import os
import sys
import numpy as np
import nibabel as nb
import nighresjava
from ..io import load_volume, save_volume
from ..utils import _output_dir_4saving, _fname_4saving, \
_check_topology_lut_dir, _check_available_memory
[docs]def levelset_fusion(levelset_images,
correct_topology=True, topology_lut_dir=None,
save_data=False, overwrite=False, output_dir=None,
file_name=None):
"""Levelset fusion
Creates an average levelset surface representations from a collection of
levelset surfaces, with same avearage volume and (optionally) spherical
topology
Parameters
----------
levelset_images: niimg
List of levelset images to combine.
correct_topology: bool, optional
Corrects the average shape to ensure correct topology (default is True)
topology_lut_dir: str, optional
Path to directory in which topology files are stored (default is stored
in TOPOLOGY_LUT_DIR)
save_data: bool, optional
Save output data to file (default is False)
overwrite: bool, optional
Overwrite existing results (default is False)
output_dir: str, optional
Path to desired output directory, will be created if it doesn't exist
file_name: str, optional
Desired base name for output files with file extension
(suffixes will be added)
Returns
----------
dict
Dictionary collecting outputs under the following keys
(suffix of output files in brackets)
* result (niimg): Levelset representation of combined surface (_lsf-avg)
Notes
----------
Original Java module by Pierre-Louis Bazin
"""
print("\nLevelset Shape Fusion")
# check topology_lut_dir and set default if not given
topology_lut_dir = _check_topology_lut_dir(topology_lut_dir)
# make sure that saving related parameters are correct
if save_data:
output_dir = _output_dir_4saving(output_dir, levelset_images[0])
levelset_file = os.path.join(output_dir,
_fname_4saving(module=__name__,file_name=file_name,
rootfile=levelset_images[0],
suffix='lsf-avg'))
print('output file: '+levelset_file)
if overwrite is False \
and os.path.isfile(levelset_file) :
print("skip computation (use existing results)")
output = {'result': levelset_file}
return output
# start virtual machine if not running
try:
mem = _check_available_memory()
nighresjava.initVM(initialheap=mem['init'], maxheap=mem['max'])
except ValueError:
pass
# initiate class
algorithm = nighresjava.ShapeLevelsetFusion()
# load the data
nsubjects = len(levelset_images)
img = load_volume(levelset_images[0])
hdr = img.header
aff = img.affine
resolution = [x.item() for x in hdr.get_zooms()]
dimensions = img.get_data().shape
algorithm.setNumberOfImages(nsubjects)
algorithm.setResolutions(resolution[0], resolution[1], resolution[2])
algorithm.setDimensions(dimensions[0], dimensions[1], dimensions[2])
levelset_data = [];
for idx in range(len(levelset_images)):
img = load_volume(levelset_images[idx])
data = img.get_data()
algorithm.setLevelsetImageAt(idx, nighresjava.JArray('float')(
(data.flatten('F')).astype(float)))
algorithm.setCorrectSkeletonTopology(correct_topology)
algorithm.setTopologyLUTdirectory(topology_lut_dir)
# execute class
try:
algorithm.execute()
except:
# if the Java module fails, reraise the error it throws
print("\n The underlying Java code did not execute cleanly: ")
print(sys.exc_info()[0])
raise
return
# collect outputs
levelset_data = np.reshape(np.array(algorithm.getLevelsetAverage(),
dtype=np.float32), dimensions, 'F')
hdr['cal_min'] = np.nanmin(levelset_data)
hdr['cal_max'] = np.nanmax(levelset_data)
levelset = nb.Nifti1Image(levelset_data, aff, hdr)
if save_data:
save_volume(levelset_file, levelset)
return {'result': levelset_file}
else:
return {'result': levelset}