Napari support for labeling components

Author: schilling40

flamingo_tools/postprocessing/label_components.py

@@ -750,6 +750,7 @@ def label_components_single(
     s3_bucket_name: Optional[str] = None,
     s3_service_endpoint: Optional[str] = None,
     custom_dic: Optional[dict] = None,
+    use_napari: bool = False,
     **_
 ):
     """Process a single cochlea using one set of parameters or a custom dictionary.
@@ -774,6 +775,7 @@ def label_components_single(
         s3_service_endpoint:
         custom_dic: Custom dictionary which allows multiple post-processing configurations and combines the
             results into final components.
+        use_napari: Visualize component labels with napari viewer.
     """
     if os.path.isdir(out_path):
         raise ValueError(f"Output path {out_path} is a directory. Provide a path to a single output file.")
@@ -820,3 +822,17 @@ def label_components_single(
             print(f"Custom component(s) have {custom_comp} {cell_type.upper()}s.")
 
         tsv_table.to_csv(out_path, sep="\t", index=False)
+
+        if use_napari:
+            import napari
+            scale_factor = 20
+            centroids = list(zip(tsv_table["anchor_x"], tsv_table["anchor_y"], tsv_table["anchor_z"]))
+            component_labels = list(tsv_table["component_labels"])
+            array_downscaled = downscaled_centroids(centroids=centroids, scale_factor=scale_factor,
+                                                    component_labels=component_labels, downsample_mode="components")
+            image_downscaled = downscaled_centroids(centroids, scale_factor=scale_factor,
+                                                    downsample_mode="accumulated")
+            viewer = napari.Viewer()
+            viewer.add_image(image_downscaled, name='3D Volume')
+            viewer.add_labels(array_downscaled, name="components")
+            napari.run()

reproducibility/label_components/repro_label_components.py

@@ -17,6 +17,7 @@ def wrapper_label_components(
     output_path: str,
     table_path: Optional[str] = None,
     ddict: Optional[str] = None,
+    use_napari: bool = False,
     s3: bool = False,
     **kwargs
 ):
@@ -25,7 +26,7 @@ def wrapper_label_components(
     and the explicit setting of parameters.
     """
     if ddict is None:
-        label_components_single(table_path, out_path=output_path, s3=s3, **kwargs)
+        label_components_single(table_path, out_path=output_path, use_napari=use_napari, s3=s3, **kwargs)
     else:
         param_dicts = _load_json_as_list(ddict)
         for params in param_dicts:
@@ -41,7 +42,7 @@ def wrapper_label_components(
                 save_path = os.path.join(output_path, "_".join([cochlea_str, f"{table_str}.tsv"]))
             else:
                 save_path = output_path
-            label_components_single(table_path=table_path, out_path=save_path, s3=s3,
+            label_components_single(table_path=table_path, out_path=save_path, s3=s3, use_napari=use_napari,
                                     **params)
 
 
@@ -65,6 +66,7 @@ def main():
     parser.add_argument("--max_edge_distance", type=float, default=30,
                         help="Maximal distance in micrometer between points to create edges for connected components.")
     parser.add_argument("-c", "--components", type=int, nargs="+", default=[1], help="List of connected components.")
+    parser.add_argument("--napari", action="store_true", help="Use napari viewer to visualize result.")
 
     # options for S3 bucket
     parser.add_argument("--s3", action="store_true", help="Flag for using S3 bucket.")
@@ -88,6 +90,7 @@ def main():
         min_component_length=args.min_component_length,
         min_size=args.min_size,
         force_overwrite=args.force,
+        use_napari=args.napari,
         s3=args.s3,
         s3_credentials=args.s3_credentials,
         s3_bucket_name=args.s3_bucket_name,