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OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition电子书

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作       者:Robert Laganiere

出  版  社:Packt Publishing

出版时间:2017-02-01

字       数:324.9万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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Recipes to help you build computer vision applications that make the most of the popular C++ library OpenCV 3 About This Book Written to the latest, gold-standard specification of OpenCV 3 Master OpenCV, the open source library of the computer vision community Master fundamental concepts in computer vision and image processing Learn about the important classes and functions of OpenCV with complete working examples applied to real images Who This Book Is For OpenCV 3 Computer Vision Application Programming Cookbook Third Edition is appropriate for novice C++ programmers who want to learn how to use the OpenCV library to build computer vision applications. It is also suitable for professional software developers who wish to be introduced to the concepts of computer vision programming. It can also be used as a companion book for university-level computer vision courses. It constitutes an excellent reference for graduate students and researchers in image processing and computer vision. What You Will Learn Install and create a program using the OpenCV library Process an image by manipulating its pixels Analyze an image using histograms Segment images into homogenous regions and extract meaningful objects Apply image filters to enhance image content Exploit the image geometry in order to relay different views of a pictured scene Calibrate the camera from different image observations Detect people and objects in images using machine learning techniques Reconstruct a 3D scene from images In Detail Making your applications see has never been easier with OpenCV. With it, you can teach your robot how to follow your cat, write a program to correctly identify the members of One Direction, or even help you find the right colors for your redecoration. OpenCV 3 Computer Vision Application Programming Cookbook Third Edition provides a complete introduction to the OpenCV library and explains how to build your first computer vision program. You will be presented with a variety of computer vision algorithms and exposed to important concepts in image and video analysis that will enable you to build your own computer vision applications. This book helps you to get started with the library, and shows you how to install and deploy the OpenCV library to write effective computer vision applications following good programming practices. You will learn how to read and write images and manipulate their pixels. Different techniques for image enhancement and shape analysis will be presented. You will learn how to detect specific image features such as lines, circles or corners. You will be introduced to the concepts of mathematical morphology and image filtering. The most recent methods for image matching and object recognition are described, and you’ll discover how to process video from files or cameras, as well as how to detect and track moving objects. Techniques to achieve camera calibration and perform multiple-view analysis will also be explained. Finally, you’ll also get acquainted with recent approaches in machine learning and object classification. Style and approach This book will arm you with the basics you need to start writing world-aware applications right from a pixel level all the way through to processing video sequences.
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OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition

OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition

Credits

About the Author

About the Reviewer

www.PacktPub.com

Why subscribe?

Customer Feedback

Preface

What this book covers

What you need for this book

Who this book is for

Sections

Getting ready

How to do it…

How it works…

There's more…

See also

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Playing with Images

Introduction

Installing the OpenCV library

Getting ready

How to do it...

How it works...

There's more...

The Visualization Toolkit and the cv::viz module

The OpenCV developer site

See also

Loading, displaying, and saving images

Getting ready

How to do it...

How it works...

There's more...

Clicking on images

Drawing on images

See also

Exploring the cv::Mat data structure

How to do it...

How it works...

There's more...

The input and output arrays

Manipulating small matrices

See also

Defining regions of interest

Getting ready

How to do it...

How it works...

There's more...

Using image masks

See also

2. Manipulating Pixels

Introduction

Accessing pixel values

Getting ready

How to do it...

How it works...

There's more...

The cv::Mat_ template class

See also

Scanning an image with pointers

Getting ready

How to do it...

How it works...

There's more...

Other color reduction formulas

Having input and output arguments

Efficient scanning of continuous images

Low-level pointer arithmetic

See also

Scanning an image with iterators

Getting ready

How to do it...

How it works...

There's more...

See also

Writing efficient image-scanning loops

How to do it...

How it works...

There's more...

See also

Scanning an image with neighbor access

Getting ready

How to do it...

How it works...

There's more...

See also

Performing simple image arithmetic

Getting ready

How to do it...

How it works...

There's more...

Overloaded image operators

Splitting the image channels

Remapping an image

How to do it...

How it works...

See also

3. Processing the Colors of an Image

Introduction

Comparing colors using the Strategy design pattern

How to do it…

How it works…

There's more…

Computing the distance between two color vectors

Using OpenCV functions

The floodFill function

Functor or function object

The OpenCV base class for algorithms

See also

Segmenting an image with the GrabCut algorithm

How to do it…

How it works…

See also

Converting color representations

How to do it…

How it works…

See also

Representing colors with hue, saturation, and brightness

How to do it...

How it works…

There's more…

Using colors for detection - skin tone detection

See also

4. Counting the Pixels with Histograms

Introduction

Computing an image histogram

Getting ready

How to do it...

How it works...

There's more...

Computing histograms of color images

See also

Applying look-up tables to modify the image's appearance

How to do it...

How it works...

There's more...

Stretching a histogram to improve the image contrast

Applying a look-up table to color images

See also

Equalizing the image histogram

How to do it...

How it works...

Backprojecting a histogram to detect specific image content

How to do it...

How it works...

There's more...

Backprojecting color histograms

See also

Using the mean shift algorithm to find an object

How to do it...

How it works...

See also

Retrieving similar images using the histogram comparison

How to do it...

How it works...

See also

Counting pixels with integral images

How to do it...

How it works...

There's more...

Adaptive thresholding

Visual tracking using histograms

See also

5. Transforming Images with Morphological Operations

Introduction

Eroding and dilating images using morphological filters

Getting ready

How to do it...

How it works...

There's more...

See also

Opening and closing images using morphological filters

How to do it...

How it works...

See also

Applying morphological operators on gray-level images

How to do it...

How it works...

See also

Segmenting images using watersheds

How to do it...

How it works...

There's more...

See also

Extracting distinctive regions using MSER

How to do it...

How it works...

See also

6. Filtering the Images

Introduction

Filtering images using low-pass filters

How to do it...

How it works...

See also

Downsampling images with filters

How to do it...

How it works...

There's more...

Interpolating pixel values

See also

Filtering images using a median filter

How to do it...

How it works...

Applying directional filters to detect edges

How to do it...

How it works...

There's more...

Gradient operators

Gaussian derivatives

See also

Computing the Laplacian of an image

How to do it...

How it works...

There's more...

Enhancing the contrast of an image using the Laplacian

Difference of Gaussians

See also

7. Extracting Lines, Contours, and Components

Introduction

Detecting image contours with the Canny operator

How to do it...

How it works...

See also

Detecting lines in images with the Hough transform

Getting ready

How to do it...

How it works...

There's more...

Detecting circles

See also

Fitting a line to a set of points

How to do it...

How it works...

There's more...

Extracting connected components

How to do it...

How it works...

There's more...

Computing components' shape descriptors

How to do it...

How it works...

There's more...

Quadrilateral detection

8. Detecting Interest Points

Introduction

Detecting corners in an image

How to do it...

How it works...

There's more...

Good features to track

See also

Detecting features quickly

How to do it...

How it works...

There's more...

See also

Detecting scale-invariant features

How to do it...

How it works...

There's more...

The SIFT feature-detection algorithm

See also

Detecting FAST features at multiple scales

How to do it...

How it works...

There's more...

The ORB feature-detection algorithm

See also

9. Describing and Matching Interest Points

Introduction

Matching local templates

How to do it...

How it works...

There's more...

Template matching

See also

Describing and matching local intensity patterns

How to do it...

How it works...

There's more...

Cross-checking matches

The ratio test

Distance thresholding

See also

Matching keypoints with binary descriptors

How to do it...

How it works...

There's more...

FREAK

See also

10. Estimating Projective Relations in Images

Introduction

Image formation

Computing the fundamental matrix of an image pair

Getting ready

How to do it...

How it works...

See also

Matching images using random sample consensus

How to do it...

How it works...

There's more...

Refining the fundamental matrix

Refining the matches

Computing a homography between two images

Getting ready

How to do it...

How it works...

There's more...

Generating image panoramas with the cv::Stitcher module

See also

Detecting a planar target in images

How to do it...

How it works...

See also

11. Reconstructing 3D Scenes

Introduction

Digital image formation

Calibrating a camera

How to do it...

How it works...

There's more...

Calibration with known intrinsic parameters

Using a grid of circles for calibration

See also

Recovering camera pose

How to do it...

How it works...

There's more...

cv::Viz, a 3D Visualizer module

See also

Reconstructing a 3D scene from calibrated cameras

How to do it...

How it works...

There's more...

Decomposing a homography

Bundle adjustment

See also

Computing depth from stereo image

Getting ready

How to do it...

How it works...

See also

12. Processing Video Sequences

Introduction

Reading video sequences

How to do it...

How it works...

There's more...

See also

Processing the video frames

How to do it...

How it works...

There's more...

Processing a sequence of images

Using a frame processor class

See also

Writing video sequences

How to do it...

How it works...

There's more...

The codec four-character code

See also

Extracting the foreground objects in a video

How to do it...

How it works...

There's more...

The Mixture of Gaussian method

See also

13. Tracking Visual Motion

Introduction

Tracing feature points in a video

How to do it...

How it works...

See also

Estimating the optical flow

Getting ready

How to do it...

How it works...

See also

Tracking an object in a video

How to do it...

How it works...

See also

14. Learning from Examples

Introduction

Recognizing faces using nearest neighbors of local binary patterns

How to do it...

How it works...

See also

Finding objects and faces with a cascade of Haar features

Getting ready

How to do it...

How it works...

There's more...

Face detection with a Haar cascade

See also

Detecting objects and people with Support Vector Machines and histograms of oriented gradients

Getting ready

How to do it...

How it works...

There's more...

HOG visualization

People detection

Deep learning and Convolutional Neural Networks

See also

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