OpenCV kmeans代码

代码：出处忘了

//
// Example 13-1. Using K-means
//
//
/* *************** License:**************************
   Oct. 3, 2008
   Right to use this code in any way you want without warrenty, support or any guarentee of it working.

   BOOK: It would be nice if you cited it:
   Learning OpenCV: Computer Vision with the OpenCV Library
     by Gary Bradski and Adrian Kaehler
     Published by O'Reilly Media, October 3, 2008

   AVAILABLE AT:
     http://www.amazon.com/Learning-OpenCV-Computer-Vision-Library/dp/0596516134
     Or: http://oreilly.com/catalog/9780596516130/
     ISBN-10: 0596516134 or: ISBN-13: 978-0596516130    

   OTHER OPENCV SITES:
   * The source code is on sourceforge at:
     http://sourceforge.net/projects/opencvlibrary/
   * The OpenCV wiki page (As of Oct 1, 2008 this is down for changing over servers, but should come back):
     http://opencvlibrary.sourceforge.net/
   * An active user group is at:
     http://tech.groups.yahoo.com/group/OpenCV/
   * The minutes of weekly OpenCV development meetings are at:
     http://pr.willowgarage.com/wiki/OpenCV
   ************************************************** */

#include "cxcore.h"
#include "highgui.h"

#pragma comment(lib,"opencv_core2410d.lib")
#pragma comment(lib,"opencv_highgui2410d.lib")
#pragma comment(lib,"opencv_ml2410d.lib")

int main( int argc, char** argv )
{
    #define MAX_CLUSTERS 5			 //最大聚类数
    CvScalar color_tab[MAX_CLUSTERS];
    IplImage* img = cvCreateImage( cvSize( 500, 500 ), 8, 3 );
    CvRNG rng = cvRNG(0xffffffff);

    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(;;)
    {
        int k, cluster_count = cvRandInt(&rng)%MAX_CLUSTERS + 1;
        int i, sample_count = cvRandInt(&rng)%1000 + 1;
        CvMat* points = cvCreateMat( sample_count, 1, CV_32FC2 );
        CvMat* clusters = cvCreateMat( sample_count, 1, CV_32SC1 );

        /* generate random sample from multivariate
           Gaussian 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 );

            cvRandArr( &rng, &point_chunk, CV_RAND_NORMAL,
                       cvScalar(center.x,center.y,0,0),
                       cvScalar(img->width/6, img->height/6,0,0) );
        }

        /* 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 );
        }

        cvKMeans2( points, cluster_count, clusters,
                   cvTermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,
                                   10, 1.0 ));
        cvZero( img );
        for( i = 0; i < sample_count; i++ )
        {
            CvPoint2D32f pt = ((CvPoint2D32f*)points->data.fl)[i];
            int cluster_idx = clusters->data.i[i];
            cvCircle( img, cvPointFrom32f(pt), 2,
                      color_tab[cluster_idx], CV_FILLED );
        }

        cvReleaseMat( &points );
        cvReleaseMat( &clusters );

        cvShowImage( "clusters", img );

        int key = cvWaitKey(0);
        if( key == 27 ) // 'ESC'
            break;
    }
}