コード例 #1
0
ファイル: AutoTestML.cs プロジェクト: Delaley/emgucv
      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);
      }
コード例 #2
0
ファイル: TestML.cs プロジェクト: pakerliu/sharp-context
        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;
        }