Keypoints are used for a variety of reasons, mainly object detection and classification. There are all kinds of keypoint detectoin algorithms inside OpenCV. Once these keypoints are detected, they can be matched to find similarity between images. Keypoints can be invariant to rotation, scale, translation amongst others, depending on the algorithm.
OpenCV Keypoint detector and matcher (SURF):
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#include
#include
#include
#include
#include
#include
using namespace cv;
int main( int argc, char** argv )
{
if(argc != 3)
{
return -1;
}
Mat img1 = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
Mat img2 = imread(argv[2], CV_LOAD_IMAGE_GRAYSCALE);
if(img1.empty() || img2.empty())
{
printf("Can't read one of the images\n");
return -1;
}
// detecting keypoints
SurfFeatureDetector detector(400);
vectorKeyPoint> keypoints1, keypoints2;
detector.detect(img1, keypoints1);
detector.detect(img2, keypoints2);
// computing descriptors
SurfDescriptorExtractor extractor;
Mat descriptors1, descriptors2;
extractor.compute(img1, keypoints1, descriptors1);
extractor.compute(img2, keypoints2, descriptors2);
// matching descriptors
BFMatcher matcher(NORM_L2);
vectorDMatch> matches;
matcher.match(descriptors1, descriptors2, matches);
// drawing the results
namedWindow("matches", 1);
Mat img_matches;
drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches);
imshow("matches", img_matches);
waitKey(0);
return 0;
}
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Output:
Python OpenCV SIFT Keypoint detector:
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# keypoint detector. talkera.org/opencv
import cv2
import numpy as np
img = cv2.imread('cat.jpg')
gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
sift = cv2.SIFT()
kp = sift.detect(gray,None)
img=cv2.drawKeypoints(gray,kp)
cv2.imwrite('sift_keypoints.jpg',img)
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