/*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.
// 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 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 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*/
#ifndef _ncv_alg_hpp_
#define _ncv_alg_hpp_
#include "opencv2/cudalegacy/NCV.hpp"
template <class T>
static void swap(T &p1, T &p2)
{
T tmp = p1;
p1 = p2;
p2 = tmp;
}
template<typename T>
static T divUp(T a, T b)
{
return (a + b - 1) / b;
}
template<typename T>
struct functorAddValues
{
static __device__ __inline__ void assign(volatile T *dst, volatile T *src)
{
//Works only for integral types. If you see compiler error here, then you have to specify how to copy your object as a set of integral fields.
*dst = *src;
}
static __device__ __inline__ void reduce(volatile T &in1out, const volatile T &in2)
{
in1out += in2;
}
};
template<typename T>
struct functorMinValues
{
static __device__ __inline__ void assign(volatile T *dst, volatile T *src)
{
//Works only for integral types. If you see compiler error here, then you have to specify how to copy your object as a set of integral fields.
*dst = *src;
}
static __device__ __inline__ void reduce(volatile T &in1out, const volatile T &in2)
{
in1out = in1out > in2 ? in2 : in1out;
}
};
template<typename T>
struct functorMaxValues
{
static __device__ __inline__ void assign(volatile T *dst, volatile T *src)
{
//Works only for integral types. If you see compiler error here, then you have to specify how to copy your object as a set of integral fields.
*dst = *src;
}
static __device__ __inline__ void reduce(volatile T &in1out, const volatile T &in2)
{
in1out = in1out > in2 ? in1out : in2;
}
};
template<typename Tdata, class Tfunc, Ncv32u nThreads>
static __device__ Tdata subReduce(Tdata threadElem)
{
Tfunc functor;
__shared__ Tdata _reduceArr[nThreads];
volatile Tdata *reduceArr = _reduceArr;
functor.assign(reduceArr + threadIdx.x, &threadElem);
__syncthreads();
if (nThreads >= 256 && threadIdx.x < 128)
{
functor.reduce(reduceArr[threadIdx.x], reduceArr[threadIdx.x + 128]);
}
__syncthreads();
if (nThreads >= 128 && threadIdx.x < 64)
{
functor.reduce(reduceArr[threadIdx.x], reduceArr[threadIdx.x + 64]);
}
__syncthreads();
if (threadIdx.x < 32)
{
if (nThreads >= 64)
{
functor.reduce(reduceArr[threadIdx.x], reduceArr[threadIdx.x + 32]);
}
if (nThreads >= 32 && threadIdx.x < 16)
{
functor.reduce(reduceArr[threadIdx.x], reduceArr[threadIdx.x + 16]);
functor.reduce(reduceArr[threadIdx.x], reduceArr[threadIdx.x + 8]);
functor.reduce(reduceArr[threadIdx.x], reduceArr[threadIdx.x + 4]);
functor.reduce(reduceArr[threadIdx.x], reduceArr[threadIdx.x + 2]);
functor.reduce(reduceArr[threadIdx.x], reduceArr[threadIdx.x + 1]);
}
}
__syncthreads();
Tdata reduceRes;
functor.assign(&reduceRes, reduceArr);
return reduceRes;
}
#endif //_ncv_alg_hpp_