public static int?EntroypSelectionThreshold(WriteableBitmap writeableBitmap)
{
ImageHistogramData bitmapHistogramData = new ImageHistogramData(writeableBitmap);
if (bitmapHistogramData.IsGrayscale)
{
var grayscaleHistogram = bitmapHistogramData.GrayscaleHistogram;
int threshold = 0;
double max_entrophy = Double.MinValue;
int length = grayscaleHistogram.Length;
for (int i = 0; i < length; ++i)
{
double background_entrophy = CalculateBackgroundEntropy(grayscaleHistogram, i);
double foreground_entrophy = CalculateForegorundEntropy(grayscaleHistogram, length, i);
Debug.WriteLine(background_entrophy + foreground_entrophy + " : " + max_entrophy);
if (background_entrophy + foreground_entrophy > max_entrophy)
{
max_entrophy = background_entrophy + foreground_entrophy;
threshold = i;
}
}
return(threshold);
}
return(null);
}
public static int GetOtsuThreshold(WriteableBitmap bmp)
{
double[] vet = new double[256];
ImageHistogramData imageHistogramData = new ImageHistogramData(bmp);
int[] hist = imageHistogramData.R;
// loop through all possible t values and maximize between class variance
for (int k = 1; k != 255; ++k)
{
double p1 = Px(0, k, hist);
double p2 = Px(k + 1, 255, hist);
double p12 = p1 * p2;
if (p12 == 0)
{
p12 = 1;
}
double diff = (Mx(0, k, hist) * p2) - (Mx(k + 1, 255, hist) * p1);
vet[k] = diff * diff / p12;
//vet[k] = (float)Math.Pow((Mx(0, k, hist) * p2) - (Mx(k + 1, 255, hist) * p1), 2) / p12;
}
return(FindMaxElementIndex(vet));
}
public static int?PercentageBlackSelectionThreshold(WriteableBitmap writeableBitmap, double percentage)
{
ImageHistogramData bitmapHistogramData = new ImageHistogramData(writeableBitmap);
if (bitmapHistogramData.IsGrayscale)
{
var grayscaleHistogram = bitmapHistogramData.GrayscaleHistogram;
int desiredPixelsCount = (int)Math.Floor(writeableBitmap.PixelHeight * writeableBitmap.PixelWidth * percentage);
for (int count = 0, i = 0; i < 256; ++i)
{
count += grayscaleHistogram[i];
if (count > desiredPixelsCount)
{
return(Math.Min(255, i));
}
}
}
return(null);
}