public void TestANN_MLP()
{
int trainSampleCount = 100;
#region Generate the traning 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> prediction = new Matrix<float>(1, 1);
Matrix<float> trainData1 = trainData.GetRows(0, trainSampleCount >> 1, 1);
trainData1.SetRandNormal(new MCvScalar(200), new MCvScalar(50));
Matrix<float> trainData2 = trainData.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainData2.SetRandNormal(new MCvScalar(300), new MCvScalar(50));
Matrix<float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount >> 1, 1);
trainClasses1.SetValue(1);
Matrix<float> trainClasses2 = trainClasses.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainClasses2.SetValue(2);
#endregion
using(Matrix<int> layerSize = new Matrix<int>(new int[] { 2, 5, 1 }))
using(Mat layerSizeMat = layerSize.Mat)
using (TrainData td = new TrainData(trainData, MlEnum.DataLayoutType.RowSample, trainClasses))
using (ANN_MLP network = new ANN_MLP())
{
network.SetLayerSizes(layerSizeMat);
network.SetActivationFunction(ANN_MLP.AnnMlpActivationFunction.SigmoidSym, 0, 0);
network.TermCriteria = new MCvTermCriteria(10, 1.0e-8);
network.SetTrainMethod(ANN_MLP.AnnMlpTrainMethod.Backprop, 0.1, 0.1);
network.Train(td, (int) Emgu.CV.ML.MlEnum.AnnMlpTrainingFlag.Default);
#if !NETFX_CORE
String fileName = Path.Combine(Path.GetTempPath(), "ann_mlp_model.xml");
network.Save(fileName);
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;
network.Predict(sample, prediction);
// estimates the response and get the neighbors' labels
float response = prediction.Data[0, 0];
// highlight the pixel depending on the accuracy (or confidence)
img[i, j] = response < 1.5 ? new Bgr(90, 0, 0) : new Bgr(0, 90, 0);
}
}
}
// display the original training samples
for (int i = 0; i < (trainSampleCount >> 1); i++)
{
PointF p1 = new PointF(trainData1[i, 0], trainData1[i, 1]);
img.Draw(new CircleF(p1, 2), new Bgr(255, 100, 100), -1);
PointF p2 = new PointF((int) trainData2[i, 0], (int) trainData2[i, 1]);
img.Draw(new CircleF(p2, 2), new Bgr(100, 255, 100), -1);
}
//Emgu.CV.UI.ImageViewer.Show(img);
}
private Image<Bgr, Byte> nn()
{
int trainSampleCount = 100;
#region Generate the traning 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> prediction = new Matrix<float>(1, 1);
Matrix<float> trainData1 = trainData.GetRows(0, trainSampleCount >> 1, 1);
trainData1.SetRandNormal(new MCvScalar(200), new MCvScalar(50));
Matrix<float> trainData2 = trainData.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainData2.SetRandNormal(new MCvScalar(300), new MCvScalar(50));
Matrix<float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount >> 1, 1);
trainClasses1.SetValue(1);
Matrix<float> trainClasses2 = trainClasses.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainClasses2.SetValue(2);
#endregion
Matrix<int> layerSize = new Matrix<int>(new int[] { 2, 5, 1 });
MCvANN_MLP_TrainParams parameters = new MCvANN_MLP_TrainParams();
parameters.term_crit = new MCvTermCriteria(10, 1.0e-8);
parameters.train_method = Emgu.CV.ML.MlEnum.ANN_MLP_TRAIN_METHOD.BACKPROP;
parameters.bp_dw_scale = 0.1;
parameters.bp_moment_scale = 0.1;
using (ANN_MLP network = new ANN_MLP(layerSize, Emgu.CV.ML.MlEnum.ANN_MLP_ACTIVATION_FUNCTION.SIGMOID_SYM, 1.0, 1.0)) {
network.Train(trainData, trainClasses, null, null, parameters, Emgu.CV.ML.MlEnum.ANN_MLP_TRAINING_FLAG.DEFAULT);
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;
network.Predict(sample, prediction);
// estimates the response and get the neighbors' labels
float response = prediction.Data[0, 0];
// highlight the pixel depending on the accuracy (or confidence)
img[i, j] = response < 1.5 ? new Bgr(90, 0, 0) : new Bgr(0, 90, 0);
}
}
}
// display the original training samples
for (int i = 0; i < (trainSampleCount >> 1); i++) {
PointF p1 = new PointF(trainData1[i, 0], trainData1[i, 1]);
img.Draw(new CircleF(p1, 2), new Bgr(255, 100, 100), -1);
PointF p2 = new PointF((int)trainData2[i, 0], (int)trainData2[i, 1]);
img.Draw(new CircleF(p2, 2), new Bgr(100, 255, 100), -1);
}
return img;
}
