public static void RegionUnificationMethodAndSave(
string sourceFilePath, byte[,,] sourceImage, double averageDiffThreshold)
{
byte[,,] resultImage = ImageAreaDivision.RegionUnificationMethod(
sourceImage, averageDiffThreshold);
string newFilePath = ProbE.NewFilePath(
sourceFilePath, $"RegionUnificationMethod-{averageDiffThreshold}");
Utility.SaveColorImage(resultImage, newFilePath);
Console.WriteLine($"Region unification method done and saved to \'{newFilePath}\'");
}
public static void KMeansMethodAndSave(
string sourceFilePath, byte[,,] sourceImage, int numOfClasses, double weightOfPos)
{
byte[,,] resultImage = ImageAreaDivision.KMeansMethod(
sourceImage, numOfClasses, weightOfPos, new Random());
string newFilePath = ProbE.NewFilePath(
sourceFilePath, $"KMeansMethod-{numOfClasses}-{weightOfPos}");
Utility.SaveColorImage(resultImage, newFilePath);
Console.WriteLine($"K-means method done and saved to \'{newFilePath}\'");
}
public static void Main(string[] args)
{
Console.Write("Input image file name: ");
string fileName = Console.ReadLine();
byte[,,] sourceImage = Utility.LoadColorImage(fileName);
Console.Write("Input the threshold: ");
double diffThreshold = double.Parse(Console.ReadLine());
ProbE.RegionUnificationMethodAndSave(fileName, sourceImage, diffThreshold);
Console.Write("Input the number of the clusters: ");
int numOfClasses = int.Parse(Console.ReadLine());
Console.Write("Weight of position: ");
double weightOfPos = double.Parse(Console.ReadLine());
ProbE.KMeansMethodAndSave(fileName, sourceImage, numOfClasses, weightOfPos);
Console.ReadKey();
}
