Introduction to the Course

About this course

Welcome to this comprehensive course on reading and writing machine-readable optical labels with Python. This course is designed to help you understand the basics of machine-readable optical labels, their applications, and how to read and write them using Python and various specialized libraries.

In the first part of this course, we will introduce you to the world of barcodes. We’ll explore the different types of barcodes, from 1D or linear barcodes to 2D or matrix barcodes, and learn how they encode information. We’ll learn how to generate and decode the most common 1D and 2D barcodes with Python.

Moving forward, we will introduce fiduciary markers, a special kind of 2D barcode that serves as a reference point for image recognition and tracking. We’ll discuss how these markers, often comprising simple geometric shapes, are easily detected and recognized by machine vision systems. We’ll then learn how we can use several Python libraries to create these markers and also how to detect and track them.

By the end of this course, you’ll understand how to apply the principles of barcodes and fiduciary markers, which are essential tools in bridging the gap between the physical and digital worlds.

Welcome aboard, and let’s start this exciting journey together!

Intended audience

• Learners studying computer science, information systems, or related fields: This course is highly beneficial for learners pursuing studies in these areas, providing them with an understanding of the applications and benefits of machine-readable optical labels.

• Professionals working in retail, supply chain management, healthcare, or libraries: Given the widespread use of barcodes in these sectors, professionals in these fields can gain valuable insights into how to better implement and utilize these technologies using Python.

• Engineers and technicians in robotics, computer vision, and augmented reality: As fiduciary markers are frequently used in these technical areas, this course can enhance the practical Python skills of these professionals.

• Software developers and IT professionals: Those who are developing applications or systems using Python that incorporate barcodes or fiduciary markers can greatly benefit from understanding these technologies.

• Tech enthusiasts: Individuals interested in the intersection of the physical and digital worlds and how they can bridge this gap using Python will find this course fascinating.

• Academics and researchers: Those researching or teaching in fields related to computer vision, augmented reality, or machine learning can use this course to stay updated on the practical applications of these technologies using Python.

Prerequisites for this course

To take this course, you should have a basic understanding of Python and be interested in machine-readable optical labels like barcodes and/or fiduciary markers.

Structure of the course

This course consists of 29 chapters. These chapters can be segmented into ten sections, listed below:

• An overview of barcodes: This section provides the basics of 1D and 2D barcodes. The most common barcodes, along with a short history and typical applications, will be discussed.

• Reading and writing 1D barcodes: In this section, we’ll look at how to read and write barcodes using Python. As an example, we will use the EAN13 type barcode, but the code applies to all other supported types.

• Reading and writing 2D barcodes: Here, we’ll learn how to use Python to generate and decode three common 2D barcodes, namely the QR codes, the DataMatrix codes, and the PDF417 codes.

• An overview of fiduciary markers: In this section, we’ll look at the basics of fiduciary markers, their applications, and the two most common types.

• Creating fiduciary markers: Here, we’ll learn how we can create both a single fiduciary marker and a whole page of markers using Python. We’ll focus on ArUco markers and AprilTags.

• Reading fiduciary markers: In this section, we’ll discuss how we can use Python to detect and read fiduciary markers. Here, it is important to also look at how to calibrate our camera and how we may improve the detection of markers.

• Fiduciary marker use cases: Here, we’ll present two examples of how to use fiduciary markers.

• Build a user interface for reading optical labels: In this section, we’ll discuss how we can use Gradio to create a simple interface for reading optical labels and how to test it.

• Roundup: We’ll conclude the course here and discuss where to go from here. We’ll also list some important references.

• Appendix: In the appendix, we have described how to install the libraries used in this course.

Course strengths

• Comprehensive coverage: The course provides a thorough understanding of both barcodes and fiduciary markers, giving learners a wide breadth of knowledge in the field of machine-readable optical labels.

• Practical application: Learners will discover not just the theoretical aspects but also how to practically generate, decode, and track these labels using Python, equipping them with skills that can be applied in real-world scenarios.

• Use of Python: Python is a popular, versatile, and easy-to-learn programming language. Learning to use Python in the context of optical labels allows learners to leverage these benefits and potentially apply their knowledge in various other domains.

• Real-world relevance: The course content emphasizes real-world applications using Python. This enhances learners’ understanding of the practical significance and potential uses of the studied technologies.

• Foundation for advanced study: The course lays a solid foundation for understanding the principles of barcodes and fiduciary markers, enabling learners to potentially delve into more advanced topics in this area.