//
// simpleMatching.cpp
// openCV_test
//
// Created by mark on 14-6-3.
// Copyright (c) 2014年 mark. All rights reserved.
//
#include "simpleMatching.h"
// #define DRAW_RICH_KEYPOINTS_MODE 1 // mark definition
// #define DRAW_OUTLIERS_MODE 1 // mark definition
const string winName = "correspondences";
enum { NONE_FILTER = 0, CROSS_CHECK_FILTER = 1 };
int getMatcherFilterType( const string& str ){
if( str == "NoneFilter" )
return NONE_FILTER;
if( str == "CrossCheckFilter" )
return CROSS_CHECK_FILTER;
CV_Error(CV_StsBadArg, "Invalid filter name");
return -1;
}
static void simpleMatching( Ptr<DescriptorMatcher>& descriptorMatcher,
const Mat& descriptors1, const Mat& descriptors2,
vector<DMatch>& matches12 ){
vector<DMatch> matches;
descriptorMatcher->match( descriptors1, descriptors2, matches12 );
}
static void crossCheckMatching( Ptr<DescriptorMatcher>& descriptorMatcher,
const Mat& descriptors1, const Mat& descriptors2,
vector<DMatch>& filteredMatches12, int knn=1 ){
filteredMatches12.clear();
vector<vector<DMatch> > matches12, matches21;
descriptorMatcher->knnMatch( descriptors1, descriptors2, matches12, knn );
descriptorMatcher->knnMatch( descriptors2, descriptors1, matches21, knn );
for( size_t m = 0; m < matches12.size(); m++ ){
bool findCrossCheck = false;
for( size_t fk = 0; fk < matches12[m].size(); fk++ ){
DMatch forward = matches12[m][fk];
for( size_t bk = 0; bk < matches21[forward.trainIdx].size(); bk++ ){
DMatch backward = matches21[forward.trainIdx][bk];
if( backward.trainIdx == forward.queryIdx ){
filteredMatches12.push_back(forward);
findCrossCheck = true;
break;
}
}
if( findCrossCheck ) break;
}
}
}
static 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() );
}
UIImage* doIteration( const Mat& img1, Mat& img2, bool isWarpPerspective,
vector<KeyPoint>& keypoints1, const Mat& descriptors1,
Ptr<FeatureDetector>& detector, Ptr<DescriptorExtractor>& descriptorExtractor,
Ptr<DescriptorMatcher>& descriptorMatcher, int matcherFilter, bool eval,
double ransacReprojThreshold, RNG& rng ){
assert( !img1.empty() );
Mat H1to2;
if( isWarpPerspective )
warpPerspectiveRand(img1, img2, H1to2, rng );
else
assert( !img2.empty() /* && img2.cols==img1.cols && img2.rows==img1.rows */ );
cout << endl << "< Extracting keypoints from second image
";
vector<KeyPoint> keypoints2;
#define mark 1
#if mark
int cols = img2.cols;
int rows = img2.rows;
int delta = 0;
img2 = Mat(img2, Range(delta, rows-delta), Range(delta, cols-delta));
detector->detect(img2, keypoints2);
/*
1 2 3 4
5 6 7 8
2 3 4 5
0 9 8 7
*/
//
// for(int i= 0; i < 2; i++){
// for(int j = 0; j < 2; j++){
// Mat tmpMat = Mat(img2, Range(rows*i/2, rows*(i+1)/2), Range(cols*j/2, cols*(j+1)/2));
// vector<KeyPoint> keypoints;
// detector->detect( tmpMat, keypoints );
// for(auto itr = keypoints.begin(); itr != keypoints.end(); ++itr){
// keypoints2.push_back(*itr);
// }
// }
// }
// /*左上角
// 1 2
// 5 6
// */
// Mat tmpMat = Mat(img2, Range(0, rows/2), Range(0, cols/2));
// vector<KeyPoint> keypoints;
// detector->detect( tmpMat, keypoints );
// for(auto itr = keypoints.begin(); itr != keypoints.end(); ++itr){
// keypoints2.push_back(*itr);
// }
// /*右下脚
// 4 5
// 8 7
// */
// tmpMat = Mat(img2, Range(rows/2, rows), Range(cols/2, cols));
// keypoints.clear();
// detector->detect( tmpMat, keypoints );
// for(auto itr = keypoints.begin(); itr != keypoints.end(); ++itr){
// keypoints2.push_back(*itr);
// }
// /*右上角
// 3 4
// 7 8
// */
// tmpMat = Mat(img2, Range(rows/2, rows), Range(0, cols/2));
// keypoints.clear();
// detector->detect( tmpMat, keypoints );
// for(auto itr = keypoints.begin(); itr != keypoints.end(); ++itr){
// keypoints2.push_back(*itr);
// }
// /*左下角
// 2 3
// 0 9
// */
// tmpMat = Mat(img2, Range(0, rows/2), Range(cols/2, cols/2));
// keypoints.clear();
// detector->detect( tmpMat, keypoints );
// for(auto itr = keypoints.begin(); itr != keypoints.end(); ++itr){
// keypoints2.push_back(*itr);
// }
#else
detector->detect( img2, keypoints2 );
#endif
cout << keypoints2.size() << " points" << ">" << endl;
if( !H1to2.empty() && eval ){
cout << "< Evaluate feature detector" << endl;
float repeatability;
int correspCount;
evaluateFeatureDetector( img1, img2, H1to2, &keypoints1, &keypoints2,
repeatability, correspCount );
cout << "repeatability = " << repeatability << endl;
cout << "correspCount = " << correspCount << endl << ">" << endl;
}
cout << "< Computing descriptors for keypoints from second image";
Mat descriptors2;
descriptorExtractor->compute( img2, keypoints2, descriptors2 );
cout << ">" << endl;
cout << "< Matching descriptors";
vector<DMatch> filteredMatches;
switch( matcherFilter ){
case CROSS_CHECK_FILTER :
crossCheckMatching( descriptorMatcher, descriptors1, descriptors2, filteredMatches, 1 );
break;
default :
simpleMatching( descriptorMatcher, descriptors1, descriptors2, filteredMatches );
}
cout << ">" << endl;
if( !H1to2.empty() && eval ){
cout << "< Evaluate descriptor matcher" << endl;
vector<Point2f> curve;
Ptr<GenericDescriptorMatcher> gdm =
new VectorDescriptorMatcher( descriptorExtractor, descriptorMatcher );
evaluateGenericDescriptorMatcher( img1, img2, H1to2,
keypoints1, keypoints2, 0, 0, curve, gdm );
Point2f firstPoint = *curve.begin();
Point2f lastPoint = *curve.rbegin();
int prevPointIndex = -1;
cout << "1-precision = " << firstPoint.x << "; recall = " << firstPoint.y << endl;
for( float l_p = 0; l_p <= 1 + FLT_EPSILON; l_p+=0.05f ){
int nearest = getNearestPoint( curve, l_p );
if( nearest >= 0 ){
Point2f curPoint = curve[nearest];
if( curPoint.x > firstPoint.x && curPoint.x < lastPoint.x &&
nearest != prevPointIndex ){
cout << "1-precision = " << curPoint.x << "; recall = " << curPoint.y << endl;
prevPointIndex = nearest;
}
}
}
cout << "1-precision = " << lastPoint.x
<< "; recall = " << lastPoint.y << endl << ">" << endl;
}
vector< int> queryIdxs( filteredMatches.size() ), trainIdxs( filteredMatches.size() );
for( size_t i = 0; i < filteredMatches.size(); i++ ){
queryIdxs[i] = filteredMatches[i].queryIdx;
trainIdxs[i] = filteredMatches[i].trainIdx;
}
if( !isWarpPerspective && ransacReprojThreshold >= 0 ){
cout << "< Computing homography (RANSAC)";
vector<Point2f> points1; KeyPoint::convert(keypoints1, points1, queryIdxs);
vector<Point2f> points2; KeyPoint::convert(keypoints2, points2, trainIdxs);
H1to2 = findHomography( Mat(points1), Mat(points2), CV_RANSAC, ransacReprojThreshold );
cout << ">" << endl;
}
Mat drawImg;
if( !H1to2.empty() ){ // filter outliers
vector< char> matchesMask( filteredMatches.size(), 0 );
vector<Point2f> points1; KeyPoint::convert(keypoints1, points1, queryIdxs);
vector<Point2f> points2; KeyPoint::convert(keypoints2, points2, trainIdxs);
Mat points1t; perspectiveTransform(Mat(points1), points1t, H1to2);
double maxInlierDist = ransacReprojThreshold < 0 ? 3 : ransacReprojThreshold;
for( size_t i1 = 0; i1 < points1.size(); i1++ ){
if( norm(points2[i1] - points1t.at<Point2f>(( int)i1,0)) <= maxInlierDist ) // inlier
matchesMask[i1] = 1;
}
// draw inliers
drawMatches( img1, keypoints1, img2, keypoints2,
filteredMatches, drawImg, CV_RGB(0, 255, 0), CV_RGB(0, 0, 255), matchesMask
#if DRAW_RICH_KEYPOINTS_MODE
, DrawMatchesFlags::DRAW_RICH_KEYPOINTS
#endif
);
#if DRAW_OUTLIERS_MODE
// draw outliers
for( size_t i1 = 0; i1 < matchesMask.size(); i1++ )
matchesMask[i1] = !matchesMask[i1];
drawMatches( img1, keypoints1, img2, keypoints2, filteredMatches, drawImg,
CV_RGB(0, 0, 255), CV_RGB(255, 0, 0), matchesMask,
DrawMatchesFlags::DRAW_OVER_OUTIMG | DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
#endif
cout << "Number of inliers: " << countNonZero(matchesMask) << endl;
} else
drawMatches( img1, keypoints1, img2, keypoints2, filteredMatches, drawImg );
return [UIImage UIImageFromCVMat: drawImg];
}
UIImage * getMatch( Mat & img1, Mat & img2){
cv::initModule_nonfree(); /// /if use SIFT or SURF
bool isWarpPerspective = 0;
double ransacReprojThreshold = 3;
cout << "< Creating detector, ";
Ptr<FeatureDetector> detector = FeatureDetector::create( "SURF" ); // "HARRIS"
cout << " descriptor extractor ";
Ptr<DescriptorExtractor> descriptorExtractor = DescriptorExtractor::create( "SURF");
cout << " and descriptor matcher";
Ptr<DescriptorMatcher> descriptorMatcher = DescriptorMatcher::create( "BruteForce");
int mactherFilterType = getMatcherFilterType( "CrossCheckFilter");
bool eval = false;
cout << ">" << endl;
if( detector.empty() || descriptorExtractor.empty() || descriptorMatcher.empty() ){
cout<<"Can not create " <<
(detector.empty() ? "detector" : descriptorExtractor ? "descriptor exstractor":"descriptor")
<< " matcher of given types" <<endl;
exit(-1);
}
if( img1.empty() || (!isWarpPerspective && img2.empty()) ){
cout << "Can not read images" << endl;
exit(-1);
}
cout << endl << "< Extracting keypoints from first image";
vector<KeyPoint> keypoints1;
detector->detect( img1, keypoints1 );
cout << keypoints1.size() << " points" << ">" << endl;
cout << "< Computing descriptors for keypoints from first image";
Mat descriptors1;
descriptorExtractor->compute( img1, keypoints1, descriptors1 );
cout << ">" << endl;
RNG rng = theRNG();
UIImage *retImg = doIteration( img1, img2, isWarpPerspective, keypoints1, descriptors1,
detector, descriptorExtractor, descriptorMatcher, mactherFilterType, eval,
ransacReprojThreshold, rng);
detector.release();
descriptorExtractor.release();
descriptorMatcher.release();
img1.release();
img2.release();
return retImg;
}
// simpleMatching.cpp
// openCV_test
//
// Created by mark on 14-6-3.
// Copyright (c) 2014年 mark. All rights reserved.
//
#include "simpleMatching.h"
// #define DRAW_RICH_KEYPOINTS_MODE 1 // mark definition
// #define DRAW_OUTLIERS_MODE 1 // mark definition
const string winName = "correspondences";
enum { NONE_FILTER = 0, CROSS_CHECK_FILTER = 1 };
int getMatcherFilterType( const string& str ){
if( str == "NoneFilter" )
return NONE_FILTER;
if( str == "CrossCheckFilter" )
return CROSS_CHECK_FILTER;
CV_Error(CV_StsBadArg, "Invalid filter name");
return -1;
}
static void simpleMatching( Ptr<DescriptorMatcher>& descriptorMatcher,
const Mat& descriptors1, const Mat& descriptors2,
vector<DMatch>& matches12 ){
vector<DMatch> matches;
descriptorMatcher->match( descriptors1, descriptors2, matches12 );
}
static void crossCheckMatching( Ptr<DescriptorMatcher>& descriptorMatcher,
const Mat& descriptors1, const Mat& descriptors2,
vector<DMatch>& filteredMatches12, int knn=1 ){
filteredMatches12.clear();
vector<vector<DMatch> > matches12, matches21;
descriptorMatcher->knnMatch( descriptors1, descriptors2, matches12, knn );
descriptorMatcher->knnMatch( descriptors2, descriptors1, matches21, knn );
for( size_t m = 0; m < matches12.size(); m++ ){
bool findCrossCheck = false;
for( size_t fk = 0; fk < matches12[m].size(); fk++ ){
DMatch forward = matches12[m][fk];
for( size_t bk = 0; bk < matches21[forward.trainIdx].size(); bk++ ){
DMatch backward = matches21[forward.trainIdx][bk];
if( backward.trainIdx == forward.queryIdx ){
filteredMatches12.push_back(forward);
findCrossCheck = true;
break;
}
}
if( findCrossCheck ) break;
}
}
}
static 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() );
}
UIImage* doIteration( const Mat& img1, Mat& img2, bool isWarpPerspective,
vector<KeyPoint>& keypoints1, const Mat& descriptors1,
Ptr<FeatureDetector>& detector, Ptr<DescriptorExtractor>& descriptorExtractor,
Ptr<DescriptorMatcher>& descriptorMatcher, int matcherFilter, bool eval,
double ransacReprojThreshold, RNG& rng ){
assert( !img1.empty() );
Mat H1to2;
if( isWarpPerspective )
warpPerspectiveRand(img1, img2, H1to2, rng );
else
assert( !img2.empty() /* && img2.cols==img1.cols && img2.rows==img1.rows */ );
cout << endl << "< Extracting keypoints from second image
";
vector<KeyPoint> keypoints2;
#define mark 1
#if mark
int cols = img2.cols;
int rows = img2.rows;
int delta = 0;
img2 = Mat(img2, Range(delta, rows-delta), Range(delta, cols-delta));
detector->detect(img2, keypoints2);
/*
1 2 3 4
5 6 7 8
2 3 4 5
0 9 8 7
*/
//
// for(int i= 0; i < 2; i++){
// for(int j = 0; j < 2; j++){
// Mat tmpMat = Mat(img2, Range(rows*i/2, rows*(i+1)/2), Range(cols*j/2, cols*(j+1)/2));
// vector<KeyPoint> keypoints;
// detector->detect( tmpMat, keypoints );
// for(auto itr = keypoints.begin(); itr != keypoints.end(); ++itr){
// keypoints2.push_back(*itr);
// }
// }
// }
// /*左上角
// 1 2
// 5 6
// */
// Mat tmpMat = Mat(img2, Range(0, rows/2), Range(0, cols/2));
// vector<KeyPoint> keypoints;
// detector->detect( tmpMat, keypoints );
// for(auto itr = keypoints.begin(); itr != keypoints.end(); ++itr){
// keypoints2.push_back(*itr);
// }
// /*右下脚
// 4 5
// 8 7
// */
// tmpMat = Mat(img2, Range(rows/2, rows), Range(cols/2, cols));
// keypoints.clear();
// detector->detect( tmpMat, keypoints );
// for(auto itr = keypoints.begin(); itr != keypoints.end(); ++itr){
// keypoints2.push_back(*itr);
// }
// /*右上角
// 3 4
// 7 8
// */
// tmpMat = Mat(img2, Range(rows/2, rows), Range(0, cols/2));
// keypoints.clear();
// detector->detect( tmpMat, keypoints );
// for(auto itr = keypoints.begin(); itr != keypoints.end(); ++itr){
// keypoints2.push_back(*itr);
// }
// /*左下角
// 2 3
// 0 9
// */
// tmpMat = Mat(img2, Range(0, rows/2), Range(cols/2, cols/2));
// keypoints.clear();
// detector->detect( tmpMat, keypoints );
// for(auto itr = keypoints.begin(); itr != keypoints.end(); ++itr){
// keypoints2.push_back(*itr);
// }
#else
detector->detect( img2, keypoints2 );
#endif
cout << keypoints2.size() << " points" << ">" << endl;
if( !H1to2.empty() && eval ){
cout << "< Evaluate feature detector
" << endl;
float repeatability;
int correspCount;
evaluateFeatureDetector( img1, img2, H1to2, &keypoints1, &keypoints2,
repeatability, correspCount );
cout << "repeatability = " << repeatability << endl;
cout << "correspCount = " << correspCount << endl << ">" << endl;
}
cout << "< Computing descriptors for keypoints from second image
";
Mat descriptors2;
descriptorExtractor->compute( img2, keypoints2, descriptors2 );
cout << ">" << endl;
cout << "< Matching descriptors
";
vector<DMatch> filteredMatches;
switch( matcherFilter ){
case CROSS_CHECK_FILTER :
crossCheckMatching( descriptorMatcher, descriptors1, descriptors2, filteredMatches, 1 );
break;
default :
simpleMatching( descriptorMatcher, descriptors1, descriptors2, filteredMatches );
}
cout << ">" << endl;
if( !H1to2.empty() && eval ){
cout << "< Evaluate descriptor matcher
" << endl;
vector<Point2f> curve;
Ptr<GenericDescriptorMatcher> gdm =
new VectorDescriptorMatcher( descriptorExtractor, descriptorMatcher );
evaluateGenericDescriptorMatcher( img1, img2, H1to2,
keypoints1, keypoints2, 0, 0, curve, gdm );
Point2f firstPoint = *curve.begin();
Point2f lastPoint = *curve.rbegin();
int prevPointIndex = -1;
cout << "1-precision = " << firstPoint.x << "; recall = " << firstPoint.y << endl;
for( float l_p = 0; l_p <= 1 + FLT_EPSILON; l_p+=0.05f ){
int nearest = getNearestPoint( curve, l_p );
if( nearest >= 0 ){
Point2f curPoint = curve[nearest];
if( curPoint.x > firstPoint.x && curPoint.x < lastPoint.x &&
nearest != prevPointIndex ){
cout << "1-precision = " << curPoint.x << "; recall = " << curPoint.y << endl;
prevPointIndex = nearest;
}
}
}
cout << "1-precision = " << lastPoint.x
<< "; recall = " << lastPoint.y << endl << ">" << endl;
}
vector< int> queryIdxs( filteredMatches.size() ), trainIdxs( filteredMatches.size() );
for( size_t i = 0; i < filteredMatches.size(); i++ ){
queryIdxs[i] = filteredMatches[i].queryIdx;
trainIdxs[i] = filteredMatches[i].trainIdx;
}
if( !isWarpPerspective && ransacReprojThreshold >= 0 ){
cout << "< Computing homography (RANSAC)
";
vector<Point2f> points1; KeyPoint::convert(keypoints1, points1, queryIdxs);
vector<Point2f> points2; KeyPoint::convert(keypoints2, points2, trainIdxs);
H1to2 = findHomography( Mat(points1), Mat(points2), CV_RANSAC, ransacReprojThreshold );
cout << ">" << endl;
}
Mat drawImg;
if( !H1to2.empty() ){ // filter outliers
vector< char> matchesMask( filteredMatches.size(), 0 );
vector<Point2f> points1; KeyPoint::convert(keypoints1, points1, queryIdxs);
vector<Point2f> points2; KeyPoint::convert(keypoints2, points2, trainIdxs);
Mat points1t; perspectiveTransform(Mat(points1), points1t, H1to2);
double maxInlierDist = ransacReprojThreshold < 0 ? 3 : ransacReprojThreshold;
for( size_t i1 = 0; i1 < points1.size(); i1++ ){
if( norm(points2[i1] - points1t.at<Point2f>(( int)i1,0)) <= maxInlierDist ) // inlier
matchesMask[i1] = 1;
}
// draw inliers
drawMatches( img1, keypoints1, img2, keypoints2,
filteredMatches, drawImg, CV_RGB(0, 255, 0), CV_RGB(0, 0, 255), matchesMask
#if DRAW_RICH_KEYPOINTS_MODE
, DrawMatchesFlags::DRAW_RICH_KEYPOINTS
#endif
);
#if DRAW_OUTLIERS_MODE
// draw outliers
for( size_t i1 = 0; i1 < matchesMask.size(); i1++ )
matchesMask[i1] = !matchesMask[i1];
drawMatches( img1, keypoints1, img2, keypoints2, filteredMatches, drawImg,
CV_RGB(0, 0, 255), CV_RGB(255, 0, 0), matchesMask,
DrawMatchesFlags::DRAW_OVER_OUTIMG | DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
#endif
cout << "Number of inliers: " << countNonZero(matchesMask) << endl;
} else
drawMatches( img1, keypoints1, img2, keypoints2, filteredMatches, drawImg );
return [UIImage UIImageFromCVMat: drawImg];
}
UIImage * getMatch( Mat & img1, Mat & img2){
cv::initModule_nonfree(); /// /if use SIFT or SURF
bool isWarpPerspective = 0;
double ransacReprojThreshold = 3;
cout << "< Creating detector, ";
Ptr<FeatureDetector> detector = FeatureDetector::create( "SURF" ); // "HARRIS"
cout << " descriptor extractor ";
Ptr<DescriptorExtractor> descriptorExtractor = DescriptorExtractor::create( "SURF");
cout << " and descriptor matcher
";
Ptr<DescriptorMatcher> descriptorMatcher = DescriptorMatcher::create( "BruteForce");
int mactherFilterType = getMatcherFilterType( "CrossCheckFilter");
bool eval = false;
cout << ">" << endl;
if( detector.empty() || descriptorExtractor.empty() || descriptorMatcher.empty() ){
cout<<"Can not create " <<
(detector.empty() ? "detector" : descriptorExtractor ? "descriptor exstractor":"descriptor")
<< " matcher of given types" <<endl;
exit(-1);
}
if( img1.empty() || (!isWarpPerspective && img2.empty()) ){
cout << "Can not read images" << endl;
exit(-1);
}
cout << endl << "< Extracting keypoints from first image
";
vector<KeyPoint> keypoints1;
detector->detect( img1, keypoints1 );
cout << keypoints1.size() << " points" << ">" << endl;
cout << "< Computing descriptors for keypoints from first image
";
Mat descriptors1;
descriptorExtractor->compute( img1, keypoints1, descriptors1 );
cout << ">" << endl;
RNG rng = theRNG();
UIImage *retImg = doIteration( img1, img2, isWarpPerspective, keypoints1, descriptors1,
detector, descriptorExtractor, descriptorMatcher, mactherFilterType, eval,
ransacReprojThreshold, rng);
detector.release();
descriptorExtractor.release();
descriptorMatcher.release();
img1.release();
img2.release();
return retImg;
}