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 test_picking_trajectories():
curves=[100*np.random.rand(10,3),100*np.random.rand(5,3),100*np.random.rand(3,3)]
curves=[100*np.array([[0,0,0],[1,0,0]]), 100*np.array([[0,1,0],[0,1,3]]),100*np.array([[0,2,0],[0,2,3]])]
'''
from nibabel import trackvis as tv
#fname='/home/eg309/Data/PROC_MR10032/subj_01/101/1312211075232351192010091419011391228126452ep2dadvdiffDSI25x25x25b4000s003a001_FA_warp.trk'
fname='/home/eg309/Data/fibers.trk'
streams,hdr=tv.read(fname)
T=[s[0] for s in streams]
curves=T[:200000]
'''
fname='/home/eg309/Data/PROC_MR10032/subj_02/101/1312211075232351192010091708112071055601107ep2dadvdiffDSI10125x25x25STs002a001_QA_native.dpy'
from dipy.io.dpy import Dpy
dpr=Dpy(fname,'r')
T=dpr.read_indexed(range(20000))
from dipy.core.track_metrics import length
curves=[t for t in T if length(t) > 20]
dpr.close()
#colors=np.random.rand(len(curves),4).astype('f4')
colors=0.5*np.ones((len(curves),4)).astype('f4')
for (i,c) in enumerate(curves):
orient=c[0]-c[-1]
orient=np.abs(orient/np.linalg.norm(orient))
colors[i,:3]=orient
c=InteractiveCurves(curves,colors=colors)
w=World()
w.add(c)
wi=Window()
wi.attach(w)
def sums_length(dname,type='64'):
for root, dirs, files in os.walk(dname):
if root.endswith(type):
for file in files:
if file.endswith('_warp.dpy'):
fname=os.path.join(root,file)
dpr=Dpy(fname,'r')
sum=0
for i in range(dpr.track_no):
sum+=length(dpr.read_track())
dpr.close()
print fname, sum
def generate_lengths():
for root, dirs, files in os.walk(dname):
for file in files:
if file.endswith('_warp.dpy'):
fname=os.path.join(root,file)
dpr=Dpy(fname,'r')
lengths=np.zeros((dpr.track_no,))
for i in range(dpr.track_no):
lengths[i]=length(dpr.read_track())
dpr.close()
fname2=fname.split('_warp.dpy')[0]+'_warp_lengths.npy'
print fname2
np.save(fname2,lengths)
def init(self):
import dipy.io.trackvis as tv
lines,hdr = tv.read(self.fname)
ras = tv.aff_from_hdr(hdr)
self.affine=ras
tracks = [l[0] for l in lines]
if self.yellow_indices != None :
tracks = [t for t in tracks if tm.length(t) > 20]
print 'tracks loaded'
#self.data = [100*np.array([[0,0,0],[1,0,0],[2,0,0]]).astype(np.float32) ,100*np.array([[0,1,0],[0,2,0],[0,3,0]]).astype(np.float32)]#tracks[:20000]
if self.dummy_data:
self.data = [100*np.array([[0,0,0],[1,0,0],[2,0,0]]).astype(np.float32) ,100*np.array([[0,1,0],[0,2,0],[0,3,0]]).astype(np.float32)]
if self.data_subset!=None:
self.data = tracks[self.data_subset[0]:self.data_subset[1]]
else:
self.data = tracks
data_stats = np.concatenate(tracks)
self.min=np.min(data_stats,axis=0)
self.max=np.max(data_stats,axis=0)
self.mean=np.mean(data_stats,axis=0)
del data_stats
del lines
self.multiple_colors()
def __init__(self,fname,colormap=None, line_width=1., shrink=None, thinning = 0,
angle_table = None, manycolors = False, brain_color=[1,1,1]):
self.position = (0,0,0)
self.fname = fname
self.manycolors = manycolors
#self.color = monocolor
self.bbox = None
self.list_index = None
self.affine = None
self.data = None
self.list_index = None
self.rot_angle = 0
self.colormap = None
self.min = None
self.max = None
self.mean = None
self.material_color = False
self.fadeout = False
self.fadein = False
self.fadeout_speed = 0.
self.fadein_speed = 0.
self.min_length = 20.
self.angle = 0.
self.angular_speed = .5
self.line_width = line_width
self.opacity = 1.
self.opacity_rate = 0.
self.near_pick = None
self.far_pick = None
self.near_pick_prev = None
self.far_pick_prev = None
self.picked_track = None
self.pick_color = [1,1,0]
#self.brain_color = [1,1,1] # white
#self.brain_color = [.941,.862,.510] # buff
self.brain_color = brain_color
self.yellow_indices = None
self.dummy_data = False
self.data_subset = [0,20000]#None
self.orbit_demo = False
self.orbit_anglez =0.
self.orbit_anglez_rate = 10.
self.orbit_anglex = 0.
self.orbit_anglex_rate = 2.
self.angle_table = angle_table
if angle_table != None:
print 'Tracks angle_table shape %s' % str(self.angle_table.shape)
self.angle_table_index = 0
#print 'angle_table_index %d' % self.angle_table_index
self.shrink = shrink
self.picking_example = False
import dipy.io.trackvis as tv
lines,hdr = tv.read(self.fname)
ras = tv.aff_from_hdr(hdr
)
self.affine=ras
tracks = [l[0] for l in lines]
print 'tracks %d loaded' % len(tracks)
self.thinning = thinning
if self.yellow_indices != None :
tracks = [t for t in tracks if tm.length(t) > 20]
if self.thinning != 0:
tracks = [tracks[k] for k in range(0,len(tracks),self.thinning)]
print '%d tracks active' % len(tracks)
#self.data = [100*np.array([[0,0,0],[1,0,0],[2,0,0]]).astype(np.float32) ,100*np.array([[0,1,0],[0,2,0],[0,3,0]]).astype(np.float32)]#tracks[:20000]
if self.dummy_data:
self.data = [100*np.array([[0,0,0],[1,0,0],[2,0,0]]).astype(np.float32) ,100*np.array([[0,1,0],[0,2,0],[0,3,0]]).astype(np.float32)]
if self.data_subset!=None:
self.data = tracks[self.data_subset[0]:self.data_subset[1]]
else:
self.data = tracks
if self.shrink != None:
self.data = [ self.shrink*t for t in self.data]
data_stats = np.concatenate(tracks)
self.min=np.min(data_stats,axis=0)
self.max=np.max(data_stats,axis=0)
self.mean=np.mean(data_stats,axis=0)
del data_stats
del lines
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]
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)
sk=Tracks(skeletals,colors_sk,line_width=3.)
sk.position=(0,0,0)
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)
sk = Tracks(skeletals, colors_sk, line_width=3.)
sk.position = (0, 0, 0)
dpr=Dpy(ftrack,'r')
print dpr.track_no
T=dpr.read_indexed([0,1,2,3,2000,1000000])
for t in T:
print t.shape
dpr.close()
track=T[4]
im2im = flirt2aff_files(flirtaff, ffa, ref_fname) #ref_name to be replaced by ffareg
print im2im
from dipy.core.track_metrics import length
print len(track)
print length(track)
#ntrack=np.dot(im2im[:3,:3],track.T)+im2im[:3,[3]]
ntrack=np.dot(track,im2im[:3,:3].T)+im2im[:3,3]
print length(ntrack)
#print length(ntrack.T)
print length(ntrack)/length(track)
#print npl.det(im2im)**(1/3.)
disimg=nib.load(fdis)
ddata=disimg.get_data()
daff=disimg.affine
from scipy.ndimage.interpolation import map_coordinates as mc
di=ddata[:,:,:,0]
dj=ddata[:,:,:,1]
dk=ddata[:,:,:,2]
mci=mc(di,ntrack.T)
def init(self, given_tracks=None):
global b1
global b2
#devel06
#b1_fname='/home/ian/Data/dipy/brain2_scan1_fiber_track_mni.trk'
#devel07
b1_fname='/home/eg01/Data_Backup/Data/PBC/pbc2009icdm/brain2/brain2_scan1_fiber_track_mni.trk'
b1=tracks.TracksModified(b1_fname,subset=[0,1000])
b1.angular_speed = 0.
b1.picking_example = True
b1.min_length = 15.
b1.opacity = 0.1
b1.manycolors = False
b1.brain_color = [1, 1, 1]
b1.init()
global texim
#devel06
#fname = '/home/ian/Data/dipy/Streaks4.bmp'
#devel07
fname = '/home/eg01/Devel/Fos/fos/core/tests/data/Streaks4.bmp'
texim = texture.Texture_Demo(fname,red=False,green=True, blue=False)
#texim.orbit = b1.data[4246]
number_of_spritetracks = 30
random_inx=np.floor(len(b1.data)*np.random.rand(number_of_spritetracks)).astype(np.int)
systematic_inx = range(0,len(b1.data),len(b1.data)/number_of_spritetracks)
#texim.orbits = [b1.data[4246],b1.data[3000],b1.data[2000],b1.data[1000]]
texim.orbits =[]
#indices = random_inx
indices = systematic_inx
for i in indices:
#print i
if tm.length(b1.data[i]) > 20.:
#print i
texim.orbits.append(b1.data[i])
global b2
b2=tracks.TracksModified(None,line_width=2,tracks=[b1.data[i] for i in indices],colormap=False,text='M')
b2.angular_speed = 0.
b2.picking_example = True
b2.min_length = 15.
b2.opacity = 0.9
b2.manycolors = False
b2.brain_color = [0.2, 0.8, 0.4]
b2.init()
texim.orbits_index = np.zeros((len(texim.orbits),),np.int)
texim.init()
#'''
self.slots={0:{'actor':texim,'slot':( 0*MS, 55*MS )},
1:{'actor':b1,'slot':(0*MS, 40*MS)},
2:{'actor':b2,'slot':(35*MS, 200*MS)}}
#'''
'''
def init(self):
br1path='/home/eg01/Data_Backup/Data/Eleftherios/CBU090133_METHODS/20090227_145404/Series_003_CBU_DTI_64D_iso_1000/dtk_dti_out/dti_FACT.trk'
br2path='/home/eg01/Data_Backup/Data/Eleftherios/CBU090134_METHODS/20090227_154122/Series_003_CBU_DTI_64D_iso_1000/dtk_dti_out/dti_FACT.trk'
br3path='/home/eg01/Data_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
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], [ 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]
t_ind=np.array(t_ind)
print 'Indices of tracks for detection', t_ind
print 'Finding corresponding tracks'
global track2track
track2track= self.corresponding_tracks(t_ind,tracks1z,tracks2z)
global track2track2
track2track2=self.corresponding_tracks(t_ind,tracks1z,tracks3z)
print 'First Correspondance Matrix'
print track2track
print 'Second Correspondance Matrix'
print track2track2
print 'first brain'
print track2track[:,1].T
print 'second brain'
print track2track[:,2].T
print 'third brain'
print track2track2[:,2].T
#fos.add(r,fos.line(tracks1,fos.red,opacity=0.01))
#fos.add(r,fos.line(tracks2,fos.cyan,opacity=0.01))
tracks1zshift = tracks1z
tracks2zshift = tracks2z
tracks3zshift = tracks3z
m1z=np.concatenate(tracks1zshift).mean(axis=0)
m2z=np.concatenate(tracks2zshift).mean(axis=0)
m3z=np.concatenate(tracks3zshift).mean(axis=0)
#tracks1zshift=[t+np.array([-70,0,0]) for t in tracks1z]
#tracks2zshift=[t+np.array([70,0,0]) for t in tracks2z]
tracks1zshift=[t-m1z for t in tracks1z]
tracks2zshift=[t-m2z for t in tracks2z]
tracks3zshift=[t-m3z for t in tracks3z]
global t1
#devel07
t1=tracks.Tracks(None,data_ext=tracks1zshift)
t1.angular_speed = 0.1
t1.brain_color=[1,0,0]
t1.manycolors=False
t1.opacity = 0.01
t1.orbit_demo=True
t1.orbit_anglez_rate = 0.
t1.orbit_anglex_rate = 0.
t1.orbit_angley_rate = .2
t1.init()
t1.position = np.array([-120,0,-30])
print 't1p',t1.position
global t2
#devel07
t2=tracks.Tracks(None,data_ext=tracks2zshift)
t2.angular_speed = 0.1
t2.brain_color=[0,1,1]
t2.manycolors=False
t2.opacity = 0.01
t2.orbit_demo=True
t2.orbit_anglez_rate = 0.
t2.orbit_anglex_rate = 0.
t2.orbit_angley_rate = .2
t2.init()
t2.position = np.array([0,0,-30])
print 't2p', t2.position
global t3
#devel07
t3=tracks.Tracks(None,data_ext=tracks3zshift)
t3.angular_speed = 0.1
t3.manycolors=False
t3.brain_color=[0,0,1]
t3.opacity = 0.01
t3.orbit_demo=True
t3.orbit_anglez_rate = 0.
t3.orbit_anglex_rate = 0.
t3.orbit_angley_rate = .2
t3.init()
#t3.position = -
#np.concatenate(tracks3zshift).mean(axis=0)+np.array([70,0,0])
t3.position = np.array([120,0,-30])
print 't3p', t3.position
self.slots={0:{'actor':t1,'slot':( 0, 800*MS ) },
1:{'actor':t2,'slot':( 0, 800*MS ) },
2:{'actor':t3,'slot':( 0, 800*MS ) }}
def init(self):
global b1
#devel06
b1_fname='/home/ian/Data/dipy/brain2_scan1_fiber_track_mni.trk'
#devel07
#b1_fname='/home/eg01/Data_Backup/Data/PBC/pbc2009icdm/brain2/brain2_scan1_fiber_track_mni.trk'
b1=tracks.Tracks(b1_fname,subset=[0,200])
b1.angular_speed = 0.
b1.picking_example = True
b1.min_length = 20.
b1.opacity = 0.8
b1.manycolors = False
b1.brain_color = [0, 0, 0]
b1.init()
global texim
#devel06
fname = '/home/ian/Data/dipy/Streaks4.bmp'
#devel07
#fname = '/home/eg01/Devel/Fos/fos/core/tests/data/Streaks4.bmp'
texim = texture.Texture_Demo(fname,red=False,green=False, blue=True)
#texim.orbit = b1.data[4246]
#print 'len(b1.data)', len(b1.data)
random_inx=np.trunc(len(b1.data)*np.random.rand(200)).astype(np.int)
#print random_inx
#texim.orbits = [b1.data[4246],b1.data[3000],b1.data[2000],b1.data[1000]]
texim.orbits =[]
for i in random_inx:
#print i
if tm.length(b1.data[i]) > 20.:
#print i
texim.orbits.append(b1.data[i])
texim.orbits_index = np.zeros((len(texim.orbits),),np.int)
texim.init()
self.slots={0:{'actor':texim,'slot':( 0, 800*MS )}}#,
dpr=Dpy(ftrack,'r')
print dpr.track_no
T=dpr.read_indexed([0,1,2,3,2000,1000000])
for t in T:
print t.shape
dpr.close()
track=T[4]
im2im = flirt2aff_files(flirtaff, ffa, ref_fname) #ref_name to be replaced by ffareg
print im2im
from dipy.core.track_metrics import length
print len(track)
print length(track)
#ntrack=np.dot(im2im[:3,:3],track.T)+im2im[:3,[3]]
ntrack=np.dot(track,im2im[:3,:3].T)+im2im[:3,3]
print length(ntrack)
#print length(ntrack.T)
print length(ntrack)/length(track)
#print npl.det(im2im)**(1/3.)
disimg=nib.load(fdis)
ddata=disimg.get_data()
daff=disimg.affine
from scipy.ndimage.interpolation import map_coordinates as mc
di=ddata[:,:,:,0]
dj=ddata[:,:,:,1]
dk=ddata[:,:,:,2]
mci=mc(di,ntrack.T)
def multiple_colors(self):
from dipy.viz.colormaps import boys2rgb
from dipy.core.track_metrics import mean_orientation, length, downsample
colors=np.random.rand(1,3).astype(np.float32)
print colors
self.list_index = gl.glGenLists(1)
gl.glNewList( self.list_index,gl.GL_COMPILE)
gl.glPushMatrix()
gl.glDisable(gl.GL_LIGHTING)
gl.glEnable(gl.GL_LINE_SMOOTH)
gl.glEnable(gl.GL_BLEND)
gl.glBlendFunc(gl.GL_SRC_ALPHA,gl.GL_ONE_MINUS_SRC_ALPHA)
#gl.glBlendFunc(gl.GL_SRC_ALPHA_SATURATE,gl.GL_ONE_MINUS_SRC_ALPHA)
#gl.glBlendFunc(gl.GL_SRC_ALPHA,gl.GL_ONE)
gl.glHint(gl.GL_LINE_SMOOTH_HINT,gl.GL_DONT_CARE)
#gl.glHint(gl.GL_LINE_SMOOTH_HINT,gl.GL_NICEST)
gl.glLineWidth(self.line_width)
#gl.glDepthMask(gl.GL_FALSE)
gl.glEnableClientState(gl.GL_VERTEX_ARRAY)
for d in self.data:
if length(d)> self.min_length:
#mo=mean_orientation(d)
if self.manycolors:
ds=downsample(d,6)
mo=ds[3]-ds[2]
mo=mo/np.sqrt(np.sum(mo**2))
mo.shape=(1,3)
color=boys2rgb(mo)
color4=np.array([color[0][0],color[0][1],color[0][2],self.opacity],np.float32)
gl.glColor4fv(color4)
else:
color4=np.array([self.brain_color[0],self.brain_color[1],\
self.brain_color[2],self.opacity],np.float32)
gl.glColor4fv(color4)
gl.glVertexPointerf(d)
gl.glDrawArrays(gl.GL_LINE_STRIP, 0, len(d))
gl.glDisableClientState(gl.GL_VERTEX_ARRAY)
#gl.glDisable(gl.GL_BLEND)
gl.glEnable(gl.GL_LIGHTING)
gl.glPopMatrix()
gl.glEndList()
def multiple_colors(self):
from dipy.viz.colormaps import boys2rgb
from dipy.core.track_metrics import mean_orientation, length, downsample
colors=np.random.rand(1,3).astype(np.float32)
print colors
self.list_index = gl.glGenLists(1)
gl.glNewList( self.list_index,gl.GL_COMPILE)
#gl.glPushMatrix()
gl.glDisable(gl.GL_LIGHTING)
#!!!gl.glEnable(gl.GL_LINE_SMOOTH)
gl.glDisable(gl.GL_DEPTH_TEST)
#gl.glDepthFunc(gl.GL_NEVER)
gl.glEnable(gl.GL_BLEND)
gl.glBlendFunc(gl.GL_SRC_ALPHA,gl.GL_ONE_MINUS_SRC_ALPHA)
#gl.glBlendFunc(gl.GL_SRC_ALPHA_SATURATE,gl.GL_ONE_MINUS_SRC_ALPHA)
#gl.glBlendFunc(gl.GL_SRC_ALPHA,gl.GL_ONE)
#!!!gl.glHint(gl.GL_LINE_SMOOTH_HINT,gl.GL_DONT_CARE)
#gl.glHint(gl.GL_LINE_SMOOTH_HINT,gl.GL_NICEST)
gl.glLineWidth(self.line_width)
#gl.glDepthMask(gl.GL_FALSE)
gl.glEnableClientState(gl.GL_VERTEX_ARRAY)
for d in self.data:
if length(d)> self.min_length:
#mo=mean_orientation(d)
if self.manycolors:
ds=downsample(d,6)
mo=ds[3]-ds[2]
mo=mo/np.sqrt(np.sum(mo**2))
mo.shape=(1,3)
color=boys2rgb(mo)
color4=np.array([color[0][0],color[0][1],color[0][2],self.opacity],np.float32)
else:
color4=np.array([self.brain_color[0],self.brain_color[1],\
self.brain_color[2],self.opacity],np.float32)
if self.fadein == True:
color4[3] += self.fadein_speed
if self.fadeout == True:
color4[3] -= self.fadeout_speed
gl.glColor4fv(color4)
gl.glVertexPointerf(d)
gl.glDrawArrays(gl.GL_LINE_STRIP, 0, len(d))
gl.glDisableClientState(gl.GL_VERTEX_ARRAY)
#gl.glDisable(gl.GL_BLEND)
gl.glEnable(gl.GL_LIGHTING)
gl.glEnable(gl.GL_DEPTH_TEST)
#gl.glPopMatrix()
gl.glEndList()
def __init__(self,fname,ang_table=None,colormap=None, line_width=3., shrink=None,subset=None,tracks=None,text=None,text_color=np.array([1,0,0])):
self.position = (0,0,0)
self.fname = fname
self.manycolors = True
self.bbox = None
self.list_index = None
self.affine = None
self.data = None
self.list_index = None
self.rot_angle = 0
self.colormap = None
self.text = text
self.min = None
self.max = None
self.mean = None
self.material_color = False
self.fadeout = False
self.fadein = False
self.fadeout_speed = 0.
self.fadein_speed = 0.
self.min_length = 20.
self.angle = 0.
self.angular_speed = .5
self.line_width = line_width
self.opacity = 1.
self.near_pick = None
self.far_pick = None
self.near_pick_prev = None
self.far_pick_prev = None
self.picked_track = None
self.pick_color = [1,1,0]
self.brain_color = [1,1,1]
self.yellow_indices = None
self.dummy_data = False
self.tracks = tracks
if subset != None:
self.data_subset = subset #[0,20000]#None
else:
self.data_subset = None
self.orbit_demo = False
self.orbit_anglez = 0.
self.orbit_anglez_rate = 10.
self.orbit_anglex = 0.
self.orbit_anglex_rate = 2.
self.angle_table = ang_table
self.angle_table_index = 0
self.shrink = shrink
self.picking_example = False
self.partial_colors = False
import dipy.io.trackvis as tv
if self.tracks == None:
lines,hdr = tv.read(self.fname)
ras = tv.aff_from_hdr(hdr)
self.affine=ras
tracks = [l[0] for l in lines]
del lines
else:
tracks = self.tracks
if self.yellow_indices != None :
tracks = [t for t in tracks if tm.length(t) > 20]
print '%d tracks loaded' % len(tracks)
#self.data = [100*np.array([[0,0,0],[1,0,0],[2,0,0]]).astype(np.float32) ,100*np.array([[0,1,0],[0,2,0],[0,3,0]]).astype(np.float32)]#tracks[:20000]
if self.dummy_data:
self.data = [100*np.array([[0,0,0],[1,0,0],[2,0,0]]).astype(np.float32) ,100*np.array([[0,1,0],[0,2,0],[0,3,0]]).astype(np.float32)]
if self.data_subset!=None:
self.data = tracks[self.data_subset[0]:self.data_subset[1]]
else:
self.data = tracks
if self.shrink != None:
self.data = [ self.shrink*t for t in self.data]
data_stats = np.concatenate(tracks)
self.min=np.min(data_stats,axis=0)
self.max=np.max(data_stats,axis=0)
self.mean=np.mean(data_stats,axis=0)
del data_stats
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]) > 30.0:
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=1.0)
t2.position = (100, 0, 0)
sk = Tracks(skeletals, colors_sk, line_width=3.0)
sk.position = (0, 0, 0)
