torchvision/transforms/functional.py
def perspective(img, startpoints, endpoints, interpolation=Image.BICUBIC):
    """Perform perspective transform of the given PIL Image.

    Args:
        img (PIL Image): Image to be transformed.
        coeffs (tuple) : 8-tuple (a, b, c, d, e, f, g, h) which contains the coefficients.
                            for a perspective transform.
        interpolation: Default- Image.BICUBIC
    Returns:
        PIL Image:  Perspectively transformed Image.
    """
    if not _is_pil_image(img):
        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))

    coeffs = _get_perspective_coeffs(startpoints, endpoints)
    return img.transform(img.size, Image.PERSPECTIVE, coeffs, interpolation)
def perspective(img, startpoints, endpoints, interpolation=Image.BICUBIC):
    if not _is_pil_image(img):
        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))

    coeffs = _get_perspective_coeffs(startpoints, endpoints)
    return img.transform(img.size, Image.PERSPECTIVE, coeffs, interpolation)
def perspective ( img , startpoints , endpoints , interpolation = Image . BICUBIC ) : if not _is_pil_image ( img ) : raise TypeError ( 'img should be PIL Image. Got {}' . format ( type ( img ) ) ) coeffs = _get_perspective_coeffs ( startpoints , endpoints ) return img . transform ( img . size , Image . PERSPECTIVE , coeffs , interpolation )
Perform perspective transform of the given PIL Image.

    Args:
        img (PIL Image): Image to be transformed.
        coeffs (tuple) : 8-tuple (a, b, c, d, e, f, g, h) which contains the coefficients.
                            for a perspective transform.
        interpolation: Default- Image.BICUBIC
    Returns:
        PIL Image:  Perspectively transformed Image.
Perform perspective transform of the given PIL Image .