Book Image

OpenCV 4 Computer Vision Application Programming Cookbook - Fourth Edition

By : David Millán Escrivá, Robert Laganiere
Book Image

OpenCV 4 Computer Vision Application Programming Cookbook - Fourth Edition

By: David Millán Escrivá, Robert Laganiere

Overview of this book

OpenCV is an image and video processing library used for all types of image and video analysis. Throughout the book, you'll work with recipes to implement a variety of tasks. With 70 self-contained tutorials, this book examines common pain points and best practices for computer vision (CV) developers. Each recipe addresses a specific problem and offers a proven, best-practice solution with insights into how it works, so that you can copy the code and configuration files and modify them to suit your needs. This book begins by guiding you through setting up OpenCV, and explaining how to manipulate pixels. You'll understand how you can process images with classes and count pixels with histograms. You'll also learn detecting, describing, and matching interest points. As you advance through the chapters, you'll get to grips with estimating projective relations in images, reconstructing 3D scenes, processing video sequences, and tracking visual motion. In the final chapters, you'll cover deep learning concepts such as face and object detection. By the end of this book, you'll have the skills you need to confidently implement a range of computer vision algorithms to meet the technical requirements of your complex CV projects.
Table of Contents (17 chapters)

Counting the Pixels with Histograms

An image is composed of pixels of different values (colors). The distribution of pixel values across the image constitutes an important characteristic of this image. This chapter introduces the concept of image histograms. You will learn how to compute a histogram and how to use it to modify the image's appearance. Histograms can also be used to characterize the image's content and detect specific objects or textures in an image. Some of these techniques will be presented in this chapter.

In this chapter, we will cover the following recipes:

  • Computing the image histogram
  • Applying lookup tables to modify the image's appearance
  • Equalizing the image histogram
  • Backprojecting a histogram to detect the specific image content
  • Using the mean shift algorithm to find an object
  • Retrieving similar images using histogram comparison
  • Counting...