Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2025-01-21 00:06:44 +01:00
Code Issues Projects Releases Wiki Activity
NVR with realtime local object detection for IP cameras
2,845 Commits 48 Branches 131 Tags 246 MiB
Python 47.8%
TypeScript 47.5%
CSS 1.4%
C++ 1.1%
Dockerfile 0.7%
Other 1.5%
e1bedf30bf
Go to file
Josh Hawkins e1bedf30bf
Make sure camera is always set in settings (#11812)
2024-06-07 13:34:29 -06:00
.cspell cspell fixes (#11447) 2024-05-20 07:37:56 -06:00
.devcontainer Auth! (#11347) 2024-05-18 10:36:13 -06:00
.github Shutdown hang (#11793) 2024-06-06 18:54:38 -05:00
.vscode
config
docker Adds support for YOLO-NAS in OpenVino (#11645) 2024-06-07 05:52:08 -06:00
docs Adds support for YOLO-NAS in OpenVino (#11645) 2024-06-07 05:52:08 -06:00
frigate Adds support for YOLO-NAS in OpenVino (#11645) 2024-06-07 05:52:08 -06:00
migrations Auth! (#11347) 2024-05-18 10:36:13 -06:00
notebooks Adds support for YOLO-NAS in OpenVino (#11645) 2024-06-07 05:52:08 -06:00
web Make sure camera is always set in settings (#11812) 2024-06-07 13:34:29 -06:00
.dockerignore Improve the devcontainer experience (#3492) 2022-11-20 07:34:12 -06:00
.gitignore upgrade to latest openvino version (#11563) 2024-05-27 14:49:35 -06:00
.pylintrc use fstr log style 2021-02-25 07:01:59 -06:00
audio-labelmap.txt
benchmark_motion.py use a different method for blur and contrast to reduce CPU (#6940) 2023-06-30 07:27:31 -05:00
benchmark.py
CODEOWNERS
cspell.json cspell fixes (#11447) 2024-05-20 07:37:56 -06:00
docker-compose.yml
labelmap.txt
LICENSE switch to MIT license 2020-07-26 12:07:47 -05:00
Makefile increment version 2024-01-31 06:23:54 -06:00
netlify.toml
process_clip.py
pyproject.toml
README.md

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 RTSP to reduce the number of connections to your camera
  • WebRTC & MSE support for low-latency live view

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