| Name | napari-xgboost JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | A plugin for pixel classification using XGBoost |
| upload_time | 2024-07-05 09:26:25 |
| maintainer | None |
| docs_url | None |
| author | Robert Haase |
| requires_python | >=3.9 |
| license | Copyright (c) 2024, Robert Haase, ScaDS.AI, Uni Leipzig All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# napari-xgboost
[](https://github.com/haesleinhuepf/napari-xgboost/raw/main/LICENSE)
[](https://pypi.org/project/napari-xgboost)
[](https://python.org)
[](https://github.com/haesleinhuepf/napari-xgboost/actions)
[](https://codecov.io/gh/haesleinhuepf/napari-xgboost)
[](https://napari-hub.org/plugins/napari-xgboost)
A plugin for pixel classification using [XGBoost](https://xgboost.readthedocs.io/en/stable/), inspired by [Digital Sreeni's Youtube video](https://www.youtube.com/watch?v=yqkNslkzLk4).
Note: This plugin is work-in-progress. Check out the [github issues](https://github.com/haesleinhuepf/napari-xgboost/issues) to see what's currently being worked on.
## Usage
Load an example image into napari. Add a Labels layer by clicking on this button:
Then, draw a sparse annotation on the image. Try to draw thin lines on background and foreground, e.g. like this:
Then click the menu `Layers > Segment > Train Pixel Classifier (XGBoost)`.
In the dialog, select the original image and the labels layer. Also enter a filename where the model should be saved.
Afterwards, click on `Run` to explore the result.
## Installation
You can install `napari-xgboost` via [pip]:
pip install napari-xgboost
To install latest development version :
pip install git+https://github.com/haesleinhuepf/napari-xgboost.git
## 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,
"napari-xgboost" 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/haesleinhuepf/napari-xgboost/issues
[napari]: https://github.com/napari/napari
[tox]: https://tox.readthedocs.io/en/latest/
[pip]: https://pypi.org/project/pip/
[PyPI]: https://pypi.org/
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"description": "# napari-xgboost\r\n\r\n[](https://github.com/haesleinhuepf/napari-xgboost/raw/main/LICENSE)\r\n[](https://pypi.org/project/napari-xgboost)\r\n[](https://python.org)\r\n[](https://github.com/haesleinhuepf/napari-xgboost/actions)\r\n[](https://codecov.io/gh/haesleinhuepf/napari-xgboost)\r\n[](https://napari-hub.org/plugins/napari-xgboost)\r\n\r\nA plugin for pixel classification using [XGBoost](https://xgboost.readthedocs.io/en/stable/), inspired by [Digital Sreeni's Youtube video](https://www.youtube.com/watch?v=yqkNslkzLk4).\r\n\r\nNote: This plugin is work-in-progress. Check out the [github issues](https://github.com/haesleinhuepf/napari-xgboost/issues) to see what's currently being worked on.\r\n\r\n## Usage\r\n\r\nLoad an example image into napari. Add a Labels layer by clicking on this button:\r\n\r\n\r\n\r\nThen, draw a sparse annotation on the image. Try to draw thin lines on background and foreground, e.g. like this:\r\n\r\n\r\n\r\nThen click the menu `Layers > Segment > Train Pixel Classifier (XGBoost)`.\r\n\r\n\r\n\r\nIn the dialog, select the original image and the labels layer. Also enter a filename where the model should be saved. \r\nAfterwards, click on `Run` to explore the result.\r\n\r\n\r\n\r\n## Installation\r\n\r\nYou can install `napari-xgboost` via [pip]:\r\n\r\n pip install napari-xgboost\r\n\r\nTo install latest development version :\r\n\r\n pip install git+https://github.com/haesleinhuepf/napari-xgboost.git\r\n\r\n\r\n## Contributing\r\n\r\nContributions are very welcome. Tests can be run with [tox], please ensure\r\nthe coverage at least stays the same before you submit a pull request.\r\n\r\n## License\r\n\r\nDistributed under the terms of the [BSD-3] license,\r\n\"napari-xgboost\" is free and open source software\r\n\r\n## Issues\r\n\r\nIf you encounter any problems, please [file an issue] along with a detailed description.\r\n\r\n[napari]: https://github.com/napari/napari\r\n[Cookiecutter]: https://github.com/audreyr/cookiecutter\r\n[@napari]: https://github.com/napari\r\n[MIT]: http://opensource.org/licenses/MIT\r\n[BSD-3]: http://opensource.org/licenses/BSD-3-Clause\r\n[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt\r\n[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt\r\n[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0\r\n[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt\r\n[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin\r\n\r\n[file an issue]: https://github.com/haesleinhuepf/napari-xgboost/issues\r\n\r\n[napari]: https://github.com/napari/napari\r\n[tox]: https://tox.readthedocs.io/en/latest/\r\n[pip]: https://pypi.org/project/pip/\r\n[PyPI]: https://pypi.org/\r\n",
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