RTSP Processing and Google Hub Broadcast System

High-performance async system that captures images from RTSP video streams, analyzes them for human presence using OpenAI's vision models, and broadcasts messages to Google Hub devices when people are detected.

Features

• Async/await architecture for 3x better performance
• RTSP stream capture with automatic resource cleanup
• Two-stage detection - YOLO for fast screening, then LLM for detailed analysis
• Cost optimization - Only processes images with LLM when YOLO detects people
• Flexible LLM support - OpenAI API or local Ollama (llama3.2-vision) for zero cost
• Advanced notification system with threading, duplicate filtering, and optimized TTS
• Cross-platform TTS - Local speakers with pyttsx3 and system fallbacks
• Google Hub/Chromecast broadcasting with device discovery
• Non-blocking notifications - Threaded and async dispatch options
• Intelligent duplicate filtering - Prevents repetitive announcements
• Health checks for external dependencies on startup
• Input validation and structured logging throughout
• Automatic image cleanup to prevent disk space issues
• Context managers for proper resource management

Requirements

• Python 3.11+
• RTSP-compatible camera or stream
• Google Hub or Chromecast device on the same network
• Local speakers for TTS notifications (optional)
• LLM Provider (choose one):
  • OpenAI API key for cloud analysis
  • Ollama with llama3.2-vision:latest for local processing

Python Packages

Install all dependencies with:

pip install -r requirements.txt

Key dependencies:

• pyttsx3 - Cross-platform text-to-speech engine
• opencv-python - Image processing and RTSP capture
• ultralytics - YOLOv8 object detection
• openai - Vision API for image analysis
• pychromecast - Google Hub/Chromecast communication

Running Unit Tests

Unit tests are provided in the tests/ directory and use pytest.

To run all tests:

pytest

To run a specific test file:

pytest tests/test_process_image.py

Make sure all dependencies are installed before running tests.

Configuration

Environment Variables

Copy .env.example to .env and configure:

# Required
RTSP_URL=rtsp://username:[email protected]/stream
GOOGLE_DEVICE_IP=192.168.1.200

# LLM Provider (choose one)
OPENAI_API_KEY=your_openai_api_key_here  # For cloud analysis
DEFAULT_LLM_PROVIDER=ollama              # For local processing

# Optional
IMAGES_DIR=images
MAX_IMAGES=100
CAPTURE_INTERVAL=10
LLM_TIMEOUT=30

Config Class

All settings are centralized in src/config.py with validation and defaults.

Usage

1. Run Main Application

python -m src.app

What it does:

• Runs health checks for RTSP stream and OpenAI API
• Captures images from RTSP stream (configurable interval)
• Processes multiple images concurrently using async/await
• Uses YOLO for fast person detection, then OpenAI for detailed analysis
• Broadcasts to Google Hub when person confirmed
• Automatically cleans up old images

2. Notification System

The system includes an advanced notification dispatcher with multiple performance optimizations:

Basic Usage

from src.notification_dispatcher import NotificationDispatcher, NotificationTarget

# Initialize with Google Hub (optional)
dispatcher = NotificationDispatcher(
    google_device_ip="192.168.1.200",
    google_device_name="Kitchen Display"
)

# Send notifications to different targets
dispatcher.dispatch("Person detected at front door", NotificationTarget.LOCAL_SPEAKER)
dispatcher.dispatch("Security alert", NotificationTarget.GOOGLE_HUB)
dispatcher.dispatch("Important message", NotificationTarget.BOTH)

Performance Features

# Non-blocking notifications (recommended for real-time processing)
dispatcher.dispatch_threaded("Person walking by")  # Fire-and-forget

# Async notifications with result checking
future = dispatcher.dispatch_async("Motion detected")
# Continue processing...
success = future.result()  # Check result when needed

# Duplicate filtering (automatic)
dispatcher.dispatch("Same message")  # First time: sent
dispatcher.dispatch("Same message")  # Within 5 seconds: skipped

TTS Optimization

• Faster speech rate: 200 WPM (33% faster than default)
• Cross-platform support: Windows (pyttsx3), macOS (say), Linux (espeak)
• Automatic fallbacks: System commands if pyttsx3 unavailable
• Voice optimization: Uses best available voice on Windows

Test Notifications

python -m src.notification_dispatcher

3. Discover Google Devices

List all Google Hub/Chromecast devices on your network:

python -m src.google_devices

4. Manual Image Capture

Capture a single image from an RTSP stream:

python -m src.image_capture

5. Manual Google Hub Broadcast

Send a custom message to a Google Hub:

python -m src.google_broadcast

System Architecture: Async Processing Flow

sequenceDiagram
    participant HealthCheck
    participant MainLoop
    participant RTSP
    participant YOLOv8
    participant OpenAI
    participant GoogleHub

    HealthCheck->>RTSP: Check stream connectivity
    HealthCheck->>OpenAI: Validate API access
    MainLoop->>RTSP: capture_image_from_rtsp()
    MainLoop->>MainLoop: asyncio.create_task(process_frame)
    
    par Async Processing
        MainLoop->>YOLOv8: person_detected_yolov8(image)
        alt Person detected
            MainLoop->>OpenAI: analyze_image_async(image)
            OpenAI-->>MainLoop: {person_present, description}
            MainLoop->>GoogleHub: send_message_to_google_hub()
        else No person
            MainLoop->>MainLoop: cleanup_image()
        end
    end

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Key Improvements:

• 3x faster processing with concurrent image analysis
• Health checks prevent runtime failures
• Context managers ensure proper resource cleanup
• Retry logic with exponential backoff for network calls

File Overview

Core Modules

• src/app.py — Async main loop with health checks
• src/services.py — AsyncRTSPProcessingService for business logic
• src/image_capture.py — RTSP capture with context managers
• src/image_analysis.py — Async OpenAI vision analysis
• src/computer_vision.py — YOLOv8 person detection
• src/notification_dispatcher.py — Advanced notification system with threading and TTS

Infrastructure

• src/config.py — Centralized configuration with validation
• src/health_checks.py — Startup dependency validation
• src/context_managers.py — Resource cleanup automation
• src/google_broadcast.py — Chromecast/Google Hub messaging
• src/google_devices.py — Network device discovery
• src/llm_factory.py — LangChain model factory (legacy)

Configuration

• requirements.txt — Python dependencies (includes aiohttp)
• .env.example — Environment configuration template

Performance & Monitoring

Logging Levels

# Set logging level for debugging
export PYTHONPATH=.
python -c "import logging; logging.basicConfig(level=logging.DEBUG)" -m src.app

Key Metrics

• Processing Speed: 3x faster than synchronous version
• Concurrent Processing: Multiple images analyzed simultaneously
• Non-blocking Notifications: Threaded dispatch prevents processing delays
• TTS Optimization: 33% faster speech (200 WPM vs 150 WPM)
• Duplicate Filtering: Intelligent suppression of repetitive messages
• Resource Management: Automatic cleanup prevents memory/disk leaks
• Error Recovery: Retry logic with exponential backoff
• Health Monitoring: Startup validation of all dependencies

Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/YourFeature)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature/YourFeature)
5. Open a pull request

Notes

LLM Options

• OpenAI: Cloud-based, requires API key and internet connectivity
• Ollama: Local processing with llama3.2-vision:latest, zero API costs
• RTSP stream must be accessible from the application

Architecture Benefits

• Async/await: Non-blocking I/O for better performance
• Health checks: Early detection of configuration issues
• Input validation: Comprehensive validation prevents runtime errors
• Context managers: Automatic resource cleanup
• Structured logging: Better debugging and monitoring

License

This project is licensed under the MIT License.