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)

Visualizing image contours

A curve that joins all the points that lie continuously along the boundary that have the same value as the color of the pixels is known as a contour. Contours are used for detecting the boundaries in an image. Contours are also used for image segmentation. Contours are usually computed using edges in an image. However, contours are closed curves and that is their main distinction from the edges in an image. It is always a good idea to apply the thresholding operation to an image before we extract contours from an image. It will increase the accuracy of the computation of the contour operation.

The cv2.findContours() function is used to compute the contours in an image. This function accepts an image array, the mode of the retrieval of the contours, and the method for the approximation of contours as arguments. It then returns a list of computer contours in the image. The contour retrieval mode can be any of the following:

  • CV_RETR_CCOMP
  • CV_RETR_TREE...