book

The Computer Vision Workshop

Name: The Computer Vision Workshop
ISBN: 9781800201774

by Hafsa Asad, Vishwesh Ravi Shrimali, Nikhil Singh, Richmond Alake, Tim Hoolihan, Harshil Jain, Geetank Raipuria, Shovon Sengupta, Sanchit Singh

July 2020

Intermediate to advanced

568 pages

9h 51m

English

Packt Publishing

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The Computer Vision Workshop
Preface
About the BookAudienceAbout the ChaptersConventionsCode PresentationSetting up Your EnvironmentDownloading Anaconda InstallerInstalling Anaconda on WindowsInstalling Anaconda on LinuxInstalling Anaconda on macOS XInstalling Other LibrariesAccessing the Code Files
1. Basics of Image Processing
IntroductionNumPy ArraysExercise 1.01: Creating NumPy ArraysPixels in ImagesPixel Location – Image Coordinate SystemImage PropertiesSize of the ImageColor Spaces and ChannelsPixel ValuesIntroduction to OpenCVImages in OpenCVImportant OpenCV FunctionsExercise 1.02: Reading, Processing, and Writing an ImageUsing Matplotlib to Display ImagesAccessing and Manipulating PixelsExercise 1.03: Creating a Water EffectActivity 1.01: Mirror Effect with a TwistSummary
2. Common Operations When Working with Images
IntroductionGeometric TransformationsImage TranslationExercise 2.01: Translation Using NumPyImage RotationFinding the Rotation MatrixFinding the Size of the Output ImageImage ResizingAffine TransformationExercise 2.02: Working with Affine TransformationPerspective TransformationExercise 2.03: Perspective TransformationImage ArithmeticImage AdditionExercise 2.04: Performing Image AdditionImage MultiplicationExercise 2.05: Image MultiplicationBinary ImagesExercise 2.06: Converting an Image into a Binary ImageBitwise Operations on ImagesExercise 2.07: Chess PiecesMaskingActivity 2.01: Masking Using Binary ImagesSummary
3. Working with Histograms
IntroductionIntroduction to MatplotlibDisplaying Images with MatplotlibPlotting Histograms with MatplotlibExercise 3.01: Plotting a Sample Image and Its Histogram with 256 binsExercise 3.02: Plotting a Sample Image and Its Histogram with 10 binsHistograms with OpenCVUser-Selected ROIExercise 3.03: Creating a Mask Image Using a User-Selected ROIA Comparison of Some Sample HistogramsWhat Is Histogram Equalization?Exercise 3.04: Histogram Equalization of a Grayscale ImageContrast Limited Adaptive Histogram Equalization (CLAHE)Exercise 3.05: Application of CLAHE on a Grayscale ImageActivity 3.01: Enhancing Images Using Histogram Equalization and CLAHEExercise 3.06: Histogram Equalization in the BGR Color SpaceThe Histogram Equalization of Color Images Using the HSV and LAB Color SpacesExercise 3.07: Histogram Equalization in the HSV Color SpaceExercise 3.08: Histogram Equalization in the LAB Color SpaceActivity 3.02: Image Enhancement in a User-Defined ROISummary
4. Working with contours
IntroductionContours – Basic Detection and PlottingExercise 4.01: Detecting Shapes and Displaying Them on BGR ImagesExercise 4.02: Detecting Shapes and Displaying Them on Black and White ImagesExercise 4.03: Displaying Different Contours with Different Colors and ThicknessesDrawing a Bounding Box around a ContourArea of a ContourDifference between Contour Detection and Edge DetectionHierarchyExercise 4.04: Detecting a Bolt and a NutExercise 4.05: Detecting a Basketball Net in an ImageContour MatchingExercise 4.06: Detecting Fruits in an ImageExercise 4.07: Identifying Bananas from the Image of FruitsExercise 4.08: Detecting an Upright Banana from the Image of FruitsActivity 4.01: Identifying a Character on a Mirrored DocumentSummary
5. Face Processing in Image and Video
IntroductionIntroduction to Haar CascadesUsing Haar Cascades for Face DetectionExercise 5.01: Face Detection Using Haar CascadesDetecting Parts of the FaceExercise 5.02: Eye Detection Using CascadesClubbing Cascades for Multiple Object DetectionActivity 5.01: Eye Detection Using Multiple CascadesActivity 5.02: Smile Detection Using Haar CascadesGrabCut TechniqueExercise 5.03: Human Body Segmentation Using GrabCut with Rectangular MaskExercise 5.04: Human Body Segmentation Using Mask and ROIActivity 5.03: Skin Segmentation Using GrabCutActivity 5.04: Emoji FilterSummary
6. Object Tracking
IntroductionNaïve TrackerExercise 6.01: Object Tracking Using Basic Image ProcessingNon-Deep Learning-Based Object TrackersKalman Filter – Predict and UpdateMeanshift – Density Seeking FilterCAMshift – Continuously Adaptive MeanshiftThe OpenCV Object Tracking APIObject Tracker SummaryExercise 6.02: Object Tracking Using the Median Flow and MIL TrackersInstalling DlibObject Tracking Using DlibExercise 6.03: Object Tracking Using DlibActivity 6.01: Implementing Autofocus Using Object TrackingSummary
7. Object Detection and Face Recognition
IntroductionFace RecognitionFace Recognition Using EigenfacesPrincipal Component AnalysisEigenfacesExercise 7.01: Facial Recognition Using EigenfacesLimitations of the Eigenface MethodFisherfaceExercise 7.02: Facial Recognition Using the Fisherface MethodLocal Binary Patterns HistogramsExercise 7.03: Facial Recognition Using the LBPH MethodObject DetectionSingle Shot DetectorMobileNetExercise 7.04: Object Detection Using MobileNet SSDObject Detection Using the LBPH MethodExercise 7.05: Object Detection Using the LBPH MethodHaar CascadesExercise 7.06: Object Detection Using Haar-Based FeaturesActivity 7.01: Object Detection in a Video Using MobileNet SSDActivity 7.02: Real-Time Facial Recognition Using LBPHSummary
8. OpenVINO with OpenCV
IntroductionExploring the OpenVINO ToolkitComponents of the OpenVINO ToolkitInstalling OpenVINO for UbuntuOpenVINO as a Backend in OpenCVThe Need for Pre-Trained ModelsOpenVINO Model ZooExercise 8.01: Downloading the Pedestrian and Vehicle Detection ModelModel SpecificationsImage Transforms Using OpenCVExercise 8.02: Image Preprocessing Using OpenCVModel Conversion Using Model OptimizerIntroduction to OpenVINO's Inference EngineExercise 8.03: Vehicle and Pedestrian DetectionActivity 8.01: Face Detection Using OpenVINO and OpenCVSummary

Appendix
1. Basics of Image ProcessingActivity 1.01: Mirror Effect with a Twist2. Common Operations When Working with ImagesActivity 2.01: Masking Using Binary Images3. Working with HistogramsActivity 3.01: Enhancing Images Using Histogram Equalization and CLAHEActivity 3.02: Image Enhancement in a User-Defined ROI4. Working with contoursActivity 4.01: Identifying a Character on a Mirrored Document5. Face Processing in Image and VideoActivity 5.01: Eye Detection Using Multiple CascadesActivity 5.02: Smile Detection Using Haar CascadesActivity 5.03: Skin Segmentation Using GrabCutActivity 5.04: Emoji Filter6. Object TrackingActivity 6.01: Implementing Autofocus Using Object Tracking7. Object Detection and Face RecognitionActivity 7.01: Object Detection in a Video Using MobileNet SSDActivity 7.02: Real-Time Facial Recognition Using LBPH8. OpenVINO with OpenCVActivity 8.01: Face Detection Using OpenVINO and OpenCV

Content preview from The Computer Vision Workshop

1. Basics of Image Processing

Overview

This chapter serves as an introduction to the amazing and colorful world of image processing. We will start by understanding images and their various components – pixels, pixel values, and channels. We will then have a look at the commonly used color spaces – red, green, and blue (RGB) and Hue, Saturation, and Value (HSV). Next, we will look at how images are loaded and represented in OpenCV and how can they be displayed using another library called Matplotlib. Finally, we will have a look at how we can access and manipulate pixels. By the end of this chapter, you will be able to implement the concepts of color space conversion.

Introduction

Welcome to the world of images. It's interesting, it's wide, ...

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The Computer Vision Workshop

by Hafsa Asad, Vishwesh Ravi Shrimali, Nikhil Singh, Richmond Alake, Tim Hoolihan, Harshil Jain, Geetank Raipuria, Shovon Sengupta, Sanchit Singh

1. Basics of Image Processing

Introduction

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.