Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By : David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot
Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By: David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot

Overview of this book

OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books: •Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millán Escrivá •Learn OpenCV 4 By Building Projects - Second Edition by David Millán Escrivá, Vinícius G. Mendonça, and Prateek Joshi
Table of Contents (28 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Chapter 21. Avoiding Common Pitfalls in OpenCV

OpenCV has been around for more than 15 years now. It contains many implementations that are outdated or unoptimized and are relics of the past. An advanced OpenCV engineer should know how to avoid basic mistakes in navigating the OpenCV APIs, and see their project to algorithmic success.

In this chapter, we will review the historic development of OpenCV, and the gradual increase in the framework and algorithmic offering, alongside the development of computer vision at large. We will use this knowledge to see how to figure out whether a newer alternative exists within OpenCV for our algorithm of choice. Lastly, we will discuss how to identify and avoid common problems or sub-optimal choices while creating computer vision systems with OpenCV.

The following topics will be covered in this chapter:

  • A historic review of OpenCV and the latest wave of computer vision research
  • Checking the date at which an algorithm became available in OpenCV, and whether...