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)

Performance measurement and the management of OpenCV

OpenCV has a lot of optimized and unoptimized code. The optimized code uses features of modern microprocessors, such as instruction pipelining and AVX.

We can check whether the optimization of OpenCV is enabled on the computer we are currently using with the cv2.useOptimized() function. We can also use the cv2.setUseOptimized() function to toggle the optimization. The cv2.getTickCount() function returns the number of clock ticks (also known as clock cycles) from the time that the computer was turned on. This function is called before and after the execution of the code snippet that we are interested in.

Then, we compute the difference between the clock cycles and it returns the number of clock cycles needed to execute the code snippet. The cv2.getTickFrequency() function returns the frequency of the clock cycles. Then, we can divide the difference between the clock cycles by the frequency of the clock cycles to obtain the...