fix names

skimage/transform/__init__.py

@@ -1,6 +1,6 @@
-from ._hough_transform import hough_circle,
+from ._hough_transform import (hough_circle,
                               hough_line,
-                              probabilistic_hough_line
+                              probabilistic_hough_line)
 from .hough_transform import *
 from .radon_transform import *
 from .finite_radon_transform import *

skimage/transform/hough_transform.py

@@ -1,4 +1,4 @@
-__all__ = ['hough_peaks']
+__all__ = ['hough_line_peaks']
 
 from itertools import izip as zip
 

skimage/transform/tests/test_hough_transform.py

@@ -3,7 +3,6 @@ from numpy.testing import *
 
 import skimage.transform as tf
 import skimage.transform.hough_transform as ht
-from skimage.transform import probabilistic_hough
 from skimage.draw import circle_perimeter, line
 
 
@@ -50,7 +49,7 @@ def test_probabilistic_hough():
     # decrease default theta sampling because similar orientations may confuse
     # as mentioned in article of Galambos et al
     theta = np.linspace(0, np.pi, 45)
-    lines = probabilistic_hough(img, theta=theta, threshold=10, line_length=10,
+    lines = tf.probabilistic_hough_line(img, theta=theta, threshold=10, line_length=10,
                                 line_gap=1)
     # sort the lines according to the x-axis
     sorted_lines = []
@@ -62,59 +61,59 @@ def test_probabilistic_hough():
     assert([(25, 25), (74, 74)] in sorted_lines)
 
 
-def test_hough_peaks():
+def test_hough_line_peaks():
     img = np.zeros((100, 150), dtype=int)
     rr, cc = line(60, 130, 80, 10)
     img[rr, cc] = 1
 
     out, angles, d = tf.hough_line(img)
 
-    out, theta, dist = tf.hough_peaks(out, angles, d)
+    out, theta, dist = tf.hough_line_peaks(out, angles, d)
 
     assert_equal(len(dist), 1)
     assert_almost_equal(dist[0], 80.723, 1)
     assert_almost_equal(theta[0], 1.41, 1)
 
 
-def test_hough_peaks_dist():
+def test_hough_line_peaks_dist():
     img = np.zeros((100, 100), dtype=np.bool_)
     img[:, 30] = True
     img[:, 40] = True
     hspace, angles, dists = tf.hough_line(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
+    assert len(tf.hough_line_peaks(hspace, angles, dists, min_distance=5)[0]) == 2
+    assert len(tf.hough_line_peaks(hspace, angles, dists, min_distance=15)[0]) == 1
 
 
-def test_hough_peaks_angle():
+def test_hough_line_peaks_angle():
     img = np.zeros((100, 100), dtype=np.bool_)
     img[:, 0] = True
     img[0, :] = True
 
     hspace, angles, dists = tf.hough_line(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
+    assert len(tf.hough_line_peaks(hspace, angles, dists, min_angle=45)[0]) == 2
+    assert len(tf.hough_line_peaks(hspace, angles, dists, min_angle=90)[0]) == 1
 
     theta = np.linspace(0, np.pi, 100)
     hspace, angles, dists = tf.hough_line(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
+    assert len(tf.hough_line_peaks(hspace, angles, dists, min_angle=45)[0]) == 2
+    assert len(tf.hough_line_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_line(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
+    assert len(tf.hough_line_peaks(hspace, angles, dists, min_angle=45)[0]) == 2
+    assert len(tf.hough_line_peaks(hspace, angles, dists, min_angle=90)[0]) == 1
 
 
-def test_hough_peaks_num():
+def test_hough_line_peaks_num():
     img = np.zeros((100, 100), dtype=np.bool_)
     img[:, 30] = True
     img[:, 40] = True
     hspace, angles, dists = tf.hough_line(img)
-    assert len(tf.hough_peaks(hspace, angles, dists, min_distance=0,
+    assert len(tf.hough_line_peaks(hspace, angles, dists, min_distance=0,
                               min_angle=0, num_peaks=1)[0]) == 1
 
 
-def test_houghcircle():
+def test_hough_circle():
     # Prepare picture
     img = np.zeros((120, 100), dtype=int)
     radius = 20