/home/li/catkin_ws/src/robot_vision/src/pipei.cpp:11:9: error: ‘SIFT’ was not declared in this scope 11 | Ptr<SIFT> sift = SIFT::create(); | ^~~~
时间: 2024-01-27 17:05:01 浏览: 43
该错误提示表明SIFT类未被声明。在OpenCV 4.0及以上版本中,SIFT和SURF算法已经被包含在了xfeatures2d模块中,因此需要使用以下代码来包含该模块:
```cpp
#include <opencv2/opencv.hpp>
#include <opencv2/xfeatures2d.hpp>
```
然后,我们可以使用xfeatures2d模块中的SIFT类来实现特征点提取。以下是修改后的示例代码:
```cpp
#include <opencv2/opencv.hpp>
#include <opencv2/xfeatures2d.hpp>
using namespace cv;
int main() {
// 读取图像
Mat img1 = imread("image1.jpg", IMREAD_GRAYSCALE);
Mat img2 = imread("image2.jpg", IMREAD_GRAYSCALE);
// 创建SIFT特征提取器
Ptr<xfeatures2d::SIFT> sift = xfeatures2d::SIFT::create();
// 检测关键点并计算描述子
std::vector<KeyPoint> keypoints1, keypoints2;
Mat descriptors1, descriptors2;
sift->detectAndCompute(img1, noArray(), keypoints1, descriptors1);
sift->detectAndCompute(img2, noArray(), keypoints2, descriptors2);
// 创建特征匹配器
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create(DescriptorMatcher::FLANNBASED);
// 匹配描述子
std::vector<DMatch> matches;
matcher->match(descriptors1, descriptors2, matches);
// 筛选匹配对
double max_dist = 0, 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;
}
std::vector<DMatch> good_matches;
for (int i = 0; i < descriptors1.rows; i++) {
if (matches[i].distance <= std::max(2 * min_dist, 0.02)) {
good_matches.push_back(matches[i]);
}
}
// 绘制匹配结果
Mat img_matches;
drawMatches(img1, keypoints1, img2, keypoints2, good_matches, img_matches);
// 显示匹配结果
imshow("Matches", img_matches);
waitKey(0);
return 0;
}
```
在修改后的代码中,我们使用xfeatures2d模块中的SIFT类来创建特征提取器,并进行特征点检测和描述子计算。其余部分与之前的示例代码相同。