C++opencv,输入原图,利用基于特征点检测匹配的方法,完成在复杂图像中特定对象检测定位,要求输出待搜索对象原图,包含对象的复杂场景图(检测对象用多边形框出)。
时间: 2023-12-11 22:04:32 浏览: 33
这个问题需要分成几个步骤来完成。
首先,需要使用OpenCV读取原始图像和待搜索对象图像。可以使用以下代码:
```c++
cv::Mat scene = cv::imread("scene.jpg");
cv::Mat object = cv::imread("object.jpg");
```
接下来,需要使用特征点检测算法(例如SIFT、SURF或ORB)在场景和对象图像中提取特征点和描述符。可以使用以下代码:
```c++
cv::Ptr<cv::FeatureDetector> detector = cv::SIFT::create();
std::vector<cv::KeyPoint> keypoints_object, keypoints_scene;
cv::Mat descriptors_object, descriptors_scene;
detector->detectAndCompute(object, cv::noArray(), keypoints_object, descriptors_object);
detector->detectAndCompute(scene, cv::noArray(), keypoints_scene, descriptors_scene);
```
然后,需要使用特征点匹配算法(例如FLANN或BFMatcher)在场景和对象图像中匹配特征点。可以使用以下代码:
```c++
cv::Ptr<cv::DescriptorMatcher> matcher = cv::DescriptorMatcher::create(cv::DescriptorMatcher::FLANNBASED);
std::vector<std::vector<cv::DMatch>> knn_matches;
matcher->knnMatch(descriptors_object, descriptors_scene, knn_matches, 2);
```
接下来,需要筛选出好的匹配点,可以使用以下代码:
```c++
const float ratio_thresh = 0.75f;
std::vector<cv::DMatch> good_matches;
for (size_t i = 0; i < knn_matches.size(); i++)
{
if (knn_matches[i][0].distance < ratio_thresh * knn_matches[i][1].distance)
{
good_matches.push_back(knn_matches[i][0]);
}
}
```
然后,需要计算出对象在场景中的位置和姿态。可以使用以下代码:
```c++
std::vector<cv::Point2f> obj;
std::vector<cv::Point2f> scene;
for (size_t i = 0; i < good_matches.size(); i++)
{
obj.push_back(keypoints_object[good_matches[i].queryIdx].pt);
scene.push_back(keypoints_scene[good_matches[i].trainIdx].pt);
}
cv::Mat H = cv::findHomography(obj, scene, cv::RANSAC);
std::vector<cv::Point2f> obj_corners(4);
obj_corners[0] = cv::Point2f(0, 0);
obj_corners[1] = cv::Point2f((float)object.cols, 0);
obj_corners[2] = cv::Point2f((float)object.cols, (float)object.rows);
obj_corners[3] = cv::Point2f(0, (float)object.rows);
std::vector<cv::Point2f> scene_corners(4);
cv::perspectiveTransform(obj_corners, scene_corners, H);
```
最后,需要在场景图像上绘制出对象的位置和姿态。可以使用以下代码:
```c++
cv::Mat img_matches;
cv::drawMatches(object, keypoints_object, scene, keypoints_scene, good_matches, img_matches, cv::Scalar::all(-1),
cv::Scalar::all(-1), std::vector<char>(), cv::DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
cv::line(img_matches, scene_corners[0] + cv::Point2f((float)object.cols, 0), scene_corners[1] + cv::Point2f((float)object.cols, 0), cv::Scalar(0, 255, 0), 4);
cv::line(img_matches, scene_corners[1] + cv::Point2f((float)object.cols, 0), scene_corners[2] + cv::Point2f((float)object.cols, 0), cv::Scalar(0, 255, 0), 4);
cv::line(img_matches, scene_corners[2] + cv::Point2f((float)object.cols, 0), scene_corners[3] + cv::Point2f((float)object.cols, 0), cv::Scalar(0, 255, 0), 4);
cv::line(img_matches, scene_corners[3] + cv::Point2f((float)object.cols, 0), scene_corners[0] + cv::Point2f((float)object.cols, 0), cv::Scalar(0, 255, 0), 4);
cv::imshow("Object detection", img_matches);
cv::waitKey();
```
这样就可以完成在复杂图像中特定对象的检测和定位,并在结果图像中用多边形框出检测到的对象。