OpenCV getting started, opencv

OpenCV getting started, opencv
OpenCV getting started-Key Point description child match Brute-force


The keypoint descriptors matching is performed after the feature vectors are extracted from the image to determine the matching degree between the specific image and the image in the training set. The BFMatcher Brute force matching class inherits from the abstract class DescriptorMatcher, "Brute-force descriptor matcher. for each descriptor in the first set, this matcher finds the closest descriptor in the second set by trying each one. this descriptor matcher supports masking permissible matches of descriptor sets ". The following shows the effect through a simple instance.

#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/nonfree/features2d.hpp> //#include <iostream>using namespace cv;using namespace std;int main(int argc, const char *argv[]){    Mat car1 = imread("car1.jpeg", 0);// load as grayscale    Mat car2 = imread("car2.jpeg", 0);    SiftFeatureDetector detector;    vector<KeyPoint> keypoints1, keypoints2;    detector.detect(car1, keypoints1);    detector.detect(car2, keypoints2);    cout << "# keypoints of car1 :" << keypoints1.size() << endl;    cout << "# keypoints of car2 :" << keypoints2.size() << endl;       Mat descriptors1,descriptors2;    Ptr<DescriptorExtractor> extractor = DescriptorExtractor::create("SIFT");    extractor->compute(car1,keypoints1,descriptors1);    extractor->compute(car2,keypoints2,descriptors2);        BFMatcher bfmatcher(NORM_L2, true);    vector<DMatch> matches;    bfmatcher.match(descriptors1, descriptors2, matches);    cout << "# matches : " << matches.size() << endl;    // show it on an image    Mat output;    drawMatches(car1, keypoints1, car2, keypoints2, matches, output);    imshow("car matches result",output);    waitKey(0);    return 0;}

Running result:



Refer:
1. cv: imread ()
2. Drawing Function of Keypoints and Matches
3. BFMatcher