perspective

torchvision.transforms.functional.perspective(img: torch.Tensor, startpoints: List[List[int]], endpoints: List[List[int]], interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.BILINEAR: 'bilinear'>, fill: Optional[List[float]] = None) → torch.Tensor

Perform perspective transform of the given image. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions.

Parameters

• img (PIL Image or Tensor) – Image to be transformed.

• startpoints (list of list of python:ints) – List containing four lists of two integers corresponding to four corners [top-left, top-right, bottom-right, bottom-left] of the original image.

• endpoints (list of list of python:ints) – List containing four lists of two integers corresponding to four corners [top-left, top-right, bottom-right, bottom-left] of the transformed image.

• interpolation (InterpolationMode) – Desired interpolation enum defined by torchvision.transforms.InterpolationMode. Default is InterpolationMode.BILINEAR. If input is Tensor, only InterpolationMode.NEAREST, InterpolationMode.BILINEAR are supported. For backward compatibility integer values (e.g. PIL.Image[.Resampling].NEAREST) are still accepted, but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.

• fill (sequence or number, optional) –

  Pixel fill value for the area outside the transformed image. If given a number, the value is used for all bands respectively.

  Note

  In torchscript mode single int/float value is not supported, please use a sequence of length 1: [value, ].

Returns

transformed Image.

Return type

PIL Image or Tensor

Examples using perspective:

Illustration of transforms

Illustration of transforms