def __call__(self, data):
mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
std = np.array(self.std)[np.newaxis, np.newaxis, :]
data['img'] = functional.normalize(data['img'], mean, std)
if 'fg' in data.get('gt_fields', []):
data['fg'] = functional.normalize(data['fg'], mean, std)
if 'bg' in data.get('gt_fields', []):
data['bg'] = functional.normalize(data['bg'], mean, std)
return data
def __call__(self, im, label=None):
"""
Args:
im (np.ndarray): The Image data.
label (np.ndarray, optional): The label data. Default: None.
Returns:
(tuple). When label is None, it returns (im, ), otherwise it returns (im, label).
"""
mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
std = np.array(self.std)[np.newaxis, np.newaxis, :]
im = functional.normalize(im, mean, std)
if label is None:
return (im, )
else:
return (im, label)
def __call__(self, im, im_info=None, label=None):
"""
Args:
im (np.ndarray): The Image data.
im_info (dict, optional): A dictionary maintains image info before this transformation. Default: None.
label (np.ndarray, optional): The label data. Default: None.
Returns:
(tuple). When label is None, it returns (im, im_info), otherwise it returns (im, im_info, label).
"""
mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
std = np.array(self.std)[np.newaxis, np.newaxis, :]
im = functional.normalize(im, mean, std)
if label is None:
return (im, im_info)
else:
return (im, im_info, label)
def __call__(self, data):
mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
std = np.array(self.std)[np.newaxis, np.newaxis, :]
data['img'] = functional.normalize(data['img'], mean, std)
return data