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)