When we started running our code on datasets of images, as opposed to a single image (or a handful of them), you learnt a neat trick. We could read the image names (image paths) off a file and then, once the image path is available to us, we could proceed with our usual image processing steps. So, the only thing that was left was to include the name of the file into our code and then pass it to the open()
function of the ifstream
class. And how did we do that (pass the filename to the code)? In some of the examples in this chapter, you would have come across something called command-line arguments that we used for this purpose. They are nothing but effective techniques used by all the programming languages (this is not something specific to just C++) to pass arguments (such as filenames) to your code. This Appendix section will be an attempt to demystify the same.
Learning OpenCV 3 Application Development
By :
Learning OpenCV 3 Application Development
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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
Free Chapter
Laying the Foundation
Image Filtering
Image Thresholding
Image Histograms
Image Derivatives and Edge Detection
Face Detection Using OpenCV
Affine Transformations and Face Alignment
Feature Descriptors in OpenCV
Machine Learning with OpenCV
Command-line Arguments in C++
Customer Reviews