void RV02::Sobel (const channel8& sPic, channel8& GradientPic, channel8& DirectionPic){
const int PicSizeY = sPic.rows();
const int PicSizeX = sPic.columns();
int x, y;
int gx, gy;
for(x = 1; x < PicSizeX - 1; x++){
for(y = 1; y < PicSizeY - 1; y++){
// Gx berechnen
gx = -sPic[y-1][x-1] + 0 + sPic[y-1][x+1]
-2*sPic[y][x-1] + 0 + 2*sPic[y][x+1]
-sPic[y+1][x-1] + 0 + sPic[y+1][x+1];
gx /= 4;
// Gy berechnen
gy = - sPic[y-1][x-1] - 2*sPic[y-1][x] - sPic[y-1][x+1]
+ 0 + 0 + 0
+ sPic[y+1][x-1] + 2*sPic[y+1][x] + sPic[y+1][x+1];
gy /= 4;
// Gradientenbetrag
GradientPic[y][x] = sqrt(gx*gx + gy*gy);
// Winkel: 180 <= angle < 540
double angle = atan2((double)gy,(double)gx)/PI*180+360.0;
// Abbildung auf die Winkelbereiche
int dir = ((int)(angle+22.5)%360)/45;
DirectionPic[y][x] = dir;
}
}
}
// On copy apply for type channel8!
bool harrisCorners::apply(const channel8& src,channel8& dest) const {
pointList pts;
if (apply(src,pts)) {
const parameters& par = getParameters();
pointList::iterator it;
dest.resize(src.size(),par.noCornerValue,false,true);
for (it=pts.begin();it!=pts.end();++it) {
dest.at(*it)=par.cornerValue;
}
return true;
}
return false;
};
// merge 8-bit channels
bool mergeOCPToImage::apply(const channel8& c1,
const channel8& c2,
const channel8& c3,
image& img) const {
point p; // coordinates
float r,g,b; // unnormed RGB channels
float RG, BY, WB; // opponent colour channels
if ((c1.size() != c2.size()) || (c1.size() != c3.size())) {
setStatusString("sizes of channels do not match");
return false;
}
img.resize(c1.size(),rgbPixel(),false,false);
for (p.y=0;p.y<img.rows();p.y++) {
for (p.x=0;p.x<img.columns();p.x++) {
RG = static_cast<float>(c1.at(p)) - 127.5f;
BY = static_cast<float>(c2.at(p)) - 127.5f;
WB = static_cast<float>(c3.at(p)) - 127.5f;
b = BY*0.66666666666667f;
//
r = WB + RG - b;
g = WB - RG - b;
b = WB + BY*1.3333333333333f;
// truncate r,g and b if the value is not in intervall [0..1]
// can happen due to rounding errors in split operation
if (r<0.0f) {
r=0.0f;
} else if (r>255.0f) {
r=255.0f;
}
if (g<0.0f) {
g=0.0f;
} else if (g>255.0f) {
g=255.0f;
}
if (b<0.0f) {
b=0.0f;
} else if (b>255.0f) {
b=255.0f;
}
img.at(p).set(static_cast<ubyte>(r),
static_cast<ubyte>(g),
static_cast<ubyte>(b),
0);
}
}
return true;
};
bool kNearestNeighFilter::apply(const channel8& src,channel8& dest) {
imatrix tmpSrc,tmpDest;
tmpSrc.castFrom(src);
apply(tmpSrc,tmpDest);
dest.castFrom(tmpDest);
return true;
}
/*
* compute the second and up iterations of a probability map
* using the given aPriori probabilites per pixel.
*/
bool probabilityMap2D::computeMap(const channel8& src1, const channel8& src2,
channel& aPrioriDest) const {
point chnl1_size = src1.size();
point chnl2_size = src2.size();
// size of src1 equals src2 ?
if ( (chnl1_size.x != chnl2_size.x) || (chnl1_size.y != chnl2_size.y) ) {
setStatusString("probabilityMap2D: channels do not match");
return false;
} else {
int y;
vector<channel8::value_type>::const_iterator srcIterator1, eit1;
vector<channel8::value_type>::const_iterator srcIterator2, eit2;
vector<channel::value_type>::iterator destIterator;
const parameters& param = getParameters();
const thistogram<double>& objModel = param.getObjectColorModel();
const thistogram<double>& nonObjModel = param.getNonObjectColorModel();
float relObjProb;
float relNonObjProb;
ivector theBin(2);
for (y=0;y<src1.rows();++y) {
srcIterator1 = src1.getRow(y).begin();
eit1 = src1.getRow(y).end();
srcIterator2 = src2.getRow(y).begin();
eit2 = src2.getRow(y).end();
destIterator = aPrioriDest.getRow(y).begin();
while (srcIterator1 != eit1) {
theBin[0] = lookupTable[0][*srcIterator1];
theBin[1] = lookupTable[1][*srcIterator2];
relObjProb = static_cast<float>(objModel.getProbability(theBin) *
(*destIterator));
relNonObjProb = static_cast<float>(nonObjModel.getProbability(theBin)*
(1.0f-(*destIterator)));
// assume non-object if no entries are given
if ((relObjProb == 0.0f) && (relNonObjProb == 0.0f)) {
(*destIterator) = 0.0f;
} else {
// bayes
(*destIterator) = relObjProb / (relObjProb + relNonObjProb);
}
srcIterator1++;
srcIterator2++;
destIterator++;
}
}
}
return true;
}
// On copy apply for type channel8!
bool distanceTransform::apply(const channel8& src,channel8& dest) const {
channel tmp;
tmp.castFrom(static_cast<matrix<ubyte> >(src));
if (apply(tmp)) {
dest.castFrom(static_cast<matrix<float> >(tmp));
return true;
}
return false;
};
// split image into 8-bit channels
// N.B.: when casting the transformation result to unsigned shorts
// (8-bit channel), major rounding errors will occur.
// As a result, the merging operation might
// produce negative values or values > 1, which are truncated subsequently.
// When accurate X, Y and Z channels are required, prefer float channels!
bool splitImageToxyY::apply(const image& img,
channel8& c1,
channel8& c2,
channel8& c3) const {
point p; // coordinates
rgbPixel pix; // single Pixel Element in RGB-values...
float Y; // channels
float X, XYZ; // help variables
// make the channels size of source image...
c1.resize(img.rows(),img.columns(),0,false,false);
c2.resize(img.rows(),img.columns(),0,false,false);
c3.resize(img.rows(),img.columns(),0,false,false);
for (p.y=0;p.y<img.rows();p.y++)
for (p.x=0;p.x<img.columns();p.x++) {
// take pixel at position p
pix = img.at(p);
// see Gonzales & Woods for explanation of magic numbers
X = (((float)(pix.getRed())) *0.412453f +
((float)(pix.getGreen())) *0.357580f +
((float)(pix.getBlue())) *0.180423f)/255.0f; // x
Y = (((float)(pix.getRed())) *0.212671f +
((float)(pix.getGreen())) *0.715160f +
((float)(pix.getBlue())) *0.072169f)/255.0f; // y
XYZ = (((float)(pix.getRed())) *0.644458f +
((float)(pix.getGreen())) *1.191933f +
((float)(pix.getBlue())) *1.202819f)/255.0f; // Y
if (XYZ>0.0f) {
c1.at(p) = (ubyte)(X/XYZ*255.0f); // x
c2.at(p) = (ubyte)(Y/XYZ*255.0f); // y
}
else {
c1.at(p) = 0; // x
c2.at(p) = 0; // y
}
c3.at(p) = (ubyte)(Y*255.0f); // Y
} // loop
return true;
}
// set the mask to use
void correlation::parameters::setMask(const channel8& aMask) {
delete mask;
mask=dynamic_cast<channel8*>(aMask.clone());
}
void RV02::Median( const channel8& sPic, // source picture
channel8& dPic, // destination picture
const int MaskSizeX, // mask size in x-direction
const int MaskSizeY // mask size in y-direction
)
{
const int PicSizeY = sPic.rows();
const int PicSizeX = sPic.columns();
const int maskWidth = (MaskSizeX % 2 == 0 ? MaskSizeX + 1 : MaskSizeX);
const int maskHeight = (MaskSizeX % 2 == 0 ? MaskSizeY + 1 : MaskSizeY);
const int maskSize = maskWidth * maskHeight;
const int maskHalfWidth = maskWidth / 2;
const int maskHalfHeight = maskHeight / 2;
// Gesuchert Wert im akkumulierten Histogramm
ubyte accuHistSearched = (maskSize + 1) / 2;
ubyte histogram[256] = {0};
ubyte accHistogram[256] = {0};
int x, y, mx, my;
for(y = maskHalfHeight; y < PicSizeY - maskHalfHeight; y++){
for(x = maskHalfWidth; x < PicSizeX - maskHalfWidth; x++){
// Alte Maskenhinstogrammwerte leeren
std::fill(histogram, histogram + 256, 0);
// Initialwerte
ubyte minGrauwert = 255;
ubyte maxGrauwert = 0;
ubyte h = 0;
// Maskenhistrogramm berechnen
for(my = y - maskHalfHeight; my <= y + maskHalfHeight; my++){
for(mx = x - maskHalfWidth; mx <= x + maskHalfWidth; mx++){
histogram[sPic[my][mx]]++;
h = sPic[my][mx];
if (h > maxGrauwert) {
maxGrauwert = h;
}
if (h < minGrauwert){
minGrauwert = h;
}
}
}
// Akkumuliertes Histogrammwert berechnen
// Der erste Index dessen Wert im akkumulierten Histogramm den accuHistValue ueberschreitet
// ist der gesuchte Medianwert.
int accuHistvalue = 0;
for(int i = minGrauwert; i <= maxGrauwert; i++){
accuHistvalue += histogram[i];
if(accuHistvalue >= accuHistSearched) {
dPic[y][x] = i;
break;
}
}
}
}
}
// return probability channel
bool probabilityMap2D::apply(const channel8& src1, const channel8& src2, channel& dest) const {
const parameters& param = getParameters();
point chnl1_size = src1.size();
point chnl2_size = src2.size();
// size of src1 equals src2 ?
if ( (chnl1_size.x != chnl2_size.x) || (chnl1_size.y != chnl2_size.y) ) {
setStatusString("probabilityMap2D: channels do not match");
return false;
}
// the color model MUST have 2 dimensions!
if (probabilityHistogram.dimensions() == 2) {
// resize probability channel
dest.resize(src1.size());
ivector theBin(2);
// compute first iteration
int y;
vector<channel8::value_type>::const_iterator srcIterator1, eit1;
vector<channel8::value_type>::const_iterator srcIterator2, eit2;
vector<channel::value_type>::iterator destIterator;
for (y=0;y<src1.rows();++y) {
srcIterator1 = src1.getRow(y).begin();
eit1 = src1.getRow(y).end();
srcIterator2 = src2.getRow(y).begin();
eit2 = src2.getRow(y).end();
destIterator = dest.getRow(y).begin();
while (srcIterator1 != eit1) {
theBin[0] = lookupTable[0][*srcIterator1];
theBin[1] = lookupTable[1][*srcIterator2];
(*destIterator)=static_cast<float>(probabilityHistogram.at(theBin));
srcIterator1++;
srcIterator2++;
destIterator++;
}
}
// compute all other iterations
if (param.iterations > 1) {
int i;
if (param.gaussian) {
gaussKernel2D<float> gk(param.windowSize,param.variance);
convolution convolver;
convolution::parameters convParam;
convParam.boundaryType = lti::Mirror;
convParam.setKernel(gk);
convolver.setParameters(convParam);
for (i=1;i<param.iterations;++i) {
convolver.apply(dest);
computeMap(src1,src2,dest);
}
} else {
squareConvolution<float> convolver;
squareConvolution<float>::parameters convParam;
convParam.boundaryType = lti::Mirror;
convParam.initSquare(param.windowSize);
convolver.setParameters(convParam);
for (i=1;i<param.iterations;++i) {
convolver.apply(dest);
computeMap(src1,src2,dest);
}
}
} // of (param.iterations > 1)
return true;
} // of (probabilityHistogram.dimensions() == 2)
setStatusString("probabilityMap2D: no models loaded");
return false;
}
// Quantization takes place here!
bool medianCut::performQuantization(const image& src,
image& dest,
channel8& mask,
palette &thePalette) const {
// parameters and const variables
const parameters& param = getParameters();
const int imageRows=src.rows(); // number of rows in src
const int imageCols=src.columns(); // number of columns in src
// resize destination containers
dest.resize(imageRows,imageCols,rgbPixel(),false,false);
mask.resize(imageRows,imageCols,ubyte(),false,false);
// Variables
int row,col; // row, column counters
int r,g,b; // red,green,blue
ivector iVec(3); // int-vector
std::list<boxInfo> theLeaves; // list of leaves (tree without root
// and nodes)
std::list<boxInfo>::iterator splitPos; // position to split
std::list<boxInfo>::iterator iter; // iterator for theLeaves
// create histogram with desired pre-quantization dimensions from src
histogram theHist(3,param.preQuant);
const float factor = param.preQuant/256.0f;
for (row = 0 ; row < imageRows ; row++) {
for (col = 0 ; col < imageCols ; col++) {
r = static_cast<int>(src.at( row,col ).getRed() * factor);
g = static_cast<int>(src.at( row,col ).getGreen() * factor);
b = static_cast<int>(src.at( row,col ).getBlue() * factor);
// insert point with quantized color
dest.at(row,col).set((r*256+128)/param.preQuant,
(g*256+128)/param.preQuant,
(b*256+128)/param.preQuant,0);
iVec[0] = r;
iVec[1] = g;
iVec[2] = b;
theHist.put(iVec);
}
}
// initialization of first box of list (the whole histogram)
boxInfo theBox(rgbPixel(0,0,0),
rgbPixel(param.preQuant-1,
param.preQuant-1,
param.preQuant-1));
computeBoxInfo(theHist,theBox);
// return, if desired number of colors smaller than colors in
// pre-quantized image
if (theBox.colors < param.numberOfColors) {
thePalette.resize(theBox.colors,rgbPixel(),false,false);
// prepare palette
int i = 0;
for (r=0;r<param.preQuant;++r) {
for (g=0;g<param.preQuant;++g) {
for (b=0;b<param.preQuant;++b) {
iVec[0] = r;
iVec[1] = g;
iVec[2] = b;
if (theHist.at(iVec) > 0) {
thePalette.at(i).set((r*256+128)/param.preQuant,
(g*256+128)/param.preQuant,
(b*256+128)/param.preQuant);
}
}
}
}
// use the palette to generate the corresponding channel
usePalette colorizer;
colorizer.apply(dest,thePalette,mask);
return true;
}
// Push first box into List
theLeaves.push_back(theBox);
// MAIN LOOP (do this until you have enough leaves (count), or no
// splittable boxes (entries))
int count, entries=1; // auxiliary variables for the main loop
for (count=1; (count<param.numberOfColors) && (entries!=0); count++) {
// find box with largest number of entries from list
entries = 0;
for (iter = theLeaves.begin() ; iter != theLeaves.end() ; iter++) {
if ( (*iter).colorFrequency > entries ) {
// Avoid choosing single colors, i.e. unsplittable boxes
if ( ((*iter).max.getRed() > (*iter).min.getRed()) ||
((*iter).max.getGreen() > (*iter).min.getGreen()) ||
((*iter).max.getBlue() > (*iter).min.getBlue()) ) {
entries = (*iter).colorFrequency;
splitPos = iter;
}
}
}
// A splittable box was found.
// The iterator "splitPos" indicates its position in the List
if (entries >0) {
// Determine next axis to split (largest variance) and box dimensions
int splitAxis; // split axis indicator
if ( ((*splitPos).var[0] >= (*splitPos).var[1]) &&
((*splitPos).var[0] >= (*splitPos).var[2]) ) {
splitAxis = 0; // red axis
}
else if ( (*splitPos).var[1] >= (*splitPos).var[2] ) {
splitAxis = 1; // green axis
}
else {
splitAxis = 2; // blue axis
}
int rMax = ((*splitPos).max.getRed());
int rMin = ((*splitPos).min.getRed());
int gMax = ((*splitPos).max.getGreen());
int gMin = ((*splitPos).min.getGreen());
int bMax = ((*splitPos).max.getBlue());
int bMin = ((*splitPos).min.getBlue());
// pass through box along the axis to split
bool found; // becomes true when split plane is found
int nrOfCols=0; // counter: number of colors of box
int prevNrOfCols=0; // forerunner of nrOfCols
rgbPixel lower1; // lower pixel from box 1
rgbPixel upper1; // upper pixel from box 1
rgbPixel lower2; // lower pixel from box 2
rgbPixel upper2; // upper pixel from box 2
switch (splitAxis) {
case 0: // red axis
nrOfCols = 0;
for (r = rMin , found = false ; (!found) && (r<=rMax) ; r++) {
prevNrOfCols = nrOfCols;
for (g = gMin ; g <= gMax ; g++) {
for (b=bMin;b<=bMax;b++) {
iVec[0] = r;
iVec[1] = g;
iVec[2] = b;
if (theHist.at(iVec) > 0.0) {
nrOfCols += static_cast<long int>(theHist.at(iVec));
}
}
}
if ( nrOfCols >= (*splitPos).colorFrequency/2 ) {
found=true;
}
}
if (fabs(prevNrOfCols -
static_cast<float>((*splitPos).colorFrequency)/2) <
fabs(nrOfCols -
static_cast<float>((*splitPos).colorFrequency)/2)) {
r--;
nrOfCols = prevNrOfCols;
}
// first box
lower1.setRed(rMin); lower1.setGreen(gMin); lower1.setBlue(bMin);
upper1.setRed(r-1); upper1.setGreen(gMax); upper1.setBlue(bMax);
// second box
lower2.setRed(r); lower2.setGreen(gMin); lower2.setBlue(bMin);
upper2.setRed(rMax); upper2.setGreen(gMax); upper2.setBlue(bMax);
break;
case 1: // g axis
nrOfCols = 0;
for (g = gMin , found = false ; (!found) && (g<=gMax) ; g++) {
prevNrOfCols = nrOfCols;
for (r = rMin ; r <= rMax ; r++) {
for (b = bMin ; b <= bMax ; b++) {
iVec[0] = r;
iVec[1] = g;
iVec[2] = b;
if (theHist.at(iVec) > 0.0) {
nrOfCols += static_cast<long int>(theHist.at(iVec));
}
}
}
if ( nrOfCols >= (*splitPos).colorFrequency/2 ) {
found=true;
}
}
if (fabs(prevNrOfCols -
static_cast<float>((*splitPos).colorFrequency)/2) <
fabs(nrOfCols -
static_cast<float>((*splitPos).colorFrequency)/2)) {
g--;
nrOfCols = prevNrOfCols;
}
// first box
lower1.setRed(rMin); lower1.setGreen(gMin); lower1.setBlue(bMin);
upper1.setRed(rMax); upper1.setGreen(g-1); upper1.setBlue(bMax);
// second box
lower2.setRed(rMin); lower2.setGreen(g); lower2.setBlue(bMin);
upper2.setRed(rMax); upper2.setGreen(gMax); upper2.setBlue(bMax);
break;
case 2: // b axis
nrOfCols = 0;
for (b = bMin , found = false ; (!found) && (b<=bMax) ; b++) {
prevNrOfCols = nrOfCols;
for (r = rMin ; r <= rMax ; r++) {
for (g = gMin ; g <= gMax ; g++) {
iVec[0] = r;
iVec[1] = g;
iVec[2] = b;
if (theHist.at(iVec) > 0.0) {
nrOfCols += static_cast<long int>(theHist.at(iVec));
}
}
}
if ( nrOfCols >= (*splitPos).colorFrequency/2 ) {
found=true;
}
}
if (fabs(prevNrOfCols -
static_cast<float>((*splitPos).colorFrequency)/2) <
fabs(nrOfCols -
static_cast<float>((*splitPos).colorFrequency)/2)) {
b--;
nrOfCols = prevNrOfCols;
}
// first box
lower1.setRed(rMin); lower1.setGreen(gMin); lower1.setBlue(bMin);
upper1.setRed(rMax); upper1.setGreen(gMax); upper1.setBlue(b-1);
// second box
lower2.setRed(rMin); lower2.setGreen(gMin); lower2.setBlue(b);
upper2.setRed(rMax); upper2.setGreen(gMax); upper2.setBlue(bMax);
break;
default:
break;
} // end of switch
// compute box info of new boxes and
// append both at the end of list
theBox.min = lower1;
theBox.max = upper1;
computeBoxInfo(theHist,theBox);
theLeaves.push_back(theBox);
theBox.min = lower2;
theBox.max = upper2;
computeBoxInfo(theHist,theBox);
theLeaves.push_back(theBox);
// delete splited box from list
theLeaves.erase(splitPos);
}
} // end of for (MAIN LOOP)
// compute block histogram and respective color palette
thePalette.resize(theLeaves.size());
int i;
for (iter = theLeaves.begin() , i=0 ;
iter != theLeaves.end() ;
iter++ , i++) {
// misuse histogram as a look-up-table
for (r = (*iter).min.getRed(); r <= (*iter).max.getRed(); r++) {
for (g = (*iter).min.getGreen(); g <= (*iter).max.getGreen(); g++) {
for (b = (*iter).min.getBlue(); b <= (*iter).max.getBlue(); b++) {
iVec[0] = r;
iVec[1] = g;
iVec[2] = b;
theHist.at(iVec) = i; // insert palette-index (refers to
// color in palette)
}
}
}
// create palette
r = (static_cast<int>((*iter).mean[0]*factor)*256+128)/param.preQuant;
g = (static_cast<int>((*iter).mean[1]*factor)*256+128)/param.preQuant;
b = (static_cast<int>((*iter).mean[2]*factor)*256+128)/param.preQuant;
thePalette[i].set(r,g,b,0); // insert color
}
// create new image with palette and theHist
dest.resize(imageRows,imageCols);
mask.resize(imageRows,imageCols,0,false,true);
// <= 256 colors? then also fill the mask
if (thePalette.size() <= 256) {
for (row = 0 ; row < imageRows ; row++) {
for (col = 0 ; col < imageCols ; col++) {
iVec[0] = static_cast<int>(src.at( row,col ).getRed() * factor);
iVec[1] = static_cast<int>(src.at( row,col ).getGreen() * factor);
iVec[2] = static_cast<int>(src.at( row,col ).getBlue() * factor);
i = static_cast<int>(theHist.at( iVec ));
dest.at(row,col) = thePalette[i];// insert point with quantized color
mask.at(row,col) = i; // insert palette index of quantized color
}
}
}
else {
for (row = 0 ; row < imageRows ; row++) {
for (col = 0 ; col < imageCols ; col++) {
iVec[0] = static_cast<int>(src.at( row,col ).getRed() * factor);
iVec[1] = static_cast<int>(src.at( row,col ).getGreen() * factor);
iVec[2] = static_cast<int>(src.at( row,col ).getBlue() * factor);
i = static_cast<int>(theHist.at( iVec ));
r = thePalette[i].getRed();
g = thePalette[i].getGreen();
b = thePalette[i].getBlue();
dest.at(row,col).set(r,g,b,0); // insert point with quantized color
}
}
}
return true;
}
// On copy apply for type channel8!
bool huMoments::apply(const channel8& src,dvector& dest, dvector& more) const {
channel8::value_type val;
dest.resize(NUM_FEAT,0,false,true);
more.resize(MORE_FEAT,0,false,true);
double m00=0.0;
double cm11,cm20,cm02,cm30,cm03,cm12,cm21;
cm11=cm20=cm02=cm30=cm03=cm12=cm21=0.0;
double xcog, ycog;
xcog=ycog=0.0;
int r, rows=src.rows();
int c, cols=src.columns();
// compute simple moments and cog
for (r=0; r<rows; r++) {
for (c=0; c<cols; c++) {
val = src.at(r,c);
m00+=val;
xcog+=c*val;
ycog+=r*val;
}
}
// end here, if no content
if (m00==0) {
return false;
}
// compute cog's
more[huMoments::xcog]=xcog=xcog/m00; //xcog
more[huMoments::ycog]=ycog=ycog/m00; //ycog
// compute central moments
for (r=0; r<rows; r++) {
for (c=0; c<cols; c++) {
val = src.at(r,c);
double x_xcog = c-xcog;
double y_ycog = r-ycog;
cm11+=(x_xcog)*(y_ycog)*val;
cm20+=(x_xcog)*(x_xcog)*val;
cm02+=(y_ycog)*(y_ycog)*val;
cm30+=(x_xcog)*(x_xcog)*(x_xcog)*val;
cm03+=(y_ycog)*(y_ycog)*(y_ycog)*val;
cm12+=(x_xcog)*(y_ycog)*(y_ycog)*val;
cm21+=(x_xcog)*(x_xcog)*(y_ycog)*val;
}
}
double m00pow2,m00pow2_5;
m00pow2 = m00*m00;
m00pow2_5 = pow(m00,2.5);
// normalized central moments
cm02 = cm02/m00pow2;
cm03 = cm03/m00pow2_5;
cm11 = cm11/m00pow2;
cm12 = cm12/m00pow2_5;
cm20 = cm20/m00pow2;
cm21 = cm21/m00pow2_5;
cm30 = cm30/m00pow2_5;
// calculate moment invariants
dest[huMoments::hu1] = cm20 + cm02;
dest[huMoments::hu2] = pow((cm20 - cm02),2) + 4*pow(cm11,2);
dest[huMoments::hu3] = pow((cm30 - 3*cm12),2) + pow((3*cm21 - cm03),2);
dest[huMoments::hu4] = pow((cm30 + cm12),2) + pow((cm21 + cm03),2);
dest[huMoments::hu5] = (cm30-3*cm12)*(cm30+cm12)*( pow((cm30+cm12),2) - 3*pow((cm21+cm03),2) )
+ (3*cm21-cm03)*(cm21+cm03)*( 3*pow((cm30+cm12),2) - pow((cm21+cm03),2) );
dest[huMoments::hu6] = (cm20-cm02)*( pow((cm30+cm12),2) - pow((cm21+cm03),2) )
+ 4*cm11*(cm30+cm12)*(cm21+cm03);
dest[huMoments::hu7] = (3*cm21-cm03)*(cm30+cm12)*( pow((cm30+cm12),2) - 3*pow((cm21+cm03),2) )
- (cm30-3*cm12)*(cm21+cm03)*( 3*pow((cm30+cm12),2) - pow((cm21+cm03),2) );
double temp = sqrt( (cm20 - cm02)*(cm20 - cm02) + 4*cm11*cm11 );
more[huMoments::eigen1]=m00*0.5*((cm20+cm02) + temp); //eigen 1
more[huMoments::eigen2]=m00*0.5*((cm20+cm02) - temp); //eigen 2
more[huMoments::orientation]=0.5*atan2(2*cm11, cm20 - cm02); //orientation
more[huMoments::m00]=m00; //m00
const parameters& param = getParameters();
if (param.scaling) {
int i;
for (i=0; i<dest.size();i++){
dest[i]=-logn(dest[i]);
}
}
return true;
}
