def _create_trackvis_header(self, nii_hdr):
hdr = tv.empty_header()
hdr['dim'] = np.array(nii_hdr.get_data_shape())
hdr['voxel_size'] = np.array(nii_hdr.get_zooms())
aff = np.eye(4)
aff[0:3, 0:3] *= np.array(nii_hdr.get_zooms())
hdr['vox_to_ras'] = aff
return hdr
def _create_trackvis_header(self, nii_hdr):
hdr = tv.empty_header()
hdr['dim'] = np.array(nii_hdr.get_data_shape())
hdr['voxel_size'] = np.array(nii_hdr.get_zooms())
aff = np.eye(4)
aff[0:3,0:3] *= np.array(nii_hdr.get_zooms())
hdr['vox_to_ras'] = aff
return hdr
def save_streamlines(self, streamlines, save_streamlines_to):
trk_hdr = empty_header()
voxel_order = orientation_to_string(nib.io_orientation(self.affine))
trk_hdr['voxel_order'] = voxel_order
trk_hdr['voxel_size'] = self.voxel_size
trk_hdr['vox_to_ras'] = self.affine
trk_hdr['dim'] = self.shape
trk_tracks = ((ii, None, None) for ii in streamlines)
write(save_streamlines_to, trk_tracks, trk_hdr)
pickle.dump(self, open(save_streamlines_to + '.p', 'wb'))
def save_streamlines(self, streamlines, save_streamlines_to):
trk_hdr = empty_header()
voxel_order = orientation_to_string(nib.io_orientation(self.affine))
trk_hdr['voxel_order'] = voxel_order
trk_hdr['voxel_size'] = self.voxel_size
trk_hdr['vox_to_ras'] = self.affine
trk_hdr['dim'] = self.shape
trk_tracks = ((ii, None, None) for ii in streamlines)
write(save_streamlines_to, trk_tracks, trk_hdr)
pickle.dump(self, open(save_streamlines_to + '.p', 'wb'))
def bundle_tracks(in_file, dist_thr=40., pts=16, skip=80.):
import subprocess
import os.path as op
from nibabel import trackvis as tv
from dipy.segment.quickbundles import QuickBundles
streams, hdr = tv.read(in_file)
streamlines = [i[0] for i in streams]
qb = QuickBundles(streamlines, float(dist_thr), int(pts))
clusters = qb.clustering
#scalars = [i[0] for i in streams]
out_files = []
name = "quickbundle_"
n_clusters = clusters.keys()
print("%d clusters found" % len(n_clusters))
new_hdr = tv.empty_header()
new_hdr['n_scalars'] = 1
for cluster in clusters:
cluster_trk = op.abspath(name + str(cluster) + ".trk")
print("Writing cluster %d to %s" % (cluster, cluster_trk))
out_files.append(cluster_trk)
clust_idxs = clusters[cluster]['indices']
new_streams = [streamlines[i] for i in clust_idxs]
for_save = [(sl, None, None) for sl in new_streams]
tv.write(cluster_trk, for_save, hdr)
out_merged_file = "MergedBundles.trk"
command_list = ["track_merge"]
command_list.extend(out_files)
command_list.append(out_merged_file)
subprocess.call(command_list)
out_scene_file = write_trackvis_scene(out_merged_file,
n_clusters=len(clusters),
skip=skip,
names=None,
out_file="NewScene.scene")
print("Merged track file written to %s" % out_merged_file)
print("Scene file written to %s" % out_scene_file)
return out_files, out_merged_file, out_scene_file
def bundle_tracks(in_file, dist_thr=40., pts = 16, skip=80.):
import subprocess
import os.path as op
from nibabel import trackvis as tv
from dipy.segment.quickbundles import QuickBundles
streams, hdr = tv.read(in_file)
streamlines = [i[0] for i in streams]
qb = QuickBundles(streamlines, float(dist_thr), int(pts))
clusters = qb.clustering
#scalars = [i[0] for i in streams]
out_files = []
name = "quickbundle_"
n_clusters = clusters.keys()
print("%d clusters found" % len(n_clusters))
new_hdr = tv.empty_header()
new_hdr['n_scalars'] = 1
for cluster in clusters:
cluster_trk = op.abspath(name + str(cluster) + ".trk")
print("Writing cluster %d to %s" % (cluster, cluster_trk))
out_files.append(cluster_trk)
clust_idxs = clusters[cluster]['indices']
new_streams = [ streamlines[i] for i in clust_idxs ]
for_save = [(sl, None, None) for sl in new_streams]
tv.write(cluster_trk, for_save, hdr)
out_merged_file = "MergedBundles.trk"
command_list = ["track_merge"]
command_list.extend(out_files)
command_list.append(out_merged_file)
subprocess.call(command_list)
out_scene_file = write_trackvis_scene(out_merged_file, n_clusters=len(clusters), skip=skip, names=None, out_file = "NewScene.scene")
print("Merged track file written to %s" % out_merged_file)
print("Scene file written to %s" % out_scene_file)
return out_files, out_merged_file, out_scene_file
def __init__(self,
fname="",
tracks=None,
header=None,
connections=np.array([]),
original_track_indices=np.array([]),
properties=None,
**traits):
"""Convenience class for loading, manipulating and storing
trackvis data.
Parameters
----------
fname:str
path to the trackvis .trk file
streams:list
If there is already a list of tracks
header:dict
Trackvis header file
connections:np.ndarray[dtype=int,ndim=1]
array with connection Id's for each track
storage_coords: {"ijk", "voxmm", "MNI", "qsdr"}
How should this object store the tracks internally? Assumes
that the original coords are in voxmm for trackvis
properties: traited_query.Scan object
information about this dataset
"""
#if not None in (tracks,header):
if not (header is None):
# Tracks and header are provided
self.header = header
self.set_tracks(tracks)
else:
if fname.endswith("trk.gz") or fname.endswith("trk"):
if fname.endswith("trk.gz"):
fl = gzip.open(fname, "r")
elif fname.endswith("trk"):
fl = open(fname, "r")
streams, self.header = trackvis.read(fl)
# Convert voxmm to ijk
self.set_tracks(
np.array([stream[0] for stream in streams],
dtype=np.object))
fl.close()
# Check for scalars, support them someday
if self.header['n_scalars'] > 0:
print "WARNING: Ignoring track scalars in %s" % fname
elif fname.endswith("txt"):
fop = open(fname, "r")
self.set_tracks( np.array(
[np.array(map(float, line.strip().split())).reshape(-1,3) for \
line in fop ], dtype=np.object ))
elif fname.endswith("mat"):
pass
if not hasattr(self, "header"):
self.header = trackvis.empty_header()
if properties is None:
from dsi2.database.traited_query import Scan
print "Warning: using default properties"
self.properties = Scan()
else:
self.properties = properties
self.connections = connections
self.clusters = []
self.original_track_indices = original_track_indices
RangeEditor, TableEditor, Handler, Include,HSplit, EnumEditor, HSplit, Action, \
CheckListEditor, ObjectColumn
from traits.api import HasTraits, Instance, Array, \
CInt, Color, Bool, List, Int, Str, Instance, Any, Enum, \
DelegatesTo, on_trait_change, Button
import cPickle as pickle
from mayavi.core.ui.api import SceneEditor
from mayavi.tools.mlab_scene_model import MlabSceneModel
from mayavi import mlab
import gzip
from .track_math import tracks_to_endpoints
from mayavi.core.api import PipelineBase, Source
mni_hdr = trackvis.empty_header()
mni_hdr['dim'] = np.array([91, 109, 91], dtype="int16")
mni_hdr['voxel_order'] = 'LAS'
mni_hdr['voxel_size'] = np.array([2., 2., 2.], dtype='float32')
mni_hdr['image_orientation_patient'] = np.array([1., 0., 0., 0., -1., 0.],
dtype='float32')
mni_hdr['vox_to_ras'] = \
np.array([[ -2., 0., 0., 90.],
[ 0., 2., 0., -126.],
[ 0., 0., 2., -72.],
[ 0., 0., 0., 1.]], dtype='float32')
qsdr_hdr = trackvis.empty_header(version=1)
qsdr_hdr['dim'] = np.array([79, 95, 69], dtype="int16")
qsdr_hdr['voxel_order'] = 'LPS'
qsdr_hdr['voxel_size'] = np.array([2., 2., 2.], dtype='float32')
def tractography_to_trackvis_file(filename,
tractography,
affine=None,
image_dimensions=None):
trk_header = trackvis.empty_header()
if affine is not None:
pass
elif hasattr(tractography, 'affine'):
affine = tractography.affine
else:
raise ValueError("Affine transform has to be provided")
trackvis.aff_to_hdr(affine, trk_header, True, True)
trk_header['origin'] = 0.
if image_dimensions is not None:
trk_header['dim'] = image_dimensions
elif hasattr(tractography, 'image_dimensions'):
trk_header['dim'] = tractography.image_dimensions
else:
raise ValueError("Image dimensions needed to save a trackvis file")
orig_data = tractography.tracts_data()
data = {}
for k, v in orig_data.items():
if not isinstance(v[0], numpy.ndarray):
continue
if (v[0].ndim > 1 and any(d > 1 for d in v[0].shape[1:])):
warn("Scalar data %s ignored as trackvis "
"format does not handle multivalued data" % k)
else:
data[k] = v
#data_new = {}
# for k, v in data.iteritems():
# if (v[0].ndim > 1 and v[0].shape[1] > 1):
# for i in xrange(v[0].shape[1]):
# data_new['%s_%02d' % (k, i)] = [
# v_[:, i] for v_ in v
# ]
# else:
# data_new[k] = v
trk_header['n_count'] = len(tractography.tracts())
trk_header['n_properties'] = 0
trk_header['n_scalars'] = len(data)
if len(data) > 10:
raise ValueError('At most 10 scalars permitted per point')
trk_header['scalar_name'][:len(data)] = numpy.array([n[:20] for n in data],
dtype='|S20')
trk_tracts = []
for i, sl in enumerate(tractography.tracts()):
scalars = None
if len(data) > 0:
scalars = numpy.vstack([
data[k.decode('utf8')][i].squeeze()
for k in trk_header['scalar_name'][:len(data)]
]).T
trk_tracts.append((sl, scalars, None))
trackvis.write(filename, trk_tracts, trk_header, points_space='rasmm')
def hdr(self):
if self._hdr is not None:
return self._hdr
else:
return trackvis.empty_header()
def tractography_to_trackvis_file(filename, tractography, affine=None, image_dimensions=None):
trk_header = trackvis.empty_header()
if affine is not None:
pass
elif hasattr(tractography, 'affine'):
affine = tractography.affine
else:
raise ValueError("Affine transform has to be provided")
trackvis.aff_to_hdr(affine, trk_header, True, True)
trk_header['origin'] = 0.
if image_dimensions is not None:
trk_header['dim'] = image_dimensions
elif hasattr(tractography, 'image_dimensions'):
trk_header['dim'] = tractography.image_dimensions
else:
raise ValueError("Image dimensions needed to save a trackvis file")
orig_data = tractography.tracts_data()
data = {}
for k, v in orig_data.items():
if not isinstance(v[0], numpy.ndarray):
continue
if (v[0].ndim > 1 and any(d > 1 for d in v[0].shape[1:])):
warn(
"Scalar data %s ignored as trackvis "
"format does not handle multivalued data" % k
)
else:
data[k] = v
#data_new = {}
# for k, v in data.iteritems():
# if (v[0].ndim > 1 and v[0].shape[1] > 1):
# for i in xrange(v[0].shape[1]):
# data_new['%s_%02d' % (k, i)] = [
# v_[:, i] for v_ in v
# ]
# else:
# data_new[k] = v
trk_header['n_count'] = len(tractography.tracts())
trk_header['n_properties'] = 0
trk_header['n_scalars'] = len(data)
if len(data) > 10:
raise ValueError('At most 10 scalars permitted per point')
trk_header['scalar_name'][:len(data)] = numpy.array(
[n[:20] for n in data],
dtype='|S20'
)
trk_tracts = []
for i, sl in enumerate(tractography.tracts()):
scalars = None
if len(data) > 0:
scalars = numpy.vstack([
data[k.decode('utf8')][i].squeeze()
for k in trk_header['scalar_name'][:len(data)]
]).T
trk_tracts.append((sl, scalars, None))
trackvis.write(filename, trk_tracts, trk_header, points_space='rasmm')
def __init__(self, fname="", tracks=None, header=None,
connections=np.array([]),
original_track_indices=np.array([]),
properties=None,**traits):
"""Convenience class for loading, manipulating and storing
trackvis data.
Parameters
----------
fname:str
path to the trackvis .trk file
streams:list
If there is already a list of tracks
header:dict
Trackvis header file
connections:np.ndarray[dtype=int,ndim=1]
array with connection Id's for each track
storage_coords: {"ijk", "voxmm", "MNI", "qsdr"}
How should this object store the tracks internally? Assumes
that the original coords are in voxmm for trackvis
properties: traited_query.Scan object
information about this dataset
"""
#if not None in (tracks,header):
if not (header is None):
# Tracks and header are provided
self.header = header
self.set_tracks(tracks)
else:
if fname.endswith("trk.gz") or fname.endswith("trk"):
if fname.endswith("trk.gz"):
fl = gzip.open(fname,"r")
elif fname.endswith("trk"):
fl = open(fname,"r")
streams, self.header = trackvis.read(fl)
# Convert voxmm to ijk
self.set_tracks(np.array([stream[0] for stream in streams],
dtype=np.object))
fl.close()
# Check for scalars, support them someday
if self.header['n_scalars'] > 0:
print "WARNING: Ignoring track scalars in %s"%fname
elif fname.endswith("txt"):
fop = open(fname,"r")
self.set_tracks( np.array(
[np.array(map(float, line.strip().split())).reshape(-1,3) for \
line in fop ], dtype=np.object ))
elif fname.endswith("mat"):
pass
if not hasattr(self,"header"):
self.header = trackvis.empty_header()
if properties is None:
from dsi2.database.traited_query import Scan
print "Warning: using default properties"
self.properties = Scan()
else:
self.properties = properties
self.connections = connections
self.clusters = []
self.original_track_indices = original_track_indices
CheckListEditor, ObjectColumn
from traits.api import HasTraits, Instance, Array, \
CInt, Color, Bool, List, Int, Str, Instance, Any, Enum, \
DelegatesTo, on_trait_change, Button
import cPickle as pickle
from mayavi.core.ui.api import SceneEditor
from mayavi.tools.mlab_scene_model import MlabSceneModel
from mayavi import mlab
import gzip
from .track_math import tracks_to_endpoints
from mayavi.core.api import PipelineBase, Source
mni_hdr = trackvis.empty_header()
mni_hdr['dim'] = np.array([91,109,91],dtype="int16")
mni_hdr['voxel_order'] = 'LAS'
mni_hdr['voxel_size'] = np.array([ 2., 2., 2.], dtype='float32')
mni_hdr['image_orientation_patient'] = np.array(
[ 1., 0., 0., 0., -1., 0.], dtype='float32')
mni_hdr['vox_to_ras'] = \
np.array([[ -2., 0., 0., 90.],
[ 0., 2., 0., -126.],
[ 0., 0., 2., -72.],
[ 0., 0., 0., 1.]], dtype='float32')
qsdr_hdr = trackvis.empty_header(version=1)
qsdr_hdr['dim'] = np.array([79, 95, 69],dtype="int16")
qsdr_hdr['voxel_order'] = 'LPS'
qsdr_hdr['voxel_size'] = np.array([ 2., 2., 2.], dtype='float32')
'''
Created on 1 Apr 2011
@author: filo
'''
import nibabel.trackvis as tv
import nibabel as nb
import numpy as np
import cPickle
pkl_file = open('streamlines.pkl', 'rb')
streamlines = cPickle.load(pkl_file)
print type(streamlines)
hdr = tv.empty_header()
nifti_filename = 'dtifit__FA.nii'
nii_hdr = nb.load(nifti_filename).get_header()
hdr['dim'] = np.array(nii_hdr.get_data_shape())
hdr['voxel_size'] = np.array(nii_hdr.get_zooms())
aff = np.eye(4)
aff[0:3, 0:3] *= np.array(nii_hdr.get_zooms())
hdr['vox_to_ras'] = aff
print hdr['version']
for i in range(len(streamlines)):
points_arr = streamlines[i][0]
#invert y
#Treat these streamlines as if they are in trackvis format and generate counts
counts_trackvis = density_map(streamlines, (4, 4, 5), (1, 1, 1))
#Treat these streamlines as if they are in nifti format and generate counts
counts_nifti = track_counts(streamlines, (4, 4, 5), (1, 1, 1),
return_elements=False)
print("saving trk files and track_count volumes")
aff = np.eye(4)
aff[0, 0] = -1
img = nib.Nifti1Image(counts_trackvis.astype('int16'), aff)
nib.save(img, 'counts_trackvis.nii.gz')
img = nib.Nifti1Image(counts_nifti.astype('int16'), aff)
nib.save(img, 'counts_nifti.nii.gz')
hdr = empty_header()
hdr['voxel_size'] = (1, 1, 1)
hdr['voxel_order'] = 'las'
hdr['vox_to_ras'] = aff
hdr['dim'] = counts_nifti.shape
#Treat these streamlines like they are in trackvis format and save them
streamlines_trackvis = ((ii, None, None) for ii in streamlines)
write('slAsTrackvis.trk', streamlines_trackvis, hdr)
#Move these streamlines from nifti to trackvis format and save them
streamlines_nifti = ((ii + .5, None, None) for ii in streamlines)
write('slAsNifti.trk', streamlines_nifti, hdr)
"""
Trackvis:
A------------
#Treat these streamlines as if they are in trackvis format and generate counts
counts_trackvis = density_map(streamlines, (4,4,5), (1,1,1))
#Treat these streamlines as if they are in nifti format and generate counts
counts_nifti = track_counts(streamlines, (4,4,5), (1,1,1),
return_elements=False)
print("saving trk files and track_count volumes")
aff = np.eye(4)
aff[0, 0] = -1
img = nib.Nifti1Image(counts_trackvis.astype('int16'), aff)
nib.save(img, 'counts_trackvis.nii.gz')
img = nib.Nifti1Image(counts_nifti.astype('int16'), aff)
nib.save(img, 'counts_nifti.nii.gz')
hdr = empty_header()
hdr['voxel_size'] = (1,1,1)
hdr['voxel_order'] = 'las'
hdr['vox_to_ras'] = aff
hdr['dim'] = counts_nifti.shape
#Treat these streamlines like they are in trackvis format and save them
streamlines_trackvis = ((ii,None,None) for ii in streamlines)
write('slAsTrackvis.trk', streamlines_trackvis, hdr)
#Move these streamlines from nifti to trackvis format and save them
streamlines_nifti = ((ii+.5,None,None) for ii in streamlines)
write('slAsNifti.trk', streamlines_nifti, hdr)
"""
Trackvis: