/* Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above copyright * notice, this list of conditions and the following disclaimer in the * documentation and/or other materials provided with the distribution. * * Neither the name of NVIDIA CORPORATION nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ //////////////////////////////////////////////////////////////////////////////// // KNN kernel //////////////////////////////////////////////////////////////////////////////// __global__ void KNN(TColor *dst, int imageW, int imageH, float Noise, float lerpC, cudaTextureObject_t texImage) { const int ix = blockDim.x * blockIdx.x + threadIdx.x; const int iy = blockDim.y * blockIdx.y + threadIdx.y; // Add half of a texel to always address exact texel centers const float x = (float)ix + 0.5f; const float y = (float)iy + 0.5f; if (ix < imageW && iy < imageH) { // Normalized counter for the weight threshold float fCount = 0; // Total sum of pixel weights float sumWeights = 0; // Result accumulator float3 clr = {0, 0, 0}; // Center of the KNN window float4 clr00 = tex2D(texImage, x, y); // Cycle through KNN window, surrounding (x, y) texel for (float i = -KNN_WINDOW_RADIUS; i <= KNN_WINDOW_RADIUS; i++) for (float j = -KNN_WINDOW_RADIUS; j <= KNN_WINDOW_RADIUS; j++) { float4 clrIJ = tex2D(texImage, x + j, y + i); float distanceIJ = vecLen(clr00, clrIJ); // Derive final weight from color distance float weightIJ = __expf( -(distanceIJ * Noise + (i * i + j * j) * INV_KNN_WINDOW_AREA)); // Accumulate (x + j, y + i) texel color with computed weight clr.x += clrIJ.x * weightIJ; clr.y += clrIJ.y * weightIJ; clr.z += clrIJ.z * weightIJ; // Sum of weights for color normalization to [0..1] range sumWeights += weightIJ; // Update weight counter, if KNN weight for current window texel // exceeds the weight threshold fCount += (weightIJ > KNN_WEIGHT_THRESHOLD) ? INV_KNN_WINDOW_AREA : 0; } // Normalize result color by sum of weights sumWeights = 1.0f / sumWeights; clr.x *= sumWeights; clr.y *= sumWeights; clr.z *= sumWeights; // Choose LERP quotient basing on how many texels // within the KNN window exceeded the weight threshold float lerpQ = (fCount > KNN_LERP_THRESHOLD) ? lerpC : 1.0f - lerpC; // Write final result to global memory clr.x = lerpf(clr.x, clr00.x, lerpQ); clr.y = lerpf(clr.y, clr00.y, lerpQ); clr.z = lerpf(clr.z, clr00.z, lerpQ); dst[imageW * iy + ix] = make_color(clr.x, clr.y, clr.z, 0); }; } extern "C" void cuda_KNN(TColor *d_dst, int imageW, int imageH, float Noise, float lerpC, cudaTextureObject_t texImage) { dim3 threads(BLOCKDIM_X, BLOCKDIM_Y); dim3 grid(iDivUp(imageW, BLOCKDIM_X), iDivUp(imageH, BLOCKDIM_Y)); KNN<<>>(d_dst, imageW, imageH, Noise, lerpC, texImage); } //////////////////////////////////////////////////////////////////////////////// // Stripped KNN kernel, only highlighting areas with different LERP directions //////////////////////////////////////////////////////////////////////////////// __global__ void KNNdiag(TColor *dst, int imageW, int imageH, float Noise, float lerpC, cudaTextureObject_t texImage) { const int ix = blockDim.x * blockIdx.x + threadIdx.x; const int iy = blockDim.y * blockIdx.y + threadIdx.y; // Add half of a texel to always address exact texel centers const float x = (float)ix + 0.5f; const float y = (float)iy + 0.5f; if (ix < imageW && iy < imageH) { // Normalized counter for the weight threshold float fCount = 0; // Center of the KNN window float4 clr00 = tex2D(texImage, x, y); // Cycle through KNN window, surrounding (x, y) texel for (float i = -KNN_WINDOW_RADIUS; i <= KNN_WINDOW_RADIUS; i++) for (float j = -KNN_WINDOW_RADIUS; j <= KNN_WINDOW_RADIUS; j++) { float4 clrIJ = tex2D(texImage, x + j, y + i); float distanceIJ = vecLen(clr00, clrIJ); // Derive final weight from color and geometric distance float weightIJ = __expf( -(distanceIJ * Noise + (i * i + j * j) * INV_KNN_WINDOW_AREA)); // Update weight counter, if KNN weight for current window texel // exceeds the weight threshold fCount += (weightIJ > KNN_WEIGHT_THRESHOLD) ? INV_KNN_WINDOW_AREA : 0.0f; } // Choose LERP quotient basing on how many texels // within the KNN window exceeded the weight threshold float lerpQ = (fCount > KNN_LERP_THRESHOLD) ? 1.0f : 0; // Write final result to global memory dst[imageW * iy + ix] = make_color(lerpQ, 0, (1.0f - lerpQ), 0); }; } extern "C" void cuda_KNNdiag(TColor *d_dst, int imageW, int imageH, float Noise, float lerpC, cudaTextureObject_t texImage) { dim3 threads(BLOCKDIM_X, BLOCKDIM_Y); dim3 grid(iDivUp(imageW, BLOCKDIM_X), iDivUp(imageH, BLOCKDIM_Y)); KNNdiag<<>>(d_dst, imageW, imageH, Noise, lerpC, texImage); }