Some advanced updates to annotator tools

micro_sam/_test_util.py

@@ -6,15 +6,15 @@ def check_layer_initialization(viewer, expected_shape):
 
     assert len(viewer.layers) == 6
     expected_layer_names = [
-        "image", "auto_segmentation", "committed_objects", "current_object", "point_prompts", "prompts"
+        "image", "auto_segmentation", "committed_objects", "current_object", "points", "geometry"
     ]
 
     for layer_name in expected_layer_names:
         assert layer_name in viewer.layers
 
     # Check prompt layers
-    assert viewer.layers["prompts"].data == []  # shape data is list, not numpy array
-    np.testing.assert_equal(viewer.layers["point_prompts"].data, 0)
+    assert viewer.layers["geometry"].data == []  # shape data is list, not numpy array
+    np.testing.assert_equal(viewer.layers["points"].data, 0)
 
     # Check segmentation layers.
     for layer_name in ["auto_segmentation", "committed_objects", "current_object"]:

micro_sam/sam_annotator/_annotator.py

@@ -73,11 +73,11 @@ def _require_layers(self, layer_choices: Optional[List[str]] = None):
 
         # Add the point layer for point prompts.
         self._point_labels = ["positive", "negative"]
-        if "point_prompts" in self._viewer.layers:
-            self._point_prompt_layer = self._viewer.layers["point_prompts"]
+        if "points" in self._viewer.layers:
+            self._point_prompt_layer = self._viewer.layers["points"]
         else:
             self._point_prompt_layer = self._viewer.add_points(
-                name="point_prompts",
+                name="points",
                 property_choices={"label": self._point_labels},
                 border_color="label",
                 border_color_cycle=vutil.LABEL_COLOR_CYCLE,
@@ -89,13 +89,13 @@ def _require_layers(self, layer_choices: Optional[List[str]] = None):
             )
             self._point_prompt_layer.border_color_mode = "cycle"
 
-        if "prompts" not in self._viewer.layers:
+        if "geometry" not in self._viewer.layers:
             # Add the shape layer for box and other shape prompts.
             self._viewer.add_shapes(
                 face_color="transparent",
                 edge_color="green",
                 edge_width=4,
-                name="prompts",
+                name="geometry",
                 ndim=self._ndim,
             )
 
@@ -106,7 +106,7 @@ def _get_widgets(self):
         )
 
     def _create_embedding_widget(self):
-        return widgets.EmbeddingWidget()
+        return widgets.EmbeddingWidget(viewer=self._viewer, roi_selection=True)
 
     def _create_widgets(self):
         # Create the embedding widget and connect all events related to it.
@@ -140,8 +140,8 @@ def _segment(viewer):
         # Note: we also need to over-write the keybindings for specific layers.
         # See https://github.com/napari/napari/issues/7302 for details.
         # Here, we need to over-write the 's' keybinding for both of the prompt layers.
-        prompt_layer = self._viewer.layers["prompts"]
-        point_prompt_layer = self._viewer.layers["point_prompts"]
+        prompt_layer = self._viewer.layers["geometry"]
+        point_prompt_layer = self._viewer.layers["points"]
 
         @prompt_layer.bind_key("s", overwrite=True)
         def _segment_prompts(event):
@@ -274,7 +274,7 @@ def _rebuild_for_ndim(self, ndim, force=False):
         self._shape = PLACEHOLDER_SHAPE[ndim]
 
         # Remove the existing micro_sam layers so they are recreated with the new ndim and shape.
-        layer_names = ("current_object", "auto_segmentation", "committed_objects", "point_prompts", "prompts")
+        layer_names = ("current_object", "auto_segmentation", "committed_objects", "points", "geometry")
         for layer_name in layer_names:
             if layer_name in self._viewer.layers:
                 del self._viewer.layers[layer_name]
@@ -319,6 +319,7 @@ def _update_image(self, segmentation_result=None):
 
         # Update the image scale.
         scale = state.image_scale
+        translate = state.image_translate
 
         # Reset all layers.
         self._viewer.layers["current_object"].data = np.zeros(
@@ -339,8 +340,16 @@ def _update_image(self, segmentation_result=None):
             self._viewer.layers["committed_objects"].data = segmentation_result
         self._viewer.layers["committed_objects"].scale = scale
 
-        self._viewer.layers["point_prompts"].scale = scale
-        self._viewer.layers["prompts"].scale = scale
+        self._viewer.layers["points"].scale = scale
+        self._viewer.layers["geometry"].scale = scale
+
+        # Keep cropped annotation layers aligned with their location in the full image. The image
+        # itself remains unchanged and visible; only the embeddings and result arrays are cropped.
+        if translate is not None:
+            for layer_name in (
+                "current_object", "auto_segmentation", "committed_objects", "points", "geometry"
+            ):
+                self._viewer.layers[layer_name].translate = translate
 
         vutil.clear_annotations(self._viewer, clear_segmentations=False)
 

micro_sam/sam_annotator/_state.py

@@ -89,6 +89,7 @@ class AnnotatorState(metaclass=Singleton):
     predictor: Optional[SamPredictor] = None
     image_shape: Optional[Tuple[int, int]] = None
     image_scale: Optional[Tuple[float, ...]] = None
+    image_translate: Optional[Tuple[float, ...]] = None
     ndim: Optional[int] = None
     image_name: Optional[str] = None
     embedding_path: Optional[str] = None
@@ -363,6 +364,7 @@ def reset_state(self):
         self.predictor = None
         self.image_shape = None
         self.image_scale = None
+        self.image_translate = None
         self.ndim = None
         self.image_name = None
         self.embedding_path = None

micro_sam/sam_annotator/_tooltips.py

@@ -10,6 +10,7 @@
         "model_family": "Select the segment anything 2 model family.",
         "model_family_advanced": "Select the advanced (non-SAM2) model family, e.g. a SAM1 family. Switched on via 'Advanced Models' in the embedding settings.",  # noqa
         "model_size": "Select the image encoder size of the segment anything 2 model.",
+        "region": "Compute embeddings for the full image or for one rectangle selected in the 'geometry' layer. The ROI becomes a new selected image layer while the source stays open. For 3D volumes and timeseries, the rectangle crops Y/X across all slices or frames.",  # noqa
         "advanced_model": "Switch the model list above to advanced models beyond the default SAM2 models (currently SAM1). Only available for the classification tools.",  # noqa
         "automatic_segmentation_mode": "Select the automatic segmentation mode.",
         "run_button": "Compute embeddings or load embeddings if embedding_save_path is specified.",