Nate Meyer 4383b883c0 Refactor to simplify support for additional detector types (#3656)
* Refactor EdgeTPU and CPU model handling to detector submodules.

* Fix selecting the correct detection device type from the config

* Remove detector type check when creating ObjectDetectProcess

* Fixes after rebasing to 0.11

* Add init file to detector folder

* Rename to detect_api

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

* Add unit test for LocalObjectDetector class

* Add configuration for model inputs
Support transforming detection regions to RGB or BGR.
Support specifying the input tensor shape.  The tensor shape has a standard format ["BHWC"] when handed to the detector, but can be transformed in the detector to match the model shape using the model  input_tensor config.

* Add documentation for new model config parameters

* Add input tensor transpose to LocalObjectDetector

* Change the model input tensor config to use an enumeration

* Updates for model config documentation

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
2022-11-03 21:23:09 -05:00
2022-03-11 07:49:06 -06:00
2022-02-27 08:04:12 -06:00
2021-02-25 07:01:59 -06:00
2020-07-26 12:07:47 -05:00
2022-10-08 19:32:45 -05:00
2021-09-26 16:43:26 -05:00

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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 RTMP to reduce the number of connections to your camera

Documentation

View the documentation at https://docs.frigate.video

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If you would like to make a donation to support development, please use Github Sponsors.

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Integration into Home Assistant

Also comes with a builtin UI:

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