The Geeky Junkyard of kingler

The graphical programming tool, Labview, is great for scientific instrumentation, because it is easy to use and yet very powerful and feature rich. However, the graphical interface sometimes can be a hassle, especially when you want to do complicated mathematical operations. Like a look at the example here for adding up two 1′s. Drawing the wires can be a headache if you are doing many many more operations.

This is when you will need the MathScript in Labview. It is similar to MATLAB syntax and does a good job complimenting the all-graphical programming of Labview.

Today, I received an email saying that NI just released an online beta demo (link). With the demo, you can actually run MathScript programs without installing Labview! It can even output 2D or 3D plots. This is great for plotting function for any school project

Try some simple codes:

a= 0 : 0.1 : 2*pi
plot(a, sin(a))

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I just found out that I didn’t do a fair enough comparison between the MATLAB built-in normxcorr2 function and the pre-compiled normxcorr2_mex in the previous post. The reason is that MATLAB is calculating the full normalized cross-correlation, while the pre-compiled MEX file only calculates the values for the “valid” region of the cross correlation. The idea of calculating only the “valid” region assumes that the feature/template that you are looking for is WITHIN the larger image. If the feature has several pixels sticking out of the larger image, this assumption will make the normxcorr_mex to give the wrong peak position.

So, to match the output of the normxcorr2 in matlab, one will need to pad the larger image with zeros before using the normxcorr2_mex. For example, instead of using
ncc = normxcorr2_mex(template, img);
The following code will give you exactly the same result as the output from the Matlab normxcorr2 function:
img_padded=padarray(img,[size(template,1)-1,size(template,2)-1]);
ncc=normxcorr2_mex(template, img_padded, 'valid');

By doing this, we are calculating more correlation values, thus trading off performance, as is indicated by the comparison plot below (still 10 times speed-up with large image sizes, compared with the matlab built-in function) :

Recently, I’ve encounter the problem of finding a template in an image, which is a common issue in image registration or computer vision.

With the built-in function “normxcorr2″ in MATLAB, it is rather easy to write a program to do this. However, the performance is not that great.

Luckily, I came across the following discussion by Daniel Eaton:
“How to do Normalized Cross-Correlation Fast”

By wrapping the C++ code from OpenCV project and making it available as a MATLAB MEX-file, Daniel claims that he achieved a huge performance boost.

I was a bit suspicious at first, but decided to give it a shot. As I am using a n Apple PowerMac G5, I found out that I need to compile the MEX-file on Mac OS X, which turns out not be a trivial task (see my dedicated post).

Anyway, the outcome really surprised me. I can assure you now, that it is well worth the time and trouble to compile the MEX-file!

By embedding a random grayscale pattern in a larger image, I was able to benchmark the performance on an Apple PowerMac G5 (Dual 2.5GHz PowerPC CPUs). Take a look:

The template stays the same (100×100 pixels), while the size of search image is increasing (from 200×200 to 800×800, indicated by the X-axis). The Y-axis shows the cpu time. Clearly, there is a speedup of 15x – 20x, using the compiled MEX-file over the built-in “normxcorr2″!

Life is better, right?

I was trying to compile the fast Normalized Cross-correlation function from Daniel Eaton, when I encountered compilation errors on my PowerMac G5. It took me some time to figure out a solution, so I thought it might be useful to other people who want to use Intel s Open Computer Vision library in MATLAB on Mac OS X Tiger.

1. Follow Christoph Seibert’s guide to compile OpenCV on Mac OS X Tiger. You will need Fink installed (use FinkCommander), as well as Apple XCode and X11.
2. Open an X11 Terminal window and type in the following command to set up the environment for pkg-config.
   export PKG_CONFIG_PATH=/usr/local/lib/pkgconfig
   Or you can add this line to your .bash_profile file.
3. Following the above command, start MATLAB by typing:
   /Application/MATLAB71/bin/matlab
   (use your own version of MATLAB, instead of 7.1 here)
4. In MATLAB command window, type in
   mex -setup
   and choose the default gcc option (# 2).
5. Open a new Terminal window and modify the matlab mexopts.sh file (~/.matlab/R14SP3/mexopts.sh for example). Now open the file with your favorite text editor (vi or pico) and find the section starting with
   mac)
#-------------------------
CC='gcc-3.3'

6. replace the CC line with the following line and save the file.
   CC='gcc-3.3 -bind_at_load `pkg-config --cflags opencv` `pkg-config --libs opencv`'
7. Okay, now you can go ahead and compile the MEX file, by using the “mex” command in MATLAB. For example:
   mex mycode.cpp

Have fun!