def surf_lab_match_bci_uscbrain(uscbrain, uscbrain_dict, bci, bci_dict,
error_ind):
uscbrain_new = copy.deepcopy(uscbrain)
class from_surf:
pass
class to_surf:
pass
for bci_lab in bci_dict:
lab_ind = (bci.labels == bci_lab)
error_ind = np.array(error_ind, dtype=bool)
to_ind = lab_ind & error_ind
from_ind = lab_ind & (~error_ind)
from_surf.vertices = uscbrain.vertices[from_ind, :]
to_surf.vertices = uscbrain.vertices[to_ind, :]
from_surf.labels = uscbrain.labels[from_ind]
to_surf = interpolate_labels(from_surf, to_surf)
uscbrain_new.labels[to_ind] = to_surf.labels
return uscbrain_new
pass
t.vertices = sp.concatenate((Xc[:, None], Yc[:, None], Zc[:, None]), axis=1)
f.vertices = sp.concatenate((left_mid.vertices, right_mid.vertices,
left_inner.vertices, right_inner.vertices,
left_pial.vertices, right_pial.vertices,
l1_vert, r1_vert, l2_vert, r2_vert))
f.labels = sp.concatenate((left_mod.attributes, right_mod.attributes,
left_mod.attributes, right_mod.attributes,
left_mod.attributes, right_mod.attributes,
left_mod.attributes, right_mod.attributes,
left_mod.attributes, right_mod.attributes))
t = interpolate_labels(fromsurf=f, tosurf=t)
# %% here make sure that hanns labels are not modified TBD
vol_attrib[ind] = t.labels
vol = image.load_img('/big_disk/ajoshi/coding_ground/hybridatlas/\
BCI-DNI_brain_atlas_refined_4_18_2017/BCI-DNI_brain.bfc.nii.gz')
prob_img = image.new_img_like(vol, vol_attrib)
prob_img.to_filename('/big_disk/ajoshi/coding_ground/hybridatlas/hBCI_DNI_fsl/\
BCI-DNI_brain-prob.nii.gz')
msk = image.threshold_img(prob_img, threshold=.5)
lab = image.load_img('/big_disk/ajoshi/coding_ground/hybridatlas/hBCI_DNI_fsl/\
BCI-DNI_brain.label.nii.gz')
lab1 = image.new_img_like(lab, lab.get_data() * (prob_img.get_data() > 0))
freq1 = data1['freq'].squeeze()
freq1 = freq1 / nSub
freq1[labs == 0] = 0
# bci_labs = reduce3_to_bci_lh(labs)
# freq1 = reduce3_to_bci_lh(freq1)
bci_labs = labs.squeeze().copy()
bci_labs_orig = bci_labs.copy()
if sp.amax(bci_labs) > 0:
bci_labs[bci_bst.labels != roino * 10] = 0
indt = (bci_bst.labels == roino * 10) & (bci_labs == 0)
ts.vertices = bci_bst.vertices[indt, :]
ind = (bci_bst.labels == roino * 10) & (bci_labs != 0)
fs.vertices = bci_bst.vertices[ind, :]
fs.labels = bci_labs[ind]
ts = interpolate_labels(fs, ts)
bci_labs[indt] = ts.labels
freq1[(bci_labs > 0) & (freq1 == 0)] = \
sp.amin(freq1[(bci_labs_orig > 0) & (freq1 != 0)])
freq[(bci_bst.labels == roino * 10)] += freq1[(bci_bst.labels == roino *
10)]
bci_bst.labels += sp.uint16(bci_labs)
freq[freq == 0] = 1
bci_bst.attributes = freq
bci_bst = patch_color_labels(bci_bst, freq=freq, cmap='Paired')
# bci_bst = smooth_patch(bci_bst, iterations=90, relaxation=10.8)
view_patch_vtk(bci_bst, show=1)
####writedfs('/home/ajoshi/data/BCI-DNI_brain_atlas/BCI-DNI_brain.left.\
freq1 = data1['freq'].squeeze()
freq1 = freq1 / nSub
freq1[labs == 0] = 0
# bci_labs = reduce3_to_bci_rh(labs)
# freq1 = reduce3_to_bci_rh(freq1)
bci_labs = labs.squeeze().copy()
bci_labs_orig = bci_labs.copy()
if sp.amax(bci_labs) > 0:
bci_labs[bci_bst.labels != roino * 10] = 0
indt = (bci_bst.labels == roino * 10) & (bci_labs == 0)
ts.vertices = bci_bst.vertices[indt, :]
ind = (bci_bst.labels == roino * 10) & (bci_labs != 0)
fs.vertices = bci_bst.vertices[ind, :]
fs.labels = bci_labs[ind]
ts = interpolate_labels(fs, ts)
bci_labs[indt] = ts.labels
freq1[(bci_labs > 0) & (freq1 == 0)] = \
sp.amin(freq1[(bci_labs_orig > 0) & (freq1 != 0)])
freq[(bci_bst.labels == roino * 10)] += freq1[(bci_bst.labels == roino *
10)]
bci_bst.labels += sp.uint16(bci_labs)
freq[freq == 0] = 1
bci_bst.attributes = freq
bci_bst = patch_color_labels(bci_bst, cmap='Paired')
# bci_bst = smooth_patch(bci_bst, iterations=90, relaxation=10.8)
view_patch_vtk(bci_bst, show=1)
####writedfs('/home/ajoshi/data/BCI-DNI_brain_atlas/BCI-DNI_brain.right.\
