`skimage.transform.hough_transform:hough_line_peaks`

source: /dev/scikit-image/src/skimage/transform/hough_transform.py :8

Signature

   def     hough_line_peaks (    hspace    ,    angles    ,    dists    ,    min_distance    =  9   ,    min_angle    =  10   ,    threshold    =  None   ,    num_peaks    =  inf     )

Summary

Return peaks in a straight line Hough transform.

Extended Summary

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

Returns

accum, angles, dists : tuple of array: Peak values in Hough space, angles and distances.

Examples

from skimage.transform import hough_line, hough_line_peaks
from skimage.draw import line
img = np.zeros((15, 15), dtype=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)

✓

Aliases

skimage.transform.hough_line_peaks