I have been running in and out of OpenCV 2.4.3 trying to figure out the extra functions and parameters that can be used to CvBGCodeBookModel based background subtraction. The documentation is not very helpful, does anyone know a resource/tutorial out there that explains CvBGCodeBookModel implemented in OpenCV along with some of its functions?
Guidance much appreciated
There is a sample in the opencv code (samples/c/bgfg_codebook.cpp) that uses CvBGCodeBookModel, it might be a good place to look.
It says the code is adapted from the book "Learning OpenCV" by O'Reilly press, so that would be another resource.
There is also this paper that describes the theory, not sure if that would be helpful to you or not.
Related
I'm intending to develop a new OF for RPL in cooja simulator, the thing is that I don't find any tutorial or example on how to do so!
Also, There are hundreds of published papers on this work, yet no guidance on how to conduct your own experiements!
Any help or tutorial i can follow.
Furthermore, i need to know what are possible tools needed to do so, like matlab, python or C++ libraries?
too much confused and cannot figure out where to start actually.
Please Help
Please Help I have been searching and reading alot, nothing found but journal papers discusses things theoritically.
I am new to GDbus programming. I need to implement a simple Dbus send-receive message (Signals) using Dbus Glib. I tried to google some sample programs, but couldn't find.
Can anyone post any such sample program or point me to some sample program tutorial?
Thanks in advance...
Thanks,
SB
I think following these steps could help:
Read the wikipedia article on DBus to get a good understanding
of the DBus architecture.
Follow it up with these slides(atleast the first few slides about the architecture). Here is the original GNOME conference video where these slides were used.
Look at a simple hello world program using GDBus here, or for something more detailed, see my example code here. I've got a detailed README explaining the details.
This should help.
:)
I've found this tutorial helpful. It starts off explaining DBus in general and continues with showing implementation examples using gdbus.
I found a book that talks about GDBus, gdbus-code gen, GVariant and all the relevant bits and pieces:
http://maemo.org/maemo_training_material/maemo4.x/html/maemo_Platform_Development_Chinook/
Simple server/client example:
https://github.com/chiehmin/gdbus_test
As outlined above, I would start with the wiki article to understand the concepts:
https://en.wikipedia.org/wiki/D-Bus
I'm Alexander Mashkovtsev, student of gymnasium "Akademy", Kyiv, Ukrane. I'm 15.
I'd like to do face recognition program using OpenCV.
I write science work about face recognition, too.
It's very interesting for me, so i search a command.
I'd like to demonstrate the work on Kyiv High-Technology Center to get help with this.
There are people who are ready help me to create this program?
I will be grateful. Also ready to to reward the person who will help me.
Thanks!
have a look at the opencv facereco docs
or, here for a small python demo (yea, i 've seen your other questions here, that's why i'm posting the latter).
but ofc, you want to write your own, if i understood that right, that's great!
It seems that Face++ SDKs are more easier than OpenCV.
You can refer to Face++ website, look through their API docs overview.
Good luck!
I am basically just starting out in computer programming; mostly fluent in basic Java. I have an idea of creating an ASL (American Sign Language) to English, and my initial problem is how to identify hand movement from a webcam then comparing them to Signs that is already stored as an image or another video. If the problem is a bit too advanced for me then please list any major concepts that I can learn. Please and thank you.
You clearly have a challenging problem ^^. Try to explain all you need to solve your problem would be very hard, mainly because there many ways to do this. I advice you to read a nice book about image processing (Gonzalez' book is a nice choice) and the OpenCV documentation (but it is implemented in C, C++ and has Python bindings; although it's a library that implements a lot of image processing techniques). Maybe you should focus your study on feature detection, motion analysis and object tracking. As sign language uses not just hand sign (static state) but also hand moviments (dynamic state) to express something, object tracking may be a good way to describe the signs.I hope these informations help you, at least a little -^.^- Bye bye.
Look at OpenCV. They have a lot of libraries that you might find handy.
http://opencv.willowgarage.com/wiki/
I want to do a project involving Computer Vision. Mostly object detection/identification. After some research, I keep coming back to OpenCV. But all of the tutorials are from 2008 (I guess it was big for a bit then). It doesn't compile in Python on the mac apparently. I'm using the C++ framework right out of Xcode, but none of the tutorials work as they're outdated and the documentation sucks from what I can parse.
Is there a better solution for what I'm doing, and does anyone have any suggestions as to learning how to to use OpenCV?
Thanks
I have had similar problems getting started with OpenCV and from my experience this is actually the biggest hurdle to learning it. Here is what worked for me:
This book: "OpenCV 2 Computer Vision Application Programming Cookbook." It's the most up-to-date book and has examples on how to solve different Computer Vision problems (You can see the table of contents on Amazon with "Look Inside!"). It really helped ease me into OpenCV and get comfortable with how the library works.
Like have others have said, the samples are very helpful. For things that the book skips or covers only briefly you can usually find more detailed examples when looking through the samples. You can also find different ways of solving the same problem between the book and the samples. For example, for finding keypoints/features, the book shows an example using FAST features:
vector<KeyPoint> keypoints;
FastFeatureDetector fast(40);
fast.detect(image, keypoints);
But in the samples you will find a much more flexible way (if you want to have the option of choosing which keypoint detection algorithm to use):
vector<KeyPoint> keypoints;
Ptr<FeatureDetector> featureDetector = FeatureDetector::create("FAST");
featureDetector->detect(image, keypoints);
From my experience things eventually start to click and for more specific questions you start finding up-to-date information on blogs or right here on StackOverflow.
Let me add a couple of things. First, I can assure you that the Python bindings to OpenCV work on a Mac. I use them every day.
Many people like OpenCV for many reasons:
The license is good, friendly to integration into commercial products, etc.
It is quite good from a technical stand point. It gives you a reference implementation of state of the art algorithms.
It tends to be quite fast compared to the alternatives (Matlab I'm looking at you).
Like everything in life, it is not perfect:
It is a good example of a software library that is a moving target.
I have a 300 line python program that uses OpenCV and every few
months when a new version of OpenCV is released I have to change it
to adapt to the new function names/calling conventions, etc. The
library does advance, a lot, however it is a pain to have to change
the same program 3 times per year.
It has a learning curve, like computer vision itself, it is quite
technical and not easy to learn.
There are alternatives (with other pros and cons) MATLAB with the Image Processing Toolbox is one such example.
The simplest answer that comes to mind, is to read the example code with a bit of understanding, and to try out if Your ideas work. The api does change, and most of the tutorials are writen for the first versions of OpenCV, and it looks that nobody bothered to rewrite them. Nevertheless the core ideas behind it are not changing. So if You find a tutorial answering Your questions, but written in old API just look in the documentation for modern replacements of used functions. It’s not easy and quick, but looks like it works. If You use the newest (actually 2.3) version, I suggest using both the 2.1 documntation and 2.3 docs + tutorials . You should also look into the samples, which should have been installed alongside the library. There are lots of hints about how to use certain structures and tricks that weren't mentioned in documentation. Finally, don't be afraid to look inside the code of the library itself (if You compiled it on Your own). Unfortunately, thats the only source I know to check for example what code corresponds to which type of Mat object.