public void TestSVM()
{
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.Params p = new SVM.Params(MlEnum.SvmType.CSvc, MlEnum.SvmKernelType.Linear, 0, 1, 0, 1, 0, 0, null, new MCvTermCriteria(100, 1.0e-6)))
using (SVM model = new SVM())
using (Matrix <int> trainClassesInt = trainClasses.Convert <int>())
using (TrainData td = new TrainData(trainData, MlEnum.DataLayoutType.RowSample, trainClassesInt))
{
model.Type = SVM.SvmType.CSvc;
model.SetKernel(SVM.SvmKernelType.Inter);
model.Degree = 0;
model.Gamma = 1;
model.Coef0 = 0;
model.C = 1;
model.Nu = 0;
model.P = 0;
model.TermCriteria = new MCvTermCriteria(100, 1.0e-6);
//bool trained = model.TrainAuto(td, 5);
model.Train(td);
#if !NETFX_CORE
String fileName = "svmModel.xml";
//String fileName = Path.Combine(Path.GetTempPath(), "svmModel.xml");
model.Save(fileName);
SVM model2 = new SVM();
FileStorage fs = new FileStorage(fileName, FileStorage.Mode.Read);
model2.Read(fs.GetFirstTopLevelNode());
if (File.Exists(fileName))
{
File.Delete(fileName);
}
#endif
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);
}
}
Mat supportVectors = model.GetSupportVectors();
//TODO: find out how to draw the support vectors
Image <Gray, float> pts = supportVectors.ToImage <Gray, float>();
PointF[] vectors = new PointF[supportVectors.Rows];
GCHandle handler = GCHandle.Alloc(vectors, GCHandleType.Pinned);
using (
Mat vMat = new Mat(supportVectors.Rows, supportVectors.Cols, DepthType.Cv32F, 1,
handler.AddrOfPinnedObject(), supportVectors.Cols * 4))
{
supportVectors.CopyTo(vMat);
}
handler.Free();
Mat supportVec = model.GetSupportVectors();
/*
* 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);
}