hough-ellipse

hough_ellipse

skimage.transform.hough_ellipse(img, threshold=4, accuracy=1, min_size=4, max_size=None) [source]

Perform an elliptical Hough transform.

Parameters:

img : (M, N) ndarray Input image with nonzero values representing edges. threshold: int, optional (default 4) Accumulator threshold value. accuracy : double, optional (default 1) Bin size on the minor axis used in the accumulator. min_size : int, optional (default 4) Minimal major axis length. max_size : int, optional Maximal minor axis length. (default None) If None, the value is set to the half of the smaller image dimension.

Returns:

result : ndarray with fields [(accumulator, y0, x0, a, b, orientation)] Where (yc, xc) is the center, (a, b) the major and minor axes, respectively. The orientation value follows skimage.draw.ellipse_perimeter convention.

Notes

The accuracy must be chosen to produce a peak in the accumulator distribution. In other words, a flat accumulator distribution with low values may be caused by a too low bin size.

References

[R367]

Xie, Yonghong, and Qiang Ji. “A new efficient ellipse detection method.” Pattern Recognition, 2002. Proceedings. 16th International Conference on. Vol. 2. IEEE, 2002

Examples

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>>> from skimage.transform import hough_ellipse >>> from skimage.draw import ellipse_perimeter >>> img = np.zeros((25, 25), dtype=np.uint8) >>> rr, cc = ellipse_perimeter(10, 10, 6, 8) >>> img[cc, rr] = 1 >>> result = hough_ellipse(img, threshold=8) >>> result.tolist() [(10, 10.0, 10.0, 8.0, 6.0, 0.0)]