, row, col, CV_ELEM_SIZE((mat).type) ) - -#define CV_MAT_ELEM( mat, elemtype, row, col ) \ - (*(elemtype*)CV_MAT_ELEM_PTR_FAST( mat, row, col, sizeof(elemtype))) - -/** @brief Returns the particular element of single-channel floating-point matrix. - -The function is a fast replacement for cvGetReal2D in the case of single-channel floating-point -matrices. It is faster because it is inline, it does fewer checks for array type and array element -type, and it checks for the row and column ranges only in debug mode. -@param mat Input matrix -@param row The zero-based index of row -@param col The zero-based index of column - */ -CV_INLINE double cvmGet( const CvMat* mat, int row, int col ) -{ - int type; - - type = CV_MAT_TYPE(mat->type); - assert( (unsigned)row < (unsigned)mat->rows && - (unsigned)col < (unsigned)mat->cols ); - - if( type == CV_32FC1 ) - return ((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col]; - else - { - assert( type == CV_64FC1 ); - return ((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col]; - } -} - -/** @brief Sets a specific element of a single-channel floating-point matrix. - -The function is a fast replacement for cvSetReal2D in the case of single-channel floating-point -matrices. It is faster because it is inline, it does fewer checks for array type and array element -type, and it checks for the row and column ranges only in debug mode. -@param mat The matrix -@param row The zero-based index of row -@param col The zero-based index of column -@param value The new value of the matrix element - */ -CV_INLINE void cvmSet( CvMat* mat, int row, int col, double value ) -{ - int type; - type = CV_MAT_TYPE(mat->type); - assert( (unsigned)row < (unsigned)mat->rows && - (unsigned)col < (unsigned)mat->cols ); - - if( type == CV_32FC1 ) - ((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = (float)value; - else - { - assert( type == CV_64FC1 ); - ((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = value; - } -} - - -CV_INLINE int cvIplDepth( int type ) -{ - int depth = CV_MAT_DEPTH(type); - return CV_ELEM_SIZE1(depth)*8 | (depth == CV_8S || depth == CV_16S || - depth == CV_32S ? IPL_DEPTH_SIGN : 0); -} - - -/****************************************************************************************\ -* Multi-dimensional dense array (CvMatND) * -\****************************************************************************************/ - -#define CV_MATND_MAGIC_VAL 0x42430000 -#define CV_TYPE_NAME_MATND "opencv-nd-matrix" - -#define CV_MAX_DIM 32 - -#ifdef __cplusplus -typedef struct CvMatND CvMatND; -CV_EXPORTS CvMatND cvMatND(const cv::Mat& m); -#endif - -/** - @deprecated consider using cv::Mat instead - */ -typedef struct -CvMatND -{ - int type; - int dims; - - int* refcount; - int hdr_refcount; - - union - { - uchar* ptr; - float* fl; - double* db; - int* i; - short* s; - } data; - - struct - { - int size; - int step; - } - dim[CV_MAX_DIM]; - -#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) - CvMatND() {} - CvMatND(const cv::Mat& m) { *this = cvMatND(m); } -#endif -} -CvMatND; - - -CV_INLINE CvMatND cvMatND() -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvMatND self = CV_STRUCT_INITIALIZER; return self; -#else - return CvMatND(); -#endif -} - -#define CV_IS_MATND_HDR(mat) \ - ((mat) != NULL && (((const CvMatND*)(mat))->type & CV_MAGIC_MASK) == CV_MATND_MAGIC_VAL) - -#define CV_IS_MATND(mat) \ - (CV_IS_MATND_HDR(mat) && ((const CvMatND*)(mat))->data.ptr != NULL) - - -/****************************************************************************************\ -* Multi-dimensional sparse array (CvSparseMat) * -\****************************************************************************************/ - -#define CV_SPARSE_MAT_MAGIC_VAL 0x42440000 -#define CV_TYPE_NAME_SPARSE_MAT "opencv-sparse-matrix" - -struct CvSet; - -typedef struct CvSparseMat -{ - int type; - int dims; - int* refcount; - int hdr_refcount; - - struct CvSet* heap; - void** hashtable; - int hashsize; - int valoffset; - int idxoffset; - int size[CV_MAX_DIM]; - -#ifdef __cplusplus - CV_EXPORTS void copyToSparseMat(cv::SparseMat& m) const; -#endif -} -CvSparseMat; - -#ifdef __cplusplus -CV_EXPORTS CvSparseMat* cvCreateSparseMat(const cv::SparseMat& m); -#endif - -#define CV_IS_SPARSE_MAT_HDR(mat) \ - ((mat) != NULL && \ - (((const CvSparseMat*)(mat))->type & CV_MAGIC_MASK) == CV_SPARSE_MAT_MAGIC_VAL) - -#define CV_IS_SPARSE_MAT(mat) \ - CV_IS_SPARSE_MAT_HDR(mat) - -/**************** iteration through a sparse array *****************/ - -typedef struct CvSparseNode -{ - unsigned hashval; - struct CvSparseNode* next; -} -CvSparseNode; - -typedef struct CvSparseMatIterator -{ - CvSparseMat* mat; - CvSparseNode* node; - int curidx; -} -CvSparseMatIterator; - -#define CV_NODE_VAL(mat,node) ((void*)((uchar*)(node) + (mat)->valoffset)) -#define CV_NODE_IDX(mat,node) ((int*)((uchar*)(node) + (mat)->idxoffset)) - -/****************************************************************************************\ -* Histogram * -\****************************************************************************************/ - -typedef int CvHistType; - -#define CV_HIST_MAGIC_VAL 0x42450000 -#define CV_HIST_UNIFORM_FLAG (1 << 10) - -/** indicates whether bin ranges are set already or not */ -#define CV_HIST_RANGES_FLAG (1 << 11) - -#define CV_HIST_ARRAY 0 -#define CV_HIST_SPARSE 1 -#define CV_HIST_TREE CV_HIST_SPARSE - -/** should be used as a parameter only, - it turns to CV_HIST_UNIFORM_FLAG of hist->type */ -#define CV_HIST_UNIFORM 1 - -typedef struct CvHistogram -{ - int type; - CvArr* bins; - float thresh[CV_MAX_DIM][2]; /**< For uniform histograms. */ - float** thresh2; /**< For non-uniform histograms. */ - CvMatND mat; /**< Embedded matrix header for array histograms. */ -} -CvHistogram; - -#define CV_IS_HIST( hist ) \ - ((hist) != NULL && \ - (((CvHistogram*)(hist))->type & CV_MAGIC_MASK) == CV_HIST_MAGIC_VAL && \ - (hist)->bins != NULL) - -#define CV_IS_UNIFORM_HIST( hist ) \ - (((hist)->type & CV_HIST_UNIFORM_FLAG) != 0) - -#define CV_IS_SPARSE_HIST( hist ) \ - CV_IS_SPARSE_MAT((hist)->bins) - -#define CV_HIST_HAS_RANGES( hist ) \ - (((hist)->type & CV_HIST_RANGES_FLAG) != 0) - -/****************************************************************************************\ -* Other supplementary data type definitions * -\****************************************************************************************/ - -/*************************************** CvRect *****************************************/ -/** @sa Rect_ */ -typedef struct CvRect -{ - int x; - int y; - int width; - int height; - -#ifdef CV__VALIDATE_UNUNITIALIZED_VARS - CvRect() __attribute__(( warning("Non-initialized variable") )) {}; - template CvRect(const std::initializer_list<_Tp> list) - { - CV_Assert(list.size() == 0 || list.size() == 4); - x = y = width = height = 0; - if (list.size() == 4) - { - x = list.begin()[0]; y = list.begin()[1]; width = list.begin()[2]; height = list.begin()[3]; - } - }; -#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) - CvRect(int _x = 0, int _y = 0, int w = 0, int h = 0): x(_x), y(_y), width(w), height(h) {} - template - CvRect(const cv::Rect_<_Tp>& r): x(cv::saturate_cast(r.x)), y(cv::saturate_cast(r.y)), width(cv::saturate_cast(r.width)), height(cv::saturate_cast(r.height)) {} -#endif -#ifdef __cplusplus - template - operator cv::Rect_<_Tp>() const { return cv::Rect_<_Tp>((_Tp)x, (_Tp)y, (_Tp)width, (_Tp)height); } -#endif -} -CvRect; - -/** constructs CvRect structure. */ -CV_INLINE CvRect cvRect( int x, int y, int width, int height ) -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvRect r = {x, y, width, height}; -#else - CvRect r(x, y , width, height); -#endif - return r; -} -#ifdef __cplusplus -CV_INLINE CvRect cvRect(const cv::Rect& rc) { return cvRect(rc.x, rc.y, rc.width, rc.height); } -#endif - -CV_INLINE IplROI cvRectToROI( CvRect rect, int coi ) -{ - IplROI roi; - roi.xOffset = rect.x; - roi.yOffset = rect.y; - roi.width = rect.width; - roi.height = rect.height; - roi.coi = coi; - - return roi; -} - - -CV_INLINE CvRect cvROIToRect( IplROI roi ) -{ - return cvRect( roi.xOffset, roi.yOffset, roi.width, roi.height ); -} - -/*********************************** CvTermCriteria *************************************/ - -#define CV_TERMCRIT_ITER 1 -#define CV_TERMCRIT_NUMBER CV_TERMCRIT_ITER -#define CV_TERMCRIT_EPS 2 - -/** @sa TermCriteria - */ -typedef struct CvTermCriteria -{ - int type; /**< may be combination of - CV_TERMCRIT_ITER - CV_TERMCRIT_EPS */ - int max_iter; - double epsilon; -#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) - CvTermCriteria(int _type = 0, int _iter = 0, double _eps = 0) : type(_type), max_iter(_iter), epsilon(_eps) {} - CvTermCriteria(const cv::TermCriteria& t) : type(t.type), max_iter(t.maxCount), epsilon(t.epsilon) {} -#endif -#ifdef __cplusplus - operator cv::TermCriteria() const { return cv::TermCriteria(type, max_iter, epsilon); } -#endif -} -CvTermCriteria; - -CV_INLINE CvTermCriteria cvTermCriteria( int type, int max_iter, double epsilon ) -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvTermCriteria t = { type, max_iter, (float)epsilon}; -#else - CvTermCriteria t(type, max_iter, epsilon); -#endif - return t; -} -#ifdef __cplusplus -CV_INLINE CvTermCriteria cvTermCriteria(const cv::TermCriteria& t) { return cvTermCriteria(t.type, t.maxCount, t.epsilon); } -#endif - - -/******************************* CvPoint and variants ***********************************/ - -typedef struct CvPoint -{ - int x; - int y; - -#ifdef CV__VALIDATE_UNUNITIALIZED_VARS - CvPoint() __attribute__(( warning("Non-initialized variable") )) {} - template CvPoint(const std::initializer_list<_Tp> list) - { - CV_Assert(list.size() == 0 || list.size() == 2); - x = y = 0; - if (list.size() == 2) - { - x = list.begin()[0]; y = list.begin()[1]; - } - }; -#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) - CvPoint(int _x = 0, int _y = 0): x(_x), y(_y) {} - template - CvPoint(const cv::Point_<_Tp>& pt): x((int)pt.x), y((int)pt.y) {} -#endif -#ifdef __cplusplus - template - operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); } -#endif -} -CvPoint; - -/** constructs CvPoint structure. */ -CV_INLINE CvPoint cvPoint( int x, int y ) -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvPoint p = {x, y}; -#else - CvPoint p(x, y); -#endif - return p; -} -#ifdef __cplusplus -CV_INLINE CvPoint cvPoint(const cv::Point& pt) { return cvPoint(pt.x, pt.y); } -#endif - -typedef struct CvPoint2D32f -{ - float x; - float y; - -#ifdef CV__VALIDATE_UNUNITIALIZED_VARS - CvPoint2D32f() __attribute__(( warning("Non-initialized variable") )) {} - template CvPoint2D32f(const std::initializer_list<_Tp> list) - { - CV_Assert(list.size() == 0 || list.size() == 2); - x = y = 0; - if (list.size() == 2) - { - x = list.begin()[0]; y = list.begin()[1]; - } - }; -#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) - CvPoint2D32f(float _x = 0, float _y = 0): x(_x), y(_y) {} - template - CvPoint2D32f(const cv::Point_<_Tp>& pt): x((float)pt.x), y((float)pt.y) {} -#endif -#ifdef __cplusplus - template - operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); } -#endif -} -CvPoint2D32f; - -/** constructs CvPoint2D32f structure. */ -CV_INLINE CvPoint2D32f cvPoint2D32f( double x, double y ) -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvPoint2D32f p = { (float)x, (float)y }; -#else - CvPoint2D32f p((float)x, (float)y); -#endif - return p; -} - -#ifdef __cplusplus -template -CvPoint2D32f cvPoint2D32f(const cv::Point_<_Tp>& pt) -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvPoint2D32f p = { (float)pt.x, (float)pt.y }; -#else - CvPoint2D32f p((float)pt.x, (float)pt.y); -#endif - return p; -} -#endif - -/** converts CvPoint to CvPoint2D32f. */ -CV_INLINE CvPoint2D32f cvPointTo32f( CvPoint point ) -{ - return cvPoint2D32f( (float)point.x, (float)point.y ); -} - -/** converts CvPoint2D32f to CvPoint. */ -CV_INLINE CvPoint cvPointFrom32f( CvPoint2D32f point ) -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvPoint ipt = { cvRound(point.x), cvRound(point.y) }; -#else - CvPoint ipt(cvRound(point.x), cvRound(point.y)); -#endif - return ipt; -} - - -typedef struct CvPoint3D32f -{ - float x; - float y; - float z; - -#ifdef CV__VALIDATE_UNUNITIALIZED_VARS - CvPoint3D32f() __attribute__(( warning("Non-initialized variable") )) {} - template CvPoint3D32f(const std::initializer_list<_Tp> list) - { - CV_Assert(list.size() == 0 || list.size() == 3); - x = y = z = 0; - if (list.size() == 3) - { - x = list.begin()[0]; y = list.begin()[1]; z = list.begin()[2]; - } - }; -#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) - CvPoint3D32f(float _x = 0, float _y = 0, float _z = 0): x(_x), y(_y), z(_z) {} - template - CvPoint3D32f(const cv::Point3_<_Tp>& pt): x((float)pt.x), y((float)pt.y), z((float)pt.z) {} -#endif -#ifdef __cplusplus - template - operator cv::Point3_<_Tp>() const { return cv::Point3_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y), cv::saturate_cast<_Tp>(z)); } -#endif -} -CvPoint3D32f; - -/** constructs CvPoint3D32f structure. */ -CV_INLINE CvPoint3D32f cvPoint3D32f( double x, double y, double z ) -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvPoint3D32f p = { (float)x, (float)y, (float)z }; -#else - CvPoint3D32f p((float)x, (float)y, (float)z); -#endif - return p; -} - -#ifdef __cplusplus -template -CvPoint3D32f cvPoint3D32f(const cv::Point3_<_Tp>& pt) -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvPoint3D32f p = { (float)pt.x, (float)pt.y, (float)pt.z }; -#else - CvPoint3D32f p((float)pt.x, (float)pt.y, (float)pt.z); -#endif - return p; -} -#endif - - -typedef struct CvPoint2D64f -{ - double x; - double y; -#ifdef CV__VALIDATE_UNUNITIALIZED_VARS - CvPoint2D64f() __attribute__(( warning("Non-initialized variable") )) {} - template CvPoint2D64f(const std::initializer_list<_Tp> list) - { - CV_Assert(list.size() == 0 || list.size() == 2); - x = y = 0; - if (list.size() == 2) - { - x = list.begin()[0]; y = list.begin()[1]; - } - }; -#endif -} -CvPoint2D64f; - -/** constructs CvPoint2D64f structure.*/ -CV_INLINE CvPoint2D64f cvPoint2D64f( double x, double y ) -{ - CvPoint2D64f p = { x, y }; - return p; -} - - -typedef struct CvPoint3D64f -{ - double x; - double y; - double z; -#ifdef CV__VALIDATE_UNUNITIALIZED_VARS - CvPoint3D64f() __attribute__(( warning("Non-initialized variable") )) {} - template CvPoint3D64f(const std::initializer_list<_Tp> list) - { - CV_Assert(list.size() == 0 || list.size() == 3); - x = y = z = 0; - if (list.size() == 3) - { - x = list.begin()[0]; y = list.begin()[1]; z = list.begin()[2]; - } - }; -#endif -} -CvPoint3D64f; - -/** constructs CvPoint3D64f structure. */ -CV_INLINE CvPoint3D64f cvPoint3D64f( double x, double y, double z ) -{ - CvPoint3D64f p = { x, y, z }; - return p; -} - - -/******************************** CvSize's & CvBox **************************************/ - -typedef struct CvSize -{ - int width; - int height; - -#ifdef CV__VALIDATE_UNUNITIALIZED_VARS - CvSize() __attribute__(( warning("Non-initialized variable") )) {} - template CvSize(const std::initializer_list<_Tp> list) - { - CV_Assert(list.size() == 0 || list.size() == 2); - width = 0; height = 0; - if (list.size() == 2) - { - width = list.begin()[0]; height = list.begin()[1]; - } - }; -#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) - CvSize(int w = 0, int h = 0): width(w), height(h) {} - template - CvSize(const cv::Size_<_Tp>& sz): width(cv::saturate_cast(sz.width)), height(cv::saturate_cast(sz.height)) {} -#endif -#ifdef __cplusplus - template - operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); } -#endif -} -CvSize; - -/** constructs CvSize structure. */ -CV_INLINE CvSize cvSize( int width, int height ) -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvSize s = { width, height }; -#else - CvSize s(width, height); -#endif - return s; -} - -#ifdef __cplusplus -CV_INLINE CvSize cvSize(const cv::Size& sz) -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvSize s = { sz.width, sz.height }; -#else - CvSize s(sz.width, sz.height); -#endif - return s; -} -#endif - -typedef struct CvSize2D32f -{ - float width; - float height; - -#ifdef CV__VALIDATE_UNUNITIALIZED_VARS - CvSize2D32f() __attribute__(( warning("Non-initialized variable") )) {} - template CvSize2D32f(const std::initializer_list<_Tp> list) - { - CV_Assert(list.size() == 0 || list.size() == 2); - width = 0; height = 0; - if (list.size() == 2) - { - width = list.begin()[0]; height = list.begin()[1]; - } - }; -#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) - CvSize2D32f(float w = 0, float h = 0): width(w), height(h) {} - template - CvSize2D32f(const cv::Size_<_Tp>& sz): width(cv::saturate_cast(sz.width)), height(cv::saturate_cast(sz.height)) {} -#endif -#ifdef __cplusplus - template - operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); } -#endif -} -CvSize2D32f; - -/** constructs CvSize2D32f structure. */ -CV_INLINE CvSize2D32f cvSize2D32f( double width, double height ) -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvSize2D32f s = { (float)width, (float)height }; -#else - CvSize2D32f s((float)width, (float)height); -#endif - return s; -} -#ifdef __cplusplus -template -CvSize2D32f cvSize2D32f(const cv::Size_<_Tp>& sz) -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvSize2D32f s = { (float)sz.width, (float)sz.height }; -#else - CvSize2D32f s((float)sz.width, (float)sz.height); -#endif - return s; -} -#endif - -/** @sa RotatedRect - */ -typedef struct CvBox2D -{ - CvPoint2D32f center; /**< Center of the box. */ - CvSize2D32f size; /**< Box width and length. */ - float angle; /**< Angle between the horizontal axis */ - /**< and the first side (i.e. length) in degrees */ - -#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) - CvBox2D(CvPoint2D32f c = CvPoint2D32f(), CvSize2D32f s = CvSize2D32f(), float a = 0) : center(c), size(s), angle(a) {} - CvBox2D(const cv::RotatedRect& rr) : center(rr.center), size(rr.size), angle(rr.angle) {} -#endif -#ifdef __cplusplus - operator cv::RotatedRect() const { return cv::RotatedRect(center, size, angle); } -#endif -} -CvBox2D; - - -#ifdef __cplusplus -CV_INLINE CvBox2D cvBox2D(CvPoint2D32f c = CvPoint2D32f(), CvSize2D32f s = CvSize2D32f(), float a = 0) -{ - CvBox2D self; - self.center = c; - self.size = s; - self.angle = a; - return self; -} -CV_INLINE CvBox2D cvBox2D(const cv::RotatedRect& rr) -{ - CvBox2D self; - self.center = cvPoint2D32f(rr.center); - self.size = cvSize2D32f(rr.size); - self.angle = rr.angle; - return self; -} -#endif - - -/** Line iterator state: */ -typedef struct CvLineIterator -{ - /** Pointer to the current point: */ - uchar* ptr; - - /* Bresenham algorithm state: */ - int err; - int plus_delta; - int minus_delta; - int plus_step; - int minus_step; -} -CvLineIterator; - - - -/************************************* CvSlice ******************************************/ -#define CV_WHOLE_SEQ_END_INDEX 0x3fffffff -#define CV_WHOLE_SEQ cvSlice(0, CV_WHOLE_SEQ_END_INDEX) - -typedef struct CvSlice -{ - int start_index, end_index; - -#ifdef CV__VALIDATE_UNUNITIALIZED_VARS - CvSlice() __attribute__(( warning("Non-initialized variable") )) {} - template CvSlice(const std::initializer_list<_Tp> list) - { - CV_Assert(list.size() == 0 || list.size() == 2); - start_index = end_index = 0; - if (list.size() == 2) - { - start_index = list.begin()[0]; end_index = list.begin()[1]; - } - }; -#endif -#if defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) && !defined(__CUDACC__) - CvSlice(int start = 0, int end = 0) : start_index(start), end_index(end) {} - CvSlice(const cv::Range& r) { *this = (r.start != INT_MIN && r.end != INT_MAX) ? CvSlice(r.start, r.end) : CvSlice(0, CV_WHOLE_SEQ_END_INDEX); } - operator cv::Range() const { return (start_index == 0 && end_index == CV_WHOLE_SEQ_END_INDEX ) ? cv::Range::all() : cv::Range(start_index, end_index); } -#endif -} -CvSlice; - -CV_INLINE CvSlice cvSlice( int start, int end ) -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) && !defined(__CUDACC__)) - CvSlice slice = { start, end }; -#else - CvSlice slice(start, end); -#endif - return slice; -} - -#if defined(__cplusplus) -CV_INLINE CvSlice cvSlice(const cv::Range& r) -{ - CvSlice slice = (r.start != INT_MIN && r.end != INT_MAX) ? cvSlice(r.start, r.end) : cvSlice(0, CV_WHOLE_SEQ_END_INDEX); - return slice; -} -#endif - - -/************************************* CvScalar *****************************************/ -/** @sa Scalar_ - */ -typedef struct CvScalar -{ - double val[4]; - -#ifdef CV__VALIDATE_UNUNITIALIZED_VARS - CvScalar() __attribute__(( warning("Non-initialized variable") )) {} - CvScalar(const std::initializer_list list) - { - CV_Assert(list.size() == 0 || list.size() == 4); - val[0] = val[1] = val[2] = val[3] = 0; - if (list.size() == 4) - { - val[0] = list.begin()[0]; val[1] = list.begin()[1]; val[2] = list.begin()[2]; val[3] = list.begin()[3]; - } - }; -#elif defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus) - CvScalar() {} - CvScalar(double d0, double d1 = 0, double d2 = 0, double d3 = 0) { val[0] = d0; val[1] = d1; val[2] = d2; val[3] = d3; } - template - CvScalar(const cv::Scalar_<_Tp>& s) { val[0] = s.val[0]; val[1] = s.val[1]; val[2] = s.val[2]; val[3] = s.val[3]; } - template - CvScalar(const cv::Vec<_Tp, cn>& v) - { - int i; - for( i = 0; i < (cn < 4 ? cn : 4); i++ ) val[i] = v.val[i]; - for( ; i < 4; i++ ) val[i] = 0; - } -#endif -#ifdef __cplusplus - template - operator cv::Scalar_<_Tp>() const { return cv::Scalar_<_Tp>(cv::saturate_cast<_Tp>(val[0]), cv::saturate_cast<_Tp>(val[1]), cv::saturate_cast<_Tp>(val[2]), cv::saturate_cast<_Tp>(val[3])); } -#endif -} -CvScalar; - -CV_INLINE CvScalar cvScalar( double val0, double val1 CV_DEFAULT(0), - double val2 CV_DEFAULT(0), double val3 CV_DEFAULT(0)) -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvScalar scalar = CV_STRUCT_INITIALIZER; -#else - CvScalar scalar; -#endif - scalar.val[0] = val0; scalar.val[1] = val1; - scalar.val[2] = val2; scalar.val[3] = val3; - return scalar; -} - -#ifdef __cplusplus -CV_INLINE CvScalar cvScalar() -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvScalar scalar = CV_STRUCT_INITIALIZER; -#else - CvScalar scalar; -#endif - scalar.val[0] = scalar.val[1] = scalar.val[2] = scalar.val[3] = 0; - return scalar; -} -CV_INLINE CvScalar cvScalar(const cv::Scalar& s) -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvScalar scalar = CV_STRUCT_INITIALIZER; -#else - CvScalar scalar; -#endif - scalar.val[0] = s.val[0]; - scalar.val[1] = s.val[1]; - scalar.val[2] = s.val[2]; - scalar.val[3] = s.val[3]; - return scalar; -} -#endif - -CV_INLINE CvScalar cvRealScalar( double val0 ) -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvScalar scalar = CV_STRUCT_INITIALIZER; -#else - CvScalar scalar; -#endif - scalar.val[0] = val0; - scalar.val[1] = scalar.val[2] = scalar.val[3] = 0; - return scalar; -} - -CV_INLINE CvScalar cvScalarAll( double val0123 ) -{ -#if !(defined(CV__ENABLE_C_API_CTORS) && defined(__cplusplus)) - CvScalar scalar = CV_STRUCT_INITIALIZER; -#else - CvScalar scalar; -#endif - scalar.val[0] = val0123; - scalar.val[1] = val0123; - scalar.val[2] = val0123; - scalar.val[3] = val0123; - return scalar; -} - -/****************************************************************************************\ -* Dynamic Data structures * -\****************************************************************************************/ - -/******************************** Memory storage ****************************************/ - -typedef struct CvMemBlock -{ - struct CvMemBlock* prev; - struct CvMemBlock* next; -} -CvMemBlock; - -#define CV_STORAGE_MAGIC_VAL 0x42890000 - -typedef struct CvMemStorage -{ - int signature; - CvMemBlock* bottom; /**< First allocated block. */ - CvMemBlock* top; /**< Current memory block - top of the stack. */ - struct CvMemStorage* parent; /**< We get new blocks from parent as needed. */ - int block_size; /**< Block size. */ - int free_space; /**< Remaining free space in current block. */ -} -CvMemStorage; - -#define CV_IS_STORAGE(storage) \ - ((storage) != NULL && \ - (((CvMemStorage*)(storage))->signature & CV_MAGIC_MASK) == CV_STORAGE_MAGIC_VAL) - - -typedef struct CvMemStoragePos -{ - CvMemBlock* top; - int free_space; -} -CvMemStoragePos; - - -/*********************************** Sequence *******************************************/ - -typedef struct CvSeqBlock -{ - struct CvSeqBlock* prev; /**< Previous sequence block. */ - struct CvSeqBlock* next; /**< Next sequence block. */ - int start_index; /**< Index of the first element in the block + */ - /**< sequence->first->start_index. */ - int count; /**< Number of elements in the block. */ - schar* data; /**< Pointer to the first element of the block. */ -} -CvSeqBlock; - - -#define CV_TREE_NODE_FIELDS(node_type) \ - int flags; /**< Miscellaneous flags. */ \ - int header_size; /**< Size of sequence header. */ \ - struct node_type* h_prev; /**< Previous sequence. */ \ - struct node_type* h_next; /**< Next sequence. */ \ - struct node_type* v_prev; /**< 2nd previous sequence. */ \ - struct node_type* v_next /**< 2nd next sequence. */ - -/** - Read/Write sequence. - Elements can be dynamically inserted to or deleted from the sequence. -*/ -#define CV_SEQUENCE_FIELDS() \ - CV_TREE_NODE_FIELDS(CvSeq); \ - int total; /**< Total number of elements. */ \ - int elem_size; /**< Size of sequence element in bytes. */ \ - schar* block_max; /**< Maximal bound of the last block. */ \ - schar* ptr; /**< Current write pointer. */ \ - int delta_elems; /**< Grow seq this many at a time. */ \ - CvMemStorage* storage; /**< Where the seq is stored. */ \ - CvSeqBlock* free_blocks; /**< Free blocks list. */ \ - CvSeqBlock* first; /**< Pointer to the first sequence block. */ - -typedef struct CvSeq -{ - CV_SEQUENCE_FIELDS() -} -CvSeq; - -#define CV_TYPE_NAME_SEQ "opencv-sequence" -#define CV_TYPE_NAME_SEQ_TREE "opencv-sequence-tree" - -/*************************************** Set ********************************************/ -/** @brief Set - Order is not preserved. There can be gaps between sequence elements. - After the element has been inserted it stays in the same place all the time. - The MSB(most-significant or sign bit) of the first field (flags) is 0 iff the element exists. -*/ -#define CV_SET_ELEM_FIELDS(elem_type) \ - int flags; \ - struct elem_type* next_free; - -typedef struct CvSetElem -{ - CV_SET_ELEM_FIELDS(CvSetElem) -} -CvSetElem; - -#define CV_SET_FIELDS() \ - CV_SEQUENCE_FIELDS() \ - CvSetElem* free_elems; \ - int active_count; - -typedef struct CvSet -{ - CV_SET_FIELDS() -} -CvSet; - - -#define CV_SET_ELEM_IDX_MASK ((1 << 26) - 1) -#define CV_SET_ELEM_FREE_FLAG (1 << (sizeof(int)*8-1)) - -/** Checks whether the element pointed by ptr belongs to a set or not */ -#define CV_IS_SET_ELEM( ptr ) (((CvSetElem*)(ptr))->flags >= 0) - -/************************************* Graph ********************************************/ - -/** @name Graph - -We represent a graph as a set of vertices. Vertices contain their adjacency lists (more exactly, -pointers to first incoming or outcoming edge (or 0 if isolated vertex)). Edges are stored in -another set. There is a singly-linked list of incoming/outcoming edges for each vertex. - -Each edge consists of: - -- Two pointers to the starting and ending vertices (vtx[0] and vtx[1] respectively). - - A graph may be oriented or not. In the latter case, edges between vertex i to vertex j are not -distinguished during search operations. - -- Two pointers to next edges for the starting and ending vertices, where next[0] points to the -next edge in the vtx[0] adjacency list and next[1] points to the next edge in the vtx[1] -adjacency list. - -@see CvGraphEdge, CvGraphVtx, CvGraphVtx2D, CvGraph -@{ -*/ -#define CV_GRAPH_EDGE_FIELDS() \ - int flags; \ - float weight; \ - struct CvGraphEdge* next[2]; \ - struct CvGraphVtx* vtx[2]; - - -#define CV_GRAPH_VERTEX_FIELDS() \ - int flags; \ - struct CvGraphEdge* first; - - -typedef struct CvGraphEdge -{ - CV_GRAPH_EDGE_FIELDS() -} -CvGraphEdge; - -typedef struct CvGraphVtx -{ - CV_GRAPH_VERTEX_FIELDS() -} -CvGraphVtx; - -typedef struct CvGraphVtx2D -{ - CV_GRAPH_VERTEX_FIELDS() - CvPoint2D32f* ptr; -} -CvGraphVtx2D; - -/** - Graph is "derived" from the set (this is set a of vertices) - and includes another set (edges) -*/ -#define CV_GRAPH_FIELDS() \ - CV_SET_FIELDS() \ - CvSet* edges; - -typedef struct CvGraph -{ - CV_GRAPH_FIELDS() -} -CvGraph; - -#define CV_TYPE_NAME_GRAPH "opencv-graph" - -/** @} */ - -/*********************************** Chain/Contour *************************************/ - -typedef struct CvChain -{ - CV_SEQUENCE_FIELDS() - CvPoint origin; -} -CvChain; - -#define CV_CONTOUR_FIELDS() \ - CV_SEQUENCE_FIELDS() \ - CvRect rect; \ - int color; \ - int reserved[3]; - -typedef struct CvContour -{ - CV_CONTOUR_FIELDS() -} -CvContour; - -typedef CvContour CvPoint2DSeq; - -/****************************************************************************************\ -* Sequence types * -\****************************************************************************************/ - -#define CV_SEQ_MAGIC_VAL 0x42990000 - -#define CV_IS_SEQ(seq) \ - ((seq) != NULL && (((CvSeq*)(seq))->flags & CV_MAGIC_MASK) == CV_SEQ_MAGIC_VAL) - -#define CV_SET_MAGIC_VAL 0x42980000 -#define CV_IS_SET(set) \ - ((set) != NULL && (((CvSeq*)(set))->flags & CV_MAGIC_MASK) == CV_SET_MAGIC_VAL) - -#define CV_SEQ_ELTYPE_BITS 12 -#define CV_SEQ_ELTYPE_MASK ((1 << CV_SEQ_ELTYPE_BITS) - 1) - -#define CV_SEQ_ELTYPE_POINT CV_32SC2 /**< (x,y) */ -#define CV_SEQ_ELTYPE_CODE CV_8UC1 /**< freeman code: 0..7 */ -#define CV_SEQ_ELTYPE_GENERIC 0 -#define CV_SEQ_ELTYPE_PTR CV_USRTYPE1 -#define CV_SEQ_ELTYPE_PPOINT CV_SEQ_ELTYPE_PTR /**< &(x,y) */ -#define CV_SEQ_ELTYPE_INDEX CV_32SC1 /**< #(x,y) */ -#define CV_SEQ_ELTYPE_GRAPH_EDGE 0 /**< &next_o, &next_d, &vtx_o, &vtx_d */ -#define CV_SEQ_ELTYPE_GRAPH_VERTEX 0 /**< first_edge, &(x,y) */ -#define CV_SEQ_ELTYPE_TRIAN_ATR 0 /**< vertex of the binary tree */ -#define CV_SEQ_ELTYPE_CONNECTED_COMP 0 /**< connected component */ -#define CV_SEQ_ELTYPE_POINT3D CV_32FC3 /**< (x,y,z) */ - -#define CV_SEQ_KIND_BITS 2 -#define CV_SEQ_KIND_MASK (((1 << CV_SEQ_KIND_BITS) - 1)<flags & CV_SEQ_ELTYPE_MASK) -#define CV_SEQ_KIND( seq ) ((seq)->flags & CV_SEQ_KIND_MASK ) - -/** flag checking */ -#define CV_IS_SEQ_INDEX( seq ) ((CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_INDEX) && \ - (CV_SEQ_KIND(seq) == CV_SEQ_KIND_GENERIC)) - -#define CV_IS_SEQ_CURVE( seq ) (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE) -#define CV_IS_SEQ_CLOSED( seq ) (((seq)->flags & CV_SEQ_FLAG_CLOSED) != 0) -#define CV_IS_SEQ_CONVEX( seq ) 0 -#define CV_IS_SEQ_HOLE( seq ) (((seq)->flags & CV_SEQ_FLAG_HOLE) != 0) -#define CV_IS_SEQ_SIMPLE( seq ) 1 - -/** type checking macros */ -#define CV_IS_SEQ_POINT_SET( seq ) \ - ((CV_SEQ_ELTYPE(seq) == CV_32SC2 || CV_SEQ_ELTYPE(seq) == CV_32FC2)) - -#define CV_IS_SEQ_POINT_SUBSET( seq ) \ - (CV_IS_SEQ_INDEX( seq ) || CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_PPOINT) - -#define CV_IS_SEQ_POLYLINE( seq ) \ - (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && CV_IS_SEQ_POINT_SET(seq)) - -#define CV_IS_SEQ_POLYGON( seq ) \ - (CV_IS_SEQ_POLYLINE(seq) && CV_IS_SEQ_CLOSED(seq)) - -#define CV_IS_SEQ_CHAIN( seq ) \ - (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && (seq)->elem_size == 1) - -#define CV_IS_SEQ_CONTOUR( seq ) \ - (CV_IS_SEQ_CLOSED(seq) && (CV_IS_SEQ_POLYLINE(seq) || CV_IS_SEQ_CHAIN(seq))) - -#define CV_IS_SEQ_CHAIN_CONTOUR( seq ) \ - (CV_IS_SEQ_CHAIN( seq ) && CV_IS_SEQ_CLOSED( seq )) - -#define CV_IS_SEQ_POLYGON_TREE( seq ) \ - (CV_SEQ_ELTYPE (seq) == CV_SEQ_ELTYPE_TRIAN_ATR && \ - CV_SEQ_KIND( seq ) == CV_SEQ_KIND_BIN_TREE ) - -#define CV_IS_GRAPH( seq ) \ - (CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_GRAPH) - -#define CV_IS_GRAPH_ORIENTED( seq ) \ - (((seq)->flags & CV_GRAPH_FLAG_ORIENTED) != 0) - -#define CV_IS_SUBDIV2D( seq ) \ - (CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_SUBDIV2D) - -/****************************************************************************************/ -/* Sequence writer & reader */ -/****************************************************************************************/ - -#define CV_SEQ_WRITER_FIELDS() \ - int header_size; \ - CvSeq* seq; /**< the sequence written */ \ - CvSeqBlock* block; /**< current block */ \ - schar* ptr; /**< pointer to free space */ \ - schar* block_min; /**< pointer to the beginning of block*/\ - schar* block_max; /**< pointer to the end of block */ - -typedef struct CvSeqWriter -{ - CV_SEQ_WRITER_FIELDS() -} -CvSeqWriter; - - -#define CV_SEQ_READER_FIELDS() \ - int header_size; \ - CvSeq* seq; /**< sequence, beign read */ \ - CvSeqBlock* block; /**< current block */ \ - schar* ptr; /**< pointer to element be read next */ \ - schar* block_min; /**< pointer to the beginning of block */\ - schar* block_max; /**< pointer to the end of block */ \ - int delta_index;/**< = seq->first->start_index */ \ - schar* prev_elem; /**< pointer to previous element */ - -typedef struct CvSeqReader -{ - CV_SEQ_READER_FIELDS() -} -CvSeqReader; - -/****************************************************************************************/ -/* Operations on sequences */ -/****************************************************************************************/ - -#define CV_SEQ_ELEM( seq, elem_type, index ) \ -/** assert gives some guarantee that parameter is valid */ \ -( assert(sizeof((seq)->first[0]) == sizeof(CvSeqBlock) && \ - (seq)->elem_size == sizeof(elem_type)), \ - (elem_type*)((seq)->first && (unsigned)index < \ - (unsigned)((seq)->first->count) ? \ - (seq)->first->data + (index) * sizeof(elem_type) : \ - cvGetSeqElem( (CvSeq*)(seq), (index) ))) -#define CV_GET_SEQ_ELEM( elem_type, seq, index ) CV_SEQ_ELEM( (seq), elem_type, (index) ) - -/** Add element to sequence: */ -#define CV_WRITE_SEQ_ELEM_VAR( elem_ptr, writer ) \ -{ \ - if( (writer).ptr >= (writer).block_max ) \ - { \ - cvCreateSeqBlock( &writer); \ - } \ - memcpy((writer).ptr, elem_ptr, (writer).seq->elem_size);\ - (writer).ptr += (writer).seq->elem_size; \ -} - -#define CV_WRITE_SEQ_ELEM( elem, writer ) \ -{ \ - assert( (writer).seq->elem_size == sizeof(elem)); \ - if( (writer).ptr >= (writer).block_max ) \ - { \ - cvCreateSeqBlock( &writer); \ - } \ - assert( (writer).ptr <= (writer).block_max - sizeof(elem));\ - memcpy((writer).ptr, &(elem), sizeof(elem)); \ - (writer).ptr += sizeof(elem); \ -} - - -/** Move reader position forward: */ -#define CV_NEXT_SEQ_ELEM( elem_size, reader ) \ -{ \ - if( ((reader).ptr += (elem_size)) >= (reader).block_max ) \ - { \ - cvChangeSeqBlock( &(reader), 1 ); \ - } \ -} - - -/** Move reader position backward: */ -#define CV_PREV_SEQ_ELEM( elem_size, reader ) \ -{ \ - if( ((reader).ptr -= (elem_size)) < (reader).block_min ) \ - { \ - cvChangeSeqBlock( &(reader), -1 ); \ - } \ -} - -/** Read element and move read position forward: */ -#define CV_READ_SEQ_ELEM( elem, reader ) \ -{ \ - assert( (reader).seq->elem_size == sizeof(elem)); \ - memcpy( &(elem), (reader).ptr, sizeof((elem))); \ - CV_NEXT_SEQ_ELEM( sizeof(elem), reader ) \ -} - -/** Read element and move read position backward: */ -#define CV_REV_READ_SEQ_ELEM( elem, reader ) \ -{ \ - assert( (reader).seq->elem_size == sizeof(elem)); \ - memcpy(&(elem), (reader).ptr, sizeof((elem))); \ - CV_PREV_SEQ_ELEM( sizeof(elem), reader ) \ -} - - -#define CV_READ_CHAIN_POINT( _pt, reader ) \ -{ \ - (_pt) = (reader).pt; \ - if( (reader).ptr ) \ - { \ - CV_READ_SEQ_ELEM( (reader).code, (reader)); \ - assert( ((reader).code & ~7) == 0 ); \ - (reader).pt.x += (reader).deltas[(int)(reader).code][0]; \ - (reader).pt.y += (reader).deltas[(int)(reader).code][1]; \ - } \ -} - -#define CV_CURRENT_POINT( reader ) (*((CvPoint*)((reader).ptr))) -#define CV_PREV_POINT( reader ) (*((CvPoint*)((reader).prev_elem))) - -#define CV_READ_EDGE( pt1, pt2, reader ) \ -{ \ - assert( sizeof(pt1) == sizeof(CvPoint) && \ - sizeof(pt2) == sizeof(CvPoint) && \ - reader.seq->elem_size == sizeof(CvPoint)); \ - (pt1) = CV_PREV_POINT( reader ); \ - (pt2) = CV_CURRENT_POINT( reader ); \ - (reader).prev_elem = (reader).ptr; \ - CV_NEXT_SEQ_ELEM( sizeof(CvPoint), (reader)); \ -} - -/************ Graph macros ************/ - -/** Return next graph edge for given vertex: */ -#define CV_NEXT_GRAPH_EDGE( edge, vertex ) \ - (assert((edge)->vtx[0] == (vertex) || (edge)->vtx[1] == (vertex)), \ - (edge)->next[(edge)->vtx[1] == (vertex)]) - - - -/****************************************************************************************\ -* Data structures for persistence (a.k.a serialization) functionality * -\****************************************************************************************/ - -/** "black box" file storage */ -typedef struct CvFileStorage CvFileStorage; - -/** Storage flags: */ -#define CV_STORAGE_READ 0 -#define CV_STORAGE_WRITE 1 -#define CV_STORAGE_WRITE_TEXT CV_STORAGE_WRITE -#define CV_STORAGE_WRITE_BINARY CV_STORAGE_WRITE -#define CV_STORAGE_APPEND 2 -#define CV_STORAGE_MEMORY 4 -#define CV_STORAGE_FORMAT_MASK (7<<3) -#define CV_STORAGE_FORMAT_AUTO 0 -#define CV_STORAGE_FORMAT_XML 8 -#define CV_STORAGE_FORMAT_YAML 16 -#define CV_STORAGE_FORMAT_JSON 24 -#define CV_STORAGE_BASE64 64 -#define CV_STORAGE_WRITE_BASE64 (CV_STORAGE_BASE64 | CV_STORAGE_WRITE) - -/** @brief List of attributes. : - -In the current implementation, attributes are used to pass extra parameters when writing user -objects (see cvWrite). XML attributes inside tags are not supported, aside from the object type -specification (type_id attribute). -@see cvAttrList, cvAttrValue - */ -typedef struct CvAttrList -{ - const char** attr; /**< NULL-terminated array of (attribute_name,attribute_value) pairs. */ - struct CvAttrList* next; /**< Pointer to next chunk of the attributes list. */ -} -CvAttrList; - -/** initializes CvAttrList structure */ -CV_INLINE CvAttrList cvAttrList( const char** attr CV_DEFAULT(NULL), - CvAttrList* next CV_DEFAULT(NULL) ) -{ - CvAttrList l; - l.attr = attr; - l.next = next; - - return l; -} - -struct CvTypeInfo; - -#define CV_NODE_NONE 0 -#define CV_NODE_INT 1 -#define CV_NODE_INTEGER CV_NODE_INT -#define CV_NODE_REAL 2 -#define CV_NODE_FLOAT CV_NODE_REAL -#define CV_NODE_STR 3 -#define CV_NODE_STRING CV_NODE_STR -#define CV_NODE_REF 4 /**< not used */ -#define CV_NODE_SEQ 5 -#define CV_NODE_MAP 6 -#define CV_NODE_TYPE_MASK 7 - -#define CV_NODE_TYPE(flags) ((flags) & CV_NODE_TYPE_MASK) - -/** file node flags */ -#define CV_NODE_FLOW 8 /**= CV_NODE_SEQ) -#define CV_NODE_IS_FLOW(flags) (((flags) & CV_NODE_FLOW) != 0) -#define CV_NODE_IS_EMPTY(flags) (((flags) & CV_NODE_EMPTY) != 0) -#define CV_NODE_IS_USER(flags) (((flags) & CV_NODE_USER) != 0) -#define CV_NODE_HAS_NAME(flags) (((flags) & CV_NODE_NAMED) != 0) - -#define CV_NODE_SEQ_SIMPLE 256 -#define CV_NODE_SEQ_IS_SIMPLE(seq) (((seq)->flags & CV_NODE_SEQ_SIMPLE) != 0) - -typedef struct CvString -{ - int len; - char* ptr; -} -CvString; - -/** All the keys (names) of elements in the read file storage - are stored in the hash to speed up the lookup operations: */ -typedef struct CvStringHashNode -{ - unsigned hashval; - CvString str; - struct CvStringHashNode* next; -} -CvStringHashNode; - -typedef struct CvGenericHash CvFileNodeHash; - -/** Basic element of the file storage - scalar or collection: */ -typedef struct CvFileNode -{ - int tag; - struct CvTypeInfo* info; /**< type information - (only for user-defined object, for others it is 0) */ - union - { - double f; /**< scalar floating-point number */ - int i; /**< scalar integer number */ - CvString str; /**< text string */ - CvSeq* seq; /**< sequence (ordered collection of file nodes) */ - CvFileNodeHash* map; /**< map (collection of named file nodes) */ - } data; -} -CvFileNode; - -#ifdef __cplusplus -extern "C" { -#endif -typedef int (CV_CDECL *CvIsInstanceFunc)( const void* struct_ptr ); -typedef void (CV_CDECL *CvReleaseFunc)( void** struct_dblptr ); -typedef void* (CV_CDECL *CvReadFunc)( CvFileStorage* storage, CvFileNode* node ); -typedef void (CV_CDECL *CvWriteFunc)( CvFileStorage* storage, const char* name, - const void* struct_ptr, CvAttrList attributes ); -typedef void* (CV_CDECL *CvCloneFunc)( const void* struct_ptr ); -#ifdef __cplusplus -} -#endif - -/** @brief Type information - -The structure contains information about one of the standard or user-defined types. Instances of the -type may or may not contain a pointer to the corresponding CvTypeInfo structure. In any case, there -is a way to find the type info structure for a given object using the cvTypeOf function. -Alternatively, type info can be found by type name using cvFindType, which is used when an object -is read from file storage. The user can register a new type with cvRegisterType that adds the type -information structure into the beginning of the type list. Thus, it is possible to create -specialized types from generic standard types and override the basic methods. - */ -typedef struct CvTypeInfo -{ - int flags; /**< not used */ - int header_size; /**< sizeof(CvTypeInfo) */ - struct CvTypeInfo* prev; /**< previous registered type in the list */ - struct CvTypeInfo* next; /**< next registered type in the list */ - const char* type_name; /**< type name, written to file storage */ - CvIsInstanceFunc is_instance; /**< checks if the passed object belongs to the type */ - CvReleaseFunc release; /**< releases object (memory etc.) */ - CvReadFunc read; /**< reads object from file storage */ - CvWriteFunc write; /**< writes object to file storage */ - CvCloneFunc clone; /**< creates a copy of the object */ -} -CvTypeInfo; - - -/**** System data types ******/ - -typedef struct CvPluginFuncInfo -{ - void** func_addr; - void* default_func_addr; - const char* func_names; - int search_modules; - int loaded_from; -} -CvPluginFuncInfo; - -typedef struct CvModuleInfo -{ - struct CvModuleInfo* next; - const char* name; - const char* version; - CvPluginFuncInfo* func_tab; -} -CvModuleInfo; - -/** @} */ - -#endif /*OPENCV_CORE_TYPES_H*/ - -/* End of file. */ diff --git a/include/opencv2/core/utility.hpp b/include/opencv2/core/utility.hpp deleted file mode 100755 index b97458d..0000000 --- a/include/opencv2/core/utility.hpp +++ /dev/null @@ -1,1237 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Copyright (C) 2015, Itseez Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_CORE_UTILITY_H -#define OPENCV_CORE_UTILITY_H - -#ifndef __cplusplus -# error utility.hpp header must be compiled as C++ -#endif - -#if defined(check) -# warning Detected Apple 'check' macro definition, it can cause build conflicts. Please, include this header before any Apple headers. -#endif - -#include "opencv2/core.hpp" -#include - -#ifdef CV_CXX11 -#include -#endif - -namespace cv -{ - -//! @addtogroup core_utils -//! @{ - -/** @brief Automatically Allocated Buffer Class - - The class is used for temporary buffers in functions and methods. - If a temporary buffer is usually small (a few K's of memory), - but its size depends on the parameters, it makes sense to create a small - fixed-size array on stack and use it if it's large enough. If the required buffer size - is larger than the fixed size, another buffer of sufficient size is allocated dynamically - and released after the processing. Therefore, in typical cases, when the buffer size is small, - there is no overhead associated with malloc()/free(). - At the same time, there is no limit on the size of processed data. - - This is what AutoBuffer does. The template takes 2 parameters - type of the buffer elements and - the number of stack-allocated elements. Here is how the class is used: - - \code - void my_func(const cv::Mat& m) - { - cv::AutoBuffer buf(1000); // create automatic buffer containing 1000 floats - - buf.allocate(m.rows); // if m.rows <= 1000, the pre-allocated buffer is used, - // otherwise the buffer of "m.rows" floats will be allocated - // dynamically and deallocated in cv::AutoBuffer destructor - ... - } - \endcode -*/ -#ifdef OPENCV_ENABLE_MEMORY_SANITIZER -template class AutoBuffer -#else -template class AutoBuffer -#endif -{ -public: - typedef _Tp value_type; - - //! the default constructor - AutoBuffer(); - //! constructor taking the real buffer size - explicit AutoBuffer(size_t _size); - - //! the copy constructor - AutoBuffer(const AutoBuffer<_Tp, fixed_size>& buf); - //! the assignment operator - AutoBuffer<_Tp, fixed_size>& operator = (const AutoBuffer<_Tp, fixed_size>& buf); - - //! destructor. calls deallocate() - ~AutoBuffer(); - - //! allocates the new buffer of size _size. if the _size is small enough, stack-allocated buffer is used - void allocate(size_t _size); - //! deallocates the buffer if it was dynamically allocated - void deallocate(); - //! resizes the buffer and preserves the content - void resize(size_t _size); - //! returns the current buffer size - size_t size() const; - //! returns pointer to the real buffer, stack-allocated or heap-allocated - inline _Tp* data() { return ptr; } - //! returns read-only pointer to the real buffer, stack-allocated or heap-allocated - inline const _Tp* data() const { return ptr; } - -#if !defined(OPENCV_DISABLE_DEPRECATED_COMPATIBILITY) // use to .data() calls instead - //! returns pointer to the real buffer, stack-allocated or heap-allocated - operator _Tp* () { return ptr; } - //! returns read-only pointer to the real buffer, stack-allocated or heap-allocated - operator const _Tp* () const { return ptr; } -#else - //! returns a reference to the element at specified location. No bounds checking is performed in Release builds. - inline _Tp& operator[] (size_t i) { CV_DbgCheckLT(i, sz, "out of range"); return ptr[i]; } - //! returns a reference to the element at specified location. No bounds checking is performed in Release builds. - inline const _Tp& operator[] (size_t i) const { CV_DbgCheckLT(i, sz, "out of range"); return ptr[i]; } -#endif - -protected: - //! pointer to the real buffer, can point to buf if the buffer is small enough - _Tp* ptr; - //! size of the real buffer - size_t sz; - //! pre-allocated buffer. At least 1 element to confirm C++ standard requirements - _Tp buf[(fixed_size > 0) ? fixed_size : 1]; -}; - -/** @brief Sets/resets the break-on-error mode. - -When the break-on-error mode is set, the default error handler issues a hardware exception, which -can make debugging more convenient. - -\return the previous state - */ -CV_EXPORTS bool setBreakOnError(bool flag); - -extern "C" typedef int (*ErrorCallback)( int status, const char* func_name, - const char* err_msg, const char* file_name, - int line, void* userdata ); - - -/** @brief Sets the new error handler and the optional user data. - - The function sets the new error handler, called from cv::error(). - - \param errCallback the new error handler. If NULL, the default error handler is used. - \param userdata the optional user data pointer, passed to the callback. - \param prevUserdata the optional output parameter where the previous user data pointer is stored - - \return the previous error handler -*/ -CV_EXPORTS ErrorCallback redirectError( ErrorCallback errCallback, void* userdata=0, void** prevUserdata=0); - -CV_EXPORTS String tempfile( const char* suffix = 0); -CV_EXPORTS void glob(String pattern, std::vector& result, bool recursive = false); - -/** @brief OpenCV will try to set the number of threads for the next parallel region. - -If threads == 0, OpenCV will disable threading optimizations and run all it's functions -sequentially. Passing threads \< 0 will reset threads number to system default. This function must -be called outside of parallel region. - -OpenCV will try to run its functions with specified threads number, but some behaviour differs from -framework: -- `TBB` - User-defined parallel constructions will run with the same threads number, if - another is not specified. If later on user creates his own scheduler, OpenCV will use it. -- `OpenMP` - No special defined behaviour. -- `Concurrency` - If threads == 1, OpenCV will disable threading optimizations and run its - functions sequentially. -- `GCD` - Supports only values \<= 0. -- `C=` - No special defined behaviour. -@param nthreads Number of threads used by OpenCV. -@sa getNumThreads, getThreadNum - */ -CV_EXPORTS_W void setNumThreads(int nthreads); - -/** @brief Returns the number of threads used by OpenCV for parallel regions. - -Always returns 1 if OpenCV is built without threading support. - -The exact meaning of return value depends on the threading framework used by OpenCV library: -- `TBB` - The number of threads, that OpenCV will try to use for parallel regions. If there is - any tbb::thread_scheduler_init in user code conflicting with OpenCV, then function returns - default number of threads used by TBB library. -- `OpenMP` - An upper bound on the number of threads that could be used to form a new team. -- `Concurrency` - The number of threads, that OpenCV will try to use for parallel regions. -- `GCD` - Unsupported; returns the GCD thread pool limit (512) for compatibility. -- `C=` - The number of threads, that OpenCV will try to use for parallel regions, if before - called setNumThreads with threads \> 0, otherwise returns the number of logical CPUs, - available for the process. -@sa setNumThreads, getThreadNum - */ -CV_EXPORTS_W int getNumThreads(); - -/** @brief Returns the index of the currently executed thread within the current parallel region. Always -returns 0 if called outside of parallel region. - -@deprecated Current implementation doesn't corresponding to this documentation. - -The exact meaning of the return value depends on the threading framework used by OpenCV library: -- `TBB` - Unsupported with current 4.1 TBB release. Maybe will be supported in future. -- `OpenMP` - The thread number, within the current team, of the calling thread. -- `Concurrency` - An ID for the virtual processor that the current context is executing on (0 - for master thread and unique number for others, but not necessary 1,2,3,...). -- `GCD` - System calling thread's ID. Never returns 0 inside parallel region. -- `C=` - The index of the current parallel task. -@sa setNumThreads, getNumThreads - */ -CV_EXPORTS_W int getThreadNum(); - -/** @brief Returns full configuration time cmake output. - -Returned value is raw cmake output including version control system revision, compiler version, -compiler flags, enabled modules and third party libraries, etc. Output format depends on target -architecture. - */ -CV_EXPORTS_W const String& getBuildInformation(); - -/** @brief Returns library version string - -For example "3.4.1-dev". - -@sa getMajorVersion, getMinorVersion, getRevisionVersion -*/ -CV_EXPORTS_W String getVersionString(); - -/** @brief Returns major library version */ -CV_EXPORTS_W int getVersionMajor(); - -/** @brief Returns minor library version */ -CV_EXPORTS_W int getVersionMinor(); - -/** @brief Returns revision field of the library version */ -CV_EXPORTS_W int getVersionRevision(); - -/** @brief Returns the number of ticks. - -The function returns the number of ticks after the certain event (for example, when the machine was -turned on). It can be used to initialize RNG or to measure a function execution time by reading the -tick count before and after the function call. -@sa getTickFrequency, TickMeter - */ -CV_EXPORTS_W int64 getTickCount(); - -/** @brief Returns the number of ticks per second. - -The function returns the number of ticks per second. That is, the following code computes the -execution time in seconds: -@code - double t = (double)getTickCount(); - // do something ... - t = ((double)getTickCount() - t)/getTickFrequency(); -@endcode -@sa getTickCount, TickMeter - */ -CV_EXPORTS_W double getTickFrequency(); - -/** @brief a Class to measure passing time. - -The class computes passing time by counting the number of ticks per second. That is, the following code computes the -execution time in seconds: -@snippet snippets/core_various.cpp TickMeter_total - -It is also possible to compute the average time over multiple runs: -@snippet snippets/core_various.cpp TickMeter_average - -@sa getTickCount, getTickFrequency -*/ -class CV_EXPORTS_W TickMeter -{ -public: - //! the default constructor - CV_WRAP TickMeter() - { - reset(); - } - - //! starts counting ticks. - CV_WRAP void start() - { - startTime = cv::getTickCount(); - } - - //! stops counting ticks. - CV_WRAP void stop() - { - int64 time = cv::getTickCount(); - if (startTime == 0) - return; - ++counter; - sumTime += (time - startTime); - startTime = 0; - } - - //! returns counted ticks. - CV_WRAP int64 getTimeTicks() const - { - return sumTime; - } - - //! returns passed time in microseconds. - CV_WRAP double getTimeMicro() const - { - return getTimeMilli()*1e3; - } - - //! returns passed time in milliseconds. - CV_WRAP double getTimeMilli() const - { - return getTimeSec()*1e3; - } - - //! returns passed time in seconds. - CV_WRAP double getTimeSec() const - { - return (double)getTimeTicks() / getTickFrequency(); - } - - //! returns internal counter value. - CV_WRAP int64 getCounter() const - { - return counter; - } - - //! returns average FPS (frames per second) value. - CV_WRAP double getFPS() const - { - const double sec = getTimeSec(); - if (sec < DBL_EPSILON) - return 0.; - return counter / sec; - } - - //! returns average time in seconds - CV_WRAP double getAvgTimeSec() const - { - if (counter <= 0) - return 0.; - return getTimeSec() / counter; - } - - //! returns average time in milliseconds - CV_WRAP double getAvgTimeMilli() const - { - return getAvgTimeSec() * 1e3; - } - - //! resets internal values. - CV_WRAP void reset() - { - startTime = 0; - sumTime = 0; - counter = 0; - } - -private: - int64 counter; - int64 sumTime; - int64 startTime; -}; - -/** @brief output operator -@code -TickMeter tm; -tm.start(); -// do something ... -tm.stop(); -std::cout << tm; -@endcode -*/ - -static inline -std::ostream& operator << (std::ostream& out, const TickMeter& tm) -{ - return out << tm.getTimeSec() << "sec"; -} - -/** @brief Returns the number of CPU ticks. - -The function returns the current number of CPU ticks on some architectures (such as x86, x64, -PowerPC). On other platforms the function is equivalent to getTickCount. It can also be used for -very accurate time measurements, as well as for RNG initialization. Note that in case of multi-CPU -systems a thread, from which getCPUTickCount is called, can be suspended and resumed at another CPU -with its own counter. So, theoretically (and practically) the subsequent calls to the function do -not necessary return the monotonously increasing values. Also, since a modern CPU varies the CPU -frequency depending on the load, the number of CPU clocks spent in some code cannot be directly -converted to time units. Therefore, getTickCount is generally a preferable solution for measuring -execution time. - */ -CV_EXPORTS_W int64 getCPUTickCount(); - -/** @brief Returns true if the specified feature is supported by the host hardware. - -The function returns true if the host hardware supports the specified feature. When user calls -setUseOptimized(false), the subsequent calls to checkHardwareSupport() will return false until -setUseOptimized(true) is called. This way user can dynamically switch on and off the optimized code -in OpenCV. -@param feature The feature of interest, one of cv::CpuFeatures - */ -CV_EXPORTS_W bool checkHardwareSupport(int feature); - -/** @brief Returns feature name by ID - -Returns empty string if feature is not defined -*/ -CV_EXPORTS_W String getHardwareFeatureName(int feature); - -/** @brief Returns list of CPU features enabled during compilation. - -Returned value is a string containing space separated list of CPU features with following markers: - -- no markers - baseline features -- prefix `*` - features enabled in dispatcher -- suffix `?` - features enabled but not available in HW - -Example: `SSE SSE2 SSE3 *SSE4.1 *SSE4.2 *FP16 *AVX *AVX2 *AVX512-SKX?` -*/ -CV_EXPORTS_W std::string getCPUFeaturesLine(); - -/** @brief Returns the number of logical CPUs available for the process. - */ -CV_EXPORTS_W int getNumberOfCPUs(); - - -/** @brief Aligns a pointer to the specified number of bytes. - -The function returns the aligned pointer of the same type as the input pointer: -\f[\texttt{(_Tp*)(((size_t)ptr + n-1) & -n)}\f] -@param ptr Aligned pointer. -@param n Alignment size that must be a power of two. - */ -template static inline _Tp* alignPtr(_Tp* ptr, int n=(int)sizeof(_Tp)) -{ - CV_DbgAssert((n & (n - 1)) == 0); // n is a power of 2 - return (_Tp*)(((size_t)ptr + n-1) & -n); -} - -/** @brief Aligns a buffer size to the specified number of bytes. - -The function returns the minimum number that is greater than or equal to sz and is divisible by n : -\f[\texttt{(sz + n-1) & -n}\f] -@param sz Buffer size to align. -@param n Alignment size that must be a power of two. - */ -static inline size_t alignSize(size_t sz, int n) -{ - CV_DbgAssert((n & (n - 1)) == 0); // n is a power of 2 - return (sz + n-1) & -n; -} - -/** @brief Integer division with result round up. - -Use this function instead of `ceil((float)a / b)` expressions. - -@sa alignSize -*/ -static inline int divUp(int a, unsigned int b) -{ - CV_DbgAssert(a >= 0); - return (a + b - 1) / b; -} -/** @overload */ -static inline size_t divUp(size_t a, unsigned int b) -{ - return (a + b - 1) / b; -} - -/** @brief Round first value up to the nearest multiple of second value. - -Use this function instead of `ceil((float)a / b) * b` expressions. - -@sa divUp -*/ -static inline int roundUp(int a, unsigned int b) -{ - CV_DbgAssert(a >= 0); - return a + b - 1 - (a + b -1) % b; -} -/** @overload */ -static inline size_t roundUp(size_t a, unsigned int b) -{ - return a + b - 1 - (a + b - 1) % b; -} - -/** @brief Alignment check of passed values - -Usage: `isAligned(...)` - -@note Alignment(N) must be a power of 2 (2**k, 2^k) -*/ -template static inline -bool isAligned(const T& data) -{ - CV_StaticAssert((N & (N - 1)) == 0, ""); // power of 2 - return (((size_t)data) & (N - 1)) == 0; -} -/** @overload */ -template static inline -bool isAligned(const void* p1) -{ - return isAligned((size_t)p1); -} -/** @overload */ -template static inline -bool isAligned(const void* p1, const void* p2) -{ - return isAligned(((size_t)p1)|((size_t)p2)); -} -/** @overload */ -template static inline -bool isAligned(const void* p1, const void* p2, const void* p3) -{ - return isAligned(((size_t)p1)|((size_t)p2)|((size_t)p3)); -} -/** @overload */ -template static inline -bool isAligned(const void* p1, const void* p2, const void* p3, const void* p4) -{ - return isAligned(((size_t)p1)|((size_t)p2)|((size_t)p3)|((size_t)p4)); -} - -/** @brief Enables or disables the optimized code. - -The function can be used to dynamically turn on and off optimized dispatched code (code that uses SSE4.2, AVX/AVX2, -and other instructions on the platforms that support it). It sets a global flag that is further -checked by OpenCV functions. Since the flag is not checked in the inner OpenCV loops, it is only -safe to call the function on the very top level in your application where you can be sure that no -other OpenCV function is currently executed. - -By default, the optimized code is enabled unless you disable it in CMake. The current status can be -retrieved using useOptimized. -@param onoff The boolean flag specifying whether the optimized code should be used (onoff=true) -or not (onoff=false). - */ -CV_EXPORTS_W void setUseOptimized(bool onoff); - -/** @brief Returns the status of optimized code usage. - -The function returns true if the optimized code is enabled. Otherwise, it returns false. - */ -CV_EXPORTS_W bool useOptimized(); - -static inline size_t getElemSize(int type) { return (size_t)CV_ELEM_SIZE(type); } - -/////////////////////////////// Parallel Primitives ////////////////////////////////// - -/** @brief Base class for parallel data processors -*/ -class CV_EXPORTS ParallelLoopBody -{ -public: - virtual ~ParallelLoopBody(); - virtual void operator() (const Range& range) const = 0; -}; - -/** @brief Parallel data processor -*/ -CV_EXPORTS void parallel_for_(const Range& range, const ParallelLoopBody& body, double nstripes=-1.); - -#ifdef CV_CXX11 -class ParallelLoopBodyLambdaWrapper : public ParallelLoopBody -{ -private: - std::function m_functor; -public: - ParallelLoopBodyLambdaWrapper(std::function functor) : - m_functor(functor) - { } - - virtual void operator() (const cv::Range& range) const CV_OVERRIDE - { - m_functor(range); - } -}; - -inline void parallel_for_(const Range& range, std::function functor, double nstripes=-1.) -{ - parallel_for_(range, ParallelLoopBodyLambdaWrapper(functor), nstripes); -} -#endif - -/////////////////////////////// forEach method of cv::Mat //////////////////////////// -template inline -void Mat::forEach_impl(const Functor& operation) { - if (false) { - operation(*reinterpret_cast<_Tp*>(0), reinterpret_cast(0)); - // If your compiler fails in this line. - // Please check that your functor signature is - // (_Tp&, const int*) <- multi-dimensional - // or (_Tp&, void*) <- in case you don't need current idx. - } - - CV_Assert(!empty()); - CV_Assert(this->total() / this->size[this->dims - 1] <= INT_MAX); - const int LINES = static_cast(this->total() / this->size[this->dims - 1]); - - class PixelOperationWrapper :public ParallelLoopBody - { - public: - PixelOperationWrapper(Mat_<_Tp>* const frame, const Functor& _operation) - : mat(frame), op(_operation) {} - virtual ~PixelOperationWrapper(){} - // ! Overloaded virtual operator - // convert range call to row call. - virtual void operator()(const Range &range) const CV_OVERRIDE - { - const int DIMS = mat->dims; - const int COLS = mat->size[DIMS - 1]; - if (DIMS <= 2) { - for (int row = range.start; row < range.end; ++row) { - this->rowCall2(row, COLS); - } - } else { - std::vector idx(DIMS); /// idx is modified in this->rowCall - idx[DIMS - 2] = range.start - 1; - - for (int line_num = range.start; line_num < range.end; ++line_num) { - idx[DIMS - 2]++; - for (int i = DIMS - 2; i >= 0; --i) { - if (idx[i] >= mat->size[i]) { - idx[i - 1] += idx[i] / mat->size[i]; - idx[i] %= mat->size[i]; - continue; // carry-over; - } - else { - break; - } - } - this->rowCall(&idx[0], COLS, DIMS); - } - } - } - private: - Mat_<_Tp>* const mat; - const Functor op; - // ! Call operator for each elements in this row. - inline void rowCall(int* const idx, const int COLS, const int DIMS) const { - int &col = idx[DIMS - 1]; - col = 0; - _Tp* pixel = &(mat->template at<_Tp>(idx)); - - while (col < COLS) { - op(*pixel, const_cast(idx)); - pixel++; col++; - } - col = 0; - } - // ! Call operator for each elements in this row. 2d mat special version. - inline void rowCall2(const int row, const int COLS) const { - union Index{ - int body[2]; - operator const int*() const { - return reinterpret_cast(this); - } - int& operator[](const int i) { - return body[i]; - } - } idx = {{row, 0}}; - // Special union is needed to avoid - // "error: array subscript is above array bounds [-Werror=array-bounds]" - // when call the functor `op` such that access idx[3]. - - _Tp* pixel = &(mat->template at<_Tp>(idx)); - const _Tp* const pixel_end = pixel + COLS; - while(pixel < pixel_end) { - op(*pixel++, static_cast(idx)); - idx[1]++; - } - } - PixelOperationWrapper& operator=(const PixelOperationWrapper &) { - CV_Assert(false); - // We can not remove this implementation because Visual Studio warning C4822. - return *this; - } - }; - - parallel_for_(cv::Range(0, LINES), PixelOperationWrapper(reinterpret_cast*>(this), operation)); -} - -/////////////////////////// Synchronization Primitives /////////////////////////////// - -class CV_EXPORTS Mutex -{ -public: - Mutex(); - ~Mutex(); - Mutex(const Mutex& m); - Mutex& operator = (const Mutex& m); - - void lock(); - bool trylock(); - void unlock(); - - struct Impl; -protected: - Impl* impl; -}; - -class CV_EXPORTS AutoLock -{ -public: - AutoLock(Mutex& m) : mutex(&m) { mutex->lock(); } - ~AutoLock() { mutex->unlock(); } -protected: - Mutex* mutex; -private: - AutoLock(const AutoLock&); - AutoLock& operator = (const AutoLock&); -}; - - -/** @brief Designed for command line parsing - -The sample below demonstrates how to use CommandLineParser: -@code - CommandLineParser parser(argc, argv, keys); - parser.about("Application name v1.0.0"); - - if (parser.has("help")) - { - parser.printMessage(); - return 0; - } - - int N = parser.get("N"); - double fps = parser.get("fps"); - String path = parser.get("path"); - - use_time_stamp = parser.has("timestamp"); - - String img1 = parser.get(0); - String img2 = parser.get(1); - - int repeat = parser.get(2); - - if (!parser.check()) - { - parser.printErrors(); - return 0; - } -@endcode - -### Keys syntax - -The keys parameter is a string containing several blocks, each one is enclosed in curly braces and -describes one argument. Each argument contains three parts separated by the `|` symbol: - --# argument names is a space-separated list of option synonyms (to mark argument as positional, prefix it with the `@` symbol) --# default value will be used if the argument was not provided (can be empty) --# help message (can be empty) - -For example: - -@code{.cpp} - const String keys = - "{help h usage ? | | print this message }" - "{@image1 | | image1 for compare }" - "{@image2 || image2 for compare }" - "{@repeat |1 | number }" - "{path |. | path to file }" - "{fps | -1.0 | fps for output video }" - "{N count |100 | count of objects }" - "{ts timestamp | | use time stamp }" - ; -} -@endcode - -Note that there are no default values for `help` and `timestamp` so we can check their presence using the `has()` method. -Arguments with default values are considered to be always present. Use the `get()` method in these cases to check their -actual value instead. - -String keys like `get("@image1")` return the empty string `""` by default - even with an empty default value. -Use the special `` default value to enforce that the returned string must not be empty. (like in `get("@image2")`) - -### Usage - -For the described keys: - -@code{.sh} - # Good call (3 positional parameters: image1, image2 and repeat; N is 200, ts is true) - $ ./app -N=200 1.png 2.jpg 19 -ts - - # Bad call - $ ./app -fps=aaa - ERRORS: - Parameter 'fps': can not convert: [aaa] to [double] -@endcode - */ -class CV_EXPORTS CommandLineParser -{ -public: - - /** @brief Constructor - - Initializes command line parser object - - @param argc number of command line arguments (from main()) - @param argv array of command line arguments (from main()) - @param keys string describing acceptable command line parameters (see class description for syntax) - */ - CommandLineParser(int argc, const char* const argv[], const String& keys); - - /** @brief Copy constructor */ - CommandLineParser(const CommandLineParser& parser); - - /** @brief Assignment operator */ - CommandLineParser& operator = (const CommandLineParser& parser); - - /** @brief Destructor */ - ~CommandLineParser(); - - /** @brief Returns application path - - This method returns the path to the executable from the command line (`argv[0]`). - - For example, if the application has been started with such a command: - @code{.sh} - $ ./bin/my-executable - @endcode - this method will return `./bin`. - */ - String getPathToApplication() const; - - /** @brief Access arguments by name - - Returns argument converted to selected type. If the argument is not known or can not be - converted to selected type, the error flag is set (can be checked with @ref check). - - For example, define: - @code{.cpp} - String keys = "{N count||}"; - @endcode - - Call: - @code{.sh} - $ ./my-app -N=20 - # or - $ ./my-app --count=20 - @endcode - - Access: - @code{.cpp} - int N = parser.get("N"); - @endcode - - @param name name of the argument - @param space_delete remove spaces from the left and right of the string - @tparam T the argument will be converted to this type if possible - - @note You can access positional arguments by their `@`-prefixed name: - @code{.cpp} - parser.get("@image"); - @endcode - */ - template - T get(const String& name, bool space_delete = true) const - { - T val = T(); - getByName(name, space_delete, ParamType::type, (void*)&val); - return val; - } - - /** @brief Access positional arguments by index - - Returns argument converted to selected type. Indexes are counted from zero. - - For example, define: - @code{.cpp} - String keys = "{@arg1||}{@arg2||}" - @endcode - - Call: - @code{.sh} - ./my-app abc qwe - @endcode - - Access arguments: - @code{.cpp} - String val_1 = parser.get(0); // returns "abc", arg1 - String val_2 = parser.get(1); // returns "qwe", arg2 - @endcode - - @param index index of the argument - @param space_delete remove spaces from the left and right of the string - @tparam T the argument will be converted to this type if possible - */ - template - T get(int index, bool space_delete = true) const - { - T val = T(); - getByIndex(index, space_delete, ParamType::type, (void*)&val); - return val; - } - - /** @brief Check if field was provided in the command line - - @param name argument name to check - */ - bool has(const String& name) const; - - /** @brief Check for parsing errors - - Returns false if error occurred while accessing the parameters (bad conversion, missing arguments, - etc.). Call @ref printErrors to print error messages list. - */ - bool check() const; - - /** @brief Set the about message - - The about message will be shown when @ref printMessage is called, right before arguments table. - */ - void about(const String& message); - - /** @brief Print help message - - This method will print standard help message containing the about message and arguments description. - - @sa about - */ - void printMessage() const; - - /** @brief Print list of errors occurred - - @sa check - */ - void printErrors() const; - -protected: - void getByName(const String& name, bool space_delete, int type, void* dst) const; - void getByIndex(int index, bool space_delete, int type, void* dst) const; - - struct Impl; - Impl* impl; -}; - -//! @} core_utils - -//! @cond IGNORED - -/////////////////////////////// AutoBuffer implementation //////////////////////////////////////// - -template inline -AutoBuffer<_Tp, fixed_size>::AutoBuffer() -{ - ptr = buf; - sz = fixed_size; -} - -template inline -AutoBuffer<_Tp, fixed_size>::AutoBuffer(size_t _size) -{ - ptr = buf; - sz = fixed_size; - allocate(_size); -} - -template inline -AutoBuffer<_Tp, fixed_size>::AutoBuffer(const AutoBuffer<_Tp, fixed_size>& abuf ) -{ - ptr = buf; - sz = fixed_size; - allocate(abuf.size()); - for( size_t i = 0; i < sz; i++ ) - ptr[i] = abuf.ptr[i]; -} - -template inline AutoBuffer<_Tp, fixed_size>& -AutoBuffer<_Tp, fixed_size>::operator = (const AutoBuffer<_Tp, fixed_size>& abuf) -{ - if( this != &abuf ) - { - deallocate(); - allocate(abuf.size()); - for( size_t i = 0; i < sz; i++ ) - ptr[i] = abuf.ptr[i]; - } - return *this; -} - -template inline -AutoBuffer<_Tp, fixed_size>::~AutoBuffer() -{ deallocate(); } - -template inline void -AutoBuffer<_Tp, fixed_size>::allocate(size_t _size) -{ - if(_size <= sz) - { - sz = _size; - return; - } - deallocate(); - sz = _size; - if(_size > fixed_size) - { - ptr = new _Tp[_size]; - } -} - -template inline void -AutoBuffer<_Tp, fixed_size>::deallocate() -{ - if( ptr != buf ) - { - delete[] ptr; - ptr = buf; - sz = fixed_size; - } -} - -template inline void -AutoBuffer<_Tp, fixed_size>::resize(size_t _size) -{ - if(_size <= sz) - { - sz = _size; - return; - } - size_t i, prevsize = sz, minsize = MIN(prevsize, _size); - _Tp* prevptr = ptr; - - ptr = _size > fixed_size ? new _Tp[_size] : buf; - sz = _size; - - if( ptr != prevptr ) - for( i = 0; i < minsize; i++ ) - ptr[i] = prevptr[i]; - for( i = prevsize; i < _size; i++ ) - ptr[i] = _Tp(); - - if( prevptr != buf ) - delete[] prevptr; -} - -template inline size_t -AutoBuffer<_Tp, fixed_size>::size() const -{ return sz; } - -template<> inline std::string CommandLineParser::get(int index, bool space_delete) const -{ - return get(index, space_delete); -} -template<> inline std::string CommandLineParser::get(const String& name, bool space_delete) const -{ - return get(name, space_delete); -} - -//! @endcond - - -// Basic Node class for tree building -template -class CV_EXPORTS Node -{ -public: - Node() - { - m_pParent = 0; - } - Node(OBJECT& payload) : m_payload(payload) - { - m_pParent = 0; - } - ~Node() - { - removeChilds(); - if (m_pParent) - { - int idx = m_pParent->findChild(this); - if (idx >= 0) - m_pParent->m_childs.erase(m_pParent->m_childs.begin() + idx); - } - } - - Node* findChild(OBJECT& payload) const - { - for(size_t i = 0; i < this->m_childs.size(); i++) - { - if(this->m_childs[i]->m_payload == payload) - return this->m_childs[i]; - } - return NULL; - } - - int findChild(Node *pNode) const - { - for (size_t i = 0; i < this->m_childs.size(); i++) - { - if(this->m_childs[i] == pNode) - return (int)i; - } - return -1; - } - - void addChild(Node *pNode) - { - if(!pNode) - return; - - CV_Assert(pNode->m_pParent == 0); - pNode->m_pParent = this; - this->m_childs.push_back(pNode); - } - - void removeChilds() - { - for(size_t i = 0; i < m_childs.size(); i++) - { - m_childs[i]->m_pParent = 0; // avoid excessive parent vector trimming - delete m_childs[i]; - } - m_childs.clear(); - } - - int getDepth() - { - int count = 0; - Node *pParent = m_pParent; - while(pParent) count++, pParent = pParent->m_pParent; - return count; - } - -public: - OBJECT m_payload; - Node* m_pParent; - std::vector*> m_childs; -}; - - -namespace samples { - -//! @addtogroup core_utils_samples -// This section describes utility functions for OpenCV samples. -// -// @note Implementation of these utilities is not thread-safe. -// -//! @{ - -/** @brief Try to find requested data file - -Search directories: - -1. Directories passed via `addSamplesDataSearchPath()` -2. OPENCV_SAMPLES_DATA_PATH_HINT environment variable -3. OPENCV_SAMPLES_DATA_PATH environment variable - If parameter value is not empty and nothing is found then stop searching. -4. Detects build/install path based on: - a. current working directory (CWD) - b. and/or binary module location (opencv_core/opencv_world, doesn't work with static linkage) -5. Scan `/{,data,samples/data}` directories if build directory is detected or the current directory is in source tree. -6. Scan `/share/OpenCV` directory if install directory is detected. - -@see cv::utils::findDataFile - -@param relative_path Relative path to data file -@param required Specify "file not found" handling. - If true, function prints information message and raises cv::Exception. - If false, function returns empty result -@param silentMode Disables messages -@return Returns path (absolute or relative to the current directory) or empty string if file is not found -*/ -CV_EXPORTS_W cv::String findFile(const cv::String& relative_path, bool required = true, bool silentMode = false); - -CV_EXPORTS_W cv::String findFileOrKeep(const cv::String& relative_path, bool silentMode = false); - -inline cv::String findFileOrKeep(const cv::String& relative_path, bool silentMode) -{ - cv::String res = findFile(relative_path, false, silentMode); - if (res.empty()) - return relative_path; - return res; -} - -/** @brief Override search data path by adding new search location - -Use this only to override default behavior -Passed paths are used in LIFO order. - -@param path Path to used samples data -*/ -CV_EXPORTS_W void addSamplesDataSearchPath(const cv::String& path); - -/** @brief Append samples search data sub directory - -General usage is to add OpenCV modules name (`/modules//samples/data` -> `/samples/data` + `modules//samples/data`). -Passed subdirectories are used in LIFO order. - -@param subdir samples data sub directory -*/ -CV_EXPORTS_W void addSamplesDataSearchSubDirectory(const cv::String& subdir); - -//! @} -} // namespace samples - -namespace utils { - -CV_EXPORTS int getThreadID(); - -} // namespace - -} //namespace cv - -#ifdef CV_COLLECT_IMPL_DATA -#include "opencv2/core/utils/instrumentation.hpp" -#else -/// Collect implementation data on OpenCV function call. Requires ENABLE_IMPL_COLLECTION build option. -#define CV_IMPL_ADD(impl) -#endif - -#ifndef DISABLE_OPENCV_24_COMPATIBILITY -#include "opencv2/core/core_c.h" -#endif - -#endif //OPENCV_CORE_UTILITY_H diff --git a/include/opencv2/core/utils/allocator_stats.hpp b/include/opencv2/core/utils/allocator_stats.hpp deleted file mode 100755 index 79e9338..0000000 --- a/include/opencv2/core/utils/allocator_stats.hpp +++ /dev/null @@ -1,29 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_CORE_ALLOCATOR_STATS_HPP -#define OPENCV_CORE_ALLOCATOR_STATS_HPP - -#include "../cvdef.h" - -namespace cv { namespace utils { - -class AllocatorStatisticsInterface -{ -protected: - AllocatorStatisticsInterface() {} - virtual ~AllocatorStatisticsInterface() {} -public: - virtual uint64_t getCurrentUsage() const = 0; - virtual uint64_t getTotalUsage() const = 0; - virtual uint64_t getNumberOfAllocations() const = 0; - virtual uint64_t getPeakUsage() const = 0; - - /** set peak usage = current usage */ - virtual void resetPeakUsage() = 0; -}; - -}} // namespace - -#endif // OPENCV_CORE_ALLOCATOR_STATS_HPP diff --git a/include/opencv2/core/utils/allocator_stats.impl.hpp b/include/opencv2/core/utils/allocator_stats.impl.hpp deleted file mode 100755 index eb5ecde..0000000 --- a/include/opencv2/core/utils/allocator_stats.impl.hpp +++ /dev/null @@ -1,158 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_CORE_ALLOCATOR_STATS_IMPL_HPP -#define OPENCV_CORE_ALLOCATOR_STATS_IMPL_HPP - -#include "./allocator_stats.hpp" - -//#define OPENCV_DISABLE_ALLOCATOR_STATS - -#ifdef CV_CXX11 - -#include - -#ifndef OPENCV_ALLOCATOR_STATS_COUNTER_TYPE -#if defined(__GNUC__) && (\ - (defined(__SIZEOF_POINTER__) && __SIZEOF_POINTER__ == 4) || \ - (defined(__GCC_HAVE_SYNC_COMPARE_AND_SWAP_4) && !defined(__GCC_HAVE_SYNC_COMPARE_AND_SWAP_8)) \ - ) -#define OPENCV_ALLOCATOR_STATS_COUNTER_TYPE int -#endif -#endif - -#ifndef OPENCV_ALLOCATOR_STATS_COUNTER_TYPE -#define OPENCV_ALLOCATOR_STATS_COUNTER_TYPE long long -#endif - -#else // CV_CXX11 - -#ifndef OPENCV_ALLOCATOR_STATS_COUNTER_TYPE -#define OPENCV_ALLOCATOR_STATS_COUNTER_TYPE int // CV_XADD supports int only -#endif - -#endif // CV_CXX11 - -namespace cv { namespace utils { - -#ifdef CV__ALLOCATOR_STATS_LOG -namespace { -#endif - -class AllocatorStatistics : public AllocatorStatisticsInterface -{ -#ifdef OPENCV_DISABLE_ALLOCATOR_STATS - -public: - AllocatorStatistics() {} - ~AllocatorStatistics() CV_OVERRIDE {} - - uint64_t getCurrentUsage() const CV_OVERRIDE { return 0; } - uint64_t getTotalUsage() const CV_OVERRIDE { return 0; } - uint64_t getNumberOfAllocations() const CV_OVERRIDE { return 0; } - uint64_t getPeakUsage() const CV_OVERRIDE { return 0; } - - /** set peak usage = current usage */ - void resetPeakUsage() CV_OVERRIDE {}; - - void onAllocate(size_t /*sz*/) {} - void onFree(size_t /*sz*/) {} - -#elif defined(CV_CXX11) - -protected: - typedef OPENCV_ALLOCATOR_STATS_COUNTER_TYPE counter_t; - std::atomic curr, total, total_allocs, peak; -public: - AllocatorStatistics() {} - ~AllocatorStatistics() CV_OVERRIDE {} - - uint64_t getCurrentUsage() const CV_OVERRIDE { return (uint64_t)curr.load(); } - uint64_t getTotalUsage() const CV_OVERRIDE { return (uint64_t)total.load(); } - uint64_t getNumberOfAllocations() const CV_OVERRIDE { return (uint64_t)total_allocs.load(); } - uint64_t getPeakUsage() const CV_OVERRIDE { return (uint64_t)peak.load(); } - - /** set peak usage = current usage */ - void resetPeakUsage() CV_OVERRIDE { peak.store(curr.load()); } - - // Controller interface - void onAllocate(size_t sz) - { -#ifdef CV__ALLOCATOR_STATS_LOG - CV__ALLOCATOR_STATS_LOG(cv::format("allocate: %lld (curr=%lld)", (long long int)sz, (long long int)curr.load())); -#endif - - counter_t new_curr = curr.fetch_add((counter_t)sz) + (counter_t)sz; - - // peak = std::max((uint64_t)peak, new_curr); - auto prev_peak = peak.load(); - while (prev_peak < new_curr) - { - if (peak.compare_exchange_weak(prev_peak, new_curr)) - break; - } - // end of peak = max(...) - - total += (counter_t)sz; - total_allocs++; - } - void onFree(size_t sz) - { -#ifdef CV__ALLOCATOR_STATS_LOG - CV__ALLOCATOR_STATS_LOG(cv::format("free: %lld (curr=%lld)", (long long int)sz, (long long int)curr.load())); -#endif - curr -= (counter_t)sz; - } - -#else // non C++11 - -protected: - typedef OPENCV_ALLOCATOR_STATS_COUNTER_TYPE counter_t; - volatile counter_t curr, total, total_allocs, peak; // overflow is possible, CV_XADD operates with 'int' only -public: - AllocatorStatistics() - : curr(0), total(0), total_allocs(0), peak(0) - {} - ~AllocatorStatistics() CV_OVERRIDE {} - - uint64_t getCurrentUsage() const CV_OVERRIDE { return (uint64_t)curr; } - uint64_t getTotalUsage() const CV_OVERRIDE { return (uint64_t)total; } - uint64_t getNumberOfAllocations() const CV_OVERRIDE { return (uint64_t)total_allocs; } - uint64_t getPeakUsage() const CV_OVERRIDE { return (uint64_t)peak; } - - void resetPeakUsage() CV_OVERRIDE { peak = curr; } - - // Controller interface - void onAllocate(size_t sz) - { -#ifdef CV__ALLOCATOR_STATS_LOG - CV__ALLOCATOR_STATS_LOG(cv::format("allocate: %lld (curr=%lld)", (long long int)sz, (long long int)curr)); -#endif - - counter_t new_curr = (counter_t)CV_XADD(&curr, (counter_t)sz) + (counter_t)sz; - - peak = std::max((counter_t)peak, new_curr); // non-thread safe - - //CV_XADD(&total, (uint64_t)sz); // overflow with int, non-reliable... - total += sz; - - CV_XADD(&total_allocs, (counter_t)1); - } - void onFree(size_t sz) - { -#ifdef CV__ALLOCATOR_STATS_LOG - CV__ALLOCATOR_STATS_LOG(cv::format("free: %lld (curr=%lld)", (long long int)sz, (long long int)curr)); -#endif - CV_XADD(&curr, (counter_t)-sz); - } -#endif -}; - -#ifdef CV__ALLOCATOR_STATS_LOG -} // namespace -#endif - -}} // namespace - -#endif // OPENCV_CORE_ALLOCATOR_STATS_IMPL_HPP diff --git a/include/opencv2/core/utils/filesystem.hpp b/include/opencv2/core/utils/filesystem.hpp deleted file mode 100755 index 00b0dd1..0000000 --- a/include/opencv2/core/utils/filesystem.hpp +++ /dev/null @@ -1,78 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_UTILS_FILESYSTEM_HPP -#define OPENCV_UTILS_FILESYSTEM_HPP - -namespace cv { namespace utils { namespace fs { - - -CV_EXPORTS bool exists(const cv::String& path); -CV_EXPORTS bool isDirectory(const cv::String& path); - -CV_EXPORTS void remove_all(const cv::String& path); - - -CV_EXPORTS cv::String getcwd(); - -/** @brief Converts path p to a canonical absolute path - * Symlinks are processed if there is support for them on running platform. - * - * @param path input path. Target file/directory should exist. - */ -CV_EXPORTS cv::String canonical(const cv::String& path); - -/** Join path components */ -CV_EXPORTS cv::String join(const cv::String& base, const cv::String& path); - -/** - * Generate a list of all files that match the globbing pattern. - * - * Result entries are prefixed by base directory path. - * - * @param directory base directory - * @param pattern filter pattern (based on '*'/'?' symbols). Use empty string to disable filtering and return all results - * @param[out] result result of globing. - * @param recursive scan nested directories too - * @param includeDirectories include directories into results list - */ -CV_EXPORTS void glob(const cv::String& directory, const cv::String& pattern, - CV_OUT std::vector& result, - bool recursive = false, bool includeDirectories = false); - -/** - * Generate a list of all files that match the globbing pattern. - * - * @param directory base directory - * @param pattern filter pattern (based on '*'/'?' symbols). Use empty string to disable filtering and return all results - * @param[out] result globbing result with relative paths from base directory - * @param recursive scan nested directories too - * @param includeDirectories include directories into results list - */ -CV_EXPORTS void glob_relative(const cv::String& directory, const cv::String& pattern, - CV_OUT std::vector& result, - bool recursive = false, bool includeDirectories = false); - - -CV_EXPORTS bool createDirectory(const cv::String& path); -CV_EXPORTS bool createDirectories(const cv::String& path); - -#ifdef __OPENCV_BUILD -// TODO -//CV_EXPORTS cv::String getTempDirectory(); - -/** - * @brief Returns directory to store OpenCV cache files - * Create sub-directory in common OpenCV cache directory if it doesn't exist. - * @param sub_directory_name name of sub-directory. NULL or "" value asks to return root cache directory. - * @param configuration_name optional name of configuration parameter name which overrides default behavior. - * @return Path to cache directory. Returns empty string if cache directories support is not available. Returns "disabled" if cache disabled by user. - */ -CV_EXPORTS cv::String getCacheDirectory(const char* sub_directory_name, const char* configuration_name = NULL); - -#endif - -}}} // namespace - -#endif // OPENCV_UTILS_FILESYSTEM_HPP diff --git a/include/opencv2/core/utils/instrumentation.hpp b/include/opencv2/core/utils/instrumentation.hpp deleted file mode 100755 index 3639867..0000000 --- a/include/opencv2/core/utils/instrumentation.hpp +++ /dev/null @@ -1,125 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_UTILS_INSTR_HPP -#define OPENCV_UTILS_INSTR_HPP - -#include -#include - -namespace cv { - -//! @addtogroup core_utils -//! @{ - -#ifdef CV_COLLECT_IMPL_DATA -CV_EXPORTS void setImpl(int flags); // set implementation flags and reset storage arrays -CV_EXPORTS void addImpl(int flag, const char* func = 0); // add implementation and function name to storage arrays -// Get stored implementation flags and functions names arrays -// Each implementation entry correspond to function name entry, so you can find which implementation was executed in which function -CV_EXPORTS int getImpl(std::vector &impl, std::vector &funName); - -CV_EXPORTS bool useCollection(); // return implementation collection state -CV_EXPORTS void setUseCollection(bool flag); // set implementation collection state - -#define CV_IMPL_PLAIN 0x01 // native CPU OpenCV implementation -#define CV_IMPL_OCL 0x02 // OpenCL implementation -#define CV_IMPL_IPP 0x04 // IPP implementation -#define CV_IMPL_MT 0x10 // multithreaded implementation - -#undef CV_IMPL_ADD -#define CV_IMPL_ADD(impl) \ - if(cv::useCollection()) \ - { \ - cv::addImpl(impl, CV_Func); \ - } -#endif - -// Instrumentation external interface -namespace instr -{ - -#if !defined OPENCV_ABI_CHECK - -enum TYPE -{ - TYPE_GENERAL = 0, // OpenCV API function, e.g. exported function - TYPE_MARKER, // Information marker - TYPE_WRAPPER, // Wrapper function for implementation - TYPE_FUN, // Simple function call -}; - -enum IMPL -{ - IMPL_PLAIN = 0, - IMPL_IPP, - IMPL_OPENCL, -}; - -struct NodeDataTls -{ - NodeDataTls() - { - m_ticksTotal = 0; - } - uint64 m_ticksTotal; -}; - -class CV_EXPORTS NodeData -{ -public: - NodeData(const char* funName = 0, const char* fileName = NULL, int lineNum = 0, void* retAddress = NULL, bool alwaysExpand = false, cv::instr::TYPE instrType = TYPE_GENERAL, cv::instr::IMPL implType = IMPL_PLAIN); - NodeData(NodeData &ref); - ~NodeData(); - NodeData& operator=(const NodeData&); - - cv::String m_funName; - cv::instr::TYPE m_instrType; - cv::instr::IMPL m_implType; - const char* m_fileName; - int m_lineNum; - void* m_retAddress; - bool m_alwaysExpand; - bool m_funError; - - volatile int m_counter; - volatile uint64 m_ticksTotal; - TLSDataAccumulator m_tls; - int m_threads; - - // No synchronization - double getTotalMs() const { return ((double)m_ticksTotal / cv::getTickFrequency()) * 1000; } - double getMeanMs() const { return (((double)m_ticksTotal/m_counter) / cv::getTickFrequency()) * 1000; } -}; -bool operator==(const NodeData& lhs, const NodeData& rhs); - -typedef Node InstrNode; - -CV_EXPORTS InstrNode* getTrace(); - -#endif // !defined OPENCV_ABI_CHECK - - -CV_EXPORTS bool useInstrumentation(); -CV_EXPORTS void setUseInstrumentation(bool flag); -CV_EXPORTS void resetTrace(); - -enum FLAGS -{ - FLAGS_NONE = 0, - FLAGS_MAPPING = 0x01, - FLAGS_EXPAND_SAME_NAMES = 0x02, -}; - -CV_EXPORTS void setFlags(FLAGS modeFlags); -static inline void setFlags(int modeFlags) { setFlags((FLAGS)modeFlags); } -CV_EXPORTS FLAGS getFlags(); - -} // namespace instr - -//! @} - -} // namespace - -#endif // OPENCV_UTILS_TLS_HPP diff --git a/include/opencv2/core/utils/logger.defines.hpp b/include/opencv2/core/utils/logger.defines.hpp deleted file mode 100755 index b2dfc41..0000000 --- a/include/opencv2/core/utils/logger.defines.hpp +++ /dev/null @@ -1,22 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_LOGGER_DEFINES_HPP -#define OPENCV_LOGGER_DEFINES_HPP - -//! @addtogroup core_logging -//! @{ - -// Supported logging levels and their semantic -#define CV_LOG_LEVEL_SILENT 0 //!< for using in setLogLevel() call -#define CV_LOG_LEVEL_FATAL 1 //!< Fatal (critical) error (unrecoverable internal error) -#define CV_LOG_LEVEL_ERROR 2 //!< Error message -#define CV_LOG_LEVEL_WARN 3 //!< Warning message -#define CV_LOG_LEVEL_INFO 4 //!< Info message -#define CV_LOG_LEVEL_DEBUG 5 //!< Debug message. Disabled in the "Release" build. -#define CV_LOG_LEVEL_VERBOSE 6 //!< Verbose (trace) messages. Requires verbosity level. Disabled in the "Release" build. - -//! @} - -#endif // OPENCV_LOGGER_DEFINES_HPP diff --git a/include/opencv2/core/utils/logger.hpp b/include/opencv2/core/utils/logger.hpp deleted file mode 100755 index 501d140..0000000 --- a/include/opencv2/core/utils/logger.hpp +++ /dev/null @@ -1,159 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_LOGGER_HPP -#define OPENCV_LOGGER_HPP - -#include -#include -#include // INT_MAX - -#include "logger.defines.hpp" - -namespace cv { -namespace utils { -namespace logging { - -//! @addtogroup core_logging -//! @{ - -//! Supported logging levels and their semantic -enum LogLevel { - LOG_LEVEL_SILENT = 0, //!< for using in setLogVevel() call - LOG_LEVEL_FATAL = 1, //!< Fatal (critical) error (unrecoverable internal error) - LOG_LEVEL_ERROR = 2, //!< Error message - LOG_LEVEL_WARNING = 3, //!< Warning message - LOG_LEVEL_INFO = 4, //!< Info message - LOG_LEVEL_DEBUG = 5, //!< Debug message. Disabled in the "Release" build. - LOG_LEVEL_VERBOSE = 6, //!< Verbose (trace) messages. Requires verbosity level. Disabled in the "Release" build. -#ifndef CV_DOXYGEN - ENUM_LOG_LEVEL_FORCE_INT = INT_MAX -#endif -}; - -/** Set global logging level -@return previous logging level -*/ -CV_EXPORTS LogLevel setLogLevel(LogLevel logLevel); -/** Get global logging level */ -CV_EXPORTS LogLevel getLogLevel(); - -namespace internal { -/** Write log message */ -CV_EXPORTS void writeLogMessage(LogLevel logLevel, const char* message); -} // namespace - -/** - * \def CV_LOG_STRIP_LEVEL - * - * Define CV_LOG_STRIP_LEVEL=CV_LOG_LEVEL_[DEBUG|INFO|WARN|ERROR|FATAL|DISABLED] to compile out anything at that and before that logging level - */ -#ifndef CV_LOG_STRIP_LEVEL -# if defined NDEBUG -# define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_DEBUG -# else -# define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_VERBOSE -# endif -#endif - -#define CV_LOG_WITH_TAG(tag, msgLevel, extra_check0, extra_check1, msg_prefix, ...) \ - for(;;) { \ - extra_check0; \ - if (cv::utils::logging::getLogLevel() < msgLevel) break; \ - extra_check1; \ - std::stringstream ss; ss msg_prefix << __VA_ARGS__; \ - cv::utils::logging::internal::writeLogMessage(msgLevel, ss.str().c_str()); \ - break; \ - } - -#define CV_LOG_FATAL(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_FATAL, , , , __VA_ARGS__) -#define CV_LOG_ERROR(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_ERROR, , , , __VA_ARGS__) -#define CV_LOG_WARNING(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_WARNING, , , , __VA_ARGS__) -#define CV_LOG_INFO(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_INFO, , , , __VA_ARGS__) -#define CV_LOG_DEBUG(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_DEBUG, , , , __VA_ARGS__) -#define CV_LOG_VERBOSE(tag, v, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_VERBOSE, , , << "[VERB" << v << ":" << cv::utils::getThreadID() << "] ", __VA_ARGS__) - -#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_INFO -#undef CV_LOG_INFO -#define CV_LOG_INFO(tag, ...) -#endif - -#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_DEBUG -#undef CV_LOG_DEBUG -#define CV_LOG_DEBUG(tag, ...) -#endif - -#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_VERBOSE -#undef CV_LOG_VERBOSE -#define CV_LOG_VERBOSE(tag, v, ...) -#endif - -//! @cond IGNORED -#define CV__LOG_ONCE_CHECK_PRE \ - static bool _cv_log_once_ ## __LINE__ = false; \ - if (_cv_log_once_ ## __LINE__) break; - -#define CV__LOG_ONCE_CHECK_POST \ - _cv_log_once_ ## __LINE__ = true; - -#define CV__LOG_IF_CHECK(logging_cond) \ - if (!(logging_cond)) break; - -//! @endcond - - -// CV_LOG_ONCE_XXX macros - -#define CV_LOG_ONCE_ERROR(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_ERROR, CV__LOG_ONCE_CHECK_PRE, CV__LOG_ONCE_CHECK_POST, , __VA_ARGS__) -#define CV_LOG_ONCE_WARNING(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_WARNING, CV__LOG_ONCE_CHECK_PRE, CV__LOG_ONCE_CHECK_POST, , __VA_ARGS__) -#define CV_LOG_ONCE_INFO(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_INFO, CV__LOG_ONCE_CHECK_PRE, CV__LOG_ONCE_CHECK_POST, , __VA_ARGS__) -#define CV_LOG_ONCE_DEBUG(tag, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_DEBUG, CV__LOG_ONCE_CHECK_PRE, CV__LOG_ONCE_CHECK_POST, , __VA_ARGS__) -#define CV_LOG_ONCE_VERBOSE(tag, v, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_VERBOSE, CV__LOG_ONCE_CHECK_PRE, CV__LOG_ONCE_CHECK_POST, << "[VERB" << v << ":" << cv::utils::getThreadID() << "] ", __VA_ARGS__) - -#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_INFO -#undef CV_LOG_ONCE_INFO -#define CV_LOG_ONCE_INFO(tag, ...) -#endif - -#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_DEBUG -#undef CV_LOG_ONCE_DEBUG -#define CV_LOG_ONCE_DEBUG(tag, ...) -#endif - -#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_VERBOSE -#undef CV_LOG_ONCE_VERBOSE -#define CV_LOG_ONCE_VERBOSE(tag, v, ...) -#endif - - -// CV_LOG_IF_XXX macros - -#define CV_LOG_IF_FATAL(tag, logging_cond, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_FATAL, , CV__LOG_IF_CHECK(logging_cond), , __VA_ARGS__) -#define CV_LOG_IF_ERROR(tag, logging_cond, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_ERROR, , CV__LOG_IF_CHECK(logging_cond), , __VA_ARGS__) -#define CV_LOG_IF_WARNING(tag, logging_cond, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_WARNING, , CV__LOG_IF_CHECK(logging_cond), , __VA_ARGS__) -#define CV_LOG_IF_INFO(tag, logging_cond, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_INFO, , CV__LOG_IF_CHECK(logging_cond), , __VA_ARGS__) -#define CV_LOG_IF_DEBUG(tag, logging_cond, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_DEBUG, , CV__LOG_IF_CHECK(logging_cond), , __VA_ARGS__) -#define CV_LOG_IF_VERBOSE(tag, v, logging_cond, ...) CV_LOG_WITH_TAG(tag, cv::utils::logging::LOG_LEVEL_VERBOSE, , CV__LOG_IF_CHECK(logging_cond), << "[VERB" << v << ":" << cv::utils::getThreadID() << "] ", __VA_ARGS__) - -#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_INFO -#undef CV_LOG_IF_INFO -#define CV_LOG_IF_INFO(tag, logging_cond, ...) -#endif - -#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_DEBUG -#undef CV_LOG_IF_DEBUG -#define CV_LOG_IF_DEBUG(tag, logging_cond, ...) -#endif - -#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_VERBOSE -#undef CV_LOG_IF_VERBOSE -#define CV_LOG_IF_VERBOSE(tag, v, logging_cond, ...) -#endif - - -//! @} - -}}} // namespace - -#endif // OPENCV_LOGGER_HPP diff --git a/include/opencv2/core/utils/tls.hpp b/include/opencv2/core/utils/tls.hpp deleted file mode 100755 index b5f1138..0000000 --- a/include/opencv2/core/utils/tls.hpp +++ /dev/null @@ -1,237 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_UTILS_TLS_HPP -#define OPENCV_UTILS_TLS_HPP - -#include - -namespace cv { - -//! @addtogroup core_utils -//! @{ - -namespace details { class TlsStorage; } - -/** TLS container base implementation - * - * Don't use directly. - * - * @sa TLSData, TLSDataAccumulator templates - */ -class CV_EXPORTS TLSDataContainer -{ -protected: - TLSDataContainer(); - virtual ~TLSDataContainer(); - - /// @deprecated use detachData() instead - void gatherData(std::vector &data) const; - /// get TLS data and detach all data from threads (similar to cleanup() call) - void detachData(std::vector& data); - - void* getData() const; - void release(); - -protected: - virtual void* createDataInstance() const = 0; - virtual void deleteDataInstance(void* pData) const = 0; - -#if OPENCV_ABI_COMPATIBILITY > 300 -private: -#else -public: -#endif - int key_; - - friend class cv::details::TlsStorage; // core/src/system.cpp - -public: - void cleanup(); //!< Release created TLS data container objects. It is similar to release() call, but it keeps TLS container valid. - -private: - // Disable copy/assign (noncopyable pattern) - TLSDataContainer(TLSDataContainer &); - TLSDataContainer& operator =(const TLSDataContainer &); -}; - - -/** @brief Simple TLS data class - * - * @sa TLSDataAccumulator - */ -template -class TLSData : protected TLSDataContainer -{ -public: - inline TLSData() {} - inline ~TLSData() { release(); } - - inline T* get() const { return (T*)getData(); } //!< Get data associated with key - inline T& getRef() const { T* ptr = (T*)getData(); CV_DbgAssert(ptr); return *ptr; } //!< Get data associated with key - - /// Release associated thread data - inline void cleanup() - { - TLSDataContainer::cleanup(); - } - -protected: - /// Wrapper to allocate data by template - virtual void* createDataInstance() const CV_OVERRIDE { return new T; } - /// Wrapper to release data by template - virtual void deleteDataInstance(void* pData) const CV_OVERRIDE { delete (T*)pData; } -}; - - -/// TLS data accumulator with gathering methods -template -class TLSDataAccumulator : public TLSData -{ - mutable cv::Mutex mutex; - mutable std::vector dataFromTerminatedThreads; - std::vector detachedData; - bool cleanupMode; -public: - TLSDataAccumulator() : cleanupMode(false) {} - ~TLSDataAccumulator() - { - release(); - } - - /** @brief Get data from all threads - * @deprecated replaced by detachData() - * - * Lifetime of vector data is valid until next detachData()/cleanup()/release() calls - * - * @param[out] data result buffer (should be empty) - */ - void gather(std::vector &data) const - { - CV_Assert(cleanupMode == false); // state is not valid - CV_Assert(data.empty()); - { - std::vector &dataVoid = reinterpret_cast&>(data); - TLSDataContainer::gatherData(dataVoid); - } - { - AutoLock lock(mutex); - data.reserve(data.size() + dataFromTerminatedThreads.size()); - for (typename std::vector::const_iterator i = dataFromTerminatedThreads.begin(); i != dataFromTerminatedThreads.end(); ++i) - { - data.push_back((T*)*i); - } - } - } - - /** @brief Get and detach data from all threads - * - * Call cleanupDetachedData() when returned vector is not needed anymore. - * - * @return Vector with associated data. Content is preserved (including lifetime of attached data pointers) until next detachData()/cleanupDetachedData()/cleanup()/release() calls - */ - std::vector& detachData() - { - CV_Assert(cleanupMode == false); // state is not valid - std::vector dataVoid; - { - TLSDataContainer::detachData(dataVoid); - } - { - AutoLock lock(mutex); - detachedData.reserve(dataVoid.size() + dataFromTerminatedThreads.size()); - for (typename std::vector::const_iterator i = dataFromTerminatedThreads.begin(); i != dataFromTerminatedThreads.end(); ++i) - { - detachedData.push_back((T*)*i); - } - dataFromTerminatedThreads.clear(); - for (typename std::vector::const_iterator i = dataVoid.begin(); i != dataVoid.end(); ++i) - { - detachedData.push_back((T*)(void*)*i); - } - } - dataVoid.clear(); - return detachedData; - } - - /// Release associated thread data returned by detachData() call - void cleanupDetachedData() - { - AutoLock lock(mutex); - cleanupMode = true; - _cleanupDetachedData(); - cleanupMode = false; - } - - /// Release associated thread data - void cleanup() - { - cleanupMode = true; - TLSDataContainer::cleanup(); - - AutoLock lock(mutex); - _cleanupDetachedData(); - _cleanupTerminatedData(); - cleanupMode = false; - } - - /// Release associated thread data and free TLS key - void release() - { - cleanupMode = true; - TLSDataContainer::release(); - { - AutoLock lock(mutex); - _cleanupDetachedData(); - _cleanupTerminatedData(); - } - } - -protected: - // synchronized - void _cleanupDetachedData() - { - for (typename std::vector::iterator i = detachedData.begin(); i != detachedData.end(); ++i) - { - deleteDataInstance((T*)*i); - } - detachedData.clear(); - } - - // synchronized - void _cleanupTerminatedData() - { - for (typename std::vector::iterator i = dataFromTerminatedThreads.begin(); i != dataFromTerminatedThreads.end(); ++i) - { - deleteDataInstance((T*)*i); - } - dataFromTerminatedThreads.clear(); - } - -protected: - virtual void* createDataInstance() const CV_OVERRIDE - { - // Note: we can collect all allocated data here, but this would require raced mutex locks - return new T; - } - virtual void deleteDataInstance(void* pData) const CV_OVERRIDE - { - if (cleanupMode) - { - delete (T*)pData; - } - else - { - AutoLock lock(mutex); - dataFromTerminatedThreads.push_back((T*)pData); - } - } -}; - - -//! @} - -} // namespace - -#endif // OPENCV_UTILS_TLS_HPP diff --git a/include/opencv2/core/utils/trace.hpp b/include/opencv2/core/utils/trace.hpp deleted file mode 100755 index ef5d35b..0000000 --- a/include/opencv2/core/utils/trace.hpp +++ /dev/null @@ -1,252 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_TRACE_HPP -#define OPENCV_TRACE_HPP - -#include - -namespace cv { -namespace utils { -namespace trace { - -//! @addtogroup core_logging -//! @{ - -//! Macro to trace function -#define CV_TRACE_FUNCTION() - -#define CV_TRACE_FUNCTION_SKIP_NESTED() - -//! Trace code scope. -//! @note Dynamic names are not supported in this macro (on stack or heap). Use string literals here only, like "initialize". -#define CV_TRACE_REGION(name_as_static_string_literal) -//! mark completed of the current opened region and create new one -//! @note Dynamic names are not supported in this macro (on stack or heap). Use string literals here only, like "step1". -#define CV_TRACE_REGION_NEXT(name_as_static_string_literal) - -//! Macro to trace argument value -#define CV_TRACE_ARG(arg_id) - -//! Macro to trace argument value (expanded version) -#define CV_TRACE_ARG_VALUE(arg_id, arg_name, value) - -//! @cond IGNORED -#define CV_TRACE_NS cv::utils::trace - -#if !defined(OPENCV_DISABLE_TRACE) && defined(__EMSCRIPTEN__) -#define OPENCV_DISABLE_TRACE 1 -#endif - -namespace details { - -#ifndef __OPENCV_TRACE -# if defined __OPENCV_BUILD && !defined __OPENCV_TESTS && !defined __OPENCV_APPS -# define __OPENCV_TRACE 1 -# else -# define __OPENCV_TRACE 0 -# endif -#endif - -#ifndef CV_TRACE_FILENAME -# define CV_TRACE_FILENAME __FILE__ -#endif - -#ifndef CV__TRACE_FUNCTION -# if defined _MSC_VER -# define CV__TRACE_FUNCTION __FUNCSIG__ -# elif defined __GNUC__ -# define CV__TRACE_FUNCTION __PRETTY_FUNCTION__ -# else -# define CV__TRACE_FUNCTION "" -# endif -#endif - -//! Thread-local instance (usually allocated on stack) -class CV_EXPORTS Region -{ -public: - struct LocationExtraData; - struct LocationStaticStorage - { - LocationExtraData** ppExtra; //< implementation specific data - const char* name; //< region name (function name or other custom name) - const char* filename; //< source code filename - int line; //< source code line - int flags; //< flags (implementation code path: Plain, IPP, OpenCL) - }; - - Region(const LocationStaticStorage& location); - inline ~Region() - { - if (implFlags != 0) - destroy(); - CV_DbgAssert(implFlags == 0); - CV_DbgAssert(pImpl == NULL); - } - - class Impl; - Impl* pImpl; // NULL if current region is not active - int implFlags; // see RegionFlag, 0 if region is ignored - - bool isActive() const { return pImpl != NULL; } - - void destroy(); -private: - Region(const Region&); // disabled - Region& operator= (const Region&); // disabled -}; - -//! Specify region flags -enum RegionLocationFlag { - REGION_FLAG_FUNCTION = (1 << 0), //< region is function (=1) / nested named region (=0) - REGION_FLAG_APP_CODE = (1 << 1), //< region is Application code (=1) / OpenCV library code (=0) - REGION_FLAG_SKIP_NESTED = (1 << 2), //< avoid processing of nested regions - - REGION_FLAG_IMPL_IPP = (1 << 16), //< region is part of IPP code path - REGION_FLAG_IMPL_OPENCL = (2 << 16), //< region is part of OpenCL code path - REGION_FLAG_IMPL_OPENVX = (3 << 16), //< region is part of OpenVX code path - - REGION_FLAG_IMPL_MASK = (15 << 16), - - REGION_FLAG_REGION_FORCE = (1 << 30), - REGION_FLAG_REGION_NEXT = (1 << 31), //< close previous region (see #CV_TRACE_REGION_NEXT macro) - - ENUM_REGION_FLAG_FORCE_INT = INT_MAX -}; - -struct CV_EXPORTS TraceArg { -public: - struct ExtraData; - ExtraData** ppExtra; - const char* name; - int flags; -}; -/** @brief Add meta information to current region (function) - * See CV_TRACE_ARG macro - * @param arg argument information structure (global static cache) - * @param value argument value (can by dynamic string literal in case of string, static allocation is not required) - */ -CV_EXPORTS void traceArg(const TraceArg& arg, const char* value); -//! @overload -CV_EXPORTS void traceArg(const TraceArg& arg, int value); -//! @overload -CV_EXPORTS void traceArg(const TraceArg& arg, int64 value); -//! @overload -CV_EXPORTS void traceArg(const TraceArg& arg, double value); - -#define CV__TRACE_LOCATION_VARNAME(loc_id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_trace_location_, loc_id), __LINE__) -#define CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_trace_location_extra_, loc_id) , __LINE__) - -#define CV__TRACE_DEFINE_LOCATION_(loc_id, name, flags) \ - static CV_TRACE_NS::details::Region::LocationExtraData* CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id) = 0; \ - static const CV_TRACE_NS::details::Region::LocationStaticStorage \ - CV__TRACE_LOCATION_VARNAME(loc_id) = { &(CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id)), name, CV_TRACE_FILENAME, __LINE__, flags}; - -#define CV__TRACE_DEFINE_LOCATION_FN(name, flags) CV__TRACE_DEFINE_LOCATION_(fn, name, ((flags) | CV_TRACE_NS::details::REGION_FLAG_FUNCTION)) - - -#define CV__TRACE_OPENCV_FUNCTION() \ - CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, 0); \ - const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); - -#define CV__TRACE_OPENCV_FUNCTION_NAME(name) \ - CV__TRACE_DEFINE_LOCATION_FN(name, 0); \ - const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); - -#define CV__TRACE_APP_FUNCTION() \ - CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \ - const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); - -#define CV__TRACE_APP_FUNCTION_NAME(name) \ - CV__TRACE_DEFINE_LOCATION_FN(name, CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \ - const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); - - -#define CV__TRACE_OPENCV_FUNCTION_SKIP_NESTED() \ - CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED); \ - const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); - -#define CV__TRACE_OPENCV_FUNCTION_NAME_SKIP_NESTED(name) \ - CV__TRACE_DEFINE_LOCATION_FN(name, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED); \ - const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); - -#define CV__TRACE_APP_FUNCTION_SKIP_NESTED() \ - CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED | CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \ - const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn)); - - -#define CV__TRACE_REGION_(name_as_static_string_literal, flags) \ - CV__TRACE_DEFINE_LOCATION_(region, name_as_static_string_literal, flags); \ - CV_TRACE_NS::details::Region CVAUX_CONCAT(__region_, __LINE__)(CV__TRACE_LOCATION_VARNAME(region)); - -#define CV__TRACE_REGION(name_as_static_string_literal) CV__TRACE_REGION_(name_as_static_string_literal, 0) -#define CV__TRACE_REGION_NEXT(name_as_static_string_literal) CV__TRACE_REGION_(name_as_static_string_literal, CV_TRACE_NS::details::REGION_FLAG_REGION_NEXT) - -#define CV__TRACE_ARG_VARNAME(arg_id) CVAUX_CONCAT(__cv_trace_arg_ ## arg_id, __LINE__) -#define CV__TRACE_ARG_EXTRA_VARNAME(arg_id) CVAUX_CONCAT(__cv_trace_arg_extra_ ## arg_id, __LINE__) - -#define CV__TRACE_DEFINE_ARG_(arg_id, name, flags) \ - static CV_TRACE_NS::details::TraceArg::ExtraData* CV__TRACE_ARG_EXTRA_VARNAME(arg_id) = 0; \ - static const CV_TRACE_NS::details::TraceArg \ - CV__TRACE_ARG_VARNAME(arg_id) = { &(CV__TRACE_ARG_EXTRA_VARNAME(arg_id)), name, flags }; - -#define CV__TRACE_ARG_VALUE(arg_id, arg_name, value) \ - CV__TRACE_DEFINE_ARG_(arg_id, arg_name, 0); \ - CV_TRACE_NS::details::traceArg((CV__TRACE_ARG_VARNAME(arg_id)), value); - -#define CV__TRACE_ARG(arg_id) CV_TRACE_ARG_VALUE(arg_id, #arg_id, (arg_id)) - -} // namespace - -#ifndef OPENCV_DISABLE_TRACE -#undef CV_TRACE_FUNCTION -#undef CV_TRACE_FUNCTION_SKIP_NESTED -#if __OPENCV_TRACE -#define CV_TRACE_FUNCTION CV__TRACE_OPENCV_FUNCTION -#define CV_TRACE_FUNCTION_SKIP_NESTED CV__TRACE_OPENCV_FUNCTION_SKIP_NESTED -#else -#define CV_TRACE_FUNCTION CV__TRACE_APP_FUNCTION -#define CV_TRACE_FUNCTION_SKIP_NESTED CV__TRACE_APP_FUNCTION_SKIP_NESTED -#endif - -#undef CV_TRACE_REGION -#define CV_TRACE_REGION CV__TRACE_REGION - -#undef CV_TRACE_REGION_NEXT -#define CV_TRACE_REGION_NEXT CV__TRACE_REGION_NEXT - -#undef CV_TRACE_ARG_VALUE -#define CV_TRACE_ARG_VALUE(arg_id, arg_name, value) \ - if (__region_fn.isActive()) \ - { \ - CV__TRACE_ARG_VALUE(arg_id, arg_name, value); \ - } - -#undef CV_TRACE_ARG -#define CV_TRACE_ARG CV__TRACE_ARG - -#endif // OPENCV_DISABLE_TRACE - -#ifdef OPENCV_TRACE_VERBOSE -#define CV_TRACE_FUNCTION_VERBOSE CV_TRACE_FUNCTION -#define CV_TRACE_REGION_VERBOSE CV_TRACE_REGION -#define CV_TRACE_REGION_NEXT_VERBOSE CV_TRACE_REGION_NEXT -#define CV_TRACE_ARG_VALUE_VERBOSE CV_TRACE_ARG_VALUE -#define CV_TRACE_ARG_VERBOSE CV_TRACE_ARG -#else -#define CV_TRACE_FUNCTION_VERBOSE(...) -#define CV_TRACE_REGION_VERBOSE(...) -#define CV_TRACE_REGION_NEXT_VERBOSE(...) -#define CV_TRACE_ARG_VALUE_VERBOSE(...) -#define CV_TRACE_ARG_VERBOSE(...) -#endif - -//! @endcond - -//! @} - -}}} // namespace - -#endif // OPENCV_TRACE_HPP diff --git a/include/opencv2/core/va_intel.hpp b/include/opencv2/core/va_intel.hpp deleted file mode 100755 index f665470..0000000 --- a/include/opencv2/core/va_intel.hpp +++ /dev/null @@ -1,78 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -// Copyright (C) 2015, Itseez, Inc., all rights reserved. -// Third party copyrights are property of their respective owners. - -#ifndef OPENCV_CORE_VA_INTEL_HPP -#define OPENCV_CORE_VA_INTEL_HPP - -#ifndef __cplusplus -# error va_intel.hpp header must be compiled as C++ -#endif - -#include "opencv2/core.hpp" -#include "ocl.hpp" - -#if defined(HAVE_VA) -# include "va/va.h" -#else // HAVE_VA -# if !defined(_VA_H_) - typedef void* VADisplay; - typedef unsigned int VASurfaceID; -# endif // !_VA_H_ -#endif // HAVE_VA - -namespace cv { namespace va_intel { - -/** @addtogroup core_va_intel -This section describes Intel VA-API/OpenCL (CL-VA) interoperability. - -To enable CL-VA interoperability support, configure OpenCV using CMake with WITH_VA_INTEL=ON . Currently VA-API is -supported on Linux only. You should also install Intel Media Server Studio (MSS) to use this feature. You may -have to specify the path(s) to MSS components for cmake in environment variables: - -- VA_INTEL_IOCL_ROOT for Intel OpenCL (default is "/opt/intel/opencl"). - -To use CL-VA interoperability you should first create VADisplay (libva), and then call initializeContextFromVA() -function to create OpenCL context and set up interoperability. -*/ -//! @{ - -/////////////////// CL-VA Interoperability Functions /////////////////// - -namespace ocl { -using namespace cv::ocl; - -// TODO static functions in the Context class -/** @brief Creates OpenCL context from VA. -@param display - VADisplay for which CL interop should be established. -@param tryInterop - try to set up for interoperability, if true; set up for use slow copy if false. -@return Returns reference to OpenCL Context - */ -CV_EXPORTS Context& initializeContextFromVA(VADisplay display, bool tryInterop = true); - -} // namespace cv::va_intel::ocl - -/** @brief Converts InputArray to VASurfaceID object. -@param display - VADisplay object. -@param src - source InputArray. -@param surface - destination VASurfaceID object. -@param size - size of image represented by VASurfaceID object. - */ -CV_EXPORTS void convertToVASurface(VADisplay display, InputArray src, VASurfaceID surface, Size size); - -/** @brief Converts VASurfaceID object to OutputArray. -@param display - VADisplay object. -@param surface - source VASurfaceID object. -@param size - size of image represented by VASurfaceID object. -@param dst - destination OutputArray. - */ -CV_EXPORTS void convertFromVASurface(VADisplay display, VASurfaceID surface, Size size, OutputArray dst); - -//! @} - -}} // namespace cv::va_intel - -#endif /* OPENCV_CORE_VA_INTEL_HPP */ diff --git a/include/opencv2/core/version.hpp b/include/opencv2/core/version.hpp deleted file mode 100755 index 7b5bb2d..0000000 --- a/include/opencv2/core/version.hpp +++ /dev/null @@ -1,26 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html. - -#ifndef OPENCV_VERSION_HPP -#define OPENCV_VERSION_HPP - -#define CV_VERSION_MAJOR 3 -#define CV_VERSION_MINOR 4 -#define CV_VERSION_REVISION 13 -#define CV_VERSION_STATUS "" - -#define CVAUX_STR_EXP(__A) #__A -#define CVAUX_STR(__A) CVAUX_STR_EXP(__A) - -#define CVAUX_STRW_EXP(__A) L ## #__A -#define CVAUX_STRW(__A) CVAUX_STRW_EXP(__A) - -#define CV_VERSION CVAUX_STR(CV_VERSION_MAJOR) "." CVAUX_STR(CV_VERSION_MINOR) "." CVAUX_STR(CV_VERSION_REVISION) CV_VERSION_STATUS - -/* old style version constants*/ -#define CV_MAJOR_VERSION CV_VERSION_MAJOR -#define CV_MINOR_VERSION CV_VERSION_MINOR -#define CV_SUBMINOR_VERSION CV_VERSION_REVISION - -#endif // OPENCV_VERSION_HPP diff --git a/include/opencv2/core/vsx_utils.hpp b/include/opencv2/core/vsx_utils.hpp deleted file mode 100755 index d796251..0000000 --- a/include/opencv2/core/vsx_utils.hpp +++ /dev/null @@ -1,1040 +0,0 @@ -// This file is part of OpenCV project. -// It is subject to the license terms in the LICENSE file found in the top-level directory -// of this distribution and at http://opencv.org/license.html - -#ifndef OPENCV_HAL_VSX_UTILS_HPP -#define OPENCV_HAL_VSX_UTILS_HPP - -#include "opencv2/core/cvdef.h" - -#ifndef SKIP_INCLUDES -# include -#endif - -//! @addtogroup core_utils_vsx -//! @{ -#if CV_VSX - -#define __VSX_S16__(c, v) (c){v, v, v, v, v, v, v, v, v, v, v, v, v, v, v, v} -#define __VSX_S8__(c, v) (c){v, v, v, v, v, v, v, v} -#define __VSX_S4__(c, v) (c){v, v, v, v} -#define __VSX_S2__(c, v) (c){v, v} - -typedef __vector unsigned char vec_uchar16; -#define vec_uchar16_set(...) (vec_uchar16){__VA_ARGS__} -#define vec_uchar16_sp(c) (__VSX_S16__(vec_uchar16, (unsigned char)c)) -#define vec_uchar16_c(v) ((vec_uchar16)(v)) -#define vec_uchar16_z vec_uchar16_sp(0) - -typedef __vector signed char vec_char16; -#define vec_char16_set(...) (vec_char16){__VA_ARGS__} -#define vec_char16_sp(c) (__VSX_S16__(vec_char16, (signed char)c)) -#define vec_char16_c(v) ((vec_char16)(v)) -#define vec_char16_z vec_char16_sp(0) - -typedef __vector unsigned short vec_ushort8; -#define vec_ushort8_set(...) (vec_ushort8){__VA_ARGS__} -#define vec_ushort8_sp(c) (__VSX_S8__(vec_ushort8, (unsigned short)c)) -#define vec_ushort8_c(v) ((vec_ushort8)(v)) -#define vec_ushort8_z vec_ushort8_sp(0) - -typedef __vector signed short vec_short8; -#define vec_short8_set(...) (vec_short8){__VA_ARGS__} -#define vec_short8_sp(c) (__VSX_S8__(vec_short8, (signed short)c)) -#define vec_short8_c(v) ((vec_short8)(v)) -#define vec_short8_z vec_short8_sp(0) - -typedef __vector unsigned int vec_uint4; -#define vec_uint4_set(...) (vec_uint4){__VA_ARGS__} -#define vec_uint4_sp(c) (__VSX_S4__(vec_uint4, (unsigned int)c)) -#define vec_uint4_c(v) ((vec_uint4)(v)) -#define vec_uint4_z vec_uint4_sp(0) - -typedef __vector signed int vec_int4; -#define vec_int4_set(...) (vec_int4){__VA_ARGS__} -#define vec_int4_sp(c) (__VSX_S4__(vec_int4, (signed int)c)) -#define vec_int4_c(v) ((vec_int4)(v)) -#define vec_int4_z vec_int4_sp(0) - -typedef __vector float vec_float4; -#define vec_float4_set(...) (vec_float4){__VA_ARGS__} -#define vec_float4_sp(c) (__VSX_S4__(vec_float4, c)) -#define vec_float4_c(v) ((vec_float4)(v)) -#define vec_float4_z vec_float4_sp(0) - -typedef __vector unsigned long long vec_udword2; -#define vec_udword2_set(...) (vec_udword2){__VA_ARGS__} -#define vec_udword2_sp(c) (__VSX_S2__(vec_udword2, (unsigned long long)c)) -#define vec_udword2_c(v) ((vec_udword2)(v)) -#define vec_udword2_z vec_udword2_sp(0) - -typedef __vector signed long long vec_dword2; -#define vec_dword2_set(...) (vec_dword2){__VA_ARGS__} -#define vec_dword2_sp(c) (__VSX_S2__(vec_dword2, (signed long long)c)) -#define vec_dword2_c(v) ((vec_dword2)(v)) -#define vec_dword2_z vec_dword2_sp(0) - -typedef __vector double vec_double2; -#define vec_double2_set(...) (vec_double2){__VA_ARGS__} -#define vec_double2_c(v) ((vec_double2)(v)) -#define vec_double2_sp(c) (__VSX_S2__(vec_double2, c)) -#define vec_double2_z vec_double2_sp(0) - -#define vec_bchar16 __vector __bool char -#define vec_bchar16_set(...) (vec_bchar16){__VA_ARGS__} -#define vec_bchar16_c(v) ((vec_bchar16)(v)) - -#define vec_bshort8 __vector __bool short -#define vec_bshort8_set(...) (vec_bshort8){__VA_ARGS__} -#define vec_bshort8_c(v) ((vec_bshort8)(v)) - -#define vec_bint4 __vector __bool int -#define vec_bint4_set(...) (vec_bint4){__VA_ARGS__} -#define vec_bint4_c(v) ((vec_bint4)(v)) - -#define vec_bdword2 __vector __bool long long -#define vec_bdword2_set(...) (vec_bdword2){__VA_ARGS__} -#define vec_bdword2_c(v) ((vec_bdword2)(v)) - -#define VSX_FINLINE(tp) extern inline tp __attribute__((always_inline)) - -#define VSX_REDIRECT_1RG(rt, rg, fnm, fn2) \ -VSX_FINLINE(rt) fnm(const rg& a) { return fn2(a); } - -#define VSX_REDIRECT_2RG(rt, rg, fnm, fn2) \ -VSX_FINLINE(rt) fnm(const rg& a, const rg& b) { return fn2(a, b); } - -/* - * GCC VSX compatibility -**/ -#if defined(__GNUG__) && !defined(__clang__) - -// inline asm helper -#define VSX_IMPL_1RG(rt, rg, opc, fnm) \ -VSX_FINLINE(rt) fnm(const rg& a) \ -{ rt rs; __asm__ __volatile__(#opc" %x0,%x1" : "=wa" (rs) : "wa" (a)); return rs; } - -#define VSX_IMPL_1VRG(rt, rg, opc, fnm) \ -VSX_FINLINE(rt) fnm(const rg& a) \ -{ rt rs; __asm__ __volatile__(#opc" %0,%1" : "=v" (rs) : "v" (a)); return rs; } - -#define VSX_IMPL_2VRG_F(rt, rg, fopc, fnm) \ -VSX_FINLINE(rt) fnm(const rg& a, const rg& b) \ -{ rt rs; __asm__ __volatile__(fopc : "=v" (rs) : "v" (a), "v" (b)); return rs; } - -#define VSX_IMPL_2VRG(rt, rg, opc, fnm) VSX_IMPL_2VRG_F(rt, rg, #opc" %0,%1,%2", fnm) - -#if __GNUG__ < 8 - - // Support for int4 -> dword2 expanding multiply was added in GCC 8. - #ifdef vec_mule - #undef vec_mule - #endif - #ifdef vec_mulo - #undef vec_mulo - #endif - - VSX_REDIRECT_2RG(vec_ushort8, vec_uchar16, vec_mule, __builtin_vec_mule) - VSX_REDIRECT_2RG(vec_short8, vec_char16, vec_mule, __builtin_vec_mule) - VSX_REDIRECT_2RG(vec_int4, vec_short8, vec_mule, __builtin_vec_mule) - VSX_REDIRECT_2RG(vec_uint4, vec_ushort8, vec_mule, __builtin_vec_mule) - VSX_REDIRECT_2RG(vec_ushort8, vec_uchar16, vec_mulo, __builtin_vec_mulo) - VSX_REDIRECT_2RG(vec_short8, vec_char16, vec_mulo, __builtin_vec_mulo) - VSX_REDIRECT_2RG(vec_int4, vec_short8, vec_mulo, __builtin_vec_mulo) - VSX_REDIRECT_2RG(vec_uint4, vec_ushort8, vec_mulo, __builtin_vec_mulo) - - // dword2 support arrived in ISA 2.07 and GCC 8+ - VSX_IMPL_2VRG(vec_dword2, vec_int4, vmulosw, vec_mule) - VSX_IMPL_2VRG(vec_udword2, vec_uint4, vmulouw, vec_mule) - VSX_IMPL_2VRG(vec_dword2, vec_int4, vmulesw, vec_mulo) - VSX_IMPL_2VRG(vec_udword2, vec_uint4, vmuleuw, vec_mulo) - -#endif - -#if __GNUG__ < 7 -// up to GCC 6 vec_mul only supports precisions and llong -# ifdef vec_mul -# undef vec_mul -# endif -/* - * there's no a direct instruction for supporting 8-bit, 16-bit multiplication in ISA 2.07, - * XLC Implement it by using instruction "multiply even", "multiply odd" and "permute" -**/ -# define VSX_IMPL_MULH(Tvec, cperm) \ - VSX_FINLINE(Tvec) vec_mul(const Tvec& a, const Tvec& b) \ - { \ - static const vec_uchar16 ev_od = {cperm}; \ - return vec_perm((Tvec)vec_mule(a, b), (Tvec)vec_mulo(a, b), ev_od); \ - } - #define VSX_IMPL_MULH_P16 0, 16, 2, 18, 4, 20, 6, 22, 8, 24, 10, 26, 12, 28, 14, 30 - VSX_IMPL_MULH(vec_char16, VSX_IMPL_MULH_P16) - VSX_IMPL_MULH(vec_uchar16, VSX_IMPL_MULH_P16) - #define VSX_IMPL_MULH_P8 0, 1, 16, 17, 4, 5, 20, 21, 8, 9, 24, 25, 12, 13, 28, 29 - VSX_IMPL_MULH(vec_short8, VSX_IMPL_MULH_P8) - VSX_IMPL_MULH(vec_ushort8, VSX_IMPL_MULH_P8) - // vmuluwm can be used for unsigned or signed integers, that's what they said - VSX_IMPL_2VRG(vec_int4, vec_int4, vmuluwm, vec_mul) - VSX_IMPL_2VRG(vec_uint4, vec_uint4, vmuluwm, vec_mul) - // redirect to GCC builtin vec_mul, since it already supports precisions and llong - VSX_REDIRECT_2RG(vec_float4, vec_float4, vec_mul, __builtin_vec_mul) - VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mul, __builtin_vec_mul) - VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mul, __builtin_vec_mul) - VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mul, __builtin_vec_mul) -#endif // __GNUG__ < 7 - -#if __GNUG__ < 6 -/* - * Instruction "compare greater than or equal" in ISA 2.07 only supports single - * and double precision. - * In XLC and new versions of GCC implement integers by using instruction "greater than" and NOR. -**/ -# ifdef vec_cmpge -# undef vec_cmpge -# endif -# ifdef vec_cmple -# undef vec_cmple -# endif -# define vec_cmple(a, b) vec_cmpge(b, a) -# define VSX_IMPL_CMPGE(rt, rg, opc, fnm) \ - VSX_IMPL_2VRG_F(rt, rg, #opc" %0,%2,%1\n\t xxlnor %x0,%x0,%x0", fnm) - - VSX_IMPL_CMPGE(vec_bchar16, vec_char16, vcmpgtsb, vec_cmpge) - VSX_IMPL_CMPGE(vec_bchar16, vec_uchar16, vcmpgtub, vec_cmpge) - VSX_IMPL_CMPGE(vec_bshort8, vec_short8, vcmpgtsh, vec_cmpge) - VSX_IMPL_CMPGE(vec_bshort8, vec_ushort8, vcmpgtuh, vec_cmpge) - VSX_IMPL_CMPGE(vec_bint4, vec_int4, vcmpgtsw, vec_cmpge) - VSX_IMPL_CMPGE(vec_bint4, vec_uint4, vcmpgtuw, vec_cmpge) - VSX_IMPL_CMPGE(vec_bdword2, vec_dword2, vcmpgtsd, vec_cmpge) - VSX_IMPL_CMPGE(vec_bdword2, vec_udword2, vcmpgtud, vec_cmpge) - -// redirect to GCC builtin cmpge, since it already supports precisions - VSX_REDIRECT_2RG(vec_bint4, vec_float4, vec_cmpge, __builtin_vec_cmpge) - VSX_REDIRECT_2RG(vec_bdword2, vec_double2, vec_cmpge, __builtin_vec_cmpge) - -// up to gcc5 vec_nor doesn't support bool long long -# undef vec_nor - template - VSX_REDIRECT_2RG(T, T, vec_nor, __builtin_vec_nor) - - VSX_FINLINE(vec_bdword2) vec_nor(const vec_bdword2& a, const vec_bdword2& b) - { return vec_bdword2_c(__builtin_vec_nor(vec_dword2_c(a), vec_dword2_c(b))); } - -// vec_packs doesn't support double words in gcc4 and old versions of gcc5 -# undef vec_packs - VSX_REDIRECT_2RG(vec_char16, vec_short8, vec_packs, __builtin_vec_packs) - VSX_REDIRECT_2RG(vec_uchar16, vec_ushort8, vec_packs, __builtin_vec_packs) - VSX_REDIRECT_2RG(vec_short8, vec_int4, vec_packs, __builtin_vec_packs) - VSX_REDIRECT_2RG(vec_ushort8, vec_uint4, vec_packs, __builtin_vec_packs) - - VSX_IMPL_2VRG_F(vec_int4, vec_dword2, "vpksdss %0,%2,%1", vec_packs) - VSX_IMPL_2VRG_F(vec_uint4, vec_udword2, "vpkudus %0,%2,%1", vec_packs) -#endif // __GNUG__ < 6 - -#if __GNUG__ < 5 -// vec_xxpermdi in gcc4 missing little-endian supports just like clang -# define vec_permi(a, b, c) vec_xxpermdi(b, a, (3 ^ (((c) & 1) << 1 | (c) >> 1))) -// same as vec_xxpermdi -# undef vec_vbpermq - VSX_IMPL_2VRG(vec_udword2, vec_uchar16, vbpermq, vec_vbpermq) - VSX_IMPL_2VRG(vec_dword2, vec_char16, vbpermq, vec_vbpermq) -#else -# define vec_permi vec_xxpermdi -#endif // __GNUG__ < 5 - -// shift left double by word immediate -#ifndef vec_sldw -# define vec_sldw __builtin_vsx_xxsldwi -#endif - -// vector population count -VSX_IMPL_1VRG(vec_uchar16, vec_uchar16, vpopcntb, vec_popcntu) -VSX_IMPL_1VRG(vec_uchar16, vec_char16, vpopcntb, vec_popcntu) -VSX_IMPL_1VRG(vec_ushort8, vec_ushort8, vpopcnth, vec_popcntu) -VSX_IMPL_1VRG(vec_ushort8, vec_short8, vpopcnth, vec_popcntu) -VSX_IMPL_1VRG(vec_uint4, vec_uint4, vpopcntw, vec_popcntu) -VSX_IMPL_1VRG(vec_uint4, vec_int4, vpopcntw, vec_popcntu) -VSX_IMPL_1VRG(vec_udword2, vec_udword2, vpopcntd, vec_popcntu) -VSX_IMPL_1VRG(vec_udword2, vec_dword2, vpopcntd, vec_popcntu) - -// converts between single and double-precision -VSX_REDIRECT_1RG(vec_float4, vec_double2, vec_cvfo, __builtin_vsx_xvcvdpsp) -VSX_REDIRECT_1RG(vec_double2, vec_float4, vec_cvfo, __builtin_vsx_xvcvspdp) - -// converts word and doubleword to double-precision -#undef vec_ctd -VSX_IMPL_1RG(vec_double2, vec_int4, xvcvsxwdp, vec_ctdo) -VSX_IMPL_1RG(vec_double2, vec_uint4, xvcvuxwdp, vec_ctdo) -VSX_IMPL_1RG(vec_double2, vec_dword2, xvcvsxddp, vec_ctd) -VSX_IMPL_1RG(vec_double2, vec_udword2, xvcvuxddp, vec_ctd) - -// converts word and doubleword to single-precision -#undef vec_ctf -VSX_IMPL_1RG(vec_float4, vec_int4, xvcvsxwsp, vec_ctf) -VSX_IMPL_1RG(vec_float4, vec_uint4, xvcvuxwsp, vec_ctf) -VSX_IMPL_1RG(vec_float4, vec_dword2, xvcvsxdsp, vec_ctfo) -VSX_IMPL_1RG(vec_float4, vec_udword2, xvcvuxdsp, vec_ctfo) - -// converts single and double precision to signed word -#undef vec_cts -VSX_IMPL_1RG(vec_int4, vec_double2, xvcvdpsxws, vec_ctso) -VSX_IMPL_1RG(vec_int4, vec_float4, xvcvspsxws, vec_cts) - -// converts single and double precision to unsigned word -#undef vec_ctu -VSX_IMPL_1RG(vec_uint4, vec_double2, xvcvdpuxws, vec_ctuo) -VSX_IMPL_1RG(vec_uint4, vec_float4, xvcvspuxws, vec_ctu) - -// converts single and double precision to signed doubleword -#undef vec_ctsl -VSX_IMPL_1RG(vec_dword2, vec_double2, xvcvdpsxds, vec_ctsl) -VSX_IMPL_1RG(vec_dword2, vec_float4, xvcvspsxds, vec_ctslo) - -// converts single and double precision to unsigned doubleword -#undef vec_ctul -VSX_IMPL_1RG(vec_udword2, vec_double2, xvcvdpuxds, vec_ctul) -VSX_IMPL_1RG(vec_udword2, vec_float4, xvcvspuxds, vec_ctulo) - -// just in case if GCC doesn't define it -#ifndef vec_xl -# define vec_xl vec_vsx_ld -# define vec_xst vec_vsx_st -#endif - -#endif // GCC VSX compatibility - -/* - * CLANG VSX compatibility -**/ -#if defined(__clang__) && !defined(__IBMCPP__) - -/* - * CLANG doesn't support %x in the inline asm template which fixes register number - * when using any of the register constraints wa, wd, wf - * - * For more explanation checkout PowerPC and IBM RS6000 in https://gcc.gnu.org/onlinedocs/gcc/Machine-Constraints.html - * Also there's already an open bug https://bugs.llvm.org/show_bug.cgi?id=31837 - * - * So we're not able to use inline asm and only use built-in functions that CLANG supports - * and use __builtin_convertvector if clang missing any of vector conversions built-in functions - * - * todo: clang asm template bug is fixed, need to reconsider the current workarounds. -*/ - -// convert vector helper -#define VSX_IMPL_CONVERT(rt, rg, fnm) \ -VSX_FINLINE(rt) fnm(const rg& a) { return __builtin_convertvector(a, rt); } - -#if __clang_major__ < 5 -// implement vec_permi in a dirty way -# define VSX_IMPL_CLANG_4_PERMI(Tvec) \ - VSX_FINLINE(Tvec) vec_permi(const Tvec& a, const Tvec& b, unsigned const char c) \ - { \ - switch (c) \ - { \ - case 0: \ - return vec_mergeh(a, b); \ - case 1: \ - return vec_mergel(vec_mergeh(a, a), b); \ - case 2: \ - return vec_mergeh(vec_mergel(a, a), b); \ - default: \ - return vec_mergel(a, b); \ - } \ - } - VSX_IMPL_CLANG_4_PERMI(vec_udword2) - VSX_IMPL_CLANG_4_PERMI(vec_dword2) - VSX_IMPL_CLANG_4_PERMI(vec_double2) - -// vec_xxsldwi is missing in clang 4 -# define vec_xxsldwi(a, b, c) vec_sld(a, b, (c) * 4) -#else -// vec_xxpermdi is missing little-endian supports in clang 4 just like gcc4 -# define vec_permi(a, b, c) vec_xxpermdi(b, a, (3 ^ (((c) & 1) << 1 | (c) >> 1))) -#endif // __clang_major__ < 5 - -// shift left double by word immediate -#ifndef vec_sldw -# define vec_sldw vec_xxsldwi -#endif - -// Implement vec_rsqrt since clang only supports vec_rsqrte -#ifndef vec_rsqrt - VSX_FINLINE(vec_float4) vec_rsqrt(const vec_float4& a) - { return vec_div(vec_float4_sp(1), vec_sqrt(a)); } - - VSX_FINLINE(vec_double2) vec_rsqrt(const vec_double2& a) - { return vec_div(vec_double2_sp(1), vec_sqrt(a)); } -#endif - -// vec_promote missing support for doubleword -VSX_FINLINE(vec_dword2) vec_promote(long long a, int b) -{ - vec_dword2 ret = vec_dword2_z; - ret[b & 1] = a; - return ret; -} - -VSX_FINLINE(vec_udword2) vec_promote(unsigned long long a, int b) -{ - vec_udword2 ret = vec_udword2_z; - ret[b & 1] = a; - return ret; -} - -// vec_popcnt should return unsigned but clang has different thought just like gcc in vec_vpopcnt -#define VSX_IMPL_POPCNTU(Tvec, Tvec2, ucast) \ -VSX_FINLINE(Tvec) vec_popcntu(const Tvec2& a) \ -{ return ucast(vec_popcnt(a)); } -VSX_IMPL_POPCNTU(vec_uchar16, vec_char16, vec_uchar16_c); -VSX_IMPL_POPCNTU(vec_ushort8, vec_short8, vec_ushort8_c); -VSX_IMPL_POPCNTU(vec_uint4, vec_int4, vec_uint4_c); -VSX_IMPL_POPCNTU(vec_udword2, vec_dword2, vec_udword2_c); -// redirect unsigned types -VSX_REDIRECT_1RG(vec_uchar16, vec_uchar16, vec_popcntu, vec_popcnt) -VSX_REDIRECT_1RG(vec_ushort8, vec_ushort8, vec_popcntu, vec_popcnt) -VSX_REDIRECT_1RG(vec_uint4, vec_uint4, vec_popcntu, vec_popcnt) -VSX_REDIRECT_1RG(vec_udword2, vec_udword2, vec_popcntu, vec_popcnt) - -// converts between single and double precision -VSX_REDIRECT_1RG(vec_float4, vec_double2, vec_cvfo, __builtin_vsx_xvcvdpsp) -VSX_REDIRECT_1RG(vec_double2, vec_float4, vec_cvfo, __builtin_vsx_xvcvspdp) - -// converts word and doubleword to double-precision -#ifdef vec_ctd -# undef vec_ctd -#endif -VSX_REDIRECT_1RG(vec_double2, vec_int4, vec_ctdo, __builtin_vsx_xvcvsxwdp) -VSX_REDIRECT_1RG(vec_double2, vec_uint4, vec_ctdo, __builtin_vsx_xvcvuxwdp) - -VSX_IMPL_CONVERT(vec_double2, vec_dword2, vec_ctd) -VSX_IMPL_CONVERT(vec_double2, vec_udword2, vec_ctd) - -// converts word and doubleword to single-precision -#if __clang_major__ > 4 -# undef vec_ctf -#endif -VSX_IMPL_CONVERT(vec_float4, vec_int4, vec_ctf) -VSX_IMPL_CONVERT(vec_float4, vec_uint4, vec_ctf) -VSX_REDIRECT_1RG(vec_float4, vec_dword2, vec_ctfo, __builtin_vsx_xvcvsxdsp) -VSX_REDIRECT_1RG(vec_float4, vec_udword2, vec_ctfo, __builtin_vsx_xvcvuxdsp) - -// converts single and double precision to signed word -#if __clang_major__ > 4 -# undef vec_cts -#endif -VSX_REDIRECT_1RG(vec_int4, vec_double2, vec_ctso, __builtin_vsx_xvcvdpsxws) -VSX_IMPL_CONVERT(vec_int4, vec_float4, vec_cts) - -// converts single and double precision to unsigned word -#if __clang_major__ > 4 -# undef vec_ctu -#endif -VSX_REDIRECT_1RG(vec_uint4, vec_double2, vec_ctuo, __builtin_vsx_xvcvdpuxws) -VSX_IMPL_CONVERT(vec_uint4, vec_float4, vec_ctu) - -// converts single and double precision to signed doubleword -#ifdef vec_ctsl -# undef vec_ctsl -#endif -VSX_IMPL_CONVERT(vec_dword2, vec_double2, vec_ctsl) -// __builtin_convertvector unable to convert, xvcvspsxds is missing on it -VSX_FINLINE(vec_dword2) vec_ctslo(const vec_float4& a) -{ return vec_ctsl(vec_cvfo(a)); } - -// converts single and double precision to unsigned doubleword -#ifdef vec_ctul -# undef vec_ctul -#endif -VSX_IMPL_CONVERT(vec_udword2, vec_double2, vec_ctul) -// __builtin_convertvector unable to convert, xvcvspuxds is missing on it -VSX_FINLINE(vec_udword2) vec_ctulo(const vec_float4& a) -{ return vec_ctul(vec_cvfo(a)); } - -#endif // CLANG VSX compatibility - -/* - * Common GCC, CLANG compatibility -**/ -#if defined(__GNUG__) && !defined(__IBMCPP__) - -#ifdef vec_cvf -# undef vec_cvf -#endif - -#define VSX_IMPL_CONV_EVEN_4_2(rt, rg, fnm, fn2) \ -VSX_FINLINE(rt) fnm(const rg& a) \ -{ return fn2(vec_sldw(a, a, 1)); } - -VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_float4, vec_cvf, vec_cvfo) -VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_int4, vec_ctd, vec_ctdo) -VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_uint4, vec_ctd, vec_ctdo) - -VSX_IMPL_CONV_EVEN_4_2(vec_dword2, vec_float4, vec_ctsl, vec_ctslo) -VSX_IMPL_CONV_EVEN_4_2(vec_udword2, vec_float4, vec_ctul, vec_ctulo) - -#define VSX_IMPL_CONV_EVEN_2_4(rt, rg, fnm, fn2) \ -VSX_FINLINE(rt) fnm(const rg& a) \ -{ \ - rt v4 = fn2(a); \ - return vec_sldw(v4, v4, 3); \ -} - -VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_double2, vec_cvf, vec_cvfo) -VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_dword2, vec_ctf, vec_ctfo) -VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_udword2, vec_ctf, vec_ctfo) - -VSX_IMPL_CONV_EVEN_2_4(vec_int4, vec_double2, vec_cts, vec_ctso) -VSX_IMPL_CONV_EVEN_2_4(vec_uint4, vec_double2, vec_ctu, vec_ctuo) - -// Only for Eigen! -/* - * changing behavior of conversion intrinsics for gcc has effect on Eigen - * so we redefine old behavior again only on gcc, clang -*/ -#if !defined(__clang__) || __clang_major__ > 4 - // ignoring second arg since Eigen only truncates toward zero -# define VSX_IMPL_CONV_2VARIANT(rt, rg, fnm, fn2) \ - VSX_FINLINE(rt) fnm(const rg& a, int only_truncate) \ - { \ - assert(only_truncate == 0); \ - CV_UNUSED(only_truncate); \ - return fn2(a); \ - } - VSX_IMPL_CONV_2VARIANT(vec_int4, vec_float4, vec_cts, vec_cts) - VSX_IMPL_CONV_2VARIANT(vec_float4, vec_int4, vec_ctf, vec_ctf) - // define vec_cts for converting double precision to signed doubleword - // which isn't compatible with xlc but its okay since Eigen only uses it for gcc - VSX_IMPL_CONV_2VARIANT(vec_dword2, vec_double2, vec_cts, vec_ctsl) -#endif // Eigen - -#endif // Common GCC, CLANG compatibility - -/* - * XLC VSX compatibility -**/ -#if defined(__IBMCPP__) - -// vector population count -#define vec_popcntu vec_popcnt - -// overload and redirect with setting second arg to zero -// since we only support conversions without the second arg -#define VSX_IMPL_OVERLOAD_Z2(rt, rg, fnm) \ -VSX_FINLINE(rt) fnm(const rg& a) { return fnm(a, 0); } - -VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_int4, vec_ctd) -VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_uint4, vec_ctd) -VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_dword2, vec_ctd) -VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_udword2, vec_ctd) - -VSX_IMPL_OVERLOAD_Z2(vec_float4, vec_int4, vec_ctf) -VSX_IMPL_OVERLOAD_Z2(vec_float4, vec_uint4, vec_ctf) -VSX_IMPL_OVERLOAD_Z2(vec_float4, vec_dword2, vec_ctf) -VSX_IMPL_OVERLOAD_Z2(vec_float4, vec_udword2, vec_ctf) - -VSX_IMPL_OVERLOAD_Z2(vec_int4, vec_double2, vec_cts) -VSX_IMPL_OVERLOAD_Z2(vec_int4, vec_float4, vec_cts) - -VSX_IMPL_OVERLOAD_Z2(vec_uint4, vec_double2, vec_ctu) -VSX_IMPL_OVERLOAD_Z2(vec_uint4, vec_float4, vec_ctu) - -VSX_IMPL_OVERLOAD_Z2(vec_dword2, vec_double2, vec_ctsl) -VSX_IMPL_OVERLOAD_Z2(vec_dword2, vec_float4, vec_ctsl) - -VSX_IMPL_OVERLOAD_Z2(vec_udword2, vec_double2, vec_ctul) -VSX_IMPL_OVERLOAD_Z2(vec_udword2, vec_float4, vec_ctul) - -// fixme: implement conversions of odd-numbered elements in a dirty way -// since xlc doesn't support VSX registers operand in inline asm. -#define VSX_IMPL_CONV_ODD_4_2(rt, rg, fnm, fn2) \ -VSX_FINLINE(rt) fnm(const rg& a) { return fn2(vec_sldw(a, a, 3)); } - -VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_float4, vec_cvfo, vec_cvf) -VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_int4, vec_ctdo, vec_ctd) -VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_uint4, vec_ctdo, vec_ctd) - -VSX_IMPL_CONV_ODD_4_2(vec_dword2, vec_float4, vec_ctslo, vec_ctsl) -VSX_IMPL_CONV_ODD_4_2(vec_udword2, vec_float4, vec_ctulo, vec_ctul) - -#define VSX_IMPL_CONV_ODD_2_4(rt, rg, fnm, fn2) \ -VSX_FINLINE(rt) fnm(const rg& a) \ -{ \ - rt v4 = fn2(a); \ - return vec_sldw(v4, v4, 1); \ -} - -VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_double2, vec_cvfo, vec_cvf) -VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_dword2, vec_ctfo, vec_ctf) -VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_udword2, vec_ctfo, vec_ctf) - -VSX_IMPL_CONV_ODD_2_4(vec_int4, vec_double2, vec_ctso, vec_cts) -VSX_IMPL_CONV_ODD_2_4(vec_uint4, vec_double2, vec_ctuo, vec_ctu) - -#endif // XLC VSX compatibility - -// ignore GCC warning that caused by -Wunused-but-set-variable in rare cases -#if defined(__GNUG__) && !defined(__clang__) -# define VSX_UNUSED(Tvec) Tvec __attribute__((__unused__)) -#else // CLANG, XLC -# define VSX_UNUSED(Tvec) Tvec -#endif - -// gcc can find his way in casting log int and XLC, CLANG ambiguous -#if defined(__clang__) || defined(__IBMCPP__) - VSX_FINLINE(vec_udword2) vec_splats(uint64 v) - { return vec_splats((unsigned long long) v); } - - VSX_FINLINE(vec_dword2) vec_splats(int64 v) - { return vec_splats((long long) v); } - - VSX_FINLINE(vec_udword2) vec_promote(uint64 a, int b) - { return vec_promote((unsigned long long) a, b); } - - VSX_FINLINE(vec_dword2) vec_promote(int64 a, int b) - { return vec_promote((long long) a, b); } -#endif - -/* - * implement vsx_ld(offset, pointer), vsx_st(vector, offset, pointer) - * load and set using offset depend on the pointer type - * - * implement vsx_ldf(offset, pointer), vsx_stf(vector, offset, pointer) - * load and set using offset depend on fixed bytes size - * - * Note: In clang vec_xl and vec_xst fails to load unaligned addresses - * so we are using vec_vsx_ld, vec_vsx_st instead -*/ - -#if defined(__clang__) && !defined(__IBMCPP__) -# define vsx_ldf vec_vsx_ld -# define vsx_stf vec_vsx_st -#else // GCC , XLC -# define vsx_ldf vec_xl -# define vsx_stf vec_xst -#endif - -#define VSX_OFFSET(o, p) ((o) * sizeof(*(p))) -#define vsx_ld(o, p) vsx_ldf(VSX_OFFSET(o, p), p) -#define vsx_st(v, o, p) vsx_stf(v, VSX_OFFSET(o, p), p) - -/* - * implement vsx_ld2(offset, pointer), vsx_st2(vector, offset, pointer) to load and store double words - * In GCC vec_xl and vec_xst it maps to vec_vsx_ld, vec_vsx_st which doesn't support long long - * and in CLANG we are using vec_vsx_ld, vec_vsx_st because vec_xl, vec_xst fails to load unaligned addresses - * - * In XLC vec_xl and vec_xst fail to cast int64(long int) to long long -*/ -#if (defined(__GNUG__) || defined(__clang__)) && !defined(__IBMCPP__) - VSX_FINLINE(vec_udword2) vsx_ld2(long o, const uint64* p) - { return vec_udword2_c(vsx_ldf(VSX_OFFSET(o, p), (unsigned int*)p)); } - - VSX_FINLINE(vec_dword2) vsx_ld2(long o, const int64* p) - { return vec_dword2_c(vsx_ldf(VSX_OFFSET(o, p), (int*)p)); } - - VSX_FINLINE(void) vsx_st2(const vec_udword2& vec, long o, uint64* p) - { vsx_stf(vec_uint4_c(vec), VSX_OFFSET(o, p), (unsigned int*)p); } - - VSX_FINLINE(void) vsx_st2(const vec_dword2& vec, long o, int64* p) - { vsx_stf(vec_int4_c(vec), VSX_OFFSET(o, p), (int*)p); } -#else // XLC - VSX_FINLINE(vec_udword2) vsx_ld2(long o, const uint64* p) - { return vsx_ldf(VSX_OFFSET(o, p), (unsigned long long*)p); } - - VSX_FINLINE(vec_dword2) vsx_ld2(long o, const int64* p) - { return vsx_ldf(VSX_OFFSET(o, p), (long long*)p); } - - VSX_FINLINE(void) vsx_st2(const vec_udword2& vec, long o, uint64* p) - { vsx_stf(vec, VSX_OFFSET(o, p), (unsigned long long*)p); } - - VSX_FINLINE(void) vsx_st2(const vec_dword2& vec, long o, int64* p) - { vsx_stf(vec, VSX_OFFSET(o, p), (long long*)p); } -#endif - -// Store lower 8 byte -#define vec_st_l8(v, p) *((uint64*)(p)) = vec_extract(vec_udword2_c(v), 0) - -// Store higher 8 byte -#define vec_st_h8(v, p) *((uint64*)(p)) = vec_extract(vec_udword2_c(v), 1) - -// Load 64-bits of integer data to lower part -#define VSX_IMPL_LOAD_L8(Tvec, Tp) \ -VSX_FINLINE(Tvec) vec_ld_l8(const Tp *p) \ -{ return ((Tvec)vec_promote(*((uint64*)p), 0)); } - -VSX_IMPL_LOAD_L8(vec_uchar16, uchar) -VSX_IMPL_LOAD_L8(vec_char16, schar) -VSX_IMPL_LOAD_L8(vec_ushort8, ushort) -VSX_IMPL_LOAD_L8(vec_short8, short) -VSX_IMPL_LOAD_L8(vec_uint4, uint) -VSX_IMPL_LOAD_L8(vec_int4, int) -VSX_IMPL_LOAD_L8(vec_float4, float) -VSX_IMPL_LOAD_L8(vec_udword2, uint64) -VSX_IMPL_LOAD_L8(vec_dword2, int64) -VSX_IMPL_LOAD_L8(vec_double2, double) - -// logical not -#define vec_not(a) vec_nor(a, a) - -// power9 yaya -// not equal -#ifndef vec_cmpne -# define vec_cmpne(a, b) vec_not(vec_cmpeq(a, b)) -#endif - -// absolute difference -#ifndef vec_absd -# define vec_absd(a, b) vec_sub(vec_max(a, b), vec_min(a, b)) -#endif - -/* - * Implement vec_unpacklu and vec_unpackhu - * since vec_unpackl, vec_unpackh only support signed integers -**/ -#define VSX_IMPL_UNPACKU(rt, rg, zero) \ -VSX_FINLINE(rt) vec_unpacklu(const rg& a) \ -{ return (rt)(vec_mergel(a, zero)); } \ -VSX_FINLINE(rt) vec_unpackhu(const rg& a) \ -{ return (rt)(vec_mergeh(a, zero)); } - -VSX_IMPL_UNPACKU(vec_ushort8, vec_uchar16, vec_uchar16_z) -VSX_IMPL_UNPACKU(vec_uint4, vec_ushort8, vec_ushort8_z) -VSX_IMPL_UNPACKU(vec_udword2, vec_uint4, vec_uint4_z) - -/* - * Implement vec_mergesqe and vec_mergesqo - * Merges the sequence values of even and odd elements of two vectors -*/ -#define VSX_IMPL_PERM(rt, fnm, ...) \ -VSX_FINLINE(rt) fnm(const rt& a, const rt& b) \ -{ static const vec_uchar16 perm = {__VA_ARGS__}; return vec_perm(a, b, perm); } - -// 16 -#define perm16_mergesqe 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30 -#define perm16_mergesqo 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31 -VSX_IMPL_PERM(vec_uchar16, vec_mergesqe, perm16_mergesqe) -VSX_IMPL_PERM(vec_uchar16, vec_mergesqo, perm16_mergesqo) -VSX_IMPL_PERM(vec_char16, vec_mergesqe, perm16_mergesqe) -VSX_IMPL_PERM(vec_char16, vec_mergesqo, perm16_mergesqo) -// 8 -#define perm8_mergesqe 0, 1, 4, 5, 8, 9, 12, 13, 16, 17, 20, 21, 24, 25, 28, 29 -#define perm8_mergesqo 2, 3, 6, 7, 10, 11, 14, 15, 18, 19, 22, 23, 26, 27, 30, 31 -VSX_IMPL_PERM(vec_ushort8, vec_mergesqe, perm8_mergesqe) -VSX_IMPL_PERM(vec_ushort8, vec_mergesqo, perm8_mergesqo) -VSX_IMPL_PERM(vec_short8, vec_mergesqe, perm8_mergesqe) -VSX_IMPL_PERM(vec_short8, vec_mergesqo, perm8_mergesqo) -// 4 -#define perm4_mergesqe 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27 -#define perm4_mergesqo 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 -VSX_IMPL_PERM(vec_uint4, vec_mergesqe, perm4_mergesqe) -VSX_IMPL_PERM(vec_uint4, vec_mergesqo, perm4_mergesqo) -VSX_IMPL_PERM(vec_int4, vec_mergesqe, perm4_mergesqe) -VSX_IMPL_PERM(vec_int4, vec_mergesqo, perm4_mergesqo) -VSX_IMPL_PERM(vec_float4, vec_mergesqe, perm4_mergesqe) -VSX_IMPL_PERM(vec_float4, vec_mergesqo, perm4_mergesqo) -// 2 -VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqe, vec_mergeh) -VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqo, vec_mergel) -VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mergesqe, vec_mergeh) -VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mergesqo, vec_mergel) -VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqe, vec_mergeh) -VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqo, vec_mergel) - -/* - * Implement vec_mergesqh and vec_mergesql - * Merges the sequence most and least significant halves of two vectors -*/ -#define VSX_IMPL_MERGESQHL(Tvec) \ -VSX_FINLINE(Tvec) vec_mergesqh(const Tvec& a, const Tvec& b) \ -{ return (Tvec)vec_mergeh(vec_udword2_c(a), vec_udword2_c(b)); } \ -VSX_FINLINE(Tvec) vec_mergesql(const Tvec& a, const Tvec& b) \ -{ return (Tvec)vec_mergel(vec_udword2_c(a), vec_udword2_c(b)); } -VSX_IMPL_MERGESQHL(vec_uchar16) -VSX_IMPL_MERGESQHL(vec_char16) -VSX_IMPL_MERGESQHL(vec_ushort8) -VSX_IMPL_MERGESQHL(vec_short8) -VSX_IMPL_MERGESQHL(vec_uint4) -VSX_IMPL_MERGESQHL(vec_int4) -VSX_IMPL_MERGESQHL(vec_float4) -VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqh, vec_mergeh) -VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesql, vec_mergel) -VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mergesqh, vec_mergeh) -VSX_REDIRECT_2RG(vec_dword2, vec_dword2, vec_mergesql, vec_mergel) -VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqh, vec_mergeh) -VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesql, vec_mergel) - - -// 2 and 4 channels interleave for all types except 2 lanes -#define VSX_IMPL_ST_INTERLEAVE(Tp, Tvec) \ -VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, Tp* ptr) \ -{ \ - vsx_stf(vec_mergeh(a, b), 0, ptr); \ - vsx_stf(vec_mergel(a, b), 16, ptr); \ -} \ -VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ - const Tvec& c, const Tvec& d, Tp* ptr) \ -{ \ - Tvec ac = vec_mergeh(a, c); \ - Tvec bd = vec_mergeh(b, d); \ - vsx_stf(vec_mergeh(ac, bd), 0, ptr); \ - vsx_stf(vec_mergel(ac, bd), 16, ptr); \ - ac = vec_mergel(a, c); \ - bd = vec_mergel(b, d); \ - vsx_stf(vec_mergeh(ac, bd), 32, ptr); \ - vsx_stf(vec_mergel(ac, bd), 48, ptr); \ -} -VSX_IMPL_ST_INTERLEAVE(uchar, vec_uchar16) -VSX_IMPL_ST_INTERLEAVE(schar, vec_char16) -VSX_IMPL_ST_INTERLEAVE(ushort, vec_ushort8) -VSX_IMPL_ST_INTERLEAVE(short, vec_short8) -VSX_IMPL_ST_INTERLEAVE(uint, vec_uint4) -VSX_IMPL_ST_INTERLEAVE(int, vec_int4) -VSX_IMPL_ST_INTERLEAVE(float, vec_float4) - -// 2 and 4 channels deinterleave for 16 lanes -#define VSX_IMPL_ST_DINTERLEAVE_8(Tp, Tvec) \ -VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b) \ -{ \ - Tvec v0 = vsx_ld(0, ptr); \ - Tvec v1 = vsx_ld(16, ptr); \ - a = vec_mergesqe(v0, v1); \ - b = vec_mergesqo(v0, v1); \ -} \ -VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, \ - Tvec& c, Tvec& d) \ -{ \ - Tvec v0 = vsx_ld(0, ptr); \ - Tvec v1 = vsx_ld(16, ptr); \ - Tvec v2 = vsx_ld(32, ptr); \ - Tvec v3 = vsx_ld(48, ptr); \ - Tvec m0 = vec_mergesqe(v0, v1); \ - Tvec m1 = vec_mergesqe(v2, v3); \ - a = vec_mergesqe(m0, m1); \ - c = vec_mergesqo(m0, m1); \ - m0 = vec_mergesqo(v0, v1); \ - m1 = vec_mergesqo(v2, v3); \ - b = vec_mergesqe(m0, m1); \ - d = vec_mergesqo(m0, m1); \ -} -VSX_IMPL_ST_DINTERLEAVE_8(uchar, vec_uchar16) -VSX_IMPL_ST_DINTERLEAVE_8(schar, vec_char16) - -// 2 and 4 channels deinterleave for 8 lanes -#define VSX_IMPL_ST_DINTERLEAVE_16(Tp, Tvec) \ -VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b) \ -{ \ - Tvec v0 = vsx_ld(0, ptr); \ - Tvec v1 = vsx_ld(8, ptr); \ - a = vec_mergesqe(v0, v1); \ - b = vec_mergesqo(v0, v1); \ -} \ -VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, \ - Tvec& c, Tvec& d) \ -{ \ - Tvec v0 = vsx_ld(0, ptr); \ - Tvec v1 = vsx_ld(8, ptr); \ - Tvec m0 = vec_mergeh(v0, v1); \ - Tvec m1 = vec_mergel(v0, v1); \ - Tvec ab0 = vec_mergeh(m0, m1); \ - Tvec cd0 = vec_mergel(m0, m1); \ - v0 = vsx_ld(16, ptr); \ - v1 = vsx_ld(24, ptr); \ - m0 = vec_mergeh(v0, v1); \ - m1 = vec_mergel(v0, v1); \ - Tvec ab1 = vec_mergeh(m0, m1); \ - Tvec cd1 = vec_mergel(m0, m1); \ - a = vec_mergesqh(ab0, ab1); \ - b = vec_mergesql(ab0, ab1); \ - c = vec_mergesqh(cd0, cd1); \ - d = vec_mergesql(cd0, cd1); \ -} -VSX_IMPL_ST_DINTERLEAVE_16(ushort, vec_ushort8) -VSX_IMPL_ST_DINTERLEAVE_16(short, vec_short8) - -// 2 and 4 channels deinterleave for 4 lanes -#define VSX_IMPL_ST_DINTERLEAVE_32(Tp, Tvec) \ -VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b) \ -{ \ - a = vsx_ld(0, ptr); \ - b = vsx_ld(4, ptr); \ - Tvec m0 = vec_mergeh(a, b); \ - Tvec m1 = vec_mergel(a, b); \ - a = vec_mergeh(m0, m1); \ - b = vec_mergel(m0, m1); \ -} \ -VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, \ - Tvec& c, Tvec& d) \ -{ \ - Tvec v0 = vsx_ld(0, ptr); \ - Tvec v1 = vsx_ld(4, ptr); \ - Tvec v2 = vsx_ld(8, ptr); \ - Tvec v3 = vsx_ld(12, ptr); \ - Tvec m0 = vec_mergeh(v0, v2); \ - Tvec m1 = vec_mergeh(v1, v3); \ - a = vec_mergeh(m0, m1); \ - b = vec_mergel(m0, m1); \ - m0 = vec_mergel(v0, v2); \ - m1 = vec_mergel(v1, v3); \ - c = vec_mergeh(m0, m1); \ - d = vec_mergel(m0, m1); \ -} -VSX_IMPL_ST_DINTERLEAVE_32(uint, vec_uint4) -VSX_IMPL_ST_DINTERLEAVE_32(int, vec_int4) -VSX_IMPL_ST_DINTERLEAVE_32(float, vec_float4) - -// 2 and 4 channels interleave and deinterleave for 2 lanes -#define VSX_IMPL_ST_D_INTERLEAVE_64(Tp, Tvec, ld_func, st_func) \ -VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, Tp* ptr) \ -{ \ - st_func(vec_mergeh(a, b), 0, ptr); \ - st_func(vec_mergel(a, b), 2, ptr); \ -} \ -VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ - const Tvec& c, const Tvec& d, Tp* ptr) \ -{ \ - st_func(vec_mergeh(a, b), 0, ptr); \ - st_func(vec_mergeh(c, d), 2, ptr); \ - st_func(vec_mergel(a, b), 4, ptr); \ - st_func(vec_mergel(c, d), 6, ptr); \ -} \ -VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b) \ -{ \ - Tvec m0 = ld_func(0, ptr); \ - Tvec m1 = ld_func(2, ptr); \ - a = vec_mergeh(m0, m1); \ - b = vec_mergel(m0, m1); \ -} \ -VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, \ - Tvec& c, Tvec& d) \ -{ \ - Tvec v0 = ld_func(0, ptr); \ - Tvec v1 = ld_func(2, ptr); \ - Tvec v2 = ld_func(4, ptr); \ - Tvec v3 = ld_func(6, ptr); \ - a = vec_mergeh(v0, v2); \ - b = vec_mergel(v0, v2); \ - c = vec_mergeh(v1, v3); \ - d = vec_mergel(v1, v3); \ -} -VSX_IMPL_ST_D_INTERLEAVE_64(int64, vec_dword2, vsx_ld2, vsx_st2) -VSX_IMPL_ST_D_INTERLEAVE_64(uint64, vec_udword2, vsx_ld2, vsx_st2) -VSX_IMPL_ST_D_INTERLEAVE_64(double, vec_double2, vsx_ld, vsx_st) - -/* 3 channels */ -#define VSX_IMPL_ST_INTERLEAVE_3CH_16(Tp, Tvec) \ -VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ - const Tvec& c, Tp* ptr) \ -{ \ - static const vec_uchar16 a12 = {0, 16, 0, 1, 17, 0, 2, 18, 0, 3, 19, 0, 4, 20, 0, 5}; \ - static const vec_uchar16 a123 = {0, 1, 16, 3, 4, 17, 6, 7, 18, 9, 10, 19, 12, 13, 20, 15}; \ - vsx_st(vec_perm(vec_perm(a, b, a12), c, a123), 0, ptr); \ - static const vec_uchar16 b12 = {21, 0, 6, 22, 0, 7, 23, 0, 8, 24, 0, 9, 25, 0, 10, 26}; \ - static const vec_uchar16 b123 = {0, 21, 2, 3, 22, 5, 6, 23, 8, 9, 24, 11, 12, 25, 14, 15}; \ - vsx_st(vec_perm(vec_perm(a, b, b12), c, b123), 16, ptr); \ - static const vec_uchar16 c12 = {0, 11, 27, 0, 12, 28, 0, 13, 29, 0, 14, 30, 0, 15, 31, 0}; \ - static const vec_uchar16 c123 = {26, 1, 2, 27, 4, 5, 28, 7, 8, 29, 10, 11, 30, 13, 14, 31}; \ - vsx_st(vec_perm(vec_perm(a, b, c12), c, c123), 32, ptr); \ -} \ -VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c) \ -{ \ - Tvec v1 = vsx_ld(0, ptr); \ - Tvec v2 = vsx_ld(16, ptr); \ - Tvec v3 = vsx_ld(32, ptr); \ - static const vec_uchar16 a12_perm = {0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 0, 0, 0, 0, 0}; \ - static const vec_uchar16 a123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 17, 20, 23, 26, 29}; \ - a = vec_perm(vec_perm(v1, v2, a12_perm), v3, a123_perm); \ - static const vec_uchar16 b12_perm = {1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 0, 0, 0, 0, 0}; \ - static const vec_uchar16 b123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 18, 21, 24, 27, 30}; \ - b = vec_perm(vec_perm(v1, v2, b12_perm), v3, b123_perm); \ - static const vec_uchar16 c12_perm = {2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 0, 0, 0, 0, 0, 0}; \ - static const vec_uchar16 c123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 16, 19, 22, 25, 28, 31}; \ - c = vec_perm(vec_perm(v1, v2, c12_perm), v3, c123_perm); \ -} -VSX_IMPL_ST_INTERLEAVE_3CH_16(uchar, vec_uchar16) -VSX_IMPL_ST_INTERLEAVE_3CH_16(schar, vec_char16) - -#define VSX_IMPL_ST_INTERLEAVE_3CH_8(Tp, Tvec) \ -VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ - const Tvec& c, Tp* ptr) \ -{ \ - static const vec_uchar16 a12 = {0, 1, 16, 17, 0, 0, 2, 3, 18, 19, 0, 0, 4, 5, 20, 21}; \ - static const vec_uchar16 a123 = {0, 1, 2, 3, 16, 17, 6, 7, 8, 9, 18, 19, 12, 13, 14, 15}; \ - vsx_st(vec_perm(vec_perm(a, b, a12), c, a123), 0, ptr); \ - static const vec_uchar16 b12 = {0, 0, 6, 7, 22, 23, 0, 0, 8, 9, 24, 25, 0, 0, 10, 11}; \ - static const vec_uchar16 b123 = {20, 21, 2, 3, 4, 5, 22, 23, 8, 9, 10, 11, 24, 25, 14, 15}; \ - vsx_st(vec_perm(vec_perm(a, b, b12), c, b123), 8, ptr); \ - static const vec_uchar16 c12 = {26, 27, 0, 0, 12, 13, 28, 29, 0, 0, 14, 15, 30, 31, 0, 0}; \ - static const vec_uchar16 c123 = {0, 1, 26, 27, 4, 5, 6, 7, 28, 29, 10, 11, 12, 13, 30, 31}; \ - vsx_st(vec_perm(vec_perm(a, b, c12), c, c123), 16, ptr); \ -} \ -VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c) \ -{ \ - Tvec v1 = vsx_ld(0, ptr); \ - Tvec v2 = vsx_ld(8, ptr); \ - Tvec v3 = vsx_ld(16, ptr); \ - static const vec_uchar16 a12_perm = {0, 1, 6, 7, 12, 13, 18, 19, 24, 25, 30, 31, 0, 0, 0, 0}; \ - static const vec_uchar16 a123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 20, 21, 26, 27}; \ - a = vec_perm(vec_perm(v1, v2, a12_perm), v3, a123_perm); \ - static const vec_uchar16 b12_perm = {2, 3, 8, 9, 14, 15, 20, 21, 26, 27, 0, 0, 0, 0, 0, 0}; \ - static const vec_uchar16 b123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 16, 17, 22, 23, 28, 29}; \ - b = vec_perm(vec_perm(v1, v2, b12_perm), v3, b123_perm); \ - static const vec_uchar16 c12_perm = {4, 5, 10, 11, 16, 17, 22, 23, 28, 29, 0, 0, 0, 0, 0, 0}; \ - static const vec_uchar16 c123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 18, 19, 24, 25, 30, 31}; \ - c = vec_perm(vec_perm(v1, v2, c12_perm), v3, c123_perm); \ -} -VSX_IMPL_ST_INTERLEAVE_3CH_8(ushort, vec_ushort8) -VSX_IMPL_ST_INTERLEAVE_3CH_8(short, vec_short8) - -#define VSX_IMPL_ST_INTERLEAVE_3CH_4(Tp, Tvec) \ -VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ - const Tvec& c, Tp* ptr) \ -{ \ - Tvec hbc = vec_mergeh(b, c); \ - static const vec_uchar16 ahbc = {0, 1, 2, 3, 16, 17, 18, 19, 20, 21, 22, 23, 4, 5, 6, 7}; \ - vsx_st(vec_perm(a, hbc, ahbc), 0, ptr); \ - Tvec lab = vec_mergel(a, b); \ - vsx_st(vec_sld(lab, hbc, 8), 4, ptr); \ - static const vec_uchar16 clab = {8, 9, 10, 11, 24, 25, 26, 27, 28, 29, 30, 31, 12, 13, 14, 15};\ - vsx_st(vec_perm(c, lab, clab), 8, ptr); \ -} \ -VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c) \ -{ \ - Tvec v1 = vsx_ld(0, ptr); \ - Tvec v2 = vsx_ld(4, ptr); \ - Tvec v3 = vsx_ld(8, ptr); \ - static const vec_uchar16 flp = {0, 1, 2, 3, 12, 13, 14, 15, 16, 17, 18, 19, 28, 29, 30, 31}; \ - a = vec_perm(v1, vec_sld(v3, v2, 8), flp); \ - static const vec_uchar16 flp2 = {28, 29, 30, 31, 0, 1, 2, 3, 12, 13, 14, 15, 16, 17, 18, 19}; \ - b = vec_perm(v2, vec_sld(v1, v3, 8), flp2); \ - c = vec_perm(vec_sld(v2, v1, 8), v3, flp); \ -} -VSX_IMPL_ST_INTERLEAVE_3CH_4(uint, vec_uint4) -VSX_IMPL_ST_INTERLEAVE_3CH_4(int, vec_int4) -VSX_IMPL_ST_INTERLEAVE_3CH_4(float, vec_float4) - -#define VSX_IMPL_ST_INTERLEAVE_3CH_2(Tp, Tvec, ld_func, st_func) \ -VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, \ - const Tvec& c, Tp* ptr) \ -{ \ - st_func(vec_mergeh(a, b), 0, ptr); \ - st_func(vec_permi(c, a, 1), 2, ptr); \ - st_func(vec_mergel(b, c), 4, ptr); \ -} \ -VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, \ - Tvec& b, Tvec& c) \ -{ \ - Tvec v1 = ld_func(0, ptr); \ - Tvec v2 = ld_func(2, ptr); \ - Tvec v3 = ld_func(4, ptr); \ - a = vec_permi(v1, v2, 1); \ - b = vec_permi(v1, v3, 2); \ - c = vec_permi(v2, v3, 1); \ -} -VSX_IMPL_ST_INTERLEAVE_3CH_2(int64, vec_dword2, vsx_ld2, vsx_st2) -VSX_IMPL_ST_INTERLEAVE_3CH_2(uint64, vec_udword2, vsx_ld2, vsx_st2) -VSX_IMPL_ST_INTERLEAVE_3CH_2(double, vec_double2, vsx_ld, vsx_st) - -#endif // CV_VSX - -//! @} - -#endif // OPENCV_HAL_VSX_UTILS_HPP diff --git a/include/opencv2/features2d.hpp b/include/opencv2/features2d.hpp deleted file mode 100755 index 293b5be..0000000 --- a/include/opencv2/features2d.hpp +++ /dev/null @@ -1,1523 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_FEATURES_2D_HPP -#define OPENCV_FEATURES_2D_HPP - -#include "opencv2/opencv_modules.hpp" -#include "opencv2/core.hpp" - -#ifdef HAVE_OPENCV_FLANN -#include "opencv2/flann/miniflann.hpp" -#endif - -/** - @defgroup features2d 2D Features Framework - @{ - @defgroup features2d_main Feature Detection and Description - @defgroup features2d_match Descriptor Matchers - -Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to -easily switch between different algorithms solving the same problem. This section is devoted to -matching descriptors that are represented as vectors in a multidimensional space. All objects that -implement vector descriptor matchers inherit the DescriptorMatcher interface. - -@note - - An example explaining keypoint matching can be found at - opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp - - An example on descriptor matching evaluation can be found at - opencv_source_code/samples/cpp/detector_descriptor_matcher_evaluation.cpp - - An example on one to many image matching can be found at - opencv_source_code/samples/cpp/matching_to_many_images.cpp - - @defgroup features2d_draw Drawing Function of Keypoints and Matches - @defgroup features2d_category Object Categorization - -This section describes approaches based on local 2D features and used to categorize objects. - -@note - - A complete Bag-Of-Words sample can be found at - opencv_source_code/samples/cpp/bagofwords_classification.cpp - - (Python) An example using the features2D framework to perform object categorization can be - found at opencv_source_code/samples/python/find_obj.py - - @defgroup feature2d_hal Hardware Acceleration Layer - @{ - @defgroup features2d_hal_interface Interface - @} - @} - */ - -namespace cv -{ - -//! @addtogroup features2d -//! @{ - -// //! writes vector of keypoints to the file storage -// CV_EXPORTS void write(FileStorage& fs, const String& name, const std::vector& keypoints); -// //! reads vector of keypoints from the specified file storage node -// CV_EXPORTS void read(const FileNode& node, CV_OUT std::vector& keypoints); - -/** @brief A class filters a vector of keypoints. - - Because now it is difficult to provide a convenient interface for all usage scenarios of the - keypoints filter class, it has only several needed by now static methods. - */ -class CV_EXPORTS KeyPointsFilter -{ -public: - KeyPointsFilter(){} - - /* - * Remove keypoints within borderPixels of an image edge. - */ - static void runByImageBorder( std::vector& keypoints, Size imageSize, int borderSize ); - /* - * Remove keypoints of sizes out of range. - */ - static void runByKeypointSize( std::vector& keypoints, float minSize, - float maxSize=FLT_MAX ); - /* - * Remove keypoints from some image by mask for pixels of this image. - */ - static void runByPixelsMask( std::vector& keypoints, const Mat& mask ); - /* - * Remove duplicated keypoints. - */ - static void removeDuplicated( std::vector& keypoints ); - /* - * Remove duplicated keypoints and sort the remaining keypoints - */ - static void removeDuplicatedSorted( std::vector& keypoints ); - - /* - * Retain the specified number of the best keypoints (according to the response) - */ - static void retainBest( std::vector& keypoints, int npoints ); -}; - - -/************************************ Base Classes ************************************/ - -/** @brief Abstract base class for 2D image feature detectors and descriptor extractors -*/ -#ifdef __EMSCRIPTEN__ -class CV_EXPORTS_W Feature2D : public Algorithm -#else -class CV_EXPORTS_W Feature2D : public virtual Algorithm -#endif -{ -public: - virtual ~Feature2D(); - - /** @brief Detects keypoints in an image (first variant) or image set (second variant). - - @param image Image. - @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set - of keypoints detected in images[i] . - @param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer - matrix with non-zero values in the region of interest. - */ - CV_WRAP virtual void detect( InputArray image, - CV_OUT std::vector& keypoints, - InputArray mask=noArray() ); - - /** @overload - @param images Image set. - @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set - of keypoints detected in images[i] . - @param masks Masks for each input image specifying where to look for keypoints (optional). - masks[i] is a mask for images[i]. - */ - CV_WRAP virtual void detect( InputArrayOfArrays images, - CV_OUT std::vector >& keypoints, - InputArrayOfArrays masks=noArray() ); - - /** @brief Computes the descriptors for a set of keypoints detected in an image (first variant) or image set - (second variant). - - @param image Image. - @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be - computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint - with several dominant orientations (for each orientation). - @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are - descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the - descriptor for keypoint j-th keypoint. - */ - CV_WRAP virtual void compute( InputArray image, - CV_OUT CV_IN_OUT std::vector& keypoints, - OutputArray descriptors ); - - /** @overload - - @param images Image set. - @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be - computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint - with several dominant orientations (for each orientation). - @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are - descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the - descriptor for keypoint j-th keypoint. - */ - CV_WRAP virtual void compute( InputArrayOfArrays images, - CV_OUT CV_IN_OUT std::vector >& keypoints, - OutputArrayOfArrays descriptors ); - - /** Detects keypoints and computes the descriptors */ - CV_WRAP virtual void detectAndCompute( InputArray image, InputArray mask, - CV_OUT std::vector& keypoints, - OutputArray descriptors, - bool useProvidedKeypoints=false ); - - CV_WRAP virtual int descriptorSize() const; - CV_WRAP virtual int descriptorType() const; - CV_WRAP virtual int defaultNorm() const; - - CV_WRAP void write( const String& fileName ) const; - - CV_WRAP void read( const String& fileName ); - - virtual void write( FileStorage&) const CV_OVERRIDE; - - // see corresponding cv::Algorithm method - CV_WRAP virtual void read( const FileNode&) CV_OVERRIDE; - - //! Return true if detector object is empty - CV_WRAP virtual bool empty() const CV_OVERRIDE; - CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; - - // see corresponding cv::Algorithm method - CV_WRAP inline void write(const Ptr& fs, const String& name = String()) const { Algorithm::write(fs, name); } -}; - -/** Feature detectors in OpenCV have wrappers with a common interface that enables you to easily switch -between different algorithms solving the same problem. All objects that implement keypoint detectors -inherit the FeatureDetector interface. */ -typedef Feature2D FeatureDetector; - -/** Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you -to easily switch between different algorithms solving the same problem. This section is devoted to -computing descriptors represented as vectors in a multidimensional space. All objects that implement -the vector descriptor extractors inherit the DescriptorExtractor interface. - */ -typedef Feature2D DescriptorExtractor; - -//! @addtogroup features2d_main -//! @{ - - -/** @brief Class for implementing the wrapper which makes detectors and extractors to be affine invariant, -described as ASIFT in @cite YM11 . -*/ -class CV_EXPORTS_W AffineFeature : public Feature2D -{ -public: - /** - @param backend The detector/extractor you want to use as backend. - @param maxTilt The highest power index of tilt factor. 5 is used in the paper as tilt sampling range n. - @param minTilt The lowest power index of tilt factor. 0 is used in the paper. - @param tiltStep Tilt sampling step \f$\delta_t\f$ in Algorithm 1 in the paper. - @param rotateStepBase Rotation sampling step factor b in Algorithm 1 in the paper. - */ - CV_WRAP static Ptr create(const Ptr& backend, - int maxTilt = 5, int minTilt = 0, float tiltStep = 1.4142135623730951f, float rotateStepBase = 72); - - CV_WRAP virtual void setViewParams(const std::vector& tilts, const std::vector& rolls) = 0; - CV_WRAP virtual void getViewParams(std::vector& tilts, std::vector& rolls) const = 0; - CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; -}; - -typedef AffineFeature AffineFeatureDetector; -typedef AffineFeature AffineDescriptorExtractor; - - -/** @brief Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform -(SIFT) algorithm by D. Lowe @cite Lowe04 . -*/ -class CV_EXPORTS_W SIFT : public Feature2D -{ -public: - /** - @param nfeatures The number of best features to retain. The features are ranked by their scores - (measured in SIFT algorithm as the local contrast) - - @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The - number of octaves is computed automatically from the image resolution. - - @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform - (low-contrast) regions. The larger the threshold, the less features are produced by the detector. - - @note The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When - nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set - this argument to 0.09. - - @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning - is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are - filtered out (more features are retained). - - @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image - is captured with a weak camera with soft lenses, you might want to reduce the number. - */ - CV_WRAP static Ptr create(int nfeatures = 0, int nOctaveLayers = 3, - double contrastThreshold = 0.04, double edgeThreshold = 10, - double sigma = 1.6); - - /** @brief Create SIFT with specified descriptorType. - @param nfeatures The number of best features to retain. The features are ranked by their scores - (measured in SIFT algorithm as the local contrast) - - @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The - number of octaves is computed automatically from the image resolution. - - @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform - (low-contrast) regions. The larger the threshold, the less features are produced by the detector. - - @note The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When - nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set - this argument to 0.09. - - @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning - is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are - filtered out (more features are retained). - - @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image - is captured with a weak camera with soft lenses, you might want to reduce the number. - - @param descriptorType The type of descriptors. Only CV_32F and CV_8U are supported. - */ - CV_WRAP static Ptr create(int nfeatures, int nOctaveLayers, - double contrastThreshold, double edgeThreshold, - double sigma, int descriptorType); - - CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; -}; - -typedef SIFT SiftFeatureDetector; -typedef SIFT SiftDescriptorExtractor; - - -/** @brief Class implementing the BRISK keypoint detector and descriptor extractor, described in @cite LCS11 . - */ -class CV_EXPORTS_W BRISK : public Feature2D -{ -public: - /** @brief The BRISK constructor - - @param thresh AGAST detection threshold score. - @param octaves detection octaves. Use 0 to do single scale. - @param patternScale apply this scale to the pattern used for sampling the neighbourhood of a - keypoint. - */ - CV_WRAP static Ptr create(int thresh=30, int octaves=3, float patternScale=1.0f); - - /** @brief The BRISK constructor for a custom pattern - - @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for - keypoint scale 1). - @param numberList defines the number of sampling points on the sampling circle. Must be the same - size as radiusList.. - @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint - scale 1). - @param dMin threshold for the long pairings used for orientation determination (in pixels for - keypoint scale 1). - @param indexChange index remapping of the bits. */ - CV_WRAP static Ptr create(const std::vector &radiusList, const std::vector &numberList, - float dMax=5.85f, float dMin=8.2f, const std::vector& indexChange=std::vector()); - - /** @brief The BRISK constructor for a custom pattern, detection threshold and octaves - - @param thresh AGAST detection threshold score. - @param octaves detection octaves. Use 0 to do single scale. - @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for - keypoint scale 1). - @param numberList defines the number of sampling points on the sampling circle. Must be the same - size as radiusList.. - @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint - scale 1). - @param dMin threshold for the long pairings used for orientation determination (in pixels for - keypoint scale 1). - @param indexChange index remapping of the bits. */ - CV_WRAP static Ptr create(int thresh, int octaves, const std::vector &radiusList, - const std::vector &numberList, float dMax=5.85f, float dMin=8.2f, - const std::vector& indexChange=std::vector()); - CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; -}; - -/** @brief Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor - -described in @cite RRKB11 . The algorithm uses FAST in pyramids to detect stable keypoints, selects -the strongest features using FAST or Harris response, finds their orientation using first-order -moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or -k-tuples) are rotated according to the measured orientation). - */ -class CV_EXPORTS_W ORB : public Feature2D -{ -public: - enum { kBytes = 32, HARRIS_SCORE=0, FAST_SCORE=1 }; - - /** @brief The ORB constructor - - @param nfeatures The maximum number of features to retain. - @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical - pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor - will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor - will mean that to cover certain scale range you will need more pyramid levels and so the speed - will suffer. - @param nlevels The number of pyramid levels. The smallest level will have linear size equal to - input_image_linear_size/pow(scaleFactor, nlevels - firstLevel). - @param edgeThreshold This is size of the border where the features are not detected. It should - roughly match the patchSize parameter. - @param firstLevel The level of pyramid to put source image to. Previous layers are filled - with upscaled source image. - @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The - default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, - so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 - random points (of course, those point coordinates are random, but they are generated from the - pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel - rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such - output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, - denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each - bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3). - @param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features - (the score is written to KeyPoint::score and is used to retain best nfeatures features); - FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, - but it is a little faster to compute. - @param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller - pyramid layers the perceived image area covered by a feature will be larger. - @param fastThreshold the fast threshold - */ - CV_WRAP static Ptr create(int nfeatures=500, float scaleFactor=1.2f, int nlevels=8, int edgeThreshold=31, - int firstLevel=0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31, int fastThreshold=20); - - CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0; - CV_WRAP virtual int getMaxFeatures() const = 0; - - CV_WRAP virtual void setScaleFactor(double scaleFactor) = 0; - CV_WRAP virtual double getScaleFactor() const = 0; - - CV_WRAP virtual void setNLevels(int nlevels) = 0; - CV_WRAP virtual int getNLevels() const = 0; - - CV_WRAP virtual void setEdgeThreshold(int edgeThreshold) = 0; - CV_WRAP virtual int getEdgeThreshold() const = 0; - - CV_WRAP virtual void setFirstLevel(int firstLevel) = 0; - CV_WRAP virtual int getFirstLevel() const = 0; - - CV_WRAP virtual void setWTA_K(int wta_k) = 0; - CV_WRAP virtual int getWTA_K() const = 0; - - CV_WRAP virtual void setScoreType(int scoreType) = 0; - CV_WRAP virtual int getScoreType() const = 0; - - CV_WRAP virtual void setPatchSize(int patchSize) = 0; - CV_WRAP virtual int getPatchSize() const = 0; - - CV_WRAP virtual void setFastThreshold(int fastThreshold) = 0; - CV_WRAP virtual int getFastThreshold() const = 0; - CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; -}; - -/** @brief Maximally stable extremal region extractor - -The class encapsulates all the parameters of the %MSER extraction algorithm (see [wiki -article](http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions)). - -- there are two different implementation of %MSER: one for grey image, one for color image - -- the grey image algorithm is taken from: @cite nister2008linear ; the paper claims to be faster -than union-find method; it actually get 1.5~2m/s on my centrino L7200 1.2GHz laptop. - -- the color image algorithm is taken from: @cite forssen2007maximally ; it should be much slower -than grey image method ( 3~4 times ); the chi_table.h file is taken directly from paper's source -code which is distributed under GPL. - -- (Python) A complete example showing the use of the %MSER detector can be found at samples/python/mser.py -*/ -class CV_EXPORTS_W MSER : public Feature2D -{ -public: - /** @brief Full constructor for %MSER detector - - @param _delta it compares \f$(size_{i}-size_{i-delta})/size_{i-delta}\f$ - @param _min_area prune the area which smaller than minArea - @param _max_area prune the area which bigger than maxArea - @param _max_variation prune the area have similar size to its children - @param _min_diversity for color image, trace back to cut off mser with diversity less than min_diversity - @param _max_evolution for color image, the evolution steps - @param _area_threshold for color image, the area threshold to cause re-initialize - @param _min_margin for color image, ignore too small margin - @param _edge_blur_size for color image, the aperture size for edge blur - */ - CV_WRAP static Ptr create( int _delta=5, int _min_area=60, int _max_area=14400, - double _max_variation=0.25, double _min_diversity=.2, - int _max_evolution=200, double _area_threshold=1.01, - double _min_margin=0.003, int _edge_blur_size=5 ); - - /** @brief Detect %MSER regions - - @param image input image (8UC1, 8UC3 or 8UC4, must be greater or equal than 3x3) - @param msers resulting list of point sets - @param bboxes resulting bounding boxes - */ - CV_WRAP virtual void detectRegions( InputArray image, - CV_OUT std::vector >& msers, - CV_OUT std::vector& bboxes ) = 0; - - CV_WRAP virtual void setDelta(int delta) = 0; - CV_WRAP virtual int getDelta() const = 0; - - CV_WRAP virtual void setMinArea(int minArea) = 0; - CV_WRAP virtual int getMinArea() const = 0; - - CV_WRAP virtual void setMaxArea(int maxArea) = 0; - CV_WRAP virtual int getMaxArea() const = 0; - - CV_WRAP virtual void setPass2Only(bool f) = 0; - CV_WRAP virtual bool getPass2Only() const = 0; - CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; -}; - -/** @overload */ -CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector& keypoints, - int threshold, bool nonmaxSuppression=true ); - -/** @brief Detects corners using the FAST algorithm - -@param image grayscale image where keypoints (corners) are detected. -@param keypoints keypoints detected on the image. -@param threshold threshold on difference between intensity of the central pixel and pixels of a -circle around this pixel. -@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners -(keypoints). -@param type one of the three neighborhoods as defined in the paper: -FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12, -FastFeatureDetector::TYPE_5_8 - -Detects corners using the FAST algorithm by @cite Rosten06 . - -@note In Python API, types are given as cv2.FAST_FEATURE_DETECTOR_TYPE_5_8, -cv2.FAST_FEATURE_DETECTOR_TYPE_7_12 and cv2.FAST_FEATURE_DETECTOR_TYPE_9_16. For corner -detection, use cv2.FAST.detect() method. - */ -CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector& keypoints, - int threshold, bool nonmaxSuppression, int type ); - -//! @} features2d_main - -//! @addtogroup features2d_main -//! @{ - -/** @brief Wrapping class for feature detection using the FAST method. : - */ -class CV_EXPORTS_W FastFeatureDetector : public Feature2D -{ -public: - enum - { - TYPE_5_8 = 0, TYPE_7_12 = 1, TYPE_9_16 = 2, - THRESHOLD = 10000, NONMAX_SUPPRESSION=10001, FAST_N=10002, - }; - - CV_WRAP static Ptr create( int threshold=10, - bool nonmaxSuppression=true, - int type=FastFeatureDetector::TYPE_9_16 ); - - CV_WRAP virtual void setThreshold(int threshold) = 0; - CV_WRAP virtual int getThreshold() const = 0; - - CV_WRAP virtual void setNonmaxSuppression(bool f) = 0; - CV_WRAP virtual bool getNonmaxSuppression() const = 0; - - CV_WRAP virtual void setType(int type) = 0; - CV_WRAP virtual int getType() const = 0; - CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; -}; - -/** @overload */ -CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector& keypoints, - int threshold, bool nonmaxSuppression=true ); - -/** @brief Detects corners using the AGAST algorithm - -@param image grayscale image where keypoints (corners) are detected. -@param keypoints keypoints detected on the image. -@param threshold threshold on difference between intensity of the central pixel and pixels of a -circle around this pixel. -@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners -(keypoints). -@param type one of the four neighborhoods as defined in the paper: -AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d, -AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16 - -For non-Intel platforms, there is a tree optimised variant of AGAST with same numerical results. -The 32-bit binary tree tables were generated automatically from original code using perl script. -The perl script and examples of tree generation are placed in features2d/doc folder. -Detects corners using the AGAST algorithm by @cite mair2010_agast . - - */ -CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector& keypoints, - int threshold, bool nonmaxSuppression, int type ); -//! @} features2d_main - -//! @addtogroup features2d_main -//! @{ - -/** @brief Wrapping class for feature detection using the AGAST method. : - */ -class CV_EXPORTS_W AgastFeatureDetector : public Feature2D -{ -public: - enum - { - AGAST_5_8 = 0, AGAST_7_12d = 1, AGAST_7_12s = 2, OAST_9_16 = 3, - THRESHOLD = 10000, NONMAX_SUPPRESSION = 10001, - }; - - CV_WRAP static Ptr create( int threshold=10, - bool nonmaxSuppression=true, - int type=AgastFeatureDetector::OAST_9_16 ); - - CV_WRAP virtual void setThreshold(int threshold) = 0; - CV_WRAP virtual int getThreshold() const = 0; - - CV_WRAP virtual void setNonmaxSuppression(bool f) = 0; - CV_WRAP virtual bool getNonmaxSuppression() const = 0; - - CV_WRAP virtual void setType(int type) = 0; - CV_WRAP virtual int getType() const = 0; - CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; -}; - -/** @brief Wrapping class for feature detection using the goodFeaturesToTrack function. : - */ -class CV_EXPORTS_W GFTTDetector : public Feature2D -{ -public: - CV_WRAP static Ptr create( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1, - int blockSize=3, bool useHarrisDetector=false, double k=0.04 ); - CV_WRAP static Ptr create( int maxCorners, double qualityLevel, double minDistance, - int blockSize, int gradiantSize, bool useHarrisDetector=false, double k=0.04 ); - CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0; - CV_WRAP virtual int getMaxFeatures() const = 0; - - CV_WRAP virtual void setQualityLevel(double qlevel) = 0; - CV_WRAP virtual double getQualityLevel() const = 0; - - CV_WRAP virtual void setMinDistance(double minDistance) = 0; - CV_WRAP virtual double getMinDistance() const = 0; - - CV_WRAP virtual void setBlockSize(int blockSize) = 0; - CV_WRAP virtual int getBlockSize() const = 0; - - CV_WRAP virtual void setHarrisDetector(bool val) = 0; - CV_WRAP virtual bool getHarrisDetector() const = 0; - - CV_WRAP virtual void setK(double k) = 0; - CV_WRAP virtual double getK() const = 0; - CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; -}; - -/** @brief Class for extracting blobs from an image. : - -The class implements a simple algorithm for extracting blobs from an image: - -1. Convert the source image to binary images by applying thresholding with several thresholds from - minThreshold (inclusive) to maxThreshold (exclusive) with distance thresholdStep between - neighboring thresholds. -2. Extract connected components from every binary image by findContours and calculate their - centers. -3. Group centers from several binary images by their coordinates. Close centers form one group that - corresponds to one blob, which is controlled by the minDistBetweenBlobs parameter. -4. From the groups, estimate final centers of blobs and their radiuses and return as locations and - sizes of keypoints. - -This class performs several filtrations of returned blobs. You should set filterBy\* to true/false -to turn on/off corresponding filtration. Available filtrations: - -- **By color**. This filter compares the intensity of a binary image at the center of a blob to -blobColor. If they differ, the blob is filtered out. Use blobColor = 0 to extract dark blobs -and blobColor = 255 to extract light blobs. -- **By area**. Extracted blobs have an area between minArea (inclusive) and maxArea (exclusive). -- **By circularity**. Extracted blobs have circularity -(\f$\frac{4*\pi*Area}{perimeter * perimeter}\f$) between minCircularity (inclusive) and -maxCircularity (exclusive). -- **By ratio of the minimum inertia to maximum inertia**. Extracted blobs have this ratio -between minInertiaRatio (inclusive) and maxInertiaRatio (exclusive). -- **By convexity**. Extracted blobs have convexity (area / area of blob convex hull) between -minConvexity (inclusive) and maxConvexity (exclusive). - -Default values of parameters are tuned to extract dark circular blobs. - */ -class CV_EXPORTS_W SimpleBlobDetector : public Feature2D -{ -public: - struct CV_EXPORTS_W_SIMPLE Params - { - CV_WRAP Params(); - CV_PROP_RW float thresholdStep; - CV_PROP_RW float minThreshold; - CV_PROP_RW float maxThreshold; - CV_PROP_RW size_t minRepeatability; - CV_PROP_RW float minDistBetweenBlobs; - - CV_PROP_RW bool filterByColor; - CV_PROP_RW uchar blobColor; - - CV_PROP_RW bool filterByArea; - CV_PROP_RW float minArea, maxArea; - - CV_PROP_RW bool filterByCircularity; - CV_PROP_RW float minCircularity, maxCircularity; - - CV_PROP_RW bool filterByInertia; - CV_PROP_RW float minInertiaRatio, maxInertiaRatio; - - CV_PROP_RW bool filterByConvexity; - CV_PROP_RW float minConvexity, maxConvexity; - - void read( const FileNode& fn ); - void write( FileStorage& fs ) const; - }; - - CV_WRAP static Ptr - create(const SimpleBlobDetector::Params ¶meters = SimpleBlobDetector::Params()); - CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; -}; - -//! @} features2d_main - -//! @addtogroup features2d_main -//! @{ - -/** @brief Class implementing the KAZE keypoint detector and descriptor extractor, described in @cite ABD12 . - -@note AKAZE descriptor can only be used with KAZE or AKAZE keypoints .. [ABD12] KAZE Features. Pablo -F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. In European Conference on Computer Vision -(ECCV), Fiorenze, Italy, October 2012. -*/ -class CV_EXPORTS_W KAZE : public Feature2D -{ -public: - enum - { - DIFF_PM_G1 = 0, - DIFF_PM_G2 = 1, - DIFF_WEICKERT = 2, - DIFF_CHARBONNIER = 3 - }; - - /** @brief The KAZE constructor - - @param extended Set to enable extraction of extended (128-byte) descriptor. - @param upright Set to enable use of upright descriptors (non rotation-invariant). - @param threshold Detector response threshold to accept point - @param nOctaves Maximum octave evolution of the image - @param nOctaveLayers Default number of sublevels per scale level - @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or - DIFF_CHARBONNIER - */ - CV_WRAP static Ptr create(bool extended=false, bool upright=false, - float threshold = 0.001f, - int nOctaves = 4, int nOctaveLayers = 4, - int diffusivity = KAZE::DIFF_PM_G2); - - CV_WRAP virtual void setExtended(bool extended) = 0; - CV_WRAP virtual bool getExtended() const = 0; - - CV_WRAP virtual void setUpright(bool upright) = 0; - CV_WRAP virtual bool getUpright() const = 0; - - CV_WRAP virtual void setThreshold(double threshold) = 0; - CV_WRAP virtual double getThreshold() const = 0; - - CV_WRAP virtual void setNOctaves(int octaves) = 0; - CV_WRAP virtual int getNOctaves() const = 0; - - CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0; - CV_WRAP virtual int getNOctaveLayers() const = 0; - - CV_WRAP virtual void setDiffusivity(int diff) = 0; - CV_WRAP virtual int getDiffusivity() const = 0; - CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; -}; - -/** @brief Class implementing the AKAZE keypoint detector and descriptor extractor, described in @cite ANB13. - -@details AKAZE descriptors can only be used with KAZE or AKAZE keypoints. This class is thread-safe. - -@note When you need descriptors use Feature2D::detectAndCompute, which -provides better performance. When using Feature2D::detect followed by -Feature2D::compute scale space pyramid is computed twice. - -@note AKAZE implements T-API. When image is passed as UMat some parts of the algorithm -will use OpenCL. - -@note [ANB13] Fast Explicit Diffusion for Accelerated Features in Nonlinear -Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien Bartoli. In -British Machine Vision Conference (BMVC), Bristol, UK, September 2013. - -*/ -class CV_EXPORTS_W AKAZE : public Feature2D -{ -public: - // AKAZE descriptor type - enum - { - DESCRIPTOR_KAZE_UPRIGHT = 2, ///< Upright descriptors, not invariant to rotation - DESCRIPTOR_KAZE = 3, - DESCRIPTOR_MLDB_UPRIGHT = 4, ///< Upright descriptors, not invariant to rotation - DESCRIPTOR_MLDB = 5 - }; - - /** @brief The AKAZE constructor - - @param descriptor_type Type of the extracted descriptor: DESCRIPTOR_KAZE, - DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT. - @param descriptor_size Size of the descriptor in bits. 0 -\> Full size - @param descriptor_channels Number of channels in the descriptor (1, 2, 3) - @param threshold Detector response threshold to accept point - @param nOctaves Maximum octave evolution of the image - @param nOctaveLayers Default number of sublevels per scale level - @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or - DIFF_CHARBONNIER - */ - CV_WRAP static Ptr create(int descriptor_type=AKAZE::DESCRIPTOR_MLDB, - int descriptor_size = 0, int descriptor_channels = 3, - float threshold = 0.001f, int nOctaves = 4, - int nOctaveLayers = 4, int diffusivity = KAZE::DIFF_PM_G2); - - CV_WRAP virtual void setDescriptorType(int dtype) = 0; - CV_WRAP virtual int getDescriptorType() const = 0; - - CV_WRAP virtual void setDescriptorSize(int dsize) = 0; - CV_WRAP virtual int getDescriptorSize() const = 0; - - CV_WRAP virtual void setDescriptorChannels(int dch) = 0; - CV_WRAP virtual int getDescriptorChannels() const = 0; - - CV_WRAP virtual void setThreshold(double threshold) = 0; - CV_WRAP virtual double getThreshold() const = 0; - - CV_WRAP virtual void setNOctaves(int octaves) = 0; - CV_WRAP virtual int getNOctaves() const = 0; - - CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0; - CV_WRAP virtual int getNOctaveLayers() const = 0; - - CV_WRAP virtual void setDiffusivity(int diff) = 0; - CV_WRAP virtual int getDiffusivity() const = 0; - CV_WRAP virtual String getDefaultName() const CV_OVERRIDE; -}; - -//! @} features2d_main - -/****************************************************************************************\ -* Distance * -\****************************************************************************************/ - -template -struct CV_EXPORTS Accumulator -{ - typedef T Type; -}; - -template<> struct Accumulator { typedef float Type; }; -template<> struct Accumulator { typedef float Type; }; -template<> struct Accumulator { typedef float Type; }; -template<> struct Accumulator { typedef float Type; }; - -/* - * Squared Euclidean distance functor - */ -template -struct CV_EXPORTS SL2 -{ - enum { normType = NORM_L2SQR }; - typedef T ValueType; - typedef typename Accumulator::Type ResultType; - - ResultType operator()( const T* a, const T* b, int size ) const - { - return normL2Sqr(a, b, size); - } -}; - -/* - * Euclidean distance functor - */ -template -struct L2 -{ - enum { normType = NORM_L2 }; - typedef T ValueType; - typedef typename Accumulator::Type ResultType; - - ResultType operator()( const T* a, const T* b, int size ) const - { - return (ResultType)std::sqrt((double)normL2Sqr(a, b, size)); - } -}; - -/* - * Manhattan distance (city block distance) functor - */ -template -struct L1 -{ - enum { normType = NORM_L1 }; - typedef T ValueType; - typedef typename Accumulator::Type ResultType; - - ResultType operator()( const T* a, const T* b, int size ) const - { - return normL1(a, b, size); - } -}; - -/****************************************************************************************\ -* DescriptorMatcher * -\****************************************************************************************/ - -//! @addtogroup features2d_match -//! @{ - -/** @brief Abstract base class for matching keypoint descriptors. - -It has two groups of match methods: for matching descriptors of an image with another image or with -an image set. - */ -class CV_EXPORTS_W DescriptorMatcher : public Algorithm -{ -public: - enum - { - FLANNBASED = 1, - BRUTEFORCE = 2, - BRUTEFORCE_L1 = 3, - BRUTEFORCE_HAMMING = 4, - BRUTEFORCE_HAMMINGLUT = 5, - BRUTEFORCE_SL2 = 6 - }; - virtual ~DescriptorMatcher(); - - /** @brief Adds descriptors to train a CPU(trainDescCollectionis) or GPU(utrainDescCollectionis) descriptor - collection. - - If the collection is not empty, the new descriptors are added to existing train descriptors. - - @param descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same - train image. - */ - CV_WRAP virtual void add( InputArrayOfArrays descriptors ); - - /** @brief Returns a constant link to the train descriptor collection trainDescCollection . - */ - CV_WRAP const std::vector& getTrainDescriptors() const; - - /** @brief Clears the train descriptor collections. - */ - CV_WRAP virtual void clear() CV_OVERRIDE; - - /** @brief Returns true if there are no train descriptors in the both collections. - */ - CV_WRAP virtual bool empty() const CV_OVERRIDE; - - /** @brief Returns true if the descriptor matcher supports masking permissible matches. - */ - CV_WRAP virtual bool isMaskSupported() const = 0; - - /** @brief Trains a descriptor matcher - - Trains a descriptor matcher (for example, the flann index). In all methods to match, the method - train() is run every time before matching. Some descriptor matchers (for example, BruteForceMatcher) - have an empty implementation of this method. Other matchers really train their inner structures (for - example, FlannBasedMatcher trains flann::Index ). - */ - CV_WRAP virtual void train(); - - /** @brief Finds the best match for each descriptor from a query set. - - @param queryDescriptors Query set of descriptors. - @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors - collection stored in the class object. - @param matches Matches. If a query descriptor is masked out in mask , no match is added for this - descriptor. So, matches size may be smaller than the query descriptors count. - @param mask Mask specifying permissible matches between an input query and train matrices of - descriptors. - - In the first variant of this method, the train descriptors are passed as an input argument. In the - second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is - used. Optional mask (or masks) can be passed to specify which query and training descriptors can be - matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if - mask.at\(i,j) is non-zero. - */ - CV_WRAP void match( InputArray queryDescriptors, InputArray trainDescriptors, - CV_OUT std::vector& matches, InputArray mask=noArray() ) const; - - /** @brief Finds the k best matches for each descriptor from a query set. - - @param queryDescriptors Query set of descriptors. - @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors - collection stored in the class object. - @param mask Mask specifying permissible matches between an input query and train matrices of - descriptors. - @param matches Matches. Each matches[i] is k or less matches for the same query descriptor. - @param k Count of best matches found per each query descriptor or less if a query descriptor has - less than k possible matches in total. - @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is - false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, - the matches vector does not contain matches for fully masked-out query descriptors. - - These extended variants of DescriptorMatcher::match methods find several best matches for each query - descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match - for the details about query and train descriptors. - */ - CV_WRAP void knnMatch( InputArray queryDescriptors, InputArray trainDescriptors, - CV_OUT std::vector >& matches, int k, - InputArray mask=noArray(), bool compactResult=false ) const; - - /** @brief For each query descriptor, finds the training descriptors not farther than the specified distance. - - @param queryDescriptors Query set of descriptors. - @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors - collection stored in the class object. - @param matches Found matches. - @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is - false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, - the matches vector does not contain matches for fully masked-out query descriptors. - @param maxDistance Threshold for the distance between matched descriptors. Distance means here - metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured - in Pixels)! - @param mask Mask specifying permissible matches between an input query and train matrices of - descriptors. - - For each query descriptor, the methods find such training descriptors that the distance between the - query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are - returned in the distance increasing order. - */ - CV_WRAP void radiusMatch( InputArray queryDescriptors, InputArray trainDescriptors, - CV_OUT std::vector >& matches, float maxDistance, - InputArray mask=noArray(), bool compactResult=false ) const; - - /** @overload - @param queryDescriptors Query set of descriptors. - @param matches Matches. If a query descriptor is masked out in mask , no match is added for this - descriptor. So, matches size may be smaller than the query descriptors count. - @param masks Set of masks. Each masks[i] specifies permissible matches between the input query - descriptors and stored train descriptors from the i-th image trainDescCollection[i]. - */ - CV_WRAP void match( InputArray queryDescriptors, CV_OUT std::vector& matches, - InputArrayOfArrays masks=noArray() ); - /** @overload - @param queryDescriptors Query set of descriptors. - @param matches Matches. Each matches[i] is k or less matches for the same query descriptor. - @param k Count of best matches found per each query descriptor or less if a query descriptor has - less than k possible matches in total. - @param masks Set of masks. Each masks[i] specifies permissible matches between the input query - descriptors and stored train descriptors from the i-th image trainDescCollection[i]. - @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is - false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, - the matches vector does not contain matches for fully masked-out query descriptors. - */ - CV_WRAP void knnMatch( InputArray queryDescriptors, CV_OUT std::vector >& matches, int k, - InputArrayOfArrays masks=noArray(), bool compactResult=false ); - /** @overload - @param queryDescriptors Query set of descriptors. - @param matches Found matches. - @param maxDistance Threshold for the distance between matched descriptors. Distance means here - metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured - in Pixels)! - @param masks Set of masks. Each masks[i] specifies permissible matches between the input query - descriptors and stored train descriptors from the i-th image trainDescCollection[i]. - @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is - false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, - the matches vector does not contain matches for fully masked-out query descriptors. - */ - CV_WRAP void radiusMatch( InputArray queryDescriptors, CV_OUT std::vector >& matches, float maxDistance, - InputArrayOfArrays masks=noArray(), bool compactResult=false ); - - - CV_WRAP void write( const String& fileName ) const - { - FileStorage fs(fileName, FileStorage::WRITE); - write(fs); - } - - CV_WRAP void read( const String& fileName ) - { - FileStorage fs(fileName, FileStorage::READ); - read(fs.root()); - } - // Reads matcher object from a file node - // see corresponding cv::Algorithm method - CV_WRAP virtual void read( const FileNode& ) CV_OVERRIDE; - // Writes matcher object to a file storage - virtual void write( FileStorage& ) const CV_OVERRIDE; - - /** @brief Clones the matcher. - - @param emptyTrainData If emptyTrainData is false, the method creates a deep copy of the object, - that is, copies both parameters and train data. If emptyTrainData is true, the method creates an - object copy with the current parameters but with empty train data. - */ - CV_WRAP virtual Ptr clone( bool emptyTrainData=false ) const = 0; - - /** @brief Creates a descriptor matcher of a given type with the default parameters (using default - constructor). - - @param descriptorMatcherType Descriptor matcher type. Now the following matcher types are - supported: - - `BruteForce` (it uses L2 ) - - `BruteForce-L1` - - `BruteForce-Hamming` - - `BruteForce-Hamming(2)` - - `FlannBased` - */ - CV_WRAP static Ptr create( const String& descriptorMatcherType ); - - CV_WRAP static Ptr create( int matcherType ); - - - // see corresponding cv::Algorithm method - CV_WRAP inline void write(const Ptr& fs, const String& name = String()) const { Algorithm::write(fs, name); } - -protected: - /** - * Class to work with descriptors from several images as with one merged matrix. - * It is used e.g. in FlannBasedMatcher. - */ - class CV_EXPORTS DescriptorCollection - { - public: - DescriptorCollection(); - DescriptorCollection( const DescriptorCollection& collection ); - virtual ~DescriptorCollection(); - - // Vector of matrices "descriptors" will be merged to one matrix "mergedDescriptors" here. - void set( const std::vector& descriptors ); - virtual void clear(); - - const Mat& getDescriptors() const; - const Mat getDescriptor( int imgIdx, int localDescIdx ) const; - const Mat getDescriptor( int globalDescIdx ) const; - void getLocalIdx( int globalDescIdx, int& imgIdx, int& localDescIdx ) const; - - int size() const; - - protected: - Mat mergedDescriptors; - std::vector startIdxs; - }; - - //! In fact the matching is implemented only by the following two methods. These methods suppose - //! that the class object has been trained already. Public match methods call these methods - //! after calling train(). - virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, - InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0; - virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, - InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0; - - static bool isPossibleMatch( InputArray mask, int queryIdx, int trainIdx ); - static bool isMaskedOut( InputArrayOfArrays masks, int queryIdx ); - - static Mat clone_op( Mat m ) { return m.clone(); } - void checkMasks( InputArrayOfArrays masks, int queryDescriptorsCount ) const; - - //! Collection of descriptors from train images. - std::vector trainDescCollection; - std::vector utrainDescCollection; -}; - -/** @brief Brute-force descriptor matcher. - -For each descriptor in the first set, this matcher finds the closest descriptor in the second set -by trying each one. This descriptor matcher supports masking permissible matches of descriptor -sets. - */ -class CV_EXPORTS_W BFMatcher : public DescriptorMatcher -{ -public: - /** @brief Brute-force matcher constructor (obsolete). Please use BFMatcher.create() - * - * - */ - CV_WRAP BFMatcher( int normType=NORM_L2, bool crossCheck=false ); - - virtual ~BFMatcher() {} - - virtual bool isMaskSupported() const CV_OVERRIDE { return true; } - - /** @brief Brute-force matcher create method. - @param normType One of NORM_L1, NORM_L2, NORM_HAMMING, NORM_HAMMING2. L1 and L2 norms are - preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and - BRIEF, NORM_HAMMING2 should be used with ORB when WTA_K==3 or 4 (see ORB::ORB constructor - description). - @param crossCheck If it is false, this is will be default BFMatcher behaviour when it finds the k - nearest neighbors for each query descriptor. If crossCheck==true, then the knnMatch() method with - k=1 will only return pairs (i,j) such that for i-th query descriptor the j-th descriptor in the - matcher's collection is the nearest and vice versa, i.e. the BFMatcher will only return consistent - pairs. Such technique usually produces best results with minimal number of outliers when there are - enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper. - */ - CV_WRAP static Ptr create( int normType=NORM_L2, bool crossCheck=false ) ; - - virtual Ptr clone( bool emptyTrainData=false ) const CV_OVERRIDE; -protected: - virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, - InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE; - virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, - InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE; - - int normType; - bool crossCheck; -}; - -#if defined(HAVE_OPENCV_FLANN) || defined(CV_DOXYGEN) - -/** @brief Flann-based descriptor matcher. - -This matcher trains cv::flann::Index on a train descriptor collection and calls its nearest search -methods to find the best matches. So, this matcher may be faster when matching a large train -collection than the brute force matcher. FlannBasedMatcher does not support masking permissible -matches of descriptor sets because flann::Index does not support this. : - */ -class CV_EXPORTS_W FlannBasedMatcher : public DescriptorMatcher -{ -public: - CV_WRAP FlannBasedMatcher( const Ptr& indexParams=makePtr(), - const Ptr& searchParams=makePtr() ); - - virtual void add( InputArrayOfArrays descriptors ) CV_OVERRIDE; - virtual void clear() CV_OVERRIDE; - - // Reads matcher object from a file node - virtual void read( const FileNode& ) CV_OVERRIDE; - // Writes matcher object to a file storage - virtual void write( FileStorage& ) const CV_OVERRIDE; - - virtual void train() CV_OVERRIDE; - virtual bool isMaskSupported() const CV_OVERRIDE; - - CV_WRAP static Ptr create(); - - virtual Ptr clone( bool emptyTrainData=false ) const CV_OVERRIDE; -protected: - static void convertToDMatches( const DescriptorCollection& descriptors, - const Mat& indices, const Mat& distances, - std::vector >& matches ); - - virtual void knnMatchImpl( InputArray queryDescriptors, std::vector >& matches, int k, - InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE; - virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector >& matches, float maxDistance, - InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE; - - Ptr indexParams; - Ptr searchParams; - Ptr flannIndex; - - DescriptorCollection mergedDescriptors; - int addedDescCount; -}; - -#endif - -//! @} features2d_match - -/****************************************************************************************\ -* Drawing functions * -\****************************************************************************************/ - -//! @addtogroup features2d_draw -//! @{ - -struct CV_EXPORTS DrawMatchesFlags -{ - enum{ DEFAULT = 0, //!< Output image matrix will be created (Mat::create), - //!< i.e. existing memory of output image may be reused. - //!< Two source image, matches and single keypoints will be drawn. - //!< For each keypoint only the center point will be drawn (without - //!< the circle around keypoint with keypoint size and orientation). - DRAW_OVER_OUTIMG = 1, //!< Output image matrix will not be created (Mat::create). - //!< Matches will be drawn on existing content of output image. - NOT_DRAW_SINGLE_POINTS = 2, //!< Single keypoints will not be drawn. - DRAW_RICH_KEYPOINTS = 4 //!< For each keypoint the circle around keypoint with keypoint size and - //!< orientation will be drawn. - }; -}; - -/** @brief Draws keypoints. - -@param image Source image. -@param keypoints Keypoints from the source image. -@param outImage Output image. Its content depends on the flags value defining what is drawn in the -output image. See possible flags bit values below. -@param color Color of keypoints. -@param flags Flags setting drawing features. Possible flags bit values are defined by -DrawMatchesFlags. See details above in drawMatches . - -@note -For Python API, flags are modified as cv2.DRAW_MATCHES_FLAGS_DEFAULT, -cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG, -cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS - */ -CV_EXPORTS_W void drawKeypoints( InputArray image, const std::vector& keypoints, InputOutputArray outImage, - const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT ); - -/** @brief Draws the found matches of keypoints from two images. - -@param img1 First source image. -@param keypoints1 Keypoints from the first source image. -@param img2 Second source image. -@param keypoints2 Keypoints from the second source image. -@param matches1to2 Matches from the first image to the second one, which means that keypoints1[i] -has a corresponding point in keypoints2[matches[i]] . -@param outImg Output image. Its content depends on the flags value defining what is drawn in the -output image. See possible flags bit values below. -@param matchColor Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1) -, the color is generated randomly. -@param singlePointColor Color of single keypoints (circles), which means that keypoints do not -have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly. -@param matchesMask Mask determining which matches are drawn. If the mask is empty, all matches are -drawn. -@param flags Flags setting drawing features. Possible flags bit values are defined by -DrawMatchesFlags. - -This function draws matches of keypoints from two images in the output image. Match is a line -connecting two keypoints (circles). See cv::DrawMatchesFlags. - */ -CV_EXPORTS_W void drawMatches( InputArray img1, const std::vector& keypoints1, - InputArray img2, const std::vector& keypoints2, - const std::vector& matches1to2, InputOutputArray outImg, - const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), - const std::vector& matchesMask=std::vector(), int flags=DrawMatchesFlags::DEFAULT ); - -/** @overload */ -CV_EXPORTS_AS(drawMatchesKnn) void drawMatches( InputArray img1, const std::vector& keypoints1, - InputArray img2, const std::vector& keypoints2, - const std::vector >& matches1to2, InputOutputArray outImg, - const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), - const std::vector >& matchesMask=std::vector >(), int flags=DrawMatchesFlags::DEFAULT ); - -//! @} features2d_draw - -/****************************************************************************************\ -* Functions to evaluate the feature detectors and [generic] descriptor extractors * -\****************************************************************************************/ - -CV_EXPORTS void evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H1to2, - std::vector* keypoints1, std::vector* keypoints2, - float& repeatability, int& correspCount, - const Ptr& fdetector=Ptr() ); - -CV_EXPORTS void computeRecallPrecisionCurve( const std::vector >& matches1to2, - const std::vector >& correctMatches1to2Mask, - std::vector& recallPrecisionCurve ); - -CV_EXPORTS float getRecall( const std::vector& recallPrecisionCurve, float l_precision ); -CV_EXPORTS int getNearestPoint( const std::vector& recallPrecisionCurve, float l_precision ); - -/****************************************************************************************\ -* Bag of visual words * -\****************************************************************************************/ - -//! @addtogroup features2d_category -//! @{ - -/** @brief Abstract base class for training the *bag of visual words* vocabulary from a set of descriptors. - -For details, see, for example, *Visual Categorization with Bags of Keypoints* by Gabriella Csurka, -Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. : - */ -class CV_EXPORTS_W BOWTrainer -{ -public: - BOWTrainer(); - virtual ~BOWTrainer(); - - /** @brief Adds descriptors to a training set. - - @param descriptors Descriptors to add to a training set. Each row of the descriptors matrix is a - descriptor. - - The training set is clustered using clustermethod to construct the vocabulary. - */ - CV_WRAP void add( const Mat& descriptors ); - - /** @brief Returns a training set of descriptors. - */ - CV_WRAP const std::vector& getDescriptors() const; - - /** @brief Returns the count of all descriptors stored in the training set. - */ - CV_WRAP int descriptorsCount() const; - - CV_WRAP virtual void clear(); - - /** @overload */ - CV_WRAP virtual Mat cluster() const = 0; - - /** @brief Clusters train descriptors. - - @param descriptors Descriptors to cluster. Each row of the descriptors matrix is a descriptor. - Descriptors are not added to the inner train descriptor set. - - The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first - variant of the method, train descriptors stored in the object are clustered. In the second variant, - input descriptors are clustered. - */ - CV_WRAP virtual Mat cluster( const Mat& descriptors ) const = 0; - -protected: - std::vector descriptors; - int size; -}; - -/** @brief kmeans -based class to train visual vocabulary using the *bag of visual words* approach. : - */ -class CV_EXPORTS_W BOWKMeansTrainer : public BOWTrainer -{ -public: - /** @brief The constructor. - - @see cv::kmeans - */ - CV_WRAP BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(), - int attempts=3, int flags=KMEANS_PP_CENTERS ); - virtual ~BOWKMeansTrainer(); - - // Returns trained vocabulary (i.e. cluster centers). - CV_WRAP virtual Mat cluster() const CV_OVERRIDE; - CV_WRAP virtual Mat cluster( const Mat& descriptors ) const CV_OVERRIDE; - -protected: - - int clusterCount; - TermCriteria termcrit; - int attempts; - int flags; -}; - -/** @brief Class to compute an image descriptor using the *bag of visual words*. - -Such a computation consists of the following steps: - -1. Compute descriptors for a given image and its keypoints set. -2. Find the nearest visual words from the vocabulary for each keypoint descriptor. -3. Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words -encountered in the image. The i-th bin of the histogram is a frequency of i-th word of the -vocabulary in the given image. - */ -class CV_EXPORTS_W BOWImgDescriptorExtractor -{ -public: - /** @brief The constructor. - - @param dextractor Descriptor extractor that is used to compute descriptors for an input image and - its keypoints. - @param dmatcher Descriptor matcher that is used to find the nearest word of the trained vocabulary - for each keypoint descriptor of the image. - */ - CV_WRAP BOWImgDescriptorExtractor( const Ptr& dextractor, - const Ptr& dmatcher ); - /** @overload */ - BOWImgDescriptorExtractor( const Ptr& dmatcher ); - virtual ~BOWImgDescriptorExtractor(); - - /** @brief Sets a visual vocabulary. - - @param vocabulary Vocabulary (can be trained using the inheritor of BOWTrainer ). Each row of the - vocabulary is a visual word (cluster center). - */ - CV_WRAP void setVocabulary( const Mat& vocabulary ); - - /** @brief Returns the set vocabulary. - */ - CV_WRAP const Mat& getVocabulary() const; - - /** @brief Computes an image descriptor using the set visual vocabulary. - - @param image Image, for which the descriptor is computed. - @param keypoints Keypoints detected in the input image. - @param imgDescriptor Computed output image descriptor. - @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that - pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary) - returned if it is non-zero. - @param descriptors Descriptors of the image keypoints that are returned if they are non-zero. - */ - void compute( InputArray image, std::vector& keypoints, OutputArray imgDescriptor, - std::vector >* pointIdxsOfClusters=0, Mat* descriptors=0 ); - /** @overload - @param keypointDescriptors Computed descriptors to match with vocabulary. - @param imgDescriptor Computed output image descriptor. - @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that - pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary) - returned if it is non-zero. - */ - void compute( InputArray keypointDescriptors, OutputArray imgDescriptor, - std::vector >* pointIdxsOfClusters=0 ); - // compute() is not constant because DescriptorMatcher::match is not constant - - CV_WRAP_AS(compute) void compute2( const Mat& image, std::vector& keypoints, CV_OUT Mat& imgDescriptor ) - { compute(image,keypoints,imgDescriptor); } - - /** @brief Returns an image descriptor size if the vocabulary is set. Otherwise, it returns 0. - */ - CV_WRAP int descriptorSize() const; - - /** @brief Returns an image descriptor type. - */ - CV_WRAP int descriptorType() const; - -protected: - Mat vocabulary; - Ptr dextractor; - Ptr dmatcher; -}; - -//! @} features2d_category - -//! @} features2d - -} /* namespace cv */ - -#endif diff --git a/include/opencv2/features2d/features2d.hpp b/include/opencv2/features2d/features2d.hpp deleted file mode 100755 index e81df0a..0000000 --- a/include/opencv2/features2d/features2d.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/features2d.hpp" diff --git a/include/opencv2/features2d/hal/interface.h b/include/opencv2/features2d/hal/interface.h deleted file mode 100755 index bc3b084..0000000 --- a/include/opencv2/features2d/hal/interface.h +++ /dev/null @@ -1,33 +0,0 @@ -#ifndef OPENCV_FEATURE2D_HAL_INTERFACE_H -#define OPENCV_FEATURE2D_HAL_INTERFACE_H - -#include "opencv2/core/cvdef.h" -//! @addtogroup features2d_hal_interface -//! @{ - -//! @name Fast feature detector types -//! @sa cv::FastFeatureDetector -//! @{ -#define CV_HAL_TYPE_5_8 0 -#define CV_HAL_TYPE_7_12 1 -#define CV_HAL_TYPE_9_16 2 -//! @} - -//! @name Key point -//! @sa cv::KeyPoint -//! @{ -struct CV_EXPORTS cvhalKeyPoint -{ - float x; - float y; - float size; - float angle; - float response; - int octave; - int class_id; -}; -//! @} - -//! @} - -#endif diff --git a/include/opencv2/flann.hpp b/include/opencv2/flann.hpp deleted file mode 100755 index 5c52bfe..0000000 --- a/include/opencv2/flann.hpp +++ /dev/null @@ -1,635 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_FLANN_HPP -#define OPENCV_FLANN_HPP - -#include "opencv2/core.hpp" -#include "opencv2/flann/miniflann.hpp" -#include "opencv2/flann/flann_base.hpp" - -/** -@defgroup flann Clustering and Search in Multi-Dimensional Spaces - -This section documents OpenCV's interface to the FLANN library. FLANN (Fast Library for Approximate -Nearest Neighbors) is a library that contains a collection of algorithms optimized for fast nearest -neighbor search in large datasets and for high dimensional features. More information about FLANN -can be found in @cite Muja2009 . -*/ - -namespace cvflann -{ - CV_EXPORTS flann_distance_t flann_distance_type(); - CV_DEPRECATED CV_EXPORTS void set_distance_type(flann_distance_t distance_type, int order); -} - - -namespace cv -{ -namespace flann -{ - - -//! @addtogroup flann -//! @{ - -template struct CvType {}; -template <> struct CvType { static int type() { return CV_8U; } }; -template <> struct CvType { static int type() { return CV_8S; } }; -template <> struct CvType { static int type() { return CV_16U; } }; -template <> struct CvType { static int type() { return CV_16S; } }; -template <> struct CvType { static int type() { return CV_32S; } }; -template <> struct CvType { static int type() { return CV_32F; } }; -template <> struct CvType { static int type() { return CV_64F; } }; - - -// bring the flann parameters into this namespace -using ::cvflann::get_param; -using ::cvflann::print_params; - -// bring the flann distances into this namespace -using ::cvflann::L2_Simple; -using ::cvflann::L2; -using ::cvflann::L1; -using ::cvflann::MinkowskiDistance; -using ::cvflann::MaxDistance; -using ::cvflann::HammingLUT; -using ::cvflann::Hamming; -using ::cvflann::Hamming2; -using ::cvflann::DNAmmingLUT; -using ::cvflann::DNAmming2; -using ::cvflann::HistIntersectionDistance; -using ::cvflann::HellingerDistance; -using ::cvflann::ChiSquareDistance; -using ::cvflann::KL_Divergence; - - -/** @brief The FLANN nearest neighbor index class. This class is templated with the type of elements for which -the index is built. - -`Distance` functor specifies the metric to be used to calculate the distance between two points. -There are several `Distance` functors that are readily available: - -cv::cvflann::L2_Simple - Squared Euclidean distance functor. -This is the simpler, unrolled version. This is preferable for very low dimensionality data (eg 3D points) - -cv::flann::L2 - Squared Euclidean distance functor, optimized version. - -cv::flann::L1 - Manhattan distance functor, optimized version. - -cv::flann::MinkowskiDistance - The Minkowsky distance functor. -This is highly optimised with loop unrolling. -The computation of squared root at the end is omitted for efficiency. - -cv::flann::MaxDistance - The max distance functor. It computes the -maximum distance between two vectors. This distance is not a valid kdtree distance, it's not -dimensionwise additive. - -cv::flann::HammingLUT - %Hamming distance functor. It counts the bit -differences between two strings using a lookup table implementation. - -cv::flann::Hamming - %Hamming distance functor. Population count is -performed using library calls, if available. Lookup table implementation is used as a fallback. - -cv::flann::Hamming2 - %Hamming distance functor. Population count is -implemented in 12 arithmetic operations (one of which is multiplication). - -cv::flann::DNAmmingLUT - %Adaptation of the Hamming distance functor to DNA comparison. -As the four bases A, C, G, T of the DNA (or A, G, C, U for RNA) can be coded on 2 bits, -it counts the bits pairs differences between two sequences using a lookup table implementation. - -cv::flann::DNAmming2 - %Adaptation of the Hamming distance functor to DNA comparison. -Bases differences count are vectorised thanks to arithmetic operations using standard -registers (AVX2 and AVX-512 should come in a near future). - -cv::flann::HistIntersectionDistance - The histogram -intersection distance functor. - -cv::flann::HellingerDistance - The Hellinger distance functor. - -cv::flann::ChiSquareDistance - The chi-square distance functor. - -cv::flann::KL_Divergence - The Kullback-Leibler divergence functor. - -Although the provided implementations cover a vast range of cases, it is also possible to use -a custom implementation. The distance functor is a class whose `operator()` computes the distance -between two features. If the distance is also a kd-tree compatible distance, it should also provide an -`accum_dist()` method that computes the distance between individual feature dimensions. - -In addition to `operator()` and `accum_dist()`, a distance functor should also define the -`ElementType` and the `ResultType` as the types of the elements it operates on and the type of the -result it computes. If a distance functor can be used as a kd-tree distance (meaning that the full -distance between a pair of features can be accumulated from the partial distances between the -individual dimensions) a typedef `is_kdtree_distance` should be present inside the distance functor. -If the distance is not a kd-tree distance, but it's a distance in a vector space (the individual -dimensions of the elements it operates on can be accessed independently) a typedef -`is_vector_space_distance` should be defined inside the functor. If neither typedef is defined, the -distance is assumed to be a metric distance and will only be used with indexes operating on -generic metric distances. - */ -template -class GenericIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - /** @brief Constructs a nearest neighbor search index for a given dataset. - - @param features Matrix of containing the features(points) to index. The size of the matrix is - num_features x feature_dimensionality and the data type of the elements in the matrix must - coincide with the type of the index. - @param params Structure containing the index parameters. The type of index that will be - constructed depends on the type of this parameter. See the description. - @param distance - - The method constructs a fast search structure from a set of features using the specified algorithm - with specified parameters, as defined by params. params is a reference to one of the following class - IndexParams descendants: - - - **LinearIndexParams** When passing an object of this type, the index will perform a linear, - brute-force search. : - @code - struct LinearIndexParams : public IndexParams - { - }; - @endcode - - **KDTreeIndexParams** When passing an object of this type the index constructed will consist of - a set of randomized kd-trees which will be searched in parallel. : - @code - struct KDTreeIndexParams : public IndexParams - { - KDTreeIndexParams( int trees = 4 ); - }; - @endcode - - **HierarchicalClusteringIndexParams** When passing an object of this type the index constructed - will be a hierarchical tree of clusters, dividing each set of points into n clusters whose centers - are picked among the points without further refinement of their position. - This algorithm fits both floating, integer and binary vectors. : - @code - struct HierarchicalClusteringIndexParams : public IndexParams - { - HierarchicalClusteringIndexParams( - int branching = 32, - flann_centers_init_t centers_init = CENTERS_RANDOM, - int trees = 4, - int leaf_size = 100); - - }; - @endcode - - **KMeansIndexParams** When passing an object of this type the index constructed will be a - hierarchical k-means tree (one tree by default), dividing each set of points into n clusters - whose barycenters are refined iteratively. - Note that this algorithm has been extended to the support of binary vectors as an alternative - to LSH when knn search speed is the criterium. It will also outperform LSH when processing - directly (i.e. without the use of MCA/PCA) datasets whose points share mostly the same values - for most of the dimensions. It is recommended to set more than one tree with binary data. : - @code - struct KMeansIndexParams : public IndexParams - { - KMeansIndexParams( - int branching = 32, - int iterations = 11, - flann_centers_init_t centers_init = CENTERS_RANDOM, - float cb_index = 0.2 ); - - KMeansIndexParams( - int branching, - int iterations, - flann_centers_init_t centers_init, - float cb_index, - int trees ); - }; - @endcode - - **CompositeIndexParams** When using a parameters object of this type the index created - combines the randomized kd-trees and the hierarchical k-means tree. : - @code - struct CompositeIndexParams : public IndexParams - { - CompositeIndexParams( - int trees = 4, - int branching = 32, - int iterations = 11, - flann_centers_init_t centers_init = CENTERS_RANDOM, - float cb_index = 0.2 ); - }; - @endcode - - **LshIndexParams** When using a parameters object of this type the index created uses - multi-probe LSH (by Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search - by Qin Lv, William Josephson, Zhe Wang, Moses Charikar, Kai Li., Proceedings of the 33rd - International Conference on Very Large Data Bases (VLDB). Vienna, Austria. September 2007). - This algorithm is designed for binary vectors. : - @code - struct LshIndexParams : public IndexParams - { - LshIndexParams( - unsigned int table_number, - unsigned int key_size, - unsigned int multi_probe_level ); - }; - @endcode - - **AutotunedIndexParams** When passing an object of this type the index created is - automatically tuned to offer the best performance, by choosing the optimal index type - (randomized kd-trees, hierarchical kmeans, linear) and parameters for the dataset provided. : - @code - struct AutotunedIndexParams : public IndexParams - { - AutotunedIndexParams( - float target_precision = 0.9, - float build_weight = 0.01, - float memory_weight = 0, - float sample_fraction = 0.1 ); - }; - @endcode - - **SavedIndexParams** This object type is used for loading a previously saved index from the - disk. : - @code - struct SavedIndexParams : public IndexParams - { - SavedIndexParams( String filename ); - }; - @endcode - */ - GenericIndex(const Mat& features, const ::cvflann::IndexParams& params, Distance distance = Distance()); - - ~GenericIndex(); - - /** @brief Performs a K-nearest neighbor search for a given query point using the index. - - @param query The query point - @param indices Vector that will contain the indices of the K-nearest neighbors found. It must have - at least knn size. - @param dists Vector that will contain the distances to the K-nearest neighbors found. It must have - at least knn size. - @param knn Number of nearest neighbors to search for. - @param params SearchParams - */ - void knnSearch(const std::vector& query, std::vector& indices, - std::vector& dists, int knn, const ::cvflann::SearchParams& params); - void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& params); - - /** @brief Performs a radius nearest neighbor search for a given query point using the index. - - @param query The query point. - @param indices Vector that will contain the indices of the nearest neighbors found. - @param dists Vector that will contain the distances to the nearest neighbors found. It has the same - number of elements as indices. - @param radius The search radius. - @param params SearchParams - - This function returns the number of nearest neighbors found. - */ - int radiusSearch(const std::vector& query, std::vector& indices, - std::vector& dists, DistanceType radius, const ::cvflann::SearchParams& params); - int radiusSearch(const Mat& query, Mat& indices, Mat& dists, - DistanceType radius, const ::cvflann::SearchParams& params); - - void save(String filename) { nnIndex->save(filename); } - - int veclen() const { return nnIndex->veclen(); } - - int size() const { return (int)nnIndex->size(); } - - ::cvflann::IndexParams getParameters() { return nnIndex->getParameters(); } - - CV_DEPRECATED const ::cvflann::IndexParams* getIndexParameters() { return nnIndex->getIndexParameters(); } - -private: - ::cvflann::Index* nnIndex; - Mat _dataset; -}; - -//! @cond IGNORED - -#define FLANN_DISTANCE_CHECK \ - if ( ::cvflann::flann_distance_type() != cvflann::FLANN_DIST_L2) { \ - printf("[WARNING] You are using cv::flann::Index (or cv::flann::GenericIndex) and have also changed "\ - "the distance using cvflann::set_distance_type. This is no longer working as expected "\ - "(cv::flann::Index always uses L2). You should create the index templated on the distance, "\ - "for example for L1 distance use: GenericIndex< L1 > \n"); \ - } - - -template -GenericIndex::GenericIndex(const Mat& dataset, const ::cvflann::IndexParams& params, Distance distance) -: _dataset(dataset) -{ - CV_Assert(dataset.type() == CvType::type()); - CV_Assert(dataset.isContinuous()); - ::cvflann::Matrix m_dataset((ElementType*)_dataset.ptr(0), _dataset.rows, _dataset.cols); - - nnIndex = new ::cvflann::Index(m_dataset, params, distance); - - FLANN_DISTANCE_CHECK - - nnIndex->buildIndex(); -} - -template -GenericIndex::~GenericIndex() -{ - delete nnIndex; -} - -template -void GenericIndex::knnSearch(const std::vector& query, std::vector& indices, std::vector& dists, int knn, const ::cvflann::SearchParams& searchParams) -{ - ::cvflann::Matrix m_query((ElementType*)&query[0], 1, query.size()); - ::cvflann::Matrix m_indices(&indices[0], 1, indices.size()); - ::cvflann::Matrix m_dists(&dists[0], 1, dists.size()); - - FLANN_DISTANCE_CHECK - - nnIndex->knnSearch(m_query,m_indices,m_dists,knn,searchParams); -} - - -template -void GenericIndex::knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams) -{ - CV_Assert(queries.type() == CvType::type()); - CV_Assert(queries.isContinuous()); - ::cvflann::Matrix m_queries((ElementType*)queries.ptr(0), queries.rows, queries.cols); - - CV_Assert(indices.type() == CV_32S); - CV_Assert(indices.isContinuous()); - ::cvflann::Matrix m_indices((int*)indices.ptr(0), indices.rows, indices.cols); - - CV_Assert(dists.type() == CvType::type()); - CV_Assert(dists.isContinuous()); - ::cvflann::Matrix m_dists((DistanceType*)dists.ptr(0), dists.rows, dists.cols); - - FLANN_DISTANCE_CHECK - - nnIndex->knnSearch(m_queries,m_indices,m_dists,knn, searchParams); -} - -template -int GenericIndex::radiusSearch(const std::vector& query, std::vector& indices, std::vector& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams) -{ - ::cvflann::Matrix m_query((ElementType*)&query[0], 1, query.size()); - ::cvflann::Matrix m_indices(&indices[0], 1, indices.size()); - ::cvflann::Matrix m_dists(&dists[0], 1, dists.size()); - - FLANN_DISTANCE_CHECK - - return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); -} - -template -int GenericIndex::radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams) -{ - CV_Assert(query.type() == CvType::type()); - CV_Assert(query.isContinuous()); - ::cvflann::Matrix m_query((ElementType*)query.ptr(0), query.rows, query.cols); - - CV_Assert(indices.type() == CV_32S); - CV_Assert(indices.isContinuous()); - ::cvflann::Matrix m_indices((int*)indices.ptr(0), indices.rows, indices.cols); - - CV_Assert(dists.type() == CvType::type()); - CV_Assert(dists.isContinuous()); - ::cvflann::Matrix m_dists((DistanceType*)dists.ptr(0), dists.rows, dists.cols); - - FLANN_DISTANCE_CHECK - - return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); -} - -/** - * @deprecated Use GenericIndex class instead - */ -template -class Index_ -{ -public: - typedef typename L2::ElementType ElementType; - typedef typename L2::ResultType DistanceType; - - CV_DEPRECATED Index_(const Mat& dataset, const ::cvflann::IndexParams& params) - { - printf("[WARNING] The cv::flann::Index_ class is deperecated, use cv::flann::GenericIndex instead\n"); - - CV_Assert(dataset.type() == CvType::type()); - CV_Assert(dataset.isContinuous()); - ::cvflann::Matrix m_dataset((ElementType*)dataset.ptr(0), dataset.rows, dataset.cols); - - if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) { - nnIndex_L1 = NULL; - nnIndex_L2 = new ::cvflann::Index< L2 >(m_dataset, params); - } - else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) { - nnIndex_L1 = new ::cvflann::Index< L1 >(m_dataset, params); - nnIndex_L2 = NULL; - } - else { - printf("[ERROR] cv::flann::Index_ only provides backwards compatibility for the L1 and L2 distances. " - "For other distance types you must use cv::flann::GenericIndex\n"); - CV_Assert(0); - } - if (nnIndex_L1) nnIndex_L1->buildIndex(); - if (nnIndex_L2) nnIndex_L2->buildIndex(); - } - CV_DEPRECATED ~Index_() - { - if (nnIndex_L1) delete nnIndex_L1; - if (nnIndex_L2) delete nnIndex_L2; - } - - CV_DEPRECATED void knnSearch(const std::vector& query, std::vector& indices, std::vector& dists, int knn, const ::cvflann::SearchParams& searchParams) - { - ::cvflann::Matrix m_query((ElementType*)&query[0], 1, query.size()); - ::cvflann::Matrix m_indices(&indices[0], 1, indices.size()); - ::cvflann::Matrix m_dists(&dists[0], 1, dists.size()); - - if (nnIndex_L1) nnIndex_L1->knnSearch(m_query,m_indices,m_dists,knn,searchParams); - if (nnIndex_L2) nnIndex_L2->knnSearch(m_query,m_indices,m_dists,knn,searchParams); - } - CV_DEPRECATED void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams) - { - CV_Assert(queries.type() == CvType::type()); - CV_Assert(queries.isContinuous()); - ::cvflann::Matrix m_queries((ElementType*)queries.ptr(0), queries.rows, queries.cols); - - CV_Assert(indices.type() == CV_32S); - CV_Assert(indices.isContinuous()); - ::cvflann::Matrix m_indices((int*)indices.ptr(0), indices.rows, indices.cols); - - CV_Assert(dists.type() == CvType::type()); - CV_Assert(dists.isContinuous()); - ::cvflann::Matrix m_dists((DistanceType*)dists.ptr(0), dists.rows, dists.cols); - - if (nnIndex_L1) nnIndex_L1->knnSearch(m_queries,m_indices,m_dists,knn, searchParams); - if (nnIndex_L2) nnIndex_L2->knnSearch(m_queries,m_indices,m_dists,knn, searchParams); - } - - CV_DEPRECATED int radiusSearch(const std::vector& query, std::vector& indices, std::vector& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams) - { - ::cvflann::Matrix m_query((ElementType*)&query[0], 1, query.size()); - ::cvflann::Matrix m_indices(&indices[0], 1, indices.size()); - ::cvflann::Matrix m_dists(&dists[0], 1, dists.size()); - - if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); - if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); - } - - CV_DEPRECATED int radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams) - { - CV_Assert(query.type() == CvType::type()); - CV_Assert(query.isContinuous()); - ::cvflann::Matrix m_query((ElementType*)query.ptr(0), query.rows, query.cols); - - CV_Assert(indices.type() == CV_32S); - CV_Assert(indices.isContinuous()); - ::cvflann::Matrix m_indices((int*)indices.ptr(0), indices.rows, indices.cols); - - CV_Assert(dists.type() == CvType::type()); - CV_Assert(dists.isContinuous()); - ::cvflann::Matrix m_dists((DistanceType*)dists.ptr(0), dists.rows, dists.cols); - - if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); - if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams); - } - - CV_DEPRECATED void save(String filename) - { - if (nnIndex_L1) nnIndex_L1->save(filename); - if (nnIndex_L2) nnIndex_L2->save(filename); - } - - CV_DEPRECATED int veclen() const - { - if (nnIndex_L1) return nnIndex_L1->veclen(); - if (nnIndex_L2) return nnIndex_L2->veclen(); - } - - CV_DEPRECATED int size() const - { - if (nnIndex_L1) return nnIndex_L1->size(); - if (nnIndex_L2) return nnIndex_L2->size(); - } - - CV_DEPRECATED ::cvflann::IndexParams getParameters() - { - if (nnIndex_L1) return nnIndex_L1->getParameters(); - if (nnIndex_L2) return nnIndex_L2->getParameters(); - - } - - CV_DEPRECATED const ::cvflann::IndexParams* getIndexParameters() - { - if (nnIndex_L1) return nnIndex_L1->getIndexParameters(); - if (nnIndex_L2) return nnIndex_L2->getIndexParameters(); - } - -private: - // providing backwards compatibility for L2 and L1 distances (most common) - ::cvflann::Index< L2 >* nnIndex_L2; - ::cvflann::Index< L1 >* nnIndex_L1; -}; - -//! @endcond - -/** @brief Clusters features using hierarchical k-means algorithm. - -@param features The points to be clustered. The matrix must have elements of type -Distance::ElementType. -@param centers The centers of the clusters obtained. The matrix must have type -Distance::CentersType. The number of rows in this matrix represents the number of clusters desired, -however, because of the way the cut in the hierarchical tree is chosen, the number of clusters -computed will be the highest number of the form (branching-1)\*k+1 that's lower than the number of -clusters desired, where branching is the tree's branching factor (see description of the -KMeansIndexParams). -@param params Parameters used in the construction of the hierarchical k-means tree. -@param d Distance to be used for clustering. - -The method clusters the given feature vectors by constructing a hierarchical k-means tree and -choosing a cut in the tree that minimizes the cluster's variance. It returns the number of clusters -found. - */ -template -int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params, - Distance d = Distance()) -{ - typedef typename Distance::ElementType ElementType; - typedef typename Distance::CentersType CentersType; - - CV_Assert(features.type() == CvType::type()); - CV_Assert(features.isContinuous()); - ::cvflann::Matrix m_features((ElementType*)features.ptr(0), features.rows, features.cols); - - CV_Assert(centers.type() == CvType::type()); - CV_Assert(centers.isContinuous()); - ::cvflann::Matrix m_centers((CentersType*)centers.ptr(0), centers.rows, centers.cols); - - return ::cvflann::hierarchicalClustering(m_features, m_centers, params, d); -} - -//! @cond IGNORED - -template -CV_DEPRECATED int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params) -{ - printf("[WARNING] cv::flann::hierarchicalClustering is deprecated, use " - "cv::flann::hierarchicalClustering instead\n"); - - if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) { - return hierarchicalClustering< L2 >(features, centers, params); - } - else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) { - return hierarchicalClustering< L1 >(features, centers, params); - } - else { - printf("[ERROR] cv::flann::hierarchicalClustering only provides backwards " - "compatibility for the L1 and L2 distances. " - "For other distance types you must use cv::flann::hierarchicalClustering\n"); - CV_Assert(0); - } -} - -//! @endcond - -//! @} flann - -} } // namespace cv::flann - -#endif diff --git a/include/opencv2/flann/all_indices.h b/include/opencv2/flann/all_indices.h deleted file mode 100755 index 03877ab..0000000 --- a/include/opencv2/flann/all_indices.h +++ /dev/null @@ -1,162 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - - -#ifndef OPENCV_FLANN_ALL_INDICES_H_ -#define OPENCV_FLANN_ALL_INDICES_H_ - -//! @cond IGNORED - -#include "general.h" - -#include "nn_index.h" -#include "kdtree_index.h" -#include "kdtree_single_index.h" -#include "kmeans_index.h" -#include "composite_index.h" -#include "linear_index.h" -#include "hierarchical_clustering_index.h" -#include "lsh_index.h" -#include "autotuned_index.h" - - -namespace cvflann -{ - -template -struct index_creator -{ - static NNIndex* create(const Matrix& dataset, const IndexParams& params, const Distance& distance) - { - flann_algorithm_t index_type = get_param(params, "algorithm"); - - NNIndex* nnIndex; - switch (index_type) { - case FLANN_INDEX_LINEAR: - nnIndex = new LinearIndex(dataset, params, distance); - break; - case FLANN_INDEX_KDTREE_SINGLE: - nnIndex = new KDTreeSingleIndex(dataset, params, distance); - break; - case FLANN_INDEX_KDTREE: - nnIndex = new KDTreeIndex(dataset, params, distance); - break; - case FLANN_INDEX_KMEANS: - nnIndex = new KMeansIndex(dataset, params, distance); - break; - case FLANN_INDEX_COMPOSITE: - nnIndex = new CompositeIndex(dataset, params, distance); - break; - case FLANN_INDEX_AUTOTUNED: - nnIndex = new AutotunedIndex(dataset, params, distance); - break; - case FLANN_INDEX_HIERARCHICAL: - nnIndex = new HierarchicalClusteringIndex(dataset, params, distance); - break; - case FLANN_INDEX_LSH: - nnIndex = new LshIndex(dataset, params, distance); - break; - default: - FLANN_THROW(cv::Error::StsBadArg, "Unknown index type"); - } - - return nnIndex; - } -}; - -template -struct index_creator -{ - static NNIndex* create(const Matrix& dataset, const IndexParams& params, const Distance& distance) - { - flann_algorithm_t index_type = get_param(params, "algorithm"); - - NNIndex* nnIndex; - switch (index_type) { - case FLANN_INDEX_LINEAR: - nnIndex = new LinearIndex(dataset, params, distance); - break; - case FLANN_INDEX_KMEANS: - nnIndex = new KMeansIndex(dataset, params, distance); - break; - case FLANN_INDEX_HIERARCHICAL: - nnIndex = new HierarchicalClusteringIndex(dataset, params, distance); - break; - case FLANN_INDEX_LSH: - nnIndex = new LshIndex(dataset, params, distance); - break; - default: - FLANN_THROW(cv::Error::StsBadArg, "Unknown index type"); - } - - return nnIndex; - } -}; - -template -struct index_creator -{ - static NNIndex* create(const Matrix& dataset, const IndexParams& params, const Distance& distance) - { - flann_algorithm_t index_type = get_param(params, "algorithm"); - - NNIndex* nnIndex; - switch (index_type) { - case FLANN_INDEX_LINEAR: - nnIndex = new LinearIndex(dataset, params, distance); - break; - case FLANN_INDEX_KMEANS: - nnIndex = new KMeansIndex(dataset, params, distance); - break; - case FLANN_INDEX_HIERARCHICAL: - nnIndex = new HierarchicalClusteringIndex(dataset, params, distance); - break; - case FLANN_INDEX_LSH: - nnIndex = new LshIndex(dataset, params, distance); - break; - default: - FLANN_THROW(cv::Error::StsBadArg, "Unknown index type"); - } - - return nnIndex; - } -}; - -template -NNIndex* create_index_by_type(const Matrix& dataset, const IndexParams& params, const Distance& distance) -{ - return index_creator::create(dataset, params,distance); -} - -} - -//! @endcond - -#endif /* OPENCV_FLANN_ALL_INDICES_H_ */ diff --git a/include/opencv2/flann/allocator.h b/include/opencv2/flann/allocator.h deleted file mode 100755 index d5870a0..0000000 --- a/include/opencv2/flann/allocator.h +++ /dev/null @@ -1,196 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_ALLOCATOR_H_ -#define OPENCV_FLANN_ALLOCATOR_H_ - -//! @cond IGNORED - -#include -#include - - -namespace cvflann -{ - -/** - * Allocates (using C's malloc) a generic type T. - * - * Params: - * count = number of instances to allocate. - * Returns: pointer (of type T*) to memory buffer - */ -template -T* allocate(size_t count = 1) -{ - T* mem = (T*) ::malloc(sizeof(T)*count); - return mem; -} - - -/** - * Pooled storage allocator - * - * The following routines allow for the efficient allocation of storage in - * small chunks from a specified pool. Rather than allowing each structure - * to be freed individually, an entire pool of storage is freed at once. - * This method has two advantages over just using malloc() and free(). First, - * it is far more efficient for allocating small objects, as there is - * no overhead for remembering all the information needed to free each - * object or consolidating fragmented memory. Second, the decision about - * how long to keep an object is made at the time of allocation, and there - * is no need to track down all the objects to free them. - * - */ - -const size_t WORDSIZE=16; -const size_t BLOCKSIZE=8192; - -class PooledAllocator -{ - /* We maintain memory alignment to word boundaries by requiring that all - allocations be in multiples of the machine wordsize. */ - /* Size of machine word in bytes. Must be power of 2. */ - /* Minimum number of bytes requested at a time from the system. Must be multiple of WORDSIZE. */ - - - int remaining; /* Number of bytes left in current block of storage. */ - void* base; /* Pointer to base of current block of storage. */ - void* loc; /* Current location in block to next allocate memory. */ - int blocksize; - - -public: - int usedMemory; - int wastedMemory; - - /** - Default constructor. Initializes a new pool. - */ - PooledAllocator(int blockSize = BLOCKSIZE) - { - blocksize = blockSize; - remaining = 0; - base = NULL; - loc = NULL; - - usedMemory = 0; - wastedMemory = 0; - } - - /** - * Destructor. Frees all the memory allocated in this pool. - */ - ~PooledAllocator() - { - void* prev; - - while (base != NULL) { - prev = *((void**) base); /* Get pointer to prev block. */ - ::free(base); - base = prev; - } - } - - /** - * Returns a pointer to a piece of new memory of the given size in bytes - * allocated from the pool. - */ - void* allocateMemory(int size) - { - int blockSize; - - /* Round size up to a multiple of wordsize. The following expression - only works for WORDSIZE that is a power of 2, by masking last bits of - incremented size to zero. - */ - size = (size + (WORDSIZE - 1)) & ~(WORDSIZE - 1); - - /* Check whether a new block must be allocated. Note that the first word - of a block is reserved for a pointer to the previous block. - */ - if (size > remaining) { - - wastedMemory += remaining; - - /* Allocate new storage. */ - blockSize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ? - size + sizeof(void*) + (WORDSIZE-1) : BLOCKSIZE; - - // use the standard C malloc to allocate memory - void* m = ::malloc(blockSize); - if (!m) { - fprintf(stderr,"Failed to allocate memory.\n"); - return NULL; - } - - /* Fill first word of new block with pointer to previous block. */ - ((void**) m)[0] = base; - base = m; - - int shift = 0; - //int shift = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) & (WORDSIZE-1))) & (WORDSIZE-1); - - remaining = blockSize - sizeof(void*) - shift; - loc = ((char*)m + sizeof(void*) + shift); - } - void* rloc = loc; - loc = (char*)loc + size; - remaining -= size; - - usedMemory += size; - - return rloc; - } - - /** - * Allocates (using this pool) a generic type T. - * - * Params: - * count = number of instances to allocate. - * Returns: pointer (of type T*) to memory buffer - */ - template - T* allocate(size_t count = 1) - { - T* mem = (T*) this->allocateMemory((int)(sizeof(T)*count)); - return mem; - } - -private: - PooledAllocator(const PooledAllocator &); // copy disabled - PooledAllocator& operator=(const PooledAllocator &); // assign disabled -}; - -} - -//! @endcond - -#endif //OPENCV_FLANN_ALLOCATOR_H_ diff --git a/include/opencv2/flann/any.h b/include/opencv2/flann/any.h deleted file mode 100755 index f5684e9..0000000 --- a/include/opencv2/flann/any.h +++ /dev/null @@ -1,334 +0,0 @@ -#ifndef OPENCV_FLANN_ANY_H_ -#define OPENCV_FLANN_ANY_H_ -/* - * (C) Copyright Christopher Diggins 2005-2011 - * (C) Copyright Pablo Aguilar 2005 - * (C) Copyright Kevlin Henney 2001 - * - * Distributed under the Boost Software License, Version 1.0. (See - * accompanying file LICENSE_1_0.txt or copy at - * http://www.boost.org/LICENSE_1_0.txt - * - * Adapted for FLANN by Marius Muja - */ - -//! @cond IGNORED - -#include "defines.h" -#include -#include -#include - -namespace cvflann -{ - -namespace anyimpl -{ - -struct bad_any_cast -{ -}; - -struct empty_any -{ -}; - -inline std::ostream& operator <<(std::ostream& out, const empty_any&) -{ - out << "[empty_any]"; - return out; -} - -struct base_any_policy -{ - virtual void static_delete(void** x) = 0; - virtual void copy_from_value(void const* src, void** dest) = 0; - virtual void clone(void* const* src, void** dest) = 0; - virtual void move(void* const* src, void** dest) = 0; - virtual void* get_value(void** src) = 0; - virtual const void* get_value(void* const * src) = 0; - virtual ::size_t get_size() = 0; - virtual const std::type_info& type() = 0; - virtual void print(std::ostream& out, void* const* src) = 0; - virtual ~base_any_policy() {} -}; - -template -struct typed_base_any_policy : base_any_policy -{ - virtual ::size_t get_size() CV_OVERRIDE { return sizeof(T); } - virtual const std::type_info& type() CV_OVERRIDE { return typeid(T); } - -}; - -template -struct small_any_policy CV_FINAL : typed_base_any_policy -{ - virtual void static_delete(void**) CV_OVERRIDE { } - virtual void copy_from_value(void const* src, void** dest) CV_OVERRIDE - { - new (dest) T(* reinterpret_cast(src)); - } - virtual void clone(void* const* src, void** dest) CV_OVERRIDE { *dest = *src; } - virtual void move(void* const* src, void** dest) CV_OVERRIDE { *dest = *src; } - virtual void* get_value(void** src) CV_OVERRIDE { return reinterpret_cast(src); } - virtual const void* get_value(void* const * src) CV_OVERRIDE { return reinterpret_cast(src); } - virtual void print(std::ostream& out, void* const* src) CV_OVERRIDE { out << *reinterpret_cast(src); } -}; - -template -struct big_any_policy CV_FINAL : typed_base_any_policy -{ - virtual void static_delete(void** x) CV_OVERRIDE - { - if (* x) delete (* reinterpret_cast(x)); - *x = NULL; - } - virtual void copy_from_value(void const* src, void** dest) CV_OVERRIDE - { - *dest = new T(*reinterpret_cast(src)); - } - virtual void clone(void* const* src, void** dest) CV_OVERRIDE - { - *dest = new T(**reinterpret_cast(src)); - } - virtual void move(void* const* src, void** dest) CV_OVERRIDE - { - (*reinterpret_cast(dest))->~T(); - **reinterpret_cast(dest) = **reinterpret_cast(src); - } - virtual void* get_value(void** src) CV_OVERRIDE { return *src; } - virtual const void* get_value(void* const * src) CV_OVERRIDE { return *src; } - virtual void print(std::ostream& out, void* const* src) CV_OVERRIDE { out << *reinterpret_cast(*src); } -}; - -template<> inline void big_any_policy::print(std::ostream& out, void* const* src) -{ - out << int(*reinterpret_cast(*src)); -} - -template<> inline void big_any_policy::print(std::ostream& out, void* const* src) -{ - out << int(*reinterpret_cast(*src)); -} - -template<> inline void big_any_policy::print(std::ostream& out, void* const* src) -{ - out << (*reinterpret_cast(*src)).c_str(); -} - -template -struct choose_policy -{ - typedef big_any_policy type; -}; - -template -struct choose_policy -{ - typedef small_any_policy type; -}; - -struct any; - -/// Choosing the policy for an any type is illegal, but should never happen. -/// This is designed to throw a compiler error. -template<> -struct choose_policy -{ - typedef void type; -}; - -/// Specializations for small types. -#define SMALL_POLICY(TYPE) \ - template<> \ - struct choose_policy { typedef small_any_policy type; \ - } - -SMALL_POLICY(signed char); -SMALL_POLICY(unsigned char); -SMALL_POLICY(signed short); -SMALL_POLICY(unsigned short); -SMALL_POLICY(signed int); -SMALL_POLICY(unsigned int); -SMALL_POLICY(signed long); -SMALL_POLICY(unsigned long); -SMALL_POLICY(float); -SMALL_POLICY(bool); - -#undef SMALL_POLICY - -template -class SinglePolicy -{ - SinglePolicy(); - SinglePolicy(const SinglePolicy& other); - SinglePolicy& operator=(const SinglePolicy& other); - -public: - static base_any_policy* get_policy(); - -private: - static typename choose_policy::type policy; -}; - -template -typename choose_policy::type SinglePolicy::policy; - -/// This function will return a different policy for each type. -template -inline base_any_policy* SinglePolicy::get_policy() { return &policy; } - -} // namespace anyimpl - -struct any -{ -private: - // fields - anyimpl::base_any_policy* policy; - void* object; - -public: - /// Initializing constructor. - template - any(const T& x) - : policy(anyimpl::SinglePolicy::get_policy()), object(NULL) - { - assign(x); - } - - /// Empty constructor. - any() - : policy(anyimpl::SinglePolicy::get_policy()), object(NULL) - { } - - /// Special initializing constructor for string literals. - any(const char* x) - : policy(anyimpl::SinglePolicy::get_policy()), object(NULL) - { - assign(x); - } - - /// Copy constructor. - any(const any& x) - : policy(anyimpl::SinglePolicy::get_policy()), object(NULL) - { - assign(x); - } - - /// Destructor. - ~any() - { - policy->static_delete(&object); - } - - /// Assignment function from another any. - any& assign(const any& x) - { - reset(); - policy = x.policy; - policy->clone(&x.object, &object); - return *this; - } - - /// Assignment function. - template - any& assign(const T& x) - { - reset(); - policy = anyimpl::SinglePolicy::get_policy(); - policy->copy_from_value(&x, &object); - return *this; - } - - /// Assignment operator. - template - any& operator=(const T& x) - { - return assign(x); - } - - /// Assignment operator. Template-based version above doesn't work as expected. We need regular assignment operator here. - any& operator=(const any& x) - { - return assign(x); - } - - /// Assignment operator, specialed for literal strings. - /// They have types like const char [6] which don't work as expected. - any& operator=(const char* x) - { - return assign(x); - } - - /// Utility functions - any& swap(any& x) - { - std::swap(policy, x.policy); - std::swap(object, x.object); - return *this; - } - - /// Cast operator. You can only cast to the original type. - template - T& cast() - { - if (policy->type() != typeid(T)) throw anyimpl::bad_any_cast(); - T* r = reinterpret_cast(policy->get_value(&object)); - return *r; - } - - /// Cast operator. You can only cast to the original type. - template - const T& cast() const - { - if (policy->type() != typeid(T)) throw anyimpl::bad_any_cast(); - const T* r = reinterpret_cast(policy->get_value(&object)); - return *r; - } - - /// Returns true if the any contains no value. - bool empty() const - { - return policy->type() == typeid(anyimpl::empty_any); - } - - /// Frees any allocated memory, and sets the value to NULL. - void reset() - { - policy->static_delete(&object); - policy = anyimpl::SinglePolicy::get_policy(); - } - - /// Returns true if the two types are the same. - bool compatible(const any& x) const - { - return policy->type() == x.policy->type(); - } - - /// Returns if the type is compatible with the policy - template - bool has_type() - { - return policy->type() == typeid(T); - } - - const std::type_info& type() const - { - return policy->type(); - } - - friend std::ostream& operator <<(std::ostream& out, const any& any_val); -}; - -inline std::ostream& operator <<(std::ostream& out, const any& any_val) -{ - any_val.policy->print(out,&any_val.object); - return out; -} - -} - -//! @endcond - -#endif // OPENCV_FLANN_ANY_H_ diff --git a/include/opencv2/flann/autotuned_index.h b/include/opencv2/flann/autotuned_index.h deleted file mode 100755 index d90f739..0000000 --- a/include/opencv2/flann/autotuned_index.h +++ /dev/null @@ -1,594 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ -#ifndef OPENCV_FLANN_AUTOTUNED_INDEX_H_ -#define OPENCV_FLANN_AUTOTUNED_INDEX_H_ - -//! @cond IGNORED - -#include - -#include "nn_index.h" -#include "ground_truth.h" -#include "index_testing.h" -#include "sampling.h" -#include "kdtree_index.h" -#include "kdtree_single_index.h" -#include "kmeans_index.h" -#include "composite_index.h" -#include "linear_index.h" -#include "logger.h" - -namespace cvflann -{ - -template -NNIndex* create_index_by_type(const Matrix& dataset, const IndexParams& params, const Distance& distance); - - -struct AutotunedIndexParams : public IndexParams -{ - AutotunedIndexParams(float target_precision = 0.8, float build_weight = 0.01, float memory_weight = 0, float sample_fraction = 0.1) - { - (*this)["algorithm"] = FLANN_INDEX_AUTOTUNED; - // precision desired (used for autotuning, -1 otherwise) - (*this)["target_precision"] = target_precision; - // build tree time weighting factor - (*this)["build_weight"] = build_weight; - // index memory weighting factor - (*this)["memory_weight"] = memory_weight; - // what fraction of the dataset to use for autotuning - (*this)["sample_fraction"] = sample_fraction; - } -}; - - -template -class AutotunedIndex : public NNIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - AutotunedIndex(const Matrix& inputData, const IndexParams& params = AutotunedIndexParams(), Distance d = Distance()) : - dataset_(inputData), distance_(d) - { - target_precision_ = get_param(params, "target_precision",0.8f); - build_weight_ = get_param(params,"build_weight", 0.01f); - memory_weight_ = get_param(params, "memory_weight", 0.0f); - sample_fraction_ = get_param(params,"sample_fraction", 0.1f); - bestIndex_ = NULL; - speedup_ = 0; - } - - AutotunedIndex(const AutotunedIndex&); - AutotunedIndex& operator=(const AutotunedIndex&); - - virtual ~AutotunedIndex() - { - if (bestIndex_ != NULL) { - delete bestIndex_; - bestIndex_ = NULL; - } - } - - /** - * Method responsible with building the index. - */ - virtual void buildIndex() CV_OVERRIDE - { - std::ostringstream stream; - bestParams_ = estimateBuildParams(); - print_params(bestParams_, stream); - Logger::info("----------------------------------------------------\n"); - Logger::info("Autotuned parameters:\n"); - Logger::info("%s", stream.str().c_str()); - Logger::info("----------------------------------------------------\n"); - - bestIndex_ = create_index_by_type(dataset_, bestParams_, distance_); - bestIndex_->buildIndex(); - speedup_ = estimateSearchParams(bestSearchParams_); - stream.str(std::string()); - print_params(bestSearchParams_, stream); - Logger::info("----------------------------------------------------\n"); - Logger::info("Search parameters:\n"); - Logger::info("%s", stream.str().c_str()); - Logger::info("----------------------------------------------------\n"); - } - - /** - * Saves the index to a stream - */ - virtual void saveIndex(FILE* stream) CV_OVERRIDE - { - save_value(stream, (int)bestIndex_->getType()); - bestIndex_->saveIndex(stream); - save_value(stream, get_param(bestSearchParams_, "checks")); - } - - /** - * Loads the index from a stream - */ - virtual void loadIndex(FILE* stream) CV_OVERRIDE - { - int index_type; - - load_value(stream, index_type); - IndexParams params; - params["algorithm"] = (flann_algorithm_t)index_type; - bestIndex_ = create_index_by_type(dataset_, params, distance_); - bestIndex_->loadIndex(stream); - int checks; - load_value(stream, checks); - bestSearchParams_["checks"] = checks; - } - - /** - * Method that searches for nearest-neighbors - */ - virtual void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE - { - int checks = get_param(searchParams,"checks",FLANN_CHECKS_AUTOTUNED); - if (checks == FLANN_CHECKS_AUTOTUNED) { - bestIndex_->findNeighbors(result, vec, bestSearchParams_); - } - else { - bestIndex_->findNeighbors(result, vec, searchParams); - } - } - - - IndexParams getParameters() const CV_OVERRIDE - { - return bestIndex_->getParameters(); - } - - SearchParams getSearchParameters() const - { - return bestSearchParams_; - } - - float getSpeedup() const - { - return speedup_; - } - - - /** - * Number of features in this index. - */ - virtual size_t size() const CV_OVERRIDE - { - return bestIndex_->size(); - } - - /** - * The length of each vector in this index. - */ - virtual size_t veclen() const CV_OVERRIDE - { - return bestIndex_->veclen(); - } - - /** - * The amount of memory (in bytes) this index uses. - */ - virtual int usedMemory() const CV_OVERRIDE - { - return bestIndex_->usedMemory(); - } - - /** - * Algorithm name - */ - virtual flann_algorithm_t getType() const CV_OVERRIDE - { - return FLANN_INDEX_AUTOTUNED; - } - -private: - - struct CostData - { - float searchTimeCost; - float buildTimeCost; - float memoryCost; - float totalCost; - IndexParams params; - }; - - void evaluate_kmeans(CostData& cost) - { - StartStopTimer t; - int checks; - const int nn = 1; - - Logger::info("KMeansTree using params: max_iterations=%d, branching=%d\n", - get_param(cost.params,"iterations"), - get_param(cost.params,"branching")); - KMeansIndex kmeans(sampledDataset_, cost.params, distance_); - // measure index build time - t.start(); - kmeans.buildIndex(); - t.stop(); - float buildTime = (float)t.value; - - // measure search time - float searchTime = test_index_precision(kmeans, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn); - - float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float)); - cost.memoryCost = (kmeans.usedMemory() + datasetMemory) / datasetMemory; - cost.searchTimeCost = searchTime; - cost.buildTimeCost = buildTime; - Logger::info("KMeansTree buildTime=%g, searchTime=%g, build_weight=%g\n", buildTime, searchTime, build_weight_); - } - - - void evaluate_kdtree(CostData& cost) - { - StartStopTimer t; - int checks; - const int nn = 1; - - Logger::info("KDTree using params: trees=%d\n", get_param(cost.params,"trees")); - KDTreeIndex kdtree(sampledDataset_, cost.params, distance_); - - t.start(); - kdtree.buildIndex(); - t.stop(); - float buildTime = (float)t.value; - - //measure search time - float searchTime = test_index_precision(kdtree, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn); - - float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float)); - cost.memoryCost = (kdtree.usedMemory() + datasetMemory) / datasetMemory; - cost.searchTimeCost = searchTime; - cost.buildTimeCost = buildTime; - Logger::info("KDTree buildTime=%g, searchTime=%g\n", buildTime, searchTime); - } - - - // struct KMeansSimpleDownhillFunctor { - // - // Autotune& autotuner; - // KMeansSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {} - // - // float operator()(int* params) { - // - // float maxFloat = numeric_limits::max(); - // - // if (params[0]<2) return maxFloat; - // if (params[1]<0) return maxFloat; - // - // CostData c; - // c.params["algorithm"] = KMEANS; - // c.params["centers-init"] = CENTERS_RANDOM; - // c.params["branching"] = params[0]; - // c.params["max-iterations"] = params[1]; - // - // autotuner.evaluate_kmeans(c); - // - // return c.timeCost; - // - // } - // }; - // - // struct KDTreeSimpleDownhillFunctor { - // - // Autotune& autotuner; - // KDTreeSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {} - // - // float operator()(int* params) { - // float maxFloat = numeric_limits::max(); - // - // if (params[0]<1) return maxFloat; - // - // CostData c; - // c.params["algorithm"] = KDTREE; - // c.params["trees"] = params[0]; - // - // autotuner.evaluate_kdtree(c); - // - // return c.timeCost; - // - // } - // }; - - - - void optimizeKMeans(std::vector& costs) - { - Logger::info("KMEANS, Step 1: Exploring parameter space\n"); - - // explore kmeans parameters space using combinations of the parameters below - int maxIterations[] = { 1, 5, 10, 15 }; - int branchingFactors[] = { 16, 32, 64, 128, 256 }; - - int kmeansParamSpaceSize = FLANN_ARRAY_LEN(maxIterations) * FLANN_ARRAY_LEN(branchingFactors); - costs.reserve(costs.size() + kmeansParamSpaceSize); - - // evaluate kmeans for all parameter combinations - for (size_t i = 0; i < FLANN_ARRAY_LEN(maxIterations); ++i) { - for (size_t j = 0; j < FLANN_ARRAY_LEN(branchingFactors); ++j) { - CostData cost; - cost.params["algorithm"] = FLANN_INDEX_KMEANS; - cost.params["centers_init"] = FLANN_CENTERS_RANDOM; - cost.params["iterations"] = maxIterations[i]; - cost.params["branching"] = branchingFactors[j]; - - evaluate_kmeans(cost); - costs.push_back(cost); - } - } - - // Logger::info("KMEANS, Step 2: simplex-downhill optimization\n"); - // - // const int n = 2; - // // choose initial simplex points as the best parameters so far - // int kmeansNMPoints[n*(n+1)]; - // float kmeansVals[n+1]; - // for (int i=0;i& costs) - { - Logger::info("KD-TREE, Step 1: Exploring parameter space\n"); - - // explore kd-tree parameters space using the parameters below - int testTrees[] = { 1, 4, 8, 16, 32 }; - - // evaluate kdtree for all parameter combinations - for (size_t i = 0; i < FLANN_ARRAY_LEN(testTrees); ++i) { - CostData cost; - cost.params["algorithm"] = FLANN_INDEX_KDTREE; - cost.params["trees"] = testTrees[i]; - - evaluate_kdtree(cost); - costs.push_back(cost); - } - - // Logger::info("KD-TREE, Step 2: simplex-downhill optimization\n"); - // - // const int n = 1; - // // choose initial simplex points as the best parameters so far - // int kdtreeNMPoints[n*(n+1)]; - // float kdtreeVals[n+1]; - // for (int i=0;i costs; - - int sampleSize = int(sample_fraction_ * dataset_.rows); - int testSampleSize = std::min(sampleSize / 10, 1000); - - Logger::info("Entering autotuning, dataset size: %d, sampleSize: %d, testSampleSize: %d, target precision: %g\n", dataset_.rows, sampleSize, testSampleSize, target_precision_); - - // For a very small dataset, it makes no sense to build any fancy index, just - // use linear search - if (testSampleSize < 10) { - Logger::info("Choosing linear, dataset too small\n"); - return LinearIndexParams(); - } - - // We use a fraction of the original dataset to speedup the autotune algorithm - sampledDataset_ = random_sample(dataset_, sampleSize); - // We use a cross-validation approach, first we sample a testset from the dataset - testDataset_ = random_sample(sampledDataset_, testSampleSize, true); - - // We compute the ground truth using linear search - Logger::info("Computing ground truth... \n"); - gt_matches_ = Matrix(new int[testDataset_.rows], testDataset_.rows, 1); - StartStopTimer t; - t.start(); - compute_ground_truth(sampledDataset_, testDataset_, gt_matches_, 0, distance_); - t.stop(); - - CostData linear_cost; - linear_cost.searchTimeCost = (float)t.value; - linear_cost.buildTimeCost = 0; - linear_cost.memoryCost = 0; - linear_cost.params["algorithm"] = FLANN_INDEX_LINEAR; - - costs.push_back(linear_cost); - - // Start parameter autotune process - Logger::info("Autotuning parameters...\n"); - - optimizeKMeans(costs); - optimizeKDTree(costs); - - float bestTimeCost = costs[0].searchTimeCost; - for (size_t i = 0; i < costs.size(); ++i) { - float timeCost = costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost; - if (timeCost < bestTimeCost) { - bestTimeCost = timeCost; - } - } - - float bestCost = costs[0].searchTimeCost / bestTimeCost; - IndexParams bestParams = costs[0].params; - if (bestTimeCost > 0) { - for (size_t i = 0; i < costs.size(); ++i) { - float crtCost = (costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost) / bestTimeCost + - memory_weight_ * costs[i].memoryCost; - if (crtCost < bestCost) { - bestCost = crtCost; - bestParams = costs[i].params; - } - } - } - - delete[] gt_matches_.data; - delete[] testDataset_.data; - delete[] sampledDataset_.data; - - return bestParams; - } - - - - /** - * Estimates the search time parameters needed to get the desired precision. - * Precondition: the index is built - * Postcondition: the searchParams will have the optimum params set, also the speedup obtained over linear search. - */ - float estimateSearchParams(SearchParams& searchParams) - { - const int nn = 1; - const size_t SAMPLE_COUNT = 1000; - - CV_Assert(bestIndex_ != NULL && "Requires a valid index"); // must have a valid index - - float speedup = 0; - - int samples = (int)std::min(dataset_.rows / 10, SAMPLE_COUNT); - if (samples > 0) { - Matrix testDataset = random_sample(dataset_, samples); - - Logger::info("Computing ground truth\n"); - - // we need to compute the ground truth first - Matrix gt_matches(new int[testDataset.rows], testDataset.rows, 1); - StartStopTimer t; - t.start(); - compute_ground_truth(dataset_, testDataset, gt_matches, 1, distance_); - t.stop(); - float linear = (float)t.value; - - int checks; - Logger::info("Estimating number of checks\n"); - - float searchTime; - float cb_index; - if (bestIndex_->getType() == FLANN_INDEX_KMEANS) { - Logger::info("KMeans algorithm, estimating cluster border factor\n"); - KMeansIndex* kmeans = (KMeansIndex*)bestIndex_; - float bestSearchTime = -1; - float best_cb_index = -1; - int best_checks = -1; - for (cb_index = 0; cb_index < 1.1f; cb_index += 0.2f) { - kmeans->set_cb_index(cb_index); - searchTime = test_index_precision(*kmeans, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1); - if ((searchTime < bestSearchTime) || (bestSearchTime == -1)) { - bestSearchTime = searchTime; - best_cb_index = cb_index; - best_checks = checks; - } - } - searchTime = bestSearchTime; - cb_index = best_cb_index; - checks = best_checks; - - kmeans->set_cb_index(best_cb_index); - Logger::info("Optimum cb_index: %g\n", cb_index); - bestParams_["cb_index"] = cb_index; - } - else { - searchTime = test_index_precision(*bestIndex_, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1); - } - - Logger::info("Required number of checks: %d \n", checks); - searchParams["checks"] = checks; - - speedup = linear / searchTime; - - delete[] gt_matches.data; - delete[] testDataset.data; - } - - return speedup; - } - -private: - NNIndex* bestIndex_; - - IndexParams bestParams_; - SearchParams bestSearchParams_; - - Matrix sampledDataset_; - Matrix testDataset_; - Matrix gt_matches_; - - float speedup_; - - /** - * The dataset used by this index - */ - const Matrix dataset_; - - /** - * Index parameters - */ - float target_precision_; - float build_weight_; - float memory_weight_; - float sample_fraction_; - - Distance distance_; - - -}; -} - -//! @endcond - -#endif /* OPENCV_FLANN_AUTOTUNED_INDEX_H_ */ diff --git a/include/opencv2/flann/composite_index.h b/include/opencv2/flann/composite_index.h deleted file mode 100755 index f1af41a..0000000 --- a/include/opencv2/flann/composite_index.h +++ /dev/null @@ -1,197 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_COMPOSITE_INDEX_H_ -#define OPENCV_FLANN_COMPOSITE_INDEX_H_ - -//! @cond IGNORED - -#include "nn_index.h" -#include "kdtree_index.h" -#include "kmeans_index.h" - -namespace cvflann -{ - -/** - * Index parameters for the CompositeIndex. - */ -struct CompositeIndexParams : public IndexParams -{ - CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11, - flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 ) - { - (*this)["algorithm"] = FLANN_INDEX_KMEANS; - // number of randomized trees to use (for kdtree) - (*this)["trees"] = trees; - // branching factor - (*this)["branching"] = branching; - // max iterations to perform in one kmeans clustering (kmeans tree) - (*this)["iterations"] = iterations; - // algorithm used for picking the initial cluster centers for kmeans tree - (*this)["centers_init"] = centers_init; - // cluster boundary index. Used when searching the kmeans tree - (*this)["cb_index"] = cb_index; - } -}; - - -/** - * This index builds a kd-tree index and a k-means index and performs nearest - * neighbour search both indexes. This gives a slight boost in search performance - * as some of the neighbours that are missed by one index are found by the other. - */ -template -class CompositeIndex : public NNIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - /** - * Index constructor - * @param inputData dataset containing the points to index - * @param params Index parameters - * @param d Distance functor - * @return - */ - CompositeIndex(const Matrix& inputData, const IndexParams& params = CompositeIndexParams(), - Distance d = Distance()) : index_params_(params) - { - kdtree_index_ = new KDTreeIndex(inputData, params, d); - kmeans_index_ = new KMeansIndex(inputData, params, d); - - } - - CompositeIndex(const CompositeIndex&); - CompositeIndex& operator=(const CompositeIndex&); - - virtual ~CompositeIndex() - { - delete kdtree_index_; - delete kmeans_index_; - } - - /** - * @return The index type - */ - flann_algorithm_t getType() const CV_OVERRIDE - { - return FLANN_INDEX_COMPOSITE; - } - - /** - * @return Size of the index - */ - size_t size() const CV_OVERRIDE - { - return kdtree_index_->size(); - } - - /** - * \returns The dimensionality of the features in this index. - */ - size_t veclen() const CV_OVERRIDE - { - return kdtree_index_->veclen(); - } - - /** - * \returns The amount of memory (in bytes) used by the index. - */ - int usedMemory() const CV_OVERRIDE - { - return kmeans_index_->usedMemory() + kdtree_index_->usedMemory(); - } - - /** - * \brief Builds the index - */ - void buildIndex() CV_OVERRIDE - { - Logger::info("Building kmeans tree...\n"); - kmeans_index_->buildIndex(); - Logger::info("Building kdtree tree...\n"); - kdtree_index_->buildIndex(); - } - - /** - * \brief Saves the index to a stream - * \param stream The stream to save the index to - */ - void saveIndex(FILE* stream) CV_OVERRIDE - { - kmeans_index_->saveIndex(stream); - kdtree_index_->saveIndex(stream); - } - - /** - * \brief Loads the index from a stream - * \param stream The stream from which the index is loaded - */ - void loadIndex(FILE* stream) CV_OVERRIDE - { - kmeans_index_->loadIndex(stream); - kdtree_index_->loadIndex(stream); - } - - /** - * \returns The index parameters - */ - IndexParams getParameters() const CV_OVERRIDE - { - return index_params_; - } - - /** - * \brief Method that searches for nearest-neighbours - */ - void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE - { - kmeans_index_->findNeighbors(result, vec, searchParams); - kdtree_index_->findNeighbors(result, vec, searchParams); - } - -private: - /** The k-means index */ - KMeansIndex* kmeans_index_; - - /** The kd-tree index */ - KDTreeIndex* kdtree_index_; - - /** The index parameters */ - const IndexParams index_params_; -}; - -} - -//! @endcond - -#endif //OPENCV_FLANN_COMPOSITE_INDEX_H_ diff --git a/include/opencv2/flann/config.h b/include/opencv2/flann/config.h deleted file mode 100755 index c9342c0..0000000 --- a/include/opencv2/flann/config.h +++ /dev/null @@ -1,42 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - - -#ifndef OPENCV_FLANN_CONFIG_H_ -#define OPENCV_FLANN_CONFIG_H_ - -//! @cond IGNORED - -#ifdef FLANN_VERSION_ -#undef FLANN_VERSION_ -#endif -#define FLANN_VERSION_ "1.6.10" - -//! @endcond - -#endif /* OPENCV_FLANN_CONFIG_H_ */ diff --git a/include/opencv2/flann/defines.h b/include/opencv2/flann/defines.h deleted file mode 100755 index 8ab8329..0000000 --- a/include/opencv2/flann/defines.h +++ /dev/null @@ -1,169 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - - -#ifndef OPENCV_FLANN_DEFINES_H_ -#define OPENCV_FLANN_DEFINES_H_ - -//! @cond IGNORED - -#include "config.h" - -#ifdef FLANN_EXPORT -#undef FLANN_EXPORT -#endif -#ifdef _WIN32 -/* win32 dll export/import directives */ - #ifdef FLANN_EXPORTS - #define FLANN_EXPORT __declspec(dllexport) - #elif defined(FLANN_STATIC) - #define FLANN_EXPORT - #else - #define FLANN_EXPORT __declspec(dllimport) - #endif -#else -/* unix needs nothing */ - #define FLANN_EXPORT -#endif - - -#undef FLANN_PLATFORM_32_BIT -#undef FLANN_PLATFORM_64_BIT -#if defined __amd64__ || defined __x86_64__ || defined _WIN64 || defined _M_X64 -#define FLANN_PLATFORM_64_BIT -#else -#define FLANN_PLATFORM_32_BIT -#endif - - -#undef FLANN_ARRAY_LEN -#define FLANN_ARRAY_LEN(a) (sizeof(a)/sizeof(a[0])) - -namespace cvflann { - -/* Nearest neighbour index algorithms */ -enum flann_algorithm_t -{ - FLANN_INDEX_LINEAR = 0, - FLANN_INDEX_KDTREE = 1, - FLANN_INDEX_KMEANS = 2, - FLANN_INDEX_COMPOSITE = 3, - FLANN_INDEX_KDTREE_SINGLE = 4, - FLANN_INDEX_HIERARCHICAL = 5, - FLANN_INDEX_LSH = 6, - FLANN_INDEX_SAVED = 254, - FLANN_INDEX_AUTOTUNED = 255, - - // deprecated constants, should use the FLANN_INDEX_* ones instead - LINEAR = 0, - KDTREE = 1, - KMEANS = 2, - COMPOSITE = 3, - KDTREE_SINGLE = 4, - SAVED = 254, - AUTOTUNED = 255 -}; - - - -enum flann_centers_init_t -{ - FLANN_CENTERS_RANDOM = 0, - FLANN_CENTERS_GONZALES = 1, - FLANN_CENTERS_KMEANSPP = 2, - FLANN_CENTERS_GROUPWISE = 3, - - // deprecated constants, should use the FLANN_CENTERS_* ones instead - CENTERS_RANDOM = 0, - CENTERS_GONZALES = 1, - CENTERS_KMEANSPP = 2 -}; - -enum flann_log_level_t -{ - FLANN_LOG_NONE = 0, - FLANN_LOG_FATAL = 1, - FLANN_LOG_ERROR = 2, - FLANN_LOG_WARN = 3, - FLANN_LOG_INFO = 4 -}; - -enum flann_distance_t -{ - FLANN_DIST_EUCLIDEAN = 1, - FLANN_DIST_L2 = 1, - FLANN_DIST_MANHATTAN = 2, - FLANN_DIST_L1 = 2, - FLANN_DIST_MINKOWSKI = 3, - FLANN_DIST_MAX = 4, - FLANN_DIST_HIST_INTERSECT = 5, - FLANN_DIST_HELLINGER = 6, - FLANN_DIST_CHI_SQUARE = 7, - FLANN_DIST_CS = 7, - FLANN_DIST_KULLBACK_LEIBLER = 8, - FLANN_DIST_KL = 8, - FLANN_DIST_HAMMING = 9, - FLANN_DIST_DNAMMING = 10, - - // deprecated constants, should use the FLANN_DIST_* ones instead - EUCLIDEAN = 1, - MANHATTAN = 2, - MINKOWSKI = 3, - MAX_DIST = 4, - HIST_INTERSECT = 5, - HELLINGER = 6, - CS = 7, - KL = 8, - KULLBACK_LEIBLER = 8 -}; - -enum flann_datatype_t -{ - FLANN_INT8 = 0, - FLANN_INT16 = 1, - FLANN_INT32 = 2, - FLANN_INT64 = 3, - FLANN_UINT8 = 4, - FLANN_UINT16 = 5, - FLANN_UINT32 = 6, - FLANN_UINT64 = 7, - FLANN_FLOAT32 = 8, - FLANN_FLOAT64 = 9 -}; - -enum -{ - FLANN_CHECKS_UNLIMITED = -1, - FLANN_CHECKS_AUTOTUNED = -2 -}; - -} - -//! @endcond - -#endif /* OPENCV_FLANN_DEFINES_H_ */ diff --git a/include/opencv2/flann/dist.h b/include/opencv2/flann/dist.h deleted file mode 100755 index 608f8a5..0000000 --- a/include/opencv2/flann/dist.h +++ /dev/null @@ -1,1292 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_DIST_H_ -#define OPENCV_FLANN_DIST_H_ - -//! @cond IGNORED - -#include -#include -#include -#ifdef _MSC_VER -typedef unsigned __int32 uint32_t; -typedef unsigned __int64 uint64_t; -#else -#include -#endif - -#include "defines.h" - -#if defined _WIN32 && (defined(_M_ARM) || defined(_M_ARM64)) -# include -#endif - -#if defined(__ARM_NEON__) && !defined(__CUDACC__) -# include "arm_neon.h" -#endif - -namespace cvflann -{ - -template -inline T abs(T x) { return (x<0) ? -x : x; } - -template<> -inline int abs(int x) { return ::abs(x); } - -template<> -inline float abs(float x) { return fabsf(x); } - -template<> -inline double abs(double x) { return fabs(x); } - - -template -inline TargetType round(float x) { return static_cast(x); } - -template<> -inline unsigned int round(float x) { return static_cast(x + 0.5f); } - -template<> -inline unsigned short round(float x) { return static_cast(x + 0.5f); } - -template<> -inline unsigned char round(float x) { return static_cast(x + 0.5f); } - -template<> -inline long long round(float x) { return static_cast(x + 0.5f); } - -template<> -inline long round(float x) { return static_cast(x + 0.5f); } - -template<> -inline int round(float x) { return static_cast(x + 0.5f) - (x<0); } - -template<> -inline short round(float x) { return static_cast(x + 0.5f) - (x<0); } - -template<> -inline char round(float x) { return static_cast(x + 0.5f) - (x<0); } - - -template -inline TargetType round(double x) { return static_cast(x); } - -template<> -inline unsigned int round(double x) { return static_cast(x + 0.5); } - -template<> -inline unsigned short round(double x) { return static_cast(x + 0.5); } - -template<> -inline unsigned char round(double x) { return static_cast(x + 0.5); } - -template<> -inline long long round(double x) { return static_cast(x + 0.5); } - -template<> -inline long round(double x) { return static_cast(x + 0.5); } - -template<> -inline int round(double x) { return static_cast(x + 0.5) - (x<0); } - -template<> -inline short round(double x) { return static_cast(x + 0.5) - (x<0); } - -template<> -inline char round(double x) { return static_cast(x + 0.5) - (x<0); } - - -template -struct Accumulator { typedef T Type; }; -template<> -struct Accumulator { typedef float Type; }; -template<> -struct Accumulator { typedef float Type; }; -template<> -struct Accumulator { typedef float Type; }; -template<> -struct Accumulator { typedef float Type; }; -template<> -struct Accumulator { typedef float Type; }; -template<> -struct Accumulator { typedef float Type; }; - -#undef True -#undef False - -class True -{ -public: - static const bool val = true; -}; - -class False -{ -public: - static const bool val = false; -}; - - -/* - * This is a "zero iterator". It basically behaves like a zero filled - * array to all algorithms that use arrays as iterators (STL style). - * It's useful when there's a need to compute the distance between feature - * and origin it and allows for better compiler optimisation than using a - * zero-filled array. - */ -template -struct ZeroIterator -{ - - T operator*() - { - return 0; - } - - T operator[](int) - { - return 0; - } - - const ZeroIterator& operator ++() - { - return *this; - } - - ZeroIterator operator ++(int) - { - return *this; - } - - ZeroIterator& operator+=(int) - { - return *this; - } - -}; - - - -/** - * Squared Euclidean distance functor. - * - * This is the simpler, unrolled version. This is preferable for - * very low dimensionality data (eg 3D points) - */ -template -struct L2_Simple -{ - typedef True is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - typedef ResultType CentersType; - - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const - { - ResultType result = ResultType(); - ResultType diff; - for(size_t i = 0; i < size; ++i ) { - diff = (ResultType)(*a++ - *b++); - result += diff*diff; - } - return result; - } - - template - inline ResultType accum_dist(const U& a, const V& b, int) const - { - return (a-b)*(a-b); - } -}; - - - -/** - * Squared Euclidean distance functor, optimized version - */ -template -struct L2 -{ - typedef True is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - typedef ResultType CentersType; - - /** - * Compute the squared Euclidean distance between two vectors. - * - * This is highly optimised, with loop unrolling, as it is one - * of the most expensive inner loops. - * - * The computation of squared root at the end is omitted for - * efficiency. - */ - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const - { - ResultType result = ResultType(); - ResultType diff0, diff1, diff2, diff3; - Iterator1 last = a + size; - Iterator1 lastgroup = last - 3; - - /* Process 4 items with each loop for efficiency. */ - while (a < lastgroup) { - diff0 = (ResultType)(a[0] - b[0]); - diff1 = (ResultType)(a[1] - b[1]); - diff2 = (ResultType)(a[2] - b[2]); - diff3 = (ResultType)(a[3] - b[3]); - result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3; - a += 4; - b += 4; - - if ((worst_dist>0)&&(result>worst_dist)) { - return result; - } - } - /* Process last 0-3 pixels. Not needed for standard vector lengths. */ - while (a < last) { - diff0 = (ResultType)(*a++ - *b++); - result += diff0 * diff0; - } - return result; - } - - /** - * Partial euclidean distance, using just one dimension. This is used by the - * kd-tree when computing partial distances while traversing the tree. - * - * Squared root is omitted for efficiency. - */ - template - inline ResultType accum_dist(const U& a, const V& b, int) const - { - return (a-b)*(a-b); - } -}; - - -/* - * Manhattan distance functor, optimized version - */ -template -struct L1 -{ - typedef True is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - typedef ResultType CentersType; - - /** - * Compute the Manhattan (L_1) distance between two vectors. - * - * This is highly optimised, with loop unrolling, as it is one - * of the most expensive inner loops. - */ - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const - { - ResultType result = ResultType(); - ResultType diff0, diff1, diff2, diff3; - Iterator1 last = a + size; - Iterator1 lastgroup = last - 3; - - /* Process 4 items with each loop for efficiency. */ - while (a < lastgroup) { - diff0 = (ResultType)abs(a[0] - b[0]); - diff1 = (ResultType)abs(a[1] - b[1]); - diff2 = (ResultType)abs(a[2] - b[2]); - diff3 = (ResultType)abs(a[3] - b[3]); - result += diff0 + diff1 + diff2 + diff3; - a += 4; - b += 4; - - if ((worst_dist>0)&&(result>worst_dist)) { - return result; - } - } - /* Process last 0-3 pixels. Not needed for standard vector lengths. */ - while (a < last) { - diff0 = (ResultType)abs(*a++ - *b++); - result += diff0; - } - return result; - } - - /** - * Partial distance, used by the kd-tree. - */ - template - inline ResultType accum_dist(const U& a, const V& b, int) const - { - return abs(a-b); - } -}; - - - -template -struct MinkowskiDistance -{ - typedef True is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - typedef ResultType CentersType; - - int order; - - MinkowskiDistance(int order_) : order(order_) {} - - /** - * Compute the Minkowsky (L_p) distance between two vectors. - * - * This is highly optimised, with loop unrolling, as it is one - * of the most expensive inner loops. - * - * The computation of squared root at the end is omitted for - * efficiency. - */ - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const - { - ResultType result = ResultType(); - ResultType diff0, diff1, diff2, diff3; - Iterator1 last = a + size; - Iterator1 lastgroup = last - 3; - - /* Process 4 items with each loop for efficiency. */ - while (a < lastgroup) { - diff0 = (ResultType)abs(a[0] - b[0]); - diff1 = (ResultType)abs(a[1] - b[1]); - diff2 = (ResultType)abs(a[2] - b[2]); - diff3 = (ResultType)abs(a[3] - b[3]); - result += pow(diff0,order) + pow(diff1,order) + pow(diff2,order) + pow(diff3,order); - a += 4; - b += 4; - - if ((worst_dist>0)&&(result>worst_dist)) { - return result; - } - } - /* Process last 0-3 pixels. Not needed for standard vector lengths. */ - while (a < last) { - diff0 = (ResultType)abs(*a++ - *b++); - result += pow(diff0,order); - } - return result; - } - - /** - * Partial distance, used by the kd-tree. - */ - template - inline ResultType accum_dist(const U& a, const V& b, int) const - { - return pow(static_cast(abs(a-b)),order); - } -}; - - - -template -struct MaxDistance -{ - typedef False is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - typedef ResultType CentersType; - - /** - * Compute the max distance (L_infinity) between two vectors. - * - * This distance is not a valid kdtree distance, it's not dimensionwise additive. - */ - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const - { - ResultType result = ResultType(); - ResultType diff0, diff1, diff2, diff3; - Iterator1 last = a + size; - Iterator1 lastgroup = last - 3; - - /* Process 4 items with each loop for efficiency. */ - while (a < lastgroup) { - diff0 = abs(a[0] - b[0]); - diff1 = abs(a[1] - b[1]); - diff2 = abs(a[2] - b[2]); - diff3 = abs(a[3] - b[3]); - if (diff0>result) {result = diff0; } - if (diff1>result) {result = diff1; } - if (diff2>result) {result = diff2; } - if (diff3>result) {result = diff3; } - a += 4; - b += 4; - - if ((worst_dist>0)&&(result>worst_dist)) { - return result; - } - } - /* Process last 0-3 pixels. Not needed for standard vector lengths. */ - while (a < last) { - diff0 = abs(*a++ - *b++); - result = (diff0>result) ? diff0 : result; - } - return result; - } - - /* This distance functor is not dimension-wise additive, which - * makes it an invalid kd-tree distance, not implementing the accum_dist method */ - -}; - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -/** - * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor - * bit count of A exclusive XOR'ed with B - */ -struct HammingLUT -{ - typedef False is_kdtree_distance; - typedef False is_vector_space_distance; - - typedef unsigned char ElementType; - typedef int ResultType; - typedef ElementType CentersType; - - /** this will count the bits in a ^ b - */ - template - ResultType operator()(const unsigned char* a, const Iterator2 b, size_t size) const - { - static const uchar popCountTable[] = - { - 0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, - 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, - 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, - 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8 - }; - ResultType result = 0; - const unsigned char* b2 = reinterpret_cast (b); - for (size_t i = 0; i < size; i++) { - result += popCountTable[a[i] ^ b2[i]]; - } - return result; - } - - - ResultType operator()(const unsigned char* a, const ZeroIterator b, size_t size) const - { - (void)b; - static const uchar popCountTable[] = - { - 0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, - 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, - 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, - 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, - 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8 - }; - ResultType result = 0; - for (size_t i = 0; i < size; i++) { - result += popCountTable[a[i]]; - } - return result; - } -}; - -/** - * Hamming distance functor (pop count between two binary vectors, i.e. xor them and count the number of bits set) - * That code was taken from brief.cpp in OpenCV - */ -template -struct Hamming -{ - typedef False is_kdtree_distance; - typedef False is_vector_space_distance; - - - typedef T ElementType; - typedef int ResultType; - typedef ElementType CentersType; - - template - ResultType operator()(const Iterator1 a, const Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const - { - ResultType result = 0; -#if defined(__ARM_NEON__) && !defined(__CUDACC__) - { - const unsigned char* a2 = reinterpret_cast (a); - const unsigned char* b2 = reinterpret_cast (b); - uint32x4_t bits = vmovq_n_u32(0); - for (size_t i = 0; i < size; i += 16) { - uint8x16_t A_vec = vld1q_u8 (a2 + i); - uint8x16_t B_vec = vld1q_u8 (b2 + i); - uint8x16_t AxorB = veorq_u8 (A_vec, B_vec); - uint8x16_t bitsSet = vcntq_u8 (AxorB); - uint16x8_t bitSet8 = vpaddlq_u8 (bitsSet); - uint32x4_t bitSet4 = vpaddlq_u16 (bitSet8); - bits = vaddq_u32(bits, bitSet4); - } - uint64x2_t bitSet2 = vpaddlq_u32 (bits); - result = vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),0); - result += vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),2); - } -#elif defined(__GNUC__) - { - //for portability just use unsigned long -- and use the __builtin_popcountll (see docs for __builtin_popcountll) - typedef unsigned long long pop_t; - const size_t modulo = size % sizeof(pop_t); - const pop_t* a2 = reinterpret_cast (a); - const pop_t* b2 = reinterpret_cast (b); - const pop_t* a2_end = a2 + (size / sizeof(pop_t)); - - for (; a2 != a2_end; ++a2, ++b2) result += __builtin_popcountll((*a2) ^ (*b2)); - - if (modulo) { - //in the case where size is not dividable by sizeof(size_t) - //need to mask off the bits at the end - pop_t a_final = 0, b_final = 0; - memcpy(&a_final, a2, modulo); - memcpy(&b_final, b2, modulo); - result += __builtin_popcountll(a_final ^ b_final); - } - } -#else // NO NEON and NOT GNUC - HammingLUT lut; - result = lut(reinterpret_cast (a), - reinterpret_cast (b), size); -#endif - return result; - } - - - template - ResultType operator()(const Iterator1 a, ZeroIterator b, size_t size, ResultType /*worst_dist*/ = -1) const - { - (void)b; - ResultType result = 0; -#if defined(__ARM_NEON__) && !defined(__CUDACC__) - { - const unsigned char* a2 = reinterpret_cast (a); - uint32x4_t bits = vmovq_n_u32(0); - for (size_t i = 0; i < size; i += 16) { - uint8x16_t A_vec = vld1q_u8 (a2 + i); - uint8x16_t bitsSet = vcntq_u8 (A_vec); - uint16x8_t bitSet8 = vpaddlq_u8 (bitsSet); - uint32x4_t bitSet4 = vpaddlq_u16 (bitSet8); - bits = vaddq_u32(bits, bitSet4); - } - uint64x2_t bitSet2 = vpaddlq_u32 (bits); - result = vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),0); - result += vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),2); - } -#elif defined(__GNUC__) - { - //for portability just use unsigned long -- and use the __builtin_popcountll (see docs for __builtin_popcountll) - typedef unsigned long long pop_t; - const size_t modulo = size % sizeof(pop_t); - const pop_t* a2 = reinterpret_cast (a); - const pop_t* a2_end = a2 + (size / sizeof(pop_t)); - - for (; a2 != a2_end; ++a2) result += __builtin_popcountll(*a2); - - if (modulo) { - //in the case where size is not dividable by sizeof(size_t) - //need to mask off the bits at the end - pop_t a_final = 0; - memcpy(&a_final, a2, modulo); - result += __builtin_popcountll(a_final); - } - } -#else // NO NEON and NOT GNUC - HammingLUT lut; - result = lut(reinterpret_cast (a), b, size); -#endif - return result; - } -}; - -template -struct Hamming2 -{ - typedef False is_kdtree_distance; - typedef False is_vector_space_distance; - - typedef T ElementType; - typedef int ResultType; - typedef ElementType CentersType; - - /** This is popcount_3() from: - * http://en.wikipedia.org/wiki/Hamming_weight */ - unsigned int popcnt32(uint32_t n) const - { - n -= ((n >> 1) & 0x55555555); - n = (n & 0x33333333) + ((n >> 2) & 0x33333333); - return (((n + (n >> 4))& 0xF0F0F0F)* 0x1010101) >> 24; - } - -#ifdef FLANN_PLATFORM_64_BIT - unsigned int popcnt64(uint64_t n) const - { - n -= ((n >> 1) & 0x5555555555555555); - n = (n & 0x3333333333333333) + ((n >> 2) & 0x3333333333333333); - return (((n + (n >> 4))& 0x0f0f0f0f0f0f0f0f)* 0x0101010101010101) >> 56; - } -#endif - - template - ResultType operator()(const Iterator1 a, const Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const - { - CV_DbgAssert(!(size % long_word_size_) && "vectors size must be multiple of long words size (i.e. 8)"); - -#ifdef FLANN_PLATFORM_64_BIT - const uint64_t* pa = reinterpret_cast(a); - const uint64_t* pb = reinterpret_cast(b); - ResultType result = 0; - size /= long_word_size_; - for(size_t i = 0; i < size; ++i ) { - result += popcnt64(*pa ^ *pb); - ++pa; - ++pb; - } -#else - const uint32_t* pa = reinterpret_cast(a); - const uint32_t* pb = reinterpret_cast(b); - ResultType result = 0; - size /= long_word_size_; - for(size_t i = 0; i < size; ++i ) { - result += popcnt32(*pa ^ *pb); - ++pa; - ++pb; - } -#endif - return result; - } - - - template - ResultType operator()(const Iterator1 a, ZeroIterator b, size_t size, ResultType /*worst_dist*/ = -1) const - { - CV_DbgAssert(!(size % long_word_size_) && "vectors size must be multiple of long words size (i.e. 8)"); - - (void)b; -#ifdef FLANN_PLATFORM_64_BIT - const uint64_t* pa = reinterpret_cast(a); - ResultType result = 0; - size /= long_word_size_; - for(size_t i = 0; i < size; ++i ) { - result += popcnt64(*pa); - ++pa; - } -#else - const uint32_t* pa = reinterpret_cast(a); - ResultType result = 0; - size /= long_word_size_; - for(size_t i = 0; i < size; ++i ) { - result += popcnt32(*pa); - ++pa; - } -#endif - return result; - } - -private: -#ifdef FLANN_PLATFORM_64_BIT - static const size_t long_word_size_ = sizeof(uint64_t)/sizeof(unsigned char); -#else - static const size_t long_word_size_ = sizeof(uint32_t)/sizeof(unsigned char); -#endif -}; - - - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -struct DNAmmingLUT -{ - typedef False is_kdtree_distance; - typedef False is_vector_space_distance; - - typedef unsigned char ElementType; - typedef int ResultType; - typedef ElementType CentersType; - - /** this will count the bits in a ^ b - */ - template - ResultType operator()(const unsigned char* a, const Iterator2 b, size_t size) const - { - static const uchar popCountTable[] = - { - 0, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, - 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, - 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, - 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, - 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, - 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, - 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, - 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4 - }; - ResultType result = 0; - const unsigned char* b2 = reinterpret_cast (b); - for (size_t i = 0; i < size; i++) { - result += popCountTable[a[i] ^ b2[i]]; - } - return result; - } - - - ResultType operator()(const unsigned char* a, const ZeroIterator b, size_t size) const - { - (void)b; - static const uchar popCountTable[] = - { - 0, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, - 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, - 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, - 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, - 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, - 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, - 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, - 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4 - }; - ResultType result = 0; - for (size_t i = 0; i < size; i++) { - result += popCountTable[a[i]]; - } - return result; - } -}; - - -template -struct DNAmming2 -{ - typedef False is_kdtree_distance; - typedef False is_vector_space_distance; - - typedef T ElementType; - typedef int ResultType; - typedef ElementType CentersType; - - /** This is popcount_3() from: - * http://en.wikipedia.org/wiki/Hamming_weight */ - unsigned int popcnt32(uint32_t n) const - { - n = ((n >> 1) | n) & 0x55555555; - n = (n & 0x33333333) + ((n >> 2) & 0x33333333); - return (((n + (n >> 4))& 0x0F0F0F0F)* 0x01010101) >> 24; - } - -#ifdef FLANN_PLATFORM_64_BIT - unsigned int popcnt64(uint64_t n) const - { - n = ((n >> 1) | n) & 0x5555555555555555; - n = (n & 0x3333333333333333) + ((n >> 2) & 0x3333333333333333); - return (((n + (n >> 4))& 0x0f0f0f0f0f0f0f0f)* 0x0101010101010101) >> 56; - } -#endif - - template - ResultType operator()(const Iterator1 a, const Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const - { - CV_DbgAssert(!(size % long_word_size_) && "vectors size must be multiple of long words size (i.e. 8)"); - -#ifdef FLANN_PLATFORM_64_BIT - const uint64_t* pa = reinterpret_cast(a); - const uint64_t* pb = reinterpret_cast(b); - ResultType result = 0; - size /= long_word_size_; - for(size_t i = 0; i < size; ++i ) { - result += popcnt64(*pa ^ *pb); - ++pa; - ++pb; - } -#else - const uint32_t* pa = reinterpret_cast(a); - const uint32_t* pb = reinterpret_cast(b); - ResultType result = 0; - size /= long_word_size_; - for(size_t i = 0; i < size; ++i ) { - result += popcnt32(*pa ^ *pb); - ++pa; - ++pb; - } -#endif - return result; - } - - - template - ResultType operator()(const Iterator1 a, ZeroIterator b, size_t size, ResultType /*worst_dist*/ = -1) const - { - CV_DbgAssert(!(size % long_word_size_) && "vectors size must be multiple of long words size (i.e. 8)"); - - (void)b; -#ifdef FLANN_PLATFORM_64_BIT - const uint64_t* pa = reinterpret_cast(a); - ResultType result = 0; - size /= long_word_size_; - for(size_t i = 0; i < size; ++i ) { - result += popcnt64(*pa); - ++pa; - } -#else - const uint32_t* pa = reinterpret_cast(a); - ResultType result = 0; - size /= long_word_size_; - for(size_t i = 0; i < size; ++i ) { - result += popcnt32(*pa); - ++pa; - } -#endif - return result; - } - -private: -#ifdef FLANN_PLATFORM_64_BIT - static const size_t long_word_size_= sizeof(uint64_t)/sizeof(unsigned char); -#else - static const size_t long_word_size_= sizeof(uint32_t)/sizeof(unsigned char); -#endif -}; - - - -template -struct HistIntersectionDistance -{ - typedef True is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - typedef ResultType CentersType; - - /** - * Compute the histogram intersection distance - */ - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const - { - ResultType result = ResultType(); - ResultType min0, min1, min2, min3; - Iterator1 last = a + size; - Iterator1 lastgroup = last - 3; - - /* Process 4 items with each loop for efficiency. */ - while (a < lastgroup) { - min0 = (ResultType)(a[0] < b[0] ? a[0] : b[0]); - min1 = (ResultType)(a[1] < b[1] ? a[1] : b[1]); - min2 = (ResultType)(a[2] < b[2] ? a[2] : b[2]); - min3 = (ResultType)(a[3] < b[3] ? a[3] : b[3]); - result += min0 + min1 + min2 + min3; - a += 4; - b += 4; - if ((worst_dist>0)&&(result>worst_dist)) { - return result; - } - } - /* Process last 0-3 pixels. Not needed for standard vector lengths. */ - while (a < last) { - min0 = (ResultType)(*a < *b ? *a : *b); - result += min0; - ++a; - ++b; - } - return result; - } - - /** - * Partial distance, used by the kd-tree. - */ - template - inline ResultType accum_dist(const U& a, const V& b, int) const - { - return a -struct HellingerDistance -{ - typedef True is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - typedef ResultType CentersType; - - /** - * Compute the Hellinger distance - */ - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const - { - ResultType result = ResultType(); - ResultType diff0, diff1, diff2, diff3; - Iterator1 last = a + size; - Iterator1 lastgroup = last - 3; - - /* Process 4 items with each loop for efficiency. */ - while (a < lastgroup) { - diff0 = sqrt(static_cast(a[0])) - sqrt(static_cast(b[0])); - diff1 = sqrt(static_cast(a[1])) - sqrt(static_cast(b[1])); - diff2 = sqrt(static_cast(a[2])) - sqrt(static_cast(b[2])); - diff3 = sqrt(static_cast(a[3])) - sqrt(static_cast(b[3])); - result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3; - a += 4; - b += 4; - } - while (a < last) { - diff0 = sqrt(static_cast(*a++)) - sqrt(static_cast(*b++)); - result += diff0 * diff0; - } - return result; - } - - /** - * Partial distance, used by the kd-tree. - */ - template - inline ResultType accum_dist(const U& a, const V& b, int) const - { - ResultType diff = sqrt(static_cast(a)) - sqrt(static_cast(b)); - return diff * diff; - } -}; - - -template -struct ChiSquareDistance -{ - typedef True is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - typedef ResultType CentersType; - - /** - * Compute the chi-square distance - */ - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const - { - ResultType result = ResultType(); - ResultType sum, diff; - Iterator1 last = a + size; - - while (a < last) { - sum = (ResultType)(*a + *b); - if (sum>0) { - diff = (ResultType)(*a - *b); - result += diff*diff/sum; - } - ++a; - ++b; - - if ((worst_dist>0)&&(result>worst_dist)) { - return result; - } - } - return result; - } - - /** - * Partial distance, used by the kd-tree. - */ - template - inline ResultType accum_dist(const U& a, const V& b, int) const - { - ResultType result = ResultType(); - ResultType sum, diff; - - sum = (ResultType)(a+b); - if (sum>0) { - diff = (ResultType)(a-b); - result = diff*diff/sum; - } - return result; - } -}; - - -template -struct KL_Divergence -{ - typedef True is_kdtree_distance; - typedef True is_vector_space_distance; - - typedef T ElementType; - typedef typename Accumulator::Type ResultType; - typedef ResultType CentersType; - - /** - * Compute the Kullback-Leibler divergence - */ - template - ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const - { - ResultType result = ResultType(); - Iterator1 last = a + size; - - while (a < last) { - if ( *a != 0 && *b != 0 ) { - ResultType ratio = (ResultType)(*a / *b); - if (ratio>0) { - result += *a * log(ratio); - } - } - ++a; - ++b; - - if ((worst_dist>0)&&(result>worst_dist)) { - return result; - } - } - return result; - } - - /** - * Partial distance, used by the kd-tree. - */ - template - inline ResultType accum_dist(const U& a, const V& b, int) const - { - ResultType result = ResultType(); - if( a != 0 && b != 0 ) { - ResultType ratio = (ResultType)(a / b); - if (ratio>0) { - result = a * log(ratio); - } - } - return result; - } -}; - - -/* - * Depending on processed distances, some of them are already squared (e.g. L2) - * and some are not (e.g.Hamming). In KMeans++ for instance we want to be sure - * we are working on ^2 distances, thus following templates to ensure that. - */ -template -struct squareDistance -{ - typedef typename Distance::ResultType ResultType; - ResultType operator()( ResultType dist ) { return dist*dist; } -}; - - -template -struct squareDistance, ElementType> -{ - typedef typename L2_Simple::ResultType ResultType; - ResultType operator()( ResultType dist ) { return dist; } -}; - -template -struct squareDistance, ElementType> -{ - typedef typename L2::ResultType ResultType; - ResultType operator()( ResultType dist ) { return dist; } -}; - - -template -struct squareDistance, ElementType> -{ - typedef typename MinkowskiDistance::ResultType ResultType; - ResultType operator()( ResultType dist ) { return dist; } -}; - -template -struct squareDistance, ElementType> -{ - typedef typename HellingerDistance::ResultType ResultType; - ResultType operator()( ResultType dist ) { return dist; } -}; - -template -struct squareDistance, ElementType> -{ - typedef typename ChiSquareDistance::ResultType ResultType; - ResultType operator()( ResultType dist ) { return dist; } -}; - - -template -typename Distance::ResultType ensureSquareDistance( typename Distance::ResultType dist ) -{ - typedef typename Distance::ElementType ElementType; - - squareDistance dummy; - return dummy( dist ); -} - - -/* - * ...a template to tell the user if the distance he is working with is actually squared - */ - -template -struct isSquareDist -{ - bool operator()() { return false; } -}; - - -template -struct isSquareDist, ElementType> -{ - bool operator()() { return true; } -}; - -template -struct isSquareDist, ElementType> -{ - bool operator()() { return true; } -}; - - -template -struct isSquareDist, ElementType> -{ - bool operator()() { return true; } -}; - -template -struct isSquareDist, ElementType> -{ - bool operator()() { return true; } -}; - -template -struct isSquareDist, ElementType> -{ - bool operator()() { return true; } -}; - - -template -bool isSquareDistance() -{ - typedef typename Distance::ElementType ElementType; - - isSquareDist dummy; - return dummy(); -} - -/* - * ...and a template to ensure the user that he will process the normal distance, - * and not squared distance, without losing processing time calling sqrt(ensureSquareDistance) - * that will result in doing actually sqrt(dist*dist) for L1 distance for instance. - */ -template -struct simpleDistance -{ - typedef typename Distance::ResultType ResultType; - ResultType operator()( ResultType dist ) { return dist; } -}; - - -template -struct simpleDistance, ElementType> -{ - typedef typename L2_Simple::ResultType ResultType; - ResultType operator()( ResultType dist ) { return sqrt(dist); } -}; - -template -struct simpleDistance, ElementType> -{ - typedef typename L2::ResultType ResultType; - ResultType operator()( ResultType dist ) { return sqrt(dist); } -}; - - -template -struct simpleDistance, ElementType> -{ - typedef typename MinkowskiDistance::ResultType ResultType; - ResultType operator()( ResultType dist ) { return sqrt(dist); } -}; - -template -struct simpleDistance, ElementType> -{ - typedef typename HellingerDistance::ResultType ResultType; - ResultType operator()( ResultType dist ) { return sqrt(dist); } -}; - -template -struct simpleDistance, ElementType> -{ - typedef typename ChiSquareDistance::ResultType ResultType; - ResultType operator()( ResultType dist ) { return sqrt(dist); } -}; - - -template -typename Distance::ResultType ensureSimpleDistance( typename Distance::ResultType dist ) -{ - typedef typename Distance::ElementType ElementType; - - simpleDistance dummy; - return dummy( dist ); -} - -} - -//! @endcond - -#endif //OPENCV_FLANN_DIST_H_ diff --git a/include/opencv2/flann/dummy.h b/include/opencv2/flann/dummy.h deleted file mode 100755 index c176f2e..0000000 --- a/include/opencv2/flann/dummy.h +++ /dev/null @@ -1,16 +0,0 @@ - -#ifndef OPENCV_FLANN_DUMMY_H_ -#define OPENCV_FLANN_DUMMY_H_ - -//! @cond IGNORED - -namespace cvflann -{ - -CV_DEPRECATED inline void dummyfunc() {} - -} - -//! @endcond - -#endif /* OPENCV_FLANN_DUMMY_H_ */ diff --git a/include/opencv2/flann/dynamic_bitset.h b/include/opencv2/flann/dynamic_bitset.h deleted file mode 100755 index a00ce1b..0000000 --- a/include/opencv2/flann/dynamic_bitset.h +++ /dev/null @@ -1,163 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -/*********************************************************************** - * Author: Vincent Rabaud - *************************************************************************/ - -#ifndef OPENCV_FLANN_DYNAMIC_BITSET_H_ -#define OPENCV_FLANN_DYNAMIC_BITSET_H_ - -//! @cond IGNORED - -#ifndef FLANN_USE_BOOST -# define FLANN_USE_BOOST 0 -#endif -//#define FLANN_USE_BOOST 1 -#if FLANN_USE_BOOST -#include -typedef boost::dynamic_bitset<> DynamicBitset; -#else - -#include - -#include "dist.h" - -namespace cvflann { - -/** Class re-implementing the boost version of it - * This helps not depending on boost, it also does not do the bound checks - * and has a way to reset a block for speed - */ -class DynamicBitset -{ -public: - /** default constructor - */ - DynamicBitset() : size_(0) - { - } - - /** only constructor we use in our code - * @param sz the size of the bitset (in bits) - */ - DynamicBitset(size_t sz) - { - resize(sz); - reset(); - } - - /** Sets all the bits to 0 - */ - void clear() - { - std::fill(bitset_.begin(), bitset_.end(), 0); - } - - /** @brief checks if the bitset is empty - * @return true if the bitset is empty - */ - bool empty() const - { - return bitset_.empty(); - } - - /** set all the bits to 0 - */ - void reset() - { - std::fill(bitset_.begin(), bitset_.end(), 0); - } - - /** @brief set one bit to 0 - * @param index - */ - void reset(size_t index) - { - bitset_[index / cell_bit_size_] &= ~(size_t(1) << (index % cell_bit_size_)); - } - - /** @brief sets a specific bit to 0, and more bits too - * This function is useful when resetting a given set of bits so that the - * whole bitset ends up being 0: if that's the case, we don't care about setting - * other bits to 0 - * @param index - */ - void reset_block(size_t index) - { - bitset_[index / cell_bit_size_] = 0; - } - - /** resize the bitset so that it contains at least sz bits - * @param sz - */ - void resize(size_t sz) - { - size_ = sz; - bitset_.resize(sz / cell_bit_size_ + 1); - } - - /** set a bit to true - * @param index the index of the bit to set to 1 - */ - void set(size_t index) - { - bitset_[index / cell_bit_size_] |= size_t(1) << (index % cell_bit_size_); - } - - /** gives the number of contained bits - */ - size_t size() const - { - return size_; - } - - /** check if a bit is set - * @param index the index of the bit to check - * @return true if the bit is set - */ - bool test(size_t index) const - { - return (bitset_[index / cell_bit_size_] & (size_t(1) << (index % cell_bit_size_))) != 0; - } - -private: - std::vector bitset_; - size_t size_; - static const unsigned int cell_bit_size_ = CHAR_BIT * sizeof(size_t); -}; - -} // namespace cvflann - -#endif - -//! @endcond - -#endif // OPENCV_FLANN_DYNAMIC_BITSET_H_ diff --git a/include/opencv2/flann/flann.hpp b/include/opencv2/flann/flann.hpp deleted file mode 100755 index 227683f..0000000 --- a/include/opencv2/flann/flann.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/flann.hpp" diff --git a/include/opencv2/flann/flann_base.hpp b/include/opencv2/flann/flann_base.hpp deleted file mode 100755 index 258ec38..0000000 --- a/include/opencv2/flann/flann_base.hpp +++ /dev/null @@ -1,299 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_BASE_HPP_ -#define OPENCV_FLANN_BASE_HPP_ - -//! @cond IGNORED - -#include -#include - -#include "general.h" -#include "matrix.h" -#include "params.h" -#include "saving.h" - -#include "all_indices.h" - -namespace cvflann -{ - -/** - * Sets the log level used for all flann functions - * @param level Verbosity level - */ -inline void log_verbosity(int level) -{ - if (level >= 0) { - Logger::setLevel(level); - } -} - -/** - * (Deprecated) Index parameters for creating a saved index. - */ -struct SavedIndexParams : public IndexParams -{ - SavedIndexParams(cv::String filename) - { - (* this)["algorithm"] = FLANN_INDEX_SAVED; - (*this)["filename"] = filename; - } -}; - - -template -NNIndex* load_saved_index(const Matrix& dataset, const cv::String& filename, Distance distance) -{ - typedef typename Distance::ElementType ElementType; - - FILE* fin = fopen(filename.c_str(), "rb"); - if (fin == NULL) { - return NULL; - } - IndexHeader header = load_header(fin); - if (header.data_type != Datatype::type()) { - fclose(fin); - FLANN_THROW(cv::Error::StsError, "Datatype of saved index is different than of the one to be created."); - } - if ((size_t(header.rows) != dataset.rows)||(size_t(header.cols) != dataset.cols)) { - fclose(fin); - FLANN_THROW(cv::Error::StsError, "The index saved belongs to a different dataset"); - } - - IndexParams params; - params["algorithm"] = header.index_type; - NNIndex* nnIndex = create_index_by_type(dataset, params, distance); - nnIndex->loadIndex(fin); - fclose(fin); - - return nnIndex; -} - - -template -class Index : public NNIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - Index(const Matrix& features, const IndexParams& params, Distance distance = Distance() ) - : index_params_(params) - { - flann_algorithm_t index_type = get_param(params,"algorithm"); - loaded_ = false; - - if (index_type == FLANN_INDEX_SAVED) { - nnIndex_ = load_saved_index(features, get_param(params,"filename"), distance); - loaded_ = true; - } - else { - nnIndex_ = create_index_by_type(features, params, distance); - } - } - - ~Index() - { - delete nnIndex_; - } - - /** - * Builds the index. - */ - void buildIndex() CV_OVERRIDE - { - if (!loaded_) { - nnIndex_->buildIndex(); - } - } - - void save(cv::String filename) - { - FILE* fout = fopen(filename.c_str(), "wb"); - if (fout == NULL) { - FLANN_THROW(cv::Error::StsError, "Cannot open file"); - } - save_header(fout, *nnIndex_); - saveIndex(fout); - fclose(fout); - } - - /** - * \brief Saves the index to a stream - * \param stream The stream to save the index to - */ - virtual void saveIndex(FILE* stream) CV_OVERRIDE - { - nnIndex_->saveIndex(stream); - } - - /** - * \brief Loads the index from a stream - * \param stream The stream from which the index is loaded - */ - virtual void loadIndex(FILE* stream) CV_OVERRIDE - { - nnIndex_->loadIndex(stream); - } - - /** - * \returns number of features in this index. - */ - size_t veclen() const CV_OVERRIDE - { - return nnIndex_->veclen(); - } - - /** - * \returns The dimensionality of the features in this index. - */ - size_t size() const CV_OVERRIDE - { - return nnIndex_->size(); - } - - /** - * \returns The index type (kdtree, kmeans,...) - */ - flann_algorithm_t getType() const CV_OVERRIDE - { - return nnIndex_->getType(); - } - - /** - * \returns The amount of memory (in bytes) used by the index. - */ - virtual int usedMemory() const CV_OVERRIDE - { - return nnIndex_->usedMemory(); - } - - - /** - * \returns The index parameters - */ - IndexParams getParameters() const CV_OVERRIDE - { - return nnIndex_->getParameters(); - } - - /** - * \brief Perform k-nearest neighbor search - * \param[in] queries The query points for which to find the nearest neighbors - * \param[out] indices The indices of the nearest neighbors found - * \param[out] dists Distances to the nearest neighbors found - * \param[in] knn Number of nearest neighbors to return - * \param[in] params Search parameters - */ - void knnSearch(const Matrix& queries, Matrix& indices, Matrix& dists, int knn, const SearchParams& params) CV_OVERRIDE - { - nnIndex_->knnSearch(queries, indices, dists, knn, params); - } - - /** - * \brief Perform radius search - * \param[in] query The query point - * \param[out] indices The indinces of the neighbors found within the given radius - * \param[out] dists The distances to the nearest neighbors found - * \param[in] radius The radius used for search - * \param[in] params Search parameters - * \returns Number of neighbors found - */ - int radiusSearch(const Matrix& query, Matrix& indices, Matrix& dists, float radius, const SearchParams& params) CV_OVERRIDE - { - return nnIndex_->radiusSearch(query, indices, dists, radius, params); - } - - /** - * \brief Method that searches for nearest-neighbours - */ - void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE - { - nnIndex_->findNeighbors(result, vec, searchParams); - } - - /** - * \brief Returns actual index - */ - CV_DEPRECATED NNIndex* getIndex() - { - return nnIndex_; - } - - /** - * \brief Returns index parameters. - * \deprecated use getParameters() instead. - */ - CV_DEPRECATED const IndexParams* getIndexParameters() - { - return &index_params_; - } - -private: - /** Pointer to actual index class */ - NNIndex* nnIndex_; - /** Indices if the index was loaded from a file */ - bool loaded_; - /** Parameters passed to the index */ - IndexParams index_params_; - - Index(const Index &); // copy disabled - Index& operator=(const Index &); // assign disabled -}; - -/** - * Performs a hierarchical clustering of the points passed as argument and then takes a cut in the - * the clustering tree to return a flat clustering. - * @param[in] points Points to be clustered - * @param centers The computed cluster centres. Matrix should be preallocated and centers.rows is the - * number of clusters requested. - * @param params Clustering parameters (The same as for cvflann::KMeansIndex) - * @param d Distance to be used for clustering (eg: cvflann::L2) - * @return number of clusters computed (can be different than clusters.rows and is the highest number - * of the form (branching-1)*K+1 smaller than clusters.rows). - */ -template -int hierarchicalClustering(const Matrix& points, Matrix& centers, - const KMeansIndexParams& params, Distance d = Distance()) -{ - KMeansIndex kmeans(points, params, d); - kmeans.buildIndex(); - - int clusterNum = kmeans.getClusterCenters(centers); - return clusterNum; -} - -} - -//! @endcond - -#endif /* OPENCV_FLANN_BASE_HPP_ */ diff --git a/include/opencv2/flann/general.h b/include/opencv2/flann/general.h deleted file mode 100755 index 29fa8be..0000000 --- a/include/opencv2/flann/general.h +++ /dev/null @@ -1,63 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_GENERAL_H_ -#define OPENCV_FLANN_GENERAL_H_ - -#if CV_VERSION_MAJOR <= 4 - -//! @cond IGNORED - -#include "opencv2/core.hpp" - -namespace cvflann -{ - -class FLANNException : public cv::Exception -{ -public: - FLANNException(const char* message) : cv::Exception(0, message, "", __FILE__, __LINE__) { } - - FLANNException(const cv::String& message) : cv::Exception(0, message, "", __FILE__, __LINE__) { } -}; - -} - -#define FLANN_THROW(TYPE, STR) throw FLANNException(STR) - -#else - -#define FLANN_THROW(TYPE, STR) CV_Error(TYPE, STR) - -#endif - -//! @endcond - -#endif /* OPENCV_FLANN_GENERAL_H_ */ diff --git a/include/opencv2/flann/ground_truth.h b/include/opencv2/flann/ground_truth.h deleted file mode 100755 index 17f2a8e..0000000 --- a/include/opencv2/flann/ground_truth.h +++ /dev/null @@ -1,98 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_GROUND_TRUTH_H_ -#define OPENCV_FLANN_GROUND_TRUTH_H_ - -//! @cond IGNORED - -#include "dist.h" -#include "matrix.h" - - -namespace cvflann -{ - -template -void find_nearest(const Matrix& dataset, typename Distance::ElementType* query, int* matches, int nn, - int skip = 0, Distance distance = Distance()) -{ - typedef typename Distance::ResultType DistanceType; - int n = nn + skip; - - std::vector match(n); - std::vector dists(n); - - dists[0] = distance(dataset[0], query, dataset.cols); - match[0] = 0; - int dcnt = 1; - - for (size_t i=1; i=1 && dists[j] -void compute_ground_truth(const Matrix& dataset, const Matrix& testset, Matrix& matches, - int skip=0, Distance d = Distance()) -{ - for (size_t i=0; i(dataset, testset[i], matches[i], (int)matches.cols, skip, d); - } -} - - -} - -//! @endcond - -#endif //OPENCV_FLANN_GROUND_TRUTH_H_ diff --git a/include/opencv2/flann/hdf5.h b/include/opencv2/flann/hdf5.h deleted file mode 100755 index 7554384..0000000 --- a/include/opencv2/flann/hdf5.h +++ /dev/null @@ -1,235 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - - -#ifndef OPENCV_FLANN_HDF5_H_ -#define OPENCV_FLANN_HDF5_H_ - -//! @cond IGNORED - -#include - -#include "matrix.h" - - -namespace cvflann -{ - -namespace -{ - -template -hid_t get_hdf5_type() -{ - throw FLANNException("Unsupported type for IO operations"); -} - -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_CHAR; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_UCHAR; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_SHORT; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_USHORT; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_INT; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_UINT; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_LONG; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_ULONG; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_FLOAT; } -template<> -hid_t get_hdf5_type() { return H5T_NATIVE_DOUBLE; } -} - - -#define CHECK_ERROR(x,y) if ((x)<0) throw FLANNException((y)); - -template -void save_to_file(const cvflann::Matrix& dataset, const String& filename, const String& name) -{ - -#if H5Eset_auto_vers == 2 - H5Eset_auto( H5E_DEFAULT, NULL, NULL ); -#else - H5Eset_auto( NULL, NULL ); -#endif - - herr_t status; - hid_t file_id; - file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, H5P_DEFAULT); - if (file_id < 0) { - file_id = H5Fcreate(filename.c_str(), H5F_ACC_EXCL, H5P_DEFAULT, H5P_DEFAULT); - } - CHECK_ERROR(file_id,"Error creating hdf5 file."); - - hsize_t dimsf[2]; // dataset dimensions - dimsf[0] = dataset.rows; - dimsf[1] = dataset.cols; - - hid_t space_id = H5Screate_simple(2, dimsf, NULL); - hid_t memspace_id = H5Screate_simple(2, dimsf, NULL); - - hid_t dataset_id; -#if H5Dcreate_vers == 2 - dataset_id = H5Dcreate2(file_id, name.c_str(), get_hdf5_type(), space_id, H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT); -#else - dataset_id = H5Dcreate(file_id, name.c_str(), get_hdf5_type(), space_id, H5P_DEFAULT); -#endif - - if (dataset_id<0) { -#if H5Dopen_vers == 2 - dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT); -#else - dataset_id = H5Dopen(file_id, name.c_str()); -#endif - } - CHECK_ERROR(dataset_id,"Error creating or opening dataset in file."); - - status = H5Dwrite(dataset_id, get_hdf5_type(), memspace_id, space_id, H5P_DEFAULT, dataset.data ); - CHECK_ERROR(status, "Error writing to dataset"); - - H5Sclose(memspace_id); - H5Sclose(space_id); - H5Dclose(dataset_id); - H5Fclose(file_id); - -} - - -template -void load_from_file(cvflann::Matrix& dataset, const String& filename, const String& name) -{ - herr_t status; - hid_t file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, H5P_DEFAULT); - CHECK_ERROR(file_id,"Error opening hdf5 file."); - - hid_t dataset_id; -#if H5Dopen_vers == 2 - dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT); -#else - dataset_id = H5Dopen(file_id, name.c_str()); -#endif - CHECK_ERROR(dataset_id,"Error opening dataset in file."); - - hid_t space_id = H5Dget_space(dataset_id); - - hsize_t dims_out[2]; - H5Sget_simple_extent_dims(space_id, dims_out, NULL); - - dataset = cvflann::Matrix(new T[dims_out[0]*dims_out[1]], dims_out[0], dims_out[1]); - - status = H5Dread(dataset_id, get_hdf5_type(), H5S_ALL, H5S_ALL, H5P_DEFAULT, dataset[0]); - CHECK_ERROR(status, "Error reading dataset"); - - H5Sclose(space_id); - H5Dclose(dataset_id); - H5Fclose(file_id); -} - - -#ifdef HAVE_MPI - -namespace mpi -{ -/** - * Loads a the hyperslice corresponding to this processor from a hdf5 file. - * @param flann_dataset Dataset where the data is loaded - * @param filename HDF5 file name - * @param name Name of dataset inside file - */ -template -void load_from_file(cvflann::Matrix& dataset, const String& filename, const String& name) -{ - MPI_Comm comm = MPI_COMM_WORLD; - MPI_Info info = MPI_INFO_NULL; - - int mpi_size, mpi_rank; - MPI_Comm_size(comm, &mpi_size); - MPI_Comm_rank(comm, &mpi_rank); - - herr_t status; - - hid_t plist_id = H5Pcreate(H5P_FILE_ACCESS); - H5Pset_fapl_mpio(plist_id, comm, info); - hid_t file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, plist_id); - CHECK_ERROR(file_id,"Error opening hdf5 file."); - H5Pclose(plist_id); - hid_t dataset_id; -#if H5Dopen_vers == 2 - dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT); -#else - dataset_id = H5Dopen(file_id, name.c_str()); -#endif - CHECK_ERROR(dataset_id,"Error opening dataset in file."); - - hid_t space_id = H5Dget_space(dataset_id); - hsize_t dims[2]; - H5Sget_simple_extent_dims(space_id, dims, NULL); - - hsize_t count[2]; - hsize_t offset[2]; - - hsize_t item_cnt = dims[0]/mpi_size+(dims[0]%mpi_size==0 ? 0 : 1); - hsize_t cnt = (mpi_rank(), memspace_id, space_id, plist_id, dataset.data); - CHECK_ERROR(status, "Error reading dataset"); - - H5Pclose(plist_id); - H5Sclose(space_id); - H5Sclose(memspace_id); - H5Dclose(dataset_id); - H5Fclose(file_id); -} -} -#endif // HAVE_MPI -} // namespace cvflann::mpi - -//! @endcond - -#endif /* OPENCV_FLANN_HDF5_H_ */ diff --git a/include/opencv2/flann/heap.h b/include/opencv2/flann/heap.h deleted file mode 100755 index ee1c682..0000000 --- a/include/opencv2/flann/heap.h +++ /dev/null @@ -1,169 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_HEAP_H_ -#define OPENCV_FLANN_HEAP_H_ - -//! @cond IGNORED - -#include -#include - -namespace cvflann -{ - -/** - * Priority Queue Implementation - * - * The priority queue is implemented with a heap. A heap is a complete - * (full) binary tree in which each parent is less than both of its - * children, but the order of the children is unspecified. - */ -template -class Heap -{ - - /** - * Storage array for the heap. - * Type T must be comparable. - */ - std::vector heap; - int length; - - /** - * Number of element in the heap - */ - int count; - - - -public: - /** - * Constructor. - * - * Params: - * sz = heap size - */ - - Heap(int sz) - { - length = sz; - heap.reserve(length); - count = 0; - } - - /** - * - * Returns: heap size - */ - int size() - { - return count; - } - - /** - * Tests if the heap is empty - * - * Returns: true is heap empty, false otherwise - */ - bool empty() - { - return size()==0; - } - - /** - * Clears the heap. - */ - void clear() - { - heap.clear(); - count = 0; - } - - struct CompareT - { - bool operator()(const T& t_1, const T& t_2) const - { - return t_2 < t_1; - } - }; - - /** - * Insert a new element in the heap. - * - * We select the next empty leaf node, and then keep moving any larger - * parents down until the right location is found to store this element. - * - * Params: - * value = the new element to be inserted in the heap - */ - void insert(T value) - { - /* If heap is full, then return without adding this element. */ - if (count == length) { - return; - } - - heap.push_back(value); - static CompareT compareT; - std::push_heap(heap.begin(), heap.end(), compareT); - ++count; - } - - - - /** - * Returns the node of minimum value from the heap (top of the heap). - * - * Params: - * value = out parameter used to return the min element - * Returns: false if heap empty - */ - bool popMin(T& value) - { - if (count == 0) { - return false; - } - - value = heap[0]; - static CompareT compareT; - std::pop_heap(heap.begin(), heap.end(), compareT); - heap.pop_back(); - --count; - - return true; /* Return old last node. */ - } -}; - -} - -//! @endcond - -#endif //OPENCV_FLANN_HEAP_H_ diff --git a/include/opencv2/flann/hierarchical_clustering_index.h b/include/opencv2/flann/hierarchical_clustering_index.h deleted file mode 100755 index 7085d69..0000000 --- a/include/opencv2/flann/hierarchical_clustering_index.h +++ /dev/null @@ -1,847 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_ -#define OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_ - -//! @cond IGNORED - -#include -#include -#include -#include - -#include "general.h" -#include "nn_index.h" -#include "dist.h" -#include "matrix.h" -#include "result_set.h" -#include "heap.h" -#include "allocator.h" -#include "random.h" -#include "saving.h" - - -namespace cvflann -{ - -struct HierarchicalClusteringIndexParams : public IndexParams -{ - HierarchicalClusteringIndexParams(int branching = 32, - flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, - int trees = 4, int leaf_size = 100) - { - (*this)["algorithm"] = FLANN_INDEX_HIERARCHICAL; - // The branching factor used in the hierarchical clustering - (*this)["branching"] = branching; - // Algorithm used for picking the initial cluster centers - (*this)["centers_init"] = centers_init; - // number of parallel trees to build - (*this)["trees"] = trees; - // maximum leaf size - (*this)["leaf_size"] = leaf_size; - } -}; - - -/** - * Hierarchical index - * - * Contains a tree constructed through a hierarchical clustering - * and other information for indexing a set of points for nearest-neighbour matching. - */ -template -class HierarchicalClusteringIndex : public NNIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - -private: - - - typedef void (HierarchicalClusteringIndex::* centersAlgFunction)(int, int*, int, int*, int&); - - /** - * The function used for choosing the cluster centers. - */ - centersAlgFunction chooseCenters; - - - - /** - * Chooses the initial centers in the k-means clustering in a random manner. - * - * Params: - * k = number of centers - * vecs = the dataset of points - * indices = indices in the dataset - * indices_length = length of indices vector - * - */ - void chooseCentersRandom(int k, int* dsindices, int indices_length, int* centers, int& centers_length) - { - UniqueRandom r(indices_length); - - int index; - for (index=0; index=0 && rnd < n); - - centers[0] = dsindices[rnd]; - - int index; - for (index=1; indexbest_val) { - best_val = dist; - best_index = j; - } - } - if (best_index!=-1) { - centers[index] = dsindices[best_index]; - } - else { - break; - } - } - centers_length = index; - } - - - /** - * Chooses the initial centers in the k-means using the algorithm - * proposed in the KMeans++ paper: - * Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding - * - * Implementation of this function was converted from the one provided in Arthur's code. - * - * Params: - * k = number of centers - * vecs = the dataset of points - * indices = indices in the dataset - * Returns: - */ - void chooseCentersKMeanspp(int k, int* dsindices, int indices_length, int* centers, int& centers_length) - { - int n = indices_length; - - double currentPot = 0; - DistanceType* closestDistSq = new DistanceType[n]; - - // Choose one random center and set the closestDistSq values - int index = rand_int(n); - CV_DbgAssert(index >=0 && index < n); - centers[0] = dsindices[index]; - - // Computing distance^2 will have the advantage of even higher probability further to pick new centers - // far from previous centers (and this complies to "k-means++: the advantages of careful seeding" article) - for (int i = 0; i < n; i++) { - closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols); - closestDistSq[i] = ensureSquareDistance( closestDistSq[i] ); - currentPot += closestDistSq[i]; - } - - - const int numLocalTries = 1; - - // Choose each center - int centerCount; - for (centerCount = 1; centerCount < k; centerCount++) { - - // Repeat several trials - double bestNewPot = -1; - int bestNewIndex = 0; - for (int localTrial = 0; localTrial < numLocalTries; localTrial++) { - - // Choose our center - have to be slightly careful to return a valid answer even accounting - // for possible rounding errors - double randVal = rand_double(currentPot); - for (index = 0; index < n-1; index++) { - if (randVal <= closestDistSq[index]) break; - else randVal -= closestDistSq[index]; - } - - // Compute the new potential - double newPot = 0; - for (int i = 0; i < n; i++) { - DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols); - newPot += std::min( ensureSquareDistance(dist), closestDistSq[i] ); - } - - // Store the best result - if ((bestNewPot < 0)||(newPot < bestNewPot)) { - bestNewPot = newPot; - bestNewIndex = index; - } - } - - // Add the appropriate center - centers[centerCount] = dsindices[bestNewIndex]; - currentPot = bestNewPot; - for (int i = 0; i < n; i++) { - DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols); - closestDistSq[i] = std::min( ensureSquareDistance(dist), closestDistSq[i] ); - } - } - - centers_length = centerCount; - - delete[] closestDistSq; - } - - - /** - * Chooses the initial centers in a way inspired by Gonzales (by Pierre-Emmanuel Viel): - * select the first point of the list as a candidate, then parse the points list. If another - * point is further than current candidate from the other centers, test if it is a good center - * of a local aggregation. If it is, replace current candidate by this point. And so on... - * - * Used with KMeansIndex that computes centers coordinates by averaging positions of clusters points, - * this doesn't make a real difference with previous methods. But used with HierarchicalClusteringIndex - * class that pick centers among existing points instead of computing the barycenters, there is a real - * improvement. - * - * Params: - * k = number of centers - * vecs = the dataset of points - * indices = indices in the dataset - * Returns: - */ - void GroupWiseCenterChooser(int k, int* dsindices, int indices_length, int* centers, int& centers_length) - { - const float kSpeedUpFactor = 1.3f; - - int n = indices_length; - - DistanceType* closestDistSq = new DistanceType[n]; - - // Choose one random center and set the closestDistSq values - int index = rand_int(n); - CV_DbgAssert(index >=0 && index < n); - centers[0] = dsindices[index]; - - for (int i = 0; i < n; i++) { - closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols); - } - - - // Choose each center - int centerCount; - for (centerCount = 1; centerCount < k; centerCount++) { - - // Repeat several trials - double bestNewPot = -1; - int bestNewIndex = 0; - DistanceType furthest = 0; - for (index = 0; index < n; index++) { - - // We will test only the potential of the points further than current candidate - if( closestDistSq[index] > kSpeedUpFactor * (float)furthest ) { - - // Compute the new potential - double newPot = 0; - for (int i = 0; i < n; i++) { - newPot += std::min( distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols) - , closestDistSq[i] ); - } - - // Store the best result - if ((bestNewPot < 0)||(newPot <= bestNewPot)) { - bestNewPot = newPot; - bestNewIndex = index; - furthest = closestDistSq[index]; - } - } - } - - // Add the appropriate center - centers[centerCount] = dsindices[bestNewIndex]; - for (int i = 0; i < n; i++) { - closestDistSq[i] = std::min( distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols) - , closestDistSq[i] ); - } - } - - centers_length = centerCount; - - delete[] closestDistSq; - } - - -public: - - - /** - * Index constructor - * - * Params: - * inputData = dataset with the input features - * params = parameters passed to the hierarchical k-means algorithm - */ - HierarchicalClusteringIndex(const Matrix& inputData, const IndexParams& index_params = HierarchicalClusteringIndexParams(), - Distance d = Distance()) - : dataset(inputData), params(index_params), root(NULL), indices(NULL), distance(d) - { - memoryCounter = 0; - - size_ = dataset.rows; - veclen_ = dataset.cols; - - branching_ = get_param(params,"branching",32); - centers_init_ = get_param(params,"centers_init", FLANN_CENTERS_RANDOM); - trees_ = get_param(params,"trees",4); - leaf_size_ = get_param(params,"leaf_size",100); - - if (centers_init_==FLANN_CENTERS_RANDOM) { - chooseCenters = &HierarchicalClusteringIndex::chooseCentersRandom; - } - else if (centers_init_==FLANN_CENTERS_GONZALES) { - chooseCenters = &HierarchicalClusteringIndex::chooseCentersGonzales; - } - else if (centers_init_==FLANN_CENTERS_KMEANSPP) { - chooseCenters = &HierarchicalClusteringIndex::chooseCentersKMeanspp; - } - else if (centers_init_==FLANN_CENTERS_GROUPWISE) { - chooseCenters = &HierarchicalClusteringIndex::GroupWiseCenterChooser; - } - else { - FLANN_THROW(cv::Error::StsError, "Unknown algorithm for choosing initial centers."); - } - - root = new NodePtr[trees_]; - indices = new int*[trees_]; - - for (int i=0; i(); - computeClustering(root[i], indices[i], (int)size_, branching_,0); - } - } - - - flann_algorithm_t getType() const CV_OVERRIDE - { - return FLANN_INDEX_HIERARCHICAL; - } - - - void saveIndex(FILE* stream) CV_OVERRIDE - { - save_value(stream, branching_); - save_value(stream, trees_); - save_value(stream, centers_init_); - save_value(stream, leaf_size_); - save_value(stream, memoryCounter); - for (int i=0; i& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE - { - - const int maxChecks = get_param(searchParams,"checks",32); - - // Priority queue storing intermediate branches in the best-bin-first search - Heap* heap = new Heap((int)size_); - - std::vector checked(size_,false); - int checks = 0; - for (int i=0; i= maxChecks) && result.full()) - break; - } - - BranchSt branch; - while (heap->popMin(branch) && (checks BranchSt; - - - - void save_tree(FILE* stream, NodePtr node, int num) - { - save_value(stream, *node); - if (node->childs==NULL) { - int indices_offset = (int)(node->indices - indices[num]); - save_value(stream, indices_offset); - } - else { - for(int i=0; ichilds[i], num); - } - } - } - - - void load_tree(FILE* stream, NodePtr& node, int num) - { - node = pool.allocate(); - load_value(stream, *node); - if (node->childs==NULL) { - int indices_offset; - load_value(stream, indices_offset); - node->indices = indices[num] + indices_offset; - } - else { - node->childs = pool.allocate(branching_); - for(int i=0; ichilds[i], num); - } - } - } - - - /** - * Release the inner elements of indices[] - */ - void free_indices() - { - if (indices!=NULL) { - for(int i=0; inew_dist) { - labels[i] = j; - dist = new_dist; - } - } - cost += dist; - } - } - - /** - * The method responsible with actually doing the recursive hierarchical - * clustering - * - * Params: - * node = the node to cluster - * indices = indices of the points belonging to the current node - * branching = the branching factor to use in the clustering - * - * TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point) - */ - void computeClustering(NodePtr node, int* dsindices, int indices_length, int branching, int level) - { - node->size = indices_length; - node->level = level; - - if (indices_length < leaf_size_) { // leaf node - node->indices = dsindices; - std::sort(node->indices,node->indices+indices_length); - node->childs = NULL; - return; - } - - std::vector centers(branching); - std::vector labels(indices_length); - - int centers_length; - (this->*chooseCenters)(branching, dsindices, indices_length, ¢ers[0], centers_length); - - if (centers_lengthindices = dsindices; - std::sort(node->indices,node->indices+indices_length); - node->childs = NULL; - return; - } - - - // assign points to clusters - DistanceType cost; - computeLabels(dsindices, indices_length, ¢ers[0], centers_length, &labels[0], cost); - - node->childs = pool.allocate(branching); - int start = 0; - int end = start; - for (int i=0; ichilds[i] = pool.allocate(); - node->childs[i]->pivot = centers[i]; - node->childs[i]->indices = NULL; - computeClustering(node->childs[i],dsindices+start, end-start, branching, level+1); - start=end; - } - } - - - - /** - * Performs one descent in the hierarchical k-means tree. The branches not - * visited are stored in a priority queue. - * - * Params: - * node = node to explore - * result = container for the k-nearest neighbors found - * vec = query points - * checks = how many points in the dataset have been checked so far - * maxChecks = maximum dataset points to checks - */ - - - void findNN(NodePtr node, ResultSet& result, const ElementType* vec, int& checks, int maxChecks, - Heap* heap, std::vector& checked) - { - if (node->childs==NULL) { - if ((checks>=maxChecks) && result.full()) { - return; - } - for (int i=0; isize; ++i) { - int index = node->indices[i]; - if (!checked[index]) { - DistanceType dist = distance(dataset[index], vec, veclen_); - result.addPoint(dist, index); - checked[index] = true; - ++checks; - } - } - } - else { - DistanceType* domain_distances = new DistanceType[branching_]; - int best_index = 0; - domain_distances[best_index] = distance(vec, dataset[node->childs[best_index]->pivot], veclen_); - for (int i=1; ichilds[i]->pivot], veclen_); - if (domain_distances[i]insert(BranchSt(node->childs[i],domain_distances[i])); - } - } - delete[] domain_distances; - findNN(node->childs[best_index],result,vec, checks, maxChecks, heap, checked); - } - } - -private: - - - /** - * The dataset used by this index - */ - const Matrix dataset; - - /** - * Parameters used by this index - */ - IndexParams params; - - - /** - * Number of features in the dataset. - */ - size_t size_; - - /** - * Length of each feature. - */ - size_t veclen_; - - /** - * The root node in the tree. - */ - NodePtr* root; - - /** - * Array of indices to vectors in the dataset. - */ - int** indices; - - - /** - * The distance - */ - Distance distance; - - /** - * Pooled memory allocator. - * - * Using a pooled memory allocator is more efficient - * than allocating memory directly when there is a large - * number small of memory allocations. - */ - PooledAllocator pool; - - /** - * Memory occupied by the index. - */ - int memoryCounter; - - /** index parameters */ - int branching_; - int trees_; - flann_centers_init_t centers_init_; - int leaf_size_; - - -}; - -} - -//! @endcond - -#endif /* OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_ */ diff --git a/include/opencv2/flann/index_testing.h b/include/opencv2/flann/index_testing.h deleted file mode 100755 index 207adef..0000000 --- a/include/opencv2/flann/index_testing.h +++ /dev/null @@ -1,321 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_INDEX_TESTING_H_ -#define OPENCV_FLANN_INDEX_TESTING_H_ - -//! @cond IGNORED - -#include -#include - -#include "matrix.h" -#include "nn_index.h" -#include "result_set.h" -#include "logger.h" -#include "timer.h" - - -namespace cvflann -{ - -inline int countCorrectMatches(int* neighbors, int* groundTruth, int n) -{ - int count = 0; - for (int i=0; i -typename Distance::ResultType computeDistanceRaport(const Matrix& inputData, typename Distance::ElementType* target, - int* neighbors, int* groundTruth, int veclen, int n, const Distance& distance) -{ - typedef typename Distance::ResultType DistanceType; - - DistanceType ret = 0; - for (int i=0; i -float search_with_ground_truth(NNIndex& index, const Matrix& inputData, - const Matrix& testData, const Matrix& matches, int nn, int checks, - float& time, typename Distance::ResultType& dist, const Distance& distance, int skipMatches) -{ - typedef typename Distance::ResultType DistanceType; - - if (matches.cols resultSet(nn+skipMatches); - SearchParams searchParams(checks); - - std::vector indices(nn+skipMatches); - std::vector dists(nn+skipMatches); - int* neighbors = &indices[skipMatches]; - - int correct = 0; - DistanceType distR = 0; - StartStopTimer t; - int repeats = 0; - while (t.value<0.2) { - repeats++; - t.start(); - correct = 0; - distR = 0; - for (size_t i = 0; i < testData.rows; i++) { - resultSet.init(&indices[0], &dists[0]); - index.findNeighbors(resultSet, testData[i], searchParams); - - correct += countCorrectMatches(neighbors,matches[i], nn); - distR += computeDistanceRaport(inputData, testData[i], neighbors, matches[i], (int)testData.cols, nn, distance); - } - t.stop(); - } - time = float(t.value/repeats); - - float precicion = (float)correct/(nn*testData.rows); - - dist = distR/(testData.rows*nn); - - Logger::info("%8d %10.4g %10.5g %10.5g %10.5g\n", - checks, precicion, time, 1000.0 * time / testData.rows, dist); - - return precicion; -} - - -template -float test_index_checks(NNIndex& index, const Matrix& inputData, - const Matrix& testData, const Matrix& matches, - int checks, float& precision, const Distance& distance, int nn = 1, int skipMatches = 0) -{ - typedef typename Distance::ResultType DistanceType; - - Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n"); - Logger::info("---------------------------------------------------------\n"); - - float time = 0; - DistanceType dist = 0; - precision = search_with_ground_truth(index, inputData, testData, matches, nn, checks, time, dist, distance, skipMatches); - - return time; -} - -template -float test_index_precision(NNIndex& index, const Matrix& inputData, - const Matrix& testData, const Matrix& matches, - float precision, int& checks, const Distance& distance, int nn = 1, int skipMatches = 0) -{ - typedef typename Distance::ResultType DistanceType; - const float SEARCH_EPS = 0.001f; - - Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n"); - Logger::info("---------------------------------------------------------\n"); - - int c2 = 1; - float p2; - int c1 = 1; - //float p1; - float time; - DistanceType dist; - - p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches); - - if (p2>precision) { - Logger::info("Got as close as I can\n"); - checks = c2; - return time; - } - - while (p2SEARCH_EPS) { - Logger::info("Start linear estimation\n"); - // after we got to values in the vecinity of the desired precision - // use linear approximation get a better estimation - - cx = (c1+c2)/2; - realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches); - while (fabs(realPrecision-precision)>SEARCH_EPS) { - - if (realPrecision -void test_index_precisions(NNIndex& index, const Matrix& inputData, - const Matrix& testData, const Matrix& matches, - float* precisions, int precisions_length, const Distance& distance, int nn = 1, int skipMatches = 0, float maxTime = 0) -{ - typedef typename Distance::ResultType DistanceType; - - const float SEARCH_EPS = 0.001; - - // make sure precisions array is sorted - std::sort(precisions, precisions+precisions_length); - - int pindex = 0; - float precision = precisions[pindex]; - - Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n"); - Logger::info("---------------------------------------------------------\n"); - - int c2 = 1; - float p2; - - int c1 = 1; - float p1; - - float time; - DistanceType dist; - - p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches); - - // if precision for 1 run down the tree is already - // better then some of the requested precisions, then - // skip those - while (precisions[pindex] 0)&&(time > maxTime)&&(p2SEARCH_EPS) { - Logger::info("Start linear estimation\n"); - // after we got to values in the vecinity of the desired precision - // use linear approximation get a better estimation - - cx = (c1+c2)/2; - realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches); - while (fabs(realPrecision-precision)>SEARCH_EPS) { - - if (realPrecision -#include -#include - -#include "nn_index.h" -#include "dynamic_bitset.h" -#include "matrix.h" -#include "result_set.h" -#include "heap.h" -#include "allocator.h" -#include "random.h" -#include "saving.h" - - -namespace cvflann -{ - -struct KDTreeIndexParams : public IndexParams -{ - KDTreeIndexParams(int trees = 4) - { - (*this)["algorithm"] = FLANN_INDEX_KDTREE; - (*this)["trees"] = trees; - } -}; - - -/** - * Randomized kd-tree index - * - * Contains the k-d trees and other information for indexing a set of points - * for nearest-neighbor matching. - */ -template -class KDTreeIndex : public NNIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - - /** - * KDTree constructor - * - * Params: - * inputData = dataset with the input features - * params = parameters passed to the kdtree algorithm - */ - KDTreeIndex(const Matrix& inputData, const IndexParams& params = KDTreeIndexParams(), - Distance d = Distance() ) : - dataset_(inputData), index_params_(params), distance_(d) - { - size_ = dataset_.rows; - veclen_ = dataset_.cols; - - trees_ = get_param(index_params_,"trees",4); - tree_roots_ = new NodePtr[trees_]; - - // Create a permutable array of indices to the input vectors. - vind_.resize(size_); - for (size_t i = 0; i < size_; ++i) { - vind_[i] = int(i); - } - - mean_ = new DistanceType[veclen_]; - var_ = new DistanceType[veclen_]; - } - - - KDTreeIndex(const KDTreeIndex&); - KDTreeIndex& operator=(const KDTreeIndex&); - - /** - * Standard destructor - */ - ~KDTreeIndex() - { - if (tree_roots_!=NULL) { - delete[] tree_roots_; - } - delete[] mean_; - delete[] var_; - } - - /** - * Builds the index - */ - void buildIndex() CV_OVERRIDE - { - /* Construct the randomized trees. */ - for (int i = 0; i < trees_; i++) { - /* Randomize the order of vectors to allow for unbiased sampling. */ -#ifndef OPENCV_FLANN_USE_STD_RAND - cv::randShuffle(vind_); -#else - std::random_shuffle(vind_.begin(), vind_.end()); -#endif - - tree_roots_[i] = divideTree(&vind_[0], int(size_) ); - } - } - - - flann_algorithm_t getType() const CV_OVERRIDE - { - return FLANN_INDEX_KDTREE; - } - - - void saveIndex(FILE* stream) CV_OVERRIDE - { - save_value(stream, trees_); - for (int i=0; i& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE - { - int maxChecks = get_param(searchParams,"checks", 32); - float epsError = 1+get_param(searchParams,"eps",0.0f); - - if (maxChecks==FLANN_CHECKS_UNLIMITED) { - getExactNeighbors(result, vec, epsError); - } - else { - getNeighbors(result, vec, maxChecks, epsError); - } - } - - IndexParams getParameters() const CV_OVERRIDE - { - return index_params_; - } - -private: - - - /*--------------------- Internal Data Structures --------------------------*/ - struct Node - { - /** - * Dimension used for subdivision. - */ - int divfeat; - /** - * The values used for subdivision. - */ - DistanceType divval; - /** - * The child nodes. - */ - Node* child1, * child2; - }; - typedef Node* NodePtr; - typedef BranchStruct BranchSt; - typedef BranchSt* Branch; - - - - void save_tree(FILE* stream, NodePtr tree) - { - save_value(stream, *tree); - if (tree->child1!=NULL) { - save_tree(stream, tree->child1); - } - if (tree->child2!=NULL) { - save_tree(stream, tree->child2); - } - } - - - void load_tree(FILE* stream, NodePtr& tree) - { - tree = pool_.allocate(); - load_value(stream, *tree); - if (tree->child1!=NULL) { - load_tree(stream, tree->child1); - } - if (tree->child2!=NULL) { - load_tree(stream, tree->child2); - } - } - - - /** - * Create a tree node that subdivides the list of vecs from vind[first] - * to vind[last]. The routine is called recursively on each sublist. - * Place a pointer to this new tree node in the location pTree. - * - * Params: pTree = the new node to create - * first = index of the first vector - * last = index of the last vector - */ - NodePtr divideTree(int* ind, int count) - { - NodePtr node = pool_.allocate(); // allocate memory - - /* If too few exemplars remain, then make this a leaf node. */ - if ( count == 1) { - node->child1 = node->child2 = NULL; /* Mark as leaf node. */ - node->divfeat = *ind; /* Store index of this vec. */ - } - else { - int idx; - int cutfeat; - DistanceType cutval; - meanSplit(ind, count, idx, cutfeat, cutval); - - node->divfeat = cutfeat; - node->divval = cutval; - node->child1 = divideTree(ind, idx); - node->child2 = divideTree(ind+idx, count-idx); - } - - return node; - } - - - /** - * Choose which feature to use in order to subdivide this set of vectors. - * Make a random choice among those with the highest variance, and use - * its variance as the threshold value. - */ - void meanSplit(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval) - { - memset(mean_,0,veclen_*sizeof(DistanceType)); - memset(var_,0,veclen_*sizeof(DistanceType)); - - /* Compute mean values. Only the first SAMPLE_MEAN values need to be - sampled to get a good estimate. - */ - int cnt = std::min((int)SAMPLE_MEAN+1, count); - for (int j = 0; j < cnt; ++j) { - ElementType* v = dataset_[ind[j]]; - for (size_t k=0; kcount/2) index = lim1; - else if (lim2 v[topind[num-1]])) { - /* Put this element at end of topind. */ - if (num < RAND_DIM) { - topind[num++] = i; /* Add to list. */ - } - else { - topind[num-1] = i; /* Replace last element. */ - } - /* Bubble end value down to right location by repeated swapping. */ - int j = num - 1; - while (j > 0 && v[topind[j]] > v[topind[j-1]]) { - std::swap(topind[j], topind[j-1]); - --j; - } - } - } - /* Select a random integer in range [0,num-1], and return that index. */ - int rnd = rand_int(num); - return (int)topind[rnd]; - } - - - /** - * Subdivide the list of points by a plane perpendicular on axe corresponding - * to the 'cutfeat' dimension at 'cutval' position. - * - * On return: - * dataset[ind[0..lim1-1]][cutfeat]cutval - */ - void planeSplit(int* ind, int count, int cutfeat, DistanceType cutval, int& lim1, int& lim2) - { - /* Move vector indices for left subtree to front of list. */ - int left = 0; - int right = count-1; - for (;; ) { - while (left<=right && dataset_[ind[left]][cutfeat]=cutval) --right; - if (left>right) break; - std::swap(ind[left], ind[right]); ++left; --right; - } - lim1 = left; - right = count-1; - for (;; ) { - while (left<=right && dataset_[ind[left]][cutfeat]<=cutval) ++left; - while (left<=right && dataset_[ind[right]][cutfeat]>cutval) --right; - if (left>right) break; - std::swap(ind[left], ind[right]); ++left; --right; - } - lim2 = left; - } - - /** - * Performs an exact nearest neighbor search. The exact search performs a full - * traversal of the tree. - */ - void getExactNeighbors(ResultSet& result, const ElementType* vec, float epsError) - { - // checkID -= 1; /* Set a different unique ID for each search. */ - - if (trees_ > 1) { - fprintf(stderr,"It doesn't make any sense to use more than one tree for exact search"); - } - if (trees_>0) { - searchLevelExact(result, vec, tree_roots_[0], 0.0, epsError); - } - CV_Assert(result.full()); - } - - /** - * Performs the approximate nearest-neighbor search. The search is approximate - * because the tree traversal is abandoned after a given number of descends in - * the tree. - */ - void getNeighbors(ResultSet& result, const ElementType* vec, int maxCheck, float epsError) - { - int i; - BranchSt branch; - - int checkCount = 0; - Heap* heap = new Heap((int)size_); - DynamicBitset checked(size_); - - /* Search once through each tree down to root. */ - for (i = 0; i < trees_; ++i) { - searchLevel(result, vec, tree_roots_[i], 0, checkCount, maxCheck, epsError, heap, checked); - } - - /* Keep searching other branches from heap until finished. */ - while ( heap->popMin(branch) && (checkCount < maxCheck || !result.full() )) { - searchLevel(result, vec, branch.node, branch.mindist, checkCount, maxCheck, epsError, heap, checked); - } - - delete heap; - - CV_Assert(result.full()); - } - - - /** - * Search starting from a given node of the tree. Based on any mismatches at - * higher levels, all exemplars below this level must have a distance of - * at least "mindistsq". - */ - void searchLevel(ResultSet& result_set, const ElementType* vec, NodePtr node, DistanceType mindist, int& checkCount, int maxCheck, - float epsError, Heap* heap, DynamicBitset& checked) - { - if (result_set.worstDist()child1 == NULL)&&(node->child2 == NULL)) { - /* Do not check same node more than once when searching multiple trees. - Once a vector is checked, we set its location in vind to the - current checkID. - */ - int index = node->divfeat; - if ( checked.test(index) || ((checkCount>=maxCheck)&& result_set.full()) ) return; - checked.set(index); - checkCount++; - - DistanceType dist = distance_(dataset_[index], vec, veclen_); - result_set.addPoint(dist,index); - - return; - } - - /* Which child branch should be taken first? */ - ElementType val = vec[node->divfeat]; - DistanceType diff = val - node->divval; - NodePtr bestChild = (diff < 0) ? node->child1 : node->child2; - NodePtr otherChild = (diff < 0) ? node->child2 : node->child1; - - /* Create a branch record for the branch not taken. Add distance - of this feature boundary (we don't attempt to correct for any - use of this feature in a parent node, which is unlikely to - happen and would have only a small effect). Don't bother - adding more branches to heap after halfway point, as cost of - adding exceeds their value. - */ - - DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat); - // if (2 * checkCount < maxCheck || !result.full()) { - if ((new_distsq*epsError < result_set.worstDist())|| !result_set.full()) { - heap->insert( BranchSt(otherChild, new_distsq) ); - } - - /* Call recursively to search next level down. */ - searchLevel(result_set, vec, bestChild, mindist, checkCount, maxCheck, epsError, heap, checked); - } - - /** - * Performs an exact search in the tree starting from a node. - */ - void searchLevelExact(ResultSet& result_set, const ElementType* vec, const NodePtr node, DistanceType mindist, const float epsError) - { - /* If this is a leaf node, then do check and return. */ - if ((node->child1 == NULL)&&(node->child2 == NULL)) { - int index = node->divfeat; - DistanceType dist = distance_(dataset_[index], vec, veclen_); - result_set.addPoint(dist,index); - return; - } - - /* Which child branch should be taken first? */ - ElementType val = vec[node->divfeat]; - DistanceType diff = val - node->divval; - NodePtr bestChild = (diff < 0) ? node->child1 : node->child2; - NodePtr otherChild = (diff < 0) ? node->child2 : node->child1; - - /* Create a branch record for the branch not taken. Add distance - of this feature boundary (we don't attempt to correct for any - use of this feature in a parent node, which is unlikely to - happen and would have only a small effect). Don't bother - adding more branches to heap after halfway point, as cost of - adding exceeds their value. - */ - - DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat); - - /* Call recursively to search next level down. */ - searchLevelExact(result_set, vec, bestChild, mindist, epsError); - - if (new_distsq*epsError<=result_set.worstDist()) { - searchLevelExact(result_set, vec, otherChild, new_distsq, epsError); - } - } - - -private: - - enum - { - /** - * To improve efficiency, only SAMPLE_MEAN random values are used to - * compute the mean and variance at each level when building a tree. - * A value of 100 seems to perform as well as using all values. - */ - SAMPLE_MEAN = 100, - /** - * Top random dimensions to consider - * - * When creating random trees, the dimension on which to subdivide is - * selected at random from among the top RAND_DIM dimensions with the - * highest variance. A value of 5 works well. - */ - RAND_DIM=5 - }; - - - /** - * Number of randomized trees that are used - */ - int trees_; - - /** - * Array of indices to vectors in the dataset. - */ - std::vector vind_; - - /** - * The dataset used by this index - */ - const Matrix dataset_; - - IndexParams index_params_; - - size_t size_; - size_t veclen_; - - - DistanceType* mean_; - DistanceType* var_; - - - /** - * Array of k-d trees used to find neighbours. - */ - NodePtr* tree_roots_; - - /** - * Pooled memory allocator. - * - * Using a pooled memory allocator is more efficient - * than allocating memory directly when there is a large - * number small of memory allocations. - */ - PooledAllocator pool_; - - Distance distance_; - - -}; // class KDTreeForest - -} - -//! @endcond - -#endif //OPENCV_FLANN_KDTREE_INDEX_H_ diff --git a/include/opencv2/flann/kdtree_single_index.h b/include/opencv2/flann/kdtree_single_index.h deleted file mode 100755 index ed95c3d..0000000 --- a/include/opencv2/flann/kdtree_single_index.h +++ /dev/null @@ -1,645 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_ -#define OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_ - -//! @cond IGNORED - -#include -#include -#include - -#include "nn_index.h" -#include "matrix.h" -#include "result_set.h" -#include "heap.h" -#include "allocator.h" -#include "random.h" -#include "saving.h" - -namespace cvflann -{ - -struct KDTreeSingleIndexParams : public IndexParams -{ - KDTreeSingleIndexParams(int leaf_max_size = 10, bool reorder = true, int dim = -1) - { - (*this)["algorithm"] = FLANN_INDEX_KDTREE_SINGLE; - (*this)["leaf_max_size"] = leaf_max_size; - (*this)["reorder"] = reorder; - (*this)["dim"] = dim; - } -}; - - -/** - * Randomized kd-tree index - * - * Contains the k-d trees and other information for indexing a set of points - * for nearest-neighbor matching. - */ -template -class KDTreeSingleIndex : public NNIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - - /** - * KDTree constructor - * - * Params: - * inputData = dataset with the input features - * params = parameters passed to the kdtree algorithm - */ - KDTreeSingleIndex(const Matrix& inputData, const IndexParams& params = KDTreeSingleIndexParams(), - Distance d = Distance() ) : - dataset_(inputData), index_params_(params), distance_(d) - { - size_ = dataset_.rows; - dim_ = dataset_.cols; - root_node_ = 0; - int dim_param = get_param(params,"dim",-1); - if (dim_param>0) dim_ = dim_param; - leaf_max_size_ = get_param(params,"leaf_max_size",10); - reorder_ = get_param(params,"reorder",true); - - // Create a permutable array of indices to the input vectors. - vind_.resize(size_); - for (size_t i = 0; i < size_; i++) { - vind_[i] = (int)i; - } - } - - KDTreeSingleIndex(const KDTreeSingleIndex&); - KDTreeSingleIndex& operator=(const KDTreeSingleIndex&); - - /** - * Standard destructor - */ - ~KDTreeSingleIndex() - { - if (reorder_) delete[] data_.data; - } - - /** - * Builds the index - */ - void buildIndex() CV_OVERRIDE - { - computeBoundingBox(root_bbox_); - root_node_ = divideTree(0, (int)size_, root_bbox_ ); // construct the tree - - if (reorder_) { - delete[] data_.data; - data_ = cvflann::Matrix(new ElementType[size_*dim_], size_, dim_); - for (size_t i=0; i& queries, Matrix& indices, Matrix& dists, int knn, const SearchParams& params) CV_OVERRIDE - { - CV_Assert(queries.cols == veclen()); - CV_Assert(indices.rows >= queries.rows); - CV_Assert(dists.rows >= queries.rows); - CV_Assert(int(indices.cols) >= knn); - CV_Assert(int(dists.cols) >= knn); - - KNNSimpleResultSet resultSet(knn); - for (size_t i = 0; i < queries.rows; i++) { - resultSet.init(indices[i], dists[i]); - findNeighbors(resultSet, queries[i], params); - } - } - - IndexParams getParameters() const CV_OVERRIDE - { - return index_params_; - } - - /** - * Find set of nearest neighbors to vec. Their indices are stored inside - * the result object. - * - * Params: - * result = the result object in which the indices of the nearest-neighbors are stored - * vec = the vector for which to search the nearest neighbors - * maxCheck = the maximum number of restarts (in a best-bin-first manner) - */ - void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE - { - float epsError = 1+get_param(searchParams,"eps",0.0f); - - std::vector dists(dim_,0); - DistanceType distsq = computeInitialDistances(vec, dists); - searchLevel(result, vec, root_node_, distsq, dists, epsError); - } - -private: - - - /*--------------------- Internal Data Structures --------------------------*/ - struct Node - { - /** - * Indices of points in leaf node - */ - int left, right; - /** - * Dimension used for subdivision. - */ - int divfeat; - /** - * The values used for subdivision. - */ - DistanceType divlow, divhigh; - /** - * The child nodes. - */ - Node* child1, * child2; - }; - typedef Node* NodePtr; - - - struct Interval - { - DistanceType low, high; - }; - - typedef std::vector BoundingBox; - - typedef BranchStruct BranchSt; - typedef BranchSt* Branch; - - - - - void save_tree(FILE* stream, NodePtr tree) - { - save_value(stream, *tree); - if (tree->child1!=NULL) { - save_tree(stream, tree->child1); - } - if (tree->child2!=NULL) { - save_tree(stream, tree->child2); - } - } - - - void load_tree(FILE* stream, NodePtr& tree) - { - tree = pool_.allocate(); - load_value(stream, *tree); - if (tree->child1!=NULL) { - load_tree(stream, tree->child1); - } - if (tree->child2!=NULL) { - load_tree(stream, tree->child2); - } - } - - - void computeBoundingBox(BoundingBox& bbox) - { - bbox.resize(dim_); - for (size_t i=0; ibbox[i].high) bbox[i].high = (DistanceType)dataset_[k][i]; - } - } - } - - - /** - * Create a tree node that subdivides the list of vecs from vind[first] - * to vind[last]. The routine is called recursively on each sublist. - * Place a pointer to this new tree node in the location pTree. - * - * Params: pTree = the new node to create - * first = index of the first vector - * last = index of the last vector - */ - NodePtr divideTree(int left, int right, BoundingBox& bbox) - { - NodePtr node = pool_.allocate(); // allocate memory - - /* If too few exemplars remain, then make this a leaf node. */ - if ( (right-left) <= leaf_max_size_) { - node->child1 = node->child2 = NULL; /* Mark as leaf node. */ - node->left = left; - node->right = right; - - // compute bounding-box of leaf points - for (size_t i=0; idataset_[vind_[k]][i]) bbox[i].low=(DistanceType)dataset_[vind_[k]][i]; - if (bbox[i].highdivfeat = cutfeat; - - BoundingBox left_bbox(bbox); - left_bbox[cutfeat].high = cutval; - node->child1 = divideTree(left, left+idx, left_bbox); - - BoundingBox right_bbox(bbox); - right_bbox[cutfeat].low = cutval; - node->child2 = divideTree(left+idx, right, right_bbox); - - node->divlow = left_bbox[cutfeat].high; - node->divhigh = right_bbox[cutfeat].low; - - for (size_t i=0; imax_elem) max_elem = val; - } - } - - void middleSplit(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval, const BoundingBox& bbox) - { - // find the largest span from the approximate bounding box - ElementType max_span = bbox[0].high-bbox[0].low; - cutfeat = 0; - cutval = (bbox[0].high+bbox[0].low)/2; - for (size_t i=1; imax_span) { - max_span = span; - cutfeat = i; - cutval = (bbox[i].high+bbox[i].low)/2; - } - } - - // compute exact span on the found dimension - ElementType min_elem, max_elem; - computeMinMax(ind, count, cutfeat, min_elem, max_elem); - cutval = (min_elem+max_elem)/2; - max_span = max_elem - min_elem; - - // check if a dimension of a largest span exists - size_t k = cutfeat; - for (size_t i=0; imax_span) { - computeMinMax(ind, count, i, min_elem, max_elem); - span = max_elem - min_elem; - if (span>max_span) { - max_span = span; - cutfeat = i; - cutval = (min_elem+max_elem)/2; - } - } - } - int lim1, lim2; - planeSplit(ind, count, cutfeat, cutval, lim1, lim2); - - if (lim1>count/2) index = lim1; - else if (lim2max_span) { - max_span = span; - } - } - DistanceType max_spread = -1; - cutfeat = 0; - for (size_t i=0; i(DistanceType)((1-EPS)*max_span)) { - ElementType min_elem, max_elem; - computeMinMax(ind, count, (int)i, min_elem, max_elem); - DistanceType spread = (DistanceType)(max_elem-min_elem); - if (spread>max_spread) { - cutfeat = (int)i; - max_spread = spread; - } - } - } - // split in the middle - DistanceType split_val = (bbox[cutfeat].low+bbox[cutfeat].high)/2; - ElementType min_elem, max_elem; - computeMinMax(ind, count, cutfeat, min_elem, max_elem); - - if (split_valmax_elem) cutval = (DistanceType)max_elem; - else cutval = split_val; - - int lim1, lim2; - planeSplit(ind, count, cutfeat, cutval, lim1, lim2); - - if (lim1>count/2) index = lim1; - else if (lim2cutval - */ - void planeSplit(int* ind, int count, int cutfeat, DistanceType cutval, int& lim1, int& lim2) - { - /* Move vector indices for left subtree to front of list. */ - int left = 0; - int right = count-1; - for (;; ) { - while (left<=right && dataset_[ind[left]][cutfeat]=cutval) --right; - if (left>right) break; - std::swap(ind[left], ind[right]); ++left; --right; - } - /* If either list is empty, it means that all remaining features - * are identical. Split in the middle to maintain a balanced tree. - */ - lim1 = left; - right = count-1; - for (;; ) { - while (left<=right && dataset_[ind[left]][cutfeat]<=cutval) ++left; - while (left<=right && dataset_[ind[right]][cutfeat]>cutval) --right; - if (left>right) break; - std::swap(ind[left], ind[right]); ++left; --right; - } - lim2 = left; - } - - DistanceType computeInitialDistances(const ElementType* vec, std::vector& dists) - { - DistanceType distsq = 0.0; - - for (size_t i = 0; i < dim_; ++i) { - if (vec[i] < root_bbox_[i].low) { - dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].low, (int)i); - distsq += dists[i]; - } - if (vec[i] > root_bbox_[i].high) { - dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].high, (int)i); - distsq += dists[i]; - } - } - - return distsq; - } - - /** - * Performs an exact search in the tree starting from a node. - */ - void searchLevel(ResultSet& result_set, const ElementType* vec, const NodePtr node, DistanceType mindistsq, - std::vector& dists, const float epsError) - { - /* If this is a leaf node, then do check and return. */ - if ((node->child1 == NULL)&&(node->child2 == NULL)) { - DistanceType worst_dist = result_set.worstDist(); - if (reorder_) { - for (int i=node->left; iright; ++i) { - DistanceType dist = distance_(vec, data_[i], dim_, worst_dist); - if (distleft; iright; ++i) { - DistanceType dist = distance_(vec, data_[vind_[i]], dim_, worst_dist); - if (distdivfeat; - ElementType val = vec[idx]; - DistanceType diff1 = val - node->divlow; - DistanceType diff2 = val - node->divhigh; - - NodePtr bestChild; - NodePtr otherChild; - DistanceType cut_dist; - if ((diff1+diff2)<0) { - bestChild = node->child1; - otherChild = node->child2; - cut_dist = distance_.accum_dist(val, node->divhigh, idx); - } - else { - bestChild = node->child2; - otherChild = node->child1; - cut_dist = distance_.accum_dist( val, node->divlow, idx); - } - - /* Call recursively to search next level down. */ - searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError); - - DistanceType dst = dists[idx]; - mindistsq = mindistsq + cut_dist - dst; - dists[idx] = cut_dist; - if (mindistsq*epsError<=result_set.worstDist()) { - searchLevel(result_set, vec, otherChild, mindistsq, dists, epsError); - } - dists[idx] = dst; - } - -private: - - /** - * The dataset used by this index - */ - const Matrix dataset_; - - IndexParams index_params_; - - int leaf_max_size_; - bool reorder_; - - - /** - * Array of indices to vectors in the dataset. - */ - std::vector vind_; - - Matrix data_; - - size_t size_; - size_t dim_; - - /** - * Array of k-d trees used to find neighbours. - */ - NodePtr root_node_; - - BoundingBox root_bbox_; - - /** - * Pooled memory allocator. - * - * Using a pooled memory allocator is more efficient - * than allocating memory directly when there is a large - * number small of memory allocations. - */ - PooledAllocator pool_; - - Distance distance_; -}; // class KDTree - -} - -//! @endcond - -#endif //OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_ diff --git a/include/opencv2/flann/kmeans_index.h b/include/opencv2/flann/kmeans_index.h deleted file mode 100755 index 10b272d..0000000 --- a/include/opencv2/flann/kmeans_index.h +++ /dev/null @@ -1,1832 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_KMEANS_INDEX_H_ -#define OPENCV_FLANN_KMEANS_INDEX_H_ - -//! @cond IGNORED - -#include -#include -#include -#include - -#include "general.h" -#include "nn_index.h" -#include "dist.h" -#include "matrix.h" -#include "result_set.h" -#include "heap.h" -#include "allocator.h" -#include "random.h" -#include "saving.h" -#include "logger.h" - -#define BITS_PER_CHAR 8 -#define BITS_PER_BASE 2 // for DNA/RNA sequences -#define BASE_PER_CHAR (BITS_PER_CHAR/BITS_PER_BASE) -#define HISTOS_PER_BASE (1< -class KMeansIndex : public NNIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - typedef typename Distance::CentersType CentersType; - - typedef typename Distance::is_kdtree_distance is_kdtree_distance; - typedef typename Distance::is_vector_space_distance is_vector_space_distance; - - - - typedef void (KMeansIndex::* centersAlgFunction)(int, int*, int, int*, int&); - - /** - * The function used for choosing the cluster centers. - */ - centersAlgFunction chooseCenters; - - - - /** - * Chooses the initial centers in the k-means clustering in a random manner. - * - * Params: - * k = number of centers - * vecs = the dataset of points - * indices = indices in the dataset - * indices_length = length of indices vector - * - */ - void chooseCentersRandom(int k, int* indices, int indices_length, int* centers, int& centers_length) - { - UniqueRandom r(indices_length); - - int index; - for (index=0; index=0 && rnd < n); - - centers[0] = indices[rnd]; - - int index; - for (index=1; indexbest_val) { - best_val = dist; - best_index = j; - } - } - if (best_index!=-1) { - centers[index] = indices[best_index]; - } - else { - break; - } - } - centers_length = index; - } - - - /** - * Chooses the initial centers in the k-means using the algorithm - * proposed in the KMeans++ paper: - * Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding - * - * Implementation of this function was converted from the one provided in Arthur's code. - * - * Params: - * k = number of centers - * vecs = the dataset of points - * indices = indices in the dataset - * Returns: - */ - void chooseCentersKMeanspp(int k, int* indices, int indices_length, int* centers, int& centers_length) - { - int n = indices_length; - - double currentPot = 0; - DistanceType* closestDistSq = new DistanceType[n]; - - // Choose one random center and set the closestDistSq values - int index = rand_int(n); - CV_DbgAssert(index >=0 && index < n); - centers[0] = indices[index]; - - for (int i = 0; i < n; i++) { - closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols); - closestDistSq[i] = ensureSquareDistance( closestDistSq[i] ); - currentPot += closestDistSq[i]; - } - - - const int numLocalTries = 1; - - // Choose each center - int centerCount; - for (centerCount = 1; centerCount < k; centerCount++) { - - // Repeat several trials - double bestNewPot = -1; - int bestNewIndex = -1; - for (int localTrial = 0; localTrial < numLocalTries; localTrial++) { - - // Choose our center - have to be slightly careful to return a valid answer even accounting - // for possible rounding errors - double randVal = rand_double(currentPot); - for (index = 0; index < n-1; index++) { - if (randVal <= closestDistSq[index]) break; - else randVal -= closestDistSq[index]; - } - - // Compute the new potential - double newPot = 0; - for (int i = 0; i < n; i++) { - DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols); - newPot += std::min( ensureSquareDistance(dist), closestDistSq[i] ); - } - - // Store the best result - if ((bestNewPot < 0)||(newPot < bestNewPot)) { - bestNewPot = newPot; - bestNewIndex = index; - } - } - - // Add the appropriate center - centers[centerCount] = indices[bestNewIndex]; - currentPot = bestNewPot; - for (int i = 0; i < n; i++) { - DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols); - closestDistSq[i] = std::min( ensureSquareDistance(dist), closestDistSq[i] ); - } - } - - centers_length = centerCount; - - delete[] closestDistSq; - } - - - -public: - - flann_algorithm_t getType() const CV_OVERRIDE - { - return FLANN_INDEX_KMEANS; - } - - template - class KMeansDistanceComputer : public cv::ParallelLoopBody - { - public: - KMeansDistanceComputer(Distance _distance, const Matrix& _dataset, - const int _branching, const int* _indices, const CentersContainerType& _dcenters, - const size_t _veclen, std::vector &_new_centroids, - std::vector &_sq_dists) - : distance(_distance) - , dataset(_dataset) - , branching(_branching) - , indices(_indices) - , dcenters(_dcenters) - , veclen(_veclen) - , new_centroids(_new_centroids) - , sq_dists(_sq_dists) - { - } - - void operator()(const cv::Range& range) const CV_OVERRIDE - { - const int begin = range.start; - const int end = range.end; - - for( int i = begin; inew_sq_dist) { - new_centroid = j; - sq_dist = new_sq_dist; - } - } - sq_dists[i] = sq_dist; - new_centroids[i] = new_centroid; - } - } - - private: - Distance distance; - const Matrix& dataset; - const int branching; - const int* indices; - const CentersContainerType& dcenters; - const size_t veclen; - std::vector &new_centroids; - std::vector &sq_dists; - KMeansDistanceComputer& operator=( const KMeansDistanceComputer & ) { return *this; } - }; - - /** - * Index constructor - * - * Params: - * inputData = dataset with the input features - * params = parameters passed to the hierarchical k-means algorithm - */ - KMeansIndex(const Matrix& inputData, const IndexParams& params = KMeansIndexParams(), - Distance d = Distance()) - : dataset_(inputData), index_params_(params), root_(NULL), indices_(NULL), distance_(d) - { - memoryCounter_ = 0; - - size_ = dataset_.rows; - veclen_ = dataset_.cols; - - branching_ = get_param(params,"branching",32); - trees_ = get_param(params,"trees",1); - iterations_ = get_param(params,"iterations",11); - if (iterations_<0) { - iterations_ = (std::numeric_limits::max)(); - } - centers_init_ = get_param(params,"centers_init",FLANN_CENTERS_RANDOM); - - if (centers_init_==FLANN_CENTERS_RANDOM) { - chooseCenters = &KMeansIndex::chooseCentersRandom; - } - else if (centers_init_==FLANN_CENTERS_GONZALES) { - chooseCenters = &KMeansIndex::chooseCentersGonzales; - } - else if (centers_init_==FLANN_CENTERS_KMEANSPP) { - chooseCenters = &KMeansIndex::chooseCentersKMeanspp; - } - else { - FLANN_THROW(cv::Error::StsBadArg, "Unknown algorithm for choosing initial centers."); - } - cb_index_ = 0.4f; - - root_ = new KMeansNodePtr[trees_]; - indices_ = new int*[trees_]; - - for (int i=0; i(); - std::memset(root_[i], 0, sizeof(KMeansNode)); - - Distance* dummy = NULL; - computeNodeStatistics(root_[i], indices_[i], (unsigned int)size_, dummy); - - computeClustering(root_[i], indices_[i], (int)size_, branching_,0); - } - } - - - void saveIndex(FILE* stream) CV_OVERRIDE - { - save_value(stream, branching_); - save_value(stream, iterations_); - save_value(stream, memoryCounter_); - save_value(stream, cb_index_); - save_value(stream, trees_); - for (int i=0; i& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE - { - - const int maxChecks = get_param(searchParams,"checks",32); - - if (maxChecks==FLANN_CHECKS_UNLIMITED) { - findExactNN(root_[0], result, vec); - } - else { - // Priority queue storing intermediate branches in the best-bin-first search - Heap* heap = new Heap((int)size_); - - int checks = 0; - for (int i=0; i= maxChecks) && result.full()) - break; - } - - BranchSt branch; - while (heap->popMin(branch) && (checks& centers) - { - int numClusters = centers.rows; - if (numClusters<1) { - FLANN_THROW(cv::Error::StsBadArg, "Number of clusters must be at least 1"); - } - - DistanceType variance; - KMeansNodePtr* clusters = new KMeansNodePtr[numClusters]; - - int clusterCount = getMinVarianceClusters(root_[0], clusters, numClusters, variance); - - Logger::info("Clusters requested: %d, returning %d\n",numClusters, clusterCount); - - for (int i=0; ipivot; - for (size_t j=0; j BranchSt; - - - - - void save_tree(FILE* stream, KMeansNodePtr node, int num) - { - save_value(stream, *node); - save_value(stream, *(node->pivot), (int)veclen_); - if (node->childs==NULL) { - int indices_offset = (int)(node->indices - indices_[num]); - save_value(stream, indices_offset); - } - else { - for(int i=0; ichilds[i], num); - } - } - } - - - void load_tree(FILE* stream, KMeansNodePtr& node, int num) - { - node = pool_.allocate(); - load_value(stream, *node); - node->pivot = new CentersType[veclen_]; - load_value(stream, *(node->pivot), (int)veclen_); - if (node->childs==NULL) { - int indices_offset; - load_value(stream, indices_offset); - node->indices = indices_[num] + indices_offset; - } - else { - node->childs = pool_.allocate(branching_); - for(int i=0; ichilds[i], num); - } - } - } - - - /** - * Helper function - */ - void free_centers(KMeansNodePtr node) - { - delete[] node->pivot; - if (node->childs!=NULL) { - for (int k=0; kchilds[k]); - } - } - } - - void free_centers() - { - if (root_ != NULL) { - for(int i=0; i(), veclen_); - } - float length = static_cast(indices_length); - for (size_t j=0; j( mean[j] / static_cast(indices_length) ); - } - variance /= static_cast( length ); - variance -= distance_(mean, ZeroIterator(), veclen_); - - DistanceType radius = 0; - for (unsigned int i=0; iradius) { - radius = tmp; - } - } - - node->variance = variance; - node->radius = radius; - node->pivot = mean; - } - - - void computeBitfieldNodeStatistics(KMeansNodePtr node, int* indices, - unsigned int indices_length) - { - const unsigned int accumulator_veclen = static_cast( - veclen_*sizeof(CentersType)*BITS_PER_CHAR); - - unsigned long long variance = 0ull; - CentersType* mean = new CentersType[veclen_]; - memoryCounter_ += int(veclen_*sizeof(CentersType)); - unsigned int* mean_accumulator = new unsigned int[accumulator_veclen]; - - memset(mean_accumulator, 0, sizeof(unsigned int)*accumulator_veclen); - - for (unsigned int i=0; i( ensureSquareDistance( - distance_(dataset_[indices[i]], ZeroIterator(), veclen_))); - unsigned char* vec = (unsigned char*)dataset_[indices[i]]; - for (size_t k=0, l=0; k>1) & 0x01; - mean_accumulator[k+2] += (vec[l]>>2) & 0x01; - mean_accumulator[k+3] += (vec[l]>>3) & 0x01; - mean_accumulator[k+4] += (vec[l]>>4) & 0x01; - mean_accumulator[k+5] += (vec[l]>>5) & 0x01; - mean_accumulator[k+6] += (vec[l]>>6) & 0x01; - mean_accumulator[k+7] += (vec[l]>>7) & 0x01; - } - } - double cnt = static_cast(indices_length); - unsigned char* char_mean = (unsigned char*)mean; - for (size_t k=0, l=0; k( - (((int)(0.5 + (double)(mean_accumulator[k]) / cnt))) - | (((int)(0.5 + (double)(mean_accumulator[k+1]) / cnt))<<1) - | (((int)(0.5 + (double)(mean_accumulator[k+2]) / cnt))<<2) - | (((int)(0.5 + (double)(mean_accumulator[k+3]) / cnt))<<3) - | (((int)(0.5 + (double)(mean_accumulator[k+4]) / cnt))<<4) - | (((int)(0.5 + (double)(mean_accumulator[k+5]) / cnt))<<5) - | (((int)(0.5 + (double)(mean_accumulator[k+6]) / cnt))<<6) - | (((int)(0.5 + (double)(mean_accumulator[k+7]) / cnt))<<7)); - } - variance = static_cast( - 0.5 + static_cast(variance) / static_cast(indices_length)); - variance -= static_cast( - ensureSquareDistance( - distance_(mean, ZeroIterator(), veclen_))); - - DistanceType radius = 0; - for (unsigned int i=0; iradius) { - radius = tmp; - } - } - - node->variance = static_cast(variance); - node->radius = radius; - node->pivot = mean; - - delete[] mean_accumulator; - } - - - void computeDnaNodeStatistics(KMeansNodePtr node, int* indices, - unsigned int indices_length) - { - const unsigned int histos_veclen = static_cast( - veclen_*sizeof(CentersType)*(HISTOS_PER_BASE*BASE_PER_CHAR)); - - unsigned long long variance = 0ull; - unsigned int* histograms = new unsigned int[histos_veclen]; - memset(histograms, 0, sizeof(unsigned int)*histos_veclen); - - for (unsigned int i=0; i( ensureSquareDistance( - distance_(dataset_[indices[i]], ZeroIterator(), veclen_))); - - unsigned char* vec = (unsigned char*)dataset_[indices[i]]; - for (size_t k=0, l=0; k>2) & 0x03)]++; - histograms[k + 8 + ((vec[l]>>4) & 0x03)]++; - histograms[k +12 + ((vec[l]>>6) & 0x03)]++; - } - } - - CentersType* mean = new CentersType[veclen_]; - memoryCounter_ += int(veclen_*sizeof(CentersType)); - unsigned char* char_mean = (unsigned char*)mean; - unsigned int* h = histograms; - for (size_t k=0, l=0; k h[k+1] ? h[k+2] > h[k+3] ? h[k] > h[k+2] ? 0x00 : 0x10 - : h[k] > h[k+3] ? 0x00 : 0x11 - : h[k+2] > h[k+3] ? h[k+1] > h[k+2] ? 0x01 : 0x10 - : h[k+1] > h[k+3] ? 0x01 : 0x11) - | (h[k+4]>h[k+5] ? h[k+6] > h[k+7] ? h[k+4] > h[k+6] ? 0x00 : 0x1000 - : h[k+4] > h[k+7] ? 0x00 : 0x1100 - : h[k+6] > h[k+7] ? h[k+5] > h[k+6] ? 0x0100 : 0x1000 - : h[k+5] > h[k+7] ? 0x0100 : 0x1100) - | (h[k+8]>h[k+9] ? h[k+10]>h[k+11] ? h[k+8] >h[k+10] ? 0x00 : 0x100000 - : h[k+8] >h[k+11] ? 0x00 : 0x110000 - : h[k+10]>h[k+11] ? h[k+9] >h[k+10] ? 0x010000 : 0x100000 - : h[k+9] >h[k+11] ? 0x010000 : 0x110000) - | (h[k+12]>h[k+13] ? h[k+14]>h[k+15] ? h[k+12] >h[k+14] ? 0x00 : 0x10000000 - : h[k+12] >h[k+15] ? 0x00 : 0x11000000 - : h[k+14]>h[k+15] ? h[k+13] >h[k+14] ? 0x01000000 : 0x10000000 - : h[k+13] >h[k+15] ? 0x01000000 : 0x11000000); - } - variance = static_cast( - 0.5 + static_cast(variance) / static_cast(indices_length)); - variance -= static_cast( - ensureSquareDistance( - distance_(mean, ZeroIterator(), veclen_))); - - DistanceType radius = 0; - for (unsigned int i=0; iradius) { - radius = tmp; - } - } - - node->variance = static_cast(variance); - node->radius = radius; - node->pivot = mean; - - delete[] histograms; - } - - - template - void computeNodeStatistics(KMeansNodePtr node, int* indices, - unsigned int indices_length, - const DistType* identifier) - { - (void)identifier; - computeNodeStatistics(node, indices, indices_length); - } - - void computeNodeStatistics(KMeansNodePtr node, int* indices, - unsigned int indices_length, - const cvflann::HammingLUT* identifier) - { - (void)identifier; - computeBitfieldNodeStatistics(node, indices, indices_length); - } - - void computeNodeStatistics(KMeansNodePtr node, int* indices, - unsigned int indices_length, - const cvflann::Hamming* identifier) - { - (void)identifier; - computeBitfieldNodeStatistics(node, indices, indices_length); - } - - void computeNodeStatistics(KMeansNodePtr node, int* indices, - unsigned int indices_length, - const cvflann::Hamming2* identifier) - { - (void)identifier; - computeBitfieldNodeStatistics(node, indices, indices_length); - } - - void computeNodeStatistics(KMeansNodePtr node, int* indices, - unsigned int indices_length, - const cvflann::DNAmmingLUT* identifier) - { - (void)identifier; - computeDnaNodeStatistics(node, indices, indices_length); - } - - void computeNodeStatistics(KMeansNodePtr node, int* indices, - unsigned int indices_length, - const cvflann::DNAmming2* identifier) - { - (void)identifier; - computeDnaNodeStatistics(node, indices, indices_length); - } - - - void refineClustering(int* indices, int indices_length, int branching, CentersType** centers, - std::vector& radiuses, int* belongs_to, int* count) - { - cv::AutoBuffer dcenters_buf(branching*veclen_); - Matrix dcenters(dcenters_buf.data(), branching, veclen_); - - bool converged = false; - int iteration = 0; - while (!converged && iteration new_centroids(indices_length); - std::vector sq_dists(indices_length); - - // reassign points to clusters - KMeansDistanceComputer > invoker( - distance_, dataset_, branching, indices, dcenters, veclen_, new_centroids, sq_dists); - parallel_for_(cv::Range(0, (int)indices_length), invoker); - - for (int i=0; i < (int)indices_length; ++i) { - DistanceType sq_dist(sq_dists[i]); - int new_centroid(new_centroids[i]); - if (sq_dist > radiuses[new_centroid]) { - radiuses[new_centroid] = sq_dist; - } - if (new_centroid != belongs_to[i]) { - count[belongs_to[i]]--; - count[new_centroid]++; - belongs_to[i] = new_centroid; - converged = false; - } - } - - for (int i=0; i& radiuses, int* belongs_to, int* count) - { - for (int i=0; i( - veclen_*sizeof(ElementType)*BITS_PER_CHAR); - cv::AutoBuffer dcenters_buf(branching*accumulator_veclen); - Matrix dcenters(dcenters_buf.data(), branching, accumulator_veclen); - - bool converged = false; - int iteration = 0; - while (!converged && iteration>1) & 0x01; - dcenter[k+2] += (vec[l]>>2) & 0x01; - dcenter[k+3] += (vec[l]>>3) & 0x01; - dcenter[k+4] += (vec[l]>>4) & 0x01; - dcenter[k+5] += (vec[l]>>5) & 0x01; - dcenter[k+6] += (vec[l]>>6) & 0x01; - dcenter[k+7] += (vec[l]>>7) & 0x01; - } - } - for (int i=0; i(count[i]); - unsigned int* dcenter = dcenters[i]; - unsigned char* charCenter = (unsigned char*)centers[i]; - for (size_t k=0, l=0; k( - (((int)(0.5 + (double)(dcenter[k]) / cnt))) - | (((int)(0.5 + (double)(dcenter[k+1]) / cnt))<<1) - | (((int)(0.5 + (double)(dcenter[k+2]) / cnt))<<2) - | (((int)(0.5 + (double)(dcenter[k+3]) / cnt))<<3) - | (((int)(0.5 + (double)(dcenter[k+4]) / cnt))<<4) - | (((int)(0.5 + (double)(dcenter[k+5]) / cnt))<<5) - | (((int)(0.5 + (double)(dcenter[k+6]) / cnt))<<6) - | (((int)(0.5 + (double)(dcenter[k+7]) / cnt))<<7)); - } - } - - std::vector new_centroids(indices_length); - std::vector dists(indices_length); - - // reassign points to clusters - KMeansDistanceComputer invoker( - distance_, dataset_, branching, indices, centers, veclen_, new_centroids, dists); - parallel_for_(cv::Range(0, (int)indices_length), invoker); - - for (int i=0; i < indices_length; ++i) { - DistanceType dist(dists[i]); - int new_centroid(new_centroids[i]); - if (dist > radiuses[new_centroid]) { - radiuses[new_centroid] = dist; - } - if (new_centroid != belongs_to[i]) { - count[belongs_to[i]]--; - count[new_centroid]++; - belongs_to[i] = new_centroid; - converged = false; - } - } - - for (int i=0; i& radiuses, int* belongs_to, int* count) - { - for (int i=0; i( - veclen_*sizeof(CentersType)*(HISTOS_PER_BASE*BASE_PER_CHAR)); - cv::AutoBuffer histos_buf(branching*histos_veclen); - Matrix histos(histos_buf.data(), branching, histos_veclen); - - bool converged = false; - int iteration = 0; - while (!converged && iteration>2) & 0x03)]++; - h[k + 8 + ((vec[l]>>4) & 0x03)]++; - h[k +12 + ((vec[l]>>6) & 0x03)]++; - } - } - for (int i=0; i h[k+1] ? h[k+2] > h[k+3] ? h[k] > h[k+2] ? 0x00 : 0x10 - : h[k] > h[k+3] ? 0x00 : 0x11 - : h[k+2] > h[k+3] ? h[k+1] > h[k+2] ? 0x01 : 0x10 - : h[k+1] > h[k+3] ? 0x01 : 0x11) - | (h[k+4]>h[k+5] ? h[k+6] > h[k+7] ? h[k+4] > h[k+6] ? 0x00 : 0x1000 - : h[k+4] > h[k+7] ? 0x00 : 0x1100 - : h[k+6] > h[k+7] ? h[k+5] > h[k+6] ? 0x0100 : 0x1000 - : h[k+5] > h[k+7] ? 0x0100 : 0x1100) - | (h[k+8]>h[k+9] ? h[k+10]>h[k+11] ? h[k+8] >h[k+10] ? 0x00 : 0x100000 - : h[k+8] >h[k+11] ? 0x00 : 0x110000 - : h[k+10]>h[k+11] ? h[k+9] >h[k+10] ? 0x010000 : 0x100000 - : h[k+9] >h[k+11] ? 0x010000 : 0x110000) - | (h[k+12]>h[k+13] ? h[k+14]>h[k+15] ? h[k+12] >h[k+14] ? 0x00 : 0x10000000 - : h[k+12] >h[k+15] ? 0x00 : 0x11000000 - : h[k+14]>h[k+15] ? h[k+13] >h[k+14] ? 0x01000000 : 0x10000000 - : h[k+13] >h[k+15] ? 0x01000000 : 0x11000000); - } - } - - std::vector new_centroids(indices_length); - std::vector dists(indices_length); - - // reassign points to clusters - KMeansDistanceComputer invoker( - distance_, dataset_, branching, indices, centers, veclen_, new_centroids, dists); - parallel_for_(cv::Range(0, (int)indices_length), invoker); - - for (int i=0; i < indices_length; ++i) { - DistanceType dist(dists[i]); - int new_centroid(new_centroids[i]); - if (dist > radiuses[new_centroid]) { - radiuses[new_centroid] = dist; - } - if (new_centroid != belongs_to[i]) { - count[belongs_to[i]]--; - count[new_centroid]++; - belongs_to[i] = new_centroid; - converged = false; - } - } - - for (int i=0; i& radiuses, int* belongs_to, int* count) - { - // compute kmeans clustering for each of the resulting clusters - node->childs = pool_.allocate(branching); - int start = 0; - int end = start; - for (int c=0; c(), veclen_); - variance += d; - mean_radius += static_cast( sqrt(d) ); - std::swap(indices[i],indices[end]); - std::swap(belongs_to[i],belongs_to[end]); - end++; - } - } - variance /= s; - mean_radius /= s; - variance -= distance_(centers[c], ZeroIterator(), veclen_); - - node->childs[c] = pool_.allocate(); - std::memset(node->childs[c], 0, sizeof(KMeansNode)); - node->childs[c]->radius = radiuses[c]; - node->childs[c]->pivot = centers[c]; - node->childs[c]->variance = variance; - node->childs[c]->mean_radius = mean_radius; - computeClustering(node->childs[c],indices+start, end-start, branching, level+1); - start=end; - } - } - - - void computeAnyBitfieldSubClustering(KMeansNodePtr node, int* indices, int indices_length, - int branching, int level, CentersType** centers, - std::vector& radiuses, int* belongs_to, int* count) - { - // compute kmeans clustering for each of the resulting clusters - node->childs = pool_.allocate(branching); - int start = 0; - int end = start; - for (int c=0; c(), veclen_); - variance += static_cast( ensureSquareDistance(d) ); - mean_radius += ensureSimpleDistance(d); - std::swap(indices[i],indices[end]); - std::swap(belongs_to[i],belongs_to[end]); - end++; - } - } - mean_radius = static_cast( - 0.5f + static_cast(mean_radius) / static_cast(s)); - variance = static_cast( - 0.5 + static_cast(variance) / static_cast(s)); - variance -= static_cast( - ensureSquareDistance( - distance_(centers[c], ZeroIterator(), veclen_))); - - node->childs[c] = pool_.allocate(); - std::memset(node->childs[c], 0, sizeof(KMeansNode)); - node->childs[c]->radius = radiuses[c]; - node->childs[c]->pivot = centers[c]; - node->childs[c]->variance = static_cast(variance); - node->childs[c]->mean_radius = mean_radius; - computeClustering(node->childs[c],indices+start, end-start, branching, level+1); - start=end; - } - } - - - template - void refineAndSplitClustering( - KMeansNodePtr node, int* indices, int indices_length, int branching, - int level, CentersType** centers, std::vector& radiuses, - int* belongs_to, int* count, const DistType* identifier) - { - (void)identifier; - refineClustering(indices, indices_length, branching, centers, radiuses, belongs_to, count); - - computeSubClustering(node, indices, indices_length, branching, - level, centers, radiuses, belongs_to, count); - } - - - /** - * The methods responsible with doing the recursive hierarchical clustering on - * binary vectors. - * As some might have heard that KMeans on binary data doesn't make sense, - * it's worth a little explanation why it actually fairly works. As - * with the Hierarchical Clustering algortihm, we seed several centers for the - * current node by picking some of its points. Then in a first pass each point - * of the node is then related to its closest center. Now let's have a look at - * the 5 central dimensions of the 9 following points: - * - * xxxxxx11100xxxxx (1) - * xxxxxx11010xxxxx (2) - * xxxxxx11001xxxxx (3) - * xxxxxx10110xxxxx (4) - * xxxxxx10101xxxxx (5) - * xxxxxx10011xxxxx (6) - * xxxxxx01110xxxxx (7) - * xxxxxx01101xxxxx (8) - * xxxxxx01011xxxxx (9) - * sum _____ - * of 1: 66555 - * - * Even if the barycenter notion doesn't apply, we can set a center - * xxxxxx11111xxxxx that will better fit the five dimensions we are focusing - * on for these points. - * - * Note that convergence isn't ensured anymore. In practice, using Gonzales - * as seeding algorithm should be fine for getting convergence ("iterations" - * value can be set to -1). But with KMeans++ seeding you should definitely - * set a maximum number of iterations (but make it higher than the "iterations" - * default value of 11). - * - * Params: - * node = the node to cluster - * indices = indices of the points belonging to the current node - * indices_length = number of points in the current node - * branching = the branching factor to use in the clustering - * level = 0 for the root node, it increases with the subdivision levels - * centers = clusters centers to compute - * radiuses = radiuses of clusters - * belongs_to = LookUp Table returning, for a given indice id, the center id it belongs to - * count = array storing the number of indices for a given center id - * identifier = dummy pointer on an instance of Distance (use to branch correctly among templates) - */ - void refineAndSplitClustering( - KMeansNodePtr node, int* indices, int indices_length, int branching, - int level, CentersType** centers, std::vector& radiuses, - int* belongs_to, int* count, const cvflann::HammingLUT* identifier) - { - (void)identifier; - refineBitfieldClustering( - indices, indices_length, branching, centers, radiuses, belongs_to, count); - - computeAnyBitfieldSubClustering(node, indices, indices_length, branching, - level, centers, radiuses, belongs_to, count); - } - - - void refineAndSplitClustering( - KMeansNodePtr node, int* indices, int indices_length, int branching, - int level, CentersType** centers, std::vector& radiuses, - int* belongs_to, int* count, const cvflann::Hamming* identifier) - { - (void)identifier; - refineBitfieldClustering( - indices, indices_length, branching, centers, radiuses, belongs_to, count); - - computeAnyBitfieldSubClustering(node, indices, indices_length, branching, - level, centers, radiuses, belongs_to, count); - } - - - void refineAndSplitClustering( - KMeansNodePtr node, int* indices, int indices_length, int branching, - int level, CentersType** centers, std::vector& radiuses, - int* belongs_to, int* count, const cvflann::Hamming2* identifier) - { - (void)identifier; - refineBitfieldClustering( - indices, indices_length, branching, centers, radiuses, belongs_to, count); - - computeAnyBitfieldSubClustering(node, indices, indices_length, branching, - level, centers, radiuses, belongs_to, count); - } - - - void refineAndSplitClustering( - KMeansNodePtr node, int* indices, int indices_length, int branching, - int level, CentersType** centers, std::vector& radiuses, - int* belongs_to, int* count, const cvflann::DNAmmingLUT* identifier) - { - (void)identifier; - refineDnaClustering( - indices, indices_length, branching, centers, radiuses, belongs_to, count); - - computeAnyBitfieldSubClustering(node, indices, indices_length, branching, - level, centers, radiuses, belongs_to, count); - } - - - void refineAndSplitClustering( - KMeansNodePtr node, int* indices, int indices_length, int branching, - int level, CentersType** centers, std::vector& radiuses, - int* belongs_to, int* count, const cvflann::DNAmming2* identifier) - { - (void)identifier; - refineDnaClustering( - indices, indices_length, branching, centers, radiuses, belongs_to, count); - - computeAnyBitfieldSubClustering(node, indices, indices_length, branching, - level, centers, radiuses, belongs_to, count); - } - - - /** - * The method responsible with actually doing the recursive hierarchical - * clustering - * - * Params: - * node = the node to cluster - * indices = indices of the points belonging to the current node - * branching = the branching factor to use in the clustering - * - * TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point) - */ - void computeClustering(KMeansNodePtr node, int* indices, int indices_length, int branching, int level) - { - node->size = indices_length; - node->level = level; - - if (indices_length < branching) { - node->indices = indices; - std::sort(node->indices,node->indices+indices_length); - node->childs = NULL; - return; - } - - cv::AutoBuffer centers_idx_buf(branching); - int* centers_idx = centers_idx_buf.data(); - int centers_length; - (this->*chooseCenters)(branching, indices, indices_length, centers_idx, centers_length); - - if (centers_lengthindices = indices; - std::sort(node->indices,node->indices+indices_length); - node->childs = NULL; - return; - } - - - std::vector radiuses(branching); - cv::AutoBuffer count_buf(branching); - int* count = count_buf.data(); - for (int i=0; i belongs_to_buf(indices_length); - int* belongs_to = belongs_to_buf.data(); - for (int i=0; inew_sq_dist) { - belongs_to[i] = j; - sq_dist = new_sq_dist; - } - } - if (sq_dist>radiuses[belongs_to[i]]) { - radiuses[belongs_to[i]] = sq_dist; - } - count[belongs_to[i]]++; - } - - CentersType** centers = new CentersType*[branching]; - - Distance* dummy = NULL; - refineAndSplitClustering(node, indices, indices_length, branching, level, - centers, radiuses, belongs_to, count, dummy); - - delete[] centers; - } - - - /** - * Performs one descent in the hierarchical k-means tree. The branches not - * visited are stored in a priority queue. - * - * Params: - * node = node to explore - * result = container for the k-nearest neighbors found - * vec = query points - * checks = how many points in the dataset have been checked so far - * maxChecks = maximum dataset points to checks - */ - - - void findNN(KMeansNodePtr node, ResultSet& result, const ElementType* vec, int& checks, int maxChecks, - Heap* heap) - { - // Ignore those clusters that are too far away - { - DistanceType bsq = distance_(vec, node->pivot, veclen_); - DistanceType rsq = node->radius; - DistanceType wsq = result.worstDist(); - - if (isSquareDistance()) - { - DistanceType val = bsq-rsq-wsq; - if ((val>0) && (val*val > 4*rsq*wsq)) - return; - } - else - { - if (bsq-rsq > wsq) - return; - } - } - - if (node->childs==NULL) { - if ((checks>=maxChecks) && result.full()) { - return; - } - checks += node->size; - for (int i=0; isize; ++i) { - int index = node->indices[i]; - DistanceType dist = distance_(dataset_[index], vec, veclen_); - result.addPoint(dist, index); - } - } - else { - DistanceType* domain_distances = new DistanceType[branching_]; - int closest_center = exploreNodeBranches(node, vec, domain_distances, heap); - delete[] domain_distances; - findNN(node->childs[closest_center],result,vec, checks, maxChecks, heap); - } - } - - /** - * Helper function that computes the nearest childs of a node to a given query point. - * Params: - * node = the node - * q = the query point - * distances = array with the distances to each child node. - * Returns: - */ - int exploreNodeBranches(KMeansNodePtr node, const ElementType* q, DistanceType* domain_distances, Heap* heap) - { - - int best_index = 0; - domain_distances[best_index] = distance_(q, node->childs[best_index]->pivot, veclen_); - for (int i=1; ichilds[i]->pivot, veclen_); - if (domain_distances[i]childs[best_index]->pivot; - for (int i=0; i( - cb_index_*node->childs[i]->variance ); - - // float dist_to_border = getDistanceToBorder(node.childs[i].pivot,best_center,q); - // if (domain_distances[i]insert(BranchSt(node->childs[i],domain_distances[i])); - } - } - - return best_index; - } - - - /** - * Function the performs exact nearest neighbor search by traversing the entire tree. - */ - void findExactNN(KMeansNodePtr node, ResultSet& result, const ElementType* vec) - { - // Ignore those clusters that are too far away - { - DistanceType bsq = distance_(vec, node->pivot, veclen_); - DistanceType rsq = node->radius; - DistanceType wsq = result.worstDist(); - - if (isSquareDistance()) - { - DistanceType val = bsq-rsq-wsq; - if ((val>0) && (val*val > 4*rsq*wsq)) - return; - } - else - { - if (bsq-rsq > wsq) - return; - } - } - - - if (node->childs==NULL) { - for (int i=0; isize; ++i) { - int index = node->indices[i]; - DistanceType dist = distance_(dataset_[index], vec, veclen_); - result.addPoint(dist, index); - } - } - else { - int* sort_indices = new int[branching_]; - - getCenterOrdering(node, vec, sort_indices); - - for (int i=0; ichilds[sort_indices[i]],result,vec); - } - - delete[] sort_indices; - } - } - - - /** - * Helper function. - * - * I computes the order in which to traverse the child nodes of a particular node. - */ - void getCenterOrdering(KMeansNodePtr node, const ElementType* q, int* sort_indices) - { - DistanceType* domain_distances = new DistanceType[branching_]; - for (int i=0; ichilds[i]->pivot, veclen_); - - int j=0; - while (domain_distances[j]j; --k) { - domain_distances[k] = domain_distances[k-1]; - sort_indices[k] = sort_indices[k-1]; - } - domain_distances[j] = dist; - sort_indices[j] = i; - } - delete[] domain_distances; - } - - /** - * Method that computes the squared distance from the query point q - * from inside region with center c to the border between this - * region and the region with center p - */ - DistanceType getDistanceToBorder(DistanceType* p, DistanceType* c, DistanceType* q) - { - DistanceType sum = 0; - DistanceType sum2 = 0; - - for (int i=0; ivariance*root->size; - - while (clusterCount::max)(); - int splitIndex = -1; - - for (int i=0; ichilds != NULL) { - - DistanceType variance = meanVariance - clusters[i]->variance*clusters[i]->size; - - for (int j=0; jchilds[j]->variance*clusters[i]->childs[j]->size; - } - if (variance clusters_length) break; - - meanVariance = minVariance; - - // split node - KMeansNodePtr toSplit = clusters[splitIndex]; - clusters[splitIndex] = toSplit->childs[0]; - for (int i=1; ichilds[i]; - } - } - - varianceValue = meanVariance/root->size; - return clusterCount; - } - -private: - /** The branching factor used in the hierarchical k-means clustering */ - int branching_; - - /** Number of kmeans trees (default is one) */ - int trees_; - - /** Maximum number of iterations to use when performing k-means clustering */ - int iterations_; - - /** Algorithm for choosing the cluster centers */ - flann_centers_init_t centers_init_; - - /** - * Cluster border index. This is used in the tree search phase when determining - * the closest cluster to explore next. A zero value takes into account only - * the cluster centres, a value greater then zero also take into account the size - * of the cluster. - */ - float cb_index_; - - /** - * The dataset used by this index - */ - const Matrix dataset_; - - /** Index parameters */ - IndexParams index_params_; - - /** - * Number of features in the dataset. - */ - size_t size_; - - /** - * Length of each feature. - */ - size_t veclen_; - - /** - * The root node in the tree. - */ - KMeansNodePtr* root_; - - /** - * Array of indices to vectors in the dataset. - */ - int** indices_; - - /** - * The distance - */ - Distance distance_; - - /** - * Pooled memory allocator. - */ - PooledAllocator pool_; - - /** - * Memory occupied by the index. - */ - int memoryCounter_; -}; - -} - -//! @endcond - -#endif //OPENCV_FLANN_KMEANS_INDEX_H_ diff --git a/include/opencv2/flann/linear_index.h b/include/opencv2/flann/linear_index.h deleted file mode 100755 index 6428c0d..0000000 --- a/include/opencv2/flann/linear_index.h +++ /dev/null @@ -1,135 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_LINEAR_INDEX_H_ -#define OPENCV_FLANN_LINEAR_INDEX_H_ - -//! @cond IGNORED - -#include "nn_index.h" - -namespace cvflann -{ - -struct LinearIndexParams : public IndexParams -{ - LinearIndexParams() - { - (* this)["algorithm"] = FLANN_INDEX_LINEAR; - } -}; - -template -class LinearIndex : public NNIndex -{ -public: - - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - - LinearIndex(const Matrix& inputData, const IndexParams& params = LinearIndexParams(), - Distance d = Distance()) : - dataset_(inputData), index_params_(params), distance_(d) - { - } - - LinearIndex(const LinearIndex&); - LinearIndex& operator=(const LinearIndex&); - - flann_algorithm_t getType() const CV_OVERRIDE - { - return FLANN_INDEX_LINEAR; - } - - - size_t size() const CV_OVERRIDE - { - return dataset_.rows; - } - - size_t veclen() const CV_OVERRIDE - { - return dataset_.cols; - } - - - int usedMemory() const CV_OVERRIDE - { - return 0; - } - - void buildIndex() CV_OVERRIDE - { - /* nothing to do here for linear search */ - } - - void saveIndex(FILE*) CV_OVERRIDE - { - /* nothing to do here for linear search */ - } - - - void loadIndex(FILE*) CV_OVERRIDE - { - /* nothing to do here for linear search */ - - index_params_["algorithm"] = getType(); - } - - void findNeighbors(ResultSet& resultSet, const ElementType* vec, const SearchParams& /*searchParams*/) CV_OVERRIDE - { - ElementType* data = dataset_.data; - for (size_t i = 0; i < dataset_.rows; ++i, data += dataset_.cols) { - DistanceType dist = distance_(data, vec, dataset_.cols); - resultSet.addPoint(dist, (int)i); - } - } - - IndexParams getParameters() const CV_OVERRIDE - { - return index_params_; - } - -private: - /** The dataset */ - const Matrix dataset_; - /** Index parameters */ - IndexParams index_params_; - /** Index distance */ - Distance distance_; - -}; - -} - -//! @endcond - -#endif // OPENCV_FLANN_LINEAR_INDEX_H_ diff --git a/include/opencv2/flann/logger.h b/include/opencv2/flann/logger.h deleted file mode 100755 index 8911812..0000000 --- a/include/opencv2/flann/logger.h +++ /dev/null @@ -1,139 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_LOGGER_H -#define OPENCV_FLANN_LOGGER_H - -//! @cond IGNORED - -#include -#include - -#include "defines.h" - - -namespace cvflann -{ - -class Logger -{ - Logger() : stream(stdout), logLevel(FLANN_LOG_WARN) {} - - ~Logger() - { - if ((stream!=NULL)&&(stream!=stdout)) { - fclose(stream); - } - } - - static Logger& instance() - { - static Logger logger; - return logger; - } - - void _setDestination(const char* name) - { - if (name==NULL) { - stream = stdout; - } - else { -#ifdef _MSC_VER - if (fopen_s(&stream, name, "w") != 0) - stream = NULL; -#else - stream = fopen(name,"w"); -#endif - if (stream == NULL) { - stream = stdout; - } - } - } - - int _log(int level, const char* fmt, va_list arglist) - { - if (level > logLevel ) return -1; - int ret = vfprintf(stream, fmt, arglist); - return ret; - } - -public: - /** - * Sets the logging level. All messages with lower priority will be ignored. - * @param level Logging level - */ - static void setLevel(int level) { instance().logLevel = level; } - - /** - * Sets the logging destination - * @param name Filename or NULL for console - */ - static void setDestination(const char* name) { instance()._setDestination(name); } - - /** - * Print log message - * @param level Log level - * @param fmt Message format - * @return - */ - static int log(int level, const char* fmt, ...) - { - va_list arglist; - va_start(arglist, fmt); - int ret = instance()._log(level,fmt,arglist); - va_end(arglist); - return ret; - } - -#define LOG_METHOD(NAME,LEVEL) \ - static int NAME(const char* fmt, ...) \ - { \ - va_list ap; \ - va_start(ap, fmt); \ - int ret = instance()._log(LEVEL, fmt, ap); \ - va_end(ap); \ - return ret; \ - } - - LOG_METHOD(fatal, FLANN_LOG_FATAL) - LOG_METHOD(error, FLANN_LOG_ERROR) - LOG_METHOD(warn, FLANN_LOG_WARN) - LOG_METHOD(info, FLANN_LOG_INFO) - -private: - FILE* stream; - int logLevel; -}; - -} - -//! @endcond - -#endif //OPENCV_FLANN_LOGGER_H diff --git a/include/opencv2/flann/lsh_index.h b/include/opencv2/flann/lsh_index.h deleted file mode 100755 index 9ff86b8..0000000 --- a/include/opencv2/flann/lsh_index.h +++ /dev/null @@ -1,403 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -/*********************************************************************** - * Author: Vincent Rabaud - *************************************************************************/ - -#ifndef OPENCV_FLANN_LSH_INDEX_H_ -#define OPENCV_FLANN_LSH_INDEX_H_ - -//! @cond IGNORED - -#include -#include -#include -#include - -#include "nn_index.h" -#include "matrix.h" -#include "result_set.h" -#include "heap.h" -#include "lsh_table.h" -#include "allocator.h" -#include "random.h" -#include "saving.h" - -#ifdef _MSC_VER -#pragma warning(push) -#pragma warning(disable: 4702) //disable unreachable code -#endif - -namespace cvflann -{ - -struct LshIndexParams : public IndexParams -{ - LshIndexParams(unsigned int table_number = 12, unsigned int key_size = 20, unsigned int multi_probe_level = 2) - { - (*this)["algorithm"] = FLANN_INDEX_LSH; - // The number of hash tables to use - (*this)["table_number"] = static_cast(table_number); - // The length of the key in the hash tables - (*this)["key_size"] = static_cast(key_size); - // Number of levels to use in multi-probe (0 for standard LSH) - (*this)["multi_probe_level"] = static_cast(multi_probe_level); - } -}; - -/** - * Locality-sensitive hashing index - * - * Contains the tables and other information for indexing a set of points - * for nearest-neighbor matching. - */ -template -class LshIndex : public NNIndex -{ -public: - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - - /** Constructor - * @param input_data dataset with the input features - * @param params parameters passed to the LSH algorithm - * @param d the distance used - */ - LshIndex(const Matrix& input_data, const IndexParams& params = LshIndexParams(), - Distance d = Distance()) : - dataset_(input_data), index_params_(params), distance_(d) - { - // cv::flann::IndexParams sets integer params as 'int', so it is used with get_param - // in place of 'unsigned int' - table_number_ = get_param(index_params_,"table_number",12); - key_size_ = get_param(index_params_,"key_size",20); - multi_probe_level_ = get_param(index_params_,"multi_probe_level",2); - - feature_size_ = (unsigned)dataset_.cols; - fill_xor_mask(0, key_size_, multi_probe_level_, xor_masks_); - } - - - LshIndex(const LshIndex&); - LshIndex& operator=(const LshIndex&); - - /** - * Builds the index - */ - void buildIndex() CV_OVERRIDE - { - tables_.resize(table_number_); - for (int i = 0; i < table_number_; ++i) { - lsh::LshTable& table = tables_[i]; - table = lsh::LshTable(feature_size_, key_size_); - - // Add the features to the table - table.add(dataset_); - } - } - - flann_algorithm_t getType() const CV_OVERRIDE - { - return FLANN_INDEX_LSH; - } - - - void saveIndex(FILE* stream) CV_OVERRIDE - { - save_value(stream,table_number_); - save_value(stream,key_size_); - save_value(stream,multi_probe_level_); - save_value(stream, dataset_); - } - - void loadIndex(FILE* stream) CV_OVERRIDE - { - load_value(stream, table_number_); - load_value(stream, key_size_); - load_value(stream, multi_probe_level_); - load_value(stream, dataset_); - // Building the index is so fast we can afford not storing it - buildIndex(); - - index_params_["algorithm"] = getType(); - index_params_["table_number"] = table_number_; - index_params_["key_size"] = key_size_; - index_params_["multi_probe_level"] = multi_probe_level_; - } - - /** - * Returns size of index. - */ - size_t size() const CV_OVERRIDE - { - return dataset_.rows; - } - - /** - * Returns the length of an index feature. - */ - size_t veclen() const CV_OVERRIDE - { - return feature_size_; - } - - /** - * Computes the index memory usage - * Returns: memory used by the index - */ - int usedMemory() const CV_OVERRIDE - { - return (int)(dataset_.rows * sizeof(int)); - } - - - IndexParams getParameters() const CV_OVERRIDE - { - return index_params_; - } - - /** - * \brief Perform k-nearest neighbor search - * \param[in] queries The query points for which to find the nearest neighbors - * \param[out] indices The indices of the nearest neighbors found - * \param[out] dists Distances to the nearest neighbors found - * \param[in] knn Number of nearest neighbors to return - * \param[in] params Search parameters - */ - virtual void knnSearch(const Matrix& queries, Matrix& indices, Matrix& dists, int knn, const SearchParams& params) CV_OVERRIDE - { - CV_Assert(queries.cols == veclen()); - CV_Assert(indices.rows >= queries.rows); - CV_Assert(dists.rows >= queries.rows); - CV_Assert(int(indices.cols) >= knn); - CV_Assert(int(dists.cols) >= knn); - - - KNNUniqueResultSet resultSet(knn); - for (size_t i = 0; i < queries.rows; i++) { - resultSet.clear(); - std::fill_n(indices[i], knn, -1); - std::fill_n(dists[i], knn, std::numeric_limits::max()); - findNeighbors(resultSet, queries[i], params); - if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices[i], dists[i], knn); - else resultSet.copy(indices[i], dists[i], knn); - } - } - - - /** - * Find set of nearest neighbors to vec. Their indices are stored inside - * the result object. - * - * Params: - * result = the result object in which the indices of the nearest-neighbors are stored - * vec = the vector for which to search the nearest neighbors - * maxCheck = the maximum number of restarts (in a best-bin-first manner) - */ - void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& /*searchParams*/) CV_OVERRIDE - { - getNeighbors(vec, result); - } - -private: - /** Defines the comparator on score and index - */ - typedef std::pair ScoreIndexPair; - struct SortScoreIndexPairOnSecond - { - bool operator()(const ScoreIndexPair& left, const ScoreIndexPair& right) const - { - return left.second < right.second; - } - }; - - /** Fills the different xor masks to use when getting the neighbors in multi-probe LSH - * @param key the key we build neighbors from - * @param lowest_index the lowest index of the bit set - * @param level the multi-probe level we are at - * @param xor_masks all the xor mask - */ - void fill_xor_mask(lsh::BucketKey key, int lowest_index, unsigned int level, - std::vector& xor_masks) - { - xor_masks.push_back(key); - if (level == 0) return; - for (int index = lowest_index - 1; index >= 0; --index) { - // Create a new key - lsh::BucketKey new_key = key | (1 << index); - fill_xor_mask(new_key, index, level - 1, xor_masks); - } - } - - /** Performs the approximate nearest-neighbor search. - * @param vec the feature to analyze - * @param do_radius flag indicating if we check the radius too - * @param radius the radius if it is a radius search - * @param do_k flag indicating if we limit the number of nn - * @param k_nn the number of nearest neighbors - * @param checked_average used for debugging - */ - void getNeighbors(const ElementType* vec, bool /*do_radius*/, float radius, bool do_k, unsigned int k_nn, - float& /*checked_average*/) - { - static std::vector score_index_heap; - - if (do_k) { - unsigned int worst_score = std::numeric_limits::max(); - typename std::vector >::const_iterator table = tables_.begin(); - typename std::vector >::const_iterator table_end = tables_.end(); - for (; table != table_end; ++table) { - size_t key = table->getKey(vec); - std::vector::const_iterator xor_mask = xor_masks_.begin(); - std::vector::const_iterator xor_mask_end = xor_masks_.end(); - for (; xor_mask != xor_mask_end; ++xor_mask) { - size_t sub_key = key ^ (*xor_mask); - const lsh::Bucket* bucket = table->getBucketFromKey(sub_key); - if (bucket == 0) continue; - - // Go over each descriptor index - std::vector::const_iterator training_index = bucket->begin(); - std::vector::const_iterator last_training_index = bucket->end(); - DistanceType hamming_distance; - - // Process the rest of the candidates - for (; training_index < last_training_index; ++training_index) { - hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols); - - if (hamming_distance < worst_score) { - // Insert the new element - score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index)); - std::push_heap(score_index_heap.begin(), score_index_heap.end()); - - if (score_index_heap.size() > (unsigned int)k_nn) { - // Remove the highest distance value as we have too many elements - std::pop_heap(score_index_heap.begin(), score_index_heap.end()); - score_index_heap.pop_back(); - // Keep track of the worst score - worst_score = score_index_heap.front().first; - } - } - } - } - } - } - else { - typename std::vector >::const_iterator table = tables_.begin(); - typename std::vector >::const_iterator table_end = tables_.end(); - for (; table != table_end; ++table) { - size_t key = table->getKey(vec); - std::vector::const_iterator xor_mask = xor_masks_.begin(); - std::vector::const_iterator xor_mask_end = xor_masks_.end(); - for (; xor_mask != xor_mask_end; ++xor_mask) { - size_t sub_key = key ^ (*xor_mask); - const lsh::Bucket* bucket = table->getBucketFromKey(sub_key); - if (bucket == 0) continue; - - // Go over each descriptor index - std::vector::const_iterator training_index = bucket->begin(); - std::vector::const_iterator last_training_index = bucket->end(); - DistanceType hamming_distance; - - // Process the rest of the candidates - for (; training_index < last_training_index; ++training_index) { - // Compute the Hamming distance - hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols); - if (hamming_distance < radius) score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index)); - } - } - } - } - } - - /** Performs the approximate nearest-neighbor search. - * This is a slower version than the above as it uses the ResultSet - * @param vec the feature to analyze - */ - void getNeighbors(const ElementType* vec, ResultSet& result) - { - typename std::vector >::const_iterator table = tables_.begin(); - typename std::vector >::const_iterator table_end = tables_.end(); - for (; table != table_end; ++table) { - size_t key = table->getKey(vec); - std::vector::const_iterator xor_mask = xor_masks_.begin(); - std::vector::const_iterator xor_mask_end = xor_masks_.end(); - for (; xor_mask != xor_mask_end; ++xor_mask) { - size_t sub_key = key ^ (*xor_mask); - const lsh::Bucket* bucket = table->getBucketFromKey((lsh::BucketKey)sub_key); - if (bucket == 0) continue; - - // Go over each descriptor index - std::vector::const_iterator training_index = bucket->begin(); - std::vector::const_iterator last_training_index = bucket->end(); - DistanceType hamming_distance; - - // Process the rest of the candidates - for (; training_index < last_training_index; ++training_index) { - // Compute the Hamming distance - hamming_distance = distance_(vec, dataset_[*training_index], (int)dataset_.cols); - result.addPoint(hamming_distance, *training_index); - } - } - } - } - - /** The different hash tables */ - std::vector > tables_; - - /** The data the LSH tables where built from */ - Matrix dataset_; - - /** The size of the features (as ElementType[]) */ - unsigned int feature_size_; - - IndexParams index_params_; - - /** table number */ - int table_number_; - /** key size */ - int key_size_; - /** How far should we look for neighbors in multi-probe LSH */ - int multi_probe_level_; - - /** The XOR masks to apply to a key to get the neighboring buckets */ - std::vector xor_masks_; - - Distance distance_; -}; -} - -#ifdef _MSC_VER -#pragma warning(pop) -#endif - -//! @endcond - -#endif //OPENCV_FLANN_LSH_INDEX_H_ diff --git a/include/opencv2/flann/lsh_table.h b/include/opencv2/flann/lsh_table.h deleted file mode 100755 index a189562..0000000 --- a/include/opencv2/flann/lsh_table.h +++ /dev/null @@ -1,525 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -/*********************************************************************** - * Author: Vincent Rabaud - *************************************************************************/ - -#ifndef OPENCV_FLANN_LSH_TABLE_H_ -#define OPENCV_FLANN_LSH_TABLE_H_ - -//! @cond IGNORED - -#include -#include -#include -#include -// TODO as soon as we use C++0x, use the code in USE_UNORDERED_MAP -#ifdef __GXX_EXPERIMENTAL_CXX0X__ -# define USE_UNORDERED_MAP 1 -#else -# define USE_UNORDERED_MAP 0 -#endif -#if USE_UNORDERED_MAP -#include -#else -#include -#endif -#include -#include - -#include "dynamic_bitset.h" -#include "matrix.h" - -#ifdef _MSC_VER -#pragma warning(push) -#pragma warning(disable: 4702) //disable unreachable code -#endif - - -namespace cvflann -{ - -namespace lsh -{ - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -/** What is stored in an LSH bucket - */ -typedef uint32_t FeatureIndex; -/** The id from which we can get a bucket back in an LSH table - */ -typedef unsigned int BucketKey; - -/** A bucket in an LSH table - */ -typedef std::vector Bucket; - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -/** POD for stats about an LSH table - */ -struct LshStats -{ - std::vector bucket_sizes_; - size_t n_buckets_; - size_t bucket_size_mean_; - size_t bucket_size_median_; - size_t bucket_size_min_; - size_t bucket_size_max_; - size_t bucket_size_std_dev; - /** Each contained vector contains three value: beginning/end for interval, number of elements in the bin - */ - std::vector > size_histogram_; -}; - -/** Overload the << operator for LshStats - * @param out the streams - * @param stats the stats to display - * @return the streams - */ -inline std::ostream& operator <<(std::ostream& out, const LshStats& stats) -{ - int w = 20; - out << "Lsh Table Stats:\n" << std::setw(w) << std::setiosflags(std::ios::right) << "N buckets : " - << stats.n_buckets_ << "\n" << std::setw(w) << std::setiosflags(std::ios::right) << "mean size : " - << std::setiosflags(std::ios::left) << stats.bucket_size_mean_ << "\n" << std::setw(w) - << std::setiosflags(std::ios::right) << "median size : " << stats.bucket_size_median_ << "\n" << std::setw(w) - << std::setiosflags(std::ios::right) << "min size : " << std::setiosflags(std::ios::left) - << stats.bucket_size_min_ << "\n" << std::setw(w) << std::setiosflags(std::ios::right) << "max size : " - << std::setiosflags(std::ios::left) << stats.bucket_size_max_; - - // Display the histogram - out << std::endl << std::setw(w) << std::setiosflags(std::ios::right) << "histogram : " - << std::setiosflags(std::ios::left); - for (std::vector >::const_iterator iterator = stats.size_histogram_.begin(), end = - stats.size_histogram_.end(); iterator != end; ++iterator) out << (*iterator)[0] << "-" << (*iterator)[1] << ": " << (*iterator)[2] << ", "; - - return out; -} - - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -/** Lsh hash table. As its key is a sub-feature, and as usually - * the size of it is pretty small, we keep it as a continuous memory array. - * The value is an index in the corpus of features (we keep it as an unsigned - * int for pure memory reasons, it could be a size_t) - */ -template -class LshTable -{ -public: - /** A container of all the feature indices. Optimized for space - */ -#if USE_UNORDERED_MAP - typedef std::unordered_map BucketsSpace; -#else - typedef std::map BucketsSpace; -#endif - - /** A container of all the feature indices. Optimized for speed - */ - typedef std::vector BucketsSpeed; - - /** Default constructor - */ - LshTable() - { - key_size_ = 0; - feature_size_ = 0; - speed_level_ = kArray; - } - - /** Default constructor - * Create the mask and allocate the memory - * @param feature_size is the size of the feature (considered as a ElementType[]) - * @param key_size is the number of bits that are turned on in the feature - */ - LshTable(unsigned int feature_size, unsigned int key_size) - { - feature_size_ = feature_size; - CV_UNUSED(key_size); - CV_Error(cv::Error::StsUnsupportedFormat, "LSH is not implemented for that type" ); - } - - /** Add a feature to the table - * @param value the value to store for that feature - * @param feature the feature itself - */ - void add(unsigned int value, const ElementType* feature) - { - // Add the value to the corresponding bucket - BucketKey key = (lsh::BucketKey)getKey(feature); - - switch (speed_level_) { - case kArray: - // That means we get the buckets from an array - buckets_speed_[key].push_back(value); - break; - case kBitsetHash: - // That means we can check the bitset for the presence of a key - key_bitset_.set(key); - buckets_space_[key].push_back(value); - break; - case kHash: - { - // That means we have to check for the hash table for the presence of a key - buckets_space_[key].push_back(value); - break; - } - } - } - - /** Add a set of features to the table - * @param dataset the values to store - */ - void add(Matrix dataset) - { -#if USE_UNORDERED_MAP - buckets_space_.rehash((buckets_space_.size() + dataset.rows) * 1.2); -#endif - // Add the features to the table - for (unsigned int i = 0; i < dataset.rows; ++i) add(i, dataset[i]); - // Now that the table is full, optimize it for speed/space - optimize(); - } - - /** Get a bucket given the key - * @param key - * @return - */ - inline const Bucket* getBucketFromKey(BucketKey key) const - { - // Generate other buckets - switch (speed_level_) { - case kArray: - // That means we get the buckets from an array - return &buckets_speed_[key]; - break; - case kBitsetHash: - // That means we can check the bitset for the presence of a key - if (key_bitset_.test(key)) return &buckets_space_.find(key)->second; - else return 0; - break; - case kHash: - { - // That means we have to check for the hash table for the presence of a key - BucketsSpace::const_iterator bucket_it, bucket_end = buckets_space_.end(); - bucket_it = buckets_space_.find(key); - // Stop here if that bucket does not exist - if (bucket_it == bucket_end) return 0; - else return &bucket_it->second; - break; - } - } - return 0; - } - - /** Compute the sub-signature of a feature - */ - size_t getKey(const ElementType* /*feature*/) const - { - CV_Error(cv::Error::StsUnsupportedFormat, "LSH is not implemented for that type" ); - return 0; - } - - /** Get statistics about the table - * @return - */ - LshStats getStats() const; - -private: - /** defines the speed fo the implementation - * kArray uses a vector for storing data - * kBitsetHash uses a hash map but checks for the validity of a key with a bitset - * kHash uses a hash map only - */ - enum SpeedLevel - { - kArray, kBitsetHash, kHash - }; - - /** Initialize some variables - */ - void initialize(size_t key_size) - { - const size_t key_size_lower_bound = 1; - //a value (size_t(1) << key_size) must fit the size_t type so key_size has to be strictly less than size of size_t - const size_t key_size_upper_bound = (std::min)(sizeof(BucketKey) * CHAR_BIT + 1, sizeof(size_t) * CHAR_BIT); - if (key_size < key_size_lower_bound || key_size >= key_size_upper_bound) - { - CV_Error(cv::Error::StsBadArg, cv::format("Invalid key_size (=%d). Valid values for your system are %d <= key_size < %d.", (int)key_size, (int)key_size_lower_bound, (int)key_size_upper_bound)); - } - - speed_level_ = kHash; - key_size_ = (unsigned)key_size; - } - - /** Optimize the table for speed/space - */ - void optimize() - { - // If we are already using the fast storage, no need to do anything - if (speed_level_ == kArray) return; - - // Use an array if it will be more than half full - if (buckets_space_.size() > ((size_t(1) << key_size_) / 2)) { - speed_level_ = kArray; - // Fill the array version of it - buckets_speed_.resize(size_t(1) << key_size_); - for (BucketsSpace::const_iterator key_bucket = buckets_space_.begin(); key_bucket != buckets_space_.end(); ++key_bucket) buckets_speed_[key_bucket->first] = key_bucket->second; - - // Empty the hash table - buckets_space_.clear(); - return; - } - - // If the bitset is going to use less than 10% of the RAM of the hash map (at least 1 size_t for the key and two - // for the vector) or less than 512MB (key_size_ <= 30) - if (((std::max(buckets_space_.size(), buckets_speed_.size()) * CHAR_BIT * 3 * sizeof(BucketKey)) / 10 - >= (size_t(1) << key_size_)) || (key_size_ <= 32)) { - speed_level_ = kBitsetHash; - key_bitset_.resize(size_t(1) << key_size_); - key_bitset_.reset(); - // Try with the BucketsSpace - for (BucketsSpace::const_iterator key_bucket = buckets_space_.begin(); key_bucket != buckets_space_.end(); ++key_bucket) key_bitset_.set(key_bucket->first); - } - else { - speed_level_ = kHash; - key_bitset_.clear(); - } - } - - /** The vector of all the buckets if they are held for speed - */ - BucketsSpeed buckets_speed_; - - /** The hash table of all the buckets in case we cannot use the speed version - */ - BucketsSpace buckets_space_; - - /** What is used to store the data */ - SpeedLevel speed_level_; - - /** If the subkey is small enough, it will keep track of which subkeys are set through that bitset - * That is just a speedup so that we don't look in the hash table (which can be mush slower that checking a bitset) - */ - DynamicBitset key_bitset_; - - /** The size of the sub-signature in bits - */ - unsigned int key_size_; - - unsigned int feature_size_; - - // Members only used for the unsigned char specialization - /** The mask to apply to a feature to get the hash key - * Only used in the unsigned char case - */ - std::vector mask_; -}; - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// -// Specialization for unsigned char - -template<> -inline LshTable::LshTable(unsigned int feature_size, unsigned int subsignature_size) -{ - feature_size_ = feature_size; - initialize(subsignature_size); - // Allocate the mask - mask_ = std::vector((feature_size * sizeof(char) + sizeof(size_t) - 1) / sizeof(size_t), 0); - - // A bit brutal but fast to code - std::vector indices(feature_size * CHAR_BIT); - for (size_t i = 0; i < feature_size * CHAR_BIT; ++i) indices[i] = (int)i; -#ifndef OPENCV_FLANN_USE_STD_RAND - cv::randShuffle(indices); -#else - std::random_shuffle(indices.begin(), indices.end()); -#endif - - // Generate a random set of order of subsignature_size_ bits - for (unsigned int i = 0; i < key_size_; ++i) { - size_t index = indices[i]; - - // Set that bit in the mask - size_t divisor = CHAR_BIT * sizeof(size_t); - size_t idx = index / divisor; //pick the right size_t index - mask_[idx] |= size_t(1) << (index % divisor); //use modulo to find the bit offset - } - - // Set to 1 if you want to display the mask for debug -#if 0 - { - size_t bcount = 0; - BOOST_FOREACH(size_t mask_block, mask_){ - out << std::setw(sizeof(size_t) * CHAR_BIT / 4) << std::setfill('0') << std::hex << mask_block - << std::endl; - bcount += __builtin_popcountll(mask_block); - } - out << "bit count : " << std::dec << bcount << std::endl; - out << "mask size : " << mask_.size() << std::endl; - return out; - } -#endif -} - -/** Return the Subsignature of a feature - * @param feature the feature to analyze - */ -template<> -inline size_t LshTable::getKey(const unsigned char* feature) const -{ - // no need to check if T is dividable by sizeof(size_t) like in the Hamming - // distance computation as we have a mask - // FIXIT: This is bad assumption, because we reading tail bytes after of the allocated features buffer - const size_t* feature_block_ptr = reinterpret_cast ((const void*)feature); - - // Figure out the subsignature of the feature - // Given the feature ABCDEF, and the mask 001011, the output will be - // 000CEF - size_t subsignature = 0; - size_t bit_index = 1; - - for (unsigned i = 0; i < feature_size_; i += sizeof(size_t)) { - // get the mask and signature blocks - size_t feature_block; - if (i <= feature_size_ - sizeof(size_t)) - { - feature_block = *feature_block_ptr; - } - else - { - size_t tmp = 0; - memcpy(&tmp, feature_block_ptr, feature_size_ - i); // preserve bytes order - feature_block = tmp; - } - size_t mask_block = mask_[i / sizeof(size_t)]; - while (mask_block) { - // Get the lowest set bit in the mask block - size_t lowest_bit = mask_block & (-(ptrdiff_t)mask_block); - // Add it to the current subsignature if necessary - subsignature += (feature_block & lowest_bit) ? bit_index : 0; - // Reset the bit in the mask block - mask_block ^= lowest_bit; - // increment the bit index for the subsignature - bit_index <<= 1; - } - // Check the next feature block - ++feature_block_ptr; - } - return subsignature; -} - -template<> -inline LshStats LshTable::getStats() const -{ - LshStats stats; - stats.bucket_size_mean_ = 0; - if ((buckets_speed_.empty()) && (buckets_space_.empty())) { - stats.n_buckets_ = 0; - stats.bucket_size_median_ = 0; - stats.bucket_size_min_ = 0; - stats.bucket_size_max_ = 0; - return stats; - } - - if (!buckets_speed_.empty()) { - for (BucketsSpeed::const_iterator pbucket = buckets_speed_.begin(); pbucket != buckets_speed_.end(); ++pbucket) { - stats.bucket_sizes_.push_back((lsh::FeatureIndex)pbucket->size()); - stats.bucket_size_mean_ += pbucket->size(); - } - stats.bucket_size_mean_ /= buckets_speed_.size(); - stats.n_buckets_ = buckets_speed_.size(); - } - else { - for (BucketsSpace::const_iterator x = buckets_space_.begin(); x != buckets_space_.end(); ++x) { - stats.bucket_sizes_.push_back((lsh::FeatureIndex)x->second.size()); - stats.bucket_size_mean_ += x->second.size(); - } - stats.bucket_size_mean_ /= buckets_space_.size(); - stats.n_buckets_ = buckets_space_.size(); - } - - std::sort(stats.bucket_sizes_.begin(), stats.bucket_sizes_.end()); - - // BOOST_FOREACH(int size, stats.bucket_sizes_) - // std::cout << size << " "; - // std::cout << std::endl; - stats.bucket_size_median_ = stats.bucket_sizes_[stats.bucket_sizes_.size() / 2]; - stats.bucket_size_min_ = stats.bucket_sizes_.front(); - stats.bucket_size_max_ = stats.bucket_sizes_.back(); - - // TODO compute mean and std - /*float mean, stddev; - stats.bucket_size_mean_ = mean; - stats.bucket_size_std_dev = stddev;*/ - - // Include a histogram of the buckets - unsigned int bin_start = 0; - unsigned int bin_end = 20; - bool is_new_bin = true; - for (std::vector::iterator iterator = stats.bucket_sizes_.begin(), end = stats.bucket_sizes_.end(); iterator - != end; ) - if (*iterator < bin_end) { - if (is_new_bin) { - stats.size_histogram_.push_back(std::vector(3, 0)); - stats.size_histogram_.back()[0] = bin_start; - stats.size_histogram_.back()[1] = bin_end - 1; - is_new_bin = false; - } - ++stats.size_histogram_.back()[2]; - ++iterator; - } - else { - bin_start += 20; - bin_end += 20; - is_new_bin = true; - } - - return stats; -} - -// End the two namespaces -} -} - -#ifdef _MSC_VER -#pragma warning(pop) -#endif - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -//! @endcond - -#endif /* OPENCV_FLANN_LSH_TABLE_H_ */ diff --git a/include/opencv2/flann/matrix.h b/include/opencv2/flann/matrix.h deleted file mode 100755 index fb871bd..0000000 --- a/include/opencv2/flann/matrix.h +++ /dev/null @@ -1,118 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_DATASET_H_ -#define OPENCV_FLANN_DATASET_H_ - -//! @cond IGNORED - -#include - -namespace cvflann -{ - -/** - * Class that implements a simple rectangular matrix stored in a memory buffer and - * provides convenient matrix-like access using the [] operators. - */ -template -class Matrix -{ -public: - typedef T type; - - size_t rows; - size_t cols; - size_t stride; - T* data; - - Matrix() : rows(0), cols(0), stride(0), data(NULL) - { - } - - Matrix(T* data_, size_t rows_, size_t cols_, size_t stride_ = 0) : - rows(rows_), cols(cols_), stride(stride_), data(data_) - { - if (stride==0) stride = cols; - } - - /** - * Convenience function for deallocating the storage data. - */ - CV_DEPRECATED void free() - { - fprintf(stderr, "The cvflann::Matrix::free() method is deprecated " - "and it does not do any memory deallocation any more. You are" - "responsible for deallocating the matrix memory (by doing" - "'delete[] matrix.data' for example)"); - } - - /** - * Operator that return a (pointer to a) row of the data. - */ - T* operator[](size_t index) const - { - return data+index*stride; - } -}; - - -class UntypedMatrix -{ -public: - size_t rows; - size_t cols; - void* data; - flann_datatype_t type; - - UntypedMatrix(void* data_, long rows_, long cols_) : - rows(rows_), cols(cols_), data(data_) - { - } - - ~UntypedMatrix() - { - } - - - template - Matrix as() - { - return Matrix((T*)data, rows, cols); - } -}; - - - -} - -//! @endcond - -#endif //OPENCV_FLANN_DATASET_H_ diff --git a/include/opencv2/flann/miniflann.hpp b/include/opencv2/flann/miniflann.hpp deleted file mode 100755 index 1deeeb9..0000000 --- a/include/opencv2/flann/miniflann.hpp +++ /dev/null @@ -1,170 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_MINIFLANN_HPP -#define OPENCV_MINIFLANN_HPP - -//! @cond IGNORED - -#include "opencv2/core.hpp" -#include "opencv2/flann/defines.h" - -namespace cv -{ - -namespace flann -{ - -struct CV_EXPORTS IndexParams -{ - IndexParams(); - ~IndexParams(); - - String getString(const String& key, const String& defaultVal=String()) const; - int getInt(const String& key, int defaultVal=-1) const; - double getDouble(const String& key, double defaultVal=-1) const; - - void setString(const String& key, const String& value); - void setInt(const String& key, int value); - void setDouble(const String& key, double value); - void setFloat(const String& key, float value); - void setBool(const String& key, bool value); - void setAlgorithm(int value); - - void getAll(std::vector& names, - std::vector& types, - std::vector& strValues, - std::vector& numValues) const; - - void* params; - -private: - IndexParams(const IndexParams &); // copy disabled - IndexParams& operator=(const IndexParams &); // assign disabled -}; - -struct CV_EXPORTS KDTreeIndexParams : public IndexParams -{ - KDTreeIndexParams(int trees=4); -}; - -struct CV_EXPORTS LinearIndexParams : public IndexParams -{ - LinearIndexParams(); -}; - -struct CV_EXPORTS CompositeIndexParams : public IndexParams -{ - CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11, - cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, float cb_index = 0.2f ); -}; - -struct CV_EXPORTS AutotunedIndexParams : public IndexParams -{ - AutotunedIndexParams(float target_precision = 0.8f, float build_weight = 0.01f, - float memory_weight = 0, float sample_fraction = 0.1f); -}; - -struct CV_EXPORTS HierarchicalClusteringIndexParams : public IndexParams -{ - HierarchicalClusteringIndexParams(int branching = 32, - cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, int trees = 4, int leaf_size = 100 ); -}; - -struct CV_EXPORTS KMeansIndexParams : public IndexParams -{ - KMeansIndexParams(int branching = 32, int iterations = 11, - cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, float cb_index = 0.2f ); -}; - -struct CV_EXPORTS LshIndexParams : public IndexParams -{ - LshIndexParams(int table_number, int key_size, int multi_probe_level); -}; - -struct CV_EXPORTS SavedIndexParams : public IndexParams -{ - SavedIndexParams(const String& filename); -}; - -struct CV_EXPORTS SearchParams : public IndexParams -{ - SearchParams( int checks = 32, float eps = 0, bool sorted = true ); -}; - -class CV_EXPORTS Index -{ -public: - Index(); - - /// @note 'features' must have extended lifetime (as cv::Mat) than this #Index instance (to avoid dangling pointers) - Index(InputArray features, const IndexParams& params, cvflann::flann_distance_t distType=cvflann::FLANN_DIST_L2); - virtual ~Index(); - - /// @note 'features' must have extended lifetime (as cv::Mat) than this #Index instance (to avoid dangling pointers) - virtual void build(InputArray features, const IndexParams& params, cvflann::flann_distance_t distType=cvflann::FLANN_DIST_L2); - - virtual void knnSearch(InputArray query, OutputArray indices, - OutputArray dists, int knn, const SearchParams& params=SearchParams()); - - virtual int radiusSearch(InputArray query, OutputArray indices, - OutputArray dists, double radius, int maxResults, - const SearchParams& params=SearchParams()); - - virtual void save(const String& filename) const; - virtual bool load(InputArray features, const String& filename); - virtual void release(); - cvflann::flann_distance_t getDistance() const; - cvflann::flann_algorithm_t getAlgorithm() const; - -protected: - cvflann::flann_distance_t distType; - cvflann::flann_algorithm_t algo; - int featureType; - void* index; -}; - -} } // namespace cv::flann - -//! @endcond - -#endif diff --git a/include/opencv2/flann/nn_index.h b/include/opencv2/flann/nn_index.h deleted file mode 100755 index f6e17d1..0000000 --- a/include/opencv2/flann/nn_index.h +++ /dev/null @@ -1,180 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_NNINDEX_H -#define OPENCV_FLANN_NNINDEX_H - -#include "matrix.h" -#include "result_set.h" -#include "params.h" - -//! @cond IGNORED - -namespace cvflann -{ - -/** - * Nearest-neighbour index base class - */ -template -class NNIndex -{ - typedef typename Distance::ElementType ElementType; - typedef typename Distance::ResultType DistanceType; - -public: - - virtual ~NNIndex() {} - - /** - * \brief Builds the index - */ - virtual void buildIndex() = 0; - - /** - * \brief Perform k-nearest neighbor search - * \param[in] queries The query points for which to find the nearest neighbors - * \param[out] indices The indices of the nearest neighbors found - * \param[out] dists Distances to the nearest neighbors found - * \param[in] knn Number of nearest neighbors to return - * \param[in] params Search parameters - */ - virtual void knnSearch(const Matrix& queries, Matrix& indices, Matrix& dists, int knn, const SearchParams& params) - { - CV_Assert(queries.cols == veclen()); - CV_Assert(indices.rows >= queries.rows); - CV_Assert(dists.rows >= queries.rows); - CV_Assert(int(indices.cols) >= knn); - CV_Assert(int(dists.cols) >= knn); - -#if 0 - KNNResultSet resultSet(knn); - for (size_t i = 0; i < queries.rows; i++) { - resultSet.init(indices[i], dists[i]); - findNeighbors(resultSet, queries[i], params); - } -#else - KNNUniqueResultSet resultSet(knn); - for (size_t i = 0; i < queries.rows; i++) { - resultSet.clear(); - findNeighbors(resultSet, queries[i], params); - if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices[i], dists[i], knn); - else resultSet.copy(indices[i], dists[i], knn); - } -#endif - } - - /** - * \brief Perform radius search - * \param[in] query The query point - * \param[out] indices The indinces of the neighbors found within the given radius - * \param[out] dists The distances to the nearest neighbors found - * \param[in] radius The radius used for search - * \param[in] params Search parameters - * \returns Number of neighbors found - */ - virtual int radiusSearch(const Matrix& query, Matrix& indices, Matrix& dists, float radius, const SearchParams& params) - { - if (query.rows != 1) { - fprintf(stderr, "I can only search one feature at a time for range search\n"); - return -1; - } - assert(query.cols == veclen()); - assert(indices.cols == dists.cols); - - int n = 0; - int* indices_ptr = NULL; - DistanceType* dists_ptr = NULL; - if (indices.cols > 0) { - n = (int)indices.cols; - indices_ptr = indices[0]; - dists_ptr = dists[0]; - } - - RadiusUniqueResultSet resultSet((DistanceType)radius); - resultSet.clear(); - findNeighbors(resultSet, query[0], params); - if (n>0) { - if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices_ptr, dists_ptr, n); - else resultSet.copy(indices_ptr, dists_ptr, n); - } - - return (int)resultSet.size(); - } - - /** - * \brief Saves the index to a stream - * \param stream The stream to save the index to - */ - virtual void saveIndex(FILE* stream) = 0; - - /** - * \brief Loads the index from a stream - * \param stream The stream from which the index is loaded - */ - virtual void loadIndex(FILE* stream) = 0; - - /** - * \returns number of features in this index. - */ - virtual size_t size() const = 0; - - /** - * \returns The dimensionality of the features in this index. - */ - virtual size_t veclen() const = 0; - - /** - * \returns The amount of memory (in bytes) used by the index. - */ - virtual int usedMemory() const = 0; - - /** - * \returns The index type (kdtree, kmeans,...) - */ - virtual flann_algorithm_t getType() const = 0; - - /** - * \returns The index parameters - */ - virtual IndexParams getParameters() const = 0; - - - /** - * \brief Method that searches for nearest-neighbours - */ - virtual void findNeighbors(ResultSet& result, const ElementType* vec, const SearchParams& searchParams) = 0; -}; - -} - -//! @endcond - -#endif //OPENCV_FLANN_NNINDEX_H diff --git a/include/opencv2/flann/object_factory.h b/include/opencv2/flann/object_factory.h deleted file mode 100755 index 5cc45ad..0000000 --- a/include/opencv2/flann/object_factory.h +++ /dev/null @@ -1,95 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_OBJECT_FACTORY_H_ -#define OPENCV_FLANN_OBJECT_FACTORY_H_ - -//! @cond IGNORED - -#include - -namespace cvflann -{ - -class CreatorNotFound -{ -}; - -template -class ObjectFactory -{ - typedef ObjectFactory ThisClass; - typedef std::map ObjectRegistry; - - // singleton class, private constructor - ObjectFactory() {} - -public: - - bool subscribe(UniqueIdType id, ObjectCreator creator) - { - if (object_registry.find(id) != object_registry.end()) return false; - - object_registry[id] = creator; - return true; - } - - bool unregister(UniqueIdType id) - { - return object_registry.erase(id) == 1; - } - - ObjectCreator create(UniqueIdType id) - { - typename ObjectRegistry::const_iterator iter = object_registry.find(id); - - if (iter == object_registry.end()) { - throw CreatorNotFound(); - } - - return iter->second; - } - - static ThisClass& instance() - { - static ThisClass the_factory; - return the_factory; - } -private: - ObjectRegistry object_registry; -}; - -} - -//! @endcond - -#endif /* OPENCV_FLANN_OBJECT_FACTORY_H_ */ diff --git a/include/opencv2/flann/params.h b/include/opencv2/flann/params.h deleted file mode 100755 index a7920d0..0000000 --- a/include/opencv2/flann/params.h +++ /dev/null @@ -1,102 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - - -#ifndef OPENCV_FLANN_PARAMS_H_ -#define OPENCV_FLANN_PARAMS_H_ - -//! @cond IGNORED - -#include "any.h" -#include "general.h" -#include -#include - - -namespace cvflann -{ - -typedef std::map IndexParams; - -struct SearchParams : public IndexParams -{ - SearchParams(int checks = 32, float eps = 0, bool sorted = true ) - { - // how many leafs to visit when searching for neighbours (-1 for unlimited) - (*this)["checks"] = checks; - // search for eps-approximate neighbours (default: 0) - (*this)["eps"] = eps; - // only for radius search, require neighbours sorted by distance (default: true) - (*this)["sorted"] = sorted; - } -}; - - -template -T get_param(const IndexParams& params, cv::String name, const T& default_value) -{ - IndexParams::const_iterator it = params.find(name); - if (it != params.end()) { - return it->second.cast(); - } - else { - return default_value; - } -} - -template -T get_param(const IndexParams& params, cv::String name) -{ - IndexParams::const_iterator it = params.find(name); - if (it != params.end()) { - return it->second.cast(); - } - else { - FLANN_THROW(cv::Error::StsBadArg, cv::String("Missing parameter '")+name+cv::String("' in the parameters given")); - } -} - -inline void print_params(const IndexParams& params, std::ostream& stream) -{ - IndexParams::const_iterator it; - - for(it=params.begin(); it!=params.end(); ++it) { - stream << it->first << " : " << it->second << std::endl; - } -} - -inline void print_params(const IndexParams& params) -{ - print_params(params, std::cout); -} - -} - -//! @endcond - -#endif /* OPENCV_FLANN_PARAMS_H_ */ diff --git a/include/opencv2/flann/random.h b/include/opencv2/flann/random.h deleted file mode 100755 index 2c1809c..0000000 --- a/include/opencv2/flann/random.h +++ /dev/null @@ -1,157 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_RANDOM_H -#define OPENCV_FLANN_RANDOM_H - -//! @cond IGNORED - -#include -#include -#include - -namespace cvflann -{ - -inline int rand() -{ -#ifndef OPENCV_FLANN_USE_STD_RAND -# if INT_MAX == RAND_MAX - int v = cv::theRNG().next() & INT_MAX; -# else - int v = cv::theRNG().uniform(0, RAND_MAX + 1); -# endif -#else - int v = std::rand(); -#endif // OPENCV_FLANN_USE_STD_RAND - return v; -} - -/** - * Seeds the random number generator - * @param seed Random seed - */ -inline void seed_random(unsigned int seed) -{ -#ifndef OPENCV_FLANN_USE_STD_RAND - cv::theRNG() = cv::RNG(seed); -#else - std::srand(seed); -#endif -} - -/* - * Generates a random double value. - */ -/** - * Generates a random double value. - * @param high Upper limit - * @param low Lower limit - * @return Random double value - */ -inline double rand_double(double high = 1.0, double low = 0) -{ - return low + ((high-low) * (rand() / (RAND_MAX + 1.0))); -} - -/** - * Generates a random integer value. - * @param high Upper limit - * @param low Lower limit - * @return Random integer value - */ -inline int rand_int(int high = RAND_MAX, int low = 0) -{ - return low + (int) ( double(high-low) * (rand() / (RAND_MAX + 1.0))); -} - -/** - * Random number generator that returns a distinct number from - * the [0,n) interval each time. - */ -class UniqueRandom -{ - std::vector vals_; - int size_; - int counter_; - -public: - /** - * Constructor. - * @param n Size of the interval from which to generate - * @return - */ - UniqueRandom(int n) - { - init(n); - } - - /** - * Initializes the number generator. - * @param n the size of the interval from which to generate random numbers. - */ - void init(int n) - { - // create and initialize an array of size n - vals_.resize(n); - size_ = n; - for (int i = 0; i < size_; ++i) vals_[i] = i; - - // shuffle the elements in the array -#ifndef OPENCV_FLANN_USE_STD_RAND - cv::randShuffle(vals_); -#else - std::random_shuffle(vals_.begin(), vals_.end()); -#endif - - counter_ = 0; - } - - /** - * Return a distinct random integer in greater or equal to 0 and less - * than 'n' on each call. It should be called maximum 'n' times. - * Returns: a random integer - */ - int next() - { - if (counter_ == size_) { - return -1; - } - else { - return vals_[counter_++]; - } - } -}; - -} - -//! @endcond - -#endif //OPENCV_FLANN_RANDOM_H diff --git a/include/opencv2/flann/result_set.h b/include/opencv2/flann/result_set.h deleted file mode 100755 index 47ad231..0000000 --- a/include/opencv2/flann/result_set.h +++ /dev/null @@ -1,548 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_RESULTSET_H -#define OPENCV_FLANN_RESULTSET_H - -//! @cond IGNORED - -#include -#include -#include -#include -#include -#include - -namespace cvflann -{ - -/* This record represents a branch point when finding neighbors in - the tree. It contains a record of the minimum distance to the query - point, as well as the node at which the search resumes. - */ - -template -struct BranchStruct -{ - T node; /* Tree node at which search resumes */ - DistanceType mindist; /* Minimum distance to query for all nodes below. */ - - BranchStruct() {} - BranchStruct(const T& aNode, DistanceType dist) : node(aNode), mindist(dist) {} - - bool operator<(const BranchStruct& rhs) const - { - return mindist -class ResultSet -{ -public: - virtual ~ResultSet() {} - - virtual bool full() const = 0; - - virtual void addPoint(DistanceType dist, int index) = 0; - - virtual DistanceType worstDist() const = 0; - -}; - -/** - * KNNSimpleResultSet does not ensure that the element it holds are unique. - * Is used in those cases where the nearest neighbour algorithm used does not - * attempt to insert the same element multiple times. - */ -template -class KNNSimpleResultSet : public ResultSet -{ - int* indices; - DistanceType* dists; - int capacity; - int count; - DistanceType worst_distance_; - -public: - KNNSimpleResultSet(int capacity_) : capacity(capacity_), count(0) - { - } - - void init(int* indices_, DistanceType* dists_) - { - indices = indices_; - dists = dists_; - count = 0; - worst_distance_ = (std::numeric_limits::max)(); - dists[capacity-1] = worst_distance_; - } - - size_t size() const - { - return count; - } - - bool full() const CV_OVERRIDE - { - return count == capacity; - } - - - void addPoint(DistanceType dist, int index) CV_OVERRIDE - { - if (dist >= worst_distance_) return; - int i; - for (i=count; i>0; --i) { -#ifdef FLANN_FIRST_MATCH - if ( (dists[i-1]>dist) || ((dist==dists[i-1])&&(indices[i-1]>index)) ) -#else - if (dists[i-1]>dist) -#endif - { - if (i -class KNNResultSet : public ResultSet -{ - int* indices; - DistanceType* dists; - int capacity; - int count; - DistanceType worst_distance_; - -public: - KNNResultSet(int capacity_) - : indices(NULL), dists(NULL), capacity(capacity_), count(0), worst_distance_(0) - { - } - - void init(int* indices_, DistanceType* dists_) - { - indices = indices_; - dists = dists_; - count = 0; - worst_distance_ = (std::numeric_limits::max)(); - dists[capacity-1] = worst_distance_; - } - - size_t size() const - { - return count; - } - - bool full() const CV_OVERRIDE - { - return count == capacity; - } - - - void addPoint(DistanceType dist, int index) CV_OVERRIDE - { - CV_DbgAssert(indices); - CV_DbgAssert(dists); - if (dist >= worst_distance_) return; - int i; - for (i = count; i > 0; --i) { -#ifdef FLANN_FIRST_MATCH - if ( (dists[i-1]<=dist) && ((dist!=dists[i-1])||(indices[i-1]<=index)) ) -#else - if (dists[i-1]<=dist) -#endif - { - // Check for duplicate indices - for (int j = i; dists[j] == dist && j--;) { - if (indices[j] == index) { - return; - } - } - break; - } - } - - if (count < capacity) ++count; - for (int j = count-1; j > i; --j) { - dists[j] = dists[j-1]; - indices[j] = indices[j-1]; - } - dists[i] = dist; - indices[i] = index; - worst_distance_ = dists[capacity-1]; - } - - DistanceType worstDist() const CV_OVERRIDE - { - return worst_distance_; - } -}; - - -/** - * A result-set class used when performing a radius based search. - */ -template -class RadiusResultSet : public ResultSet -{ - DistanceType radius; - int* indices; - DistanceType* dists; - size_t capacity; - size_t count; - -public: - RadiusResultSet(DistanceType radius_, int* indices_, DistanceType* dists_, int capacity_) : - radius(radius_), indices(indices_), dists(dists_), capacity(capacity_) - { - init(); - } - - ~RadiusResultSet() - { - } - - void init() - { - count = 0; - } - - size_t size() const - { - return count; - } - - bool full() const - { - return true; - } - - void addPoint(DistanceType dist, int index) - { - if (dist0)&&(count < capacity)) { - dists[count] = dist; - indices[count] = index; - } - count++; - } - } - - DistanceType worstDist() const - { - return radius; - } - -}; - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -/** Class that holds the k NN neighbors - * Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays - */ -template -class UniqueResultSet : public ResultSet -{ -public: - struct DistIndex - { - DistIndex(DistanceType dist, unsigned int index) : - dist_(dist), index_(index) - { - } - bool operator<(const DistIndex dist_index) const - { - return (dist_ < dist_index.dist_) || ((dist_ == dist_index.dist_) && index_ < dist_index.index_); - } - DistanceType dist_; - unsigned int index_; - }; - - /** Default constructor */ - UniqueResultSet() : - is_full_(false), worst_distance_(std::numeric_limits::max()) - { - } - - /** Check the status of the set - * @return true if we have k NN - */ - inline bool full() const CV_OVERRIDE - { - return is_full_; - } - - /** Remove all elements in the set - */ - virtual void clear() = 0; - - /** Copy the set to two C arrays - * @param indices pointer to a C array of indices - * @param dist pointer to a C array of distances - * @param n_neighbors the number of neighbors to copy - */ - virtual void copy(int* indices, DistanceType* dist, int n_neighbors = -1) const - { - if (n_neighbors < 0) { - for (typename std::set::const_iterator dist_index = dist_indices_.begin(), dist_index_end = - dist_indices_.end(); dist_index != dist_index_end; ++dist_index, ++indices, ++dist) { - *indices = dist_index->index_; - *dist = dist_index->dist_; - } - } - else { - int i = 0; - for (typename std::set::const_iterator dist_index = dist_indices_.begin(), dist_index_end = - dist_indices_.end(); (dist_index != dist_index_end) && (i < n_neighbors); ++dist_index, ++indices, ++dist, ++i) { - *indices = dist_index->index_; - *dist = dist_index->dist_; - } - } - } - - /** Copy the set to two C arrays but sort it according to the distance first - * @param indices pointer to a C array of indices - * @param dist pointer to a C array of distances - * @param n_neighbors the number of neighbors to copy - */ - virtual void sortAndCopy(int* indices, DistanceType* dist, int n_neighbors = -1) const - { - copy(indices, dist, n_neighbors); - } - - /** The number of neighbors in the set - * @return - */ - size_t size() const - { - return dist_indices_.size(); - } - - /** The distance of the furthest neighbor - * If we don't have enough neighbors, it returns the max possible value - * @return - */ - inline DistanceType worstDist() const CV_OVERRIDE - { - return worst_distance_; - } -protected: - /** Flag to say if the set is full */ - bool is_full_; - - /** The worst distance found so far */ - DistanceType worst_distance_; - - /** The best candidates so far */ - std::set dist_indices_; -}; - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -/** Class that holds the k NN neighbors - * Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays - */ -template -class KNNUniqueResultSet : public UniqueResultSet -{ -public: - /** Constructor - * @param capacity the number of neighbors to store at max - */ - KNNUniqueResultSet(unsigned int capacity) : capacity_(capacity) - { - this->is_full_ = false; - this->clear(); - } - - /** Add a possible candidate to the best neighbors - * @param dist distance for that neighbor - * @param index index of that neighbor - */ - inline void addPoint(DistanceType dist, int index) CV_OVERRIDE - { - // Don't do anything if we are worse than the worst - if (dist >= worst_distance_) return; - dist_indices_.insert(DistIndex(dist, index)); - - if (is_full_) { - if (dist_indices_.size() > capacity_) { - dist_indices_.erase(*dist_indices_.rbegin()); - worst_distance_ = dist_indices_.rbegin()->dist_; - } - } - else if (dist_indices_.size() == capacity_) { - is_full_ = true; - worst_distance_ = dist_indices_.rbegin()->dist_; - } - } - - /** Remove all elements in the set - */ - void clear() CV_OVERRIDE - { - dist_indices_.clear(); - worst_distance_ = std::numeric_limits::max(); - is_full_ = false; - } - -protected: - typedef typename UniqueResultSet::DistIndex DistIndex; - using UniqueResultSet::is_full_; - using UniqueResultSet::worst_distance_; - using UniqueResultSet::dist_indices_; - - /** The number of neighbors to keep */ - unsigned int capacity_; -}; - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -/** Class that holds the radius nearest neighbors - * It is more accurate than RadiusResult as it is not limited in the number of neighbors - */ -template -class RadiusUniqueResultSet : public UniqueResultSet -{ -public: - /** Constructor - * @param radius the maximum distance of a neighbor - */ - RadiusUniqueResultSet(DistanceType radius) : - radius_(radius) - { - is_full_ = true; - } - - /** Add a possible candidate to the best neighbors - * @param dist distance for that neighbor - * @param index index of that neighbor - */ - void addPoint(DistanceType dist, int index) CV_OVERRIDE - { - if (dist <= radius_) dist_indices_.insert(DistIndex(dist, index)); - } - - /** Remove all elements in the set - */ - inline void clear() CV_OVERRIDE - { - dist_indices_.clear(); - } - - - /** Check the status of the set - * @return alwys false - */ - inline bool full() const CV_OVERRIDE - { - return true; - } - - /** The distance of the furthest neighbor - * If we don't have enough neighbors, it returns the max possible value - * @return - */ - inline DistanceType worstDist() const CV_OVERRIDE - { - return radius_; - } -private: - typedef typename UniqueResultSet::DistIndex DistIndex; - using UniqueResultSet::dist_indices_; - using UniqueResultSet::is_full_; - - /** The furthest distance a neighbor can be */ - DistanceType radius_; -}; - -//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// - -/** Class that holds the k NN neighbors within a radius distance - */ -template -class KNNRadiusUniqueResultSet : public KNNUniqueResultSet -{ -public: - /** Constructor - * @param capacity the number of neighbors to store at max - * @param radius the maximum distance of a neighbor - */ - KNNRadiusUniqueResultSet(unsigned int capacity, DistanceType radius) - { - this->capacity_ = capacity; - this->radius_ = radius; - this->dist_indices_.reserve(capacity_); - this->clear(); - } - - /** Remove all elements in the set - */ - void clear() - { - dist_indices_.clear(); - worst_distance_ = radius_; - is_full_ = false; - } -private: - using KNNUniqueResultSet::dist_indices_; - using KNNUniqueResultSet::is_full_; - using KNNUniqueResultSet::worst_distance_; - - /** The maximum number of neighbors to consider */ - unsigned int capacity_; - - /** The maximum distance of a neighbor */ - DistanceType radius_; -}; -} - -//! @endcond - -#endif //OPENCV_FLANN_RESULTSET_H diff --git a/include/opencv2/flann/sampling.h b/include/opencv2/flann/sampling.h deleted file mode 100755 index 4e452b9..0000000 --- a/include/opencv2/flann/sampling.h +++ /dev/null @@ -1,84 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - - -#ifndef OPENCV_FLANN_SAMPLING_H_ -#define OPENCV_FLANN_SAMPLING_H_ - -//! @cond IGNORED - -#include "matrix.h" -#include "random.h" - -namespace cvflann -{ - -template -Matrix random_sample(Matrix& srcMatrix, long size, bool remove = false) -{ - Matrix newSet(new T[size * srcMatrix.cols], size,srcMatrix.cols); - - T* src,* dest; - for (long i=0; i -Matrix random_sample(const Matrix& srcMatrix, size_t size) -{ - UniqueRandom rand((int)srcMatrix.rows); - Matrix newSet(new T[size * srcMatrix.cols], size,srcMatrix.cols); - - T* src,* dest; - for (size_t i=0; i -#include - -#include "general.h" -#include "nn_index.h" - -#ifdef FLANN_SIGNATURE_ -#undef FLANN_SIGNATURE_ -#endif -#define FLANN_SIGNATURE_ "FLANN_INDEX" - -namespace cvflann -{ - -template -struct Datatype {}; -template<> -struct Datatype { static flann_datatype_t type() { return FLANN_INT8; } }; -template<> -struct Datatype { static flann_datatype_t type() { return FLANN_INT16; } }; -template<> -struct Datatype { static flann_datatype_t type() { return FLANN_INT32; } }; -template<> -struct Datatype { static flann_datatype_t type() { return FLANN_UINT8; } }; -template<> -struct Datatype { static flann_datatype_t type() { return FLANN_UINT16; } }; -template<> -struct Datatype { static flann_datatype_t type() { return FLANN_UINT32; } }; -template<> -struct Datatype { static flann_datatype_t type() { return FLANN_FLOAT32; } }; -template<> -struct Datatype { static flann_datatype_t type() { return FLANN_FLOAT64; } }; - - -/** - * Structure representing the index header. - */ -struct IndexHeader -{ - char signature[16]; - char version[16]; - flann_datatype_t data_type; - flann_algorithm_t index_type; - size_t rows; - size_t cols; -}; - -/** - * Saves index header to stream - * - * @param stream - Stream to save to - * @param index - The index to save - */ -template -void save_header(FILE* stream, const NNIndex& index) -{ - IndexHeader header; - memset(header.signature, 0, sizeof(header.signature)); - strcpy(header.signature, FLANN_SIGNATURE_); - memset(header.version, 0, sizeof(header.version)); - strcpy(header.version, FLANN_VERSION_); - header.data_type = Datatype::type(); - header.index_type = index.getType(); - header.rows = index.size(); - header.cols = index.veclen(); - - std::fwrite(&header, sizeof(header),1,stream); -} - - -/** - * - * @param stream - Stream to load from - * @return Index header - */ -inline IndexHeader load_header(FILE* stream) -{ - IndexHeader header; - size_t read_size = fread(&header,sizeof(header),1,stream); - - if (read_size!=(size_t)1) { - FLANN_THROW(cv::Error::StsError, "Invalid index file, cannot read"); - } - - if (strcmp(header.signature,FLANN_SIGNATURE_)!=0) { - FLANN_THROW(cv::Error::StsError, "Invalid index file, wrong signature"); - } - - return header; - -} - - -template -void save_value(FILE* stream, const T& value, size_t count = 1) -{ - fwrite(&value, sizeof(value),count, stream); -} - -template -void save_value(FILE* stream, const cvflann::Matrix& value) -{ - fwrite(&value, sizeof(value),1, stream); - fwrite(value.data, sizeof(T),value.rows*value.cols, stream); -} - -template -void save_value(FILE* stream, const std::vector& value) -{ - size_t size = value.size(); - fwrite(&size, sizeof(size_t), 1, stream); - fwrite(&value[0], sizeof(T), size, stream); -} - -template -void load_value(FILE* stream, T& value, size_t count = 1) -{ - size_t read_cnt = fread(&value, sizeof(value), count, stream); - if (read_cnt != count) { - FLANN_THROW(cv::Error::StsParseError, "Cannot read from file"); - } -} - -template -void load_value(FILE* stream, cvflann::Matrix& value) -{ - size_t read_cnt = fread(&value, sizeof(value), 1, stream); - if (read_cnt != 1) { - FLANN_THROW(cv::Error::StsParseError, "Cannot read from file"); - } - value.data = new T[value.rows*value.cols]; - read_cnt = fread(value.data, sizeof(T), value.rows*value.cols, stream); - if (read_cnt != (size_t)(value.rows*value.cols)) { - FLANN_THROW(cv::Error::StsParseError, "Cannot read from file"); - } -} - - -template -void load_value(FILE* stream, std::vector& value) -{ - size_t size; - size_t read_cnt = fread(&size, sizeof(size_t), 1, stream); - if (read_cnt!=1) { - FLANN_THROW(cv::Error::StsError, "Cannot read from file"); - } - value.resize(size); - read_cnt = fread(&value[0], sizeof(T), size, stream); - if (read_cnt != size) { - FLANN_THROW(cv::Error::StsError, "Cannot read from file"); - } -} - -} - -//! @endcond - -#endif /* OPENCV_FLANN_SAVING_H_ */ diff --git a/include/opencv2/flann/simplex_downhill.h b/include/opencv2/flann/simplex_downhill.h deleted file mode 100755 index 0297014..0000000 --- a/include/opencv2/flann/simplex_downhill.h +++ /dev/null @@ -1,190 +0,0 @@ -/*********************************************************************** - * Software License Agreement (BSD License) - * - * Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved. - * Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved. - * - * THE BSD LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR - * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES - * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, - * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF - * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - *************************************************************************/ - -#ifndef OPENCV_FLANN_SIMPLEX_DOWNHILL_H_ -#define OPENCV_FLANN_SIMPLEX_DOWNHILL_H_ - -//! @cond IGNORED - -namespace cvflann -{ - -/** - Adds val to array vals (and point to array points) and keeping the arrays sorted by vals. - */ -template -void addValue(int pos, float val, float* vals, T* point, T* points, int n) -{ - vals[pos] = val; - for (int i=0; i0 && vals[j] -float optimizeSimplexDownhill(T* points, int n, F func, float* vals = NULL ) -{ - const int MAX_ITERATIONS = 10; - - CV_DbgAssert(n>0); - - T* p_o = new T[n]; - T* p_r = new T[n]; - T* p_e = new T[n]; - - int alpha = 1; - - int iterations = 0; - - bool ownVals = false; - if (vals == NULL) { - ownVals = true; - vals = new float[n+1]; - for (int i=0; i MAX_ITERATIONS) break; - - // compute average of simplex points (except the highest point) - for (int j=0; j=vals[0])&&(val_r=vals[n]) { - for (int i=0; i -#include "opencv2/core.hpp" -#include "opencv2/core/utility.hpp" - -namespace cvflann -{ - -/** - * A start-stop timer class. - * - * Can be used to time portions of code. - */ -class StartStopTimer -{ - int64 startTime; - -public: - /** - * Value of the timer. - */ - double value; - - - /** - * Constructor. - */ - StartStopTimer() - : startTime(0) - { - reset(); - } - - /** - * Starts the timer. - */ - void start() - { - startTime = cv::getTickCount(); - } - - /** - * Stops the timer and updates timer value. - */ - void stop() - { - int64 stopTime = cv::getTickCount(); - value += ( (double)stopTime - startTime) / cv::getTickFrequency(); - } - - /** - * Resets the timer value to 0. - */ - void reset() - { - value = 0; - } - -}; - -} - -//! @endcond - -#endif // FLANN_TIMER_H diff --git a/include/opencv2/highgui.hpp b/include/opencv2/highgui.hpp deleted file mode 100755 index 6a8c598..0000000 --- a/include/opencv2/highgui.hpp +++ /dev/null @@ -1,857 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_HIGHGUI_HPP -#define OPENCV_HIGHGUI_HPP - -#include "opencv2/core.hpp" -#ifdef HAVE_OPENCV_IMGCODECS -#include "opencv2/imgcodecs.hpp" -#endif -#ifdef HAVE_OPENCV_VIDEOIO -#include "opencv2/videoio.hpp" -#endif - -/** -@defgroup highgui High-level GUI - -While OpenCV was designed for use in full-scale applications and can be used within functionally -rich UI frameworks (such as Qt\*, WinForms\*, or Cocoa\*) or without any UI at all, sometimes there -it is required to try functionality quickly and visualize the results. This is what the HighGUI -module has been designed for. - -It provides easy interface to: - -- Create and manipulate windows that can display images and "remember" their content (no need to - handle repaint events from OS). -- Add trackbars to the windows, handle simple mouse events as well as keyboard commands. - -@{ - @defgroup highgui_window_flags Flags related creating and manipulating HighGUI windows and mouse events - @defgroup highgui_opengl OpenGL support - @defgroup highgui_qt Qt New Functions - - ![image](pics/qtgui.png) - - This figure explains new functionality implemented with Qt\* GUI. The new GUI provides a statusbar, - a toolbar, and a control panel. The control panel can have trackbars and buttonbars attached to it. - If you cannot see the control panel, press Ctrl+P or right-click any Qt window and select **Display - properties window**. - - - To attach a trackbar, the window name parameter must be NULL. - - - To attach a buttonbar, a button must be created. If the last bar attached to the control panel - is a buttonbar, the new button is added to the right of the last button. If the last bar - attached to the control panel is a trackbar, or the control panel is empty, a new buttonbar is - created. Then, a new button is attached to it. - - See below the example used to generate the figure: - @code - int main(int argc, char *argv[]) - { - - int value = 50; - int value2 = 0; - - - namedWindow("main1",WINDOW_NORMAL); - namedWindow("main2",WINDOW_AUTOSIZE | WINDOW_GUI_NORMAL); - createTrackbar( "track1", "main1", &value, 255, NULL); - - String nameb1 = "button1"; - String nameb2 = "button2"; - - createButton(nameb1,callbackButton,&nameb1,QT_CHECKBOX,1); - createButton(nameb2,callbackButton,NULL,QT_CHECKBOX,0); - createTrackbar( "track2", NULL, &value2, 255, NULL); - createButton("button5",callbackButton1,NULL,QT_RADIOBOX,0); - createButton("button6",callbackButton2,NULL,QT_RADIOBOX,1); - - setMouseCallback( "main2",on_mouse,NULL ); - - Mat img1 = imread("files/flower.jpg"); - VideoCapture video; - video.open("files/hockey.avi"); - - Mat img2,img3; - - while( waitKey(33) != 27 ) - { - img1.convertTo(img2,-1,1,value); - video >> img3; - - imshow("main1",img2); - imshow("main2",img3); - } - - destroyAllWindows(); - - return 0; - } - @endcode - - - @defgroup highgui_winrt WinRT support - - This figure explains new functionality implemented with WinRT GUI. The new GUI provides an Image control, - and a slider panel. Slider panel holds trackbars attached to it. - - Sliders are attached below the image control. Every new slider is added below the previous one. - - See below the example used to generate the figure: - @code - void sample_app::MainPage::ShowWindow() - { - static cv::String windowName("sample"); - cv::winrt_initContainer(this->cvContainer); - cv::namedWindow(windowName); // not required - - cv::Mat image = cv::imread("Assets/sample.jpg"); - cv::Mat converted = cv::Mat(image.rows, image.cols, CV_8UC4); - cv::cvtColor(image, converted, COLOR_BGR2BGRA); - cv::imshow(windowName, converted); // this will create window if it hasn't been created before - - int state = 42; - cv::TrackbarCallback callback = [](int pos, void* userdata) - { - if (pos == 0) { - cv::destroyWindow(windowName); - } - }; - cv::TrackbarCallback callbackTwin = [](int pos, void* userdata) - { - if (pos >= 70) { - cv::destroyAllWindows(); - } - }; - cv::createTrackbar("Sample trackbar", windowName, &state, 100, callback); - cv::createTrackbar("Twin brother", windowName, &state, 100, callbackTwin); - } - @endcode - - @defgroup highgui_c C API -@} -*/ - -///////////////////////// graphical user interface ////////////////////////// -namespace cv -{ - -//! @addtogroup highgui -//! @{ - -//! @addtogroup highgui_window_flags -//! @{ - -//! Flags for cv::namedWindow -enum WindowFlags { - WINDOW_NORMAL = 0x00000000, //!< the user can resize the window (no constraint) / also use to switch a fullscreen window to a normal size. - WINDOW_AUTOSIZE = 0x00000001, //!< the user cannot resize the window, the size is constrainted by the image displayed. - WINDOW_OPENGL = 0x00001000, //!< window with opengl support. - - WINDOW_FULLSCREEN = 1, //!< change the window to fullscreen. - WINDOW_FREERATIO = 0x00000100, //!< the image expends as much as it can (no ratio constraint). - WINDOW_KEEPRATIO = 0x00000000, //!< the ratio of the image is respected. - WINDOW_GUI_EXPANDED=0x00000000, //!< status bar and tool bar - WINDOW_GUI_NORMAL = 0x00000010, //!< old fashious way - }; - -//! Flags for cv::setWindowProperty / cv::getWindowProperty -enum WindowPropertyFlags { - WND_PROP_FULLSCREEN = 0, //!< fullscreen property (can be WINDOW_NORMAL or WINDOW_FULLSCREEN). - WND_PROP_AUTOSIZE = 1, //!< autosize property (can be WINDOW_NORMAL or WINDOW_AUTOSIZE). - WND_PROP_ASPECT_RATIO = 2, //!< window's aspect ration (can be set to WINDOW_FREERATIO or WINDOW_KEEPRATIO). - WND_PROP_OPENGL = 3, //!< opengl support. - WND_PROP_VISIBLE = 4, //!< checks whether the window exists and is visible - WND_PROP_TOPMOST = 5 //!< property to toggle normal window being topmost or not - }; - -//! Mouse Events see cv::MouseCallback -enum MouseEventTypes { - EVENT_MOUSEMOVE = 0, //!< indicates that the mouse pointer has moved over the window. - EVENT_LBUTTONDOWN = 1, //!< indicates that the left mouse button is pressed. - EVENT_RBUTTONDOWN = 2, //!< indicates that the right mouse button is pressed. - EVENT_MBUTTONDOWN = 3, //!< indicates that the middle mouse button is pressed. - EVENT_LBUTTONUP = 4, //!< indicates that left mouse button is released. - EVENT_RBUTTONUP = 5, //!< indicates that right mouse button is released. - EVENT_MBUTTONUP = 6, //!< indicates that middle mouse button is released. - EVENT_LBUTTONDBLCLK = 7, //!< indicates that left mouse button is double clicked. - EVENT_RBUTTONDBLCLK = 8, //!< indicates that right mouse button is double clicked. - EVENT_MBUTTONDBLCLK = 9, //!< indicates that middle mouse button is double clicked. - EVENT_MOUSEWHEEL = 10,//!< positive and negative values mean forward and backward scrolling, respectively. - EVENT_MOUSEHWHEEL = 11 //!< positive and negative values mean right and left scrolling, respectively. - }; - -//! Mouse Event Flags see cv::MouseCallback -enum MouseEventFlags { - EVENT_FLAG_LBUTTON = 1, //!< indicates that the left mouse button is down. - EVENT_FLAG_RBUTTON = 2, //!< indicates that the right mouse button is down. - EVENT_FLAG_MBUTTON = 4, //!< indicates that the middle mouse button is down. - EVENT_FLAG_CTRLKEY = 8, //!< indicates that CTRL Key is pressed. - EVENT_FLAG_SHIFTKEY = 16,//!< indicates that SHIFT Key is pressed. - EVENT_FLAG_ALTKEY = 32 //!< indicates that ALT Key is pressed. - }; - -//! @} highgui_window_flags - -//! @addtogroup highgui_qt -//! @{ - -//! Qt font weight -enum QtFontWeights { - QT_FONT_LIGHT = 25, //!< Weight of 25 - QT_FONT_NORMAL = 50, //!< Weight of 50 - QT_FONT_DEMIBOLD = 63, //!< Weight of 63 - QT_FONT_BOLD = 75, //!< Weight of 75 - QT_FONT_BLACK = 87 //!< Weight of 87 - }; - -//! Qt font style -enum QtFontStyles { - QT_STYLE_NORMAL = 0, //!< Normal font. - QT_STYLE_ITALIC = 1, //!< Italic font. - QT_STYLE_OBLIQUE = 2 //!< Oblique font. - }; - -//! Qt "button" type -enum QtButtonTypes { - QT_PUSH_BUTTON = 0, //!< Push button. - QT_CHECKBOX = 1, //!< Checkbox button. - QT_RADIOBOX = 2, //!< Radiobox button. - QT_NEW_BUTTONBAR = 1024 //!< Button should create a new buttonbar - }; - -//! @} highgui_qt - -/** @brief Callback function for mouse events. see cv::setMouseCallback -@param event one of the cv::MouseEventTypes constants. -@param x The x-coordinate of the mouse event. -@param y The y-coordinate of the mouse event. -@param flags one of the cv::MouseEventFlags constants. -@param userdata The optional parameter. - */ -typedef void (*MouseCallback)(int event, int x, int y, int flags, void* userdata); - -/** @brief Callback function for Trackbar see cv::createTrackbar -@param pos current position of the specified trackbar. -@param userdata The optional parameter. - */ -typedef void (*TrackbarCallback)(int pos, void* userdata); - -/** @brief Callback function defined to be called every frame. See cv::setOpenGlDrawCallback -@param userdata The optional parameter. - */ -typedef void (*OpenGlDrawCallback)(void* userdata); - -/** @brief Callback function for a button created by cv::createButton -@param state current state of the button. It could be -1 for a push button, 0 or 1 for a check/radio box button. -@param userdata The optional parameter. - */ -typedef void (*ButtonCallback)(int state, void* userdata); - -/** @brief Creates a window. - -The function namedWindow creates a window that can be used as a placeholder for images and -trackbars. Created windows are referred to by their names. - -If a window with the same name already exists, the function does nothing. - -You can call cv::destroyWindow or cv::destroyAllWindows to close the window and de-allocate any associated -memory usage. For a simple program, you do not really have to call these functions because all the -resources and windows of the application are closed automatically by the operating system upon exit. - -@note - -Qt backend supports additional flags: - - **WINDOW_NORMAL or WINDOW_AUTOSIZE:** WINDOW_NORMAL enables you to resize the - window, whereas WINDOW_AUTOSIZE adjusts automatically the window size to fit the - displayed image (see imshow ), and you cannot change the window size manually. - - **WINDOW_FREERATIO or WINDOW_KEEPRATIO:** WINDOW_FREERATIO adjusts the image - with no respect to its ratio, whereas WINDOW_KEEPRATIO keeps the image ratio. - - **WINDOW_GUI_NORMAL or WINDOW_GUI_EXPANDED:** WINDOW_GUI_NORMAL is the old way to draw the window - without statusbar and toolbar, whereas WINDOW_GUI_EXPANDED is a new enhanced GUI. -By default, flags == WINDOW_AUTOSIZE | WINDOW_KEEPRATIO | WINDOW_GUI_EXPANDED - -@param winname Name of the window in the window caption that may be used as a window identifier. -@param flags Flags of the window. The supported flags are: (cv::WindowFlags) - */ -CV_EXPORTS_W void namedWindow(const String& winname, int flags = WINDOW_AUTOSIZE); - -/** @brief Destroys the specified window. - -The function destroyWindow destroys the window with the given name. - -@param winname Name of the window to be destroyed. - */ -CV_EXPORTS_W void destroyWindow(const String& winname); - -/** @brief Destroys all of the HighGUI windows. - -The function destroyAllWindows destroys all of the opened HighGUI windows. - */ -CV_EXPORTS_W void destroyAllWindows(); - -CV_EXPORTS_W int startWindowThread(); - -/** @brief Similar to #waitKey, but returns full key code. - -@note - -Key code is implementation specific and depends on used backend: QT/GTK/Win32/etc - -*/ -CV_EXPORTS_W int waitKeyEx(int delay = 0); - -/** @brief Waits for a pressed key. - -The function waitKey waits for a key event infinitely (when \f$\texttt{delay}\leq 0\f$ ) or for delay -milliseconds, when it is positive. Since the OS has a minimum time between switching threads, the -function will not wait exactly delay ms, it will wait at least delay ms, depending on what else is -running on your computer at that time. It returns the code of the pressed key or -1 if no key was -pressed before the specified time had elapsed. - -@note - -This function is the only method in HighGUI that can fetch and handle events, so it needs to be -called periodically for normal event processing unless HighGUI is used within an environment that -takes care of event processing. - -@note - -The function only works if there is at least one HighGUI window created and the window is active. -If there are several HighGUI windows, any of them can be active. - -@param delay Delay in milliseconds. 0 is the special value that means "forever". - */ -CV_EXPORTS_W int waitKey(int delay = 0); - -/** @brief Displays an image in the specified window. - -The function imshow displays an image in the specified window. If the window was created with the -cv::WINDOW_AUTOSIZE flag, the image is shown with its original size, however it is still limited by the screen resolution. -Otherwise, the image is scaled to fit the window. The function may scale the image, depending on its depth: - -- If the image is 8-bit unsigned, it is displayed as is. -- If the image is 16-bit unsigned or 32-bit integer, the pixels are divided by 256. That is, the - value range [0,255\*256] is mapped to [0,255]. -- If the image is 32-bit or 64-bit floating-point, the pixel values are multiplied by 255. That is, the - value range [0,1] is mapped to [0,255]. - -If window was created with OpenGL support, cv::imshow also support ogl::Buffer , ogl::Texture2D and -cuda::GpuMat as input. - -If the window was not created before this function, it is assumed creating a window with cv::WINDOW_AUTOSIZE. - -If you need to show an image that is bigger than the screen resolution, you will need to call namedWindow("", WINDOW_NORMAL) before the imshow. - -@note This function should be followed by cv::waitKey function which displays the image for specified -milliseconds. Otherwise, it won't display the image. For example, **waitKey(0)** will display the window -infinitely until any keypress (it is suitable for image display). **waitKey(25)** will display a frame -for 25 ms, after which display will be automatically closed. (If you put it in a loop to read -videos, it will display the video frame-by-frame) - -@note - -[__Windows Backend Only__] Pressing Ctrl+C will copy the image to the clipboard. - -[__Windows Backend Only__] Pressing Ctrl+S will show a dialog to save the image. - -@param winname Name of the window. -@param mat Image to be shown. - */ -CV_EXPORTS_W void imshow(const String& winname, InputArray mat); - -/** @brief Resizes the window to the specified size - -@note - -- The specified window size is for the image area. Toolbars are not counted. -- Only windows created without cv::WINDOW_AUTOSIZE flag can be resized. - -@param winname Window name. -@param width The new window width. -@param height The new window height. - */ -CV_EXPORTS_W void resizeWindow(const String& winname, int width, int height); - -/** @overload -@param winname Window name. -@param size The new window size. -*/ -CV_EXPORTS_W void resizeWindow(const String& winname, const cv::Size& size); - -/** @brief Moves the window to the specified position - -@param winname Name of the window. -@param x The new x-coordinate of the window. -@param y The new y-coordinate of the window. - */ -CV_EXPORTS_W void moveWindow(const String& winname, int x, int y); - -/** @brief Changes parameters of a window dynamically. - -The function setWindowProperty enables changing properties of a window. - -@param winname Name of the window. -@param prop_id Window property to edit. The supported operation flags are: (cv::WindowPropertyFlags) -@param prop_value New value of the window property. The supported flags are: (cv::WindowFlags) - */ -CV_EXPORTS_W void setWindowProperty(const String& winname, int prop_id, double prop_value); - -/** @brief Updates window title -@param winname Name of the window. -@param title New title. -*/ -CV_EXPORTS_W void setWindowTitle(const String& winname, const String& title); - -/** @brief Provides parameters of a window. - -The function getWindowProperty returns properties of a window. - -@param winname Name of the window. -@param prop_id Window property to retrieve. The following operation flags are available: (cv::WindowPropertyFlags) - -@sa setWindowProperty - */ -CV_EXPORTS_W double getWindowProperty(const String& winname, int prop_id); - -/** @brief Provides rectangle of image in the window. - -The function getWindowImageRect returns the client screen coordinates, width and height of the image rendering area. - -@param winname Name of the window. - -@sa resizeWindow moveWindow - */ -CV_EXPORTS_W Rect getWindowImageRect(const String& winname); - -/** @example samples/cpp/create_mask.cpp -This program demonstrates using mouse events and how to make and use a mask image (black and white) . -*/ -/** @brief Sets mouse handler for the specified window - -@param winname Name of the window. -@param onMouse Callback function for mouse events. See OpenCV samples on how to specify and use the callback. -@param userdata The optional parameter passed to the callback. - */ -CV_EXPORTS void setMouseCallback(const String& winname, MouseCallback onMouse, void* userdata = 0); - -/** @brief Gets the mouse-wheel motion delta, when handling mouse-wheel events cv::EVENT_MOUSEWHEEL and -cv::EVENT_MOUSEHWHEEL. - -For regular mice with a scroll-wheel, delta will be a multiple of 120. The value 120 corresponds to -a one notch rotation of the wheel or the threshold for action to be taken and one such action should -occur for each delta. Some high-precision mice with higher-resolution freely-rotating wheels may -generate smaller values. - -For cv::EVENT_MOUSEWHEEL positive and negative values mean forward and backward scrolling, -respectively. For cv::EVENT_MOUSEHWHEEL, where available, positive and negative values mean right and -left scrolling, respectively. - -@note - -Mouse-wheel events are currently supported only on Windows. - -@param flags The mouse callback flags parameter. - */ -CV_EXPORTS int getMouseWheelDelta(int flags); - -/** @brief Allows users to select a ROI on the given image. - -The function creates a window and allows users to select a ROI using the mouse. -Controls: use `space` or `enter` to finish selection, use key `c` to cancel selection (function will return the zero cv::Rect). - -@param windowName name of the window where selection process will be shown. -@param img image to select a ROI. -@param showCrosshair if true crosshair of selection rectangle will be shown. -@param fromCenter if true center of selection will match initial mouse position. In opposite case a corner of -selection rectangle will correspont to the initial mouse position. -@return selected ROI or empty rect if selection canceled. - -@note The function sets it's own mouse callback for specified window using cv::setMouseCallback(windowName, ...). -After finish of work an empty callback will be set for the used window. - */ -CV_EXPORTS_W Rect selectROI(const String& windowName, InputArray img, bool showCrosshair = true, bool fromCenter = false); - -/** @overload - */ -CV_EXPORTS_W Rect selectROI(InputArray img, bool showCrosshair = true, bool fromCenter = false); - -/** @brief Allows users to select multiple ROIs on the given image. - -The function creates a window and allows users to select multiple ROIs using the mouse. -Controls: use `space` or `enter` to finish current selection and start a new one, -use `esc` to terminate multiple ROI selection process. - -@param windowName name of the window where selection process will be shown. -@param img image to select a ROI. -@param boundingBoxes selected ROIs. -@param showCrosshair if true crosshair of selection rectangle will be shown. -@param fromCenter if true center of selection will match initial mouse position. In opposite case a corner of -selection rectangle will correspont to the initial mouse position. - -@note The function sets it's own mouse callback for specified window using cv::setMouseCallback(windowName, ...). -After finish of work an empty callback will be set for the used window. - */ -CV_EXPORTS_W void selectROIs(const String& windowName, InputArray img, - CV_OUT std::vector& boundingBoxes, bool showCrosshair = true, bool fromCenter = false); - -/** @brief Creates a trackbar and attaches it to the specified window. - -The function createTrackbar creates a trackbar (a slider or range control) with the specified name -and range, assigns a variable value to be a position synchronized with the trackbar and specifies -the callback function onChange to be called on the trackbar position change. The created trackbar is -displayed in the specified window winname. - -@note - -[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar should be attached to the -control panel. - -Clicking the label of each trackbar enables editing the trackbar values manually. - -@param trackbarname Name of the created trackbar. -@param winname Name of the window that will be used as a parent of the created trackbar. -@param value Optional pointer to an integer variable whose value reflects the position of the -slider. Upon creation, the slider position is defined by this variable. -@param count Maximal position of the slider. The minimal position is always 0. -@param onChange Pointer to the function to be called every time the slider changes position. This -function should be prototyped as void Foo(int,void\*); , where the first parameter is the trackbar -position and the second parameter is the user data (see the next parameter). If the callback is -the NULL pointer, no callbacks are called, but only value is updated. -@param userdata User data that is passed as is to the callback. It can be used to handle trackbar -events without using global variables. - */ -CV_EXPORTS int createTrackbar(const String& trackbarname, const String& winname, - int* value, int count, - TrackbarCallback onChange = 0, - void* userdata = 0); - -/** @brief Returns the trackbar position. - -The function returns the current position of the specified trackbar. - -@note - -[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control -panel. - -@param trackbarname Name of the trackbar. -@param winname Name of the window that is the parent of the trackbar. - */ -CV_EXPORTS_W int getTrackbarPos(const String& trackbarname, const String& winname); - -/** @brief Sets the trackbar position. - -The function sets the position of the specified trackbar in the specified window. - -@note - -[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control -panel. - -@param trackbarname Name of the trackbar. -@param winname Name of the window that is the parent of trackbar. -@param pos New position. - */ -CV_EXPORTS_W void setTrackbarPos(const String& trackbarname, const String& winname, int pos); - -/** @brief Sets the trackbar maximum position. - -The function sets the maximum position of the specified trackbar in the specified window. - -@note - -[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control -panel. - -@param trackbarname Name of the trackbar. -@param winname Name of the window that is the parent of trackbar. -@param maxval New maximum position. - */ -CV_EXPORTS_W void setTrackbarMax(const String& trackbarname, const String& winname, int maxval); - -/** @brief Sets the trackbar minimum position. - -The function sets the minimum position of the specified trackbar in the specified window. - -@note - -[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control -panel. - -@param trackbarname Name of the trackbar. -@param winname Name of the window that is the parent of trackbar. -@param minval New minimum position. - */ -CV_EXPORTS_W void setTrackbarMin(const String& trackbarname, const String& winname, int minval); - -//! @addtogroup highgui_opengl OpenGL support -//! @{ - -/** @brief Displays OpenGL 2D texture in the specified window. - -@param winname Name of the window. -@param tex OpenGL 2D texture data. - */ -CV_EXPORTS void imshow(const String& winname, const ogl::Texture2D& tex); - -/** @brief Sets a callback function to be called to draw on top of displayed image. - -The function setOpenGlDrawCallback can be used to draw 3D data on the window. See the example of -callback function below: -@code - void on_opengl(void* param) - { - glLoadIdentity(); - - glTranslated(0.0, 0.0, -1.0); - - glRotatef( 55, 1, 0, 0 ); - glRotatef( 45, 0, 1, 0 ); - glRotatef( 0, 0, 0, 1 ); - - static const int coords[6][4][3] = { - { { +1, -1, -1 }, { -1, -1, -1 }, { -1, +1, -1 }, { +1, +1, -1 } }, - { { +1, +1, -1 }, { -1, +1, -1 }, { -1, +1, +1 }, { +1, +1, +1 } }, - { { +1, -1, +1 }, { +1, -1, -1 }, { +1, +1, -1 }, { +1, +1, +1 } }, - { { -1, -1, -1 }, { -1, -1, +1 }, { -1, +1, +1 }, { -1, +1, -1 } }, - { { +1, -1, +1 }, { -1, -1, +1 }, { -1, -1, -1 }, { +1, -1, -1 } }, - { { -1, -1, +1 }, { +1, -1, +1 }, { +1, +1, +1 }, { -1, +1, +1 } } - }; - - for (int i = 0; i < 6; ++i) { - glColor3ub( i*20, 100+i*10, i*42 ); - glBegin(GL_QUADS); - for (int j = 0; j < 4; ++j) { - glVertex3d(0.2 * coords[i][j][0], 0.2 * coords[i][j][1], 0.2 * coords[i][j][2]); - } - glEnd(); - } - } -@endcode - -@param winname Name of the window. -@param onOpenGlDraw Pointer to the function to be called every frame. This function should be -prototyped as void Foo(void\*) . -@param userdata Pointer passed to the callback function.(__Optional__) - */ -CV_EXPORTS void setOpenGlDrawCallback(const String& winname, OpenGlDrawCallback onOpenGlDraw, void* userdata = 0); - -/** @brief Sets the specified window as current OpenGL context. - -@param winname Name of the window. - */ -CV_EXPORTS void setOpenGlContext(const String& winname); - -/** @brief Force window to redraw its context and call draw callback ( See cv::setOpenGlDrawCallback ). - -@param winname Name of the window. - */ -CV_EXPORTS void updateWindow(const String& winname); - -//! @} highgui_opengl - -//! @addtogroup highgui_qt -//! @{ - -/** @brief QtFont available only for Qt. See cv::fontQt - */ -struct QtFont -{ - const char* nameFont; //!< Name of the font - Scalar color; //!< Color of the font. Scalar(blue_component, green_component, red_component[, alpha_component]) - int font_face; //!< See cv::QtFontStyles - const int* ascii; //!< font data and metrics - const int* greek; - const int* cyrillic; - float hscale, vscale; - float shear; //!< slope coefficient: 0 - normal, >0 - italic - int thickness; //!< See cv::QtFontWeights - float dx; //!< horizontal interval between letters - int line_type; //!< PointSize -}; - -/** @brief Creates the font to draw a text on an image. - -The function fontQt creates a cv::QtFont object. This cv::QtFont is not compatible with putText . - -A basic usage of this function is the following: : -@code - QtFont font = fontQt("Times"); - addText( img1, "Hello World !", Point(50,50), font); -@endcode - -@param nameFont Name of the font. The name should match the name of a system font (such as -*Times*). If the font is not found, a default one is used. -@param pointSize Size of the font. If not specified, equal zero or negative, the point size of the -font is set to a system-dependent default value. Generally, this is 12 points. -@param color Color of the font in BGRA where A = 255 is fully transparent. Use the macro CV_RGB -for simplicity. -@param weight Font weight. Available operation flags are : cv::QtFontWeights You can also specify a positive integer for better control. -@param style Font style. Available operation flags are : cv::QtFontStyles -@param spacing Spacing between characters. It can be negative or positive. - */ -CV_EXPORTS QtFont fontQt(const String& nameFont, int pointSize = -1, - Scalar color = Scalar::all(0), int weight = QT_FONT_NORMAL, - int style = QT_STYLE_NORMAL, int spacing = 0); - -/** @brief Draws a text on the image. - -The function addText draws *text* on the image *img* using a specific font *font* (see example cv::fontQt -) - -@param img 8-bit 3-channel image where the text should be drawn. -@param text Text to write on an image. -@param org Point(x,y) where the text should start on an image. -@param font Font to use to draw a text. - */ -CV_EXPORTS void addText( const Mat& img, const String& text, Point org, const QtFont& font); - -/** @brief Draws a text on the image. - -@param img 8-bit 3-channel image where the text should be drawn. -@param text Text to write on an image. -@param org Point(x,y) where the text should start on an image. -@param nameFont Name of the font. The name should match the name of a system font (such as -*Times*). If the font is not found, a default one is used. -@param pointSize Size of the font. If not specified, equal zero or negative, the point size of the -font is set to a system-dependent default value. Generally, this is 12 points. -@param color Color of the font in BGRA where A = 255 is fully transparent. -@param weight Font weight. Available operation flags are : cv::QtFontWeights You can also specify a positive integer for better control. -@param style Font style. Available operation flags are : cv::QtFontStyles -@param spacing Spacing between characters. It can be negative or positive. - */ -CV_EXPORTS_W void addText(const Mat& img, const String& text, Point org, const String& nameFont, int pointSize = -1, Scalar color = Scalar::all(0), - int weight = QT_FONT_NORMAL, int style = QT_STYLE_NORMAL, int spacing = 0); - -/** @brief Displays a text on a window image as an overlay for a specified duration. - -The function displayOverlay displays useful information/tips on top of the window for a certain -amount of time *delayms*. The function does not modify the image, displayed in the window, that is, -after the specified delay the original content of the window is restored. - -@param winname Name of the window. -@param text Overlay text to write on a window image. -@param delayms The period (in milliseconds), during which the overlay text is displayed. If this -function is called before the previous overlay text timed out, the timer is restarted and the text -is updated. If this value is zero, the text never disappears. - */ -CV_EXPORTS_W void displayOverlay(const String& winname, const String& text, int delayms = 0); - -/** @brief Displays a text on the window statusbar during the specified period of time. - -The function displayStatusBar displays useful information/tips on top of the window for a certain -amount of time *delayms* . This information is displayed on the window statusbar (the window must be -created with the CV_GUI_EXPANDED flags). - -@param winname Name of the window. -@param text Text to write on the window statusbar. -@param delayms Duration (in milliseconds) to display the text. If this function is called before -the previous text timed out, the timer is restarted and the text is updated. If this value is -zero, the text never disappears. - */ -CV_EXPORTS_W void displayStatusBar(const String& winname, const String& text, int delayms = 0); - -/** @brief Saves parameters of the specified window. - -The function saveWindowParameters saves size, location, flags, trackbars value, zoom and panning -location of the window windowName. - -@param windowName Name of the window. - */ -CV_EXPORTS void saveWindowParameters(const String& windowName); - -/** @brief Loads parameters of the specified window. - -The function loadWindowParameters loads size, location, flags, trackbars value, zoom and panning -location of the window windowName. - -@param windowName Name of the window. - */ -CV_EXPORTS void loadWindowParameters(const String& windowName); - -CV_EXPORTS int startLoop(int (*pt2Func)(int argc, char *argv[]), int argc, char* argv[]); - -CV_EXPORTS void stopLoop(); - -/** @brief Attaches a button to the control panel. - -The function createButton attaches a button to the control panel. Each button is added to a -buttonbar to the right of the last button. A new buttonbar is created if nothing was attached to the -control panel before, or if the last element attached to the control panel was a trackbar or if the -QT_NEW_BUTTONBAR flag is added to the type. - -See below various examples of the cv::createButton function call: : -@code - createButton(NULL,callbackButton);//create a push button "button 0", that will call callbackButton. - createButton("button2",callbackButton,NULL,QT_CHECKBOX,0); - createButton("button3",callbackButton,&value); - createButton("button5",callbackButton1,NULL,QT_RADIOBOX); - createButton("button6",callbackButton2,NULL,QT_PUSH_BUTTON,1); - createButton("button6",callbackButton2,NULL,QT_PUSH_BUTTON|QT_NEW_BUTTONBAR);// create a push button in a new row -@endcode - -@param bar_name Name of the button. -@param on_change Pointer to the function to be called every time the button changes its state. -This function should be prototyped as void Foo(int state,\*void); . *state* is the current state -of the button. It could be -1 for a push button, 0 or 1 for a check/radio box button. -@param userdata Pointer passed to the callback function. -@param type Optional type of the button. Available types are: (cv::QtButtonTypes) -@param initial_button_state Default state of the button. Use for checkbox and radiobox. Its -value could be 0 or 1. (__Optional__) -*/ -CV_EXPORTS int createButton( const String& bar_name, ButtonCallback on_change, - void* userdata = 0, int type = QT_PUSH_BUTTON, - bool initial_button_state = false); - -//! @} highgui_qt - -//! @} highgui - -} // cv - -#ifndef DISABLE_OPENCV_24_COMPATIBILITY -#include "opencv2/highgui/highgui_c.h" -#endif - -#endif diff --git a/include/opencv2/imgcodecs.hpp b/include/opencv2/imgcodecs.hpp deleted file mode 100755 index 7f6b24f..0000000 --- a/include/opencv2/imgcodecs.hpp +++ /dev/null @@ -1,276 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_IMGCODECS_HPP -#define OPENCV_IMGCODECS_HPP - -#include "opencv2/core.hpp" - -/** - @defgroup imgcodecs Image file reading and writing - @{ - @defgroup imgcodecs_c C API - @defgroup imgcodecs_flags Flags used for image file reading and writing - @defgroup imgcodecs_ios iOS glue - @} -*/ - -//////////////////////////////// image codec //////////////////////////////// -namespace cv -{ - -//! @addtogroup imgcodecs -//! @{ - -//! @addtogroup imgcodecs_flags -//! @{ - -//! Imread flags -enum ImreadModes { - IMREAD_UNCHANGED = -1, //!< If set, return the loaded image as is (with alpha channel, otherwise it gets cropped). Ignore EXIF orientation. - IMREAD_GRAYSCALE = 0, //!< If set, always convert image to the single channel grayscale image (codec internal conversion). - IMREAD_COLOR = 1, //!< If set, always convert image to the 3 channel BGR color image. - IMREAD_ANYDEPTH = 2, //!< If set, return 16-bit/32-bit image when the input has the corresponding depth, otherwise convert it to 8-bit. - IMREAD_ANYCOLOR = 4, //!< If set, the image is read in any possible color format. - IMREAD_LOAD_GDAL = 8, //!< If set, use the gdal driver for loading the image. - IMREAD_REDUCED_GRAYSCALE_2 = 16, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/2. - IMREAD_REDUCED_COLOR_2 = 17, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/2. - IMREAD_REDUCED_GRAYSCALE_4 = 32, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/4. - IMREAD_REDUCED_COLOR_4 = 33, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/4. - IMREAD_REDUCED_GRAYSCALE_8 = 64, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/8. - IMREAD_REDUCED_COLOR_8 = 65, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/8. - IMREAD_IGNORE_ORIENTATION = 128 //!< If set, do not rotate the image according to EXIF's orientation flag. - }; - -//! Imwrite flags -enum ImwriteFlags { - IMWRITE_JPEG_QUALITY = 1, //!< For JPEG, it can be a quality from 0 to 100 (the higher is the better). Default value is 95. - IMWRITE_JPEG_PROGRESSIVE = 2, //!< Enable JPEG features, 0 or 1, default is False. - IMWRITE_JPEG_OPTIMIZE = 3, //!< Enable JPEG features, 0 or 1, default is False. - IMWRITE_JPEG_RST_INTERVAL = 4, //!< JPEG restart interval, 0 - 65535, default is 0 - no restart. - IMWRITE_JPEG_LUMA_QUALITY = 5, //!< Separate luma quality level, 0 - 100, default is 0 - don't use. - IMWRITE_JPEG_CHROMA_QUALITY = 6, //!< Separate chroma quality level, 0 - 100, default is 0 - don't use. - IMWRITE_PNG_COMPRESSION = 16, //!< For PNG, it can be the compression level from 0 to 9. A higher value means a smaller size and longer compression time. If specified, strategy is changed to IMWRITE_PNG_STRATEGY_DEFAULT (Z_DEFAULT_STRATEGY). Default value is 1 (best speed setting). - IMWRITE_PNG_STRATEGY = 17, //!< One of cv::ImwritePNGFlags, default is IMWRITE_PNG_STRATEGY_RLE. - IMWRITE_PNG_BILEVEL = 18, //!< Binary level PNG, 0 or 1, default is 0. - IMWRITE_PXM_BINARY = 32, //!< For PPM, PGM, or PBM, it can be a binary format flag, 0 or 1. Default value is 1. - IMWRITE_EXR_TYPE = (3 << 4) + 0, /* 48 */ //!< override EXR storage type (FLOAT (FP32) is default) - IMWRITE_WEBP_QUALITY = 64, //!< For WEBP, it can be a quality from 1 to 100 (the higher is the better). By default (without any parameter) and for quality above 100 the lossless compression is used. - IMWRITE_PAM_TUPLETYPE = 128,//!< For PAM, sets the TUPLETYPE field to the corresponding string value that is defined for the format - IMWRITE_TIFF_RESUNIT = 256,//!< For TIFF, use to specify which DPI resolution unit to set; see libtiff documentation for valid values. - IMWRITE_TIFF_XDPI = 257,//!< For TIFF, use to specify the X direction DPI. - IMWRITE_TIFF_YDPI = 258, //!< For TIFF, use to specify the Y direction DPI. - IMWRITE_TIFF_COMPRESSION = 259 //!< For TIFF, use to specify the image compression scheme. See libtiff for integer constants corresponding to compression formats. Note, for images whose depth is CV_32F, only libtiff's SGILOG compression scheme is used. For other supported depths, the compression scheme can be specified by this flag; LZW compression is the default. - }; - -enum ImwriteEXRTypeFlags { - /*IMWRITE_EXR_TYPE_UNIT = 0, //!< not supported */ - IMWRITE_EXR_TYPE_HALF = 1, //!< store as HALF (FP16) - IMWRITE_EXR_TYPE_FLOAT = 2 //!< store as FP32 (default) - }; - -//! Imwrite PNG specific flags used to tune the compression algorithm. -/** These flags will be modify the way of PNG image compression and will be passed to the underlying zlib processing stage. - -- The effect of IMWRITE_PNG_STRATEGY_FILTERED is to force more Huffman coding and less string matching; it is somewhat intermediate between IMWRITE_PNG_STRATEGY_DEFAULT and IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY. -- IMWRITE_PNG_STRATEGY_RLE is designed to be almost as fast as IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY, but give better compression for PNG image data. -- The strategy parameter only affects the compression ratio but not the correctness of the compressed output even if it is not set appropriately. -- IMWRITE_PNG_STRATEGY_FIXED prevents the use of dynamic Huffman codes, allowing for a simpler decoder for special applications. -*/ -enum ImwritePNGFlags { - IMWRITE_PNG_STRATEGY_DEFAULT = 0, //!< Use this value for normal data. - IMWRITE_PNG_STRATEGY_FILTERED = 1, //!< Use this value for data produced by a filter (or predictor).Filtered data consists mostly of small values with a somewhat random distribution. In this case, the compression algorithm is tuned to compress them better. - IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY = 2, //!< Use this value to force Huffman encoding only (no string match). - IMWRITE_PNG_STRATEGY_RLE = 3, //!< Use this value to limit match distances to one (run-length encoding). - IMWRITE_PNG_STRATEGY_FIXED = 4 //!< Using this value prevents the use of dynamic Huffman codes, allowing for a simpler decoder for special applications. - }; - -//! Imwrite PAM specific tupletype flags used to define the 'TUPETYPE' field of a PAM file. -enum ImwritePAMFlags { - IMWRITE_PAM_FORMAT_NULL = 0, - IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1, - IMWRITE_PAM_FORMAT_GRAYSCALE = 2, - IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3, - IMWRITE_PAM_FORMAT_RGB = 4, - IMWRITE_PAM_FORMAT_RGB_ALPHA = 5, - }; - -//! @} imgcodecs_flags - -/** @brief Loads an image from a file. - -@anchor imread - -The function imread loads an image from the specified file and returns it. If the image cannot be -read (because of missing file, improper permissions, unsupported or invalid format), the function -returns an empty matrix ( Mat::data==NULL ). - -Currently, the following file formats are supported: - -- Windows bitmaps - \*.bmp, \*.dib (always supported) -- JPEG files - \*.jpeg, \*.jpg, \*.jpe (see the *Note* section) -- JPEG 2000 files - \*.jp2 (see the *Note* section) -- Portable Network Graphics - \*.png (see the *Note* section) -- WebP - \*.webp (see the *Note* section) -- Portable image format - \*.pbm, \*.pgm, \*.ppm \*.pxm, \*.pnm (always supported) -- Sun rasters - \*.sr, \*.ras (always supported) -- TIFF files - \*.tiff, \*.tif (see the *Note* section) -- OpenEXR Image files - \*.exr (see the *Note* section) -- Radiance HDR - \*.hdr, \*.pic (always supported) -- Raster and Vector geospatial data supported by GDAL (see the *Note* section) - -@note -- The function determines the type of an image by the content, not by the file extension. -- In the case of color images, the decoded images will have the channels stored in **B G R** order. -- When using IMREAD_GRAYSCALE, the codec's internal grayscale conversion will be used, if available. - Results may differ to the output of cvtColor() -- On Microsoft Windows\* OS and MacOSX\*, the codecs shipped with an OpenCV image (libjpeg, - libpng, libtiff, and libjasper) are used by default. So, OpenCV can always read JPEGs, PNGs, - and TIFFs. On MacOSX, there is also an option to use native MacOSX image readers. But beware - that currently these native image loaders give images with different pixel values because of - the color management embedded into MacOSX. -- On Linux\*, BSD flavors and other Unix-like open-source operating systems, OpenCV looks for - codecs supplied with an OS image. Install the relevant packages (do not forget the development - files, for example, "libjpeg-dev", in Debian\* and Ubuntu\*) to get the codec support or turn - on the OPENCV_BUILD_3RDPARTY_LIBS flag in CMake. -- In the case you set *WITH_GDAL* flag to true in CMake and @ref IMREAD_LOAD_GDAL to load the image, - then the [GDAL](http://www.gdal.org) driver will be used in order to decode the image, supporting - the following formats: [Raster](http://www.gdal.org/formats_list.html), - [Vector](http://www.gdal.org/ogr_formats.html). -- If EXIF information is embedded in the image file, the EXIF orientation will be taken into account - and thus the image will be rotated accordingly except if the flags @ref IMREAD_IGNORE_ORIENTATION - or @ref IMREAD_UNCHANGED are passed. -- By default number of pixels must be less than 2^30. Limit can be set using system - variable OPENCV_IO_MAX_IMAGE_PIXELS - -@param filename Name of file to be loaded. -@param flags Flag that can take values of cv::ImreadModes -*/ -CV_EXPORTS_W Mat imread( const String& filename, int flags = IMREAD_COLOR ); - -/** @brief Loads a multi-page image from a file. - -The function imreadmulti loads a multi-page image from the specified file into a vector of Mat objects. -@param filename Name of file to be loaded. -@param flags Flag that can take values of cv::ImreadModes, default with cv::IMREAD_ANYCOLOR. -@param mats A vector of Mat objects holding each page, if more than one. -@sa cv::imread -*/ -CV_EXPORTS_W bool imreadmulti(const String& filename, CV_OUT std::vector& mats, int flags = IMREAD_ANYCOLOR); - -/** @brief Saves an image to a specified file. - -The function imwrite saves the image to the specified file. The image format is chosen based on the -filename extension (see cv::imread for the list of extensions). In general, only 8-bit -single-channel or 3-channel (with 'BGR' channel order) images -can be saved using this function, with these exceptions: - -- 16-bit unsigned (CV_16U) images can be saved in the case of PNG, JPEG 2000, and TIFF formats -- 32-bit float (CV_32F) images can be saved in TIFF, OpenEXR, and Radiance HDR formats; 3-channel -(CV_32FC3) TIFF images will be saved using the LogLuv high dynamic range encoding (4 bytes per pixel) -- PNG images with an alpha channel can be saved using this function. To do this, create -8-bit (or 16-bit) 4-channel image BGRA, where the alpha channel goes last. Fully transparent pixels -should have alpha set to 0, fully opaque pixels should have alpha set to 255/65535 (see the code sample below). -- Multiple images (vector of Mat) can be saved in TIFF format (see the code sample below). - -If the format, depth or channel order is different, use -Mat::convertTo and cv::cvtColor to convert it before saving. Or, use the universal FileStorage I/O -functions to save the image to XML or YAML format. - -The sample below shows how to create a BGRA image, how to set custom compression parameters and save it to a PNG file. -It also demonstrates how to save multiple images in a TIFF file: -@include snippets/imgcodecs_imwrite.cpp -@param filename Name of the file. -@param img (Mat or vector of Mat) Image or Images to be saved. -@param params Format-specific parameters encoded as pairs (paramId_1, paramValue_1, paramId_2, paramValue_2, ... .) see cv::ImwriteFlags -*/ -CV_EXPORTS_W bool imwrite( const String& filename, InputArray img, - const std::vector& params = std::vector()); - -/// @overload multi-image overload for bindings -CV_WRAP static inline -bool imwritemulti(const String& filename, InputArrayOfArrays img, - const std::vector& params = std::vector()) -{ - return imwrite(filename, img, params); -} - -/** @brief Reads an image from a buffer in memory. - -The function imdecode reads an image from the specified buffer in the memory. If the buffer is too short or -contains invalid data, the function returns an empty matrix ( Mat::data==NULL ). - -See cv::imread for the list of supported formats and flags description. - -@note In the case of color images, the decoded images will have the channels stored in **B G R** order. -@param buf Input array or vector of bytes. -@param flags The same flags as in cv::imread, see cv::ImreadModes. -*/ -CV_EXPORTS_W Mat imdecode( InputArray buf, int flags ); - -/** @overload -@param buf -@param flags -@param dst The optional output placeholder for the decoded matrix. It can save the image -reallocations when the function is called repeatedly for images of the same size. -*/ -CV_EXPORTS Mat imdecode( InputArray buf, int flags, Mat* dst); - -/** @brief Encodes an image into a memory buffer. - -The function imencode compresses the image and stores it in the memory buffer that is resized to fit the -result. See cv::imwrite for the list of supported formats and flags description. - -@param ext File extension that defines the output format. -@param img Image to be written. -@param buf Output buffer resized to fit the compressed image. -@param params Format-specific parameters. See cv::imwrite and cv::ImwriteFlags. -*/ -CV_EXPORTS_W bool imencode( const String& ext, InputArray img, - CV_OUT std::vector& buf, - const std::vector& params = std::vector()); - -//! @} imgcodecs - -} // cv - -#endif //OPENCV_IMGCODECS_HPP diff --git a/include/opencv2/imgproc.hpp b/include/opencv2/imgproc.hpp deleted file mode 100755 index 2739d28..0000000 --- a/include/opencv2/imgproc.hpp +++ /dev/null @@ -1,4946 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_IMGPROC_HPP -#define OPENCV_IMGPROC_HPP - -#include "opencv2/core.hpp" - -/** - @defgroup imgproc Image Processing - -This module includes image-processing functions. - - @{ - @defgroup imgproc_filter Image Filtering - -Functions and classes described in this section are used to perform various linear or non-linear -filtering operations on 2D images (represented as Mat's). It means that for each pixel location -\f$(x,y)\f$ in the source image (normally, rectangular), its neighborhood is considered and used to -compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of -morphological operations, it is the minimum or maximum values, and so on. The computed response is -stored in the destination image at the same location \f$(x,y)\f$. It means that the output image -will be of the same size as the input image. Normally, the functions support multi-channel arrays, -in which case every channel is processed independently. Therefore, the output image will also have -the same number of channels as the input one. - -Another common feature of the functions and classes described in this section is that, unlike -simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For -example, if you want to smooth an image using a Gaussian \f$3 \times 3\f$ filter, then, when -processing the left-most pixels in each row, you need pixels to the left of them, that is, outside -of the image. You can let these pixels be the same as the left-most image pixels ("replicated -border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant -border" extrapolation method), and so on. OpenCV enables you to specify the extrapolation method. -For details, see #BorderTypes - -@anchor filter_depths -### Depth combinations -Input depth (src.depth()) | Output depth (ddepth) ---------------------------|---------------------- -CV_8U | -1/CV_16S/CV_32F/CV_64F -CV_16U/CV_16S | -1/CV_32F/CV_64F -CV_32F | -1/CV_32F/CV_64F -CV_64F | -1/CV_64F - -@note when ddepth=-1, the output image will have the same depth as the source. - - @defgroup imgproc_transform Geometric Image Transformations - -The functions in this section perform various geometrical transformations of 2D images. They do not -change the image content but deform the pixel grid and map this deformed grid to the destination -image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from -destination to the source. That is, for each pixel \f$(x, y)\f$ of the destination image, the -functions compute coordinates of the corresponding "donor" pixel in the source image and copy the -pixel value: - -\f[\texttt{dst} (x,y)= \texttt{src} (f_x(x,y), f_y(x,y))\f] - -In case when you specify the forward mapping \f$\left: \texttt{src} \rightarrow -\texttt{dst}\f$, the OpenCV functions first compute the corresponding inverse mapping -\f$\left: \texttt{dst} \rightarrow \texttt{src}\f$ and then use the above formula. - -The actual implementations of the geometrical transformations, from the most generic remap and to -the simplest and the fastest resize, need to solve two main problems with the above formula: - -- Extrapolation of non-existing pixels. Similarly to the filtering functions described in the -previous section, for some \f$(x,y)\f$, either one of \f$f_x(x,y)\f$, or \f$f_y(x,y)\f$, or both -of them may fall outside of the image. In this case, an extrapolation method needs to be used. -OpenCV provides the same selection of extrapolation methods as in the filtering functions. In -addition, it provides the method #BORDER_TRANSPARENT. This means that the corresponding pixels in -the destination image will not be modified at all. - -- Interpolation of pixel values. Usually \f$f_x(x,y)\f$ and \f$f_y(x,y)\f$ are floating-point -numbers. This means that \f$\left\f$ can be either an affine or perspective -transformation, or radial lens distortion correction, and so on. So, a pixel value at fractional -coordinates needs to be retrieved. In the simplest case, the coordinates can be just rounded to the -nearest integer coordinates and the corresponding pixel can be used. This is called a -nearest-neighbor interpolation. However, a better result can be achieved by using more -sophisticated [interpolation methods](http://en.wikipedia.org/wiki/Multivariate_interpolation) , -where a polynomial function is fit into some neighborhood of the computed pixel \f$(f_x(x,y), -f_y(x,y))\f$, and then the value of the polynomial at \f$(f_x(x,y), f_y(x,y))\f$ is taken as the -interpolated pixel value. In OpenCV, you can choose between several interpolation methods. See -resize for details. - -@note The geometrical transformations do not work with `CV_8S` or `CV_32S` images. - - @defgroup imgproc_misc Miscellaneous Image Transformations - @defgroup imgproc_draw Drawing Functions - -Drawing functions work with matrices/images of arbitrary depth. The boundaries of the shapes can be -rendered with antialiasing (implemented only for 8-bit images for now). All the functions include -the parameter color that uses an RGB value (that may be constructed with the Scalar constructor ) -for color images and brightness for grayscale images. For color images, the channel ordering is -normally *Blue, Green, Red*. This is what imshow, imread, and imwrite expect. So, if you form a -color using the Scalar constructor, it should look like: - -\f[\texttt{Scalar} (blue \_ component, green \_ component, red \_ component[, alpha \_ component])\f] - -If you are using your own image rendering and I/O functions, you can use any channel ordering. The -drawing functions process each channel independently and do not depend on the channel order or even -on the used color space. The whole image can be converted from BGR to RGB or to a different color -space using cvtColor . - -If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also, -many drawing functions can handle pixel coordinates specified with sub-pixel accuracy. This means -that the coordinates can be passed as fixed-point numbers encoded as integers. The number of -fractional bits is specified by the shift parameter and the real point coordinates are calculated as -\f$\texttt{Point}(x,y)\rightarrow\texttt{Point2f}(x*2^{-shift},y*2^{-shift})\f$ . This feature is -especially effective when rendering antialiased shapes. - -@note The functions do not support alpha-transparency when the target image is 4-channel. In this -case, the color[3] is simply copied to the repainted pixels. Thus, if you want to paint -semi-transparent shapes, you can paint them in a separate buffer and then blend it with the main -image. - - @defgroup imgproc_color_conversions Color Space Conversions - @defgroup imgproc_colormap ColorMaps in OpenCV - -The human perception isn't built for observing fine changes in grayscale images. Human eyes are more -sensitive to observing changes between colors, so you often need to recolor your grayscale images to -get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your -computer vision application. - -In OpenCV you only need applyColorMap to apply a colormap on a given image. The following sample -code reads the path to an image from command line, applies a Jet colormap on it and shows the -result: - -@include snippets/imgproc_applyColorMap.cpp - -@see #ColormapTypes - - @defgroup imgproc_subdiv2d Planar Subdivision - -The Subdiv2D class described in this section is used to perform various planar subdivision on -a set of 2D points (represented as vector of Point2f). OpenCV subdivides a plane into triangles -using the Delaunay's algorithm, which corresponds to the dual graph of the Voronoi diagram. -In the figure below, the Delaunay's triangulation is marked with black lines and the Voronoi -diagram with red lines. - -![Delaunay triangulation (black) and Voronoi (red)](pics/delaunay_voronoi.png) - -The subdivisions can be used for the 3D piece-wise transformation of a plane, morphing, fast -location of points on the plane, building special graphs (such as NNG,RNG), and so forth. - - @defgroup imgproc_hist Histograms - @defgroup imgproc_shape Structural Analysis and Shape Descriptors - @defgroup imgproc_motion Motion Analysis and Object Tracking - @defgroup imgproc_feature Feature Detection - @defgroup imgproc_object Object Detection - @defgroup imgproc_c C API - @defgroup imgproc_hal Hardware Acceleration Layer - @{ - @defgroup imgproc_hal_functions Functions - @defgroup imgproc_hal_interface Interface - @} - @} -*/ - -namespace cv -{ - -/** @addtogroup imgproc -@{ -*/ - -//! @addtogroup imgproc_filter -//! @{ - -//! type of morphological operation -enum MorphTypes{ - MORPH_ERODE = 0, //!< see #erode - MORPH_DILATE = 1, //!< see #dilate - MORPH_OPEN = 2, //!< an opening operation - //!< \f[\texttt{dst} = \mathrm{open} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \mathrm{erode} ( \texttt{src} , \texttt{element} ))\f] - MORPH_CLOSE = 3, //!< a closing operation - //!< \f[\texttt{dst} = \mathrm{close} ( \texttt{src} , \texttt{element} )= \mathrm{erode} ( \mathrm{dilate} ( \texttt{src} , \texttt{element} ))\f] - MORPH_GRADIENT = 4, //!< a morphological gradient - //!< \f[\texttt{dst} = \mathrm{morph\_grad} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \texttt{src} , \texttt{element} )- \mathrm{erode} ( \texttt{src} , \texttt{element} )\f] - MORPH_TOPHAT = 5, //!< "top hat" - //!< \f[\texttt{dst} = \mathrm{tophat} ( \texttt{src} , \texttt{element} )= \texttt{src} - \mathrm{open} ( \texttt{src} , \texttt{element} )\f] - MORPH_BLACKHAT = 6, //!< "black hat" - //!< \f[\texttt{dst} = \mathrm{blackhat} ( \texttt{src} , \texttt{element} )= \mathrm{close} ( \texttt{src} , \texttt{element} )- \texttt{src}\f] - MORPH_HITMISS = 7 //!< "hit or miss" - //!< .- Only supported for CV_8UC1 binary images. A tutorial can be found in the documentation -}; - -//! shape of the structuring element -enum MorphShapes { - MORPH_RECT = 0, //!< a rectangular structuring element: \f[E_{ij}=1\f] - MORPH_CROSS = 1, //!< a cross-shaped structuring element: - //!< \f[E_{ij} = \begin{cases} 1 & \texttt{if } {i=\texttt{anchor.y } {or } {j=\texttt{anchor.x}}} \\0 & \texttt{otherwise} \end{cases}\f] - MORPH_ELLIPSE = 2 //!< an elliptic structuring element, that is, a filled ellipse inscribed - //!< into the rectangle Rect(0, 0, esize.width, 0.esize.height) -}; - -//! @} imgproc_filter - -//! @addtogroup imgproc_transform -//! @{ - -//! interpolation algorithm -enum InterpolationFlags{ - /** nearest neighbor interpolation */ - INTER_NEAREST = 0, - /** bilinear interpolation */ - INTER_LINEAR = 1, - /** bicubic interpolation */ - INTER_CUBIC = 2, - /** resampling using pixel area relation. It may be a preferred method for image decimation, as - it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST - method. */ - INTER_AREA = 3, - /** Lanczos interpolation over 8x8 neighborhood */ - INTER_LANCZOS4 = 4, - /** Bit exact bilinear interpolation */ - INTER_LINEAR_EXACT = 5, - /** Bit exact nearest neighbor interpolation. This will produce same results as - the nearest neighbor method in PIL, scikit-image or Matlab. */ - INTER_NEAREST_EXACT = 6, - /** mask for interpolation codes */ - INTER_MAX = 7, - /** flag, fills all of the destination image pixels. If some of them correspond to outliers in the - source image, they are set to zero */ - WARP_FILL_OUTLIERS = 8, - /** flag, inverse transformation - - For example, #linearPolar or #logPolar transforms: - - flag is __not__ set: \f$dst( \rho , \phi ) = src(x,y)\f$ - - flag is set: \f$dst(x,y) = src( \rho , \phi )\f$ - */ - WARP_INVERSE_MAP = 16 -}; - -/** \brief Specify the polar mapping mode -@sa warpPolar -*/ -enum WarpPolarMode -{ - WARP_POLAR_LINEAR = 0, ///< Remaps an image to/from polar space. - WARP_POLAR_LOG = 256 ///< Remaps an image to/from semilog-polar space. -}; - -enum InterpolationMasks { - INTER_BITS = 5, - INTER_BITS2 = INTER_BITS * 2, - INTER_TAB_SIZE = 1 << INTER_BITS, - INTER_TAB_SIZE2 = INTER_TAB_SIZE * INTER_TAB_SIZE - }; - -//! @} imgproc_transform - -//! @addtogroup imgproc_misc -//! @{ - -//! Distance types for Distance Transform and M-estimators -//! @see distanceTransform, fitLine -enum DistanceTypes { - DIST_USER = -1, //!< User defined distance - DIST_L1 = 1, //!< distance = |x1-x2| + |y1-y2| - DIST_L2 = 2, //!< the simple euclidean distance - DIST_C = 3, //!< distance = max(|x1-x2|,|y1-y2|) - DIST_L12 = 4, //!< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) - DIST_FAIR = 5, //!< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 - DIST_WELSCH = 6, //!< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 - DIST_HUBER = 7 //!< distance = |x| \texttt{thresh}\)}{0}{otherwise}\f] - THRESH_BINARY_INV = 1, //!< \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{maxval}}{otherwise}\f] - THRESH_TRUNC = 2, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{threshold}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f] - THRESH_TOZERO = 3, //!< \f[\texttt{dst} (x,y) = \fork{\texttt{src}(x,y)}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f] - THRESH_TOZERO_INV = 4, //!< \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f] - THRESH_MASK = 7, - THRESH_OTSU = 8, //!< flag, use Otsu algorithm to choose the optimal threshold value - THRESH_TRIANGLE = 16 //!< flag, use Triangle algorithm to choose the optimal threshold value -}; - -//! adaptive threshold algorithm -//! @see adaptiveThreshold -enum AdaptiveThresholdTypes { - /** the threshold value \f$T(x,y)\f$ is a mean of the \f$\texttt{blockSize} \times - \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$ minus C */ - ADAPTIVE_THRESH_MEAN_C = 0, - /** the threshold value \f$T(x, y)\f$ is a weighted sum (cross-correlation with a Gaussian - window) of the \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$ - minus C . The default sigma (standard deviation) is used for the specified blockSize . See - #getGaussianKernel*/ - ADAPTIVE_THRESH_GAUSSIAN_C = 1 -}; - -//! cv::undistort mode -enum UndistortTypes { - PROJ_SPHERICAL_ORTHO = 0, - PROJ_SPHERICAL_EQRECT = 1 - }; - -//! class of the pixel in GrabCut algorithm -enum GrabCutClasses { - GC_BGD = 0, //!< an obvious background pixels - GC_FGD = 1, //!< an obvious foreground (object) pixel - GC_PR_BGD = 2, //!< a possible background pixel - GC_PR_FGD = 3 //!< a possible foreground pixel -}; - -//! GrabCut algorithm flags -enum GrabCutModes { - /** The function initializes the state and the mask using the provided rectangle. After that it - runs iterCount iterations of the algorithm. */ - GC_INIT_WITH_RECT = 0, - /** The function initializes the state using the provided mask. Note that GC_INIT_WITH_RECT - and GC_INIT_WITH_MASK can be combined. Then, all the pixels outside of the ROI are - automatically initialized with GC_BGD .*/ - GC_INIT_WITH_MASK = 1, - /** The value means that the algorithm should just resume. */ - GC_EVAL = 2, - /** The value means that the algorithm should just run the grabCut algorithm (a single iteration) with the fixed model */ - GC_EVAL_FREEZE_MODEL = 3 -}; - -//! distanceTransform algorithm flags -enum DistanceTransformLabelTypes { - /** each connected component of zeros in src (as well as all the non-zero pixels closest to the - connected component) will be assigned the same label */ - DIST_LABEL_CCOMP = 0, - /** each zero pixel (and all the non-zero pixels closest to it) gets its own label. */ - DIST_LABEL_PIXEL = 1 -}; - -//! floodfill algorithm flags -enum FloodFillFlags { - /** If set, the difference between the current pixel and seed pixel is considered. Otherwise, - the difference between neighbor pixels is considered (that is, the range is floating). */ - FLOODFILL_FIXED_RANGE = 1 << 16, - /** If set, the function does not change the image ( newVal is ignored), and only fills the - mask with the value specified in bits 8-16 of flags as described above. This option only make - sense in function variants that have the mask parameter. */ - FLOODFILL_MASK_ONLY = 1 << 17 -}; - -//! @} imgproc_misc - -//! @addtogroup imgproc_shape -//! @{ - -//! connected components statistics -enum ConnectedComponentsTypes { - CC_STAT_LEFT = 0, //!< The leftmost (x) coordinate which is the inclusive start of the bounding - //!< box in the horizontal direction. - CC_STAT_TOP = 1, //!< The topmost (y) coordinate which is the inclusive start of the bounding - //!< box in the vertical direction. - CC_STAT_WIDTH = 2, //!< The horizontal size of the bounding box - CC_STAT_HEIGHT = 3, //!< The vertical size of the bounding box - CC_STAT_AREA = 4, //!< The total area (in pixels) of the connected component -#ifndef CV_DOXYGEN - CC_STAT_MAX = 5 //!< Max enumeration value. Used internally only for memory allocation -#endif -}; - -//! connected components algorithm -enum ConnectedComponentsAlgorithmsTypes { - CCL_WU = 0, //!< SAUF @cite Wu2009 algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity - CCL_DEFAULT = -1, //!< BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity - CCL_GRANA = 1 //!< BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity -}; - -//! mode of the contour retrieval algorithm -enum RetrievalModes { - /** retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for - all the contours. */ - RETR_EXTERNAL = 0, - /** retrieves all of the contours without establishing any hierarchical relationships. */ - RETR_LIST = 1, - /** retrieves all of the contours and organizes them into a two-level hierarchy. At the top - level, there are external boundaries of the components. At the second level, there are - boundaries of the holes. If there is another contour inside a hole of a connected component, it - is still put at the top level. */ - RETR_CCOMP = 2, - /** retrieves all of the contours and reconstructs a full hierarchy of nested contours.*/ - RETR_TREE = 3, - RETR_FLOODFILL = 4 //!< -}; - -//! the contour approximation algorithm -enum ContourApproximationModes { - /** stores absolutely all the contour points. That is, any 2 subsequent points (x1,y1) and - (x2,y2) of the contour will be either horizontal, vertical or diagonal neighbors, that is, - max(abs(x1-x2),abs(y2-y1))==1. */ - CHAIN_APPROX_NONE = 1, - /** compresses horizontal, vertical, and diagonal segments and leaves only their end points. - For example, an up-right rectangular contour is encoded with 4 points. */ - CHAIN_APPROX_SIMPLE = 2, - /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */ - CHAIN_APPROX_TC89_L1 = 3, - /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */ - CHAIN_APPROX_TC89_KCOS = 4 -}; - -/** @brief Shape matching methods - -\f$A\f$ denotes object1,\f$B\f$ denotes object2 - -\f$\begin{array}{l} m^A_i = \mathrm{sign} (h^A_i) \cdot \log{h^A_i} \\ m^B_i = \mathrm{sign} (h^B_i) \cdot \log{h^B_i} \end{array}\f$ - -and \f$h^A_i, h^B_i\f$ are the Hu moments of \f$A\f$ and \f$B\f$ , respectively. -*/ -enum ShapeMatchModes { - CONTOURS_MATCH_I1 =1, //!< \f[I_1(A,B) = \sum _{i=1...7} \left | \frac{1}{m^A_i} - \frac{1}{m^B_i} \right |\f] - CONTOURS_MATCH_I2 =2, //!< \f[I_2(A,B) = \sum _{i=1...7} \left | m^A_i - m^B_i \right |\f] - CONTOURS_MATCH_I3 =3 //!< \f[I_3(A,B) = \max _{i=1...7} \frac{ \left| m^A_i - m^B_i \right| }{ \left| m^A_i \right| }\f] -}; - -//! @} imgproc_shape - -//! @addtogroup imgproc_feature -//! @{ - -//! Variants of a Hough transform -enum HoughModes { - - /** classical or standard Hough transform. Every line is represented by two floating-point - numbers \f$(\rho, \theta)\f$ , where \f$\rho\f$ is a distance between (0,0) point and the line, - and \f$\theta\f$ is the angle between x-axis and the normal to the line. Thus, the matrix must - be (the created sequence will be) of CV_32FC2 type */ - HOUGH_STANDARD = 0, - /** probabilistic Hough transform (more efficient in case if the picture contains a few long - linear segments). It returns line segments rather than the whole line. Each segment is - represented by starting and ending points, and the matrix must be (the created sequence will - be) of the CV_32SC4 type. */ - HOUGH_PROBABILISTIC = 1, - /** multi-scale variant of the classical Hough transform. The lines are encoded the same way as - HOUGH_STANDARD. */ - HOUGH_MULTI_SCALE = 2, - HOUGH_GRADIENT = 3 //!< basically *21HT*, described in @cite Yuen90 -}; - -//! Variants of Line Segment %Detector -enum LineSegmentDetectorModes { - LSD_REFINE_NONE = 0, //!< No refinement applied - LSD_REFINE_STD = 1, //!< Standard refinement is applied. E.g. breaking arches into smaller straighter line approximations. - LSD_REFINE_ADV = 2 //!< Advanced refinement. Number of false alarms is calculated, lines are - //!< refined through increase of precision, decrement in size, etc. -}; - -//! @} imgproc_feature - -/** Histogram comparison methods - @ingroup imgproc_hist -*/ -enum HistCompMethods { - /** Correlation - \f[d(H_1,H_2) = \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}\f] - where - \f[\bar{H_k} = \frac{1}{N} \sum _J H_k(J)\f] - and \f$N\f$ is a total number of histogram bins. */ - HISTCMP_CORREL = 0, - /** Chi-Square - \f[d(H_1,H_2) = \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}\f] */ - HISTCMP_CHISQR = 1, - /** Intersection - \f[d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))\f] */ - HISTCMP_INTERSECT = 2, - /** Bhattacharyya distance - (In fact, OpenCV computes Hellinger distance, which is related to Bhattacharyya coefficient.) - \f[d(H_1,H_2) = \sqrt{1 - \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}\f] */ - HISTCMP_BHATTACHARYYA = 3, - HISTCMP_HELLINGER = HISTCMP_BHATTACHARYYA, //!< Synonym for HISTCMP_BHATTACHARYYA - /** Alternative Chi-Square - \f[d(H_1,H_2) = 2 * \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)+H_2(I)}\f] - This alternative formula is regularly used for texture comparison. See e.g. @cite Puzicha1997 */ - HISTCMP_CHISQR_ALT = 4, - /** Kullback-Leibler divergence - \f[d(H_1,H_2) = \sum _I H_1(I) \log \left(\frac{H_1(I)}{H_2(I)}\right)\f] */ - HISTCMP_KL_DIV = 5 -}; - -/** the color conversion codes -@see @ref imgproc_color_conversions -@ingroup imgproc_color_conversions - */ -enum ColorConversionCodes { - COLOR_BGR2BGRA = 0, //!< add alpha channel to RGB or BGR image - COLOR_RGB2RGBA = COLOR_BGR2BGRA, - - COLOR_BGRA2BGR = 1, //!< remove alpha channel from RGB or BGR image - COLOR_RGBA2RGB = COLOR_BGRA2BGR, - - COLOR_BGR2RGBA = 2, //!< convert between RGB and BGR color spaces (with or without alpha channel) - COLOR_RGB2BGRA = COLOR_BGR2RGBA, - - COLOR_RGBA2BGR = 3, - COLOR_BGRA2RGB = COLOR_RGBA2BGR, - - COLOR_BGR2RGB = 4, - COLOR_RGB2BGR = COLOR_BGR2RGB, - - COLOR_BGRA2RGBA = 5, - COLOR_RGBA2BGRA = COLOR_BGRA2RGBA, - - COLOR_BGR2GRAY = 6, //!< convert between RGB/BGR and grayscale, @ref color_convert_rgb_gray "color conversions" - COLOR_RGB2GRAY = 7, - COLOR_GRAY2BGR = 8, - COLOR_GRAY2RGB = COLOR_GRAY2BGR, - COLOR_GRAY2BGRA = 9, - COLOR_GRAY2RGBA = COLOR_GRAY2BGRA, - COLOR_BGRA2GRAY = 10, - COLOR_RGBA2GRAY = 11, - - COLOR_BGR2BGR565 = 12, //!< convert between RGB/BGR and BGR565 (16-bit images) - COLOR_RGB2BGR565 = 13, - COLOR_BGR5652BGR = 14, - COLOR_BGR5652RGB = 15, - COLOR_BGRA2BGR565 = 16, - COLOR_RGBA2BGR565 = 17, - COLOR_BGR5652BGRA = 18, - COLOR_BGR5652RGBA = 19, - - COLOR_GRAY2BGR565 = 20, //!< convert between grayscale to BGR565 (16-bit images) - COLOR_BGR5652GRAY = 21, - - COLOR_BGR2BGR555 = 22, //!< convert between RGB/BGR and BGR555 (16-bit images) - COLOR_RGB2BGR555 = 23, - COLOR_BGR5552BGR = 24, - COLOR_BGR5552RGB = 25, - COLOR_BGRA2BGR555 = 26, - COLOR_RGBA2BGR555 = 27, - COLOR_BGR5552BGRA = 28, - COLOR_BGR5552RGBA = 29, - - COLOR_GRAY2BGR555 = 30, //!< convert between grayscale and BGR555 (16-bit images) - COLOR_BGR5552GRAY = 31, - - COLOR_BGR2XYZ = 32, //!< convert RGB/BGR to CIE XYZ, @ref color_convert_rgb_xyz "color conversions" - COLOR_RGB2XYZ = 33, - COLOR_XYZ2BGR = 34, - COLOR_XYZ2RGB = 35, - - COLOR_BGR2YCrCb = 36, //!< convert RGB/BGR to luma-chroma (aka YCC), @ref color_convert_rgb_ycrcb "color conversions" - COLOR_RGB2YCrCb = 37, - COLOR_YCrCb2BGR = 38, - COLOR_YCrCb2RGB = 39, - - COLOR_BGR2HSV = 40, //!< convert RGB/BGR to HSV (hue saturation value), @ref color_convert_rgb_hsv "color conversions" - COLOR_RGB2HSV = 41, - - COLOR_BGR2Lab = 44, //!< convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions" - COLOR_RGB2Lab = 45, - - COLOR_BGR2Luv = 50, //!< convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions" - COLOR_RGB2Luv = 51, - COLOR_BGR2HLS = 52, //!< convert RGB/BGR to HLS (hue lightness saturation), @ref color_convert_rgb_hls "color conversions" - COLOR_RGB2HLS = 53, - - COLOR_HSV2BGR = 54, //!< backward conversions to RGB/BGR - COLOR_HSV2RGB = 55, - - COLOR_Lab2BGR = 56, - COLOR_Lab2RGB = 57, - COLOR_Luv2BGR = 58, - COLOR_Luv2RGB = 59, - COLOR_HLS2BGR = 60, - COLOR_HLS2RGB = 61, - - COLOR_BGR2HSV_FULL = 66, - COLOR_RGB2HSV_FULL = 67, - COLOR_BGR2HLS_FULL = 68, - COLOR_RGB2HLS_FULL = 69, - - COLOR_HSV2BGR_FULL = 70, - COLOR_HSV2RGB_FULL = 71, - COLOR_HLS2BGR_FULL = 72, - COLOR_HLS2RGB_FULL = 73, - - COLOR_LBGR2Lab = 74, - COLOR_LRGB2Lab = 75, - COLOR_LBGR2Luv = 76, - COLOR_LRGB2Luv = 77, - - COLOR_Lab2LBGR = 78, - COLOR_Lab2LRGB = 79, - COLOR_Luv2LBGR = 80, - COLOR_Luv2LRGB = 81, - - COLOR_BGR2YUV = 82, //!< convert between RGB/BGR and YUV - COLOR_RGB2YUV = 83, - COLOR_YUV2BGR = 84, - COLOR_YUV2RGB = 85, - - //! YUV 4:2:0 family to RGB - COLOR_YUV2RGB_NV12 = 90, - COLOR_YUV2BGR_NV12 = 91, - COLOR_YUV2RGB_NV21 = 92, - COLOR_YUV2BGR_NV21 = 93, - COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21, - COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21, - - COLOR_YUV2RGBA_NV12 = 94, - COLOR_YUV2BGRA_NV12 = 95, - COLOR_YUV2RGBA_NV21 = 96, - COLOR_YUV2BGRA_NV21 = 97, - COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21, - COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21, - - COLOR_YUV2RGB_YV12 = 98, - COLOR_YUV2BGR_YV12 = 99, - COLOR_YUV2RGB_IYUV = 100, - COLOR_YUV2BGR_IYUV = 101, - COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV, - COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV, - COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12, - COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12, - - COLOR_YUV2RGBA_YV12 = 102, - COLOR_YUV2BGRA_YV12 = 103, - COLOR_YUV2RGBA_IYUV = 104, - COLOR_YUV2BGRA_IYUV = 105, - COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV, - COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV, - COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12, - COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12, - - COLOR_YUV2GRAY_420 = 106, - COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420, - COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420, - COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420, - COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420, - COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420, - COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420, - COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420, - - //! YUV 4:2:2 family to RGB - COLOR_YUV2RGB_UYVY = 107, - COLOR_YUV2BGR_UYVY = 108, - //COLOR_YUV2RGB_VYUY = 109, - //COLOR_YUV2BGR_VYUY = 110, - COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY, - COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY, - COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY, - COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY, - - COLOR_YUV2RGBA_UYVY = 111, - COLOR_YUV2BGRA_UYVY = 112, - //COLOR_YUV2RGBA_VYUY = 113, - //COLOR_YUV2BGRA_VYUY = 114, - COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY, - COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY, - COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY, - COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY, - - COLOR_YUV2RGB_YUY2 = 115, - COLOR_YUV2BGR_YUY2 = 116, - COLOR_YUV2RGB_YVYU = 117, - COLOR_YUV2BGR_YVYU = 118, - COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2, - COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2, - COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2, - COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2, - - COLOR_YUV2RGBA_YUY2 = 119, - COLOR_YUV2BGRA_YUY2 = 120, - COLOR_YUV2RGBA_YVYU = 121, - COLOR_YUV2BGRA_YVYU = 122, - COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2, - COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2, - COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2, - COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2, - - COLOR_YUV2GRAY_UYVY = 123, - COLOR_YUV2GRAY_YUY2 = 124, - //CV_YUV2GRAY_VYUY = CV_YUV2GRAY_UYVY, - COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY, - COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY, - COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2, - COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2, - COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2, - - //! alpha premultiplication - COLOR_RGBA2mRGBA = 125, - COLOR_mRGBA2RGBA = 126, - - //! RGB to YUV 4:2:0 family - COLOR_RGB2YUV_I420 = 127, - COLOR_BGR2YUV_I420 = 128, - COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420, - COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420, - - COLOR_RGBA2YUV_I420 = 129, - COLOR_BGRA2YUV_I420 = 130, - COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420, - COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420, - COLOR_RGB2YUV_YV12 = 131, - COLOR_BGR2YUV_YV12 = 132, - COLOR_RGBA2YUV_YV12 = 133, - COLOR_BGRA2YUV_YV12 = 134, - - //! Demosaicing - COLOR_BayerBG2BGR = 46, - COLOR_BayerGB2BGR = 47, - COLOR_BayerRG2BGR = 48, - COLOR_BayerGR2BGR = 49, - - COLOR_BayerBG2RGB = COLOR_BayerRG2BGR, - COLOR_BayerGB2RGB = COLOR_BayerGR2BGR, - COLOR_BayerRG2RGB = COLOR_BayerBG2BGR, - COLOR_BayerGR2RGB = COLOR_BayerGB2BGR, - - COLOR_BayerBG2GRAY = 86, - COLOR_BayerGB2GRAY = 87, - COLOR_BayerRG2GRAY = 88, - COLOR_BayerGR2GRAY = 89, - - //! Demosaicing using Variable Number of Gradients - COLOR_BayerBG2BGR_VNG = 62, - COLOR_BayerGB2BGR_VNG = 63, - COLOR_BayerRG2BGR_VNG = 64, - COLOR_BayerGR2BGR_VNG = 65, - - COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG, - COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG, - COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG, - COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG, - - //! Edge-Aware Demosaicing - COLOR_BayerBG2BGR_EA = 135, - COLOR_BayerGB2BGR_EA = 136, - COLOR_BayerRG2BGR_EA = 137, - COLOR_BayerGR2BGR_EA = 138, - - COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA, - COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA, - COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA, - COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA, - - //! Demosaicing with alpha channel - COLOR_BayerBG2BGRA = 139, - COLOR_BayerGB2BGRA = 140, - COLOR_BayerRG2BGRA = 141, - COLOR_BayerGR2BGRA = 142, - - COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA, - COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA, - COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA, - COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA, - - COLOR_COLORCVT_MAX = 143 -}; - -//! @addtogroup imgproc_shape -//! @{ - -//! types of intersection between rectangles -enum RectanglesIntersectTypes { - INTERSECT_NONE = 0, //!< No intersection - INTERSECT_PARTIAL = 1, //!< There is a partial intersection - INTERSECT_FULL = 2 //!< One of the rectangle is fully enclosed in the other -}; - -/** @brief finds arbitrary template in the grayscale image using Generalized Hough Transform -*/ -class CV_EXPORTS_W GeneralizedHough : public Algorithm -{ -public: - //! set template to search - CV_WRAP virtual void setTemplate(InputArray templ, Point templCenter = Point(-1, -1)) = 0; - CV_WRAP virtual void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1)) = 0; - - //! find template on image - CV_WRAP virtual void detect(InputArray image, OutputArray positions, OutputArray votes = noArray()) = 0; - CV_WRAP virtual void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes = noArray()) = 0; - - //! Canny low threshold. - CV_WRAP virtual void setCannyLowThresh(int cannyLowThresh) = 0; - CV_WRAP virtual int getCannyLowThresh() const = 0; - - //! Canny high threshold. - CV_WRAP virtual void setCannyHighThresh(int cannyHighThresh) = 0; - CV_WRAP virtual int getCannyHighThresh() const = 0; - - //! Minimum distance between the centers of the detected objects. - CV_WRAP virtual void setMinDist(double minDist) = 0; - CV_WRAP virtual double getMinDist() const = 0; - - //! Inverse ratio of the accumulator resolution to the image resolution. - CV_WRAP virtual void setDp(double dp) = 0; - CV_WRAP virtual double getDp() const = 0; - - //! Maximal size of inner buffers. - CV_WRAP virtual void setMaxBufferSize(int maxBufferSize) = 0; - CV_WRAP virtual int getMaxBufferSize() const = 0; -}; - -/** @brief finds arbitrary template in the grayscale image using Generalized Hough Transform - -Detects position only without translation and rotation @cite Ballard1981 . -*/ -class CV_EXPORTS_W GeneralizedHoughBallard : public GeneralizedHough -{ -public: - //! R-Table levels. - CV_WRAP virtual void setLevels(int levels) = 0; - CV_WRAP virtual int getLevels() const = 0; - - //! The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected. - CV_WRAP virtual void setVotesThreshold(int votesThreshold) = 0; - CV_WRAP virtual int getVotesThreshold() const = 0; -}; - -/** @brief finds arbitrary template in the grayscale image using Generalized Hough Transform - -Detects position, translation and rotation @cite Guil1999 . -*/ -class CV_EXPORTS_W GeneralizedHoughGuil : public GeneralizedHough -{ -public: - //! Angle difference in degrees between two points in feature. - CV_WRAP virtual void setXi(double xi) = 0; - CV_WRAP virtual double getXi() const = 0; - - //! Feature table levels. - CV_WRAP virtual void setLevels(int levels) = 0; - CV_WRAP virtual int getLevels() const = 0; - - //! Maximal difference between angles that treated as equal. - CV_WRAP virtual void setAngleEpsilon(double angleEpsilon) = 0; - CV_WRAP virtual double getAngleEpsilon() const = 0; - - //! Minimal rotation angle to detect in degrees. - CV_WRAP virtual void setMinAngle(double minAngle) = 0; - CV_WRAP virtual double getMinAngle() const = 0; - - //! Maximal rotation angle to detect in degrees. - CV_WRAP virtual void setMaxAngle(double maxAngle) = 0; - CV_WRAP virtual double getMaxAngle() const = 0; - - //! Angle step in degrees. - CV_WRAP virtual void setAngleStep(double angleStep) = 0; - CV_WRAP virtual double getAngleStep() const = 0; - - //! Angle votes threshold. - CV_WRAP virtual void setAngleThresh(int angleThresh) = 0; - CV_WRAP virtual int getAngleThresh() const = 0; - - //! Minimal scale to detect. - CV_WRAP virtual void setMinScale(double minScale) = 0; - CV_WRAP virtual double getMinScale() const = 0; - - //! Maximal scale to detect. - CV_WRAP virtual void setMaxScale(double maxScale) = 0; - CV_WRAP virtual double getMaxScale() const = 0; - - //! Scale step. - CV_WRAP virtual void setScaleStep(double scaleStep) = 0; - CV_WRAP virtual double getScaleStep() const = 0; - - //! Scale votes threshold. - CV_WRAP virtual void setScaleThresh(int scaleThresh) = 0; - CV_WRAP virtual int getScaleThresh() const = 0; - - //! Position votes threshold. - CV_WRAP virtual void setPosThresh(int posThresh) = 0; - CV_WRAP virtual int getPosThresh() const = 0; -}; - -//! @} imgproc_shape - -//! @addtogroup imgproc_hist -//! @{ - -/** @brief Base class for Contrast Limited Adaptive Histogram Equalization. -*/ -class CV_EXPORTS_W CLAHE : public Algorithm -{ -public: - /** @brief Equalizes the histogram of a grayscale image using Contrast Limited Adaptive Histogram Equalization. - - @param src Source image of type CV_8UC1 or CV_16UC1. - @param dst Destination image. - */ - CV_WRAP virtual void apply(InputArray src, OutputArray dst) = 0; - - /** @brief Sets threshold for contrast limiting. - - @param clipLimit threshold value. - */ - CV_WRAP virtual void setClipLimit(double clipLimit) = 0; - - //! Returns threshold value for contrast limiting. - CV_WRAP virtual double getClipLimit() const = 0; - - /** @brief Sets size of grid for histogram equalization. Input image will be divided into - equally sized rectangular tiles. - - @param tileGridSize defines the number of tiles in row and column. - */ - CV_WRAP virtual void setTilesGridSize(Size tileGridSize) = 0; - - //!@brief Returns Size defines the number of tiles in row and column. - CV_WRAP virtual Size getTilesGridSize() const = 0; - - CV_WRAP virtual void collectGarbage() = 0; -}; - -//! @} imgproc_hist - -//! @addtogroup imgproc_subdiv2d -//! @{ - -class CV_EXPORTS_W Subdiv2D -{ -public: - /** Subdiv2D point location cases */ - enum { PTLOC_ERROR = -2, //!< Point location error - PTLOC_OUTSIDE_RECT = -1, //!< Point outside the subdivision bounding rect - PTLOC_INSIDE = 0, //!< Point inside some facet - PTLOC_VERTEX = 1, //!< Point coincides with one of the subdivision vertices - PTLOC_ON_EDGE = 2 //!< Point on some edge - }; - - /** Subdiv2D edge type navigation (see: getEdge()) */ - enum { NEXT_AROUND_ORG = 0x00, - NEXT_AROUND_DST = 0x22, - PREV_AROUND_ORG = 0x11, - PREV_AROUND_DST = 0x33, - NEXT_AROUND_LEFT = 0x13, - NEXT_AROUND_RIGHT = 0x31, - PREV_AROUND_LEFT = 0x20, - PREV_AROUND_RIGHT = 0x02 - }; - - /** creates an empty Subdiv2D object. - To create a new empty Delaunay subdivision you need to use the #initDelaunay function. - */ - CV_WRAP Subdiv2D(); - - /** @overload - - @param rect Rectangle that includes all of the 2D points that are to be added to the subdivision. - - The function creates an empty Delaunay subdivision where 2D points can be added using the function - insert() . All of the points to be added must be within the specified rectangle, otherwise a runtime - error is raised. - */ - CV_WRAP Subdiv2D(Rect rect); - - /** @brief Creates a new empty Delaunay subdivision - - @param rect Rectangle that includes all of the 2D points that are to be added to the subdivision. - - */ - CV_WRAP void initDelaunay(Rect rect); - - /** @brief Insert a single point into a Delaunay triangulation. - - @param pt Point to insert. - - The function inserts a single point into a subdivision and modifies the subdivision topology - appropriately. If a point with the same coordinates exists already, no new point is added. - @returns the ID of the point. - - @note If the point is outside of the triangulation specified rect a runtime error is raised. - */ - CV_WRAP int insert(Point2f pt); - - /** @brief Insert multiple points into a Delaunay triangulation. - - @param ptvec Points to insert. - - The function inserts a vector of points into a subdivision and modifies the subdivision topology - appropriately. - */ - CV_WRAP void insert(const std::vector& ptvec); - - /** @brief Returns the location of a point within a Delaunay triangulation. - - @param pt Point to locate. - @param edge Output edge that the point belongs to or is located to the right of it. - @param vertex Optional output vertex the input point coincides with. - - The function locates the input point within the subdivision and gives one of the triangle edges - or vertices. - - @returns an integer which specify one of the following five cases for point location: - - The point falls into some facet. The function returns #PTLOC_INSIDE and edge will contain one of - edges of the facet. - - The point falls onto the edge. The function returns #PTLOC_ON_EDGE and edge will contain this edge. - - The point coincides with one of the subdivision vertices. The function returns #PTLOC_VERTEX and - vertex will contain a pointer to the vertex. - - The point is outside the subdivision reference rectangle. The function returns #PTLOC_OUTSIDE_RECT - and no pointers are filled. - - One of input arguments is invalid. A runtime error is raised or, if silent or "parent" error - processing mode is selected, #PTLOC_ERROR is returned. - */ - CV_WRAP int locate(Point2f pt, CV_OUT int& edge, CV_OUT int& vertex); - - /** @brief Finds the subdivision vertex closest to the given point. - - @param pt Input point. - @param nearestPt Output subdivision vertex point. - - The function is another function that locates the input point within the subdivision. It finds the - subdivision vertex that is the closest to the input point. It is not necessarily one of vertices - of the facet containing the input point, though the facet (located using locate() ) is used as a - starting point. - - @returns vertex ID. - */ - CV_WRAP int findNearest(Point2f pt, CV_OUT Point2f* nearestPt = 0); - - /** @brief Returns a list of all edges. - - @param edgeList Output vector. - - The function gives each edge as a 4 numbers vector, where each two are one of the edge - vertices. i.e. org_x = v[0], org_y = v[1], dst_x = v[2], dst_y = v[3]. - */ - CV_WRAP void getEdgeList(CV_OUT std::vector& edgeList) const; - - /** @brief Returns a list of the leading edge ID connected to each triangle. - - @param leadingEdgeList Output vector. - - The function gives one edge ID for each triangle. - */ - CV_WRAP void getLeadingEdgeList(CV_OUT std::vector& leadingEdgeList) const; - - /** @brief Returns a list of all triangles. - - @param triangleList Output vector. - - The function gives each triangle as a 6 numbers vector, where each two are one of the triangle - vertices. i.e. p1_x = v[0], p1_y = v[1], p2_x = v[2], p2_y = v[3], p3_x = v[4], p3_y = v[5]. - */ - CV_WRAP void getTriangleList(CV_OUT std::vector& triangleList) const; - - /** @brief Returns a list of all Voronoi facets. - - @param idx Vector of vertices IDs to consider. For all vertices you can pass empty vector. - @param facetList Output vector of the Voronoi facets. - @param facetCenters Output vector of the Voronoi facets center points. - - */ - CV_WRAP void getVoronoiFacetList(const std::vector& idx, CV_OUT std::vector >& facetList, - CV_OUT std::vector& facetCenters); - - /** @brief Returns vertex location from vertex ID. - - @param vertex vertex ID. - @param firstEdge Optional. The first edge ID which is connected to the vertex. - @returns vertex (x,y) - - */ - CV_WRAP Point2f getVertex(int vertex, CV_OUT int* firstEdge = 0) const; - - /** @brief Returns one of the edges related to the given edge. - - @param edge Subdivision edge ID. - @param nextEdgeType Parameter specifying which of the related edges to return. - The following values are possible: - - NEXT_AROUND_ORG next around the edge origin ( eOnext on the picture below if e is the input edge) - - NEXT_AROUND_DST next around the edge vertex ( eDnext ) - - PREV_AROUND_ORG previous around the edge origin (reversed eRnext ) - - PREV_AROUND_DST previous around the edge destination (reversed eLnext ) - - NEXT_AROUND_LEFT next around the left facet ( eLnext ) - - NEXT_AROUND_RIGHT next around the right facet ( eRnext ) - - PREV_AROUND_LEFT previous around the left facet (reversed eOnext ) - - PREV_AROUND_RIGHT previous around the right facet (reversed eDnext ) - - ![sample output](pics/quadedge.png) - - @returns edge ID related to the input edge. - */ - CV_WRAP int getEdge( int edge, int nextEdgeType ) const; - - /** @brief Returns next edge around the edge origin. - - @param edge Subdivision edge ID. - - @returns an integer which is next edge ID around the edge origin: eOnext on the - picture above if e is the input edge). - */ - CV_WRAP int nextEdge(int edge) const; - - /** @brief Returns another edge of the same quad-edge. - - @param edge Subdivision edge ID. - @param rotate Parameter specifying which of the edges of the same quad-edge as the input - one to return. The following values are possible: - - 0 - the input edge ( e on the picture below if e is the input edge) - - 1 - the rotated edge ( eRot ) - - 2 - the reversed edge (reversed e (in green)) - - 3 - the reversed rotated edge (reversed eRot (in green)) - - @returns one of the edges ID of the same quad-edge as the input edge. - */ - CV_WRAP int rotateEdge(int edge, int rotate) const; - CV_WRAP int symEdge(int edge) const; - - /** @brief Returns the edge origin. - - @param edge Subdivision edge ID. - @param orgpt Output vertex location. - - @returns vertex ID. - */ - CV_WRAP int edgeOrg(int edge, CV_OUT Point2f* orgpt = 0) const; - - /** @brief Returns the edge destination. - - @param edge Subdivision edge ID. - @param dstpt Output vertex location. - - @returns vertex ID. - */ - CV_WRAP int edgeDst(int edge, CV_OUT Point2f* dstpt = 0) const; - -protected: - int newEdge(); - void deleteEdge(int edge); - int newPoint(Point2f pt, bool isvirtual, int firstEdge = 0); - void deletePoint(int vtx); - void setEdgePoints( int edge, int orgPt, int dstPt ); - void splice( int edgeA, int edgeB ); - int connectEdges( int edgeA, int edgeB ); - void swapEdges( int edge ); - int isRightOf(Point2f pt, int edge) const; - void calcVoronoi(); - void clearVoronoi(); - void checkSubdiv() const; - - struct CV_EXPORTS Vertex - { - Vertex(); - Vertex(Point2f pt, bool _isvirtual, int _firstEdge=0); - bool isvirtual() const; - bool isfree() const; - - int firstEdge; - int type; - Point2f pt; - }; - - struct CV_EXPORTS QuadEdge - { - QuadEdge(); - QuadEdge(int edgeidx); - bool isfree() const; - - int next[4]; - int pt[4]; - }; - - //! All of the vertices - std::vector vtx; - //! All of the edges - std::vector qedges; - int freeQEdge; - int freePoint; - bool validGeometry; - - int recentEdge; - //! Top left corner of the bounding rect - Point2f topLeft; - //! Bottom right corner of the bounding rect - Point2f bottomRight; -}; - -//! @} imgproc_subdiv2d - -//! @addtogroup imgproc_feature -//! @{ - -/** @brief Line segment detector class - -following the algorithm described at @cite Rafael12 . - -@note Implementation has been removed due original code license conflict - -*/ -class CV_EXPORTS_W LineSegmentDetector : public Algorithm -{ -public: - - /** @brief Finds lines in the input image. - - This is the output of the default parameters of the algorithm on the above shown image. - - ![image](pics/building_lsd.png) - - @param _image A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use: - `lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);` - @param _lines A vector of Vec4i or Vec4f elements specifying the beginning and ending point of a line. Where - Vec4i/Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly - oriented depending on the gradient. - @param width Vector of widths of the regions, where the lines are found. E.g. Width of line. - @param prec Vector of precisions with which the lines are found. - @param nfa Vector containing number of false alarms in the line region, with precision of 10%. The - bigger the value, logarithmically better the detection. - - -1 corresponds to 10 mean false alarms - - 0 corresponds to 1 mean false alarm - - 1 corresponds to 0.1 mean false alarms - This vector will be calculated only when the objects type is #LSD_REFINE_ADV. - */ - CV_WRAP virtual void detect(InputArray _image, OutputArray _lines, - OutputArray width = noArray(), OutputArray prec = noArray(), - OutputArray nfa = noArray()) = 0; - - /** @brief Draws the line segments on a given image. - @param _image The image, where the lines will be drawn. Should be bigger or equal to the image, - where the lines were found. - @param lines A vector of the lines that needed to be drawn. - */ - CV_WRAP virtual void drawSegments(InputOutputArray _image, InputArray lines) = 0; - - /** @brief Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels. - - @param size The size of the image, where lines1 and lines2 were found. - @param lines1 The first group of lines that needs to be drawn. It is visualized in blue color. - @param lines2 The second group of lines. They visualized in red color. - @param _image Optional image, where the lines will be drawn. The image should be color(3-channel) - in order for lines1 and lines2 to be drawn in the above mentioned colors. - */ - CV_WRAP virtual int compareSegments(const Size& size, InputArray lines1, InputArray lines2, InputOutputArray _image = noArray()) = 0; - - virtual ~LineSegmentDetector() { } -}; - -/** @brief Creates a smart pointer to a LineSegmentDetector object and initializes it. - -The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want -to edit those, as to tailor it for their own application. - -@param _refine The way found lines will be refined, see #LineSegmentDetectorModes -@param _scale The scale of the image that will be used to find the lines. Range (0..1]. -@param _sigma_scale Sigma for Gaussian filter. It is computed as sigma = _sigma_scale/_scale. -@param _quant Bound to the quantization error on the gradient norm. -@param _ang_th Gradient angle tolerance in degrees. -@param _log_eps Detection threshold: -log10(NFA) \> log_eps. Used only when advance refinement -is chosen. -@param _density_th Minimal density of aligned region points in the enclosing rectangle. -@param _n_bins Number of bins in pseudo-ordering of gradient modulus. - -@note Implementation has been removed due original code license conflict - */ -CV_EXPORTS_W Ptr createLineSegmentDetector( - int _refine = LSD_REFINE_STD, double _scale = 0.8, - double _sigma_scale = 0.6, double _quant = 2.0, double _ang_th = 22.5, - double _log_eps = 0, double _density_th = 0.7, int _n_bins = 1024); - -//! @} imgproc_feature - -//! @addtogroup imgproc_filter -//! @{ - -/** @brief Returns Gaussian filter coefficients. - -The function computes and returns the \f$\texttt{ksize} \times 1\f$ matrix of Gaussian filter -coefficients: - -\f[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\f] - -where \f$i=0..\texttt{ksize}-1\f$ and \f$\alpha\f$ is the scale factor chosen so that \f$\sum_i G_i=1\f$. - -Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize -smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. -You may also use the higher-level GaussianBlur. -@param ksize Aperture size. It should be odd ( \f$\texttt{ksize} \mod 2 = 1\f$ ) and positive. -@param sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as -`sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8`. -@param ktype Type of filter coefficients. It can be CV_32F or CV_64F . -@sa sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur - */ -CV_EXPORTS_W Mat getGaussianKernel( int ksize, double sigma, int ktype = CV_64F ); - -/** @brief Returns filter coefficients for computing spatial image derivatives. - -The function computes and returns the filter coefficients for spatial image derivatives. When -`ksize=CV_SCHARR`, the Scharr \f$3 \times 3\f$ kernels are generated (see #Scharr). Otherwise, Sobel -kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to - -@param kx Output matrix of row filter coefficients. It has the type ktype . -@param ky Output matrix of column filter coefficients. It has the type ktype . -@param dx Derivative order in respect of x. -@param dy Derivative order in respect of y. -@param ksize Aperture size. It can be CV_SCHARR, 1, 3, 5, or 7. -@param normalize Flag indicating whether to normalize (scale down) the filter coefficients or not. -Theoretically, the coefficients should have the denominator \f$=2^{ksize*2-dx-dy-2}\f$. If you are -going to filter floating-point images, you are likely to use the normalized kernels. But if you -compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve -all the fractional bits, you may want to set normalize=false . -@param ktype Type of filter coefficients. It can be CV_32f or CV_64F . - */ -CV_EXPORTS_W void getDerivKernels( OutputArray kx, OutputArray ky, - int dx, int dy, int ksize, - bool normalize = false, int ktype = CV_32F ); - -/** @brief Returns Gabor filter coefficients. - -For more details about gabor filter equations and parameters, see: [Gabor -Filter](http://en.wikipedia.org/wiki/Gabor_filter). - -@param ksize Size of the filter returned. -@param sigma Standard deviation of the gaussian envelope. -@param theta Orientation of the normal to the parallel stripes of a Gabor function. -@param lambd Wavelength of the sinusoidal factor. -@param gamma Spatial aspect ratio. -@param psi Phase offset. -@param ktype Type of filter coefficients. It can be CV_32F or CV_64F . - */ -CV_EXPORTS_W Mat getGaborKernel( Size ksize, double sigma, double theta, double lambd, - double gamma, double psi = CV_PI*0.5, int ktype = CV_64F ); - -//! returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation. -static inline Scalar morphologyDefaultBorderValue() { return Scalar::all(DBL_MAX); } - -/** @brief Returns a structuring element of the specified size and shape for morphological operations. - -The function constructs and returns the structuring element that can be further passed to #erode, -#dilate or #morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as -the structuring element. - -@param shape Element shape that could be one of #MorphShapes -@param ksize Size of the structuring element. -@param anchor Anchor position within the element. The default value \f$(-1, -1)\f$ means that the -anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor -position. In other cases the anchor just regulates how much the result of the morphological -operation is shifted. - */ -CV_EXPORTS_W Mat getStructuringElement(int shape, Size ksize, Point anchor = Point(-1,-1)); - -/** @example samples/cpp/tutorial_code/ImgProc/Smoothing/Smoothing.cpp -Sample code for simple filters -![Sample screenshot](Smoothing_Tutorial_Result_Median_Filter.jpg) -Check @ref tutorial_gausian_median_blur_bilateral_filter "the corresponding tutorial" for more details - */ - -/** @brief Blurs an image using the median filter. - -The function smoothes an image using the median filter with the \f$\texttt{ksize} \times -\texttt{ksize}\f$ aperture. Each channel of a multi-channel image is processed independently. -In-place operation is supported. - -@note The median filter uses #BORDER_REPLICATE internally to cope with border pixels, see #BorderTypes - -@param src input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be -CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U. -@param dst destination array of the same size and type as src. -@param ksize aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ... -@sa bilateralFilter, blur, boxFilter, GaussianBlur - */ -CV_EXPORTS_W void medianBlur( InputArray src, OutputArray dst, int ksize ); - -/** @brief Blurs an image using a Gaussian filter. - -The function convolves the source image with the specified Gaussian kernel. In-place filtering is -supported. - -@param src input image; the image can have any number of channels, which are processed -independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. -@param dst output image of the same size and type as src. -@param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be -positive and odd. Or, they can be zero's and then they are computed from sigma. -@param sigmaX Gaussian kernel standard deviation in X direction. -@param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be -equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, -respectively (see #getGaussianKernel for details); to fully control the result regardless of -possible future modifications of all this semantics, it is recommended to specify all of ksize, -sigmaX, and sigmaY. -@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. - -@sa sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur - */ -CV_EXPORTS_W void GaussianBlur( InputArray src, OutputArray dst, Size ksize, - double sigmaX, double sigmaY = 0, - int borderType = BORDER_DEFAULT ); - -/** @brief Applies the bilateral filter to an image. - -The function applies bilateral filtering to the input image, as described in -http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html -bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is -very slow compared to most filters. - -_Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\< -10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very -strong effect, making the image look "cartoonish". - -_Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time -applications, and perhaps d=9 for offline applications that need heavy noise filtering. - -This filter does not work inplace. -@param src Source 8-bit or floating-point, 1-channel or 3-channel image. -@param dst Destination image of the same size and type as src . -@param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, -it is computed from sigmaSpace. -@param sigmaColor Filter sigma in the color space. A larger value of the parameter means that -farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting -in larger areas of semi-equal color. -@param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that -farther pixels will influence each other as long as their colors are close enough (see sigmaColor -). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is -proportional to sigmaSpace. -@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes - */ -CV_EXPORTS_W void bilateralFilter( InputArray src, OutputArray dst, int d, - double sigmaColor, double sigmaSpace, - int borderType = BORDER_DEFAULT ); - -/** @brief Blurs an image using the box filter. - -The function smooths an image using the kernel: - -\f[\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\f] - -where - -\f[\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}\f] - -Unnormalized box filter is useful for computing various integral characteristics over each pixel -neighborhood, such as covariance matrices of image derivatives (used in dense optical flow -algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral. - -@param src input image. -@param dst output image of the same size and type as src. -@param ddepth the output image depth (-1 to use src.depth()). -@param ksize blurring kernel size. -@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel -center. -@param normalize flag, specifying whether the kernel is normalized by its area or not. -@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported. -@sa blur, bilateralFilter, GaussianBlur, medianBlur, integral - */ -CV_EXPORTS_W void boxFilter( InputArray src, OutputArray dst, int ddepth, - Size ksize, Point anchor = Point(-1,-1), - bool normalize = true, - int borderType = BORDER_DEFAULT ); - -/** @brief Calculates the normalized sum of squares of the pixel values overlapping the filter. - -For every pixel \f$ (x, y) \f$ in the source image, the function calculates the sum of squares of those neighboring -pixel values which overlap the filter placed over the pixel \f$ (x, y) \f$. - -The unnormalized square box filter can be useful in computing local image statistics such as the the local -variance and standard deviation around the neighborhood of a pixel. - -@param src input image -@param dst output image of the same size and type as _src -@param ddepth the output image depth (-1 to use src.depth()) -@param ksize kernel size -@param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel -center. -@param normalize flag, specifying whether the kernel is to be normalized by it's area or not. -@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported. -@sa boxFilter -*/ -CV_EXPORTS_W void sqrBoxFilter( InputArray src, OutputArray dst, int ddepth, - Size ksize, Point anchor = Point(-1, -1), - bool normalize = true, - int borderType = BORDER_DEFAULT ); - -/** @brief Blurs an image using the normalized box filter. - -The function smooths an image using the kernel: - -\f[\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\f] - -The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(), ksize, -anchor, true, borderType)`. - -@param src input image; it can have any number of channels, which are processed independently, but -the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. -@param dst output image of the same size and type as src. -@param ksize blurring kernel size. -@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel -center. -@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported. -@sa boxFilter, bilateralFilter, GaussianBlur, medianBlur - */ -CV_EXPORTS_W void blur( InputArray src, OutputArray dst, - Size ksize, Point anchor = Point(-1,-1), - int borderType = BORDER_DEFAULT ); - -/** @brief Convolves an image with the kernel. - -The function applies an arbitrary linear filter to an image. In-place operation is supported. When -the aperture is partially outside the image, the function interpolates outlier pixel values -according to the specified border mode. - -The function does actually compute correlation, not the convolution: - -\f[\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\f] - -That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip -the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows - -anchor.y - 1)`. - -The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or -larger) and the direct algorithm for small kernels. - -@param src input image. -@param dst output image of the same size and the same number of channels as src. -@param ddepth desired depth of the destination image, see @ref filter_depths "combinations" -@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point -matrix; if you want to apply different kernels to different channels, split the image into -separate color planes using split and process them individually. -@param anchor anchor of the kernel that indicates the relative position of a filtered point within -the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor -is at the kernel center. -@param delta optional value added to the filtered pixels before storing them in dst. -@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. -@sa sepFilter2D, dft, matchTemplate - */ -CV_EXPORTS_W void filter2D( InputArray src, OutputArray dst, int ddepth, - InputArray kernel, Point anchor = Point(-1,-1), - double delta = 0, int borderType = BORDER_DEFAULT ); - -/** @brief Applies a separable linear filter to an image. - -The function applies a separable linear filter to the image. That is, first, every row of src is -filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D -kernel kernelY. The final result shifted by delta is stored in dst . - -@param src Source image. -@param dst Destination image of the same size and the same number of channels as src . -@param ddepth Destination image depth, see @ref filter_depths "combinations" -@param kernelX Coefficients for filtering each row. -@param kernelY Coefficients for filtering each column. -@param anchor Anchor position within the kernel. The default value \f$(-1,-1)\f$ means that the anchor -is at the kernel center. -@param delta Value added to the filtered results before storing them. -@param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. -@sa filter2D, Sobel, GaussianBlur, boxFilter, blur - */ -CV_EXPORTS_W void sepFilter2D( InputArray src, OutputArray dst, int ddepth, - InputArray kernelX, InputArray kernelY, - Point anchor = Point(-1,-1), - double delta = 0, int borderType = BORDER_DEFAULT ); - -/** @example samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp -Sample code using Sobel and/or Scharr OpenCV functions to make a simple Edge Detector -![Sample screenshot](Sobel_Derivatives_Tutorial_Result.jpg) -Check @ref tutorial_sobel_derivatives "the corresponding tutorial" for more details -*/ - -/** @brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. - -In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to -calculate the derivative. When \f$\texttt{ksize = 1}\f$, the \f$3 \times 1\f$ or \f$1 \times 3\f$ -kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first -or the second x- or y- derivatives. - -There is also the special value `ksize = #CV_SCHARR (-1)` that corresponds to the \f$3\times3\f$ Scharr -filter that may give more accurate results than the \f$3\times3\f$ Sobel. The Scharr aperture is - -\f[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\f] - -for the x-derivative, or transposed for the y-derivative. - -The function calculates an image derivative by convolving the image with the appropriate kernel: - -\f[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\f] - -The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less -resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) -or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first -case corresponds to a kernel of: - -\f[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\f] - -The second case corresponds to a kernel of: - -\f[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\f] - -@param src input image. -@param dst output image of the same size and the same number of channels as src . -@param ddepth output image depth, see @ref filter_depths "combinations"; in the case of - 8-bit input images it will result in truncated derivatives. -@param dx order of the derivative x. -@param dy order of the derivative y. -@param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7. -@param scale optional scale factor for the computed derivative values; by default, no scaling is -applied (see #getDerivKernels for details). -@param delta optional delta value that is added to the results prior to storing them in dst. -@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. -@sa Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar - */ -CV_EXPORTS_W void Sobel( InputArray src, OutputArray dst, int ddepth, - int dx, int dy, int ksize = 3, - double scale = 1, double delta = 0, - int borderType = BORDER_DEFAULT ); - -/** @brief Calculates the first order image derivative in both x and y using a Sobel operator - -Equivalent to calling: - -@code -Sobel( src, dx, CV_16SC1, 1, 0, 3 ); -Sobel( src, dy, CV_16SC1, 0, 1, 3 ); -@endcode - -@param src input image. -@param dx output image with first-order derivative in x. -@param dy output image with first-order derivative in y. -@param ksize size of Sobel kernel. It must be 3. -@param borderType pixel extrapolation method, see #BorderTypes. - Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported. - -@sa Sobel - */ - -CV_EXPORTS_W void spatialGradient( InputArray src, OutputArray dx, - OutputArray dy, int ksize = 3, - int borderType = BORDER_DEFAULT ); - -/** @brief Calculates the first x- or y- image derivative using Scharr operator. - -The function computes the first x- or y- spatial image derivative using the Scharr operator. The -call - -\f[\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\f] - -is equivalent to - -\f[\texttt{Sobel(src, dst, ddepth, dx, dy, CV_SCHARR, scale, delta, borderType)} .\f] - -@param src input image. -@param dst output image of the same size and the same number of channels as src. -@param ddepth output image depth, see @ref filter_depths "combinations" -@param dx order of the derivative x. -@param dy order of the derivative y. -@param scale optional scale factor for the computed derivative values; by default, no scaling is -applied (see #getDerivKernels for details). -@param delta optional delta value that is added to the results prior to storing them in dst. -@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. -@sa cartToPolar - */ -CV_EXPORTS_W void Scharr( InputArray src, OutputArray dst, int ddepth, - int dx, int dy, double scale = 1, double delta = 0, - int borderType = BORDER_DEFAULT ); - -/** @example samples/cpp/laplace.cpp -An example using Laplace transformations for edge detection -*/ - -/** @brief Calculates the Laplacian of an image. - -The function calculates the Laplacian of the source image by adding up the second x and y -derivatives calculated using the Sobel operator: - -\f[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\f] - -This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image -with the following \f$3 \times 3\f$ aperture: - -\f[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\f] - -@param src Source image. -@param dst Destination image of the same size and the same number of channels as src . -@param ddepth Desired depth of the destination image. -@param ksize Aperture size used to compute the second-derivative filters. See #getDerivKernels for -details. The size must be positive and odd. -@param scale Optional scale factor for the computed Laplacian values. By default, no scaling is -applied. See #getDerivKernels for details. -@param delta Optional delta value that is added to the results prior to storing them in dst . -@param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. -@sa Sobel, Scharr - */ -CV_EXPORTS_W void Laplacian( InputArray src, OutputArray dst, int ddepth, - int ksize = 1, double scale = 1, double delta = 0, - int borderType = BORDER_DEFAULT ); - -//! @} imgproc_filter - -//! @addtogroup imgproc_feature -//! @{ - -/** @example samples/cpp/edge.cpp -This program demonstrates usage of the Canny edge detector - -Check @ref tutorial_canny_detector "the corresponding tutorial" for more details -*/ - -/** @brief Finds edges in an image using the Canny algorithm @cite Canny86 . - -The function finds edges in the input image and marks them in the output map edges using the -Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The -largest value is used to find initial segments of strong edges. See - - -@param image 8-bit input image. -@param edges output edge map; single channels 8-bit image, which has the same size as image . -@param threshold1 first threshold for the hysteresis procedure. -@param threshold2 second threshold for the hysteresis procedure. -@param apertureSize aperture size for the Sobel operator. -@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm -\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude ( -L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough ( -L2gradient=false ). - */ -CV_EXPORTS_W void Canny( InputArray image, OutputArray edges, - double threshold1, double threshold2, - int apertureSize = 3, bool L2gradient = false ); - -/** \overload - -Finds edges in an image using the Canny algorithm with custom image gradient. - -@param dx 16-bit x derivative of input image (CV_16SC1 or CV_16SC3). -@param dy 16-bit y derivative of input image (same type as dx). -@param edges output edge map; single channels 8-bit image, which has the same size as image . -@param threshold1 first threshold for the hysteresis procedure. -@param threshold2 second threshold for the hysteresis procedure. -@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm -\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude ( -L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough ( -L2gradient=false ). - */ -CV_EXPORTS_W void Canny( InputArray dx, InputArray dy, - OutputArray edges, - double threshold1, double threshold2, - bool L2gradient = false ); - -/** @brief Calculates the minimal eigenvalue of gradient matrices for corner detection. - -The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal -eigenvalue of the covariance matrix of derivatives, that is, \f$\min(\lambda_1, \lambda_2)\f$ in terms -of the formulae in the cornerEigenValsAndVecs description. - -@param src Input single-channel 8-bit or floating-point image. -@param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as -src . -@param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ). -@param ksize Aperture parameter for the Sobel operator. -@param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported. - */ -CV_EXPORTS_W void cornerMinEigenVal( InputArray src, OutputArray dst, - int blockSize, int ksize = 3, - int borderType = BORDER_DEFAULT ); - -/** @brief Harris corner detector. - -The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and -cornerEigenValsAndVecs , for each pixel \f$(x, y)\f$ it calculates a \f$2\times2\f$ gradient covariance -matrix \f$M^{(x,y)}\f$ over a \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood. Then, it -computes the following characteristic: - -\f[\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)} - k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\f] - -Corners in the image can be found as the local maxima of this response map. - -@param src Input single-channel 8-bit or floating-point image. -@param dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same -size as src . -@param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ). -@param ksize Aperture parameter for the Sobel operator. -@param k Harris detector free parameter. See the formula above. -@param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported. - */ -CV_EXPORTS_W void cornerHarris( InputArray src, OutputArray dst, int blockSize, - int ksize, double k, - int borderType = BORDER_DEFAULT ); - -/** @brief Calculates eigenvalues and eigenvectors of image blocks for corner detection. - -For every pixel \f$p\f$ , the function cornerEigenValsAndVecs considers a blockSize \f$\times\f$ blockSize -neighborhood \f$S(p)\f$ . It calculates the covariation matrix of derivatives over the neighborhood as: - -\f[M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}dI/dx dI/dy \\ \sum _{S(p)}dI/dx dI/dy & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\f] - -where the derivatives are computed using the Sobel operator. - -After that, it finds eigenvectors and eigenvalues of \f$M\f$ and stores them in the destination image as -\f$(\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)\f$ where - -- \f$\lambda_1, \lambda_2\f$ are the non-sorted eigenvalues of \f$M\f$ -- \f$x_1, y_1\f$ are the eigenvectors corresponding to \f$\lambda_1\f$ -- \f$x_2, y_2\f$ are the eigenvectors corresponding to \f$\lambda_2\f$ - -The output of the function can be used for robust edge or corner detection. - -@param src Input single-channel 8-bit or floating-point image. -@param dst Image to store the results. It has the same size as src and the type CV_32FC(6) . -@param blockSize Neighborhood size (see details below). -@param ksize Aperture parameter for the Sobel operator. -@param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported. - -@sa cornerMinEigenVal, cornerHarris, preCornerDetect - */ -CV_EXPORTS_W void cornerEigenValsAndVecs( InputArray src, OutputArray dst, - int blockSize, int ksize, - int borderType = BORDER_DEFAULT ); - -/** @brief Calculates a feature map for corner detection. - -The function calculates the complex spatial derivative-based function of the source image - -\f[\texttt{dst} = (D_x \texttt{src} )^2 \cdot D_{yy} \texttt{src} + (D_y \texttt{src} )^2 \cdot D_{xx} \texttt{src} - 2 D_x \texttt{src} \cdot D_y \texttt{src} \cdot D_{xy} \texttt{src}\f] - -where \f$D_x\f$,\f$D_y\f$ are the first image derivatives, \f$D_{xx}\f$,\f$D_{yy}\f$ are the second image -derivatives, and \f$D_{xy}\f$ is the mixed derivative. - -The corners can be found as local maximums of the functions, as shown below: -@code - Mat corners, dilated_corners; - preCornerDetect(image, corners, 3); - // dilation with 3x3 rectangular structuring element - dilate(corners, dilated_corners, Mat(), 1); - Mat corner_mask = corners == dilated_corners; -@endcode - -@param src Source single-channel 8-bit of floating-point image. -@param dst Output image that has the type CV_32F and the same size as src . -@param ksize %Aperture size of the Sobel . -@param borderType Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported. - */ -CV_EXPORTS_W void preCornerDetect( InputArray src, OutputArray dst, int ksize, - int borderType = BORDER_DEFAULT ); - -/** @brief Refines the corner locations. - -The function iterates to find the sub-pixel accurate location of corners or radial saddle -points as described in @cite forstner1987fast, and as shown on the figure below. - -![image](pics/cornersubpix.png) - -Sub-pixel accurate corner locator is based on the observation that every vector from the center \f$q\f$ -to a point \f$p\f$ located within a neighborhood of \f$q\f$ is orthogonal to the image gradient at \f$p\f$ -subject to image and measurement noise. Consider the expression: - -\f[\epsilon _i = {DI_{p_i}}^T \cdot (q - p_i)\f] - -where \f${DI_{p_i}}\f$ is an image gradient at one of the points \f$p_i\f$ in a neighborhood of \f$q\f$ . The -value of \f$q\f$ is to be found so that \f$\epsilon_i\f$ is minimized. A system of equations may be set up -with \f$\epsilon_i\f$ set to zero: - -\f[\sum _i(DI_{p_i} \cdot {DI_{p_i}}^T) \cdot q - \sum _i(DI_{p_i} \cdot {DI_{p_i}}^T \cdot p_i)\f] - -where the gradients are summed within a neighborhood ("search window") of \f$q\f$ . Calling the first -gradient term \f$G\f$ and the second gradient term \f$b\f$ gives: - -\f[q = G^{-1} \cdot b\f] - -The algorithm sets the center of the neighborhood window at this new center \f$q\f$ and then iterates -until the center stays within a set threshold. - -@param image Input single-channel, 8-bit or float image. -@param corners Initial coordinates of the input corners and refined coordinates provided for -output. -@param winSize Half of the side length of the search window. For example, if winSize=Size(5,5) , -then a \f$(5*2+1) \times (5*2+1) = 11 \times 11\f$ search window is used. -@param zeroZone Half of the size of the dead region in the middle of the search zone over which -the summation in the formula below is not done. It is used sometimes to avoid possible -singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such -a size. -@param criteria Criteria for termination of the iterative process of corner refinement. That is, -the process of corner position refinement stops either after criteria.maxCount iterations or when -the corner position moves by less than criteria.epsilon on some iteration. - */ -CV_EXPORTS_W void cornerSubPix( InputArray image, InputOutputArray corners, - Size winSize, Size zeroZone, - TermCriteria criteria ); - -/** @brief Determines strong corners on an image. - -The function finds the most prominent corners in the image or in the specified image region, as -described in @cite Shi94 - -- Function calculates the corner quality measure at every source image pixel using the - #cornerMinEigenVal or #cornerHarris . -- Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are - retained). -- The corners with the minimal eigenvalue less than - \f$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\f$ are rejected. -- The remaining corners are sorted by the quality measure in the descending order. -- Function throws away each corner for which there is a stronger corner at a distance less than - maxDistance. - -The function can be used to initialize a point-based tracker of an object. - -@note If the function is called with different values A and B of the parameter qualityLevel , and -A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector -with qualityLevel=B . - -@param image Input 8-bit or floating-point 32-bit, single-channel image. -@param corners Output vector of detected corners. -@param maxCorners Maximum number of corners to return. If there are more corners than are found, -the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set -and all detected corners are returned. -@param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The -parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue -(see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the -quality measure less than the product are rejected. For example, if the best corner has the -quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure -less than 15 are rejected. -@param minDistance Minimum possible Euclidean distance between the returned corners. -@param mask Optional region of interest. If the image is not empty (it needs to have the type -CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. -@param blockSize Size of an average block for computing a derivative covariation matrix over each -pixel neighborhood. See cornerEigenValsAndVecs . -@param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris) -or #cornerMinEigenVal. -@param k Free parameter of the Harris detector. - -@sa cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform, - */ - -CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners, - int maxCorners, double qualityLevel, double minDistance, - InputArray mask = noArray(), int blockSize = 3, - bool useHarrisDetector = false, double k = 0.04 ); - -CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners, - int maxCorners, double qualityLevel, double minDistance, - InputArray mask, int blockSize, - int gradientSize, bool useHarrisDetector = false, - double k = 0.04 ); -/** @example samples/cpp/tutorial_code/ImgTrans/houghlines.cpp -An example using the Hough line detector -![Sample input image](Hough_Lines_Tutorial_Original_Image.jpg) ![Output image](Hough_Lines_Tutorial_Result.jpg) -*/ - -/** @brief Finds lines in a binary image using the standard Hough transform. - -The function implements the standard or standard multi-scale Hough transform algorithm for line -detection. See for a good explanation of Hough -transform. - -@param image 8-bit, single-channel binary source image. The image may be modified by the function. -@param lines Output vector of lines. Each line is represented by a 2 or 3 element vector -\f$(\rho, \theta)\f$ or \f$(\rho, \theta, \textrm{votes})\f$ . \f$\rho\f$ is the distance from the coordinate origin \f$(0,0)\f$ (top-left corner of -the image). \f$\theta\f$ is the line rotation angle in radians ( -\f$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\f$ ). -\f$\textrm{votes}\f$ is the value of accumulator. -@param rho Distance resolution of the accumulator in pixels. -@param theta Angle resolution of the accumulator in radians. -@param threshold Accumulator threshold parameter. Only those lines are returned that get enough -votes ( \f$>\texttt{threshold}\f$ ). -@param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho . -The coarse accumulator distance resolution is rho and the accurate accumulator resolution is -rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these -parameters should be positive. -@param stn For the multi-scale Hough transform, it is a divisor for the distance resolution theta. -@param min_theta For standard and multi-scale Hough transform, minimum angle to check for lines. -Must fall between 0 and max_theta. -@param max_theta For standard and multi-scale Hough transform, maximum angle to check for lines. -Must fall between min_theta and CV_PI. - */ -CV_EXPORTS_W void HoughLines( InputArray image, OutputArray lines, - double rho, double theta, int threshold, - double srn = 0, double stn = 0, - double min_theta = 0, double max_theta = CV_PI ); - -/** @brief Finds line segments in a binary image using the probabilistic Hough transform. - -The function implements the probabilistic Hough transform algorithm for line detection, described -in @cite Matas00 - -See the line detection example below: -@include snippets/imgproc_HoughLinesP.cpp -This is a sample picture the function parameters have been tuned for: - -![image](pics/building.jpg) - -And this is the output of the above program in case of the probabilistic Hough transform: - -![image](pics/houghp.png) - -@param image 8-bit, single-channel binary source image. The image may be modified by the function. -@param lines Output vector of lines. Each line is represented by a 4-element vector -\f$(x_1, y_1, x_2, y_2)\f$ , where \f$(x_1,y_1)\f$ and \f$(x_2, y_2)\f$ are the ending points of each detected -line segment. -@param rho Distance resolution of the accumulator in pixels. -@param theta Angle resolution of the accumulator in radians. -@param threshold Accumulator threshold parameter. Only those lines are returned that get enough -votes ( \f$>\texttt{threshold}\f$ ). -@param minLineLength Minimum line length. Line segments shorter than that are rejected. -@param maxLineGap Maximum allowed gap between points on the same line to link them. - -@sa LineSegmentDetector - */ -CV_EXPORTS_W void HoughLinesP( InputArray image, OutputArray lines, - double rho, double theta, int threshold, - double minLineLength = 0, double maxLineGap = 0 ); - -/** @brief Finds lines in a set of points using the standard Hough transform. - -The function finds lines in a set of points using a modification of the Hough transform. -@include snippets/imgproc_HoughLinesPointSet.cpp -@param _point Input vector of points. Each vector must be encoded as a Point vector \f$(x,y)\f$. Type must be CV_32FC2 or CV_32SC2. -@param _lines Output vector of found lines. Each vector is encoded as a vector \f$(votes, rho, theta)\f$. -The larger the value of 'votes', the higher the reliability of the Hough line. -@param lines_max Max count of hough lines. -@param threshold Accumulator threshold parameter. Only those lines are returned that get enough -votes ( \f$>\texttt{threshold}\f$ ) -@param min_rho Minimum Distance value of the accumulator in pixels. -@param max_rho Maximum Distance value of the accumulator in pixels. -@param rho_step Distance resolution of the accumulator in pixels. -@param min_theta Minimum angle value of the accumulator in radians. -@param max_theta Maximum angle value of the accumulator in radians. -@param theta_step Angle resolution of the accumulator in radians. - */ -CV_EXPORTS_W void HoughLinesPointSet( InputArray _point, OutputArray _lines, int lines_max, int threshold, - double min_rho, double max_rho, double rho_step, - double min_theta, double max_theta, double theta_step ); - -/** @example samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp -An example using the Hough circle detector -*/ - -/** @brief Finds circles in a grayscale image using the Hough transform. - -The function finds circles in a grayscale image using a modification of the Hough transform. - -Example: : -@include snippets/imgproc_HoughLinesCircles.cpp - -@note Usually the function detects the centers of circles well. However, it may fail to find correct -radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if -you know it. Or, you may set maxRadius to a negative number to return centers only without radius -search, and find the correct radius using an additional procedure. - -@param image 8-bit, single-channel, grayscale input image. -@param circles Output vector of found circles. Each vector is encoded as 3 or 4 element -floating-point vector \f$(x, y, radius)\f$ or \f$(x, y, radius, votes)\f$ . -@param method Detection method, see #HoughModes. Currently, the only implemented method is #HOUGH_GRADIENT -@param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if -dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has -half as big width and height. -@param minDist Minimum distance between the centers of the detected circles. If the parameter is -too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is -too large, some circles may be missed. -@param param1 First method-specific parameter. In case of #HOUGH_GRADIENT , it is the higher -threshold of the two passed to the Canny edge detector (the lower one is twice smaller). -@param param2 Second method-specific parameter. In case of #HOUGH_GRADIENT , it is the -accumulator threshold for the circle centers at the detection stage. The smaller it is, the more -false circles may be detected. Circles, corresponding to the larger accumulator values, will be -returned first. -@param minRadius Minimum circle radius. -@param maxRadius Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, returns -centers without finding the radius. - -@sa fitEllipse, minEnclosingCircle - */ -CV_EXPORTS_W void HoughCircles( InputArray image, OutputArray circles, - int method, double dp, double minDist, - double param1 = 100, double param2 = 100, - int minRadius = 0, int maxRadius = 0 ); - -//! @} imgproc_feature - -//! @addtogroup imgproc_filter -//! @{ - -/** @example samples/cpp/tutorial_code/ImgProc/Morphology_2.cpp -Advanced morphology Transformations sample code -![Sample screenshot](Morphology_2_Tutorial_Result.jpg) -Check @ref tutorial_opening_closing_hats "the corresponding tutorial" for more details -*/ - -/** @brief Erodes an image by using a specific structuring element. - -The function erodes the source image using the specified structuring element that determines the -shape of a pixel neighborhood over which the minimum is taken: - -\f[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f] - -The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In -case of multi-channel images, each channel is processed independently. - -@param src input image; the number of channels can be arbitrary, but the depth should be one of -CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. -@param dst output image of the same size and type as src. -@param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular -structuring element is used. Kernel can be created using #getStructuringElement. -@param anchor position of the anchor within the element; default value (-1, -1) means that the -anchor is at the element center. -@param iterations number of times erosion is applied. -@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. -@param borderValue border value in case of a constant border -@sa dilate, morphologyEx, getStructuringElement - */ -CV_EXPORTS_W void erode( InputArray src, OutputArray dst, InputArray kernel, - Point anchor = Point(-1,-1), int iterations = 1, - int borderType = BORDER_CONSTANT, - const Scalar& borderValue = morphologyDefaultBorderValue() ); - -/** @example samples/cpp/tutorial_code/ImgProc/Morphology_1.cpp -Erosion and Dilation sample code -![Sample Screenshot-Erosion](Morphology_1_Tutorial_Erosion_Result.jpg)![Sample Screenshot-Dilation](Morphology_1_Tutorial_Dilation_Result.jpg) -Check @ref tutorial_erosion_dilatation "the corresponding tutorial" for more details -*/ - -/** @brief Dilates an image by using a specific structuring element. - -The function dilates the source image using the specified structuring element that determines the -shape of a pixel neighborhood over which the maximum is taken: -\f[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f] - -The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In -case of multi-channel images, each channel is processed independently. - -@param src input image; the number of channels can be arbitrary, but the depth should be one of -CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. -@param dst output image of the same size and type as src. -@param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular -structuring element is used. Kernel can be created using #getStructuringElement -@param anchor position of the anchor within the element; default value (-1, -1) means that the -anchor is at the element center. -@param iterations number of times dilation is applied. -@param borderType pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported. -@param borderValue border value in case of a constant border -@sa erode, morphologyEx, getStructuringElement - */ -CV_EXPORTS_W void dilate( InputArray src, OutputArray dst, InputArray kernel, - Point anchor = Point(-1,-1), int iterations = 1, - int borderType = BORDER_CONSTANT, - const Scalar& borderValue = morphologyDefaultBorderValue() ); - -/** @brief Performs advanced morphological transformations. - -The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as -basic operations. - -Any of the operations can be done in-place. In case of multi-channel images, each channel is -processed independently. - -@param src Source image. The number of channels can be arbitrary. The depth should be one of -CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. -@param dst Destination image of the same size and type as source image. -@param op Type of a morphological operation, see #MorphTypes -@param kernel Structuring element. It can be created using #getStructuringElement. -@param anchor Anchor position with the kernel. Negative values mean that the anchor is at the -kernel center. -@param iterations Number of times erosion and dilation are applied. -@param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported. -@param borderValue Border value in case of a constant border. The default value has a special -meaning. -@sa dilate, erode, getStructuringElement -@note The number of iterations is the number of times erosion or dilatation operation will be applied. -For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply -successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate). - */ -CV_EXPORTS_W void morphologyEx( InputArray src, OutputArray dst, - int op, InputArray kernel, - Point anchor = Point(-1,-1), int iterations = 1, - int borderType = BORDER_CONSTANT, - const Scalar& borderValue = morphologyDefaultBorderValue() ); - -//! @} imgproc_filter - -//! @addtogroup imgproc_transform -//! @{ - -/** @brief Resizes an image. - -The function resize resizes the image src down to or up to the specified size. Note that the -initial dst type or size are not taken into account. Instead, the size and type are derived from -the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst, -you may call the function as follows: -@code - // explicitly specify dsize=dst.size(); fx and fy will be computed from that. - resize(src, dst, dst.size(), 0, 0, interpolation); -@endcode -If you want to decimate the image by factor of 2 in each direction, you can call the function this -way: -@code - // specify fx and fy and let the function compute the destination image size. - resize(src, dst, Size(), 0.5, 0.5, interpolation); -@endcode -To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to -enlarge an image, it will generally look best with c#INTER_CUBIC (slow) or #INTER_LINEAR -(faster but still looks OK). - -@param src input image. -@param dst output image; it has the size dsize (when it is non-zero) or the size computed from -src.size(), fx, and fy; the type of dst is the same as of src. -@param dsize output image size; if it equals zero, it is computed as: - \f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f] - Either dsize or both fx and fy must be non-zero. -@param fx scale factor along the horizontal axis; when it equals 0, it is computed as -\f[\texttt{(double)dsize.width/src.cols}\f] -@param fy scale factor along the vertical axis; when it equals 0, it is computed as -\f[\texttt{(double)dsize.height/src.rows}\f] -@param interpolation interpolation method, see #InterpolationFlags - -@sa warpAffine, warpPerspective, remap - */ -CV_EXPORTS_W void resize( InputArray src, OutputArray dst, - Size dsize, double fx = 0, double fy = 0, - int interpolation = INTER_LINEAR ); - -/** @brief Applies an affine transformation to an image. - -The function warpAffine transforms the source image using the specified matrix: - -\f[\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\f] - -when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted -with #invertAffineTransform and then put in the formula above instead of M. The function cannot -operate in-place. - -@param src input image. -@param dst output image that has the size dsize and the same type as src . -@param M \f$2\times 3\f$ transformation matrix. -@param dsize size of the output image. -@param flags combination of interpolation methods (see #InterpolationFlags) and the optional -flag #WARP_INVERSE_MAP that means that M is the inverse transformation ( -\f$\texttt{dst}\rightarrow\texttt{src}\f$ ). -@param borderMode pixel extrapolation method (see #BorderTypes); when -borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to -the "outliers" in the source image are not modified by the function. -@param borderValue value used in case of a constant border; by default, it is 0. - -@sa warpPerspective, resize, remap, getRectSubPix, transform - */ -CV_EXPORTS_W void warpAffine( InputArray src, OutputArray dst, - InputArray M, Size dsize, - int flags = INTER_LINEAR, - int borderMode = BORDER_CONSTANT, - const Scalar& borderValue = Scalar()); - -/** @example samples/cpp/warpPerspective_demo.cpp -An example program shows using cv::getPerspectiveTransform and cv::warpPerspective for image warping -*/ - -/** @brief Applies a perspective transformation to an image. - -The function warpPerspective transforms the source image using the specified matrix: - -\f[\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , - \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\f] - -when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert -and then put in the formula above instead of M. The function cannot operate in-place. - -@param src input image. -@param dst output image that has the size dsize and the same type as src . -@param M \f$3\times 3\f$ transformation matrix. -@param dsize size of the output image. -@param flags combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the -optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation ( -\f$\texttt{dst}\rightarrow\texttt{src}\f$ ). -@param borderMode pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE). -@param borderValue value used in case of a constant border; by default, it equals 0. - -@sa warpAffine, resize, remap, getRectSubPix, perspectiveTransform - */ -CV_EXPORTS_W void warpPerspective( InputArray src, OutputArray dst, - InputArray M, Size dsize, - int flags = INTER_LINEAR, - int borderMode = BORDER_CONSTANT, - const Scalar& borderValue = Scalar()); - -/** @brief Applies a generic geometrical transformation to an image. - -The function remap transforms the source image using the specified map: - -\f[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\f] - -where values of pixels with non-integer coordinates are computed using one of available -interpolation methods. \f$map_x\f$ and \f$map_y\f$ can be encoded as separate floating-point maps -in \f$map_1\f$ and \f$map_2\f$ respectively, or interleaved floating-point maps of \f$(x,y)\f$ in -\f$map_1\f$, or fixed-point maps created by using convertMaps. The reason you might want to -convert from floating to fixed-point representations of a map is that they can yield much faster -(\~2x) remapping operations. In the converted case, \f$map_1\f$ contains pairs (cvFloor(x), -cvFloor(y)) and \f$map_2\f$ contains indices in a table of interpolation coefficients. - -This function cannot operate in-place. - -@param src Source image. -@param dst Destination image. It has the same size as map1 and the same type as src . -@param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 , -CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point -representation to fixed-point for speed. -@param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map -if map1 is (x,y) points), respectively. -@param interpolation Interpolation method (see #InterpolationFlags). The methods #INTER_AREA -and #INTER_LINEAR_EXACT are not supported by this function. -@param borderMode Pixel extrapolation method (see #BorderTypes). When -borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image that -corresponds to the "outliers" in the source image are not modified by the function. -@param borderValue Value used in case of a constant border. By default, it is 0. -@note -Due to current implementation limitations the size of an input and output images should be less than 32767x32767. - */ -CV_EXPORTS_W void remap( InputArray src, OutputArray dst, - InputArray map1, InputArray map2, - int interpolation, int borderMode = BORDER_CONSTANT, - const Scalar& borderValue = Scalar()); - -/** @brief Converts image transformation maps from one representation to another. - -The function converts a pair of maps for remap from one representation to another. The following -options ( (map1.type(), map2.type()) \f$\rightarrow\f$ (dstmap1.type(), dstmap2.type()) ) are -supported: - -- \f$\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. This is the -most frequently used conversion operation, in which the original floating-point maps (see remap ) -are converted to a more compact and much faster fixed-point representation. The first output array -contains the rounded coordinates and the second array (created only when nninterpolation=false ) -contains indices in the interpolation tables. - -- \f$\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. The same as above but -the original maps are stored in one 2-channel matrix. - -- Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same -as the originals. - -@param map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 . -@param map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix), -respectively. -@param dstmap1 The first output map that has the type dstmap1type and the same size as src . -@param dstmap2 The second output map. -@param dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or -CV_32FC2 . -@param nninterpolation Flag indicating whether the fixed-point maps are used for the -nearest-neighbor or for a more complex interpolation. - -@sa remap, undistort, initUndistortRectifyMap - */ -CV_EXPORTS_W void convertMaps( InputArray map1, InputArray map2, - OutputArray dstmap1, OutputArray dstmap2, - int dstmap1type, bool nninterpolation = false ); - -/** @brief Calculates an affine matrix of 2D rotation. - -The function calculates the following matrix: - -\f[\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot \texttt{center.x} - \beta \cdot \texttt{center.y} \\ - \beta & \alpha & \beta \cdot \texttt{center.x} + (1- \alpha ) \cdot \texttt{center.y} \end{bmatrix}\f] - -where - -\f[\begin{array}{l} \alpha = \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta = \texttt{scale} \cdot \sin \texttt{angle} \end{array}\f] - -The transformation maps the rotation center to itself. If this is not the target, adjust the shift. - -@param center Center of the rotation in the source image. -@param angle Rotation angle in degrees. Positive values mean counter-clockwise rotation (the -coordinate origin is assumed to be the top-left corner). -@param scale Isotropic scale factor. - -@sa getAffineTransform, warpAffine, transform - */ -CV_EXPORTS_W Mat getRotationMatrix2D( Point2f center, double angle, double scale ); - -//! returns 3x3 perspective transformation for the corresponding 4 point pairs. -CV_EXPORTS Mat getPerspectiveTransform( const Point2f src[], const Point2f dst[] ); - -/** @brief Calculates an affine transform from three pairs of the corresponding points. - -The function calculates the \f$2 \times 3\f$ matrix of an affine transform so that: - -\f[\begin{bmatrix} x'_i \\ y'_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f] - -where - -\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2\f] - -@param src Coordinates of triangle vertices in the source image. -@param dst Coordinates of the corresponding triangle vertices in the destination image. - -@sa warpAffine, transform - */ -CV_EXPORTS Mat getAffineTransform( const Point2f src[], const Point2f dst[] ); - -/** @brief Inverts an affine transformation. - -The function computes an inverse affine transformation represented by \f$2 \times 3\f$ matrix M: - -\f[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ a_{21} & a_{22} & b_2 \end{bmatrix}\f] - -The result is also a \f$2 \times 3\f$ matrix of the same type as M. - -@param M Original affine transformation. -@param iM Output reverse affine transformation. - */ -CV_EXPORTS_W void invertAffineTransform( InputArray M, OutputArray iM ); - -/** @brief Calculates a perspective transform from four pairs of the corresponding points. - -The function calculates the \f$3 \times 3\f$ matrix of a perspective transform so that: - -\f[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f] - -where - -\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\f] - -@param src Coordinates of quadrangle vertices in the source image. -@param dst Coordinates of the corresponding quadrangle vertices in the destination image. - -@sa findHomography, warpPerspective, perspectiveTransform - */ -CV_EXPORTS_W Mat getPerspectiveTransform( InputArray src, InputArray dst ); - -CV_EXPORTS_W Mat getAffineTransform( InputArray src, InputArray dst ); - -/** @brief Retrieves a pixel rectangle from an image with sub-pixel accuracy. - -The function getRectSubPix extracts pixels from src: - -\f[patch(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\f] - -where the values of the pixels at non-integer coordinates are retrieved using bilinear -interpolation. Every channel of multi-channel images is processed independently. Also -the image should be a single channel or three channel image. While the center of the -rectangle must be inside the image, parts of the rectangle may be outside. - -@param image Source image. -@param patchSize Size of the extracted patch. -@param center Floating point coordinates of the center of the extracted rectangle within the -source image. The center must be inside the image. -@param patch Extracted patch that has the size patchSize and the same number of channels as src . -@param patchType Depth of the extracted pixels. By default, they have the same depth as src . - -@sa warpAffine, warpPerspective - */ -CV_EXPORTS_W void getRectSubPix( InputArray image, Size patchSize, - Point2f center, OutputArray patch, int patchType = -1 ); - -/** @example samples/cpp/polar_transforms.cpp -An example using the cv::linearPolar and cv::logPolar operations -*/ - -/** @brief Remaps an image to semilog-polar coordinates space. - -@deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG); - -@internal -Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image d)"): -\f[\begin{array}{l} - dst( \rho , \phi ) = src(x,y) \\ - dst.size() \leftarrow src.size() -\end{array}\f] - -where -\f[\begin{array}{l} - I = (dx,dy) = (x - center.x,y - center.y) \\ - \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\ - \phi = Kangle \cdot \texttt{angle} (I) \\ -\end{array}\f] - -and -\f[\begin{array}{l} - M = src.cols / log_e(maxRadius) \\ - Kangle = src.rows / 2\Pi \\ -\end{array}\f] - -The function emulates the human "foveal" vision and can be used for fast scale and -rotation-invariant template matching, for object tracking and so forth. -@param src Source image -@param dst Destination image. It will have same size and type as src. -@param center The transformation center; where the output precision is maximal -@param M Magnitude scale parameter. It determines the radius of the bounding circle to transform too. -@param flags A combination of interpolation methods, see #InterpolationFlags - -@note -- The function can not operate in-place. -- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. - -@sa cv::linearPolar -@endinternal -*/ -CV_EXPORTS_W void logPolar( InputArray src, OutputArray dst, - Point2f center, double M, int flags ); - -/** @brief Remaps an image to polar coordinates space. - -@deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags) - -@internal -Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image c)"): -\f[\begin{array}{l} - dst( \rho , \phi ) = src(x,y) \\ - dst.size() \leftarrow src.size() -\end{array}\f] - -where -\f[\begin{array}{l} - I = (dx,dy) = (x - center.x,y - center.y) \\ - \rho = Kmag \cdot \texttt{magnitude} (I) ,\\ - \phi = angle \cdot \texttt{angle} (I) -\end{array}\f] - -and -\f[\begin{array}{l} - Kx = src.cols / maxRadius \\ - Ky = src.rows / 2\Pi -\end{array}\f] - - -@param src Source image -@param dst Destination image. It will have same size and type as src. -@param center The transformation center; -@param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too. -@param flags A combination of interpolation methods, see #InterpolationFlags - -@note -- The function can not operate in-place. -- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. - -@sa cv::logPolar -@endinternal -*/ -CV_EXPORTS_W void linearPolar( InputArray src, OutputArray dst, - Point2f center, double maxRadius, int flags ); - - -/** \brief Remaps an image to polar or semilog-polar coordinates space - -@anchor polar_remaps_reference_image -![Polar remaps reference](pics/polar_remap_doc.png) - -Transform the source image using the following transformation: -\f[ -dst(\rho , \phi ) = src(x,y) -\f] - -where -\f[ -\begin{array}{l} -\vec{I} = (x - center.x, \;y - center.y) \\ -\phi = Kangle \cdot \texttt{angle} (\vec{I}) \\ -\rho = \left\{\begin{matrix} -Klin \cdot \texttt{magnitude} (\vec{I}) & default \\ -Klog \cdot log_e(\texttt{magnitude} (\vec{I})) & if \; semilog \\ -\end{matrix}\right. -\end{array} -\f] - -and -\f[ -\begin{array}{l} -Kangle = dsize.height / 2\Pi \\ -Klin = dsize.width / maxRadius \\ -Klog = dsize.width / log_e(maxRadius) \\ -\end{array} -\f] - - -\par Linear vs semilog mapping - -Polar mapping can be linear or semi-log. Add one of #WarpPolarMode to `flags` to specify the polar mapping mode. - -Linear is the default mode. - -The semilog mapping emulates the human "foveal" vision that permit very high acuity on the line of sight (central vision) -in contrast to peripheral vision where acuity is minor. - -\par Option on `dsize`: - -- if both values in `dsize <=0 ` (default), -the destination image will have (almost) same area of source bounding circle: -\f[\begin{array}{l} -dsize.area \leftarrow (maxRadius^2 \cdot \Pi) \\ -dsize.width = \texttt{cvRound}(maxRadius) \\ -dsize.height = \texttt{cvRound}(maxRadius \cdot \Pi) \\ -\end{array}\f] - - -- if only `dsize.height <= 0`, -the destination image area will be proportional to the bounding circle area but scaled by `Kx * Kx`: -\f[\begin{array}{l} -dsize.height = \texttt{cvRound}(dsize.width \cdot \Pi) \\ -\end{array} -\f] - -- if both values in `dsize > 0 `, -the destination image will have the given size therefore the area of the bounding circle will be scaled to `dsize`. - - -\par Reverse mapping - -You can get reverse mapping adding #WARP_INVERSE_MAP to `flags` -\snippet polar_transforms.cpp InverseMap - -In addiction, to calculate the original coordinate from a polar mapped coordinate \f$(rho, phi)->(x, y)\f$: -\snippet polar_transforms.cpp InverseCoordinate - -@param src Source image. -@param dst Destination image. It will have same type as src. -@param dsize The destination image size (see description for valid options). -@param center The transformation center. -@param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too. -@param flags A combination of interpolation methods, #InterpolationFlags + #WarpPolarMode. - - Add #WARP_POLAR_LINEAR to select linear polar mapping (default) - - Add #WARP_POLAR_LOG to select semilog polar mapping - - Add #WARP_INVERSE_MAP for reverse mapping. -@note -- The function can not operate in-place. -- To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees. -- This function uses #remap. Due to current implementation limitations the size of an input and output images should be less than 32767x32767. - -@sa cv::remap -*/ -CV_EXPORTS_W void warpPolar(InputArray src, OutputArray dst, Size dsize, - Point2f center, double maxRadius, int flags); - - -//! @} imgproc_transform - -//! @addtogroup imgproc_misc -//! @{ - -/** @overload */ -CV_EXPORTS_W void integral( InputArray src, OutputArray sum, int sdepth = -1 ); - -/** @overload */ -CV_EXPORTS_AS(integral2) void integral( InputArray src, OutputArray sum, - OutputArray sqsum, int sdepth = -1, int sqdepth = -1 ); - -/** @brief Calculates the integral of an image. - -The function calculates one or more integral images for the source image as follows: - -\f[\texttt{sum} (X,Y) = \sum _{x - -Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed -with getOptimalDFTSize. - -The function performs the following equations: -- First it applies a Hanning window (see ) to each -image to remove possible edge effects. This window is cached until the array size changes to speed -up processing time. -- Next it computes the forward DFTs of each source array: -\f[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\f] -where \f$\mathcal{F}\f$ is the forward DFT. -- It then computes the cross-power spectrum of each frequency domain array: -\f[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\f] -- Next the cross-correlation is converted back into the time domain via the inverse DFT: -\f[r = \mathcal{F}^{-1}\{R\}\f] -- Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to -achieve sub-pixel accuracy. -\f[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\f] -- If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 -centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single -peak) and will be smaller when there are multiple peaks. - -@param src1 Source floating point array (CV_32FC1 or CV_64FC1) -@param src2 Source floating point array (CV_32FC1 or CV_64FC1) -@param window Floating point array with windowing coefficients to reduce edge effects (optional). -@param response Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional). -@returns detected phase shift (sub-pixel) between the two arrays. - -@sa dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow - */ -CV_EXPORTS_W Point2d phaseCorrelate(InputArray src1, InputArray src2, - InputArray window = noArray(), CV_OUT double* response = 0); - -/** @brief This function computes a Hanning window coefficients in two dimensions. - -See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function) -for more information. - -An example is shown below: -@code - // create hanning window of size 100x100 and type CV_32F - Mat hann; - createHanningWindow(hann, Size(100, 100), CV_32F); -@endcode -@param dst Destination array to place Hann coefficients in -@param winSize The window size specifications (both width and height must be > 1) -@param type Created array type - */ -CV_EXPORTS_W void createHanningWindow(OutputArray dst, Size winSize, int type); - -//! @} imgproc_motion - -//! @addtogroup imgproc_misc -//! @{ - -/** @brief Applies a fixed-level threshold to each array element. - -The function applies fixed-level thresholding to a multiple-channel array. The function is typically -used to get a bi-level (binary) image out of a grayscale image ( #compare could be also used for -this purpose) or for removing a noise, that is, filtering out pixels with too small or too large -values. There are several types of thresholding supported by the function. They are determined by -type parameter. - -Also, the special values #THRESH_OTSU or #THRESH_TRIANGLE may be combined with one of the -above values. In these cases, the function determines the optimal threshold value using the Otsu's -or Triangle algorithm and uses it instead of the specified thresh. - -@note Currently, the Otsu's and Triangle methods are implemented only for 8-bit single-channel images. - -@param src input array (multiple-channel, 8-bit or 32-bit floating point). -@param dst output array of the same size and type and the same number of channels as src. -@param thresh threshold value. -@param maxval maximum value to use with the #THRESH_BINARY and #THRESH_BINARY_INV thresholding -types. -@param type thresholding type (see #ThresholdTypes). -@return the computed threshold value if Otsu's or Triangle methods used. - -@sa adaptiveThreshold, findContours, compare, min, max - */ -CV_EXPORTS_W double threshold( InputArray src, OutputArray dst, - double thresh, double maxval, int type ); - - -/** @brief Applies an adaptive threshold to an array. - -The function transforms a grayscale image to a binary image according to the formulae: -- **THRESH_BINARY** - \f[dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\f] -- **THRESH_BINARY_INV** - \f[dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\f] -where \f$T(x,y)\f$ is a threshold calculated individually for each pixel (see adaptiveMethod parameter). - -The function can process the image in-place. - -@param src Source 8-bit single-channel image. -@param dst Destination image of the same size and the same type as src. -@param maxValue Non-zero value assigned to the pixels for which the condition is satisfied -@param adaptiveMethod Adaptive thresholding algorithm to use, see #AdaptiveThresholdTypes. -The #BORDER_REPLICATE | #BORDER_ISOLATED is used to process boundaries. -@param thresholdType Thresholding type that must be either #THRESH_BINARY or #THRESH_BINARY_INV, -see #ThresholdTypes. -@param blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the -pixel: 3, 5, 7, and so on. -@param C Constant subtracted from the mean or weighted mean (see the details below). Normally, it -is positive but may be zero or negative as well. - -@sa threshold, blur, GaussianBlur - */ -CV_EXPORTS_W void adaptiveThreshold( InputArray src, OutputArray dst, - double maxValue, int adaptiveMethod, - int thresholdType, int blockSize, double C ); - -//! @} imgproc_misc - -//! @addtogroup imgproc_filter -//! @{ - -/** @example samples/cpp/tutorial_code/ImgProc/Pyramids/Pyramids.cpp -An example using pyrDown and pyrUp functions -*/ - -/** @brief Blurs an image and downsamples it. - -By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in -any case, the following conditions should be satisfied: - -\f[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\f] - -The function performs the downsampling step of the Gaussian pyramid construction. First, it -convolves the source image with the kernel: - -\f[\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\f] - -Then, it downsamples the image by rejecting even rows and columns. - -@param src input image. -@param dst output image; it has the specified size and the same type as src. -@param dstsize size of the output image. -@param borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported) - */ -CV_EXPORTS_W void pyrDown( InputArray src, OutputArray dst, - const Size& dstsize = Size(), int borderType = BORDER_DEFAULT ); - -/** @brief Upsamples an image and then blurs it. - -By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any -case, the following conditions should be satisfied: - -\f[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\f] - -The function performs the upsampling step of the Gaussian pyramid construction, though it can -actually be used to construct the Laplacian pyramid. First, it upsamples the source image by -injecting even zero rows and columns and then convolves the result with the same kernel as in -pyrDown multiplied by 4. - -@param src input image. -@param dst output image. It has the specified size and the same type as src . -@param dstsize size of the output image. -@param borderType Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported) - */ -CV_EXPORTS_W void pyrUp( InputArray src, OutputArray dst, - const Size& dstsize = Size(), int borderType = BORDER_DEFAULT ); - -/** @brief Constructs the Gaussian pyramid for an image. - -The function constructs a vector of images and builds the Gaussian pyramid by recursively applying -pyrDown to the previously built pyramid layers, starting from `dst[0]==src`. - -@param src Source image. Check pyrDown for the list of supported types. -@param dst Destination vector of maxlevel+1 images of the same type as src. dst[0] will be the -same as src. dst[1] is the next pyramid layer, a smoothed and down-sized src, and so on. -@param maxlevel 0-based index of the last (the smallest) pyramid layer. It must be non-negative. -@param borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported) - */ -CV_EXPORTS void buildPyramid( InputArray src, OutputArrayOfArrays dst, - int maxlevel, int borderType = BORDER_DEFAULT ); - -//! @} imgproc_filter - -//! @addtogroup imgproc_transform -//! @{ - -/** @brief Transforms an image to compensate for lens distortion. - -The function transforms an image to compensate radial and tangential lens distortion. - -The function is simply a combination of #initUndistortRectifyMap (with unity R ) and #remap -(with bilinear interpolation). See the former function for details of the transformation being -performed. - -Those pixels in the destination image, for which there is no correspondent pixels in the source -image, are filled with zeros (black color). - -A particular subset of the source image that will be visible in the corrected image can be regulated -by newCameraMatrix. You can use #getOptimalNewCameraMatrix to compute the appropriate -newCameraMatrix depending on your requirements. - -The camera matrix and the distortion parameters can be determined using #calibrateCamera. If -the resolution of images is different from the resolution used at the calibration stage, \f$f_x, -f_y, c_x\f$ and \f$c_y\f$ need to be scaled accordingly, while the distortion coefficients remain -the same. - -@param src Input (distorted) image. -@param dst Output (corrected) image that has the same size and type as src . -@param cameraMatrix Input camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . -@param distCoeffs Input vector of distortion coefficients -\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ -of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. -@param newCameraMatrix Camera matrix of the distorted image. By default, it is the same as -cameraMatrix but you may additionally scale and shift the result by using a different matrix. - */ -CV_EXPORTS_W void undistort( InputArray src, OutputArray dst, - InputArray cameraMatrix, - InputArray distCoeffs, - InputArray newCameraMatrix = noArray() ); - -/** @brief Computes the undistortion and rectification transformation map. - -The function computes the joint undistortion and rectification transformation and represents the -result in the form of maps for remap. The undistorted image looks like original, as if it is -captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a -monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by -#getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera, -newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify . - -Also, this new camera is oriented differently in the coordinate space, according to R. That, for -example, helps to align two heads of a stereo camera so that the epipolar lines on both images -become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera). - -The function actually builds the maps for the inverse mapping algorithm that is used by remap. That -is, for each pixel \f$(u, v)\f$ in the destination (corrected and rectified) image, the function -computes the corresponding coordinates in the source image (that is, in the original image from -camera). The following process is applied: -\f[ -\begin{array}{l} -x \leftarrow (u - {c'}_x)/{f'}_x \\ -y \leftarrow (v - {c'}_y)/{f'}_y \\ -{[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\ -x' \leftarrow X/W \\ -y' \leftarrow Y/W \\ -r^2 \leftarrow x'^2 + y'^2 \\ -x'' \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} -+ 2p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4\\ -y'' \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} -+ p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ -s\vecthree{x'''}{y'''}{1} = -\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} -{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} -{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ -map_x(u,v) \leftarrow x''' f_x + c_x \\ -map_y(u,v) \leftarrow y''' f_y + c_y -\end{array} -\f] -where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ -are the distortion coefficients. - -In case of a stereo camera, this function is called twice: once for each camera head, after -stereoRectify, which in its turn is called after #stereoCalibrate. But if the stereo camera -was not calibrated, it is still possible to compute the rectification transformations directly from -the fundamental matrix using #stereoRectifyUncalibrated. For each camera, the function computes -homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D -space. R can be computed from H as -\f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f] -where cameraMatrix can be chosen arbitrarily. - -@param cameraMatrix Input camera matrix \f$A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . -@param distCoeffs Input vector of distortion coefficients -\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ -of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. -@param R Optional rectification transformation in the object space (3x3 matrix). R1 or R2 , -computed by #stereoRectify can be passed here. If the matrix is empty, the identity transformation -is assumed. In cvInitUndistortMap R assumed to be an identity matrix. -@param newCameraMatrix New camera matrix \f$A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\f$. -@param size Undistorted image size. -@param m1type Type of the first output map that can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps -@param map1 The first output map. -@param map2 The second output map. - */ -CV_EXPORTS_W void initUndistortRectifyMap( InputArray cameraMatrix, InputArray distCoeffs, - InputArray R, InputArray newCameraMatrix, - Size size, int m1type, OutputArray map1, OutputArray map2 ); - -//! initializes maps for #remap for wide-angle -CV_EXPORTS_W float initWideAngleProjMap( InputArray cameraMatrix, InputArray distCoeffs, - Size imageSize, int destImageWidth, - int m1type, OutputArray map1, OutputArray map2, - int projType = PROJ_SPHERICAL_EQRECT, double alpha = 0); - -/** @brief Returns the default new camera matrix. - -The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when -centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true). - -In the latter case, the new camera matrix will be: - -\f[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\f] - -where \f$f_x\f$ and \f$f_y\f$ are \f$(0,0)\f$ and \f$(1,1)\f$ elements of cameraMatrix, respectively. - -By default, the undistortion functions in OpenCV (see #initUndistortRectifyMap, #undistort) do not -move the principal point. However, when you work with stereo, it is important to move the principal -points in both views to the same y-coordinate (which is required by most of stereo correspondence -algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for -each view where the principal points are located at the center. - -@param cameraMatrix Input camera matrix. -@param imgsize Camera view image size in pixels. -@param centerPrincipalPoint Location of the principal point in the new camera matrix. The -parameter indicates whether this location should be at the image center or not. - */ -CV_EXPORTS_W Mat getDefaultNewCameraMatrix( InputArray cameraMatrix, Size imgsize = Size(), - bool centerPrincipalPoint = false ); - -/** @brief Computes the ideal point coordinates from the observed point coordinates. - -The function is similar to #undistort and #initUndistortRectifyMap but it operates on a -sparse set of points instead of a raster image. Also the function performs a reverse transformation -to projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a -planar object, it does, up to a translation vector, if the proper R is specified. - -For each observed point coordinate \f$(u, v)\f$ the function computes: -\f[ -\begin{array}{l} -x^{"} \leftarrow (u - c_x)/f_x \\ -y^{"} \leftarrow (v - c_y)/f_y \\ -(x',y') = undistort(x^{"},y^{"}, \texttt{distCoeffs}) \\ -{[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\ -x \leftarrow X/W \\ -y \leftarrow Y/W \\ -\text{only performed if P is specified:} \\ -u' \leftarrow x {f'}_x + {c'}_x \\ -v' \leftarrow y {f'}_y + {c'}_y -\end{array} -\f] - -where *undistort* is an approximate iterative algorithm that estimates the normalized original -point coordinates out of the normalized distorted point coordinates ("normalized" means that the -coordinates do not depend on the camera matrix). - -The function can be used for both a stereo camera head or a monocular camera (when R is empty). -@param src Observed point coordinates, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or -vector\ ). -@param dst Output ideal point coordinates (1xN/Nx1 2-channel or vector\ ) after undistortion and reverse perspective -transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates. -@param cameraMatrix Camera matrix \f$\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . -@param distCoeffs Input vector of distortion coefficients -\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ -of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed. -@param R Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by -#stereoRectify can be passed here. If the matrix is empty, the identity transformation is used. -@param P New camera matrix (3x3) or new projection matrix (3x4) \f$\begin{bmatrix} {f'}_x & 0 & {c'}_x & t_x \\ 0 & {f'}_y & {c'}_y & t_y \\ 0 & 0 & 1 & t_z \end{bmatrix}\f$. P1 or P2 computed by -#stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used. - */ -CV_EXPORTS_W void undistortPoints( InputArray src, OutputArray dst, - InputArray cameraMatrix, InputArray distCoeffs, - InputArray R = noArray(), InputArray P = noArray() ); -/** @overload - @note Default version of #undistortPoints does 5 iterations to compute undistorted points. - - */ -CV_EXPORTS_AS(undistortPointsIter) void undistortPoints( InputArray src, OutputArray dst, - InputArray cameraMatrix, InputArray distCoeffs, - InputArray R, InputArray P, TermCriteria criteria ); - -//! @} imgproc_transform - -//! @addtogroup imgproc_hist -//! @{ - -/** @example samples/cpp/demhist.cpp -An example for creating histograms of an image -*/ - -/** @brief Calculates a histogram of a set of arrays. - -The function cv::calcHist calculates the histogram of one or more arrays. The elements of a tuple used -to increment a histogram bin are taken from the corresponding input arrays at the same location. The -sample below shows how to compute a 2D Hue-Saturation histogram for a color image. : -@include snippets/imgproc_calcHist.cpp - -@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same -size. Each of them can have an arbitrary number of channels. -@param nimages Number of source images. -@param channels List of the dims channels used to compute the histogram. The first array channels -are numerated from 0 to images[0].channels()-1 , the second array channels are counted from -images[0].channels() to images[0].channels() + images[1].channels()-1, and so on. -@param mask Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size -as images[i] . The non-zero mask elements mark the array elements counted in the histogram. -@param hist Output histogram, which is a dense or sparse dims -dimensional array. -@param dims Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS -(equal to 32 in the current OpenCV version). -@param histSize Array of histogram sizes in each dimension. -@param ranges Array of the dims arrays of the histogram bin boundaries in each dimension. When the -histogram is uniform ( uniform =true), then for each dimension i it is enough to specify the lower -(inclusive) boundary \f$L_0\f$ of the 0-th histogram bin and the upper (exclusive) boundary -\f$U_{\texttt{histSize}[i]-1}\f$ for the last histogram bin histSize[i]-1 . That is, in case of a -uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not uniform ( -uniform=false ), then each of ranges[i] contains histSize[i]+1 elements: -\f$L_0, U_0=L_1, U_1=L_2, ..., U_{\texttt{histSize[i]}-2}=L_{\texttt{histSize[i]}-1}, U_{\texttt{histSize[i]}-1}\f$ -. The array elements, that are not between \f$L_0\f$ and \f$U_{\texttt{histSize[i]}-1}\f$ , are not -counted in the histogram. -@param uniform Flag indicating whether the histogram is uniform or not (see above). -@param accumulate Accumulation flag. If it is set, the histogram is not cleared in the beginning -when it is allocated. This feature enables you to compute a single histogram from several sets of -arrays, or to update the histogram in time. -*/ -CV_EXPORTS void calcHist( const Mat* images, int nimages, - const int* channels, InputArray mask, - OutputArray hist, int dims, const int* histSize, - const float** ranges, bool uniform = true, bool accumulate = false ); - -/** @overload - -this variant uses %SparseMat for output -*/ -CV_EXPORTS void calcHist( const Mat* images, int nimages, - const int* channels, InputArray mask, - SparseMat& hist, int dims, - const int* histSize, const float** ranges, - bool uniform = true, bool accumulate = false ); - -/** @overload */ -CV_EXPORTS_W void calcHist( InputArrayOfArrays images, - const std::vector& channels, - InputArray mask, OutputArray hist, - const std::vector& histSize, - const std::vector& ranges, - bool accumulate = false ); - -/** @brief Calculates the back projection of a histogram. - -The function cv::calcBackProject calculates the back project of the histogram. That is, similarly to -#calcHist , at each location (x, y) the function collects the values from the selected channels -in the input images and finds the corresponding histogram bin. But instead of incrementing it, the -function reads the bin value, scales it by scale , and stores in backProject(x,y) . In terms of -statistics, the function computes probability of each element value in respect with the empirical -probability distribution represented by the histogram. See how, for example, you can find and track -a bright-colored object in a scene: - -- Before tracking, show the object to the camera so that it covers almost the whole frame. -Calculate a hue histogram. The histogram may have strong maximums, corresponding to the dominant -colors in the object. - -- When tracking, calculate a back projection of a hue plane of each input video frame using that -pre-computed histogram. Threshold the back projection to suppress weak colors. It may also make -sense to suppress pixels with non-sufficient color saturation and too dark or too bright pixels. - -- Find connected components in the resulting picture and choose, for example, the largest -component. - -This is an approximate algorithm of the CamShift color object tracker. - -@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same -size. Each of them can have an arbitrary number of channels. -@param nimages Number of source images. -@param channels The list of channels used to compute the back projection. The number of channels -must match the histogram dimensionality. The first array channels are numerated from 0 to -images[0].channels()-1 , the second array channels are counted from images[0].channels() to -images[0].channels() + images[1].channels()-1, and so on. -@param hist Input histogram that can be dense or sparse. -@param backProject Destination back projection array that is a single-channel array of the same -size and depth as images[0] . -@param ranges Array of arrays of the histogram bin boundaries in each dimension. See #calcHist . -@param scale Optional scale factor for the output back projection. -@param uniform Flag indicating whether the histogram is uniform or not (see above). - -@sa calcHist, compareHist - */ -CV_EXPORTS void calcBackProject( const Mat* images, int nimages, - const int* channels, InputArray hist, - OutputArray backProject, const float** ranges, - double scale = 1, bool uniform = true ); - -/** @overload */ -CV_EXPORTS void calcBackProject( const Mat* images, int nimages, - const int* channels, const SparseMat& hist, - OutputArray backProject, const float** ranges, - double scale = 1, bool uniform = true ); - -/** @overload */ -CV_EXPORTS_W void calcBackProject( InputArrayOfArrays images, const std::vector& channels, - InputArray hist, OutputArray dst, - const std::vector& ranges, - double scale ); - -/** @brief Compares two histograms. - -The function cv::compareHist compares two dense or two sparse histograms using the specified method. - -The function returns \f$d(H_1, H_2)\f$ . - -While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable -for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling -problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms -or more general sparse configurations of weighted points, consider using the #EMD function. - -@param H1 First compared histogram. -@param H2 Second compared histogram of the same size as H1 . -@param method Comparison method, see #HistCompMethods - */ -CV_EXPORTS_W double compareHist( InputArray H1, InputArray H2, int method ); - -/** @overload */ -CV_EXPORTS double compareHist( const SparseMat& H1, const SparseMat& H2, int method ); - -/** @brief Equalizes the histogram of a grayscale image. - -The function equalizes the histogram of the input image using the following algorithm: - -- Calculate the histogram \f$H\f$ for src . -- Normalize the histogram so that the sum of histogram bins is 255. -- Compute the integral of the histogram: -\f[H'_i = \sum _{0 \le j < i} H(j)\f] -- Transform the image using \f$H'\f$ as a look-up table: \f$\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\f$ - -The algorithm normalizes the brightness and increases the contrast of the image. - -@param src Source 8-bit single channel image. -@param dst Destination image of the same size and type as src . - */ -CV_EXPORTS_W void equalizeHist( InputArray src, OutputArray dst ); - -/** @brief Creates a smart pointer to a cv::CLAHE class and initializes it. - -@param clipLimit Threshold for contrast limiting. -@param tileGridSize Size of grid for histogram equalization. Input image will be divided into -equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column. - */ -CV_EXPORTS_W Ptr createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8)); - -/** @brief Computes the "minimal work" distance between two weighted point configurations. - -The function computes the earth mover distance and/or a lower boundary of the distance between the -two weighted point configurations. One of the applications described in @cite RubnerSept98, -@cite Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation -problem that is solved using some modification of a simplex algorithm, thus the complexity is -exponential in the worst case, though, on average it is much faster. In the case of a real metric -the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used -to determine roughly whether the two signatures are far enough so that they cannot relate to the -same object. - -@param signature1 First signature, a \f$\texttt{size1}\times \texttt{dims}+1\f$ floating-point matrix. -Each row stores the point weight followed by the point coordinates. The matrix is allowed to have -a single column (weights only) if the user-defined cost matrix is used. The weights must be -non-negative and have at least one non-zero value. -@param signature2 Second signature of the same format as signature1 , though the number of rows -may be different. The total weights may be different. In this case an extra "dummy" point is added -to either signature1 or signature2. The weights must be non-negative and have at least one non-zero -value. -@param distType Used metric. See #DistanceTypes. -@param cost User-defined \f$\texttt{size1}\times \texttt{size2}\f$ cost matrix. Also, if a cost matrix -is used, lower boundary lowerBound cannot be calculated because it needs a metric function. -@param lowerBound Optional input/output parameter: lower boundary of a distance between the two -signatures that is a distance between mass centers. The lower boundary may not be calculated if -the user-defined cost matrix is used, the total weights of point configurations are not equal, or -if the signatures consist of weights only (the signature matrices have a single column). You -**must** initialize \*lowerBound . If the calculated distance between mass centers is greater or -equal to \*lowerBound (it means that the signatures are far enough), the function does not -calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on -return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound -should be set to 0. -@param flow Resultant \f$\texttt{size1} \times \texttt{size2}\f$ flow matrix: \f$\texttt{flow}_{i,j}\f$ is -a flow from \f$i\f$ -th point of signature1 to \f$j\f$ -th point of signature2 . - */ -CV_EXPORTS float EMD( InputArray signature1, InputArray signature2, - int distType, InputArray cost=noArray(), - float* lowerBound = 0, OutputArray flow = noArray() ); - -CV_EXPORTS_AS(EMD) float wrapperEMD( InputArray signature1, InputArray signature2, - int distType, InputArray cost=noArray(), - CV_IN_OUT Ptr lowerBound = Ptr(), OutputArray flow = noArray() ); - -//! @} imgproc_hist - -/** @example samples/cpp/watershed.cpp -An example using the watershed algorithm -*/ - -/** @brief Performs a marker-based image segmentation using the watershed algorithm. - -The function implements one of the variants of watershed, non-parametric marker-based segmentation -algorithm, described in @cite Meyer92 . - -Before passing the image to the function, you have to roughly outline the desired regions in the -image markers with positive (\>0) indices. So, every region is represented as one or more connected -components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary -mask using #findContours and #drawContours (see the watershed.cpp demo). The markers are "seeds" of -the future image regions. All the other pixels in markers , whose relation to the outlined regions -is not known and should be defined by the algorithm, should be set to 0's. In the function output, -each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the -regions. - -@note Any two neighbor connected components are not necessarily separated by a watershed boundary -(-1's pixels); for example, they can touch each other in the initial marker image passed to the -function. - -@param image Input 8-bit 3-channel image. -@param markers Input/output 32-bit single-channel image (map) of markers. It should have the same -size as image . - -@sa findContours - -@ingroup imgproc_misc - */ -CV_EXPORTS_W void watershed( InputArray image, InputOutputArray markers ); - -//! @addtogroup imgproc_filter -//! @{ - -/** @brief Performs initial step of meanshift segmentation of an image. - -The function implements the filtering stage of meanshift segmentation, that is, the output of the -function is the filtered "posterized" image with color gradients and fine-grain texture flattened. -At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes -meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is -considered: - -\f[(x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}\f] - -where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively -(though, the algorithm does not depend on the color space used, so any 3-component color space can -be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector -(R',G',B') are found and they act as the neighborhood center on the next iteration: - -\f[(X,Y)~(X',Y'), (R,G,B)~(R',G',B').\f] - -After the iterations over, the color components of the initial pixel (that is, the pixel from where -the iterations started) are set to the final value (average color at the last iteration): - -\f[I(X,Y) <- (R*,G*,B*)\f] - -When maxLevel \> 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is -run on the smallest layer first. After that, the results are propagated to the larger layer and the -iterations are run again only on those pixels where the layer colors differ by more than sr from the -lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the -results will be actually different from the ones obtained by running the meanshift procedure on the -whole original image (i.e. when maxLevel==0). - -@param src The source 8-bit, 3-channel image. -@param dst The destination image of the same format and the same size as the source. -@param sp The spatial window radius. -@param sr The color window radius. -@param maxLevel Maximum level of the pyramid for the segmentation. -@param termcrit Termination criteria: when to stop meanshift iterations. - */ -CV_EXPORTS_W void pyrMeanShiftFiltering( InputArray src, OutputArray dst, - double sp, double sr, int maxLevel = 1, - TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) ); - -//! @} - -//! @addtogroup imgproc_misc -//! @{ - -/** @example samples/cpp/grabcut.cpp -An example using the GrabCut algorithm -![Sample Screenshot](grabcut_output1.jpg) -*/ - -/** @brief Runs the GrabCut algorithm. - -The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut). - -@param img Input 8-bit 3-channel image. -@param mask Input/output 8-bit single-channel mask. The mask is initialized by the function when -mode is set to #GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses. -@param rect ROI containing a segmented object. The pixels outside of the ROI are marked as -"obvious background". The parameter is only used when mode==#GC_INIT_WITH_RECT . -@param bgdModel Temporary array for the background model. Do not modify it while you are -processing the same image. -@param fgdModel Temporary arrays for the foreground model. Do not modify it while you are -processing the same image. -@param iterCount Number of iterations the algorithm should make before returning the result. Note -that the result can be refined with further calls with mode==#GC_INIT_WITH_MASK or -mode==GC_EVAL . -@param mode Operation mode that could be one of the #GrabCutModes - */ -CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect, - InputOutputArray bgdModel, InputOutputArray fgdModel, - int iterCount, int mode = GC_EVAL ); - -/** @example samples/cpp/distrans.cpp -An example on using the distance transform -*/ - -/** @brief Calculates the distance to the closest zero pixel for each pixel of the source image. - -The function cv::distanceTransform calculates the approximate or precise distance from every binary -image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero. - -When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the -algorithm described in @cite Felzenszwalb04 . This algorithm is parallelized with the TBB library. - -In other cases, the algorithm @cite Borgefors86 is used. This means that for a pixel the function -finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, -diagonal, or knight's move (the latest is available for a \f$5\times 5\f$ mask). The overall -distance is calculated as a sum of these basic distances. Since the distance function should be -symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all -the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the -same cost (denoted as `c`). For the #DIST_C and #DIST_L1 types, the distance is calculated -precisely, whereas for #DIST_L2 (Euclidean distance) the distance can be calculated only with a -relative error (a \f$5\times 5\f$ mask gives more accurate results). For `a`,`b`, and `c`, OpenCV -uses the values suggested in the original paper: -- DIST_L1: `a = 1, b = 2` -- DIST_L2: - - `3 x 3`: `a=0.955, b=1.3693` - - `5 x 5`: `a=1, b=1.4, c=2.1969` -- DIST_C: `a = 1, b = 1` - -Typically, for a fast, coarse distance estimation #DIST_L2, a \f$3\times 3\f$ mask is used. For a -more accurate distance estimation #DIST_L2, a \f$5\times 5\f$ mask or the precise algorithm is used. -Note that both the precise and the approximate algorithms are linear on the number of pixels. - -This variant of the function does not only compute the minimum distance for each pixel \f$(x, y)\f$ -but also identifies the nearest connected component consisting of zero pixels -(labelType==#DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==#DIST_LABEL_PIXEL). Index of the -component/pixel is stored in `labels(x, y)`. When labelType==#DIST_LABEL_CCOMP, the function -automatically finds connected components of zero pixels in the input image and marks them with -distinct labels. When labelType==#DIST_LABEL_PIXEL, the function scans through the input image and -marks all the zero pixels with distinct labels. - -In this mode, the complexity is still linear. That is, the function provides a very fast way to -compute the Voronoi diagram for a binary image. Currently, the second variant can use only the -approximate distance transform algorithm, i.e. maskSize=#DIST_MASK_PRECISE is not supported -yet. - -@param src 8-bit, single-channel (binary) source image. -@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, -single-channel image of the same size as src. -@param labels Output 2D array of labels (the discrete Voronoi diagram). It has the type -CV_32SC1 and the same size as src. -@param distanceType Type of distance, see #DistanceTypes -@param maskSize Size of the distance transform mask, see #DistanceTransformMasks. -#DIST_MASK_PRECISE is not supported by this variant. In case of the #DIST_L1 or #DIST_C distance type, -the parameter is forced to 3 because a \f$3\times 3\f$ mask gives the same result as \f$5\times -5\f$ or any larger aperture. -@param labelType Type of the label array to build, see #DistanceTransformLabelTypes. - */ -CV_EXPORTS_AS(distanceTransformWithLabels) void distanceTransform( InputArray src, OutputArray dst, - OutputArray labels, int distanceType, int maskSize, - int labelType = DIST_LABEL_CCOMP ); - -/** @overload -@param src 8-bit, single-channel (binary) source image. -@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point, -single-channel image of the same size as src . -@param distanceType Type of distance, see #DistanceTypes -@param maskSize Size of the distance transform mask, see #DistanceTransformMasks. In case of the -#DIST_L1 or #DIST_C distance type, the parameter is forced to 3 because a \f$3\times 3\f$ mask gives -the same result as \f$5\times 5\f$ or any larger aperture. -@param dstType Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for -the first variant of the function and distanceType == #DIST_L1. -*/ -CV_EXPORTS_W void distanceTransform( InputArray src, OutputArray dst, - int distanceType, int maskSize, int dstType=CV_32F); - -/** @example samples/cpp/ffilldemo.cpp -An example using the FloodFill technique -*/ - -/** @overload - -variant without `mask` parameter -*/ -CV_EXPORTS int floodFill( InputOutputArray image, - Point seedPoint, Scalar newVal, CV_OUT Rect* rect = 0, - Scalar loDiff = Scalar(), Scalar upDiff = Scalar(), - int flags = 4 ); - -/** @brief Fills a connected component with the given color. - -The function cv::floodFill fills a connected component starting from the seed point with the specified -color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The -pixel at \f$(x,y)\f$ is considered to belong to the repainted domain if: - -- in case of a grayscale image and floating range -\f[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\f] - - -- in case of a grayscale image and fixed range -\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\f] - - -- in case of a color image and floating range -\f[\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\f] -\f[\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\f] -and -\f[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\f] - - -- in case of a color image and fixed range -\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\f] -\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\f] -and -\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\f] - - -where \f$src(x',y')\f$ is the value of one of pixel neighbors that is already known to belong to the -component. That is, to be added to the connected component, a color/brightness of the pixel should -be close enough to: -- Color/brightness of one of its neighbors that already belong to the connected component in case -of a floating range. -- Color/brightness of the seed point in case of a fixed range. - -Use these functions to either mark a connected component with the specified color in-place, or build -a mask and then extract the contour, or copy the region to another image, and so on. - -@param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the -function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See -the details below. -@param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels -taller than image. Since this is both an input and output parameter, you must take responsibility -of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, -an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the -mask corresponding to filled pixels in the image are set to 1 or to the a value specified in flags -as described below. Additionally, the function fills the border of the mask with ones to simplify -internal processing. It is therefore possible to use the same mask in multiple calls to the function -to make sure the filled areas do not overlap. -@param seedPoint Starting point. -@param newVal New value of the repainted domain pixels. -@param loDiff Maximal lower brightness/color difference between the currently observed pixel and -one of its neighbors belonging to the component, or a seed pixel being added to the component. -@param upDiff Maximal upper brightness/color difference between the currently observed pixel and -one of its neighbors belonging to the component, or a seed pixel being added to the component. -@param rect Optional output parameter set by the function to the minimum bounding rectangle of the -repainted domain. -@param flags Operation flags. The first 8 bits contain a connectivity value. The default value of -4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A -connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) -will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill -the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest -neighbours and fill the mask with a value of 255. The following additional options occupy higher -bits and therefore may be further combined with the connectivity and mask fill values using -bit-wise or (|), see #FloodFillFlags. - -@note Since the mask is larger than the filled image, a pixel \f$(x, y)\f$ in image corresponds to the -pixel \f$(x+1, y+1)\f$ in the mask . - -@sa findContours - */ -CV_EXPORTS_W int floodFill( InputOutputArray image, InputOutputArray mask, - Point seedPoint, Scalar newVal, CV_OUT Rect* rect=0, - Scalar loDiff = Scalar(), Scalar upDiff = Scalar(), - int flags = 4 ); - -//! Performs linear blending of two images: -//! \f[ \texttt{dst}(i,j) = \texttt{weights1}(i,j)*\texttt{src1}(i,j) + \texttt{weights2}(i,j)*\texttt{src2}(i,j) \f] -//! @param src1 It has a type of CV_8UC(n) or CV_32FC(n), where n is a positive integer. -//! @param src2 It has the same type and size as src1. -//! @param weights1 It has a type of CV_32FC1 and the same size with src1. -//! @param weights2 It has a type of CV_32FC1 and the same size with src1. -//! @param dst It is created if it does not have the same size and type with src1. -CV_EXPORTS void blendLinear(InputArray src1, InputArray src2, InputArray weights1, InputArray weights2, OutputArray dst); - -//! @} imgproc_misc - -//! @addtogroup imgproc_color_conversions -//! @{ - -/** @brief Converts an image from one color space to another. - -The function converts an input image from one color space to another. In case of a transformation -to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note -that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the -bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue -component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and -sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on. - -The conventional ranges for R, G, and B channel values are: -- 0 to 255 for CV_8U images -- 0 to 65535 for CV_16U images -- 0 to 1 for CV_32F images - -In case of linear transformations, the range does not matter. But in case of a non-linear -transformation, an input RGB image should be normalized to the proper value range to get the correct -results, for example, for RGB \f$\rightarrow\f$ L\*u\*v\* transformation. For example, if you have a -32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will -have the 0..255 value range instead of 0..1 assumed by the function. So, before calling #cvtColor , -you need first to scale the image down: -@code - img *= 1./255; - cvtColor(img, img, COLOR_BGR2Luv); -@endcode -If you use #cvtColor with 8-bit images, the conversion will have some information lost. For many -applications, this will not be noticeable but it is recommended to use 32-bit images in applications -that need the full range of colors or that convert an image before an operation and then convert -back. - -If conversion adds the alpha channel, its value will set to the maximum of corresponding channel -range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F. - -@param src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision -floating-point. -@param dst output image of the same size and depth as src. -@param code color space conversion code (see #ColorConversionCodes). -@param dstCn number of channels in the destination image; if the parameter is 0, the number of the -channels is derived automatically from src and code. - -@see @ref imgproc_color_conversions - */ -CV_EXPORTS_W void cvtColor( InputArray src, OutputArray dst, int code, int dstCn = 0 ); - -/** @brief Converts an image from one color space to another where the source image is -stored in two planes. - -This function only supports YUV420 to RGB conversion as of now. - -@param src1: 8-bit image (#CV_8U) of the Y plane. -@param src2: image containing interleaved U/V plane. -@param dst: output image. -@param code: Specifies the type of conversion. It can take any of the following values: -- #COLOR_YUV2BGR_NV12 -- #COLOR_YUV2RGB_NV12 -- #COLOR_YUV2BGRA_NV12 -- #COLOR_YUV2RGBA_NV12 -- #COLOR_YUV2BGR_NV21 -- #COLOR_YUV2RGB_NV21 -- #COLOR_YUV2BGRA_NV21 -- #COLOR_YUV2RGBA_NV21 -*/ -CV_EXPORTS_W void cvtColorTwoPlane( InputArray src1, InputArray src2, OutputArray dst, int code ); - -/** @brief main function for all demosaicing processes - -@param src input image: 8-bit unsigned or 16-bit unsigned. -@param dst output image of the same size and depth as src. -@param code Color space conversion code (see the description below). -@param dstCn number of channels in the destination image; if the parameter is 0, the number of the -channels is derived automatically from src and code. - -The function can do the following transformations: - -- Demosaicing using bilinear interpolation - - #COLOR_BayerBG2BGR , #COLOR_BayerGB2BGR , #COLOR_BayerRG2BGR , #COLOR_BayerGR2BGR - - #COLOR_BayerBG2GRAY , #COLOR_BayerGB2GRAY , #COLOR_BayerRG2GRAY , #COLOR_BayerGR2GRAY - -- Demosaicing using Variable Number of Gradients. - - #COLOR_BayerBG2BGR_VNG , #COLOR_BayerGB2BGR_VNG , #COLOR_BayerRG2BGR_VNG , #COLOR_BayerGR2BGR_VNG - -- Edge-Aware Demosaicing. - - #COLOR_BayerBG2BGR_EA , #COLOR_BayerGB2BGR_EA , #COLOR_BayerRG2BGR_EA , #COLOR_BayerGR2BGR_EA - -- Demosaicing with alpha channel - - #COLOR_BayerBG2BGRA , #COLOR_BayerGB2BGRA , #COLOR_BayerRG2BGRA , #COLOR_BayerGR2BGRA - -@sa cvtColor -*/ -CV_EXPORTS_W void demosaicing(InputArray src, OutputArray dst, int code, int dstCn = 0); - -//! @} imgproc_color_conversions - -//! @addtogroup imgproc_shape -//! @{ - -/** @brief Calculates all of the moments up to the third order of a polygon or rasterized shape. - -The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The -results are returned in the structure cv::Moments. - -@param array Raster image (single-channel, 8-bit or floating-point 2D array) or an array ( -\f$1 \times N\f$ or \f$N \times 1\f$ ) of 2D points (Point or Point2f ). -@param binaryImage If it is true, all non-zero image pixels are treated as 1's. The parameter is -used for images only. -@returns moments. - -@note Only applicable to contour moments calculations from Python bindings: Note that the numpy -type for the input array should be either np.int32 or np.float32. - -@sa contourArea, arcLength - */ -CV_EXPORTS_W Moments moments( InputArray array, bool binaryImage = false ); - -/** @brief Calculates seven Hu invariants. - -The function calculates seven Hu invariants (introduced in @cite Hu62; see also -) defined as: - -\f[\begin{array}{l} hu[0]= \eta _{20}+ \eta _{02} \\ hu[1]=( \eta _{20}- \eta _{02})^{2}+4 \eta _{11}^{2} \\ hu[2]=( \eta _{30}-3 \eta _{12})^{2}+ (3 \eta _{21}- \eta _{03})^{2} \\ hu[3]=( \eta _{30}+ \eta _{12})^{2}+ ( \eta _{21}+ \eta _{03})^{2} \\ hu[4]=( \eta _{30}-3 \eta _{12})( \eta _{30}+ \eta _{12})[( \eta _{30}+ \eta _{12})^{2}-3( \eta _{21}+ \eta _{03})^{2}]+(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ hu[5]=( \eta _{20}- \eta _{02})[( \eta _{30}+ \eta _{12})^{2}- ( \eta _{21}+ \eta _{03})^{2}]+4 \eta _{11}( \eta _{30}+ \eta _{12})( \eta _{21}+ \eta _{03}) \\ hu[6]=(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}]-( \eta _{30}-3 \eta _{12})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ \end{array}\f] - -where \f$\eta_{ji}\f$ stands for \f$\texttt{Moments::nu}_{ji}\f$ . - -These values are proved to be invariants to the image scale, rotation, and reflection except the -seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of -infinite image resolution. In case of raster images, the computed Hu invariants for the original and -transformed images are a bit different. - -@param moments Input moments computed with moments . -@param hu Output Hu invariants. - -@sa matchShapes - */ -CV_EXPORTS void HuMoments( const Moments& moments, double hu[7] ); - -/** @overload */ -CV_EXPORTS_W void HuMoments( const Moments& m, OutputArray hu ); - -//! @} imgproc_shape - -//! @addtogroup imgproc_object -//! @{ - -//! type of the template matching operation -enum TemplateMatchModes { - TM_SQDIFF = 0, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2\f] - TM_SQDIFF_NORMED = 1, //!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y')-I(x+x',y+y'))^2}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f] - TM_CCORR = 2, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y'))\f] - TM_CCORR_NORMED = 3, //!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I(x+x',y+y'))}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f] - TM_CCOEFF = 4, //!< \f[R(x,y)= \sum _{x',y'} (T'(x',y') \cdot I'(x+x',y+y'))\f] - //!< where - //!< \f[\begin{array}{l} T'(x',y')=T(x',y') - 1/(w \cdot h) \cdot \sum _{x'',y''} T(x'',y'') \\ I'(x+x',y+y')=I(x+x',y+y') - 1/(w \cdot h) \cdot \sum _{x'',y''} I(x+x'',y+y'') \end{array}\f] - TM_CCOEFF_NORMED = 5 //!< \f[R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{ \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2} }\f] -}; - -/** @example samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp -An example using Template Matching algorithm -*/ - -/** @brief Compares a template against overlapped image regions. - -The function slides through image , compares the overlapped patches of size \f$w \times h\f$ against -templ using the specified method and stores the comparison results in result . Here are the formulae -for the available comparison methods ( \f$I\f$ denotes image, \f$T\f$ template, \f$R\f$ result ). The summation -is done over template and/or the image patch: \f$x' = 0...w-1, y' = 0...h-1\f$ - -After the function finishes the comparison, the best matches can be found as global minimums (when -#TM_SQDIFF was used) or maximums (when #TM_CCORR or #TM_CCOEFF was used) using the -#minMaxLoc function. In case of a color image, template summation in the numerator and each sum in -the denominator is done over all of the channels and separate mean values are used for each channel. -That is, the function can take a color template and a color image. The result will still be a -single-channel image, which is easier to analyze. - -@param image Image where the search is running. It must be 8-bit or 32-bit floating-point. -@param templ Searched template. It must be not greater than the source image and have the same -data type. -@param result Map of comparison results. It must be single-channel 32-bit floating-point. If image -is \f$W \times H\f$ and templ is \f$w \times h\f$ , then result is \f$(W-w+1) \times (H-h+1)\f$ . -@param method Parameter specifying the comparison method, see #TemplateMatchModes -@param mask Mask of searched template. It must have the same datatype and size with templ. It is -not set by default. Currently, only the #TM_SQDIFF and #TM_CCORR_NORMED methods are supported. - */ -CV_EXPORTS_W void matchTemplate( InputArray image, InputArray templ, - OutputArray result, int method, InputArray mask = noArray() ); - -//! @} - -//! @addtogroup imgproc_shape -//! @{ - -/** @example samples/cpp/connected_components.cpp -This program demonstrates connected components and use of the trackbar -*/ - -/** @brief computes the connected components labeled image of boolean image - -image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 -represents the background label. ltype specifies the output label image type, an important -consideration based on the total number of labels or alternatively the total number of pixels in -the source image. ccltype specifies the connected components labeling algorithm to use, currently -Grana (BBDT) and Wu's (SAUF) @cite Wu2009 algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes -for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not. -This function uses parallel version of both Grana and Wu's algorithms if at least one allowed -parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. - -@param image the 8-bit single-channel image to be labeled -@param labels destination labeled image -@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively -@param ltype output image label type. Currently CV_32S and CV_16U are supported. -@param ccltype connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes). -*/ -CV_EXPORTS_AS(connectedComponentsWithAlgorithm) int connectedComponents(InputArray image, OutputArray labels, - int connectivity, int ltype, int ccltype); - - -/** @overload - -@param image the 8-bit single-channel image to be labeled -@param labels destination labeled image -@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively -@param ltype output image label type. Currently CV_32S and CV_16U are supported. -*/ -CV_EXPORTS_W int connectedComponents(InputArray image, OutputArray labels, - int connectivity = 8, int ltype = CV_32S); - - -/** @brief computes the connected components labeled image of boolean image and also produces a statistics output for each label - -image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 -represents the background label. ltype specifies the output label image type, an important -consideration based on the total number of labels or alternatively the total number of pixels in -the source image. ccltype specifies the connected components labeling algorithm to use, currently -Grana's (BBDT) and Wu's (SAUF) @cite Wu2009 algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes -for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not. -This function uses parallel version of both Grana and Wu's algorithms (statistics included) if at least one allowed -parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs. - -@param image the 8-bit single-channel image to be labeled -@param labels destination labeled image -@param stats statistics output for each label, including the background label. -Statistics are accessed via stats(label, COLUMN) where COLUMN is one of -#ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. -@param centroids centroid output for each label, including the background label. Centroids are -accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. -@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively -@param ltype output image label type. Currently CV_32S and CV_16U are supported. -@param ccltype connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes). -*/ -CV_EXPORTS_AS(connectedComponentsWithStatsWithAlgorithm) int connectedComponentsWithStats(InputArray image, OutputArray labels, - OutputArray stats, OutputArray centroids, - int connectivity, int ltype, int ccltype); - -/** @overload -@param image the 8-bit single-channel image to be labeled -@param labels destination labeled image -@param stats statistics output for each label, including the background label. -Statistics are accessed via stats(label, COLUMN) where COLUMN is one of -#ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S. -@param centroids centroid output for each label, including the background label. Centroids are -accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F. -@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively -@param ltype output image label type. Currently CV_32S and CV_16U are supported. -*/ -CV_EXPORTS_W int connectedComponentsWithStats(InputArray image, OutputArray labels, - OutputArray stats, OutputArray centroids, - int connectivity = 8, int ltype = CV_32S); - - -/** @brief Finds contours in a binary image. - -The function retrieves contours from the binary image using the algorithm @cite Suzuki85 . The contours -are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the -OpenCV sample directory. -@note Since opencv 3.2 source image is not modified by this function. - -@param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero -pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold , -#adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one. -If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1). -@param contours Detected contours. Each contour is stored as a vector of points (e.g. -std::vector >). -@param hierarchy Optional output vector (e.g. std::vector), containing information about the image topology. It has -as many elements as the number of contours. For each i-th contour contours[i], the elements -hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices -in contours of the next and previous contours at the same hierarchical level, the first child -contour and the parent contour, respectively. If for the contour i there are no next, previous, -parent, or nested contours, the corresponding elements of hierarchy[i] will be negative. -@param mode Contour retrieval mode, see #RetrievalModes -@param method Contour approximation method, see #ContourApproximationModes -@param offset Optional offset by which every contour point is shifted. This is useful if the -contours are extracted from the image ROI and then they should be analyzed in the whole image -context. - */ -CV_EXPORTS_W void findContours( InputOutputArray image, OutputArrayOfArrays contours, - OutputArray hierarchy, int mode, - int method, Point offset = Point()); - -/** @overload */ -CV_EXPORTS void findContours( InputOutputArray image, OutputArrayOfArrays contours, - int mode, int method, Point offset = Point()); - -/** @example samples/cpp/squares.cpp -A program using pyramid scaling, Canny, contours and contour simplification to find -squares in a list of images (pic1-6.png). Returns sequence of squares detected on the image. -*/ - -/** @example samples/tapi/squares.cpp -A program using pyramid scaling, Canny, contours and contour simplification to find -squares in the input image. -*/ - -/** @brief Approximates a polygonal curve(s) with the specified precision. - -The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less -vertices so that the distance between them is less or equal to the specified precision. It uses the -Douglas-Peucker algorithm - -@param curve Input vector of a 2D point stored in std::vector or Mat -@param approxCurve Result of the approximation. The type should match the type of the input curve. -@param epsilon Parameter specifying the approximation accuracy. This is the maximum distance -between the original curve and its approximation. -@param closed If true, the approximated curve is closed (its first and last vertices are -connected). Otherwise, it is not closed. - */ -CV_EXPORTS_W void approxPolyDP( InputArray curve, - OutputArray approxCurve, - double epsilon, bool closed ); - -/** @brief Calculates a contour perimeter or a curve length. - -The function computes a curve length or a closed contour perimeter. - -@param curve Input vector of 2D points, stored in std::vector or Mat. -@param closed Flag indicating whether the curve is closed or not. - */ -CV_EXPORTS_W double arcLength( InputArray curve, bool closed ); - -/** @brief Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image. - -The function calculates and returns the minimal up-right bounding rectangle for the specified point set or -non-zero pixels of gray-scale image. - -@param array Input gray-scale image or 2D point set, stored in std::vector or Mat. - */ -CV_EXPORTS_W Rect boundingRect( InputArray array ); - -/** @brief Calculates a contour area. - -The function computes a contour area. Similarly to moments , the area is computed using the Green -formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using -#drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong -results for contours with self-intersections. - -Example: -@code - vector contour; - contour.push_back(Point2f(0, 0)); - contour.push_back(Point2f(10, 0)); - contour.push_back(Point2f(10, 10)); - contour.push_back(Point2f(5, 4)); - - double area0 = contourArea(contour); - vector approx; - approxPolyDP(contour, approx, 5, true); - double area1 = contourArea(approx); - - cout << "area0 =" << area0 << endl << - "area1 =" << area1 << endl << - "approx poly vertices" << approx.size() << endl; -@endcode -@param contour Input vector of 2D points (contour vertices), stored in std::vector or Mat. -@param oriented Oriented area flag. If it is true, the function returns a signed area value, -depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can -determine orientation of a contour by taking the sign of an area. By default, the parameter is -false, which means that the absolute value is returned. - */ -CV_EXPORTS_W double contourArea( InputArray contour, bool oriented = false ); - -/** @brief Finds a rotated rectangle of the minimum area enclosing the input 2D point set. - -The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a -specified point set. Developer should keep in mind that the returned RotatedRect can contain negative -indices when data is close to the containing Mat element boundary. - -@param points Input vector of 2D points, stored in std::vector\<\> or Mat - */ -CV_EXPORTS_W RotatedRect minAreaRect( InputArray points ); - -/** @brief Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle. - -The function finds the four vertices of a rotated rectangle. This function is useful to draw the -rectangle. In C++, instead of using this function, you can directly use RotatedRect::points method. Please -visit the @ref tutorial_bounding_rotated_ellipses "tutorial on Creating Bounding rotated boxes and ellipses for contours" for more information. - -@param box The input rotated rectangle. It may be the output of -@param points The output array of four vertices of rectangles. - */ -CV_EXPORTS_W void boxPoints(RotatedRect box, OutputArray points); - -/** @brief Finds a circle of the minimum area enclosing a 2D point set. - -The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm. - -@param points Input vector of 2D points, stored in std::vector\<\> or Mat -@param center Output center of the circle. -@param radius Output radius of the circle. - */ -CV_EXPORTS_W void minEnclosingCircle( InputArray points, - CV_OUT Point2f& center, CV_OUT float& radius ); - -/** @example samples/cpp/minarea.cpp -*/ - -/** @brief Finds a triangle of minimum area enclosing a 2D point set and returns its area. - -The function finds a triangle of minimum area enclosing the given set of 2D points and returns its -area. The output for a given 2D point set is shown in the image below. 2D points are depicted in -*red* and the enclosing triangle in *yellow*. - -![Sample output of the minimum enclosing triangle function](pics/minenclosingtriangle.png) - -The implementation of the algorithm is based on O'Rourke's @cite ORourke86 and Klee and Laskowski's -@cite KleeLaskowski85 papers. O'Rourke provides a \f$\theta(n)\f$ algorithm for finding the minimal -enclosing triangle of a 2D convex polygon with n vertices. Since the #minEnclosingTriangle function -takes a 2D point set as input an additional preprocessing step of computing the convex hull of the -2D point set is required. The complexity of the #convexHull function is \f$O(n log(n))\f$ which is higher -than \f$\theta(n)\f$. Thus the overall complexity of the function is \f$O(n log(n))\f$. - -@param points Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector\<\> or Mat -@param triangle Output vector of three 2D points defining the vertices of the triangle. The depth -of the OutputArray must be CV_32F. - */ -CV_EXPORTS_W double minEnclosingTriangle( InputArray points, CV_OUT OutputArray triangle ); - -/** @brief Compares two shapes. - -The function compares two shapes. All three implemented methods use the Hu invariants (see #HuMoments) - -@param contour1 First contour or grayscale image. -@param contour2 Second contour or grayscale image. -@param method Comparison method, see #ShapeMatchModes -@param parameter Method-specific parameter (not supported now). - */ -CV_EXPORTS_W double matchShapes( InputArray contour1, InputArray contour2, - int method, double parameter ); - -/** @example samples/cpp/convexhull.cpp -An example using the convexHull functionality -*/ - -/** @brief Finds the convex hull of a point set. - -The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm @cite Sklansky82 -that has *O(N logN)* complexity in the current implementation. - -@param points Input 2D point set, stored in std::vector or Mat. -@param hull Output convex hull. It is either an integer vector of indices or vector of points. In -the first case, the hull elements are 0-based indices of the convex hull points in the original -array (since the set of convex hull points is a subset of the original point set). In the second -case, hull elements are the convex hull points themselves. -@param clockwise Orientation flag. If it is true, the output convex hull is oriented clockwise. -Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing -to the right, and its Y axis pointing upwards. -@param returnPoints Operation flag. In case of a matrix, when the flag is true, the function -returns convex hull points. Otherwise, it returns indices of the convex hull points. When the -output array is std::vector, the flag is ignored, and the output depends on the type of the -vector: std::vector\ implies returnPoints=false, std::vector\ implies -returnPoints=true. - -@note `points` and `hull` should be different arrays, inplace processing isn't supported. - -Check @ref tutorial_hull "the corresponding tutorial" for more details. - -useful links: - -https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/ - */ -CV_EXPORTS_W void convexHull( InputArray points, OutputArray hull, - bool clockwise = false, bool returnPoints = true ); - -/** @brief Finds the convexity defects of a contour. - -The figure below displays convexity defects of a hand contour: - -![image](pics/defects.png) - -@param contour Input contour. -@param convexhull Convex hull obtained using convexHull that should contain indices of the contour -points that make the hull. -@param convexityDefects The output vector of convexity defects. In C++ and the new Python/Java -interface each convexity defect is represented as 4-element integer vector (a.k.a. #Vec4i): -(start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices -in the original contour of the convexity defect beginning, end and the farthest point, and -fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the -farthest contour point and the hull. That is, to get the floating-point value of the depth will be -fixpt_depth/256.0. - */ -CV_EXPORTS_W void convexityDefects( InputArray contour, InputArray convexhull, OutputArray convexityDefects ); - -/** @brief Tests a contour convexity. - -The function tests whether the input contour is convex or not. The contour must be simple, that is, -without self-intersections. Otherwise, the function output is undefined. - -@param contour Input vector of 2D points, stored in std::vector\<\> or Mat - */ -CV_EXPORTS_W bool isContourConvex( InputArray contour ); - -/** @example samples/cpp/intersectExample.cpp -Examples of how intersectConvexConvex works -*/ - -/** @brief Finds intersection of two convex polygons - -@param _p1 First polygon -@param _p2 Second polygon -@param _p12 Output polygon describing the intersecting area -@param handleNested When true, an intersection is found if one of the polygons is fully enclosed in the other. -When false, no intersection is found. If the polygons share a side or the vertex of one polygon lies on an edge -of the other, they are not considered nested and an intersection will be found regardless of the value of handleNested. - -@returns Absolute value of area of intersecting polygon - -@note intersectConvexConvex doesn't confirm that both polygons are convex and will return invalid results if they aren't. - */ -CV_EXPORTS_W float intersectConvexConvex( InputArray _p1, InputArray _p2, - OutputArray _p12, bool handleNested = true ); - -/** @example samples/cpp/fitellipse.cpp -An example using the fitEllipse technique -*/ - -/** @brief Fits an ellipse around a set of 2D points. - -The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of -all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by @cite Fitzgibbon95 -is used. Developer should keep in mind that it is possible that the returned -ellipse/rotatedRect data contains negative indices, due to the data points being close to the -border of the containing Mat element. - -@param points Input 2D point set, stored in std::vector\<\> or Mat - */ -CV_EXPORTS_W RotatedRect fitEllipse( InputArray points ); - -/** @brief Fits an ellipse around a set of 2D points. - - The function calculates the ellipse that fits a set of 2D points. - It returns the rotated rectangle in which the ellipse is inscribed. - The Approximate Mean Square (AMS) proposed by @cite Taubin1991 is used. - - For an ellipse, this basis set is \f$ \chi= \left(x^2, x y, y^2, x, y, 1\right) \f$, - which is a set of six free coefficients \f$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \f$. - However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \f$ (a,b) \f$, - the position \f$ (x_0,y_0) \f$, and the orientation \f$ \theta \f$. This is because the basis set includes lines, - quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. - If the fit is found to be a parabolic or hyperbolic function then the standard #fitEllipse method is used. - The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves - by imposing the condition that \f$ A^T ( D_x^T D_x + D_y^T D_y) A = 1 \f$ where - the matrices \f$ Dx \f$ and \f$ Dy \f$ are the partial derivatives of the design matrix \f$ D \f$ with - respect to x and y. The matrices are formed row by row applying the following to - each of the points in the set: - \f{align*}{ - D(i,:)&=\left\{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right\} & - D_x(i,:)&=\left\{2 x_i,y_i,0,1,0,0\right\} & - D_y(i,:)&=\left\{0,x_i,2 y_i,0,1,0\right\} - \f} - The AMS method minimizes the cost function - \f{equation*}{ - \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T } - \f} - - The minimum cost is found by solving the generalized eigenvalue problem. - - \f{equation*}{ - D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A - \f} - - @param points Input 2D point set, stored in std::vector\<\> or Mat - */ -CV_EXPORTS_W RotatedRect fitEllipseAMS( InputArray points ); - - -/** @brief Fits an ellipse around a set of 2D points. - - The function calculates the ellipse that fits a set of 2D points. - It returns the rotated rectangle in which the ellipse is inscribed. - The Direct least square (Direct) method by @cite Fitzgibbon1999 is used. - - For an ellipse, this basis set is \f$ \chi= \left(x^2, x y, y^2, x, y, 1\right) \f$, - which is a set of six free coefficients \f$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \f$. - However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \f$ (a,b) \f$, - the position \f$ (x_0,y_0) \f$, and the orientation \f$ \theta \f$. This is because the basis set includes lines, - quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. - The Direct method confines the fit to ellipses by ensuring that \f$ 4 A_{xx} A_{yy}- A_{xy}^2 > 0 \f$. - The condition imposed is that \f$ 4 A_{xx} A_{yy}- A_{xy}^2=1 \f$ which satisfies the inequality - and as the coefficients can be arbitrarily scaled is not overly restrictive. - - \f{equation*}{ - \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix} - 0 & 0 & 2 & 0 & 0 & 0 \\ - 0 & -1 & 0 & 0 & 0 & 0 \\ - 2 & 0 & 0 & 0 & 0 & 0 \\ - 0 & 0 & 0 & 0 & 0 & 0 \\ - 0 & 0 & 0 & 0 & 0 & 0 \\ - 0 & 0 & 0 & 0 & 0 & 0 - \end{matrix} \right) - \f} - - The minimum cost is found by solving the generalized eigenvalue problem. - - \f{equation*}{ - D^T D A = \lambda \left( C\right) A - \f} - - The system produces only one positive eigenvalue \f$ \lambda\f$ which is chosen as the solution - with its eigenvector \f$\mathbf{u}\f$. These are used to find the coefficients - - \f{equation*}{ - A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u} - \f} - The scaling factor guarantees that \f$A^T C A =1\f$. - - @param points Input 2D point set, stored in std::vector\<\> or Mat - */ -CV_EXPORTS_W RotatedRect fitEllipseDirect( InputArray points ); - -/** @brief Fits a line to a 2D or 3D point set. - -The function fitLine fits a line to a 2D or 3D point set by minimizing \f$\sum_i \rho(r_i)\f$ where -\f$r_i\f$ is a distance between the \f$i^{th}\f$ point, the line and \f$\rho(r)\f$ is a distance function, one -of the following: -- DIST_L2 -\f[\rho (r) = r^2/2 \quad \text{(the simplest and the fastest least-squares method)}\f] -- DIST_L1 -\f[\rho (r) = r\f] -- DIST_L12 -\f[\rho (r) = 2 \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\f] -- DIST_FAIR -\f[\rho \left (r \right ) = C^2 \cdot \left ( \frac{r}{C} - \log{\left(1 + \frac{r}{C}\right)} \right ) \quad \text{where} \quad C=1.3998\f] -- DIST_WELSCH -\f[\rho \left (r \right ) = \frac{C^2}{2} \cdot \left ( 1 - \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right ) \quad \text{where} \quad C=2.9846\f] -- DIST_HUBER -\f[\rho (r) = \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\f] - -The algorithm is based on the M-estimator ( ) technique -that iteratively fits the line using the weighted least-squares algorithm. After each iteration the -weights \f$w_i\f$ are adjusted to be inversely proportional to \f$\rho(r_i)\f$ . - -@param points Input vector of 2D or 3D points, stored in std::vector\<\> or Mat. -@param line Output line parameters. In case of 2D fitting, it should be a vector of 4 elements -(like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and -(x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like -Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line -and (x0, y0, z0) is a point on the line. -@param distType Distance used by the M-estimator, see #DistanceTypes -@param param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value -is chosen. -@param reps Sufficient accuracy for the radius (distance between the coordinate origin and the line). -@param aeps Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps. - */ -CV_EXPORTS_W void fitLine( InputArray points, OutputArray line, int distType, - double param, double reps, double aeps ); - -/** @brief Performs a point-in-contour test. - -The function determines whether the point is inside a contour, outside, or lies on an edge (or -coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) -value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. -Otherwise, the return value is a signed distance between the point and the nearest contour edge. - -See below a sample output of the function where each image pixel is tested against the contour: - -![sample output](pics/pointpolygon.png) - -@param contour Input contour. -@param pt Point tested against the contour. -@param measureDist If true, the function estimates the signed distance from the point to the -nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not. - */ -CV_EXPORTS_W double pointPolygonTest( InputArray contour, Point2f pt, bool measureDist ); - -/** @brief Finds out if there is any intersection between two rotated rectangles. - -If there is then the vertices of the intersecting region are returned as well. - -Below are some examples of intersection configurations. The hatched pattern indicates the -intersecting region and the red vertices are returned by the function. - -![intersection examples](pics/intersection.png) - -@param rect1 First rectangle -@param rect2 Second rectangle -@param intersectingRegion The output array of the vertices of the intersecting region. It returns -at most 8 vertices. Stored as std::vector\ or cv::Mat as Mx1 of type CV_32FC2. -@returns One of #RectanglesIntersectTypes - */ -CV_EXPORTS_W int rotatedRectangleIntersection( const RotatedRect& rect1, const RotatedRect& rect2, OutputArray intersectingRegion ); - -/** @brief Creates a smart pointer to a cv::GeneralizedHoughBallard class and initializes it. -*/ -CV_EXPORTS_W Ptr createGeneralizedHoughBallard(); - -/** @brief Creates a smart pointer to a cv::GeneralizedHoughGuil class and initializes it. -*/ -CV_EXPORTS_W Ptr createGeneralizedHoughGuil(); - -//! @} imgproc_shape - -//! @addtogroup imgproc_colormap -//! @{ - -//! GNU Octave/MATLAB equivalent colormaps -enum ColormapTypes -{ - COLORMAP_AUTUMN = 0, //!< ![autumn](pics/colormaps/colorscale_autumn.jpg) - COLORMAP_BONE = 1, //!< ![bone](pics/colormaps/colorscale_bone.jpg) - COLORMAP_JET = 2, //!< ![jet](pics/colormaps/colorscale_jet.jpg) - COLORMAP_WINTER = 3, //!< ![winter](pics/colormaps/colorscale_winter.jpg) - COLORMAP_RAINBOW = 4, //!< ![rainbow](pics/colormaps/colorscale_rainbow.jpg) - COLORMAP_OCEAN = 5, //!< ![ocean](pics/colormaps/colorscale_ocean.jpg) - COLORMAP_SUMMER = 6, //!< ![summer](pics/colormaps/colorscale_summer.jpg) - COLORMAP_SPRING = 7, //!< ![spring](pics/colormaps/colorscale_spring.jpg) - COLORMAP_COOL = 8, //!< ![cool](pics/colormaps/colorscale_cool.jpg) - COLORMAP_HSV = 9, //!< ![HSV](pics/colormaps/colorscale_hsv.jpg) - COLORMAP_PINK = 10, //!< ![pink](pics/colormaps/colorscale_pink.jpg) - COLORMAP_HOT = 11, //!< ![hot](pics/colormaps/colorscale_hot.jpg) - COLORMAP_PARULA = 12, //!< ![parula](pics/colormaps/colorscale_parula.jpg) - COLORMAP_MAGMA = 13, //!< ![magma](pics/colormaps/colorscale_magma.jpg) - COLORMAP_INFERNO = 14, //!< ![inferno](pics/colormaps/colorscale_inferno.jpg) - COLORMAP_PLASMA = 15, //!< ![plasma](pics/colormaps/colorscale_plasma.jpg) - COLORMAP_VIRIDIS = 16, //!< ![viridis](pics/colormaps/colorscale_viridis.jpg) - COLORMAP_CIVIDIS = 17, //!< ![cividis](pics/colormaps/colorscale_cividis.jpg) - COLORMAP_TWILIGHT = 18, //!< ![twilight](pics/colormaps/colorscale_twilight.jpg) - COLORMAP_TWILIGHT_SHIFTED = 19, //!< ![twilight shifted](pics/colormaps/colorscale_twilight_shifted.jpg) - COLORMAP_TURBO = 20, //!< ![turbo](pics/colormaps/colorscale_turbo.jpg) - COLORMAP_DEEPGREEN = 21 //!< ![deepgreen](pics/colormaps/colorscale_deepgreen.jpg) -}; - -/** @example samples/cpp/falsecolor.cpp -An example using applyColorMap function -*/ - -/** @brief Applies a GNU Octave/MATLAB equivalent colormap on a given image. - -@param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3. -@param dst The result is the colormapped source image. Note: Mat::create is called on dst. -@param colormap The colormap to apply, see #ColormapTypes -*/ -CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, int colormap); - -/** @brief Applies a user colormap on a given image. - -@param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3. -@param dst The result is the colormapped source image. Note: Mat::create is called on dst. -@param userColor The colormap to apply of type CV_8UC1 or CV_8UC3 and size 256 -*/ -CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, InputArray userColor); - -//! @} imgproc_colormap - -//! @addtogroup imgproc_draw -//! @{ - - -/** OpenCV color channel order is BGR[A] */ -#define CV_RGB(r, g, b) cv::Scalar((b), (g), (r), 0) - -/** @brief Draws a line segment connecting two points. - -The function line draws the line segment between pt1 and pt2 points in the image. The line is -clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected -or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased -lines are drawn using Gaussian filtering. - -@param img Image. -@param pt1 First point of the line segment. -@param pt2 Second point of the line segment. -@param color Line color. -@param thickness Line thickness. -@param lineType Type of the line. See #LineTypes. -@param shift Number of fractional bits in the point coordinates. - */ -CV_EXPORTS_W void line(InputOutputArray img, Point pt1, Point pt2, const Scalar& color, - int thickness = 1, int lineType = LINE_8, int shift = 0); - -/** @brief Draws a arrow segment pointing from the first point to the second one. - -The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line. - -@param img Image. -@param pt1 The point the arrow starts from. -@param pt2 The point the arrow points to. -@param color Line color. -@param thickness Line thickness. -@param line_type Type of the line. See #LineTypes -@param shift Number of fractional bits in the point coordinates. -@param tipLength The length of the arrow tip in relation to the arrow length - */ -CV_EXPORTS_W void arrowedLine(InputOutputArray img, Point pt1, Point pt2, const Scalar& color, - int thickness=1, int line_type=8, int shift=0, double tipLength=0.1); - -/** @brief Draws a simple, thick, or filled up-right rectangle. - -The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners -are pt1 and pt2. - -@param img Image. -@param pt1 Vertex of the rectangle. -@param pt2 Vertex of the rectangle opposite to pt1 . -@param color Rectangle color or brightness (grayscale image). -@param thickness Thickness of lines that make up the rectangle. Negative values, like #FILLED, -mean that the function has to draw a filled rectangle. -@param lineType Type of the line. See #LineTypes -@param shift Number of fractional bits in the point coordinates. - */ -CV_EXPORTS_W void rectangle(InputOutputArray img, Point pt1, Point pt2, - const Scalar& color, int thickness = 1, - int lineType = LINE_8, int shift = 0); - -/** @overload - -use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and -r.br()-Point(1,1)` are opposite corners -*/ -CV_EXPORTS void rectangle(CV_IN_OUT Mat& img, Rect rec, - const Scalar& color, int thickness = 1, - int lineType = LINE_8, int shift = 0); - -/** @example samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_2.cpp -An example using drawing functions -*/ - -/** @brief Draws a circle. - -The function cv::circle draws a simple or filled circle with a given center and radius. -@param img Image where the circle is drawn. -@param center Center of the circle. -@param radius Radius of the circle. -@param color Circle color. -@param thickness Thickness of the circle outline, if positive. Negative values, like #FILLED, -mean that a filled circle is to be drawn. -@param lineType Type of the circle boundary. See #LineTypes -@param shift Number of fractional bits in the coordinates of the center and in the radius value. - */ -CV_EXPORTS_W void circle(InputOutputArray img, Point center, int radius, - const Scalar& color, int thickness = 1, - int lineType = LINE_8, int shift = 0); - -/** @brief Draws a simple or thick elliptic arc or fills an ellipse sector. - -The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic -arc, or a filled ellipse sector. The drawing code uses general parametric form. -A piecewise-linear curve is used to approximate the elliptic arc -boundary. If you need more control of the ellipse rendering, you can retrieve the curve using -#ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first -variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and -`endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains -the meaning of the parameters to draw the blue arc. - -![Parameters of Elliptic Arc](pics/ellipse.svg) - -@param img Image. -@param center Center of the ellipse. -@param axes Half of the size of the ellipse main axes. -@param angle Ellipse rotation angle in degrees. -@param startAngle Starting angle of the elliptic arc in degrees. -@param endAngle Ending angle of the elliptic arc in degrees. -@param color Ellipse color. -@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that -a filled ellipse sector is to be drawn. -@param lineType Type of the ellipse boundary. See #LineTypes -@param shift Number of fractional bits in the coordinates of the center and values of axes. - */ -CV_EXPORTS_W void ellipse(InputOutputArray img, Point center, Size axes, - double angle, double startAngle, double endAngle, - const Scalar& color, int thickness = 1, - int lineType = LINE_8, int shift = 0); - -/** @overload -@param img Image. -@param box Alternative ellipse representation via RotatedRect. This means that the function draws -an ellipse inscribed in the rotated rectangle. -@param color Ellipse color. -@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that -a filled ellipse sector is to be drawn. -@param lineType Type of the ellipse boundary. See #LineTypes -*/ -CV_EXPORTS_W void ellipse(InputOutputArray img, const RotatedRect& box, const Scalar& color, - int thickness = 1, int lineType = LINE_8); - -/* ----------------------------------------------------------------------------------------- */ -/* ADDING A SET OF PREDEFINED MARKERS WHICH COULD BE USED TO HIGHLIGHT POSITIONS IN AN IMAGE */ -/* ----------------------------------------------------------------------------------------- */ - -//! Possible set of marker types used for the cv::drawMarker function -enum MarkerTypes -{ - MARKER_CROSS = 0, //!< A crosshair marker shape - MARKER_TILTED_CROSS = 1, //!< A 45 degree tilted crosshair marker shape - MARKER_STAR = 2, //!< A star marker shape, combination of cross and tilted cross - MARKER_DIAMOND = 3, //!< A diamond marker shape - MARKER_SQUARE = 4, //!< A square marker shape - MARKER_TRIANGLE_UP = 5, //!< An upwards pointing triangle marker shape - MARKER_TRIANGLE_DOWN = 6 //!< A downwards pointing triangle marker shape -}; - -/** @brief Draws a marker on a predefined position in an image. - -The function cv::drawMarker draws a marker on a given position in the image. For the moment several -marker types are supported, see #MarkerTypes for more information. - -@param img Image. -@param position The point where the crosshair is positioned. -@param color Line color. -@param markerType The specific type of marker you want to use, see #MarkerTypes -@param thickness Line thickness. -@param line_type Type of the line, See #LineTypes -@param markerSize The length of the marker axis [default = 20 pixels] - */ -CV_EXPORTS_W void drawMarker(CV_IN_OUT Mat& img, Point position, const Scalar& color, - int markerType = MARKER_CROSS, int markerSize=20, int thickness=1, - int line_type=8); - -/* ----------------------------------------------------------------------------------------- */ -/* END OF MARKER SECTION */ -/* ----------------------------------------------------------------------------------------- */ - -/** @overload */ -CV_EXPORTS void fillConvexPoly(Mat& img, const Point* pts, int npts, - const Scalar& color, int lineType = LINE_8, - int shift = 0); - -/** @brief Fills a convex polygon. - -The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the -function #fillPoly . It can fill not only convex polygons but any monotonic polygon without -self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) -twice at the most (though, its top-most and/or the bottom edge could be horizontal). - -@param img Image. -@param points Polygon vertices. -@param color Polygon color. -@param lineType Type of the polygon boundaries. See #LineTypes -@param shift Number of fractional bits in the vertex coordinates. - */ -CV_EXPORTS_W void fillConvexPoly(InputOutputArray img, InputArray points, - const Scalar& color, int lineType = LINE_8, - int shift = 0); - -/** @overload */ -CV_EXPORTS void fillPoly(Mat& img, const Point** pts, - const int* npts, int ncontours, - const Scalar& color, int lineType = LINE_8, int shift = 0, - Point offset = Point() ); - -/** @example samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp -An example using drawing functions -Check @ref tutorial_random_generator_and_text "the corresponding tutorial" for more details -*/ - -/** @brief Fills the area bounded by one or more polygons. - -The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill -complex areas, for example, areas with holes, contours with self-intersections (some of their -parts), and so forth. - -@param img Image. -@param pts Array of polygons where each polygon is represented as an array of points. -@param color Polygon color. -@param lineType Type of the polygon boundaries. See #LineTypes -@param shift Number of fractional bits in the vertex coordinates. -@param offset Optional offset of all points of the contours. - */ -CV_EXPORTS_W void fillPoly(InputOutputArray img, InputArrayOfArrays pts, - const Scalar& color, int lineType = LINE_8, int shift = 0, - Point offset = Point() ); - -/** @overload */ -CV_EXPORTS void polylines(Mat& img, const Point* const* pts, const int* npts, - int ncontours, bool isClosed, const Scalar& color, - int thickness = 1, int lineType = LINE_8, int shift = 0 ); - -/** @brief Draws several polygonal curves. - -@param img Image. -@param pts Array of polygonal curves. -@param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed, -the function draws a line from the last vertex of each curve to its first vertex. -@param color Polyline color. -@param thickness Thickness of the polyline edges. -@param lineType Type of the line segments. See #LineTypes -@param shift Number of fractional bits in the vertex coordinates. - -The function cv::polylines draws one or more polygonal curves. - */ -CV_EXPORTS_W void polylines(InputOutputArray img, InputArrayOfArrays pts, - bool isClosed, const Scalar& color, - int thickness = 1, int lineType = LINE_8, int shift = 0 ); - -/** @example samples/cpp/contours2.cpp -An example program illustrates the use of cv::findContours and cv::drawContours -\image html WindowsQtContoursOutput.png "Screenshot of the program" -*/ - -/** @example samples/cpp/segment_objects.cpp -An example using drawContours to clean up a background segmentation result -*/ - -/** @brief Draws contours outlines or filled contours. - -The function draws contour outlines in the image if \f$\texttt{thickness} \ge 0\f$ or fills the area -bounded by the contours if \f$\texttt{thickness}<0\f$ . The example below shows how to retrieve -connected components from the binary image and label them: : -@include snippets/imgproc_drawContours.cpp - -@param image Destination image. -@param contours All the input contours. Each contour is stored as a point vector. -@param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn. -@param color Color of the contours. -@param thickness Thickness of lines the contours are drawn with. If it is negative (for example, -thickness=#FILLED ), the contour interiors are drawn. -@param lineType Line connectivity. See #LineTypes -@param hierarchy Optional information about hierarchy. It is only needed if you want to draw only -some of the contours (see maxLevel ). -@param maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn. -If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function -draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This -parameter is only taken into account when there is hierarchy available. -@param offset Optional contour shift parameter. Shift all the drawn contours by the specified -\f$\texttt{offset}=(dx,dy)\f$ . -@note When thickness=#FILLED, the function is designed to handle connected components with holes correctly -even when no hierarchy date is provided. This is done by analyzing all the outlines together -using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved -contours. In order to solve this problem, you need to call #drawContours separately for each sub-group -of contours, or iterate over the collection using contourIdx parameter. - */ -CV_EXPORTS_W void drawContours( InputOutputArray image, InputArrayOfArrays contours, - int contourIdx, const Scalar& color, - int thickness = 1, int lineType = LINE_8, - InputArray hierarchy = noArray(), - int maxLevel = INT_MAX, Point offset = Point() ); - -/** @brief Clips the line against the image rectangle. - -The function cv::clipLine calculates a part of the line segment that is entirely within the specified -rectangle. it returns false if the line segment is completely outside the rectangle. Otherwise, -it returns true . -@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . -@param pt1 First line point. -@param pt2 Second line point. - */ -CV_EXPORTS bool clipLine(Size imgSize, CV_IN_OUT Point& pt1, CV_IN_OUT Point& pt2); - -/** @overload -@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) . -@param pt1 First line point. -@param pt2 Second line point. -*/ -CV_EXPORTS bool clipLine(Size2l imgSize, CV_IN_OUT Point2l& pt1, CV_IN_OUT Point2l& pt2); - -/** @overload -@param imgRect Image rectangle. -@param pt1 First line point. -@param pt2 Second line point. -*/ -CV_EXPORTS_W bool clipLine(Rect imgRect, CV_OUT CV_IN_OUT Point& pt1, CV_OUT CV_IN_OUT Point& pt2); - -/** @brief Approximates an elliptic arc with a polyline. - -The function ellipse2Poly computes the vertices of a polyline that approximates the specified -elliptic arc. It is used by #ellipse. If `arcStart` is greater than `arcEnd`, they are swapped. - -@param center Center of the arc. -@param axes Half of the size of the ellipse main axes. See #ellipse for details. -@param angle Rotation angle of the ellipse in degrees. See #ellipse for details. -@param arcStart Starting angle of the elliptic arc in degrees. -@param arcEnd Ending angle of the elliptic arc in degrees. -@param delta Angle between the subsequent polyline vertices. It defines the approximation -accuracy. -@param pts Output vector of polyline vertices. - */ -CV_EXPORTS_W void ellipse2Poly( Point center, Size axes, int angle, - int arcStart, int arcEnd, int delta, - CV_OUT std::vector& pts ); - -/** @overload -@param center Center of the arc. -@param axes Half of the size of the ellipse main axes. See #ellipse for details. -@param angle Rotation angle of the ellipse in degrees. See #ellipse for details. -@param arcStart Starting angle of the elliptic arc in degrees. -@param arcEnd Ending angle of the elliptic arc in degrees. -@param delta Angle between the subsequent polyline vertices. It defines the approximation accuracy. -@param pts Output vector of polyline vertices. -*/ -CV_EXPORTS void ellipse2Poly(Point2d center, Size2d axes, int angle, - int arcStart, int arcEnd, int delta, - CV_OUT std::vector& pts); - -/** @brief Draws a text string. - -The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered -using the specified font are replaced by question marks. See #getTextSize for a text rendering code -example. - -@param img Image. -@param text Text string to be drawn. -@param org Bottom-left corner of the text string in the image. -@param fontFace Font type, see #HersheyFonts. -@param fontScale Font scale factor that is multiplied by the font-specific base size. -@param color Text color. -@param thickness Thickness of the lines used to draw a text. -@param lineType Line type. See #LineTypes -@param bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise, -it is at the top-left corner. - */ -CV_EXPORTS_W void putText( InputOutputArray img, const String& text, Point org, - int fontFace, double fontScale, Scalar color, - int thickness = 1, int lineType = LINE_8, - bool bottomLeftOrigin = false ); - -/** @brief Calculates the width and height of a text string. - -The function cv::getTextSize calculates and returns the size of a box that contains the specified text. -That is, the following code renders some text, the tight box surrounding it, and the baseline: : -@code - String text = "Funny text inside the box"; - int fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX; - double fontScale = 2; - int thickness = 3; - - Mat img(600, 800, CV_8UC3, Scalar::all(0)); - - int baseline=0; - Size textSize = getTextSize(text, fontFace, - fontScale, thickness, &baseline); - baseline += thickness; - - // center the text - Point textOrg((img.cols - textSize.width)/2, - (img.rows + textSize.height)/2); - - // draw the box - rectangle(img, textOrg + Point(0, baseline), - textOrg + Point(textSize.width, -textSize.height), - Scalar(0,0,255)); - // ... and the baseline first - line(img, textOrg + Point(0, thickness), - textOrg + Point(textSize.width, thickness), - Scalar(0, 0, 255)); - - // then put the text itself - putText(img, text, textOrg, fontFace, fontScale, - Scalar::all(255), thickness, 8); -@endcode - -@param text Input text string. -@param fontFace Font to use, see #HersheyFonts. -@param fontScale Font scale factor that is multiplied by the font-specific base size. -@param thickness Thickness of lines used to render the text. See #putText for details. -@param[out] baseLine y-coordinate of the baseline relative to the bottom-most text -point. -@return The size of a box that contains the specified text. - -@see putText - */ -CV_EXPORTS_W Size getTextSize(const String& text, int fontFace, - double fontScale, int thickness, - CV_OUT int* baseLine); - - -/** @brief Calculates the font-specific size to use to achieve a given height in pixels. - -@param fontFace Font to use, see cv::HersheyFonts. -@param pixelHeight Pixel height to compute the fontScale for -@param thickness Thickness of lines used to render the text.See putText for details. -@return The fontSize to use for cv::putText - -@see cv::putText -*/ -CV_EXPORTS_W double getFontScaleFromHeight(const int fontFace, - const int pixelHeight, - const int thickness = 1); - -/** @brief Line iterator - -The class is used to iterate over all the pixels on the raster line -segment connecting two specified points. - -The class LineIterator is used to get each pixel of a raster line. It -can be treated as versatile implementation of the Bresenham algorithm -where you can stop at each pixel and do some extra processing, for -example, grab pixel values along the line or draw a line with an effect -(for example, with XOR operation). - -The number of pixels along the line is stored in LineIterator::count. -The method LineIterator::pos returns the current position in the image: - -@code{.cpp} -// grabs pixels along the line (pt1, pt2) -// from 8-bit 3-channel image to the buffer -LineIterator it(img, pt1, pt2, 8); -LineIterator it2 = it; -vector buf(it.count); - -for(int i = 0; i < it.count; i++, ++it) - buf[i] = *(const Vec3b*)*it; - -// alternative way of iterating through the line -for(int i = 0; i < it2.count; i++, ++it2) -{ - Vec3b val = img.at(it2.pos()); - CV_Assert(buf[i] == val); -} -@endcode -*/ -class CV_EXPORTS LineIterator -{ -public: - /** @brief initializes the iterator - - creates iterators for the line connecting pt1 and pt2 - the line will be clipped on the image boundaries - the line is 8-connected or 4-connected - If leftToRight=true, then the iteration is always done - from the left-most point to the right most, - not to depend on the ordering of pt1 and pt2 parameters - */ - LineIterator( const Mat& img, Point pt1, Point pt2, - int connectivity = 8, bool leftToRight = false ); - /** @brief returns pointer to the current pixel - */ - uchar* operator *(); - /** @brief prefix increment operator (++it). shifts iterator to the next pixel - */ - LineIterator& operator ++(); - /** @brief postfix increment operator (it++). shifts iterator to the next pixel - */ - LineIterator operator ++(int); - /** @brief returns coordinates of the current pixel - */ - Point pos() const; - - uchar* ptr; - const uchar* ptr0; - int step, elemSize; - int err, count; - int minusDelta, plusDelta; - int minusStep, plusStep; -}; - -//! @cond IGNORED - -// === LineIterator implementation === - -inline -uchar* LineIterator::operator *() -{ - return ptr; -} - -inline -LineIterator& LineIterator::operator ++() -{ - int mask = err < 0 ? -1 : 0; - err += minusDelta + (plusDelta & mask); - ptr += minusStep + (plusStep & mask); - return *this; -} - -inline -LineIterator LineIterator::operator ++(int) -{ - LineIterator it = *this; - ++(*this); - return it; -} - -inline -Point LineIterator::pos() const -{ - Point p; - p.y = (int)((ptr - ptr0)/step); - p.x = (int)(((ptr - ptr0) - p.y*step)/elemSize); - return p; -} - -//! @endcond - -//! @} imgproc_draw - -//! @} imgproc - -} // cv - -#ifndef DISABLE_OPENCV_24_COMPATIBILITY -#include "opencv2/imgproc/imgproc_c.h" -#endif - -#endif diff --git a/include/opencv2/imgproc/hal/hal.hpp b/include/opencv2/imgproc/hal/hal.hpp deleted file mode 100755 index a435fd6..0000000 --- a/include/opencv2/imgproc/hal/hal.hpp +++ /dev/null @@ -1,241 +0,0 @@ -#ifndef CV_IMGPROC_HAL_HPP -#define CV_IMGPROC_HAL_HPP - -#include "opencv2/core/cvdef.h" -#include "opencv2/core/cvstd.hpp" -#include "opencv2/core/hal/interface.h" - -namespace cv { namespace hal { - -//! @addtogroup imgproc_hal_functions -//! @{ - -//--------------------------- -//! @cond IGNORED - -struct CV_EXPORTS Filter2D -{ - CV_DEPRECATED static Ptr create(uchar * , size_t , int , - int , int , - int , int , - int , int , - int , double , - int , int , - bool , bool ); - virtual void apply(uchar * , size_t , - uchar * , size_t , - int , int , - int , int , - int , int ) = 0; - virtual ~Filter2D() {} -}; - -struct CV_EXPORTS SepFilter2D -{ - CV_DEPRECATED static Ptr create(int , int , int , - uchar * , int , - uchar * , int , - int , int , - double , int ); - virtual void apply(uchar * , size_t , - uchar * , size_t , - int , int , - int , int , - int , int ) = 0; - virtual ~SepFilter2D() {} -}; - - -struct CV_EXPORTS Morph -{ - CV_DEPRECATED static Ptr create(int , int , int , int , int , - int , uchar * , size_t , - int , int , - int , int , - int , const double *, - int , bool , bool ); - virtual void apply(uchar * , size_t , uchar * , size_t , int , int , - int , int , int , int , - int , int , int , int ) = 0; - virtual ~Morph() {} -}; - -//! @endcond -//--------------------------- - -CV_EXPORTS void filter2D(int stype, int dtype, int kernel_type, - uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int full_width, int full_height, - int offset_x, int offset_y, - uchar * kernel_data, size_t kernel_step, - int kernel_width, int kernel_height, - int anchor_x, int anchor_y, - double delta, int borderType, - bool isSubmatrix); - -CV_EXPORTS void sepFilter2D(int stype, int dtype, int ktype, - uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int full_width, int full_height, - int offset_x, int offset_y, - uchar * kernelx_data, int kernelx_len, - uchar * kernely_data, int kernely_len, - int anchor_x, int anchor_y, - double delta, int borderType); - -CV_EXPORTS void morph(int op, int src_type, int dst_type, - uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int roi_width, int roi_height, int roi_x, int roi_y, - int roi_width2, int roi_height2, int roi_x2, int roi_y2, - int kernel_type, uchar * kernel_data, size_t kernel_step, - int kernel_width, int kernel_height, int anchor_x, int anchor_y, - int borderType, const double borderValue[4], - int iterations, bool isSubmatrix); - - -CV_EXPORTS void resize(int src_type, - const uchar * src_data, size_t src_step, int src_width, int src_height, - uchar * dst_data, size_t dst_step, int dst_width, int dst_height, - double inv_scale_x, double inv_scale_y, int interpolation); - -CV_EXPORTS void warpAffine(int src_type, - const uchar * src_data, size_t src_step, int src_width, int src_height, - uchar * dst_data, size_t dst_step, int dst_width, int dst_height, - const double M[6], int interpolation, int borderType, const double borderValue[4]); - -CV_EXPORTS void warpPerspectve(int src_type, - const uchar * src_data, size_t src_step, int src_width, int src_height, - uchar * dst_data, size_t dst_step, int dst_width, int dst_height, - const double M[9], int interpolation, int borderType, const double borderValue[4]); - -CV_EXPORTS void cvtBGRtoBGR(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int depth, int scn, int dcn, bool swapBlue); - -CV_EXPORTS void cvtBGRtoBGR5x5(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int scn, bool swapBlue, int greenBits); - -CV_EXPORTS void cvtBGR5x5toBGR(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int dcn, bool swapBlue, int greenBits); - -CV_EXPORTS void cvtBGRtoGray(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int depth, int scn, bool swapBlue); - -CV_EXPORTS void cvtGraytoBGR(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int depth, int dcn); - -CV_EXPORTS void cvtBGR5x5toGray(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int greenBits); - -CV_EXPORTS void cvtGraytoBGR5x5(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int greenBits); -CV_EXPORTS void cvtBGRtoYUV(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int depth, int scn, bool swapBlue, bool isCbCr); - -CV_EXPORTS void cvtYUVtoBGR(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int depth, int dcn, bool swapBlue, bool isCbCr); - -CV_EXPORTS void cvtBGRtoXYZ(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int depth, int scn, bool swapBlue); - -CV_EXPORTS void cvtXYZtoBGR(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int depth, int dcn, bool swapBlue); - -CV_EXPORTS void cvtBGRtoHSV(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int depth, int scn, bool swapBlue, bool isFullRange, bool isHSV); - -CV_EXPORTS void cvtHSVtoBGR(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int depth, int dcn, bool swapBlue, bool isFullRange, bool isHSV); - -CV_EXPORTS void cvtBGRtoLab(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int depth, int scn, bool swapBlue, bool isLab, bool srgb); - -CV_EXPORTS void cvtLabtoBGR(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int depth, int dcn, bool swapBlue, bool isLab, bool srgb); - -CV_EXPORTS void cvtTwoPlaneYUVtoBGR(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int dst_width, int dst_height, - int dcn, bool swapBlue, int uIdx); - -//! Separate Y and UV planes -CV_EXPORTS void cvtTwoPlaneYUVtoBGR(const uchar * y_data, const uchar * uv_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int dst_width, int dst_height, - int dcn, bool swapBlue, int uIdx); - -CV_EXPORTS void cvtThreePlaneYUVtoBGR(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int dst_width, int dst_height, - int dcn, bool swapBlue, int uIdx); - -CV_EXPORTS void cvtBGRtoThreePlaneYUV(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int scn, bool swapBlue, int uIdx); - -//! Separate Y and UV planes -CV_EXPORTS void cvtBGRtoTwoPlaneYUV(const uchar * src_data, size_t src_step, - uchar * y_data, uchar * uv_data, size_t dst_step, - int width, int height, - int scn, bool swapBlue, int uIdx); - -CV_EXPORTS void cvtOnePlaneYUVtoBGR(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height, - int dcn, bool swapBlue, int uIdx, int ycn); - -CV_EXPORTS void cvtRGBAtoMultipliedRGBA(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height); - -CV_EXPORTS void cvtMultipliedRGBAtoRGBA(const uchar * src_data, size_t src_step, - uchar * dst_data, size_t dst_step, - int width, int height); - -CV_EXPORTS void integral(int depth, int sdepth, int sqdepth, - const uchar* src, size_t srcstep, - uchar* sum, size_t sumstep, - uchar* sqsum, size_t sqsumstep, - uchar* tilted, size_t tstep, - int width, int height, int cn); - -//! @} - -}} - -#endif // CV_IMGPROC_HAL_HPP diff --git a/include/opencv2/imgproc/hal/interface.h b/include/opencv2/imgproc/hal/interface.h deleted file mode 100755 index f8dbcfe..0000000 --- a/include/opencv2/imgproc/hal/interface.h +++ /dev/null @@ -1,46 +0,0 @@ -#ifndef OPENCV_IMGPROC_HAL_INTERFACE_H -#define OPENCV_IMGPROC_HAL_INTERFACE_H - -//! @addtogroup imgproc_hal_interface -//! @{ - -//! @name Interpolation modes -//! @sa cv::InterpolationFlags -//! @{ -#define CV_HAL_INTER_NEAREST 0 -#define CV_HAL_INTER_LINEAR 1 -#define CV_HAL_INTER_CUBIC 2 -#define CV_HAL_INTER_AREA 3 -#define CV_HAL_INTER_LANCZOS4 4 -//! @} - -//! @name Morphology operations -//! @sa cv::MorphTypes -//! @{ -#define CV_HAL_MORPH_ERODE 0 -#define CV_HAL_MORPH_DILATE 1 -//! @} - -//! @name Threshold types -//! @sa cv::ThresholdTypes -//! @{ -#define CV_HAL_THRESH_BINARY 0 -#define CV_HAL_THRESH_BINARY_INV 1 -#define CV_HAL_THRESH_TRUNC 2 -#define CV_HAL_THRESH_TOZERO 3 -#define CV_HAL_THRESH_TOZERO_INV 4 -#define CV_HAL_THRESH_MASK 7 -#define CV_HAL_THRESH_OTSU 8 -#define CV_HAL_THRESH_TRIANGLE 16 -//! @} - -//! @name Adaptive threshold algorithm -//! @sa cv::AdaptiveThresholdTypes -//! @{ -#define CV_HAL_ADAPTIVE_THRESH_MEAN_C 0 -#define CV_HAL_ADAPTIVE_THRESH_GAUSSIAN_C 1 -//! @} - -//! @} - -#endif diff --git a/include/opencv2/imgproc/imgproc.hpp b/include/opencv2/imgproc/imgproc.hpp deleted file mode 100755 index 4175bd0..0000000 --- a/include/opencv2/imgproc/imgproc.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/imgproc.hpp" diff --git a/include/opencv2/imgproc/imgproc_c.h b/include/opencv2/imgproc/imgproc_c.h deleted file mode 100755 index 2fe85b6..0000000 --- a/include/opencv2/imgproc/imgproc_c.h +++ /dev/null @@ -1,1210 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_IMGPROC_IMGPROC_C_H -#define OPENCV_IMGPROC_IMGPROC_C_H - -#include "opencv2/imgproc/types_c.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @addtogroup imgproc_c -@{ -*/ - -/*********************** Background statistics accumulation *****************************/ - -/** @brief Adds image to accumulator -@see cv::accumulate -*/ -CVAPI(void) cvAcc( const CvArr* image, CvArr* sum, - const CvArr* mask CV_DEFAULT(NULL) ); - -/** @brief Adds squared image to accumulator -@see cv::accumulateSquare -*/ -CVAPI(void) cvSquareAcc( const CvArr* image, CvArr* sqsum, - const CvArr* mask CV_DEFAULT(NULL) ); - -/** @brief Adds a product of two images to accumulator -@see cv::accumulateProduct -*/ -CVAPI(void) cvMultiplyAcc( const CvArr* image1, const CvArr* image2, CvArr* acc, - const CvArr* mask CV_DEFAULT(NULL) ); - -/** @brief Adds image to accumulator with weights: acc = acc*(1-alpha) + image*alpha -@see cv::accumulateWeighted -*/ -CVAPI(void) cvRunningAvg( const CvArr* image, CvArr* acc, double alpha, - const CvArr* mask CV_DEFAULT(NULL) ); - -/****************************************************************************************\ -* Image Processing * -\****************************************************************************************/ - -/** Copies source 2D array inside of the larger destination array and - makes a border of the specified type (IPL_BORDER_*) around the copied area. */ -CVAPI(void) cvCopyMakeBorder( const CvArr* src, CvArr* dst, CvPoint offset, - int bordertype, CvScalar value CV_DEFAULT(cvScalarAll(0))); - -/** @brief Smooths the image in one of several ways. - -@param src The source image -@param dst The destination image -@param smoothtype Type of the smoothing, see SmoothMethod_c -@param size1 The first parameter of the smoothing operation, the aperture width. Must be a -positive odd number (1, 3, 5, ...) -@param size2 The second parameter of the smoothing operation, the aperture height. Ignored by -CV_MEDIAN and CV_BILATERAL methods. In the case of simple scaled/non-scaled and Gaussian blur if -size2 is zero, it is set to size1. Otherwise it must be a positive odd number. -@param sigma1 In the case of a Gaussian parameter this parameter may specify Gaussian \f$\sigma\f$ -(standard deviation). If it is zero, it is calculated from the kernel size: -\f[\sigma = 0.3 (n/2 - 1) + 0.8 \quad \text{where} \quad n= \begin{array}{l l} \mbox{\texttt{size1} for horizontal kernel} \\ \mbox{\texttt{size2} for vertical kernel} \end{array}\f] -Using standard sigma for small kernels ( \f$3\times 3\f$ to \f$7\times 7\f$ ) gives better speed. If -sigma1 is not zero, while size1 and size2 are zeros, the kernel size is calculated from the -sigma (to provide accurate enough operation). -@param sigma2 additional parameter for bilateral filtering - -@see cv::GaussianBlur, cv::blur, cv::medianBlur, cv::bilateralFilter. - */ -CVAPI(void) cvSmooth( const CvArr* src, CvArr* dst, - int smoothtype CV_DEFAULT(CV_GAUSSIAN), - int size1 CV_DEFAULT(3), - int size2 CV_DEFAULT(0), - double sigma1 CV_DEFAULT(0), - double sigma2 CV_DEFAULT(0)); - -/** @brief Convolves an image with the kernel. - -@param src input image. -@param dst output image of the same size and the same number of channels as src. -@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point -matrix; if you want to apply different kernels to different channels, split the image into -separate color planes using split and process them individually. -@param anchor anchor of the kernel that indicates the relative position of a filtered point within -the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor -is at the kernel center. - -@see cv::filter2D - */ -CVAPI(void) cvFilter2D( const CvArr* src, CvArr* dst, const CvMat* kernel, - CvPoint anchor CV_DEFAULT(cvPoint(-1,-1))); - -/** @brief Finds integral image: SUM(X,Y) = sum(x \texttt{hist1}(I)\)}{\frac{\texttt{hist2}(I) \cdot \texttt{scale}}{\texttt{hist1}(I)}}{if \(\texttt{hist1}(I) \ne 0\) and \(\texttt{hist2}(I) \le \texttt{hist1}(I)\)}\f] - -@param hist1 First histogram (the divisor). -@param hist2 Second histogram. -@param dst_hist Destination histogram. -@param scale Scale factor for the destination histogram. - */ -CVAPI(void) cvCalcProbDensity( const CvHistogram* hist1, const CvHistogram* hist2, - CvHistogram* dst_hist, double scale CV_DEFAULT(255) ); - -/** @brief equalizes histogram of 8-bit single-channel image -@see cv::equalizeHist -*/ -CVAPI(void) cvEqualizeHist( const CvArr* src, CvArr* dst ); - - -/** @brief Applies distance transform to binary image -@see cv::distanceTransform -*/ -CVAPI(void) cvDistTransform( const CvArr* src, CvArr* dst, - int distance_type CV_DEFAULT(CV_DIST_L2), - int mask_size CV_DEFAULT(3), - const float* mask CV_DEFAULT(NULL), - CvArr* labels CV_DEFAULT(NULL), - int labelType CV_DEFAULT(CV_DIST_LABEL_CCOMP)); - - -/** @brief Applies fixed-level threshold to grayscale image. - - This is a basic operation applied before retrieving contours -@see cv::threshold -*/ -CVAPI(double) cvThreshold( const CvArr* src, CvArr* dst, - double threshold, double max_value, - int threshold_type ); - -/** @brief Applies adaptive threshold to grayscale image. - - The two parameters for methods CV_ADAPTIVE_THRESH_MEAN_C and - CV_ADAPTIVE_THRESH_GAUSSIAN_C are: - neighborhood size (3, 5, 7 etc.), - and a constant subtracted from mean (...,-3,-2,-1,0,1,2,3,...) -@see cv::adaptiveThreshold -*/ -CVAPI(void) cvAdaptiveThreshold( const CvArr* src, CvArr* dst, double max_value, - int adaptive_method CV_DEFAULT(CV_ADAPTIVE_THRESH_MEAN_C), - int threshold_type CV_DEFAULT(CV_THRESH_BINARY), - int block_size CV_DEFAULT(3), - double param1 CV_DEFAULT(5)); - -/** @brief Fills the connected component until the color difference gets large enough -@see cv::floodFill -*/ -CVAPI(void) cvFloodFill( CvArr* image, CvPoint seed_point, - CvScalar new_val, CvScalar lo_diff CV_DEFAULT(cvScalarAll(0)), - CvScalar up_diff CV_DEFAULT(cvScalarAll(0)), - CvConnectedComp* comp CV_DEFAULT(NULL), - int flags CV_DEFAULT(4), - CvArr* mask CV_DEFAULT(NULL)); - -/****************************************************************************************\ -* Feature detection * -\****************************************************************************************/ - -/** @brief Runs canny edge detector -@see cv::Canny -*/ -CVAPI(void) cvCanny( const CvArr* image, CvArr* edges, double threshold1, - double threshold2, int aperture_size CV_DEFAULT(3) ); - -/** @brief Calculates constraint image for corner detection - - Dx^2 * Dyy + Dxx * Dy^2 - 2 * Dx * Dy * Dxy. - Applying threshold to the result gives coordinates of corners -@see cv::preCornerDetect -*/ -CVAPI(void) cvPreCornerDetect( const CvArr* image, CvArr* corners, - int aperture_size CV_DEFAULT(3) ); - -/** @brief Calculates eigen values and vectors of 2x2 - gradient covariation matrix at every image pixel -@see cv::cornerEigenValsAndVecs -*/ -CVAPI(void) cvCornerEigenValsAndVecs( const CvArr* image, CvArr* eigenvv, - int block_size, int aperture_size CV_DEFAULT(3) ); - -/** @brief Calculates minimal eigenvalue for 2x2 gradient covariation matrix at - every image pixel -@see cv::cornerMinEigenVal -*/ -CVAPI(void) cvCornerMinEigenVal( const CvArr* image, CvArr* eigenval, - int block_size, int aperture_size CV_DEFAULT(3) ); - -/** @brief Harris corner detector: - - Calculates det(M) - k*(trace(M)^2), where M is 2x2 gradient covariation matrix for each pixel -@see cv::cornerHarris -*/ -CVAPI(void) cvCornerHarris( const CvArr* image, CvArr* harris_response, - int block_size, int aperture_size CV_DEFAULT(3), - double k CV_DEFAULT(0.04) ); - -/** @brief Adjust corner position using some sort of gradient search -@see cv::cornerSubPix -*/ -CVAPI(void) cvFindCornerSubPix( const CvArr* image, CvPoint2D32f* corners, - int count, CvSize win, CvSize zero_zone, - CvTermCriteria criteria ); - -/** @brief Finds a sparse set of points within the selected region - that seem to be easy to track -@see cv::goodFeaturesToTrack -*/ -CVAPI(void) cvGoodFeaturesToTrack( const CvArr* image, CvArr* eig_image, - CvArr* temp_image, CvPoint2D32f* corners, - int* corner_count, double quality_level, - double min_distance, - const CvArr* mask CV_DEFAULT(NULL), - int block_size CV_DEFAULT(3), - int use_harris CV_DEFAULT(0), - double k CV_DEFAULT(0.04) ); - -/** @brief Finds lines on binary image using one of several methods. - - line_storage is either memory storage or 1 x _max number of lines_ CvMat, its - number of columns is changed by the function. - method is one of CV_HOUGH_*; - rho, theta and threshold are used for each of those methods; - param1 ~ line length, param2 ~ line gap - for probabilistic, - param1 ~ srn, param2 ~ stn - for multi-scale -@see cv::HoughLines -*/ -CVAPI(CvSeq*) cvHoughLines2( CvArr* image, void* line_storage, int method, - double rho, double theta, int threshold, - double param1 CV_DEFAULT(0), double param2 CV_DEFAULT(0), - double min_theta CV_DEFAULT(0), double max_theta CV_DEFAULT(CV_PI)); - -/** @brief Finds circles in the image -@see cv::HoughCircles -*/ -CVAPI(CvSeq*) cvHoughCircles( CvArr* image, void* circle_storage, - int method, double dp, double min_dist, - double param1 CV_DEFAULT(100), - double param2 CV_DEFAULT(100), - int min_radius CV_DEFAULT(0), - int max_radius CV_DEFAULT(0)); - -/** @brief Fits a line into set of 2d or 3d points in a robust way (M-estimator technique) -@see cv::fitLine -*/ -CVAPI(void) cvFitLine( const CvArr* points, int dist_type, double param, - double reps, double aeps, float* line ); - -/****************************************************************************************\ -* Drawing * -\****************************************************************************************/ - -/****************************************************************************************\ -* Drawing functions work with images/matrices of arbitrary type. * -* For color images the channel order is BGR[A] * -* Antialiasing is supported only for 8-bit image now. * -* All the functions include parameter color that means rgb value (that may be * -* constructed with CV_RGB macro) for color images and brightness * -* for grayscale images. * -* If a drawn figure is partially or completely outside of the image, it is clipped.* -\****************************************************************************************/ - -#define CV_FILLED -1 - -#define CV_AA 16 - -/** @brief Draws 4-connected, 8-connected or antialiased line segment connecting two points -@see cv::line -*/ -CVAPI(void) cvLine( CvArr* img, CvPoint pt1, CvPoint pt2, - CvScalar color, int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); - -/** @brief Draws a rectangle given two opposite corners of the rectangle (pt1 & pt2) - - if thickness<0 (e.g. thickness == CV_FILLED), the filled box is drawn -@see cv::rectangle -*/ -CVAPI(void) cvRectangle( CvArr* img, CvPoint pt1, CvPoint pt2, - CvScalar color, int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8), - int shift CV_DEFAULT(0)); - -/** @brief Draws a rectangle specified by a CvRect structure -@see cv::rectangle -*/ -CVAPI(void) cvRectangleR( CvArr* img, CvRect r, - CvScalar color, int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8), - int shift CV_DEFAULT(0)); - - -/** @brief Draws a circle with specified center and radius. - - Thickness works in the same way as with cvRectangle -@see cv::circle -*/ -CVAPI(void) cvCircle( CvArr* img, CvPoint center, int radius, - CvScalar color, int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); - -/** @brief Draws ellipse outline, filled ellipse, elliptic arc or filled elliptic sector - - depending on _thickness_, _start_angle_ and _end_angle_ parameters. The resultant figure - is rotated by _angle_. All the angles are in degrees -@see cv::ellipse -*/ -CVAPI(void) cvEllipse( CvArr* img, CvPoint center, CvSize axes, - double angle, double start_angle, double end_angle, - CvScalar color, int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); - -CV_INLINE void cvEllipseBox( CvArr* img, CvBox2D box, CvScalar color, - int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ) -{ - CvSize axes = cvSize( - cvRound(box.size.width*0.5), - cvRound(box.size.height*0.5) - ); - - cvEllipse( img, cvPointFrom32f( box.center ), axes, box.angle, - 0, 360, color, thickness, line_type, shift ); -} - -/** @brief Fills convex or monotonous polygon. -@see cv::fillConvexPoly -*/ -CVAPI(void) cvFillConvexPoly( CvArr* img, const CvPoint* pts, int npts, CvScalar color, - int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0)); - -/** @brief Fills an area bounded by one or more arbitrary polygons -@see cv::fillPoly -*/ -CVAPI(void) cvFillPoly( CvArr* img, CvPoint** pts, const int* npts, - int contours, CvScalar color, - int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); - -/** @brief Draws one or more polygonal curves -@see cv::polylines -*/ -CVAPI(void) cvPolyLine( CvArr* img, CvPoint** pts, const int* npts, int contours, - int is_closed, CvScalar color, int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) ); - -#define cvDrawRect cvRectangle -#define cvDrawLine cvLine -#define cvDrawCircle cvCircle -#define cvDrawEllipse cvEllipse -#define cvDrawPolyLine cvPolyLine - -/** @brief Clips the line segment connecting *pt1 and *pt2 - by the rectangular window - - (0<=xptr will point to pt1 (or pt2, see left_to_right description) location in -the image. Returns the number of pixels on the line between the ending points. -@see cv::LineIterator -*/ -CVAPI(int) cvInitLineIterator( const CvArr* image, CvPoint pt1, CvPoint pt2, - CvLineIterator* line_iterator, - int connectivity CV_DEFAULT(8), - int left_to_right CV_DEFAULT(0)); - -#define CV_NEXT_LINE_POINT( line_iterator ) \ -{ \ - int _line_iterator_mask = (line_iterator).err < 0 ? -1 : 0; \ - (line_iterator).err += (line_iterator).minus_delta + \ - ((line_iterator).plus_delta & _line_iterator_mask); \ - (line_iterator).ptr += (line_iterator).minus_step + \ - ((line_iterator).plus_step & _line_iterator_mask); \ -} - - -#define CV_FONT_HERSHEY_SIMPLEX 0 -#define CV_FONT_HERSHEY_PLAIN 1 -#define CV_FONT_HERSHEY_DUPLEX 2 -#define CV_FONT_HERSHEY_COMPLEX 3 -#define CV_FONT_HERSHEY_TRIPLEX 4 -#define CV_FONT_HERSHEY_COMPLEX_SMALL 5 -#define CV_FONT_HERSHEY_SCRIPT_SIMPLEX 6 -#define CV_FONT_HERSHEY_SCRIPT_COMPLEX 7 - -#define CV_FONT_ITALIC 16 - -#define CV_FONT_VECTOR0 CV_FONT_HERSHEY_SIMPLEX - - -/** Font structure */ -typedef struct CvFont -{ - const char* nameFont; //Qt:nameFont - CvScalar color; //Qt:ColorFont -> cvScalar(blue_component, green_component, red_component[, alpha_component]) - int font_face; //Qt: bool italic /** =CV_FONT_* */ - const int* ascii; //!< font data and metrics - const int* greek; - const int* cyrillic; - float hscale, vscale; - float shear; //!< slope coefficient: 0 - normal, >0 - italic - int thickness; //!< Qt: weight /** letters thickness */ - float dx; //!< horizontal interval between letters - int line_type; //!< Qt: PointSize -} -CvFont; - -/** @brief Initializes font structure (OpenCV 1.x API). - -The function initializes the font structure that can be passed to text rendering functions. - -@param font Pointer to the font structure initialized by the function -@param font_face Font name identifier. See cv::HersheyFonts and corresponding old CV_* identifiers. -@param hscale Horizontal scale. If equal to 1.0f , the characters have the original width -depending on the font type. If equal to 0.5f , the characters are of half the original width. -@param vscale Vertical scale. If equal to 1.0f , the characters have the original height depending -on the font type. If equal to 0.5f , the characters are of half the original height. -@param shear Approximate tangent of the character slope relative to the vertical line. A zero -value means a non-italic font, 1.0f means about a 45 degree slope, etc. -@param thickness Thickness of the text strokes -@param line_type Type of the strokes, see line description - -@sa cvPutText - */ -CVAPI(void) cvInitFont( CvFont* font, int font_face, - double hscale, double vscale, - double shear CV_DEFAULT(0), - int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8)); - -CV_INLINE CvFont cvFont( double scale, int thickness CV_DEFAULT(1) ) -{ - CvFont font; - cvInitFont( &font, CV_FONT_HERSHEY_PLAIN, scale, scale, 0, thickness, CV_AA ); - return font; -} - -/** @brief Renders text stroke with specified font and color at specified location. - CvFont should be initialized with cvInitFont -@see cvInitFont, cvGetTextSize, cvFont, cv::putText -*/ -CVAPI(void) cvPutText( CvArr* img, const char* text, CvPoint org, - const CvFont* font, CvScalar color ); - -/** @brief Calculates bounding box of text stroke (useful for alignment) -@see cv::getTextSize -*/ -CVAPI(void) cvGetTextSize( const char* text_string, const CvFont* font, - CvSize* text_size, int* baseline ); - -/** @brief Unpacks color value - -if arrtype is CV_8UC?, _color_ is treated as packed color value, otherwise the first channels -(depending on arrtype) of destination scalar are set to the same value = _color_ -*/ -CVAPI(CvScalar) cvColorToScalar( double packed_color, int arrtype ); - -/** @brief Returns the polygon points which make up the given ellipse. - -The ellipse is define by the box of size 'axes' rotated 'angle' around the 'center'. A partial -sweep of the ellipse arc can be done by specifying arc_start and arc_end to be something other than -0 and 360, respectively. The input array 'pts' must be large enough to hold the result. The total -number of points stored into 'pts' is returned by this function. -@see cv::ellipse2Poly -*/ -CVAPI(int) cvEllipse2Poly( CvPoint center, CvSize axes, - int angle, int arc_start, int arc_end, CvPoint * pts, int delta ); - -/** @brief Draws contour outlines or filled interiors on the image -@see cv::drawContours -*/ -CVAPI(void) cvDrawContours( CvArr *img, CvSeq* contour, - CvScalar external_color, CvScalar hole_color, - int max_level, int thickness CV_DEFAULT(1), - int line_type CV_DEFAULT(8), - CvPoint offset CV_DEFAULT(cvPoint(0,0))); - -/** @} */ - -#ifdef __cplusplus -} -#endif - -#endif diff --git a/include/opencv2/imgproc/types_c.h b/include/opencv2/imgproc/types_c.h deleted file mode 100755 index d3e55f5..0000000 --- a/include/opencv2/imgproc/types_c.h +++ /dev/null @@ -1,659 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_IMGPROC_TYPES_C_H -#define OPENCV_IMGPROC_TYPES_C_H - -#include "opencv2/core/core_c.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @addtogroup imgproc_c - @{ -*/ - -/** Connected component structure */ -typedef struct CvConnectedComp -{ - double area; /** DBL_EPSILON ? 1./std::sqrt(am00) : 0; - } - operator cv::Moments() const - { - return cv::Moments(m00, m10, m01, m20, m11, m02, m30, m21, m12, m03); - } -#endif -} -CvMoments; - -#ifdef __cplusplus -} // extern "C" - -CV_INLINE CvMoments cvMoments() -{ -#if !defined(CV__ENABLE_C_API_CTORS) - CvMoments self = CV_STRUCT_INITIALIZER; return self; -#else - return CvMoments(); -#endif -} - -CV_INLINE CvMoments cvMoments(const cv::Moments& m) -{ -#if !defined(CV__ENABLE_C_API_CTORS) - double am00 = std::abs(m.m00); - CvMoments self = { - m.m00, m.m10, m.m01, m.m20, m.m11, m.m02, m.m30, m.m21, m.m12, m.m03, - m.mu20, m.mu11, m.mu02, m.mu30, m.mu21, m.mu12, m.mu03, - am00 > DBL_EPSILON ? 1./std::sqrt(am00) : 0 - }; - return self; -#else - return CvMoments(m); -#endif -} - -extern "C" { -#endif // __cplusplus - -/** Hu invariants */ -typedef struct CvHuMoments -{ - double hu1, hu2, hu3, hu4, hu5, hu6, hu7; /**< Hu invariants */ -} -CvHuMoments; - -/** Template matching methods */ -enum -{ - CV_TM_SQDIFF =0, - CV_TM_SQDIFF_NORMED =1, - CV_TM_CCORR =2, - CV_TM_CCORR_NORMED =3, - CV_TM_CCOEFF =4, - CV_TM_CCOEFF_NORMED =5 -}; - -typedef float (CV_CDECL * CvDistanceFunction)( const float* a, const float* b, void* user_param ); - -/** Contour retrieval modes */ -enum -{ - CV_RETR_EXTERNAL=0, - CV_RETR_LIST=1, - CV_RETR_CCOMP=2, - CV_RETR_TREE=3, - CV_RETR_FLOODFILL=4 -}; - -/** Contour approximation methods */ -enum -{ - CV_CHAIN_CODE=0, - CV_CHAIN_APPROX_NONE=1, - CV_CHAIN_APPROX_SIMPLE=2, - CV_CHAIN_APPROX_TC89_L1=3, - CV_CHAIN_APPROX_TC89_KCOS=4, - CV_LINK_RUNS=5 -}; - -/* -Internal structure that is used for sequential retrieving contours from the image. -It supports both hierarchical and plane variants of Suzuki algorithm. -*/ -typedef struct _CvContourScanner* CvContourScanner; - -/** Freeman chain reader state */ -typedef struct CvChainPtReader -{ - CV_SEQ_READER_FIELDS() - char code; - CvPoint pt; - schar deltas[8][2]; -} -CvChainPtReader; - -/** initializes 8-element array for fast access to 3x3 neighborhood of a pixel */ -#define CV_INIT_3X3_DELTAS( deltas, step, nch ) \ - ((deltas)[0] = (nch), (deltas)[1] = -(step) + (nch), \ - (deltas)[2] = -(step), (deltas)[3] = -(step) - (nch), \ - (deltas)[4] = -(nch), (deltas)[5] = (step) - (nch), \ - (deltas)[6] = (step), (deltas)[7] = (step) + (nch)) - - -/** Contour approximation algorithms */ -enum -{ - CV_POLY_APPROX_DP = 0 -}; - -/** Shape matching methods */ -enum -{ - CV_CONTOURS_MATCH_I1 =1, //!< \f[I_1(A,B) = \sum _{i=1...7} \left | \frac{1}{m^A_i} - \frac{1}{m^B_i} \right |\f] - CV_CONTOURS_MATCH_I2 =2, //!< \f[I_2(A,B) = \sum _{i=1...7} \left | m^A_i - m^B_i \right |\f] - CV_CONTOURS_MATCH_I3 =3 //!< \f[I_3(A,B) = \max _{i=1...7} \frac{ \left| m^A_i - m^B_i \right| }{ \left| m^A_i \right| }\f] -}; - -/** Shape orientation */ -enum -{ - CV_CLOCKWISE =1, - CV_COUNTER_CLOCKWISE =2 -}; - - -/** Convexity defect */ -typedef struct CvConvexityDefect -{ - CvPoint* start; /**< point of the contour where the defect begins */ - CvPoint* end; /**< point of the contour where the defect ends */ - CvPoint* depth_point; /**< the farthest from the convex hull point within the defect */ - float depth; /**< distance between the farthest point and the convex hull */ -} CvConvexityDefect; - - -/** Histogram comparison methods */ -enum -{ - CV_COMP_CORREL =0, - CV_COMP_CHISQR =1, - CV_COMP_INTERSECT =2, - CV_COMP_BHATTACHARYYA =3, - CV_COMP_HELLINGER =CV_COMP_BHATTACHARYYA, - CV_COMP_CHISQR_ALT =4, - CV_COMP_KL_DIV =5 -}; - -/** Mask size for distance transform */ -enum -{ - CV_DIST_MASK_3 =3, - CV_DIST_MASK_5 =5, - CV_DIST_MASK_PRECISE =0 -}; - -/** Content of output label array: connected components or pixels */ -enum -{ - CV_DIST_LABEL_CCOMP = 0, - CV_DIST_LABEL_PIXEL = 1 -}; - -/** Distance types for Distance Transform and M-estimators */ -enum -{ - CV_DIST_USER =-1, /**< User defined distance */ - CV_DIST_L1 =1, /**< distance = |x1-x2| + |y1-y2| */ - CV_DIST_L2 =2, /**< the simple euclidean distance */ - CV_DIST_C =3, /**< distance = max(|x1-x2|,|y1-y2|) */ - CV_DIST_L12 =4, /**< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) */ - CV_DIST_FAIR =5, /**< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 */ - CV_DIST_WELSCH =6, /**< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 */ - CV_DIST_HUBER =7 /**< distance = |x| threshold ? max_value : 0 */ - CV_THRESH_BINARY_INV =1, /**< value = value > threshold ? 0 : max_value */ - CV_THRESH_TRUNC =2, /**< value = value > threshold ? threshold : value */ - CV_THRESH_TOZERO =3, /**< value = value > threshold ? value : 0 */ - CV_THRESH_TOZERO_INV =4, /**< value = value > threshold ? 0 : value */ - CV_THRESH_MASK =7, - CV_THRESH_OTSU =8, /**< use Otsu algorithm to choose the optimal threshold value; - combine the flag with one of the above CV_THRESH_* values */ - CV_THRESH_TRIANGLE =16 /**< use Triangle algorithm to choose the optimal threshold value; - combine the flag with one of the above CV_THRESH_* values, but not - with CV_THRESH_OTSU */ -}; - -/** Adaptive threshold methods */ -enum -{ - CV_ADAPTIVE_THRESH_MEAN_C =0, - CV_ADAPTIVE_THRESH_GAUSSIAN_C =1 -}; - -/** FloodFill flags */ -enum -{ - CV_FLOODFILL_FIXED_RANGE =(1 << 16), - CV_FLOODFILL_MASK_ONLY =(1 << 17) -}; - - -/** Canny edge detector flags */ -enum -{ - CV_CANNY_L2_GRADIENT =(1 << 31) -}; - -/** Variants of a Hough transform */ -enum -{ - CV_HOUGH_STANDARD =0, - CV_HOUGH_PROBABILISTIC =1, - CV_HOUGH_MULTI_SCALE =2, - CV_HOUGH_GRADIENT =3 -}; - - -/* Fast search data structures */ -struct CvFeatureTree; -struct CvLSH; -struct CvLSHOperations; - -/** @} */ - -#ifdef __cplusplus -} -#endif - -#endif diff --git a/include/opencv2/objdetect.hpp b/include/opencv2/objdetect.hpp deleted file mode 100755 index 0387b10..0000000 --- a/include/opencv2/objdetect.hpp +++ /dev/null @@ -1,840 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_OBJDETECT_HPP -#define OPENCV_OBJDETECT_HPP - -#include "opencv2/core.hpp" - -/** -@defgroup objdetect Object Detection - -Haar Feature-based Cascade Classifier for Object Detection ----------------------------------------------------------- - -The object detector described below has been initially proposed by Paul Viola @cite Viola01 and -improved by Rainer Lienhart @cite Lienhart02 . - -First, a classifier (namely a *cascade of boosted classifiers working with haar-like features*) is -trained with a few hundred sample views of a particular object (i.e., a face or a car), called -positive examples, that are scaled to the same size (say, 20x20), and negative examples - arbitrary -images of the same size. - -After a classifier is trained, it can be applied to a region of interest (of the same size as used -during the training) in an input image. The classifier outputs a "1" if the region is likely to show -the object (i.e., face/car), and "0" otherwise. To search for the object in the whole image one can -move the search window across the image and check every location using the classifier. The -classifier is designed so that it can be easily "resized" in order to be able to find the objects of -interest at different sizes, which is more efficient than resizing the image itself. So, to find an -object of an unknown size in the image the scan procedure should be done several times at different -scales. - -The word "cascade" in the classifier name means that the resultant classifier consists of several -simpler classifiers (*stages*) that are applied subsequently to a region of interest until at some -stage the candidate is rejected or all the stages are passed. The word "boosted" means that the -classifiers at every stage of the cascade are complex themselves and they are built out of basic -classifiers using one of four different boosting techniques (weighted voting). Currently Discrete -Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported. The basic classifiers are -decision-tree classifiers with at least 2 leaves. Haar-like features are the input to the basic -classifiers, and are calculated as described below. The current algorithm uses the following -Haar-like features: - -![image](pics/haarfeatures.png) - -The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within -the region of interest and the scale (this scale is not the same as the scale used at the detection -stage, though these two scales are multiplied). For example, in the case of the third line feature -(2c) the response is calculated as the difference between the sum of image pixels under the -rectangle covering the whole feature (including the two white stripes and the black stripe in the -middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to -compensate for the differences in the size of areas. The sums of pixel values over a rectangular -regions are calculated rapidly using integral images (see below and the integral description). - -To see the object detector at work, have a look at the facedetect demo: - - -The following reference is for the detection part only. There is a separate application called -opencv_traincascade that can train a cascade of boosted classifiers from a set of samples. - -@note In the new C++ interface it is also possible to use LBP (local binary pattern) features in -addition to Haar-like features. .. [Viola01] Paul Viola and Michael J. Jones. Rapid Object Detection -using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. The paper is available online at - - -@{ - @defgroup objdetect_c C API -@} - */ - -typedef struct CvHaarClassifierCascade CvHaarClassifierCascade; - -namespace cv -{ - -//! @addtogroup objdetect -//! @{ - -///////////////////////////// Object Detection //////////////////////////// - -//! class for grouping object candidates, detected by Cascade Classifier, HOG etc. -//! instance of the class is to be passed to cv::partition (see cxoperations.hpp) -class CV_EXPORTS SimilarRects -{ -public: - SimilarRects(double _eps) : eps(_eps) {} - inline bool operator()(const Rect& r1, const Rect& r2) const - { - double delta = eps * ((std::min)(r1.width, r2.width) + (std::min)(r1.height, r2.height)) * 0.5; - return std::abs(r1.x - r2.x) <= delta && - std::abs(r1.y - r2.y) <= delta && - std::abs(r1.x + r1.width - r2.x - r2.width) <= delta && - std::abs(r1.y + r1.height - r2.y - r2.height) <= delta; - } - double eps; -}; - -/** @brief Groups the object candidate rectangles. - -@param rectList Input/output vector of rectangles. Output vector includes retained and grouped -rectangles. (The Python list is not modified in place.) -@param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a -group of rectangles to retain it. -@param eps Relative difference between sides of the rectangles to merge them into a group. - -The function is a wrapper for the generic function partition . It clusters all the input rectangles -using the rectangle equivalence criteria that combines rectangles with similar sizes and similar -locations. The similarity is defined by eps. When eps=0 , no clustering is done at all. If -\f$\texttt{eps}\rightarrow +\inf\f$ , all the rectangles are put in one cluster. Then, the small -clusters containing less than or equal to groupThreshold rectangles are rejected. In each other -cluster, the average rectangle is computed and put into the output rectangle list. - */ -CV_EXPORTS void groupRectangles(std::vector& rectList, int groupThreshold, double eps = 0.2); -/** @overload */ -CV_EXPORTS_W void groupRectangles(CV_IN_OUT std::vector& rectList, CV_OUT std::vector& weights, - int groupThreshold, double eps = 0.2); -/** @overload */ -CV_EXPORTS void groupRectangles(std::vector& rectList, int groupThreshold, - double eps, std::vector* weights, std::vector* levelWeights ); -/** @overload */ -CV_EXPORTS void groupRectangles(std::vector& rectList, std::vector& rejectLevels, - std::vector& levelWeights, int groupThreshold, double eps = 0.2); -/** @overload */ -CV_EXPORTS void groupRectangles_meanshift(std::vector& rectList, std::vector& foundWeights, - std::vector& foundScales, - double detectThreshold = 0.0, Size winDetSize = Size(64, 128)); - -template<> CV_EXPORTS void DefaultDeleter::operator ()(CvHaarClassifierCascade* obj) const; - -enum { CASCADE_DO_CANNY_PRUNING = 1, - CASCADE_SCALE_IMAGE = 2, - CASCADE_FIND_BIGGEST_OBJECT = 4, - CASCADE_DO_ROUGH_SEARCH = 8 - }; - -class CV_EXPORTS_W BaseCascadeClassifier : public Algorithm -{ -public: - virtual ~BaseCascadeClassifier(); - virtual bool empty() const CV_OVERRIDE = 0; - virtual bool load( const String& filename ) = 0; - virtual void detectMultiScale( InputArray image, - CV_OUT std::vector& objects, - double scaleFactor, - int minNeighbors, int flags, - Size minSize, Size maxSize ) = 0; - - virtual void detectMultiScale( InputArray image, - CV_OUT std::vector& objects, - CV_OUT std::vector& numDetections, - double scaleFactor, - int minNeighbors, int flags, - Size minSize, Size maxSize ) = 0; - - virtual void detectMultiScale( InputArray image, - CV_OUT std::vector& objects, - CV_OUT std::vector& rejectLevels, - CV_OUT std::vector& levelWeights, - double scaleFactor, - int minNeighbors, int flags, - Size minSize, Size maxSize, - bool outputRejectLevels ) = 0; - - virtual bool isOldFormatCascade() const = 0; - virtual Size getOriginalWindowSize() const = 0; - virtual int getFeatureType() const = 0; - virtual void* getOldCascade() = 0; - - class CV_EXPORTS MaskGenerator - { - public: - virtual ~MaskGenerator() {} - virtual Mat generateMask(const Mat& src)=0; - virtual void initializeMask(const Mat& /*src*/) { } - }; - virtual void setMaskGenerator(const Ptr& maskGenerator) = 0; - virtual Ptr getMaskGenerator() = 0; -}; - -/** @example samples/cpp/facedetect.cpp -This program demonstrates usage of the Cascade classifier class -\image html Cascade_Classifier_Tutorial_Result_Haar.jpg "Sample screenshot" width=321 height=254 -*/ -/** @brief Cascade classifier class for object detection. - */ -class CV_EXPORTS_W CascadeClassifier -{ -public: - CV_WRAP CascadeClassifier(); - /** @brief Loads a classifier from a file. - - @param filename Name of the file from which the classifier is loaded. - */ - CV_WRAP CascadeClassifier(const String& filename); - ~CascadeClassifier(); - /** @brief Checks whether the classifier has been loaded. - */ - CV_WRAP bool empty() const; - /** @brief Loads a classifier from a file. - - @param filename Name of the file from which the classifier is loaded. The file may contain an old - HAAR classifier trained by the haartraining application or a new cascade classifier trained by the - traincascade application. - */ - CV_WRAP bool load( const String& filename ); - /** @brief Reads a classifier from a FileStorage node. - - @note The file may contain a new cascade classifier (trained traincascade application) only. - */ - CV_WRAP bool read( const FileNode& node ); - - /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list - of rectangles. - - @param image Matrix of the type CV_8U containing an image where objects are detected. - @param objects Vector of rectangles where each rectangle contains the detected object, the - rectangles may be partially outside the original image. - @param scaleFactor Parameter specifying how much the image size is reduced at each image scale. - @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have - to retain it. - @param flags Parameter with the same meaning for an old cascade as in the function - cvHaarDetectObjects. It is not used for a new cascade. - @param minSize Minimum possible object size. Objects smaller than that are ignored. - @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale. - - The function is parallelized with the TBB library. - - @note - - (Python) A face detection example using cascade classifiers can be found at - opencv_source_code/samples/python/facedetect.py - */ - CV_WRAP void detectMultiScale( InputArray image, - CV_OUT std::vector& objects, - double scaleFactor = 1.1, - int minNeighbors = 3, int flags = 0, - Size minSize = Size(), - Size maxSize = Size() ); - - /** @overload - @param image Matrix of the type CV_8U containing an image where objects are detected. - @param objects Vector of rectangles where each rectangle contains the detected object, the - rectangles may be partially outside the original image. - @param numDetections Vector of detection numbers for the corresponding objects. An object's number - of detections is the number of neighboring positively classified rectangles that were joined - together to form the object. - @param scaleFactor Parameter specifying how much the image size is reduced at each image scale. - @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have - to retain it. - @param flags Parameter with the same meaning for an old cascade as in the function - cvHaarDetectObjects. It is not used for a new cascade. - @param minSize Minimum possible object size. Objects smaller than that are ignored. - @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale. - */ - CV_WRAP_AS(detectMultiScale2) void detectMultiScale( InputArray image, - CV_OUT std::vector& objects, - CV_OUT std::vector& numDetections, - double scaleFactor=1.1, - int minNeighbors=3, int flags=0, - Size minSize=Size(), - Size maxSize=Size() ); - - /** @overload - This function allows you to retrieve the final stage decision certainty of classification. - For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter. - For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage. - This value can then be used to separate strong from weaker classifications. - - A code sample on how to use it efficiently can be found below: - @code - Mat img; - vector weights; - vector levels; - vector detections; - CascadeClassifier model("/path/to/your/model.xml"); - model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true); - cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl; - @endcode - */ - CV_WRAP_AS(detectMultiScale3) void detectMultiScale( InputArray image, - CV_OUT std::vector& objects, - CV_OUT std::vector& rejectLevels, - CV_OUT std::vector& levelWeights, - double scaleFactor = 1.1, - int minNeighbors = 3, int flags = 0, - Size minSize = Size(), - Size maxSize = Size(), - bool outputRejectLevels = false ); - - CV_WRAP bool isOldFormatCascade() const; - CV_WRAP Size getOriginalWindowSize() const; - CV_WRAP int getFeatureType() const; - void* getOldCascade(); - - CV_WRAP static bool convert(const String& oldcascade, const String& newcascade); - - void setMaskGenerator(const Ptr& maskGenerator); - Ptr getMaskGenerator(); - - Ptr cc; -}; - -CV_EXPORTS Ptr createFaceDetectionMaskGenerator(); - -//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector ////////////// - -//! struct for detection region of interest (ROI) -struct DetectionROI -{ - //! scale(size) of the bounding box - double scale; - //! set of requested locations to be evaluated - std::vector locations; - //! vector that will contain confidence values for each location - std::vector confidences; -}; - -/**@brief Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector. - -the HOG descriptor algorithm introduced by Navneet Dalal and Bill Triggs @cite Dalal2005 . - -useful links: - -https://hal.inria.fr/inria-00548512/document/ - -https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients - -https://software.intel.com/en-us/ipp-dev-reference-histogram-of-oriented-gradients-hog-descriptor - -http://www.learnopencv.com/histogram-of-oriented-gradients - -http://www.learnopencv.com/handwritten-digits-classification-an-opencv-c-python-tutorial - - */ -struct CV_EXPORTS_W HOGDescriptor -{ -public: - enum { L2Hys = 0 //!< Default histogramNormType - }; - enum { DEFAULT_NLEVELS = 64 //!< Default nlevels value. - }; - /**@brief Creates the HOG descriptor and detector with default params. - - aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9, 1 ) - */ - CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8), - cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1), - histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true), - free_coef(-1.f), nlevels(HOGDescriptor::DEFAULT_NLEVELS), signedGradient(false) - {} - - /** @overload - @param _winSize sets winSize with given value. - @param _blockSize sets blockSize with given value. - @param _blockStride sets blockStride with given value. - @param _cellSize sets cellSize with given value. - @param _nbins sets nbins with given value. - @param _derivAperture sets derivAperture with given value. - @param _winSigma sets winSigma with given value. - @param _histogramNormType sets histogramNormType with given value. - @param _L2HysThreshold sets L2HysThreshold with given value. - @param _gammaCorrection sets gammaCorrection with given value. - @param _nlevels sets nlevels with given value. - @param _signedGradient sets signedGradient with given value. - */ - CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, - Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1, - int _histogramNormType=HOGDescriptor::L2Hys, - double _L2HysThreshold=0.2, bool _gammaCorrection=false, - int _nlevels=HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient=false) - : winSize(_winSize), blockSize(_blockSize), blockStride(_blockStride), cellSize(_cellSize), - nbins(_nbins), derivAperture(_derivAperture), winSigma(_winSigma), - histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold), - gammaCorrection(_gammaCorrection), free_coef(-1.f), nlevels(_nlevels), signedGradient(_signedGradient) - {} - - /** @overload - @param filename the file name containing HOGDescriptor properties and coefficients of the trained classifier - */ - CV_WRAP HOGDescriptor(const String& filename) - { - load(filename); - } - - /** @overload - @param d the HOGDescriptor which cloned to create a new one. - */ - HOGDescriptor(const HOGDescriptor& d) - { - d.copyTo(*this); - } - - /**@brief Default destructor. - */ - virtual ~HOGDescriptor() {} - - /**@brief Returns the number of coefficients required for the classification. - */ - CV_WRAP size_t getDescriptorSize() const; - - /** @brief Checks if detector size equal to descriptor size. - */ - CV_WRAP bool checkDetectorSize() const; - - /** @brief Returns winSigma value - */ - CV_WRAP double getWinSigma() const; - - /**@example samples/cpp/peopledetect.cpp - */ - /**@brief Sets coefficients for the linear SVM classifier. - @param _svmdetector coefficients for the linear SVM classifier. - */ - CV_WRAP virtual void setSVMDetector(InputArray _svmdetector); - - /** @brief Reads HOGDescriptor parameters from a file node. - @param fn File node - */ - virtual bool read(FileNode& fn); - - /** @brief Stores HOGDescriptor parameters in a file storage. - @param fs File storage - @param objname Object name - */ - virtual void write(FileStorage& fs, const String& objname) const; - - /** @brief loads coefficients for the linear SVM classifier from a file - @param filename Name of the file to read. - @param objname The optional name of the node to read (if empty, the first top-level node will be used). - */ - CV_WRAP virtual bool load(const String& filename, const String& objname = String()); - - /** @brief saves coefficients for the linear SVM classifier to a file - @param filename File name - @param objname Object name - */ - CV_WRAP virtual void save(const String& filename, const String& objname = String()) const; - - /** @brief clones the HOGDescriptor - @param c cloned HOGDescriptor - */ - virtual void copyTo(HOGDescriptor& c) const; - - /**@example samples/cpp/train_HOG.cpp - */ - /** @brief Computes HOG descriptors of given image. - @param img Matrix of the type CV_8U containing an image where HOG features will be calculated. - @param descriptors Matrix of the type CV_32F - @param winStride Window stride. It must be a multiple of block stride. - @param padding Padding - @param locations Vector of Point - */ - CV_WRAP virtual void compute(InputArray img, - CV_OUT std::vector& descriptors, - Size winStride = Size(), Size padding = Size(), - const std::vector& locations = std::vector()) const; - - /** @brief Performs object detection without a multi-scale window. - @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. - @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries. - @param weights Vector that will contain confidence values for each detected object. - @param hitThreshold Threshold for the distance between features and SVM classifying plane. - Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). - But if the free coefficient is omitted (which is allowed), you can specify it manually here. - @param winStride Window stride. It must be a multiple of block stride. - @param padding Padding - @param searchLocations Vector of Point includes set of requested locations to be evaluated. - */ - CV_WRAP virtual void detect(const Mat& img, CV_OUT std::vector& foundLocations, - CV_OUT std::vector& weights, - double hitThreshold = 0, Size winStride = Size(), - Size padding = Size(), - const std::vector& searchLocations = std::vector()) const; - - /** @brief Performs object detection without a multi-scale window. - @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. - @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries. - @param hitThreshold Threshold for the distance between features and SVM classifying plane. - Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). - But if the free coefficient is omitted (which is allowed), you can specify it manually here. - @param winStride Window stride. It must be a multiple of block stride. - @param padding Padding - @param searchLocations Vector of Point includes locations to search. - */ - virtual void detect(const Mat& img, CV_OUT std::vector& foundLocations, - double hitThreshold = 0, Size winStride = Size(), - Size padding = Size(), - const std::vector& searchLocations=std::vector()) const; - - /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list - of rectangles. - @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. - @param foundLocations Vector of rectangles where each rectangle contains the detected object. - @param foundWeights Vector that will contain confidence values for each detected object. - @param hitThreshold Threshold for the distance between features and SVM classifying plane. - Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). - But if the free coefficient is omitted (which is allowed), you can specify it manually here. - @param winStride Window stride. It must be a multiple of block stride. - @param padding Padding - @param scale Coefficient of the detection window increase. - @param finalThreshold Final threshold - @param useMeanshiftGrouping indicates grouping algorithm - */ - CV_WRAP virtual void detectMultiScale(InputArray img, CV_OUT std::vector& foundLocations, - CV_OUT std::vector& foundWeights, double hitThreshold = 0, - Size winStride = Size(), Size padding = Size(), double scale = 1.05, - double finalThreshold = 2.0,bool useMeanshiftGrouping = false) const; - - /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list - of rectangles. - @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. - @param foundLocations Vector of rectangles where each rectangle contains the detected object. - @param hitThreshold Threshold for the distance between features and SVM classifying plane. - Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). - But if the free coefficient is omitted (which is allowed), you can specify it manually here. - @param winStride Window stride. It must be a multiple of block stride. - @param padding Padding - @param scale Coefficient of the detection window increase. - @param finalThreshold Final threshold - @param useMeanshiftGrouping indicates grouping algorithm - */ - virtual void detectMultiScale(InputArray img, CV_OUT std::vector& foundLocations, - double hitThreshold = 0, Size winStride = Size(), - Size padding = Size(), double scale = 1.05, - double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const; - - /** @brief Computes gradients and quantized gradient orientations. - @param img Matrix contains the image to be computed - @param grad Matrix of type CV_32FC2 contains computed gradients - @param angleOfs Matrix of type CV_8UC2 contains quantized gradient orientations - @param paddingTL Padding from top-left - @param paddingBR Padding from bottom-right - */ - CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs, - Size paddingTL = Size(), Size paddingBR = Size()) const; - - /** @brief Returns coefficients of the classifier trained for people detection (for 64x128 windows). - */ - CV_WRAP static std::vector getDefaultPeopleDetector(); - - /**@example samples/tapi/hog.cpp - */ - /** @brief Returns coefficients of the classifier trained for people detection (for 48x96 windows). - */ - CV_WRAP static std::vector getDaimlerPeopleDetector(); - - //! Detection window size. Align to block size and block stride. Default value is Size(64,128). - CV_PROP Size winSize; - - //! Block size in pixels. Align to cell size. Default value is Size(16,16). - CV_PROP Size blockSize; - - //! Block stride. It must be a multiple of cell size. Default value is Size(8,8). - CV_PROP Size blockStride; - - //! Cell size. Default value is Size(8,8). - CV_PROP Size cellSize; - - //! Number of bins used in the calculation of histogram of gradients. Default value is 9. - CV_PROP int nbins; - - //! not documented - CV_PROP int derivAperture; - - //! Gaussian smoothing window parameter. - CV_PROP double winSigma; - - //! histogramNormType - CV_PROP int histogramNormType; - - //! L2-Hys normalization method shrinkage. - CV_PROP double L2HysThreshold; - - //! Flag to specify whether the gamma correction preprocessing is required or not. - CV_PROP bool gammaCorrection; - - //! coefficients for the linear SVM classifier. - CV_PROP std::vector svmDetector; - - //! coefficients for the linear SVM classifier used when OpenCL is enabled - UMat oclSvmDetector; - - //! not documented - float free_coef; - - //! Maximum number of detection window increases. Default value is 64 - CV_PROP int nlevels; - - //! Indicates signed gradient will be used or not - CV_PROP bool signedGradient; - - /** @brief evaluate specified ROI and return confidence value for each location - @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. - @param locations Vector of Point - @param foundLocations Vector of Point where each Point is detected object's top-left point. - @param confidences confidences - @param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually - it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if - the free coefficient is omitted (which is allowed), you can specify it manually here - @param winStride winStride - @param padding padding - */ - virtual void detectROI(const cv::Mat& img, const std::vector &locations, - CV_OUT std::vector& foundLocations, CV_OUT std::vector& confidences, - double hitThreshold = 0, cv::Size winStride = Size(), - cv::Size padding = Size()) const; - - /** @brief evaluate specified ROI and return confidence value for each location in multiple scales - @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. - @param foundLocations Vector of rectangles where each rectangle contains the detected object. - @param locations Vector of DetectionROI - @param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified - in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here. - @param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it. - */ - virtual void detectMultiScaleROI(const cv::Mat& img, - CV_OUT std::vector& foundLocations, - std::vector& locations, - double hitThreshold = 0, - int groupThreshold = 0) const; - - /** @brief read/parse Dalal's alt model file - @param modelfile Path of Dalal's alt model file. - */ - void readALTModel(String modelfile); - - /** @brief Groups the object candidate rectangles. - @param rectList Input/output vector of rectangles. Output vector includes retained and grouped rectangles. (The Python list is not modified in place.) - @param weights Input/output vector of weights of rectangles. Output vector includes weights of retained and grouped rectangles. (The Python list is not modified in place.) - @param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it. - @param eps Relative difference between sides of the rectangles to merge them into a group. - */ - void groupRectangles(std::vector& rectList, std::vector& weights, int groupThreshold, double eps) const; -}; - -class CV_EXPORTS_W QRCodeDetector -{ -public: - CV_WRAP QRCodeDetector(); - ~QRCodeDetector(); - - /** @brief sets the epsilon used during the horizontal scan of QR code stop marker detection. - @param epsX Epsilon neighborhood, which allows you to determine the horizontal pattern - of the scheme 1:1:3:1:1 according to QR code standard. - */ - CV_WRAP void setEpsX(double epsX); - /** @brief sets the epsilon used during the vertical scan of QR code stop marker detection. - @param epsY Epsilon neighborhood, which allows you to determine the vertical pattern - of the scheme 1:1:3:1:1 according to QR code standard. - */ - CV_WRAP void setEpsY(double epsY); - - /** @brief Detects QR code in image and returns the quadrangle containing the code. - @param img grayscale or color (BGR) image containing (or not) QR code. - @param points Output vector of vertices of the minimum-area quadrangle containing the code. - */ - CV_WRAP bool detect(InputArray img, OutputArray points) const; - - /** @brief Decodes QR code in image once it's found by the detect() method. - - Returns UTF8-encoded output string or empty string if the code cannot be decoded. - @param img grayscale or color (BGR) image containing QR code. - @param points Quadrangle vertices found by detect() method (or some other algorithm). - @param straight_qrcode The optional output image containing rectified and binarized QR code - */ - CV_WRAP cv::String decode(InputArray img, InputArray points, OutputArray straight_qrcode = noArray()); - - /** @brief Decodes QR code on a curved surface in image once it's found by the detect() method. - - Returns UTF8-encoded output string or empty string if the code cannot be decoded. - @param img grayscale or color (BGR) image containing QR code. - @param points Quadrangle vertices found by detect() method (or some other algorithm). - @param straight_qrcode The optional output image containing rectified and binarized QR code - */ - CV_WRAP cv::String decodeCurved(InputArray img, InputArray points, OutputArray straight_qrcode = noArray()); - - /** @brief Both detects and decodes QR code - - @param img grayscale or color (BGR) image containing QR code. - @param points optional output array of vertices of the found QR code quadrangle. Will be empty if not found. - @param straight_qrcode The optional output image containing rectified and binarized QR code - */ - CV_WRAP cv::String detectAndDecode(InputArray img, OutputArray points=noArray(), - OutputArray straight_qrcode = noArray()); - - /** @brief Both detects and decodes QR code on a curved surface - - @param img grayscale or color (BGR) image containing QR code. - @param points optional output array of vertices of the found QR code quadrangle. Will be empty if not found. - @param straight_qrcode The optional output image containing rectified and binarized QR code - */ - CV_WRAP cv::String detectAndDecodeCurved(InputArray img, OutputArray points=noArray(), - OutputArray straight_qrcode = noArray()); - - /** @brief Detects QR codes in image and returns the vector of the quadrangles containing the codes. - @param img grayscale or color (BGR) image containing (or not) QR codes. - @param points Output vector of vector of vertices of the minimum-area quadrangle containing the codes. - */ - CV_WRAP - bool detectMulti(InputArray img, OutputArray points) const; - - /** @brief Decodes QR codes in image once it's found by the detect() method. - @param img grayscale or color (BGR) image containing QR codes. - @param decoded_info UTF8-encoded output vector of string or empty vector of string if the codes cannot be decoded. - @param points vector of Quadrangle vertices found by detect() method (or some other algorithm). - @param straight_qrcode The optional output vector of images containing rectified and binarized QR codes - */ - CV_WRAP - bool decodeMulti( - InputArray img, InputArray points, - CV_OUT std::vector& decoded_info, - OutputArrayOfArrays straight_qrcode = noArray() - ) const; - - /** @brief Both detects and decodes QR codes - @param img grayscale or color (BGR) image containing QR codes. - @param decoded_info UTF8-encoded output vector of string or empty vector of string if the codes cannot be decoded. - @param points optional output vector of vertices of the found QR code quadrangles. Will be empty if not found. - @param straight_qrcode The optional output vector of images containing rectified and binarized QR codes - */ - CV_WRAP - bool detectAndDecodeMulti( - InputArray img, CV_OUT std::vector& decoded_info, - OutputArray points = noArray(), - OutputArrayOfArrays straight_qrcode = noArray() - ) const; - - -#ifndef CV_DOXYGEN // COMPATIBILITY - inline bool decodeMulti( - InputArray img, InputArray points, - CV_OUT std::vector& decoded_info, - OutputArrayOfArrays straight_qrcode = noArray() - ) const - { - std::vector decoded_info_; - bool res = decodeMulti(img, points, decoded_info_, straight_qrcode); - decoded_info.resize(decoded_info_.size()); - for (size_t i = 0; i < decoded_info.size(); ++i) - { - cv::String s; std::swap(s, decoded_info_[i]); - decoded_info[i] = s; - } - return res; - } - - inline bool detectAndDecodeMulti( - InputArray img, CV_OUT std::vector& decoded_info, - OutputArray points = noArray(), - OutputArrayOfArrays straight_qrcode = noArray() - ) const - { - std::vector decoded_info_; - bool res = detectAndDecodeMulti(img, decoded_info_, points, straight_qrcode); - decoded_info.resize(decoded_info_.size()); - for (size_t i = 0; i < decoded_info.size(); ++i) - { - cv::String s; std::swap(s, decoded_info_[i]); - decoded_info[i] = s; - } - return res; - } -#endif - -protected: - struct Impl; - Ptr p; -}; - -/** @brief Detect QR code in image and return minimum area of quadrangle that describes QR code. - @param in Matrix of the type CV_8UC1 containing an image where QR code are detected. - @param points Output vector of vertices of a quadrangle of minimal area that describes QR code. - @param eps_x Epsilon neighborhood, which allows you to determine the horizontal pattern of the scheme 1:1:3:1:1 according to QR code standard. - @param eps_y Epsilon neighborhood, which allows you to determine the vertical pattern of the scheme 1:1:3:1:1 according to QR code standard. - */ -CV_EXPORTS bool detectQRCode(InputArray in, std::vector &points, double eps_x = 0.2, double eps_y = 0.1); - -/** @brief Decode QR code in image and return text that is encrypted in QR code. - @param in Matrix of the type CV_8UC1 containing an image where QR code are detected. - @param points Input vector of vertices of a quadrangle of minimal area that describes QR code. - @param decoded_info String information that is encrypted in QR code. - @param straight_qrcode Matrix of the type CV_8UC1 containing an binary straight QR code. - */ -CV_EXPORTS bool decodeQRCode(InputArray in, InputArray points, std::string &decoded_info, OutputArray straight_qrcode = noArray()); - -/** @brief Decode QR code on a curved surface in image and return text that is encrypted in QR code. - @param in Matrix of the type CV_8UC1 containing an image where QR code are detected. - @param points Input vector of vertices of a quadrangle of minimal area that describes QR code. - @param decoded_info String information that is encrypted in QR code. - @param straight_qrcode Matrix of the type CV_8UC1 containing an binary straight QR code. - */ -CV_EXPORTS bool decodeCurvedQRCode(InputArray in, InputArray points, std::string &decoded_info, OutputArray straight_qrcode = noArray()); - -//! @} objdetect -} - -#include "opencv2/objdetect/detection_based_tracker.hpp" - -#ifndef DISABLE_OPENCV_24_COMPATIBILITY -#include "opencv2/objdetect/objdetect_c.h" -#endif - -#endif diff --git a/include/opencv2/objdetect/detection_based_tracker.hpp b/include/opencv2/objdetect/detection_based_tracker.hpp deleted file mode 100755 index 07dd587..0000000 --- a/include/opencv2/objdetect/detection_based_tracker.hpp +++ /dev/null @@ -1,227 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_OBJDETECT_DBT_HPP -#define OPENCV_OBJDETECT_DBT_HPP - -#include - -// After this condition removal update blacklist for bindings: modules/python/common.cmake -#if defined(__linux__) || defined(LINUX) || defined(__APPLE__) || defined(__ANDROID__) || \ - defined(CV_CXX11) - -#include - -namespace cv -{ - -//! @addtogroup objdetect -//! @{ - -class CV_EXPORTS DetectionBasedTracker -{ - public: - struct CV_EXPORTS Parameters - { - int maxTrackLifetime; - int minDetectionPeriod; //the minimal time between run of the big object detector (on the whole frame) in ms (1000 mean 1 sec), default=0 - - Parameters(); - }; - - class IDetector - { - public: - IDetector(): - minObjSize(96, 96), - maxObjSize(INT_MAX, INT_MAX), - minNeighbours(2), - scaleFactor(1.1f) - {} - - virtual void detect(const cv::Mat& image, std::vector& objects) = 0; - - void setMinObjectSize(const cv::Size& min) - { - minObjSize = min; - } - void setMaxObjectSize(const cv::Size& max) - { - maxObjSize = max; - } - cv::Size getMinObjectSize() const - { - return minObjSize; - } - cv::Size getMaxObjectSize() const - { - return maxObjSize; - } - float getScaleFactor() - { - return scaleFactor; - } - void setScaleFactor(float value) - { - scaleFactor = value; - } - int getMinNeighbours() - { - return minNeighbours; - } - void setMinNeighbours(int value) - { - minNeighbours = value; - } - virtual ~IDetector() {} - - protected: - cv::Size minObjSize; - cv::Size maxObjSize; - int minNeighbours; - float scaleFactor; - }; - - DetectionBasedTracker(cv::Ptr mainDetector, cv::Ptr trackingDetector, const Parameters& params); - virtual ~DetectionBasedTracker(); - - virtual bool run(); - virtual void stop(); - virtual void resetTracking(); - - virtual void process(const cv::Mat& imageGray); - - bool setParameters(const Parameters& params); - const Parameters& getParameters() const; - - - typedef std::pair Object; - virtual void getObjects(std::vector& result) const; - virtual void getObjects(std::vector& result) const; - - enum ObjectStatus - { - DETECTED_NOT_SHOWN_YET, - DETECTED, - DETECTED_TEMPORARY_LOST, - WRONG_OBJECT - }; - struct ExtObject - { - int id; - cv::Rect location; - ObjectStatus status; - ExtObject(int _id, cv::Rect _location, ObjectStatus _status) - :id(_id), location(_location), status(_status) - { - } - }; - virtual void getObjects(std::vector& result) const; - - - virtual int addObject(const cv::Rect& location); //returns id of the new object - - protected: - class SeparateDetectionWork; - cv::Ptr separateDetectionWork; - friend void* workcycleObjectDetectorFunction(void* p); - - struct InnerParameters - { - int numLastPositionsToTrack; - int numStepsToWaitBeforeFirstShow; - int numStepsToTrackWithoutDetectingIfObjectHasNotBeenShown; - int numStepsToShowWithoutDetecting; - - float coeffTrackingWindowSize; - float coeffObjectSizeToTrack; - float coeffObjectSpeedUsingInPrediction; - - InnerParameters(); - }; - Parameters parameters; - InnerParameters innerParameters; - - struct TrackedObject - { - typedef std::vector PositionsVector; - - PositionsVector lastPositions; - - int numDetectedFrames; - int numFramesNotDetected; - int id; - - TrackedObject(const cv::Rect& rect):numDetectedFrames(1), numFramesNotDetected(0) - { - lastPositions.push_back(rect); - id=getNextId(); - }; - - static int getNextId() - { - static int _id=0; - return _id++; - } - }; - - int numTrackedSteps; - std::vector trackedObjects; - - std::vector weightsPositionsSmoothing; - std::vector weightsSizesSmoothing; - - cv::Ptr cascadeForTracking; - - void updateTrackedObjects(const std::vector& detectedObjects); - cv::Rect calcTrackedObjectPositionToShow(int i) const; - cv::Rect calcTrackedObjectPositionToShow(int i, ObjectStatus& status) const; - void detectInRegion(const cv::Mat& img, const cv::Rect& r, std::vector& detectedObjectsInRegions); -}; - -//! @} objdetect - -} //end of cv namespace -#endif - -#endif diff --git a/include/opencv2/objdetect/objdetect.hpp b/include/opencv2/objdetect/objdetect.hpp deleted file mode 100755 index 3ee284f..0000000 --- a/include/opencv2/objdetect/objdetect.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/objdetect.hpp" diff --git a/include/opencv2/objdetect/objdetect_c.h b/include/opencv2/objdetect/objdetect_c.h deleted file mode 100755 index 67dc2f4..0000000 --- a/include/opencv2/objdetect/objdetect_c.h +++ /dev/null @@ -1,166 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_OBJDETECT_C_H -#define OPENCV_OBJDETECT_C_H - -#include "opencv2/core/core_c.h" - -#ifdef __cplusplus -#include -#include - -extern "C" { -#endif - -/** @addtogroup objdetect_c - @{ - */ - -/****************************************************************************************\ -* Haar-like Object Detection functions * -\****************************************************************************************/ - -#define CV_HAAR_MAGIC_VAL 0x42500000 -#define CV_TYPE_NAME_HAAR "opencv-haar-classifier" - -#define CV_IS_HAAR_CLASSIFIER( haar ) \ - ((haar) != NULL && \ - (((const CvHaarClassifierCascade*)(haar))->flags & CV_MAGIC_MASK)==CV_HAAR_MAGIC_VAL) - -#define CV_HAAR_FEATURE_MAX 3 -#define CV_HAAR_STAGE_MAX 1000 - -typedef struct CvHaarFeature -{ - int tilted; - struct - { - CvRect r; - float weight; - } rect[CV_HAAR_FEATURE_MAX]; -} CvHaarFeature; - -typedef struct CvHaarClassifier -{ - int count; - CvHaarFeature* haar_feature; - float* threshold; - int* left; - int* right; - float* alpha; -} CvHaarClassifier; - -typedef struct CvHaarStageClassifier -{ - int count; - float threshold; - CvHaarClassifier* classifier; - - int next; - int child; - int parent; -} CvHaarStageClassifier; - -typedef struct CvHidHaarClassifierCascade CvHidHaarClassifierCascade; - -typedef struct CvHaarClassifierCascade -{ - int flags; - int count; - CvSize orig_window_size; - CvSize real_window_size; - double scale; - CvHaarStageClassifier* stage_classifier; - CvHidHaarClassifierCascade* hid_cascade; -} CvHaarClassifierCascade; - -typedef struct CvAvgComp -{ - CvRect rect; - int neighbors; -} CvAvgComp; - -/* Loads haar classifier cascade from a directory. - It is obsolete: convert your cascade to xml and use cvLoad instead */ -CVAPI(CvHaarClassifierCascade*) cvLoadHaarClassifierCascade( - const char* directory, CvSize orig_window_size); - -CVAPI(void) cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** cascade ); - -#define CV_HAAR_DO_CANNY_PRUNING 1 -#define CV_HAAR_SCALE_IMAGE 2 -#define CV_HAAR_FIND_BIGGEST_OBJECT 4 -#define CV_HAAR_DO_ROUGH_SEARCH 8 - -CVAPI(CvSeq*) cvHaarDetectObjects( const CvArr* image, - CvHaarClassifierCascade* cascade, CvMemStorage* storage, - double scale_factor CV_DEFAULT(1.1), - int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0), - CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0))); - -/* sets images for haar classifier cascade */ -CVAPI(void) cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade, - const CvArr* sum, const CvArr* sqsum, - const CvArr* tilted_sum, double scale ); - -/* runs the cascade on the specified window */ -CVAPI(int) cvRunHaarClassifierCascade( const CvHaarClassifierCascade* cascade, - CvPoint pt, int start_stage CV_DEFAULT(0)); - -/** @} objdetect_c */ - -#ifdef __cplusplus -} - -CV_EXPORTS CvSeq* cvHaarDetectObjectsForROC( const CvArr* image, - CvHaarClassifierCascade* cascade, CvMemStorage* storage, - std::vector& rejectLevels, std::vector& levelWeightds, - double scale_factor = 1.1, - int min_neighbors = 3, int flags = 0, - CvSize min_size = cvSize(0, 0), CvSize max_size = cvSize(0, 0), - bool outputRejectLevels = false ); - -#endif - -#endif /* OPENCV_OBJDETECT_C_H */ diff --git a/include/opencv2/opencv.hpp b/include/opencv2/opencv.hpp deleted file mode 100755 index 4048158..0000000 --- a/include/opencv2/opencv.hpp +++ /dev/null @@ -1,139 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009-2010, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_ALL_HPP -#define OPENCV_ALL_HPP - -// File that defines what modules where included during the build of OpenCV -// These are purely the defines of the correct HAVE_OPENCV_modulename values -#include "opencv2/opencv_modules.hpp" - -// Then the list of defines is checked to include the correct headers -// Core library is always included --> without no OpenCV functionality available -#include "opencv2/core.hpp" - -// Then the optional modules are checked -#ifdef HAVE_OPENCV_CALIB3D -#include "opencv2/calib3d.hpp" -#endif -#ifdef HAVE_OPENCV_FEATURES2D -#include "opencv2/features2d.hpp" -#endif -#ifdef HAVE_OPENCV_DNN -#include "opencv2/dnn.hpp" -#endif -#ifdef HAVE_OPENCV_FLANN -#include "opencv2/flann.hpp" -#endif -#ifdef HAVE_OPENCV_HIGHGUI -#include "opencv2/highgui.hpp" -#endif -#ifdef HAVE_OPENCV_IMGCODECS -#include "opencv2/imgcodecs.hpp" -#endif -#ifdef HAVE_OPENCV_IMGPROC -#include "opencv2/imgproc.hpp" -#endif -#ifdef HAVE_OPENCV_ML -#include "opencv2/ml.hpp" -#endif -#ifdef HAVE_OPENCV_OBJDETECT -#include "opencv2/objdetect.hpp" -#endif -#ifdef HAVE_OPENCV_PHOTO -#include "opencv2/photo.hpp" -#endif -#ifdef HAVE_OPENCV_SHAPE -#include "opencv2/shape.hpp" -#endif -#ifdef HAVE_OPENCV_STITCHING -#include "opencv2/stitching.hpp" -#endif -#ifdef HAVE_OPENCV_SUPERRES -#include "opencv2/superres.hpp" -#endif -#ifdef HAVE_OPENCV_VIDEO -#include "opencv2/video.hpp" -#endif -#ifdef HAVE_OPENCV_VIDEOIO -#include "opencv2/videoio.hpp" -#endif -#ifdef HAVE_OPENCV_VIDEOSTAB -#include "opencv2/videostab.hpp" -#endif -#ifdef HAVE_OPENCV_VIZ -#include "opencv2/viz.hpp" -#endif - -// Finally CUDA specific entries are checked and added -#ifdef HAVE_OPENCV_CUDAARITHM -#include "opencv2/cudaarithm.hpp" -#endif -#ifdef HAVE_OPENCV_CUDABGSEGM -#include "opencv2/cudabgsegm.hpp" -#endif -#ifdef HAVE_OPENCV_CUDACODEC -#include "opencv2/cudacodec.hpp" -#endif -#ifdef HAVE_OPENCV_CUDAFEATURES2D -#include "opencv2/cudafeatures2d.hpp" -#endif -#ifdef HAVE_OPENCV_CUDAFILTERS -#include "opencv2/cudafilters.hpp" -#endif -#ifdef HAVE_OPENCV_CUDAIMGPROC -#include "opencv2/cudaimgproc.hpp" -#endif -#ifdef HAVE_OPENCV_CUDAOBJDETECT -#include "opencv2/cudaobjdetect.hpp" -#endif -#ifdef HAVE_OPENCV_CUDAOPTFLOW -#include "opencv2/cudaoptflow.hpp" -#endif -#ifdef HAVE_OPENCV_CUDASTEREO -#include "opencv2/cudastereo.hpp" -#endif -#ifdef HAVE_OPENCV_CUDAWARPING -#include "opencv2/cudawarping.hpp" -#endif - -#endif diff --git a/include/opencv2/opencv_modules.hpp b/include/opencv2/opencv_modules.hpp deleted file mode 100755 index d0f2dc5..0000000 --- a/include/opencv2/opencv_modules.hpp +++ /dev/null @@ -1,32 +0,0 @@ -/* - * ** File generated automatically, do not modify ** - * - * This file defines the list of modules available in current build configuration - * - * -*/ - -// This definition means that OpenCV is built with enabled non-free code. -// For example, patented algorithms for non-profit/non-commercial use only. -/* #undef OPENCV_ENABLE_NONFREE */ - -#define HAVE_OPENCV_CALIB3D -#define HAVE_OPENCV_CORE -#define HAVE_OPENCV_DNN -#define HAVE_OPENCV_FEATURES2D -#define HAVE_OPENCV_FLANN -#define HAVE_OPENCV_HIGHGUI -#define HAVE_OPENCV_IMGCODECS -#define HAVE_OPENCV_IMGPROC -#define HAVE_OPENCV_ML -#define HAVE_OPENCV_OBJDETECT -#define HAVE_OPENCV_PHOTO -#define HAVE_OPENCV_SHAPE -#define HAVE_OPENCV_STITCHING -#define HAVE_OPENCV_SUPERRES -#define HAVE_OPENCV_VIDEO -#define HAVE_OPENCV_VIDEOIO -#define HAVE_OPENCV_VIDEOSTAB -#define HAVE_OPENCV_WORLD - - diff --git a/include/opencv2/photo.hpp b/include/opencv2/photo.hpp deleted file mode 100755 index a457295..0000000 --- a/include/opencv2/photo.hpp +++ /dev/null @@ -1,862 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_PHOTO_HPP -#define OPENCV_PHOTO_HPP - -#include "opencv2/core.hpp" -#include "opencv2/imgproc.hpp" - -/** -@defgroup photo Computational Photography - -This module includes photo processing algorithms -@{ - @defgroup photo_inpaint Inpainting - @defgroup photo_denoise Denoising - @defgroup photo_hdr HDR imaging - -This section describes high dynamic range imaging algorithms namely tonemapping, exposure alignment, -camera calibration with multiple exposures and exposure fusion. - - @defgroup photo_decolor Contrast Preserving Decolorization - -Useful links: - -http://www.cse.cuhk.edu.hk/leojia/projects/color2gray/index.html - - @defgroup photo_clone Seamless Cloning - -Useful links: - -https://www.learnopencv.com/seamless-cloning-using-opencv-python-cpp - - @defgroup photo_render Non-Photorealistic Rendering - -Useful links: - -http://www.inf.ufrgs.br/~eslgastal/DomainTransform - -https://www.learnopencv.com/non-photorealistic-rendering-using-opencv-python-c/ - - @defgroup photo_c C API -@} - */ - -namespace cv -{ - -//! @addtogroup photo -//! @{ - -//! @addtogroup photo_inpaint -//! @{ -//! the inpainting algorithm -enum -{ - INPAINT_NS = 0, //!< Use Navier-Stokes based method - INPAINT_TELEA = 1 //!< Use the algorithm proposed by Alexandru Telea @cite Telea04 -}; - -/** @brief Restores the selected region in an image using the region neighborhood. - -@param src Input 8-bit, 16-bit unsigned or 32-bit float 1-channel or 8-bit 3-channel image. -@param inpaintMask Inpainting mask, 8-bit 1-channel image. Non-zero pixels indicate the area that -needs to be inpainted. -@param dst Output image with the same size and type as src . -@param inpaintRadius Radius of a circular neighborhood of each point inpainted that is considered -by the algorithm. -@param flags Inpainting method that could be cv::INPAINT_NS or cv::INPAINT_TELEA - -The function reconstructs the selected image area from the pixel near the area boundary. The -function may be used to remove dust and scratches from a scanned photo, or to remove undesirable -objects from still images or video. See for more details. - -@note - - An example using the inpainting technique can be found at - opencv_source_code/samples/cpp/inpaint.cpp - - (Python) An example using the inpainting technique can be found at - opencv_source_code/samples/python/inpaint.py - */ -CV_EXPORTS_W void inpaint( InputArray src, InputArray inpaintMask, - OutputArray dst, double inpaintRadius, int flags ); - -//! @} photo_inpaint - -//! @addtogroup photo_denoise -//! @{ - -/** @brief Perform image denoising using Non-local Means Denoising algorithm - with several computational -optimizations. Noise expected to be a gaussian white noise - -@param src Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image. -@param dst Output image with the same size and type as src . -@param templateWindowSize Size in pixels of the template patch that is used to compute weights. -Should be odd. Recommended value 7 pixels -@param searchWindowSize Size in pixels of the window that is used to compute weighted average for -given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater -denoising time. Recommended value 21 pixels -@param h Parameter regulating filter strength. Big h value perfectly removes noise but also -removes image details, smaller h value preserves details but also preserves some noise - -This function expected to be applied to grayscale images. For colored images look at -fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored -image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting -image to CIELAB colorspace and then separately denoise L and AB components with different h -parameter. - */ -CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst, float h = 3, - int templateWindowSize = 7, int searchWindowSize = 21); - -/** @brief Perform image denoising using Non-local Means Denoising algorithm - with several computational -optimizations. Noise expected to be a gaussian white noise - -@param src Input 8-bit or 16-bit (only with NORM_L1) 1-channel, -2-channel, 3-channel or 4-channel image. -@param dst Output image with the same size and type as src . -@param templateWindowSize Size in pixels of the template patch that is used to compute weights. -Should be odd. Recommended value 7 pixels -@param searchWindowSize Size in pixels of the window that is used to compute weighted average for -given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater -denoising time. Recommended value 21 pixels -@param h Array of parameters regulating filter strength, either one -parameter applied to all channels or one per channel in dst. Big h value -perfectly removes noise but also removes image details, smaller h -value preserves details but also preserves some noise -@param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 - -This function expected to be applied to grayscale images. For colored images look at -fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored -image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting -image to CIELAB colorspace and then separately denoise L and AB components with different h -parameter. - */ -CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst, - const std::vector& h, - int templateWindowSize = 7, int searchWindowSize = 21, - int normType = NORM_L2); - -/** @brief Modification of fastNlMeansDenoising function for colored images - -@param src Input 8-bit 3-channel image. -@param dst Output image with the same size and type as src . -@param templateWindowSize Size in pixels of the template patch that is used to compute weights. -Should be odd. Recommended value 7 pixels -@param searchWindowSize Size in pixels of the window that is used to compute weighted average for -given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater -denoising time. Recommended value 21 pixels -@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly -removes noise but also removes image details, smaller h value preserves details but also preserves -some noise -@param hColor The same as h but for color components. For most images value equals 10 -will be enough to remove colored noise and do not distort colors - -The function converts image to CIELAB colorspace and then separately denoise L and AB components -with given h parameters using fastNlMeansDenoising function. - */ -CV_EXPORTS_W void fastNlMeansDenoisingColored( InputArray src, OutputArray dst, - float h = 3, float hColor = 3, - int templateWindowSize = 7, int searchWindowSize = 21); - -/** @brief Modification of fastNlMeansDenoising function for images sequence where consecutive images have been -captured in small period of time. For example video. This version of the function is for grayscale -images or for manual manipulation with colorspaces. For more details see - - -@param srcImgs Input 8-bit 1-channel, 2-channel, 3-channel or -4-channel images sequence. All images should have the same type and -size. -@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence -@param temporalWindowSize Number of surrounding images to use for target image denoising. Should -be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to -imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise -srcImgs[imgToDenoiseIndex] image. -@param dst Output image with the same size and type as srcImgs images. -@param templateWindowSize Size in pixels of the template patch that is used to compute weights. -Should be odd. Recommended value 7 pixels -@param searchWindowSize Size in pixels of the window that is used to compute weighted average for -given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater -denoising time. Recommended value 21 pixels -@param h Parameter regulating filter strength. Bigger h value -perfectly removes noise but also removes image details, smaller h -value preserves details but also preserves some noise - */ -CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst, - int imgToDenoiseIndex, int temporalWindowSize, - float h = 3, int templateWindowSize = 7, int searchWindowSize = 21); - -/** @brief Modification of fastNlMeansDenoising function for images sequence where consecutive images have been -captured in small period of time. For example video. This version of the function is for grayscale -images or for manual manipulation with colorspaces. For more details see - - -@param srcImgs Input 8-bit or 16-bit (only with NORM_L1) 1-channel, -2-channel, 3-channel or 4-channel images sequence. All images should -have the same type and size. -@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence -@param temporalWindowSize Number of surrounding images to use for target image denoising. Should -be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to -imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise -srcImgs[imgToDenoiseIndex] image. -@param dst Output image with the same size and type as srcImgs images. -@param templateWindowSize Size in pixels of the template patch that is used to compute weights. -Should be odd. Recommended value 7 pixels -@param searchWindowSize Size in pixels of the window that is used to compute weighted average for -given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater -denoising time. Recommended value 21 pixels -@param h Array of parameters regulating filter strength, either one -parameter applied to all channels or one per channel in dst. Big h value -perfectly removes noise but also removes image details, smaller h -value preserves details but also preserves some noise -@param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1 - */ -CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst, - int imgToDenoiseIndex, int temporalWindowSize, - const std::vector& h, - int templateWindowSize = 7, int searchWindowSize = 21, - int normType = NORM_L2); - -/** @brief Modification of fastNlMeansDenoisingMulti function for colored images sequences - -@param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and -size. -@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence -@param temporalWindowSize Number of surrounding images to use for target image denoising. Should -be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to -imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise -srcImgs[imgToDenoiseIndex] image. -@param dst Output image with the same size and type as srcImgs images. -@param templateWindowSize Size in pixels of the template patch that is used to compute weights. -Should be odd. Recommended value 7 pixels -@param searchWindowSize Size in pixels of the window that is used to compute weighted average for -given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater -denoising time. Recommended value 21 pixels -@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly -removes noise but also removes image details, smaller h value preserves details but also preserves -some noise. -@param hColor The same as h but for color components. - -The function converts images to CIELAB colorspace and then separately denoise L and AB components -with given h parameters using fastNlMeansDenoisingMulti function. - */ -CV_EXPORTS_W void fastNlMeansDenoisingColoredMulti( InputArrayOfArrays srcImgs, OutputArray dst, - int imgToDenoiseIndex, int temporalWindowSize, - float h = 3, float hColor = 3, - int templateWindowSize = 7, int searchWindowSize = 21); - -/** @brief Primal-dual algorithm is an algorithm for solving special types of variational problems (that is, -finding a function to minimize some functional). As the image denoising, in particular, may be seen -as the variational problem, primal-dual algorithm then can be used to perform denoising and this is -exactly what is implemented. - -It should be noted, that this implementation was taken from the July 2013 blog entry -@cite MA13 , which also contained (slightly more general) ready-to-use source code on Python. -Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end -of July 2013 and finally it was slightly adapted by later authors. - -Although the thorough discussion and justification of the algorithm involved may be found in -@cite ChambolleEtAl, it might make sense to skim over it here, following @cite MA13 . To begin -with, we consider the 1-byte gray-level images as the functions from the rectangular domain of -pixels (it may be seen as set -\f$\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\f$ for some -\f$m,\;n\in\mathbb{N}\f$) into \f$\{0,1,\dots,255\}\f$. We shall denote the noised images as \f$f_i\f$ and with -this view, given some image \f$x\f$ of the same size, we may measure how bad it is by the formula - -\f[\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\f] - -\f$\|\|\cdot\|\|\f$ here denotes \f$L_2\f$-norm and as you see, the first addend states that we want our -image to be smooth (ideally, having zero gradient, thus being constant) and the second states that -we want our result to be close to the observations we've got. If we treat \f$x\f$ as a function, this is -exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play. - -@param observations This array should contain one or more noised versions of the image that is to -be restored. -@param result Here the denoised image will be stored. There is no need to do pre-allocation of -storage space, as it will be automatically allocated, if necessary. -@param lambda Corresponds to \f$\lambda\f$ in the formulas above. As it is enlarged, the smooth -(blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly -speaking, as it becomes smaller, the result will be more blur but more sever outliers will be -removed. -@param niters Number of iterations that the algorithm will run. Of course, as more iterations as -better, but it is hard to quantitatively refine this statement, so just use the default and -increase it if the results are poor. - */ -CV_EXPORTS_W void denoise_TVL1(const std::vector& observations,Mat& result, double lambda=1.0, int niters=30); - -//! @} photo_denoise - -//! @addtogroup photo_hdr -//! @{ - -enum { LDR_SIZE = 256 }; - -/** @brief Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range. - */ -class CV_EXPORTS_W Tonemap : public Algorithm -{ -public: - /** @brief Tonemaps image - - @param src source image - CV_32FC3 Mat (float 32 bits 3 channels) - @param dst destination image - CV_32FC3 Mat with values in [0, 1] range - */ - CV_WRAP virtual void process(InputArray src, OutputArray dst) = 0; - - CV_WRAP virtual float getGamma() const = 0; - CV_WRAP virtual void setGamma(float gamma) = 0; -}; - -/** @brief Creates simple linear mapper with gamma correction - -@param gamma positive value for gamma correction. Gamma value of 1.0 implies no correction, gamma -equal to 2.2f is suitable for most displays. -Generally gamma \> 1 brightens the image and gamma \< 1 darkens it. - */ -CV_EXPORTS_W Ptr createTonemap(float gamma = 1.0f); - -/** @brief Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in -logarithmic domain. - -Since it's a global operator the same function is applied to all the pixels, it is controlled by the -bias parameter. - -Optional saturation enhancement is possible as described in @cite FL02 . - -For more information see @cite DM03 . - */ -class CV_EXPORTS_W TonemapDrago : public Tonemap -{ -public: - - CV_WRAP virtual float getSaturation() const = 0; - CV_WRAP virtual void setSaturation(float saturation) = 0; - - CV_WRAP virtual float getBias() const = 0; - CV_WRAP virtual void setBias(float bias) = 0; -}; - -/** @brief Creates TonemapDrago object - -@param gamma gamma value for gamma correction. See createTonemap -@param saturation positive saturation enhancement value. 1.0 preserves saturation, values greater -than 1 increase saturation and values less than 1 decrease it. -@param bias value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best -results, default value is 0.85. - */ -CV_EXPORTS_W Ptr createTonemapDrago(float gamma = 1.0f, float saturation = 1.0f, float bias = 0.85f); - - -/** @brief This is a global tonemapping operator that models human visual system. - -Mapping function is controlled by adaptation parameter, that is computed using light adaptation and -color adaptation. - -For more information see @cite RD05 . - */ -class CV_EXPORTS_W TonemapReinhard : public Tonemap -{ -public: - CV_WRAP virtual float getIntensity() const = 0; - CV_WRAP virtual void setIntensity(float intensity) = 0; - - CV_WRAP virtual float getLightAdaptation() const = 0; - CV_WRAP virtual void setLightAdaptation(float light_adapt) = 0; - - CV_WRAP virtual float getColorAdaptation() const = 0; - CV_WRAP virtual void setColorAdaptation(float color_adapt) = 0; -}; - -/** @brief Creates TonemapReinhard object - -@param gamma gamma value for gamma correction. See createTonemap -@param intensity result intensity in [-8, 8] range. Greater intensity produces brighter results. -@param light_adapt light adaptation in [0, 1] range. If 1 adaptation is based only on pixel -value, if 0 it's global, otherwise it's a weighted mean of this two cases. -@param color_adapt chromatic adaptation in [0, 1] range. If 1 channels are treated independently, -if 0 adaptation level is the same for each channel. - */ -CV_EXPORTS_W Ptr -createTonemapReinhard(float gamma = 1.0f, float intensity = 0.0f, float light_adapt = 1.0f, float color_adapt = 0.0f); - -/** @brief This algorithm transforms image to contrast using gradients on all levels of gaussian pyramid, -transforms contrast values to HVS response and scales the response. After this the image is -reconstructed from new contrast values. - -For more information see @cite MM06 . - */ -class CV_EXPORTS_W TonemapMantiuk : public Tonemap -{ -public: - CV_WRAP virtual float getScale() const = 0; - CV_WRAP virtual void setScale(float scale) = 0; - - CV_WRAP virtual float getSaturation() const = 0; - CV_WRAP virtual void setSaturation(float saturation) = 0; -}; - -/** @brief Creates TonemapMantiuk object - -@param gamma gamma value for gamma correction. See createTonemap -@param scale contrast scale factor. HVS response is multiplied by this parameter, thus compressing -dynamic range. Values from 0.6 to 0.9 produce best results. -@param saturation saturation enhancement value. See createTonemapDrago - */ -CV_EXPORTS_W Ptr -createTonemapMantiuk(float gamma = 1.0f, float scale = 0.7f, float saturation = 1.0f); - -/** @brief The base class for algorithms that align images of the same scene with different exposures - */ -class CV_EXPORTS_W AlignExposures : public Algorithm -{ -public: - /** @brief Aligns images - - @param src vector of input images - @param dst vector of aligned images - @param times vector of exposure time values for each image - @param response 256x1 matrix with inverse camera response function for each pixel value, it should - have the same number of channels as images. - */ - CV_WRAP virtual void process(InputArrayOfArrays src, std::vector& dst, - InputArray times, InputArray response) = 0; -}; - -/** @brief This algorithm converts images to median threshold bitmaps (1 for pixels brighter than median -luminance and 0 otherwise) and than aligns the resulting bitmaps using bit operations. - -It is invariant to exposure, so exposure values and camera response are not necessary. - -In this implementation new image regions are filled with zeros. - -For more information see @cite GW03 . - */ -class CV_EXPORTS_W AlignMTB : public AlignExposures -{ -public: - CV_WRAP virtual void process(InputArrayOfArrays src, std::vector& dst, - InputArray times, InputArray response) CV_OVERRIDE = 0; - - /** @brief Short version of process, that doesn't take extra arguments. - - @param src vector of input images - @param dst vector of aligned images - */ - CV_WRAP virtual void process(InputArrayOfArrays src, std::vector& dst) = 0; - - /** @brief Calculates shift between two images, i. e. how to shift the second image to correspond it with the - first. - - @param img0 first image - @param img1 second image - */ - CV_WRAP virtual Point calculateShift(InputArray img0, InputArray img1) = 0; - /** @brief Helper function, that shift Mat filling new regions with zeros. - - @param src input image - @param dst result image - @param shift shift value - */ - CV_WRAP virtual void shiftMat(InputArray src, OutputArray dst, const Point shift) = 0; - /** @brief Computes median threshold and exclude bitmaps of given image. - - @param img input image - @param tb median threshold bitmap - @param eb exclude bitmap - */ - CV_WRAP virtual void computeBitmaps(InputArray img, OutputArray tb, OutputArray eb) = 0; - - CV_WRAP virtual int getMaxBits() const = 0; - CV_WRAP virtual void setMaxBits(int max_bits) = 0; - - CV_WRAP virtual int getExcludeRange() const = 0; - CV_WRAP virtual void setExcludeRange(int exclude_range) = 0; - - CV_WRAP virtual bool getCut() const = 0; - CV_WRAP virtual void setCut(bool value) = 0; -}; - -/** @brief Creates AlignMTB object - -@param max_bits logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are -usually good enough (31 and 63 pixels shift respectively). -@param exclude_range range for exclusion bitmap that is constructed to suppress noise around the -median value. -@param cut if true cuts images, otherwise fills the new regions with zeros. - */ -CV_EXPORTS_W Ptr createAlignMTB(int max_bits = 6, int exclude_range = 4, bool cut = true); - -/** @brief The base class for camera response calibration algorithms. - */ -class CV_EXPORTS_W CalibrateCRF : public Algorithm -{ -public: - /** @brief Recovers inverse camera response. - - @param src vector of input images - @param dst 256x1 matrix with inverse camera response function - @param times vector of exposure time values for each image - */ - CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0; -}; - -/** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective -function as linear system. Objective function is constructed using pixel values on the same position -in all images, extra term is added to make the result smoother. - -For more information see @cite DM97 . - */ -class CV_EXPORTS_W CalibrateDebevec : public CalibrateCRF -{ -public: - CV_WRAP virtual float getLambda() const = 0; - CV_WRAP virtual void setLambda(float lambda) = 0; - - CV_WRAP virtual int getSamples() const = 0; - CV_WRAP virtual void setSamples(int samples) = 0; - - CV_WRAP virtual bool getRandom() const = 0; - CV_WRAP virtual void setRandom(bool random) = 0; -}; - -/** @brief Creates CalibrateDebevec object - -@param samples number of pixel locations to use -@param lambda smoothness term weight. Greater values produce smoother results, but can alter the -response. -@param random if true sample pixel locations are chosen at random, otherwise they form a -rectangular grid. - */ -CV_EXPORTS_W Ptr createCalibrateDebevec(int samples = 70, float lambda = 10.0f, bool random = false); - -/** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective -function as linear system. This algorithm uses all image pixels. - -For more information see @cite RB99 . - */ -class CV_EXPORTS_W CalibrateRobertson : public CalibrateCRF -{ -public: - CV_WRAP virtual int getMaxIter() const = 0; - CV_WRAP virtual void setMaxIter(int max_iter) = 0; - - CV_WRAP virtual float getThreshold() const = 0; - CV_WRAP virtual void setThreshold(float threshold) = 0; - - CV_WRAP virtual Mat getRadiance() const = 0; -}; - -/** @brief Creates CalibrateRobertson object - -@param max_iter maximal number of Gauss-Seidel solver iterations. -@param threshold target difference between results of two successive steps of the minimization. - */ -CV_EXPORTS_W Ptr createCalibrateRobertson(int max_iter = 30, float threshold = 0.01f); - -/** @brief The base class algorithms that can merge exposure sequence to a single image. - */ -class CV_EXPORTS_W MergeExposures : public Algorithm -{ -public: - /** @brief Merges images. - - @param src vector of input images - @param dst result image - @param times vector of exposure time values for each image - @param response 256x1 matrix with inverse camera response function for each pixel value, it should - have the same number of channels as images. - */ - CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, - InputArray times, InputArray response) = 0; -}; - -/** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure -values and camera response. - -For more information see @cite DM97 . - */ -class CV_EXPORTS_W MergeDebevec : public MergeExposures -{ -public: - CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, - InputArray times, InputArray response) CV_OVERRIDE = 0; - CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0; -}; - -/** @brief Creates MergeDebevec object - */ -CV_EXPORTS_W Ptr createMergeDebevec(); - -/** @brief Pixels are weighted using contrast, saturation and well-exposedness measures, than images are -combined using laplacian pyramids. - -The resulting image weight is constructed as weighted average of contrast, saturation and -well-exposedness measures. - -The resulting image doesn't require tonemapping and can be converted to 8-bit image by multiplying -by 255, but it's recommended to apply gamma correction and/or linear tonemapping. - -For more information see @cite MK07 . - */ -class CV_EXPORTS_W MergeMertens : public MergeExposures -{ -public: - CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, - InputArray times, InputArray response) CV_OVERRIDE = 0; - /** @brief Short version of process, that doesn't take extra arguments. - - @param src vector of input images - @param dst result image - */ - CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst) = 0; - - CV_WRAP virtual float getContrastWeight() const = 0; - CV_WRAP virtual void setContrastWeight(float contrast_weiht) = 0; - - CV_WRAP virtual float getSaturationWeight() const = 0; - CV_WRAP virtual void setSaturationWeight(float saturation_weight) = 0; - - CV_WRAP virtual float getExposureWeight() const = 0; - CV_WRAP virtual void setExposureWeight(float exposure_weight) = 0; -}; - -/** @brief Creates MergeMertens object - -@param contrast_weight contrast measure weight. See MergeMertens. -@param saturation_weight saturation measure weight -@param exposure_weight well-exposedness measure weight - */ -CV_EXPORTS_W Ptr -createMergeMertens(float contrast_weight = 1.0f, float saturation_weight = 1.0f, float exposure_weight = 0.0f); - -/** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure -values and camera response. - -For more information see @cite RB99 . - */ -class CV_EXPORTS_W MergeRobertson : public MergeExposures -{ -public: - CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, - InputArray times, InputArray response) CV_OVERRIDE = 0; - CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0; -}; - -/** @brief Creates MergeRobertson object - */ -CV_EXPORTS_W Ptr createMergeRobertson(); - -//! @} photo_hdr - -//! @addtogroup photo_decolor -//! @{ - -/** @brief Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized -black-and-white photograph rendering, and in many single channel image processing applications -@cite CL12 . - -@param src Input 8-bit 3-channel image. -@param grayscale Output 8-bit 1-channel image. -@param color_boost Output 8-bit 3-channel image. - -This function is to be applied on color images. - */ -CV_EXPORTS_W void decolor( InputArray src, OutputArray grayscale, OutputArray color_boost); - -//! @} photo_decolor - -//! @addtogroup photo_clone -//! @{ - - -//! seamlessClone algorithm flags -enum -{ - /** The power of the method is fully expressed when inserting objects with complex outlines into a new background*/ - NORMAL_CLONE = 1, - /** The classic method, color-based selection and alpha masking might be time consuming and often leaves an undesirable - halo. Seamless cloning, even averaged with the original image, is not effective. Mixed seamless cloning based on a loose selection proves effective.*/ - MIXED_CLONE = 2, - /** Monochrome transfer allows the user to easily replace certain features of one object by alternative features.*/ - MONOCHROME_TRANSFER = 3}; - - -/** @example samples/cpp/tutorial_code/photo/seamless_cloning/cloning_demo.cpp -An example using seamlessClone function -*/ -/** @brief Image editing tasks concern either global changes (color/intensity corrections, filters, -deformations) or local changes concerned to a selection. Here we are interested in achieving local -changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless -manner. The extent of the changes ranges from slight distortions to complete replacement by novel -content @cite PM03 . - -@param src Input 8-bit 3-channel image. -@param dst Input 8-bit 3-channel image. -@param mask Input 8-bit 1 or 3-channel image. -@param p Point in dst image where object is placed. -@param blend Output image with the same size and type as dst. -@param flags Cloning method that could be cv::NORMAL_CLONE, cv::MIXED_CLONE or cv::MONOCHROME_TRANSFER - */ -CV_EXPORTS_W void seamlessClone( InputArray src, InputArray dst, InputArray mask, Point p, - OutputArray blend, int flags); - -/** @brief Given an original color image, two differently colored versions of this image can be mixed -seamlessly. - -@param src Input 8-bit 3-channel image. -@param mask Input 8-bit 1 or 3-channel image. -@param dst Output image with the same size and type as src . -@param red_mul R-channel multiply factor. -@param green_mul G-channel multiply factor. -@param blue_mul B-channel multiply factor. - -Multiplication factor is between .5 to 2.5. - */ -CV_EXPORTS_W void colorChange(InputArray src, InputArray mask, OutputArray dst, float red_mul = 1.0f, - float green_mul = 1.0f, float blue_mul = 1.0f); - -/** @brief Applying an appropriate non-linear transformation to the gradient field inside the selection and -then integrating back with a Poisson solver, modifies locally the apparent illumination of an image. - -@param src Input 8-bit 3-channel image. -@param mask Input 8-bit 1 or 3-channel image. -@param dst Output image with the same size and type as src. -@param alpha Value ranges between 0-2. -@param beta Value ranges between 0-2. - -This is useful to highlight under-exposed foreground objects or to reduce specular reflections. - */ -CV_EXPORTS_W void illuminationChange(InputArray src, InputArray mask, OutputArray dst, - float alpha = 0.2f, float beta = 0.4f); - -/** @brief By retaining only the gradients at edge locations, before integrating with the Poisson solver, one -washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge %Detector is used. - -@param src Input 8-bit 3-channel image. -@param mask Input 8-bit 1 or 3-channel image. -@param dst Output image with the same size and type as src. -@param low_threshold %Range from 0 to 100. -@param high_threshold Value \> 100. -@param kernel_size The size of the Sobel kernel to be used. - -@note -The algorithm assumes that the color of the source image is close to that of the destination. This -assumption means that when the colors don't match, the source image color gets tinted toward the -color of the destination image. - */ -CV_EXPORTS_W void textureFlattening(InputArray src, InputArray mask, OutputArray dst, - float low_threshold = 30, float high_threshold = 45, - int kernel_size = 3); - -//! @} photo_clone - -//! @addtogroup photo_render -//! @{ - -//! Edge preserving filters -enum -{ - RECURS_FILTER = 1, //!< Recursive Filtering - NORMCONV_FILTER = 2 //!< Normalized Convolution Filtering -}; - -/** @brief Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing -filters are used in many different applications @cite EM11 . - -@param src Input 8-bit 3-channel image. -@param dst Output 8-bit 3-channel image. -@param flags Edge preserving filters: cv::RECURS_FILTER or cv::NORMCONV_FILTER -@param sigma_s %Range between 0 to 200. -@param sigma_r %Range between 0 to 1. - */ -CV_EXPORTS_W void edgePreservingFilter(InputArray src, OutputArray dst, int flags = 1, - float sigma_s = 60, float sigma_r = 0.4f); - -/** @brief This filter enhances the details of a particular image. - -@param src Input 8-bit 3-channel image. -@param dst Output image with the same size and type as src. -@param sigma_s %Range between 0 to 200. -@param sigma_r %Range between 0 to 1. - */ -CV_EXPORTS_W void detailEnhance(InputArray src, OutputArray dst, float sigma_s = 10, - float sigma_r = 0.15f); - -/** @example samples/cpp/tutorial_code/photo/non_photorealistic_rendering/npr_demo.cpp -An example using non-photorealistic line drawing functions -*/ -/** @brief Pencil-like non-photorealistic line drawing - -@param src Input 8-bit 3-channel image. -@param dst1 Output 8-bit 1-channel image. -@param dst2 Output image with the same size and type as src. -@param sigma_s %Range between 0 to 200. -@param sigma_r %Range between 0 to 1. -@param shade_factor %Range between 0 to 0.1. - */ -CV_EXPORTS_W void pencilSketch(InputArray src, OutputArray dst1, OutputArray dst2, - float sigma_s = 60, float sigma_r = 0.07f, float shade_factor = 0.02f); - -/** @brief Stylization aims to produce digital imagery with a wide variety of effects not focused on -photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low -contrast while preserving, or enhancing, high-contrast features. - -@param src Input 8-bit 3-channel image. -@param dst Output image with the same size and type as src. -@param sigma_s %Range between 0 to 200. -@param sigma_r %Range between 0 to 1. - */ -CV_EXPORTS_W void stylization(InputArray src, OutputArray dst, float sigma_s = 60, - float sigma_r = 0.45f); - -//! @} photo_render - -//! @} photo - -} // cv - -#ifndef DISABLE_OPENCV_24_COMPATIBILITY -#include "opencv2/photo/photo_c.h" -#endif - -#endif diff --git a/include/opencv2/photo/cuda.hpp b/include/opencv2/photo/cuda.hpp deleted file mode 100755 index a2f3816..0000000 --- a/include/opencv2/photo/cuda.hpp +++ /dev/null @@ -1,132 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_PHOTO_CUDA_HPP -#define OPENCV_PHOTO_CUDA_HPP - -#include "opencv2/core/cuda.hpp" - -namespace cv { namespace cuda { - -//! @addtogroup photo_denoise -//! @{ - -/** @brief Performs pure non local means denoising without any simplification, and thus it is not fast. - -@param src Source image. Supports only CV_8UC1, CV_8UC2 and CV_8UC3. -@param dst Destination image. -@param h Filter sigma regulating filter strength for color. -@param search_window Size of search window. -@param block_size Size of block used for computing weights. -@param borderMode Border type. See borderInterpolate for details. BORDER_REFLECT101 , -BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now. -@param stream Stream for the asynchronous version. - -@sa - fastNlMeansDenoising - */ -CV_EXPORTS void nonLocalMeans(InputArray src, OutputArray dst, - float h, - int search_window = 21, - int block_size = 7, - int borderMode = BORDER_DEFAULT, - Stream& stream = Stream::Null()); - -/** @brief Perform image denoising using Non-local Means Denoising algorithm - with several computational -optimizations. Noise expected to be a gaussian white noise - -@param src Input 8-bit 1-channel, 2-channel or 3-channel image. -@param dst Output image with the same size and type as src . -@param h Parameter regulating filter strength. Big h value perfectly removes noise but also -removes image details, smaller h value preserves details but also preserves some noise -@param search_window Size in pixels of the window that is used to compute weighted average for -given pixel. Should be odd. Affect performance linearly: greater search_window - greater -denoising time. Recommended value 21 pixels -@param block_size Size in pixels of the template patch that is used to compute weights. Should be -odd. Recommended value 7 pixels -@param stream Stream for the asynchronous invocations. - -This function expected to be applied to grayscale images. For colored images look at -FastNonLocalMeansDenoising::labMethod. - -@sa - fastNlMeansDenoising - */ -CV_EXPORTS void fastNlMeansDenoising(InputArray src, OutputArray dst, - float h, - int search_window = 21, - int block_size = 7, - Stream& stream = Stream::Null()); - -/** @brief Modification of fastNlMeansDenoising function for colored images - -@param src Input 8-bit 3-channel image. -@param dst Output image with the same size and type as src . -@param h_luminance Parameter regulating filter strength. Big h value perfectly removes noise but -also removes image details, smaller h value preserves details but also preserves some noise -@param photo_render float The same as h but for color components. For most images value equals 10 will be -enough to remove colored noise and do not distort colors -@param search_window Size in pixels of the window that is used to compute weighted average for -given pixel. Should be odd. Affect performance linearly: greater search_window - greater -denoising time. Recommended value 21 pixels -@param block_size Size in pixels of the template patch that is used to compute weights. Should be -odd. Recommended value 7 pixels -@param stream Stream for the asynchronous invocations. - -The function converts image to CIELAB colorspace and then separately denoise L and AB components -with given h parameters using FastNonLocalMeansDenoising::simpleMethod function. - -@sa - fastNlMeansDenoisingColored - */ -CV_EXPORTS void fastNlMeansDenoisingColored(InputArray src, OutputArray dst, - float h_luminance, float photo_render, - int search_window = 21, - int block_size = 7, - Stream& stream = Stream::Null()); - -//! @} photo - -}} // namespace cv { namespace cuda { - -#endif /* OPENCV_PHOTO_CUDA_HPP */ diff --git a/include/opencv2/photo/photo.hpp b/include/opencv2/photo/photo.hpp deleted file mode 100755 index 8af5e9f..0000000 --- a/include/opencv2/photo/photo.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/photo.hpp" diff --git a/include/opencv2/photo/photo_c.h b/include/opencv2/photo/photo_c.h deleted file mode 100755 index cd623c1..0000000 --- a/include/opencv2/photo/photo_c.h +++ /dev/null @@ -1,74 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_PHOTO_C_H -#define OPENCV_PHOTO_C_H - -#include "opencv2/core/core_c.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @addtogroup photo_c - @{ - */ - -/* Inpainting algorithms */ -enum InpaintingModes -{ - CV_INPAINT_NS =0, - CV_INPAINT_TELEA =1 -}; - - -/* Inpaints the selected region in the image */ -CVAPI(void) cvInpaint( const CvArr* src, const CvArr* inpaint_mask, - CvArr* dst, double inpaintRange, int flags ); - -/** @} */ - -#ifdef __cplusplus -} //extern "C" -#endif - -#endif //OPENCV_PHOTO_C_H diff --git a/include/opencv2/video.hpp b/include/opencv2/video.hpp deleted file mode 100755 index aa644a9..0000000 --- a/include/opencv2/video.hpp +++ /dev/null @@ -1,63 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_VIDEO_HPP -#define OPENCV_VIDEO_HPP - -/** - @defgroup video Video Analysis - @{ - @defgroup video_motion Motion Analysis - @defgroup video_track Object Tracking - @defgroup video_c C API - @} -*/ - -#include "opencv2/video/tracking.hpp" -#include "opencv2/video/background_segm.hpp" - -#ifndef DISABLE_OPENCV_24_COMPATIBILITY -#include "opencv2/video/tracking_c.h" -#endif - -#endif //OPENCV_VIDEO_HPP diff --git a/include/opencv2/video/background_segm.hpp b/include/opencv2/video/background_segm.hpp deleted file mode 100755 index e1dfa15..0000000 --- a/include/opencv2/video/background_segm.hpp +++ /dev/null @@ -1,317 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_BACKGROUND_SEGM_HPP -#define OPENCV_BACKGROUND_SEGM_HPP - -#include "opencv2/core.hpp" - -namespace cv -{ - -//! @addtogroup video_motion -//! @{ - -/** @brief Base class for background/foreground segmentation. : - -The class is only used to define the common interface for the whole family of background/foreground -segmentation algorithms. - */ -class CV_EXPORTS_W BackgroundSubtractor : public Algorithm -{ -public: - /** @brief Computes a foreground mask. - - @param image Next video frame. - @param fgmask The output foreground mask as an 8-bit binary image. - @param learningRate The value between 0 and 1 that indicates how fast the background model is - learnt. Negative parameter value makes the algorithm to use some automatically chosen learning - rate. 0 means that the background model is not updated at all, 1 means that the background model - is completely reinitialized from the last frame. - */ - CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) = 0; - - /** @brief Computes a background image. - - @param backgroundImage The output background image. - - @note Sometimes the background image can be very blurry, as it contain the average background - statistics. - */ - CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const = 0; -}; - - -/** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm. - -The class implements the Gaussian mixture model background subtraction described in @cite Zivkovic2004 -and @cite Zivkovic2006 . - */ -class CV_EXPORTS_W BackgroundSubtractorMOG2 : public BackgroundSubtractor -{ -public: - /** @brief Returns the number of last frames that affect the background model - */ - CV_WRAP virtual int getHistory() const = 0; - /** @brief Sets the number of last frames that affect the background model - */ - CV_WRAP virtual void setHistory(int history) = 0; - - /** @brief Returns the number of gaussian components in the background model - */ - CV_WRAP virtual int getNMixtures() const = 0; - /** @brief Sets the number of gaussian components in the background model. - - The model needs to be reinitalized to reserve memory. - */ - CV_WRAP virtual void setNMixtures(int nmixtures) = 0;//needs reinitialization! - - /** @brief Returns the "background ratio" parameter of the algorithm - - If a foreground pixel keeps semi-constant value for about backgroundRatio\*history frames, it's - considered background and added to the model as a center of a new component. It corresponds to TB - parameter in the paper. - */ - CV_WRAP virtual double getBackgroundRatio() const = 0; - /** @brief Sets the "background ratio" parameter of the algorithm - */ - CV_WRAP virtual void setBackgroundRatio(double ratio) = 0; - - /** @brief Returns the variance threshold for the pixel-model match - - The main threshold on the squared Mahalanobis distance to decide if the sample is well described by - the background model or not. Related to Cthr from the paper. - */ - CV_WRAP virtual double getVarThreshold() const = 0; - /** @brief Sets the variance threshold for the pixel-model match - */ - CV_WRAP virtual void setVarThreshold(double varThreshold) = 0; - - /** @brief Returns the variance threshold for the pixel-model match used for new mixture component generation - - Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the - existing components (corresponds to Tg in the paper). If a pixel is not close to any component, it - is considered foreground or added as a new component. 3 sigma =\> Tg=3\*3=9 is default. A smaller Tg - value generates more components. A higher Tg value may result in a small number of components but - they can grow too large. - */ - CV_WRAP virtual double getVarThresholdGen() const = 0; - /** @brief Sets the variance threshold for the pixel-model match used for new mixture component generation - */ - CV_WRAP virtual void setVarThresholdGen(double varThresholdGen) = 0; - - /** @brief Returns the initial variance of each gaussian component - */ - CV_WRAP virtual double getVarInit() const = 0; - /** @brief Sets the initial variance of each gaussian component - */ - CV_WRAP virtual void setVarInit(double varInit) = 0; - - CV_WRAP virtual double getVarMin() const = 0; - CV_WRAP virtual void setVarMin(double varMin) = 0; - - CV_WRAP virtual double getVarMax() const = 0; - CV_WRAP virtual void setVarMax(double varMax) = 0; - - /** @brief Returns the complexity reduction threshold - - This parameter defines the number of samples needed to accept to prove the component exists. CT=0.05 - is a default value for all the samples. By setting CT=0 you get an algorithm very similar to the - standard Stauffer&Grimson algorithm. - */ - CV_WRAP virtual double getComplexityReductionThreshold() const = 0; - /** @brief Sets the complexity reduction threshold - */ - CV_WRAP virtual void setComplexityReductionThreshold(double ct) = 0; - - /** @brief Returns the shadow detection flag - - If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for - details. - */ - CV_WRAP virtual bool getDetectShadows() const = 0; - /** @brief Enables or disables shadow detection - */ - CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0; - - /** @brief Returns the shadow value - - Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0 - in the mask always means background, 255 means foreground. - */ - CV_WRAP virtual int getShadowValue() const = 0; - /** @brief Sets the shadow value - */ - CV_WRAP virtual void setShadowValue(int value) = 0; - - /** @brief Returns the shadow threshold - - A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in - the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel - is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiara, - *Detecting Moving Shadows...*, IEEE PAMI,2003. - */ - CV_WRAP virtual double getShadowThreshold() const = 0; - /** @brief Sets the shadow threshold - */ - CV_WRAP virtual void setShadowThreshold(double threshold) = 0; - - /** @brief Computes a foreground mask. - - @param image Next video frame. Floating point frame will be used without scaling and should be in range \f$[0,255]\f$. - @param fgmask The output foreground mask as an 8-bit binary image. - @param learningRate The value between 0 and 1 that indicates how fast the background model is - learnt. Negative parameter value makes the algorithm to use some automatically chosen learning - rate. 0 means that the background model is not updated at all, 1 means that the background model - is completely reinitialized from the last frame. - */ - CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0; -}; - -/** @brief Creates MOG2 Background Subtractor - -@param history Length of the history. -@param varThreshold Threshold on the squared Mahalanobis distance between the pixel and the model -to decide whether a pixel is well described by the background model. This parameter does not -affect the background update. -@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the -speed a bit, so if you do not need this feature, set the parameter to false. - */ -CV_EXPORTS_W Ptr - createBackgroundSubtractorMOG2(int history=500, double varThreshold=16, - bool detectShadows=true); - -/** @brief K-nearest neighbours - based Background/Foreground Segmentation Algorithm. - -The class implements the K-nearest neighbours background subtraction described in @cite Zivkovic2006 . -Very efficient if number of foreground pixels is low. - */ -class CV_EXPORTS_W BackgroundSubtractorKNN : public BackgroundSubtractor -{ -public: - /** @brief Returns the number of last frames that affect the background model - */ - CV_WRAP virtual int getHistory() const = 0; - /** @brief Sets the number of last frames that affect the background model - */ - CV_WRAP virtual void setHistory(int history) = 0; - - /** @brief Returns the number of data samples in the background model - */ - CV_WRAP virtual int getNSamples() const = 0; - /** @brief Sets the number of data samples in the background model. - - The model needs to be reinitalized to reserve memory. - */ - CV_WRAP virtual void setNSamples(int _nN) = 0;//needs reinitialization! - - /** @brief Returns the threshold on the squared distance between the pixel and the sample - - The threshold on the squared distance between the pixel and the sample to decide whether a pixel is - close to a data sample. - */ - CV_WRAP virtual double getDist2Threshold() const = 0; - /** @brief Sets the threshold on the squared distance - */ - CV_WRAP virtual void setDist2Threshold(double _dist2Threshold) = 0; - - /** @brief Returns the number of neighbours, the k in the kNN. - - K is the number of samples that need to be within dist2Threshold in order to decide that that - pixel is matching the kNN background model. - */ - CV_WRAP virtual int getkNNSamples() const = 0; - /** @brief Sets the k in the kNN. How many nearest neighbours need to match. - */ - CV_WRAP virtual void setkNNSamples(int _nkNN) = 0; - - /** @brief Returns the shadow detection flag - - If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorKNN for - details. - */ - CV_WRAP virtual bool getDetectShadows() const = 0; - /** @brief Enables or disables shadow detection - */ - CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0; - - /** @brief Returns the shadow value - - Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0 - in the mask always means background, 255 means foreground. - */ - CV_WRAP virtual int getShadowValue() const = 0; - /** @brief Sets the shadow value - */ - CV_WRAP virtual void setShadowValue(int value) = 0; - - /** @brief Returns the shadow threshold - - A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in - the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel - is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiara, - *Detecting Moving Shadows...*, IEEE PAMI,2003. - */ - CV_WRAP virtual double getShadowThreshold() const = 0; - /** @brief Sets the shadow threshold - */ - CV_WRAP virtual void setShadowThreshold(double threshold) = 0; -}; - -/** @brief Creates KNN Background Subtractor - -@param history Length of the history. -@param dist2Threshold Threshold on the squared distance between the pixel and the sample to decide -whether a pixel is close to that sample. This parameter does not affect the background update. -@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the -speed a bit, so if you do not need this feature, set the parameter to false. - */ -CV_EXPORTS_W Ptr - createBackgroundSubtractorKNN(int history=500, double dist2Threshold=400.0, - bool detectShadows=true); - -//! @} video_motion - -} // cv - -#endif diff --git a/include/opencv2/video/tracking.hpp b/include/opencv2/video/tracking.hpp deleted file mode 100755 index 9f344be..0000000 --- a/include/opencv2/video/tracking.hpp +++ /dev/null @@ -1,654 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_TRACKING_HPP -#define OPENCV_TRACKING_HPP - -#include "opencv2/core.hpp" -#include "opencv2/imgproc.hpp" - -namespace cv -{ - -//! @addtogroup video_track -//! @{ - -enum { OPTFLOW_USE_INITIAL_FLOW = 4, - OPTFLOW_LK_GET_MIN_EIGENVALS = 8, - OPTFLOW_FARNEBACK_GAUSSIAN = 256 - }; - -/** @brief Finds an object center, size, and orientation. - -@param probImage Back projection of the object histogram. See calcBackProject. -@param window Initial search window. -@param criteria Stop criteria for the underlying meanShift. -returns -(in old interfaces) Number of iterations CAMSHIFT took to converge -The function implements the CAMSHIFT object tracking algorithm @cite Bradski98 . First, it finds an -object center using meanShift and then adjusts the window size and finds the optimal rotation. The -function returns the rotated rectangle structure that includes the object position, size, and -orientation. The next position of the search window can be obtained with RotatedRect::boundingRect() - -See the OpenCV sample camshiftdemo.c that tracks colored objects. - -@note -- (Python) A sample explaining the camshift tracking algorithm can be found at - opencv_source_code/samples/python/camshift.py - */ -CV_EXPORTS_W RotatedRect CamShift( InputArray probImage, CV_IN_OUT Rect& window, - TermCriteria criteria ); -/** @example samples/cpp/camshiftdemo.cpp -An example using the mean-shift tracking algorithm -*/ - -/** @brief Finds an object on a back projection image. - -@param probImage Back projection of the object histogram. See calcBackProject for details. -@param window Initial search window. -@param criteria Stop criteria for the iterative search algorithm. -returns -: Number of iterations CAMSHIFT took to converge. -The function implements the iterative object search algorithm. It takes the input back projection of -an object and the initial position. The mass center in window of the back projection image is -computed and the search window center shifts to the mass center. The procedure is repeated until the -specified number of iterations criteria.maxCount is done or until the window center shifts by less -than criteria.epsilon. The algorithm is used inside CamShift and, unlike CamShift , the search -window size or orientation do not change during the search. You can simply pass the output of -calcBackProject to this function. But better results can be obtained if you pre-filter the back -projection and remove the noise. For example, you can do this by retrieving connected components -with findContours , throwing away contours with small area ( contourArea ), and rendering the -remaining contours with drawContours. - - */ -CV_EXPORTS_W int meanShift( InputArray probImage, CV_IN_OUT Rect& window, TermCriteria criteria ); - -/** @brief Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK. - -@param img 8-bit input image. -@param pyramid output pyramid. -@param winSize window size of optical flow algorithm. Must be not less than winSize argument of -calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels. -@param maxLevel 0-based maximal pyramid level number. -@param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is -constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. -@param pyrBorder the border mode for pyramid layers. -@param derivBorder the border mode for gradients. -@param tryReuseInputImage put ROI of input image into the pyramid if possible. You can pass false -to force data copying. -@return number of levels in constructed pyramid. Can be less than maxLevel. - */ -CV_EXPORTS_W int buildOpticalFlowPyramid( InputArray img, OutputArrayOfArrays pyramid, - Size winSize, int maxLevel, bool withDerivatives = true, - int pyrBorder = BORDER_REFLECT_101, - int derivBorder = BORDER_CONSTANT, - bool tryReuseInputImage = true ); - -/** @example samples/cpp/lkdemo.cpp -An example using the Lucas-Kanade optical flow algorithm -*/ - -/** @brief Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with -pyramids. - -@param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid. -@param nextImg second input image or pyramid of the same size and the same type as prevImg. -@param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be -single-precision floating-point numbers. -@param nextPts output vector of 2D points (with single-precision floating-point coordinates) -containing the calculated new positions of input features in the second image; when -OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input. -@param status output status vector (of unsigned chars); each element of the vector is set to 1 if -the flow for the corresponding features has been found, otherwise, it is set to 0. -@param err output vector of errors; each element of the vector is set to an error for the -corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't -found then the error is not defined (use the status parameter to find such cases). -@param winSize size of the search window at each pyramid level. -@param maxLevel 0-based maximal pyramid level number; if set to 0, pyramids are not used (single -level), if set to 1, two levels are used, and so on; if pyramids are passed to input then -algorithm will use as many levels as pyramids have but no more than maxLevel. -@param criteria parameter, specifying the termination criteria of the iterative search algorithm -(after the specified maximum number of iterations criteria.maxCount or when the search window -moves by less than criteria.epsilon. -@param flags operation flags: - - **OPTFLOW_USE_INITIAL_FLOW** uses initial estimations, stored in nextPts; if the flag is - not set, then prevPts is copied to nextPts and is considered the initial estimate. - - **OPTFLOW_LK_GET_MIN_EIGENVALS** use minimum eigen values as an error measure (see - minEigThreshold description); if the flag is not set, then L1 distance between patches - around the original and a moved point, divided by number of pixels in a window, is used as a - error measure. -@param minEigThreshold the algorithm calculates the minimum eigen value of a 2x2 normal matrix of -optical flow equations (this matrix is called a spatial gradient matrix in @cite Bouguet00), divided -by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding -feature is filtered out and its flow is not processed, so it allows to remove bad points and get a -performance boost. - -The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See -@cite Bouguet00 . The function is parallelized with the TBB library. - -@note - -- An example using the Lucas-Kanade optical flow algorithm can be found at - opencv_source_code/samples/cpp/lkdemo.cpp -- (Python) An example using the Lucas-Kanade optical flow algorithm can be found at - opencv_source_code/samples/python/lk_track.py -- (Python) An example using the Lucas-Kanade tracker for homography matching can be found at - opencv_source_code/samples/python/lk_homography.py - */ -CV_EXPORTS_W void calcOpticalFlowPyrLK( InputArray prevImg, InputArray nextImg, - InputArray prevPts, InputOutputArray nextPts, - OutputArray status, OutputArray err, - Size winSize = Size(21,21), int maxLevel = 3, - TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), - int flags = 0, double minEigThreshold = 1e-4 ); - -/** @brief Computes a dense optical flow using the Gunnar Farneback's algorithm. - -@param prev first 8-bit single-channel input image. -@param next second input image of the same size and the same type as prev. -@param flow computed flow image that has the same size as prev and type CV_32FC2. -@param pyr_scale parameter, specifying the image scale (\<1) to build pyramids for each image; -pyr_scale=0.5 means a classical pyramid, where each next layer is twice smaller than the previous -one. -@param levels number of pyramid layers including the initial image; levels=1 means that no extra -layers are created and only the original images are used. -@param winsize averaging window size; larger values increase the algorithm robustness to image -noise and give more chances for fast motion detection, but yield more blurred motion field. -@param iterations number of iterations the algorithm does at each pyramid level. -@param poly_n size of the pixel neighborhood used to find polynomial expansion in each pixel; -larger values mean that the image will be approximated with smoother surfaces, yielding more -robust algorithm and more blurred motion field, typically poly_n =5 or 7. -@param poly_sigma standard deviation of the Gaussian that is used to smooth derivatives used as a -basis for the polynomial expansion; for poly_n=5, you can set poly_sigma=1.1, for poly_n=7, a -good value would be poly_sigma=1.5. -@param flags operation flags that can be a combination of the following: - - **OPTFLOW_USE_INITIAL_FLOW** uses the input flow as an initial flow approximation. - - **OPTFLOW_FARNEBACK_GAUSSIAN** uses the Gaussian \f$\texttt{winsize}\times\texttt{winsize}\f$ - filter instead of a box filter of the same size for optical flow estimation; usually, this - option gives z more accurate flow than with a box filter, at the cost of lower speed; - normally, winsize for a Gaussian window should be set to a larger value to achieve the same - level of robustness. - -The function finds an optical flow for each prev pixel using the @cite Farneback2003 algorithm so that - -\f[\texttt{prev} (y,x) \sim \texttt{next} ( y + \texttt{flow} (y,x)[1], x + \texttt{flow} (y,x)[0])\f] - -@note - -- An example using the optical flow algorithm described by Gunnar Farneback can be found at - opencv_source_code/samples/cpp/fback.cpp -- (Python) An example using the optical flow algorithm described by Gunnar Farneback can be - found at opencv_source_code/samples/python/opt_flow.py - */ -CV_EXPORTS_W void calcOpticalFlowFarneback( InputArray prev, InputArray next, InputOutputArray flow, - double pyr_scale, int levels, int winsize, - int iterations, int poly_n, double poly_sigma, - int flags ); - -/** @brief Computes an optimal affine transformation between two 2D point sets. - -@param src First input 2D point set stored in std::vector or Mat, or an image stored in Mat. -@param dst Second input 2D point set of the same size and the same type as A, or another image. -@param fullAffine If true, the function finds an optimal affine transformation with no additional -restrictions (6 degrees of freedom). Otherwise, the class of transformations to choose from is -limited to combinations of translation, rotation, and uniform scaling (4 degrees of freedom). - -The function finds an optimal affine transform *[A|b]* (a 2 x 3 floating-point matrix) that -approximates best the affine transformation between: - -* Two point sets -* Two raster images. In this case, the function first finds some features in the src image and - finds the corresponding features in dst image. After that, the problem is reduced to the first - case. -In case of point sets, the problem is formulated as follows: you need to find a 2x2 matrix *A* and -2x1 vector *b* so that: - -\f[[A^*|b^*] = arg \min _{[A|b]} \sum _i \| \texttt{dst}[i] - A { \texttt{src}[i]}^T - b \| ^2\f] -where src[i] and dst[i] are the i-th points in src and dst, respectively -\f$[A|b]\f$ can be either arbitrary (when fullAffine=true ) or have a form of -\f[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ -a_{12} & a_{11} & b_2 \end{bmatrix}\f] -when fullAffine=false. - -@sa -estimateAffine2D, estimateAffinePartial2D, getAffineTransform, getPerspectiveTransform, findHomography - */ -CV_EXPORTS_W Mat estimateRigidTransform( InputArray src, InputArray dst, bool fullAffine); -CV_EXPORTS_W Mat estimateRigidTransform( InputArray src, InputArray dst, bool fullAffine, int ransacMaxIters, double ransacGoodRatio, - int ransacSize0); - - -enum -{ - MOTION_TRANSLATION = 0, - MOTION_EUCLIDEAN = 1, - MOTION_AFFINE = 2, - MOTION_HOMOGRAPHY = 3 -}; - -/** @brief Computes the Enhanced Correlation Coefficient value between two images @cite EP08 . - -@param templateImage single-channel template image; CV_8U or CV_32F array. -@param inputImage single-channel input image to be warped to provide an image similar to - templateImage, same type as templateImage. -@param inputMask An optional mask to indicate valid values of inputImage. - -@sa -findTransformECC - */ - -CV_EXPORTS_W double computeECC(InputArray templateImage, InputArray inputImage, InputArray inputMask = noArray()); - -/** @example samples/cpp/image_alignment.cpp -An example using the image alignment ECC algorithm -*/ - -/** @brief Finds the geometric transform (warp) between two images in terms of the ECC criterion @cite EP08 . - -@param templateImage single-channel template image; CV_8U or CV_32F array. -@param inputImage single-channel input image which should be warped with the final warpMatrix in -order to provide an image similar to templateImage, same type as templateImage. -@param warpMatrix floating-point \f$2\times 3\f$ or \f$3\times 3\f$ mapping matrix (warp). -@param motionType parameter, specifying the type of motion: - - **MOTION_TRANSLATION** sets a translational motion model; warpMatrix is \f$2\times 3\f$ with - the first \f$2\times 2\f$ part being the unity matrix and the rest two parameters being - estimated. - - **MOTION_EUCLIDEAN** sets a Euclidean (rigid) transformation as motion model; three - parameters are estimated; warpMatrix is \f$2\times 3\f$. - - **MOTION_AFFINE** sets an affine motion model (DEFAULT); six parameters are estimated; - warpMatrix is \f$2\times 3\f$. - - **MOTION_HOMOGRAPHY** sets a homography as a motion model; eight parameters are - estimated;\`warpMatrix\` is \f$3\times 3\f$. -@param criteria parameter, specifying the termination criteria of the ECC algorithm; -criteria.epsilon defines the threshold of the increment in the correlation coefficient between two -iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion). -Default values are shown in the declaration above. -@param inputMask An optional mask to indicate valid values of inputImage. -@param gaussFiltSize An optional value indicating size of gaussian blur filter; (DEFAULT: 5) - -The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion -(@cite EP08), that is - -\f[\texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\f] - -where - -\f[\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}\f] - -(the equation holds with homogeneous coordinates for homography). It returns the final enhanced -correlation coefficient, that is the correlation coefficient between the template image and the -final warped input image. When a \f$3\times 3\f$ matrix is given with motionType =0, 1 or 2, the third -row is ignored. - -Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an -area-based alignment that builds on intensity similarities. In essence, the function updates the -initial transformation that roughly aligns the images. If this information is missing, the identity -warp (unity matrix) is used as an initialization. Note that if images undergo strong -displacements/rotations, an initial transformation that roughly aligns the images is necessary -(e.g., a simple euclidean/similarity transform that allows for the images showing the same image -content approximately). Use inverse warping in the second image to take an image close to the first -one, i.e. use the flag WARP_INVERSE_MAP with warpAffine or warpPerspective. See also the OpenCV -sample image_alignment.cpp that demonstrates the use of the function. Note that the function throws -an exception if algorithm does not converges. - -@sa -computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography - */ -CV_EXPORTS_W double findTransformECC( InputArray templateImage, InputArray inputImage, - InputOutputArray warpMatrix, int motionType, - TermCriteria criteria, - InputArray inputMask, int gaussFiltSize); - -/** @overload */ -CV_EXPORTS -double findTransformECC(InputArray templateImage, InputArray inputImage, - InputOutputArray warpMatrix, int motionType = MOTION_AFFINE, - TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001), - InputArray inputMask = noArray()); - -/** @example samples/cpp/kalman.cpp -An example using the standard Kalman filter -*/ - -/** @brief Kalman filter class. - -The class implements a standard Kalman filter , -@cite Welch95 . However, you can modify transitionMatrix, controlMatrix, and measurementMatrix to get -an extended Kalman filter functionality. -@note In C API when CvKalman\* kalmanFilter structure is not needed anymore, it should be released -with cvReleaseKalman(&kalmanFilter) - */ -class CV_EXPORTS_W KalmanFilter -{ -public: - CV_WRAP KalmanFilter(); - /** @overload - @param dynamParams Dimensionality of the state. - @param measureParams Dimensionality of the measurement. - @param controlParams Dimensionality of the control vector. - @param type Type of the created matrices that should be CV_32F or CV_64F. - */ - CV_WRAP KalmanFilter( int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F ); - - /** @brief Re-initializes Kalman filter. The previous content is destroyed. - - @param dynamParams Dimensionality of the state. - @param measureParams Dimensionality of the measurement. - @param controlParams Dimensionality of the control vector. - @param type Type of the created matrices that should be CV_32F or CV_64F. - */ - void init( int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F ); - - /** @brief Computes a predicted state. - - @param control The optional input control - */ - CV_WRAP const Mat& predict( const Mat& control = Mat() ); - - /** @brief Updates the predicted state from the measurement. - - @param measurement The measured system parameters - */ - CV_WRAP const Mat& correct( const Mat& measurement ); - - CV_PROP_RW Mat statePre; //!< predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k) - CV_PROP_RW Mat statePost; //!< corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) - CV_PROP_RW Mat transitionMatrix; //!< state transition matrix (A) - CV_PROP_RW Mat controlMatrix; //!< control matrix (B) (not used if there is no control) - CV_PROP_RW Mat measurementMatrix; //!< measurement matrix (H) - CV_PROP_RW Mat processNoiseCov; //!< process noise covariance matrix (Q) - CV_PROP_RW Mat measurementNoiseCov;//!< measurement noise covariance matrix (R) - CV_PROP_RW Mat errorCovPre; //!< priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/ - CV_PROP_RW Mat gain; //!< Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R) - CV_PROP_RW Mat errorCovPost; //!< posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k) - - // temporary matrices - Mat temp1; - Mat temp2; - Mat temp3; - Mat temp4; - Mat temp5; -}; - - -class CV_EXPORTS_W DenseOpticalFlow : public Algorithm -{ -public: - /** @brief Calculates an optical flow. - - @param I0 first 8-bit single-channel input image. - @param I1 second input image of the same size and the same type as prev. - @param flow computed flow image that has the same size as prev and type CV_32FC2. - */ - CV_WRAP virtual void calc( InputArray I0, InputArray I1, InputOutputArray flow ) = 0; - /** @brief Releases all inner buffers. - */ - CV_WRAP virtual void collectGarbage() = 0; -}; - -/** @brief Base interface for sparse optical flow algorithms. - */ -class CV_EXPORTS_W SparseOpticalFlow : public Algorithm -{ -public: - /** @brief Calculates a sparse optical flow. - - @param prevImg First input image. - @param nextImg Second input image of the same size and the same type as prevImg. - @param prevPts Vector of 2D points for which the flow needs to be found. - @param nextPts Output vector of 2D points containing the calculated new positions of input features in the second image. - @param status Output status vector. Each element of the vector is set to 1 if the - flow for the corresponding features has been found. Otherwise, it is set to 0. - @param err Optional output vector that contains error response for each point (inverse confidence). - */ - CV_WRAP virtual void calc(InputArray prevImg, InputArray nextImg, - InputArray prevPts, InputOutputArray nextPts, - OutputArray status, - OutputArray err = cv::noArray()) = 0; -}; - -/** @brief "Dual TV L1" Optical Flow Algorithm. - -The class implements the "Dual TV L1" optical flow algorithm described in @cite Zach2007 and -@cite Javier2012 . -Here are important members of the class that control the algorithm, which you can set after -constructing the class instance: - -- member double tau - Time step of the numerical scheme. - -- member double lambda - Weight parameter for the data term, attachment parameter. This is the most relevant - parameter, which determines the smoothness of the output. The smaller this parameter is, - the smoother the solutions we obtain. It depends on the range of motions of the images, so - its value should be adapted to each image sequence. - -- member double theta - Weight parameter for (u - v)\^2, tightness parameter. It serves as a link between the - attachment and the regularization terms. In theory, it should have a small value in order - to maintain both parts in correspondence. The method is stable for a large range of values - of this parameter. - -- member int nscales - Number of scales used to create the pyramid of images. - -- member int warps - Number of warpings per scale. Represents the number of times that I1(x+u0) and grad( - I1(x+u0) ) are computed per scale. This is a parameter that assures the stability of the - method. It also affects the running time, so it is a compromise between speed and - accuracy. - -- member double epsilon - Stopping criterion threshold used in the numerical scheme, which is a trade-off between - precision and running time. A small value will yield more accurate solutions at the - expense of a slower convergence. - -- member int iterations - Stopping criterion iterations number used in the numerical scheme. - -C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow". -Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation". -*/ -class CV_EXPORTS_W DualTVL1OpticalFlow : public DenseOpticalFlow -{ -public: - //! @brief Time step of the numerical scheme - /** @see setTau */ - CV_WRAP virtual double getTau() const = 0; - /** @copybrief getTau @see getTau */ - CV_WRAP virtual void setTau(double val) = 0; - //! @brief Weight parameter for the data term, attachment parameter - /** @see setLambda */ - CV_WRAP virtual double getLambda() const = 0; - /** @copybrief getLambda @see getLambda */ - CV_WRAP virtual void setLambda(double val) = 0; - //! @brief Weight parameter for (u - v)^2, tightness parameter - /** @see setTheta */ - CV_WRAP virtual double getTheta() const = 0; - /** @copybrief getTheta @see getTheta */ - CV_WRAP virtual void setTheta(double val) = 0; - //! @brief coefficient for additional illumination variation term - /** @see setGamma */ - CV_WRAP virtual double getGamma() const = 0; - /** @copybrief getGamma @see getGamma */ - CV_WRAP virtual void setGamma(double val) = 0; - //! @brief Number of scales used to create the pyramid of images - /** @see setScalesNumber */ - CV_WRAP virtual int getScalesNumber() const = 0; - /** @copybrief getScalesNumber @see getScalesNumber */ - CV_WRAP virtual void setScalesNumber(int val) = 0; - //! @brief Number of warpings per scale - /** @see setWarpingsNumber */ - CV_WRAP virtual int getWarpingsNumber() const = 0; - /** @copybrief getWarpingsNumber @see getWarpingsNumber */ - CV_WRAP virtual void setWarpingsNumber(int val) = 0; - //! @brief Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time - /** @see setEpsilon */ - CV_WRAP virtual double getEpsilon() const = 0; - /** @copybrief getEpsilon @see getEpsilon */ - CV_WRAP virtual void setEpsilon(double val) = 0; - //! @brief Inner iterations (between outlier filtering) used in the numerical scheme - /** @see setInnerIterations */ - CV_WRAP virtual int getInnerIterations() const = 0; - /** @copybrief getInnerIterations @see getInnerIterations */ - CV_WRAP virtual void setInnerIterations(int val) = 0; - //! @brief Outer iterations (number of inner loops) used in the numerical scheme - /** @see setOuterIterations */ - CV_WRAP virtual int getOuterIterations() const = 0; - /** @copybrief getOuterIterations @see getOuterIterations */ - CV_WRAP virtual void setOuterIterations(int val) = 0; - //! @brief Use initial flow - /** @see setUseInitialFlow */ - CV_WRAP virtual bool getUseInitialFlow() const = 0; - /** @copybrief getUseInitialFlow @see getUseInitialFlow */ - CV_WRAP virtual void setUseInitialFlow(bool val) = 0; - //! @brief Step between scales (<1) - /** @see setScaleStep */ - CV_WRAP virtual double getScaleStep() const = 0; - /** @copybrief getScaleStep @see getScaleStep */ - CV_WRAP virtual void setScaleStep(double val) = 0; - //! @brief Median filter kernel size (1 = no filter) (3 or 5) - /** @see setMedianFiltering */ - CV_WRAP virtual int getMedianFiltering() const = 0; - /** @copybrief getMedianFiltering @see getMedianFiltering */ - CV_WRAP virtual void setMedianFiltering(int val) = 0; - - /** @brief Creates instance of cv::DualTVL1OpticalFlow*/ - CV_WRAP static Ptr create( - double tau = 0.25, - double lambda = 0.15, - double theta = 0.3, - int nscales = 5, - int warps = 5, - double epsilon = 0.01, - int innnerIterations = 30, - int outerIterations = 10, - double scaleStep = 0.8, - double gamma = 0.0, - int medianFiltering = 5, - bool useInitialFlow = false); -}; - -/** @brief Creates instance of cv::DenseOpticalFlow -*/ -CV_EXPORTS_W Ptr createOptFlow_DualTVL1(); - -/** @brief Class computing a dense optical flow using the Gunnar Farneback's algorithm. - */ -class CV_EXPORTS_W FarnebackOpticalFlow : public DenseOpticalFlow -{ -public: - CV_WRAP virtual int getNumLevels() const = 0; - CV_WRAP virtual void setNumLevels(int numLevels) = 0; - - CV_WRAP virtual double getPyrScale() const = 0; - CV_WRAP virtual void setPyrScale(double pyrScale) = 0; - - CV_WRAP virtual bool getFastPyramids() const = 0; - CV_WRAP virtual void setFastPyramids(bool fastPyramids) = 0; - - CV_WRAP virtual int getWinSize() const = 0; - CV_WRAP virtual void setWinSize(int winSize) = 0; - - CV_WRAP virtual int getNumIters() const = 0; - CV_WRAP virtual void setNumIters(int numIters) = 0; - - CV_WRAP virtual int getPolyN() const = 0; - CV_WRAP virtual void setPolyN(int polyN) = 0; - - CV_WRAP virtual double getPolySigma() const = 0; - CV_WRAP virtual void setPolySigma(double polySigma) = 0; - - CV_WRAP virtual int getFlags() const = 0; - CV_WRAP virtual void setFlags(int flags) = 0; - - CV_WRAP static Ptr create( - int numLevels = 5, - double pyrScale = 0.5, - bool fastPyramids = false, - int winSize = 13, - int numIters = 10, - int polyN = 5, - double polySigma = 1.1, - int flags = 0); -}; - - -/** @brief Class used for calculating a sparse optical flow. - -The class can calculate an optical flow for a sparse feature set using the -iterative Lucas-Kanade method with pyramids. - -@sa calcOpticalFlowPyrLK - -*/ -class CV_EXPORTS_W SparsePyrLKOpticalFlow : public SparseOpticalFlow -{ -public: - CV_WRAP virtual Size getWinSize() const = 0; - CV_WRAP virtual void setWinSize(Size winSize) = 0; - - CV_WRAP virtual int getMaxLevel() const = 0; - CV_WRAP virtual void setMaxLevel(int maxLevel) = 0; - - CV_WRAP virtual TermCriteria getTermCriteria() const = 0; - CV_WRAP virtual void setTermCriteria(TermCriteria& crit) = 0; - - CV_WRAP virtual int getFlags() const = 0; - CV_WRAP virtual void setFlags(int flags) = 0; - - CV_WRAP virtual double getMinEigThreshold() const = 0; - CV_WRAP virtual void setMinEigThreshold(double minEigThreshold) = 0; - - CV_WRAP static Ptr create( - Size winSize = Size(21, 21), - int maxLevel = 3, TermCriteria crit = - TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), - int flags = 0, - double minEigThreshold = 1e-4); -}; - -//! @} video_track - -} // cv - -#endif diff --git a/include/opencv2/video/tracking_c.h b/include/opencv2/video/tracking_c.h deleted file mode 100755 index 3e32fbd..0000000 --- a/include/opencv2/video/tracking_c.h +++ /dev/null @@ -1,232 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_TRACKING_C_H -#define OPENCV_TRACKING_C_H - -#include "opencv2/imgproc/types_c.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @addtogroup video_c - @{ -*/ - -/****************************************************************************************\ -* Motion Analysis * -\****************************************************************************************/ - -/************************************ optical flow ***************************************/ - -#define CV_LKFLOW_PYR_A_READY 1 -#define CV_LKFLOW_PYR_B_READY 2 -#define CV_LKFLOW_INITIAL_GUESSES 4 -#define CV_LKFLOW_GET_MIN_EIGENVALS 8 - -/* It is Lucas & Kanade method, modified to use pyramids. - Also it does several iterations to get optical flow for - every point at every pyramid level. - Calculates optical flow between two images for certain set of points (i.e. - it is a "sparse" optical flow, which is opposite to the previous 3 methods) */ -CVAPI(void) cvCalcOpticalFlowPyrLK( const CvArr* prev, const CvArr* curr, - CvArr* prev_pyr, CvArr* curr_pyr, - const CvPoint2D32f* prev_features, - CvPoint2D32f* curr_features, - int count, - CvSize win_size, - int level, - char* status, - float* track_error, - CvTermCriteria criteria, - int flags ); - - -/* Modification of a previous sparse optical flow algorithm to calculate - affine flow */ -CVAPI(void) cvCalcAffineFlowPyrLK( const CvArr* prev, const CvArr* curr, - CvArr* prev_pyr, CvArr* curr_pyr, - const CvPoint2D32f* prev_features, - CvPoint2D32f* curr_features, - float* matrices, int count, - CvSize win_size, int level, - char* status, float* track_error, - CvTermCriteria criteria, int flags ); - -/* Estimate rigid transformation between 2 images or 2 point sets */ -CVAPI(int) cvEstimateRigidTransform( const CvArr* A, const CvArr* B, - CvMat* M, int full_affine ); - -/* Estimate optical flow for each pixel using the two-frame G. Farneback algorithm */ -CVAPI(void) cvCalcOpticalFlowFarneback( const CvArr* prev, const CvArr* next, - CvArr* flow, double pyr_scale, int levels, - int winsize, int iterations, int poly_n, - double poly_sigma, int flags ); - -/********************************* motion templates *************************************/ - -/****************************************************************************************\ -* All the motion template functions work only with single channel images. * -* Silhouette image must have depth IPL_DEPTH_8U or IPL_DEPTH_8S * -* Motion history image must have depth IPL_DEPTH_32F, * -* Gradient mask - IPL_DEPTH_8U or IPL_DEPTH_8S, * -* Motion orientation image - IPL_DEPTH_32F * -* Segmentation mask - IPL_DEPTH_32F * -* All the angles are in degrees, all the times are in milliseconds * -\****************************************************************************************/ - -/* Updates motion history image given motion silhouette */ -CVAPI(void) cvUpdateMotionHistory( const CvArr* silhouette, CvArr* mhi, - double timestamp, double duration ); - -/* Calculates gradient of the motion history image and fills - a mask indicating where the gradient is valid */ -CVAPI(void) cvCalcMotionGradient( const CvArr* mhi, CvArr* mask, CvArr* orientation, - double delta1, double delta2, - int aperture_size CV_DEFAULT(3)); - -/* Calculates average motion direction within a selected motion region - (region can be selected by setting ROIs and/or by composing a valid gradient mask - with the region mask) */ -CVAPI(double) cvCalcGlobalOrientation( const CvArr* orientation, const CvArr* mask, - const CvArr* mhi, double timestamp, - double duration ); - -/* Splits a motion history image into a few parts corresponding to separate independent motions - (e.g. left hand, right hand) */ -CVAPI(CvSeq*) cvSegmentMotion( const CvArr* mhi, CvArr* seg_mask, - CvMemStorage* storage, - double timestamp, double seg_thresh ); - -/****************************************************************************************\ -* Tracking * -\****************************************************************************************/ - -/* Implements CAMSHIFT algorithm - determines object position, size and orientation - from the object histogram back project (extension of meanshift) */ -CVAPI(int) cvCamShift( const CvArr* prob_image, CvRect window, - CvTermCriteria criteria, CvConnectedComp* comp, - CvBox2D* box CV_DEFAULT(NULL) ); - -/* Implements MeanShift algorithm - determines object position - from the object histogram back project */ -CVAPI(int) cvMeanShift( const CvArr* prob_image, CvRect window, - CvTermCriteria criteria, CvConnectedComp* comp ); - -/* -standard Kalman filter (in G. Welch' and G. Bishop's notation): - - x(k)=A*x(k-1)+B*u(k)+w(k) p(w)~N(0,Q) - z(k)=H*x(k)+v(k), p(v)~N(0,R) -*/ -typedef struct CvKalman -{ - int MP; /* number of measurement vector dimensions */ - int DP; /* number of state vector dimensions */ - int CP; /* number of control vector dimensions */ - - /* backward compatibility fields */ -#if 1 - float* PosterState; /* =state_pre->data.fl */ - float* PriorState; /* =state_post->data.fl */ - float* DynamMatr; /* =transition_matrix->data.fl */ - float* MeasurementMatr; /* =measurement_matrix->data.fl */ - float* MNCovariance; /* =measurement_noise_cov->data.fl */ - float* PNCovariance; /* =process_noise_cov->data.fl */ - float* KalmGainMatr; /* =gain->data.fl */ - float* PriorErrorCovariance;/* =error_cov_pre->data.fl */ - float* PosterErrorCovariance;/* =error_cov_post->data.fl */ - float* Temp1; /* temp1->data.fl */ - float* Temp2; /* temp2->data.fl */ -#endif - - CvMat* state_pre; /* predicted state (x'(k)): - x(k)=A*x(k-1)+B*u(k) */ - CvMat* state_post; /* corrected state (x(k)): - x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) */ - CvMat* transition_matrix; /* state transition matrix (A) */ - CvMat* control_matrix; /* control matrix (B) - (it is not used if there is no control)*/ - CvMat* measurement_matrix; /* measurement matrix (H) */ - CvMat* process_noise_cov; /* process noise covariance matrix (Q) */ - CvMat* measurement_noise_cov; /* measurement noise covariance matrix (R) */ - CvMat* error_cov_pre; /* priori error estimate covariance matrix (P'(k)): - P'(k)=A*P(k-1)*At + Q)*/ - CvMat* gain; /* Kalman gain matrix (K(k)): - K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)*/ - CvMat* error_cov_post; /* posteriori error estimate covariance matrix (P(k)): - P(k)=(I-K(k)*H)*P'(k) */ - CvMat* temp1; /* temporary matrices */ - CvMat* temp2; - CvMat* temp3; - CvMat* temp4; - CvMat* temp5; -} CvKalman; - -/* Creates Kalman filter and sets A, B, Q, R and state to some initial values */ -CVAPI(CvKalman*) cvCreateKalman( int dynam_params, int measure_params, - int control_params CV_DEFAULT(0)); - -/* Releases Kalman filter state */ -CVAPI(void) cvReleaseKalman( CvKalman** kalman); - -/* Updates Kalman filter by time (predicts future state of the system) */ -CVAPI(const CvMat*) cvKalmanPredict( CvKalman* kalman, - const CvMat* control CV_DEFAULT(NULL)); - -/* Updates Kalman filter by measurement - (corrects state of the system and internal matrices) */ -CVAPI(const CvMat*) cvKalmanCorrect( CvKalman* kalman, const CvMat* measurement ); - -#define cvKalmanUpdateByTime cvKalmanPredict -#define cvKalmanUpdateByMeasurement cvKalmanCorrect - -/** @} video_c */ - -#ifdef __cplusplus -} // extern "C" -#endif - - -#endif // OPENCV_TRACKING_C_H diff --git a/include/opencv2/video/video.hpp b/include/opencv2/video/video.hpp deleted file mode 100755 index 8267b85..0000000 --- a/include/opencv2/video/video.hpp +++ /dev/null @@ -1,48 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Copyright (C) 2013, OpenCV Foundation, all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifdef __OPENCV_BUILD -#error this is a compatibility header which should not be used inside the OpenCV library -#endif - -#include "opencv2/video.hpp" diff --git a/include/opencv2/videoio.hpp b/include/opencv2/videoio.hpp deleted file mode 100755 index df1d181..0000000 --- a/include/opencv2/videoio.hpp +++ /dev/null @@ -1,1000 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_VIDEOIO_HPP -#define OPENCV_VIDEOIO_HPP - -#include "opencv2/core.hpp" - -/** - @defgroup videoio Video I/O - - @brief Read and write video or images sequence with OpenCV - - ### See also: - - @ref videoio_overview - - Tutorials: @ref tutorial_table_of_content_videoio - @{ - @defgroup videoio_flags_base Flags for video I/O - @defgroup videoio_flags_others Additional flags for video I/O API backends - @defgroup videoio_c C API for video I/O - @defgroup videoio_ios iOS glue for video I/O - @defgroup videoio_winrt WinRT glue for video I/O - @defgroup videoio_registry Query I/O API backends registry - @} -*/ - -////////////////////////////////// video io ///////////////////////////////// - -typedef struct CvCapture CvCapture; -typedef struct CvVideoWriter CvVideoWriter; - -namespace cv -{ - -//! @addtogroup videoio -//! @{ - -//! @addtogroup videoio_flags_base -//! @{ - - -/** @brief %VideoCapture API backends identifier. - -Select preferred API for a capture object. -To be used in the VideoCapture::VideoCapture() constructor or VideoCapture::open() - -@note Backends are available only if they have been built with your OpenCV binaries. -See @ref videoio_overview for more information. -*/ -enum VideoCaptureAPIs { - CAP_ANY = 0, //!< Auto detect == 0 - CAP_VFW = 200, //!< Video For Windows (platform native) - CAP_V4L = 200, //!< V4L/V4L2 capturing support via libv4l - CAP_V4L2 = CAP_V4L, //!< Same as CAP_V4L - CAP_FIREWIRE = 300, //!< IEEE 1394 drivers - CAP_FIREWARE = CAP_FIREWIRE, //!< Same as CAP_FIREWIRE - CAP_IEEE1394 = CAP_FIREWIRE, //!< Same as CAP_FIREWIRE - CAP_DC1394 = CAP_FIREWIRE, //!< Same as CAP_FIREWIRE - CAP_CMU1394 = CAP_FIREWIRE, //!< Same as CAP_FIREWIRE - CAP_QT = 500, //!< QuickTime - CAP_UNICAP = 600, //!< Unicap drivers - CAP_DSHOW = 700, //!< DirectShow (via videoInput) - CAP_PVAPI = 800, //!< PvAPI, Prosilica GigE SDK - CAP_OPENNI = 900, //!< OpenNI (for Kinect) - CAP_OPENNI_ASUS = 910, //!< OpenNI (for Asus Xtion) - CAP_ANDROID = 1000, //!< Android - not used - CAP_XIAPI = 1100, //!< XIMEA Camera API - CAP_AVFOUNDATION = 1200, //!< AVFoundation framework for iOS (OS X Lion will have the same API) - CAP_GIGANETIX = 1300, //!< Smartek Giganetix GigEVisionSDK - CAP_MSMF = 1400, //!< Microsoft Media Foundation (via videoInput) - CAP_WINRT = 1410, //!< Microsoft Windows Runtime using Media Foundation - CAP_INTELPERC = 1500, //!< Intel Perceptual Computing SDK - CAP_OPENNI2 = 1600, //!< OpenNI2 (for Kinect) - CAP_OPENNI2_ASUS = 1610, //!< OpenNI2 (for Asus Xtion and Occipital Structure sensors) - CAP_GPHOTO2 = 1700, //!< gPhoto2 connection - CAP_GSTREAMER = 1800, //!< GStreamer - CAP_FFMPEG = 1900, //!< Open and record video file or stream using the FFMPEG library - CAP_IMAGES = 2000, //!< OpenCV Image Sequence (e.g. img_%02d.jpg) - CAP_ARAVIS = 2100, //!< Aravis SDK - CAP_OPENCV_MJPEG = 2200, //!< Built-in OpenCV MotionJPEG codec - CAP_INTEL_MFX = 2300, //!< Intel MediaSDK - CAP_XINE = 2400, //!< XINE engine (Linux) - }; - -/** @brief %VideoCapture generic properties identifier. - - Reading / writing properties involves many layers. Some unexpected result might happens along this chain. - Effective behaviour depends from device hardware, driver and API Backend. - @sa videoio_flags_others, VideoCapture::get(), VideoCapture::set() -*/ -enum VideoCaptureProperties { - CAP_PROP_POS_MSEC =0, //!< Current position of the video file in milliseconds. - CAP_PROP_POS_FRAMES =1, //!< 0-based index of the frame to be decoded/captured next. - CAP_PROP_POS_AVI_RATIO =2, //!< Relative position of the video file: 0=start of the film, 1=end of the film. - CAP_PROP_FRAME_WIDTH =3, //!< Width of the frames in the video stream. - CAP_PROP_FRAME_HEIGHT =4, //!< Height of the frames in the video stream. - CAP_PROP_FPS =5, //!< Frame rate. - CAP_PROP_FOURCC =6, //!< 4-character code of codec. see VideoWriter::fourcc . - CAP_PROP_FRAME_COUNT =7, //!< Number of frames in the video file. - CAP_PROP_FORMAT =8, //!< Format of the %Mat objects (see Mat::type()) returned by VideoCapture::retrieve(). - //!< Set value -1 to fetch undecoded RAW video streams (as Mat 8UC1). - CAP_PROP_MODE =9, //!< Backend-specific value indicating the current capture mode. - CAP_PROP_BRIGHTNESS =10, //!< Brightness of the image (only for those cameras that support). - CAP_PROP_CONTRAST =11, //!< Contrast of the image (only for cameras). - CAP_PROP_SATURATION =12, //!< Saturation of the image (only for cameras). - CAP_PROP_HUE =13, //!< Hue of the image (only for cameras). - CAP_PROP_GAIN =14, //!< Gain of the image (only for those cameras that support). - CAP_PROP_EXPOSURE =15, //!< Exposure (only for those cameras that support). - CAP_PROP_CONVERT_RGB =16, //!< Boolean flags indicating whether images should be converted to RGB.
- //!< *GStreamer note*: The flag is ignored in case if custom pipeline is used. It's user responsibility to interpret pipeline output. - CAP_PROP_WHITE_BALANCE_BLUE_U =17, //!< Currently unsupported. - CAP_PROP_RECTIFICATION =18, //!< Rectification flag for stereo cameras (note: only supported by DC1394 v 2.x backend currently). - CAP_PROP_MONOCHROME =19, - CAP_PROP_SHARPNESS =20, - CAP_PROP_AUTO_EXPOSURE =21, //!< DC1394: exposure control done by camera, user can adjust reference level using this feature. - CAP_PROP_GAMMA =22, - CAP_PROP_TEMPERATURE =23, - CAP_PROP_TRIGGER =24, - CAP_PROP_TRIGGER_DELAY =25, - CAP_PROP_WHITE_BALANCE_RED_V =26, - CAP_PROP_ZOOM =27, - CAP_PROP_FOCUS =28, - CAP_PROP_GUID =29, - CAP_PROP_ISO_SPEED =30, - CAP_PROP_BACKLIGHT =32, - CAP_PROP_PAN =33, - CAP_PROP_TILT =34, - CAP_PROP_ROLL =35, - CAP_PROP_IRIS =36, - CAP_PROP_SETTINGS =37, //!< Pop up video/camera filter dialog (note: only supported by DSHOW backend currently. The property value is ignored) - CAP_PROP_BUFFERSIZE =38, - CAP_PROP_AUTOFOCUS =39, - CAP_PROP_SAR_NUM =40, //!< Sample aspect ratio: num/den (num) - CAP_PROP_SAR_DEN =41, //!< Sample aspect ratio: num/den (den) - CAP_PROP_BACKEND =42, //!< Current backend (enum VideoCaptureAPIs). Read-only property - CAP_PROP_CHANNEL =43, //!< Video input or Channel Number (only for those cameras that support) - CAP_PROP_AUTO_WB =44, //!< enable/ disable auto white-balance - CAP_PROP_WB_TEMPERATURE=45, //!< white-balance color temperature - CAP_PROP_CODEC_PIXEL_FORMAT =46, //!< (read-only) codec's pixel format. 4-character code - see VideoWriter::fourcc . Subset of [AV_PIX_FMT_*](https://github.com/FFmpeg/FFmpeg/blob/master/libavcodec/raw.c) or -1 if unknown - CAP_PROP_BITRATE =47, //!< (read-only) Video bitrate in kbits/s - CAP_PROP_ORIENTATION_META=48, //!< (read-only) Frame rotation defined by stream meta (applicable for FFmpeg back-end only) - CAP_PROP_ORIENTATION_AUTO=49, //!< if true - rotates output frames of CvCapture considering video file's metadata (applicable for FFmpeg back-end only) (https://github.com/opencv/opencv/issues/15499) -#ifndef CV_DOXYGEN - CV__CAP_PROP_LATEST -#endif - }; - - -/** @brief Generic camera output modes identifier. -@note Currently, these are supported through the libv4l backend only. -*/ -enum VideoCaptureModes { - CAP_MODE_BGR = 0, //!< BGR24 (default) - CAP_MODE_RGB = 1, //!< RGB24 - CAP_MODE_GRAY = 2, //!< Y8 - CAP_MODE_YUYV = 3 //!< YUYV - }; - -/** @brief %VideoWriter generic properties identifier. - @sa VideoWriter::get(), VideoWriter::set() -*/ -enum VideoWriterProperties { - VIDEOWRITER_PROP_QUALITY = 1, //!< Current quality (0..100%) of the encoded videostream. Can be adjusted dynamically in some codecs. - VIDEOWRITER_PROP_FRAMEBYTES = 2, //!< (Read-only): Size of just encoded video frame. Note that the encoding order may be different from representation order. - VIDEOWRITER_PROP_NSTRIPES = 3 //!< Number of stripes for parallel encoding. -1 for auto detection. -}; - -//! @} videoio_flags_base - -//! @addtogroup videoio_flags_others -//! @{ - -/** @name IEEE 1394 drivers - @{ -*/ - -/** @brief Modes of the IEEE 1394 controlling registers -(can be: auto, manual, auto single push, absolute Latter allowed with any other mode) -every feature can have only one mode turned on at a time -*/ -enum { CAP_PROP_DC1394_OFF = -4, //!< turn the feature off (not controlled manually nor automatically). - CAP_PROP_DC1394_MODE_MANUAL = -3, //!< set automatically when a value of the feature is set by the user. - CAP_PROP_DC1394_MODE_AUTO = -2, - CAP_PROP_DC1394_MODE_ONE_PUSH_AUTO = -1, - CAP_PROP_DC1394_MAX = 31 - }; - -//! @} IEEE 1394 drivers - -/** @name OpenNI (for Kinect) - @{ -*/ - -//! OpenNI map generators -enum { CAP_OPENNI_DEPTH_GENERATOR = 1 << 31, - CAP_OPENNI_IMAGE_GENERATOR = 1 << 30, - CAP_OPENNI_IR_GENERATOR = 1 << 29, - CAP_OPENNI_GENERATORS_MASK = CAP_OPENNI_DEPTH_GENERATOR + CAP_OPENNI_IMAGE_GENERATOR + CAP_OPENNI_IR_GENERATOR - }; - -//! Properties of cameras available through OpenNI backend -enum { CAP_PROP_OPENNI_OUTPUT_MODE = 100, - CAP_PROP_OPENNI_FRAME_MAX_DEPTH = 101, //!< In mm - CAP_PROP_OPENNI_BASELINE = 102, //!< In mm - CAP_PROP_OPENNI_FOCAL_LENGTH = 103, //!< In pixels - CAP_PROP_OPENNI_REGISTRATION = 104, //!< Flag that synchronizes the remapping depth map to image map - //!< by changing depth generator's view point (if the flag is "on") or - //!< sets this view point to its normal one (if the flag is "off"). - CAP_PROP_OPENNI_REGISTRATION_ON = CAP_PROP_OPENNI_REGISTRATION, - CAP_PROP_OPENNI_APPROX_FRAME_SYNC = 105, - CAP_PROP_OPENNI_MAX_BUFFER_SIZE = 106, - CAP_PROP_OPENNI_CIRCLE_BUFFER = 107, - CAP_PROP_OPENNI_MAX_TIME_DURATION = 108, - CAP_PROP_OPENNI_GENERATOR_PRESENT = 109, - CAP_PROP_OPENNI2_SYNC = 110, - CAP_PROP_OPENNI2_MIRROR = 111 - }; - -//! OpenNI shortcuts -enum { CAP_OPENNI_IMAGE_GENERATOR_PRESENT = CAP_OPENNI_IMAGE_GENERATOR + CAP_PROP_OPENNI_GENERATOR_PRESENT, - CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE = CAP_OPENNI_IMAGE_GENERATOR + CAP_PROP_OPENNI_OUTPUT_MODE, - CAP_OPENNI_DEPTH_GENERATOR_PRESENT = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_GENERATOR_PRESENT, - CAP_OPENNI_DEPTH_GENERATOR_BASELINE = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_BASELINE, - CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_FOCAL_LENGTH, - CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_REGISTRATION, - CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION_ON = CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION, - CAP_OPENNI_IR_GENERATOR_PRESENT = CAP_OPENNI_IR_GENERATOR + CAP_PROP_OPENNI_GENERATOR_PRESENT, - }; - -//! OpenNI data given from depth generator -enum { CAP_OPENNI_DEPTH_MAP = 0, //!< Depth values in mm (CV_16UC1) - CAP_OPENNI_POINT_CLOUD_MAP = 1, //!< XYZ in meters (CV_32FC3) - CAP_OPENNI_DISPARITY_MAP = 2, //!< Disparity in pixels (CV_8UC1) - CAP_OPENNI_DISPARITY_MAP_32F = 3, //!< Disparity in pixels (CV_32FC1) - CAP_OPENNI_VALID_DEPTH_MASK = 4, //!< CV_8UC1 - - CAP_OPENNI_BGR_IMAGE = 5, //!< Data given from RGB image generator - CAP_OPENNI_GRAY_IMAGE = 6, //!< Data given from RGB image generator - - CAP_OPENNI_IR_IMAGE = 7 //!< Data given from IR image generator - }; - -//! Supported output modes of OpenNI image generator -enum { CAP_OPENNI_VGA_30HZ = 0, - CAP_OPENNI_SXGA_15HZ = 1, - CAP_OPENNI_SXGA_30HZ = 2, - CAP_OPENNI_QVGA_30HZ = 3, - CAP_OPENNI_QVGA_60HZ = 4 - }; - -//! @} OpenNI - -/** @name GStreamer - @{ -*/ - -enum { CAP_PROP_GSTREAMER_QUEUE_LENGTH = 200 //!< Default is 1 - }; - -//! @} GStreamer - -/** @name PvAPI, Prosilica GigE SDK - @{ -*/ - -//! PVAPI -enum { CAP_PROP_PVAPI_MULTICASTIP = 300, //!< IP for enable multicast master mode. 0 for disable multicast. - CAP_PROP_PVAPI_FRAMESTARTTRIGGERMODE = 301, //!< FrameStartTriggerMode: Determines how a frame is initiated. - CAP_PROP_PVAPI_DECIMATIONHORIZONTAL = 302, //!< Horizontal sub-sampling of the image. - CAP_PROP_PVAPI_DECIMATIONVERTICAL = 303, //!< Vertical sub-sampling of the image. - CAP_PROP_PVAPI_BINNINGX = 304, //!< Horizontal binning factor. - CAP_PROP_PVAPI_BINNINGY = 305, //!< Vertical binning factor. - CAP_PROP_PVAPI_PIXELFORMAT = 306 //!< Pixel format. - }; - -//! PVAPI: FrameStartTriggerMode -enum { CAP_PVAPI_FSTRIGMODE_FREERUN = 0, //!< Freerun - CAP_PVAPI_FSTRIGMODE_SYNCIN1 = 1, //!< SyncIn1 - CAP_PVAPI_FSTRIGMODE_SYNCIN2 = 2, //!< SyncIn2 - CAP_PVAPI_FSTRIGMODE_FIXEDRATE = 3, //!< FixedRate - CAP_PVAPI_FSTRIGMODE_SOFTWARE = 4 //!< Software - }; - -//! PVAPI: DecimationHorizontal, DecimationVertical -enum { CAP_PVAPI_DECIMATION_OFF = 1, //!< Off - CAP_PVAPI_DECIMATION_2OUTOF4 = 2, //!< 2 out of 4 decimation - CAP_PVAPI_DECIMATION_2OUTOF8 = 4, //!< 2 out of 8 decimation - CAP_PVAPI_DECIMATION_2OUTOF16 = 8 //!< 2 out of 16 decimation - }; - -//! PVAPI: PixelFormat -enum { CAP_PVAPI_PIXELFORMAT_MONO8 = 1, //!< Mono8 - CAP_PVAPI_PIXELFORMAT_MONO16 = 2, //!< Mono16 - CAP_PVAPI_PIXELFORMAT_BAYER8 = 3, //!< Bayer8 - CAP_PVAPI_PIXELFORMAT_BAYER16 = 4, //!< Bayer16 - CAP_PVAPI_PIXELFORMAT_RGB24 = 5, //!< Rgb24 - CAP_PVAPI_PIXELFORMAT_BGR24 = 6, //!< Bgr24 - CAP_PVAPI_PIXELFORMAT_RGBA32 = 7, //!< Rgba32 - CAP_PVAPI_PIXELFORMAT_BGRA32 = 8, //!< Bgra32 - }; - -//! @} PvAPI - -/** @name XIMEA Camera API - @{ -*/ - -//! Properties of cameras available through XIMEA SDK backend -enum { CAP_PROP_XI_DOWNSAMPLING = 400, //!< Change image resolution by binning or skipping. - CAP_PROP_XI_DATA_FORMAT = 401, //!< Output data format. - CAP_PROP_XI_OFFSET_X = 402, //!< Horizontal offset from the origin to the area of interest (in pixels). - CAP_PROP_XI_OFFSET_Y = 403, //!< Vertical offset from the origin to the area of interest (in pixels). - CAP_PROP_XI_TRG_SOURCE = 404, //!< Defines source of trigger. - CAP_PROP_XI_TRG_SOFTWARE = 405, //!< Generates an internal trigger. PRM_TRG_SOURCE must be set to TRG_SOFTWARE. - CAP_PROP_XI_GPI_SELECTOR = 406, //!< Selects general purpose input. - CAP_PROP_XI_GPI_MODE = 407, //!< Set general purpose input mode. - CAP_PROP_XI_GPI_LEVEL = 408, //!< Get general purpose level. - CAP_PROP_XI_GPO_SELECTOR = 409, //!< Selects general purpose output. - CAP_PROP_XI_GPO_MODE = 410, //!< Set general purpose output mode. - CAP_PROP_XI_LED_SELECTOR = 411, //!< Selects camera signalling LED. - CAP_PROP_XI_LED_MODE = 412, //!< Define camera signalling LED functionality. - CAP_PROP_XI_MANUAL_WB = 413, //!< Calculates White Balance(must be called during acquisition). - CAP_PROP_XI_AUTO_WB = 414, //!< Automatic white balance. - CAP_PROP_XI_AEAG = 415, //!< Automatic exposure/gain. - CAP_PROP_XI_EXP_PRIORITY = 416, //!< Exposure priority (0.5 - exposure 50%, gain 50%). - CAP_PROP_XI_AE_MAX_LIMIT = 417, //!< Maximum limit of exposure in AEAG procedure. - CAP_PROP_XI_AG_MAX_LIMIT = 418, //!< Maximum limit of gain in AEAG procedure. - CAP_PROP_XI_AEAG_LEVEL = 419, //!< Average intensity of output signal AEAG should achieve(in %). - CAP_PROP_XI_TIMEOUT = 420, //!< Image capture timeout in milliseconds. - CAP_PROP_XI_EXPOSURE = 421, //!< Exposure time in microseconds. - CAP_PROP_XI_EXPOSURE_BURST_COUNT = 422, //!< Sets the number of times of exposure in one frame. - CAP_PROP_XI_GAIN_SELECTOR = 423, //!< Gain selector for parameter Gain allows to select different type of gains. - CAP_PROP_XI_GAIN = 424, //!< Gain in dB. - CAP_PROP_XI_DOWNSAMPLING_TYPE = 426, //!< Change image downsampling type. - CAP_PROP_XI_BINNING_SELECTOR = 427, //!< Binning engine selector. - CAP_PROP_XI_BINNING_VERTICAL = 428, //!< Vertical Binning - number of vertical photo-sensitive cells to combine together. - CAP_PROP_XI_BINNING_HORIZONTAL = 429, //!< Horizontal Binning - number of horizontal photo-sensitive cells to combine together. - CAP_PROP_XI_BINNING_PATTERN = 430, //!< Binning pattern type. - CAP_PROP_XI_DECIMATION_SELECTOR = 431, //!< Decimation engine selector. - CAP_PROP_XI_DECIMATION_VERTICAL = 432, //!< Vertical Decimation - vertical sub-sampling of the image - reduces the vertical resolution of the image by the specified vertical decimation factor. - CAP_PROP_XI_DECIMATION_HORIZONTAL = 433, //!< Horizontal Decimation - horizontal sub-sampling of the image - reduces the horizontal resolution of the image by the specified vertical decimation factor. - CAP_PROP_XI_DECIMATION_PATTERN = 434, //!< Decimation pattern type. - CAP_PROP_XI_TEST_PATTERN_GENERATOR_SELECTOR = 587, //!< Selects which test pattern generator is controlled by the TestPattern feature. - CAP_PROP_XI_TEST_PATTERN = 588, //!< Selects which test pattern type is generated by the selected generator. - CAP_PROP_XI_IMAGE_DATA_FORMAT = 435, //!< Output data format. - CAP_PROP_XI_SHUTTER_TYPE = 436, //!< Change sensor shutter type(CMOS sensor). - CAP_PROP_XI_SENSOR_TAPS = 437, //!< Number of taps. - CAP_PROP_XI_AEAG_ROI_OFFSET_X = 439, //!< Automatic exposure/gain ROI offset X. - CAP_PROP_XI_AEAG_ROI_OFFSET_Y = 440, //!< Automatic exposure/gain ROI offset Y. - CAP_PROP_XI_AEAG_ROI_WIDTH = 441, //!< Automatic exposure/gain ROI Width. - CAP_PROP_XI_AEAG_ROI_HEIGHT = 442, //!< Automatic exposure/gain ROI Height. - CAP_PROP_XI_BPC = 445, //!< Correction of bad pixels. - CAP_PROP_XI_WB_KR = 448, //!< White balance red coefficient. - CAP_PROP_XI_WB_KG = 449, //!< White balance green coefficient. - CAP_PROP_XI_WB_KB = 450, //!< White balance blue coefficient. - CAP_PROP_XI_WIDTH = 451, //!< Width of the Image provided by the device (in pixels). - CAP_PROP_XI_HEIGHT = 452, //!< Height of the Image provided by the device (in pixels). - CAP_PROP_XI_REGION_SELECTOR = 589, //!< Selects Region in Multiple ROI which parameters are set by width, height, ... ,region mode. - CAP_PROP_XI_REGION_MODE = 595, //!< Activates/deactivates Region selected by Region Selector. - CAP_PROP_XI_LIMIT_BANDWIDTH = 459, //!< Set/get bandwidth(datarate)(in Megabits). - CAP_PROP_XI_SENSOR_DATA_BIT_DEPTH = 460, //!< Sensor output data bit depth. - CAP_PROP_XI_OUTPUT_DATA_BIT_DEPTH = 461, //!< Device output data bit depth. - CAP_PROP_XI_IMAGE_DATA_BIT_DEPTH = 462, //!< bitdepth of data returned by function xiGetImage. - CAP_PROP_XI_OUTPUT_DATA_PACKING = 463, //!< Device output data packing (or grouping) enabled. Packing could be enabled if output_data_bit_depth > 8 and packing capability is available. - CAP_PROP_XI_OUTPUT_DATA_PACKING_TYPE = 464, //!< Data packing type. Some cameras supports only specific packing type. - CAP_PROP_XI_IS_COOLED = 465, //!< Returns 1 for cameras that support cooling. - CAP_PROP_XI_COOLING = 466, //!< Start camera cooling. - CAP_PROP_XI_TARGET_TEMP = 467, //!< Set sensor target temperature for cooling. - CAP_PROP_XI_CHIP_TEMP = 468, //!< Camera sensor temperature. - CAP_PROP_XI_HOUS_TEMP = 469, //!< Camera housing temperature. - CAP_PROP_XI_HOUS_BACK_SIDE_TEMP = 590, //!< Camera housing back side temperature. - CAP_PROP_XI_SENSOR_BOARD_TEMP = 596, //!< Camera sensor board temperature. - CAP_PROP_XI_CMS = 470, //!< Mode of color management system. - CAP_PROP_XI_APPLY_CMS = 471, //!< Enable applying of CMS profiles to xiGetImage (see XI_PRM_INPUT_CMS_PROFILE, XI_PRM_OUTPUT_CMS_PROFILE). - CAP_PROP_XI_IMAGE_IS_COLOR = 474, //!< Returns 1 for color cameras. - CAP_PROP_XI_COLOR_FILTER_ARRAY = 475, //!< Returns color filter array type of RAW data. - CAP_PROP_XI_GAMMAY = 476, //!< Luminosity gamma. - CAP_PROP_XI_GAMMAC = 477, //!< Chromaticity gamma. - CAP_PROP_XI_SHARPNESS = 478, //!< Sharpness Strength. - CAP_PROP_XI_CC_MATRIX_00 = 479, //!< Color Correction Matrix element [0][0]. - CAP_PROP_XI_CC_MATRIX_01 = 480, //!< Color Correction Matrix element [0][1]. - CAP_PROP_XI_CC_MATRIX_02 = 481, //!< Color Correction Matrix element [0][2]. - CAP_PROP_XI_CC_MATRIX_03 = 482, //!< Color Correction Matrix element [0][3]. - CAP_PROP_XI_CC_MATRIX_10 = 483, //!< Color Correction Matrix element [1][0]. - CAP_PROP_XI_CC_MATRIX_11 = 484, //!< Color Correction Matrix element [1][1]. - CAP_PROP_XI_CC_MATRIX_12 = 485, //!< Color Correction Matrix element [1][2]. - CAP_PROP_XI_CC_MATRIX_13 = 486, //!< Color Correction Matrix element [1][3]. - CAP_PROP_XI_CC_MATRIX_20 = 487, //!< Color Correction Matrix element [2][0]. - CAP_PROP_XI_CC_MATRIX_21 = 488, //!< Color Correction Matrix element [2][1]. - CAP_PROP_XI_CC_MATRIX_22 = 489, //!< Color Correction Matrix element [2][2]. - CAP_PROP_XI_CC_MATRIX_23 = 490, //!< Color Correction Matrix element [2][3]. - CAP_PROP_XI_CC_MATRIX_30 = 491, //!< Color Correction Matrix element [3][0]. - CAP_PROP_XI_CC_MATRIX_31 = 492, //!< Color Correction Matrix element [3][1]. - CAP_PROP_XI_CC_MATRIX_32 = 493, //!< Color Correction Matrix element [3][2]. - CAP_PROP_XI_CC_MATRIX_33 = 494, //!< Color Correction Matrix element [3][3]. - CAP_PROP_XI_DEFAULT_CC_MATRIX = 495, //!< Set default Color Correction Matrix. - CAP_PROP_XI_TRG_SELECTOR = 498, //!< Selects the type of trigger. - CAP_PROP_XI_ACQ_FRAME_BURST_COUNT = 499, //!< Sets number of frames acquired by burst. This burst is used only if trigger is set to FrameBurstStart. - CAP_PROP_XI_DEBOUNCE_EN = 507, //!< Enable/Disable debounce to selected GPI. - CAP_PROP_XI_DEBOUNCE_T0 = 508, //!< Debounce time (x * 10us). - CAP_PROP_XI_DEBOUNCE_T1 = 509, //!< Debounce time (x * 10us). - CAP_PROP_XI_DEBOUNCE_POL = 510, //!< Debounce polarity (pol = 1 t0 - falling edge, t1 - rising edge). - CAP_PROP_XI_LENS_MODE = 511, //!< Status of lens control interface. This shall be set to XI_ON before any Lens operations. - CAP_PROP_XI_LENS_APERTURE_VALUE = 512, //!< Current lens aperture value in stops. Examples: 2.8, 4, 5.6, 8, 11. - CAP_PROP_XI_LENS_FOCUS_MOVEMENT_VALUE = 513, //!< Lens current focus movement value to be used by XI_PRM_LENS_FOCUS_MOVE in motor steps. - CAP_PROP_XI_LENS_FOCUS_MOVE = 514, //!< Moves lens focus motor by steps set in XI_PRM_LENS_FOCUS_MOVEMENT_VALUE. - CAP_PROP_XI_LENS_FOCUS_DISTANCE = 515, //!< Lens focus distance in cm. - CAP_PROP_XI_LENS_FOCAL_LENGTH = 516, //!< Lens focal distance in mm. - CAP_PROP_XI_LENS_FEATURE_SELECTOR = 517, //!< Selects the current feature which is accessible by XI_PRM_LENS_FEATURE. - CAP_PROP_XI_LENS_FEATURE = 518, //!< Allows access to lens feature value currently selected by XI_PRM_LENS_FEATURE_SELECTOR. - CAP_PROP_XI_DEVICE_MODEL_ID = 521, //!< Returns device model id. - CAP_PROP_XI_DEVICE_SN = 522, //!< Returns device serial number. - CAP_PROP_XI_IMAGE_DATA_FORMAT_RGB32_ALPHA = 529, //!< The alpha channel of RGB32 output image format. - CAP_PROP_XI_IMAGE_PAYLOAD_SIZE = 530, //!< Buffer size in bytes sufficient for output image returned by xiGetImage. - CAP_PROP_XI_TRANSPORT_PIXEL_FORMAT = 531, //!< Current format of pixels on transport layer. - CAP_PROP_XI_SENSOR_CLOCK_FREQ_HZ = 532, //!< Sensor clock frequency in Hz. - CAP_PROP_XI_SENSOR_CLOCK_FREQ_INDEX = 533, //!< Sensor clock frequency index. Sensor with selected frequencies have possibility to set the frequency only by this index. - CAP_PROP_XI_SENSOR_OUTPUT_CHANNEL_COUNT = 534, //!< Number of output channels from sensor used for data transfer. - CAP_PROP_XI_FRAMERATE = 535, //!< Define framerate in Hz. - CAP_PROP_XI_COUNTER_SELECTOR = 536, //!< Select counter. - CAP_PROP_XI_COUNTER_VALUE = 537, //!< Counter status. - CAP_PROP_XI_ACQ_TIMING_MODE = 538, //!< Type of sensor frames timing. - CAP_PROP_XI_AVAILABLE_BANDWIDTH = 539, //!< Calculate and returns available interface bandwidth(int Megabits). - CAP_PROP_XI_BUFFER_POLICY = 540, //!< Data move policy. - CAP_PROP_XI_LUT_EN = 541, //!< Activates LUT. - CAP_PROP_XI_LUT_INDEX = 542, //!< Control the index (offset) of the coefficient to access in the LUT. - CAP_PROP_XI_LUT_VALUE = 543, //!< Value at entry LUTIndex of the LUT. - CAP_PROP_XI_TRG_DELAY = 544, //!< Specifies the delay in microseconds (us) to apply after the trigger reception before activating it. - CAP_PROP_XI_TS_RST_MODE = 545, //!< Defines how time stamp reset engine will be armed. - CAP_PROP_XI_TS_RST_SOURCE = 546, //!< Defines which source will be used for timestamp reset. Writing this parameter will trigger settings of engine (arming). - CAP_PROP_XI_IS_DEVICE_EXIST = 547, //!< Returns 1 if camera connected and works properly. - CAP_PROP_XI_ACQ_BUFFER_SIZE = 548, //!< Acquisition buffer size in buffer_size_unit. Default bytes. - CAP_PROP_XI_ACQ_BUFFER_SIZE_UNIT = 549, //!< Acquisition buffer size unit in bytes. Default 1. E.g. Value 1024 means that buffer_size is in KiBytes. - CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_SIZE = 550, //!< Acquisition transport buffer size in bytes. - CAP_PROP_XI_BUFFERS_QUEUE_SIZE = 551, //!< Queue of field/frame buffers. - CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_COMMIT = 552, //!< Number of buffers to commit to low level. - CAP_PROP_XI_RECENT_FRAME = 553, //!< GetImage returns most recent frame. - CAP_PROP_XI_DEVICE_RESET = 554, //!< Resets the camera to default state. - CAP_PROP_XI_COLUMN_FPN_CORRECTION = 555, //!< Correction of column FPN. - CAP_PROP_XI_ROW_FPN_CORRECTION = 591, //!< Correction of row FPN. - CAP_PROP_XI_SENSOR_MODE = 558, //!< Current sensor mode. Allows to select sensor mode by one integer. Setting of this parameter affects: image dimensions and downsampling. - CAP_PROP_XI_HDR = 559, //!< Enable High Dynamic Range feature. - CAP_PROP_XI_HDR_KNEEPOINT_COUNT = 560, //!< The number of kneepoints in the PWLR. - CAP_PROP_XI_HDR_T1 = 561, //!< Position of first kneepoint(in % of XI_PRM_EXPOSURE). - CAP_PROP_XI_HDR_T2 = 562, //!< Position of second kneepoint (in % of XI_PRM_EXPOSURE). - CAP_PROP_XI_KNEEPOINT1 = 563, //!< Value of first kneepoint (% of sensor saturation). - CAP_PROP_XI_KNEEPOINT2 = 564, //!< Value of second kneepoint (% of sensor saturation). - CAP_PROP_XI_IMAGE_BLACK_LEVEL = 565, //!< Last image black level counts. Can be used for Offline processing to recall it. - CAP_PROP_XI_HW_REVISION = 571, //!< Returns hardware revision number. - CAP_PROP_XI_DEBUG_LEVEL = 572, //!< Set debug level. - CAP_PROP_XI_AUTO_BANDWIDTH_CALCULATION = 573, //!< Automatic bandwidth calculation. - CAP_PROP_XI_FFS_FILE_ID = 594, //!< File number. - CAP_PROP_XI_FFS_FILE_SIZE = 580, //!< Size of file. - CAP_PROP_XI_FREE_FFS_SIZE = 581, //!< Size of free camera FFS. - CAP_PROP_XI_USED_FFS_SIZE = 582, //!< Size of used camera FFS. - CAP_PROP_XI_FFS_ACCESS_KEY = 583, //!< Setting of key enables file operations on some cameras. - CAP_PROP_XI_SENSOR_FEATURE_SELECTOR = 585, //!< Selects the current feature which is accessible by XI_PRM_SENSOR_FEATURE_VALUE. - CAP_PROP_XI_SENSOR_FEATURE_VALUE = 586, //!< Allows access to sensor feature value currently selected by XI_PRM_SENSOR_FEATURE_SELECTOR. - }; - -//! @} XIMEA - -/** @name AVFoundation framework for iOS - OS X Lion will have the same API - @{ -*/ - -//! Properties of cameras available through AVFOUNDATION backend -enum { CAP_PROP_IOS_DEVICE_FOCUS = 9001, - CAP_PROP_IOS_DEVICE_EXPOSURE = 9002, - CAP_PROP_IOS_DEVICE_FLASH = 9003, - CAP_PROP_IOS_DEVICE_WHITEBALANCE = 9004, - CAP_PROP_IOS_DEVICE_TORCH = 9005 - }; - -/** @name Smartek Giganetix GigEVisionSDK - @{ -*/ - -//! Properties of cameras available through Smartek Giganetix Ethernet Vision backend -/* --- Vladimir Litvinenko (litvinenko.vladimir@gmail.com) --- */ -enum { CAP_PROP_GIGA_FRAME_OFFSET_X = 10001, - CAP_PROP_GIGA_FRAME_OFFSET_Y = 10002, - CAP_PROP_GIGA_FRAME_WIDTH_MAX = 10003, - CAP_PROP_GIGA_FRAME_HEIGH_MAX = 10004, - CAP_PROP_GIGA_FRAME_SENS_WIDTH = 10005, - CAP_PROP_GIGA_FRAME_SENS_HEIGH = 10006 - }; - -//! @} Smartek - -/** @name Intel Perceptual Computing SDK - @{ -*/ -enum { CAP_PROP_INTELPERC_PROFILE_COUNT = 11001, - CAP_PROP_INTELPERC_PROFILE_IDX = 11002, - CAP_PROP_INTELPERC_DEPTH_LOW_CONFIDENCE_VALUE = 11003, - CAP_PROP_INTELPERC_DEPTH_SATURATION_VALUE = 11004, - CAP_PROP_INTELPERC_DEPTH_CONFIDENCE_THRESHOLD = 11005, - CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_HORZ = 11006, - CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_VERT = 11007 - }; - -//! Intel Perceptual Streams -enum { CAP_INTELPERC_DEPTH_GENERATOR = 1 << 29, - CAP_INTELPERC_IMAGE_GENERATOR = 1 << 28, - CAP_INTELPERC_GENERATORS_MASK = CAP_INTELPERC_DEPTH_GENERATOR + CAP_INTELPERC_IMAGE_GENERATOR - }; - -enum { CAP_INTELPERC_DEPTH_MAP = 0, //!< Each pixel is a 16-bit integer. The value indicates the distance from an object to the camera's XY plane or the Cartesian depth. - CAP_INTELPERC_UVDEPTH_MAP = 1, //!< Each pixel contains two 32-bit floating point values in the range of 0-1, representing the mapping of depth coordinates to the color coordinates. - CAP_INTELPERC_IR_MAP = 2, //!< Each pixel is a 16-bit integer. The value indicates the intensity of the reflected laser beam. - CAP_INTELPERC_IMAGE = 3 - }; - -//! @} Intel Perceptual - -/** @name gPhoto2 connection - @{ -*/ - -/** @brief gPhoto2 properties - -If `propertyId` is less than 0 then work on widget with that __additive inversed__ camera setting ID -Get IDs by using CAP_PROP_GPHOTO2_WIDGET_ENUMERATE. -@see CvCaptureCAM_GPHOTO2 for more info -*/ -enum { CAP_PROP_GPHOTO2_PREVIEW = 17001, //!< Capture only preview from liveview mode. - CAP_PROP_GPHOTO2_WIDGET_ENUMERATE = 17002, //!< Readonly, returns (const char *). - CAP_PROP_GPHOTO2_RELOAD_CONFIG = 17003, //!< Trigger, only by set. Reload camera settings. - CAP_PROP_GPHOTO2_RELOAD_ON_CHANGE = 17004, //!< Reload all settings on set. - CAP_PROP_GPHOTO2_COLLECT_MSGS = 17005, //!< Collect messages with details. - CAP_PROP_GPHOTO2_FLUSH_MSGS = 17006, //!< Readonly, returns (const char *). - CAP_PROP_SPEED = 17007, //!< Exposure speed. Can be readonly, depends on camera program. - CAP_PROP_APERTURE = 17008, //!< Aperture. Can be readonly, depends on camera program. - CAP_PROP_EXPOSUREPROGRAM = 17009, //!< Camera exposure program. - CAP_PROP_VIEWFINDER = 17010 //!< Enter liveview mode. - }; - -//! @} gPhoto2 - - -/** @name Images backend - @{ -*/ - -/** @brief Images backend properties - -*/ -enum { CAP_PROP_IMAGES_BASE = 18000, - CAP_PROP_IMAGES_LAST = 19000 // excluding - }; - -//! @} Images - -//! @} videoio_flags_others - - -class IVideoCapture; - -/** @brief Class for video capturing from video files, image sequences or cameras. - -The class provides C++ API for capturing video from cameras or for reading video files and image sequences. - -Here is how the class can be used: -@include samples/cpp/videocapture_basic.cpp - -@note In @ref videoio_c "C API" the black-box structure `CvCapture` is used instead of %VideoCapture. -@note -- (C++) A basic sample on using the %VideoCapture interface can be found at - `OPENCV_SOURCE_CODE/samples/cpp/videocapture_starter.cpp` -- (Python) A basic sample on using the %VideoCapture interface can be found at - `OPENCV_SOURCE_CODE/samples/python/video.py` -- (Python) A multi threaded video processing sample can be found at - `OPENCV_SOURCE_CODE/samples/python/video_threaded.py` -- (Python) %VideoCapture sample showcasing some features of the Video4Linux2 backend - `OPENCV_SOURCE_CODE/samples/python/video_v4l2.py` - */ -class CV_EXPORTS_W VideoCapture -{ -public: - /** @brief Default constructor - @note In @ref videoio_c "C API", when you finished working with video, release CvCapture structure with - cvReleaseCapture(), or use Ptr\ that calls cvReleaseCapture() automatically in the - destructor. - */ - CV_WRAP VideoCapture(); - - /** @overload - @brief Open video file or image file sequence or a capturing device or a IP video stream for video capturing - - Same as VideoCapture(const String& filename, int apiPreference) but using default Capture API backends - */ - CV_WRAP VideoCapture(const String& filename); - - /** @overload - @brief Open video file or a capturing device or a IP video stream for video capturing with API Preference - - @param filename it can be: - - name of video file (eg. `video.avi`) - - or image sequence (eg. `img_%02d.jpg`, which will read samples like `img_00.jpg, img_01.jpg, img_02.jpg, ...`) - - or URL of video stream (eg. `protocol://host:port/script_name?script_params|auth`) - - or GStreamer pipeline string in gst-launch tool format in case if GStreamer is used as backend - Note that each video stream or IP camera feed has its own URL scheme. Please refer to the - documentation of source stream to know the right URL. - @param apiPreference preferred Capture API backends to use. Can be used to enforce a specific reader - implementation if multiple are available: e.g. cv::CAP_FFMPEG or cv::CAP_IMAGES or cv::CAP_DSHOW. - - @sa cv::VideoCaptureAPIs - */ - CV_WRAP VideoCapture(const String& filename, int apiPreference); - - /** @overload - @brief Open a camera for video capturing - - @param index camera_id + domain_offset (CAP_*) id of the video capturing device to open. To open default camera using default backend just pass 0. - Use a `domain_offset` to enforce a specific reader implementation if multiple are available like cv::CAP_FFMPEG or cv::CAP_IMAGES or cv::CAP_DSHOW. - e.g. to open Camera 1 using the MS Media Foundation API use `index = 1 + cv::CAP_MSMF` - - @sa cv::VideoCaptureAPIs - */ - CV_WRAP VideoCapture(int index); - - /** @overload - @brief Opens a camera for video capturing - - @param index id of the video capturing device to open. To open default camera using default backend just pass 0. - (to backward compatibility usage of camera_id + domain_offset (CAP_*) is valid when apiPreference is CAP_ANY) - @param apiPreference preferred Capture API backends to use. Can be used to enforce a specific reader - implementation if multiple are available: e.g. cv::CAP_DSHOW or cv::CAP_MSMF or cv::CAP_V4L2. - - @sa cv::VideoCaptureAPIs - */ - CV_WRAP VideoCapture(int index, int apiPreference); - - /** @brief Default destructor - - The method first calls VideoCapture::release to close the already opened file or camera. - */ - virtual ~VideoCapture(); - - /** @brief Open video file or a capturing device or a IP video stream for video capturing - - @overload - - Parameters are same as the constructor VideoCapture(const String& filename) - @return `true` if the file has been successfully opened - - The method first calls VideoCapture::release to close the already opened file or camera. - */ - CV_WRAP virtual bool open(const String& filename); - - /** @brief Open a camera for video capturing - - @overload - - Parameters are same as the constructor VideoCapture(int index) - @return `true` if the camera has been successfully opened. - - The method first calls VideoCapture::release to close the already opened file or camera. - */ - CV_WRAP virtual bool open(int index); - - /** @brief Open a camera for video capturing - - @overload - - Parameters are similar as the constructor VideoCapture(int index),except it takes an additional argument apiPreference. - Definitely, is same as open(int index) where `index=cameraNum + apiPreference` - @return `true` if the camera has been successfully opened. - */ - CV_WRAP bool open(int cameraNum, int apiPreference); - - /** @brief Returns true if video capturing has been initialized already. - - If the previous call to VideoCapture constructor or VideoCapture::open() succeeded, the method returns - true. - */ - CV_WRAP virtual bool isOpened() const; - - /** @brief Closes video file or capturing device. - - The method is automatically called by subsequent VideoCapture::open and by VideoCapture - destructor. - - The C function also deallocates memory and clears \*capture pointer. - */ - CV_WRAP virtual void release(); - - /** @brief Grabs the next frame from video file or capturing device. - - @return `true` (non-zero) in the case of success. - - The method/function grabs the next frame from video file or camera and returns true (non-zero) in - the case of success. - - The primary use of the function is in multi-camera environments, especially when the cameras do not - have hardware synchronization. That is, you call VideoCapture::grab() for each camera and after that - call the slower method VideoCapture::retrieve() to decode and get frame from each camera. This way - the overhead on demosaicing or motion jpeg decompression etc. is eliminated and the retrieved frames - from different cameras will be closer in time. - - Also, when a connected camera is multi-head (for example, a stereo camera or a Kinect device), the - correct way of retrieving data from it is to call VideoCapture::grab() first and then call - VideoCapture::retrieve() one or more times with different values of the channel parameter. - - @ref tutorial_kinect_openni - */ - CV_WRAP virtual bool grab(); - - /** @brief Decodes and returns the grabbed video frame. - - @param [out] image the video frame is returned here. If no frames has been grabbed the image will be empty. - @param flag it could be a frame index or a driver specific flag - @return `false` if no frames has been grabbed - - The method decodes and returns the just grabbed frame. If no frames has been grabbed - (camera has been disconnected, or there are no more frames in video file), the method returns false - and the function returns an empty image (with %cv::Mat, test it with Mat::empty()). - - @sa read() - - @note In @ref videoio_c "C API", functions cvRetrieveFrame() and cv.RetrieveFrame() return image stored inside the video - capturing structure. It is not allowed to modify or release the image! You can copy the frame using - cvCloneImage and then do whatever you want with the copy. - */ - CV_WRAP virtual bool retrieve(OutputArray image, int flag = 0); - - /** @brief Stream operator to read the next video frame. - @sa read() - */ - virtual VideoCapture& operator >> (CV_OUT Mat& image); - - /** @overload - @sa read() - */ - virtual VideoCapture& operator >> (CV_OUT UMat& image); - - /** @brief Grabs, decodes and returns the next video frame. - - @param [out] image the video frame is returned here. If no frames has been grabbed the image will be empty. - @return `false` if no frames has been grabbed - - The method/function combines VideoCapture::grab() and VideoCapture::retrieve() in one call. This is the - most convenient method for reading video files or capturing data from decode and returns the just - grabbed frame. If no frames has been grabbed (camera has been disconnected, or there are no more - frames in video file), the method returns false and the function returns empty image (with %cv::Mat, test it with Mat::empty()). - - @note In @ref videoio_c "C API", functions cvRetrieveFrame() and cv.RetrieveFrame() return image stored inside the video - capturing structure. It is not allowed to modify or release the image! You can copy the frame using - cvCloneImage and then do whatever you want with the copy. - */ - CV_WRAP virtual bool read(OutputArray image); - - /** @brief Sets a property in the VideoCapture. - - @param propId Property identifier from cv::VideoCaptureProperties (eg. cv::CAP_PROP_POS_MSEC, cv::CAP_PROP_POS_FRAMES, ...) - or one from @ref videoio_flags_others - @param value Value of the property. - @return `true` if the property is supported by backend used by the VideoCapture instance. - @note Even if it returns `true` this doesn't ensure that the property - value has been accepted by the capture device. See note in VideoCapture::get() - */ - CV_WRAP virtual bool set(int propId, double value); - - /** @brief Returns the specified VideoCapture property - - @param propId Property identifier from cv::VideoCaptureProperties (eg. cv::CAP_PROP_POS_MSEC, cv::CAP_PROP_POS_FRAMES, ...) - or one from @ref videoio_flags_others - @return Value for the specified property. Value 0 is returned when querying a property that is - not supported by the backend used by the VideoCapture instance. - - @note Reading / writing properties involves many layers. Some unexpected result might happens - along this chain. - @code{.txt} - VideoCapture -> API Backend -> Operating System -> Device Driver -> Device Hardware - @endcode - The returned value might be different from what really used by the device or it could be encoded - using device dependent rules (eg. steps or percentage). Effective behaviour depends from device - driver and API Backend - - */ - CV_WRAP virtual double get(int propId) const; - - /** @brief Open video file or a capturing device or a IP video stream for video capturing with API Preference - - @overload - - Parameters are same as the constructor VideoCapture(const String& filename, int apiPreference) - @return `true` if the file has been successfully opened - - The method first calls VideoCapture::release to close the already opened file or camera. - */ - CV_WRAP virtual bool open(const String& filename, int apiPreference); - - /** @brief Returns used backend API name - - @note Stream should be opened. - */ - CV_WRAP String getBackendName() const; - -protected: - Ptr cap; - Ptr icap; -}; - -class IVideoWriter; - -/** @example samples/cpp/tutorial_code/videoio/video-write/video-write.cpp -Check @ref tutorial_video_write "the corresponding tutorial" for more details -*/ - -/** @example samples/cpp/videowriter_basic.cpp -An example using VideoCapture and VideoWriter class -*/ - -/** @brief Video writer class. - -The class provides C++ API for writing video files or image sequences. -*/ -class CV_EXPORTS_W VideoWriter -{ -public: - /** @brief Default constructors - - The constructors/functions initialize video writers. - - On Linux FFMPEG is used to write videos; - - On Windows FFMPEG or VFW is used; - - On MacOSX QTKit is used. - */ - CV_WRAP VideoWriter(); - - /** @overload - @param filename Name of the output video file. - @param fourcc 4-character code of codec used to compress the frames. For example, - VideoWriter::fourcc('P','I','M','1') is a MPEG-1 codec, VideoWriter::fourcc('M','J','P','G') is a - motion-jpeg codec etc. List of codes can be obtained at [Video Codecs by - FOURCC](http://www.fourcc.org/codecs.php) page. FFMPEG backend with MP4 container natively uses - other values as fourcc code: see [ObjectType](http://mp4ra.org/#/codecs), - so you may receive a warning message from OpenCV about fourcc code conversion. - @param fps Framerate of the created video stream. - @param frameSize Size of the video frames. - @param isColor If it is not zero, the encoder will expect and encode color frames, otherwise it - will work with grayscale frames (the flag is currently supported on Windows only). - - @b Tips: - - With some backends `fourcc=-1` pops up the codec selection dialog from the system. - - To save image sequence use a proper filename (eg. `img_%02d.jpg`) and `fourcc=0` - OR `fps=0`. Use uncompressed image format (eg. `img_%02d.BMP`) to save raw frames. - - Most codecs are lossy. If you want lossless video file you need to use a lossless codecs - (eg. FFMPEG FFV1, Huffman HFYU, Lagarith LAGS, etc...) - - If FFMPEG is enabled, using `codec=0; fps=0;` you can create an uncompressed (raw) video file. - */ - CV_WRAP VideoWriter(const String& filename, int fourcc, double fps, - Size frameSize, bool isColor = true); - - /** @overload - The `apiPreference` parameter allows to specify API backends to use. Can be used to enforce a specific reader implementation - if multiple are available: e.g. cv::CAP_FFMPEG or cv::CAP_GSTREAMER. - */ - CV_WRAP VideoWriter(const String& filename, int apiPreference, int fourcc, double fps, - Size frameSize, bool isColor = true); - - /** @brief Default destructor - - The method first calls VideoWriter::release to close the already opened file. - */ - virtual ~VideoWriter(); - - /** @brief Initializes or reinitializes video writer. - - The method opens video writer. Parameters are the same as in the constructor - VideoWriter::VideoWriter. - @return `true` if video writer has been successfully initialized - - The method first calls VideoWriter::release to close the already opened file. - */ - CV_WRAP virtual bool open(const String& filename, int fourcc, double fps, - Size frameSize, bool isColor = true); - - /** @overload - */ - CV_WRAP bool open(const String& filename, int apiPreference, int fourcc, double fps, - Size frameSize, bool isColor = true); - - /** @brief Returns true if video writer has been successfully initialized. - */ - CV_WRAP virtual bool isOpened() const; - - /** @brief Closes the video writer. - - The method is automatically called by subsequent VideoWriter::open and by the VideoWriter - destructor. - */ - CV_WRAP virtual void release(); - - /** @brief Stream operator to write the next video frame. - @sa write - */ - virtual VideoWriter& operator << (const Mat& image); - - /** @brief Writes the next video frame - - @param image The written frame. In general, color images are expected in BGR format. - - The function/method writes the specified image to video file. It must have the same size as has - been specified when opening the video writer. - */ - CV_WRAP virtual void write(const Mat& image); - - /** @brief Sets a property in the VideoWriter. - - @param propId Property identifier from cv::VideoWriterProperties (eg. cv::VIDEOWRITER_PROP_QUALITY) - or one of @ref videoio_flags_others - - @param value Value of the property. - @return `true` if the property is supported by the backend used by the VideoWriter instance. - */ - CV_WRAP virtual bool set(int propId, double value); - - /** @brief Returns the specified VideoWriter property - - @param propId Property identifier from cv::VideoWriterProperties (eg. cv::VIDEOWRITER_PROP_QUALITY) - or one of @ref videoio_flags_others - - @return Value for the specified property. Value 0 is returned when querying a property that is - not supported by the backend used by the VideoWriter instance. - */ - CV_WRAP virtual double get(int propId) const; - - /** @brief Concatenates 4 chars to a fourcc code - - @return a fourcc code - - This static method constructs the fourcc code of the codec to be used in the constructor - VideoWriter::VideoWriter or VideoWriter::open. - */ - CV_WRAP static int fourcc(char c1, char c2, char c3, char c4); - - /** @brief Returns used backend API name - - @note Stream should be opened. - */ - CV_WRAP String getBackendName() const; - -protected: - Ptr writer; - Ptr iwriter; - - static Ptr create(const String& filename, int fourcc, double fps, - Size frameSize, bool isColor = true); -}; - -template<> CV_EXPORTS void DefaultDeleter::operator ()(CvCapture* obj) const; -template<> CV_EXPORTS void DefaultDeleter::operator ()(CvVideoWriter* obj) const; - -//! @} videoio - -} // cv - -#endif //OPENCV_VIDEOIO_HPP diff --git a/include/opencv2/videostab.hpp b/include/opencv2/videostab.hpp deleted file mode 100755 index ca3f5ad..0000000 --- a/include/opencv2/videostab.hpp +++ /dev/null @@ -1,81 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef OPENCV_VIDEOSTAB_HPP -#define OPENCV_VIDEOSTAB_HPP - -/** - @defgroup videostab Video Stabilization - -The video stabilization module contains a set of functions and classes that can be used to solve the -problem of video stabilization. There are a few methods implemented, most of them are described in -the papers @cite OF06 and @cite G11 . However, there are some extensions and deviations from the original -paper methods. - -### References - - 1. "Full-Frame Video Stabilization with Motion Inpainting" - Yasuyuki Matsushita, Eyal Ofek, Weina Ge, Xiaoou Tang, Senior Member, and Heung-Yeung Shum - 2. "Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths" - Matthias Grundmann, Vivek Kwatra, Irfan Essa - - @{ - @defgroup videostab_motion Global Motion Estimation - -The video stabilization module contains a set of functions and classes for global motion estimation -between point clouds or between images. In the last case features are extracted and matched -internally. For the sake of convenience the motion estimation functions are wrapped into classes. -Both the functions and the classes are available. - - @defgroup videostab_marching Fast Marching Method - -The Fast Marching Method @cite Telea04 is used in of the video stabilization routines to do motion and -color inpainting. The method is implemented is a flexible way and it's made public for other users. - - @} - -*/ - -#include "opencv2/videostab/stabilizer.hpp" -#include "opencv2/videostab/ring_buffer.hpp" - -#endif diff --git a/ocrweb/readme.md b/ocrweb/readme.md old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_mobile_v1_det/__init__.py b/python/ch_ppocr_mobile_v1_det/__init__.py old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_mobile_v1_det/text_detect.py b/python/ch_ppocr_mobile_v1_det/text_detect.py old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_mobile_v1_det/utils.py b/python/ch_ppocr_mobile_v1_det/utils.py old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_mobile_v2_cls/__init__.py b/python/ch_ppocr_mobile_v2_cls/__init__.py old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_mobile_v2_cls/text_cls.py b/python/ch_ppocr_mobile_v2_cls/text_cls.py old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_mobile_v2_cls/utils.py b/python/ch_ppocr_mobile_v2_cls/utils.py old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_mobile_v2_det/__init__.py b/python/ch_ppocr_mobile_v2_det/__init__.py old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_mobile_v2_det/text_detect.py b/python/ch_ppocr_mobile_v2_det/text_detect.py old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_mobile_v2_det/utils.py b/python/ch_ppocr_mobile_v2_det/utils.py old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_mobile_v2_rec/__init__.py b/python/ch_ppocr_mobile_v2_rec/__init__.py old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_mobile_v2_rec/ppocr_keys_v1.txt b/python/ch_ppocr_mobile_v2_rec/ppocr_keys_v1.txt old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_mobile_v2_rec/text_recognize.py b/python/ch_ppocr_mobile_v2_rec/text_recognize.py old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_mobile_v2_rec/utils.py b/python/ch_ppocr_mobile_v2_rec/utils.py old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_server_v2_det/__init__.py b/python/ch_ppocr_server_v2_det/__init__.py old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_server_v2_det/text_detect.py b/python/ch_ppocr_server_v2_det/text_detect.py old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_server_v2_det/utils.py b/python/ch_ppocr_server_v2_det/utils.py old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_server_v2_rec/__init__.py b/python/ch_ppocr_server_v2_rec/__init__.py old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_server_v2_rec/ppocr_keys_v1.txt b/python/ch_ppocr_server_v2_rec/ppocr_keys_v1.txt old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_server_v2_rec/text_recognize.py b/python/ch_ppocr_server_v2_rec/text_recognize.py old mode 100644 new mode 100755 diff --git a/python/ch_ppocr_server_v2_rec/utils.py b/python/ch_ppocr_server_v2_rec/utils.py old mode 100644 new mode 100755 diff --git a/python/models/模型位置.md b/python/models/模型位置.md old mode 100644 new mode 100755 diff --git a/python/requirements.txt b/python/requirements.txt old mode 100644 new mode 100755 diff --git a/python/resources/下载显示结果所有字体.txt b/python/resources/下载显示结果所有字体.txt old mode 100644 new mode 100755 diff --git a/python/test_images/det_images/1.jpg b/python/test_images/det_images/1.jpg old mode 100644 new mode 100755 diff --git a/python/test_images/det_images/v1_det_results.jpg b/python/test_images/det_images/v1_det_results.jpg old mode 100644 new mode 100755 diff --git a/python/test_images/rec_images/2021-01-18_14-22-32.png b/python/test_images/rec_images/2021-01-18_14-22-32.png old mode 100644 new mode 100755 diff --git a/python/test_images/rec_images/2021-01-19_13-44-34.png b/python/test_images/rec_images/2021-01-19_13-44-34.png old mode 100644 new mode 100755 diff --git a/test_imgs/test_multi_language.png b/test_imgs/test_multi_language.png old mode 100644 new mode 100755