Metadata-Version: 2.4
Name: algorave
Version: 2.0.9
Summary: Fast, flexible, and advanced augmentation library for deep learning, computer vision, and medical imaging. Algorave offers a wide range of transformations for both 2D (images, masks, bboxes, keypoints) and 3D (volumes, volumetric masks, keypoints) data, with optimized performance and seamless integration into ML workflows.
Author-email: Ben Elfner <algorave@belfner.com>
Maintainer-email: Ben Elfner <algorave@belfner.com>
License: MIT License
        
        Copyright (c) 2017 Vladimir Iglovikov, Alexander Buslaev, Alexander Parinov,
        
        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
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        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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Project-URL: Homepage, https://github.com/belfner/algorave
Keywords: 2D augmentation,3D augmentation,aerial photography,anomaly detection,artificial intelligence,autonomous driving,bounding boxes,classification,computer vision,computer vision library,data augmentation,data preprocessing,data science,deep learning,deep learning library,depth estimation,face recognition,fast augmentation,image augmentation,image processing,image transformation,images,instance segmentation,keras,keypoint detection,keypoints,machine learning,machine learning tools,masks,medical imaging,microscopy,object counting,object detection,optimized performance,panoptic segmentation,pose estimation,python library,pytorch,quality inspection,real-time processing,robotics vision,satellite imagery,semantic segmentation,tensorflow,volumes,volumetric data,volumetric masks
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Topic :: Scientific/Engineering :: Atmospheric Science
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Typing :: Typed
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.24.4
Requires-Dist: scipy>=1.10.0
Requires-Dist: PyYAML
Requires-Dist: typing-extensions>=4.9.0; python_version < "3.10"
Requires-Dist: pydantic>=2.9.2
Requires-Dist: algocore==0.0.28
Requires-Dist: eval-type-backport; python_version < "3.10"
Requires-Dist: opencv-python>=4.9.0.80
Provides-Extra: hub
Requires-Dist: huggingface-hub; extra == "hub"
Provides-Extra: pytorch
Requires-Dist: torch; extra == "pytorch"
Provides-Extra: text
Requires-Dist: pillow; extra == "text"
Dynamic: license-file
Dynamic: requires-dist

# Algorave

A fast and flexible image augmentation library for deep learning, computer vision, and machine learning workflows.

Algorave is a fork of the [Albumentations](https://github.com/albumentations-team/albumentations) library, providing powerful image transformation capabilities with a focus on performance and ease of use.

## Installation

```bash
pip install algorave
```

For development installation:
```bash
git clone https://github.com/your-username/algorave.git
cd algorave
pip install -e .
```

## Features

- **Fast and efficient**: Optimized for performance with NumPy and OpenCV backends
- **Flexible**: Supports a wide range of image augmentations for various computer vision tasks
- **Easy to use**: Simple, intuitive API that integrates seamlessly with popular deep learning frameworks
- **Extensible**: Easy to add custom augmentations
- **Battle-tested**: Based on the proven Albumentations library used in numerous production systems

## Quick Start

```python
import algorave as A
import cv2

# Define an augmentation pipeline
transform = A.Compose([
    A.RandomCrop(width=256, height=256),
    A.HorizontalFlip(p=0.5),
    A.RandomBrightnessContrast(p=0.2),
])

# Read an image
image = cv2.imread("image.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Apply augmentations
transformed = transform(image=image)
transformed_image = transformed["image"]
```

## Supported Data Types

Algorave supports augmentation of:
- Images
- Masks
- Bounding boxes
- Keypoints

## Requirements

- Python >= 3.9
- NumPy
- OpenCV
- PyYAML
- scikit-image (optional)

## Contributing

Contributions are welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Acknowledgments

Algorave is based on [Albumentations](https://github.com/albumentations-team/albumentations), originally created by the Albumentations team. This fork was created from commit [66212d7](https://github.com/albumentations-team/albumentations/commit/66212d75638f25dae1842ad3db069cf3bf4f8449).

Special thanks to the original Albumentations authors:
- Vladimir Iglovikov
- Alexander Buslaev
- Alex Parinov
- Eugene Khvedchenya
- Mikhail Druzhinin
