From 068293962da3d5dc271534f21d98855dcfc8b6f9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Tue, 9 Oct 2012 11:00:39 +0200 Subject: [PATCH] Add hough transform peak detection --- skimage/transform/hough_transform.py | 106 +++++++++++++++++- .../transform/tests/test_hough_transform.py | 29 +++++ 2 files changed, 134 insertions(+), 1 deletion(-) diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py index 4e3acd6e..71454a21 100644 --- a/skimage/transform/hough_transform.py +++ b/skimage/transform/hough_transform.py @@ -1,4 +1,4 @@ -__all__ = ['hough', 'probabilistic_hough'] +__all__ = ['hough', 'hough_peaks', 'probabilistic_hough'] from itertools import izip as zip @@ -135,3 +135,107 @@ def hough(img, theta=None): """ return _hough(img, theta) + + +def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10, + threshold_abs=0, threshold_rel=0.5, num_peaks=np.inf): + """Return peaks in hough transform. + + Identifies most prominent lines separated by a certain angle and distance in + a hough transform. Non-maximum suppresion 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` function. + angles : (M,) array + Angles returned by the `hough` function. + dists : (N, ) array + Distances returned by the `hough` 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_abs : float + Minimum intensity of peaks in hough space. + threshold_rel : float + Minimum intensity of peaks calculated as `max(hspace) * threshold_rel`. + 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 + -------- + >>> import numpy as np + >>> from skimage.transform import hough, hough_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(img) + >>> hspace, angles, dists = hough_peaks(hspace, angles, dists) + >>> angles + array([ 0.74590887, -0.79856126]) + >>> dists + array([ 10.74418605, 0.51162791]) + + """ + + hspace = hspace.copy() + rows, cols = hspace.shape + + threshold = max(threshold_abs, threshold_rel * np.max(hspace)) + + # sort accumulators from large to small + hspace_max = np.argsort(hspace.flat)[::-1] + hspace_max = np.column_stack(np.unravel_index(hspace_max, hspace.shape)) + + hspace_peaks = [] + dist_peaks = [] + angle_peaks = [] + + # relative coordinate grid for local neighbourhood suppresion + dist_ext, angle_ext = np.mgrid[- min_distance:min_distance + 1, + - min_angle:min_angle + 1] + + for dist_idx, angle_idx in hspace_max: + accum = hspace[dist_idx, angle_idx] + if accum > threshold: + # absolute coordinate grid for local neighbourhood suppresion + dist_nh = dist_idx + dist_ext + angle_nh = angle_idx + angle_ext + + # no reflection for distance neighbourhood + dist_in = np.logical_and(dist_nh > 0, dist_nh < rows) + dist_nh = dist_nh[dist_in] + angle_nh = angle_nh[dist_in] + + # reflect angles and assume angles are continuous, e.g. + # (..., 88, 89, -90, -89, ..., 89, -90, -89, ...) + angle_low = angle_nh < 0 + dist_nh[angle_low] = rows - dist_nh[angle_low] + angle_nh[angle_low] += cols + angle_high = angle_nh >= cols + dist_nh[angle_high] = rows - dist_nh[angle_high] + angle_nh[angle_high] -= cols + + # suppress neighbourhood + hspace[dist_nh, angle_nh] = 0 + + # add current line to peaks + hspace_peaks.append(accum) + dist_peaks.append(dists[dist_idx]) + angle_peaks.append(angles[angle_idx]) + + return np.array(hspace_peaks), np.array(dist_peaks), np.array(angle_peaks) diff --git a/skimage/transform/tests/test_hough_transform.py b/skimage/transform/tests/test_hough_transform.py index 03b63d07..3fb0a6f3 100644 --- a/skimage/transform/tests/test_hough_transform.py +++ b/skimage/transform/tests/test_hough_transform.py @@ -72,5 +72,34 @@ def test_probabilistic_hough(): assert([(25, 25), (74, 74)] in sorted_lines) +def test_hough_peaks_dist(): + img = np.zeros((100, 100), dtype=np.bool_) + img[:, 30] = True + img[:, 40] = True + hspace, angles, dists = tf.hough(img) + assert len(tf.hough_peaks(hspace, angles, dists, min_distance=5)[0]) == 2 + assert len(tf.hough_peaks(hspace, angles, dists, min_distance=15)[0]) == 1 + + +def test_hough_peaks_angle(): + img = np.zeros((100, 100), dtype=np.bool_) + img[:, 0] = True + img[0, :] = True + + hspace, angles, dists = tf.hough(img) + assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2 + assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1 + + theta = np.linspace(0, np.pi, 100) + hspace, angles, dists = tf.hough(img, theta) + assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2 + assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1 + + theta = np.linspace(np.pi / 3, 4. / 3 * np.pi, 100) + hspace, angles, dists = tf.hough(img, theta) + assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2 + assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1 + + if __name__ == "__main__": run_module_suite()