Book Image

Learn OpenCV 4 By Building Projects - Second Edition

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

Learn OpenCV 4 By Building Projects - Second Edition

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

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. Whether you’re completely new to computer vision, or have a basic understanding of its concepts, Learn OpenCV 4 by Building Projects – Second edition will be your guide to understanding OpenCV concepts and algorithms through real-world examples and projects. You’ll begin with the installation of OpenCV and the basics of image processing. Then, you’ll cover user interfaces and get deeper into image processing. As you progress through the book, you'll learn complex computer vision algorithms and explore machine learning and face detection. The book then guides you in creating optical flow video analysis and background subtraction in complex scenes. In the concluding chapters, you'll also learn about text segmentation and recognition and understand the basics of the new and improved deep learning module. By the end of this book, you'll be familiar with the basics of Open CV, such as matrix operations, filters, and histograms, and you'll have mastered commonly used computer vision techniques to build OpenCV projects from scratch.
Table of Contents (14 chapters)

Preprocessing stage

Software that identifies letters does so by comparing text with previously recorded data. Classification results can be improved greatly if the input text is clear, if the letters are in a vertical position, and if there's no other elements, such as images sent to the classification software. In this section, we'll learn how to adjust text by using preprocessing.

Thresholding the image

We usually start preprocessing by thresholding the image. This eliminates all color information. Most OpenCV functions consider information to be written in white, and the background to be black. So, let's start by creating a threshold function to match this criteria:

#include opencv2/opencv.hpp; 
#include...