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),
Raises:
TypeError: When the 'img' type is not numpy.
ValueError: When the length of "im" shape is not 3.
"""
if not isinstance(im, np.ndarray):
raise TypeError("Resize: image type is not numpy.")
if len(im.shape) != 3:
raise ValueError('Resize: image is not 3-dimensional.')
if self.interp == "RANDOM":
interp = random.choice(list(self.interp_dict.keys()))
else:
interp = self.interp
im = functional.resize(im, self.target_size, self.interp_dict[interp])
if label is not None:
label = functional.resize(label, self.target_size,
cv2.INTER_NEAREST)
if label is None:
return (im, )
else:
return (im, label)
def __call__(self, data):
height = data['img'].shape[0]
width = data['img'].shape[1]
new_im = np.zeros((max(height, width), max(
height, width), 3)) + self.im_padding_value
if 'label' in data['gt_fields']:
new_label = np.zeros((max(height, width), max(
height, width))) + self.label_padding_value
if height > width:
padding = int((height - width) / 2)
new_im[:, padding:padding + width, :] = data['img']
if 'label' in data['gt_fields']:
new_label[:, padding:padding + width] = data['label']
else:
padding = int((width - height) / 2)
new_im[padding:padding + height, :, :] = data['img']
if 'label' in data['gt_fields']:
new_label[padding:padding + height, :] = data['label']
data['img'] = np.uint8(new_im)
data['img'] = functional.resize(data['img'],
self.target_size,
interp=cv2.INTER_CUBIC)
if 'label' in data['gt_fields']:
data['label'] = np.uint8(new_label)
data['label'] = functional.resize(data['label'],
self.target_size,
interp=cv2.INTER_CUBIC)
return data
def __call__(self, data):
if self.min_scale_factor == self.max_scale_factor:
scale_factor = self.min_scale_factor
elif self.scale_step_size == 0:
scale_factor = np.random.uniform(self.min_scale_factor,
self.max_scale_factor)
else:
num_steps = int((self.max_scale_factor - self.min_scale_factor) /
self.scale_step_size + 1)
scale_factors = np.linspace(self.min_scale_factor,
self.max_scale_factor,
num_steps).tolist()
np.random.shuffle(scale_factors)
scale_factor = scale_factors[0]
w = int(round(scale_factor * data['img'].shape[1]))
h = int(round(scale_factor * data['img'].shape[0]))
data['img'] = functional.resize(data['img'], (w, h), cv2.INTER_LINEAR)
for key in data.get('gt_fields', []):
data[key] = functional.resize(data[key], (w, h), cv2.INTER_NEAREST)
return data
def __call__(self, data):
data['trans_info'].append(('resize', data['img'].shape[0:2]))
data['img'] = functional.resize(data['img'], self.target_size)
for key in data.get('gt_fields', []):
if key == 'trimap':
data[key] = functional.resize(data[key], self.target_size,
cv2.INTER_NEAREST)
else:
data[key] = functional.resize(data[key], self.target_size)
return data
def __call__(self, data):
data['trans_info'].append(('resize', data['img'].shape[0:2]))
h, w = data['img'].shape[0:2]
rw = w - w % 32
rh = h - h % 32
data['img'] = functional.resize(data['img'], (rw, rh))
for key in data.get('gt_fields', []):
data[key] = functional.resize(data[key], (rw, rh))
return data
def __call__(self, data):
data['trans_info'].append(('resize', data['img'].shape[0:2]))
if self.interp == "RANDOM":
interp = random.choice(list(self.interp_dict.keys()))
else:
interp = self.interp
data['img'] = functional.resize(data['img'], self.target_size,
self.interp_dict[interp])
for key in data.get('gt_fields', []):
data[key] = functional.resize(data[key], self.target_size,
cv2.INTER_NEAREST)
return data
def __call__(self, data):
data['trans_info'].append(('resize', data['img'].shape[0:2]))
h, w = data['img'].shape[0:2]
rw = w - w % self.mult_int
rh = h - h % self.mult_int
data['img'] = functional.resize(data['img'], (rw, rh))
for key in data.get('gt_fields', []):
if key == 'trimap':
data[key] = functional.resize(data[key], (rw, rh),
cv2.INTER_NEAREST)
else:
data[key] = functional.resize(data[key], (rw, rh))
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).
"""
height = im.shape[0]
width = im.shape[1]
new_im = np.zeros((max(height, width), max(
height, width), 3)) + self.im_padding_value
if label is not None:
new_label = np.zeros((max(height, width), max(
height, width))) + self.label_padding_value
if height > width:
padding = int((height - width) / 2)
new_im[:, padding:padding + width, :] = im
if label is not None:
new_label[:, padding:padding + width] = label
else:
padding = int((width - height) / 2)
new_im[padding:padding + height, :, :] = im
if label is not None:
new_label[padding:padding + height, :] = label
im = np.uint8(new_im)
im = functional.resize(im, self.target_size, interp=cv2.INTER_CUBIC)
if label is not None:
label = np.uint8(new_label)
label = functional.resize(label,
self.target_size,
interp=cv2.INTER_CUBIC)
if label is None:
return (im, )
else:
return (im, label)
def __call__(self, data):
h, w = data['img'].shape[:2]
if self.scale is not None:
scale = np.random.uniform(self.scale[0], self.scale[1])
else:
scale = 1.
if self.size is not None:
scale_factor = max(self.size[0] / w, self.size[1] / h)
else:
scale_factor = 1
scale = scale * scale_factor
w = int(round(w * scale))
h = int(round(h * scale))
data['img'] = functional.resize(data['img'], (w, h))
for key in data.get('gt_fields', []):
if key == 'trimap':
data[key] = functional.resize(data[key], (w, h),
cv2.INTER_NEAREST)
else:
data[key] = functional.resize(data[key], (w, h))
return data
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_scale_factor == self.max_scale_factor:
scale_factor = self.min_scale_factor
elif self.scale_step_size == 0:
scale_factor = np.random.uniform(self.min_scale_factor,
self.max_scale_factor)
else:
num_steps = int((self.max_scale_factor - self.min_scale_factor) /
self.scale_step_size + 1)
scale_factors = np.linspace(self.min_scale_factor,
self.max_scale_factor,
num_steps).tolist()
np.random.shuffle(scale_factors)
scale_factor = scale_factors[0]
w = int(round(scale_factor * im.shape[1]))
h = int(round(scale_factor * im.shape[0]))
im = functional.resize(im, (w, h), cv2.INTER_LINEAR)
if label is not None:
label = functional.resize(label, (w, h), cv2.INTER_NEAREST)
if label is None:
return (im, im_info)
else:
return (im, im_info, 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),
where im_info["shape_before_resize"] is updated to the size of the image before this transformation.
Raises:
TypeError: When the 'img' type is not numpy.
ValueError: When the length of "im" shape is not 3.
"""
if im_info is None:
im_info = list()
im_info.append(('resize', im.shape[:2]))
if not isinstance(im, np.ndarray):
raise TypeError("Resize: image type is not numpy.")
if len(im.shape) != 3:
raise ValueError('Resize: image is not 3-dimensional.')
if self.interp == "RANDOM":
interp = random.choice(list(self.interp_dict.keys()))
else:
interp = self.interp
im = functional.resize(im, self.target_size, self.interp_dict[interp])
if label is not None:
label = functional.resize(label, self.target_size,
cv2.INTER_NEAREST)
if label is None:
return (im, im_info)
else:
return (im, im_info, label)
def __call__(self, data):
data['trans_info'].append(('resize', data['img'].shape[0:2]))
data['img'] = functional.resize(data['img'], self.target_size)
for key in data.get('gt_fields', []):
data[key] = functional.resize(data[key], self.target_size)
return data