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 8: High-Pass Filters and Feature Detection

In the previous chapter, we learned about kernels and low-pass filters and their applications. We learned about and demonstrated how to use low-pass filters in blurring, smoothing, and de-noising images.

In this chapter, we will learn about and demonstrate the uses of high-pass filters. This includes their application in image processing and computer vision. First, we will explore the Laplacian, Scharr, and Sobel high-pass filters. Then, we will learn about the Canny edge detection algorithm. We will also demonstrate Hough transforms for circles and lines. We will conclude by looking at corner detection with the Harris algorithm.

The following is a list of the topics we will cover in this chapter:

  • Exploring high-pass filters
  • Working with the Canny edge detector
  • Finding circles and lines with Hough transforms
  • Harris corner detection

After following this chapter, you will be able to use high-pass filters...