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import argparse
import os.path as osp
from functools import partial
import mmcv
import numpy as np
from detail import Detail
from PIL import Image
_mapping = np.sort(
np.array([
0, 2, 259, 260, 415, 324, 9, 258, 144, 18, 19, 22, 23, 397, 25, 284,
158, 159, 416, 33, 162, 420, 454, 295, 296, 427, 44, 45, 46, 308, 59,
440, 445, 31, 232, 65, 354, 424, 68, 326, 72, 458, 34, 207, 80, 355,
85, 347, 220, 349, 360, 98, 187, 104, 105, 366, 189, 368, 113, 115
]))
_key = np.array(range(len(_mapping))).astype('uint8')
def generate_labels(img_id, detail, out_dir):
def _class_to_index(mask, _mapping, _key):
# assert the values
values = np.unique(mask)
for i in range(len(values)):
assert (values[i] in _mapping)
index = np.digitize(mask.ravel(), _mapping, right=True)
return _key[index].reshape(mask.shape)
mask = Image.fromarray(
_class_to_index(detail.getMask(img_id), _mapping=_mapping, _key=_key))
filename = img_id['file_name']
mask.save(osp.join(out_dir, filename.replace('jpg', 'png')))
return osp.splitext(osp.basename(filename))[0]
def parse_args():
parser = argparse.ArgumentParser(
description='Convert PASCAL VOC annotations to mmsegmentation format')
parser.add_argument('devkit_path', help='pascal voc devkit path')
parser.add_argument('json_path', help='annoation json filepath')
parser.add_argument('-o', '--out_dir', help='output path')
args = parser.parse_args()
return args
def main():
args = parse_args()
devkit_path = args.devkit_path
if args.out_dir is None:
out_dir = osp.join(devkit_path, 'VOC2010', 'SegmentationClassContext')
else:
out_dir = args.out_dir
json_path = args.json_path
mmcv.mkdir_or_exist(out_dir)
img_dir = osp.join(devkit_path, 'VOC2010', 'JPEGImages')
train_detail = Detail(json_path, img_dir, 'train')
train_ids = train_detail.getImgs()
val_detail = Detail(json_path, img_dir, 'val')
val_ids = val_detail.getImgs()
mmcv.mkdir_or_exist(
osp.join(devkit_path, 'VOC2010/ImageSets/SegmentationContext'))
train_list = mmcv.track_progress(
partial(generate_labels, detail=train_detail, out_dir=out_dir),
train_ids)
with open(
osp.join(devkit_path, 'VOC2010/ImageSets/SegmentationContext',
'train.txt'), 'w') as f:
f.writelines(line + '\n' for line in sorted(train_list))
val_list = mmcv.track_progress(
partial(generate_labels, detail=val_detail, out_dir=out_dir), val_ids)
with open(
osp.join(devkit_path, 'VOC2010/ImageSets/SegmentationContext',
'val.txt'), 'w') as f:
f.writelines(line + '\n' for line in sorted(val_list))
print('Done!')
if __name__ == '__main__':
main()
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