# Image Augmentation Package
## Overview
This package provides a set of functions for image augmentation using various techniques. Users can integrate these functions into their projects to preprocess images for machine learning tasks or other applications.
## Features
- **Image Augmentation Functions**: Includes functions for flipping, rotating, shearing, cropping, blurring, adjusting exposure, adding noise, and more.
- **Flexible Integration**: Functions can be used individually or combined based on user requirements.
- **Output Options**: Generates augmented images in a specified output directory, with options to create ZIP or TAR.GZ archives.
## Installation
To install the package, you can use pip:
```bash
pip install amplifyy==0.0.1
```
## Usage
### Example Usage
```python
from amplifyy import (
get_custom_augmented_images,
get_augmentation_descriptions,
apply_all_augmentations,
create_zip,
create_tar_gz_with_timestamp
)
input_dir = 'path/to/input/directory'
output_dir = 'path/to/output/directory'
# Display available augmentations with their descriptions
augmentation_descriptions = get_augmentation_descriptions()
for method, description in augmentation_descriptions.items():
print(f'{method}: {description}')
# User selects augmentation methods based on the provided descriptions
user_choices = [1, 2, 3, 4, 5] # Example choices
# Apply specific augmentations
get_custom_augmented_images(input_dir, output_dir, user_choices)
# Apply all augmentations
apply_all_augmentations(input_dir, output_dir)
# Create a zip file of augmented images
create_zip('augmented_images.zip', output_dir)
# Create a tar.gz file of augmented images with a timestamp
create_tar_gz_with_timestamp(output_dir)
```
### Functions
- **`get_augmentation_descriptions()`**: Returns all augmentation options that can be select by user.
- **`get_custom_augmented_images(input_dir, output_dir, user_choices)`**: Applies selected augmentations to images in `input_dir` and saves augmented images to `output_dir`.
- **`apply_all_augmentations(input_dir, output_dir)`**: Applies all available augmentations to images in `input_dir` and saves them to `output_dir`.
- **`create_zip(zip_filename, output_dir)`**: Creates a ZIP archive containing augmented images from `output_dir`.
- **`create_tar_gz_with_timestamp(output_dir)`**: Creates a TAR.GZ archive with a timestamp containing augmented images from `output_dir`.
Additionally, we have a special command that you can run after installing the package:
```bash
amplifyy
```
What it does is a secret, so try it out and see for yourself!
## License
This project is licensed under the License - see the [LICENSE](https://github.com/Adarshh9/Amplify/blob/main/LICENSE) file for details.
## Acknowledgments
- The script uses the Pillow and OpenCV libraries for image processing.
- Image augmentation functions are adapted from common techniques used in data augmentation for computer vision.
## Contributing
Contributions are welcome! Please fork the repository and submit a pull request with your enhancements.
## Support
For any questions or issues, please [open an issue](https://github.com/Adarshh9/Amplify/issues) on GitHub.
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"description": "# Image Augmentation Package\n\n## Overview\n\nThis package provides a set of functions for image augmentation using various techniques. Users can integrate these functions into their projects to preprocess images for machine learning tasks or other applications.\n\n## Features\n\n- **Image Augmentation Functions**: Includes functions for flipping, rotating, shearing, cropping, blurring, adjusting exposure, adding noise, and more.\n- **Flexible Integration**: Functions can be used individually or combined based on user requirements.\n- **Output Options**: Generates augmented images in a specified output directory, with options to create ZIP or TAR.GZ archives.\n\n## Installation\n\nTo install the package, you can use pip:\n\n```bash\npip install amplifyy==0.0.1\n```\n\n## Usage\n\n### Example Usage\n\n```python\nfrom amplifyy import (\n get_custom_augmented_images,\n get_augmentation_descriptions,\n apply_all_augmentations,\n create_zip,\n create_tar_gz_with_timestamp\n)\n\ninput_dir = 'path/to/input/directory'\noutput_dir = 'path/to/output/directory'\n\n# Display available augmentations with their descriptions\naugmentation_descriptions = get_augmentation_descriptions()\nfor method, description in augmentation_descriptions.items():\n print(f'{method}: {description}')\n\n# User selects augmentation methods based on the provided descriptions\nuser_choices = [1, 2, 3, 4, 5] # Example choices\n\n# Apply specific augmentations\nget_custom_augmented_images(input_dir, output_dir, user_choices)\n\n# Apply all augmentations\napply_all_augmentations(input_dir, output_dir)\n\n# Create a zip file of augmented images\ncreate_zip('augmented_images.zip', output_dir)\n\n# Create a tar.gz file of augmented images with a timestamp\ncreate_tar_gz_with_timestamp(output_dir)\n```\n\n### Functions\n\n- **`get_augmentation_descriptions()`**: Returns all augmentation options that can be select by user.\n\n- **`get_custom_augmented_images(input_dir, output_dir, user_choices)`**: Applies selected augmentations to images in `input_dir` and saves augmented images to `output_dir`.\n \n- **`apply_all_augmentations(input_dir, output_dir)`**: Applies all available augmentations to images in `input_dir` and saves them to `output_dir`.\n \n- **`create_zip(zip_filename, output_dir)`**: Creates a ZIP archive containing augmented images from `output_dir`.\n \n- **`create_tar_gz_with_timestamp(output_dir)`**: Creates a TAR.GZ archive with a timestamp containing augmented images from `output_dir`.\n\nAdditionally, we have a special command that you can run after installing the package:\n\n```bash\namplifyy\n```\n\nWhat it does is a secret, so try it out and see for yourself!\n\n## License\n\nThis project is licensed under the License - see the [LICENSE](https://github.com/Adarshh9/Amplify/blob/main/LICENSE) file for details.\n\n## Acknowledgments\n\n- The script uses the Pillow and OpenCV libraries for image processing.\n- Image augmentation functions are adapted from common techniques used in data augmentation for computer vision.\n\n## Contributing\n\nContributions are welcome! Please fork the repository and submit a pull request with your enhancements.\n\n## Support\n\nFor any questions or issues, please [open an issue](https://github.com/Adarshh9/Amplify/issues) on GitHub.\n",
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