mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
a468ed316d
* Added stop_event to util.Process util.Process will take care of receiving signals when the stop_event is accessed in the subclass. If it never is, SystemExit is raised instead. This has the effect of still behaving like multiprocessing.Process when stop_event is not accessed, while still allowing subclasses to not deal with the hassle of setting it up. * Give each util.Process their own logger This will help to reduce boilerplate in subclasses. * Give explicit types to util.Process.__init__ This gives better type hinting in the editor. * Use util.Process facilities in AudioProcessor Boilerplate begone! * Removed pointless check in util.Process The log_listener.queue should never be None, unless something has gone extremely wrong in the log setup code. If we're that far gone, crashing is better. * Make sure faulthandler is enabled in all processes This has no effect currently since we're using the fork start_method. However, when we inevidably switch to forkserver (either by choice, or by upgrading to python 3.14+) not having this makes for some really fun failure modes :D |
||
|---|---|---|
| .cspell | ||
| .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 | ||
| labelmap.txt | ||
| LICENSE | ||
| Makefile | ||
| netlify.toml | ||
| package-lock.json | ||
| process_clip.py | ||
| pyproject.toml | ||
| README.md | ||
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.