Building Computer Vision Projects with OpenCV 4 and C++电子书

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Copyright and Credits

Building Computer Vision Projects with OpenCV 4 and C++

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Contributors

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Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Conventions used

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Getting Started with OpenCV

Understanding the human visual system

How do humans understand image content?

Why is it difficult for machines to understand image content?

What can you do with OpenCV?

Inbuilt data structures and input/output

Image processing operations

GUI

Video analysis

3D reconstruction

Feature extraction

Object detection

Machine learning

Computational photography

Shape analysis

Optical flow algorithms

Face and object recognition

Surface matching

Text detection and recognition

Deep learning

Installing OpenCV

Windows

Mac OS X

Linux

Summary

An Introduction to the Basics of OpenCV

Technical requirements

Basic CMake configuration file

Creating a library

Managing dependencies

Making the script more complex

Images and matrices

Reading/writing images

Reading videos and cameras

Other basic object types

Vec object type

Scalar object type

Point object type

Size object type

Rect object type

RotatedRect object type

Basic matrix operations

Basic data persistence and storage

Writing to FileStorage

Summary

Learning Graphical User Interfaces

Technical requirements

Introducing the OpenCV user interface

Basic graphical user interface with OpenCV

Adding slider and mouse events to our interfaces

Graphic user interface with Qt

Adding buttons to the user interface

OpenGL support

Summary

Delving into Histogram and Filters

Technical requirements

Generating a CMake script file

Creating the graphical user interface

Drawing a histogram

Image color equalization

Lomography effect

Cartoonize effect

Summary

Automated Optical Inspection, Object Segmentation, and Detection

Technical requirements

Isolating objects in a scene

Creating an application for AOI

Preprocessing the input image

Noise removal

Removing the background using the light pattern for segmentation

Thresholding

Segmenting our input image

The connected components algorithm

The findContours algorithm

Summary

Learning Object Classification

Technical requirements

Introducing machine learning concepts

OpenCV machine learning algorithms

Computer vision and the machine learning workflow

Automatic object inspection classification example

Feature extraction

Training an SVM model

Input image prediction

Summary

Detecting Face Parts and Overlaying Masks

Technical requirements

Understanding Haar cascades

What are integral images?

Overlaying a face mask in a live video

What happened in the code?

Get your sunglasses on

Looking inside the code

Tracking the nose, mouth, and ears

Summary

Video Surveillance, Background Modeling, and Morphological Operations

Technical requirements

Understanding background subtraction

Naive background subtraction

Does it work well?

Frame differencing

How well does it work?

The Mixture of Gaussians approach

What happened in the code?

Morphological image processing

What's the underlying principle?

Slimming the shapes

Thickening the shapes

Other morphological operators

Morphological opening

Morphological closing

Drawing the boundary

Top Hat transform

Black Hat transform

Summary

Learning Object Tracking

Technical requirements

Tracking objects of a specific color

Building an interactive object tracker

Detecting points using the Harris corner detector

Good features to track

Feature-based tracking

Lucas-Kanade method

Farneback algorithm

Summary

Developing Segmentation Algorithms for Text Recognition

Technical requirements

Introducing optical character recognition

Preprocessing stage

Thresholding the image

Text segmentation

Creating connected areas

Identifying paragraph blocks

Text extraction and skewing adjustment

Installing Tesseract OCR on your operating system

Installing Tesseract on Windows

Building the latest library

Setting up Tesseract in Visual Studio

Static linking

Installing Tesseract on Mac

Using the Tesseract OCR library

Creating an OCR function

Sending the output to a file

Summary

Text Recognition with Tesseract

Technical requirements

How the text API works

The scene detection problem

Extremal regions

Extremal region filtering

Using the text API

Text detection

Text extraction

Text recognition

Summary

Deep Learning with OpenCV

Technical requirements

Introduction to deep learning

What is a neural network and how can we learn from data?

Convolutional neural networks

Deep learning in OpenCV

YOLO – real-time object detection

YOLO v3 deep learning model architecture

The YOLO dataset, vocabulary, and model

Importing YOLO into OpenCV

Face detection with SSD

SSD model architecture

Importing SSD face detection into OpenCV

Summary

Cartoonifier and Skin Color Analysis on the RaspberryPi

Accessing the webcam

Main camera processing loop for a desktop app

Generating a black and white sketch

Generating a color painting and a cartoon

Generating an evil mode using edge filters

Generating an alien mode using skin detection

Skin detection algorithm

Showing the user where to put their face

Implementation of the skin color changer

Reducing the random pepper noise from the sketch image

Porting from desktop to an embedded device

Equipment setup to develop code for an embedded device

Configuring a new Raspberry Pi

Installing OpenCV on an embedded device

Using the Raspberry Pi Camera Module

Installing the Raspberry Pi Camera Module driver

Making Cartoonifier run in fullscreen

Hiding the mouse cursor

Running Cartoonifier automatically after bootup

Speed comparison of Cartoonifier on desktop versus embedded

Changing the camera and camera resolution

Power draw of Cartoonifier running on desktop versus embedded system

Streaming video from Raspberry Pi to a powerful computer

Customizing your embedded system!

Summary

Explore Structure from Motion with the SfM Module

Technical requirements

Core concepts of SfM

Calibrated cameras and epipolar geometry

Stereo reconstruction and SfM

Implementing SfM in OpenCV

Image feature matching

Finding feature tracks

3D reconstruction and visualization

MVS for dense reconstruction

Summary

Face Landmark and Pose with the Face Module

Technical requirements

Theory and context

Active appearance models and constrained local models

Regression methods

Facial landmark detection in OpenCV

Measuring error

Estimating face direction from landmarks

Estimated pose calculation

Projecting the pose on the image

Summary

Number Plate Recognition with Deep Convolutional Networks

Introduction to ANPR

ANPR algorithm

Plate detection

Segmentation

Classification

Plate recognition

OCR segmentation

Character classification using a convolutional neural network

Creating and training a convolutional neural network with TensorFlow

Preparing the data

Creating a TensorFlow model

Preparing a model for OpenCV

Import and use model in OpenCV C++ code

Summary

Face Detection and Recognition with the DNN Module

Introduction to face detection and face recognition

Face detection

Implementing face detection using OpenCV cascade classifiers

Loading a Haar or LBP detector for object or face detection

Accessing the webcam

Detecting an object using the Haar or LBP classifier

Detecting the face

Implementing face detection using the OpenCV deep learning module

Face preprocessing

Eye detection

Eye search regions

Geometrical transformation

Separate histogram equalization for left and right sides

Smoothing

Elliptical mask

Collecting faces and learning from them

Collecting preprocessed faces for training

Training the face recognition system from collected faces

Viewing the learned knowledge

Average face

Eigenvalues, Eigenfaces, and Fisherfaces

Face recognition

Face identification – recognizing people from their faces

Face verification—validating that it is the claimed person

Finishing touches—saving and loading files

Finishing touches—making a nice and interactive GUI

Drawing the GUI elements

Startup mode

Detection mode

Collection mode

Training mode

Recognition mode

Checking and handling mouse clicks

Summary

References

Android Camera Calibration and AR Using the ArUco Module

Technical requirements

Augmented reality and pose estimation

Camera calibration

Augmented reality markers for planar reconstruction

Camera access in Android OS

Finding and opening the camera

Camera calibration with ArUco

Augmented reality with jMonkeyEngine

Summary

iOS Panoramas with the Stitching Module

Technical requirements

Panoramic image stitching methods

Feature extraction and robust matching for panoramas

Affine constraint

Random sample consensus (RANSAC)

Homography constraint

Bundle Adjustment

Warping images for panorama creation

Project overview

Setting up an iOS OpenCV project with CocoaPods

iOS UI for panorama capture

OpenCV stitching in an Objective-C++ wrapper

Summary

Further reading

Finding the Best OpenCV Algorithm for the Job

Technical requirements

Is it covered in OpenCV?

Algorithm options in OpenCV

Which algorithm is best?

Example comparative performance test of algorithms

Summary

Avoiding Common Pitfalls in OpenCV

History of OpenCV from v1 to v4

OpenCV and the data revolution in computer vision

Historic algorithms in OpenCV

How to check when an algorithm was added to OpenCV

Common pitfalls and suggested solutions

Summary

Further reading

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