mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2025-01-13 16:44:05 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity
5f175f9594
opencv/modules/gpu/src/cuda/element_operations.cu

581 lines
23 KiB
Plaintext
Raw Normal View History

gpu module refactoring: moved per-element operations into separated file
2010-12-20 17:07:19 +08:00
/*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*/
#include "opencv2/gpu/device/vecmath.hpp"
added gpu threshold.
2011-01-24 18:11:02 +08:00
#include "opencv2/gpu/device/transform.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
gpu module refactoring: moved per-element operations into separated file
2010-12-20 17:07:19 +08:00
#include "internal_shared.hpp"
using namespace cv::gpu;
using namespace cv::gpu::device;
namespace cv { namespace gpu { namespace mathfunc
{
//////////////////////////////////////////////////////////////////////////////////////
// Compare
template <typename T1, typename T2>
struct NotEqual
{
__device__ uchar operator()(const T1& src1, const T2& src2)
{
return static_cast<uchar>(static_cast<int>(src1 != src2) * 255);
}
};
template <typename T1, typename T2>
inline void compare_ne(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst)
{
NotEqual<T1, T2> op;
transform(static_cast< DevMem2D_<T1> >(src1), static_cast< DevMem2D_<T2> >(src2), dst, op, 0);
}
void compare_ne_8uc4(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst)
{
compare_ne<uint, uint>(src1, src2, dst);
}
void compare_ne_32f(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst)
{
compare_ne<float, float>(src1, src2, dst);
}
//////////////////////////////////////////////////////////////////////////
// Unary bitwise logical matrix operations
enum { UN_OP_NOT };
template <typename T, int opid>
struct UnOp;
template <typename T>
struct UnOp<T, UN_OP_NOT>
{
static __device__ T call(T v) { return ~v; }
};
template <int opid>
__global__ void bitwiseUnOpKernel(int rows, int width, const PtrStep src, PtrStep dst)
{
const int x = (blockDim.x * blockIdx.x + threadIdx.x) * 4;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (y < rows)
{
uchar* dst_ptr = dst.ptr(y) + x;
const uchar* src_ptr = src.ptr(y) + x;
if (x + sizeof(uint) - 1 < width)
{
*(uint*)dst_ptr = UnOp<uint, opid>::call(*(uint*)src_ptr);
}
else
{
const uchar* src_end = src.ptr(y) + width;
while (src_ptr < src_end)
{
*dst_ptr++ = UnOp<uchar, opid>::call(*src_ptr++);
}
}
}
}
template <int opid>
void bitwiseUnOp(int rows, int width, const PtrStep src, PtrStep dst,
cudaStream_t stream)
{
dim3 threads(16, 16);
dim3 grid(divUp(width, threads.x * sizeof(uint)),
divUp(rows, threads.y));
bitwiseUnOpKernel<opid><<<grid, threads>>>(rows, width, src, dst);
if (stream == 0)
cudaSafeCall(cudaThreadSynchronize());
}
template <typename T, int opid>
__global__ void bitwiseUnOpKernel(int rows, int cols, int cn, const PtrStep src,
const PtrStep mask, PtrStep dst)
{
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x < cols && y < rows && mask.ptr(y)[x / cn])
{
T* dst_row = (T*)dst.ptr(y);
const T* src_row = (const T*)src.ptr(y);
dst_row[x] = UnOp<T, opid>::call(src_row[x]);
}
}
template <typename T, int opid>
void bitwiseUnOp(int rows, int cols, int cn, const PtrStep src,
const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
dim3 threads(16, 16);
dim3 grid(divUp(cols, threads.x), divUp(rows, threads.y));
bitwiseUnOpKernel<T, opid><<<grid, threads>>>(rows, cols, cn, src, mask, dst);
if (stream == 0)
cudaSafeCall(cudaThreadSynchronize());
}
void bitwiseNotCaller(int rows, int cols, int elem_size1, int cn,
const PtrStep src, PtrStep dst, cudaStream_t stream)
{
bitwiseUnOp<UN_OP_NOT>(rows, cols * elem_size1 * cn, src, dst, stream);
}
template <typename T>
void bitwiseMaskNotCaller(int rows, int cols, int cn, const PtrStep src,
const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
bitwiseUnOp<T, UN_OP_NOT>(rows, cols * cn, cn, src, mask, dst, stream);
}
template void bitwiseMaskNotCaller<uchar>(int, int, int, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwiseMaskNotCaller<ushort>(int, int, int, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwiseMaskNotCaller<uint>(int, int, int, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
//////////////////////////////////////////////////////////////////////////
// Binary bitwise logical matrix operations
enum { BIN_OP_OR, BIN_OP_AND, BIN_OP_XOR };
template <typename T, int opid>
struct BinOp;
template <typename T>
struct BinOp<T, BIN_OP_OR>
{
static __device__ T call(T a, T b) { return a | b; }
};
template <typename T>
struct BinOp<T, BIN_OP_AND>
{
static __device__ T call(T a, T b) { return a & b; }
};
template <typename T>
struct BinOp<T, BIN_OP_XOR>
{
static __device__ T call(T a, T b) { return a ^ b; }
};
template <int opid>
__global__ void bitwiseBinOpKernel(int rows, int width, const PtrStep src1,
const PtrStep src2, PtrStep dst)
{
const int x = (blockDim.x * blockIdx.x + threadIdx.x) * 4;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (y < rows)
{
uchar* dst_ptr = dst.ptr(y) + x;
const uchar* src1_ptr = src1.ptr(y) + x;
const uchar* src2_ptr = src2.ptr(y) + x;
if (x + sizeof(uint) - 1 < width)
{
*(uint*)dst_ptr = BinOp<uint, opid>::call(*(uint*)src1_ptr, *(uint*)src2_ptr);
}
else
{
const uchar* src1_end = src1.ptr(y) + width;
while (src1_ptr < src1_end)
{
*dst_ptr++ = BinOp<uchar, opid>::call(*src1_ptr++, *src2_ptr++);
}
}
}
}
template <int opid>
void bitwiseBinOp(int rows, int width, const PtrStep src1, const PtrStep src2,
PtrStep dst, cudaStream_t stream)
{
dim3 threads(16, 16);
dim3 grid(divUp(width, threads.x * sizeof(uint)), divUp(rows, threads.y));
bitwiseBinOpKernel<opid><<<grid, threads>>>(rows, width, src1, src2, dst);
if (stream == 0)
cudaSafeCall(cudaThreadSynchronize());
}
template <typename T, int opid>
__global__ void bitwiseBinOpKernel(
int rows, int cols, int cn, const PtrStep src1, const PtrStep src2,
const PtrStep mask, PtrStep dst)
{
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x < cols && y < rows && mask.ptr(y)[x / cn])
{
T* dst_row = (T*)dst.ptr(y);
const T* src1_row = (const T*)src1.ptr(y);
const T* src2_row = (const T*)src2.ptr(y);
dst_row[x] = BinOp<T, opid>::call(src1_row[x], src2_row[x]);
}
}
template <typename T, int opid>
void bitwiseBinOp(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2,
const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
dim3 threads(16, 16);
dim3 grid(divUp(cols, threads.x), divUp(rows, threads.y));
bitwiseBinOpKernel<T, opid><<<grid, threads>>>(rows, cols, cn, src1, src2, mask, dst);
if (stream == 0)
cudaSafeCall(cudaThreadSynchronize());
}
void bitwiseOrCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1,
const PtrStep src2, PtrStep dst, cudaStream_t stream)
{
bitwiseBinOp<BIN_OP_OR>(rows, cols * elem_size1 * cn, src1, src2, dst, stream);
}
template <typename T>
void bitwiseMaskOrCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2,
const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
bitwiseBinOp<T, BIN_OP_OR>(rows, cols * cn, cn, src1, src2, mask, dst, stream);
}
template void bitwiseMaskOrCaller<uchar>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwiseMaskOrCaller<ushort>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwiseMaskOrCaller<uint>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
void bitwiseAndCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1,
const PtrStep src2, PtrStep dst, cudaStream_t stream)
{
bitwiseBinOp<BIN_OP_AND>(rows, cols * elem_size1 * cn, src1, src2, dst, stream);
}
template <typename T>
void bitwiseMaskAndCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2,
const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
bitwiseBinOp<T, BIN_OP_AND>(rows, cols * cn, cn, src1, src2, mask, dst, stream);
}
template void bitwiseMaskAndCaller<uchar>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwiseMaskAndCaller<ushort>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwiseMaskAndCaller<uint>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
void bitwiseXorCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1,
const PtrStep src2, PtrStep dst, cudaStream_t stream)
{
bitwiseBinOp<BIN_OP_XOR>(rows, cols * elem_size1 * cn, src1, src2, dst, stream);
}
template <typename T>
void bitwiseMaskXorCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2,
const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
bitwiseBinOp<T, BIN_OP_XOR>(rows, cols * cn, cn, src1, src2, mask, dst, stream);
}
template void bitwiseMaskXorCaller<uchar>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwiseMaskXorCaller<ushort>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwiseMaskXorCaller<uint>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
refactoring: moved gpu reduction-based functions into separated file
2010-12-20 17:51:25 +08:00
//////////////////////////////////////////////////////////////////////////
// min/max
struct MinOp
{
template <typename T>
__device__ T operator()(T a, T b)
{
return min(a, b);
}
__device__ float operator()(float a, float b)
{
return fmin(a, b);
}
__device__ double operator()(double a, double b)
{
return fmin(a, b);
}
};
struct MaxOp
{
template <typename T>
__device__ T operator()(T a, T b)
{
return max(a, b);
}
__device__ float operator()(float a, float b)
{
return fmax(a, b);
}
__device__ double operator()(double a, double b)
{
return fmax(a, b);
}
};
struct ScalarMinOp
{
double s;
explicit ScalarMinOp(double s_) : s(s_) {}
template <typename T>
__device__ T operator()(T a)
{
return saturate_cast<T>(fmin((double)a, s));
}
};
struct ScalarMaxOp
{
double s;
explicit ScalarMaxOp(double s_) : s(s_) {}
template <typename T>
__device__ T operator()(T a)
{
return saturate_cast<T>(fmax((double)a, s));
}
};
template <typename T>
void min_gpu(const DevMem2D_<T>& src1, const DevMem2D_<T>& src2, const DevMem2D_<T>& dst, cudaStream_t stream)
{
MinOp op;
transform(src1, src2, dst, op, stream);
}
template void min_gpu<uchar >(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst, cudaStream_t stream);
template void min_gpu<char >(const DevMem2D_<char>& src1, const DevMem2D_<char>& src2, const DevMem2D_<char>& dst, cudaStream_t stream);
template void min_gpu<ushort>(const DevMem2D_<ushort>& src1, const DevMem2D_<ushort>& src2, const DevMem2D_<ushort>& dst, cudaStream_t stream);
template void min_gpu<short >(const DevMem2D_<short>& src1, const DevMem2D_<short>& src2, const DevMem2D_<short>& dst, cudaStream_t stream);
template void min_gpu<int >(const DevMem2D_<int>& src1, const DevMem2D_<int>& src2, const DevMem2D_<int>& dst, cudaStream_t stream);
template void min_gpu<float >(const DevMem2D_<float>& src1, const DevMem2D_<float>& src2, const DevMem2D_<float>& dst, cudaStream_t stream);
template void min_gpu<double>(const DevMem2D_<double>& src1, const DevMem2D_<double>& src2, const DevMem2D_<double>& dst, cudaStream_t stream);
template <typename T>
void max_gpu(const DevMem2D_<T>& src1, const DevMem2D_<T>& src2, const DevMem2D_<T>& dst, cudaStream_t stream)
{
MaxOp op;
transform(src1, src2, dst, op, stream);
}
template void max_gpu<uchar >(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst, cudaStream_t stream);
template void max_gpu<char >(const DevMem2D_<char>& src1, const DevMem2D_<char>& src2, const DevMem2D_<char>& dst, cudaStream_t stream);
template void max_gpu<ushort>(const DevMem2D_<ushort>& src1, const DevMem2D_<ushort>& src2, const DevMem2D_<ushort>& dst, cudaStream_t stream);
template void max_gpu<short >(const DevMem2D_<short>& src1, const DevMem2D_<short>& src2, const DevMem2D_<short>& dst, cudaStream_t stream);
template void max_gpu<int >(const DevMem2D_<int>& src1, const DevMem2D_<int>& src2, const DevMem2D_<int>& dst, cudaStream_t stream);
template void max_gpu<float >(const DevMem2D_<float>& src1, const DevMem2D_<float>& src2, const DevMem2D_<float>& dst, cudaStream_t stream);
template void max_gpu<double>(const DevMem2D_<double>& src1, const DevMem2D_<double>& src2, const DevMem2D_<double>& dst, cudaStream_t stream);
template <typename T>
void min_gpu(const DevMem2D_<T>& src1, double src2, const DevMem2D_<T>& dst, cudaStream_t stream)
{
ScalarMinOp op(src2);
transform(src1, dst, op, stream);
}
template void min_gpu<uchar >(const DevMem2D& src1, double src2, const DevMem2D& dst, cudaStream_t stream);
template void min_gpu<char >(const DevMem2D_<char>& src1, double src2, const DevMem2D_<char>& dst, cudaStream_t stream);
template void min_gpu<ushort>(const DevMem2D_<ushort>& src1, double src2, const DevMem2D_<ushort>& dst, cudaStream_t stream);
template void min_gpu<short >(const DevMem2D_<short>& src1, double src2, const DevMem2D_<short>& dst, cudaStream_t stream);
template void min_gpu<int >(const DevMem2D_<int>& src1, double src2, const DevMem2D_<int>& dst, cudaStream_t stream);
template void min_gpu<float >(const DevMem2D_<float>& src1, double src2, const DevMem2D_<float>& dst, cudaStream_t stream);
template void min_gpu<double>(const DevMem2D_<double>& src1, double src2, const DevMem2D_<double>& dst, cudaStream_t stream);
template <typename T>
void max_gpu(const DevMem2D_<T>& src1, double src2, const DevMem2D_<T>& dst, cudaStream_t stream)
{
ScalarMaxOp op(src2);
transform(src1, dst, op, stream);
}
template void max_gpu<uchar >(const DevMem2D& src1, double src2, const DevMem2D& dst, cudaStream_t stream);
template void max_gpu<char >(const DevMem2D_<char>& src1, double src2, const DevMem2D_<char>& dst, cudaStream_t stream);
template void max_gpu<ushort>(const DevMem2D_<ushort>& src1, double src2, const DevMem2D_<ushort>& dst, cudaStream_t stream);
template void max_gpu<short >(const DevMem2D_<short>& src1, double src2, const DevMem2D_<short>& dst, cudaStream_t stream);
template void max_gpu<int >(const DevMem2D_<int>& src1, double src2, const DevMem2D_<int>& dst, cudaStream_t stream);
template void max_gpu<float >(const DevMem2D_<float>& src1, double src2, const DevMem2D_<float>& dst, cudaStream_t stream);
template void max_gpu<double>(const DevMem2D_<double>& src1, double src2, const DevMem2D_<double>& dst, cudaStream_t stream);
added gpu threshold.
2011-01-24 18:11:02 +08:00
//////////////////////////////////////////////////////////////////////////
// threshold
class ThreshOp
{
public:
ThreshOp(float thresh_, float maxVal_) : thresh(thresh_), maxVal(maxVal_) {}
protected:
float thresh;
float maxVal;
};
class ThreshBinary : public ThreshOp
{
public:
ThreshBinary(float thresh_, float maxVal_) : ThreshOp(thresh_, maxVal_) {}
template<typename T>
__device__ T operator()(const T& src) const
{
return (float)src > thresh ? saturate_cast<T>(maxVal) : 0;
}
};
class ThreshBinaryInv : public ThreshOp
{
public:
ThreshBinaryInv(float thresh_, float maxVal_) : ThreshOp(thresh_, maxVal_) {}
template<typename T>
__device__ T operator()(const T& src) const
{
return (float)src > thresh ? 0 : saturate_cast<T>(maxVal);
}
};
class ThreshTrunc : public ThreshOp
{
public:
ThreshTrunc(float thresh_, float maxVal_) : ThreshOp(thresh_, maxVal_) {}
template<typename T>
__device__ T operator()(const T& src) const
{
return saturate_cast<T>(fmin((float)src, thresh));
}
};
class ThreshToZero : public ThreshOp
{
public:
ThreshToZero(float thresh_, float maxVal_) : ThreshOp(thresh_, maxVal_) {}
template<typename T>
__device__ T operator()(const T& src) const
{
return (float)src > thresh ? src : 0;
}
};
class ThreshToZeroInv : public ThreshOp
{
public:
ThreshToZeroInv(float thresh_, float maxVal_) : ThreshOp(thresh_, maxVal_) {}
template<typename T>
__device__ T operator()(const T& src) const
{
return (float)src > thresh ? 0 : src;
}
};
template <class Op, typename T>
void threshold_caller(const DevMem2D_<T>& src, const DevMem2D_<T>& dst, float thresh, float maxVal,
cudaStream_t stream)
{
Op op(thresh, maxVal);
transform(src, dst, op, stream);
}
template <typename T>
void threshold_gpu(const DevMem2D& src, const DevMem2D& dst, float thresh, float maxVal, int type,
cudaStream_t stream)
{
typedef void (*caller_t)(const DevMem2D_<T>& src, const DevMem2D_<T>& dst, float thresh, float maxVal,
cudaStream_t stream);
static const caller_t callers[] =
{
threshold_caller<ThreshBinary, T>,
threshold_caller<ThreshBinaryInv, T>,
threshold_caller<ThreshTrunc, T>,
threshold_caller<ThreshToZero, T>,
threshold_caller<ThreshToZeroInv, T>
};
callers[type]((DevMem2D_<T>)src, (DevMem2D_<T>)dst, thresh, maxVal, stream);
}
template void threshold_gpu<uchar>(const DevMem2D& src, const DevMem2D& dst, float thresh, float maxVal, int type, cudaStream_t stream);
template void threshold_gpu<schar>(const DevMem2D& src, const DevMem2D& dst, float thresh, float maxVal, int type, cudaStream_t stream);
template void threshold_gpu<ushort>(const DevMem2D& src, const DevMem2D& dst, float thresh, float maxVal, int type, cudaStream_t stream);
template void threshold_gpu<short>(const DevMem2D& src, const DevMem2D& dst, float thresh, float maxVal, int type, cudaStream_t stream);
template void threshold_gpu<int>(const DevMem2D& src, const DevMem2D& dst, float thresh, float maxVal, int type, cudaStream_t stream);
template void threshold_gpu<float>(const DevMem2D& src, const DevMem2D& dst, float thresh, float maxVal, int type, cudaStream_t stream);
gpu module refactoring: moved per-element operations into separated file
2010-12-20 17:07:19 +08:00
}}}
Reference in New Issue Copy Permalink