opencv K邻近分类器的使用

下面是手册中给出的K邻近分类器使用的例子，该例子是以CvMat形式实现的。通过下面的例子可以知道如何使用Opencv自带的分类器、矩阵数据如何访问、如何画图、如何使用Opencv的随机数生成函数等内容。在第二个例子中已将这些代码部分做了注释。

#include "ml.h"#include "highgui.h"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);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 samplescvGetRows( trainData, &trainData1, 0, train_sample_count/2 );cvRandArr( &rng_state, &trainData1, CV_RAND_NORMAL, cvScalar( 200, 200), cvScalar( 50, 50) );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 classifierCvKNearest 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;// estimate the response and get the neighbors’ labelsresponse = knn.find_nearest( &sample,K, 0, 0,nearests, 0);// compute the number of neighbors representing the majorityfor( 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 samplesfor( 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;}

下面是自己修改的基于Mat数据类型的K邻近分类器使用例子

#include <opencv.hpp>int main( int argc, char** argv ){const int K = 10;int i, j, k, accuracy;float response;int train_sample_count = 100;RNG rng_state = RNG( - 1);//如何使用随机函数Mat trainData=Mat::zeros( train_sample_count, 2, CV_32FC1 );Mat trainClasses =Mat::zeros( train_sample_count, 1, CV_32FC1 );Mat img = Mat::zeros( 500,500,CV_8UC3);float _sample[ 2];Mat sample=Mat( 1, 2, CV_32FC1, _sample );Mat trainData1, trainData2, trainClasses1, trainClasses2;// form the training samplestrainData1=trainData.rowRange(0,train_sample_count/2);//如何从已有矩阵中提取部分矩阵rng_state.fill( trainData1, CV_RAND_NORMAL,Mat(1,1,CV_64F,cvScalar(200, 200)), Mat(1,1,CV_64F,cvScalar(50, 50)) );//如何利用随机函数赋值已有矩阵trainData2=trainData.rowRange(train_sample_count/2,train_sample_count);rng_state.fill( trainData2, CV_RAND_NORMAL,Mat(1,1,CV_64F,cvScalar(300, 300)), Mat(1,1,CV_64F,cvScalar(50, 50)) );trainClasses1=trainClasses.rowRange(0,train_sample_count/2);trainClasses1.setTo(Scalar( 1));//如何初始化矩阵为同一个数值trainClasses2=trainClasses.rowRange(train_sample_count/2,train_sample_count);trainClasses2.setTo(Scalar( 2));// learn classifierCvKNearest knn( trainData, trainClasses,Mat(), false, K );//如何建立分类器的训练对象Mat nearests ( 1, K, CV_32FC1);for( i = 0; i < img.rows; i++ ){for( j = 0; j < img.cols; j ++ ){ sample.at<float>(0,0) = ( float)j;sample.at<float>(0,1) = ( float)i;// estimate the response and get the neighbors’ labelsresponse = knn.find_nearest( sample,K, 0, 0,&nearests, 0);//利用训练好的分类器分类新的数据// compute the number of neighbors representing the majorityfor( k = 0, accuracy = 0; k < K; k++ ){if( nearests.at<float>(0,k) == response)accuracy++;}// highlight the pixel depending on the accuracy (or confidence)img.at<Vec3b>(i,j)[2]=response == 1 ?(accuracy>5?180:180):(accuracy>5?0:120);//如何访问多通道的矩阵img.at<Vec3b>(i,j)[1]=response == 1 ?(accuracy>5?0:120):(accuracy>5?180:120);img.at<Vec3b>(i,j)[0]=response == 1 ?(accuracy>5?0:0):(accuracy>5?0:0);}}// display the original training samplesfor( i = 0; i < train_sample_count/2; i++ ){CvPoint pt;pt.x = cvRound(trainData1.at<float>(i,0));pt.y = cvRound(trainData1.at<float>(i,1));circle( img, pt, 2, CV_RGB( 255, 0, 0), CV_FILLED );//在Mat数据类型中画图pt.x = cvRound(trainData2.at<float>(i,0));pt.y = cvRound(trainData2.at<float>(i,1));circle( img, pt, 2, CV_RGB( 0, 255, 0), CV_FILLED );}imshow( " classifier result" , img );cvWaitKey( 0);return 0;｝

下面是分类器分类结果