hough_circle
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skimage.transform.hough_circle(image, radius, normalize=True, full_output=False)
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Perform a circular Hough transform.
Parameters: image : (M, N) ndarray
Input image with nonzero values representing edges.
radius : scalar or sequence of scalars
Radii at which to compute the Hough transform. Floats are converted to integers.
normalize : boolean, optional (default True)
Normalize the accumulator with the number of pixels used to draw the radius.
full_output : boolean, optional (default False)
Extend the output size by twice the largest radius in order to detect centers outside the input picture.
Returns: H : 3D ndarray (radius index, (M + 2R, N + 2R) ndarray)
Hough transform accumulator for each radius. R designates the larger radius if full_output is True. Otherwise, R = 0.
Examples
>>> from skimage.transform import hough_circle >>> from skimage.draw import circle_perimeter >>> img = np.zeros((100, 100), dtype=np.bool_) >>> rr, cc = circle_perimeter(25, 35, 23) >>> img[rr, cc] = 1 >>> try_radii = np.arange(5, 50) >>> res = hough_circle(img, try_radii) >>> ridx, r, c = np.unravel_index(np.argmax(res), res.shape) >>> r, c, try_radii[ridx] (25, 35, 23)
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