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Mastering OpenCV 4 with Python电子书

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作       者:Alberto Fernández Villán

出  版  社:Packt Publishing

出版时间:2019-03-29

字       数:58.3万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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Create advanced applications with Python and OpenCV, exploring the potential of facial recognition, machine learning, deep learning, web computing and augmented reality. Key Features * Develop your computer vision skills by mastering algorithms in Open Source Computer Vision 4 (OpenCV 4)and Python * Apply machine learning and deep learning techniques with TensorFlow, Keras, and PyTorch * Discover the modern design patterns you should avoid when developing efficient computer vision applications Book Description OpenCV is considered to be one of the best open source computer vision and machine learning software libraries. It helps developers build complete projects in relation to image processing, motion detection, or image segmentation, among many others. OpenCV for Python enables you to run computer vision algorithms smoothly in real time, combining the best of the OpenCV C++ API and the Python language. In this book, you'll get started by setting up OpenCV and delving into the key concepts of computer vision. You'll then proceed to study more advanced concepts and discover the full potential of OpenCV. The book will also introduce you to the creation of advanced applications using Python and OpenCV, enabling you to develop applications that include facial recognition, target tracking, or augmented reality. Next, you'll learn machine learning techniques and concepts, understand how to apply them in real-world examples, and also explore their benefits, including real-time data production and faster data processing. You'll also discover how to translate the functionality provided by OpenCV into optimized application code projects using Python bindings. Toward the concluding chapters, you'll explore the application of artificial intelligence and deep learning techniques using the popular Python libraries TensorFlow, and Keras. By the end of this book, you'll be able to develop advanced computer vision applications to meet your customers' demands. What you will learn * Handle files and images, and explore various image processing techniques * Explore image transformations, including translation, resizing, and cropping * Gain insights into building histograms * Brush up on contour detection, filtering, and drawing * Work with Augmented Reality to build marker-based and markerless applications * Work with the main machine learning algorithms in OpenCV * Explore the deep learning Python libraries and OpenCV deep learning capabilities * Create computer vision and deep learning web applications Who this book is for This book is designed for computer vision developers, engineers, and researchers who want to develop modern computer vision applications. Basic experience of OpenCV and Python programming is a must.
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About Packt

Why subscribe?

Packt.com

Contributors

About the author

About the reviewers

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

Section 1: Introduction to OpenCV 4 and Python

Setting Up OpenCV

Technical requirements

Code testing specifications

Hardware specifications

Understanding Python

Introducing OpenCV

Contextualizing the reader

A theoretical introduction to the OpenCV library

OpenCV modules

OpenCV users

OpenCV applications

Why citing OpenCV in your research work

Installing OpenCV, Python, and other packages

Installing Python, OpenCV, and other packages globally

Installing Python

Installing Python on Linux

Installing Python on Windows

Installing OpenCV

Installing OpenCV on Linux

Installing OpenCV on Windows

Testing the installation

Installing Python, OpenCV, and other packages with virtualenv

Python IDEs to create virtual environments with virtualenv

Anaconda/Miniconda distributions and conda package–and environment-management system

Packages for scientific computing, data science, machine learning, deep learning, and computer vision

Jupyter Notebook

Trying Jupiter Notebook online

Installing the Jupyter Notebook

Installing Jupyter using Anaconda

Installing Jupyter with pip

The OpenCV and Python project structure

Our first Python and OpenCV project

Summary

Questions

Further reading

Image Basics in OpenCV

Technical requirements

A theoretical introduction to image basics

Main problems in image processing

Image-processing steps

Images formulation

Concepts of pixels, colors, channels, images, and color spaces

File extensions

The coordinate system in OpenCV

Accessing and manipulating pixels in OpenCV

Accessing and manipulating pixels in OpenCV with BGR images

Accessing and manipulating pixels in OpenCV with grayscale images

BGR order in OpenCV

Summary

Questions

Further reading

Handling Files and Images

Technical requirements

An introduction to handling files and images

sys.argv

Argparse – command-line option and argument parsing

Reading and writing images

Reading images in OpenCV

Reading and writing images in OpenCV

Reading camera frames and video files

Reading camera frames

Accessing some properties of the capture object

Saving camera frames

Reading a video file

Reading from an IP camera

Writing a video file

Calculating frames per second

Considerations for writing a video file

Playing with video capture properties

Getting all the properties from the video capture object

Using the properties – playing a video backwards

Summary

Questions

Further reading

Constructing Basic Shapes in OpenCV

Technical requirements

A theoretical introduction to drawing in OpenCV

Drawing shapes

Basic shapes – lines, rectangles, and circles

Drawing lines

Drawing rectangles

Drawing circles

Understanding advanced shapes

Drawing a clip line

Drawing arrows

Drawing ellipses

Drawing polygons

Shift parameter in drawing functions

lineType parameter in drawing functions

Writing text

Drawing text

Using all OpenCV text fonts

More functions related to text

Dynamic drawing with mouse events

Drawing dynamic shapes

Drawing both text and shapes

Event handling with Matplotlib

Advanced drawing

Summary

Questions

Further reading

Section 2: Image Processing in OpenCV

Image Processing Techniques

Technical requirements

Splitting and merging channels in OpenCV

Geometric transformations of images

Scaling an image

Translating an image

Rotating an image

Affine transformation of an image

Perspective transformation of an image

Cropping an image

Image filtering

Applying arbitrary kernels

Smoothing images

Averaging filter

Gaussian filtering

Median filtering

Bilateral filtering

Sharpening images

Common kernels in image processing

Creating cartoonized images

Arithmetic with images

Saturation arithmetic

Image addition and subtraction

Image blending

Bitwise operations

Morphological transformations

Dilation operation

Erosion operation

Opening operation

Closing operation

Morphological gradient operation

Top hat operation

Black hat operation

Structuring element

Applying morphological transformations to images

Color spaces

Showing color spaces

Skin segmentation in different color spaces

Color maps

Color maps in OpenCV

Custom color maps

Showing the legend for the custom color map

Summary

Questions

Further reading

Constructing and Building Histograms

Technical requirements

A theoretical introduction to histograms

Histogram terminology

Grayscale histograms

Grayscale histograms without a mask

Grayscale histograms with a mask

Color histograms

Custom visualizations of histograms

Comparing OpenCV, NumPy, and Matplotlib histograms

Histogram equalization

Grayscale histogram equalization

Color histogram equalization

Contrast Limited Adaptive Histogram Equalization

Comparing CLAHE and histogram equalization

Histogram comparison

Summary

Questions

Further reading

Thresholding Techniques

Technical requirements

Installing scikit-image

Installing SciPy

Introducing thresholding techniques

Simple thresholding

Thresholding types

Simple thresholding applied to a real image

Adaptive thresholding

Otsu's thresholding algorithm

The triangle binarization algorithm

Thresholding color images

Thresholding algorithms using scikit-image

Introducing thresholding with scikit-image

Trying out more thresholding techniques with scikit-image

Summary

Questions

Further reading

Contour Detection, Filtering, and Drawing

Technical requirements

An introduction to contours

Compressing contours

Image moments

Some object features based on moments

Hu moment invariants

Zernike moments

More functionality related to contours

Filtering contours

Recognizing contours

Matching contours

Summary

Questions

Further reading

Augmented Reality

Technical requirements

An introduction to augmented reality

Markerless-based augmented reality

Feature detection

Feature matching

Feature matching and homography computation to find objects

Marker-based augmented reality

Creating markers and dictionaries

Detecting markers

Camera calibration

Camera pose estimation

Camera pose estimation and basic augmentation

Camera pose estimation and more advanced augmentation

Snapchat-based augmented reality

Snapchat-based augmented reality OpenCV moustache overlay

Snapchat-based augmented reality OpenCV glasses overlay

QR code detection

Summary

Questions

Further reading

Section 3: Machine Learning and Deep Learning in OpenCV

Machine Learning with OpenCV

Technical requirements

An introduction to machine learning

Supervised machine learning

Unsupervised machine learning

Semi-supervised machine learning

k-means clustering

Understanding k-means clustering

Color quantization using k-means clustering

k-nearest neighbor

Understanding k-nearest neighbors

Recognizing handwritten digits using k-nearest neighbor

Support vector machine

Understanding SVM

Handwritten digit recognition using SVM

Summary

Questions

Further reading

Face Detection, Tracking, and Recognition

Technical requirements

Installing dlib

Installing the face_recognition package

Installing the cvlib package

Face processing introduction

Face detection

Face detection with OpenCV

Face detection with dlib

Face detection with face_recognition

Face detection with cvlib

Detecting facial landmarks

Detecting facial landmarks with OpenCV

Detecting facial landmarks with dlib

Detecting facial landmarks with face_recognition

Face tracking

Face tracking with the dlib DCF-based tracker

Object tracking with the dlib DCF-based tracker

Face recognition

Face recognition with OpenCV

Face recognition with dlib

Face recognition with face_recognition

Summary

Questions

Further reading

Introduction to Deep Learning

Technical requirements

Installing TensorFlow

Installing Keras

Deep learning overview for computer vision tasks

Deep learning characteristics

Deep learning explosion

Deep learning for image classification

Deep learning for object detection

Deep learning in OpenCV

Understanding cv2.dnn.blobFromImage()

Complete examples using the OpenCV DNN face detector

OpenCV deep learning classification

AlexNet for image classification

GoogLeNet for image classification

ResNet for image classification

SqueezeNet for image classification

OpenCV deep learning object detection

MobileNet-SSD for object detection

YOLO for object detection

The TensorFlow library

Introduction example to TensorFlow

Linear regression in TensorFlow

Handwritten digits recognition using TensorFlow

The Keras library

Linear regression in Keras

Handwritten digit recognition in Keras

Summary

Questions

Further reading

Section 4: Mobile and Web Computer Vision

Mobile and Web Computer Vision with Python and OpenCV

Technical requirements

Installing the packages

Introduction to Python web frameworks

Introduction to Flask

Web computer vision applications using OpenCV and Flask

A minimal example to introduce OpenCV and Flask

Minimal face API using OpenCV

Deep learning cat detection API using OpenCV

Deep learning API using Keras and Flask

Keras applications

Deep learning REST API using Keras Applications

Deploying a Flask application to the cloud

Summary

Questions

Further reading

Assessments

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

Chapter 10

Chapter 11

Chapter 12

Chapter 13

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