[Code segments] OpenCV3.0 SVM with C ++ interface

[Code segments] OpenCV3.0 SVM with C ++ interface

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/************************************************************************//* Name   : OpenCV SVM test                                             *//* Date   : 2015/11/7                                                   *//* Author : aban                                                        *//************************************************************************/// note : the code is modified from internet. #include 
  
   #include 
   
    #include 
    
     using namespace std;#include 
     
      #include 
      
       using namespace cv;bool plotSupportVectors = true;int numTrainingPoints = 200;int numTestPoints = 2000;int size = 200;int eq = 0;// accuracyfloat evaluate(cv::Mat& predicted, cv::Mat& actual) { assert(predicted.rows == actual.rows); int t = 0; int f = 0; for (int i = 0; i < actual.rows; i++) { float p = predicted.at
       
        (i, 0); float a = actual.at
        
         (i, 0); if ((p >= 0.0 && a >= 0.0) || (p <= 0.0 && a <= 0.0)) { t++; } else { f++; } } return (t * 1.0) / (t + f);}// plot data and classvoid plot_binary(cv::Mat& data, cv::Mat& classes, string name) { cv::Mat plot(size, size, CV_8UC3); plot.setTo(cv::Scalar(255.0, 255.0, 255.0)); for (int i = 0; i < data.rows; i++) { float x = data.at
         
          (i, 0) * size; float y = data.at
          
           (i, 1) * size; if (classes.at
           
            (i, 0) > 0) { cv::circle(plot, Point(x, y), 2, CV_RGB(255, 0, 0), 1); } else { cv::circle(plot, Point(x, y), 2, CV_RGB(0, 255, 0), 1); } } cv::namedWindow(name, CV_WINDOW_KEEPRATIO); cv::imshow(name, plot);}// function to learnint f(float x, float y, int equation) { switch (equation) { case 0: return y > sin(x * 10) ? -1 : 1; break; case 1: return y > cos(x * 10) ? -1 : 1; break; case 2: return y > 2 * x ? -1 : 1; break; case 3: return y > tan(x * 10) ? -1 : 1; break; default: return y > cos(x * 10) ? -1 : 1; }}// label data with equationcv::Mat labelData(cv::Mat points, int equation) { cv::Mat labels(points.rows, 1, CV_32FC1); for (int i = 0; i < points.rows; i++) { float x = points.at
            
             (i, 0); float y = points.at
             
              (i, 1); labels.at
              
               (i, 0) = f(x, y, equation); } return labels;}void svm(cv::Mat& trainingData, cv::Mat& trainingClasses, cv::Mat& testData, cv::Mat& testClasses) { Mat traning_label(trainingClasses.rows, 1, CV_32SC1); for (int i = 0; i < trainingClasses.rows; i++){ traning_label.at
               
                (i, 0) = trainingClasses.at
                
                 (i, 0); } cv::Ptr
                 
                   svm = ml::SVM::create(); svm->setType(ml::SVM::Types::C_SVC); svm->setKernel(ml::SVM::KernelTypes::RBF); //svm->setDegree(0); // for poly svm->setGamma(20); // for poly/rbf/sigmoid //svm->setCoef0(0); // for poly/sigmoid svm->setC(7); // for CV_SVM_C_SVC, CV_SVM_EPS_SVR and CV_SVM_NU_SVR //svm->setNu(0); // for CV_SVM_NU_SVC, CV_SVM_ONE_CLASS, and CV_SVM_NU_SVR //svm->setP(0); // for CV_SVM_EPS_SVR svm->setTermCriteria(TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 1000, 1E-6)); svm->train(trainingData, ml::SampleTypes::ROW_SAMPLE, traning_label); cv::Mat predicted(testClasses.rows, 1, CV_32F); svm->predict(testData, predicted); cout << "Accuracy_{SVM} = " << evaluate(predicted, testClasses) << endl; plot_binary(testData, predicted, "Predictions SVM"); // plot support vectors if (plotSupportVectors) { cv::Mat plot_sv(size, size, CV_8UC3); plot_sv.setTo(cv::Scalar(255.0, 255.0, 255.0)); Mat support_vectors = svm->getSupportVectors(); for (int vecNum = 0; vecNum < support_vectors.rows; vecNum++){ cv::circle(plot_sv, Point(support_vectors.row(vecNum).at
                  
                   (0)*size, support_vectors.row(vecNum).at
                   
                    (1)*size), 3, CV_RGB(0, 0, 0)); } namedWindow("Support Vectors", CV_WINDOW_KEEPRATIO); cv::imshow("Support Vectors", plot_sv); }}int main(){ cv::Mat trainingData(numTrainingPoints, 2, CV_32FC1); cv::Mat testData(numTestPoints, 2, CV_32FC1); cv::randu(trainingData, 0, 1); cv::randu(testData, 0, 1); cv::Mat trainingClasses = labelData(trainingData, eq); cv::Mat testClasses = labelData(testData, eq); plot_binary(trainingData, trainingClasses, "Training Data"); plot_binary(testData, testClasses, "Test Data"); svm(trainingData, trainingClasses, testData, testClasses); waitKey(0); return 0;}