Book Image

Learning OpenCV 3 Application Development

By : Samyak Datta
Book Image

Learning OpenCV 3 Application Development

By: Samyak Datta

Overview of this book

Computer vision and machine learning concepts are frequently used in practical computer vision based projects. If you’re a novice, this book provides the steps to build and deploy an end-to-end application in the domain of computer vision using OpenCV/C++. At the outset, we explain how to install OpenCV and demonstrate how to run some simple programs. You will start with images (the building blocks of image processing applications), and see how they are stored and processed by OpenCV. You’ll get comfortable with OpenCV-specific jargon (Mat Point, Scalar, and more), and get to know how to traverse images and perform basic pixel-wise operations. Building upon this, we introduce slightly more advanced image processing concepts such as filtering, thresholding, and edge detection. In the latter parts, the book touches upon more complex and ubiquitous concepts such as face detection (using Haar cascade classifiers), interest point detection algorithms, and feature descriptors. You will now begin to appreciate the true power of the library in how it reduces mathematically non-trivial algorithms to a single line of code! The concluding sections touch upon OpenCV’s Machine Learning module. You will witness not only how OpenCV helps you pre-process and extract features from images that are relevant to the problems you are trying to solve, but also how to use Machine Learning algorithms that work on these features to make intelligent predictions from visual data!
Table of Contents (16 chapters)
Learning OpenCV 3 Application Development
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Face alignment - the first step in facial analysis


So, we have taken our first steps in getting to know the dataset and also getting a feel of how to operate upon it. At this point, we know that all the images in our dataset contain faces (that can be detected by the cascaded classifier from Chapter 6, Face Detection Using OpenCV). Although, you might feel that this is an unnecessary step because we have already told you that the dataset contains facial images of celebrities belonging to both the gender categories. However, along the way, we picked up important skills that are going to help us in the course of our work. For example, if you are working with some other bigger dataset that has noise, then the preceding step can act as a filter to remove the images where the faces can't be detected so that they do not pose problems in the subsequent stages of our solution pipeline. Now, it's time to analyze faces!

When we are working with faces, the first step in any analysis pipeline is face...