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OpenCV 4 Computer Vision Application Programming Cookbook

By : Millán Escrivá, Robert Laganiere

5 (1)

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.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Sections

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Free Chapter

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

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

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

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

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

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

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

Detecting Interest Points

Detecting Interest Points

Detecting corners in an image

Detecting features quickly

Detecting scale-invariant features

Detecting FAST features at multiple scales

Describing and Matching Interest Points

Describing and Matching Interest Points

Matching local templates

Describing local intensity patterns

Describing keypoints with binary features

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

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

Processing Video Sequences

Processing Video Sequences

Reading video sequences

Processing video frames

Writing video sequences

Extracting the foreground objects in a video

Tracking Visual Motion

Tracking Visual Motion

Tracing feature points in a video

Estimating the optical flow

Tracking an object in a video

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

OpenCV Advanced Features

OpenCV Advanced Features

Face detection using deep learning

Object detection with YOLOv3

Enabling Halide to improve efficiency

OpenCV.js introduction

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