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Use OpenCV's brute-force match with cross-check instead of KNN match with ratio check and symmetry check #479

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Use OpenCV's brute-force match with cross-check instead of KNN match with ratio check and symmetry check #479

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55 changes: 3 additions & 52 deletions ov_core/src/track/TrackDescriptor.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -479,21 +479,11 @@ void TrackDescriptor::perform_detection_stereo(const cv::Mat &img0, const cv::Ma

void TrackDescriptor::robust_match(const std::vector<cv::KeyPoint> &pts0, const std::vector<cv::KeyPoint> &pts1, const cv::Mat &desc0,
const cv::Mat &desc1, size_t id0, size_t id1, std::vector<cv::DMatch> &matches) {
if (pts0.empty() || pts1.empty())
return;

// Our 1to2 and 2to1 match vectors
std::vector<std::vector<cv::DMatch>> matches0to1, matches1to0;

// Match descriptors (return 2 nearest neighbours)
matcher->knnMatch(desc0, desc1, matches0to1, 2);
matcher->knnMatch(desc1, desc0, matches1to0, 2);

// Do a ratio test for both matches
robust_ratio_test(matches0to1);
robust_ratio_test(matches1to0);

// Finally do a symmetry test
std::vector<cv::DMatch> matches_good;
robust_symmetry_test(matches0to1, matches1to0, matches_good);
matcher->match(desc0, desc1, matches_good);

// Convert points into points for RANSAC
std::vector<cv::Point2f> pts0_rsc, pts1_rsc;
Expand Down Expand Up @@ -534,42 +524,3 @@ void TrackDescriptor::robust_match(const std::vector<cv::KeyPoint> &pts0, const
matches.push_back(matches_good.at(i));
}
}

void TrackDescriptor::robust_ratio_test(std::vector<std::vector<cv::DMatch>> &matches) {
// Loop through all matches
for (auto &match : matches) {
// If 2 NN has been identified, else remove this feature
if (match.size() > 1) {
// check distance ratio, remove it if the ratio is larger
if (match[0].distance / match[1].distance > knn_ratio) {
match.clear();
}
} else {
// does not have 2 neighbours, so remove it
match.clear();
}
}
}

void TrackDescriptor::robust_symmetry_test(std::vector<std::vector<cv::DMatch>> &matches1, std::vector<std::vector<cv::DMatch>> &matches2,
std::vector<cv::DMatch> &good_matches) {
// for all matches image 1 -> image 2
for (auto &match1 : matches1) {
// ignore deleted matches
if (match1.empty() || match1.size() < 2)
continue;
// for all matches image 2 -> image 1
for (auto &match2 : matches2) {
// ignore deleted matches
if (match2.empty() || match2.size() < 2)
continue;
// Match symmetry test
if (match1[0].queryIdx == match2[0].trainIdx && match2[0].queryIdx == match1[0].trainIdx) {
// add symmetrical match
good_matches.emplace_back(cv::DMatch(match1[0].queryIdx, match1[0].trainIdx, match1[0].distance));
// next match in image 1 -> image 2
break;
}
}
}
}
17 changes: 5 additions & 12 deletions ov_core/src/track/TrackDescriptor.h
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Original file line number Diff line number Diff line change
Expand Up @@ -127,21 +127,14 @@ class TrackDescriptor : public TrackBase {
* @param id1 id of the second camera
* @param matches vector of matches that we have found
*
* This will perform a "robust match" between the two sets of points (slow but has great results).
* First we do a simple KNN match from 1to2 and 2to1, which is followed by a ratio check and symmetry check.
* Original code is from the "RobustMatcher" in the opencv examples, and seems to give very good results in the matches.
* https://github.com/opencv/opencv/blob/master/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/RobustMatcher.cpp
* This will perform a "brute-force match with cross-check" between the two sets of features.
* When there are only a few hundred features, this is faster than KNN match followed by a ratio check and symmetry check.
* See BFMatcher::create from opencv for more details.
* https://docs.opencv.org/4.x/d3/da1/classcv_1_1BFMatcher.html#a02ef4d594b33d091767cbfe442aefb8a
*/
void robust_match(const std::vector<cv::KeyPoint> &pts0, const std::vector<cv::KeyPoint> &pts1, const cv::Mat &desc0,
const cv::Mat &desc1, size_t id0, size_t id1, std::vector<cv::DMatch> &matches);

// Helper functions for the robust_match function
// Original code is from the "RobustMatcher" in the opencv examples
// https://github.com/opencv/opencv/blob/master/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/RobustMatcher.cpp
void robust_ratio_test(std::vector<std::vector<cv::DMatch>> &matches);
void robust_symmetry_test(std::vector<std::vector<cv::DMatch>> &matches1, std::vector<std::vector<cv::DMatch>> &matches2,
std::vector<cv::DMatch> &good_matches);

// Timing variables
boost::posix_time::ptime rT1, rT2, rT3, rT4, rT5, rT6, rT7;

Expand All @@ -150,7 +143,7 @@ class TrackDescriptor : public TrackBase {
cv::Ptr<cv::ORB> orb1 = cv::ORB::create();

// Our descriptor matcher
cv::Ptr<cv::DescriptorMatcher> matcher = cv::DescriptorMatcher::create("BruteForce-Hamming");
cv::Ptr<cv::BFMatcher> matcher = cv::BFMatcher::create(cv::NORM_HAMMING, true);

// Parameters for our FAST grid detector
int threshold;
Expand Down
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