Developing OpenCV computer vision apps for the Android platform

Eric Gregori, BDTI

January 31, 2013

Eric Gregori, BDTI

Continued from page 2

OpenCV for Tegra
OpenCV for Tegra is a version of OpenCV for Android that NVIDIA has optimized for Tegra 3 platforms running the Android operating system. It currently supports Android API levels 9 through 16, and contains optimizations that enable OpenCV for Tegra to often run several times faster on Tegra 3 than does the generic open-source OpenCV for Android implementation. The TADP includes a SDK package for OpenCV for Tegra.

Figure 8 shows the OpenCV for Tegra Demo available for download from the Google Play store. Only the Sobel and Morphology algorithms are shown, although the demo supports additional algorithms such as various blurs and optical flow. The screen shots show performance both with and without Tegra optimizations enabled. Notice in Figures 8b and 8c that the Sobel algorithm runs twice as fast using the NVIDIA optimized version of OpenCV. Figures 8d and 8e show edge detection using morphology operators. The operations are listed in red at the bottom of the image. In this case, the NVIDIA-optimized OpenCV library executes the specified operators five times faster than the standard ARM version of OpenCV.

Figure 8: The OpenCV for Tegra demo is also available on Google Play (a). The included Sobel algorithm can be run either with Tegra optimizations off (b) or on (c); in the latter case it's twice as fast. Similarly, the morphology algorithm is five times (e).

Sample applications
The OpenCV4Android SDK includes four sample applications and five tutorials to help you get started in developing OpenCV applications for Android. The tutorials are meant to serve as frameworks or foundations for your specific application: simply open an appropriate tutorial project and start adding your code:

• Android Camera: This tutorial is a skeleton application for all of the others. It does not use OpenCV at all, but gives an example Android Java application working with a camera.
• Add OpenCV: This tutorial shows the simplest way to add a Java OpenCV call to the Android application.
• Use OpenCV Camera: This tutorial functions exactly the same as the previous one, but uses OpenCV’s native camera for video capture.
• Add Native OpenCV: This tutorial demonstrates how you can use OpenCV in the native part of your application, through JNI.
• Mix Java + Native OpenCV: This tutorial shows you how to use both the C++ and Java OpenCV APIs within a single application.

The sample applications, on the other hand, are complete applications that you can build and run:

• Image-manipulations: This sample demonstrates how you can use OpenCV as an image processing and manipulation library. It supports several filters and demonstrates color space conversions and working with histograms.
• 15-puzzle: This sample shows how you can implement a simple game with just a few calls to OpenCV. It is also available on Google Play.
• Face-detection: This sample is the simplest implementation of the face detection functionality on Android. It supports two modes of execution: an available-by-default Java wrapper for the cascade classifier, and a manually crafted JNI call to a native class which supports tracking. Even the Java version is able to deliver close to real-time performance on a Google Nexus One device.
• Color-blob-detection: This sample shows a trivial implementation of a color blob tracker. After the user points to a particular image region, the algorithm attempts to select the whole blob of a similar color.

Conclusion
We are at a notable point in the evolution of computing. Modern smartphones and tablets are now quite capable of running useful computer vision algorithms. And by delivering significant advancements to OpenCV4Android, NVIDIA has brought the power of OpenCV to the smartphone and tablet. Developers can implement their algorithms using either Java or native C++ API’s. The Java API in particular exposes computer vision to a whole new level of developers. This is a very exciting time to be involved with computer vision!

Eric Gregori is a Senior Software Engineer and Embedded Vision Specialist with Berkeley Design Technology, Inc. (BDTI), which provides engineering services for embedded vision applications. He is a robot enthusiast with over 17 years of embedded firmware design experience, with specialties in computer vision, artificial intelligence, and programming for Windows Embedded CE, Linux, and Android operating systems.


1. Smart phones overtake client PCs in 2011

2. Gartner Says Worldwide Sales of Mobile Phones Declined 3 Percent in Third Quarter of 2012; Smartphone Sales Increased 47 Percent

3. Gartner Says 821 Million Smart Devices Will Be Purchased Worldwide in 2012; Sales to Rise to 1.2 Billion in 2013

4. December 2012 Embedded Vision Alliance Member Summit Technology Trends Presentation (requires registration)

5. Google Play hits 25 billion downloads

6. July 2012 Embedded Vision Alliance Member Summit Technology Trends Presentation on OpenCL (requires registration)

7. Introduction To Computer Vision Using OpenCV (registration required)

8. Home page of OpenCV.org

9. September 2012 Embedded Vision Summit Afternoon Keynote: Gary Bradski, OpenCV Foundation (requires registration)

10. Bradski, Industrial Perception (requires registration)

11. Introduction into Android Development

12. OpenCV Change Logs

13. Android Release Notes 2.3.1 (beta1) 

14. OpenCV4Android Usage Models

15. OpenCV4Android Reference

16. OpenCV4Android SDK

17. Android development with OpenCV