public void Run()
{
Bitmap bitmap = GetImage();
int width = bitmap.Width;
int height = bitmap.Height;
double[][] image = ImageTools.BitmapToArray(bitmap);
var kMeans = new Clustering.KMeans(20, 6, 0, 255, image, SCDE);
int[,] map = new int[width, height];
int currentCluster = 0;
foreach (var cluster in kMeans.clusters)
{
foreach (int index in cluster.indeces)
{
int x = (int)Math.Floor(index / (double)bitmap.Height);
int y = index % bitmap.Height;
map[x, y] = currentCluster;
}
currentCluster++;
}
Save(FeatureExtraction.CellularAutomata(map, 12, 0, 0, 6), kMeans, "4-0-0-6");
//map = FloodFill.Fill(map);
/*
* foreach (var cluster in kMeans.clusters) {
*
* double[] mean = cluster.GetMean();
* Color color = Color.FromArgb((int)mean[0],(int)mean[1],(int)mean[2]);
*
* foreach (int index in cluster.indeces) {
*
* int x = (int)Math.Floor(index / (double)bitmap.Height);
* int y = index % bitmap.Height;
*
* newImage.SetPixel(x,y,color);
* }
* }
*/
}
public void Save(int[,] map, Clustering.KMeans kMeans, string name)
{
Bitmap newImage = new Bitmap(map.GetLength(0), map.GetLength(1));
Color[] colors = new Color[kMeans.clusters.Length];
for (int i = 0; i < colors.Length; i++)
{
double[] mean = kMeans.clusters[i].GetMean();
colors[i] = Color.FromArgb((int)mean[0], (int)mean[1], (int)mean[2]);
}
for (int x = 0; x < map.GetLength(0); x++)
{
for (int y = 0; y < map.GetLength(1); y++)
{
newImage.SetPixel(x, y, colors[map[x, y]]);
}
}
newImage.Save(rootPath + name + " - " + random.Next() + ".png", ImageFormat.Png);
}
