Source code for kornia.filters.gaussian

from typing import Tuple

import torch
import torch.nn as nn

from .filter import filter2d, filter2d_separable
from .kernels import get_gaussian_kernel1d, get_gaussian_kernel2d


[docs]def gaussian_blur2d(input: torch.Tensor, kernel_size: Tuple[int, int], sigma: Tuple[float, float], border_type: str = 'reflect', separable: bool = True) -> torch.Tensor: r"""Create an operator that blurs a tensor using a Gaussian filter. .. image:: _static/img/gaussian_blur2d.png The operator smooths the given tensor with a gaussian kernel by convolving it to each channel. It supports batched operation. Arguments: 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'``. Default: ``'reflect'``. separable: run as composition of two 1d-convolutions. Returns: the blurred tensor with shape :math:`(B, C, H, W)`. .. note:: See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/ gaussian_blur.html>`__. Examples: >>> input = torch.rand(2, 4, 5, 5) >>> output = gaussian_blur2d(input, (3, 3), (1.5, 1.5)) >>> output.shape torch.Size([2, 4, 5, 5]) """ if separable: kernel_x: torch.Tensor = get_gaussian_kernel1d(kernel_size[1], sigma[1]) kernel_y: torch.Tensor = get_gaussian_kernel1d(kernel_size[0], sigma[0]) out = filter2d_separable(input, kernel_x[None], kernel_y[None], border_type) else: kernel: torch.Tensor = get_gaussian_kernel2d(kernel_size, sigma) out = filter2d(input, kernel[None], border_type) return out
[docs]class GaussianBlur2d(nn.Module): r"""Create an operator that blurs a tensor using a Gaussian filter. The operator smooths the given tensor with a gaussian kernel by convolving it to each channel. It supports batched operation. Arguments: 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'``. Default: ``'reflect'``. separable: run as composition of two 1d-convolutions. Returns: the blurred tensor. Shape: - Input: :math:`(B, C, H, W)` - Output: :math:`(B, C, H, W)` Examples:: >>> input = torch.rand(2, 4, 5, 5) >>> gauss = GaussianBlur2d((3, 3), (1.5, 1.5)) >>> output = gauss(input) # 2x4x5x5 >>> output.shape torch.Size([2, 4, 5, 5]) """ def __init__(self, kernel_size: Tuple[int, int], sigma: Tuple[float, float], border_type: str = 'reflect', separable: bool = True) -> None: super().__init__() self.kernel_size: Tuple[int, int] = kernel_size self.sigma: Tuple[float, float] = sigma self.border_type = border_type self.separable = separable def __repr__(self) -> str: return ( self.__class__.__name__ + '(kernel_size=' + str(self.kernel_size) + ', ' + 'sigma=' + str(self.sigma) + ', ' + 'border_type=' + self.border_type + 'separable=' + str(self.separable) + ')' ) def forward(self, input: torch.Tensor) -> torch.Tensor: return gaussian_blur2d(input, self.kernel_size, self.sigma, self.border_type, self.separable)