def main():
start_time = datetime.now()
args = cells_standard_space_cli_parser().parse_args()
args.paths = prep.Paths(args.output_dir)
args.paths.standard_space_output_folder = args.output_dir
args.paths.cells_in_standard_space = join(args.paths.output_dir,
"cells_in_standard_space.xml")
cli_path_update(args.paths, args)
args.paths.make_invert_cell_position_paths()
args = define_pixel_sizes(args)
# TODO: implement a recursive function to remove the need to do this
# (probably using pathlib)
ensure_directory_exists(args.paths.output_dir)
ensure_directory_exists(args.paths.standard_space_output_folder)
tools.start_logging(
args.paths.output_dir,
args=args,
verbose=args.debug,
filename="cells_to_standard_space",
log_header="CELL TRANSFORMATION TO STANDARD SPACE LOG",
)
logging.info("Starting transformation of cell positions")
transform_cells_to_standard_space(args)
logging.info("Finished. Total time taken: %s", datetime.now() - start_time)
def main():
args = region_summary_cli_parser().parse_args()
args = define_pixel_sizes(args)
args.paths = Paths(args, args.output_dir)
args.paths.registered_atlas_path = args.registered_atlas_path
args.paths.hemispheres_atlas_path = args.hemispheres_atlas_path
args.paths.classification_out_file = args.xml_file_path
analysis_run(args)
def run():
start_time = datetime.now()
args = register_cli_parser().parse_args()
args = define_pixel_sizes(args)
args, additional_images_downsample = prep_registration(args)
args = make_paths_absolute(args)
fancylog.start_logging(
args.registration_output_folder,
program_for_log,
variables=[args],
verbose=args.debug,
log_header="AMAP LOG",
multiprocessing_aware=False,
)
logging.info("Starting registration")
register(
args.registration_config,
args.image_paths,
args.registration_output_folder,
x_pixel_um=args.x_pixel_um,
y_pixel_um=args.y_pixel_um,
z_pixel_um=args.z_pixel_um,
orientation=args.orientation,
flip_x=args.flip_x,
flip_y=args.flip_y,
flip_z=args.flip_z,
rotation=args.rotation,
affine_n_steps=args.affine_n_steps,
affine_use_n_steps=args.affine_use_n_steps,
freeform_n_steps=args.freeform_n_steps,
freeform_use_n_steps=args.freeform_use_n_steps,
bending_energy_weight=args.bending_energy_weight,
grid_spacing=args.grid_spacing,
smoothing_sigma_reference=args.smoothing_sigma_reference,
smoothing_sigma_floating=args.smoothing_sigma_floating,
histogram_n_bins_floating=args.histogram_n_bins_floating,
histogram_n_bins_reference=args.histogram_n_bins_reference,
sort_input_file=args.sort_input_file,
n_free_cpus=args.n_free_cpus,
save_downsampled=not (args.no_save_downsampled),
boundaries=not (args.no_boundaries),
additional_images_downsample=additional_images_downsample,
debug=args.debug,
)
logging.info("Finished. Total time taken: %s", datetime.now() - start_time)
def test_define_pixel_sizes():
args = Args()
args.set_all_pixel_sizes()
args = meta.define_pixel_sizes(args)
assert isclose(1, args.x_pixel_um, abs_tol=VOX_DIM_TOLERANCE)
assert isclose(2, args.y_pixel_um, abs_tol=VOX_DIM_TOLERANCE)
assert isclose(3, args.z_pixel_um, abs_tol=VOX_DIM_TOLERANCE)
# baking tray
args = Args()
args.set_some_pixel_sizes_w_meta_baking_tray()
args = meta.define_pixel_sizes(args)
assert isclose(10, args.x_pixel_um, abs_tol=VOX_DIM_TOLERANCE)
assert isclose(2.14, args.y_pixel_um, abs_tol=VOX_DIM_TOLERANCE)
assert isclose(40, args.z_pixel_um, abs_tol=VOX_DIM_TOLERANCE)
# mesospim
args = Args()
args.set_some_pixel_sizes_w_meta_mesospim()
args = meta.define_pixel_sizes(args)
assert isclose(100, args.x_pixel_um, abs_tol=VOX_DIM_TOLERANCE)
assert isclose(8.23, args.y_pixel_um, abs_tol=VOX_DIM_TOLERANCE)
assert isclose(10, args.z_pixel_um, abs_tol=VOX_DIM_TOLERANCE)
# cellfinder
args = Args()
args.set_some_pixel_sizes_w_meta_cellfinder()
args = meta.define_pixel_sizes(args)
assert isclose(2, args.x_pixel_um, abs_tol=VOX_DIM_TOLERANCE)
assert isclose(0.2, args.y_pixel_um, abs_tol=VOX_DIM_TOLERANCE)
assert isclose(40, args.z_pixel_um, abs_tol=VOX_DIM_TOLERANCE)
# no metadata
args = Args()
args.set_some_pixel_sizes_no_meta()
with pytest.raises(CommandLineInputError):
assert meta.define_pixel_sizes(args)
# unsupported metadata
args = Args()
args.set_some_pixel_sizes_unsupported_meta()
with pytest.raises(CommandLineInputError):
assert meta.define_pixel_sizes(args)
# missing metadata
args = Args()
args.set_some_pixel_sizes_missing_meta()
with pytest.raises(CommandLineInputError):
assert meta.define_pixel_sizes(args)
def main():
start_time = datetime.now()
args = register_cli_parser().parse_args()
arg_groups = get_arg_groups(args, register_cli_parser())
args = define_pixel_sizes(args)
args, additional_images_downsample = prep_registration(args)
paths = Paths(args.brainreg_directory)
log_metadata(paths.metadata_path, args)
fancylog.start_logging(
paths.registration_output_folder,
program_for_log,
variables=[args],
verbose=args.debug,
log_header="BRAINREG LOG",
multiprocessing_aware=False,
)
logging.info("Starting registration")
atlas = BrainGlobeAtlas(args.atlas)
register(
atlas,
args.orientation,
args.image_paths,
paths,
arg_groups["NiftyReg registration backend options"],
x_pixel_um=args.x_pixel_um,
y_pixel_um=args.y_pixel_um,
z_pixel_um=args.z_pixel_um,
sort_input_file=args.sort_input_file,
n_free_cpus=args.n_free_cpus,
additional_images_downsample=additional_images_downsample,
backend=args.backend,
debug=args.debug,
)
logging.info("Finished. Total time taken: %s", datetime.now() - start_time)
def prep_cellfinder_general():
args = parser.cellfinder_parser().parse_args()
args = define_pixel_sizes(args)
check_input_arg_existance(args)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
args.paths = Paths(args.output_dir)
args.paths.make_reg_paths()
fancylog.start_logging(
args.output_dir,
program_for_log,
variables=[args, args.paths],
verbose=args.debug,
log_header="CELLFINDER LOG",
)
what_to_run = CalcWhatToRun(args)
args.signal_ch_ids, args.background_ch_id = check_and_return_ch_ids(
args.signal_ch_ids, args.background_ch_id, args.signal_planes_paths)
return args, what_to_run
def main():
args = parser().parse_args()
args = define_pixel_sizes(args)
if args.output is None:
output = Path(args.cells_xml)
output_directory = output.parent
print(f"No output directory given, so setting output "
f"directory to: {output_directory}")
else:
output_directory = args.output
ensure_directory_exists(output_directory)
output_filename = output_directory / OUTPUT_NAME
img_paths = get_sorted_file_paths(args.signal_image_paths,
file_extension=".tif")
cells, labels = get_cell_labels_arrays(args.cells_xml)
properties = {"cell": labels}
with napari.gui_qt():
viewer = napari.Viewer(title="Cellfinder cell curation")
images = magic_imread(img_paths, use_dask=True, stack=True)
viewer.add_image(images)
face_color_cycle = ["lightskyblue", "lightgoldenrodyellow"]
points_layer = viewer.add_points(
cells,
properties=properties,
symbol=args.symbol,
n_dimensional=True,
size=args.marker_size,
face_color="cell",
face_color_cycle=face_color_cycle,
name="Cell candidates",
)
@viewer.bind_key("t")
def toggle_point_property(viewer):
"""Toggle point type"""
selected_points = viewer.layers[1].selected_data
if selected_points:
selected_properties = viewer.layers[1].properties["cell"][
selected_points]
toggled_properties = np.logical_not(selected_properties)
viewer.layers[1].properties["cell"][
selected_points] = toggled_properties
# Add curated cells to list
CURATED_POINTS.extend(selected_points)
print(f"{len(selected_points)} points "
f"toggled and added to the list ")
# refresh the properties colour
viewer.layers[1].refresh_colors(update_color_mapping=False)
@viewer.bind_key("c")
def confirm_point_property(viewer):
"""Confirm point type"""
selected_points = viewer.layers[1].selected_data
if selected_points:
# Add curated cells to list
CURATED_POINTS.extend(selected_points)
print(f"{len(selected_points)} points "
f"confirmed and added to the list ")
@viewer.bind_key("Control-S")
def save_curation(viewer):
"""Save file"""
if not CURATED_POINTS:
print("No cells have been confirmed or toggled, not saving")
else:
unique_cells = unique_elements_lists(CURATED_POINTS)
points = viewer.layers[1].data[unique_cells]
labels = viewer.layers[1].properties["cell"][unique_cells]
labels = labels.astype("int")
labels = labels + 1
cells_to_save = []
for idx, point in enumerate(points):
cell = Cell([point[2], point[1], point[0]], labels[idx])
cells_to_save.append(cell)
print(f"Saving results to: {output_filename}")
save_cells(cells_to_save, output_filename)
@viewer.bind_key("Alt-E")
def start_cube_extraction(viewer):
"""Extract cubes for training"""
if not output_filename.exists():
print("No curation results have been saved. "
"Please save before extracting cubes")
else:
print(f"Saving cubes to: {output_directory}")
run_extraction(
output_filename,
output_directory,
args.signal_image_paths,
args.background_image_paths,
args.cube_depth,
args.cube_width,
args.cube_height,
args.x_pixel_um,
args.y_pixel_um,
args.z_pixel_um,
args.x_pixel_um_network,
args.y_pixel_um_network,
args.z_pixel_um_network,
args.max_ram,
args.n_free_cpus,
args.save_empty_cubes,
)
print("Saving yaml file to use for training")
save_yaml_file(output_directory)
print("Closing window")
QApplication.closeAllWindows()
print("Finished! You may now annotate more "
"datasets, or go straight to training")