def test_modes_in_random_walker():
img = np.zeros((30, 30, 30)) + 0.1 * np.random.randn(30, 30, 30)
img[9:21, 9:21, 9:21] = 1
img[10:20, 10:20, 10:20] = 0
labels = np.zeros_like(img)
labels[6, 6, 6] = 1
labels[14, 15, 16] = 2
# default mode = cg
random_walker_cg = _random_walker(img, labels, beta=90)
assert (random_walker_cg.reshape(img.shape)[6, 6, 6] == 1).all()
assert img.shape == random_walker_cg.shape
# test `mask` strategy of sub function _mask_edges_weights in laplacian
labels[5:25, 26:29, 26:29] = -1
random_walker_inactive = _random_walker(img, labels, beta=30)
def test_modes_in_random_walker():
img = np.zeros((30, 30, 30)) + 0.1 * np.random.randn(30, 30, 30)
img[9:21, 9:21, 9:21] = 1
img[10:20, 10:20, 10:20] = 0
labels = np.zeros_like(img)
labels[6, 6, 6] = 1
labels[14, 15, 16] = 2
# default mode = cg
random_walker_cg = _random_walker(img, labels, beta=90)
assert (random_walker_cg.reshape(img.shape)[6, 6, 6] == 1).all()
assert img.shape == random_walker_cg.shape
# test `mask` strategy of sub function _mask_edges_weights in laplacian
labels[5:25, 26:29, 26:29] = -1
random_walker_inactive = _random_walker(img, labels, beta=30)
def test_trivial_cases():
# When all voxels are labeled
img = np.ones((10, 10, 10))
labels = np.ones((10, 10, 10))
# It returns same labels which are provided
pass_through = _random_walker(img, labels)
np.testing.assert_array_equal(pass_through, labels)
def test_trivial_cases():
# When all voxels are labeled
img = np.ones((10, 10, 10))
labels = np.ones((10, 10, 10))
# It returns same labels which are provided
pass_through = _random_walker(img, labels)
np.testing.assert_array_equal(pass_through, labels)
def test_trivial_cases():
# When all voxels are labeled
img = np.ones((10, 10, 10))
labels = np.ones((10, 10, 10))
# It returns same labels which are provided
pass_through = _random_walker(img, labels)
np.testing.assert_array_equal(pass_through, labels)
# When there is no seed
# Case 1: only masked pixels
labels = -np.ones((10, 10, 10))
# It returns the labels
np.testing.assert_array_equal(_random_walker(img, labels), labels)
# Case 2: only unlabeled pixels
labels = np.zeros((10, 10, 10))
# It return the labels
np.testing.assert_array_equal(_random_walker(img, labels), labels)
def test_reorder_labels():
# When labels have non-consecutive integers, we make them consecutive
# by reordering them to make no gaps/differences between integers. We expect
# labels to be of same shape even if they are reordered.
# Issue #938, comment #14.
data = np.zeros((5, 5)) + 0.1 * np.random.randn(5, 5)
data[1:5, 1:5] = 1
labels = np.zeros_like(data)
labels[3, 3] = 1
labels[1, 4] = 4 # giving integer which is non-consecutive
labels = _random_walker(data, labels)
assert data.shape == labels.shape
def test_reorder_labels():
# When labels have non-consecutive integers, we make them consecutive
# by reordering them to make no gaps/differences between integers. We expect
# labels to be of same shape even if they are reordered.
# Issue #938, comment #14.
data = np.zeros((5, 5)) + 0.1 * np.random.randn(5, 5)
data[1:5, 1:5] = 1
labels = np.zeros_like(data)
labels[3, 3] = 1
labels[1, 4] = 4 # giving integer which is non-consecutive
labels = _random_walker(data, labels)
assert data.shape == labels.shape
def test_isolated_pixel():
data = np.random.random((3, 3))
# Build the following labels with an isolated seed
# in the bottom-right corner:
# array([[ 0., -1., -1.],
# [-1., -1., -1.],
# [-1., -1., 1.]])
labels = -np.ones((3, 3))
# Point
labels[0, 0] = 0
# Make a seed
labels[2, 2] = 1
# The expected result is:
# array([[ 0., -1., -1.],
# [-1., -1., -1.],
# [-1., -1., 1.]])
expected = np.array([[0., -1., -1.], [-1., -1., -1.], [-1., -1., 1.]])
np.testing.assert_array_equal(expected, _random_walker(data, labels))
def test_isolated_seed():
data = np.random.random((3, 3))
# Build the following labels with an isolated seed
# in the bottom-right corner:
# array([[ 0., 1., -1.],
# [-1., -1., -1.],
# [-1., -1., 2.]])
labels = -np.ones((3, 3))
# Make a seed
labels[0, 1] = 1
# Point next to the seed
labels[0, 0] = 0
# Set a seed in the middle, it is surrounded by masked pixels
labels[2, 2] = 2
# The expected result is:
# array([[ 1., 1., -1.],
# [-1., -1., -1.],
# [-1., -1., -1.]])
expected = np.array([[1., 1., -1.], [-1., -1., -1.], [-1., -1., -1.]])
np.testing.assert_array_equal(expected, _random_walker(data, labels))
def test_bad_inputs():
# Too few dimensions
img = np.ones(10)
labels = np.arange(10)
with pytest.raises(ValueError):
_random_walker(img, labels)
# Too many dimensions
rng = np.random.RandomState(42)
img = rng.normal(size=(3, 3, 3, 3, 3))
labels = np.arange(3 ** 5).reshape(img.shape)
with pytest.raises(ValueError):
_random_walker(img, labels)
# Spacing incorrect length
img = rng.normal(size=(10, 10))
labels = np.zeros((10, 10))
labels[2, 4] = 2
labels[6, 8] = 5
with pytest.raises(ValueError):
_random_walker(img, labels, spacing=(1,))
