def __call__(self, img, target):
"""
Args:
img (PIL Image): Image to be scaled.
Returns:
PIL Image: Rescaled image.
"""
return F.resize(img, self.size, self.interpolation), F.resize(target, self.size, self.interpolation)
def __call__(self, image, target):
min_h, min_w = self.min_size
max_h, max_w = self.max_size
h = random.randint(min_h, max_h)
w = random.randint(min_w, max_w)
image = F.resize(image, (h, w), interpolation=Image.LINEAR)
target = target if type(target) == str else F.resize(
target, (h, w), interpolation=Image.NEAREST)
return image, target
def __call__(self, img):
"""
Args:
img (numpy ndarray): Image to be scaled.
Returns:
numpy ndarray: Rescaled image.
"""
return F.resize(img, self.size, self.interpolation)
def __call__(self, sample):
"""
Args:
img (PIL Image): Image to be scaled.
Returns:
PIL Image: Rescaled image.
"""
return F.resize(sample, self.size, self.interpolation)
def __call__(self, img, boxes=None, labels=None):
"""
:param img:
:param boxes:
:param labels:
:return:
"""
h, w = img.height, img.width
img, oh, ow = F.resize(img, self.size, self.interpolation)
if boxes is not None:
boxes = boxes.copy()
scale_h, scale_w = oh / h, ow / w
boxes[:, ::2] *= scale_h
boxes[:, 1::2] *= scale_w
return img, boxes, labels
def __call__(self, image, target):
image = F.resize(image, self.size_image, interpolation=Image.LINEAR)
target = target if type(target) == str else F.resize(
target, self.size_label, interpolation=Image.NEAREST)
return image, target
def __call__(self, img):
return F.resize(img, self.size)