save_as_csv(agg_metric, fname_output, fname_in=fname_data, append=append_csv)
append_csv = True # when looping across labels, need to append results in the same file
sct.display_open(fname_output)
if __name__ == "__main__":
init_sct()
param_default = Param()
parser = get_parser()
arguments = parser.parse_args(args=None if sys.argv[1:] else ['--help'])
overwrite = 0 # TODO: Not used. Why?
fname_data = sct.get_absolute_path(arguments.i)
path_label = arguments.f
method = arguments.method
fname_output = arguments.o
append_csv = arguments.append
combine_labels = arguments.combine
labels_user = arguments.l
adv_param_user = arguments.param # TODO: Not used. Why?
slices_of_interest = arguments.z
vertebral_levels = arguments.vert
fname_vertebral_labeling = arguments.vertfile
perslice = arguments.perslice
perlevel = arguments.perlevel
fname_normalizing_label = arguments.norm_file # TODO: Not used. Why?
normalization_method = arguments.norm_method # TODO: Not used. Why?
label_to_fix = arguments.fix_label # TODO: Not used. Why?
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():
#Initialization
directory = ""
fname_template = ''
n_l = 0
verbose = param.verbose
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:t:n:v:')
except getopt.GetoptError:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ("-i"):
directory = arg
elif opt in ("-t"):
fname_template = arg
elif opt in ('-n'):
n_l = int(arg)
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_template == '' or directory == '':
usage()
# check existence of input files
print '\nCheck if file exists ...\n'
sct.check_file_exist(fname_template)
sct.check_folder_exist(directory)
path_template, file_template, ext_template = sct.extract_fname(
fname_template)
template_absolute_path = sct.get_absolute_path(fname_template)
os.chdir(directory)
n_i = len([
name for name in os.listdir('.')
if (os.path.isfile(name) and name.endswith(".nii.gz")
and name != 'template_landmarks.nii.gz')
]) # number of landmark images
average = zeros((n_i, n_l))
compteur = 0
for file in os.listdir('.'):
if file.endswith(".nii.gz") and file != 'template_landmarks.nii.gz':
print file
img = nibabel.load(file)
data = img.get_data()
X, Y, Z = (data > 0).nonzero()
Z = [Z[i] for i in Z.argsort()]
Z.reverse()
for i in xrange(n_l):
if i < len(Z):
average[compteur][i] = Z[i]
compteur = compteur + 1
average = array([
int(round(mean([average[average[:, i] > 0, i]]))) for i in xrange(n_l)
])
#print average
print template_absolute_path
print '\nGet dimensions of template...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(template_absolute_path)
print '.. matrix size: ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)
print '.. voxel size: ' + str(px) + 'mm x ' + str(py) + 'mm x ' + str(
pz) + 'mm'
img = nibabel.load(template_absolute_path)
data = img.get_data()
hdr = img.get_header()
data[:, :, :] = 0
compteur = 1
for i in average:
print int(round(nx / 2.0)), int(round(ny / 2.0)), int(round(i)), int(
round(compteur))
data[int(round(nx / 2.0)),
int(round(ny / 2.0)),
int(round(i))] = int(round(compteur))
compteur = compteur + 1
print '\nSave volume ...'
#hdr.set_data_dtype('float32') # set imagetype to uint8
# save volume
#data = data.astype(float32, copy =False)
img = nibabel.Nifti1Image(data, None, hdr)
file_name = 'template_landmarks.nii.gz'
nibabel.save(img, file_name)
print '\nFile created : ' + file_name
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():
#Initialization
directory = ""
fname_template = ''
n_l = 0
verbose = param.verbose
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:t:n:v:')
except getopt.GetoptError:
usage()
for opt, arg in opts :
if opt == '-h':
usage()
elif opt in ("-i"):
directory = arg
elif opt in ("-t"):
fname_template = arg
elif opt in ('-n'):
n_l = int(arg)
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_template == '' or directory == '':
usage()
# check existence of input files
print'\nCheck if file exists ...\n'
sct.check_file_exist(fname_template)
sct.check_folder_exist(directory)
path_template, file_template, ext_template = sct.extract_fname(fname_template)
template_absolute_path = sct.get_absolute_path(fname_template)
os.chdir(directory)
n_i = len([name for name in os.listdir('.') if (os.path.isfile(name) and name.endswith(".nii.gz") and name!='template_landmarks.nii.gz')]) # number of landmark images
average = zeros((n_i,n_l))
compteur = 0
for file in os.listdir('.'):
if file.endswith(".nii.gz") and file != 'template_landmarks.nii.gz':
print file
img = nibabel.load(file)
data = img.get_data()
X,Y,Z = (data>0).nonzero()
Z = [Z[i] for i in Z.argsort()]
Z.reverse()
for i in xrange(n_l):
if i < len(Z):
average[compteur][i] = Z[i]
compteur = compteur + 1
average = array([int(round(mean([average[average[:,i]>0,i]]))) for i in xrange(n_l)])
#print average
print template_absolute_path
print '\nGet dimensions of template...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(template_absolute_path)
print '.. matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz)
print '.. voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm'
img = nibabel.load(template_absolute_path)
data = img.get_data()
hdr = img.get_header()
data[:,:,:] = 0
compteur = 1
for i in average:
print int(round(nx/2.0)),int(round(ny/2.0)),int(round(i)),int(round(compteur))
data[int(round(nx/2.0)),int(round(ny/2.0)),int(round(i))] = int(round(compteur))
compteur = compteur + 1
print '\nSave volume ...'
#hdr.set_data_dtype('float32') # set imagetype to uint8
# save volume
#data = data.astype(float32, copy =False)
img = nibabel.Nifti1Image(data, None, hdr)
file_name = 'template_landmarks.nii.gz'
nibabel.save(img,file_name)
print '\nFile created : ' + file_name
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=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 = sct.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 = process_seg.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')
sct.display_open(file_out)
save_as_csv(agg_metric, fname_output, fname_in=fname_data, append=append)
append = True # when looping across labels, need to append results in the same file
sct.display_open(fname_output)
if __name__ == "__main__":
sct.init_sct()
param_default = Param()
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
overwrite = 0
fname_data = sct.get_absolute_path(arguments['-i'])
path_label = arguments['-f']
method = arguments['-method']
fname_output = arguments['-o']
if '-append' in arguments:
append = int(arguments['-append'])
else:
append = 0
if '-l' in arguments:
labels_user = arguments['-l']
else:
labels_user = ''
if '-param' in arguments:
adv_param_user = arguments['-param']
else:
adv_param_user = ''
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)