VRAQ is an advanced PCB (Printed Circuit Board) defect detection system that combines cutting-edge computer vision with immersive VR visualization. It automatically identifies missing, misaligned, or defective components by comparing test PCBs against reference images, providing both traditional web-based analysis and revolutionary VR experiences for quality assurance teams.
- 🎯 Automated Defect Detection - AI-powered identification of missing, misaligned, and defective components
- 🖼️ Side-by-Side Comparison - Visual comparison with precise defect markers
- 🥽 Immersive VR Experience - 3D virtual reality PCB inspection environment
- 📊 Detailed Analytics - Comprehensive defect reporting and statistics
- 🔌 RESTful API - Integration with manufacturing systems
- 📱 Cross-Platform - Works on desktop, mobile, and VR devices
- Python 3.11 or higher
- Modern web browser (Chrome, Firefox, Edge)
- VR headset (optional, for VR experience)
-
Clone the repository
git clone https://github.com/your-username/vraq.git cd vraq -
Install dependencies
pip install flask flask-cors opencv-python numpy pillow werkzeug gunicorn
-
Run the application
python main.py
-
Access the system
- Web Interface:
http://localhost:5000 - VR Interface:
http://localhost:5000/vr
- Web Interface:
- PNG (Recommended for best quality)
- JPEG/JPG (Good for general use)
- TIFF (Professional imaging)
- Maximum file size: 10MB per image
- Recommended resolution: 1920x1080 or higher
- Quality: High contrast, well-lit PCB images
Features:
- Drag-and-drop file upload
- Side-by-side image comparison
- Interactive defect markers
- Detailed component analysis table
- Export results to JSON
VRAQ - VR Interface
Features:
- Immersive 3D PCB visualization
- Floating defect markers in 3D space
- Voice commands and hand tracking
- Teleportation controls
- In-VR file upload system
| Defect Type | Description | Visual Indicator |
|---|---|---|
| 🔴 Missing | Component absent from expected location | Red X marker |
| 🟠 Misaligned | Component positioned incorrectly | Orange arrow showing direction |
| 🟢 OK | Component present and correctly positioned | Green checkmark |
- Production Line Integration: Automated inspection at scale
- Batch Processing: Analyze multiple PCBs simultaneously
- Trend Analysis: Track defect patterns over time
- VR Training: Immersive defect identification training
- Remote Assistance: Expert guidance via VR collaboration
- Documentation: Visual defect catalogs for training materials
- Process Optimization: Identify improvement opportunities
- Failure Analysis: Root cause investigation
- Prototype Validation: Early-stage design verification
- Flask - Web framework
- OpenCV - Computer vision
- NumPy - Numerical computing
- Pillow - Image processing
- Bootstrap - UI framework
- JavaScript - Interactive features
- A-Frame - VR framework
- Three.js - 3D graphics
- Template Matching - Component identification
- Correlation Analysis - Defect classification
- Geometric Validation - Position verification
This project is licensed under the MIT License - see the LICENSE file for details.