rgb2gray

Convert RGB image or colormap to grayscale

Syntax

I = rgb2gray(RGB)

newmap = rgb2gray(map)

Description

I = rgb2gray(RGB) converts the truecolor image RGB to the grayscale image I. The rgb2gray function converts RGB images to grayscale by eliminating the hue and saturation information while retaining the luminance. If you have Parallel Computing Toolbox™ installed, rgb2gray can perform this conversion on a GPU.

newmap = rgb2gray(map) returns a grayscale colormap equivalent to map.

Examples

Convert RGB Image to Grayscale Image

Read and display an RGB image, and then convert it to grayscale.

Read the sample file, peppers.png, and display the RGB image.

RGB = imread('peppers.png');
imshow(RGB)

Convert the RGB image to a grayscale image and display it.

I = rgb2gray(RGB);
figure
imshow(I)

Convert RGB Colormap to Grayscale Colormap

Read an indexed image with an RGB colormap. Then, convert the colormap to grayscale.

Read the sample file, corn.tif, which is an indexed image with an RGB colormap.

[X,map] = imread('corn.tif');

Display the image.

imshow(X,map)

Convert the RGB colormap to a grayscale colormap and redisplay the image.

newmap = rgb2gray(map);
imshow(X,newmap)

Input Arguments

RGB — Truecolor image

m-by-n-by-3 numeric array

Truecolor image, specified as an m-by-n-by-3 numeric array.

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

Data Types: single | double | uint8 | uint16

map — Colormap

c-by-3 numeric matrix

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

I — Grayscale image

m-by-n numeric array

Grayscale image, returned as an m-by-n numeric array.

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

newmap — Grayscale colormap

c-by-3 numeric matrix

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

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

Data Types: double

Tips

• rgb2gray supports the generation of C code using MATLAB^® Coder™.

Algorithms

rgb2gray converts RGB values to grayscale values by forming a weighted sum of the R, G, and B components:

0.2989 * R + 0.5870 * G + 0.1140 * B 

These are the same weights used by the rgb2ntsc (Image Processing Toolbox) function to compute the Y component.

The coefficients used to calculate grayscale values in rgb2gray are identical to those used to calculate luminance (E'y) in Rec.ITU-R BT.601-7 after rounding to 3 decimal places.

Rec.ITU-R BT.601-7 calculates E'y using the following formula:

0.299 * R + 0.587 * G + 0.114 * B

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

When generating code, if you choose the generic MATLAB Host Computer target platform, rgb2gray generates code that uses a precompiled, platform-specific shared library. Use of a shared library preserves performance optimizations but limits the target platforms for which code can be generated.

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™.

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).