hog_svm code

http://blog.csdn.net/zhazhiqiang/article/details/18664417
http://blog.csdn.net/hujingshuang/article/details/47337707/
http://blog.csdn.net/orsinozhu/article/details/40554211
http://blog.csdn.net/qq_14845119/article/details/52187774
http://blog.csdn.net/leifeng_soul/article/details/52608575
http://blog.csdn.net/zhazhiqiang/article/details/20723425
http://blog.csdn.net/alvine008/article/details/9097105
http://blog.csdn.net/love_linney/article/details/25192909

#ifndef MY_HOG_SVM_H#define MY_HOG_SVM_H#include <QObject>#include "opencv2/core/core.hpp"#include "opencv2/imgproc/imgproc.hpp"#include "opencv2/highgui/highgui.hpp"#include "opencv2/calib3d/calib3d.hpp"#include "opencv2/objdetect/objdetect.hpp"#include "opencv/cv.h"#include <QDebug>#include <QTime>#include <QDateTime>#include <QTimer>#include <QtCore/qmath.h>#include "opencv/ml.h"#include <iostream>#include <fstream>#include <string>#include <vector>using namespace cv;using namespace std;class Mysvm : public CvSVM{public:    //获得SVM的决策函数中的alpha数组    double * get_alpha_vector()    {        return this->decision_func->alpha;    }    //获得SVM的决策函数中的rho参数,即偏移量    float get_rho()    {        return this->decision_func->rho;    }};class Mysvm;class My_Hog_Svm : public QObject{    Q_OBJECTpublic:    explicit My_Hog_Svm(QObject *parent = 0);private:    const int m_iImgHeight = 96;    const int m_iImgWidht =96;    const int m_iBlockSizeWidth = 32;    const int m_iCellSizeWidth =16;    const int m_iStrideSizeWidth =16;private:    void MyTrain();    void Detection();    void GetFeatureVector();    void DrawBox();};#endif // MY_HOG_SVM_H

#include "my_hog_svm.h"//#include "mysvm.h"My_Hog_Svm::My_Hog_Svm(QObject *parent) : QObject(parent){//    //1：训练//    this->MyTrain();//    //2：检测//    this->Detection();//    //3：获得特征向量//    this->GetFeatureVector();    //4：画框    this->DrawBox();}void My_Hog_Svm::MyTrain(){    vector<string> img_path;    //样本路径    vector<int> img_catg;       //标记正负样本    int nLine = 0;  //样本总共的个数    string buf;    ifstream svm_data_true( "./TRAIN_HEAD/Pos.txt" );   //正样本路径    ifstream svm_data_false( "./TRAIN_HEAD/Neg.txt" );  //负样本路径    unsigned long n;    //获取样本的路径    while(svm_data_true)    //正样本    {        if( getline( svm_data_true, buf ) )        {            nLine ++;            img_catg.push_back(1);            img_path.push_back( buf );        }    }    while(svm_data_false)   //负样本    {        if(getline(svm_data_false, buf))        {            nLine ++;            img_catg.push_back(0);            img_path.push_back( buf );        }    }    svm_data_true.close();//关闭文件    svm_data_false.close();    Mat data_mat;   //存放特征值的矩阵    Mat res_mat;    //存放正负样本的标识    int nImgNum = nLine;            //读入样本数量    //类型矩阵,存储每个样本的类型标志    res_mat = Mat::zeros( nImgNum, 1, CV_32FC1);    Mat src;    Mat trainImg = Mat::zeros(m_iImgHeight, m_iImgWidht, CV_8UC3);//需要分析的图片    //获取每一个文件的特征值矩阵    for( string::size_type i = 0; i != img_path.size(); i++ )    {        src = imread(img_path[i].c_str(), 0);        resize(src, trainImg, cv::Size(m_iImgWidht,m_iImgHeight), 0, 0, INTER_CUBIC); //调整训练的图片        HOGDescriptor *hog=new HOGDescriptor(cvSize(m_iImgWidht,m_iImgHeight) //参数：窗口大小，块的大小，块滑动增量，cell的大小，每个bin的特征值                                             ,cvSize(m_iBlockSizeWidth,m_iBlockSizeWidth),cvSize(m_iCellSizeWidth,m_iCellSizeWidth),cvSize(m_iStrideSizeWidth,m_iStrideSizeWidth), 9);  //具体意思见参考文章1,2        vector<float>descriptors;//结果数组，特征值的个数        hog->compute(trainImg, descriptors, Size(m_iStrideSizeWidth,m_iStrideSizeWidth), Size(0,0)); //调用计算函数开始计算，滑动块增量        if (i==0)        {            //初始化存放所有图片特征值的容器            data_mat = Mat::zeros( nImgNum, descriptors.size(), CV_32FC1 ); //根据输入图片大小进行分配空间        }        n=0;        for(vector<float>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)        {            //为容器赋值            data_mat.at<float>(i,n) = *iter;            n++;        }        res_mat.at<float>(i, 0) =  img_catg[i];        cout<<" end processing "<<img_path[i].c_str()<<"    label:"<<img_catg[i]<<" HOG dims: "<<descriptors.size()<<endl;    }    //svm训练    Mysvm* svm = new Mysvm();    //训练SVM分类器    //迭代终止条件，当迭代满1000次或误差小于FLT_EPSILON时停止迭代    CvTermCriteria criteria = cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 1000, FLT_EPSILON);    //SVM参数：SVM类型为C_SVC；线性核函数；松弛因子C=0.01    CvSVMParams param(CvSVM::C_SVC, CvSVM::LINEAR, 0, 1, 0, 0.01, 0, 0, 0, criteria);    //☆☆☆☆☆☆☆☆☆(5)  SVM学习 ☆☆☆☆☆☆☆☆☆☆☆☆    svm->train( data_mat, res_mat, Mat(), Mat(), param );    //☆☆利用训练数据和确定的学习参数,进行SVM学习☆☆☆☆    svm->save( "./TRAIN_HEAD/SVM_DATA.xml" );    qDebug() << "Finish!";}void My_Hog_Svm::Detection(){    Mysvm* svm = new Mysvm();    svm->load("./TRAIN_HEAD/SVM_DATA.xml");    Mat trainImg = Mat::zeros(m_iImgHeight, m_iImgWidht, CV_8UC3);//需要分析的图片    string buf;    //检测样本    vector<string> img_tst_path;    ifstream img_tstNeg( "./TRAIN_HEAD/testNeg.txt" );    ifstream img_tstPos( "./TRAIN_HEAD/testPos.txt" );    while( img_tstNeg )    {        if( getline( img_tstNeg, buf ) )        {            img_tst_path.push_back( buf );        }    }    img_tstNeg.close();    while( img_tstPos )    {        if( getline( img_tstPos, buf ) )        {            img_tst_path.push_back( buf );        }    }    img_tstPos.close();    Mat test;    char line[512];    ofstream predict_txt( "./TRAIN_HEAD/SVM_PREDICT.txt" );    for( string::size_type j = 0; j != img_tst_path.size(); j++ )    {        test = imread( img_tst_path[j].c_str(), 0);//读入图像        resize(test, trainImg, cv::Size(m_iImgWidht,m_iImgHeight), 0, 0, INTER_CUBIC);//要搞成同样的大小才可以检测到        HOGDescriptor *hog=new HOGDescriptor(cvSize(m_iImgWidht,m_iImgHeight)                                             ,cvSize(m_iBlockSizeWidth,m_iBlockSizeWidth),cvSize(m_iCellSizeWidth,m_iCellSizeWidth),cvSize(m_iStrideSizeWidth,m_iStrideSizeWidth), 9);        vector<float>descriptors;//结果数组        hog->compute(trainImg, descriptors,Size(m_iStrideSizeWidth,m_iStrideSizeWidth), Size(0,0)); //调用计算函数开始计算        cout<<"The Detection Result:"<<endl;        Mat SVMtrainMat =  Mat::zeros(1,descriptors.size(),CV_32FC1);        int n=0;        for(vector<float>::iterator iter=descriptors.begin();iter!=descriptors.end();iter++)        {            SVMtrainMat.at<float>(0,n) = *iter;            n++;        }        int ret = svm->predict(SVMtrainMat);        std::sprintf( line, "%s %d\r\n", img_tst_path[j].c_str(), ret );        printf("%s %d\r\n", img_tst_path[j].c_str(), ret);        predict_txt<<line;    }    predict_txt.close();    cout << "Finish" <<endl;    return ;}/*************************************************************************************************    线性SVM训练完成后得到的XML文件里面，有一个数组，叫做support vector，还有一个数组，叫做alpha,有一个浮点数，叫做rho;    将alpha矩阵同support vector相乘，注意，alpha*supportVector,将得到一个列向量。之后，再该列向量的最后添加一个元素rho。    如此，变得到了一个分类器，利用该分类器，直接替换opencv中行人检测默认的那个分类器（cv::HOGDescriptor::setSVMDetector()），    就可以利用你的训练样本训练出来的分类器进行行人检测了。***************************************************************************************************/void My_Hog_Svm::GetFeatureVector(){    Mysvm* svm = new Mysvm();    svm->load("./TRAIN_HEAD/SVM_DATA.xml");                                   //获取特征值的个数    int l_iFeatureNum = svm->get_var_count();//特征向量的维数，即HOG描述子的维数    int supportVectorNum = svm->get_support_vector_count();//支持向量的个数    qDebug()<<"支持向量个数："<<supportVectorNum;    Mat alphaMat = Mat::zeros(1, supportVectorNum, CV_32FC1);//alpha向量，长度等于支持向量个数    Mat supportVectorMat = Mat::zeros(supportVectorNum, l_iFeatureNum, CV_32FC1);//支持向量矩阵    Mat resultMat = Mat::zeros(1, l_iFeatureNum, CV_32FC1);//alpha向量乘以支持向量矩阵的结果    //将支持向量的数据复制到supportVectorMat矩阵中    for(int i=0; i<supportVectorNum; i++)    {        const float * pSVData = svm->get_support_vector(i);//返回第i个支持向量的数据指针        for(int j=0; j<l_iFeatureNum; j++)        {            supportVectorMat.at<float>(i,j) = pSVData[j];        }    }    //将alpha向量的数据复制到alphaMat中    double * pAlphaData = svm->get_alpha_vector();//返回SVM的决策函数中的alpha向量    for(int i=0; i<supportVectorNum; i++)    {        alphaMat.at<float>(0,i) = pAlphaData[i];    }    //gemm(alphaMat, supportVectorMat, -1, 0, 1, resultMat);//不知道为什么加负号？    resultMat = -1 * alphaMat * supportVectorMat;    //得到最终的setSVMDetector(const vector<float>& detector)参数中可用的检测子    vector<float> myDetector;    //将resultMat中的数据复制到数组myDetector中    for(int i=0; i<l_iFeatureNum; i++)    {        myDetector.push_back(resultMat.at<float>(0,i));    }    //最后添加偏移量rho，得到检测子//    myDetector.push_back(svm->get_rho());    qDebug()<<"检测子维数："<<myDetector.size() +1;    //保存检测子参数到文件    FILE* fp = fopen("./TRAIN_HEAD/hogSVMDetector-peopleFlow.txt","wb");    if( NULL == fp )    {        return ;    }    for(int i=0; i<myDetector.size(); i++)    {        fprintf(fp, "%f \n", myDetector[i]);    }    fprintf(fp, "%f", svm->get_rho());    fclose(fp);    qDebug() << "Finish!";    return;}void My_Hog_Svm::DrawBox(){        vector<Rect> found;        Mat img = imread("./11.jpg");        vector<float> myDetector;        ifstream fileIn("./TRAIN_HEAD/hogSVMDetector-peopleFlow.txt", ios::in);        float val = 0.0f;        while(!fileIn.eof())        {            fileIn>>val;            myDetector.push_back(val);        }        fileIn.close();        HOGDescriptor defaultHog(cvSize(m_iImgWidht,m_iImgHeight) //参数：窗口大小，块的大小，块滑动增量，cell的大小，每个bin的特征值                                  ,cvSize(m_iBlockSizeWidth,m_iBlockSizeWidth),cvSize(m_iCellSizeWidth,m_iCellSizeWidth),cvSize(m_iStrideSizeWidth,m_iStrideSizeWidth), 9);        defaultHog.setSVMDetector(myDetector);        //进行检测        defaultHog.detectMultiScale(img, found);        //画长方形，框出行人        for(int i = 0; i < found.size(); i++)        {            Rect r = found[i];            rectangle(img, r.tl(), r.br(), Scalar(255, 255, 255), 3);        }        namedWindow("检测行人", CV_WINDOW_AUTOSIZE);        imshow("检测行人", img);        waitKey(0);//        Mat l_pImageEle;//        namedWindow("Video");//        VideoCapture capture("./a.avi");//        while(1)    //循环每一帧//        {//            static int l_iNum = 0;//            if(!capture.read(l_pImageEle))//            {//                return;//            }//            if(l_iNum%34 ==0)//            {//                //进行检测//                defaultHog.detectMultiScale(l_pImageEle, found);//                //画长方形，框出行人//                for(int i = 0; i < found.size(); i++)//                {//                    Rect r = found[i];//                    rectangle(l_pImageEle, r.tl(), r.br(), Scalar(255, 255, 255), 3);//                }//            }//            imshow("Video", l_pImageEle);//            cvWaitKey(34);//        }}