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 You can still install and use it (version 0.1.2 was last tested with napari 0.4.12) but this project is no longer maintained and supported.
conda install pyopencl
pip install napari-mahotas-image-processing==0.1.2
Check out these napari plugins which have similar functionality:
- napari-zelda
- napari-allencell-segmenter
- napari-simpleitk-image-processing
- napari-segment-blobs-and-things-with-membranes
- napari_pyclesperanto_assistant
napari-mahotas-image-processing (n-mahotas)
Image processing based using the Mahotas library in napari
Usage
Gaussian blur
Applies a Gaussian blur to an image. This might be useful for denoising, e.g. before applying the Threshold-Otsu method.
Otsu's threshold
Binarizes an image using scikit-image's threshold Otsu algorithm, also known as Otsu's method.
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.
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.
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.
This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.
Installation
Before installing this napari plugin, please install mahotas, 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.
Metadata
Release files for napari-mahotas-image-processing 0.1.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| napari-mahotas-image-processing-0.1.3.tar.gz | 9.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| napari_mahotas_image_processing-0.1.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 18.5 kB
Release files / napari-mahotas-image-processing-0.1.3.tar.gz
| Download URL | napari-mahotas-image-processing-0.1.3.tar.gz |
|---|---|
| Size | 9.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / napari_mahotas_image_processing-0.1.3-py3-none-any.whl
| Download URL | napari_mahotas_image_processing-0.1.3-py3-none-any.whl |
|---|---|
| Size | 9.1 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
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| Uploaded via |
twine/3.7.1 importlib_metadata/4.6.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12
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