frigate-event-handler 0.3.0

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Frigate Event Handler

A tool that listens to Frigate events and generates AI-powered descriptions of detected events using vision and language models.

Features

• Connects to Frigate via MQTT to receive real-time events
• Processes event video clips using AI vision models
• Generates natural language descriptions of events
• Supports multiple cameras with camera-specific configurations
• Configurable frame processing (resizing, similarity detection, grid layout)
• Customizable prompts for different camera contexts

Installation

pip install frigate-event-handler

Usage

frigate-event-handler -c config.yml

Command Line Options

usage: frigate-event-handler [-h] [-V] [-v] [--debug] [-c CONFIG]

Frigate event handler.

options:
  -h, --help            show this help message and exit
  -V, --version         show program's version number and exit
  -v, --verbose         Logging verbosity level
  --debug               Enable debug mode
  -c CONFIG, --config CONFIG
                        Configuration file

Configuration

The tool uses a YAML configuration file to specify connection details and behavior. Here's a minimal configuration example:

mqtt:
  host: localhost
  port: 1883
  topic: frigate/events

frigate:
  base_url: http://localhost:5000/api

vision_agent:
  api_key: your-llm-api-key
  vision_prompt: |
    Describe what you see in these surveillance camera frames.
  refine_prompt: |
    Rewrite this surveillance event description for a notification.

See reference config for a complete configuration file with all available options and their descriptions.

Camera-Specific Configuration

You can override global vision agent settings for specific cameras:

vision_agent:
  # Global settings here
  cameras:
    front_door:
      prompt_context: |
        This camera faces the front door entrance.
    backyard:
      prompt_context: |
        This camera overlooks the backyard area.

How It Works

1. The tool subscribes to Frigate's MQTT events
2. When an event is received, it:
   • Downloads the event video clip from Frigate
   • Extracts frames from the video
   • Processes frames (resize, similarity detection, etc.)
   • Sends frames to the vision model for analysis
   • Refines the description using a language model
3. The resulting description is then posted back to frigate

Frame Processing Options

Frame Similarity Detection

The tool can remove similar frames before sending them to the vision model:

vision_agent:
  remove_similar_frames: true
  hashing_max_frames: 200
  hash_size: 12  # Lower = more aggressive similarity matching

Grid Layout

Frames can be arranged in a grid:

vision_agent:
  stack_grid: true
  stack_grid_size: [3, 3]  # 3x3 grid

Frame Resizing

Control frame dimensions sent to the vision model:

vision_agent:
  resize_video: [640, 360]  # [width, height]

Debug Mode

Enable debug mode to save processed frames and API responses:

frigate-event-handler --debug -c config.yml

Debug files will be saved to ./debug by default.

Requirements

• Python 3.12+
• MQTT broker
• Frigate instance
• Access to an LLM API (OpenAI compatible)