Least Squares fitting of ellipses, python routine

based on the publication Halir, R., Flusser, J.: 'Numerically Stable Direct Least Squares Fitting of Ellipses'

Install

pip install lsq-ellipse

https://pypi.org/project/lsq-ellipse/

Example execution

import numpy as np
from ellipse import LsqEllipse
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse

if __name__ == '__main__':
    # avalible in the `example.py` script in this repo
    X1, X2 = example.make_test_ellipse()

    X = np.array(list(zip(X1, X2)))
    reg = LsqEllipse().fit(X)
    center, width, height, phi = reg.as_parameters()

    print(f'center: {center[0]:.3f}, {center[1]:.3f}')
    print(f'width: {width:.3f}')
    print(f'height: {height:.3f}')
    print(f'phi: {phi:.3f}')

    fig = plt.figure(figsize=(6, 6))
    ax = plt.subplot()
    ax.axis('equal')
    ax.plot(X1, X2, 'ro', zorder=1)
    ellipse = Ellipse(
        xy=center, width=2*width, height=2*height, angle=np.rad2deg(phi),
        edgecolor='b', fc='None', lw=2, label='Fit', zorder=2
    )
    ax.add_patch(ellipse)

    plt.xlabel('$X_1$')
    plt.ylabel('$X_2$')

    plt.legend()
    plt.show()

Cite this work

@software{ben_hammel_2020_3723294,
  author       = {Ben Hammel and Nick Sullivan-Molina},
  title        = {bdhammel/least-squares-ellipse-fitting: v2.0.0},
  month        = mar,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v2.0.0},
  doi          = {10.5281/zenodo.3723294},
  url          = {https://doi.org/10.5281/zenodo.3723294}
}

Ben Hammel, & Nick Sullivan-Molina. (2020, March 21). bdhammel/least-squares-ellipse-fitting: v2.0.0 (Version v2.0.0). Zenodo. http://doi.org/10.5281/zenodo.3723294