#tractography2 = target_cst_ext[:num_pro*2]
print "Source", len(tractography1)
print "Target", len(tractography2)
#print "Computing the distance matrices for each tractography."
dm1 = bundles_distances_mam(tractography1, tractography1)
dm2 = bundles_distances_mam(tractography2, tractography2)
size1 = len(tractography1)
size2 = len(tractography2)
if vis:
ren = fvtk.ren()
ren = visualize_tract(ren, tractography1, fvtk.yellow)
ren = visualize_tract(ren, tractography2, fvtk.blue)
fvtk.show(ren)
y_dm1 = dm1
x_dm2 = dm2
L = []
print 'Optimizing ...........................'
max_nfe = 50000
#optimizing with SLSPQ ---------------------
#-----------with bounds and (constrains OR NO constrains)
for t in np.arange(1):
L = []
visualize = False#True#True#False#True# False
print '---------------------------------------------------------------------------'
print 'Source \t Len \t Entropy '
for s_id in np.arange(len(source_ids)):
source = str(source_ids[s_id])
#tract_file = '/home/bao/Personal/PhD_at_Trento/Research/ALS_Nivedita_Bao/Code/Segmentation/ROI/' + source + '/cst_right.trk'
#tract = load_whole_tract_trk(tract_file)
#tracks_ind_file = '/home/bao/tiensy/Tractography_Mapping/data/ROI_seg/CST_ROI_R_control/' + source + '_CST_ROI_R_3M.pkl'
#tracks_ind_file = '/home/bao/tiensy/Tractography_Mapping/data/ROI_seg/CST_ROI_L_control/' + source + '_CST_ROI_L_3M.pkl'
#tracks_ind_file = '/home/bao/tiensy/Tractography_Mapping/data/BOI_seg/' + source + '_corticospinal_R_3M.pkl'
#tract_file = '/home/bao/tiensy/Tractography_Mapping/data/' + source + '_tracks_dti_3M.dpy'
#tract = load_tract(tract_file,tracks_ind_file)
tracks_ind_file = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/ROI_seg_tvis/ROI_seg_tvis_native/' + source + '_corticospinal_R_tvis.pkl'
tract_file = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/tvis_tractography/' + source + '_tracks_dti_tvis.trk'
tract = load_tract_trk(tract_file,tracks_ind_file)
entropy = Shannon_entropy(tract)
print source, '\t', len(tract), '\t ', entropy
if (visualize==True):
ren = fvtk.ren()
ren = visualize_tract(ren, tract, fvtk.yellow)
fvtk.show(ren)
for s_id in np.arange(len(source_ids)):
print '-----------------------------------------------------------------------------------------'
print 'Source: ', source_ids[s_id]
source = str(source_ids[s_id])
flirt_filename = '/home/bao/Personal/PhD_at_Trento/Research/ALS_Nivedita_Bao/Code/data/' + source+'/DIFF2DEPI_EKJ_64dirs_14/DTI/flirt.mat'
fa_filename= '/home/bao/Personal/PhD_at_Trento/Research/ALS_Nivedita_Bao/Segmentation_CST_francesca/' + source + '/DTI/dti_fa_brain.nii.gz'
tracks_filename = '/home/bao/Personal/PhD_at_Trento/Research/ALS_Nivedita_Bao/Segmentation_CST_francesca/' + source + '/DTI/dti.trk'
linear_filename = '/home/bao/Personal/PhD_at_Trento/Research/ALS_Nivedita_Bao/Segmentation_CST_francesca/' + source + '/DTI/dti_linear.dpy'
warp_tracks_linearly(flirt_filename,fa_filename, tracks_filename,linear_filename)
print 'Saved ', linear_filename
tract_linear = load_whole_tract(linear_filename)
if save:
from common_functions import save_tract_trk
fa_warped = '/home/bao/Personal/PhD_at_Trento/Research/ALS_Nivedita_Bao/Code/data/' + source+'/DIFF2DEPI_EKJ_64dirs_14/DTI/fa_warped.nii.gz'
fname_out = '/home/bao/Personal/PhD_at_Trento/Research/ALS_Nivedita_Bao/Segmentation_CST_francesca/' + source + '/DTI/dti_linear.trk'
save_tract_trk(tract_linear, fa_warped, fname_out )
print 'Saved ', fname_out
if vis:
tract = load_whole_tract(tracks_filename)
from dipy.viz import fvtk
from common_functions import visualize_tract
ren = fvtk.ren()
visualize_tract(ren, tract,color=fvtk.red)
visualize_tract(ren, tract_linear,color=fvtk.blue)
fvtk.show(ren)
clearall()
#Step 4: Segment the cst_s in the target using nearest neighbor of each fiber_source
# in the space of the kdt_t_ext - result is nn_cst_s_in_t
k = 1
dst_nn, idx_nn = kdt_t_ext.query(dis_s, k)
#print 'Distance'
#print dst_nn
#print 'Index '
#print idx_nn
mapped_s_cst = [t_cst_ext[idx_nn[i][0]] for i in np.arange(cst_len)]
#Step 5: Compute JAC and BFN between cst_t and nn_cst_s_in_t
jac0, bfn0 = Jac_BFN(s_cst, t_cst, vol_dims, disp=False)
jac1, bfn1 = Jac_BFN(mapped_s_cst, t_cst, vol_dims, disp=False)
print "\t\t", target_ids[t_id], "\t", jac0,"\t", bfn0, "\t", jac1,"\t", bfn1
print "Before mapping: ", jac0, bfn0
print "After mapping: ", jac1, bfn1
if vis:
#visualize target cst and mapped source cst - yellow and blue
ren = fvtk.ren()
ren = visualize_tract(ren, s_cst, fvtk.yellow)
ren = visualize_tract(ren, t_cst, fvtk.blue)
ren = visualize_tract(ren, mapped_s_cst, fvtk.red)
fvtk.show(ren)
ROIs = ROIs_subject[source]
print ROIs
#common = intersec_ROIs_dpy(tracks, ROIs, Rs, vis = True)
common = intersec_ROIs(tracks, ROIs, Rs, vis = True)
print "\t Total ", len(tracks), " and the number of fibers cross the ROIs ", len(common)
tracks_ind_file = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/ROI_seg_tvis_MNI/' + source + '_corticospinal_L_tvis_MNI.pkl'
save_pickle(tracks_ind_file, common)
print "Saved file", tracks_ind_file
segment = [tracks[i] for i in common]
'''
if save==True:
fa_file = '/home/bao/Personal/PhD_at_Trento/Research/ALS_Nivedita_Bao/Segmentation_CST_francesca/' + source + '/DTI/dti_fa.nii'
fname = '/home/bao/Personal/PhD_at_Trento/Research/ALS_Nivedita_Bao/Code/Segmentation/ROI/' + source + '/' + source + '_cst_right_ROI_tvis_native.trk'
from common_functions import save_tract_trk
save_tract_trk(segment, fa_file, fname)
print 'Saved ', fname
'''
if (visualize==True):
ren = fvtk.ren()
ren = visualize_tract(ren, segment, fvtk.yellow)
fvtk.add(ren, fvtk.sphere(ROIs[0],Rs[0],color = fvtk.red, opacity=1.0))
fvtk.add(ren, fvtk.sphere(ROIs[1],Rs[1],color = fvtk.blue, opacity=1.0))
fvtk.show(ren)
#clearall()
#t_tracts = '/home/bao/tiensy/Tractography_Mapping/data/' + target + '_tracks_dti_3M.dpy'
t_idx = '/home/bao/tiensy/Tractography_Mapping/data/' + target + '_corticospinal_L_3M.pkl'
#t_cst = load_tract_trk(t_tracts,t_idx)
t_cst = load_tract(t_tracts,t_idx)
'''
dm = bundles_distances_mam(s_cst, t_cst)
dm = np.array(dm, dtype=float)
avg_dis = np.sum(dm) / (len(s_cst)*len(t_cst))
min_dis = dm.min()
max_dis = dm.max()
s_qb = QuickBundles(s_cst,200,18)
s_medoid = s_qb.centroids[0]
t_qb = QuickBundles(t_cst,200,18)
t_medoid = t_qb.centroids[0]
#print len(s_cst), '\t', len(t_cst), '\t', min_dis, '\t', max_dis, '\t', avg_dis, '\t', bundles_distances_mam([s_medoid],[t_medoid])[0][0]
print min_dis, '\t', max_dis, '\t', avg_dis, '\t', bundles_distances_mam([s_medoid],[t_medoid])[0][0]
'''
if (vis==True):
ren = fvtk.ren()
ren = visualize_tract(ren,s_cst,fvtk.red)
ren = visualize_tract(ren, t_cst, fvtk.blue)
fvtk.show(ren)
else:
print ' '
