mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-04-05 22:57:40 +03:00
NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
f9885df0e4
The previous approach (useEffect → onActiveMotionChange callback →
parent state update) was unreliable: the dot only appeared if motion
was active at the moment of initial mount but did not react to
subsequent WS motion events.
Root cause: the intermediate state chain breaks because React's
useEffect batching and component re-render timing can cause the
parent state to lag behind or miss updates when motion changes after
mount.
Fix: replace the mechanism entirely with a dedicated CameraMotionDot
component that calls useCameraActivity directly. Being a proper React
component it subscribes to the {camera}/motion WS topic via
useSyncExternalStore and re-renders immediately and reliably whenever
motion state changes — no intermediate callbacks or parent state needed.
- Remove onActiveMotionChange prop from LivePlayer; add showMotionDot
boolean prop (default true) to suppress the internal dot in grid view
- Remove cameraMotionStates state and setCameraMotionStates from
DraggableGridLayout
- Add CameraMotionDot component with direct useCameraActivity hook
https://claude.ai/code/session_019B4dJXtcxvHn97ZaqHUB62
|
||
|---|---|---|
| .cspell | ||
| .cursor/rules | ||
| .devcontainer | ||
| .github | ||
| .vscode | ||
| config | ||
| docker | ||
| docs | ||
| frigate | ||
| migrations | ||
| notebooks | ||
| web | ||
| .dockerignore | ||
| .gitignore | ||
| .pylintrc | ||
| audio-labelmap.txt | ||
| benchmark_motion.py | ||
| benchmark.py | ||
| CODEOWNERS | ||
| cspell.json | ||
| docker-compose.yml | ||
| generate_config_translations.py | ||
| labelmap.txt | ||
| LICENSE | ||
| Makefile | ||
| netlify.toml | ||
| package-lock.json | ||
| process_clip.py | ||
| pyproject.toml | ||
| README_CN.md | ||
| README.md | ||
| TRADEMARK.md | ||
Frigate NVR™ - Realtime Object Detection for IP Cameras
EnglishA 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 GPU or AI accelerator is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead. See Frigate's supported object detectors.
- 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.
License
This project is licensed under the MIT License.
- Code: The source code, configuration files, and documentation in this repository are available under the MIT License. You are free to use, modify, and distribute the code as long as you include the original copyright notice.
- Trademarks: The "Frigate" name, the "Frigate NVR" brand, and the Frigate logo are trademarks of Frigate, Inc. and are not covered by the MIT License.
Please see our Trademark Policy for details on acceptable use of our brand assets.
Screenshots
Live dashboard
Streamlined review workflow
Multi-camera scrubbing
Built-in mask and zone editor
Translations
We use Weblate to support language translations. Contributions are always welcome.
Copyright © 2026 Frigate, Inc.