def check_csv(file, force):
with open(file) as f:
content = f.read()
for line, row in enumerate(content.split()):
dwi_mask = [element for element in row.split(',')
if element] # handling w/space
if len(dwi_mask) != 2:
raise FileNotFoundError(
f'Columns don\'t have same number of entries: check line {line} in {file}'
)
dirCheckFlag = 1
for img in dwi_mask:
if not exists(img):
raise FileNotFoundError(
f'{img} does not exist: check line {line} in {file}')
elif dirCheckFlag:
# create DTI and harmonization directory
dtiPath = pjoin(dirname(img), 'dti')
check_dir(dtiPath, force)
harmPath = pjoin(dirname(img), 'harm')
check_dir(harmPath, force)
dirCheckFlag = 0
def play(samples_or_snd, sr=None, compress=True):
if sr is None:
samples = samples_or_snd.samples
sr = samples_or_snd.rate
else:
samples = samples_or_snd
snd = Sound(None, sr, samples) # .unnormalized('int16')
path = ut.pjoin(imtable.get_www_path(), 'sounds')
if compress:
with ut.temp_file('.wav') as wav_fname:
fname = ut.make_temp('.mp3', dir=path)
#scipy.io.wavfile.write(wav_fname, snd.rate, snd.samples)
snd.save(wav_fname)
os.system('ffmpeg -loglevel error -y -i "%s" "%s"' %
(wav_fname, fname))
else:
fname = ut.make_temp('.wav', dir=path)
scipy.io.wavfile.write(fname, snd.rate, snd.samples)
os.system('chmod a+rwx %s' % fname)
#url = ut.pjoin(imtable.PUBLIC_URL, 'sounds', os.path.split(fname)[1])
url = ut.pjoin(imtable.get_url(), 'sounds', os.path.split(fname)[1])
print(url)
return url
def test_tour(par=1):
ip.reset(par)
scene = 'disrupt-11'
scan = dset.Scan('../data/%s' % scene)
texel_colors = ut.load(ut.pjoin(figures.make_path('noloo', 'interior-wide', scene), 'data.pk'))['ret'][0]
mesh = box.load_from_mat(ut.pjoin(scan.path, 'cube.mat'))
tour(scan, mesh, texel_colors, [0, 1, 2], plane_idx = 0, outline_start = 0, par = par)
def test_tour(par=1):
ip.reset(par)
scene = 'disrupt-11'
scan = dset.Scan('../data/%s' % scene)
texel_colors = ut.load(
ut.pjoin(figures.make_path('noloo', 'interior-wide', scene),
'data.pk'))['ret'][0]
mesh = box.load_from_mat(ut.pjoin(scan.path, 'cube.mat'))
tour(scan,
mesh,
texel_colors, [0, 1, 2],
plane_idx=0,
outline_start=0,
par=par)
def play(samples_or_snd, sr=None):
if sr is None:
samples = samples_or_snd.samples
sr = samples_or_snd.rate
else:
samples = samples_or_snd
snd = Sound(None, sr, samples).unnormalized('int16')
path = ut.pjoin(imtable.WWW_PATH, 'sounds')
fname = ut.make_temp('.wav', dir=path)
scipy.io.wavfile.write(fname, snd.rate, snd.samples)
os.system('chmod a+rwx %s' % fname)
url = ut.pjoin(imtable.PUBLIC_URL, 'sounds', os.path.split(fname)[1])
print url
return url
def rigid_registration(dim, moving, fixed, outPrefix):
check_call((' ').join([
pjoin(FILEDIR, 'antsRegistrationSyNMI.sh'), '-d',
str(dim), '-t', 'r', '-m', moving, '-f', fixed, '-o', outPrefix
]),
shell=True)
def make_video(out_fname, ims, fps, tmp_ext = '.ppm'):
assert tmp_ext.startswith('.')
in_dir = ut.make_temp_dir_big()
# for i, x in enumerate(ims):
# # pad to be even
# if x.shape[0] % 2 == 1 or x.shape[1] % 2 == 1:
# x = ig.pad_corner(x, x.shape[1] % 2, x.shape[0] % 2)
# ig.save(ut.pjoin(in_dir, 'ao-video-frame%05d%s' % (i, tmp_ext)), x)
ut.parmap(make_video_helper, [(i, x, in_dir, tmp_ext) for i, x in enumerate(ims)])
cmd = 'ffmpeg -loglevel warning -f image2 -r %f -i %s/ao-video-frame%%05d%s -pix_fmt yuv420p -vcodec h264 -acodec aac -strict 2 -y %s' % (fps, in_dir, tmp_ext, out_fname)
# try to make it work well in keynote (hack)
#cmd = 'ffmpeg -r %f -i %s/ao-video-frame%%05d%s -y -qscale:v 0 %s' % (fps, in_dir, tmp_ext, out_fname)
print cmd
#print cmd
os.system(cmd)
#os.system('ffmpeg -f image2 -r %f -i %s/ao-video-frame%%05d%s -pix_fmt yuv420p -vcodec h264 -acodec aac -strict 2 -y %s' % (fps, in_dir, tmp_ext, out_fname))
#os.system('ffmpeg -f image2 -r %f -i %s/ao-video-frame%%05d%s -pix_fmt yuv420p -vcodec h264 -acodec aac -strict 2 -y %s' % (fps, in_dir, tmp_ext, out_fname))
if 0:
print 'HACK'
else:
for x in glob.glob(ut.pjoin(in_dir, 'ao-video-frame*%s' % tmp_ext)):
os.remove(x)
os.rmdir(in_dir)
def main(self):
prefix = self.dwi_file.name.split('.')[0]
outPrefix = pjoin(self.outDir._path, prefix + '_Ed')
if self.b0_brain_mask == 'None':
logging.info('Mask not provided, creating mask ...')
self.b0_brain_mask = outPrefix + '_mask.nii.gz'
bet_mask(self.dwi_file,
self.b0_brain_mask,
4,
bvalFile=self.bvals_file,
BET_THRESHOLD=self.betThreshold)
eddy_openmp[f'--imain={self.dwi_file}', f'--mask={self.b0_brain_mask}',
f'--acqp={self.acqparams_file}',
f'--index={self.index_file}', f'--bvecs={self.bvecs_file}',
f'--bvals={self.bvals_file}', f'--out={outPrefix}',
'--verbose',
eddy_openmp_params.split()] & FG
# copy bval,bvec to have same prefix as that of eddy corrected volume
copyfile(outPrefix + '.eddy_rotated_bvecs', outPrefix + '.bvec')
copyfile(self.bvals_file, outPrefix + '.bval')
def make_video(out_fname, ims, fps, tmp_ext='.ppm', sound=None):
# Compressed images
if type(ims[0]) == type(''):
#tmp_ext = '.jpg'
tmp_ext = '.png'
assert tmp_ext.startswith('.')
in_dir = ut.make_temp_dir_big()
ut.parmap(make_video_helper,
[(i, x, in_dir, tmp_ext) for i, x in enumerate(ims)])
with ut.temp_file('.wav') as tmp_wav:
if sound is None:
sound_flags = ''
else:
if type(sound) != type(''):
sound.save(tmp_wav)
sound = tmp_wav
sound_flags = '-i "%s" -shortest' % sound
# removed aac flag from above, for compatibility with some wav files
cmd = (
'ffmpeg -loglevel warning -f image2 -r %f -i %s/ao-video-frame%%05d%s %s '
'-pix_fmt yuv420p -vcodec h264 -strict -2 -y %s') % (
fps, in_dir, tmp_ext, sound_flags, out_fname)
print cmd
if 0 == os.system(cmd):
for x in glob.glob(ut.pjoin(in_dir,
'ao-video-frame*%s' % tmp_ext)):
os.remove(x)
os.rmdir(in_dir)
else:
raise RuntimeError()
class App(cli.Application):
"""Runs tract_querier. Output is <out>/*.vtk"""
ukf = cli.SwitchAttr(
['-i', '--in'],
cli.ExistingFile,
help='tractography file (.vtk or .vtk.gz), must be in RAS space',
mandatory=True)
fsindwi = cli.SwitchAttr(['-f', '--fsindwi'],
cli.ExistingFile,
help='Freesurfer labelmap in DWI space (nifti)',
mandatory=True)
query = cli.SwitchAttr(['-q', '--query'],
help='tract_querier query file (e.g. wmql-2.0.qry)',
mandatory=False,
default=pjoin(FILEDIR, 'wmql-2.0.qry'))
out = cli.SwitchAttr(['-o', '--out'],
help='output directory',
mandatory=True)
nproc = cli.SwitchAttr(
['-n', '--nproc'],
help='''number of threads to use, if other processes in your computer
becomes sluggish/you run into memory error, reduce --nproc''',
default=N_PROC)
def main(self):
with TemporaryDirectory() as t:
t = local.path(t)
ukf = self.ukf
fsindwi = self.fsindwi
if '.gz' in self.ukf.suffix:
ukf = t / 'ukf.vtk'
from plumbum.cmd import gunzip
(gunzip['-c', self.ukf] > ukf)()
tract_querier = local['tract_querier']
tract_math = local['tract_math']
ukfpruned = t / 'ukfpruned.vtk'
# tract_math(ukf, 'tract_remove_short_tracts', '2', ukfpruned)
tract_math[ukf, 'tract_remove_short_tracts', '2', ukfpruned] & FG
if not ukfpruned.exists():
raise Exception(
"tract_math failed to make '{}'".format(ukfpruned))
self.out = local.path(self.out)
if self.out.exists():
self.out.delete()
self.out.mkdir()
tract_querier['-t', ukfpruned, '-a', fsindwi, '-q', self.query,
'-o', self.out / '_'] & FG
logging.info('Convert vtk field data to tensor data')
# use the following multi-processed loop
pool = Pool(int(self.nproc))
pool.map_async(_activateTensors_py, self.out.glob('*.vtk'))
pool.close()
pool.join()
def put(self, input_file):
sha = util.hash_file(input_file)
target_path = util.pjoin(self.url_components.path or "/",
"db", sha[0:3], sha, "contents")
self.bucket.upload_file(input_file, target_path.lstrip("/"))
return sha
def verifyNshmForAll(csvFile, N_shm):
for imgPath in read_imgs_masks(csvFile)[0]:
directory = dirname(imgPath)
prefix = basename(imgPath).split('.nii')[0]
bvalFile = pjoin(directory, prefix + '.bval')
verifyNshm(N_shm, bvalFile)
def make_video(out_fname, ims, fps, tmp_ext='.ppm'):
assert tmp_ext.startswith('.')
in_dir = ut.make_temp_dir_big()
# for i, x in enumerate(ims):
# # pad to be even
# if x.shape[0] % 2 == 1 or x.shape[1] % 2 == 1:
# x = ig.pad_corner(x, x.shape[1] % 2, x.shape[0] % 2)
# ig.save(ut.pjoin(in_dir, 'ao-video-frame%05d%s' % (i, tmp_ext)), x)
ut.parmap(make_video_helper,
[(i, x, in_dir, tmp_ext) for i, x in enumerate(ims)])
cmd = 'ffmpeg -loglevel warning -f image2 -r %f -i %s/ao-video-frame%%05d%s -pix_fmt yuv420p -vcodec h264 -acodec aac -strict 2 -y %s' % (
fps, in_dir, tmp_ext, out_fname)
# try to make it work well in keynote (hack)
#cmd = 'ffmpeg -r %f -i %s/ao-video-frame%%05d%s -y -qscale:v 0 %s' % (fps, in_dir, tmp_ext, out_fname)
print cmd
#print cmd
os.system(cmd)
#os.system('ffmpeg -f image2 -r %f -i %s/ao-video-frame%%05d%s -pix_fmt yuv420p -vcodec h264 -acodec aac -strict 2 -y %s' % (fps, in_dir, tmp_ext, out_fname))
#os.system('ffmpeg -f image2 -r %f -i %s/ao-video-frame%%05d%s -pix_fmt yuv420p -vcodec h264 -acodec aac -strict 2 -y %s' % (fps, in_dir, tmp_ext, out_fname))
if 0:
print 'HACK'
else:
for x in glob.glob(ut.pjoin(in_dir, 'ao-video-frame*%s' % tmp_ext)):
os.remove(x)
os.rmdir(in_dir)
def render_scene(scene, texel_colors, mesh=None):
scan = dset.Scan(scene)
if mesh is None:
mesh = load_from_mat(ut.pjoin(scan.path, 'cube.mat'))
# click to toggle between the rendered image and the mesh
ig.show([('cycle',
[mesh.render(scan, frame, texel_colors),
scan.im(frame)]) for frame in xrange(scan.length)])
def make_video_helper((i, x, in_dir, tmp_ext)):
out_fname = ut.pjoin(in_dir, 'ao-video-frame%05d%s' % (i, tmp_ext))
if type(x) == type(''):
f = open(out_fname, 'wb')
f.write(x)
f.close()
else:
# pad to be even
x = pad_im_even(x)
ig.save(out_fname, x)
def joinBshells(imgPath, ref_bvals_file=None, ref_bvals=None, sep_prefix=None):
if ref_bvals_file:
print('Reading reference b-shell file ...')
ref_bvals = read_bvals(ref_bvals_file)
print('Joining b-shells for', imgPath)
imgPath = local.path(imgPath)
img = load(imgPath._path)
dim = img.header['dim'][1:5]
inPrefix = abspath(imgPath).split('.nii')[0]
directory = dirname(inPrefix)
prefix = basename(inPrefix)
bvalFile = inPrefix + '.bval'
bvecFile = inPrefix + '.bvec'
if sep_prefix:
harmPrefix = pjoin(directory, sep_prefix + prefix)
else:
harmPrefix = inPrefix
if not isfile(harmPrefix + '.bval'):
copyfile(bvalFile, harmPrefix + '.bval')
if not isfile(harmPrefix + '.bvec'):
copyfile(bvecFile, harmPrefix + '.bvec')
bvals = np.array(read_bvals(inPrefix + '.bval'))
joinedDwi = np.zeros((dim[0], dim[1], dim[2], dim[3]), dtype='float32')
for bval in ref_bvals:
# ind= np.where(bval==bvals)[0]
ind = np.where(abs(bval - bvals) <= BSHELL_MIN_DIST)[0]
if bval == 0.:
b0Img = load(inPrefix + '_b0.nii.gz')
b0 = b0Img.get_data()
for i in ind:
joinedDwi[:, :, :, i] = b0
else:
b0_bshell = load(harmPrefix + f'_b{int(bval)}.nii.gz').get_data()
joinedDwi[:, :, :, ind] = b0_bshell[:, :, :, 1:]
if not isfile(harmPrefix + '.nii.gz'):
save_nifti(harmPrefix + '.nii.gz', joinedDwi, b0Img.affine,
b0Img.header)
else:
print(harmPrefix + '.nii.gz', 'already exists, not overwritten.')
def main(self):
with TemporaryDirectory() as tmpdir:
tmpdir = local.path(tmpdir)
pre = tmpdir / 'ants'
rigidxfm = pre + '0GenericAffine.mat'
check_call((' ').join([
pjoin(FILEDIR, 'antsRegistrationSyNMI.sh'), '-f', self.target,
'-m', self.infile, '-t', 'r', '-o', pre, '-n', ANTSREG_THREADS
]),
shell=True)
antsApplyTransforms['-d', '3', '-i', self.labelmap, '-t', rigidxfm,
'-r', self.target, '-o', self.out,
'--interpolation', 'NearestNeighbor'] & FG
def registerFs2Dwi(tmpdir, namePrefix, b0masked, brain, wmparc, wmparc_out):
pre = tmpdir / namePrefix
affine = pre + '0GenericAffine.mat'
warp = pre + '1Warp.nii.gz'
print('Computing warp from brain.nii.gz to (resampled) baseline')
check_call((' ').join([
pjoin(FILEDIR, 'antsRegistrationSyNMI.sh'), '-d', '3', '-m', brain,
'-f', b0masked, '-o', pre, '-n', ANTSREG_THREADS
]),
shell=True)
print(
'Applying warp to wmparc.nii.gz to create (resampled) wmparcindwi.nii.gz'
)
antsApplyTransforms('-d', '3', '-i', wmparc, '-t', warp, affine, '-r',
b0masked, '-o', wmparc_out, '--interpolation',
'NearestNeighbor')
print('Made ' + wmparc_out)
def main(self):
with TemporaryDirectory() as tmpdir:
tmpdir = local.path(tmpdir)
pre = tmpdir / 'ants'
warp = pre + '1Warp.nii.gz'
affine = pre + '0GenericAffine.mat'
check_call((' ').join([
pjoin(FILEDIR, 'antsRegistrationSyNMI.sh'), '-f', self.target,
'-m', self.infile,
'-t r' if self.reg_method == 'rigid' else '', '-o', pre
]),
shell=True)
xfrms = f'-t {warp} -t {affine}' if self.reg_method == 'SyN' else f'-t {affine}'
antsApplyTransforms['-d', '3', '-i', self.labelmap,
xfrms.split(), '-r', self.target, '-o',
self.out, '--interpolation',
'NearestNeighbor'] & FG
def make_video_helper((i, x, in_dir, tmp_ext)):
# pad to be even
if x.shape[0] % 2 == 1 or x.shape[1] % 2 == 1:
x = ig.pad_corner(x, x.shape[1] % 2, x.shape[0] % 2)
ig.save(ut.pjoin(in_dir, 'ao-video-frame%05d%s' % (i, tmp_ext)), x)
def render_scene(scene, texel_colors, mesh = None):
scan = dset.Scan(scene)
if mesh is None:
mesh = load_from_mat(ut.pjoin(scan.path, 'cube.mat'))
# click to toggle between the rendered image and the mesh
ig.show([('cycle', [mesh.render(scan, frame, texel_colors), scan.im(frame)]) for frame in xrange(scan.length)])
def mesh_from_path(path): return load_from_mat(ut.pjoin(path, 'cube.mat'))
def get(self, sha, output_file):
target_path = util.pjoin(self.url_components.path or "/",
"db", sha[0:3], sha, "contents")
self.bucket.download_file(target_path.lstrip("/"), output_file)
def _activateTensors_py(vtk):
vtknew = vtk.dirname / (vtk.stem[2:] + ''.join(vtk.suffixes))
check_call((' ').join([pjoin(FILEDIR, 'activateTensors.py'), vtk, vtknew]),
shell=True)
vtk.delete()
def main(self):
if self.N_proc == '-1':
self.N_proc = N_CPU
# check directory existence
check_dir(self.templatePath, self.force)
## check consistency of b-shells and spatial resolution
ref_bvals_file = pjoin(self.templatePath, 'ref_bshell_bvalues.txt')
ref_res_file = pjoin(self.templatePath, 'ref_res_file.npy')
if self.ref_csv:
if isfile(ref_bvals_file) and isfile(ref_res_file):
remove(ref_bvals_file)
remove(ref_res_file)
consistencyCheck(self.ref_csv, ref_bvals_file, ref_res_file)
if self.target_csv:
consistencyCheck(self.target_csv, ref_bvals_file, ref_res_file)
## separate b-shells
if self.ref_csv:
refListOutPrefix = separateShellsWrapper(self.ref_csv,
ref_bvals_file,
self.N_proc)
if self.target_csv:
tarListOutPrefix = separateShellsWrapper(self.target_csv,
ref_bvals_file,
self.N_proc)
## define variables for template creation and data harmonization
# variables common to all ref_bvals
pipeline_vars = [
'--tar_name',
self.target,
'--nshm',
self.N_shm,
'--nproc',
self.N_proc,
'--template',
self.templatePath,
]
if self.reference:
pipeline_vars.append(f'--ref_name {self.reference}')
if self.N_zero:
pipeline_vars.append(f'--nzero {self.N_zero}')
if self.bvalMap:
pipeline_vars.append(f'--bvalMap {self.bvalMap}')
if self.resample:
pipeline_vars.append(f'--resample {self.resample}')
if self.denoise:
pipeline_vars.append('--denoise')
if self.travelHeads:
pipeline_vars.append('--travelHeads')
if self.force:
pipeline_vars.append('--force')
if self.debug:
pipeline_vars.append('--debug')
if self.verbose:
pipeline_vars.append('--verbose')
# the b-shell bvalues are sorted in descending order because we want to perform registration with highest bval
ref_bvals = read_bvals(ref_bvals_file)[::-1]
for bval in ref_bvals[:-1]: # pass the last bval which is 0.
if self.create and not self.process:
print('## template creation ##')
check_call((' ').join([
pjoin(SCRIPTDIR, 'harmonization.py'), '--tar_list',
tarListOutPrefix + f'_b{int(bval)}.csv', '--bshell_b',
str(int(bval)), '--ref_list', refListOutPrefix +
f'_b{int(bval)}.csv', '--create'
] + pipeline_vars),
shell=True)
elif not self.create and self.process:
print('## data harmonization ##')
check_call((' ').join([
pjoin(SCRIPTDIR, 'harmonization.py'), '--tar_list',
tarListOutPrefix + f'_b{int(bval)}.csv',
f'--ref_list {refListOutPrefix}_b{int(bval)}.csv' if self.
ref_csv else '', '--bshell_b',
str(int(bval)), '--process'
] + pipeline_vars),
shell=True)
elif self.create and self.process:
check_call((' ').join([
pjoin(SCRIPTDIR, 'harmonization.py'), '--tar_list',
tarListOutPrefix + f'_b{int(bval)}.csv', '--bshell_b',
str(int(bval)), '--ref_list', refListOutPrefix +
f'_b{int(bval)}.csv', '--create', '--process'
] + pipeline_vars),
shell=True)
if '--force' in pipeline_vars:
pipeline_vars.remove('--force')
## join harmonized data
if self.process:
joinAllBshells(self.target_csv, ref_bvals_file, 'harmonized_',
self.N_proc)
if self.debug and self.ref_csv:
joinAllBshells(self.ref_csv, ref_bvals_file, 'reconstructed_',
self.N_proc)
def main(self):
self.out = local.path(self.out)
if not self.force and self.out.exists():
logging.error(
'{} already exists, use --force to force overwrite.'.format(
self.out))
sys.exit(1)
with TemporaryDirectory() as tmpdir:
tmpdir = local.path(tmpdir)
bse = tmpdir / 'maskedbse.nii.gz'
t2masked = tmpdir / 'maskedt2.nii.gz'
t2inbse = tmpdir / 't2inbse.nii.gz'
epiwarp = tmpdir / 'epiwarp.nii.gz'
t2tobse_rigid = tmpdir / 't2tobse_rigid'
affine = tmpdir / 't2tobse_rigid0GenericAffine.mat'
logging.info('1. Extract B0 and and mask it')
check_call((' ').join([
pjoin(FILEDIR, 'bse.py'), '-m', self.dwimask, '-i', self.dwi,
'-o', bse
]),
shell=True)
logging.info('2. Mask the T2')
fslmaths(self.t2mask, '-mul', self.t2, t2masked)
logging.info(
'3. Compute a rigid registration from the T2 to the DWI baseline'
)
rigid_registration(3, t2masked, bse, t2tobse_rigid)
antsApplyTransforms('-d', '3', '-i', t2masked, '-o', t2inbse, '-r',
bse, '-t', affine)
logging.info(
'4. Compute 1d nonlinear registration from the DWI to T2-in-bse along the phase direction'
)
moving = bse
fixed = t2inbse
pre = tmpdir / 'epi'
dwiepi = tmpdir / 'dwiepi.nii.gz'
antsRegistration('-d', '3', '-m',
'cc[' + str(fixed) + ',' + str(moving) + ',1,2]',
'-t', 'SyN[0.25,3,0]', '-c', '50x50x10', '-f',
'4x2x1', '-s', '2x1x0', '--restrict-deformation',
'0x1x0', '-v', '1', '-o', pre)
local.path(str(pre) + '0Warp.nii.gz').move(epiwarp)
logging.info('5. Apply warp to the DWI')
check_call((' ').join([
pjoin(FILEDIR,
'antsApplyTransformsDWI.py'), '-i', self.dwi, '-m',
self.dwimask, '-t', epiwarp, '-o', dwiepi, '-n', self.nproc
]),
shell=True)
# WarpTimeSeriesImageMultiTransform can also be used
# dwimasked = tmpdir / 'masked_dwi.nii.gz'
# fslmaths(self.dwi, '-mul', self.dwimask, dwimasked)
# WarpTimeSeriesImageMultiTransform('4', dwimasked, dwiepi, '-R', dwimasked, '-i', epiwarp)
logging.info('6. Apply warp to the DWI mask')
epimask = self.out._path + '-mask.nii.gz'
antsApplyTransforms('-d', '3', '-i', self.dwimask, '-o', epimask,
'-n', 'NearestNeighbor', '-r', bse, '-t',
epiwarp)
fslmaths(epimask, '-mul', '1', epimask, '-odt', 'char')
dwiepi.move(self.out._path + '.nii.gz')
self.bvals_file.copy(self.out._path + '.bval')
self.bvecs_file.copy(self.out._path + '.bvec')
if self.debug:
tmpdir.copy(self.out.dirname / ('epidebug-' + str(getpid())))
def main(self):
self.out = local.path(self.out)
if self.out.exists():
if self.overwrite:
self.out.delete()
else:
logging.error("{} exists, use '--force' to overwrite it".format(self.out))
sys.exit(1)
outxfms = self.out.dirname / self.out.stem+'_xfms.tgz'
with TemporaryDirectory() as tmpdir, local.cwd(tmpdir):
tmpdir = local.path(tmpdir)
dicePrefix = 'vol'
logging.info('Dice the DWI')
fslsplit[self.dwi] & FG
logging.info('Extract the B0')
check_call((' ').join([pjoin(FILEDIR,'bse.py'), '-i', self.dwi._path, '-o', 'b0.nii.gz']), shell= True)
logging.info('Register each volume to the B0')
vols = sorted(tmpdir // (dicePrefix + '*.nii.gz'))
# use the following multi-processed loop
pool= Pool(int(self.nproc))
res= pool.map_async(_Register_vol, vols)
volsRegistered= res.get()
pool.close()
pool.join()
# or use the following for loop
# volsRegistered = []
# for vol in vols:
# volnii = vol.with_suffix('.nii.gz')
# logging.info('Run FSL flirt affine registration')
# flirt('-interp' ,'sinc'
# ,'-sincwidth' ,'7'
# ,'-sincwindow' ,'blackman'
# ,'-in', volnii
# ,'-ref', 'b0.nii.gz'
# ,'-nosearch'
# ,'-o', volnii
# ,'-omat', volnii.with_suffix('.txt', depth=2)
# ,'-paddingsize', '1')
# volsRegistered.append(volnii)
fslmerge('-t', 'EddyCorrect-DWI.nii.gz', volsRegistered)
transforms = tmpdir.glob(dicePrefix+'*.txt')
transforms.sort()
logging.info('Extract the rotations and realign the gradients')
bvecs= read_bvecs(self.bvecFile._path)
bvecs_new= bvecs.copy()
for (i,t) in enumerate(transforms):
logging.info('Apply ' + t)
tra = np.loadtxt(t)
# removes the translation
aff = np.matrix(tra[0:3,0:3])
# computes the finite strain of aff to get the rotation
rot = aff*aff.T
# compute the square root of rot
[el, ev] = np.linalg.eig(rot)
eL = np.identity(3)*np.sqrt(el)
sq = ev*eL*ev.I
# finally the rotation is defined as
rot = sq.I*aff
bvecs_new[i] = np.dot(rot,bvecs[i]).tolist()[0]
tar('cvzf', outxfms, transforms)
# save modified bvecs
write_bvecs(self.out._path+'.bvec', bvecs_new)
# save EddyCorrect-DWI
local.path('EddyCorrect-DWI.nii.gz').copy(self.out._path+'.nii.gz')
# copy bvals
self.bvalFile.copy(self.out._path+'.bval')
if self.debug:
tmpdir.copy(pjoin(dirname(self.out),"eddy-debug-"+str(getpid())))
def main(self):
from plumbum.cmd import eddy_openmp
if self.useGpu:
try:
from plumbum.cmd import nvcc
nvcc['--version'] & FG
print('\nCUDA found, looking for available GPU\n')
from GPUtil import getFirstAvailable
getFirstAvailable()
print('available GPU found, looking for eddy_cuda executable\n'
'make sure you have created a softlink according to '
'https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/eddy/UsersGuide')
from plumbum.cmd import eddy_cuda as eddy_openmp
print('\neddy_cuda executable found\n')
except:
print(
'nvcc, available GPU, and/or eddy_cuda was not found, using eddy_openmp'
)
prefix = self.dwi_file.name.split('.')[0]
self.outDir.mkdir()
outPrefix = pjoin(self.outDir._path, prefix + '_Ed')
if not self.b0_brain_mask:
logging.info('Mask not provided, creating mask ...')
self.b0_brain_mask = outPrefix + '_mask.nii.gz'
bet_mask(self.dwi_file,
self.b0_brain_mask,
4,
bvalFile=self.bvals_file,
BET_THRESHOLD=self.betThreshold)
_, _, eddy_openmp_params = obtain_fsl_eddy_params(
self.eddy_config_file._path)
print('eddy_openmp/cuda parameters')
print(eddy_openmp_params)
print('')
eddy_openmp[f'--imain={self.dwi_file}', f'--mask={self.b0_brain_mask}',
f'--acqp={self.acqparams_file}',
f'--index={self.index_file}', f'--bvecs={self.bvecs_file}',
f'--bvals={self.bvals_file}', f'--out={outPrefix}',
eddy_openmp_params.split()] & FG
# free space, see https://github.com/pnlbwh/pnlNipype/issues/82
if '--repol' in eddy_openmp_params:
rm[f'{outPrefix}.eddy_outlier_free_data.nii.gz'] & FG
bvals = np.array(read_bvals(self.bvals_file))
ind = [
i for i in range(len(bvals))
if bvals[i] > B0_THRESHOLD and bvals[i] <= REPOL_BSHELL_GREATER
]
if '--repol' in eddy_openmp_params and len(ind):
print(
'\nDoing eddy_openmp/cuda again without --repol option '
'to obtain eddy correction w/o outlier replacement for b<=500 shells\n'
)
eddy_openmp_params = eddy_openmp_params.split()
eddy_openmp_params.remove('--repol')
print(eddy_openmp_params)
print('')
wo_repol_outDir = self.outDir.join('wo_repol')
wo_repol_outDir.mkdir()
wo_repol_outPrefix = pjoin(wo_repol_outDir, prefix + '_Ed')
eddy_openmp[
f'--imain={self.dwi_file}', f'--mask={self.b0_brain_mask}',
f'--acqp={self.acqparams_file}', f'--index={self.index_file}',
f'--bvecs={self.bvecs_file}', f'--bvals={self.bvals_file}',
f'--out={wo_repol_outPrefix}', eddy_openmp_params] & FG
repol_bvecs = np.array(
read_bvecs(outPrefix + '.eddy_rotated_bvecs'))
wo_repol_bvecs = np.array(
read_bvecs(wo_repol_outPrefix + '.eddy_rotated_bvecs'))
merged_bvecs = repol_bvecs.copy()
merged_bvecs[ind, :] = wo_repol_bvecs[ind, :]
repol_data = load_nifti(outPrefix + '.nii.gz')
wo_repol_data = load_nifti(wo_repol_outPrefix + '.nii.gz')
merged_data = repol_data.get_fdata().copy()
merged_data[..., ind] = wo_repol_data.get_fdata()[..., ind]
save_nifti(outPrefix + '.nii.gz',
merged_data,
repol_data.affine,
hdr=repol_data.header)
# copy bval,bvec to have same prefix as that of eddy corrected volume
write_bvecs(outPrefix + '.bvec', merged_bvecs)
copyfile(self.bvals_file, outPrefix + '.bval')
# clean up
rm['-r', wo_repol_outDir] & FG
else:
# copy bval,bvec to have same prefix as that of eddy corrected volume
copyfile(outPrefix + '.eddy_rotated_bvecs', outPrefix + '.bvec')
copyfile(self.bvals_file, outPrefix + '.bval')
def make_video_helper((i, x, in_dir, tmp_ext)):
# pad to be even
if x.shape[0] % 2 == 1 or x.shape[1] % 2 == 1:
x = ig.pad_corner(x, x.shape[1] % 2, x.shape[0] % 2)
ig.save(ut.pjoin(in_dir, 'ao-video-frame%05d%s' % (i, tmp_ext)), x)
def makeAtlases(target, trainingTable, outPrefix, fusion, threads, debug):
with TemporaryDirectory() as tmpdir:
tmpdir = local.path(tmpdir)
L= len(trainingTable)
multiDataFrame= pd.concat([trainingTable, pd.DataFrame({'tmpdir': [tmpdir]*L, 'target': [str(target)]*L})], axis= 1)
logging.info('Create {} atlases: compute transforms from images to target and apply over images'.format(L))
pool = multiprocessing.Pool(threads) # Use all available cores, otherwise specify the number you want as an argument
pool.map_async(train2target, multiDataFrame.iterrows())
pool.close()
pool.join()
logging.info('Fuse warped labelmaps to compute output labelmaps')
atlasimages = tmpdir // 'atlas*.nii.gz'
# sorting is required for applying weight to corresponding labelmap
atlasimages.sort()
if fusion.lower() == 'wavg':
ALPHA_DEFAULT= 0.45
logging.info('Compute MI between warped images and target')
pool = multiprocessing.Pool(threads)
for img in atlasimages:
print('MI between {} and target'.format(img))
miFile= img+'.txt'
pool.apply_async(func= computeMI, args= (target, img, miFile, ))
pool.close()
pool.join()
mis= []
with open(tmpdir+'/MI.txt','w') as fw:
for img in atlasimages:
with open(img+'.txt') as f:
mi= f.read().strip()
fw.write(img+','+mi+'\n')
mis.append(float(mi))
weights = weightsFromMIExp(mis, ALPHA_DEFAULT)
target_header= load_nifti(target._path).header
pool = multiprocessing.Pool(threads) # Use all available cores, otherwise specify the number you want as an argument
for labelname in list(trainingTable)[1:]: # list(d) gets column names
out = os.path.abspath(outPrefix+ f'_{labelname}.nii.gz')
if os.path.exists(out):
os.remove(out)
labelmaps = tmpdir // (labelname + '*')
labelmaps.sort()
if fusion.lower() == 'avg':
print(' ')
# parellelize
# fuseAvg(labelmaps, out, target_header)
pool.apply_async(func= fuseAvg, args= (labelmaps, out, target_header, ))
elif fusion.lower() == 'antsjointfusion':
print(' ')
# atlasimages are the warped images
# labelmaps are the warped labels
# parellelize
# fuseAntsJointFusion(target, atlasimages, labelmaps, out)
pool.apply_async(func= fuseAntsJointFusion, args= (target, atlasimages, labelmaps, out, ))
elif fusion.lower() == 'wavg':
print(' ')
# parellelize
# fuseWeightedAvg(labelmaps, weights, out, target_header)
pool.apply_async(func= fuseWeightedAvg, args= (labelmaps, weights, out, target_header, ))
else:
print('Unrecognized fusion option: {}. Skipping.'.format(fusion))
pool.close()
pool.join()
if debug:
tmpdir.copy(pjoin(dirname(outPrefix), 'atlas-debug-' + str(os.getpid())))
def mesh_from_path(path):
return load_from_mat(ut.pjoin(path, 'cube.mat'))
def main(self):
with tempfile.TemporaryDirectory() as tmpdir:
tmpdir = local.path(tmpdir)
b0masked = tmpdir / "b0masked.nii.gz" # Sylvain wants both
b0maskedbrain = tmpdir / "b0maskedbrain.nii.gz"
brain = tmpdir / "brain.nii.gz"
wmparc = tmpdir / "wmparc.nii.gz"
brainmgz = self.parent.fsdir / 'mri/brain.mgz'
wmparcmgz = self.parent.fsdir / 'mri/wmparc.mgz'
wmparcindwi = tmpdir / 'wmparcInDwi.nii.gz' # Sylvain wants both
wmparcinbrain = tmpdir / 'wmparcInBrain.nii.gz'
print(
"Making brain.nii.gz and wmparc.nii.gz from their mgz versions"
)
vol2vol = local[self.parent.fshome / 'bin/mri_vol2vol']
label2vol = local[self.parent.fshome / 'bin/mri_label2vol']
with local.env(SUBJECTS_DIR=''):
vol2vol('--mov', brainmgz, '--targ', brainmgz, '--regheader',
'--o', brain)
label2vol('--seg', wmparcmgz, '--temp', brainmgz,
'--regheader', wmparcmgz, '--o', wmparc)
print('Extracting B0 from DWI and masking it')
check_call((' ').join([
pjoin(FILEDIR, 'bse.py'), '-i', self.parent.dwi, '-m',
self.parent.dwimask, '-o', b0masked
]),
shell=True)
print('Made masked B0')
dwi_res = load_nifti(
str(b0masked)).header['pixdim'][1:4].round(decimals=2)
brain_res = load_nifti(
str(brain)).header['pixdim'][1:4].round(decimals=2)
print(f'DWI resolution: {dwi_res}')
print(f'FreeSurfer brain resolution: {brain_res}')
print('Registering wmparc to B0')
registerFs2Dwi(tmpdir, 'fsbrainToB0', b0masked, brain, wmparc,
wmparcindwi)
if (dwi_res != brain_res).any():
print(
'DWI resolution is different from FreeSurfer brain resolution'
)
print(
'wmparc wil be registered to both DWI and brain resolution'
)
print(
'Check output files wmparcInDwi.nii.gz and wmparcInBrain.nii.gz'
)
print('Resampling B0 to brain resolution')
ResampleImageBySpacing('3', b0masked, b0maskedbrain,
brain_res.tolist())
print('Registering wmparc to resampled B0')
registerFs2Dwi(tmpdir, 'fsbrainToResampledB0', b0maskedbrain,
brain, wmparc, wmparcinbrain)
# copying images to outDir
b0masked.copy(self.parent.out)
wmparcindwi.copy(self.parent.out)
if b0maskedbrain.exists():
b0maskedbrain.copy(self.parent.out)
wmparcinbrain.copy(self.parent.out)
if self.parent.debug:
tmpdir.copy(self.parent.out,
'fs2dwi-debug-' + str(os.getpid()))
print('See output files in ', self.parent.out._path)
class App(cli.Application):
"""Convenient script to run Freesurfer segmentation"""
VERSION = __version__
t1 = cli.SwitchAttr(['-i', '--input'],
cli.ExistingFile,
help='t1 image in nifti format (nii, nii.gz)',
mandatory=True)
t1mask = cli.SwitchAttr(
['-m', '--mask'],
cli.ExistingFile,
help=
'mask the t1 before running Freesurfer; if not provided, -skullstrip is enabled with Freesurfer segmentation',
mandatory=False)
t2 = cli.SwitchAttr(['--t2'],
cli.ExistingFile,
help='t2 image in nifti format (nii, nii.gz)',
mandatory=False)
t2mask = cli.SwitchAttr(
['--t2mask'],
cli.ExistingFile,
help=
'mask the t2 before running Freesurfer, if t2 is provided but not its mask, -skullstrip is enabled with Freesurfer segmentation',
mandatory=False)
force = cli.Flag(
['-f', '--force'],
help='if --force is used, any previous output will be overwritten')
out = cli.SwitchAttr(['-o', '--outDir'],
help='output directory',
mandatory=True)
ncpu = cli.SwitchAttr(
['-n', '--nproc'],
help=
'number of processes/threads to use (-1 for all available) for Freesurfer segmentation',
default=1)
expert_file = cli.SwitchAttr(
['--expert'],
cli.ExistingFile,
help=
'expert options to use with recon-all for high-resolution data, see https://surfer.nmr.mgh.harvard.edu/fswiki/SubmillimeterRecon',
default=pjoin(FILEDIR, 'expert_file.txt'))
no_hires = cli.Flag(
['--nohires'],
help=
'omit high resolution freesurfer segmentation i.e. do not use -hires flag'
)
no_skullstrip = cli.Flag(
['--noskullstrip'],
help=
'if you do not provide --mask but --input is already masked, omit further skull stripping by freesurfer'
)
subfields = cli.Flag(['--subfields'],
help='FreeSurfer 7 supported -subfields')
no_rand = cli.Flag(
['--norandomness'],
help='use the same random seed for certain binaries run under recon-all'
)
def main(self):
fshome = local.path(os.getenv('FREESURFER_HOME'))
if not fshome:
logging.error('Set FREESURFER_HOME first.')
sys.exit(1)
if not self.force and os.path.exists(self.out):
logging.error(
'Output directory exists, use -f/--force to force an overwrite.'
)
sys.exit(1)
if self.t2mask and not self.t2:
raise AttributeError('--t2mask is invalid without --t2')
with TemporaryDirectory() as tmpdir, local.env(SUBJECTS_DIR=tmpdir,
FSFAST_HOME='',
MNI_DIR=''):
tmpdir = local.path(tmpdir)
if self.t1mask:
logging.info('Mask the t1')
t1 = tmpdir / 't1masked.nii.gz'
ImageMath('3', t1, 'm', self.t1, self.t1mask)
else:
t1 = tmpdir / 't1.nii.gz'
self.t1.copy(t1)
subjid = self.t1.stem
common_params = ['-s', subjid]
autorecon3_params = []
if self.t2:
if self.t2mask:
logging.info('Mask the t2')
t2 = tmpdir / 't2masked.nii.gz'
ImageMath('3', t2, 'm', self.t2, self.t2mask)
else:
t2 = tmpdir / 't2.nii.gz'
self.t2.copy(t2)
autorecon3_params = ['-T2', t2, '-T2pial']
if self.subfields:
autorecon3_params += ['-subfields']
logging.info("Running freesurfer on " + t1)
if self.ncpu == '-1':
self.ncpu = str(N_CPU)
if int(self.ncpu) > 1:
common_params += ['-parallel', '-openmp', self.ncpu]
autorecon1_params = []
if not self.no_hires:
common_params.append('-hires')
autorecon1_params = ['-expert', self.expert_file]
if self.no_rand:
common_params.append('-norandomness')
# run recon_all in three steps so we can overwrite/provide MABS masked T1
# -noskullstrip is used with -autorecon1 only, so we need to check whether T1 is masked/T1 mask is provided
# irrespective of masked/unmasked T2
if self.t1mask or self.no_skullstrip:
recon_all['-i', t1, common_params, '-autorecon1',
autorecon1_params, '-noskullstrip'] & FG
(tmpdir / subjid / 'mri/T1.mgz').copy(tmpdir / subjid /
'mri/brainmask.mgz')
else:
recon_all['-i', t1, common_params, '-autorecon1',
autorecon1_params] & FG
recon_all[common_params, '-autorecon2'] & FG
recon_all[common_params, '-autorecon3', autorecon3_params] & FG
logging.info("Freesurfer done.")
(tmpdir / subjid).copy(
self.out, override=True) # overwrites any existing directory
logging.info("Made " + self.out)
def main(self):
with tempfile.TemporaryDirectory() as tmpdir:
tmpdir = local.path(tmpdir)
b0masked = tmpdir / "b0masked.nii.gz" # Sylvain wants both
b0maskedbrain = tmpdir / "b0maskedbrain.nii.gz"
t2masked = tmpdir / 't2masked.nii.gz'
print('Masking the T2')
ImageMath(3, t2masked, 'm', self.t2, self.t2mask)
brain = tmpdir / "brain.nii.gz"
wmparc = tmpdir / "wmparc.nii.gz"
brainmgz = self.parent.fsdir / 'mri/brain.mgz'
wmparcmgz = self.parent.fsdir / 'mri/wmparc.mgz'
wmparcindwi = tmpdir / 'wmparcInDwi.nii.gz' # Sylvain wants both
wmparcinbrain = tmpdir / 'wmparcInBrain.nii.gz'
print(
"Making brain.nii.gz and wmparc.nii.gz from their mgz versions"
)
vol2vol = local[self.parent.fshome / 'bin/mri_vol2vol']
label2vol = local[self.parent.fshome / 'bin/mri_label2vol']
with local.env(SUBJECTS_DIR=''):
vol2vol('--mov', brainmgz, '--targ', brainmgz, '--regheader',
'--o', brain)
label2vol('--seg', wmparcmgz, '--temp', brainmgz,
'--regheader', wmparcmgz, '--o', wmparc)
print('Extracting B0 from DWI and masking it')
check_call((' ').join([
pjoin(FILEDIR, 'bse.py'), '-i', self.parent.dwi, '-m',
self.parent.dwimask, '-o', b0masked
]),
shell=True)
print('Made masked B0')
# rigid registration from t2 to brain.nii.gz
pre = tmpdir / 'BrainToT2'
BrainToT2Affine = pre + '0GenericAffine.mat'
print('Computing rigid registration from brain.nii.gz to t2')
rigid_registration(3, brain, t2masked, pre)
# generates three files for rigid registration:
# pre0GenericAffine.mat preInverseWarped.nii.gz preWarped.nii.gz
# generates five files for default(rigid+affine+deformable syn) registration:
# pre0GenericAffine.mat pre1Warp.nii.gz preWarped.nii.gz pre1InverseWarp.nii.gz preInverseWarped.nii.gz
dwi_res = load_nifti(
str(b0masked)).header['pixdim'][1:4].round(decimals=2)
brain_res = load_nifti(
str(brain)).header['pixdim'][1:4].round(decimals=2)
print(f'DWI resolution: {dwi_res}')
print(f'FreeSurfer brain resolution: {brain_res}')
print('Registering wmparc to B0 through T2')
registerFs2Dwi_T2(tmpdir, 'fsbrainToT2ToB0', b0masked, t2masked,
BrainToT2Affine, wmparc, wmparcindwi)
if (dwi_res != brain_res).any():
print(
'DWI resolution is different from FreeSurfer brain resolution'
)
print(
'wmparc wil be registered to both DWI and brain resolution'
)
print(
'Check output files wmparcInDwi.nii.gz and wmparcInBrain.nii.gz'
)
print('Resampling B0 to brain resolution')
ResampleImageBySpacing('3', b0masked, b0maskedbrain,
brain_res.tolist())
print('Registering wmparc to resampled B0')
registerFs2Dwi_T2(tmpdir, 'fsbrainToT2ToResampledB0',
b0maskedbrain, t2masked, BrainToT2Affine,
wmparc, wmparcinbrain)
# copying images to outDir
b0masked.copy(self.parent.out)
wmparcindwi.copy(self.parent.out)
if b0maskedbrain.exists():
b0maskedbrain.copy(self.parent.out)
wmparcinbrain.copy(self.parent.out)
if self.parent.debug:
tmpdir.copy(self.parent.out,
'fs2dwi-debug-' + str(os.getpid()))
print('See output files in ', self.parent.out._path)
def get(self, sha, output_file):
target_path = util.pjoin(self.url.rstrip("/"), "db", sha[0:3], sha,
"contents")
self.download(target_path, output_file)
