/// <summary>
/// Computes the thresholded gradient (Snatos97).
/// </summary>
/// <returns>The focus measure value</returns>
double BasicFM::computeGRAT()
{
if (checkInput()) {
cv::Mat dH = mSrcImg(cv::Range::all(), cv::Range(1, mSrcImg.cols)) - mSrcImg(cv::Range::all(), cv::Range(0, mSrcImg.cols - 1));
cv::Mat dV = mSrcImg(cv::Range(1, mSrcImg.rows), cv::Range::all()) - mSrcImg(cv::Range(0, mSrcImg.rows - 1), cv::Range::all());
//dH = cv::abs(dH);
//dV = cv::abs(dV);
//cv::Mat FM = cv::max(dH, dV);
cv::Mat FM = cv::max(dH(cv::Range(0, dH.rows - 1), cv::Range::all()), dV(cv::Range::all(), cv::Range(0, dV.cols - 1)));
double thr = 0;
cv::Mat mask = FM >= thr;
mask.convertTo(mask, CV_32FC1, 255.0);
FM = FM.mul(mask);
cv::Scalar fm = cv::sum(FM) / cv::sum(mask);
//normalize
mVal = fm[0] / 255.0;
}
return mVal;
}
/// <summary>
/// Computes the squared gradient in horizontal direction (Eskicioglu95)
/// </summary>
/// <returns>The focus measure value</returns>
double BasicFM::computeGRAS()
{
if (checkInput()) {
cv::Mat dH = mSrcImg(cv::Range::all(), cv::Range(1, mSrcImg.cols)) - mSrcImg(cv::Range::all(), cv::Range(0, mSrcImg.cols - 1));
dH = dH.mul(dH);
cv::Scalar fm = cv::mean(dH);
mVal = fm[0] / (255.0*255.0);
}
return mVal;
}
/// <summary>
/// Computes Brenner's focus measure and determines the ratio of the median/mean.
/// </summary>
/// <returns>The focus measure value</returns>
double BasicFM::computeROGR()
{
if (checkInput()) {
cv::Mat dH = mSrcImg(cv::Range::all(), cv::Range(1, mSrcImg.cols)) - mSrcImg(cv::Range::all(), cv::Range(0, mSrcImg.cols - 1));
cv::Mat dV = mSrcImg(cv::Range(1, mSrcImg.rows), cv::Range::all()) - mSrcImg(cv::Range(0, mSrcImg.rows - 1), cv::Range::all());
dH = cv::abs(dH);
dV = cv::abs(dV);
cv::Mat FM = cv::max(dH(cv::Range(0, dH.rows - 1), cv::Range::all()), dV(cv::Range::all(), cv::Range(0, dV.cols - 1)));
FM = FM.mul(FM);
cv::Scalar m = cv::mean(FM);
cv::Mat tmp;
FM.convertTo(tmp, CV_32F);
double r = 255.0*255.0;
//mVal = r > 0 ? m[0] / r : m[0];
mVal = m[0] / r;
}
return mVal;
}
void SIG_VResample(nm8s* pSrcImg, int nSrcStride, int nSrcWidth, int nSrcHeight, int nP, int nQ, int nK, int nWindow, nm16s* pDstImg)
{
nmmtr8s mSrcImg(pSrcImg,nSrcHeight,nSrcWidth);
nmmtr16s mDstImg(pDstImg,nSrcHeight*nP/nQ,nSrcWidth);
const double PI=3.1415926535897932384626433832795;
double fP=nP;
double v;
int k,i,j;
int nFixpoint=64;
if (nP>=nQ)
{
nmvec16s vKernel(nP*nK+1);
vec<double> vHamming(nP*nK+1);
vec<double> vImpulse(nP*nK+1);
vec<double> vEnergy(nP);
if(nK==1)
{
vImpulse.reset();
for(k=0;k<vKernel.size-1;k++)
{
vImpulse[k]=1;
}
}
else
{
ASSERTE(nK%2==0);
// Impulse function generation
vImpulse[vImpulse.size/2]=1;
for(k=1;k<=vKernel.size/2;k++)
{
v=sin(PI*double(k)/fP)/(PI*k/fP); //1.0/nP*
vImpulse[vImpulse.size/2+k]=v;
vImpulse[vImpulse.size/2-k]=v;
}
if (nWindow)
{
// Hamming window function generation
for(k=0;k<vHamming.size;k++)
{
vHamming[k]=0.54-0.46*cos(2*PI*k/(vHamming.size-1));
}
// multiplication of impulse response by Hamming window
for(k=0;k<vImpulse.size;k++)
{
vImpulse[k]*=vHamming[k];
}
}
}
vEnergy.reset();
for(i=0;i<nP;i++)
for(k=i;k<vImpulse.size;k+=nP)
vEnergy[i]+=vImpulse[k];
for(i=0;i<nP;i++)
for(k=i;k<vImpulse.size;k+=nP)
vImpulse[k]/=vEnergy[i];
// Filxed point kernel coefficient initialization
for(k=0;k<vImpulse.size;k++)
vKernel[k]=floor(vImpulse[k]*nFixpoint+0.5);
nmvec8s vSrcUpsample(nSrcHeight*nP,nP*nK); // Up-Resampled vSrcVec by nP times
nmvec16s vDstUpsample(nSrcHeight*nP); // Filtered vSrcUpsample
vSrcUpsample.reset();
for(int x=0;x<nSrcWidth;x++)
{
int y;
// Up-resampling by nP times
for( y=0;y<nSrcHeight;y++)
{
vSrcUpsample[y*nP]= mSrcImg[y][x];
}
// Filtration
for(i=0;i<vSrcUpsample.size;i++)
{
nmvec8s vLocalSrcUpsample(vSrcUpsample.m_data+i-vKernel.size/2,vKernel.size);
vDstUpsample[i]=vLocalSrcUpsample*vKernel;
}
// Decimation by 4
for(y=0,i=0;y<mDstImg.m_height;i+=nQ,y++)
{
mDstImg[y][x]=vDstUpsample[i].m_value;
}
}
}
}
