有没有大神们搞改进ORB算法的,或者对已有ORB算法的改进的代码,求部分代码
附上opencv中的ORB算法:
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/legacy/legacy.hpp>
#include <iostream>
#include <vector>
using namespace cv;
using namespace std;
int main()
{
Mat img_1 = imread("frame_0.png");
Mat img_2 = imread("frame_1.png");
if (!img_1.data || !img_2.data)
{
cout << "error reading images " << endl;
return -1;
}
ORB orb;
vector<KeyPoint> keyPoints_1, keyPoints_2;
Mat descriptors_1, descriptors_2;
orb(img_1, Mat(), keyPoints_1, descriptors_1);
orb(img_2, Mat(), keyPoints_2, descriptors_2);
BruteForceMatcher<HammingLUT> matcher;
vector<DMatch> matches;
matcher.match(descriptors_1, descriptors_2, matches);
double max_dist = 0; double min_dist = 100;
//-- Quick calculation of max and min distances between keypoints
for (int i = 0; i < descriptors_1.rows; i++)
{
double dist = matches.distance;
if (dist < min_dist) min_dist = dist;
if (dist > max_dist) max_dist = dist;
}
printf("-- Max dist : %f \n", max_dist);
printf("-- Min dist : %f \n", min_dist);
//-- Draw only "good" matches (i.e. whose distance is less than 0.6*max_dist )
//-- PS.- radiusMatch can also be used here.
std::vector< DMatch > good_matches;
for (int i = 0; i < descriptors_1.rows; i++)
{
if (matches.distance < 0.6*max_dist)
{
good_matches.push_back(matches);
}
}
Mat img_matches;
drawMatches(img_1, keyPoints_1, img_2, keyPoints_2,
good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
imshow("Match", img_matches);
cvWaitKey();
return 0;
}
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