hough_line_peaks(hspace, angles, dists, min_distance=9, min_angle=10, threshold=None, num_peaks=inf)
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.
Hough space returned by the hough_line
function.
Angles returned by the hough_line
function. Assumed to be continuous. (:None:None:`angles[-1] - angles[0] == PI`).
Distances returned by the hough_line
function.
Minimum distance separating lines (maximum filter size for first dimension of hough space).
Minimum angle separating lines (maximum filter size for second dimension of hough space).
Minimum intensity of peaks. Default is :None:None:`0.5 * max(hspace)`.
Maximum number of peaks. When the number of peaks exceeds :None:None:`num_peaks`, return :None:None:`num_peaks` coordinates based on peak intensity.
Peak values in Hough space, angles and distances.
Return peaks in a straight line Hough transform.
>>> from skimage.transform import hough_line, hough_line_peaksSee :
... 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
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.transform.hough_transform.hough_line_peaks
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