Source code for kornia.filters.median

from typing import Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F
from kornia.filters.kernels import get_binary_kernel2d

def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]:
    r"""Utility function that computes zero padding tuple."""
    computed: Tuple[int, ...] = tuple([(k - 1) // 2 for k in kernel_size])
    return computed[0], computed[1]

[docs]class MedianBlur(nn.Module): r"""Blurs an image using the median filter. Args: kernel_size (Tuple[int, int]): the blurring kernel size. Returns: torch.Tensor: the blurred input tensor. Shape: - Input: :math:`(B, C, H, W)` - Output: :math:`(B, C, H, W)` Example: >>> input = torch.rand(2, 4, 5, 7) >>> blur = kornia.filters.MedianBlur((3, 3)) >>> output = blur(input) # 2x4x5x7 """ def __init__(self, kernel_size: Tuple[int, int]) -> None: super(MedianBlur, self).__init__() self.kernel: torch.Tensor = get_binary_kernel2d(kernel_size) self.padding: Tuple[int, int] = _compute_zero_padding(kernel_size) def forward(self, input: torch.Tensor): # type: ignore if not torch.is_tensor(input): raise TypeError("Input type is not a torch.Tensor. Got {}" .format(type(input))) if not len(input.shape) == 4: raise ValueError("Invalid input shape, we expect BxCxHxW. Got: {}" .format(input.shape)) # prepare kernel b, c, h, w = input.shape kernel: torch.Tensor = # map the local window to single vector features: torch.Tensor = F.conv2d( input.reshape(b * c, 1, h, w), kernel, padding=self.padding, stride=1) features = features.view(b, c, -1, h, w) # BxCx(K_h * K_w)xHxW # compute the median along the feature axis median: torch.Tensor = torch.median(features, dim=2)[0] return median
# functiona api
[docs]def median_blur(input: torch.Tensor, kernel_size: Tuple[int, int]) -> torch.Tensor: r"""Blurs an image using the median filter. See :class:`~kornia.filters.MedianBlur` for details. """ return MedianBlur(kernel_size)(input)