Effortlessly Organize and Analyze Event Photos with Advanced Facial Recognition
FaceLinker is an advanced image processing and facial recognition application designed to streamline the organization and analysis of event photos. With FaceLinker, users can:
- Upload multiple images from various events.
- Extract faces from these images.
- Group similar faces together for easy organization and analysis.
- Bulk Image Upload: Upload multiple images from different events for batch processing.
- Facial Extraction: Automatically detect and extract faces from uploaded images.
- Face Grouping: Organize photos by grouping similar faces together.
- Event Management: Keep your event photos organized by categorizing them into different events.
Frontend: HTML, Tailwind, JavaScript
Backend: Python Flask
Database: MongoDB
Facial Recognition: DeepFace
Image Storage: Supabase
To get started with FaceLinker, follow these steps:
Prerequisites
- Python installed on your machine.
- MongoDB installed and running.
- Supabase account and project set up.
git clone https://github.com/yourusername/FaceLinker.gitGo to the project directory
cd FaceLinkerCreate and activate a virtual environment:
python3 -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`Install the required Python packages:
pip install -r requirements.txtTo run this project, you will need to add the following environment variables to your .env file
SECRET_KEY
MONGO_URI
SUPABASE_URL
SUPABASE_KEY
SUPABASE_BUCKET
flask runThe application will be available at http://localhost:5000.
FaceLinker.mp4
Contributions are welcome! Please follow these steps:
- Fork the repository.
- Create a new branch (
git checkout -b feature/your-feature-name). - Commit your changes (
git commit -m 'Add some feature'). - Push to the branch (
git push origin feature/your-feature-name). - Open a Pull Request.
For any questions or suggestions, please feel free to contact us.