| Name | albucore JSON |
| Version |
0.0.21
JSON |
| download |
| home_page | None |
| Summary | High-performance image processing functions for deep learning and computer vision. |
| upload_time | 2024-11-01 21:47:04 |
| maintainer | Vladimir Iglovikov |
| docs_url | None |
| author | Vladimir Iglovikov |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2024 Vladimir Iglovikov 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. |
| keywords |
deep learning
image processing
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 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.
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"description": "# Albucore: High-Performance Image Processing Functions\n\nAlbucore 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.\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",
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