- torchvision.transforms.functional.resized_crop(img: Tensor, top: int, left: int, height: int, width: int, size: List[int], interpolation: InterpolationMode = InterpolationMode.BILINEAR, antialias: Optional[bool] = True) Tensor ¶
Crop the given image and resize it to desired size. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions
Notably used in
img (PIL Image or Tensor) – Image to be cropped. (0,0) denotes the top left corner of the image.
top (int) – Vertical component of the top left corner of the crop box.
left (int) – Horizontal component of the top left corner of the crop box.
height (int) – Height of the crop box.
width (int) – Width of the crop box.
size (sequence or int) – Desired output size. Same semantics as
interpolation (InterpolationMode) – Desired interpolation enum defined by
torchvision.transforms.InterpolationMode. Default is
InterpolationMode.BILINEAR. If input is Tensor, only
InterpolationMode.BICUBICare supported. The corresponding Pillow integer constants, e.g.
PIL.Image.BILINEARare accepted as well.
antialias (bool, optional) –
Whether to apply antialiasing. It only affects tensors with bilinear or bicubic modes and it is ignored otherwise: on PIL images, antialiasing is always applied on bilinear or bicubic modes; on other modes (for PIL images and tensors), antialiasing makes no sense and this parameter is ignored. Possible values are:
True(default): will apply antialiasing for bilinear or bicubic modes. Other mode aren’t affected. This is probably what you want to use.
False: will not apply antialiasing for tensors on any mode. PIL images are still antialiased on bilinear or bicubic modes, because PIL doesn’t support no antialias.
None: equivalent to
Falsefor tensors and
Truefor PIL images. This value exists for legacy reasons and you probably don’t want to use it unless you really know what you are doing.
The default value changed from
Truein v0.17, for the PIL and Tensor backends to be consistent.
- Return type:
PIL Image or Tensor
resized_crop:Illustration of transforms