Mastering OpenCV 4 with Python: A practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7

Cover
Title Page
Copyright and Credits
About Packt
Contributors
Table of Contents
Preface
Section 1: Introduction to OpenCV 4 and Python
Chapter 1: Setting Up OpenCV
	Technical requirements
		Code testing specifications
		Hardware specifications
	Understanding Python
		Introducing OpenCV
		Contextualizing the reader
	A theoretical introduction to the OpenCV library
		OpenCV modules
		OpenCV users
		OpenCV applications
		Why citing OpenCV in your research work
	Installing OpenCV, Python, and other packages
		Installing Python, OpenCV, and other packages globally
		Installing Python
			Installing Python on Linux
			Installing Python on Windows
			Installing OpenCV
			Installing OpenCV on Linux
			Installing OpenCV on Windows
		Testing the installation
	Installing Python, OpenCV, and other packages with virtualenv
	Python IDEs to create virtual environments with virtualenv
	Anaconda/Miniconda distributions and conda package–and environment-management system 
	Packages for scientific computing, data science, machine learning, deep learning, and computer vision
	Jupyter Notebook
		Trying Jupiter Notebook online 
		Installing the Jupyter Notebook
		Installing Jupyter using Anaconda
		Installing Jupyter with pip
	The OpenCV and Python project structure
	Our first Python and OpenCV project
	Summary
	Questions
	Further reading
Chapter 2: Image Basics in OpenCV
	Technical requirements
	A theoretical introduction to image basics
		Main problems in image processing
		Image-processing steps
		Images formulation
	Concepts of pixels, colors, channels, images, and color spaces
		File extensions
	The coordinate system in OpenCV
	Accessing and manipulating pixels in OpenCV
		Accessing and manipulating pixels in OpenCV with BGR images
		Accessing and manipulating pixels in OpenCV with grayscale images
	BGR order in OpenCV
	Summary
	Questions
	Further reading
Chapter 3: Handling Files and Images
	Technical requirements
	An introduction to handling files and images
		sys.argv
		Argparse – command-line option and argument parsing
	Reading and writing images
		Reading images in OpenCV
		Reading and writing images in OpenCV
	Reading camera frames and video files
		Reading camera frames
		Accessing some properties of the capture object
		Saving camera frames
		Reading a video file
		Reading from an IP camera
	Writing a video file
		Calculating frames per second
		Considerations for writing a video file
	Playing with video capture properties
		Getting all the properties from the video capture object
		Using the properties – playing a video backwards
	Summary
	Questions
	Further reading
Chapter 4: Constructing Basic Shapes in OpenCV
	Technical requirements
	A theoretical introduction to drawing in OpenCV
	Drawing shapes
		Basic shapes – lines, rectangles, and circles
			Drawing lines
			Drawing rectangles
			Drawing circles
		Understanding advanced shapes
			Drawing a clip line
			Drawing arrows
			Drawing ellipses
			Drawing polygons
		Shift parameter in drawing functions
		lineType parameter in drawing functions
	Writing text
		Drawing text
		Using all OpenCV text fonts
		More functions related to text
	Dynamic drawing with mouse events
		Drawing dynamic shapes
		Drawing both text and shapes
		Event handling with Matplotlib
	Advanced drawing
	Summary
	Questions
	Further reading
Section 2: Image Processing in OpenCV
Chapter 5: Image Processing Techniques
	Technical requirements
	Splitting and merging channels in OpenCV
	Geometric transformations of images
		Scaling an image
		Translating an image
		Rotating an image
		Affine transformation of an image
		Perspective transformation of an image
		Cropping an image
	Image filtering
		Applying arbitrary kernels
		Smoothing images
			Averaging filter
			Gaussian filtering
			Median filtering
			Bilateral filtering
		Sharpening images
		Common kernels in image processing
		Creating cartoonized images
	Arithmetic with images
		Saturation arithmetic
		Image addition and subtraction
		Image blending
		Bitwise operations
	Morphological transformations
		Dilation operation
		Erosion operation
		Opening operation
		Closing operation
		Morphological gradient operation
		Top hat operation
		Black hat operation
		Structuring element
		Applying morphological transformations to images
	Color spaces
		Showing color spaces
		Skin segmentation in different color spaces
	Color maps
		Color maps in OpenCV
		Custom color maps
		Showing the legend for the custom color map
	Summary
	Questions
	Further reading
Chapter 6: Constructing and Building Histograms
	Technical requirements
	A theoretical introduction to histograms
		Histogram terminology
	Grayscale histograms
		Grayscale histograms without a mask
		Grayscale histograms with a mask
	Color histograms
	Custom visualizations of histograms
	Comparing OpenCV, NumPy, and Matplotlib histograms
	Histogram equalization
		Grayscale histogram equalization
		Color histogram equalization
	Contrast Limited Adaptive Histogram Equalization 
	Comparing CLAHE and histogram equalization
	Histogram comparison
	Summary
	Questions
	Further reading
Chapter 7: Thresholding Techniques
	Technical requirements
		Installing scikit-image
		Installing SciPy
	Introducing thresholding techniques
	Simple thresholding 
		Thresholding types
		Simple thresholding applied to a real image
	Adaptive thresholding
	Otsu's thresholding algorithm
	The triangle binarization algorithm
	Thresholding color images
	Thresholding algorithms using scikit-image
		Introducing thresholding with scikit-image
		Trying out more thresholding techniques with scikit-image
	Summary
	Questions
	Further reading
Chapter 8: Contour Detection, Filtering, and Drawing
	Technical requirements
	An introduction to contours
	Compressing contours
	Image moments
		Some object features based on moments
		Hu moment invariants
		Zernike moments
	More functionality related to contours
	Filtering contours
	Recognizing contours
	Matching contours
	Summary
	Questions
	Further reading
Chapter 9: Augmented Reality
	Technical requirements
	An introduction to augmented reality
	Markerless-based augmented reality
		Feature detection
		Feature matching
		Feature matching and homography computation to find objects
	Marker-based augmented reality
		Creating markers and dictionaries
		Detecting markers
		Camera calibration
		Camera pose estimation
		Camera pose estimation and basic augmentation
		Camera pose estimation and more advanced augmentation
	Snapchat-based augmented reality
		Snapchat-based augmented reality OpenCV moustache overlay
		Snapchat-based augmented reality OpenCV glasses overlay
	QR code detection
	Summary
	Questions
	Further reading
Section 3: Machine Learning and Deep Learning in OpenCV
Chapter 10: Machine Learning with OpenCV
	Technical requirements
	An introduction to machine learning
		Supervised machine learning
		Unsupervised machine learning
		Semi-supervised machine learning
	k-means clustering
		Understanding k-means clustering
		Color quantization using k-means clustering
	k-nearest neighbor
		Understanding k-nearest neighbors
		Recognizing handwritten digits using k-nearest neighbor 
	Support vector machine
		Understanding SVM
		Handwritten digit recognition using SVM
	Summary
	Questions
	Further reading
Chapter 11: Face Detection, Tracking, and Recognition
	Technical requirements
		Installing dlib
		Installing the face_recognition package
		Installing the cvlib package
	Face processing introduction
	Face detection
		Face detection with OpenCV
		Face detection with dlib
		Face detection with face_recognition
		Face detection with cvlib
	Detecting facial landmarks
		Detecting facial landmarks with OpenCV
		Detecting facial landmarks with dlib
		Detecting facial landmarks with face_recognition
	Face tracking
		Face tracking with the dlib DCF-based tracker
		Object tracking with the dlib DCF-based tracker
	Face recognition
		Face recognition with OpenCV
		Face recognition with dlib
		Face recognition with face_recognition
	Summary
	Questions
	Further reading
Chapter 12: Introduction to Deep Learning
	Technical requirements
		Installing TensorFlow
		Installing Keras
	Deep learning overview for computer vision tasks
		Deep learning characteristics
		Deep learning explosion
		Deep learning for image classification
		Deep learning for object detection 
	Deep learning in OpenCV
		Understanding cv2.dnn.blobFromImage()
		Complete examples using the OpenCV DNN face detector
		OpenCV deep learning classification
			AlexNet for image classification
			GoogLeNet for image classification
			ResNet for image classification
			SqueezeNet for image classification
		OpenCV deep learning object detection
			MobileNet-SSD for object detection
			YOLO for object detection
	The TensorFlow library
		Introduction example to TensorFlow
		Linear regression in TensorFlow
		Handwritten digits recognition using TensorFlow
	The Keras library
		Linear regression in Keras
		Handwritten digit recognition in Keras
	Summary
	Questions
	Further reading
Section 4: Mobile and Web Computer Vision
Chapter 13: Mobile and Web Computer Vision with Python and OpenCV
	Technical requirements
		Installing the packages
	Introduction to Python web frameworks
	Introduction to Flask
	Web computer vision applications using OpenCV and Flask
		A minimal example to introduce OpenCV and Flask 
		Minimal face API using OpenCV
		Deep learning cat detection API using OpenCV
	Deep learning API using Keras and Flask
		Keras applications
		Deep learning REST API using Keras Applications
	Deploying a Flask application to the cloud
	Summary
	Questions
	Further reading
Assessments
Other Books You May Enjoy
Index