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https://github.com/NVIDIA/cuda-samples.git
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224 lines
6.5 KiB
Plaintext
224 lines
6.5 KiB
Plaintext
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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/*
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Recursive Gaussian filter
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*/
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#ifndef _RECURSIVEGAUSSIAN_KERNEL_CU_
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#define _RECURSIVEGAUSSIAN_KERNEL_CU_
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#include <stdlib.h>
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#include <stdio.h>
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#include <string.h>
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#include <cooperative_groups.h>
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namespace cg = cooperative_groups;
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#include <helper_cuda.h>
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#include <helper_math.h>
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#define BLOCK_DIM 16
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#define CLAMP_TO_EDGE 1
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// Transpose kernel (see transpose CUDA Sample for details)
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__global__ void d_transpose(uint *odata, uint *idata, int width, int height) {
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// Handle to thread block group
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cg::thread_block cta = cg::this_thread_block();
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__shared__ uint block[BLOCK_DIM][BLOCK_DIM + 1];
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// read the matrix tile into shared memory
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unsigned int xIndex = blockIdx.x * BLOCK_DIM + threadIdx.x;
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unsigned int yIndex = blockIdx.y * BLOCK_DIM + threadIdx.y;
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if ((xIndex < width) && (yIndex < height)) {
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unsigned int index_in = yIndex * width + xIndex;
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block[threadIdx.y][threadIdx.x] = idata[index_in];
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}
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cg::sync(cta);
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// write the transposed matrix tile to global memory
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xIndex = blockIdx.y * BLOCK_DIM + threadIdx.x;
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yIndex = blockIdx.x * BLOCK_DIM + threadIdx.y;
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if ((xIndex < height) && (yIndex < width)) {
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unsigned int index_out = yIndex * height + xIndex;
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odata[index_out] = block[threadIdx.x][threadIdx.y];
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}
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}
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// RGBA version
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// reads from 32-bit uint array holding 8-bit RGBA
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// convert floating point rgba color to 32-bit integer
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__device__ uint rgbaFloatToInt(float4 rgba) {
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rgba.x = __saturatef(rgba.x); // clamp to [0.0, 1.0]
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rgba.y = __saturatef(rgba.y);
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rgba.z = __saturatef(rgba.z);
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rgba.w = __saturatef(rgba.w);
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return (uint(rgba.w * 255) << 24) | (uint(rgba.z * 255) << 16) |
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(uint(rgba.y * 255) << 8) | uint(rgba.x * 255);
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}
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// convert from 32-bit int to float4
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__device__ float4 rgbaIntToFloat(uint c) {
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float4 rgba;
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rgba.x = (c & 0xff) / 255.0f;
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rgba.y = ((c >> 8) & 0xff) / 255.0f;
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rgba.z = ((c >> 16) & 0xff) / 255.0f;
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rgba.w = ((c >> 24) & 0xff) / 255.0f;
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return rgba;
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}
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/*
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simple 1st order recursive filter
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- processes one image column per thread
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parameters:
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id - pointer to input data (RGBA image packed into 32-bit integers)
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od - pointer to output data
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w - image width
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h - image height
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a - blur parameter
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*/
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__global__ void d_simpleRecursive_rgba(uint *id, uint *od, int w, int h,
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float a) {
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unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
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if (x >= w) return;
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id += x; // advance pointers to correct column
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od += x;
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// forward pass
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float4 yp = rgbaIntToFloat(*id); // previous output
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for (int y = 0; y < h; y++) {
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float4 xc = rgbaIntToFloat(*id);
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float4 yc =
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xc + a * (yp - xc); // simple lerp between current and previous value
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*od = rgbaFloatToInt(yc);
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id += w;
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od += w; // move to next row
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yp = yc;
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}
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// reset pointers to point to last element in column
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id -= w;
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od -= w;
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// reverse pass
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// ensures response is symmetrical
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yp = rgbaIntToFloat(*id);
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for (int y = h - 1; y >= 0; y--) {
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float4 xc = rgbaIntToFloat(*id);
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float4 yc = xc + a * (yp - xc);
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*od = rgbaFloatToInt((rgbaIntToFloat(*od) + yc) * 0.5f);
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id -= w;
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od -= w; // move to previous row
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yp = yc;
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}
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}
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/*
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recursive Gaussian filter
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parameters:
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id - pointer to input data (RGBA image packed into 32-bit integers)
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od - pointer to output data
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w - image width
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h - image height
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a0-a3, b1, b2, coefp, coefn - filter parameters
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*/
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__global__ void d_recursiveGaussian_rgba(uint *id, uint *od, int w, int h,
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float a0, float a1, float a2, float a3,
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float b1, float b2, float coefp,
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float coefn) {
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unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
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if (x >= w) return;
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id += x; // advance pointers to correct column
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od += x;
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// forward pass
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float4 xp = make_float4(0.0f); // previous input
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float4 yp = make_float4(0.0f); // previous output
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float4 yb = make_float4(0.0f); // previous output by 2
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#if CLAMP_TO_EDGE
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xp = rgbaIntToFloat(*id);
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yb = coefp * xp;
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yp = yb;
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#endif
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for (int y = 0; y < h; y++) {
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float4 xc = rgbaIntToFloat(*id);
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float4 yc = a0 * xc + a1 * xp - b1 * yp - b2 * yb;
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*od = rgbaFloatToInt(yc);
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id += w;
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od += w; // move to next row
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xp = xc;
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yb = yp;
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yp = yc;
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}
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// reset pointers to point to last element in column
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id -= w;
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od -= w;
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// reverse pass
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// ensures response is symmetrical
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float4 xn = make_float4(0.0f);
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float4 xa = make_float4(0.0f);
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float4 yn = make_float4(0.0f);
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float4 ya = make_float4(0.0f);
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#if CLAMP_TO_EDGE
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xn = xa = rgbaIntToFloat(*id);
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yn = coefn * xn;
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ya = yn;
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#endif
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for (int y = h - 1; y >= 0; y--) {
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float4 xc = rgbaIntToFloat(*id);
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float4 yc = a2 * xn + a3 * xa - b1 * yn - b2 * ya;
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xa = xn;
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xn = xc;
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ya = yn;
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yn = yc;
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*od = rgbaFloatToInt(rgbaIntToFloat(*od) + yc);
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id -= w;
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od -= w; // move to previous row
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}
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}
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#endif // #ifndef _GAUSSIAN_KERNEL_H_
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