Create a three-column HSV matrix that specifies five shades of blue. In this case, hue and value are constant, while saturation varies between 1.0 and 0.0.
HSV image, specified 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.
Data Types: double | single | logical
hsvmap — HSV colormap c-by-3 numeric matrix
HSV colormap, specified 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.
RGB image, returned as an
m-by-n-by-3 numeric array with values
in the range [0, 1]. The third dimension of RGB defines
the red, green, and blue intensity of each pixel, respectively. The image
has the same data type as the HSV image, HSV.
Data Types: double | single
rgbmap — RGB colormap c-by-3 numeric matrix
RGB colormap, returned 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 ref, green, and blue components
of a single color of the colormap. The colormap has the same data type as
the HSV colormap, hsvmap.
Data Types: double | single
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™.
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™.
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