Packt+ | Advance your knowledge in tech

You're reading from Building Computer Vision Projects with OpenCV 4 and C++ Implement complex computer vision algorithms and explore deep learning and face detection

Product type Course

Published in Mar 2019

Publisher

ISBN-13 9781838644673

Length 538 pages

Edition 1st Edition

Languages

C++

Tools

OpenCV

Concepts

Computer Vision

Authors (4):

Roy Shilkrot

David Millán Escrivá

Vinícius G. Mendonça

Prateek Joshi

View More author details

Table of Contents (28) Chapters

Title Page

About Packt

Contributors

Preface

1. Getting Started with OpenCV FREE CHAPTER

2. An Introduction to the Basics of OpenCV

3. Learning Graphical User Interfaces

4. Delving into Histogram and Filters

5. Automated Optical Inspection, Object Segmentation, and Detection

6. Learning Object Classification

7. Detecting Face Parts and Overlaying Masks

8. Video Surveillance, Background Modeling, and Morphological Operations

9. Learning Object Tracking

10. Developing Segmentation Algorithms for Text Recognition

11. Text Recognition with Tesseract

12. Deep Learning with OpenCV

13. Cartoonifier and Skin Color Analysis on the RaspberryPi

14. Explore Structure from Motion with the SfM Module

15. Face Landmark and Pose with the Face Module

16. Number Plate Recognition with Deep Convolutional Networks

17. Face Detection and Recognition with the DNN Module

18. Android Camera Calibration and AR Using the ArUco Module

19. iOS Panoramas with the Stitching Module

20. Finding the Best OpenCV Algorithm for the Job

21. Avoiding Common Pitfalls in OpenCV

1. Other Books You May Enjoy

Leave a review - let other readers know what you think

Index

Technical requirements

The technologies and softwares used in this chapter are the following:

OpenCV v3 or v4 Android SDK compiled with the ArUco contrib module: https://github.com/Mainvooid/opencv-android-sdk-with-contrib
Android Studio v3.2+
Android device running Android OS v6.0+

Build instructions for these components, as well as the code to implement the concepts presented in this chapter, will be provided in the accompanying code repository.

To run the examples, a printed calibration board is required. The board image can be generated programmatically with the ArUco cv::aruco::CharucoBoard::draw function, and can then be printed using a home printer. The board works best if it is glued to a hard surface, such as a cardboard or plastic sheet. After printing the board, precise measurements of the board marker's size should be taken (with a ruler or caliper), to make the calibration results more accurate and true to the real world.

The code for this chapter can be accessed through GitHub: https...

The rest of the chapter is locked

You're reading from Building Computer Vision Projects with OpenCV 4 and C++ Implement complex computer vision algorithms and explore deep learning and face detection

Table of Contents (28) Chapters

Technical requirements

Unlock this book and the full library FREE for 7 days

Authors (4)

Personalised recommendations for you