bioimage-cpp
Image processing and segmentation functionality in C++ with light-weight python bindings through nanobind and minimal dependencies to enable distribution via pip.
The package includes dependency-free triangle-mesh extraction from 3D volumes
and segmentation masks, plus Laplacian mesh smoothing, under bioimage_cpp.mesh.
It also includes exact masked-grid Dijkstra paths under bioimage_cpp.distance
and binary-forest plus semantic multi-label 3D TEASAR skeletonization under
bioimage_cpp.skeleton.
The bioimage_cpp python library can be installed via pip:
pip install bioimage-cpp
Or via conda-forge:
conda install -c conda-forge bioimage-cpp
Please refer to the documentation for details.
Disclaimer: The functionality of this library was implemented mainly by coding agents (Claude Code and OpenAI Codex). We have made our best efforts to test the implementations thoroughly and are already using it heavily in our day-to-day research and have integrated with other software tools. Nevertheless, it may contain bugs or unintended behavior (as most software does). If you find such a problem, please open an issue on github. We are committed to improving and mainting this software.
1 maintainer
Metadata
1 maintainer
Release files for bioimage-cpp 0.9.0
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Source distribution (sdist)
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Built distributions (wheels)
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Release files / bioimage_cpp-0.9.0.tar.gz
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76a561f41773d14acf3f4206c3bee2eb790b52a5342ca05a2bbf12b34afd1691
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twine/7.0.0 CPython/3.13.14
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Transparency logRelease files / bioimage_cpp-0.9.0-cp310-cp310-macosx_10_13_x86_64.whl
| Download URL | bioimage_cpp-0.9.0-cp310-cp310-macosx_10_13_x86_64.whl |
|---|---|
| Size | 1.7 MB |
| Tags | CPython 3.10 macOS 10.13+ x86-64 |
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SHA-256 checksum How to use checksums |
cec061b7a596d12a9de67b775f290840c47dc52121923b93cbe09a76e9da0323
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BLAKE2b-256 checksum How to use checksums |
b59a3403862bf85f64e041b4f04b5c3b6075163b3dc784e0d9d8bbe5332b4b46
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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
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