pub fn fit_ellipse_ams(points: &dyn ToInputArray) -> Result<RotatedRect>
Fits an ellipse around a set of 2D points.
The function calculates the ellipse that fits a set of 2D points.
It returns the rotated rectangle in which the ellipse is inscribed.
The Approximate Mean Square (AMS) proposed by Taubin1991 is used.
For an ellipse, this basis set is 
,
which is a set of six free coefficients 
.
However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths 
,
the position 
, and the orientation 
. This is because the basis set includes lines,
quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits.
If the fit is found to be a parabolic or hyperbolic function then the standard #fitEllipse method is used.
The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves
by imposing the condition that 
where
the matrices 
and 
are the partial derivatives of the design matrix 
with
respect to x and y. The matrices are formed row by row applying the following to
each of the points in the set:
\f{align*}{
D(i,:)&=\left{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right} &
D_x(i,:)&=\left{2 x_i,y_i,0,1,0,0\right} &
D_y(i,:)&=\left{0,x_i,2 y_i,0,1,0\right}
\f}
The AMS method minimizes the cost function
\f{equation*}{
\epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T }
\f}
The minimum cost is found by solving the generalized eigenvalue problem.
\f{equation*}{
D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A
\f}
- points: Input 2D point set, stored in std::vector<> or Mat