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:input (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.
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: