def test_compute_shape(im_seg, expected, params):
metrics = process_seg.compute_shape(im_seg,
algo_fitting=PARAM.algo_fitting,
angle_correction=params['angle_corr'],
verbose=VERBOSE)
for key in expected.keys():
# fetch obtained_value
if 'slice' in params:
obtained_value = float(metrics['area'].data[params['slice']])
else:
obtained_value = float(np.mean(metrics[key].data))
# fetch expected_value
if expected[key] is np.nan:
assert math.isnan(obtained_value)
break
else:
expected_value = pytest.approx(expected[key], rel=0.05)
assert obtained_value == expected_value
def test_compute_shape_noangle(dummy_segmentation):
"""Test computation of cross-sectional area from input segmentation."""
# Using hanning because faster
metrics = process_seg.compute_shape(dummy_segmentation(shape='ellipse',
angle=0,
a=50.0,
b=30.0),
algo_fitting=PARAM.algo_fitting,
verbose=VERBOSE)
assert np.mean(metrics['area'].data[30:70]) == pytest.approx(47.01,
rel=0.05)
assert np.mean(metrics['AP_diameter'].data[30:70]) == pytest.approx(
6.0, rel=0.05)
assert np.mean(metrics['RL_diameter'].data[30:70]) == pytest.approx(
10.0, rel=0.05)
assert np.mean(metrics['ratio_minor_major'].data[30:70]) == pytest.approx(
0.6, rel=0.05)
assert np.mean(metrics['eccentricity'].data[30:70]) == pytest.approx(
0.8, rel=0.05)
assert np.mean(metrics['orientation'].data[30:70]) == pytest.approx(
0.0, rel=0.05)
assert np.mean(metrics['solidity'].data[30:70]) == pytest.approx(1.0,
rel=0.05)
def test_compute_shape(im_seg, expected, params):
metrics, fit_results = process_seg.compute_shape(im_seg,
angle_correction=params['angle_corr'],
param_centerline=ParamCenterline(),
verbose=VERBOSE)
for key in expected.keys():
# fetch obtained_value
if 'slice' in params:
obtained_value = float(metrics['area'].data[params['slice']])
else:
if key == 'length':
# when computing length, sums values across slices
obtained_value = metrics[key].data.sum()
else:
# otherwise, average across slices
obtained_value = metrics[key].data.mean()
# fetch expected_value
if expected[key] is np.nan:
assert math.isnan(obtained_value)
break
else:
expected_value = pytest.approx(expected[key], rel=0.05)
assert obtained_value == expected_value
def main(args=None):
parser = get_parser()
if args:
arguments = parser.parse_args(args)
else:
arguments = parser.parse_args(
args=None if sys.argv[1:] else ['--help'])
# Initialization
slices = ''
group_funcs = (('MEAN', func_wa), ('STD', func_std)
) # functions to perform when aggregating metrics along S-I
fname_segmentation = get_absolute_path(arguments.i)
fname_vert_levels = ''
if arguments.o is not None:
file_out = os.path.abspath(arguments.o)
else:
file_out = ''
if arguments.append is not None:
append = arguments.append
else:
append = 0
if arguments.vert is not None:
vert_levels = arguments.vert
else:
vert_levels = ''
remove_temp_files = arguments.r
if arguments.vertfile is not None:
fname_vert_levels = arguments.vertfile
if arguments.perlevel is not None:
perlevel = arguments.perlevel
else:
perlevel = None
if arguments.z is not None:
slices = arguments.z
if arguments.perslice is not None:
perslice = arguments.perslice
else:
perslice = None
angle_correction = arguments.angle_corr
param_centerline = ParamCenterline(algo_fitting=arguments.centerline_algo,
smooth=arguments.centerline_smooth,
minmax=True)
path_qc = arguments.qc
qc_dataset = arguments.qc_dataset
qc_subject = arguments.qc_subject
verbose = int(arguments.v)
init_sct(log_level=verbose, update=True) # Update log level
# update fields
metrics_agg = {}
if not file_out:
file_out = 'csa.csv'
metrics, fit_results = compute_shape(fname_segmentation,
angle_correction=angle_correction,
param_centerline=param_centerline,
verbose=verbose)
for key in metrics:
if key == 'length':
# For computing cord length, slice-wise length needs to be summed across slices
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=(('SUM', func_sum), ))
else:
# For other metrics, we compute the average and standard deviation across slices
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)
# QC report (only show CSA for clarity)
if path_qc is not None:
generate_qc(fname_segmentation,
args=args,
path_qc=os.path.abspath(path_qc),
dataset=qc_dataset,
subject=qc_subject,
path_img=_make_figure(metrics_agg_merged, fit_results),
process='sct_process_segmentation')
display_open(file_out)
def main(args):
parser = get_parser()
arguments = parser.parse(args)
# Initialization
slices = ''
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'])
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 = None
if '-z' in arguments:
slices = arguments['-z']
if '-perslice' in arguments:
perslice = arguments['-perslice']
else:
perslice = None
if '-angle-corr' in arguments:
if arguments['-angle-corr'] == '1':
angle_correction = True
elif arguments['-angle-corr'] == '0':
angle_correction = False
param_centerline = ParamCenterline(
algo_fitting=arguments['-centerline-algo'],
smooth=arguments['-centerline-smooth'],
minmax=True)
path_qc = arguments.get("-qc", None)
qc_dataset = arguments.get("-qc-dataset", None)
qc_subject = arguments.get("-qc-subject", None)
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
# update fields
metrics_agg = {}
if not file_out:
file_out = 'csa.csv'
metrics, fit_results = process_seg.compute_shape(fname_segmentation,
angle_correction=angle_correction,
param_centerline=param_centerline,
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)
# QC report (only show CSA for clarity)
if path_qc is not None:
generate_qc(fname_segmentation, args=args, path_qc=os.path.abspath(path_qc), dataset=qc_dataset,
subject=qc_subject, path_img=_make_figure(metrics_agg_merged, fit_results),
process='sct_process_segmentation')
sct.display_open(file_out)
def main(args):
parser = get_parser()
arguments = parser.parse(args)
param = Param()
# Initialization
slices = param.slices
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'])
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 '-z' in arguments:
slices = arguments['-z']
if '-perslice' in arguments:
perslice = arguments['-perslice']
else:
perslice = Param().perslice
if '-angle-corr' in arguments:
if arguments['-angle-corr'] == '1':
angle_correction = True
elif arguments['-angle-corr'] == '0':
angle_correction = False
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
# update fields
metrics_agg = {}
if not file_out:
file_out = 'csa.csv'
metrics = process_seg.compute_shape(fname_segmentation,
algo_fitting='bspline',
angle_correction=angle_correction,
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.display_open(file_out)
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')
def main(args):
parser = get_parser()
arguments = parser.parse(args)
param = Param()
# Initialization
slices = param.slices
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'])
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 '-z' in arguments:
slices = arguments['-z']
if '-perslice' in arguments:
perslice = arguments['-perslice']
else:
perslice = Param().perslice
if '-angle-corr' in arguments:
if arguments['-angle-corr'] == '1':
angle_correction = True
elif arguments['-angle-corr'] == '0':
angle_correction = False
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
# update fields
metrics_agg = {}
if not file_out:
file_out = 'csa.csv'
metrics = process_seg.compute_shape(fname_segmentation,
algo_fitting='bspline',
angle_correction=angle_correction,
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.display_open(file_out)