public void createMatrix()
{
int N = 159;
similarityMatrix = new double[N, N];
Quantize q = new Quantize();
for (int index1 = 0; index1 < N; index1++)
{
for (int index2 = 0; index2 < N; index2++)
{
LUVClass luv1 = q.getLUVfromIndex(index1);
LUVClass luv2 = q.getLUVfromIndex(index2);
double distance = getEuclideanDistance(luv1, luv2);
if (distance > tColor)
{
similarityMatrix[index1, index2] = 0;
}
else
{
similarityMatrix[index1, index2] = 1 - distance / tColor;
}
}
}
//return similarityMatrix;
}
private void checkNeighbors(LUVClass[,] luvMatrix, int luvIndex, int x, int y)
{
Quantize q = new Quantize();
if (q.IndexOf(luvMatrix[x, y].L, luvMatrix[x, y].u, luvMatrix[x, y].v) == luvIndex)
{
coherenceCounter++;
marked[x, y] = true;
if (x + 1 < this.width - 1 && !marked[x + 1, y])
{
checkNeighbors(luvMatrix, luvIndex, x + 1, y);
}
if (y + 1 < this.height - 1 && !marked[x, y + 1])
{
checkNeighbors(luvMatrix, luvIndex, x, y + 1);
}
}
}
public Dictionary <int, float> quantizeColors(Dictionary <LUVClass, float> histogram, int imgDimensions)
{
Dictionary <int, float> normalizedHist = new Dictionary <int, float>();
Quantize q = new Quantize();
foreach (LUVClass luv in histogram.Keys)
{
int luvIndex = q.IndexOf(luv.L, luv.u, luv.v);
if (normalizedHist.ContainsKey(luvIndex))
{
normalizedHist[luvIndex] = normalizedHist[luvIndex] + histogram[luv];
}
else
{
normalizedHist.Add(luvIndex, histogram[luv]);
}
}
return(normalizedHist);
}
private void initializeMaxDistance()
{
int N = 159;
Quantize q = new Quantize();
double max = 0;
for (int index1 = 0; index1 < N; index1++)
{
for (int index2 = 0; index2 < N; index2++)
{
LUVClass luv1 = q.getLUVfromIndex(index1);
LUVClass luv2 = q.getLUVfromIndex(index2);
double distance = getEuclideanDistance(luv1, luv2);
if (distance > max)
{
max = distance;
}
}
}
dMax = max;
tColor = p * dMax;
}
private Dictionary <int, CoherenceUnit> checkCoherence(LUVClass[,] luvMatrix, int width, int height)
{
//Console.WriteLine("wh: " + width + "," + height);
Dictionary <int, CoherenceUnit> ccv = new Dictionary <int, CoherenceUnit>();
Quantize q = new Quantize();
int T = Convert.ToInt32(width * height * 0.01);
marked = new Boolean[width, height];
for (int i = 0; i < width; i++)
{
for (int j = 0; j < height; j++)
{
marked[i, j] = false;
}
}
for (int x = 0; x < width; x++)
{
for (int y = 0; y < height; y++)
{
if (!marked[x, y])
{
coherenceCounter = 0;
float a = 0; float b = 0;
int luvIndex = q.IndexOf(luvMatrix[x, y].L, luvMatrix[x, y].u, luvMatrix[x, y].v);
checkNeighbors(luvMatrix, luvIndex, x, y);
//Console.WriteLine("coherence: " + coherenceCounter);
if (coherenceCounter >= T)
{
a += coherenceCounter;
}
else
{
b += coherenceCounter;
}
if (ccv.ContainsKey(luvIndex))
{
ccv[luvIndex] = new CoherenceUnit(
ccv[luvIndex].aCoherentValue + a,
ccv[luvIndex].bIncoherentValue + b
);
}
else
{
ccv.Add(luvIndex, new CoherenceUnit(a, b));
}
}
}
}
//normalize
Dictionary <int, CoherenceUnit> ccvNorm = new Dictionary <int, CoherenceUnit>();
int dim = width * height;
foreach (KeyValuePair <int, CoherenceUnit> k in ccv)
{
CoherenceUnit c = new CoherenceUnit(ccv[k.Key].aCoherentValue / dim, ccv[k.Key].bIncoherentValue / dim);
ccvNorm[k.Key] = c;
}
return(ccvNorm);
}
