Book Image

OpenCV By Example

By : Prateek Joshi, David Millán Escrivá, Vinícius G. Mendonça
Book Image

OpenCV By Example

By: Prateek Joshi, David Millán Escrivá, Vinícius G. Mendonça

Overview of this book

Open CV is a cross-platform, free-for-use library that is primarily used for real-time Computer Vision and image processing. It is considered to be one of the best open source libraries that helps developers focus on constructing complete projects on image processing, motion detection, and image segmentation. Whether you are completely new to the concept of Computer Vision or have a basic understanding of it, this book will be your guide to understanding the basic OpenCV concepts and algorithms through amazing real-world examples and projects. Starting from the installation of OpenCV on your system and understanding the basics of image processing, we swiftly move on to creating optical flow video analysis or text recognition in complex scenes, and will take you through the commonly used Computer Vision techniques to build your own Open CV projects from scratch. By the end of this book, you will be familiar with the basics of Open CV such as matrix operations, filters, and histograms, as well as more advanced concepts such as segmentation, machine learning, complex video analysis, and text recognition.

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OpenCV By Example
Prateek Joshi | David Millán Escrivá | Vinícius G. Mendonça
Jan, 2016 | 296 pages
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Table of Contents (18 chapters)
OpenCV By Example
OpenCV By Example
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Started with OpenCV
Getting Started with OpenCV
Understanding the human visual system
How do humans understand image content?
What can you do with OpenCV?
Installing OpenCV
Summary
2
An Introduction to the Basics of OpenCV
An Introduction to the Basics of OpenCV
Basic CMake configuration files
Creating a library
Managing dependencies
Making the script more complex
Images and matrices
Reading/writing images
Reading videos and cameras
Other basic object types
Basic matrix operations
Basic data persistence and storage
Summary
3
Learning the Graphical User Interface and Basic Filtering
Learning the Graphical User Interface and Basic Filtering
Introducing the OpenCV user interface
A basic graphical user interface with OpenCV
The graphical user interface with QT
Adding slider and mouse events to our interfaces
Adding buttons to a user interface
OpenGL support
Summary
4
Delving into Histograms and Filters
Delving into Histograms and Filters
Generating a CMake script file
Creating the Graphical User Interface
Drawing a histogram
Image color equalization
Lomography effect
The cartoonize effect
Summary
5
Automated Optical Inspection, Object Segmentation, and Detection
Automated Optical Inspection, Object Segmentation, and Detection
Isolating objects in a scene
Creating an application for AOI
Preprocessing the input image
Segmenting our input image
Summary
6
Learning Object Classification
Learning Object Classification
Introducing machine learning concepts
Computer Vision and the machine learning workflow
Automatic object inspection classification example
Feature extraction
Summary
7
Detecting Face Parts and Overlaying Masks
Detecting Face Parts and Overlaying Masks
Understanding Haar cascades
What are integral images?
Overlaying a facemask in a live video
Get your sunglasses on
Tracking your nose, mouth, and ears
Summary
8
Video Surveillance, Background Modeling, and Morphological Operations
Video Surveillance, Background Modeling, and Morphological Operations
Understanding background subtraction
Naive background subtraction
Frame differencing
The Mixture of Gaussians approach
Morphological image processing
Slimming the shapes
Thickening the shapes
Other morphological operators
Summary
9
Learning Object Tracking
Learning Object Tracking
Tracking objects of a specific color
Building an interactive object tracker
Detecting points using the Harris corner detector
Shi-Tomasi Corner Detector
Feature-based tracking
Summary
10
Developing Segmentation Algorithms for Text Recognition
Developing Segmentation Algorithms for Text Recognition
Introducing optical character recognition
The preprocessing step
Installing Tesseract OCR on your operating system
Using Tesseract OCR library
Summary
11
Text Recognition with Tesseract
Text Recognition with Tesseract
How the text API works
Using the text API
Summary
Index
Index

Drawing a histogram

A histogram is a statistical graphic representation of variable distribution. This allows us to understand the density estimation and probability distribution of data. The histogram is created by dividing the entire range of variable values into a fixed number of intervals and then counting how many values fall into each interval.

If we apply this histogram concept to an image, it seems to be complex to understand, but it is really very simple. In a gray image, our variable values can take any possible gray value ranging from 0 to 255, and the density is the number of pixels in the image that have this value. This means that we have to count the number of image pixels that have the value 0, count the number of pixels of value 1, and so on.

The callback function that shows the histogram of the input image is called showHistoCallback. This function calculates the histogram of each channel image and shows the result of each histogram channel in a new image.

Now, let's check...

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