Build platform: Ubuntu 9.10
Target platform: Android

Download and prepare OpenCV library source code.

1. Download the latest version of OpenCV (http://sourceforge.net/project/showfiles.php?group_id=22870).

2. As build platform is Linux, select linux version (for example OpenCV2.1.0.tat.bz).

nike.avi

This is a demo video of the invariant orientation and scale fast object detection algorithm. The algorithm is a robust in cases when the object is deformed a little 🙂

I would like to represent an executable demo  which was described at  http://www.computer-vision-software.com/blog/2009/07/barcode-recognition/.

This is demo application for Rhonda barcode recognition library. It’s cross-platform library written on C++ language. It was tested on ARM Cortex-A8, ARM11 and x86 platforms.

Average recognition time on 640×480 frames:

P4 – 3GHz:  25ms

In OpenCV/Samples there is  facedetect program.  This program can detect  faces on images and video.  It’s very fun, but its speed leaves much to be desired =(.  Of course  with OpenMP,  it works  faster; on Intel Core Duo 2.7GHZ, it works fast;  but will it work fast on ARM? I have big doubts.  I compiled facedetect without OpenMP and on average it takes 600 ms for 640×480 resolution to find one face.   I wanted to find out, if it’s possible to improve this time by software means or not…  After some investigations, code refactoring and improvements, facedetect started to work 2.5 time faster, even on ARM.  Of course, without big quality loss =)

This time OpenCV was ported to the Apple iPhone platform.

First of all we need to compile OpenCV library itself so that it can be used on the iPhone. There are two ways here:

1. Use OpenCV as a private framework.
2. Compile OpenCV as a static library.

First approach looks more comfortable for using, though I was not able to make it work properly on the iPhone (it works fine on the simulator, but not on the real hardware).

But anyway, let’s see how both approaches can be followed.

Compiling OpenCV

• Build platform: Windows XP SP3
• Target platform: ARM9 fixed point

Contents

• Download and prepare OpenCV library source code
• Download and prepare compiler
• Create/Modify Makefile
• Compile and link
• Run and Test
• Performance
• Profiling

Here is a brief description of compiling and running OpenCV’s facedetect sample on Pocket PC.

Compiling

I used Visual Studio 2005 Team Suite edition to compile OpenCV 1.1 pre 1. It compiled without any trouble for Win32.

Then I tried to compile for Pocket PC (set Platform to Pocket PC 2003 (ARMV4)). Some compilation errors occured. As I only needed to compile a facedetect sample, I decided to compile the necessary files only.

I played with two ARM9-based single board computers (SBC) recently to investigate how OpenCV would operate on embedded platforms. The SBCs are – TS-7800 and SBC2440-III.

OpenCV uses floating point operations a lot, but not all of the ARM processors have FP coprocessor, so developers should use either some FP library, or as in my case – use Linux kernel FP emulation. There are two types of such emulation OABI and EABI. More details can be found here and here. Kernel release 2.6.16 was the first one to include ARM EABI support.

Unfortunately, CPUs of both SBCs do not support floating point in hardware, but luckily enough, TS-7800 has Debian Linux with 2.6.21 kernel. So I had a chance to compare OpenCV performance for OABI and EABI.

Both SBCs have necessary tool-chains in package – TS-7800 has Linux and Windows(Cygwin) tool-chains, SBC-2440 – only Linux one.  In addition to these I downloaded newer release of Codesourcery ARM tool-chain for Windows, because the one from TS-7800 SW package didn’t work properly with Cygwin.