Applies a GNU Octave/MATLAB equivalent colormap on a given image.
Parameters: |
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Currently the following GNU Octave/MATLAB equivalent colormaps are implemented:
enum
{
COLORMAP_AUTUMN = 0,
COLORMAP_BONE = 1,
COLORMAP_JET = 2,
COLORMAP_WINTER = 3,
COLORMAP_RAINBOW = 4,
COLORMAP_OCEAN = 5,
COLORMAP_SUMMER = 6,
COLORMAP_SPRING = 7,
COLORMAP_COOL = 8,
COLORMAP_HSV = 9,
COLORMAP_PINK = 10,
COLORMAP_HOT = 11
}
The human perception isn’t built for observing fine changes in grayscale images. Human eyes are more sensitive to observing changes between colors, so you often need to recolor your grayscale images to get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your computer vision application.
In OpenCV you only need applyColorMap() to apply a colormap on a given image. The following sample code reads the path to an image from command line, applies a Jet colormap on it and shows the result:
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
using namespace cv;
#include <iostream>
using namespace std;
int main(int argc, const char *argv[])
{
// We need an input image. (can be grayscale or color)
if (argc < 2)
{
cerr << "We need an image to process here. Please run: colorMap [path_to_image]" << endl;
return -1;
}
Mat img_in = imread(argv[1]);
if(img_in.empty())
{
cerr << "Sample image (" << argv[1] << ") is empty. Please adjust your path, so it points to a valid input image!" << endl;
return -1;
}
// Holds the colormap version of the image:
Mat img_color;
// Apply the colormap:
applyColorMap(img_in, img_color, COLORMAP_JET);
// Show the result:
imshow("colorMap", img_color);
waitKey(0);
return 0;
}
And here are the color scales for each of the available colormaps:
Class | Scale |
---|---|
COLORMAP_AUTUMN | |
COLORMAP_BONE | |
COLORMAP_COOL | |
COLORMAP_HOT | |
COLORMAP_HSV | |
COLORMAP_JET | |
COLORMAP_OCEAN | |
COLORMAP_PINK | |
COLORMAP_RAINBOW | |
COLORMAP_SPRING | |
COLORMAP_SUMMER | |
COLORMAP_WINTER |