def main():
parser = _build_arg_parser()
args = parser.parse_args()
assert_inputs_exist(parser, args.in_tractogram)
assert_outputs_exist(parser, args, args.out_tractogram)
check_tracts_same_format(parser, args.in_tractogram, args.out_tractogram)
if args.error_rate < 0.001 or args.error_rate > 1:
logging.warning(
'You are using an error rate of {}.\n'
'We recommend setting it between 0.001 and 1.\n'
'0.001 will do almost nothing to the streamlines\n'
'while 1 will highly compress/linearize the streamlines'.format(
args.error_rate))
in_tractogram = nib.streamlines.load(args.in_tractogram, lazy_load=True)
compressed_streamlines = compress_streamlines_wrapper(
in_tractogram, args.error_rate)
out_tractogram = LazyTractogram(compressed_streamlines,
affine_to_rasmm=np.eye(4))
nib.streamlines.save(out_tractogram,
args.out_tractogram,
header=in_tractogram.header)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
if args.verbose:
logging.basicConfig(level=logging.INFO)
assert_inputs_exist(parser, args.in_tractograms)
assert_outputs_exist(parser, args, args.out_tractogram,
optional=args.save_indices)
if args.operation == 'lazy_concatenate':
logging.info('Using lazy_concatenate, no spatial or metadata related '
'checks are performed.\nMetadata will be lost, only '
'trk/tck file are supported.')
def list_generator_from_nib(filenames):
for in_file in filenames:
tractogram_file = nib.streamlines.load(in_file, lazy_load=True)
for s in tractogram_file.streamlines:
yield s
header = None
for in_file in args.in_tractograms:
_, ext = os.path.splitext(in_file)
if ext == '.trk':
if header is None:
header = nib.streamlines.load(
in_file, lazy_load=True).header
elif not is_header_compatible(header, in_file):
logging.warning('Incompatible headers in the list.')
generator = list_generator_from_nib(args.in_tractograms)
out_tractogram = LazyTractogram(lambda: generator,
affine_to_rasmm=np.eye(4))
nib.streamlines.save(out_tractogram, args.out_tractogram,
header=header)
return
# Load all input streamlines.
sft_list = []
for f in args.in_tractograms:
sft_list.append(load_tractogram_with_reference(
parser, args, f, bbox_check=not args.ignore_invalid))
# Apply the requested operation to each input file.
logging.info('Performing operation \'{}\'.'.format(args.operation))
new_sft = concatenate_sft(sft_list, args.no_metadata, args.fake_metadata)
if args.operation == 'concatenate':
indices = np.arange(len(new_sft), dtype=np.uint32)
else:
streamlines_list = [sft.streamlines for sft in sft_list]
op_name = args.operation
if args.robust:
op_name += '_robust'
_, indices = OPERATIONS[op_name](streamlines_list,
precision=args.precision)
else:
_, indices = perform_streamlines_operation(
OPERATIONS[op_name], streamlines_list,
precision=args.precision)
# Save the indices to a file if requested.
if args.save_indices:
start = 0
out_dict = {}
streamlines_len_cumsum = [len(sft) for sft in sft_list]
for name, nb in zip(args.in_tractograms, streamlines_len_cumsum):
end = start + nb
# Switch to int32 for json
out_dict[name] = [int(i - start)
for i in indices if start <= i < end]
start = end
with open(args.save_indices, 'wt') as f:
json.dump(out_dict, f,
indent=args.indent,
sort_keys=args.sort_keys)
# Save the new streamlines (and metadata)
logging.info('Saving {} streamlines to {}.'.format(len(indices),
args.out_tractogram))
save_tractogram(new_sft[indices], args.out_tractogram,
bbox_valid_check=not args.ignore_invalid)
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)
