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Merge pull request #500 from JDWarner/fix_preserve_rw_input_shape

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BUG: Preserve input image shape upon executing `random_walker`.
This commit is contained in:
Stefan van der Walt
2013-04-14 21:27:55 -07:00
parent baae1d9a48 e3c3a69560
commit 73e73c4bfb
2 changed files with 43 additions and 30 deletions
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skimage/segmentation/random_walker_segmentation.py
+10 -9
View File
@@ -69,7 +69,7 @@ def _compute_weights_3d(data, beta=130, eps=1.e-6, depth=1.,
gradients = 0
for channel in range(0, data.shape[-1]):
gradients += _compute_gradients_3d(data[..., channel],
depth=depth) ** 2
depth=depth) ** 2
# All channels considered together in this standard deviation
beta /= 10 * data.std()
if multichannel:
@@ -334,17 +334,18 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
# Parse input data
if not multichannel:
# We work with 4-D arrays of floats
assert data.ndim > 1 and data.ndim < 4, 'For non-multichannel input, \
data must be of dimension 2 \
or 3.'
dims = data.shape
data = np.atleast_3d(img_as_float(data))
data.shape += (1,)
data = np.atleast_3d(img_as_float(data))[..., np.newaxis]
else:
dims = data[..., 0].shape
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))
data = data[..., np.newaxis].transpose((0, 1, 3, 2))
if copy:
labels = np.copy(labels)
@@ -378,9 +379,9 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
if mode == 'cg_mg':
if not amg_loaded:
warnings.warn(
"""pyamg (http://code.google.com/p/pyamg/)) is needed to use
this mode, but is not installed. The 'cg' mode will be used
instead.""")
"""pyamg (http://code.google.com/p/pyamg/)) is needed to use
this mode, but is not installed. The 'cg' mode will be used
instead.""")
X = _solve_cg(lap_sparse, B, tol=tol,
return_full_prob=return_full_prob)
else:
@@ -411,7 +412,7 @@ 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())\
X = np.array([solver(np.array((-B[i]).todense()).ravel())
for i in range(len(B))])
if not return_full_prob:
X = np.argmax(X, axis=0)
skimage/segmentation/tests/test_random_walker.py
+33 -21
View File
@@ -1,15 +1,11 @@
import numpy as np
from skimage.segmentation import random_walker
try:
import pyamg
amg_loaded = True
except ImportError:
amg_loaded = False
def make_2d_syntheticdata(lx, ly=None):
if ly is 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,
@@ -29,6 +25,7 @@ def make_3d_syntheticdata(lx, ly=None, lz=None):
ly = lx
if lz is 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,
@@ -40,8 +37,8 @@ def make_3d_syntheticdata(lx, ly=None, lz=None):
# 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
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
@@ -54,17 +51,21 @@ def test_2d_bf():
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)
return_full_prob=True)
assert (full_prob_bf[1, 25:45, 40:60] >=
full_prob_bf[0, 25:45, 40:60]).all()
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)
return_full_prob=True)
assert (full_prob_bf[1, 25:45, 40:60] >=
full_prob_bf[0, 25:45, 40:60]).all()
full_prob_bf[0, 25:45, 40:60]).all()
assert len(full_prob_bf) == 3
assert data.shape == labels.shape
def test_2d_cg():
lx = 70
@@ -72,10 +73,12 @@ def test_2d_cg():
data, labels = make_2d_syntheticdata(lx, ly)
labels_cg = random_walker(data, labels, beta=90, mode='cg')
assert (labels_cg[25:45, 40:60] == 2).all()
assert data.shape == labels.shape
full_prob = random_walker(data, labels, beta=90, mode='cg',
return_full_prob=True)
return_full_prob=True)
assert (full_prob[1, 25:45, 40:60] >=
full_prob[0, 25:45, 40:60]).all()
full_prob[0, 25:45, 40:60]).all()
assert data.shape == labels.shape
return data, labels_cg
@@ -85,10 +88,12 @@ def test_2d_cg_mg():
data, labels = make_2d_syntheticdata(lx, ly)
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
full_prob = random_walker(data, labels, beta=90, mode='cg_mg',
return_full_prob=True)
return_full_prob=True)
assert (full_prob[1, 25:45, 40:60] >=
full_prob[0, 25:45, 40:60]).all()
full_prob[0, 25:45, 40:60]).all()
assert data.shape == labels.shape
return data, labels_cg_mg
@@ -100,6 +105,7 @@ def test_types():
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()
assert data.shape == labels.shape
return data, labels_cg_mg
@@ -110,6 +116,7 @@ def test_reorder_labels():
labels[labels == 2] = 4
labels_bf = random_walker(data, labels, beta=90, mode='bf')
assert (labels_bf[25:45, 40:60] == 2).all()
assert data.shape == labels.shape
return data, labels_bf
@@ -121,6 +128,7 @@ def test_2d_inactive():
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
@@ -130,6 +138,7 @@ def test_3d():
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
@@ -142,18 +151,19 @@ def test_3d_inactive():
after_labels = np.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_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
data = data[..., np.newaxis].repeat(2, axis=-1) # Expect identical output
multi_labels = random_walker(data, labels, mode='cg', multichannel=True)
single_labels = random_walker(data2, labels, mode='cg')
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
@@ -161,14 +171,16 @@ def test_multispectral_3d():
n = 30
lx, ly, lz = n, n, n
data, labels = make_3d_syntheticdata(lx, ly, lz)
data.shape += (1,)
data = data.repeat(2, axis=3) # Result should be identical
data = data[..., np.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
if __name__ == '__main__':
from numpy import testing
testing.run_module_suite()