hough-line-peaks

hough_line_peaks

skimage.transform.hough_line_peaks(hspace, angles, dists, min_distance=9, min_angle=10, threshold=None, num_peaks=inf) [source]

Return peaks in hough transform.

Identifies most prominent lines separated by a certain angle and distance in a hough transform. Non-maximum suppression with different sizes is applied separately in the first (distances) and second (angles) dimension of the hough space to identify peaks.

Parameters:

hspace : (N, M) array Hough space returned by the hough_line function. angles : (M,) array Angles returned by the hough_line function. Assumed to be continuous. (angles[-1] - angles[0] == PI). dists : (N, ) array Distances returned by the hough_line function. min_distance : int Minimum distance separating lines (maximum filter size for first dimension of hough space). min_angle : int Minimum angle separating lines (maximum filter size for second dimension of hough space). threshold : float Minimum intensity of peaks. Default is 0.5 * max(hspace). num_peaks : int Maximum number of peaks. When the number of peaks exceeds num_peaks, return num_peaks coordinates based on peak intensity.

Returns:

hspace, angles, dists : tuple of array Peak values in hough space, angles and distances.

Examples

1 2 3 4 5 6 7 8 9 10 11

>>> from skimage.transform import hough_line, hough_line_peaks >>> from skimage.draw import line >>> img = np.zeros((15, 15), dtype=np.bool_) >>> rr, cc = line(0, 0, 14, 14) >>> img[rr, cc] = 1 >>> rr, cc = line(0, 14, 14, 0) >>> img[cc, rr] = 1 >>> hspace, angles, dists = hough_line(img) >>> hspace, angles, dists = hough_line_peaks(hspace, angles, dists) >>> len(angles) 2