rgb2hsv

Convert RGB colors to HSV

Syntax

HSV = rgb2hsv(RGB)

hsvmap = rgb2hsv(rgbmap)

Description

HSV = rgb2hsv(RGB) converts the red, green, and blue values of an RGB image to hue, saturation, and value (HSV) values of an HSV image.

hsvmap = rgb2hsv(rgbmap) converts an RGB colormap to an HSV colormap.

Examples

Convert Colormap to HSV

Get a down-sampled version of the prism colormap.

rgb = prism(6)

rgb = 6×3

    1.0000         0         0
    1.0000    0.5000         0
    1.0000    1.0000         0
         0    1.0000         0
         0         0    1.0000
    0.6667         0    1.0000

Convert the RGB values in the colormap to HSV.

hsv = rgb2hsv(rgb)

hsv = 6×3

         0    1.0000    1.0000
    0.0833    1.0000    1.0000
    0.1667    1.0000    1.0000
    0.3333    1.0000    1.0000
    0.6667    1.0000    1.0000
    0.7778    1.0000    1.0000

Convert Truecolor Image to HSV

Create a 2-by-2 truecolor image.

rgb(:,:,1) = [1 1; 0 .5];
rgb(:,:,2) = [0 1; 0 .5];
rgb(:,:,3) = [0 0; 1 .5];
image(rgb);

Convert the image to an HSV array.

hsv = rgb2hsv(rgb)

hsv = 
hsv(:,:,1) =

         0    0.1667
    0.6667         0


hsv(:,:,2) =

     1     1
     1     0


hsv(:,:,3) =

    1.0000    1.0000
    1.0000    0.5000

Input Arguments

RGB — RGB image

m-by-n-by-3 numeric array

RGB image to convert, specified as an m-by-n-by-3 numeric array. The third dimension of RGB defines the red, green, and blue intensity of each pixel, respectively.

The rgb2hsv function expects truecolor images of data type double and single to have values in the range [0, 1].

Data Types: single | double | uint8 | uint16

rgbmap — RGB colormap

c-by-3 numeric matrix

RGB colormap, specified as a c-by-3 numeric matrix with values in the range [0, 1]. Each row of rgbmap is a three-element RGB triplet that specifies the red, green, and blue components of a single color of the colormap.

Data Types: double

Output Arguments

HSV — HSV image

m-by-n-by-3 numeric array

HSV image, returned as an m-by-n-by-3 numeric array with values in the range [0, 1]. The third dimension of HSV defines the hue, saturation, and value for each pixel, respectively, as described in the table.

Attribute	Description
Hue	Value from 0 to 1 that corresponds to the color’s position on a color wheel. As hue increases from 0 to 1, the color transitions from red to orange, yellow, green, cyan, blue, magenta, and finally back to red.
Saturation	Amount of hue or departure from neutral. 0 indicates a neutral shade, whereas 1 indicates maximum saturation.
Value	Maximum value among the red, green, and blue components of a specific color.

The data type of HSV depends on the data type of RGB:

• If RGB is of data type single, then HSV is of data type single.

• Otherwise, HSV is of data type double.

Data Types: double | single

hsvmap — HSV colormap

c-by-3 numeric matrix

HSV colormap, returned as a c-by-3 numeric matrix with values in the range [0, 1]. Each row of hsvmap is a three-element HSV triplet that specifies the hue, saturation, and value components of a single color of the colormap.

The data type of hsvmap depends on the data type of rgbmap:

• If rgbmap is of data type single, then hsvmap is of data type single.

• Otherwise, hsvmap is of data type double.

Data Types: single | double

References

[1] Smith, A. R. “Color Gamut Transform Pairs”. SIGGRAPH 78 Conference Proceedings. 1978, pp. 12–19.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Thread-Based Environment

Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

The rgb2hsv function fully supports GPU arrays. To run the function on a GPU, specify the input data as a gpuArray (Parallel Computing Toolbox). For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Distributed Arrays

Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).

Version History

Introduced before R2006a