cellcast_python
This crate contains the Python bindings (via PyO3) for the cellcast core Rust library. Cellcast is a recast of cell segmentation models built on the Burn tensor and deep learning framework. The goal of this project is to modernize (i.e. recast) established cell segmentation models with a WebGPU backend. Cellcast aims to make access to cell segmentation models easy and reproducible.
Installation
Requirements
The cellcast Python package currently supports the following architectures:
| Operating System | Architecture |
|---|---|
| Linux | x86-64, arm64 |
| macOS | intel, arm64 |
| Windows | x86-64 |
Cellcast is compatible with Python >=3.8 and requires only NumPy.
cellcast from PyPI
You can install the cellcast Python package from PyPI with:
$ pip install cellcast
Build cellcast_python from source
To build the cellcat_python package from source, use the maturin build tool
(this requires the Rust toolchain). If you're using uv to manage your Python
virtual environments (venv) add numpy and maturin to your environment and run the
maturin develop command in the cellcast_python directory of the
cellcast repository with your venv activated:
$ source ~/path/to/myenv/.venv/bin/activate
$ (myenv) cd cellcast_python
$ maturin develop
Alernatively if you're using conda or mamba you can do the following:
$ cd cellcat_python
$ mamba activate myenv
(myenv) $ mamba install numpy maturin
...
(myenv) $ maturin develop
This will compile a non-optimized cellcast binaries. Pass the --release flag to
compile optimized binaries (note that compilation time may take upwards of 10 minutes).
Usage
Using cellcast
The following example demonstrates how to use cellcast's StarDist2D model in Python with fetched versatile fluo pre-trained weights (note: here we assume you have your data in a 2D NumPy array):
from cellcast.models import StarDist2D
# assuming "data" is a 2D NumPy array
sd = StarDist2D.init_fluo(gpu=True)
labels = sd.predict_fluo(data)
Run help() on the predict_fluo() function to see the full function signature and default values. To initialize a model with custom weights, provide
the path to the weights in burnpack format (.bpk) when creating a model instance.
sd = StarDist2D.init_fluo("path/to/custom_weights.bpk", True)
License
Cellcast itself is a dual-licensed project with your choice of:
- MIT License (see LICENSE-MIT)
- The Unlicense (see LICENSE-UNLICENSE)
These licenses only apply to the cellcast project and do not apply to the individual models supported by cellcast. You can find each model's associated license listed in the MODEL-LICENSES file.
Metadata
Release files for cellcast 0.3.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| cellcast-0.3.1.tar.gz | 81.4 kB | With provenance | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| cellcast-0.3.1-cp38-abi3-win_amd64.whl | CPython 3.8 | abi3 | Windows x86-64 | Details |
| cellcast-0.3.1-cp38-abi3-manylinux_2_28_aarch64.whl | CPython 3.8 | abi3 | Linux glibc 2.28+ ARM64 | Details |
| cellcast-0.3.1-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.8 | abi3 | Linux glibc 2.17+ x86-64 | Details |
| cellcast-0.3.1-cp38-abi3-macosx_11_0_arm64.whl | CPython 3.8 | abi3 | macOS 11.0+ ARM64 | Details |
| cellcast-0.3.1-cp38-abi3-macosx_10_12_x86_64.whl | CPython 3.8 | abi3 | macOS 10.12+ x86-64 | Details |
Total release size: 58.9 MB
Release files / cellcast-0.3.1.tar.gz
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| Tags | Source |
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