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Code Issues Packages Projects Releases Wiki Activity

Handle more warnings and reset io plugins as needed

Browse Source 
Reset plugins prior to running collections test

Handle warnings in morphology pkg

Add __init__ for morpohology tests

Handle warnings for novice pkg

Handle warnings for restoration pkg

Handle warnings for segmentation pkg

Handle warnings for _shared pkg

Handle warnings for transform pkg

Handle warnings for util pkg

Handle warnings in viewer module
This commit is contained in:
Steven Silvester
2014-12-23 16:48:16 -06:00
parent 9e8f91930e
commit 0debedd82c
21 changed files with 161 additions and 75 deletions
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skimage/segmentation/tests/__init__.py
+2
View File
@@ -0,0 +1,2 @@
import warnings
warnings.simplefilter('error')
skimage/segmentation/tests/test_random_walker.py
+39 -19
View File
@@ -1,6 +1,7 @@
import numpy as np
from skimage.segmentation import random_walker
from skimage.transform import resize
from skimage._shared.utils import all_warnings
def make_2d_syntheticdata(lx, ly=None):
@@ -74,10 +75,12 @@ def test_2d_cg():
lx = 70
ly = 100
data, labels = make_2d_syntheticdata(lx, ly)
labels_cg = random_walker(data, labels, beta=90, mode='cg')
with all_warnings(): # cg mode
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',
with all_warnings(): # cg mode
full_prob = random_walker(data, labels, beta=90, mode='cg',
return_full_prob=True)
assert (full_prob[1, 25:45, 40:60] >=
full_prob[0, 25:45, 40:60]).all()
@@ -89,10 +92,12 @@ def test_2d_cg_mg():
lx = 70
ly = 100
data, labels = make_2d_syntheticdata(lx, ly)
labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg')
with all_warnings(): # pyamg optional
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',
with all_warnings(): # pyamg optional
full_prob = random_walker(data, labels, beta=90, mode='cg_mg',
return_full_prob=True)
assert (full_prob[1, 25:45, 40:60] >=
full_prob[0, 25:45, 40:60]).all()
@@ -106,7 +111,8 @@ def test_types():
data, labels = make_2d_syntheticdata(lx, ly)
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')
with all_warnings(): # pyamg optional
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
@@ -139,7 +145,8 @@ def test_3d():
n = 30
lx, ly, lz = n, n, n
data, labels = make_3d_syntheticdata(lx, ly, lz)
labels = random_walker(data, labels, mode='cg')
with all_warnings(): # cg mode
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
@@ -152,7 +159,8 @@ def test_3d_inactive():
old_labels = np.copy(labels)
labels[5:25, 26:29, 26:29] = -1
after_labels = np.copy(labels)
labels = random_walker(data, labels, mode='cg')
with all_warnings(): # cg mode
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
@@ -162,9 +170,12 @@ def test_multispectral_2d():
lx, ly = 70, 100
data, labels = make_2d_syntheticdata(lx, ly)
data = data[..., np.newaxis].repeat(2, axis=-1) # Expect identical output
multi_labels = random_walker(data, labels, mode='cg', multichannel=True)
with all_warnings(): # cg mode
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')
with all_warnings(): # cg mode
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
@@ -175,9 +186,12 @@ def test_multispectral_3d():
lx, ly, lz = n, n, n
data, labels = make_3d_syntheticdata(lx, ly, lz)
data = data[..., np.newaxis].repeat(2, axis=-1) # Expect identical output
multi_labels = random_walker(data, labels, mode='cg', multichannel=True)
with all_warnings(): # cg mode
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')
with all_warnings(): # cg mode
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
@@ -203,7 +217,8 @@ def test_spacing_0():
lz // 4 - small_l // 8] = 2
# Test with `spacing` kwarg
labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg',
with all_warnings(): # cg mode
labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg',
spacing=(1., 1., 0.5))
assert (labels_aniso[13:17, 13:17, 7:9] == 2).all()
@@ -230,8 +245,9 @@ def test_spacing_1():
# Test with `spacing` kwarg
# First, anisotropic along Y
labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg',
spacing=(1., 2., 1.))
with all_warnings(): # using cd mode
labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg',
spacing=(1., 2., 1.))
assert (labels_aniso[13:17, 26:34, 13:17] == 2).all()
# Rescale `data` along X axis
@@ -249,9 +265,10 @@ def test_spacing_1():
lz // 2 - small_l // 4] = 2
# Anisotropic along X
labels_aniso2 = random_walker(data_aniso,
labels_aniso2,
mode='cg', spacing=(2., 1., 1.))
with all_warnings(): # cg mode
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()
@@ -259,14 +276,17 @@ 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)
with all_warnings(): # using provided labels
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)
with all_warnings(): # using provided labels
test = random_walker(img, labels, return_full_prob=True)
np.testing.assert_array_equal(test, expected)