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https://github.com/wassname/scikit-image.git
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ENH: Clean up whitespace and remove class in ctmf tests.
This commit is contained in:
Stefan van der Walt
@@ -5,95 +5,94 @@ from numpy.testing import *
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from scikits.image.filter import median_filter
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class TestMedianFilter():
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def test_00_00_zeros(self):
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'''The median filter on an array of all zeros should be zero'''
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result = median_filter(np.zeros((10,10)), np.ones((10,10),bool), 3)
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assert np.all(result == 0)
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def test_00_01_all_masked(self):
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'''Test a completely masked image
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Regression test of IMG-1029'''
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result = median_filter(np.zeros((10,10)), np.zeros((10,10), bool), 3)
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assert (np.all(result == 0))
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def test_00_02_all_but_one_masked(self):
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mask = np.zeros((10,10), bool)
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mask[5,5] = True
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result = median_filter(np.zeros((10,10)), mask, 3)
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def test_01_01_mask(self):
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'''The median filter, masking a single value'''
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img = np.zeros((10,10))
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img[5,5] = 1
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mask = np.ones((10,10),bool)
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mask[5,5] = False
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result = median_filter(img, mask, 3)
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assert (np.all(result[mask] == 0))
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assert_equal(result[5,5], 1)
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def test_02_01_median(self):
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'''A median filter larger than the image = median of image'''
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np.random.seed(0)
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img = np.random.uniform(size=(9,9))
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result = median_filter(img, np.ones((9,9),bool), 20)
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assert_equal(result[0,0], np.median(img))
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assert (np.all(result == np.median(img)))
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def test_02_02_median_bigger(self):
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'''Use an image of more than 255 values to test approximation'''
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np.random.seed(0)
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img = np.random.uniform(size=(20,20))
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result = median_filter(img, np.ones((20,20),bool),40)
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sorted = np.ravel(img)
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sorted.sort()
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min_acceptable = sorted[198]
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max_acceptable = sorted[202]
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assert (np.all(result >= min_acceptable))
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assert (np.all(result <= max_acceptable))
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def test_03_01_shape(self):
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'''Make sure the median filter is the expected octagonal shape'''
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radius = 5
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a_2 = int(radius / 2.414213)
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i,j = np.mgrid[-10:11,-10:11]
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octagon = np.ones((21,21), bool)
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#
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# constrain the octagon mask to be the points that are on
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# the correct side of the 8 edges
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#
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octagon[i < -radius] = False
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octagon[i > radius] = False
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octagon[j < -radius] = False
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octagon[j > radius] = False
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octagon[i+j < -radius-a_2] = False
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octagon[j-i > radius+a_2] = False
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octagon[i+j > radius+a_2] = False
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octagon[i-j > radius+a_2] = False
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np.random.seed(0)
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img = np.random.uniform(size=(21,21))
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result = median_filter(img, np.ones((21,21),bool), radius)
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sorted = img[octagon]
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sorted.sort()
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min_acceptable = sorted[len(sorted)/2-1]
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max_acceptable = sorted[len(sorted)/2+1]
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assert (result[10,10] >= min_acceptable)
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assert (result[10,10] <= max_acceptable)
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def test_04_01_half_masked(self):
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'''Make sure that the median filter can handle large masked areas.'''
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img = np.ones((20, 20))
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mask = np.ones((20, 20),bool)
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mask[10:, :] = False
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img[~ mask] = 2
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img[1, 1] = 0 # to prevent short circuit for uniform data.
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result = median_filter(img, mask, 5)
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# in partial coverage areas, the result should be only from the masked pixels
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assert (np.all(result[:14, :] == 1))
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# in zero coverage areas, the result should be the lowest valud in the valid area
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assert (np.all(result[15:, :] == np.min(img[mask])))
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def test_00_00_zeros():
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'''The median filter on an array of all zeros should be zero'''
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result = median_filter(np.zeros((10,10)), np.ones((10,10),bool), 3)
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assert np.all(result == 0)
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def test_00_01_all_masked():
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'''Test a completely masked image
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Regression test of IMG-1029'''
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result = median_filter(np.zeros((10,10)), np.zeros((10,10), bool), 3)
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assert (np.all(result == 0))
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def test_00_02_all_but_one_masked():
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mask = np.zeros((10,10), bool)
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mask[5,5] = True
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result = median_filter(np.zeros((10,10)), mask, 3)
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def test_01_01_mask():
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'''The median filter, masking a single value'''
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img = np.zeros((10,10))
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img[5,5] = 1
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mask = np.ones((10,10),bool)
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mask[5,5] = False
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result = median_filter(img, mask, 3)
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assert (np.all(result[mask] == 0))
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assert_equal(result[5,5], 1)
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def test_02_01_median():
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'''A median filter larger than the image = median of image'''
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np.random.seed(0)
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img = np.random.uniform(size=(9,9))
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result = median_filter(img, np.ones((9,9),bool), 20)
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assert_equal(result[0,0], np.median(img))
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assert (np.all(result == np.median(img)))
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def test_02_02_median_bigger():
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'''Use an image of more than 255 values to test approximation'''
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np.random.seed(0)
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img = np.random.uniform(size=(20,20))
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result = median_filter(img, np.ones((20,20),bool),40)
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sorted = np.ravel(img)
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sorted.sort()
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min_acceptable = sorted[198]
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max_acceptable = sorted[202]
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assert (np.all(result >= min_acceptable))
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assert (np.all(result <= max_acceptable))
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def test_03_01_shape():
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'''Make sure the median filter is the expected octagonal shape'''
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radius = 5
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a_2 = int(radius / 2.414213)
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i,j = np.mgrid[-10:11,-10:11]
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octagon = np.ones((21,21), bool)
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#
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# constrain the octagon mask to be the points that are on
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# the correct side of the 8 edges
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#
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octagon[i < -radius] = False
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octagon[i > radius] = False
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octagon[j < -radius] = False
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octagon[j > radius] = False
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octagon[i+j < -radius-a_2] = False
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octagon[j-i > radius+a_2] = False
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octagon[i+j > radius+a_2] = False
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octagon[i-j > radius+a_2] = False
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np.random.seed(0)
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img = np.random.uniform(size=(21,21))
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result = median_filter(img, np.ones((21,21),bool), radius)
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sorted = img[octagon]
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sorted.sort()
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min_acceptable = sorted[len(sorted)/2-1]
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max_acceptable = sorted[len(sorted)/2+1]
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assert (result[10,10] >= min_acceptable)
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assert (result[10,10] <= max_acceptable)
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def test_04_01_half_masked():
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'''Make sure that the median filter can handle large masked areas.'''
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img = np.ones((20, 20))
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mask = np.ones((20, 20),bool)
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mask[10:, :] = False
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img[~ mask] = 2
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img[1, 1] = 0 # to prevent short circuit for uniform data.
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result = median_filter(img, mask, 5)
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# in partial coverage areas, the result should be only from the masked pixels
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assert (np.all(result[:14, :] == 1))
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# in zero coverage areas, the result should be the lowest valud in the valid area
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assert (np.all(result[15:, :] == np.min(img[mask])))
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if __name__ == "__main__":
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run_module_suite()
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