Fix, improve and extend histogram test cases

2026-07-06 05:16:40 +08:00 · 2012-11-10 09:43:15 +01:00
parent 7ea3a754e0
commit c00fedde6c
1 changed files with 89 additions and 46 deletions
@@ -1,57 +1,100 @@
-import sys
-print sys.path
-import skimage
-print skimage
-
-import unittest
-
 import numpy as np
-from skimage.filter import rank
+from numpy.testing import run_module_suite, assert_array_equal

-from skimage import data
-from skimage.morphology import cmorph,disk
 from skimage.filter.rank import _crank8, _crank16
-from skimage.filter.rank import _crank16_percentiles


-class TestSequenceFunctions(unittest.TestCase):
+def test_trivial_selem8():
+    # check that min, max and mean returns identity if structuring element
+    # contains only central pixel

-    def setUp(self):
-        pass
+    image = np.zeros((5, 5), dtype=np.uint8)
+    out = np.zeros_like(image)
+    mask = np.ones_like(image, dtype=np.uint8)
+    image[2,2] = 255
+    image[2,3] = 128
+    image[1,2] = 16

-    def test_trivial_selem(self):
-        # check that min, max and mean returns identity if structuring element contains only central pixel
-
-        a = np.zeros((5,5),dtype='uint8')
-        a[2,2] = 255
-        a[2,3] = 128
-        a[1,2] = 16
-        elem = np.asarray([[0,0,0],[0,1,0],[0,0,0]],dtype='uint8')
-        f = _crank8.mean(image=a,selem = elem,shift_x=0,shift_y=0)
-        np.testing.assert_array_equal(a,f)
-        f = _crank8.minimum(image=a,selem = elem,shift_x=0,shift_y=0)
-        np.testing.assert_array_equal(a,f)
-        f = _crank8.maximum(image=a,selem = elem,shift_x=0,shift_y=0)
-        np.testing.assert_array_equal(a,f)
-
-    def test_smallest_selem(self):
-        # check that min, max and mean returns identity if structuring element contains only central pixel
-
-        a = np.zeros((5,5),dtype='uint8')
-        a[2,2] = 255
-        a[2,3] = 128
-        a[1,2] = 16
-        elem = np.asarray([[1]],dtype='uint8')
-        f = _crank8.mean(image=a,selem = elem,shift_x=0,shift_y=0)
-        np.testing.assert_array_equal(a,f)
-        f = _crank8.minimum(image=a,selem = elem,shift_x=0,shift_y=0)
-        np.testing.assert_array_equal(a,f)
-        f = _crank8.maximum(image=a,selem = elem,shift_x=0,shift_y=0)
-        np.testing.assert_array_equal(a,f)
+    elem = np.array([[0, 0, 0], [0, 1, 0],[0, 0, 0]], dtype=np.uint8)
+    _crank8.mean(image=image, selem=elem, out=out, mask=mask,
+                 shift_x=0, shift_y=0)
+    assert_array_equal(image, out)
+    _crank8.minimum(image=image, selem=elem, out=out, mask=mask,
+                    shift_x=0, shift_y=0)
+    assert_array_equal(image, out)
+    _crank8.maximum(image=image, selem=elem, out=out, mask=mask,
+                    shift_x=0, shift_y=0)
+    assert_array_equal(image, out)


+def test_trivial_selem16():
+    # check that min, max and mean returns identity if structuring element
+    # contains only central pixel

-if __name__ == '__main__':
+    image = np.zeros((5, 5), dtype=np.uint16)
+    out = np.zeros_like(image)
+    mask = np.ones_like(image, dtype=np.uint8)
+    image[2,2] = 255
+    image[2,3] = 128
+    image[1,2] = 16

-    suite = unittest.TestLoader().loadTestsFromTestCase(TestSequenceFunctions)
-    unittest.TextTestRunner(verbosity=2).run(suite)
+    elem = np.array([[0, 0, 0], [0, 1, 0],[0, 0, 0]], dtype=np.uint8)
+    _crank16.mean(image=image, selem=elem, out=out, mask=mask,
+                  shift_x=0, shift_y=0)
+    assert_array_equal(image, out)
+    _crank16.minimum(image=image, selem=elem, out=out, mask=mask,
+                     shift_x=0, shift_y=0)
+    assert_array_equal(image, out)
+    _crank16.maximum(image=image, selem=elem, out=out, mask=mask,
+                     shift_x=0, shift_y=0)
+    assert_array_equal(image, out)
+
+
+def test_smallest_selem8():
+    # check that min, max and mean returns identity if structuring element
+    # contains only central pixel
+
+    image = np.zeros((5, 5), dtype=np.uint8)
+    out = np.zeros_like(image)
+    mask = np.ones_like(image, dtype=np.uint8)
+    image[2,2] = 255
+    image[2,3] = 128
+    image[1,2] = 16
+
+    elem = np.array([[1]], dtype=np.uint8)
+    _crank8.mean(image=image, selem=elem, out=out, mask=mask,
+                 shift_x=0, shift_y=0)
+    assert_array_equal(image, out)
+    _crank8.minimum(image=image, selem=elem, out=out, mask=mask,
+                    shift_x=0, shift_y=0)
+    assert_array_equal(image, out)
+    _crank8.maximum(image=image, selem=elem, out=out, mask=mask,
+                    shift_x=0, shift_y=0)
+    assert_array_equal(image, out)
+
+
+def test_smallest_selem16():
+    # check that min, max and mean returns identity if structuring element
+    # contains only central pixel
+
+    image = np.zeros((5, 5), dtype=np.uint16)
+    out = np.zeros_like(image)
+    mask = np.ones_like(image, dtype=np.uint8)
+    image[2,2] = 255
+    image[2,3] = 128
+    image[1,2] = 16
+
+    elem = np.array([[1]], dtype=np.uint8)
+    _crank16.mean(image=image, selem=elem, out=out, mask=mask,
+                  shift_x=0, shift_y=0)
+    assert_array_equal(image, out)
+    _crank16.minimum(image=image, selem=elem, out=out, mask=mask,
+                     shift_x=0, shift_y=0)
+    assert_array_equal(image, out)
+    _crank16.maximum(image=image, selem=elem, out=out, mask=mask,
+                     shift_x=0, shift_y=0)
+    assert_array_equal(image, out)
+
+
+if __name__ == "__main__":
+    run_module_suite()