Merge pull request #49 from fractal-napari-plugins-collection/41_napa…

…ri_050_support

Add napari 0.5.0 support & update prediction layer display

.github/workflows/test_and_deploy.yaml

@@ -23,7 +23,7 @@ jobs:
     strategy:
       matrix:
         platform: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: ['3.9', '3.10']
+        python-version: ['3.9', '3.10', '3.11']
 
     steps:
       - uses: actions/checkout@v3

README.md

@@ -12,7 +12,7 @@ An interactive classifier plugin that allows the user to assign objects in a lab
 ## Usage
 <p align="center"><img src="https://github.com/fractal-napari-plugins-collection/napari-feature-classifier/assets/18033446/1ebf0890-1a7b-4e4b-a21c-88ca8f1dd800" /></p>
 
-To use the napari-feature-classifier, you need to have a label image and corresponding measurements: as a csv file, loaded to layer.features or in an [OME-Zarr Anndata table loaded with another plugin](https://github.com/jluethi/napari-ome-zarr-roi-loader). Your feature measurements need to contain a `label` column that matches the label objects in the label image.
+To use the napari-feature-classifier, you need to have a label image and corresponding measurements: as a csv file, loaded to layer.features or in an [OME-Zarr Anndata table loaded with another plugin](https://github.com/fractal-napari-plugins-collection/napari-ome-zarr-navigator). Your feature measurements need to contain a `label` column that matches the label objects in the label image.
 These interactive classification workflows are well suited to visually define cell types, find mitotic cells in images, do quality control by automatically detecting missegmented cells and other tasks where a user can easily assign objects to groups.
 
 #### Prepare the label layer:

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = napari-feature-classifier
-version = 0.1.2
+version = 0.2.0
 author = Joel Luethi and Max Hess
 author_email = [email protected]
 url = https://github.com/fractal-napari-plugins-collection/napari-feature-classifier
@@ -37,7 +37,7 @@ package_dir =
 # add your package requirements here
 install_requires =
     numpy < 2.0
-    napari < 0.4.19
+    napari
     matplotlib
     magicgui
     pandas

src/napari_feature_classifier/__init__.py

@@ -1,2 +1,2 @@
 """Init"""
-__version__ = "0.1.2"
+__version__ = "0.2.0"

src/napari_feature_classifier/annotator_widget.py

@@ -2,6 +2,7 @@
 import warnings
 from enum import Enum
 from functools import partial
+from packaging import version
 from pathlib import Path
 from typing import Optional, Sequence, cast
 
@@ -24,7 +25,8 @@
 # pylint: disable=R0801
 from napari_feature_classifier.utils import (
     get_colormap,
-    reset_display_colormaps,
+    reset_display_colormaps_legacy,
+    reset_display_colormaps_modern,
     get_valid_label_layers,
     get_selected_or_valid_label_layer,
     napari_info,
@@ -245,7 +247,11 @@ def toggle_label(self, labels_layer, event):
             ] = np.NaN
 
         # Update only the single color value that changed
-        self.update_single_color(labels_layer, label)
+        napari_version = version.parse(napari.__version__)
+        if napari_version >= version.parse("0.4.19"):
+            self.update_single_color_slow(labels_layer, label)
+        else:
+            self.update_single_color_legacy(labels_layer, label)
 
     def set_class_n(self, event, n: int):  # pylint: disable=C0103
         self._class_selector.value = self.ClassSelection[
@@ -276,13 +282,23 @@ def _init_annotation(self, label_layer: napari.layers.Labels):
         self._annotations_layer.scale = label_layer.scale
         self._annotations_layer.translate = label_layer.translate
 
-        reset_display_colormaps(
-            label_layer,
-            feature_col="annotations",
-            display_layer=self._annotations_layer,
-            label_column=self._label_column,
-            cmap=self.cmap,
-        )
+        napari_version = version.parse(napari.__version__)
+        if napari_version >= version.parse("0.4.19"):
+            reset_display_colormaps_modern(
+                label_layer,
+                feature_col="annotations",
+                display_layer=self._annotations_layer,
+                label_column=self._label_column,
+                cmap=self.cmap,
+            )
+        else:
+            reset_display_colormaps_legacy(
+                label_layer,
+                feature_col="annotations",
+                display_layer=self._annotations_layer,
+                label_column=self._label_column,
+                cmap=self.cmap,
+            )
         label_layer.mouse_drag_callbacks.append(self.toggle_label)
 
         # keybindings for the available classes (0 = deselect)
@@ -300,7 +316,7 @@ def _update_save_destination(self, label_layer: napari.layers.Labels):
         base_path = Path(self._save_destination.value).parent
         self._save_destination.value = base_path / f"{label_layer.name}_annotation.csv"
 
-    def update_single_color(self, label_layer, label):
+    def update_single_color_legacy(self, label_layer, label):
         """
         Update the color of a single object in the annotations layer.
         """
@@ -317,6 +333,33 @@ def update_single_color(self, label_layer, label):
         self._annotations_layer.opacity = 1.0
         self._annotations_layer.color_mode = "direct"
 
+    def update_single_color_slow(self, label_layer, label):
+        """
+        Update the color of a single object in the annotations layer.
+
+        napari >= 0.4.19 does not have a direct API to only update a single
+        color. It always validates & updates the whole colormap.
+        Therefore, this update mode scales badly with the number of unique
+        labels.
+        See details in https://github.com/napari/napari/issues/6732
+
+        """
+        color = self.cmap(
+            float(
+                label_layer.features.loc[
+                    label_layer.features[self._label_column] == label,
+                    "annotations",
+                ].iloc[0]
+            )
+            / len(self.cmap.colors)
+        )
+        from napari.utils.colormaps import DirectLabelColormap
+
+        colordict = self._annotations_layer.colormap.color_dict
+        colordict[label] = color
+        self._annotations_layer.colormap = DirectLabelColormap(color_dict=colordict)
+        self._annotations_layer.opacity = 1.0
+
     def _on_save_clicked(self):
         """
         Save annotations to a csv file.

src/napari_feature_classifier/classifier_widget.py

@@ -2,6 +2,7 @@
 import logging
 import pickle
 
+from packaging import version
 from pathlib import Path
 from typing import Optional
 
@@ -27,7 +28,8 @@
 from napari_feature_classifier.classifier import Classifier
 from napari_feature_classifier.utils import (
     get_colormap,
-    reset_display_colormaps,
+    reset_display_colormaps_modern,
+    reset_display_colormaps_legacy,
     get_valid_label_layers,
     get_selected_or_valid_label_layer,
     napari_info,
@@ -256,6 +258,7 @@ def __init__(
             name="Predictions",
             translate=self._last_selected_label_layer.translate,
         )
+        self._prediction_layer.contour = 2
 
         # Set the label selection to a valid label layer => Running into proxy bug
         self._viewer.layers.selection.active = self._last_selected_label_layer
@@ -294,9 +297,9 @@ def __init__(
         self._init_prediction_layer(self._last_selected_label_layer)
         # Whenever the label layer is clicked, hide the prediction layer
         # (e.g. new annotations are made)
-        self._last_selected_label_layer.mouse_drag_callbacks.append(
-            self.hide_prediction_layer
-        )
+        # self._last_selected_label_layer.mouse_drag_callbacks.append(
+        #     self.hide_prediction_layer
+        # )
 
     def run(self):
         """
@@ -396,9 +399,9 @@ def selection_changed(self):
             ):
                 self._last_selected_label_layer = self._viewer.layers.selection.active
                 self._init_prediction_layer(self._viewer.layers.selection.active)
-                self._last_selected_label_layer.mouse_drag_callbacks.append(
-                    self.hide_prediction_layer
-                )
+                # self._last_selected_label_layer.mouse_drag_callbacks.append(
+                #     self.hide_prediction_layer
+                # )
                 self._update_export_destination(self._last_selected_label_layer)
 
     def _init_prediction_layer(self, label_layer: napari.layers.Labels):
@@ -427,19 +430,29 @@ def _init_prediction_layer(self, label_layer: napari.layers.Labels):
         self._prediction_layer.translate = label_layer.translate
 
         # Update the colormap of the prediction layer
-        reset_display_colormaps(
-            label_layer,
-            feature_col="prediction",
-            display_layer=self._prediction_layer,
-            label_column=self._label_column,
-            cmap=get_colormap(),
-        )
+        napari_version = version.parse(napari.__version__)
+        if napari_version >= version.parse("0.4.19"):
+            reset_display_colormaps_modern(
+                label_layer,
+                feature_col="prediction",
+                display_layer=self._prediction_layer,
+                label_column=self._label_column,
+                cmap=get_colormap(),
+            )
+        else:
+            reset_display_colormaps_legacy(
+                label_layer,
+                feature_col="prediction",
+                display_layer=self._prediction_layer,
+                label_column=self._label_column,
+                cmap=get_colormap(),
+            )
 
-    def hide_prediction_layer(self, labels_layer, event):
-        """
-        Hide the prediction layer
-        """
-        self._prediction_layer.visible = False
+    # def hide_prediction_layer(self, labels_layer, event):
+    #     """
+    #     Hide the prediction layer
+    #     """
+    #     self._prediction_layer.visible = False
 
     def get_relevant_label_layers(self):
         relevant_label_layers = []

src/napari_feature_classifier/utils.py

@@ -70,7 +70,7 @@ def get_colormap(matplotlib_colormap="Set1"):
     return cmap
 
 
-def reset_display_colormaps(
+def reset_display_colormaps_legacy(
     label_layer, feature_col, display_layer, label_column, cmap
 ):
     """
@@ -85,25 +85,25 @@ def reset_display_colormaps(
     display_layer.color_mode = "direct"
 
 
-#     # Check if it runs in napari
-#     # This currently triggers an exception.
-#     # Find a new way to ensure the warning is also shown in the napari
-#     # interface    # if _ipython_has_eventloop():
-#     NapariQtNotification(message, 'INFO').show()
-
-
-# def napari_warn(message):
-#     # Wrapper function to ensure a message o
-#     warnings.warn(message)
-#     show_info(message)
-#     print('test')
-#     # This currently triggers an exception.
-#     # Find a new way to ensure the warning is also shown in the napari
-#     # interface
-#     if _ipython_has_eventloop():
-#         pass
-#         # NapariQtNotification(message, 'WARNING').show()
-#
+def reset_display_colormaps_modern(
+    label_layer, feature_col, display_layer, label_column, cmap
+):
+    """
+    Reset the colormap based on the annotations in
+    label_layer.features['annotation'] and sends the updated colormap
+    to the annotation label layer
+
+    Modern version to support napari >= 0.4.19
+    """
+    from napari.utils.colormaps import DirectLabelColormap
+
+    colors = cmap(label_layer.features[feature_col].astype(float) / len(cmap.colors))
+    colordict = dict(zip(label_layer.features[label_column], colors))
+    colordict[None] = [0, 0, 0, 0]
+    display_layer.colormap = DirectLabelColormap(color_dict=colordict)
+    display_layer.opacity = 1.0
+
+
 def napari_info(message):
     """
     Info message wrapper.

tox.ini

@@ -1,13 +1,13 @@
 # For more information about tox, see https://tox.readthedocs.io/en/latest/
 [tox]
-envlist = py{38,39,310}-{linux,macos,windows}
+envlist = py{39,310,311}-{linux,macos,windows}
 isolated_build=true
 
 [gh-actions]
 python =
-    3.8: py38
     3.9: py39
     3.10: py310
+    3.11: py311
 
 [gh-actions:env]
 PLATFORM =