def skeletonize_both():
from dipy.viz import fos
from dipy.core.track_metrics import downsample
from dipy.core.track_performance import local_skeleton_clustering, most_similar_track_mam
froi='/home/eg309/Data/ICBM_Wmpm/ICBM_WMPM.nii'
wI=get_roi(froi,9,0) #4 is genu
fname='/home/eg309/Data/PROC_MR10032/subj_03/101/1312211075232351192010092217244332311282470ep2dadvdiffDSI10125x25x25STs004a001_QA_warp.dpy'
#fname='/home/eg309/Data/PROC_MR10032/subj_03/101/1312211075232351192010092217244332311282470ep2dadvdiffDSI10125x25x25STs004a001_QA_native.dpy'
#fname='/home/eg309/Data/PROC_MR10032/subj_06/101/13122110752323511930000010092916083910900000227ep2dadvdiffDSI10125x25x25STs004a001_QA_native.dpy'
fname2='/home/eg309/Data/PROC_MR10032/subj_06/101/13122110752323511930000010092916083910900000227ep2dadvdiffDSI10125x25x25STs004a001_QA_warp.dpy'
r=fos.ren()
#'''
dpr=Dpy(fname,'r')
T=dpr.read_indexed(range(2*10**4))
dpr.close()
print len(T)
Td=[downsample(t,3) for t in T if length(t)>40]
C=local_skeleton_clustering(Td,d_thr=20.)
for c in C:
#color=np.random.rand(3)
color=fos.red
if C[c]['N']>0:
Ttmp=[]
for i in C[c]['indices']:
Ttmp.append(T[i])
si,s=most_similar_track_mam(Ttmp,'avg')
print si,C[c]['N']
fos.add(r,fos.line(Ttmp[si],color))
dpr=Dpy(fname2,'r')
T=dpr.read_indexed(range(2*10**4))
dpr.close()
print len(T)
Td=[downsample(t,3) for t in T if length(t)>40]
C=local_skeleton_clustering(Td,d_thr=20.)
#r=fos.ren()
for c in C:
#color=np.random.rand(3)
color=fos.yellow
if C[c]['N']>0:
Ttmp=[]
for i in C[c]['indices']:
Ttmp.append(T[i])
si,s=most_similar_track_mam(Ttmp,'avg')
print si,C[c]['N']
fos.add(r,fos.line(Ttmp[si],color))
#'''
fos.add(r,fos.point(wI,fos.green))
fos.show(r)
def bloat_bundle(self,tracks):
tracksn=[]
#c=np.array([0,0,0],np.float32)
import dipy.core.track_performance as pf
#c=tracks[0][-4]
ct=tracks[pf.most_similar_track_mam(tracks,'avg')[0]]
c=ct[len(ct)/2]
#tracks2=[tm.downsample(t,200) for t in tracks]
for X in tracks2:
Xn = X + np.multiply(10/np.sum((X-c)**2,axis=1).reshape(len(X),1),X-c)
#Xn = X + 2*(X-c)
tracksn.append(Xn)
from dipy.io import pickles as pkl
pkl.save_pickle('bundles.pkl',{'init':tracks[10],'after':tracksn[10]})
return tracksn
def skeletonize():
froi='/home/eg309/Data/ICBM_Wmpm/ICBM_WMPM.nii'
wI=get_roi(froi,35,1) #4 is genu
#fname='/home/eg309/Data/PROC_MR10032/subj_03/101/1312211075232351192010092217244332311282470ep2dadvdiffDSI10125x25x25STs004a001_QA_warp.dpy'
#fname='/home/eg309/Data/PROC_MR10032/subj_03/101/1312211075232351192010092217244332311282470ep2dadvdiffDSI10125x25x25STs004a001_QA_native.dpy'
#fname='/home/eg309/Data/PROC_MR10032/subj_06/101/13122110752323511930000010092916083910900000227ep2dadvdiffDSI10125x25x25STs004a001_QA_native.dpy'
fname='/home/eg309/Data/PROC_MR10032/subj_06/101/13122110752323511930000010092916083910900000227ep2dadvdiffDSI10125x25x25STs004a001_QA_warp.dpy'
dpr=Dpy(fname,'r')
T=dpr.read_indexed(range(2*10**4))
dpr.close()
print len(T)
from dipy.core.track_metrics import downsample
from dipy.core.track_performance import local_skeleton_clustering, most_similar_track_mam
Td=[downsample(t,3) for t in T]
C=local_skeleton_clustering(Td,d_thr=20.)
#Tobject=np.array(T,dtype=np.object)
from dipy.viz import fos
r=fos.ren()
#skeleton=[]
for c in C:
color=np.random.rand(3)
if C[c]['N']>0:
Ttmp=[]
for i in C[c]['indices']:
Ttmp.append(T[i])
si,s=most_similar_track_mam(Ttmp,'avg')
print si,C[c]['N']
fos.add(r,fos.line(Ttmp[si],color))
#print len(skeleton)
#fos.add(r,fos.line(skeleton,color))
#fos.add(r,fos.line(T,fos.red))
fos.show(r)
colors2 = len(data)*[None]
colors_sk = []#len(C.keys())*[None]
for c in C:
color=np.random.rand(3)
r,g,b = color
bundle=[]
for i in C[c]['indices']:
colors2[i]=np.tile(np.array([r,g,b,opacity],'f'),(len(data[i]),1))
bundle.append(data[i])
bi=pf.most_similar_track_mam(bundle)[0]
C[c]['skeletal']=bundle[bi]
if len(C[c]['indices'])>100 and tm.length(bundle[bi])>30.:
colors_sk.append( np.tile(np.array([r,g,b,opacity],'f'),(len(bundle[bi]),1)) )
skeletals.append(bundle[bi])
print 'len_data', len(data)
print 'len_skeletals', len(skeletals)
print 'len_colors2', len(colors2)
print 'len_colors_sk', len(colors_sk)
t2=Tracks(data,colors2,line_width=1.)
colors2 = len(data) * [None]
colors_sk = [] #len(C.keys())*[None]
for c in C:
color = np.random.rand(3)
r, g, b = color
bundle = []
for i in C[c]['indices']:
colors2[i] = np.tile(np.array([r, g, b, opacity], 'f'),
(len(data[i]), 1))
bundle.append(data[i])
bi = pf.most_similar_track_mam(bundle)[0]
C[c]['skeletal'] = bundle[bi]
if len(C[c]['indices']) > 100 and tm.length(bundle[bi]) > 30.:
colors_sk.append(
np.tile(np.array([r, g, b, opacity], 'f'), (len(bundle[bi]), 1)))
skeletals.append(bundle[bi])
print 'len_data', len(data)
print 'len_skeletals', len(skeletals)
print 'len_colors2', len(colors2)
print 'len_colors_sk', len(colors_sk)
t2 = Tracks(data, colors2, line_width=1.)
t2.position = (100, 0, 0)
