mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-07-18 18:01:14 +03:00
NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
f1028d0c36
* preserve runtime camera toggles across config saves Runtime toggles (camera on/off, detect, recordings, snapshots, audio) mutate the in-memory config and persist an override to .runtime_state.json. /api/config/set re-parses yaml into a fresh FrigateConfig and swaps it in, re-applying the yaml and profile layers but dropping the runtime layer, so a camera turned off from the dashboard came back on when an unrelated camera was saved. The workers were never notified, so it only appeared to come back: the UI streamed go2rtc while ffmpeg stayed stopped. Extract the startup replay into Dispatcher.apply_runtime_state() and call it from config_set after the swap, re-layering the overrides and republishing them so workers and the UI reconverge. Remove the broad clear_runtime_state() from ProfileManager.update_config, which is only ever reached from config_set: with a profile active, every save wiped every camera's overrides from disk. The broad wipe stays in activate_profile, where a real profile switch does invalidate the steady state. Saves still clear the keys they rewrote via clear_runtime_state_for_yaml_keys, so yaml wins where the two disagree. * sync runtime config on camera delete and prune its overrides Deleting a camera re-parsed yaml into a fresh FrigateConfig but only rebound app.frigate_config and genai_manager, never dispatcher.config (nor profile_manager, stats_emitter, or the runtime overrides). The API and the dispatcher then drifted onto different config objects until the next config save re-synced them, so the API reported surviving cameras with their yaml enabled state while the dispatcher still acted on their real runtime state. Extract the config swap that config_set already does into a shared swap_runtime_config helper and call it from both sites, so every collaborator is rebound and the surviving cameras' runtime toggles are re-layered. Also drop the deleted camera's persisted overrides via a new clear_camera so a camera later added under the same name does not inherit them. |
||
|---|---|---|
| .cspell | ||
| .cursor/rules | ||
| .devcontainer | ||
| .github | ||
| .vscode | ||
| config | ||
| docker | ||
| docs | ||
| frigate | ||
| migrations | ||
| notebooks | ||
| testing-scripts | ||
| web | ||
| .dockerignore | ||
| .gitignore | ||
| .pylintrc | ||
| AGENTS.md | ||
| audio-labelmap.txt | ||
| CLAUDE.md | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| cspell.json | ||
| docker-compose.yml | ||
| generate_api_auth_spec.py | ||
| generate_config_translations.py | ||
| labelmap.txt | ||
| LICENSE | ||
| Makefile | ||
| netlify.toml | ||
| package-lock.json | ||
| 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.