Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2024-11-21 19:07:46 +01:00
Code Issues Projects Releases Wiki Activity
NVR with realtime local object detection for IP cameras
761 Commits 60 Branches 126 Tags 151 MiB
Python 47.8%
TypeScript 47.5%
CSS 1.4%
C++ 1.1%
Dockerfile 0.7%
Other 1.5%
40c4ca305e
Go to file
Jason Hunter 40c4ca305e add playback rate adjustment and seek buttons
2021-06-05 07:30:18 -05:00
.devcontainer allow logger daemon process to be killed with the main thread, thus allowing us to continue logging during shutdown 2021-05-06 07:01:33 -05:00
.github Add paularmstrong to funding.yml 2021-02-24 20:58:44 -06:00
docker Add support for NGINX VOD Module 2021-05-22 07:48:44 -05:00
docs update HTTP API docs 2021-05-22 07:48:44 -05:00
frigate restyle to match Material Design List specs 2021-06-05 07:30:18 -05:00
migrations formatting cleanup 2021-02-25 07:01:57 -06:00
nginx We need to use relative URLs for Ingress to work 2021-06-05 07:30:18 -05:00
web add playback rate adjustment and seek buttons 2021-06-05 07:30:18 -05:00
.dockerignore add devcontainer setup 2021-02-25 07:00:59 -06:00
.gitignore Use dataclasses for config handling 2021-05-23 20:38:57 -05:00
.pylintrc use fstr log style 2021-02-25 07:01:59 -06:00
benchmark.py support multiple coral devices (fixes #100) 2020-10-18 13:47:13 -05:00
docker-compose.yml Add support for NGINX VOD Module 2021-05-22 07:48:44 -05:00
labelmap.txt refactor and reduce false positives 2020-09-17 07:37:27 -05:00
LICENSE
Makefile add --push so the images actually get published for nginx since they are not saved locally 2021-05-22 07:48:44 -05:00
README.md clarifying addon docs 2021-01-28 07:45:09 -06:00
run.sh Add support for NGINX VOD Module 2021-05-22 07:48:44 -05:00

README.md

logo

Frigate - NVR With Realtime Object Detection for IP Cameras

A complete and local NVR designed for HomeAssistant 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 HomeAssistant 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 clips of detected objects
  • 24/7 recording
  • Re-streaming via RTMP to reduce the number of connections to your camera

Documentation

View the documentation at https://blakeblackshear.github.io/frigate

Donations

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

Screenshots

Integration into HomeAssistant

Also comes with a builtin UI:

Events