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NVR with realtime local object detection for IP cameras
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Custom classes for Process and Metrics (#13950) 
* Subclass Process for audio_process

* Introduce custom mp.Process subclass

In preparation to switch the multiprocessing startup method away from
"fork", we cannot rely on os.fork cloning the log state at fork time.
Instead, we have to set up logging before we run the business logic of
each process.

* Make camera_metrics into a class

* Make ptz_metrics into a class

* Fixed PtzMotionEstimator.ptz_metrics type annotation

* Removed pointless variables

* Do not start audio processor when no audio cameras are configured
2024-09-27 07:53:23 -05:00
.cspell Work through most of the cspell warnings in python (#13794) 2024-09-17 10:41:46 -05:00
.devcontainer Removed usage of PyYAML for config parsing. (#13883) 2024-09-22 10:56:57 -05:00
.github Rocm yolonas (#13816) 2024-09-18 19:34:07 -05:00
.vscode Fix vscode launch configuration (#13795) 2024-09-17 10:42:10 -05:00
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docker Search functionality and UI tweaks (#13978) 2024-09-26 15:30:56 -05:00
docs Update object_detectors.md (#13994) 2024-09-26 18:58:58 -05:00
frigate Custom classes for Process and Metrics (#13950) 2024-09-27 07:53:23 -05:00
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benchmark_motion.py
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README.md

README.md

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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.

Screenshots

Live dashboard

Live dashboard

Streamlined review workflow

Streamlined review workflow

Multi-camera scrubbing

Multi-camera scrubbing

Built-in mask and zone editor

Multi-camera scrubbing