Ensure valid layers exist for each annotator click menu

micro_sam/sam_annotator/_annotator.py

@@ -1,6 +1,8 @@
-import napari
+from typing import Optional, List
+
 import numpy as np
 
+import napari
 from qtpy import QtWidgets
 from magicgui.widgets import Widget, Container, FunctionGui
 
@@ -16,19 +18,39 @@ class _AnnotatorBase(QtWidgets.QScrollArea):
     The annotators differ in their data dimensionality and the widgets.
     """
 
-    def _create_layers(self):
+    def _require_layers(self, layer_choices: Optional[List[str]] = None):
+
+        # Check whether the image is initialized already. And use the image shape and scale for the layers.
+        state = AnnotatorState()
+        shape = self._shape if state.image_shape is None else state.image_shape
+
         # Add the label layers for the current object, the automatic segmentation and the committed segmentation.
-        dummy_data = np.zeros(self._shape, dtype="uint32")
+        dummy_data = np.zeros(shape, dtype="uint32")
+        image_scale = state.image_scale
 
         # Before adding new layers, we always check whether a layer with this name already exists or not.
         if "current_object" not in self._viewer.layers:
+            if layer_choices and "current_object" in layer_choices:  # Check at 'commit' call button.
+                widgets._validation_window_for_missing_layer("current_object")
             self._viewer.add_labels(data=dummy_data, name="current_object")
+            if image_scale is not None:
+                self.layers["current_objects"].scale = image_scale
+
         if "auto_segmentation" not in self._viewer.layers:
+            if layer_choices and "auto_segmentation" in layer_choices:  # Check at 'commit' call button.
+                widgets._validation_window_for_missing_layer("auto_segmentation")
             self._viewer.add_labels(data=dummy_data, name="auto_segmentation")
+            if image_scale is not None:
+                self.layers["auto_segmentation"].scale = image_scale
+
         if "committed_objects" not in self._viewer.layers:
+            if layer_choices and "committed_objects" in layer_choices:  # Check at 'commit' call button.
+                widgets._validation_window_for_missing_layer("committed_objects")
             self._viewer.add_labels(data=dummy_data, name="committed_objects")
             # Randomize colors so it is easy to see when object committed.
             self._viewer.layers["committed_objects"].new_colormap()
+            if image_scale is not None:
+                self.layers["committed_objects"].scale = image_scale
 
         # Add the point layer for point prompts.
         self._point_labels = ["positive", "negative"]
@@ -70,7 +92,7 @@ def _create_widgets(self):
         # Create the prompt widget. (The same for all plugins.)
         self._prompt_widget = widgets.create_prompt_menu(self._point_prompt_layer, self._point_labels)
 
-        # Create the dictionray for the widgets and get the widgets of the child plugin.
+        # Create the dictionary for the widgets and get the widgets of the child plugin.
         self._widgets = {"embeddings": self._embedding_widget, "prompts": self._prompt_widget}
         self._widgets.update(self._get_widgets())
 
@@ -131,7 +153,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", ndim: int) -> None:
         # Initialize with a dummy shape, which is reset to the correct shape once an image is set.
         self._ndim = ndim
         self._shape = (256, 256) if ndim == 2 else (16, 256, 256)
-        self._create_layers()
+        self._require_layers()
 
         # Create all the widgets and add them to the layout.
         self._create_widgets()
@@ -179,6 +201,9 @@ def _update_image(self, segmentation_result=None):
                 )
             self._shape = state.image_shape
 
+        # Before we reset the layers, we ensure all expected layers exist.
+        self._require_layers()
+
         # Update the image scale.
         scale = state.image_scale
 
@@ -187,12 +212,15 @@ def _update_image(self, segmentation_result=None):
         self._viewer.layers["current_object"].scale = scale
         self._viewer.layers["auto_segmentation"].data = np.zeros(self._shape, dtype="uint32")
         self._viewer.layers["auto_segmentation"].scale = scale
+
         if segmentation_result is None or segmentation_result is False:
             self._viewer.layers["committed_objects"].data = np.zeros(self._shape, dtype="uint32")
         else:
             assert segmentation_result.shape == self._shape
             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
+
         vutil.clear_annotations(self._viewer, clear_segmentations=False)

micro_sam/sam_annotator/_state.py

@@ -14,6 +14,7 @@
 
 import torch.nn as nn
 
+import micro_sam
 import micro_sam.util as util
 from micro_sam.instance_segmentation import AMGBase, get_decoder
 from micro_sam.precompute_state import cache_amg_state, cache_is_state
@@ -69,6 +70,9 @@ class AnnotatorState(metaclass=Singleton):
     # z-range to limit the data being committed in 3d / tracking.
     z_range: Optional[Tuple[int, int]] = None
 
+    # annotator_class
+    annotator: Optional["micro_sam.sam_annotator._annotator._AnnotatorBase"] = None
+
     def initialize_predictor(
         self,
         image_data,

micro_sam/sam_annotator/_widgets.py

@@ -30,9 +30,9 @@
 # from napari.qt.threading import thread_worker
 from napari.utils import progress
 
-from ._state import AnnotatorState
 from . import util as vutil
 from ._tooltips import get_tooltip
+from ._state import AnnotatorState
 from .. import instance_segmentation, util
 from ..multi_dimensional_segmentation import (
     segment_mask_in_volume, merge_instance_segmentation_3d, track_across_frames, PROJECTION_MODES, get_napari_track_data
@@ -496,8 +496,12 @@ def _mask_matched_objects(seg, prev_seg, preservation_threshold):
 
 
 def _commit_impl(viewer, layer, preserve_mode, preservation_threshold):
-    # Check if we have a z_range. If yes, use it to set a bounding box.
     state = AnnotatorState()
+
+    # Check whether all layers exist as expected or create new ones automatically.
+    state.annotator._require_layers(layer_choices=[layer, "committed_objects"])
+
+    # Check if we have a z_range. If yes, use it to set a bounding box.
     if state.z_range is None:
         bb = np.s_[:]
     else:
@@ -750,6 +754,7 @@ def commit(
         commit_path: Select a file path where the committed results and prompts will be saved.
             This feature is still experimental.
     """
+    # Commit the segmentation layer.
     _, seg, mask, bb = _commit_impl(viewer, layer, preserve_mode, preservation_threshold)
 
     if commit_path is not None:
@@ -964,12 +969,27 @@ def _validate_embeddings(viewer: "napari.viewer.Viewer"):
     #     return False
 
 
-def _validate_prompts(viewer: "napari.viewer.Viewer") -> bool:
-    if len(viewer.layers["prompts"].data) == 0 and len(viewer.layers["point_prompts"].data) == 0:
-        msg = "No prompts were given. Please provide prompts to run interactive segmentation."
-        return _generate_message("error", msg)
+def _validation_window_for_missing_layer(layer_choice):
+    if layer_choice == "committed_objects":
+        msg = "The 'committed_objects' layer to commit masks is missing. Please try to commit again."
     else:
-        return False
+        msg = f"The '{layer_choice}' layer to commit is missing. Please re-annotate and try again."
+
+    return _generate_message(message_type="error", message=msg)
+
+
+def _validate_layers(viewer: "napari.viewer.Viewer", automatic_segmentation: bool = False) -> bool:
+    # Check whether all layers exist as expected or create new ones automatically.
+    state = AnnotatorState()
+    state.annotator._require_layers()
+
+    if not automatic_segmentation:
+        # Check prompts layer.
+        if len(viewer.layers["prompts"].data) == 0 and len(viewer.layers["point_prompts"].data) == 0:
+            msg = "No prompts were given. Please provide prompts to run interactive segmentation."
+            return _generate_message("error", msg)
+        else:
+            return False
 
 
 @magic_factory(call_button="Segment Object [S]")
@@ -982,7 +1002,7 @@ def segment(viewer: "napari.viewer.Viewer", batched: bool = False) -> None:
     """
     if _validate_embeddings(viewer):
         return None
-    if _validate_prompts(viewer):
+    if _validate_layers(viewer):
         return None
 
     shape = viewer.layers["current_object"].data.shape
@@ -1016,7 +1036,7 @@ def segment_slice(viewer: "napari.viewer.Viewer") -> None:
     """
     if _validate_embeddings(viewer):
         return None
-    if _validate_prompts(viewer):
+    if _validate_layers(viewer):
         return None
 
     shape = viewer.layers["current_object"].data.shape[1:]
@@ -1057,8 +1077,9 @@ def segment_frame(viewer: "napari.viewer.Viewer") -> None:
     """
     if _validate_embeddings(viewer):
         return None
-    if _validate_prompts(viewer):
+    if _validate_layers(viewer):
         return None
+
     state = AnnotatorState()
     shape = state.image_shape[1:]
     position = viewer.dims.point
@@ -1626,8 +1647,9 @@ def update_segmentation(seg):
     def __call__(self):
         if _validate_embeddings(self._viewer):
             return None
-        if _validate_prompts(self._viewer):
+        if _validate_layers(self._viewer):
             return None
+
         if self.tracking:
             return self._run_tracking()
         else:
@@ -1889,6 +1911,9 @@ def update_segmentation(seg):
                 self._viewer.layers["auto_segmentation"].data[i] = seg
             self._viewer.layers["auto_segmentation"].refresh()
 
+        # Validate all layers.
+        _validate_layers(self._viewer, automatic_segmentation=True)
+
         seg = seg_impl()
         update_segmentation(seg)
         # worker = seg_impl()

micro_sam/sam_annotator/annotator_2d.py

@@ -24,9 +24,18 @@ def _get_widgets(self):
             "clear": widgets.clear(),
         }
 
-    def __init__(self, viewer: "napari.viewer.Viewer") -> None:
+    def __init__(self, viewer: "napari.viewer.Viewer", reset_state: bool = True) -> None:
         super().__init__(viewer=viewer, ndim=2)
 
+        # Set the expected annotator class to the state.
+        state = AnnotatorState()
+
+        # Reset the state.
+        if reset_state:
+            state.reset_state()
+
+        state.annotator = self
+
 
 def annotator_2d(
     image: np.ndarray,
@@ -85,7 +94,7 @@ def annotator_2d(
         viewer = napari.Viewer()
 
     viewer.add_image(image, name="image")
-    annotator = Annotator2d(viewer)
+    annotator = Annotator2d(viewer, reset_state=False)
 
     # Trigger layer update of the annotator so that layers have the correct shape.
     # And initialize the 'committed_objects' with the segmentation result if it was given.

micro_sam/sam_annotator/annotator_3d.py

@@ -24,10 +24,19 @@ def _get_widgets(self):
             "clear": widgets.clear_volume(),
         }
 
-    def __init__(self, viewer: "napari.viewer.Viewer") -> None:
+    def __init__(self, viewer: "napari.viewer.Viewer", reset_state: bool = True) -> None:
         self._with_decoder = AnnotatorState().decoder is not None
         super().__init__(viewer=viewer, ndim=3)
 
+        # Set the expected annotator class to the state.
+        state = AnnotatorState()
+
+        # Reset the state.
+        if reset_state:
+            state.reset_state()
+
+        state.annotator = self
+
     def _update_image(self, segmentation_result=None):
         super()._update_image(segmentation_result=segmentation_result)
         # Load the amg state from the embedding path.
@@ -95,7 +104,7 @@ def annotator_3d(
         viewer = napari.Viewer()
 
     viewer.add_image(image, name="image")
-    annotator = Annotator3d(viewer)
+    annotator = Annotator3d(viewer, reset_state=False)
 
     # Trigger layer update of the annotator so that layers have the correct shape.
     # And initialize the 'committed_objects' with the segmentation result if it was given.