舉報 
會員
Mastering OpenCV 4
MasteringOpenCV,nowinitsthirdedition,targetscomputervisionengineerstakingtheirfirststepstowardmasteringOpenCV.Keepingthemathematicalformulationstoasolidbutbareminimum,thebookdeliverscompleteprojectsfromideationtorunningcode,targetingcurrenthottopicsincomputervisionsuchasfacerecognition,landmarkdetectionandposeestimation,andnumberrecognitionwithdeepconvolutionalnetworks.You’lllearnfromexperiencedOpenCVexpertshowtoimplementcomputervisionproductsandprojectsbothinacademiaandindustryinacomfortablepackage.You’llgetacquaintedwithAPIfunctionalityandgaininsightsintodesignchoicesinacompletecomputervisionproject.You’llalsogobeyondthebasicsofcomputervisiontoimplementsolutionsforcompleximageprocessingprojects.Bytheendofthebook,youwillhavecreatedvariousworkingprototypeswiththehelpofprojectsinthebookandbewellversedwiththenewfeaturesofOpenCV4.
目錄(174章)
倒序
- coverpage
- Title Page
- Copyright and Credits
- Mastering OpenCV 4 Third Edition
- Dedication
- About Packt
- Why subscribe?
- Packt.com
- Contributors
- About the authors
- 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
- 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
- Introduction to Web Computer Vision with OpenCV.js
- What is OpenCV.js?
- Compile OpenCV.js
- Basic introduction to OpenCV.js development
- Accessing webcam streams
- Image processing and basic user interface
- Threshold filter
- Gaussian filter
- Canny filter
- Optical flow in your browser
- Face detection using a Haar cascade classifier in your browser
- Summary
- 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
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-07-02 14:48:09
推薦閱讀
- 多媒體CAI課件設計與制作導論（第二版）
- 算法訓練營：入門篇（全彩版）
- ASP.NET Core 2 and Vue.js
- Mastering Julia
- 高級C/C++編譯技術（典藏版）
- 快速念咒：MySQL入門指南與進階實戰
- 青少年學Python（第1冊）
- Getting Started with LLVM Core Libraries
- NoSQL數據庫原理
- C#程序設計（項目教學版）
- JavaScript悟道
- Maven for Eclipse
- JavaScript前端開發基礎教程
- 用Python動手學統計學
- 系統分析師UML用例實戰
- Python人工智能項目實戰
- 微信小程序開發圖解案例教程：附精講視頻（第3版）
- Java語言程序設計與實現（微課版）
- Python Geospatial Analysis Cookbook
- Spring 5.0 Cookbook
- ATmega16單片機C語言程序設計經典實例
- Learning AirWatch
- 深入理解Android網絡編程：技術詳解與最佳實踐
- Dapr與.NET微服務實戰
- JavaScript核心概念及實踐
- Java Data Analysis
- Unsupervised Learning with R
- 高級前端程序員面試筆試真題庫
- 大學信息基礎
- Go程序開發實戰寶典
