خوشه یابی با الگوریتم K میانگین در OpenCV

 
#ifdef _CH_
#pragma package 
#endif
 
#define CV_NO_BACKWARD_COMPATIBILITY
 
#ifndef _EiC
#include "cv.h"
#include "highgui.h"
#include 
#endif
 
#ifdef _DEBUG
#pragma comment (lib, "highguid.lib")
#pragma comment (lib, "cxcored.lib")
#else
#pragma comment (lib, "highgui.lib")
#pragma comment (lib, "cxcore.lib")
#endif
 
int main( int argc, char** argv )
{
#define MAX_CLUSTERS 5
	CvScalar color_tab[MAX_CLUSTERS];
	IplImage* img = cvCreateImage( cvSize( 500, 500 ), 8, 3 );
	CvRNG rng = cvRNG(-1);
	CvPoint ipt;
 
	color_tab[0] = CV_RGB(255,0,0);
	color_tab[1] = CV_RGB(0,255,0);
	color_tab[2] = CV_RGB(100,100,255);
	color_tab[3] = CV_RGB(255,0,255);
	color_tab[4] = CV_RGB(255,255,0);
 
	cvNamedWindow( "clusters", 1 );
 
	for(;;)
	{
  char key;
  int k, cluster_count = cvRandInt(&rng)%MAX_CLUSTERS + 1;
  int i, sample_count = cvRandInt(&rng)%1000 + 1;
 
 
  cv::Mat _points (sample_count, 2, CV_32FC1);
  cv::Mat _clusters (sample_count, 1, CV_32SC1);
  //cv::RNG& rng = cv::theRNG();
  //rng.fill(_points, cv::RNG::NORMAL, param1, param2 );
  for(int i = 0; i < sample_count; i++){
   CvPoint center;
   CvMat point_chunk;
   center.x = cvRandInt(&rng)%img->width;
   center.y = cvRandInt(&rng)%img->height;
  }
 
  CvMat* points = cvCreateMat( sample_count, 1, CV_32FC2 );
  CvMat* clusters = cvCreateMat( sample_count, 1, CV_32SC1 );
  cluster_count = MIN(cluster_count, sample_count);
 
  /* generate random sample from multigaussian distribution */
  for( k = 0; k < cluster_count; k++ )
  {
   CvPoint center;
   CvMat point_chunk;
   center.x = cvRandInt(&rng)%img->width;
   center.y = cvRandInt(&rng)%img->height;
   cvGetRows( points, &point_chunk, k*sample_count/cluster_count,
    k == cluster_count - 1 ? sample_count :
    (k+1)*sample_count/cluster_count, 1 );
 
   cvRandArr( &rng, &point_chunk, CV_RAND_NORMAL,
    cvScalar(center.x,center.y,0,0),
    cvScalar(img->width*0.1,img->height*0.1,0,0));
  }
 
  /* shuffle samples */
  for( i = 0; i < sample_count/2; i++ )
  {
   CvPoint2D32f* pt1 = (CvPoint2D32f*)points->data.fl + cvRandInt(&rng)%sample_count;
   CvPoint2D32f* pt2 = (CvPoint2D32f*)points->data.fl + cvRandInt(&rng)%sample_count;
   CvPoint2D32f temp;
   CV_SWAP( *pt1, *pt2, temp );
  }
 
 
  for(int i = 0; i < sample_count; i++){   
   _points.at(i, 0) = points->data.fl[i*2];
   _points.at(i, 1) = points->data.fl[i*2+1];
  }
 
 
  //old OpenCV Style
  //cvKMeans2( points, cluster_count, clusters,
  //	cvTermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10, 1.0 ),
  //	5, 0, 0, 0, 0 );
 
  //New OpenCV Style
  cv::kmeans( _points, cluster_count, _clusters,
   cvTermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10, 1.0 ),
   5, 0, 0);
 
  cvZero( img );
 
  for( i = 0; i < sample_count; i++ )
  {
   //int cluster_idx = clusters->data.i[i];
   //ipt.x = (int)points->data.fl[i*2];
   //ipt.y = (int)points->data.fl[i*2+1];
 
   int cluster_idx = _clusters.at(i, 0);
   ipt.x = (int)_points.at (i, 0);
   ipt.y = (int)_points.at (i, 1);
 
   cvCircle( img, ipt, 2, color_tab[cluster_idx], CV_FILLED, CV_AA, 0 );
  }
 
  cvReleaseMat( &points );
  cvReleaseMat( &clusters );
 
  cvShowImage( "clusters", img );
 
  key = (char) cvWaitKey(0);
  if( key == 27 || key == 'q' || key == 'Q' ) // 'ESC'
   break;
	}
 
	cvDestroyWindow( "clusters" );
	return 0;
}
 
#ifdef _EiC
main(1,"kmeans.c");
#endif