{"name":"napari-svetlana","display_name":"napari-svetlana","visibility":"public","icon":"","categories":[],"schema_version":"0.2.0","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"napari-svetlana.Annotation","title":"Create Annotation","python_name":"napari_svetlana._batch_dock_widget:Annotation","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-svetlana.Training","title":"Create Training","python_name":"napari_svetlana._batch_dock_widget:Training","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-svetlana.Prediction","title":"Create Prediction","python_name":"napari_svetlana._batch_dock_widget:Prediction","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-svetlana.get_reader","title":"Get Reader","python_name":"napari_svetlana._reader:napari_get_reader","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-svetlana.write_image","title":"Write Image","python_name":"napari_svetlana._writer:napari_write_image","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":[{"command":"napari-svetlana.get_reader","filename_patterns":["*.npy"],"accepts_directories":true}],"writers":[{"command":"napari-svetlana.write_image","layer_types":["image"],"filename_extensions":[],"display_name":"image"}],"widgets":[{"command":"napari-svetlana.Annotation","display_name":"Annotation","autogenerate":false},{"command":"napari-svetlana.Training","display_name":"Training","autogenerate":false},{"command":"napari-svetlana.Prediction","display_name":"Prediction","autogenerate":false}],"sample_data":null,"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.1","name":"napari-svetlana","version":"0.3.2","dynamic":null,"platform":null,"supported_platform":null,"summary":"A classification plugin for the ROIs of a segmentation mask.","description":"# napari_svetlana\n\n[![License](https://img.shields.io/pypi/l/napari_svetlana.svg?color=green)](https://bitbucket.org/koopa31/napari_svetlana/src/main/LICENSE)\n[![PyPI](https://img.shields.io/pypi/v/napari_svetlana.svg?color=green)](https://pypi.org/project/napari_svetlana)\n[![Python Version](https://img.shields.io/pypi/pyversions/napari_svetlana.svg?color=green)](https://python.org)\n[![tests](https://bitbucket.org/koopa31/napari_svetlana/workflows/tests/badge.svg)](https://bitbucket.org/koopa31/napari_svetlana/actions)\n[![codecov](https://codecov.io/gh/koopa31/napari_svetlana/branch/main/graph/badge.svg)](https://codecov.io/gh/koopa31/napari_svetlana)\n[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-svetlana)](https://napari-hub.org/plugins/napari-svetlana)\n[![Documentation](https://readthedocs.org/projects/svetlana-documentation/badge/?version=latest)](https://svetlana-documentation.readthedocs.io/en/latest/)\n\nThe aim of this plugin is to classify the output of a segmentation algorithm.\nThe inputs are :\n<ul>\n  <li>A folder of raw images</li>\n  <li>Their segmentation masks where each ROI has its own label.</li>\n</ul>\n\nSvetlana can process 2D, 3D and multichannel image. If you want to use it to work on cell images, we strongly\nrecommend the use of [Cellpose](https://www.cellpose.org) for the segmentation part, as it provides excellent quality results and a standard output format\naccepted by Svetlana (labels masks). \n\nIf you use this plugin please cite the [paper](https://hal.inria.fr/hal-03927879): \n\nCazorla, C., Weiss, P., & Morin, R. (2023). Svetlana: a Supervised Segmentation Classifier for Napari.\n\n```bibtex\n@unpublished{weiss:hal-03927879,\n  TITLE = {{Svetlana: a Supervised Segmentation Classifier for Napari}},\n  AUTHOR = {Weiss, Pierre and Cazorla, Cl{\\'e}ment and Morin, Renaud},\n  URL = {https://hal.inria.fr/hal-03927879},\n  NOTE = {working paper or preprint},\n  YEAR = {2023},\n  MONTH = Jan,\n  PDF = {https://hal.inria.fr/hal-03927879/file/main_nature.pdf},\n  HAL_ID = {hal-03927879},\n  HAL_VERSION = {v1},\n}\n\n```\n\n\n![](https://bitbucket.org/koopa31/napari_svetlana/raw/bca8788111b38d97bd172c7caac87cc488ace699/images/Videogif.gif)\n\n\n----------------------------------\n\nThis [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.\n\n<!--\nDon't miss the full getting started guide to set up your new package:\nhttps://github.com/napari/cookiecutter-napari-plugin#getting-started\n\nand review the napari docs for plugin developers:\nhttps://napari.org/plugins/stable/index.html\n-->\n\n## Installation\n\nFirst install Napari in a Python 3.9 Conda environment following these instructions :\n\n```bash\nconda create -n svetlana_env python=3.9\nconda activate svetlana_env\nconda install pip\npython -m pip install \"napari[all]\"==0.4.17\n```\n\nThen, you can install `napari_svetlana` via [pip](https://pypi.org/project/napari-svetlana/), or directly from the Napari plugin manager (see Napari documentation):\n```bash\npip install napari_svetlana\n```\nWARNING:\n\nIf you have a Cuda compatible GPU on your computer, some computations may be accelerated\nusing [Cupy](https://pypi.org/project/cupy/). Unfortunately, Cupy needs Cudatoolkit to be installed. This library can only be installed via \nConda while the plugin is a pip plugin, so it must be installed manually for the moment:\n```bash\nconda install cudatoolkit=10.2 \n```\nAlso note that the library ([Cucim](https://pypi.org/project/cucim/)) that we use to improve these performances, computing morphological operations on GPU\nis unfortunately only available for Linux systems. Hence, if you are a Windows user, this installation is not necessary.\n\n## Tutorial\n\nMany advanced features are available in Svetlana, such as data augmentation or contextual information reduction, to optimize the performance of your classifier. Thus, we strongly encourage you to\ncheck our [Youtube tutorial](https://www.youtube.com/watch?v=u_FKuHta-RE) and\nour [documentation](https://svetlana-documentation.readthedocs.io/en/latest/).\nA button called **TRY ON DEMO IMAGE** is available in the annotation plugin and enables you to apply the YouTube\ntutorial to the same test images to learn how to use the plugin. Feel free to try it to test all the features\nthat Svetlana offers.\n\n## Similar Napari plugins\n\nJoel Luethi developed a similar method for objects classification called [napari feature classifier](https://www.napari-hub.org/plugins/napari-feature-classifier).\nAlso, [apoc](https://www.napari-hub.org/plugins/napari-accelerated-pixel-and-object-classification) by Robert Haase is available in Napari for pixels and objects classification.\n\n## Contributing\n\nContributions are very welcome.\n\n## License\n\nDistributed under the terms of the [BSD-3] license,\n\"napari_svetlana\" is free and open source software\n\n## Acknowledgements\n\nThe method was developed by [Clément Cazorla](https://koopa31.github.io/), [Renaud Morin](https://www.linkedin.com/in/renaud-morin-6a42665b/?originalSubdomain=fr) and [Pierre Weiss](https://www.math.univ-toulouse.fr/~weiss/). And the plugin was written by\nClément Cazorla. The project is co-funded by [Imactiv-3D](https://www.imactiv-3d.com/) and [CNRS](https://www.cnrs.fr/fr).\n\n## Issues\n\nIf you encounter any problems, please [file an issue](https://bitbucket.org/koopa31/napari_svetlana/issues?status=new&status=open) along with a detailed description.\n\n[napari]: https://github.com/napari/napari\n[Cookiecutter]: https://github.com/audreyr/cookiecutter\n[@napari]: https://github.com/napari\n[MIT]: http://opensource.org/licenses/MIT\n[BSD-3]: http://opensource.org/licenses/BSD-3-Clause\n[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt\n[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt\n[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0\n[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt\n[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin\n\n[napari]: https://github.com/napari/napari\n[tox]: https://tox.readthedocs.io/en/latest/\n[pip]: https://pypi.org/project/pip/\n[PyPI]: https://pypi.org/\n","description_content_type":"text/markdown","keywords":null,"home_page":null,"download_url":null,"author":"Clément Cazorla","author_email":"clement.cazorla31@gmail.com","maintainer":null,"maintainer_email":null,"license":"GPL-3.0-only","classifier":["Development Status :: 2 - Pre-Alpha","Intended Audience :: Developers","Framework :: napari","Topic :: Software Development :: Testing","Programming Language :: Python","Programming Language :: Python :: 3","Programming Language :: Python :: 3.7","Programming Language :: Python :: 3.8","Programming Language :: Python :: 3.9","Operating System :: OS Independent","License :: OSI Approved :: GNU General Public License v3 (GPLv3)"],"requires_dist":["napari-plugin-engine >=0.1.4","numpy","albumentations ==1.0.3","joblib ==1.2.0","light-the-torch","matplotlib","opencv-python ==4.8.1.78","PyQt5","cupy-cuda102 ==10.6.0","xlsxwriter","pandas","npe2","pooch","cucim ==22.6.0 ; platform_system == \"Linux\""],"requires_python":">=3.9","requires_external":null,"project_url":["Bug Tracker, https://bitbucket.org/koopa31/napari_svetlana/issues?status=new&status=open","Documentation, https://svetlana-documentation.readthedocs.io/en/latest/","Source Code, https://bitbucket.org/koopa31/napari_svetlana/src/main/","User Support, https://bitbucket.org/koopa31/napari_svetlana/issues?status=new&status=open"],"provides_extra":null,"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}