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Added multispectral random walker test.

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Since the multispectral path is equivalent except for gradient calcs,
only one test case is needed.  This test is modeled on the 3-D
non-multispectral version.  If deemed necessary, adding a 2-D case
would be simple.
This commit is contained in:
JDWarner
2012-08-27 13:41:41 -05:00
parent feca48cc49
commit 682d0535cd
1 changed files with 12 additions and 0 deletions
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skimage/segmentation/tests/test_random_walker.py
+12
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@@ -138,6 +138,18 @@ def test_3d_inactive():
assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all()
return data, labels, old_labels, after_labels
def test_multispectral():
n = 30
lx, ly, lz = n, n, n
data, labels = make_3d_syntheticdata( lx, ly, lz )
data = [data, data] # Result should be identical
multi_labels = random_walker(data, labels, mode='cg')
single_labels = random_walker(data[0], labels, mode='cg')
assert (multi_labels.reshape(data[0].shape)[13:17, 13:17, 13:17] == 2).all()
assert (single_labels.reshape(data[0].shape)[13:17, 13:17, 13:17] == 2).all()
return data, multi_labels, single_labels, labels
if __name__ == '__main__':
from numpy import testing
testing.run_module_suite()