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Raspberry Pi Computer Vision Programming

Raspberry Pi Computer Vision Programming

By : Ashwin Pajankar
3.8 (4)
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Raspberry Pi Computer Vision Programming

Raspberry Pi Computer Vision Programming

3.8 (4)
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)
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Chapter 7: Let's Make Some Noise

In the previous chapter, we learned and demonstrated the concepts of colorspaces and converting them, mathematical transformations, and thresholding operations.

In this chapter, we will learn and demonstrate the concepts related to noise and filtering. This entire chapter is dedicated to understanding the concept of noise in detail. First, we will learn how to simulate various types of noise pattern in depth. Then, we will learn and demonstrate how to use image kernels and the convolution operation. We will also learn how to use the convolution operation to apply various types of filters. Finally, we will learn the basics of low pass filters and demonstrate how to use them to perform blurring and noise removal operations.

We will also use GPIO for demonstrations. In this chapter, we will cover the following topics:

  • Noise
  • Working with kernels
  • 2D convolution with the Signal Processing module in SciPy
  • Filtering and blurring...

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