OpenCV实现基于边缘的模板匹配--适用部分遮挡和光照变化情形(附源码)

cvSobel( src, gx, 1,0, 3 ); //gradient in X directioncvSobel( src, gy, 0, 1, 3 ); //gradient in Y direction
for( i = 1; i < Ssize.height-1; i++ ){    for( j = 1; j < Ssize.width-1; j++ )    {                  _sdx = (short*)(gx->data.ptr + gx->step*i);        _sdy = (short*)(gy->data.ptr + gy->step*i);        fdx = _sdx[j]; fdy = _sdy[j];        // read x, y derivatives
        //Magnitude = Sqrt(gx^2 +gy^2)        MagG = sqrt((float)(fdx*fdx) + (float)(fdy*fdy));        //Direction = invtan (Gy / Gx)        direction =cvFastArctan((float)fdy,(float)fdx);        magMat[i][j] = MagG;                    if(MagG>MaxGradient)            MaxGradient=MagG;            // get maximum gradient value for normalizing.
                    // get closest angle from 0, 45, 90, 135 set        if ( (direction>0 && direction < 22.5) ||               (direction >157.5 && direction < 202.5) ||               (direction>337.5 && direction<360)  )            direction = 0;        else if ( (direction>22.5 && direction < 67.5) ||                   (direction >202.5 && direction <247.5)  )            direction = 45;        else if ( (direction >67.5 && direction < 112.5)||                  (direction>247.5 && direction<292.5) )            direction = 90;        else if ( (direction >112.5 && direction < 157.5)||                  (direction>292.5 && direction<337.5) )            direction = 135;        else            direction = 0;                    orients[count] = (int)direction;        count++;    }}

for( i = 1; i < Ssize.height-1; i++ ){    for( j = 1; j < Ssize.width-1; j++ )    {        switch ( orients[count] )        {            case 0:                leftPixel  = magMat[i][j-1];                rightPixel = magMat[i][j+1];                break;            case 45:                leftPixel  = magMat[i-1][j+1];                rightPixel = magMat[i+1][j-1];                break;            case 90:                leftPixel  = magMat[i-1][j];                rightPixel = magMat[i+1][j];                break;            case 135:                leftPixel  = magMat[i-1][j-1];                rightPixel = magMat[i+1][j+1];                break;        }        // compare current pixels value with adjacent pixels        if (( magMat[i][j] < leftPixel ) || (magMat[i][j] < rightPixel ) )            (nmsEdges->data.ptr + nmsEdges->step*i)[j]=0;        Else            (nmsEdges->data.ptr + nmsEdges->step*i)[j]=                                 (uchar)(magMat[i][j]/MaxGradient*255);        count++;    }}

_sdx = (short*)(gx->data.ptr + gx->step*i);_sdy = (short*)(gy->data.ptr + gy->step*i);fdx = _sdx[j]; fdy = _sdy[j];    MagG = sqrt(fdx*fdx + fdy*fdy); //Magnitude = Sqrt(gx^2 +gy^2)DirG =cvFastArctan((float)fdy,(float)fdx);     //Direction = tan(y/x)
////((uchar*)(imgGDir->imageData + imgGDir->widthStep*i))[j]= MagG;flag=1;if(((double)((nmsEdges->data.ptr + nmsEdges->step*i))[j]) < maxContrast){    if(((double)((nmsEdges->data.ptr + nmsEdges->step*i))[j])< minContrast)    {        (nmsEdges->data.ptr + nmsEdges->step*i)[j]=0;        flag=0; // remove from edge        ////((uchar*)(imgGDir->imageData + imgGDir->widthStep*i))[j]=0;    }    else    {   // if any of 8 neighboring pixel is not greater than max contraxt remove from edge        if( (((double)((nmsEdges->data.ptr + nmsEdges->step*(i-1)))[j-1]) < maxContrast) &&            (((double)((nmsEdges->data.ptr + nmsEdges->step*(i-1)))[j]) < maxContrast)   &&            (((double)((nmsEdges->data.ptr + nmsEdges->step*(i-1)))[j+1]) < maxContrast) &&            (((double)((nmsEdges->data.ptr + nmsEdges->step*i))[j-1]) < maxContrast)     &&            (((double)((nmsEdges->data.ptr + nmsEdges->step*i))[j+1]) < maxContrast)     &&            (((double)((nmsEdges->data.ptr + nmsEdges->step*(i+1)))[j-1]) < maxContrast) &&            (((double)((nmsEdges->data.ptr + nmsEdges->step*(i+1)))[j]) < maxContrast)   &&            (((double)((nmsEdges->data.ptr + nmsEdges->step*(i+1)))[j+1]) < maxContrast))        {            (nmsEdges->data.ptr + nmsEdges->step*i)[j]=0;            flag=0;            ////((uchar*)(imgGDir->imageData + imgGDir->widthStep*i))[j]=0;        }    }}

cvSobel( src, Sdx, 1, 0, 3 );  // find X derivativescvSobel( src, Sdy, 0, 1, 3 ); // find Y derivativesfor( i = 0; i < Ssize.height; i++ ){    for( j = 0; j < Ssize.width; j++ )    {         partialSum = 0; // initilize partialSum measure        for(m=0;m<noOfCordinates;m++)        {            curX    = i + cordinates[m].x ;    // template X coordinate            curY    = j + cordinates[m].y ; // template Y coordinate            iTx    = edgeDerivativeX[m];    // template X derivative            iTy    = edgeDerivativeY[m];    // template Y derivative
            if(curX<0 ||curY<0||curX>Ssize.height-1 ||curY>Ssize.width-1)                continue;                                 _Sdx = (short*)(Sdx->data.ptr + Sdx->step*(curX));            _Sdy = (short*)(Sdy->data.ptr + Sdy->step*(curX));                                    iSx=_Sdx[curY]; // get curresponding  X derivative from source image            iSy=_Sdy[curY];// get curresponding  Y derivative from source image                                    if((iSx!=0 || iSy!=0) && (iTx!=0 || iTy!=0))            {                //partial Sum  = Sum of(((Source X derivative* Template X drivative)                //+ Source Y derivative * Template Y derivative)) / Edge                //magnitude of(Template)* edge magnitude of(Source))                partialSum = partialSum + ((iSx*iTx)+(iSy*iTy))*                            (edgeMagnitude[m] * matGradMag[curX][curY]);                                                }

// stoping criterias to search for modeldouble normMinScore = minScore /noOfCordinates; // precompute minumum score double normGreediness = ((1- greediness * minScore)/(1-greediness)) /noOfCordinates;// precompute greedniness
sumOfCoords = m + 1;partialScore = partialSum /sumOfCoords ;// check termination criteria// if partial score score is less than the score than// needed to make the required score at that position// break serching at that coordinate.if( partialScore < (MIN((minScore -1) +         normGreediness*sumOfCoords,normMinScore*  sumOfCoords)))    break;