Real-Time Computer Vision Analytics Platform
VisionForge AI is a production-ready computer vision analytics platform that provides real-time detection, tracking, and analysis capabilities. Built with OpenCV, Streamlit, and modern Python best practices, it offers a comprehensive suite of tools for face detection, object tracking, lane detection, and traffic analytics.
- Face Detection: Real-time face and eye detection using Haar Cascade classifiers
- Object Tracking: HSV color-based tracking and centroid tracking algorithms
- Lane Detection: Road lane detection using Canny edge detection and Hough Transform
- Traffic Analytics: Vehicle counting, density estimation, speed measurement, and congestion analysis
- Performance Benchmarking: FPS, latency, CPU, and memory profiling
- Interactive Dashboard: Streamlit-based web interface for real-time visualization
- Analytics Engine: Comprehensive data collection and statistical analysis
- Visualization: Plotly-powered charts and graphs for data exploration
VisionForge AI follows a modular architecture with clear separation of concerns:
VisionForge-AI/
├── app.py # Streamlit dashboard
├── src/
│ ├── __init__.py
│ ├── face_detection.py # Face and eye detection module
│ ├── object_tracking.py # Object tracking algorithms
│ ├── lane_detection.py # Lane detection for roads
│ ├── traffic_analysis.py # Traffic analytics engine
│ ├── analytics.py # Analytics and statistics
│ ├── benchmarking.py # Performance benchmarking
│ ├── visualization.py # Plotly visualizations
│ └── utils.py # Utility functions
├── datasets/ # Sample datasets
├── assets/ # Static assets
├── docs/ # Documentation
├── results/ # Analysis results
├── tests/ # Unit tests
├── legacy/ # Original demo code
└── requirements.txt # Python dependencies
- Python 3.8 or higher
- pip package manager
- Clone the repository:
git clone https://github.com/yourusername/VisionForge-AI.git
cd VisionForge-AI- Install dependencies:
pip install -r requirements.txt- Verify installation:
python -c "import cv2; print(f'OpenCV version: {cv2.__version__}')"Start the Streamlit dashboard:
streamlit run app.pyThe dashboard will open in your browser at http://localhost:8501
- Overview: System metrics and platform features
- Image Analysis: Upload images for face and lane detection
- Video Analysis: Process videos for traffic and lane analysis
- Webcam Mode: Real-time camera stream with live detection
- Benchmark Dashboard: Performance profiling and metrics
from src.face_detection import FaceDetector
from src.lane_detection import LaneDetector
from src.traffic_analysis import TrafficAnalytics
from src.analytics import AnalyticsEngine
# Face Detection
detector = FaceDetector()
results = detector.detect_faces_with_eyes(image)
# Lane Detection
lane_detector = LaneDetector()
lane_results = lane_detector.process_frame(frame)
# Traffic Analytics
traffic = TrafficAnalytics()
traffic_results = traffic.process_frame(frame)
# Analytics
analytics = AnalyticsEngine()
analytics.record_face_detection(results)
report = analytics.generate_detection_summary()- Haar Cascade-based face detection
- Eye detection within face regions
- Bounding box visualization
- Face statistics (count, size, area)
- Processing time metrics
- HSV color space tracking
- Centroid tracking algorithm
- Object counting across lines
- Tracking history and duration
- Tracking accuracy metrics
- Canny edge detection
- Region of Interest (ROI) masking
- Hough Transform line detection
- Left/right lane separation
- Lane curvature estimation
- Deviation from center calculation
- Background subtraction for vehicle detection
- Traffic density estimation
- Vehicle speed estimation
- Congestion scoring (free to severe)
- Comprehensive analytics reports
- FPS measurement
- Latency profiling (P50, P95, P99)
- CPU and memory monitoring
- Video processing throughput
- Baseline comparison
- Detection count charts
- Traffic density graphs
- Congestion level distribution
- FPS and latency plots
- CPU/memory usage charts
- Benchmark comparison visualizations
Run the test suite:
pytest tests/ -v --cov=srcRun specific test modules:
pytest tests/test_face_detection.py -v
pytest tests/test_analytics.py -vTypical performance on modern hardware:
| Module | FPS | Latency | Memory |
|---|---|---|---|
| Face Detection | 30-45 | 15-25ms | 150MB |
| Object Tracking | 25-40 | 20-30ms | 180MB |
| Lane Detection | 20-35 | 25-35ms | 200MB |
| Traffic Analytics | 15-30 | 30-45ms | 250MB |
Benchmarks vary based on hardware and input resolution
- Deep learning model integration (YOLO, SSD)
- Multi-camera support
- Cloud deployment options
- Real-time alert system
- Mobile application
- REST API endpoints
- Database integration for analytics storage
- Advanced visualization dashboard
Contributions are welcome! Please read the Contributing Guide for details on our code of conduct and the process for submitting pull requests.
Anany Tripathi
AI Engineer | Computer Vision Specialist
- OpenCV community for excellent computer vision tools
- Streamlit team for the amazing dashboard framework
- Plotly for interactive visualization capabilities
For questions, suggestions, or collaboration opportunities, please open an issue on GitHub.
VisionForge AI - Transforming Computer Vision into Actionable Insights