ALPR Database

<h4 align="center">A Fully-Featured Automated License Plate Recognition Database for Blue Iris + CodeProject AI Server</h4>

<h4 align="center"> ⭐ Please star the repository if you find the project useful ⭐</h4>    </div> <br>

App Screens

:star2: 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 OCR 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.

Features:

Searchable database & fuzzy search
Build labeled training sets from your traffic
Live recognition feed
Traffic Analytics
Categorization and filtering
Store information on known vehicles
Push notifications
Automation rules
Customizable tagging
Configurable retention
Flexible API
HomeAssistant integration
Permissioned users

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🔧 Installation

The application is packaged as a Docker image. This is the fastest and most reliable way to deploy. Below is a done-for-you installation script that will create a Docker stack with both the application and a PostgreSQL database. The installation script is recommended and more carefully maintained, but manual installation instructions are also available here.

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Prerequisites

In order to send data and use the application, you will need ALREADY WORKING ALPR within Blue Iris. If you are not getting plate numbers in your alert memos, please configure and ensure your ALPR is working before beginning the setup.

You will also need the following installed on your system.

Docker
Docker Compose
Docker engine enabled and running

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[!TIP]
If unfamiliar with Docker, an easy way to check all three of these boxes at once is to install Docker Desktop, which has a GUI and bunch of nice tools.

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Linux/MacOS

Create a new directory wherever you would like to store your ALPR data. Enter the directory in your terminal and paste in the below command. After that, everything will be set up automatically!

bash
curl -sSL https://raw.githubusercontent.com/algertc/ALPR-Database/main/install.sh | bash

Or, if you prefer:

bash
wget -qO- https://raw.githubusercontent.com/algertc/ALPR-Database/main/install.sh | bash

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:bangbang: Note for Linux:

If your user is not in the Docker group, you will need to run with sudo using the command below:

bash
curl -sSL https://raw.githubusercontent.com/algertc/ALPR-Database/main/install.sh | sudo bash

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Windows

Create a new directory wherever you would like to store your ALPR data. Open PowerShell with administrator priveleges and cd into your new install directory.

Paste in the below commands. After that, everything will be set up automatically!

shell
Set-ExecutionPolicy RemoteSigned

shell
irm https://raw.githubusercontent.com/algertc/ALPR-Database/main/install.ps1 | iex

⚙️ Setup

Get Your API Key

To start sending data, log in to the application and 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 in Blue Iris.

enter image description here

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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:

{ "ai_dump":&JSON, "Image":"&ALERT_JPEG", "camera":"&CAM", "ALERT_PATH": "&ALERT_PATH", "ALERT_CLIP": "&ALERT_CLIP", "timestamp":"&ALERT_TIME" }

Set your API key with the x-api-key header as seen below.
enter image description here

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

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:camera: Screenshots

Live Viewer

Dashboard
TPMS
Plate Database
Insights

:warning: Disclaimer

This is meant to be a helpful project and is still a work-in-progress. There's a good amount of spaghetti vibe coding in here and random things left over from the initial release. Not to be relied on for anything critical.