def preprocess(self, mode):
assert mode in ['train', 'val', 'test']
pascal_data_path = get_data_path('pascal')
sbd_data_path = get_data_path('sbd')
items = []
if mode == 'train':
p = open(
os.path.join(pascal_data_path, 'ImageSets', 'Person',
'train.txt')).readlines()
s = open(os.path.join(sbd_data_path, 'dataset',
'train.txt')).readlines()
lines = list(set(p).intersection(s))
data_list = [l.strip('\n') for l in lines]
elif mode == 'val':
p = open(
os.path.join(pascal_data_path, 'ImageSets', 'Person',
'val.txt')).readlines()
s = open(os.path.join(sbd_data_path, 'dataset',
'val.txt')).readlines()
lines = list(set(p).intersection(s))
data_list = [l.strip('\n') for l in lines]
img_path = os.path.join(sbd_data_path, 'dataset', 'img')
semseg_mask_path = os.path.join(sbd_data_path, 'dataset', 'cls')
parts_mask_path = os.path.join(pascal_data_path, 'ImageSets', 'Person',
'gt')
for it in data_list:
item = (os.path.join(img_path, it + '.jpg'),
os.path.join(semseg_mask_path, it + '.mat'),
os.path.join(parts_mask_path, it + '.png'))
items.append(item)
return items
def preprocess(self, mode):
assert mode in ['train', 'val', 'test']
items = []
data_path = get_data_path('lip')
if mode == 'train':
img_path = os.path.join(data_path, 'multi-person', 'Training',
'Images')
mask_path = os.path.join(data_path, 'multi-person', 'Training',
'Category_ids')
data_list = [
l.strip('\n') for l in open(
os.path.join(data_path, 'multi-person', 'Training',
'train_id.txt')).readlines()
]
for it in data_list:
item = (os.path.join(img_path, it + '.jpg'),
os.path.join(mask_path, it + '.png'))
items.append(item)
elif mode == 'val':
img_path = os.path.join(data_path, 'multi-person', 'Validation',
'Images')
mask_path = os.path.join(data_path, 'multi-person', 'Validation',
'Category_ids')
data_list = [
l.strip('\n') for l in open(
os.path.join(data_path, 'multi-person', 'Validation',
'val_id.txt')).readlines()
]
for it in data_list:
item = (os.path.join(img_path, it + '.jpg'),
os.path.join(mask_path, it + '.png'))
items.append(item)
return items[0:11716]
def preprocess(self, mode):
assert mode in ['train', 'val', 'test']
data_path = get_data_path('pascalparts')
if mode == 'train':
data_list = [
l.strip('\n') for l in open(
os.path.join(data_path, 'ImageSets', 'Person',
'train.txt')).readlines()
]
elif mode == 'val':
data_list = [
l.strip('\n') for l in open(
os.path.join(data_path, 'ImageSets', 'Person',
'val.txt')).readlines()
]
items = []
img_path = os.path.join(data_path, 'JPEGImages')
mask_path = os.path.join(data_path, 'ImageSets', 'Person', 'gt')
for it in data_list:
item = (os.path.join(img_path, it + '.jpg'),
os.path.join(mask_path, it + '.png'))
items.append(item)
return items
def preprocess(self, mode):
assert mode in ['train', 'val', 'test']
items = []
sbd_path = get_data_path('sbd')
sbd_img_path = os.path.join(sbd_path, 'dataset', 'img')
sbd_mask_path = os.path.join(sbd_path, 'dataset', 'cls')
voc_path = get_data_path('pascal')
voc_test_path = get_data_path('pascal_test')
voc_img_path = os.path.join(voc_path, 'JPEGImages')
voc_mask_path = os.path.join(voc_path, 'SegmentationClass')
# Train data = VOC_train + SBD_train + SBD_val
if mode == 'train':
sbd_data_list = [
l.strip('\n')
for l in open(os.path.join(sbd_path, 'dataset',
'trainval.txt')).readlines()
]
# SBD dataset contains some of the voc_val samples, so we have to remove them
voc_val_data_list = [
l.strip('\n') for l in open(
os.path.join(voc_path, 'ImageSets', 'Segmentation',
'val.txt')).readlines()
]
sbd_data_list = list(set(sbd_data_list) - set(voc_val_data_list))
for it in sbd_data_list:
item = (os.path.join(sbd_img_path, it + '.jpg'),
os.path.join(sbd_mask_path, it + '.mat'))
items.append(item)
voc_data_list = [
l.strip('\n') for l in open(
os.path.join(voc_path, 'ImageSets', 'Segmentation',
'train.txt')).readlines()
]
for it in voc_data_list:
item = (os.path.join(voc_img_path, it + '.jpg'),
os.path.join(voc_mask_path, it + '.png'))
items.append(item)
# Val data = VOC_val
elif mode == 'val':
data_list = [
l.strip('\n') for l in open(
os.path.join(voc_path, 'ImageSets', 'Segmentation',
'val.txt')).readlines()
]
for it in data_list:
item = (os.path.join(voc_img_path, it + '.jpg'),
os.path.join(voc_mask_path, it + '.png'))
items.append(item)
# Test data = VOC_test
else:
img_path = os.path.join(voc_test_path, 'JPEGImages')
data_list = [
l.strip('\n') for l in open(
os.path.join(voc_path, 'ImageSets', 'Segmentation',
'test.txt')).readlines()
]
for it in data_list:
items.append((img_path, it))
return items