我测试了 ORB、SIFT 和 SURF 匹配。
SIFT 最好，SURF 次之，ORB 次之。
但是人们说ORB比SIFT好。
就我而言，我想知道为什么？
原图也附上。
我的代码如下。
int imagematching(Mat &img1, Mat & img2){
if( !img1.data || !img2.data )
{
std::cout<< " --(!) Error reading images " << std::endl;
return FAILED_IN_LOAD_IMAGES;
}
int max_keypoints = 500;
#if defined(USE_SURF)
Ptr<SURF> detector = SURF::create( max_keypoints );
Ptr<SURF> extractor = SURF::create();
#elif defined(USE_SIFT)
Ptr<SIFT> detector = SIFT::create();
Ptr<SIFT> extractor = SIFT::create();
#elif defined(USE_ORB)
Ptr<ORB> detector = ORB::create();
#endif
//--Step 1: Key point detection
std::vector<KeyPoint> keypoints1, keypoints2;
//-- Step 2: Calculate descriptors (feature vectors)
Mat descriptors1, descriptors2;
#if defined(USE_SURF) || defined(USE_SIFT)
detector->detect( img1, keypoints1 );
detector->detect( img2, keypoints2 );
extractor->compute(img1, keypoints1, descriptors1);
extractor->compute(img2, keypoints2, descriptors2);
#elif defined(USE_ORB)
Mat dp1, dp2;
detector->detectAndCompute(img1, Mat(), keypoints1, dp1);
detector->detectAndCompute(img2, Mat(), keypoints2, dp2);
if(dp1.type()!=CV_32F) {
dp1.convertTo(descriptors1, CV_32F);
}
if(dp2.type()!=CV_32F) {
dp2.convertTo(descriptors2, CV_32F);
}
#endif
#if defined(USE_FLANN)
FlannBasedMatcher matcher;
#endif
vector<DMatch> matches;
matcher.match(descriptors1, descriptors2, matches);
double max_dist = 0; double min_dist = 999999;
//-- 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;
}
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 < descriptors1.rows; i++ )
{
if( matches[i].distance < 3*min_dist )
{ good_matches.push_back( matches[i]); }
}
matches.clear();
Mat img_matches;
drawMatches( img1, keypoints1, img2, keypoints2,
good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
descriptors1.release();
descriptors2.release();
//-- Localize the object
std::vector<Point2f> first_keypoints;
std::vector<Point2f> second_keypoints;
for( int i = 0; i < good_matches.size(); i++ )
{
//-- Get the keypoints from the good matches
first_keypoints.push_back( keypoints1[ good_matches[i].queryIdx ].pt );
second_keypoints.push_back( keypoints2[ good_matches[i].trainIdx ].pt );
}
keypoints1.clear();
keypoints2.clear();
good_matches.clear();
Mat H = findHomography( first_keypoints, second_keypoints, CV_RANSAC );
first_keypoints.clear();
second_keypoints.clear();
//-- Get the corners from the image_1 ( the object to be "detected" )
std::vector<Point2f> first_image_corners(4);
first_image_corners[0] = cvPoint(0,0); first_image_corners[1] = cvPoint( img1.cols, 0 );
first_image_corners[2] = cvPoint( img1.cols, img1.rows ); first_image_corners[3] = cvPoint( 0, img1.rows );
std::vector<Point2f> second_image_corners(4);
perspectiveTransform( first_image_corners, second_image_corners, H);
//-- Draw lines between the corners (the mapped object in the scene - image_2 )
line( img_matches, second_image_corners[0] + Point2f( img1.cols, 0), second_image_corners[1] + Point2f( img1.cols, 0), Scalar(0, 255, 0), 4 );
line( img_matches, second_image_corners[1] + Point2f( img1.cols, 0), second_image_corners[2] + Point2f( img1.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, second_image_corners[2] + Point2f( img1.cols, 0), second_image_corners[3] + Point2f( img1.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, second_image_corners[3] + Point2f( img1.cols, 0), second_image_corners[0] + Point2f( img1.cols, 0), Scalar( 0, 255, 0), 4 );
//-- Show detected matches
imshow( "ORB Good Matches & Object detection", img_matches );
waitKey(0);
img_matches.release();
first_image_corners.clear();
second_image_corners.clear();
return SUCCESS;
}
我的代码有问题吗？
谢谢