{"name":"napari-feature-classifier","display_name":"napari feature classifier","visibility":"public","icon":"","categories":[],"schema_version":"0.2.1","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"napari-feature-classifier.annotator_init_widget","title":"Annotator","python_name":"napari_feature_classifier.annotator_init_widget:InitializeLabelAnnotatorWidget","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-feature-classifier.initialize_classifier_new","title":"Initialize a Classifier","python_name":"napari_feature_classifier.classifier_widget:ClassifierWidget","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-feature-classifier.load_classifier","title":"Load a Classifier","python_name":"napari_feature_classifier.classifier_widget:LoadClassifierContainer","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-feature-classifier.load_csv_features","title":"CSV Feature loader","python_name":"napari_feature_classifier.feature_loader_widget:load_features_factory","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":null,"writers":null,"widgets":[{"command":"napari-feature-classifier.annotator_init_widget","display_name":"Annotator","autogenerate":false},{"command":"napari-feature-classifier.initialize_classifier_new","display_name":"Initialize a Classifier","autogenerate":false},{"command":"napari-feature-classifier.load_classifier","display_name":"Load Classifier","autogenerate":false},{"command":"napari-feature-classifier.load_csv_features","display_name":"CSV Feature loader","autogenerate":false}],"sample_data":null,"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.4","name":"napari-feature-classifier","version":"0.4.2","dynamic":null,"platform":null,"supported_platform":null,"summary":"An interactive classifier plugin to use with label images and feature measurements","description":"# napari-feature-classifier\n\n[](https://github.com/fractal-napari-plugins-collection/napari-feature-classifier/raw/main/LICENSE)\n[](https://pypi.org/project/napari-feature-classifier)\n[](https://python.org)\n[](https://github.com/fractal-napari-plugins-collection/napari-feature-classifier/actions)\n[](https://codecov.io/gh/fractal-napari-plugins-collection/napari-feature-classifier)\n[](https://napari-hub.org/plugins/napari-feature-classifier)\n\nAn interactive classifier plugin for napari that lets you annotate objects in a label image and train a random forest classifier to generalize those annotations across all objects — without leaving the viewer.\n\n\n\n## When to use this\n\nThis plugin is designed for tasks where visual inspection defines the ground truth:\n- Classifying cell types (e.g. mitotic vs. interphase cells)\n- Quality control (flagging mis-segmented objects)\n- Any labeling task where you can recognize the classes by eye but want to scale it to thousands of objects automatically\n\nYou need: a **label image** and a **feature table** — measurements per object (area, intensity, shape descriptors, etc.) stored in `layer.features`. The classifier learns from your manual annotations and applies those patterns to every object.\n\n## Usage\n\n### 1. Prepare your label layer\n\nLoad your label image into napari and attach feature measurements to `layer.features` of that layer. You can have multiple label layers open at once — the classifier handles them all.\n\nYour feature table must have:\n- A `label` column matching the integer labels in the image\n- A `roi_id` column identifying which image each row belongs to (used when training on multiple images)\n\n**Ways to load features:**\n- From an OME-Zarr file: use [napari-ome-zarr-navigator](https://github.com/fractal-napari-plugins-collection/napari-ome-zarr-navigator), which handles correct loading of both the label image and the feature table and populates the `roi_id` column automatically.\n- From a CSV file: `Plugins → napari-feature-classifier → CSV Feature Loader`, then select the label layer and point to the CSV.\n- Programmatically: `label_layer.features = your_dataframe`\n\n### 2. Initialize a classifier\n\nGo to `Plugins → napari-feature-classifier → Initialize a Classifier`.\n\n![\"classifier_init\"](\"https://github.com/user-attachments/assets/05a2495a-b7fd-40be-bd9c-607177c0aa68\")
\n\n\n- Select the features to use for training. Hold Cmd/Ctrl to select multiple. The feature list reflects the currently selected label layer.\n- Name your classes (e.g. \"Mitotic\", \"Interphase\"). Classes without a name won't be created.\n- Click **Initialize**.\n\n> Feature selection is fixed after initialization. If you want different features, start a new classifier.\n\n### 3. Annotate and train\n\n![\"classifier_annotation\"](\"https://github.com/user-attachments/assets/25a9c90a-0d58-4fc2-92df-9006cdae00aa\")
\n\n\n- Select your label layer in the napari layer list.\n- Pick a class using the panel buttons or keyboard shortcuts (keys **1–9** for classes, **0** to deselect).\n- Click on label objects in the viewer to annotate them.\n- The live count display shows how many objects you've annotated per class across all open images.\n- Once you have at least a handful of examples per class (aim for 10+), click **Run Classifier**.\n\nThe classifier splits your annotations 80/20 into training and test sets, trains a random forest, and applies it to all objects. Predictions appear as a color-coded **Predictions** layer.\n\n![\"classifier-predict\"](\"https://github.com/user-attachments/assets/273d696f-669f-4d01-88e4-e74e204a066d\")
\n\nCorrect mistakes the classifier made and click Run Classifier again to improve it. Iterative annotation is the intended workflow.\n\n### 4. Save and reload\n\nAfter each run, the classifier auto-saves to a `.clf` file named after the label layer (in the current working directory). To save to a different location: expand **▶ Saving & Export** and click **Save Classifier As…**.\n\nTo resume work or apply a trained classifier to new images:\n`Plugins → napari-feature-classifier → Load Classifier`\n\nSelect the `.clf` file, make sure your label layers with features are already open, and click **Load Classifier**. \n\n### 5. Export results\n\nExpand **▶ Saving & Export** and click **Export Results As…** to save predictions for the currently selected layer as a CSV file.\n\nThe exported CSV contains:\n- `label` — integer object ID\n- `prediction` — classifier prediction (1–N for each class; NaN for objects with missing features)\n- `annotations` — your manual annotations (NaN = not annotated, −1 = explicitly deselected, 1–N = class)\n- One column per annotation class name\n\n![\"classifier-save\"](\"https://github.com/user-attachments/assets/62236f0d-7cc5-4760-8158-efe65e52109a\")
\n\n\n### 6. Standalone annotator\n\nYou can use the annotation tool independently from the classifier:\n`Plugins → napari-feature-classifier → Annotator`\n\nName up to 9 classes, click **Initialize**, then annotate as above. Annotations are stored in `layer.features[\"annotations\"]` and can be saved to CSV via **▶ Save Annotations**.\n\n![\"annotator\"](\"https://github.com/user-attachments/assets/05c3bebc-3d9c-4a78-bfde-b821e8f565cc\")
\n\n### Batch / scripted use\n\nClassifiers can be applied programmatically without the napari UI. See [examples/simple_classifier_example.ipynb](examples/simple_classifier_example.ipynb) for a worked example.\n\n## Installation\n\nRequires Python ≥ 3.10 and napari ≥ 0.6.0.\n\nWe recommend installing into a dedicated environment to avoid dependency conflicts:\n\n```bash\n# With conda\nconda create -n napari-feature-classifier -c conda-forge napari python=3.12 -y\nconda activate napari-feature-classifier\npip install napari-feature-classifier\n```\n\nOr with [pixi](https://pixi.sh):\n\n```bash\npixi init my-project\npixi add napari napari-feature-classifier\npixi run napari\n```\n\nOr into an existing environment:\n\n```bash\npip install napari-feature-classifier\n```\n\n## Similar napari plugins\n\n- [napari-convpaint](https://github.com/guiwitz/napari-convpaint) — deep feature-based pixel and object classifier by Guillaume Witz, @quasar1357 & others\n- [napari-accelerated-pixel-and-object-classification (APOC)](https://github.com/haesleinhuepf/napari-accelerated-pixel-and-object-classification) — pixel and object classifier by Robert Haase\n- [napari-svetlana](https://www.napari-hub.org/plugins/napari-svetlana) — deep learning based classifier by Clément Cazorla\n\n## Release process\n\n1. Tag a release on GitHub with the new version number (e.g. `v0.3.2`). The version is set automatically from the git tag via `hatch-vcs`.\n2. Once CI tests pass, the package is automatically deployed to PyPI.\n3. A conda-forge PR will be opened automatically within 1–2 days — review and merge it at [napari-feature-classifier-feedstock](https://github.com/conda-forge/napari-feature-classifier-feedstock).\n\n## Contributing\n\nContributions are very welcome. Please open an issue to discuss significant changes before starting work.\n\n## License\n\nDistributed under the terms of the [BSD-3-Clause](LICENSE) license.\n\n## Issues\n\nIf you encounter any problems, please [file an issue](https://github.com/fractal-napari-plugins-collection/napari-feature-classifier/issues) along with a detailed description.\n\n## Contributors\n\n[Joel Lüthi](https://github.com/jluethi) & [Max Hess](https://github.com/MaksHess)\n","description_content_type":"text/markdown","keywords":null,"home_page":null,"download_url":null,"author":null,"author_email":"Joel Luethi and Max Hess ","maintainer":null,"maintainer_email":null,"license":"BSD-3-Clause","classifier":["Development Status :: 4 - Beta","Framework :: napari","Intended Audience :: Developers","License :: OSI Approved :: BSD License","Operating System :: OS Independent","Programming Language :: Python","Programming Language :: Python :: 3","Programming Language :: Python :: 3.10","Programming Language :: Python :: 3.11","Programming Language :: Python :: 3.12","Topic :: Software Development :: Testing"],"requires_dist":["magicgui","matplotlib","napari>=0.6.0","numpy","pandas<3.0.0,>=2.2.0","pandera","scikit-learn>=1.2.2","xxhash","hypothesis; extra == 'testing'","pytest; extra == 'testing'","pytest-cov; extra == 'testing'","pytest-qt; extra == 'testing'","qtpy; extra == 'testing'"],"requires_python":">=3.10","requires_external":null,"project_url":["Bug Tracker, https://github.com/fractal-napari-plugins-collection/napari-feature-classifier/issues","Documentation, https://github.com/fractal-napari-plugins-collection/napari-feature-classifier#napari-feature-classifier","Source Code, https://github.com/fractal-napari-plugins-collection/napari-feature-classifier","User Support, https://github.com/fractal-napari-plugins-collection/napari-feature-classifier/issues"],"provides_extra":["testing"],"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}