def test_03_02_all_masked(self):
# Regression test of 1286 - exception if totally masked image
#
i = np.linspace(-1, 1, 11)**2
i, j = i[:, np.newaxis], i[np.newaxis, :]
image = np.sqrt(i * i + j * j)
mask = np.zeros(image.shape, bool)
F.convex_hull_transform(image, levels=8, mask=mask)
def test_01_05_concave(self):
'''The convex hull transform of a concave figure is the convex hull'''
expected = np.zeros((10, 20))
expected[2:8, 7:14] = 1
image = expected.copy()
image[4:6, 7:10] = .5
self.assertTrue(np.all(F.convex_hull_transform(image) == expected))
def test_04_02_two_pass(self):
'''Test the two-pass at multiple levels chunk looping'''
np.random.seed(42)
#
# Make an image that monotonically decreases from the center
#
i, j = np.mgrid[-50:51, -50:51].astype(float) / 100.
image = 1 - np.sqrt(i**2 + j**2)
#
# Riddle it with holes
#
holes = np.random.uniform(size=image.shape) < .01
image[holes] = 0
result = F.convex_hull_transform(image,
levels=256,
chunksize=1000,
pass_cutoff=256)
expected = F.convex_hull_transform(image, pass_cutoff=256)
np.testing.assert_equal(result, expected)
def test_02_01_two_levels(self):
'''Test operation on two grayscale levels'''
expected = np.zeros((20, 30))
expected[3:18, 3:27] = .5
expected[8:15, 10:20] = 1
image = expected.copy()
image[:, 15] = 0
image[10, :] = 0
# need an odd # of bins in order to have .5 be a bin
self.assertTrue(np.all(F.convex_hull_transform(image, 7) == expected))
def test_04_01_many_chunks(self):
'''Test the two-pass at a single level chunk looping'''
np.random.seed(41)
#
# Make an image that monotonically decreases from the center
#
i, j = np.mgrid[-50:51, -50:51].astype(float) / 100.
image = 1 - np.sqrt(i**2 + j**2)
expected = image.copy()
#
# Riddle it with holes
#
holes = np.random.uniform(size=image.shape) < .01
image[holes] = 0
result = F.convex_hull_transform(image,
levels=256,
chunksize=1000,
pass_cutoff=256)
diff = np.abs(result - expected)
self.assertTrue(np.sum(diff > 1 / 256.) <= np.sum(holes))
expected = F.convex_hull_transform(image, pass_cutoff=256)
np.testing.assert_equal(result, expected)
def test_03_01_masked(self):
'''Test operation on a masked image'''
expected = np.zeros((20, 30))
expected[3:18, 3:27] = .5
expected[8:15, 10:20] = 1
image = expected.copy()
image[:, 15] = 0
image[10, :] = 0
mask = np.ones((20, 30), bool)
mask[:, 0] = False
image[:, 0] = .75
result = F.convex_hull_transform(image, levels=7, mask=mask)
self.assertTrue(np.all(result == expected))
def test_01_04_convex(self):
'''The convex hull transform of a convex figure is itself'''
image = np.zeros((10, 20))
image[2:7, 7:14] = 1
self.assertTrue(np.all(F.convex_hull_transform(image) == image))
def test_01_03_line(self):
'''The convex hull transform of a line of foreground pixels is itself'''
image = np.zeros((10, 20))
image[5, 7:14] = 1
self.assertTrue(np.all(F.convex_hull_transform(image) == image))
def test_01_02_point(self):
'''The convex hull transform of 1 foreground pixel is itself'''
image = np.zeros((10, 20))
image[5, 10] = 1
self.assertTrue(np.all(F.convex_hull_transform(image) == image))
def test_01_01_zeros(self):
'''The convex hull transform of an array of identical values is itself'''
self.assertTrue(
np.all(F.convex_hull_transform(np.zeros((10, 20))) == 0))
