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#!/usr/bin/env python
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# Copyright (c) 2022 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 os
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import os.path as osp
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import cv2
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import paddle
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import paddlers
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from tqdm import tqdm
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import custom_model
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import custom_trainer
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def read_file_list(file_list, sep=' '):
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with open(file_list, 'r') as f:
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for line in f:
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line = line.strip()
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parts = line.split(sep)
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yield parts
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model_dir", default=None, type=str, help="Path of saved model.")
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parser.add_argument("--data_dir", type=str, help="Path of input dataset.")
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parser.add_argument("--file_list", type=str, help="Path of file list.")
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parser.add_argument(
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"--save_dir",
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default='./exp/predict',
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type=str,
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help="Path of directory to save prediction results.")
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parser.add_argument(
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"--ext",
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default='.png',
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type=str,
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help="Extension name of the saved image file.")
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return parser.parse_args()
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if __name__ == '__main__':
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args = parse_args()
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model = paddlers.tasks.load_model(args.model_dir)
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if not osp.exists(args.save_dir):
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os.makedirs(args.save_dir)
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with paddle.no_grad():
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for parts in tqdm(read_file_list(args.file_list)):
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im1_path = osp.join(args.data_dir, parts[0])
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im2_path = osp.join(args.data_dir, parts[1])
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pred = model.predict((im1_path, im2_path))
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cm = pred['label_map']
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# {0,1} -> {0,255}
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cm[cm > 0] = 255
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cm = cm.astype('uint8')
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if len(parts) > 2:
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name = osp.basename(parts[2])
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else:
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name = osp.basename(im1_path)
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name = osp.splitext(name)[0] + args.ext
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out_path = osp.join(args.save_dir, name)
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cv2.imwrite(out_path, cm)
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