def test_compute_csa_ellipse(dummy_segmentation):
"""Test computation of cross-sectional area from input segmentation"""
metrics = process_seg.compute_csa(dummy_segmentation(shape='ellipse',
angle=0,
a=50.0,
b=30.0),
algo_fitting=PARAM.algo_fitting,
angle_correction=True,
use_phys_coord=False,
verbose=VERBOSE)
assert np.mean(metrics['csa'].data[30:70]) == pytest.approx(47.01,
rel=0.01)
assert np.mean(metrics['angle'].data[30:70]) == pytest.approx(0.0,
rel=0.01)
def test_compute_csa_noangle(dummy_segmentation):
"""Test computation of cross-sectional area from input segmentation"""
metrics = process_seg.compute_csa(dummy_segmentation(shape='rectangle',
angle=0,
a=50.0,
b=30.0),
algo_fitting='hanning',
window_length=5,
angle_correction=False,
use_phys_coord=True,
verbose=0)
assert np.isnan(metrics['csa'].data[95])
assert np.mean(metrics['csa'].data[20:80]) == pytest.approx(61.61,
rel=0.01)
assert np.mean(metrics['angle'].data[20:80]) == pytest.approx(0.0,
rel=0.01)
def test_compute_csa(dummy_segmentation):
"""Test computation of cross-sectional area from input segmentation
Note: here, compared to the previous tests with no angle, we use smaller hanning window and smaller range for
computing the mean, because the smoothing creates spurious errors at edges."""
metrics = process_seg.compute_csa(dummy_segmentation(shape='rectangle',
angle=15,
a=50.0,
b=30.0),
algo_fitting=PARAM.algo_fitting,
angle_correction=True,
use_phys_coord=False,
verbose=VERBOSE)
assert np.mean(metrics['csa'].data[30:70]) == pytest.approx(
61.61, rel=0.01) # theoretical: 61.61
assert np.mean(metrics['angle'].data[30:70]) == pytest.approx(15.00,
rel=0.02)
def main(args):
parser = get_parser()
arguments = parser.parse(args)
param = Param()
# Initialization
slices = param.slices
angle_correction = True
use_phys_coord = True
group_funcs = (('MEAN', func_wa), ('STD', func_std)) # functions to perform when aggregating metrics along S-I
fname_segmentation = sct.get_absolute_path(arguments['-i'])
name_process = arguments['-p']
fname_vert_levels = ''
if '-o' in arguments:
file_out = os.path.abspath(arguments['-o'])
else:
file_out = ''
if '-append' in arguments:
append = int(arguments['-append'])
else:
append = 0
if '-vert' in arguments:
vert_levels = arguments['-vert']
else:
vert_levels = ''
if '-r' in arguments:
remove_temp_files = arguments['-r']
if '-vertfile' in arguments:
fname_vert_levels = arguments['-vertfile']
if '-perlevel' in arguments:
perlevel = arguments['-perlevel']
else:
perlevel = Param().perlevel
if '-v' in arguments:
verbose = int(arguments['-v'])
if '-z' in arguments:
slices = arguments['-z']
if '-perslice' in arguments:
perslice = arguments['-perslice']
else:
perslice = Param().perslice
if '-a' in arguments:
param.algo_fitting = arguments['-a']
if '-no-angle' in arguments:
if arguments['-no-angle'] == '1':
angle_correction = False
elif arguments['-no-angle'] == '0':
angle_correction = True
if '-use-image-coord' in arguments:
if arguments['-use-image-coord'] == '1':
use_phys_coord = False
if arguments['-use-image-coord'] == '0':
use_phys_coord = True
# update fields
param.verbose = verbose
metrics_agg = {}
if not file_out:
file_out = name_process + '.csv'
if name_process == 'centerline':
process_seg.extract_centerline(fname_segmentation, verbose=param.verbose,
algo_fitting=param.algo_fitting, use_phys_coord=use_phys_coord,
file_out=file_out)
if name_process == 'csa':
metrics = process_seg.compute_csa(fname_segmentation, algo_fitting=param.algo_fitting,
type_window=param.type_window, window_length=param.window_length,
angle_correction=angle_correction, use_phys_coord=use_phys_coord,
remove_temp_files=remove_temp_files, verbose=verbose)
for key in metrics:
metrics_agg[key] = aggregate_per_slice_or_level(metrics[key], slices=parse_num_list(slices),
levels=parse_num_list(vert_levels), perslice=perslice,
perlevel=perlevel, vert_level=fname_vert_levels,
group_funcs=group_funcs)
metrics_agg_merged = merge_dict(metrics_agg)
save_as_csv(metrics_agg_merged, file_out, fname_in=fname_segmentation, append=append)
sct.printv('\nFile created: '+file_out, verbose=1, type='info')
if name_process == 'label-vert':
if '-discfile' in arguments:
fname_discs = arguments['-discfile']
else:
sct.printv('\nERROR: Disc label file is mandatory (flag: -discfile).\n', 1, 'error')
process_seg.label_vert(fname_segmentation, fname_discs, verbose=verbose)
if name_process == 'shape':
fname_discs = None
if '-discfile' in arguments:
fname_discs = arguments['-discfile']
metrics = process_seg.compute_shape(fname_segmentation, remove_temp_files=remove_temp_files, verbose=verbose)
for key in metrics:
metrics_agg[key] = aggregate_per_slice_or_level(metrics[key], slices=parse_num_list(slices),
levels=parse_num_list(vert_levels), perslice=perslice,
perlevel=perlevel, vert_level=fname_vert_levels,
group_funcs=group_funcs)
metrics_agg_merged = merge_dict(metrics_agg)
save_as_csv(metrics_agg_merged, file_out, fname_in=fname_segmentation, append=append)
sct.printv('\nFile created: ' + file_out, verbose=1, type='info')