[FEATURE] Add borderType option to kornia.filters like OpenCV

kornia.filters operations have no option like borderType on OpenCV. In my experience, borderType is the most critical argument in the arguments that is not in kornia.filters. To reproduce it, I suggest the changes like below:

class BoxBlur(nn.Module):
    def __init__(self, kernel_size: Tuple[int, int], border_type='zero': str) -> None:
        super(BoxBlur, self).__init__()
        self.kernel: torch.Tensor = _get_box_filter(kernel_size)
        self.padding: Tuple[int, int] = _compute_zero_padding(kernel_size)
        self.border_type = border_type

    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
        tmp_kernel: torch.Tensor = self.kernel.to(input.device).to(input.dtype)
        kernel: torch.Tensor = tmp_kernel.repeat(c, 1, 1, 1)

        return conv2d(input, kernel, padding=self.padding, padding_mode=self.border_type, stride=1, groups=c)

[edgarriba]

Yeap, it’s something we needed for a while. I’ve seen you implemented YCbCr, are you planning to send a PR? The is a lot of color stuff to be done 😆

[edgarriba]

@S-aiueo32 you are absolutely right. However, torch.conv2d only implements zeros or circular padding modes. To address this issue, as @ducha-aiki suggested in https://github.com/arraiyopensource/kornia/pull/168, we need to manually pad the input using torch.pad which already implement more advanced padding techniques. In order to generalize the other filtering operators, I will implement this strategy in a filter2d operator which is BTW already implemented in OpenCV.