OpenCv K近邻算法

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 <cv.h>
#include <highgui.h>
#include <ml.h>
#include <iostream>
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; 
}