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_HOG.xml"); //或者svm = Statmodel::load("SVM_HOG.xml"); 在3.0里，SVM::load 是一个static function
// svm->load("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 
#include 
#include 
#include 
#include 
#include 
#include 

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 true

int 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::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; ifloat>(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: "<"DATA/INRIAPerson/Neg/" + ImgName;
            Mat src = imread(ImgName);

            vector<float> descriptors;
            hog.compute(src,descriptors,Size(8,8));

            for(int i=0; ifloat>(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: "<"DATA/INRIAPerson/HardNeg/" + ImgName;
                Mat src = imread(ImgName);

                vector<float> descriptors;
                hog.compute(src,descriptors,Size(8,8));

                for(int i=0; ifloat>(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..."<train(sampleFeatureMat, ROW_SAMPLE, sampleLabelMat);
        cout<<"Finishing training..."<save("SVM_HOG.xml");

    }
    else {
        svm = SVM::load("SVM_HOG.xml"); //或者svm = Statmodel::load("SVM_HOG.xml"); static function
        // svm->load("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 found, found_filtered;
    hog_test.detectMultiScale(src, found, 0, Size(8,8), Size(32,32), 1.05, 2);

    cout<<"found.size : "<//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; i0.1);
        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;
    }