#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <opencv2/nonfree/features2d.hpp>
#include<opencv2/legacy/legacy.hpp>
using namespace cv;
void readme();
/** @function main */
int main(int argc, char** argv)
{
/*if (argc != 3)
{
readme(); return -1;
}*/
//Mat img_1 = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
//Mat img_2 = imread(argv[2], CV_LOAD_IMAGE_GRAYSCALE);
Mat img_1 = imread("color51.bmp", CV_LOAD_IMAGE_GRAYSCALE);
Mat img_2 = imread("color52.bmp", CV_LOAD_IMAGE_GRAYSCALE);
if (!img_1.data || !img_2.data)
{
std::cout << " --(!) Error reading images " << std::endl; return -1;
}
//-- Step 1: Detect the keypoints using SURF Detector
int minHessian = 400;
SurfFeatureDetector detector(minHessian);
std::vector<KeyPoint> keypoints_1, keypoints_2;
detector.detect(img_1, keypoints_1);
detector.detect(img_2, keypoints_2);
//-- Step 2: Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;
Mat descriptors_1, descriptors_2;
extractor.compute(img_1, keypoints_1, descriptors_1);
extractor.compute(img_2, keypoints_2, descriptors_2);
//-- Step 3: Matching descriptor vectors using FLANN matcher
FlannBasedMatcher matcher;
std::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[i].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 2*min_dist )
//-- PS.- radiusMatch can also be used here.
std::vector< DMatch > good_matches;
for (int i = 0; i < descriptors_1.rows; i++)
{
if (matches[i].distance < 10 * min_dist)//通过距离判定为好的匹配特征
{
good_matches.push_back(matches[i]);
}
}
//-- Draw only "good" 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);
//-- Show detected matches
imshow("Good Matches", img_matches);
for (int i = 0; i < good_matches.size(); i++)
{
printf("-- Good Match [%d] Keypoint 1: %d -- Keypoint 2: %d \n", i, good_matches[i].queryIdx, good_matches[i].trainIdx);
}
waitKey(0);
return 0;
}
/** @function readme */
FLANN进行特征点匹配
最新推荐文章于 2025-02-13 21:54:23 发布