Work on practical computer vision projects covering advanced object detector techniques and modern deep learning and machine learning algorithms

Key Features

• Learn about the new features that help unlock the full potential of OpenCV 4
• Build face detection applications with a cascade classifier using face landmarks
• Create an optical character recognition (OCR) model using deep learning and convolutional neural networks

Book Description

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.

What you will learn

• Build real-world computer vision problems with working OpenCV code samples
• Uncover best practices in engineering and maintaining OpenCV projects
• Explore algorithmic design approaches for complex computer vision tasks
• Work with OpenCV's most updated API (v4.0.0) through projects
• Understand 3D scene reconstruction and Structure from Motion (SfM)
• Study camera calibration and overlay AR using the ArUco Module

Who this book is for

This book is for those who have a basic knowledge of OpenCV and are competent C++ programmers. You need to have an understanding of some of the more theoretical/mathematical concepts, as we move quite quickly throughout the book.

Table of Contents

1. Cartoonifier and Skin Color Analysis on the RaspberryPi
2. Exploring Structure from Motion with the SfM Module
3. Face Landmark and Pose Estimation with the Face Module
4. Number Plate Recognition with Deep Convolutional Networks
5. Face Recognition with the DNN Module
6. Introduction to Web Computer Vision with OpenCv.js
7. Android Camera Calibration and AR using the ARUco Module
8. iOS Image Stitching with the Stitching Module
9. Finding the Best OpenCV Algorithm for the Job
10. Avoiding Common Pitfalls in OpenCV