OpenCV3的接口变化挺大的，原来OpenCV2.4.X版本的SVM不能用了。
OpenCV3中使用SVM方法如下：
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");1234567891011121314151617181234567891011121314151617181，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 }}转自：http://blog.csdn.net/heroacool/article/details/50579955新闻热点
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