def summary_run(args):
# if args.structures_file_path is None:
# args.structures_file_path = get_structures_path()
# reference_structures_table = pd.read_csv(args.structures_file_path)
# else:
# raise NotImplementedError(
# "Only the Allen adult mouse atlas is " "currently supported."
# )
reference_structures_table = pd.read_csv(get_structures_path())
if not args.regions and not args.regions_list:
# regions = list(pd.read_csv(args.structures_file_path)["name"])
regions = list(pd.read_csv(get_structures_path())["name"])
else:
regions = []
if args.regions:
regions = regions + args.regions
if args.regions_list:
regions = regions + list(pd.read_csv(args.regions_list)["name"])
regions = remove_empty_string(regions)
regions = unique_elements_lists(regions)
csvs_folder = args.csv_dir
destination_folder = os.path.join(args.csv_dir, "summary")
csvs_names = [f for f in os.listdir(csvs_folder) if f.endswith(".csv")]
if len(csvs_names) == 0:
raise FileNotFoundError(
"No CSV files were found in the directory: "
"{}. Please check the arguments".format(csvs_folder))
csvs_paths = [os.path.join(csvs_folder, f) for f in csvs_names]
if not os.path.exists(destination_folder):
os.makedirs(destination_folder)
for csv_file_path in csvs_paths:
print("CSV file: {}".format(os.path.basename(csv_file_path)))
summary = make_summary_df_for_brain(
csv_file_path,
regions,
reference_structures_table,
sum_regions=args.sum_regions,
)
filename = os.path.basename(csv_file_path)
dest_path = os.path.join(destination_folder, filename)
summary.to_csv(dest_path, index=False)
print("Done!")
def save_analyse_regions(viewer):
ensure_directory_exists(paths.regions_directory)
delete_directory_contents(str(paths.regions_directory))
if volumes:
annotations = load_any(paths.annotations)
hemispheres = load_any(paths.hemispheres)
structures_reference_df = load_structures_as_df(
get_structures_path())
print(
f"\nSaving summary volumes to: {paths.regions_directory}")
for label_layer in label_layers:
analyse_region_brain_areas(
label_layer,
paths.regions_directory,
annotations,
hemispheres,
structures_reference_df,
)
print(f"\nSaving regions to: {paths.regions_directory}")
for label_layer in label_layers:
save_regions_to_file(
label_layer,
paths.regions_directory,
paths.downsampled_image,
)
close_viewer(viewer)
def analysis_run(args, file_name="summary_cell_counts.csv"):
args = prep_atlas_conf(args)
# if args.structures_file_path is None:
# args.structures_file_path = get_structures_path()
atlas = brainio.load_any(args.paths.registered_atlas_path)
hemisphere = brainio.load_any(args.paths.hemispheres_atlas_path)
cells = get_cells_data(
args.paths.classification_out_file,
cells_only=args.cells_only,
)
max_coords = get_max_coords(cells) # Useful for debugging dimensions
# structures_reference_df = load_structures_as_df(args.structures_file_path)
structures_reference_df = load_structures_as_df(get_structures_path())
atlas_pixel_sizes = get_atlas_pixel_sizes(args.atlas_config)
sample_pixel_sizes = args.x_pixel_um, args.y_pixel_um, args.z_pixel_um
scales = get_scales(sample_pixel_sizes, atlas_pixel_sizes,
args.scale_cell_coordinates)
structures_with_cells = set()
for i, cell in enumerate(tqdm(cells)):
transform_cell_coords(atlas, cell, scales)
structure_id = get_structure_from_coordinates(
atlas,
cell,
max_coords,
order=args.coordinates_order,
structures_reference_df=structures_reference_df,
)
if structure_id is not None:
cell.structure_id = structure_id
structures_with_cells.add(structure_id)
else:
continue
cell.hemisphere = get_structure_from_coordinates(
hemisphere, cell, max_coords, order=args.coordinates_order)
sorted_cell_numbers = get_cells_nbs_df(cells, structures_reference_df,
structures_with_cells)
combined_hemispheres = combine_df_hemispheres(sorted_cell_numbers)
df = calculate_densities(combined_hemispheres, args.paths.volume_csv_path)
df = sanitise_df(df)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
output_file = os.path.join(args.output_dir, file_name)
df.to_csv(output_file, index=False)
def main():
print("Starting amap viewer")
args = parser().parse_args()
structures_df = load_structures_as_df(get_structures_path())
if not args.memory:
print(
"By default amap_vis does not load data into memory. "
"To speed up visualisation, use the '-m' flag. Be aware "
"this will make the viewer slower to open initially."
)
paths = Paths(args.amap_directory)
with napari.gui_qt():
v = napari.Viewer(title="amap viewer")
if (
Path(paths.registered_atlas_path).exists()
and Path(paths.boundaries_file_path).exists()
):
if args.raw:
image_scales = display_raw(v, args)
else:
if Path(paths.downsampled_brain_path).exists():
image_scales = display_downsampled(v, args, paths)
else:
raise FileNotFoundError(
f"The downsampled image: "
f"{paths.downsampled_brain_path} could not be found. "
f"Please ensure this is the correct "
f"directory and that amap has completed. "
)
region_labels = display_registration(
v,
paths.registered_atlas_path,
paths.boundaries_file_path,
image_scales,
memory=args.memory,
)
@region_labels.mouse_move_callbacks.append
def display_region_name(layer, event):
display_brain_region_name(layer, structures_df)
else:
raise FileNotFoundError(
f"The directory: '{args.amap_directory}' does not "
f"appear to be complete. Please ensure this is the correct "
f"directory and that amap has completed."
)
def load_registration(self):
self.status_label.setText("Loading...")
self.region_labels = display_registration(
self.viewer,
self.registration_paths.registered_atlas_path,
self.registration_paths.boundaries_file_path,
self.image_scales,
memory=memory,
)
self.structures_df = load_structures_as_df(get_structures_path())
self.status_label.setText("Ready")
@self.region_labels.mouse_move_callbacks.append
def display_region_name(layer, event):
display_brain_region_name(layer, self.structures_df)
def load_amap_directory(self):
self.select_nii_file()
self.registration_directory = self.downsampled_file.parent
self.status_label.setText(f"Loading ...")
self.paths = Paths(self.registration_directory, self.downsampled_file)
if not self.paths.tmp__inverse_transformed_image.exists():
print(
f"The image: '{self.downsampled_file}' has not been transformed into standard "
f"space, and so must be transformed before segmentation.\n")
transform_image_to_standard_space(
self.registration_directory,
image_to_transform_fname=self.downsampled_file,
output_fname=self.paths.tmp__inverse_transformed_image,
log_file_path=self.paths.tmp__inverse_transform_log_path,
error_file_path=self.paths.tmp__inverse_transform_error_path,
)
else:
print("Registered image exists, skipping registration\n")
self.registered_image = prepare_load_nii(
self.paths.tmp__inverse_transformed_image, memory=memory)
self.base_layer = display_channel(
self.viewer,
self.registration_directory,
self.paths.tmp__inverse_transformed_image,
memory=memory,
name="Image in standard space",
)
self.initialise_image_view()
self.structures_df = load_structures_as_df(get_structures_path())
self.load_button.setMinimumWidth(0)
self.load_atlas_button.setVisible(True)
self.save_button.setVisible(True)
self.initialise_region_segmentation()
self.initialise_track_tracing()
self.status_label.setText(f"Ready")
def xml_crop(args, df_query="name"):
args = prep_atlas_conf(args)
if args.structures_file_path is None:
args.structures_file_path = get_structures_path()
reference_struct_df = pd.read_csv(get_structures_path())
curate_struct_df = pd.read_csv(args.structures_file_path)
curate_struct_df = reference_struct_df[
reference_struct_df[df_query].isin(curate_struct_df[df_query])
]
curated_ids = list(curate_struct_df["structure_id_path"])
atlas = brainio.load_any(args.registered_atlas_path)
hemisphere = brainio.load_any(args.hemispheres_atlas_path)
structures_reference_df = load_structures_as_df(get_structures_path())
atlas_pixel_sizes = get_atlas_pixel_sizes(args.atlas_config)
sample_pixel_sizes = args.x_pixel_um, args.y_pixel_um, args.z_pixel_um
scales = cells_regions.get_scales(sample_pixel_sizes, atlas_pixel_sizes)
destination_folder = os.path.join(args.xml_dir, "xml_crop")
if not os.path.exists(destination_folder):
os.makedirs(destination_folder)
xml_names = [f for f in os.listdir(args.xml_dir) if f.endswith(".xml")]
xml_paths = [os.path.join(args.xml_dir, f) for f in xml_names]
for idx, xml_path in enumerate(xml_paths):
print("Curating file: {}".format(xml_names[idx]))
cells = cells_regions.get_cells_data(
xml_path, cells_only=args.cells_only,
)
max_coords = cells_regions.get_max_coords(cells)
curated_cells = []
for i, cell in enumerate(cells):
cells_regions.transform_cell_coords(atlas, cell, scales)
structure_id = cells_regions.get_structure_from_coordinates(
atlas,
cell,
max_coords,
order=args.coordinates_order,
structures_reference_df=structures_reference_df,
)
if structure_id in curated_ids:
if args.hemisphere_query in [1, 2]:
hemisphere = cells_regions.get_structure_from_coordinates(
hemisphere,
cell,
max_coords,
order=args.coordinates_order,
)
if hemisphere is args.hemisphere_query:
curated_cells.append(cell)
else:
curated_cells.append(cell)
cells_to_xml(
curated_cells,
os.path.join(destination_folder, xml_names[idx]),
artifact_keep=True,
)
print("Done!")
from neuro.atlas_tools import paths as reg_paths
from neuro.atlas_tools.misc import get_voxel_volume, get_atlas_pixel_sizes
import napari
import numpy as np
amap_output_dir = (
"/media/adam/Storage/cellfinder/analysis/inj_segment/CT_CX_142_2_test")
region_acronym = "RSP"
visual_check = False
left_hemisphere_value = 2
right_hemisphere_value = 1
properties_to_fetch = ["area", "bbox", "centroid"]
structures_reference_df = load_structures_as_df(get_structures_path())
region_value = int(structures_reference_df[structures_reference_df["acronym"]
== region_acronym]["id"])
region_search_string = "/" + str(region_value) + "/"
sub_regions = structures_reference_df[structures_reference_df[
"structure_id_path"].str.contains(region_search_string)]
amap_output_dir = Path(amap_output_dir)
annotations_image = load_any(amap_output_dir / reg_paths.ANNOTATIONS)
midpoint = int(annotations_image.shape[0] // 2)
hemispheres_image = load_any(amap_output_dir / reg_paths.HEMISPHERES)
sub_region_values = list(sub_regions["id"])
def generate_region_volume(structure_names,
atlas_path,
output_path,
atlas_config,
glass=False):
structure_csv_file = get_structures_path()
reference_structures_table = pd.read_csv(structure_csv_file)
# ensure all names are valid
for indv_structure_name in structure_names:
try:
get_substructures(indv_structure_name, reference_structures_table)
except IndexError:
raise ValueError(
f"Brain region: '{indv_structure_name}' cannot be found "
f"in file: {structure_csv_file}. Please choose "
f"another structure.")
print(f"Loading atlas from: {atlas_path}")
atlas = brainio.load_nii(atlas_path, as_array=False)
atlas_scale = atlas.header.get_zooms()
atlas = atlas.get_data()
transformation_matrix = nii.get_transformation_matrix(atlas_config)
if len(structure_names) > 1:
# Initialise an image to add each subimage to.
final_image = np.zeros_like(atlas)
for indv_structure_name in structure_names:
print(f"Analysing brain region: {indv_structure_name}")
substructures = get_substructures(indv_structure_name,
reference_structures_table)
print("This includes structures:")
indv_substructure_names = substructures["name"].values
for indv_substructure_name in indv_substructure_names:
print(indv_substructure_name)
list_vals = substructures["id"].values
print("Generating image with specified regions \n")
sub_image = np.isin(atlas, list_vals)
if glass:
print("Generating glass brain")
sub_image = sk_segmentation.find_boundaries(sub_image)
# If multiple structures, add them together
if len(structure_names) > 1:
final_image = np.logical_or(final_image, sub_image)
else:
final_image = sub_image
print("Converting image to 16 bit")
final_image = scale_and_convert_to_16_bits(final_image)
print("Saving image")
brainio.to_nii(
final_image,
output_path,
scale=atlas_scale,
affine_transform=transformation_matrix,
)
print(f"Saved image at: {output_path}")
def main():
print("Starting amap viewer")
args = parser().parse_args()
structures_path = get_structures_path()
structures_df = load_structures_as_df(structures_path)
if not args.memory:
print(
"By default amap_vis does not load data into memory. "
"To speed up visualisation, use the '-m' flag. Be aware "
"this will make the viewer slower to open initially."
)
paths = Paths(args.amap_directory)
with napari.gui_qt():
v = napari.Viewer(title="amap viewer")
if (
Path(paths.registered_atlas_path).exists()
and Path(paths.boundaries_file_path).exists()
):
if args.raw:
image_scales = display_raw(v, args)
else:
if Path(paths.downsampled_brain_path).exists():
image_scales = display_downsampled(v, args, paths)
else:
raise FileNotFoundError(
f"The downsampled image: "
f"{paths.downsampled_brain_path} could not be found. "
f"Please ensure this is the correct "
f"directory and that amap has completed. "
)
labels = display_registration(
v,
paths.registered_atlas_path,
paths.boundaries_file_path,
image_scales,
memory=args.memory,
)
@labels.mouse_move_callbacks.append
def get_connected_component_shape(layer, event):
val = layer.get_value()
if val != 0 and val is not None:
try:
region = atlas_value_to_name(val, structures_df)
msg = f"{region}"
except UnknownAtlasValue:
msg = "Unknown region"
else:
msg = "No label here!"
layer.help = msg
else:
raise FileNotFoundError(
f"The directory: '{args.amap_directory}' does not "
f"appear to be complete. Please ensure this is the correct "
f"directory and that amap has completed."
)