mirror of
https://github.com/RapidAI/RapidOCR.git
synced 2026-11-12 05:50:49 +08:00
134 lines
4.9 KiB
Python
Executable File
134 lines
4.9 KiB
Python
Executable File
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import math
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import time
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from pathlib import Path
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import cv2
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import numpy as np
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import onnxruntime
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try:
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from .utils import CTCLabelDecode, check_and_read_gif, get_image_file_list
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except:
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from utils import CTCLabelDecode, check_and_read_gif, get_image_file_list
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root_path = Path(__file__).parent
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class TextRecognizer(object):
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def __init__(self, rec_model_path):
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self.rec_image_shape = [3, 32, 320]
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self.rec_batch_num = 6
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self.character_dict_path = str(root_path / 'ppocr_keys_v1.txt')
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self.postprocess_op = CTCLabelDecode(self.character_dict_path)
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self.session = onnxruntime.InferenceSession(rec_model_path)
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def resize_norm_img(self, img, max_wh_ratio):
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img_channel, img_height, img_width = self.rec_image_shape
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assert img_channel == img.shape[2]
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img_width = int((32 * max_wh_ratio))
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max_wh_ratio = 1
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h, w = img.shape[:2]
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ratio = w / float(h)
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if math.ceil(img_height * ratio) > img_width:
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resized_w = img_width
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else:
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resized_w = int(math.ceil(img_height * ratio))
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resized_image = cv2.resize(img, (resized_w, img_height))
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resized_image = resized_image.astype('float32')
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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padding_im = np.zeros((img_channel, img_height, img_width),
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dtype=np.float32)
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padding_im[:, :, 0:resized_w] = resized_image
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return padding_im
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@staticmethod
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def load_image(image_dir):
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image_file_list = get_image_file_list(image_dir)
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valid_image_file_list = []
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img_list = []
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for image_file in image_file_list:
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img, flag = check_and_read_gif(image_file)
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if not flag:
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img = cv2.imread(image_file)
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if img is None:
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print(f"error in loading image:{image_file}")
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continue
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valid_image_file_list.append(image_file)
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img_list.append(img)
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return img_list
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def __call__(self, image_dir):
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if not isinstance(image_dir, list):
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img_list = self.load_image(image_dir)
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else:
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img_list = image_dir
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img_num = len(img_list)
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# Calculate the aspect ratio of all text bars
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width_list = []
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for img in img_list:
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width_list.append(img.shape[1] / float(img.shape[0]))
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# Sorting can speed up the recognition process
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indices = np.argsort(np.array(width_list))
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# rec_res = []
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rec_res = [['', 0.0]] * img_num
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batch_num = self.rec_batch_num
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elapse = 0
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for beg_img_no in range(0, img_num, batch_num):
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end_img_no = min(img_num, beg_img_no + batch_num)
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norm_img_batch = []
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max_wh_ratio = 0
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for ino in range(beg_img_no, end_img_no):
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# h, w = img_list[ino].shape[0:2]
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h, w = img_list[indices[ino]].shape[0:2]
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wh_ratio = w * 1.0 / h
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max_wh_ratio = max(max_wh_ratio, wh_ratio)
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for ino in range(beg_img_no, end_img_no):
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norm_img = self.resize_norm_img(img_list[indices[ino]],
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max_wh_ratio)
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norm_img = norm_img[np.newaxis, :]
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norm_img_batch.append(norm_img)
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norm_img_batch = np.concatenate(norm_img_batch)
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norm_img_batch = norm_img_batch.copy()
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starttime = time.time()
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ort_inputs = {self.session.get_inputs()[0].name: norm_img_batch}
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preds = self.session.run(None, ort_inputs)[0]
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rec_result = self.postprocess_op(preds)
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for rno in range(len(rec_result)):
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rec_res[indices[beg_img_no + rno]] = rec_result[rno]
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elapse += time.time() - starttime
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return rec_res, elapse
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--image_path', type=str, help='image_dir|image_path')
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parser.add_argument('--model_path', type=str, help='rec_model_path')
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args = parser.parse_args()
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text_recognizer = TextRecognizer(args.model_path)
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rec_res, predict_time = text_recognizer(args.image_path)
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print(f'识别结果: {rec_res}\t cost: {predict_time}s')
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