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* update LoveDA dataset api * revised lint errors in dataset_prepare.md * revised lint errors in loveda.py * revised lint errors in loveda.py * revised lint errors in dataset_prepare.md * revised lint errors in dataset_prepare.md * checked with isort and yapf * checked with isort and yapf * checked with isort and yapf * Revert "checked with isort and yapf" This reverts commit 686a51d9 * Revert "checked with isort and yapf" This reverts commit b877e121bb2935ceefc503c09675019489829feb. * Revert "revised lint errors in dataset_prepare.md" This reverts commit 2289e27c * Revert "checked with isort and yapf" This reverts commit 159db2f8 * Revert "checked with isort and yapf" This reverts commit 159db2f8 * add configs & fix bugs * update new branch * upload models&logs and add format-only * change pretraied model path of HRNet * fix the errors in dataset_prepare.md * fix the errors in dataset_prepare.md and configs in loveda.py * change the description in docs_zh-CN/dataset_prepare.md * use init_cfg * fix test converage * adding pseudo loveda dataset * adding pseudo loveda dataset * adding pseudo loveda dataset * adding pseudo loveda dataset * adding pseudo loveda dataset * adding pseudo loveda dataset * Update docs/dataset_prepare.md Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn> * Update docs_zh-CN/dataset_prepare.md Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn> * Update docs_zh-CN/dataset_prepare.md Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn> * Delete unused lines of unittest and Add docs * add convert .py file * add downloading links from zenodo * move place of LoveDA and Cityscapes in doc * move place of LoveDA and Cityscapes in doc Co-authored-by: MengzhangLI <mcmong@pku.edu.cn> Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
74 lines
2.6 KiB
Python
74 lines
2.6 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 shutil
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import tempfile
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import zipfile
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import mmcv
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Convert LoveDA dataset to mmsegmentation format')
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parser.add_argument('dataset_path', help='LoveDA folder path')
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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', 'loveDA')
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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, 'img_dir'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'train'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'val'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'test'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'train'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))
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assert 'Train.zip' in os.listdir(dataset_path), \
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'Train.zip is not in {}'.format(dataset_path)
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assert 'Val.zip' in os.listdir(dataset_path), \
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'Val.zip is not in {}'.format(dataset_path)
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assert 'Test.zip' in os.listdir(dataset_path), \
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'Test.zip is not in {}'.format(dataset_path)
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with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
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for dataset in ['Train', 'Val', 'Test']:
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zip_file = zipfile.ZipFile(
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os.path.join(dataset_path, dataset + '.zip'))
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zip_file.extractall(tmp_dir)
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data_type = dataset.lower()
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for location in ['Rural', 'Urban']:
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for image_type in ['images_png', 'masks_png']:
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if image_type == 'images_png':
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dst = osp.join(out_dir, 'img_dir', data_type)
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else:
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dst = osp.join(out_dir, 'ann_dir', data_type)
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if dataset == 'Test' and image_type == 'masks_png':
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continue
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else:
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src_dir = osp.join(tmp_dir, dataset, location,
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image_type)
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src_lst = os.listdir(src_dir)
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for file in src_lst:
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shutil.move(osp.join(src_dir, file), dst)
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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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