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

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

talk is cheap, show you the code:

/************************************************************************/
/* 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;

// accuracy
float 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 class
void 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 learn
int 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 equation
cv::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;
}