TST: Test both Cython and Python versions of the hough tf.

scikits/image/transform/hough_transform.py

@@ -3,7 +3,6 @@ __all__ = ['hough']
 from itertools import izip
 import numpy as np
 
-
 def _hough(img, theta=None):
     if img.ndim != 2:
         raise ValueError('The input image must be 2-D')
@@ -50,6 +49,7 @@ def _hough(img, theta=None):
 
     return out, theta, bins
 
+_py_hough = _hough
 
 # try to import and use the faster Cython version if it exists
 try:

scikits/image/transform/tests/test_hough_transform.py

@@ -1,7 +1,16 @@
 import numpy as np
 from numpy.testing import *
 
-from scikits.image.transform import *
+import scikits.image.transform as tf
+import scikits.image.transform.hough_transform as ht
+
+def append_desc(func, description):
+    """Append the test function ``func`` and append
+    ``description`` to its name.
+    """
+    func.description = func.__module__ + '.' + func.func_name + description
+
+    return func
 
 def test_hough():
     # Generate a test image
@@ -9,7 +18,7 @@ def test_hough():
     for i in range(25, 75):
         img[100 - i, i] = 1
 
-    out, angles, d = hough(img)
+    out, angles, d = tf.hough(img)
 
     y, x = np.where(out == out.max())
     dist = d[y[0]]
@@ -22,10 +31,18 @@ def test_hough_angles():
     img = np.zeros((10, 10))
     img[0, 0] = 1
 
-    out, angles, d = hough(img, np.linspace(0, 360, 10))
+    out, angles, d = tf.hough(img, np.linspace(0, 360, 10))
 
     assert_equal(len(angles), 10)
 
+def test_py_hough():
+    ht._hough, fast_hough = ht._py_hough, ht._hough
+
+    yield append_desc(test_hough, '_python')
+    yield append_desc(test_hough_angles, '_python')
+
+    tf._hough = fast_hough
+
 if __name__ == "__main__":
     run_module_suite()