Metadata-Version: 2.4
Name: graph_segment
Version: 0.0.2
Summary: Graph-based image segmentation
Keywords: graph,segmentation,tracking
Author-Email: Xiangyang Ju <xju@lbl.gov>
License-Expression: MIT
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: C++
Project-URL: Homepage, https://github.com/xju2/graph_segment_for_gnn_tracking
Provides-Extra: dev
Requires-Dist: pre-commit; extra == "dev"
Requires-Dist: ruff>=0.9.7; extra == "dev"
Requires-Dist: pytest; extra == "dev"
Requires-Dist: pytest-cov; extra == "dev"
Requires-Dist: cibuildwheel; extra == "dev"
Requires-Dist: twine; extra == "dev"
Requires-Dist: wheel; extra == "dev"
Description-Content-Type: text/markdown

# Graph Segment Algorithms for Graph Neural Network-based Tracking

### Installation
```bash
pip install graph_segment
```

### Test
Download the data `python3 download_data.py`. And then `./build/bin/walk_through data/debug_graph.dot`.


### Developer Guide
Install a Python virtual environment and activate it:
```bash
python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install cibuildwheel build twine
```

Then you can run
```bash
pipx run cibuildwheel --platform linux
```

### Packing and Uploading
```bash
python3 -m build
twine upload dist/*
```
#### Note
The code was improved by `OpenAI o1Pro` model. The execution time was reduced from 403 ms to 190 ms.
