ALPR Database

A Fully-Featured Automated License Plate Recognition Database for Blue Iris + CodeProject AI Server

Overview

I've been using CodeProject AI with Mike Lud's license plate model on Blue Iris for a couple years now, but in this setup, the ALPR doesn't really do a whole lot. Really, you have more of a license plate camera with some AI as a bonus, and no nice way to take advantage the data other than parsing Blue Iris logs or paying $600+/year for PlateMinder or Rekor ALPR.

This project serves as a complement to a CodeProject Blue Iris setup, giving you a full-featured database to store and actually use your ALPR data, completely for free. Complete with the following it has a very solid initial feature set and is a huge upgrade over the standard setup.

Please star and share the project :)

Features:

• Searchable Database & Fuzzy Search
• Live recognition feed with images
• Add vehicles you know to a known plates table
• Custom tags
• Configurable retention
• Configurable Push Notifications
• Flexible API
• Detailed system insights
• HomeAssistant Dashboard Integration

Install

Docker is the easiest and fastest way to deploy. Below is a docker-compose.yml file that will create a stack with both the application and a database. Just run the compose and you will have everything you need. If you prefer to use a separate database, you can either just spin up the container on its own from the image or use the docker-compose-without-database.yml in the repository.

Quick Start:

1. Ensure you have Docker installed on your system.

2. In a new directory, create a file named docker-compose.yml and paste in the content below, changing the variables to the passwords you would like to use.

3. Create two new directories / folders in this directory called "config" and "auth". These will ensure that your settings are saved separately and not lost during updates.

4. Download the required database schema: curl -O https://raw.githubusercontent.com/algertc/ALPR-Database/refs/heads/main/schema.sql Or, if you prefer a download link, click here to download the schema from this repository. Place it in the same directory as your docker-compose.yml.

5. Start the application: docker compose up -d

6. Access the application at http://localhost:3000

Docker Compose

version: "3.8"
services:
  app:
    image: algertc/alpr-dashboard:latest
    restart: unless-stopped
    ports:
      - "3000:3000"  # Change the first port to the port you want to expose
    environment:
      - NODE_ENV=production
      - ADMIN_PASSWORD=password  # Change this to a secure password
      - DB_PASSWORD=password  # Change this to match your postgres password
    depends_on:
      - db
    volumes:
      - app-auth:/app/auth
      - app-config:/app/config

  db:
    image: postgres:13
    environment:
      - POSTGRES_DB=postgres
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=password  # Change this to a secure password
    volumes:
      - db-data:/var/lib/postgresql/data
      - ./schema.sql:/docker-entrypoint-initdb.d/schema.sql
      - ./migrations.sql:/migrations.sql

    # Make sure you download the migrations.sql file if you are updating your existing database.
    # Place it in the same directory as your docker-compose.yml and schema.sql files. If you changed the user or database name, you will need to plug those values into the command below.
    command: >
      bash -c "
        docker-entrypoint.sh postgres &
        until pg_isready; do sleep 1; done;
        psql -U postgres -d postgres -f /migrations.sql;
        wait
      "
    ports:
      - "5432:5432"
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U postgres"]
      interval: 10s
      timeout: 5s
      retries: 5

volumes:
  db-data:
  app-auth:
    driver: local
    driver_opts:
      type: none
      o: bind
      device: ./auth
  app-config:
    driver: local
    driver_opts:
      type: none
      o: bind
      device: ./config

Setup

There is currently support for both API Posts and MQTT, however, the API is significantly more reliable. Ingestion via MQTT is not recommended.

Get Your API Key

To start sending data, spin up the docker containers and log in to the application. Navigate to settings -> security in the bottom left hand corner. At the bottom of the page you should see an API key. Click the eye to reveal the key and copy it down for use on Blue Iris.

Set up an alert action within Blue Iris:

ALPR recognitions are sent to the api/plate-reads endpoint.

We can make use of the built-in macros to dynamically get the alert data and send it as our payload. It should look like this:

{ "plate_number":"&PLATE", "Image":"&ALERT_JPEG", "camera":"&CAM", "timestamp":"&ALERT_TIME" }

Set your API key with the x-api-key header as seen below.

Note: The &PLATE macro will only send one plate number per alert. If you need to detect multiple plates in a single alert/image, you can optionally use the memo instead of the plate number. Your payload should look like this:

{ "memo":"&MEMO", "Image":"&ALERT_JPEG", "timestamp":"&ALERT_TIME" }

Thats it! You're now collecting and storing your ALPR data.

Future Considerations

• Better image storage instead of giant base64 in database.
• Ability to share your plate database with others

Screenshots

Disclaimer

This is meant to be a helpful project. It is not an official release. It is not secure and should not be exposed outside your network.