If you need help configuring your development environment for OpenCV, I highly recommend that you read my pip install OpenCV guide - it will have you up and running in a matter of minutes. Luckily, OpenCV is pip-installable: $ pip install opencv-contrib-python In order to perform real-time augmented reality with OpenCV, you need to have the OpenCV library installed.

We’ll then read frames from the second video stream and then transform them into the first.īy the end of this tutorial, you will have a fully functional OpenCV augmented reality project running in real time! Configuring your development environment.

The first video stream will act as our “eyes” into the real world (i.e., what our camera sees).

Take a source image and apply a perspective transform to map the source input onto the frame, thus creating our augmented reality output!Īnd just to make this project even more fun and interesting, we’ll utilize two video streams:.

Detect ArUco markers in each input frame.

Since OpenCV is geared to work with real-time image processing, we can also use OpenCV to facilitate real-time augmented reality.įor the purposes of this tutorial we will: The library accepts input images/frames, processes them as quickly as possible, and then returns the results. The very reason the OpenCV library exists is to facilitate real-time image processing. Figure 1: OpenCV can be used to apply augmented reality to real-time video streams.

How to install project reality