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Raspberry Pi Computer Vision Programming. - Second Edition

You're reading from  Raspberry Pi Computer Vision Programming. - Second Edition

Product type Book
Published in Jun 2020
Publisher Packt
ISBN-13 9781800207219
Pages 306 pages
Edition 2nd Edition
Languages
Author (1):
Ashwin Pajankar Ashwin Pajankar
Profile icon Ashwin Pajankar

Table of Contents (15) Chapters

Preface 1. Chapter 1: Introduction to Computer Vision and the Raspberry Pi 2. Chapter 2: Preparing the Raspberry Pi for Computer Vision 3. Chapter 3: Introduction to Python Programming 4. Chapter 4: Getting Started with Computer Vision 5. Chapter 5: Basics of Image Processing 6. Chapter 6: Colorspaces, Transformations, and Thresholding 7. Chapter 7: Let's Make Some Noise 8. Chapter 8: High-Pass Filters and Feature Detection 9. Chapter 9: Image Restoration, Segmentation, and Depth Maps 10. Chapter 10: Histograms, Contours, and Morphological Transformations 11. Chapter 11: Real-Life Applications of Computer Vision 12. Chapter 12: Working with Mahotas and Jupyter 13. Chapter 13: Appendix 14. Other Books You May Enjoy

Detecting barcodes in images

A barcode is a way that information is represented visually and is easy to understand for purpose-made machines. There are many barcode formats. The usual format has parallel vertical lines of different thicknesses and different amounts of space in between them.

In this section, we will demonstrate how to detect a simple parallel-lines formatted barcode from a still image. We will use the following image of a soda can:

Figure 11.6 – The original source image

  1. Let's read the source image of a soda can using the following code:
    import numpy as np
    import cv2
    image=cv2.imread('/home/pi/book/dataset/barcode.jpeg', 1)
    input = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  2. The horizontal image of a barcode has a low and a high vertical gradient. So, the candidate image must have the region that fits this criterion. We will use the cv2.Sobel() function to compute the horizontal and vertical derivatives and...
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