Open Source Computer Vision Library https://opencv.org/
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#include "opencv2/highgui/highgui.hpp"
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
using namespace cv;
using namespace std;
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void help(char** argv)
{
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cout << "\nDemonstrate mean-shift based color segmentation in spatial pyramid.\n"
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<< "Call:\n " << argv[0] << " image\n"
<< "This program allows you to set the spatial and color radius\n"
<< "of the mean shift window as well as the number of pyramid reduction levels explored\n"
<< endl;
}
//This colors the segmentations
void floodFillPostprocess( Mat& img, const Scalar& colorDiff=Scalar::all(1) )
{
CV_Assert( !img.empty() );
RNG rng = theRNG();
Mat mask( img.rows+2, img.cols+2, CV_8UC1, Scalar::all(0) );
for( int y = 0; y < img.rows; y++ )
{
for( int x = 0; x < img.cols; x++ )
{
if( mask.at<uchar>(y+1, x+1) == 0 )
{
Scalar newVal( rng(256), rng(256), rng(256) );
floodFill( img, mask, Point(x,y), newVal, 0, colorDiff, colorDiff );
}
}
}
}
string winName = "meanshift";
int spatialRad, colorRad, maxPyrLevel;
Mat img, res;
void meanShiftSegmentation( int, void* )
{
cout << "spatialRad=" << spatialRad << "; "
<< "colorRad=" << colorRad << "; "
<< "maxPyrLevel=" << maxPyrLevel << endl;
pyrMeanShiftFiltering( img, res, spatialRad, colorRad, maxPyrLevel );
floodFillPostprocess( res, Scalar::all(2) );
imshow( winName, res );
}
int main(int argc, char** argv)
{
if( argc !=2 )
{
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help(argv);
return -1;
}
img = imread( argv[1] );
if( img.empty() )
return -1;
spatialRad = 10;
colorRad = 10;
maxPyrLevel = 1;
namedWindow( winName, CV_WINDOW_AUTOSIZE );
createTrackbar( "spatialRad", winName, &spatialRad, 80, meanShiftSegmentation );
createTrackbar( "colorRad", winName, &colorRad, 60, meanShiftSegmentation );
createTrackbar( "maxPyrLevel", winName, &maxPyrLevel, 5, meanShiftSegmentation );
meanShiftSegmentation(0, 0);
waitKey();
return 0;
}