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;
}