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https://github.com/haesleinhuepf/napari-mahotas-image-processing


https://github.com/haesleinhuepf/napari-mahotas-image-processing

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# Note: This is a decommissioned napari plugin

This project is discontinued. You can still install and use it (version 0.1.2 was last tested with napari 0.4.12) but this project is
no longer maintained and supported.

```python
conda install pyopencl
pip install napari-mahotas-image-processing==0.1.2
```

Check out these napari plugins which have similar functionality:

* [napari-zelda](https://github.com/RoccoDAnt/napari-zelda)
* [napari-allencell-segmenter](https://github.com/AllenCell/napari-allencell-segmenter)
* [napari-simpleitk-image-processing](https://github.com/haesleinhuepf/napari-simpleitk-image-processing)
* [napari-segment-blobs-and-things-with-membranes](https://github.com/haesleinhuepf/napari-segment-blobs-and-things-with-membranes)
* [napari_pyclesperanto_assistant](https://github.com/clesperanto/napari_pyclesperanto_assistant)

# napari-mahotas-image-processing (n-mahotas)

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

Image processing based using the [Mahotas library](https://github.com/luispedro/mahotas) in [napari](https://napari.org)

## Usage

### Gaussian blur

Applies a [Gaussian blur](https://mahotas.readthedocs.io/en/latest/api.html#mahotas.gaussian_filter) to an
image. This might be useful for denoising, e.g. before applying the Threshold-Otsu method.

![img.png](https://github.com/haesleinhuepf/napari-mahotas-image-processing/raw/main/docs/gaussian_blur.png)

### Otsu's threshold

Binarizes an image using [scikit-image's threshold Otsu algorithm](https://mahotas.readthedocs.io/en/latest/api.html#mahotas.otsu), also known as
[Otsu's method](https://ieeexplore.ieee.org/document/4310076).

![img.png](https://github.com/haesleinhuepf/napari-mahotas-image-processing/raw/main/docs/threshold_otsu.png)

### Split connected objects

In case objects stick together after thresholding, this tool might help.
It aims to deliver similar results as [ImageJ's watershed implementation](https://imagej.nih.gov/ij/docs/menus/process.html#watershed).

![img.png](https://github.com/haesleinhuepf/napari-mahotas-image-processing/raw/main/docs/split_touching_objects.png)

### Connected component labeling

Takes a binary image and produces a label image with all separated objects labeled differently. Under the hood, it uses
[mahotas' label function](https://mahotas.readthedocs.io/en/latest/api.html#mahotas.label).

![img.png](https://github.com/haesleinhuepf/napari-mahotas-image-processing/raw/main/docs/connected_component_labeling.png)

### Seeded watershed

Starting from an image showing high-intensity membranes and a seed-image where objects have been labeled,
objects are labeled that are constrained by the membranes. Hint: you may want to blur the membrane channel a bit in advance.

![img.png](https://github.com/haesleinhuepf/napari-mahotas-image-processing/raw/main/docs/seeded_watershed.png)

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

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

## Installation

Before installing this napari plugin, please [install `mahotas`](https://github.com/luispedro/mahotas#install), e.g. using conda:

```
conda config --add channels conda-forge
conda install mahotas
```

Afterwards, you can install `napari-mahotas-image-processing` via [pip]:

pip install napari-mahotas-image-processing

To install latest development version :

pip install git+https://github.com/haesleinhuepf/napari-mahotas-image-processing.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-mahotas-image-processing" 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-mahotas-image-processing/issues

[napari]: https://github.com/napari/napari
[tox]: https://tox.readthedocs.io/en/latest/
[pip]: https://pypi.org/project/pip/
[PyPI]: https://pypi.org/