About

About DistanceTransforms

DistanceTransforms is a high-performance library for computing distance transforms, available for both Julia and Python. The library is designed to be efficient, versatile, and easy to use, with support for multi-threading and GPU acceleration.

Project History

DistanceTransforms.jl was developed by Dale Black and contributors to address the need for fast, GPU-accelerated distance transforms in Julia. The Python wrapper, py-distance-transforms, was later added to make these capabilities accessible to the Python community.

Features

Fast CPU implementation using the Felzenszwalb algorithm
Multi-threading support for enhanced CPU performance
GPU acceleration for NVIDIA (CUDA), AMD (ROCm), and Apple (Metal)
Support for arrays of various dimensions (1D, 2D, 3D)
Clean, simple API in both Julia and Python
Comprehensive documentation and examples

Applications

Distance transforms have numerous applications across different fields:

Image Processing: Segmentation, skeletonization, feature extraction
Computer Vision: Object recognition, shape analysis
Medical Imaging: Registration, segmentation, analysis
Computational Geometry: Voronoi diagrams, medial axis transforms
Machine Learning: Distance-based features, loss functions

Implementation Details

The core algorithm implemented in DistanceTransforms.jl is based on the work of Felzenszwalb and Huttenlocher, which provides an efficient method for computing the Euclidean distance transform in O(n) time complexity.

For GPU acceleration, the library uses KernelAbstractions.jl, which allows the code to be hardware-agnostic and run efficiently across different GPU architectures.

Acknowledgments

DistanceTransforms.jl and its Python wrapper wouldn’t be possible without the contributions of many individuals and the support of the Julia and Python communities. We’d like to specifically acknowledge:

The original authors of the distance transform algorithms
Contributors to related Julia packages like ImageMorphology.jl
The developers of KernelAbstractions.jl, which enables the hardware-agnostic GPU implementation
PythonCall.jl and juliacall for enabling the Python wrapper

Citation

If you use DistanceTransforms in your research, please cite:

@software{DistanceTransforms,
  author = {Black, Dale and Contributors},
  title = {DistanceTransforms: Fast Distance Transforms for Julia and Python},
  url = {https://github.com/Dale-Black/DistanceTransforms.jl},
  year = {2023}
}

License

DistanceTransforms is available under the MIT License.

MIT License

Copyright (c) 2023 Dale Black and contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Contact

For questions, issues, or contributions: - GitHub: DistanceTransforms.jl - GitHub: py-distance-transforms

--- title: "About" format: html: toc: true --- # About DistanceTransforms DistanceTransforms is a high-performance library for computing distance transforms, available for both Julia and Python. The library is designed to be efficient, versatile, and easy to use, with support for multi-threading and GPU acceleration. ## Project History DistanceTransforms.jl was developed by Dale Black and contributors to address the need for fast, GPU-accelerated distance transforms in Julia. The Python wrapper, py-distance-transforms, was later added to make these capabilities accessible to the Python community. ## Features - Fast CPU implementation using the Felzenszwalb algorithm - Multi-threading support for enhanced CPU performance - GPU acceleration for NVIDIA (CUDA), AMD (ROCm), and Apple (Metal) - Support for arrays of various dimensions (1D, 2D, 3D) - Clean, simple API in both Julia and Python - Comprehensive documentation and examples ## Applications Distance transforms have numerous applications across different fields: - **Image Processing**: Segmentation, skeletonization, feature extraction - **Computer Vision**: Object recognition, shape analysis - **Medical Imaging**: Registration, segmentation, analysis - **Computational Geometry**: Voronoi diagrams, medial axis transforms - **Machine Learning**: Distance-based features, loss functions ## Implementation Details The core algorithm implemented in DistanceTransforms.jl is based on the work of Felzenszwalb and Huttenlocher, which provides an efficient method for computing the Euclidean distance transform in O(n) time complexity. For GPU acceleration, the library uses KernelAbstractions.jl, which allows the code to be hardware-agnostic and run efficiently across different GPU architectures. ## Acknowledgments DistanceTransforms.jl and its Python wrapper wouldn't be possible without the contributions of many individuals and the support of the Julia and Python communities. We'd like to specifically acknowledge: - The original authors of the distance transform algorithms - Contributors to related Julia packages like ImageMorphology.jl - The developers of KernelAbstractions.jl, which enables the hardware-agnostic GPU implementation - PythonCall.jl and juliacall for enabling the Python wrapper ## Citation If you use DistanceTransforms in your research, please cite: ```bibtex @software{DistanceTransforms, author = {Black, Dale and Contributors}, title = {DistanceTransforms: Fast Distance Transforms for Julia and Python}, url = {https://github.com/Dale-Black/DistanceTransforms.jl}, year = {2023} } ``` ## License DistanceTransforms is available under the MIT License. ``` MIT License Copyright (c) 2023 Dale Black and contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ``` ## Contact For questions, issues, or contributions: - GitHub: [DistanceTransforms.jl](https://github.com/Dale-Black/DistanceTransforms.jl) - GitHub: [py-distance-transforms](https://github.com/Dale-Black/py-distance-transforms)