root/modules/shape/src/sc_dis.cpp

DEFINITIONS

1. setAngularBins
2. getAngularBins
3. setRadialBins
4. getRadialBins
5. setInnerRadius
6. getInnerRadius
7. setOuterRadius
8. getOuterRadius
9. setRotationInvariant
10. getRotationInvariant
11. setCostExtractor
12. getCostExtractor
13. setShapeContextWeight
14. getShapeContextWeight
15. setImageAppearanceWeight
16. getImageAppearanceWeight
17. setBendingEnergyWeight
18. getBendingEnergyWeight
19. setStdDev
20. getStdDev
21. setImages
22. getImages
23. setIterations
24. getIterations
25. setTransformAlgorithm
26. getTransformAlgorithm
27. write
28. read
29. computeDistance
30. createShapeContextDistanceExtractor
31. extractSCD
32. logarithmicSpaces
33. angularSpaces
34. buildNormalizedDistanceMatrix
35. buildAngleMatrix
36. matchDescriptors
37. buildCostMatrix
38. hungarian

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/*
 * Implementation of the paper Shape Matching and Object Recognition Using Shape Contexts
 * Belongie et al., 2002 by Juan Manuel Perez for GSoC 2013.
 */

#include "precomp.hpp"
#include "opencv2/core.hpp"
#include "scd_def.hpp"
#include <limits>

namespace cv
{
class ShapeContextDistanceExtractorImpl : public ShapeContextDistanceExtractor
{
public:
    /* Constructors */
    ShapeContextDistanceExtractorImpl(int _nAngularBins, int _nRadialBins, float _innerRadius, float _outerRadius, int _iterations,
                                      const Ptr<HistogramCostExtractor> &_comparer, const Ptr<ShapeTransformer> &_transformer)
    {
        nAngularBins=_nAngularBins;
        nRadialBins=_nRadialBins;
        innerRadius=_innerRadius;
        outerRadius=_outerRadius;
        rotationInvariant=false;
        comparer=_comparer;
        iterations=_iterations;
        transformer=_transformer;
        bendingEnergyWeight=0.3f;
        imageAppearanceWeight=0.0f;
        shapeContextWeight=1.0f;
        sigma=10.0f;
        name_ = "ShapeDistanceExtractor.SCD";
    }

    /* Destructor */
    ~ShapeContextDistanceExtractorImpl()
    {
    }

    //! the main operator
    virtual float computeDistance(InputArray contour1, InputArray contour2);

    //! Setters/Getters
    virtual void setAngularBins(int _nAngularBins){CV_Assert(_nAngularBins>0); nAngularBins=_nAngularBins;}
    virtual int getAngularBins() const {return nAngularBins;}

    virtual void setRadialBins(int _nRadialBins){CV_Assert(_nRadialBins>0); nRadialBins=_nRadialBins;}
    virtual int getRadialBins() const {return nRadialBins;}

    virtual void setInnerRadius(float _innerRadius) {CV_Assert(_innerRadius>0); innerRadius=_innerRadius;}
    virtual float getInnerRadius() const {return innerRadius;}

    virtual void setOuterRadius(float _outerRadius) {CV_Assert(_outerRadius>0); outerRadius=_outerRadius;}
    virtual float getOuterRadius() const {return outerRadius;}

    virtual void setRotationInvariant(bool _rotationInvariant) {rotationInvariant=_rotationInvariant;}
    virtual bool getRotationInvariant() const {return rotationInvariant;}

    virtual void setCostExtractor(Ptr<HistogramCostExtractor> _comparer) { comparer = _comparer; }
    virtual Ptr<HistogramCostExtractor> getCostExtractor() const { return comparer; }

    virtual void setShapeContextWeight(float _shapeContextWeight) {shapeContextWeight=_shapeContextWeight;}
    virtual float getShapeContextWeight() const {return shapeContextWeight;}

    virtual void setImageAppearanceWeight(float _imageAppearanceWeight) {imageAppearanceWeight=_imageAppearanceWeight;}
    virtual float getImageAppearanceWeight() const {return imageAppearanceWeight;}

    virtual void setBendingEnergyWeight(float _bendingEnergyWeight) {bendingEnergyWeight=_bendingEnergyWeight;}
    virtual float getBendingEnergyWeight() const {return bendingEnergyWeight;}

    virtual void setStdDev(float _sigma) {sigma=_sigma;}
    virtual float getStdDev() const {return sigma;}

    virtual void setImages(InputArray _image1, InputArray _image2)
    {
        Mat image1_=_image1.getMat(), image2_=_image2.getMat();
        CV_Assert((image1_.depth()==0) && (image2_.depth()==0));
        image1=image1_;
        image2=image2_;
    }

    virtual void getImages(OutputArray _image1, OutputArray _image2) const
    {
        CV_Assert((!image1.empty()) && (!image2.empty()));
        _image1.create(image1.size(), image1.type());
        _image2.create(image2.size(), image2.type());
        _image1.getMat()=image1;
        _image2.getMat()=image2;
    }

    virtual void setIterations(int _iterations) {CV_Assert(_iterations>0); iterations=_iterations;}
    virtual int getIterations() const {return iterations;}

    virtual void setTransformAlgorithm(Ptr<ShapeTransformer> _transformer) {transformer=_transformer;}
    virtual Ptr<ShapeTransformer> getTransformAlgorithm() const {return transformer;}

    //! write/read
    virtual void write(FileStorage& fs) const
    {
        fs << "name" << name_
           << "nRads" << nRadialBins
           << "nAngs" << nAngularBins
           << "iters" << iterations
           << "img_1" << image1
           << "img_2" << image2
           << "beWei" << bendingEnergyWeight
           << "scWei" << shapeContextWeight
           << "iaWei" << imageAppearanceWeight
           << "costF" << costFlag
           << "rotIn" << rotationInvariant
           << "sigma" << sigma;
    }

    virtual void read(const FileNode& fn)
    {
        CV_Assert( (String)fn["name"] == name_ );
        nRadialBins = (int)fn["nRads"];
        nAngularBins = (int)fn["nAngs"];
        iterations = (int)fn["iters"];
        bendingEnergyWeight = (float)fn["beWei"];
        shapeContextWeight = (float)fn["scWei"];
        imageAppearanceWeight = (float)fn["iaWei"];
        costFlag = (int)fn["costF"];
        sigma = (float)fn["sigma"];
    }

protected:
    int nAngularBins;
    int nRadialBins;
    float innerRadius;
    float outerRadius;
    bool rotationInvariant;
    int costFlag;
    int iterations;
    Ptr<ShapeTransformer> transformer;
    Ptr<HistogramCostExtractor> comparer;
    Mat image1;
    Mat image2;
    float bendingEnergyWeight;
    float imageAppearanceWeight;
    float shapeContextWeight;
    float sigma;
    String name_;
};

float ShapeContextDistanceExtractorImpl::computeDistance(InputArray contour1, InputArray contour2)
{
    // Checking //
    Mat sset1=contour1.getMat(), sset2=contour2.getMat(), set1, set2;
    if (set1.type() != CV_32F)
        sset1.convertTo(set1, CV_32F);
    else
        sset1.copyTo(set1);

    if (set2.type() != CV_32F)
        sset2.convertTo(set2, CV_32F);
    else
        sset2.copyTo(set2);

    CV_Assert((set1.channels()==2) && (set1.cols>0));
    CV_Assert((set2.channels()==2) && (set2.cols>0));
    if (imageAppearanceWeight!=0)
    {
        CV_Assert((!image1.empty()) && (!image2.empty()));
    }

    // Initializing Extractor, Descriptor structures and Matcher //
    SCD set1SCE(nAngularBins, nRadialBins, innerRadius, outerRadius, rotationInvariant);
    Mat set1SCD;
    SCD set2SCE(nAngularBins, nRadialBins, innerRadius, outerRadius, rotationInvariant);
    Mat set2SCD;
    SCDMatcher matcher;
    std::vector<DMatch> matches;

    // Distance components (The output is a linear combination of these 3) //
    float sDistance=0, bEnergy=0, iAppearance=0;
    float beta;

    // Initializing some variables //
    std::vector<int> inliers1, inliers2;

    Ptr<ThinPlateSplineShapeTransformer> transDown = transformer.dynamicCast<ThinPlateSplineShapeTransformer>();

    Mat warpedImage;
    int ii, jj, pt;

    for (ii=0; ii<iterations; ii++)
    {
        // Extract SCD descriptor in the set1 //
        set1SCE.extractSCD(set1, set1SCD, inliers1);

        // Extract SCD descriptor of the set2 (TARGET) //
        set2SCE.extractSCD(set2, set2SCD, inliers2, set1SCE.getMeanDistance());

        // regularization parameter with annealing rate annRate //
        beta=set1SCE.getMeanDistance();
        beta *= beta;

        // match //
        matcher.matchDescriptors(set1SCD, set2SCD, matches, comparer, inliers1, inliers2);

        // apply TPS transform //
        if ( !transDown.empty() )
            transDown->setRegularizationParameter(beta);
        transformer->estimateTransformation(set1, set2, matches);
        bEnergy += transformer->applyTransformation(set1, set1);

        // Image appearance //
        if (imageAppearanceWeight!=0)
        {
            // Have to accumulate the transformation along all the iterations
            if (ii==0)
            {
                if ( !transDown.empty() )
                {
                    image2.copyTo(warpedImage);
                }
                else
                {
                    image1.copyTo(warpedImage);
                }
            }
            transformer->warpImage(warpedImage, warpedImage);
        }
    }

    Mat gaussWindow, diffIm;
    if (imageAppearanceWeight!=0)
    {
        // compute appearance cost
        if ( !transDown.empty() )
        {
            resize(warpedImage, warpedImage, image1.size());
            Mat temp=(warpedImage-image1);
            multiply(temp, temp, diffIm);
        }
        else
        {
            resize(warpedImage, warpedImage, image2.size());
            Mat temp=(warpedImage-image2);
            multiply(temp, temp, diffIm);
        }
        gaussWindow = Mat::zeros(warpedImage.rows, warpedImage.cols, CV_32F);
        for (pt=0; pt<sset1.cols; pt++)
        {
            Point2f p = sset1.at<Point2f>(0,pt);
            for (ii=0; ii<diffIm.rows; ii++)
            {
                for (jj=0; jj<diffIm.cols; jj++)
                {
                    float val = float(std::exp( -float( (p.x-jj)*(p.x-jj) + (p.y-ii)*(p.y-ii) )/(2*sigma*sigma) ) / (sigma*sigma*2*CV_PI));
                    gaussWindow.at<float>(ii,jj) += val;
                }
            }
        }

        Mat appIm(diffIm.rows, diffIm.cols, CV_32F);
        for (ii=0; ii<diffIm.rows; ii++)
        {
            for (jj=0; jj<diffIm.cols; jj++)
            {
                float elema=float( diffIm.at<uchar>(ii,jj) )/255;
                float elemb=gaussWindow.at<float>(ii,jj);
                appIm.at<float>(ii,jj) = elema*elemb;
            }
        }
        iAppearance = float(cv::sum(appIm)[0]/sset1.cols);
    }
    sDistance = matcher.getMatchingCost();

    return (sDistance*shapeContextWeight+bEnergy*bendingEnergyWeight+iAppearance*imageAppearanceWeight);
}

Ptr <ShapeContextDistanceExtractor> createShapeContextDistanceExtractor(int nAngularBins, int nRadialBins, float innerRadius, float outerRadius, int iterations,
                                                                        const Ptr<HistogramCostExtractor> &comparer, const Ptr<ShapeTransformer> &transformer)
{
    return Ptr <ShapeContextDistanceExtractor> ( new ShapeContextDistanceExtractorImpl(nAngularBins, nRadialBins, innerRadius,
                                                                                       outerRadius, iterations, comparer, transformer) );
}

//! SCD
void SCD::extractSCD(cv::Mat &contour, cv::Mat &descriptors, const std::vector<int> &queryInliers, const float _meanDistance)
{
    cv::Mat contourMat = contour;
    cv::Mat disMatrix = cv::Mat::zeros(contourMat.cols, contourMat.cols, CV_32F);
    cv::Mat angleMatrix = cv::Mat::zeros(contourMat.cols, contourMat.cols, CV_32F);

    std::vector<double> logspaces, angspaces;
    logarithmicSpaces(logspaces);
    angularSpaces(angspaces);
    buildNormalizedDistanceMatrix(contourMat, disMatrix, queryInliers, _meanDistance);
    buildAngleMatrix(contourMat, angleMatrix);

    // Now, build the descriptor matrix (each row is a point) //
    descriptors = cv::Mat::zeros(contourMat.cols, descriptorSize(), CV_32F);

    for (int ptidx=0; ptidx<contourMat.cols; ptidx++)
    {
        for (int cmp=0; cmp<contourMat.cols; cmp++)
        {
            if (ptidx==cmp) continue;
            if ((int)queryInliers.size()>0)
            {
                if (queryInliers[ptidx]==0 || queryInliers[cmp]==0) continue; //avoid outliers
            }

            int angidx=-1, radidx=-1;
            for (int i=0; i<nRadialBins; i++)
            {
                if (disMatrix.at<float>(ptidx, cmp)<logspaces[i])
                {
                    radidx=i;
                    break;
                }
            }
            for (int i=0; i<nAngularBins; i++)
            {
                if (angleMatrix.at<float>(ptidx, cmp)<angspaces[i])
                {
                    angidx=i;
                    break;
                }
            }
            if (angidx!=-1 && radidx!=-1)
            {
                int idx = angidx+radidx*nAngularBins;
                descriptors.at<float>(ptidx, idx)++;
            }
        }
    }
}

void SCD::logarithmicSpaces(std::vector<double> &vecSpaces) const
{
    double logmin=log10(innerRadius);
    double logmax=log10(outerRadius);
    double delta=(logmax-logmin)/(nRadialBins-1);
    double accdelta=0;

    for (int i=0; i<nRadialBins; i++)
    {
        double val = std::pow(10,logmin+accdelta);
        vecSpaces.push_back(val);
        accdelta += delta;
    }
}

void SCD::angularSpaces(std::vector<double> &vecSpaces) const
{
    double delta=2*CV_PI/nAngularBins;
    double val=0;

    for (int i=0; i<nAngularBins; i++)
    {
        val += delta;
        vecSpaces.push_back(val);
    }
}

void SCD::buildNormalizedDistanceMatrix(cv::Mat &contour, cv::Mat &disMatrix, const std::vector<int> &queryInliers, const float _meanDistance)
{
    cv::Mat contourMat = contour;
    cv::Mat mask(disMatrix.rows, disMatrix.cols, CV_8U);

    for (int i=0; i<contourMat.cols; i++)
    {
      for (int j=0; j<contourMat.cols; j++)
      {
          disMatrix.at<float>(i,j) = (float)norm( cv::Mat(contourMat.at<cv::Point2f>(0,i)-contourMat.at<cv::Point2f>(0,j)), cv::NORM_L2 );
          if (_meanDistance<0)
          {
              if (queryInliers.size()>0)
              {
                  mask.at<char>(i,j)=char(queryInliers[j] && queryInliers[i]);
              }
              else
              {
                  mask.at<char>(i,j)=1;
              }
          }
      }
    }

    if (_meanDistance<0)
    {
      meanDistance=(float)mean(disMatrix, mask)[0];
    }
    else
    {
      meanDistance=_meanDistance;
    }
    disMatrix/=meanDistance+FLT_EPSILON;
}

void SCD::buildAngleMatrix(cv::Mat &contour, cv::Mat &angleMatrix) const
{
    cv::Mat contourMat = contour;

    // if descriptor is rotationInvariant compute massCenter //
    cv::Point2f massCenter(0,0);
    if (rotationInvariant)
    {
        for (int i=0; i<contourMat.cols; i++)
        {
            massCenter+=contourMat.at<cv::Point2f>(0,i);
        }
        massCenter.x=massCenter.x/(float)contourMat.cols;
        massCenter.y=massCenter.y/(float)contourMat.cols;
    }


    for (int i=0; i<contourMat.cols; i++)
    {
        for (int j=0; j<contourMat.cols; j++)
        {
            if (i==j)
            {
                angleMatrix.at<float>(i,j)=0.0;
            }
            else
            {
                cv::Point2f dif = contourMat.at<cv::Point2f>(0,i) - contourMat.at<cv::Point2f>(0,j);
                angleMatrix.at<float>(i,j) = std::atan2(dif.y, dif.x);

                if (rotationInvariant)
                {
                    cv::Point2f refPt = contourMat.at<cv::Point2f>(0,i) - massCenter;
                    float refAngle = atan2(refPt.y, refPt.x);
                    angleMatrix.at<float>(i,j) -= refAngle;
                }
                angleMatrix.at<float>(i,j) = float(fmod(double(angleMatrix.at<float>(i,j)+(double)FLT_EPSILON),2*CV_PI)+CV_PI);
            }
        }
    }
}

//! SCDMatcher
void SCDMatcher::matchDescriptors(cv::Mat &descriptors1, cv::Mat &descriptors2, std::vector<cv::DMatch> &matches,
                                  cv::Ptr<cv::HistogramCostExtractor> &comparer, std::vector<int> &inliers1, std::vector<int> &inliers2)
{
    matches.clear();

    // Build the cost Matrix between descriptors //
    cv::Mat costMat;
    buildCostMatrix(descriptors1, descriptors2, costMat, comparer);

    // Solve the matching problem using the hungarian method //
    hungarian(costMat, matches, inliers1, inliers2, descriptors1.rows, descriptors2.rows);
}

void SCDMatcher::buildCostMatrix(const cv::Mat &descriptors1, const cv::Mat &descriptors2,
                                 cv::Mat &costMatrix, cv::Ptr<cv::HistogramCostExtractor> &comparer) const
{
    comparer->buildCostMatrix(descriptors1, descriptors2, costMatrix);
}

void SCDMatcher::hungarian(cv::Mat &costMatrix, std::vector<cv::DMatch> &outMatches, std::vector<int> &inliers1,
                           std::vector<int> &inliers2, int sizeScd1, int sizeScd2)
{
    std::vector<int> free(costMatrix.rows, 0), collist(costMatrix.rows, 0);
    std::vector<int> matches(costMatrix.rows, 0), colsol(costMatrix.rows), rowsol(costMatrix.rows);
    std::vector<float> d(costMatrix.rows), pred(costMatrix.rows), v(costMatrix.rows);

    const float LOWV = 1e-10f;
    bool unassignedfound;
    int  i=0, imin=0, numfree=0, prvnumfree=0, f=0, i0=0, k=0, freerow=0;
    int  j=0, j1=0, j2=0, endofpath=0, last=0, low=0, up=0;
    float min=0, h=0, umin=0, usubmin=0, v2=0;

    // COLUMN REDUCTION //
    for (j = costMatrix.rows-1; j >= 0; j--)
    {
        // find minimum cost over rows.
        min = costMatrix.at<float>(0,j);
        imin = 0;
        for (i = 1; i < costMatrix.rows; i++)
        if (costMatrix.at<float>(i,j) < min)
        {
            min = costMatrix.at<float>(i,j);
            imin = i;
        }
        v[j] = min;

        if (++matches[imin] == 1)
        {
            rowsol[imin] = j;
            colsol[j] = imin;
        }
        else
        {
            colsol[j]=-1;
        }
    }

    // REDUCTION TRANSFER //
    for (i=0; i<costMatrix.rows; i++)
    {
        if (matches[i] == 0)
        {
            free[numfree++] = i;
        }
        else
        {
            if (matches[i] == 1)
            {
                j1=rowsol[i];
                min=std::numeric_limits<float>::max();
                for (j=0; j<costMatrix.rows; j++)
                {
                    if (j!=j1)
                    {
                        if (costMatrix.at<float>(i,j)-v[j] < min)
                        {
                            min=costMatrix.at<float>(i,j)-v[j];
                        }
                    }
                }
                v[j1] = v[j1]-min;
            }
        }
    }
    // AUGMENTING ROW REDUCTION //
    int loopcnt = 0;
    do
    {
        loopcnt++;
        k=0;
        prvnumfree=numfree;
        numfree=0;
        while (k < prvnumfree)
        {
            i=free[k];
            k++;
            umin = costMatrix.at<float>(i,0)-v[0];
            j1=0;
            usubmin = std::numeric_limits<float>::max();
            for (j=1; j<costMatrix.rows; j++)
            {
                h = costMatrix.at<float>(i,j)-v[j];
                if (h < usubmin)
                {
                    if (h >= umin)
                    {
                        usubmin = h;
                        j2 = j;
                    }
                    else
                    {
                        usubmin = umin;
                        umin = h;
                        j2 = j1;
                        j1 = j;
                    }
                }
            }
            i0 = colsol[j1];

            if (fabs(umin-usubmin) > LOWV) //if( umin < usubmin )
            {
                v[j1] = v[j1] - (usubmin - umin);
            }
            else // minimum and subminimum equal.
            {
                if (i0 >= 0) // minimum column j1 is assigned.
                {
                    j1 = j2;
                    i0 = colsol[j2];
                }
            }
            // (re-)assign i to j1, possibly de-assigning an i0.
            rowsol[i]=j1;
            colsol[j1]=i;

            if (i0 >= 0)
            {
                //if( umin < usubmin )
                if (fabs(umin-usubmin) > LOWV)
                {
                    free[--k] = i0;
                }
                else
                {
                    free[numfree++] = i0;
                }
            }
        }
    }while (loopcnt<2); // repeat once.

    // AUGMENT SOLUTION for each free row //
    for (f = 0; f<numfree; f++)
    {
        freerow = free[f];       // start row of augmenting path.
        // Dijkstra shortest path algorithm.
        // runs until unassigned column added to shortest path tree.
        for (j = 0; j < costMatrix.rows; j++)
        {
            d[j] = costMatrix.at<float>(freerow,j) - v[j];
            pred[j] = float(freerow);
            collist[j] = j;        // init column list.
        }

        low=0; // columns in 0..low-1 are ready, now none.
        up=0;  // columns in low..up-1 are to be scanned for current minimum, now none.
        unassignedfound = false;
        do
        {
            if (up == low)
            {
                last=low-1;
                min = d[collist[up++]];
                for (k = up; k < costMatrix.rows; k++)
                {
                    j = collist[k];
                    h = d[j];
                    if (h <= min)
                    {
                        if (h < min) // new minimum.
                        {
                            up = low; // restart list at index low.
                            min = h;
                        }
                        collist[k] = collist[up];
                        collist[up++] = j;
                    }
                }
                for (k=low; k<up; k++)
                {
                    if (colsol[collist[k]] < 0)
                    {
                        endofpath = collist[k];
                        unassignedfound = true;
                        break;
                    }
                }
            }

            if (!unassignedfound)
            {
                // update 'distances' between freerow and all unscanned columns, via next scanned column.
                j1 = collist[low];
                low++;
                i = colsol[j1];
                h = costMatrix.at<float>(i,j1)-v[j1]-min;

                for (k = up; k < costMatrix.rows; k++)
                {
                    j = collist[k];
                    v2 = costMatrix.at<float>(i,j) - v[j] - h;
                    if (v2 < d[j])
                    {
                        pred[j] = float(i);
                        if (v2 == min)
                        {
                            if (colsol[j] < 0)
                            {
                                // if unassigned, shortest augmenting path is complete.
                                endofpath = j;
                                unassignedfound = true;
                                break;
                            }
                            else
                            {
                                collist[k] = collist[up];
                                collist[up++] = j;
                            }
                        }
                        d[j] = v2;
                    }
                }
            }
        }while (!unassignedfound);

        // update column prices.
        for (k = 0; k <= last; k++)
        {
            j1 = collist[k];
            v[j1] = v[j1] + d[j1] - min;
        }

        // reset row and column assignments along the alternating path.
        do
        {
            i = int(pred[endofpath]);
            colsol[endofpath] = i;
            j1 = endofpath;
            endofpath = rowsol[i];
            rowsol[i] = j1;
        }while (i != freerow);
    }

    // calculate symmetric shape context cost
    cv::Mat trueCostMatrix(costMatrix, cv::Rect(0,0,sizeScd1, sizeScd2));
    float leftcost = 0;
    for (int nrow=0; nrow<trueCostMatrix.rows; nrow++)
    {
        double minval;
        minMaxIdx(trueCostMatrix.row(nrow), &minval);
        leftcost+=float(minval);
    }
    leftcost /= trueCostMatrix.rows;

    float rightcost = 0;
    for (int ncol=0; ncol<trueCostMatrix.cols; ncol++)
    {
        double minval;
        minMaxIdx(trueCostMatrix.col(ncol), &minval);
        rightcost+=float(minval);
    }
    rightcost /= trueCostMatrix.cols;

    minMatchCost = std::max(leftcost,rightcost);

    // Save in a DMatch vector
    for (i=0;i<costMatrix.cols;i++)
    {
        cv::DMatch singleMatch(colsol[i],i,costMatrix.at<float>(colsol[i],i));//queryIdx,trainIdx,distance
        outMatches.push_back(singleMatch);
    }

    // Update inliers
    inliers1.reserve(sizeScd1);
    for (size_t kc = 0; kc<inliers1.size(); kc++)
    {
        if (rowsol[kc]<sizeScd1) // if a real match
            inliers1[kc]=1;
        else
            inliers1[kc]=0;
    }
    inliers2.reserve(sizeScd2);
    for (size_t kc = 0; kc<inliers2.size(); kc++)
    {
        if (colsol[kc]<sizeScd2) // if a real match
            inliers2[kc]=1;
        else
            inliers2[kc]=0;
    }
}

}