{"repo":"albumentations-team/albucore","free":true,"listed":false,"github":"https://github.com/albumentations-team/albucore","clone":"git clone https://github.com/albumentations-team/albucore.git","description":"A high-performance image processing library designed to optimize and extend the Albumentations library with specialized functions for advanced image transformations. Perfect for developers working in computer vision who require efficient and scalable image augmentation.","language":"Python","stars":122,"topics":["albumentations","artificial-intelligence","automation","computer-vision","data-augmentation","deep-learning","efficiency","high-performance-computing","image-augmentation","image-processing"],"license":"MIT","category":"machine-learning","readme_excerpt":"Albucore: High-Performance Image Processing Functions Albucore is a library of optimized atomic functions designed for efficient image processing. These functions serve as the foundation for AlbumentationsX, an image augmentation library. Citing AlbumentationsX If you use Albucore as part of an AlbumentationsX pipeline, please cite AlbumentationsX: One Augmentation Pipeline for Images and Related Annotations. Your citation makes the project's research impact visible to funders and helps sustain maintenance. Overview Image processing operations can be implemented in several ways, with performance depending on dtype, size, layout, and channel count. Albucore routes each operation to a benchmark-selected NumPy, OpenCV, NumKong, StringZilla, or eligible CPU PyTorch implementation. Most image-processing routers support uint8 and float32 . The elementwise exp , log , and sqrt routers are float32 -only; conversion helpers also support additional integer dtypes. Key features: - Optimized atomic image processing functions - Automatic selection of the fastest implementation based on input image characteristics - Seamless integration with AlbumentationsX - Reproducible micro-benchmarks and committed routing reports (see benchmarks/README.md) Installation Requires Python 3.10+. Choose and install the PyTorch build for your CPU, CUDA, or MPS environment first. Then install Albucore with an OpenCV extra. For a Linux CPU-only headless application: CUDA or macOS (MPS): Select and install the","default_branch":null,"files":null,"tree":[],"storefront":"/r/albumentations-team","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/albumentations-team/albucore/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}