SURF opencv的描述符计算

Question

我正在做一个关于SURF的项目，到目前为止我已经成功地实现了SURF功能，并且我也正确地完成了功能评估。但我不知道如何做描述符评估…我正在使用c++/opencv svn.

在这里你可以找到opencv svn的示例代码(这显示了如何使用EVALUATOR，但我不能在我的代码中使用它…

我代码:

#include "cv.h" // include standard OpenCV headers, same as before
#include "highgui.h"
#include "ml.h"
#include <stdio.h>
#include <iostream>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <vector>
//#include "precomp.hpp"

using namespace cv; // all the new API is put into "cv" namespace. Export its content
using namespace std;
using std::cout;
using std::cerr;
using std::endl;
using std::vector;
// enable/disable use of mixed API in the code below.
#define DEMO_MIXED_API_USE 1
void warpPerspectiveRand( const Mat& src, Mat& dst, Mat& H, RNG& rng )
{
    H.create(3, 3, CV_32FC1);
    H.at<float>(0,0) = rng.uniform( 0.8f, 1.2f);
    H.at<float>(0,1) = rng.uniform(-0.1f, 0.1f);
    H.at<float>(0,2) = rng.uniform(-0.1f, 0.1f)*src.cols;
    H.at<float>(1,0) = rng.uniform(-0.1f, 0.1f);
    H.at<float>(1,1) = rng.uniform( 0.8f, 1.2f);
    H.at<float>(1,2) = rng.uniform(-0.1f, 0.1f)*src.rows;
    H.at<float>(2,0) = rng.uniform( -1e-4f, 1e-4f);
    H.at<float>(2,1) = rng.uniform( -1e-4f, 1e-4f);
    H.at<float>(2,2) = rng.uniform( 0.8f, 1.2f);
    warpPerspective( src, dst, H, src.size() );
}


double match(const vector<KeyPoint>& /*kpts_train*/, const vector<KeyPoint>& /*kpts_query*/, DescriptorMatcher& matcher,
            const Mat& train, const Mat& query, vector<DMatch>& matches)
{
  double t = (double)getTickCount();
  matcher.match(query, train, matches); //Using features2d
  return ((double)getTickCount() - t) / getTickFrequency();
}

void simpleMatching( Ptr<DescriptorMatcher>& descriptorMatcher,
                     const Mat& descriptors1, const Mat& descriptors2,
                     vector<DMatch>& matches12 );
int main( int argc, char** argv )
{
string im1_name, im2_name;
  im1_name = "lena.jpg";
  im2_name = "lena.jpg";
Mat img1 = imread(im1_name, 1);
Mat img2 = imread(im2_name, 1);
RNG rng = theRNG();
Mat H12;
warpPerspectiveRand(img1, img2, H12, rng );


    SurfFeatureDetector detector(2000);
    vector<KeyPoint> keypoints1, keypoints2;
    detector.detect(img1, keypoints1);
    detector.detect(img2, keypoints2);

float repeatability;
int correspCount;
evaluateFeatureDetector( img1, img2, H12, &keypoints1, &keypoints2, repeatability, correspCount );
cout << "repeatability = " << repeatability << endl;
        cout << "correspCount = " << correspCount << endl;
    // computing descriptors
    SurfDescriptorExtractor extractor;
    Mat descriptors1, descriptors2;
    extractor.compute(img1, keypoints1, descriptors1);
    extractor.compute(img2, keypoints2, descriptors2);

  return 0;
}

所以我的问题是:如何评估描述符SURF(如何做到这一点)我尝试了很多方法，但我不能这样做…

Thank you so much

Answer 1

使用描述符匹配器

cv::BruteForceMatcher< cv::L2<float> > matcher;
std::vector<cv::DMatch> matches;
matcher.match(descriptors1, descriptors2, matches);

这将得到一个匹配向量。请查看DMatch的文档。

还可以看看这个函数:

cv::drawMatches(image1, keypoints1, image2, keypoints2, matches, outimage);
cv::imshow("foo", outimage);