Metadata-Version: 2.1
Name: cut-detector
Version: 1.0.2
Summary: Automatic Cut Detector
Home-page: https://github.com/15bonte/cut-detector
Author: Thomas Bonte
Author-email: thomas.bonte@mines-paristech.fr
License: BSD-3-Clause
Project-URL: Bug Tracker, https://github.com/15bonte/cut-detector/issues
Project-URL: Documentation, https://github.com/15bonte/cut-detector#README.md
Project-URL: Source Code, https://github.com/15bonte/cut-detector
Project-URL: User Support, https://github.com/15bonte/cut-detector/issues
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Framework :: napari
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering :: Image Processing
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: cellpose ==3.0.9
Requires-Dist: pyimagej
Requires-Dist: cnn-framework ==0.0.16
Requires-Dist: magicgui
Requires-Dist: pydantic ==1.10.12
Requires-Dist: xmltodict
Requires-Dist: shapely
Requires-Dist: aicsimageio ==4.14.0
Requires-Dist: fsspec ==2023.6.0
Requires-Dist: charset-normalizer ==3.3.0
Requires-Dist: napari[all]
Requires-Dist: laptrack ==0.16.2
Requires-Dist: scikit-learn ==1.5.0
Requires-Dist: numba >=0.59.1
Provides-Extra: testing
Requires-Dist: tox ; extra == 'testing'
Requires-Dist: pytest ; extra == 'testing'
Requires-Dist: pytest-cov ; extra == 'testing'
Requires-Dist: pytest-qt ; extra == 'testing'
Requires-Dist: napari ; extra == 'testing'
Requires-Dist: pyqt5 ; extra == 'testing'

# Cut Detector

[![License BSD-3](https://img.shields.io/pypi/l/cut-detector.svg?color=green)](https://github.com/15bonte/cut-detector/raw/main/LICENSE)
[![PyPI](https://img.shields.io/pypi/v/cut-detector.svg?color=green)](https://pypi.org/project/cut-detector)
[![Python Version](https://img.shields.io/pypi/pyversions/cut-detector.svg?color=green)](https://python.org)
[![tests](https://github.com/15bonte/cut-detector/workflows/tests/badge.svg)](https://github.com/15bonte/cut-detector/actions)
[![codecov](https://codecov.io/gh/15bonte/cut-detector/branch/main/graph/badge.svg)](https://codecov.io/gh/15bonte/cut-detector)
[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/cut-detector)](https://napari-hub.org/plugins/cut-detector)

Automatic micro-tubules cut detector.

https://github.com/15bonte/cut-detector/assets/28538099/3c59fbee-e599-4a9c-81a1-01e38a670fc1

---

This [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.

<!--
Don't miss the full getting started guide to set up your new package:
https://github.com/napari/cookiecutter-napari-plugin#getting-started

and review the napari docs for plugin developers:
https://napari.org/stable/plugins/index.html
-->

<video width="640" height="480" controls>
  <source src="https://github.com/15bonte/cut-detector-models/blob/main/demo.mp4" type="video/mp4">
  Your browser does not support the video tag.
</video>

## Installation

### Conda environment

It is highly recommended to create a dedicated conda environment, by following these few steps:

1. Install an [Anaconda] distribution of Python. Note you might need to use an anaconda prompt if you did not add anaconda to the path.

2. Open an Anaconda prompt as admin to create a new environment using [conda]. We advice to use python 3.10 and conda 23.10.0, to get conda-libmamba-solver as default solver.

```
conda create --name cut_detector python=3.10 conda=23.10.0
conda activate cut_detector
```

### Package installation

Once in a dedicated environment, our package can be installed via [pip]:

```
pip install cut_detector
```

Alternatively, you can clone the github repo to access to playground scripts.

```
git clone https://github.com/15bonte/cut-detector.git
cd cut-detector
pip install -e .
```

### GPU

We 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].

Next we need to remove the CPU version of torch:

```
pip uninstall torch
```

The 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:

```
conda install pytorch==1.12.1 torchvision pytorch-cuda=11.6 -c pytorch -c nvidia
```

## Update

To update cut-detector to the latest version, open an Anaconda prompt and use the following commands:

```
conda activate cut_detector
pip install cut-detector --upgrade
```

## Contributing

Contributions are very welcome. Tests can be run with [tox], please ensure
the coverage at least stays the same before you submit a pull request.

## License

Distributed under the terms of the [BSD-3] license,
"cut-detector" is free and open source software

## Issues

If you encounter any problems, please [file an issue] along with a detailed description.

[napari]: https://github.com/napari/napari
[Cookiecutter]: https://github.com/audreyr/cookiecutter
[@napari]: https://github.com/napari
[MIT]: http://opensource.org/licenses/MIT
[BSD-3]: http://opensource.org/licenses/BSD-3-Clause
[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt
[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt
[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0
[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt
[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin
[file an issue]: https://github.com/15bonte/cut-detector/issues
[napari]: https://github.com/napari/napari
[tox]: https://tox.readthedocs.io/en/latest/
[pip]: https://pypi.org/project/pip/
[PyPI]: https://pypi.org/
[Anaconda]: https://www.anaconda.com/products/distribution
[Fiji]: https://imagej.net/software/fiji/
[NVIDIA]: https://www.nvidia.com/Download/index.aspx?lang=en-us
[conda]: https://docs.conda.io/en/latest/
