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Small API changes for training (#70)
* Add optional train/val split for supervised models * Update worker_training.py * Change name to WNet3D * Update worker_training.py * Replace CellSeg3d with CellSeg3D * Update test_training.py * Update test_training.py * Fix missing Wnet3d + fix tests (utils)
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.napari/DESCRIPTION.md

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## Getting Help
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If you would like to report an issue with the plugin,
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please open an [issue on Github](https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues)
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please open an [issue on Github](https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues)
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<!--
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This section should point users to your preferred support tools, whether this be raising
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an issue on GitHub, asking a question on image.sc, or using some other method of contact.

.napari/config.yml

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# note : remember to add napari-hub preview app to repo
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project_urls:
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Bug Tracker: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues
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Documentation: https://adaptivemotorcontrollab.github.io/CellSeg3d/res/welcome.html
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Source Code: https://github.com/AdaptiveMotorControlLab/CellSeg3d
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Project Site: https://github.com/AdaptiveMotorControlLab/CellSeg3d
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User Support: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues
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Bug Tracker: https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues
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Documentation: https://adaptivemotorcontrollab.github.io/CellSeg3D/res/welcome.html
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Source Code: https://github.com/AdaptiveMotorControlLab/CellSeg3D
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Project Site: https://github.com/AdaptiveMotorControlLab/CellSeg3D
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User Support: https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues
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# Add labels from the EDAM Bioimaging ontology
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labels:

README.md

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[![PyPI](https://img.shields.io/pypi/v/napari-cellseg3d.svg?color=green)](https://pypi.org/project/napari-cellseg3d)
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[![Downloads](https://static.pepy.tech/badge/napari-cellseg3d)](https://pepy.tech/project/napari-cellseg3d)
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[![Downloads](https://static.pepy.tech/badge/napari-cellseg3d/month)](https://pepy.tech/project/napari-cellseg3d)
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[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/AdaptiveMotorControlLab/CellSeg3d/raw/main/LICENSE)
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[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/AdaptiveMotorControlLab/CellSeg3D/raw/main/LICENSE)
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[![Python Version](https://img.shields.io/pypi/pyversions/napari-cellseg-annotator.svg?color=green)](https://python.org)
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[![codecov](https://codecov.io/gh/AdaptiveMotorControlLab/CellSeg3d/branch/main/graph/badge.svg?token=hzUcn3XN8F)](https://codecov.io/gh/AdaptiveMotorControlLab/CellSeg3d)
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[![codecov](https://codecov.io/gh/AdaptiveMotorControlLab/CellSeg3D/branch/main/graph/badge.svg?token=hzUcn3XN8F)](https://codecov.io/gh/AdaptiveMotorControlLab/CellSeg3D)
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[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-cellseg3d)](https://www.napari-hub.org/plugins/napari-cellseg3d)
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- A napari plugin for 3D cell segmentation: training, inference, and data review. In particular, this project was developed for analysis of mesoSPIM-acquired (cleared tissue + lightsheet) datasets.
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## Documentation
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📚 A lot of documentation is available at https://AdaptiveMotorControlLab.github.io/CellSeg3d
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📚 A lot of documentation is available at https://AdaptiveMotorControlLab.github.io/CellSeg3D
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You can also generate docs by running ``make html`` in the docs/ folder.
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conda env create -f conda/CellSeg3D_ARM64.yml
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conda activate napari_CellSeg3D_ARM64
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2) Install a Qt backend (PySide or PyQt5)
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3) Launch napari, the plugin should be available in the plugins menu.
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[napari-hub]: https://www.napari-hub.org/plugins/napari-cellseg3d
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[file an issue]: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues
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[file an issue]: https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues
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[napari]: https://github.com/napari/napari
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[Cookiecutter]: https://github.com/audreyr/cookiecutter
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[@napari]: https://github.com/napari
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[tox]: https://tox.readthedocs.io/en/latest/
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[pip]: https://pypi.org/project/pip/
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[PyPI]: https://pypi.org/
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[Installation page]: https://adaptivemotorcontrollab.github.io/CellSeg3d/source/guides/installation_guide.html
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[Installation page]: https://adaptivemotorcontrollab.github.io/CellSeg3D/source/guides/installation_guide.html
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[the PyTorch website for installation instructions]: https://pytorch.org/get-started/locally/
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[PyTorch]: https://pytorch.org/get-started/locally/
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[MONAI's optional dependencies]: https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies

docs/_config.yml

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# Information about where the book exists on the web
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repository:
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url: https://github.com/AdaptiveMotorControlLab/CellSeg3d # Online location of your book
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url: https://github.com/AdaptiveMotorControlLab/CellSeg3D # Online location of your book
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path_to_book: docs # Optional path to your book, relative to the repository root
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branch: main # Which branch of the repository should be used when creating links (optional)
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docs/source/guides/custom_model_template.rst

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.. note::
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**WIP** : Currently you must modify :doc:`model_framework.py <../code/_autosummary/napari_cellseg3d.code_models.model_framework>` as well : import your model class and add it to the ``model_dict`` attribute
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.. _file an issue: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues
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.. _file an issue: https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues

docs/source/guides/detailed_walkthrough.rst

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.. note::
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Too plot your results, check out the `provided notebooks`_
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.. _provided notebooks: https://github.com/AdaptiveMotorControlLab/CellSeg3d/tree/main/notebooks
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.. _provided notebooks: https://github.com/AdaptiveMotorControlLab/CellSeg3D/tree/main/notebooks
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You can then launch inference and the results will be saved in your specified folder.
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Example of the plot present in the notebooks.
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Coordinates are based on centroids, the size represents the volume, the color, and the sphericity.
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.. _notebooks folder of the repository: https://github.com/AdaptiveMotorControlLab/CellSeg3d/tree/main/notebooks
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.. _notebooks folder of the repository: https://github.com/AdaptiveMotorControlLab/CellSeg3D/tree/main/notebooks
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With this complete, you can repeat the workflow as needed.

docs/source/guides/installation_guide.rst

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.. warning::
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If you encounter any issues during installation, feel free to open an issue on our `GitHub repository`_.
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.. _GitHub repository: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues
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.. _GitHub repository: https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues
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Installing pre-requisites
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.. _miniconda3: https://docs.conda.io/projects/conda/en/latest/user-guide/install/macos.html
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1. **Clone the repository** (`link <https://github.com/AdaptiveMotorControlLab/CellSeg3d>`_):
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1. **Clone the repository** (`link <https://github.com/AdaptiveMotorControlLab/CellSeg3D>`_):
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.. code-block::
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git clone https://github.com/AdaptiveMotorControlLab/CellSeg3d.git
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git clone https://github.com/AdaptiveMotorControlLab/CellSeg3D.git
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In the terminal, navigate to the CellSeg3D folder:

docs/source/guides/training_wnet.rst

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.. _WNet, A Deep Model for Fully Unsupervised Image Segmentation: https://arxiv.org/abs/1711.08506
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.. _open an issue: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues
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.. _open an issue: https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues

examples/README.md

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You can install, launch `napari`, activate the CellSeg3D plugin app, and drag & drop this volume into the canvas.
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See [CellSeg3D documentation](https://adaptivemotorcontrollab.github.io/CellSeg3d/welcome.html) for more details.
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See [CellSeg3D documentation](https://adaptivemotorcontrollab.github.io/CellSeg3D/welcome.html) for more details.
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napari_cellseg3d/_tests/test_plugin_inference.py

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widget.model_choice.setCurrentText("WNet")
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widget.model_choice.setCurrentText("WNet3D")
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widget._restrict_window_size_for_model()
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assert widget.use_window_choice.isChecked()
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assert widget.window_size_choice.currentText() == "64"

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