# kornia.filters#

The functions in this sections perform various image filtering operations.

## Blurring#

kornia.filters.blur_pool2d(input, kernel_size, stride=2)[source]#

Compute blurs and downsample a given feature map.

See BlurPool2D for details.

See [Zha19] for more details.

Parameters
• kernel_size (int) – the kernel size for max pooling..

• ceil_mode – should be true to match output size of conv2d with same kernel size.

Shape:
• Input: $$(B, C, H, W)$$

• Output: $$(N, C, H_{out}, W_{out})$$, where

$H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{kernel\_size//2}[0] - \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor$
$W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{kernel\_size//2}[1] - \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor$
Returns

the transformed tensor.

Note

This function is tested against https://github.com/adobe/antialiased-cnns.

Note

See a working example here.

Examples

>>> input = torch.eye(5)[None, None]
>>> blur_pool2d(input, 3)
tensor([[[[0.3125, 0.0625, 0.0000],
[0.0625, 0.3750, 0.0625],
[0.0000, 0.0625, 0.3125]]]])

kornia.filters.box_blur(input, kernel_size, border_type='reflect', normalized=True)[source]#

Blur an image using the box filter.

The function smooths an image using the kernel:

$\begin{split}K = \frac{1}{\text{kernel_size}_x * \text{kernel_size}_y} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \vdots & \vdots & \vdots & \ddots & \vdots & \vdots \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\end{split}$
Parameters
• image – the image to blur with shape $$(B,C,H,W)$$.

• kernel_size (Tuple[int, int]) – the blurring kernel size.

• border_type (str, optional) – the padding mode to be applied before convolving. The expected modes are: 'constant', 'reflect', 'replicate' or 'circular'. Default: 'reflect'

• normalized (bool, optional) – if True, L1 norm of the kernel is set to 1. Default: True

Return type

Tensor

Returns

the blurred tensor with shape $$(B,C,H,W)$$.

Note

See a working example here.

Example

>>> input = torch.rand(2, 4, 5, 7)
>>> output = box_blur(input, (3, 3))  # 2x4x5x7
>>> output.shape
torch.Size([2, 4, 5, 7])

kornia.filters.gaussian_blur2d(input, kernel_size, sigma, border_type='reflect', separable=True)[source]#

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.

Parameters
Return type

Tensor

Returns

the blurred tensor with shape $$(B, C, H, W)$$.

Note

See a working example here.

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])

kornia.filters.max_blur_pool2d(input, kernel_size, stride=2, max_pool_size=2, ceil_mode=False)[source]#

Compute pools and blurs and downsample a given feature map.

See MaxBlurPool2D for details.

Parameters
• kernel_size (int) – the kernel size for max pooling.

• stride (int, optional) – stride for pooling. Default: 2

• max_pool_size (int, optional) – the kernel size for max pooling. Default: 2

• ceil_mode (bool, optional) – should be true to match output size of conv2d with same kernel size. Default: False

Note

This function is tested against https://github.com/adobe/antialiased-cnns.

Note

See a working example here.

Examples

>>> input = torch.eye(5)[None, None]
>>> max_blur_pool2d(input, 3)
tensor([[[[0.5625, 0.3125],
[0.3125, 0.8750]]]])

Return type

Tensor

kornia.filters.median_blur(input, kernel_size)[source]#

Blur an image using the median filter.

Parameters
Return type

Tensor

Returns

the blurred input tensor with shape $$(B,C,H,W)$$.

Note

See a working example here.

Example

>>> input = torch.rand(2, 4, 5, 7)
>>> output = median_blur(input, (3, 3))
>>> output.shape
torch.Size([2, 4, 5, 7])

kornia.filters.motion_blur(input, kernel_size, angle, direction, border_type='constant', mode='nearest')[source]#

Perform motion blur on tensor images.

Parameters
• input (Tensor) – the input tensor with shape $$(B, C, H, W)$$.

• kernel_size (int) – motion kernel width and height. It should be odd and positive.

• angle (Union[torch.Tensor, float]) – angle of the motion blur in degrees (anti-clockwise rotation). If tensor, it must be $$(B,)$$.

• direction (Union[float, Tensor]) – forward/backward direction of the motion blur. Lower values towards -1.0 will point the motion blur towards the back (with angle provided via angle), while higher values towards 1.0 will point the motion blur forward. A value of 0.0 leads to a uniformly (but still angled) motion blur. If tensor, it must be $$(B,)$$.

• border_type (str, optional) – the padding mode to be applied before convolving. The expected modes are: 'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'.

• mode (str, optional) – interpolation mode for rotating the kernel. 'bilinear' or 'nearest'. Default: 'nearest'

Return type

Tensor

Returns

the blurred image with shape $$(B, C, H, W)$$.

Example

>>> input = torch.randn(1, 3, 80, 90).repeat(2, 1, 1, 1)
>>> # perform exact motion blur across the batch
>>> out_1 = motion_blur(input, 5, 90., 1)
>>> torch.allclose(out_1[0], out_1[1])
True
>>> # perform element-wise motion blur across the batch
>>> out_1 = motion_blur(input, 5, torch.tensor([90., 180,]), torch.tensor([1., -1.]))
>>> torch.allclose(out_1[0], out_1[1])
False

kornia.filters.unsharp_mask(input, kernel_size, sigma, border_type='reflect')[source]#

Create an operator that sharpens a tensor by applying operation out = 2 * image - gaussian_blur2d(image).

Parameters
Return type

Tensor

Returns

the blurred tensor with shape $$(B,C,H,W)$$.

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])


## Edge detection#

kornia.filters.canny(input, low_threshold=0.1, high_threshold=0.2, kernel_size=(5, 5), sigma=(1, 1), hysteresis=True, eps=1e-06)[source]#

Find edges of the input image and filters them using the Canny algorithm.

Parameters
Return type
Returns

• the canny edge magnitudes map, shape of $$(B,1,H,W)$$.

• the canny edge detection filtered by thresholds and hysteresis, shape of $$(B,1,H,W)$$.

Note

See a working example here.

Example

>>> input = torch.rand(5, 3, 4, 4)
>>> magnitude, edges = canny(input)  # 5x3x4x4
>>> magnitude.shape
torch.Size([5, 1, 4, 4])
>>> edges.shape
torch.Size([5, 1, 4, 4])

kornia.filters.laplacian(input, kernel_size, border_type='reflect', normalized=True)[source]#

Create an operator that returns a tensor using a Laplacian filter.

The operator smooths the given tensor with a laplacian kernel by convolving it to each channel. It supports batched operation.

Parameters
• input (Tensor) – the input image tensor with shape $$(B, C, H, W)$$.

• kernel_size (int) – the size of the kernel.

• border_type (str, optional) – the padding mode to be applied before convolving. The expected modes are: 'constant', 'reflect', 'replicate' or 'circular'. Default: 'reflect'

• normalized (bool, optional) – if True, L1 norm of the kernel is set to 1. Default: True

Return type

Tensor

Returns

the blurred image with shape $$(B, C, H, W)$$.

Note

See a working example here.

Examples

>>> input = torch.rand(2, 4, 5, 5)
>>> output = laplacian(input, 3)
>>> output.shape
torch.Size([2, 4, 5, 5])

kornia.filters.sobel(input, normalized=True, eps=1e-06)[source]#

Compute the Sobel operator and returns the magnitude per channel.

Parameters
• input (Tensor) – the input image with shape $$(B,C,H,W)$$.

• normalized (bool, optional) – if True, L1 norm of the kernel is set to 1. Default: True

• eps (float, optional) – regularization number to avoid NaN during backprop. Default: 1e-06

Return type

Tensor

Returns

the sobel edge gradient magnitudes map with shape $$(B,C,H,W)$$.

Note

See a working example here.

Example

>>> input = torch.rand(1, 3, 4, 4)
>>> output = sobel(input)  # 1x3x4x4
>>> output.shape
torch.Size([1, 3, 4, 4])

kornia.filters.spatial_gradient(input, mode='sobel', order=1, normalized=True)[source]#

Compute the first order image derivative in both x and y using a Sobel operator.

Parameters
• input (Tensor) – input image tensor with shape $$(B, C, H, W)$$.

• mode (str, optional) – derivatives modality, can be: sobel or diff. Default: 'sobel'

• order (int, optional) – the order of the derivatives. Default: 1

• normalized (bool, optional) – whether the output is normalized. Default: True

Return type

Tensor

Returns

the derivatives of the input feature map. with shape $$(B, C, 2, H, W)$$.

Note

See a working example here.

Examples

>>> input = torch.rand(1, 3, 4, 4)
>>> output = spatial_gradient(input)  # 1x3x2x4x4
>>> output.shape
torch.Size([1, 3, 2, 4, 4])

kornia.filters.spatial_gradient3d(input, mode='diff', order=1)[source]#

Compute the first and second order volume derivative in x, y and d using a diff operator.

Parameters
• input (Tensor) – input features tensor with shape $$(B, C, D, H, W)$$.

• mode (str, optional) – derivatives modality, can be: sobel or diff. Default: 'diff'

• order (int, optional) – the order of the derivatives. Default: 1

Returns

(B, C, 3, D, H, W) or $$(B, C, 6, D, H, W)$$.

Return type

the spatial gradients of the input feature map with shape math

Examples

>>> input = torch.rand(1, 4, 2, 4, 4)
>>> output = spatial_gradient3d(input)
>>> output.shape
torch.Size([1, 4, 3, 2, 4, 4])


## Filtering API#

kornia.filters.filter2d(input, kernel, border_type='reflect', normalized=False, padding='same')[source]#

Convolve a tensor with a 2d kernel.

The function applies a given kernel to a tensor. The kernel is applied independently at each depth channel of the tensor. Before applying the kernel, the function applies padding according to the specified mode so that the output remains in the same shape.

Parameters
• input (Tensor) – the input tensor with shape of $$(B, C, H, W)$$.

• kernel (Tensor) – the kernel to be convolved with the input tensor. The kernel shape must be $$(1, kH, kW)$$ or $$(B, kH, kW)$$.

• border_type (str, optional) – the padding mode to be applied before convolving. The expected modes are: 'constant', 'reflect', 'replicate' or 'circular'. Default: 'reflect'

• normalized (bool, optional) – If True, kernel will be L1 normalized. Default: False

• padding (str, optional) – This defines the type of padding. 2 modes available 'same' or 'valid'. Default: 'same'

Returns

the convolved tensor of same size and numbers of channels as the input with shape $$(B, C, H, W)$$.

Return type

torch.Tensor

Example

>>> input = torch.tensor([[[
...    [0., 0., 0., 0., 0.],
...    [0., 0., 0., 0., 0.],
...    [0., 0., 5., 0., 0.],
...    [0., 0., 0., 0., 0.],
...    [0., 0., 0., 0., 0.],]]])
>>> kernel = torch.ones(1, 3, 3)
>>> filter2d(input, kernel, padding='same')
tensor([[[[0., 0., 0., 0., 0.],
[0., 5., 5., 5., 0.],
[0., 5., 5., 5., 0.],
[0., 5., 5., 5., 0.],
[0., 0., 0., 0., 0.]]]])

kornia.filters.filter2d_separable(input, kernel_x, kernel_y, border_type='reflect', normalized=False, padding='same')[source]#

Convolve a tensor with two 1d kernels, in x and y directions.

The function applies a given kernel to a tensor. The kernel is applied independently at each depth channel of the tensor. Before applying the kernel, the function applies padding according to the specified mode so that the output remains in the same shape.

Parameters
• input (Tensor) – the input tensor with shape of $$(B, C, H, W)$$.

• kernel_x (Tensor) – the kernel to be convolved with the input tensor. The kernel shape must be $$(1, kW)$$ or $$(B, kW)$$.

• kernel_y (Tensor) – the kernel to be convolved with the input tensor. The kernel shape must be $$(1, kH)$$ or $$(B, kH)$$.

• border_type (str, optional) – the padding mode to be applied before convolving. The expected modes are: 'constant', 'reflect', 'replicate' or 'circular'. Default: 'reflect'

• normalized (bool, optional) – If True, kernel will be L1 normalized. Default: False

• padding (str, optional) – This defines the type of padding. 2 modes available 'same' or 'valid'. Default: 'same'

Returns

the convolved tensor of same size and numbers of channels as the input with shape $$(B, C, H, W)$$.

Return type

torch.Tensor

Example

>>> input = torch.tensor([[[
...    [0., 0., 0., 0., 0.],
...    [0., 0., 0., 0., 0.],
...    [0., 0., 5., 0., 0.],
...    [0., 0., 0., 0., 0.],
...    [0., 0., 0., 0., 0.],]]])
>>> kernel = torch.ones(1, 3)

>>> filter2d_separable(input, kernel, kernel, padding='same')
tensor([[[[0., 0., 0., 0., 0.],
[0., 5., 5., 5., 0.],
[0., 5., 5., 5., 0.],
[0., 5., 5., 5., 0.],
[0., 0., 0., 0., 0.]]]])

kornia.filters.filter3d(input, kernel, border_type='replicate', normalized=False)[source]#

Convolve a tensor with a 3d kernel.

The function applies a given kernel to a tensor. The kernel is applied independently at each depth channel of the tensor. Before applying the kernel, the function applies padding according to the specified mode so that the output remains in the same shape.

Parameters
• input (Tensor) – the input tensor with shape of $$(B, C, D, H, W)$$.

• kernel (Tensor) – the kernel to be convolved with the input tensor. The kernel shape must be $$(1, kD, kH, kW)$$ or $$(B, kD, kH, kW)$$.

• border_type (str, optional) – the padding mode to be applied before convolving. The expected modes are: 'constant', 'replicate' or 'circular'. Default: 'replicate'

• normalized (bool, optional) – If True, kernel will be L1 normalized. Default: False

Return type

Tensor

Returns

the convolved tensor of same size and numbers of channels as the input with shape $$(B, C, D, H, W)$$.

Example

>>> input = torch.tensor([[[
...    [[0., 0., 0., 0., 0.],
...     [0., 0., 0., 0., 0.],
...     [0., 0., 0., 0., 0.],
...     [0., 0., 0., 0., 0.],
...     [0., 0., 0., 0., 0.]],
...    [[0., 0., 0., 0., 0.],
...     [0., 0., 0., 0., 0.],
...     [0., 0., 5., 0., 0.],
...     [0., 0., 0., 0., 0.],
...     [0., 0., 0., 0., 0.]],
...    [[0., 0., 0., 0., 0.],
...     [0., 0., 0., 0., 0.],
...     [0., 0., 0., 0., 0.],
...     [0., 0., 0., 0., 0.],
...     [0., 0., 0., 0., 0.]]
... ]]])
>>> kernel = torch.ones(1, 3, 3, 3)
>>> filter3d(input, kernel)
tensor([[[[[0., 0., 0., 0., 0.],
[0., 5., 5., 5., 0.],
[0., 5., 5., 5., 0.],
[0., 5., 5., 5., 0.],
[0., 0., 0., 0., 0.]],

[[0., 0., 0., 0., 0.],
[0., 5., 5., 5., 0.],
[0., 5., 5., 5., 0.],
[0., 5., 5., 5., 0.],
[0., 0., 0., 0., 0.]],

[[0., 0., 0., 0., 0.],
[0., 5., 5., 5., 0.],
[0., 5., 5., 5., 0.],
[0., 5., 5., 5., 0.],
[0., 0., 0., 0., 0.]]]]])


## Kernels#

kornia.filters.get_gaussian_kernel1d(kernel_size, sigma, force_even=False)[source]#

Function that returns Gaussian filter coefficients.

Parameters
Return type

Tensor

Returns

1D tensor with gaussian filter coefficients.

Shape:
• Output: $$(\text{kernel_size})$$

Examples

>>> get_gaussian_kernel1d(3, 2.5)
tensor([0.3243, 0.3513, 0.3243])

>>> get_gaussian_kernel1d(5, 1.5)
tensor([0.1201, 0.2339, 0.2921, 0.2339, 0.1201])

kornia.filters.get_gaussian_erf_kernel1d(kernel_size, sigma, force_even=False)[source]#

Function that returns Gaussian filter coefficients by interpolating the error function, adapted from: https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/layers/convutils.py.

Parameters
Return type

Tensor

Returns

1D tensor with gaussian filter coefficients.

Shape:
• Output: $$(\text{kernel_size})$$

Examples

>>> get_gaussian_erf_kernel1d(3, 2.5)
tensor([0.3245, 0.3511, 0.3245])

>>> get_gaussian_erf_kernel1d(5, 1.5)
tensor([0.1226, 0.2331, 0.2887, 0.2331, 0.1226])

kornia.filters.get_gaussian_discrete_kernel1d(kernel_size, sigma, force_even=False)[source]#

Function that returns Gaussian filter coefficients based on the modified Bessel functions. Adapted from: https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/layers/convutils.py.

Parameters
Return type

Tensor

Returns

1D tensor with gaussian filter coefficients.

Shape:
• Output: $$(\text{kernel_size})$$

Examples

>>> get_gaussian_discrete_kernel1d(3, 2.5)
tensor([0.3235, 0.3531, 0.3235])

>>> get_gaussian_discrete_kernel1d(5, 1.5)
tensor([0.1096, 0.2323, 0.3161, 0.2323, 0.1096])

kornia.filters.get_gaussian_kernel2d(kernel_size, sigma, force_even=False)[source]#

Function that returns Gaussian filter matrix coefficients.

Parameters
Return type

Tensor

Returns

2D tensor with gaussian filter matrix coefficients.

Shape:
• Output: $$(\text{kernel_size}_x, \text{kernel_size}_y)$$

Examples

>>> get_gaussian_kernel2d((3, 3), (1.5, 1.5))
tensor([[0.0947, 0.1183, 0.0947],
[0.1183, 0.1478, 0.1183],
[0.0947, 0.1183, 0.0947]])
>>> get_gaussian_kernel2d((3, 5), (1.5, 1.5))
tensor([[0.0370, 0.0720, 0.0899, 0.0720, 0.0370],
[0.0462, 0.0899, 0.1123, 0.0899, 0.0462],
[0.0370, 0.0720, 0.0899, 0.0720, 0.0370]])

kornia.filters.get_hanning_kernel1d(kernel_size, device=torch.device('cpu'), dtype=torch.float)[source]#

Returns Hanning (also known as Hann) kernel, used in signal processing and KCF tracker.

$\begin{split}w(n) = 0.5 - 0.5cos\\left(\\frac{2\\pi{n}}{M-1}\\right) \\qquad 0 \\leq n \\leq M-1\end{split}$

See further in numpy docs https://numpy.org/doc/stable/reference/generated/numpy.hanning.html

Parameters

kernel_size (int) – The size the of the kernel. It should be positive.

Return type

Tensor

Returns

1D tensor with Hanning filter coefficients.
$\begin{split}w(n) = 0.5 - 0.5cos\\left(\\frac{2\\pi{n}}{M-1}\\right)\end{split}$

Shape:
• Output: math:(text{kernel_size})

Examples

>>> get_hanning_kernel1d(4)
tensor([0.0000, 0.7500, 0.7500, 0.0000])

kornia.filters.get_hanning_kernel2d(kernel_size, device=torch.device('cpu'), dtype=torch.float)[source]#

Returns 2d Hanning kernel, used in signal processing and KCF tracker.

Parameters

kernel_size (Tuple[int, int]) – The size of the kernel for the filter. It should be positive.

Return type

Tensor

Returns

2D tensor with Hanning filter coefficients.
$\begin{split}w(n) = 0.5 - 0.5cos\\left(\\frac{2\\pi{n}}{M-1}\\right)\end{split}$

Shape:
• Output: math:(text{kernel_size[0], kernel_size[1]})

kornia.filters.get_laplacian_kernel1d(kernel_size)[source]#

Function that returns the coefficients of a 1D Laplacian filter.

Parameters

kernel_size (int) – filter size. It should be odd and positive.

Return type

Tensor

Returns

1D tensor with laplacian filter coefficients.

Shape:
• Output: math:(text{kernel_size})

Examples

>>> get_laplacian_kernel1d(3)
tensor([ 1., -2.,  1.])
>>> get_laplacian_kernel1d(5)
tensor([ 1.,  1., -4.,  1.,  1.])

kornia.filters.get_laplacian_kernel2d(kernel_size)[source]#

Function that returns Gaussian filter matrix coefficients.

Parameters

kernel_size (int) – filter size should be odd.

Return type

Tensor

Returns

2D tensor with laplacian filter matrix coefficients.

Shape:
• Output: $$(\text{kernel_size}_x, \text{kernel_size}_y)$$

Examples

>>> get_laplacian_kernel2d(3)
tensor([[ 1.,  1.,  1.],
[ 1., -8.,  1.],
[ 1.,  1.,  1.]])
>>> get_laplacian_kernel2d(5)
tensor([[  1.,   1.,   1.,   1.,   1.],
[  1.,   1.,   1.,   1.,   1.],
[  1.,   1., -24.,   1.,   1.],
[  1.,   1.,   1.,   1.,   1.],
[  1.,   1.,   1.,   1.,   1.]])

kornia.filters.get_motion_kernel2d(kernel_size, angle, direction=0.0, mode='nearest')[source]#

Return 2D motion blur filter.

Parameters
• kernel_size (int) – motion kernel width and height. It should be odd and positive.

• angle (Union[Tensor, float]) – angle of the motion blur in degrees (anti-clockwise rotation).

• direction (Union[Tensor, float], optional) – forward/backward direction of the motion blur. Lower values towards -1.0 will point the motion blur towards the back (with angle provided via angle), while higher values towards 1.0 will point the motion blur forward. A value of 0.0 leads to a uniformly (but still angled) motion blur. Default: 0.0

• mode (str, optional) – interpolation mode for rotating the kernel. 'bilinear' or 'nearest'. Default: 'nearest'

Return type

Tensor

Returns

The motion blur kernel of shape $$(B, k_\text{size}, k_\text{size})$$.

Examples

>>> get_motion_kernel2d(5, 0., 0.)
tensor([[[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.2000, 0.2000, 0.2000, 0.2000, 0.2000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000]]])

>>> get_motion_kernel2d(3, 215., -0.5)
tensor([[[0.0000, 0.0000, 0.1667],
[0.0000, 0.3333, 0.0000],
[0.5000, 0.0000, 0.0000]]])


## Module#

class kornia.filters.BlurPool2D(kernel_size, stride=2)[source]#

Compute blur (anti-aliasing) and downsample a given feature map.

See [Zha19] for more details.

Parameters
Shape:
• Input: $$(B, C, H, W)$$

• Output: $$(N, C, H_{out}, W_{out})$$, where

$H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{kernel\_size//2}[0] - \text{kernel\_size}[0]}{\text{stride}[0]} + 1\right\rfloor$
$W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{kernel\_size//2}[1] - \text{kernel\_size}[1]}{\text{stride}[1]} + 1\right\rfloor$

Examples

>>> from kornia.filters.blur_pool import BlurPool2D
>>> input = torch.eye(5)[None, None]
>>> bp = BlurPool2D(kernel_size=3, stride=2)
>>> bp(input)
tensor([[[[0.3125, 0.0625, 0.0000],
[0.0625, 0.3750, 0.0625],
[0.0000, 0.0625, 0.3125]]]])

class kornia.filters.BoxBlur(kernel_size, border_type='reflect', normalized=True)[source]#

Blur an image using the box filter.

The function smooths an image using the kernel:

$\begin{split}K = \frac{1}{\text{kernel_size}_x * \text{kernel_size}_y} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \vdots & \vdots & \vdots & \ddots & \vdots & \vdots \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\end{split}$
Parameters
• kernel_size (Tuple[int, int]) – the blurring kernel size.

• border_type (str, optional) – the padding mode to be applied before convolving. The expected modes are: 'constant', 'reflect', 'replicate' or 'circular'. Default: 'reflect'.

• normalized (bool, optional) – if True, L1 norm of the kernel is set to 1. Default: True

Returns

the blurred input tensor.

Shape:
• Input: $$(B, C, H, W)$$

• Output: $$(B, C, H, W)$$

Example

>>> input = torch.rand(2, 4, 5, 7)
>>> blur = BoxBlur((3, 3))
>>> output = blur(input)  # 2x4x5x7
>>> output.shape
torch.Size([2, 4, 5, 7])

class kornia.filters.MaxBlurPool2D(kernel_size, stride=2, max_pool_size=2, ceil_mode=False)[source]#

Compute pools and blurs and downsample a given feature map.

Equivalent to nn.Sequential(nn.MaxPool2d(...), BlurPool2D(...))

See [Zha19] for more details.

Parameters
• kernel_size (int) – the kernel size for max pooling.

• stride (int, optional) – stride for pooling. Default: 2

• max_pool_size (int, optional) – the kernel size for max pooling. Default: 2

• ceil_mode (bool, optional) – should be true to match output size of conv2d with same kernel size. Default: False

Shape:
• Input: $$(B, C, H, W)$$

• Output: $$(B, C, H / stride, W / stride)$$

Returns

the transformed tensor.

Return type

torch.Tensor

Examples

>>> import torch.nn as nn
>>> from kornia.filters.blur_pool import BlurPool2D
>>> input = torch.eye(5)[None, None]
>>> mbp = MaxBlurPool2D(kernel_size=3, stride=2, max_pool_size=2, ceil_mode=False)
>>> mbp(input)
tensor([[[[0.5625, 0.3125],
[0.3125, 0.8750]]]])
>>> seq = nn.Sequential(nn.MaxPool2d(kernel_size=2, stride=1), BlurPool2D(kernel_size=3, stride=2))
>>> seq(input)
tensor([[[[0.5625, 0.3125],
[0.3125, 0.8750]]]])

class kornia.filters.MedianBlur(kernel_size)[source]#

Blur an image using the median filter.

Parameters

kernel_size (Tuple[int, int]) – the blurring kernel size.

Returns

the blurred input tensor.

Shape:
• Input: $$(B, C, H, W)$$

• Output: $$(B, C, H, W)$$

Example

>>> input = torch.rand(2, 4, 5, 7)
>>> blur = MedianBlur((3, 3))
>>> output = blur(input)
>>> output.shape
torch.Size([2, 4, 5, 7])

class kornia.filters.GaussianBlur2d(kernel_size, sigma, border_type='reflect', separable=True)[source]#

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.

Parameters
Returns

the blurred tensor.

Shape:
• Input: $$(B, C, H, W)$$

• Output: $$(B, C, H, W)$$

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])

class kornia.filters.Laplacian(kernel_size, border_type='reflect', normalized=True)[source]#

Create an operator that returns a tensor using a Laplacian filter.

The operator smooths the given tensor with a laplacian kernel by convolving it to each channel. It supports batched operation.

Parameters
• kernel_size (int) – the size of the kernel.

• border_type (str, optional) – the padding mode to be applied before convolving. The expected modes are: 'constant', 'reflect', 'replicate' or 'circular'. Default: 'reflect'

• normalized (bool, optional) – if True, L1 norm of the kernel is set to 1. Default: True

Shape:
• Input: $$(B, C, H, W)$$

• Output: $$(B, C, H, W)$$

Examples

>>> input = torch.rand(2, 4, 5, 5)
>>> laplace = Laplacian(5)
>>> output = laplace(input)
>>> output.shape
torch.Size([2, 4, 5, 5])

class kornia.filters.Sobel(normalized=True, eps=1e-06)[source]#

Compute the Sobel operator and returns the magnitude per channel.

Parameters
• normalized (bool, optional) – if True, L1 norm of the kernel is set to 1. Default: True

• eps (float, optional) – regularization number to avoid NaN during backprop. Default: 1e-06

Returns

the sobel edge gradient magnitudes map.

Shape:
• Input: $$(B, C, H, W)$$

• Output: $$(B, C, H, W)$$

Examples

>>> input = torch.rand(1, 3, 4, 4)
>>> output = Sobel()(input)  # 1x3x4x4

class kornia.filters.Canny(low_threshold=0.1, high_threshold=0.2, kernel_size=(5, 5), sigma=(1, 1), hysteresis=True, eps=1e-06)[source]#

Module that finds edges of the input image and filters them using the Canny algorithm.

Parameters
• input – input image tensor with shape $$(B,C,H,W)$$.

• low_threshold (float, optional) – lower threshold for the hysteresis procedure. Default: 0.1

• high_threshold (float, optional) – upper threshold for the hysteresis procedure. Default: 0.2

• kernel_size (Tuple[int, int], optional) – the size of the kernel for the gaussian blur. Default: (5, 5)

• sigma (Tuple[float, float], optional) – the standard deviation of the kernel for the gaussian blur. Default: (1, 1)

• hysteresis (bool, optional) – if True, applies the hysteresis edge tracking. Otherwise, the edges are divided between weak (0.5) and strong (1) edges. Default: True

• eps (float, optional) – regularization number to avoid NaN during backprop. Default: 1e-06

Returns

• the canny edge magnitudes map, shape of $$(B,1,H,W)$$.

• the canny edge detection filtered by thresholds and hysteresis, shape of $$(B,1,H,W)$$.

Example

>>> input = torch.rand(5, 3, 4, 4)
>>> magnitude, edges = Canny()(input)  # 5x3x4x4
>>> magnitude.shape
torch.Size([5, 1, 4, 4])
>>> edges.shape
torch.Size([5, 1, 4, 4])

class kornia.filters.SpatialGradient(mode='sobel', order=1, normalized=True)[source]#

Compute the first order image derivative in both x and y using a Sobel operator.

Parameters
• mode (str, optional) – derivatives modality, can be: sobel or diff. Default: 'sobel'

• order (int, optional) – the order of the derivatives. Default: 1

• normalized (bool, optional) – whether the output is normalized. Default: True

Returns

the sobel edges of the input feature map.

Shape:
• Input: $$(B, C, H, W)$$

• Output: $$(B, C, 2, H, W)$$

Examples

>>> input = torch.rand(1, 3, 4, 4)
>>> output = SpatialGradient()(input)  # 1x3x2x4x4

class kornia.filters.SpatialGradient3d(mode='diff', order=1)[source]#

Compute the first and second order volume derivative in x, y and d using a diff operator.

Parameters
• mode (str, optional) – derivatives modality, can be: sobel or diff. Default: 'diff'

• order (int, optional) – the order of the derivatives. Default: 1

Returns

the spatial gradients of the input feature map.

Shape:
• Input: $$(B, C, D, H, W)$$. D, H, W are spatial dimensions, gradient is calculated w.r.t to them.

• Output: $$(B, C, 3, D, H, W)$$ or $$(B, C, 6, D, H, W)$$

Examples

>>> input = torch.rand(1, 4, 2, 4, 4)
>>> output = SpatialGradient3d()(input)
>>> output.shape
torch.Size([1, 4, 3, 2, 4, 4])

class kornia.filters.MotionBlur(kernel_size, angle, direction, border_type='constant')[source]#

Blur 2D images (4D tensor) using the motion filter.

Parameters
• kernel_size (int) – motion kernel width and height. It should be odd and positive.

• angle (float) – angle of the motion blur in degrees (anti-clockwise rotation).

• direction (float) – forward/backward direction of the motion blur. Lower values towards -1.0 will point the motion blur towards the back (with angle provided via angle), while higher values towards 1.0 will point the motion blur forward. A value of 0.0 leads to a uniformly (but still angled) motion blur.

• border_type (str, optional) – the padding mode to be applied before convolving. The expected modes are: 'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'

Returns

the blurred input tensor.

Shape:
• Input: $$(B, C, H, W)$$

• Output: $$(B, C, H, W)$$

Examples

>>> input = torch.rand(2, 4, 5, 7)
>>> motion_blur = MotionBlur(3, 35., 0.5)
>>> output = motion_blur(input)  # 2x4x5x7

class kornia.filters.UnsharpMask(kernel_size, sigma, border_type='reflect')[source]#

Create an operator that sharpens image with: out = 2 * image - gaussian_blur2d(image).

Parameters
Returns

the sharpened tensor with shape $$(B,C,H,W)$$.

Shape:
• Input: $$(B, C, H, W)$$

• Output: $$(B, C, H, W)$$

Note

See a working example here.

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])