text: #include "opencv2/highgui/highgui.hpp"
  #include "opencv2/core/core.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);
          Mat 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, CV_RAND_NORMAL, Scalar(center.x, center.y), Scalar(img.cols*0.05, img.rows*0.05));
          }
  
          randShuffle(points, 1, &rng);
  
          kmeans(points, clusterCount, labels,
                 TermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 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], CV_FILLED, CV_AA );
          }
  
          imshow("clusters", img);
  
          char key = (char)waitKey();
          if( key == 27 || key == 'q' || key == 'Q' ) // 'ESC'
              break;
      }
  
      return 0;
  }
  