https://github.com/haesleinhuepf/devbio-napari

napari-plugin
Last synced: 9 days ago
JSON representation
Host: GitHub
URL: https://github.com/haesleinhuepf/devbio-napari
Owner: haesleinhuepf
License: bsd-3-clause
Created: 2021-06-08T07:17:55.000Z (almost 4 years ago)
Default Branch: master
Last Pushed: 2024-10-18T09:45:43.000Z (6 months ago)
Last Synced: 2025-03-30T03:03:29.576Z (16 days ago)
Topics: napari-plugin
Language: Python
Homepage:
Size: 1.65 MB
Stars: 47
Watchers: 3
Forks: 14
Open Issues: 23
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

awesome-scientific-image-analysis - devbio-napari - Comprehensive image processing toolbox. (🏝️ Napari / Plugins)
README

        # devbio-napari

[![License](https://img.shields.io/pypi/l/devbio-napari.svg?color=green)](https://github.com/haesleinhuepf/devbio-napari/raw/master/LICENSE)

[![PyPI](https://img.shields.io/pypi/v/devbio-napari.svg?color=green)](https://pypi.org/project/devbio-napari)

[![Python Version](https://img.shields.io/pypi/pyversions/devbio-napari.svg?color=green)](https://python.org)

[![tests](https://github.com/haesleinhuepf/devbio-napari/workflows/tests/badge.svg)](https://github.com/haesleinhuepf/napari-plot-profile/actions)

[![codecov](https://codecov.io/gh/haesleinhuepf/devbio-napari/branch/master/graph/badge.svg)](https://codecov.io/gh/haesleinhuepf/devbio-napari)

[![Development Status](https://img.shields.io/pypi/status/devbio-napari.svg)](https://en.wikipedia.org/wiki/Software_release_life_cycle#Alpha)

[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/devbio-napari)](https://napari-hub.org/plugins/devbio-napari)

 

A bundle of napari plugins useful for 3D+t image processing and analysis for studying developmental biology.

* [accelerated-pixel-and-object-classification](https://www.napari-hub.org/plugins/napari-accelerated-pixel-and-object-classification)

  * Instance segmentation

  * Semantic segmentation

  * Object classification

  * Random Forest Classifier training

* [animation](https://www.napari-hub.org/plugins/napari-animation) 

  * Visualization

* [blob-detection](https://www.napari-hub.org/plugins/napari-blob-detection)

  * Detection

* [brightness-contrast](https://www.napari-hub.org/plugins/napari-brightness-contrast)

  * Visualization

* [clusters-plotter](https://www.napari-hub.org/plugins/napari-clusters-plotter)

  * Visualization

  * Plotting

  * Semantic object segmentation

  * Dimensionality reduction

  * Unsupervised machine learning

* [crop](https://www.napari-hub.org/plugins/napari-crop)

  * Transformation

* [curtain](https://www.napari-hub.org/plugins/napari-curtain)

  * Visualization 

* [czifile2](https://www.napari-hub.org/plugins/napari-czifile2)

  * File input/output

* [folder-browser](https://www.napari-hub.org/plugins/napari-folder-browser)

  * File input/output

* [layer-details-display](https://www.napari-hub.org/plugins/napari-layer-details-display)

  * Visualization

* [mouse-controls](https://www.napari-hub.org/plugins/napari-mouse-controls)

  * Interaction

* [PlatyMatch](https://www.napari-hub.org/plugins/PlatyMatch)

  * Image registration

* [plot-profile](https://www.napari-hub.org/plugins/napari-plot-profile)

  * Visualization

  * Quantification

* [plugin-search](https://www.napari-hub.org/plugins/napari-plugin-search)

  * Interaction

* [pyclesperanto-assistant](https://www.napari-hub.org/plugins/napari-pyclesperanto-assistant)

  * Filtering

  * Instance segmentation

  * Semantic segmentation

  * Quantification

* [pystackreg](https://www.napari-hub.org/plugins/napari-pystackreg)

  * Image registration

  * Motion correction

* [RedLionfish](https://www.napari-hub.org/plugins/RedLionfish)

  * Deconvolution

  * Processing

* [roi](https://www.napari-hub.org/plugins/napari-roi)

  * Manual segmentation

* [segment-blobs-and-things-with-membranes](https://www.napari-hub.org/plugins/napari-segment-blobs-and-things-with-membranes)

  * Filtering

  * Instance segmentation

  * Semantic segmentation

* [simpleitk-image-processing](https://www.napari-hub.org/plugins/napari-simpleitk-image-processing)

  * Filtering

  * Instance segmentation

  * Semantic segmentation

  * Quantification

* [skimage-regionprops](https://www.napari-hub.org/plugins/napari-skimage-regionprops)

  * Quantification

* [stl-exporter](https://www.napari-hub.org/plugins/napari-stl-exporter)

  * File input/output

* [tabu](https://www.napari-hub.org/plugins/napari-tabu)

  * Interaction

* [the-segmentation-game](https://www.napari-hub.org/plugins/the-segmentation-game)

  * Quantification

  * Segmentation quality assurance

* [workflow-inspector](https://www.napari-hub.org/plugins/napari-workflow-inspector)

  * Visualization

* [workflow-optimizer](https://www.napari-hub.org/plugins/napari-workflow-optimizer)

  * Interaction

  * Optimization

----------------------------------

## Installation

You can install `devbio-napari` via conda/mamba. If you have never used conda before, please [read this guide first](https://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html).  

Start by creating an environment using mamba.

```

mamba create --name devbio-napari-env napari=0.4.19 python=3.9 devbio-napari pyqt -c conda-forge -c pytorch

```

Afterwards, activate the environment like this:

```

mamba activate devbio-napari-env

```

Afterwards, run this command from the command line

```

naparia

```

This window should open. It shows the [Assistant](https://www.napari-hub.org/plugins/napari-assistant) graphical user interface. 

Read more about how to use it in its [documentation](https://www.napari-hub.org/plugins/napari-assistant).

![img.png](https://github.com/haesleinhuepf/devbio-napari/raw/master/docs/screenshot.png)

## Troubleshooting: Graphics cards drivers

In case error messages contains "ImportError: DLL load failed while importing cl: The specified procedure could not be found" [see also](https://github.com/clEsperanto/pyclesperanto_prototype/issues/55) or ""clGetPlatformIDs failed: PLATFORM_NOT_FOUND_KHR", please install recent drivers for your graphics card and/or OpenCL device. Select the right driver source depending on your hardware from this list:

* [AMD drivers](https://www.amd.com/en/support)

* [NVidia drivers](https://www.nvidia.com/download/index.aspx)

* [Intel GPU drivers](https://www.intel.com/content/www/us/en/download/726609/intel-arc-graphics-windows-dch-driver.html)

* [Intel CPU OpenCL drivers](https://www.intel.com/content/www/us/en/developer/articles/tool/opencl-drivers.html#latest_CPU_runtime)

* [Microsoft Windows OpenCL support](https://www.microsoft.com/en-us/p/opencl-and-opengl-compatibility-pack/9nqpsl29bfff)

Sometimes, mac-users need to install this:

    conda install -c conda-forge ocl_icd_wrapper_apple

Sometimes, linux users need to install this:

    conda install -c conda-forge ocl-icd-system

In case installation didn't work in the first attempt, you may have to call this command line to reset the napari configuration:

```

napari --reset

```

## Troubleshooting: pytorch

In case pytorch-related plugins fail, install pytorch as explained on [its website](https://pytorch.org/get-started/locally/). Consider replacing `conda` with `mamba` in given instructions.

For example if you have an NVidia GPU at hand, install pytorch like this:

```

mamba install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

```

Or if not:

```

mamba install pytorch torchvision torchaudio cpuonly -c pytorch

```

## Contributing

Contributions are very welcome. 

If you want to [suggest a new napari plugin](https://github.com/haesleinhuepf/devbio-napari/pulls) to become part of this distribution, please make sure it interoperates nicely with the other plugins. 

For example, if the plugin you suggest provided cell segmentation algorithms, please check if the resulting segmented cells can be analysed using napari-skimage-regionprops.

Furthermore, please make sure the README of the plugin you are proposing comes with user documentation, e.g. a step-by-step guide with screenshots explaining what users can do with the plugin and how to use it. 

It is recommended to provide example data as well so that end-users can try out the plugin under conditions it was developed for.

## License

Distributed under the terms of the [BSD-3] license,

"devbio-napari" 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/devbio/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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