Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying OpenCV Computer Vision Application Programming Cookbook Second Edition
  • Table Of Contents Toc
  • Feedback & Rating feedback
OpenCV Computer Vision Application Programming Cookbook Second Edition

OpenCV Computer Vision Application Programming Cookbook Second Edition

By : Robert Laganiere
3.7 (3)
Buy this Book
close
close
OpenCV Computer Vision Application Programming Cookbook Second Edition

OpenCV Computer Vision Application Programming Cookbook Second Edition

3.7 (3)
By: Robert Laganiere
Buy this Book

Overview of this book

OpenCV 3 Computer Vision Application Programming Cookbook 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 wishing to be introduced to the concepts of computer vision programming. It can also be used as a companion book in a university-level computer vision courses. It constitutes an excellent reference for graduate students and researchers in image processing and computer vision.
Table of Contents (13 chapters)
close
close
close
Preface
Icon
Preface
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Free Chapter
1
1. Playing with Images
Icon
1. Playing with Images
Icon
Introduction
Icon
Installing the OpenCV library
Icon
Loading, displaying, and saving images
Icon
Exploring the cv::Mat data structure
Icon
Defining regions of interest
2
2. Manipulating Pixels
Icon
2. Manipulating Pixels
Icon
Introduction
Icon
Accessing pixel values
Icon
Scanning an image with pointers
Icon
Scanning an image with iterators
Icon
Writing efficient image-scanning loops
Icon
Scanning an image with neighbor access
Icon
Performing simple image arithmetic
Icon
Remapping an image
3
3. Processing Color Images with Classes
chevron up
Icon
3. Processing Color Images with Classes
Icon
Introduction
Icon
Using the Strategy pattern in an algorithm design
Icon
Using a Controller design pattern to communicate with processing modules
Icon
Converting color representations
Icon
Representing colors with hue, saturation, and brightness
4
4. Counting the Pixels with Histograms
Icon
4. Counting the Pixels with Histograms
Icon
Introduction
Icon
Computing the image histogram
Icon
Applying look-up tables to modify the image appearance
Icon
Equalizing the image histogram
Icon
Backprojecting a histogram to detect specific image content
Icon
Using the mean shift algorithm to find an object
Icon
Retrieving similar images using the histogram comparison
Icon
Counting pixels with integral images
5
5. Transforming Images with Morphological Operations
Icon
5. Transforming Images with Morphological Operations
Icon
Introduction
Icon
Eroding and dilating images using morphological filters
Icon
Opening and closing images using morphological filters
Icon
Detecting edges and corners using morphological filters
Icon
Segmenting images using watersheds
Icon
Extracting distinctive regions using MSER
Icon
Extracting foreground objects with the GrabCut algorithm
6
6. Filtering the Images
Icon
6. Filtering the Images
Icon
Introduction
Icon
Filtering images using low-pass filters
Icon
Filtering images using a median filter
Icon
Applying directional filters to detect edges
Icon
Computing the Laplacian of an image
7
7. Extracting Lines, Contours, and Components
Icon
7. Extracting Lines, Contours, and Components
Icon
Introduction
Icon
Detecting image contours with the Canny operator
Icon
Detecting lines in images with the Hough transform
Icon
Fitting a line to a set of points
Icon
Extracting the components' contours
Icon
Computing components' shape descriptors
8
8. Detecting Interest Points
Icon
8. Detecting Interest Points
Icon
Introduction
Icon
Detecting corners in an image
Icon
Detecting features quickly
Icon
Detecting scale-invariant features
Icon
Detecting FAST features at multiple scales
9
9. Describing and Matching Interest Points
Icon
9. Describing and Matching Interest Points
Icon
Introduction
Icon
Matching local templates
Icon
Describing local intensity patterns
Icon
Describing keypoints with binary features
10
10. Estimating Projective Relations in Images
Icon
10. Estimating Projective Relations in Images
Icon
Introduction
Icon
Calibrating a camera
Icon
Computing the fundamental matrix of an image pair
Icon
Matching images using a random sample consensus
Icon
Computing a homography between two images
11
11. Processing Video Sequences
Icon
11. Processing Video Sequences
Icon
Introduction
Icon
Reading video sequences
Icon
Processing the video frames
Icon
Writing video sequences
Icon
Tracking feature points in a video
Icon
Extracting the foreground objects in a video
12
Index
Icon
Index

Converting color representations

The earlier recipes taught you how to encapsulate an algorithm into a class. This way, the algorithm becomes easier to use through a simplified interface. Encapsulation also permits you to modify an algorithm's implementation without impacting the classes that use it. This principle is illustrated in the next recipe, where we will modify the ColorDetector class algorithm in order to use another color space. Therefore, this recipe will also be an opportunity to introduce color conversions with OpenCV.

Getting ready

The RGB color space is based on the use of the red, green, and blue additive primary colors. These have been selected because when they are combined together, they can produce a wide gamut of different colors. In fact, the human visual system is also based on the trichromatic perception of colors, with cone cell sensitivity located around the red, green, and blue spectrum. It is often the default color space in digital imagery because that is...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
Start a 7-day FREE trial
End of Section 5
Next Section

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist download
font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY