def __init__(self,
data_dir='auto',
split='train',
year='2012',
use_difficult=False,
return_difficult=False):
if data_dir == 'auto' and year in ['2007', '2012']:
data_dir = voc_utils.get_voc(year, split)
if split not in ['train', 'trainval', 'val']:
if not (split == 'test' and year == '2007'):
warnings.warn(
'please pick split from \'train\', \'trainval\', \'val\''
'for 2012 dataset. For 2007 dataset, you can pick \'test\''
' in addition to the above mentioned splits.')
id_list_file = os.path.join(data_dir,
'ImageSets/Main/{0}.txt'.format(split))
# self.ids = [id_.strip() for id_ in open(id_list_file)]
import re
self.ids = list()
for id_ in open(id_list_file):
id_ = id_.strip()
id_ = re.split(" +", id_)
if id_[1] == "1":
self.ids.append(id_[0])
self.data_dir = data_dir
self.use_difficult = use_difficult
self.return_difficult = return_difficult
def __init__(self, data_dir='auto', split='train', year='2012',
use_difficult=False, return_difficult=False):
super(VOCBboxDataset, self).__init__()
if data_dir == 'auto' and year in ['2007', '2012']:
data_dir = voc_utils.get_voc(year, split)
if split not in ['train', 'trainval', 'val']:
if not (split == 'test' and year == '2007'):
warnings.warn(
'please pick split from \'train\', \'trainval\', \'val\''
'for 2012 dataset. For 2007 dataset, you can pick \'test\''
' in addition to the above mentioned splits.'
)
id_list_file = os.path.join(
data_dir, 'ImageSets/Main/{0}.txt'.format(split))
self.ids = [id_.strip() for id_ in open(id_list_file)]
self.data_dir = data_dir
self.use_difficult = use_difficult
self.add_getter('img', self._get_image)
self.add_getter(('bbox', 'label', 'difficult'), self._get_annotations)
if not return_difficult:
self.keys = ('img', 'bbox', 'label')
def __init__(self, data_dir='auto', split='train'):
if split not in ['train', 'trainval', 'val']:
raise ValueError(
'please pick split from \'train\', \'trainval\', \'val\'')
if data_dir == 'auto':
data_dir = voc_utils.get_voc('2012', split)
id_list_file = os.path.join(
data_dir, 'ImageSets/Segmentation/{0}.txt'.format(split))
self.ids = [id_.strip() for id_ in open(id_list_file)]
self.data_dir = data_dir
def __init__(self, data_dir='auto', split='train'):
if split not in ['train', 'trainval', 'val']:
raise ValueError(
'please pick split from \'train\', \'trainval\', \'val\'')
if data_dir == 'auto':
data_dir = voc_utils.get_voc('2012', split)
id_list_file = os.path.join(
data_dir, 'ImageSets/Segmentation/{0}.txt'.format(split))
self.ids = [id_.strip() for id_ in open(id_list_file)]
self.data_dir = data_dir
def __init__(self, data_dir='auto', split='train'):
super(VOCInstanceSegmentationDataset, self).__init__()
if split not in ['train', 'trainval', 'val']:
raise ValueError(
'please pick split from \'train\', \'trainval\', \'val\'')
if data_dir == 'auto':
data_dir = voc_utils.get_voc('2012', split)
id_list_file = os.path.join(
data_dir, 'ImageSets/Segmentation/{0}.txt'.format(split))
self.ids = [id_.strip() for id_ in open(id_list_file)]
self.data_dir = data_dir
self.add_getter('img', self._get_image)
self.add_getter(('mask', 'label'), self._get_annotations)
def __init__(self, data_dir='auto', split='train', year='2012',
use_difficult=False, return_difficult=False):
if data_dir == 'auto' and year in ['2007', '2012']:
data_dir = voc_utils.get_voc(year, split)
if split not in ['train', 'trainval', 'val']:
if not (split == 'test' and year == '2007'):
warnings.warn(
'please pick split from \'train\', \'trainval\', \'val\''
'for 2012 dataset. For 2007 dataset, you can pick \'test\''
' in addition to the above mentioned splits.'
)
id_list_file = os.path.join(
data_dir, 'ImageSets/Main/{0}.txt'.format(split))
self.ids = [id_.strip() for id_ in open(id_list_file)]
self.data_dir = data_dir
self.use_difficult = use_difficult
self.return_difficult = return_difficult
def __init__(self, data_dir='auto', split='aug'):
super(VOCSemanticSegmentationWithBboxDataset, self).__init__()
if data_dir == 'auto':
data_dir = voc_utils.get_voc('2012', split)
if split == 'aug':
file_dir = os.path.dirname(os.path.realpath(__file__))
f = open(os.path.join(file_dir, 'splits/voc_trainaug.txt'))
self.ids = []
for l in f.readlines():
path = l.split()[0]
self.ids.append(os.path.split(os.path.splitext(path)[0])[1])
elif split == 'val':
id_list_file = os.path.join(
data_dir, 'ImageSets/Segmentation/{0}.txt'.format(split))
self.ids = [id_.strip() for id_ in open(id_list_file)]
self.data_dir = data_dir
self.add_getter('img', self._get_image)
self.add_getter('label_map', self._get_label)
self.add_getter(('bbox', 'label'), self._get_annotations)
def __init__(self,
data_dir='auto',
split='train',
year='2012',
use_difficult=False,
return_difficult=False):
super(VOCBboxDataset, self).__init__()
if data_dir == 'auto' and year in ['2007', '2012']:
data_dir = voc_utils.get_voc(year, split)
id_list_file = os.path.join(data_dir,
'ImageSets/Main/{0}.txt'.format(split))
self.ids = [id_.strip() for id_ in open(id_list_file)]
self.data_dir = data_dir
self.use_difficult = use_difficult
self.add_getter('img', self._get_image)
self.add_getter(('bbox', 'label', 'difficult'), self._get_annotations)
if not return_difficult:
self.keys = ('img', 'bbox', 'label')
