fun_mPb_theta.cpp
2.42 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
53
54
55
56
57
58
59
60
61
62
63
#include <stim/image/image.h>
#include <cmath>
#include <stim/visualization/colormap.h>
#include <stim/image/image_contour_detection.h>
#include <sstream>
void array_multiply(float* lhs, float rhs, unsigned int N);
void array_add(float* ptr1, float* ptr2, float* sum, unsigned int N);
/// This function evaluates the theta-dependent multicale Pb given an multi-channel image
/// @param img is the multi-channel image
/// @param theta is the angle used for computing the gradient
/// @param r is an array of radii for different scaled discs(filters)
/// @param alpha is is an array of weights for different scaled discs(filters)
/// @param s is the number of scales
stim::image<float> func_mPb_theta(stim::image<float> img, float theta, int* r, float* alpha, int s){
unsigned int w = img.width(); // get the width of picture
unsigned int h = img.height(); // get the height of picture
unsigned int c = img.channels(); // get the channels of picture
stim::image<float> mPb_theta(w, h, 1); // allocate space for theta-dependent multicale Pb
unsigned size = mPb_theta.size(); // get the size of theta-dependent multicale Pb
memset ( mPb_theta.data(), 0, size * sizeof(float)); // initialize all the pixels of theta-dependent multicale Pb to 0
unsigned int N = w * h; // get the number of pixels
int sigma_n = 3; // set the number of standard deviations used to define the sigma
std::ostringstream ss; // (optional) set the stream to designate the test result file
stim::image<float> temp; // set the temporary image to store the addtion result
for (int i = 0; i < c; i++){
for (int j = 0; j < s; j++){
//ss << "data_output/mPb_theta_slice"<< i*s + j << ".bmp"; // set the name for test result file
//std::string sss = ss.str();
// get the gaussian gradient by convolving each image slice with the mask
temp = gaussian_derivative_filter_odd(img.channel(i), r[i*s + j], sigma_n, theta);
// (optional) output the test result of each slice
//stim::cpu2image(temp.data(), sss, w, h, stim::cmBrewer);
// multiply each gaussian gradient with its weight
array_multiply(temp.data(), alpha[i*s + j], N);
// add up all the weighted gaussian gradients
array_add(mPb_theta.data(), temp.data(), mPb_theta.data(), N);
//ss.str(""); //(optional) clear the space for stream
}
}
return mPb_theta;
}