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  • OpenCV 4 Computer Vision Application Programming Cookbook
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OpenCV 4 Computer Vision Application Programming Cookbook

OpenCV 4 Computer Vision Application Programming Cookbook

By : Millán Escrivá, Robert Laganiere
5 (1)
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OpenCV 4 Computer Vision Application Programming Cookbook

OpenCV 4 Computer Vision Application Programming Cookbook

5 (1)
By: Millán Escrivá, Robert Laganiere

Overview of this book

OpenCV is an image and video processing library used for all types of image and video analysis. Throughout the book, you'll work with recipes to implement a variety of tasks. With 70 self-contained tutorials, this book examines common pain points and best practices for computer vision (CV) developers. Each recipe addresses a specific problem and offers a proven, best-practice solution with insights into how it works, so that you can copy the code and configuration files and modify them to suit your needs. This book begins by guiding you through setting up OpenCV, and explaining how to manipulate pixels. You'll understand how you can process images with classes and count pixels with histograms. You'll also learn detecting, describing, and matching interest points. As you advance through the chapters, you'll get to grips with estimating projective relations in images, reconstructing 3D scenes, processing video sequences, and tracking visual motion. In the final chapters, you'll cover deep learning concepts such as face and object detection. By the end of this book, you'll have the skills you need to confidently implement a range of computer vision algorithms to meet the technical requirements of your complex CV projects.
Table of Contents (17 chapters)
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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Sections
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1
Playing with Images
Playing with Images
Installing the OpenCV library
Loading, displaying, and saving images
Exploring the cv::Mat data structure
Defining regions of interest
2
Manipulating the Pixels
Manipulating the Pixels
Accessing pixel values
Scanning an image with pointers
Scanning an image with iterators
Writing efficient image-scanning loops
Scanning an image with neighbor access
Performing simple image arithmetic
Remapping an image
3
Processing Color Images with Classes
Processing Color Images with Classes
Comparing colors using the strategy design pattern
Segmenting an image with the GrabCut algorithm
Converting color representations
Representing colors with hue, saturation, and brightness
4
Counting the Pixels with Histograms
Counting the Pixels with Histograms
Computing the image histogram
Applying lookup tables to modify the image's appearance
Equalizing the image histogram
Backprojecting a histogram to detect specific image content
Using the mean shift algorithm to find an object
Retrieving similar images using histogram comparison
Counting pixels with integral images
5
Transforming Images with Morphological Operations
Transforming Images with Morphological Operations
Eroding and dilating images using morphological filters
Opening and closing images using morphological filters
Detecting edges and corners using morphological filters
Segmenting images using watersheds
Extracting distinctive regions using MSER
Extracting foreground objects with the GrabCut algorithm
6
Filtering the Images
Filtering the Images
Filtering images using low-pass filters
Downsampling an image
Filtering images using a median filter
Applying directional filters to detect edges
Computing the Laplacian of an image
7
Extracting Lines, Contours, and Components
Extracting Lines, Contours, and Components
Detecting image contours with the Canny operator
Detecting lines in images with the Hough transform
Fitting a line to a set of points
Extracting the components' contours
Computing components' shape descriptors
8
Detecting Interest Points
Detecting Interest Points
Detecting corners in an image
Detecting features quickly
Detecting scale-invariant features
Detecting FAST features at multiple scales
9
Describing and Matching Interest Points
Describing and Matching Interest Points
Matching local templates
Describing local intensity patterns
Describing keypoints with binary features
10
Estimating Projective Relations in Images
Estimating Projective Relations in Images
Computing the fundamental matrix of an image pair
Matching images using a random sample consensus
Computing a homography between two images
Detecting planar targets in an image
11
Reconstructing 3D Scenes
Reconstructing 3D Scenes
Digital image formation
Calibrating a camera
Recovering the camera pose
Reconstructing a 3D scene from calibrated cameras
Computing depth from a stereo image
12
Processing Video Sequences
Processing Video Sequences
Reading video sequences
Processing video frames
Writing video sequences
Extracting the foreground objects in a video
13
Tracking Visual Motion
Tracking Visual Motion
Tracing feature points in a video
Estimating the optical flow
Tracking an object in a video
14
Learning from Examples
Learning from Examples
Recognizing faces using the nearest neighbors of local binary patterns
Finding objects and faces with a cascade of Haar features
Detecting objects and people using SVMs and histograms of oriented gradients
15
OpenCV Advanced Features
OpenCV Advanced Features
Face detection using deep learning
Object detection with YOLOv3
Enabling Halide to improve efficiency
OpenCV.js introduction
16
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