{"name":"cut-detector","display_name":"Cut Detector","visibility":"public","icon":"","categories":[],"schema_version":"0.2.0","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"cut-detector.whole_process","title":"0 - Whole Process","python_name":"cut_detector._widget:whole_process","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"cut-detector.segmentation_tracking","title":"1 - Segmentation & Tracking","python_name":"cut_detector._widget:segmentation_tracking","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"cut-detector.mitosis_track_generation","title":"2 - Mitosis Track Generation","python_name":"cut_detector._widget:mitosis_track_generation","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"cut-detector.mid_body_detection","title":"3 - Mid-body Detection","python_name":"cut_detector._widget:mid_body_detection","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"cut-detector.micro_tubules_cut_detection","title":"4 - Micro Tubules Cut Detection","python_name":"cut_detector._widget:micro_tubules_cut_detection","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"cut-detector.results_saving","title":"5 - Results Saving","python_name":"cut_detector._widget:results_saving","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":null,"writers":null,"widgets":[{"command":"cut-detector.whole_process","display_name":"0 - Whole Process","autogenerate":false},{"command":"cut-detector.segmentation_tracking","display_name":"1 - Segmentation & Tracking","autogenerate":false},{"command":"cut-detector.mitosis_track_generation","display_name":"2 - Mitosis Track Generation","autogenerate":false},{"command":"cut-detector.mid_body_detection","display_name":"3 - Mid-body Detection","autogenerate":false},{"command":"cut-detector.micro_tubules_cut_detection","display_name":"4 - Micro Tubules Cut Detection","autogenerate":false},{"command":"cut-detector.results_saving","display_name":"5 - Results Saving","autogenerate":false}],"sample_data":null,"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.1","name":"cut-detector","version":"0.0.4","dynamic":null,"platform":null,"supported_platform":null,"summary":"Automatic Cut Detector","description":"# Cut Detector\n\n[![License BSD-3](https://img.shields.io/pypi/l/cut-detector.svg?color=green)](https://github.com/15bonte/cut-detector/raw/main/LICENSE)\n[![PyPI](https://img.shields.io/pypi/v/cut-detector.svg?color=green)](https://pypi.org/project/cut-detector)\n[![Python Version](https://img.shields.io/pypi/pyversions/cut-detector.svg?color=green)](https://python.org)\n[![tests](https://github.com/15bonte/cut-detector/workflows/tests/badge.svg)](https://github.com/15bonte/cut-detector/actions)\n[![codecov](https://codecov.io/gh/15bonte/cut-detector/branch/main/graph/badge.svg)](https://codecov.io/gh/15bonte/cut-detector)\n[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/cut-detector)](https://napari-hub.org/plugins/cut-detector)\n\nAutomatic micro-tubules cut detector.\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/stable/plugins/index.html\n-->\n\n## Installation\n\n### Conda environment\n\nIt is highly recommended to create a dedicated conda environment, by following these few steps:\n\n1. Install an [Anaconda] distribution of Python. Note you might need to use an anaconda prompt if you did not add anaconda to the path.\n\n2. Open an anaconda prompt and create a new environment with:\n\n```\n   conda create --name cut-detector python=3.9\n```\n\n3. Activate the newly created environment:\n\n```\nconda activate cut-detector\n```\n\n### Package installation\n\nOnce in a dedicated environment, our package can be installed via [pip]:\n\n```\npip install cut_detector\n```\n\n### Fiji\n\nThis package relies on [Trackmate] to perform cell tracking. Trackmate is called through [Fiji], which has to be installed independently. Please follow the steps [here] to install it.\n\n### GPU\n\nWe highly recommend to use GPU to speed up segmentation. To use your NVIDIA GPU, the first step is to download the dedicated driver from [NVIDIA].\n\nNext we need to remove the CPU version of torch:\n\n```\npip uninstall torch\n```\n\nThe GPU version of torch to be installed can be found [here](https://pytorch.org/get-started/locally/). You may choose the CUDA version supported by your GPU, and install it with conda. This package has been developed with the version 11.6, installed with this command:\n\n```\nconda install numpy==1.25 pytorch==1.12.1 torchvision pytorch-cuda=11.6 -c pytorch -c nvidia\n```\n\nNote that we have added numpy here to prevent conda from installing a version higher than 1.25, which is not supported by numba.\n\nIf the previous results in an inifinite \"Solving environment\", consider using mamba instead of conda.\n\n## Contributing\n\nContributions are very welcome. Tests can be run with [tox], please ensure\nthe coverage at least stays the same before you submit a pull request.\n\n## License\n\nDistributed under the terms of the [BSD-3] license,\n\"cut-detector\" is free and open source software\n\n## Issues\n\nIf you encounter any problems, please [file an issue] 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[file an issue]: https://github.com/15bonte/cut-detector/issues\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[Anaconda]: (https://www.anaconda.com/products/distribution)\n[Trackmate]: (https://imagej.net/plugins/trackmate/)\n[Fiji]: (https://imagej.net/software/fiji/)\n[here]: (https://imagej.net/software/fiji/downloads)\n[NVIDIA]: (https://www.nvidia.com/Download/index.aspx?lang=en-us)\n","description_content_type":"text/markdown","keywords":null,"home_page":"https://github.com/15bonte/cut-detector","download_url":null,"author":"Thomas Bonte","author_email":"thomas.bonte@mines-paristech.fr","maintainer":null,"maintainer_email":null,"license":"BSD-3-Clause","classifier":["Development Status :: 2 - Pre-Alpha","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 :: Only","Programming Language :: Python :: 3.9","Topic :: Scientific/Engineering :: Image Processing"],"requires_dist":["cellpose >=2.2.3","pyimagej","scyjava","numpy <=1.24","cnn-framework","magicgui","pydantic ==1.10.12","xmltodict","shapely","aicsimageio","scikit-learn ==1.2.2","napari[all]","tox ; extra == 'testing'","pytest ; extra == 'testing'","pytest-cov ; extra == 'testing'","pytest-qt ; extra == 'testing'","napari ; extra == 'testing'","pyqt5 ; extra == 'testing'"],"requires_python":">=3.8","requires_external":null,"project_url":["Bug Tracker, https://github.com/15bonte/cut-detector/issues","Documentation, https://github.com/15bonte/cut-detector#README.md","Source Code, https://github.com/15bonte/cut-detector","User Support, https://github.com/15bonte/cut-detector/issues"],"provides_extra":["testing"],"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}