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FIX: Shortcut output or catch trivial cases in random_walker

Browse Source 
Also:
* New tests to cover these new checks
* Improvements to docstrings and user warnings
* Generalize handling of `sampling` in accordance with docstring
* Some extra whitespace to improve readability
This commit is contained in:
Josh Warner (Mac)
2014-04-18 18:47:20 -05:00
parent 7161175e43
commit 7847287f46
2 changed files with 95 additions and 27 deletions
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skimage/segmentation/random_walker_segmentation.py
+51 -25
View File
@@ -9,7 +9,7 @@ significantly the performance.
"""
import warnings
from numbers import Number
import numpy as np
from scipy import sparse, ndimage
@@ -216,14 +216,14 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
beta : float
Penalization coefficient for the random walker motion
(the greater `beta`, the more difficult the diffusion).
mode : {'bf', 'cg_mg', 'cg'} (default: 'bf')
Mode for solving the linear system in the random walker
algorithm.
mode : string, available options {'cg_mg', 'cg', 'bf'}
Mode for solving the linear system in the random walker algorithm.
If no preference given, automatically attempt to use the fastest
option available ('cg_mg' from pyamg >> 'cg' with UMFPACK > 'bf').
- 'bf' (brute force, default): an LU factorization of the Laplacian is
- 'bf' (brute force): an LU factorization of the Laplacian is
computed. This is fast for small images (<1024x1024), but very slow
(due to the memory cost) and memory-consuming for big images (in 3-D
for example).
and memory-intensive for large images (e.g., 3-D volumes).
- 'cg' (conjugate gradient): the linear system is solved iteratively
using the Conjugate Gradient method from scipy.sparse.linalg. This is
less memory-consuming than the brute force method for large images,
@@ -316,11 +316,11 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
Examples
--------
>>> a = np.zeros((10, 10)) + 0.2*np.random.random((10, 10))
>>> a = np.zeros((10, 10)) + 0.2 * np.random.random((10, 10))
>>> a[5:8, 5:8] += 1
>>> b = np.zeros_like(a)
>>> b[3,3] = 1 #Marker for first phase
>>> b[6,6] = 2 #Marker for second phase
>>> b[3, 3] = 1 # Marker for first phase
>>> b[6, 6] = 2 # Marker for second phase
>>> random_walker(a, b)
array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
@@ -334,7 +334,7 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32)
"""
# Parse input data
if mode is None:
if amg_loaded:
mode = 'cg_mg'
@@ -351,40 +351,64 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
'You may also install pyamg and run the random_walker '
'function in "cg_mg" mode (see docstring).')
# Spacing kwarg checks
if spacing is None:
spacing = (1., 1., 1.)
elif len(spacing) == 3:
pass
else:
raise ValueError('Input argument `spacing` incorrect, should be an '
'iterable of length 3.')
if (labels == 0).sum() == 0:
warnings.warn('Random walker only segments unlabeled areas, where '
'labels == 0. No zero valued areas in labels were '
'found. Returning provided labels.')
if return_full_prob:
# Find and iterate over valid labels
unique_labels = np.unique(labels)
unique_labels = unique_labels[unique_labels > 0]
out_labels = np.empty(labels.shape + (0, ))
for i in unique_labels:
out_labels = np.concatenate(
(out_labels, (labels == i)[..., np.newaxis]), axis=-1)
else:
out_labels = labels
return out_labels
# Parse input data
if not multichannel:
# We work with 4-D arrays of floats
if data.ndim < 1 or data.ndim > 4:
if data.ndim < 2 or data.ndim > 3:
raise ValueError('For non-multichannel input, data must be of '
'dimension 2 or 3.')
dims = data.shape
dims = data.shape # To reshape final labeled result
data = np.atleast_3d(img_as_float(data))[..., np.newaxis]
else:
if data.ndim < 2:
raise ValueError('For multichannel input, data must have >= 3 '
if data.ndim < 3:
raise ValueError('For multichannel input, data must have 3 or 4 '
'dimensions.')
dims = data[..., 0].shape
dims = data[..., 0].shape # To reshape final labeled result
data = img_as_float(data)
if data.ndim == 3:
data = data[..., np.newaxis].transpose((0, 1, 3, 2))
# Spacing kwarg checks
if spacing is None:
spacing = (1., ) * 3
elif len(spacing) == len(dims):
for i in spacing:
if not isinstance(i, Number):
raise ValueError('Input `spacing` contained %s, which is not '
'a number.' % (i))
if len(spacing) == 2:
spacing += (1., )
else:
raise ValueError('Input argument `spacing` incorrect, should be an '
'iterable of length 3.')
if copy:
labels = np.copy(labels)
label_values = np.unique(labels)
# Reorder label values to have consecutive integers (no gaps)
if np.any(np.diff(label_values) != 1):
mask = labels >= 0
labels[mask] = rank_order(labels[mask])[0].astype(labels.dtype)
labels = labels.astype(np.int32)
# If the array has pruned zones, be sure that no isolated pixels
# exist between pruned zones (they could not be determined)
if np.any(labels < 0):
@@ -399,6 +423,7 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
lap_sparse = _build_laplacian(data, spacing, beta=beta,
multichannel=multichannel)
lap_sparse, B = _buildAB(lap_sparse, labels)
# We solve the linear system
# lap_sparse X = B
# where X[i, j] is the probability that a marker of label i arrives
@@ -420,6 +445,7 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
if mode == 'bf':
X = _solve_bf(lap_sparse, B,
return_full_prob=return_full_prob)
# Clean up results
if return_full_prob:
labels = labels.astype(np.float)
skimage/segmentation/tests/test_random_walker.py
+44 -2
View File
@@ -255,6 +255,48 @@ def test_spacing_1():
assert (labels_aniso2[26:34, 13:17, 13:17] == 2).all()
def test_trivial_cases():
# When all voxels are labeled
img = np.ones((10, 10))
labels = np.ones((10, 10))
pass_through = random_walker(img, labels)
np.testing.assert_array_equal(pass_through, labels)
# When all voxels are labeled AND return_full_prob is True
labels[:, :5] = 3
expected = np.concatenate(((labels == 1)[..., np.newaxis],
(labels == 3)[..., np.newaxis]), axis=2)
test = random_walker(img, labels, return_full_prob=True)
np.testing.assert_array_equal(test, expected)
def test_bad_inputs():
# Too few dimensions
img = np.ones(10)
labels = np.arange(10)
np.testing.assert_raises(ValueError, random_walker, img, labels)
np.testing.assert_raises(ValueError,
random_walker, img, labels, multichannel=True)
# Too many dimensions
img = np.random.normal(size=(3, 3, 3, 3, 3))
labels = np.arange(3 ** 5).reshape(img.shape)
np.testing.assert_raises(ValueError, random_walker, img, labels)
np.testing.assert_raises(ValueError,
random_walker, img, labels, multichannel=True)
# Spacing incorrect length
img = np.random.normal(size=(10, 10))
labels = np.zeros((10, 10))
labels[2, 4] = 2
labels[6, 8] = 5
np.testing.assert_raises(ValueError,
random_walker, img, labels, spacing=(1,))
# Spacing contains unacceptable information
np.testing.assert_raises(
ValueError, random_walker, img, labels, spacing=(1, 'chickens'))
if __name__ == '__main__':
from numpy import testing
testing.run_module_suite()
np.testing.run_module_suite()