Question

有谁知道，检测功能如何工作？例如：

Mat img = imread (...);    
SurfFeatureDetector detector(400); 
vector<KeyPoint> keypoints;
detector.detect(img, keypoints);

特别是关键点如何写作。

我需要在不使用

的情况下在关键点中编写一些坐标

detector.detect(...);

这不起作用

keypoints.push_back(KeyPoint(i,j);

下一个问题： 我有这个功能：

void trajkovic(Mat img, vector<KeyPoint> keypoints)
{    for( int i = 0; i < img.rows-3; i++ )
        for( int j = 0; j < img.cols-3; j++ )
        {   Point2f keyPointLocation(i, j);
            keypoints.push_back(KeyPoint(keyPointLocation, 1)); } }

int main()
{   Mat img_object = imread( ".../box.png", CV_LOAD_IMAGE_GRAYSCALE );
    Mat img_scene = imread( ".../box_in_scene.png", CV_LOAD_IMAGE_GRAYSCALE );

    std::vector<KeyPoint> keypoints_object, keypoints_scene;
    trajkovic(img_object, keypoints_object);
    trajkovic(img_scene, keypoints_scene);

代替（* detector.detect（img_object，keypoints_object）; *）

//-- 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 from img_1 in img_2
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 );

cvWaitKey(0);
return 0;

}

findHomography中的

我有一个错误：“断言失败（npoints＆gt; = 0＆amp;＆amp; points2.Vector（2）== npoints））在findHomography中，文件/modules/calib3d/src/fundam.cpp，line 1062"

怎么了？

我猜错了

keypoints.push_back(KeyPoint(keyPointLocation, 1));

非常感谢

Answer 1

首先你应该抛出opencv documentation。 cv::KeyPoint是用于存储cv::FeatureDetector对象检测到的点的结构。它包含关键点的坐标，有意义的邻域的直径，方向和响应。关键点的计算取决于探测器的定义（Sift，Surf，Mser，...）

开始here！

Answer 2

如果您想手动创建KeyPoint's，这里有一个小样本可以帮助您入门：

vector<KeyPoint> keyPoints;
for(int i = 0; i < 10; i++)
{
    for(int j = 0; j < 10; j++)
    {
        Point2f keyPointLocation(i, j);
        float meaningfulNeighborhoodDiameter = 5.0;
        keyPoints.push_back(KeyPoint(keyPointLocation, meaningfulNeighborhoodDiameter));
    }
}

希望有所帮助！

功能检测功能2d

2 个答案: