#include #include #include using namespace cv; int main() { // Data for visual representation int width = 512, height = 512; Mat image = Mat::zeros(height, width, CV_8UC3); // Set up training data 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); // Set up SVM's parameters CvSVMParams params; params.svm_type = CvSVM::C_SVC; params.kernel_type = CvSVM::LINEAR; params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6); // Train the SVM CvSVM SVM; SVM.train(trainingDataMat, labelsMat, Mat(), Mat(), params); Vec3b green(0,255,0), blue (255,0,0); // Show the decision regions given by the SVM for (int i = 0; i < image.rows; ++i) for (int j = 0; j < image.cols; ++j) { Mat sampleMat = (Mat_(1,2) << j,i); float response = SVM.predict(sampleMat); if (response == 1) image.at(i,j) = green; else if (response == -1) image.at(i,j) = blue; } // Show the training data int thickness = -1; int lineType = 8; for (int i = 0; i < trainingDataMat.rows; i++) { const CvScalar color = (labels[i] == 1) ? CV_RGB(255, 255, 255) : CV_RGB(0, 0, 0); circle(image, Point(trainingData[i][0], trainingData[i][1]), 5, color, thickness, lineType); } // Show support vectors thickness = 2; lineType = 8; int c = SVM.get_support_vector_count(); for (int i = 0; i < c; ++i) { const float* v = SVM.get_support_vector(i); circle( image, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thickness, lineType); } imwrite("result.png", image); // save the image imshow("SVM Simple Example", image); // show it to the user waitKey(0); }