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).
"""
if self.min_value == self.max_value:
random_size = self.max_value
else:
random_size = int(
np.random.uniform(self.min_value, self.max_value) + 0.5)
im = functional.resize_long(im, random_size, cv2.INTER_LINEAR)
if label is not None:
label = functional.resize_long(label, random_size,
cv2.INTER_NEAREST)
if label is None:
return (im, im_info)
else:
return (im, im_info, label)
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).
"""
h, w = im.shape[0], im.shape[1]
long_edge = max(h, w)
target = long_edge
if (self.max_long is not None) and (long_edge > self.max_long):
target = self.max_long
elif (self.min_long is not None) and (long_edge < self.min_long):
target = self.min_long
if target != long_edge:
im = functional.resize_long(im, target)
if label is not None:
label = functional.resize_long(label, target,
cv2.INTER_NEAREST)
if label is None:
return (im, )
else:
return (im, label)
def __call__(self, data):
data['trans_info'].append(('resize', data['img'].shape[0:2]))
data['img'] = functional.resize_long(data['img'], self.long_size)
for key in data.get('gt_fields', []):
data[key] = functional.resize_long(data[key], self.long_size,
cv2.INTER_NEAREST)
return data
def __call__(self, data):
if self.min_value == self.max_value:
random_size = self.max_value
else:
random_size = int(
np.random.uniform(self.min_value, self.max_value) + 0.5)
data['img'] = functional.resize_long(data['img'], random_size,
cv2.INTER_LINEAR)
for key in data.get('gt_fields', []):
data[key] = functional.resize_long(data[key], random_size,
cv2.INTER_NEAREST)
return data
def __call__(self, data):
h, w = data['img'].shape[:2]
long_edge = max(h, w)
target = long_edge
if (self.max_long is not None) and (long_edge > self.max_long):
target = self.max_long
elif (self.min_long is not None) and (long_edge < self.min_long):
target = self.min_long
if target != long_edge:
data['trans_info'].append(('resize', data['img'].shape[0:2]))
data['img'] = functional.resize_long(data['img'], target)
for key in data.get('gt_fields', []):
data[key] = functional.resize_long(data[key], target)
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).
"""
im = functional.resize_long(im, self.long_size)
if label is not None:
label = functional.resize_long(label, self.long_size,
cv2.INTER_NEAREST)
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).
"""
if im_info is None:
im_info = list()
im_info.append(('resize', im.shape[:2]))
im = functional.resize_long(im, self.long_size)
if label is not None:
label = functional.resize_long(label, self.long_size,
cv2.INTER_NEAREST)
if label is None:
return (im, im_info)
else:
return (im, im_info, label)