# Driver DSMS/ADAS Real-Time Validator

A Streamlit-based application for real-time Driver State Monitoring System (DSMS) and Advanced Driver Assistance System (ADAS) validation using computer vision and deep learning.

## 📋 Project Status

**Current Status**: ⚠️ **Requires Critical Fixes Before Use**

- **Dependencies**: 2/11 installed (18%)
- **Code Quality**: Multiple critical bugs identified
- **Performance**: Not optimized for low-spec CPUs
- **Functionality**: Non-functional (will crash on execution)

## 🚀 Quick Start

### 1. Check Current Status

```bash
python3 check_dependencies.py
```

### 2. Install Dependencies

```bash
pip install -r requirements.txt
```

**Note**: This will download ~2GB and require ~5GB disk space.

### 3. Configure

Edit `track_drive.py` and set your Roboflow API key:
```python
'roboflow_api_key': 'YOUR_ACTUAL_KEY_HERE',
```

### 4. ⚠️ **DO NOT RUN YET**

The code has critical bugs that must be fixed first. See [ASSESSMENT_REPORT.md](ASSESSMENT_REPORT.md) for details.

## 📚 Documentation

- **[ASSESSMENT_REPORT.md](ASSESSMENT_REPORT.md)** - Comprehensive evaluation, issues, and improvement plan
- **[QUICK_START.md](QUICK_START.md)** - Installation and setup guide
- **[requirements.txt](requirements.txt)** - Python dependencies

## 🔍 What This Project Does

### DSMS (Driver State Monitoring)
- Drowsiness detection (PERCLOS)
- Distraction detection (phone use, looking away)
- Smoking detection
- Seatbelt detection
- Driver absence detection

### ADAS (Advanced Driver Assistance)
- Forward Collision Warning (FCW)
- Lane Departure Warning (LDW)
- Pedestrian detection
- Tailgating detection
- Hard braking/acceleration detection
- Overspeed detection

## 🛠️ Technology Stack

- **Streamlit**: Web UI framework
- **YOLOv8n**: Object detection (vehicles, pedestrians, phones)
- **MediaPipe**: Face mesh analysis for PERCLOS
- **OpenCV**: Image processing and optical flow
- **Roboflow**: Seatbelt detection API
- **VideoMAE**: Action recognition (⚠️ too heavy, needs replacement)
- **scikit-learn**: Anomaly detection

## ⚠️ Known Issues

### Critical Bugs (Must Fix)
1. **Optical Flow API Error**: `calcOpticalFlowPyrLK` used incorrectly - will crash
2. **VideoMAE JIT Scripting**: Will fail - transformers can't be JIT scripted
3. **YOLO ONNX Parsing**: Incorrect output format assumption
4. **ONNX Export**: Runs on every load instead of conditionally

### Performance Issues
1. **VideoMAE Too Heavy**: Not suitable for low-spec CPUs
2. **All Models Load at Startup**: Slow initialization
3. **No Model Quantization**: VideoMAE runs in FP32
4. **Untrained Isolation Forest**: Produces random predictions

See [ASSESSMENT_REPORT.md](ASSESSMENT_REPORT.md) for complete analysis.

## 📊 Performance Targets

**Target Hardware**: Low-spec CPU (4 cores, 2GHz, 8GB RAM)

**Current (Estimated After Fixes)**:
- FPS: 5-8
- Memory: 4-6GB
- CPU: 70-90%

**Target (After Optimizations)**:
- FPS: 12-15
- Memory: 2-3GB
- CPU: <80%
- Accuracy: >90% precision, >85% recall

## 🗺️ Implementation Roadmap

### Phase 1: Critical Fixes (Week 1)
- Fix optical flow implementation
- Remove VideoMAE JIT scripting
- Fix YOLO ONNX parsing
- Add error handling
- Install and test dependencies

### Phase 2: Performance Optimization (Week 2)
- Replace VideoMAE with lightweight alternative
- Implement lazy model loading
- Optimize frame processing pipeline
- Add smart frame skipping

### Phase 3: Accuracy Improvements (Week 3)
- Train Isolation Forest
- Improve PERCLOS calculation
- Add temporal smoothing
- Fix distance estimation

### Phase 4: Testing & Validation (Week 4)
- Unit tests
- Integration tests
- Performance benchmarking
- Documentation

## 🧪 Testing

After fixes are implemented:

```bash
# Run dependency check
python3 check_dependencies.py

# Run application
streamlit run track_drive.py
```

## 📝 Requirements

- Python 3.8+
- ~5GB disk space
- Webcam or video file
- Roboflow API key (free tier available)

## 🤝 Contributing

Before making changes:
1. Read [ASSESSMENT_REPORT.md](ASSESSMENT_REPORT.md)
2. Follow the implementation plan
3. Test on low-spec hardware
4. Document changes

## 📄 License

[Add your license here]

## 🙏 Acknowledgments

- Ultralytics for YOLOv8
- Google for MediaPipe
- Hugging Face for transformers
- Roboflow for model hosting

---

**Last Updated**: November 2024
**Status**: Assessment Complete - Awaiting Implementation