def main(argv=None):
"""
Main function
:param fname_anat:
:param fname_centerline:
:param verbose:
:return:
"""
parser = get_parser()
arguments = parser.parse_args(argv)
verbose = arguments.v
set_global_loglevel(verbose=verbose)
fname_anat = arguments.i
fname_centerline = arguments.s
# load input images
im_anat = Image(fname_anat)
im_centerline = Image(fname_centerline)
# flatten sagittal
im_anat_flattened = flatten_sagittal(im_anat, im_centerline, verbose)
# save output
fname_out = add_suffix(fname_anat, '_flatten')
im_anat_flattened.save(fname_out)
display_viewer_syntax([fname_anat, fname_out])
def main(argv=None):
parser = get_parser()
arguments = parser.parse_args(argv if argv else ['--help'])
verbose = arguments.v
set_global_loglevel(verbose=verbose)
printv('Load data...', verbose)
nii_mt = Image(arguments.mt)
nii_pd = Image(arguments.pd)
nii_t1 = Image(arguments.t1)
if arguments.b1map is None:
nii_b1map = None
else:
nii_b1map = Image(arguments.b1map)
if arguments.trmt is None:
arguments.trmt = fetch_metadata(
get_json_file_name(arguments.mt, check_exist=True),
'RepetitionTime')
if arguments.trpd is None:
arguments.trpd = fetch_metadata(
get_json_file_name(arguments.pd, check_exist=True),
'RepetitionTime')
if arguments.trt1 is None:
arguments.trt1 = fetch_metadata(
get_json_file_name(arguments.t1, check_exist=True),
'RepetitionTime')
if arguments.famt is None:
arguments.famt = fetch_metadata(
get_json_file_name(arguments.mt, check_exist=True), 'FlipAngle')
if arguments.fapd is None:
arguments.fapd = fetch_metadata(
get_json_file_name(arguments.pd, check_exist=True), 'FlipAngle')
if arguments.fat1 is None:
arguments.fat1 = fetch_metadata(
get_json_file_name(arguments.t1, check_exist=True), 'FlipAngle')
# compute MTsat
nii_mtsat, nii_t1map = compute_mtsat(nii_mt,
nii_pd,
nii_t1,
arguments.trmt,
arguments.trpd,
arguments.trt1,
arguments.famt,
arguments.fapd,
arguments.fat1,
nii_b1map=nii_b1map)
# Output MTsat and T1 maps
printv('Generate output files...', verbose)
nii_mtsat.save(arguments.omtsat)
nii_t1map.save(arguments.ot1map)
display_viewer_syntax([arguments.omtsat, arguments.ot1map],
colormaps=['gray', 'gray'],
minmax=['-10,10', '0, 3'],
opacities=['1', '1'],
verbose=verbose)
def main(argv=None):
parser = get_parser()
arguments = parser.parse_args(argv)
verbose = arguments.v
set_global_loglevel(verbose=verbose)
fname_mtr = arguments.o
# compute MTR
printv('\nCompute MTR...', verbose)
nii_mtr = compute_mtr(nii_mt1=Image(arguments.mt1), nii_mt0=Image(arguments.mt0), threshold_mtr=arguments.thr)
# save MTR file
nii_mtr.save(fname_mtr, dtype='float32')
display_viewer_syntax([arguments.mt0, arguments.mt1, fname_mtr])
def main(argv=None):
parser = get_parser()
arguments = parser.parse_args(argv if argv else ['--help'])
verbose = arguments.v
set_global_loglevel(verbose=verbose)
input_filename = arguments.i
if arguments.o is not None:
output_filename = arguments.o
else:
output_filename = add_suffix(input_filename, '_gmseg')
use_tta = arguments.t
model_name = arguments.m
threshold = arguments.thr
if threshold > 1.0 or threshold < 0.0:
raise RuntimeError("Threshold should be between 0.0 and 1.0.")
# Threshold zero means no thresholding
if threshold == 0.0:
threshold = None
from spinalcordtoolbox.deepseg_gm import deepseg_gm
deepseg_gm.check_backend()
out_fname = deepseg_gm.segment_file(input_filename,
output_filename, model_name, threshold,
int(verbose), use_tta)
path_qc = arguments.qc
qc_dataset = arguments.qc_dataset
qc_subject = arguments.qc_subject
if path_qc is not None:
generate_qc(fname_in1=input_filename,
fname_seg=out_fname,
args=sys.argv[1:],
path_qc=os.path.abspath(path_qc),
dataset=qc_dataset,
subject=qc_subject,
process='sct_deepseg_gm')
display_viewer_syntax([input_filename, format(out_fname)],
colormaps=['gray', 'red'],
opacities=['1', '0.7'],
verbose=verbose)
def resample_file(fname_data,
fname_out,
new_size,
new_size_type,
interpolation,
verbose,
fname_ref=None):
"""This function will resample the specified input
image file to the target size.
Can deal with 2d, 3d or 4d image objects.
:param fname_data: The input image filename.
:param fname_out: The output image filename.
:param new_size: The target size, i.e. 0.25x0.25
:param new_size_type: Unit of resample (mm, vox, factor)
:param interpolation: The interpolation type
:param verbose: verbosity level
:param fname_ref: Reference image to resample input image to
"""
# Load data
logger.info('load data...')
nii = nib.load(fname_data)
if fname_ref is not None:
nii_ref = nib.load(fname_ref)
else:
nii_ref = None
nii_r = resample_nib(nii,
new_size.split('x'),
new_size_type,
image_dest=nii_ref,
interpolation=interpolation)
# build output file name
if fname_out == '':
fname_out = add_suffix(fname_data, '_r')
else:
fname_out = fname_out
# save data
nib.save(nii_r, fname_out)
# to view results
display_viewer_syntax([fname_out], verbose=verbose)
return nii_r
def main():
# Check input parameters
parser = get_parser()
args = parser.parse_args(args=None if sys.argv[1:] else ['--help'])
fname_mtr = args.o
verbose = args.v
# compute MTR
printv('\nCompute MTR...', verbose)
nii_mtr = compute_mtr(nii_mt1=Image(args.mt1),
nii_mt0=Image(args.mt0),
threshold_mtr=args.thr)
# save MTR file
nii_mtr.save(fname_mtr, dtype='float32')
display_viewer_syntax([args.mt0, args.mt1, fname_mtr])
def main(fname_anat, fname_centerline, verbose):
"""
Main function
:param fname_anat:
:param fname_centerline:
:param verbose:
:return:
"""
# load input images
im_anat = Image(fname_anat)
im_centerline = Image(fname_centerline)
# flatten sagittal
im_anat_flattened = flatten_sagittal(im_anat, im_centerline, verbose)
# save output
fname_out = add_suffix(fname_anat, '_flatten')
im_anat_flattened.save(fname_out)
display_viewer_syntax([fname_anat, fname_out])
def main(argv=None):
"""
Main function
:param argv:
:return:
"""
parser = get_parser()
arguments = parser.parse_args(argv if argv else ['--help'])
verbose = arguments.v
set_global_loglevel(verbose=verbose)
# initialize ImageCropper
cropper = ImageCropper(Image(arguments.i))
cropper.verbose = verbose
# Switch across cropping methods
if arguments.g:
cropper.get_bbox_from_gui()
elif arguments.m:
cropper.get_bbox_from_mask(Image(arguments.m))
elif arguments.ref:
cropper.get_bbox_from_ref(Image(arguments.ref))
else:
cropper.get_bbox_from_minmax(
BoundingBox(arguments.xmin, arguments.xmax,
arguments.ymin, arguments.ymax,
arguments.zmin, arguments.zmax)
)
# Crop image
img_crop = cropper.crop(background=arguments.b)
# Write cropped image to file
if arguments.o is None:
fname_out = add_suffix(arguments.i, '_crop')
else:
fname_out = arguments.o
img_crop.save(fname_out)
display_viewer_syntax([arguments.i, fname_out])
def main(args=None):
"""
Main function
:param args:
:return:
"""
# get parser args
if args is None:
args = None if sys.argv[1:] else ['--help']
parser = get_parser()
arguments = parser.parse_args(args=args)
# initialize ImageCropper
cropper = ImageCropper(Image(arguments.i))
cropper.verbose = arguments.v
init_sct(log_level=cropper.verbose, update=True) # Update log level
# Switch across cropping methods
if arguments.g:
cropper.get_bbox_from_gui()
elif arguments.m:
cropper.get_bbox_from_mask(Image(arguments.m))
elif arguments.ref:
cropper.get_bbox_from_ref(Image(arguments.ref))
else:
cropper.get_bbox_from_minmax(
BoundingBox(arguments.xmin, arguments.xmax, arguments.ymin,
arguments.ymax, arguments.zmin, arguments.zmax))
# Crop image
img_crop = cropper.crop(background=arguments.b)
# Write cropped image to file
if arguments.o is None:
fname_out = add_suffix(arguments.i, '_crop')
else:
fname_out = arguments.o
img_crop.save(fname_out)
display_viewer_syntax([arguments.i, fname_out])
def compute(self):
fname_data = self.fmri
# open data
nii_data = Image(fname_data)
data = nii_data.data
# compute mean
data_mean = np.mean(data, 3)
# compute STD
data_std = np.std(data, 3, ddof=1)
# compute TSNR
data_tsnr = data_mean / data_std
# save TSNR
fname_tsnr = self.out
nii_tsnr = empty_like(nii_data)
nii_tsnr.data = data_tsnr
nii_tsnr.save(fname_tsnr, dtype=np.float32)
display_viewer_syntax([fname_tsnr])
