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)

Implementing the chroma key effect

Chroma keying is also known as chroma key compositing. It is also colloquially known as the green screen or blue screen effect due to the green or blue background that we use while creating this effect. It is a post-production technique and can also be used on still images and live videos. In the chroma key effect, we place an object or a person in the foreground and capture an image or footage. The background is usually a green- or blue-colored fabric or wall. Then, we replace the green or blue color in the captured image or footage with another video or an image. This makes the viewers feel that the person or the object in the foreground is at a different location than the studio where they were filmed. This effect is one of the most used effects in film-making and live weather forecasts in news broadcasts:

  1. Let's start by importing all the needed libraries and initiating a video capture object:
    import numpy as np
    import cv2
    cap = cv2...