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)
Free Chapter
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

What's new in OpenCV 5

If you are an OpenCV veteran, you might want to know more about OpenCV 5's changes before you decide to install it. Here are some of the highlights:

  • The C++ implementation of OpenCV has been updated to C++14. OpenCV's Python bindings wrap the C++ implementation, so as Python users, we may gain some performance advantages from this update, even though we are not using C++ directly.
  • Support for Python 2 has been dropped because this Python version is obsolete and no longer maintained. If you have any old Python 2 projects, you should upgrade them to Python 3.
  • For many algorithms, support for named parameters has been added in order to make the initialization code more concise and readable.
  • For the pip package installer, the opencv-python and opencv-contrib-python packages are now maintained by the core OpenCV team rather than a third party. The opencv-contrib-python-nonfree package has been discontinued due to patent restrictions.
  • The SIFT algorithm...