GPU implementation of CLAHE
This commit is contained in:
Vladislav Vinogradov
186
modules/gpu/src/cuda/clahe.cu
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186
modules/gpu/src/cuda/clahe.cu
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#if !defined CUDA_DISABLER
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#include "opencv2/gpu/device/common.hpp"
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#include "opencv2/gpu/device/functional.hpp"
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#include "opencv2/gpu/device/emulation.hpp"
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#include "opencv2/gpu/device/scan.hpp"
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#include "opencv2/gpu/device/reduce.hpp"
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#include "opencv2/gpu/device/saturate_cast.hpp"
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using namespace cv::gpu;
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using namespace cv::gpu::device;
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namespace clahe
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{
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__global__ void calcLutKernel(const PtrStepb src, PtrStepb lut,
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const int2 tileSize, const int tilesX,
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const int clipLimit, const float lutScale)
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{
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__shared__ int smem[512];
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const int tx = blockIdx.x;
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const int ty = blockIdx.y;
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const unsigned int tid = threadIdx.y * blockDim.x + threadIdx.x;
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smem[tid] = 0;
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__syncthreads();
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for (int i = threadIdx.y; i < tileSize.y; i += blockDim.y)
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{
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const uchar* srcPtr = src.ptr(ty * tileSize.y + i) + tx * tileSize.x;
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for (int j = threadIdx.x; j < tileSize.x; j += blockDim.x)
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{
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const int data = srcPtr[j];
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Emulation::smem::atomicAdd(&smem[data], 1);
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}
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}
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__syncthreads();
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int tHistVal = smem[tid];
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__syncthreads();
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if (clipLimit > 0)
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{
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// clip histogram bar
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int clipped = 0;
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if (tHistVal > clipLimit)
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{
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clipped = tHistVal - clipLimit;
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tHistVal = clipLimit;
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}
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// find number of overall clipped samples
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reduce<256>(smem, clipped, tid, plus<int>());
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// broadcast evaluated value
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__shared__ int totalClipped;
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if (tid == 0)
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totalClipped = clipped;
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__syncthreads();
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// redistribute clipped samples evenly
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int redistBatch = totalClipped / 256;
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tHistVal += redistBatch;
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int residual = totalClipped - redistBatch * 256;
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if (tid < residual)
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++tHistVal;
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}
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const int lutVal = blockScanInclusive<256>(tHistVal, smem, tid);
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lut(ty * tilesX + tx, tid) = saturate_cast<uchar>(__float2int_rn(lutScale * lutVal));
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}
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void calcLut(PtrStepSzb src, PtrStepb lut, int tilesX, int tilesY, int2 tileSize, int clipLimit, float lutScale, cudaStream_t stream)
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{
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const dim3 block(32, 8);
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const dim3 grid(tilesX, tilesY);
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calcLutKernel<<<grid, block, 0, stream>>>(src, lut, tileSize, tilesX, clipLimit, lutScale);
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cudaSafeCall( cudaGetLastError() );
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if (stream == 0)
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cudaSafeCall( cudaDeviceSynchronize() );
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}
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__global__ void tranformKernel(const PtrStepSzb src, PtrStepb dst, const PtrStepb lut, const int2 tileSize, const int tilesX, const int tilesY)
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{
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const int x = blockIdx.x * blockDim.x + threadIdx.x;
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const int y = blockIdx.y * blockDim.y + threadIdx.y;
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if (x >= src.cols || y >= src.rows)
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return;
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const float tyf = (static_cast<float>(y) / tileSize.y) - 0.5f;
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int ty1 = __float2int_rd(tyf);
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int ty2 = ty1 + 1;
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const float ya = tyf - ty1;
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ty1 = ::max(ty1, 0);
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ty2 = ::min(ty2, tilesY - 1);
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const float txf = (static_cast<float>(x) / tileSize.x) - 0.5f;
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int tx1 = __float2int_rd(txf);
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int tx2 = tx1 + 1;
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const float xa = txf - tx1;
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tx1 = ::max(tx1, 0);
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tx2 = ::min(tx2, tilesX - 1);
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const int srcVal = src(y, x);
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float res = 0;
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res += lut(ty1 * tilesX + tx1, srcVal) * ((1.0f - xa) * (1.0f - ya));
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res += lut(ty1 * tilesX + tx2, srcVal) * ((xa) * (1.0f - ya));
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res += lut(ty2 * tilesX + tx1, srcVal) * ((1.0f - xa) * (ya));
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res += lut(ty2 * tilesX + tx2, srcVal) * ((xa) * (ya));
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dst(y, x) = saturate_cast<uchar>(res);
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}
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void transform(PtrStepSzb src, PtrStepSzb dst, PtrStepb lut, int tilesX, int tilesY, int2 tileSize, cudaStream_t stream)
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{
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const dim3 block(32, 8);
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const dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y));
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cudaSafeCall( cudaFuncSetCacheConfig(tranformKernel, cudaFuncCachePreferL1) );
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tranformKernel<<<grid, block, 0, stream>>>(src, dst, lut, tileSize, tilesX, tilesY);
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cudaSafeCall( cudaGetLastError() );
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if (stream == 0)
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cudaSafeCall( cudaDeviceSynchronize() );
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}
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}
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#endif // CUDA_DISABLER
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@@ -96,6 +96,7 @@ void cv::gpu::Canny(const GpuMat&, const GpuMat&, GpuMat&, double, double, bool)
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void cv::gpu::Canny(const GpuMat&, const GpuMat&, CannyBuf&, GpuMat&, double, double, bool) { throw_nogpu(); }
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void cv::gpu::CannyBuf::create(const Size&, int) { throw_nogpu(); }
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void cv::gpu::CannyBuf::release() { throw_nogpu(); }
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cv::Ptr<cv::gpu::CLAHE> cv::gpu::createCLAHE(double, cv::Size) { throw_nogpu(); return cv::Ptr<cv::gpu::CLAHE>(); }
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#else /* !defined (HAVE_CUDA) */
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@@ -1559,4 +1560,136 @@ void cv::gpu::Canny(const GpuMat& dx, const GpuMat& dy, CannyBuf& buf, GpuMat& d
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CannyCaller(dx, dy, buf, dst, static_cast<float>(low_thresh), static_cast<float>(high_thresh));
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}
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////////////////////////////////////////////////////////////////////////
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// CLAHE
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namespace clahe
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{
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void calcLut(PtrStepSzb src, PtrStepb lut, int tilesX, int tilesY, int2 tileSize, int clipLimit, float lutScale, cudaStream_t stream);
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void transform(PtrStepSzb src, PtrStepSzb dst, PtrStepb lut, int tilesX, int tilesY, int2 tileSize, cudaStream_t stream);
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}
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namespace
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{
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class CLAHE_Impl : public cv::gpu::CLAHE
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{
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public:
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CLAHE_Impl(double clipLimit = 40.0, int tilesX = 8, int tilesY = 8);
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cv::AlgorithmInfo* info() const;
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void apply(cv::InputArray src, cv::OutputArray dst);
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void apply(InputArray src, OutputArray dst, Stream& stream);
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void setClipLimit(double clipLimit);
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double getClipLimit() const;
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void setTilesGridSize(cv::Size tileGridSize);
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cv::Size getTilesGridSize() const;
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void collectGarbage();
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private:
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double clipLimit_;
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int tilesX_;
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int tilesY_;
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GpuMat srcExt_;
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GpuMat lut_;
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};
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CLAHE_Impl::CLAHE_Impl(double clipLimit, int tilesX, int tilesY) :
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clipLimit_(clipLimit), tilesX_(tilesX), tilesY_(tilesY)
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{
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}
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CV_INIT_ALGORITHM(CLAHE_Impl, "CLAHE_GPU",
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obj.info()->addParam(obj, "clipLimit", obj.clipLimit_);
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obj.info()->addParam(obj, "tilesX", obj.tilesX_);
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obj.info()->addParam(obj, "tilesY", obj.tilesY_))
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void CLAHE_Impl::apply(cv::InputArray _src, cv::OutputArray _dst)
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{
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apply(_src, _dst, Stream::Null());
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}
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void CLAHE_Impl::apply(InputArray _src, OutputArray _dst, Stream& s)
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{
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GpuMat src = _src.getGpuMat();
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CV_Assert( src.type() == CV_8UC1 );
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_dst.create( src.size(), src.type() );
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GpuMat dst = _dst.getGpuMat();
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const int histSize = 256;
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ensureSizeIsEnough(tilesX_ * tilesY_, histSize, CV_8UC1, lut_);
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cudaStream_t stream = StreamAccessor::getStream(s);
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cv::Size tileSize;
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GpuMat srcForLut;
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if (src.cols % tilesX_ == 0 && src.rows % tilesY_ == 0)
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{
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tileSize = cv::Size(src.cols / tilesX_, src.rows / tilesY_);
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srcForLut = src;
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}
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else
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{
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cv::gpu::copyMakeBorder(src, srcExt_, 0, tilesY_ - (src.rows % tilesY_), 0, tilesX_ - (src.cols % tilesX_), cv::BORDER_REFLECT_101, cv::Scalar(), s);
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tileSize = cv::Size(srcExt_.cols / tilesX_, srcExt_.rows / tilesY_);
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srcForLut = srcExt_;
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}
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const int tileSizeTotal = tileSize.area();
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const float lutScale = static_cast<float>(histSize - 1) / tileSizeTotal;
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int clipLimit = 0;
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if (clipLimit_ > 0.0)
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{
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clipLimit = static_cast<int>(clipLimit_ * tileSizeTotal / histSize);
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clipLimit = std::max(clipLimit, 1);
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}
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clahe::calcLut(srcForLut, lut_, tilesX_, tilesY_, make_int2(tileSize.width, tileSize.height), clipLimit, lutScale, stream);
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clahe::transform(src, dst, lut_, tilesX_, tilesY_, make_int2(tileSize.width, tileSize.height), stream);
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}
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void CLAHE_Impl::setClipLimit(double clipLimit)
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{
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clipLimit_ = clipLimit;
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}
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double CLAHE_Impl::getClipLimit() const
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{
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return clipLimit_;
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}
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void CLAHE_Impl::setTilesGridSize(cv::Size tileGridSize)
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{
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tilesX_ = tileGridSize.width;
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tilesY_ = tileGridSize.height;
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}
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cv::Size CLAHE_Impl::getTilesGridSize() const
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{
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return cv::Size(tilesX_, tilesY_);
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}
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void CLAHE_Impl::collectGarbage()
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{
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srcExt_.release();
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lut_.release();
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}
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}
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cv::Ptr<cv::gpu::CLAHE> cv::gpu::createCLAHE(double clipLimit, cv::Size tileGridSize)
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{
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return new CLAHE_Impl(clipLimit, tileGridSize.width, tileGridSize.height);
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}
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#endif /* !defined (HAVE_CUDA) */
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