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

# Generating ArUco markers

In the following example, we'll learn how to generate ArUco markers.


import cv2 
import numpy as np

print(cv2.__version__)

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

marker_id = 42  
marker_size = 200  

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)

cv2.imwrite("output/aruco_marker_42.png", marker_image)

**Lines 1–2:** To generate markers, we first need to import the required libraries, `cv2` and `numpy`.

**Line 4:** We print the version of the `cv2` library to make sure that we have installed the correct version.

Then, we’ll get the dictionary containing the specific ArUco markers that we are interested in. For the ArUco markers, we can choose between multiple dictionaries. These dictionaries contain ArUco markers of sizes:
* 4 x 4
* 5 x 5
* 6 x 6
* 7 x 7

The number of markers needed for dictionaries is as follows:
* 50
* 100
* 250
* 1,000

The name of a specific ArUco dictionary can be inferred by combining the above sizes and numbers into the following pattern: `DICT_[size]_[number]`. For example, an ArUco dictionary with 100 markers of size 5 x 5 will have the name: `DICT_5X5_100`.

**Line 6:** We use the 6 x 6 marker dictionary that has 250 different markers available. This dictionary is defined using the `aruco_dict` variable.

**Line 8:** We draw marker number `42` (out of 250), so we’ll put this number in a variable called `marker_id`.

**Line 9:** To determine the size in pixels of the marker, we set the variable `marker_size` to `200` (pixels).

**Line 11:** The actual marker image will be created as a `numpy` matrix of zeros. The size is `marker_size` by `marker_size`. The type is set to `uint8`.

**Line 14:** Then, we draw the marker using the `drawMarker()` function from the OpenCV ArUco library. 

**Line 16:** Finally, we save the image using the `imwrite()` from OpenCV.

 Learn how to draw ArUco markers and AprilTags.

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

Tracking Moving Objects Using Python

Creating Single ArUco Markers and AprilTags with OpenCV

Generating ArUco markers