GUI version of OpenCV for feature-detection (SIFT etc.) prototyping before actual project development?

Question

I had an idea for which I need to be able to recognize certain objects or models from a rendered three dimensional digital movie.

After limited research, I know now that what I need is called feature detection in the field of Computer Vision.

So, what I want to do is:

1. create a few screenshots of a certain character in the movie (eg. front/back/leftSide/rightSide)
2. play the movie
3. while playing the movie, continuously create new screenshots of the movie
4. for each screenshot, perform feature detection (SIFT?, with openCV?) to see if any of our character appearances are there (they must still be recognized if the character is further away and thus appears smaller, or if the character is eg. lying down).
5. give a notice whenever the character is found

This would be possible with OpenCV, right?

The "issue" is that I would have to learn c++ or python to develop this application. This is not a problem if my movie and screenshots are applicable for what I want to do.

So, I would like to first test my screenshots of the movie. Is there a GUI version of OpenCV that I can input my test data and then execute it's feature detection algorithms manually as a means of prototyping?

Any feedback is appreciated. Thanks.

Answer 1

There is no GUI of OpenCV able to do what you want. You will be able to use OpenCV for some aspects of your problem, but there is no ready-made solution waiting there for you.

While it's definitely possible to solve your problem, the learning curve for this problem is quite long. If you're a professional, then an alternative to learning about it yourself would be to hire an expert to do it for you. It would cost money, but save you time.

EDIT

As far as template matching goes, you wouldn't normally use it to solve such a problem because the thing you're looking for is changing appearance and shape. There aren't really any "dynamic parameters to set". The closest thing you could try is have a massive template collection that would try to cover the expected forms that your target may take. But it would hardly be an elegant solution. Plus it wouldn't scale.

Next, to your point about face recognition. This is kind of related, but most facial recognition applications deal with a controlled environment: lighting, distance, pose, angle, etc. Outside of that controlled environment face detection effectiveness drops significantly. If you're detecting objects in a movie, then your environment isn't really controlled.

You may want to first try a simpler problem of accurately detecting where the characters are, without determining who they are (video surveillance, essentially). While it may sound simple, you'll find that it's actually non-trivial for arbitrary scenes. The result of solving that problem may be useful in identifying the characters.

Answer 2

Well, using OpenCV you would of taking a frame of a video file and do any computations on it.

You can do several different methods of detecting a character on that image, but it's not so easy to have it as flexible so you can even get that person if it's lying on the floor for example, if you only entered reference images of that character standing.

Basically you could try extracting all important features from your set of reference pictures and have a (in your case supervised) learning algorithm that gets a good feature-vector of that character for classification.

You then need to write your code that plays the video and which takes a video frame let's say each 500ms (or other as you desire), gets a segmentation of the object you thing would be that character and compare it with the reference values you get from your learning algorithm. If there's a match, your code can yell "Yehaaawww!" or do other things...

But all this depends on how flexible you want this to be. You could also try a template match or cross-correlation which basically shifts the reference image(s) over the frame and checks how equal both parts are. But this unfortunately is very sensitive for rotation, deformations or other noise... so you wouldn't get that person if its i.e. laying down. And I doubt you can get all those calculations done in realtime...

Basically: Yes OpenCV is good to use for your image processing/computer vision tasks. But it offers a lot of methods and ways and you'd need to find a way that works for your images... it's not a trivial task though...

Hope that helps...