018b00d6
 David Mayerich
adapted bsds500 f...
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#ifndef STIM_CUDA_DG1_CONV2_CUH
#define STIM_CUDA_DG1_CONV2_CUH
#include <stim/image/image.h>
//#include <cmath>
#include <stim/visualization/colormap.h>
//#include <stim/image/image_contour_detection.h>
#include <stim/cuda/templates/conv2sep.cuh>
#define SIGMA_N 3
/// This function generates the first-order gaussian derivative filter gx gy,
/// convolves the image with gx gy,
/// and returns an image class which channel(0) is Ix and channel(1) is Iy
/// @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> dG1_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
int r = SIGMA_N * sigma;
int winsize = 2 * SIGMA_N * sigma + 1; // set the winsdow size of filter
stim::image<float> I(w, h, 1, 2); // allocate space for return image class
stim::image<float> Ix(w, h); // allocate space for Ix
stim::image<float> Iy(w, h); // allocate space for Iy
Ix = image; // initialize Ix
Iy = image; // initialize Iy
float* array_x1;
array_x1 = new float[winsize]; //allocate space for the 1D x-oriented gaussian derivative filter array_x1 for gx
float* array_y1;
array_y1 = new float[winsize]; //allocate space for the 1D y-oriented gaussian derivative filter array_y1 for gx
float* array_x2;
array_x2 = new float[winsize]; //allocate space for the 1D x-oriented gaussian derivative filter array_x2 for gy
float* array_y2;
array_y2 = new float[winsize]; //allocate space for the 1D y-oriented gaussian derivative filter array_y2 for gy
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 gx
array_x1[i] = (-1) * x * exp((-1)*(pow(x, 2))/(2*pow(sigma, 2)));
// create the 1D y-oriented gaussian derivative filter array_y1 for gx
array_y1[i] = exp((-1)*(pow(y, 2))/(2*pow(sigma, 2)));
// create the 1D x-oriented gaussian derivative filter array_x2 for gy
array_x2[i] = exp((-1)*(pow(x, 2))/(2*pow(sigma, 2)));
// create the 1D y-oriented gaussian derivative filter array_y2 for gy
array_y2[i] = (-1) * y * 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 Ix by convolving the image with gx
//conv2_sep(Ix.data(), w, h, array_x1, winsize, array_y1, winsize);
stim::cuda::cpu_conv2sep(Ix.data(), w, h, array_x1, winsize, array_y1, winsize);
//stim::cpu2image(Ix.data(), "data_output/Ix_0915.bmp", w, h, stim::cmBrewer);
// get Iy by convolving the image with gy
//conv2_sep(Iy.data(), w, h, array_x2, winsize, array_y2, winsize);
stim::cuda::cpu_conv2sep(Iy.data(), w, h, array_x2, winsize, array_y2, winsize);
//stim::cpu2image(Iy.data(), "data_output/Iy_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, Ix.data());
I.set_channel(1, Iy.data());
return I;
}
#endif
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