https://github.com/alfa-echo-niner-ait/drowsiness-detection
A web-based drowsiness detection system that uses facial landmarks from webcam video to detect eye closure using Eye Aspect Ratio (EAR). Works in both desktop and mobile browsers.
https://github.com/alfa-echo-niner-ait/drowsiness-detection
flask mediapipe opencv python
Last synced: 3 months ago
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A web-based drowsiness detection system that uses facial landmarks from webcam video to detect eye closure using Eye Aspect Ratio (EAR). Works in both desktop and mobile browsers.
- Host: GitHub
- URL: https://github.com/alfa-echo-niner-ait/drowsiness-detection
- Owner: alfa-echo-niner-ait
- License: mit
- Created: 2025-05-23T09:26:23.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-23T14:26:02.000Z (about 1 year ago)
- Last Synced: 2025-06-17T08:07:31.887Z (about 1 year ago)
- Topics: flask, mediapipe, opencv, python
- Language: Python
- Homepage:
- Size: 60.1 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Driver Drowsiness Detection Web App
**Real-Time Eye Closure Detection Using MediaPipe, OpenCV & Flask**
A web-based drowsiness detection system that uses facial landmarks from webcam video to detect eye closure using **Eye Aspect Ratio (EAR)**. Works in both **desktop and mobile browsers**.
### Perfect for:
🧠 Research in driver safety
💻 Computer vision demos
📱 Mobile-friendly applications
📐 EAR-based fatigue detection
---
## 🔍 Overview
This project implements a lightweight, real-time **driver drowsiness detection system** using:
- **MediaPipe FaceMesh** – for 468 facial landmarks
- **OpenCV** – For image processing, drawing eye landmarks, and video manipulation
- **Flask** – Backend processing
- **HTML + JavaScript** – Frontend UI with live webcam access
- **Eye Aspect Ratio (EAR)** – For blink/drowsiness detection
- **Audio alert** – Visual + sound alert when eyes are closed too long
No need for extra hardware, all we need is a **webcam-enabled device** and a modern **browser**.
---
## 🧩 Features
✅ Real-time webcam feed
✅ EAR calculation per frame
✅ Visual + audio alert on drowsiness
✅ Responsive design (mobile/desktop friendly)
✅ Easy setup – no complex dependencies
✅ Works over local network or via Ngrok
---
## 🛠️ Tools Versions
| Tool | Version |
|------|---------|
| Python | 3.12.1 |
| Flask | 3.1.1 |
| MediaPipe | 0.10.21 |
| OpenCV | 4.11.0.86 |
| SciPy | 1.15.3 |
| Base64 | Built-in |
| ngrok (optional) | For HTTPS tunneling |
---
## 🚀 How to Run
1. Clone or download the repo
2. Install dependencies:
```bash
pip install -r requirements.txt
```
Or,
```bash
pip install flask mediapipe opencv-python numpy scipy
```
3. Start the Flask server:
```bash
python app.py
```
4. Open browser at:
```
http://localhost:5000
```
5. (Optional) Use [Ngrok](https://ngrok.com/downloads/) for mobile testing:
```bash
ngrok http 5000
```
Open the generated URL on any device.
---
## 🖥️ Usage
1. Allow camera access when prompted.
2. The app will calculate EAR every 200ms.
3. If EAR < threshold (`0.22`) for 10+ frames → "DROWSINESS DETECTED!" alert appears.
4. Audio alarm plays automatically.
5. Once eyes reopen, status resets to "Awake".
---
## ⚙️ Technical Details
### Eye Landmarks Used
```python
LEFT_EYE = [362, 385, 387, 263, 373, 380]
RIGHT_EYE = [33, 160, 158, 133, 153, 144]
```
### EAR Formula
$$
\text{EAR} = \frac{(A + B)}{(2 \times C)}
$$
Where:
- $ A = \text{Top-left to bottom-left vertical distance} $
- $ B = \text{Top-right to bottom-right vertical distance} $
- $ C = \text{Left corner to right corner horizontal distance} $
Threshold: `0.22`
Frame Count to Trigger Alert: `10`
---
## 🎨 UI Preview
| Feature | Description |
|--------|-------------|
| Webcam Video | Flipped to match natural view |
| EAR Display | Real-time eye aspect ratio shown |
| Status Text | Shows "Awake" or "Drowsy" |
| Red Glow + Flash | Visual alert when drowsy |
| Audio Alert | Plays sound when drowsiness detected |
---
## 📋 License
MIT License – see [LICENSE](LICENSE)
---
## 📬 Feedback & Contributions
Contributions are welcome! Feel free to open issues or PRs.
👤 **Maintainer**: Ayub Ali Emon (emon@sdust.edu.cn), member of [CogTwins Lab](https://cogtwins.github.io/), Shandong University of Science and Technology.