//*******************************************************************
// ilEuclidNorm
// normalizes vectors of dim=n in mxn input matrix with the
// Euclidean norm
//*******************************************************************
void ilEuclidNorm(pMat const& src, pMat dst)
{
int m = src->rows;
int n = src->cols;
int type = src->type;
pMat ones = CreateMat(n, 1, type);
pMat res1 = CreateMat(m, n, type);
pMat norm = CreateMat(m, 1, type);
SetValue(ones, cvScalar(1.0));
//*** compute the norm
Multiply(src, src, res1);
MatrixMultiply(res1, ones, norm);
PowerMatrix(norm, norm, .5);
//*** normalize columns of src
#if 0 //*** matrix version
pMat normmat = CreateMat(m, n, CV_32FC1);
pMat onesrow = CreateMat(1, n, CV_32FC1);
SetValue(onesrow, cvScalar(1.0));
MatrixMultiply(norm, onesrow, normmat);
Divide(src, normmat, dst);
#endif
#if 1 // *** column version
CvMat colmat1 = cvMat(0, 0, 0, 0);
CvMat colmat2 = cvMat(0, 0, 0, 0);
for (int i = 0; i<n; ++i)
{
cvGetCol(src.get(), &colmat1, i);
cvGetCol(dst.get(), &colmat2, i);
Divide(dummyGuard(&colmat1), norm, dummyGuard(&colmat2));
}
#endif
}
//------------------------------------------------------
// Spectrum Matrix Analysis Drive
//------------------------------------------------------
void ArtStoryCore::SpectrumMatrixAnalysis(){
/* Available Infos:
melodyStoragesVec
*/
float max, min;
PowerMatrix(NormalizationPower); //power up and normalize
GetMatrixMaxMin(max, min); //shifts to ensure all zero or positive
ScalerAddMatrix(-min); //integrate HighFreqCompensation inside
//HighFreqCompensation();
//BandedNormalizeMatrix(FrequencyBandSeparatorVec); //integrate HardDecisionPolarizeRule
//HardDecisionPolarizeRule(dynamicThreshold,valueBottom,valueTop); // hard decision rule
StepFreRegistration(); //Including the Tone Translation
};
//************************************************************************
// ilEuclidDist
// Computes Euclidean distance between sets of points in src1 and src2.
// src1 is m1*n array; src2 is m2*n array where m1 and m2 are
// the number of points in each set and n is the dimensionality
// of the Euclidean space. The result dst is an m1*m2 matrix
// with distances between all pairs of points.
// If m1=m2=1 Euclidean distance between two vectors is computed.
//************************************************************************
void ilEuclidDist(pMat const& src1, pMat const& src2, pMat dst)
{
int m1 = src1->rows;
int m2 = src2->rows;
int n = src1->cols;
int type=src1->type;
#if 0
//*** matrix version
pMat ones_nm2 = CreateMat(n, m2, type);
pMat ones_m1n = CreateMat(m1, n, type);
pMat src1sq = CreateMat(m1, n, type);
pMat src2sq = CreateMat(m2, n, type);
pMat res1 = CreateMat(m1, m2, type);
pMat res2 = CreateMat(m1, m2, type);
Multiply(src1, src1, src1sq);
Multiply(src2, src2, src2sq);
SetValue(ones_nm2, cvScalar(1.0));
SetValue(ones_m1n, cvScalar(1.0));
//*** Euclidean distance with matrix multiplications
MatrixMultiply(src1sq, ones_nm2, res1);
GEMM(ones_m1n, src2sq, 1.0, res1, 1, res2, CV_GEMM_B_T);
GEMM(src1, src2, -2.0, res2, 1, dst, CV_GEMM_B_T);
PowerMatrix(dst, dst, .5);
#endif
#if 1
//*** element-wise version
pMat src1sq = CreateMat(m1, n, type);
pMat src2sq = CreateMat(m2, n, type);
Multiply(src1, src1, src1sq);
Multiply(src2, src2, src2sq);
float *ptrsrc1sq, *ptrsrc2sq, *ptrdst;
ptrsrc1sq = Ptr2D<float>(src1sq);
ptrsrc2sq = Ptr2D<float>(src2sq);
ptrdst = Ptr2D<float>(dst);
int indsrc1sq, indsrc2sq, inddst=0;
float v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, vdst;
for(int i = 0; i<m1; ++i)
{
indsrc1sq = i * n;
v0 = ptrsrc1sq[indsrc1sq + 0];
v1 = ptrsrc1sq[indsrc1sq + 1];
v2 = ptrsrc1sq[indsrc1sq + 2];
v3 = ptrsrc1sq[indsrc1sq + 3];
v4 = ptrsrc1sq[indsrc1sq + 4];
v5 = ptrsrc1sq[indsrc1sq + 5];
v6 = ptrsrc1sq[indsrc1sq + 6];
v7 = ptrsrc1sq[indsrc1sq + 7];
v8 = ptrsrc1sq[indsrc1sq + 8];
v9 = ptrsrc1sq[indsrc1sq + 9];
for (int j=0; j<m2; ++j)
{
inddst = i * m2 + j;
indsrc2sq = j * n;
vdst = (v0-ptrsrc2sq[indsrc2sq]) * (v0-ptrsrc2sq[indsrc2sq]);
vdst += (v1-ptrsrc2sq[indsrc2sq+1]) * (v1-ptrsrc2sq[indsrc2sq+1]);
vdst += (v2-ptrsrc2sq[indsrc2sq+2]) * (v2-ptrsrc2sq[indsrc2sq+2]);
vdst += (v3-ptrsrc2sq[indsrc2sq+3]) * (v3-ptrsrc2sq[indsrc2sq+3]);
vdst += (v4-ptrsrc2sq[indsrc2sq+4]) * (v4-ptrsrc2sq[indsrc2sq+4]);
vdst += (v5-ptrsrc2sq[indsrc2sq+5]) * (v5-ptrsrc2sq[indsrc2sq+5]);
vdst += (v6-ptrsrc2sq[indsrc2sq+6]) * (v6-ptrsrc2sq[indsrc2sq+6]);
vdst += (v7-ptrsrc2sq[indsrc2sq+7]) * (v7-ptrsrc2sq[indsrc2sq+7]);
vdst += (v8-ptrsrc2sq[indsrc2sq+8]) * (v8-ptrsrc2sq[indsrc2sq+8]);
vdst += (v9-ptrsrc2sq[indsrc2sq+9]) * (v9-ptrsrc2sq[indsrc2sq+9]);
ptrdst[inddst] = vdst;
}
}
#endif
}
