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Prototype for annotator plugins (#304)
Refactor napari functionality into napari plugins.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,22 @@ | ||
| import numpy as np | ||
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| def check_layer_initialization(viewer, expected_shape): | ||
| """Utility function to check the initial layer setup is correct.""" | ||
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| assert len(viewer.layers) == 6 | ||
| expected_layer_names = [ | ||
| "image", "auto_segmentation", "committed_objects", "current_object", "point_prompts", "prompts" | ||
| ] | ||
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| for layer_name in expected_layer_names: | ||
| assert layer_name in viewer.layers | ||
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| # 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) | ||
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| # Check segmentation layers. | ||
| for layer_name in ["auto_segmentation", "committed_objects", "current_object"]: | ||
| assert viewer.layers[layer_name].data.shape == expected_shape | ||
| np.testing.assert_equal(viewer.layers[layer_name].data, 0) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,160 @@ | ||
| import numpy as np | ||
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| from magicgui.widgets import Container, Widget | ||
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| from . import _widgets as widgets | ||
| from . import util as vutil | ||
| from ._state import AnnotatorState | ||
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| from typing import TYPE_CHECKING, Optional | ||
| if TYPE_CHECKING: | ||
| import napari | ||
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| class _AnnotatorBase(Container): | ||
| """Base class for micro_sam annotation plugins. | ||
| Implements the logic for the 2d, 3d and tracking annotator. | ||
| The annotators differ in their data dimensionality and the widgets. | ||
| """ | ||
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| def _create_layers(self, segmentation_result): | ||
| # Add the point layer for point prompts. | ||
| self._point_labels = ["positive", "negative"] | ||
| self._point_prompt_layer = self._viewer.add_points( | ||
| name="point_prompts", | ||
| property_choices={"label": self._point_labels}, | ||
| edge_color="label", | ||
| edge_color_cycle=vutil.LABEL_COLOR_CYCLE, | ||
| symbol="o", | ||
| face_color="transparent", | ||
| edge_width=0.5, | ||
| size=12, | ||
| ndim=self._ndim, | ||
| ) | ||
| self._point_prompt_layer.edge_color_mode = "cycle" | ||
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| # 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", ndim=self._ndim, | ||
| ) | ||
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| # Add the label layers for the current object, the automatic segmentation and the committed segmentation. | ||
| dummy_data = np.zeros(self._shape, dtype="uint32") | ||
| self._viewer.add_labels(data=dummy_data, name="current_object") | ||
| self._viewer.add_labels(data=dummy_data, name="auto_segmentation") | ||
| self._viewer.add_labels( | ||
| data=dummy_data if segmentation_result is None else segmentation_result, name="committed_objects" | ||
| ) | ||
| # Randomize colors so it is easy to see when object committed. | ||
| self._viewer.layers["committed_objects"].new_colormap() | ||
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| def _create_widgets(self, segment_widget, segment_nd_widget, autosegment_widget, commit_widget, clear_widget): | ||
| self._embedding_widget = widgets.embedding_widget() | ||
| # Connect the call button of the embedding widget with a function | ||
| # that updates all relevant layers when the image changes. | ||
| self._embedding_widget.call_button.changed.connect(self._update_image) | ||
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| self._prompt_widget = widgets.create_prompt_menu(self._point_prompt_layer, self._point_labels) | ||
| self._segment_widget = segment_widget() | ||
| widget_list = [self._embedding_widget, self._prompt_widget, self._segment_widget] | ||
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| if segment_nd_widget is not None: | ||
| self._segment_nd_widget = segment_nd_widget() | ||
| widget_list.append(self._segment_nd_widget) | ||
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| if autosegment_widget is not None: | ||
| self._autosegment_widget = autosegment_widget() | ||
| widget_list.append(self._autosegment_widget) | ||
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| self._commit_widget = commit_widget() | ||
| self._clear_widget = clear_widget() | ||
| widget_list.extend([self._commit_widget, self._clear_widget]) | ||
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| # Add the widgets to the container. | ||
| self.extend(widget_list) | ||
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| def _create_keybindings(self): | ||
| @self._viewer.bind_key("s") | ||
| def _segment(viewer): | ||
| self._segment_widget(viewer) | ||
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| @self._viewer.bind_key("c") | ||
| def _commit(viewer): | ||
| self._commit_widget(viewer) | ||
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| @self._viewer.bind_key("t") | ||
| def _toggle_label(event=None): | ||
| vutil.toggle_label(self._point_prompt_layer) | ||
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| @self._viewer.bind_key("Shift-C") | ||
| def _clear_annotations(viewer): | ||
| self._clear_widget(viewer) | ||
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| if hasattr(self, "_segment_nd_widget"): | ||
| @self._viewer.bind_key("Shift-S") | ||
| def _seg_nd(viewer): | ||
| self._segment_nd_widget(viewer) | ||
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| # TODO | ||
| # We could implement a better way of initializing the segmentation result, | ||
| # so that instead of just passing a numpy array an existing layer from the napari | ||
| # viewer can be chosen. | ||
| # See https://github.com/computational-cell-analytics/micro-sam/issues/335 | ||
| def __init__( | ||
| self, | ||
| viewer: "napari.viewer.Viewer", | ||
| ndim: int, | ||
| segment_widget: Widget, | ||
| segment_nd_widget: Optional[Widget] = None, | ||
| autosegment_widget: Optional[Widget] = None, | ||
| commit_widget: Widget = widgets.commit_segmentation_widget, | ||
| clear_widget: Widget = widgets.clear_widget, | ||
| segmentation_result: Optional[np.ndarray] = None, | ||
| ) -> None: | ||
| """ | ||
| Args: | ||
| viewer: | ||
| ndim: | ||
| segment_widget: | ||
| segment_nd_widget: | ||
| autosegment_widget: | ||
| commit_widget: | ||
| clear_widget: | ||
| segmentation_result: | ||
| """ | ||
| super().__init__() | ||
| self._viewer = viewer | ||
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| # Add the layers for prompts and segmented obejcts. | ||
| # We initialize these 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(segmentation_result) | ||
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| # Add the widgets in common between all annotators. | ||
| self._create_widgets( | ||
| segment_widget, segment_nd_widget, autosegment_widget, commit_widget, clear_widget, | ||
| ) | ||
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| # Add the key bindings in common between all annotators. | ||
| self._create_keybindings() | ||
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| def _update_image(self): | ||
| state = AnnotatorState() | ||
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| # Update the image shape if it has changed. | ||
| if state.image_shape != self._shape: | ||
| if len(state.image_shape) != self._ndim: | ||
| raise RuntimeError( | ||
| f"The dim of the annotator {self._ndim} does not match the image data of shape {state.image_shape}." | ||
| ) | ||
| self._shape = state.image_shape | ||
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| # Reset all layers. | ||
| self._viewer.layers["current_object"].data = np.zeros(self._shape, dtype="uint32") | ||
| self._viewer.layers["committed_objects"].data = np.zeros(self._shape, dtype="uint32") | ||
| self._viewer.layers["auto_segmentation"].data = np.zeros(self._shape, dtype="uint32") | ||
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| vutil.clear_annotations(self._viewer, clear_segmentations=False) |
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