Franklin

OpenCV 4 computer vision application programming cookbook : build complex computer vision applications with OpenCV and C++ / David Millán Escrivá and Robert Laganiere.

Author/Creator:
Escrivá, David Millán, author.
Publication:
Birmingham ; Mumbai : Packt Publishing Ltd, 2019.
Format/Description:
Book
1 online resource (479 pages)
Edition:
Fourth edition.
Status/Location:
Loading...

Options
Location Notes Your Loan Policy

Details

Subjects:
Computer vision.
Image processing -- Digital techniques.
Form/Genre:
Electronic books.
System Details:
text file
Summary:
Discover interesting recipes to help you understand the concepts of object detection, image processing, and facial detection Key Features Explore the latest features and APIs in OpenCV 4 and build computer vision algorithms Develop effective, robust, and fail-safe vision for your applications Build computer vision algorithms with machine learning capabilities Book Description OpenCV is an image and video processing library used for all types of image and video analysis. Throughout the book, you'll work through recipes that 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 setting up OpenCV, and explains 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 the book, you'll be able to confidently implement a range of computer vision algorithms to meet the technical requirements of your complex CV projects. What you will learn Install and create a program using the OpenCV library Segment images into homogenous regions and extract meaningful objects Apply image filters to enhance image content Exploit image geometry 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 Explore face detection using deep learning Who this book is for If you’re a CV developer or professional who already uses or would like to use OpenCV for building computer vision software, this book is for you. You’ll also find this book useful if you’re a C++ programmer looking to extend your computer vision skillset by learning OpenCV. Downloading the example code for this ebook: You can download the example code files for this ebook on GitHub at the following link: https://github...
Contents:
Cover
Title Page
Copyright and Credits
About Packt
Contributors
Table of Contents
Preface
Chapter 1: Playing with Images
Installing the OpenCV library
Getting ready
How to do it...
How it works...
There's more...
Using Qt for OpenCV developments
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
Running the example with Qt
See also
Exploring the cv::Mat data structure
How to do it...
How it works...
There's more...
The input and output arrays
See also
Defining regions of interest
Getting ready
How to do it...
How it works...
There's more...
Using image masks
See also
Chapter 2: Manipulating the Pixels
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 arithmetics
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.
Chapter 3: Processing Color Images with Classes
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 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
Getting ready
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
Chapter 4: Counting the Pixels with Histograms
Computing the image histogram
Getting started
How to do it...
How it works...
There's more...
Computing histograms of color images
See also
Applying lookup 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 lookup table on color images
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
Using the mean shift algorithm to find an object
How to do it...
How it works...
See also
Retrieving similar images using 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
Chapter 5: Transforming Images with Morphological Operations
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
Detecting edges and corners using morphological filters
Getting ready
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
Extracting foreground objects with the GrabCut algorithm
How to do it...
How it works...
See also
Chapter 6: Filtering the Images
Filtering images using low-pass filters
How to do it...
How it works...
See also
Downsampling an image
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
Chapter 7: Extracting Lines, Contours, and Components
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 the components' contours
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
Chapter 8: Detecting Interest Points
Detecting corners in an image
How to do it...
How it works...
There's more...
Good features to track
The feature detector's common interface
See also
Detecting features quickly
How to do it...
How it works...
There's more...
Adapted feature detection
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
Chapter 9: Describing and Matching Interest Points
Matching local templates
How to do it...
How it works...
There's more...
Template matching
See also
Describing local intensity patterns
How to do it...
How it works...
There's more...
Cross-checking matches
The ratio test
Distance thresholding
See also
Describing keypoints with binary features
How to do it...
How it works...
There's more...
FREAK
See also
Chapter 10: Estimating Projective Relations in Images
Computing the fundamental matrix of an image pair
Getting ready
How to do it...
How it works...
See also
Matching images using a 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...
Detecting planar targets in an image
How to do it...
See also
Chapter 11: Reconstructing 3D Scenes
Digital image formation
Calibrating a camera
Getting ready
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 the 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 a stereo image
Getting ready
How to do it...
How it works...
See also
Chapter 12: Processing Video Sequences
Reading video sequences
How to do it...
How it works...
There's more...
See also
Processing 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
Chapter 13: Tracking Visual Motion
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
Chapter 14: Learning from Examples
Recognizing faces using the 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 using SVMs and histograms of oriented gradients
Getting ready
How to do it...
How it works...
There's more...
Notes:
Description based on print version record.
Contributor:
Laganiere, Robert, author.
ISBN:
1-78934-528-6
OCLC:
1101042394