| ellipse fitting? [message #30371] |
Sat, 27 April 2002 06:52  |
tom
Messages: 28
Registered: April 1995
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Junior Member |
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I found a matlab function for ellipse, but it is not easy for me translate
to IDl. For example,
% Solve eigensystem
[gevec, geval] = eig(S,C);
are there any function like eig(S,C) in IDL?
The matlab for ellips fitting is as following, who have a idl version?
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function a = fitellipse(X,Y)
% FITELLIPSE Least-squares fit of ellipse to 2D points.
% A = FITELLIPSE(X,Y) returns the parameters of the best-fit
% ellipse to 2D points (X,Y).
% The returned vector A contains the center, radii, and orientation
% of the ellipse, stored as (Cx, Cy, Rx, Ry, theta_radians)
%
% Authors: Andrew Fitzgibbon, Maurizio Pilu, Bob Fisher
% Reference: "Direct Least Squares Fitting of Ellipses", IEEE T-PAMI, 1999
%
% This is a more bulletproof version than that in the paper, incorporating
% scaling to reduce roundoff error, correction of behaviour when the input
% data are on a perfect hyperbola, and returns the geometric parameters
% of the ellipse, rather than the coefficients of the quadratic form.
%
% Example: Run fitellipse without any arguments to get a demo
if nargin == 0
% Create an ellipse
t = linspace(0,2);
Rx = 300
Ry = 200
Cx = 250
Cy = 150
Rotation = .4 % Radians
x = Rx * cos(t);
y = Ry * sin(t);
nx = x*cos(Rotation)-y*sin(Rotation) + Cx;
ny = x*sin(Rotation)+y*cos(Rotation) + Cy;
% Draw it
plot(nx,ny,'o');
% Fit it
fitellipse(nx,ny)
% Note it returns (Rotation - pi/2) and swapped radii, this is fine.
return
end
% normalize data
mx = mean(X);
my = mean(Y);
sx = (max(X)-min(X))/2;
sy = (max(Y)-min(Y))/2;
x = (X-mx)/sx;
y = (Y-my)/sy;
% Force to column vectors
x = x(:);
y = y(:);
% Build design matrix
D = [ x.*x x.*y y.*y x y ones(size(x)) ];
% Build scatter matrix
S = D'*D;
% Build 6x6 constraint matrix
C(6,6) = 0; C(1,3) = -2; C(2,2) = 1; C(3,1) = -2;
% Solve eigensystem
[gevec, geval] = eig(S,C);
% Find the negative eigenvalue
I = find(real(diag(geval)) < 1e-8 & ~isinf(diag(geval)));
% Extract eigenvector corresponding to negative eigenvalue
A = real(gevec(:,I));
% unnormalize
par = [
A(1)*sy*sy, ...
A(2)*sx*sy, ...
A(3)*sx*sx, ...
-2*A(1)*sy*sy*mx - A(2)*sx*sy*my + A(4)*sx*sy*sy, ...
-A(2)*sx*sy*mx - 2*A(3)*sx*sx*my + A(5)*sx*sx*sy, ...
A(1)*sy*sy*mx*mx + A(2)*sx*sy*mx*my + A(3)*sx*sx*my*my ...
- A(4)*sx*sy*sy*mx - A(5)*sx*sx*sy*my ...
+ A(6)*sx*sx*sy*sy ...
]';
% Convert to geometric radii, and centers
thetarad = 0.5*atan2(par(2),par(1) - par(3));
cost = cos(thetarad);
sint = sin(thetarad);
sin_squared = sint.*sint;
cos_squared = cost.*cost;
cos_sin = sint .* cost;
Ao = par(6);
Au = par(4) .* cost + par(5) .* sint;
Av = - par(4) .* sint + par(5) .* cost;
Auu = par(1) .* cos_squared + par(3) .* sin_squared + par(2) .* cos_sin;
Avv = par(1) .* sin_squared + par(3) .* cos_squared - par(2) .* cos_sin;
% ROTATED = [Ao Au Av Auu Avv]
tuCentre = - Au./(2.*Auu);
tvCentre = - Av./(2.*Avv);
wCentre = Ao - Auu.*tuCentre.*tuCentre - Avv.*tvCentre.*tvCentre;
uCentre = tuCentre .* cost - tvCentre .* sint;
vCentre = tuCentre .* sint + tvCentre .* cost;
Ru = -wCentre./Auu;
Rv = -wCentre./Avv;
Ru = sqrt(abs(Ru)).*sign(Ru);
Rv = sqrt(abs(Rv)).*sign(Rv);
a = [uCentre, vCentre, Ru, Rv, thetarad];
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