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所以我的问题是:如何评估SURF描述符(如何做到这一点)?我尝试了很多方法,但是我无法做到...非常感谢。
我正在做一个关于SURF的项目,目前我已经成功实现了SURF特征,并且我也正确地完成了特征评估。但是我不知道如何进行DESCRIPTOR评估...我正在使用c++/opencv svn。
在这里你可以找到来自opencv svn的示例代码(展示如何使用EVALUATOR),但我无法在我的代码中使用它...
我的代码:
#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SURF描述符(如何做到这一点)?我尝试了很多方法,但是我无法做到...非常感谢。