opencv/modules/gpu/src/cuda/nlm.cu

171 lines
7.7 KiB
Plaintext
Raw Normal View History

/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 1993-2011, NVIDIA Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's 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.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or bpied warranties, including, but not limited to, the bpied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation 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.
//
//M*/
#include "internal_shared.hpp"
#include "opencv2/gpu/device/vec_traits.hpp"
#include "opencv2/gpu/device/vec_math.hpp"
#include "opencv2/gpu/device/border_interpolate.hpp"
using namespace cv::gpu;
typedef unsigned char uchar;
typedef unsigned short ushort;
//////////////////////////////////////////////////////////////////////////////////
//// Non Local Means Denosing
namespace cv { namespace gpu { namespace device
{
namespace imgproc
{
__device__ __forceinline__ float norm2(const float& v) { return v*v; }
__device__ __forceinline__ float norm2(const float2& v) { return v.x*v.x + v.y*v.y; }
__device__ __forceinline__ float norm2(const float3& v) { return v.x*v.x + v.y*v.y + v.z*v.z; }
__device__ __forceinline__ float norm2(const float4& v) { return v.x*v.x + v.y*v.y + v.z*v.z + v.w*v.w; }
template<typename T, typename B>
__global__ void nlm_kernel(const PtrStepSz<T> src, PtrStep<T> dst, const B b, int search_radius, int block_radius, float h2_inv_half)
{
typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type value_type;
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x >= src.cols || y >= src.rows)
return;
float block_radius2_inv = -1.f/(block_radius * block_radius);
value_type sum1 = VecTraits<value_type>::all(0);
float sum2 = 0.f;
if (x - search_radius - block_radius >=0 && y - search_radius - block_radius >=0 &&
x + search_radius + block_radius < src.cols && y + search_radius + block_radius < src.rows)
{
for(float cy = -search_radius; cy <= search_radius; ++cy)
for(float cx = -search_radius; cx <= search_radius; ++cx)
{
float color2 = 0;
for(float by = -block_radius; by <= block_radius; ++by)
for(float bx = -block_radius; bx <= block_radius; ++bx)
{
value_type v1 = saturate_cast<value_type>(src(y + by, x + bx));
value_type v2 = saturate_cast<value_type>(src(y + cy + by, x + cx + bx));
color2 += norm2(v1 - v2);
}
float dist2 = cx * cx + cy * cy;
float w = __expf(color2 * h2_inv_half + dist2 * block_radius2_inv);
sum1 = sum1 + saturate_cast<value_type>(src(y + cy, x + cy)) * w;
sum2 += w;
}
}
else
{
for(float cy = -search_radius; cy <= search_radius; ++cy)
for(float cx = -search_radius; cx <= search_radius; ++cx)
{
float color2 = 0;
for(float by = -block_radius; by <= block_radius; ++by)
for(float bx = -block_radius; bx <= block_radius; ++bx)
{
value_type v1 = saturate_cast<value_type>(b.at(y + by, x + bx, src.data, src.step));
value_type v2 = saturate_cast<value_type>(b.at(y + cy + by, x + cx + bx, src.data, src.step));
color2 += norm2(v1 - v2);
}
float dist2 = cx * cx + cy * cy;
float w = __expf(color2 * h2_inv_half + dist2 * block_radius2_inv);
sum1 = sum1 + saturate_cast<value_type>(b.at(y + cy, x + cy, src.data, src.step)) * w;
sum2 += w;
}
}
dst(y, x) = saturate_cast<T>(sum1 / sum2);
}
template<typename T, template <typename> class B>
void nlm_caller(const PtrStepSzb src, PtrStepSzb dst, int search_radius, int block_radius, float h, cudaStream_t stream)
{
dim3 block (32, 8);
dim3 grid (divUp (src.cols, block.x), divUp (src.rows, block.y));
B<T> b(src.rows, src.cols);
float h2_inv_half = -0.5f/(h * h * VecTraits<T>::cn);
cudaSafeCall( cudaFuncSetCacheConfig (nlm_kernel<T, B<T> >, cudaFuncCachePreferL1) );
nlm_kernel<<<grid, block>>>((PtrStepSz<T>)src, (PtrStepSz<T>)dst, b, search_radius, block_radius, h2_inv_half);
cudaSafeCall ( cudaGetLastError () );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
template<typename T>
void nlm_bruteforce_gpu(const PtrStepSzb& src, PtrStepSzb dst, int search_radius, int block_radius, float h, int borderMode, cudaStream_t stream)
{
typedef void (*func_t)(const PtrStepSzb src, PtrStepSzb dst, int search_radius, int block_radius, float h, cudaStream_t stream);
static func_t funcs[] =
{
nlm_caller<T, BrdReflect101>,
nlm_caller<T, BrdReplicate>,
nlm_caller<T, BrdConstant>,
nlm_caller<T, BrdReflect>,
nlm_caller<T, BrdWrap>,
};
funcs[borderMode](src, dst, search_radius, block_radius, h, stream);
}
template void nlm_bruteforce_gpu<uchar>(const PtrStepSzb&, PtrStepSzb, int, int, float, int, cudaStream_t);
template void nlm_bruteforce_gpu<uchar3>(const PtrStepSzb&, PtrStepSzb, int, int, float, int, cudaStream_t);
}
}}}