OpenCV-Python dense SIFT

Question

OpenCV has very good documentation on generating SIFT descriptors, but this is a version of "weak SIFT", where the key points are detected by the original Lowe algorithm. The OpenCV example reads something like:

img = cv2.imread('home.jpg')
gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

sift = cv2.SIFT()
kp = sift.detect(gray,None)
kp,des = sift.compute(gray,kp)

What I'm looking for is strong/dense SIFT, which does not detect keypoints but instead calculates SIFT descriptors for a set of patches (e.g. 16x16 pixels, 8 pixels padding) covering an image as a grid. As I understand it, there are two ways to do this in OpenCV:

• I could divide the image in a grid myself, and somehow convert those patches to KeyPoints
• I could use a grid-based feature detector

In other words, I'd have to replace the sift.detect() line with something that gives me the keypoints I require.

My problem is that the rest of the OpenCV documentation, especially wrt Python, is severely lacking, so I have no idea how to achieve either of these things. I see in the C++ documentation that there are keypoint detectors for grid, but I don't know how to use these from Python.

The alternative is to switch to VLFeat, which has a very good DSift/PHOW implementation but means that I'll have to switch from python to matlab.

Any ideas? Thanks.

Answer 1

You can use Dense Sift in opencv 2.4.6 <. Creates a feature detector by its name.

cv2.FeatureDetector_create(detectorType)

Then "Dense" string in place of detectorType

eg:-

dense=cv2.FeatureDetector_create("Dense")
kp=dense.detect(imgGray)
kp,des=sift.compute(imgGray,kp)

Answer 2

I'm not sure what your goal is here, but be warned, the SIFT descriptor calculation is extremely slow and was never designed to be used in a dense fashion. That being said, OpenCV makes it fairly trivial to do so.

Basically instead of using sift.detect(), you just fill in the keypoint array yourself by making a grid a keypoints however dense you want them. Then a descriptor will be calculated for each keypoint when you pass the keypoints to sift.compute().

Depending on the size of your image and the speed of your machine, this might take a very long time. If copmutational time is a factor, I suggest you look at some of the binary descriptors OpenCV has to offer.

Answer 3

Inspite of the OpenCV way being the standard, it was too slow for me. So for that, I used pyvlfeat, which is basically python bindings to VL-FEAT. The functions carry similar syntax as the Matlab functions