#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; }
from: http://blog.csdn.net/yangtrees/article/details/7471222
时间: 2024-10-13 19:02:03