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ismrm2010_save.py
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ismrm2010_save.py
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import numpy as np
from dipy.viz import fos
from dipy.core import track_metrics as tm
from dipy.core import track_learning as tl
from dipy.io import trackvis as tv
import dipy.core.performance as pf
def corresponding_bundles(indices,tracks1,tracks2):
''' Detect similar bundles
'''
pass
def corresponding_tracks(indices,tracks1,tracks2):
''' Detect similar tracks in different brains
'''
li=len(indices)
track2track=np.zeros((li,3))
cnt=0
for i in indices:
rt=[pf.zhang_distances(tracks1[i],t,'avg') for t in tracks2]
rt=np.array(rt)
track2track[cnt-1]=np.array([cnt,i,rt.argmin()])
cnt+=1
return track2track.astype(int)
#br1path='/backup/Data/Eleftherios/CBU090133_METHODS/20090227_145404/Series_003_CBU_DTI_64D_iso_1000/dtk_dti_out/dti_RK2.trk'
#br2path='/backup/Data/Eleftherios/CBU090134_METHODS/20090227_154122/Series_003_CBU_DTI_64D_iso_1000/dtk_dti_out/dti_RK2.trk'
#br1path='/backup/Data/Eleftherios/CBU090133_METHODS/20090227_145404/Series_003_CBU_DTI_64D_iso_1000/dtk_dti_out/dti_FACT.trk'
#br2path='/backup/Data/Eleftherios/CBU090134_METHODS/20090227_154122/Series_003_CBU_DTI_64D_iso_1000/dtk_dti_out/dti_FACT.trk'
br1path='/backup/Data/Eleftherios/CBU090133_METHODS/20090227_145404/Series_003_CBU_DTI_64D_iso_1000/dtk_dti_out/dti_FACT.trk'
br2path='/backup/Data/Eleftherios/CBU090134_METHODS/20090227_154122/Series_003_CBU_DTI_64D_iso_1000/dtk_dti_out/dti_FACT.trk'
br3path='/backup/Data/Eleftherios/CBU090133_METHODS/20090227_145404/Series_003_CBU_DTI_64D_iso_1000/dtk_dti_out/dti_RK2.trk'
min_len=20
down=20
rand_tracks=-1 #default 10
min_search_len=70
max_search_len=140
'''
corr_mat_demo=np.array([[ 1, 10560, 3609],[ 2, 17872, 15377],[ 3, 6447, 3897],
[4, 18854, 6409], [ 5, 14416, 4515], [ 6, 6071, 12155], [ 7, 9956, 13913],
[8, 10853, 15572], [ 9, 13280, 8461], [ 0, 11275, 9224]])
'''
corr_mat_demo=np.array([[ 1, 10560, 3609],[ 2, 17872, 15377],[ 3, 6447, 3897],
[4, 18854, 6409], [ 5, 14416, 4515], [ 7, 9956, 13913],
[8, 10853, 15572], [ 9, 13280, 8461], [ 0, 11275, 9224]])
print 'Minimum track length', min_len, 'mm'
print 'Number of segments for downsampling',down
print 'Number of tracks for detection',rand_tracks
print 'Minimum searched track length', min_search_len, 'mm'
print 'Maximum searched track length', max_search_len, 'mm'
tracks1,hdr1=tv.read(br1path)
tracks2,hdr2=tv.read(br2path)
tracks3,hdr3=tv.read(br3path)
#Load only track points, no scalars or parameters.
tracks1=[t[0] for t in tracks1]
tracks2=[t[0] for t in tracks2]
tracks3=[t[0] for t in tracks3]
print 'Before thresholding'
print len(tracks1)
print len(tracks2)
print len(tracks3)
print hdr1['dim']
print hdr2['dim']
print hdr3['dim']
#Apply thresholds
tracks1=[t for t in tracks1 if tm.length(t) > min_len]
tracks2=[t for t in tracks2 if tm.length(t) > min_len]
tracks3=[t for t in tracks3 if tm.length(t) > min_len]
print 'After thresholding'
print len(tracks1)
print len(tracks2)
print len(tracks3)
print 'Downsampling'
tracks1z=[tm.downsample(t,down) for t in tracks1]
tracks2z=[tm.downsample(t,down) for t in tracks2]
tracks3z=[tm.downsample(t,down) for t in tracks3]
print 'Detecting random tracks'
lt1=len(tracks1)
lt2=len(tracks2)
lt3=len(tracks3)
if rand_tracks==-1:
#use already stored indices
t_ind=corr_mat_demo[:,1]
else:
#find the size in number of tracks of the smallest dataset
mlt=np.min(lt1,lt2)
#find some random tracks
rt=0
t_ind=[]
while rt < rand_tracks:
ind=mlt*np.random.rand()
#indices of random fibers
ind=int(round(ind))
if tm.length(tracks1[ind]) >= min_search_len and tm.length(tracks1[ind]) <= max_search_len:
t_ind.append(ind)
rt+=1
t_ind=np.array(t_ind)
print 'Indices of tracks for detection', t_ind
print 'Finding corresponding tracks'
track2track=corresponding_tracks(t_ind,tracks1z,tracks2z)
track2track2=corresponding_tracks(t_ind,tracks1z,tracks3z)
print 'First Correspondance Matrix'
print track2track
print 'Second Correspondance Matrix'
print track2track2
print 'Fos loading'
#fos.add(r,fos.line(tracks1,fos.red,opacity=0.01))
#fos.add(r,fos.line(tracks2,fos.cyan,opacity=0.01))
tracks1zshift=[t+np.array([-70,0,0]) for t in tracks1z]
tracks2zshift=[t+np.array([70,0,0]) for t in tracks2z]
tracks3zshift=[t+np.array([210,0,0]) for t in tracks3z]
import pbc
pbc.save_pickle('t1zs.pkl',tracks1zshift)
pbc.save_pickle('t2zs.pkl',tracks2zshift)
pbc.save_pickle('t3zs.pkl',tracks3zshift)
pbc.save_pickle('t2t.pkl',track2track)
pbc.save_pickle('t2t2.pkl',track2track2)
'''
r=fos.ren()
fos.add(r,fos.line(tracks1zshift,fos.red,opacity=0.02))
fos.add(r,fos.line(tracks2zshift,fos.cyan,opacity=0.02))
fos.add(r,fos.line(tracks3zshift,fos.blue,opacity=0.02))
print 'Show track to track correspondence br1 br2'
for i in track2track:
fos.add(r,fos.line(tracks1zshift[i[1]],fos.yellow,opacity=0.5,linewidth=3))
fos.label(r,str(i[0]),tracks1zshift[i[1]][0],(4,4,4),fos.white)
fos.add(r,fos.line(tracks2zshift[i[2]],fos.yellow,opacity=0.5,linewidth=3))
fos.label(r,str(i[0]),tracks2zshift[i[2]][0],(4,4,4),fos.white)
print 'Show track to track correspondence br1_FACT and br2_RK2'
for i in track2track2:
fos.add(r,fos.line(tracks3zshift[i[2]],fos.yellow,opacity=0.5,linewidth=3))
fos.label(r,str(i[0]),tracks3zshift[i[2]][0],(4,4,4),fos.white)
fos.show(r)
'''