Frigate NVR

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Frigate NVR

Docker

Download and save the Docker repository signing key: 

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | gpg --dearmor -o /etc/apt/keyrings/docker.gpg

Add the Docker repository in /etc/apt/sources.list.d/docker.list: 

deb [arch=amd64 signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu jammy stable

Allow unattended updates from this repository by add to /etc/apt/apt.conf.d/50unattended-upgrades: 

Unattended-Upgrade::Allowed-Origins { 
        // Append to end of existing entries
        "Docker:${distro_codename}";
};

Update the repositories and install the package: 

apt update
apt install docker-ce docker-ce-cli containerd.io docker-compose-plugin

Check all is working with: 

docker version

Stop the services: 

systemctl stop docker.service
systemctl stop docker.socket
systemctl stop containerd.service

Move the Docker root directory: 

mv /var/lib/docker /srv/data

Edit the systemd service for Docker: 

systemctl edit docker.service

Add the following to the top of the override file: 

[Service]
ExecStart=
ExecStart=/usr/bin/dockerd -–data-root=/srv/data/docker -H fd:// --containerd=/run/containerd/containerd.sock

Restart Docker: 

systemctl daemon-reload
systemctl start docker.service

Check it still works with: 

docker run hello-world


GPU Hardware Acceleration

Download and save the NVIDIA repository signing key: 

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | gpg --dearmor -o /etc/apt/keyrings/nvidia-container.gpg

Add the Docker repository in /etc/apt/sources.list.d/nvidia-container.list: 

deb [signed-by=/etc/apt/keyrings/nvidia-container.gpg] https://nvidia.github.io/libnvidia-container/stable/ubuntu18.04/$(ARCH) /

Allow unattended updates from this repository by add to /etc/apt/apt.conf.d/50unattended-upgrades: 

Unattended-Upgrade::Origins-Pattern {
        "site=nvidia.github.io";
};

Update the repositories and install the package: 

apt update
apt install nvidia-docker2

Restart Docker: 

systemctl start docker.service


Frigate

If using ZFS, create a filesystem to store the video recordings (not used currently) 

zfs create -o recordsize=1M -o compression=off -o quota=256G data/cctv


Create a configuration directory for Frigate: 

mkdir /etc/frigate


If using NVidia TensorRT object detection, download and train the models:


Get the training image 

 cd /etc/frigate
 wget https://github.com/blakeblackshear/frigate/raw/master/docker/tensorrt_models.sh
 chmod +x tensorrt_models.sh

Run the script to train. 

 docker run --gpus=all --rm -it -e USE_FP16=False -v `pwd`/trt-models:/tensorrt_models -v `pwd`/tensorrt_models.sh:/tensorrt_models.sh nvcr.io/nvidia/tensorrt:22.07-py3 /tensorrt_models.sh

Cleanup the image used to train with this. 

 docker image prune -a


Create /etc/frigate/docker-compose.yml: 

version: "3.9"
services:
  frigate:
    container_name: frigate
    privileged: true # this may not be necessary for all setups
    restart: unless-stopped
    image: ghcr.io/blakeblackshear/frigate:stable-tensorrt
    network_mode: bridge
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    shm_size: "128mb" # update for your cameras based on calculation above
    devices:
#      - /dev/bus/usb:/dev/bus/usb # passes the USB Coral, needs to be modified for other versions
    volumes:
      - /etc/localtime:/etc/localtime:ro
      - /etc/frigate/trt-models:/trt-models:ro
      - /etc/frigate/config.yml:/config/config.yml
      - /srv/cctv:/media/frigate
      - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
        target: /tmp/cache
        tmpfs:
          size: 1000000000
    ports:
	  - "5000:5000"
	  - "8554:8554" # RTSP feeds
	  - "8555:8555/tcp" # WebRTC over tcp
	  - "8555:8555/udp" # WebRTC over udp
    environment:
      FRIGATE_RTSP_PASSWORD: "<Strong Password>"

Create /etc/frigate/config.yml: 

mqtt:
  host: mqtt.scottworld.net
  user: scottbroker
  password: mqtt1

#detectors:
#  coral:
#    type: edgetpu
#    device: usb

	detectors:
	  tensorrt:
	    type: tensorrt
	    device: 0

	model:
	  path: /trt-models/yolov7-tiny-416.trt
	  input_tensor: nchw
	  input_pixel_format: rgb
	  width: 416
	  height: 416



	ffmpeg:
	  hwaccel_args: preset-nvidia-h264
	  input_args: preset-http-reolink
	  output_args:
	    record: preset-record-generic-audio-copy

	timestamp_style:
	  format: "%Y-%m-%d %H:%M:%S"


	go2rtc:
	  streams:
	    FrontCam:
	        - http://frontcam.scottworld.net/flv?port-1935&app=bcs&stream=channel0_main.bcs&user=camera&password=camera
	    BackCam:
	        - http://backcam.scottworld.net/flv?port-1935&app=bcs&stream=channel0_main.bcs&user=camera&password=camera
	    Doorbell:
	        - http://doorbell.scottworld.net/flv?port-1935&app=bcs&stream=channel0_main.bcs&user=camera&password=camera




cameras:
  FrontCam:
    ffmpeg:
      inputs:
        - path: http://frontcam.scottworld.net/flv?port-1935&app=bcs&stream=channel0_ext.bcs&user=camera&password=camera
          roles:
            - detect
	    - path: rtsp://127.0.0.1:8554/FrontCam?video=copy&audio=aac
	      input_args: preset-rtsp-restream
          roles:
            - record
    motion:
      mask:
        - 640,0,443,0,438,25,640,22
    objects:
      track:
        - person
        - car
        - bicycle
        - motorcycle
    zones:
      front_drive:
        coordinates: 640,333,640,480,0,480,0,106,168,27,180,94,328,155
        objects:
          - person
          - car

  BackCam:
    ffmpeg:
      inputs:
        - path: http://backcam.scottworld.net/flv?port-1935&app=bcs&stream=channel0_ext.bcs&user=camera&password=camera
          roles:
            - detect
	    - path: rtsp://127.0.0.1:8554/BackCam?video=copy&audio=aac
	      input_args: preset-rtsp-restream
          roles:
            - record
    motion:
      mask:
        - 640,0,640,31,436,31,436,0
    objects:
      track:
        - person
        - cat
        - bird
    zones:
      back_garden:
        coordinates: 572,480,0,480,0,0,514,0,449,188,568,443
        objects:
          - person
          - cat
          - bird

  Doorbell:
    ffmpeg:
      inputs:
        - path: http://doorbell.scottworld.net/flv?port-1935&app=bcs&stream=channel0_ext.bcs&user=camera&password=camera
          roles:
            - detect
	    - path: rtsp://127.0.0.1:8554/Doorbell?video=copy&audio=aac
	      input_args: preset-rtsp-restream
          roles:
            - record
    motion:
      mask:
        - 640,0,640,40,0,40,0,0
    objects:
      track:
        - person
        - cat
        - bird
    zones:
      front_drive:
        coordinates: 640,480,0,480,0,202,36,222,36,305,174,315,294,31,640,27,640,426
        objects:
          - person
          - cat
          - bird

birdseye:
  enabled: True
  mode: motion

record:
  enabled: True
  retain:
    days: 14
    mode: motion
  events:
    retain:
      default: 90
      mode: active_objects

snapshots:
  enabled: True

Start Frigate with: 

docker compose -f /etc/frigate/docker-compose.yml up -d

Visit the Frigate interface via: 

http://server:5000/
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