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Mastering OpenCV 4
MasteringOpenCV,nowinitsthirdedition,targetscomputervisionengineerstakingtheirfirststepstowardmasteringOpenCV.Keepingthemathematicalformulationstoasolidbutbareminimum,thebookdeliverscompleteprojectsfromideationtorunningcode,targetingcurrenthottopicsincomputervisionsuchasfacerecognition,landmarkdetectionandposeestimation,andnumberrecognitionwithdeepconvolutionalnetworks.You’lllearnfromexperiencedOpenCVexpertshowtoimplementcomputervisionproductsandprojectsbothinacademiaandindustryinacomfortablepackage.You’llgetacquaintedwithAPIfunctionalityandgaininsightsintodesignchoicesinacompletecomputervisionproject.You’llalsogobeyondthebasicsofcomputervisiontoimplementsolutionsforcompleximageprocessingprojects.Bytheendofthebook,youwillhavecreatedvariousworkingprototypeswiththehelpofprojectsinthebookandbewellversedwiththenewfeaturesofOpenCV4.
最新章節
- Leave a review - let other readers know what you think
- Other Books You May Enjoy
- Further reading
- Summary
- Common pitfalls and suggested solutions
- How to check when an algorithm was added to OpenCV
品牌：中圖公司
上架時間：2021-07-02 12:38:30
出版社：Packt Publishing
本書數字版權由中圖公司提供，并由其授權上海閱文信息技術有限公司制作發行
- Leave a review - let other readers know what you think 更新時間：2021-07-02 14:48:09
- Other Books You May Enjoy
- Further reading
- Summary
- Common pitfalls and suggested solutions
- How to check when an algorithm was added to OpenCV
- Historic algorithms in OpenCV
- OpenCV and the data revolution in computer vision
- History of OpenCV from v1 to v4
- Avoiding Common Pitfalls in OpenCV
- Summary
- Example comparative performance test of algorithms
- Which algorithm is best?
- Algorithm options in OpenCV
- Is it covered in OpenCV?
- Technical requirements
- Finding the Best OpenCV Algorithm for the Job
- Further reading
- Summary
- OpenCV stitching in an Objective-C++ wrapper
- iOS UI for panorama capture
- Setting up an iOS OpenCV project with CocoaPods
- Project overview
- Warping images for panorama creation
- Bundle Adjustment
- Homography constraint
- Random sample consensus (RANSAC)
- Affine constraint
- Feature extraction and robust matching for panoramas
- Panoramic image stitching methods
- Technical requirements
- iOS Panoramas with the Stitching Module
- Summary
- Augmented reality with jMonkeyEngine
- Camera calibration with ArUco
- Finding and opening the camera
- Camera access in Android OS
- Augmented reality markers for planar reconstruction
- Camera calibration
- Augmented reality and pose estimation
- Technical requirements
- Android Camera Calibration and AR Using the ArUco Module
- Summary
- Face detection using a Haar cascade classifier in your browser
- Optical flow in your browser
- Canny filter
- Gaussian filter
- Threshold filter
- Image processing and basic user interface
- Accessing webcam streams
- Basic introduction to OpenCV.js development
- Compile OpenCV.js
- What is OpenCV.js?
- Introduction to Web Computer Vision with OpenCV.js
- References
- Summary
- Checking and handling mouse clicks
- Recognition mode
- Training mode
- Collection mode
- Detection mode
- Startup mode
- Drawing the GUI elements
- Finishing touches—making a nice and interactive GUI
- Finishing touches—saving and loading files
- Face verification—validating that it is the claimed person
- Face identification – recognizing people from their faces
- Face recognition
- Eigenvalues Eigenfaces and Fisherfaces
- Average face
- Viewing the learned knowledge
- Training the face recognition system from collected faces
- Collecting preprocessed faces for training
- Collecting faces and learning from them
- Elliptical mask
- Smoothing
- Separate histogram equalization for left and right sides
- Geometrical transformation
- Eye search regions
- Eye detection
- Face preprocessing
- Implementing face detection using the OpenCV deep learning module
- Detecting the face
- Detecting an object using the Haar or LBP classifier
- Accessing the webcam
- Loading a Haar or LBP detector for object or face detection
- Implementing face detection using OpenCV cascade classifiers
- Face detection
- Introduction to face detection and face recognition
- Face Detection and Recognition with the DNN Module
- Summary
- Import and use model in OpenCV C++ code
- Preparing a model for OpenCV
- Creating a TensorFlow model
- Preparing the data
- Creating and training a convolutional neural network with TensorFlow
- Character classification using a convolutional neural network
- OCR segmentation
- Plate recognition
- Classification
- Segmentation
- Plate detection
- ANPR algorithm
- Introduction to ANPR
- Number Plate Recognition with Deep Convolutional Networks
- Summary
- Projecting the pose on the image
- Estimated pose calculation
- Estimating face direction from landmarks
- Measuring error
- Facial landmark detection in OpenCV
- Regression methods
- Active appearance models and constrained local models
- Theory and context
- Technical requirements
- Face Landmark and Pose with the Face Module
- Summary
- MVS for dense reconstruction
- 3D reconstruction and visualization
- Finding feature tracks
- Image feature matching
- Implementing SfM in OpenCV
- Stereo reconstruction and SfM
- Calibrated cameras and epipolar geometry
- Core concepts of SfM
- Technical requirements
- Explore Structure from Motion with the SfM Module
- Summary
- Customizing your embedded system!
- Streaming video from Raspberry Pi to a powerful computer
- Power draw of Cartoonifier running on desktop versus embedded system
- Changing the camera and camera resolution
- Speed comparison of Cartoonifier on desktop versus embedded
- Running Cartoonifier automatically after bootup
- Hiding the mouse cursor
- Making Cartoonifier run in fullscreen
- Installing the Raspberry Pi Camera Module driver
- Using the Raspberry Pi Camera Module
- Installing OpenCV on an embedded device
- Configuring a new Raspberry Pi
- Equipment setup to develop code for an embedded device
- Porting from desktop to an embedded device
- Reducing the random pepper noise from the sketch image
- Implementation of the skin color changer
- Showing the user where to put their face
- Skin detection algorithm
- Generating an alien mode using skin detection
- Generating an evil mode using edge filters
- Generating a color painting and a cartoon
- Generating a black and white sketch
- Main camera processing loop for a desktop app
- Accessing the webcam
- Cartoonifier and Skin Color Analysis on the RaspberryPi
- Reviews
- Get in touch
- Conventions used
- Download the color images
- Download the example code files
- To get the most out of this book
- What this book covers
- Who this book is for
- Preface
- Packt is searching for authors like you
- About the reviewers
- About the authors
- Contributors
- Packt.com
- Why subscribe?
- About Packt
- Dedication
- Mastering OpenCV 4 Third Edition
- Copyright and Credits
- Title Page
- coverpage
- 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
