我正在使用这个 FLANN 匹配器算法来匹配 2 张图片中的兴趣点,代码如下所示)。
代码找到匹配点列表的时刻:
std::vector<DMatch> good_matches;
我想获得两张图片中的点定位 (x,y)。创建置换贴图。 我如何访问这些点本地化?
干杯,
#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/nonfree/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
using namespace cv;
void readme();
/** @function main */
int main(int argc, char** argv) {
if (argc != 3) {
readme();
return -1;
}
// Transform in GrayScale
Mat img_1 = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
Mat img_2 = imread(argv[2], CV_LOAD_IMAGE_GRAYSCALE);
// Checks if the image could be loaded
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;
// printf("-- DISTANCE = [%f]\n", dist);
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 < 2 * min_dist) {
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 */
void readme() {
std::cout << " Usage: ./SURF_FlannMatcher <img1> <img2>" << std::endl;
}
最佳答案
matched_points1 和 2 将是左右图像中的对应点。然后,您可以找到左侧图像的 idx1=good_matches[i].trainIdx 和右侧图像的 idx2=good_matches[i].queryIdx 的 good_matches 索引。然后只需将相应的点添加到您的 matched_points vector 即可获得匹配的 x,y 点 vector 。
long num_matches = good_matches.size();
vector<Point2f> matched_points1;
vector<Point2f> matched_points2;
for (int i=0;i<num_matches;i++)
{
int idx1=good_matches[i].trainIdx;
int idx2=good_matches[i].queryIdx;
matched_points1.push_back(points1[idx1]);
matched_points2.push_back(points2[idx2]);
}
现在你有两个匹配点的 vector 。我想这就是你要问的?
关于c++ - 如何访问 OpenCV 匹配器上的点位置?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/12937490/