def test_one(self):
shape = (128, 128, 128)
points = tuple(np.array(s // 2) for s in shape)
binary = synthetic.points_to_binary(points=points, shape=shape)
self.assertEqual(binary.shape, shape)
self.assertTrue(np.all(binary[points] == 255))
self.assertTrue(np.all(binary[0, 0, 0] == 0))
def test_random(self):
n = 100
shape = (128, 128, 128)
points = synthetic.random_points(n=n, shape=shape)
binary = synthetic.points_to_binary(points=points, shape=shape)
self.assertEqual(binary.shape, shape)
self.assertTrue(np.all(binary[points] == 255))
self.assertTrue(binary.min() == 0)
def watershed_centers(image, centers, mask, **watershed_kwargs):
seeds = points_to_binary(tuple(centers.T), image.shape, cval=1)
markers = ndi.label(seeds)[0]
labels = morphology.watershed(-image,
markers,
mask=mask,
**watershed_kwargs)
return labels
def test_one(self):
shape = (64, 64, 64)
sigmas = [1, 2, 3, 4]
offset = 1
targets = [0.22313017, 0.68728930, 0.84648174, 0.91051036]
points = tuple(np.array(s // 2) for s in shape)
test_points = tuple(np.array(s // 2 + offset) for s in shape)
binary = synthetic.points_to_binary(points=points, shape=shape)
for sigma, target in zip(sigmas, targets):
blobs = synthetic.binary_to_blobs(binary, sigma)
self.assertEqual(blobs[points], 1)
self.assertAlmostEqual(blobs[test_points], target)
def test_random(self):
n = 20
shape = (128, 128, 128)
sigmas = [1, 2, 3, 4]
thresh = 0.7
points = synthetic.random_points(n, shape)
binary = synthetic.points_to_binary(points, shape)
old_nb_foreground = 0
for sigma in sigmas:
blobs = synthetic.binary_to_blobs(binary, sigma)
self.assertEqual(blobs.shape, shape)
self.assertAlmostEqual(blobs.max(), 1)
self.assertAlmostEqual(blobs.min(), 0)
idx = np.where(blobs.ravel() > thresh)[0]
new_nb_foreground = len(idx)
self.assertTrue(old_nb_foreground < new_nb_foreground)
old_nb_foreground = new_nb_foreground