public static Bitmap FoursConnection(this Bitmap map)
{
Bitmap dst = new Bitmap(map.Width, map.Height);
var array = map.GetArrayOfPixels();
var imageObjects = new List <ImageObject>();
int currentColor = 2;
for (int i = 0; i < map.Width; i++)
{
for (int j = 0; j < map.Height; j++)
{
dst.SetPixel(i, j, Color.White);
if (array[i, j] == 1)
{
array = ConnectMap(array, i, j, currentColor, map.Width, imageObjects);
currentColor++;
}
}
}
var imageEngineService = new ImageEngineService();
imageObjects.ForEach(imageEngineService.SetImageProperties);
var kMeansProperies = new KMeansProperties(false, false, true, true);
var clusters = imageEngineService.KMeans(imageObjects, 6, 0.0001, kMeansProperies);
Random rnd = new Random();
foreach (var cluster in clusters)
{
var RRand = rnd.Next(0, 255);
var GRand = rnd.Next(0, 255);
var BRand = rnd.Next(0, 255);
foreach (var clusterImage in cluster.Images)
{
clusterImage.Pixels.ForEach(x => setRColor(x, RRand, GRand, BRand, dst));
}
}
return(dst);
}
public List <ImageCluster> KMeans(List <ImageObject> images, int countClusters, double stopValue, KMeansProperties properties)
{
var clusters = new List <ImageCluster>(countClusters);
for (int i = 0; i < countClusters; i++)
{
clusters.Add(new ImageCluster());
}
InitializeCluster(clusters, images);
while (clusters.Any(x => x.IsNeedToRecalculate))
{
CalculateClusters(clusters, images, properties);
clusters.ForEach(x => x.ReCalculateCenterOfCluster(stopValue));
}
return(clusters);
}
private ImageCluster FindClusterWithMinDistance(ImageCluster firstCluster, ImageCluster secondCluster, ImageObject image, KMeansProperties properties)
{
var firstDistance = properties.EvalByProperites(firstCluster, image);
var secondDistance = properties.EvalByProperites(secondCluster, image);
return((firstDistance < secondDistance) ? firstCluster : secondCluster);
}
private void CalculateClusters(List <ImageCluster> clusters, List <ImageObject> images, KMeansProperties properties)
{
clusters.ForEach(x => x.ClearImageItems());
images.ForEach(image => clusters.Aggregate((x, y) => FindClusterWithMinDistance(x, y, image, properties)).Images.Add(image));
}
public static Bitmap ConnectByKMeans(this Bitmap map, List <ImageObject> images, KMeansProperties kMeansProperties, int countOfCluster)
{
Bitmap dst = new Bitmap(map.Width, map.Height);
for (int i = 0; i < map.Width; i++)
{
for (int j = 0; j < map.Height; j++)
{
dst.SetPixel(i, j, Color.White);
}
}
var imageEngineService = new ImageEngineService();
images.ForEach(imageEngineService.SetImageProperties);
var clusters = imageEngineService.KMeans(images, countOfCluster, 0.0001, kMeansProperties);
Random rnd = new Random();
foreach (var cluster in clusters)
{
var RRand = rnd.Next(0, 255);
var GRand = rnd.Next(0, 255);
var BRand = rnd.Next(0, 255);
foreach (var clusterImage in cluster.Images)
{
clusterImage.Pixels.ForEach(x => setRColor(x, RRand, GRand, BRand, dst));
}
}
return(dst);
}
