特征匹配评估的c++代码
时间: 2023-09-23 08:03:57 浏览: 35
以下是特征匹配评估的C++代码示例:
```c++
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{
// 读取两张待匹配的图像
Mat img1 = imread("image1.jpg", IMREAD_GRAYSCALE);
Mat img2 = imread("image2.jpg", IMREAD_GRAYSCALE);
// 初始化特征检测器和描述符提取器
Ptr<FeatureDetector> detector = ORB::create();
Ptr<DescriptorExtractor> extractor = ORB::create();
// 检测特征点并提取特征描述符
vector<KeyPoint> keypoints1, keypoints2;
Mat descriptors1, descriptors2;
detector->detect(img1, keypoints1);
detector->detect(img2, keypoints2);
extractor->compute(img1, keypoints1, descriptors1);
extractor->compute(img2, keypoints2, descriptors2);
// 进行特征点匹配
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
vector<DMatch> matches;
matcher->match(descriptors1, descriptors2, matches);
// 计算匹配结果的正确匹配率和误匹配率
double max_dist = 0;
double min_dist = 100;
for (int i = 0; i < descriptors1.rows; i++) {
double dist = matches[i].distance;
if (dist < min_dist) min_dist = dist;
if (dist > max_dist) max_dist = dist;
}
cout << "最大距离:" << max_dist << endl;
cout << "最小距离:" << min_dist << endl;
vector<DMatch> good_matches;
for (int i = 0; i < descriptors1.rows; i++) {
if (matches[i].distance <= max(2 * min_dist, 0.02)) {
good_matches.push_back(matches[i]);
}
}
double accuracy = double(good_matches.size()) / matches.size();
double error_rate = 1 - accuracy;
cout << "正确匹配率:" << accuracy << endl;
cout << "误匹配率:" << error_rate << endl;
// 显示匹配结果
Mat img_matches;
drawMatches(img1, keypoints1, img2, keypoints2, good_matches, img_matches);
imshow("特征匹配结果", img_matches);
waitKey(0);
return 0;
}
```
在这个示例中,我们使用ORB算法来检测特征点并提取特征描述符,使用暴力匹配算法进行特征点匹配,并计算匹配结果的正确匹配率和误匹配率。最后,我们将匹配结果可视化并显示在窗口中。