Gain insights into using Python to read and write optical labels, explore 1D and 2D barcodes, and fiduciary markers for augmented reality, and discover relevant Python libraries and applications.

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

g.tar.gz

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

In this lesson, we’ll learn to read barcodes from images using the `pyzbar` library. Images read with both the PIL and the OpenCV libraries can be used as input for the `pyzbar.decode()` function. It is also possible to only look for specific barcodes in the input images.

> **Note:** **Pillow** is a fork of the Python Imaging Library (PIL), created to add new features and support for newer Python versions when PIL was discontinued. It’s fully backward-compatible with PIL and is now the preferred Python library for image processing due to its active maintenance and expanded features.

## Using PIL to read barcode images

First, we need to import the `Image` class from the `PIL` library (also commonly referred to as Pillow; see the note above) and the decode function from the `pyzbar` library.

```
import pyzbar 
from PIL import Image 
from pyzbar.pyzbar import decode
```

We now open the image in which we wish to detect and decode a barcode. 



Below is an example of how to read an image and decode the barcode that is present in that image.



from PIL import Image 
from pyzbar.pyzbar import decode

im1 = 'resources/EAN13.jpg' 
img = Image.open(im1) 

decoded_list = decode(img) 
print("decoded:", decoded_list) 

print("data:", decoded_list[0].data.decode())

**Lines 4–5:** Once the image has been opened using the `Image.open()` method from the `PIL` library, it can be supplied to the `decode()` function as a parameter.

**Line 7:** The `decode()` function returns a list of all the barcodes that it has detected. 

**Line 8:** We print this list to see what it gives us. In the output, we can see a list containing one object called `Decoded` that contains multiple fields. These fields are: `data`, `type`, `rect`, and `polygon`. Here, we can see that the barcode is of type `EAN13` and that the data is `2090378762431`.

**Line 10:** We then select the first (and only) barcode that was returned, subsequently decode it using `data.decode()`, and print it. The result is a string containing the `data` from the `Decoded` object.

Learn how to use the pyzbar library in Python to read barcode images.

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 Various Barcodes with Python

Using PIL to read barcode images