Free eBook: Mastering OpenCV 4 - Third Edition

Mastering OpenCV 4 - Third Edition
Work on practical computer vision projects covering advanced object detector techniques and modern deep learning and machine learning algorithms

Roy Shilkrot and David Millán Escrivá, 280 pages, Dec 2018

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


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.

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Chapter 1


Cartoonifier and Skin Color Analysis on the RaspberryPi

Learn how to write some image processing filters for desktops and for small embedded systems such as Raspberry Pi. First, we develop for the desktop (in C/C++) and then port the project to Raspberry Pi, since this is the recommended scenario when developing for embedded devices.

Chapter 2


Explore Structure from Motion with the SfM Module

Learn how to use multiple view stereo (MVS) and structure from motion (SfM) for 3D reconstruction, and how to export the final result in OpenMVG format.

Chapter 3


Face Landmark and Pose with the Face Module

See how to use OpenCV's face contrib module to detect facial landmarks in photos, as well as detecting the direction a face is pointing with the solvePnP function.

Chapter 4


Number Plate Recognition with Deep Convolutional Networks

Introduction to the steps needed to create an application for Automatic Number Plate Recognition (ANPR). There are different approaches and techniques based on different situations; for example, an infrared camera, fixed car position, and light conditions.

Chapter 5


Face Detection and Recognition with the DNN Module

Learn the main techniques of face detection and recognition. Face detection is the process whereby faces are located in a whole image. In this chapter, we are going to cover different techniques to detect faces in images, from classic algorithms using cascade classifiers with Haar features to new...

Chapter 6


Introduction to Web Computer Vision with OpenCV.js

Understand what OpenCV.js is and the benefits of client browser code and develop basic algorithms for image manipulation. Learn how to manipulate frames with OpenCV.js and how to detect faces in real time.

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Deep Learning for Computer Vision

Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks

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