/// <summary>
/// Calculates the centroids of the clusters
/// </summary>
public void CalculateClusterCentroids()
{
//Console.WriteLine("Cluster Centroid calculation:");
for (int j = 0; j < this.Clusters.Count; j++)
{
ClusterCentroid c = this.Clusters[j];
double l = 0.0;
c.PixelCount = 1;
c.RSum = 0;
c.GSum = 0;
c.BSum = 0;
c.MembershipSum = 0;
for (int i = 0; i < this.Points.Count; i++)
{
ClusterPoint p = this.Points[i];
l = Math.Pow(U[i, j], this.Fuzzyness);
c.RSum += l * p.PixelColor.R;
c.GSum += l * p.PixelColor.G;
c.BSum += l * p.PixelColor.B;
c.MembershipSum += l;
if (U[i, j] == p.ClusterIndex)
{
c.PixelCount += 1;
}
}
c.PixelColor = Color.FromArgb((byte)(c.RSum / c.MembershipSum), (byte)(c.GSum / c.MembershipSum), (byte)(c.BSum / c.MembershipSum));
}
//update the original image
Bitmap tempImage = new Bitmap(myImageWidth, myImageHeight, PixelFormat.Format32bppRgb);
for (int j = 0; j < this.Points.Count; j++)
{
for (int i = 0; i < this.Clusters.Count; i++)
{
ClusterPoint p = this.Points[j];
if (U[j, i] == p.ClusterIndex)
{
tempImage.SetPixel((int)p.X, (int)p.Y, this.Clusters[i].PixelColor);
}
}
}
processedImage = tempImage;
}
/// <summary>
/// Initialize the algorithm with points and initial clusters
/// </summary>
/// <param name="points">The list of Points objects</param>
/// <param name="clusters">The list of Clusters objects</param>
/// <param name="fuzzy">The fuzzyness factor to be used, constant</param>
/// <param name="myImage">A working image, so that the GUI working image can be updated</param>
/// <param name="numCluster">The number of clusters requested by the user from the GUI</param>
public KMeansAlgorithm(List <ClusterPoint> points, List <ClusterCentroid> clusters, float fuzzy, Bitmap myImage, int numCluster)
{
if (points == null)
{
throw new ArgumentNullException("points");
}
if (clusters == null)
{
throw new ArgumentNullException("clusters");
}
processedImage = (Bitmap)myImage.Clone();
this.Points = points;
this.Clusters = clusters;
this.myImageHeight = myImage.Height;
this.myImageWidth = myImage.Width;
this.myImage = new Bitmap(myImageWidth, myImageHeight, PixelFormat.Format32bppRgb);
U = new double[this.Points.Count, this.Clusters.Count];
this.Fuzzyness = fuzzy;
double diff;
// Iterate through all points to create initial U matrix
for (int i = 0; i < this.Points.Count; i++)
{
ClusterPoint p = this.Points[i];
double sum = 0.0;
for (int j = 0; j < this.Clusters.Count; j++)
{
ClusterCentroid c = this.Clusters[j];
diff = Math.Sqrt(Math.Pow(CalculateEuclideanDistance(p, c), 2.0));
U[i, j] = (diff == 0) ? Eps : diff;
sum += U[i, j];
}
}
this.RecalculateClusterMembershipValues();
}
/// <summary>
/// Calculates Euclidean Distance distance between a point and a cluster centroid
/// </summary>
/// <param name="p">Point</param>
/// <param name="c">Centroid</param>
/// <returns>Calculated distance</returns>
private double CalculateEuclideanDistance(ClusterPoint p, ClusterCentroid c)
{
return Math.Sqrt(Math.Pow(p.PixelColor.R - c.PixelColor.R, 2.0) + Math.Pow(p.PixelColor.G - c.PixelColor.G, 2.0) + Math.Pow(p.PixelColor.B - c.PixelColor.B, 2.0));
}
/// <summary>
/// Calculates Euclidean Distance distance between a point and a cluster centroid
/// </summary>
/// <param name="p">Point</param>
/// <param name="c">Centroid</param>
/// <returns>Calculated distance</returns>
private double CalculateEuclideanDistance(ClusterPoint p, ClusterCentroid c)
{
return(Math.Sqrt(Math.Pow(p.PixelColor.R - c.PixelColor.R, 2.0) + Math.Pow(p.PixelColor.G - c.PixelColor.G, 2.0) + Math.Pow(p.PixelColor.B - c.PixelColor.B, 2.0)));
}
// 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)
{
Database dataBase = new Database();
backgroundWorker.ReportProgress(0, "Working...");
filteredImage = (Bitmap)picPreview.Image.Clone();
int numClusters = (int)txtNumClusters.Value;
int maxIterations = (int)txtIterations.Value;
double accuracy = 0.00001;
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)));
}
/*foreach (ClusterPoint p in points)
* {
* List<SqlParameter> listParam = new List<SqlParameter>(4);
* listParam.Add(new SqlParameter("@x",p.X));
* listParam.Add(new SqlParameter("@y", p.Y));
* listParam.Add(new SqlParameter("@Color", p.PixelColor.ToArgb()));
* listParam.Add(new SqlParameter("@ImageId ", 1));
* dataBase.RunProcedure("usp_ClusterPointsInsert", listParam.ToArray());
* }*/
KMeansAlgorithm alg = new KMeansAlgorithm(points, centroids, 2, filteredImage, (int)txtNumClusters.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;
picProcessed.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
picProcessed.Image = (Bitmap)alg.getProcessedImage.Clone();
// 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);
}
}
}
k = Application.StartupPath.IndexOf("\\bin");
string sub = Application.StartupPath.Substring(k);
string ImagePath = Application.StartupPath.Substring(0, Application.StartupPath.Length - sub.Length) + "\\AnhData\\";
DirectoryInfo dirInfo = new DirectoryInfo(ImagePath);
if (dirInfo.Exists)
{
dirInfo.Create();
}
List <SqlParameter> listParam = new List <SqlParameter>(4);
string imageName = ofdCluster.FileName.Substring(ofdCluster.FileName.LastIndexOf(@"\") + 1);
listParam.Add(new SqlParameter("@ImageName", imageName.Substring(0, ofdCluster.FileName.Substring(ofdCluster.FileName.LastIndexOf(@"\") + 1).Length - imageName.Substring(imageName.LastIndexOf(".")).Length) + "_segmented.png"));
listParam.Add(new SqlParameter("@Url", "\\AnhData\\" + imageName.Substring(0, ofdCluster.FileName.Substring(ofdCluster.FileName.LastIndexOf(@"\") + 1).Length - imageName.Substring(imageName.LastIndexOf(".")).Length) + "_segmented.png"));
listParam.Add(new SqlParameter("@Width", picPreview.Image.Width));
listParam.Add(new SqlParameter("@Height", picPreview.Image.Height));
dataBase.RunProcedure("usp_ImageInsert", listParam.ToArray());
alg.getProcessedImage.Save(ImagePath + imageName.Substring(0, ofdCluster.FileName.Substring(ofdCluster.FileName.LastIndexOf(@"\") + 1).Length - imageName.Substring(imageName.LastIndexOf(".")).Length) + "_segmented.png");
// Save the image for each segmented cluster
for (int i = 0; i < centroids.Count; i++)
{
bmapArray[i].Save(ImagePath + imageName.Substring(0, ofdCluster.FileName.Substring(ofdCluster.FileName.LastIndexOf(@"\") + 1).Length - imageName.Substring(imageName.LastIndexOf(".")).Length) + "_" + i + ".png");
List <SqlParameter> listParam1 = new List <SqlParameter>(4);
listParam1.Add(new SqlParameter("@ImageName", imageName.Substring(0, ofdCluster.FileName.Substring(ofdCluster.FileName.LastIndexOf(@"\") + 1).Length - imageName.Substring(imageName.LastIndexOf(".")).Length) + "_" + i + ".png"));
listParam1.Add(new SqlParameter("@Url", "\\AnhData\\" + imageName.Substring(0, ofdCluster.FileName.Substring(ofdCluster.FileName.LastIndexOf(@"\") + 1).Length - imageName.Substring(imageName.LastIndexOf(".")).Length) + "_" + i + ".png"));
listParam1.Add(new SqlParameter("@Width", picPreview.Image.Width));
listParam1.Add(new SqlParameter("@Height", picPreview.Image.Height));
dataBase.RunProcedure("usp_ImageInsert", listParam1.ToArray());
/*DataTable dt = dataBase.RunProcedureGet("usp_ImageGetAfterInsert");
* if (dt.Rows.Count > 0)
* {
*
* List<SqlParameter> listParam1 = new List<SqlParameter>(4);
* listParam1.Add(new SqlParameter("@x", centroids[i].X));
* listParam1.Add(new SqlParameter("@y", centroids[i].Y));
* listParam1.Add(new SqlParameter("@Color", centroids[i].PixelColor.ToArgb()));
* listParam1.Add(new SqlParameter("@ImageId ", dt.Rows[0][0]));
* dataBase.RunProcedure("usp_ClusterCentroidsInsert", listParam1.ToArray());
* }*/
}
// 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();
}
