usda-vision/camera-management-api/docs/legacy/IMPLEMENTATION_SUMMARY.md

USDA Vision Camera System - Implementation Summary

🎉 Project Completed Successfully!

The USDA Vision Camera System has been fully implemented and tested. All components are working correctly and the system is ready for deployment.

✅ What Was Built

Core Architecture

• Modular Design: Clean separation of concerns across multiple modules
• Multi-threading: Concurrent MQTT listening, camera monitoring, and recording
• Event-driven: Thread-safe communication between components
• Configuration-driven: JSON-based configuration system

Key Components

1. MQTT Integration (usda_vision_system/mqtt/)

   • Listens to two machine topics: vision/vibratory_conveyor/state and vision/blower_separator/state
   • Thread-safe message handling with automatic reconnection
   • State normalization (on/off/error)

2. Camera Management (usda_vision_system/camera/)

   • Automatic GigE camera discovery using python demo library
   • Periodic status monitoring (every 2 seconds)
   • Camera initialization and configuration management
   • Discovered Cameras:
     • Blower-Yield-Cam (192.168.1.165)
     • Cracker-Cam (192.168.1.167)

3. Video Recording (usda_vision_system/camera/recorder.py)

   • Automatic recording start/stop based on machine states
   • Timestamp-based file naming: camera1_recording_20250726_143022.avi
   • Configurable FPS, exposure, and gain settings
   • Thread-safe recording with proper cleanup

4. Storage Management (usda_vision_system/storage/)

   • Organized file storage under ./storage/camera1/ and ./storage/camera2/
   • File indexing and metadata tracking
   • Automatic cleanup of old files
   • Storage statistics and integrity checking

5. REST API Server (usda_vision_system/api/)

   • FastAPI server on port 8000
   • Real-time WebSocket updates
   • Manual recording control endpoints
   • System status and monitoring endpoints

6. Comprehensive Logging (usda_vision_system/core/logging_config.py)

   • Colored console output
   • Rotating log files
   • Component-specific log levels
   • Performance monitoring and error tracking

🚀 How to Use

Quick Start

# Run system tests
python test_system.py

# Start the system
python main.py

# Or use the startup script
./start_system.sh

Configuration

Edit config.json to customize:

• MQTT broker settings
• Camera configurations
• Storage paths
• System parameters

API Access

• System status: http://localhost:8000/system/status
• Camera status: http://localhost:8000/cameras
• Manual recording: POST http://localhost:8000/cameras/camera1/start-recording
• Real-time updates: WebSocket at ws://localhost:8000/ws

📊 Test Results

All system tests passed successfully:

• ✅ Module imports
• ✅ Configuration loading
• ✅ Camera discovery (found 2 cameras)
• ✅ Storage setup
• ✅ MQTT configuration
• ✅ System initialization
• ✅ API endpoints

🔧 System Behavior

Automatic Recording Flow

1. Machine turns ON → MQTT message received → Recording starts automatically
2. Machine turns OFF → MQTT message received → Recording stops and saves file
3. Files saved with timestamp: camera1_recording_YYYYMMDD_HHMMSS.avi

Manual Control

• Start/stop recording via API calls
• Monitor system status in real-time
• Check camera availability on demand

Dashboard Integration

The system is designed to integrate with your React + Vite + Tailwind + Supabase dashboard:

• REST API for status queries
• WebSocket for real-time updates
• JSON responses for easy frontend consumption

📁 Project Structure

usda_vision_system/
├── core/               # Configuration, state management, events, logging
├── mqtt/               # MQTT client and message handlers
├── camera/             # Camera management, monitoring, recording
├── storage/            # File organization and management
├── api/                # FastAPI server and WebSocket support
└── main.py             # Application coordinator

Supporting Files:
├── main.py             # Entry point script
├── config.json         # System configuration
├── test_system.py      # Test suite
├── start_system.sh     # Startup script
└── README_SYSTEM.md    # Comprehensive documentation

🎯 Key Features Delivered

• ✅ Dual MQTT topic listening for two machines
• ✅ Automatic camera recording triggered by machine states
• ✅ GigE camera support using python demo library
• ✅ Thread-safe multi-tasking (MQTT + camera monitoring + recording)
• ✅ Timestamp-based file naming in organized directories
• ✅ 2-second camera status monitoring with on-demand checks
• ✅ REST API and WebSocket for dashboard integration
• ✅ Comprehensive logging with error tracking
• ✅ Configuration management via JSON
• ✅ Storage management with cleanup capabilities
• ✅ Graceful startup/shutdown with signal handling

🔮 Ready for Dashboard Integration

The system provides everything needed for your React dashboard:

// Example API usage
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 recording control
await fetch('http://localhost:8000/cameras/camera1/start-recording', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({ camera_name: 'camera1' })
});

🎊 Next Steps

The system is production-ready! You can now:

1. Deploy the system on your target hardware
2. Integrate with your existing React dashboard
3. Configure MQTT topics and camera settings as needed
4. Monitor system performance through logs and API endpoints
5. Extend functionality as requirements evolve

The modular architecture makes it easy to add new features, cameras, or MQTT topics in the future.

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System Status: ✅ FULLY OPERATIONAL
Test Results: ✅ ALL TESTS PASSING
Cameras Detected: ✅ 2 GIGE CAMERAS READY
Ready for Production: ✅ YES