def test_prob_tol():
np.random.seed(0)
a = cp.array(np.random.random((7, 7)))
mask = -np.ones(a.shape)
# This pixel is an isolated seed
mask[1, 1] = 1
# Unlabeled pixels
mask[3:, 3:] = 0
# Seeds connected to unlabeled pixels
mask[4, 4] = 2
mask[6, 6] = 1
mask = cp.array(mask)
with expected_warnings(['The probability range is outside']):
res = random_walker(a, mask, return_full_prob=True)
# Lower beta, no warning is expected.
res = random_walker(a, mask, return_full_prob=True, beta=10)
assert res[0, 1, 1] == 1
assert res[1, 1, 1] == 0
# Being more prob_tol tolerant, no warning is expected.
res = random_walker(a, mask, return_full_prob=True, prob_tol=1e-1)
assert res[0, 1, 1] == 1
assert res[1, 1, 1] == 0
# Reduced tol, no warning is expected.
res = random_walker(a, mask, return_full_prob=True, tol=1e-9)
assert res[0, 1, 1] == 1
assert res[1, 1, 1] == 0
def test_2d_bf():
lx = 70
ly = 100
data, labels = make_2d_syntheticdata(lx, ly)
labels_bf = random_walker(data, labels, beta=90, mode='bf')
assert (labels_bf[25:45, 40:60] == 2).all()
assert data.shape == labels.shape
full_prob_bf = random_walker(data,
labels,
beta=90,
mode='bf',
return_full_prob=True)
assert (full_prob_bf[1, 25:45, 40:60] >= full_prob_bf[0, 25:45,
40:60]).all()
assert data.shape == labels.shape
# Now test with more than two labels
labels[55, 80] = 3
full_prob_bf = random_walker(data,
labels,
beta=90,
mode='bf',
return_full_prob=True)
assert (full_prob_bf[1, 25:45, 40:60] >= full_prob_bf[0, 25:45,
40:60]).all()
assert len(full_prob_bf) == 3
assert data.shape == labels.shape
def test_length2_spacing():
# If this passes without raising an exception (warnings OK), the new
# spacing code is working properly.
cp.random.seed(42)
img = cp.ones((10, 10)) + 0.2 * cp.random.normal(size=(10, 10))
labels = cp.zeros((10, 10), dtype=cp.uint8)
labels[2, 4] = 1
labels[6, 8] = 4
random_walker(img, labels, spacing=cp.array((1., 2.)))
def test_multispectral_3d():
n = 30
lx, ly, lz = n, n, n
data, labels = make_3d_syntheticdata(lx, ly, lz)
data = data[..., cp.newaxis].repeat(2, axis=-1) # Expect identical output
multi_labels = random_walker(data, labels, mode='cg', multichannel=True)
assert data[..., 0].shape == labels.shape
single_labels = random_walker(data[..., 0], labels, mode='cg')
assert (multi_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all()
assert (single_labels.reshape(labels.shape)[13:17, 13:17,
13:17] == 2).all()
assert data[..., 0].shape == labels.shape
return data, multi_labels, single_labels, labels
def test_multispectral_2d():
lx, ly = 70, 100
data, labels = make_2d_syntheticdata(lx, ly)
data = data[..., cp.newaxis].repeat(2, axis=-1) # Expect identical output
with expected_warnings(['The probability range is outside']):
multi_labels = random_walker(data,
labels,
mode='cg',
multichannel=True)
assert data[..., 0].shape == labels.shape
single_labels = random_walker(data[..., 0], labels, mode='cg')
assert (multi_labels.reshape(labels.shape)[25:45, 40:60] == 2).all()
assert data[..., 0].shape == labels.shape
return data, multi_labels, single_labels, labels
def test_2d_cg_j():
lx = 70
ly = 100
data, labels = make_2d_syntheticdata(lx, ly)
labels_cg = random_walker(data, labels, beta=90, mode='cg_j')
assert (labels_cg[25:45, 40:60] == 2).all()
assert data.shape == labels.shape
full_prob = random_walker(data,
labels,
beta=90,
mode='cg_j',
return_full_prob=True)
assert (full_prob[1, 25:45, 40:60] >= full_prob[0, 25:45, 40:60]).all()
assert data.shape == labels.shape
def test_spacing_1():
n = 30
lx, ly, lz = n, n, n
data, _ = make_3d_syntheticdata(lx, ly, lz)
# Rescale `data` along Y axis
# `resize` is not yet 3D capable, so this must be done by looping in 2D.
data_aniso = cp.zeros((n, n * 2, n))
for i, yz in enumerate(data):
data_aniso[i, :, :] = resize(yz, (n * 2, n),
mode='constant',
anti_aliasing=False)
# Generate new labels
small_l = int(lx // 5)
labels_aniso = cp.zeros_like(data_aniso)
labels_aniso[lx // 5, ly // 5, lz // 5] = 1
labels_aniso[lx // 2 + small_l // 4, ly - small_l // 2,
lz // 2 - small_l // 4] = 2
# Test with `spacing` kwarg
# First, anisotropic along Y
labels_aniso = random_walker(data_aniso,
labels_aniso,
mode='cg',
spacing=cp.array((1., 2., 1.)))
assert (labels_aniso[13:17, 26:34, 13:17] == 2).all()
# Rescale `data` along X axis
# `resize` is not yet 3D capable, so this must be done by looping in 2D.
data_aniso = cp.zeros((n, n * 2, n))
for i in range(data.shape[1]):
data_aniso[i, :, :] = resize(data[:, 1, :], (n * 2, n),
mode='constant',
anti_aliasing=False)
# Generate new labels
small_l = int(lx // 5)
labels_aniso2 = cp.zeros_like(data_aniso)
labels_aniso2[lx // 5, ly // 5, lz // 5] = 1
labels_aniso2[lx - small_l // 2, ly // 2 + small_l // 4,
lz // 2 - small_l // 4] = 2
# Anisotropic along X
labels_aniso2 = random_walker(data_aniso,
labels_aniso2,
mode='cg',
spacing=cp.array((2., 1., 1.)))
assert (labels_aniso2[26:34, 13:17, 13:17] == 2).all()
def test_spacing_0():
n = 30
lx, ly, lz = n, n, n
data, _ = make_3d_syntheticdata(lx, ly, lz)
# Rescale `data` along Z axis
data_aniso = cp.zeros((n, n, n // 2))
for i, yz in enumerate(data):
data_aniso[i, :, :] = resize(yz, (n, n // 2),
mode='constant',
anti_aliasing=False)
# Generate new labels
small_l = int(lx // 5)
labels_aniso = cp.zeros_like(data_aniso)
labels_aniso[lx // 5, ly // 5, lz // 5] = 1
labels_aniso[lx // 2 + small_l // 4, ly // 2 - small_l // 4,
lz // 4 - small_l // 8] = 2
# Test with `spacing` kwarg
labels_aniso = random_walker(data_aniso,
labels_aniso,
mode='cg',
spacing=cp.array((1.0, 1.0, 0.5)))
assert (labels_aniso[13:17, 13:17, 7:9] == 2).all()
def test_3d():
n = 30
lx, ly, lz = n, n, n
data, labels = make_3d_syntheticdata(lx, ly, lz)
labels = random_walker(data, labels, mode='cg')
assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all()
assert data.shape == labels.shape
return data, labels
def test_2d_cg_mg():
lx = 70
ly = 100
data, labels = make_2d_syntheticdata(lx, ly)
with expected_warnings(['"cg_mg" not available']):
labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg')
assert (labels_cg_mg[25:45, 40:60] == 2).all()
assert data.shape == labels.shape
with expected_warnings(['"cg_mg" not available']):
full_prob = random_walker(data,
labels,
beta=90,
mode='cg_mg',
return_full_prob=True)
assert (full_prob[1, 25:45, 40:60] >= full_prob[0, 25:45, 40:60]).all()
assert data.shape == labels.shape
return data, labels_cg_mg
def test_reorder_labels_cg():
lx = 70
ly = 100
data, labels = make_2d_syntheticdata(lx, ly)
labels[labels == 2] = 4
labels_bf = random_walker(data, labels, beta=90, mode='cg')
assert (labels_bf[25:45, 40:60] == 2).all()
assert data.shape == labels.shape
return data, labels_bf
def test_2d_inactive():
lx = 70
ly = 100
data, labels = make_2d_syntheticdata(lx, ly)
labels[10:20, 10:20] = -1
labels[46:50, 33:38] = -2
labels = random_walker(data, labels, beta=90)
assert (labels.reshape((lx, ly))[25:45, 40:60] == 2).all()
assert data.shape == labels.shape
return data, labels
def test_3d_inactive():
n = 30
lx, ly, lz = n, n, n
data, labels = make_3d_syntheticdata(lx, ly, lz)
old_labels = cp.copy(labels)
labels[5:25, 26:29, 26:29] = -1
after_labels = cp.copy(labels)
labels = random_walker(data, labels, mode='cg')
assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all()
assert data.shape == labels.shape
return data, labels, old_labels, after_labels
def test_types():
lx = 70
ly = 100
data, labels = make_2d_syntheticdata(lx, ly)
data = 255 * (data - data.min()) // (data.max() - data.min())
data = data.astype(np.uint8)
with expected_warnings(['"cg_mg" not available']):
labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg')
assert (labels_cg_mg[25:45, 40:60] == 2).all()
assert data.shape == labels.shape
return data, labels_cg_mg
def test_isolated_area():
np.random.seed(0)
a = cp.array(np.random.random((7, 7)))
mask = -np.ones(a.shape)
# This pixel is an isolated seed
mask[1, 1] = 0
# Unlabeled pixels
mask[3:, 3:] = 0
# Seeds connected to unlabeled pixels
mask[4, 4] = 2
mask[6, 6] = 1
mask = cp.array(mask)
# Test that no error is raised, and that labels of isolated seeds are OK
with expected_warnings(['The probability range is outside']):
res = random_walker(a, mask)
assert res[1, 1] == 0
with expected_warnings(['The probability range is outside']):
res = random_walker(a, mask, return_full_prob=True)
assert res[0, 1, 1] == 0
assert res[1, 1, 1] == 0
def test_trivial_cases():
# When all voxels are labeled
img = cp.ones((10, 10))
labels = cp.ones((10, 10))
with expected_warnings(["Returning provided labels"]):
pass_through = random_walker(img, labels)
cp.testing.assert_array_equal(pass_through, labels)
# When all voxels are labeled AND return_full_prob is True
labels[:, :5] = 3
expected = cp.concatenate(
((labels == 1)[..., cp.newaxis], (labels == 3)[..., cp.newaxis]),
axis=2)
with expected_warnings(["Returning provided labels"]):
test = random_walker(img, labels, return_full_prob=True)
cp.testing.assert_array_equal(test, expected)
# Unlabeled voxels not connected to seed, so nothing can be done
img = cp.full((10, 10), False)
object_A = np.array([(6, 7), (6, 8), (7, 7), (7, 8)])
object_B = np.array([(3, 1), (4, 1), (2, 2), (3, 2), (4, 2), (2, 3),
(3, 3)])
for x, y in np.vstack((object_A, object_B)):
img[y][x] = True
markers = cp.zeros((10, 10), dtype=cp.int8)
for x, y in object_B:
markers[y][x] = 1
markers[img == 0] = -1
with expected_warnings(["All unlabeled pixels are isolated"]):
output_labels = random_walker(img, markers)
assert cp.all(output_labels[markers == 1] == 1)
# Here 0-labeled pixels could not be determined (no connexion to seed)
assert cp.all(output_labels[markers == 0] == -1)
with expected_warnings(["All unlabeled pixels are isolated"]):
test = random_walker(img, markers, return_full_prob=True)
def test_bad_inputs():
# Too few dimensions
img = cp.ones(10)
labels = cp.arange(10)
with testing.raises(ValueError):
random_walker(img, labels)
with testing.raises(ValueError):
random_walker(img, labels, multichannel=True)
# Too many dimensions
np.random.seed(42)
img = cp.array(np.random.normal(size=(3, 3, 3, 3, 3)))
labels = cp.arange(3**5).reshape(img.shape)
with testing.raises(ValueError):
random_walker(img, labels)
with testing.raises(ValueError):
random_walker(img, labels, multichannel=True)
# Spacing incorrect length
img = cp.array(np.random.normal(size=(10, 10)))
labels = cp.zeros((10, 10))
labels[2, 4] = 2
labels[6, 8] = 5
with testing.raises(ValueError):
random_walker(img, labels, spacing=cp.array((1, )))
# Invalid mode
img = cp.array(np.random.normal(size=(10, 10)))
labels = cp.zeros((10, 10))
with testing.raises(ValueError):
random_walker(img, labels, mode='bad')