018b00d6
 David Mayerich
adapted bsds500 f...
|
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
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
|
#ifndef STIM_CUDA_DG2_CONV2_CUH
#define STIM_CUDA_DG2_CONV2_CUH
#include <stim/image/image.h>
//#include <cmath>
#include <stim/visualization/colormap.h>
//#include <stim/image/image_contour_detection.h>
#define SIGMA_N 3
/// This function generates the second-order gaussian derivative filter gxx gyy,
/// convolves the image with gxx gyy,
/// and returns an image class which channel(0) is Ixx and channel(1) is Iyy
/// @param img is the one-channel image
/// @param sigma is the parameter for gaussian function
//void conv2_sep(float* img, unsigned int x, unsigned int y, float* kernel0, unsigned int k0, float* kernel1, unsigned int k1);
//void array_abs(float* img, unsigned int N);
stim::image<float> dG2_conv2(stim::image<float> image, int sigma){
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 * SIGMA_N * sigma + 1; // set the winsdow size of filter
int r = SIGMA_N * sigma;
stim::image<float> I(w, h, 1, 2); // allocate space for return image class
stim::image<float> Ixx(w, h); // allocate space for Ixx
stim::image<float> Iyy(w, h); // allocate space for Iyy
Ixx = image; // initialize Ixx
Iyy = image; // initialize Iyy
float* array_x1;
array_x1 = new float[winsize]; //allocate space for the 1D x-oriented gaussian derivative filter array_x1 for gxx
float* array_y1;
array_y1 = new float[winsize]; //allocate space for the 1D y-oriented gaussian derivative filter array_y1 for gxx
float* array_x2;
array_x2 = new float[winsize]; //allocate space for the 1D x-oriented gaussian derivative filter array_x2 for gyy
float* array_y2;
array_y2 = new float[winsize]; //allocate space for the 1D y-oriented gaussian derivative filter array_y2 for gyy
for (int i = 0; i < winsize; i++){
int x = i - r; //range of x
int y = i - r; //range of y
// create the 1D x-oriented gaussian derivative filter array_x1 for gxx
array_x1[i] = (-1) * (1 - pow(x, 2)) * exp((-1)*(pow(x, 2))/(2*pow(sigma, 2)));
// create the 1D y-oriented gaussian derivative filter array_y1 for gxx
array_y1[i] = exp((-1)*(pow(y, 2))/(2*pow(sigma, 2)));
// create the 1D x-oriented gaussian derivative filter array_x2 for gyy
array_x2[i] = exp((-1)*(pow(x, 2))/(2*pow(sigma, 2)));
// create the 1D y-oriented gaussian derivative filter array_y2 for gyy
array_y2[i] = (-1) * (1 - pow(y, 2)) * exp((-1)*(pow(y, 2))/(2*pow(sigma, 2)));
}
//stim::cpu2image(array_x1, "data_output/array_x1_0915.bmp", winsize, 1, stim::cmBrewer); // (optional) show the mask result
//stim::cpu2image(array_y1, "data_output/array_y1_0915.bmp", winsize, 1, stim::cmBrewer); // (optional) show the mask result
//stim::cpu2image(array_x2, "data_output/array_x2_0915.bmp", winsize, 1, stim::cmBrewer); // (optional) show the mask result
//stim::cpu2image(array_y2, "data_output/array_y2_0915.bmp", winsize, 1, stim::cmBrewer); // (optional) show the mask result
// get Ixx by convolving the image with gxx
conv2_sep(Ixx.data(), w, h, array_x1, winsize, array_y1, winsize);
//stim::cpu2image(Ixx.data(), "data_output/Ixx_0915.bmp", w, h, stim::cmBrewer);
// get Iyy by convolving the image with gyy
conv2_sep(Iyy.data(), w, h, array_x2, winsize, array_y2, winsize);
//stim::cpu2image(Iyy.data(), "data_output/Iyy_0915.bmp", w, h, stim::cmBrewer);
delete [] array_x1; //free the memory of array_x1
delete [] array_y1; //free the memory of array_y1
delete [] array_x2; //free the memory of array_x2
delete [] array_y2; //free the memory of array_y2
I.set_channel(0, Ixx.data());
I.set_channel(1, Iyy.data());
return I;
}
#endif
|
