参考文献：

http://www.aiseminar.cn/bbs/forum.php?mod=viewthread&tid=824

http://www.cnblogs.com/v-July-v/archive/2012/11/20/3125419.html



//运行环境：winXP + VS2008 + openCV2.1.0 #include "stdafx.h" #include  #include  #include  #include  using namespace std;  int main( int argc, char** argv )  {      	const int K = 10;      	int i, j, k, accuracy;      	float response;      	int train_sample_count = 100;      	CvRNG rng_state = cvRNG(-1);//初始化随机数生成器状态     	CvMat* trainData = cvCreateMat( train_sample_count, 2, CV_32FC1 );      	CvMat* trainClasses = cvCreateMat( train_sample_count, 1, CV_32FC1 );      	IplImage* img = cvCreateImage( cvSize( 500, 500 ), 8, 3 );      	float _sample[2];      	CvMat sample = cvMat( 1, 2, CV_32FC1, _sample );      	cvZero( img );    	CvMat trainData1, trainData2, trainClasses1, trainClasses2;      	// form the training samples      	cvGetRows( trainData, &trainData1, 0, train_sample_count/2 ); //返回数组的一行或在一定跨度内的行     	cvRandArr( &rng_state, &trainData1, CV_RAND_NORMAL, cvScalar(200,200), cvScalar(50,50) ); //用随机数填充数组并更新 RNG 状态       	cvGetRows( trainData, &trainData2, train_sample_count/2, train_sample_count );      	cvRandArr( &rng_state, &trainData2, CV_RAND_NORMAL, cvScalar(300,300), cvScalar(50,50) );    	cvGetRows( trainClasses, &trainClasses1, 0, train_sample_count/2 );      	cvSet( &trainClasses1, cvScalar(1) );       	cvGetRows( trainClasses, &trainClasses2, train_sample_count/2, train_sample_count );      	cvSet( &trainClasses2, cvScalar(2) );     	// learn classifier      	CvKNearest knn( trainData, trainClasses, 0, false, K );     	CvMat* nearests = cvCreateMat( 1, K, CV_32FC1);    	for( i = 0; i < img->height; i++ )      	{          		for( j = 0; j < img->width; j++ )          		{              			sample.data.fl[0] = (float)j;              			sample.data.fl[1] = (float)i;     			// estimates the response and get the neighbors' labels              			response = knn.find_nearest(&sample,K,0,0,nearests,0);        			// compute the number of neighbors representing the majority              			for( k = 0, accuracy = 0; k < K; k++ )              			{                  				if( nearests->data.fl[k] == response)                      					accuracy++;              			}     			// highlight the pixel depending on the accuracy (or confidence)              			cvSet2D( img, i, j, response == 1 ?                  				(accuracy > 5 ? CV_RGB(180,0,0) : CV_RGB(180,120,0)) :                  				(accuracy > 5 ? CV_RGB(0,180,0) : CV_RGB(120,120,0)) );          		}      	}        	 	// display the original training samples      	for( i = 0; i < train_sample_count/2; i++ )      	{          		CvPoint pt;          		pt.x = cvRound(trainData1.data.fl[i*2]);          		pt.y = cvRound(trainData1.data.fl[i*2+1]);          		cvCircle( img, pt, 2, CV_RGB(255,0,0), CV_FILLED );    		pt.x = cvRound(trainData2.data.fl[i*2]);          		pt.y = cvRound(trainData2.data.fl[i*2+1]);          		cvCircle( img, pt, 2, CV_RGB(0,255,0), CV_FILLED );      	}       	cvNamedWindow( "classifier result", 1 );      	cvShowImage( "classifier result", img );      	cvWaitKey(0);       	cvReleaseMat( &trainClasses );      	cvReleaseMat( &trainData );      	return 0;  }