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Mini docker stack that allows you to easily record, clean and serve CCTV recordings made over RSTP while using a minimal amount of system resources.

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Mini CCTV NRV

A mini docker stack that allows you to easily record, clean and serve CCTV recordings made over RSTP while using a minimal amount of system resources.

This stack is mainly intended to be used as a backup when other and more complete solutions crash or need to be shutdown.

Preamble

This stack records the camera's streams as-is and doens't re-encode or compress it which uses more disk space.
See "Usage statistics example" for an example.

If served out of your LAN, the web server should be behind a secure reverse-proxy that requires authentication.

Setup

All of the setup is done through environment variables in the docker-compose.yml file.

It should only take 2-3 minutes if you already have the RTSP URL on hand.
If you don't have them, you should see your camera's user manual and test the URLs with VLC.

Cameras

Each recording container needs to be given a RSTP stream URL and a unique folder into which the recordings will go.

The URL must be given via the NP_CCTV_URL environment variable, and the output folder via a mounted volume that is mounted as /data in the container.

Example:

  cctv_recorder_cam1:
    container_name: cctv-recorder-cam1
    build:
      context: .
      dockerfile: Dockerfile_recorder
    environment:
      - TZ=Europe/Brussels
      - "NP_CCTV_URL=rtsp://user:password@address:554/sub-path"
    volumes:
      - ./recordings/cam1:/data
    restart: unless-stopped

This example will use the rtsp://user:password@address:554/sub-path URL and will put its recordings in ./recordings/cam1.

Cleaner

The cleaner script named cleaner.py only requires you to set 1 environment variable named NP_MAX_FILE_AGE_HOURS to the max amount of hours any recording should be kept.

If not set, the script will simply clean any recordings older than 72 hours.

Example

  cctv_cleaner:
    container_name: cctv-cleaner
    build:
      context: .
      dockerfile: Dockerfile_cleaner
    environment:
      - TZ=Europe/Brussels
      - "NP_MAX_FILE_AGE_HOURS=72"
    volumes:
      - ./recordings:/data
      - ./cleaner.py:/app/app.py:ro
    restart: unless-stopped

Web interface

The web interface provides more customization options, but at its core, it only requires the camera's environment variables to be set.

Each camera requires one of the following environment variable:
NP_CAM_<camId> = <Camera's name>

Here is an example for cam1 if named as Camera #1:
NP_CAM_cam1 = Camera #1

Other variables

Variable Description
NP_TITLE Page's title
NP_FOOTER Page's footer HTML content

Example:

  cctv_web:
    container_name: cctv-web
    image: php:apache
    ports:
      - 26880:80
    environment:
      - TZ=Europe/Brussels
      - "NP_CAM_cam1=Camera #1"
      - "NP_CAM_cam2=Camera #2"
      - "NP_TITLE=NibblePoker's Mini CCTV NVR"
      - "NP_FOOTER=Made by <i>BOZET Herwin</i>"
    volumes:
      - ./htdocs:/var/www/html  # Cannot be ":ro" since the recordings are mounted into it.
      - ./apache2.conf:/etc/apache2/apache2.conf:ro
      - ./recordings:/var/www/html/data:ro
    restart: unless-stopped

Startup

Once you have finished setting up the stack, you can simply run the following command:

docker-compose up --build -d

Screenshots

alt text alt text

Usage statistics example

  • NanoPi R4S 4GB
    • Uses 0.008 kWh / 8 Watts with other containers and USB HDD & USB SSD
  • 4 IP Cameras
    • All H.256 4k RTSP TCP streams
    • Around 220 GB of data per day
      • Around 20.4 Mbit/s or 2.6 MB/s
  • Less than 200MB of RAM usage
    • ~32 MB per recorder
    • 4 MB for cleaner
    • 4 MB for web server
  • Uses ~10% of CPU on average over 6 cores
    • Average of 15% per recorder
    • Average of 1-5% on cleaner and web server

License

This software, as well as the Simplette CSS Stylesheet used for the web interface are both licensed under Unlicense.

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Mini docker stack that allows you to easily record, clean and serve CCTV recordings made over RSTP while using a minimal amount of system resources.

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