cuda-samples/Samples/recursiveGaussian/recursiveGaussian_cuda.cu

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/*
Recursive Gaussian filter
sgreen 8/1/08
This code sample implements a Gaussian blur using Deriche's recursive method:
http://citeseer.ist.psu.edu/deriche93recursively.html
This is similar to the box filter sample in the SDK, but it uses the previous
outputs of the filter as well as the previous inputs. This is also known as an
IIR (infinite impulse response) filter, since its response to an input impulse
can last forever.
The main advantage of this method is that the execution time is independent of
the filter width.
The GPU processes columns of the image in parallel. To avoid uncoalesced reads
for the row pass we transpose the image and then transpose it back again
afterwards.
The implementation is based on code from the CImg library:
http://cimg.sourceforge.net/
Thanks to David Tschumperl<72> and all the CImg contributors!
*/
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <cuda_runtime.h>
#include <helper_cuda.h>
#include <helper_math.h>
#include "recursiveGaussian_kernel.cuh"
#define USE_SIMPLE_FILTER 0
// Round a / b to nearest higher integer value
int iDivUp(int a, int b) { return (a % b != 0) ? (a / b + 1) : (a / b); }
/*
Transpose a 2D array (see SDK transpose example)
*/
extern "C" void transpose(uint *d_src, uint *d_dest, uint width, int height) {
dim3 grid(iDivUp(width, BLOCK_DIM), iDivUp(height, BLOCK_DIM), 1);
dim3 threads(BLOCK_DIM, BLOCK_DIM, 1);
d_transpose<<<grid, threads>>>(d_dest, d_src, width, height);
getLastCudaError("Kernel execution failed");
}
/*
Perform Gaussian filter on a 2D image using CUDA
Parameters:
d_src - pointer to input image in device memory
d_dest - pointer to destination image in device memory
d_temp - pointer to temporary storage in device memory
width - image width
height - image height
sigma - sigma of Gaussian
order - filter order (0, 1 or 2)
*/
// 8-bit RGBA version
extern "C" void gaussianFilterRGBA(uint *d_src, uint *d_dest, uint *d_temp,
int width, int height, float sigma,
int order, int nthreads) {
// compute filter coefficients
const float nsigma = sigma < 0.1f ? 0.1f : sigma, alpha = 1.695f / nsigma,
ema = (float)std::exp(-alpha), ema2 = (float)std::exp(-2 * alpha),
b1 = -2 * ema, b2 = ema2;
float a0 = 0, a1 = 0, a2 = 0, a3 = 0, coefp = 0, coefn = 0;
switch (order) {
case 0: {
const float k = (1 - ema) * (1 - ema) / (1 + 2 * alpha * ema - ema2);
a0 = k;
a1 = k * (alpha - 1) * ema;
a2 = k * (alpha + 1) * ema;
a3 = -k * ema2;
} break;
case 1: {
const float k = (1 - ema) * (1 - ema) / ema;
a0 = k * ema;
a1 = a3 = 0;
a2 = -a0;
} break;
case 2: {
const float ea = (float)std::exp(-alpha),
k = -(ema2 - 1) / (2 * alpha * ema),
kn = (-2 * (-1 + 3 * ea - 3 * ea * ea + ea * ea * ea) /
(3 * ea + 1 + 3 * ea * ea + ea * ea * ea));
a0 = kn;
a1 = -kn * (1 + k * alpha) * ema;
a2 = kn * (1 - k * alpha) * ema;
a3 = -kn * ema2;
} break;
default:
fprintf(stderr, "gaussianFilter: invalid order parameter!\n");
return;
}
coefp = (a0 + a1) / (1 + b1 + b2);
coefn = (a2 + a3) / (1 + b1 + b2);
// process columns
#if USE_SIMPLE_FILTER
d_simpleRecursive_rgba<<<iDivUp(width, nthreads), nthreads>>>(
d_src, d_temp, width, height, ema);
#else
d_recursiveGaussian_rgba<<<iDivUp(width, nthreads), nthreads>>>(
d_src, d_temp, width, height, a0, a1, a2, a3, b1, b2, coefp, coefn);
#endif
getLastCudaError("Kernel execution failed");
transpose(d_temp, d_dest, width, height);
getLastCudaError("transpose: Kernel execution failed");
// process rows
#if USE_SIMPLE_FILTER
d_simpleRecursive_rgba<<<iDivUp(height, nthreads), nthreads>>>(
d_dest, d_temp, height, width, ema);
#else
d_recursiveGaussian_rgba<<<iDivUp(height, nthreads), nthreads>>>(
d_dest, d_temp, height, width, a0, a1, a2, a3, b1, b2, coefp, coefn);
#endif
getLastCudaError("Kernel execution failed");
transpose(d_temp, d_dest, height, width);
}