skimage2.transform.hough_line_peaks#

skimage2.transform.hough_line_peaks(hspace, angles, dists, min_distance=9, min_angle=10, threshold=None, num_peaks=inf)[source]#

Return peaks in a straight line 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:

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

Returns:

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

Examples

>>> from _skimage2.transform import hough_line, hough_line_peaks
>>> from _skimage2.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)
2