def __init__(self,
name,
has_3d,
reorder=None,
face_and_shoulder=None,
lsp_only=False):
# for custom regressors add the corresponding keypoints to universal order
# this will force all datasets to add the given keypoint if available else
# will pad it with [0, 0] and visibility 0
self.universal_order = [
'toes_r', 'ankle_r', 'knee_r', 'hip_r', 'hip_l', 'knee_l',
'ankle_l', 'toes_l', 'wrist_r', 'elbow_r', 'shoulder_r',
'shoulder_l', 'elbow_l', 'wrist_l', 'neck', 'brain'
]
default_face_and_shoulder = [
'shoulder_l', 'shoulder_r', 'neck', 'brain'
]
if face_and_shoulder is not None:
# merge face and shoulder kps without duplicate
face_and_shoulder = list(
set(face_and_shoulder + default_face_and_shoulder))
else:
face_and_shoulder = default_face_and_shoulder
self.body_idx = [
i for i, kp in enumerate(self.universal_order)
if kp not in face_and_shoulder
]
self.lsp_only = lsp_only
self.name = check_type('name', name, str)
self.has_3d = check_type('has_3d', has_3d, int)
self.reorder = self.__create_reorder_idx(
reorder) if reorder is not None else None
def __init__(self,
config: DataSetConfig,
image_paths,
kps_2d,
vis=None,
kps_3d=None,
seqs=None):
self.config = config
self.image_paths = check_type('image_paths', image_paths, np.ndarray)
self.kps_2d = check_type('2d keypoints', kps_2d, np.ndarray)
self.vis = check_type('visibility', vis,
np.ndarray) if vis is not None else None
self.kps_3d = check_type('3d keypoints', kps_3d,
np.ndarray) if kps_3d is not None else None
self.seqs = check_type('sequences', seqs,
np.ndarray) if seqs is not None else None
def __init__(self, config: SmplDataSetConfig, poses, shapes=None):
self.config = config
self.poses = check_type('poses', poses, np.ndarray)
self.shapes = check_type('shapes', shapes,
np.ndarray) if shapes is not None else None
def __init__(self, name):
self.name = check_type('name', name, str)
