def add_existing_label_layers(
viewer,
label_file,
selected_label=1,
num_colors=10,
brush_size=30,
memory=False,
):
"""
Loads an existing (nii) image as a napari labels layer
:param viewer: Napari viewer instance
:param label_file: Filename of the image to be loaded
:param int selected_label: Label ID to be preselected
:param int num_colors: How many colors (labels)
:param int brush_size: Default size of the label brush
:return label_layer: napari labels layer
"""
label_file = Path(label_file)
labels = prepare_load_nii(label_file, memory=memory)
label_layer = viewer.add_labels(labels,
num_colors=num_colors,
name=label_file.stem)
label_layer.selected_label = selected_label
label_layer.brush_size = brush_size
return label_layer
def load_main_downsampled_channel(self):
self.viewer.add_image(
prepare_load_nii(
self.registration_paths.downsampled_brain_path,
memory=memory,
),
scale=self.image_scales,
name="Raw data (downsampled)",
)
def load_heatmap(self):
self.status_label.setText("Loading...")
self.viewer.add_image(
prepare_load_nii(
self.heatmap_path,
memory=memory,
),
scale=self.image_scales,
name="Heatmap",
)
self.status_label.setText("Ready")
def load_atlas(self):
if not self.region_labels:
self.status_label.setText(f"Loading ...")
self.region_labels = self.viewer.add_labels(
prepare_load_nii(self.paths.annotations, memory=memory),
name="Region labels",
opacity=0.2,
)
self.region_labels.editable = False
@self.region_labels.mouse_move_callbacks.append
def display_region_name(layer, event):
display_brain_region_name(layer, self.structures_df)
self.status_label.setText(f"Ready")
def load_additional_downsampled_channels(self,
search_string="downsampled_",
extension=".nii"):
for file in self.registration_directory.iterdir():
if ((file.suffix == extension)
and file.name.startswith(search_string) and file != Path(
self.registration_paths.downsampled_brain_path)
and file != Path(
self.registration_paths.tmp__downsampled_filtered)):
print(f"Found additional downsampled image: {file.name}, "
f"adding to viewer")
name = (file.name.strip(search_string).strip(extension) +
" (Downsampled)")
self.viewer.add_image(
prepare_load_nii(file, memory=memory),
name=name,
scale=self.image_scales,
)
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")