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OpenCV 3 Computer Vision Application Programming Cookbook

OpenCV 3 Computer Vision Application Programming Cookbook

By : Robert Laganiere
3.5 (2)
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OpenCV 3 Computer Vision Application Programming Cookbook

OpenCV 3 Computer Vision Application Programming Cookbook

3.5 (2)
By: Robert Laganiere

Overview of this book

Making your applications see has never been easier with OpenCV. With it, you can teach your robot how to follow your cat, write a program to correctly identify the members of One Direction, or even help you find the right colors for your redecoration. OpenCV 3 Computer Vision Application Programming Cookbook Third Edition provides a complete introduction to the OpenCV library and explains how to build your first computer vision program. You will be presented with a variety of computer vision algorithms and exposed to important concepts in image and video analysis that will enable you to build your own computer vision applications. This book helps you to get started with the library, and shows you how to install and deploy the OpenCV library to write effective computer vision applications following good programming practices. You will learn how to read and write images and manipulate their pixels. Different techniques for image enhancement and shape analysis will be presented. You will learn how to detect specific image features such as lines, circles or corners. You will be introduced to the concepts of mathematical morphology and image filtering. The most recent methods for image matching and object recognition are described, and you’ll discover how to process video from files or cameras, as well as how to detect and track moving objects. Techniques to achieve camera calibration and perform multiple-view analysis will also be explained. Finally, you’ll also get acquainted with recent approaches in machine learning and object classification.
Table of Contents (15 chapters)
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Writing efficient image-scanning loops

In the previous recipes of this chapter, we presented different ways of scanning an image in order to process its pixels. In this recipe, we will compare the efficiency of these different approaches.

When you write an image-processing function, efficiency is often a concern. When you design your function, you will frequently need to check the computational efficiency of your code in order to detect any bottleneck in your processing that might slow down your program.

However, it is important to note that unless necessary, optimization should not be done at the price of reducing code clarity. Simple code is indeed, always easier to debug and maintain. Only code portions that are critical to a program's efficiency should be heavily optimized.

How to do it...

In order to measure the execution time of a function or a portion of code, there exists a very convenient OpenCV function called cv::getTickCount(). This function gives you the number of clock cycles...

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