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Since October 1, 2024, this version is no longer actively maintained and will not be updated. New plugins and plugin updates will continue to be listed.

ImageGrains

napari-imagegrains

An interactive napari plugin for the ImageGrains software.

    Workflow step:
    Image annotation
    Image segmentation

    License BSD-3 PyPI Python Version tests codecov napari hub

    An interactive napari plugin for the ImageGrains software.


    This napari plugin was generated with copier using the napari-plugin-template.

    Installation

    We recommend to install the plugin in an isolated environment as provided by conda. For conda create an appropriate environment with (do not use Python more recent than 3.11):

    conda create -n napari-imagegrains -c conda-forge python=3.11 napari pyqt
    conda activate napari-imagegrains

    :warning: This is a work in progress and the plugin is available neither on PyPi nor in the napari plugin manager. You can install napari-imagegrains via pip:

    pip install napari-imagegrains

    To install latest development version :

    pip install git+https://github.com/guiwitz/napari-imagegrains.git

    Or if you want to contribute to the plugin, fork the repository, clone it locally and install it in editable mode:

    pip install -e .

    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-imagegrains" is free and open source software

    Issues

    If you encounter any problems, please file an issue along with a detailed description.

    Authors

    The original software ImageGrain was developed by David Mair, Institute of Geological Sciences, University of Bern. The current plugin, a user-interface for the ImageGrains software, was developed by Guillaume Witz and Michael Horn, Data Science Lab, University of Bern in collaboration with David Mair.

    Citation

    If you use this software, please cite the following publication: Mair, D., Witz, G., Do Prado, A.H., Garefalakis, P. & Schlunegger, F. (2023) Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning. Earth Surface Processes and Landforms, 1–18. https://doi.org/10.1002/esp.5755.

    Version:

    • 0.1.0

    Last updated:

    • 26 June 2025

    First released:

    • 26 June 2025

    License:

    Supported data:

    • Information not submitted

    Plugin type:

    GitHub activity:

    • Stars: 0
    • Forks: 1
    • Issues + PRs: 0

    Python versions supported:

    Operating system:

    Requirements:

    • numpy
    • magicgui
    • qtpy
    • superqt
    • napari_matplotlib
    • scikit-image
    • seaborn
    • pandas
    • imagegrains