コード例 #1
0
ファイル: Form1.cs プロジェクト: harshanad112/KmeansDemo
        // This method will run on a thread other than the UI thread.
        // Be sure not to manipulate any Windows Forms controls created
        // on the UI thread from this method.
        private void backgroundWorker1_DoWork(object sender, DoWorkEventArgs e)
        {
            backgroundWorker.ReportProgress(0, "Working...");

            filteredImage = (Bitmap)pictureBox1.Image.Clone();
            int numClusters = (int)numericUpDown2.Value;
            int maxIterations = (int)numericUpDown3.Value;
            double accuracy = (double)numericUpDown4.Value;

            List<ClusterPoint> points = new List<ClusterPoint>();

            for (int row = 0; row < originalImage.Width; ++row)
            {
                for (int col = 0; col < originalImage.Height; ++col)
                {

                    Color c2 = originalImage.GetPixel(row, col);
                    points.Add(new ClusterPoint(row, col, c2));

                }
            }

            List<ClusterCentroid> centroids = new List<ClusterCentroid>();

            //Create random points to use a the cluster centroids
            Random random = new Random();
            for (int i = 0; i < numClusters; i++)
            {
                int randomNumber1 = random.Next(sourceImage.Width);
                int randomNumber2 = random.Next(sourceImage.Height);
                centroids.Add(new ClusterCentroid(randomNumber1, randomNumber2, filteredImage.GetPixel(randomNumber1, randomNumber2)));
            }
            FCM alg = new FCM(points, centroids, 2, filteredImage, (int)numericUpDown2.Value);

            int k = 0;
            do
            {
                if ((backgroundWorker.CancellationPending == true))
                {
                    e.Cancel = true;
                    break;
                }
                else
                {

                    k++;
                    alg.J = alg.CalculateObjectiveFunction();
                    alg.CalculateClusterCentroids();
                    alg.Step();
                    double Jnew = alg.CalculateObjectiveFunction();
                    Console.WriteLine("Run method i={0} accuracy = {1} delta={2}", k, alg.J, Math.Abs(alg.J - Jnew));
                    toolStripStatusLabel2.Text = "Precision " + Math.Abs(alg.J - Jnew);

                    // Format and display the TimeSpan value.
                    string elapsedTime = String.Format("{0:00}:{1:00}:{2:00}.{3:00}", stopWatch.Elapsed.Hours, stopWatch.Elapsed.Minutes, stopWatch.Elapsed.Seconds, stopWatch.Elapsed.Milliseconds / 10);
                    toolStripStatusLabel3.Text = "Duration: " + elapsedTime;

                    pictureBox2.Image = (Bitmap)alg.getProcessedImage;
                    backgroundWorker.ReportProgress((100 * k) / maxIterations, "Iteration " + k);

                    if (Math.Abs(alg.J - Jnew) < accuracy) break;
                }
            }
            while (maxIterations > k);
            Console.WriteLine("Done.");

            stopWatch.Stop();
            // Get the elapsed time as a TimeSpan value.
            TimeSpan ts = stopWatch.Elapsed;

               // Save the segmented image
               pictureBox2.Image = (Bitmap)alg.getProcessedImage.Clone();
               alg.getProcessedImage.Save("segmented.png");

               // Create a new image for each cluster in order to extract the features from the original image
               double[,] Matrix = alg.U;
               Bitmap[] bmapArray = new Bitmap[centroids.Count];
               for (int i = 0; i < centroids.Count; i++)
               {
               bmapArray[i] = new Bitmap(sourceImage.Width, sourceImage.Height, PixelFormat.Format32bppRgb);
               }

               for (int j = 0; j < points.Count; j++)
               {
               for (int i = 0; i < centroids.Count; i++)
               {
                   ClusterPoint p = points[j];
                   if (Matrix[j, i] == p.ClusterIndex)
                   {
                        bmapArray[i].SetPixel((int)p.X, (int)p.Y, p.OriginalPixelColor);
                   }
               }
            }

               // Save the image for each segmented cluster
               for (int i = 0; i < centroids.Count; i++)
               {
               bmapArray[i].Save("Cluster" + i + ".png");
               }

               // Resource cleanup...more work to do here to avoid memory problems!!!
               backgroundWorker.ReportProgress(100, "Done in " + k + " iterations.");
               ////alg.Dispose();
               for (int i = 0; i < points.Count; i++)
               {
               points[i] = null;
               }
               for (int i = 0; i < centroids.Count; i++)
               {
               centroids[i] = null;
               }
               alg = null;
            //centroids.Clear();
            //points.Clear();
        }
コード例 #2
0
ファイル: Form1.cs プロジェクト: harshanad112/KmeansDemo
        // This method will run on a thread other than the UI thread.
        // Be sure not to manipulate any Windows Forms controls created
        // on the UI thread from this method.
        private void backgroundWorker1_DoWork(object sender, DoWorkEventArgs e)
        {
            backgroundWorker.ReportProgress(0, "Working...");

            filteredImage = (Bitmap)pictureBox1.Image.Clone();
            int    numClusters   = (int)numericUpDown2.Value;
            int    maxIterations = (int)numericUpDown3.Value;
            double accuracy      = (double)numericUpDown4.Value;



            List <ClusterPoint> points = new List <ClusterPoint>();


            for (int row = 0; row < originalImage.Width; ++row)
            {
                for (int col = 0; col < originalImage.Height; ++col)
                {
                    Color c2 = originalImage.GetPixel(row, col);
                    points.Add(new ClusterPoint(row, col, c2));
                }
            }



            List <ClusterCentroid> centroids = new List <ClusterCentroid>();

            //Create random points to use a the cluster centroids
            Random random = new Random();

            for (int i = 0; i < numClusters; i++)
            {
                int randomNumber1 = random.Next(sourceImage.Width);
                int randomNumber2 = random.Next(sourceImage.Height);
                centroids.Add(new ClusterCentroid(randomNumber1, randomNumber2, filteredImage.GetPixel(randomNumber1, randomNumber2)));
            }
            FCM alg = new FCM(points, centroids, 2, filteredImage, (int)numericUpDown2.Value);


            int k = 0;

            do
            {
                if ((backgroundWorker.CancellationPending == true))
                {
                    e.Cancel = true;
                    break;
                }
                else
                {
                    k++;
                    alg.J = alg.CalculateObjectiveFunction();
                    alg.CalculateClusterCentroids();
                    alg.Step();
                    double Jnew = alg.CalculateObjectiveFunction();
                    Console.WriteLine("Run method i={0} accuracy = {1} delta={2}", k, alg.J, Math.Abs(alg.J - Jnew));
                    toolStripStatusLabel2.Text = "Precision " + Math.Abs(alg.J - Jnew);

                    // Format and display the TimeSpan value.
                    string elapsedTime = String.Format("{0:00}:{1:00}:{2:00}.{3:00}", stopWatch.Elapsed.Hours, stopWatch.Elapsed.Minutes, stopWatch.Elapsed.Seconds, stopWatch.Elapsed.Milliseconds / 10);
                    toolStripStatusLabel3.Text = "Duration: " + elapsedTime;

                    pictureBox2.Image = (Bitmap)alg.getProcessedImage;
                    backgroundWorker.ReportProgress((100 * k) / maxIterations, "Iteration " + k);

                    if (Math.Abs(alg.J - Jnew) < accuracy)
                    {
                        break;
                    }
                }
            }while (maxIterations > k);
            Console.WriteLine("Done.");

            stopWatch.Stop();
            // Get the elapsed time as a TimeSpan value.
            TimeSpan ts = stopWatch.Elapsed;

            // Save the segmented image
            pictureBox2.Image = (Bitmap)alg.getProcessedImage.Clone();
            alg.getProcessedImage.Save("segmented.png");


            // Create a new image for each cluster in order to extract the features from the original image
            double[,] Matrix = alg.U;
            Bitmap[] bmapArray = new Bitmap[centroids.Count];
            for (int i = 0; i < centroids.Count; i++)
            {
                bmapArray[i] = new Bitmap(sourceImage.Width, sourceImage.Height, PixelFormat.Format32bppRgb);
            }

            for (int j = 0; j < points.Count; j++)
            {
                for (int i = 0; i < centroids.Count; i++)
                {
                    ClusterPoint p = points[j];
                    if (Matrix[j, i] == p.ClusterIndex)
                    {
                        bmapArray[i].SetPixel((int)p.X, (int)p.Y, p.OriginalPixelColor);
                    }
                }
            }

            // Save the image for each segmented cluster
            for (int i = 0; i < centroids.Count; i++)
            {
                bmapArray[i].Save("Cluster" + i + ".png");
            }


            // Resource cleanup...more work to do here to avoid memory problems!!!
            backgroundWorker.ReportProgress(100, "Done in " + k + " iterations.");
            ////alg.Dispose();
            for (int i = 0; i < points.Count; i++)
            {
                points[i] = null;
            }
            for (int i = 0; i < centroids.Count; i++)
            {
                centroids[i] = null;
            }
            alg = null;
            //centroids.Clear();
            //points.Clear();
        }