Image Processing with Lenia

This project implements image processing using Lenia cellular automata with a Flask backend for intelligent image compression.

Features

• Lenia-Enhanced Compression: Uses cellular automata patterns for predictive image compression
• Real Compression: Achieves 2:1 to 4:1 compression ratios with maintained quality
• Multi-format Support: Handles PNG, JPG, and JPEG images
• Color Preservation: Maintains color information in compressed images
• Web Interface: Simple drag-and-drop interface for image processing

Requirements

• Python 3.7+
• Flask
• OpenCV (cv2)
• NumPy
• SciPy
• scikit-learn

Setup

For Windows Users

1. Clone this repository

   git clone <repository-url>
   cd <repository-name>

2. Create and activate a virtual environment:

   python -m venv venv
   venv\Scripts\activate

3. Install dependencies:

   pip install flask flask-cors opencv-python numpy scipy scikit-learn werkzeug

4. Run the Flask app:

   python main.py

5. Open the web interface:

   • Open index.html in your web browser
   • Or navigate to http://127.0.0.1:5000 if serving the HTML through Flask

For macOS/Linux Users

1. Clone this repository

   git clone <repository-url>
   cd <repository-name>

2. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies:

   pip install flask flask-cors opencv-python numpy scipy scikit-learn werkzeug

4. Run the Flask app:

   python3 main.py

5. Open the web interface:

   • Open index.html in your web browser
   • Or navigate to http://127.0.0.1:5000 if serving the HTML through Flask

Usage

1. Upload an Image: Click "Choose File" and select a PNG, JPG, or JPEG image
2. View Compression: The system will show:
   • Original file size
   • Compressed file size
   • Compression ratio achieved
3. Download Result: Click "Download Processed" to get the compressed image
4. File Management: Previously uploaded files are stored locally and can be re-downloaded

How It Works

The system uses Lenia Cellular Automata for intelligent image compression:

1. Predictive Analysis: Lenia kernels analyze image patterns to predict pixel values
2. Multi-scale Processing: Different kernel sizes handle various image features
3. Residual Encoding: Only unpredictable differences are stored
4. Smart Reconstruction: Combines Lenia predictions with stored residuals
5. Quality Enhancement: Applies bilateral filtering for final image improvement

Project Structure

├── main.py              # Flask backend server
├── index.html           # Web interface
├── script.js           # Frontend JavaScript
├── uploads/            # Temporary upload folder (auto-created)
├── processed/          # Compressed images storage (auto-created)
└── README.md           # This file

API Endpoints

• POST /upload - Upload and compress an image
• GET /download/<filename> - Download compressed image
• GET /info/<filename> - Get compression information

Troubleshooting

Common Issues

"Module not found" errors:

pip install --upgrade pip
pip install flask flask-cors opencv-python numpy scipy scikit-learn werkzeug

CORS errors in browser:

• Make sure Flask server is running on http://127.0.0.1:5000
• Check that CORS is enabled in the Flask app

File upload fails:

• Ensure the image format is PNG, JPG, or JPEG
• Check file size (very large images may take longer to process)

Windows-specific issues:

• Use python instead of python3 in commands
• Use venv\Scripts\activate to activate virtual environment
• Ensure Python is added to system PATH

License

This project is open source and available under the MIT License.

Acknowledgments

• Lenia cellular automata research by Bert Wang-Chak Chan
• OpenCV for image processing capabilities
• Flask for the web framework