OpenCV3.0 HOG+SVM行人检测器

介绍什么的请参考:利用Hog特征和SVM分类器进行行人检测我只说一下Opencv3.0里面，需要注意的地方。
本人接触OpenCV很短的时间，新手。

OpenCV3.0相比2.X，接口更加清晰，还是有很大的改动的。
主要有几个需要注意的地方：
1. sampleLabelMat的数据类型必须为有符号整数型。
2. 加载已经训练好的分类器，需要注意：
svm = SVM::load<SVM>("SVM_HOG.xml"); //或者svm = Statmodel::load<SVM>("SVM_HOG.xml"); 在3.0里，SVM::load 是一个static function
// svm->load<SVM>("SVM_HOG.xml"); 这样使用不行
3. 一些新的接口：
Mat svecsmat = svm ->getSupportVectors();//svecsmat元素的数据类型为float
int svdim = svm ->getVarCount();//特征向量位数
4. getDecisionFunction()
Mat alphamat = Mat::zeros(numofsv, svdim, CV_32F);//alphamat和svindex必须初始化，否则getDecisionFunction()函数会报错
Mat svindex = Mat::zeros(1, numofsv,CV_64F);
Mat Result;
double rho = svm ->getDecisionFunction(0, alphamat, svindex);
5. alphamat.convertTo(alphamat, CV_32F);//将alphamat元素的数据类型重新转成CV_32F,经过getDecisionFunction后alphamat的数据类型会发生改变，为了后续的矩阵乘法，这里要修改其元素的数据类型
6. 还有一个问题，没想明白，为什么Result = -1 * alphamat * svecsmat;要乘以-1？？？？求告知，谢谢！

#include <iostream>#include <fstream>#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/imgproc/imgproc.hpp>#include <opencv2/objdetect/objdetect.hpp>#include <opencv2/ml/ml.hpp>using namespace std;using namespace cv;using namespace cv::ml;#define PosSamNO 2400  //original positive num#define NegSamNO 2400 // original negative num#define HardExampleNO 3600 // hard negative num#define AugPosSamNO 2400 //Aug positive num#define TRAIN false #define CENTRAL_CROP trueint main(){    //winsize(64,128),blocksize(16,16),blockstep(8,8),cellsize(8,8),bins9    HOGDescriptor hog(Size(64,128),Size(16,16),Size(8,8),Size(8,8),9);    int DescriptorDim;    Ptr<SVM> svm = SVM::create();    if(TRAIN)    {        string ImgName;        ifstream finPos("DATA/INRIAPerson96X160PosList.txt");        ifstream finNeg("DATA/NoPersonFromINRIAList.txt");        if (!finPos || !finNeg)            {                cout << "Pos/Neg imglist reading failed..." << endl;                return 1;            }        Mat sampleFeatureMat;        Mat sampleLabelMat;        //loading original positive examples...        for(int num=0; num < PosSamNO && getline(finPos,ImgName); num++)        {            cout <<"Now processing original positive image: " << ImgName << endl;            ImgName = "DataSet/INRIAPerson/train_64x128_H96/pos/" + ImgName;            Mat src = imread(ImgName);            if(CENTRAL_CROP)                src = src(Rect(16,16,64,128));            vector<float> descriptors;            hog.compute(src, descriptors, Size(8,8));//计算HOG描述子，检测窗口移动步长(8,8)            if( 0 == num )            {                DescriptorDim = descriptors.size();                sampleFeatureMat = Mat::zeros(PosSamNO +AugPosSamNO +NegSamNO +HardExampleNO, DescriptorDim, CV_32FC1);                sampleLabelMat = Mat::zeros(PosSamNO +AugPosSamNO +NegSamNO +HardExampleNO, 1, CV_32SC1);//sampleLabelMat的数据类型必须为有符号整数型            }            //将计算好的HOG描述子复制到样本特征矩阵sampleFeatureMat            for(int i=0; i<DescriptorDim; i++)                sampleFeatureMat.at<float>(num,i) = descriptors[i];            sampleLabelMat.at<int>(num,0) = 1;        }        finPos.close();        //positive examples augmenting...        if (AugPosSamNO > 0)        {            ifstream finAug("DATA/AugPosImgList.txt");            if (!finAug)            {                cout << "Aug positive imglist reading failed..." << endl;                return 1;            }            for (int num = 0; num < AugPosSamNO && getline(finAug, ImgName); ++num)            {                cout << "Now processing Aug positive image: " << ImgName << endl;                ImgName = "DATA/INRIAPerson/AugPos/" + ImgName;                Mat src = imread(ImgName);                vector<float> descriptors;                hog.compute(src, descriptors, Size(8,8));                for (int i = 0; i < DescriptorDim; ++i)                    sampleFeatureMat.at<float>(num +PosSamNO, i) = descriptors[i];                sampleLabelMat.at<int>(num +PosSamNO, 0) = 1;            }            finAug.close();        }        //loading original negative examples...        for(int num = 0; num < NegSamNO && getline(finNeg,ImgName); num++)        {            cout<<"Now processing original negative image: "<<ImgName<<endl;            ImgName = "DATA/INRIAPerson/Neg/" + ImgName;            Mat src = imread(ImgName);            vector<float> descriptors;            hog.compute(src,descriptors,Size(8,8));            for(int i=0; i<DescriptorDim; i++)                sampleFeatureMat.at<float>(num+PosSamNO,i) = descriptors[i];            sampleLabelMat.at<int>(num +PosSamNO +AugPosSamNO, 0) = -1;        }        finNeg.close();        //loading hard examples...        if(HardExampleNO > 0)        {            ifstream finHardExample("DATA/INRIAPersonHardNegList.txt");            if (!finHardExample)            {                cout << "HardExample list reading failed..." << endl;                return 1;            }            for(int num=0; num < HardExampleNO && getline(finHardExample, ImgName); num++)            {                cout<<"Now processing hard negative image: "<<ImgName<<endl;                ImgName = "DATA/INRIAPerson/HardNeg/" + ImgName;                Mat src = imread(ImgName);                vector<float> descriptors;                hog.compute(src,descriptors,Size(8,8));                for(int i=0; i<DescriptorDim; i++)                    sampleFeatureMat.at<float>(num+PosSamNO+NegSamNO,i) = descriptors[i];                sampleLabelMat.at<int>(num +PosSamNO +AugPosSamNO +NegSamNO, 0) = -1;            }            finHardExample.close();        }        svm ->setType(SVM::C_SVC);        svm ->setC(0.01);        svm ->setKernel(SVM::LINEAR);        svm ->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 3000, 1e-6));        cout<<"Starting training..."<<endl;        svm ->train(sampleFeatureMat, ROW_SAMPLE, sampleLabelMat);        cout<<"Finishing training..."<<endl;        svm ->save("SVM_HOG.xml");    }    else {        svm = SVM::load<SVM>("SVM_HOG.xml"); //或者svm = Statmodel::load<SVM>("SVM_HOG.xml"); static function        // svm->load<SVM>("SVM_HOG.xml"); 这样使用不行    }    Mat svecsmat = svm ->getSupportVectors();//svecsmat元素的数据类型为float    int svdim = svm ->getVarCount();//特征向量位数    int numofsv = svecsmat.rows;    Mat alphamat = Mat::zeros(numofsv, svdim, CV_32F);//alphamat和svindex必须初始化，否则getDecisionFunction()函数会报错    Mat svindex = Mat::zeros(1, numofsv,CV_64F);    Mat Result;    double rho = svm ->getDecisionFunction(0, alphamat, svindex);    alphamat.convertTo(alphamat, CV_32F);//将alphamat元素的数据类型重新转成CV_32F    Result = -1 * alphamat * svecsmat;//float    vector<float> vec;    for (int i = 0; i < svdim; ++i)    {        vec.push_back(Result.at<float>(0, i));    }    vec.push_back(rho);    //saving HOGDetectorForOpenCV.txt    ofstream fout("HOGDetectorForOpenCV.txt");    for (int i = 0; i < vec.size(); ++i)    {        fout << vec[i] << endl;    }    /*********************************Testing**************************************************/    HOGDescriptor hog_test;    hog_test.setSVMDetector(vec);    Mat src = imread("person_138.png");    vector<Rect> found, found_filtered;    hog_test.detectMultiScale(src, found, 0, Size(8,8), Size(32,32), 1.05, 2);    cout<<"found.size : "<<found.size()<<endl;    //找出所有没有嵌套的矩形框r,并放入found_filtered中,如果有嵌套的话,则取外面最大的那个矩形框放入found_filtered中    for(int i=0; i < found.size(); i++)    {        Rect r = found[i];        int j=0;        for(; j < found.size(); j++)            if(j != i && (r & found[j]) == r)                break;        if( j == found.size())            found_filtered.push_back(r);    }    //画矩形框，因为hog检测出的矩形框比实际人体框要稍微大些,所以这里需要做一些调整    for(int i=0; i<found_filtered.size(); i++)    {        Rect r = found_filtered[i];        r.x += cvRound(r.width*0.1);        r.width = cvRound(r.width*0.8);        r.y += cvRound(r.height*0.07);        r.height = cvRound(r.height*0.8);        rectangle(src, r.tl(), r.br(), Scalar(0,255,0), 3);    }    imwrite("ImgProcessed.jpg",src);    namedWindow("src",0);    imshow("src",src);    waitKey();    return 0;    }