TSTFIX: Fix imports in hough_ellipse doctest

skimage/transform/_hough_transform.pyx

@@ -12,9 +12,6 @@ from libc.stdlib cimport rand
 
 from ..draw import circle_perimeter
 
-cdef double PI_2 = 1.5707963267948966
-cdef double NEG_PI_2 = -PI_2
-
 from .._shared.interpolation cimport round
 
 

skimage/transform/hough_transform.py

@@ -59,7 +59,7 @@ def hough_line(img, theta=None):
 
     if theta is None:
         # These values are approximations of pi/2
-        theta = np.linspace(-1.5707963267948966, 1.5707963267948966, 180)
+        theta = np.linspace(-np.pi / 2, np.pi / 2, 180)
 
     return _hough_line(img, theta=theta)
 
@@ -225,7 +225,7 @@ def probabilistic_hough_line(img, threshold=10, line_length=50, line_gap=10,
         raise ValueError('The input image `img` must be 2D.')
 
     if theta is None:
-        theta = 1.5707963267948966 - np.arange(180) / 180.0 * np.pi
+        theta = np.pi / 2 - np.arange(180) / 180.0 * np.pi
 
     return _prob_hough_line(img, threshold=threshold, line_length=line_length,
                             line_gap=line_gap, theta=theta)
@@ -304,11 +304,14 @@ def hough_ellipse(img, threshold=4, accuracy=1, min_size=4, max_size=None):
 
     Examples
     --------
+    >>> from skimage.transform import hough_ellipse
+    >>> from skimage.draw import ellipse_perimeter
     >>> img = np.zeros((25, 25), dtype=np.uint8)
     >>> rr, cc = ellipse_perimeter(10, 10, 6, 8)
     >>> img[cc, rr] = 1
     >>> result = hough_ellipse(img, threshold=8)
-    [(10, 10.0, 8.0, 6.0, 0.0, 10.0)]
+    >>> result.tolist()
+    [(10, 10.0, 10.0, 8.0, 6.0, 0.0)]
 
     Notes
     -----

skimage/transform/tests/test_hough_transform.py

@@ -1,5 +1,5 @@
 import numpy as np
-from numpy.testing import assert_almost_equal, assert_equal
+from numpy.testing import assert_almost_equal, assert_equal, assert_raises
 
 import skimage.transform as tf
 from skimage.draw import line, circle_perimeter, ellipse_perimeter
@@ -7,15 +7,6 @@ from skimage._shared._warnings import expected_warnings
 from skimage._shared.testing import test_parallel
 
 
-def append_desc(func, description):
-    """Append the test function ``func`` and append
-    ``description`` to its name.
-    """
-    func.description = func.__module__ + '.' + func.__name__ + description
-
-    return func
-
-
 @test_parallel()
 def test_hough_line():
     # Generate a test image
@@ -42,12 +33,21 @@ def test_hough_line_angles():
     assert_equal(len(angles), 10)
 
 
+def test_hough_line_bad_input():
+    img = np.zeros(100)
+    img[10] = 1
+
+    # Expected error, img must be 2D
+    assert_raises(ValueError, tf.hough_line, img, np.linspace(0, 360, 10))
+
+
 def test_probabilistic_hough():
     # Generate a test image
     img = np.zeros((100, 100), dtype=int)
     for i in range(25, 75):
         img[100 - i, i] = 100
         img[i, i] = 100
+
     # decrease default theta sampling because similar orientations may confuse
     # as mentioned in article of Galambos et al
     theta = np.linspace(0, np.pi, 45)
@@ -59,9 +59,21 @@ def test_probabilistic_hough():
         line = list(line)
         line.sort(key=lambda x: x[0])
         sorted_lines.append(line)
+
     assert([(25, 75), (74, 26)] in sorted_lines)
     assert([(25, 25), (74, 74)] in sorted_lines)
 
+    # Execute with default theta
+    tf.probabilistic_hough_line(img, line_length=10, line_gap=3)
+
+
+def test_probabilistic_hough_bad_input():
+    img = np.zeros(100)
+    img[10] = 1
+
+    # Expected error, img must be 2D
+    assert_raises(ValueError, tf.probabilistic_hough_line, img)
+
 
 def test_hough_line_peaks():
     img = np.zeros((100, 150), dtype=int)