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FeatureMatcher1.cpp
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FeatureMatcher1.cpp
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#include "FeatureMatcher.h"
FeatureMatcher::FeatureMatcher()
{
use_orb=true;
debug=true;
enableRatioTest=true;
homographyReprojectionThreshold=3;
if(use_orb)
{
//detector=new cv::OrbFeatureDetector();
//extractor=new cv::OrbDescriptorExtractor();
detector=new cv::ORB(1000);
extractor=new cv::FREAK(false,false);
//matcher=cv::DescriptorMatcher::create("BruteForce-Hamming");
matcher=new cv::BFMatcher();
}
else
{
//using surf as defalut
detector= new cv::SurfFeatureDetector();
extractor= new cv::SurfDescriptorExtractor();
//matcher=cv::DescriptorMatcher::create("FlannBased");
matcher=new cv::FlannBasedMatcher();
}
}
FeatureMatcher::~FeatureMatcher()
{
}
void FeatureMatcher::getGray(cv::Mat& image, cv::Mat& gray)
{
if (image.channels() == 3)
cv::cvtColor(image, gray, CV_BGR2GRAY);
else if (image.channels() == 4)
cv::cvtColor(image, gray, CV_BGRA2GRAY);
else if (image.channels() == 1)
gray = image;
}
void FeatureMatcher::detectFeatures(cv::Mat& image,vector<cv::KeyPoint>& keypoints)
{
detector->detect(image,keypoints);
cout<<"detect "<<keypoints.size()<<" keypoints!"<<endl;
}
void FeatureMatcher::extractFeatrues(cv::Mat& image,vector<cv::KeyPoint>& keypoints,cv::Mat& descriptors)
{
extractor->compute(image,keypoints,descriptors);
cout<<"extracted "<<descriptors.rows<<" descriptors"<<endl;
}
//get the refined and point2f
void FeatureMatcher::finalRefine(cv::Mat& src,cv::Mat& tgt,vector<cv::DMatch>& matches,
vector<cv::Point2f>& src_points,vector<cv::Point2f>& tgt_points)
{
cv::Mat srcGray,tgtGray;
getGray(src,srcGray);
getGray(tgt,tgtGray);
//extract
vector<cv::KeyPoint> src_keypoints,tgt_keypoints;
cv::Mat src_descriptors,tgt_descriptors;
detectFeatures(srcGray,src_keypoints);
extractFeatrues(srcGray,src_keypoints,src_descriptors);
detectFeatures(tgtGray,tgt_keypoints);
extractFeatrues(tgtGray,tgt_keypoints,tgt_descriptors);
//get matches
//vector<cv::DMatch> matches;
findMatches(src_descriptors,tgt_descriptors,matches);
//debug
if(debug)
{
cv::Mat img_matches;
cv::drawMatches(src,src_keypoints,tgt,tgt_keypoints,matches,img_matches);
cv::imshow("knn-matching",img_matches);
cv::waitKey(10);
}
//rough homography
cv::Mat m_roughHomography;
bool homographyFound=refineMatchesWithHomography(src_keypoints,tgt_keypoints,
homographyReprojectionThreshold,
matches,
m_roughHomography); //storw homography matrix
if(debug)
{
cv::Mat img_matches;
cv::drawMatches(src,src_keypoints,tgt,tgt_keypoints,matches,img_matches);
cv::imshow("homgraphy matching",img_matches);
cv::waitKey(10);
}
cout<<"matches size is: "<<matches.size()<<endl;
//get the point2f
for(size_t i=0;i<matches.size();++i)
{
src_points.push_back(src_keypoints[matches[i].queryIdx].pt);
tgt_points.push_back(tgt_keypoints[matches[i].trainIdx].pt);
}
}
void FeatureMatcher::findMatches(cv::Mat& queryDescriptors,cv::Mat& trainDescriptors,
std::vector<cv::DMatch>& matches)
{
matches.clear();
if (enableRatioTest)
{
// To avoid NaN's when best match has zero distance we will use inversed ratio.
const float minRatio = 1.f / 1.5f;
// KNN match will return 2 nearest matches for each query descriptor
std::vector< std::vector<cv::DMatch> > m_knnMatches;
matcher->knnMatch(queryDescriptors,trainDescriptors, m_knnMatches, 2);
for (size_t i=0; i<m_knnMatches.size(); i++)
{
const cv::DMatch& bestMatch = m_knnMatches[i][0];
const cv::DMatch& betterMatch = m_knnMatches[i][1];
float distanceRatio = bestMatch.distance / betterMatch.distance;
// Pass only matches where distance ratio between
// nearest matches is greater than 1.5 (distinct criteria)
if (distanceRatio < minRatio)
{
matches.push_back(bestMatch);
}
}
}
else
{
// Perform regular match
matcher->match(queryDescriptors, trainDescriptors,matches);
}
}
bool FeatureMatcher::refineMatchesWithHomography(std::vector<cv::KeyPoint>& queryKeypoints,
std::vector<cv::KeyPoint>& trainKeypoints,
float reprojectionThreshold,
std::vector<cv::DMatch>& matches,
cv::Mat& homography)
{
const int minNumberMatchesAllowed = 8;
if (matches.size() < minNumberMatchesAllowed)
return false;
// Prepare data for cv::findHomography
std::vector<cv::Point2f> srcPoints(matches.size());
std::vector<cv::Point2f> dstPoints(matches.size());
for (size_t i = 0; i < matches.size(); i++)
{
srcPoints[i] = trainKeypoints[matches[i].trainIdx].pt;
dstPoints[i] = queryKeypoints[matches[i].queryIdx].pt;
}
// Find homography matrix and get inliers mask
std::vector<unsigned char> inliersMask(srcPoints.size());
homography = cv::findHomography(srcPoints,
dstPoints,
CV_FM_RANSAC,
reprojectionThreshold,
inliersMask);
std::vector<cv::DMatch> inliers;
for (size_t i=0; i<inliersMask.size(); i++)
{
if (inliersMask[i])
inliers.push_back(matches[i]);
}
matches.swap(inliers);
return matches.size() > minNumberMatchesAllowed;
}
void FeatureMatcher::findMatches(const std::vector<cv::KeyPoint>& source_keypoints,
const cv::Mat& source_descriptors,
const std::vector<cv::KeyPoint>& target_keypoints,
const cv::Mat& target_descriptors,
const int image_height,
const int image_width,
std::vector<cv::DMatch >& matches)
{
}