【OpenCV】K-Means非监督聚类

// K_Means.cpp : 定义控制台应用程序的入口点。
//

#include "stdafx.h"
#include "cv.h"
#include "highgui.h"
#include <stdio.h>

/*
   OpenCV中K-Means分类函数参数详解
   void cvKMeans2(  
	const CvArr* samples,  //输入样本的浮点矩阵，每个样本一行。  
	int cluster_count,   //所给定的聚类数目   
	* labels,     //输出整数向量：每个样本对应的类别标识 ,注意：该变量的行数必须与samples的行数是一致的 
	CvTermCriteria termcrit //指定聚类的最大迭代次数和／或精度（两次迭代引起的聚类中心的移动距离） 
	);  
*/

int _tmain(int argc, _TCHAR* 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 cluster_count = 3;
		int i, sample_count = /*cvRandInt(&rng)%1000 + 1*/1000;
		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 );
		}

		printf( "iterations=%d\n", cvKMeans2( points, cluster_count, clusters,
			cvTermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10, 1.0 ),
			10, 0, 0, 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];
			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;
}
// K_Means.cpp : 定义控制台应用程序的入口点。
//

#include "s