Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By : David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot
Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By: David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot

Overview of this book

OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books: •Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millán Escrivá •Learn OpenCV 4 By Building Projects - Second Edition by David Millán Escrivá, Vinícius G. Mendonça, and Prateek Joshi
Table of Contents (28 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Basic data persistence and storage


Before finishing this chapter, we will explore the OpenCV functions to store and read our data. In many applications, such as calibration or machine learning, when we finish performing a number of calculations, we need to save these results to retrieve them in subsequent operations. OpenCV provides an XML/YAML persistence layer to this end.

Writing to FileStorage

To write a file with some OpenCV or other numeric data, we can use the FileStorage class, using a streaming << operator such as STL streaming:

#include "opencv2/opencv.hpp" 
using namespace cv; 
 
int main(int, char** argv) 
{ 
   // create our writer 
    FileStorage fs("test.yml", FileStorage::WRITE); 
    // Save an int 
    int fps= 5; 
    fs << "fps" << fps; 
    // Create some mat sample 
    Mat m1= Mat::eye(2,3, CV_32F); 
    Mat m2= Mat::ones(3,2, CV_32F); 
    Mat result= (m1+1).mul(m1+3); 
    // write the result 
    fs << "Result" << result; 
    // release...