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

Using your own filters in OpenCV


So far, we have talked about a couple of different filtering techniques: box filtering and Gaussian filtering. Both of them had their own set of rules for defining a filter and also had a dedicated set of functions to help you apply the filters to images. When we introduced the concept of filtering, we said that different operations can be performed on our images by simply changing the value of the filter. So, if we design our own custom filter, how do we apply that to our image? There needs to exist a function that is more generic than boxFilter(), blur(), or GaussianBlur() and that will help us in applying the filter that we have designed to our input image. And OpenCV has the answer for you--the filter2D() function.

We have already hinted at what the filter2D() hopes to accomplish, so let's jump right into the code! I think at this point, I really don't need to say what the first few lines should look like:

#include <iostream> 
#include <opencv2...