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ENH: spacing kwarg for random_walker and improved tests

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This commit is contained in:
Josh Warner (Mac)
2013-10-12 00:41:33 -05:00
parent e5e1918a2b
commit f25ca3a835
2 changed files with 46 additions and 36 deletions
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skimage/segmentation/random_walker_segmentation.py
+13 -5
View File
@@ -169,13 +169,13 @@ def _mask_edges_weights(edges, weights, mask):
# Reassign edges labels to 0, 1, ... edges_number - 1
order = np.searchsorted(np.unique(edges.ravel()),
np.arange(max_node_index + 1))
edges = order[edges]
edges = order[edges.astype(np.int64)]
return edges, weights
def _build_laplacian(data, spacing, mask=None, beta=50,
multichannel=False):
l_x, l_y, l_z = tuple(data.shape[i] * spacing[i] for i in range(3))
l_x, l_y, l_z = tuple(data.shape[i] for i in range(3))
edges = _make_graph_edges_3d(l_x, l_y, l_z)
weights = _compute_weights_3d(data, spacing, beta=beta, eps=1.e-10,
multichannel=multichannel)
@@ -257,7 +257,7 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
out-of-plane voxel spacing represents the third spatial dimension.
`depth` is deprecated as of 0.9, in favor of `spacing`.
spacing : iterable of floats
spacing between voxels in each spatial dimension. If `None`, then
Spacing between voxels in each spatial dimension. If `None`, then
the spacing between pixels/voxels in each dimension is assumed 1.
Returns
@@ -357,9 +357,17 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
'(see the docstrings)')
if depth != 1.:
warnings.warn('`depth` kwarg is deprecated, and will be removed in the'
' next major version. Use `spacing` instead.')
' next major release. Use `spacing` instead.')
# Spacing kwarg checks
if spacing is None:
spacing = (1., 1.) + (depth, )
spacing = (1., 1.) + (depth, )
elif len(spacing) == 2:
spacing = tuple(spacing) + (depth, )
elif len(spacing) == 3:
pass
else:
raise ValueError('Input argument `spacing` incorrect, see docstring.')
# Parse input data
if not multichannel:
skimage/segmentation/tests/test_random_walker.py
+33 -31
View File
@@ -8,16 +8,16 @@ def make_2d_syntheticdata(lx, ly=None):
ly = lx
np.random.seed(1234)
data = np.zeros((lx, ly)) + 0.1 * np.random.randn(lx, ly)
small_l = int(lx / 5)
data[lx / 2 - small_l:lx / 2 + small_l,
ly / 2 - small_l:ly / 2 + small_l] = 1
data[lx / 2 - small_l + 1:lx / 2 + small_l - 1,
ly / 2 - small_l + 1:ly / 2 + small_l - 1] = \
0.1 * np.random.randn(2 * small_l - 2, 2 * small_l - 2)
data[lx / 2 - small_l, ly / 2 - small_l / 8:ly / 2 + small_l / 8] = 0
small_l = int(lx // 5)
data[lx // 2 - small_l:lx // 2 + small_l,
ly // 2 - small_l:ly // 2 + small_l] = 1
data[lx // 2 - small_l + 1:lx // 2 + small_l - 1,
ly // 2 - small_l + 1:ly // 2 + small_l - 1] = (
0.1 * np.random.randn(2 * small_l - 2, 2 * small_l - 2))
data[lx // 2 - small_l, ly // 2 - small_l // 8:ly // 2 + small_l // 8] = 0
seeds = np.zeros_like(data)
seeds[lx / 5, ly / 5] = 1
seeds[lx / 2 + small_l / 4, ly / 2 - small_l / 4] = 2
seeds[lx // 5, ly // 5] = 1
seeds[lx // 2 + small_l // 4, ly // 2 - small_l // 4] = 2
return data, seeds
@@ -28,21 +28,23 @@ def make_3d_syntheticdata(lx, ly=None, lz=None):
lz = lx
np.random.seed(1234)
data = np.zeros((lx, ly, lz)) + 0.1 * np.random.randn(lx, ly, lz)
small_l = int(lx / 5)
data[lx / 2 - small_l:lx / 2 + small_l,
ly / 2 - small_l:ly / 2 + small_l,
lz / 2 - small_l:lz / 2 + small_l] = 1
data[lx / 2 - small_l + 1:lx / 2 + small_l - 1,
ly / 2 - small_l + 1:ly / 2 + small_l - 1,
lz / 2 - small_l + 1:lz / 2 + small_l - 1] = 0
small_l = int(lx // 5)
data[lx // 2 - small_l:lx // 2 + small_l,
ly // 2 - small_l:ly // 2 + small_l,
lz // 2 - small_l:lz // 2 + small_l] = 1
data[lx // 2 - small_l + 1:lx // 2 + small_l - 1,
ly // 2 - small_l + 1:ly // 2 + small_l - 1,
lz // 2 - small_l + 1:lz // 2 + small_l - 1] = 0
# make a hole
hole_size = np.max([1, small_l / 8])
data[lx / 2 - small_l,
ly / 2 - hole_size:ly / 2 + hole_size,
lz / 2 - hole_size:lz / 2 + hole_size] = 0
hole_size = np.max([1, small_l // 8])
data[lx // 2 - small_l,
ly // 2 - hole_size:ly // 2 + hole_size,
lz // 2 - hole_size:lz // 2 + hole_size] = 0
seeds = np.zeros_like(data)
seeds[lx / 5, ly / 5, lz / 5] = 1
seeds[lx / 2 + small_l / 4, ly / 2 - small_l / 4, lz / 2 - small_l / 4] = 2
seeds[lx // 5, ly // 5, lz // 5] = 1
seeds[lx // 2 + small_l // 4,
ly // 2 - small_l // 4,
lz // 2 - small_l // 4] = 2
return data, seeds
@@ -102,7 +104,7 @@ def test_types():
lx = 70
ly = 100
data, labels = make_2d_syntheticdata(lx, ly)
data = 255 * (data - data.min()) / (data.max() - data.min())
data = 255 * (data - data.min()) // (data.max() - data.min())
data = data.astype(np.uint8)
labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg')
assert (labels_cg_mg[25:45, 40:60] == 2).all()
@@ -236,19 +238,19 @@ def test_spacing():
# `resize` is not yet 3D capable, so this must be done by looping in 2D.
data_aniso = np.zeros((n, n * 2, n))
for i in range(data.shape[1]):
data_aniso[i, :, :] = resize(data[:, 1, :], (n * 1.5, n))
data_aniso[i, :, :] = resize(data[:, 1, :], (n * 2, n))
# Generate new labels
small_l = int(lx // 5)
labels_aniso = np.zeros_like(data_aniso)
labels_aniso[lx // 5, ly // 5, lz // 5] = 1
labels_aniso[lx - small_l // 2,
ly // 2 + small_l // 4,
lz // 2 - small_l // 4] = 2
labels_aniso2 = np.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(np.rollaxis(data_aniso, 1).copy(),
np.rollaxis(labels_aniso, 1).copy(),
labels_aniso2 = random_walker(data_aniso,
labels_aniso2,
mode='cg', spacing=(2., 1., 1.))
assert (labels_aniso2[26:34, 13:17, 13:17] == 2).all()