Metadata-Version: 2.4
Name: volsegtools
Version: 0.2.3
License-File: LICENSE
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Typing :: Typed
Requires-Python: <3.13,>=3.12
Requires-Dist: ciftools
Requires-Dist: dask
Requires-Dist: dask-image
Requires-Dist: h5py>=3.14.0
Requires-Dist: isort>=6.1.0
Requires-Dist: molviewspec>=1.8.1
Requires-Dist: mrcfile
Requires-Dist: nibabel
Requires-Dist: numpy
Requires-Dist: ome-zarr>=0.10.2
Requires-Dist: pydantic
Requires-Dist: pyometiff>=1.0.1
Requires-Dist: scikit-image>=0.24.0
Requires-Dist: sfftk-rw>=0.8.1
Requires-Dist: tifffile>=2024.8.30
Requires-Dist: trimesh>=4.11.5
Requires-Dist: typer
Requires-Dist: vrmlxpy>=1.0.2
Requires-Dist: zarr
Provides-Extra: dev
Requires-Dist: pytest; extra == 'dev'
Description-Content-Type: text/markdown

# VolSeg Tools

A library used for processing of volumes and segmentations.

## Instalation

If you cannot, or don't want to use the [PyPi Package](https://pypi.org/project/volsegtools/), you can install the project locally using [uv](https://docs.astral.sh/uv/):

```
    git clone https://github.com/DanielKriz/volseg-tools
    cd volseg-tools
    uv venv
    source .venv/bin/activate
    uv pip install .
```

## Usage

The library is intended for definition of processing pipelines for volumetric
data and segmentations.

Next to that a CLI preprocessing application is provided. After the installation
you can run:

```
molstar-preprocessor --help
```

If you wish to be more informed about the processing you might add `--verbose`
option.


Given that we have downloaded some volumetric data, e.g.
[EMD-53130](https://www.ebi.ac.uk/emdb/EMD-53130), we can start the processing
with some downsampling method:

```
molstar-preprocessor --vs EMD-53130.map --strategy tricubic --bundle mvsx --output-path out
```

This is going to create downsampled version of the provided dataset in directory
`out/` in the MVSX archive.

After this step it is possible to drop this archive into, e.g. [Mol*
Viewer](https://molstar.org/viewer/) for visualization.

To get the list of supported downsampling method you can run
`--list-strategies`.

Current implementation is sensitive to the contents of the working directory.
You might want to run `--overwrite-tmp` to ignore the data already stored in the
working directory and run the processing again (effectively overwriting the
contents of the working directory).

## Support

### Input Formats

- MRC
- NiBabel
- SFF
- Imaris Bitplane
- STL, OBJ, PLY
- VRML
- OME-TIFF
- OME-NGFF

### Serialization Formats

- MRC
- BCIF

### Bundling Formats

- MVSX
- Zip per resolution
- Zip

## License

This project is licensed under the **MIT**. See the [LICENSE](LICENSE) for more
information.
