89 lines
3.1 KiB
Python
89 lines
3.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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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 tempfile
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import zipfile
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import mmcv
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CHASE_DB1_LEN = 28 * 3
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TRAINING_LEN = 60
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Convert CHASE_DB1 dataset to mmsegmentation format')
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parser.add_argument('dataset_path', help='path of CHASEDB1.zip')
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parser.add_argument('--tmp_dir', help='path of the temporary directory')
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parser.add_argument('-o', '--out_dir', help='output path')
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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dataset_path = args.dataset_path
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if args.out_dir is None:
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out_dir = osp.join('data', 'CHASE_DB1')
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else:
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out_dir = args.out_dir
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print('Making directories...')
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mmcv.mkdir_or_exist(out_dir)
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mmcv.mkdir_or_exist(osp.join(out_dir, 'images'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'training'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'validation'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations', 'training'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations', 'validation'))
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with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
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print('Extracting CHASEDB1.zip...')
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zip_file = zipfile.ZipFile(dataset_path)
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zip_file.extractall(tmp_dir)
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print('Generating training dataset...')
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assert len(os.listdir(tmp_dir)) == CHASE_DB1_LEN, \
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'len(os.listdir(tmp_dir)) != {}'.format(CHASE_DB1_LEN)
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for img_name in sorted(os.listdir(tmp_dir))[:TRAINING_LEN]:
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img = mmcv.imread(osp.join(tmp_dir, img_name))
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if osp.splitext(img_name)[1] == '.jpg':
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mmcv.imwrite(
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img,
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osp.join(out_dir, 'images', 'training',
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osp.splitext(img_name)[0] + '.png'))
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else:
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# The annotation img should be divided by 128, because some of
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# the annotation imgs are not standard. We should set a
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# threshold to convert the nonstandard annotation imgs. The
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# value divided by 128 is equivalent to '1 if value >= 128
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# else 0'
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mmcv.imwrite(
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img[:, :, 0] // 128,
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osp.join(out_dir, 'annotations', 'training',
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osp.splitext(img_name)[0] + '.png'))
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for img_name in sorted(os.listdir(tmp_dir))[TRAINING_LEN:]:
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img = mmcv.imread(osp.join(tmp_dir, img_name))
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if osp.splitext(img_name)[1] == '.jpg':
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mmcv.imwrite(
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img,
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osp.join(out_dir, 'images', 'validation',
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osp.splitext(img_name)[0] + '.png'))
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else:
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mmcv.imwrite(
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img[:, :, 0] // 128,
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osp.join(out_dir, 'annotations', 'validation',
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osp.splitext(img_name)[0] + '.png'))
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print('Removing the temporary files...')
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print('Done!')
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if __name__ == '__main__':
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main()
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