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Mastering OpenCV Android Application Programming
Mastering OpenCV Android Application Programming

Mastering OpenCV Android Application Programming: Master the art of implementing computer vision algorithms on Android platforms to build robust and efficient applications

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Mastering OpenCV Android Application Programming

Chapter 2. Detecting Basic Features in Images

After reading about the basics of image processing and manipulation in the previous chapter, we will take a look at some of the most widely used algorithms used to extract meaningful information from the images in the form of edges, lines, circles, ellipses, blobs or contours, user defined shapes, and corners. In context of computer vision and image processing, such information is often termed as features. In this chapter, we will take a look at the various feature detection algorithms, such as Edge and Corner detection algorithms, Hough transformations, and Contour detection algorithms and their implementations on an Android platform using OpenCV.

To make our lives simpler, and have a clear understanding of this chapter, we will first create a basic Android application to which we will keep adding implementations of different feature detection algorithms. This will reduce the amount of extra code that we would otherwise have to write...

Creating our application

Let's create a very basic Android application that will read images from your phone's gallery and display them on the screen using the ImageView control. The application will also have a menu option to open the gallery to choose an image.

We will start off by creating a new Eclipse (or an Android Studio) project with a blank activity, and let's call our application Features App.

Note

Before doing anything to the application, initialize OpenCV in your application (refer to Chapter 1, Applying Effects to Images, on how to initialize OpenCV in an Android project).

To the blank activity, add an ImageView control (used to display the image), as shown in the following code snippet:

<ImageView
        android:layout_width="fill_parent"
        android:layout_height="fill_parent"
        android:id="@+id/image_view"
        android:visibility="visible"/>

In the application menu, add an OpenGallery menu option to open...

Edge and Corner detection

Edge detection and Corner detection are two of the most basic feature detection algorithms, and are very useful ones too. Having information about the edges in an image can be of great help in applications, where you want to find boundaries of different objects in an image, or you need to find corners in an image when you want to analyze how an object rotates or moves in a given sequence of images (or videos). In this section, we will take a look at the techniques and implementations of various Edge and Corner detection algorithms, such as Difference of Gaussian, Canny Edge detector, Sobel Operator, and Harris Corners.

The Difference of Gaussian technique

Let's start with the easiest and the most rudimentary technique. Before we understand how Difference of Gaussian (DoG) works, let's take a look at what exactly edges are. To put it simply, edges are points in an image where the pixel intensity changes appreciably. We will exploit this property of edges...

Hough transformations

So far, we looked at how to detect edges and corners in an image. Sometimes, for image analysis apart from edges and corners, you want to detect shapes, such as lines, circles, ellipses, or any other shape for that matter. Say for example, you want to detect coins in an image, or you want to detect a box or a grid in an image. A technique that comes handy in such scenarios is Hough transformations. It is a widely used technique that detects shapes in an image using their mathematical equations in their parameterized forms.

The generalized Hough transformation is capable of detecting any shape for which we can provide an equation in the parameterized form. As the shapes start getting complex (with an increase in the number of dimensions), such as spheres or ellipsoids, it gets computationally expensive; hence, we generally look at standard Hough transformations for simple 2D shapes, such as lines and circles.

In this section, we will take a look at Hough transformations...

Contours

We are often required to break down the image into smaller segments to have a more focused view of the object of interest. Say for instance, you have an image with balls from different sports, such as a golf ball, cricket ball, tennis ball, and football. However, you are only interested in analyzing the football. One way of doing this could be by using Hough circles that we looked at in the last section. Another way of doing this is using contour detection to segment the image into smaller parts, with each segment representing a particular ball.

The next step is to choose the segment having the largest area, that is, your football (it is safe to assume that the football would be the largest of all!).

Contours are nothing but connected curves in an image or boundaries of connected components in an image. Contours are often computed using edges in an image, but a subtle difference between edges and contours is that contours are closed, whereas edges can be anything. The concept of...

Project – detecting a Sudoku puzzle in an image

Let's try to apply our learning from this chapter and create a simple application for detecting a Sudoku grid in an image. You see Sudoku puzzles every day in newspapers or magazines; sometimes they provide a solution and sometimes they don't. Why not build an application for your mobile phone that can click a picture of the Sudoku, analyze the numbers, run some intelligent algorithms to solve the puzzle, and within seconds, you have the solution on your mobile screen.

After reading this chapter, we can easily work on the first part of the application; that is, localizing (detecting) the Sudoku grid from an image. As you read through this book, you will come across algorithms and techniques that will help you build the other parts of this application.

Let's break down our problem statement into subproblems and work each one out to have a fully functioning Sudoku localizing application:

  • Capture the image using a camera or...

Creating our application


Let's create a very basic Android application that will read images from your phone's gallery and display them on the screen using the ImageView control. The application will also have a menu option to open the gallery to choose an image.

We will start off by creating a new Eclipse (or an Android Studio) project with a blank activity, and let's call our application Features App.

Note

Before doing anything to the application, initialize OpenCV in your application (refer to Chapter 1, Applying Effects to Images, on how to initialize OpenCV in an Android project).

To the blank activity, add an ImageView control (used to display the image), as shown in the following code snippet:

<ImageView
        android:layout_width="fill_parent"
        android:layout_height="fill_parent"
        android:id="@+id/image_view"
        android:visibility="visible"/>

In the application menu, add an OpenGallery menu option to open the phone's gallery and help us pick an image. For this...

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Description

If you are a Java and Android developer looking to enhance your skills by learning the latest features of OpenCV Android application programming, then this book is for you.

Who is this book for?

If you are a Java and Android developer looking to enhance your skills by learning the latest features of OpenCV Android application programming, then this book is for you.

What you will learn

  • Understand image processing using OpenCV
  • Detect specific objects in an image or video using various stateoftheart featurematching algorithms such as SIFT, SURF, and ORB
  • Perform image transformations such as changing color, space, resizing, applying filters like Gaussian blur, and likes
  • Use mobile phone cameras to interact with the real world
  • Explore face detection, object detection, and image stitching in OpenCV Android programming
  • Build smarter applications by using machine learning algorithms
  • Learn to debug applications and create optimal custom algorithms by understanding how data is stored internally
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Publication date : Jul 29, 2015
Length: 216 pages
Edition : 1st
Language : English
ISBN-13 : 9781783988204
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Table of Contents

10 Chapters
1. Applying Effects to Images Chevron down icon Chevron up icon
2. Detecting Basic Features in Images Chevron down icon Chevron up icon
3. Detecting Objects Chevron down icon Chevron up icon
4. Drilling Deeper into Object Detection – Using Cascade Classifiers Chevron down icon Chevron up icon
5. Tracking Objects in Videos Chevron down icon Chevron up icon
6. Working with Image Alignment and Stitching Chevron down icon Chevron up icon
7. Bringing Your Apps to Life with OpenCV Machine Learning Chevron down icon Chevron up icon
8. Troubleshooting and Best Practices Chevron down icon Chevron up icon
9. Developing a Document Scanning App Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(4 Ratings)
5 star 75%
4 star 0%
3 star 25%
2 star 0%
1 star 0%
Carles Sep 04, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is aimed to both mobile app developers who want to improve the graphic functionality to a new level (3d gaming, augmented reality, etc) or to opengl desktop developers who want to extent his projects/products to mobile platforms. Both would benefit from all the know-how inside this book, that goes deep into not only the OpenCV framework fundamentals but also into some math required for creating powerful 2d/3d code and behaviours. A warn for beginners, you should know at least some C/C++ basic coding and some algebra (arrays, matrix and so) to not get lost while reading this book. Otherwise the book is a very useful and deep source of knowledge regarding OpenCV application to develop mobile apps.
Amazon Verified review Amazon
Gabriel Humpire Mamani Sep 24, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I had a chance to read "Mastering OpenCV Android Application Programming" recently on a request of Packt Publishing. I hope to help readers with this review. Let's go, I got my copy from Packt Publishing, where additional formats to download it are available. In my case, I preferred it in Kindle Edition, also they provide me the source code for each chapter to a better understanding of each topic.This book is suitable for people who have at least basic knowledge in Android, OpenCV and Computer Vision, if you are new in Android maybe you should have to learn it before reading this book. Let's begin with the main review. The first chapters are about OpenCV basic concepts (brightness, contrast, color space, arithmetic and geometrical transforms) and how to set OpenCV in your Android project step by step. For every topic, they show the code, explanation and results.Until chapter 5, the content was about main OpenCV functions with Android interactions, but here on-wards the interesting and useful projects are explained such as Image alignment and stitching with notable improvements. Moreover, a Machine Learning chapter explains how to use its potential for Android apps. A nice chapter explains the best practices for Android projects as well for debugging using logcat logs. Finally, an entire chapter dedicated for an Optical Character Recognition is detailed.Afterwards, I found this book very useful and recommendable. Overall, this is a must-read book for Android programmers, and Computer Vision specialists.
Amazon Verified review Amazon
André Aug 18, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is recommended for those who wants to delve into the image processing techniques in the context of mobile applications. The book also provides a valuable examples and guide you step by step over the contents.
Amazon Verified review Amazon
erick lestrange Nov 05, 2017
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
cuatro copypastes de interner que ni siquiera se han molestado en ocultar, formulas que ni siquiera son las que usaras, pocos ejemplos practicos y aplicaciones... casi que sale mejo verse tutoriales en youtube
Amazon Verified review Amazon
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