def test_get_centerline_nurbs(img_ctl, expected, params):
"""Test centerline fitting using nurbs"""
img, img_sub = [img_ctl[0].copy(), img_ctl[1].copy()]
img_out, arr_out, arr_deriv_out, fit_results = get_centerline(
img_sub, ParamCenterline(algo_fitting='nurbs', minmax=False), verbose=VERBOSE)
assert np.median(find_and_sort_coord(img) - find_and_sort_coord(img_out)) == expected['median']
assert fit_results.laplacian_max < expected['laplacian']
def test_get_centerline_linear(img_ctl, expected):
"""Test centerline fitting using linear interpolation"""
deg = 3
img, img_sub = [img_ctl[0].copy(), img_ctl[1].copy()]
img_out, arr_out, _ = get_centerline(img_sub, algo_fitting='linear', param=ParamCenterline(degree=deg),
minmax=False, verbose=VERBOSE)
assert np.linalg.norm(find_and_sort_coord(img) - find_and_sort_coord(img_out)) < expected
def test_get_centerline_optic():
"""Test extraction of metrics aggregation across slices: All slices by default"""
fname_t2 = os.path.join(__sct_dir__, 'sct_testing_data/t2/t2.nii.gz'
) # install: sct_download_data -d sct_testing_data
img_t2 = Image(fname_t2)
# Add non-numerical values at the top corner of the image for testing purpose
img_t2.change_type('float32')
img_t2.data[0, 0, 0] = np.nan
img_t2.data[1, 0, 0] = np.inf
img_out, arr_out, _, _ = get_centerline(img_t2,
ParamCenterline(
algo_fitting='optic',
contrast='t2',
minmax=False),
verbose=VERBOSE)
# Open ground truth segmentation and compare
fname_t2_seg = os.path.join(__sct_dir__,
'sct_testing_data/t2/t2_seg.nii.gz')
img_seg_out, arr_seg_out, _, _ = get_centerline(Image(fname_t2_seg),
ParamCenterline(
algo_fitting='bspline',
minmax=False),
verbose=VERBOSE)
assert np.linalg.norm(
find_and_sort_coord(img_seg_out) - find_and_sort_coord(img_out)) < 3.5
def test_get_centerline_nurbs(img_ctl, expected):
"""Test centerline fitting using nurbs"""
img, img_sub = [img_ctl[0].copy(), img_ctl[1].copy()]
# Here we need a try/except because nurbs crashes with too few points.
try:
img_out, arr_out, _ = get_centerline(img_sub, algo_fitting='nurbs', minmax=False, verbose=VERBOSE)
assert np.linalg.norm(find_and_sort_coord(img) - find_and_sort_coord(img_out)) < expected
except ArithmeticError as e:
print(e)
def test_get_centerline_polyfit(img_ctl, expected, params):
"""Test centerline fitting using polyfit"""
img, img_sub = [img_ctl[0].copy(), img_ctl[1].copy()]
img_out, arr_out, arr_deriv_out, fit_results = get_centerline(
img_sub, ParamCenterline(algo_fitting='polyfit', minmax=False), verbose=VERBOSE)
assert np.median(find_and_sort_coord(img) - find_and_sort_coord(img_out)) == expected['median']
assert np.max(np.absolute(np.diff(arr_deriv_out))) < expected['laplacian']
# check arr_out only if input orientation is RPI (because the output array is always in RPI)
if img.orientation == 'RPI':
assert np.linalg.norm(find_and_sort_coord(img) - arr_out) < expected['norm']
def test_get_centerline_nurbs(img_ctl, expected):
"""Test centerline fitting using nurbs"""
img, img_sub = [img_ctl[0].copy(), img_ctl[1].copy()]
# Here we need a try/except because nurbs crashes with too few points.
try:
img_out, arr_out, arr_deriv_out = get_centerline(img_sub, algo_fitting='nurbs', minmax=False, verbose=VERBOSE)
assert np.median(find_and_sort_coord(img) - find_and_sort_coord(img_out)) == expected['median']
assert np.max(np.absolute(np.diff(arr_deriv_out))) < expected['laplacian']
except ArithmeticError as e:
print(e)
def test_get_centerline_linear(img_ctl, expected, params):
"""Test centerline fitting using linear interpolation"""
img, img_sub = [img_ctl[0].copy(), img_ctl[1].copy()]
if params:
img_out, arr_out, arr_deriv_out = get_centerline(img_sub, algo_fitting='polyfit', minmax=False,
degree=params['degree'], verbose=VERBOSE)
else:
img_out, arr_out, arr_deriv_out = get_centerline(img_sub, algo_fitting='polyfit', minmax=False, verbose=VERBOSE)
assert np.median(find_and_sort_coord(img) - find_and_sort_coord(img_out)) == expected['median']
assert np.max(np.absolute(np.diff(arr_deriv_out))) < expected['laplacian']
def test_get_centerline_polyfit(img_ctl, expected):
"""Test centerline fitting using polyfit"""
deg = 3
img, img_sub = [img_ctl[0].copy(), img_ctl[1].copy()]
img_out, arr_out, _ = get_centerline(img_sub, algo_fitting='polyfit', param=ParamCenterline(degree=deg),
minmax=False, verbose=VERBOSE)
assert np.linalg.norm(find_and_sort_coord(img) - find_and_sort_coord(img_out)) < expected
# check arr_out and arr_out_deriv only if input orientation is RPI (because the output array is always in RPI)
if img.orientation == 'RPI':
assert np.linalg.norm(find_and_sort_coord(img) - arr_out) < expected
def test_get_centerline_bspline(img_ctl, expected, params):
"""Test centerline fitting using bspline"""
img, img_sub = [img_ctl[0].copy(), img_ctl[1].copy()]
if params:
img_out, arr_out, arr_deriv_out = get_centerline(img_sub, algo_fitting='bspline', minmax=False,
smooth=params['smooth'], verbose=VERBOSE)
else:
img_out, arr_out, arr_deriv_out = get_centerline(img_sub, algo_fitting='bspline', minmax=False, verbose=VERBOSE)
assert np.median(find_and_sort_coord(img) - find_and_sort_coord(img_out)) == expected['median']
assert np.max(np.absolute(np.diff(arr_deriv_out))) < expected['laplacian']
def test_get_centerline_polyfit(img_ctl, expected, params):
"""Test centerline fitting using polyfit"""
img, img_sub = [img_ctl[0].copy(), img_ctl[1].copy()]
if params:
img_out, arr_out, arr_deriv_out = get_centerline(img_sub, algo_fitting='polyfit', minmax=False,
degree=params['degree'], verbose=VERBOSE)
else:
img_out, arr_out, arr_deriv_out = get_centerline(img_sub, algo_fitting='polyfit', minmax=False, verbose=VERBOSE)
assert np.median(find_and_sort_coord(img) - find_and_sort_coord(img_out)) == expected['median']
assert np.max(np.absolute(np.diff(arr_deriv_out))) < expected['laplacian']
# check arr_out only if input orientation is RPI (because the output array is always in RPI)
if img.orientation == 'RPI':
assert np.linalg.norm(find_and_sort_coord(img) - arr_out) < expected['norm']
def test_get_centerline_optic():
"""Test extraction of metrics aggregation across slices: All slices by default"""
fname_t2 = os.path.join(sct.__sct_dir__, 'sct_testing_data/t2/t2.nii.gz') # install: sct_download_data -d sct_testing_data
img_t2 = Image(fname_t2)
# Add non-numerical values at the top corner of the image for testing purpose
img_t2.change_type('float32')
img_t2.data[0, 0, 0] = np.nan
img_t2.data[1, 0, 0] = np.inf
img_out, arr_out, _ = get_centerline(img_t2, algo_fitting='optic', contrast='t2', minmax=False, verbose=VERBOSE)
# Open ground truth segmentation and compare
fname_t2_seg = os.path.join(sct.__sct_dir__, 'sct_testing_data/t2/t2_seg.nii.gz')
img_seg_out, arr_seg_out, _ = get_centerline(Image(fname_t2_seg), algo_fitting='bspline', minmax=False,
verbose=VERBOSE)
assert np.linalg.norm(find_and_sort_coord(img_seg_out) - find_and_sort_coord(img_out)) < 3.5
def test_get_centerline_nurbs(img_ctl, expected):
"""Test centerline fitting using nurbs"""
img, img_sub = [img_ctl[0].copy(), img_ctl[1].copy()]
# Here we need a try/except because nurbs crashes with too few points.
try:
img_out, arr_out, arr_deriv_out = get_centerline(img_sub,
algo_fitting='nurbs',
minmax=False,
verbose=VERBOSE)
assert np.median(
find_and_sort_coord(img) -
find_and_sort_coord(img_out)) == expected['median']
assert np.max(np.absolute(
np.diff(arr_deriv_out))) < expected['laplacian']
except ArithmeticError as e:
print(e)
def test_get_centerline_linear(img_ctl, expected, params):
"""Test centerline fitting using linear interpolation"""
img, img_sub = [img_ctl[0].copy(), img_ctl[1].copy()]
if params:
img_out, arr_out, arr_deriv_out = get_centerline(
img_sub,
algo_fitting='polyfit',
minmax=False,
degree=params['degree'],
verbose=VERBOSE)
else:
img_out, arr_out, arr_deriv_out = get_centerline(
img_sub, algo_fitting='polyfit', minmax=False, verbose=VERBOSE)
assert np.median(find_and_sort_coord(img) -
find_and_sort_coord(img_out)) == expected['median']
assert np.max(np.absolute(np.diff(arr_deriv_out))) < expected['laplacian']
def test_get_centerline_bspline(img_ctl, expected, params):
"""Test centerline fitting using bspline"""
img, img_sub = [img_ctl[0].copy(), img_ctl[1].copy()]
if params:
img_out, arr_out, arr_deriv_out = get_centerline(
img_sub,
algo_fitting='bspline',
minmax=False,
smooth=params['smooth'],
verbose=VERBOSE)
else:
img_out, arr_out, arr_deriv_out = get_centerline(
img_sub, algo_fitting='bspline', minmax=False, verbose=VERBOSE)
assert np.median(find_and_sort_coord(img) -
find_and_sort_coord(img_out)) == expected['median']
assert np.max(np.absolute(np.diff(arr_deriv_out))) < expected['laplacian']
def test_find_and_sort_coord(img_ctl, expected):
img = img_ctl[0].copy()
centermass = find_and_sort_coord(img)
assert centermass.shape == (3, 9)
assert np.linalg.norm(centermass - img_ctl[2]) == 0
def test_find_and_sort_coord(img_ctl, expected):
img = img_ctl[0].copy()
centermass = find_and_sort_coord(img)
assert centermass.shape == (3, 9)
assert np.linalg.norm(centermass - img_ctl[2]) == 0
