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A Docker container that runs DeepStack AI to process images from a watch folder and then fire web request calls, MQTT events, and Telegram messages if the image matches a defined list of triggers.
This project was heavily inspired by GentlePumpkin's post on ipcamtalk.com that triggers BlueIris video survelliance using DeepStack as the motion sensing system. While the C# application is nifty it was difficult to install as a service and only ran on Windows. This project is an attempt to take the idea behind the C# app, make it a bit more general purpose, and provide quick deployment via Docker.
- Copy the
docker-compose.yml
,mqtt.json
andtriggers.json
files from thesampleConfiguration
directory locally. - Edit the
docker-compose.yml
file to modify the mount point for source images, set the timezone and optionally enable MQTT and telegram. - Edit
triggers.json
to define the triggers you want to use. - Edit
mqtt.json
to specify the connection information for your MQTT server (if using MQTT). - Edit
telegram.json
to specify the connection information for your Telegram bot server (if using Telegram).
Setting the timezone via the TZ
environment variable in docker-compose.yml
is important for
every thing to work smoothly. By default Docker containers are in UTC and that messes up
logic to skip existing images on restart. A list of valid timezones is available on
Wikipedia. Use any value
from the TZ database name
column.
Editing the .json files in Visual Studio Code or some other editor that understands JSON Schemas is recommended: you'll get full auto-complete and documentation as you type.
After modifying the configuration files run docker-compose up -d
and everything should
spin up and work. Use your Docker client to review log messages from the trigger
container: there's ample logging to help track down any configuration errors that
may exist.
See the configuration page for information on how to configure the system, including how to enable MQTT and Telegram messages. See the defining triggers page for details on how to set up triggers to detect motion and then fire notifications.
Interested in building this locally? Check out the contributing guide for quick steps on how to clone and run in under five minutes.