def __init__(self, split, align=False, partition='all'):
if not partition == 'all':
name = 'person_' + partition + '_' + split
elif partition == 'all':
name = 'person_' + split
if align and (partition == 'all' or partition == 'face'):
name += '_align'
Imdb.__init__(self, name)
# Load two children dataset wrappers
self._face = CelebA(split, align=align)
self._clothes = DeepFashion(split)
# The class list is a combination of face and clothing attributes
self._classes = self._face.classes + self._clothes.classes
self._face_class_idx = range(self._face.num_classes)
self._clothes_class_idx = range(
self._face.num_classes,
self._face.num_classes + self._clothes.num_classes)
# load data path
self._data_path = os.path.join(self.data_path, 'imdb_PersonAttributes')
# load the image lists and attributes.
self._load_dataset(split, align, partition)
def __init__(self, split):
name = 'celeba_plus_webcam_cls_' + split
Imdb.__init__(self, name)
# object classes
self._classes = ['Bald', 'Hat', 'Hair']
# load image paths and annotations
self._data_path = osp.join(self.data_path, 'imdb_CelebA+Webcam')
self._load_dataset(split)
def __init__(self, split):
name = 'mnist_' + split
Imdb.__init__(self, name)
# load image paths
self._data_path = os.path.join(self.data_path, 'imdb_mnist')
# attribute classes
self._classes = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
# load annotations
self._load_dataset(split)
def __init__(self, split, align=False):
name = 'celeba_' + split
if align is True:
name += '_align'
Imdb.__init__(self, name)
# attribute classes
self._classes = \
['5_o_Clock_Shadow', 'Arched_Eyebrows', 'Attractive', 'Bags_Under_Eyes', 'Bald', 'Bangs', 'Big_Lips', 'Big_Nose', 'Black_Hair', 'Blond_Hair', 'Blurry', 'Brown_Hair', 'Bushy_Eyebrows', 'Chubby', 'Double_Chin', 'Eyeglasses', 'Goatee', 'Gray_Hair', 'Heavy_Makeup', 'High_Cheekbones', 'Male', 'Mouth_Slightly_Open', 'Mustache', 'Narrow_Eyes', 'No_Beard', 'Oval_Face', 'Pale_Skin', 'Pointy_Nose', 'Receding_Hairline', 'Rosy_Cheeks', 'Sideburns', 'Smiling', 'Straight_Hair', 'Wavy_Hair', 'Wearing_Earrings', 'Wearing_Hat', 'Wearing_Lipstick', 'Wearing_Necklace', 'Wearing_Necktie', 'Young']
# load image paths and annotations
self._data_path = os.path.join(self.data_path, 'imdb_CelebA')
self._load_dataset(split, align)
def __init__(self, split):
name = 'IBMattributes_' + split
Imdb.__init__(self, name)
# attribute classes
self._classes = [
'Bald', 'Hat', 'Hair', 'Blackhair', 'Blondehair', 'Facialhair',
'Asian', 'Black', 'White', 'NoGlasses', 'SunGlasses',
'VisionGlasses'
]
self._split = split
# load image paths and annotations
self._data_path = osp.join(self.data_path, 'imdb_IBMAttributes')
self._load_config()
self._load_dataset(split)
def __init__(self, split):
name = 'deepfashion_' + split
Imdb.__init__(self, name)
# load image paths
self._data_path = os.path.join(self.data_path, 'imdb_DeepFashion')
# attribute classes
self._classes = []
self._class_types = []
attr_file = os.path.join(self.data_path, 'Anno',
'list_category_cloth.txt')
with open(attr_file, 'r') as fid:
# skip first two lines
next(fid)
next(fid)
# read class list
for line in fid:
parsed_line = line.split()
self._classes.append(' '.join(parsed_line[:-1]))
self._class_types.append(int(parsed_line[-1]))
# load annotations
self._load_dataset(split)