def _all_orientations_helper(arr, expected_result=None):
# different 90 degree rotations of a 3D volume and test if number of objects are as
# expected after thinning
for reoriented in reorders(arr):
thinned_volume = thin_volume.get_thinned(reoriented, mode='constant')
result = _get_count_objects(thinned_volume)
nose.tools.assert_equal(result, expected_result)
def _getCountObjects(image):
# count number of 26 or 8 connected objects in the skeletonized image
if image.ndim == 2:
skel = getskeletonize2d(image)
label_skel, countObjects = ndimage.measurements.label(skel, structure=np.ones((3, 3), dtype=bool))
else:
skel = get_thinned(image)
label_skel, countObjects = ndimage.measurements.label(skel, structure=np.ones((3, 3, 3), dtype=bool))
return countObjects
def test_wide_lines():
test_images = get_thick_lines()
for image, thickness, center in test_images:
result = thin_volume.get_thinned(image, mode='reflect')
nose.tools.assert_equal(result.sum(), image.sum() // thickness)
nzc = image_tools.list_of_tuples_of_val(result, 1)
prev_slope = np.subtract(nzc[0], nzc[1])
for p1, p2 in zip(nzc[:-1], nzc[1:]):
slope = np.subtract(p1, p2)
# all points lie on same straight line
np.testing.assert_array_equal(slope, prev_slope)
# all points are in the center of the thick line
nose.tools.assert_in(center, p1)
nose.tools.assert_in(center, p2)
def setThinningOutput(self, mode="reflect"):
# Thinning output
self.skeletonStack = get_thinned(self.inputStack, mode)
def test_rectangles():
test_images = get_stationary_3d_rectangles(width=0)
for image in test_images:
np.testing.assert_array_equal(
image, thin_volume.get_thinned(image, mode='constant'))
def test_random_images():
for image in get_rand_images():
thin_volume.get_thinned(image, mode='constant')