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Dawid_zad_
@ -17,42 +17,8 @@ PNM* BinarizationGradient::transform()
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PNM* newImage = new PNM(width, height, QImage::Format_Mono);
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int Gmax = 0;
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int numerator = 0;
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int denominator = 0;
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for(int x=1; x<width-1; x++){
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for(int y=1; y<height-1; y++){
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int t = (qRed(image->pixel(x,y)) + qGreen(image->pixel(x,y)) + qBlue(image->pixel(x,y))) / 3;
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int Gxa = (qRed(image->pixel(x-1,y)) + qGreen(image->pixel(x-1,y)) + qBlue(image->pixel(x-1,y))) / 3;
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int Gxb = (qRed(image->pixel(x+1,y)) + qGreen(image->pixel(x+1,y)) + qBlue(image->pixel(x+1,y))) / 3;
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int Gya = (qRed(image->pixel(x,y+1)) + qGreen(image->pixel(x,y+1)) + qBlue(image->pixel(x,y+1))) / 3;
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int Gyb= (qRed(image->pixel(x,y-1)) + qGreen(image->pixel(x,y-1)) + qBlue(image->pixel(x,y-1))) / 3;
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qDebug() << Q_FUNC_INFO << "Not implemented yet!";
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int Gx = Gxa - Gxb;
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int Gy = Gya - Gyb;
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if (Gx > Gy) Gmax = Gx;
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else Gmax = Gy;
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numerator += t * Gmax;
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denominator += Gmax;
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}
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}
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int T = numerator/denominator;
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// create new image
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for(int i=0; i<width; i++){
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for(int j=0; j<height; j++){
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QRgb pixel = image->pixel(i,j);
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int tempValue = (qRed(pixel) + qGreen(pixel) + qBlue(pixel)) / 3;
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if ( tempValue > T ){
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newImage->setPixel(i, j, Qt::color1);
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}
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else {
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newImage->setPixel(i, j, Qt::color0);
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}
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}
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}
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return newImage;
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}
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@ -41,8 +41,9 @@ math::matrix<float> BlurGaussian::getMask(int size, Mode)
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}
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float BlurGaussian::getGauss(int x, int y, float sigma)
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{
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{
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float gauss= exp(-(x*x+y*y)/(2*sigma*sigma))/(2*M_PI*sigma*sigma);
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return gauss;
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}
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@ -137,7 +137,7 @@ const math::matrix<float> Convolution::join(math::matrix<float> A, math::matrix<
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for (int x=0; x<size; x++)
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for (int y=0; y<size; y++)
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{
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{
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C[x][y]=A[x][y]*B[x][y];
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}
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@ -181,5 +181,4 @@ const math::matrix<float> Convolution::reflection(const math::matrix<float> A)
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}
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return C;
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}
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@ -24,115 +24,9 @@ PNM* CornerHarris::transform()
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int width = image->width(),
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height = image->height();
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//Krok1 Zdef i wypelij zerami
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math::matrix<float> Ixx(width, height);
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math::matrix<float> Iyy(width, height);
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math::matrix<float> Ixy(width, height);
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math::matrix<float> CC(width, height);
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math::matrix<float> CNS(width, height);
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for (int x=0; x<width; x++)
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for (int y=0; y<height; y++)
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{
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Ixx[x][y]=0;
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Iyy[x][y]=0;
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Ixy[x][y]=0;
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CC[x][y]=0;
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CNS[x][y]=0;
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}
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//Krok 2 skala szarosci
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ConversionGrayscale cg(image);
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PNM* gray_image=cg.transform();
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//Krok 3 rozmyj
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BlurGaussian bg(gray_image);
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bg.setParameter("size",3);
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bg.setParameter("sigma",1.6);
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PNM* blur_image=bg.transform();
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//Krok 4 sobel
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EdgeSobel es(blur_image);
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math::matrix<float> *Gx=es.rawHorizontalDetection();
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math::matrix<float> *Gy=es.rawVerticalDetection();
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//Krok 5 wzorek
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for (int x=0; x<width; x++)
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for (int y=0; y<height; y++)
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{
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Ixx[x][y]=(*Gx)[x][y] * (*Gx)[x][y];
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Iyy[x][y]=(*Gy)[x][y] * (*Gy)[x][y];
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Ixy[x][y]=(*Gx)[x][y] * (*Gy)[x][y];
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}
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//Krok 6 wzorek
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for (int x=1; x<width-1; x++)
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for (int y=1; y<height-1; y++)
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{
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float Sxx=0;
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float Syy=0;
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float Sxy=0;
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math::matrix<float> H(2, 2);
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for(int z=-1;z<=1;z++) //Znacznik sumy
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for(int a=-1;a<=1;a++)
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{
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Sxx+=Ixx[x+z][y+a]*BlurGaussian::getGauss(z,a,sigma);
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Syy+=Iyy[x+z][y+a]*BlurGaussian::getGauss(z,a,sigma);
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Sxy+=Ixy[x+z][y+a]*BlurGaussian::getGauss(z,a,sigma);
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}
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Sxx=Sxx/sigma_weight;
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Syy=Syy/sigma_weight;
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Sxy=Sxy/sigma_weight;
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H[0][0]=Sxx;
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H[0][1]=Sxy;
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H[1][0]=Sxy;
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H[1][1]=Syy;
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float detH=H[0][0]*H[1][1]-H[0][1]*H[1][0];
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//Wyznaczkik + suma po przekatnej kwadratowej
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float r = detH - k_param * pow(H[0][0] + H[1][1], 2);
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if(r>threshold) CC[x][y]=r;
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}
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bool search=1;
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//Krok 8
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while(search==1)
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{
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search=0;
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for (int x=1; x<width-1; x++)
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for (int y=1; y<height-1; y++)
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{
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float max=CC[x][y];
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if(max > CC[x][y-1] && max > CC[x][y+1] && max > CC[x+1][y-1] && max > CC[x+1][y] && max > CC[x+1][y+1] && max > CC[x-1][y-1] && max > CC[x-1][y] && max > CC[x-1][y+1])
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CNS[x][y]=CC[x][y];
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else {
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if(CC[x][y]>0)
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{
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CNS[x][y]=0;
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search=1;
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}
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}
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}
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CC=CNS;
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}
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PNM* newImage = new PNM(width, height, QImage::Format_Mono);
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//Krok 9
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for (int x=1; x<width-1; x++)
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for (int y=1; y<height-1; y++)
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{
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if(CC[x][y]==0) newImage->setPixel(x,y,0);
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else newImage->setPixel(x,y,1);
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}
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qDebug() << Q_FUNC_INFO << "Not implemented yet!";
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return newImage;
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}
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@ -10,12 +10,9 @@ EdgeLaplacian::EdgeLaplacian(PNM* img, ImageViewer* iv) :
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{
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}
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math::matrix<float> EdgeLaplacian::getMask(int size , Mode)
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math::matrix<float> EdgeLaplacian::getMask(int, Mode)
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{
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if( getParameter("size").toInt() > size )
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size =getParameter("size").toInt();
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int size = getParameter("size").toInt();
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math::matrix<float> mask(size, size);
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int r=size/2;
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@ -12,14 +12,11 @@ EdgeLaplaceOfGauss::EdgeLaplaceOfGauss(PNM* img, ImageViewer* iv) :
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{
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}
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math::matrix<float> EdgeLaplaceOfGauss::getMask(int size, Mode)
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math::matrix<float> EdgeLaplaceOfGauss::getMask(int, Mode)
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{
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if( getParameter("size").toInt() > size )
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size =getParameter("size").toInt();
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size = getParameter("size").toInt();
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double sigma = getParameter("sigma").toDouble();
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math::matrix<float> mask(size, size);
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int r=size/2;
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@ -51,7 +51,7 @@ math::matrix<float>* EdgeSobel::rawHorizontalDetection()
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for (int y = 0; y < height; y++)
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{
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math::matrix<float> temp = getWindow(x, y, 3, LChannel, NullEdge);
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(*x_gradient)(x, y) = Convolution::sum(Convolution::join(g_x, temp));
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x_gradient[x][y] = Convolution::sum(Convolution::join(g_x, temp));
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}
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}
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@ -71,7 +71,7 @@ math::matrix<float>* EdgeSobel::rawVerticalDetection()
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for (int y = 0; y < height; y++)
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{
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math::matrix<float> temp = getWindow(x, y, 3, LChannel, NullEdge);
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(*y_gradient)(x, y) = Convolution::sum(Convolution::join(g_x, temp));
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y_gradient[x][y] = Convolution::sum(Convolution::join(g_x, temp));
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}
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}
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@ -17,50 +17,7 @@ PNM* Hough::transform()
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{
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int thetaDensity = getParameter("theta_density").toInt();
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int width = image->width();
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int height = image->height();
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PNM* tempImage = new PNM(width, height, QImage::Format_Indexed8);
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tempImage = ConversionGrayscale(image).transform();
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qDebug() << Q_FUNC_INFO << "Not implemented yet!";
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if (!getParameter("skip_edge_detection").toBool()) {
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tempImage = EdgeLaplacian(tempImage).transform();
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}
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int pmax =int(sqrt(height*height + width*width));
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int thetaSize= 180 * thetaDensity;
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int lenght = pmax * 2 + 1;
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double theta, p;
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int max = 0;
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math::matrix<int> hough(thetaSize,lenght);
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for (int i = 0; i < thetaSize; i++) {
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for (int j = 0 ; j < lenght ; j++) {
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hough[i][j] = 0;
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}
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}
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for (int i = 0; i < width; i++) {
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for (int j = 0; j < height; j++) {
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if (qGray(tempImage->pixel(i, j)) > 0) {
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for (int k = 0; k < thetaSize; k++) {
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theta = (k * 3.14) / (180 * thetaDensity);
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p = i*cos(theta) + j*sin(theta);
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hough[k][p+pmax]++;
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if (hough[k][p+pmax] > max) {
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max = hough[k][p+pmax];
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}
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}
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}
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}
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}
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PNM* newImage = new PNM(thetaSize,lenght, QImage::Format_Grayscale8);
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for (int i = 0; i < thetaSize; i++) {
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for (int j = 0; j < lenght; j++) {
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int value = int(((hough[i][j] * 255)/ max));
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newImage->setPixel(i, j, QColor(value,value,value).rgb());
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}
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}
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return newImage;
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return 0;
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}
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@ -24,34 +24,7 @@ PNM* HoughLines::transform()
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PNM* newImage = new PNM(image->copy());
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// step 1
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EdgeLaplacian* edgeLA = new EdgeLaplacian(image);
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edgeLA->setParameter("size", 3);
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PNM* tempImage = edgeLA->transform();
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// step 2
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BinarizationGradient* binarizationGradient = new BinarizationGradient(tempImage);
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PNM* binImage = binarizationGradient->transform();
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// step 3
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Hough* hough = new Hough(binImage);
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hough->setParameter("theta_density", 3);
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hough->setParameter("skip_edge_detection", true);
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tempImage = hough->transform();
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// step 4
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QPainter p(newImage);
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p.setPen(Qt::red);
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for(int i=0; i < tempImage->width(); i++) {
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for(int j=0; j < tempImage->height(); j++) {
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if(qGray(tempImage->pixel(i, j)) > threshold) {
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double rtheta = ((double)i/3.0)*M_PI/180.0;
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int rrho = j - tempImage->height()/2;
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p.drawLine(0, round(rrho/sin(rtheta)), newImage->width()-1, round((rrho - (newImage->width()-1)*cos(rtheta))/sin(rtheta)));
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}
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if (!drawWholeLines == true){
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if (qRed(binImage->pixel(i, j)) == 0){
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newImage->setPixel(i, j, image->pixel(i, j));
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}
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}
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}
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}
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qDebug() << Q_FUNC_INFO << "Not implemented yet!";
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return newImage;
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}
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