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

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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

# Creating a single marker

Let’s put the relevant functions in a function so we can easily create markers in an iterative manner.

In the example below, we’ll create such a function with the name `create_marker()`.





import cv2 
import numpy as np

def create_marker(aruco_dict, marker_id, marker_size): 
    marker_image = np.zeros((marker_size, marker_size), dtype=np.uint8) 
    marker_image = cv2.aruco.drawMarker(aruco_dict, marker_id, marker_size, marker_image, borderBits=1) 
    return marker_image

aruco_dict = cv2.aruco.Dictionary_get(cv2.aruco.DICT_6X6_250) 

marker_id = 0
marker_size = 200

cv2.imwrite("output/onemarker.png", create_marker(aruco_dict, marker_id, marker_size))


**Line 5:** An empty `marker_image` will be created as a `numpy` matrix of zeros. The size will be `marker_size` in both dimensions. The type is set to `uint8`.

**Line 6:** Then we draw the marker using the `drawMarker()` function from the OpenCV ArUco library. 

**Line 9:** Next, we use the `6X6` marker dictionary with  250 different markers and call it `aruco_dict`.

**Lines 11–12:** We define `marker_id` as marker number `0` (out of 250) and also set the variable `marker_size` to `200` (pixels).

**Line 14:** Finally, we save the image created with our function `create_marker()` using `imwrite()` from OpenCV.

Learn how to generate a page filled with ArUco markers.

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

Creating a Page of ArUco Markers With OpenCV

Creating a single marker