初始化数据
int width = 512, height = 512; Mat image = Mat::zeros(height, width, CV_8UC3);设置训练数据
float labels[4] = {1.0, -1.0, -1.0, -1.0}; Mat labelsMat(4, 1, CV_32FC1, labels); float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} }; Mat trainingDataMat(4, 2, CV_32FC1, trainingData);SVM初始化
CvSVMParams params; params.svm_type = CvSVM::C_SVC; params.kernel_type = CvSVM::LINEAR; params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);SVM训练
CvSVM SVM; SVM.train(trainingDataMat, labelsMat, Mat(), Mat(), params);SVM结果显示
Vec3b green(0,255,0), blue (255,0,0); for (int i = 0; i < image.rows; ++i) for (int j = 0; j < image.cols; ++j) { Mat sampleMat = (Mat_<float>(1,2) << i,j); float response = SVM.predict(sampleMat); if (response == 1) image.at<Vec3b>(j, i) = green; else if (response == -1) image.at<Vec3b>(j, i) = blue; } // Show the training data int thickness = -1; int lineType = 8; circle( image, Point(501, 10), 5, Scalar( 0, 0, 0), thickness, lineType); circle( image, Point(255, 10), 5, Scalar(255, 255, 255), thickness, lineType); circle( image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType); circle( image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness, lineType);