matcher.h

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/*------------------------------------------------------------------------------------------*\
   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
\*------------------------------------------------------------------------------------------*/

#ifndef 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>
#include <opencv2/nonfree/features2d.hpp>
#include <opencv2/legacy/legacy.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)
      bool verbose; //print stuff or not

  public:

      RobustMatcher() : ratio(0.65f), refineF(true), distance(3.0),confidence(0.99), verbose(false) {

                  // 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 verbose
      void setVerbose(bool verb)
      {
          verbose = verb;
      }

          // 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));
            }
        cv::Mat fundemental;
                // Compute F matrix using RANSAC
                std::vector<uchar> inliers(points1.size(),0);
        if (inliers.size()<8)
        {
            if(verbose)
                std::cout << "Too few matches" << std::endl;
            return fundemental;
        }
        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);
                        }
                }
        if(verbose)
            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 features
        detector->detect(image1,keypoints1);
        detector->detect(image2,keypoints2);
        if(verbose)
        {
            std::cout << "Number of points (1): " << keypoints1.size() << std::endl;
            std::cout << "Number of 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);
        if(verbose)
            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
        if(verbose)
        {
            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);
        if(verbose)
            std::cout << "Number of matched points 1->2 (ratio test) : " << matches1.size()-removed << std::endl;
        // clean image 2 -> image 1 matches
        removed= ratioTest(matches2);
        if(verbose)
            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);

        if(verbose)
            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;
    }

      // Match feature points using symmetry test and RANSAC
      // returns fundemental matrix
      cv::Mat match(std::vector<cv::DMatch>& matches, // output matches and keypoints
                    std::vector<cv::KeyPoint>& keypoints1, std::vector<cv::KeyPoint>& keypoints2,
                    cv::Mat descriptors1,cv::Mat descriptors2) {

        // 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
        if(verbose)
        {
            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);
        if(verbose)
            std::cout << "Number of matched points 1->2 (ratio test) : " << matches1.size()-removed << std::endl;
        // clean image 2 -> image 1 matches
        removed= ratioTest(matches2);
        if(verbose)
            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);

        if(verbose)
            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