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
Below is a minimal example: #include <opencv/cv.h> #include <opencv/highgui.h> int main(int argc, const char* argv[]) { const cv::Mat input = cv::imread(“input.jpg”, 0); //Load as grayscale cv::SiftFeatureDetector detector; std::vector<cv::KeyPoint> keypoints; detector.detect(input, keypoints); // Add results to image and save. cv::Mat output; cv::drawKeypoints(input, keypoints, output); cv::imwrite(“sift_result.jpg”, output); return 0; } Tested on OpenCV 2.3
Short version: each keypoint of the first image is matched with a number of keypoints from the second image. We keep the 2 best matches for each keypoint (best matches = the ones with the smallest distance measurement). Lowe’s test checks that the two distances are sufficiently different. If they are not, then the keypoint … Read more
First, aren’t you supposed to be using vl_sift instead of sift? Second, you can use SIFT feature matching to find correspondences in the two images. Here’s some sample code: I = imread(‘p1.jpg’); J = imread(‘p2.jpg’); I = single(rgb2gray(I)); % Conversion to single is recommended J = single(rgb2gray(J)); % in the documentation [F1 D1] = vl_sift(I); … 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