Mastering OpenCV 4

By : Roy Shilkrot, Millán Escrivá

2.7 (3)

Buy this Book

Mastering OpenCV 4

2.7 (3)

By: Roy Shilkrot, Millán Escrivá

Buy this Book

Overview of this book

Mastering OpenCV, now in its third edition, targets computer vision engineers taking their first steps toward mastering OpenCV. Keeping the mathematical formulations to a solid but bare minimum, the book delivers complete projects from ideation to running code, targeting current hot topics in computer vision such as face recognition, landmark detection and pose estimation, and number recognition with deep convolutional networks. You’ll learn from experienced OpenCV experts how to implement computer vision products and projects both in academia and industry in a comfortable package. You’ll get acquainted with API functionality and gain insights into design choices in a complete computer vision project. You’ll also go beyond the basics of computer vision to implement solutions for complex image processing projects. By the end of the book, you will have created various working prototypes with the help of projects in the book and be well versed with the new features of OpenCV4.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

Cartoonifier and Skin Color Analysis on the RaspberryPi

Accessing the webcam

Main camera processing loop for a desktop app

Implementation of the skin color changer

Porting from desktop to an embedded device

Summary

Explore Structure from Motion with the SfM Module

Technical requirements

Core concepts of SfM

Implementing SfM in OpenCV

Summary

Face Landmark and Pose with the Face Module

Technical requirements

Theory and context

Facial landmark detection in OpenCV

Estimating face direction from landmarks

Summary

Number Plate Recognition with Deep Convolutional Networks

Introduction to ANPR

ANPR algorithm

Plate detection

Plate recognition

Summary

Face Detection and Recognition with the DNN Module

Introduction to face detection and face recognition

Summary

References

Introduction to Web Computer Vision with OpenCV.js

What is OpenCV.js?

Compile OpenCV.js

Basic introduction to OpenCV.js development

Accessing webcam streams

Image processing and basic user interface

Optical flow in your browser

Face detection using a Haar cascade classifier in your browser

Summary

Android Camera Calibration and AR Using the ArUco Module

Technical requirements

Augmented reality and pose estimation

Camera access in Android OS

Camera calibration with ArUco

Augmented reality with jMonkeyEngine

Summary

iOS Panoramas with the Stitching Module

Technical requirements

Panoramic image stitching methods

Project overview

Setting up an iOS OpenCV project with CocoaPods

iOS UI for panorama capture

OpenCV stitching in an Objective-C++ wrapper

Summary

Technical requirements

The following technologies and installations are required to build the code in this chapter:

OpenCV v4 (compiled with the face contrib module)
Boost v1.66+

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

To run the facemark detector, a pre-trained model is required. Although training the detector model is certainly possible with the APIs provided in OpenCV, some pre-trained models are offered for download. One such model can be obtained from https://raw.githubusercontent.com/kurnianggoro/GSOC2017/master/data/lbfmodel.yaml, supplied by the contributor of the algorithm implementation to OpenCV (during the 2017 Google Summer of Code (GSoC)).

The facemark detector can work with any image; however, we can use a prescribed dataset...