Book Image

Learning OpenCV 5 Computer Vision with Python, Fourth Edition - Fourth Edition

By : Joseph Howse, Joe Minichino
5 (2)
Book Image

Learning OpenCV 5 Computer Vision with Python, Fourth Edition - Fourth Edition

5 (2)
By: Joseph Howse, Joe Minichino

Overview of this book

Computer vision is a rapidly evolving science in the field of artificial intelligence, encompassing diverse use cases and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You'll be able to put theory into practice by building apps with OpenCV 5 and Python 3. You'll start by setting up OpenCV 5 with Python 3 on various platforms. Next, you'll learn how to perform basic operations such as reading, writing, manipulating, and displaying images, videos, and camera feeds. From taking you through image processing, video analysis, depth estimation, and segmentation, to helping you gain practice by building a GUI app, this book ensures you'll have opportunities for hands-on activities. You'll tackle two popular challenges: face detection and face recognition. You'll also learn about object classification and machine learning, which will enable you to create and use object detectors and even track moving objects in real time. Later, you'll develop your skills in augmented reality and real-world 3D navigation. Finally, you'll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age, and you'll deploy your solutions to the Cloud. By the end of this book, you'll have the skills you need to execute real-world computer vision projects.
Table of Contents (12 chapters)
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1
Learning OpenCV 5 Computer Vision with Python, Fourth Edition: Tackle tools, techniques, and algorithms for computer vision and machine learning
Appendix A: Bending Color Space with the Curves Filter

Optimizing OpenCV for specific hardware

Generally, the setup instructions in this chapter are compatible with any hardware architecture that is supported by OpenCV and the operating system. For example, the setup instructions that target Linux operating systems will work equally well on x64, x86, ARM, or RISC-V hardware (except that the optional OpenNI package is unavailable for RISC-V).

That said, OpenCV supports many optional optimizations for specific hardware. By following the instructions in this chapter, you will get an OpenCV installation that uses a default set of optimizations for your hardware. The defaults are generally good, but for some applications, you could get even better performance with a more highly customized installation of OpenCV. Here are a few examples of use cases:

  • For x64, x86, or ARM processors, OpenCV can optionally integrate with Intel Thread Building Blocks (TBB), a multithreading library. OpenCV can use TBB to speed up certain parallel algorithms, notably...