public void TestGBTrees() { 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 (GBTrees classifier = new GBTrees()) { classifier.Train(trainData, MlEnum.DataLayoutType.RowSample, trainClasses.Convert <float>(), null, null, MCvGBTreesParams.GetDefaultParameter(), false); #if !NETFX_CORE String fileName = Path.Combine(Path.GetTempPath(), "GBTrees.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)Math.Round(classifier.Predict(sample, null, null, MCvSlice.WholeSeq, false)); 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); }