Function get_gaussian_kernel2d

src/modules/pixie/utils/util.py:275–314

r"""Function that returns Gaussian filter matrix coefficients. Args: kernel_size (Tuple[int, int]): filter sizes in the x and y direction. Sizes should be odd and positive. sigma (Tuple[int, int]): gaussian standard deviation in the x and y direction. Retu

(kernel_size, sigma)

    return window_1d

def get_gaussian_kernel2d(kernel_size, sigma):
    r"""Function that returns Gaussian filter matrix coefficients.

    Args:
        kernel_size (Tuple[int, int]): filter sizes in the x and y direction.
         Sizes should be odd and positive.
        sigma (Tuple[int, int]): gaussian standard deviation in the x and y
         direction.

    Returns:
        Tensor: 2D tensor with gaussian filter matrix coefficients.

    Shape:
        - Output: :math:`(\text{kernel_size}_x, \text{kernel_size}_y)`

    Examples::

        >>> kornia.image.get_gaussian_kernel2d((3, 3), (1.5, 1.5))
        tensor([[0.0947, 0.1183, 0.0947],
                [0.1183, 0.1478, 0.1183],
                [0.0947, 0.1183, 0.0947]])

        >>> kornia.image.get_gaussian_kernel2d((3, 5), (1.5, 1.5))
        tensor([[0.0370, 0.0720, 0.0899, 0.0720, 0.0370],
                [0.0462, 0.0899, 0.1123, 0.0899, 0.0462],
                [0.0370, 0.0720, 0.0899, 0.0720, 0.0370]])
    """
    if not isinstance(kernel_size, tuple) or len(kernel_size) != 2:
        raise TypeError("kernel_size must be a tuple of length two. Got {}"
                        .format(kernel_size))
    if not isinstance(sigma, tuple) or len(sigma) != 2:
        raise TypeError("sigma must be a tuple of length two. Got {}"
                        .format(sigma))
    ksize_x, ksize_y = kernel_size
    sigma_x, sigma_y = sigma
    kernel_x = get_gaussian_kernel(ksize_x, sigma_x)
    kernel_y = get_gaussian_kernel(ksize_y, sigma_y)
    kernel_2d = torch.matmul(
        kernel_x.unsqueeze(-1), kernel_y.unsqueeze(-1).t())
    return kernel_2d

def gaussian_blur(x, kernel_size=(5,5), sigma=(1.3,1.3)):
    b, c, h, w = x.shape