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Building Computer Vision Projects with OpenCV 4 and C++
David Millán Escrivá Prateek Joshi Vinícius G. Mendon?a Roy Shilkrot 著
更新時間：2021-07-02 12:29:33
開會員，本書免費讀 >
OpenCVisoneofthebestopensourcelibrariesavailableandcanhelpyoufocusonconstructingcompleteprojectsonimageprocessing,motiondetection,andimagesegmentation.ThisLearningPathisyourguidetounderstandingOpenCVconceptsandalgorithmsthroughreal-worldexamplesandactivities.Throughvariousprojects,you'llalsodiscoverhowtousecomplexcomputervisionandmachinelearningalgorithmsandfacedetectiontoextractthemaximumamountofinformationfromimagesandvideos.Inlaterchapters,you'lllearntoenhanceyourvideosandimageswithopticalflowanalysisandbackgroundsubtraction.SectionsintheLearningPathwillhelpyougettogripswithtextsegmentationandrecognition,inadditiontoguidingyouthroughthebasicsofthenewandimproveddeeplearningmodules.BytheendofthisLearningPath,youwillhavemasteredcommonlyusedcomputervisiontechniquestobuildOpenCVprojectsfromscratch.ThisLearningPathincludescontentfromthefollowingPacktbooks:MasteringOpenCV4-ThirdEditionbyRoyShilkrotandDavidMillánEscrivá.LearnOpenCV4ByBuildingProjects-SecondEditionbyDavidMillánEscrivá,ViníciusG.Mendon?a,andPrateekJoshi.
最新章節(jié)
- 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 11:59:16
出版社：Packt Publishing
本書數(shù)字版權(quán)由中圖公司提供，并由其授權(quán)上海閱文信息技術(shù)有限公司制作發(fā)行
- Leave a review - let other readers know what you think 更新時間：2021-07-02 12:29:33
- 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
- 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
- Summary
- Importing SSD face detection into OpenCV
- SSD model architecture
- Face detection with SSD
- Importing YOLO into OpenCV
- The YOLO dataset vocabulary and model
- YOLO v3 deep learning model architecture
- YOLO – real-time object detection
- Deep learning in OpenCV
- Convolutional neural networks
- What is a neural network and how can we learn from data?
- Introduction to deep learning
- Technical requirements
- Deep Learning with OpenCV
- Summary
- Text recognition
- Text extraction
- Text detection
- Using the text API
- Extremal region filtering
- Extremal regions
- The scene detection problem
- How the text API works
- Technical requirements
- Text Recognition with Tesseract
- Summary
- Sending the output to a file
- Creating an OCR function
- Using the Tesseract OCR library
- Installing Tesseract on Mac
- Static linking
- Setting up Tesseract in Visual Studio
- Building the latest library
- Installing Tesseract on Windows
- Installing Tesseract OCR on your operating system
- Text extraction and skewing adjustment
- Identifying paragraph blocks
- Creating connected areas
- Text segmentation
- Thresholding the image
- Preprocessing stage
- Introducing optical character recognition
- Technical requirements
- Developing Segmentation Algorithms for Text Recognition
- Summary
- Farneback algorithm
- Lucas-Kanade method
- Feature-based tracking
- Good features to track
- Detecting points using the Harris corner detector
- Building an interactive object tracker
- Tracking objects of a specific color
- Technical requirements
- Learning Object Tracking
- Summary
- Black Hat transform
- Top Hat transform
- Drawing the boundary
- Morphological closing
- Morphological opening
- Other morphological operators
- Thickening the shapes
- Slimming the shapes
- What's the underlying principle?
- Morphological image processing
- What happened in the code?
- The Mixture of Gaussians approach
- How well does it work?
- Frame differencing
- Does it work well?
- Naive background subtraction
- Understanding background subtraction
- Technical requirements
- Video Surveillance Background Modeling and Morphological Operations
- Summary
- Tracking the nose mouth and ears
- Looking inside the code
- Get your sunglasses on
- What happened in the code?
- Overlaying a face mask in a live video
- What are integral images?
- Understanding Haar cascades
- Technical requirements
- Detecting Face Parts and Overlaying Masks
- Summary
- Input image prediction
- Training an SVM model
- Feature extraction
- Automatic object inspection classification example
- Computer vision and the machine learning workflow
- OpenCV machine learning algorithms
- Introducing machine learning concepts
- Technical requirements
- Learning Object Classification
- Summary
- The findContours algorithm
- The connected components algorithm
- Segmenting our input image
- Thresholding
- Removing the background using the light pattern for segmentation
- Noise removal
- Preprocessing the input image
- Creating an application for AOI
- Isolating objects in a scene
- Technical requirements
- Automated Optical Inspection Object Segmentation and Detection
- Summary
- Cartoonize effect
- Lomography effect
- Image color equalization
- Drawing a histogram
- Creating the graphical user interface
- Generating a CMake script file
- Technical requirements
- Delving into Histogram and Filters
- Summary
- OpenGL support
- Adding buttons to the user interface
- Graphic user interface with Qt
- Adding slider and mouse events to our interfaces
- Basic graphical user interface with OpenCV
- Introducing the OpenCV user interface
- Technical requirements
- Learning Graphical User Interfaces
- Summary
- Writing to FileStorage
- Basic data persistence and storage
- Basic matrix operations
- RotatedRect object type
- Rect object type
- Size object type
- Point object type
- Scalar object type
- Vec object type
- Other basic object types
- Reading videos and cameras
- Reading/writing images
- Images and matrices
- Making the script more complex
- Managing dependencies
- Creating a library
- Basic CMake configuration file
- Technical requirements
- An Introduction to the Basics of OpenCV
- Summary
- Linux
- Mac OS X
- Windows
- Installing OpenCV
- Deep learning
- Text detection and recognition
- Surface matching
- Face and object recognition
- Optical flow algorithms
- Shape analysis
- Computational photography
- Machine learning
- Object detection
- Feature extraction
- 3D reconstruction
- Video analysis
- GUI
- Image processing operations
- Inbuilt data structures and input/output
- What can you do with OpenCV?
- Why is it difficult for machines to understand image content?
- How do humans understand image content?
- Understanding the human visual system
- Getting Started with OpenCV
- Reviews
- Get in touch
- Conventions used
- 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 authors
- Contributors
- Packt.com
- Why subscribe?
- About Packt
- Building Computer Vision Projects with OpenCV 4 and C++
- Copyright and Credits
- Title Page
- coverpage
- coverpage
- Title Page
- Copyright and Credits
- Building Computer Vision Projects with OpenCV 4 and C++
- About Packt
- Why subscribe?
- Packt.com
- Contributors
- About the authors
- 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
- Conventions used
- Get in touch
- Reviews
- 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
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-07-02 12:29:33
