When it was designed it was intended to be faster, but actually, the differences are not relevant for real-time applications with standard cameras. By the way, FAST detector is faster and quite robust. I am programming for real-time augmented reality on phones, and we use a combination of SIFT (initialization) and FAST (pyramidal FAST for … Read more
The feature data you mention (position, laplacian, size, orientation, hessian) is insufficient for your purpose (these are actually the less relevant parts of the descriptor if you want to do matching). The data you want to look at are the “descriptors” (the 4th argument): void cvExtractSURF(const CvArr* image, const CvArr* mask, CvSeq** keypoints, CvSeq** descriptors, … Read more
There is a pip source that makes this very easy. If you have another version of opencv-python installed use this command to remove it to avoid conflicts: pip uninstall opencv-python Then install the contrib version with this: pip install opencv-contrib-python SIFT usage: import cv2 sift = cv2.xfeatures2d.SIFT_create()
Let’s tackle each point one by one: My question is what actually are these keypoints? Keypoints are the same thing as interest points. They are spatial locations, or points in the image that define what is interesting or what stand out in the image. Interest point detection is actually a subset of blob detection, which … Read more