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)