Zhang-Suen thinning algorithm C#

Question

I am trying to write a Zhang-Suen thinning algorithm in C# following this guideline, without processing the margins.

Answer 1

bwang22's answer is very slow. Try this instead:

public readonly struct ConnectivityData
{
    public readonly int[] N;
    public readonly int NumNeighbors;
    public readonly int NumChanges;

    public ConnectivityData(in int[] n, in int numNeighbors, in int numChanges)
    {
        N = n;
        NumNeighbors = numNeighbors;
        NumChanges = numChanges;
    }
}


public static void ZhangSuen(in HashSet pixels)
{        
    while (true)
    {
        // Pass #1:
        List mark1 = new List();
        foreach (Pixel p in pixels)
        {
            ConnectivityData conn = ComputeConnectivity(p, pixels);

            if (conn.NumNeighbors > 1 && 
                conn.NumNeighbors < 7 && 
                conn.NumChanges == 1 &&
                conn.N[0] * conn.N[2] * conn.N[4] == 0 &&
                conn.N[2] * conn.N[4] * conn.N[6] == 0)
            {
                mark1.Add(p);
            }
        }

        //delete all marked:
        foreach (Pixel p in mark1)
        {
            pixels.Remove(p);
        }

        // PASS #2:
        List mark2 = new List();
        foreach (Pixel p in pixels)
        {
            ConnectivityData conn = ComputeConnectivity(p, pixels);

            if (conn.NumNeighbors > 1 &&
                conn.NumNeighbors < 7 &&
                conn.NumChanges == 1 &&
                conn.N[0] * conn.N[2] * conn.N[6] == 0 &&
                conn.N[0] * conn.N[4] * conn.N[6] == 0)
            {
                mark2.Add(p);
            }
        }

        //delete all marked:
        foreach (Pixel p in mark2)
        {
            pixels.Remove(p);
        }

        if (mark1.Count == 0 && mark2.Count == 0)
        {
            break;
        }
    }

}

private static ConnectivityData ComputeConnectivity(
    in Pixel p, 
    in HashSet pixels)
{
    // calculate #neighbors and number of changes:
    int[] n = new int[8];
    if (pixels.Contains(new Pixel(p.X, p.Y - 1)))
    {
        n[0] = 1;
    }
    if (pixels.Contains(new Pixel(p.X + 1, p.Y - 1)))
    {
        n[1] = 1;
    }
    if (pixels.Contains(new Pixel(p.X + 1, p.Y)))
    {
        n[2] = 1;
    }
    if (pixels.Contains(new Pixel(p.X + 1, p.Y + 1)))
    {
        n[3] = 1;
    }
    if (pixels.Contains(new Pixel(p.X, p.Y + 1)))
    {
        n[4] = 1;
    }
    if (pixels.Contains(new Pixel(p.X - 1, p.Y + 1)))
    {
        n[5] = 1;
    }
    if (pixels.Contains(new Pixel(p.X - 1, p.Y)))
    {
        n[6] = 1;
    }
    if (pixels.Contains(new Pixel(p.X - 1, p.Y - 1)))
    {
        n[7] = 1;
    }

    return new ConnectivityData(
            n,
            n[0] + n[1] + n[2] + n[3] + n[4] + n[5] + n[6] + n[7],
            ComputeNumberOfChanges(n));
}

private static int ComputeNumberOfChanges(in int[] n)
{
    int numberOfChanges = 0;

    // Iterate over each location and see if it is has changed from 0 to 1:
    int current = n[0];
    for (int i = 1; i < 8; i++)
    {
        if (n[i] == 1 && current == 0)
        {
             numberOfChanges++;
        }
        current = n[i];
    }

    // Also consider the change over the discontinuity between n[7] and n[0]:
    if (n[0] == 1 && n[7] == 0)
    {
        numberOfChanges++;
    }

    return numberOfChanges;
}

To use:

From your Bitmap etc, create a hash set of type Pixel, (which contains all the black pixels you want to thin) eg:

public class Pixel
{
    public int X;
    public int Y;

    public Pixel(in int x, in int y)
    {
        X = x;
        Y = y;
    }

    public override bool Equals(object pixel)
    {
        Pixel b = pixel as Pixel;
        return X == b.X && Y == b.Y;
    }

    public override int GetHashCode()
    {
        //return (a.X << 2) ^ a.Y; // this is also commonly used as a pixel hash code
        return X * 100000 + Y; // a bit hacky [will fail if bitmap width is > 100000]
    }
}

...then call ZhangSuen(pixels). This will delete the appropriate pixels from the set.

Note that this method does not work perfectly on all images. It makes parts of some images disappear. Specifically, I am having problems with downward-right pointing diagonal lines of thickness around 11 pixels wide.

I am currently working on a way to improve this, but it performs better than the similar Staniford algorithm on most files I have tested it with (CAD files).