Mastering opencv android application programming

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For Android development, from beginner to beginner.

Saturday, September 12, 2015

Mastering OpenCV Android Application Programming

Master the art of implementing computer vision algorithms on Android platforms to build robust and efficient applications

Mastering OpenCV Android Application Programming
About This Book

• Understand and utilise the features of OpenCV, Android SDK, and OpenGL
• Detect and track specific objects in a video using Optical Flow and Lucas Kanade Tracker
• An advanced guide full of real-world examples, helping you to build smart OpenCV Android applications

Who This Book Is 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 state-of-the-art feature-matching 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

In Detail
OpenCV is a famous computer vision library, used to analyze and transform copious amounts of image data, even in real time and on a mobile device.

This book focuses on leveraging mobile platforms to build interactive and useful applications. The book starts off with an introduction to OpenCV and Android and how they interact with each other using OpenCV’s Java API. You’ll also discover basic image processing techniques such as erosion and dilation of images, before walking through how to build more complex applications, such as object detection, image stitching, and face detection. As you progress, you will be introduced to OpenCV’s machine learning framework, enabling you to make your applications smarter.

The book ends with a short chapter covering useful Android tips and tricks and some common errors and solutions that people might face while building an application. By the end of the book, readers will have gained more expertise in building their own OpenCV projects for the Android platform and integrating OpenCV application programming into existing projects.

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Mastering OpenCV Android Application Programming

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Book description

Master the art of implementing computer vision algorithms on Android platforms to build robust and efficient applications

OpenCV is a famous computer vision library, used to analyze and transform copious amounts of image data, even in real time and on a mobile device.

This book focuses on leveraging mobile platforms to build interactive and useful applications. The book starts off with an introduction to OpenCV and Android and how they interact with each other using OpenCV’s Java API. You’ll also discover basic image processing techniques such as erosion and dilation of images, before walking through how to build more complex applications, such as object detection, image stitching, and face detection. As you progress, you will be introduced to OpenCV’s machine learning framework, enabling you to make your applications smarter.

The book ends with a short chapter covering useful Android tips and tricks and some common errors and solutions that people might face while building an application. By the end of the book, readers will have gained more expertise in building their own OpenCV projects for the Android platform and integrating OpenCV application programming into existing projects.

What You Will Learn

• Understand image processing using OpenCV
• Detect specific objects in an image or video using various state-of-the-art feature-matching 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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Android

Want a Quick Start link? Use this tutorial: “OpenCV for Android SDK”.

What you should know

There are two major types of OpenCV4Android beginners, first group is new to Android, and the second – to OpenCV. We’ll try to provide some advice for both:

1. If you’re an experienced OpenCV adept and you want to start with Android, you should remember, that Android is not desktop OS, you should prepare yourself for mobile development. We’re not aiming to teach you all about Android, so in case you’re not really familiar with the platform, you may consider consulting the official website for developers, or some short introduction like this one. At the same time, we’ve prepared a special “Introduction into Android Development” tutorial and a set of samples helping you to become familiar with Android specifics. What’s important, is that you can reuse your C++ desktop code. It’s even recommended to develop and debug your algorithms in familiar environment, using your PC and favourite IDE. Of course you have to keep efficiency in mind, but please avoid premature optimization. OpenCV was designed to be high-performance, so measure your actual performance before you start to worry. Keep in mind, that majority of modern mobile devices is surprisingly powerful.
2. If you’re a confident Android developer, but you need some information on OpenCV, please have a look at the documentation and use the user-support resources if needed: forum and issue tracker. And do not forget to look into tutorials, they will help you to quickly understand what you can easily accomplish with OpenCV. Computer Vision field has a long history, but some problems are still unsolved. If you’re not sure if OpenCV could help you with your task, just ask your question at our forum, people there like to solve quirky problems.

How to start

OpenCv4Android is available as a SDK with a set of samples and Javadoc documentation for OpenCV Java API. It also contains prebuilt apk-files, which you can run on your device instantly. There are three OpenCV tutorials aimed to help you start:

1. “Introduction into Android Development” would be useful for the absolute beginner, because it shows you how to setup Android development environment.
2. Detailed instructions on SDK are available in the “OpenCV for Android SDK” tutorial. You’ll see a couple of sample applications, which you can use as a basis for your own developments.
3. “Android development with OpenCV” shows you how to add OpenCV functionality into your Android application. For those who want to reuse their C++ code, we’ve created a special section: “Native/C++”. We propose this way for the professional developers, since native development is a bit harder, but gives you larger opportunities. Face detection sample shows an example of wrapping a C++ class to the Java.

If you need additional information on OpenCV usage models, you can check this page.

Online resources

User Communities:

• OpenCV Q&A forum: http://answers.opencv.org. Use it as a major resource for Computer Vision and OpenCV consultancy.
• Read-only OpenCV4Android group: https://groups.google.com/group/android-opencv. This group was replaced by the Q&A forum from above, but its archive is still available.

OpenCV4Android documentation:

• Tutorials: “Introduction into Android Development”, “OpenCV for Android SDK”, “Android development with OpenCV”.
• Documentation on “OpenCV Manager”: please check these slides.
• Javadoc html-files are included into the distribution and available online.
• Android Samples
• Android Best Practices

Information on OpenCV:

• Official documentation for the latest public release: http://docs.opencv.org.
• Daily documentation build: http://docs.opencv.org/master/.
• And please pay special attention to the tutorials and C++ cheatsheet!

3rd-party samples and tutorials:

• Open Source Google Glass samples by Jared Burrows and Andre Compagno
• Eyes detection and tracking on Android sample by Roman Hošek (the “face-detection” sample extension)

Providing feedback

1. You know, we love contributions, especially pull requests on the GitHub!
2. If you think you’ve found a new bug, let’s double-check it:
3. Please check that you use the latest version of OpenCV4Android.
4. Please check the open “Android issues” on the tracker.
5. Ask OpenCV community about your problem.
6. If you’re still suspecting that you’re probably the first human who met such problem, let’s file a bug! Instructions are here.

Contacts

• Use OpenCV Q&A forum for most of your questions. And please help others, this is good for your karma!
• Email: android at opencv dot org. Again, please use the group first of all, we do not provide private consultancy!
• Twitter: https://twitter.com/OpenCV4Android.

Stay up to date on OpenCV and Computer Vision news

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Mastering opencv android application programming

Easy way to integrate OpenCV into your Android project via Gradle.
No NDK dependency needed — just include this library and you are good to go.

• OpenCV Contribution’s package naming has been changed to make it as per the naming guideline.
  • Old: opencv:VERSION-contrib
  • New: opencv-contrib:VERSION

Each versions is available in only OpenCV as well as OpenCV with contributions.

• 4.5.3.0 ‼️
• 4.5.2
• 4.5.1
• 4.5.0
• 4.4.0

‼️ Please use 4.5.3.0 instead of 4.5.3 . They are both the same versions, however, 4.5.3 has some runtime issues on some of the Android versions while 4.5.3.0 works fine.

• 3.4.15
• 3.4.14
• 3.4.13
• 3.4.12
• 3.4.5
• 3.4.1

• Add mavenCentral to your project’s build.gradle file’s repositories section if not already present.

• Add either the normal or contributions version in the dependencies of your module.

• Initialize OpenCV before using it.

‼️ Want to use OpenCV as source Dependency?

• Checkout the Repository. There will be an empty project with placeholder for the opencv source.
• Download the source from your desired release.
• Unzip the folder. The name of the folder will be opencv_VERSION or opencv-contrib_VERSION . Copy this folder to the project and keep it at the same level as the app folder.
• Open settings.gradle and replace the VERSION with the OpenCV version you have downloaded.

• Open app/build.gradle and replace the VERSION with the OpenCV version you have downloaded

• Sync and run the project. You should see the screen with the text OpenCV Loaded Successfully .

🛠 None of the released versions meet your requirements?

About

Easy way to integrate OpenCv into your Android project via Gradle

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Mastering opencv android application programming

Full source-code for the book.

To build & run the projects for the book:

Install OpenCV (versions between 2.4.2 to 2.4.11 are supported, whereas OpenCV 3.0 is not yet supported). eg: go to «http://opencv.org/», click on Downloads, download the latest OpenCV 2.4 version (including prebuilt library), and extract it to «C:\OpenCV» for Windows or » /OpenCV» for Linux. In OpenCV v2.4.3, the prebuilt OpenCV library is in «C:\OpenCV\build» or » /OpenCV/build», such as «C:\OpenCV\build\x64\vc9» for MS Visual Studio 2008 (or «vs10» folder for MS Visual Studio 2010, or the «x86» parent folder for 32-bit Windows).

Install all the source code of the book. eg: extract the code to «C:\MasteringOpenCV» for Windows or «

/MasteringOpenCV» for Linux.

Install CMake v2.8 or later from «http://www.cmake.org/».

Each chapter of the book is for a separate project. Therefore there are 9 projects for the 9 chapters (remember that Chapter 9 is an online chapter that can be downloaded from «http://www.packtpub.com/cool-projects-with-opencv/book»). You can run each project separately, they each contain a README.md text file describing how to build that project, using CMake in most cases, because CMake can be used with many compilers and many operating systems.

• Ch1) Cartoonifier and Skin Changer for Android, by Shervin Emami.
• Ch2) Marker-based Augmented Reality on iPhone or iPad, by Khvedchenia Ievgen.
• Ch3) Marker-less Augmented Reality, by Khvedchenia Ievgen.
• Ch4) Exploring Structure from Motion using OpenCV, by Roy Shilkrot.
• Ch5) Number Plate Recognition using SVM and Neural Networks, by David Escrivá.
• Ch6) Non-rigid Face Tracking, by Jason Saragih.
• Ch7) 3D Head Pose Estimation using AAM and POSIT, by Daniel Lélis Baggio.
• Ch8) Face Recognition using Eigenfaces or Fisherfaces, by Shervin Emami.
• Ch9) Developing Fluid Wall using the Microsoft Kinect, by Naureen Mahmood.

What you need for this book:

You don’t need to have special knowledge in computer vision to read this book, but you should have good C/C++ programming skills and basic experience with OpenCV before reading this book. Readers without experience in OpenCV may wish to read the book Learning OpenCV for an introduction to the OpenCV features, or read «OpenCV 2 Cookbook» for examples on how to use OpenCV with recommended C/C++ patterns, because «Mastering OpenCV with Practical Computer Vision Projects» will show you how to solve real problems, assuming you are already familiar with the basics of OpenCV and C/C++ development.

In addition to C/C++ and OpenCV experience, you will also need a computer, and an IDE of your choice (such as Visual Studio, XCode, Eclipse, or QtCreator, running on Windows, Mac or Linux). Some chapters have further requirements, particularly:

• To develop the Android app, you will need an Android device, Android development tools, and basic Android development experience.
• To develop the iOS app, you will need an iPhone, iPad, or iPod Touch device, iOS development tools (including an Apple computer, XCode IDE, and an Apple Developer Certificate), and basic iOS and Objective-C development experience.
• Several desktop projects require a webcam connected to your computer. Any common USB webcam should suffice, but a webcam of at least 1 megapixel may be desirable.
• CMake is used in most projects, including OpenCV itself, to build across operating systems and compilers. A basic understanding of build systems is required, and knowledge of cross-platform building is recommended.
• An understanding of linear algebra is expected, such as basic vector and matrix operations and eigen decomposition.

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