mirror of
https://github.com/RapidAI/RapidOCR.git
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100 lines
3.4 KiB
Python
100 lines
3.4 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 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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from .utils import (OrtInferSession, PicoDetPostProcess, create_operators,
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read_yaml, transform, vis_layout)
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root_dir = Path(__file__).resolve().parent
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class RapidLayout():
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def __init__(self,
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model_path: str = None):
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config_path = str(root_dir / 'config.yaml')
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config = read_yaml(config_path)
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if model_path is None:
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model_path = str(root_dir / 'models' / 'layout_cdla.onnx')
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config['model_path'] = model_path
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session_instance = OrtInferSession(config)
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self.predictor = session_instance.session
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self.input_name = session_instance.get_input_name()
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labels = session_instance.get_metadata()['character'].splitlines()
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self.preprocess_op = create_operators(config['pre_process'])
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self.postprocess_op = PicoDetPostProcess(labels,
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**config['post_process'])
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def __call__(self, img):
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ori_im = img.copy()
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data = {'image': img}
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data = transform(data, self.preprocess_op)
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img = data[0]
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if img is None:
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return None, 0
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img = np.expand_dims(img, axis=0)
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img = img.copy()
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preds, elapse = 0, 1
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starttime = time.time()
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input_dict = {self.input_name: img}
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preds_onnx = self.predictor.run(None, input_dict)
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np_score_list, np_boxes_list = [], []
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num_outs = int(len(preds_onnx) / 2)
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for out_idx in range(num_outs):
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np_score_list.append(preds_onnx[out_idx])
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np_boxes_list.append(preds_onnx[out_idx + num_outs])
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preds = dict(boxes=np_score_list, boxes_num=np_boxes_list)
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post_preds = self.postprocess_op(ori_im, img, preds)
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elapse = time.time() - starttime
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return post_preds, elapse
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('-v', '--vis', action='store_true',
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help='Wheter to visualize the layout results.')
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parser.add_argument('-img', '--img_path', type=str, required=True,
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help='Path to image for layout.')
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parser.add_argument('-m', '--model_path', type=str,
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default=str(root_dir / 'models' / 'layout_cdla.onnx'),
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help='The model path used for inference.')
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args = parser.parse_args()
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layout_engine = RapidLayout(args.model_path)
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img = cv2.imread(args.img_path)
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layout_res, elapse = layout_engine(img)
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print(layout_res)
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if args.vis:
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img_path = Path(args.img_path)
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save_path = img_path.resolve().parent / f'vis_{img_path.name}'
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vis_layout(img, layout_res, str(save_path))
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if __name__ == '__main__':
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main()
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