branch_detection2.cuh
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#include <stim/cuda/templates/gaussian_blur.cuh>
#include <stim/cuda/templates/gradient.cuh>
#include <stim/cuda/arraymath.cuh>
#include <stim/cuda/ivote.cuh>
void atan_2(float* cpuTable, unsigned int rmax){
//initialize the width and height of the window which atan2 are computed in.
int xsize = 2*rmax +1;
int ysize = 2*rmax +1;
// assign the center coordinates of the atan2 window to yi and xi
int yi = rmax;
int xi = rmax;
for (int xt = 0; xt < xsize; xt++){
for(int yt = 0; yt < ysize; yt++){
//convert the current 2D coordinates to 1D
int id = yt * xsize + xt;
// calculate the distance between the pixel and the center of the atan2 window
float xd = xi - xt;
float yd = yi - yt;
// calculate the angle between the pixel and the center of the atan2 window and store the result.
float atan_2d_vote = atan2(yd, xd);
cpuTable[id] = atan_2d_vote;
}
}
}
std::vector<stim::vec<float> >
find_branch(GLint texbufferID, GLenum texType, unsigned int x, unsigned int y)
{
float* cpuTable = (float
unsigned int pixels = x * y;
unsigned int bytes = sizeof(float) * pixels;
//calculate the number of bytes in the atan2 table
unsigned int bytes_table = (2*rmax+1) * (2*rmax+1) * sizeof(float);
//allocate space on the GPU for the atan2 table
float* gpuTable;
cudaMalloc(&gpuTable, bytes_table);
cudaMemcpy(gpuTable, cpuTable, bytes_table, cudaMemcpyHostToDevice);
unsigned int sigma_ds = 1/resize;
unsigned int x_ds = (x/sigma_ds + (x %sigma_ds == 0 ? 0:1));
unsigned int y_ds = (y/sigma_ds + (y %sigma_ds == 0 ? 0:1));
unsigned int bytes_ds = sizeof(float) * x_ds * y_ds;
float* gpuI;
cudaMalloc(&gpuI, bytes_ds);
float* gpuGrad;
cudaMalloc(&gpuGrad, bytes_ds*2);
float* gpuVote;
cudaMalloc(&gpuVote, bytes_ds);
// allocate space on the GPU for the detected cell centes
float* gpuCenters;
cudaMalloc(&gpuCenters, bytes_ds);
stim::cuda::gpu_down_sample<float>(gpuI, gpuI0, resize, x , y);
cudaMemcpy(cpuResize, gpuI, bytes_ds, cudaMemcpyDeviceToHost);
x = x_ds;
y = y_ds;
t = t * resize;
//sigma = sigma * resize;
cudaDeviceSynchronize();
stim::cuda::gpu_gaussian_blur2<float>(gpuI,sigma, x, y);
cudaDeviceSynchronize();
cudaMemcpy(cpuBlur, gpuI, bytes_ds, cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
stim::cuda::gpu_gradient_2d<float>(gpuGrad, gpuI, x, y);
cudaDeviceSynchronize();
cudaMemcpy(cpuGradient, gpuGrad, bytes_ds*2, cudaMemcpyDeviceToHost);
stim::cuda::gpu_cart2polar<float>(gpuGrad, x, y);
cudaDeviceSynchronize();
cudaMemcpy(cpuCart2Polar, gpuGrad, bytes_ds*2, cudaMemcpyDeviceToHost);
//multiply the gradient by a constant and calculate the absolute value (to save an image)
stim::cuda::cpu_multiply<float>(cpuCart2Polar, 40, x * y * 2);
cudaDeviceSynchronize();
stim::cuda::cpu_abs<float>(cpuCart2Polar, x * y * 2);
cudaDeviceSynchronize();
for (int i =0; i<iter; i++){
stim::cuda::gpu_vote<float>(gpuVote, gpuGrad, gpuTable, phi, rmax, x, y);
cudaDeviceSynchronize();
stim::cuda::gpu_update_dir<float>(gpuVote, gpuGrad, gpuTable, phi, rmax, x, y);
cudaDeviceSynchronize();
switch (i){
case 0 : cudaMemcpy(cpuVote1, gpuVote, bytes_ds, cudaMemcpyDeviceToHost);
break;
case 1 : cudaMemcpy(cpuVote2, gpuVote, bytes_ds, cudaMemcpyDeviceToHost);
break;
case 2 : cudaMemcpy(cpuVote3, gpuVote, bytes_ds, cudaMemcpyDeviceToHost);
break;
case 3 : cudaMemcpy(cpuVote4, gpuVote, bytes_ds, cudaMemcpyDeviceToHost);
break;
case 4 : cudaMemcpy(cpuVote5, gpuVote, bytes_ds, cudaMemcpyDeviceToHost);
break;
default : cudaMemcpy(cpuVote5, gpuVote, bytes_ds, cudaMemcpyDeviceToHost);
break;
}
phi = phi - dphi;
}
stim::cuda::gpu_local_max<float>(gpuCenters, gpuVote, t, conn, x, y);
cudaMemcpy(cpuCenters, gpuCenters, bytes_ds, cudaMemcpyDeviceToHost);
}