Image Data Augmentation App 🖼️

A powerful and user-friendly Streamlit application for performing various image augmentation techniques on multiple images simultaneously. This tool is perfect for machine learning practitioners, data scientists, and anyone working with image datasets who needs to expand their training data through augmentation.

• Hosting Link:
  • Verify that the hosting link ([demo_link] (https://data-image-augmentation.streamlit.app/)) is correct and active.

✨ Features

• Multiple Image Processing: Upload and process multiple images simultaneously
• Various Augmentation Techniques:
  • Rotation (adjustable degrees)
  • Random horizontal flip
  • Random crop with resizing
  • Color jitter (brightness, contrast, saturation)
  • Grayscale conversion
  • Sepia filter
• Batch Processing: Process all uploaded images with the same augmentation parameters
• Real-time Preview: See augmented versions immediately in the web interface
• Progress Tracking: Visual progress bar during processing
• Bulk Download: Download all original and augmented images in an organized ZIP file
• User-Friendly Interface: Easy-to-use sidebar controls for augmentation parameters

🚀 Installation

1. Clone the repository:

git clone https://github.com/yourusername/image-augmentation-app.git
cd image-augmentation-app

2. Create and activate a virtual environment (optional but recommended):

# Windows
python -m venv env
env\Scripts\activate

# Linux/Mac
python3 -m venv env
source env/bin/activate

3. Install required packages:

pip install -r requirements.txt

📦 Requirements

Create a requirements.txt file with the following dependencies:

streamlit>=1.0.0
torch>=1.8.0
torchvision>=0.9.0
Pillow>=8.0.0
numpy>=1.19.0

💻 Usage

1. Start the Streamlit app:

streamlit run app.py

2. Open your web browser and navigate to the URL shown in your terminal (typically http://localhost:8501)

3. Use the app:

   • Upload one or multiple images using the file uploader
   • Adjust augmentation parameters in the sidebar
   • Click "Generate Augmentations for All Images"
   • Preview the results
   • Download all images as a ZIP file

📁 Output Structure

The downloaded ZIP file will be organized as follows:

augmented_images_TIMESTAMP.zip
├── image_1_filename1/
│   ├── original_filename1.jpg
│   ├── augmented_1_filename1.jpg
│   ├── augmented_2_filename1.jpg
│   └── ...
├── image_2_filename2/
│   ├── original_filename2.jpg
│   ├── augmented_1_filename2.jpg
│   └── ...
└── ...

🎛️ Available Parameters

Parameter	Description	Range
Number of augmentations	Number of augmented versions per image	1-10
Rotation degrees	Maximum rotation angle	0-180°
Brightness variation	Brightness adjustment range	0.0-1.0
Contrast variation	Contrast adjustment range	0.0-1.0
Saturation variation	Saturation adjustment range	0.0-1.0

🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a new branch (git checkout -b feature/improvement)
3. Make your changes
4. Commit your changes (git commit -am 'Add new feature')
5. Push to the branch (git push origin feature/improvement)
6. Create a Pull Request

🐛 Bug Reports

If you find a bug, please create an issue with:

• A clear description of the bug
• Steps to reproduce
• Expected behavior
• Screenshots (if applicable)

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built with Streamlit
• Image processing powered by PyTorch and torchvision
• Image manipulation using Pillow

📞 Contact

• EL ANSARI Mostapha - [elansarimostapha011@gmail.com]
• Project Link: https://github.com/yourusername/image-augmentation-app

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