/* Convolve image with a Gaussian of width sigma and store result back
in image. This routine creates the Gaussian kernel, and then applies
it sequentially in horizontal and vertical directions.
*/
void GaussianBlur(SiftImage image, double sigma)
{
double x, kernel[100], sum = 0.0;
int ksize, i;
/* The Gaussian kernel is truncated at GaussTruncate sigmas from
center. The kernel size should be odd.
*/
ksize = (int)(2.0 * GaussTruncate * sigma + 1.0);
ksize = MAX_SIFT(3, ksize); /* Kernel must be at least 3. */
if (ksize % 2 == 0) /* Make kernel size odd. */
ksize++;
assert(ksize < 100);
/* Fill in kernel values. */
for (i = 0; i <= ksize; i++) {
x = i - ksize / 2;
kernel[i] = exp(- x * x / (2.0 * sigma * sigma));
sum += kernel[i];
}
/* Normalize kernel values to sum to 1.0. */
for (i = 0; i < ksize; i++)
kernel[i] /= sum;
ConvHorizontal(image, kernel, ksize);
ConvVertical(image, kernel, ksize);
}
/* 1D Convolve image with a Gaussian of width sigma and store result back
in image. This routine creates the Gaussian kernel, and then applies
it in horizontal (flag_dir=0) OR vertical directions (flag_dir!=0).
*/
void GaussianBlur1D(vector<float>& image, int width, int height, float sigma, int flag_dir)
{
float x, kernel[100], sum = 0.0;
int ksize, i;
/* The Gaussian kernel is truncated at GaussTruncate sigmas from
center. The kernel size should be odd.
*/
ksize = (int)(2.0 * GaussTruncate1 * sigma + 1.0);
ksize = MAX(3, ksize); /* Kernel must be at least 3. */
if (ksize % 2 == 0) /* Make kernel size odd. */
ksize++;
assert(ksize < 100);
/* Fill in kernel values. */
for (i = 0; i <= ksize; i++) {
x = i - ksize / 2;
kernel[i] = exp(- x * x / (2.0 * sigma * sigma));
sum += kernel[i];
}
/* Normalize kernel values to sum to 1.0. */
for (i = 0; i < ksize; i++)
kernel[i] /= sum;
if (flag_dir == 0)
{
ConvHorizontal(image, width, height, kernel, ksize);
}
else
{
ConvVertical(image, width, height, kernel, ksize);
}
}
/* Convolve image with a Gaussian of width sigma and store result back
in image. This routine creates the Gaussian kernel, and then applies
it sequentially in horizontal and vertical directions.
*/
void gauss_convol(LWImage<flnum>& image, flnum sigma)
{
/*float x, kernel[100], sum = 0.0;*/
flnum x, kernel[1000], sum = 0.0; /*TANG*/
int ksize, i;
/* The Gaussian kernel is truncated at GaussTruncate sigmas from
center. The kernel size should be odd.
*/
ksize = (int)(2.0 * GaussTruncate * sigma + 1.0);
/*ksize = MAX(3, ksize);*/ /* Kernel must be at least 3. */
ksize = ksize > 3 ? ksize : 3;
if (ksize % 2 == 0) /* Make kernel size odd. */
ksize++;
/*assert(ksize < 100);*/
assert(ksize < 1000); /*TANG*/
/* Fill in kernel values. */
for (i = 0; i <= ksize; i++) {
x = i - ksize/2;
kernel[i] = exp(- x * x / (2 * sigma * sigma));
sum += kernel[i];
}
/* Normalize kernel values to sum to 1.0. */
for (i = 0; i < ksize; i++)
kernel[i] /= sum;
ConvHorizontal(image, kernel, ksize);
ConvVertical(image, kernel, ksize);
}
