#print "\t\t", target_ids[t_id], "\t", len(source), "\t", len(target),"\t", jac0,"\t", bfn0, "\t", jac1,"\t", bfn1
#print "Before mapping: ", jac0, bfn0
#print "After mapping: ", jac1, bfn1
cor0, ncor0 = vol_corr_notcorr(s_cst, t_cst, vol_dims, disp=False)
cor1, ncor1 = vol_corr_notcorr(mapped_s_cst, t_cst, vol_dims, disp=False)
print "\t\t", target_ids[t_id], "\t", cor0,"\t", ncor0, "\t", cor1,"\t", ncor1
if vis:
s_file_native = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/tvis_tractography/' + source_sub + '_tracks_dti_tvis.trk'
t_file_native = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/tvis_tractography/' + target_sub + '_tracks_dti_tvis.trk'
t_cst_ext_idx_file = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/50_SFF_in_ext/ROI_seg_native/' + target_sub + '_cst_L_tvis_ext.pkl'
s_cst_native = load_tract(s_file_native, s_cst_idx_file)
t_cst_native = load_tract(t_file_native, t_cst_idx_file)
t_cst_ext_native = load_tract(t_file_native, t_cst_ext_idx_file)
TP_mapped,FP_mapped,TP_source, FN_source = TP_FP_TP_FN(s_cst_native, t_cst_native, t_cst_ext_native, mapped)
#TP_mapped,FP_mapped,TP_source, FN_source = TP_FP_TP_FN(s_cst, t_cst, t_cst_ext, mapped)
from common_functions import show_both_bundles
show_both_bundles([TP_mapped, FP_mapped],
#colors=[fvtk.colors.orange, fvtk.colors.red],
colors=[fvtk.colors.red, fvtk.colors.green],
show=True,
fname='Lauren_reg_1NN_' + source_sub + '_'+ target_sub + '_L_TP_red_FP_green_in_mapped_show_in_native.png')
show_both_bundles([TP_source, FN_source],
s_file = args.inputSourceTractography s_ind = args.inputSourceCSTSFFIndex t_file = args.inputTargetTractography t_ind = args.inputTargetCSTExtIndex t_cst = args.inputTargetCSTIndex num_pro = args.inputNumPrototypes map_prob = args.outputMap_prob vis = False source_cst = load_tract(s_file,s_ind) target_cst_ext = load_tract(t_file,t_ind) print len(source_cst), len(target_cst_ext) tractography1 = source_cst[-num_pro:] tractography2 = target_cst_ext[:num_pro] #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)
t_cst_ext_idx = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/50_SFF_in_ext/ROI_seg_native/' + target + '_cst_L_tvis_ext.pkl'
#map_file = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/results/result_cst_sff_in_ext_2_cst_ext/50_SFF_MNI/map_best_' + source + '_' + target + '_cst_L_ann_' + str(anneal[a_id]) + '_MNI.txt'
map_file = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/results/result_cst_sff_in_ext_2_cst_ext/50_SFF_MNI/map_1nn_' + source + '_' + target + '_cst_L_ann_' + str(anneal[a_id]) + '_MNI.txt'
'''
#Right
s_file = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/tvis_tractography/' + source + '_tracks_dti_tvis_linear.trk'
t_file = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/tvis_tractography/' + target + '_tracks_dti_tvis_linear.trk'
s_cst_idx = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/ROI_seg_tvis/ROI_seg_tvis_native/' + source + '_corticospinal_R_tvis.pkl'
t_cst_idx = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/ROI_seg_tvis/ROI_seg_tvis_native/' + target + '_corticospinal_R_tvis.pkl'
t_cst_ext_idx = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/50_SFF_in_ext/ROI_seg_native/' + target + '_cst_R_tvis_ext.pkl'
#map_file = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/results/result_cst_sff_in_ext_2_cst_ext/50_SFF_MNI/map_best_' + source + '_' + target + '_cst_R_ann_' + str(anneal[a_id]) + '_MNI.txt'
map_file = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/results/result_cst_sff_in_ext_2_cst_ext/50_SFF_MNI/map_1nn_' + source + '_' + target + '_cst_R_ann_' + str(anneal[a_id]) + '_MNI.txt'
'''
s_cst = load_tract(s_file, s_cst_idx)
t_cst = load_tract(t_file, t_cst_idx)
t_cst_ext = load_tract(t_file, t_cst_ext_idx)
map_all = load_pickle(map_file)
#this is only for mapping - to conver voxel size from 1,1,1 to 2,2,2
s_cst = .5 * s_cst
t_cst = .5 * t_cst
t_cst_ext = .5 * t_cst_ext
#vol_dims = [182*2,218*2,182*2]
# end of this is only for mapping - to conver voxel size from 1,1,1 to 2,2,2
cst_len = len(s_cst)
save_tract_trk(cst_tract_ext, s_fa, cst_ext_trk_fname)
#converting trk file to vtk file
input_anatomy_ref = '/home/bao/tiensy/Lauren_registration/data_compare_mapping/anatomy/' + subj + '_data_brain.nii.gz'
#it is a link to '/home/bao/Personal/PhD_at_Trento/Research/ALS_Nivedita_Bao/Segmentation_CST_francesca/' + subj + '/DTI/data_brain.nii.gz'
#cst_vtk_fname = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/ROI_seg_tvis/ROI_seg_tvis_native/' + subj + '_corticospinal_L_tvis.vtk'
cst_ext_vtk_fname = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/ROI_seg_tvis/ROI_seg_tvis_native/' + subj + '_cst_L_tvis_ext.vtk'
'''
#CST_Right
#saving CST as trk file
s_file = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/tvis_tractography/' + subj + '_tracks_dti_tvis.trk'
#cst_ind = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/ROI_seg_tvis/ROI_seg_tvis_native/' + subj + '_corticospinal_R_tvis.pkl'
#cst_tract = load_tract(s_file,cst_ind)
cst_ext_ind = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/50_SFF_in_ext/ROI_seg_native/' + subj + '_cst_R_tvis_ext.pkl'
cst_tract_ext = load_tract(s_file,cst_ext_ind)
s_fa = '/home/bao/Personal/PhD_at_Trento/Research/ALS_Nivedita_Bao/Segmentation_CST_francesca/' + subj + '/DTI/dti_fa_brain.nii.gz'
#cst_trk_fname = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/ROI_seg_tvis/ROI_seg_tvis_native/' + subj + '_corticospinal_R_tvis.trk'
#save_tract_trk(cst_tract, s_fa, cst_trk_fname)
cst_ext_trk_fname = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/ROI_seg_tvis/ROI_seg_tvis_native/' + subj + '_cst_R_tvis_ext.trk'
save_tract_trk(cst_tract_ext, s_fa, cst_ext_trk_fname)
#converting trk file to vtk file
input_anatomy_ref = '/home/bao/tiensy/Lauren_registration/data_compare_mapping/anatomy/' + subj + '_data_brain.nii.gz'
#it is a link to '/home/bao/Personal/PhD_at_Trento/Research/ALS_Nivedita_Bao/Segmentation_CST_francesca/' + subj + '/DTI/data_brain.nii.gz'
#cst_vtk_fname = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/ROI_seg_tvis/ROI_seg_tvis_native/' + subj + '_corticospinal_R_tvis.vtk'
cst_ext_vtk_fname = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/ROI_seg_tvis/ROI_seg_tvis_native/' + subj + '_cst_R_tvis_ext.vtk'
in_format = str(".trk")
#print "Source tractography: ", args.inputSourceTractography
print "Source tract index: ", args.inputSourceTractIndex
#print "Target tractography: ", args.inputTargetTractography
print "Target tract index: ", args.inputTargetTractIndex
print "Out put source tract aligned: ", args.outputSourceTractAligned
#print "=========================="
s_file = args.inputSourceTractography
s_ind = args.inputSourceTractIndex
t_file = args.inputTargetTractography
t_ind = args.inputTargetTractIndex
out_file = args.outputSourceTractAligned
s_tract = load_tract(s_file,s_ind)
t_tract = load_tract(t_file,t_ind)
from dipy.align.streamlinear import (StreamlineLinearRegistration,
vectorize_streamlines,
BundleMinDistance)
"""
An important step before running the registration is to resample the streamlines
so that they both have the same number of points per streamline. Here we will
use 10 points.
"""
points = 20#10#20
s_tract = vectorize_streamlines(s_tract, points)
s_cst_idx = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/ROI_seg_tvis/ROI_seg_tvis_native/' + source + '_corticospinal_R_tvis.pkl'
t_cst_idx = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/ROI_seg_tvis/ROI_seg_tvis_native/' + target + '_corticospinal_R_tvis.pkl'
s_cst_sff_in_ext_idx = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/50_SFF_in_ext/ROI_seg_native/' + source + '_cst_R_tvis_sff_in_ext.pkl'
t_cst_ext_idx = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/50_SFF_in_ext/ROI_seg_native/' + target + '_cst_R_tvis_ext.pkl'
#annealing
#map_file = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/results/result_cst_sff_in_ext_2_cst_ext/50_SFF_MNI/map_best_' + source + '_' + target + '_cst_R_ann_' + str(anneal[a_id]) + '_MNI.txt'
#map_file = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/results/result_cst_sff_in_ext_2_cst_ext/50_SFF_MNI/map_1nn_' + source + '_' + target + '_cst_R_ann_' + str(anneal[a_id]) + '_MNI.txt'
#probability
nn = 10
map_file = '/home/bao/tiensy/Tractography_Mapping/code/results/result_prob_map/prob_map_prob_map_' + source + '_' + target + '_cst_R_MNI_full_full' + '_sparse_density_' + str(nn) + '_neighbors.txt'
s_cst = load_tract(s_file, s_cst_idx)
t_cst = load_tract(t_file, t_cst_idx)
s_cst_sff_in_ext = load_tract(s_file, s_cst_sff_in_ext_idx)
t_cst_ext = load_tract(t_file, t_cst_ext_idx)
#---------------------------------------------------
#for normal mapping
map_all = load_pickle(map_file)
#-----------------------------------------------
cst_len = len(s_cst)
pr_s = s_cst_sff_in_ext[-num_pro:]
'''
source = '213'
target = '202'
s_file = '/home/bao/tiensy/Tractography_Mapping/data/' + source + '_tracks_dti_3M.dpy'
s_idx = '/home/bao/tiensy/Tractography_Mapping/data/ROI_seg/CST_ROI_R_control/' + source + '_CST_ROI_R_3M.pkl'
#s_idx = '/home/bao/tiensy/Tractography_Mapping/data/50_SFF_plus_ext/ROI_seg/' + source + '_cst_R_3M_plus_sff.pkl'
t_file = '/home/bao/tiensy/Tractography_Mapping/data/' + target + '_tracks_dti_3M.dpy'
t_idx = '/home/bao/tiensy/Tractography_Mapping/data/ROI_seg/CST_ROI_R_control/' + target + '_CST_ROI_R_3M.pkl'
#s_idx = '/home/bao/tiensy/Tractography_Mapping/data/50_SFF_plus_ext/ROI_seg/' + source + '_cst_R_3M_plus_sff.pkl'
from common_functions import load_tract
cb_subj1 = load_tract(s_file,s_idx)
cb_subj2 = load_tract(t_file,t_idx)
from dipy.align.streamlinear import (StreamlineLinearRegistration,
vectorize_streamlines,
BundleMinDistance)
"""
An important step before running the registration is to resample the streamlines
so that they both have the same number of points per streamline. Here we will
use 20 points.
"""
cb_subj1 = vectorize_streamlines(cb_subj1, 20)
for s_id in np.arange(len(source_ids)):
print '-----------------------------------------------------------------------------------------'
source = str(source_ids[s_id])
print 'Source: ', source
for t_id in np.arange(len(target_ids)):
if target_ids[t_id] != source_ids[s_id]:
target = str(target_ids[t_id])
print 'Target: ', target
s_tracts = '/home/bao/tiensy/Tractography_Mapping/data/' + source + '_tracks_dti_3M_linear.dpy'
#s_tracts = '/home/bao/tiensy/Tractography_Mapping/data/' + source + '_tracks_dti_3M.dpy'
s_idx = '/home/bao/tiensy/Tractography_Mapping/data/' + source + '_corticospinal_L_3M.pkl'
#s_cst = load_tract_trk(s_tracts,s_idx)
s_cst = load_tract(s_tracts,s_idx)
t_tracts = '/home/bao/tiensy/Tractography_Mapping/data/' + target + '_tracks_dti_3M_linear.dpy'
#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)
#CST Left
cst_ind = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/ROI_seg_tvis/ROI_seg_tvis_native/' + sub + '_corticospinal_L_tvis.pkl'
cst_sff_ind = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/50_SFF_in_ext/ROI_seg_native/' + sub + '_cst_L_tvis_sff_in_ext.pkl'
cst_ext_ind = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/50_SFF_in_ext/ROI_seg_native/' + sub + '_cst_L_tvis_ext.pkl'
'''
#CST Right
cst_ind = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/ROI_seg_tvis/ROI_seg_tvis_native/' + sub + '_corticospinal_R_tvis.pkl'
cst_sff_ind = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/50_SFF_in_ext/ROI_seg_native/' + sub + '_cst_R_tvis_sff_in_ext.pkl'
cst_ext_ind = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/50_SFF_in_ext/ROI_seg_native/' + sub + '_cst_R_tvis_ext.pkl'
'''
cst = load_tract(tractography_file, cst_ind)
cst_sff = load_tract(tractography_file, cst_sff_ind)
cst_ext = load_tract(tractography_file, cst_ext_ind)
#cst_len_min = length_min(cst)
#cst_len_max = length_max(cst)
#cst_len_avg = length_avg(cst)
#cst_ext_len_min = length_min(cst_ext)
#cst_ext_len_max = length_max(cst_ext)
#cst_ext_len_avg = length_avg(cst_ext)
cst_len_min = truth_length_min(cst)
cst_len_max = truth_length_max(cst)
cst_len_avg = truth_length_avg(cst)
cst_ext_len_min = truth_length_min(cst_ext)
cst_ext_len_max = truth_length_max(cst_ext)
