filter.cuh
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#ifndef STIM_FILTER_H
#define STIM_FILTER_H
#include <assert.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <stdio.h>
#include <stim/visualization/colormap.h>
#include <sstream>
#include <stim/math/constants.h>
#include <stim/cuda/cudatools/devices.h>
#include <stim/cuda/cudatools/threads.h>
#include <stim/cuda/cuda_texture.cuh>
#include <stim/cuda/ivote.cuh>
#include <stim/cuda/arraymath.cuh>
#define IMAD(a,b,c) ( __mul24((a), (b)) + (c) )
namespace stim
{
namespace cuda
{
float* gpuLoG;
float* LoG;
float* res;
float* centers;
stim::cuda::cuda_texture tx;
void initArray(int DIM_X, int DIM_Y, int kl)
{
LoG = (float*) malloc(kl*kl*sizeof(float));
HANDLE_ERROR(
cudaMalloc( (void**) &gpuLoG, kl*kl*sizeof(float))
);
// checkCUDAerrors("Memory Allocation, LoG");
HANDLE_ERROR(
cudaMalloc( (void**) &res, DIM_Y*DIM_X*sizeof(float))
);
HANDLE_ERROR(
cudaMalloc( (void**) ¢ers, DIM_Y*DIM_X*sizeof(float))
);
// checkCUDAerrors("Memory Allocation, Result");
}
void cleanUP()
{
HANDLE_ERROR(
cudaFree(gpuLoG)
);
HANDLE_ERROR(
cudaFree(res)
);
HANDLE_ERROR(
cudaFree(centers)
);
free(LoG);
}
void
filterKernel (float kl, float sigma, float *LoG)
{
float t = 0.0;
float kr = kl/2;
float x, y;
int idx;
for(int i = 0; i < kl; i++){
for(int j = 0; j < kl; j++){
idx = j*kl+i;
x = i - kr - 0.5;
y = j - kr - 0.5;
LoG[idx] = (-1.0/PI/powf(sigma, 4))* (1 - (powf(x,2)+powf(y,2))/2.0/powf(sigma, 2))
*expf(-(powf(x,2)+powf(y,2))/2/powf(sigma,2));
t +=LoG[idx];
}
}
for(int i = 0; i < kl*kl; i++)
{
LoG[i] = LoG[i]/t;
}
}
//Shared memory would be better.
__global__
void
applyFilter(cudaTextureObject_t texIn, unsigned int DIM_X, unsigned int DIM_Y, int kr, int kl, float *res, float* gpuLoG){
//R = floor(size/2)
//THIS IS A NAIVE WAY TO DO IT, and there is a better way)
__shared__ float shared[7][7];
int x = blockIdx.x;
int y = blockIdx.y;
int xi = threadIdx.x;
int yi = threadIdx.y;
// float val = 0;
float tu = (x-kr+xi)/(float)DIM_X;
float tv = (y-kr+yi)/(float)DIM_Y;
shared[xi][yi] = gpuLoG[yi*kl+xi]*(255.0-(float)tex2D<unsigned char>(texIn, tu, tv));
__syncthreads();
//x = max(0,x);
//x = min(x, width-1);
//y = max(y, 0);
//y = min(y, height - 1);
int idx = y*DIM_X+x;
// int k_idx;
for(unsigned int step = blockDim.x/2; step >= 1; step >>= 1)
{
__syncthreads();
if (xi < step)
{
shared[xi][yi] += shared[xi + step][yi];
}
__syncthreads();
}
__syncthreads();
for(unsigned int step = blockDim.y/2; step >= 1; step >>= 1)
{
__syncthreads();
if(yi < step)
{
shared[xi][yi] += shared[xi][yi + step];
}
__syncthreads();
}
__syncthreads();
if(xi == 0 && yi == 0)
res[idx] = shared[0][0];
}
extern "C"
float *
get_centers(GLint texbufferID, GLenum texType, int DIM_X, int DIM_Y, int sizeK, float sigma, float conn, float threshold)
{
tx.SetTextureCoordinates(1);
tx.SetAddressMode(1, 3);
tx.MapCudaTexture(texbufferID, texType);
float* result = (float*) malloc(DIM_X*DIM_Y*sizeof(float));
initArray(DIM_X, DIM_Y, sizeK);
filterKernel(sizeK, sigma, LoG);
cudaMemcpy(gpuLoG, LoG, sizeK*sizeK*sizeof(float), cudaMemcpyHostToDevice);
dim3 numBlocks(DIM_X, DIM_Y);
dim3 threadsPerBlock(sizeK, sizeK);
applyFilter <<< numBlocks, threadsPerBlock >>> (tx.getTexture(), DIM_X, DIM_Y, floor(sizeK/2), sizeK, res, gpuLoG);
stim::cuda::gpu_local_max<float>(centers, res, threshold, conn, DIM_X, DIM_Y);
cudaDeviceSynchronize();
cudaMemcpy(result, centers, DIM_X*DIM_Y*sizeof(float), cudaMemcpyDeviceToHost);
tx.UnmapCudaTexture();
cleanUP();
return result;
}
}
}
#endif