mirror of
https://github.com/RapidAI/RapidOCR.git
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83 lines
2.6 KiB
Python
83 lines
2.6 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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# -*- encoding: utf-8 -*-
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# @Author: SWHL
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# @Contact: liekkaskono@163.com
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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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import yaml
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from .utils import OrtInferSession, create_operators
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root_dir = Path(__file__).resolve().parent
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class RapidOrientation():
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def __init__(self, model_path: str = None):
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config_path = str(root_dir / 'config.yaml')
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config = self.read_yaml(config_path)
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if model_path is None:
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model_path = str(root_dir / 'models' / 'rapid_orientation.onnx')
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config['model_path'] = model_path
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self.session = OrtInferSession(config)
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self.labels = self.session.get_metadata()['character'].splitlines()
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self.preprocess_ops = create_operators(config["PreProcess"])
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def __call__(self, images):
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s = time.time()
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for ops in self.preprocess_ops:
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images = ops(images)
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image = np.array(images)[None, ...]
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pred_output = self.session(image)[0]
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pred_output = pred_output.squeeze()
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pred_idx = np.argmax(pred_output)
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pred_txt = self.labels[pred_idx]
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elapse = time.time() - s
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return pred_txt, elapse
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@staticmethod
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def read_yaml(yaml_path):
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with open(yaml_path, 'rb') as f:
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data = yaml.load(f, Loader=yaml.Loader)
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return data
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def main():
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parser = argparse.ArgumentParser()
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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' /
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'rapid_orientation.onnx'),
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help='The model path used for inference.')
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args = parser.parse_args()
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orientation_engine = RapidOrientation(args.model_path)
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img = cv2.imread(args.img_path)
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orientaion_result, _ = orientation_engine(img)
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print(orientaion_result)
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if __name__ == '__main__':
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main()
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