mirror of
https://github.com/open-mmlab/mmsegmentation.git
synced 2025-06-03 22:03:48 +08:00
48d4222412
* 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.
|
|
import argparse
|
|
import os
|
|
import os.path as osp
|
|
import shutil
|
|
import tempfile
|
|
import zipfile
|
|
|
|
import mmcv
|
|
|
|
|
|
def parse_args():
|
|
parser = argparse.ArgumentParser(
|
|
description='Convert LoveDA dataset to mmsegmentation format')
|
|
parser.add_argument('dataset_path', help='LoveDA folder path')
|
|
parser.add_argument('--tmp_dir', help='path of the temporary directory')
|
|
parser.add_argument('-o', '--out_dir', help='output path')
|
|
args = parser.parse_args()
|
|
return args
|
|
|
|
|
|
def main():
|
|
args = parse_args()
|
|
dataset_path = args.dataset_path
|
|
if args.out_dir is None:
|
|
out_dir = osp.join('data', 'loveDA')
|
|
else:
|
|
out_dir = args.out_dir
|
|
|
|
print('Making directories...')
|
|
mmcv.mkdir_or_exist(out_dir)
|
|
mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir'))
|
|
mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'train'))
|
|
mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'val'))
|
|
mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'test'))
|
|
mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir'))
|
|
mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'train'))
|
|
mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))
|
|
|
|
assert 'Train.zip' in os.listdir(dataset_path), \
|
|
'Train.zip is not in {}'.format(dataset_path)
|
|
assert 'Val.zip' in os.listdir(dataset_path), \
|
|
'Val.zip is not in {}'.format(dataset_path)
|
|
assert 'Test.zip' in os.listdir(dataset_path), \
|
|
'Test.zip is not in {}'.format(dataset_path)
|
|
|
|
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
|
|
for dataset in ['Train', 'Val', 'Test']:
|
|
zip_file = zipfile.ZipFile(
|
|
os.path.join(dataset_path, dataset + '.zip'))
|
|
zip_file.extractall(tmp_dir)
|
|
data_type = dataset.lower()
|
|
for location in ['Rural', 'Urban']:
|
|
for image_type in ['images_png', 'masks_png']:
|
|
if image_type == 'images_png':
|
|
dst = osp.join(out_dir, 'img_dir', data_type)
|
|
else:
|
|
dst = osp.join(out_dir, 'ann_dir', data_type)
|
|
if dataset == 'Test' and image_type == 'masks_png':
|
|
continue
|
|
else:
|
|
src_dir = osp.join(tmp_dir, dataset, location,
|
|
image_type)
|
|
src_lst = os.listdir(src_dir)
|
|
for file in src_lst:
|
|
shutil.move(osp.join(src_dir, file), dst)
|
|
print('Removing the temporary files...')
|
|
|
|
print('Done!')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|