如何访问OpenCV匹配器上的点位置

How to Access Points location on OpenCV Matcher?

本文关键字：位置 OpenCV 何访问 访问      更新时间：2023-10-16

我正在使用此Flann Matcher算法与2个图片中的兴趣点匹配代码在下面显示）。

有一个时刻，代码找到匹配点的列表：

std::vector<DMatch> good_matches;

我想在这两个图片中获取点本地化（x，y）。创建位移图。我如何访问这些点本地化？

欢呼，

#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;
}

匹配的_points1和2将是左图和右图像中的相应点。然后，您可以使用idx1 = good_matches [i] .trainidx找到good_matches的索引，左图和idx2 = good_matches [i] .queryIdx用于正确的图像。然后只需将相应的点添加到您的匹配_points向量即可获得匹配的x，y点向量。

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]);
}

现在您有两个匹配点的向量。我认为这就是您要问的？

相关文章：