def io_tractogram(extension):
with InTemporaryDirectory():
fname = 'test.{}'.format(extension)
in_affine = np.eye(4)
in_dimensions = np.array([50, 50, 50])
in_voxel_sizes = np.array([2, 1.5, 1.5])
nii_header = create_nifti_header(in_affine, in_dimensions,
in_voxel_sizes)
sft = StatefulTractogram(streamlines, nii_header, space=Space.RASMM)
save_tractogram(sft, fname, bbox_valid_check=False)
if extension == 'trk':
reference = 'same'
else:
reference = nii_header
sft = load_tractogram(fname, reference, bbox_valid_check=False)
affine, dimensions, voxel_sizes, _ = sft.space_attribute
npt.assert_array_equal(in_affine, affine)
npt.assert_array_equal(in_voxel_sizes, voxel_sizes)
npt.assert_array_equal(in_dimensions, dimensions)
npt.assert_equal(len(sft), len(streamlines))
npt.assert_array_almost_equal(sft.streamlines[1], streamline,
decimal=4)
def reference_info_zero_affine():
header = create_nifti_header(np.zeros((4, 4)), [10, 10, 10], [1, 1, 1])
try:
get_reference_info(header)
return True
except ValueError:
return False
def test_horizon_events():
# using here MNI template affine 2009a
affine = np.array([[1., 0., 0., -98.],
[0., 1., 0., -134.],
[0., 0., 1., -72.],
[0., 0., 0., 1.]])
data = 255 * np.random.rand(197, 233, 189)
vox_size = (1., 1., 1.)
images = [(data, affine)]
# images = None
from dipy.segment.tests.test_bundles import setup_module
setup_module()
from dipy.segment.tests.test_bundles import f1
streamlines = f1.copy()
streamlines._data += np.array([-98., -134., -72.])
header = create_nifti_header(affine, data.shape, vox_size)
sft = StatefulTractogram(streamlines, header, Space.RASMM)
tractograms = [sft]
# select all centroids and expand and click everything else
# do not press the key shortcuts as vtk generates warning that
# blocks recording
fname = os.path.join(DATA_DIR, 'record_horizon.log.gz')
horizon(tractograms=tractograms, images=images, pams=None,
cluster=True, cluster_thr=5.0,
random_colors=False, length_gt=0, length_lt=np.inf,
clusters_gt=0, clusters_lt=np.inf,
world_coords=True, interactive=True, out_png='tmp.png',
recorded_events=fname)
def _afd_rd_wrapper(args):
in_hdf5_filename = args[0]
key = args[1]
fodf_img = args[2]
sh_basis = args[3]
length_weighting = args[4]
with h5py.File(in_hdf5_filename, 'r') as in_hdf5_file:
affine = in_hdf5_file.attrs['affine']
dimensions = in_hdf5_file.attrs['dimensions']
voxel_sizes = in_hdf5_file.attrs['voxel_sizes']
streamlines = reconstruct_streamlines_from_hdf5(in_hdf5_file, key)
if len(streamlines) == 0:
return key, 0
header = create_nifti_header(affine, dimensions, voxel_sizes)
sft = StatefulTractogram(streamlines,
header,
Space.VOX,
origin=Origin.TRACKVIS)
afd_mean_map, rd_mean_map = afd_map_along_streamlines(
sft, fodf_img, sh_basis, length_weighting)
afd_mean = np.average(afd_mean_map[afd_mean_map > 0])
return key, afd_mean
def main():
parser = _build_arg_parser()
args = parser.parse_args()
assert_inputs_exist(parser, args.in_hdf5)
assert_output_dirs_exist_and_empty(parser,
args,
args.out_dir,
create_dir=True)
hdf5_file = h5py.File(args.in_hdf5, 'r')
for key in hdf5_file.keys():
affine = hdf5_file.attrs['affine']
dimensions = hdf5_file.attrs['dimensions']
voxel_sizes = hdf5_file.attrs['voxel_sizes']
streamlines = reconstruct_streamlines_from_hdf5(hdf5_file, key)
header = create_nifti_header(affine, dimensions, voxel_sizes)
sft = StatefulTractogram(streamlines,
header,
Space.VOX,
origin=Origin.TRACKVIS)
if args.include_dps:
for dps_key in hdf5_file[key].keys():
if dps_key not in ['data', 'offsets', 'lengths']:
sft.data_per_streamline[dps_key] = hdf5_file[key][dps_key]
save_tractogram(sft, '{}.trk'.format(os.path.join(args.out_dir, key)))
hdf5_file.close()
def main():
parser = _build_arg_parser()
args = parser.parse_args()
assert_inputs_exist(parser, args.in_hdf5)
assert_output_dirs_exist_and_empty(parser,
args,
args.out_dir,
create_dir=True)
if args.save_empty and args.labels_list is None:
parser.error("The option --save_empty requires --labels_list.")
with h5py.File(args.in_hdf5, 'r') as hdf5_file:
if args.save_empty:
all_labels = np.loadtxt(args.labels_list, dtype='str')
comb_list = list(itertools.combinations(all_labels, r=2))
comb_list.extend(zip(all_labels, all_labels))
keys = [i[0] + '_' + i[1] for i in comb_list]
else:
keys = hdf5_file.keys()
if args.edge_keys is not None:
selected_keys = [key for key in keys if key in args.edge_keys]
elif args.node_keys is not None:
selected_keys = []
for node in args.node_keys:
selected_keys.extend([
key for key in keys
if key.startswith(node + '_') or key.endswith('_' + node)
])
else:
selected_keys = keys
affine = hdf5_file.attrs['affine']
dimensions = hdf5_file.attrs['dimensions']
voxel_sizes = hdf5_file.attrs['voxel_sizes']
header = create_nifti_header(affine, dimensions, voxel_sizes)
for key in selected_keys:
streamlines = reconstruct_streamlines_from_hdf5(hdf5_file, key)
if len(streamlines) == 0 and not args.save_empty:
continue
sft = StatefulTractogram(streamlines,
header,
Space.VOX,
origin=Origin.TRACKVIS)
if args.include_dps:
for dps_key in hdf5_file[key].keys():
if dps_key not in ['data', 'offsets', 'lengths']:
sft.data_per_streamline[dps_key] = hdf5_file[key][
dps_key]
save_tractogram(sft,
'{}.trk'.format(os.path.join(args.out_dir, key)))
def density_map(tractogram, n_sls=None, to_vox=False, normalize=False):
"""
Create a streamline density map.
based on:
https://dipy.org/documentation/1.1.1./examples_built/streamline_formats/
Parameters
----------
tractogram : StatefulTractogram
Stateful tractogram whose streamlines are used to make
the density map.
n_sls : int or None, optional
n_sls to randomly select to make the density map.
If None, all streamlines are used.
Default: None
to_vox : bool, optional
Whether to put the stateful tractogram in VOX space before making
the density map.
Default: False
normalize : bool, optional
Whether to normalize maximum values to 1.
Default: False
Returns
-------
Nifti1Image containing the density map.
"""
if to_vox:
tractogram.to_vox()
sls = tractogram.streamlines
if n_sls is not None:
sls = select_random_set_of_streamlines(sls, n_sls)
affine, vol_dims, voxel_sizes, voxel_order = get_reference_info(tractogram)
tractogram_density = dtu.density_map(sls, np.eye(4), vol_dims)
if normalize:
tractogram_density = tractogram_density / tractogram_density.max()
nifti_header = create_nifti_header(affine, vol_dims, voxel_sizes)
density_map_img = nib.Nifti1Image(tractogram_density, affine, nifti_header)
return density_map_img
def main():
parser = _build_arg_parser()
args = parser.parse_args()
assert_inputs_exist(parser, [args.in_hdf5, args.in_target_file,
args.in_transfo], args.in_deformation)
assert_outputs_exist(parser, args, args.out_hdf5)
# HDF5 will not overwrite the file
if os.path.isfile(args.out_hdf5):
os.remove(args.out_hdf5)
with h5py.File(args.in_hdf5, 'r') as in_hdf5_file:
shutil.copy(args.in_hdf5, args.out_hdf5)
with h5py.File(args.out_hdf5, 'a') as out_hdf5_file:
transfo = load_matrix_in_any_format(args.in_transfo)
deformation_data = None
if args.in_deformation is not None:
deformation_data = np.squeeze(nib.load(
args.in_deformation).get_fdata(dtype=np.float32))
target_img = nib.load(args.in_target_file)
for key in in_hdf5_file.keys():
affine = in_hdf5_file.attrs['affine']
dimensions = in_hdf5_file.attrs['dimensions']
voxel_sizes = in_hdf5_file.attrs['voxel_sizes']
streamlines = reconstruct_streamlines_from_hdf5(
in_hdf5_file, key)
if len(streamlines) == 0:
continue
header = create_nifti_header(affine, dimensions, voxel_sizes)
moving_sft = StatefulTractogram(streamlines, header, Space.VOX,
origin=Origin.TRACKVIS)
new_sft = transform_warp_streamlines(
moving_sft, transfo, target_img,
inverse=args.inverse,
deformation_data=deformation_data,
remove_invalid=not args.cut_invalid,
cut_invalid=args.cut_invalid)
new_sft.to_vox()
new_sft.to_corner()
affine, dimensions, voxel_sizes, voxel_order = get_reference_info(
target_img)
out_hdf5_file.attrs['affine'] = affine
out_hdf5_file.attrs['dimensions'] = dimensions
out_hdf5_file.attrs['voxel_sizes'] = voxel_sizes
out_hdf5_file.attrs['voxel_order'] = voxel_order
group = out_hdf5_file[key]
del group['data']
group.create_dataset('data',
data=new_sft.streamlines.get_data())
del group['offsets']
group.create_dataset('offsets',
data=new_sft.streamlines._offsets)
del group['lengths']
group.create_dataset('lengths',
data=new_sft.streamlines._lengths)
def run(self,
input_files,
cluster=False,
cluster_thr=15.,
random_colors=False,
length_gt=0,
length_lt=1000,
clusters_gt=0,
clusters_lt=10**8,
native_coords=False,
stealth=False,
emergency_header='icbm_2009a',
bg_color=(0, 0, 0),
disable_order_transparency=False,
buan=False,
buan_thr=0.5,
buan_highlight=(1, 0, 0),
out_dir='',
out_stealth_png='tmp.png'):
""" Interactive medical visualization - Invert the Horizon!
Interact with any number of .trk, .tck or .dpy tractograms and anatomy
files .nii or .nii.gz. Cluster streamlines on loading.
Parameters
----------
input_files : variable string
cluster : bool, optional
Enable QuickBundlesX clustering.
cluster_thr : float, optional
Distance threshold used for clustering. Default value 15.0 for
small animal brains you may need to use something smaller such
as 2.0. The distance is in mm. For this parameter to be active
``cluster`` should be enabled.
random_colors : bool, optional
Given multiple tractograms have been included then each tractogram
will be shown with different color.
length_gt : float, optional
Clusters with average length greater than ``length_gt`` amount
in mm will be shown.
length_lt : float, optional
Clusters with average length less than ``length_lt`` amount in
mm will be shown.
clusters_gt : int, optional
Clusters with size greater than ``clusters_gt`` will be shown.
clusters_lt : int, optional
Clusters with size less than ``clusters_gt`` will be shown.
native_coords : bool, optional
Show results in native coordinates.
stealth : bool, optional
Do not use interactive mode just save figure.
emergency_header : str, optional
If no anatomy reference is provided an emergency header is
provided. Current options 'icbm_2009a' and 'icbm_2009c'.
bg_color : variable float, optional
Define the background color of the scene. Colors can be defined
with 1 or 3 values and should be between [0-1].
disable_order_transparency : bool, optional
Use depth peeling to sort transparent objects.
If True also enables anti-aliasing.
buan : bool, optional
Enables BUAN framework visualization.
buan_thr : float, optional
Uses the threshold value to highlight segments on the
bundle which have pvalues less than this threshold.
buan_highlight : variable float, optional
Define the bundle highlight area color. Colors can be defined
with 1 or 3 values and should be between [0-1].
For example, a value of (1, 0, 0) would mean the red color.
out_dir : str, optional
Output directory. (default current directory)
out_stealth_png : str, optional
Filename of saved picture.
References
----------
.. [Horizon_ISMRM19] Garyfallidis E., M-A. Cote, B.Q. Chandio,
S. Fadnavis, J. Guaje, R. Aggarwal, E. St-Onge, K.S. Juneja,
S. Koudoro, D. Reagan, DIPY Horizon: fast, modular, unified and
adaptive visualization, Proceedings of: International Society of
Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 2019.
"""
verbose = True
tractograms = []
images = []
pams = []
numpy_files = []
interactive = not stealth
world_coords = not native_coords
bundle_colors = None
mni_2009a = {}
mni_2009a['affine'] = np.array([[1., 0., 0.,
-98.], [0., 1., 0., -134.],
[0., 0., 1., -72.], [0., 0., 0., 1.]])
mni_2009a['dims'] = (197, 233, 189)
mni_2009a['vox_size'] = (1., 1., 1.)
mni_2009a['vox_space'] = 'RAS'
mni_2009c = {}
mni_2009c['affine'] = np.array([[1., 0., 0.,
-96.], [0., 1., 0., -132.],
[0., 0., 1., -78.], [0., 0., 0., 1.]])
mni_2009c['dims'] = (193, 229, 193)
mni_2009c['vox_size'] = (1., 1., 1.)
mni_2009c['vox_space'] = 'RAS'
if emergency_header == 'icbm_2009a':
hdr = mni_2009c
else:
hdr = mni_2009c
emergency_ref = create_nifti_header(hdr['affine'], hdr['dims'],
hdr['vox_size'])
io_it = self.get_io_iterator()
for input_output in io_it:
fname = input_output[0]
if verbose:
print('Loading file ...')
print(fname)
print('\n')
fl = fname.lower()
ends = fl.endswith
if ends('.trk'):
sft = load_tractogram(fname, 'same', bbox_valid_check=False)
tractograms.append(sft)
if ends('.dpy') or ends('.tck'):
sft = load_tractogram(fname, emergency_ref)
tractograms.append(sft)
if ends('.nii.gz') or ends('.nii'):
data, affine = load_nifti(fname)
images.append((data, affine))
if verbose:
print('Affine to RAS')
np.set_printoptions(3, suppress=True)
print(affine)
np.set_printoptions()
if ends(".pam5"):
pam = load_peaks(fname)
pams.append(pam)
if verbose:
print('Peak_dirs shape')
print(pam.peak_dirs.shape)
if ends(".npy"):
data = np.load(fname)
numpy_files.append(data)
if verbose:
print('numpy array length')
print(len(data))
if buan:
bundle_colors = []
for i in range(len(numpy_files)):
n = len(numpy_files[i])
pvalues = numpy_files[i]
bundle = tractograms[i].streamlines
indx = assignment_map(bundle, bundle, n)
ind = np.array(indx)
nb_lines = len(bundle)
lines_range = range(nb_lines)
points_per_line = [len(bundle[i]) for i in lines_range]
points_per_line = np.array(points_per_line, np.intp)
cols_arr = line_colors(bundle)
colors_mapper = np.repeat(lines_range, points_per_line, axis=0)
vtk_colors = numpy_to_vtk_colors(255 * cols_arr[colors_mapper])
colors = numpy_support.vtk_to_numpy(vtk_colors)
colors = (colors - np.min(colors)) / np.ptp(colors)
for i in range(n):
if pvalues[i] < buan_thr:
colors[ind == i] = buan_highlight
bundle_colors.append(colors)
if len(bg_color) == 1:
bg_color *= 3
elif len(bg_color) != 3:
raise ValueError('You need 3 values to set up backgound color. '
'e.g --bg_color 0.5 0.5 0.5')
order_transparent = not disable_order_transparency
horizon(tractograms=tractograms,
images=images,
pams=pams,
cluster=cluster,
cluster_thr=cluster_thr,
random_colors=random_colors,
bg_color=bg_color,
order_transparent=order_transparent,
length_gt=length_gt,
length_lt=length_lt,
clusters_gt=clusters_gt,
clusters_lt=clusters_lt,
world_coords=world_coords,
interactive=interactive,
buan=buan,
buan_colors=bundle_colors,
out_png=pjoin(out_dir, out_stealth_png))
def test_horizon_flow():
s1 = 10 * np.array([[0, 0, 0], [1, 0, 0], [2, 0, 0], [3, 0, 0], [4, 0, 0]],
dtype='f8')
s2 = 10 * np.array([[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0], [0, 4, 0]],
dtype='f8')
s3 = 10 * np.array(
[[0, 0, 0], [1, 0.2, 0], [2, 0.2, 0], [3, 0.2, 0], [4, 0.2, 0]],
dtype='f8')
affine = np.array([[1., 0., 0., -98.], [0., 1., 0., -134.],
[0., 0., 1., -72.], [0., 0., 0., 1.]])
data = 255 * np.random.rand(197, 233, 189)
vox_size = (1., 1., 1.)
streamlines = Streamlines()
streamlines.append(s1)
streamlines.append(s2)
streamlines.append(s3)
header = create_nifti_header(affine, data.shape, vox_size)
sft = StatefulTractogram(streamlines, header, Space.RASMM)
tractograms = [sft]
images = None
horizon(tractograms,
images=images,
cluster=True,
cluster_thr=5,
random_colors=False,
length_lt=np.inf,
length_gt=0,
clusters_lt=np.inf,
clusters_gt=0,
world_coords=True,
interactive=False)
buan_colors = np.ones(streamlines.get_data().shape)
horizon(tractograms,
buan=True,
buan_colors=buan_colors,
world_coords=True,
interactive=False)
data = 255 * np.random.rand(197, 233, 189)
images = [(data, affine)]
horizon(tractograms,
images=images,
cluster=True,
cluster_thr=5,
random_colors=False,
length_lt=np.inf,
length_gt=0,
clusters_lt=np.inf,
clusters_gt=0,
world_coords=True,
interactive=False)
with TemporaryDirectory() as out_dir:
fimg = os.path.join(out_dir, 'test.nii.gz')
ftrk = os.path.join(out_dir, 'test.trk')
fnpy = os.path.join(out_dir, 'test.npy')
save_nifti(fimg, data, affine)
dimensions = data.shape
nii_header = create_nifti_header(affine, dimensions, vox_size)
sft = StatefulTractogram(streamlines, nii_header, space=Space.RASMM)
save_tractogram(sft, ftrk, bbox_valid_check=False)
pvalues = np.random.uniform(low=0, high=1, size=(10, ))
np.save(fnpy, pvalues)
input_files = [ftrk, fimg]
npt.assert_equal(len(input_files), 2)
hz_flow = HorizonFlow()
hz_flow.run(input_files=input_files,
stealth=True,
out_dir=out_dir,
out_stealth_png='tmp_x.png')
npt.assert_equal(os.path.exists(os.path.join(out_dir, 'tmp_x.png')),
True)
npt.assert_raises(ValueError,
hz_flow.run,
input_files=input_files,
bg_color=(0.2, 0.2))
hz_flow.run(input_files=input_files,
stealth=True,
bg_color=[
0.5,
],
out_dir=out_dir,
out_stealth_png='tmp_x.png')
npt.assert_equal(os.path.exists(os.path.join(out_dir, 'tmp_x.png')),
True)
input_files = [ftrk, fnpy]
npt.assert_equal(len(input_files), 2)
hz_flow.run(input_files=input_files,
stealth=True,
bg_color=[
0.5,
],
buan=True,
buan_thr=0.5,
buan_highlight=(1, 1, 0),
out_dir=out_dir,
out_stealth_png='tmp_x.png')
npt.assert_equal(os.path.exists(os.path.join(out_dir, 'tmp_x.png')),
True)
npt.assert_raises(ValueError,
hz_flow.run,
input_files=input_files,
roi_colors=(0.2, 0.2))
hz_flow.run(input_files=input_files,
stealth=True,
roi_colors=[
0.5,
],
out_dir=out_dir,
out_stealth_png='tmp_x.png')
npt.assert_equal(os.path.exists(os.path.join(out_dir, 'tmp_x.png')),
True)
def test_horizon_flow():
s1 = 10 * np.array([[0, 0, 0], [1, 0, 0], [2, 0, 0], [3, 0, 0], [4, 0, 0]],
dtype='f8')
s2 = 10 * np.array([[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0], [0, 4, 0]],
dtype='f8')
s3 = 10 * np.array(
[[0, 0, 0], [1, 0.2, 0], [2, 0.2, 0], [3, 0.2, 0], [4, 0.2, 0]],
dtype='f8')
print(s1.shape)
print(s2.shape)
print(s3.shape)
streamlines = Streamlines()
streamlines.append(s1)
streamlines.append(s2)
streamlines.append(s3)
tractograms = [streamlines]
images = None
horizon(tractograms,
images=images,
cluster=True,
cluster_thr=5,
random_colors=False,
length_lt=np.inf,
length_gt=0,
clusters_lt=np.inf,
clusters_gt=0,
world_coords=False,
interactive=False)
#
affine = np.diag([2., 1, 1, 1]).astype('f8')
#
data = 255 * np.random.rand(150, 150, 150)
#
images = [(data, affine)]
horizon(tractograms,
images=images,
cluster=True,
cluster_thr=5,
random_colors=False,
length_lt=np.inf,
length_gt=0,
clusters_lt=np.inf,
clusters_gt=0,
world_coords=True,
interactive=False)
with TemporaryDirectory() as out_dir:
fimg = os.path.join(out_dir, 'test.nii.gz')
ftrk = os.path.join(out_dir, 'test.trk')
save_nifti(fimg, data, affine)
dimensions = data.shape
voxel_sizes = np.array([2, 1.0, 1.0])
nii_header = create_nifti_header(affine, dimensions, voxel_sizes)
sft = StatefulTractogram(streamlines, nii_header, space=Space.RASMM)
save_tractogram(sft, ftrk, bbox_valid_check=False)
input_files = [ftrk, fimg]
npt.assert_equal(len(input_files), 2)
hz_flow = HorizonFlow()
hz_flow.run(input_files=input_files,
stealth=True,
out_dir=out_dir,
out_stealth_png='tmp_x.png')
npt.assert_equal(os.path.exists(os.path.join(out_dir, 'tmp_x.png')),
True)
def test_horizon():
s1 = 10 * np.array([[0, 0, 0],
[1, 0, 0],
[2, 0, 0],
[3, 0, 0],
[4, 0, 0]], dtype='f8')
s2 = 10 * np.array([[0, 0, 0],
[0, 1, 0],
[0, 2, 0],
[0, 3, 0],
[0, 4, 0]], dtype='f8')
s3 = 10 * np.array([[0, 0, 0],
[1, 0.2, 0],
[2, 0.2, 0],
[3, 0.2, 0],
[4, 0.2, 0]], dtype='f8')
streamlines = Streamlines()
streamlines.append(s1)
streamlines.append(s2)
streamlines.append(s3)
affine = np.array([[1., 0., 0., -98.],
[0., 1., 0., -134.],
[0., 0., 1., -72.],
[0., 0., 0., 1.]])
data = 255 * np.random.rand(197, 233, 189)
vox_size = (1., 1., 1.)
streamlines._data += np.array([-98., -134., -72.])
header = create_nifti_header(affine, data.shape, vox_size)
sft = StatefulTractogram(streamlines, header, Space.RASMM)
# only tractograms
tractograms = [sft]
images = None
horizon(tractograms, images=images, cluster=True, cluster_thr=5,
random_colors=False, length_lt=np.inf, length_gt=0,
clusters_lt=np.inf, clusters_gt=0,
world_coords=True, interactive=False)
images = [(data, affine)]
# tractograms in native coords (not supported for now)
with npt.assert_raises(ValueError) as ve:
horizon(tractograms, images=images, cluster=True, cluster_thr=5,
random_colors=False, length_lt=np.inf, length_gt=0,
clusters_lt=np.inf, clusters_gt=0,
world_coords=False, interactive=False)
msg = 'Currently native coordinates are not supported for streamlines'
npt.assert_(msg in str(ve.exception))
# only images
tractograms = None
horizon(tractograms, images=images, cluster=True, cluster_thr=5,
random_colors=False, length_lt=np.inf, length_gt=0,
clusters_lt=np.inf, clusters_gt=0,
world_coords=True, interactive=False)
# no clustering tractograms and images
horizon(tractograms, images=images, cluster=False, cluster_thr=5,
random_colors=False, length_lt=np.inf, length_gt=0,
clusters_lt=np.inf, clusters_gt=0,
world_coords=True, interactive=False)
to be considered valid all fields must correspond between the headers.
It can be easily verified using this function, which also accept
the same variety of input as ``get_reference_info``
"""
print(is_header_compatible(reference_anatomy, bundles_filename[0]))
"""
If a TRK was generated with a valid header, but the reference NIFTI was lost
a header can be generated to then generate a fake NIFTI file.
If you wish to manually save Trk and Tck file using nibabel streamlines
API for more freedom of action (not recommended for beginners) you can
create a valid header using create_tractogram_header
"""
nifti_header = create_nifti_header(affine, dimensions, voxel_sizes)
nib.save(nib.Nifti1Image(np.zeros(dimensions), affine, nifti_header),
'fake.nii.gz')
nib.save(reference_anatomy, os.path.basename(ref_anat_filename))
"""
Once loaded, no matter the original file format, the stateful tractogram is
self-contained and maintains a valid state. By requiring a reference the
tractogram's spatial transformation can be easily manipulated.
Let's save all files as TRK to visualize in TrackVis for example.
However, when loaded the lpt and rpt files contain invalid streamlines and
for particular operations/tools/functions it is safer to remove them
"""
save_tractogram(cc_sft, 'cc.trk')
save_tractogram(laf_sft, 'laf.trk')
def run(self,
input_files,
cluster=False,
cluster_thr=15.,
random_colors=False,
length_gt=0,
length_lt=1000,
clusters_gt=0,
clusters_lt=10**8,
native_coords=False,
stealth=False,
emergency_header='icbm_2009a',
bg_color=(0, 0, 0),
disable_order_transparency=False,
out_dir='',
out_stealth_png='tmp.png'):
""" Interactive medical visualization - Invert the Horizon!
Interact with any number of .trk, .tck or .dpy tractograms and anatomy
files .nii or .nii.gz. Cluster streamlines on loading.
Parameters
----------
input_files : variable string
cluster : bool, optional
Enable QuickBundlesX clustering
cluster_thr : float, optional
Distance threshold used for clustering. Default value 15.0 for
small animal brains you may need to use something smaller such
as 2.0. The distance is in mm. For this parameter to be active
``cluster`` should be enabled
random_colors : bool, optional
Given multiple tractograms have been included then each tractogram
will be shown with different color
length_gt : float, optional
Clusters with average length greater than ``length_gt`` amount
in mm will be shown
length_lt : float, optional
Clusters with average length less than ``length_lt`` amount in
mm will be shown
clusters_gt : int, optional
Clusters with size greater than ``clusters_gt`` will be shown.
clusters_lt : int, optional
Clusters with size less than ``clusters_gt`` will be shown.
native_coords : bool, optional
Show results in native coordinates
stealth : bool, optional
Do not use interactive mode just save figure.
emergency_header : str, optional
If no anatomy reference is provided an emergency header is
provided. Current options 'icbm_2009a' and 'icbm_2009c'.
bg_color : variable float, optional
Define the background color of the scene. Colors can be defined
with 1 or 3 values and should be between [0-1].
Default is black (e.g --bg_color 0 0 0 or --bg_color 0).
disable_order_transparency : bool, optional
Default False. Use depth peeling to sort transparent objects.
If True also enables anti-aliasing.
out_dir : str, optional
Output directory. Default current directory.
out_stealth_png : str, optional
Filename of saved picture.
References
----------
.. [Horizon_ISMRM19] Garyfallidis E., M-A. Cote, B.Q. Chandio,
S. Fadnavis, J. Guaje, R. Aggarwal, E. St-Onge, K.S. Juneja,
S. Koudoro, D. Reagan, DIPY Horizon: fast, modular, unified and
adaptive visualization, Proceedings of: International Society of
Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 2019.
"""
verbose = True
tractograms = []
images = []
pams = []
interactive = not stealth
world_coords = not native_coords
mni_2009a = {}
mni_2009a['affine'] = np.array([[1., 0., 0.,
-98.], [0., 1., 0., -134.],
[0., 0., 1., -72.], [0., 0., 0., 1.]])
mni_2009a['dims'] = (197, 233, 189)
mni_2009a['vox_size'] = (1., 1., 1.)
mni_2009a['vox_space'] = 'RAS'
mni_2009c = {}
mni_2009c['affine'] = np.array([[1., 0., 0.,
-96.], [0., 1., 0., -132.],
[0., 0., 1., -78.], [0., 0., 0., 1.]])
mni_2009c['dims'] = (193, 229, 193)
mni_2009c['vox_size'] = (1., 1., 1.)
mni_2009c['vox_space'] = 'RAS'
if emergency_header == 'icbm_2009a':
hdr = mni_2009c
else:
hdr = mni_2009c
emergency_ref = create_nifti_header(hdr['affine'], hdr['dims'],
hdr['vox_size'])
io_it = self.get_io_iterator()
for input_output in io_it:
fname = input_output[0]
if verbose:
print('Loading file ...')
print(fname)
print('\n')
fl = fname.lower()
ends = fl.endswith
if ends('.trk'):
sft = load_tractogram(fname, 'same', bbox_valid_check=False)
tractograms.append(sft)
if ends('.dpy') or ends('.tck'):
sft = load_tractogram(fname, emergency_ref)
tractograms.append(sft)
if ends('.nii.gz') or ends('.nii'):
data, affine = load_nifti(fname)
images.append((data, affine))
if verbose:
print('Affine to RAS')
np.set_printoptions(3, suppress=True)
print(affine)
np.set_printoptions()
if ends(".pam5"):
pam = load_peaks(fname)
pams.append(pam)
if verbose:
print('Peak_dirs shape')
print(pam.peak_dirs.shape)
if len(bg_color) == 1:
bg_color *= 3
elif len(bg_color) != 3:
raise ValueError('You need 3 values to set up backgound color. '
'e.g --bg_color 0.5 0.5 0.5')
order_transparent = not disable_order_transparency
horizon(tractograms=tractograms,
images=images,
pams=pams,
cluster=cluster,
cluster_thr=cluster_thr,
random_colors=random_colors,
bg_color=bg_color,
order_transparent=order_transparent,
length_gt=length_gt,
length_lt=length_lt,
clusters_gt=clusters_gt,
clusters_lt=clusters_lt,
world_coords=world_coords,
interactive=interactive,
out_png=pjoin(out_dir, out_stealth_png))
