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NVR with realtime local object detection for IP cameras
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AMD GPU support with the rocm detector and YOLOv8 pretrained model download (#9762) 
* ROCm AMD/GPU based build and detector, WIP

* detectors/rocm: separate yolov8 postprocessing into own function; fix box scaling; use cv2.dnn.blobForImage for preprocessing; assert on required model parameters

* AMD/ROCm: add couple of more ultralytics models; comments

* docker/rocm: make imported model files readable by all

* docker/rocm: readme about running on AMD GPUs

* docker/rocm: updated README

* docker/rocm: updated README

* docker/rocm: updated README

* detectors/rocm: separated preprocessing functions into yolo_utils.py

* detector/plugins: added onnx cpu plugin

* docker/rocm: updated container with limite label sets

* example detectors view

* docker/rocm: updated README.md

* docker/rocm: update README.md

* docker/rocm: do not set HSA_OVERRIDE_GFX_VERSION at all for the general version as the empty value broke rocm

* detectors: simplified/optimized yolov8_postprocess

* detector/yolo_utils: indentation, remove unused variable

* detectors/rocm: default option to conserve cpu usage at the expense of latency

* detectors/yolo_utils: use nms to prefilter overlapping boxes if too many detected

* detectors/edgetpu_tfl: add support for yolov8

* util/download_models: script to download yolov8 model files

* docker/main: add download-models overlay into s6 startup

* detectors/rocm: assume models are in /config/model_cache/yolov8/

* docker/rocm: compile onnx files into mxr files at startup

* switch model download into bash script

* detectors/rocm: automatically override HSA_OVERRIDE_GFX_VERSION for couple of known chipsets

* docs: rocm detector first notes

* typos

* describe builds (harakas temporary)

* docker/rocm: also build a version for gfx1100

* docker/rocm: use cp instead of tar

* docker.rocm: remove README as it is now in detector config

* frigate/detectors: renamed yolov8_preprocess->preprocess, pass input tensor element type

* docker/main: use newer openvino (2023.3.0)

* detectors: implement class aggregation

* update yolov8 model

* add openvino/yolov8 support for label aggregation

* docker: remove pointless s6/timeout-up files

* Revert "detectors: implement class aggregation"

This reverts commit dcfe6bbf6f.

* detectors/openvino: remove class aggregation

* detectors: increase yolov8 postprocessing score trershold to 0.5

* docker/rocm: separate rocm distributed files into its own build stage

* Update object_detectors.md

* updated CODEOWNERS file for rocm

* updated build names for documentation

* Revert "docker/main: use newer openvino (2023.3.0)"

This reverts commit dee95de908.

* reverrted openvino detector

* reverted edgetpu detector

* scratched rocm docs from any mention of edgetpu or openvino

* Update docs/docs/configuration/object_detectors.md

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

* renamed frigate.detectors.yolo_utils.py -> frigate.detectors.util.py

* clarified rocm example performance

* Improved wording and clarified text

* Mentioned rocm detector for AMD GPUs

* applied ruff formating

* applied ruff suggested fixes

* docker/rocm: fix missing argument resulting in larger docker image sizes

* docs/configuration/object_detectors: fix links to yolov8 release files

---------

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
2024-02-10 06:41:46 -06:00
.devcontainer Remove deprecated RTMP port 1935 (#9137) 2024-01-31 12:56:11 +00:00
.github Use new UI (#8983) 2024-01-31 12:56:11 +00:00
.vscode Set User Agent for FFmpeg calls (#4555) 2022-11-30 16:53:45 -06:00
config Improve the devcontainer experience (#3492) 2022-11-20 07:34:12 -06:00
docker AMD GPU support with the rocm detector and YOLOv8 pretrained model download (#9762) 2024-02-10 06:41:46 -06:00
docs AMD GPU support with the rocm detector and YOLOv8 pretrained model download (#9762) 2024-02-10 06:41:46 -06:00
frigate AMD GPU support with the rocm detector and YOLOv8 pretrained model download (#9762) 2024-02-10 06:41:46 -06:00
migrations Write a low resolution low fps stream from decoded frames (#8673) 2024-01-31 12:56:11 +00:00
web Streamline live view (#9772) 2024-02-10 05:30:53 -07:00
web-old Use new UI (#8983) 2024-01-31 12:56:11 +00:00
.dockerignore Improve the devcontainer experience (#3492) 2022-11-20 07:34:12 -06:00
.gitignore Small autotracking changes (#9571) 2024-02-02 06:23:14 -06:00
.pylintrc use fstr log style 2021-02-25 07:01:59 -06:00
audio-labelmap.txt Audio events (#6848) 2023-07-01 08:18:33 -05:00
benchmark_motion.py use a different method for blur and contrast to reduce CPU (#6940) 2023-06-30 07:27:31 -05:00
benchmark.py Add isort and ruff linter (#6575) 2023-05-29 05:31:17 -05:00
CODEOWNERS AMD GPU support with the rocm detector and YOLOv8 pretrained model download (#9762) 2024-02-10 06:41:46 -06:00
docker-compose.yml Docs improvements (#8641) 2023-11-18 08:04:43 -06:00
labelmap.txt Cleanup Detector labelmap (#4932) 2023-01-06 07:03:16 -06:00
LICENSE switch to MIT license 2020-07-26 12:07:47 -05:00
Makefile increment version 2024-01-31 06:23:54 -06:00
netlify.toml Docs improvements (#8641) 2023-11-18 08:04:43 -06:00
process_clip.py Remove rtmp (#8941) 2024-01-31 12:56:11 +00:00
pyproject.toml Docs improvements (#8641) 2023-11-18 08:04:43 -06:00
README.md Clarify docs about rtmp (#5052) 2023-01-13 07:20:25 -06:00

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 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

Integration into Home Assistant

Also comes with a builtin UI:

Events