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https://github.com/albumentations-team/albucore
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.
https://github.com/albumentations-team/albucore
albumentations artificial-intelligence automation computer-vision data-augmentation deep-learning efficiency high-performance-computing image-augmentation image-processing machine-learning neural-networks opencv performance-optimization python
Last synced: about 2 months ago
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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.
- Host: GitHub
- URL: https://github.com/albumentations-team/albucore
- Owner: albumentations-team
- License: mit
- Created: 2024-05-12T21:52:40.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-10-23T18:41:46.000Z (2 months ago)
- Last Synced: 2024-10-24T02:59:56.868Z (2 months ago)
- Topics: albumentations, artificial-intelligence, automation, computer-vision, data-augmentation, deep-learning, efficiency, high-performance-computing, image-augmentation, image-processing, machine-learning, neural-networks, opencv, performance-optimization, python
- Language: Python
- Homepage:
- Size: 117 KB
- Stars: 10
- Watchers: 5
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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README
# 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 [Albumentations](https://github.com/albumentations-team/albumentations), a popular image augmentation library.
## Overview
Image 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.
Key features:
- Optimized atomic image processing functions
- Automatic selection of the fastest implementation based on input image characteristics
- Seamless integration with Albumentations
- Extensive benchmarking for performance validation
## Installation
```bash
pip install albucore
```
## Usage
```python
import numpy as np
import albucore
# Create a sample image
image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
# Apply a function
result = albucore.multiply(image, 1.5)
```
Albucore automatically selects the most efficient implementation based on the input image type and characteristics.
## Functions
Albucore includes optimized implementations for various image processing operations, including:
- Arithmetic operations (add, multiply, power)
- Normalization (per-channel, global)
- Geometric transformations (vertical flip, horizontal flip)
- Helper decorators (to_float, to_uint8)
## Performance
Albucore uses a combination of techniques to achieve high performance:
1. **Multiple Implementations**: Each function may have several implementations using different backends (NumPy, OpenCV, custom code).
2. **Automatic Selection**: The library automatically chooses the fastest implementation based on the input image type, size, and number of channels.
3. **Optimized Algorithms**: Custom implementations are optimized for specific use cases, often outperforming general-purpose libraries.
### Benchmarks
We 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.
## License
MIT
## Acknowledgements
Albucore 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.