{"id":20607309,"url":"https://github.com/albumentations-team/albucore","last_synced_at":"2025-06-16T08:04:42.949Z","repository":{"id":239508223,"uuid":"799690272","full_name":"albumentations-team/albucore","owner":"albumentations-team","description":"A high-performance image processing library designed to optimize and extend the Albumentations library with specialized functions for advanced image transformations. 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These functions serve as the foundation for [Albumentations](https://github.com/albumentations-team/albumentations), a popular image augmentation library.\n\n## Overview\n\nImage processing operations can be implemented in various ways, each with its own performance characteristics depending on the image type, size, and number of channels. Albucore aims to provide the fastest implementation for each operation by leveraging different backends such as NumPy, OpenCV, and custom optimized code.\n\nKey features:\n\n- Optimized atomic image processing functions\n- Automatic selection of the fastest implementation based on input image characteristics\n- Seamless integration with Albumentations\n- Extensive benchmarking for performance validation\n\n## Installation\n\n```bash\npip install albucore\n```\n\n## Usage\n\n```python\nimport numpy as np\nimport albucore\n# Create a sample image\nimage = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)\n# Apply a function\nresult = albucore.multiply(image, 1.5)\n```\n\nAlbucore automatically selects the most efficient implementation based on the input image type and characteristics.\n\n## Functions\n\nAlbucore includes optimized implementations for various image processing operations, including:\n\n- Arithmetic operations (add, multiply, power)\n- Normalization (per-channel, global)\n- Geometric transformations (vertical flip, horizontal flip)\n- Helper decorators (to_float, to_uint8)\n\n## Performance\n\nAlbucore uses a combination of techniques to achieve high performance:\n\n1. **Multiple Implementations**: Each function may have several implementations using different backends (NumPy, OpenCV, custom code).\n2. **Automatic Selection**: The library automatically chooses the fastest implementation based on the input image type, size, and number of channels.\n3. **Optimized Algorithms**: Custom implementations are optimized for specific use cases, often outperforming general-purpose libraries.\n\n### Benchmarks\n\nWe maintain an extensive benchmark suite to ensure Albucore's performance across various scenarios. You can find the benchmarks and their results in the [benchmarks](./benchmarks/README.md) directory.\n\n## License\n\nMIT\n\n## Acknowledgements\n\nAlbucore is part of the [Albumentations](https://github.com/albumentations-team/albumentations) project. We'd like to thank all contributors to [Albumentations](https://albumentations.ai/) and the broader computer vision community for their inspiration and support.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falbumentations-team%2Falbucore","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falbumentations-team%2Falbucore","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falbumentations-team%2Falbucore/lists"}