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NVR with realtime local object detection for IP cameras
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Add object filter ratio (#2952) 
* Add object ratio config parameters

Issue: #2948

* Add config test for object filter ratios

Issue: #2948

* Address review comments

- Accept `ratio` default
- Rename `bounds` to `box` for consistency
- Add migration for new field

Issue: #2948

* Fix logical errors

- field migrations require default values
- `clipped` referenced the wrong index for region, since it shifted
- missed an inclusion of `ratio` for detections in `process_frames`
- revert naming `o[2]` as `box` since it is out of scope!

This has now been test-run against a video, so I believe the kinks are
worked out.

Issue: #2948

* Update contributing notes for `make`

Issue: #2948

* Fix migration

- Ensure that defaults match between Event and migration script
- Deconflict migration script number (from rebase)

Issue: #2948

* Filter objects out of ratio bounds

Issue: #2948

* Update migration file to 009

Issue: #2948
2022-04-10 08:25:18 -05:00
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.github update to node 16 2022-03-11 07:49:06 -06:00
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docs Add object filter ratio (#2952) 2022-04-10 08:25:18 -05:00
frigate Add object filter ratio (#2952) 2022-04-10 08:25:18 -05:00
migrations Add object filter ratio (#2952) 2022-04-10 08:25:18 -05:00
web Fix tiny timing bug. (#2994) 2022-03-21 06:43:27 -05:00
.dockerignore swr events refactor 2022-03-11 07:49:06 -06:00
.gitignore feat: Timeline UI (#2830) 2022-02-27 08:04:12 -06:00
.pylintrc use fstr log style 2021-02-25 07:01:59 -06:00
benchmark.py
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process_clip.py fix process_clip 2022-02-18 21:18:26 -06:00
README.md update docs url 2021-09-26 16:43:26 -05:00

README.md

logo

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 RTMP to reduce the number of connections to your camera

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.

Screenshots

Integration into Home Assistant

Also comes with a builtin UI:

Events