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#include "opencv2/highgui.hpp"
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
using namespace cv;
using namespace std;
// static void help()
// {
// cout << "\nThis program demonstrates kmeans clustering.\n"
// "It generates an image with random points, then assigns a random number of cluster\n"
// "centers and uses kmeans to move those cluster centers to their representitive location\n"
// "Call\n"
// "./kmeans\n" << endl;
// }
int main( int /*argc*/, char** /*argv*/ )
{
const int MAX_CLUSTERS = 5;
Scalar colorTab[] =
{
Scalar(0, 0, 255),
Scalar(0,255,0),
Scalar(255,100,100),
Scalar(255,0,255),
Scalar(0,255,255)
};
Mat img(500, 500, CV_8UC3);
RNG rng(12345);
for(;;)
{
int k, clusterCount = rng.uniform(2, MAX_CLUSTERS+1);
int i, sampleCount = rng.uniform(1, 1001);
Mat points(sampleCount, 1, CV_32FC2), labels;
clusterCount = MIN(clusterCount, sampleCount);
std::vector<Point2f> centers;
/* generate random sample from multigaussian distribution */
for( k = 0; k < clusterCount; k++ )
{
Point center;
center.x = rng.uniform(0, img.cols);
center.y = rng.uniform(0, img.rows);
Mat pointChunk = points.rowRange(k*sampleCount/clusterCount,
k == clusterCount - 1 ? sampleCount :
(k+1)*sampleCount/clusterCount);
rng.fill(pointChunk, RNG::NORMAL, Scalar(center.x, center.y), Scalar(img.cols*0.05, img.rows*0.05));
}
randShuffle(points, 1, &rng);
double compactness = kmeans(points, clusterCount, labels,
TermCriteria( TermCriteria::EPS+TermCriteria::COUNT, 10, 1.0),
3, KMEANS_PP_CENTERS, centers);
img = Scalar::all(0);
for( i = 0; i < sampleCount; i++ )
{
int clusterIdx = labels.at<int>(i);
Point ipt = points.at<Point2f>(i);
circle( img, ipt, 2, colorTab[clusterIdx], FILLED, LINE_AA );
}
for (i = 0; i < (int)centers.size(); ++i)
{
Point2f c = centers[i];
circle( img, c, 40, colorTab[i], 1, LINE_AA );
}
cout << "Compactness: " << compactness << endl;
imshow("clusters", img);
char key = (char)waitKey();
if( key == 27 || key == 'q' || key == 'Q' ) // 'ESC'
break;
}
return 0;
}
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