kornia.color¶

The functions in this section perform various color space conversions.

rgb_to_grayscale(input: torch.Tensor) → torch.Tensor[source]¶

Convert an RGB image to grayscale.

See RgbToGrayscale for details.

Parameters:	input (torch.Tensor) – Image to be converted to grayscale.
Returns:	Grayscale version of the image.
Return type:	torch.Tensor

rgb_to_hsv(image)[source]¶

Convert an RGB image to HSV.

Parameters:	input (torch.Tensor) – RGB Image to be converted to HSV.
Returns:	HSV version of the image.
Return type:	torch.Tensor

hsv_to_rgb(image)[source]¶

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

Parameters:	input (torch.Tensor) – RGB Image to be converted to HSV.
Returns:	HSV version of the image.
Return type:	torch.Tensor

rgb_to_bgr(image: torch.Tensor) → torch.Tensor[source]¶

Convert a RGB image to BGR.

See RgbToBgr for details.

Parameters:	image (torch.Tensor) – RGB Image to be converted to BGR.
Returns:	BGR version of the image.
Return type:	torch.Tensor

bgr_to_rgb(image: torch.Tensor) → torch.Tensor[source]¶

Convert a BGR image to RGB.

See BgrToRgb for details.

Parameters:	input (torch.Tensor) – BGR Image to be converted to RGB.
Returns:	RGB version of the image.
Return type:	torch.Tensor

normalize(data: torch.Tensor, mean: torch.Tensor, std: torch.Tensor) → torch.Tensor[source]¶

Normalise the image with channel-wise mean and standard deviation.

See Normalize for details.

Parameters:	data (torch.Tensor) – The image tensor to be normalised. mean (torch.Tensor) – Mean for each channel. std (torch.Tensor) – Standard deviations for each channel. Returns – torch.Tensor: The normalised image tensor.

adjust_brightness(image: torch.Tensor, brightness_factor: torch.Tensor) → torch.Tensor[source]¶

Adjust Brightness of an Image

See AdjustBrightness for details.

Parameters:	image (torch.Tensor) – Image to be adjusted. brightness_factor (torch.Tensor) – Brightness adjust factor. 0 generates a compleatly black image, 1 does not modify the input image while any other non-negative number modify the brightness by this factor.
Returns:	Adjusted image.
Return type:	torch.Tensor

class RgbToGrayscale[source]¶

convert image to grayscale version of image.

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

Parameters:	input (torch.Tensor) – image to be converted to grayscale.
Returns:	grayscale version of the image.
Return type:	torch.Tensor

shape:

input: \((*, 3, H, W)\)
output: \((*, 1, H, W)\)

Examples:

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

class RgbToHsv[source]¶

Convert image from RGB to HSV.

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

Parameters:	image (torch.Tensor) – RGB image to be converted to HSV.
Returns:	HSV version of the image.
Return type:	torch.tensor

shape:

image: \((*, 3, H, W)\)
output: \((*, 3, H, W)\)

class HsvToRgb[source]¶

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

Parameters:	image (torch.Tensor) – RGB image to be converted to HSV.
Returns:	HSV version of the image.
Return type:	torch.tensor

shape:

image: \((*, 3, H, W)\)
output: \((*, 3, H, W)\)

class RgbToBgr[source]¶

Convert image from RGB to BGR.

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

Parameters:	image (torch.Tensor) – RGB image to be converted to BGR
Returns:	BGR version of the image.
Return type:	torch.Tensor

shape:

image: \((*, 3, H, W)\)
output: \((*, 3, H, W)\)

class BgrToRgb[source]¶

Convert image from BGR to RGB.

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

Parameters:	image (torch.Tensor) – BGR image to be converted to RGB.
Returns:	RGB version of the image.
Return type:	torch.Tensor

shape:

image: \((*, 3, H, W)\)
output: \((*, 3, H, W)\)

class Normalize(mean: torch.Tensor, std: torch.Tensor)[source]¶

Normalize a tensor image or a batch of tensor images with mean and standard deviation. Input must be a tensor of shape (C, H, W) or a batch of tensors \((*, C, H, W)\).

Given mean: (M1,...,Mn) and std: (S1,..,Sn) for n channels, this transform will normalize each channel of the input torch.Tensor i.e. input[channel] = (input[channel] - mean[channel]) / std[channel]

Parameters:	mean (torch.Tensor) – Mean for each channel. std (torch.Tensor) – Standard deviation for each channel.

class AdjustBrightness[source]¶

Adjust Brightness of an Image

See AdjustBrightness for details.

Parameters:	image (torch.Tensor) – Image to be adjusted. brightness_factor (torch.Tensor) – Brightness adjust factor. 0 generates a compleatly black image, 1 does not modify the input image while any other non-negative number modify the brightness by this factor.
Returns:	Adjusted image.
Return type:	torch.Tensor