Merge branch 'master' into v0.12.x

Conflicts:
	bento.info
	skimage/__init__.py

TODO.txt

@@ -13,7 +13,6 @@ Version 0.14
   parameters `(dist, theta)`, LineModelND has the more general parameters
   `(origin, direction)`.
 * Remove deprecated old syntax support for ``skimage.transform.integrate``.
-* Remove deprecated ``skimage.data.lena`` and corresponding data files.
 * Remove deprecated ``skimage.measure.structural_similarity`` alias and
   deprecation warning test for this alias.
 * Remove deprecated ``sigma_range`` kwargs in ``skimage.restoration.denoise_bilateral``

doc/source/api_changes.txt

@@ -14,6 +14,7 @@ Version 0.12
   `skimage.transform.PiecewiseAffineTransform.affines_*` attributes
   have been removed.
 - `skimage.filters.denoise_*` have moved to `skimage.restoration.denoise_*`.
+- `skimage.data.lena` has been removed.
 
 Version 0.11
 ------------

skimage/_build.py

@@ -1,7 +1,7 @@
 import sys
 import os
 import hashlib
-import subprocess
+from distutils.version import LooseVersion
 
 # WindowsError is not defined on unix systems
 try:
@@ -25,6 +25,10 @@ def cython(pyx_files, working_path=''):
         return
 
     try:
+        from Cython import __version__
+        if LooseVersion(__version__) < '0.23':
+            raise RuntimeError('Cython >= 0.23 needed to build scikit-image')
+
         from Cython.Build import cythonize
     except ImportError:
         # If cython is not found, we do nothing -- the build will make use of

skimage/data/__init__.py

@@ -61,13 +61,16 @@ def camera():
 def lena():
     """Colour "Lena" image.
 
+    **This image has been removed from scikit-image due to copyright
+    concerns.**
+
     The standard, yet sometimes controversial Lena test image was
     scanned from the November 1972 edition of Playboy magazine.  From
     an image processing perspective, this image is useful because it
     contains smooth, textured, shaded as well as detail areas.
 
     """
-    return load("lena.png")
+    raise RuntimeError("This image has been removed due to copyright concerns.")
 
 
 def astronaut():

skimage/data/tests/test_data.py

@@ -1,12 +1,11 @@
 import numpy as np
 import skimage.data as data
-from numpy.testing import assert_equal, assert_almost_equal
+from numpy.testing import assert_equal, assert_almost_equal, assert_raises
 
 
-def test_lena():
-    """ Test that "Lena" image can be loaded. """
-    lena = data.lena()
-    assert_equal(lena.shape, (512, 512, 3))
+def test_lena_removed():
+    """ Test that "Lena" has been removed """
+    assert_raises(RuntimeError, data.lena)
 
 
 def test_astronaut():

skimage/feature/tests/test_match.py

@@ -25,9 +25,9 @@ def test_binary_descriptors():
     assert_equal(matches, [[0, 0], [1, 1]])
 
 
-def test_binary_descriptors_lena_rotation_crosscheck_false():
+def test_binary_descriptors_rotation_crosscheck_false():
     """Verify matched keypoints and their corresponding masks results between
-    lena image and its rotated version with the expected keypoint pairs with
+    image and its rotated version with the expected keypoint pairs with
     cross_check disabled."""
     img = data.astronaut()
     img = rgb2gray(img)
@@ -60,9 +60,9 @@ def test_binary_descriptors_lena_rotation_crosscheck_false():
     assert_equal(matches[:, 1], exp_matches2)
 
 
-def test_binary_descriptors_lena_rotation_crosscheck_true():
+def test_binary_descriptors_rotation_crosscheck_true():
     """Verify matched keypoints and their corresponding masks results between
-    lena image and its rotated version with the expected keypoint pairs with
+    image and its rotated version with the expected keypoint pairs with
     cross_check enabled."""
     img = data.astronaut()
     img = rgb2gray(img)

skimage/io/collection.py

@@ -143,11 +143,11 @@ class ImageCollection(object):
     >>> import skimage.io as io
     >>> from skimage import data_dir
 
-    >>> coll = io.ImageCollection(data_dir + '/lena*.png')
+    >>> coll = io.ImageCollection(data_dir + '/chess*.png')
     >>> len(coll)
     2
     >>> coll[0].shape
-    (512, 512, 3)
+    (200, 200)
 
     >>> ic = io.ImageCollection('/tmp/work/*.png:/tmp/other/*.jpg')
 

skimage/morphology/tests/test_grey.py

@@ -4,66 +4,46 @@ import numpy as np
 from numpy import testing
 from scipy import ndimage as ndi
 
-from skimage import data_dir, img_as_uint
+from skimage import color, data, transform
+from skimage import img_as_uint, img_as_ubyte, data_dir
 from skimage.morphology import grey, selem
 from skimage._shared._warnings import expected_warnings
 
 
-lena = np.load(os.path.join(data_dir, 'lena_GRAY_U8.npy'))
-bw_lena = lena > 100
-
-
 class TestMorphology():
 
-    def morph_worker(self, img, fn, morph_func, strel_func):
-        matlab_results = np.load(os.path.join(data_dir, fn))
-        k = 0
-        for arrname in sorted(matlab_results):
-            expected_result = matlab_results[arrname]
-            mask = strel_func(k)
-            actual_result = morph_func(lena, mask)
-            testing.assert_equal(expected_result, actual_result)
-            k = k + 1
+    # These expected outputs were generated with skimage v0.12.1
+    # using:
+    #
+    #   from skimage.morphology.tests.test_grey import TestMorphology
+    #   import numpy as np
+    #   output = TestMorphology()._build_expected_output()
+    #   np.savez_compressed('gray_morph_output.npz', **output)
 
-    def test_erode_diamond(self):
-        self.morph_worker(lena, "diamond-erode-matlab-output.npz",
-                          grey.erosion, selem.diamond)
 
-    def test_dilate_diamond(self):
-        self.morph_worker(lena, "diamond-dilate-matlab-output.npz",
-                          grey.dilation, selem.diamond)
+    def _build_expected_output(self):
+        funcs = (grey.erosion, grey.dilation, grey.opening, grey.closing,
+                 grey.white_tophat, grey.black_tophat)
+        selems_2D = (selem.square, selem.diamond,
+                     selem.disk, selem.star)
 
-    def test_open_diamond(self):
-        self.morph_worker(lena, "diamond-open-matlab-output.npz",
-                          grey.opening, selem.diamond)
+        with expected_warnings(['Possible precision loss']):
+            image = img_as_ubyte(transform.downscale_local_mean(
+                color.rgb2gray(data.coffee()), (20, 20)))
 
-    def test_close_diamond(self):
-        self.morph_worker(lena, "diamond-close-matlab-output.npz",
-                          grey.closing, selem.diamond)
+        output = {}
+        for n in range(1, 4):
+            for strel in selems_2D:
+                for func in funcs:
+                    key = '{0}_{1}_{2}'.format(strel.__name__, n, func.__name__)
+                    output[key] = func(image, strel(n))
 
-    def test_tophat_diamond(self):
-        self.morph_worker(lena, "diamond-tophat-matlab-output.npz",
-                          grey.white_tophat, selem.diamond)
+        return output
 
-    def test_bothat_diamond(self):
-        self.morph_worker(lena, "diamond-bothat-matlab-output.npz",
-                          grey.black_tophat, selem.diamond)
-
-    def test_erode_disk(self):
-        self.morph_worker(lena, "disk-erode-matlab-output.npz",
-                          grey.erosion, selem.disk)
-
-    def test_dilate_disk(self):
-        self.morph_worker(lena, "disk-dilate-matlab-output.npz",
-                          grey.dilation, selem.disk)
-
-    def test_open_disk(self):
-        self.morph_worker(lena, "disk-open-matlab-output.npz",
-                          grey.opening, selem.disk)
-
-    def test_close_disk(self):
-        self.morph_worker(lena, "disk-close-matlab-output.npz",
-                          grey.closing, selem.disk)
+    def test_gray_morphology(self):
+        expected = dict(np.load(os.path.join(data_dir, 'gray_morph_output.npz')))
+        calculated = self._build_expected_output()
+        testing.assert_equal(expected, calculated)
 
 
 class TestEccentricStructuringElements():
@@ -119,6 +99,7 @@ class TestEccentricStructuringElements():
             tophat = grey.black_tophat(self.white_pixel, s)
             assert np.all(tophat == 0)
 
+
 def test_default_selem():
     functions = [grey.erosion, grey.dilation,
                  grey.opening, grey.closing,
@@ -142,6 +123,7 @@ def test_default_selem():
         im_test = function(image)
         yield testing.assert_array_equal, im_expected, im_test
 
+
 def test_3d_fallback_default_selem():
     # 3x3x3 cube inside a 7x7x7 image:
     image = np.zeros((7, 7, 7), np.bool)
@@ -154,6 +136,7 @@ def test_3d_fallback_default_selem():
     image_expected[2:5, 2:5, 2:5] = ndi.generate_binary_structure(3, 1)
     testing.assert_array_equal(opened, image_expected)
 
+
 def test_3d_fallback_cube_selem():
     # 3x3x3 cube inside a 7x7x7 image:
     image = np.zeros((7, 7, 7), np.bool)
@@ -165,6 +148,7 @@ def test_3d_fallback_cube_selem():
         new_image = function(image, cube)
         yield testing.assert_array_equal, new_image, image
 
+
 def test_3d_fallback_white_tophat():
     image = np.zeros((7, 7, 7), dtype=bool)
     image[2, 2:4, 2:4] = 1
@@ -178,6 +162,7 @@ def test_3d_fallback_white_tophat():
         image_expected = ndi.white_tophat(image,footprint=footprint)
     testing.assert_array_equal(new_image, image_expected)
 
+
 def test_3d_fallback_black_tophat():
     image = np.ones((7, 7, 7), dtype=bool)
     image[2, 2:4, 2:4] = 0
@@ -191,6 +176,7 @@ def test_3d_fallback_black_tophat():
         image_expected = ndi.black_tophat(image,footprint=footprint)
     testing.assert_array_equal(new_image, image_expected)
 
+
 def test_2d_ndimage_equivalence():
     image = np.zeros((9, 9), np.uint8)
     image[2:-2, 2:-2] = 128
@@ -207,6 +193,7 @@ def test_2d_ndimage_equivalence():
     testing.assert_array_equal(opened, ndimage_opened)
     testing.assert_array_equal(closed, ndimage_closed)
 
+
 # float test images
 im = np.array([[ 0.55,  0.72,  0.6 ,  0.54,  0.42],
                [ 0.65,  0.44,  0.89,  0.96,  0.38],
@@ -246,8 +233,9 @@ def test_float():
 
 
 def test_uint16():
-    im16, eroded16, dilated16, opened16, closed16 = (
-                    map(img_as_uint, [im, eroded, dilated, opened, closed]))
+    with expected_warnings(['Possible precision loss']):
+        im16, eroded16, dilated16, opened16, closed16 = (
+            map(img_as_uint, [im, eroded, dilated, opened, closed]))
     np.testing.assert_allclose(grey.erosion(im16), eroded16)
     np.testing.assert_allclose(grey.dilation(im16), dilated16)
     np.testing.assert_allclose(grey.opening(im16), opened16)