Question

我使用以下代码在MSV2012中使用C ++检测opencv 2.4.10中的描述和评估功能。

 #include <iostream>
    #include <fstream>
    #include <vector>
    #include <opencv2/opencv.hpp>
    #include <opencv2/core/core.hpp>
    #include <opencv2/highgui/highgui.hpp>
    #include <opencv2/imgproc/imgproc.hpp>
    #include <opencv2/nonfree/features2d.hpp>
    #include <opencv2/legacy/legacy.hpp> 

    using namespace cv;
    using namespace std;

    int main( int argc, char** argv )

{

    Mat image1;
    image1 = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);   // Read the first file

    Mat image2;
    image2 = imread(argv[2], CV_LOAD_IMAGE_GRAYSCALE);   

    if ( (!image1.data) || (!image2.data)){
        std:: cout << "ERROR: Cannot load images in\n" << argv[1] << "\n" << argv[2] << endl;
        return -1;
    }

     vector<KeyPoint> keypoints1, keypoints2;
     cv:: Mat descriptors1, descriptors2;

     /** Construction of the feature detector
     */

     double ExTime = (double)cv::getTickCount(); 
     cv::SurfFeatureDetector surf(800);

     /** Detection of the features
     */
     surf.detect(image1,keypoints1);
     surf.detect(image2,keypoints2);

    cv:: SurfDescriptorExtractor surfDesc;
    surfDesc.compute(image1,keypoints1,descriptors1);
    surfDesc.compute(image2,keypoints2,descriptors2);

    //Calculate the time needed for code execution
    ExTime = ((double)cv::getTickCount() - ExTime)/cv::getTickFrequency();  

    /** Draw the keypoints
    */
    Mat ImageKP1, ImageKP2;

    drawKeypoints(image1,keypoints1,ImageKP1, cv::Scalar(255,0,255),cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
    drawKeypoints(image2,keypoints2,ImageKP2, cv::Scalar(255,0,255),cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);

    // Construction of the matcher 
    cv::Mat ImageMatch;
    cv::FlannBasedMatcher matcher;

    // Match the two image descriptors  
    std::vector<DMatch> matches;
    matcher.match(descriptors1,descriptors2, matches);

    double max_dist = 0; double min_dist = 100;

    //-- Quick calculation of max and min distances between keypoints
      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;
        }


  //-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist,
  //-- or a small arbitary value ( 0.02 ) in the event that min_dist is very
  //-- small)
  std::vector< DMatch > good_matches;

  for( int i = 0; i < descriptors1.rows; i++ )
  { if( matches[i].distance <=  2*min_dist )
    { good_matches.push_back(matches[i]); }
  }

    cout << "Number of good matches: " << good_matches.size() << endl;

    drawMatches(image1,keypoints1,image2,keypoints2, good_matches, ImageMatch, cv::Scalar(255,0,255),cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);

    /** Evaluation of detected points
    */
    std::cout << ">" << std::endl;
    cout << "Evaluating feature detector..." << endl;
    float repeatability;
    int corrCounter;
    cv::Mat Homog;

    std::vector<cv::Point2f> srcKey;
    std::vector<cv::Point2f> refKey;

    for (int i = 0; i < matches.size(); i++) {
        srcKey.push_back(keypoints1[matches[i].queryIdx].pt);
        refKey.push_back(keypoints2[matches[i].queryIdx].pt);
    }

    Homog = cv::findHomography(srcKey,refKey,CV_RANSAC,1);

    cv::evaluateFeatureDetector(image1, image2, Homog, &keypoints1, &keypoints2, repeatability, corrCounter);

    std::cout << "repeatability = " << repeatability << std::endl;
    std::cout << "correspCount = " << corrCounter << std::endl; 
    std::cout << ">" << std::endl;

    system ("pause");

    return 0;   
}

问题是，重复率始终为-1，以及correspCount。我使用的图像有很大的重叠，检测到很多功能。我在OpenCv官方网站上找不到关于该功能的教程

cv::EvaluateFeatureDetector

但只是像这样的网站中的一些教程。有一些类似的问题，但没有人总是-1作为回报。可能有什么问题？

Answer 1

如果您检查evaluateFeatureDetector calculateRepeatability功能，它会检查关键点并调用correspondencesCount = -1; repeatability = -1.f; if( overlaps.empty() ) return;功能。在该职能中，在第436行：

const

值设置为-1，如果两个图像中的过滤关键点之间没有重叠，则返回这些值。在这种情况下，由于你的实际关键点向量不是空的，我会说你的单应性是不正确的。这可能是因为图像不够相似，或者单应性计算不正确。既然你保证第一个不是这种情况，我建议你怀疑产生的单应矩阵。您可以使用the source code通过生成的单应性在透视变换下绘制图像，这样如果图像意外失真，您将对上述函数失败的方式/原因有所了解。

OpenCV中的重复率

1 个答案: