/*
in input image
size size of filter kernel
spatial true if convolution shall be performed in spatial domain, false otherwise
return smoothed image
*/
Mat Dip3::mySmooth(Mat& in, int size, bool spatial){
// create filter kernel
Mat kernel = createGaussianKernel(size);
// perform convoltion
if (spatial)
return spatialConvolution(in, kernel);
else
return frequencyConvolution(in, kernel);
}
void Dip3::test_createGaussianKernel(void){
Mat k = createGaussianKernel(11);
if ( abs(sum(k).val[0] - 1) > 0.0001){
cout << "ERROR: Dip3::createGaussianKernel(): Sum of all kernel elements is not one!" << endl;
return;
}
if (sum(k >= k.at<float>(5,5)).val[0]/255 != 1){
cout << "ERROR: Dip3::createGaussianKernel(): Seems like kernel is not centered!" << endl;
return;
}
cout << "Message: Dip3::createGaussianKernel() seems to be correct" << endl;
}
pixel3f *Image::createGaussian(float sigma)
{
pixel3f *gaussian = new pixel3f[_width * _height]();
// Precompute constants.
float variance = sigma * sigma;
float denomiator = 2 * M_PI * variance;
// Radius of filter.
int r = 2.0f * sigma;
float *kernel = createGaussianKernel(r + 1, variance);
// Seperable x component of Gaussian filter.
for (int x = 0; x < _width; x++) {
for (int y = 0; y < _height; y++) {
float sum = 0;
for (int i = -r; i <= r; i++) {
sum += pixelAt(_pixels, x + i, y)->L * kernel[abs(i)];
}
// Do not divide by the regular denominator.
pixelAt(_copy, x, y)->L = sum;
}
}
// Seperable y component of Gaussian filter.
for (int x = 0; x < _width; x++) {
for (int y = 0; y < _height; y++) {
float sum = 0;
for (int j = -r; j <= r; j++) {
sum += pixelAt(_copy, x, y + j)->L * kernel[abs(j)];
}
// Divided by the denominator squared only once rather than twice.
pixelAt(gaussian, x, y)->L = sum / denomiator;
}
}
// Free temporary data.
delete[] kernel;
return gaussian;
}
void Image::bilateral()
{
// Gaussian kernel used to model geometric similarity.
float *kernel = createGaussianKernel(RADIUS + 1, VARIANCE);
for (int x = 0; x < _width; x++) {
for (int y = 0; y < _height; y++) {
pixel3f *center = pixelAt(_pixels, x, y);
float numerator = 0;
float denominator = 0;
for (int i = -RADIUS; i <= RADIUS; i++) {
pixel3f *neighbor = pixelAt(_pixels, x + i, y);
// Compute euclidean distance between Lab colors to determine photometric similarity.
float dL = center->L - neighbor->L;
float da = center->a - center->a;
float db = center->b - center->b;
float distance = dL * dL + da * da + db * db;
float photometric = expf(distance / PHOTOMETRIC);
float similarity = photometric * kernel[abs(i)];
// Denominator serves to normalize values.
denominator += similarity;
numerator += similarity * neighbor->L;
}
// Copy a and b data over as well to use for the y-filter pass.
pixel3f *target = pixelAt(_copy, x, y);
target->L = numerator / denominator;
target->a = center->a;
target->b = center->b;
}
}
for (int x = 0; x < _width; x++) {
for (int y = 0; y < _height; y++) {
pixel3f *center = pixelAt(_copy, x, y);
float numerator = 0;
float denominator = 0;
for (int j = -RADIUS; j <= RADIUS; j++) {
pixel3f *neighbor = pixelAt(_copy, x, y + j);
float dL = center->L - neighbor->L;
float da = center->a - center->a;
float db = center->b - center->b;
float distance = dL * dL + da * da + db * db;
float photometric = expf(distance / PHOTOMETRIC);
float similarity = photometric * kernel[abs(j)];
denominator += similarity;
numerator += similarity * neighbor->L;
}
pixelAt(_pixels, x, y)->L = numerator / denominator;
}
}
delete[] kernel;
}
