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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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Summary

In this chapter, we learned and demonstrated the concepts of histograms in general and saw how to create a simple histogram from a simple single-dimensional array. Then, we saw how to visualize a histogram for grayscale and color images. We also demonstrated how to use image contours. Finally, we visually demonstrated the operations that are performed in the area of mathematical morphology. These morphological operations will be extremely useful for real-life applications, some of which we will demonstrate in Chapter 11, Real-Life Applications of Computer Vision.

In the next chapter, we will demonstrate many of the concepts we learned in this and the earlier chapters by building real-life applications such as movement detectors, chroma keys with a green screen, and barcode detection in still images. It will be an exciting and interesting chapter that culminates all the knowledge we have gained so far.

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