The Computer Vision Workshop: Develop the skills you need to use computer vision algorithms in your own artificial intelligence projects

Cover
FM
Copyright
Table of Contents
Preface
Chapter 1: Basics ofImage Processing
	Introduction
	NumPy Arrays
		Exercise 1.01: Creating NumPy Arrays
	Pixels in Images
		Pixel Location – Image Coordinate System
		Image Properties
			Size of the Image
			Color Spaces and Channels
		Pixel Values
	Introduction to OpenCV
		Images in OpenCV
		Important OpenCV Functions
		Exercise 1.02: Reading, Processing, and Writing an Image
		Using Matplotlib to Display Images
		Accessing and Manipulating Pixels
		Exercise 1.03: Creating a Water Effect
		Activity 1.01: Mirror Effect with a Twist
	Summary
Chapter 2: Common Operations When Working with Images
	Introduction
	Geometric Transformations
		Image Translation
		Exercise 2.01: Translation Using NumPy
		Image Rotation
			Finding the Rotation Matrix
			Finding the Size of the Output Image
		Image Resizing
		Affine Transformation
		Exercise 2.02: Working with Affine Transformation
		Perspective Transformation
		Exercise 2.03: Perspective Transformation
	Image Arithmetic
		Image Addition
		Exercise 2.04: Performing Image Addition
		Image Multiplication
		Exercise 2.05: Image Multiplication
	Binary Images
		Exercise 2.06: Converting an Image into a Binary Image
		Bitwise Operations on Images
		Exercise 2.07: Chess Pieces
		Masking
		Activity 2.01: Masking Using Binary Images
	Summary
Chapter 3: Working with Histograms
	Introduction
	Introduction to Matplotlib
		Displaying Images with Matplotlib
		Plotting Histograms with Matplotlib
		Exercise 3.01: Plotting a Sample Image and Its Histogram with 256 bins
		Exercise 3.02: Plotting a Sample Image and Its Histogram with 10 bins
	Histograms with OpenCV
		User-Selected ROI
		Exercise 3.03: Creating a Mask Image Using a User-Selected ROI
		A Comparison of Some Sample Histograms
		What Is Histogram Equalization?
		Exercise 3.04: Histogram Equalization of a Grayscale Image
		Contrast Limited Adaptive Histogram Equalization (CLAHE)
		Exercise 3.05: Application of CLAHE on a Grayscale Image
		Activity 3.01: Enhancing Images Using Histogram Equalization and CLAHE
		Exercise 3.06: Histogram Equalization in the BGR Color Space
		The Histogram Equalization of Color Images Using the HSV and LAB Color Spaces
		Exercise 3.07: Histogram Equalization in the HSV Color Space
		Exercise 3.08: Histogram Equalization in the LAB Color Space
		Activity 3.02: Image Enhancement in a User-Defined ROI
	Summary
Chapter 4: Working with contours
	Introduction
	Contours – Basic Detection and Plotting
		Exercise 4.01: Detecting Shapes and Displaying Them on BGR Images
		Exercise 4.02: Detecting Shapes and Displaying Them on Black and White Images
		Exercise 4.03: Displaying Different Contours with Different Colors and Thicknesses
		Drawing a Bounding Box around a Contour
		Area of a Contour
		Difference between Contour Detection and Edge Detection
	Hierarchy
		Exercise 4.04: Detecting a Bolt and a Nut
		Exercise 4.05: Detecting a Basketball Net in an Image
	Contour Matching
		Exercise 4.06: Detecting Fruits in an Image
		Exercise 4.07: Identifying Bananas from the Image of Fruits
		Exercise 4.08: Detecting an Upright Banana from the Image of Fruits
		Activity 4.01: Identifying a Character on a Mirrored Document
	Summary
Chapter 5: Face Processing in Image and Video
	Introduction
	Introduction to Haar Cascades
		Using Haar Cascades for Face Detection
		Exercise 5.01: Face Detection Using Haar Cascades
		Detecting Parts of the Face
		Exercise 5.02: Eye Detection Using Cascades
		Clubbing Cascades for Multiple Object Detection
		Activity 5.01: Eye Detection Using Multiple Cascades
		Activity 5.02: Smile Detection Using Haar Cascades
	GrabCut Technique
		Exercise 5.03: Human Body Segmentation Using GrabCut with Rectangular Mask
		Exercise 5.04: Human Body Segmentation Using Mask and ROI
		Activity 5.03: Skin Segmentation Using GrabCut
		Activity 5.04: Emoji Filter
	Summary
Chapter 6: Object Tracking
	Introduction
		Naïve Tracker
		Exercise 6.01: Object Tracking Using Basic Image Processing
		Non-Deep Learning-Based Object Trackers
		Kalman Filter – Predict and Update
		Meanshift – Density Seeking Filter
		CAMshift – Continuously Adaptive Meanshift
		The OpenCV Object Tracking API
		Object Tracker Summary
		Exercise 6.02: Object Tracking Using the Median Flow and MIL Trackers
		Installing Dlib
		Object Tracking Using Dlib
		Exercise 6.03: Object Tracking Using Dlib
		Activity 6.01: Implementing Autofocus Using Object Tracking
	Summary
Chapter 7: Object Detection and Face Recognition
	Introduction
	Face Recognition
		Face Recognition Using Eigenfaces
		Principal Component Analysis
		Eigenfaces
		Exercise 7.01: Facial Recognition Using Eigenfaces
		Limitations of the Eigenface Method
		Fisherface
		Exercise 7.02: Facial Recognition Using the Fisherface Method
		Local Binary Patterns Histograms
		Exercise 7.03: Facial Recognition Using the LBPH Method
	Object Detection
		Single Shot Detector
		MobileNet
		Exercise 7.04: Object Detection Using MobileNet SSD
		Object Detection Using the LBPH Method
		Exercise 7.05: Object Detection Using the LBPH Method
		Haar Cascades
		Exercise 7.06: Object Detection Using Haar-Based Features
		Activity 7.01: Object Detection in a Video Using MobileNet SSD
		Activity 7.02: Real-Time Facial Recognition Using LBPH
	Summary
Chapter 8: OpenVINO with OpenCV
	Introduction
	Exploring the OpenVINO Toolkit
		Components of the OpenVINO Toolkit
		Installing OpenVINO for Ubuntu
		OpenVINO as a Backend in OpenCV
		The Need for Pre-Trained Models
		OpenVINO Model Zoo
		Exercise 8.01: Downloading the Pedestrian and Vehicle Detection Model
		Model Specifications
		Image Transforms Using OpenCV
		Exercise 8.02: Image Preprocessing Using OpenCV
	Model Conversion Using Model Optimizer
		Introduction to OpenVINO's Inference Engine
		Exercise 8.03: Vehicle and Pedestrian Detection
		Activity 8.01: Face Detection Using OpenVINO and OpenCV
	Summary
Appendix
Index