def test_volume_calc(tmpdir, test_config_path):
tmpdir = str(tmpdir)
atlas = Atlas(test_config_path)
structures_file_path = atlas.get_structures_path()
registration_config = test_config_path
volumes_csv_path = os.path.join(tmpdir, "volumes.csv")
calculate_volumes(
registered_atlas_path,
registered_hemispheres_path,
structures_file_path,
registration_config,
volumes_csv_path,
)
volumes_validate = pd.read_csv(volumes_validate_path,
sep=",",
header=0,
quotechar='"')
volumes_test = pd.read_csv(volumes_csv_path,
sep=",",
header=0,
quotechar='"')
assert (volumes_validate == volumes_test).all().all()
def __init__(
self,
config_path,
affine_n_steps=6,
affine_use_n_steps=5,
freeform_n_steps=6,
freeform_use_n_steps=4,
bending_energy_weight=0.95,
grid_spacing_x=-10,
smoothing_sigma_reference=-1.0,
smoothing_sigma_floating=-1.0,
histogram_n_bins_floating=128,
histogram_n_bins_reference=128,
):
self.config = get_config_ob(config_path)
self.transform_program_path = self.__get_binary("transform")
self.affine_reg_program_path = self.__get_binary("affine")
self.freeform_reg_program_path = self.__get_binary("freeform")
self.segmentation_program_path = self.__get_binary("segmentation")
# affine (reg_aladin)
self.affine_reg_pyramid_steps = ("-ln", affine_n_steps)
self.affine_reg_used_pyramid_steps = ("-lp", affine_use_n_steps)
# freeform (ref_f3d)
self.freeform_reg_pyramid_steps = ("-ln", freeform_n_steps)
self.freeform_reg_used_pyramid_steps = ("-lp", freeform_use_n_steps)
self.freeform_reg_grid_spacing_x = ("-sx", grid_spacing_x)
self.bending_energy_penalty_weight = ("-be", bending_energy_weight)
self.reference_image_smoothing_sigma = (
"-smooR",
smoothing_sigma_reference,
)
self.floating_image_smoothing_sigma = (
"-smooF",
smoothing_sigma_floating,
)
self.reference_image_histo_n_bins = (
"--rbn",
histogram_n_bins_reference,
)
self.floating_image_histo_n_bins = ("--fbn", histogram_n_bins_floating)
# segmentation (reg_resample)
self.segmentation_interpolation_order = ("-inter", 0)
# The atlas has been saved to the output folder
atlas = Atlas(config_path)
# self.atlas_path = atlas.get_path()
# self.atlas_brain_path = atlas.get_brain_path()
# self.hemispheres_path = atlas.get_hemispheres_path()
pixel_sizes = atlas.get_pixel_sizes_from_config()
self.atlas_x_pix_size = pixel_sizes["x"]
self.atlas_y_pix_size = pixel_sizes["y"]
self.atlas_z_pix_size = pixel_sizes["z"]
def get_scaling(self):
logging.debug("Determining scaling of the figures to the raw image")
atlas = Atlas(self._registration_config)
atlas_pixel_sizes = atlas.pix_sizes
self.atlas_pixel_x_um = atlas_pixel_sizes["x"]
self.atlas_pixel_y_um = atlas_pixel_sizes["y"]
self.atlas_pixel_z_um = atlas_pixel_sizes["z"]
self.x_scaling = round(
self.x_pixel_um / self.atlas_pixel_x_um,
self.scaling_rounding_decimals,
)
self.y_scaling = round(
self.y_pixel_um / self.atlas_pixel_y_um,
self.scaling_rounding_decimals,
)
self.z_scaling = round(
self.z_pixel_um / self.atlas_pixel_z_um,
self.scaling_rounding_decimals,
)
self.position_scaling = [
self.x_scaling,
self.y_scaling,
self.z_scaling,
]
def get_structures_path(config=None):
if config is None:
config = source_custom_config()
atlas = Atlas(config)
return atlas.get_structures_path()
def main():
start_time = datetime.now()
print("Starting ROI transformation")
args = get_parser().parse_args()
rois = args.rois
print(f"ROI file is: {rois}")
if args.registration_config is None:
args.registration_config = source_custom_config()
atlas = Atlas(args.registration_config)
source_image = args.reg_dir / SOURCE_IMAGE_NAME
print(f"Source image is: {source_image}")
destination_image = args.reg_dir / atlas.atlas_conf["default_brain_name"]
print(f"Destination image is: {destination_image}")
control_point_file = args.reg_dir / DEFAULT_CONTROL_POINT_FILE
print(f"Transformation file is: {control_point_file}")
if args.output_filename is None:
output_filename = rois.parent / DEFAULT_OUTPUT_FILE_NAME
temp_output_filename = rois.parent / DEFAULT_TEMP_FILE_NAME
log_file_path = rois.parent / "roi_transform_log.txt"
error_file_path = rois.parent / "roi_transform_error.txt"
else:
output_filename = args.output_filename
temp_output_filename = (args.output_filename.parent /
DEFAULT_TEMP_FILE_NAME)
log_file_path = args.output_filename.parent / "roi_transform_log.txt"
error_file_path = (args.output_filename.parent /
"roi_transform_error.txt")
if not output_filename.parent.exists():
output_filename.parent.mkdir()
print(f"Output file is: {output_filename}")
atlas = brainio.load_nii(str(destination_image), as_array=False)
atlas_scale = atlas.header.get_zooms()
atlas_pixel_sizes = cells_regions.get_atlas_pixel_sizes(
args.registration_config)
transformation_matrix = np.eye(4)
for i, axis in enumerate(("x", "y", "z")):
transformation_matrix[i, i] = atlas_pixel_sizes[axis]
transform_rois(
rois,
source_image,
destination_image,
control_point_file,
output_filename,
temp_output_filename,
log_file_path,
error_file_path,
roi_reference_image=args.reference_image,
selem_size=args.selem_size,
nii_scale=atlas_scale,
transformation_matrix=transformation_matrix,
debug=args.debug,
z_filter_padding=args.z_filter_padding,
)
print("Finished. Total time taken: {}".format(datetime.now() - start_time))
def main(
registration_config,
target_brain_path,
registration_output_folder,
x_pixel_um=0.02,
y_pixel_um=0.02,
z_pixel_um=0.05,
orientation="coronal",
flip_x=False,
flip_y=False,
flip_z=False,
affine_n_steps=6,
affine_use_n_steps=5,
freeform_n_steps=6,
freeform_use_n_steps=4,
bending_energy_weight=0.95,
grid_spacing=-10,
smoothing_sigma_reference=-1.0,
smoothing_sigma_floating=-1.0,
histogram_n_bins_floating=128,
histogram_n_bins_reference=128,
n_free_cpus=2,
sort_input_file=False,
save_downsampled=True,
additional_images_downsample=None,
boundaries=True,
debug=False,
):
"""
The main function that will perform the library calls and
register the atlas to the brain given on the CLI
:param registration_config:
:param target_brain_path:
:param registration_output_folder:
:param filtered_brain_path:
:param x_pixel_um:
:param y_pixel_um:
:param z_pixel_um:
:param orientation:
:param flip_x:
:param flip_y:
:param flip_z:
:param n_free_cpus:
:param sort_input_file:
:param save_downsampled:
:param additional_images_downsample: dict of
{image_name: image_to_be_downsampled}
:return:
"""
n_processes = get_num_processes(min_free_cpu_cores=n_free_cpus)
load_parallel = n_processes > 1
paths = Paths(registration_output_folder)
atlas = Atlas(registration_config, dest_folder=registration_output_folder)
run = Run(paths, atlas, boundaries=boundaries, debug=debug)
if run.preprocess:
logging.info("Preprocessing data for registration")
logging.info("Loading data")
brain = BrainProcessor(
atlas,
target_brain_path,
registration_output_folder,
x_pixel_um,
y_pixel_um,
z_pixel_um,
original_orientation=orientation,
load_parallel=load_parallel,
sort_input_file=sort_input_file,
n_free_cpus=n_free_cpus,
)
# reorients the atlas to the orientation of the sample
brain.swap_atlas_orientation_to_self()
# reorients atlas to the nifti (origin is the most ventral, posterior,
# left voxel) coordinate framework
flip = flips[orientation]
brain.flip_atlas(flip)
# flips if the input data doesnt match the nifti standard
brain.flip_atlas((flip_x, flip_y, flip_z))
brain.atlas.save_all()
if save_downsampled:
brain.target_brain = brain.target_brain.astype(
np.uint16, copy=False
)
logging.info("Saving downsampled image")
brain.save(paths.downsampled_brain_path)
brain.filter()
logging.info("Saving filtered image")
brain.save(paths.tmp__downsampled_filtered)
del brain
if additional_images_downsample:
for name, image in additional_images_downsample.items():
if not check_downsampled(registration_output_folder, name):
save_downsampled_image(
image,
name,
registration_output_folder,
atlas,
x_pixel_um=x_pixel_um,
y_pixel_um=y_pixel_um,
z_pixel_um=z_pixel_um,
orientation=orientation,
n_free_cpus=n_free_cpus,
sort_input_file=sort_input_file,
load_parallel=load_parallel,
)
else:
logging.info(f"Image: {name} already downsampled, skipping.")
if run.register:
logging.info("Registering")
if any(
[
run.affine,
run.freeform,
run.segment,
run.hemispheres,
run.inverse_transform,
]
):
registration_params = RegistrationParams(
registration_config,
affine_n_steps=affine_n_steps,
affine_use_n_steps=affine_use_n_steps,
freeform_n_steps=freeform_n_steps,
freeform_use_n_steps=freeform_use_n_steps,
bending_energy_weight=bending_energy_weight,
grid_spacing=grid_spacing,
smoothing_sigma_reference=smoothing_sigma_reference,
smoothing_sigma_floating=smoothing_sigma_floating,
histogram_n_bins_floating=histogram_n_bins_floating,
histogram_n_bins_reference=histogram_n_bins_reference,
)
brain_reg = BrainRegistration(
registration_config,
paths,
registration_params,
n_processes=n_processes,
)
if run.affine:
logging.info("Starting affine registration")
brain_reg.register_affine()
if run.freeform:
logging.info("Starting freeform registration")
brain_reg.register_freeform()
if run.segment:
logging.info("Starting segmentation")
brain_reg.segment()
if run.hemispheres:
logging.info("Segmenting hemispheres")
brain_reg.register_hemispheres()
if run.inverse_transform:
logging.info("Generating inverse (sample to atlas) transforms")
brain_reg.generate_inverse_transforms()
if run.volumes:
logging.info("Calculating volumes of each brain area")
calculate_volumes(
paths.registered_atlas_path,
paths.hemispheres_atlas_path,
atlas.get_structures_path(),
registration_config,
paths.volume_csv_path,
left_hemisphere_value=atlas.get_left_hemisphere_value(),
right_hemisphere_value=atlas.get_right_hemisphere_value(),
)
if run.boundaries:
logging.info("Generating boundary image")
calc_boundaries(
paths.registered_atlas_path,
paths.boundaries_file_path,
atlas_config=registration_config,
)
if run.delete_temp:
logging.info("Removing registration temp files")
delete_temp(paths.registration_output_folder, paths)
logging.info(
f"amap completed. Results can be found here: "
f"{registration_output_folder}"
)
