hough_ellipse(image, threshold=4, accuracy=1, min_size=4, max_size=None)
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.
Input image with nonzero values representing edges.
Accumulator threshold value.
Bin size on the minor axis used in the accumulator.
Minimal major axis length.
Maximal minor axis length. If None, the value is set to the half of the smaller image dimension.
Where (yc, xc)
is the center, (a, b)
the major and minor axes, respectively. The :None:None:`orientation` value follows skimage.draw.ellipse_perimeter
convention.
Perform an elliptical Hough transform.
>>> from skimage.transform import hough_ellipseSee :
... 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)]
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.transform.hough_transform.hough_ellipse
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