After installation, just on a lark, I tried to use ADB to see if I can run my application. And surprisingly, it connected!!

The process I used was I ran an application and when I was running the app I issued the command in command prompt


adb connect 127.0.0.1:5555


and that’s it! With adb connected, I was able to successfully use bluestacks instead of the slow as molasses(tbh when compared to Bluestacks) Emulator!

I know what I am using as emulator from tomorrow( until I break it.)

Thanks for the BlueStacks team for the great application! I just can’t wait for it to be in final release…

The best part, however, is that the paper provides a MATLAB implementation for the algorithm that Fits an ellipse in Canonical form

A x² + Bxy + Cy² + Dx + Ey + F = 0

The program gives the estimates for parameters A..F.

The algorithm looks fairly straightforward to implement. The only wrinkle is the calculation of the eigenvalues and vectors for a matrix. So, in order to avoid re-inventing the wheel, I used the excellent library Meta.Numerics available at codeplex.

So here is the class that does it all
EllipseFit.cs

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using Meta.Numerics.Matrices;
using Meta.Numerics;

namespace FitEllipse
{
    public class EllipseFit
    {
        public Matrix Fit(PointCollection points)
        {
            int numPoints = points.Count;

            Matrix D1 = new Matrix(numPoints, 3);
            Matrix D2 = new Matrix(numPoints, 3);
            SquareMatrix S1 = new SquareMatrix(3);
            SquareMatrix S2 = new SquareMatrix(3);
            SquareMatrix S3 = new SquareMatrix(3);
            SquareMatrix T = new SquareMatrix(3);
            SquareMatrix M = new SquareMatrix(3);
            SquareMatrix C1 = new SquareMatrix(3);
            Matrix a1 = new Matrix(3, 1);
            Matrix a2 = new Matrix(3, 1);
            Matrix result = new Matrix(6, 1);
            Matrix temp;

            C1[0, 0] = 0;
            C1[0, 1] = 0;
            C1[0, 2] = 0.5;
            C1[1, 0] = 0;
            C1[1, 1] = -1;
            C1[1, 2] = 0;
            C1[2, 0] = 0.5;
            C1[2, 1] = 0;
            C1[2, 2] = 0;

            //2 D1 = [x .ˆ 2, x .* y, y .ˆ 2]; % quadratic part of the design matrix
            //3 D2 = [x, y, ones(size(x))]; % linear part of the design matrix
            for (int xx = 0; xx < points.Count; xx++)
            {
                Point p = points[xx];
                D1[xx, 0] = p.X * p.X;
                D1[xx, 1] = p.X * p.Y;
                D1[xx, 2] = p.Y * p.Y;

                D2[xx, 0] = p.X;
                D2[xx, 1] = p.Y;
                D2[xx, 2] = 1;
            }

            //4 S1 = D1’ * D1; % quadratic part of the scatter matrix
            temp = D1.Transpose() * D1;
            for (int xx = 0; xx < 3; xx++)
                for (int yy = 0; yy < 3; yy++)
                    S1[xx, yy] = temp[xx, yy];

            //5 S2 = D1’ * D2; % combined part of the scatter matrix
            temp = D1.Transpose() * D2;
            for (int xx = 0; xx < 3; xx++)
                for (int yy = 0; yy < 3; yy++)
                    S2[xx, yy] = temp[xx, yy];

            //6 S3 = D2’ * D2; % linear part of the scatter matrix
            temp = D2.Transpose() * D2;
            for (int xx = 0; xx < 3; xx++)
                for (int yy = 0; yy < 3; yy++)
                    S3[xx, yy] = temp[xx, yy];

            //7 T = - inv(S3) * S2’; % for getting a2 from a1
            T = -1 * S3.Inverse() * S2.Transpose();
            
            //8 M = S1 + S2 * T; % reduced scatter matrix
            M = S1 + S2 * T;
            
            //9 M = [M(3, :) ./ 2; - M(2, :); M(1, :) ./ 2]; % premultiply by inv(C1)
            M = C1 * M;
            
            //10 [evec, eval] = eig(M); % solve eigensystem
            ComplexEigensystem eigenSystem = M.Eigensystem();

            //11 cond = 4 * evec(1, :) .* evec(3, :) - evec(2, :) .ˆ 2; % evaluate a’Ca
            //12 a1 = evec(:, find(cond > 0)); % eigenvector for min. pos. eigenvalue
            for (int xx = 0; xx < eigenSystem.Dimension; xx++)
            {
               Vector<Complex> vector =  eigenSystem.Eigenvector(xx);
               Complex condition = 4 * vector[0] * vector[2] - vector[1] * vector[1];
               if (condition.Im == 0 && condition.Re > 0)
               {
                   // Solution is found
                   Console.WriteLine("\nSolution Found!");
                   for (int yy = 0; yy < vector.Count(); yy++)
                   {
                       Console.Write("{0}, ", vector[yy]);
                       a1[yy, 0] = vector[yy].Re;
                   }
               }
            }
            //13 a2 = T * a1; % ellipse coefficients
            a2 = T * a1;

            //14 a = [a1; a2]; % ellipse coefficients
            result[0, 0] = a1[0, 0];
            result[1, 0] = a1[1, 0];
            result[2, 0] = a1[2, 0];
            result[3, 0] = a2[0, 0];
            result[4, 0] = a2[1, 0];
            result[5, 0] = a2[2, 0];

            return result;
        }
    }
}

Point.cs

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;

namespace FitEllipse
{
    public class Point
    {
        public double X { get; set; }
        public double Y {get; set;  }
    }

    public class PointCollection : List<Point>
    {
    }
}

The complete project is available for download here. Just download the file and rename it to zip.
 FitEllipse.zip

All code is made available under the MS-PL.
Any comments to improve the execution time are always welcomed.