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Copy file name to clipboardExpand all lines: .github/workflows/test_and_deploy.yml
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## This workflows will upload a Python Package using Twine when a release is created
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## For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
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#
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# This workflows will upload a Python Package using Twine when a release is created
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# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
Copy file name to clipboardExpand all lines: .napari/DESCRIPTION.md
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<!-- This file is designed to provide you with a starting template for documenting
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<!---->
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<!--
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TODO : complete
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This file is designed to provide you with a starting template for documenting
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the functionality of your plugin. Its content will be rendered on your plugin's
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napari hub page.
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The sections below are given as a guide for the flow of information only, and
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are in no way prescriptive. You should feel free to merge, remove, add and
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rename sections at will to make this document work best for your plugin.
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-->
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## Description
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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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A detailed walk-through and description is available [on the documentation website](https://adaptivemotorcontrollab.github.io/cellseg3d-docs/res/welcome.html).
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<!--
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This should be a detailed description of the context of your plugin and its
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intended purpose.
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Here is an example of an mp4 video embedded this way.
This plugin requires basic knowledge in machine learning;
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all the concepts required for the parameters of the plugin are still covered and explained for their contextual use in the plugin.
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Currently, this plugin requires 3D volumes as .tif files, for review and cropping 2D stacks as .tif or .png are supported as well.
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Feel free to open an issue on Github if you'd like to discuss implementation of a specific file type !
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<!--
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This section should describe the target audience for this plugin (any knowledge,
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skills and experience required), as well as a description of the types of data
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supported by this plugin.
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If you know of researchers, groups or labs using your plugin, or if it has been cited
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anywhere, feel free to also include this information here.
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-->
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<!--
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## Quickstart
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This section should go through step-by-step examples of how your plugin should be used.
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quick overview of the plugin's functionality, but you should definitely link out to
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more complex and in-depth tutorials highlighting any intricacies of your plugin, and
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more detailed documentation if you have it.
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-->
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## Additional Install Steps
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## Additional Install Steps (uncommon)
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**Python >= 3.8 required**
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Requires manual installation of **pytorch** and **MONAI**.
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For Pytorch, please see [PyTorch's website for installation instructions](https://pytorch.org/get-started/locally/).
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A **CUDA-capable GPU** is not needed but very strongly recommended, especially for training.
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Simply follow the instructions on Pytorch's install page.
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If you get errors from MONAI regarding missing readers, please see [MONAI's optional dependencies](https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies) page for instructions on getting the readers required by your images.
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<!--
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We will be providing installation instructions on the hub, which will be sufficient
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for the majority of plugins. They will include instructions to pip install, and
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to install via napari itself.
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over in `setup.cfg`. However, if your plugin has any more complex dependencies, or
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requires any additional preparation before (or after) installation, you should add
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this information here.
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-->
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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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<!--
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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.
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If you distinguish between usage support and bug/feature support, you should state that
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here.
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-->
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## How to Cite
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<!--
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Many plugins may be used in the course of published (or publishable) research, as well as
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during conference talks and other public facing events. If you'd like to be cited in
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a particular format, or have a DOI you'd like used, you should provide that information here. -->
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a particular format, or have a DOI you'd like used, you should provide that information here.
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The developer has not yet provided a napari-hub specific description.
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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**Pre-Alpha version, please expect bugs and issues. Reporting them on the Github repository would help us a lot!**
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----------------------------------
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## Installation
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You can install `napari-cellseg-3d` via [pip]:
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You can install `napari-cellseg-3d` via [pip] (pypi-test placeholder):
Then go into Plugins > napari-cellseg-3d, and choose which tool to use.
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-**Review**: This module allows you to review your labels, from predictions or manual labeling, and correct them if needed. It then saves the status of each file in a csv, for easier monitoring.
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-**Infer**: This module allows you to use pre-trained segmentation algorithms on volumes to automatically label cells.
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-**Inference**: This module allows you to use pre-trained segmentation algorithms on volumes to automatically label cells and compute statistics.
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-**Train**: This module allows you to train segmentation algorithms from labeled volumes.
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-**Utilities**: This module allows you to perform several actions like cropping your volumes and labels dynamically, by selecting a fixed size volume and moving it around the image; computing prediction scores from ground truth and predicition labels; or converting labels from instance to segmentation and the opposite.
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## Requirements
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**Python >= 3.8 required**
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Requires manual installation of **pytorch** and **MONAI**.
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For Pytorch, please see [PyTorch's website for installation instructions].
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A CUDA-capable GPU is not needed but very strongly recommended, especially for training.
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If you get errors from MONAI regarding missing readers, please see [MONAI's optional dependencies] page for instructions on getting the readers required by your images.
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## Issues
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If you encounter any problems, please [file an issue] along with a detailed description.
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## Testing
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To run tests locally:
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## Contributing
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Contributions are very welcome.
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Please ensure the coverage at least stays the same before you submit a pull request.
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For local installation, please run:
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For local installation from Github cloning, please run:
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```
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pip install -e .
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```
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## License
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Distributed under the terms of the [MIT] license,
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"napari-cellseg-3d" is free and open source software
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Distributed under the terms of the [MIT] license.
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## Issues
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If you encounter any problems, please [file an issue] along with a detailed description.
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"napari-cellseg-3d" is free and open source software.
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## Requirements
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**Python >= 3.8 required**
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Requires manual installation of **pytorch** and **MONAI**.
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For Pytorch, please see [PyTorch's website for installation instructions].
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A CUDA-capable GPU is not needed but very strongly recommended, especially for training.
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If you get errors from MONAI regarding missing readers, please see [MONAI's optional dependencies] page for instructions on getting the readers required by your images.
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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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## Acknowledgements
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This plugin was developed by Cyril Achard & Maxime Vidal.
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This [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template. This work was funded, in part, from the Wyss Center to the Adaptive Motor Control Lab.
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This work was funded, in part, from the Wyss Center to the Adaptive Motor Control Lab.
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## Plugin base
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This [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.
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<!--
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Don't miss the full getting started guide to set up your new package:
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