def aseg_to_srf(subject, subjects_dir, output_fol, region_id, mask_fname, norm_fname,
overwrite_subcortical_objs=False):
ret = True
tmp_fol = op.join(subjects_dir, subject, 'tmp', utils.rand_letters(6))
utils.make_dir(tmp_fol)
rs = utils.partial_run_script(locals())
output_fname = op.join(output_fol, '{}.srf'.format(region_id))
tmp_output_fname = op.join(tmp_fol, '{}.asc'.format(region_id))
if overwrite_subcortical_objs:
utils.remove_file(output_fname)
try:
rs(mri_pretess)
rs(mri_tessellate)
rs(mris_smooth)
rs(mris_convert)
if op.isfile(tmp_output_fname):
shutil.move(tmp_output_fname, output_fname)
shutil.rmtree(tmp_fol)
else:
ret = False
except:
print('Error in aseg_to_srf! subject: {}'.format(subject))
print(traceback.format_exc())
ret = False
return ret
def aseg_to_srf(subject,
subjects_dir,
output_fol,
region_id,
lookup,
mask_fname,
norm_fname,
overwrite_subcortical_objs=False,
**kargs):
from src.utils import geometry_utils as gu
ret = True
tmp_fol = op.join(subjects_dir, subject, 'tmp', utils.rand_letters(6))
utils.make_dir(tmp_fol)
rs = utils.partial_run_script(locals())
# output_fname = op.join(output_fol, '{}.srf'.format(region_id))
# tmp_output_fname = op.join(tmp_fol, '{}.asc'.format(region_id))
# if overwrite_subcortical_objs:
# utils.remove_file(output_fname)
try:
rs(mri_pretess)
rs(mri_tessellate)
rs(mris_smooth)
fs_file = op.join(tmp_fol, '{}_smooth'.format(region_id))
verts, faces = gu.read_surface(fs_file)
# verts, faces = nib_fs.read_geometry(fs_file)
num = int(op.basename(fs_file).split('_')[0])
if num not in lookup:
print('Error in the subcorticals lookup table!')
return False
new_name = lookup.get(num, '')
utils.write_ply_file(verts, faces,
op.join(output_fol, '{}.ply'.format(new_name)),
True)
# mris_convert = 'mris_convert {tmp_fol}/{region_id}_smooth {tmp_fol}/{region_id}.asc'
# rs(mris_convert)
# if op.isfile(tmp_output_fname):
# shutil.move(tmp_output_fname, output_fname)
if op.isdir(tmp_fol):
shutil.rmtree(tmp_fol)
else:
ret = False
except:
print('Error in aseg_to_srf! subject: {}'.format(subject))
print(traceback.format_exc())
ret = False
return ret
def find_electrodes(subject,
n_components,
n_groups,
ct_name='',
brain_mask_fname='',
output_fol=None,
clustering_method='knn',
max_iters=5,
cylinder_error_radius=3,
min_elcs_for_lead=4,
max_dist_between_electrodes=20,
min_cylinders_ang=0.1,
thresholds=(99, 99.9, 99.95, 99.99, 99.995, 99.999),
min_joined_items_num=1,
min_distance_beteen_electrodes=2,
overwrite=False,
debug=False):
from src.misc.dell import find_electrodes_in_ct
if n_components <= 0 or n_groups <= 0:
print('Both n_components and n_groups should be > 0!')
return False
if ct_name == '':
ct_name = 'ct_reg_to_mr.mgz'
ct_fname = op.join(MMVT_DIR, subject, 'ct', ct_name)
if brain_mask_fname == '':
brain_mask_fname = op.join(SUBJECTS_DIR, subject, 'mri', 'brain.mgz')
if output_fol is None:
output_fol = utils.make_dir(
op.join(MMVT_DIR, subject, 'ct', 'finding_electrodes_in_ct',
utils.rand_letters(5)))
electrodes, groups, groups_hemis = find_electrodes_in_ct.find_depth_electrodes_in_ct(
subject, ct_fname, brain_mask_fname, n_components, n_groups,
output_fol, clustering_method, max_iters, cylinder_error_radius,
min_elcs_for_lead, max_dist_between_electrodes, min_cylinders_ang,
thresholds, min_joined_items_num, min_distance_beteen_electrodes,
overwrite, debug)
if output_fol == '':
return all(x is not None for x in [electrodes, groups, groups_hemis])
else:
return op.isfile(op.join(output_fol, 'objects.pkl'))
