Building Computer Vision Projects with OpenCV 4 and C++: Implement complex computer vision algorithms and explore deep learning and face detection (English Edition)

Delve into practical computer vision and image processing projects and get up to speed with advanced object detection techniques and machine learning algorithms

Key Features

Discover best practices for engineering and maintaining OpenCV projects

Explore important deep learning tools for image classification

Understand basic image matrix formats and filters

Book Description

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

What you will learn

Stay up-to-date with algorithmic design approaches for complex computer vision tasks

Work with OpenCV's most up-to-date API through various projects

Understand 3D scene reconstruction and Structure from Motion (SfM)

Study camera calibration and overlay augmented reality (AR) using the ArUco module

Create CMake scripts to compile your C++ application

Explore segmentation and feature extraction techniques

Remove backgrounds from static scenes to identify moving objects for surveillance

Work with new OpenCV functions to detect and recognize text with Tesseract

Who this book is for

If you are a software developer with a basic understanding of computer vision and image processing and want to develop interesting computer vision applications with OpenCV, this Learning Path is for you. Prior knowledge of C++ and familiarity with mathematical concepts will help you better understand the concepts in this Learning Path. Table of Contents

Getting Started with OpenCV

An Introduction to the Basics of OpenCV

Learning Graphical User Interfaces

Delving into Histogram and Filters

Automated Optical Inspection, Object Segmentation, and Detection

Learning Object Classification

Detecting Face Parts and Overlaying Masks

Video Surveillance, Background Modeling, and Morphological Operations

Learning Object Tracking

Developing Segmentation Algorithms for Text Recognition

Text Recognition with Tesseract

Deep Learning with OpenCV

Cartoonifier and Skin Color Analysis on the RaspberryPi

Explore Structure from Motion with the SfM Module

Face Landmark and Pose with the Face Module

Number Plate Recognition with Deep Convolutional Networks

Face Detection and Recognition with the DNN Module

Android Camera Calibration and AR Using the ArUco Module

iOS Panoramas with the Stitching Module

Finding the Best OpenCV Algorithm for the Job

Avoiding Common Pitfalls in OpenCV

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