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
path_NAC = working_folder + '/recon/NAC/'
path_sino = working_folder + '/sino/'
path_rando = working_folder + '/rando/'
if not os.path.exists(path_NAC):
os.makedirs(path_NAC, mode=0o770)
print('Create Folder: {}'.format(path_NAC))
#%% Settings for reconstruction
# set acq_model
acq_model = Pet.AcquisitionModelUsingRayTracingMatrix()
acq_model.set_num_tangential_LORs(5)
# set recon, OSEM
recon = Pet.OSMAPOSLReconstructor()
num_subsets = 7
num_subiterations = 4
recon.set_num_subsets(num_subsets)
recon.set_num_subiterations(num_subiterations)
# definitions for detector sensitivity modelling
asm_norm = Pet.AcquisitionSensitivityModel(norm_file)
acq_model.set_acquisition_sensitivity(asm_norm)
#%% redirect STIR messages to some files
# you can check these if things go wrong
msg_red = Pet.MessageRedirector('info.txt', 'warn.txt')
#%% List of sino and rand
list_sino = [
