AI Face Swapper with Celebrity Faces

Your ultimate tool for seamless facial transformations in photos and videos, powered by cutting-edge machine learning!

📹 Demo

(https://github.com/2100031988/AI-Face-Swapper-using-Celebrity/blob/38fa1d6a0be5c94687110a5c0b4134805b4426a6/video_demonstration.mp4)

✨ Introduction

AI Lecture Face Swapper is a versatile, cross-platform application built using Python. It leverages advanced deep learning algorithms to detect, learn, and swap faces in images or videos. Whether you're a developer, hobbyist, or AI enthusiast, this tool provides endless opportunities to explore facial transformations creatively and efficiently.

🌟 Key Features

• Face Detection: Automatically locate and extract faces from input images or video frames with high accuracy.
• Deep Learning Model Training: Build a custom model tailored to deliver precise facial transformations.
• Face Conversion: Seamlessly swap faces in the target media using advanced AI models.
• User-Friendly Interface: Choose between a command-line interface or a sleek graphical interface for easy operation.
• Batch Processing: Effortlessly handle multiple images and video frames in one go for large-scale projects.

⚙️ System Requirements

• An NVIDIA GPU with CUDA support for optimized performance.
• Software: Python, Node.js, TensorFlow, Keras, and OpenCV libraries.
• Platforms: Windows, macOS, and Linux are fully supported.

🚀 Quick Start Guide

🛠️ Installation

1. Clone the Project:

   git clone https://github.com/2100031988/AI-Face-Swapper-using-Celebrity.git

2. Navigate to the Project Directory:

   cd ai-face-transformer

3. Install Dependencies:

   • Python dependencies:

     pip install -r requirements.txt

   • Node.js dependencies:

     npm install

4. Run the Project:

   • For command-line usage:

     python face_transformer.py

   • For graphical interface:

     npm run start

5. Build the Frontend (Optional):

   npm run build

6. Additional Configuration:

   • Customize configuration files in the config/ directory as needed for your specific use case.

💡 Usage Tips

• Model Training: The more diverse and extensive your dataset, the better your transformation results will be!
• Batch Processing: Place multiple images in the input folder to process all in one session.
• Settings: Adjust configurations in the config/ directory to fine-tune performance.

🤝 Contributing

We welcome contributions from the community! Fork the repository, experiment with the tool, and propose new features or improvements via pull requests. Please follow our contribution guidelines in CONTRIBUTING.md.

📜 License

This project is licensed under the GPL License – see the LICENSE file for details.

©️ Copyright

© 2024 Sabyasachi Kumar. All rights reserved

🙏 Acknowledgements

We extend our heartfelt thanks to the open-source community and all contributors for their unwavering support and feedback in refining and enhancing this project.

🎉 Happy Coding! 🎉