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OpenALPR is an open source Automatic License Plate Recognition library written in C++ with bindings in C#, Java, Node.js, Go, and Python.

#Anpr open source software

Follow these next schematic diagram:Īfter having your Raspberry Pi prepared and Node-RED software configured, you can continue with this project. You should also have Node-RED installed in your Pi and the node-red-contrib-camerapi node Installed:Īssemble the circuit to test this project. Make sure the camera is connected in the right orientation with the ribbon blue letters facing up (you need to enable the camera in your Raspbian OS). With the Pi shutdown, connect the camera to the Pi CSI port as shown in the following figure. You can use the preceding links or go directly to /tools to find all the parts for your projects at the best price! Note: at the moment, we don’t have an automatic garage, so we’ll use an LED to mimic the event triggering (we know it is not the same thing, but you get the idea).

Mini PIR Motion Sensor or PIR Motion Sensor.

Raspberry Pi Board – read Best Raspberry Pi Starter Kits.

Parts Requiredįor this project you need the following parts (click the links below to find the best price at Maker Advisor):

#Anpr open source download

If you like home automation and you want to learn more about Node-RED, Raspberry Pi, ESP8266 and Arduino, we recommend that you download our course: Build a Home Automation System for $100.

You need Node-RED installed on your Pi and Node-RED prepared to take photos with the Pi Camera.

We’ll be using the Raspberry Pi Camera V2 Module, we recommend reading: Guide to Raspberry Pi Camera V2 Module.

You should have the Raspbian or Raspbian Lite operating system installed in your Raspberry Pi – read Installing Raspbian Lite, Enabling and Connecting with SSH.

You should be familiar with the Raspberry Pi – read Getting Started with Raspberry Pi.

Then, you need to add several verifications to check if the car has already entered the garage. After that, we wait a determined period of time until the car enters the garage. If they match, we’ll check if the car is in the list of authorized vehicles. Here’s what happens: after the car has been identified by OpenALPR, we’ll check if the license plate and the car model match. The following image contains a flowchart showing the process. Then, the OpenALPR API returns the car details like: plate number, model, color, and the confidence of the results.Īfter identifying a car, we’ll do some verifications, and if we found an authorized car, we’ll trigger an event (that can be open the garage, for example). After that, the Pi sends a request to OpenALPR with the car photo to be identified. When the sensor detects motion, the Raspberry Pi camera takes a photo.

Active infrared detectors: detects the presence of an object by detecting the reflection of infrared light.

Ultrasonic sensor: detects distance to an object.

Hall effect sensor: senses changes in magnetic field when the car is near.

There are other sensors that may be more suitable to detect a car, for example: In this example, we use a PIR motion sensor to detect that the car arrives home.

The following image shows how the detection process will work:

Then, we’ll trigger an event based on the detected car (for example, open the garage when it detects that your car arrived home).

First, we’ll identify a car using OpenALPR and Node-RED.

For this project we’ll be using a software called OpenALPR (Automatic License Place Recognition) that has an API you can use to identify car plates and car models based on an image.

#Anpr open source how to

No registration, license key or internet connection is required.In this project you’re going to learn how to build a car recognition system using a Raspberry Pi and Node-RED.

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You can try our SDK on your own devices ( Android, Raspberry Pi, NVIDIA Jetson, Linux and Windows) using the code released on Github with full documentation hosted here. The code is accelerated on CPU, GPU, VPU and FPGA, thanks to CUDA, TensorRT and OpenVINO. Image Enhancement for Night-Vision ( IENV), License Plate Recognition ( LPR), License Plate Country Identification ( LPCI), Vehicle Color Recognition ( VCR), Vehicle Make Model Recognition ( VMMR), Vehicle Body Style Recognition ( VBSR), Vehicle Direction Tracking ( VDT its) and Vehicle Speed Estimation ( VSE its)Īt up to 237fps on x86-64 CPUs (no GPU required). This is the kind of information you can get from our Intelligent Transportation System (ITS). "Black Mercedes-Benz SLS year 2014, license plate KMY256 from USA, state of New York, moving to the north at 91km/h speed". World's fastest and most accurate Automatic Number/License Plate Recognition (ANPR / ALPR) using deep learning. Back home Pricing Automatic Number/License Plate Recognition ( ANPR / ALPR)