def test_03_01_shape():
'''Make sure the median filter is the expected octagonal shape'''
radius = 5
a_2 = int(radius / 2.414213)
i,j = np.mgrid[-10:11,-10:11]
octagon = np.ones((21,21), bool)
#
# constrain the octagon mask to be the points that are on
# the correct side of the 8 edges
#
octagon[i < -radius] = False
octagon[i > radius] = False
octagon[j < -radius] = False
octagon[j > radius] = False
octagon[i+j < -radius-a_2] = False
octagon[j-i > radius+a_2] = False
octagon[i+j > radius+a_2] = False
octagon[i-j > radius+a_2] = False
np.random.seed(0)
img = np.random.uniform(size=(21,21))
result = median_filter(img, np.ones((21,21),bool), radius)
sorted = img[octagon]
sorted.sort()
min_acceptable = sorted[len(sorted)/2-1]
max_acceptable = sorted[len(sorted)/2+1]
assert (result[10,10] >= min_acceptable)
assert (result[10,10] <= max_acceptable)
def test_02_01_median():
'''A median filter larger than the image = median of image'''
np.random.seed(0)
img = np.random.uniform(size=(9,9))
result = median_filter(img, np.ones((9,9),bool), 20)
assert_equal(result[0,0], np.median(img))
assert (np.all(result == np.median(img)))
def test_01_01_mask():
'''The median filter, masking a single value'''
img = np.zeros((10,10))
img[5,5] = 1
mask = np.ones((10,10),bool)
mask[5,5] = False
result = median_filter(img, mask, 3)
assert (np.all(result[mask] == 0))
assert_equal(result[5,5], 1)
def test_02_02_median_bigger():
'''Use an image of more than 255 values to test approximation'''
np.random.seed(0)
img = np.random.uniform(size=(20,20))
result = median_filter(img, np.ones((20,20),bool),40)
sorted = np.ravel(img)
sorted.sort()
min_acceptable = sorted[198]
max_acceptable = sorted[202]
assert (np.all(result >= min_acceptable))
assert (np.all(result <= max_acceptable))
def test_04_01_half_masked():
'''Make sure that the median filter can handle large masked areas.'''
img = np.ones((20, 20))
mask = np.ones((20, 20),bool)
mask[10:, :] = False
img[~ mask] = 2
img[1, 1] = 0 # to prevent short circuit for uniform data.
result = median_filter(img, mask, 5)
# in partial coverage areas, the result should be only from the masked pixels
assert (np.all(result[:14, :] == 1))
# in zero coverage areas, the result should be the lowest valud in the valid area
assert (np.all(result[15:, :] == np.min(img[mask])))
def test_default_values():
img = (np.random.random((20, 20)) * 255).astype(np.uint8)
mask = np.ones((20, 20), dtype=np.uint8)
result1 = median_filter(img, mask, radius=1, percent=50)
result2 = median_filter(img)
assert_array_equal(result1, result2)
def test_00_00_zeros():
'''The median filter on an array of all zeros should be zero'''
result = median_filter(np.zeros((10,10)), np.ones((10,10),bool), 3)
assert np.all(result == 0)
def test_00_02_all_but_one_masked():
mask = np.zeros((10,10), bool)
mask[5,5] = True
result = median_filter(np.zeros((10,10)), mask, 3)
def test_00_01_all_masked():
'''Test a completely masked image
Regression test of IMG-1029'''
result = median_filter(np.zeros((10,10)), np.zeros((10,10), bool), 3)
assert (np.all(result == 0))
