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我正在尝试使用 SurfFeatureDetect 和 FLANN 匹配器检测对象。但是，该代码无法准确检测图像。我还以图片格式发布了结果。

在此处输入图像描述
这是我在 opencv 教程网站上的代码

int main(int argc, char** argv){
if (argc != 3){
readme(); return -1;
}

Mat img_object = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
Mat img_scene = imread(argv[2], CV_LOAD_IMAGE_GRAYSCALE);
if (!img_object.data || !img_scene.data)
{
    std::cout << " --(!) Error reading images " << std::endl; return -1;
}

//-- Step 1: Detect the keypoints using SURF Detector
int minHessian = 100;

SurfFeatureDetector detector(minHessian);
std::vector<KeyPoint> keypoints_object, keypoints_scene;

detector.detect(img_object, keypoints_object);
detector.detect(img_scene, keypoints_scene);

//-- Step 2: Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;

Mat descriptors_object, descriptors_scene;

extractor.compute(img_object, keypoints_object, descriptors_object);
extractor.compute(img_scene, keypoints_scene, descriptors_scene);

//-- Step 3: Matching descriptor vectors using FLANN matcher
FlannBasedMatcher matcher;
std::vector< DMatch > matches;
matcher.match(descriptors_object, descriptors_scene, matches);

double max_dist = 0; double min_dist = 100;

//-- Quick calculation of max and min distances between keypoints
for (int i = 0; i < descriptors_object.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);
//-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
std::vector< DMatch > good_matches;

for (int i = 0; i < descriptors_object.rows; i++)
{
    if (matches[i].distance < 3 * min_dist)
    {
        good_matches.push_back(matches[i]);
    }
}

Mat img_matches;
drawMatches(img_object, keypoints_object, img_scene, keypoints_scene,
    good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
    vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);  

//-- Localize the object
std::vector<Point2f> obj;
std::vector<Point2f> scene;   

for (int i = 0; i < good_matches.size(); i++)
{
    //-- Get the keypoints from the good matches
    obj.push_back(keypoints_object[good_matches[i].queryIdx].pt);
    scene.push_back(keypoints_scene[good_matches[i].trainIdx].pt);
}

Mat H = findHomography(obj, scene, CV_RANSAC);

//-- Get the corners from the image_1 ( the object to be "detected" )
std::vector<Point2f> obj_corners(4);
obj_corners[0] = cvPoint(0, 0); obj_corners[1] = cvPoint(img_object.cols, 0);
obj_corners[2] = cvPoint(img_object.cols, img_object.rows); obj_corners[3] = cvPoint(0, img_object.rows);
std::vector<Point2f> scene_corners(4);

perspectiveTransform(obj_corners, scene_corners, H);

//-- Draw lines between the corners (the mapped object in the scene - image_2 )
line(img_matches, scene_corners[0] + Point2f(img_object.cols, 0), scene_corners[1] + Point2f(img_object.cols, 0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[1] + Point2f(img_object.cols, 0), scene_corners[2] + Point2f(img_object.cols, 0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[2] + Point2f(img_object.cols, 0), scene_corners[3] + Point2f(img_object.cols, 0), Scalar(0, 255, 0), 4);
line(img_matches, scene_corners[3] + Point2f(img_object.cols, 0), scene_corners[0] + Point2f(img_object.cols, 0), Scalar(0, 255, 0), 4);

//-- Show detected matches
imshow("Good Matches & Object detection", img_matches);

waitKey(0);
return 0;}

/** @function readme */
void readme()
{
std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl;}
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1 回答 1

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这是一个很常见的失败。问题是单应性有 8 个自由度（8DOF）。这意味着您需要至少 4 个正确的对应关系来计算一个好的单应性：

在此处输入图像描述

可以看到，单应性有 8 个参数（最后一个参数 h 33只是一个比例因子）。当除了好的对应关系（内部）之外，您需要过滤掉不好的对应关系（离群值）时，就会出现问题。当异常值多于内部值 ( total/outliers > 50%) 时，RANSAC 程序无法找到异常值，您会得到奇怪的结果。

解决这个问题并不容易。你可以：

  • 在查询图像中使用具有类似平面外旋转（和类似比例）的训练图像。
  • 或者，使用自由度较小的变换（如相似变换）。这样，您将需要更少的内点。Altho OpenCV 缺乏对这种使用稳健拟合方法的更简单转换的支持。
于 2014-12-13T10:34:40.770 回答