7.2 KiB
7.2 KiB
🎉 USDA Vision Camera System - PROJECT COMPLETE!
✅ Final Status: READY FOR PRODUCTION
The USDA Vision Camera System has been successfully implemented, tested, and documented. All requirements have been met and the system is production-ready.
📋 Completed Requirements
✅ Core Functionality
- MQTT Integration: Dual topic listening for machine states
- Automatic Recording: Camera recording triggered by machine on/off states
- GigE Camera Support: Full integration with camera SDK library
- Multi-threading: Concurrent MQTT + camera monitoring + recording
- File Management: Timestamp-based naming in organized directories
✅ Advanced Features
- REST API: Complete FastAPI server with all endpoints
- WebSocket Support: Real-time updates for dashboard integration
- Time Synchronization: Atlanta, Georgia timezone with NTP sync
- Storage Management: File indexing, cleanup, and statistics
- Comprehensive Logging: Rotating logs with error tracking
- Configuration System: JSON-based configuration management
✅ Documentation & Testing
- Complete README: Installation, usage, API docs, troubleshooting
- Test Suite: Comprehensive system testing (
test_system.py) - Time Verification: Timezone and sync testing (
check_time.py) - Startup Scripts: Easy deployment with
start_system.sh - Clean Repository: Organized structure with proper .gitignore
🏗️ Final Project Structure
USDA-Vision-Cameras/
├── README.md # Complete documentation
├── main.py # System entry point
├── config.json # System configuration
├── requirements.txt # Python dependencies
├── pyproject.toml # UV package configuration
├── .gitignore # Git ignore rules
├── start_system.sh # Startup script
├── setup_timezone.sh # Time sync setup
├── test_system.py # System test suite
├── check_time.py # Time verification
├── test_timezone.py # Timezone testing
├── usda_vision_system/ # Main application
│ ├── core/ # Core functionality
│ ├── mqtt/ # MQTT integration
│ ├── camera/ # Camera management
│ ├── storage/ # File management
│ ├── api/ # REST API server
│ └── main.py # Application coordinator
├── camera_sdk/ # GigE camera SDK library
├── demos/ # Demo and example code
│ ├── cv_grab*.py # Camera SDK usage examples
│ └── mqtt_*.py # MQTT demo scripts
├── storage/ # Recording storage
│ ├── camera1/ # Camera 1 recordings
│ └── camera2/ # Camera 2 recordings
├── tests/ # Test files and legacy tests
├── notebooks/ # Jupyter notebooks
└── docs/ # Documentation files
🚀 How to Deploy
1. Clone and Setup
git clone https://github.com/your-username/USDA-Vision-Cameras.git
cd USDA-Vision-Cameras
uv sync
2. Configure System
# Edit config.json for your environment
# Set MQTT broker, camera settings, storage paths
3. Setup Time Sync
./setup_timezone.sh
4. Test System
python test_system.py
5. Start System
./start_system.sh
🌐 API Integration
Dashboard Integration
// React component example
const systemStatus = await fetch('http://localhost:8000/system/status');
const cameras = await fetch('http://localhost:8000/cameras');
// WebSocket for real-time updates
const ws = new WebSocket('ws://localhost:8000/ws');
ws.onmessage = (event) => {
const update = JSON.parse(event.data);
// Handle real-time system updates
};
Manual Control
# Start recording manually
curl -X POST http://localhost:8000/cameras/camera1/start-recording
# Stop recording manually
curl -X POST http://localhost:8000/cameras/camera1/stop-recording
# Get system status
curl http://localhost:8000/system/status
📊 System Capabilities
Discovered Hardware
- 2 GigE Cameras: Blower-Yield-Cam, Cracker-Cam
- Network Ready: Cameras accessible at 192.168.1.165, 192.168.1.167
- MQTT Ready: Configured for broker at 192.168.1.110
Recording Features
- Automatic Start/Stop: Based on MQTT machine states
- Timezone Aware: Atlanta time timestamps (EST/EDT)
- Organized Storage: Separate directories per camera
- File Naming:
camera1_recording_20250725_213000.avi - Manual Control: API endpoints for manual recording
Monitoring Features
- Real-time Status: Camera and machine state monitoring
- Health Checks: Automatic system health verification
- Performance Tracking: Recording metrics and system stats
- Error Handling: Comprehensive error tracking and recovery
🔧 Maintenance
Regular Tasks
- Log Monitoring: Check
usda_vision_system.log - Storage Cleanup: Automatic cleanup of old recordings
- Time Sync: Automatic NTP synchronization
- Health Checks: Built-in system monitoring
Troubleshooting
- Test Suite:
python test_system.py - Time Check:
python check_time.py - API Health:
curl http://localhost:8000/health - Debug Mode:
python main.py --log-level DEBUG
🎯 Production Readiness
✅ All Tests Passing
- System initialization: ✅
- Camera discovery: ✅ (2 cameras found)
- MQTT configuration: ✅
- Storage setup: ✅
- Time synchronization: ✅
- API endpoints: ✅
✅ Documentation Complete
- Installation guide: ✅
- Configuration reference: ✅
- API documentation: ✅
- Troubleshooting guide: ✅
- Integration examples: ✅
✅ Production Features
- Error handling: ✅
- Logging system: ✅
- Time synchronization: ✅
- Storage management: ✅
- API security: ✅
- Performance monitoring: ✅
🚀 Next Steps
The system is now ready for:
- Production Deployment: Deploy on target hardware
- Dashboard Integration: Connect to React + Supabase dashboard
- MQTT Configuration: Connect to production MQTT broker
- Camera Calibration: Fine-tune camera settings for production
- Monitoring Setup: Configure production monitoring and alerts
📞 Support
For ongoing support:
- Documentation: Complete README.md with troubleshooting
- Test Suite: Comprehensive diagnostic tools
- Logging: Detailed system logs for debugging
- API Health: Built-in health check endpoints
🎊 PROJECT STATUS: COMPLETE AND PRODUCTION-READY! 🎊
The USDA Vision Camera System is fully implemented, tested, and documented. All original requirements have been met, and the system is ready for production deployment with your React dashboard integration.
Key Achievements:
- ✅ Dual MQTT topic monitoring
- ✅ Automatic camera recording
- ✅ Atlanta timezone synchronization
- ✅ Complete REST API
- ✅ Comprehensive documentation
- ✅ Production-ready deployment