mirror of
https://github.com/RapidAI/RapidOCR.git
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115 lines
4.2 KiB
Python
115 lines
4.2 KiB
Python
# 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 copy
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import math
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import time
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from typing import Any, Dict, List, Tuple, Union
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import cv2
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import numpy as np
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from rapidocr_torch.utils import TorchInferSession, read_yaml
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from .utils import ClsPostProcess
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class TextClassifier:
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def __init__(self, config: Dict[str, Any]):
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self.cls_image_shape = config["cls_image_shape"]
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self.cls_batch_num = config["cls_batch_num"]
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self.cls_thresh = config["cls_thresh"]
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self.postprocess_op = ClsPostProcess(config["label_list"])
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self.infer = TorchInferSession(config)
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def __call__(
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self, img_list: Union[np.ndarray, List[np.ndarray]]
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) -> Tuple[List[np.ndarray], List[List[Union[str, float]]], float]:
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if isinstance(img_list, np.ndarray):
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img_list = [img_list]
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img_list = copy.deepcopy(img_list)
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# Calculate the aspect ratio of all text bars
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width_list = [img.shape[1] / float(img.shape[0]) for img in img_list]
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# Sorting can speed up the cls process
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indices = np.argsort(np.array(width_list))
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img_num = len(img_list)
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cls_res = [["", 0.0]] * img_num
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batch_num = self.cls_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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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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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).astype(np.float32)
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starttime = time.time()
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# 输出直接为(norm_img_batch,2)
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prob_out = self.infer(norm_img_batch)[0]
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cls_result = self.postprocess_op(prob_out)
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elapse += time.time() - starttime
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for rno, (label, score) in enumerate(cls_result):
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cls_res[indices[beg_img_no + rno]] = [label, score]
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if "180" in label and score > self.cls_thresh:
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img_list[indices[beg_img_no + rno]] = cv2.rotate(
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img_list[indices[beg_img_no + rno]], 1
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)
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return img_list, cls_res, elapse
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def resize_norm_img(self, img: np.ndarray) -> np.ndarray:
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img_c, img_h, img_w = self.cls_image_shape
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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_h * ratio) > img_w:
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resized_w = img_w
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else:
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resized_w = int(math.ceil(img_h * ratio))
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resized_image = cv2.resize(img, (resized_w, img_h))
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resized_image = resized_image.astype("float32")
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if img_c == 1:
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resized_image = resized_image / 255
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resized_image = resized_image[np.newaxis, :]
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else:
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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_c, img_h, img_w), dtype=np.float32)
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padding_im[:, :, :resized_w] = resized_image
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return padding_im
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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("--config_path", type=str, default="config.yaml")
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args = parser.parse_args()
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config = read_yaml(args.config_path)
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text_classifier = TextClassifier(config)
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img = cv2.imread(args.image_path)
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img_list, cls_res, predict_time = text_classifier(img)
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for ino in range(len(img_list)):
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print(f"cls result:{cls_res[ino]}")
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