K Nearest Neighbors in CSharp

This example requires Emgu CV 1.5.0.0

What is a K Nearest Neighbors Classifier

According to wikipedia,

In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a method for classifying objects based on closest training examples in the feature space. k-NN is a type of instance-based learning, or lazy learning where the function is only approximated locally and all computation is deferred until classification. It can also be used for regression.

Source Code

using System.Drawing;
using Emgu.CV.Structure;
using Emgu.CV.ML;
using Emgu.CV.ML.Structure;

...

int K = 10;
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> 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<float> results, neighborResponses;
results = new Matrix<float>(sample.Rows, 1);
neighborResponses = new Matrix<float>(sample.Rows, K);
//dist = new Matrix<float>(sample.Rows, K);

using (KNearest knn = new KNearest(trainData, trainClasses, null, false, K))
{
   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;

         //Matrix<float> nearestNeighbors = new Matrix<float>(K* sample.Rows, sample.Cols);
         // estimates the response and get the neighbors' labels
         float response = knn.FindNearest(sample, K, results, null, neighborResponses, null);

         int accuracy = 0;
         // compute the number of neighbors representing the majority
         for (int k = 0; k < K; k++)
         {
            if (neighborResponses.Data[0, k] == response)
               accuracy++;
         }
         // highlight the pixel depending on the accuracy (or confidence)
         img[i, j] =
         response == 1 ?
             (accuracy > 5 ? new Bgr(90, 0, 0) : new Bgr(90, 60, 0)) :
             (accuracy > 5 ? new Bgr(0, 90, 0) : new Bgr(60, 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, 2.0f), new Bgr(255, 100, 100), -1);
   PointF p2 = new PointF(trainData2[i, 0], trainData2[i, 1]);
   img.Draw(new CircleF(p2, 2.0f), new Bgr(100, 255, 100), -1);
}

Emgu.CV.UI.ImageViewer.Show(img);

Result