void GetFDoG(imatrix& image, ETF& e, double sigma, double sigma3, double tau)
{
int i, j;
int image_x = image.getRow();
int image_y = image.getCol();
myvec GAU1, GAU2, GAU3;
MakeGaussianVector(sigma, GAU1);
MakeGaussianVector(sigma*1.6, GAU2);
int half_w1, half_w2, half_l;
half_w1 = GAU1.getMax()-1;
half_w2 = GAU2.getMax()-1;
MakeGaussianVector(sigma3, GAU3);
half_l = GAU3.getMax()-1;
mymatrix tmp(image_x, image_y);
mymatrix dog(image_x, image_y);
GetDirectionalDoG(image, e, dog, GAU1, GAU2, tau);
GetFlowDoG(e, dog, tmp, GAU3);
for (i = 0; i < image_x; i++) {
for (j = 0; j < image_y; j++) {
image[i][j] = round(tmp[i][j] * 255.);
}
}
}
void GaussSmoothSep(imatrix& image, double sigma)
{
int i, j;
double g, max_g, min_g;
int s, t;
int x, y;
double weight, w_sum;
int image_x = image.getRow();
int image_y = image.getCol();
myvec GAU1;
MakeGaussianVector(sigma, GAU1);
int half = GAU1.getMax()-1;
mymatrix tmp(image_x, image_y);
max_g = -1;
min_g = 10000000;
for (j = 0; j < image_y; j++) {
for (i = 0; i < image_x; i++) {
g = 0.0;
weight = w_sum = 0.0;
for (s = -half; s <= half; s++) {
x = i+s; y = j;
if (x > image_x-1) x = image_x-1;
else if (x < 0) x = 0;
if (y > image_y-1) y = image_y-1;
else if (y < 0) y = 0;
weight = GAU1[ABS(s)];
g += weight * image[x][y];
w_sum += weight;
}
g /= w_sum;
if (g > max_g) max_g = g;
if (g < min_g) min_g = g;
tmp[i][j] = g;
}
}
for (j = 0; j < image_y; j++) {
for (i = 0; i < image_x; i++) {
g = 0.0;
weight = w_sum = 0.0;
for (t = -half; t <= half; t++) {
x = i; y = j+t;
if (x > image_x-1) x = image_x-1;
else if (x < 0) x = 0;
if (y > image_y-1) y = image_y-1;
else if (y < 0) y = 0;
weight = GAU1[ABS(t)];
g += weight * tmp[x][y];
w_sum += weight;
}
g /= w_sum;
if (g > max_g) max_g = g;
if (g < min_g) min_g = g;
image[i][j] = round(g);
}
}
}
void ConstructMergedImage(imatrix& image, imatrix& gray, imatrix& merged)
{
int x, y;
int image_x = image.getRow();
int image_y = image.getCol();
for (y = 0; y < image_y; y++) {
for (x = 0; x < image_x; x++) {
if (gray[x][y] == 0) merged[x][y] = 0;
else merged[x][y] = image[x][y];
}
}
}
void GrayThresholding(imatrix& image, double thres)
{
int i, j;
double val;
int image_x = image.getRow();
int image_y = image.getCol();
for (i = 0; i < image_x; i++) {
for (j = 0; j < image_y; j++) {
val = image[i][j] / 255.0;
if (val < thres)
image[i][j] = round(val * 255.0);
else image[i][j] = 255;
}
}
}
void Binarize(imatrix& image, double thres)
{
int i, j;
double val;
int image_x = image.getRow();
int image_y = image.getCol();
for (i = 0; i < image_x; i++) {
for (j = 0; j < image_y; j++) {
val = image[i][j] / 255.0;
if (val < thres)
image[i][j] = 0;
else image[i][j] = 255;
}
}
}
void ConstructMergedImageMult(imatrix& image, imatrix& gray, imatrix& merged)
// using multiplication
{
int x, y;
double gray_val, line_darkness;
int image_x = image.getRow();
int image_y = image.getCol();
for (y = 0; y < image_y; y++) {
for (x = 0; x < image_x; x++) {
gray_val = image[x][y] / 255.0;
line_darkness = gray[x][y] / 255.0;
gray_val *= line_darkness;
merged[x][y] = round(gray_val * 255.0);
}
}
}
void GetDirectionalDoG(imatrix& image, ETF& e, mymatrix& dog, myvec& GAU1, myvec& GAU2, double tau)
{
myvec vn(2);
double x, y, d_x, d_y;
double weight1, weight2, w_sum1, sum1, sum2, w_sum2;
int s;
int x1, y1;
int i, j;
int dd;
double val;
int half_w1, half_w2;
half_w1 = GAU1.getMax()-1;
half_w2 = GAU2.getMax()-1;
int image_x, image_y;
image_x = image.getRow();
image_y = image.getCol();
for (i = 0; i < image_x; i++) {
for (j = 0; j < image_y; j++) {
sum1 = sum2 = 0.0;
w_sum1 = w_sum2 = 0.0;
weight1 = weight2 = 0.0;
vn[0] = -e[i][j].ty;
vn[1] = e[i][j].tx;
if (vn[0] == 0.0 && vn[1] == 0.0) {
sum1 = 255.0;
sum2 = 255.0;
dog[i][j] = sum1 - tau * sum2;
continue;
}
d_x = i; d_y = j;
////////////////////////////////////////
for (s = -half_w2; s <= half_w2; s++) {
////////////////////////
x = d_x + vn[0] * s;
y = d_y + vn[1] * s;
/////////////////////////////////////////////////////
if (x > (double)image_x-1 || x < 0.0 || y > (double)image_y-1 || y < 0.0)
continue;
x1 = round(x); if (x1 < 0) x1 = 0; if (x1 > image_x-1) x1 = image_x-1;
y1 = round(y); if (y1 < 0) y1 = 0; if (y1 > image_y-1) y1 = image_y-1;
val = image[x1][y1];
/////////////////////////////////////////////////////////
dd = ABS(s);
if (dd > half_w1) weight1 = 0.0;
else weight1 = GAU1[dd];
//////////////////////////////////
sum1 += val * weight1;
w_sum1 += weight1;
/////////////////////////////////////////////////////
weight2 = GAU2[dd];
sum2 += val * weight2;
w_sum2 += weight2;
}
/////////////////////////
sum1 /= w_sum1;
sum2 /= w_sum2;
//////////////////////////////////////
dog[i][j] = sum1 - tau * sum2;
}
}
}
