gauss_deriv_even.cpp
2.24 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
#include <stim/image/image.h>
#include <cmath>
#include <stim/visualization/colormap.h>
#include <stim/image/image_contour_detection.h>
/// This function evaluates the theta-dependent even-symmetric gaussian derivative gradient of an one-channel image
/// @param img is the one-channel image
/// @param r is an array of radii for different scaled discs(filters)
/// @param sigma_n is the number of standard deviations used to define the sigma
/// @param theta is angle used for computing the gradient
void conv2(float* img, float* mask, float* cpu_copy, unsigned int w, unsigned int h, unsigned int M);
void array_abs(float* img, unsigned int N);
stim::image<float> Gd_even(stim::image<float> image, int r, unsigned int sigma_n, float theta){
unsigned int w = image.width(); // get the width of picture
unsigned int h = image.height(); // get the height of picture
unsigned N = w * h; // get the number of pixels of picture
int winsize = 2 * r + 1; // set the winsdow size of disc(filter)
float sigma = float(r)/float(sigma_n); // calculate the sigma used in gaussian function
stim::image<float> I(w, h, 1, 2); // allocate space for return image class
stim::image<float> Gd_even_theta(w, h); // allocate space for Gd_even_theta
stim::image<float> mask_x(winsize, winsize); // allocate space for x-axis-oriented filter
stim::image<float> mask_r(winsize, winsize); // allocate space for theta-oriented filter
for (int j = 0; j < winsize; j++){
for (int i = 0; i< winsize; i++){
int x = i - r; //range of x
int y = j - r; //range of y
// create the x-oriented gaussian derivative filter mask_x
mask_x.data()[j*winsize + i] = (-1) * (1 - pow(x, 2)) * exp((-1)*(pow(x, 2))/(2*pow(sigma, 2))) * exp((-1)*(pow(y, 2))/(2*pow(sigma, 2)));
}
}
mask_r = mask_x.rotate(theta, r, r);
//mask_r = mask_x.rotate(45, r, r);
//stim::cpu2image(mask_r.data(), "data_output/mask_r_0919.bmp", winsize, winsize, stim::cmBrewer);
// do the 2D convolution with image and mask
conv2(image.data(), mask_r.data(), Gd_even_theta.data(), w, h, winsize);
array_abs(Gd_even_theta.data(), N);
//stim::cpu2image(Gd_even_theta.data(), "data_output/Gd_even_theta_0919.bmp", w, h, stim::cmGrayscale);
return Gd_even_theta;
}