def test_main(rec=False, verb=False, throw=True):
pet.MessageRedirector()
for scheme in ("file", "memory"):
pet.AcquisitionData.set_storage_scheme(scheme)
original_verb = pet.get_verbosity()
pet.set_verbosity(False)
# create an acq_model that is explicitly a RayTracingMatrix
am = pet.AcquisitionModelUsingRayTracingMatrix()
# load sample data
data_path = pet.examples_data_path('PET')
raw_data_file = pet.existing_filepath(data_path,
'Utahscat600k_ca_seg4.hs')
ad = pet.AcquisitionData(raw_data_file)
# create sample image
image = pet.ImageData()
image.initialise(dim=(31, 111, 111), vsize=(2.25, 2.25, 2.25))
# set up Acquisition Model
am.set_up(ad, image)
# test for adjointnesss
if not is_operator_adjoint(am, verbose=verb):
raise AssertionError(
'AcquisitionModelUsingRayTracingMatrix is not adjoint')
# Reset original verbose-ness
pet.set_verbosity(original_verb)
return 0, 1
def test_main(rec=False, verb=False, throw=True):
# Set STIR verbosity to off
original_verb = pet.get_verbosity()
pet.set_verbosity(1)
time.sleep(0.5)
sys.stderr.write("Testing NiftyPET projector...")
time.sleep(0.5)
# Get image
image = get_image()
# Get AM
try:
acq_model = pet.AcquisitionModelUsingNiftyPET()
except:
return 1, 1
acq_model.set_cuda_verbosity(verb)
data_path = examples_data_path('PET')
# raw_data_path = pet.existing_filepath(os.path.join(data_path, 'mMR'), 'mMR_template_span11.hs')
raw_data_path = os.path.join(data_path, 'mMR')
template_acq_data = pet.AcquisitionData(
os.path.join(raw_data_path, 'mMR_template_span11.hs'))
acq_model.set_up(template_acq_data, image)
# Test operator adjointness
if verb:
print('testing adjointness')
if not is_operator_adjoint(acq_model, num_tests=1, verbose=True):
raise AssertionError('NiftyPet AcquisitionModel is not adjoint')
# Generate test data
simulated_acq_data = acq_model.forward(image)
simulated_acq_data_w_noise = add_noise(simulated_acq_data, 10)
obj_fun = pet.make_Poisson_loglikelihood(template_acq_data)
obj_fun.set_acquisition_model(acq_model)
recon = pet.OSMAPOSLReconstructor()
recon.set_objective_function(obj_fun)
recon.set_num_subsets(1)
recon.set_num_subiterations(1)
recon.set_input(simulated_acq_data_w_noise)
if verb:
print('setting up, please wait...')
initial_estimate = image.get_uniform_copy()
recon.set_up(initial_estimate)
if verb:
print('reconstructing...')
recon.set_current_estimate(initial_estimate)
recon.process()
reconstructed_im = recon.get_output()
if not reconstructed_im:
raise AssertionError()
# Reset original verbose-ness
pet.set_verbosity(original_verb)
return 0, 1
convert = lambda text: int(text) if text.isdigit() else text.lower()
alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]
return sorted(data, key=alphanum_key, reverse=False)
# generate a list, contains the time intervalls (sum of frames)
def sum_list(lists):
sum_liste = []
add = int(0)
sum_liste.append(add)
for frame in lists[0:]:
add += frame
sum_liste.append(add)
return sum_liste
# Set the STIR verbosity
Pet.set_verbosity(1)
#%% data path and set filenames
# data path to list-mode and normalisation files
data_path_LM = '/home/rich/Documents/ReCo/UKL_data/LM/20190417/'
data_path_MR = '/home/rich/Documents/ReCo/UKL_data/Processed/MedPhys_MoCo_Test_20190417_7/'
# avoid cluttering of files, delete working-folder and start from scratch
working_folder = '/home/rich/Documents/ReCo/working'
if os.path.exists(working_folder):
shutil.rmtree(working_folder)
if not os.path.exists(working_folder):
os.makedirs(working_folder, mode=0o770)
# change the current working directory to the given path
os.chdir(working_folder)
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#=========================================================================
import os, shutil, numpy
import unittest
import sirf.STIR as pet
from sirf.Utilities import examples_data_path
pet.AcquisitionData.set_storage_scheme('file')
pet.set_verbosity(0)
class TestSTIRObjectiveFunction(unittest.TestCase):
def setUp(self):
data_path = os.path.join(examples_data_path('PET'),
'thorax_single_slice')
image = pet.ImageData(os.path.join(data_path, 'emission.hv'))
am = pet.AcquisitionModelUsingRayTracingMatrix()
am.set_num_tangential_LORs(5)
templ = pet.AcquisitionData(
os.path.join(data_path, 'template_sinogram.hs'))
am.set_up(templ, image)
acquired_data = am.forward(image)
# Output file
outp_prefix = args['--outp']
# Initial estimate
initial_estimate = args['--initial']
visualisations = True if args['--visualisations'] and have_pylab else False
nifti = True if args['--nifti'] else False
use_gpu = True if args['--gpu'] else False
descriptive_fname = True if args['--descriptive_fname'] else False
update_obj_fn_interval = int(args['--update_obj_fn_interval'])
# Verbosity
verbosity = int(args['--verbosity'])
pet.set_verbosity(verbosity)
if verbosity == 0:
msg_red = pet.MessageRedirector(None, None, None)
# Save interval
save_interval = int(args['--save_interval'])
save_interval = min(save_interval, num_iters)
# Convergence variables
r_alpha = float(args['--alpha'])
precond = True if args['--precond'] else False
def get_resampler(image, ref=None, trans=None):
"""returns a NiftyResampler object for the specified transform and image"""
if ref is None:
