Designing visionary mobile apps using Tegra Android Development Pack
By default, all the Eclipse OpenCV examples in the TADP come pre-configured to use the OpenCV for Tegra libraries (via static or dynamic linking). Note that if the Tegra optimized libraries are statically linked in, they are not guaranteed to run on non-Tegra 3-based device. Hence, it is advisable to always use dynamic linking via the OpenCV Manager service in your published apps to ensure their compatibility on different hardware platforms.
To determine whether the Tegra optimized functions are indeed being used in your application, check for the presence of the following message in the Android systems logs. You can use the adb logcat command to write out the system logs.
E/OpenCV_for_Tegra(28465): Tegra platform detected, optimizations are switched ON!
Additionally, the OpenCV Manager installed on your device will display the information about your detected Tegra hardware (Figure 1).
Click on image to enlarge.
Figure 1: The OpenCV Manager displays information about your detected Tegra hardware
To check out the speedups you can get with the OpenCV for Tegra, download and run the OpenCV for Tegra Demo App. You can download it from Google Play or find its OpenCVBenchmark.apk in the OpenCV-2.4.2-Tegra-sdk/apk/ folder of your TADP installation directory. The app continuously grabs preview frames from the camera, processes them in an asynchronous processing thread, and displays the processed frames.
Different processing modes (medianBlur, GaussianBlur, etc.) can be selected from the menu options. OpenCV for Tegra optimizations can be turned on or off by touching on the NVIDIA logo on the lower right of the display. When Tegra optimizations are enabled, the NVIDIA logo will turn green. Check the information displayed in the upper part of the display for your hardware's performance statistics.
Shalini Gupta is a Senior Mobile Computer Vision Engineer at NVIDIA. Her previous experience includes two years as an Imaging and Architecture Scientist at Texas Instruments, along with work at AT&T Laboratories and Advanced Digital Imaging Research, LLC (where she developed successful novel algorithms for 3D facial recognition). Shalini obtained her Bachelors Degree in Electronics and Electrical Communication Engineering at Punjab Engineering College, and her Masters Degree and Ph.D. in Electrical and Computer Engineering at the University of Texas at Austin.
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