def main():
time_start = ph.start_timing()
# Set print options for numpy
np.set_printoptions(precision=3)
# Read input
input_parser = InputArgparser(
description="Volumetric MRI reconstruction framework to reconstruct "
"an isotropic, high-resolution 3D volume from multiple "
"motion-corrected (or static) stacks of low-resolution slices.", )
input_parser.add_dir_input()
input_parser.add_filenames()
input_parser.add_image_selection()
input_parser.add_dir_output(required=True)
input_parser.add_prefix_output(default="SRR_")
input_parser.add_suffix_mask(default="_mask")
input_parser.add_target_stack_index(default=0)
input_parser.add_extra_frame_target(default=10)
input_parser.add_isotropic_resolution(default=None)
input_parser.add_reconstruction_space(default=None)
input_parser.add_minimizer(default="lsmr")
input_parser.add_iter_max(default=10)
input_parser.add_reconstruction_type(default="TK1L2")
input_parser.add_data_loss(default="linear")
input_parser.add_data_loss_scale(default=1)
input_parser.add_alpha(default=0.02 # TK1L2
# default=0.006 #TVL2, HuberL2
)
input_parser.add_rho(default=0.5)
input_parser.add_tv_solver(default="PD")
input_parser.add_pd_alg_type(default="ALG2")
input_parser.add_iterations(default=15)
input_parser.add_subfolder_comparison()
input_parser.add_provide_comparison(default=0)
input_parser.add_log_script_execution(default=1)
input_parser.add_verbose(default=0)
args = input_parser.parse_args()
input_parser.print_arguments(args)
# Write script execution call
if args.log_script_execution:
input_parser.write_performed_script_execution(
os.path.abspath(__file__))
# --------------------------------Read Data--------------------------------
ph.print_title("Read Data")
# Neither '--dir-input' nor '--filenames' was specified
if args.filenames is not None and args.dir_input is not None:
raise IOError("Provide input by either '--dir-input' or '--filenames' "
"but not both together")
# '--dir-input' specified
elif args.dir_input is not None:
data_reader = dr.ImageSlicesDirectoryReader(
path_to_directory=args.dir_input,
suffix_mask=args.suffix_mask,
image_selection=args.image_selection)
# '--filenames' specified
elif args.filenames is not None:
data_reader = dr.MultipleImagesReader(args.filenames,
suffix_mask=args.suffix_mask)
else:
raise IOError("Provide input by either '--dir-input' or '--filenames'")
if args.reconstruction_type not in ["TK1L2", "TVL2", "HuberL2"]:
raise IOError("Reconstruction type unknown")
data_reader.read_data()
stacks = data_reader.get_data()
ph.print_info("%d input stacks read for further processing" % len(stacks))
# Reconstruction space is given isotropically resampled target stack
if args.reconstruction_space is None:
recon0 = \
stacks[args.target_stack_index].get_isotropically_resampled_stack(
resolution=args.isotropic_resolution,
extra_frame=args.extra_frame_target)
# Reconstruction space was provided by user
else:
recon0 = st.Stack.from_filename(args.reconstruction_space,
extract_slices=False)
# Change resolution for isotropic resolution if provided by user
if args.isotropic_resolution is not None:
recon0 = recon0.get_isotropically_resampled_stack(
args.isotropic_resolution)
# Use image information of selected target stack as recon0 serves
# as initial value for reconstruction
recon0 = \
stacks[args.target_stack_index].get_resampled_stack(recon0.sitk)
recon0 = recon0.get_stack_multiplied_with_mask()
if args.reconstruction_type in ["TVL2", "HuberL2"]:
ph.print_title("Compute Initial value for %s" %
args.reconstruction_type)
SRR0 = tk.TikhonovSolver(
stacks=stacks,
reconstruction=recon0,
alpha=args.alpha,
iter_max=args.iter_max,
reg_type="TK1",
minimizer=args.minimizer,
data_loss=args.data_loss,
data_loss_scale=args.data_loss_scale,
# verbose=args.verbose,
)
SRR0.run()
recon = SRR0.get_reconstruction()
recon.set_filename(SRR0.get_setting_specific_filename(args.prefix_output))
recon.write(args.dir_output)
# List to store SRRs
recons = []
for i in range(0, len(stacks)):
recons.append(stacks[i])
recons.insert(0, recon)
if args.reconstruction_type in ["TVL2", "HuberL2"]:
ph.print_title("Compute %s reconstruction" % args.reconstruction_type)
if args.tv_solver == "ADMM":
SRR = admm.ADMMSolver(
stacks=stacks,
reconstruction=st.Stack.from_stack(SRR0.get_reconstruction()),
minimizer=args.minimizer,
alpha=args.alpha,
iter_max=args.iter_max,
rho=args.rho,
data_loss=args.data_loss,
iterations=args.iterations,
verbose=args.verbose,
)
SRR.run()
recon = SRR.get_reconstruction()
recon.set_filename(
SRR.get_setting_specific_filename(args.prefix_output))
recons.insert(0, recon)
recon.write(args.dir_output)
else:
SRR = pd.PrimalDualSolver(
stacks=stacks,
reconstruction=st.Stack.from_stack(SRR0.get_reconstruction()),
minimizer=args.minimizer,
alpha=args.alpha,
iter_max=args.iter_max,
iterations=args.iterations,
alg_type=args.pd_alg_type,
reg_type="TV"
if args.reconstruction_type == "TVL2" else "huber",
data_loss=args.data_loss,
verbose=args.verbose,
)
SRR.run()
recon = SRR.get_reconstruction()
recon.set_filename(
SRR.get_setting_specific_filename(args.prefix_output))
recons.insert(0, recon)
recon.write(args.dir_output)
if args.verbose and not args.provide_comparison:
sitkh.show_stacks(recons)
# Show SRR together with linearly resampled input data.
# Additionally, a script is generated to open files
if args.provide_comparison:
sitkh.show_stacks(
recons,
show_comparison_file=args.provide_comparison,
dir_output=os.path.join(args.dir_output,
args.subfolder_comparison),
)
ph.print_line_separator()
elapsed_time = ph.stop_timing(time_start)
ph.print_title("Summary")
print("Computational Time for Volumetric Reconstruction: %s" %
(elapsed_time))
return 0
def main():
input_parser = InputArgparser(
description="Simulate stacks from obtained reconstruction. "
"Script simulates/projects the slices at estimated positions "
"within reconstructed volume. Ideally, if motion correction was "
"correct, the resulting stack of such obtained projected slices, "
"corresponds to the originally acquired (motion corrupted) data.",
)
input_parser.add_dir_input(required=True)
input_parser.add_reconstruction(required=True)
input_parser.add_dir_output(required=True)
input_parser.add_suffix_mask(default="_mask")
input_parser.add_prefix_output(default="Simulated_")
input_parser.add_option(
option_string="--copy-data",
type=int,
help="Turn on/off copying of original data (including masks) to "
"output folder.",
default=0)
input_parser.add_option(
option_string="--reconstruction-mask",
type=str,
help="If given, reconstruction image mask is propagated to "
"simulated stack(s) of slices as well",
default=None)
input_parser.add_interpolator(
option_string="--interpolator-mask",
help="Choose the interpolator type to propagate the reconstruction "
"mask (%s)." % (INTERPOLATOR_TYPES),
default="NearestNeighbor")
input_parser.add_verbose(default=0)
args = input_parser.parse_args()
input_parser.print_arguments(args)
if args.interpolator_mask not in ALLOWED_INTERPOLATORS:
raise IOError(
"Unknown interpolator provided. Possible choices are %s" % (
INTERPOLATOR_TYPES))
# Read motion corrected data
data_reader = dr.ImageSlicesDirectoryReader(
path_to_directory=args.dir_input,
suffix_mask=args.suffix_mask)
data_reader.read_data()
stacks = data_reader.get_data()
reconstruction = st.Stack.from_filename(
args.reconstruction, args.reconstruction_mask, extract_slices=False)
linear_operators = lin_op.LinearOperators()
for i, stack in enumerate(stacks):
# initialize image data array(s)
nda = np.zeros_like(sitk.GetArrayFromImage(stack.sitk))
if args.reconstruction_mask:
nda_mask = np.zeros_like(sitk.GetArrayFromImage(stack.sitk_mask))
# Simulate slices at estimated positions within reconstructed volume
simulated_slices = [
linear_operators.A(
reconstruction, s, interpolator_mask=args.interpolator_mask)
for s in stack.get_slices()
]
# Fill stack information "as if slice was acquired consecutively"
# Therefore, simulated stack slices correspond to acquired slices
# (in case motion correction was correct)
for j, simulated_slice in enumerate(simulated_slices):
nda[j, :, :] = sitk.GetArrayFromImage(simulated_slice.sitk)
if args.reconstruction_mask:
nda_mask[j, :, :] = sitk.GetArrayFromImage(
simulated_slice.sitk_mask)
# Create nifti image with same image header as original stack
simulated_stack_sitk = sitk.GetImageFromArray(nda)
simulated_stack_sitk.CopyInformation(stack.sitk)
if args.reconstruction_mask:
simulated_stack_sitk_mask = sitk.GetImageFromArray(nda_mask)
simulated_stack_sitk_mask.CopyInformation(stack.sitk_mask)
else:
simulated_stack_sitk_mask = None
simulated_stack = st.Stack.from_sitk_image(
image_sitk=simulated_stack_sitk,
image_sitk_mask=simulated_stack_sitk_mask,
filename=args.prefix_output + stack.get_filename(),
extract_slices=False)
if args.verbose:
sitkh.show_stacks([
stack, simulated_stack],
segmentation=simulated_stack
if args.reconstruction_mask else None)
simulated_stack.write(
args.dir_output,
write_mask=True,
write_slices=False,
suffix_mask=args.suffix_mask)
if args.copy_data:
stack.write(
args.dir_output,
write_mask=True,
write_slices=False,
suffix_mask=args.suffix_mask)
return 0
def main():
time_start = ph.start_timing()
# Set print options for numpy
np.set_printoptions(precision=3)
# Read input
input_parser = InputArgparser(
description="Volumetric MRI reconstruction framework to reconstruct "
"an isotropic, high-resolution 3D volume from multiple stacks of 2D "
"slices with motion correction. The resolution of the computed "
"Super-Resolution Reconstruction (SRR) is given by the in-plane "
"spacing of the selected target stack. A region of interest can be "
"specified by providing a mask for the selected target stack. Only "
"this region will then be reconstructed by the SRR algorithm which "
"can substantially reduce the computational time.", )
input_parser.add_dir_input()
input_parser.add_filenames()
input_parser.add_dir_output(required=True)
input_parser.add_suffix_mask(default="_mask")
input_parser.add_target_stack_index(default=0)
input_parser.add_search_angle(default=90)
input_parser.add_multiresolution(default=0)
input_parser.add_shrink_factors(default=[2, 1])
input_parser.add_smoothing_sigmas(default=[1, 0])
input_parser.add_sigma(default=0.9)
input_parser.add_reconstruction_type(default="TK1L2")
input_parser.add_iterations(default=15)
input_parser.add_alpha(default=0.02)
input_parser.add_alpha_first(default=0.05)
input_parser.add_iter_max(default=10)
input_parser.add_iter_max_first(default=5)
input_parser.add_dilation_radius(default=3)
input_parser.add_extra_frame_target(default=10)
input_parser.add_bias_field_correction(default=0)
input_parser.add_intensity_correction(default=0)
input_parser.add_isotropic_resolution(default=None)
input_parser.add_log_script_execution(default=1)
input_parser.add_subfolder_motion_correction()
input_parser.add_provide_comparison(default=0)
input_parser.add_subfolder_comparison()
input_parser.add_write_motion_correction(default=1)
input_parser.add_verbose(default=0)
input_parser.add_two_step_cycles(default=3)
input_parser.add_use_masks_srr(default=1)
input_parser.add_boundary_stacks(default=[10, 10, 0])
input_parser.add_reference()
input_parser.add_reference_mask()
args = input_parser.parse_args()
input_parser.print_arguments(args)
# Write script execution call
if args.log_script_execution:
input_parser.write_performed_script_execution(
os.path.abspath(__file__))
# Use FLIRT for volume-to-volume reg. step. Otherwise, RegAladin is used.
use_flirt_for_v2v_registration = True
# --------------------------------Read Data--------------------------------
ph.print_title("Read Data")
# Neither '--dir-input' nor '--filenames' was specified
if args.filenames is not None and args.dir_input is not None:
raise IOError("Provide input by either '--dir-input' or '--filenames' "
"but not both together")
# '--dir-input' specified
elif args.dir_input is not None:
data_reader = dr.ImageDirectoryReader(args.dir_input,
suffix_mask=args.suffix_mask)
# '--filenames' specified
elif args.filenames is not None:
data_reader = dr.MultipleImagesReader(args.filenames,
suffix_mask=args.suffix_mask)
else:
raise IOError("Provide input by either '--dir-input' or '--filenames'")
if len(args.boundary_stacks) is not 3:
raise IOError(
"Provide exactly three values for '--boundary-stacks' to define "
"cropping in i-, j-, and k-dimension of the input stacks")
data_reader.read_data()
stacks = data_reader.get_data()
ph.print_info("%d input stacks read for further processing" % len(stacks))
if all(s.is_unity_mask() is True for s in stacks):
ph.print_warning("No mask is provided! "
"Generated reconstruction space may be very big!")
# ---------------------------Data Preprocessing---------------------------
ph.print_title("Data Preprocessing")
segmentation_propagator = segprop.SegmentationPropagation(
# registration_method=regflirt.FLIRT(use_verbose=args.verbose),
dilation_radius=args.dilation_radius,
dilation_kernel="Ball",
)
data_preprocessing = dp.DataPreprocessing(
stacks=stacks,
segmentation_propagator=segmentation_propagator,
use_cropping_to_mask=True,
use_N4BiasFieldCorrector=args.bias_field_correction,
use_intensity_correction=args.intensity_correction,
target_stack_index=args.target_stack_index,
boundary_i=args.boundary_stacks[0],
boundary_j=args.boundary_stacks[1],
boundary_k=args.boundary_stacks[2],
unit="mm",
)
data_preprocessing.run()
time_data_preprocessing = data_preprocessing.get_computational_time()
# Get preprocessed stacks
stacks = data_preprocessing.get_preprocessed_stacks()
# Define reference/target stack for registration and reconstruction
if args.reference is not None:
reference = st.Stack.from_filename(file_path=args.reference,
file_path_mask=args.reference_mask,
extract_slices=False)
else:
reference = st.Stack.from_stack(stacks[args.target_stack_index])
# ------------------------Volume-to-Volume Registration--------------------
if args.two_step_cycles > 0:
# Define search angle ranges for FLIRT in all three dimensions
search_angles = [
"-searchr%s -%d %d" % (x, args.search_angle, args.search_angle)
for x in ["x", "y", "z"]
]
search_angles = (" ").join(search_angles)
if use_flirt_for_v2v_registration:
vol_registration = regflirt.FLIRT(
registration_type="Rigid",
use_fixed_mask=True,
use_moving_mask=True,
options=search_angles,
use_verbose=False,
)
else:
vol_registration = niftyreg.RegAladin(
registration_type="Rigid",
use_fixed_mask=True,
use_moving_mask=True,
use_verbose=False,
)
v2vreg = pipeline.VolumeToVolumeRegistration(
stacks=stacks,
reference=reference,
registration_method=vol_registration,
verbose=args.verbose,
)
v2vreg.run()
stacks = v2vreg.get_stacks()
time_registration = v2vreg.get_computational_time()
else:
time_registration = ph.get_zero_time()
# ---------------------------Create first volume---------------------------
time_tmp = ph.start_timing()
# Isotropic resampling to define HR target space
ph.print_title("Reconstruction Space Generation")
HR_volume = reference.get_isotropically_resampled_stack(
resolution=args.isotropic_resolution)
ph.print_info(
"Isotropic reconstruction space with %g mm resolution is created" %
HR_volume.sitk.GetSpacing()[0])
if args.reference is None:
# Create joint image mask in target space
joint_image_mask_builder = imb.JointImageMaskBuilder(
stacks=stacks,
target=HR_volume,
dilation_radius=1,
)
joint_image_mask_builder.run()
HR_volume = joint_image_mask_builder.get_stack()
ph.print_info("Isotropic reconstruction space is centered around "
"joint stack masks. ")
# Crop to space defined by mask (plus extra margin)
HR_volume = HR_volume.get_cropped_stack_based_on_mask(
boundary_i=args.extra_frame_target,
boundary_j=args.extra_frame_target,
boundary_k=args.extra_frame_target,
unit="mm",
)
# Scattered Data Approximation to get first estimate of HR volume
ph.print_title("First Estimate of HR Volume")
SDA = sda.ScatteredDataApproximation(stacks,
HR_volume,
sigma=args.sigma)
SDA.run()
HR_volume = SDA.get_reconstruction()
time_reconstruction = ph.stop_timing(time_tmp)
if args.verbose:
tmp = list(stacks)
tmp.insert(0, HR_volume)
sitkh.show_stacks(tmp, segmentation=HR_volume)
# ----------------Two-step Slice-to-Volume Registration SRR----------------
SRR = tk.TikhonovSolver(
stacks=stacks,
reconstruction=HR_volume,
reg_type="TK1",
minimizer="lsmr",
alpha=args.alpha_first,
iter_max=args.iter_max_first,
verbose=True,
use_masks=args.use_masks_srr,
)
if args.two_step_cycles > 0:
registration = regsitk.SimpleItkRegistration(
moving=HR_volume,
use_fixed_mask=True,
use_moving_mask=True,
use_verbose=args.verbose,
interpolator="Linear",
metric="Correlation",
use_multiresolution_framework=args.multiresolution,
shrink_factors=args.shrink_factors,
smoothing_sigmas=args.smoothing_sigmas,
initializer_type="SelfGEOMETRY",
optimizer="ConjugateGradientLineSearch",
optimizer_params={
"learningRate": 1,
"numberOfIterations": 100,
"lineSearchUpperLimit": 2,
},
scales_estimator="Jacobian",
)
two_step_s2v_reg_recon = \
pipeline.TwoStepSliceToVolumeRegistrationReconstruction(
stacks=stacks,
reference=HR_volume,
registration_method=registration,
reconstruction_method=SRR,
cycles=args.two_step_cycles,
alpha_range=[args.alpha_first, args.alpha],
verbose=args.verbose,
)
two_step_s2v_reg_recon.run()
HR_volume_iterations = \
two_step_s2v_reg_recon.get_iterative_reconstructions()
time_registration += \
two_step_s2v_reg_recon.get_computational_time_registration()
time_reconstruction += \
two_step_s2v_reg_recon.get_computational_time_reconstruction()
else:
HR_volume_iterations = []
# Write motion-correction results
if args.write_motion_correction:
for stack in stacks:
stack.write(
os.path.join(args.dir_output,
args.subfolder_motion_correction),
write_mask=True,
write_slices=True,
write_transforms=True,
suffix_mask=args.suffix_mask,
)
# ------------------Final Super-Resolution Reconstruction------------------
ph.print_title("Final Super-Resolution Reconstruction")
if args.reconstruction_type in ["TVL2", "HuberL2"]:
SRR = pd.PrimalDualSolver(
stacks=stacks,
reconstruction=HR_volume,
reg_type="TV" if args.reconstruction_type == "TVL2" else "huber",
iterations=args.iterations,
)
else:
SRR = tk.TikhonovSolver(
stacks=stacks,
reconstruction=HR_volume,
reg_type="TK1" if args.reconstruction_type == "TK1L2" else "TK0",
use_masks=args.use_masks_srr,
)
SRR.set_alpha(args.alpha)
SRR.set_iter_max(args.iter_max)
SRR.set_verbose(True)
SRR.run()
time_reconstruction += SRR.get_computational_time()
elapsed_time_total = ph.stop_timing(time_start)
# Write SRR result
HR_volume_final = SRR.get_reconstruction()
HR_volume_final.set_filename(SRR.get_setting_specific_filename())
HR_volume_final.write(args.dir_output,
write_mask=True,
suffix_mask=args.suffix_mask)
HR_volume_iterations.insert(0, HR_volume_final)
for stack in stacks:
HR_volume_iterations.append(stack)
if args.verbose and not args.provide_comparison:
sitkh.show_stacks(HR_volume_iterations, segmentation=HR_volume)
# HR_volume_final.show()
# Show SRR together with linearly resampled input data.
# Additionally, a script is generated to open files
if args.provide_comparison:
sitkh.show_stacks(
HR_volume_iterations,
segmentation=HR_volume,
show_comparison_file=args.provide_comparison,
dir_output=os.path.join(args.dir_output,
args.subfolder_comparison),
)
# Summary
ph.print_title("Summary")
print("Computational Time for Data Preprocessing: %s" %
(time_data_preprocessing))
print("Computational Time for Registrations: %s" % (time_registration))
print("Computational Time for Reconstructions: %s" % (time_reconstruction))
print("Computational Time for Entire Reconstruction Pipeline: %s" %
(elapsed_time_total))
ph.print_line_separator()
return 0
def main():
time_start = ph.start_timing()
# Set print options for numpy
np.set_printoptions(precision=3)
# Read input
input_parser = InputArgparser(
description="Script to study reconstruction parameters and their "
"impact on the volumetric reconstruction quality.",
)
input_parser.add_dir_input()
input_parser.add_filenames()
input_parser.add_image_selection()
input_parser.add_dir_output(required=True)
input_parser.add_suffix_mask(default="_mask")
input_parser.add_reconstruction_space()
input_parser.add_reference(
help="Path to reference NIfTI image file. If given the volumetric "
"reconstructed is performed in this physical space. "
"Either a reconstruction space or a reference must be provided",
required=False)
input_parser.add_reference_mask(default=None)
input_parser.add_study_name()
input_parser.add_reconstruction_type(default="TK1L2")
input_parser.add_measures(default=["PSNR", "RMSE", "SSIM", "NCC", "NMI"])
input_parser.add_tv_solver(default="PD")
input_parser.add_iterations(default=50)
input_parser.add_rho(default=0.1)
input_parser.add_iter_max(default=10)
input_parser.add_minimizer(default="lsmr")
input_parser.add_alpha(default=0.01)
input_parser.add_data_loss(default="linear")
input_parser.add_data_loss_scale(default=1)
input_parser.add_log_script_execution(default=1)
input_parser.add_verbose(default=1)
# Range for parameter sweeps
input_parser.add_alpha_range(default=[0.001, 0.05, 20]) # TK1L2
# input_parser.add_alpha_range(default=[0.001, 0.003, 10]) # TVL2, HuberL2
input_parser.add_data_losses(
# default=["linear", "arctan"]
)
input_parser.add_data_loss_scale_range(
# default=[0.1, 1.5, 2]
)
args = input_parser.parse_args()
input_parser.print_arguments(args)
if args.reference is None and args.reconstruction_space is None:
raise IOError("Either reference (--reference) or reconstruction space "
"(--reconstruction-space) must be provided.")
# Write script execution call
if args.log_script_execution:
input_parser.write_performed_script_execution(
os.path.abspath(__file__))
# --------------------------------Read Data--------------------------------
ph.print_title("Read Data")
# Neither '--dir-input' nor '--filenames' was specified
if args.filenames is not None and args.dir_input is not None:
raise IOError(
"Provide input by either '--dir-input' or '--filenames' "
"but not both together")
# '--dir-input' specified
elif args.dir_input is not None:
data_reader = dr.ImageSlicesDirectoryReader(
path_to_directory=args.dir_input,
suffix_mask=args.suffix_mask,
image_selection=args.image_selection)
# '--filenames' specified
elif args.filenames is not None:
data_reader = dr.MultipleImagesReader(
args.filenames, suffix_mask=args.suffix_mask)
else:
raise IOError(
"Provide input by either '--dir-input' or '--filenames'")
data_reader.read_data()
stacks = data_reader.get_data()
ph.print_info("%d input stacks read for further processing" % len(stacks))
if args.reference is not None:
reference = st.Stack.from_filename(
file_path=args.reference,
file_path_mask=args.reference_mask,
extract_slices=False)
reconstruction_space = stacks[0].get_resampled_stack(reference.sitk)
reconstruction_space = \
reconstruction_space.get_stack_multiplied_with_mask()
x_ref = sitk.GetArrayFromImage(reference.sitk).flatten()
x_ref_mask = sitk.GetArrayFromImage(reference.sitk_mask).flatten()
else:
reconstruction_space = st.Stack.from_filename(
file_path=args.reconstruction_space,
extract_slices=False)
reconstruction_space = stacks[0].get_resampled_stack(
reconstruction_space.sitk)
reconstruction_space = \
reconstruction_space.get_stack_multiplied_with_mask()
x_ref = None
x_ref_mask = None
# ----------------------------Set Up Parameters----------------------------
parameters = {}
parameters["alpha"] = np.linspace(
args.alpha_range[0], args.alpha_range[1], int(args.alpha_range[2]))
if args.data_losses is not None:
parameters["data_loss"] = args.data_losses
if args.data_loss_scale_range is not None:
parameters["data_loss_scale"] = np.linspace(
args.data_loss_scale_range[0],
args.data_loss_scale_range[1],
int(args.data_loss_scale_range[2]))
# --------------------------Set Up Parameter Study-------------------------
if args.study_name is None:
name = args.reconstruction_type
else:
name = args.study_name
reconstruction_info = {
"shape": reconstruction_space.sitk.GetSize()[::-1],
"origin": reconstruction_space.sitk.GetOrigin(),
"spacing": reconstruction_space.sitk.GetSpacing(),
"direction": reconstruction_space.sitk.GetDirection(),
}
# Create Tikhonov solver from which all information can be extracted
# (also for other reconstruction types)
tmp = tk.TikhonovSolver(
stacks=stacks,
reconstruction=reconstruction_space,
alpha=args.alpha,
iter_max=args.iter_max,
data_loss=args.data_loss,
data_loss_scale=args.data_loss_scale,
reg_type="TK1",
minimizer=args.minimizer,
verbose=args.verbose,
)
solver = tmp.get_solver()
parameter_study_interface = \
deconv_interface.DeconvolutionParameterStudyInterface(
A=solver.get_A(),
A_adj=solver.get_A_adj(),
D=solver.get_B(),
D_adj=solver.get_B_adj(),
b=solver.get_b(),
x0=solver.get_x0(),
alpha=solver.get_alpha(),
x_scale=solver.get_x_scale(),
data_loss=solver.get_data_loss(),
data_loss_scale=solver.get_data_loss_scale(),
iter_max=solver.get_iter_max(),
minimizer=solver.get_minimizer(),
iterations=args.iterations,
measures=args.measures,
dimension=3,
L2=16./reconstruction_space.sitk.GetSpacing()[0]**2,
reconstruction_type=args.reconstruction_type,
rho=args.rho,
dir_output=args.dir_output,
parameters=parameters,
name=name,
reconstruction_info=reconstruction_info,
x_ref=x_ref,
x_ref_mask=x_ref_mask,
tv_solver=args.tv_solver,
verbose=args.verbose,
)
parameter_study_interface.set_up_parameter_study()
parameter_study = parameter_study_interface.get_parameter_study()
# Run parameter study
parameter_study.run()
print("\nComputational time for Deconvolution Parameter Study %s: %s" %
(name, parameter_study.get_computational_time()))
return 0
def main():
input_parser = InputArgparser(
description="Script to show the evaluated similarity between "
"simulated stack from obtained reconstruction and original stack. "
"This function takes the result of "
"evaluate_simulated_stack_similarity.py as input. "
"Provide --dir-output in order to save the results."
)
input_parser.add_dir_input(required=True)
input_parser.add_dir_output(required=False)
args = input_parser.parse_args()
input_parser.print_arguments(args)
if not ph.directory_exists(args.dir_input):
raise exceptions.DirectoryNotExistent(args.dir_input)
# --------------------------------Read Data--------------------------------
pattern = "Similarity_(" + REGEX_FILENAMES + ")[.]txt"
p = re.compile(pattern)
dic_filenames = {
p.match(f).group(1): p.match(f).group(0)
for f in os.listdir(args.dir_input) if p.match(f)
}
dic_stacks = {}
for filename in dic_filenames.keys():
path_to_file = os.path.join(args.dir_input, dic_filenames[filename])
# Extract evaluated measures written as header in second line
measures = open(path_to_file).readlines()[1]
measures = re.sub("#\t", "", measures)
measures = re.sub("\n", "", measures)
measures = measures.split("\t")
# Extract errors
similarities = np.loadtxt(path_to_file, skiprows=2)
# Build dictionary holding all similarity information for stack
dic_stack_similarity = {
measures[i]: similarities[:, i] for i in range(len(measures))
}
# dic_stack_similarity["measures"] = measures
# Store information of to dictionary
dic_stacks[filename] = dic_stack_similarity
# -----------Visualize stacks individually per similarity measure----------
ctr = [0]
N_stacks = len(dic_stacks)
N_measures = len(measures)
rows = 2 if N_measures < 6 else 3
filenames = natsorted(dic_stacks.keys(), key=lambda y: y.lower())
for i, filename in enumerate(filenames):
fig = plt.figure(ph.add_one(ctr))
fig.clf()
for m, measure in enumerate(measures):
ax = plt.subplot(rows, np.ceil(N_measures/float(rows)), m+1)
y = dic_stacks[filename][measure]
x = range(1, y.size+1)
lines = plt.plot(x, y)
line = lines[0]
line.set_linestyle("")
line.set_marker(ph.MARKERS[0])
# line.set_markerfacecolor("w")
plt.xlabel("Slice")
plt.ylabel(measure)
ax.set_xticks(x)
if measure in ["SSIM", "NCC"]:
ax.set_ylim([0, 1])
plt.suptitle(filename)
try:
# Open windows (and also save them) in full screen
manager = plt.get_current_fig_manager()
manager.full_screen_toggle()
except:
pass
plt.show(block=False)
if args.dir_output is not None:
filename = "Similarity_%s.pdf" % filename
ph.save_fig(fig, args.dir_output, filename)
# -----------All in one (meaningful in case of similar scaling)----------
fig = plt.figure(ph.add_one(ctr))
fig.clf()
data = {}
for m, measure in enumerate(measures):
for i, filename in enumerate(filenames):
similarities = dic_stacks[filename][measure]
labels = [filename] * similarities.size
if m == 0:
if "Stack" not in data.keys():
data["Stack"] = labels
else:
data["Stack"] = np.concatenate((data["Stack"], labels))
if measure not in data.keys():
data[measure] = similarities
else:
data[measure] = np.concatenate(
(data[measure], similarities))
df_melt = pd.DataFrame(data).melt(
id_vars="Stack",
var_name="",
value_name=" ",
value_vars=measures,
)
ax = plt.subplot(1, 1, 1)
b = sns.boxplot(
data=df_melt,
hue="Stack", # different colors for different "Stack"
x="",
y=" ",
order=measures,
)
ax.set_axisbelow(True)
try:
# Open windows (and also save them) in full screen
manager = plt.get_current_fig_manager()
manager.full_screen_toggle()
except:
pass
plt.show(block=False)
if args.dir_output is not None:
filename = "Boxplot.pdf"
ph.save_fig(fig, args.dir_output, filename)
# # -------------Boxplot: Plot individual similarity measures v1----------
# for m, measure in enumerate(measures):
# fig = plt.figure(ph.add_one(ctr))
# fig.clf()
# data = {}
# for i, filename in enumerate(filenames):
# similarities = dic_stacks[filename][measure]
# labels = [filename] * similarities.size
# if "Stack" not in data.keys():
# data["Stack"] = labels
# else:
# data["Stack"] = np.concatenate((data["Stack"], labels))
# if measure not in data.keys():
# data[measure] = similarities
# else:
# data[measure] = np.concatenate(
# (data[measure], similarities))
# df_melt = pd.DataFrame(data).melt(
# id_vars="Stack",
# var_name="",
# value_name=measure,
# )
# ax = plt.subplot(1, 1, 1)
# b = sns.boxplot(
# data=df_melt,
# hue="Stack", # different colors for different "Stack"
# x="",
# y=measure,
# )
# ax.set_axisbelow(True)
# plt.show(block=False)
# # -------------Boxplot: Plot individual similarity measures v2----------
# for m, measure in enumerate(measures):
# fig = plt.figure(ph.add_one(ctr))
# fig.clf()
# data = {}
# for i, filename in enumerate(filenames):
# similarities = dic_stacks[filename][measure]
# labels = [filename] * len(filenames)
# if filename not in data.keys():
# data[filename] = similarities
# else:
# data[filename] = np.concatenate(
# (data[filename], similarities))
# for filename in filenames:
# data[filename] = pd.Series(data[filename])
# df = pd.DataFrame(data)
# df_melt = df.melt(
# var_name="",
# value_name=measure,
# value_vars=filenames,
# )
# ax = plt.subplot(1, 1, 1)
# b = sns.boxplot(
# data=df_melt,
# x="",
# y=measure,
# order=filenames,
# )
# ax.set_axisbelow(True)
# plt.show(block=False)
return 0
def main():
time_start = ph.start_timing()
np.set_printoptions(precision=3)
input_parser = InputArgparser(
description="Register an obtained reconstruction (moving) "
"to a template image/space (fixed) using rigid registration. "
"The resulting registration can optionally be applied to previously "
"obtained motion correction slice transforms so that a volumetric "
"reconstruction is possible in the (standard anatomical) space "
"defined by the fixed.", )
input_parser.add_fixed(required=True)
input_parser.add_moving(
required=True,
nargs="+",
help="Specify moving image to be warped to fixed space. "
"If multiple images are provided, all images will be transformed "
"uniformly according to the registration obtained for the first one.")
input_parser.add_dir_output(required=True)
input_parser.add_dir_input()
input_parser.add_suffix_mask(default="_mask")
input_parser.add_search_angle(default=180)
input_parser.add_option(
option_string="--transform-only",
type=int,
help="Turn on/off functionality to transform moving image(s) to fixed "
"image only, i.e. no resampling to fixed image space",
default=0)
input_parser.add_option(
option_string="--write-transform",
type=int,
help="Turn on/off functionality to write registration transform",
default=0)
input_parser.add_verbose(default=0)
args = input_parser.parse_args()
input_parser.print_arguments(args)
use_reg_aladin_for_refinement = True
# --------------------------------Read Data--------------------------------
ph.print_title("Read Data")
data_reader = dr.MultipleImagesReader(args.moving, suffix_mask="_mask")
data_reader.read_data()
moving = data_reader.get_data()
data_reader = dr.MultipleImagesReader([args.fixed], suffix_mask="_mask")
data_reader.read_data()
fixed = data_reader.get_data()[0]
# -------------------Register Reconstruction to Template-------------------
ph.print_title("Register Reconstruction to Template")
# Define search angle ranges for FLIRT in all three dimensions
search_angles = [
"-searchr%s -%d %d" % (x, args.search_angle, args.search_angle)
for x in ["x", "y", "z"]
]
search_angles = (" ").join(search_angles)
options_args = []
options_args.append(search_angles)
# cost = "mutualinfo"
# options_args.append("-searchcost %s -cost %s" % (cost, cost))
registration = regflirt.FLIRT(
fixed=moving[0],
moving=fixed,
# use_fixed_mask=True,
# use_moving_mask=True, # moving mask only seems to work for SB cases
registration_type="Rigid",
use_verbose=False,
options=(" ").join(options_args),
)
ph.print_info("Run Registration (FLIRT) ... ", newline=False)
registration.run()
print("done")
transform_sitk = registration.get_registration_transform_sitk()
if args.write_transform:
path_to_transform = os.path.join(args.dir_output,
"registration_transform_sitk.txt")
sitk.WriteTransform(transform_sitk, path_to_transform)
# Apply rigidly transform to align reconstruction (moving) with template
# (fixed)
for m in moving:
m.update_motion_correction(transform_sitk)
# Additionally, use RegAladin for more accurate alignment
# Rationale: FLIRT has better capture range, but RegAladin seems to
# find better alignment once it is within its capture range.
if use_reg_aladin_for_refinement:
registration = niftyreg.RegAladin(
fixed=m,
use_fixed_mask=True,
moving=fixed,
registration_type="Rigid",
use_verbose=False,
)
ph.print_info("Run Registration (RegAladin) ... ", newline=False)
registration.run()
print("done")
transform2_sitk = registration.get_registration_transform_sitk()
m.update_motion_correction(transform2_sitk)
transform_sitk = sitkh.get_composite_sitk_affine_transform(
transform2_sitk, transform_sitk)
if args.transform_only:
for m in moving:
m.write(args.dir_output, write_mask=False)
ph.exit()
# Resample reconstruction (moving) to template space (fixed)
warped_moving = [
m.get_resampled_stack(fixed.sitk, interpolator="Linear")
for m in moving
]
for wm in warped_moving:
wm.set_filename(wm.get_filename() + "ResamplingToTemplateSpace")
if args.verbose:
sitkh.show_stacks([fixed, wm], segmentation=fixed)
# Write resampled reconstruction (moving)
wm.write(args.dir_output, write_mask=False)
if args.dir_input is not None:
data_reader = dr.ImageSlicesDirectoryReader(
path_to_directory=args.dir_input, suffix_mask=args.suffix_mask)
data_reader.read_data()
stacks = data_reader.get_data()
for i, stack in enumerate(stacks):
stack.update_motion_correction(transform_sitk)
ph.print_info("Stack %d/%d: All slice transforms updated" %
(i + 1, len(stacks)))
# Write transformed slices
stack.write(
os.path.join(args.dir_output, "motion_correction"),
write_mask=True,
write_slices=True,
write_transforms=True,
suffix_mask=args.suffix_mask,
)
elapsed_time_total = ph.stop_timing(time_start)
# Summary
ph.print_title("Summary")
print("Computational Time: %s" % (elapsed_time_total))
return 0
def main():
time_start = ph.start_timing()
np.set_printoptions(precision=3)
input_parser = InputArgparser(
description="Run reconstruction pipeline including "
"(i) preprocessing (bias field correction + intensity correction), "
"(ii) volumetric reconstruction in subject space, "
"and (iii) volumetric reconstruction in template space.",
)
input_parser.add_dir_input(required=True)
input_parser.add_dir_mask(required=True)
input_parser.add_dir_output(required=True)
input_parser.add_suffix_mask(default="_mask")
input_parser.add_target_stack(required=False)
input_parser.add_alpha(default=0.01)
input_parser.add_verbose(default=0)
input_parser.add_gestational_age(required=False)
input_parser.add_prefix_output(default="")
input_parser.add_search_angle(default=180)
input_parser.add_multiresolution(default=0)
input_parser.add_log_script_execution(default=1)
input_parser.add_dir_input_templates(default=DIR_TEMPLATES)
input_parser.add_isotropic_resolution()
input_parser.add_reference()
input_parser.add_reference_mask()
input_parser.add_bias_field_correction(default=1)
input_parser.add_intensity_correction(default=1)
input_parser.add_iter_max(default=10)
input_parser.add_two_step_cycles(default=3)
input_parser.add_option(
option_string="--run-recon-subject-space",
type=int,
help="Turn on/off reconstruction in subject space",
default=1)
input_parser.add_option(
option_string="--run-recon-template-space",
type=int,
help="Turn on/off reconstruction in template space",
default=1)
input_parser.add_option(
option_string="--run-data-vs-simulated-data",
type=int,
help="Turn on/off comparison of data vs data simulated from the "
"obtained volumetric reconstruction",
default=1)
input_parser.add_outlier_rejection(default=0)
input_parser.add_use_robust_registration(default=0)
args = input_parser.parse_args()
input_parser.print_arguments(args)
# Write script execution call
if args.log_script_execution:
input_parser.write_performed_script_execution(
os.path.abspath(__file__))
dir_output_recon_subject_space = os.path.join(
args.dir_output, "recon_subject_space")
dir_output_recon_template_space = os.path.join(
args.dir_output, "recon_template_space")
dir_output_data_vs_simulatd_data = os.path.join(
args.dir_output, "data_vs_simulated_data")
# if args.run_recon_template_space and args.gestational_age is None:
# raise IOError("Gestational age must be set in order to pick the "
# "right template")
# get input stack names
files = os.listdir(args.dir_input)
input_files = []
mask_files = []
for file in files:
if (".nii" in file):
input_files.append("{0:}/{1:}".format(args.dir_input, file))
file_prefix = file[:-7] if (".nii.gz" in file) else file[:-4]
mask_name = "{0:}/{1:}.nii.gz".format(args.dir_mask, file_prefix)
if(not os.path.isfile(mask_name)):
mask_name = "{0:}/{1:}.nii".format(args.dir_mask, file_prefix)
assert(os.path.isfile(mask_name))
mask_files.append(mask_name)
if args.target_stack is None:
target_stack = input_files[0]
else:
target_stack = input_files
if args.run_recon_subject_space:
target_stack_index = input_files.index(target_stack)
cmd_args = []
cmd_args.append("--filenames %s" % (" ").join(input_files))
cmd_args.append("--filenames-masks %s" % (" ").join(mask_files))
cmd_args.append("--multiresolution %d" % args.multiresolution)
cmd_args.append("--target-stack-index %d" % target_stack_index)
cmd_args.append("--dir-output %s" % dir_output_recon_subject_space)
# cmd_args.append("--suffix-mask %s" % args.suffix_mask)
cmd_args.append("--intensity-correction %d" %
args.intensity_correction)
cmd_args.append("--alpha %s" % args.alpha)
cmd_args.append("--iter-max %d" % args.iter_max)
cmd_args.append("--two-step-cycles %d" % args.two_step_cycles)
cmd_args.append("--outlier-rejection %d" %
args.outlier_rejection)
cmd_args.append("--use-robust-registration %d" %
args.use_robust_registration)
cmd_args.append("--verbose %d" % args.verbose)
if args.isotropic_resolution is not None:
cmd_args.append("--isotropic-resolution %f" %
args.isotropic_resolution)
if args.reference is not None:
cmd_args.append("--reference %s" % args.reference)
if args.reference_mask is not None:
cmd_args.append("--reference-mask %s" % args.reference_mask)
cmd = "niftymic_reconstruct_volume %s" % (" ").join(cmd_args)
time_start_volrec = ph.start_timing()
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Reconstruction in subject space failed")
elapsed_time_volrec = ph.stop_timing(time_start_volrec)
else:
elapsed_time_volrec = ph.get_zero_time()
if args.run_recon_template_space:
# register recon to template space
pattern = "[a-zA-Z0-9_]+(stacks)[a-zA-Z0-9_]+(.nii.gz)"
p = re.compile(pattern)
reconstruction = [
os.path.join(
dir_output_recon_subject_space, p.match(f).group(0))
for f in os.listdir(dir_output_recon_subject_space)
if p.match(f)][0]
if('mask_manual' in args.dir_output):
# find the corresponding template by volume matching
reconstruction_mask = reconstruction
if(not ("_mask" in reconstruction)):
reconstruction_mask = ph.append_to_filename(reconstruction, "_mask")
template_stack_estimator = \
tse.TemplateStackEstimator.from_mask(
reconstruction_mask,
args.dir_input_templates)
template_mask = template_stack_estimator.get_path_to_template()
template = template_mask.replace('_mask_dil.nii.gz', '.nii.gz')
print('template name', template)
# template = os.path.join(
# args.dir_input_templates,
# "STA%d.nii.gz" % args.gestational_age)
# template_mask = os.path.join(
# args.dir_input_templates,
# "STA%d_mask.nii.gz" % args.gestational_age)
else:
template_folder = args.dir_output + "/../../mask_manual/reconstruct_outlier_gpr/"
file_names = os.listdir(template_folder)
template_names = [item for item in file_names if ("nii.gz" in item) and ("Masked" not in item)]
mask_names = [item for item in file_names if ("nii.gz" in item) and ("Masked" in item)]
template = os.path.join(template_folder, template_names[0])
template_mask = os.path.join(template_folder, mask_names[0])
cmd_args = []
cmd_args.append("--moving %s" % reconstruction)
cmd_args.append("--fixed %s" % template)
# if(use_spatiotemporal_template is False):
# cmd_args.append("--use-fixed-mask 1") # added by Guotai
# cmd_args.append("--template-mask %s" % template_mask) # micheal's code
cmd_args.append("--dir-input %s" % os.path.join(
dir_output_recon_subject_space,
"motion_correction"))
cmd_args.append("--dir-output %s" % dir_output_recon_template_space)
cmd_args.append("--suffix-mask %s" % args.suffix_mask)
cmd_args.append("--verbose %s" % args.verbose)
cmd = "niftymic_register_image %s" % (" ").join(cmd_args)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Registration to template space failed")
# reconstruct volume in template space
# pattern = "[a-zA-Z0-9_.]+(ResamplingToTemplateSpace.nii.gz)"
# p = re.compile(pattern)
# reconstruction_space = [
# os.path.join(dir_output_recon_template_space, p.match(f).group(0))
# for f in os.listdir(dir_output_recon_template_space)
# if p.match(f)][0]
dir_input = os.path.join(
dir_output_recon_template_space, "motion_correction")
cmd_args = []
cmd_args.append("--dir-input %s" % dir_input)
cmd_args.append("--dir-output %s" % dir_output_recon_template_space)
cmd_args.append("--reconstruction-space %s" % template)
cmd_args.append("--iter-max %d" % args.iter_max)
cmd_args.append("--alpha %s" % args.alpha)
cmd_args.append("--suffix-mask %s" % args.suffix_mask)
cmd = "niftymic_reconstruct_volume_from_slices %s" % \
(" ").join(cmd_args)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Reconstruction in template space failed")
pattern = "[a-zA-Z0-9_.]+(stacks[0-9]+).*(.nii.gz)"
p = re.compile(pattern)
reconstruction = {
p.match(f).group(1):
os.path.join(
dir_output_recon_template_space, p.match(f).group(0))
for f in os.listdir(dir_output_recon_template_space)
if p.match(f) and not p.match(f).group(0).endswith(
"ResamplingToTemplateSpace.nii.gz")}
key = reconstruction.keys()[0]
path_to_recon = reconstruction[key]
# Copy SRR to output directory
output = "%sSRR_%s_GW%d.nii.gz" % (
args.prefix_output, key, args.gestational_age)
path_to_output = os.path.join(args.dir_output, output)
cmd = "cp -p %s %s" % (path_to_recon, path_to_output)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Copy of SRR to output directory failed")
# Multiply template mask with reconstruction
cmd_args = []
cmd_args.append("--filename %s" % path_to_output)
cmd_args.append("--gestational-age %s" % args.gestational_age)
cmd_args.append("--verbose %s" % args.verbose)
cmd_args.append("--dir-input-templates %s " % args.dir_input_templates)
cmd = "niftymic_multiply_stack_with_mask %s" % (
" ").join(cmd_args)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("SRR brain masking failed")
else:
elapsed_time_template = ph.get_zero_time()
if args.run_data_vs_simulated_data:
dir_input = os.path.join(
dir_output_recon_template_space, "motion_correction")
pattern = "[a-zA-Z0-9_.]+(stacks[0-9]+).*(.nii.gz)"
# pattern = "Masked_[a-zA-Z0-9_.]+(stacks[0-9]+).*(.nii.gz)"
p = re.compile(pattern)
reconstruction = {
p.match(f).group(1):
os.path.join(
dir_output_recon_template_space, p.match(f).group(0))
for f in os.listdir(dir_output_recon_template_space)
if p.match(f) and not p.match(f).group(0).endswith(
"ResamplingToTemplateSpace.nii.gz")}
key = reconstruction.keys()[0]
path_to_recon = reconstruction[key]
# Get simulated/projected slices
cmd_args = []
cmd_args.append("--dir-input %s" % dir_input)
cmd_args.append("--dir-output %s" % dir_output_data_vs_simulatd_data)
cmd_args.append("--reconstruction %s" % path_to_recon)
cmd_args.append("--copy-data 1")
cmd_args.append("--suffix-mask %s" % args.suffix_mask)
# cmd_args.append("--verbose %s" % args.verbose)
exe = os.path.abspath(simulate_stacks_from_reconstruction.__file__)
cmd = "python %s %s" % (exe, (" ").join(cmd_args))
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("SRR slice projections failed")
filenames_simulated = [
os.path.join(dir_output_data_vs_simulatd_data, os.path.basename(f))
for f in input_files]
dir_output_evaluation = os.path.join(
dir_output_data_vs_simulatd_data, "evaluation")
dir_output_figures = os.path.join(
dir_output_data_vs_simulatd_data, "figures")
dir_output_side_by_side = os.path.join(
dir_output_figures, "side-by-side")
# Evaluate slice similarities to ground truth
cmd_args = []
cmd_args.append("--filenames %s" % (" ").join(filenames_simulated))
cmd_args.append("--suffix-mask %s" % args.suffix_mask)
cmd_args.append("--measures NCC SSIM")
cmd_args.append("--dir-output %s" % dir_output_evaluation)
exe = os.path.abspath(evaluate_simulated_stack_similarity.__file__)
cmd = "python %s %s" % (exe, (" ").join(cmd_args))
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Evaluation of slice similarities failed")
# Generate figures showing the quantitative comparison
cmd_args = []
cmd_args.append("--dir-input %s" % dir_output_evaluation)
cmd_args.append("--dir-output %s" % dir_output_figures)
exe = os.path.abspath(
show_evaluated_simulated_stack_similarity.__file__)
cmd = "python %s %s" % (exe, (" ").join(cmd_args))
exit_code = ph.execute_command(cmd)
if exit_code != 0:
ph.print_warning("Visualization of slice similarities failed")
# Generate pdfs showing all the side-by-side comparisons
cmd_args = []
cmd_args.append("--filenames %s" % (" ").join(filenames_simulated))
cmd_args.append("--dir-output %s" % dir_output_side_by_side)
exe = os.path.abspath(
export_side_by_side_simulated_vs_original_slice_comparison.__file__)
cmd = "python %s %s" % (exe, (" ").join(cmd_args))
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Generation of PDF overview failed")
ph.print_title("Summary")
print("Computational Time for Volumetric Reconstruction: %s" %
elapsed_time_volrec)
print("Computational Time for Pipeline: %s" %
ph.stop_timing(time_start))
return 0
