I am about to start a project in visual image-processing and have no had experience with Matlab, Aforge, OpenCV and was wondering if anyone had any experiences with these different software packages.
I was also wondering which of the three packages were most efficient I assume OpenCV but has anyone had any experience?
Thanks
Jamie.
The question you need to ask yourself is which is more important - your time or the computer's time. If your task is really simple, you may be able to code it up in MATLAB and have it work right off the bat. MATLAB is by far the easiest for development - a scripted language with built-in memory management, a huge array of provided functions, and a great interface for displaying and manipulating data while debugging.
On the other hand, MATLAB is at least an order of magnitude slower than compiled openCV code for many tasks. This is especially true if you use the intel performance primitives libraries.
If you know how to code in MATLAB, I would suggest writing and debugging your algorithms in that language, then porting them to c/c++ with openCV for speed. If there are only a couple of simple functions that you need to speed up, you can call c code from MATLAB, but it's hard to get this working right the first few times you try it, so you're probably better off just rewriting your finished code entirely in c/c++
First, please elaborate about your project's needs. It has the biggest impact on the choice, in addition to other factors - your general programming knowledge (If you haven't dealt with dot net but just with C++, AForge is not a good choice, for example).
Generally,
Both AForge and OpenCV has a built-in interface to .Net, and OpenCV also with C++, python, and more. Matlab might be more efficient, but if you don't have any experience with it - you should also learn its syntax. Take it into consideration.
Matlab probably has the largest variety of functions, but it is more complicated than the other projects. OpenCV and AForge themselves have some differences - see them described in this StackOverflow question/ answers.
I worked last year in two similar projects with cars on the highway. Afaik, Matlab allows to process only one picture frame at a time (surely you could elaborate an algorithm to compute a stream) but using Simulink you can process the stream directly.
On the other hand, i found AForge a lot friendlier and easier to use since you can easily adjust the processing parameters from a GUI (not so fast/easy) to do in Matlab/simulink.
I'd go for Aforge.Net. It's also fast enough if you're worrying about processing speed. (using 640x480)
If you are asking about using one of these in .net,easily you can get info by this:
1-matlab mostly used in simulation of projects not the End-prototype project; my numer : 30;
2-aforge (as I'v used in many project) if you do not need the circular process like capturing image, or recognition of something in images or ... you'll find it very good, cause it is easy to use but useful for single processes; my number : 50
3-opencv very good at speed and useful for circular processes, for example you can capture images from a webcam and Instantly cartoonize it without any delay, But not easy-to-use as aforge. I like it anyway cause of its speed and MANY functions it gives us mostly anything we need in programming; my number : 80
Dr.Taha - Tahasoft.net
Related
Does anyone have a pointer to measurements of how fast a behavioral model written in SystemC is compared to the same model written in SystemVerilog? Or at least first hand experience of the relative simulation speed of the two, when modeling the same thing?
To clarify, this is for high level system simulation, where the model is the minimum code to mimic the behavior. It's decidedly not synthesizable. In System Verilog, it would use as many non-synthesizable behavioral constructs as possible, for maximum execution speed. The point is, we're asking about an apples to apples comparison that someone did, where they tried to do both: stay high level (non-synthesizable), and make the code as close to equivalent as possible between the two languages.
It would be great if they stated the SystemVerilog environment used, and what machine each was run on, in order to be able to translate. Hopefully can be useful to a variety of people in choosing approach.
Thanks!
Like with other language benchmarks it is not possible to answer in general, because it depends on multiple factors like modeling style, compiler vendor and version, compilation options.
Also there are tools to convert Verilog to C++ and C++ to Verilog. So you can always match simulation performance by converting from one language to other.
Verilog can be converted to C++/SystemC using Verilator https://www.veripool.org/wiki/verilator
To convert C++/SystemC into Verilog you can use HLS (High-level synthesis) toos.
I am working on reducing dimentionality of a set of (Boolean) vectors with both the number and dimentionality of vectors tending to be of the order of 10^5-10^6 using autoencoders. Hence even though speed is not of essence (it is supposed to be a pre-computation for a clustering algorithm) but obviously one would expect that the computations take a reasonable amount of time. Seeing how the library itself was written in c++ would it be a good idea to stick to it or to code in Java (Since the rest of the code is written in Java)? Or would it not matter at all?
That question is difficult to answer. It depends on:
How computationally demanding will be your code? If the hard part is done by the library and your code is only to generate the input and post-process the output, Java would be a valid choice. Compare it to Matlab: The language is very slow but the built-in algorithms are super-fast.
How skilled are you (or your team, or your future students) in Java and C++. Consider learning C++ takes a lot of time. If you have only a small scaled project, it could be easier to buy a bigger machine or wait two days instead of one, to get the results.
Have you legacy code in one of the languages you want to couple or maybe re-use?
Overall, I would advice you to set up a benchmark example in whatever language you like more. Then give it a try. If the speed is ok, stick to it. If you wait to long, think about alternatives (new hardware, parallel execution, different language).
I'm trying to write a program in iPhone than can take two audio files (e.g. WAV) as inputs, compare them, and spit out a number that tells you how similar the audio files are.
If someone has done something like this, know how to go about doing it, or just have some ideas, please let me know. Anything will be greatly appreciated.
Specific questions: What language is suitable? How hard is it to do (how many
hours, roughly)? Where can I find a good source of audio library/tools?
Thanks!
I'd say it's pretty hard, not so much the implementation, but coming up with a reasonable definition of 'similar'.
That said, you're probably looking at techniques like autocorrelation and FFT, both of which are CPU-intensive tasks, so I'd say a fully-compiled language (C, C++, don't know about Objective-C) would be most suitable at least for the actual calculations. Also, you're facing a somewhat underpowered platform for such tasks (if only because uncompressed audio files are pretty large), so you're in for quite some optimization.
This book: http://www.dspguide.com/ is quite concise reading for all things DSP-related.
Sounds similar to what 'Shazam' does - awesome iPhone app by the way, check it out if you haven't already (it's free too).
A while ago there was an article on how Shazam works, read it here. It takes an acoustic fingerprint and compares it to other songs' fingerprints, returning the closest match.
I would say there is a lot of math, probably some matrices and maybe Fourier transforms involved in fingerprinting and then trying to compare the audio.
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Probably would take a good while to program. If your math skills are up to it though, sounds like a good challenge :-)
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EDIT: turns out there was some source code on the site I linked. It's in Java but would be well worth a look through before you start writing your own. Source code here
I am working on something similar in Java on a speech recognition app.
I would recommend using MFCC (requires calculating FFT) for feature extraction and Neural Networks or some other sort of machine learning technique for training and recognition. You train the NN with the features extracted from the reference wav file, more precisely from consecutive equal lenght slices/windows of that audio file. Then you use the NN to detect if another file, also split into slices, has the same features.
This is the basic idea upon which you can elaborate to further your own specifications, or exactly what you want your app to do.
In terms of libraries in Objective C I think you can find a few for the signal processing part (FFT and such) as for the machine learning part I have no idea about what you could find.
As for programming time it's hard to estimate because it depends on a lot of details. I would say somewhere about a week, but that's just a fair estimation.
ps: MFCC stands for Mel-Frequency Coeficients: http://en.wikipedia.org/wiki/Mel-frequency_cepstrum
I have a project that requires lots of image processing and wanted to add GPU support to speed things up.
I was wondering if i compiled my matlab into c++ shared library and called it from within OpenCL program, does that mean that the matlab code is going to be run on GPU?
My own (semi-educated) guess is that you are going to find this very difficult to do. But, others have trodden the same path. This paper might be a good place to start your research. And Googling turned up Accelereyes and a couple of references to items on the Mathworks File Exchange which you might want to follow up.
Everything in jacket is written in c/ c++ / cuda.
Infact we now have a beta version libjacket (http://www.accelereyes.com/downloadLibjacket) which can be used to extend not just matlab but other languages if you are willing.
#OSaad
Most of our functions are the fastest options out there. Be it in C or matlab.
The Parallel Computing Toolbox in the upcoming release R2010b (due September 1st) supports GPU processing for several functions. Unfortunately, it only supports CUDA (version 1.3 and later), so with an ATI graphics card, you're out of luck. However, you may just want to buy a dedicated GPU, anyway.
Typically, if you can write your Matlab code in a "vectorized" way, then the packages like AccelerEyes and Jacket have a reasonable chance of making things run on the GPU. You can verify this to some extent beforehand by checking whether Matlab itself is able to run on multiple cores on the CPU (these days Matlab will use multiple cores if things are parallelizable in an obvious way).
If that doesn't work, then you need to drop down to C/C++ via mex and then, from there, call OpenCL yourself. Mex is how Matlab talks to C code, so you write C code that is called by Matlab (and receives the matrices, etc), then initialises and calls OpenCL. This is more work, but may be your only route (and, even if the automated packages work to some extent, this approach can still give more speedups because you can be smarter about memory management, for example, if you know what your are doing).
I've been using F# for a while now to model algorithms before coding them in C++, and also using it afterwards to check the results of the C++ code, and also against real-world recorded data.
For the modeling side of things, it's very handy, but for the 'data mashup' kind of stuff, pulling in data from CSV and other sources, generating statistics, drawing charts etc., my colleague teases me no end ("why are you coding that yourself? It's built in to MatLab").
And I have another colleague who swears by R, which also has charting stuff 'built-in'.
I know that MatLab, R and F# are not strictly comparable, so I'm not asking for a 'feature comparison shoot out'. I just wondered what other people are using for these kind of pre- and post-analysis scenarios, and how happy they are with it.
(If there's anyone out there working on wrapping Microsoft Charts into something F#-friendly, let me know, I'd be happy to participate...)
(Note: answers to this question will be subjective, but based on experience, please)
I have very little experience with F#, but regarding C++/Matlab/R: If the speed of your program's execution is the most important, use C++. If speed of implementation is the most important, use Matlab or R. This is true for a number of reasons, not the least of which is their massive libraries of math/stats packages.
Both Matlab and R can be sped up through parallelism: so generally, I think that speed and quality of implementation should be a bigger concern. That's where the real "value" of programming is taking place, in the design of the application. It's not a minor proposition if you can write 3 or 4 good R programs in the same time it takes you to write 1 good C++ program.
Regarding F#: so far as it is part of Microsoft's framework, it must have a lot to offer. If you're developing in Visual Studio or working on a big .Net project (for instance), it might make sense to use F#. On the other hand, you can call both Matlab and R from .Net applications, so I would probably argue that their libraries should be a bigger concern. For instance, see this article as an example for R and the Matlab Builder.
Long story short: comparing F# and Matlab/R isn't a good comparison. F# is a general purpose programming language, while Matlab/R can be viewed as massive mathematical/data analysis toolkits. Some people call Matlab or R from F# in order to take advantage of each language's benefits (e.g. see this discussion, this article on Matlab/F#, or this article on R/F#).
So far as charting is concerned: R is extremely strong on this front. Have a look at the graphics view on CRAN and this series of posts on the LearnR blog about Lattice and ggplot2.
I've worked a bit with matlab and python/pylab for these purposes. What these tools have 'built-in' is a programming environment, a shell, and gui tools designed for quickly looking at data from a variety of sources.
In a few commands, you can go from having a csv file to interactive plots on the screen, then to an image export in just about any format. It takes a minute or two to go from data to visualization once you have the hang of it. I would imagine this is uncommon in the C++ world (although I have seen some professors with pretty impressive work-flows).
I've tried R, but I can't say much useful about it. It seems to offer about the same set of features, but it may be troublesome to Google for support.
If you are spending more than a couple minutes getting from data to plot using your current method, it's definitely worth learning one of these environments. The best choice depends on your colleagues, your work environment, experience, and your budget.
This is a reasonable close double to the previous question on suitable functional language for scientific/statistical computing so you may want to peruse the long and detailed answers there.
Answers depends, as so often, on your experience and prior language training. I very much prefer R for data munging / modeling / visualization.
I use R because on the one hand it has everything built in and on the other hand you can still manipulate almost everything or start from scratch. Nevertheless, R is rather slow for heavy calculations (although I do all my Monte Carlo simulations in it).
I would say that Matlab is best for the availability of mathematical functionalities in general, R is best for data input/manipulation/visualisation/analysis/etc., and C++ for high-speed subroutines. You can by the way easily integrate C++ (or C, fortran, ...) code in R. Why not read and manipulate input data in R, apply the models in C++, and analyse/visualize output back in R?
I always prototype my models in MATLAB. If my prototype is fast enough, I refactor and it's done. If not, I go back and implement certain functions in C to be called by MATLAB. This requires knowledge of a low level language, which I think is always going to be the case if you are doing anything that is technically challenging.
I'm intrigued with this Lisp flavor if it ever gets off the ground.