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

To calibrate a camera using OpenCV, we need to perform the following steps:

1. Read a series of images of a chessboard pattern in different orientations, taken with the camera, that needs to be calibrated.
2. Identify a chessboard pattern in each of these images.
3. Use this information to calculate the camera matrix and distortion coefficients of the chosen camera. 
4. Save the camera matrix and distortion coefficients for future use.

To subsequently undistort an image, we need to do the following:

1. Load the camera matrix and distortion coefficients of our camera.
2. Calculate a new optimal camera matrix.
3. Undistort the image using the original camera matrix, the distortion coefficients, and the new camera matrix.

# Calibrating a camera

Let’s look at an example of how to calibrate a camera:

import cv2 
import numpy as np 
import glob
from datetime import datetime

num_corners = (10, 7)

objp = np.zeros((num_corners[1] * num_corners[0], 3), np.float32) 
objp[:, :2] = np.mgrid[0:num_corners[0], 0:num_corners[1]].T.reshape(-1, 2)
object_points = []
image_points = []

images = glob.glob('./resources/calibration_images/*.png')
print("nr. of calibration images found:", len(images))

for image_file in images:
    image = cv2.imread(image_file)
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    chessboard_flags = cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_FAST_CHECK + cv2.CALIB_CB_NORMALIZE_IMAGE
    ret, corners = cv2.findChessboardCorners(gray_image, num_corners, chessboard_flags)
    
    if ret:
        object_points.append(objp)
        image_points.append(corners)
        image_corners = cv2.drawChessboardCorners(image, num_corners, corners, ret)
        #cv2.imwrite(f"./output/chessboard_corners_{datetime.now().strftime('%Y%m%d_%H%M%S.%f')}.png", image_corners)

image_size = gray_image.shape[::-1] 
ret, camera_matrix, distortion_coeffs, rvecs, tvecs = cv2.calibrateCamera(object_points, image_points, image_size, None, None)

np.savez("./output/calibration_data.npz", camera_matrix=camera_matrix, distortion_coeffs=distortion_coeffs)


**Lines 1–4:** First, we import the required libraries. These libraries are `cv2` (OpenCV), `numpy`, `glob`, and `datetime`. 

We’ll use OpenCV for computer vision. `numpy` is a package for scientific computing, `glob` is used for retrieving files/pathnames matching a specified pattern, and `datetime` is for manipulating date and time values.

**Line 6:** Next, we define the number of corners, `num_corners`, on the chessboard (width x height) as a tuple (10, 7). Note that the width and height of the corners are one less than the number of squares on the chessboard, which is 11 x 8.

**Lines 8–9:** The 3D points in the real-world space, `objp`, are set up as zeros initially and are filled with actual values later. The `objp` then becomes an array of 3D points, like (0,0,0), (1,0,0), (2,0,0), and (6,5,0). The array will represent the physical coordinates of the chessboard corners in 3D space. These points are ordered (x, y, z), where z is always 0 (as the chessboard is on a flat surface).

**Lines 10–11:** Two empty lists, `object_points` and `image_points`, are created for storing the object points (3D points in the real world space) and image points (2D points in the image plane), respectively.

**Line 13:** Next, we fetch the calibration images using `glob`. The script looks for all the `.png` files in the `./resources/calibration_images/` directory and prints the number of images found. These calibration images are captured with the camera that we wish to calibrate. In the appendix, there are instructions on how to do that.




Learn how to calibrate a camera and undistort an image with Python and OpenCV.

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

Calibrating Our Camera with OpenCV

Calibrating a camera