Mat Kernel::extractDescriptors(Mat image){
int w = image.cols, h=image.rows, size=32, delta=16;
Mat result, temp, img;
int i = (patches/14)*delta, j = (patches%14)*delta;
if(filter=="DoG")
img = DoGFilter(image);
else if(filter=="CSDN")
img = CSDNFilter(image);
else if(filter=="Gaussian")
img = GaussianFilter(image);
else
cerr << "Error, no filter choosed" << endl;
img = img(Rect(i,j,size,size));
//img.convertTo(img, CV_32F); // Retirar isso depois
//dct(img,img); // Isso tambem
if(descriptor=="SIFT")
calcSIFTDescriptors(img,temp);
else if(descriptor=="MLBP")
calcLBPHistogram(img,temp);
else
cerr << "Error, no descriptor choosed" << endl;
normalize(temp,temp,1);
result = temp.t();
return result;
}
void testNoise (void) {
GIMAGE* input = Gtype(readGrey("./TestRepo/00.Test_Images/lena.bmp"));
GIMAGE* output = createImage(input->width,input->height,1);
double *pdf = new double [NOISE_LENGTH];
GaussianFilter (NOISE_LENGTH/2, (double)NOISE_LENGTH/3.0, 0, NOISE_LENGTH, pdf); // Gaussian white noise
// Uniform White noise
//for (int i = 0; i < NOISE_LENGTH; i++)
// pdf [i] = 1.0/NOISE_LENGTH;
// Impulse
//for (int i = 0; i < NOISE_LENGTH; i++)
// pdf [i] = 0;
//pdf [(int)NOISE_LENGTH-1] = 1;
double *inv_cdf = inverse_cdf (-NOISE_AMP, NOISE_AMP, NOISE_LENGTH, pdf);
addWhiteNoise (input, NOISE_LENGTH, inv_cdf, output);
writeImage ("./TestRepo/noise.bmp", output);
}
// GaussianFilterByFreq -----------------------
int GaussianFilterByFreq(quint16 * pnData, long lPoints,
long InFreq,long CutoffFreq)
{
return GaussianFilter(pnData,lPoints,double(CutoffFreq)/InFreq);
};
