def transform_tractogram(in_filename, ref_filename, transfo, filename_to_save):
tractogram = nib.streamlines.load(in_filename)
_, out_extension = split_name_with_nii(filename_to_save)
if out_extension == '.trk':
# Only TRK/NII can be a reference, because they have an affine
_, ref_extension = split_name_with_nii(ref_filename)
if ref_extension == '.trk':
ref_tractogram = nib.streamlines.load(ref_filename, lazy_load=True)
ref_header = ref_tractogram.header
else:
ref_img = nib.load(ref_filename)
ref_header = create_header_from_anat(ref_img)
elif out_extension == '.tck':
ref_header = nib.streamlines.TckFile.create_empty_header()
tractogram.tractogram.apply_affine(transfo)
new_tractogram = nib.streamlines.Tractogram(tractogram.streamlines,
affine_to_rasmm=np.eye(4))
nib.streamlines.save(new_tractogram, filename_to_save, header=ref_header)
def transform_tractogram(in_filename, ref_filename, def_filename,
filename_to_save, field_source):
in_tractogram = nib.streamlines.load(in_filename)
_, out_extension = split_name_with_nii(filename_to_save)
if out_extension == '.trk':
# Only TRK/NII can be a reference, because they have an affine
_, ref_extension = split_name_with_nii(ref_filename)
if ref_extension == '.trk':
ref_tractogram = nib.streamlines.load(ref_filename, lazy_load=True)
ref_header = ref_tractogram.header
else:
ref_img = nib.load(ref_filename)
ref_header = create_header_from_anat(ref_img)
elif out_extension == '.tck':
ref_header = nib.streamlines.TckFile.create_empty_header()
deformation = nib.load(def_filename)
deformation_data = np.squeeze(deformation.get_data())
if not np.allclose(deformation.affine,
in_tractogram.header["voxel_to_rasmm"]):
raise ValueError('Both affines are not equal')
if not np.array_equal(deformation_data.shape[0:3],
in_tractogram.header["dimensions"]):
raise ValueError('Both dimensions are not equal')
transfo = in_tractogram.header["voxel_to_rasmm"]
# Warning: Apply warp in-place
warp_tractogram(in_tractogram.streamlines, transfo, deformation_data,
field_source)
new_tractogram = nib.streamlines.Tractogram(in_tractogram.streamlines,
affine_to_rasmm=np.eye(4))
nib.streamlines.save(new_tractogram, filename_to_save, header=ref_header)
def main():
parser = _build_args_parser()
args = parser.parse_args()
if args.isVerbose:
logging.basicConfig(level=logging.DEBUG)
assert_inputs_exist(parser, [
args.sh_file, args.seed_file, args.map_include_file,
args.map_exclude_file
])
assert_outputs_exist(parser, args, [args.output_file])
if not nib.streamlines.is_supported(args.output_file):
parser.error('Invalid output streamline file format (must be trk or ' +
'tck): {0}'.format(args.output_file))
if not args.min_length > 0:
parser.error('minL must be > 0, {}mm was provided.'.format(
args.min_length))
if args.max_length < args.min_length:
parser.error(
'maxL must be > than minL, (minL={}mm, maxL={}mm).'.format(
args.min_length, args.max_length))
if args.compress:
if args.compress < 0.001 or args.compress > 1:
logging.warning(
'You are using an error rate of {}.\nWe recommend setting it '
'between 0.001 and 1.\n0.001 will do almost nothing to the '
'tracts while 1 will higly compress/linearize the tracts'.
format(args.compress))
if args.particles <= 0:
parser.error('--particles must be >= 1.')
if args.back_tracking <= 0:
parser.error('PFT backtracking distance must be > 0.')
if args.forward_tracking <= 0:
parser.error('PFT forward tracking distance must be > 0.')
if args.npv and args.npv <= 0:
parser.error('Number of seeds per voxel must be > 0.')
if args.nt and args.nt <= 0:
parser.error('Total number of seeds must be > 0.')
fodf_sh_img = nib.load(args.sh_file)
fodf_sh_img = nib.load(args.sh_file)
if not np.allclose(np.mean(fodf_sh_img.header.get_zooms()[:3]),
fodf_sh_img.header.get_zooms()[0],
atol=1.e-3):
parser.error(
'SH file is not isotropic. Tracking cannot be ran robustly.')
tracking_sphere = HemiSphere.from_sphere(get_sphere('repulsion724'))
# Check if sphere is unit, since we couldn't find such check in Dipy.
if not np.allclose(np.linalg.norm(tracking_sphere.vertices, axis=1), 1.):
raise RuntimeError('Tracking sphere should be unit normed.')
sh_basis = args.sh_basis
if args.algo == 'det':
dgklass = DeterministicMaximumDirectionGetter
else:
dgklass = ProbabilisticDirectionGetter
theta = get_theta(args.theta, args.algo)
# Reminder for the future:
# pmf_threshold == clip pmf under this
# relative_peak_threshold is for initial directions filtering
# min_separation_angle is the initial separation angle for peak extraction
dg = dgklass.from_shcoeff(fodf_sh_img.get_data().astype(np.double),
max_angle=theta,
sphere=tracking_sphere,
basis_type=sh_basis,
pmf_threshold=args.sf_threshold,
relative_peak_threshold=args.sf_threshold_init)
map_include_img = nib.load(args.map_include_file)
map_exclude_img = nib.load(args.map_exclude_file)
voxel_size = np.average(map_include_img.get_header()['pixdim'][1:4])
tissue_classifier = None
if not args.act:
tissue_classifier = CmcTissueClassifier(map_include_img.get_data(),
map_exclude_img.get_data(),
step_size=args.step_size,
average_voxel_size=voxel_size)
else:
tissue_classifier = ActTissueClassifier(map_include_img.get_data(),
map_exclude_img.get_data())
if args.npv:
nb_seeds = args.npv
seed_per_vox = True
elif args.nt:
nb_seeds = args.nt
seed_per_vox = False
else:
nb_seeds = 1
seed_per_vox = True
voxel_size = fodf_sh_img.header.get_zooms()[0]
vox_step_size = args.step_size / voxel_size
seed_img = nib.load(args.seed_file)
seeds = track_utils.random_seeds_from_mask(
seed_img.get_data(),
seeds_count=nb_seeds,
seed_count_per_voxel=seed_per_vox,
random_seed=args.seed)
# Note that max steps is used once for the forward pass, and
# once for the backwards. This doesn't, in fact, control the real
# max length
max_steps = int(args.max_length / args.step_size) + 1
pft_streamlines = ParticleFilteringTracking(
dg,
tissue_classifier,
seeds,
np.eye(4),
max_cross=1,
step_size=vox_step_size,
maxlen=max_steps,
pft_back_tracking_dist=args.back_tracking,
pft_front_tracking_dist=args.forward_tracking,
particle_count=args.particles,
return_all=args.keep_all,
random_seed=args.seed)
scaled_min_length = args.min_length / voxel_size
scaled_max_length = args.max_length / voxel_size
filtered_streamlines = (
s for s in pft_streamlines
if scaled_min_length <= length(s) <= scaled_max_length)
if args.compress:
filtered_streamlines = (compress_streamlines(s, args.compress)
for s in filtered_streamlines)
tractogram = LazyTractogram(lambda: filtered_streamlines,
affine_to_rasmm=seed_img.affine)
filetype = nib.streamlines.detect_format(args.output_file)
header = create_header_from_anat(seed_img, base_filetype=filetype)
# Use generator to save the streamlines on-the-fly
nib.streamlines.save(tractogram, args.output_file, header=header)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
if args.isVerbose:
logging.basicConfig(level=logging.DEBUG)
assert_inputs_exist(parser, [args.sh_file, args.seed_file, args.mask_file])
assert_outputs_exist(parser, args, [args.output_file])
if not nib.streamlines.is_supported(args.output_file):
parser.error('Invalid output streamline file format (must be trk or ' +
'tck): {0}'.format(args.output_file))
if not args.min_length > 0:
parser.error('minL must be > 0, {}mm was provided.'.format(
args.min_length))
if args.max_length < args.min_length:
parser.error(
'maxL must be > than minL, (minL={}mm, maxL={}mm).'.format(
args.min_length, args.max_length))
if args.compress:
if args.compress < 0.001 or args.compress > 1:
logging.warning(
'You are using an error rate of {}.\nWe recommend setting it '
'between 0.001 and 1.\n0.001 will do almost nothing to the '
'tracts while 1 will higly compress/linearize the tracts'.
format(args.compress))
if args.npv and args.npv <= 0:
parser.error('Number of seeds per voxel must be > 0.')
if args.nt and args.nt <= 0:
parser.error('Total number of seeds must be > 0.')
mask_img = nib.load(args.mask_file)
mask_data = mask_img.get_data()
# Make sure the mask is isotropic. Else, the strategy used
# when providing information to dipy (i.e. working as if in voxel space)
# will not yield correct results.
fodf_sh_img = nib.load(args.sh_file)
if not np.allclose(np.mean(fodf_sh_img.header.get_zooms()[:3]),
fodf_sh_img.header.get_zooms()[0],
atol=1.e-3):
parser.error(
'SH file is not isotropic. Tracking cannot be ran robustly.')
if args.npv:
nb_seeds = args.npv
seed_per_vox = True
elif args.nt:
nb_seeds = args.nt
seed_per_vox = False
else:
nb_seeds = 1
seed_per_vox = True
voxel_size = fodf_sh_img.header.get_zooms()[0]
vox_step_size = args.step_size / voxel_size
seed_img = nib.load(args.seed_file)
seeds = track_utils.random_seeds_from_mask(
seed_img.get_data(),
seeds_count=nb_seeds,
seed_count_per_voxel=seed_per_vox,
random_seed=args.seed)
# Tracking is performed in voxel space
max_steps = int(args.max_length / args.step_size) + 1
streamlines = LocalTracking(_get_direction_getter(args, mask_data),
BinaryTissueClassifier(mask_data),
seeds,
np.eye(4),
step_size=vox_step_size,
max_cross=1,
maxlen=max_steps,
fixedstep=True,
return_all=True,
random_seed=args.seed)
scaled_min_length = args.min_length / voxel_size
scaled_max_length = args.max_length / voxel_size
filtered_streamlines = (
s for s in streamlines
if scaled_min_length <= length(s) <= scaled_max_length)
if args.compress:
filtered_streamlines = (compress_streamlines(s, args.compress)
for s in filtered_streamlines)
tractogram = LazyTractogram(lambda: filtered_streamlines,
affine_to_rasmm=seed_img.affine)
filetype = nib.streamlines.detect_format(args.output_file)
header = create_header_from_anat(seed_img, base_filetype=filetype)
# Use generator to save the streamlines on-the-fly
nib.streamlines.save(tractogram, args.output_file, header=header)
