torchvision/transforms/functional.py
def _get_perspective_coeffs(startpoints, endpoints):
    """Helper function to get the coefficients (a, b, c, d, e, f, g, h) for the perspective transforms.

    In Perspective Transform each pixel (x, y) in the orignal image gets transformed as,
     (x, y) -> ( (ax + by + c) / (gx + hy + 1), (dx + ey + f) / (gx + hy + 1) )

    Args:
        List containing [top-left, top-right, bottom-right, bottom-left] of the orignal image,
        List containing [top-left, top-right, bottom-right, bottom-left] of the transformed
                   image
    Returns:
        octuple (a, b, c, d, e, f, g, h) for transforming each pixel.
    """
    matrix = []

    for p1, p2 in zip(endpoints, startpoints):
        matrix.append([p1[0], p1[1], 1, 0, 0, 0, -p2[0] * p1[0], -p2[0] * p1[1]])
        matrix.append([0, 0, 0, p1[0], p1[1], 1, -p2[1] * p1[0], -p2[1] * p1[1]])

    A = torch.tensor(matrix, dtype=torch.float)
    B = torch.tensor(startpoints, dtype=torch.float).view(8)
    res = torch.gels(B, A)[0]
    return res.squeeze_(1).tolist()
def _get_perspective_coeffs(startpoints, endpoints):
    """Helper function to get the coefficients (a, b, c, d, e, f, g, h) for the perspective transforms.

    In Perspective Transform each pixel (x, y) in the orignal image gets transformed as,
     (x, y) -> ( (ax + by + c) / (gx + hy + 1), (dx + ey + f) / (gx + hy + 1) )

    Args:
        List containing [top-left, top-right, bottom-right, bottom-left] of the orignal image,
        List containing [top-left, top-right, bottom-right, bottom-left] of the transformed
                   image
    Returns:
        octuple (a, b, c, d, e, f, g, h) for transforming each pixel.
    """
    matrix = []

    for p1, p2 in zip(endpoints, startpoints):
        matrix.append([p1[0], p1[1], 1, 0, 0, 0, -p2[0] * p1[0], -p2[0] * p1[1]])
        matrix.append([0, 0, 0, p1[0], p1[1], 1, -p2[1] * p1[0], -p2[1] * p1[1]])

    A = torch.tensor(matrix, dtype=torch.float)
    B = torch.tensor(startpoints, dtype=torch.float).view(8)
    res = torch.gels(B, A)[0]
    return res.squeeze_(1).tolist()
Helper function to get the coefficients (a, b, c, d, e, f, g, h) for the perspective transforms.

    In Perspective Transform each pixel (x, y) in the orignal image gets transformed as,
     (x, y) -> ( (ax + by + c) / (gx + hy + 1), (dx + ey + f) / (gx + hy + 1) )

    Args:
        List containing [top-left, top-right, bottom-right, bottom-left] of the orignal image,
        List containing [top-left, top-right, bottom-right, bottom-left] of the transformed
                   image
    Returns:
        octuple (a, b, c, d, e, f, g, h) for transforming each pixel.