public static Func<byte, byte, byte, Pixel> Otsu(PNM image)
{
double[] histogram = image.GetHistogramLuminosity();
object compareLock = new object();
double[] variances = new double[256];
Parallel.For(0, 256, i =>
{
double[] background = histogram.Take(i).ToArray();
double[] foreground = histogram.Skip(i).ToArray();
double backgroundWeight = background.Sum();
double foregroundWeight = 1 - backgroundWeight;
double backgroundMean = background.Length == 0 ? 0 : background.Select((d, idx) => d * idx).Average();
double foregroundMean = foreground.Select((d, idx) => d * idx).Average();
double variance = backgroundWeight * foregroundWeight * Math.Pow(foregroundMean - backgroundMean, 2);
variances[i] = variance;
});
byte threshold = 0;
double maxVariance = Double.NegativeInfinity;
for(int i =0; i< 256;i++)
{
if (variances[i] > maxVariance)
{
maxVariance = variances[i];
threshold = (byte)i;
}
}
return (r, g, b) => Plain(r, g, b, threshold);
}
public static Func <byte, byte, byte, Pixel> Otsu(PNM image)
{
double[] histogram = image.GetHistogramLuminosity();
object compareLock = new object();
double[] variances = new double[256];
Parallel.For(0, 256, i =>
{
double[] background = histogram.Take(i).ToArray();
double[] foreground = histogram.Skip(i).ToArray();
double backgroundWeight = background.Sum();
double foregroundWeight = 1 - backgroundWeight;
double backgroundMean = background.Length == 0 ? 0 : background.Select((d, idx) => d * idx).Average();
double foregroundMean = foreground.Select((d, idx) => d * idx).Average();
double variance = backgroundWeight * foregroundWeight * Math.Pow(foregroundMean - backgroundMean, 2);
variances[i] = variance;
});
byte threshold = 0;
double maxVariance = Double.NegativeInfinity;
for (int i = 0; i < 256; i++)
{
if (variances[i] > maxVariance)
{
maxVariance = variances[i];
threshold = (byte)i;
}
}
return((r, g, b) => Plain(r, g, b, threshold));
}
public static Func <byte, byte, byte, Pixel> Triangle(PNM image)
{
double[] histogram = image.GetHistogramLuminosity();
int minx = 0, maxx = 0;
double minValue = Double.PositiveInfinity, maxValue = Double.NegativeInfinity;
// find min max
for (int i = 0; i < 256; i++)
{
if (histogram[i] >= maxValue)
{
maxValue = histogram[i];
maxx = i;
}
if (histogram[i] < minValue)
{
minValue = histogram[i];
minx = i;
}
}
// find line
double a = -(maxValue - minValue) / (maxx - minx);
double b = 1;
double c = (minx * (maxValue - minValue) / (maxx - minx)) - minValue;
// find the furthest point
double distance, maxDistance = Double.NegativeInfinity;
byte threshold = 0;
int from, to;
if (minx < maxx)
{
from = minx + 1;
to = maxx;
}
else
{
to = minx + 1;
from = maxx;
}
for (int i = from; i < to; i++)
{
distance = PointToLineDistance(a, b, c, i, histogram[i]);
if (distance >= maxDistance)
{
maxDistance = distance;
threshold = (byte)i;
}
}
return((pr, pg, pb) => Plain(pr, pg, pb, threshold));
}
public static Func<byte, byte, byte, Pixel> Triangle(PNM image)
{
double[] histogram = image.GetHistogramLuminosity();
int minx=0, maxx=0;
double minValue = Double.PositiveInfinity, maxValue = Double.NegativeInfinity;
// find min max
for (int i = 0; i < 256; i++)
{
if (histogram[i] >= maxValue)
{
maxValue = histogram[i];
maxx = i;
}
if (histogram[i] < minValue)
{
minValue = histogram[i];
minx = i;
}
}
// find line
double a = -(maxValue - minValue) / (maxx - minx);
double b = 1;
double c = (minx * (maxValue - minValue) / (maxx - minx)) - minValue;
// find the furthest point
double distance, maxDistance = Double.NegativeInfinity;
byte threshold = 0;
int from, to;
if (minx < maxx)
{
from = minx + 1;
to = maxx;
}
else
{
to = minx + 1;
from = maxx;
}
for(int i = from; i< to; i++)
{
distance = PointToLineDistance(a,b,c,i,histogram[i]);
if (distance >= maxDistance)
{
maxDistance = distance;
threshold = (byte)i;
}
}
return (pr, pg, pb) => Plain(pr, pg, pb, threshold);
}
// Kapur's method
public static Func <byte, byte, byte, Pixel> Entropy(PNM image)
{
double[] histogram = image.GetHistogramLuminosity();
double entropySum = Double.NegativeInfinity;
int threshold = 0;
for (int i = 0; i < 256; i++)
{
double[] background = histogram.Take(i + 1).ToArray();
double[] foreground = histogram.Skip(i + 1).ToArray();
double sumB = background.Sum();
double sumF = foreground.Sum();
double entropyB = -background.Select(p =>
{
if (p == 0)
{
return(0);
}
return((p / sumB) * Math.Log(p / sumB, 2));
})
.Sum();
double entropyF = -foreground.Select(p =>
{
if (p == 0)
{
return(0);
}
return((p / (1 - sumB)) * Math.Log(p / (1 - sumB), 2));
})
.Sum();
double sum = entropyB + entropyF;
if (sum > entropySum)
{
entropySum = sum;
threshold = i;
}
}
return((r, g, b) => Plain(r, g, b, (byte)threshold));
}
// Kapur's method
public static Func<byte, byte, byte, Pixel> Entropy(PNM image)
{
double[] histogram = image.GetHistogramLuminosity();
double entropySum = Double.NegativeInfinity;
int threshold = 0;
for (int i = 0; i < 256; i++)
{
double[] background = histogram.Take(i + 1).ToArray();
double[] foreground = histogram.Skip(i + 1).ToArray();
double sumB = background.Sum();
double sumF = foreground.Sum();
double entropyB = -background.Select(p =>
{
if(p == 0)
return 0;
return (p / sumB) * Math.Log(p / sumB, 2);
})
.Sum();
double entropyF = -foreground.Select(p =>
{
if(p == 0)
return 0;
return (p / (1 - sumB)) * Math.Log(p / (1 - sumB), 2);
})
.Sum();
double sum = entropyB + entropyF;
if (sum > entropySum)
{
entropySum = sum;
threshold = i;
}
}
return (r, g, b) => Plain(r, g, b, (byte)threshold);
}
