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* Move database and config from homeassistant /config to addon /config * Re-implement config migration for the add-on * Align some terms * Improve function name * Use local variables * Add model.path migration * Fix homeassistant config path * Ensure migration scripts run before go2rtc and frigate * Migrate all files I know * Add ffmpeg.path migration * Update docker/main/rootfs/etc/s6-overlay/s6-rc.d/prepare/run Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> * Improve some variable names and organization * Update docs to reflect addon config dir * Update live.md with /addon_configs * Move addon config section to configuration doc * Align several terminologies and improve text * Fix webrtc example config title * Capitalize Add-on in more places * Improve specific add-on config dir docs * Align bash and python scripts to prefer config.yml over config.yaml * Support config.json in migration shell scripts * Change docs to reflect config.yml is preferred over config.yaml * If previous config was yaml, migrate to yaml * Fix typo in edgetpu.md * Fix formatting of Python files * Remove HailoRT Beta add-on variant from docs * Add migration for labelmap and certs * Fix variable name * Fix new_config_file var unset * Fix addon config directories table * Improve db migration to avoid migrating files like .db.bak * Fix echo location --------- Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
Frigate - NVR With Realtime Object Detection for IP Cameras
[English] | 简体中文
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.