public void TestEM2()
{
Random r = new Random(DateTime.Now.Millisecond);
int N = 2000;
int D = 20;
int G = 10;
EM Em = new EM();
Matrix <int> labels = new Matrix <int>(N, 1);
Matrix <float> featuresM = new Matrix <float>(N, D);
for (int i = 0; i < N; i++)
{
for (int j = 0; j < D; j++)
{
featuresM[i, j] = 100 * (float)r.NextDouble() - 50;
}
}
EMParams pars = new EMParams();
pars.CovMatType = Emgu.CV.ML.MlEnum.EM_COVARIAN_MATRIX_TYPE.COV_MAT_DIAGONAL;
pars.Nclusters = G;
pars.StartStep = Emgu.CV.ML.MlEnum.EM_INIT_STEP_TYPE.START_AUTO_STEP;
pars.TermCrit = new MCvTermCriteria(100, 1.0e-6);
Em.Train(featuresM, null, pars, labels);
}
public void TestEM2()
{
Random r = new Random(DateTime.Now.Millisecond);
int N = 2000;
int D = 20;
int G = 10;
EM Em = new EM();
Matrix<int> labels = new Matrix<int>(N, 1);
Matrix<float> featuresM = new Matrix<float>(N, D);
for (int i = 0; i < N; i++)
for (int j = 0; j < D; j++)
featuresM[i, j] = 100 * (float)r.NextDouble() - 50;
EMParams pars = new EMParams();
pars.CovMatType = Emgu.CV.ML.MlEnum.EM_COVARIAN_MATRIX_TYPE.COV_MAT_DIAGONAL;
pars.Nclusters = G;
pars.StartStep = Emgu.CV.ML.MlEnum.EM_INIT_STEP_TYPE.START_AUTO_STEP;
pars.TermCrit = new MCvTermCriteria(100, 1.0e-6);
Em.Train(featuresM, null, pars, labels);
}
public void TestEM()
{
int N = 4; //number of clusters
int N1 = (int)Math.Sqrt((double)4);
Bgr[] colors = new Bgr[] {
new Bgr(0, 0, 255),
new Bgr(0, 255, 0),
new Bgr(0, 255, 255),
new Bgr(255, 255, 0)};
int nSamples = 100;
Matrix<float> samples = new Matrix<float>(nSamples, 2);
Matrix<Int32> labels = new Matrix<int>(nSamples, 1);
Image<Bgr, Byte> img = new Image<Bgr,byte>(500, 500);
Matrix<float> sample = new Matrix<float>(1, 2);
CvInvoke.cvReshape(samples.Ptr, samples.Ptr, 2, 0);
for (int i = 0; i < N; i++)
{
Matrix<float> rows = samples.GetRows(i * nSamples / N, (i + 1) * nSamples / N, 1);
double scale = ((i % N1) + 1.0) / (N1 + 1);
MCvScalar mean = new MCvScalar(scale * img.Width, scale * img.Height);
MCvScalar sigma = new MCvScalar(30, 30);
rows.SetRandNormal(mean, sigma);
}
CvInvoke.cvReshape(samples.Ptr, samples.Ptr, 1, 0);
using (EM emModel1 = new EM())
using (EM emModel2 = new EM())
{
EMParams parameters1 = new EMParams();
parameters1.Nclusters = N;
parameters1.CovMatType = Emgu.CV.ML.MlEnum.EM_COVARIAN_MATRIX_TYPE.COV_MAT_DIAGONAL;
parameters1.StartStep = Emgu.CV.ML.MlEnum.EM_INIT_STEP_TYPE.START_AUTO_STEP;
parameters1.TermCrit = new MCvTermCriteria(10, 0.01);
emModel1.Train(samples, null, parameters1, labels);
EMParams parameters2 = new EMParams();
parameters2.Nclusters = N;
parameters2.CovMatType = Emgu.CV.ML.MlEnum.EM_COVARIAN_MATRIX_TYPE.COV_MAT_GENERIC;
parameters2.StartStep = Emgu.CV.ML.MlEnum.EM_INIT_STEP_TYPE.START_E_STEP;
parameters2.TermCrit = new MCvTermCriteria(100, 1.0e-6);
parameters2.Means = emModel1.Means;
parameters2.Covs = emModel1.GetCovariances();
parameters2.Weights = emModel1.Weights;
emModel2.Train(samples, null, parameters2, labels);
//TODO: Find out when saving of EM model will be enable
//emModel2.Save("emModel.xml");
#region Classify every image pixel
for (int i = 0; i < img.Height; i++)
for (int j = 0; j < img.Width; j++)
{
sample.Data[0, 0] = i;
sample.Data[0, 1] = j;
int response = (int) emModel2.Predict(sample, null);
Bgr color = colors[response];
img[j, i] = new Bgr(color.Blue*0.5, color.Green * 0.5, color.Red * 0.5 );
}
#endregion
#region draw the clustered samples
for (int i = 0; i < nSamples; i++)
{
img.Draw(new CircleF(new PointF(samples.Data[i, 0], samples.Data[i, 1]), 1), colors[labels.Data[i, 0]], 0);
}
#endregion
}
}
public void TestEM()
{
int N = 4; //number of clusters
int N1 = (int)Math.Sqrt((double)4);
Bgr[] colors = new Bgr[] {
new Bgr(0, 0, 255),
new Bgr(0, 255, 0),
new Bgr(0, 255, 255),
new Bgr(255, 255, 0)
};
int nSamples = 100;
Matrix <float> samples = new Matrix <float>(nSamples, 2);
Matrix <Int32> labels = new Matrix <int>(nSamples, 1);
Image <Bgr, Byte> img = new Image <Bgr, byte>(500, 500);
Matrix <float> sample = new Matrix <float>(1, 2);
CvInvoke.cvReshape(samples.Ptr, samples.Ptr, 2, 0);
for (int i = 0; i < N; i++)
{
Matrix <float> rows = samples.GetRows(i * nSamples / N, (i + 1) * nSamples / N, 1);
double scale = ((i % N1) + 1.0) / (N1 + 1);
MCvScalar mean = new MCvScalar(scale * img.Width, scale * img.Height);
MCvScalar sigma = new MCvScalar(30, 30);
rows.SetRandNormal(mean, sigma);
}
CvInvoke.cvReshape(samples.Ptr, samples.Ptr, 1, 0);
using (EM emModel1 = new EM())
using (EM emModel2 = new EM())
{
EMParams parameters1 = new EMParams();
parameters1.Nclusters = N;
parameters1.CovMatType = Emgu.CV.ML.MlEnum.EM_COVARIAN_MATRIX_TYPE.COV_MAT_DIAGONAL;
parameters1.StartStep = Emgu.CV.ML.MlEnum.EM_INIT_STEP_TYPE.START_AUTO_STEP;
parameters1.TermCrit = new MCvTermCriteria(10, 0.01);
emModel1.Train(samples, null, parameters1, labels);
EMParams parameters2 = new EMParams();
parameters2.Nclusters = N;
parameters2.CovMatType = Emgu.CV.ML.MlEnum.EM_COVARIAN_MATRIX_TYPE.COV_MAT_GENERIC;
parameters2.StartStep = Emgu.CV.ML.MlEnum.EM_INIT_STEP_TYPE.START_E_STEP;
parameters2.TermCrit = new MCvTermCriteria(100, 1.0e-6);
parameters2.Means = emModel1.Means;
parameters2.Covs = emModel1.GetCovariances();
parameters2.Weights = emModel1.Weights;
emModel2.Train(samples, null, parameters2, labels);
//TODO: Find out when saving of EM model will be enable
//emModel2.Save("emModel.xml");
#region Classify every image pixel
for (int i = 0; i < img.Height; i++)
{
for (int j = 0; j < img.Width; j++)
{
sample.Data[0, 0] = i;
sample.Data[0, 1] = j;
int response = (int)emModel2.Predict(sample, null);
Bgr color = colors[response];
img[j, i] = new Bgr(color.Blue * 0.5, color.Green * 0.5, color.Red * 0.5);
}
}
#endregion
#region draw the clustered samples
for (int i = 0; i < nSamples; i++)
{
img.Draw(new CircleF(new PointF(samples.Data[i, 0], samples.Data[i, 1]), 1), colors[labels.Data[i, 0]], 0);
}
#endregion
}
}
