ind2rgb
Convert indexed image to RGB image
Syntax
Description
Examples
Read the first image in a sample indexed image file.
[A,map] = imread("corn.tif"); whos A map
Name Size Bytes Class Attributes A 415x312 129480 uint8 map 256x3 6144 double
The indexed image A is a 415-by-312 matrix of type uint8, and the colormap map is a 256-by-3 matrix of type double. The dimensions of map indicate that the indexed image contains up to 256 colors.
Display the image.
imshow(A,map)

Convert the indexed image to an RGB image. The result is a 415-by-312-by-3 array of type double.
RGB = ind2rgb(A,map);
Check that the values of the RGB image are in the range [0, 1].
[minVal,maxVal] = bounds(RGB(:))
minVal = 0.0078
maxVal = 0.9765
Input Arguments
Indexed image, specified as an m-by-n matrix of integers.
If you specify
Xas an array of integer data type, then the value 0 corresponds to the first color in the colormapmap. For a colormap containing c colors, values of imageXare clipped to the range [0, c-1].If you specify
Xas an array of data typesingleordouble, then the value 1 corresponds to the first color in the colormap. For a colormap containing c colors, values of imageXare clipped to the range [1, c].
Data Types: single | double | uint8 | uint16
Colormap associated with indexed image X, specified as a
c-by-3 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.
Data Types: double
Output Arguments
RGB image, returned as an m-by-n-by-3 numeric array with values in the range [0, 1].
Data Types: double
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.
The ind2rgb 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).
This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).
Version History
Introduced before R2006a
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