public SymImage addItem(int[,] imageMatrix)
{
//Check if image matrix size is correct
if (imageMatrix.GetLength(0) != matrixSize || imageMatrix.GetLength(0) != matrixSize)
{
throw new ArgumentException("The image matrix must be " + matrixSize + "x" + matrixSize + ".");
}
//Create a new SymImage
SymImage symImage = new SymImage(imageMatrix, generateID());
//Calculate distance to all other items in the list
foreach (SymImage sImg in symImages)
{
symDistances.Add(new SymImageDistance(symImage, sImg));
}
//Add item to symImages list. It is added after distance calculations are done, so it is note compared to itself.
symImages.Add(symImage);
//Perform hierarchy analysis
createHierarchy();
//Return the new SymImage
return(symImage);
}
public void removeItem(SymImage symImage)
{
//Remove from symDistances list
symDistances.RemoveAll(p => p.symImageA == symImage || p.symImageB == symImage);
//Remove from symImages list
symImages.Remove(symImage);
}
public void createHierarchy()
{
//Create copy of original data
List <SymImage> clusters = symImages.ToList();
List <SymImageDistance> clusterDistances = symDistances.ToList();
//Merge all pairs and until list is empty
int AutoStop = 10;
while (clusterDistances.Count > 0)
{
AutoStop--;
if (AutoStop == 0)
{
AutoStop = 0;
}
//Sort the distance list
clusterDistances = clusterDistances.OrderBy(p => p.Distance).ToList();
//Get first distance entry, since it is the nearest pair.
SymImageDistance distEntry = clusterDistances[0];
//Remove all items referencing these two items.
clusters.Remove(distEntry.symImageA);
clusters.Remove(distEntry.symImageB);
clusterDistances.RemoveAll(p => p.symImageA == distEntry.symImageA || p.symImageA == distEntry.symImageB);
clusterDistances.RemoveAll(p => p.symImageB == distEntry.symImageA || p.symImageB == distEntry.symImageB);
//Create a new cluster from the distEntry items
SymImage newCluster = new SymImage(new List <SymImage>()
{
distEntry.symImageA, distEntry.symImageB
}, generateID());
//Calculate distance to all existing clusters
foreach (SymImage cluster in clusters)
{
clusterDistances.Add(new SymImageDistance(newCluster, cluster));
}
//Add new cluster to list
clusters.Add(newCluster);
}
//Save results
clusterHierarchy = clusters.ToList();
}
//Constructors
public SymImageDistance(SymImage symImage1, SymImage symImage2)
{
//Compare IDs and put lower ID in position A
if (symImage1.ID < symImage2.ID)
{
a = symImage1;
b = symImage2;
}
else
{
b = symImage1;
a = symImage2;
}
//Calculate distance and save
distance = a.distanceFrom(b);
}
//Methods - Public
public int distanceFrom(SymImage symImageB)
{
//Result variable
int distance = 0;
//Cycle through every X pixel of image A
for (int aX = 0; aX < matrixSize; aX++)
{
//Cycle through every Y pixel of image A
for (int aY = 0; aY < matrixSize; aY++)
{
//Check if pixel A is white. Skip these
if (this.ImageMatrix[aX, aY] == 0)
{
continue;
}
//Cycle through every X pixel of image B
for (int bX = 0; bX < matrixSize; bX++)
{
//Cycle through every Y pixel of image B
for (int bY = 0; bY < matrixSize; bY++)
{
//Check if pixel B is white. Skip these
if (symImageB.ImageMatrix[bX, bY] == 0)
{
continue;
}
//Calculate difference between coordinates
distance += Convert.ToInt32(Math.Sqrt(Math.Pow(aX - bX, 2) + Math.Pow(aY - bY, 2)));
}
}
}
}
//Normalized the distance by dividing by number of black pixels
distance = distance / ((this.numBlackPixels + symImageB.numBlackPixels) / 2);
return(distance);
}
