if not os.path.isdir(track_outdir):
os.mkdir(track_outdir)
if not os.path.isfile(track_file):
deterministic(diffusion_file,
bvecs_file,
bvals_file,
track_outdir,
mask_file=mask_file,
order=4,
nb_seeds_per_voxel=nb_seeds_per_voxel,
step=0.5,
fmt="%.4f")
if use_vtk:
tracks = loadtxt(track_file)
actor = pvtk.line(tracks, scalar=1)
pvtk.add(ren, actor)
pvtk.record(ren, qcdir, "fibers", az_ang=45, n_frames=2)
pvtk.clear(ren)
connect_outdir = os.path.join(outdir, "det_region_connectivity")
if not os.path.isdir(connect_outdir):
os.mkdir(connect_outdir)
if len(os.listdir(connect_outdir)) == 0:
if use_t1_space:
proba_file, network_file = anatomical_connectivity_matrix(
track_file,
label_file,
t1_file,
fa_file,
trf_file,
np.array([[0,0.2,0],[1,0.2,0],[2,0.2,0]], dtype=np.single),
np.array([[2,0.2,0],[1,0.2,0],[0,0.2,0]], dtype=np.single),
np.array([[0,0,0],[0,1,0],[0,2,0]], dtype=np.single)]
if key == 0:
clusters = agglomerative_clustering(
tracks, "single", cutoff=0.5, nb_samples=10, nb_cpu=1,
preserve_input=False)
ren = pvtk.ren()
nb_of_clusters = len(clusters)
for cluster_id, tracks_index in clusters.iteritems():
fibers = []
for index in tracks_index:
fibers.append(tracks[index])
actor = pvtk.line(fibers, float(cluster_id) / nb_of_clusters)
pvtk.add(ren, actor)
pvtk.show(ren)
elif key==1:
tracks *= 1000
print len(tracks)
print clustering_metric_resample(np.asarray(tracks), nb_of_threads=24)
print clustering_metric(tracks, nb_of_threads=24, zhang_thr=0.,
distance_metric="max")
elif key==2:
tracks *= 30
clusters = local_skeleton_clustering(tracks, cutoff=0.7, nb_samples=10,
nb_of_threads=1)
print clusters
for cnt, cluster in enumerate(clusters_):
clusters[str(cnt)] = {"indices": cluster.indices}
with open(cluster_file, "w") as open_file:
json.dump(clusters, open_file, indent=4)
else:
with open(cluster_file) as open_file:
clusters = json.load(open_file)
if 1: #use_vtk:
ren = pvtk.ren()
colors = numpy.ones((len(fibers),))
nb_clusters = len(clusters)
for clusterid, item in clusters.items():
indices = item["indices"]
colors[indices] = numpy.random.rand()
actor = pvtk.line(fibers, colors.tolist())
actor.RotateX(actor_ang[0])
actor.RotateY(actor_ang[1])
actor.RotateZ(actor_ang[2])
pvtk.add(ren, actor)
ren.SetBackground(1, 1, 1)
#pvtk.record(ren, qcdir, "clusters", az_ang=45, n_frames=2)
pvtk.record(ren, qcdir, "clusters", n_frames=36, az_ang=10, animate=True,
delay=25)
pvtk.show(ren)
pvtk.clear(ren)
print(stop)
"""
Bundle statistic
os.mkdir(track_outdir)
if not os.path.isfile(track_file):
deterministic(
diffusion_file,
bvecs_file,
bvals_file,
track_outdir,
mask_file=mask_file,
order=4,
nb_seeds_per_voxel=nb_seeds_per_voxel,
step=0.5,
fmt="%.4f")
if use_vtk:
tracks = loadtxt(track_file)
actor = pvtk.line(tracks, scalar=1)
pvtk.add(ren, actor)
pvtk.record(ren, qcdir, "fibers", az_ang=45, n_frames=2)
pvtk.clear(ren)
connect_outdir = os.path.join(outdir, "det_region_connectivity")
if not os.path.isdir(connect_outdir):
os.mkdir(connect_outdir)
if len(os.listdir(connect_outdir)) == 0:
if use_t1_space:
proba_file, network_file = anatomical_connectivity_matrix(
track_file,
label_file,
t1_file,
fa_file,