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;
}
}
}
/*
* 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;
}
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;
}
// 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;
}