# kornia.color#

The functions in this section perform various color space conversions.

Note

Check a tutorial for color space conversions here.

## Grayscale#

kornia.color.rgb_to_grayscale(image, rgb_weights=None)#

Convert a RGB image to grayscale version of image.

The image data is assumed to be in the range of (0, 1).

Parameters:
• image (Tensor) – RGB image to be converted to grayscale with shape $$(*,3,H,W)$$.

• rgb_weights (, optional) – Weights that will be applied on each channel (RGB). The sum of the weights should add up to one. Default: None

Return type:

Tensor

Returns:

grayscale version of the image with shape $$(*,1,H,W)$$.

Note

See a working example here.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> gray = rgb_to_grayscale(input) # 2x1x4x5

kornia.color.bgr_to_grayscale(image)#

Convert a BGR image to grayscale.

The image data is assumed to be in the range of (0, 1). First flips to RGB, then converts.

Parameters:

image (Tensor) – BGR image to be converted to grayscale with shape $$(*,3,H,W)$$.

Return type:

Tensor

Returns:

grayscale version of the image with shape $$(*,1,H,W)$$.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> gray = bgr_to_grayscale(input) # 2x1x4x5

kornia.color.grayscale_to_rgb(image)#

Convert a grayscale image to RGB version of image.

The image data is assumed to be in the range of (0, 1).

Parameters:

image (Tensor) – grayscale image tensor to be converted to RGB with shape $$(*,1,H,W)$$.

Return type:

Tensor

Returns:

RGB version of the image with shape $$(*,3,H,W)$$.

Example

>>> input = torch.randn(2, 1, 4, 5)
>>> gray = grayscale_to_rgb(input) # 2x3x4x5

kornia.color.apply_colormap(input_tensor, colormap)

Apply to a gray tensor a colormap.

Parameters:
Return type:

Tensor

Returns:

A RGB tensor with the applied color map into the input_tensor.

Raises:

ValueError – If colormap is not a ColorMap object.

Note

The image data is assumed to be integer values in range of [0-255].

Example

>>> input_tensor = torch.tensor([[[0, 1, 2], [25, 50, 63]]])
>>> colormap = ColorMap(base='autumn')
>>> apply_colormap(input_tensor, colormap)
tensor([[[1.0000, 1.0000, 1.0000],
[1.0000, 1.0000, 1.0000]],

[[0.0000, 0.0159, 0.0317],
[0.3968, 0.7937, 1.0000]],

[[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000]]])

class kornia.color.GrayscaleToRgb(*args, **kwargs)#

Module to convert a grayscale image to RGB version of image.

The image data is assumed to be in the range of (0, 1).

Shape:
• image: $$(*, 1, H, W)$$

• output: $$(*, 3, H, W)$$

reference:

https://docs.opencv.org/4.0.1/de/d25/imgproc_color_conversions.html

Example

>>> input = torch.rand(2, 1, 4, 5)
>>> rgb = GrayscaleToRgb()
>>> output = rgb(input)  # 2x3x4x5

class kornia.color.RgbToGrayscale(rgb_weights=None)#

Module to convert a RGB image to grayscale version of image.

The image data is assumed to be in the range of (0, 1).

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 1, H, W)$$

reference:

https://docs.opencv.org/4.0.1/de/d25/imgproc_color_conversions.html

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> gray = RgbToGrayscale()
>>> output = gray(input)  # 2x1x4x5

class kornia.color.BgrToGrayscale(*args, **kwargs)#

Module to convert a BGR image to grayscale version of image.

The image data is assumed to be in the range of (0, 1). First flips to RGB, then converts.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 1, H, W)$$

reference:

https://docs.opencv.org/4.0.1/de/d25/imgproc_color_conversions.html

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> gray = BgrToGrayscale()
>>> output = gray(input)  # 2x1x4x5

class kornia.color.ApplyColorMap(colormap)

Class for applying a colormap to images.

Parameters:
• colormap (ColorMap) – Either the name of a built-in colormap or a ColorMap object.

• num_colors – Number of colors in the colormap. Default is 256.

• device – The device to put the generated colormap on.

• dtype – The data type of the generated colormap.

Returns:

A RGB tensor with the applied color map into the input_tensor

Raises:

ValueError – If colormap is not a ColorMap object.

Note

The image data is assumed to be integer values in range of [0-255].

Example

>>> input_tensor = torch.tensor([[[0, 1, 2], [25, 50, 63]]])
>>> colormap = ColorMap(base='autumn')
>>> ApplyColorMap(colormap=colormap)(input_tensor)
tensor([[[1.0000, 1.0000, 1.0000],
[1.0000, 1.0000, 1.0000]],

[[0.0000, 0.0159, 0.0317],
[0.3968, 0.7937, 1.0000]],

[[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000]]])


## RGB#

Tip

kornia.color.rgb_to_bgr(image)#

Convert a RGB image to BGR.

Parameters:

image (Tensor) – RGB Image to be converted to BGRof of shape $$(*,3,H,W)$$.

Return type:

Tensor

Returns:

BGR version of the image with shape of shape $$(*,3,H,W)$$.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = rgb_to_bgr(input) # 2x3x4x5

kornia.color.bgr_to_rgb(image)#

Convert a BGR image to RGB.

Parameters:

image (Tensor) – BGR Image to be converted to BGR of shape $$(*,3,H,W)$$.

Return type:

Tensor

Returns:

RGB version of the image with shape of shape $$(*,3,H,W)$$.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = bgr_to_rgb(input) # 2x3x4x5

kornia.color.rgb_to_linear_rgb(image)#

Convert an sRGB image to linear RGB. Used in colorspace conversions.

Parameters:

image (Tensor) – sRGB Image to be converted to linear RGB of shape $$(*,3,H,W)$$.

Return type:

Tensor

Returns:

linear RGB version of the image with shape of $$(*,3,H,W)$$.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = rgb_to_linear_rgb(input) # 2x3x4x5

kornia.color.linear_rgb_to_rgb(image)#

Convert a linear RGB image to sRGB. Used in colorspace conversions.

Parameters:

image (Tensor) – linear RGB Image to be converted to sRGB of shape $$(*,3,H,W)$$.

Return type:

Tensor

Returns:

sRGB version of the image with shape of shape $$(*,3,H,W)$$.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = linear_rgb_to_rgb(input) # 2x3x4x5

class kornia.color.RgbToBgr(*args, **kwargs)#

Convert an image from RGB to BGR.

The image data is assumed to be in the range of (0, 1).

Returns:

BGR version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> bgr = RgbToBgr()
>>> output = bgr(input)  # 2x3x4x5

class kornia.color.BgrToRgb(*args, **kwargs)#

Convert image from BGR to RGB.

The image data is assumed to be in the range of (0, 1).

Returns:

RGB version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> rgb = BgrToRgb()
>>> output = rgb(input)  # 2x3x4x5

class kornia.color.LinearRgbToRgb(*args, **kwargs)#

Convert a linear RGB image to sRGB.

Applies gamma correction to linear RGB values, at the end of colorspace conversions, to get sRGB.

Returns:

sRGB version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> srgb = LinearRgbToRgb()
>>> output = srgb(input)  # 2x3x4x5


References

class kornia.color.RgbToLinearRgb(*args, **kwargs)#

Convert an image from sRGB to linear RGB.

Reverses the gamma correction of sRGB to get linear RGB values for colorspace conversions. The image data is assumed to be in the range of $$[0, 1]$$

Returns:

Linear RGB version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> rgb_lin = RgbToLinearRgb()
>>> output = rgb_lin(input)  # 2x3x4x5


References

## RGBA#

Tip

kornia.color.bgr_to_rgba(image, alpha_val)#

Convert an image from BGR to RGBA.

Parameters:
• image (Tensor) – BGR Image to be converted to RGBA of shape $$(*,3,H,W)$$.

• alpha_val () – A float number for the alpha value or a tensor of shape $$(*,1,H,W)$$.

Return type:

Tensor

Returns:

RGBA version of the image with shape $$(*,4,H,W)$$.

Note

The current functionality is NOT supported by Torchscript.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = bgr_to_rgba(input, 1.) # 2x4x4x5

kornia.color.rgb_to_rgba(image, alpha_val)#

Convert an image from RGB to RGBA.

Parameters:
• image (Tensor) – RGB Image to be converted to RGBA of shape $$(*,3,H,W)$$.

• alpha_val (float, torch.Tensor) – A float number for the alpha value or a tensor of shape $$(*,1,H,W)$$.

Return type:

Tensor

Returns:

RGBA version of the image with shape $$(*,4,H,W)$$.

Note

The current functionality is NOT supported by Torchscript.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = rgb_to_rgba(input, 1.) # 2x4x4x5

kornia.color.rgba_to_rgb(image)#

Convert an image from RGBA to RGB.

Parameters:

image (Tensor) – RGBA Image to be converted to RGB of shape $$(*,4,H,W)$$.

Return type:

Tensor

Returns:

RGB version of the image with shape $$(*,3,H,W)$$.

Example

>>> input = torch.rand(2, 4, 4, 5)
>>> output = rgba_to_rgb(input) # 2x3x4x5

kornia.color.rgba_to_bgr(image)#

Convert an image from RGBA to BGR.

Parameters:

image (Tensor) – RGBA Image to be converted to BGR of shape $$(*,4,H,W)$$.

Return type:

Tensor

Returns:

RGB version of the image with shape $$(*,3,H,W)$$.

Example

>>> input = torch.rand(2, 4, 4, 5)
>>> output = rgba_to_bgr(input) # 2x3x4x5

class kornia.color.RgbToRgba(alpha_val)#

Convert an image from RGB to RGBA.

Add an alpha channel to existing RGB image.

Parameters:

alpha_val () – A float number for the alpha value or a tensor of shape $$(*,1,H,W)$$.

Returns:

RGBA version of the image with shape $$(*,4,H,W)$$.

Return type:

torch.Tensor

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 4, H, W)$$

Note

The current functionality is NOT supported by Torchscript.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> rgba = RgbToRgba(1.)
>>> output = rgba(input)  # 2x4x4x5

class kornia.color.BgrToRgba(alpha_val)#

Convert an image from BGR to RGBA.

Add an alpha channel to existing RGB image.

Parameters:

alpha_val () – A float number for the alpha value or a tensor of shape $$(*,1,H,W)$$.

Returns:

RGBA version of the image with shape $$(*,4,H,W)$$.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 4, H, W)$$

Note

The current functionality is NOT supported by Torchscript.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> rgba = BgrToRgba(1.)
>>> output = rgba(input)  # 2x4x4x5

class kornia.color.RgbaToRgb(*args, **kwargs)#

Convert an image from RGBA to RGB.

Remove an alpha channel from RGB image.

Returns:

RGB version of the image.

Shape:
• image: $$(*, 4, H, W)$$

• output: $$(*, 3, H, W)$$

Example

>>> input = torch.rand(2, 4, 4, 5)
>>> rgba = RgbaToRgb()
>>> output = rgba(input)  # 2x3x4x5

class kornia.color.RgbaToBgr(*args, **kwargs)#

Convert an image from RGBA to BGR.

Remove an alpha channel from BGR image.

Returns:

BGR version of the image.

Shape:
• image: $$(*, 4, H, W)$$

• output: $$(*, 3, H, W)$$

Example

>>> input = torch.rand(2, 4, 4, 5)
>>> rgba = RgbaToBgr()
>>> output = rgba(input)  # 2x3x4x5


## HLS#

kornia.color.rgb_to_hls(image, eps=1e-8)#

Convert an RGB image to HLS.

The image data is assumed to be in the range of (0, 1).

NOTE: this method cannot be compiled with JIT in pytohrch < 1.7.0

Parameters:
• image (Tensor) – RGB image to be converted to HLS with shape $$(*, 3, H, W)$$.

• eps (float, optional) – epsilon value to avoid div by zero. Default: 1e-8

Return type:

Tensor

Returns:

HLS version of the image with shape $$(*, 3, H, W)$$.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = rgb_to_hls(input)  # 2x3x4x5

kornia.color.hls_to_rgb(image)#

Convert a HLS image to RGB.

The image data is assumed to be in the range of (0, 1).

Parameters:

image (Tensor) – HLS image to be converted to RGB with shape $$(*, 3, H, W)$$.

Return type:

Tensor

Returns:

RGB version of the image with shape $$(*, 3, H, W)$$.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = hls_to_rgb(input)  # 2x3x4x5

class kornia.color.RgbToHls(*args, **kwargs)#

Convert an image from RGB to HLS.

The image data is assumed to be in the range of (0, 1).

Returns:

HLS version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Examples

>>> input = torch.rand(2, 3, 4, 5)
>>> hls = RgbToHls()
>>> output = hls(input)  # 2x3x4x5

class kornia.color.HlsToRgb(*args, **kwargs)#

Convert an image from HLS to RGB.

The image data is assumed to be in the range of (0, 1).

Returns:

RGB version of the image.

Shape:
• input: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Reference:

https://en.wikipedia.org/wiki/HSL_and_HSV

Examples

>>> input = torch.rand(2, 3, 4, 5)
>>> rgb = HlsToRgb()
>>> output = rgb(input)  # 2x3x4x5


## HSV#

kornia.color.rgb_to_hsv(image, eps=1e-8)#

Convert an image from RGB to HSV.

The image data is assumed to be in the range of (0, 1).

Parameters:
• image (Tensor) – RGB Image to be converted to HSV with shape of $$(*, 3, H, W)$$.

• eps (float, optional) – scalar to enforce numarical stability. Default: 1e-8

Return type:

Tensor

Returns:

HSV version of the image with shape of $$(*, 3, H, W)$$. The H channel values are in the range 0..2pi. S and V are in the range 0..1.

Note

See a working example here.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = rgb_to_hsv(input)  # 2x3x4x5

kornia.color.hsv_to_rgb(image)#

Convert an image from HSV to RGB.

The H channel values are assumed to be in the range 0..2pi. S and V are in the range 0..1.

Parameters:

image (Tensor) – HSV Image to be converted to HSV with shape of $$(*, 3, H, W)$$.

Return type:

Tensor

Returns:

RGB version of the image with shape of $$(*, 3, H, W)$$.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = hsv_to_rgb(input)  # 2x3x4x5

class kornia.color.RgbToHsv(eps=1e-6)#

Convert an image from RGB to HSV.

The image data is assumed to be in the range of (0, 1).

Parameters:

eps (float, optional) – scalar to enforce numarical stability. Default: 1e-6

Returns:

HSV version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> hsv = RgbToHsv()
>>> output = hsv(input)  # 2x3x4x5

class kornia.color.HsvToRgb(*args, **kwargs)#

Convert an image from HSV to RGB.

H channel values are assumed to be in the range 0..2pi. S and V are in the range 0..1.

Returns:

RGB version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> rgb = HsvToRgb()
>>> output = rgb(input)  # 2x3x4x5


## LUV#

kornia.color.rgb_to_luv(image, eps=1e-12)#

Convert a RGB image to Luv.

The image data is assumed to be in the range of $$[0, 1]$$. Luv color is computed using the D65 illuminant and Observer 2.

Parameters:
• image (Tensor) – RGB Image to be converted to Luv with shape $$(*, 3, H, W)$$.

• eps (float, optional) – for numerically stability when dividing. Default: 1e-12

Return type:

Tensor

Returns:

Luv version of the image with shape $$(*, 3, H, W)$$.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = rgb_to_luv(input)  # 2x3x4x5

kornia.color.luv_to_rgb(image, eps=1e-12)#

Convert a Luv image to RGB.

Parameters:
• image (Tensor) – Luv image to be converted to RGB with shape $$(*, 3, H, W)$$.

• eps (float, optional) – for numerically stability when dividing. Default: 1e-12

Return type:

Tensor

Returns:

Luv version of the image with shape $$(*, 3, H, W)$$.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = luv_to_rgb(input)  # 2x3x4x5

class kornia.color.RgbToLuv(*args, **kwargs)#

Convert an image from RGB to Luv.

The image data is assumed to be in the range of $$[0, 1]$$. Luv color is computed using the D65 illuminant and Observer 2.

Returns:

Luv version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Examples

>>> input = torch.rand(2, 3, 4, 5)
>>> luv = RgbToLuv()
>>> output = luv(input)  # 2x3x4x5

Reference:
class kornia.color.LuvToRgb(*args, **kwargs)#

Convert an image from Luv to RGB.

Returns:

RGB version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Examples

>>> input = torch.rand(2, 3, 4, 5)
>>> rgb = LuvToRgb()
>>> output = rgb(input)  # 2x3x4x5


References

## Lab#

Tip

kornia.color.rgb_to_lab(image)#

Convert a RGB image to Lab.

The input RGB image is assumed to be in the range of $$[0, 1]$$. Lab color is computed using the D65 illuminant and Observer 2.

Parameters:

image (Tensor) – RGB Image to be converted to Lab with shape $$(*, 3, H, W)$$.

Return type:

Tensor

Returns:

Lab version of the image with shape $$(*, 3, H, W)$$. The L channel values are in the range 0..100. a and b are in the range -128..127.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = rgb_to_lab(input)  # 2x3x4x5

kornia.color.lab_to_rgb(image, clip=True)#

Convert a Lab image to RGB.

The L channel is assumed to be in the range of $$[0, 100]$$. a and b channels are in the range of $$[-128, 127]$$.

Parameters:
• image (Tensor) – Lab image to be converted to RGB with shape $$(*, 3, H, W)$$.

• clip (bool, optional) – Whether to apply clipping to insure output RGB values in range $$[0, 1]$$. Default: True

Return type:

Tensor

Returns:

Lab version of the image with shape $$(*, 3, H, W)$$. The output RGB image are in the range of $$[0, 1]$$.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = lab_to_rgb(input)  # 2x3x4x5

class kornia.color.RgbToLab(*args, **kwargs)#

Convert an image from RGB to Lab.

The image data is assumed to be in the range of $$[0, 1]$$. Lab color is computed using the D65 illuminant and Observer 2.

Returns:

Lab version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Examples

>>> input = torch.rand(2, 3, 4, 5)
>>> lab = RgbToLab()
>>> output = lab(input)  # 2x3x4x5

Reference:
class kornia.color.LabToRgb(*args, **kwargs)#

Convert an image from Lab to RGB.

Returns:

RGB version of the image. Range may not be in $$[0, 1]$$.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Examples

>>> input = torch.rand(2, 3, 4, 5)
>>> rgb = LabToRgb()
>>> output = rgb(input)  # 2x3x4x5


References

## YCbCr#

kornia.color.rgb_to_ycbcr(image)#

Convert an RGB image to YCbCr.

Parameters:

image (Tensor) – RGB Image to be converted to YCbCr with shape $$(*, 3, H, W)$$.

Return type:

Tensor

Returns:

YCbCr version of the image with shape $$(*, 3, H, W)$$.

Examples

>>> input = torch.rand(2, 3, 4, 5)
>>> output = rgb_to_ycbcr(input)  # 2x3x4x5

kornia.color.ycbcr_to_rgb(image)#

Convert an YCbCr image to RGB.

The image data is assumed to be in the range of (0, 1).

Parameters:

image (Tensor) – YCbCr Image to be converted to RGB with shape $$(*, 3, H, W)$$.

Return type:

Tensor

Returns:

RGB version of the image with shape $$(*, 3, H, W)$$.

Examples

>>> input = torch.rand(2, 3, 4, 5)
>>> output = ycbcr_to_rgb(input)  # 2x3x4x5

class kornia.color.YcbcrToRgb(*args, **kwargs)#

Convert an image from YCbCr to Rgb.

The image data is assumed to be in the range of (0, 1).

Returns:

RGB version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Examples

>>> input = torch.rand(2, 3, 4, 5)
>>> rgb = YcbcrToRgb()
>>> output = rgb(input)  # 2x3x4x5

class kornia.color.RgbToYcbcr(*args, **kwargs)#

Convert an image from RGB to YCbCr.

The image data is assumed to be in the range of (0, 1).

Returns:

YCbCr version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Examples

>>> input = torch.rand(2, 3, 4, 5)
>>> ycbcr = RgbToYcbcr()
>>> output = ycbcr(input)  # 2x3x4x5


## YUV#

kornia.color.rgb_to_yuv(image)#

Convert an RGB image to YUV.

The image data is assumed to be in the range of (0, 1).

Parameters:

image (Tensor) – RGB Image to be converted to YUV with shape $$(*, 3, H, W)$$.

Return type:

Tensor

Returns:

YUV version of the image with shape $$(*, 3, H, W)$$.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = rgb_to_yuv(input)  # 2x3x4x5

kornia.color.yuv_to_rgb(image)#

Convert an YUV image to RGB.

The image data is assumed to be in the range of (0, 1) for luma and (-0.5, 0.5) for chroma.

Parameters:

image (Tensor) – YUV Image to be converted to RGB with shape $$(*, 3, H, W)$$.

Return type:

Tensor

Returns:

RGB version of the image with shape $$(*, 3, H, W)$$.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = yuv_to_rgb(input)  # 2x3x4x5

class kornia.color.RgbToYuv(*args, **kwargs)#

Convert an image from RGB to YUV.

The image data is assumed to be in the range of (0, 1).

Returns:

YUV version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Examples

>>> input = torch.rand(2, 3, 4, 5)
>>> yuv = RgbToYuv()
>>> output = yuv(input)  # 2x3x4x5

Reference::
class kornia.color.YuvToRgb(*args, **kwargs)#

Convert an image from YUV to RGB.

The image data is assumed to be in the range of (0, 1) for luma and (-0.5, 0.5) for chroma.

Returns:

RGB version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Examples

>>> input = torch.rand(2, 3, 4, 5)
>>> rgb = YuvToRgb()
>>> output = rgb(input)  # 2x3x4x5


## YUV420#

Tip

kornia.color.rgb_to_yuv420(image)#

Convert an RGB image to YUV 420 (subsampled).

The image data is assumed to be in the range of (0, 1). Input need to be padded to be evenly divisible by 2 horizontal and vertical. This function will output chroma siting (0.5,0.5)

Parameters:

image (Tensor) – RGB Image to be converted to YUV with shape $$(*, 3, H, W)$$.

Return type:

Returns:

A Tensor containing the Y plane with shape $$(*, 1, H, W)$$ A Tensor containing the UV planes with shape $$(*, 2, H/2, W/2)$$

Example

>>> input = torch.rand(2, 3, 4, 6)
>>> output = rgb_to_yuv420(input)  # (2x1x4x6, 2x2x2x3)

kornia.color.yuv420_to_rgb(imagey, imageuv)#

Convert an YUV420 image to RGB.

The image data is assumed to be in the range of (0, 1) for luma and (-0.5, 0.5) for chroma. Input need to be padded to be evenly divisible by 2 horizontal and vertical. This function assumed chroma siting is (0.5, 0.5)

Parameters:
• imagey (Tensor) – Y (luma) Image plane to be converted to RGB with shape $$(*, 1, H, W)$$.

• imageuv (Tensor) – UV (chroma) Image planes to be converted to RGB with shape $$(*, 2, H/2, W/2)$$.

Return type:

Tensor

Returns:

RGB version of the image with shape $$(*, 3, H, W)$$.

Example

>>> inputy = torch.rand(2, 1, 4, 6)
>>> inputuv = torch.rand(2, 2, 2, 3)
>>> output = yuv420_to_rgb(inputy, inputuv)  # 2x3x4x6

class kornia.color.RgbToYuv420(*args, **kwargs)#

Convert an image from RGB to YUV420.

The image data is assumed to be in the range of (0, 1). Width and Height evenly divisible by 2.

Returns:

YUV420 version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 1, H, W)$$ and $$(*, 2, H/2, W/2)$$

Examples

>>> yuvinput = torch.rand(2, 3, 4, 6)
>>> yuv = RgbToYuv420()
>>> output = yuv(yuvinput)  # # (2x1x4x6, 2x1x2x3)

Reference::
class kornia.color.Yuv420ToRgb(*args, **kwargs)#

Convert an image from YUV to RGB.

The image data is assumed to be in the range of (0, 1) for luma and (-0.5, 0.5) for chroma. Width and Height evenly divisible by 2.

Returns:

RGB version of the image.

Shape:
• imagey: $$(*, 1, H, W)$$

• imageuv: $$(*, 2, H/2, W/2)$$

• output: $$(*, 3, H, W)$$

Examples

>>> inputy = torch.rand(2, 1, 4, 6)
>>> inputuv = torch.rand(2, 2, 2, 3)
>>> rgb = Yuv420ToRgb()
>>> output = rgb(inputy, inputuv)  # 2x3x4x6


## YUV422#

Tip

kornia.color.rgb_to_yuv422(image)#

Convert an RGB image to YUV 422 (subsampled).

The image data is assumed to be in the range of (0, 1). Input need to be padded to be evenly divisible by 2 vertical. This function will output chroma siting (0.5)

Parameters:

image (Tensor) – RGB Image to be converted to YUV with shape $$(*, 3, H, W)$$.

Return type:

Returns:

A Tensor containing the Y plane with shape $$(*, 1, H, W)$$ A Tensor containing the UV planes with shape $$(*, 2, H, W/2)$$

Example

>>> input = torch.rand(2, 3, 4, 6)
>>> output = rgb_to_yuv420(input)  # (2x1x4x6, 2x1x4x3)

kornia.color.yuv422_to_rgb(imagey, imageuv)#

Convert an YUV422 image to RGB.

The image data is assumed to be in the range of (0, 1) for luma and (-0.5, 0.5) for chroma. Input need to be padded to be evenly divisible by 2 vertical. This function assumed chroma siting is (0.5)

Parameters:
• imagey (Tensor) – Y (luma) Image plane to be converted to RGB with shape $$(*, 1, H, W)$$.

• imageuv (Tensor) – UV (luma) Image planes to be converted to RGB with shape $$(*, 2, H, W/2)$$.

Return type:

Tensor

Returns:

RGB version of the image with shape $$(*, 3, H, W)$$.

Example

>>> inputy = torch.rand(2, 1, 4, 6)
>>> inputuv = torch.rand(2, 2, 2, 3)
>>> output = yuv420_to_rgb(inputy, inputuv)  # 2x3x4x5

class kornia.color.RgbToYuv422(*args, **kwargs)#

Convert an image from RGB to YUV422.

The image data is assumed to be in the range of (0, 1). Width evenly disvisible by 2.

Returns:

YUV422 version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 1, H, W)$$ and $$(*, 2, H, W/2)$$

Examples

>>> yuvinput = torch.rand(2, 3, 4, 6)
>>> yuv = RgbToYuv422()
>>> output = yuv(yuvinput)  # # (2x1x4x6, 2x2x4x3)

Reference::
class kornia.color.Yuv422ToRgb(*args, **kwargs)#

Convert an image from YUV to RGB.

The image data is assumed to be in the range of (0, 1) for luma and (-0.5, 0.5) for chroma. Width evenly divisible by 2.

Returns:

RGB version of the image.

Shape:
• imagey: $$(*, 1, H, W)$$

• imageuv: $$(*, 2, H, W/2)$$

• output: $$(*, 3, H, W)$$

Examples

>>> inputy = torch.rand(2, 1, 4, 6)
>>> inputuv = torch.rand(2, 2, 4, 3)
>>> rgb = Yuv422ToRgb()
>>> output = rgb(inputy, inputuv)  # 2x3x4x6


## XYZ#

kornia.color.rgb_to_xyz(image)#

Convert a RGB image to XYZ.

Parameters:

image (Tensor) – RGB Image to be converted to XYZ with shape $$(*, 3, H, W)$$.

Return type:

Tensor

Returns:

XYZ version of the image with shape $$(*, 3, H, W)$$.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = rgb_to_xyz(input)  # 2x3x4x5

kornia.color.xyz_to_rgb(image)#

Convert a XYZ image to RGB.

Parameters:

image (Tensor) – XYZ Image to be converted to RGB with shape $$(*, 3, H, W)$$.

Return type:

Tensor

Returns:

RGB version of the image with shape $$(*, 3, H, W)$$.

Example

>>> input = torch.rand(2, 3, 4, 5)
>>> output = xyz_to_rgb(input)  # 2x3x4x5

class kornia.color.RgbToXyz(*args, **kwargs)#

Convert an image from RGB to XYZ.

The image data is assumed to be in the range of (0, 1).

Returns:

XYZ version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Examples

>>> input = torch.rand(2, 3, 4, 5)
>>> xyz = RgbToXyz()
>>> output = xyz(input)  # 2x3x4x5

Reference:
class kornia.color.XyzToRgb(*args, **kwargs)#

Converts an image from XYZ to RGB.

Returns:

RGB version of the image.

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 3, H, W)$$

Examples

>>> input = torch.rand(2, 3, 4, 5)
>>> rgb = XyzToRgb()
>>> output = rgb(input)  # 2x3x4x5

Reference:

## Bayer RAW#

class kornia.color.CFA(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)#

Define the configuration of the color filter array.

So far only bayer images is supported and the enum sets the pixel order for bayer. Note that this can change due to things like rotations and cropping of images. Take care if including the translations in pipeline. This implementations is optimized to be reasonably fast, look better than simple nearest neighbour. On top of this care is taken to make it reversible going raw -> rgb -> raw. the raw samples remain intact during conversion and only unknown samples are interpolated.

The names are based on the OpenCV convention where the BG indicates pixel 1,1 (counting from 0,0) is blue and its neighbour to the right is green. In that case the top left pixel is red. Other options are GB, RG and GR

reference:

https://en.wikipedia.org/wiki/Color_filter_array

BG = 0#
GB = 1#
GR = 3#
RG = 2#
kornia.color.rgb_to_raw(image, cfa)#

Convert a RGB image to RAW version of image with the specified color filter array.

The image data is assumed to be in the range of (0, 1).

Parameters:
• image (Tensor) – RGB image to be converted to bayer raw with shape $$(*,3,H,W)$$.

• cfa (CFA) – Which color filter array do we want the output to mimic. I.e. which pixels are red/green/blue.

Return type:

Tensor

Returns:

raw version of the image with shape $$(*,1,H,W)$$.

Example

>>> rgbinput = torch.rand(2, 3, 4, 6)
>>> raw = rgb_to_raw(rgbinput, CFA.BG) # 2x1x4x6

kornia.color.raw_to_rgb(image, cfa)#

Convert a raw bayer image to RGB version of image.

We are assuming a CFA with 2 green, 1 red, 1 blue. A bilinear interpolation is used for R/G and a fix convolution for the green pixels. To simplify calculations we expect the Height Width to be evenly divisible by 2.

The image data is assumed to be in the range of (0, 1). Image H/W is assumed to be evenly divisible by 2. for simplicity reasons

Parameters:
Return type:

Tensor

Returns:

RGB version of the image with shape $$(*,3,H,W)$$.

Example

>>> rawinput = torch.randn(2, 1, 4, 6)
>>> rgb = raw_to_rgb(rawinput, CFA.RG) # 2x3x4x6

kornia.color.raw_to_rgb_2x2_downscaled(image, cfa)#

Convert the raw bayer image to RGB version of it and resize width and height by half.

This is done efficiently by converting each superpixel of bayer image to the corresponding rgb triplet. R and B channels of the raw image are left as are, while two G channels of raw image are averaged to obtain the output G channel.

We are assuming a CFA with 2 green, 1 red, 1 blue. The image data is assumed to be in the range of (0, 1). Image H/W is assumed to be evenly divisible by 2 for simplicity reasons.

Parameters:
Return type:

Tensor

Returns:

downscaled RGB version of the image with shape $$(*,3,\frac{H}{2},\frac{W}{2})$$.

Example

>>> rawinput = torch.randn(2, 1, 4, 6)
>>> rgb = raw_to_rgb_2x2_downscaled(rawinput, CFA.RG) # 2x3x2x3

class kornia.color.RawToRgb(cfa)#

Module to convert a bayer raw image to RGB version of image.

The image data is assumed to be in the range of (0, 1).

Shape:
• image: $$(*, 1, H, W)$$

• output: $$(*, 3, H, W)$$

Example

>>> rawinput = torch.rand(2, 1, 4, 6)
>>> rgb = RawToRgb(CFA.RG)
>>> output = rgb(rawinput)  # 2x3x4x5

class kornia.color.RgbToRaw(cfa)#

Module to convert a RGB image to bayer raw version of image.

The image data is assumed to be in the range of (0, 1).

Shape:
• image: $$(*, 3, H, W)$$

• output: $$(*, 1, H, W)$$

reference:

https://docs.opencv.org/4.0.1/de/d25/imgproc_color_conversions.html

Example

>>> rgbinput = torch.rand(2, 3, 4, 6)
>>> raw = RgbToRaw(CFA.GB)
>>> output = raw(rgbinput)  # 2x1x4x6

class kornia.color.RawToRgb2x2Downscaled(cfa)#

Module version of the raw_to_rgb_2x2_downscaled() function.

The image width and height have to be divisible by two. The image data is assumed to be in the range of (0, 1).

Shape:
• image: $$(*, 1, H, W)$$

• output: $$(*, 3, \frac{H}{2}, \frac{W}{2})$$

Example

>>> rawinput = torch.rand(2, 1, 4, 6)
>>> rgb_downscale = RawToRgb2x2Downscaled(CFA.RG)
>>> output = rgb_downscale(rawinput)  # 2x3x2x3


## Sepia#

class kornia.color.Sepia(rescale=True, eps=1e-6)#

Module that apply the sepia filter to tensors.

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

• rescale (bool, optional) – If True, the output tensor will be rescaled (max values be 1. or 255). Default: True

• eps (float, optional) – scalar to enforce numerical stability. Default: 1e-6

Returns:

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

Return type:

Tensor

Example

>>>
>>> input = torch.ones(3, 1, 1)
>>> Sepia(rescale=False)(input)
tensor([[[1.3510]],

[[1.2030]],

[[0.9370]]])

kornia.color.sepia(input, rescale=True, eps=1e-6)#

Apply to a tensor the sepia filter.

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

• rescale (bool, optional) – If True, the output tensor will be rescaled (max values be 1. or 255). Default: True

• eps (float, optional) – scalar to enforce numerical stability. Default: 1e-6

Returns:

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

Return type:

Tensor

Example

>>> input = torch.ones(3, 1, 1)
>>> sepia_from_rgb(input, rescale=False)
tensor([[[1.3510]],

[[1.2030]],

[[0.9370]]])


## Color Maps#

class kornia.color.ColorMap(base, num_colors=64, device=None, dtype=None)#

Class to represent a colour map. It can be created or selected from the built-in colour map. Please refer to the ColorMapType enum class to view all available colormaps.

Parameters:
• base () – A list of RGB colors to define a new custom colormap or

• class. (the name of a built-in colormap as str or using ColorMapType) –

• num_colors (int, optional) – Number of colors in the colormap. Default: 64

• device (, optional) – The device to put the generated colormap on. Default: None

• dtype (, optional) – The data type of the generated colormap. Default: None

Returns:

An object of the colormap with the num_colors length.

Examples

>>> ColorMap(base='viridis', num_colors=8).colors
tensor([[0.2813, 0.2621, 0.2013, 0.1505, 0.1210, 0.2463, 0.5259, 0.8557],
[0.0842, 0.2422, 0.3836, 0.5044, 0.6258, 0.7389, 0.8334, 0.8886],
[0.4072, 0.5207, 0.5543, 0.5574, 0.5334, 0.4519, 0.2880, 0.0989]])

Create a color map from the first color (RGB with range[0-1]) to the last one with num_colors length.
>>> ColorMap(base=[[0., 0.5 , 1.0], [1., 0.5, 0.]], num_colors=8).colors
tensor([[0.0000, 0.0000, 0.1250, 0.3750, 0.6250, 0.8750, 1.0000, 1.0000],
[0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000],
[1.0000, 1.0000, 0.8750, 0.6250, 0.3750, 0.1250, 0.0000, 0.0000]])

kornia.color.RGBColor#

alias of List[float]

Color maps available:

class kornia.color.ColorMapType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)#

An enumeration for available colormaps.

List of available colormaps:

autumn = 1#
bone = 2#
jet = 3#
winter = 4#
rainbow = 5#
ocean = 6#
summer = 7#
spring = 8#
cool = 9#
hsv = 10#
brg = 11#
pink = 12#
hot = 13#
plasma = 14#
viridis = 15#
cividis = 16#
twilight = 17#
turbo = 18#
seismic = 19#
classmethod list()#

Returns a list of names of enumeration members.

Return type:

Returns:

A list containing the names of enumeration members.

Functions and modules to use the color maps:

kornia.color.apply_colormap(input_tensor, colormap)#

Apply to a gray tensor a colormap.

Parameters:
Return type:

Tensor

Returns:

A RGB tensor with the applied color map into the input_tensor.

Raises:

ValueError – If colormap is not a ColorMap object.

Note

The image data is assumed to be integer values in range of [0-255].

Example

>>> input_tensor = torch.tensor([[[0, 1, 2], [25, 50, 63]]])
>>> colormap = ColorMap(base='autumn')
>>> apply_colormap(input_tensor, colormap)
tensor([[[1.0000, 1.0000, 1.0000],
[1.0000, 1.0000, 1.0000]],

[[0.0000, 0.0159, 0.0317],
[0.3968, 0.7937, 1.0000]],

[[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000]]])

class kornia.color.ApplyColorMap(colormap)#

Class for applying a colormap to images.

Parameters:
• colormap (ColorMap) – Either the name of a built-in colormap or a ColorMap object.

• num_colors – Number of colors in the colormap. Default is 256.

• device – The device to put the generated colormap on.

• dtype – The data type of the generated colormap.

Returns:

A RGB tensor with the applied color map into the input_tensor

Raises:

ValueError – If colormap is not a ColorMap object.

Note

The image data is assumed to be integer values in range of [0-255].

Example

>>> input_tensor = torch.tensor([[[0, 1, 2], [25, 50, 63]]])
>>> colormap = ColorMap(base='autumn')
>>> ApplyColorMap(colormap=colormap)(input_tensor)
tensor([[[1.0000, 1.0000, 1.0000],
[1.0000, 1.0000, 1.0000]],

[[0.0000, 0.0159, 0.0317],
[0.3968, 0.7937, 1.0000]],

[[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000]]])