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@wzpan wzpan/RobustMatcher.h
Last active Sep 24, 2018

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CV - match images using random sample consensus(RANSAC).
/*------------------------------------------------------------------------------------------*\
This file contains material supporting chapter 9 of the cookbook:
Computer Vision Programming using the OpenCV Library.
by Robert Laganiere, Packt Publishing, 2011.
This program is free software; permission is hereby granted to use, copy, modify,
and distribute this source code, or portions thereof, for any purpose, without fee,
subject to the restriction that the copyright notice may not be removed
or altered from any source or altered source distribution.
The software is released on an as-is basis and without any warranties of any kind.
In particular, the software is not guaranteed to be fault-tolerant or free from failure.
The author disclaims all warranties with regard to this software, any use,
and any consequent failure, is purely the responsibility of the user.
Copyright (C) 2010-2011 Robert Laganiere, www.laganiere.name
\*------------------------------------------------------------------------------------------*/
#if !defined MATCHER
#define MATCHER
#include <vector>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
class RobustMatcher {
private:
// pointer to the feature point detector object
cv::Ptr<cv::FeatureDetector> detector;
// pointer to the feature descriptor extractor object
cv::Ptr<cv::DescriptorExtractor> extractor;
float ratio; // max ratio between 1st and 2nd NN
bool refineF; // if true will refine the F matrix
double distance; // min distance to epipolar
double confidence; // confidence level (probability)
public:
RobustMatcher() : ratio(0.65f), refineF(true), confidence(0.99), distance(3.0) {
// SURF is the default feature
detector= new cv::SurfFeatureDetector();
extractor= new cv::SurfDescriptorExtractor();
}
// Set the feature detector
void setFeatureDetector(cv::Ptr<cv::FeatureDetector>& detect) {
detector= detect;
}
// Set descriptor extractor
void setDescriptorExtractor(cv::Ptr<cv::DescriptorExtractor>& desc) {
extractor= desc;
}
// Set the minimum distance to epipolar in RANSAC
void setMinDistanceToEpipolar(double d) {
distance= d;
}
// Set confidence level in RANSAC
void setConfidenceLevel(double c) {
confidence= c;
}
// Set the NN ratio
void setRatio(float r) {
ratio= r;
}
// if you want the F matrix to be recalculated
void refineFundamental(bool flag) {
refineF= flag;
}
// Clear matches for which NN ratio is > than threshold
// return the number of removed points
// (corresponding entries being cleared, i.e. size will be 0)
int ratioTest(std::vector<std::vector<cv::DMatch>>& matches) {
int removed=0;
// for all matches
for (std::vector<std::vector<cv::DMatch>>::iterator matchIterator= matches.begin();
matchIterator!= matches.end(); ++matchIterator) {
// if 2 NN has been identified
if (matchIterator->size() > 1) {
// check distance ratio
if ((*matchIterator)[0].distance/(*matchIterator)[1].distance > ratio) {
matchIterator->clear(); // remove match
removed++;
}
} else { // does not have 2 neighbours
matchIterator->clear(); // remove match
removed++;
}
}
return removed;
}
// Insert symmetrical matches in symMatches vector
void symmetryTest(const std::vector<std::vector<cv::DMatch>>& matches1,
const std::vector<std::vector<cv::DMatch>>& matches2,
std::vector<cv::DMatch>& symMatches) {
// for all matches image 1 -> image 2
for (std::vector<std::vector<cv::DMatch>>::const_iterator matchIterator1= matches1.begin();
matchIterator1!= matches1.end(); ++matchIterator1) {
if (matchIterator1->size() < 2) // ignore deleted matches
continue;
// for all matches image 2 -> image 1
for (std::vector<std::vector<cv::DMatch>>::const_iterator matchIterator2= matches2.begin();
matchIterator2!= matches2.end(); ++matchIterator2) {
if (matchIterator2->size() < 2) // ignore deleted matches
continue;
// Match symmetry test
if ((*matchIterator1)[0].queryIdx == (*matchIterator2)[0].trainIdx &&
(*matchIterator2)[0].queryIdx == (*matchIterator1)[0].trainIdx) {
// add symmetrical match
symMatches.push_back(cv::DMatch((*matchIterator1)[0].queryIdx,
(*matchIterator1)[0].trainIdx,
(*matchIterator1)[0].distance));
break; // next match in image 1 -> image 2
}
}
}
}
// Identify good matches using RANSAC
// Return fundemental matrix
cv::Mat ransacTest(const std::vector<cv::DMatch>& matches,
const std::vector<cv::KeyPoint>& keypoints1,
const std::vector<cv::KeyPoint>& keypoints2,
std::vector<cv::DMatch>& outMatches) {
// Convert keypoints into Point2f
std::vector<cv::Point2f> points1, points2;
for (std::vector<cv::DMatch>::const_iterator it= matches.begin();
it!= matches.end(); ++it) {
// Get the position of left keypoints
float x= keypoints1[it->queryIdx].pt.x;
float y= keypoints1[it->queryIdx].pt.y;
points1.push_back(cv::Point2f(x,y));
// Get the position of right keypoints
x= keypoints2[it->trainIdx].pt.x;
y= keypoints2[it->trainIdx].pt.y;
points2.push_back(cv::Point2f(x,y));
}
// Compute F matrix using RANSAC
std::vector<uchar> inliers(points1.size(),0);
cv::Mat fundemental= cv::findFundamentalMat(
cv::Mat(points1),cv::Mat(points2), // matching points
inliers, // match status (inlier ou outlier)
CV_FM_RANSAC, // RANSAC method
distance, // distance to epipolar line
confidence); // confidence probability
// extract the surviving (inliers) matches
std::vector<uchar>::const_iterator itIn= inliers.begin();
std::vector<cv::DMatch>::const_iterator itM= matches.begin();
// for all matches
for ( ;itIn!= inliers.end(); ++itIn, ++itM) {
if (*itIn) { // it is a valid match
outMatches.push_back(*itM);
}
}
std::cout << "Number of matched points (after cleaning): " << outMatches.size() << std::endl;
if (refineF) {
// The F matrix will be recomputed with all accepted matches
// Convert keypoints into Point2f for final F computation
points1.clear();
points2.clear();
for (std::vector<cv::DMatch>::const_iterator it= outMatches.begin();
it!= outMatches.end(); ++it) {
// Get the position of left keypoints
float x= keypoints1[it->queryIdx].pt.x;
float y= keypoints1[it->queryIdx].pt.y;
points1.push_back(cv::Point2f(x,y));
// Get the position of right keypoints
x= keypoints2[it->trainIdx].pt.x;
y= keypoints2[it->trainIdx].pt.y;
points2.push_back(cv::Point2f(x,y));
}
// Compute 8-point F from all accepted matches
fundemental= cv::findFundamentalMat(
cv::Mat(points1),cv::Mat(points2), // matching points
CV_FM_8POINT); // 8-point method
}
return fundemental;
}
// Match feature points using symmetry test and RANSAC
// returns fundemental matrix
cv::Mat match(cv::Mat& image1, cv::Mat& image2, // input images
std::vector<cv::DMatch>& matches, // output matches and keypoints
std::vector<cv::KeyPoint>& keypoints1, std::vector<cv::KeyPoint>& keypoints2) {
// 1a. Detection of the SURF features
detector->detect(image1,keypoints1);
detector->detect(image2,keypoints2);
std::cout << "Number of SURF points (1): " << keypoints1.size() << std::endl;
std::cout << "Number of SURF points (2): " << keypoints2.size() << std::endl;
// 1b. Extraction of the SURF descriptors
cv::Mat descriptors1, descriptors2;
extractor->compute(image1,keypoints1,descriptors1);
extractor->compute(image2,keypoints2,descriptors2);
std::cout << "descriptor matrix size: " << descriptors1.rows << " by " << descriptors1.cols << std::endl;
// 2. Match the two image descriptors
// Construction of the matcher
cv::BruteForceMatcher<cv::L2<float>> matcher;
// from image 1 to image 2
// based on k nearest neighbours (with k=2)
std::vector<std::vector<cv::DMatch>> matches1;
matcher.knnMatch(descriptors1,descriptors2,
matches1, // vector of matches (up to 2 per entry)
2); // return 2 nearest neighbours
// from image 2 to image 1
// based on k nearest neighbours (with k=2)
std::vector<std::vector<cv::DMatch>> matches2;
matcher.knnMatch(descriptors2,descriptors1,
matches2, // vector of matches (up to 2 per entry)
2); // return 2 nearest neighbours
std::cout << "Number of matched points 1->2: " << matches1.size() << std::endl;
std::cout << "Number of matched points 2->1: " << matches2.size() << std::endl;
// 3. Remove matches for which NN ratio is > than threshold
// clean image 1 -> image 2 matches
int removed= ratioTest(matches1);
std::cout << "Number of matched points 1->2 (ratio test) : " << matches1.size()-removed << std::endl;
// clean image 2 -> image 1 matches
removed= ratioTest(matches2);
std::cout << "Number of matched points 1->2 (ratio test) : " << matches2.size()-removed << std::endl;
// 4. Remove non-symmetrical matches
std::vector<cv::DMatch> symMatches;
symmetryTest(matches1,matches2,symMatches);
std::cout << "Number of matched points (symmetry test): " << symMatches.size() << std::endl;
// 5. Validate matches using RANSAC
cv::Mat fundemental= ransacTest(symMatches, keypoints1, keypoints2, matches);
// return the found fundemental matrix
return fundemental;
}
};
#endif
@eranshulpareek

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commented Sep 24, 2018

i,m using opencv3.4.0.i m not able to run this code .please suggest.
// SURF is the default feature
detector= new cv::SurfFeatureDetector();
extractor= new cv::SurfDescriptorExtractor();

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