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Merge branch 'Zadanie_06_Dawid' of s452126/lpo-projekt2020 into master

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Dawid Kreft 2020-04-06 21:49:59 +00:00 committed by Gogs
commit 17f063074c
3 changed files with 99 additions and 13 deletions
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2
src/core/transformations/convolution.cpp
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@ -177,4 +177,4 @@ const math::matrix<float> Convolution::reflection(const math::matrix<float> A)
return C; return C;
} }

63
src/core/transformations/noise_bilateral.cpp
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@ -21,28 +21,73 @@ PNM* NoiseBilateral::transform()
sigma_r = getParameter("sigma_r").toInt(); sigma_r = getParameter("sigma_r").toInt();
radius = sigma_d; radius = sigma_d;
qDebug() << Q_FUNC_INFO << "Not implemented yet!"; for(int x = 0 ; x < width; x++){
for (int y = 0 ; y < height; y++) {
int g = calcVal(x,y, GChannel);
int r = calcVal(x,y, RChannel);
int b = calcVal(x,y, BChannel);
newImage->setPixel(x,y,QColor(r,g,b).rgba());
}
}
return newImage; return newImage;
} }
int NoiseBilateral::calcVal(int x, int y, Channel channel) int NoiseBilateral::calcVal(int x, int y, Channel channel)
{ {
qDebug() << Q_FUNC_INFO << "Not implemented yet!"; double sumNumerator = 0;
double sumDenominator = 0;
return 0; double colorClosenes = 0;
double spatialClosennes = 0;
int colorValue;
int colorValueRef;
for ( int i = x- radius ; i <= x+radius ; i++) {
for ( int j = y- radius ; j <= y+radius ; j++) {
if ( i+radius >= image->width() ||
i-radius < 0 ||
j+radius >= image->height() ||
j-radius < 0 ) {
continue;
}
if (channel==LChannel){
colorValue = qAlpha(image->pixel(i,j));
colorValueRef = qAlpha(image->pixel(x,y));
}
if (channel==RChannel){
colorValue = (qRed(image->pixel(i,j)));
colorValueRef = qRed(image->pixel(x,y));
}
if (channel==GChannel){
colorValueRef = qGreen(image->pixel(x,y));
colorValue = (qGreen(image->pixel(i,j)));
}
if (channel==BChannel){
colorValueRef = qBlue(image->pixel(x,y));
colorValue = ( qBlue(image->pixel(i,j)));
}
colorClosenes = colorCloseness(colorValue,colorValueRef);
spatialClosennes = spatialCloseness( QPoint(i,j),QPoint(x,y));
sumNumerator = sumNumerator +( colorValue* colorClosenes * spatialClosennes);
sumDenominator =sumDenominator + ( colorClosenes * spatialClosennes);
}
}
return sumNumerator/sumDenominator;
} }
float NoiseBilateral::colorCloseness(int val1, int val2) float NoiseBilateral::colorCloseness(int val1, int val2)
{ {
qDebug() << Q_FUNC_INFO << "Not implemented yet!"; return exp(-(((val1-val2)^2)/(2*sigma_r^2)));
return 0;
} }
float NoiseBilateral::spatialCloseness(QPoint point1, QPoint point2) float NoiseBilateral::spatialCloseness(QPoint point1, QPoint point2)
{ {
qDebug() << Q_FUNC_INFO << "Not implemented yet!"; return exp(-((point1.x() - point2.x())^2 + ( point1.y() - point2.y())^2)/(2*sigma_d^2));
return 0;
} }

47
src/core/transformations/noise_median.cpp
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@ -17,16 +17,57 @@ PNM* NoiseMedian::transform()
PNM* newImage = new PNM(width, height, image->format()); PNM* newImage = new PNM(width, height, image->format());
qDebug() << Q_FUNC_INFO << "Not implemented yet!"; for(int x = 0 ; x < width; x++){
for (int y = 0 ; y < height; y++) {
int g = getMedian(x,y, GChannel);
int r = getMedian(x,y, RChannel);
int b = getMedian(x,y, BChannel);
newImage->setPixel(x,y,QColor(r,g,b).rgba());
}
}
return newImage; return newImage;
} }
int NoiseMedian::getMedian(int x, int y, Channel channel) int NoiseMedian::getMedian(int x, int y, Channel channel)
{ {
int radius = getParameter("radius").toInt(); int radius = getParameter("radius").toInt();
QSet<int> set;
qDebug() << Q_FUNC_INFO << "Not implemented yet!"; QList<int> list = QList<int>::fromSet(set);
std::sort(list.begin(), list.end());
return 0;
for ( int i = x- radius ; i <= x+radius ; i++) {
for ( int j = y- radius ; j <= y+radius ; j++) {
if ( i+radius >= image->width() ||
i-radius < 0 ||
j+radius >= image->height() ||
j-radius < 0 ) {
continue;
}
if (channel==LChannel) set.insert(qAlpha(image->pixel(i,j)));
if (channel==RChannel) set.insert(qRed(image->pixel(i,j)));
if (channel==GChannel) set.insert(qGreen(image->pixel(i,j)));
if (channel==BChannel) set.insert( qBlue(image->pixel(i,j)));
}
}
QList<int> data = QList<int>::fromSet(set);
std::sort(data.begin(), data.end());
int result =0 ;
if( data.size() %2 == 1){
result = data.at(data.size()/2);
}else if(data.size()>3){
int first_med = data.at((data.size()/2 )-1);
int sec_dem = data.at((data.size()/2 ));
result = (first_med+sec_dem)/2;
}
data.clear();
set.clear();
return result;
} }