napari-skimage
napari-skimage gives easy access to scikit-image functions in napari. The main goal of the plugin is to allow new users of napari, especially without coding experience, to easily explore basic image processing, in a similar way to what is possible in Fiji.
This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.
Philosophy
The plugin is still in early development and does not cover all functions of scikit-image. If you are interested in a specific function, please open an issue or a pull request. scikit-image functions are turned into interactive widgets mostly via magicgui, a tool that allows to create GUIs from functions in a simple way (in particular not requiring Qt knowledge). The code avoids on purpose complex approaches, e.g. to automate the creation of widgets, in order to keep the code simple and easy to understand for beginners.
Installation
You can install napari-skimage via pip:
pip install napari-skimage
To install latest development version :
pip install git+https://github.com/guiwitz/napari-skimage.git
Usage
The plugin function can be accessed under Plugins -> napari-skimage. Each function will appear as a widget on the right of the napari window. Some functions such as Gaussian Filter give access to a single operation and its options. Some functions such as Thresholding give access to variants of the same operation via a dropdown menu. Currently the plugin does not support multi-channel processing and will consider those as stacks. At the moment, the plugin offers access to the following operation types.
Filtering
A set of classical filters: Gaussian, Prewitt, Laplace etc. as well as rank filters such as median, minimum, maximum etc.
Thresholding
A set of thresholding methods: Otsu, Li, Yen etc.
Binary morphological operations
A set of binary morphology operations: binary erosion, binary dilation etc.
Morphological operations
A set of morphological operations: erosion, dilation, opening, closing etc.
Restoration
A set of restoration operations such as rolling ball, or non-local means denoising.
Mathematics
In addition the plugin provides a set of simple mathematical operators to:
- operate on single images e.g. square, square root, log etc.
- operate on two images e.g. add, subtract, multiply etc.
Code structure
Each set of functions is grouped in a separate module. For example all filtering operations are grouped in src/napari_skimge/skimage_filter_widget.py. A set of test in src/_tests/test_basic_widgets.py simply check that each widget can be created and generated an output of the correct size using the default settings.
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-skimage" 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-skimage 0.7.1
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_skimage-0.7.1.tar.gz | 1.6 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| napari_skimage-0.7.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.6 MB
Release files / napari_skimage-0.7.1.tar.gz
| Download URL | napari_skimage-0.7.1.tar.gz |
|---|---|
| Size | 1.6 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
9212dcc58ee28cc800210ba6c54022eaa8382f592556aaeb2515a74f620b4615
|
|
BLAKE2b-256 checksum How to use checksums |
b36b576be66d81fbec12b36f5d7f0800cd68918d290b09c4c298d7c117060a3c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 4, 2026.
Transparency logRelease files / napari_skimage-0.7.1-py3-none-any.whl
| Download URL | napari_skimage-0.7.1-py3-none-any.whl |
|---|---|
| Size | 31.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
bb093a0d7b76bd9f829a14fdb3b2bf5a5f9604807b656ce1a1cb9c8e34c69a32
|
|
BLAKE2b-256 checksum How to use checksums |
3413394e470c5c186741d4ba41586e657ae08e3c2e490e1792a57592a550a4a1
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 4, 2026.
Transparency log