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

Exploring the dataset


In the previous chapter, we talked about the role of a dataset in image classification systems. We saw that any classifier needs to see some examples of the images that it wants to classify so that it can use the domain knowledge to learn its own set of rules. These rules would eventually help the classifier to make predictions for the new images during its course of operation. We also shared a small dataset comprising of 200 images in the previous chapter. Let's now take a closer look at the contents.

As mentioned, the dataset contains 200 images--100 images of male and 100 of female faces. Since our goal here is to recognize the gender of faces, our dataset must have representations from both the gender categories. All the facial images belong to celebrities (politicians, actors, sportspersons, and so on). Here is a list of celebrity names (categorized according to their genders) whose faces appear in the dataset that we have curated for our project:

Male

Female...