/*
in input image
type integer defining how convolution for smoothing operation is done
0 <==> spatial domain; 1 <==> frequency domain
size size of used smoothing kernel
thresh minimal intensity difference to perform operation
scale scaling of edge enhancement
return enhanced image
*/
Mat Dip3::usm(Mat& in, int type, int size, double thresh, double scale){
// some temporary images
Mat tmp(in.rows, in.cols, CV_32FC1);
// calculate edge enhancement
// 1: smooth original image
// save result in tmp for subsequent usage
switch(type){
case 0:
tmp = mySmooth(in, size, true);
break;
case 1:
tmp = mySmooth(in, size, false);
break;
default:
GaussianBlur(in, tmp, Size(floor(size/2)*2+1, floor(size/2)*2+1), size/5., size/5.);
}
subtract(in, tmp, tmp);
for (int x = 0; x < in.rows; x++) for (int y = 0; y < in.cols; y++) {
if (tmp.at<float>(x, y) > thresh) {
in.at<float>(x, y) = in.at<float>(x, y) + tmp.at<float>(x, y) * scale;
}
}
return in;
}
/*
Performs UnSharp Masking to enhance fine image structures
in input image
type integer defining how convolution for smoothing operation is done
0 <==> spatial domain; 1 <==> frequency domain
size size of used smoothing kernel
thresh minimal intensity difference to perform operation
scale scaling of edge enhancement
return enhanced image
*/
Mat usm(Mat& in, int type, int size, double thresh, double scale) {
const bool spatial = (type == 0);
Mat diff = mySmooth(in, size, spatial);
diff -= in;
diff *= scale;
Mat res = in + diff;
int ltt = 0;
int htt = 0;
const int w = in.rows;
const int h = in.cols;
for (int x = 0; x < w; ++x) { // image.x
for (int y = 0; y < h; ++y) { // image.y
// reset to input if the difference is below the threshold
float newVal;
if (diff.at<float>(x, y) < thresh) {
newVal = in.at<float>(x, y);
ltt++;
} else {
newVal = res.at<float>(x, y);
htt++;
}
res.at<float>(x, y) = newVal;
}
}
// cout << "higher then threshold: " << htt << " / lower: " << ltt << endl;
return res;
}
/*
in input image
type integer defining how convolution for smoothing operation is done
0 <==> spatial domain; 1 <==> frequency domain
size size of used smoothing kernel
thresh minimal intensity difference to perform operation
scale scaling of edge enhancement
return enhanced image
*/
Mat Dip3::usm(Mat& in, int type, int size, double thresh, double scale){
// some temporary images
Mat tmp(in.rows, in.cols, CV_32FC1);
// calculate edge enhancement
// 1: smooth original image
// save result in tmp for subsequent usage
switch(type){
case 0:
tmp = mySmooth(in, size, true);
break;
case 1:
tmp = mySmooth(in, size, false);
break;
default:
GaussianBlur(in, tmp, Size(floor(size/2)*2+1, floor(size/2)*2+1), size/5., size/5.);
}
// TO DO !!!
//2. subtract smoothed image
Mat subtractedImage = in.clone();
subtractedImage -= tmp;
//subtract(in, tmp, subtractedImage);
//3. Thresholding
Mat scaledImage = Mat::zeros(in.size(), CV_32FC1);
threshold(subtractedImage, scaledImage, thresh, 255, THRESH_TOZERO);
scaledImage *= scale;
////for (int i = 0; i < in.rows; i++){
//// for (int j = 0; j < in.cols; j++){
//// if (subtractedImage.at<float>(i, j)>thresh){
//// scaledImage.at<float>(i, j) = scale*subtractedImage.at<float>(i, j);
//// }
//// }
////}
Mat enhancedImage = in.clone();
enhancedImage += scaledImage;
//add()
return enhancedImage;
// return in;
}