Supports skimage_range. Displays deprecated warning when used. Two test cases to test this functionality.

DEPENDS.txt

@@ -2,7 +2,7 @@ Build Requirements
 ------------------
 * `Python >= 2.6 <http://python.org>`__
 * `Numpy >= 1.7.2 <http://numpy.scipy.org/>`__
-* `Cython >= 0.21 <http://www.cython.org/>`__
+* `Cython >= 0.23 <http://www.cython.org/>`__
 * `Six >=1.4 <https://pypi.python.org/pypi/six>`__
 * `SciPy >=0.9 <http://scipy.org>`__
 

skimage/restoration/_denoise.py

@@ -3,12 +3,12 @@ import numpy as np
 from math import ceil
 from .. import img_as_float
 from ..restoration._denoise_cy import _denoise_bilateral, _denoise_tv_bregman
-from .._shared.utils import _mode_deprecations
+from .._shared.utils import _mode_deprecations, skimage_deprecation, warn
 import warnings
 
 
 def denoise_bilateral(image, win_size=None, sigma_color=None, sigma_spatial=1,
-                      bins=10000, mode='constant', cval=0, multichannel=True):
+                      bins=10000, mode='constant', cval=0, multichannel=True, sigma_range=None):
     """Denoise image using bilateral filter.
 
     This is an edge-preserving and noise reducing denoising filter. It averages
@@ -29,7 +29,7 @@ def denoise_bilateral(image, win_size=None, sigma_color=None, sigma_spatial=1,
     win_size : int
         Window size for filtering.
         If win_size is not specified, it is calculated as max(5, 2*ceil(3*sigma_spatial)+1)
-    sigma_range : float
+    sigma_color : float
         Standard deviation for grayvalue/color distance (radiometric
         similarity). A larger value results in averaging of pixels with larger
         radiometric differences. Note, that the image will be converted using
@@ -101,6 +101,14 @@ def denoise_bilateral(image, win_size=None, sigma_color=None, sigma_spatial=1,
                              "``multichannel=True`` for 2-D RGB "
                              "images.".format(image.shape))
 
+    if sigma_range is not None:
+        warn('`sigma_range` have been deprecated in favor of '
+             '`sigma_color`. The `sigma_range` keyword argument '
+             'will be removed in v0.14', skimage_deprecation)
+
+        #If sigma_range is provided, assign it to sigma_color
+        sigma_color = sigma_range
+
     if win_size is None:
         win_size = max(5, 2*int(ceil(3*sigma_spatial))+1)
 

skimage/restoration/tests/test_denoise.py

@@ -4,7 +4,6 @@ from numpy.testing import run_module_suite, assert_raises, assert_equal
 from skimage import restoration, data, color, img_as_float, measure
 from skimage._shared._warnings import expected_warnings
 
-
 np.random.seed(1234)
 
 
@@ -212,6 +211,31 @@ def test_denoise_bilateral_nan():
     out = restoration.denoise_bilateral(img, multichannel=False)
     assert_equal(img, out)
 
+def test_denoise_sigma_range():
+    img = checkerboard_gray.copy()
+    # add some random noise
+    img += 0.5 * img.std() * np.random.rand(*img.shape)
+    img = np.clip(img, 0, 1)
+    out1 = restoration.denoise_bilateral(img, sigma_color=0.1,
+                                         sigma_spatial=20, multichannel=False)
+    with expected_warnings('`sigma_range` have been deprecated in favor of `sigma_color`. '
+                           'The `sigma_range` keyword argument will be removed in v0.14'):
+        out2 = restoration.denoise_bilateral(img, sigma_range=0.1,
+                                             sigma_spatial=20, multichannel=False)
+    assert_equal(out1, out2)
+
+def test_denoise_sigma_range_and_sigma_color():
+    img = checkerboard_gray.copy()
+    # add some random noise
+    img += 0.5 * img.std() * np.random.rand(*img.shape)
+    img = np.clip(img, 0, 1)
+    out1 = restoration.denoise_bilateral(img, sigma_color=0.1,
+                                         sigma_spatial=20, multichannel=False)
+    with expected_warnings('`sigma_range` have been deprecated in favor of `sigma_color`. '
+                           'The `sigma_range` keyword argument will be removed in v0.14'):
+        out2 = restoration.denoise_bilateral(img, sigma_color=0.2, sigma_range=0.1,
+                                             sigma_spatial=20, multichannel=False)
+    assert_equal(out1, out2)
 
 def test_nl_means_denoising_2d():
     img = np.zeros((40, 40))