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OpenCV By Example

OpenCV By Example

By : Joshi, Millán Escrivá, Vinícius G. Mendonça
3.8 (5)
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OpenCV By Example

OpenCV By Example

3.8 (5)
By: Joshi, 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.
Table of Contents (13 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Getting Started with OpenCV
1. 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
2. An Introduction to the Basics of OpenCV
2. 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
3. Learning the Graphical User Interface and Basic Filtering
3. 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
4. Delving into Histograms and Filters
4. 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
5. Automated Optical Inspection, Object Segmentation, and Detection
5. 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
6. Learning Object Classification
6. Learning Object Classification
Introducing machine learning concepts
Computer Vision and the machine learning workflow
Automatic object inspection classification example
Feature extraction
Summary
7
7. Detecting Face Parts and Overlaying Masks
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7. 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
8. Video Surveillance, Background Modeling, and Morphological Operations
8. 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
9. Learning Object Tracking
9. 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
10. Developing Segmentation Algorithms for Text Recognition
10. 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
11. Text Recognition with Tesseract
11. Text Recognition with Tesseract
How the text API works
Using the text API
Summary
12
Index
Index

Get your sunglasses on

Now that we understand how to detect faces, we can generalize this concept to detect different parts of the face. We will use an eye detector to overlay sunglasses in a live video. It's important to understand that the Viola-Jones framework can be applied to any object. The accuracy and robustness will depend on the uniqueness of the object. For example, a human face has very unique characteristics, so it's easy to train our system to be robust. On the other hand, an object such as a towel is too generic, and there are no distinguishing characteristics as such. So, it's more difficult to build a robust towel detector.

Once you build the eye detector and overlay glasses on top of it, it will look something like this:

Let's take a look at the main parts of the code:

int main(int argc, char* argv[])
{
    string faceCascadeName = argv[1];
    string eyeCascadeName = argv[2];

    // Variable declarations and initializations
 
    // Face detection code
        
        vector...

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