def bundle_thre_seg(self,
streamlines=None,
cluster_thre=10,
dist_thre=10.0,
pts=12):
"""
QuickBundles-based segmentation
Parameters
----------
streamlines: streamline data
cluster_thre: remove small cluster
dist_thre: clustering threshold (distance mm)
pts: each streamlines are divided into sections
Return
------
sort_index: sort of clusters according to y mean of cluster's centroids
data_cluster: cluster data corresponding to sort_index
"""
if streamlines is None:
streamlines = self._fasciculus.get_data()
else:
streamlines = streamlines
bundles = QuickBundles(streamlines, dist_thre, pts)
bundles.remove_small_clusters(cluster_thre)
clusters = bundles.clusters()
data_clusters = []
for key in clusters.keys():
data_clusters.append(streamlines[clusters[key]['indices']])
centroids = bundles.centroids
clusters_y_mean = [clu[:, 1].mean() for clu in centroids]
sort_index = np.argsort(clusters_y_mean)
return sort_index, data_clusters
def bundle_centroids(self,
streamlines=None,
cluster_thre=10,
dist_thre=10.0,
pts=12):
"""
QuickBundles-based segmentation
Parameters
----------
streamlines: streamline data
cluster_thre: remove small cluster
dist_thre: clustering threshold (distance mm)
pts: each streamlines are divided into sections
Return
------
centroids: cluster's centroids
"""
if streamlines is None:
streamlines = self._fasciculus.get_data()
else:
streamlines = streamlines
bundles = QuickBundles(streamlines, dist_thre, pts)
bundles.remove_small_clusters(cluster_thre)
centroids = bundles.centroids
return nibas.ArraySequence(centroids)
def load_PX_tracks():
roi = "LH_premotor"
dn = "/home/hadron/from_John_mon12thmarch"
dname = "/extra_probtrackX_analyses/_subject_id_subj05_101_32/particle2trackvis_" + roi + "_native/"
fname = dn + dname + "tract_samples.trk"
from nibabel import trackvis as tv
points_space = [None, "voxel", "rasmm"]
streamlines, hdr = tv.read(fname, as_generator=True, points_space="voxel")
tracks = [s[0] for s in streamlines]
del streamlines
# return tracks
qb = QuickBundles(tracks, 25.0 / 2.5, 18)
# tl=Line(qb.exemplars()[0],line_width=1)
del tracks
qb.remove_small_clusters(20)
tl = TrackLabeler(qb, qb.downsampled_tracks(), vol_shape=None, tracks_line_width=3.0, tracks_alpha=1)
# put the seeds together
# seeds=np.vstack((seeds,seeds2))
# shif the seeds
# seeds=np.dot(mat[:3,:3],seeds.T).T + mat[:3,3]
# seeds=seeds-shift
# seeds2=np.dot(mat[:3,:3],seeds2.T).T + mat[:3,3]
# seeds2=seeds2-shift
# msk = Point(seeds,colors=(1,0,0,1.),pointsize=2.)
# msk2 = Point(seeds2,colors=(1,0,.ppppp2,1.),pointsize=2.)
w = World()
w.add(tl)
# w.add(msk)
# w.add(msk2)
# w.add(sl)
# create window
wi = Window(caption="Fos", bgcolor=(0.3, 0.3, 0.6, 1.0), width=1600, height=900)
wi.attach(w)
# create window manager
wm = WindowManager()
wm.add(wi)
wm.run()
if reduce_length:
T=[t for t in T if track_range(100,200)]
#iT=np.random.randint(0,len(T),5000)
#T=[T[i] for i in iT]
#stop
#center
shift=(np.array(data.shape)-1)/2.
T=[t-shift for t in T]
#load initial QuickBundles with threshold 30mm
#fpkl = 'data/subj_05/101_32/DTI/qb_gqi_1M_linear_30.pkl'
qb=QuickBundles(T,25.,30)
print len(qb.clustering)
#qb=load_pickle(fpkl)
qb.remove_small_clusters(1000)
print len(qb.clustering)
#create the interaction system for tracks
tl = TrackLabeler(qb,qb.downsampled_tracks(),vol_shape=data.shape,tracks_line_width=3.,tracks_alpha=1.)
#add a interactive slicing/masking tool
sl = Slicer(affine,data)
#add one way communication between tl and sl
tl.slicer=sl
#OpenGL coordinate system axes
ax = Axes(100)
x,y,z=data.shape
#add the actors to the world
w=World()
w.add(tl)
