Merge pull request #583 from AllenCell/refactor/thresholdingmodel_com…

…poses_fileinputmodel

Make ThresholdingModel compose FileInputModel, Add ability to remove binary maps

src/allencell_ml_segmenter/_tests/fakes/fake_viewer.py

@@ -145,3 +145,6 @@ def get_all_layers_containing_prob_map(self) -> list[Layer]:
             if getattr(layer, "metadata", None)
             and "prob_map" in layer.metadata
         ]
+
+    def clear_binary_map_from_layer(self, layer: Layer) -> None:
+        self.threshold_inserted.pop(f"[threshold] {layer.name}")

src/allencell_ml_segmenter/_tests/main/test_viewer.py

@@ -0,0 +1,12 @@
+from napari.layers import Labels
+import numpy as np
+
+from allencell_ml_segmenter.main.viewer import Viewer
+
+
+def test_clear_binary_map_from_layer() -> None:
+    layer: Labels = Labels(np.ones((10, 10, 10), dtype=bool))
+
+    Viewer.clear_binary_map_from_layer(Viewer, layer)
+
+    assert np.all(layer.data == 0)  # assert all values are now zeros

src/allencell_ml_segmenter/_tests/thresholding/test_thresholding_model.py

@@ -16,29 +16,27 @@ def thresholding_model() -> ThresholdingModel:
 def test_set_thresholding_value_dispatches_event(thresholding_model):
     fake_subscriber: FakeSubscriber = FakeSubscriber()
     thresholding_model.subscribe(
-        Event.ACTION_THRESHOLDING_VALUE_CHANGED,
+        Event.ACTION_EXECUTE_THRESHOLDING,
         fake_subscriber,
         fake_subscriber.handle,
     )
 
     thresholding_model.set_thresholding_value(2)
 
-    assert fake_subscriber.was_handled(Event.ACTION_THRESHOLDING_VALUE_CHANGED)
+    assert fake_subscriber.was_handled(Event.ACTION_EXECUTE_THRESHOLDING)
 
 
 def test_set_autothresholding_enabled_dispatches_event(thresholding_model):
     fake_subscriber: FakeSubscriber = FakeSubscriber()
     thresholding_model.subscribe(
-        Event.ACTION_THRESHOLDING_AUTOTHRESHOLDING_SELECTED,
+        Event.ACTION_EXECUTE_THRESHOLDING,
         fake_subscriber,
         fake_subscriber.handle,
     )
 
     thresholding_model.set_autothresholding_enabled(True)
 
-    assert fake_subscriber.was_handled(
-        Event.ACTION_THRESHOLDING_AUTOTHRESHOLDING_SELECTED
-    )
+    assert fake_subscriber.was_handled(Event.ACTION_EXECUTE_THRESHOLDING)
 
 
 def test_dispatch_save_thresholded_images(thresholding_model):

src/allencell_ml_segmenter/_tests/thresholding/test_thresholding_service.py

@@ -1,7 +1,6 @@
 import pytest
 import numpy as np
 
-from allencell_ml_segmenter.core.file_input_model import FileInputModel
 from allencell_ml_segmenter._tests.fakes.fake_experiments_model import (
     FakeExperimentsModel,
 )
@@ -29,7 +28,6 @@ def test_on_threshold_changed_non_prediction(test_image):
     thresholding_service: ThresholdingService = ThresholdingService(
         thresholding_model,
         FakeExperimentsModel(),
-        FileInputModel(),
         MainModel(),
         viewer,
         task_executor=SynchroTaskExecutor.global_instance(),
@@ -60,11 +58,9 @@ def test_on_threshold_changed_non_prediction(test_image):
     viewer: FakeViewer = FakeViewer()
     main_model: MainModel = MainModel()
     main_model.set_predictions_in_viewer(True)
-    file_input_model: FileInputModel = FileInputModel()
     thresholding_service: ThresholdingService = ThresholdingService(
         thresholding_model,
         FakeExperimentsModel(),
-        file_input_model,
         main_model,
         viewer,
         task_executor=SynchroTaskExecutor.global_instance(),
@@ -84,10 +80,14 @@ def test_on_threshold_changed_non_prediction(test_image):
         metadata={"prob_map": test_image},
     )
     viewer.add_image(test_image, name="donotthreshold")
-    file_input_model.set_selected_idx([0, 1])
+    thresholding_model.set_thresholding_layers(
+        viewer.get_all_layers_containing_prob_map()
+    )
+    thresholding_model.set_selected_idx([0, 1])
 
     # ACT set a threshold to trigger
     thresholding_model.set_thresholding_value(50)
+    thresholding_model.set_threshold_enabled(True)
 
     # Verify a threshold layer is added for each seg layer
     assert "[threshold] [seg] test_layer 1" in viewer.threshold_inserted
@@ -110,3 +110,121 @@ def test_on_threshold_changed_non_prediction(test_image):
     assert np.array_equal(seg_data, (test_image > 100).astype(int))
     # verify that raw layers do not get thresholded
     assert len(viewer.threshold_inserted) == 2
+
+
+def test_none_selected_removes_binary_maps(test_image):
+    """
+    Test that the thresholding service does not add a threshold layer for a layer that is not a probability map
+    """
+    # ARRANGE
+    thresholding_model: ThresholdingModel = ThresholdingModel()
+    viewer: FakeViewer = FakeViewer()
+    main_model: MainModel = MainModel()
+    main_model.set_predictions_in_viewer(True)
+    thresholding_service: ThresholdingService = ThresholdingService(
+        thresholding_model,
+        FakeExperimentsModel(),
+        main_model,
+        viewer,
+        task_executor=SynchroTaskExecutor.global_instance(),
+    )
+
+    # Only the [seg] layers below should produce a threshold layer since they have prob map metadata
+    viewer.add_image(test_image, name="[raw] test_layer 1")
+    viewer.add_image(
+        test_image,
+        name="[seg] test_layer 1",
+        metadata={"prob_map": test_image},
+    )
+    viewer.add_image(test_image, name="[raw] test_layer 2")
+    viewer.add_image(
+        test_image,
+        name="[seg] test_layer 2",
+        metadata={"prob_map": test_image},
+    )
+    viewer.add_image(test_image, name="donotthreshold")
+    thresholding_model.set_thresholding_layers(
+        viewer.get_all_layers_containing_prob_map()
+    )
+    thresholding_model.set_selected_idx([0, 1])
+
+    # create binary maps and add to viewer
+    thresholding_model.set_thresholding_value(50)
+    thresholding_model.set_threshold_enabled(True)
+
+    # Verify a threshold layer is added for each seg layer
+    assert "[threshold] [seg] test_layer 1" in viewer.threshold_inserted
+    seg_data = viewer.threshold_inserted["[threshold] [seg] test_layer 1"]
+    assert np.array_equal(seg_data, (test_image > 50).astype(int))
+    assert "[threshold] [seg] test_layer 2" in viewer.threshold_inserted
+    seg_data = viewer.threshold_inserted["[threshold] [seg] test_layer 2"]
+    assert np.array_equal(seg_data, (test_image > 50).astype(int))
+    # verify that raw layers do not get thresholded
+    assert len(viewer.threshold_inserted) == 2
+
+    # ACT remove all binary maps by selecting None
+    thresholding_model.disable_all()
+
+    # Assert
+    assert len(viewer.threshold_inserted) == 0
+
+
+def test_unselection_removes_binary_maps(test_image):
+    """
+    Test that the thresholding service does not add a threshold layer for a layer that is not a probability map
+    """
+    # ARRANGE
+    thresholding_model: ThresholdingModel = ThresholdingModel()
+    viewer: FakeViewer = FakeViewer()
+    main_model: MainModel = MainModel()
+    main_model.set_predictions_in_viewer(True)
+    thresholding_service: ThresholdingService = ThresholdingService(
+        thresholding_model,
+        FakeExperimentsModel(),
+        main_model,
+        viewer,
+        task_executor=SynchroTaskExecutor.global_instance(),
+    )
+
+    # Only the [seg] layers below should produce a threshold layer since they have prob map metadata
+    viewer.add_image(test_image, name="[raw] test_layer 1")
+    viewer.add_image(
+        test_image,
+        name="[seg] test_layer 1",
+        metadata={"prob_map": test_image},
+    )
+    viewer.add_image(test_image, name="[raw] test_layer 2")
+    viewer.add_image(
+        test_image,
+        name="[seg] test_layer 2",
+        metadata={"prob_map": test_image},
+    )
+    viewer.add_image(test_image, name="donotthreshold")
+    thresholding_model.set_thresholding_layers(
+        viewer.get_all_layers_containing_prob_map()
+    )
+    thresholding_model.set_selected_idx([0, 1])
+
+    # create binary maps and add to viewer
+    thresholding_model.set_thresholding_value(50)
+    thresholding_model.set_threshold_enabled(True)
+
+    # Verify a threshold layer is added for each seg layer
+    assert "[threshold] [seg] test_layer 1" in viewer.threshold_inserted
+    seg_data = viewer.threshold_inserted["[threshold] [seg] test_layer 1"]
+    assert np.array_equal(seg_data, (test_image > 50).astype(int))
+    assert "[threshold] [seg] test_layer 2" in viewer.threshold_inserted
+    seg_data = viewer.threshold_inserted["[threshold] [seg] test_layer 2"]
+    assert np.array_equal(seg_data, (test_image > 50).astype(int))
+    # verify that raw layers do not get thresholded
+    assert len(viewer.threshold_inserted) == 2
+
+    # ACT remove binary map by unselecting index 1
+    thresholding_model.set_selected_idx(
+        [0]
+    )  # remove 1, which was previously selected
+
+    # Assert
+    assert len(viewer.threshold_inserted) == 1
+    assert "[threshold] [seg] test_layer 2" not in viewer.threshold_inserted
+    assert "[threshold] [seg] test_layer 1" in viewer.threshold_inserted

src/allencell_ml_segmenter/_tests/thresholding/test_thresholding_view.py

@@ -13,7 +13,6 @@
     ThresholdingModel,
 )
 from allencell_ml_segmenter.core.file_input_model import (
-    FileInputModel,
     InputMode,
 )
 from allencell_ml_segmenter._tests.fakes.fake_viewer import FakeViewer
@@ -28,16 +27,9 @@ def main_model() -> MainModel:
 
 
 @pytest.fixture
-def thresholding_model() -> ThresholdingModel:
+def thresholding_model(tmp_path: Path) -> ThresholdingModel:
     model = ThresholdingModel()
     model.set_thresholding_value(128)
-    return model
-
-
-@pytest.fixture
-# tmp_path is a builtin pytest fixture for a faked path
-def file_input_model(tmp_path: Path) -> FileInputModel:
-    model = FileInputModel()
     model.set_output_directory(tmp_path / "output")
     model.set_input_image_path(tmp_path / "input")
     model.set_input_mode(InputMode.FROM_PATH)
@@ -58,15 +50,13 @@ def viewer() -> FakeViewer:
 def thresholding_view(
     main_model,
     thresholding_model,
-    file_input_model,
     experiments_model,
     viewer,
     qtbot,
 ):
     view = ThresholdingView(
         main_model,
         thresholding_model,
-        file_input_model,
         experiments_model,
         viewer,
     )
@@ -176,15 +166,18 @@ def test_update_state_from_radios(thresholding_view, thresholding_model):
 
 
 def test_check_able_to_threshold_valid(
-    main_model, file_input_model, experiments_model, viewer
+    main_model, experiments_model, viewer, tmp_path
 ):
     thresholding_model: ThresholdingModel = ThresholdingModel()
     thresholding_model.set_threshold_enabled(True)
     thresholding_model.set_thresholding_value(100)
+    thresholding_model.set_output_directory(tmp_path / "output")
+    thresholding_model.set_input_image_path(tmp_path / "input")
+    thresholding_model.set_input_mode(InputMode.FROM_PATH)
+
     thresholding_view: ThresholdingView = ThresholdingView(
         main_model,
         thresholding_model,
-        file_input_model,
         experiments_model,
         viewer,
     )
@@ -198,13 +191,11 @@ def test_check_able_to_threshold_no_output_dir(
     thresholding_model: ThresholdingModel = ThresholdingModel()
     thresholding_model.set_threshold_enabled(True)
     thresholding_model.set_thresholding_value(100)
-    file_input_model: FileInputModel = FileInputModel()
-    file_input_model.set_input_mode(InputMode.FROM_PATH)
-    file_input_model.set_input_image_path(Path("fake_path"))
+    thresholding_model.set_input_mode(InputMode.FROM_PATH)
+    thresholding_model.set_input_image_path(Path("fake_path"))
     thresholding_view: ThresholdingView = ThresholdingView(
         main_model,
         thresholding_model,
-        file_input_model,
         experiments_model,
         viewer,
     )
@@ -218,13 +209,11 @@ def test_check_able_to_threshold_no_input_dir(
     thresholding_model: ThresholdingModel = ThresholdingModel()
     thresholding_model.set_threshold_enabled(True)
     thresholding_model.set_thresholding_value(100)
-    file_input_model: FileInputModel = FileInputModel()
-    file_input_model.set_input_mode(InputMode.FROM_PATH)
-    file_input_model.set_output_directory(Path("fake_path"))
+    thresholding_model.set_input_mode(InputMode.FROM_PATH)
+    thresholding_model.set_output_directory(Path("fake_path"))
     thresholding_view: ThresholdingView = ThresholdingView(
         main_model,
         thresholding_model,
-        file_input_model,
         experiments_model,
         viewer,
     )
@@ -238,13 +227,11 @@ def test_check_able_to_threshold_no_input_method(
     thresholding_model: ThresholdingModel = ThresholdingModel()
     thresholding_model.set_threshold_enabled(True)
     thresholding_model.set_thresholding_value(100)
-    file_input_model: FileInputModel = FileInputModel()
-    file_input_model.set_input_image_path(Path("fake_path"))
-    file_input_model.set_output_directory(Path("fake_path"))
+    thresholding_model.set_input_image_path(Path("fake_path"))
+    thresholding_model.set_output_directory(Path("fake_path"))
     thresholding_view: ThresholdingView = ThresholdingView(
         main_model,
         thresholding_model,
-        file_input_model,
         experiments_model,
         viewer,
     )

src/allencell_ml_segmenter/core/event.py

@@ -65,9 +65,9 @@ class Event(Enum):
     ACTION_CURATION_RAW_THREAD_ERROR = "curation_raw_thread_error"
 
     # Thresholding events
-    ACTION_THRESHOLDING_VALUE_CHANGED = "thresholding_value_changed"
-    ACTION_THRESHOLDING_AUTOTHRESHOLDING_SELECTED = "autothresholding_selected"
+    ACTION_EXECUTE_THRESHOLDING = "execute_thresholding"
     ACTION_SAVE_THRESHOLDING_IMAGES = "save_thresholding_images"
+    ACTION_THRESHOLDING_DISABLED = "disable_thresholding"
 
     # View selection events. These can stem from a user action, or from a process (i.e. prediction process ends, and a new view is shown automatically).
     VIEW_SELECTION_TRAINING = "training_selected"

src/allencell_ml_segmenter/core/file_input_model.py

@@ -93,9 +93,3 @@ def get_input_files_as_list(self) -> List[Path]:
             if selected_paths is not None:
                 return selected_paths
         return []
-
-    def set_selected_idx(self, selected_idx: list[int]) -> None:
-        self._selected_idx = selected_idx
-
-    def get_selected_idx(self) -> Optional[list[int]]:
-        return self._selected_idx

src/allencell_ml_segmenter/core/file_input_widget.py

@@ -50,14 +50,14 @@ def __init__(
         self,
         model: FileInputModel,
         viewer: IViewer,
-        service: ModelFileService,
+        service: Optional[ModelFileService],
         include_channel_selection: bool = True,
     ):
         super().__init__()
         self._include_channel_selection: bool = include_channel_selection
         self._model: FileInputModel = model
         self._viewer: IViewer = viewer
-        self._service: ModelFileService = service
+        self._service: Optional[ModelFileService] = service
         layout: QVBoxLayout = QVBoxLayout()
         self.setLayout(layout)
         layout.setContentsMargins(0, 0, 0, 0)
@@ -271,7 +271,8 @@ def _process_checked_signal(self, row: int, state: Qt.CheckState) -> None:
                     len(selected_indices) == 0
                     and self._include_channel_selection
                 ):
-                    self._service.stop_channel_extraction()  # stop so combobox doesn't reset after thread is finished
+                    if self._service:
+                        self._service.stop_channel_extraction()  # stop so combobox doesn't reset after thread is finished
                     self._reset_channel_combobox()
 
                 self._model.set_selected_paths(