Object Tracking: MATLAB vs. Python Numpy

Question

I will soon be starting a final year Engineering project, consisting of the real-time tracking of objects moving on a 2D-surface. The objects will be registered by my algorithm using feature extraction.

I am trying to do some research to decide whether I should use MATLAB or use Python Numpy (Numerical Python). Some of the factors I am taking into account:

1.) Experience

I have reasonable experience in both, but perhaps more experience in image processing using Numpy. However, I have always found MATLAB to be very intuitive and easy to pick up.

2.) Real-Time abilities

It is very important that my choice be able to support the real-time acquisition of video data from an external camera. I found this link for MATLAB showing how to do it. I am sure that the same would be possible for Python, perhaps using the OpenCV library?

3.) Performance

I have heard, although never used, that MATLAB can easily split independent calculations across multiple cores. I should think that this would be very useful, and I am not sure whether the same is equally simple for Numpy?

4.) Price

I know that there is a cost associated with MATLAB, but I will be working at a university and thus will have access to full MATLAB without any cost to myself, so price is not a factor.

I would greatly appreciate any input from anyone who has done something similar, and what your experience was. Thanks!

Answer 1

I would recommend python.

I switched from MATLAB -> python about 1/2 way through my phd, and do not regret it. At the most simplistic, python is a much nicer language, has real objects, etc.

If you expect to be doing any parts of your code in c/c++ I would definitely recommend python. The mex interface works, but if your build gets complicated/big it starts to be a pain and I never sorted out how to effectively debug it. I also had great difficulty with mex+allocating large blocks interacting with matlab's memory management (my inability to fix that issue is what drove me to switch).

As a side note/self promotion, I have Crocker-Grier in c++ (with swig wrappers) and pure python.

Answer 2

Python (with NumPy, SciPy and MatPlotLib) is the new Matlab. So I strongly recommend Python over Matlab.

I made the change over a year ago and I am very happy with the results.

Here it is a short pro/con list for Python and Matlab

Python pros:

• Object Oriented
• Easy to write large and "real" programs
• Open Source (so it's completely free to use)
• Fast (most of the heavy computation algorithms have a python wrapper to connect with C libraries e.g. NumPy, SciPy, SciKits, libSVM, libLINEAR)
• Comfortable environment, highly configurable (iPython, python module for VIM, ...)
• Fast growing community of Python users. Tons of documentation and people willing to help

Python cons:

• Could be a pain to install (especially some modules in OS X)
• Plot manipulation is not as nice/easy as in Matlab, especially 3D plots or animations
• It's still a script language, so only use it for (fast) prototyping
• Python is not designed for multicore programming

Matlab pros:

• Very easy to install
• Powerful Toolboxes (e.g. SignalProcessing, Systems Biology)
• Unified documentation, and personalized support as long as you buy the licence
• Easy to have plot animations and interactive graphics (that I find really useful for running experiments)

Matlab cons:

• Not free (and expensive)
• Based on Java + X11, which looks extremely ugly (ok, I accept I'm completely biased here)
• Difficult to write large and extensible programs
• A lot of Matlab users are switching to Python :)

Answer 3

1. If you're experienced with both languages it's not really a decision criterion.

2. Matlab has problems coping with real time settings especially since most computer vision algorithms are very costly. This is the advantage of using a tried and tested library such as OpenCV where many of the algorithms you'll be using are efficiently implemented. Matlab offers the possibility of compiling code into Mex-files but that is a lot of work.

3. Matlab has parallel for loops parfor which makes multicore processing easy (or at least easier). But the question is if that will suffice to get real-time speeds.

4. No comment.

5. The main advantage of Matlab is that you'll obtain a running program very quickly due to its good documentation. But I found that code reusability is bad with Matlab unless you put a heavy emphasis on it.

I think the final decision has to be if you have to/can run your algorithm real-time which I doubt in Matlab, but that depends on what methods you're planning to use.

Answer 4

Others have made a lot of great comments (I've opined on this topic before in another answer https://stackoverflow.com/a/5065585/392949) , but I just wanted to point out that Python has a number of really excellent tools for parallel computing/splitting up work across multiple cores. Here's a short and by no means comprehensive list:

• IPython Parallel toolkit: http://ipython.org/ipython-doc/dev/parallel/index.html

• mpi4py: https://code.google.com/p/mpi4py

• The multiprocessing module in the standard library: http://docs.python.org/library/multiprocessing.html

• pyzmq: http://zeromq.github.com/pyzmq/ (what the IPython parallel toolkit is based on)

• parallel python (pp): http://www.parallelpython.com/

• Cython's wrapping of openmp: http://docs.cython.org/src/userguide/parallelism.html

You will also probably find cython to be much to be a vastly superior tool compared to what Matlab has to offer if you ever need to interface external C-libraries or write C-extensions, and it has excellent numpy support built right in.

There is a list with a bunch of other options here: http://wiki.python.org/moin/ParallelProcessing