SVM (Support Vector Machine) in CSharp

This example is contributed by Albert G. It requires Emgu CV 1.5.0.0

using System.Drawing;
using Emgu.CV.Structure;
using Emgu.CV.ML;
using Emgu.CV.ML.Structure;

...

int trainSampleCount = 150;
int sigma = 60;

#region Generate the training data and classes

Matrix<float> trainData = new Matrix<float>(trainSampleCount, 2);
Matrix<float> trainClasses = new Matrix<float>(trainSampleCount, 1);

Image<Bgr, Byte> img = new Image<Bgr, byte>(500, 500);

Matrix<float> sample = new Matrix<float>(1, 2);

Matrix<float> trainData1 = trainData.GetRows(0, trainSampleCount / 3, 1);
trainData1.GetCols(0, 1).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));
trainData1.GetCols(1, 2).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));

Matrix<float> trainData2 = trainData.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainData2.SetRandNormal(new MCvScalar(400), new MCvScalar(sigma));

Matrix<float> trainData3 = trainData.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainData3.GetCols(0, 1).SetRandNormal(new MCvScalar(300), new MCvScalar(sigma));
trainData3.GetCols(1, 2).SetRandNormal(new MCvScalar(100), new MCvScalar(sigma));

Matrix<float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount / 3, 1);
trainClasses1.SetValue(1);
Matrix<float> trainClasses2 = trainClasses.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainClasses2.SetValue(2);
Matrix<float> trainClasses3 = trainClasses.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainClasses3.SetValue(3);

#endregion

using (SVM model = new SVM())
{
   SVMParams p = new SVMParams();
   p.KernelType = Emgu.CV.ML.MlEnum.SVM_KERNEL_TYPE.LINEAR;
   p.SVMType = Emgu.CV.ML.MlEnum.SVM_TYPE.C_SVC;
   p.C = 1;
   p.TermCrit = new MCvTermCriteria(100, 0.00001);

   //bool trained = model.Train(trainData, trainClasses, null, null, p);
   bool trained = model.TrainAuto(trainData, trainClasses, null, null, p.MCvSVMParams, 5);
   
   for (int i = 0; i < img.Height; i++)
   {
      for (int j = 0; j < img.Width; j++)
      {
         sample.Data[0, 0] = j;
         sample.Data[0, 1] = i;

         float response = model.Predict(sample);

         img[i, j] =
            response == 1 ? new Bgr(90, 0, 0) :
            response == 2 ? new Bgr(0, 90, 0) :
            new Bgr(0, 0, 90);
      }
   }

   int c = model.GetSupportVectorCount();
   for (int i = 0; i < c; i++)
   {
      float[] v = model.GetSupportVector(i);
      PointF p1 = new PointF(v[0], v[1]);
      img.Draw(new CircleF(p1, 4), new Bgr(128, 128, 128), 2);
   }
}

// display the original training samples
for (int i = 0; i < (trainSampleCount / 3); i++)
{
   PointF p1 = new PointF(trainData1[i, 0], trainData1[i, 1]);
   img.Draw(new CircleF(p1, 2.0f), new Bgr(255, 100, 100), -1);
   PointF p2 = new PointF(trainData2[i, 0], trainData2[i, 1]);
   img.Draw(new CircleF(p2, 2.0f), new Bgr(100, 255, 100), -1);
   PointF p3 = new PointF(trainData3[i, 0], trainData3[i, 1]);
   img.Draw(new CircleF(p3, 2.0f), new Bgr(100, 100, 255), -1);
}

Emgu.CV.UI.ImageViewer.Show(img);