👁️ Driver Drowsiness Monitor

A high-performance, real-time safety system that monitors driver alertness using computer vision and provides instant feedback through a modern web dashboard and Telegram notifications.

🌟 Features

• Real-time Eye Tracking: Uses MediaPipe and EAR (Eye Aspect Ratio) calculation for high-precision drowsiness detection.
• Stable Live Feed: Optimized React-based vision system with manual Start/Stop controls.
• Intelligent Alerting:
  • Warning State: Triggered after ~2 seconds of sustained drowsiness (Visual amber glow).
  • Critical Alert: Triggered after ~6 seconds of sustained drowsiness (Visual red pulse + alarming).
• Telegram Integration: Automated alerts sent to @EARdrowsines_alert including JPEG snapshots of the driver and precise timestamps.
• Modern Analytics Dashboard: Real-time graphs for EAR value, system health status, and a detailed event log.
• Customizable Thresholds: Adjustable EAR sensitivity and notification cooldowns via the settings panel.

🛠️ Tech Stack

• Frontend: React 18, TypeScript, Tailwind CSS, Lucide React.
• Backend: Flask (Python), MediaPipe, OpenCV, Asyncio.
• Communication: REST API, Telegram Bot API.
• Deployment: Vercel ready (Backend & Frontend).

🚀 Installation & Setup

1. Prerequisites

• Python 3.10+
• Node.js 18+
• A webcam

2. Backend Setup

cd MainLogic
pip install -r requirements.txt --break-system-packages
# Ensure face_landmarker.task is in the MainLogic folder
python Drowniness_detect.py

3. Frontend Setup

cd Client
npm install
npm run dev

⚙️ Configuration

Telegram Bot

The system is pre-configured to send alerts to the @EARdrowsines_alert channel. To use your own bot:

1. Update BOT_TOKEN and CHAT_ID in MainLogic/Drowniness_detect.py.
2. Ensure your bot has permission to post to the specified channel.

Accuracy Tuning

• EAR Threshold: Default is set to 0.20. Lower values make the system less sensitive (requiring more eye closure).
• Notification Cooldown: Default is 60s to prevent alert spamming.

🏗️ Project Structure

├── Client/                 # React Frontend (Vite + TypeScript)
│   ├── src/
│   │   ├── components/     # UI Components (CameraFeed, EventLog, etc.)
│   │   ├── services/       # API Integration
│   │   └── types/          # TypeScript Definitions
├── MainLogic/              # Python Backend (Flask)
│   ├── Drowniness_detect.py # Core logic and API
│   ├── face_landmarker.task # MediaPipe model file
│   └── requirements.txt     # Backend dependencies
└── vercel.json             # Deployment configuration

📝 Deployment

This project is optimized for deployment on Vercel:

1. Connect your repository to Vercel.
2. The vercel.json file in the root will automatically configure the Python serverless functions.
3. Ensure serverless functions environment allows for enough memory for MediaPipe (at least 512MB recommended).

📜 License

MIT License - See LICENSE for details............