libsvm Minist Hog 手写体识别（源码文件）

以上是我上一篇文章中的代码实现，里面分别用了opencv中的SVM和LibSVM，opencv的SVM用起来更方便，但貌似内部其实也是基于Libsvm，同样的参数训练出来的结果是一致的，里面有Libsvm的调用过程，如果用libsvm需要在工程里面添加libsvm的源码文件分别是svm.h和svm.cpp，林智仁的库里自带的那两个核心文件即可。
libsvm的用法让人更感觉是在用C的写法，opencv封装过的易用性更好，稍后我会把工程文件放到github上供大家下载，若有什么错误，还请批评指教~，这里用的是Minist的数据集，图像时我经过处理后的单张图像，大小为20*20，里面有详细的函数使用说明

[https://github.com/YihangLou/SVM-Minist-HandWriting-Recognition] Github上的工程链接

// DigitsRec_HOG_SVM.cpp : 定义控制台应用程序的入口点。
#include "opencv2/opencv.hpp"
#include "fstream"
#include "svm.h"
using namespace std;
using namespace cv;

#define srcfeature

vector<string> trainImageList;//训练图像列表，此处路径
vector<int> trainLabelList;   //标签
vector<string> testImageList;//训练图像列表，此处路径
string trainImageFile= "D:\\WorkSpace\\homework\\PatternRecognization\\第一次作业\\minist\\train_image\\imagelist.txt";
string testImageFile = "D:\\WorkSpace\\homework\\PatternRecognization\\第一次作业\\minist\\test_image\\imagelist.txt";
string testBasePath = "D:\\WorkSpace\\homework\\PatternRecognization\\第一次作业\\minist\\test_image\\";
string trainBasePath = "D:\\WorkSpace\\homework\\PatternRecognization\\第一次作业\\minist\\train_image\\";
string SVMModel ="svm_model.xml";
CvMat * dataMat;
CvMat * labelMat;

//***************************************************************
// 名称:    readTrainFileList
// 功能:    读取训练的图像列表和图像的位置
// 权限:    public 
// 返回值:  void
// 参数:    string trainImageFile 文件列表
// 参数:    string basePath 基地址
// 参数:    vector<string> & trainImageList 图像路径list
// 参数:    vector<int> & trainLabelList 图像标签list
//***************************************************************
void readTrainFileList(string trainImageFile, string basePath, vector<string> &trainImageList,  vector<int> &trainLabelList)
{
    ifstream readData( trainImageFile );
    string buffer;
    while( readData )
    {    
        if( getline( readData, buffer))    
        {    
            int label = int((buffer[0])-'0');//在我这里路径中第一个文件夹就是类别
            trainLabelList.push_back( label);  
            trainImageList.push_back( buffer );//图像路径       
        }    
    }    
    readData.close();
    cout<<"Read Train Data Complete"<<endl;
}

//***************************************************************
// 名称:    readTestFileList
// 功能:    读测试文件
// 权限:    public 
// 返回值:  void
// 参数:    string testImageFile
// 参数:    string basePath
// 参数:    vector<string> & testImageList 测试图像列表
//***************************************************************
void readTestFileList(string testImageFile, string basePath, vector<string> &testImageList)
{
    ifstream readData( testImageFile );  //加载测试图片集合
    string buffer;
    while( readData )
    {    
        if( getline( readData, buffer))    
        {    
            testImageList.push_back( buffer );//图像路径       
        }    
    }    
    readData.close();
    cout<<"Read Test Data Complete"<<endl;
}

//***************************************************************
// 名称:    processHogFeature
// 功能:    计算Hog特征
// 权限:    public 
// 返回值:  void
// 参数:    vector<string> trainImageList
// 参数:    vector<int> trainLabelList
// 参数:    CvMat *  & dataMat
// 参数:    CvMat *  & labelMat
//***************************************************************
void processHogFeature(vector<string> trainImageList,vector<int> trainLabelList, CvMat * &dataMat,CvMat * &labelMat)
{
    
     int trainSampleNum = trainImageList.size();
     dataMat = cvCreateMat( trainSampleNum, 324, CV_32FC1 );  //324为Hog feature Size
     cvSetZero( dataMat );     
     labelMat = cvCreateMat( trainSampleNum, 1, CV_32FC1 );    
     cvSetZero( labelMat );    
     IplImage* src;   
     IplImage* trainImg=cvCreateImage(cvSize(20,20),8,3);//20 20

    for( int i = 0; i != trainImageList.size(); i++ ) 
    {    
        src=cvLoadImage( (trainBasePath  + trainImageList[i]).c_str(),1);    
        if( src == NULL )    
        {    
            cout<<" can not load the image: "<<(trainBasePath  + trainImageList[i]).c_str()<<endl;    
            continue;    
        }    
        //cout<<"Calculate Hog Feature "<<(trainBasePath  + trainImageList[i]).c_str()<<endl;    

        cvResize(src,trainImg);     
        HOGDescriptor *hog=new HOGDescriptor(cvSize(20,20),cvSize(10,10),cvSize(5,5),cvSize(5,5),9);      
        vector<float>descriptors;    
        hog->compute(trainImg, descriptors,Size(1,1), Size(0,0));     

        int j =0; 
        for(vector<float>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)    
        {    
            cvmSet(dataMat,i,j,*iter);//存储HOG特征 
            j++;    
        }       
        cvmSet( labelMat, i, 0, trainLabelList[i] );    
        //cout<<"Image and label "<<trainImageList[i].c_str()<<" "<<trainLabelList[i]<<endl;    
    }    
    cout<<"Calculate Hog Feature Complete"<<endl;
    cout<<dataMat<<endl;
}

void processNonFeature(vector<string> trainImageList,vector<int> trainLabelList, CvMat * &dataMat,CvMat * &labelMat)
{

    int trainSampleNum = trainImageList.size();
    dataMat = cvCreateMat( trainSampleNum, 400, CV_32FC1 );  //324为Hog feature 大小，需提前设置 
    cvSetZero( dataMat );     
    labelMat = cvCreateMat( trainSampleNum, 1, CV_32FC1 );    
    cvSetZero( labelMat );    
    IplImage* src;   
    IplImage* resizeImg=cvCreateImage(cvSize(20,20),8,3);//20 20是训练样本的大小

    for( int i = 0; i != trainImageList.size(); i++ ) 
    {    
        src=cvLoadImage( (trainBasePath  + trainImageList[i]).c_str(),1);    
        if( src == NULL )    
        {    
            cout<<" can not load the image: "<<(trainBasePath  + trainImageList[i]).c_str()<<endl;    
            continue;    
        }    
        //cout<<"Calculate Hog Feature "<<(trainBasePath  + trainImageList[i]).c_str()<<endl;    

        cvResize(src,resizeImg);     
        IplImage * grayImage = cvCreateImage(cvGetSize(resizeImg), IPL_DEPTH_8U, 1);
        cvCvtColor(resizeImg,grayImage,CV_BGR2GRAY);
        //二值化图像
        IplImage * binaryImage = cvCreateImage(cvGetSize(grayImage),IPL_DEPTH_8U,1);
        cvThreshold(grayImage,binaryImage,25,255,CV_THRESH_BINARY);
        //cvNamedWindow("src");
        //cvShowImage("src", src);

        //cvNamedWindow("show");
        //cvShowImage("show", binaryImage);
        //cvWaitKey(0);//这里是看一下二值化的效果怎么样
        HOGDescriptor *hog=new HOGDescriptor(cvSize(20,20),cvSize(10,10),cvSize(5,5),cvSize(5,5),9);      
        vector<float>descriptors;    


        int j =0; //j为矩阵的水平坐标，要把特征从vector中拷贝过来
        uchar * tmp = new uchar;
        for(int n=0;n<binaryImage->height;n++)  
        {   for(int m=0;m<binaryImage->width;m++)  
        {   
            *tmp=((uchar *)(binaryImage->imageData + n*binaryImage->widthStep))[m];  
            cvmSet(dataMat,i,j,*tmp);//存储HOG特征 
            j++;
        }
        }  

        cvmSet( labelMat, i, 0, trainLabelList[i] );    
        //cout<<"Image and label "<<trainImageList[i].c_str()<<" "<<trainLabelList[i]<<endl;    
    }    
    cout<<"Calculate Hog Feature Complete"<<endl;

}

//***************************************************************
// 名称:    trainSVM
// 功能:    此处用的是opencv的SVM训练
// 权限:    public 
// 返回值:  void
// 参数:    CvMat *  & dataMat
// 参数:    CvMat *  & labelMat
//***************************************************************
void trainSVM(CvMat * & dataMat,CvMat * & labelMat )
{
    cout<<"train svm start"<<endl;
    cout<<dataMat<<endl;
    CvSVM svm;
    CvSVMParams param;//这里是SVM训练相关参数  
    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.train( dataMat, labelMat, NULL, NULL, param );//训练数据          
    svm.save( SVMModel.c_str());  
    cout<<"SVM Training Complete"<<endl;
}

//***************************************************************
// 名称:    trainLibSVM
// 功能:    此处用的是LibSVM库的SVM训练
// 权限:    public 
// 返回值:  void
// 参数:    CvMat * & dataMat
// 参数:    CvMat *  & labelMat
//***************************************************************
void trainLibSVM(CvMat *& dataMat, CvMat * & labelMat)
{
    cout<<"LibSVM start"<<endl;
    //配置SVM参数
    svm_parameter param;
    //param.svm_type = C_SVC;
    param.svm_type = EPSILON_SVR;
    param.kernel_type = RBF;
    param.degree = 10.0;
    param.gamma = 0.09;
    param.coef0 = 1.0;
    param.nu = 0.5;
    param.cache_size = 1000;
    param.C = 10.0;
    param.eps = 1e-3;
    param.p = 1.0;

    //svm_prob读取
    svm_problem svm_prob;
    
    int sampleNum = dataMat->rows;
    int vectorLength = dataMat->cols;

    svm_prob.l = sampleNum;
    svm_prob.y = new double [sampleNum];

    for (int i = 0; i < sampleNum; i++)
    {
        svm_prob.y[i] = cvmGet(labelMat,i,0);
    }

    cout<<"LibSVM middle"<<endl;
    svm_prob.x = new  svm_node * [sampleNum];

    for (int i = 0; i < sampleNum; i++)
    {
        svm_node * x_space = new svm_node [vectorLength + 1];
        for (int j = 0; j < vectorLength; j++)
        {
            x_space[j].index = j;
            x_space[j].value = cvmGet(dataMat,i,j);
            
        }
        x_space[vectorLength].index = -1;//注意，结束符号，一开始忘记加了

        svm_prob.x[i] = x_space;
    }

    cout<<"LibSVM end"<<endl;
    svm_model * svm_model = svm_train(&svm_prob, &param);
#ifdef srcfeature
    svm_save_model("libsvm_minist_src_feature_model_.model",svm_model);
#else
    svm_save_model("libsvm_minist_model.model",svm_model);
#endif
    for (int i=0 ; i < sampleNum; i++)
    {
        delete [] svm_prob.x[i];
    }

    delete [] svm_prob.y;
    svm_free_model_content(svm_model);
}

//***************************************************************
// 名称:    testSVM
// 功能:    测试opencv训练的SVM准确率
// 权限:    public 
// 返回值:  void
// 参数:    vector<string> testImageList
// 参数:    string SVMModel
//***************************************************************
void testSVM(vector<string> testImageList, string SVMModel)
{
    CvSVM svm;
    svm.load(SVMModel.c_str());//加载模型文件

    IplImage* testImage;  
    IplImage* tempImage;
    char buffer[512]; 

    ofstream ResultOutput( "predict_result.txt" );//把预测结果存储在这个文本中   
    for( int j = 0; j != testImageList.size(); j++ )//依次遍历所有的待检测图片    
    {    
        testImage = cvLoadImage( (testBasePath+testImageList[j]).c_str(), 1);    
        if( testImage == NULL )    
        {    
            cout<<" can not load the image: "<<(testBasePath+testImageList[j]).c_str()<<endl;    
            continue;    
        }
        tempImage =cvCreateImage(cvSize(20,20),8,3);
        cvZero(tempImage);    
        cvResize(testImage,tempImage);    
        HOGDescriptor *hog=new HOGDescriptor(cvSize(20,20),cvSize(10,10),cvSize(5,5),cvSize(5,5),9);       
        vector<float>descriptors; 

        hog->compute(tempImage, descriptors,Size(1,1), Size(0,0));       
        CvMat* TempMat=cvCreateMat(1,descriptors.size(),CV_32FC1);    
        int n=0;    
        for(vector<float>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)    
        {    
            cvmSet(TempMat,0,n,*iter);    
            n++;    
        }       

        int resultLabel = svm.predict(TempMat);//检测结果
        sprintf( buffer, "%s  %d\r\n",testImageList[j].c_str(),resultLabel );
        ResultOutput<<buffer;  
    }
    cvReleaseImage(&testImage);
    cvReleaseImage(&tempImage);
    ResultOutput.close();   
    cout<<"SVM Predict Complete"<<endl;
}

//***************************************************************
// 名称:    testLibSVM
// 功能:    测试LisbSVM训练的模型的分类性能
// 权限:    public 
// 返回值:  void
// 参数:    string LibSVMModelFile
// 参数:    vector<string> testImageList
// 参数:    string SVMModel
//***************************************************************
void testLibSVM(string LibSVMModelFile, vector<string> testImageList, string SVMModel)
{

    svm_model * svm = svm_load_model(LibSVMModelFile.c_str());

    IplImage* testImage;  
    IplImage* tempImage;
    char buffer[512]; 

    ofstream ResultOutput( "libsvm_predict_result.txt" ); 
    for( int j = 0; j != testImageList.size(); j++ )//依次遍历所有的待检测图片    
    {    
        testImage = cvLoadImage( (testBasePath+testImageList[j]).c_str(), 1);    
        if( testImage == NULL )    
        {    
            cout<<" can not load the image: "<<(testBasePath+testImageList[j]).c_str()<<endl;    
            continue;    
        }
        tempImage =cvCreateImage(cvSize(20,20),8,3);
        cvZero(tempImage);    
        cvResize(testImage,tempImage);    
        HOGDescriptor *hog=new HOGDescriptor(cvSize(20,20),cvSize(10,10),cvSize(5,5),cvSize(5,5),9);       
        vector<float>descriptors; 

        hog->compute(tempImage, descriptors,Size(1,1), Size(0,0));  

        svm_node * inputVector = new svm_node [ descriptors.size()+1];
        int n = 0;
        for(vector<float>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)    
        {     
            inputVector[n].index = n;
            inputVector[n].value = *iter;
            n++;
        }       
        inputVector[n].index = -1;
        
        int resultLabel = svm_predict(svm,inputVector);//分类结果
        sprintf( buffer, "%s  %d\r\n",testImageList[j].c_str(),resultLabel );
        ResultOutput<<buffer;  
        delete [] inputVector;
    }
    svm_free_model_content(svm);
    cvReleaseImage(&testImage);
    cvReleaseImage(&tempImage);
    ResultOutput.close();   
    cout<<"SVM Predict Complete"<<endl;
}

//***************************************************************
// 名称:    releaseAll
// 功能:    释放相应的资源
// 权限:    public 
// 返回值:  void
//***************************************************************
void releaseAll()
{

    cvReleaseMat( &dataMat ); 
    cvReleaseMat( &labelMat);
    cout<<"Release All Complete"<<endl;
}

//***************************************************************
// 名称:    main
// 功能:    这里用了两种SVM，一种是opencv中的，一种是libsvm中的，训练测试需要选择相对应的svm
// 权限:    public 
// 返回值:  int
//***************************************************************
int main()
{

    readTrainFileList(trainImageFile,trainBasePath,trainImageList,trainLabelList);      
    processHogFeature(trainImageList,trainLabelList, dataMat,labelMat);
    //trainSVM(dataMat,labelMat );
    //processNonFeature(trainImageList,trainLabelList, dataMat,labelMat);
    trainLibSVM(dataMat,labelMat);
    //readTestFileList( testImageFile,  testBasePath, testImageList);
    testLibSVM("libsvm_minist_model.model",testImageList,SVMModel);
    //testSVM( testImageList, SVMModel);
    releaseAll();
    return 0;
}