changing function and varible names to use binary map/prob map

src/allencell_ml_segmenter/_tests/fakes/fake_viewer.py

@@ -118,7 +118,7 @@ def get_seg_layers(self) -> list[Layer]:
             if layer.name.startswith("[seg]")
         ]
 
-    def insert_threshold(
+    def insert_binary_map_into_layer(
         self, layer: ImageLayer, img: np.ndarray, seg_layers: bool = False
     ) -> None:
         self.threshold_inserted[f"[threshold] {layer.name}"] = img
@@ -138,7 +138,7 @@ def get_source_path(self, layer: Layer) -> Optional[Path]:
 
         return None
 
-    def get_all_segmentation_labels(self) -> list[Layer]:
+    def get_all_layers_containing_prob_map(self) -> list[Layer]:
         return [
             layer
             for layer in self.get_all_images()

src/allencell_ml_segmenter/core/prediction_result_input_widget.py

@@ -30,7 +30,9 @@ def __init__(
     def _update_layer_list(self, event: Optional[NapariEvent] = None) -> None:
         previous_selections: list[int] = self._image_list.get_checked_rows()
         self._image_list.clear()
-        self._prediction_layers = self._viewer.get_all_segmentation_labels()
+        self._prediction_layers = (
+            self._viewer.get_all_layers_containing_prob_map()
+        )
         for idx, prediction_output_layer in enumerate(self._prediction_layers):
             self._image_list.add_item(
                 prediction_output_layer.name,

src/allencell_ml_segmenter/main/i_viewer.py

@@ -83,7 +83,7 @@ def get_seg_layers(self) -> list[Layer]:
         pass
 
     @abstractmethod
-    def insert_threshold(
+    def insert_binary_map_into_layer(
         self, layer_name: str, img: np.ndarray, seg_layers: bool = False
     ) -> None:
         """
@@ -107,5 +107,5 @@ def get_source_path(self, layer: Layer) -> Optional[Path]:
         pass
 
     @abstractmethod
-    def get_all_segmentation_labels(self) -> list[Layer]:
+    def get_all_layers_containing_prob_map(self) -> list[Layer]:
         pass

src/allencell_ml_segmenter/main/viewer.py

@@ -141,7 +141,7 @@ def get_seg_layers(self) -> list[Layer]:
             if layer.name.startswith("[seg]")
         ]
 
-    def insert_threshold(
+    def insert_binary_map_into_layer(
         self,
         layer: Layer,
         image: np.ndarray,
@@ -176,7 +176,7 @@ def get_source_path(self, layer: Layer) -> Optional[Path]:
             return Path(layer.metadata["source_path"])
         return None
 
-    def get_all_segmentation_labels(self) -> list[Layer]:
+    def get_all_layers_containing_prob_map(self) -> list[Layer]:
         """
         Get all segmentation labels layers that currently exist in the viewer.
         """

src/allencell_ml_segmenter/thresholding/thresholding_service.py

@@ -69,8 +69,8 @@ def _handle_thresholding_error(self, error: Exception) -> None:
         show_info("Thresholding failed: " + str(error))
 
     def _on_threshold_changed(self, _: Event) -> None:
-        segmentation_labels: list[Layer] = (
-            self._viewer.get_all_segmentation_labels()
+        layers_containing_prob_map: list[Layer] = (
+            self._viewer.get_all_layers_containing_prob_map()
         )
 
         # determine thresholding function to use
@@ -80,11 +80,15 @@ def _on_threshold_changed(self, _: Event) -> None:
             )
         else:
             thresh_function = self._threshold_image
-        for idx, layer in enumerate(segmentation_labels):
-            selected_idx: Optional[list[int]] = (
-                self._file_input_model.get_selected_idx()
-            )
-            if selected_idx is not None and idx in selected_idx:
+
+        selected_idx: Optional[list[int]] = (
+            self._file_input_model.get_selected_idx()
+        )
+
+        if selected_idx is not None:
+            for idx in selected_idx:
+                layer: Layer = layers_containing_prob_map[idx]
+
                 # Creating helper functions for mypy strict typing
                 def thresholding_task(
                     layer_instance: Layer = layer,
@@ -98,16 +102,16 @@ def thresholding_task(
                         raise ValueError(
                             "Layer metadata must be a dictionary containing the 'prob_map' key in order to threshold."
                         )
-
+                    # This thresholding task returns a binary map
                     return thresh_function(layer_instance.metadata["prob_map"])
 
                 def on_return(
-                    threshold_output: np.ndarray,
+                    resulting_binary_map: np.ndarray,
                     layer_instance: Layer = layer,
                 ) -> None:
-                    self._viewer.insert_threshold(
+                    self._viewer.insert_binary_map_into_layer(
                         layer_instance,
-                        threshold_output,
+                        resulting_binary_map,
                         self._main_model.are_predictions_in_viewer(),
                     )