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83 lines
2.6 KiB
C++
83 lines
2.6 KiB
C++
#include <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/ml/ml.hpp>
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using namespace cv;
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int main()
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{
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// Data for visual representation
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int width = 512, height = 512;
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Mat image = Mat::zeros(height, width, CV_8UC3);
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// Set up training data
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Mat labelsMat = (Mat_<float>(11, 1) <<
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1.0, -1.0, -1.0, 1.0, 1.0, -1.0, -1.0, 1.0, 1.0, 1.0, 1.0);
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Mat trainingDataMat = (Mat_<float>(11, 2) <<
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501, 10, 255, 255, 255, 305, 10, 1, 10, 500, 290, 290, 180, 290, 200, 200, 400, 400, 332, 143, 125, 350);
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assert(labelsMat.rows == trainingDataMat.rows);
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// Set up SVM's parameters
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CvSVMParams params;
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params.svm_type = CvSVM::C_SVC;
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params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 1000, 1e-6);
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// Kernel
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//params.kernel_type = CvSVM::POLY;
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params.kernel_type = CvSVM::RBF;
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//params.kernel_type = CvSVM::LINEAR;
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params.gamma = .0001; // for poly/rbf/sigmoid
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params.degree = 4; // for poly XXX
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params.C = 7; // for CV_SVM_C_SVC, CV_SVM_EPS_SVR and CV_SVM_NU_SVR
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params.nu = 0.0; // for CV_SVM_NU_SVC, CV_SVM_ONE_CLASS, and CV_SVM_NU_SVR
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params.p = 0.0; // for CV_SVM_EPS_SVR
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// Train the SVM
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CvSVM SVM;
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SVM.train(trainingDataMat, labelsMat, Mat(), Mat(), params);
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Vec3b whiteish(200,200,200), blackish (55,55,55);
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// Show the decision regions given by the SVM
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for (int i = 0; i < image.rows; ++i)
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for (int j = 0; j < image.cols; ++j)
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{
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Mat sampleMat = (Mat_<float>(1,2) << j,i);
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float response = SVM.predict(sampleMat);
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if (response == 1)
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image.at<Vec3b>(i,j) = whiteish;
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else if (response == -1)
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image.at<Vec3b>(i,j) = blackish;
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}
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// Show the training data
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int thickness = -1;
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int lineType = 8;
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for (int i = 0; i < trainingDataMat.rows; i++) {
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const CvScalar color = (labelsMat.at<float>(i) == 1) ?
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CV_RGB(255, 255, 255) : CV_RGB(0, 0, 0);
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circle(image, Point(trainingDataMat.at<float>(i, 0), trainingDataMat.at<float>(i, 1)), 5,
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color, thickness, lineType);
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}
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// Show support vectors
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thickness = 2;
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lineType = 8;
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int c = SVM.get_support_vector_count();
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for (int i = 0; i < c; ++i)
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{
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const float* v = SVM.get_support_vector(i);
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circle( image, Point( (int) v[0], (int) v[1]), 6, Scalar(0, 0, 128), thickness, lineType);
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}
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imwrite("SVMTest.png", image); // save the image
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imshow("SVM Non-Linear Example", image); // show it to the user
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waitKey(0);
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}
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