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

Implementing Vignetting in OpenCV


Now that you know about the type of changes that a Vignette mask brings about in images, we can start to think about devising a strategy for the same and ultimately go about implementing the Vignetting operation. As we have discussed, the Vignette mask leaves the central portion of an image bright and darkens the borders in all directions as we move out of the center. Now, the input image (grayscale) that we will be dealing with will have a fixed intensity value for every pixel. What we essentially need to do is modify the value of every pixel in such a manner that the pixels in the center remain at their original intensity levels while the surrounding pixels get progressively darker as we approach the borders.

How can we bring about such a transformation? Well, one way to do that would be to multiply each pixel value with a scaling constant between 0 and 1. The pixels whose intensities need to be kept as is (the ones near the center of the image) should...