def gaussian_blur()

in torchvision/transforms/functional.py [0:0]

• 34 lines of code
• 22 McCabe index (conditional complexity)

def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None) -> Tensor:
    """Performs Gaussian blurring on the image by given kernel.
    If the image is torch Tensor, it is expected
    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.

    Args:
        img (PIL Image or Tensor): Image to be blurred
        kernel_size (sequence of ints or int): Gaussian kernel size. Can be a sequence of integers
            like ``(kx, ky)`` or a single integer for square kernels.

            .. note::
                In torchscript mode kernel_size as single int is not supported, use a sequence of
                length 1: ``[ksize, ]``.
        sigma (sequence of floats or float, optional): Gaussian kernel standard deviation. Can be a
            sequence of floats like ``(sigma_x, sigma_y)`` or a single float to define the
            same sigma in both X/Y directions. If None, then it is computed using
            ``kernel_size`` as ``sigma = 0.3 * ((kernel_size - 1) * 0.5 - 1) + 0.8``.
            Default, None.

            .. note::
                In torchscript mode sigma as single float is
                not supported, use a sequence of length 1: ``[sigma, ]``.

    Returns:
        PIL Image or Tensor: Gaussian Blurred version of the image.
    """
    if not torch.jit.is_scripting() and not torch.jit.is_tracing():
        _log_api_usage_once(gaussian_blur)
    if not isinstance(kernel_size, (int, list, tuple)):
        raise TypeError(f"kernel_size should be int or a sequence of integers. Got {type(kernel_size)}")
    if isinstance(kernel_size, int):
        kernel_size = [kernel_size, kernel_size]
    if len(kernel_size) != 2:
        raise ValueError(f"If kernel_size is a sequence its length should be 2. Got {len(kernel_size)}")
    for ksize in kernel_size:
        if ksize % 2 == 0 or ksize < 0:
            raise ValueError(f"kernel_size should have odd and positive integers. Got {kernel_size}")

    if sigma is None:
        sigma = [ksize * 0.15 + 0.35 for ksize in kernel_size]

    if sigma is not None and not isinstance(sigma, (int, float, list, tuple)):
        raise TypeError(f"sigma should be either float or sequence of floats. Got {type(sigma)}")
    if isinstance(sigma, (int, float)):
        sigma = [float(sigma), float(sigma)]
    if isinstance(sigma, (list, tuple)) and len(sigma) == 1:
        sigma = [sigma[0], sigma[0]]
    if len(sigma) != 2:
        raise ValueError(f"If sigma is a sequence, its length should be 2. Got {len(sigma)}")
    for s in sigma:
        if s <= 0.0:
            raise ValueError(f"sigma should have positive values. Got {sigma}")

    t_img = img
    if not isinstance(img, torch.Tensor):
        if not F_pil._is_pil_image(img):
            raise TypeError(f"img should be PIL Image or Tensor. Got {type(img)}")

        t_img = to_tensor(img)

    output = F_t.gaussian_blur(t_img, kernel_size, sigma)

    if not isinstance(img, torch.Tensor):
        output = to_pil_image(output)
    return output