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
cffc431bf0
* POC: Added FastAPI with one endpoint (get /logs/service) * POC: Revert error_log * POC: Converted preview related endpoints to FastAPI * POC: Converted two more endpoints to FastAPI * POC: lint * Convert all media endpoints to FastAPI. Added /media prefix (/media/camera && media/events && /media/preview) * Convert all notifications API endpoints to FastAPI * Convert first review API endpoints to FastAPI * Convert remaining review API endpoints to FastAPI * Convert export endpoints to FastAPI * Fix path parameters * Convert events endpoints to FastAPI * Use body for multiple events endpoints * Use body for multiple events endpoints (create and end event) * Convert app endpoints to FastAPI * Convert app endpoints to FastAPI * Convert auth endpoints to FastAPI * Removed flask app in favour of FastAPI app. Implemented FastAPI middleware to check CSRF, connect and disconnect from DB. Added middleware x-forwared-for headers * Added starlette plugin to expose custom headers * Use slowapi as the limiter * Use query parameters for the frame latest endpoint * Use query parameters for the media snapshot.jpg endpoint * Use query parameters for the media MJPEG feed endpoint * Revert initial nginx.conf change * Added missing even_id for /events/search endpoint * Removed left over comment * Use FastAPI TestClient * severity query parameter should be a string * Use the same pattern for all tests * Fix endpoint * Revert media routers to old names. Order routes to make sure the dynamic ones from media.py are only used whenever there's no match on auth/etc * Reverted paths for media on tsx files * Deleted file * Fix test_http to use TestClient * Formatting * Bind timeline to DB * Fix http tests * Replace filename with pathvalidate * Fix latest.ext handling and disable uvicorn access logs * Add cosntraints to api provided values * Formatting * Remove unused * Remove unused * Get rate limiter working --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> |
||
|---|---|---|
| .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.