Book Image

Raspberry Pi Computer Vision Programming - Second Edition

By : Ashwin Pajankar
5 (1)
Book Image

Raspberry Pi Computer Vision Programming - Second Edition

5 (1)
By: Ashwin Pajankar

Overview of this book

Raspberry Pi is one of the popular single-board computers of our generation. All the major image processing and computer vision algorithms and operations can be implemented easily with OpenCV on Raspberry Pi. This updated second edition is packed with cutting-edge examples and new topics, and covers the latest versions of key technologies such as Python 3, Raspberry Pi, and OpenCV. This book will equip you with the skills required to successfully design and implement your own OpenCV, Raspberry Pi, and Python-based computer vision projects. At the start, you'll learn the basics of Python 3, and the fundamentals of single-board computers and NumPy. Next, you'll discover how to install OpenCV 4 for Python 3 on Raspberry Pi, before covering major techniques and algorithms in image processing, manipulation, and computer vision. By working through the steps in each chapter, you'll understand essential OpenCV features. Later sections will take you through creating graphical user interface (GUI) apps with GPIO and OpenCV. You'll also learn to use the new computer vision library, Mahotas, to perform various image processing operations. Finally, you'll explore the Jupyter Notebook and how to set up a Windows computer and Ubuntu for computer vision. By the end of this book, you'll be able to confidently build and deploy computer vision apps.
Table of Contents (15 chapters)

Chapter 11: Real-Life Applications of Computer Vision

In the previous chapter, we studied various advanced concepts in computer vision such as morphological operations and contours.

This chapter is the culmination of all the computer vision concepts we've learned and demonstrated in the earlier chapters. In this chapter, we will use the computer vision operation we learned about earlier in detail to implement a few real-life projects. We will also learn about a few new concepts such as background subtraction and the computation of optical flow and then demonstrate them for small applications. This chapter contains a lot of hands-on programming examples, as well as detailed explanations of the code and new functionality.

In this chapter, we will learn and demonstrate the code for the following topics:

  • Implementing the Max RGB filter
  • Implementing background subtraction
  • Computing the optical flow
  • Detecting and tracking motion
  • Detecting barcodes in images...