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 10: Histograms, Contours, and Morphological Transformations

In the previous chapter, we learned about and demonstrated the basic- and intermediate-level concepts surrounding the areas of image processing and computer vision.

From this chapter onward, we will learn about and demonstrate advanced concepts that will prepare us for writing programs for applications in real life. First, we will look at the theoretical foundations of computing histograms with an ndarray. Then, we will learn how to compute it for grayscale and color image channels. We will also learn how to compute and visualize contours. Finally, we will learn about various mathematical morphological operations in detail and demonstrate how to use them with various structuring elements. We will learn about and demonstrate the following topics:

  • Computing and visualizing histograms
  • Visualizing image contours
  • Applying morphological transformations to images

After completing this chapter, you...