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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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Implementing the Max RGB filter

We know that filters allow and block signals or data, depending on some criteria. Let's manually write the code for implementing a special filter based on the value of the intensity of the colors of pixels. This is known as the Max RGB filter. In a Max RGB filter, we compare the intensities of all the color channels of a color image for every pixel.

Then, we keep the intensity of the channel(s) with the maximum intensity and reduce the intensities of all the other channels to zero. This happens for every pixel in an image. Suppose, for a pixel, the intensities are (30, 200, 120). Then, after applying the Max RGB filter, it will be (0, 200, 0). Let's take a look at a program that will implement this with the NumPy and OpenCV functions:

import cv2
import numpy as np
def maxRGB(img):
    b = img[:, :, 0]
    g = img[:, :, 1]
    r = img[:, :, 2]
    M = np.maximum...

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