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
4bb420d049
* Add service manager infrastructure The changes are (This will be a bit long): - A ServiceManager class that spawns a background thread and deals with service lifecycle management. The idea is that service lifecycle code will run in async functions, so a single thread is enough to manage any (reasonable) amount of services. - A Service class, that offers start(), stop() and restart() methods that simply notify the service manager to... well. Start, stop or restart a service. (!) Warning: Note that this differs from mp.Process.start/stop in that the service commands are sent asynchronously and will complete "eventually". This is good because it means that business logic is fast when booting up and shutting down, but we need to make sure that code does not rely on start() and stop() being instant (Mainly pid assignments). Subclasses of the Service class should use the on_start and on_stop methods to monitor for service events. These will be run by the service manager thread, so we need to be careful not to block execution here. Standard async stuff. (!) Note on service names: Service names should be unique within a ServiceManager. Make sure that you pass the name you want to super().__init__(name="...") if you plan to spawn multiple instances of a service. - A ServiceProcess class: A Service that wraps a multiprocessing.Process into a Service. It offers a run() method subclasses can override and can support in-place restarting using the service manager. And finally, I lied a bit about this whole thing using a single thread. I can't find any way to run python multiprocessing in async, so there is a MultiprocessingWaiter thread that waits for multiprocessing events and notifies any pending futures. This was uhhh... fun? No, not really. But it works. Using this part of the code just involves calling the provided wait method. See the implementation of ServiceProcess for more details. Mirror util.Process hooks onto service process Remove Service.__name attribute Do not serialize process object on ServiceProcess start. asd * Update frigate dictionary * Convert AudioProcessor to service process |
||
|---|---|---|
| .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.