def __init__(self, arg, dataset, split, pca_components=20, trainset_sim=None): self.arg = arg self.dataset = dataset self.split = split self.pca_components = pca_components self.list = get_annotations_list(self.arg.dataset_route, dataset, split, arg.crop_size, ispdb=arg.PDB) #[:1024] self.shapes = None self.pose_params = None self.aligned_shapes = None self.aligned_pose_params = None self.init_aligned_shapes(arg.crop_size) self.tree = None if trainset_sim is not None: self.trainset_sim = trainset_sim self.tree = KDTree(np.float32(self.trainset_sim.shapes))
def __init__(self, arg, dataset, split): self.arg = arg self.dataset = dataset self.split = split self.list = get_annotations_list(self.arg.dataset_route, dataset, split, arg.crop_size, ispdb=arg.PDB)
def __init__(self, arg, dataset, split): self.arg = arg self.dataset = dataset self.split = split self.type = arg.type self.mean_color, self.std_color = get_mean_std_color( self.dataset, self.split) self.mean_gray, self.std_gray = get_mean_std_gray( self.dataset, self.split) self.list = get_annotations_list(self.arg.dataset_route, dataset, split, arg.crop_size, ispdb=arg.PDB) if len(arg.dataset_indexes) > 0: self.list = [self.list[i] for i in arg.dataset_indexes]
def __init__(self, arg, dataset, split): self.arg = arg self.dataset = dataset self.split = split self.mean = arg.flame_dataset_mean_shape self.list = get_annotations_list(self.arg.dataset_route, dataset, split, arg.crop_size, ispdb=arg.PDB) self.shape_params = [] self.pose_params = [] self.expression_params = [] self.cam_params = [] self.mean_shape_params = None self.load_flame_params()
def __init__(self, dataset='WFLW', split='train'): self.dataset = dataset self.split = split self.list = get_annotations_list(dataset, split, ispdb=args.PDB)