Image Segmentation
Transformers
PyTorch
upernet
Inference Endpoints
test2 / tools /convert_datasets /cityscapes.py
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import argparse
import os.path as osp
import mmcv
from cityscapesscripts.preparation.json2labelImg import json2labelImg
def convert_json_to_label(json_file):
label_file = json_file.replace('_polygons.json', '_labelTrainIds.png')
json2labelImg(json_file, label_file, 'trainIds')
def parse_args():
parser = argparse.ArgumentParser(
description='Convert Cityscapes annotations to TrainIds')
parser.add_argument('cityscapes_path', help='cityscapes data path')
parser.add_argument('--gt-dir', default='gtFine', type=str)
parser.add_argument('-o', '--out-dir', help='output path')
parser.add_argument(
'--nproc', default=1, type=int, help='number of process')
args = parser.parse_args()
return args
def main():
args = parse_args()
cityscapes_path = args.cityscapes_path
out_dir = args.out_dir if args.out_dir else cityscapes_path
mmcv.mkdir_or_exist(out_dir)
gt_dir = osp.join(cityscapes_path, args.gt_dir)
poly_files = []
for poly in mmcv.scandir(gt_dir, '_polygons.json', recursive=True):
poly_file = osp.join(gt_dir, poly)
poly_files.append(poly_file)
if args.nproc > 1:
mmcv.track_parallel_progress(convert_json_to_label, poly_files,
args.nproc)
else:
mmcv.track_progress(convert_json_to_label, poly_files)
split_names = ['train', 'val', 'test']
for split in split_names:
filenames = []
for poly in mmcv.scandir(
osp.join(gt_dir, split), '_polygons.json', recursive=True):
filenames.append(poly.replace('_gtFine_polygons.json', ''))
with open(osp.join(out_dir, f'{split}.txt'), 'w') as f:
f.writelines(f + '\n' for f in filenames)
if __name__ == '__main__':
main()