OpenCV--使用SVM

OpenCV3的接口变化挺大的，是原来OpenCV2.4.X版本的SVM不能用了，网上找了一下，找到了解决办法

SVM训练过程：

1, 注意其中训练和自动训练的接口，还有labelMat一定要用CV_32SC1的类型。

    Ptr<SVM> svm = SVM::create();    svm->setType(SVM::C_SVC);    svm->setKernel(SVM::RBF);    TermCriteria criteria = cvTermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 1000, FLT_EPSILON);    svm->setTermCriteria(criteria);    Mat labelMat1(labelMat.rows, labelMat.cols, CV_32SC1);    for (int i = 0; i < labelMat.rows; i++){        for (int j = 0; j < labelMat.cols; j++){            labelMat1.at<int>(i, j) = labelMat.at<float>(i, j);        }    }    //svm->train(trainMat, ROW_SAMPLE, labelMat);    Ptr<TrainData> traindata = ml::TrainData::create(trainMat, ROW_SAMPLE, labelMat1);    svm->trainAuto(traindata, 10);    svm->save("svm.xml");

SVM预测过程：

1，注意load模型文件的时候用法。

#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;class MySVM : public  ml::SVM{public:    //获得SVM的决策函数中的alpha数组    double get_svm_rho()    {        return this->getDecisionFunction(0, svm_alpha, svm_svidx);    }    //获得SVM的决策函数中的rho参数,即偏移量    vector<float> svm_alpha;    vector<float> svm_svidx;    float  svm_rho;};int main(){    namedWindow("src", 0);    //检测窗口(64,128),块尺寸(16,16),块步长(8,8),cell尺寸(8,8),直方图bin个数9    //HOGDescriptor hog(Size(64, 128), Size(16, 16), Size(8, 8), Size(8, 8), 9);//HOG检测器，用来计算HOG描述子的    int DescriptorDim;//HOG描述子的维数，由图片大小、检测窗口大小、块大小、细胞单元中直方图bin个数决定    //Ptr svm = ml::SVM::create();    Ptr<ml::SVM>svm = ml::SVM::load<ml::SVM>("svm.xml");    DescriptorDim = svm->getVarCount();//特征向量的维数，即HOG描述子的维数    Mat supportVector = svm->getSupportVectors();//支持向量的个数    int supportVectorNum = supportVector.rows;    cout << "支持向量个数：" << supportVectorNum << endl;    //-------------------------------------------------    vector<float> svm_alpha;    vector<float> svm_svidx;    float  svm_rho;    svm_rho = svm->getDecisionFunction(0, svm_alpha, svm_svidx);    //-------------------------------------------------    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, DescriptorDim, CV_32FC1);//支持向量矩阵    Mat resultMat = Mat::zeros(1, DescriptorDim, CV_32FC1);//alpha向量乘以支持向量矩阵的结果    supportVectorMat = supportVector;    ////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) = svm_alpha[i];    }    //计算-(alphaMat * supportVectorMat),结果放到resultMat中    //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& 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 < DescriptorDim; 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_rho);    cout << "检测子维数：" << myDetector.size() << endl;    //设置HOGDescriptor的检测子    HOGDescriptor myHOG;    //myHOG.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());    myHOG.setSVMDetector(myDetector);    //myHOG.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());    /**************读入图片进行HOG行人检测******************/    //Mat src = imread("00000.jpg");    //Mat src = imread("2007_000423.jpg");    Size s1(128, 128);    Size s2(64, 64);    myHOG.winSize = s1;    myHOG.blockSize = s1;    myHOG.blockStride = s1;    myHOG.cellSize = s2;    myHOG.nbins = 9;    Mat frame;    while (true)    {        Mat src = imread("2.jpg");        vector<Rect> found, found_filtered;//矩形框数组        //cout << "进行多尺度HOG人体检测" << endl;        myHOG.detectMultiScale(src, found, 0, Size(32, 32), Size(32, 32), 1.05, 2);//对图片进行多尺度行人检测        //cout << "找到的矩形框个数：" << 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(255, 255, 255), 3);        }        imshow("src", src);        waitKey(0);//注意：imshow之后必须加waitKey，否则无法显示图像    }}

参考资料：
1，http://www.geekylin.com/195.html