// Shrink the color palette.
public static List <RGB5> ShrinkColorPalette(int numCols, List <RGB5> colors, out List <int> closestIndices)
{
//Run the algorithm.
int bucketsFilled = 1;
// We must convert to CIELAB first as RGB sucks at representing what the human eye sees.
List <CIELAB> cols = new List <CIELAB>();
foreach (var c in colors)
{
cols.Add(new CIELAB(c));
}
List <List <CIELAB> > buckets = new List <List <CIELAB> >();
buckets.Add(cols);
// Run until we get the desired number of colors. What we do here is split the bucket with the most variance.
while (bucketsFilled < numCols)
{
List <CIELAB> largestBucketVar = null;
double largestVar = double.MinValue;
foreach (var b in buckets)
{
double variance = CIELAB.CalculateVariance(b);
if (variance >= largestVar)
{
largestBucketVar = b;
largestVar = variance;
}
}
buckets.Remove(largestBucketVar);
List <CIELAB> v1, v2;
CIELAB.SplitBucket(largestBucketVar, out v1, out v2);
buckets.Add(v1);
buckets.Add(v2);
bucketsFilled++;
}
// Convert each bucket into RGB5.
List <RGB5> outputRGB = new List <RGB5>();
List <CIELAB> outputCIE = new List <CIELAB>();
foreach (var b in buckets)
{
var avg = CIELAB.AverageColor(b);
outputCIE.Add(avg);
outputRGB.Add(avg.ToRGB5());
}
// Get closest value for RGB indices.
closestIndices = new List <int>();
for (int i = 0; i < cols.Count; i++)
{
closestIndices.Add(cols[i].ClosestColorIndex(outputCIE));
}
// Return the final buckets.
return(outputRGB);
}
// The variance of a group of CIELab colors.
public static double CalculateVariance(List <CIELAB> colors)
{
double ret = 0;
CIELAB avg = AverageColor(colors);
foreach (var c in colors)
{
ret += c.DeltaESquared(avg); // (x - xBar)^2
}
return(ret / (colors.Count - 1)); // Use sample variance instead of population. Stats!
}
// Distance squared
public double DeltaESquared(CIELAB other)
{
var deltaL = L - other.L;
var deltaA = A - other.A;
var deltaB = B - other.B;
var c1 = Math.Sqrt(A * A + B * B);
var c2 = Math.Sqrt(other.A * other.A + other.B * other.B);
var deltaC = c1 - c2;
var deltaH = deltaA * deltaA + deltaB * deltaB - deltaC * deltaC;
deltaH = deltaH < 0 ? 0 : Math.Sqrt(deltaH);
var sc = 1.0 + 0.045 * c1;
var sh = 1.0 + 0.015 * c1;
var deltaLKlsl = deltaL / (1.0);
var deltaCkcsc = deltaC / (sc);
var deltaHkhsh = deltaH / (sh);
var i = deltaLKlsl * deltaLKlsl + deltaCkcsc * deltaCkcsc + deltaHkhsh * deltaHkhsh;
return(i);
}