Source code for kornia.filters.unsharp

from typing import Tuple

import torch
import torch.nn as nn

from kornia.filters import gaussian_blur2d


[docs]def unsharp_mask( input: torch.Tensor, kernel_size: Tuple[int, int], sigma: Tuple[float, float], border_type: str = 'reflect' ) -> torch.Tensor: r"""Creates an operator that blurs a tensor using the existing Gaussian filter available with the Kornia library. .. image:: _static/img/unsharp_mask.png Args: input: the input tensor with shape :math:`(B,C,H,W)`. kernel_size: the size of the kernel. sigma: the standard deviation of the kernel. border_type: the padding mode to be applied before convolving. The expected modes are: ``'constant'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Returns: the blurred tensor with shape :math:`(B,C,H,W)`. Examples: >>> input = torch.rand(2, 4, 5, 5) >>> output = unsharp_mask(input, (3, 3), (1.5, 1.5)) >>> output.shape torch.Size([2, 4, 5, 5]) """ data_blur: torch.Tensor = gaussian_blur2d(input, kernel_size, sigma) data_sharpened: torch.Tensor = input + (input - data_blur) return data_sharpened
[docs]class UnsharpMask(nn.Module): r"""Creates an operator that sharpens image using the existing Gaussian filter available with the Kornia library.. Args: kernel_size: the size of the kernel. sigma: the standard deviation of the kernel. border_type: the padding mode to be applied before convolving. The expected modes are: ``'constant'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Returns: the sharpened tensor with shape :math:`(B,C,H,W)`. Shape: - Input: :math:`(B, C, H, W)` - Output: :math:`(B, C, H, W)` .. note:: See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/ unsharp_mask.html>`__. Examples: >>> input = torch.rand(2, 4, 5, 5) >>> sharpen = UnsharpMask((3, 3), (1.5, 1.5)) >>> output = sharpen(input) >>> output.shape torch.Size([2, 4, 5, 5]) """ def __init__(self, kernel_size: Tuple[int, int], sigma: Tuple[float, float], border_type: str = 'reflect') -> None: super().__init__() self.kernel_size: Tuple[int, int] = kernel_size self.sigma: Tuple[float, float] = sigma self.border_type = border_type def forward(self, input: torch.Tensor) -> torch.Tensor: return unsharp_mask(input, self.kernel_size, self.sigma, self.border_type)