Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2026-02-04 20:09:12 +01:00
Code Issues Projects Releases Wiki Activity
NVR with realtime local object detection for IP cameras
1,418 Commits 54 Branches 151 Tags 715 MiB
TypeScript 51.2%
Python 46.3%
CSS 0.8%
Shell 0.6%
Dockerfile 0.5%
Other 0.5%
6c0978498d
Go to file
Nicolas Mowen 6c0978498d
Abstract MQTT from communication and make mqtt optional (#4462) 
* Add option for mqtt config

* Setup communication layer

* Have a dispatcher which is responsible for handling and sending messages

* Move mqtt to communication

* Separate ws communications module

* Make ws client conform to communicator

* Cleanup imports

* Migrate to new dispatcher

* Clean up

* Need to set topic prefix

* Remove references to mqtt in dispatcher

* Don't start mqtt until dispatcher is subscribed

* Cleanup

* Shorten package

* Formatting

* Remove unused

* Cleanup

* Rename mqtt to ws on web

* Fix ws mypy

* Fix mypy

* Reformat

* Cleanup if/else chain

* Catch bad set commands
2022-11-23 20:03:20 -06:00
.devcontainer
.github
config
docker
docs Abstract MQTT from communication and make mqtt optional (#4462) 2022-11-23 20:03:20 -06:00
frigate Abstract MQTT from communication and make mqtt optional (#4462) 2022-11-23 20:03:20 -06:00
migrations
web Abstract MQTT from communication and make mqtt optional (#4462) 2022-11-23 20:03:20 -06:00
.dockerignore
.gitignore
.pylintrc
benchmark.py
docker-compose.yml
Dockerfile
labelmap.txt
LICENSE
Makefile
process_clip.py
README.md
requirements-dev.txt
requirements-wheels.txt
requirements.txt
test.db-journal

README.md

logo

Frigate - NVR With Realtime Object Detection for IP Cameras

A complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.

Use of a Google Coral Accelerator is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead.

  • Tight integration with Home Assistant via a custom component
  • Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
  • Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
  • Uses a very low overhead motion detection to determine where to run object detection
  • Object detection with TensorFlow runs in separate processes for maximum FPS
  • Communicates over MQTT for easy integration into other systems
  • Records video with retention settings based on detected objects
  • 24/7 recording
  • Re-streaming via RTMP to reduce the number of connections to your camera

Documentation

View the documentation at https://docs.frigate.video

Donations

If you would like to make a donation to support development, please use Github Sponsors.

Screenshots

Integration into Home Assistant

Also comes with a builtin UI:

Events