Extracting LBPFeatures in OpenCV - c++

I am trying to extract the LBPFeatures of an image using OpenCV and C++, but there seems to be no in-built function to extract the features.
Can anyone help me?
I need to find the feature points and not the histogram.

The LBP authors give an optimized C code in one of their papers: here is the link.

you can find an old implementation of OpenCV in https://github.com/bytefish/opencv/tree/master/lbp
That libraries are included in newer versions of OpenCV, but the link have an example implementation

Related

Using OpenCV in ARToolkit5 (Linux)

I wanted to know if anyone has managed to use OpenCV in the example codes given by ARToolKit5. I was wishing to use it mainly in the nftSimple example. But since the artoolkit5 code is written in C and using opencv in C is not easy, was wondering if anyone has any ideas, or could give me some sample code to start off with. Thanks in advance!
Check out https://github.com/artoolkitx/artoolkitx/tree/master/Source/ARX/OCVT this is the newer image tracker based on OpenCV and using jpeg image files.

what library is able to extract SIFT features in Python?

In python which library is able to extract SIFT visual descriptors? I know opencv has an implementation but it is not free to use and skimage does not include SIFT particularly.
I would like to suggest VLFeat, another open source vision library. It also has a python wrapper. The implementation of SIFT in VLFeat is modified from the original algorithm, but I think the performance is good.
OpenCV is free to use.
But SIFT itself as algorithm is patented, so if you would make your own implementation of SIFT, not based on Lowe`s code, you still could not use it in commercial application. So, unless you have got a license for SIFT, no library with it, is free.
But you can consult with patent guys - some countries like Russia does not allow to patent algorithms - so you can you SIFT inside such country.

Opencv 2.4 snakeImage C++

I have looked at the opencv 2.4 reference and I cannot find an equivalent of snakeImage. Therefore what other methods can I implement to achieve the same result?
Many Thanks
You can use the definition of the snake of previous releases (eg. opencv2.0).
snake opencv 2.0
It works efficiently.
This is one approach: Active Contour (Snake) with OpenCV
But search on Google, you'll find more interesting results.
It was moved to legacy.
Python binding: cv2.cv.SnakeImage()

opencv c and c++ interface conversion table

i'l learning opencv with c++ and so i'm trying to use new c++ interface. but a lot of code i found on internet is based on old c interface.
for example i fond a lot of algorithm based on
IplImage, cvCvtPixToPlane, cvThreshold
and i have to translate them in
cv::Mat, cv::threshold, ..
in here (i think the official manual) i haven't find anything of really complete.
and each time i have to google for get the right conversion in the new c++ interface.
where can i find a conversion table?
Try OpenCV manuals, It seems they are the best way to find equivalent functions.
Version 2.1 : http://www.comp.nus.edu.sg/~cs4243/doc/opencv.pdf
All C,C++ and python API is available as single pdf, so you can just search for function names to find equivalent in C or C++
Version 2.3 : https://shimcv.googlecode.com/files/opencv2refman.pdf
Online version of 2.3 : http://opencv.itseez.com/
The cheatsheet doesn't have conversions but is the most useful documentation I've found and used!

is there a DWT function in opencv?

is there a DWT (discrete wavelet transform ) function in opencv ?? else if anyone have link of its implementation in c++
No, I don't believe OpenCV has that functionality.
This page might be useful.
It appears that openCV does not have an implementation of the DWT algorithm, but a quick google search turns up two results which may be relevant.
First is a result from Koders code search, which is an implementation designed to process MPEG4 frames for an image decoding program.
There is also a google code project, wavelet1d which is a version designed to process a 1d array of data.
You may be able to use those two implementations to build your own suitable for your uses?