def test_accuracy():
''' Verify that our implementation returns exactly the same as scikit
'''
base_dir = '/home/omar/data/DATA_NeoBrainS12/'
neo_subject = '30wCoronal/example2/'
# Read subject files
t2CurrentSubjectName = base_dir + 'trainingDataNeoBrainS12/'+neo_subject+'T2_1-1.nii.gz'
t2CurrentSubject_data = nib.load(t2CurrentSubjectName).get_data()
affineT2CS = nib.load(t2CurrentSubjectName).get_affine()
zoomsT2CS = nib.load(t2CurrentSubjectName).get_header().get_zooms()[:3]
n_zooms = (zoomsT2CS[0],zoomsT2CS[0],zoomsT2CS[0])
t2CurrentSubject_data,affineT2CS = reslice(t2CurrentSubject_data,affineT2CS,zoomsT2CS,n_zooms)
S = t2CurrentSubject_data.astype(np.float64)
max_radius = 4
D = SequencialSphereDilation(S)
for r in range(1, 1+max_radius):
D.expand(S)
expected = dilation(S, ball(r))
actual = D.get_current_dilation()
assert_array_equal(expected, actual)
expected = closing(S, ball(r))
actual = D.get_current_closing()
assert_array_equal(expected, actual)
def test_performance():
''' Compare execution time against scikit, sequencial closing case
'''
base_dir = '/home/omar/data/DATA_NeoBrainS12/'
neo_subject = '30wCoronal/example2/'
# Read subject files
t2CurrentSubjectName = base_dir + 'trainingDataNeoBrainS12/' + neo_subject + 'T2_1-1.nii.gz'
t2CurrentSubject_data = nib.load(t2CurrentSubjectName).get_data()
affineT2CS = nib.load(t2CurrentSubjectName).get_affine()
zoomsT2CS = nib.load(t2CurrentSubjectName).get_header().get_zooms()[:3]
# Step 1.4 - Resampling for isotropic voxels
n_zooms = (zoomsT2CS[0], zoomsT2CS[0], zoomsT2CS[0])
t2CurrentSubject_data, affineT2CS = reslice(t2CurrentSubject_data,
affineT2CS, zoomsT2CS, n_zooms)
S = t2CurrentSubject_data.astype(np.float64)
S = S[:S.shape[0] // 4, :S.shape[1] // 4, :S.shape[2] // 4]
###########compare times#########
# in-house
start = time.time()
max_radius = 11
D = SequencialSphereDilation(S)
for r in range(max_radius):
print('Computing radius %d...' % (r + 1, ))
D.expand(S)
actual = D.get_current_closing()
del actual
del D
end = time.time()
print('Elapsed (in-home): %f' % (end - start, ))
# scikit
start = time.time()
for r in range(max_radius):
print('Computing radius %d...' % (1 + r, ))
expected = closing(S, ball(1 + r))
del expected
end = time.time()
print('Elapsed (scikit): %f' % (end - start, ))
def test_accuracy():
''' Verify that our implementation returns exactly the same as scikit
'''
base_dir = '/home/omar/data/DATA_NeoBrainS12/'
neo_subject = '30wCoronal/example2/'
# Read subject files
t2CurrentSubjectName = base_dir + 'trainingDataNeoBrainS12/' + neo_subject + 'T2_1-1.nii.gz'
t2CurrentSubject_data = nib.load(t2CurrentSubjectName).get_data()
affineT2CS = nib.load(t2CurrentSubjectName).get_affine()
zoomsT2CS = nib.load(t2CurrentSubjectName).get_header().get_zooms()[:3]
n_zooms = (zoomsT2CS[0], zoomsT2CS[0], zoomsT2CS[0])
t2CurrentSubject_data, affineT2CS = reslice(t2CurrentSubject_data,
affineT2CS, zoomsT2CS, n_zooms)
S = t2CurrentSubject_data.astype(np.float64)
max_radius = 4
D = SequencialSphereDilation(S)
for r in range(1, 1 + max_radius):
D.expand(S)
expected = dilation(S, ball(r))
actual = D.get_current_dilation()
assert_array_equal(expected, actual)
expected = closing(S, ball(r))
actual = D.get_current_closing()
assert_array_equal(expected, actual)
def test_performance():
''' Compare execution time against scikit, sequencial closing case
'''
base_dir = '/home/omar/data/DATA_NeoBrainS12/'
neo_subject = '30wCoronal/example2/'
# Read subject files
t2CurrentSubjectName = base_dir + 'trainingDataNeoBrainS12/'+neo_subject+'T2_1-1.nii.gz'
t2CurrentSubject_data = nib.load(t2CurrentSubjectName).get_data()
affineT2CS = nib.load(t2CurrentSubjectName).get_affine()
zoomsT2CS = nib.load(t2CurrentSubjectName).get_header().get_zooms()[:3]
# Step 1.4 - Resampling for isotropic voxels
n_zooms = (zoomsT2CS[0],zoomsT2CS[0],zoomsT2CS[0])
t2CurrentSubject_data,affineT2CS = reslice(t2CurrentSubject_data,affineT2CS,zoomsT2CS,n_zooms)
S = t2CurrentSubject_data.astype(np.float64)
S = S[:S.shape[0]//4, :S.shape[1]//4, :S.shape[2]//4]
###########compare times#########
# in-house
start = time.time()
max_radius = 11
D = SequencialSphereDilation(S)
for r in range(max_radius):
print('Computing radius %d...'%(r+1,))
D.expand(S)
actual = D.get_current_closing()
del actual
del D
end = time.time()
print('Elapsed (in-home): %f'%(end-start,))
# scikit
start = time.time()
for r in range(max_radius):
print('Computing radius %d...'%(1+r,))
expected = closing(S, ball(1+r))
del expected
end = time.time()
print('Elapsed (scikit): %f'%(end-start,))
