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PEP8 compliance, removed scaling, different data parsing.

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This commit represents all recommended changes since the last
commit, notably:

* PEP8 compliance (in new sections; a few old ones still
  noncompliant w/indentations)

* Moved `depth` kwarg to end of list and in docstring.
  Clarified `depth` docstring, and added section in Notes
  further explaining this parameter.

* Added section in Notes warning that for multichannel inputs,
  all channels are combined during scaling.  The user must
  separately normalize each channel prior to calling
  random_walker()

* New method for parsing data, allowing more elegant gradient
  calculation code. Probably also more extensible. The 2D
  multispectral case forced this change.

* New test: `test_multispectral_2d()`
This commit is contained in:
JDWarner
2012-08-31 14:14:46 -05:00
parent 99238c44a5
commit e8ddcefae3
2 changed files with 61 additions and 59 deletions
Download Patch File Download Diff File Expand all files Collapse all files
skimage/segmentation/random_walker_segmentation.py
+43 -54
View File
@@ -67,23 +67,17 @@ def _make_graph_edges_3d(n_x, n_y, n_z):
def _compute_weights_3d(data, beta=130, eps=1.e-6, depth=1.,
multichannel=False):
# Weight calculation is main difference in multispectral version
# Original gradient**2 replaced with sqrt( sum of gradients**2 )
if not multichannel:
gradients = _compute_gradients_3d( data, depth=depth )**2
else:
for channel in range(data.shape[-1]):
if channel == 0:
gradients = _compute_gradients_3d(data[..., channel],
depth=depth)**2
else:
gradients += _compute_gradients_3d(data[..., channel],
depth=depth)**2
# Original gradient**2 replaced with sum of gradients ** 2
gradients = 0
for channel in range(0, data.shape[-1]):
gradients += _compute_gradients_3d(data[..., channel],
depth=depth) ** 2
# All channels considered together in this standard deviation
beta /= 10 * data.std()
if multichannel:
# New final term in beta to give == results in trivial case where
# multiple identical spectra are passed.
beta /= np.sqrt( data.shape[-1] )
beta /= np.sqrt(data.shape[-1])
gradients *= beta
weights = np.exp(- gradients)
weights += eps
@@ -166,10 +160,7 @@ def _mask_edges_weights(edges, weights, mask):
def _build_laplacian(data, mask=None, beta=50, depth=1., multichannel=False):
if not multichannel:
l_x, l_y, l_z = data.shape
else:
l_x, l_y, l_z = data.shape[0], data.shape[1], data.shape[2]
l_x, l_y, l_z = data.shape[:3]
edges = _make_graph_edges_3d(l_x, l_y, l_z)
weights = _compute_weights_3d(data, beta=beta, eps=1.e-10, depth=depth,
multichannel=multichannel)
@@ -183,21 +174,21 @@ def _build_laplacian(data, mask=None, beta=50, depth=1., multichannel=False):
#----------- Random walker algorithm --------------------------------
def random_walker(data, labels, beta=130, depth=1., mode='bf', tol=1.e-3,
copy=True, multichannel=False, scaling='all',
return_full_prob=False):
def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
multichannel=False, return_full_prob=False, depth=1.):
"""
Multichannel random walker algorithm for segmentation from markers.
Random walker algorithm for segmentation from markers, for gray-level or
multichannel images.
Parameters
----------
data : array_like
Image to be segmented in phases. Gray-level`data` can be two- or
Image to be segmented in phases. Gray-level `data` can be two- or
three-dimensional; multichannel data can be three- or four-
dimensional (requires multichannel=True) with the highest
dimension denoting channels. Data spacing is assumed isotropic
unless depth keyword argument is used.
dimensional (multichannel=True) with the highest dimension denoting
channels. Data spacing is assumed isotropic unless depth keyword
argument is used.
labels : array of ints, of same shape as `data`
Array of seed markers labeled with different positive integers
@@ -211,11 +202,6 @@ def random_walker(data, labels, beta=130, depth=1., mode='bf', tol=1.e-3,
Penalization coefficient for the random walker motion
(the greater `beta`, the more difficult the diffusion).
depth : float, default 1.
Correction for non-isotropic voxel depths in 3D volumes.
Default (1.) implies isotropy. This factor is derived as follows:
depth = (slice thickness) / (in-plane voxel dimension)
mode : {'bf', 'cg_mg', 'cg'} (default: 'bf')
Mode for solving the linear system in the random walker
algorithm.
@@ -250,21 +236,17 @@ def random_walker(data, labels, beta=130, depth=1., mode='bf', tol=1.e-3,
If True, input data is parsed as multichannel data (see 'data' above
for proper input format in this case)
scaling : string, default 'all'
Controls input scaling if multichannel=True (otherwise no effect).
- 'all' (default): Data from all channels is combined when scaling
input data to the range [0,1] as type np.float64. Recommended
option for RGB(A) inputs.
- 'separate': Each channel is scaled individually, separate from the
others, to the range [0,1]. Select this if the channels are very
different, for example if one were x-ray CT and another MRI data.
return_full_prob : bool, default False
If True, the probability that a pixel belongs to each of the labels
will be returned, instead of only the most likely label.
depth : float, default 1.
Correction for non-isotropic voxel depths in 3D volumes.
Default (1.) implies isotropy. This factor is derived as follows:
depth = (out-of-plane voxel spacing) / (in-plane voxel spacing), where
in-plane voxel spacing represents the first two spatial dimensions and
out-of-plane voxel spacing represents the third spatial dimension.
Returns
-------
@@ -286,6 +268,16 @@ def random_walker(data, labels, beta=130, depth=1., mode='bf', tol=1.e-3,
Notes
-----
Multichannel inputs are scaled with all channel data combined. Ensure all
channels are separately normalized prior to running this algorithm.
The `depth` argument is specifically for certain types of 3-dimensional
volumes which, due to how they were acquired, have different spacing
along in-plane and out-of-plane dimensions. This is commonly encountered
in medical imaging. The `depth` argument corrects gradients calculated
along the third spatial dimension for the otherwise inherent assumption
that all points are equally spaced.
The algorithm was first proposed in *Random walks for image
segmentation*, Leo Grady, IEEE Trans Pattern Anal Mach Intell.
2006 Nov;28(11):1768-83.
@@ -346,21 +338,18 @@ def random_walker(data, labels, beta=130, depth=1., mode='bf', tol=1.e-3,
"""
# Parse input data
if not multichannel:
# We work with 3-D arrays of floats
# We work with 4-D arrays of floats
dims = data.shape
data = np.atleast_3d( img_as_float(data) )
data = np.atleast_3d(img_as_float(data))
data.shape += (1,)
else:
dims = data[..., 0].shape
data = np.atleast_3d( data ) # Should never be needed
if scaling.lower().strip() == 'all':
data = img_as_float( data )
else:
newdata = np.zeros(data.shape, dtype=np.float64)
for channel in range( data.shape[-1] ):
newdata[..., channel] = img_as_float( data[..., channel] )
del data
data = newdata
del newdata
assert multichannel and data.ndim > 2, 'For multichannel input, data \
must have >= 3 dimensions.'
data = img_as_float(data)
if data.ndim == 3:
data.shape += (1,)
data = data.transpose((0, 1, 3, 2))
if copy:
labels = np.copy(labels)
@@ -409,7 +398,7 @@ def random_walker(data, labels, beta=130, depth=1., mode='bf', tol=1.e-3,
if return_full_prob:
labels = labels.astype(np.float)
X = np.array([_clean_labels_ar(Xline, labels,
copy=True).reshape(dims) for Xline in X])
copy=True).reshape(dims) for Xline in X])
for i in range(1, int(labels.max()) + 1):
mask_i = np.squeeze(labels == i)
X[i - 1, mask_i] = 1
@@ -429,7 +418,7 @@ def _solve_bf(lap_sparse, B, return_full_prob=False):
lap_sparse = lap_sparse.tocsc()
solver = sparse.linalg.factorized(lap_sparse.astype(np.double))
X = np.array([solver(np.array((-B[i]).todense()).ravel())\
for i in range(len(B))])
for i in range(len(B))])
if not return_full_prob:
X = np.argmax(X, axis=0)
return X
skimage/segmentation/tests/test_random_walker.py
+18 -5
View File
@@ -86,6 +86,7 @@ def test_2d_cg_mg():
full_prob[0, 25:45, 40:60]).all()
return data, labels_cg_mg
def test_types():
lx = 70
ly = 100
@@ -96,6 +97,7 @@ def test_types():
assert (labels_cg_mg[25:45, 40:60] == 2).all()
return data, labels_cg_mg
def test_reorder_labels():
lx = 70
ly = 100
@@ -106,7 +108,6 @@ def test_reorder_labels():
return data, labels_bf
def test_2d_inactive():
lx = 70
ly = 100
@@ -139,14 +140,26 @@ def test_3d_inactive():
return data, labels, old_labels, after_labels
def test_multispectral():
def test_multispectral_2d():
lx, ly = 70, 100
data, labels = make_2d_syntheticdata(lx, ly)
data2 = data.copy()
data.shape += (1,)
data = data.repeat(2, axis=2) # Result should be identical
multi_labels = random_walker(data, labels, mode='cg', multichannel=True)
single_labels = random_walker(data2, labels, mode='cg')
assert (multi_labels.reshape(labels.shape)[25:45, 40:60] == 2).all()
return data, multi_labels, single_labels, labels
def test_multispectral_3d():
n = 30
lx, ly, lz = n, n, n
data, labels = make_3d_syntheticdata( lx, ly, lz )
data, labels = make_3d_syntheticdata(lx, ly, lz)
data.shape += (1,)
data = data.repeat(2, axis=3) # Result should be identical
data = data.repeat(2, axis=3) # Result should be identical
multi_labels = random_walker(data, labels, mode='cg', multichannel=True)
single_labels = random_walker(data[:,:,:,0], labels, mode='cg')
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()
return data, multi_labels, single_labels, labels