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我正在尝试使用 FlannBased 匹配器实现 SURF 检测和跟踪。我的代码在检测部分正常工作，但问题在于跟踪。

在此处输入图像描述

您可以在上图中看到跟踪矩形没有聚焦在正确的对象上。此外，即使我移动相机，矩形也保持静止。我不确定我哪里出错了。

这是我实现的代码

void surf_detection::surf_detect(){

UMat img_extractor, snap_extractor;

if (crop_image_.empty())
    cv_snapshot.copyTo(dst);
else
    crop_image_.copyTo(dst);
//dst = QImagetocv(crop_image_);

imshow("dst", dst);

Ptr<SURF> detector = SURF::create(minHessian);
Ptr<DescriptorExtractor> extractor = SURF::create(minHessian);

cvtColor(dst, src, CV_BGR2GRAY);
cvtColor(frame, gray_image, CV_BGR2GRAY);


detector->detect(src, keypoints_1);
//printf("Object: %d keypoints detected\n", (int)keypoints_1.size());
detector->detect(gray_image, keypoints_2);
//printf("Object: %d keypoints detected\n", (int)keypoints_1.size());

extractor->compute(src, keypoints_1, img_extractor);
// printf("Object: %d descriptors extracted\n", img_extractor.rows);
extractor->compute(gray_image, keypoints_2, snap_extractor);

std::vector<Point2f> scene_corners(4);
std::vector<Point2f> obj_corners(4);

obj_corners[0] = (cvPoint(0, 0));
obj_corners[1] = (cvPoint(src.cols, 0));
obj_corners[2] = (cvPoint(src.cols, src.rows));
obj_corners[3] = (cvPoint(0, src.rows));

vector<DMatch> matches;
matcher.match(img_extractor, snap_extractor, matches);

double max_dist = 0; double min_dist = 100;

//-- Quick calculation of max and min distances between keypoints
for (int i = 0; i < img_extractor.rows; i++)
{
    double dist = matches[i].distance;
    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);

vector< DMatch > good_matches;

for (int i = 0; i < img_extractor.rows; i++)
{
    if (matches[i].distance <= max(2 * min_dist, 0.02))
    {
        good_matches.push_back(matches[i]);
    }
}

UMat img_matches;
drawMatches(src, keypoints_1, gray_image, keypoints_2,
    good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
    vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);

if (good_matches.size() >= 4){

    for (int i = 0; i<good_matches.size(); i++){

        //get the keypoints from good matches
        obj.push_back(keypoints_1[good_matches[i].queryIdx].pt);
        scene.push_back(keypoints_2[good_matches[i].trainIdx].pt);

    }
}

H = findHomography(obj, scene, CV_RANSAC);

perspectiveTransform(obj_corners, scene_corners, H);

line(img_matches, scene_corners[0], scene_corners[1], Scalar(0, 255, 0), 4);

line(img_matches, scene_corners[1], scene_corners[2], Scalar(0, 255, 0), 4);

line(img_matches, scene_corners[2], scene_corners[3], Scalar(0, 255, 0), 4);

line(img_matches, scene_corners[3], scene_corners[0], Scalar(0, 255, 0), 4);

imshow("Good matches", img_matches);



}
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1 回答 1

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你的匹配是正确的，你只是显示它们是错误的。匹配是指gray_image坐标系，但您在img_matches坐标系中显示它们。

因此，基本上，您需要按src宽度翻译它们：

line(img_matches, scene_corners[0] + Point2f(src.cols,0), scene_corners[1] + Point2f(src.cols,0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[1] + Point2f(src.cols,0), scene_corners[2] + Point2f(src.cols,0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[2] + Point2f(src.cols,0), scene_corners[3] + Point2f(src.cols,0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[3] + Point2f(src.cols,0), scene_corners[0] + Point2f(src.cols,0), Scalar(0, 255, 0), 4);

另请参阅此相关答案。

于 2016-01-12T16:28:50.083 回答