public bool trainingNaiveBayes(Matrix <float> inputData, Matrix <int> outputData, string modelName)
{
try
{
using (NormalBayesClassifier classifier = new NormalBayesClassifier())
{
TrainData training = new TrainData(inputData, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, outputData); // Creating training data
classifier.Train(training);
String fileName = modelName + ".xml";
classifier.Save(fileName);
}
return(true);
}
catch (Exception ee)
{
throw ee;
}
}
public void TestNormalBayesClassifier()
{
Bgr[] colors = new Bgr[]
{
new Bgr(0, 0, 255),
new Bgr(0, 255, 0),
new Bgr(255, 0, 0)
};
int trainSampleCount = 150;
#region Generate the training data and classes
Matrix <float> trainData = new Matrix <float>(trainSampleCount, 2);
Matrix <int> trainClasses = new Matrix <int>(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(50));
trainData1.GetCols(1, 2).SetRandNormal(new MCvScalar(300), new MCvScalar(50));
Matrix <float> trainData2 = trainData.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainData2.SetRandNormal(new MCvScalar(400), new MCvScalar(50));
Matrix <float> trainData3 = trainData.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainData3.GetCols(0, 1).SetRandNormal(new MCvScalar(300), new MCvScalar(50));
trainData3.GetCols(1, 2).SetRandNormal(new MCvScalar(100), new MCvScalar(50));
Matrix <int> trainClasses1 = trainClasses.GetRows(0, trainSampleCount / 3, 1);
trainClasses1.SetValue(1);
Matrix <int> trainClasses2 = trainClasses.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainClasses2.SetValue(2);
Matrix <int> trainClasses3 = trainClasses.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainClasses3.SetValue(3);
#endregion
using (TrainData td = new TrainData(trainData, MlEnum.DataLayoutType.RowSample, trainClasses))
using (NormalBayesClassifier classifier = new NormalBayesClassifier())
{
//ParamDef[] defs = classifier.GetParams();
classifier.Train(trainData, MlEnum.DataLayoutType.RowSample, trainClasses);
classifier.Clear();
classifier.Train(td);
#if !NETFX_CORE
String fileName = Path.Combine(Path.GetTempPath(), "normalBayes.xml");
classifier.Save(fileName);
if (File.Exists(fileName))
{
File.Delete(fileName);
}
#endif
#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)classifier.Predict(sample, null);
Bgr color = colors[response - 1];
img[j, i] = new Bgr(color.Blue * 0.5, color.Green * 0.5, color.Red * 0.5);
}
}
#endregion
}
// 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), colors[0], -1);
PointF p2 = new PointF(trainData2[i, 0], trainData2[i, 1]);
img.Draw(new CircleF(p2, 2.0f), colors[1], -1);
PointF p3 = new PointF(trainData3[i, 0], trainData3[i, 1]);
img.Draw(new CircleF(p3, 2.0f), colors[2], -1);
}
//Emgu.CV.UI.ImageViewer.Show(img);
}
private Image <Bgr, Byte> bayes()
{
Bgr[] colors = new Bgr[] {
new Bgr(0, 0, 255),
new Bgr(0, 255, 0),
new Bgr(255, 0, 0)
};
int trainSampleCount = 150;
#region Generate the traning data and classes
Matrix <float> trainData = new Matrix <float>(trainSampleCount, 2);
Matrix <int> trainClasses = new Matrix <int>(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(50));
trainData1.GetCols(1, 2).SetRandNormal(new MCvScalar(300), new MCvScalar(50));
Matrix <float> trainData2 = trainData.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainData2.SetRandNormal(new MCvScalar(400), new MCvScalar(50));
Matrix <float> trainData3 = trainData.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainData3.GetCols(0, 1).SetRandNormal(new MCvScalar(300), new MCvScalar(50));
trainData3.GetCols(1, 2).SetRandNormal(new MCvScalar(100), new MCvScalar(50));
Matrix <int> trainClasses1 = trainClasses.GetRows(0, trainSampleCount / 3, 1);
trainClasses1.SetValue(1);
Matrix <int> trainClasses2 = trainClasses.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainClasses2.SetValue(2);
Matrix <int> trainClasses3 = trainClasses.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainClasses3.SetValue(3);
#endregion
using (NormalBayesClassifier classifier = new NormalBayesClassifier()) {
classifier.Train(trainData, trainClasses, null, null, false);
#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)classifier.Predict(sample, null);
Bgr color = colors[response - 1];
img[j, i] = new Bgr(color.Blue * 0.5, color.Green * 0.5, color.Red * 0.5);
}
}
#endregion
}
// 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), colors[0], -1);
PointF p2 = new PointF(trainData2[i, 0], trainData2[i, 1]);
img.Draw(new CircleF(p2, 2.0f), colors[1], -1);
PointF p3 = new PointF(trainData3[i, 0], trainData3[i, 1]);
img.Draw(new CircleF(p3, 2.0f), colors[2], -1);
}
return(img);
}
public void TestNormalBayesClassifier()
{
Bgr[] colors = new Bgr[] {
new Bgr(0, 0, 255),
new Bgr(0, 255, 0),
new Bgr(255, 0, 0)};
int trainSampleCount = 150;
#region Generate the training data and classes
Matrix<float> trainData = new Matrix<float>(trainSampleCount, 2);
Matrix<int> trainClasses = new Matrix<int>(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(50));
trainData1.GetCols(1, 2).SetRandNormal(new MCvScalar(300), new MCvScalar(50));
Matrix<float> trainData2 = trainData.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainData2.SetRandNormal(new MCvScalar(400), new MCvScalar(50));
Matrix<float> trainData3 = trainData.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainData3.GetCols(0, 1).SetRandNormal(new MCvScalar(300), new MCvScalar(50));
trainData3.GetCols(1, 2).SetRandNormal(new MCvScalar(100), new MCvScalar(50));
Matrix<int> trainClasses1 = trainClasses.GetRows(0, trainSampleCount / 3, 1);
trainClasses1.SetValue(1);
Matrix<int> trainClasses2 = trainClasses.GetRows(trainSampleCount / 3, 2 * trainSampleCount / 3, 1);
trainClasses2.SetValue(2);
Matrix<int> trainClasses3 = trainClasses.GetRows(2 * trainSampleCount / 3, trainSampleCount, 1);
trainClasses3.SetValue(3);
#endregion
using (NormalBayesClassifier classifier = new NormalBayesClassifier() )
{
classifier.Train(trainData, trainClasses, null, null, false);
classifier.Save("normalBayes.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) classifier.Predict(sample, null);
Bgr color = colors[response -1];
img[j, i] = new Bgr(color.Blue * 0.5, color.Green * 0.5, color.Red * 0.5);
}
#endregion
}
// 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), colors[0], -1);
PointF p2 = new PointF(trainData2[i, 0], trainData2[i, 1]);
img.Draw(new CircleF(p2, 2.0f), colors[1], -1);
PointF p3 = new PointF(trainData3[i, 0], trainData3[i, 1]);
img.Draw(new CircleF(p3, 2.0f), colors[2], -1);
}
}