#include #include #include #include #include using namespace cv; using namespace cv::ml; int main(int, char**) { // Set up training data //! [setup1] int labels[4] = {1, -1, -1, -1}; float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} }; //! [setup1] //! [setup2] Mat trainingDataMat(4, 2, CV_32F, trainingData); Mat labelsMat(4, 1, CV_32SC1, labels); //! [setup2] // Train the SVM //! [init] Ptr svm = SVM::create(); svm->setType(SVM::C_SVC); svm->setKernel(SVM::LINEAR); svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6)); //! [init] //! [train] svm->train(trainingDataMat, ROW_SAMPLE, labelsMat); //! [train] // Data for visual representation int width = 512, height = 512; Mat image = Mat::zeros(height, width, CV_8UC3); // Show the decision regions given by the SVM //! [show] 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_(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] // Show the training data //! [show_data] int thickness = -1; circle( image, Point(501, 10), 5, Scalar( 0, 0, 0), thickness ); circle( image, Point(255, 10), 5, Scalar(255, 255, 255), thickness ); circle( image, Point(501, 255), 5, Scalar(255, 255, 255), thickness ); circle( image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness ); //! [show_data] // Show support vectors //! [show_vectors] thickness = 2; Mat sv = svm->getUncompressedSupportVectors(); for (int i = 0; i < sv.rows; i++) { const float* v = sv.ptr(i); circle(image, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thickness); } //! [show_vectors] imwrite("result.png", image); // save the image imshow("SVM Simple Example", image); // show it to the user waitKey(); return 0; }