A Flask web application that uses YOLOv8 to recognize and classify traffic signs in real-time using your computer's camera. The application includes user authentication, a collection system to track discovered signs, and a web interface for easy interaction.

• 🔐 User Authentication: Secure login and registration system
• 📷 Real-time Camera Recognition: Live traffic sign detection using your webcam
• 🎯 YOLOv8 Integration: Advanced object detection and classification
• 📊 Collection System: Track and display discovered traffic signs
• 🌐 Web Interface: User-friendly Flask web application
• 📱 Responsive Design: Works on desktop and mobile devices

Add screenshots of your application here

Before running this application, make sure you have:

• Python 3.7 or higher
• A webcam for real-time recognition
• Git (for cloning the repository)

1. Clone the repository

   git clone https://github.com/yesminehe/traffic-signs-recognition.git
   cd traffic-signs-recognition

2. Create a virtual environment

   python -m venv .venv

3. Activate the virtual environment

   Windows:

   .venv\Scripts\activate

   macOS/Linux:

   source .venv/bin/activate

4. Install dependencies

   pip install -r requirements.txt

5. Download the YOLO model

   You'll need to obtain the best.pt model file. This should be placed in the root directory of the project.

1. Start the application

   python app.py

2. Open your web browser Navigate to http://localhost:5000

3. Create an account or login

   • Register a new account or use existing credentials
   • The system will redirect you to the main dashboard

4. Use the camera recognition

   • Click on the camera section to start real-time recognition
   • Point your camera at traffic signs to detect and classify them
   • Discovered signs are automatically added to your collection

5. View your collection

   • Check the collection page to see all discovered traffic signs
   • Track your progress in finding different types of signs

traffic-signs-recognition/
├── app.py                 # Main Flask application
├── models.py              # User authentication models
├── requirements.txt       # Python dependencies
├── class_descriptions.json # Traffic sign class descriptions
├── best.pt               # YOLOv8 trained model (not included)
├── templates/            # HTML templates
│   ├── index.html
│   ├── login.html
│   ├── signup.html
│   └── ...
├── static/               # Static files (CSS, JS, images)
│   ├── css/
│   ├── js/
│   └── uploads/
└── README.md

You can set the following environment variables:

• SECRET_KEY: Flask secret key for session management
• DEBUG: Set to True for development mode

The application uses a YOLOv8 model trained on traffic sign datasets. The model file (best.pt) should be placed in the root directory.

• GET / - Main dashboard (requires authentication)
• GET /login - Login page
• POST /login - Login form submission
• GET /signup - Registration page
• POST /signup - Registration form submission
• GET /logout - Logout user
• GET /camera - Camera recognition page
• POST /start_camera - Start camera stream
• POST /stop_camera - Stop camera stream
• GET /video_feed