cmap2gray

Convert RGB colormap to grayscale colormap

Since R2020b

Syntax

newmap = cmap2gray(map)

Description

newmap = cmap2gray(map) converts the RGB colormap map into the equivalent grayscale colormap newmap.

Examples

Convert RGB Colormap to Grayscale Colormap Using cmap2gray

Load an indexed image with its colormap into the workspace. The load command returns a structure containing the image data and the colormap. View the image.

indImage = load('clown.mat');
figure
imshow(indImage.X,indImage.map)

Convert the RGB colormap that came with the image into a grayscale colormap.

newmap = cmap2gray(indImage.map);

Display the image with the grayscale colormap.

figure;
imshow(indImage.X,newmap)

Input Arguments

map — 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 map is a three-element RGB triplet that specifies the red, green, and blue components of a single color of the colormap.

If you have Parallel Computing Toolbox™ installed, map can also be a gpuArray.

Data Types: double

Output Arguments

newmap — Grayscale colormap

c-by-3 numeric matrix

Grayscale colormap, returned as a c-by-3 numeric matrix with values in the range [0, 1]. The three columns of newmap are identical, so that each row specifies a single intensity value.

If you have Parallel Computing Toolbox installed, then newmap can also be a gpuArray.

Data Types: double

Extended Capabilities

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

This function fully supports GPU arrays. 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 in R2020b