PolyGoneNMS is a library for efficient and distributed polygon Non-Maximum Suppression (NMS) in Python. It supports various NMS methods, intersection calculations, and can handle large numbers of polygons in 1D, 2D, and 3D spaces. PolyGoneNMS uses R-tree data structures and shapely polygon objects for optimal performance.
- Efficient polygon NMS for large numbers of polygons.
- Support for various NMS methods: Default, Soft, and Class Agnostic.
- Support for different intersection methods: IOU, IOS, and Dice.
- R-tree data structure for efficient spatial indexing and querying.
- Distributed processing support using Ray and Dask.
- Comprehensive documentation and examples.
You can install PolyGoneNMS using pip:
pip install polygone-nmsimport numpy as np
from polygone_nms import polygone_nms
# Example input data
data = np.array([
[0, 0, 1, 1, 0, 1, 0, 0, 1, 0.9],
[0.5, 0.5, 1.5, 1.5, 0.5, 1.5, 0, 0, 1, 0.8],
])
# Apply NMS
results = nms(data, distributed=None, nms_method="Default", intersection_method="IOU")
print("Filtered indices:", results)
# Filtered data
print("Filtered data:")
print(data[results])For a more detailed guide on using PolyGoneNMS, please see the Quickstart in the documentation.
Detailed documentation is available at:
We welcome contributions to the project! Please follow the usual GitHub process for submitting issues or pull requests.
This project is licensed under the MIT License.