Ellipse model parameters in the following order :None:None:`xc`, :None:None:`yc`, a, :None:None:`b`, :None:None:`theta`.
The functional model of the ellipse is:
xt = xc + a*cos(theta)*cos(t) - b*sin(theta)*sin(t) yt = yc + a*sin(theta)*cos(t) + b*cos(theta)*sin(t) d = sqrt((x - xt)**2 + (y - yt)**2)
where (xt, yt)
is the closest point on the ellipse to (x, y)
. Thus d is the shortest distance from the point to the ellipse.
The estimator is based on a least squares minimization. The optimal solution is computed directly, no iterations are required. This leads to a simple, stable and robust fitting method.
The params
attribute contains the parameters in the following order:
xc, yc, a, b, theta
Total least squares estimator for 2D ellipses.
>>> xy = EllipseModel().predict_xy(np.linspace(0, 2 * np.pi, 25),This example is valid syntax, but we were not able to check execution
... params=(10, 15, 4, 8, np.deg2rad(30)))
... ellipse = EllipseModel()
... ellipse.estimate(xy) True
>>> np.round(ellipse.params, 2) array([10. , 15. , 4. , 8. , 0.52])This example is valid syntax, but we were not able to check execution
>>> np.round(abs(ellipse.residuals(xy)), 5) array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])See :
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skimage.measure.fit.EllipseModel
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