Reading _ Writing Machine-Readable Optical Labels with Python .jpg

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Course

Gradio

Gradio-2

Gradio-3

Machine-readable optical labels are visual representations of data that machines can read and interpret. These labels, often composed of simple geometric shapes or lines, are designed to encode various kinds of information, such as product details or reference numbers.

In this course, you’ll be introduced to Python-based reading and writing of 1D and 2D barcodes and fiduciary markers. You’ll then learn the practical applications of barcodes and fiduciary markers in augmented reality and the creation of user interfaces for reading barcodes. Next, you’ll learn about different Python libraries and their usage to process different optical labels.

By the end of the course, you’ll be proficient in using Python for handling barcodes and fiduciary markers. You’ll also have hands-on experience installing and using different Python libraries to manipulate optical labels.

Using Python for Reading and Writing Optical Labels

The Python code we’ll examine is designed to load an image containing ArUco markers, detect these markers, estimate their pose, and then visualize the detected markers and their pose in 3D. 

We’ll discuss two versions of the same code. In the first version, we’ll read markers without using a calibrated camera. In the second version, we’ll use a calibrated camera.

Whether we need to use a calibrated camera or not depends on our specific use case.

Learn how to read fiduciary markers with OpenCV and Python using a camera.

Introduction to Machine-Readable Optical Labels

An Overview of Barcodes

Reading and Writing 1D Barcodes With Python

Reading and Writing 2D Barcodes With Python

Creating Fiduciary Markers With Python

Reading Fiduciary Markers With Python

Fiduciary Marker Use Cases

Build a User Interface With Gradio For Reading Barcodes

Build a Barcode Writing Application with Python

Roundup

Appendix

Reading ArUco Markers and AprilTags