# Source code for kornia.filters.gaussian

```from __future__ import annotations

from typing import Any

from kornia.core import Module, Tensor, tensor
from kornia.core.check import KORNIA_CHECK_IS_TENSOR
from kornia.utils import deprecated

from .filter import filter2d, filter2d_separable
from .kernels import _unpack_2d_ks, get_gaussian_kernel1d, get_gaussian_kernel2d

[docs]def gaussian_blur2d(
input: Tensor,
kernel_size: tuple[int, int] | int,
sigma: tuple[float, float] | Tensor,
border_type: str = 'reflect',
separable: bool = True,
) -> 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.github.io/tutorials/nbs/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])

>>> output = gaussian_blur2d(input, (3, 3), torch.tensor([[1.5, 1.5]]))
>>> output.shape
torch.Size([2, 4, 5, 5])
"""
KORNIA_CHECK_IS_TENSOR(input)

if isinstance(sigma, tuple):
sigma = tensor([sigma], device=input.device, dtype=input.dtype)
else:
KORNIA_CHECK_IS_TENSOR(sigma)
sigma = sigma.to(device=input.device, dtype=input.dtype)

if separable:
ky, kx = _unpack_2d_ks(kernel_size)
bs = sigma.shape[0]
kernel_x = get_gaussian_kernel1d(kx, sigma[:, 1].view(bs, 1))
kernel_y = get_gaussian_kernel1d(ky, sigma[:, 0].view(bs, 1))
out = filter2d_separable(input, kernel_x, kernel_y, border_type)
else:
kernel = get_gaussian_kernel2d(kernel_size, sigma)
out = filter2d(input, kernel, border_type)

return out

[docs]class GaussianBlur2d(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] | int,
sigma: tuple[float, float] | Tensor,
border_type: str = 'reflect',
separable: bool = True,
) -> None:
super().__init__()
self.kernel_size = kernel_size
self.sigma = sigma
self.border_type = border_type
self.separable = separable

def __repr__(self) -> str:
return (
f"{self.__class__.__name__}"
f"(kernel_size={self.kernel_size}, "
f"sigma={self.sigma}, "
f"border_type={self.border_type}, "
f"separable={self.separable})"
)

def forward(self, input: Tensor) -> Tensor:
return gaussian_blur2d(input, self.kernel_size, self.sigma, self.border_type, self.separable)

@deprecated(replace_with='gaussian_blur2d', version='6.9.10')
def gaussian_blur2d_t(*args: Any, **kwargs: Any) -> Tensor:
return gaussian_blur2d(*args, **kwargs)
```