学习OpenCV——HOG+SVM

 #include "cv.h"
 #include "highgui.h"
 #include "stdafx.h"
 #include <ml.h>
 #include <iostream>
 #include <fstream>
 #include <string>
 #include <vector>
 using namespace cv;
 using namespace std;


 int main(int argc, char** argv)
 {
     vector<string> img_path;
     vector<int> img_catg;
     int nLine = 0;
     string buf;
     ifstream svm_data( "E:/SVM_DATA.txt" );
     unsigned long n;

     while( svm_data )
     {
         if( getline( svm_data, buf ) )
         {
             nLine ++;
             if( nLine % 2 == 0 )
             {
                  img_catg.push_back( atoi( buf.c_str() ) );//atoi将字符串转换成整型，标志（0,1）
             }
             else
             {
                 img_path.push_back( buf );//图像路径
             }
         }
     }
     svm_data.close();//关闭文件

     CvMat *data_mat, *res_mat;
     int nImgNum = nLine / 2;            //读入样本数量
     ////样本矩阵，nImgNum：横坐标是样本数量， WIDTH * HEIGHT：样本特征向量，即图像大小
     data_mat = cvCreateMat( nImgNum, 1764, CV_32FC1 );
     cvSetZero( data_mat );
     //类型矩阵,存储每个样本的类型标志
     res_mat = cvCreateMat( nImgNum, 1, CV_32FC1 );
     cvSetZero( res_mat );

     IplImage* src;
     IplImage* trainImg=cvCreateImage(cvSize(64,64),8,3);//需要分析的图片

     for( string::size_type i = 0; i != img_path.size(); i++ )
     {
             src=cvLoadImage(img_path[i].c_str(),1);
             if( src == NULL )
             {
                 cout<<" can not load the image: "<<img_path[i].c_str()<<endl;
                 continue;
             }

             cout<<" processing "<<img_path[i].c_str()<<endl;

             cvResize(src,trainImg);   //读取图片
             HOGDescriptor *hog=new HOGDescriptor(cvSize(64,64),cvSize(16,16),cvSize(8,8),cvSize(8,8),9);  //具体意思见参考文章1,2
             vector<float>descriptors;//结果数组
             hog->compute(trainImg, descriptors,Size(1,1), Size(0,0)); //调用计算函数开始计算
             cout<<"HOG dims: "<<descriptors.size()<<endl;
             //CvMat* SVMtrainMat=cvCreateMat(descriptors.size(),1,CV_32FC1);
             n=0;
             for(vector<float>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
             {
                 cvmSet(data_mat,i,n,*iter);
                 n++;
             }
                 //cout<<SVMtrainMat->rows<<endl;
             cvmSet( res_mat, i, 0, img_catg[i] );
             cout<<" end processing "<<img_path[i].c_str()<<" "<<img_catg[i]<<endl;
     }


     CvSVM svm = CvSVM();
     CvSVMParams param;
     CvTermCriteria criteria;
     criteria = cvTermCriteria( CV_TERMCRIT_EPS, 1000, FLT_EPSILON );
     param = CvSVMParams( CvSVM::C_SVC, CvSVM::RBF, 10.0, 0.09, 1.0, 10.0, 0.5, 1.0, NULL, criteria );
 /*
     SVM种类：CvSVM::C_SVC
     Kernel的种类：CvSVM::RBF
     degree：10.0（此次不使用）
     gamma：8.0
     coef0：1.0（此次不使用）
     C：10.0
     nu：0.5（此次不使用）
     p：0.1（此次不使用）
     然后对训练数据正规化处理，并放在CvMat型的数组里。
                                                         */
     //☆☆☆☆☆☆☆☆☆(5)SVM学习☆☆☆☆☆☆☆☆☆☆☆☆
     svm.train( data_mat, res_mat, NULL, NULL, param );
     //☆☆利用训练数据和确定的学习参数,进行SVM学习☆☆☆☆
     svm.save( "SVM_DATA.xml" );

     //检测样本
     IplImage *test;
     vector<string> img_tst_path;
     ifstream img_tst( "E:/SVM_TEST.txt" );
     while( img_tst )
     {
         if( getline( img_tst, buf ) )
         {
             img_tst_path.push_back( buf );
         }
     }
     img_tst.close();



     CvMat *test_hog = cvCreateMat( 1, 1764, CV_32FC1 );
     char line[512];
     ofstream predict_txt( "SVM_PREDICT.txt" );
     for( string::size_type j = 0; j != img_tst_path.size(); j++ )
     {
         test = cvLoadImage( img_tst_path[j].c_str(), 1);
         if( test == NULL )
         {
              cout<<" can not load the image: "<<img_tst_path[j].c_str()<<endl;
                continue;
          }

         cvZero(trainImg);
         cvResize(test,trainImg);   //读取图片
         HOGDescriptor *hog=new HOGDescriptor(cvSize(64,64),cvSize(16,16),cvSize(8,8),cvSize(8,8),9);  //具体意思见参考文章1,2
         vector<float>descriptors;//结果数组
         hog->compute(trainImg, descriptors,Size(1,1), Size(0,0)); //调用计算函数开始计算
         cout<<"HOG dims: "<<descriptors.size()<<endl;
         CvMat* SVMtrainMat=cvCreateMat(1,descriptors.size(),CV_32FC1);
         n=0;
         for(vector<float>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)
             {
                 cvmSet(SVMtrainMat,0,n,*iter);
                 n++;
             }

         int ret = svm.predict(SVMtrainMat);
         sprintf( line, "%s %d\r\n", img_tst_path[j].c_str(), ret );
          predict_txt<<line;
     }
     predict_txt.close();

 //cvReleaseImage( &src);
 //cvReleaseImage( &sampleImg );
 //cvReleaseImage( &tst );
 //cvReleaseImage( &tst_tmp );
 cvReleaseMat( &data_mat );
 cvReleaseMat( &res_mat );

 return 0;
 }