Implement Real-time Product Detection and Recognition System with AR Overlay

[bunny1810]

Project Overview

Building a Python-based computer vision application that performs real-time product detection and identification through webcam feed, with augmented reality (AR) product information overlay.

Core Features

1. Real-time Video Capture and Processing

   • Implement webcam feed capture using OpenCV
   • Set up frame processing pipeline
   • Optimize frame rate and resolution handling

2. Object Detection System

   • Integrate object detection model (options: YOLO, SSD, or Faster R-CNN)
   • Implement frame extraction for detected objects
   • Add bounding box drawing functionality
   • Setup confidence threshold controls

3. Product Recognition

   • Implement visual recognition model integration (options):
     • Google Vision API
     • OpenAI CLIP
     • Custom-trained model
   • Add image preprocessing and normalization
   • Implement caching for frequently recognized products

4. Product Information Retrieval

   • Design product database integration system
   • Implement API clients for:
     • Amazon Product API
     • SerpAPI
     • Custom product database
   • Add rate limiting and error handling
   • Implement async product data fetching

5. AR Overlay System

   • Design overlay UI components
   • Implement real-time information display
   • Add interactive elements (clickable links)
   • Create smooth transition animations

6. User Interface

   • Create main application window
   • Implement video feed display
   • Add product information panels
   • Create settings/configuration interface
   • Add user controls for:
     • Camera selection
     • Detection sensitivity
     • Display preferences

Technical Requirements

Dependencies

• Python 3.8+
• OpenCV (cv2)
• UI Framework (choose one):
  • Tkinter
  • PyQt5
  • Streamlit
• Machine Learning:
  • TensorFlow/PyTorch
  • Required model dependencies
• API clients and utilities

System Requirements

• Webcam access
• GPU recommended for real-time processing
• Internet connection for API calls
• Sufficient storage for model files

Implementation Plan

1. Phase 1: Core Setup

   • Set up project structure
   • Implement basic webcam capture
   • Create basic UI framework
   • Add configuration management

2. Phase 2: Detection System

   • Integrate object detection model
   • Implement frame processing
   • Add basic overlay system
   • Set up object tracking

3. Phase 3: Recognition and API

   • Implement product recognition
   • Set up API integrations
   • Create product database handler
   • Add caching system

4. Phase 4: UI and AR

   • Complete UI implementation
   • Add AR overlay features
   • Implement interactive elements
   • Add user settings and controls

5. Phase 5: Optimization

   • Performance optimization
   • Error handling
   • User experience improvements
   • Testing and documentation

Success Criteria

• Smooth real-time video processing (minimum 15 FPS)
• Accurate object detection (>85% confidence)
• Product recognition accuracy >80%
• UI response time <100ms
• Stable AR overlay with no visible lag
• Successful API integration with fallback handling

Additional Considerations

• Privacy and data handling
• Error logging and monitoring
• Performance metrics tracking
• User feedback collection
• Documentation and setup guides

Future Enhancements

• Multiple camera support
• Custom product database integration
• Offline mode capabilities
• Mobile device support
• Extended AR features