static int getK(MST mst)
{
int clusters = 1;
double size = mst.getEdgesHeap().getSize();
int distictcolors = (int)size;
double cost = mst.totalCost;
double brevSTDev = 0;
double curSTDev = mst.STDev;
double Mean = mst.Mean;
Edge[] edges = mst.getEdgesHeap().Sortedges();
int j = edges.Length - 1;
int i = 0;
double curSTDevRed = 0, brevSTDevRed = 0;
while (Math.Abs(curSTDev - brevSTDev) > 0.0001)
{
brevSTDev = curSTDev;
if (Math.Abs(edges[i].weight - Mean) > Math.Abs(edges[j].weight - Mean))
{
cost -= edges[i].weight;
i++;
}
else
{
cost -= edges[j].weight;
j--;
}
size--;
Mean = cost / size;
double sum = 0;
for (int k = i; k <= j; k++)
{
sum += Math.Abs(edges[k].weight - Mean) * Math.Abs(edges[k].weight - Mean);
}
curSTDev = Math.Sqrt(sum / Convert.ToDouble(size - 1));
// curSTDevRed = Math.Abs(curSTDev - brevSTDev);
clusters++;
}
if (distictcolors > clusters)
{
return(clusters);
}
return(distictcolors);
}
/// <summary>
/// Apply Gaussian smoothing filter to enhance the edge detection
/// </summary>
/// <param name="ImageMatrix">Colored image matrix</param>
/// <param name="filterSize">Gaussian mask size</param>
/// <param name="sigma">Gaussian sigma</param>
/// <returns>smoothed color image</returns>
public static RGBPixel[,] GaussianFilter1D(RGBPixel[,] ImageMatrix, int filterSize, double sigma)
{
/////hereeeeeeeeeeeeeee
List <RGBPixelD> listRGP = findDistinctColors(ImageMatrix); //O(N^2)
MST mst = new MST(listRGP);
mst.prim(); //O(E log V)
cLustring = new CLustring(mst.getEdgesHeap(), listRGP, mst.getMSTList());
///AutoClustring autoClustring =new AutoClustring(listRGP);
Console.WriteLine();
cLustring.buildCustring(getK(mst));
// CLustring cLustring = new CLustring(autoClustring.getListOfColors(), autoClustring.getMSTDictionary());
// cLustring.buildAutoCustring();
///return replaceNewColors(autoClustring.autoClustring(filterSize, listRGP, listRGP.Count), ImageMatrix);
//return replaceNewColors(cLustring.getMatrix(), ImageMatrix);
return(ImageMatrix);
}