WARP_SHUFFLE_FUNCTIONS = FEATURE_SET_COMPUTE_30
};
+ CV_EXPORTS bool deviceSupports(FeatureSet feature_set);
+
// Gives information about what GPU archs this OpenCV GPU module was
// compiled for
class CV_EXPORTS TargetArchs
#include "opencv2/gpu/device/saturate_cast.hpp"
#include "opencv2/gpu/device/transform.hpp"
#include "opencv2/gpu/device/functional.hpp"
+#include "opencv2/gpu/device/type_traits.hpp"
namespace cv { namespace gpu { namespace device
{
void writeScalar(const int*);
void writeScalar(const float*);
void writeScalar(const double*);
+ void copyToWithMask_gpu(PtrStepSzb src, PtrStepSzb dst, size_t elemSize1, int cn, PtrStepSzb mask, bool colorMask, cudaStream_t stream);
void convert_gpu(PtrStepSzb, int, PtrStepSzb, int, double, double, cudaStream_t);
}}}
//////////////////////////////// ConvertTo ////////////////////////////////
///////////////////////////////////////////////////////////////////////////
- template <typename T, typename D> struct Convertor : unary_function<T, D>
+ template <typename T, typename D, typename S> struct Convertor : unary_function<T, D>
{
- Convertor(double alpha_, double beta_) : alpha(alpha_), beta(beta_) {}
+ Convertor(S alpha_, S beta_) : alpha(alpha_), beta(beta_) {}
- __device__ __forceinline__ D operator()(const T& src) const
+ __device__ __forceinline__ D operator()(typename TypeTraits<T>::ParameterType src) const
{
return saturate_cast<D>(alpha * src + beta);
}
- double alpha, beta;
+ S alpha, beta;
};
namespace detail
};
}
- template <typename T, typename D> struct TransformFunctorTraits< Convertor<T, D> > : detail::ConvertTraits< Convertor<T, D> >
+ template <typename T, typename D, typename S> struct TransformFunctorTraits< Convertor<T, D, S> > : detail::ConvertTraits< Convertor<T, D, S> >
{
};
- template<typename T, typename D>
+ template<typename T, typename D, typename S>
void cvt_(PtrStepSzb src, PtrStepSzb dst, double alpha, double beta, cudaStream_t stream)
{
cudaSafeCall( cudaSetDoubleForDevice(&alpha) );
cudaSafeCall( cudaSetDoubleForDevice(&beta) );
- Convertor<T, D> op(alpha, beta);
+ Convertor<T, D, S> op(static_cast<S>(alpha), static_cast<S>(beta));
cv::gpu::device::transform((PtrStepSz<T>)src, (PtrStepSz<D>)dst, op, WithOutMask(), stream);
}
{
typedef void (*caller_t)(PtrStepSzb src, PtrStepSzb dst, double alpha, double beta, cudaStream_t stream);
- static const caller_t tab[8][8] =
+ static const caller_t tab[7][7] =
{
- {cvt_<uchar, uchar>, cvt_<uchar, schar>, cvt_<uchar, ushort>, cvt_<uchar, short>,
- cvt_<uchar, int>, cvt_<uchar, float>, cvt_<uchar, double>, 0},
-
- {cvt_<schar, uchar>, cvt_<schar, schar>, cvt_<schar, ushort>, cvt_<schar, short>,
- cvt_<schar, int>, cvt_<schar, float>, cvt_<schar, double>, 0},
-
- {cvt_<ushort, uchar>, cvt_<ushort, schar>, cvt_<ushort, ushort>, cvt_<ushort, short>,
- cvt_<ushort, int>, cvt_<ushort, float>, cvt_<ushort, double>, 0},
-
- {cvt_<short, uchar>, cvt_<short, schar>, cvt_<short, ushort>, cvt_<short, short>,
- cvt_<short, int>, cvt_<short, float>, cvt_<short, double>, 0},
-
- {cvt_<int, uchar>, cvt_<int, schar>, cvt_<int, ushort>,
- cvt_<int, short>, cvt_<int, int>, cvt_<int, float>, cvt_<int, double>, 0},
-
- {cvt_<float, uchar>, cvt_<float, schar>, cvt_<float, ushort>,
- cvt_<float, short>, cvt_<float, int>, cvt_<float, float>, cvt_<float, double>, 0},
-
- {cvt_<double, uchar>, cvt_<double, schar>, cvt_<double, ushort>,
- cvt_<double, short>, cvt_<double, int>, cvt_<double, float>, cvt_<double, double>, 0},
-
- {0,0,0,0,0,0,0,0}
+ {
+ cvt_<uchar, uchar, float>,
+ cvt_<uchar, schar, float>,
+ cvt_<uchar, ushort, float>,
+ cvt_<uchar, short, float>,
+ cvt_<uchar, int, float>,
+ cvt_<uchar, float, float>,
+ cvt_<uchar, double, double>
+ },
+ {
+ cvt_<schar, uchar, float>,
+ cvt_<schar, schar, float>,
+ cvt_<schar, ushort, float>,
+ cvt_<schar, short, float>,
+ cvt_<schar, int, float>,
+ cvt_<schar, float, float>,
+ cvt_<schar, double, double>
+ },
+ {
+ cvt_<ushort, uchar, float>,
+ cvt_<ushort, schar, float>,
+ cvt_<ushort, ushort, float>,
+ cvt_<ushort, short, float>,
+ cvt_<ushort, int, float>,
+ cvt_<ushort, float, float>,
+ cvt_<ushort, double, double>
+ },
+ {
+ cvt_<short, uchar, float>,
+ cvt_<short, schar, float>,
+ cvt_<short, ushort, float>,
+ cvt_<short, short, float>,
+ cvt_<short, int, float>,
+ cvt_<short, float, float>,
+ cvt_<short, double, double>
+ },
+ {
+ cvt_<int, uchar, float>,
+ cvt_<int, schar, float>,
+ cvt_<int, ushort, float>,
+ cvt_<int, short, float>,
+ cvt_<int, int, double>,
+ cvt_<int, float, double>,
+ cvt_<int, double, double>
+ },
+ {
+ cvt_<float, uchar, float>,
+ cvt_<float, schar, float>,
+ cvt_<float, ushort, float>,
+ cvt_<float, short, float>,
+ cvt_<float, int, float>,
+ cvt_<float, float, float>,
+ cvt_<float, double, double>
+ },
+ {
+ cvt_<double, uchar, double>,
+ cvt_<double, schar, double>,
+ cvt_<double, ushort, double>,
+ cvt_<double, short, double>,
+ cvt_<double, int, double>,
+ cvt_<double, float, double>,
+ cvt_<double, double, double>
+ }
};
caller_t func = tab[sdepth][ddepth];
- if (!func)
- cv::gpu::error("Unsupported convert operation", __FILE__, __LINE__, "convert_gpu");
-
func(src, dst, alpha, beta, stream);
}
#include <iostream>
#ifdef HAVE_CUDA
- #include <cuda.h>
- #include <cuda_runtime_api.h>
+ #include <cuda_runtime.h>
#include <npp.h>
#define CUDART_MINIMUM_REQUIRED_VERSION 4010
namespace
{
- // Compares value to set using the given comparator. Returns true if
- // there is at least one element x in the set satisfying to: x cmp value
- // predicate.
- template <typename Comparer>
- bool compareToSet(const std::string& set_as_str, int value, Comparer cmp)
+ class CudaArch
+ {
+ public:
+ CudaArch();
+
+ bool builtWith(FeatureSet feature_set) const;
+ bool hasPtx(int major, int minor) const;
+ bool hasBin(int major, int minor) const;
+ bool hasEqualOrLessPtx(int major, int minor) const;
+ bool hasEqualOrGreaterPtx(int major, int minor) const;
+ bool hasEqualOrGreaterBin(int major, int minor) const;
+
+ private:
+ static void fromStr(const string& set_as_str, vector<int>& arr);
+
+ vector<int> bin;
+ vector<int> ptx;
+ vector<int> features;
+ };
+
+ const CudaArch cudaArch;
+
+ CudaArch::CudaArch()
+ {
+ #ifdef HAVE_CUDA
+ fromStr(CUDA_ARCH_BIN, bin);
+ fromStr(CUDA_ARCH_PTX, ptx);
+ fromStr(CUDA_ARCH_FEATURES, features);
+ #endif
+ }
+
+ bool CudaArch::builtWith(FeatureSet feature_set) const
+ {
+ return !features.empty() && (features.back() >= feature_set);
+ }
+
+ bool CudaArch::hasPtx(int major, int minor) const
+ {
+ return find(ptx.begin(), ptx.end(), major * 10 + minor) != ptx.end();
+ }
+
+ bool CudaArch::hasBin(int major, int minor) const
+ {
+ return find(bin.begin(), bin.end(), major * 10 + minor) != bin.end();
+ }
+
+ bool CudaArch::hasEqualOrLessPtx(int major, int minor) const
+ {
+ return !ptx.empty() && (ptx.front() <= major * 10 + minor);
+ }
+
+ bool CudaArch::hasEqualOrGreaterPtx(int major, int minor) const
+ {
+ return !ptx.empty() && (ptx.back() >= major * 10 + minor);
+ }
+
+ bool CudaArch::hasEqualOrGreaterBin(int major, int minor) const
+ {
+ return !bin.empty() && (bin.back() >= major * 10 + minor);
+ }
+
+ void CudaArch::fromStr(const string& set_as_str, vector<int>& arr)
{
if (set_as_str.find_first_not_of(" ") == string::npos)
- return false;
+ return;
- std::stringstream stream(set_as_str);
+ istringstream stream(set_as_str);
int cur_value;
while (!stream.eof())
{
stream >> cur_value;
- if (cmp(cur_value, value))
- return true;
+ arr.push_back(cur_value);
}
- return false;
+ sort(arr.begin(), arr.end());
}
}
bool cv::gpu::TargetArchs::builtWith(cv::gpu::FeatureSet feature_set)
{
#if defined (HAVE_CUDA)
- return ::compareToSet(CUDA_ARCH_FEATURES, feature_set, std::greater_equal<int>());
+ return cudaArch.builtWith(feature_set);
#else
(void)feature_set;
return false;
bool cv::gpu::TargetArchs::hasPtx(int major, int minor)
{
#if defined (HAVE_CUDA)
- return ::compareToSet(CUDA_ARCH_PTX, major * 10 + minor, std::equal_to<int>());
+ return cudaArch.hasPtx(major, minor);
#else
(void)major;
(void)minor;
bool cv::gpu::TargetArchs::hasBin(int major, int minor)
{
#if defined (HAVE_CUDA)
- return ::compareToSet(CUDA_ARCH_BIN, major * 10 + minor, std::equal_to<int>());
+ return cudaArch.hasBin(major, minor);
#else
(void)major;
(void)minor;
bool cv::gpu::TargetArchs::hasEqualOrLessPtx(int major, int minor)
{
#if defined (HAVE_CUDA)
- return ::compareToSet(CUDA_ARCH_PTX, major * 10 + minor,
- std::less_equal<int>());
+ return cudaArch.hasEqualOrLessPtx(major, minor);
#else
(void)major;
(void)minor;
bool cv::gpu::TargetArchs::hasEqualOrGreater(int major, int minor)
{
- return hasEqualOrGreaterPtx(major, minor) ||
- hasEqualOrGreaterBin(major, minor);
+ return hasEqualOrGreaterPtx(major, minor) || hasEqualOrGreaterBin(major, minor);
}
bool cv::gpu::TargetArchs::hasEqualOrGreaterPtx(int major, int minor)
{
#if defined (HAVE_CUDA)
- return ::compareToSet(CUDA_ARCH_PTX, major * 10 + minor, std::greater_equal<int>());
+ return cudaArch.hasEqualOrGreaterPtx(major, minor);
#else
(void)major;
(void)minor;
bool cv::gpu::TargetArchs::hasEqualOrGreaterBin(int major, int minor)
{
#if defined (HAVE_CUDA)
- return ::compareToSet(CUDA_ARCH_BIN, major * 10 + minor,
- std::greater_equal<int>());
+ return cudaArch.hasEqualOrGreaterBin(major, minor);
#else
(void)major;
(void)minor;
#endif
}
+bool cv::gpu::deviceSupports(FeatureSet feature_set)
+{
+ static int versions[] =
+ {
+ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1
+ };
+ static const int cache_size = static_cast<int>(sizeof(versions) / sizeof(versions[0]));
+
+ const int devId = getDevice();
+
+ int version;
+
+ if (devId < cache_size && versions[devId] >= 0)
+ version = versions[devId];
+ else
+ {
+ DeviceInfo dev(devId);
+ version = dev.majorVersion() * 10 + dev.minorVersion();
+ if (devId < cache_size)
+ versions[devId] = version;
+ }
+
+ return TargetArchs::builtWith(feature_set) && (version >= feature_set);
+}
+
#if !defined (HAVE_CUDA)
#define throw_nogpu CV_Error(CV_GpuNotSupported, "The library is compiled without CUDA support")
--- /dev/null
+/*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 __OPENCV_GPU_REDUCE_DETAIL_HPP__
+#define __OPENCV_GPU_REDUCE_DETAIL_HPP__
+
+#include <thrust/tuple.h>
+#include "../warp.hpp"
+#include "../warp_shuffle.hpp"
+
+namespace cv { namespace gpu { namespace device
+{
+ namespace reduce_detail
+ {
+ template <typename T> struct GetType;
+ template <typename T> struct GetType<T*>
+ {
+ typedef T type;
+ };
+ template <typename T> struct GetType<volatile T*>
+ {
+ typedef T type;
+ };
+ template <typename T> struct GetType<T&>
+ {
+ typedef T type;
+ };
+
+ template <unsigned int I, unsigned int N>
+ struct For
+ {
+ template <class PointerTuple, class ValTuple>
+ static __device__ void loadToSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid)
+ {
+ thrust::get<I>(smem)[tid] = thrust::get<I>(val);
+
+ For<I + 1, N>::loadToSmem(smem, val, tid);
+ }
+ template <class PointerTuple, class ValTuple>
+ static __device__ void loadFromSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid)
+ {
+ thrust::get<I>(val) = thrust::get<I>(smem)[tid];
+
+ For<I + 1, N>::loadFromSmem(smem, val, tid);
+ }
+
+ template <class PointerTuple, class ValTuple, class OpTuple>
+ static __device__ void merge(const PointerTuple& smem, const ValTuple& val, unsigned int tid, unsigned int delta, const OpTuple& op)
+ {
+ typename GetType<typename thrust::tuple_element<I, PointerTuple>::type>::type reg = thrust::get<I>(smem)[tid + delta];
+ thrust::get<I>(smem)[tid] = thrust::get<I>(val) = thrust::get<I>(op)(thrust::get<I>(val), reg);
+
+ For<I + 1, N>::merge(smem, val, tid, delta, op);
+ }
+ template <class ValTuple, class OpTuple>
+ static __device__ void mergeShfl(const ValTuple& val, unsigned int delta, unsigned int width, const OpTuple& op)
+ {
+ typename GetType<typename thrust::tuple_element<I, ValTuple>::type>::type reg = shfl_down(thrust::get<I>(val), delta, width);
+ thrust::get<I>(val) = thrust::get<I>(op)(thrust::get<I>(val), reg);
+
+ For<I + 1, N>::mergeShfl(val, delta, width, op);
+ }
+ };
+ template <unsigned int N>
+ struct For<N, N>
+ {
+ template <class PointerTuple, class ValTuple>
+ static __device__ void loadToSmem(const PointerTuple&, const ValTuple&, unsigned int)
+ {
+ }
+ template <class PointerTuple, class ValTuple>
+ static __device__ void loadFromSmem(const PointerTuple&, const ValTuple&, unsigned int)
+ {
+ }
+
+ template <class PointerTuple, class ValTuple, class OpTuple>
+ static __device__ void merge(const PointerTuple&, const ValTuple&, unsigned int, unsigned int, const OpTuple&)
+ {
+ }
+ template <class ValTuple, class OpTuple>
+ static __device__ void mergeShfl(const ValTuple&, unsigned int, unsigned int, const OpTuple&)
+ {
+ }
+ };
+
+ template <typename T>
+ __device__ __forceinline__ void loadToSmem(volatile T* smem, T& val, unsigned int tid)
+ {
+ smem[tid] = val;
+ }
+ template <typename T>
+ __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& val, unsigned int tid)
+ {
+ val = smem[tid];
+ }
+ template <typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
+ typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9>
+ __device__ __forceinline__ void loadToSmem(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
+ const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
+ unsigned int tid)
+ {
+ For<0, thrust::tuple_size<thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9> >::value>::loadToSmem(smem, val, tid);
+ }
+ template <typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
+ typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9>
+ __device__ __forceinline__ void loadFromSmem(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
+ const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
+ unsigned int tid)
+ {
+ For<0, thrust::tuple_size<thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9> >::value>::loadFromSmem(smem, val, tid);
+ }
+
+ template <typename T, class Op>
+ __device__ __forceinline__ void merge(volatile T* smem, T& val, unsigned int tid, unsigned int delta, const Op& op)
+ {
+ T reg = smem[tid + delta];
+ smem[tid] = val = op(val, reg);
+ }
+ template <typename T, class Op>
+ __device__ __forceinline__ void mergeShfl(T& val, unsigned int delta, unsigned int width, const Op& op)
+ {
+ T reg = shfl_down(val, delta, width);
+ val = op(val, reg);
+ }
+ template <typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
+ typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9,
+ class Op0, class Op1, class Op2, class Op3, class Op4, class Op5, class Op6, class Op7, class Op8, class Op9>
+ __device__ __forceinline__ void merge(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
+ const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
+ unsigned int tid,
+ unsigned int delta,
+ const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>& op)
+ {
+ For<0, thrust::tuple_size<thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9> >::value>::merge(smem, val, tid, delta, op);
+ }
+ template <typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9,
+ class Op0, class Op1, class Op2, class Op3, class Op4, class Op5, class Op6, class Op7, class Op8, class Op9>
+ __device__ __forceinline__ void mergeShfl(const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
+ unsigned int delta,
+ unsigned int width,
+ const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>& op)
+ {
+ For<0, thrust::tuple_size<thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9> >::value>::mergeShfl(val, delta, width, op);
+ }
+
+ template <unsigned int N> struct Generic
+ {
+ template <typename Pointer, typename Reference, class Op>
+ static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op)
+ {
+ loadToSmem(smem, val, tid);
+ if (N >= 32)
+ __syncthreads();
+
+ if (N >= 2048)
+ {
+ if (tid < 1024)
+ merge(smem, val, tid, 1024, op);
+
+ __syncthreads();
+ }
+ if (N >= 1024)
+ {
+ if (tid < 512)
+ merge(smem, val, tid, 512, op);
+
+ __syncthreads();
+ }
+ if (N >= 512)
+ {
+ if (tid < 256)
+ merge(smem, val, tid, 256, op);
+
+ __syncthreads();
+ }
+ if (N >= 256)
+ {
+ if (tid < 128)
+ merge(smem, val, tid, 128, op);
+
+ __syncthreads();
+ }
+ if (N >= 128)
+ {
+ if (tid < 64)
+ merge(smem, val, tid, 64, op);
+
+ __syncthreads();
+ }
+ if (N >= 64)
+ {
+ if (tid < 32)
+ merge(smem, val, tid, 32, op);
+ }
+
+ if (tid < 16)
+ {
+ merge(smem, val, tid, 16, op);
+ merge(smem, val, tid, 8, op);
+ merge(smem, val, tid, 4, op);
+ merge(smem, val, tid, 2, op);
+ merge(smem, val, tid, 1, op);
+ }
+ }
+ };
+
+ template <unsigned int I, typename Pointer, typename Reference, class Op>
+ struct Unroll
+ {
+ static __device__ void loopShfl(Reference val, Op op, unsigned int N)
+ {
+ mergeShfl(val, I, N, op);
+ Unroll<I / 2, Pointer, Reference, Op>::loopShfl(val, op, N);
+ }
+ static __device__ void loop(Pointer smem, Reference val, unsigned int tid, Op op)
+ {
+ merge(smem, val, tid, I, op);
+ Unroll<I / 2, Pointer, Reference, Op>::loop(smem, val, tid, op);
+ }
+ };
+ template <typename Pointer, typename Reference, class Op>
+ struct Unroll<0, Pointer, Reference, Op>
+ {
+ static __device__ void loopShfl(Reference, Op, unsigned int)
+ {
+ }
+ static __device__ void loop(Pointer, Reference, unsigned int, Op)
+ {
+ }
+ };
+
+ template <unsigned int N> struct WarpOptimized
+ {
+ template <typename Pointer, typename Reference, class Op>
+ static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op)
+ {
+ #if __CUDA_ARCH__ >= 300
+ (void) smem;
+ (void) tid;
+
+ Unroll<N / 2, Pointer, Reference, Op>::loopShfl(val, op, N);
+ #else
+ loadToSmem(smem, val, tid);
+
+ if (tid < N / 2)
+ Unroll<N / 2, Pointer, Reference, Op>::loop(smem, val, tid, op);
+ #endif
+ }
+ };
+
+ template <unsigned int N> struct GenericOptimized32
+ {
+ enum { M = N / 32 };
+
+ template <typename Pointer, typename Reference, class Op>
+ static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op)
+ {
+ const unsigned int laneId = Warp::laneId();
+
+ #if __CUDA_ARCH__ >= 300
+ Unroll<16, Pointer, Reference, Op>::loopShfl(val, op, warpSize);
+
+ if (laneId == 0)
+ loadToSmem(smem, val, tid / 32);
+ #else
+ loadToSmem(smem, val, tid);
+
+ if (laneId < 16)
+ Unroll<16, Pointer, Reference, Op>::loop(smem, val, tid, op);
+
+ __syncthreads();
+
+ if (laneId == 0)
+ loadToSmem(smem, val, tid / 32);
+ #endif
+
+ __syncthreads();
+
+ loadFromSmem(smem, val, tid);
+
+ if (tid < 32)
+ {
+ #if __CUDA_ARCH__ >= 300
+ Unroll<M / 2, Pointer, Reference, Op>::loopShfl(val, op, M);
+ #else
+ Unroll<M / 2, Pointer, Reference, Op>::loop(smem, val, tid, op);
+ #endif
+ }
+ }
+ };
+
+ template <bool val, class T1, class T2> struct StaticIf;
+ template <class T1, class T2> struct StaticIf<true, T1, T2>
+ {
+ typedef T1 type;
+ };
+ template <class T1, class T2> struct StaticIf<false, T1, T2>
+ {
+ typedef T2 type;
+ };
+
+ template <unsigned int N> struct IsPowerOf2
+ {
+ enum { value = ((N != 0) && !(N & (N - 1))) };
+ };
+
+ template <unsigned int N> struct Dispatcher
+ {
+ typedef typename StaticIf<
+ (N <= 32) && IsPowerOf2<N>::value,
+ WarpOptimized<N>,
+ typename StaticIf<
+ (N <= 1024) && IsPowerOf2<N>::value,
+ GenericOptimized32<N>,
+ Generic<N>
+ >::type
+ >::type reductor;
+ };
+ }
+}}}
+
+#endif // __OPENCV_GPU_REDUCE_DETAIL_HPP__
--- /dev/null
+/*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 __OPENCV_GPU_PRED_VAL_REDUCE_DETAIL_HPP__
+#define __OPENCV_GPU_PRED_VAL_REDUCE_DETAIL_HPP__
+
+#include <thrust/tuple.h>
+#include "../warp.hpp"
+#include "../warp_shuffle.hpp"
+
+namespace cv { namespace gpu { namespace device
+{
+ namespace reduce_key_val_detail
+ {
+ template <typename T> struct GetType;
+ template <typename T> struct GetType<T*>
+ {
+ typedef T type;
+ };
+ template <typename T> struct GetType<volatile T*>
+ {
+ typedef T type;
+ };
+ template <typename T> struct GetType<T&>
+ {
+ typedef T type;
+ };
+
+ template <unsigned int I, unsigned int N>
+ struct For
+ {
+ template <class PointerTuple, class ReferenceTuple>
+ static __device__ void loadToSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid)
+ {
+ thrust::get<I>(smem)[tid] = thrust::get<I>(data);
+
+ For<I + 1, N>::loadToSmem(smem, data, tid);
+ }
+ template <class PointerTuple, class ReferenceTuple>
+ static __device__ void loadFromSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid)
+ {
+ thrust::get<I>(data) = thrust::get<I>(smem)[tid];
+
+ For<I + 1, N>::loadFromSmem(smem, data, tid);
+ }
+
+ template <class ReferenceTuple>
+ static __device__ void copyShfl(const ReferenceTuple& val, unsigned int delta, int width)
+ {
+ thrust::get<I>(val) = shfl_down(thrust::get<I>(val), delta, width);
+
+ For<I + 1, N>::copyShfl(val, delta, width);
+ }
+ template <class PointerTuple, class ReferenceTuple>
+ static __device__ void copy(const PointerTuple& svals, const ReferenceTuple& val, unsigned int tid, unsigned int delta)
+ {
+ thrust::get<I>(svals)[tid] = thrust::get<I>(val) = thrust::get<I>(svals)[tid + delta];
+
+ For<I + 1, N>::copy(svals, val, tid, delta);
+ }
+
+ template <class KeyReferenceTuple, class ValReferenceTuple, class CmpTuple>
+ static __device__ void mergeShfl(const KeyReferenceTuple& key, const ValReferenceTuple& val, const CmpTuple& cmp, unsigned int delta, int width)
+ {
+ typename GetType<typename thrust::tuple_element<I, KeyReferenceTuple>::type>::type reg = shfl_down(thrust::get<I>(key), delta, width);
+
+ if (thrust::get<I>(cmp)(reg, thrust::get<I>(key)))
+ {
+ thrust::get<I>(key) = reg;
+ thrust::get<I>(val) = shfl_down(thrust::get<I>(val), delta, width);
+ }
+
+ For<I + 1, N>::mergeShfl(key, val, cmp, delta, width);
+ }
+ template <class KeyPointerTuple, class KeyReferenceTuple, class ValPointerTuple, class ValReferenceTuple, class CmpTuple>
+ static __device__ void merge(const KeyPointerTuple& skeys, const KeyReferenceTuple& key,
+ const ValPointerTuple& svals, const ValReferenceTuple& val,
+ const CmpTuple& cmp,
+ unsigned int tid, unsigned int delta)
+ {
+ typename GetType<typename thrust::tuple_element<I, KeyPointerTuple>::type>::type reg = thrust::get<I>(skeys)[tid + delta];
+
+ if (thrust::get<I>(cmp)(reg, thrust::get<I>(key)))
+ {
+ thrust::get<I>(skeys)[tid] = thrust::get<I>(key) = reg;
+ thrust::get<I>(svals)[tid] = thrust::get<I>(val) = thrust::get<I>(svals)[tid + delta];
+ }
+
+ For<I + 1, N>::merge(skeys, key, svals, val, cmp, tid, delta);
+ }
+ };
+ template <unsigned int N>
+ struct For<N, N>
+ {
+ template <class PointerTuple, class ReferenceTuple>
+ static __device__ void loadToSmem(const PointerTuple&, const ReferenceTuple&, unsigned int)
+ {
+ }
+ template <class PointerTuple, class ReferenceTuple>
+ static __device__ void loadFromSmem(const PointerTuple&, const ReferenceTuple&, unsigned int)
+ {
+ }
+
+ template <class ReferenceTuple>
+ static __device__ void copyShfl(const ReferenceTuple&, unsigned int, int)
+ {
+ }
+ template <class PointerTuple, class ReferenceTuple>
+ static __device__ void copy(const PointerTuple&, const ReferenceTuple&, unsigned int, unsigned int)
+ {
+ }
+
+ template <class KeyReferenceTuple, class ValReferenceTuple, class CmpTuple>
+ static __device__ void mergeShfl(const KeyReferenceTuple&, const ValReferenceTuple&, const CmpTuple&, unsigned int, int)
+ {
+ }
+ template <class KeyPointerTuple, class KeyReferenceTuple, class ValPointerTuple, class ValReferenceTuple, class CmpTuple>
+ static __device__ void merge(const KeyPointerTuple&, const KeyReferenceTuple&,
+ const ValPointerTuple&, const ValReferenceTuple&,
+ const CmpTuple&,
+ unsigned int, unsigned int)
+ {
+ }
+ };
+
+ //////////////////////////////////////////////////////
+ // loadToSmem
+
+ template <typename T>
+ __device__ __forceinline__ void loadToSmem(volatile T* smem, T& data, unsigned int tid)
+ {
+ smem[tid] = data;
+ }
+ template <typename T>
+ __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& data, unsigned int tid)
+ {
+ data = smem[tid];
+ }
+ template <typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
+ typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
+ __device__ __forceinline__ void loadToSmem(const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& smem,
+ const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& data,
+ unsigned int tid)
+ {
+ For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::loadToSmem(smem, data, tid);
+ }
+ template <typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
+ typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
+ __device__ __forceinline__ void loadFromSmem(const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& smem,
+ const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& data,
+ unsigned int tid)
+ {
+ For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::loadFromSmem(smem, data, tid);
+ }
+
+ //////////////////////////////////////////////////////
+ // copyVals
+
+ template <typename V>
+ __device__ __forceinline__ void copyValsShfl(V& val, unsigned int delta, int width)
+ {
+ val = shfl_down(val, delta, width);
+ }
+ template <typename V>
+ __device__ __forceinline__ void copyVals(volatile V* svals, V& val, unsigned int tid, unsigned int delta)
+ {
+ svals[tid] = val = svals[tid + delta];
+ }
+ template <typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
+ __device__ __forceinline__ void copyValsShfl(const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
+ unsigned int delta,
+ int width)
+ {
+ For<0, thrust::tuple_size<thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9> >::value>::copyShfl(val, delta, width);
+ }
+ template <typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
+ typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
+ __device__ __forceinline__ void copyVals(const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
+ const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
+ unsigned int tid, unsigned int delta)
+ {
+ For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::copy(svals, val, tid, delta);
+ }
+
+ //////////////////////////////////////////////////////
+ // merge
+
+ template <typename K, typename V, class Cmp>
+ __device__ __forceinline__ void mergeShfl(K& key, V& val, const Cmp& cmp, unsigned int delta, int width)
+ {
+ K reg = shfl_down(key, delta, width);
+
+ if (cmp(reg, key))
+ {
+ key = reg;
+ copyValsShfl(val, delta, width);
+ }
+ }
+ template <typename K, typename V, class Cmp>
+ __device__ __forceinline__ void merge(volatile K* skeys, K& key, volatile V* svals, V& val, const Cmp& cmp, unsigned int tid, unsigned int delta)
+ {
+ K reg = skeys[tid + delta];
+
+ if (cmp(reg, key))
+ {
+ skeys[tid] = key = reg;
+ copyVals(svals, val, tid, delta);
+ }
+ }
+ template <typename K,
+ typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
+ class Cmp>
+ __device__ __forceinline__ void mergeShfl(K& key,
+ const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
+ const Cmp& cmp,
+ unsigned int delta, int width)
+ {
+ K reg = shfl_down(key, delta, width);
+
+ if (cmp(reg, key))
+ {
+ key = reg;
+ copyValsShfl(val, delta, width);
+ }
+ }
+ template <typename K,
+ typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
+ typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
+ class Cmp>
+ __device__ __forceinline__ void merge(volatile K* skeys, K& key,
+ const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
+ const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
+ const Cmp& cmp, unsigned int tid, unsigned int delta)
+ {
+ K reg = skeys[tid + delta];
+
+ if (cmp(reg, key))
+ {
+ skeys[tid] = key = reg;
+ copyVals(svals, val, tid, delta);
+ }
+ }
+ template <typename KR0, typename KR1, typename KR2, typename KR3, typename KR4, typename KR5, typename KR6, typename KR7, typename KR8, typename KR9,
+ typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
+ class Cmp0, class Cmp1, class Cmp2, class Cmp3, class Cmp4, class Cmp5, class Cmp6, class Cmp7, class Cmp8, class Cmp9>
+ __device__ __forceinline__ void mergeShfl(const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>& key,
+ const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
+ const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>& cmp,
+ unsigned int delta, int width)
+ {
+ For<0, thrust::tuple_size<thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9> >::value>::mergeShfl(key, val, cmp, delta, width);
+ }
+ template <typename KP0, typename KP1, typename KP2, typename KP3, typename KP4, typename KP5, typename KP6, typename KP7, typename KP8, typename KP9,
+ typename KR0, typename KR1, typename KR2, typename KR3, typename KR4, typename KR5, typename KR6, typename KR7, typename KR8, typename KR9,
+ typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
+ typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
+ class Cmp0, class Cmp1, class Cmp2, class Cmp3, class Cmp4, class Cmp5, class Cmp6, class Cmp7, class Cmp8, class Cmp9>
+ __device__ __forceinline__ void merge(const thrust::tuple<KP0, KP1, KP2, KP3, KP4, KP5, KP6, KP7, KP8, KP9>& skeys,
+ const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>& key,
+ const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
+ const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
+ const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>& cmp,
+ unsigned int tid, unsigned int delta)
+ {
+ For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::merge(skeys, key, svals, val, cmp, tid, delta);
+ }
+
+ //////////////////////////////////////////////////////
+ // Generic
+
+ template <unsigned int N> struct Generic
+ {
+ template <class KP, class KR, class VP, class VR, class Cmp>
+ static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
+ {
+ loadToSmem(skeys, key, tid);
+ loadValsToSmem(svals, val, tid);
+ if (N >= 32)
+ __syncthreads();
+
+ if (N >= 2048)
+ {
+ if (tid < 1024)
+ merge(skeys, key, svals, val, cmp, tid, 1024);
+
+ __syncthreads();
+ }
+ if (N >= 1024)
+ {
+ if (tid < 512)
+ merge(skeys, key, svals, val, cmp, tid, 512);
+
+ __syncthreads();
+ }
+ if (N >= 512)
+ {
+ if (tid < 256)
+ merge(skeys, key, svals, val, cmp, tid, 256);
+
+ __syncthreads();
+ }
+ if (N >= 256)
+ {
+ if (tid < 128)
+ merge(skeys, key, svals, val, cmp, tid, 128);
+
+ __syncthreads();
+ }
+ if (N >= 128)
+ {
+ if (tid < 64)
+ merge(skeys, key, svals, val, cmp, tid, 64);
+
+ __syncthreads();
+ }
+ if (N >= 64)
+ {
+ if (tid < 32)
+ merge(skeys, key, svals, val, cmp, tid, 32);
+ }
+
+ if (tid < 16)
+ {
+ merge(skeys, key, svals, val, cmp, tid, 16);
+ merge(skeys, key, svals, val, cmp, tid, 8);
+ merge(skeys, key, svals, val, cmp, tid, 4);
+ merge(skeys, key, svals, val, cmp, tid, 2);
+ merge(skeys, key, svals, val, cmp, tid, 1);
+ }
+ }
+ };
+
+ template <unsigned int I, class KP, class KR, class VP, class VR, class Cmp>
+ struct Unroll
+ {
+ static __device__ void loopShfl(KR key, VR val, Cmp cmp, unsigned int N)
+ {
+ mergeShfl(key, val, cmp, I, N);
+ Unroll<I / 2, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, N);
+ }
+ static __device__ void loop(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
+ {
+ merge(skeys, key, svals, val, cmp, tid, I);
+ Unroll<I / 2, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
+ }
+ };
+ template <class KP, class KR, class VP, class VR, class Cmp>
+ struct Unroll<0, KP, KR, VP, VR, Cmp>
+ {
+ static __device__ void loopShfl(KR, VR, Cmp, unsigned int)
+ {
+ }
+ static __device__ void loop(KP, KR, VP, VR, unsigned int, Cmp)
+ {
+ }
+ };
+
+ template <unsigned int N> struct WarpOptimized
+ {
+ template <class KP, class KR, class VP, class VR, class Cmp>
+ static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
+ {
+ #if 0 // __CUDA_ARCH__ >= 300
+ (void) skeys;
+ (void) svals;
+ (void) tid;
+
+ Unroll<N / 2, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, N);
+ #else
+ loadToSmem(skeys, key, tid);
+ loadToSmem(svals, val, tid);
+
+ if (tid < N / 2)
+ Unroll<N / 2, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
+ #endif
+ }
+ };
+
+ template <unsigned int N> struct GenericOptimized32
+ {
+ enum { M = N / 32 };
+
+ template <class KP, class KR, class VP, class VR, class Cmp>
+ static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
+ {
+ const unsigned int laneId = Warp::laneId();
+
+ #if 0 // __CUDA_ARCH__ >= 300
+ Unroll<16, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, warpSize);
+
+ if (laneId == 0)
+ {
+ loadToSmem(skeys, key, tid / 32);
+ loadToSmem(svals, val, tid / 32);
+ }
+ #else
+ loadToSmem(skeys, key, tid);
+ loadToSmem(svals, val, tid);
+
+ if (laneId < 16)
+ Unroll<16, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
+
+ __syncthreads();
+
+ if (laneId == 0)
+ {
+ loadToSmem(skeys, key, tid / 32);
+ loadToSmem(svals, val, tid / 32);
+ }
+ #endif
+
+ __syncthreads();
+
+ loadFromSmem(skeys, key, tid);
+
+ if (tid < 32)
+ {
+ #if 0 // __CUDA_ARCH__ >= 300
+ loadFromSmem(svals, val, tid);
+
+ Unroll<M / 2, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, M);
+ #else
+ Unroll<M / 2, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
+ #endif
+ }
+ }
+ };
+
+ template <bool val, class T1, class T2> struct StaticIf;
+ template <class T1, class T2> struct StaticIf<true, T1, T2>
+ {
+ typedef T1 type;
+ };
+ template <class T1, class T2> struct StaticIf<false, T1, T2>
+ {
+ typedef T2 type;
+ };
+
+ template <unsigned int N> struct IsPowerOf2
+ {
+ enum { value = ((N != 0) && !(N & (N - 1))) };
+ };
+
+ template <unsigned int N> struct Dispatcher
+ {
+ typedef typename StaticIf<
+ (N <= 32) && IsPowerOf2<N>::value,
+ WarpOptimized<N>,
+ typename StaticIf<
+ (N <= 1024) && IsPowerOf2<N>::value,
+ GenericOptimized32<N>,
+ Generic<N>
+ >::type
+ >::type reductor;
+ };
+ }
+}}}
+
+#endif // __OPENCV_GPU_PRED_VAL_REDUCE_DETAIL_HPP__
+++ /dev/null
-/*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 __OPENCV_GPU_REDUCTION_DETAIL_HPP__
-#define __OPENCV_GPU_REDUCTION_DETAIL_HPP__
-
-namespace cv { namespace gpu { namespace device
-{
- namespace utility_detail
- {
- ///////////////////////////////////////////////////////////////////////////////
- // Reductor
-
- template <int n> struct WarpReductor
- {
- template <typename T, typename Op> static __device__ __forceinline__ void reduce(volatile T* data, T& partial_reduction, int tid, const Op& op)
- {
- if (tid < n)
- data[tid] = partial_reduction;
- if (n > 32) __syncthreads();
-
- if (n > 32)
- {
- if (tid < n - 32)
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 32]);
- if (tid < 16)
- {
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 16]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 8]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 4]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 2]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 1]);
- }
- }
- else if (n > 16)
- {
- if (tid < n - 16)
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 16]);
- if (tid < 8)
- {
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 8]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 4]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 2]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 1]);
- }
- }
- else if (n > 8)
- {
- if (tid < n - 8)
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 8]);
- if (tid < 4)
- {
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 4]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 2]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 1]);
- }
- }
- else if (n > 4)
- {
- if (tid < n - 4)
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 4]);
- if (tid < 2)
- {
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 2]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 1]);
- }
- }
- else if (n > 2)
- {
- if (tid < n - 2)
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 2]);
- if (tid < 2)
- {
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 1]);
- }
- }
- }
- };
- template <> struct WarpReductor<64>
- {
- template <typename T, typename Op> static __device__ void reduce(volatile T* data, T& partial_reduction, int tid, const Op& op)
- {
- data[tid] = partial_reduction;
- __syncthreads();
-
- if (tid < 32)
- {
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 32]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 16]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 8 ]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 4 ]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 2 ]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 1 ]);
- }
- }
- };
- template <> struct WarpReductor<32>
- {
- template <typename T, typename Op> static __device__ void reduce(volatile T* data, T& partial_reduction, int tid, const Op& op)
- {
- data[tid] = partial_reduction;
-
- if (tid < 16)
- {
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 16]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 8 ]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 4 ]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 2 ]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 1 ]);
- }
- }
- };
- template <> struct WarpReductor<16>
- {
- template <typename T, typename Op> static __device__ void reduce(volatile T* data, T& partial_reduction, int tid, const Op& op)
- {
- data[tid] = partial_reduction;
-
- if (tid < 8)
- {
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 8 ]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 4 ]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 2 ]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 1 ]);
- }
- }
- };
- template <> struct WarpReductor<8>
- {
- template <typename T, typename Op> static __device__ void reduce(volatile T* data, T& partial_reduction, int tid, const Op& op)
- {
- data[tid] = partial_reduction;
-
- if (tid < 4)
- {
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 4 ]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 2 ]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 1 ]);
- }
- }
- };
-
- template <bool warp> struct ReductionDispatcher;
- template <> struct ReductionDispatcher<true>
- {
- template <int n, typename T, typename Op> static __device__ void reduce(volatile T* data, T& partial_reduction, int tid, const Op& op)
- {
- WarpReductor<n>::reduce(data, partial_reduction, tid, op);
- }
- };
- template <> struct ReductionDispatcher<false>
- {
- template <int n, typename T, typename Op> static __device__ void reduce(volatile T* data, T& partial_reduction, int tid, const Op& op)
- {
- if (tid < n)
- data[tid] = partial_reduction;
- __syncthreads();
-
-
- if (n == 512) { if (tid < 256) { data[tid] = partial_reduction = op(partial_reduction, data[tid + 256]); } __syncthreads(); }
- if (n >= 256) { if (tid < 128) { data[tid] = partial_reduction = op(partial_reduction, data[tid + 128]); } __syncthreads(); }
- if (n >= 128) { if (tid < 64) { data[tid] = partial_reduction = op(partial_reduction, data[tid + 64]); } __syncthreads(); }
-
- if (tid < 32)
- {
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 32]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 16]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 8]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 4]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 2]);
- data[tid] = partial_reduction = op(partial_reduction, data[tid + 1]);
- }
- }
- };
-
- ///////////////////////////////////////////////////////////////////////////////
- // PredValWarpReductor
-
- template <int n> struct PredValWarpReductor;
- template <> struct PredValWarpReductor<64>
- {
- template <typename T, typename V, typename Pred>
- static __device__ void reduce(T& myData, V& myVal, volatile T* sdata, V* sval, int tid, const Pred& pred)
- {
- if (tid < 32)
- {
- myData = sdata[tid];
- myVal = sval[tid];
-
- T reg = sdata[tid + 32];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 32];
- }
-
- reg = sdata[tid + 16];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 16];
- }
-
- reg = sdata[tid + 8];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 8];
- }
-
- reg = sdata[tid + 4];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 4];
- }
-
- reg = sdata[tid + 2];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 2];
- }
-
- reg = sdata[tid + 1];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 1];
- }
- }
- }
- };
- template <> struct PredValWarpReductor<32>
- {
- template <typename T, typename V, typename Pred>
- static __device__ void reduce(T& myData, V& myVal, volatile T* sdata, V* sval, int tid, const Pred& pred)
- {
- if (tid < 16)
- {
- myData = sdata[tid];
- myVal = sval[tid];
-
- T reg = sdata[tid + 16];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 16];
- }
-
- reg = sdata[tid + 8];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 8];
- }
-
- reg = sdata[tid + 4];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 4];
- }
-
- reg = sdata[tid + 2];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 2];
- }
-
- reg = sdata[tid + 1];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 1];
- }
- }
- }
- };
-
- template <> struct PredValWarpReductor<16>
- {
- template <typename T, typename V, typename Pred>
- static __device__ void reduce(T& myData, V& myVal, volatile T* sdata, V* sval, int tid, const Pred& pred)
- {
- if (tid < 8)
- {
- myData = sdata[tid];
- myVal = sval[tid];
-
- T reg = reg = sdata[tid + 8];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 8];
- }
-
- reg = sdata[tid + 4];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 4];
- }
-
- reg = sdata[tid + 2];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 2];
- }
-
- reg = sdata[tid + 1];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 1];
- }
- }
- }
- };
- template <> struct PredValWarpReductor<8>
- {
- template <typename T, typename V, typename Pred>
- static __device__ void reduce(T& myData, V& myVal, volatile T* sdata, V* sval, int tid, const Pred& pred)
- {
- if (tid < 4)
- {
- myData = sdata[tid];
- myVal = sval[tid];
-
- T reg = reg = sdata[tid + 4];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 4];
- }
-
- reg = sdata[tid + 2];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 2];
- }
-
- reg = sdata[tid + 1];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 1];
- }
- }
- }
- };
-
- template <bool warp> struct PredValReductionDispatcher;
- template <> struct PredValReductionDispatcher<true>
- {
- template <int n, typename T, typename V, typename Pred> static __device__ void reduce(T& myData, V& myVal, volatile T* sdata, V* sval, int tid, const Pred& pred)
- {
- PredValWarpReductor<n>::reduce(myData, myVal, sdata, sval, tid, pred);
- }
- };
- template <> struct PredValReductionDispatcher<false>
- {
- template <int n, typename T, typename V, typename Pred> static __device__ void reduce(T& myData, V& myVal, volatile T* sdata, V* sval, int tid, const Pred& pred)
- {
- myData = sdata[tid];
- myVal = sval[tid];
-
- if (n >= 512 && tid < 256)
- {
- T reg = sdata[tid + 256];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 256];
- }
- __syncthreads();
- }
- if (n >= 256 && tid < 128)
- {
- T reg = sdata[tid + 128];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 128];
- }
- __syncthreads();
- }
- if (n >= 128 && tid < 64)
- {
- T reg = sdata[tid + 64];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 64];
- }
- __syncthreads();
- }
-
- if (tid < 32)
- {
- if (n >= 64)
- {
- T reg = sdata[tid + 32];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 32];
- }
- }
- if (n >= 32)
- {
- T reg = sdata[tid + 16];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 16];
- }
- }
- if (n >= 16)
- {
- T reg = sdata[tid + 8];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 8];
- }
- }
- if (n >= 8)
- {
- T reg = sdata[tid + 4];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 4];
- }
- }
- if (n >= 4)
- {
- T reg = sdata[tid + 2];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 2];
- }
- }
- if (n >= 2)
- {
- T reg = sdata[tid + 1];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval[tid] = myVal = sval[tid + 1];
- }
- }
- }
- }
- };
-
- ///////////////////////////////////////////////////////////////////////////////
- // PredVal2WarpReductor
-
- template <int n> struct PredVal2WarpReductor;
- template <> struct PredVal2WarpReductor<64>
- {
- template <typename T, typename V1, typename V2, typename Pred>
- static __device__ void reduce(T& myData, V1& myVal1, V2& myVal2, volatile T* sdata, V1* sval1, V2* sval2, int tid, const Pred& pred)
- {
- if (tid < 32)
- {
- myData = sdata[tid];
- myVal1 = sval1[tid];
- myVal2 = sval2[tid];
-
- T reg = sdata[tid + 32];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 32];
- sval2[tid] = myVal2 = sval2[tid + 32];
- }
-
- reg = sdata[tid + 16];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 16];
- sval2[tid] = myVal2 = sval2[tid + 16];
- }
-
- reg = sdata[tid + 8];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 8];
- sval2[tid] = myVal2 = sval2[tid + 8];
- }
-
- reg = sdata[tid + 4];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 4];
- sval2[tid] = myVal2 = sval2[tid + 4];
- }
-
- reg = sdata[tid + 2];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 2];
- sval2[tid] = myVal2 = sval2[tid + 2];
- }
-
- reg = sdata[tid + 1];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 1];
- sval2[tid] = myVal2 = sval2[tid + 1];
- }
- }
- }
- };
- template <> struct PredVal2WarpReductor<32>
- {
- template <typename T, typename V1, typename V2, typename Pred>
- static __device__ void reduce(T& myData, V1& myVal1, V2& myVal2, volatile T* sdata, V1* sval1, V2* sval2, int tid, const Pred& pred)
- {
- if (tid < 16)
- {
- myData = sdata[tid];
- myVal1 = sval1[tid];
- myVal2 = sval2[tid];
-
- T reg = sdata[tid + 16];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 16];
- sval2[tid] = myVal2 = sval2[tid + 16];
- }
-
- reg = sdata[tid + 8];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 8];
- sval2[tid] = myVal2 = sval2[tid + 8];
- }
-
- reg = sdata[tid + 4];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 4];
- sval2[tid] = myVal2 = sval2[tid + 4];
- }
-
- reg = sdata[tid + 2];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 2];
- sval2[tid] = myVal2 = sval2[tid + 2];
- }
-
- reg = sdata[tid + 1];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 1];
- sval2[tid] = myVal2 = sval2[tid + 1];
- }
- }
- }
- };
-
- template <> struct PredVal2WarpReductor<16>
- {
- template <typename T, typename V1, typename V2, typename Pred>
- static __device__ void reduce(T& myData, V1& myVal1, V2& myVal2, volatile T* sdata, V1* sval1, V2* sval2, int tid, const Pred& pred)
- {
- if (tid < 8)
- {
- myData = sdata[tid];
- myVal1 = sval1[tid];
- myVal2 = sval2[tid];
-
- T reg = reg = sdata[tid + 8];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 8];
- sval2[tid] = myVal2 = sval2[tid + 8];
- }
-
- reg = sdata[tid + 4];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 4];
- sval2[tid] = myVal2 = sval2[tid + 4];
- }
-
- reg = sdata[tid + 2];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 2];
- sval2[tid] = myVal2 = sval2[tid + 2];
- }
-
- reg = sdata[tid + 1];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 1];
- sval2[tid] = myVal2 = sval2[tid + 1];
- }
- }
- }
- };
- template <> struct PredVal2WarpReductor<8>
- {
- template <typename T, typename V1, typename V2, typename Pred>
- static __device__ void reduce(T& myData, V1& myVal1, V2& myVal2, volatile T* sdata, V1* sval1, V2* sval2, int tid, const Pred& pred)
- {
- if (tid < 4)
- {
- myData = sdata[tid];
- myVal1 = sval1[tid];
- myVal2 = sval2[tid];
-
- T reg = reg = sdata[tid + 4];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 4];
- sval2[tid] = myVal2 = sval2[tid + 4];
- }
-
- reg = sdata[tid + 2];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 2];
- sval2[tid] = myVal2 = sval2[tid + 2];
- }
-
- reg = sdata[tid + 1];
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 1];
- sval2[tid] = myVal2 = sval2[tid + 1];
- }
- }
- }
- };
-
- template <bool warp> struct PredVal2ReductionDispatcher;
- template <> struct PredVal2ReductionDispatcher<true>
- {
- template <int n, typename T, typename V1, typename V2, typename Pred>
- static __device__ void reduce(T& myData, V1& myVal1, V2& myVal2, volatile T* sdata, V1* sval1, V2* sval2, int tid, const Pred& pred)
- {
- PredVal2WarpReductor<n>::reduce(myData, myVal1, myVal2, sdata, sval1, sval2, tid, pred);
- }
- };
- template <> struct PredVal2ReductionDispatcher<false>
- {
- template <int n, typename T, typename V1, typename V2, typename Pred>
- static __device__ void reduce(T& myData, V1& myVal1, V2& myVal2, volatile T* sdata, V1* sval1, V2* sval2, int tid, const Pred& pred)
- {
- myData = sdata[tid];
- myVal1 = sval1[tid];
- myVal2 = sval2[tid];
-
- if (n >= 512 && tid < 256)
- {
- T reg = sdata[tid + 256];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 256];
- sval2[tid] = myVal2 = sval2[tid + 256];
- }
- __syncthreads();
- }
- if (n >= 256 && tid < 128)
- {
- T reg = sdata[tid + 128];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 128];
- sval2[tid] = myVal2 = sval2[tid + 128];
- }
- __syncthreads();
- }
- if (n >= 128 && tid < 64)
- {
- T reg = sdata[tid + 64];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 64];
- sval2[tid] = myVal2 = sval2[tid + 64];
- }
- __syncthreads();
- }
-
- if (tid < 32)
- {
- if (n >= 64)
- {
- T reg = sdata[tid + 32];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 32];
- sval2[tid] = myVal2 = sval2[tid + 32];
- }
- }
- if (n >= 32)
- {
- T reg = sdata[tid + 16];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 16];
- sval2[tid] = myVal2 = sval2[tid + 16];
- }
- }
- if (n >= 16)
- {
- T reg = sdata[tid + 8];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 8];
- sval2[tid] = myVal2 = sval2[tid + 8];
- }
- }
- if (n >= 8)
- {
- T reg = sdata[tid + 4];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 4];
- sval2[tid] = myVal2 = sval2[tid + 4];
- }
- }
- if (n >= 4)
- {
- T reg = sdata[tid + 2];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 2];
- sval2[tid] = myVal2 = sval2[tid + 2];
- }
- }
- if (n >= 2)
- {
- T reg = sdata[tid + 1];
-
- if (pred(reg, myData))
- {
- sdata[tid] = myData = reg;
- sval1[tid] = myVal1 = sval1[tid + 1];
- sval2[tid] = myVal2 = sval2[tid + 1];
- }
- }
- }
- }
- };
- } // namespace utility_detail
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif // __OPENCV_GPU_REDUCTION_DETAIL_HPP__
--- /dev/null
+/*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 __OPENCV_GPU_REDUCE_HPP__
+#define __OPENCV_GPU_REDUCE_HPP__
+
+#include <thrust/tuple.h>
+#include "detail/reduce.hpp"
+#include "detail/reduce_key_val.hpp"
+
+namespace cv { namespace gpu { namespace device
+{
+ template <int N, typename T, class Op>
+ __device__ __forceinline__ void reduce(volatile T* smem, T& val, unsigned int tid, const Op& op)
+ {
+ reduce_detail::Dispatcher<N>::reductor::template reduce<volatile T*, T&, const Op&>(smem, val, tid, op);
+ }
+ template <int N,
+ typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
+ typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9,
+ class Op0, class Op1, class Op2, class Op3, class Op4, class Op5, class Op6, class Op7, class Op8, class Op9>
+ __device__ __forceinline__ void reduce(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
+ const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
+ unsigned int tid,
+ const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>& op)
+ {
+ reduce_detail::Dispatcher<N>::reductor::template reduce<
+ const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>&,
+ const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>&,
+ const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>&>(smem, val, tid, op);
+ }
+
+ template <unsigned int N, typename K, typename V, class Cmp>
+ __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key, volatile V* svals, V& val, unsigned int tid, const Cmp& cmp)
+ {
+ reduce_key_val_detail::Dispatcher<N>::reductor::template reduce<volatile K*, K&, volatile V*, V&, const Cmp&>(skeys, key, svals, val, tid, cmp);
+ }
+ template <unsigned int N,
+ typename K,
+ typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
+ typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
+ class Cmp>
+ __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key,
+ const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
+ const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
+ unsigned int tid, const Cmp& cmp)
+ {
+ reduce_key_val_detail::Dispatcher<N>::reductor::template reduce<volatile K*, K&,
+ const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>&,
+ const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>&,
+ const Cmp&>(skeys, key, svals, val, tid, cmp);
+ }
+ template <unsigned int N,
+ typename KP0, typename KP1, typename KP2, typename KP3, typename KP4, typename KP5, typename KP6, typename KP7, typename KP8, typename KP9,
+ typename KR0, typename KR1, typename KR2, typename KR3, typename KR4, typename KR5, typename KR6, typename KR7, typename KR8, typename KR9,
+ typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
+ typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
+ class Cmp0, class Cmp1, class Cmp2, class Cmp3, class Cmp4, class Cmp5, class Cmp6, class Cmp7, class Cmp8, class Cmp9>
+ __device__ __forceinline__ void reduceKeyVal(const thrust::tuple<KP0, KP1, KP2, KP3, KP4, KP5, KP6, KP7, KP8, KP9>& skeys,
+ const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>& key,
+ const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
+ const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
+ unsigned int tid,
+ const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>& cmp)
+ {
+ reduce_key_val_detail::Dispatcher<N>::reductor::template reduce<
+ const thrust::tuple<KP0, KP1, KP2, KP3, KP4, KP5, KP6, KP7, KP8, KP9>&,
+ const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>&,
+ const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>&,
+ const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>&,
+ const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>&
+ >(skeys, key, svals, val, tid, cmp);
+ }
+
+ // smem_tuple
+
+ template <typename T0>
+ __device__ __forceinline__
+ thrust::tuple<volatile T0*>
+ smem_tuple(T0* t0)
+ {
+ return thrust::make_tuple((volatile T0*) t0);
+ }
+
+ template <typename T0, typename T1>
+ __device__ __forceinline__
+ thrust::tuple<volatile T0*, volatile T1*>
+ smem_tuple(T0* t0, T1* t1)
+ {
+ return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1);
+ }
+
+ template <typename T0, typename T1, typename T2>
+ __device__ __forceinline__
+ thrust::tuple<volatile T0*, volatile T1*, volatile T2*>
+ smem_tuple(T0* t0, T1* t1, T2* t2)
+ {
+ return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2);
+ }
+
+ template <typename T0, typename T1, typename T2, typename T3>
+ __device__ __forceinline__
+ thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*>
+ smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3)
+ {
+ return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3);
+ }
+
+ template <typename T0, typename T1, typename T2, typename T3, typename T4>
+ __device__ __forceinline__
+ thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*>
+ smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4)
+ {
+ return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4);
+ }
+
+ template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5>
+ __device__ __forceinline__
+ thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*>
+ smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5)
+ {
+ return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5);
+ }
+
+ template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6>
+ __device__ __forceinline__
+ thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*>
+ smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6)
+ {
+ return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6);
+ }
+
+ template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6, typename T7>
+ __device__ __forceinline__
+ thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*, volatile T7*>
+ smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7)
+ {
+ return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7);
+ }
+
+ template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6, typename T7, typename T8>
+ __device__ __forceinline__
+ thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*, volatile T7*, volatile T8*>
+ smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8)
+ {
+ return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8);
+ }
+
+ template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6, typename T7, typename T8, typename T9>
+ __device__ __forceinline__
+ thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*, volatile T7*, volatile T8*, volatile T9*>
+ smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8, T9* t9)
+ {
+ return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8, (volatile T9*) t9);
+ }
+}}}
+
+#endif // __OPENCV_GPU_UTILITY_HPP__
template<> __device__ __forceinline__ uchar saturate_cast<uchar>(schar v)
{
- return (uchar) ::max((int)v, 0);
+ uint res = 0;
+ int vi = v;
+ asm("cvt.sat.u8.s8 %0, %1;" : "=r"(res) : "r"(vi));
+ return res;
+ }
+ template<> __device__ __forceinline__ uchar saturate_cast<uchar>(short v)
+ {
+ uint res = 0;
+ asm("cvt.sat.u8.s16 %0, %1;" : "=r"(res) : "h"(v));
+ return res;
}
template<> __device__ __forceinline__ uchar saturate_cast<uchar>(ushort v)
{
- return (uchar) ::min((uint)v, (uint)UCHAR_MAX);
+ uint res = 0;
+ asm("cvt.sat.u8.u16 %0, %1;" : "=r"(res) : "h"(v));
+ return res;
}
template<> __device__ __forceinline__ uchar saturate_cast<uchar>(int v)
{
- return (uchar)((uint)v <= UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0);
+ uint res = 0;
+ asm("cvt.sat.u8.s32 %0, %1;" : "=r"(res) : "r"(v));
+ return res;
}
template<> __device__ __forceinline__ uchar saturate_cast<uchar>(uint v)
{
- return (uchar) ::min(v, (uint)UCHAR_MAX);
+ uint res = 0;
+ asm("cvt.sat.u8.u32 %0, %1;" : "=r"(res) : "r"(v));
+ return res;
}
- template<> __device__ __forceinline__ uchar saturate_cast<uchar>(short v)
- {
- return saturate_cast<uchar>((uint)v);
- }
-
template<> __device__ __forceinline__ uchar saturate_cast<uchar>(float v)
{
- int iv = __float2int_rn(v);
- return saturate_cast<uchar>(iv);
+ uint res = 0;
+ asm("cvt.rni.sat.u8.f32 %0, %1;" : "=r"(res) : "f"(v));
+ return res;
}
template<> __device__ __forceinline__ uchar saturate_cast<uchar>(double v)
{
- #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
- int iv = __double2int_rn(v);
- return saturate_cast<uchar>(iv);
+ #if __CUDA_ARCH__ >= 130
+ uint res = 0;
+ asm("cvt.rni.sat.u8.f64 %0, %1;" : "=r"(res) : "d"(v));
+ return res;
#else
return saturate_cast<uchar>((float)v);
#endif
template<> __device__ __forceinline__ schar saturate_cast<schar>(uchar v)
{
- return (schar) ::min((int)v, SCHAR_MAX);
+ uint res = 0;
+ uint vi = v;
+ asm("cvt.sat.s8.u8 %0, %1;" : "=r"(res) : "r"(vi));
+ return res;
}
- template<> __device__ __forceinline__ schar saturate_cast<schar>(ushort v)
+ template<> __device__ __forceinline__ schar saturate_cast<schar>(short v)
{
- return (schar) ::min((uint)v, (uint)SCHAR_MAX);
+ uint res = 0;
+ asm("cvt.sat.s8.s16 %0, %1;" : "=r"(res) : "h"(v));
+ return res;
}
- template<> __device__ __forceinline__ schar saturate_cast<schar>(int v)
+ template<> __device__ __forceinline__ schar saturate_cast<schar>(ushort v)
{
- return (schar)((uint)(v-SCHAR_MIN) <= (uint)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN);
+ uint res = 0;
+ asm("cvt.sat.s8.u16 %0, %1;" : "=r"(res) : "h"(v));
+ return res;
}
- template<> __device__ __forceinline__ schar saturate_cast<schar>(short v)
+ template<> __device__ __forceinline__ schar saturate_cast<schar>(int v)
{
- return saturate_cast<schar>((int)v);
+ uint res = 0;
+ asm("cvt.sat.s8.s32 %0, %1;" : "=r"(res) : "r"(v));
+ return res;
}
template<> __device__ __forceinline__ schar saturate_cast<schar>(uint v)
{
- return (schar) ::min(v, (uint)SCHAR_MAX);
+ uint res = 0;
+ asm("cvt.sat.s8.u32 %0, %1;" : "=r"(res) : "r"(v));
+ return res;
}
-
template<> __device__ __forceinline__ schar saturate_cast<schar>(float v)
{
- int iv = __float2int_rn(v);
- return saturate_cast<schar>(iv);
+ uint res = 0;
+ asm("cvt.rni.sat.s8.f32 %0, %1;" : "=r"(res) : "f"(v));
+ return res;
}
template<> __device__ __forceinline__ schar saturate_cast<schar>(double v)
{
- #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
- int iv = __double2int_rn(v);
- return saturate_cast<schar>(iv);
+ #if __CUDA_ARCH__ >= 130
+ uint res = 0;
+ asm("cvt.rni.sat.s8.f64 %0, %1;" : "=r"(res) : "d"(v));
+ return res;
#else
return saturate_cast<schar>((float)v);
#endif
template<> __device__ __forceinline__ ushort saturate_cast<ushort>(schar v)
{
- return (ushort) ::max((int)v, 0);
+ ushort res = 0;
+ int vi = v;
+ asm("cvt.sat.u16.s8 %0, %1;" : "=h"(res) : "r"(vi));
+ return res;
}
template<> __device__ __forceinline__ ushort saturate_cast<ushort>(short v)
{
- return (ushort) ::max((int)v, 0);
+ ushort res = 0;
+ asm("cvt.sat.u16.s16 %0, %1;" : "=h"(res) : "h"(v));
+ return res;
}
template<> __device__ __forceinline__ ushort saturate_cast<ushort>(int v)
{
- return (ushort)((uint)v <= (uint)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0);
+ ushort res = 0;
+ asm("cvt.sat.u16.s32 %0, %1;" : "=h"(res) : "r"(v));
+ return res;
}
template<> __device__ __forceinline__ ushort saturate_cast<ushort>(uint v)
{
- return (ushort) ::min(v, (uint)USHRT_MAX);
+ ushort res = 0;
+ asm("cvt.sat.u16.u32 %0, %1;" : "=h"(res) : "r"(v));
+ return res;
}
template<> __device__ __forceinline__ ushort saturate_cast<ushort>(float v)
{
- int iv = __float2int_rn(v);
- return saturate_cast<ushort>(iv);
+ ushort res = 0;
+ asm("cvt.rni.sat.u16.f32 %0, %1;" : "=h"(res) : "f"(v));
+ return res;
}
template<> __device__ __forceinline__ ushort saturate_cast<ushort>(double v)
{
- #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
- int iv = __double2int_rn(v);
- return saturate_cast<ushort>(iv);
+ #if __CUDA_ARCH__ >= 130
+ ushort res = 0;
+ asm("cvt.rni.sat.u16.f64 %0, %1;" : "=h"(res) : "d"(v));
+ return res;
#else
return saturate_cast<ushort>((float)v);
#endif
template<> __device__ __forceinline__ short saturate_cast<short>(ushort v)
{
- return (short) ::min((int)v, SHRT_MAX);
+ short res = 0;
+ asm("cvt.sat.s16.u16 %0, %1;" : "=h"(res) : "h"(v));
+ return res;
}
template<> __device__ __forceinline__ short saturate_cast<short>(int v)
{
- return (short)((uint)(v - SHRT_MIN) <= (uint)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN);
+ short res = 0;
+ asm("cvt.sat.s16.s32 %0, %1;" : "=h"(res) : "r"(v));
+ return res;
}
template<> __device__ __forceinline__ short saturate_cast<short>(uint v)
{
- return (short) ::min(v, (uint)SHRT_MAX);
+ short res = 0;
+ asm("cvt.sat.s16.u32 %0, %1;" : "=h"(res) : "r"(v));
+ return res;
}
template<> __device__ __forceinline__ short saturate_cast<short>(float v)
{
- int iv = __float2int_rn(v);
- return saturate_cast<short>(iv);
+ short res = 0;
+ asm("cvt.rni.sat.s16.f32 %0, %1;" : "=h"(res) : "f"(v));
+ return res;
}
template<> __device__ __forceinline__ short saturate_cast<short>(double v)
{
- #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
- int iv = __double2int_rn(v);
- return saturate_cast<short>(iv);
+ #if __CUDA_ARCH__ >= 130
+ short res = 0;
+ asm("cvt.rni.sat.s16.f64 %0, %1;" : "=h"(res) : "d"(v));
+ return res;
#else
return saturate_cast<short>((float)v);
#endif
}
+ template<> __device__ __forceinline__ int saturate_cast<int>(uint v)
+ {
+ int res = 0;
+ asm("cvt.sat.s32.u32 %0, %1;" : "=r"(res) : "r"(v));
+ return res;
+ }
template<> __device__ __forceinline__ int saturate_cast<int>(float v)
{
return __float2int_rn(v);
#endif
}
+ template<> __device__ __forceinline__ uint saturate_cast<uint>(schar v)
+ {
+ uint res = 0;
+ int vi = v;
+ asm("cvt.sat.u32.s8 %0, %1;" : "=r"(res) : "r"(vi));
+ return res;
+ }
+ template<> __device__ __forceinline__ uint saturate_cast<uint>(short v)
+ {
+ uint res = 0;
+ asm("cvt.sat.u32.s16 %0, %1;" : "=r"(res) : "h"(v));
+ return res;
+ }
+ template<> __device__ __forceinline__ uint saturate_cast<uint>(int v)
+ {
+ uint res = 0;
+ asm("cvt.sat.u32.s32 %0, %1;" : "=r"(res) : "r"(v));
+ return res;
+ }
template<> __device__ __forceinline__ uint saturate_cast<uint>(float v)
{
return __float2uint_rn(v);
#include "saturate_cast.hpp"
#include "datamov_utils.hpp"
-#include "detail/reduction_detail.hpp"
namespace cv { namespace gpu { namespace device
{
};
///////////////////////////////////////////////////////////////////////////////
- // Reduction
-
- template <int n, typename T, typename Op> __device__ __forceinline__ void reduce(volatile T* data, T& partial_reduction, int tid, const Op& op)
- {
- StaticAssert<n >= 8 && n <= 512>::check();
- utility_detail::ReductionDispatcher<n <= 64>::reduce<n>(data, partial_reduction, tid, op);
- }
-
- template <int n, typename T, typename V, typename Pred>
- __device__ __forceinline__ void reducePredVal(volatile T* sdata, T& myData, V* sval, V& myVal, int tid, const Pred& pred)
- {
- StaticAssert<n >= 8 && n <= 512>::check();
- utility_detail::PredValReductionDispatcher<n <= 64>::reduce<n>(myData, myVal, sdata, sval, tid, pred);
- }
-
- template <int n, typename T, typename V1, typename V2, typename Pred>
- __device__ __forceinline__ void reducePredVal2(volatile T* sdata, T& myData, V1* sval1, V1& myVal1, V2* sval2, V2& myVal2, int tid, const Pred& pred)
- {
- StaticAssert<n >= 8 && n <= 512>::check();
- utility_detail::PredVal2ReductionDispatcher<n <= 64>::reduce<n>(myData, myVal1, myVal2, sdata, sval1, sval2, tid, pred);
- }
-
- ///////////////////////////////////////////////////////////////////////////////
// Solve linear system
// solve 2x2 linear system Ax=b
#ifndef __OPENCV_GPU_VEC_DISTANCE_HPP__
#define __OPENCV_GPU_VEC_DISTANCE_HPP__
-#include "utility.hpp"
+#include "reduce.hpp"
#include "functional.hpp"
#include "detail/vec_distance_detail.hpp"
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
{
- reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile int>());
+ reduce<THREAD_DIM>(smem, mySum, tid, plus<int>());
}
__device__ __forceinline__ operator int() const
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
{
- reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile float>());
+ reduce<THREAD_DIM>(smem, mySum, tid, plus<float>());
}
__device__ __forceinline__ operator float() const
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
{
- reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile float>());
+ reduce<THREAD_DIM>(smem, mySum, tid, plus<float>());
}
__device__ __forceinline__ operator float() const
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
{
- reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile int>());
+ reduce<THREAD_DIM>(smem, mySum, tid, plus<int>());
}
__device__ __forceinline__ operator int() const
--- /dev/null
+/*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 __OPENCV_GPU_WARP_SHUFFLE_HPP__
+#define __OPENCV_GPU_WARP_SHUFFLE_HPP__
+
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T>
+ __device__ __forceinline__ T shfl(T val, int srcLane, int width = warpSize)
+ {
+ #if __CUDA_ARCH__ >= 300
+ return __shfl(val, srcLane, width);
+ #else
+ return T();
+ #endif
+ }
+ __device__ __forceinline__ unsigned int shfl(unsigned int val, int srcLane, int width = warpSize)
+ {
+ #if __CUDA_ARCH__ >= 300
+ return (unsigned int) __shfl((int) val, srcLane, width);
+ #else
+ return 0;
+ #endif
+ }
+ __device__ __forceinline__ double shfl(double val, int srcLane, int width = warpSize)
+ {
+ #if __CUDA_ARCH__ >= 300
+ int lo = __double2loint(val);
+ int hi = __double2hiint(val);
+
+ lo = __shfl(lo, srcLane, width);
+ hi = __shfl(hi, srcLane, width);
+
+ return __hiloint2double(hi, lo);
+ #else
+ return 0.0;
+ #endif
+ }
+
+ template <typename T>
+ __device__ __forceinline__ T shfl_down(T val, unsigned int delta, int width = warpSize)
+ {
+ #if __CUDA_ARCH__ >= 300
+ return __shfl_down(val, delta, width);
+ #else
+ return T();
+ #endif
+ }
+ __device__ __forceinline__ unsigned int shfl_down(unsigned int val, unsigned int delta, int width = warpSize)
+ {
+ #if __CUDA_ARCH__ >= 300
+ return (unsigned int) __shfl_down((int) val, delta, width);
+ #else
+ return 0;
+ #endif
+ }
+ __device__ __forceinline__ double shfl_down(double val, unsigned int delta, int width = warpSize)
+ {
+ #if __CUDA_ARCH__ >= 300
+ int lo = __double2loint(val);
+ int hi = __double2hiint(val);
+
+ lo = __shfl_down(lo, delta, width);
+ hi = __shfl_down(hi, delta, width);
+
+ return __hiloint2double(hi, lo);
+ #else
+ return 0.0;
+ #endif
+ }
+
+ template <typename T>
+ __device__ __forceinline__ T shfl_up(T val, unsigned int delta, int width = warpSize)
+ {
+ #if __CUDA_ARCH__ >= 300
+ return __shfl_up(val, delta, width);
+ #else
+ return T();
+ #endif
+ }
+ __device__ __forceinline__ unsigned int shfl_up(unsigned int val, unsigned int delta, int width = warpSize)
+ {
+ #if __CUDA_ARCH__ >= 300
+ return (unsigned int) __shfl_up((int) val, delta, width);
+ #else
+ return 0;
+ #endif
+ }
+ __device__ __forceinline__ double shfl_up(double val, unsigned int delta, int width = warpSize)
+ {
+ #if __CUDA_ARCH__ >= 300
+ int lo = __double2loint(val);
+ int hi = __double2hiint(val);
+
+ lo = __shfl_up(lo, delta, width);
+ hi = __shfl_up(hi, delta, width);
+
+ return __hiloint2double(hi, lo);
+ #else
+ return 0.0;
+ #endif
+ }
+}}}
+
+#endif // __OPENCV_GPU_WARP_SHUFFLE_HPP__
void cv::gpu::gemm(const GpuMat& src1, const GpuMat& src2, double alpha, const GpuMat& src3, double beta, GpuMat& dst, int flags, Stream& stream)
{
#ifndef HAVE_CUBLAS
- (void)src1; (void)src2; (void)alpha; (void)src3; (void)beta; (void)dst; (void)flags; (void)stream;
+ (void)src1;
+ (void)src2;
+ (void)alpha;
+ (void)src3;
+ (void)beta;
+ (void)dst;
+ (void)flags;
+ (void)stream;
CV_Error(CV_StsNotImplemented, "The library was build without CUBLAS");
-
#else
-
// CUBLAS works with column-major matrices
CV_Assert(src1.type() == CV_32FC1 || src1.type() == CV_32FC2 || src1.type() == CV_64FC1 || src1.type() == CV_64FC2);
if (src1.depth() == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
}
cublasSafeCall( cublasDestroy_v2(handle) );
-
#endif
}
}
else // if (src.elemSize() == 8)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
NppStStreamHandler h(stream);
{
template <typename T> void matchL1_gpu(const PtrStepSzb& query, const PtrStepSzb& train, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
template <typename T> void matchL2_gpu(const PtrStepSzb& query, const PtrStepSzb& train, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
template <typename T> void matchHamming_gpu(const PtrStepSzb& query, const PtrStepSzb& train, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
template <typename T> void matchL1_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
template <typename T> void matchL2_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
template <typename T> void matchHamming_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
}
namespace bf_knnmatch
{
template <typename T> void matchL1_gpu(const PtrStepSzb& query, const PtrStepSzb& train, int k, const PtrStepSzb& mask,
const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
template <typename T> void matchL2_gpu(const PtrStepSzb& query, const PtrStepSzb& train, int k, const PtrStepSzb& mask,
const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
template <typename T> void matchHamming_gpu(const PtrStepSzb& query, const PtrStepSzb& train, int k, const PtrStepSzb& mask,
const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
template <typename T> void match2L1_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
template <typename T> void match2L2_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
template <typename T> void match2Hamming_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
}
namespace bf_radius_match
{
template <typename T> void matchL1_gpu(const PtrStepSzb& query, const PtrStepSzb& train, float maxDistance, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
template <typename T> void matchL2_gpu(const PtrStepSzb& query, const PtrStepSzb& train, float maxDistance, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
template <typename T> void matchHamming_gpu(const PtrStepSzb& query, const PtrStepSzb& train, float maxDistance, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
template <typename T> void matchL1_gpu(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
template <typename T> void matchL2_gpu(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
template <typename T> void matchHamming_gpu(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
}
}}}
if (query.empty() || train.empty())
return;
- using namespace ::cv::gpu::device::bf_match;
+ using namespace cv::gpu::device::bf_match;
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& train, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
static const caller_t callers[3][6] =
{
caller_t func = callers[distType][query.depth()];
CV_Assert(func != 0);
- DeviceInfo info;
- int cc = info.majorVersion() * 10 + info.minorVersion();
-
- func(query, train, mask, trainIdx, distance, cc, StreamAccessor::getStream(stream));
+ func(query, train, mask, trainIdx, distance, StreamAccessor::getStream(stream));
}
void cv::gpu::BruteForceMatcher_GPU_base::matchDownload(const GpuMat& trainIdx, const GpuMat& distance, vector<DMatch>& matches)
if (query.empty() || trainCollection.empty())
return;
- using namespace ::cv::gpu::device::bf_match;
+ using namespace cv::gpu::device::bf_match;
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
static const caller_t callers[3][6] =
{
caller_t func = callers[distType][query.depth()];
CV_Assert(func != 0);
- DeviceInfo info;
- int cc = info.majorVersion() * 10 + info.minorVersion();
-
- func(query, trainCollection, masks, trainIdx, imgIdx, distance, cc, StreamAccessor::getStream(stream));
+ func(query, trainCollection, masks, trainIdx, imgIdx, distance, StreamAccessor::getStream(stream));
}
void cv::gpu::BruteForceMatcher_GPU_base::matchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, vector<DMatch>& matches)
if (query.empty() || train.empty())
return;
- using namespace ::cv::gpu::device::bf_knnmatch;
+ using namespace cv::gpu::device::bf_knnmatch;
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& train, int k, const PtrStepSzb& mask,
const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
static const caller_t callers[3][6] =
{
caller_t func = callers[distType][query.depth()];
CV_Assert(func != 0);
- DeviceInfo info;
- int cc = info.majorVersion() * 10 + info.minorVersion();
-
- func(query, train, k, mask, trainIdx, distance, allDist, cc, StreamAccessor::getStream(stream));
+ func(query, train, k, mask, trainIdx, distance, allDist, StreamAccessor::getStream(stream));
}
void cv::gpu::BruteForceMatcher_GPU_base::knnMatchDownload(const GpuMat& trainIdx, const GpuMat& distance,
if (query.empty() || trainCollection.empty())
return;
- using namespace ::cv::gpu::device::bf_knnmatch;
+ using namespace cv::gpu::device::bf_knnmatch;
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
static const caller_t callers[3][6] =
{
caller_t func = callers[distType][query.depth()];
CV_Assert(func != 0);
- DeviceInfo info;
- int cc = info.majorVersion() * 10 + info.minorVersion();
-
- func(query, trainCollection, maskCollection, trainIdx, imgIdx, distance, cc, StreamAccessor::getStream(stream));
+ func(query, trainCollection, maskCollection, trainIdx, imgIdx, distance, StreamAccessor::getStream(stream));
}
void cv::gpu::BruteForceMatcher_GPU_base::knnMatch2Download(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance,
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& train, float maxDistance, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
static const caller_t callers[3][6] =
{
}
};
- DeviceInfo info;
- int cc = info.majorVersion() * 10 + info.minorVersion();
-
- if (!TargetArchs::builtWith(GLOBAL_ATOMICS) || !DeviceInfo().supports(GLOBAL_ATOMICS))
- CV_Error(CV_StsNotImplemented, "The device doesn't support global atomics");
-
const int nQuery = query.rows;
const int nTrain = train.rows;
caller_t func = callers[distType][query.depth()];
CV_Assert(func != 0);
- func(query, train, maxDistance, mask, trainIdx, distance, nMatches, cc, StreamAccessor::getStream(stream));
+ func(query, train, maxDistance, mask, trainIdx, distance, nMatches, StreamAccessor::getStream(stream));
}
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& distance, const GpuMat& nMatches,
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
- int cc, cudaStream_t stream);
+ cudaStream_t stream);
static const caller_t callers[3][6] =
{
}
};
- DeviceInfo info;
- int cc = info.majorVersion() * 10 + info.minorVersion();
-
- if (!TargetArchs::builtWith(GLOBAL_ATOMICS) || !DeviceInfo().supports(GLOBAL_ATOMICS))
- CV_Error(CV_StsNotImplemented, "The device doesn't support global atomics");
-
const int nQuery = query.rows;
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
vector<PtrStepSzb> masks_(masks.begin(), masks.end());
func(query, &trains_[0], static_cast<int>(trains_.size()), maxDistance, masks_.size() == 0 ? 0 : &masks_[0],
- trainIdx, imgIdx, distance, nMatches, cc, StreamAccessor::getStream(stream));
+ trainIdx, imgIdx, distance, nMatches, StreamAccessor::getStream(stream));
}
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, const GpuMat& nMatches,
#if !defined CUDA_DISABLER
-#include "internal_shared.hpp"
+#include "opencv2/gpu/device/common.hpp"
+#include "opencv2/gpu/device/utility.hpp"
+#include "opencv2/gpu/device/reduce.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/vec_distance.hpp"
#include "opencv2/gpu/device/datamov_utils.hpp"
+#include "opencv2/gpu/device/warp_shuffle.hpp"
namespace cv { namespace gpu { namespace device
{
int& bestTrainIdx1, int& bestTrainIdx2,
float* s_distance, int* s_trainIdx)
{
+ #if __CUDA_ARCH__ >= 300
+ (void) s_distance;
+ (void) s_trainIdx;
+
+ float d1, d2;
+ int i1, i2;
+
+ #pragma unroll
+ for (int i = BLOCK_SIZE / 2; i >= 1; i /= 2)
+ {
+ d1 = shfl_down(bestDistance1, i, BLOCK_SIZE);
+ d2 = shfl_down(bestDistance2, i, BLOCK_SIZE);
+ i1 = shfl_down(bestTrainIdx1, i, BLOCK_SIZE);
+ i2 = shfl_down(bestTrainIdx2, i, BLOCK_SIZE);
+
+ if (bestDistance1 < d1)
+ {
+ if (d1 < bestDistance2)
+ {
+ bestDistance2 = d1;
+ bestTrainIdx2 = i1;
+ }
+ }
+ else
+ {
+ bestDistance2 = bestDistance1;
+ bestTrainIdx2 = bestTrainIdx1;
+
+ bestDistance1 = d1;
+ bestTrainIdx1 = i1;
+
+ if (d2 < bestDistance2)
+ {
+ bestDistance2 = d2;
+ bestTrainIdx2 = i2;
+ }
+ }
+ }
+ #else
float myBestDistance1 = numeric_limits<float>::max();
float myBestDistance2 = numeric_limits<float>::max();
int myBestTrainIdx1 = -1;
bestTrainIdx1 = myBestTrainIdx1;
bestTrainIdx2 = myBestTrainIdx2;
+ #endif
}
template <int BLOCK_SIZE>
int& bestImgIdx1, int& bestImgIdx2,
float* s_distance, int* s_trainIdx, int* s_imgIdx)
{
+ #if __CUDA_ARCH__ >= 300
+ (void) s_distance;
+ (void) s_trainIdx;
+ (void) s_imgIdx;
+
+ float d1, d2;
+ int i1, i2;
+ int j1, j2;
+
+ #pragma unroll
+ for (int i = BLOCK_SIZE / 2; i >= 1; i /= 2)
+ {
+ d1 = shfl_down(bestDistance1, i, BLOCK_SIZE);
+ d2 = shfl_down(bestDistance2, i, BLOCK_SIZE);
+ i1 = shfl_down(bestTrainIdx1, i, BLOCK_SIZE);
+ i2 = shfl_down(bestTrainIdx2, i, BLOCK_SIZE);
+ j1 = shfl_down(bestImgIdx1, i, BLOCK_SIZE);
+ j2 = shfl_down(bestImgIdx2, i, BLOCK_SIZE);
+
+ if (bestDistance1 < d1)
+ {
+ if (d1 < bestDistance2)
+ {
+ bestDistance2 = d1;
+ bestTrainIdx2 = i1;
+ bestImgIdx2 = j1;
+ }
+ }
+ else
+ {
+ bestDistance2 = bestDistance1;
+ bestTrainIdx2 = bestTrainIdx1;
+ bestImgIdx2 = bestImgIdx1;
+
+ bestDistance1 = d1;
+ bestTrainIdx1 = i1;
+ bestImgIdx1 = j1;
+
+ if (d2 < bestDistance2)
+ {
+ bestDistance2 = d2;
+ bestTrainIdx2 = i2;
+ bestImgIdx2 = j2;
+ }
+ }
+ }
+ #else
float myBestDistance1 = numeric_limits<float>::max();
float myBestDistance2 = numeric_limits<float>::max();
int myBestTrainIdx1 = -1;
bestImgIdx1 = myBestImgIdx1;
bestImgIdx2 = myBestImgIdx2;
+ #endif
}
///////////////////////////////////////////////////////////////////////////////
template <typename Dist, typename T, typename Mask>
void match2Dispatcher(const PtrStepSz<T>& query, const PtrStepSz<T>& train, const Mask& mask,
const PtrStepSzb& trainIdx, const PtrStepSzb& distance,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
- (void)cc;
if (query.cols <= 64)
{
matchUnrolledCached<16, 64, Dist>(query, train, mask, static_cast< PtrStepSz<int2> >(trainIdx), static_cast< PtrStepSz<float2> > (distance), stream);
template <typename Dist, typename T, typename Mask>
void match2Dispatcher(const PtrStepSz<T>& query, const PtrStepSz<T>* trains, int n, const Mask& mask,
const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
- (void)cc;
if (query.cols <= 64)
{
matchUnrolledCached<16, 64, Dist>(query, trains, n, mask, static_cast< PtrStepSz<int2> >(trainIdx), static_cast< PtrStepSz<int2> >(imgIdx), static_cast< PtrStepSz<float2> > (distance), stream);
template <typename Dist, typename T, typename Mask>
void calcDistanceDispatcher(const PtrStepSz<T>& query, const PtrStepSz<T>& train, const Mask& mask,
const PtrStepSzf& allDist,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
- (void)cc;
if (query.cols <= 64)
{
calcDistanceUnrolled<16, 64, Dist>(query, train, mask, allDist, stream);
s_trainIdx[threadIdx.x] = bestIdx;
__syncthreads();
- reducePredVal<BLOCK_SIZE>(s_dist, dist, s_trainIdx, bestIdx, threadIdx.x, less<volatile float>());
+ reduceKeyVal<BLOCK_SIZE>(s_dist, dist, s_trainIdx, bestIdx, threadIdx.x, less<float>());
if (threadIdx.x == 0)
{
cudaSafeCall( cudaDeviceSynchronize() );
}
- void findKnnMatchDispatcher(int k, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream)
+ void findKnnMatchDispatcher(int k, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream)
{
findKnnMatch<256>(k, static_cast<PtrStepSzi>(trainIdx), static_cast<PtrStepSzf>(distance), allDist, stream);
}
template <typename Dist, typename T, typename Mask>
void matchDispatcher(const PtrStepSz<T>& query, const PtrStepSz<T>& train, int k, const Mask& mask,
const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
if (k == 2)
{
- match2Dispatcher<Dist>(query, train, mask, trainIdx, distance, cc, stream);
+ match2Dispatcher<Dist>(query, train, mask, trainIdx, distance, stream);
}
else
{
- calcDistanceDispatcher<Dist>(query, train, mask, allDist, cc, stream);
- findKnnMatchDispatcher(k, trainIdx, distance, allDist, cc, stream);
+ calcDistanceDispatcher<Dist>(query, train, mask, allDist, stream);
+ findKnnMatchDispatcher(k, trainIdx, distance, allDist, stream);
}
}
template <typename T> void matchL1_gpu(const PtrStepSzb& query, const PtrStepSzb& train, int k, const PtrStepSzb& mask,
const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
if (mask.data)
- matchDispatcher< L1Dist<T> >(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), k, SingleMask(mask), trainIdx, distance, allDist, cc, stream);
+ matchDispatcher< L1Dist<T> >(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), k, SingleMask(mask), trainIdx, distance, allDist, stream);
else
- matchDispatcher< L1Dist<T> >(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), k, WithOutMask(), trainIdx, distance, allDist, cc, stream);
+ matchDispatcher< L1Dist<T> >(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), k, WithOutMask(), trainIdx, distance, allDist, stream);
}
- template void matchL1_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream);
- //template void matchL1_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream);
- template void matchL1_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream);
- template void matchL1_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream);
- template void matchL1_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream);
- template void matchL1_gpu<float >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream);
+ template void matchL1_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream);
+ //template void matchL1_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream);
+ template void matchL1_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream);
+ template void matchL1_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream);
+ template void matchL1_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream);
+ template void matchL1_gpu<float >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream);
template <typename T> void matchL2_gpu(const PtrStepSzb& query, const PtrStepSzb& train, int k, const PtrStepSzb& mask,
const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
if (mask.data)
- matchDispatcher<L2Dist>(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), k, SingleMask(mask), trainIdx, distance, allDist, cc, stream);
+ matchDispatcher<L2Dist>(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), k, SingleMask(mask), trainIdx, distance, allDist, stream);
else
- matchDispatcher<L2Dist>(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), k, WithOutMask(), trainIdx, distance, allDist, cc, stream);
+ matchDispatcher<L2Dist>(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), k, WithOutMask(), trainIdx, distance, allDist, stream);
}
- //template void matchL2_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream);
- //template void matchL2_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream);
- //template void matchL2_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream);
- //template void matchL2_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream);
- //template void matchL2_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream);
- template void matchL2_gpu<float >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream);
+ //template void matchL2_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream);
+ //template void matchL2_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream);
+ //template void matchL2_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream);
+ //template void matchL2_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream);
+ //template void matchL2_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream);
+ template void matchL2_gpu<float >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream);
template <typename T> void matchHamming_gpu(const PtrStepSzb& query, const PtrStepSzb& train, int k, const PtrStepSzb& mask,
const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
if (mask.data)
- matchDispatcher<HammingDist>(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), k, SingleMask(mask), trainIdx, distance, allDist, cc, stream);
+ matchDispatcher<HammingDist>(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), k, SingleMask(mask), trainIdx, distance, allDist, stream);
else
- matchDispatcher<HammingDist>(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), k, WithOutMask(), trainIdx, distance, allDist, cc, stream);
+ matchDispatcher<HammingDist>(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), k, WithOutMask(), trainIdx, distance, allDist, stream);
}
- template void matchHamming_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream);
- //template void matchHamming_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream);
- template void matchHamming_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream);
- //template void matchHamming_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream);
- template void matchHamming_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, int cc, cudaStream_t stream);
+ template void matchHamming_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream);
+ //template void matchHamming_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream);
+ template void matchHamming_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream);
+ //template void matchHamming_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream);
+ template void matchHamming_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, int k, const PtrStepSzb& mask, const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist, cudaStream_t stream);
template <typename T> void match2L1_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
if (masks.data)
- match2Dispatcher< L1Dist<T> >(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, MaskCollection(masks.data), trainIdx, imgIdx, distance, cc, stream);
+ match2Dispatcher< L1Dist<T> >(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, MaskCollection(masks.data), trainIdx, imgIdx, distance, stream);
else
- match2Dispatcher< L1Dist<T> >(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, WithOutMask(), trainIdx, imgIdx, distance, cc, stream);
+ match2Dispatcher< L1Dist<T> >(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, WithOutMask(), trainIdx, imgIdx, distance, stream);
}
- template void match2L1_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, int cc, cudaStream_t stream);
- //template void match2L1_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, int cc, cudaStream_t stream);
- template void match2L1_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, int cc, cudaStream_t stream);
- template void match2L1_gpu<short >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, int cc, cudaStream_t stream);
- template void match2L1_gpu<int >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, int cc, cudaStream_t stream);
- template void match2L1_gpu<float >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, int cc, cudaStream_t stream);
+ template void match2L1_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, cudaStream_t stream);
+ //template void match2L1_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, cudaStream_t stream);
+ template void match2L1_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, cudaStream_t stream);
+ template void match2L1_gpu<short >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, cudaStream_t stream);
+ template void match2L1_gpu<int >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, cudaStream_t stream);
+ template void match2L1_gpu<float >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, cudaStream_t stream);
template <typename T> void match2L2_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
if (masks.data)
- match2Dispatcher<L2Dist>(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, MaskCollection(masks.data), trainIdx, imgIdx, distance, cc, stream);
+ match2Dispatcher<L2Dist>(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, MaskCollection(masks.data), trainIdx, imgIdx, distance, stream);
else
- match2Dispatcher<L2Dist>(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, WithOutMask(), trainIdx, imgIdx, distance, cc, stream);
+ match2Dispatcher<L2Dist>(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, WithOutMask(), trainIdx, imgIdx, distance, stream);
}
- //template void match2L2_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, int cc, cudaStream_t stream);
- //template void match2L2_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, int cc, cudaStream_t stream);
- //template void match2L2_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, int cc, cudaStream_t stream);
- //template void match2L2_gpu<short >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, int cc, cudaStream_t stream);
- //template void match2L2_gpu<int >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzb& distance, int cc, cudaStream_t stream);
- template void match2L2_gpu<float >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, int cc, cudaStream_t stream);
+ //template void match2L2_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, cudaStream_t stream);
+ //template void match2L2_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, cudaStream_t stream);
+ //template void match2L2_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, cudaStream_t stream);
+ //template void match2L2_gpu<short >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, cudaStream_t stream);
+ //template void match2L2_gpu<int >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzb& distance, cudaStream_t stream);
+ template void match2L2_gpu<float >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, cudaStream_t stream);
template <typename T> void match2Hamming_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
if (masks.data)
- match2Dispatcher<HammingDist>(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, MaskCollection(masks.data), trainIdx, imgIdx, distance, cc, stream);
+ match2Dispatcher<HammingDist>(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, MaskCollection(masks.data), trainIdx, imgIdx, distance, stream);
else
- match2Dispatcher<HammingDist>(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, WithOutMask(), trainIdx, imgIdx, distance, cc, stream);
+ match2Dispatcher<HammingDist>(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, WithOutMask(), trainIdx, imgIdx, distance, stream);
}
- template void match2Hamming_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, int cc, cudaStream_t stream);
- //template void match2Hamming_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, int cc, cudaStream_t stream);
- template void match2Hamming_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, int cc, cudaStream_t stream);
- //template void match2Hamming_gpu<short >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, int cc, cudaStream_t stream);
- template void match2Hamming_gpu<int >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, int cc, cudaStream_t stream);
+ template void match2Hamming_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, cudaStream_t stream);
+ //template void match2Hamming_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, cudaStream_t stream);
+ template void match2Hamming_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, cudaStream_t stream);
+ //template void match2Hamming_gpu<short >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, cudaStream_t stream);
+ template void match2Hamming_gpu<int >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance, cudaStream_t stream);
} // namespace bf_knnmatch
}}} // namespace cv { namespace gpu { namespace device {
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+#endif /* CUDA_DISABLER */
#if !defined CUDA_DISABLER
-#include "internal_shared.hpp"
+#include "opencv2/gpu/device/common.hpp"
+#include "opencv2/gpu/device/utility.hpp"
+#include "opencv2/gpu/device/reduce.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/vec_distance.hpp"
#include "opencv2/gpu/device/datamov_utils.hpp"
s_distance += threadIdx.y * BLOCK_SIZE;
s_trainIdx += threadIdx.y * BLOCK_SIZE;
- s_distance[threadIdx.x] = bestDistance;
- s_trainIdx[threadIdx.x] = bestTrainIdx;
-
- __syncthreads();
-
- reducePredVal<BLOCK_SIZE>(s_distance, bestDistance, s_trainIdx, bestTrainIdx, threadIdx.x, less<volatile float>());
+ reduceKeyVal<BLOCK_SIZE>(s_distance, bestDistance, s_trainIdx, bestTrainIdx, threadIdx.x, less<float>());
}
template <int BLOCK_SIZE>
s_trainIdx += threadIdx.y * BLOCK_SIZE;
s_imgIdx += threadIdx.y * BLOCK_SIZE;
- s_distance[threadIdx.x] = bestDistance;
- s_trainIdx[threadIdx.x] = bestTrainIdx;
- s_imgIdx [threadIdx.x] = bestImgIdx;
-
- __syncthreads();
-
- reducePredVal2<BLOCK_SIZE>(s_distance, bestDistance, s_trainIdx, bestTrainIdx, s_imgIdx, bestImgIdx, threadIdx.x, less<volatile float>());
+ reduceKeyVal<BLOCK_SIZE>(s_distance, bestDistance, smem_tuple(s_trainIdx, s_imgIdx), thrust::tie(bestTrainIdx, bestImgIdx), threadIdx.x, less<float>());
}
///////////////////////////////////////////////////////////////////////////////
template <typename Dist, typename T, typename Mask>
void matchDispatcher(const PtrStepSz<T>& query, const PtrStepSz<T>& train, const Mask& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
- (void)cc;
if (query.cols <= 64)
{
matchUnrolledCached<16, 64, Dist>(query, train, mask, trainIdx, distance, stream);
template <typename Dist, typename T, typename Mask>
void matchDispatcher(const PtrStepSz<T>& query, const PtrStepSz<T>* trains, int n, const Mask& mask,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
- (void)cc;
if (query.cols <= 64)
{
matchUnrolledCached<16, 64, Dist>(query, trains, n, mask, trainIdx, imgIdx, distance, stream);
template <typename T> void matchL1_gpu(const PtrStepSzb& query, const PtrStepSzb& train, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
if (mask.data)
{
matchDispatcher< L1Dist<T> >(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), SingleMask(mask),
trainIdx, distance,
- cc, stream);
+ stream);
}
else
{
matchDispatcher< L1Dist<T> >(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), WithOutMask(),
trainIdx, distance,
- cc, stream);
+ stream);
}
}
- template void matchL1_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- //template void matchL1_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- template void matchL1_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- template void matchL1_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- template void matchL1_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- template void matchL1_gpu<float >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
+ template void matchL1_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ //template void matchL1_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ template void matchL1_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ template void matchL1_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ template void matchL1_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ template void matchL1_gpu<float >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, cudaStream_t stream);
template <typename T> void matchL2_gpu(const PtrStepSzb& query, const PtrStepSzb& train, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
if (mask.data)
{
matchDispatcher<L2Dist>(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), SingleMask(mask),
trainIdx, distance,
- cc, stream);
+ stream);
}
else
{
matchDispatcher<L2Dist>(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), WithOutMask(),
trainIdx, distance,
- cc, stream);
+ stream);
}
}
- //template void matchL2_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- //template void matchL2_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- //template void matchL2_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- //template void matchL2_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- //template void matchL2_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- template void matchL2_gpu<float >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
+ //template void matchL2_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ //template void matchL2_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ //template void matchL2_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ //template void matchL2_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ //template void matchL2_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ template void matchL2_gpu<float >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, cudaStream_t stream);
template <typename T> void matchHamming_gpu(const PtrStepSzb& query, const PtrStepSzb& train, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
if (mask.data)
{
matchDispatcher<HammingDist>(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), SingleMask(mask),
trainIdx, distance,
- cc, stream);
+ stream);
}
else
{
matchDispatcher<HammingDist>(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), WithOutMask(),
trainIdx, distance,
- cc, stream);
+ stream);
}
}
- template void matchHamming_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- //template void matchHamming_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- template void matchHamming_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- //template void matchHamming_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- template void matchHamming_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
+ template void matchHamming_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ //template void matchHamming_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ template void matchHamming_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ //template void matchHamming_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ template void matchHamming_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, cudaStream_t stream);
template <typename T> void matchL1_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
if (masks.data)
{
matchDispatcher< L1Dist<T> >(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, MaskCollection(masks.data),
trainIdx, imgIdx, distance,
- cc, stream);
+ stream);
}
else
{
matchDispatcher< L1Dist<T> >(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, WithOutMask(),
trainIdx, imgIdx, distance,
- cc, stream);
+ stream);
}
}
- template void matchL1_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- //template void matchL1_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- template void matchL1_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- template void matchL1_gpu<short >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- template void matchL1_gpu<int >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- template void matchL1_gpu<float >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
+ template void matchL1_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ //template void matchL1_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ template void matchL1_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ template void matchL1_gpu<short >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ template void matchL1_gpu<int >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ template void matchL1_gpu<float >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, cudaStream_t stream);
template <typename T> void matchL2_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
if (masks.data)
{
matchDispatcher<L2Dist>(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, MaskCollection(masks.data),
trainIdx, imgIdx, distance,
- cc, stream);
+ stream);
}
else
{
matchDispatcher<L2Dist>(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, WithOutMask(),
trainIdx, imgIdx, distance,
- cc, stream);
+ stream);
}
}
- //template void matchL2_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- //template void matchL2_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- //template void matchL2_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- //template void matchL2_gpu<short >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- //template void matchL2_gpu<int >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- template void matchL2_gpu<float >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& maskCollection, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
+ //template void matchL2_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ //template void matchL2_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ //template void matchL2_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ //template void matchL2_gpu<short >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ //template void matchL2_gpu<int >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ template void matchL2_gpu<float >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& maskCollection, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, cudaStream_t stream);
template <typename T> void matchHamming_gpu(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
if (masks.data)
{
matchDispatcher<HammingDist>(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, MaskCollection(masks.data),
trainIdx, imgIdx, distance,
- cc, stream);
+ stream);
}
else
{
matchDispatcher<HammingDist>(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains.ptr(), trains.cols, WithOutMask(),
trainIdx, imgIdx, distance,
- cc, stream);
+ stream);
}
}
- template void matchHamming_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- //template void matchHamming_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- template void matchHamming_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- //template void matchHamming_gpu<short >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
- template void matchHamming_gpu<int >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, int cc, cudaStream_t stream);
+ template void matchHamming_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ //template void matchHamming_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ template void matchHamming_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ //template void matchHamming_gpu<short >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, cudaStream_t stream);
+ template void matchHamming_gpu<int >(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, cudaStream_t stream);
} // namespace bf_match
}}} // namespace cv { namespace gpu { namespace device {
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+#endif /* CUDA_DISABLER */
#if !defined CUDA_DISABLER
-#include "internal_shared.hpp"
+#include "opencv2/gpu/device/common.hpp"
+#include "opencv2/gpu/device/utility.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/vec_distance.hpp"
#include "opencv2/gpu/device/datamov_utils.hpp"
__global__ void matchUnrolled(const PtrStepSz<T> query, int imgIdx, const PtrStepSz<T> train, float maxDistance, const Mask mask,
PtrStepi bestTrainIdx, PtrStepi bestImgIdx, PtrStepf bestDistance, unsigned int* nMatches, int maxCount)
{
- #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
-
extern __shared__ int smem[];
const int queryIdx = blockIdx.y * BLOCK_SIZE + threadIdx.y;
bestDistance.ptr(queryIdx)[ind] = distVal;
}
}
-
- #endif
}
template <int BLOCK_SIZE, int MAX_DESC_LEN, typename Dist, typename T, typename Mask>
__global__ void match(const PtrStepSz<T> query, int imgIdx, const PtrStepSz<T> train, float maxDistance, const Mask mask,
PtrStepi bestTrainIdx, PtrStepi bestImgIdx, PtrStepf bestDistance, unsigned int* nMatches, int maxCount)
{
- #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
-
extern __shared__ int smem[];
const int queryIdx = blockIdx.y * BLOCK_SIZE + threadIdx.y;
bestDistance.ptr(queryIdx)[ind] = distVal;
}
}
-
- #endif
}
template <int BLOCK_SIZE, typename Dist, typename T, typename Mask>
template <typename Dist, typename T, typename Mask>
void matchDispatcher(const PtrStepSz<T>& query, const PtrStepSz<T>& train, float maxDistance, const Mask& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
- (void)cc;
if (query.cols <= 64)
{
matchUnrolled<16, 64, Dist>(query, train, maxDistance, mask, trainIdx, distance, nMatches, stream);
template <typename Dist, typename T>
void matchDispatcher(const PtrStepSz<T>& query, const PtrStepSz<T>* trains, int n, float maxDistance, const PtrStepSzb* masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
- (void)cc;
if (query.cols <= 64)
{
matchUnrolled<16, 64, Dist>(query, trains, n, maxDistance, masks, trainIdx, imgIdx, distance, nMatches, stream);
template <typename T> void matchL1_gpu(const PtrStepSzb& query, const PtrStepSzb& train, float maxDistance, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
if (mask.data)
{
matchDispatcher< L1Dist<T> >(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), maxDistance, SingleMask(mask),
trainIdx, distance, nMatches,
- cc, stream);
+ stream);
}
else
{
matchDispatcher< L1Dist<T> >(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), maxDistance, WithOutMask(),
trainIdx, distance, nMatches,
- cc, stream);
+ stream);
}
}
- template void matchL1_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- //template void matchL1_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- template void matchL1_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- template void matchL1_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- template void matchL1_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- template void matchL1_gpu<float >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
+ template void matchL1_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ //template void matchL1_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ template void matchL1_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ template void matchL1_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ template void matchL1_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ template void matchL1_gpu<float >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
template <typename T> void matchL2_gpu(const PtrStepSzb& query, const PtrStepSzb& train, float maxDistance, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
if (mask.data)
{
matchDispatcher<L2Dist>(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), maxDistance, SingleMask(mask),
trainIdx, distance, nMatches,
- cc, stream);
+ stream);
}
else
{
matchDispatcher<L2Dist>(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), maxDistance, WithOutMask(),
trainIdx, distance, nMatches,
- cc, stream);
+ stream);
}
}
- //template void matchL2_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- //template void matchL2_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- //template void matchL2_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- //template void matchL2_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- //template void matchL2_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- template void matchL2_gpu<float >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
+ //template void matchL2_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ //template void matchL2_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ //template void matchL2_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ //template void matchL2_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ //template void matchL2_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ template void matchL2_gpu<float >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
template <typename T> void matchHamming_gpu(const PtrStepSzb& query, const PtrStepSzb& train, float maxDistance, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
if (mask.data)
{
matchDispatcher<HammingDist>(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), maxDistance, SingleMask(mask),
trainIdx, distance, nMatches,
- cc, stream);
+ stream);
}
else
{
matchDispatcher<HammingDist>(static_cast< PtrStepSz<T> >(query), static_cast< PtrStepSz<T> >(train), maxDistance, WithOutMask(),
trainIdx, distance, nMatches,
- cc, stream);
+ stream);
}
}
- template void matchHamming_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- //template void matchHamming_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- template void matchHamming_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- //template void matchHamming_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- template void matchHamming_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
+ template void matchHamming_gpu<uchar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ //template void matchHamming_gpu<schar >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ template void matchHamming_gpu<ushort>(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ //template void matchHamming_gpu<short >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ template void matchHamming_gpu<int >(const PtrStepSzb& queryDescs, const PtrStepSzb& trainDescs, float maxDistance, const PtrStepSzb& mask, const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
template <typename T> void matchL1_gpu(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
matchDispatcher< L1Dist<T> >(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains, n, maxDistance, masks,
trainIdx, imgIdx, distance, nMatches,
- cc, stream);
+ stream);
}
- template void matchL1_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- //template void matchL1_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- template void matchL1_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- template void matchL1_gpu<short >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- template void matchL1_gpu<int >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- template void matchL1_gpu<float >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
+ template void matchL1_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ //template void matchL1_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ template void matchL1_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ template void matchL1_gpu<short >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ template void matchL1_gpu<int >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ template void matchL1_gpu<float >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
template <typename T> void matchL2_gpu(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
matchDispatcher<L2Dist>(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains, n, maxDistance, masks,
trainIdx, imgIdx, distance, nMatches,
- cc, stream);
+ stream);
}
- //template void matchL2_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- //template void matchL2_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- //template void matchL2_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- //template void matchL2_gpu<short >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- //template void matchL2_gpu<int >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- template void matchL2_gpu<float >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
+ //template void matchL2_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ //template void matchL2_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ //template void matchL2_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ //template void matchL2_gpu<short >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ //template void matchL2_gpu<int >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ template void matchL2_gpu<float >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
template <typename T> void matchHamming_gpu(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
- int cc, cudaStream_t stream)
+ cudaStream_t stream)
{
matchDispatcher<HammingDist>(static_cast< PtrStepSz<T> >(query), (const PtrStepSz<T>*)trains, n, maxDistance, masks,
trainIdx, imgIdx, distance, nMatches,
- cc, stream);
+ stream);
}
- template void matchHamming_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- //template void matchHamming_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- template void matchHamming_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- //template void matchHamming_gpu<short >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
- template void matchHamming_gpu<int >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, int cc, cudaStream_t stream);
+ template void matchHamming_gpu<uchar >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ //template void matchHamming_gpu<schar >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ template void matchHamming_gpu<ushort>(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ //template void matchHamming_gpu<short >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
+ template void matchHamming_gpu<int >(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks, const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches, cudaStream_t stream);
} // namespace bf_radius_match
}}} // namespace cv { namespace gpu { namespace device
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+#endif /* CUDA_DISABLER */
#if !defined CUDA_DISABLER
-#include "internal_shared.hpp"
+#include "opencv2/gpu/device/common.hpp"
#include "opencv2/gpu/device/transform.hpp"
#include "opencv2/gpu/device/functional.hpp"
+#include "opencv2/gpu/device/reduce.hpp"
namespace cv { namespace gpu { namespace device
{
crot1.x * p.x + crot1.y * p.y + crot1.z * p.z + ctransl.y,
crot2.x * p.x + crot2.y * p.y + crot2.z * p.z + ctransl.z);
}
+ __device__ __forceinline__ TransformOp() {}
+ __device__ __forceinline__ TransformOp(const TransformOp&) {}
};
void call(const PtrStepSz<float3> src, const float* rot,
(cproj0.x * t.x + cproj0.y * t.y) / t.z + cproj0.z,
(cproj1.x * t.x + cproj1.y * t.y) / t.z + cproj1.z);
}
+ __device__ __forceinline__ ProjectOp() {}
+ __device__ __forceinline__ ProjectOp(const ProjectOp&) {}
};
void call(const PtrStepSz<float3> src, const float* rot,
return x * x;
}
+ template <int BLOCK_SIZE>
__global__ void computeHypothesisScoresKernel(
const int num_points, const float3* object, const float2* image,
const float dist_threshold, int* g_num_inliers)
++num_inliers;
}
- extern __shared__ float s_num_inliers[];
- s_num_inliers[threadIdx.x] = num_inliers;
- __syncthreads();
-
- for (int step = blockDim.x / 2; step > 0; step >>= 1)
- {
- if (threadIdx.x < step)
- s_num_inliers[threadIdx.x] += s_num_inliers[threadIdx.x + step];
- __syncthreads();
- }
+ __shared__ int s_num_inliers[BLOCK_SIZE];
+ reduce<BLOCK_SIZE>(s_num_inliers, num_inliers, threadIdx.x, plus<int>());
if (threadIdx.x == 0)
- g_num_inliers[blockIdx.x] = s_num_inliers[0];
+ g_num_inliers[blockIdx.x] = num_inliers;
}
void computeHypothesisScores(
dim3 threads(256);
dim3 grid(num_hypotheses);
- int smem_size = threads.x * sizeof(float);
- computeHypothesisScoresKernel<<<grid, threads, smem_size>>>(
+ computeHypothesisScoresKernel<256><<<grid, threads>>>(
num_points, object, image, dist_threshold, hypothesis_scores);
cudaSafeCall( cudaGetLastError() );
}}} // namespace cv { namespace gpu { namespace device
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+#endif /* CUDA_DISABLER */
#if !defined CUDA_DISABLER
#include <utility>
-#include <algorithm>
-#include "internal_shared.hpp"
+#include "opencv2/gpu/device/common.hpp"
+#include "opencv2/gpu/device/emulation.hpp"
+#include "opencv2/gpu/device/transform.hpp"
+#include "opencv2/gpu/device/functional.hpp"
+#include "opencv2/gpu/device/utility.hpp"
-namespace cv { namespace gpu { namespace device
+using namespace cv::gpu;
+using namespace cv::gpu::device;
+
+namespace canny
{
- namespace canny
+ struct L1 : binary_function<int, int, float>
{
- __global__ void calcSobelRowPass(const PtrStepb src, PtrStepi dx_buf, PtrStepi dy_buf, int rows, int cols)
+ __device__ __forceinline__ float operator ()(int x, int y) const
{
- __shared__ int smem[16][18];
-
- const int j = blockIdx.x * blockDim.x + threadIdx.x;
- const int i = blockIdx.y * blockDim.y + threadIdx.y;
-
- if (i < rows)
- {
- smem[threadIdx.y][threadIdx.x + 1] = src.ptr(i)[j];
- if (threadIdx.x == 0)
- {
- smem[threadIdx.y][0] = src.ptr(i)[::max(j - 1, 0)];
- smem[threadIdx.y][17] = src.ptr(i)[::min(j + 16, cols - 1)];
- }
- __syncthreads();
-
- if (j < cols)
- {
- dx_buf.ptr(i)[j] = -smem[threadIdx.y][threadIdx.x] + smem[threadIdx.y][threadIdx.x + 2];
- dy_buf.ptr(i)[j] = smem[threadIdx.y][threadIdx.x] + 2 * smem[threadIdx.y][threadIdx.x + 1] + smem[threadIdx.y][threadIdx.x + 2];
- }
- }
+ return ::abs(x) + ::abs(y);
}
- void calcSobelRowPass_gpu(PtrStepb src, PtrStepi dx_buf, PtrStepi dy_buf, int rows, int cols)
+ __device__ __forceinline__ L1() {}
+ __device__ __forceinline__ L1(const L1&) {}
+ };
+ struct L2 : binary_function<int, int, float>
+ {
+ __device__ __forceinline__ float operator ()(int x, int y) const
{
- dim3 block(16, 16, 1);
- dim3 grid(divUp(cols, block.x), divUp(rows, block.y), 1);
+ return ::sqrtf(x * x + y * y);
+ }
- calcSobelRowPass<<<grid, block>>>(src, dx_buf, dy_buf, rows, cols);
- cudaSafeCall( cudaGetLastError() );
+ __device__ __forceinline__ L2() {}
+ __device__ __forceinline__ L2(const L2&) {}
+ };
+}
- cudaSafeCall( cudaDeviceSynchronize() );
- }
+namespace cv { namespace gpu { namespace device
+{
+ template <> struct TransformFunctorTraits<canny::L1> : DefaultTransformFunctorTraits<canny::L1>
+ {
+ enum { smart_shift = 4 };
+ };
+ template <> struct TransformFunctorTraits<canny::L2> : DefaultTransformFunctorTraits<canny::L2>
+ {
+ enum { smart_shift = 4 };
+ };
+}}}
- struct L1
- {
- static __device__ __forceinline__ float calc(int x, int y)
- {
- return ::abs(x) + ::abs(y);
- }
- };
- struct L2
- {
- static __device__ __forceinline__ float calc(int x, int y)
- {
- return ::sqrtf(x * x + y * y);
- }
- };
+namespace canny
+{
+ texture<uchar, cudaTextureType2D, cudaReadModeElementType> tex_src(false, cudaFilterModePoint, cudaAddressModeClamp);
+ struct SrcTex
+ {
+ const int xoff;
+ const int yoff;
+ __host__ SrcTex(int _xoff, int _yoff) : xoff(_xoff), yoff(_yoff) {}
- template <typename Norm> __global__ void calcMagnitude(const PtrStepi dx_buf, const PtrStepi dy_buf,
- PtrStepi dx, PtrStepi dy, PtrStepf mag, int rows, int cols)
+ __device__ __forceinline__ int operator ()(int y, int x) const
{
- __shared__ int sdx[18][16];
- __shared__ int sdy[18][16];
+ return tex2D(tex_src, x + xoff, y + yoff);
+ }
+ };
- const int j = blockIdx.x * blockDim.x + threadIdx.x;
- const int i = blockIdx.y * blockDim.y + threadIdx.y;
+ template <class Norm> __global__
+ void calcMagnitudeKernel(const SrcTex src, PtrStepi dx, PtrStepi dy, PtrStepSzf mag, const Norm norm)
+ {
+ const int x = blockIdx.x * blockDim.x + threadIdx.x;
+ const int y = blockIdx.y * blockDim.y + threadIdx.y;
- if (j < cols)
- {
- sdx[threadIdx.y + 1][threadIdx.x] = dx_buf.ptr(i)[j];
- sdy[threadIdx.y + 1][threadIdx.x] = dy_buf.ptr(i)[j];
- if (threadIdx.y == 0)
- {
- sdx[0][threadIdx.x] = dx_buf.ptr(::max(i - 1, 0))[j];
- sdx[17][threadIdx.x] = dx_buf.ptr(::min(i + 16, rows - 1))[j];
+ if (y >= mag.rows || x >= mag.cols)
+ return;
- sdy[0][threadIdx.x] = dy_buf.ptr(::max(i - 1, 0))[j];
- sdy[17][threadIdx.x] = dy_buf.ptr(::min(i + 16, rows - 1))[j];
- }
- __syncthreads();
+ int dxVal = (src(y - 1, x + 1) + 2 * src(y, x + 1) + src(y + 1, x + 1)) - (src(y - 1, x - 1) + 2 * src(y, x - 1) + src(y + 1, x - 1));
+ int dyVal = (src(y + 1, x - 1) + 2 * src(y + 1, x) + src(y + 1, x + 1)) - (src(y - 1, x - 1) + 2 * src(y - 1, x) + src(y - 1, x + 1));
- if (i < rows)
- {
- int x = sdx[threadIdx.y][threadIdx.x] + 2 * sdx[threadIdx.y + 1][threadIdx.x] + sdx[threadIdx.y + 2][threadIdx.x];
- int y = -sdy[threadIdx.y][threadIdx.x] + sdy[threadIdx.y + 2][threadIdx.x];
+ dx(y, x) = dxVal;
+ dy(y, x) = dyVal;
- dx.ptr(i)[j] = x;
- dy.ptr(i)[j] = y;
+ mag(y, x) = norm(dxVal, dyVal);
+ }
- mag.ptr(i + 1)[j + 1] = Norm::calc(x, y);
- }
- }
- }
+ void calcMagnitude(PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, bool L2Grad)
+ {
+ const dim3 block(16, 16);
+ const dim3 grid(divUp(mag.cols, block.x), divUp(mag.rows, block.y));
- void calcMagnitude_gpu(PtrStepi dx_buf, PtrStepi dy_buf, PtrStepi dx, PtrStepi dy, PtrStepf mag, int rows, int cols, bool L2Grad)
- {
- dim3 block(16, 16, 1);
- dim3 grid(divUp(cols, block.x), divUp(rows, block.y), 1);
+ bindTexture(&tex_src, srcWhole);
+ SrcTex src(xoff, yoff);
- if (L2Grad)
- calcMagnitude<L2><<<grid, block>>>(dx_buf, dy_buf, dx, dy, mag, rows, cols);
- else
- calcMagnitude<L1><<<grid, block>>>(dx_buf, dy_buf, dx, dy, mag, rows, cols);
+ if (L2Grad)
+ {
+ L2 norm;
+ calcMagnitudeKernel<<<grid, block>>>(src, dx, dy, mag, norm);
+ }
+ else
+ {
+ L1 norm;
+ calcMagnitudeKernel<<<grid, block>>>(src, dx, dy, mag, norm);
+ }
- cudaSafeCall( cudaGetLastError() );
+ cudaSafeCall( cudaGetLastError() );
- cudaSafeCall(cudaThreadSynchronize());
- }
+ cudaSafeCall(cudaThreadSynchronize());
+ }
- template <typename Norm> __global__ void calcMagnitude(PtrStepi dx, PtrStepi dy, PtrStepf mag, int rows, int cols)
+ void calcMagnitude(PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, bool L2Grad)
+ {
+ if (L2Grad)
{
- const int j = blockIdx.x * blockDim.x + threadIdx.x;
- const int i = blockIdx.y * blockDim.y + threadIdx.y;
-
- if (i < rows && j < cols)
- mag.ptr(i + 1)[j + 1] = Norm::calc(dx.ptr(i)[j], dy.ptr(i)[j]);
+ L2 norm;
+ transform(dx, dy, mag, norm, WithOutMask(), 0);
}
-
- void calcMagnitude_gpu(PtrStepi dx, PtrStepi dy, PtrStepf mag, int rows, int cols, bool L2Grad)
+ else
{
- dim3 block(16, 16, 1);
- dim3 grid(divUp(cols, block.x), divUp(rows, block.y), 1);
+ L1 norm;
+ transform(dx, dy, mag, norm, WithOutMask(), 0);
+ }
+ }
+}
- if (L2Grad)
- calcMagnitude<L2><<<grid, block>>>(dx, dy, mag, rows, cols);
- else
- calcMagnitude<L1><<<grid, block>>>(dx, dy, mag, rows, cols);
+//////////////////////////////////////////////////////////////////////////////////////////
- cudaSafeCall( cudaGetLastError() );
+namespace canny
+{
+ texture<float, cudaTextureType2D, cudaReadModeElementType> tex_mag(false, cudaFilterModePoint, cudaAddressModeClamp);
- cudaSafeCall( cudaDeviceSynchronize() );
- }
+ __global__ void calcMapKernel(const PtrStepSzi dx, const PtrStepi dy, PtrStepi map, const float low_thresh, const float high_thresh)
+ {
+ const int CANNY_SHIFT = 15;
+ const int TG22 = (int)(0.4142135623730950488016887242097*(1<<CANNY_SHIFT) + 0.5);
- //////////////////////////////////////////////////////////////////////////////////////////
+ const int x = blockIdx.x * blockDim.x + threadIdx.x;
+ const int y = blockIdx.y * blockDim.y + threadIdx.y;
- #define CANNY_SHIFT 15
- #define TG22 (int)(0.4142135623730950488016887242097*(1<<CANNY_SHIFT) + 0.5)
+ if (x == 0 || x >= dx.cols - 1 || y == 0 || y >= dx.rows - 1)
+ return;
- __global__ void calcMap(const PtrStepi dx, const PtrStepi dy, const PtrStepf mag, PtrStepi map, int rows, int cols, float low_thresh, float high_thresh)
- {
- __shared__ float smem[18][18];
+ int dxVal = dx(y, x);
+ int dyVal = dy(y, x);
- const int j = blockIdx.x * 16 + threadIdx.x;
- const int i = blockIdx.y * 16 + threadIdx.y;
+ const int s = (dxVal ^ dyVal) < 0 ? -1 : 1;
+ const float m = tex2D(tex_mag, x, y);
- const int tid = threadIdx.y * 16 + threadIdx.x;
- const int lx = tid % 18;
- const int ly = tid / 18;
+ dxVal = ::abs(dxVal);
+ dyVal = ::abs(dyVal);
- if (ly < 14)
- smem[ly][lx] = mag.ptr(blockIdx.y * 16 + ly)[blockIdx.x * 16 + lx];
+ // 0 - the pixel can not belong to an edge
+ // 1 - the pixel might belong to an edge
+ // 2 - the pixel does belong to an edge
+ int edge_type = 0;
- if (ly < 4 && blockIdx.y * 16 + ly + 14 <= rows && blockIdx.x * 16 + lx <= cols)
- smem[ly + 14][lx] = mag.ptr(blockIdx.y * 16 + ly + 14)[blockIdx.x * 16 + lx];
+ if (m > low_thresh)
+ {
+ const int tg22x = dxVal * TG22;
+ const int tg67x = tg22x + ((dxVal + dxVal) << CANNY_SHIFT);
- __syncthreads();
+ dyVal <<= CANNY_SHIFT;
- if (i < rows && j < cols)
+ if (dyVal < tg22x)
{
- int x = dx.ptr(i)[j];
- int y = dy.ptr(i)[j];
- const int s = (x ^ y) < 0 ? -1 : 1;
- const float m = smem[threadIdx.y + 1][threadIdx.x + 1];
+ if (m > tex2D(tex_mag, x - 1, y) && m >= tex2D(tex_mag, x + 1, y))
+ edge_type = 1 + (int)(m > high_thresh);
+ }
+ else if(dyVal > tg67x)
+ {
+ if (m > tex2D(tex_mag, x, y - 1) && m >= tex2D(tex_mag, x, y + 1))
+ edge_type = 1 + (int)(m > high_thresh);
+ }
+ else
+ {
+ if (m > tex2D(tex_mag, x - s, y - 1) && m >= tex2D(tex_mag, x + s, y + 1))
+ edge_type = 1 + (int)(m > high_thresh);
+ }
+ }
- x = ::abs(x);
- y = ::abs(y);
+ map(y, x) = edge_type;
+ }
- // 0 - the pixel can not belong to an edge
- // 1 - the pixel might belong to an edge
- // 2 - the pixel does belong to an edge
- int edge_type = 0;
+ void calcMap(PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, PtrStepSzi map, float low_thresh, float high_thresh)
+ {
+ const dim3 block(16, 16);
+ const dim3 grid(divUp(dx.cols, block.x), divUp(dx.rows, block.y));
- if (m > low_thresh)
- {
- const int tg22x = x * TG22;
- const int tg67x = tg22x + ((x + x) << CANNY_SHIFT);
-
- y <<= CANNY_SHIFT;
-
- if (y < tg22x)
- {
- if (m > smem[threadIdx.y + 1][threadIdx.x] && m >= smem[threadIdx.y + 1][threadIdx.x + 2])
- edge_type = 1 + (int)(m > high_thresh);
- }
- else if( y > tg67x )
- {
- if (m > smem[threadIdx.y][threadIdx.x + 1] && m >= smem[threadIdx.y + 2][threadIdx.x + 1])
- edge_type = 1 + (int)(m > high_thresh);
- }
- else
- {
- if (m > smem[threadIdx.y][threadIdx.x + 1 - s] && m > smem[threadIdx.y + 2][threadIdx.x + 1 + s])
- edge_type = 1 + (int)(m > high_thresh);
- }
- }
+ bindTexture(&tex_mag, mag);
- map.ptr(i + 1)[j + 1] = edge_type;
- }
- }
+ calcMapKernel<<<grid, block>>>(dx, dy, map, low_thresh, high_thresh);
+ cudaSafeCall( cudaGetLastError() );
- #undef CANNY_SHIFT
- #undef TG22
+ cudaSafeCall( cudaDeviceSynchronize() );
+ }
+}
- void calcMap_gpu(PtrStepi dx, PtrStepi dy, PtrStepf mag, PtrStepi map, int rows, int cols, float low_thresh, float high_thresh)
+//////////////////////////////////////////////////////////////////////////////////////////
+
+namespace canny
+{
+ __device__ int counter = 0;
+
+ __global__ void edgesHysteresisLocalKernel(PtrStepSzi map, ushort2* st)
+ {
+ __shared__ volatile int smem[18][18];
+
+ const int x = blockIdx.x * blockDim.x + threadIdx.x;
+ const int y = blockIdx.y * blockDim.y + threadIdx.y;
+
+ smem[threadIdx.y + 1][threadIdx.x + 1] = x < map.cols && y < map.rows ? map(y, x) : 0;
+ if (threadIdx.y == 0)
+ smem[0][threadIdx.x + 1] = y > 0 ? map(y - 1, x) : 0;
+ if (threadIdx.y == blockDim.y - 1)
+ smem[blockDim.y + 1][threadIdx.x + 1] = y + 1 < map.rows ? map(y + 1, x) : 0;
+ if (threadIdx.x == 0)
+ smem[threadIdx.y + 1][0] = x > 0 ? map(y, x - 1) : 0;
+ if (threadIdx.x == blockDim.x - 1)
+ smem[threadIdx.y + 1][blockDim.x + 1] = x + 1 < map.cols ? map(y, x + 1) : 0;
+ if (threadIdx.x == 0 && threadIdx.y == 0)
+ smem[0][0] = y > 0 && x > 0 ? map(y - 1, x - 1) : 0;
+ if (threadIdx.x == blockDim.x - 1 && threadIdx.y == 0)
+ smem[0][blockDim.x + 1] = y > 0 && x + 1 < map.cols ? map(y - 1, x + 1) : 0;
+ if (threadIdx.x == 0 && threadIdx.y == blockDim.y - 1)
+ smem[blockDim.y + 1][0] = y + 1 < map.rows && x > 0 ? map(y + 1, x - 1) : 0;
+ if (threadIdx.x == blockDim.x - 1 && threadIdx.y == blockDim.y - 1)
+ smem[blockDim.y + 1][blockDim.x + 1] = y + 1 < map.rows && x + 1 < map.cols ? map(y + 1, x + 1) : 0;
+
+ __syncthreads();
+
+ if (x >= map.cols || y >= map.rows)
+ return;
+
+ int n;
+
+ #pragma unroll
+ for (int k = 0; k < 16; ++k)
{
- dim3 block(16, 16, 1);
- dim3 grid(divUp(cols, block.x), divUp(rows, block.y), 1);
+ n = 0;
- calcMap<<<grid, block>>>(dx, dy, mag, map, rows, cols, low_thresh, high_thresh);
- cudaSafeCall( cudaGetLastError() );
+ if (smem[threadIdx.y + 1][threadIdx.x + 1] == 1)
+ {
+ n += smem[threadIdx.y ][threadIdx.x ] == 2;
+ n += smem[threadIdx.y ][threadIdx.x + 1] == 2;
+ n += smem[threadIdx.y ][threadIdx.x + 2] == 2;
- cudaSafeCall( cudaDeviceSynchronize() );
- }
+ n += smem[threadIdx.y + 1][threadIdx.x ] == 2;
+ n += smem[threadIdx.y + 1][threadIdx.x + 2] == 2;
- //////////////////////////////////////////////////////////////////////////////////////////
+ n += smem[threadIdx.y + 2][threadIdx.x ] == 2;
+ n += smem[threadIdx.y + 2][threadIdx.x + 1] == 2;
+ n += smem[threadIdx.y + 2][threadIdx.x + 2] == 2;
+ }
- __device__ unsigned int counter = 0;
+ if (n > 0)
+ smem[threadIdx.y + 1][threadIdx.x + 1] = 2;
+ }
- __global__ void edgesHysteresisLocal(PtrStepi map, ushort2* st, int rows, int cols)
- {
- #if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 120)
+ const int e = smem[threadIdx.y + 1][threadIdx.x + 1];
- __shared__ int smem[18][18];
+ map(y, x) = e;
- const int j = blockIdx.x * 16 + threadIdx.x;
- const int i = blockIdx.y * 16 + threadIdx.y;
+ n = 0;
- const int tid = threadIdx.y * 16 + threadIdx.x;
- const int lx = tid % 18;
- const int ly = tid / 18;
+ if (e == 2)
+ {
+ n += smem[threadIdx.y ][threadIdx.x ] == 1;
+ n += smem[threadIdx.y ][threadIdx.x + 1] == 1;
+ n += smem[threadIdx.y ][threadIdx.x + 2] == 1;
- if (ly < 14)
- smem[ly][lx] = map.ptr(blockIdx.y * 16 + ly)[blockIdx.x * 16 + lx];
+ n += smem[threadIdx.y + 1][threadIdx.x ] == 1;
+ n += smem[threadIdx.y + 1][threadIdx.x + 2] == 1;
- if (ly < 4 && blockIdx.y * 16 + ly + 14 <= rows && blockIdx.x * 16 + lx <= cols)
- smem[ly + 14][lx] = map.ptr(blockIdx.y * 16 + ly + 14)[blockIdx.x * 16 + lx];
+ n += smem[threadIdx.y + 2][threadIdx.x ] == 1;
+ n += smem[threadIdx.y + 2][threadIdx.x + 1] == 1;
+ n += smem[threadIdx.y + 2][threadIdx.x + 2] == 1;
+ }
- __syncthreads();
+ if (n > 0)
+ {
+ const int ind = ::atomicAdd(&counter, 1);
+ st[ind] = make_ushort2(x, y);
+ }
+ }
- if (i < rows && j < cols)
- {
- int n;
+ void edgesHysteresisLocal(PtrStepSzi map, ushort2* st1)
+ {
+ void* counter_ptr;
+ cudaSafeCall( cudaGetSymbolAddress(&counter_ptr, counter) );
- #pragma unroll
- for (int k = 0; k < 16; ++k)
- {
- n = 0;
+ cudaSafeCall( cudaMemset(counter_ptr, 0, sizeof(int)) );
- if (smem[threadIdx.y + 1][threadIdx.x + 1] == 1)
- {
- n += smem[threadIdx.y ][threadIdx.x ] == 2;
- n += smem[threadIdx.y ][threadIdx.x + 1] == 2;
- n += smem[threadIdx.y ][threadIdx.x + 2] == 2;
+ const dim3 block(16, 16);
+ const dim3 grid(divUp(map.cols, block.x), divUp(map.rows, block.y));
- n += smem[threadIdx.y + 1][threadIdx.x ] == 2;
- n += smem[threadIdx.y + 1][threadIdx.x + 2] == 2;
+ edgesHysteresisLocalKernel<<<grid, block>>>(map, st1);
+ cudaSafeCall( cudaGetLastError() );
- n += smem[threadIdx.y + 2][threadIdx.x ] == 2;
- n += smem[threadIdx.y + 2][threadIdx.x + 1] == 2;
- n += smem[threadIdx.y + 2][threadIdx.x + 2] == 2;
- }
+ cudaSafeCall( cudaDeviceSynchronize() );
+ }
+}
- if (n > 0)
- smem[threadIdx.y + 1][threadIdx.x + 1] = 2;
- }
+//////////////////////////////////////////////////////////////////////////////////////////
- const int e = smem[threadIdx.y + 1][threadIdx.x + 1];
+namespace canny
+{
+ __constant__ int c_dx[8] = {-1, 0, 1, -1, 1, -1, 0, 1};
+ __constant__ int c_dy[8] = {-1, -1, -1, 0, 0, 1, 1, 1};
- map.ptr(i + 1)[j + 1] = e;
+ __global__ void edgesHysteresisGlobalKernel(PtrStepSzi map, ushort2* st1, ushort2* st2, const int count)
+ {
+ const int stack_size = 512;
- n = 0;
+ __shared__ int s_counter;
+ __shared__ int s_ind;
+ __shared__ ushort2 s_st[stack_size];
- if (e == 2)
- {
- n += smem[threadIdx.y ][threadIdx.x ] == 1;
- n += smem[threadIdx.y ][threadIdx.x + 1] == 1;
- n += smem[threadIdx.y ][threadIdx.x + 2] == 1;
+ if (threadIdx.x == 0)
+ s_counter = 0;
- n += smem[threadIdx.y + 1][threadIdx.x ] == 1;
- n += smem[threadIdx.y + 1][threadIdx.x + 2] == 1;
+ __syncthreads();
- n += smem[threadIdx.y + 2][threadIdx.x ] == 1;
- n += smem[threadIdx.y + 2][threadIdx.x + 1] == 1;
- n += smem[threadIdx.y + 2][threadIdx.x + 2] == 1;
- }
+ int ind = blockIdx.y * gridDim.x + blockIdx.x;
- if (n > 0)
- {
- const unsigned int ind = atomicInc(&counter, (unsigned int)(-1));
- st[ind] = make_ushort2(j + 1, i + 1);
- }
- }
+ if (ind >= count)
+ return;
- #endif
- }
+ ushort2 pos = st1[ind];
- void edgesHysteresisLocal_gpu(PtrStepi map, ushort2* st1, int rows, int cols)
+ if (threadIdx.x < 8)
{
- void* counter_ptr;
- cudaSafeCall( cudaGetSymbolAddress(&counter_ptr, counter) );
-
- cudaSafeCall( cudaMemset(counter_ptr, 0, sizeof(unsigned int)) );
+ pos.x += c_dx[threadIdx.x];
+ pos.y += c_dy[threadIdx.x];
- dim3 block(16, 16, 1);
- dim3 grid(divUp(cols, block.x), divUp(rows, block.y), 1);
+ if (pos.x > 0 && pos.x < map.cols && pos.y > 0 && pos.y < map.rows && map(pos.y, pos.x) == 1)
+ {
+ map(pos.y, pos.x) = 2;
- edgesHysteresisLocal<<<grid, block>>>(map, st1, rows, cols);
- cudaSafeCall( cudaGetLastError() );
+ ind = Emulation::smem::atomicAdd(&s_counter, 1);
- cudaSafeCall( cudaDeviceSynchronize() );
+ s_st[ind] = pos;
+ }
}
- __constant__ int c_dx[8] = {-1, 0, 1, -1, 1, -1, 0, 1};
- __constant__ int c_dy[8] = {-1, -1, -1, 0, 0, 1, 1, 1};
+ __syncthreads();
- __global__ void edgesHysteresisGlobal(PtrStepi map, ushort2* st1, ushort2* st2, int rows, int cols, int count)
+ while (s_counter > 0 && s_counter <= stack_size - blockDim.x)
{
- #if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 120
+ const int subTaskIdx = threadIdx.x >> 3;
+ const int portion = ::min(s_counter, blockDim.x >> 3);
- const int stack_size = 512;
+ if (subTaskIdx < portion)
+ pos = s_st[s_counter - 1 - subTaskIdx];
- __shared__ unsigned int s_counter;
- __shared__ unsigned int s_ind;
- __shared__ ushort2 s_st[stack_size];
+ __syncthreads();
if (threadIdx.x == 0)
- s_counter = 0;
- __syncthreads();
+ s_counter -= portion;
- int ind = blockIdx.y * gridDim.x + blockIdx.x;
+ __syncthreads();
- if (ind < count)
+ if (subTaskIdx < portion)
{
- ushort2 pos = st1[ind];
+ pos.x += c_dx[threadIdx.x & 7];
+ pos.y += c_dy[threadIdx.x & 7];
- if (pos.x > 0 && pos.x <= cols && pos.y > 0 && pos.y <= rows)
+ if (pos.x > 0 && pos.x < map.cols && pos.y > 0 && pos.y < map.rows && map(pos.y, pos.x) == 1)
{
- if (threadIdx.x < 8)
- {
- pos.x += c_dx[threadIdx.x];
- pos.y += c_dy[threadIdx.x];
-
- if (map.ptr(pos.y)[pos.x] == 1)
- {
- map.ptr(pos.y)[pos.x] = 2;
-
- ind = atomicInc(&s_counter, (unsigned int)(-1));
-
- s_st[ind] = pos;
- }
- }
- __syncthreads();
-
- while (s_counter > 0 && s_counter <= stack_size - blockDim.x)
- {
- const int subTaskIdx = threadIdx.x >> 3;
- const int portion = ::min(s_counter, blockDim.x >> 3);
-
- pos.x = pos.y = 0;
-
- if (subTaskIdx < portion)
- pos = s_st[s_counter - 1 - subTaskIdx];
- __syncthreads();
-
- if (threadIdx.x == 0)
- s_counter -= portion;
- __syncthreads();
-
- if (pos.x > 0 && pos.x <= cols && pos.y > 0 && pos.y <= rows)
- {
- pos.x += c_dx[threadIdx.x & 7];
- pos.y += c_dy[threadIdx.x & 7];
-
- if (map.ptr(pos.y)[pos.x] == 1)
- {
- map.ptr(pos.y)[pos.x] = 2;
-
- ind = atomicInc(&s_counter, (unsigned int)(-1));
-
- s_st[ind] = pos;
- }
- }
- __syncthreads();
- }
-
- if (s_counter > 0)
- {
- if (threadIdx.x == 0)
- {
- ind = atomicAdd(&counter, s_counter);
- s_ind = ind - s_counter;
- }
- __syncthreads();
-
- ind = s_ind;
-
- for (int i = threadIdx.x; i < s_counter; i += blockDim.x)
- {
- st2[ind + i] = s_st[i];
- }
- }
+ map(pos.y, pos.x) = 2;
+
+ ind = Emulation::smem::atomicAdd(&s_counter, 1);
+
+ s_st[ind] = pos;
}
}
- #endif
+ __syncthreads();
}
- void edgesHysteresisGlobal_gpu(PtrStepi map, ushort2* st1, ushort2* st2, int rows, int cols)
+ if (s_counter > 0)
{
- void* counter_ptr;
- cudaSafeCall( cudaGetSymbolAddress(&counter_ptr, counter) );
-
- unsigned int count;
- cudaSafeCall( cudaMemcpy(&count, counter_ptr, sizeof(unsigned int), cudaMemcpyDeviceToHost) );
-
- while (count > 0)
+ if (threadIdx.x == 0)
{
- cudaSafeCall( cudaMemset(counter_ptr, 0, sizeof(unsigned int)) );
-
- dim3 block(128, 1, 1);
- dim3 grid(std::min(count, 65535u), divUp(count, 65535), 1);
- edgesHysteresisGlobal<<<grid, block>>>(map, st1, st2, rows, cols, count);
- cudaSafeCall( cudaGetLastError() );
+ ind = ::atomicAdd(&counter, s_counter);
+ s_ind = ind - s_counter;
+ }
- cudaSafeCall( cudaDeviceSynchronize() );
+ __syncthreads();
- cudaSafeCall( cudaMemcpy(&count, counter_ptr, sizeof(unsigned int), cudaMemcpyDeviceToHost) );
+ ind = s_ind;
- std::swap(st1, st2);
- }
+ for (int i = threadIdx.x; i < s_counter; i += blockDim.x)
+ st2[ind + i] = s_st[i];
}
+ }
- __global__ void getEdges(PtrStepi map, PtrStepb dst, int rows, int cols)
- {
- const int j = blockIdx.x * 16 + threadIdx.x;
- const int i = blockIdx.y * 16 + threadIdx.y;
+ void edgesHysteresisGlobal(PtrStepSzi map, ushort2* st1, ushort2* st2)
+ {
+ void* counter_ptr;
+ cudaSafeCall( cudaGetSymbolAddress(&counter_ptr, canny::counter) );
- if (i < rows && j < cols)
- dst.ptr(i)[j] = (uchar)(-(map.ptr(i + 1)[j + 1] >> 1));
- }
+ int count;
+ cudaSafeCall( cudaMemcpy(&count, counter_ptr, sizeof(int), cudaMemcpyDeviceToHost) );
- void getEdges_gpu(PtrStepi map, PtrStepb dst, int rows, int cols)
+ while (count > 0)
{
- dim3 block(16, 16, 1);
- dim3 grid(divUp(cols, block.x), divUp(rows, block.y), 1);
+ cudaSafeCall( cudaMemset(counter_ptr, 0, sizeof(int)) );
+
+ const dim3 block(128);
+ const dim3 grid(::min(count, 65535u), divUp(count, 65535), 1);
- getEdges<<<grid, block>>>(map, dst, rows, cols);
+ edgesHysteresisGlobalKernel<<<grid, block>>>(map, st1, st2, count);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
+
+ cudaSafeCall( cudaMemcpy(&count, counter_ptr, sizeof(int), cudaMemcpyDeviceToHost) );
+
+ std::swap(st1, st2);
}
- } // namespace canny
-}}} // namespace cv { namespace gpu { namespace device
+ }
+}
+
+//////////////////////////////////////////////////////////////////////////////////////////
+namespace canny
+{
+ struct GetEdges : unary_function<int, uchar>
+ {
+ __device__ __forceinline__ uchar operator ()(int e) const
+ {
+ return (uchar)(-(e >> 1));
+ }
+
+ __device__ __forceinline__ GetEdges() {}
+ __device__ __forceinline__ GetEdges(const GetEdges&) {}
+ };
+}
+
+namespace cv { namespace gpu { namespace device
+{
+ template <> struct TransformFunctorTraits<canny::GetEdges> : DefaultTransformFunctorTraits<canny::GetEdges>
+ {
+ enum { smart_shift = 4 };
+ };
+}}}
+
+namespace canny
+{
+ void getEdges(PtrStepSzi map, PtrStepSzb dst)
+ {
+ transform(map, dst, GetEdges(), WithOutMask(), 0);
+ }
+}
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+#endif /* CUDA_DISABLER */
#if !defined CUDA_DISABLER
-#include "internal_shared.hpp"
+#include "opencv2/gpu/device/common.hpp"
#include "opencv2/gpu/device/functional.hpp"
#include "opencv2/gpu/device/vec_math.hpp"
#include "opencv2/gpu/device/transform.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
-namespace cv { namespace gpu { namespace device
-{
- //////////////////////////////////////////////////////////////////////////
- // add
+using namespace cv::gpu;
+using namespace cv::gpu::device;
- template <typename T, typename D> struct Add : binary_function<T, T, D>
+namespace arithm
+{
+ template <size_t src_size, size_t dst_size> struct ArithmFuncTraits
{
- __device__ __forceinline__ D operator ()(T a, T b) const
- {
- return saturate_cast<D>(a + b);
- }
+ enum { simple_block_dim_x = 32 };
+ enum { simple_block_dim_y = 8 };
+
+ enum { smart_block_dim_x = 32 };
+ enum { smart_block_dim_y = 8 };
+ enum { smart_shift = 1 };
};
- template <> struct TransformFunctorTraits< Add<ushort, ushort> > : DefaultTransformFunctorTraits< Add<ushort, ushort> >
+ template <> struct ArithmFuncTraits<1, 1>
{
+ enum { simple_block_dim_x = 32 };
+ enum { simple_block_dim_y = 8 };
+
+ enum { smart_block_dim_x = 32 };
enum { smart_block_dim_y = 8 };
enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< Add<short, short> > : DefaultTransformFunctorTraits< Add<short, short> >
+ template <> struct ArithmFuncTraits<1, 2>
{
+ enum { simple_block_dim_x = 32 };
+ enum { simple_block_dim_y = 8 };
+
+ enum { smart_block_dim_x = 32 };
enum { smart_block_dim_y = 8 };
enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< Add<int, int> > : DefaultTransformFunctorTraits< Add<int, int> >
+ template <> struct ArithmFuncTraits<1, 4>
{
+ enum { simple_block_dim_x = 32 };
+ enum { simple_block_dim_y = 8 };
+
+ enum { smart_block_dim_x = 32 };
enum { smart_block_dim_y = 8 };
enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< Add<float, float> > : DefaultTransformFunctorTraits< Add<float, float> >
+
+ template <> struct ArithmFuncTraits<2, 1>
{
+ enum { simple_block_dim_x = 32 };
+ enum { simple_block_dim_y = 8 };
+
+ enum { smart_block_dim_x = 32 };
enum { smart_block_dim_y = 8 };
enum { smart_shift = 4 };
};
-
- template <typename T, typename D> void add_gpu(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream)
+ template <> struct ArithmFuncTraits<2, 2>
{
- if (mask.data)
- cv::gpu::device::transform((PtrStepSz<T>)src1, (PtrStepSz<T>)src2, (PtrStepSz<D>)dst, Add<T, D>(), SingleMask(mask), stream);
- else
- cv::gpu::device::transform((PtrStepSz<T>)src1, (PtrStepSz<T>)src2, (PtrStepSz<D>)dst, Add<T, D>(), WithOutMask(), stream);
- }
+ enum { simple_block_dim_x = 32 };
+ enum { simple_block_dim_y = 8 };
- template void add_gpu<uchar, uchar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<uchar, schar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<uchar, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<uchar, short>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<uchar, int>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<uchar, float>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<uchar, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void add_gpu<schar, uchar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<schar, schar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<schar, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<schar, short>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<schar, int>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<schar, float>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<schar, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void add_gpu<ushort, uchar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<ushort, schar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<ushort, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<ushort, short>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<ushort, int>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<ushort, float>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<ushort, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void add_gpu<short, uchar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<short, schar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<short, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<short, short>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<short, int>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<short, float>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<short, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void add_gpu<int, uchar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<int, schar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<int, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<int, short>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<int, int>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<int, float>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<int, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void add_gpu<float, uchar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<float, schar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<float, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<float, short>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<float, int>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<float, float>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<float, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void add_gpu<double, uchar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<double, schar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<double, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<double, short>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<double, int>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<double, float>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<double, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- template <typename T, typename D> struct AddScalar : unary_function<T, D>
- {
- AddScalar(double val_) : val(val_) {}
- __device__ __forceinline__ D operator ()(T a) const
- {
- return saturate_cast<D>(a + val);
- }
- const double val;
+ enum { smart_block_dim_x = 32 };
+ enum { smart_block_dim_y = 8 };
+ enum { smart_shift = 4 };
};
-
- template <> struct TransformFunctorTraits< AddScalar<ushort, ushort> > : DefaultTransformFunctorTraits< AddScalar<ushort, ushort> >
+ template <> struct ArithmFuncTraits<2, 4>
{
+ enum { simple_block_dim_x = 32 };
+ enum { simple_block_dim_y = 8 };
+
+ enum { smart_block_dim_x = 32 };
enum { smart_block_dim_y = 8 };
enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< AddScalar<short, short> > : DefaultTransformFunctorTraits< AddScalar<short, short> >
+
+ template <> struct ArithmFuncTraits<4, 1>
{
+ enum { simple_block_dim_x = 32 };
+ enum { simple_block_dim_y = 8 };
+
+ enum { smart_block_dim_x = 32 };
enum { smart_block_dim_y = 8 };
enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< AddScalar<int, int> > : DefaultTransformFunctorTraits< AddScalar<int, int> >
+ template <> struct ArithmFuncTraits<4, 2>
{
+ enum { simple_block_dim_x = 32 };
+ enum { simple_block_dim_y = 8 };
+
+ enum { smart_block_dim_x = 32 };
enum { smart_block_dim_y = 8 };
enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< AddScalar<float, float> > : DefaultTransformFunctorTraits< AddScalar<float, float> >
+ template <> struct ArithmFuncTraits<4, 4>
{
+ enum { simple_block_dim_x = 32 };
+ enum { simple_block_dim_y = 8 };
+
+ enum { smart_block_dim_x = 32 };
enum { smart_block_dim_y = 8 };
enum { smart_shift = 4 };
};
+}
- template <typename T, typename D> void add_gpu(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream)
- {
- cudaSafeCall( cudaSetDoubleForDevice(&val) );
- AddScalar<T, D> op(val);
- if (mask.data)
- cv::gpu::device::transform((PtrStepSz<T>)src1, (PtrStepSz<D>)dst, op, SingleMask(mask), stream);
- else
- cv::gpu::device::transform((PtrStepSz<T>)src1, (PtrStepSz<D>)dst, op, WithOutMask(), stream);
- }
+//////////////////////////////////////////////////////////////////////////
+// addMat
- template void add_gpu<uchar, uchar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<uchar, schar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<uchar, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<uchar, short >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<uchar, int >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<uchar, float >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<uchar, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void add_gpu<schar, uchar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<schar, schar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<schar, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<schar, short>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<schar, int>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<schar, float>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<schar, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void add_gpu<ushort, uchar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<ushort, schar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<ushort, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<ushort, short>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<ushort, int>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<ushort, float>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<ushort, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void add_gpu<short, uchar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<short, schar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<short, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<short, short>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<short, int>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<short, float>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<short, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void add_gpu<int, uchar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<int, schar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<int, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<int, short>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<int, int>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<int, float>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<int, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void add_gpu<float, uchar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<float, schar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<float, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<float, short>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<float, int>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<float, float>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<float, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void add_gpu<double, uchar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<double, schar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<double, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<double, short>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<double, int>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void add_gpu<double, float>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void add_gpu<double, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //////////////////////////////////////////////////////////////////////////
- // subtract
-
- template <typename T, typename D> struct Subtract : binary_function<T, T, D>
+namespace arithm
+{
+ template <typename T, typename D> struct VAdd4;
+ template <> struct VAdd4<uint, uint> : binary_function<uint, uint, uint>
{
- __device__ __forceinline__ D operator ()(T a, T b) const
+ __device__ __forceinline__ uint operator ()(uint a, uint b) const
{
- return saturate_cast<D>(a - b);
+ uint res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vadd4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vadd.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vadd.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vadd.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vadd.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
}
- };
- template <> struct TransformFunctorTraits< Subtract<ushort, ushort> > : DefaultTransformFunctorTraits< Subtract<ushort, ushort> >
+ __device__ __forceinline__ VAdd4() {}
+ __device__ __forceinline__ VAdd4(const VAdd4<uint, uint>& other) {}
+ };
+ template <> struct VAdd4<int, uint> : binary_function<int, int, uint>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
+ __device__ __forceinline__ uint operator ()(int a, int b) const
+ {
+ uint res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vadd4.u32.s32.s32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vadd.u32.s32.s32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vadd.u32.s32.s32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vadd.u32.s32.s32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vadd.u32.s32.s32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
+ }
+
+ __device__ __forceinline__ VAdd4() {}
+ __device__ __forceinline__ VAdd4(const VAdd4<int, uint>& other) {}
};
- template <> struct TransformFunctorTraits< Subtract<short, short> > : DefaultTransformFunctorTraits< Subtract<short, short> >
+ template <> struct VAdd4<uint, int> : binary_function<uint, uint, int>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
+ __device__ __forceinline__ int operator ()(uint a, uint b) const
+ {
+ int res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vadd4.s32.u32.u32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vadd.s32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vadd.s32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vadd.s32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vadd.s32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
+ }
+
+ __device__ __forceinline__ VAdd4() {}
+ __device__ __forceinline__ VAdd4(const VAdd4<uint, int>& other) {}
};
- template <> struct TransformFunctorTraits< Subtract<int, int> > : DefaultTransformFunctorTraits< Subtract<int, int> >
+ template <> struct VAdd4<int, int> : binary_function<int, int, int>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
+ __device__ __forceinline__ int operator ()(int a, int b) const
+ {
+ int res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vadd4.s32.s32.s32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vadd.s32.s32.s32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vadd.s32.s32.s32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vadd.s32.s32.s32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vadd.s32.s32.s32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
+ }
+
+ __device__ __forceinline__ VAdd4() {}
+ __device__ __forceinline__ VAdd4(const VAdd4<int, int>& other) {}
};
- template <> struct TransformFunctorTraits< Subtract<float, float> > : DefaultTransformFunctorTraits< Subtract<float, float> >
+
+ ////////////////////////////////////
+
+ template <typename T, typename D> struct VAdd2;
+ template <> struct VAdd2<uint, uint> : binary_function<uint, uint, uint>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
+ __device__ __forceinline__ uint operator ()(uint a, uint b) const
+ {
+ uint res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vadd2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vadd.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vadd.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
+ }
+
+ __device__ __forceinline__ VAdd2() {}
+ __device__ __forceinline__ VAdd2(const VAdd2<uint, uint>& other) {}
};
+ template <> struct VAdd2<uint, int> : binary_function<uint, uint, int>
+ {
+ __device__ __forceinline__ int operator ()(uint a, uint b) const
+ {
+ int res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vadd2.s32.u32.u32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vadd.s32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vadd.s32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
+ }
- template <typename T, typename D> void subtract_gpu(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream)
+ __device__ __forceinline__ VAdd2() {}
+ __device__ __forceinline__ VAdd2(const VAdd2<uint, int>& other) {}
+ };
+ template <> struct VAdd2<int, uint> : binary_function<int, int, uint>
{
- if (mask.data)
- cv::gpu::device::transform((PtrStepSz<T>)src1, (PtrStepSz<T>)src2, (PtrStepSz<D>)dst, Subtract<T, D>(), SingleMask(mask), stream);
- else
- cv::gpu::device::transform((PtrStepSz<T>)src1, (PtrStepSz<T>)src2, (PtrStepSz<D>)dst, Subtract<T, D>(), WithOutMask(), stream);
- }
+ __device__ __forceinline__ uint operator ()(int a, int b) const
+ {
+ uint res = 0;
- template void subtract_gpu<uchar, uchar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<uchar, schar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<uchar, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<uchar, short>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<uchar, int>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<uchar, float>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<uchar, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void subtract_gpu<schar, uchar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<schar, schar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<schar, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<schar, short>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<schar, int>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<schar, float>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<schar, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void subtract_gpu<ushort, uchar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<ushort, schar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<ushort, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<ushort, short>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<ushort, int>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<ushort, float>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<ushort, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void subtract_gpu<short, uchar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<short, schar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<short, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<short, short>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<short, int>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<short, float>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<short, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void subtract_gpu<int, uchar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<int, schar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<int, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<int, short>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<int, int>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<int, float>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<int, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void subtract_gpu<float, uchar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<float, schar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<float, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<float, short>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<float, int>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<float, float>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<float, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void subtract_gpu<double, uchar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<double, schar>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<double, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<double, short>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<double, int>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<double, float>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<double, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- template <typename T, typename D> struct SubtractScalar : unary_function<T, D>
- {
- SubtractScalar(double val_) : val(val_) {}
- __device__ __forceinline__ D operator ()(T a) const
+ #if __CUDA_ARCH__ >= 300
+ asm("vadd2.u32.s32.s32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vadd.u32.s32.s32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vadd.u32.s32.s32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
+ }
+
+ __device__ __forceinline__ VAdd2() {}
+ __device__ __forceinline__ VAdd2(const VAdd2<int, uint>& other) {}
+ };
+ template <> struct VAdd2<int, int> : binary_function<int, int, int>
+ {
+ __device__ __forceinline__ int operator ()(int a, int b) const
{
- return saturate_cast<D>(a - val);
+ int res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vadd2.s32.s32.s32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vadd.s32.s32.s32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vadd.s32.s32.s32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
}
- const double val;
+
+ __device__ __forceinline__ VAdd2() {}
+ __device__ __forceinline__ VAdd2(const VAdd2<int, int>& other) {}
};
- template <> struct TransformFunctorTraits< SubtractScalar<ushort, ushort> > : DefaultTransformFunctorTraits< SubtractScalar<ushort, ushort> >
+ ////////////////////////////////////
+
+ template <typename T, typename D> struct AddMat : binary_function<T, T, D>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
+ __device__ __forceinline__ D operator ()(T a, T b) const
+ {
+ return saturate_cast<D>(a + b);
+ }
+
+ __device__ __forceinline__ AddMat() {}
+ __device__ __forceinline__ AddMat(const AddMat& other) {}
};
- template <> struct TransformFunctorTraits< SubtractScalar<short, short> > : DefaultTransformFunctorTraits< SubtractScalar<short, short> >
+}
+
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T, typename D> struct TransformFunctorTraits< arithm::VAdd4<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< SubtractScalar<int, int> > : DefaultTransformFunctorTraits< SubtractScalar<int, int> >
+
+ ////////////////////////////////////
+
+ template <typename T, typename D> struct TransformFunctorTraits< arithm::VAdd2<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< SubtractScalar<float, float> > : DefaultTransformFunctorTraits< SubtractScalar<float, float> >
+
+ ////////////////////////////////////
+
+ template <typename T, typename D> struct TransformFunctorTraits< arithm::AddMat<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
};
+}}}
+
+namespace arithm
+{
+ template <typename T, typename D>
+ void vadd4(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
+ {
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<D>) dst, VAdd4<T, D>(), WithOutMask(), stream);
+ }
+
+ template void vadd4<uint, uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vadd4<uint, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vadd4<int, uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vadd4<int, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+
+ template <typename T, typename D>
+ void vadd2(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
+ {
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<D>) dst, VAdd2<T, D>(), WithOutMask(), stream);
+ }
+
+ template void vadd2<uint, uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vadd2<uint, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vadd2<int, uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vadd2<int, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template <typename T, typename D> void subtract_gpu(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream)
+ template <typename T, typename D>
+ void addMat(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream)
{
- cudaSafeCall( cudaSetDoubleForDevice(&val) );
- SubtractScalar<T, D> op(val);
if (mask.data)
- cv::gpu::device::transform((PtrStepSz<T>)src1, (PtrStepSz<D>)dst, op, SingleMask(mask), stream);
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<D>) dst, AddMat<T, D>(), mask, stream);
else
- cv::gpu::device::transform((PtrStepSz<T>)src1, (PtrStepSz<D>)dst, op, WithOutMask(), stream);
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<D>) dst, AddMat<T, D>(), WithOutMask(), stream);
}
- template void subtract_gpu<uchar, uchar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<uchar, schar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<uchar, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<uchar, short >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<uchar, int >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<uchar, float >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<uchar, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void subtract_gpu<schar, uchar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<schar, schar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<schar, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<schar, short>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<schar, int>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<schar, float>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<schar, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void subtract_gpu<ushort, uchar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<ushort, schar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<ushort, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<ushort, short>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<ushort, int>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<ushort, float>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<ushort, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void subtract_gpu<short, uchar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<short, schar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<short, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<short, short>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<short, int>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<short, float>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<short, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void subtract_gpu<int, uchar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<int, schar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<int, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<int, short>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<int, int>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<int, float>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<int, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void subtract_gpu<float, uchar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<float, schar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<float, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<float, short>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<float, int>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<float, float>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<float, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //template void subtract_gpu<double, uchar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<double, schar>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<double, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<double, short>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<double, int>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- //template void subtract_gpu<double, float>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
- template void subtract_gpu<double, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-
- //////////////////////////////////////////////////////////////////////////
- // multiply
-
- struct multiply_8uc4_32f : binary_function<uint, float, uint>
+ template void addMat<uchar, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<uchar, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<uchar, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<uchar, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<uchar, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<uchar, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<uchar, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ template void addMat<schar, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<schar, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<schar, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<schar, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<schar, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<schar, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<schar, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void addMat<ushort, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addMat<ushort, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<ushort, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<ushort, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<ushort, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<ushort, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<ushort, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void addMat<short, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addMat<short, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<short, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<short, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<short, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<short, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<short, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void addMat<int, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addMat<int, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addMat<int, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addMat<int, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<int, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<int, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<int, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void addMat<float, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addMat<float, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addMat<float, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addMat<float, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addMat<float, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<float, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<float, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void addMat<double, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addMat<double, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addMat<double, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addMat<double, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addMat<double, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addMat<double, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addMat<double, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+}
+
+//////////////////////////////////////////////////////////////////////////
+// addScalar
+
+namespace arithm
+{
+ template <typename T, typename S, typename D> struct AddScalar : unary_function<T, D>
{
- __device__ __forceinline__ uint operator ()(uint a, float b) const
- {
- uint res = 0;
+ S val;
- res |= (saturate_cast<uchar>((0xffu & (a )) * b) );
- res |= (saturate_cast<uchar>((0xffu & (a >> 8)) * b) << 8);
- res |= (saturate_cast<uchar>((0xffu & (a >> 16)) * b) << 16);
- res |= (saturate_cast<uchar>((0xffu & (a >> 24)) * b) << 24);
+ explicit AddScalar(S val_) : val(val_) {}
- return res;
+ __device__ __forceinline__ D operator ()(T a) const
+ {
+ return saturate_cast<D>(a + val);
}
};
+}
- OPENCV_GPU_TRANSFORM_FUNCTOR_TRAITS(multiply_8uc4_32f)
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T, typename S, typename D> struct TransformFunctorTraits< arithm::AddScalar<T, S, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
- enum { smart_block_dim_x = 8 };
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 8 };
};
+}}}
- void multiply_gpu(const PtrStepSz<uchar4>& src1, const PtrStepSzf& src2, const PtrStepSz<uchar4>& dst, cudaStream_t stream)
+namespace arithm
+{
+ template <typename T, typename S, typename D>
+ void addScalar(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream)
{
- cv::gpu::device::transform(static_cast< PtrStepSz<uint> >(src1), src2, static_cast< PtrStepSz<uint> >(dst), multiply_8uc4_32f(), WithOutMask(), stream);
+ AddScalar<T, S, D> op(static_cast<S>(val));
+
+ if (mask.data)
+ transform((PtrStepSz<T>) src1, (PtrStepSz<D>) dst, op, mask, stream);
+ else
+ transform((PtrStepSz<T>) src1, (PtrStepSz<D>) dst, op, WithOutMask(), stream);
}
- struct multiply_16sc4_32f : binary_function<short4, float, short4>
+ template void addScalar<uchar, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<uchar, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<uchar, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<uchar, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<uchar, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<uchar, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<uchar, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ template void addScalar<schar, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<schar, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<schar, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<schar, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<schar, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<schar, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<schar, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void addScalar<ushort, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addScalar<ushort, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<ushort, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<ushort, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<ushort, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<ushort, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<ushort, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void addScalar<short, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addScalar<short, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<short, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<short, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<short, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<short, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<short, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void addScalar<int, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addScalar<int, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addScalar<int, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addScalar<int, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<int, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<int, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<int, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void addScalar<float, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addScalar<float, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addScalar<float, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addScalar<float, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addScalar<float, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<float, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<float, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void addScalar<double, double, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addScalar<double, double, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addScalar<double, double, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addScalar<double, double, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addScalar<double, double, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void addScalar<double, double, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void addScalar<double, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+}
+
+//////////////////////////////////////////////////////////////////////////
+// subMat
+
+namespace arithm
+{
+ template <typename T, typename D> struct VSub4;
+ template <> struct VSub4<uint, uint> : binary_function<uint, uint, uint>
{
- __device__ __forceinline__ short4 operator ()(short4 a, float b) const
+ __device__ __forceinline__ uint operator ()(uint a, uint b) const
{
- return make_short4(saturate_cast<short>(a.x * b), saturate_cast<short>(a.y * b),
- saturate_cast<short>(a.z * b), saturate_cast<short>(a.w * b));
- }
- };
+ uint res = 0;
- OPENCV_GPU_TRANSFORM_FUNCTOR_TRAITS(multiply_16sc4_32f)
- {
- enum { smart_block_dim_x = 8 };
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 8 };
- };
+ #if __CUDA_ARCH__ >= 300
+ asm("vsub4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vsub.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vsub.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vsub.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vsub.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
- void multiply_gpu(const PtrStepSz<short4>& src1, const PtrStepSzf& src2, const PtrStepSz<short4>& dst, cudaStream_t stream)
- {
- cv::gpu::device::transform(static_cast< PtrStepSz<short4> >(src1), src2, static_cast< PtrStepSz<short4> >(dst), multiply_16sc4_32f(), WithOutMask(), stream);
- }
+ return res;
+ }
- template <typename T, typename D> struct Multiply : binary_function<T, T, D>
+ __device__ __forceinline__ VSub4() {}
+ __device__ __forceinline__ VSub4(const VSub4<uint, uint>& other) {}
+ };
+ template <> struct VSub4<int, uint> : binary_function<int, int, uint>
{
- Multiply(float scale_) : scale(scale_) {}
- __device__ __forceinline__ D operator ()(T a, T b) const
+ __device__ __forceinline__ uint operator ()(int a, int b) const
{
- return saturate_cast<D>(scale * a * b);
+ uint res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vsub4.u32.s32.s32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vsub.u32.s32.s32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vsub.u32.s32.s32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vsub.u32.s32.s32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vsub.u32.s32.s32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
}
- const float scale;
+
+ __device__ __forceinline__ VSub4() {}
+ __device__ __forceinline__ VSub4(const VSub4<int, uint>& other) {}
};
- template <typename T> struct Multiply<T, double> : binary_function<T, T, double>
+ template <> struct VSub4<uint, int> : binary_function<uint, uint, int>
{
- Multiply(double scale_) : scale(scale_) {}
- __device__ __forceinline__ double operator ()(T a, T b) const
+ __device__ __forceinline__ int operator ()(uint a, uint b) const
{
- return scale * a * b;
+ int res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vsub4.s32.u32.u32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vsub.s32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vsub.s32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vsub.s32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vsub.s32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
}
- const double scale;
+
+ __device__ __forceinline__ VSub4() {}
+ __device__ __forceinline__ VSub4(const VSub4<uint, int>& other) {}
};
- template <> struct Multiply<int, int> : binary_function<int, int, int>
+ template <> struct VSub4<int, int> : binary_function<int, int, int>
{
- Multiply(double scale_) : scale(scale_) {}
__device__ __forceinline__ int operator ()(int a, int b) const
{
- return saturate_cast<int>(scale * a * b);
+ int res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vsub4.s32.s32.s32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vsub.s32.s32.s32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vsub.s32.s32.s32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vsub.s32.s32.s32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vsub.s32.s32.s32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
}
- const double scale;
- };
- template <> struct TransformFunctorTraits< Multiply<ushort, ushort> > : DefaultTransformFunctorTraits< Multiply<ushort, ushort> >
- {
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
- };
- template <> struct TransformFunctorTraits< Multiply<short, short> > : DefaultTransformFunctorTraits< Multiply<short, short> >
- {
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
- };
- template <> struct TransformFunctorTraits< Multiply<int, int> > : DefaultTransformFunctorTraits< Multiply<int, int> >
- {
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
- };
- template <> struct TransformFunctorTraits< Multiply<float, float> > : DefaultTransformFunctorTraits< Multiply<float, float> >
- {
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
+ __device__ __forceinline__ VSub4() {}
+ __device__ __forceinline__ VSub4(const VSub4<int, int>& other) {}
};
- template <typename T, typename D> struct MultiplyCaller
+ ////////////////////////////////////
+
+ template <typename T, typename D> struct VSub2;
+ template <> struct VSub2<uint, uint> : binary_function<uint, uint, uint>
{
- static void call(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream)
+ __device__ __forceinline__ uint operator ()(uint a, uint b) const
{
- Multiply<T, D> op(static_cast<float>(scale));
- cv::gpu::device::transform((PtrStepSz<T>)src1, (PtrStepSz<T>)src2, (PtrStepSz<D>)dst, op, WithOutMask(), stream);
+ uint res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vsub2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vsub.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vsub.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
}
+
+ __device__ __forceinline__ VSub2() {}
+ __device__ __forceinline__ VSub2(const VSub2<uint, uint>& other) {}
};
- template <typename T> struct MultiplyCaller<T, double>
+ template <> struct VSub2<uint, int> : binary_function<uint, uint, int>
{
- static void call(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream)
+ __device__ __forceinline__ int operator ()(uint a, uint b) const
{
- cudaSafeCall( cudaSetDoubleForDevice(&scale) );
- Multiply<T, double> op(scale);
- cv::gpu::device::transform((PtrStepSz<T>)src1, (PtrStepSz<T>)src2, (PtrStepSz<double>)dst, op, WithOutMask(), stream);
+ int res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vsub2.s32.u32.u32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vsub.s32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vsub.s32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
}
+
+ __device__ __forceinline__ VSub2() {}
+ __device__ __forceinline__ VSub2(const VSub2<uint, int>& other) {}
};
- template <> struct MultiplyCaller<int, int>
+ template <> struct VSub2<int, uint> : binary_function<int, int, uint>
{
- static void call(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream)
+ __device__ __forceinline__ uint operator ()(int a, int b) const
{
- cudaSafeCall( cudaSetDoubleForDevice(&scale) );
- Multiply<int, int> op(scale);
- cv::gpu::device::transform((PtrStepSz<int>)src1, (PtrStepSz<int>)src2, (PtrStepSz<int>)dst, op, WithOutMask(), stream);
+ uint res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vsub2.u32.s32.s32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vsub.u32.s32.s32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vsub.u32.s32.s32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
}
- };
- template <typename T, typename D> void multiply_gpu(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream)
+ __device__ __forceinline__ VSub2() {}
+ __device__ __forceinline__ VSub2(const VSub2<int, uint>& other) {}
+ };
+ template <> struct VSub2<int, int> : binary_function<int, int, int>
{
- MultiplyCaller<T, D>::call(src1, src2, dst, scale, stream);
- }
-
- template void multiply_gpu<uchar, uchar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<uchar, schar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<uchar, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<uchar, short >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<uchar, int >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<uchar, float >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<uchar, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void multiply_gpu<schar, uchar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<schar, schar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<schar, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<schar, short >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<schar, int >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<schar, float >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<schar, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void multiply_gpu<ushort, uchar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<ushort, schar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<ushort, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<ushort, short >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<ushort, int >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<ushort, float >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<ushort, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void multiply_gpu<short, uchar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<short, schar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<short, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<short, short >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<short, int >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<short, float >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<short, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void multiply_gpu<int, uchar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<int, schar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<int, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<int, short >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<int, int >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<int, float >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<int, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void multiply_gpu<float, uchar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<float, schar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<float, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<float, short >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<float, int >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<float, float >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<float, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void multiply_gpu<double, uchar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<double, schar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<double, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<double, short >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<double, int >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<double, float >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<double, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- template <typename T, typename D> struct MultiplyScalar : unary_function<T, D>
- {
- MultiplyScalar(double val_, double scale_) : val(val_), scale(scale_) {}
- __device__ __forceinline__ D operator ()(T a) const
+ __device__ __forceinline__ int operator ()(int a, int b) const
{
- return saturate_cast<D>(scale * a * val);
+ int res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vsub2.s32.s32.s32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vsub.s32.s32.s32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vsub.s32.s32.s32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
}
- const double val;
- const double scale;
+
+ __device__ __forceinline__ VSub2() {}
+ __device__ __forceinline__ VSub2(const VSub2<int, int>& other) {}
};
- template <> struct TransformFunctorTraits< MultiplyScalar<ushort, ushort> > : DefaultTransformFunctorTraits< MultiplyScalar<ushort, ushort> >
+ ////////////////////////////////////
+
+ template <typename T, typename D> struct SubMat : binary_function<T, T, D>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
+ __device__ __forceinline__ D operator ()(T a, T b) const
+ {
+ return saturate_cast<D>(a - b);
+ }
+
+ __device__ __forceinline__ SubMat() {}
+ __device__ __forceinline__ SubMat(const SubMat& other) {}
};
- template <> struct TransformFunctorTraits< MultiplyScalar<short, short> > : DefaultTransformFunctorTraits< MultiplyScalar<short, short> >
+}
+
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T, typename D> struct TransformFunctorTraits< arithm::VSub4<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< MultiplyScalar<int, int> > : DefaultTransformFunctorTraits< MultiplyScalar<int, int> >
+
+ ////////////////////////////////////
+
+ template <typename T, typename D> struct TransformFunctorTraits< arithm::VSub2<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< MultiplyScalar<float, float> > : DefaultTransformFunctorTraits< MultiplyScalar<float, float> >
+
+ ////////////////////////////////////
+
+ template <typename T, typename D> struct TransformFunctorTraits< arithm::SubMat<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
};
+}}}
- template <typename T, typename D> void multiply_gpu(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream)
+namespace arithm
+{
+ template <typename T, typename D>
+ void vsub4(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
{
- cudaSafeCall( cudaSetDoubleForDevice(&val) );
- cudaSafeCall( cudaSetDoubleForDevice(&scale) );
- MultiplyScalar<T, D> op(val, scale);
- cv::gpu::device::transform((PtrStepSz<T>)src1, (PtrStepSz<D>)dst, op, WithOutMask(), stream);
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<D>) dst, VSub4<T, D>(), WithOutMask(), stream);
}
- template void multiply_gpu<uchar, uchar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<uchar, schar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<uchar, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<uchar, short >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<uchar, int >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<uchar, float >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<uchar, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void multiply_gpu<schar, uchar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<schar, schar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<schar, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<schar, short >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<schar, int >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<schar, float >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<schar, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void multiply_gpu<ushort, uchar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<ushort, schar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<ushort, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<ushort, short >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<ushort, int >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<ushort, float >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<ushort, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void multiply_gpu<short, uchar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<short, schar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<short, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<short, short >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<short, int >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<short, float >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<short, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void multiply_gpu<int, uchar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<int, schar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<int, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<int, short >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<int, int >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<int, float >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<int, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void multiply_gpu<float, uchar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<float, schar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<float, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<float, short >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<float, int >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<float, float >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<float, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void multiply_gpu<double, uchar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<double, schar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<double, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<double, short >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<double, int >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void multiply_gpu<double, float >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void multiply_gpu<double, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //////////////////////////////////////////////////////////////////////////
- // divide
-
- struct divide_8uc4_32f : binary_function<uchar4, float, uchar4>
- {
- __device__ __forceinline__ uchar4 operator ()(uchar4 a, float b) const
- {
- return b != 0 ? make_uchar4(saturate_cast<uchar>(a.x / b), saturate_cast<uchar>(a.y / b),
- saturate_cast<uchar>(a.z / b), saturate_cast<uchar>(a.w / b))
- : make_uchar4(0,0,0,0);
- }
- };
+ template void vsub4<uint, uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vsub4<uint, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vsub4<int, uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vsub4<int, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- OPENCV_GPU_TRANSFORM_FUNCTOR_TRAITS(divide_8uc4_32f)
+ template <typename T, typename D>
+ void vsub2(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
{
- enum { smart_block_dim_x = 8 };
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 8 };
- };
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<D>) dst, VSub2<T, D>(), WithOutMask(), stream);
+ }
+
+ template void vsub2<uint, uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vsub2<uint, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vsub2<int, uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vsub2<int, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- void divide_gpu(const PtrStepSz<uchar4>& src1, const PtrStepSzf& src2, const PtrStepSz<uchar4>& dst, cudaStream_t stream)
+ template <typename T, typename D>
+ void subMat(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream)
{
- cv::gpu::device::transform(static_cast< PtrStepSz<uchar4> >(src1), src2, static_cast< PtrStepSz<uchar4> >(dst), divide_8uc4_32f(), WithOutMask(), stream);
+ if (mask.data)
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<D>) dst, SubMat<T, D>(), mask, stream);
+ else
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<D>) dst, SubMat<T, D>(), WithOutMask(), stream);
}
-
- struct divide_16sc4_32f : binary_function<short4, float, short4>
+ template void subMat<uchar, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<uchar, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<uchar, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<uchar, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<uchar, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<uchar, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<uchar, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ template void subMat<schar, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<schar, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<schar, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<schar, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<schar, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<schar, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<schar, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void subMat<ushort, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subMat<ushort, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<ushort, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<ushort, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<ushort, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<ushort, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<ushort, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void subMat<short, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subMat<short, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<short, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<short, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<short, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<short, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<short, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void subMat<int, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subMat<int, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subMat<int, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subMat<int, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<int, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<int, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<int, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void subMat<float, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subMat<float, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subMat<float, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subMat<float, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subMat<float, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<float, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<float, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void subMat<double, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subMat<double, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subMat<double, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subMat<double, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subMat<double, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subMat<double, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subMat<double, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+}
+
+//////////////////////////////////////////////////////////////////////////
+// subScalar
+
+namespace arithm
+{
+ template <typename T, typename S, typename D>
+ void subScalar(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream)
{
- __device__ __forceinline__ short4 operator ()(short4 a, float b) const
+ AddScalar<T, S, D> op(-static_cast<S>(val));
+
+ if (mask.data)
+ transform((PtrStepSz<T>) src1, (PtrStepSz<D>) dst, op, mask, stream);
+ else
+ transform((PtrStepSz<T>) src1, (PtrStepSz<D>) dst, op, WithOutMask(), stream);
+ }
+
+ template void subScalar<uchar, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<uchar, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<uchar, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<uchar, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<uchar, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<uchar, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<uchar, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ template void subScalar<schar, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<schar, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<schar, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<schar, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<schar, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<schar, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<schar, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void subScalar<ushort, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subScalar<ushort, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<ushort, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<ushort, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<ushort, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<ushort, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<ushort, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void subScalar<short, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subScalar<short, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<short, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<short, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<short, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<short, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<short, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void subScalar<int, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subScalar<int, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subScalar<int, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subScalar<int, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<int, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<int, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<int, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void subScalar<float, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subScalar<float, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subScalar<float, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subScalar<float, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subScalar<float, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<float, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<float, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+
+ //template void subScalar<double, double, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subScalar<double, double, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subScalar<double, double, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subScalar<double, double, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subScalar<double, double, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ //template void subScalar<double, double, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void subScalar<double, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+}
+
+//////////////////////////////////////////////////////////////////////////
+// mulMat
+
+namespace arithm
+{
+ struct Mul_8uc4_32f : binary_function<uint, float, uint>
+ {
+ __device__ __forceinline__ uint operator ()(uint a, float b) const
{
- return b != 0 ? make_short4(saturate_cast<short>(a.x / b), saturate_cast<short>(a.y / b),
- saturate_cast<short>(a.z / b), saturate_cast<short>(a.w / b))
- : make_short4(0,0,0,0);
+ uint res = 0;
+
+ res |= (saturate_cast<uchar>((0xffu & (a )) * b) );
+ res |= (saturate_cast<uchar>((0xffu & (a >> 8)) * b) << 8);
+ res |= (saturate_cast<uchar>((0xffu & (a >> 16)) * b) << 16);
+ res |= (saturate_cast<uchar>((0xffu & (a >> 24)) * b) << 24);
+
+ return res;
}
+
+ __device__ __forceinline__ Mul_8uc4_32f() {}
+ __device__ __forceinline__ Mul_8uc4_32f(const Mul_8uc4_32f& other) {}
};
- OPENCV_GPU_TRANSFORM_FUNCTOR_TRAITS(divide_16sc4_32f)
+ struct Mul_16sc4_32f : binary_function<short4, float, short4>
{
- enum { smart_block_dim_x = 8 };
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 8 };
+ __device__ __forceinline__ short4 operator ()(short4 a, float b) const
+ {
+ return make_short4(saturate_cast<short>(a.x * b), saturate_cast<short>(a.y * b),
+ saturate_cast<short>(a.z * b), saturate_cast<short>(a.w * b));
+ }
+
+ __device__ __forceinline__ Mul_16sc4_32f() {}
+ __device__ __forceinline__ Mul_16sc4_32f(const Mul_16sc4_32f& other) {}
};
- void divide_gpu(const PtrStepSz<short4>& src1, const PtrStepSzf& src2, const PtrStepSz<short4>& dst, cudaStream_t stream)
+ template <typename T, typename D> struct Mul : binary_function<T, T, D>
{
- cv::gpu::device::transform(static_cast< PtrStepSz<short4> >(src1), src2, static_cast< PtrStepSz<short4> >(dst), divide_16sc4_32f(), WithOutMask(), stream);
- }
+ __device__ __forceinline__ D operator ()(T a, T b) const
+ {
+ return saturate_cast<D>(a * b);
+ }
- template <typename T, typename D> struct Divide : binary_function<T, T, D>
+ __device__ __forceinline__ Mul() {}
+ __device__ __forceinline__ Mul(const Mul& other) {}
+ };
+
+ template <typename T, typename S, typename D> struct MulScale : binary_function<T, T, D>
{
- Divide(double scale_) : scale(scale_) {}
+ S scale;
+
+ explicit MulScale(S scale_) : scale(scale_) {}
+
__device__ __forceinline__ D operator ()(T a, T b) const
{
- return b != 0 ? saturate_cast<D>(a * scale / b) : 0;
+ return saturate_cast<D>(scale * a * b);
}
- const double scale;
};
+}
- template <> struct TransformFunctorTraits< Divide<ushort, ushort> > : DefaultTransformFunctorTraits< Divide<ushort, ushort> >
+namespace cv { namespace gpu { namespace device
+{
+ template <> struct TransformFunctorTraits<arithm::Mul_8uc4_32f> : arithm::ArithmFuncTraits<sizeof(uint), sizeof(uint)>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< Divide<short, short> > : DefaultTransformFunctorTraits< Divide<short, short> >
+
+ template <typename T, typename D> struct TransformFunctorTraits< arithm::Mul<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< Divide<int, int> > : DefaultTransformFunctorTraits< Divide<int, int> >
+
+ template <typename T, typename S, typename D> struct TransformFunctorTraits< arithm::MulScale<T, S, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< Divide<float, float> > : DefaultTransformFunctorTraits< Divide<float, float> >
+}}}
+
+namespace arithm
+{
+ void mulMat_8uc4_32f(PtrStepSz<uint> src1, PtrStepSzf src2, PtrStepSz<uint> dst, cudaStream_t stream)
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
- };
+ transform(src1, src2, dst, Mul_8uc4_32f(), WithOutMask(), stream);
+ }
- template <typename T, typename D> void divide_gpu(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream)
+ void mulMat_16sc4_32f(PtrStepSz<short4> src1, PtrStepSzf src2, PtrStepSz<short4> dst, cudaStream_t stream)
{
- cudaSafeCall( cudaSetDoubleForDevice(&scale) );
- Divide<T, D> op(scale);
- cv::gpu::device::transform((PtrStepSz<T>)src1, (PtrStepSz<T>)src2, (PtrStepSz<D>)dst, op, WithOutMask(), stream);
+ transform(src1, src2, dst, Mul_16sc4_32f(), WithOutMask(), stream);
}
- template void divide_gpu<uchar, uchar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<uchar, schar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<uchar, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<uchar, short >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<uchar, int >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<uchar, float >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<uchar, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void divide_gpu<schar, uchar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<schar, schar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<schar, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<schar, short >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<schar, int >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<schar, float >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<schar, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void divide_gpu<ushort, uchar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<ushort, schar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<ushort, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<ushort, short >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<ushort, int >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<ushort, float >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<ushort, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void divide_gpu<short, uchar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<short, schar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<short, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<short, short >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<short, int >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<short, float >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<short, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void divide_gpu<int, uchar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<int, schar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<int, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<int, short >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<int, int >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<int, float >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<int, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void divide_gpu<float, uchar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<float, schar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<float, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<float, short >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<float, int >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<float, float >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<float, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void divide_gpu<double, uchar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<double, schar >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<double, ushort>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<double, short >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<double, int >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<double, float >(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<double, double>(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- template <typename T, typename D> struct DivideScalar : unary_function<T, D>
- {
- DivideScalar(double val_, double scale_) : val(val_), scale(scale_) {}
- __device__ __forceinline__ D operator ()(T a) const
+ template <typename T, typename S, typename D>
+ void mulMat(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream)
+ {
+ if (scale == 1)
{
- return saturate_cast<D>(scale * a / val);
+ Mul<T, D> op;
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<D>) dst, op, WithOutMask(), stream);
}
- const double val;
- const double scale;
- };
+ else
+ {
+ MulScale<T, S, D> op(static_cast<S>(scale));
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<D>) dst, op, WithOutMask(), stream);
+ }
+ }
- template <> struct TransformFunctorTraits< DivideScalar<ushort, ushort> > : DefaultTransformFunctorTraits< DivideScalar<ushort, ushort> >
- {
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
- };
- template <> struct TransformFunctorTraits< DivideScalar<short, short> > : DefaultTransformFunctorTraits< DivideScalar<short, short> >
- {
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
- };
- template <> struct TransformFunctorTraits< DivideScalar<int, int> > : DefaultTransformFunctorTraits< DivideScalar<int, int> >
- {
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
- };
- template <> struct TransformFunctorTraits< DivideScalar<float, float> > : DefaultTransformFunctorTraits< DivideScalar<float, float> >
+ template void mulMat<uchar, float, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<uchar, float, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<uchar, float, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<uchar, float, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<uchar, float, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<uchar, float, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<uchar, double, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+
+ template void mulMat<schar, float, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<schar, float, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<schar, float, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<schar, float, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<schar, float, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<schar, float, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<schar, double, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+
+ //template void mulMat<ushort, float, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void mulMat<ushort, float, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<ushort, float, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<ushort, float, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<ushort, float, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<ushort, float, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<ushort, double, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+
+ //template void mulMat<short, float, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void mulMat<short, float, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<short, float, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<short, float, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<short, float, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<short, float, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<short, double, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+
+ //template void mulMat<int, float, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void mulMat<int, float, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void mulMat<int, float, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void mulMat<int, float, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<int, float, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<int, float, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<int, double, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+
+ //template void mulMat<float, float, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void mulMat<float, float, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void mulMat<float, float, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void mulMat<float, float, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void mulMat<float, float, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<float, float, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<float, double, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+
+ //template void mulMat<double, double, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void mulMat<double, double, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void mulMat<double, double, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void mulMat<double, double, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void mulMat<double, double, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void mulMat<double, double, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void mulMat<double, double, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+}
+
+//////////////////////////////////////////////////////////////////////////
+// mulScalar
+
+namespace arithm
+{
+ template <typename T, typename S, typename D> struct MulScalar : unary_function<T, D>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
- };
+ S val;
- template <typename T, typename D> void divide_gpu(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream)
- {
- cudaSafeCall( cudaSetDoubleForDevice(&val) );
- cudaSafeCall( cudaSetDoubleForDevice(&scale) );
- DivideScalar<T, D> op(val, scale);
- cv::gpu::device::transform((PtrStepSz<T>)src1, (PtrStepSz<D>)dst, op, WithOutMask(), stream);
- }
+ explicit MulScalar(S val_) : val(val_) {}
- template void divide_gpu<uchar, uchar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<uchar, schar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<uchar, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<uchar, short >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<uchar, int >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<uchar, float >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<uchar, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void divide_gpu<schar, uchar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<schar, schar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<schar, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<schar, short >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<schar, int >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<schar, float >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<schar, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void divide_gpu<ushort, uchar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<ushort, schar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<ushort, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<ushort, short >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<ushort, int >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<ushort, float >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<ushort, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void divide_gpu<short, uchar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<short, schar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<short, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<short, short >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<short, int >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<short, float >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<short, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void divide_gpu<int, uchar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<int, schar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<int, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<int, short >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<int, int >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<int, float >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<int, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void divide_gpu<float, uchar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<float, schar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<float, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<float, short >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<float, int >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<float, float >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<float, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- //template void divide_gpu<double, uchar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<double, schar >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<double, ushort>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<double, short >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<double, int >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- //template void divide_gpu<double, float >(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
- template void divide_gpu<double, double>(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-
- template <typename T, typename D> struct Reciprocal : unary_function<T, D>
- {
- Reciprocal(double scale_) : scale(scale_) {}
__device__ __forceinline__ D operator ()(T a) const
{
- return a != 0 ? saturate_cast<D>(scale / a) : 0;
+ return saturate_cast<D>(a * val);
}
- const double scale;
};
+}
- template <> struct TransformFunctorTraits< Reciprocal<ushort, ushort> > : DefaultTransformFunctorTraits< Reciprocal<ushort, ushort> >
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T, typename S, typename D> struct TransformFunctorTraits< arithm::MulScalar<T, S, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< Reciprocal<short, short> > : DefaultTransformFunctorTraits< Reciprocal<short, short> >
+}}}
+
+namespace arithm
+{
+ template <typename T, typename S, typename D>
+ void mulScalar(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream)
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
- };
- template <> struct TransformFunctorTraits< Reciprocal<int, int> > : DefaultTransformFunctorTraits< Reciprocal<int, int> >
+ MulScalar<T, S, D> op(static_cast<S>(val));
+ transform((PtrStepSz<T>) src1, (PtrStepSz<D>) dst, op, WithOutMask(), stream);
+ }
+
+ template void mulScalar<uchar, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<uchar, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<uchar, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<uchar, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<uchar, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<uchar, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<uchar, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ template void mulScalar<schar, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<schar, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<schar, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<schar, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<schar, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<schar, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<schar, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ //template void mulScalar<ushort, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void mulScalar<ushort, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<ushort, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<ushort, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<ushort, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<ushort, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<ushort, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ //template void mulScalar<short, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void mulScalar<short, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<short, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<short, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<short, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<short, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<short, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ //template void mulScalar<int, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void mulScalar<int, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void mulScalar<int, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void mulScalar<int, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<int, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<int, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<int, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ //template void mulScalar<float, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void mulScalar<float, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void mulScalar<float, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void mulScalar<float, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void mulScalar<float, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<float, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<float, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ //template void mulScalar<double, double, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void mulScalar<double, double, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void mulScalar<double, double, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void mulScalar<double, double, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void mulScalar<double, double, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void mulScalar<double, double, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void mulScalar<double, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+}
+
+//////////////////////////////////////////////////////////////////////////
+// divMat
+
+namespace arithm
+{
+ struct Div_8uc4_32f : binary_function<uint, float, uint>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
+ __device__ __forceinline__ uint operator ()(uint a, float b) const
+ {
+ uint res = 0;
+
+ if (b != 0)
+ {
+ b = 1.0f / b;
+ res |= (saturate_cast<uchar>((0xffu & (a )) * b) );
+ res |= (saturate_cast<uchar>((0xffu & (a >> 8)) * b) << 8);
+ res |= (saturate_cast<uchar>((0xffu & (a >> 16)) * b) << 16);
+ res |= (saturate_cast<uchar>((0xffu & (a >> 24)) * b) << 24);
+ }
+
+ return res;
+ }
};
- template <> struct TransformFunctorTraits< Reciprocal<float, float> > : DefaultTransformFunctorTraits< Reciprocal<float, float> >
+
+ struct Div_16sc4_32f : binary_function<short4, float, short4>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
+ __device__ __forceinline__ short4 operator ()(short4 a, float b) const
+ {
+ return b != 0 ? make_short4(saturate_cast<short>(a.x / b), saturate_cast<short>(a.y / b),
+ saturate_cast<short>(a.z / b), saturate_cast<short>(a.w / b))
+ : make_short4(0,0,0,0);
+ }
};
- template <typename T, typename D> void divide_gpu(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream)
+ template <typename T, typename D> struct Div : binary_function<T, T, D>
{
- cudaSafeCall( cudaSetDoubleForDevice(&scalar) );
- Reciprocal<T, D> op(scalar);
- cv::gpu::device::transform((PtrStepSz<T>)src2, (PtrStepSz<D>)dst, op, WithOutMask(), stream);
- }
-
- template void divide_gpu<uchar, uchar >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<uchar, schar >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<uchar, ushort>(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<uchar, short >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<uchar, int >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<uchar, float >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<uchar, double>(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
-
- //template void divide_gpu<schar, uchar >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<schar, schar >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<schar, ushort>(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<schar, short >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<schar, int >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<schar, float >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<schar, double>(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
-
- //template void divide_gpu<ushort, uchar >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<ushort, schar >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<ushort, ushort>(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<ushort, short >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<ushort, int >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<ushort, float >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<ushort, double>(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
-
- //template void divide_gpu<short, uchar >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<short, schar >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<short, ushort>(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<short, short >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<short, int >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<short, float >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<short, double>(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
-
- //template void divide_gpu<int, uchar >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<int, schar >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<int, ushort>(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<int, short >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<int, int >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<int, float >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<int, double>(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
-
- //template void divide_gpu<float, uchar >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<float, schar >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<float, ushort>(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<float, short >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<float, int >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<float, float >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<float, double>(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
-
- //template void divide_gpu<double, uchar >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<double, schar >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<double, ushort>(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<double, short >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<double, int >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- //template void divide_gpu<double, float >(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
- template void divide_gpu<double, double>(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
-
- //////////////////////////////////////////////////////////////////////////
- // absdiff
-
- template <typename T> struct Absdiff : binary_function<T, T, T>
- {
- static __device__ __forceinline__ int abs(int a)
+ __device__ __forceinline__ D operator ()(T a, T b) const
{
- return ::abs(a);
+ return b != 0 ? saturate_cast<D>(a / b) : 0;
}
- static __device__ __forceinline__ float abs(float a)
+
+ __device__ __forceinline__ Div() {}
+ __device__ __forceinline__ Div(const Div& other) {}
+ };
+ template <typename T> struct Div<T, float> : binary_function<T, T, float>
+ {
+ __device__ __forceinline__ float operator ()(T a, T b) const
{
- return ::fabsf(a);
+ return b != 0 ? static_cast<float>(a) / b : 0;
}
- static __device__ __forceinline__ double abs(double a)
+
+ __device__ __forceinline__ Div() {}
+ __device__ __forceinline__ Div(const Div& other) {}
+ };
+ template <typename T> struct Div<T, double> : binary_function<T, T, double>
+ {
+ __device__ __forceinline__ double operator ()(T a, T b) const
{
- return ::fabs(a);
+ return b != 0 ? static_cast<double>(a) / b : 0;
}
- __device__ __forceinline__ T operator ()(T a, T b) const
+ __device__ __forceinline__ Div() {}
+ __device__ __forceinline__ Div(const Div& other) {}
+ };
+
+ template <typename T, typename S, typename D> struct DivScale : binary_function<T, T, D>
+ {
+ S scale;
+
+ explicit DivScale(S scale_) : scale(scale_) {}
+
+ __device__ __forceinline__ D operator ()(T a, T b) const
{
- return saturate_cast<T>(::abs(a - b));
+ return b != 0 ? saturate_cast<D>(scale * a / b) : 0;
}
};
+}
- template <> struct TransformFunctorTraits< Absdiff<ushort> > : DefaultTransformFunctorTraits< Absdiff<ushort> >
+namespace cv { namespace gpu { namespace device
+{
+ template <> struct TransformFunctorTraits<arithm::Div_8uc4_32f> : arithm::ArithmFuncTraits<sizeof(uint), sizeof(uint)>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< Absdiff<short> > : DefaultTransformFunctorTraits< Absdiff<short> >
+
+ template <typename T, typename D> struct TransformFunctorTraits< arithm::Div<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< Absdiff<int> > : DefaultTransformFunctorTraits< Absdiff<int> >
+
+ template <typename T, typename S, typename D> struct TransformFunctorTraits< arithm::DivScale<T, S, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< Absdiff<float> > : DefaultTransformFunctorTraits< Absdiff<float> >
+}}}
+
+namespace arithm
+{
+ void divMat_8uc4_32f(PtrStepSz<uint> src1, PtrStepSzf src2, PtrStepSz<uint> dst, cudaStream_t stream)
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
- };
+ transform(src1, src2, dst, Div_8uc4_32f(), WithOutMask(), stream);
+ }
- template <typename T> void absdiff_gpu(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
+ void divMat_16sc4_32f(PtrStepSz<short4> src1, PtrStepSzf src2, PtrStepSz<short4> dst, cudaStream_t stream)
{
- cv::gpu::device::transform((PtrStepSz<T>)src1, (PtrStepSz<T>)src2, (PtrStepSz<T>)dst, Absdiff<T>(), WithOutMask(), stream);
+ transform(src1, src2, dst, Div_16sc4_32f(), WithOutMask(), stream);
}
- //template void absdiff_gpu<uchar >(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void absdiff_gpu<schar >(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- //template void absdiff_gpu<ushort>(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void absdiff_gpu<short >(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void absdiff_gpu<int >(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- //template void absdiff_gpu<float >(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void absdiff_gpu<double>(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template <typename T, typename S, typename D>
+ void divMat(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream)
+ {
+ if (scale == 1)
+ {
+ Div<T, D> op;
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<D>) dst, op, WithOutMask(), stream);
+ }
+ else
+ {
+ DivScale<T, S, D> op(static_cast<S>(scale));
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<D>) dst, op, WithOutMask(), stream);
+ }
+ }
- template <typename T> struct AbsdiffScalar : unary_function<T, T>
+ template void divMat<uchar, float, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<uchar, float, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<uchar, float, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<uchar, float, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<uchar, float, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<uchar, float, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<uchar, double, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+
+ template void divMat<schar, float, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<schar, float, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<schar, float, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<schar, float, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<schar, float, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<schar, float, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<schar, double, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+
+ //template void divMat<ushort, float, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void divMat<ushort, float, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<ushort, float, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<ushort, float, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<ushort, float, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<ushort, float, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<ushort, double, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+
+ //template void divMat<short, float, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void divMat<short, float, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<short, float, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<short, float, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<short, float, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<short, float, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<short, double, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+
+ //template void divMat<int, float, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void divMat<int, float, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void divMat<int, float, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void divMat<int, float, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<int, float, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<int, float, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<int, double, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+
+ //template void divMat<float, float, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void divMat<float, float, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void divMat<float, float, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void divMat<float, float, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void divMat<float, float, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<float, float, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<float, double, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+
+ //template void divMat<double, double, uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void divMat<double, double, schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void divMat<double, double, ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void divMat<double, double, short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void divMat<double, double, int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ //template void divMat<double, double, float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+ template void divMat<double, double, double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+}
+
+//////////////////////////////////////////////////////////////////////////
+// divScalar
+
+namespace arithm
+{
+ template <typename T, typename S, typename D>
+ void divScalar(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream)
{
- AbsdiffScalar(double val_) : val(val_) {}
- __device__ __forceinline__ T operator ()(T a) const
+ MulScalar<T, S, D> op(static_cast<S>(1.0 / val));
+ transform((PtrStepSz<T>) src1, (PtrStepSz<D>) dst, op, WithOutMask(), stream);
+ }
+
+ template void divScalar<uchar, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<uchar, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<uchar, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<uchar, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<uchar, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<uchar, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<uchar, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ template void divScalar<schar, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<schar, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<schar, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<schar, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<schar, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<schar, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<schar, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ //template void divScalar<ushort, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divScalar<ushort, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<ushort, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<ushort, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<ushort, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<ushort, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<ushort, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ //template void divScalar<short, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divScalar<short, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<short, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<short, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<short, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<short, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<short, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ //template void divScalar<int, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divScalar<int, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divScalar<int, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divScalar<int, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<int, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<int, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<int, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ //template void divScalar<float, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divScalar<float, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divScalar<float, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divScalar<float, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divScalar<float, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<float, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<float, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ //template void divScalar<double, double, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divScalar<double, double, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divScalar<double, double, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divScalar<double, double, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divScalar<double, double, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divScalar<double, double, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divScalar<double, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+}
+
+//////////////////////////////////////////////////////////////////////////
+// divInv
+
+namespace arithm
+{
+ template <typename T, typename S, typename D> struct DivInv : unary_function<T, D>
+ {
+ S val;
+
+ explicit DivInv(S val_) : val(val_) {}
+
+ __device__ __forceinline__ D operator ()(T a) const
{
- return saturate_cast<T>(::fabs(a - val));
+ return a != 0 ? saturate_cast<D>(val / a) : 0;
}
- double val;
};
+}
- template <> struct TransformFunctorTraits< AbsdiffScalar<ushort> > : DefaultTransformFunctorTraits< AbsdiffScalar<ushort> >
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T, typename S, typename D> struct TransformFunctorTraits< arithm::DivInv<T, S, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< AbsdiffScalar<short> > : DefaultTransformFunctorTraits< AbsdiffScalar<short> >
+}}}
+
+namespace arithm
+{
+ template <typename T, typename S, typename D>
+ void divInv(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream)
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
- };
- template <> struct TransformFunctorTraits< AbsdiffScalar<int> > : DefaultTransformFunctorTraits< AbsdiffScalar<int> >
+ DivInv<T, S, D> op(static_cast<S>(val));
+ transform((PtrStepSz<T>) src1, (PtrStepSz<D>) dst, op, WithOutMask(), stream);
+ }
+
+ template void divInv<uchar, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<uchar, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<uchar, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<uchar, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<uchar, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<uchar, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<uchar, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ template void divInv<schar, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<schar, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<schar, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<schar, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<schar, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<schar, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<schar, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ //template void divInv<ushort, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divInv<ushort, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<ushort, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<ushort, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<ushort, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<ushort, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<ushort, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ //template void divInv<short, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divInv<short, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<short, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<short, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<short, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<short, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<short, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ //template void divInv<int, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divInv<int, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divInv<int, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divInv<int, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<int, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<int, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<int, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ //template void divInv<float, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divInv<float, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divInv<float, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divInv<float, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divInv<float, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<float, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<float, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+
+ //template void divInv<double, double, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divInv<double, double, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divInv<double, double, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divInv<double, double, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divInv<double, double, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ //template void divInv<double, double, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void divInv<double, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+}
+
+//////////////////////////////////////////////////////////////////////////
+// absDiffMat
+
+namespace arithm
+{
+ template <typename T, typename D> struct VAbsDiff4;
+ template <> struct VAbsDiff4<uint, uint> : binary_function<uint, uint, uint>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
+ __device__ __forceinline__ uint operator ()(uint a, uint b) const
+ {
+ uint res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vabsdiff4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vabsdiff.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vabsdiff.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vabsdiff.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vabsdiff.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
+ }
+
+ __device__ __forceinline__ VAbsDiff4() {}
+ __device__ __forceinline__ VAbsDiff4(const VAbsDiff4<uint, uint>& other) {}
};
- template <> struct TransformFunctorTraits< AbsdiffScalar<float> > : DefaultTransformFunctorTraits< AbsdiffScalar<float> >
+ template <> struct VAbsDiff4<int, int> : binary_function<int, int, int>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
+ __device__ __forceinline__ int operator ()(int a, int b) const
+ {
+ int res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vabsdiff4.s32.s32.s32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vabsdiff.s32.s32.s32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vabsdiff.s32.s32.s32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vabsdiff.s32.s32.s32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vabsdiff.s32.s32.s32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
+ }
+
+ __device__ __forceinline__ VAbsDiff4() {}
+ __device__ __forceinline__ VAbsDiff4(const VAbsDiff4<int, int>& other) {}
};
- template <typename T> void absdiff_gpu(const PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream)
+ ////////////////////////////////////
+
+ template <typename T, typename D> struct VAbsDiff2;
+ template <> struct VAbsDiff2<uint, uint> : binary_function<uint, uint, uint>
{
- cudaSafeCall( cudaSetDoubleForDevice(&val) );
- AbsdiffScalar<T> op(val);
- cv::gpu::device::transform((PtrStepSz<T>)src1, (PtrStepSz<T>)dst, op, WithOutMask(), stream);
- }
+ __device__ __forceinline__ uint operator ()(uint a, uint b) const
+ {
+ uint res = 0;
- //template void absdiff_gpu<uchar >(const PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
- template void absdiff_gpu<schar >(const PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
- //template void absdiff_gpu<ushort>(const PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
- template void absdiff_gpu<short >(const PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
- template void absdiff_gpu<int >(const PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
- //template void absdiff_gpu<float >(const PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
- template void absdiff_gpu<double>(const PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ #if __CUDA_ARCH__ >= 300
+ asm("vabsdiff2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vabsdiff.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vabsdiff.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
- //////////////////////////////////////////////////////////////////////////////////////
- // Compare
+ return res;
+ }
- template <template <typename> class Op, typename T>
- struct Compare: binary_function<T, T, uchar>
+ __device__ __forceinline__ VAbsDiff2() {}
+ __device__ __forceinline__ VAbsDiff2(const VAbsDiff2<uint, uint>& other) {}
+ };
+ template <> struct VAbsDiff2<int, int> : binary_function<int, int, int>
{
- __device__ __forceinline__ uchar operator()(T src1, T src2) const
+ __device__ __forceinline__ int operator ()(int a, int b) const
{
- Op<T> op;
- return static_cast<uchar>(static_cast<int>(op(src1, src2)) * 255);
+ int res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vabsdiff2.s32.s32.s32.sat %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vabsdiff.s32.s32.s32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vabsdiff.s32.s32.s32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
}
+
+ __device__ __forceinline__ VAbsDiff2() {}
+ __device__ __forceinline__ VAbsDiff2(const VAbsDiff2<int, int>& other) {}
};
-#define IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(op, type, block_dim_y, shift) \
- template <> struct TransformFunctorTraits< Compare<op, type> > : DefaultTransformFunctorTraits< Compare<op, type> > \
- { \
- enum { smart_block_dim_y = block_dim_y }; \
- enum { smart_shift = shift }; \
+ ////////////////////////////////////
+
+ __device__ __forceinline__ int _abs(int a)
+ {
+ return ::abs(a);
+ }
+ __device__ __forceinline__ float _abs(float a)
+ {
+ return ::fabsf(a);
+ }
+ __device__ __forceinline__ double _abs(double a)
+ {
+ return ::fabs(a);
+ }
+
+ template <typename T> struct AbsDiffMat : binary_function<T, T, T>
+ {
+ __device__ __forceinline__ T operator ()(T a, T b) const
+ {
+ return saturate_cast<T>(_abs(a - b));
+ }
+
+ __device__ __forceinline__ AbsDiffMat() {}
+ __device__ __forceinline__ AbsDiffMat(const AbsDiffMat& other) {}
};
+}
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(equal_to, int, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(equal_to, float, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(not_equal_to, int, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(not_equal_to, float, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(greater, int, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(greater, float, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(less, int, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(less, float, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(greater_equal, int, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(greater_equal, float, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(less_equal, int, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(less_equal, float, 8, 4)
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T, typename D> struct TransformFunctorTraits< arithm::VAbsDiff4<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
+ {
+ };
-#undef IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS
+ ////////////////////////////////////
- template <template <typename> class Op, typename T> void compare(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
+ template <typename T, typename D> struct TransformFunctorTraits< arithm::VAbsDiff2<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
- Compare<Op, T> op;
- cv::gpu::device::transform(static_cast< PtrStepSz<T> >(src1), static_cast< PtrStepSz<T> >(src2), dst, op, WithOutMask(), stream);
- }
+ };
+
+ ////////////////////////////////////
- template <typename T> void compare_eq(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
+ template <typename T> struct TransformFunctorTraits< arithm::AbsDiffMat<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
- compare<equal_to, T>(src1, src2, dst, stream);
- }
- template <typename T> void compare_ne(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
+ };
+}}}
+
+namespace arithm
+{
+ template <typename T>
+ void vabsDiff4(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
{
- compare<not_equal_to, T>(src1, src2, dst, stream);
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, VAbsDiff4<T, T>(), WithOutMask(), stream);
}
- template <typename T> void compare_lt(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
+
+ template void vabsDiff4<uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vabsDiff4<int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+
+ template <typename T>
+ void vabsDiff2(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
{
- compare<less, T>(src1, src2, dst, stream);
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, VAbsDiff2<T, T>(), WithOutMask(), stream);
}
- template <typename T> void compare_le(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
+
+ template void vabsDiff2<uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vabsDiff2<int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+
+ template <typename T>
+ void absDiffMat(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
{
- compare<less_equal, T>(src1, src2, dst, stream);
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, AbsDiffMat<T>(), WithOutMask(), stream);
}
- template void compare_eq<uchar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_eq<schar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_eq<ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_eq<short >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_eq<int >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_eq<float >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_eq<double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
-
- template void compare_ne<uchar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_ne<schar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_ne<ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_ne<short >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_ne<int >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_ne<float >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_ne<double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
-
- template void compare_lt<uchar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_lt<schar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_lt<ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_lt<short >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_lt<int >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_lt<float >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_lt<double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
-
- template void compare_le<uchar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_le<schar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_le<ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_le<short >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_le<int >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_le<float >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void compare_le<double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
-
-#define TYPE_VEC(type, cn) typename TypeVec<type, cn>::vec_type
-
- template <template <typename> class Op, typename T, int cn> struct CompareScalar;
- template <template <typename> class Op, typename T>
- struct CompareScalar<Op, T, 1>: unary_function<T, uchar>
+ template void absDiffMat<uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void absDiffMat<schar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void absDiffMat<ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void absDiffMat<short>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void absDiffMat<int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void absDiffMat<float>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void absDiffMat<double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+}
+
+//////////////////////////////////////////////////////////////////////////
+// absDiffScalar
+
+namespace arithm
+{
+ template <typename T, typename S> struct AbsDiffScalar : unary_function<T, T>
{
- const T val;
+ S val;
- __host__ explicit CompareScalar(T val_) : val(val_) {}
+ explicit AbsDiffScalar(S val_) : val(val_) {}
- __device__ __forceinline__ uchar operator()(T src) const
+ __device__ __forceinline__ T operator ()(T a) const
{
- Op<T> op;
- return static_cast<uchar>(static_cast<int>(op(src, val)) * 255);
+ abs_func<S> f;
+ return saturate_cast<T>(f(a - val));
}
};
- template <template <typename> class Op, typename T>
- struct CompareScalar<Op, T, 2>: unary_function<TYPE_VEC(T, 2), TYPE_VEC(uchar, 2)>
+}
+
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T, typename S> struct TransformFunctorTraits< arithm::AbsDiffScalar<T, S> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
+ {
+ };
+}}}
+
+namespace arithm
+{
+ template <typename T, typename S>
+ void absDiffScalar(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream)
{
- const TYPE_VEC(T, 2) val;
+ AbsDiffScalar<T, S> op(static_cast<S>(val));
- __host__ explicit CompareScalar(TYPE_VEC(T, 2) val_) : val(val_) {}
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) dst, op, WithOutMask(), stream);
+ }
- __device__ __forceinline__ TYPE_VEC(uchar, 2) operator()(const TYPE_VEC(T, 2) & src) const
- {
- Op<T> op;
- return VecTraits<TYPE_VEC(uchar, 2)>::make(
- static_cast<uchar>(static_cast<int>(op(src.x, val.x)) * 255),
- static_cast<uchar>(static_cast<int>(op(src.y, val.y)) * 255));
- }
+ template void absDiffScalar<uchar, float>(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void absDiffScalar<schar, float>(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void absDiffScalar<ushort, float>(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void absDiffScalar<short, float>(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void absDiffScalar<int, float>(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void absDiffScalar<float, float>(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void absDiffScalar<double, double>(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+}
+
+//////////////////////////////////////////////////////////////////////////
+// absMat
+
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T> struct TransformFunctorTraits< abs_func<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
+ {
};
- template <template <typename> class Op, typename T>
- struct CompareScalar<Op, T, 3>: unary_function<TYPE_VEC(T, 3), TYPE_VEC(uchar, 3)>
+}}}
+
+namespace arithm
+{
+ template <typename T>
+ void absMat(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream)
{
- const TYPE_VEC(T, 3) val;
+ transform((PtrStepSz<T>) src, (PtrStepSz<T>) dst, abs_func<T>(), WithOutMask(), stream);
+ }
- __host__ explicit CompareScalar(TYPE_VEC(T, 3) val_) : val(val_) {}
+ template void absMat<uchar>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void absMat<schar>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void absMat<ushort>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void absMat<short>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void absMat<int>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void absMat<float>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void absMat<double>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+}
- __device__ __forceinline__ TYPE_VEC(uchar, 3) operator()(const TYPE_VEC(T, 3) & src) const
+//////////////////////////////////////////////////////////////////////////
+// sqrMat
+
+namespace arithm
+{
+ template <typename T> struct Sqr : unary_function<T, T>
+ {
+ __device__ __forceinline__ T operator ()(T x) const
{
- Op<T> op;
- return VecTraits<TYPE_VEC(uchar, 3)>::make(
- static_cast<uchar>(static_cast<int>(op(src.x, val.x)) * 255),
- static_cast<uchar>(static_cast<int>(op(src.y, val.y)) * 255),
- static_cast<uchar>(static_cast<int>(op(src.z, val.z)) * 255));
+ return saturate_cast<T>(x * x);
}
+
+ __device__ __forceinline__ Sqr() {}
+ __device__ __forceinline__ Sqr(const Sqr& other) {}
};
- template <template <typename> class Op, typename T>
- struct CompareScalar<Op, T, 4>: unary_function<TYPE_VEC(T, 4), TYPE_VEC(uchar, 4)>
+}
+
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T> struct TransformFunctorTraits< arithm::Sqr<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
- const TYPE_VEC(T, 4) val;
+ };
+}}}
- __host__ explicit CompareScalar(TYPE_VEC(T, 4) val_) : val(val_) {}
+namespace arithm
+{
+ template <typename T>
+ void sqrMat(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream)
+ {
+ transform((PtrStepSz<T>) src, (PtrStepSz<T>) dst, Sqr<T>(), WithOutMask(), stream);
+ }
- __device__ __forceinline__ TYPE_VEC(uchar, 4) operator()(const TYPE_VEC(T, 4) & src) const
- {
- Op<T> op;
- return VecTraits<TYPE_VEC(uchar, 4)>::make(
- static_cast<uchar>(static_cast<int>(op(src.x, val.x)) * 255),
- static_cast<uchar>(static_cast<int>(op(src.y, val.y)) * 255),
- static_cast<uchar>(static_cast<int>(op(src.z, val.z)) * 255),
- static_cast<uchar>(static_cast<int>(op(src.w, val.w)) * 255));
- }
- };
+ template void sqrMat<uchar>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void sqrMat<schar>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void sqrMat<ushort>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void sqrMat<short>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void sqrMat<int>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void sqrMat<float>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void sqrMat<double>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+}
-#undef TYPE_VEC
+//////////////////////////////////////////////////////////////////////////
+// sqrtMat
-#define IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(op, type, block_dim_y, shift) \
- template <> struct TransformFunctorTraits< CompareScalar<op, type, 1> > : DefaultTransformFunctorTraits< CompareScalar<op, type, 1> > \
- { \
- enum { smart_block_dim_y = block_dim_y }; \
- enum { smart_shift = shift }; \
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T> struct TransformFunctorTraits< sqrt_func<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
+ {
};
+}}}
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(equal_to, int, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(equal_to, float, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(not_equal_to, int, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(not_equal_to, float, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(greater, int, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(greater, float, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(less, int, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(less, float, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(greater_equal, int, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(greater_equal, float, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(less_equal, int, 8, 4)
- IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS(less_equal, float, 8, 4)
+namespace arithm
+{
+ template <typename T>
+ void sqrtMat(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream)
+ {
+ transform((PtrStepSz<T>) src, (PtrStepSz<T>) dst, sqrt_func<T>(), WithOutMask(), stream);
+ }
-#undef IMPLEMENT_COMPARE_TRANSFORM_FUNCTOR_TRAITS
+ template void sqrtMat<uchar>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void sqrtMat<schar>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void sqrtMat<ushort>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void sqrtMat<short>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void sqrtMat<int>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void sqrtMat<float>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void sqrtMat<double>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+}
- template <template <typename> class Op, typename T, int cn> void compare(PtrStepSzb src, double val[4], PtrStepSzb dst, cudaStream_t stream)
+//////////////////////////////////////////////////////////////////////////
+// logMat
+
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T> struct TransformFunctorTraits< log_func<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
- typedef typename TypeVec<T, cn>::vec_type src_t;
- typedef typename TypeVec<uchar, cn>::vec_type dst_t;
+ };
+}}}
- T sval[] = {static_cast<T>(val[0]), static_cast<T>(val[1]), static_cast<T>(val[2]), static_cast<T>(val[3])};
- src_t val1 = VecTraits<src_t>::make(sval);
+namespace arithm
+{
+ template <typename T>
+ void logMat(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream)
+ {
+ transform((PtrStepSz<T>) src, (PtrStepSz<T>) dst, log_func<T>(), WithOutMask(), stream);
+ }
- CompareScalar<Op, T, cn> op(val1);
+ template void logMat<uchar>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void logMat<schar>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void logMat<ushort>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void logMat<short>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void logMat<int>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void logMat<float>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void logMat<double>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+}
- cv::gpu::device::transform(static_cast< PtrStepSz<src_t> >(src), static_cast< PtrStepSz<dst_t> >(dst), op, WithOutMask(), stream);
- }
+//////////////////////////////////////////////////////////////////////////
+// expMat
- template <typename T> void compare_eq(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream)
+namespace arithm
+{
+ template <typename T> struct Exp : unary_function<T, T>
{
- typedef void (*func_t)(PtrStepSzb src, double val[4], PtrStepSzb dst, cudaStream_t stream);
- static const func_t funcs[] =
+ __device__ __forceinline__ T operator ()(T x) const
{
- 0,
- compare<equal_to, T, 1>,
- compare<equal_to, T, 2>,
- compare<equal_to, T, 3>,
- compare<equal_to, T, 4>
- };
+ exp_func<T> f;
+ return saturate_cast<T>(f(x));
+ }
- funcs[cn](src, val, dst, stream);
- }
- template <typename T> void compare_ne(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream)
+ __device__ __forceinline__ Exp() {}
+ __device__ __forceinline__ Exp(const Exp& other) {}
+ };
+}
+
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T> struct TransformFunctorTraits< arithm::Exp<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
- typedef void (*func_t)(PtrStepSzb src, double val[4], PtrStepSzb dst, cudaStream_t stream);
- static const func_t funcs[] =
- {
- 0,
- compare<not_equal_to, T, 1>,
- compare<not_equal_to, T, 2>,
- compare<not_equal_to, T, 3>,
- compare<not_equal_to, T, 4>
- };
+ };
+}}}
- funcs[cn](src, val, dst, stream);
+namespace arithm
+{
+ template <typename T>
+ void expMat(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream)
+ {
+ transform((PtrStepSz<T>) src, (PtrStepSz<T>) dst, Exp<T>(), WithOutMask(), stream);
}
- template <typename T> void compare_lt(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream)
+
+ template void expMat<uchar>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void expMat<schar>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void expMat<ushort>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void expMat<short>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void expMat<int>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void expMat<float>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ template void expMat<double>(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+}
+
+//////////////////////////////////////////////////////////////////////////////////////
+// cmpMat
+
+namespace arithm
+{
+ template <class Op, typename T>
+ struct Cmp : binary_function<T, T, uchar>
{
- typedef void (*func_t)(PtrStepSzb src, double val[4], PtrStepSzb dst, cudaStream_t stream);
- static const func_t funcs[] =
+ __device__ __forceinline__ uchar operator()(T a, T b) const
{
- 0,
- compare<less, T, 1>,
- compare<less, T, 2>,
- compare<less, T, 3>,
- compare<less, T, 4>
- };
+ Op op;
+ return -op(a, b);
+ }
+ };
+}
- funcs[cn](src, val, dst, stream);
- }
- template <typename T> void compare_le(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream)
+namespace cv { namespace gpu { namespace device
+{
+ template <class Op, typename T> struct TransformFunctorTraits< arithm::Cmp<Op, T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(uchar)>
{
- typedef void (*func_t)(PtrStepSzb src, double val[4], PtrStepSzb dst, cudaStream_t stream);
- static const func_t funcs[] =
- {
- 0,
- compare<less_equal, T, 1>,
- compare<less_equal, T, 2>,
- compare<less_equal, T, 3>,
- compare<less_equal, T, 4>
- };
+ };
+}}}
- funcs[cn](src, val, dst, stream);
- }
- template <typename T> void compare_gt(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream)
+namespace arithm
+{
+ template <template <typename> class Op, typename T>
+ void cmpMat(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
{
- typedef void (*func_t)(PtrStepSzb src, double val[4], PtrStepSzb dst, cudaStream_t stream);
- static const func_t funcs[] =
- {
- 0,
- compare<greater, T, 1>,
- compare<greater, T, 2>,
- compare<greater, T, 3>,
- compare<greater, T, 4>
- };
+ Cmp<Op<T>, T> op;
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, dst, op, WithOutMask(), stream);
+ }
- funcs[cn](src, val, dst, stream);
+ template <typename T> void cmpMatEq(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
+ {
+ cmpMat<equal_to, T>(src1, src2, dst, stream);
}
- template <typename T> void compare_ge(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream)
+ template <typename T> void cmpMatNe(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
{
- typedef void (*func_t)(PtrStepSzb src, double val[4], PtrStepSzb dst, cudaStream_t stream);
- static const func_t funcs[] =
- {
- 0,
- compare<greater_equal, T, 1>,
- compare<greater_equal, T, 2>,
- compare<greater_equal, T, 3>,
- compare<greater_equal, T, 4>
- };
-
- funcs[cn](src, val, dst, stream);
+ cmpMat<not_equal_to, T>(src1, src2, dst, stream);
}
+ template <typename T> void cmpMatLt(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
+ {
+ cmpMat<less, T>(src1, src2, dst, stream);
+ }
+ template <typename T> void cmpMatLe(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
+ {
+ cmpMat<less_equal, T>(src1, src2, dst, stream);
+ }
+
+ template void cmpMatEq<uchar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatEq<schar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatEq<ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatEq<short >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatEq<int >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatEq<float >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatEq<double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+
+ template void cmpMatNe<uchar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatNe<schar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatNe<ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatNe<short >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatNe<int >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatNe<float >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatNe<double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+
+ template void cmpMatLt<uchar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatLt<schar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatLt<ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatLt<short >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatLt<int >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatLt<float >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatLt<double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+
+ template void cmpMatLe<uchar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatLe<schar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatLe<ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatLe<short >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatLe<int >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatLe<float >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void cmpMatLe<double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+}
+
+//////////////////////////////////////////////////////////////////////////////////////
+// bitMat
- template void compare_eq<uchar >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_eq<schar >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_eq<ushort>(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_eq<short >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_eq<int >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_eq<float >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_eq<double>(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
-
- template void compare_ne<uchar >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_ne<schar >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_ne<ushort>(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_ne<short >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_ne<int >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_ne<float >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_ne<double>(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
-
- template void compare_lt<uchar >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_lt<schar >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_lt<ushort>(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_lt<short >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_lt<int >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_lt<float >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_lt<double>(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
-
- template void compare_le<uchar >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_le<schar >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_le<ushort>(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_le<short >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_le<int >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_le<float >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_le<double>(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
-
- template void compare_gt<uchar >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_gt<schar >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_gt<ushort>(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_gt<short >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_gt<int >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_gt<float >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_gt<double>(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
-
- template void compare_ge<uchar >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_ge<schar >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_ge<ushort>(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_ge<short >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_ge<int >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_ge<float >(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template void compare_ge<double>(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
-
- //////////////////////////////////////////////////////////////////////////
- // Unary bitwise logical matrix operations
-
- enum { UN_OP_NOT };
-
- template <typename T, int opid>
- struct UnOp;
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T> struct TransformFunctorTraits< bit_not<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
+ {
+ };
- template <typename T>
- struct UnOp<T, UN_OP_NOT>
+ template <typename T> struct TransformFunctorTraits< bit_and<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
- static __device__ __forceinline__ T call(T v) { return ~v; }
};
+ template <typename T> struct TransformFunctorTraits< bit_or<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
+ {
+ };
- template <int opid>
- __global__ void bitwiseUnOpKernel(int rows, int width, const PtrStepb src, PtrStepb dst)
+ template <typename T> struct TransformFunctorTraits< bit_xor<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
- 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++);
- }
- }
- }
+namespace arithm
+{
+ template <typename T> void bitMatNot(PtrStepSzb src, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream)
+ {
+ if (mask.data)
+ transform((PtrStepSz<T>) src, (PtrStepSz<T>) dst, bit_not<T>(), mask, stream);
+ else
+ transform((PtrStepSz<T>) src, (PtrStepSz<T>) dst, bit_not<T>(), WithOutMask(), stream);
}
-
- template <int opid>
- void bitwiseUnOp(int rows, int width, const PtrStepb src, PtrStepb dst,
- cudaStream_t stream)
+ template <typename T> void bitMatAnd(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream)
{
- dim3 threads(16, 16);
- dim3 grid(divUp(width, threads.x * sizeof(uint)),
- divUp(rows, threads.y));
+ if (mask.data)
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, bit_and<T>(), mask, stream);
+ else
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, bit_and<T>(), WithOutMask(), stream);
+ }
- bitwiseUnOpKernel<opid><<<grid, threads>>>(rows, width, src, dst);
- cudaSafeCall( cudaGetLastError() );
+ template <typename T> void bitMatOr(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream)
+ {
+ if (mask.data)
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, bit_or<T>(), mask, stream);
+ else
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, bit_or<T>(), WithOutMask(), stream);
+ }
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
+ template <typename T> void bitMatXor(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream)
+ {
+ if (mask.data)
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, bit_xor<T>(), mask, stream);
+ else
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, bit_xor<T>(), WithOutMask(), stream);
}
+ template void bitMatNot<uchar>(PtrStepSzb src, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void bitMatNot<ushort>(PtrStepSzb src, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void bitMatNot<uint>(PtrStepSzb src, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
- template <typename T, int opid>
- __global__ void bitwiseUnOpKernel(int rows, int cols, int cn, const PtrStepb src,
- const PtrStepb mask, PtrStepb dst)
- {
- const int x = blockDim.x * blockIdx.x + threadIdx.x;
- const int y = blockDim.y * blockIdx.y + threadIdx.y;
+ template void bitMatAnd<uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void bitMatAnd<ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void bitMatAnd<uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
- 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);
+ template void bitMatOr<uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void bitMatOr<ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void bitMatOr<uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
- dst_row[x] = UnOp<T, opid>::call(src_row[x]);
- }
- }
+ template void bitMatXor<uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void bitMatXor<ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template void bitMatXor<uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+}
+//////////////////////////////////////////////////////////////////////////////////////
+// bitScalar
- template <typename T, int opid>
- void bitwiseUnOp(int rows, int cols, int cn, const PtrStepb src,
- const PtrStepb mask, PtrStepb dst, cudaStream_t stream)
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T> struct TransformFunctorTraits< binder2nd< bit_and<T> > > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
- 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);
- cudaSafeCall( cudaGetLastError() );
+ };
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
- }
+ template <typename T> struct TransformFunctorTraits< binder2nd< bit_or<T> > > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
+ {
+ };
+ template <typename T> struct TransformFunctorTraits< binder2nd< bit_xor<T> > > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
+ {
+ };
+}}}
- void bitwiseNotCaller(int rows, int cols, size_t elem_size1, int cn,
- const PtrStepb src, PtrStepb dst, cudaStream_t stream)
+namespace arithm
+{
+ template <typename T> void bitScalarAnd(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream)
{
- bitwiseUnOp<UN_OP_NOT>(rows, static_cast<int>(cols * elem_size1 * cn), src, dst, stream);
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) dst, cv::gpu::device::bind2nd(bit_and<T>(), src2), WithOutMask(), stream);
}
-
- template <typename T>
- void bitwiseMaskNotCaller(int rows, int cols, int cn, const PtrStepb src,
- const PtrStepb mask, PtrStepb dst, cudaStream_t stream)
+ template <typename T> void bitScalarOr(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream)
{
- bitwiseUnOp<T, UN_OP_NOT>(rows, cols * cn, cn, src, mask, dst, stream);
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) dst, cv::gpu::device::bind2nd(bit_or<T>(), src2), WithOutMask(), stream);
}
- template void bitwiseMaskNotCaller<uchar>(int, int, int, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
- template void bitwiseMaskNotCaller<ushort>(int, int, int, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
- template void bitwiseMaskNotCaller<uint>(int, int, int, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
+ template <typename T> void bitScalarXor(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream)
+ {
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) dst, cv::gpu::device::bind2nd(bit_xor<T>(), src2), WithOutMask(), stream);
+ }
+ template void bitScalarAnd<uchar>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
+ template void bitScalarAnd<ushort>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
+ template void bitScalarAnd<uint>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
- //////////////////////////////////////////////////////////////////////////
- // Binary bitwise logical matrix operations
+ template void bitScalarOr<uchar>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
+ template void bitScalarOr<ushort>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
+ template void bitScalarOr<uint>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
- enum { BIN_OP_OR, BIN_OP_AND, BIN_OP_XOR };
+ template void bitScalarXor<uchar>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
+ template void bitScalarXor<ushort>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
+ template void bitScalarXor<uint>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
+}
- template <typename T, int opid>
- struct BinOp;
+//////////////////////////////////////////////////////////////////////////
+// min
- template <typename T>
- struct BinOp<T, BIN_OP_OR>
+namespace arithm
+{
+ template <typename T> struct VMin4;
+ template <> struct VMin4<uint> : binary_function<uint, uint, uint>
{
- static __device__ __forceinline__ T call(T a, T b) { return a | b; }
- };
+ __device__ __forceinline__ uint operator ()(uint a, uint b) const
+ {
+ uint res = 0;
+ #if __CUDA_ARCH__ >= 300
+ asm("vmin4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vmin.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vmin.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vmin.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vmin.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
- template <typename T>
- struct BinOp<T, BIN_OP_AND>
- {
- static __device__ __forceinline__ T call(T a, T b) { return a & b; }
- };
+ return res;
+ }
- template <typename T>
- struct BinOp<T, BIN_OP_XOR>
+ __device__ __forceinline__ VMin4() {}
+ __device__ __forceinline__ VMin4(const VMin4& other) {}
+ };
+ template <> struct VMin4<int> : binary_function<int, int, int>
{
- static __device__ __forceinline__ T call(T a, T b) { return a ^ b; }
+ __device__ __forceinline__ int operator ()(int a, int b) const
+ {
+ int res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vmin4.s32.s32.s32 %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vmin.s32.s32.s32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vmin.s32.s32.s32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vmin.s32.s32.s32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vmin.s32.s32.s32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
+ }
+
+ __device__ __forceinline__ VMin4() {}
+ __device__ __forceinline__ VMin4(const VMin4& other) {}
};
+ ////////////////////////////////////
- template <int opid>
- __global__ void bitwiseBinOpKernel(int rows, int width, const PtrStepb src1,
- const PtrStepb src2, PtrStepb dst)
+ template <typename T> struct VMin2;
+ template <> struct VMin2<uint> : binary_function<uint, uint, uint>
{
- const int x = (blockDim.x * blockIdx.x + threadIdx.x) * 4;
- const int y = blockDim.y * blockIdx.y + threadIdx.y;
-
- if (y < rows)
+ __device__ __forceinline__ uint operator ()(uint a, uint b) const
{
- uchar* dst_ptr = dst.ptr(y) + x;
- const uchar* src1_ptr = src1.ptr(y) + x;
- const uchar* src2_ptr = src2.ptr(y) + x;
+ uint res = 0;
- 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++);
- }
- }
- }
- }
+ #if __CUDA_ARCH__ >= 300
+ asm("vmin2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vmin.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vmin.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+ return res;
+ }
- template <int opid>
- void bitwiseBinOp(int rows, int width, const PtrStepb src1, const PtrStepb src2,
- PtrStepb dst, cudaStream_t stream)
+ __device__ __forceinline__ VMin2() {}
+ __device__ __forceinline__ VMin2(const VMin2& other) {}
+ };
+ template <> struct VMin2<int> : binary_function<int, int, int>
{
- dim3 threads(16, 16);
- dim3 grid(divUp(width, threads.x * sizeof(uint)), divUp(rows, threads.y));
+ __device__ __forceinline__ int operator ()(int a, int b) const
+ {
+ int res = 0;
- bitwiseBinOpKernel<opid><<<grid, threads>>>(rows, width, src1, src2, dst);
- cudaSafeCall( cudaGetLastError() );
+ #if __CUDA_ARCH__ >= 300
+ asm("vmin2.s32.s32.s32 %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vmin.s32.s32.s32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vmin.s32.s32.s32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
- }
+ return res;
+ }
+ __device__ __forceinline__ VMin2() {}
+ __device__ __forceinline__ VMin2(const VMin2& other) {}
+ };
+}
- template <typename T, int opid>
- __global__ void bitwiseBinOpKernel(
- int rows, int cols, int cn, const PtrStepb src1, const PtrStepb src2,
- const PtrStepb mask, PtrStepb dst)
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T> struct TransformFunctorTraits< arithm::VMin4<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
- 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> struct TransformFunctorTraits< arithm::VMin2<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
+ {
+ };
+ ////////////////////////////////////
- template <typename T, int opid>
- void bitwiseBinOp(int rows, int cols, int cn, const PtrStepb src1, const PtrStepb src2,
- const PtrStepb mask, PtrStepb dst, cudaStream_t stream)
+ template <typename T> struct TransformFunctorTraits< minimum<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
- 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);
- cudaSafeCall( cudaGetLastError() );
+ template <typename T> struct TransformFunctorTraits< binder2nd< minimum<T> > > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
+ {
+ };
+}}}
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
+namespace arithm
+{
+ template <typename T> void vmin4(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
+ {
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, VMin4<T>(), WithOutMask(), stream);
}
-
- void bitwiseOrCaller(int rows, int cols, size_t elem_size1, int cn, const PtrStepb src1,
- const PtrStepb src2, PtrStepb dst, cudaStream_t stream)
+ template <typename T> void vmin2(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
{
- bitwiseBinOp<BIN_OP_OR>(rows, static_cast<int>(cols * elem_size1 * cn), src1, src2, dst, stream);
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, VMin2<T>(), WithOutMask(), stream);
}
-
- template <typename T>
- void bitwiseMaskOrCaller(int rows, int cols, int cn, const PtrStepb src1, const PtrStepb src2,
- const PtrStepb mask, PtrStepb dst, cudaStream_t stream)
+ template <typename T> void minMat(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
{
- bitwiseBinOp<T, BIN_OP_OR>(rows, cols * cn, cn, src1, src2, mask, dst, stream);
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, minimum<T>(), WithOutMask(), stream);
}
- template void bitwiseMaskOrCaller<uchar>(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
- template void bitwiseMaskOrCaller<ushort>(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
- template void bitwiseMaskOrCaller<uint>(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
+ template void vmin4<uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vmin4<int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+
+ template void vmin2<uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vmin2<int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void minMat<uchar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void minMat<schar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void minMat<ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void minMat<short >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void minMat<int >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void minMat<float >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void minMat<double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- void bitwiseAndCaller(int rows, int cols, size_t elem_size1, int cn, const PtrStepb src1,
- const PtrStepb src2, PtrStepb dst, cudaStream_t stream)
+ template <typename T> void minScalar(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream)
{
- bitwiseBinOp<BIN_OP_AND>(rows, static_cast<int>(cols * elem_size1 * cn), src1, src2, dst, stream);
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) dst, cv::gpu::device::bind2nd(minimum<T>(), src2), WithOutMask(), stream);
}
+ template void minScalar<uchar >(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void minScalar<schar >(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void minScalar<ushort>(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void minScalar<short >(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void minScalar<int >(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void minScalar<float >(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void minScalar<double>(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+}
- template <typename T>
- void bitwiseMaskAndCaller(int rows, int cols, int cn, const PtrStepb src1, const PtrStepb src2,
- const PtrStepb mask, PtrStepb dst, cudaStream_t stream)
+//////////////////////////////////////////////////////////////////////////
+// max
+
+namespace arithm
+{
+ template <typename T> struct VMax4;
+ template <> struct VMax4<uint> : binary_function<uint, uint, uint>
{
- bitwiseBinOp<T, BIN_OP_AND>(rows, cols * cn, cn, src1, src2, mask, dst, stream);
- }
+ __device__ __forceinline__ uint operator ()(uint a, uint b) const
+ {
+ uint res = 0;
- template void bitwiseMaskAndCaller<uchar>(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
- template void bitwiseMaskAndCaller<ushort>(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
- template void bitwiseMaskAndCaller<uint>(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
+ #if __CUDA_ARCH__ >= 300
+ asm("vmax4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vmax.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vmax.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vmax.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vmax.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+ return res;
+ }
- void bitwiseXorCaller(int rows, int cols, size_t elem_size1, int cn, const PtrStepb src1,
- const PtrStepb src2, PtrStepb dst, cudaStream_t stream)
+ __device__ __forceinline__ VMax4() {}
+ __device__ __forceinline__ VMax4(const VMax4& other) {}
+ };
+ template <> struct VMax4<int> : binary_function<int, int, int>
{
- bitwiseBinOp<BIN_OP_XOR>(rows, static_cast<int>(cols * elem_size1 * cn), src1, src2, dst, stream);
- }
+ __device__ __forceinline__ int operator ()(int a, int b) const
+ {
+ int res = 0;
+ #if __CUDA_ARCH__ >= 300
+ asm("vmax4.s32.s32.s32 %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vmax.s32.s32.s32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vmax.s32.s32.s32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vmax.s32.s32.s32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vmax.s32.s32.s32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
- template <typename T>
- void bitwiseMaskXorCaller(int rows, int cols, int cn, const PtrStepb src1, const PtrStepb src2,
- const PtrStepb mask, PtrStepb dst, cudaStream_t stream)
- {
- bitwiseBinOp<T, BIN_OP_XOR>(rows, cols * cn, cn, src1, src2, mask, dst, stream);
- }
+ return res;
+ }
- template void bitwiseMaskXorCaller<uchar>(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
- template void bitwiseMaskXorCaller<ushort>(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
- template void bitwiseMaskXorCaller<uint>(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
+ __device__ __forceinline__ VMax4() {}
+ __device__ __forceinline__ VMax4(const VMax4& other) {}
+ };
- //////////////////////////////////////////////////////////////////////////
- // min/max
+ ////////////////////////////////////
- namespace detail
+ template <typename T> struct VMax2;
+ template <> struct VMax2<uint> : binary_function<uint, uint, uint>
{
- template <size_t size, typename F> struct MinMaxTraits : DefaultTransformFunctorTraits<F>
- {
- };
- template <typename F> struct MinMaxTraits<2, F> : DefaultTransformFunctorTraits<F>
+ __device__ __forceinline__ uint operator ()(uint a, uint b) const
{
- enum { smart_shift = 4 };
- };
- template <typename F> struct MinMaxTraits<4, F> : DefaultTransformFunctorTraits<F>
- {
- enum { smart_block_dim_y = 4 };
- enum { smart_shift = 4 };
- };
- }
+ uint res = 0;
- template <typename T> struct TransformFunctorTraits< minimum<T> > : detail::MinMaxTraits< sizeof(T), minimum<T> >
- {
+ #if __CUDA_ARCH__ >= 300
+ asm("vmax2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vmax.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vmax.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
+ }
+
+ __device__ __forceinline__ VMax2() {}
+ __device__ __forceinline__ VMax2(const VMax2& other) {}
};
- template <typename T> struct TransformFunctorTraits< maximum<T> > : detail::MinMaxTraits< sizeof(T), maximum<T> >
+ template <> struct VMax2<int> : binary_function<int, int, int>
{
+ __device__ __forceinline__ int operator ()(int a, int b) const
+ {
+ int res = 0;
+
+ #if __CUDA_ARCH__ >= 300
+ asm("vmax2.s32.s32.s32 %0, %1, %2, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #elif __CUDA_ARCH__ >= 200
+ asm("vmax.s32.s32.s32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ asm("vmax.s32.s32.s32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(res) : "r"(a), "r"(b), "r"(res));
+ #endif
+
+ return res;
+ }
+
+ __device__ __forceinline__ VMax2() {}
+ __device__ __forceinline__ VMax2(const VMax2& other) {}
};
- template <typename T> struct TransformFunctorTraits< binder2nd< minimum<T> > > : detail::MinMaxTraits< sizeof(T), binder2nd< minimum<T> > >
+}
+
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T> struct TransformFunctorTraits< arithm::VMax4<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
- template <typename T> struct TransformFunctorTraits< binder2nd< maximum<T> > > : detail::MinMaxTraits< sizeof(T), binder2nd< maximum<T> > >
+
+ ////////////////////////////////////
+
+ template <typename T> struct TransformFunctorTraits< arithm::VMax2<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
- template <typename T>
- void min_gpu(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
+ ////////////////////////////////////
+
+ template <typename T> struct TransformFunctorTraits< maximum<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
- cv::gpu::device::transform((PtrStepSz<T>)src1, (PtrStepSz<T>)src2, (PtrStepSz<T>)dst, minimum<T>(), WithOutMask(), stream);
- }
+ };
- template void min_gpu<uchar >(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void min_gpu<schar >(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void min_gpu<ushort>(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void min_gpu<short >(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void min_gpu<int >(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void min_gpu<float >(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void min_gpu<double>(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template <typename T> struct TransformFunctorTraits< binder2nd< maximum<T> > > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
+ {
+ };
+}}}
- template <typename T>
- void max_gpu(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
+namespace arithm
+{
+ template <typename T> void vmax4(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
{
- cv::gpu::device::transform((PtrStepSz<T>)src1, (PtrStepSz<T>)src2, (PtrStepSz<T>)dst, maximum<T>(), WithOutMask(), stream);
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, VMax4<T>(), WithOutMask(), stream);
}
- template void max_gpu<uchar >(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void max_gpu<schar >(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void max_gpu<ushort>(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void max_gpu<short >(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void max_gpu<int >(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void max_gpu<float >(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template void max_gpu<double>(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
-
- template <typename T>
- void min_gpu(const PtrStepSzb src, T val, PtrStepSzb dst, cudaStream_t stream)
+ template <typename T> void vmax2(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
{
- cv::gpu::device::transform((PtrStepSz<T>)src, (PtrStepSz<T>)dst, device::bind2nd(minimum<T>(), val), WithOutMask(), stream);
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, VMax2<T>(), WithOutMask(), stream);
}
- template void min_gpu<uchar >(const PtrStepSzb src, uchar val, PtrStepSzb dst, cudaStream_t stream);
- template void min_gpu<schar >(const PtrStepSzb src, schar val, PtrStepSzb dst, cudaStream_t stream);
- template void min_gpu<ushort>(const PtrStepSzb src, ushort val, PtrStepSzb dst, cudaStream_t stream);
- template void min_gpu<short >(const PtrStepSzb src, short val, PtrStepSzb dst, cudaStream_t stream);
- template void min_gpu<int >(const PtrStepSzb src, int val, PtrStepSzb dst, cudaStream_t stream);
- template void min_gpu<float >(const PtrStepSzb src, float val, PtrStepSzb dst, cudaStream_t stream);
- template void min_gpu<double>(const PtrStepSzb src, double val, PtrStepSzb dst, cudaStream_t stream);
-
- template <typename T>
- void max_gpu(const PtrStepSzb src, T val, PtrStepSzb dst, cudaStream_t stream)
+ template <typename T> void maxMat(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
{
- cv::gpu::device::transform((PtrStepSz<T>)src, (PtrStepSz<T>)dst, device::bind2nd(maximum<T>(), val), WithOutMask(), stream);
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, maximum<T>(), WithOutMask(), stream);
}
- template void max_gpu<uchar >(const PtrStepSzb src, uchar val, PtrStepSzb dst, cudaStream_t stream);
- template void max_gpu<schar >(const PtrStepSzb src, schar val, PtrStepSzb dst, cudaStream_t stream);
- template void max_gpu<ushort>(const PtrStepSzb src, ushort val, PtrStepSzb dst, cudaStream_t stream);
- template void max_gpu<short >(const PtrStepSzb src, short val, PtrStepSzb dst, cudaStream_t stream);
- template void max_gpu<int >(const PtrStepSzb src, int val, PtrStepSzb dst, cudaStream_t stream);
- template void max_gpu<float >(const PtrStepSzb src, float val, PtrStepSzb dst, cudaStream_t stream);
- template void max_gpu<double>(const PtrStepSzb src, double val, PtrStepSzb dst, cudaStream_t stream);
+ template void vmax4<uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vmax4<int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- //////////////////////////////////////////////////////////////////////////
- // threshold
+ template void vmax2<uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void vmax2<int>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- namespace detail
+ template void maxMat<uchar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void maxMat<schar >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void maxMat<ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void maxMat<short >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void maxMat<int >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void maxMat<float >(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template void maxMat<double>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+
+ template <typename T> void maxScalar(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream)
{
- template <size_t size, typename F> struct ThresholdTraits : DefaultTransformFunctorTraits<F>
- {
- };
- template <typename F> struct ThresholdTraits<2, F> : DefaultTransformFunctorTraits<F>
- {
- enum { smart_shift = 4 };
- };
- template <typename F> struct ThresholdTraits<4, F> : DefaultTransformFunctorTraits<F>
- {
- enum { smart_block_dim_y = 4 };
- enum { smart_shift = 4 };
- };
+ transform((PtrStepSz<T>) src1, (PtrStepSz<T>) dst, cv::gpu::device::bind2nd(maximum<T>(), src2), WithOutMask(), stream);
}
- template <typename T> struct TransformFunctorTraits< thresh_binary_func<T> > : detail::ThresholdTraits< sizeof(T), thresh_binary_func<T> >
+ template void maxScalar<uchar >(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void maxScalar<schar >(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void maxScalar<ushort>(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void maxScalar<short >(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void maxScalar<int >(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void maxScalar<float >(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ template void maxScalar<double>(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+}
+
+//////////////////////////////////////////////////////////////////////////
+// threshold
+
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T> struct TransformFunctorTraits< thresh_binary_func<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
- template <typename T> struct TransformFunctorTraits< thresh_binary_inv_func<T> > : detail::ThresholdTraits< sizeof(T), thresh_binary_inv_func<T> >
+
+ template <typename T> struct TransformFunctorTraits< thresh_binary_inv_func<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
- template <typename T> struct TransformFunctorTraits< thresh_trunc_func<T> > : detail::ThresholdTraits< sizeof(T), thresh_trunc_func<T> >
+
+ template <typename T> struct TransformFunctorTraits< thresh_trunc_func<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
- template <typename T> struct TransformFunctorTraits< thresh_to_zero_func<T> > : detail::ThresholdTraits< sizeof(T), thresh_to_zero_func<T> >
+
+ template <typename T> struct TransformFunctorTraits< thresh_to_zero_func<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
- template <typename T> struct TransformFunctorTraits< thresh_to_zero_inv_func<T> > : detail::ThresholdTraits< sizeof(T), thresh_to_zero_inv_func<T> >
+
+ template <typename T> struct TransformFunctorTraits< thresh_to_zero_inv_func<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
+}}}
+namespace arithm
+{
template <template <typename> class Op, typename T>
- void threshold_caller(const PtrStepSz<T>& src, const PtrStepSz<T>& dst, T thresh, T maxVal, cudaStream_t stream)
+ void threshold_caller(PtrStepSz<T> src, PtrStepSz<T> dst, T thresh, T maxVal, cudaStream_t stream)
{
Op<T> op(thresh, maxVal);
- cv::gpu::device::transform(src, dst, op, WithOutMask(), stream);
+ transform(src, dst, op, WithOutMask(), stream);
}
template <typename T>
- void threshold_gpu(const PtrStepSzb& src, const PtrStepSzb& dst, T thresh, T maxVal, int type,
- cudaStream_t stream)
+ void threshold(PtrStepSzb src, PtrStepSzb dst, double thresh, double maxVal, int type, cudaStream_t stream)
{
- typedef void (*caller_t)(const PtrStepSz<T>& src, const PtrStepSz<T>& dst, T thresh, T maxVal, cudaStream_t stream);
+ typedef void (*caller_t)(PtrStepSz<T> src, PtrStepSz<T> dst, T thresh, T maxVal, cudaStream_t stream);
static const caller_t callers[] =
{
threshold_caller<thresh_to_zero_inv_func, T>
};
- callers[type]((PtrStepSz<T>)src, (PtrStepSz<T>)dst, thresh, maxVal, stream);
+ callers[type]((PtrStepSz<T>) src, (PtrStepSz<T>) dst, static_cast<T>(thresh), static_cast<T>(maxVal), stream);
}
- template void threshold_gpu<uchar>(const PtrStepSzb& src, const PtrStepSzb& dst, uchar thresh, uchar maxVal, int type, cudaStream_t stream);
- template void threshold_gpu<schar>(const PtrStepSzb& src, const PtrStepSzb& dst, schar thresh, schar maxVal, int type, cudaStream_t stream);
- template void threshold_gpu<ushort>(const PtrStepSzb& src, const PtrStepSzb& dst, ushort thresh, ushort maxVal, int type, cudaStream_t stream);
- template void threshold_gpu<short>(const PtrStepSzb& src, const PtrStepSzb& dst, short thresh, short maxVal, int type, cudaStream_t stream);
- template void threshold_gpu<int>(const PtrStepSzb& src, const PtrStepSzb& dst, int thresh, int maxVal, int type, cudaStream_t stream);
- template void threshold_gpu<float>(const PtrStepSzb& src, const PtrStepSzb& dst, float thresh, float maxVal, int type, cudaStream_t stream);
- template void threshold_gpu<double>(const PtrStepSzb& src, const PtrStepSzb& dst, double thresh, double maxVal, int type, cudaStream_t stream);
+ template void threshold<uchar>(PtrStepSzb src, PtrStepSzb dst, double thresh, double maxVal, int type, cudaStream_t stream);
+ template void threshold<schar>(PtrStepSzb src, PtrStepSzb dst, double thresh, double maxVal, int type, cudaStream_t stream);
+ template void threshold<ushort>(PtrStepSzb src, PtrStepSzb dst, double thresh, double maxVal, int type, cudaStream_t stream);
+ template void threshold<short>(PtrStepSzb src, PtrStepSzb dst, double thresh, double maxVal, int type, cudaStream_t stream);
+ template void threshold<int>(PtrStepSzb src, PtrStepSzb dst, double thresh, double maxVal, int type, cudaStream_t stream);
+ template void threshold<float>(PtrStepSzb src, PtrStepSzb dst, double thresh, double maxVal, int type, cudaStream_t stream);
+ template void threshold<double>(PtrStepSzb src, PtrStepSzb dst, double thresh, double maxVal, int type, cudaStream_t stream);
+}
- //////////////////////////////////////////////////////////////////////////
- // pow
+//////////////////////////////////////////////////////////////////////////
+// pow
- template<typename T, bool Signed = device::numeric_limits<T>::is_signed> struct PowOp : unary_function<T, T>
+namespace arithm
+{
+ template<typename T, bool Signed = numeric_limits<T>::is_signed> struct PowOp : unary_function<T, T>
{
- const float power;
+ float power;
PowOp(double power_) : power(static_cast<float>(power_)) {}
};
template<typename T> struct PowOp<T, true> : unary_function<T, T>
{
- const float power;
+ float power;
PowOp(double power_) : power(static_cast<float>(power_)) {}
};
template<> struct PowOp<double> : unary_function<double, double>
{
- const double power;
+ double power;
PowOp(double power_) : power(power_) {}
return ::pow(::fabs(e), power);
}
};
+}
- namespace detail
- {
- template <size_t size, typename T> struct PowOpTraits : DefaultTransformFunctorTraits< PowOp<T> >
- {
- };
- template <typename T> struct PowOpTraits<1, T> : DefaultTransformFunctorTraits< PowOp<T> >
- {
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 8 };
- };
- template <typename T> struct PowOpTraits<2, T> : DefaultTransformFunctorTraits< PowOp<T> >
- {
- enum { smart_shift = 4 };
- };
- template <typename T> struct PowOpTraits<4, T> : DefaultTransformFunctorTraits< PowOp<T> >
- {
- enum { smart_block_dim_y = 4 };
- enum { smart_shift = 4 };
- };
- }
-
- template <typename T> struct TransformFunctorTraits< PowOp<T> > : detail::PowOpTraits<sizeof(T), T>
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T> struct TransformFunctorTraits< arithm::PowOp<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
+}}}
+namespace arithm
+{
template<typename T>
- void pow_caller(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream)
+ void pow(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream)
{
- cv::gpu::device::transform((PtrStepSz<T>)src, (PtrStepSz<T>)dst, PowOp<T>(power), WithOutMask(), stream);
+ transform((PtrStepSz<T>) src, (PtrStepSz<T>) dst, PowOp<T>(power), WithOutMask(), stream);
}
- template void pow_caller<uchar>(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream);
- template void pow_caller<schar>(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream);
- template void pow_caller<short>(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream);
- template void pow_caller<ushort>(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream);
- template void pow_caller<int>(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream);
- template void pow_caller<float>(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream);
- template void pow_caller<double>(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream);
+ template void pow<uchar>(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream);
+ template void pow<schar>(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream);
+ template void pow<short>(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream);
+ template void pow<ushort>(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream);
+ template void pow<int>(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream);
+ template void pow<float>(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream);
+ template void pow<double>(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream);
+}
- //////////////////////////////////////////////////////////////////////////
- // addWeighted
+//////////////////////////////////////////////////////////////////////////
+// addWeighted
- namespace detail
+namespace arithm
+{
+ template <typename T> struct UseDouble_
{
- template <typename T> struct UseDouble
- {
- enum {value = 0};
- };
- template <> struct UseDouble<int>
- {
- enum {value = 1};
- };
- template <> struct UseDouble<float>
- {
- enum {value = 1};
- };
- template <> struct UseDouble<double>
- {
- enum {value = 1};
- };
- }
+ enum {value = 0};
+ };
+ template <> struct UseDouble_<double>
+ {
+ enum {value = 1};
+ };
template <typename T1, typename T2, typename D> struct UseDouble
{
- enum {value = (detail::UseDouble<T1>::value || detail::UseDouble<T2>::value || detail::UseDouble<D>::value)};
+ enum {value = (UseDouble_<T1>::value || UseDouble_<T2>::value || UseDouble_<D>::value)};
};
- namespace detail
+ template <typename T1, typename T2, typename D, bool useDouble> struct AddWeighted_;
+ template <typename T1, typename T2, typename D> struct AddWeighted_<T1, T2, D, false> : binary_function<T1, T2, D>
{
- template <typename T1, typename T2, typename D, bool useDouble> struct AddWeighted;
- template <typename T1, typename T2, typename D> struct AddWeighted<T1, T2, D, false> : binary_function<T1, T2, D>
- {
- AddWeighted(double alpha_, double beta_, double gamma_) : alpha(static_cast<float>(alpha_)), beta(static_cast<float>(beta_)), gamma(static_cast<float>(gamma_)) {}
+ float alpha;
+ float beta;
+ float gamma;
- __device__ __forceinline__ D operator ()(T1 a, T2 b) const
- {
- return saturate_cast<D>(a * alpha + b * beta + gamma);
- }
+ AddWeighted_(double alpha_, double beta_, double gamma_) : alpha(static_cast<float>(alpha_)), beta(static_cast<float>(beta_)), gamma(static_cast<float>(gamma_)) {}
- const float alpha;
- const float beta;
- const float gamma;
- };
- template <typename T1, typename T2, typename D> struct AddWeighted<T1, T2, D, true> : binary_function<T1, T2, D>
+ __device__ __forceinline__ D operator ()(T1 a, T2 b) const
{
- AddWeighted(double alpha_, double beta_, double gamma_) : alpha(alpha_), beta(beta_), gamma(gamma_) {}
-
- __device__ __forceinline__ D operator ()(T1 a, T2 b) const
- {
- return saturate_cast<D>(a * alpha + b * beta + gamma);
- }
-
- const double alpha;
- const double beta;
- const double gamma;
- };
- }
- template <typename T1, typename T2, typename D> struct AddWeighted : detail::AddWeighted<T1, T2, D, UseDouble<T1, T2, D>::value>
- {
- AddWeighted(double alpha_, double beta_, double gamma_) : detail::AddWeighted<T1, T2, D, UseDouble<T1, T2, D>::value>(alpha_, beta_, gamma_) {}
- };
-
- template <> struct TransformFunctorTraits< AddWeighted<ushort, ushort, ushort> > : DefaultTransformFunctorTraits< AddWeighted<ushort, ushort, ushort> >
- {
- enum { smart_shift = 4 };
- };
- template <> struct TransformFunctorTraits< AddWeighted<ushort, ushort, short> > : DefaultTransformFunctorTraits< AddWeighted<ushort, ushort, short> >
- {
- enum { smart_shift = 4 };
- };
- template <> struct TransformFunctorTraits< AddWeighted<ushort, short, ushort> > : DefaultTransformFunctorTraits< AddWeighted<ushort, short, ushort> >
- {
- enum { smart_shift = 4 };
- };
- template <> struct TransformFunctorTraits< AddWeighted<ushort, short, short> > : DefaultTransformFunctorTraits< AddWeighted<ushort, short, short> >
- {
- enum { smart_shift = 4 };
- };
- template <> struct TransformFunctorTraits< AddWeighted<short, short, ushort> > : DefaultTransformFunctorTraits< AddWeighted<short, short, ushort> >
- {
- enum { smart_shift = 4 };
+ return saturate_cast<D>(a * alpha + b * beta + gamma);
+ }
};
- template <> struct TransformFunctorTraits< AddWeighted<short, short, short> > : DefaultTransformFunctorTraits< AddWeighted<short, short, short> >
+ template <typename T1, typename T2, typename D> struct AddWeighted_<T1, T2, D, true> : binary_function<T1, T2, D>
{
- enum { smart_shift = 4 };
- };
+ double alpha;
+ double beta;
+ double gamma;
- template <> struct TransformFunctorTraits< AddWeighted<int, int, int> > : DefaultTransformFunctorTraits< AddWeighted<int, int, int> >
- {
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
- };
- template <> struct TransformFunctorTraits< AddWeighted<int, int, float> > : DefaultTransformFunctorTraits< AddWeighted<int, int, float> >
- {
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
+ AddWeighted_(double alpha_, double beta_, double gamma_) : alpha(alpha_), beta(beta_), gamma(gamma_) {}
+
+ __device__ __forceinline__ D operator ()(T1 a, T2 b) const
+ {
+ return saturate_cast<D>(a * alpha + b * beta + gamma);
+ }
};
- template <> struct TransformFunctorTraits< AddWeighted<int, float, int> > : DefaultTransformFunctorTraits< AddWeighted<int, float, int> >
+ template <typename T1, typename T2, typename D> struct AddWeighted : AddWeighted_<T1, T2, D, UseDouble<T1, T2, D>::value>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
+ AddWeighted(double alpha_, double beta_, double gamma_) : AddWeighted_<T1, T2, D, UseDouble<T1, T2, D>::value>(alpha_, beta_, gamma_) {}
};
- template <> struct TransformFunctorTraits< AddWeighted<int, float, float> > : DefaultTransformFunctorTraits< AddWeighted<int, float, float> >
+}
+
+namespace cv { namespace gpu { namespace device
+{
+ template <typename T1, typename T2, typename D, size_t src1_size, size_t src2_size, size_t dst_size> struct AddWeightedTraits : DefaultTransformFunctorTraits< arithm::AddWeighted<T1, T2, D> >
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< AddWeighted<float, float, int> > : DefaultTransformFunctorTraits< AddWeighted<float, float, float> >
+ template <typename T1, typename T2, typename D, size_t src_size, size_t dst_size> struct AddWeightedTraits<T1, T2, D, src_size, src_size, dst_size> : arithm::ArithmFuncTraits<src_size, dst_size>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
};
- template <> struct TransformFunctorTraits< AddWeighted<float, float, float> > : DefaultTransformFunctorTraits< AddWeighted<float, float, float> >
+
+ template <typename T1, typename T2, typename D> struct TransformFunctorTraits< arithm::AddWeighted<T1, T2, D> > : AddWeightedTraits<T1, T2, D, sizeof(T1), sizeof(T2), sizeof(D)>
{
- enum { smart_block_dim_y = 8 };
- enum { smart_shift = 4 };
};
+}}}
+namespace arithm
+{
template <typename T1, typename T2, typename D>
- void addWeighted_gpu(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream)
+ void addWeighted(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream)
{
- if (UseDouble<T1, T2, D>::value)
- {
- cudaSafeCall( cudaSetDoubleForDevice(&alpha) );
- cudaSafeCall( cudaSetDoubleForDevice(&beta) );
- cudaSafeCall( cudaSetDoubleForDevice(&gamma) );
- }
-
AddWeighted<T1, T2, D> op(alpha, beta, gamma);
- cv::gpu::device::transform(static_cast< PtrStepSz<T1> >(src1), static_cast< PtrStepSz<T2> >(src2), static_cast< PtrStepSz<D> >(dst), op, WithOutMask(), stream);
+ transform((PtrStepSz<T1>) src1, (PtrStepSz<T2>) src2, (PtrStepSz<D>) dst, op, WithOutMask(), stream);
}
- template void addWeighted_gpu<uchar, uchar, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, uchar, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, uchar, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, uchar, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, uchar, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, uchar, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, uchar, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
-
- template void addWeighted_gpu<uchar, schar, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, schar, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, schar, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, schar, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, schar, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, schar, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, schar, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
-
- template void addWeighted_gpu<uchar, ushort, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, ushort, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, ushort, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, ushort, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, ushort, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, ushort, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, ushort, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
-
- template void addWeighted_gpu<uchar, short, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, short, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, short, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, short, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, short, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, short, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, short, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
-
- template void addWeighted_gpu<uchar, int, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, int, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, int, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, int, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, int, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, int, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, int, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
-
- template void addWeighted_gpu<uchar, float, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, float, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, float, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, float, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, float, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, float, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, float, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
-
- template void addWeighted_gpu<uchar, double, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, double, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, double, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, double, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, double, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, double, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<uchar, double, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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- template void addWeighted_gpu<schar, schar, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, schar, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, schar, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, schar, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, schar, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, schar, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, schar, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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- template void addWeighted_gpu<schar, ushort, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, ushort, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, ushort, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, ushort, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, ushort, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, ushort, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, ushort, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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- template void addWeighted_gpu<schar, short, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, short, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, short, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, short, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, short, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, short, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, short, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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- template void addWeighted_gpu<schar, int, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, int, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, int, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, int, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, int, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, int, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, int, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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- template void addWeighted_gpu<schar, float, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, float, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, float, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, float, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, float, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, float, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, float, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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- template void addWeighted_gpu<schar, double, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, double, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, double, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, double, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, double, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, double, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<schar, double, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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- template void addWeighted_gpu<ushort, ushort, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, ushort, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, ushort, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, ushort, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, ushort, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, ushort, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, ushort, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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- template void addWeighted_gpu<ushort, short, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, short, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, short, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, short, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, short, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, short, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, short, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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- template void addWeighted_gpu<ushort, int, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, int, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, int, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, int, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, int, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, int, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, int, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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- template void addWeighted_gpu<ushort, float, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, float, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, float, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, float, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, float, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, float, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, float, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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- template void addWeighted_gpu<ushort, double, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, double, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, double, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, double, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, double, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, double, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<ushort, double, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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-
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- template void addWeighted_gpu<short, short, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, short, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, short, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, short, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, short, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, short, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, short, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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- template void addWeighted_gpu<short, int, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, int, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, int, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, int, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, int, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, int, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, int, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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- template void addWeighted_gpu<short, float, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, float, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, float, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, float, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, float, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, float, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, float, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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- template void addWeighted_gpu<short, double, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, double, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, double, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, double, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, double, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, double, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<short, double, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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-
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- template void addWeighted_gpu<int, int, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, int, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, int, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, int, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, int, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, int, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, int, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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- template void addWeighted_gpu<int, float, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, float, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, float, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, float, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, float, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, float, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, float, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
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- template void addWeighted_gpu<int, double, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, double, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, double, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, double, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, double, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, double, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<int, double, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
-
-
-
- template void addWeighted_gpu<float, float, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<float, float, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<float, float, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<float, float, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<float, float, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<float, float, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<float, float, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
-
- template void addWeighted_gpu<float, double, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<float, double, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<float, double, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<float, double, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<float, double, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<float, double, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<float, double, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
-
-
-
- template void addWeighted_gpu<double, double, uchar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<double, double, schar>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<double, double, ushort>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<double, double, short>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<double, double, int>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<double, double, float>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
- template void addWeighted_gpu<double, double, double>(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
-}}} // namespace cv { namespace gpu { namespace device
-
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+ template void addWeighted<uchar, uchar, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, uchar, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, uchar, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, uchar, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, uchar, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, uchar, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, uchar, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<uchar, schar, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, schar, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, schar, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, schar, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, schar, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, schar, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, schar, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<uchar, ushort, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, ushort, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, ushort, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, ushort, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, ushort, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, ushort, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, ushort, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<uchar, short, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, short, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, short, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, short, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, short, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, short, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, short, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<uchar, int, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, int, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, int, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, int, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, int, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, int, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, int, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<uchar, float, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, float, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, float, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, float, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, float, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, float, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, float, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<uchar, double, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, double, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, double, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, double, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, double, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, double, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<uchar, double, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+
+
+ template void addWeighted<schar, schar, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, schar, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, schar, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, schar, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, schar, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, schar, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, schar, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<schar, ushort, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, ushort, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, ushort, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, ushort, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, ushort, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, ushort, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, ushort, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<schar, short, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, short, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, short, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, short, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, short, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, short, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, short, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<schar, int, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, int, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, int, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, int, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, int, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, int, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, int, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<schar, float, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, float, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, float, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, float, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, float, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, float, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, float, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<schar, double, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, double, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, double, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, double, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, double, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, double, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<schar, double, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+
+
+ template void addWeighted<ushort, ushort, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, ushort, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, ushort, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, ushort, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, ushort, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, ushort, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, ushort, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<ushort, short, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, short, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, short, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, short, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, short, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, short, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, short, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<ushort, int, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, int, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, int, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, int, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, int, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, int, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, int, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<ushort, float, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, float, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, float, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, float, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, float, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, float, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, float, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<ushort, double, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, double, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, double, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, double, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, double, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, double, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<ushort, double, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+
+
+ template void addWeighted<short, short, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, short, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, short, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, short, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, short, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, short, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, short, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<short, int, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, int, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, int, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, int, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, int, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, int, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, int, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<short, float, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, float, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, float, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, float, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, float, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, float, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, float, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<short, double, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, double, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, double, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, double, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, double, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, double, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<short, double, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+
+
+ template void addWeighted<int, int, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, int, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, int, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, int, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, int, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, int, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, int, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<int, float, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, float, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, float, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, float, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, float, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, float, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, float, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<int, double, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, double, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, double, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, double, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, double, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, double, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<int, double, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+
+
+ template void addWeighted<float, float, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<float, float, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<float, float, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<float, float, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<float, float, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<float, float, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<float, float, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+ template void addWeighted<float, double, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<float, double, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<float, double, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<float, double, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<float, double, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<float, double, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<float, double, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+
+
+
+ template void addWeighted<double, double, uchar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<double, double, schar>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<double, double, ushort>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<double, double, short>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<double, double, int>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<double, double, float>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+ template void addWeighted<double, double, double>(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+}
+
+#endif /* CUDA_DISABLER */
#include "opencv2/gpu/device/vec_math.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/utility.hpp"
+#include "opencv2/gpu/device/reduce.hpp"
+#include "opencv2/gpu/device/functional.hpp"
#include "fgd_bgfg_common.hpp"
using namespace cv::gpu;
__shared__ unsigned int data1[MERGE_THREADBLOCK_SIZE];
__shared__ unsigned int data2[MERGE_THREADBLOCK_SIZE];
- data0[threadIdx.x] = sum0;
- data1[threadIdx.x] = sum1;
- data2[threadIdx.x] = sum2;
- __syncthreads();
-
- if (threadIdx.x < 128)
- {
- data0[threadIdx.x] = sum0 += data0[threadIdx.x + 128];
- data1[threadIdx.x] = sum1 += data1[threadIdx.x + 128];
- data2[threadIdx.x] = sum2 += data2[threadIdx.x + 128];
- }
- __syncthreads();
-
- if (threadIdx.x < 64)
- {
- data0[threadIdx.x] = sum0 += data0[threadIdx.x + 64];
- data1[threadIdx.x] = sum1 += data1[threadIdx.x + 64];
- data2[threadIdx.x] = sum2 += data2[threadIdx.x + 64];
- }
- __syncthreads();
-
- if (threadIdx.x < 32)
- {
- volatile unsigned int* vdata0 = data0;
- volatile unsigned int* vdata1 = data1;
- volatile unsigned int* vdata2 = data2;
-
- vdata0[threadIdx.x] = sum0 += vdata0[threadIdx.x + 32];
- vdata1[threadIdx.x] = sum1 += vdata1[threadIdx.x + 32];
- vdata2[threadIdx.x] = sum2 += vdata2[threadIdx.x + 32];
-
- vdata0[threadIdx.x] = sum0 += vdata0[threadIdx.x + 16];
- vdata1[threadIdx.x] = sum1 += vdata1[threadIdx.x + 16];
- vdata2[threadIdx.x] = sum2 += vdata2[threadIdx.x + 16];
-
- vdata0[threadIdx.x] = sum0 += vdata0[threadIdx.x + 8];
- vdata1[threadIdx.x] = sum1 += vdata1[threadIdx.x + 8];
- vdata2[threadIdx.x] = sum2 += vdata2[threadIdx.x + 8];
-
- vdata0[threadIdx.x] = sum0 += vdata0[threadIdx.x + 4];
- vdata1[threadIdx.x] = sum1 += vdata1[threadIdx.x + 4];
- vdata2[threadIdx.x] = sum2 += vdata2[threadIdx.x + 4];
-
- vdata0[threadIdx.x] = sum0 += vdata0[threadIdx.x + 2];
- vdata1[threadIdx.x] = sum1 += vdata1[threadIdx.x + 2];
- vdata2[threadIdx.x] = sum2 += vdata2[threadIdx.x + 2];
-
- vdata0[threadIdx.x] = sum0 += vdata0[threadIdx.x + 1];
- vdata1[threadIdx.x] = sum1 += vdata1[threadIdx.x + 1];
- vdata2[threadIdx.x] = sum2 += vdata2[threadIdx.x + 1];
- }
+ plus<unsigned int> op;
+ reduce<MERGE_THREADBLOCK_SIZE>(smem_tuple(data0, data1, data2), thrust::tie(sum0, sum1, sum2), threadIdx.x, thrust::make_tuple(op, op, op));
if(threadIdx.x == 0)
{
void calcDiffHistogram_gpu(PtrStepSzb prevFrame, PtrStepSzb curFrame,
unsigned int* hist0, unsigned int* hist1, unsigned int* hist2,
unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2,
- int cc, cudaStream_t stream)
+ bool cc20, cudaStream_t stream)
{
- const int HISTOGRAM_WARP_COUNT = cc < 20 ? 4 : 6;
+ const int HISTOGRAM_WARP_COUNT = cc20 ? 6 : 4;
const int HISTOGRAM_THREADBLOCK_SIZE = HISTOGRAM_WARP_COUNT * WARP_SIZE;
calcPartialHistogram<PT, CT><<<PARTIAL_HISTOGRAM_COUNT, HISTOGRAM_THREADBLOCK_SIZE, 0, stream>>>(
cudaSafeCall( cudaDeviceSynchronize() );
}
- template void calcDiffHistogram_gpu<uchar3, uchar3>(PtrStepSzb prevFrame, PtrStepSzb curFrame, unsigned int* hist0, unsigned int* hist1, unsigned int* hist2, unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2, int cc, cudaStream_t stream);
- template void calcDiffHistogram_gpu<uchar3, uchar4>(PtrStepSzb prevFrame, PtrStepSzb curFrame, unsigned int* hist0, unsigned int* hist1, unsigned int* hist2, unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2, int cc, cudaStream_t stream);
- template void calcDiffHistogram_gpu<uchar4, uchar3>(PtrStepSzb prevFrame, PtrStepSzb curFrame, unsigned int* hist0, unsigned int* hist1, unsigned int* hist2, unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2, int cc, cudaStream_t stream);
- template void calcDiffHistogram_gpu<uchar4, uchar4>(PtrStepSzb prevFrame, PtrStepSzb curFrame, unsigned int* hist0, unsigned int* hist1, unsigned int* hist2, unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2, int cc, cudaStream_t stream);
+ template void calcDiffHistogram_gpu<uchar3, uchar3>(PtrStepSzb prevFrame, PtrStepSzb curFrame, unsigned int* hist0, unsigned int* hist1, unsigned int* hist2, unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2, bool cc20, cudaStream_t stream);
+ template void calcDiffHistogram_gpu<uchar3, uchar4>(PtrStepSzb prevFrame, PtrStepSzb curFrame, unsigned int* hist0, unsigned int* hist1, unsigned int* hist2, unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2, bool cc20, cudaStream_t stream);
+ template void calcDiffHistogram_gpu<uchar4, uchar3>(PtrStepSzb prevFrame, PtrStepSzb curFrame, unsigned int* hist0, unsigned int* hist1, unsigned int* hist2, unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2, bool cc20, cudaStream_t stream);
+ template void calcDiffHistogram_gpu<uchar4, uchar4>(PtrStepSzb prevFrame, PtrStepSzb curFrame, unsigned int* hist0, unsigned int* hist1, unsigned int* hist2, unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2, bool cc20, cudaStream_t stream);
/////////////////////////////////////////////////////////////////////////
// calcDiffThreshMask
template void updateBackgroundModel_gpu<uchar4, uchar4, uchar4>(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground, PtrStepSzb background, int deltaC, int deltaCC, float alpha1, float alpha2, float alpha3, int N1c, int N1cc, int N2c, int N2cc, float T, cudaStream_t stream);
}
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+#endif /* CUDA_DISABLER */
void calcDiffHistogram_gpu(cv::gpu::PtrStepSzb prevFrame, cv::gpu::PtrStepSzb curFrame,
unsigned int* hist0, unsigned int* hist1, unsigned int* hist2,
unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2,
- int cc, cudaStream_t stream);
+ bool cc20, cudaStream_t stream);
template <typename PT, typename CT>
void calcDiffThreshMask_gpu(cv::gpu::PtrStepSzb prevFrame, cv::gpu::PtrStepSzb curFrame, uchar3 bestThres, cv::gpu::PtrStepSzb changeMask, cudaStream_t stream);
#if !defined CUDA_DISABLER
-#include "thrust/device_ptr.h"
-#include "thrust/remove.h"
-#include "thrust/functional.h"
-#include "internal_shared.hpp"
-
-using namespace thrust;
+#include <thrust/device_ptr.h>
+#include <thrust/remove.h>
+#include <thrust/functional.h>
+#include "opencv2/gpu/device/common.hpp"
namespace cv { namespace gpu { namespace device { namespace globmotion {
thrust::device_ptr<float2> dpoints1((float2*)points1);
thrust::device_ptr<const uchar> dmask(mask);
- return thrust::remove_if(thrust::make_zip_iterator(thrust::make_tuple(dpoints0, dpoints1)),
+ return (int)(thrust::remove_if(thrust::make_zip_iterator(thrust::make_tuple(dpoints0, dpoints1)),
thrust::make_zip_iterator(thrust::make_tuple(dpoints0 + N, dpoints1 + N)),
dmask, thrust::not1(thrust::identity<uchar>()))
- - make_zip_iterator(make_tuple(dpoints0, dpoints1));
+ - thrust::make_zip_iterator(make_tuple(dpoints0, dpoints1)));
}
}}}}
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+#endif /* CUDA_DISABLER */
#if !defined CUDA_DISABLER
-#include "internal_shared.hpp"
-#include "opencv2/gpu/device/utility.hpp"
-#include "opencv2/gpu/device/saturate_cast.hpp"
+#include "opencv2/gpu/device/common.hpp"
+#include "opencv2/gpu/device/functional.hpp"
+#include "opencv2/gpu/device/emulation.hpp"
+#include "opencv2/gpu/device/transform.hpp"
-namespace cv { namespace gpu { namespace device
-{
- #define UINT_BITS 32U
-
- //Warps == subhistograms per threadblock
- #define WARP_COUNT 6
-
- //Threadblock size
- #define HISTOGRAM256_THREADBLOCK_SIZE (WARP_COUNT * OPENCV_GPU_WARP_SIZE)
- #define HISTOGRAM256_BIN_COUNT 256
-
- //Shared memory per threadblock
- #define HISTOGRAM256_THREADBLOCK_MEMORY (WARP_COUNT * HISTOGRAM256_BIN_COUNT)
-
- #define PARTIAL_HISTOGRAM256_COUNT 240
-
- #define MERGE_THREADBLOCK_SIZE 256
+using namespace cv::gpu;
+using namespace cv::gpu::device;
- #define USE_SMEM_ATOMICS (defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 120))
-
- namespace hist
+namespace hist
+{
+ __global__ void histogram256Kernel(const uchar* src, int cols, int rows, size_t step, int* hist)
{
- #if (!USE_SMEM_ATOMICS)
-
- #define TAG_MASK ( (1U << (UINT_BITS - OPENCV_GPU_LOG_WARP_SIZE)) - 1U )
-
- __forceinline__ __device__ void addByte(volatile uint* s_WarpHist, uint data, uint threadTag)
- {
- uint count;
- do
- {
- count = s_WarpHist[data] & TAG_MASK;
- count = threadTag | (count + 1);
- s_WarpHist[data] = count;
- } while (s_WarpHist[data] != count);
- }
-
- #else
+ __shared__ int shist[256];
- #define TAG_MASK 0xFFFFFFFFU
+ const int y = blockIdx.x * blockDim.y + threadIdx.y;
+ const int tid = threadIdx.y * blockDim.x + threadIdx.x;
- __forceinline__ __device__ void addByte(uint* s_WarpHist, uint data, uint threadTag)
- {
- atomicAdd(s_WarpHist + data, 1);
- }
+ shist[tid] = 0;
+ __syncthreads();
- #endif
-
- __forceinline__ __device__ void addWord(uint* s_WarpHist, uint data, uint tag, uint pos_x, uint cols)
+ if (y < rows)
{
- uint x = pos_x << 2;
-
- if (x + 0 < cols) addByte(s_WarpHist, (data >> 0) & 0xFFU, tag);
- if (x + 1 < cols) addByte(s_WarpHist, (data >> 8) & 0xFFU, tag);
- if (x + 2 < cols) addByte(s_WarpHist, (data >> 16) & 0xFFU, tag);
- if (x + 3 < cols) addByte(s_WarpHist, (data >> 24) & 0xFFU, tag);
- }
+ const unsigned int* rowPtr = (const unsigned int*) (src + y * step);
- __global__ void histogram256(const PtrStep<uint> d_Data, uint* d_PartialHistograms, uint dataCount, uint cols)
- {
- //Per-warp subhistogram storage
- __shared__ uint s_Hist[HISTOGRAM256_THREADBLOCK_MEMORY];
- uint* s_WarpHist= s_Hist + (threadIdx.x >> OPENCV_GPU_LOG_WARP_SIZE) * HISTOGRAM256_BIN_COUNT;
-
- //Clear shared memory storage for current threadblock before processing
- #pragma unroll
- for (uint i = 0; i < (HISTOGRAM256_THREADBLOCK_MEMORY / HISTOGRAM256_THREADBLOCK_SIZE); i++)
- s_Hist[threadIdx.x + i * HISTOGRAM256_THREADBLOCK_SIZE] = 0;
-
- //Cycle through the entire data set, update subhistograms for each warp
- const uint tag = threadIdx.x << (UINT_BITS - OPENCV_GPU_LOG_WARP_SIZE);
-
- __syncthreads();
- const uint colsui = d_Data.step / sizeof(uint);
- for(uint pos = blockIdx.x * blockDim.x + threadIdx.x; pos < dataCount; pos += blockDim.x * gridDim.x)
+ const int cols_4 = cols / 4;
+ for (int x = threadIdx.x; x < cols_4; x += blockDim.x)
{
- uint pos_y = pos / colsui;
- uint pos_x = pos % colsui;
- uint data = d_Data.ptr(pos_y)[pos_x];
- addWord(s_WarpHist, data, tag, pos_x, cols);
+ unsigned int data = rowPtr[x];
+
+ Emulation::smem::atomicAdd(&shist[(data >> 0) & 0xFFU], 1);
+ Emulation::smem::atomicAdd(&shist[(data >> 8) & 0xFFU], 1);
+ Emulation::smem::atomicAdd(&shist[(data >> 16) & 0xFFU], 1);
+ Emulation::smem::atomicAdd(&shist[(data >> 24) & 0xFFU], 1);
}
- //Merge per-warp histograms into per-block and write to global memory
- __syncthreads();
- for(uint bin = threadIdx.x; bin < HISTOGRAM256_BIN_COUNT; bin += HISTOGRAM256_THREADBLOCK_SIZE)
+ if (cols % 4 != 0 && threadIdx.x == 0)
{
- uint sum = 0;
-
- for (uint i = 0; i < WARP_COUNT; i++)
- sum += s_Hist[bin + i * HISTOGRAM256_BIN_COUNT] & TAG_MASK;
-
- d_PartialHistograms[blockIdx.x * HISTOGRAM256_BIN_COUNT + bin] = sum;
+ for (int x = cols_4 * 4; x < cols; ++x)
+ {
+ unsigned int data = ((const uchar*)rowPtr)[x];
+ Emulation::smem::atomicAdd(&shist[data], 1);
+ }
}
}
- ////////////////////////////////////////////////////////////////////////////////
- // Merge histogram256() output
- // Run one threadblock per bin; each threadblock adds up the same bin counter
- // from every partial histogram. Reads are uncoalesced, but mergeHistogram256
- // takes only a fraction of total processing time
- ////////////////////////////////////////////////////////////////////////////////
-
- __global__ void mergeHistogram256(const uint* d_PartialHistograms, int* d_Histogram)
- {
- uint sum = 0;
+ __syncthreads();
- #pragma unroll
- for (uint i = threadIdx.x; i < PARTIAL_HISTOGRAM256_COUNT; i += MERGE_THREADBLOCK_SIZE)
- sum += d_PartialHistograms[blockIdx.x + i * HISTOGRAM256_BIN_COUNT];
+ const int histVal = shist[tid];
+ if (histVal > 0)
+ ::atomicAdd(hist + tid, histVal);
+ }
- __shared__ uint data[MERGE_THREADBLOCK_SIZE];
- data[threadIdx.x] = sum;
-
- for (uint stride = MERGE_THREADBLOCK_SIZE / 2; stride > 0; stride >>= 1)
- {
- __syncthreads();
- if(threadIdx.x < stride)
- data[threadIdx.x] += data[threadIdx.x + stride];
- }
-
- if(threadIdx.x == 0)
- d_Histogram[blockIdx.x] = saturate_cast<int>(data[0]);
- }
+ void histogram256(PtrStepSzb src, int* hist, cudaStream_t stream)
+ {
+ const dim3 block(32, 8);
+ const dim3 grid(divUp(src.rows, block.y));
- void histogram256_gpu(PtrStepSzb src, int* hist, uint* buf, cudaStream_t stream)
- {
- histogram256<<<PARTIAL_HISTOGRAM256_COUNT, HISTOGRAM256_THREADBLOCK_SIZE, 0, stream>>>(
- PtrStepSz<uint>(src),
- buf,
- static_cast<uint>(src.rows * src.step / sizeof(uint)),
- src.cols);
+ histogram256Kernel<<<grid, block, 0, stream>>>(src.data, src.cols, src.rows, src.step, hist);
+ cudaSafeCall( cudaGetLastError() );
- cudaSafeCall( cudaGetLastError() );
+ if (stream == 0)
+ cudaSafeCall( cudaDeviceSynchronize() );
+ }
+}
- mergeHistogram256<<<HISTOGRAM256_BIN_COUNT, MERGE_THREADBLOCK_SIZE, 0, stream>>>(buf, hist);
+/////////////////////////////////////////////////////////////////////////
- cudaSafeCall( cudaGetLastError() );
+namespace hist
+{
+ __constant__ int c_lut[256];
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
- }
+ struct EqualizeHist : unary_function<uchar, uchar>
+ {
+ float scale;
- __constant__ int c_lut[256];
+ __host__ EqualizeHist(float _scale) : scale(_scale) {}
- __global__ void equalizeHist(const PtrStepSzb src, PtrStepb dst)
+ __device__ __forceinline__ uchar operator ()(uchar val) const
{
- const int x = blockIdx.x * blockDim.x + threadIdx.x;
- const int y = blockIdx.y * blockDim.y + threadIdx.y;
-
- if (x < src.cols && y < src.rows)
- {
- const uchar val = src.ptr(y)[x];
- const int lut = c_lut[val];
- dst.ptr(y)[x] = __float2int_rn(255.0f / (src.cols * src.rows) * lut);
- }
+ const int lut = c_lut[val];
+ return __float2int_rn(scale * lut);
}
+ };
+}
- void equalizeHist_gpu(PtrStepSzb src, PtrStepSzb dst, const int* lut, cudaStream_t stream)
- {
- dim3 block(16, 16);
- dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y));
+namespace cv { namespace gpu { namespace device
+{
+ template <> struct TransformFunctorTraits<hist::EqualizeHist> : DefaultTransformFunctorTraits<hist::EqualizeHist>
+ {
+ enum { smart_shift = 4 };
+ };
+}}}
+namespace hist
+{
+ void equalizeHist(PtrStepSzb src, PtrStepSzb dst, const int* lut, cudaStream_t stream)
+ {
+ if (stream == 0)
cudaSafeCall( cudaMemcpyToSymbol(c_lut, lut, 256 * sizeof(int), 0, cudaMemcpyDeviceToDevice) );
+ else
+ cudaSafeCall( cudaMemcpyToSymbolAsync(c_lut, lut, 256 * sizeof(int), 0, cudaMemcpyDeviceToDevice, stream) );
- equalizeHist<<<grid, block, 0, stream>>>(src, dst);
- cudaSafeCall( cudaGetLastError() );
-
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
- }
- } // namespace hist
-}}} // namespace cv { namespace gpu { namespace device
+ const float scale = 255.0f / (src.cols * src.rows);
+ transform(src, dst, EqualizeHist(scale), WithOutMask(), stream);
+ }
+}
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+#endif /* CUDA_DISABLER */
#if !defined CUDA_DISABLER
-#include "internal_shared.hpp"
+#include "opencv2/gpu/device/common.hpp"
+#include "opencv2/gpu/device/reduce.hpp"
+#include "opencv2/gpu/device/functional.hpp"
+#include "opencv2/gpu/device/warp_shuffle.hpp"
namespace cv { namespace gpu { namespace device
{
template<int size>
- __device__ float reduce_smem(volatile float* smem)
+ __device__ float reduce_smem(float* smem, float val)
{
unsigned int tid = threadIdx.x;
- float sum = smem[tid];
+ float sum = val;
- if (size >= 512) { if (tid < 256) smem[tid] = sum = sum + smem[tid + 256]; __syncthreads(); }
- if (size >= 256) { if (tid < 128) smem[tid] = sum = sum + smem[tid + 128]; __syncthreads(); }
- if (size >= 128) { if (tid < 64) smem[tid] = sum = sum + smem[tid + 64]; __syncthreads(); }
+ reduce<size>(smem, sum, tid, plus<float>());
- if (tid < 32)
+ if (size == 32)
{
- if (size >= 64) smem[tid] = sum = sum + smem[tid + 32];
- if (size >= 32) smem[tid] = sum = sum + smem[tid + 16];
- if (size >= 16) smem[tid] = sum = sum + smem[tid + 8];
- if (size >= 8) smem[tid] = sum = sum + smem[tid + 4];
- if (size >= 4) smem[tid] = sum = sum + smem[tid + 2];
- if (size >= 2) smem[tid] = sum = sum + smem[tid + 1];
+ #if __CUDA_ARCH__ >= 300
+ return shfl(sum, 0);
+ #else
+ return smem[0];
+ #endif
}
+ else
+ {
+ #if __CUDA_ARCH__ >= 300
+ if (threadIdx.x == 0)
+ smem[0] = sum;
+ #endif
- __syncthreads();
- sum = smem[0];
+ __syncthreads();
- return sum;
+ return smem[0];
+ }
}
if (threadIdx.x < block_hist_size)
elem = hist[0];
- squares[threadIdx.x] = elem * elem;
-
- __syncthreads();
- float sum = reduce_smem<nthreads>(squares);
+ float sum = reduce_smem<nthreads>(squares, elem * elem);
float scale = 1.0f / (::sqrtf(sum) + 0.1f * block_hist_size);
elem = ::min(elem * scale, threshold);
- __syncthreads();
- squares[threadIdx.x] = elem * elem;
+ sum = reduce_smem<nthreads>(squares, elem * elem);
- __syncthreads();
- sum = reduce_smem<nthreads>(squares);
scale = 1.0f / (::sqrtf(sum) + 1e-3f);
if (threadIdx.x < block_hist_size)
// return confidence values not just positive location
template <int nthreads, // Number of threads per one histogram block
- int nblocks> // Number of histogram block processed by single GPU thread block
+ int nblocks> // Number of histogram block processed by single GPU thread block
__global__ void compute_confidence_hists_kernel_many_blocks(const int img_win_width, const int img_block_width,
const int win_block_stride_x, const int win_block_stride_y,
const float* block_hists, const float* coefs,
float free_coef, float threshold, float* confidences)
{
- const int win_x = threadIdx.z;
- if (blockIdx.x * blockDim.z + win_x >= img_win_width)
- return;
-
- const float* hist = block_hists + (blockIdx.y * win_block_stride_y * img_block_width +
- blockIdx.x * win_block_stride_x * blockDim.z + win_x) *
- cblock_hist_size;
-
- float product = 0.f;
- for (int i = threadIdx.x; i < cdescr_size; i += nthreads)
- {
- int offset_y = i / cdescr_width;
- int offset_x = i - offset_y * cdescr_width;
- product += coefs[i] * hist[offset_y * img_block_width * cblock_hist_size + offset_x];
- }
-
- __shared__ float products[nthreads * nblocks];
-
- const int tid = threadIdx.z * nthreads + threadIdx.x;
- products[tid] = product;
-
- __syncthreads();
-
- if (nthreads >= 512)
- {
- if (threadIdx.x < 256) products[tid] = product = product + products[tid + 256];
- __syncthreads();
- }
- if (nthreads >= 256)
- {
- if (threadIdx.x < 128) products[tid] = product = product + products[tid + 128];
- __syncthreads();
- }
- if (nthreads >= 128)
- {
- if (threadIdx.x < 64) products[tid] = product = product + products[tid + 64];
- __syncthreads();
- }
-
- if (threadIdx.x < 32)
- {
- volatile float* smem = products;
- if (nthreads >= 64) smem[tid] = product = product + smem[tid + 32];
- if (nthreads >= 32) smem[tid] = product = product + smem[tid + 16];
- if (nthreads >= 16) smem[tid] = product = product + smem[tid + 8];
- if (nthreads >= 8) smem[tid] = product = product + smem[tid + 4];
- if (nthreads >= 4) smem[tid] = product = product + smem[tid + 2];
- if (nthreads >= 2) smem[tid] = product = product + smem[tid + 1];
- }
-
- if (threadIdx.x == 0)
- confidences[blockIdx.y * img_win_width + blockIdx.x * blockDim.z + win_x]
- = (float)(product + free_coef);
+ const int win_x = threadIdx.z;
+ if (blockIdx.x * blockDim.z + win_x >= img_win_width)
+ return;
+
+ const float* hist = block_hists + (blockIdx.y * win_block_stride_y * img_block_width +
+ blockIdx.x * win_block_stride_x * blockDim.z + win_x) *
+ cblock_hist_size;
+
+ float product = 0.f;
+ for (int i = threadIdx.x; i < cdescr_size; i += nthreads)
+ {
+ int offset_y = i / cdescr_width;
+ int offset_x = i - offset_y * cdescr_width;
+ product += coefs[i] * hist[offset_y * img_block_width * cblock_hist_size + offset_x];
+ }
+
+ __shared__ float products[nthreads * nblocks];
+
+ const int tid = threadIdx.z * nthreads + threadIdx.x;
+
+ reduce<nthreads>(products, product, tid, plus<float>());
+
+ if (threadIdx.x == 0)
+ confidences[blockIdx.y * img_win_width + blockIdx.x * blockDim.z + win_x] = product + free_coef;
}
int win_stride_y, int win_stride_x, int height, int width, float* block_hists,
float* coefs, float free_coef, float threshold, float *confidences)
{
- const int nthreads = 256;
- const int nblocks = 1;
-
- int win_block_stride_x = win_stride_x / block_stride_x;
- int win_block_stride_y = win_stride_y / block_stride_y;
- int img_win_width = (width - win_width + win_stride_x) / win_stride_x;
- int img_win_height = (height - win_height + win_stride_y) / win_stride_y;
-
- dim3 threads(nthreads, 1, nblocks);
- dim3 grid(divUp(img_win_width, nblocks), img_win_height);
-
- cudaSafeCall(cudaFuncSetCacheConfig(compute_confidence_hists_kernel_many_blocks<nthreads, nblocks>,
- cudaFuncCachePreferL1));
-
- int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) /
- block_stride_x;
- compute_confidence_hists_kernel_many_blocks<nthreads, nblocks><<<grid, threads>>>(
- img_win_width, img_block_width, win_block_stride_x, win_block_stride_y,
- block_hists, coefs, free_coef, threshold, confidences);
- cudaSafeCall(cudaThreadSynchronize());
+ const int nthreads = 256;
+ const int nblocks = 1;
+
+ int win_block_stride_x = win_stride_x / block_stride_x;
+ int win_block_stride_y = win_stride_y / block_stride_y;
+ int img_win_width = (width - win_width + win_stride_x) / win_stride_x;
+ int img_win_height = (height - win_height + win_stride_y) / win_stride_y;
+
+ dim3 threads(nthreads, 1, nblocks);
+ dim3 grid(divUp(img_win_width, nblocks), img_win_height);
+
+ cudaSafeCall(cudaFuncSetCacheConfig(compute_confidence_hists_kernel_many_blocks<nthreads, nblocks>,
+ cudaFuncCachePreferL1));
+
+ int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) /
+ block_stride_x;
+ compute_confidence_hists_kernel_many_blocks<nthreads, nblocks><<<grid, threads>>>(
+ img_win_width, img_block_width, win_block_stride_x, win_block_stride_y,
+ block_hists, coefs, free_coef, threshold, confidences);
+ cudaSafeCall(cudaThreadSynchronize());
}
template <int nthreads, // Number of threads per one histogram block
- int nblocks> // Number of histogram block processed by single GPU thread block
+ int nblocks> // Number of histogram block processed by single GPU thread block
__global__ void classify_hists_kernel_many_blocks(const int img_win_width, const int img_block_width,
const int win_block_stride_x, const int win_block_stride_y,
const float* block_hists, const float* coefs,
__shared__ float products[nthreads * nblocks];
const int tid = threadIdx.z * nthreads + threadIdx.x;
- products[tid] = product;
- __syncthreads();
-
- if (nthreads >= 512)
- {
- if (threadIdx.x < 256) products[tid] = product = product + products[tid + 256];
- __syncthreads();
- }
- if (nthreads >= 256)
- {
- if (threadIdx.x < 128) products[tid] = product = product + products[tid + 128];
- __syncthreads();
- }
- if (nthreads >= 128)
- {
- if (threadIdx.x < 64) products[tid] = product = product + products[tid + 64];
- __syncthreads();
- }
-
- if (threadIdx.x < 32)
- {
- volatile float* smem = products;
- if (nthreads >= 64) smem[tid] = product = product + smem[tid + 32];
- if (nthreads >= 32) smem[tid] = product = product + smem[tid + 16];
- if (nthreads >= 16) smem[tid] = product = product + smem[tid + 8];
- if (nthreads >= 8) smem[tid] = product = product + smem[tid + 4];
- if (nthreads >= 4) smem[tid] = product = product + smem[tid + 2];
- if (nthreads >= 2) smem[tid] = product = product + smem[tid + 1];
- }
+ reduce<nthreads>(products, product, tid, plus<float>());
if (threadIdx.x == 0)
labels[blockIdx.y * img_win_width + blockIdx.x * blockDim.z + win_x] = (product + free_coef >= threshold);
}}} // namespace cv { namespace gpu { namespace device
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+#endif /* CUDA_DISABLER */
#if !defined CUDA_DISABLER
-#include "internal_shared.hpp"
+#include "opencv2/gpu/device/common.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
+#include "opencv2/gpu/device/vec_traits.hpp"
#include "opencv2/gpu/device/vec_math.hpp"
+#include "opencv2/gpu/device/reduce.hpp"
+#include "opencv2/gpu/device/functional.hpp"
+#include "opencv2/gpu/device/utility.hpp"
+#include "opencv2/gpu/device/type_traits.hpp"
-namespace cv { namespace gpu { namespace device
+using namespace cv::gpu;
+using namespace cv::gpu::device;
+
+namespace detail
{
- namespace matrix_reductions
+ template <int cn> struct Unroll;
+ template <> struct Unroll<1>
{
- // Performs reduction in shared memory
- template <int size, typename T>
- __device__ void sumInSmem(volatile T* data, const uint tid)
+ template <int BLOCK_SIZE, typename R>
+ static __device__ __forceinline__ volatile R* smem_tuple(R* smem)
{
- T sum = data[tid];
-
- if (size >= 512) { if (tid < 256) { data[tid] = sum = sum + data[tid + 256]; } __syncthreads(); }
- if (size >= 256) { if (tid < 128) { data[tid] = sum = sum + data[tid + 128]; } __syncthreads(); }
- if (size >= 128) { if (tid < 64) { data[tid] = sum = sum + data[tid + 64]; } __syncthreads(); }
+ return smem;
+ }
- if (tid < 32)
- {
- if (size >= 64) data[tid] = sum = sum + data[tid + 32];
- if (size >= 32) data[tid] = sum = sum + data[tid + 16];
- if (size >= 16) data[tid] = sum = sum + data[tid + 8];
- if (size >= 8) data[tid] = sum = sum + data[tid + 4];
- if (size >= 4) data[tid] = sum = sum + data[tid + 2];
- if (size >= 2) data[tid] = sum = sum + data[tid + 1];
- }
+ template <typename R>
+ static __device__ __forceinline__ R& tie(R& val)
+ {
+ return val;
}
- struct Mask8U
+ template <class Op>
+ static __device__ __forceinline__ const Op& op(const Op& op)
+ {
+ return op;
+ }
+ };
+ template <> struct Unroll<2>
+ {
+ template <int BLOCK_SIZE, typename R>
+ static __device__ __forceinline__ thrust::tuple<volatile R*, volatile R*> smem_tuple(R* smem)
{
- explicit Mask8U(PtrStepb mask_): mask(mask_) {}
+ return cv::gpu::device::smem_tuple(smem, smem + BLOCK_SIZE);
+ }
- __device__ __forceinline__ bool operator()(int y, int x) const
- {
- return mask.ptr(y)[x];
- }
+ template <typename R>
+ static __device__ __forceinline__ thrust::tuple<typename VecTraits<R>::elem_type&, typename VecTraits<R>::elem_type&> tie(R& val)
+ {
+ return thrust::tie(val.x, val.y);
+ }
- PtrStepb mask;
- };
+ template <class Op>
+ static __device__ __forceinline__ const thrust::tuple<Op, Op> op(const Op& op)
+ {
+ return thrust::make_tuple(op, op);
+ }
+ };
+ template <> struct Unroll<3>
+ {
+ template <int BLOCK_SIZE, typename R>
+ static __device__ __forceinline__ thrust::tuple<volatile R*, volatile R*, volatile R*> smem_tuple(R* smem)
+ {
+ return cv::gpu::device::smem_tuple(smem, smem + BLOCK_SIZE, smem + 2 * BLOCK_SIZE);
+ }
- struct MaskTrue
+ template <typename R>
+ static __device__ __forceinline__ thrust::tuple<typename VecTraits<R>::elem_type&, typename VecTraits<R>::elem_type&, typename VecTraits<R>::elem_type&> tie(R& val)
{
- __device__ __forceinline__ bool operator()(int y, int x) const
- {
- return true;
- }
- __device__ __forceinline__ MaskTrue(){}
- __device__ __forceinline__ MaskTrue(const MaskTrue& mask_){}
- };
+ return thrust::tie(val.x, val.y, val.z);
+ }
- //////////////////////////////////////////////////////////////////////////////
- // Min max
-
- // To avoid shared bank conflicts we convert each value into value of
- // appropriate type (32 bits minimum)
- template <typename T> struct MinMaxTypeTraits {};
- template <> struct MinMaxTypeTraits<uchar> { typedef int best_type; };
- template <> struct MinMaxTypeTraits<char> { typedef int best_type; };
- template <> struct MinMaxTypeTraits<ushort> { typedef int best_type; };
- template <> struct MinMaxTypeTraits<short> { typedef int best_type; };
- template <> struct MinMaxTypeTraits<int> { typedef int best_type; };
- template <> struct MinMaxTypeTraits<float> { typedef float best_type; };
- template <> struct MinMaxTypeTraits<double> { typedef double best_type; };
-
- namespace minmax
+ template <class Op>
+ static __device__ __forceinline__ const thrust::tuple<Op, Op, Op> op(const Op& op)
{
- __constant__ int ctwidth;
- __constant__ int ctheight;
+ return thrust::make_tuple(op, op, op);
+ }
+ };
+ template <> struct Unroll<4>
+ {
+ template <int BLOCK_SIZE, typename R>
+ static __device__ __forceinline__ thrust::tuple<volatile R*, volatile R*, volatile R*, volatile R*> smem_tuple(R* smem)
+ {
+ return cv::gpu::device::smem_tuple(smem, smem + BLOCK_SIZE, smem + 2 * BLOCK_SIZE, smem + 3 * BLOCK_SIZE);
+ }
- // Global counter of blocks finished its work
- __device__ uint blocks_finished = 0;
+ template <typename R>
+ static __device__ __forceinline__ thrust::tuple<typename VecTraits<R>::elem_type&, typename VecTraits<R>::elem_type&, typename VecTraits<R>::elem_type&, typename VecTraits<R>::elem_type&> tie(R& val)
+ {
+ return thrust::tie(val.x, val.y, val.z, val.w);
+ }
+ template <class Op>
+ static __device__ __forceinline__ const thrust::tuple<Op, Op, Op, Op> op(const Op& op)
+ {
+ return thrust::make_tuple(op, op, op, op);
+ }
+ };
+}
- // Estimates good thread configuration
- // - threads variable satisfies to threads.x * threads.y == 256
- void estimateThreadCfg(int cols, int rows, dim3& threads, dim3& grid)
- {
- threads = dim3(32, 8);
- grid = dim3(divUp(cols, threads.x * 8), divUp(rows, threads.y * 32));
- grid.x = std::min(grid.x, threads.x);
- grid.y = std::min(grid.y, threads.y);
- }
+/////////////////////////////////////////////////////////////
+// sum
+namespace sum
+{
+ __device__ unsigned int blocks_finished = 0;
- // Returns required buffer sizes
- void getBufSizeRequired(int cols, int rows, int elem_size, int& bufcols, int& bufrows)
- {
- dim3 threads, grid;
- estimateThreadCfg(cols, rows, threads, grid);
- bufcols = grid.x * grid.y * elem_size;
- bufrows = 2;
- }
+ template <typename R, int cn> struct AtomicAdd;
+ template <typename R> struct AtomicAdd<R, 1>
+ {
+ static __device__ void run(R* ptr, R val)
+ {
+ ::atomicAdd(ptr, val);
+ }
+ };
+ template <typename R> struct AtomicAdd<R, 2>
+ {
+ typedef typename TypeVec<R, 2>::vec_type val_type;
+ static __device__ void run(R* ptr, val_type val)
+ {
+ ::atomicAdd(ptr, val.x);
+ ::atomicAdd(ptr + 1, val.y);
+ }
+ };
+ template <typename R> struct AtomicAdd<R, 3>
+ {
+ typedef typename TypeVec<R, 3>::vec_type val_type;
- // Estimates device constants which are used in the kernels using specified thread configuration
- void setKernelConsts(int cols, int rows, const dim3& threads, const dim3& grid)
- {
- int twidth = divUp(divUp(cols, grid.x), threads.x);
- int theight = divUp(divUp(rows, grid.y), threads.y);
- cudaSafeCall(cudaMemcpyToSymbol(ctwidth, &twidth, sizeof(ctwidth)));
- cudaSafeCall(cudaMemcpyToSymbol(ctheight, &theight, sizeof(ctheight)));
- }
+ static __device__ void run(R* ptr, val_type val)
+ {
+ ::atomicAdd(ptr, val.x);
+ ::atomicAdd(ptr + 1, val.y);
+ ::atomicAdd(ptr + 2, val.z);
+ }
+ };
+ template <typename R> struct AtomicAdd<R, 4>
+ {
+ typedef typename TypeVec<R, 4>::vec_type val_type;
+ static __device__ void run(R* ptr, val_type val)
+ {
+ ::atomicAdd(ptr, val.x);
+ ::atomicAdd(ptr + 1, val.y);
+ ::atomicAdd(ptr + 2, val.z);
+ ::atomicAdd(ptr + 3, val.w);
+ }
+ };
- // Does min and max in shared memory
- template <typename T>
- __device__ __forceinline__ void merge(uint tid, uint offset, volatile T* minval, volatile T* maxval)
- {
- minval[tid] = ::min(minval[tid], minval[tid + offset]);
- maxval[tid] = ::max(maxval[tid], maxval[tid + offset]);
- }
+ template <int BLOCK_SIZE, typename R, int cn>
+ struct GlobalReduce
+ {
+ typedef typename TypeVec<R, cn>::vec_type result_type;
+ static __device__ void run(result_type& sum, result_type* result, int tid, int bid, R* smem)
+ {
+ #if __CUDA_ARCH__ >= 200
+ if (tid == 0)
+ AtomicAdd<R, cn>::run((R*) result, sum);
+ #else
+ __shared__ bool is_last;
- template <int size, typename T>
- __device__ void findMinMaxInSmem(volatile T* minval, volatile T* maxval, const uint tid)
+ if (tid == 0)
{
- if (size >= 512) { if (tid < 256) { merge(tid, 256, minval, maxval); } __syncthreads(); }
- if (size >= 256) { if (tid < 128) { merge(tid, 128, minval, maxval); } __syncthreads(); }
- if (size >= 128) { if (tid < 64) { merge(tid, 64, minval, maxval); } __syncthreads(); }
+ result[bid] = sum;
- if (tid < 32)
- {
- if (size >= 64) merge(tid, 32, minval, maxval);
- if (size >= 32) merge(tid, 16, minval, maxval);
- if (size >= 16) merge(tid, 8, minval, maxval);
- if (size >= 8) merge(tid, 4, minval, maxval);
- if (size >= 4) merge(tid, 2, minval, maxval);
- if (size >= 2) merge(tid, 1, minval, maxval);
- }
+ __threadfence();
+
+ unsigned int ticket = ::atomicAdd(&blocks_finished, 1);
+ is_last = (ticket == gridDim.x * gridDim.y - 1);
}
+ __syncthreads();
- template <int nthreads, typename T, typename Mask>
- __global__ void minMaxKernel(const PtrStepSzb src, Mask mask, T* minval, T* maxval)
+ if (is_last)
{
- typedef typename MinMaxTypeTraits<T>::best_type best_type;
- __shared__ best_type sminval[nthreads];
- __shared__ best_type smaxval[nthreads];
-
- uint x0 = blockIdx.x * blockDim.x * ctwidth + threadIdx.x;
- uint y0 = blockIdx.y * blockDim.y * ctheight + threadIdx.y;
- uint tid = threadIdx.y * blockDim.x + threadIdx.x;
-
- T mymin = numeric_limits<T>::max();
- T mymax = numeric_limits<T>::is_signed ? -numeric_limits<T>::max() : numeric_limits<T>::min();
- uint y_end = ::min(y0 + (ctheight - 1) * blockDim.y + 1, src.rows);
- uint x_end = ::min(x0 + (ctwidth - 1) * blockDim.x + 1, src.cols);
- for (uint y = y0; y < y_end; y += blockDim.y)
- {
- const T* src_row = (const T*)src.ptr(y);
- for (uint x = x0; x < x_end; x += blockDim.x)
- {
- T val = src_row[x];
- if (mask(y, x))
- {
- mymin = ::min(mymin, val);
- mymax = ::max(mymax, val);
- }
- }
- }
-
- sminval[tid] = mymin;
- smaxval[tid] = mymax;
- __syncthreads();
+ sum = tid < gridDim.x * gridDim.y ? result[tid] : VecTraits<result_type>::all(0);
- findMinMaxInSmem<nthreads, best_type>(sminval, smaxval, tid);
+ device::reduce<BLOCK_SIZE>(detail::Unroll<cn>::template smem_tuple<BLOCK_SIZE>(smem), detail::Unroll<cn>::tie(sum), tid, detail::Unroll<cn>::op(plus<R>()));
if (tid == 0)
{
- minval[blockIdx.y * gridDim.x + blockIdx.x] = (T)sminval[0];
- maxval[blockIdx.y * gridDim.x + blockIdx.x] = (T)smaxval[0];
+ result[0] = sum;
+ blocks_finished = 0;
}
-
- #if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
- __shared__ bool is_last;
-
- if (tid == 0)
- {
- minval[blockIdx.y * gridDim.x + blockIdx.x] = (T)sminval[0];
- maxval[blockIdx.y * gridDim.x + blockIdx.x] = (T)smaxval[0];
- __threadfence();
-
- uint ticket = atomicInc(&blocks_finished, gridDim.x * gridDim.y);
- is_last = ticket == gridDim.x * gridDim.y - 1;
- }
-
- __syncthreads();
-
- if (is_last)
- {
- uint idx = ::min(tid, gridDim.x * gridDim.y - 1);
-
- sminval[tid] = minval[idx];
- smaxval[tid] = maxval[idx];
- __syncthreads();
-
- findMinMaxInSmem<nthreads, best_type>(sminval, smaxval, tid);
-
- if (tid == 0)
- {
- minval[0] = (T)sminval[0];
- maxval[0] = (T)smaxval[0];
- blocks_finished = 0;
- }
- }
- #else
- if (tid == 0)
- {
- minval[blockIdx.y * gridDim.x + blockIdx.x] = (T)sminval[0];
- maxval[blockIdx.y * gridDim.x + blockIdx.x] = (T)smaxval[0];
- }
- #endif
- }
-
-
- template <typename T>
- void minMaxMaskCaller(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf)
- {
- dim3 threads, grid;
- estimateThreadCfg(src.cols, src.rows, threads, grid);
- setKernelConsts(src.cols, src.rows, threads, grid);
-
- T* minval_buf = (T*)buf.ptr(0);
- T* maxval_buf = (T*)buf.ptr(1);
-
- minMaxKernel<256, T, Mask8U><<<grid, threads>>>(src, Mask8U(mask), minval_buf, maxval_buf);
- cudaSafeCall( cudaGetLastError() );
-
- cudaSafeCall( cudaDeviceSynchronize() );
-
- T minval_, maxval_;
- cudaSafeCall( cudaMemcpy(&minval_, minval_buf, sizeof(T), cudaMemcpyDeviceToHost) );
- cudaSafeCall( cudaMemcpy(&maxval_, maxval_buf, sizeof(T), cudaMemcpyDeviceToHost) );
- *minval = minval_;
- *maxval = maxval_;
}
+ #endif
+ }
+ };
+ template <int BLOCK_SIZE, int cn>
+ struct GlobalReduce<BLOCK_SIZE, double, cn>
+ {
+ typedef typename TypeVec<double, cn>::vec_type result_type;
- template void minMaxMaskCaller<uchar>(const PtrStepSzb, const PtrStepb, double*, double*, PtrStepb);
- template void minMaxMaskCaller<char>(const PtrStepSzb, const PtrStepb, double*, double*, PtrStepb);
- template void minMaxMaskCaller<ushort>(const PtrStepSzb, const PtrStepb, double*, double*, PtrStepb);
- template void minMaxMaskCaller<short>(const PtrStepSzb, const PtrStepb, double*, double*, PtrStepb);
- template void minMaxMaskCaller<int>(const PtrStepSzb, const PtrStepb, double*, double*, PtrStepb);
- template void minMaxMaskCaller<float>(const PtrStepSzb, const PtrStepb, double*, double*, PtrStepb);
- template void minMaxMaskCaller<double>(const PtrStepSzb, const PtrStepb, double*, double*, PtrStepb);
-
+ static __device__ void run(result_type& sum, result_type* result, int tid, int bid, double* smem)
+ {
+ __shared__ bool is_last;
- template <typename T>
- void minMaxCaller(const PtrStepSzb src, double* minval, double* maxval, PtrStepb buf)
+ if (tid == 0)
{
- dim3 threads, grid;
- estimateThreadCfg(src.cols, src.rows, threads, grid);
- setKernelConsts(src.cols, src.rows, threads, grid);
-
- T* minval_buf = (T*)buf.ptr(0);
- T* maxval_buf = (T*)buf.ptr(1);
-
- minMaxKernel<256, T, MaskTrue><<<grid, threads>>>(src, MaskTrue(), minval_buf, maxval_buf);
- cudaSafeCall( cudaGetLastError() );
+ result[bid] = sum;
- cudaSafeCall( cudaDeviceSynchronize() );
+ __threadfence();
- T minval_, maxval_;
- cudaSafeCall( cudaMemcpy(&minval_, minval_buf, sizeof(T), cudaMemcpyDeviceToHost) );
- cudaSafeCall( cudaMemcpy(&maxval_, maxval_buf, sizeof(T), cudaMemcpyDeviceToHost) );
- *minval = minval_;
- *maxval = maxval_;
+ unsigned int ticket = ::atomicAdd(&blocks_finished, 1);
+ is_last = (ticket == gridDim.x * gridDim.y - 1);
}
- template void minMaxCaller<uchar>(const PtrStepSzb, double*, double*, PtrStepb);
- template void minMaxCaller<char>(const PtrStepSzb, double*, double*, PtrStepb);
- template void minMaxCaller<ushort>(const PtrStepSzb, double*, double*, PtrStepb);
- template void minMaxCaller<short>(const PtrStepSzb, double*, double*, PtrStepb);
- template void minMaxCaller<int>(const PtrStepSzb, double*, double*, PtrStepb);
- template void minMaxCaller<float>(const PtrStepSzb, double*,double*, PtrStepb);
- template void minMaxCaller<double>(const PtrStepSzb, double*, double*, PtrStepb);
-
+ __syncthreads();
- template <int nthreads, typename T>
- __global__ void minMaxPass2Kernel(T* minval, T* maxval, int size)
+ if (is_last)
{
- typedef typename MinMaxTypeTraits<T>::best_type best_type;
- __shared__ best_type sminval[nthreads];
- __shared__ best_type smaxval[nthreads];
-
- uint tid = threadIdx.y * blockDim.x + threadIdx.x;
- uint idx = ::min(tid, size - 1);
-
- sminval[tid] = minval[idx];
- smaxval[tid] = maxval[idx];
- __syncthreads();
+ sum = tid < gridDim.x * gridDim.y ? result[tid] : VecTraits<result_type>::all(0);
- findMinMaxInSmem<nthreads, best_type>(sminval, smaxval, tid);
+ device::reduce<BLOCK_SIZE>(detail::Unroll<cn>::template smem_tuple<BLOCK_SIZE>(smem), detail::Unroll<cn>::tie(sum), tid, detail::Unroll<cn>::op(plus<double>()));
if (tid == 0)
{
- minval[0] = (T)sminval[0];
- maxval[0] = (T)smaxval[0];
+ result[0] = sum;
+ blocks_finished = 0;
}
}
+ }
+ };
+ template <int BLOCK_SIZE, typename src_type, typename result_type, class Op>
+ __global__ void kernel(const PtrStepSz<src_type> src, result_type* result, const Op op, const int twidth, const int theight)
+ {
+ typedef typename VecTraits<src_type>::elem_type T;
+ typedef typename VecTraits<result_type>::elem_type R;
+ const int cn = VecTraits<src_type>::cn;
- template <typename T>
- void minMaxMaskMultipassCaller(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf)
- {
- dim3 threads, grid;
- estimateThreadCfg(src.cols, src.rows, threads, grid);
- setKernelConsts(src.cols, src.rows, threads, grid);
-
- T* minval_buf = (T*)buf.ptr(0);
- T* maxval_buf = (T*)buf.ptr(1);
-
- minMaxKernel<256, T, Mask8U><<<grid, threads>>>(src, Mask8U(mask), minval_buf, maxval_buf);
- cudaSafeCall( cudaGetLastError() );
- minMaxPass2Kernel<256, T><<<1, 256>>>(minval_buf, maxval_buf, grid.x * grid.y);
- cudaSafeCall( cudaGetLastError() );
-
- cudaSafeCall(cudaDeviceSynchronize());
-
- T minval_, maxval_;
- cudaSafeCall( cudaMemcpy(&minval_, minval_buf, sizeof(T), cudaMemcpyDeviceToHost) );
- cudaSafeCall( cudaMemcpy(&maxval_, maxval_buf, sizeof(T), cudaMemcpyDeviceToHost) );
- *minval = minval_;
- *maxval = maxval_;
- }
-
- template void minMaxMaskMultipassCaller<uchar>(const PtrStepSzb, const PtrStepb, double*, double*, PtrStepb);
- template void minMaxMaskMultipassCaller<char>(const PtrStepSzb, const PtrStepb, double*, double*, PtrStepb);
- template void minMaxMaskMultipassCaller<ushort>(const PtrStepSzb, const PtrStepb, double*, double*, PtrStepb);
- template void minMaxMaskMultipassCaller<short>(const PtrStepSzb, const PtrStepb, double*, double*, PtrStepb);
- template void minMaxMaskMultipassCaller<int>(const PtrStepSzb, const PtrStepb, double*, double*, PtrStepb);
- template void minMaxMaskMultipassCaller<float>(const PtrStepSzb, const PtrStepb, double*, double*, PtrStepb);
-
-
- template <typename T>
- void minMaxMultipassCaller(const PtrStepSzb src, double* minval, double* maxval, PtrStepb buf)
- {
- dim3 threads, grid;
- estimateThreadCfg(src.cols, src.rows, threads, grid);
- setKernelConsts(src.cols, src.rows, threads, grid);
-
- T* minval_buf = (T*)buf.ptr(0);
- T* maxval_buf = (T*)buf.ptr(1);
-
- minMaxKernel<256, T, MaskTrue><<<grid, threads>>>(src, MaskTrue(), minval_buf, maxval_buf);
- cudaSafeCall( cudaGetLastError() );
- minMaxPass2Kernel<256, T><<<1, 256>>>(minval_buf, maxval_buf, grid.x * grid.y);
- cudaSafeCall( cudaGetLastError() );
-
- cudaSafeCall( cudaDeviceSynchronize() );
+ __shared__ R smem[BLOCK_SIZE * cn];
- T minval_, maxval_;
- cudaSafeCall( cudaMemcpy(&minval_, minval_buf, sizeof(T), cudaMemcpyDeviceToHost) );
- cudaSafeCall( cudaMemcpy(&maxval_, maxval_buf, sizeof(T), cudaMemcpyDeviceToHost) );
- *minval = minval_;
- *maxval = maxval_;
- }
+ const int x0 = blockIdx.x * blockDim.x * twidth + threadIdx.x;
+ const int y0 = blockIdx.y * blockDim.y * theight + threadIdx.y;
- template void minMaxMultipassCaller<uchar>(const PtrStepSzb, double*, double*, PtrStepb);
- template void minMaxMultipassCaller<char>(const PtrStepSzb, double*, double*, PtrStepb);
- template void minMaxMultipassCaller<ushort>(const PtrStepSzb, double*, double*, PtrStepb);
- template void minMaxMultipassCaller<short>(const PtrStepSzb, double*, double*, PtrStepb);
- template void minMaxMultipassCaller<int>(const PtrStepSzb, double*, double*, PtrStepb);
- template void minMaxMultipassCaller<float>(const PtrStepSzb, double*, double*, PtrStepb);
- } // namespace minmax
+ const int tid = threadIdx.y * blockDim.x + threadIdx.x;
+ const int bid = blockIdx.y * gridDim.x + blockIdx.x;
- ///////////////////////////////////////////////////////////////////////////////
- // minMaxLoc
+ result_type sum = VecTraits<result_type>::all(0);
- namespace minmaxloc
+ for (int i = 0, y = y0; i < theight && y < src.rows; ++i, y += blockDim.y)
{
- __constant__ int ctwidth;
- __constant__ int ctheight;
+ const src_type* ptr = src.ptr(y);
- // Global counter of blocks finished its work
- __device__ uint blocks_finished = 0;
-
-
- // Estimates good thread configuration
- // - threads variable satisfies to threads.x * threads.y == 256
- void estimateThreadCfg(int cols, int rows, dim3& threads, dim3& grid)
+ for (int j = 0, x = x0; j < twidth && x < src.cols; ++j, x += blockDim.x)
{
- threads = dim3(32, 8);
- grid = dim3(divUp(cols, threads.x * 8), divUp(rows, threads.y * 32));
- grid.x = std::min(grid.x, threads.x);
- grid.y = std::min(grid.y, threads.y);
- }
-
+ const src_type srcVal = ptr[x];
- // Returns required buffer sizes
- void getBufSizeRequired(int cols, int rows, int elem_size, int& b1cols,
- int& b1rows, int& b2cols, int& b2rows)
- {
- dim3 threads, grid;
- estimateThreadCfg(cols, rows, threads, grid);
- b1cols = grid.x * grid.y * elem_size; // For values
- b1rows = 2;
- b2cols = grid.x * grid.y * sizeof(int); // For locations
- b2rows = 2;
+ sum = sum + op(saturate_cast<result_type>(srcVal));
}
+ }
+ device::reduce<BLOCK_SIZE>(detail::Unroll<cn>::template smem_tuple<BLOCK_SIZE>(smem), detail::Unroll<cn>::tie(sum), tid, detail::Unroll<cn>::op(plus<R>()));
- // Estimates device constants which are used in the kernels using specified thread configuration
- void setKernelConsts(int cols, int rows, const dim3& threads, const dim3& grid)
- {
- int twidth = divUp(divUp(cols, grid.x), threads.x);
- int theight = divUp(divUp(rows, grid.y), threads.y);
- cudaSafeCall(cudaMemcpyToSymbol(ctwidth, &twidth, sizeof(ctwidth)));
- cudaSafeCall(cudaMemcpyToSymbol(ctheight, &theight, sizeof(ctheight)));
- }
+ GlobalReduce<BLOCK_SIZE, R, cn>::run(sum, result, tid, bid, smem);
+ }
+ const int threads_x = 32;
+ const int threads_y = 8;
- template <typename T>
- __device__ void merge(uint tid, uint offset, volatile T* minval, volatile T* maxval,
- volatile uint* minloc, volatile uint* maxloc)
- {
- T val = minval[tid + offset];
- if (val < minval[tid])
- {
- minval[tid] = val;
- minloc[tid] = minloc[tid + offset];
- }
- val = maxval[tid + offset];
- if (val > maxval[tid])
- {
- maxval[tid] = val;
- maxloc[tid] = maxloc[tid + offset];
- }
- }
+ void getLaunchCfg(int cols, int rows, dim3& block, dim3& grid)
+ {
+ block = dim3(threads_x, threads_y);
+ grid = dim3(divUp(cols, block.x * block.y),
+ divUp(rows, block.y * block.x));
- template <int size, typename T>
- __device__ void findMinMaxLocInSmem(volatile T* minval, volatile T* maxval, volatile uint* minloc,
- volatile uint* maxloc, const uint tid)
- {
- if (size >= 512) { if (tid < 256) { merge(tid, 256, minval, maxval, minloc, maxloc); } __syncthreads(); }
- if (size >= 256) { if (tid < 128) { merge(tid, 128, minval, maxval, minloc, maxloc); } __syncthreads(); }
- if (size >= 128) { if (tid < 64) { merge(tid, 64, minval, maxval, minloc, maxloc); } __syncthreads(); }
+ grid.x = ::min(grid.x, block.x);
+ grid.y = ::min(grid.y, block.y);
+ }
- if (tid < 32)
- {
- if (size >= 64) merge(tid, 32, minval, maxval, minloc, maxloc);
- if (size >= 32) merge(tid, 16, minval, maxval, minloc, maxloc);
- if (size >= 16) merge(tid, 8, minval, maxval, minloc, maxloc);
- if (size >= 8) merge(tid, 4, minval, maxval, minloc, maxloc);
- if (size >= 4) merge(tid, 2, minval, maxval, minloc, maxloc);
- if (size >= 2) merge(tid, 1, minval, maxval, minloc, maxloc);
- }
- }
+ void getBufSize(int cols, int rows, int cn, int& bufcols, int& bufrows)
+ {
+ dim3 block, grid;
+ getLaunchCfg(cols, rows, block, grid);
+ bufcols = grid.x * grid.y * sizeof(double) * cn;
+ bufrows = 1;
+ }
- template <int nthreads, typename T, typename Mask>
- __global__ void minMaxLocKernel(const PtrStepSzb src, Mask mask, T* minval, T* maxval,
- uint* minloc, uint* maxloc)
- {
- typedef typename MinMaxTypeTraits<T>::best_type best_type;
- __shared__ best_type sminval[nthreads];
- __shared__ best_type smaxval[nthreads];
- __shared__ uint sminloc[nthreads];
- __shared__ uint smaxloc[nthreads];
-
- uint x0 = blockIdx.x * blockDim.x * ctwidth + threadIdx.x;
- uint y0 = blockIdx.y * blockDim.y * ctheight + threadIdx.y;
- uint tid = threadIdx.y * blockDim.x + threadIdx.x;
-
- T mymin = numeric_limits<T>::max();
- T mymax = numeric_limits<T>::is_signed ? -numeric_limits<T>::max() : numeric_limits<T>::min();
- uint myminloc = 0;
- uint mymaxloc = 0;
- uint y_end = ::min(y0 + (ctheight - 1) * blockDim.y + 1, src.rows);
- uint x_end = ::min(x0 + (ctwidth - 1) * blockDim.x + 1, src.cols);
-
- for (uint y = y0; y < y_end; y += blockDim.y)
- {
- const T* ptr = (const T*)src.ptr(y);
- for (uint x = x0; x < x_end; x += blockDim.x)
- {
- if (mask(y, x))
- {
- T val = ptr[x];
- if (val <= mymin) { mymin = val; myminloc = y * src.cols + x; }
- if (val >= mymax) { mymax = val; mymaxloc = y * src.cols + x; }
- }
- }
- }
+ template <typename T, typename R, int cn, template <typename> class Op>
+ void caller(PtrStepSzb src_, void* buf_, double* out)
+ {
+ typedef typename TypeVec<T, cn>::vec_type src_type;
+ typedef typename TypeVec<R, cn>::vec_type result_type;
- sminval[tid] = mymin;
- smaxval[tid] = mymax;
- sminloc[tid] = myminloc;
- smaxloc[tid] = mymaxloc;
- __syncthreads();
+ PtrStepSz<src_type> src(src_);
+ result_type* buf = (result_type*) buf_;
- findMinMaxLocInSmem<nthreads, best_type>(sminval, smaxval, sminloc, smaxloc, tid);
+ dim3 block, grid;
+ getLaunchCfg(src.cols, src.rows, block, grid);
- #if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
- __shared__ bool is_last;
+ const int twidth = divUp(divUp(src.cols, grid.x), block.x);
+ const int theight = divUp(divUp(src.rows, grid.y), block.y);
- if (tid == 0)
- {
- minval[blockIdx.y * gridDim.x + blockIdx.x] = (T)sminval[0];
- maxval[blockIdx.y * gridDim.x + blockIdx.x] = (T)smaxval[0];
- minloc[blockIdx.y * gridDim.x + blockIdx.x] = sminloc[0];
- maxloc[blockIdx.y * gridDim.x + blockIdx.x] = smaxloc[0];
- __threadfence();
-
- uint ticket = atomicInc(&blocks_finished, gridDim.x * gridDim.y);
- is_last = ticket == gridDim.x * gridDim.y - 1;
- }
+ Op<result_type> op;
- __syncthreads();
+ kernel<threads_x * threads_y><<<grid, block>>>(src, buf, op, twidth, theight);
+ cudaSafeCall( cudaGetLastError() );
- if (is_last)
- {
- uint idx = ::min(tid, gridDim.x * gridDim.y - 1);
+ cudaSafeCall( cudaDeviceSynchronize() );
- sminval[tid] = minval[idx];
- smaxval[tid] = maxval[idx];
- sminloc[tid] = minloc[idx];
- smaxloc[tid] = maxloc[idx];
- __syncthreads();
+ R result[4] = {0, 0, 0, 0};
+ cudaSafeCall( cudaMemcpy(&result, buf, sizeof(result_type), cudaMemcpyDeviceToHost) );
- findMinMaxLocInSmem<nthreads, best_type>(sminval, smaxval, sminloc, smaxloc, tid);
+ out[0] = result[0];
+ out[1] = result[1];
+ out[2] = result[2];
+ out[3] = result[3];
+ }
- if (tid == 0)
- {
- minval[0] = (T)sminval[0];
- maxval[0] = (T)smaxval[0];
- minloc[0] = sminloc[0];
- maxloc[0] = smaxloc[0];
- blocks_finished = 0;
- }
- }
- #else
- if (tid == 0)
- {
- minval[blockIdx.y * gridDim.x + blockIdx.x] = (T)sminval[0];
- maxval[blockIdx.y * gridDim.x + blockIdx.x] = (T)smaxval[0];
- minloc[blockIdx.y * gridDim.x + blockIdx.x] = sminloc[0];
- maxloc[blockIdx.y * gridDim.x + blockIdx.x] = smaxloc[0];
- }
- #endif
- }
+ template <typename T> struct SumType;
+ template <> struct SumType<uchar> { typedef unsigned int R; };
+ template <> struct SumType<schar> { typedef int R; };
+ template <> struct SumType<ushort> { typedef unsigned int R; };
+ template <> struct SumType<short> { typedef int R; };
+ template <> struct SumType<int> { typedef int R; };
+ template <> struct SumType<float> { typedef float R; };
+ template <> struct SumType<double> { typedef double R; };
+ template <typename T, int cn>
+ void run(PtrStepSzb src, void* buf, double* out)
+ {
+ typedef typename SumType<T>::R R;
+ caller<T, R, cn, identity>(src, buf, out);
+ }
+
+ template void run<uchar, 1>(PtrStepSzb src, void* buf, double* out);
+ template void run<uchar, 2>(PtrStepSzb src, void* buf, double* out);
+ template void run<uchar, 3>(PtrStepSzb src, void* buf, double* out);
+ template void run<uchar, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void run<schar, 1>(PtrStepSzb src, void* buf, double* out);
+ template void run<schar, 2>(PtrStepSzb src, void* buf, double* out);
+ template void run<schar, 3>(PtrStepSzb src, void* buf, double* out);
+ template void run<schar, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void run<ushort, 1>(PtrStepSzb src, void* buf, double* out);
+ template void run<ushort, 2>(PtrStepSzb src, void* buf, double* out);
+ template void run<ushort, 3>(PtrStepSzb src, void* buf, double* out);
+ template void run<ushort, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void run<short, 1>(PtrStepSzb src, void* buf, double* out);
+ template void run<short, 2>(PtrStepSzb src, void* buf, double* out);
+ template void run<short, 3>(PtrStepSzb src, void* buf, double* out);
+ template void run<short, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void run<int, 1>(PtrStepSzb src, void* buf, double* out);
+ template void run<int, 2>(PtrStepSzb src, void* buf, double* out);
+ template void run<int, 3>(PtrStepSzb src, void* buf, double* out);
+ template void run<int, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void run<float, 1>(PtrStepSzb src, void* buf, double* out);
+ template void run<float, 2>(PtrStepSzb src, void* buf, double* out);
+ template void run<float, 3>(PtrStepSzb src, void* buf, double* out);
+ template void run<float, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void run<double, 1>(PtrStepSzb src, void* buf, double* out);
+ template void run<double, 2>(PtrStepSzb src, void* buf, double* out);
+ template void run<double, 3>(PtrStepSzb src, void* buf, double* out);
+ template void run<double, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template <typename T, int cn>
+ void runAbs(PtrStepSzb src, void* buf, double* out)
+ {
+ typedef typename SumType<T>::R R;
+ caller<T, R, cn, abs_func>(src, buf, out);
+ }
+
+ template void runAbs<uchar, 1>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<uchar, 2>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<uchar, 3>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<uchar, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void runAbs<schar, 1>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<schar, 2>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<schar, 3>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<schar, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void runAbs<ushort, 1>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<ushort, 2>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<ushort, 3>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<ushort, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void runAbs<short, 1>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<short, 2>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<short, 3>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<short, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void runAbs<int, 1>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<int, 2>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<int, 3>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<int, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void runAbs<float, 1>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<float, 2>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<float, 3>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<float, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void runAbs<double, 1>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<double, 2>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<double, 3>(PtrStepSzb src, void* buf, double* out);
+ template void runAbs<double, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template <typename T> struct Sqr : unary_function<T, T>
+ {
+ __device__ __forceinline__ T operator ()(T x) const
+ {
+ return x * x;
+ }
+ };
- template <typename T>
- void minMaxLocMaskCaller(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval,
- int minloc[2], int maxloc[2], PtrStepb valbuf, PtrStepb locbuf)
- {
- dim3 threads, grid;
- estimateThreadCfg(src.cols, src.rows, threads, grid);
- setKernelConsts(src.cols, src.rows, threads, grid);
-
- T* minval_buf = (T*)valbuf.ptr(0);
- T* maxval_buf = (T*)valbuf.ptr(1);
- uint* minloc_buf = (uint*)locbuf.ptr(0);
- uint* maxloc_buf = (uint*)locbuf.ptr(1);
-
- minMaxLocKernel<256, T, Mask8U><<<grid, threads>>>(src, Mask8U(mask), minval_buf, maxval_buf,
- minloc_buf, maxloc_buf);
- cudaSafeCall( cudaGetLastError() );
-
- cudaSafeCall( cudaDeviceSynchronize() );
-
- T minval_, maxval_;
- cudaSafeCall( cudaMemcpy(&minval_, minval_buf, sizeof(T), cudaMemcpyDeviceToHost) );
- cudaSafeCall( cudaMemcpy(&maxval_, maxval_buf, sizeof(T), cudaMemcpyDeviceToHost) );
- *minval = minval_;
- *maxval = maxval_;
-
- uint minloc_, maxloc_;
- cudaSafeCall( cudaMemcpy(&minloc_, minloc_buf, sizeof(int), cudaMemcpyDeviceToHost) );
- cudaSafeCall( cudaMemcpy(&maxloc_, maxloc_buf, sizeof(int), cudaMemcpyDeviceToHost) );
- minloc[1] = minloc_ / src.cols; minloc[0] = minloc_ - minloc[1] * src.cols;
- maxloc[1] = maxloc_ / src.cols; maxloc[0] = maxloc_ - maxloc[1] * src.cols;
- }
+ template <typename T, int cn>
+ void runSqr(PtrStepSzb src, void* buf, double* out)
+ {
+ caller<T, double, cn, Sqr>(src, buf, out);
+ }
+
+ template void runSqr<uchar, 1>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<uchar, 2>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<uchar, 3>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<uchar, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void runSqr<schar, 1>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<schar, 2>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<schar, 3>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<schar, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void runSqr<ushort, 1>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<ushort, 2>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<ushort, 3>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<ushort, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void runSqr<short, 1>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<short, 2>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<short, 3>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<short, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void runSqr<int, 1>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<int, 2>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<int, 3>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<int, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void runSqr<float, 1>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<float, 2>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<float, 3>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<float, 4>(PtrStepSzb src, void* buf, double* out);
+
+ template void runSqr<double, 1>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<double, 2>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<double, 3>(PtrStepSzb src, void* buf, double* out);
+ template void runSqr<double, 4>(PtrStepSzb src, void* buf, double* out);
+}
+
+/////////////////////////////////////////////////////////////
+// minMax
+
+namespace minMax
+{
+ __device__ unsigned int blocks_finished = 0;
+
+ // To avoid shared bank conflicts we convert each value into value of
+ // appropriate type (32 bits minimum)
+ template <typename T> struct MinMaxTypeTraits;
+ template <> struct MinMaxTypeTraits<uchar> { typedef int best_type; };
+ template <> struct MinMaxTypeTraits<schar> { typedef int best_type; };
+ template <> struct MinMaxTypeTraits<ushort> { typedef int best_type; };
+ template <> struct MinMaxTypeTraits<short> { typedef int best_type; };
+ template <> struct MinMaxTypeTraits<int> { typedef int best_type; };
+ template <> struct MinMaxTypeTraits<float> { typedef float best_type; };
+ template <> struct MinMaxTypeTraits<double> { typedef double best_type; };
+
+ template <int BLOCK_SIZE, typename R>
+ struct GlobalReduce
+ {
+ static __device__ void run(R& mymin, R& mymax, R* minval, R* maxval, int tid, int bid, R* sminval, R* smaxval)
+ {
+ __shared__ bool is_last;
- template void minMaxLocMaskCaller<uchar>(const PtrStepSzb, const PtrStepb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocMaskCaller<char>(const PtrStepSzb, const PtrStepb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocMaskCaller<ushort>(const PtrStepSzb, const PtrStepb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocMaskCaller<short>(const PtrStepSzb, const PtrStepb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocMaskCaller<int>(const PtrStepSzb, const PtrStepb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocMaskCaller<float>(const PtrStepSzb, const PtrStepb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocMaskCaller<double>(const PtrStepSzb, const PtrStepb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
+ if (tid == 0)
+ {
+ minval[bid] = mymin;
+ maxval[bid] = mymax;
+ __threadfence();
- template <typename T>
- void minMaxLocCaller(const PtrStepSzb src, double* minval, double* maxval,
- int minloc[2], int maxloc[2], PtrStepb valbuf, PtrStepb locbuf)
- {
- dim3 threads, grid;
- estimateThreadCfg(src.cols, src.rows, threads, grid);
- setKernelConsts(src.cols, src.rows, threads, grid);
-
- T* minval_buf = (T*)valbuf.ptr(0);
- T* maxval_buf = (T*)valbuf.ptr(1);
- uint* minloc_buf = (uint*)locbuf.ptr(0);
- uint* maxloc_buf = (uint*)locbuf.ptr(1);
-
- minMaxLocKernel<256, T, MaskTrue><<<grid, threads>>>(src, MaskTrue(), minval_buf, maxval_buf,
- minloc_buf, maxloc_buf);
- cudaSafeCall( cudaGetLastError() );
-
- cudaSafeCall( cudaDeviceSynchronize() );
-
- T minval_, maxval_;
- cudaSafeCall(cudaMemcpy(&minval_, minval_buf, sizeof(T), cudaMemcpyDeviceToHost));
- cudaSafeCall(cudaMemcpy(&maxval_, maxval_buf, sizeof(T), cudaMemcpyDeviceToHost));
- *minval = minval_;
- *maxval = maxval_;
-
- uint minloc_, maxloc_;
- cudaSafeCall(cudaMemcpy(&minloc_, minloc_buf, sizeof(int), cudaMemcpyDeviceToHost));
- cudaSafeCall(cudaMemcpy(&maxloc_, maxloc_buf, sizeof(int), cudaMemcpyDeviceToHost));
- minloc[1] = minloc_ / src.cols; minloc[0] = minloc_ - minloc[1] * src.cols;
- maxloc[1] = maxloc_ / src.cols; maxloc[0] = maxloc_ - maxloc[1] * src.cols;
+ unsigned int ticket = ::atomicAdd(&blocks_finished, 1);
+ is_last = (ticket == gridDim.x * gridDim.y - 1);
}
- template void minMaxLocCaller<uchar>(const PtrStepSzb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocCaller<char>(const PtrStepSzb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocCaller<ushort>(const PtrStepSzb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocCaller<short>(const PtrStepSzb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocCaller<int>(const PtrStepSzb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocCaller<float>(const PtrStepSzb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocCaller<double>(const PtrStepSzb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
-
+ __syncthreads();
- // This kernel will be used only when compute capability is 1.0
- template <int nthreads, typename T>
- __global__ void minMaxLocPass2Kernel(T* minval, T* maxval, uint* minloc, uint* maxloc, int size)
+ if (is_last)
{
- typedef typename MinMaxTypeTraits<T>::best_type best_type;
- __shared__ best_type sminval[nthreads];
- __shared__ best_type smaxval[nthreads];
- __shared__ uint sminloc[nthreads];
- __shared__ uint smaxloc[nthreads];
-
- uint tid = threadIdx.y * blockDim.x + threadIdx.x;
- uint idx = ::min(tid, size - 1);
+ int idx = ::min(tid, gridDim.x * gridDim.y - 1);
- sminval[tid] = minval[idx];
- smaxval[tid] = maxval[idx];
- sminloc[tid] = minloc[idx];
- smaxloc[tid] = maxloc[idx];
- __syncthreads();
+ mymin = minval[idx];
+ mymax = maxval[idx];
- findMinMaxLocInSmem<nthreads, best_type>(sminval, smaxval, sminloc, smaxloc, tid);
+ const minimum<R> minOp;
+ const maximum<R> maxOp;
+ device::reduce<BLOCK_SIZE>(smem_tuple(sminval, smaxval), thrust::tie(mymin, mymax), tid, thrust::make_tuple(minOp, maxOp));
if (tid == 0)
{
- minval[0] = (T)sminval[0];
- maxval[0] = (T)smaxval[0];
- minloc[0] = sminloc[0];
- maxloc[0] = smaxloc[0];
- }
- }
-
-
- template <typename T>
- void minMaxLocMaskMultipassCaller(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval,
- int minloc[2], int maxloc[2], PtrStepb valbuf, PtrStepb locbuf)
- {
- dim3 threads, grid;
- estimateThreadCfg(src.cols, src.rows, threads, grid);
- setKernelConsts(src.cols, src.rows, threads, grid);
-
- T* minval_buf = (T*)valbuf.ptr(0);
- T* maxval_buf = (T*)valbuf.ptr(1);
- uint* minloc_buf = (uint*)locbuf.ptr(0);
- uint* maxloc_buf = (uint*)locbuf.ptr(1);
-
- minMaxLocKernel<256, T, Mask8U><<<grid, threads>>>(src, Mask8U(mask), minval_buf, maxval_buf,
- minloc_buf, maxloc_buf);
- cudaSafeCall( cudaGetLastError() );
- minMaxLocPass2Kernel<256, T><<<1, 256>>>(minval_buf, maxval_buf, minloc_buf, maxloc_buf, grid.x * grid.y);
- cudaSafeCall( cudaGetLastError() );
-
- cudaSafeCall( cudaDeviceSynchronize() );
-
- T minval_, maxval_;
- cudaSafeCall(cudaMemcpy(&minval_, minval_buf, sizeof(T), cudaMemcpyDeviceToHost));
- cudaSafeCall(cudaMemcpy(&maxval_, maxval_buf, sizeof(T), cudaMemcpyDeviceToHost));
- *minval = minval_;
- *maxval = maxval_;
-
- uint minloc_, maxloc_;
- cudaSafeCall(cudaMemcpy(&minloc_, minloc_buf, sizeof(int), cudaMemcpyDeviceToHost));
- cudaSafeCall(cudaMemcpy(&maxloc_, maxloc_buf, sizeof(int), cudaMemcpyDeviceToHost));
- minloc[1] = minloc_ / src.cols; minloc[0] = minloc_ - minloc[1] * src.cols;
- maxloc[1] = maxloc_ / src.cols; maxloc[0] = maxloc_ - maxloc[1] * src.cols;
- }
+ minval[0] = mymin;
+ maxval[0] = mymax;
- template void minMaxLocMaskMultipassCaller<uchar>(const PtrStepSzb, const PtrStepb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocMaskMultipassCaller<char>(const PtrStepSzb, const PtrStepb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocMaskMultipassCaller<ushort>(const PtrStepSzb, const PtrStepb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocMaskMultipassCaller<short>(const PtrStepSzb, const PtrStepb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocMaskMultipassCaller<int>(const PtrStepSzb, const PtrStepb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocMaskMultipassCaller<float>(const PtrStepSzb, const PtrStepb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
-
-
- template <typename T>
- void minMaxLocMultipassCaller(const PtrStepSzb src, double* minval, double* maxval,
- int minloc[2], int maxloc[2], PtrStepb valbuf, PtrStepb locbuf)
- {
- dim3 threads, grid;
- estimateThreadCfg(src.cols, src.rows, threads, grid);
- setKernelConsts(src.cols, src.rows, threads, grid);
-
- T* minval_buf = (T*)valbuf.ptr(0);
- T* maxval_buf = (T*)valbuf.ptr(1);
- uint* minloc_buf = (uint*)locbuf.ptr(0);
- uint* maxloc_buf = (uint*)locbuf.ptr(1);
-
- minMaxLocKernel<256, T, MaskTrue><<<grid, threads>>>(src, MaskTrue(), minval_buf, maxval_buf,
- minloc_buf, maxloc_buf);
- cudaSafeCall( cudaGetLastError() );
- minMaxLocPass2Kernel<256, T><<<1, 256>>>(minval_buf, maxval_buf, minloc_buf, maxloc_buf, grid.x * grid.y);
- cudaSafeCall( cudaGetLastError() );
-
- cudaSafeCall( cudaDeviceSynchronize() );
-
- T minval_, maxval_;
- cudaSafeCall(cudaMemcpy(&minval_, minval_buf, sizeof(T), cudaMemcpyDeviceToHost));
- cudaSafeCall(cudaMemcpy(&maxval_, maxval_buf, sizeof(T), cudaMemcpyDeviceToHost));
- *minval = minval_;
- *maxval = maxval_;
-
- uint minloc_, maxloc_;
- cudaSafeCall(cudaMemcpy(&minloc_, minloc_buf, sizeof(int), cudaMemcpyDeviceToHost));
- cudaSafeCall(cudaMemcpy(&maxloc_, maxloc_buf, sizeof(int), cudaMemcpyDeviceToHost));
- minloc[1] = minloc_ / src.cols; minloc[0] = minloc_ - minloc[1] * src.cols;
- maxloc[1] = maxloc_ / src.cols; maxloc[0] = maxloc_ - maxloc[1] * src.cols;
+ blocks_finished = 0;
+ }
}
-
- template void minMaxLocMultipassCaller<uchar>(const PtrStepSzb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocMultipassCaller<char>(const PtrStepSzb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocMultipassCaller<ushort>(const PtrStepSzb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocMultipassCaller<short>(const PtrStepSzb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocMultipassCaller<int>(const PtrStepSzb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- template void minMaxLocMultipassCaller<float>(const PtrStepSzb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- } // namespace minmaxloc
-
- //////////////////////////////////////////////////////////////////////////////////////////////////////////
- // countNonZero
-
- namespace countnonzero
+ }
+ };
+ template <int BLOCK_SIZE>
+ struct GlobalReduce<BLOCK_SIZE, int>
+ {
+ static __device__ void run(int& mymin, int& mymax, int* minval, int* maxval, int tid, int bid, int* sminval, int* smaxval)
{
- __constant__ int ctwidth;
- __constant__ int ctheight;
-
- __device__ uint blocks_finished = 0;
-
- void estimateThreadCfg(int cols, int rows, dim3& threads, dim3& grid)
+ #if __CUDA_ARCH__ >= 200
+ if (tid == 0)
{
- threads = dim3(32, 8);
- grid = dim3(divUp(cols, threads.x * 8), divUp(rows, threads.y * 32));
- grid.x = std::min(grid.x, threads.x);
- grid.y = std::min(grid.y, threads.y);
+ ::atomicMin(minval, mymin);
+ ::atomicMax(maxval, mymax);
}
+ #else
+ __shared__ bool is_last;
-
- void getBufSizeRequired(int cols, int rows, int& bufcols, int& bufrows)
+ if (tid == 0)
{
- dim3 threads, grid;
- estimateThreadCfg(cols, rows, threads, grid);
- bufcols = grid.x * grid.y * sizeof(int);
- bufrows = 1;
- }
+ minval[bid] = mymin;
+ maxval[bid] = mymax;
+ __threadfence();
- void setKernelConsts(int cols, int rows, const dim3& threads, const dim3& grid)
- {
- int twidth = divUp(divUp(cols, grid.x), threads.x);
- int theight = divUp(divUp(rows, grid.y), threads.y);
- cudaSafeCall(cudaMemcpyToSymbol(ctwidth, &twidth, sizeof(twidth)));
- cudaSafeCall(cudaMemcpyToSymbol(ctheight, &theight, sizeof(theight)));
+ unsigned int ticket = ::atomicAdd(&blocks_finished, 1);
+ is_last = (ticket == gridDim.x * gridDim.y - 1);
}
+ __syncthreads();
- template <int nthreads, typename T>
- __global__ void countNonZeroKernel(const PtrStepSzb src, volatile uint* count)
+ if (is_last)
{
- __shared__ uint scount[nthreads];
+ int idx = ::min(tid, gridDim.x * gridDim.y - 1);
- uint x0 = blockIdx.x * blockDim.x * ctwidth + threadIdx.x;
- uint y0 = blockIdx.y * blockDim.y * ctheight + threadIdx.y;
- uint tid = threadIdx.y * blockDim.x + threadIdx.x;
+ mymin = minval[idx];
+ mymax = maxval[idx];
- uint cnt = 0;
- for (uint y = 0; y < ctheight && y0 + y * blockDim.y < src.rows; ++y)
- {
- const T* ptr = (const T*)src.ptr(y0 + y * blockDim.y);
- for (uint x = 0; x < ctwidth && x0 + x * blockDim.x < src.cols; ++x)
- cnt += ptr[x0 + x * blockDim.x] != 0;
- }
-
- scount[tid] = cnt;
- __syncthreads();
-
- sumInSmem<nthreads, uint>(scount, tid);
-
- #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
- __shared__ bool is_last;
+ const minimum<int> minOp;
+ const maximum<int> maxOp;
+ device::reduce<BLOCK_SIZE>(smem_tuple(sminval, smaxval), thrust::tie(mymin, mymax), tid, thrust::make_tuple(minOp, maxOp));
if (tid == 0)
{
- count[blockIdx.y * gridDim.x + blockIdx.x] = scount[0];
- __threadfence();
-
- uint ticket = atomicInc(&blocks_finished, gridDim.x * gridDim.y);
- is_last = ticket == gridDim.x * gridDim.y - 1;
- }
-
- __syncthreads();
-
- if (is_last)
- {
- scount[tid] = tid < gridDim.x * gridDim.y ? count[tid] : 0;
- __syncthreads();
-
- sumInSmem<nthreads, uint>(scount, tid);
+ minval[0] = mymin;
+ maxval[0] = mymax;
- if (tid == 0)
- {
- count[0] = scount[0];
- blocks_finished = 0;
- }
+ blocks_finished = 0;
}
- #else
- if (tid == 0) count[blockIdx.y * gridDim.x + blockIdx.x] = scount[0];
- #endif
- }
-
-
- template <typename T>
- int countNonZeroCaller(const PtrStepSzb src, PtrStepb buf)
- {
- dim3 threads, grid;
- estimateThreadCfg(src.cols, src.rows, threads, grid);
- setKernelConsts(src.cols, src.rows, threads, grid);
-
- uint* count_buf = (uint*)buf.ptr(0);
-
- countNonZeroKernel<256, T><<<grid, threads>>>(src, count_buf);
- cudaSafeCall( cudaGetLastError() );
-
- cudaSafeCall( cudaDeviceSynchronize() );
-
- uint count;
- cudaSafeCall(cudaMemcpy(&count, count_buf, sizeof(int), cudaMemcpyDeviceToHost));
-
- return count;
- }
-
- template int countNonZeroCaller<uchar>(const PtrStepSzb, PtrStepb);
- template int countNonZeroCaller<char>(const PtrStepSzb, PtrStepb);
- template int countNonZeroCaller<ushort>(const PtrStepSzb, PtrStepb);
- template int countNonZeroCaller<short>(const PtrStepSzb, PtrStepb);
- template int countNonZeroCaller<int>(const PtrStepSzb, PtrStepb);
- template int countNonZeroCaller<float>(const PtrStepSzb, PtrStepb);
- template int countNonZeroCaller<double>(const PtrStepSzb, PtrStepb);
-
-
- template <int nthreads, typename T>
- __global__ void countNonZeroPass2Kernel(uint* count, int size)
- {
- __shared__ uint scount[nthreads];
- uint tid = threadIdx.y * blockDim.x + threadIdx.x;
-
- scount[tid] = tid < size ? count[tid] : 0;
- __syncthreads();
-
- sumInSmem<nthreads, uint>(scount, tid);
-
- if (tid == 0)
- count[0] = scount[0];
}
+ #endif
+ }
+ };
+ template <int BLOCK_SIZE, typename T, typename R, class Mask>
+ __global__ void kernel(const PtrStepSz<T> src, const Mask mask, R* minval, R* maxval, const int twidth, const int theight)
+ {
+ __shared__ R sminval[BLOCK_SIZE];
+ __shared__ R smaxval[BLOCK_SIZE];
- template <typename T>
- int countNonZeroMultipassCaller(const PtrStepSzb src, PtrStepb buf)
- {
- dim3 threads, grid;
- estimateThreadCfg(src.cols, src.rows, threads, grid);
- setKernelConsts(src.cols, src.rows, threads, grid);
-
- uint* count_buf = (uint*)buf.ptr(0);
-
- countNonZeroKernel<256, T><<<grid, threads>>>(src, count_buf);
- cudaSafeCall( cudaGetLastError() );
- countNonZeroPass2Kernel<256, T><<<1, 256>>>(count_buf, grid.x * grid.y);
- cudaSafeCall( cudaGetLastError() );
-
- cudaSafeCall( cudaDeviceSynchronize() );
-
- uint count;
- cudaSafeCall(cudaMemcpy(&count, count_buf, sizeof(int), cudaMemcpyDeviceToHost));
-
- return count;
- }
-
- template int countNonZeroMultipassCaller<uchar>(const PtrStepSzb, PtrStepb);
- template int countNonZeroMultipassCaller<char>(const PtrStepSzb, PtrStepb);
- template int countNonZeroMultipassCaller<ushort>(const PtrStepSzb, PtrStepb);
- template int countNonZeroMultipassCaller<short>(const PtrStepSzb, PtrStepb);
- template int countNonZeroMultipassCaller<int>(const PtrStepSzb, PtrStepb);
- template int countNonZeroMultipassCaller<float>(const PtrStepSzb, PtrStepb);
+ const int x0 = blockIdx.x * blockDim.x * twidth + threadIdx.x;
+ const int y0 = blockIdx.y * blockDim.y * theight + threadIdx.y;
- } // namespace countnonzero
+ const int tid = threadIdx.y * blockDim.x + threadIdx.x;
+ const int bid = blockIdx.y * gridDim.x + blockIdx.x;
+ R mymin = numeric_limits<R>::max();
+ R mymax = -numeric_limits<R>::max();
- //////////////////////////////////////////////////////////////////////////
- // Sum
+ const minimum<R> minOp;
+ const maximum<R> maxOp;
- namespace sum
+ for (int i = 0, y = y0; i < theight && y < src.rows; ++i, y += blockDim.y)
{
- template <typename T> struct SumType {};
- template <> struct SumType<uchar> { typedef uint R; };
- template <> struct SumType<char> { typedef int R; };
- template <> struct SumType<ushort> { typedef uint R; };
- template <> struct SumType<short> { typedef int R; };
- template <> struct SumType<int> { typedef int R; };
- template <> struct SumType<float> { typedef float R; };
- template <> struct SumType<double> { typedef double R; };
+ const T* ptr = src.ptr(y);
- template <typename R>
- struct IdentityOp { static __device__ __forceinline__ R call(R x) { return x; } };
-
- template <typename R>
- struct AbsOp { static __device__ __forceinline__ R call(R x) { return ::abs(x); } };
-
- template <>
- struct AbsOp<uint> { static __device__ __forceinline__ uint call(uint x) { return x; } };
-
- template <typename R>
- struct SqrOp { static __device__ __forceinline__ R call(R x) { return x * x; } };
-
- __constant__ int ctwidth;
- __constant__ int ctheight;
- __device__ uint blocks_finished = 0;
-
- const int threads_x = 32;
- const int threads_y = 8;
-
- void estimateThreadCfg(int cols, int rows, dim3& threads, dim3& grid)
+ for (int j = 0, x = x0; j < twidth && x < src.cols; ++j, x += blockDim.x)
{
- threads = dim3(threads_x, threads_y);
- grid = dim3(divUp(cols, threads.x * threads.y),
- divUp(rows, threads.y * threads.x));
- grid.x = std::min(grid.x, threads.x);
- grid.y = std::min(grid.y, threads.y);
- }
-
-
- void getBufSizeRequired(int cols, int rows, int cn, int& bufcols, int& bufrows)
- {
- dim3 threads, grid;
- estimateThreadCfg(cols, rows, threads, grid);
- bufcols = grid.x * grid.y * sizeof(double) * cn;
- bufrows = 1;
- }
-
-
- void setKernelConsts(int cols, int rows, const dim3& threads, const dim3& grid)
- {
- int twidth = divUp(divUp(cols, grid.x), threads.x);
- int theight = divUp(divUp(rows, grid.y), threads.y);
- cudaSafeCall(cudaMemcpyToSymbol(ctwidth, &twidth, sizeof(twidth)));
- cudaSafeCall(cudaMemcpyToSymbol(ctheight, &theight, sizeof(theight)));
- }
-
- template <typename T, typename R, typename Op, int nthreads>
- __global__ void sumKernel(const PtrStepSzb src, R* result)
- {
- __shared__ R smem[nthreads];
-
- const int x0 = blockIdx.x * blockDim.x * ctwidth + threadIdx.x;
- const int y0 = blockIdx.y * blockDim.y * ctheight + threadIdx.y;
- const int tid = threadIdx.y * blockDim.x + threadIdx.x;
- const int bid = blockIdx.y * gridDim.x + blockIdx.x;
-
- R sum = 0;
- for (int y = 0; y < ctheight && y0 + y * blockDim.y < src.rows; ++y)
- {
- const T* ptr = (const T*)src.ptr(y0 + y * blockDim.y);
- for (int x = 0; x < ctwidth && x0 + x * blockDim.x < src.cols; ++x)
- sum += Op::call(ptr[x0 + x * blockDim.x]);
- }
-
- smem[tid] = sum;
- __syncthreads();
-
- sumInSmem<nthreads, R>(smem, tid);
-
- #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
- __shared__ bool is_last;
-
- if (tid == 0)
- {
- result[bid] = smem[0];
- __threadfence();
-
- uint ticket = atomicInc(&blocks_finished, gridDim.x * gridDim.y);
- is_last = (ticket == gridDim.x * gridDim.y - 1);
- }
-
- __syncthreads();
-
- if (is_last)
+ if (mask(y, x))
{
- smem[tid] = tid < gridDim.x * gridDim.y ? result[tid] : 0;
- __syncthreads();
+ const R srcVal = ptr[x];
- sumInSmem<nthreads, R>(smem, tid);
-
- if (tid == 0)
- {
- result[0] = smem[0];
- blocks_finished = 0;
- }
+ mymin = minOp(mymin, srcVal);
+ mymax = maxOp(mymax, srcVal);
}
- #else
- if (tid == 0) result[bid] = smem[0];
- #endif
}
+ }
+ device::reduce<BLOCK_SIZE>(smem_tuple(sminval, smaxval), thrust::tie(mymin, mymax), tid, thrust::make_tuple(minOp, maxOp));
- template <typename T, typename R, int nthreads>
- __global__ void sumPass2Kernel(R* result, int size)
- {
- __shared__ R smem[nthreads];
- int tid = threadIdx.y * blockDim.x + threadIdx.x;
-
- smem[tid] = tid < size ? result[tid] : 0;
- __syncthreads();
-
- sumInSmem<nthreads, R>(smem, tid);
+ GlobalReduce<BLOCK_SIZE, R>::run(mymin, mymax, minval, maxval, tid, bid, sminval, smaxval);
+ }
- if (tid == 0)
- result[0] = smem[0];
- }
+ const int threads_x = 32;
+ const int threads_y = 8;
+ void getLaunchCfg(int cols, int rows, dim3& block, dim3& grid)
+ {
+ block = dim3(threads_x, threads_y);
- template <typename T, typename R, typename Op, int nthreads>
- __global__ void sumKernel_C2(const PtrStepSzb src, typename TypeVec<R, 2>::vec_type* result)
- {
- typedef typename TypeVec<T, 2>::vec_type SrcType;
- typedef typename TypeVec<R, 2>::vec_type DstType;
+ grid = dim3(divUp(cols, block.x * block.y),
+ divUp(rows, block.y * block.x));
- __shared__ R smem[nthreads * 2];
+ grid.x = ::min(grid.x, block.x);
+ grid.y = ::min(grid.y, block.y);
+ }
- const int x0 = blockIdx.x * blockDim.x * ctwidth + threadIdx.x;
- const int y0 = blockIdx.y * blockDim.y * ctheight + threadIdx.y;
- const int tid = threadIdx.y * blockDim.x + threadIdx.x;
- const int bid = blockIdx.y * gridDim.x + blockIdx.x;
+ void getBufSize(int cols, int rows, int& bufcols, int& bufrows)
+ {
+ dim3 block, grid;
+ getLaunchCfg(cols, rows, block, grid);
- SrcType val;
- DstType sum = VecTraits<DstType>::all(0);
- for (int y = 0; y < ctheight && y0 + y * blockDim.y < src.rows; ++y)
- {
- const SrcType* ptr = (const SrcType*)src.ptr(y0 + y * blockDim.y);
- for (int x = 0; x < ctwidth && x0 + x * blockDim.x < src.cols; ++x)
- {
- val = ptr[x0 + x * blockDim.x];
- sum = sum + VecTraits<DstType>::make(Op::call(val.x), Op::call(val.y));
- }
- }
+ bufcols = grid.x * grid.y * sizeof(double);
+ bufrows = 2;
+ }
- smem[tid] = sum.x;
- smem[tid + nthreads] = sum.y;
- __syncthreads();
+ __global__ void setDefaultKernel(int* minval_buf, int* maxval_buf)
+ {
+ *minval_buf = numeric_limits<int>::max();
+ *maxval_buf = numeric_limits<int>::min();
+ }
- sumInSmem<nthreads, R>(smem, tid);
- sumInSmem<nthreads, R>(smem + nthreads, tid);
+ template <typename R>
+ void setDefault(R*, R*)
+ {
+ }
+ void setDefault(int* minval_buf, int* maxval_buf)
+ {
+ setDefaultKernel<<<1, 1>>>(minval_buf, maxval_buf);
+ }
- #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
- __shared__ bool is_last;
+ template <typename T>
+ void run(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf)
+ {
+ typedef typename MinMaxTypeTraits<T>::best_type R;
- if (tid == 0)
- {
- DstType res;
- res.x = smem[0];
- res.y = smem[nthreads];
- result[bid] = res;
- __threadfence();
-
- uint ticket = atomicInc(&blocks_finished, gridDim.x * gridDim.y);
- is_last = (ticket == gridDim.x * gridDim.y - 1);
- }
+ dim3 block, grid;
+ getLaunchCfg(src.cols, src.rows, block, grid);
- __syncthreads();
+ const int twidth = divUp(divUp(src.cols, grid.x), block.x);
+ const int theight = divUp(divUp(src.rows, grid.y), block.y);
- if (is_last)
- {
- DstType res = tid < gridDim.x * gridDim.y ? result[tid] : VecTraits<DstType>::all(0);
- smem[tid] = res.x;
- smem[tid + nthreads] = res.y;
- __syncthreads();
+ R* minval_buf = (R*) buf.ptr(0);
+ R* maxval_buf = (R*) buf.ptr(1);
- sumInSmem<nthreads, R>(smem, tid);
- sumInSmem<nthreads, R>(smem + nthreads, tid);
+ setDefault(minval_buf, maxval_buf);
- if (tid == 0)
- {
- res.x = smem[0];
- res.y = smem[nthreads];
- result[0] = res;
- blocks_finished = 0;
- }
- }
- #else
- if (tid == 0)
- {
- DstType res;
- res.x = smem[0];
- res.y = smem[nthreads];
- result[bid] = res;
- }
- #endif
- }
+ if (mask.data)
+ kernel<threads_x * threads_y><<<grid, block>>>((PtrStepSz<T>) src, SingleMask(mask), minval_buf, maxval_buf, twidth, theight);
+ else
+ kernel<threads_x * threads_y><<<grid, block>>>((PtrStepSz<T>) src, WithOutMask(), minval_buf, maxval_buf, twidth, theight);
+ cudaSafeCall( cudaGetLastError() );
- template <typename T, typename R, int nthreads>
- __global__ void sumPass2Kernel_C2(typename TypeVec<R, 2>::vec_type* result, int size)
- {
- typedef typename TypeVec<R, 2>::vec_type DstType;
+ cudaSafeCall( cudaDeviceSynchronize() );
- __shared__ R smem[nthreads * 2];
+ R minval_, maxval_;
+ cudaSafeCall( cudaMemcpy(&minval_, minval_buf, sizeof(R), cudaMemcpyDeviceToHost) );
+ cudaSafeCall( cudaMemcpy(&maxval_, maxval_buf, sizeof(R), cudaMemcpyDeviceToHost) );
+ *minval = minval_;
+ *maxval = maxval_;
+ }
- const int tid = threadIdx.y * blockDim.x + threadIdx.x;
+ template void run<uchar >(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
+ template void run<schar >(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
+ template void run<ushort>(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
+ template void run<short >(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
+ template void run<int >(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
+ template void run<float >(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
+ template void run<double>(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
+}
- DstType res = tid < size ? result[tid] : VecTraits<DstType>::all(0);
- smem[tid] = res.x;
- smem[tid + nthreads] = res.y;
- __syncthreads();
+/////////////////////////////////////////////////////////////
+// minMaxLoc
- sumInSmem<nthreads, R>(smem, tid);
- sumInSmem<nthreads, R>(smem + nthreads, tid);
+namespace minMaxLoc
+{
+ __device__ unsigned int blocks_finished = 0;
+
+ // To avoid shared bank conflicts we convert each value into value of
+ // appropriate type (32 bits minimum)
+ template <typename T> struct MinMaxTypeTraits;
+ template <> struct MinMaxTypeTraits<uchar> { typedef int best_type; };
+ template <> struct MinMaxTypeTraits<schar> { typedef int best_type; };
+ template <> struct MinMaxTypeTraits<ushort> { typedef int best_type; };
+ template <> struct MinMaxTypeTraits<short> { typedef int best_type; };
+ template <> struct MinMaxTypeTraits<int> { typedef int best_type; };
+ template <> struct MinMaxTypeTraits<float> { typedef float best_type; };
+ template <> struct MinMaxTypeTraits<double> { typedef double best_type; };
+
+ template <int BLOCK_SIZE, typename T, class Mask>
+ __global__ void kernel(const PtrStepSz<T> src, const Mask mask, T* minval, T* maxval, unsigned int* minloc, unsigned int* maxloc, const int twidth, const int theight)
+ {
+ typedef typename MinMaxTypeTraits<T>::best_type work_type;
- if (tid == 0)
- {
- res.x = smem[0];
- res.y = smem[nthreads];
- result[0] = res;
- }
- }
+ __shared__ work_type sminval[BLOCK_SIZE];
+ __shared__ work_type smaxval[BLOCK_SIZE];
+ __shared__ unsigned int sminloc[BLOCK_SIZE];
+ __shared__ unsigned int smaxloc[BLOCK_SIZE];
+ __shared__ bool is_last;
+ const int x0 = blockIdx.x * blockDim.x * twidth + threadIdx.x;
+ const int y0 = blockIdx.y * blockDim.y * theight + threadIdx.y;
- template <typename T, typename R, typename Op, int nthreads>
- __global__ void sumKernel_C3(const PtrStepSzb src, typename TypeVec<R, 3>::vec_type* result)
- {
- typedef typename TypeVec<T, 3>::vec_type SrcType;
- typedef typename TypeVec<R, 3>::vec_type DstType;
+ const int tid = threadIdx.y * blockDim.x + threadIdx.x;
+ const int bid = blockIdx.y * gridDim.x + blockIdx.x;
- __shared__ R smem[nthreads * 3];
+ work_type mymin = numeric_limits<work_type>::max();
+ work_type mymax = -numeric_limits<work_type>::max();
+ unsigned int myminloc = 0;
+ unsigned int mymaxloc = 0;
- const int x0 = blockIdx.x * blockDim.x * ctwidth + threadIdx.x;
- const int y0 = blockIdx.y * blockDim.y * ctheight + threadIdx.y;
- const int tid = threadIdx.y * blockDim.x + threadIdx.x;
- const int bid = blockIdx.y * gridDim.x + blockIdx.x;
+ for (int i = 0, y = y0; i < theight && y < src.rows; ++i, y += blockDim.y)
+ {
+ const T* ptr = src.ptr(y);
- SrcType val;
- DstType sum = VecTraits<DstType>::all(0);
- for (int y = 0; y < ctheight && y0 + y * blockDim.y < src.rows; ++y)
+ for (int j = 0, x = x0; j < twidth && x < src.cols; ++j, x += blockDim.x)
+ {
+ if (mask(y, x))
{
- const SrcType* ptr = (const SrcType*)src.ptr(y0 + y * blockDim.y);
- for (int x = 0; x < ctwidth && x0 + x * blockDim.x < src.cols; ++x)
+ const work_type srcVal = ptr[x];
+
+ if (srcVal < mymin)
{
- val = ptr[x0 + x * blockDim.x];
- sum = sum + VecTraits<DstType>::make(Op::call(val.x), Op::call(val.y), Op::call(val.z));
+ mymin = srcVal;
+ myminloc = y * src.cols + x;
}
- }
-
- smem[tid] = sum.x;
- smem[tid + nthreads] = sum.y;
- smem[tid + 2 * nthreads] = sum.z;
- __syncthreads();
-
- sumInSmem<nthreads, R>(smem, tid);
- sumInSmem<nthreads, R>(smem + nthreads, tid);
- sumInSmem<nthreads, R>(smem + 2 * nthreads, tid);
-
- #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 110
- __shared__ bool is_last;
-
- if (tid == 0)
- {
- DstType res;
- res.x = smem[0];
- res.y = smem[nthreads];
- res.z = smem[2 * nthreads];
- result[bid] = res;
- __threadfence();
-
- uint ticket = atomicInc(&blocks_finished, gridDim.x * gridDim.y);
- is_last = (ticket == gridDim.x * gridDim.y - 1);
- }
-
- __syncthreads();
-
- if (is_last)
- {
- DstType res = tid < gridDim.x * gridDim.y ? result[tid] : VecTraits<DstType>::all(0);
- smem[tid] = res.x;
- smem[tid + nthreads] = res.y;
- smem[tid + 2 * nthreads] = res.z;
- __syncthreads();
-
- sumInSmem<nthreads, R>(smem, tid);
- sumInSmem<nthreads, R>(smem + nthreads, tid);
- sumInSmem<nthreads, R>(smem + 2 * nthreads, tid);
- if (tid == 0)
+ if (srcVal > mymax)
{
- res.x = smem[0];
- res.y = smem[nthreads];
- res.z = smem[2 * nthreads];
- result[0] = res;
- blocks_finished = 0;
+ mymax = srcVal;
+ mymaxloc = y * src.cols + x;
}
}
- #else
- if (tid == 0)
- {
- DstType res;
- res.x = smem[0];
- res.y = smem[nthreads];
- res.z = smem[2 * nthreads];
- result[bid] = res;
- }
- #endif
- }
-
-
- template <typename T, typename R, int nthreads>
- __global__ void sumPass2Kernel_C3(typename TypeVec<R, 3>::vec_type* result, int size)
- {
- typedef typename TypeVec<R, 3>::vec_type DstType;
-
- __shared__ R smem[nthreads * 3];
-
- const int tid = threadIdx.y * blockDim.x + threadIdx.x;
-
- DstType res = tid < size ? result[tid] : VecTraits<DstType>::all(0);
- smem[tid] = res.x;
- smem[tid + nthreads] = res.y;
- smem[tid + 2 * nthreads] = res.z;
- __syncthreads();
-
- sumInSmem<nthreads, R>(smem, tid);
- sumInSmem<nthreads, R>(smem + nthreads, tid);
- sumInSmem<nthreads, R>(smem + 2 * nthreads, tid);
-
- if (tid == 0)
- {
- res.x = smem[0];
- res.y = smem[nthreads];
- res.z = smem[2 * nthreads];
- result[0] = res;
- }
}
+ }
- template <typename T, typename R, typename Op, int nthreads>
- __global__ void sumKernel_C4(const PtrStepSzb src, typename TypeVec<R, 4>::vec_type* result)
- {
- typedef typename TypeVec<T, 4>::vec_type SrcType;
- typedef typename TypeVec<R, 4>::vec_type DstType;
+ reduceKeyVal<BLOCK_SIZE>(smem_tuple(sminval, smaxval), thrust::tie(mymin, mymax),
+ smem_tuple(sminloc, smaxloc), thrust::tie(myminloc, mymaxloc),
+ tid,
+ thrust::make_tuple(less<work_type>(), greater<work_type>()));
- __shared__ R smem[nthreads * 4];
+ if (tid == 0)
+ {
+ minval[bid] = (T) mymin;
+ maxval[bid] = (T) mymax;
+ minloc[bid] = myminloc;
+ maxloc[bid] = mymaxloc;
- const int x0 = blockIdx.x * blockDim.x * ctwidth + threadIdx.x;
- const int y0 = blockIdx.y * blockDim.y * ctheight + threadIdx.y;
- const int tid = threadIdx.y * blockDim.x + threadIdx.x;
- const int bid = blockIdx.y * gridDim.x + blockIdx.x;
+ __threadfence();
- SrcType val;
- DstType sum = VecTraits<DstType>::all(0);
- for (int y = 0; y < ctheight && y0 + y * blockDim.y < src.rows; ++y)
- {
- const SrcType* ptr = (const SrcType*)src.ptr(y0 + y * blockDim.y);
- for (int x = 0; x < ctwidth && x0 + x * blockDim.x < src.cols; ++x)
- {
- val = ptr[x0 + x * blockDim.x];
- sum = sum + VecTraits<DstType>::make(Op::call(val.x), Op::call(val.y),
- Op::call(val.z), Op::call(val.w));
- }
- }
+ unsigned int ticket = ::atomicInc(&blocks_finished, gridDim.x * gridDim.y);
+ is_last = (ticket == gridDim.x * gridDim.y - 1);
+ }
- smem[tid] = sum.x;
- smem[tid + nthreads] = sum.y;
- smem[tid + 2 * nthreads] = sum.z;
- smem[tid + 3 * nthreads] = sum.w;
- __syncthreads();
+ __syncthreads();
- sumInSmem<nthreads, R>(smem, tid);
- sumInSmem<nthreads, R>(smem + nthreads, tid);
- sumInSmem<nthreads, R>(smem + 2 * nthreads, tid);
- sumInSmem<nthreads, R>(smem + 3 * nthreads, tid);
+ if (is_last)
+ {
+ unsigned int idx = ::min(tid, gridDim.x * gridDim.y - 1);
- #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
- __shared__ bool is_last;
+ mymin = minval[idx];
+ mymax = maxval[idx];
+ myminloc = minloc[idx];
+ mymaxloc = maxloc[idx];
- if (tid == 0)
- {
- DstType res;
- res.x = smem[0];
- res.y = smem[nthreads];
- res.z = smem[2 * nthreads];
- res.w = smem[3 * nthreads];
- result[bid] = res;
- __threadfence();
-
- uint ticket = atomicInc(&blocks_finished, gridDim.x * gridDim.y);
- is_last = (ticket == gridDim.x * gridDim.y - 1);
- }
+ reduceKeyVal<BLOCK_SIZE>(smem_tuple(sminval, smaxval), thrust::tie(mymin, mymax),
+ smem_tuple(sminloc, smaxloc), thrust::tie(myminloc, mymaxloc),
+ tid,
+ thrust::make_tuple(less<work_type>(), greater<work_type>()));
- __syncthreads();
+ if (tid == 0)
+ {
+ minval[0] = (T) mymin;
+ maxval[0] = (T) mymax;
+ minloc[0] = myminloc;
+ maxloc[0] = mymaxloc;
- if (is_last)
- {
- DstType res = tid < gridDim.x * gridDim.y ? result[tid] : VecTraits<DstType>::all(0);
- smem[tid] = res.x;
- smem[tid + nthreads] = res.y;
- smem[tid + 2 * nthreads] = res.z;
- smem[tid + 3 * nthreads] = res.w;
- __syncthreads();
-
- sumInSmem<nthreads, R>(smem, tid);
- sumInSmem<nthreads, R>(smem + nthreads, tid);
- sumInSmem<nthreads, R>(smem + 2 * nthreads, tid);
- sumInSmem<nthreads, R>(smem + 3 * nthreads, tid);
-
- if (tid == 0)
- {
- res.x = smem[0];
- res.y = smem[nthreads];
- res.z = smem[2 * nthreads];
- res.w = smem[3 * nthreads];
- result[0] = res;
- blocks_finished = 0;
- }
- }
- #else
- if (tid == 0)
- {
- DstType res;
- res.x = smem[0];
- res.y = smem[nthreads];
- res.z = smem[2 * nthreads];
- res.w = smem[3 * nthreads];
- result[bid] = res;
- }
- #endif
+ blocks_finished = 0;
}
+ }
+ }
+ const int threads_x = 32;
+ const int threads_y = 8;
- template <typename T, typename R, int nthreads>
- __global__ void sumPass2Kernel_C4(typename TypeVec<R, 4>::vec_type* result, int size)
- {
- typedef typename TypeVec<R, 4>::vec_type DstType;
+ void getLaunchCfg(int cols, int rows, dim3& block, dim3& grid)
+ {
+ block = dim3(threads_x, threads_y);
- __shared__ R smem[nthreads * 4];
+ grid = dim3(divUp(cols, block.x * block.y),
+ divUp(rows, block.y * block.x));
- const int tid = threadIdx.y * blockDim.x + threadIdx.x;
+ grid.x = ::min(grid.x, block.x);
+ grid.y = ::min(grid.y, block.y);
+ }
- DstType res = tid < size ? result[tid] : VecTraits<DstType>::all(0);
- smem[tid] = res.x;
- smem[tid + nthreads] = res.y;
- smem[tid + 2 * nthreads] = res.z;
- smem[tid + 3 * nthreads] = res.w;
- __syncthreads();
+ void getBufSize(int cols, int rows, size_t elem_size, int& b1cols, int& b1rows, int& b2cols, int& b2rows)
+ {
+ dim3 block, grid;
+ getLaunchCfg(cols, rows, block, grid);
- sumInSmem<nthreads, R>(smem, tid);
- sumInSmem<nthreads, R>(smem + nthreads, tid);
- sumInSmem<nthreads, R>(smem + 2 * nthreads, tid);
- sumInSmem<nthreads, R>(smem + 3 * nthreads, tid);
+ // For values
+ b1cols = (int)(grid.x * grid.y * elem_size);
+ b1rows = 2;
- if (tid == 0)
- {
- res.x = smem[0];
- res.y = smem[nthreads];
- res.z = smem[2 * nthreads];
- res.w = smem[3 * nthreads];
- result[0] = res;
- }
- }
+ // For locations
+ b2cols = grid.x * grid.y * sizeof(int);
+ b2rows = 2;
+ }
- template <typename T>
- void sumMultipassCaller(const PtrStepSzb src, PtrStepb buf, double* sum, int cn)
- {
- typedef typename SumType<T>::R R;
+ template <typename T>
+ void run(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, int* minloc, int* maxloc, PtrStepb valbuf, PtrStep<unsigned int> locbuf)
+ {
+ dim3 block, grid;
+ getLaunchCfg(src.cols, src.rows, block, grid);
+
+ const int twidth = divUp(divUp(src.cols, grid.x), block.x);
+ const int theight = divUp(divUp(src.rows, grid.y), block.y);
+
+ T* minval_buf = (T*) valbuf.ptr(0);
+ T* maxval_buf = (T*) valbuf.ptr(1);
+ unsigned int* minloc_buf = locbuf.ptr(0);
+ unsigned int* maxloc_buf = locbuf.ptr(1);
+
+ if (mask.data)
+ kernel<threads_x * threads_y><<<grid, block>>>((PtrStepSz<T>) src, SingleMask(mask), minval_buf, maxval_buf, minloc_buf, maxloc_buf, twidth, theight);
+ else
+ kernel<threads_x * threads_y><<<grid, block>>>((PtrStepSz<T>) src, WithOutMask(), minval_buf, maxval_buf, minloc_buf, maxloc_buf, twidth, theight);
+
+ cudaSafeCall( cudaGetLastError() );
+
+ cudaSafeCall( cudaDeviceSynchronize() );
+
+ T minval_, maxval_;
+ cudaSafeCall( cudaMemcpy(&minval_, minval_buf, sizeof(T), cudaMemcpyDeviceToHost) );
+ cudaSafeCall( cudaMemcpy(&maxval_, maxval_buf, sizeof(T), cudaMemcpyDeviceToHost) );
+ *minval = minval_;
+ *maxval = maxval_;
+
+ unsigned int minloc_, maxloc_;
+ cudaSafeCall( cudaMemcpy(&minloc_, minloc_buf, sizeof(unsigned int), cudaMemcpyDeviceToHost) );
+ cudaSafeCall( cudaMemcpy(&maxloc_, maxloc_buf, sizeof(unsigned int), cudaMemcpyDeviceToHost) );
+ minloc[1] = minloc_ / src.cols; minloc[0] = minloc_ - minloc[1] * src.cols;
+ maxloc[1] = maxloc_ / src.cols; maxloc[0] = maxloc_ - maxloc[1] * src.cols;
+ }
+
+ template void run<uchar >(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, int* minloc, int* maxloc, PtrStepb valbuf, PtrStep<unsigned int> locbuf);
+ template void run<schar >(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, int* minloc, int* maxloc, PtrStepb valbuf, PtrStep<unsigned int> locbuf);
+ template void run<ushort>(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, int* minloc, int* maxloc, PtrStepb valbuf, PtrStep<unsigned int> locbuf);
+ template void run<short >(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, int* minloc, int* maxloc, PtrStepb valbuf, PtrStep<unsigned int> locbuf);
+ template void run<int >(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, int* minloc, int* maxloc, PtrStepb valbuf, PtrStep<unsigned int> locbuf);
+ template void run<float >(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, int* minloc, int* maxloc, PtrStepb valbuf, PtrStep<unsigned int> locbuf);
+ template void run<double>(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, int* minloc, int* maxloc, PtrStepb valbuf, PtrStep<unsigned int> locbuf);
+}
+
+/////////////////////////////////////////////////////////////
+// countNonZero
+
+namespace countNonZero
+{
+ __device__ unsigned int blocks_finished = 0;
- dim3 threads, grid;
- estimateThreadCfg(src.cols, src.rows, threads, grid);
- setKernelConsts(src.cols, src.rows, threads, grid);
+ template <int BLOCK_SIZE, typename T>
+ __global__ void kernel(const PtrStepSz<T> src, unsigned int* count, const int twidth, const int theight)
+ {
+ __shared__ unsigned int scount[BLOCK_SIZE];
- switch (cn)
- {
- case 1:
- sumKernel<T, R, IdentityOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 1>::vec_type*)buf.ptr(0));
- cudaSafeCall( cudaGetLastError() );
-
- sumPass2Kernel<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(
- (typename TypeVec<R, 1>::vec_type*)buf.ptr(0), grid.x * grid.y);
- cudaSafeCall( cudaGetLastError() );
-
- break;
- case 2:
- sumKernel_C2<T, R, IdentityOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 2>::vec_type*)buf.ptr(0));
- cudaSafeCall( cudaGetLastError() );
-
- sumPass2Kernel_C2<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(
- (typename TypeVec<R, 2>::vec_type*)buf.ptr(0), grid.x * grid.y);
- cudaSafeCall( cudaGetLastError() );
-
- break;
- case 3:
- sumKernel_C3<T, R, IdentityOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 3>::vec_type*)buf.ptr(0));
- cudaSafeCall( cudaGetLastError() );
-
- sumPass2Kernel_C3<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(
- (typename TypeVec<R, 3>::vec_type*)buf.ptr(0), grid.x * grid.y);
- cudaSafeCall( cudaGetLastError() );
-
- break;
- case 4:
- sumKernel_C4<T, R, IdentityOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 4>::vec_type*)buf.ptr(0));
- cudaSafeCall( cudaGetLastError() );
-
- sumPass2Kernel_C4<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(
- (typename TypeVec<R, 4>::vec_type*)buf.ptr(0), grid.x * grid.y);
- cudaSafeCall( cudaGetLastError() );
-
- break;
- }
- cudaSafeCall( cudaDeviceSynchronize() );
+ const int x0 = blockIdx.x * blockDim.x * twidth + threadIdx.x;
+ const int y0 = blockIdx.y * blockDim.y * theight + threadIdx.y;
- R result[4] = {0, 0, 0, 0};
- cudaSafeCall(cudaMemcpy(&result, buf.ptr(0), sizeof(R) * cn, cudaMemcpyDeviceToHost));
+ const int tid = threadIdx.y * blockDim.x + threadIdx.x;
- sum[0] = result[0];
- sum[1] = result[1];
- sum[2] = result[2];
- sum[3] = result[3];
- }
-
- template void sumMultipassCaller<uchar>(const PtrStepSzb, PtrStepb, double*, int);
- template void sumMultipassCaller<char>(const PtrStepSzb, PtrStepb, double*, int);
- template void sumMultipassCaller<ushort>(const PtrStepSzb, PtrStepb, double*, int);
- template void sumMultipassCaller<short>(const PtrStepSzb, PtrStepb, double*, int);
- template void sumMultipassCaller<int>(const PtrStepSzb, PtrStepb, double*, int);
- template void sumMultipassCaller<float>(const PtrStepSzb, PtrStepb, double*, int);
+ unsigned int mycount = 0;
+ for (int i = 0, y = y0; i < theight && y < src.rows; ++i, y += blockDim.y)
+ {
+ const T* ptr = src.ptr(y);
- template <typename T>
- void sumCaller(const PtrStepSzb src, PtrStepb buf, double* sum, int cn)
+ for (int j = 0, x = x0; j < twidth && x < src.cols; ++j, x += blockDim.x)
{
- typedef typename SumType<T>::R R;
-
- dim3 threads, grid;
- estimateThreadCfg(src.cols, src.rows, threads, grid);
- setKernelConsts(src.cols, src.rows, threads, grid);
-
- switch (cn)
- {
- case 1:
- sumKernel<T, R, IdentityOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 1>::vec_type*)buf.ptr(0));
- break;
- case 2:
- sumKernel_C2<T, R, IdentityOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 2>::vec_type*)buf.ptr(0));
- break;
- case 3:
- sumKernel_C3<T, R, IdentityOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 3>::vec_type*)buf.ptr(0));
- break;
- case 4:
- sumKernel_C4<T, R, IdentityOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 4>::vec_type*)buf.ptr(0));
- break;
- }
- cudaSafeCall( cudaGetLastError() );
-
- cudaSafeCall( cudaDeviceSynchronize() );
-
- R result[4] = {0, 0, 0, 0};
- cudaSafeCall(cudaMemcpy(&result, buf.ptr(0), sizeof(R) * cn, cudaMemcpyDeviceToHost));
+ const T srcVal = ptr[x];
- sum[0] = result[0];
- sum[1] = result[1];
- sum[2] = result[2];
- sum[3] = result[3];
+ mycount += (srcVal != 0);
}
+ }
- template void sumCaller<uchar>(const PtrStepSzb, PtrStepb, double*, int);
- template void sumCaller<char>(const PtrStepSzb, PtrStepb, double*, int);
- template void sumCaller<ushort>(const PtrStepSzb, PtrStepb, double*, int);
- template void sumCaller<short>(const PtrStepSzb, PtrStepb, double*, int);
- template void sumCaller<int>(const PtrStepSzb, PtrStepb, double*, int);
- template void sumCaller<float>(const PtrStepSzb, PtrStepb, double*, int);
+ device::reduce<BLOCK_SIZE>(scount, mycount, tid, plus<unsigned int>());
+ #if __CUDA_ARCH__ >= 200
+ if (tid == 0)
+ ::atomicAdd(count, mycount);
+ #else
+ __shared__ bool is_last;
+ const int bid = blockIdx.y * gridDim.x + blockIdx.x;
- template <typename T>
- void absSumMultipassCaller(const PtrStepSzb src, PtrStepb buf, double* sum, int cn)
- {
- typedef typename SumType<T>::R R;
+ if (tid == 0)
+ {
+ count[bid] = mycount;
- dim3 threads, grid;
- estimateThreadCfg(src.cols, src.rows, threads, grid);
- setKernelConsts(src.cols, src.rows, threads, grid);
+ __threadfence();
- switch (cn)
- {
- case 1:
- sumKernel<T, R, AbsOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 1>::vec_type*)buf.ptr(0));
- cudaSafeCall( cudaGetLastError() );
-
- sumPass2Kernel<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(
- (typename TypeVec<R, 1>::vec_type*)buf.ptr(0), grid.x * grid.y);
- cudaSafeCall( cudaGetLastError() );
-
- break;
- case 2:
- sumKernel_C2<T, R, AbsOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 2>::vec_type*)buf.ptr(0));
- cudaSafeCall( cudaGetLastError() );
-
- sumPass2Kernel_C2<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(
- (typename TypeVec<R, 2>::vec_type*)buf.ptr(0), grid.x * grid.y);
- cudaSafeCall( cudaGetLastError() );
-
- break;
- case 3:
- sumKernel_C3<T, R, AbsOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 3>::vec_type*)buf.ptr(0));
- cudaSafeCall( cudaGetLastError() );
-
- sumPass2Kernel_C3<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(
- (typename TypeVec<R, 3>::vec_type*)buf.ptr(0), grid.x * grid.y);
- cudaSafeCall( cudaGetLastError() );
-
- break;
- case 4:
- sumKernel_C4<T, R, AbsOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 4>::vec_type*)buf.ptr(0));
- cudaSafeCall( cudaGetLastError() );
-
- sumPass2Kernel_C4<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(
- (typename TypeVec<R, 4>::vec_type*)buf.ptr(0), grid.x * grid.y);
- cudaSafeCall( cudaGetLastError() );
-
- break;
- }
- cudaSafeCall( cudaDeviceSynchronize() );
-
- R result[4] = {0, 0, 0, 0};
- cudaSafeCall(cudaMemcpy(result, buf.ptr(0), sizeof(R) * cn, cudaMemcpyDeviceToHost));
+ unsigned int ticket = ::atomicInc(&blocks_finished, gridDim.x * gridDim.y);
+ is_last = (ticket == gridDim.x * gridDim.y - 1);
+ }
- sum[0] = result[0];
- sum[1] = result[1];
- sum[2] = result[2];
- sum[3] = result[3];
- }
+ __syncthreads();
- template void absSumMultipassCaller<uchar>(const PtrStepSzb, PtrStepb, double*, int);
- template void absSumMultipassCaller<char>(const PtrStepSzb, PtrStepb, double*, int);
- template void absSumMultipassCaller<ushort>(const PtrStepSzb, PtrStepb, double*, int);
- template void absSumMultipassCaller<short>(const PtrStepSzb, PtrStepb, double*, int);
- template void absSumMultipassCaller<int>(const PtrStepSzb, PtrStepb, double*, int);
- template void absSumMultipassCaller<float>(const PtrStepSzb, PtrStepb, double*, int);
+ if (is_last)
+ {
+ mycount = tid < gridDim.x * gridDim.y ? count[tid] : 0;
+ device::reduce<BLOCK_SIZE>(scount, mycount, tid, plus<unsigned int>());
- template <typename T>
- void absSumCaller(const PtrStepSzb src, PtrStepb buf, double* sum, int cn)
+ if (tid == 0)
{
- typedef typename SumType<T>::R R;
-
- dim3 threads, grid;
- estimateThreadCfg(src.cols, src.rows, threads, grid);
- setKernelConsts(src.cols, src.rows, threads, grid);
-
- switch (cn)
- {
- case 1:
- sumKernel<T, R, AbsOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 1>::vec_type*)buf.ptr(0));
- break;
- case 2:
- sumKernel_C2<T, R, AbsOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 2>::vec_type*)buf.ptr(0));
- break;
- case 3:
- sumKernel_C3<T, R, AbsOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 3>::vec_type*)buf.ptr(0));
- break;
- case 4:
- sumKernel_C4<T, R, AbsOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 4>::vec_type*)buf.ptr(0));
- break;
- }
- cudaSafeCall( cudaGetLastError() );
-
- cudaSafeCall( cudaDeviceSynchronize() );
-
- R result[4] = {0, 0, 0, 0};
- cudaSafeCall(cudaMemcpy(result, buf.ptr(0), sizeof(R) * cn, cudaMemcpyDeviceToHost));
+ count[0] = mycount;
- sum[0] = result[0];
- sum[1] = result[1];
- sum[2] = result[2];
- sum[3] = result[3];
+ blocks_finished = 0;
}
+ }
+ #endif
+ }
- template void absSumCaller<uchar>(const PtrStepSzb, PtrStepb, double*, int);
- template void absSumCaller<char>(const PtrStepSzb, PtrStepb, double*, int);
- template void absSumCaller<ushort>(const PtrStepSzb, PtrStepb, double*, int);
- template void absSumCaller<short>(const PtrStepSzb, PtrStepb, double*, int);
- template void absSumCaller<int>(const PtrStepSzb, PtrStepb, double*, int);
- template void absSumCaller<float>(const PtrStepSzb, PtrStepb, double*, int);
-
+ const int threads_x = 32;
+ const int threads_y = 8;
- template <typename T>
- void sqrSumMultipassCaller(const PtrStepSzb src, PtrStepb buf, double* sum, int cn)
- {
- typedef typename SumType<T>::R R;
+ void getLaunchCfg(int cols, int rows, dim3& block, dim3& grid)
+ {
+ block = dim3(threads_x, threads_y);
- dim3 threads, grid;
- estimateThreadCfg(src.cols, src.rows, threads, grid);
- setKernelConsts(src.cols, src.rows, threads, grid);
+ grid = dim3(divUp(cols, block.x * block.y),
+ divUp(rows, block.y * block.x));
- switch (cn)
- {
- case 1:
- sumKernel<T, R, SqrOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 1>::vec_type*)buf.ptr(0));
- cudaSafeCall( cudaGetLastError() );
-
- sumPass2Kernel<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(
- (typename TypeVec<R, 1>::vec_type*)buf.ptr(0), grid.x * grid.y);
- cudaSafeCall( cudaGetLastError() );
-
- break;
- case 2:
- sumKernel_C2<T, R, SqrOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 2>::vec_type*)buf.ptr(0));
- cudaSafeCall( cudaGetLastError() );
-
- sumPass2Kernel_C2<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(
- (typename TypeVec<R, 2>::vec_type*)buf.ptr(0), grid.x * grid.y);
- cudaSafeCall( cudaGetLastError() );
-
- break;
- case 3:
- sumKernel_C3<T, R, SqrOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 3>::vec_type*)buf.ptr(0));
- cudaSafeCall( cudaGetLastError() );
-
- sumPass2Kernel_C3<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(
- (typename TypeVec<R, 3>::vec_type*)buf.ptr(0), grid.x * grid.y);
- cudaSafeCall( cudaGetLastError() );
-
- break;
- case 4:
- sumKernel_C4<T, R, SqrOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 4>::vec_type*)buf.ptr(0));
- cudaSafeCall( cudaGetLastError() );
-
- sumPass2Kernel_C4<T, R, threads_x * threads_y><<<1, threads_x * threads_y>>>(
- (typename TypeVec<R, 4>::vec_type*)buf.ptr(0), grid.x * grid.y);
- cudaSafeCall( cudaGetLastError() );
-
- break;
- }
- cudaSafeCall( cudaDeviceSynchronize() );
+ grid.x = ::min(grid.x, block.x);
+ grid.y = ::min(grid.y, block.y);
+ }
- R result[4] = {0, 0, 0, 0};
- cudaSafeCall(cudaMemcpy(result, buf.ptr(0), sizeof(R) * cn, cudaMemcpyDeviceToHost));
+ void getBufSize(int cols, int rows, int& bufcols, int& bufrows)
+ {
+ dim3 block, grid;
+ getLaunchCfg(cols, rows, block, grid);
- sum[0] = result[0];
- sum[1] = result[1];
- sum[2] = result[2];
- sum[3] = result[3];
- }
+ bufcols = grid.x * grid.y * sizeof(int);
+ bufrows = 1;
+ }
- template void sqrSumMultipassCaller<uchar>(const PtrStepSzb, PtrStepb, double*, int);
- template void sqrSumMultipassCaller<char>(const PtrStepSzb, PtrStepb, double*, int);
- template void sqrSumMultipassCaller<ushort>(const PtrStepSzb, PtrStepb, double*, int);
- template void sqrSumMultipassCaller<short>(const PtrStepSzb, PtrStepb, double*, int);
- template void sqrSumMultipassCaller<int>(const PtrStepSzb, PtrStepb, double*, int);
- template void sqrSumMultipassCaller<float>(const PtrStepSzb, PtrStepb, double*, int);
+ template <typename T>
+ int run(const PtrStepSzb src, PtrStep<unsigned int> buf)
+ {
+ dim3 block, grid;
+ getLaunchCfg(src.cols, src.rows, block, grid);
+ const int twidth = divUp(divUp(src.cols, grid.x), block.x);
+ const int theight = divUp(divUp(src.rows, grid.y), block.y);
- template <typename T>
- void sqrSumCaller(const PtrStepSzb src, PtrStepb buf, double* sum, int cn)
- {
- typedef double R;
+ unsigned int* count_buf = buf.ptr(0);
- dim3 threads, grid;
- estimateThreadCfg(src.cols, src.rows, threads, grid);
- setKernelConsts(src.cols, src.rows, threads, grid);
+ cudaSafeCall( cudaMemset(count_buf, 0, sizeof(unsigned int)) );
- switch (cn)
- {
- case 1:
- sumKernel<T, R, SqrOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 1>::vec_type*)buf.ptr(0));
- break;
- case 2:
- sumKernel_C2<T, R, SqrOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 2>::vec_type*)buf.ptr(0));
- break;
- case 3:
- sumKernel_C3<T, R, SqrOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 3>::vec_type*)buf.ptr(0));
- break;
- case 4:
- sumKernel_C4<T, R, SqrOp<R>, threads_x * threads_y><<<grid, threads>>>(
- src, (typename TypeVec<R, 4>::vec_type*)buf.ptr(0));
- break;
- }
- cudaSafeCall( cudaGetLastError() );
+ kernel<threads_x * threads_y><<<grid, block>>>((PtrStepSz<T>) src, count_buf, twidth, theight);
+ cudaSafeCall( cudaGetLastError() );
- cudaSafeCall( cudaDeviceSynchronize() );
+ cudaSafeCall( cudaDeviceSynchronize() );
- R result[4] = {0, 0, 0, 0};
- cudaSafeCall(cudaMemcpy(result, buf.ptr(0), sizeof(R) * cn, cudaMemcpyDeviceToHost));
+ unsigned int count;
+ cudaSafeCall(cudaMemcpy(&count, count_buf, sizeof(unsigned int), cudaMemcpyDeviceToHost));
- sum[0] = result[0];
- sum[1] = result[1];
- sum[2] = result[2];
- sum[3] = result[3];
- }
+ return count;
+ }
- template void sqrSumCaller<uchar>(const PtrStepSzb, PtrStepb, double*, int);
- template void sqrSumCaller<char>(const PtrStepSzb, PtrStepb, double*, int);
- template void sqrSumCaller<ushort>(const PtrStepSzb, PtrStepb, double*, int);
- template void sqrSumCaller<short>(const PtrStepSzb, PtrStepb, double*, int);
- template void sqrSumCaller<int>(const PtrStepSzb, PtrStepb, double*, int);
- template void sqrSumCaller<float>(const PtrStepSzb, PtrStepb, double*, int);
- } // namespace sum
+ template int run<uchar >(const PtrStepSzb src, PtrStep<unsigned int> buf);
+ template int run<schar >(const PtrStepSzb src, PtrStep<unsigned int> buf);
+ template int run<ushort>(const PtrStepSzb src, PtrStep<unsigned int> buf);
+ template int run<short >(const PtrStepSzb src, PtrStep<unsigned int> buf);
+ template int run<int >(const PtrStepSzb src, PtrStep<unsigned int> buf);
+ template int run<float >(const PtrStepSzb src, PtrStep<unsigned int> buf);
+ template int run<double>(const PtrStepSzb src, PtrStep<unsigned int> buf);
+}
- //////////////////////////////////////////////////////////////////////////////
- // reduce
+//////////////////////////////////////////////////////////////////////////////
+// reduce
- template <typename S> struct SumReductor
+namespace reduce
+{
+ struct Sum
+ {
+ template <typename T>
+ __device__ __forceinline__ T startValue() const
{
- __device__ __forceinline__ S startValue() const
- {
- return 0;
- }
-
- __device__ __forceinline__ SumReductor(const SumReductor& other){}
- __device__ __forceinline__ SumReductor(){}
-
- __device__ __forceinline__ S operator ()(volatile S a, volatile S b) const
- {
- return a + b;
- }
-
- __device__ __forceinline__ S result(S r, double) const
- {
- return r;
- }
- };
+ return VecTraits<T>::all(0);
+ }
- template <typename S> struct AvgReductor
+ template <typename T>
+ __device__ __forceinline__ T operator ()(T a, T b) const
{
- __device__ __forceinline__ S startValue() const
- {
- return 0;
- }
+ return a + b;
+ }
- __device__ __forceinline__ AvgReductor(const AvgReductor& other){}
- __device__ __forceinline__ AvgReductor(){}
+ template <typename T>
+ __device__ __forceinline__ T result(T r, double) const
+ {
+ return r;
+ }
- __device__ __forceinline__ S operator ()(volatile S a, volatile S b) const
- {
- return a + b;
- }
+ __device__ __forceinline__ Sum() {}
+ __device__ __forceinline__ Sum(const Sum&) {}
+ };
- __device__ __forceinline__ double result(S r, double sz) const
- {
- return r / sz;
- }
- };
+ struct Avg
+ {
+ template <typename T>
+ __device__ __forceinline__ T startValue() const
+ {
+ return VecTraits<T>::all(0);
+ }
- template <typename S> struct MinReductor
+ template <typename T>
+ __device__ __forceinline__ T operator ()(T a, T b) const
{
- __device__ __forceinline__ S startValue() const
- {
- return numeric_limits<S>::max();
- }
+ return a + b;
+ }
- __device__ __forceinline__ MinReductor(const MinReductor& other){}
- __device__ __forceinline__ MinReductor(){}
+ template <typename T>
+ __device__ __forceinline__ typename TypeVec<double, VecTraits<T>::cn>::vec_type result(T r, double sz) const
+ {
+ return r / sz;
+ }
- template <typename T> __device__ __forceinline__ T operator ()(volatile T a, volatile T b) const
- {
- return saturate_cast<T>(::min(a, b));
- }
- __device__ __forceinline__ float operator ()(volatile float a, volatile float b) const
- {
- return ::fmin(a, b);
- }
+ __device__ __forceinline__ Avg() {}
+ __device__ __forceinline__ Avg(const Avg&) {}
+ };
- __device__ __forceinline__ S result(S r, double) const
- {
- return r;
- }
- };
+ struct Min
+ {
+ template <typename T>
+ __device__ __forceinline__ T startValue() const
+ {
+ return VecTraits<T>::all(numeric_limits<typename VecTraits<T>::elem_type>::max());
+ }
- template <typename S> struct MaxReductor
+ template <typename T>
+ __device__ __forceinline__ T operator ()(T a, T b) const
{
- __device__ __forceinline__ S startValue() const
- {
- return numeric_limits<S>::min();
- }
+ minimum<T> minOp;
+ return minOp(a, b);
+ }
- __device__ __forceinline__ MaxReductor(const MaxReductor& other){}
- __device__ __forceinline__ MaxReductor(){}
+ template <typename T>
+ __device__ __forceinline__ T result(T r, double) const
+ {
+ return r;
+ }
- template <typename T> __device__ __forceinline__ int operator ()(volatile T a, volatile T b) const
- {
- return ::max(a, b);
- }
- __device__ __forceinline__ float operator ()(volatile float a, volatile float b) const
- {
- return ::fmax(a, b);
- }
+ __device__ __forceinline__ Min() {}
+ __device__ __forceinline__ Min(const Min&) {}
+ };
- __device__ __forceinline__ S result(S r, double) const
- {
- return r;
- }
- };
+ struct Max
+ {
+ template <typename T>
+ __device__ __forceinline__ T startValue() const
+ {
+ return VecTraits<T>::all(-numeric_limits<typename VecTraits<T>::elem_type>::max());
+ }
- template <class Op, typename T, typename S, typename D> __global__ void reduceRows(const PtrStepSz<T> src, D* dst, const Op op)
+ template <typename T>
+ __device__ __forceinline__ T operator ()(T a, T b) const
{
- __shared__ S smem[16 * 16];
+ maximum<T> maxOp;
+ return maxOp(a, b);
+ }
- const int x = blockIdx.x * 16 + threadIdx.x;
+ template <typename T>
+ __device__ __forceinline__ T result(T r, double) const
+ {
+ return r;
+ }
- S myVal = op.startValue();
+ __device__ __forceinline__ Max() {}
+ __device__ __forceinline__ Max(const Max&) {}
+ };
- if (x < src.cols)
- {
- for (int y = threadIdx.y; y < src.rows; y += 16)
- myVal = op(myVal, src.ptr(y)[x]);
- }
+ ///////////////////////////////////////////////////////////
- smem[threadIdx.x * 16 + threadIdx.y] = myVal;
- __syncthreads();
+ template <typename T, typename S, typename D, class Op>
+ __global__ void rowsKernel(const PtrStepSz<T> src, D* dst, const Op op)
+ {
+ __shared__ S smem[16 * 16];
- if (threadIdx.x < 8)
- {
- volatile S* srow = smem + threadIdx.y * 16;
- srow[threadIdx.x] = op(srow[threadIdx.x], srow[threadIdx.x + 8]);
- srow[threadIdx.x] = op(srow[threadIdx.x], srow[threadIdx.x + 4]);
- srow[threadIdx.x] = op(srow[threadIdx.x], srow[threadIdx.x + 2]);
- srow[threadIdx.x] = op(srow[threadIdx.x], srow[threadIdx.x + 1]);
- }
- __syncthreads();
+ const int x = blockIdx.x * 16 + threadIdx.x;
- if (threadIdx.y == 0 && x < src.cols)
- dst[x] = saturate_cast<D>(op.result(smem[threadIdx.x * 16], src.rows));
- }
+ S myVal = op.template startValue<S>();
- template <template <typename> class Op, typename T, typename S, typename D> void reduceRows_caller(const PtrStepSz<T>& src, PtrStepSz<D> dst, cudaStream_t stream)
+ if (x < src.cols)
{
- const dim3 block(16, 16);
- const dim3 grid(divUp(src.cols, block.x));
-
- Op<S> op;
- reduceRows<Op<S>, T, S, D><<<grid, block, 0, stream>>>(src, dst.data, op);
- cudaSafeCall( cudaGetLastError() );
-
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
-
+ for (int y = threadIdx.y; y < src.rows; y += 16)
+ {
+ S srcVal = src(y, x);
+ myVal = op(myVal, srcVal);
+ }
}
- template <typename T, typename S, typename D> void reduceRows_gpu(const PtrStepSzb& src, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream)
- {
- typedef void (*caller_t)(const PtrStepSz<T>& src, PtrStepSz<D> dst, cudaStream_t stream);
+ smem[threadIdx.x * 16 + threadIdx.y] = myVal;
- static const caller_t callers[] =
- {
- reduceRows_caller<SumReductor, T, S, D>,
- reduceRows_caller<AvgReductor, T, S, D>,
- reduceRows_caller<MaxReductor, T, S, D>,
- reduceRows_caller<MinReductor, T, S, D>
- };
+ __syncthreads();
- callers[reduceOp](static_cast< PtrStepSz<T> >(src), static_cast< PtrStepSz<D> >(dst), stream);
- }
+ volatile S* srow = smem + threadIdx.y * 16;
- template void reduceRows_gpu<uchar, int, uchar>(const PtrStepSzb& src, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
- template void reduceRows_gpu<uchar, int, int>(const PtrStepSzb& src, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
- template void reduceRows_gpu<uchar, int, float>(const PtrStepSzb& src, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
+ myVal = srow[threadIdx.x];
+ device::reduce<16>(srow, myVal, threadIdx.x, op);
- template void reduceRows_gpu<ushort, int, ushort>(const PtrStepSzb& src, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
- template void reduceRows_gpu<ushort, int, int>(const PtrStepSzb& src, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
- template void reduceRows_gpu<ushort, int, float>(const PtrStepSzb& src, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
+ if (threadIdx.x == 0)
+ srow[0] = myVal;
- template void reduceRows_gpu<short, int, short>(const PtrStepSzb& src, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
- template void reduceRows_gpu<short, int, int>(const PtrStepSzb& src, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
- template void reduceRows_gpu<short, int, float>(const PtrStepSzb& src, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
+ __syncthreads();
- template void reduceRows_gpu<int, int, int>(const PtrStepSzb& src, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
- template void reduceRows_gpu<int, int, float>(const PtrStepSzb& src, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
+ if (threadIdx.y == 0 && x < src.cols)
+ dst[x] = (D) op.result(smem[threadIdx.x * 16], src.rows);
+ }
- template void reduceRows_gpu<float, float, float>(const PtrStepSzb& src, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
+ template <typename T, typename S, typename D, class Op>
+ void rowsCaller(PtrStepSz<T> src, D* dst, cudaStream_t stream)
+ {
+ const dim3 block(16, 16);
+ const dim3 grid(divUp(src.cols, block.x));
+ Op op;
+ rowsKernel<T, S, D, Op><<<grid, block, 0, stream>>>(src, dst, op);
+ cudaSafeCall( cudaGetLastError() );
+ if (stream == 0)
+ cudaSafeCall( cudaDeviceSynchronize() );
+ }
- template <int cn, class Op, typename T, typename S, typename D> __global__ void reduceCols(const PtrStepSz<T> src, D* dst, const Op op)
+ template <typename T, typename S, typename D>
+ void rows(PtrStepSzb src, void* dst, int op, cudaStream_t stream)
+ {
+ typedef void (*func_t)(PtrStepSz<T> src, D* dst, cudaStream_t stream);
+ static const func_t funcs[] =
{
- __shared__ S smem[256 * cn];
+ rowsCaller<T, S, D, Sum>,
+ rowsCaller<T, S, D, Avg>,
+ rowsCaller<T, S, D, Max>,
+ rowsCaller<T, S, D, Min>
+ };
- const int y = blockIdx.x;
+ funcs[op]((PtrStepSz<T>) src, (D*) dst, stream);
+ }
- const T* src_row = src.ptr(y);
+ template void rows<unsigned char, int, unsigned char>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
+ template void rows<unsigned char, int, int>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
+ template void rows<unsigned char, float, float>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
+ template void rows<unsigned char, double, double>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
- S myVal[cn];
+ template void rows<unsigned short, int, unsigned short>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
+ template void rows<unsigned short, int, int>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
+ template void rows<unsigned short, float, float>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
+ template void rows<unsigned short, double, double>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
- #pragma unroll
- for (int c = 0; c < cn; ++c)
- myVal[c] = op.startValue();
+ template void rows<short, int, short>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
+ template void rows<short, int, int>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
+ template void rows<short, float, float>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
+ template void rows<short, double, double>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
- #if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 200
+ template void rows<int, int, int>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
+ template void rows<int, float, float>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
+ template void rows<int, double, double>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
- // For cc >= 2.0 prefer L1 cache
- for (int x = threadIdx.x; x < src.cols; x += 256)
- {
- #pragma unroll
- for (int c = 0; c < cn; ++c)
- myVal[c] = op(myVal[c], src_row[x * cn + c]);
- }
+ template void rows<float, float, float>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
+ template void rows<float, double, double>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
- #else // __CUDA_ARCH__ >= 200
+ template void rows<double, double, double>(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
- // For older arch use shared memory for cache
- for (int x = 0; x < src.cols; x += 256)
- {
- #pragma unroll
- for (int c = 0; c < cn; ++c)
- {
- smem[c * 256 + threadIdx.x] = op.startValue();
- const int load_x = x * cn + c * 256 + threadIdx.x;
- if (load_x < src.cols * cn)
- smem[c * 256 + threadIdx.x] = src_row[load_x];
- }
- __syncthreads();
+ ///////////////////////////////////////////////////////////
- #pragma unroll
- for (int c = 0; c < cn; ++c)
- myVal[c] = op(myVal[c], smem[threadIdx.x * cn + c]);
- __syncthreads();
- }
+ template <int BLOCK_SIZE, typename T, typename S, typename D, int cn, class Op>
+ __global__ void colsKernel(const PtrStepSz<typename TypeVec<T, cn>::vec_type> src, typename TypeVec<D, cn>::vec_type* dst, const Op op)
+ {
+ typedef typename TypeVec<T, cn>::vec_type src_type;
+ typedef typename TypeVec<S, cn>::vec_type work_type;
+ typedef typename TypeVec<D, cn>::vec_type dst_type;
- #endif // __CUDA_ARCH__ >= 200
+ __shared__ S smem[BLOCK_SIZE * cn];
- #pragma unroll
- for (int c = 0; c < cn; ++c)
- smem[c * 256 + threadIdx.x] = myVal[c];
- __syncthreads();
+ const int y = blockIdx.x;
- if (threadIdx.x < 128)
- {
- #pragma unroll
- for (int c = 0; c < cn; ++c)
- smem[c * 256 + threadIdx.x] = op(smem[c * 256 + threadIdx.x], smem[c * 256 + threadIdx.x + 128]);
- }
- __syncthreads();
+ const src_type* srcRow = src.ptr(y);
- if (threadIdx.x < 64)
- {
- #pragma unroll
- for (int c = 0; c < cn; ++c)
- smem[c * 256 + threadIdx.x] = op(smem[c * 256 + threadIdx.x], smem[c * 256 + threadIdx.x + 64]);
- }
- __syncthreads();
+ work_type myVal = op.template startValue<work_type>();
- volatile S* sdata = smem;
+ for (int x = threadIdx.x; x < src.cols; x += BLOCK_SIZE)
+ myVal = op(myVal, saturate_cast<work_type>(srcRow[x]));
- if (threadIdx.x < 32)
- {
- #pragma unroll
- for (int c = 0; c < cn; ++c)
- {
- sdata[c * 256 + threadIdx.x] = op(sdata[c * 256 + threadIdx.x], sdata[c * 256 + threadIdx.x + 32]);
- sdata[c * 256 + threadIdx.x] = op(sdata[c * 256 + threadIdx.x], sdata[c * 256 + threadIdx.x + 16]);
- sdata[c * 256 + threadIdx.x] = op(sdata[c * 256 + threadIdx.x], sdata[c * 256 + threadIdx.x + 8]);
- sdata[c * 256 + threadIdx.x] = op(sdata[c * 256 + threadIdx.x], sdata[c * 256 + threadIdx.x + 4]);
- sdata[c * 256 + threadIdx.x] = op(sdata[c * 256 + threadIdx.x], sdata[c * 256 + threadIdx.x + 2]);
- sdata[c * 256 + threadIdx.x] = op(sdata[c * 256 + threadIdx.x], sdata[c * 256 + threadIdx.x + 1]);
- }
- }
- __syncthreads();
+ device::reduce<BLOCK_SIZE>(detail::Unroll<cn>::template smem_tuple<BLOCK_SIZE>(smem), detail::Unroll<cn>::tie(myVal), threadIdx.x, detail::Unroll<cn>::op(op));
- if (threadIdx.x < cn)
- dst[y * cn + threadIdx.x] = saturate_cast<D>(op.result(smem[threadIdx.x * 256], src.cols));
- }
+ if (threadIdx.x == 0)
+ dst[y] = saturate_cast<dst_type>(op.result(myVal, src.cols));
+ }
- template <int cn, template <typename> class Op, typename T, typename S, typename D> void reduceCols_caller(const PtrStepSz<T>& src, PtrStepSz<D> dst, cudaStream_t stream)
- {
- const dim3 block(256);
- const dim3 grid(src.rows);
+ template <typename T, typename S, typename D, int cn, class Op> void colsCaller(PtrStepSzb src, void* dst, cudaStream_t stream)
+ {
+ const int BLOCK_SIZE = 256;
- Op<S> op;
- reduceCols<cn, Op<S>, T, S, D><<<grid, block, 0, stream>>>(src, dst.data, op);
- cudaSafeCall( cudaGetLastError() );
+ const dim3 block(BLOCK_SIZE);
+ const dim3 grid(src.rows);
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
+ Op op;
+ colsKernel<BLOCK_SIZE, T, S, D, cn, Op><<<grid, block, 0, stream>>>((PtrStepSz<typename TypeVec<T, cn>::vec_type>) src, (typename TypeVec<D, cn>::vec_type*) dst, op);
+ cudaSafeCall( cudaGetLastError() );
- }
+ if (stream == 0)
+ cudaSafeCall( cudaDeviceSynchronize() );
- template <typename T, typename S, typename D> void reduceCols_gpu(const PtrStepSzb& src, int cn, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream)
+ }
+
+ template <typename T, typename S, typename D> void cols(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream)
+ {
+ typedef void (*func_t)(PtrStepSzb src, void* dst, cudaStream_t stream);
+ static const func_t funcs[5][4] =
{
- typedef void (*caller_t)(const PtrStepSz<T>& src, PtrStepSz<D> dst, cudaStream_t stream);
+ {0,0,0,0},
+ {colsCaller<T, S, D, 1, Sum>, colsCaller<T, S, D, 1, Avg>, colsCaller<T, S, D, 1, Max>, colsCaller<T, S, D, 1, Min>},
+ {colsCaller<T, S, D, 2, Sum>, colsCaller<T, S, D, 2, Avg>, colsCaller<T, S, D, 2, Max>, colsCaller<T, S, D, 2, Min>},
+ {colsCaller<T, S, D, 3, Sum>, colsCaller<T, S, D, 3, Avg>, colsCaller<T, S, D, 3, Max>, colsCaller<T, S, D, 3, Min>},
+ {colsCaller<T, S, D, 4, Sum>, colsCaller<T, S, D, 4, Avg>, colsCaller<T, S, D, 4, Max>, colsCaller<T, S, D, 4, Min>},
+ };
- static const caller_t callers[4][4] =
- {
- {reduceCols_caller<1, SumReductor, T, S, D>, reduceCols_caller<1, AvgReductor, T, S, D>, reduceCols_caller<1, MaxReductor, T, S, D>, reduceCols_caller<1, MinReductor, T, S, D>},
- {reduceCols_caller<2, SumReductor, T, S, D>, reduceCols_caller<2, AvgReductor, T, S, D>, reduceCols_caller<2, MaxReductor, T, S, D>, reduceCols_caller<2, MinReductor, T, S, D>},
- {reduceCols_caller<3, SumReductor, T, S, D>, reduceCols_caller<3, AvgReductor, T, S, D>, reduceCols_caller<3, MaxReductor, T, S, D>, reduceCols_caller<3, MinReductor, T, S, D>},
- {reduceCols_caller<4, SumReductor, T, S, D>, reduceCols_caller<4, AvgReductor, T, S, D>, reduceCols_caller<4, MaxReductor, T, S, D>, reduceCols_caller<4, MinReductor, T, S, D>},
- };
+ funcs[cn][op](src, dst, stream);
+ }
- callers[cn - 1][reduceOp](static_cast< PtrStepSz<T> >(src), static_cast< PtrStepSz<D> >(dst), stream);
- }
+ template void cols<unsigned char, int, unsigned char>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
+ template void cols<unsigned char, int, int>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
+ template void cols<unsigned char, float, float>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
+ template void cols<unsigned char, double, double>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
- template void reduceCols_gpu<uchar, int, uchar>(const PtrStepSzb& src, int cn, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
- template void reduceCols_gpu<uchar, int, int>(const PtrStepSzb& src, int cn, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
- template void reduceCols_gpu<uchar, int, float>(const PtrStepSzb& src, int cn, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
+ template void cols<unsigned short, int, unsigned short>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
+ template void cols<unsigned short, int, int>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
+ template void cols<unsigned short, float, float>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
+ template void cols<unsigned short, double, double>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
- template void reduceCols_gpu<ushort, int, ushort>(const PtrStepSzb& src, int cn, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
- template void reduceCols_gpu<ushort, int, int>(const PtrStepSzb& src, int cn, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
- template void reduceCols_gpu<ushort, int, float>(const PtrStepSzb& src, int cn, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
+ template void cols<short, int, short>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
+ template void cols<short, int, int>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
+ template void cols<short, float, float>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
+ template void cols<short, double, double>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
- template void reduceCols_gpu<short, int, short>(const PtrStepSzb& src, int cn, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
- template void reduceCols_gpu<short, int, int>(const PtrStepSzb& src, int cn, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
- template void reduceCols_gpu<short, int, float>(const PtrStepSzb& src, int cn, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
+ template void cols<int, int, int>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
+ template void cols<int, float, float>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
+ template void cols<int, double, double>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
- template void reduceCols_gpu<int, int, int>(const PtrStepSzb& src, int cn, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
- template void reduceCols_gpu<int, int, float>(const PtrStepSzb& src, int cn, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
+ template void cols<float, float, float>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
+ template void cols<float, double, double>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
- template void reduceCols_gpu<float, float, float>(const PtrStepSzb& src, int cn, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
- } // namespace mattrix_reductions
-}}} // namespace cv { namespace gpu { namespace device
+ template void cols<double, double, double>(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
+}
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+#endif /* CUDA_DISABLER */
#if !defined CUDA_DISABLER
-#include "internal_shared.hpp"
-
+#include "opencv2/gpu/device/common.hpp"
#include "opencv2/gpu/device/vec_traits.hpp"
#include "opencv2/gpu/device/vec_math.hpp"
-#include "opencv2/gpu/device/block.hpp"
+#include "opencv2/gpu/device/functional.hpp"
+#include "opencv2/gpu/device/reduce.hpp"
#include "opencv2/gpu/device/border_interpolate.hpp"
using namespace cv::gpu;
{
namespace imgproc
{
+
+ template <int cn> struct Unroll;
+ template <> struct Unroll<1>
+ {
+ template <int BLOCK_SIZE>
+ static __device__ __forceinline__ thrust::tuple<volatile float*, volatile float*> smem_tuple(float* smem)
+ {
+ return cv::gpu::device::smem_tuple(smem, smem + BLOCK_SIZE);
+ }
+
+ static __device__ __forceinline__ thrust::tuple<float&, float&> tie(float& val1, float& val2)
+ {
+ return thrust::tie(val1, val2);
+ }
+
+ static __device__ __forceinline__ const thrust::tuple<plus<float>, plus<float> > op()
+ {
+ plus<float> op;
+ return thrust::make_tuple(op, op);
+ }
+ };
+ template <> struct Unroll<2>
+ {
+ template <int BLOCK_SIZE>
+ static __device__ __forceinline__ thrust::tuple<volatile float*, volatile float*, volatile float*> smem_tuple(float* smem)
+ {
+ return cv::gpu::device::smem_tuple(smem, smem + BLOCK_SIZE, smem + 2 * BLOCK_SIZE);
+ }
+
+ static __device__ __forceinline__ thrust::tuple<float&, float&, float&> tie(float& val1, float2& val2)
+ {
+ return thrust::tie(val1, val2.x, val2.y);
+ }
+
+ static __device__ __forceinline__ const thrust::tuple<plus<float>, plus<float>, plus<float> > op()
+ {
+ plus<float> op;
+ return thrust::make_tuple(op, op, op);
+ }
+ };
+ template <> struct Unroll<3>
+ {
+ template <int BLOCK_SIZE>
+ static __device__ __forceinline__ thrust::tuple<volatile float*, volatile float*, volatile float*, volatile float*> smem_tuple(float* smem)
+ {
+ return cv::gpu::device::smem_tuple(smem, smem + BLOCK_SIZE, smem + 2 * BLOCK_SIZE, smem + 3 * BLOCK_SIZE);
+ }
+
+ static __device__ __forceinline__ thrust::tuple<float&, float&, float&, float&> tie(float& val1, float3& val2)
+ {
+ return thrust::tie(val1, val2.x, val2.y, val2.z);
+ }
+
+ static __device__ __forceinline__ const thrust::tuple<plus<float>, plus<float>, plus<float>, plus<float> > op()
+ {
+ plus<float> op;
+ return thrust::make_tuple(op, op, op, op);
+ }
+ };
+ template <> struct Unroll<4>
+ {
+ template <int BLOCK_SIZE>
+ static __device__ __forceinline__ thrust::tuple<volatile float*, volatile float*, volatile float*, volatile float*, volatile float*> smem_tuple(float* smem)
+ {
+ return cv::gpu::device::smem_tuple(smem, smem + BLOCK_SIZE, smem + 2 * BLOCK_SIZE, smem + 3 * BLOCK_SIZE, smem + 4 * BLOCK_SIZE);
+ }
+
+ static __device__ __forceinline__ thrust::tuple<float&, float&, float&, float&, float&> tie(float& val1, float4& val2)
+ {
+ return thrust::tie(val1, val2.x, val2.y, val2.z, val2.w);
+ }
+
+ static __device__ __forceinline__ const thrust::tuple<plus<float>, plus<float>, plus<float>, plus<float>, plus<float> > op()
+ {
+ plus<float> op;
+ return thrust::make_tuple(op, op, op, op, op);
+ }
+ };
+
__device__ __forceinline__ int calcDist(const uchar& a, const uchar& b) { return (a-b)*(a-b); }
__device__ __forceinline__ int calcDist(const uchar2& a, const uchar2& b) { return (a.x-b.x)*(a.x-b.x) + (a.y-b.y)*(a.y-b.y); }
__device__ __forceinline__ int calcDist(const uchar3& a, const uchar3& b) { return (a.x-b.x)*(a.x-b.x) + (a.y-b.y)*(a.y-b.y) + (a.z-b.z)*(a.z-b.z); }
sum = sum + weight * saturate_cast<sum_type>(src(sy + y, sx + x));
}
- volatile __shared__ float cta_buffer[CTA_SIZE];
-
- int tid = threadIdx.x;
+ __shared__ float cta_buffer[CTA_SIZE * (VecTraits<T>::cn + 1)];
- cta_buffer[tid] = weights_sum;
- __syncthreads();
- Block::reduce<CTA_SIZE>(cta_buffer, plus());
- weights_sum = cta_buffer[0];
-
- __syncthreads();
-
-
- for(int n = 0; n < VecTraits<T>::cn; ++n)
- {
- cta_buffer[tid] = reinterpret_cast<float*>(&sum)[n];
- __syncthreads();
- Block::reduce<CTA_SIZE>(cta_buffer, plus());
- reinterpret_cast<float*>(&sum)[n] = cta_buffer[0];
-
- __syncthreads();
- }
+ reduce<CTA_SIZE>(Unroll<VecTraits<T>::cn>::template smem_tuple<CTA_SIZE>(cta_buffer),
+ Unroll<VecTraits<T>::cn>::tie(weights_sum, sum),
+ threadIdx.x,
+ Unroll<VecTraits<T>::cn>::op());
- if (tid == 0)
- dst = saturate_cast<T>(sum/weights_sum);
+ if (threadIdx.x == 0)
+ dst = saturate_cast<T>(sum / weights_sum);
}
__device__ __forceinline__ void operator()(PtrStepSz<T>& dst) const
}}}
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+#endif /* CUDA_DISABLER */
#if !defined CUDA_DISABLER
+#include <thrust/device_ptr.h>
#include <thrust/sort.h>
#include "opencv2/gpu/device/common.hpp"
-#include "opencv2/gpu/device/utility.hpp"
+#include "opencv2/gpu/device/reduce.hpp"
#include "opencv2/gpu/device/functional.hpp"
namespace cv { namespace gpu { namespace device
__global__ void HarrisResponses(const PtrStepb img, const short2* loc_, float* response, const int npoints, const int blockSize, const float harris_k)
{
- __shared__ int smem[8 * 32];
-
- volatile int* srow = smem + threadIdx.y * blockDim.x;
+ __shared__ int smem0[8 * 32];
+ __shared__ int smem1[8 * 32];
+ __shared__ int smem2[8 * 32];
const int ptidx = blockIdx.x * blockDim.y + threadIdx.y;
c += Ix * Iy;
}
- reduce<32>(srow, a, threadIdx.x, plus<volatile int>());
- reduce<32>(srow, b, threadIdx.x, plus<volatile int>());
- reduce<32>(srow, c, threadIdx.x, plus<volatile int>());
+ int* srow0 = smem0 + threadIdx.y * blockDim.x;
+ int* srow1 = smem1 + threadIdx.y * blockDim.x;
+ int* srow2 = smem2 + threadIdx.y * blockDim.x;
+
+ plus<int> op;
+ reduce<32>(smem_tuple(srow0, srow1, srow2), thrust::tie(a, b, c), threadIdx.x, thrust::make_tuple(op, op, op));
if (threadIdx.x == 0)
{
__global__ void IC_Angle(const PtrStepb image, const short2* loc_, float* angle, const int npoints, const int half_k)
{
- __shared__ int smem[8 * 32];
+ __shared__ int smem0[8 * 32];
+ __shared__ int smem1[8 * 32];
+
+ int* srow0 = smem0 + threadIdx.y * blockDim.x;
+ int* srow1 = smem1 + threadIdx.y * blockDim.x;
- volatile int* srow = smem + threadIdx.y * blockDim.x;
+ plus<int> op;
const int ptidx = blockIdx.x * blockDim.y + threadIdx.y;
for (int u = threadIdx.x - half_k; u <= half_k; u += blockDim.x)
m_10 += u * image(loc.y, loc.x + u);
- reduce<32>(srow, m_10, threadIdx.x, plus<volatile int>());
+ reduce<32>(srow0, m_10, threadIdx.x, op);
for (int v = 1; v <= half_k; ++v)
{
m_sum += u * (val_plus + val_minus);
}
- reduce<32>(srow, v_sum, threadIdx.x, plus<volatile int>());
- reduce<32>(srow, m_sum, threadIdx.x, plus<volatile int>());
+ reduce<32>(smem_tuple(srow0, srow1), thrust::tie(v_sum, m_sum), threadIdx.x, thrust::make_tuple(op, op));
m_10 += m_sum;
m_01 += v * v_sum;
}
}}}
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+#endif /* CUDA_DISABLER */
#include "opencv2/gpu/device/functional.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/vec_math.hpp"
+#include "opencv2/gpu/device/reduce.hpp"
-namespace cv { namespace gpu { namespace device
+using namespace cv::gpu;
+using namespace cv::gpu::device;
+
+namespace pyrlk
{
- namespace pyrlk
- {
- __constant__ int c_winSize_x;
- __constant__ int c_winSize_y;
+ __constant__ int c_winSize_x;
+ __constant__ int c_winSize_y;
+ __constant__ int c_halfWin_x;
+ __constant__ int c_halfWin_y;
+ __constant__ int c_iters;
- __constant__ int c_halfWin_x;
- __constant__ int c_halfWin_y;
+ texture<float, cudaTextureType2D, cudaReadModeElementType> tex_If(false, cudaFilterModeLinear, cudaAddressModeClamp);
+ texture<float4, cudaTextureType2D, cudaReadModeElementType> tex_If4(false, cudaFilterModeLinear, cudaAddressModeClamp);
+ texture<uchar, cudaTextureType2D, cudaReadModeElementType> tex_Ib(false, cudaFilterModePoint, cudaAddressModeClamp);
- __constant__ int c_iters;
+ texture<float, cudaTextureType2D, cudaReadModeElementType> tex_Jf(false, cudaFilterModeLinear, cudaAddressModeClamp);
+ texture<float4, cudaTextureType2D, cudaReadModeElementType> tex_Jf4(false, cudaFilterModeLinear, cudaAddressModeClamp);
- void loadConstants(int2 winSize, int iters)
+ template <int cn> struct Tex_I;
+ template <> struct Tex_I<1>
+ {
+ static __device__ __forceinline__ float read(float x, float y)
{
- cudaSafeCall( cudaMemcpyToSymbol(c_winSize_x, &winSize.x, sizeof(int)) );
- cudaSafeCall( cudaMemcpyToSymbol(c_winSize_y, &winSize.y, sizeof(int)) );
-
- int2 halfWin = make_int2((winSize.x - 1) / 2, (winSize.y - 1) / 2);
- cudaSafeCall( cudaMemcpyToSymbol(c_halfWin_x, &halfWin.x, sizeof(int)) );
- cudaSafeCall( cudaMemcpyToSymbol(c_halfWin_y, &halfWin.y, sizeof(int)) );
-
- cudaSafeCall( cudaMemcpyToSymbol(c_iters, &iters, sizeof(int)) );
+ return tex2D(tex_If, x, y);
}
-
- __device__ void reduce(float& val1, float& val2, float& val3, float* smem1, float* smem2, float* smem3, int tid)
+ };
+ template <> struct Tex_I<4>
+ {
+ static __device__ __forceinline__ float4 read(float x, float y)
{
- smem1[tid] = val1;
- smem2[tid] = val2;
- smem3[tid] = val3;
- __syncthreads();
+ return tex2D(tex_If4, x, y);
+ }
+ };
-#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ > 110)
- if (tid < 128)
- {
- smem1[tid] = val1 += smem1[tid + 128];
- smem2[tid] = val2 += smem2[tid + 128];
- smem3[tid] = val3 += smem3[tid + 128];
- }
- __syncthreads();
-#endif
+ template <int cn> struct Tex_J;
+ template <> struct Tex_J<1>
+ {
+ static __device__ __forceinline__ float read(float x, float y)
+ {
+ return tex2D(tex_Jf, x, y);
+ }
+ };
+ template <> struct Tex_J<4>
+ {
+ static __device__ __forceinline__ float4 read(float x, float y)
+ {
+ return tex2D(tex_Jf4, x, y);
+ }
+ };
- if (tid < 64)
- {
- smem1[tid] = val1 += smem1[tid + 64];
- smem2[tid] = val2 += smem2[tid + 64];
- smem3[tid] = val3 += smem3[tid + 64];
- }
- __syncthreads();
+ __device__ __forceinline__ void accum(float& dst, float val)
+ {
+ dst += val;
+ }
+ __device__ __forceinline__ void accum(float& dst, const float4& val)
+ {
+ dst += val.x + val.y + val.z;
+ }
- if (tid < 32)
- {
- volatile float* vmem1 = smem1;
- volatile float* vmem2 = smem2;
- volatile float* vmem3 = smem3;
+ __device__ __forceinline__ float abs_(float a)
+ {
+ return ::fabsf(a);
+ }
+ __device__ __forceinline__ float4 abs_(const float4& a)
+ {
+ return abs(a);
+ }
- vmem1[tid] = val1 += vmem1[tid + 32];
- vmem2[tid] = val2 += vmem2[tid + 32];
- vmem3[tid] = val3 += vmem3[tid + 32];
+ template <int cn, int PATCH_X, int PATCH_Y, bool calcErr>
+ __global__ void sparseKernel(const float2* prevPts, float2* nextPts, uchar* status, float* err, const int level, const int rows, const int cols)
+ {
+ #if __CUDA_ARCH__ <= 110
+ const int BLOCK_SIZE = 128;
+ #else
+ const int BLOCK_SIZE = 256;
+ #endif
- vmem1[tid] = val1 += vmem1[tid + 16];
- vmem2[tid] = val2 += vmem2[tid + 16];
- vmem3[tid] = val3 += vmem3[tid + 16];
+ __shared__ float smem1[BLOCK_SIZE];
+ __shared__ float smem2[BLOCK_SIZE];
+ __shared__ float smem3[BLOCK_SIZE];
- vmem1[tid] = val1 += vmem1[tid + 8];
- vmem2[tid] = val2 += vmem2[tid + 8];
- vmem3[tid] = val3 += vmem3[tid + 8];
+ const unsigned int tid = threadIdx.y * blockDim.x + threadIdx.x;
- vmem1[tid] = val1 += vmem1[tid + 4];
- vmem2[tid] = val2 += vmem2[tid + 4];
- vmem3[tid] = val3 += vmem3[tid + 4];
+ float2 prevPt = prevPts[blockIdx.x];
+ prevPt.x *= (1.0f / (1 << level));
+ prevPt.y *= (1.0f / (1 << level));
- vmem1[tid] = val1 += vmem1[tid + 2];
- vmem2[tid] = val2 += vmem2[tid + 2];
- vmem3[tid] = val3 += vmem3[tid + 2];
+ if (prevPt.x < 0 || prevPt.x >= cols || prevPt.y < 0 || prevPt.y >= rows)
+ {
+ if (tid == 0 && level == 0)
+ status[blockIdx.x] = 0;
- vmem1[tid] = val1 += vmem1[tid + 1];
- vmem2[tid] = val2 += vmem2[tid + 1];
- vmem3[tid] = val3 += vmem3[tid + 1];
- }
+ return;
}
- __device__ void reduce(float& val1, float& val2, float* smem1, float* smem2, int tid)
- {
- smem1[tid] = val1;
- smem2[tid] = val2;
- __syncthreads();
+ prevPt.x -= c_halfWin_x;
+ prevPt.y -= c_halfWin_y;
-#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ > 110)
- if (tid < 128)
- {
- smem1[tid] = val1 += smem1[tid + 128];
- smem2[tid] = val2 += smem2[tid + 128];
- }
- __syncthreads();
-#endif
+ // extract the patch from the first image, compute covariation matrix of derivatives
- if (tid < 64)
- {
- smem1[tid] = val1 += smem1[tid + 64];
- smem2[tid] = val2 += smem2[tid + 64];
- }
- __syncthreads();
+ float A11 = 0;
+ float A12 = 0;
+ float A22 = 0;
+
+ typedef typename TypeVec<float, cn>::vec_type work_type;
- if (tid < 32)
+ work_type I_patch [PATCH_Y][PATCH_X];
+ work_type dIdx_patch[PATCH_Y][PATCH_X];
+ work_type dIdy_patch[PATCH_Y][PATCH_X];
+
+ for (int yBase = threadIdx.y, i = 0; yBase < c_winSize_y; yBase += blockDim.y, ++i)
+ {
+ for (int xBase = threadIdx.x, j = 0; xBase < c_winSize_x; xBase += blockDim.x, ++j)
{
- volatile float* vmem1 = smem1;
- volatile float* vmem2 = smem2;
+ float x = prevPt.x + xBase + 0.5f;
+ float y = prevPt.y + yBase + 0.5f;
- vmem1[tid] = val1 += vmem1[tid + 32];
- vmem2[tid] = val2 += vmem2[tid + 32];
+ I_patch[i][j] = Tex_I<cn>::read(x, y);
- vmem1[tid] = val1 += vmem1[tid + 16];
- vmem2[tid] = val2 += vmem2[tid + 16];
+ // Sharr Deriv
- vmem1[tid] = val1 += vmem1[tid + 8];
- vmem2[tid] = val2 += vmem2[tid + 8];
+ work_type dIdx = 3.0f * Tex_I<cn>::read(x+1, y-1) + 10.0f * Tex_I<cn>::read(x+1, y) + 3.0f * Tex_I<cn>::read(x+1, y+1) -
+ (3.0f * Tex_I<cn>::read(x-1, y-1) + 10.0f * Tex_I<cn>::read(x-1, y) + 3.0f * Tex_I<cn>::read(x-1, y+1));
- vmem1[tid] = val1 += vmem1[tid + 4];
- vmem2[tid] = val2 += vmem2[tid + 4];
+ work_type dIdy = 3.0f * Tex_I<cn>::read(x-1, y+1) + 10.0f * Tex_I<cn>::read(x, y+1) + 3.0f * Tex_I<cn>::read(x+1, y+1) -
+ (3.0f * Tex_I<cn>::read(x-1, y-1) + 10.0f * Tex_I<cn>::read(x, y-1) + 3.0f * Tex_I<cn>::read(x+1, y-1));
- vmem1[tid] = val1 += vmem1[tid + 2];
- vmem2[tid] = val2 += vmem2[tid + 2];
+ dIdx_patch[i][j] = dIdx;
+ dIdy_patch[i][j] = dIdy;
- vmem1[tid] = val1 += vmem1[tid + 1];
- vmem2[tid] = val2 += vmem2[tid + 1];
+ accum(A11, dIdx * dIdx);
+ accum(A12, dIdx * dIdy);
+ accum(A22, dIdy * dIdy);
}
}
- __device__ void reduce(float& val1, float* smem1, int tid)
- {
- smem1[tid] = val1;
- __syncthreads();
-
-#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ > 110)
- if (tid < 128)
- {
- smem1[tid] = val1 += smem1[tid + 128];
- }
- __syncthreads();
-#endif
+ reduce<BLOCK_SIZE>(smem_tuple(smem1, smem2, smem3), thrust::tie(A11, A12, A22), tid, thrust::make_tuple(plus<float>(), plus<float>(), plus<float>()));
- if (tid < 64)
- {
- smem1[tid] = val1 += smem1[tid + 64];
- }
- __syncthreads();
-
- if (tid < 32)
- {
- volatile float* vmem1 = smem1;
-
- vmem1[tid] = val1 += vmem1[tid + 32];
- vmem1[tid] = val1 += vmem1[tid + 16];
- vmem1[tid] = val1 += vmem1[tid + 8];
- vmem1[tid] = val1 += vmem1[tid + 4];
- vmem1[tid] = val1 += vmem1[tid + 2];
- vmem1[tid] = val1 += vmem1[tid + 1];
- }
+ #if __CUDA_ARCH__ >= 300
+ if (tid == 0)
+ {
+ smem1[0] = A11;
+ smem2[0] = A12;
+ smem3[0] = A22;
}
+ #endif
- texture<float, cudaTextureType2D, cudaReadModeElementType> tex_If(false, cudaFilterModeLinear, cudaAddressModeClamp);
- texture<float4, cudaTextureType2D, cudaReadModeElementType> tex_If4(false, cudaFilterModeLinear, cudaAddressModeClamp);
- texture<uchar, cudaTextureType2D, cudaReadModeElementType> tex_Ib(false, cudaFilterModePoint, cudaAddressModeClamp);
+ __syncthreads();
- texture<float, cudaTextureType2D, cudaReadModeElementType> tex_Jf(false, cudaFilterModeLinear, cudaAddressModeClamp);
- texture<float4, cudaTextureType2D, cudaReadModeElementType> tex_Jf4(false, cudaFilterModeLinear, cudaAddressModeClamp);
+ A11 = smem1[0];
+ A12 = smem2[0];
+ A22 = smem3[0];
- template <int cn> struct Tex_I;
- template <> struct Tex_I<1>
- {
- static __device__ __forceinline__ float read(float x, float y)
- {
- return tex2D(tex_If, x, y);
- }
- };
- template <> struct Tex_I<4>
- {
- static __device__ __forceinline__ float4 read(float x, float y)
- {
- return tex2D(tex_If4, x, y);
- }
- };
+ float D = A11 * A22 - A12 * A12;
- template <int cn> struct Tex_J;
- template <> struct Tex_J<1>
- {
- static __device__ __forceinline__ float read(float x, float y)
- {
- return tex2D(tex_Jf, x, y);
- }
- };
- template <> struct Tex_J<4>
+ if (D < numeric_limits<float>::epsilon())
{
- static __device__ __forceinline__ float4 read(float x, float y)
- {
- return tex2D(tex_Jf4, x, y);
- }
- };
+ if (tid == 0 && level == 0)
+ status[blockIdx.x] = 0;
- __device__ __forceinline__ void accum(float& dst, float val)
- {
- dst += val;
- }
- __device__ __forceinline__ void accum(float& dst, const float4& val)
- {
- dst += val.x + val.y + val.z;
+ return;
}
- __device__ __forceinline__ float abs_(float a)
- {
- return ::fabs(a);
- }
- __device__ __forceinline__ float4 abs_(const float4& a)
- {
- return abs(a);
- }
+ D = 1.f / D;
+
+ A11 *= D;
+ A12 *= D;
+ A22 *= D;
- template <int cn, int PATCH_X, int PATCH_Y, bool calcErr>
- __global__ void lkSparse(const float2* prevPts, float2* nextPts, uchar* status, float* err, const int level, const int rows, const int cols)
+ float2 nextPt = nextPts[blockIdx.x];
+ nextPt.x *= 2.f;
+ nextPt.y *= 2.f;
+
+ nextPt.x -= c_halfWin_x;
+ nextPt.y -= c_halfWin_y;
+
+ for (int k = 0; k < c_iters; ++k)
{
-#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ <= 110)
- __shared__ float smem1[128];
- __shared__ float smem2[128];
- __shared__ float smem3[128];
-#else
- __shared__ float smem1[256];
- __shared__ float smem2[256];
- __shared__ float smem3[256];
-#endif
-
- const int tid = threadIdx.y * blockDim.x + threadIdx.x;
-
- float2 prevPt = prevPts[blockIdx.x];
- prevPt.x *= (1.0f / (1 << level));
- prevPt.y *= (1.0f / (1 << level));
-
- if (prevPt.x < 0 || prevPt.x >= cols || prevPt.y < 0 || prevPt.y >= rows)
+ if (nextPt.x < -c_halfWin_x || nextPt.x >= cols || nextPt.y < -c_halfWin_y || nextPt.y >= rows)
{
if (tid == 0 && level == 0)
status[blockIdx.x] = 0;
return;
}
- prevPt.x -= c_halfWin_x;
- prevPt.y -= c_halfWin_y;
-
- // extract the patch from the first image, compute covariation matrix of derivatives
+ float b1 = 0;
+ float b2 = 0;
- float A11 = 0;
- float A12 = 0;
- float A22 = 0;
-
- typedef typename TypeVec<float, cn>::vec_type work_type;
-
- work_type I_patch [PATCH_Y][PATCH_X];
- work_type dIdx_patch[PATCH_Y][PATCH_X];
- work_type dIdy_patch[PATCH_Y][PATCH_X];
-
- for (int yBase = threadIdx.y, i = 0; yBase < c_winSize_y; yBase += blockDim.y, ++i)
+ for (int y = threadIdx.y, i = 0; y < c_winSize_y; y += blockDim.y, ++i)
{
- for (int xBase = threadIdx.x, j = 0; xBase < c_winSize_x; xBase += blockDim.x, ++j)
+ for (int x = threadIdx.x, j = 0; x < c_winSize_x; x += blockDim.x, ++j)
{
- float x = prevPt.x + xBase + 0.5f;
- float y = prevPt.y + yBase + 0.5f;
-
- I_patch[i][j] = Tex_I<cn>::read(x, y);
-
- // Sharr Deriv
-
- work_type dIdx = 3.0f * Tex_I<cn>::read(x+1, y-1) + 10.0f * Tex_I<cn>::read(x+1, y) + 3.0f * Tex_I<cn>::read(x+1, y+1) -
- (3.0f * Tex_I<cn>::read(x-1, y-1) + 10.0f * Tex_I<cn>::read(x-1, y) + 3.0f * Tex_I<cn>::read(x-1, y+1));
+ work_type I_val = I_patch[i][j];
+ work_type J_val = Tex_J<cn>::read(nextPt.x + x + 0.5f, nextPt.y + y + 0.5f);
- work_type dIdy = 3.0f * Tex_I<cn>::read(x-1, y+1) + 10.0f * Tex_I<cn>::read(x, y+1) + 3.0f * Tex_I<cn>::read(x+1, y+1) -
- (3.0f * Tex_I<cn>::read(x-1, y-1) + 10.0f * Tex_I<cn>::read(x, y-1) + 3.0f * Tex_I<cn>::read(x+1, y-1));
+ work_type diff = (J_val - I_val) * 32.0f;
- dIdx_patch[i][j] = dIdx;
- dIdy_patch[i][j] = dIdy;
-
- accum(A11, dIdx * dIdx);
- accum(A12, dIdx * dIdy);
- accum(A22, dIdy * dIdy);
+ accum(b1, diff * dIdx_patch[i][j]);
+ accum(b2, diff * dIdy_patch[i][j]);
}
}
- reduce(A11, A12, A22, smem1, smem2, smem3, tid);
- __syncthreads();
-
- A11 = smem1[0];
- A12 = smem2[0];
- A22 = smem3[0];
+ reduce<BLOCK_SIZE>(smem_tuple(smem1, smem2), thrust::tie(b1, b2), tid, thrust::make_tuple(plus<float>(), plus<float>()));
- float D = A11 * A22 - A12 * A12;
-
- if (D < numeric_limits<float>::epsilon())
+ #if __CUDA_ARCH__ >= 300
+ if (tid == 0)
{
- if (tid == 0 && level == 0)
- status[blockIdx.x] = 0;
-
- return;
+ smem1[0] = b1;
+ smem2[0] = b2;
}
+ #endif
- D = 1.f / D;
+ __syncthreads();
+
+ b1 = smem1[0];
+ b2 = smem2[0];
- A11 *= D;
- A12 *= D;
- A22 *= D;
+ float2 delta;
+ delta.x = A12 * b2 - A22 * b1;
+ delta.y = A12 * b1 - A11 * b2;
- float2 nextPt = nextPts[blockIdx.x];
- nextPt.x *= 2.f;
- nextPt.y *= 2.f;
+ nextPt.x += delta.x;
+ nextPt.y += delta.y;
- nextPt.x -= c_halfWin_x;
- nextPt.y -= c_halfWin_y;
+ if (::fabs(delta.x) < 0.01f && ::fabs(delta.y) < 0.01f)
+ break;
+ }
- for (int k = 0; k < c_iters; ++k)
+ float errval = 0;
+ if (calcErr)
+ {
+ for (int y = threadIdx.y, i = 0; y < c_winSize_y; y += blockDim.y, ++i)
{
- if (nextPt.x < -c_halfWin_x || nextPt.x >= cols || nextPt.y < -c_halfWin_y || nextPt.y >= rows)
+ for (int x = threadIdx.x, j = 0; x < c_winSize_x; x += blockDim.x, ++j)
{
- if (tid == 0 && level == 0)
- status[blockIdx.x] = 0;
+ work_type I_val = I_patch[i][j];
+ work_type J_val = Tex_J<cn>::read(nextPt.x + x + 0.5f, nextPt.y + y + 0.5f);
+
+ work_type diff = J_val - I_val;
- return;
+ accum(errval, abs_(diff));
}
+ }
- float b1 = 0;
- float b2 = 0;
+ reduce<BLOCK_SIZE>(smem1, errval, tid, plus<float>());
+ }
- for (int y = threadIdx.y, i = 0; y < c_winSize_y; y += blockDim.y, ++i)
- {
- for (int x = threadIdx.x, j = 0; x < c_winSize_x; x += blockDim.x, ++j)
- {
- work_type I_val = I_patch[i][j];
- work_type J_val = Tex_J<cn>::read(nextPt.x + x + 0.5f, nextPt.y + y + 0.5f);
+ if (tid == 0)
+ {
+ nextPt.x += c_halfWin_x;
+ nextPt.y += c_halfWin_y;
- work_type diff = (J_val - I_val) * 32.0f;
+ nextPts[blockIdx.x] = nextPt;
- accum(b1, diff * dIdx_patch[i][j]);
- accum(b2, diff * dIdy_patch[i][j]);
- }
- }
-
- reduce(b1, b2, smem1, smem2, tid);
- __syncthreads();
+ if (calcErr)
+ err[blockIdx.x] = static_cast<float>(errval) / (cn * c_winSize_x * c_winSize_y);
+ }
+ }
- b1 = smem1[0];
- b2 = smem2[0];
+ template <int cn, int PATCH_X, int PATCH_Y>
+ void sparse_caller(int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
+ int level, dim3 block, cudaStream_t stream)
+ {
+ dim3 grid(ptcount);
- float2 delta;
- delta.x = A12 * b2 - A22 * b1;
- delta.y = A12 * b1 - A11 * b2;
+ if (level == 0 && err)
+ sparseKernel<cn, PATCH_X, PATCH_Y, true><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
+ else
+ sparseKernel<cn, PATCH_X, PATCH_Y, false><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
- nextPt.x += delta.x;
- nextPt.y += delta.y;
+ cudaSafeCall( cudaGetLastError() );
- if (::fabs(delta.x) < 0.01f && ::fabs(delta.y) < 0.01f)
- break;
- }
+ if (stream == 0)
+ cudaSafeCall( cudaDeviceSynchronize() );
+ }
- float errval = 0;
- if (calcErr)
- {
- for (int y = threadIdx.y, i = 0; y < c_winSize_y; y += blockDim.y, ++i)
- {
- for (int x = threadIdx.x, j = 0; x < c_winSize_x; x += blockDim.x, ++j)
- {
- work_type I_val = I_patch[i][j];
- work_type J_val = Tex_J<cn>::read(nextPt.x + x + 0.5f, nextPt.y + y + 0.5f);
+ template <bool calcErr>
+ __global__ void denseKernel(PtrStepf u, PtrStepf v, const PtrStepf prevU, const PtrStepf prevV, PtrStepf err, const int rows, const int cols)
+ {
+ extern __shared__ int smem[];
- work_type diff = J_val - I_val;
+ const int patchWidth = blockDim.x + 2 * c_halfWin_x;
+ const int patchHeight = blockDim.y + 2 * c_halfWin_y;
- accum(errval, abs_(diff));
- }
- }
+ int* I_patch = smem;
+ int* dIdx_patch = I_patch + patchWidth * patchHeight;
+ int* dIdy_patch = dIdx_patch + patchWidth * patchHeight;
- reduce(errval, smem1, tid);
- }
+ const int xBase = blockIdx.x * blockDim.x;
+ const int yBase = blockIdx.y * blockDim.y;
- if (tid == 0)
+ for (int i = threadIdx.y; i < patchHeight; i += blockDim.y)
+ {
+ for (int j = threadIdx.x; j < patchWidth; j += blockDim.x)
{
- nextPt.x += c_halfWin_x;
- nextPt.y += c_halfWin_y;
+ float x = xBase - c_halfWin_x + j + 0.5f;
+ float y = yBase - c_halfWin_y + i + 0.5f;
- nextPts[blockIdx.x] = nextPt;
+ I_patch[i * patchWidth + j] = tex2D(tex_Ib, x, y);
- if (calcErr)
- err[blockIdx.x] = static_cast<float>(errval) / (cn * c_winSize_x * c_winSize_y);
+ // Sharr Deriv
+
+ dIdx_patch[i * patchWidth + j] = 3 * tex2D(tex_Ib, x+1, y-1) + 10 * tex2D(tex_Ib, x+1, y) + 3 * tex2D(tex_Ib, x+1, y+1) -
+ (3 * tex2D(tex_Ib, x-1, y-1) + 10 * tex2D(tex_Ib, x-1, y) + 3 * tex2D(tex_Ib, x-1, y+1));
+
+ dIdy_patch[i * patchWidth + j] = 3 * tex2D(tex_Ib, x-1, y+1) + 10 * tex2D(tex_Ib, x, y+1) + 3 * tex2D(tex_Ib, x+1, y+1) -
+ (3 * tex2D(tex_Ib, x-1, y-1) + 10 * tex2D(tex_Ib, x, y-1) + 3 * tex2D(tex_Ib, x+1, y-1));
}
}
- template <int cn, int PATCH_X, int PATCH_Y>
- void lkSparse_caller(int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
- int level, dim3 block, cudaStream_t stream)
- {
- dim3 grid(ptcount);
+ __syncthreads();
- if (level == 0 && err)
- lkSparse<cn, PATCH_X, PATCH_Y, true><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
- else
- lkSparse<cn, PATCH_X, PATCH_Y, false><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
+ const int x = xBase + threadIdx.x;
+ const int y = yBase + threadIdx.y;
- cudaSafeCall( cudaGetLastError() );
+ if (x >= cols || y >= rows)
+ return;
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
- }
+ int A11i = 0;
+ int A12i = 0;
+ int A22i = 0;
- void lkSparse1_gpu(PtrStepSzf I, PtrStepSzf J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
- int level, dim3 block, dim3 patch, cudaStream_t stream)
+ for (int i = 0; i < c_winSize_y; ++i)
{
- typedef void (*func_t)(int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
- int level, dim3 block, cudaStream_t stream);
-
- static const func_t funcs[5][5] =
+ for (int j = 0; j < c_winSize_x; ++j)
{
- {lkSparse_caller<1, 1, 1>, lkSparse_caller<1, 2, 1>, lkSparse_caller<1, 3, 1>, lkSparse_caller<1, 4, 1>, lkSparse_caller<1, 5, 1>},
- {lkSparse_caller<1, 1, 2>, lkSparse_caller<1, 2, 2>, lkSparse_caller<1, 3, 2>, lkSparse_caller<1, 4, 2>, lkSparse_caller<1, 5, 2>},
- {lkSparse_caller<1, 1, 3>, lkSparse_caller<1, 2, 3>, lkSparse_caller<1, 3, 3>, lkSparse_caller<1, 4, 3>, lkSparse_caller<1, 5, 3>},
- {lkSparse_caller<1, 1, 4>, lkSparse_caller<1, 2, 4>, lkSparse_caller<1, 3, 4>, lkSparse_caller<1, 4, 4>, lkSparse_caller<1, 5, 4>},
- {lkSparse_caller<1, 1, 5>, lkSparse_caller<1, 2, 5>, lkSparse_caller<1, 3, 5>, lkSparse_caller<1, 4, 5>, lkSparse_caller<1, 5, 5>}
- };
-
- bindTexture(&tex_If, I);
- bindTexture(&tex_Jf, J);
-
- funcs[patch.y - 1][patch.x - 1](I.rows, I.cols, prevPts, nextPts, status, err, ptcount,
- level, block, stream);
- }
-
- void lkSparse4_gpu(PtrStepSz<float4> I, PtrStepSz<float4> J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
- int level, dim3 block, dim3 patch, cudaStream_t stream)
- {
- typedef void (*func_t)(int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
- int level, dim3 block, cudaStream_t stream);
+ int dIdx = dIdx_patch[(threadIdx.y + i) * patchWidth + (threadIdx.x + j)];
+ int dIdy = dIdy_patch[(threadIdx.y + i) * patchWidth + (threadIdx.x + j)];
- static const func_t funcs[5][5] =
- {
- {lkSparse_caller<4, 1, 1>, lkSparse_caller<4, 2, 1>, lkSparse_caller<4, 3, 1>, lkSparse_caller<4, 4, 1>, lkSparse_caller<4, 5, 1>},
- {lkSparse_caller<4, 1, 2>, lkSparse_caller<4, 2, 2>, lkSparse_caller<4, 3, 2>, lkSparse_caller<4, 4, 2>, lkSparse_caller<4, 5, 2>},
- {lkSparse_caller<4, 1, 3>, lkSparse_caller<4, 2, 3>, lkSparse_caller<4, 3, 3>, lkSparse_caller<4, 4, 3>, lkSparse_caller<4, 5, 3>},
- {lkSparse_caller<4, 1, 4>, lkSparse_caller<4, 2, 4>, lkSparse_caller<4, 3, 4>, lkSparse_caller<4, 4, 4>, lkSparse_caller<4, 5, 4>},
- {lkSparse_caller<4, 1, 5>, lkSparse_caller<4, 2, 5>, lkSparse_caller<4, 3, 5>, lkSparse_caller<4, 4, 5>, lkSparse_caller<4, 5, 5>}
- };
-
- bindTexture(&tex_If4, I);
- bindTexture(&tex_Jf4, J);
-
- funcs[patch.y - 1][patch.x - 1](I.rows, I.cols, prevPts, nextPts, status, err, ptcount,
- level, block, stream);
+ A11i += dIdx * dIdx;
+ A12i += dIdx * dIdy;
+ A22i += dIdy * dIdy;
+ }
}
- template <bool calcErr>
- __global__ void lkDense(PtrStepf u, PtrStepf v, const PtrStepf prevU, const PtrStepf prevV, PtrStepf err, const int rows, const int cols)
- {
- extern __shared__ int smem[];
-
- const int patchWidth = blockDim.x + 2 * c_halfWin_x;
- const int patchHeight = blockDim.y + 2 * c_halfWin_y;
-
- int* I_patch = smem;
- int* dIdx_patch = I_patch + patchWidth * patchHeight;
- int* dIdy_patch = dIdx_patch + patchWidth * patchHeight;
+ float A11 = A11i;
+ float A12 = A12i;
+ float A22 = A22i;
- const int xBase = blockIdx.x * blockDim.x;
- const int yBase = blockIdx.y * blockDim.y;
+ float D = A11 * A22 - A12 * A12;
- for (int i = threadIdx.y; i < patchHeight; i += blockDim.y)
- {
- for (int j = threadIdx.x; j < patchWidth; j += blockDim.x)
- {
- float x = xBase - c_halfWin_x + j + 0.5f;
- float y = yBase - c_halfWin_y + i + 0.5f;
-
- I_patch[i * patchWidth + j] = tex2D(tex_Ib, x, y);
+ if (D < numeric_limits<float>::epsilon())
+ {
+ if (calcErr)
+ err(y, x) = numeric_limits<float>::max();
- // Sharr Deriv
+ return;
+ }
- dIdx_patch[i * patchWidth + j] = 3 * tex2D(tex_Ib, x+1, y-1) + 10 * tex2D(tex_Ib, x+1, y) + 3 * tex2D(tex_Ib, x+1, y+1) -
- (3 * tex2D(tex_Ib, x-1, y-1) + 10 * tex2D(tex_Ib, x-1, y) + 3 * tex2D(tex_Ib, x-1, y+1));
+ D = 1.f / D;
- dIdy_patch[i * patchWidth + j] = 3 * tex2D(tex_Ib, x-1, y+1) + 10 * tex2D(tex_Ib, x, y+1) + 3 * tex2D(tex_Ib, x+1, y+1) -
- (3 * tex2D(tex_Ib, x-1, y-1) + 10 * tex2D(tex_Ib, x, y-1) + 3 * tex2D(tex_Ib, x+1, y-1));
- }
- }
+ A11 *= D;
+ A12 *= D;
+ A22 *= D;
- __syncthreads();
+ float2 nextPt;
+ nextPt.x = x + prevU(y/2, x/2) * 2.0f;
+ nextPt.y = y + prevV(y/2, x/2) * 2.0f;
- const int x = xBase + threadIdx.x;
- const int y = yBase + threadIdx.y;
+ for (int k = 0; k < c_iters; ++k)
+ {
+ if (nextPt.x < 0 || nextPt.x >= cols || nextPt.y < 0 || nextPt.y >= rows)
+ {
+ if (calcErr)
+ err(y, x) = numeric_limits<float>::max();
- if (x >= cols || y >= rows)
return;
+ }
- int A11i = 0;
- int A12i = 0;
- int A22i = 0;
+ int b1 = 0;
+ int b2 = 0;
for (int i = 0; i < c_winSize_y; ++i)
{
for (int j = 0; j < c_winSize_x; ++j)
{
+ int I = I_patch[(threadIdx.y + i) * patchWidth + threadIdx.x + j];
+ int J = tex2D(tex_Jf, nextPt.x - c_halfWin_x + j + 0.5f, nextPt.y - c_halfWin_y + i + 0.5f);
+
+ int diff = (J - I) * 32;
+
int dIdx = dIdx_patch[(threadIdx.y + i) * patchWidth + (threadIdx.x + j)];
int dIdy = dIdy_patch[(threadIdx.y + i) * patchWidth + (threadIdx.x + j)];
- A11i += dIdx * dIdx;
- A12i += dIdx * dIdy;
- A22i += dIdy * dIdy;
+ b1 += diff * dIdx;
+ b2 += diff * dIdy;
}
}
- float A11 = A11i;
- float A12 = A12i;
- float A22 = A22i;
-
- float D = A11 * A22 - A12 * A12;
+ float2 delta;
+ delta.x = A12 * b2 - A22 * b1;
+ delta.y = A12 * b1 - A11 * b2;
- if (D < numeric_limits<float>::epsilon())
- {
- if (calcErr)
- err(y, x) = numeric_limits<float>::max();
+ nextPt.x += delta.x;
+ nextPt.y += delta.y;
- return;
- }
-
- D = 1.f / D;
+ if (::fabs(delta.x) < 0.01f && ::fabs(delta.y) < 0.01f)
+ break;
+ }
- A11 *= D;
- A12 *= D;
- A22 *= D;
+ u(y, x) = nextPt.x - x;
+ v(y, x) = nextPt.y - y;
- float2 nextPt;
- nextPt.x = x + prevU(y/2, x/2) * 2.0f;
- nextPt.y = y + prevV(y/2, x/2) * 2.0f;
+ if (calcErr)
+ {
+ int errval = 0;
- for (int k = 0; k < c_iters; ++k)
+ for (int i = 0; i < c_winSize_y; ++i)
{
- if (nextPt.x < 0 || nextPt.x >= cols || nextPt.y < 0 || nextPt.y >= rows)
+ for (int j = 0; j < c_winSize_x; ++j)
{
- if (calcErr)
- err(y, x) = numeric_limits<float>::max();
+ int I = I_patch[(threadIdx.y + i) * patchWidth + threadIdx.x + j];
+ int J = tex2D(tex_Jf, nextPt.x - c_halfWin_x + j + 0.5f, nextPt.y - c_halfWin_y + i + 0.5f);
- return;
+ errval += ::abs(J - I);
}
+ }
- int b1 = 0;
- int b2 = 0;
-
- for (int i = 0; i < c_winSize_y; ++i)
- {
- for (int j = 0; j < c_winSize_x; ++j)
- {
- int I = I_patch[(threadIdx.y + i) * patchWidth + threadIdx.x + j];
- int J = tex2D(tex_Jf, nextPt.x - c_halfWin_x + j + 0.5f, nextPt.y - c_halfWin_y + i + 0.5f);
-
- int diff = (J - I) * 32;
+ err(y, x) = static_cast<float>(errval) / (c_winSize_x * c_winSize_y);
+ }
+ }
- int dIdx = dIdx_patch[(threadIdx.y + i) * patchWidth + (threadIdx.x + j)];
- int dIdy = dIdy_patch[(threadIdx.y + i) * patchWidth + (threadIdx.x + j)];
+ void loadConstants(int2 winSize, int iters)
+ {
+ cudaSafeCall( cudaMemcpyToSymbol(c_winSize_x, &winSize.x, sizeof(int)) );
+ cudaSafeCall( cudaMemcpyToSymbol(c_winSize_y, &winSize.y, sizeof(int)) );
- b1 += diff * dIdx;
- b2 += diff * dIdy;
- }
- }
+ int2 halfWin = make_int2((winSize.x - 1) / 2, (winSize.y - 1) / 2);
+ cudaSafeCall( cudaMemcpyToSymbol(c_halfWin_x, &halfWin.x, sizeof(int)) );
+ cudaSafeCall( cudaMemcpyToSymbol(c_halfWin_y, &halfWin.y, sizeof(int)) );
- float2 delta;
- delta.x = A12 * b2 - A22 * b1;
- delta.y = A12 * b1 - A11 * b2;
+ cudaSafeCall( cudaMemcpyToSymbol(c_iters, &iters, sizeof(int)) );
+ }
- nextPt.x += delta.x;
- nextPt.y += delta.y;
+ void sparse1(PtrStepSzf I, PtrStepSzf J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
+ int level, dim3 block, dim3 patch, cudaStream_t stream)
+ {
+ typedef void (*func_t)(int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
+ int level, dim3 block, cudaStream_t stream);
- if (::fabs(delta.x) < 0.01f && ::fabs(delta.y) < 0.01f)
- break;
- }
+ static const func_t funcs[5][5] =
+ {
+ {sparse_caller<1, 1, 1>, sparse_caller<1, 2, 1>, sparse_caller<1, 3, 1>, sparse_caller<1, 4, 1>, sparse_caller<1, 5, 1>},
+ {sparse_caller<1, 1, 2>, sparse_caller<1, 2, 2>, sparse_caller<1, 3, 2>, sparse_caller<1, 4, 2>, sparse_caller<1, 5, 2>},
+ {sparse_caller<1, 1, 3>, sparse_caller<1, 2, 3>, sparse_caller<1, 3, 3>, sparse_caller<1, 4, 3>, sparse_caller<1, 5, 3>},
+ {sparse_caller<1, 1, 4>, sparse_caller<1, 2, 4>, sparse_caller<1, 3, 4>, sparse_caller<1, 4, 4>, sparse_caller<1, 5, 4>},
+ {sparse_caller<1, 1, 5>, sparse_caller<1, 2, 5>, sparse_caller<1, 3, 5>, sparse_caller<1, 4, 5>, sparse_caller<1, 5, 5>}
+ };
- u(y, x) = nextPt.x - x;
- v(y, x) = nextPt.y - y;
+ bindTexture(&tex_If, I);
+ bindTexture(&tex_Jf, J);
- if (calcErr)
- {
- int errval = 0;
+ funcs[patch.y - 1][patch.x - 1](I.rows, I.cols, prevPts, nextPts, status, err, ptcount,
+ level, block, stream);
+ }
- for (int i = 0; i < c_winSize_y; ++i)
- {
- for (int j = 0; j < c_winSize_x; ++j)
- {
- int I = I_patch[(threadIdx.y + i) * patchWidth + threadIdx.x + j];
- int J = tex2D(tex_Jf, nextPt.x - c_halfWin_x + j + 0.5f, nextPt.y - c_halfWin_y + i + 0.5f);
+ void sparse4(PtrStepSz<float4> I, PtrStepSz<float4> J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
+ int level, dim3 block, dim3 patch, cudaStream_t stream)
+ {
+ typedef void (*func_t)(int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
+ int level, dim3 block, cudaStream_t stream);
- errval += ::abs(J - I);
- }
- }
+ static const func_t funcs[5][5] =
+ {
+ {sparse_caller<4, 1, 1>, sparse_caller<4, 2, 1>, sparse_caller<4, 3, 1>, sparse_caller<4, 4, 1>, sparse_caller<4, 5, 1>},
+ {sparse_caller<4, 1, 2>, sparse_caller<4, 2, 2>, sparse_caller<4, 3, 2>, sparse_caller<4, 4, 2>, sparse_caller<4, 5, 2>},
+ {sparse_caller<4, 1, 3>, sparse_caller<4, 2, 3>, sparse_caller<4, 3, 3>, sparse_caller<4, 4, 3>, sparse_caller<4, 5, 3>},
+ {sparse_caller<4, 1, 4>, sparse_caller<4, 2, 4>, sparse_caller<4, 3, 4>, sparse_caller<4, 4, 4>, sparse_caller<4, 5, 4>},
+ {sparse_caller<4, 1, 5>, sparse_caller<4, 2, 5>, sparse_caller<4, 3, 5>, sparse_caller<4, 4, 5>, sparse_caller<4, 5, 5>}
+ };
- err(y, x) = static_cast<float>(errval) / (c_winSize_x * c_winSize_y);
- }
- }
+ bindTexture(&tex_If4, I);
+ bindTexture(&tex_Jf4, J);
- void lkDense_gpu(PtrStepSzb I, PtrStepSzf J, PtrStepSzf u, PtrStepSzf v, PtrStepSzf prevU, PtrStepSzf prevV,
- PtrStepSzf err, int2 winSize, cudaStream_t stream)
- {
- dim3 block(16, 16);
- dim3 grid(divUp(I.cols, block.x), divUp(I.rows, block.y));
+ funcs[patch.y - 1][patch.x - 1](I.rows, I.cols, prevPts, nextPts, status, err, ptcount,
+ level, block, stream);
+ }
- bindTexture(&tex_Ib, I);
- bindTexture(&tex_Jf, J);
+ void dense(PtrStepSzb I, PtrStepSzf J, PtrStepSzf u, PtrStepSzf v, PtrStepSzf prevU, PtrStepSzf prevV, PtrStepSzf err, int2 winSize, cudaStream_t stream)
+ {
+ dim3 block(16, 16);
+ dim3 grid(divUp(I.cols, block.x), divUp(I.rows, block.y));
- int2 halfWin = make_int2((winSize.x - 1) / 2, (winSize.y - 1) / 2);
- const int patchWidth = block.x + 2 * halfWin.x;
- const int patchHeight = block.y + 2 * halfWin.y;
- size_t smem_size = 3 * patchWidth * patchHeight * sizeof(int);
+ bindTexture(&tex_Ib, I);
+ bindTexture(&tex_Jf, J);
- if (err.data)
- {
- lkDense<true><<<grid, block, smem_size, stream>>>(u, v, prevU, prevV, err, I.rows, I.cols);
- cudaSafeCall( cudaGetLastError() );
- }
- else
- {
- lkDense<false><<<grid, block, smem_size, stream>>>(u, v, prevU, prevV, PtrStepf(), I.rows, I.cols);
- cudaSafeCall( cudaGetLastError() );
- }
+ int2 halfWin = make_int2((winSize.x - 1) / 2, (winSize.y - 1) / 2);
+ const int patchWidth = block.x + 2 * halfWin.x;
+ const int patchHeight = block.y + 2 * halfWin.y;
+ size_t smem_size = 3 * patchWidth * patchHeight * sizeof(int);
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
+ if (err.data)
+ {
+ denseKernel<true><<<grid, block, smem_size, stream>>>(u, v, prevU, prevV, err, I.rows, I.cols);
+ cudaSafeCall( cudaGetLastError() );
+ }
+ else
+ {
+ denseKernel<false><<<grid, block, smem_size, stream>>>(u, v, prevU, prevV, PtrStepf(), I.rows, I.cols);
+ cudaSafeCall( cudaGetLastError() );
}
+
+ if (stream == 0)
+ cudaSafeCall( cudaDeviceSynchronize() );
}
-}}}
+}
#endif /* CUDA_DISABLER */
template <template <typename> class Filter, template <typename> class B, typename T> struct RemapDispatcherStream
{
- static void call(PtrStepSz<T> src, PtrStepSzf mapx, PtrStepSzf mapy, PtrStepSz<T> dst, const float* borderValue, cudaStream_t stream, int)
+ static void call(PtrStepSz<T> src, PtrStepSzf mapx, PtrStepSzf mapy, PtrStepSz<T> dst, const float* borderValue, cudaStream_t stream, bool)
{
typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type work_type;
template <template <typename> class Filter, template <typename> class B, typename T> struct RemapDispatcherNonStream
{
- static void call(PtrStepSz<T> src, PtrStepSz<T> srcWhole, int xoff, int yoff, PtrStepSzf mapx, PtrStepSzf mapy, PtrStepSz<T> dst, const float* borderValue, int)
+ static void call(PtrStepSz<T> src, PtrStepSz<T> srcWhole, int xoff, int yoff, PtrStepSzf mapx, PtrStepSzf mapy, PtrStepSz<T> dst, const float* borderValue, bool)
{
(void)srcWhole;
(void)xoff;
template <template <typename> class Filter, template <typename> class B> struct RemapDispatcherNonStream<Filter, B, type> \
{ \
static void call(PtrStepSz< type > src, PtrStepSz< type > srcWhole, int xoff, int yoff, PtrStepSzf mapx, PtrStepSzf mapy, \
- PtrStepSz< type > dst, const float* borderValue, int cc) \
+ PtrStepSz< type > dst, const float* borderValue, bool cc20) \
{ \
typedef typename TypeVec<float, VecTraits< type >::cn>::vec_type work_type; \
- dim3 block(32, cc >= 20 ? 8 : 4); \
+ dim3 block(32, cc20 ? 8 : 4); \
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y)); \
bindTexture(&tex_remap_ ## type , srcWhole); \
tex_remap_ ## type ##_reader texSrc(xoff, yoff); \
template <template <typename> class Filter> struct RemapDispatcherNonStream<Filter, BrdReplicate, type> \
{ \
static void call(PtrStepSz< type > src, PtrStepSz< type > srcWhole, int xoff, int yoff, PtrStepSzf mapx, PtrStepSzf mapy, \
- PtrStepSz< type > dst, const float*, int) \
+ PtrStepSz< type > dst, const float*, bool) \
{ \
dim3 block(32, 8); \
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y)); \
template <template <typename> class Filter, template <typename> class B, typename T> struct RemapDispatcher
{
static void call(PtrStepSz<T> src, PtrStepSz<T> srcWhole, int xoff, int yoff, PtrStepSzf mapx, PtrStepSzf mapy,
- PtrStepSz<T> dst, const float* borderValue, cudaStream_t stream, int cc)
+ PtrStepSz<T> dst, const float* borderValue, cudaStream_t stream, bool cc20)
{
if (stream == 0)
- RemapDispatcherNonStream<Filter, B, T>::call(src, srcWhole, xoff, yoff, mapx, mapy, dst, borderValue, cc);
+ RemapDispatcherNonStream<Filter, B, T>::call(src, srcWhole, xoff, yoff, mapx, mapy, dst, borderValue, cc20);
else
- RemapDispatcherStream<Filter, B, T>::call(src, mapx, mapy, dst, borderValue, stream, cc);
+ RemapDispatcherStream<Filter, B, T>::call(src, mapx, mapy, dst, borderValue, stream, cc20);
}
};
template <typename T> void remap_gpu(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap,
- PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc)
+ PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20)
{
typedef void (*caller_t)(PtrStepSz<T> src, PtrStepSz<T> srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap,
- PtrStepSz<T> dst, const float* borderValue, cudaStream_t stream, int cc);
+ PtrStepSz<T> dst, const float* borderValue, cudaStream_t stream, bool cc20);
static const caller_t callers[3][5] =
{
};
callers[interpolation][borderMode](static_cast< PtrStepSz<T> >(src), static_cast< PtrStepSz<T> >(srcWhole), xoff, yoff, xmap, ymap,
- static_cast< PtrStepSz<T> >(dst), borderValue, stream, cc);
+ static_cast< PtrStepSz<T> >(dst), borderValue, stream, cc20);
}
- template void remap_gpu<uchar >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void remap_gpu<uchar2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void remap_gpu<uchar3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void remap_gpu<uchar4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
-
- //template void remap_gpu<schar>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void remap_gpu<char2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void remap_gpu<char3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void remap_gpu<char4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
-
- template void remap_gpu<ushort >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void remap_gpu<ushort2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void remap_gpu<ushort3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void remap_gpu<ushort4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
-
- template void remap_gpu<short >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void remap_gpu<short2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void remap_gpu<short3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void remap_gpu<short4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
-
- //template void remap_gpu<int >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void remap_gpu<int2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void remap_gpu<int3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void remap_gpu<int4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
-
- template void remap_gpu<float >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void remap_gpu<float2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void remap_gpu<float3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void remap_gpu<float4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
+ template void remap_gpu<uchar >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void remap_gpu<uchar2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void remap_gpu<uchar3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void remap_gpu<uchar4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+
+ //template void remap_gpu<schar>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void remap_gpu<char2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void remap_gpu<char3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void remap_gpu<char4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+
+ template void remap_gpu<ushort >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void remap_gpu<ushort2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void remap_gpu<ushort3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void remap_gpu<ushort4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+
+ template void remap_gpu<short >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void remap_gpu<short2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void remap_gpu<short3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void remap_gpu<short4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+
+ //template void remap_gpu<int >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void remap_gpu<int2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void remap_gpu<int3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void remap_gpu<int4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+
+ template void remap_gpu<float >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void remap_gpu<float2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void remap_gpu<float3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void remap_gpu<float4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
} // namespace imgproc
}}} // namespace cv { namespace gpu { namespace device
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+#endif /* CUDA_DISABLER */
#if !defined CUDA_DISABLER
-#include "internal_shared.hpp"
+#include "opencv2/gpu/device/common.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
#include "opencv2/gpu/device/limits.hpp"
+#include "opencv2/gpu/device/reduce.hpp"
+#include "opencv2/gpu/device/functional.hpp"
namespace cv { namespace gpu { namespace device
{
}
extern __shared__ float smem[];
- float* dline = smem + winsz * threadIdx.z;
- dline[tid] = val;
-
- __syncthreads();
-
- if (winsz >= 256) { if (tid < 128) { dline[tid] += dline[tid + 128]; } __syncthreads(); }
- if (winsz >= 128) { if (tid < 64) { dline[tid] += dline[tid + 64]; } __syncthreads(); }
-
- volatile float* vdline = smem + winsz * threadIdx.z;
-
- if (winsz >= 64) if (tid < 32) vdline[tid] += vdline[tid + 32];
- if (winsz >= 32) if (tid < 16) vdline[tid] += vdline[tid + 16];
- if (winsz >= 16) if (tid < 8) vdline[tid] += vdline[tid + 8];
- if (winsz >= 8) if (tid < 4) vdline[tid] += vdline[tid + 4];
- if (winsz >= 4) if (tid < 2) vdline[tid] += vdline[tid + 2];
- if (winsz >= 2) if (tid < 1) vdline[tid] += vdline[tid + 1];
+ reduce<winsz>(smem + winsz * threadIdx.z, val, tid, plus<float>());
T* data_cost = (T*)ctemp + y_out * cmsg_step + x_out;
if (tid == 0)
- data_cost[cdisp_step1 * d] = saturate_cast<T>(dline[0]);
+ data_cost[cdisp_step1 * d] = saturate_cast<T>(val);
}
}
}
extern __shared__ float smem[];
- float* dline = smem + winsz * threadIdx.z;
- dline[tid] = val;
-
- __syncthreads();
-
- if (winsz >= 256) { if (tid < 128) { dline[tid] += dline[tid + 128]; } __syncthreads(); }
- if (winsz >= 128) { if (tid < 64) { dline[tid] += dline[tid + 64]; } __syncthreads(); }
-
- volatile float* vdline = smem + winsz * threadIdx.z;
-
- if (winsz >= 64) if (tid < 32) vdline[tid] += vdline[tid + 32];
- if (winsz >= 32) if (tid < 16) vdline[tid] += vdline[tid + 16];
- if (winsz >= 16) if (tid < 8) vdline[tid] += vdline[tid + 8];
- if (winsz >= 8) if (tid < 4) vdline[tid] += vdline[tid + 4];
- if (winsz >= 4) if (tid < 2) vdline[tid] += vdline[tid + 2];
- if (winsz >= 2) if (tid < 1) vdline[tid] += vdline[tid + 1];
+ reduce<winsz>(smem + winsz * threadIdx.z, val, tid, plus<float>());
if (tid == 0)
- data_cost[cdisp_step1 * d] = saturate_cast<T>(dline[0]);
+ data_cost[cdisp_step1 * d] = saturate_cast<T>(val);
}
}
} // namespace stereocsbp
}}} // namespace cv { namespace gpu { namespace device {
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+#endif /* CUDA_DISABLER */
#if !defined CUDA_DISABLER
-#include "internal_shared.hpp"
+#include "opencv2/gpu/device/common.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
+#include "opencv2/gpu/device/reduce.hpp"
#include "opencv2/gpu/device/utility.hpp"
#include "opencv2/gpu/device/functional.hpp"
#include "opencv2/gpu/device/filters.hpp"
-#include <float.h>
namespace cv { namespace gpu { namespace device
{
float bestx = 0, besty = 0, best_mod = 0;
+ #if __CUDA_ARCH__ >= 200
#pragma unroll
+ #endif
for (int i = 0; i < 18; ++i)
{
const int dir = (i * 4 + threadIdx.y) * ORI_SEARCH_INC;
sumy += s_Y[threadIdx.x + 96];
}
- device::reduce<32>(s_sumx + threadIdx.y * 32, sumx, threadIdx.x, plus<volatile float>());
- device::reduce<32>(s_sumy + threadIdx.y * 32, sumy, threadIdx.x, plus<volatile float>());
+ plus<float> op;
+ device::reduce<32>(smem_tuple(s_sumx + threadIdx.y * 32, s_sumy + threadIdx.y * 32),
+ thrust::tie(sumx, sumy), threadIdx.x, thrust::make_tuple(op, op));
const float temp_mod = sumx * sumx + sumy * sumy;
if (temp_mod > best_mod)
kp_dir *= 180.0f / CV_PI_F;
kp_dir = 360.0f - kp_dir;
- if (::fabsf(kp_dir - 360.f) < FLT_EPSILON)
+ if (::fabsf(kp_dir - 360.f) < numeric_limits<float>::epsilon())
kp_dir = 0.f;
featureDir[blockIdx.x] = kp_dir;
{
typedef uchar elem_type;
- __device__ __forceinline__ WinReader(float centerX_, float centerY_, float win_offset_, float cos_dir_, float sin_dir_) :
- centerX(centerX_), centerY(centerY_), win_offset(win_offset_), cos_dir(cos_dir_), sin_dir(sin_dir_)
- {
- }
-
__device__ __forceinline__ uchar operator ()(int i, int j) const
{
float pixel_x = centerX + (win_offset + j) * cos_dir + (win_offset + i) * sin_dir;
float win_offset;
float cos_dir;
float sin_dir;
+ int width;
+ int height;
};
- __device__ void calc_dx_dy(float s_dx_bin[25], float s_dy_bin[25],
- const float* featureX, const float* featureY, const float* featureSize, const float* featureDir)
+ __device__ void calc_dx_dy(const float* featureX, const float* featureY, const float* featureSize, const float* featureDir,
+ float& dx, float& dy)
{
- __shared__ float s_PATCH[6][6];
+ __shared__ float s_PATCH[PATCH_SZ + 1][PATCH_SZ + 1];
- const float centerX = featureX[blockIdx.x];
- const float centerY = featureY[blockIdx.x];
- const float size = featureSize[blockIdx.x];
- float descriptor_dir = 360.0f - featureDir[blockIdx.x];
- if (std::abs(descriptor_dir - 360.f) < FLT_EPSILON)
- descriptor_dir = 0.f;
- descriptor_dir *= (float)(CV_PI_F / 180.0f);
+ dx = dy = 0.0f;
- /* The sampling intervals and wavelet sized for selecting an orientation
- and building the keypoint descriptor are defined relative to 's' */
- const float s = size * 1.2f / 9.0f;
+ WinReader win;
- /* Extract a window of pixels around the keypoint of size 20s */
- const int win_size = (int)((PATCH_SZ + 1) * s);
+ win.centerX = featureX[blockIdx.x];
+ win.centerY = featureY[blockIdx.x];
- float sin_dir;
- float cos_dir;
- sincosf(descriptor_dir, &sin_dir, &cos_dir);
+ // The sampling intervals and wavelet sized for selecting an orientation
+ // and building the keypoint descriptor are defined relative to 's'
+ const float s = featureSize[blockIdx.x] * 1.2f / 9.0f;
- /* Nearest neighbour version (faster) */
- const float win_offset = -(float)(win_size - 1) / 2;
+ // Extract a window of pixels around the keypoint of size 20s
+ const int win_size = (int)((PATCH_SZ + 1) * s);
- // Compute sampling points
- // since grids are 2D, need to compute xBlock and yBlock indices
- const int xBlock = (blockIdx.y & 3); // blockIdx.y % 4
- const int yBlock = (blockIdx.y >> 2); // floor(blockIdx.y/4)
- const int xIndex = xBlock * 5 + threadIdx.x;
- const int yIndex = yBlock * 5 + threadIdx.y;
+ win.width = win.height = win_size;
- const float icoo = ((float)yIndex / (PATCH_SZ + 1)) * win_size;
- const float jcoo = ((float)xIndex / (PATCH_SZ + 1)) * win_size;
+ // Nearest neighbour version (faster)
+ win.win_offset = -(win_size - 1.0f) / 2.0f;
- LinearFilter<WinReader> filter(WinReader(centerX, centerY, win_offset, cos_dir, sin_dir));
+ float descriptor_dir = 360.0f - featureDir[blockIdx.x];
+ if (::fabsf(descriptor_dir - 360.f) < numeric_limits<float>::epsilon())
+ descriptor_dir = 0.f;
+ descriptor_dir *= CV_PI_F / 180.0f;
+ sincosf(descriptor_dir, &win.sin_dir, &win.cos_dir);
- s_PATCH[threadIdx.y][threadIdx.x] = filter(icoo, jcoo);
+ const int tid = threadIdx.y * blockDim.x + threadIdx.x;
- __syncthreads();
+ const int xLoadInd = tid % (PATCH_SZ + 1);
+ const int yLoadInd = tid / (PATCH_SZ + 1);
- if (threadIdx.x < 5 && threadIdx.y < 5)
+ if (yLoadInd < (PATCH_SZ + 1))
{
- const int tid = threadIdx.y * 5 + threadIdx.x;
-
- const float dw = c_DW[yIndex * PATCH_SZ + xIndex];
+ if (s > 1)
+ {
+ AreaFilter<WinReader> filter(win, s, s);
+ s_PATCH[yLoadInd][xLoadInd] = filter(yLoadInd, xLoadInd);
+ }
+ else
+ {
+ LinearFilter<WinReader> filter(win);
+ s_PATCH[yLoadInd][xLoadInd] = filter(yLoadInd * s, xLoadInd * s);
+ }
+ }
- const float vx = (s_PATCH[threadIdx.y ][threadIdx.x + 1] - s_PATCH[threadIdx.y][threadIdx.x] + s_PATCH[threadIdx.y + 1][threadIdx.x + 1] - s_PATCH[threadIdx.y + 1][threadIdx.x ]) * dw;
- const float vy = (s_PATCH[threadIdx.y + 1][threadIdx.x ] - s_PATCH[threadIdx.y][threadIdx.x] + s_PATCH[threadIdx.y + 1][threadIdx.x + 1] - s_PATCH[threadIdx.y ][threadIdx.x + 1]) * dw;
+ __syncthreads();
- s_dx_bin[tid] = vx;
- s_dy_bin[tid] = vy;
- }
- }
+ const int xPatchInd = threadIdx.x % 5;
+ const int yPatchInd = threadIdx.x / 5;
- __device__ void reduce_sum25(volatile float* sdata1, volatile float* sdata2, volatile float* sdata3, volatile float* sdata4, int tid)
- {
- // first step is to reduce from 25 to 16
- if (tid < 9) // use 9 threads
+ if (yPatchInd < 5)
{
- sdata1[tid] += sdata1[tid + 16];
- sdata2[tid] += sdata2[tid + 16];
- sdata3[tid] += sdata3[tid + 16];
- sdata4[tid] += sdata4[tid + 16];
- }
+ const int xBlockInd = threadIdx.y % 4;
+ const int yBlockInd = threadIdx.y / 4;
- // sum (reduce) from 16 to 1 (unrolled - aligned to a half-warp)
- if (tid < 8)
- {
- sdata1[tid] += sdata1[tid + 8];
- sdata1[tid] += sdata1[tid + 4];
- sdata1[tid] += sdata1[tid + 2];
- sdata1[tid] += sdata1[tid + 1];
-
- sdata2[tid] += sdata2[tid + 8];
- sdata2[tid] += sdata2[tid + 4];
- sdata2[tid] += sdata2[tid + 2];
- sdata2[tid] += sdata2[tid + 1];
-
- sdata3[tid] += sdata3[tid + 8];
- sdata3[tid] += sdata3[tid + 4];
- sdata3[tid] += sdata3[tid + 2];
- sdata3[tid] += sdata3[tid + 1];
-
- sdata4[tid] += sdata4[tid + 8];
- sdata4[tid] += sdata4[tid + 4];
- sdata4[tid] += sdata4[tid + 2];
- sdata4[tid] += sdata4[tid + 1];
+ const int xInd = xBlockInd * 5 + xPatchInd;
+ const int yInd = yBlockInd * 5 + yPatchInd;
+
+ const float dw = c_DW[yInd * PATCH_SZ + xInd];
+
+ dx = (s_PATCH[yInd ][xInd + 1] - s_PATCH[yInd][xInd] + s_PATCH[yInd + 1][xInd + 1] - s_PATCH[yInd + 1][xInd ]) * dw;
+ dy = (s_PATCH[yInd + 1][xInd ] - s_PATCH[yInd][xInd] + s_PATCH[yInd + 1][xInd + 1] - s_PATCH[yInd ][xInd + 1]) * dw;
}
}
- __global__ void compute_descriptors64(PtrStepf descriptors, const float* featureX, const float* featureY, const float* featureSize, const float* featureDir)
+ __global__ void compute_descriptors_64(PtrStep<float4> descriptors, const float* featureX, const float* featureY, const float* featureSize, const float* featureDir)
{
- // 2 floats (dx,dy) for each thread (5x5 sample points in each sub-region)
- __shared__ float sdx[25];
- __shared__ float sdy[25];
- __shared__ float sdxabs[25];
- __shared__ float sdyabs[25];
+ __shared__ float smem[32 * 16];
- calc_dx_dy(sdx, sdy, featureX, featureY, featureSize, featureDir);
- __syncthreads();
+ float* sRow = smem + threadIdx.y * 32;
+ float dx, dy;
+ calc_dx_dy(featureX, featureY, featureSize, featureDir, dx, dy);
- const int tid = threadIdx.y * blockDim.x + threadIdx.x;
+ float dxabs = ::fabsf(dx);
+ float dyabs = ::fabsf(dy);
- if (tid < 25)
- {
- sdxabs[tid] = ::fabs(sdx[tid]); // |dx| array
- sdyabs[tid] = ::fabs(sdy[tid]); // |dy| array
- __syncthreads();
+ plus<float> op;
- reduce_sum25(sdx, sdy, sdxabs, sdyabs, tid);
- __syncthreads();
+ reduce<32>(sRow, dx, threadIdx.x, op);
+ reduce<32>(sRow, dy, threadIdx.x, op);
+ reduce<32>(sRow, dxabs, threadIdx.x, op);
+ reduce<32>(sRow, dyabs, threadIdx.x, op);
- float* descriptors_block = descriptors.ptr(blockIdx.x) + (blockIdx.y << 2);
+ float4* descriptors_block = descriptors.ptr(blockIdx.x) + threadIdx.y;
- // write dx, dy, |dx|, |dy|
- if (tid == 0)
- {
- descriptors_block[0] = sdx[0];
- descriptors_block[1] = sdy[0];
- descriptors_block[2] = sdxabs[0];
- descriptors_block[3] = sdyabs[0];
- }
- }
+ // write dx, dy, |dx|, |dy|
+ if (threadIdx.x == 0)
+ *descriptors_block = make_float4(dx, dy, dxabs, dyabs);
}
- __global__ void compute_descriptors128(PtrStepf descriptors, const float* featureX, const float* featureY, const float* featureSize, const float* featureDir)
+ __global__ void compute_descriptors_128(PtrStep<float4> descriptors, const float* featureX, const float* featureY, const float* featureSize, const float* featureDir)
{
- // 2 floats (dx,dy) for each thread (5x5 sample points in each sub-region)
- __shared__ float sdx[25];
- __shared__ float sdy[25];
+ __shared__ float smem[32 * 16];
- // sum (reduce) 5x5 area response
- __shared__ float sd1[25];
- __shared__ float sd2[25];
- __shared__ float sdabs1[25];
- __shared__ float sdabs2[25];
+ float* sRow = smem + threadIdx.y * 32;
- calc_dx_dy(sdx, sdy, featureX, featureY, featureSize, featureDir);
- __syncthreads();
-
- const int tid = threadIdx.y * blockDim.x + threadIdx.x;
+ float dx, dy;
+ calc_dx_dy(featureX, featureY, featureSize, featureDir, dx, dy);
- if (tid < 25)
- {
- if (sdy[tid] >= 0)
- {
- sd1[tid] = sdx[tid];
- sdabs1[tid] = ::fabs(sdx[tid]);
- sd2[tid] = 0;
- sdabs2[tid] = 0;
- }
- else
- {
- sd1[tid] = 0;
- sdabs1[tid] = 0;
- sd2[tid] = sdx[tid];
- sdabs2[tid] = ::fabs(sdx[tid]);
- }
- __syncthreads();
+ float4* descriptors_block = descriptors.ptr(blockIdx.x) + threadIdx.y * 2;
- reduce_sum25(sd1, sd2, sdabs1, sdabs2, tid);
- __syncthreads();
+ plus<float> op;
- float* descriptors_block = descriptors.ptr(blockIdx.x) + (blockIdx.y << 3);
+ float d1 = 0.0f;
+ float d2 = 0.0f;
+ float abs1 = 0.0f;
+ float abs2 = 0.0f;
- // write dx (dy >= 0), |dx| (dy >= 0), dx (dy < 0), |dx| (dy < 0)
- if (tid == 0)
- {
- descriptors_block[0] = sd1[0];
- descriptors_block[1] = sdabs1[0];
- descriptors_block[2] = sd2[0];
- descriptors_block[3] = sdabs2[0];
- }
- __syncthreads();
+ if (dy >= 0)
+ {
+ d1 = dx;
+ abs1 = ::fabsf(dx);
+ }
+ else
+ {
+ d2 = dx;
+ abs2 = ::fabsf(dx);
+ }
- if (sdx[tid] >= 0)
- {
- sd1[tid] = sdy[tid];
- sdabs1[tid] = ::fabs(sdy[tid]);
- sd2[tid] = 0;
- sdabs2[tid] = 0;
- }
- else
- {
- sd1[tid] = 0;
- sdabs1[tid] = 0;
- sd2[tid] = sdy[tid];
- sdabs2[tid] = ::fabs(sdy[tid]);
- }
- __syncthreads();
+ reduce<32>(sRow, d1, threadIdx.x, op);
+ reduce<32>(sRow, d2, threadIdx.x, op);
+ reduce<32>(sRow, abs1, threadIdx.x, op);
+ reduce<32>(sRow, abs2, threadIdx.x, op);
- reduce_sum25(sd1, sd2, sdabs1, sdabs2, tid);
- __syncthreads();
+ // write dx (dy >= 0), |dx| (dy >= 0), dx (dy < 0), |dx| (dy < 0)
+ if (threadIdx.x == 0)
+ descriptors_block[0] = make_float4(d1, abs1, d2, abs2);
- // write dy (dx >= 0), |dy| (dx >= 0), dy (dx < 0), |dy| (dx < 0)
- if (tid == 0)
- {
- descriptors_block[4] = sd1[0];
- descriptors_block[5] = sdabs1[0];
- descriptors_block[6] = sd2[0];
- descriptors_block[7] = sdabs2[0];
- }
+ if (dx >= 0)
+ {
+ d1 = dy;
+ abs1 = ::fabsf(dy);
+ d2 = 0.0f;
+ abs2 = 0.0f;
}
+ else
+ {
+ d1 = 0.0f;
+ abs1 = 0.0f;
+ d2 = dy;
+ abs2 = ::fabsf(dy);
+ }
+
+ reduce<32>(sRow, d1, threadIdx.x, op);
+ reduce<32>(sRow, d2, threadIdx.x, op);
+ reduce<32>(sRow, abs1, threadIdx.x, op);
+ reduce<32>(sRow, abs2, threadIdx.x, op);
+
+ // write dy (dx >= 0), |dy| (dx >= 0), dy (dx < 0), |dy| (dx < 0)
+ if (threadIdx.x == 0)
+ descriptors_block[1] = make_float4(d1, abs1, d2, abs2);
}
template <int BLOCK_DIM_X> __global__ void normalize_descriptors(PtrStepf descriptors)
{
+ __shared__ float smem[BLOCK_DIM_X];
+ __shared__ float s_len;
+
// no need for thread ID
float* descriptor_base = descriptors.ptr(blockIdx.x);
// read in the unnormalized descriptor values (squared)
- __shared__ float sqDesc[BLOCK_DIM_X];
- const float lookup = descriptor_base[threadIdx.x];
- sqDesc[threadIdx.x] = lookup * lookup;
- __syncthreads();
+ const float val = descriptor_base[threadIdx.x];
- if (BLOCK_DIM_X >= 128)
- {
- if (threadIdx.x < 64)
- sqDesc[threadIdx.x] += sqDesc[threadIdx.x + 64];
- __syncthreads();
- }
+ float len = val * val;
+ reduce<BLOCK_DIM_X>(smem, len, threadIdx.x, plus<float>());
- // reduction to get total
- if (threadIdx.x < 32)
- {
- volatile float* smem = sqDesc;
-
- smem[threadIdx.x] += smem[threadIdx.x + 32];
- smem[threadIdx.x] += smem[threadIdx.x + 16];
- smem[threadIdx.x] += smem[threadIdx.x + 8];
- smem[threadIdx.x] += smem[threadIdx.x + 4];
- smem[threadIdx.x] += smem[threadIdx.x + 2];
- smem[threadIdx.x] += smem[threadIdx.x + 1];
- }
-
- // compute length (square root)
- __shared__ float len;
if (threadIdx.x == 0)
- {
- len = sqrtf(sqDesc[0]);
- }
+ s_len = ::sqrtf(len);
+
__syncthreads();
// normalize and store in output
- descriptor_base[threadIdx.x] = lookup / len;
+ descriptor_base[threadIdx.x] = val / s_len;
}
- void compute_descriptors_gpu(const PtrStepSzf& descriptors,
- const float* featureX, const float* featureY, const float* featureSize, const float* featureDir, int nFeatures)
+ void compute_descriptors_gpu(PtrStepSz<float4> descriptors, const float* featureX, const float* featureY, const float* featureSize, const float* featureDir, int nFeatures)
{
// compute unnormalized descriptors, then normalize them - odd indexing since grid must be 2D
if (descriptors.cols == 64)
{
- compute_descriptors64<<<dim3(nFeatures, 16, 1), dim3(6, 6, 1)>>>(descriptors, featureX, featureY, featureSize, featureDir);
+ compute_descriptors_64<<<nFeatures, dim3(32, 16)>>>(descriptors, featureX, featureY, featureSize, featureDir);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
- normalize_descriptors<64><<<dim3(nFeatures, 1, 1), dim3(64, 1, 1)>>>(descriptors);
+ normalize_descriptors<64><<<nFeatures, 64>>>((PtrStepSzf) descriptors);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
else
{
- compute_descriptors128<<<dim3(nFeatures, 16, 1), dim3(6, 6, 1)>>>(descriptors, featureX, featureY, featureSize, featureDir);
+ compute_descriptors_128<<<nFeatures, dim3(32, 16)>>>(descriptors, featureX, featureY, featureSize, featureDir);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
- normalize_descriptors<128><<<dim3(nFeatures, 1, 1), dim3(128, 1, 1)>>>(descriptors);
+ normalize_descriptors<128><<<nFeatures, 128>>>((PtrStepSzf) descriptors);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
template <class Transform, template <typename> class Filter, template <typename> class B, typename T> struct WarpDispatcherStream
{
- static void call(PtrStepSz<T> src, PtrStepSz<T> dst, const float* borderValue, cudaStream_t stream, int)
+ static void call(PtrStepSz<T> src, PtrStepSz<T> dst, const float* borderValue, cudaStream_t stream, bool)
{
typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type work_type;
template <class Transform, template <typename> class Filter, template <typename> class B, typename T> struct WarpDispatcherNonStream
{
- static void call(PtrStepSz<T> src, PtrStepSz<T> srcWhole, int xoff, int yoff, PtrStepSz<T> dst, const float* borderValue, int)
+ static void call(PtrStepSz<T> src, PtrStepSz<T> srcWhole, int xoff, int yoff, PtrStepSz<T> dst, const float* borderValue, bool)
{
(void)xoff;
(void)yoff;
}; \
template <class Transform, template <typename> class Filter, template <typename> class B> struct WarpDispatcherNonStream<Transform, Filter, B, type> \
{ \
- static void call(PtrStepSz< type > src, PtrStepSz< type > srcWhole, int xoff, int yoff, PtrStepSz< type > dst, const float* borderValue, int cc) \
+ static void call(PtrStepSz< type > src, PtrStepSz< type > srcWhole, int xoff, int yoff, PtrStepSz< type > dst, const float* borderValue, bool cc20) \
{ \
typedef typename TypeVec<float, VecTraits< type >::cn>::vec_type work_type; \
- dim3 block(32, cc >= 20 ? 8 : 4); \
+ dim3 block(32, cc20 ? 8 : 4); \
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y)); \
bindTexture(&tex_warp_ ## type , srcWhole); \
tex_warp_ ## type ##_reader texSrc(xoff, yoff); \
}; \
template <class Transform, template <typename> class Filter> struct WarpDispatcherNonStream<Transform, Filter, BrdReplicate, type> \
{ \
- static void call(PtrStepSz< type > src, PtrStepSz< type > srcWhole, int xoff, int yoff, PtrStepSz< type > dst, const float*, int) \
+ static void call(PtrStepSz< type > src, PtrStepSz< type > srcWhole, int xoff, int yoff, PtrStepSz< type > dst, const float*, bool) \
{ \
dim3 block(32, 8); \
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y)); \
template <class Transform, template <typename> class Filter, template <typename> class B, typename T> struct WarpDispatcher
{
- static void call(PtrStepSz<T> src, PtrStepSz<T> srcWhole, int xoff, int yoff, PtrStepSz<T> dst, const float* borderValue, cudaStream_t stream, int cc)
+ static void call(PtrStepSz<T> src, PtrStepSz<T> srcWhole, int xoff, int yoff, PtrStepSz<T> dst, const float* borderValue, cudaStream_t stream, bool cc20)
{
if (stream == 0)
- WarpDispatcherNonStream<Transform, Filter, B, T>::call(src, srcWhole, xoff, yoff, dst, borderValue, cc);
+ WarpDispatcherNonStream<Transform, Filter, B, T>::call(src, srcWhole, xoff, yoff, dst, borderValue, cc20);
else
- WarpDispatcherStream<Transform, Filter, B, T>::call(src, dst, borderValue, stream, cc);
+ WarpDispatcherStream<Transform, Filter, B, T>::call(src, dst, borderValue, stream, cc20);
}
};
template <class Transform, typename T>
void warp_caller(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzb dst, int interpolation,
- int borderMode, const float* borderValue, cudaStream_t stream, int cc)
+ int borderMode, const float* borderValue, cudaStream_t stream, bool cc20)
{
- typedef void (*func_t)(PtrStepSz<T> src, PtrStepSz<T> srcWhole, int xoff, int yoff, PtrStepSz<T> dst, const float* borderValue, cudaStream_t stream, int cc);
+ typedef void (*func_t)(PtrStepSz<T> src, PtrStepSz<T> srcWhole, int xoff, int yoff, PtrStepSz<T> dst, const float* borderValue, cudaStream_t stream, bool cc20);
static const func_t funcs[3][5] =
{
};
funcs[interpolation][borderMode](static_cast< PtrStepSz<T> >(src), static_cast< PtrStepSz<T> >(srcWhole), xoff, yoff,
- static_cast< PtrStepSz<T> >(dst), borderValue, stream, cc);
+ static_cast< PtrStepSz<T> >(dst), borderValue, stream, cc20);
}
template <typename T> void warpAffine_gpu(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation,
- int borderMode, const float* borderValue, cudaStream_t stream, int cc)
+ int borderMode, const float* borderValue, cudaStream_t stream, bool cc20)
{
cudaSafeCall( cudaMemcpyToSymbol(c_warpMat, coeffs, 2 * 3 * sizeof(float)) );
- warp_caller<AffineTransform, T>(src, srcWhole, xoff, yoff, dst, interpolation, borderMode, borderValue, stream, cc);
+ warp_caller<AffineTransform, T>(src, srcWhole, xoff, yoff, dst, interpolation, borderMode, borderValue, stream, cc20);
}
- template void warpAffine_gpu<uchar >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpAffine_gpu<uchar2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void warpAffine_gpu<uchar3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void warpAffine_gpu<uchar4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
+ template void warpAffine_gpu<uchar >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpAffine_gpu<uchar2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void warpAffine_gpu<uchar3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void warpAffine_gpu<uchar4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
- //template void warpAffine_gpu<schar>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpAffine_gpu<char2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpAffine_gpu<char3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpAffine_gpu<char4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
+ //template void warpAffine_gpu<schar>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpAffine_gpu<char2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpAffine_gpu<char3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpAffine_gpu<char4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
- template void warpAffine_gpu<ushort >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpAffine_gpu<ushort2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void warpAffine_gpu<ushort3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void warpAffine_gpu<ushort4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
+ template void warpAffine_gpu<ushort >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpAffine_gpu<ushort2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void warpAffine_gpu<ushort3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void warpAffine_gpu<ushort4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
- template void warpAffine_gpu<short >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpAffine_gpu<short2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void warpAffine_gpu<short3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void warpAffine_gpu<short4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
+ template void warpAffine_gpu<short >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpAffine_gpu<short2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void warpAffine_gpu<short3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void warpAffine_gpu<short4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
- //template void warpAffine_gpu<int >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpAffine_gpu<int2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpAffine_gpu<int3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpAffine_gpu<int4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
+ //template void warpAffine_gpu<int >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpAffine_gpu<int2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpAffine_gpu<int3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpAffine_gpu<int4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
- template void warpAffine_gpu<float >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpAffine_gpu<float2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void warpAffine_gpu<float3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void warpAffine_gpu<float4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
+ template void warpAffine_gpu<float >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpAffine_gpu<float2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void warpAffine_gpu<float3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void warpAffine_gpu<float4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
template <typename T> void warpPerspective_gpu(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation,
- int borderMode, const float* borderValue, cudaStream_t stream, int cc)
+ int borderMode, const float* borderValue, cudaStream_t stream, bool cc20)
{
cudaSafeCall( cudaMemcpyToSymbol(c_warpMat, coeffs, 3 * 3 * sizeof(float)) );
- warp_caller<PerspectiveTransform, T>(src, srcWhole, xoff, yoff, dst, interpolation, borderMode, borderValue, stream, cc);
+ warp_caller<PerspectiveTransform, T>(src, srcWhole, xoff, yoff, dst, interpolation, borderMode, borderValue, stream, cc20);
}
- template void warpPerspective_gpu<uchar >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpPerspective_gpu<uchar2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void warpPerspective_gpu<uchar3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void warpPerspective_gpu<uchar4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
-
- //template void warpPerspective_gpu<schar>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpPerspective_gpu<char2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpPerspective_gpu<char3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpPerspective_gpu<char4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
-
- template void warpPerspective_gpu<ushort >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpPerspective_gpu<ushort2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void warpPerspective_gpu<ushort3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void warpPerspective_gpu<ushort4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
-
- template void warpPerspective_gpu<short >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpPerspective_gpu<short2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void warpPerspective_gpu<short3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void warpPerspective_gpu<short4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
-
- //template void warpPerspective_gpu<int >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpPerspective_gpu<int2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpPerspective_gpu<int3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpPerspective_gpu<int4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
-
- template void warpPerspective_gpu<float >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- //template void warpPerspective_gpu<float2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void warpPerspective_gpu<float3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
- template void warpPerspective_gpu<float4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
+ template void warpPerspective_gpu<uchar >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpPerspective_gpu<uchar2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void warpPerspective_gpu<uchar3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void warpPerspective_gpu<uchar4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+
+ //template void warpPerspective_gpu<schar>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpPerspective_gpu<char2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpPerspective_gpu<char3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpPerspective_gpu<char4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+
+ template void warpPerspective_gpu<ushort >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpPerspective_gpu<ushort2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void warpPerspective_gpu<ushort3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void warpPerspective_gpu<ushort4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+
+ template void warpPerspective_gpu<short >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpPerspective_gpu<short2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void warpPerspective_gpu<short3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void warpPerspective_gpu<short4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+
+ //template void warpPerspective_gpu<int >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpPerspective_gpu<int2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpPerspective_gpu<int3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpPerspective_gpu<int4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+
+ template void warpPerspective_gpu<float >(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ //template void warpPerspective_gpu<float2>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void warpPerspective_gpu<float3>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
+ template void warpPerspective_gpu<float4>(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
} // namespace imgproc
}}} // namespace cv { namespace gpu { namespace device
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+#endif /* CUDA_DISABLER */
template<> struct NppTypeTraits<CV_32F> { typedef Npp32f npp_t; typedef Npp32fc npp_complex_type; };
template<> struct NppTypeTraits<CV_64F> { typedef Npp64f npp_t; typedef Npp64fc npp_complex_type; };
- template <int DEPTH> struct NppArithmFunc
- {
- typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
-
- typedef NppStatus (*func_t)(const npp_t* pSrc1, int nSrc1Step, const npp_t* pSrc2, int nSrc2Step, npp_t* pDst, int nDstStep, NppiSize oSizeROI, int nScaleFactor);
- };
- template <> struct NppArithmFunc<CV_32F>
- {
- typedef NppTypeTraits<CV_32F>::npp_t npp_t;
-
- typedef NppStatus (*func_t)(const Npp32f* pSrc1, int nSrc1Step, const Npp32f* pSrc2, int nSrc2Step, Npp32f* pDst, int nDstStep, NppiSize oSizeROI);
- };
-
- template <int DEPTH, typename NppArithmFunc<DEPTH>::func_t func> struct NppArithm
- {
- typedef typename NppArithmFunc<DEPTH>::npp_t npp_t;
-
- static void call(const PtrStepSzb src1, const PtrStepb src2, PtrStepb dst, cudaStream_t stream)
- {
- NppStreamHandler h(stream);
-
- NppiSize sz;
- sz.width = src1.cols;
- sz.height = src1.rows;
-
- nppSafeCall( func((const npp_t*)src1.data, static_cast<int>(src1.step), (const npp_t*)src2.data, static_cast<int>(src2.step),
- (npp_t*)dst.data, static_cast<int>(dst.step), sz, 0) );
-
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
- }
- };
- template <typename NppArithmFunc<CV_32F>::func_t func> struct NppArithm<CV_32F, func>
- {
- typedef typename NppArithmFunc<CV_32F>::npp_t npp_t;
-
- static void call(const PtrStepSzb src1, const PtrStepb src2, PtrStepb dst, cudaStream_t stream)
- {
- NppStreamHandler h(stream);
-
- NppiSize sz;
- sz.width = src1.cols;
- sz.height = src1.rows;
-
- nppSafeCall( func((const npp_t*)src1.data, static_cast<int>(src1.step), (const npp_t*)src2.data, static_cast<int>(src2.step),
- (npp_t*)dst.data, static_cast<int>(dst.step), sz) );
-
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
- }
- };
-
template<int DEPTH, int cn> struct NppArithmScalarFunc
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
////////////////////////////////////////////////////////////////////////
// add
-namespace cv { namespace gpu { namespace device
+namespace arithm
{
template <typename T, typename D>
- void add_gpu(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
+ void vadd4(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+
+ template <typename T, typename D>
+ void vadd2(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
template <typename T, typename D>
- void add_gpu(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-}}}
+ void addMat(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+}
void cv::gpu::add(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, int dtype, Stream& s)
{
- using namespace cv::gpu::device;
+ using namespace arithm;
- typedef void (*func_t)(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
+ typedef void (*func_t)(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
static const func_t funcs[7][7] =
{
- {add_gpu<unsigned char, unsigned char> , 0 /*add_gpu<unsigned char, signed char>*/ , add_gpu<unsigned char, unsigned short> , add_gpu<unsigned char, short> , add_gpu<unsigned char, int> , add_gpu<unsigned char, float> , add_gpu<unsigned char, double> },
- {0 /*add_gpu<signed char, unsigned char>*/ , 0 /*add_gpu<signed char, signed char>*/ , 0 /*add_gpu<signed char, unsigned short>*/, 0 /*add_gpu<signed char, short>*/ , 0 /*add_gpu<signed char, int>*/, 0 /*add_gpu<signed char, float>*/, 0 /*add_gpu<signed char, double>*/},
- {0 /*add_gpu<unsigned short, unsigned char>*/, 0 /*add_gpu<unsigned short, signed char>*/, add_gpu<unsigned short, unsigned short> , 0 /*add_gpu<unsigned short, short>*/, add_gpu<unsigned short, int> , add_gpu<unsigned short, float> , add_gpu<unsigned short, double> },
- {0 /*add_gpu<short, unsigned char>*/ , 0 /*add_gpu<short, signed char>*/ , 0 /*add_gpu<short, unsigned short>*/ , add_gpu<short, short> , add_gpu<short, int> , add_gpu<short, float> , add_gpu<short, double> },
- {0 /*add_gpu<int, unsigned char>*/ , 0 /*add_gpu<int, signed char>*/ , 0 /*add_gpu<int, unsigned short>*/ , 0 /*add_gpu<int, short>*/ , add_gpu<int, int> , add_gpu<int, float> , add_gpu<int, double> },
- {0 /*add_gpu<float, unsigned char>*/ , 0 /*add_gpu<float, signed char>*/ , 0 /*add_gpu<float, unsigned short>*/ , 0 /*add_gpu<float, short>*/ , 0 /*add_gpu<float, int>*/ , add_gpu<float, float> , add_gpu<float, double> },
- {0 /*add_gpu<double, unsigned char>*/ , 0 /*add_gpu<double, signed char>*/ , 0 /*add_gpu<double, unsigned short>*/ , 0 /*add_gpu<double, short>*/ , 0 /*add_gpu<double, int>*/ , 0 /*add_gpu<double, float>*/ , add_gpu<double, double> }
+ {
+ addMat<unsigned char, unsigned char>,
+ addMat<unsigned char, signed char>,
+ addMat<unsigned char, unsigned short>,
+ addMat<unsigned char, short>,
+ addMat<unsigned char, int>,
+ addMat<unsigned char, float>,
+ addMat<unsigned char, double>
+ },
+ {
+ addMat<signed char, unsigned char>,
+ addMat<signed char, signed char>,
+ addMat<signed char, unsigned short>,
+ addMat<signed char, short>,
+ addMat<signed char, int>,
+ addMat<signed char, float>,
+ addMat<signed char, double>
+ },
+ {
+ 0 /*addMat<unsigned short, unsigned char>*/,
+ 0 /*addMat<unsigned short, signed char>*/,
+ addMat<unsigned short, unsigned short>,
+ addMat<unsigned short, short>,
+ addMat<unsigned short, int>,
+ addMat<unsigned short, float>,
+ addMat<unsigned short, double>
+ },
+ {
+ 0 /*addMat<short, unsigned char>*/,
+ 0 /*addMat<short, signed char>*/,
+ addMat<short, unsigned short>,
+ addMat<short, short>,
+ addMat<short, int>,
+ addMat<short, float>,
+ addMat<short, double>
+ },
+ {
+ 0 /*addMat<int, unsigned char>*/,
+ 0 /*addMat<int, signed char>*/,
+ 0 /*addMat<int, unsigned short>*/,
+ 0 /*addMat<int, short>*/,
+ addMat<int, int>,
+ addMat<int, float>,
+ addMat<int, double>
+ },
+ {
+ 0 /*addMat<float, unsigned char>*/,
+ 0 /*addMat<float, signed char>*/,
+ 0 /*addMat<float, unsigned short>*/,
+ 0 /*addMat<float, short>*/,
+ 0 /*addMat<float, int>*/,
+ addMat<float, float>,
+ addMat<float, double>
+ },
+ {
+ 0 /*addMat<double, unsigned char>*/,
+ 0 /*addMat<double, signed char>*/,
+ 0 /*addMat<double, unsigned short>*/,
+ 0 /*addMat<double, short>*/,
+ 0 /*addMat<double, int>*/,
+ 0 /*addMat<double, float>*/,
+ addMat<double, double>
+ }
};
- typedef void (*npp_func_t)(const PtrStepSzb src1, const PtrStepb src2, PtrStepb dst, cudaStream_t stream);
- static const npp_func_t npp_funcs[] =
+ typedef void (*vfunc_t)(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ static const vfunc_t vfuncs4[4][4] =
{
- NppArithm<CV_8U , nppiAdd_8u_C1RSfs >::call,
- 0,
- NppArithm<CV_16U, nppiAdd_16u_C1RSfs>::call,
- NppArithm<CV_16S, nppiAdd_16s_C1RSfs>::call,
- NppArithm<CV_32S, nppiAdd_32s_C1RSfs>::call,
- NppArithm<CV_32F, nppiAdd_32f_C1R >::call
+ {
+ vadd4<unsigned int, unsigned int>,
+ vadd4<unsigned int, int>,
+ 0,
+ 0
+ },
+ {
+ vadd4<int, unsigned int>,
+ vadd4<int, int>,
+ 0,
+ 0
+ },
+ {
+ 0,
+ 0,
+ 0,
+ 0
+ },
+ {
+ 0,
+ 0,
+ 0,
+ 0
+ }
+ };
+ static const vfunc_t vfuncs2[4][4] =
+ {
+ {
+ 0,
+ 0,
+ 0,
+ 0
+ },
+ {
+ 0,
+ 0,
+ 0,
+ 0
+ },
+ {
+ 0,
+ 0,
+ vadd2<unsigned int, unsigned int>,
+ vadd2<unsigned int, int>
+ },
+ {
+ 0,
+ 0,
+ vadd2<int, unsigned int>,
+ vadd2<int, int>
+ }
};
if (dtype < 0)
dtype = src1.depth();
- CV_Assert(src1.depth() <= CV_64F && CV_MAT_DEPTH(dtype) <= CV_64F);
- CV_Assert(src1.type() == src2.type() && src1.size() == src2.size());
- CV_Assert(mask.empty() || (src1.channels() == 1 && mask.size() == src1.size() && mask.type() == CV_8U));
+ const int sdepth = src1.depth();
+ const int ddepth = CV_MAT_DEPTH(dtype);
+ const int cn = src1.channels();
+
+ CV_Assert( sdepth <= CV_64F && ddepth <= CV_64F );
+ CV_Assert( src2.type() == src1.type() && src2.size() == src1.size() );
+ CV_Assert( mask.empty() || (cn == 1 && mask.size() == src1.size() && mask.type() == CV_8U) );
- if (src1.depth() == CV_64F || CV_MAT_DEPTH(dtype) == CV_64F)
+ if (sdepth == CV_64F || ddepth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
- dst.create(src1.size(), CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src1.channels()));
+ dst.create(src1.size(), CV_MAKE_TYPE(ddepth, cn));
cudaStream_t stream = StreamAccessor::getStream(s);
- if (mask.empty() && dst.type() == src1.type() && src1.depth() <= CV_32F)
+ PtrStepSzb src1_(src1.rows, src1.cols * cn, src1.data, src1.step);
+ PtrStepSzb src2_(src1.rows, src1.cols * cn, src2.data, src2.step);
+ PtrStepSzb dst_(src1.rows, src1.cols * cn, dst.data, dst.step);
+
+ if (mask.empty() && sdepth < CV_32S && ddepth < CV_32S)
{
- npp_funcs[src1.depth()](src1.reshape(1), src2.reshape(1), dst.reshape(1), stream);
- return;
+ const intptr_t src1ptr = reinterpret_cast<intptr_t>(src1_.data);
+ const intptr_t src2ptr = reinterpret_cast<intptr_t>(src2_.data);
+ const intptr_t dstptr = reinterpret_cast<intptr_t>(dst_.data);
+
+ const bool isAllAligned = (src1ptr & 31) == 0 && (src2ptr & 31) == 0 && (dstptr & 31) == 0;
+
+ if (deviceSupports(FEATURE_SET_COMPUTE_20) && isAllAligned)
+ {
+ const vfunc_t vfunc4 = vfuncs4[sdepth][ddepth];
+ const vfunc_t vfunc2 = vfuncs2[sdepth][ddepth];
+
+ if (vfunc4 != 0 && (src1_.cols & 3) == 0)
+ {
+ const int vcols = src1_.cols >> 2;
+
+ vfunc4(PtrStepSzb(src1_.rows, vcols, src1_.data, src1_.step),
+ PtrStepSzb(src1_.rows, vcols, src2_.data, src2_.step),
+ PtrStepSzb(src1_.rows, vcols, dst_.data, dst_.step),
+ stream);
+
+ return;
+ }
+
+ if (vfunc2 != 0 && (src1_.cols & 1) == 0)
+ {
+ const int vcols = src1_.cols >> 1;
+
+ vfunc2(PtrStepSzb(src1_.rows, vcols, src1_.data, src1_.step),
+ PtrStepSzb(src1_.rows, vcols, src2_.data, src2_.step),
+ PtrStepSzb(src1_.rows, vcols, dst_.data, dst_.step),
+ stream);
+
+ return;
+ }
+ }
}
- const func_t func = funcs[src1.depth()][dst.depth()];
+ const func_t func = funcs[sdepth][ddepth];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of source and destination types");
- func(src1.reshape(1), src2.reshape(1), dst.reshape(1), mask, stream);
+ func(src1_, src2_, dst_, mask, stream);
+}
+
+namespace arithm
+{
+ template <typename T, typename S, typename D>
+ void addScalar(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
}
void cv::gpu::add(const GpuMat& src, const Scalar& sc, GpuMat& dst, const GpuMat& mask, int dtype, Stream& s)
{
- using namespace cv::gpu::device;
+ using namespace arithm;
- typedef void (*func_t)(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
+ typedef void (*func_t)(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
static const func_t funcs[7][7] =
{
- {add_gpu<unsigned char, unsigned char> , 0 /*add_gpu<unsigned char, signed char>*/ , add_gpu<unsigned char, unsigned short> , add_gpu<unsigned char, short> , add_gpu<unsigned char, int> , add_gpu<unsigned char, float> , add_gpu<unsigned char, double> },
- {0 /*add_gpu<signed char, unsigned char>*/ , 0 /*add_gpu<signed char, signed char>*/ , 0 /*add_gpu<signed char, unsigned short>*/, 0 /*add_gpu<signed char, short>*/ , 0 /*add_gpu<signed char, int>*/, 0 /*add_gpu<signed char, float>*/, 0 /*add_gpu<signed char, double>*/},
- {0 /*add_gpu<unsigned short, unsigned char>*/, 0 /*add_gpu<unsigned short, signed char>*/, add_gpu<unsigned short, unsigned short> , 0 /*add_gpu<unsigned short, short>*/, add_gpu<unsigned short, int> , add_gpu<unsigned short, float> , add_gpu<unsigned short, double> },
- {0 /*add_gpu<short, unsigned char>*/ , 0 /*add_gpu<short, signed char>*/ , 0 /*add_gpu<short, unsigned short>*/ , add_gpu<short, short> , add_gpu<short, int> , add_gpu<short, float> , add_gpu<short, double> },
- {0 /*add_gpu<int, unsigned char>*/ , 0 /*add_gpu<int, signed char>*/ , 0 /*add_gpu<int, unsigned short>*/ , 0 /*add_gpu<int, short>*/ , add_gpu<int, int> , add_gpu<int, float> , add_gpu<int, double> },
- {0 /*add_gpu<float, unsigned char>*/ , 0 /*add_gpu<float, signed char>*/ , 0 /*add_gpu<float, unsigned short>*/ , 0 /*add_gpu<float, short>*/ , 0 /*add_gpu<float, int>*/ , add_gpu<float, float> , add_gpu<float, double> },
- {0 /*add_gpu<double, unsigned char>*/ , 0 /*add_gpu<double, signed char>*/ , 0 /*add_gpu<double, unsigned short>*/ , 0 /*add_gpu<double, short>*/ , 0 /*add_gpu<double, int>*/ , 0 /*add_gpu<double, float>*/ , add_gpu<double, double> }
+ {
+ addScalar<unsigned char, float, unsigned char>,
+ addScalar<unsigned char, float, signed char>,
+ addScalar<unsigned char, float, unsigned short>,
+ addScalar<unsigned char, float, short>,
+ addScalar<unsigned char, float, int>,
+ addScalar<unsigned char, float, float>,
+ addScalar<unsigned char, double, double>
+ },
+ {
+ addScalar<signed char, float, unsigned char>,
+ addScalar<signed char, float, signed char>,
+ addScalar<signed char, float, unsigned short>,
+ addScalar<signed char, float, short>,
+ addScalar<signed char, float, int>,
+ addScalar<signed char, float, float>,
+ addScalar<signed char, double, double>
+ },
+ {
+ 0 /*addScalar<unsigned short, float, unsigned char>*/,
+ 0 /*addScalar<unsigned short, float, signed char>*/,
+ addScalar<unsigned short, float, unsigned short>,
+ addScalar<unsigned short, float, short>,
+ addScalar<unsigned short, float, int>,
+ addScalar<unsigned short, float, float>,
+ addScalar<unsigned short, double, double>
+ },
+ {
+ 0 /*addScalar<short, float, unsigned char>*/,
+ 0 /*addScalar<short, float, signed char>*/,
+ addScalar<short, float, unsigned short>,
+ addScalar<short, float, short>,
+ addScalar<short, float, int>,
+ addScalar<short, float, float>,
+ addScalar<short, double, double>
+ },
+ {
+ 0 /*addScalar<int, float, unsigned char>*/,
+ 0 /*addScalar<int, float, signed char>*/,
+ 0 /*addScalar<int, float, unsigned short>*/,
+ 0 /*addScalar<int, float, short>*/,
+ addScalar<int, float, int>,
+ addScalar<int, float, float>,
+ addScalar<int, double, double>
+ },
+ {
+ 0 /*addScalar<float, float, unsigned char>*/,
+ 0 /*addScalar<float, float, signed char>*/,
+ 0 /*addScalar<float, float, unsigned short>*/,
+ 0 /*addScalar<float, float, short>*/,
+ 0 /*addScalar<float, float, int>*/,
+ addScalar<float, float, float>,
+ addScalar<float, double, double>
+ },
+ {
+ 0 /*addScalar<double, double, unsigned char>*/,
+ 0 /*addScalar<double, double, signed char>*/,
+ 0 /*addScalar<double, double, unsigned short>*/,
+ 0 /*addScalar<double, double, short>*/,
+ 0 /*addScalar<double, double, int>*/,
+ 0 /*addScalar<double, double, float>*/,
+ addScalar<double, double, double>
+ }
};
typedef void (*npp_func_t)(const PtrStepSzb src, Scalar sc, PtrStepb dst, cudaStream_t stream);
if (dtype < 0)
dtype = src.depth();
- CV_Assert(src.depth() <= CV_64F && CV_MAT_DEPTH(dtype) <= CV_64F);
- CV_Assert(src.channels() <= 4);
- CV_Assert(mask.empty() || (src.channels() == 1 && mask.size() == src.size() && mask.type() == CV_8U));
+ const int sdepth = src.depth();
+ const int ddepth = CV_MAT_DEPTH(dtype);
+ const int cn = src.channels();
+
+ CV_Assert( sdepth <= CV_64F && ddepth <= CV_64F );
+ CV_Assert( cn <= 4 );
+ CV_Assert( mask.empty() || (cn == 1 && mask.size() == src.size() && mask.type() == CV_8U) );
- if (src.depth() == CV_64F || CV_MAT_DEPTH(dtype) == CV_64F)
+ if (sdepth == CV_64F || ddepth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
- dst.create(src.size(), CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()));
+ dst.create(src.size(), CV_MAKE_TYPE(ddepth, cn));
cudaStream_t stream = StreamAccessor::getStream(s);
- if (mask.empty() && dst.type() == src.type())
+ const npp_func_t npp_func = npp_funcs[sdepth][cn - 1];
+ if (ddepth == sdepth && cn > 1 && npp_func != 0)
{
- const npp_func_t npp_func = npp_funcs[src.depth()][src.channels() - 1];
-
- if (npp_func)
- {
- npp_func(src, sc, dst, stream);
- return;
- }
+ npp_func(src, sc, dst, stream);
+ return;
}
- CV_Assert(src.channels() == 1);
+ CV_Assert( cn == 1 );
- const func_t func = funcs[src.depth()][dst.depth()];
+ const func_t func = funcs[sdepth][ddepth];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of source and destination types");
////////////////////////////////////////////////////////////////////////
// subtract
-namespace cv { namespace gpu { namespace device
+namespace arithm
{
template <typename T, typename D>
- void subtract_gpu(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
+ void vsub4(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+
+ template <typename T, typename D>
+ void vsub2(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
template <typename T, typename D>
- void subtract_gpu(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
-}}}
+ void subMat(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+}
void cv::gpu::subtract(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, int dtype, Stream& s)
{
- using namespace cv::gpu::device;
+ using namespace arithm;
- typedef void (*func_t)(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
+ typedef void (*func_t)(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
static const func_t funcs[7][7] =
{
- {subtract_gpu<unsigned char, unsigned char> , 0 /*subtract_gpu<unsigned char, signed char>*/ , subtract_gpu<unsigned char, unsigned short> , subtract_gpu<unsigned char, short> , subtract_gpu<unsigned char, int> , subtract_gpu<unsigned char, float> , subtract_gpu<unsigned char, double> },
- {0 /*subtract_gpu<signed char, unsigned char>*/ , 0 /*subtract_gpu<signed char, signed char>*/ , 0 /*subtract_gpu<signed char, unsigned short>*/, 0 /*subtract_gpu<signed char, short>*/ , 0 /*subtract_gpu<signed char, int>*/, 0 /*subtract_gpu<signed char, float>*/, 0 /*subtract_gpu<signed char, double>*/},
- {0 /*subtract_gpu<unsigned short, unsigned char>*/, 0 /*subtract_gpu<unsigned short, signed char>*/, subtract_gpu<unsigned short, unsigned short> , 0 /*subtract_gpu<unsigned short, short>*/, subtract_gpu<unsigned short, int> , subtract_gpu<unsigned short, float> , subtract_gpu<unsigned short, double> },
- {0 /*subtract_gpu<short, unsigned char>*/ , 0 /*subtract_gpu<short, signed char>*/ , 0 /*subtract_gpu<short, unsigned short>*/ , subtract_gpu<short, short> , subtract_gpu<short, int> , subtract_gpu<short, float> , subtract_gpu<short, double> },
- {0 /*subtract_gpu<int, unsigned char>*/ , 0 /*subtract_gpu<int, signed char>*/ , 0 /*subtract_gpu<int, unsigned short>*/ , 0 /*subtract_gpu<int, short>*/ , subtract_gpu<int, int> , subtract_gpu<int, float> , subtract_gpu<int, double> },
- {0 /*subtract_gpu<float, unsigned char>*/ , 0 /*subtract_gpu<float, signed char>*/ , 0 /*subtract_gpu<float, unsigned short>*/ , 0 /*subtract_gpu<float, short>*/ , 0 /*subtract_gpu<float, int>*/ , subtract_gpu<float, float> , subtract_gpu<float, double> },
- {0 /*subtract_gpu<double, unsigned char>*/ , 0 /*subtract_gpu<double, signed char>*/ , 0 /*subtract_gpu<double, unsigned short>*/ , 0 /*subtract_gpu<double, short>*/ , 0 /*subtract_gpu<double, int>*/ , 0 /*subtract_gpu<double, float>*/ , subtract_gpu<double, double> }
+ {
+ subMat<unsigned char, unsigned char>,
+ subMat<unsigned char, signed char>,
+ subMat<unsigned char, unsigned short>,
+ subMat<unsigned char, short>,
+ subMat<unsigned char, int>,
+ subMat<unsigned char, float>,
+ subMat<unsigned char, double>
+ },
+ {
+ subMat<signed char, unsigned char>,
+ subMat<signed char, signed char>,
+ subMat<signed char, unsigned short>,
+ subMat<signed char, short>,
+ subMat<signed char, int>,
+ subMat<signed char, float>,
+ subMat<signed char, double>
+ },
+ {
+ 0 /*subMat<unsigned short, unsigned char>*/,
+ 0 /*subMat<unsigned short, signed char>*/,
+ subMat<unsigned short, unsigned short>,
+ subMat<unsigned short, short>,
+ subMat<unsigned short, int>,
+ subMat<unsigned short, float>,
+ subMat<unsigned short, double>
+ },
+ {
+ 0 /*subMat<short, unsigned char>*/,
+ 0 /*subMat<short, signed char>*/,
+ subMat<short, unsigned short>,
+ subMat<short, short>,
+ subMat<short, int>,
+ subMat<short, float>,
+ subMat<short, double>
+ },
+ {
+ 0 /*subMat<int, unsigned char>*/,
+ 0 /*subMat<int, signed char>*/,
+ 0 /*subMat<int, unsigned short>*/,
+ 0 /*subMat<int, short>*/,
+ subMat<int, int>,
+ subMat<int, float>,
+ subMat<int, double>
+ },
+ {
+ 0 /*subMat<float, unsigned char>*/,
+ 0 /*subMat<float, signed char>*/,
+ 0 /*subMat<float, unsigned short>*/,
+ 0 /*subMat<float, short>*/,
+ 0 /*subMat<float, int>*/,
+ subMat<float, float>,
+ subMat<float, double>
+ },
+ {
+ 0 /*subMat<double, unsigned char>*/,
+ 0 /*subMat<double, signed char>*/,
+ 0 /*subMat<double, unsigned short>*/,
+ 0 /*subMat<double, short>*/,
+ 0 /*subMat<double, int>*/,
+ 0 /*subMat<double, float>*/,
+ subMat<double, double>
+ }
};
- typedef void (*npp_func_t)(const PtrStepSzb src1, const PtrStepb src2, PtrStepb dst, cudaStream_t stream);
- static const npp_func_t npp_funcs[6] =
+ typedef void (*vfunc_t)(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ static const vfunc_t vfuncs4[4][4] =
{
- NppArithm<CV_8U , nppiSub_8u_C1RSfs>::call,
- 0,
- NppArithm<CV_16U, nppiSub_16u_C1RSfs>::call,
- NppArithm<CV_16S, nppiSub_16s_C1RSfs>::call,
- NppArithm<CV_32S, nppiSub_32s_C1RSfs>::call,
- NppArithm<CV_32F, nppiSub_32f_C1R >::call
+ {
+ vsub4<unsigned int, unsigned int>,
+ vsub4<unsigned int, int>,
+ 0,
+ 0
+ },
+ {
+ vsub4<int, unsigned int>,
+ vsub4<int, int>,
+ 0,
+ 0
+ },
+ {
+ 0,
+ 0,
+ 0,
+ 0
+ },
+ {
+ 0,
+ 0,
+ 0,
+ 0
+ }
+ };
+ static const vfunc_t vfuncs2[4][4] =
+ {
+ {
+ 0,
+ 0,
+ 0,
+ 0
+ },
+ {
+ 0,
+ 0,
+ 0,
+ 0
+ },
+ {
+ 0,
+ 0,
+ vsub2<unsigned int, unsigned int>,
+ vsub2<unsigned int, int>
+ },
+ {
+ 0,
+ 0,
+ vsub2<int, unsigned int>,
+ vsub2<int, int>
+ }
};
if (dtype < 0)
dtype = src1.depth();
- CV_Assert(src1.depth() <= CV_64F && CV_MAT_DEPTH(dtype) <= CV_64F);
- CV_Assert(src1.type() == src2.type() && src1.size() == src2.size());
- CV_Assert(mask.empty() || (src1.channels() == 1 && mask.size() == src1.size() && mask.type() == CV_8U));
+ const int sdepth = src1.depth();
+ const int ddepth = CV_MAT_DEPTH(dtype);
+ const int cn = src1.channels();
+
+ CV_Assert( sdepth <= CV_64F && ddepth <= CV_64F );
+ CV_Assert( src2.type() == src1.type() && src2.size() == src1.size() );
+ CV_Assert( mask.empty() || (cn == 1 && mask.size() == src1.size() && mask.type() == CV_8U) );
- if (src1.depth() == CV_64F || CV_MAT_DEPTH(dtype) == CV_64F)
+ if (sdepth == CV_64F || ddepth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
- dst.create(src1.size(), CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src1.channels()));
+ dst.create(src1.size(), CV_MAKE_TYPE(ddepth, cn));
cudaStream_t stream = StreamAccessor::getStream(s);
- if (mask.empty() && dst.type() == src1.type() && src1.depth() <= CV_32F)
+ PtrStepSzb src1_(src1.rows, src1.cols * cn, src1.data, src1.step);
+ PtrStepSzb src2_(src1.rows, src1.cols * cn, src2.data, src2.step);
+ PtrStepSzb dst_(src1.rows, src1.cols * cn, dst.data, dst.step);
+
+ if (mask.empty() && sdepth < CV_32S && ddepth < CV_32S)
{
- npp_funcs[src1.depth()](src2.reshape(1), src1.reshape(1), dst.reshape(1), stream);
- return;
+ const intptr_t src1ptr = reinterpret_cast<intptr_t>(src1_.data);
+ const intptr_t src2ptr = reinterpret_cast<intptr_t>(src2_.data);
+ const intptr_t dstptr = reinterpret_cast<intptr_t>(dst_.data);
+
+ const bool isAllAligned = (src1ptr & 31) == 0 && (src2ptr & 31) == 0 && (dstptr & 31) == 0;
+
+ if (deviceSupports(FEATURE_SET_COMPUTE_20) && isAllAligned)
+ {
+ const vfunc_t vfunc4 = vfuncs4[sdepth][ddepth];
+ const vfunc_t vfunc2 = vfuncs2[sdepth][ddepth];
+
+ if (vfunc4 != 0 && (src1_.cols & 3) == 0)
+ {
+ const int vcols = src1_.cols >> 2;
+
+ vfunc4(PtrStepSzb(src1_.rows, vcols, src1_.data, src1_.step),
+ PtrStepSzb(src1_.rows, vcols, src2_.data, src2_.step),
+ PtrStepSzb(src1_.rows, vcols, dst_.data, dst_.step),
+ stream);
+
+ return;
+ }
+
+ if (vfunc2 != 0 && (src1_.cols & 1) == 0)
+ {
+ const int vcols = src1_.cols >> 1;
+
+ vfunc2(PtrStepSzb(src1_.rows, vcols, src1_.data, src1_.step),
+ PtrStepSzb(src1_.rows, vcols, src2_.data, src2_.step),
+ PtrStepSzb(src1_.rows, vcols, dst_.data, dst_.step),
+ stream);
+
+ return;
+ }
+ }
}
- const func_t func = funcs[src1.depth()][dst.depth()];
+ const func_t func = funcs[sdepth][ddepth];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of source and destination types");
- func(src1.reshape(1), src2.reshape(1), dst.reshape(1), mask, stream);
+ func(src1_, src2_, dst_, mask, stream);
+}
+
+namespace arithm
+{
+ template <typename T, typename S, typename D>
+ void subScalar(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
}
void cv::gpu::subtract(const GpuMat& src, const Scalar& sc, GpuMat& dst, const GpuMat& mask, int dtype, Stream& s)
{
- using namespace cv::gpu::device;
+ using namespace arithm;
- typedef void (*func_t)(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, const PtrStepb& mask, cudaStream_t stream);
+ typedef void (*func_t)(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
static const func_t funcs[7][7] =
{
- {subtract_gpu<unsigned char, unsigned char> , 0 /*subtract_gpu<unsigned char, signed char>*/ , subtract_gpu<unsigned char, unsigned short> , subtract_gpu<unsigned char, short> , subtract_gpu<unsigned char, int> , subtract_gpu<unsigned char, float> , subtract_gpu<unsigned char, double> },
- {0 /*subtract_gpu<signed char, unsigned char>*/ , 0 /*subtract_gpu<signed char, signed char>*/ , 0 /*subtract_gpu<signed char, unsigned short>*/, 0 /*subtract_gpu<signed char, short>*/ , 0 /*subtract_gpu<signed char, int>*/, 0 /*subtract_gpu<signed char, float>*/, 0 /*subtract_gpu<signed char, double>*/},
- {0 /*subtract_gpu<unsigned short, unsigned char>*/, 0 /*subtract_gpu<unsigned short, signed char>*/, subtract_gpu<unsigned short, unsigned short> , 0 /*subtract_gpu<unsigned short, short>*/, subtract_gpu<unsigned short, int> , subtract_gpu<unsigned short, float> , subtract_gpu<unsigned short, double> },
- {0 /*subtract_gpu<short, unsigned char>*/ , 0 /*subtract_gpu<short, signed char>*/ , 0 /*subtract_gpu<short, unsigned short>*/ , subtract_gpu<short, short> , subtract_gpu<short, int> , subtract_gpu<short, float> , subtract_gpu<short, double> },
- {0 /*subtract_gpu<int, unsigned char>*/ , 0 /*subtract_gpu<int, signed char>*/ , 0 /*subtract_gpu<int, unsigned short>*/ , 0 /*subtract_gpu<int, short>*/ , subtract_gpu<int, int> , subtract_gpu<int, float> , subtract_gpu<int, double> },
- {0 /*subtract_gpu<float, unsigned char>*/ , 0 /*subtract_gpu<float, signed char>*/ , 0 /*subtract_gpu<float, unsigned short>*/ , 0 /*subtract_gpu<float, short>*/ , 0 /*subtract_gpu<float, int>*/ , subtract_gpu<float, float> , subtract_gpu<float, double> },
- {0 /*subtract_gpu<double, unsigned char>*/ , 0 /*subtract_gpu<double, signed char>*/ , 0 /*subtract_gpu<double, unsigned short>*/ , 0 /*subtract_gpu<double, short>*/ , 0 /*subtract_gpu<double, int>*/ , 0 /*subtract_gpu<double, float>*/ , subtract_gpu<double, double> }
+ {
+ subScalar<unsigned char, float, unsigned char>,
+ subScalar<unsigned char, float, signed char>,
+ subScalar<unsigned char, float, unsigned short>,
+ subScalar<unsigned char, float, short>,
+ subScalar<unsigned char, float, int>,
+ subScalar<unsigned char, float, float>,
+ subScalar<unsigned char, double, double>
+ },
+ {
+ subScalar<signed char, float, unsigned char>,
+ subScalar<signed char, float, signed char>,
+ subScalar<signed char, float, unsigned short>,
+ subScalar<signed char, float, short>,
+ subScalar<signed char, float, int>,
+ subScalar<signed char, float, float>,
+ subScalar<signed char, double, double>
+ },
+ {
+ 0 /*subScalar<unsigned short, float, unsigned char>*/,
+ 0 /*subScalar<unsigned short, float, signed char>*/,
+ subScalar<unsigned short, float, unsigned short>,
+ subScalar<unsigned short, float, short>,
+ subScalar<unsigned short, float, int>,
+ subScalar<unsigned short, float, float>,
+ subScalar<unsigned short, double, double>
+ },
+ {
+ 0 /*subScalar<short, float, unsigned char>*/,
+ 0 /*subScalar<short, float, signed char>*/,
+ subScalar<short, float, unsigned short>,
+ subScalar<short, float, short>,
+ subScalar<short, float, int>,
+ subScalar<short, float, float>,
+ subScalar<short, double, double>
+ },
+ {
+ 0 /*subScalar<int, float, unsigned char>*/,
+ 0 /*subScalar<int, float, signed char>*/,
+ 0 /*subScalar<int, float, unsigned short>*/,
+ 0 /*subScalar<int, float, short>*/,
+ subScalar<int, float, int>,
+ subScalar<int, float, float>,
+ subScalar<int, double, double>
+ },
+ {
+ 0 /*subScalar<float, float, unsigned char>*/,
+ 0 /*subScalar<float, float, signed char>*/,
+ 0 /*subScalar<float, float, unsigned short>*/,
+ 0 /*subScalar<float, float, short>*/,
+ 0 /*subScalar<float, float, int>*/,
+ subScalar<float, float, float>,
+ subScalar<float, double, double>
+ },
+ {
+ 0 /*subScalar<double, double, unsigned char>*/,
+ 0 /*subScalar<double, double, signed char>*/,
+ 0 /*subScalar<double, double, unsigned short>*/,
+ 0 /*subScalar<double, double, short>*/,
+ 0 /*subScalar<double, double, int>*/,
+ 0 /*subScalar<double, double, float>*/,
+ subScalar<double, double, double>
+ }
};
typedef void (*npp_func_t)(const PtrStepSzb src, Scalar sc, PtrStepb dst, cudaStream_t stream);
if (dtype < 0)
dtype = src.depth();
- CV_Assert(src.depth() <= CV_64F && CV_MAT_DEPTH(dtype) <= CV_64F);
- CV_Assert(src.channels() <= 4);
- CV_Assert(mask.empty() || (src.channels() == 1 && mask.size() == src.size() && mask.type() == CV_8U));
+ const int sdepth = src.depth();
+ const int ddepth = CV_MAT_DEPTH(dtype);
+ const int cn = src.channels();
+
+ CV_Assert( sdepth <= CV_64F && ddepth <= CV_64F );
+ CV_Assert( cn <= 4 );
+ CV_Assert( mask.empty() || (cn == 1 && mask.size() == src.size() && mask.type() == CV_8U) );
- if (src.depth() == CV_64F || CV_MAT_DEPTH(dtype) == CV_64F)
+ if (sdepth == CV_64F || ddepth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
- dst.create(src.size(), CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()));
+ dst.create(src.size(), CV_MAKE_TYPE(ddepth, cn));
cudaStream_t stream = StreamAccessor::getStream(s);
- if (mask.empty() && dst.type() == src.type())
+ const npp_func_t npp_func = npp_funcs[sdepth][cn - 1];
+ if (ddepth == sdepth && cn > 1 && npp_func != 0)
{
- const npp_func_t npp_func = npp_funcs[src.depth()][src.channels() - 1];
-
- if (npp_func)
- {
- npp_func(src, sc, dst, stream);
- return;
- }
+ npp_func(src, sc, dst, stream);
+ return;
}
- CV_Assert(src.channels() == 1);
+ CV_Assert( cn == 1 );
- const func_t func = funcs[src.depth()][dst.depth()];
+ const func_t func = funcs[sdepth][ddepth];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of source and destination types");
////////////////////////////////////////////////////////////////////////
// multiply
-namespace cv { namespace gpu { namespace device
+namespace arithm
{
- void multiply_gpu(const PtrStepSz<uchar4>& src1, const PtrStepSzf& src2, const PtrStepSz<uchar4>& dst, cudaStream_t stream);
- void multiply_gpu(const PtrStepSz<short4>& src1, const PtrStepSzf& src2, const PtrStepSz<short4>& dst, cudaStream_t stream);
+ void mulMat_8uc4_32f(PtrStepSz<unsigned int> src1, PtrStepSzf src2, PtrStepSz<unsigned int> dst, cudaStream_t stream);
- template <typename T, typename D>
- void multiply_gpu(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
+ void mulMat_16sc4_32f(PtrStepSz<short4> src1, PtrStepSzf src2, PtrStepSz<short4> dst, cudaStream_t stream);
- template <typename T, typename D>
- void multiply_gpu(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
-}}}
+ template <typename T, typename S, typename D>
+ void mulMat(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+}
void cv::gpu::multiply(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, double scale, int dtype, Stream& s)
{
- using namespace cv::gpu::device;
+ using namespace arithm;
cudaStream_t stream = StreamAccessor::getStream(s);
if (src1.type() == CV_8UC4 && src2.type() == CV_32FC1)
{
- CV_Assert(src1.size() == src2.size());
+ CV_Assert( src1.size() == src2.size() );
dst.create(src1.size(), src1.type());
- multiply_gpu(static_cast<PtrStepSz<uchar4> >(src1), static_cast<PtrStepSzf>(src2), static_cast<PtrStepSz<uchar4> >(dst), stream);
+ mulMat_8uc4_32f(src1, src2, dst, stream);
}
else if (src1.type() == CV_16SC4 && src2.type() == CV_32FC1)
{
- CV_Assert(src1.size() == src2.size());
+ CV_Assert( src1.size() == src2.size() );
dst.create(src1.size(), src1.type());
- multiply_gpu(static_cast<PtrStepSz<short4> >(src1), static_cast<PtrStepSzf>(src2), static_cast<PtrStepSz<short4> >(dst), stream);
+ mulMat_16sc4_32f(src1, src2, dst, stream);
}
else
{
- typedef void (*func_t)(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
+ typedef void (*func_t)(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
static const func_t funcs[7][7] =
{
- {multiply_gpu<unsigned char, unsigned char> , 0 /*multiply_gpu<unsigned char, signed char>*/ , multiply_gpu<unsigned char, unsigned short> , multiply_gpu<unsigned char, short> , multiply_gpu<unsigned char, int> , multiply_gpu<unsigned char, float> , multiply_gpu<unsigned char, double> },
- {0 /*multiply_gpu<signed char, unsigned char>*/ , 0 /*multiply_gpu<signed char, signed char>*/ , 0 /*multiply_gpu<signed char, unsigned short>*/, 0 /*multiply_gpu<signed char, short>*/ , 0 /*multiply_gpu<signed char, int>*/, 0 /*multiply_gpu<signed char, float>*/, 0 /*multiply_gpu<signed char, double>*/},
- {0 /*multiply_gpu<unsigned short, unsigned char>*/, 0 /*multiply_gpu<unsigned short, signed char>*/, multiply_gpu<unsigned short, unsigned short> , 0 /*multiply_gpu<unsigned short, short>*/, multiply_gpu<unsigned short, int> , multiply_gpu<unsigned short, float> , multiply_gpu<unsigned short, double> },
- {0 /*multiply_gpu<short, unsigned char>*/ , 0 /*multiply_gpu<short, signed char>*/ , 0 /*multiply_gpu<short, unsigned short>*/ , multiply_gpu<short, short> , multiply_gpu<short, int> , multiply_gpu<short, float> , multiply_gpu<short, double> },
- {0 /*multiply_gpu<int, unsigned char>*/ , 0 /*multiply_gpu<int, signed char>*/ , 0 /*multiply_gpu<int, unsigned short>*/ , 0 /*multiply_gpu<int, short>*/ , multiply_gpu<int, int> , multiply_gpu<int, float> , multiply_gpu<int, double> },
- {0 /*multiply_gpu<float, unsigned char>*/ , 0 /*multiply_gpu<float, signed char>*/ , 0 /*multiply_gpu<float, unsigned short>*/ , 0 /*multiply_gpu<float, short>*/ , 0 /*multiply_gpu<float, int>*/ , multiply_gpu<float, float> , multiply_gpu<float, double> },
- {0 /*multiply_gpu<double, unsigned char>*/ , 0 /*multiply_gpu<double, signed char>*/ , 0 /*multiply_gpu<double, unsigned short>*/ , 0 /*multiply_gpu<double, short>*/ , 0 /*multiply_gpu<double, int>*/ , 0 /*multiply_gpu<double, float>*/ , multiply_gpu<double, double> }
- };
-
- typedef void (*npp_func_t)(const PtrStepSzb src1, const PtrStepb src2, PtrStepb dst, cudaStream_t stream);
- static const npp_func_t npp_funcs[] =
- {
- NppArithm<CV_8U , nppiMul_8u_C1RSfs >::call,
- 0,
- NppArithm<CV_16U, nppiMul_16u_C1RSfs>::call,
- NppArithm<CV_16S, nppiMul_16s_C1RSfs>::call,
- NppArithm<CV_32S, nppiMul_32s_C1RSfs>::call,
- NppArithm<CV_32F, nppiMul_32f_C1R >::call
+ {
+ mulMat<unsigned char, float, unsigned char>,
+ mulMat<unsigned char, float, signed char>,
+ mulMat<unsigned char, float, unsigned short>,
+ mulMat<unsigned char, float, short>,
+ mulMat<unsigned char, float, int>,
+ mulMat<unsigned char, float, float>,
+ mulMat<unsigned char, double, double>
+ },
+ {
+ mulMat<signed char, float, unsigned char>,
+ mulMat<signed char, float, signed char>,
+ mulMat<signed char, float, unsigned short>,
+ mulMat<signed char, float, short>,
+ mulMat<signed char, float, int>,
+ mulMat<signed char, float, float>,
+ mulMat<signed char, double, double>
+ },
+ {
+ 0 /*mulMat<unsigned short, float, unsigned char>*/,
+ 0 /*mulMat<unsigned short, float, signed char>*/,
+ mulMat<unsigned short, float, unsigned short>,
+ mulMat<unsigned short, float, short>,
+ mulMat<unsigned short, float, int>,
+ mulMat<unsigned short, float, float>,
+ mulMat<unsigned short, double, double>
+ },
+ {
+ 0 /*mulMat<short, float, unsigned char>*/,
+ 0 /*mulMat<short, float, signed char>*/,
+ mulMat<short, float, unsigned short>,
+ mulMat<short, float, short>,
+ mulMat<short, float, int>,
+ mulMat<short, float, float>,
+ mulMat<short, double, double>
+ },
+ {
+ 0 /*mulMat<int, float, unsigned char>*/,
+ 0 /*mulMat<int, float, signed char>*/,
+ 0 /*mulMat<int, float, unsigned short>*/,
+ 0 /*mulMat<int, float, short>*/,
+ mulMat<int, float, int>,
+ mulMat<int, float, float>,
+ mulMat<int, double, double>
+ },
+ {
+ 0 /*mulMat<float, float, unsigned char>*/,
+ 0 /*mulMat<float, float, signed char>*/,
+ 0 /*mulMat<float, float, unsigned short>*/,
+ 0 /*mulMat<float, float, short>*/,
+ 0 /*mulMat<float, float, int>*/,
+ mulMat<float, float, float>,
+ mulMat<float, double, double>
+ },
+ {
+ 0 /*mulMat<double, double, unsigned char>*/,
+ 0 /*mulMat<double, double, signed char>*/,
+ 0 /*mulMat<double, double, unsigned short>*/,
+ 0 /*mulMat<double, double, short>*/,
+ 0 /*mulMat<double, double, int>*/,
+ 0 /*mulMat<double, double, float>*/,
+ mulMat<double, double, double>
+ }
};
if (dtype < 0)
dtype = src1.depth();
- CV_Assert(src1.depth() <= CV_64F && CV_MAT_DEPTH(dtype) <= CV_64F);
- CV_Assert(src1.type() == src2.type() && src1.size() == src2.size());
+ const int sdepth = src1.depth();
+ const int ddepth = CV_MAT_DEPTH(dtype);
+ const int cn = src1.channels();
+
+ CV_Assert( sdepth <= CV_64F && ddepth <= CV_64F );
+ CV_Assert( src2.type() == src1.type() && src2.size() == src1.size() );
- if (src1.depth() == CV_64F || CV_MAT_DEPTH(dtype) == CV_64F)
+ if (sdepth == CV_64F || ddepth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
- dst.create(src1.size(), CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src1.channels()));
+ dst.create(src1.size(), CV_MAKE_TYPE(ddepth, cn));
-#if (CUDA_VERSION <= 4020)
- if (scale == 1 && dst.type() == src1.type() && src1.depth() <= CV_32F)
-#else
- if (scale == 1 && dst.type() == src1.type() && src1.depth() <= CV_32F && src1.depth() > CV_8U)
-#endif
- {
- npp_funcs[src1.depth()](src1.reshape(1), src2.reshape(1), dst.reshape(1), stream);
- return;
- }
+ PtrStepSzb src1_(src1.rows, src1.cols * cn, src1.data, src1.step);
+ PtrStepSzb src2_(src1.rows, src1.cols * cn, src2.data, src2.step);
+ PtrStepSzb dst_(src1.rows, src1.cols * cn, dst.data, dst.step);
- const func_t func = funcs[src1.depth()][dst.depth()];
+ const func_t func = funcs[sdepth][ddepth];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of source and destination types");
- func(src1.reshape(1), src2.reshape(1), dst.reshape(1), scale, stream);
+ func(src1_, src2_, dst_, scale, stream);
}
}
-namespace
+namespace arithm
{
- inline bool isIntScalar(Scalar sc)
- {
- return sc.val[0] == static_cast<int>(sc.val[0]) && sc.val[1] == static_cast<int>(sc.val[1]) && sc.val[2] == static_cast<int>(sc.val[2]) && sc.val[3] == static_cast<int>(sc.val[3]);
- }
+ template <typename T, typename S, typename D>
+ void mulScalar(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
}
void cv::gpu::multiply(const GpuMat& src, const Scalar& sc, GpuMat& dst, double scale, int dtype, Stream& s)
{
- using namespace cv::gpu::device;
+ using namespace arithm;
- typedef void (*func_t)(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
+ typedef void (*func_t)(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[7][7] =
{
- {multiply_gpu<unsigned char, unsigned char> , 0 /*multiply_gpu<unsigned char, signed char>*/ , multiply_gpu<unsigned char, unsigned short> , multiply_gpu<unsigned char, short> , multiply_gpu<unsigned char, int> , multiply_gpu<unsigned char, float> , multiply_gpu<unsigned char, double> },
- {0 /*multiply_gpu<signed char, unsigned char>*/ , 0 /*multiply_gpu<signed char, signed char>*/ , 0 /*multiply_gpu<signed char, unsigned short>*/, 0 /*multiply_gpu<signed char, short>*/ , 0 /*multiply_gpu<signed char, int>*/, 0 /*multiply_gpu<signed char, float>*/, 0 /*multiply_gpu<signed char, double>*/},
- {0 /*multiply_gpu<unsigned short, unsigned char>*/, 0 /*multiply_gpu<unsigned short, signed char>*/, multiply_gpu<unsigned short, unsigned short> , 0 /*multiply_gpu<unsigned short, short>*/, multiply_gpu<unsigned short, int> , multiply_gpu<unsigned short, float> , multiply_gpu<unsigned short, double> },
- {0 /*multiply_gpu<short, unsigned char>*/ , 0 /*multiply_gpu<short, signed char>*/ , 0 /*multiply_gpu<short, unsigned short>*/ , multiply_gpu<short, short> , multiply_gpu<short, int> , multiply_gpu<short, float> , multiply_gpu<short, double> },
- {0 /*multiply_gpu<int, unsigned char>*/ , 0 /*multiply_gpu<int, signed char>*/ , 0 /*multiply_gpu<int, unsigned short>*/ , 0 /*multiply_gpu<int, short>*/ , multiply_gpu<int, int> , multiply_gpu<int, float> , multiply_gpu<int, double> },
- {0 /*multiply_gpu<float, unsigned char>*/ , 0 /*multiply_gpu<float, signed char>*/ , 0 /*multiply_gpu<float, unsigned short>*/ , 0 /*multiply_gpu<float, short>*/ , 0 /*multiply_gpu<float, int>*/ , multiply_gpu<float, float> , multiply_gpu<float, double> },
- {0 /*multiply_gpu<double, unsigned char>*/ , 0 /*multiply_gpu<double, signed char>*/ , 0 /*multiply_gpu<double, unsigned short>*/ , 0 /*multiply_gpu<double, short>*/ , 0 /*multiply_gpu<double, int>*/ , 0 /*multiply_gpu<double, float>*/ , multiply_gpu<double, double> }
+ {
+ mulScalar<unsigned char, float, unsigned char>,
+ mulScalar<unsigned char, float, signed char>,
+ mulScalar<unsigned char, float, unsigned short>,
+ mulScalar<unsigned char, float, short>,
+ mulScalar<unsigned char, float, int>,
+ mulScalar<unsigned char, float, float>,
+ mulScalar<unsigned char, double, double>
+ },
+ {
+ mulScalar<signed char, float, unsigned char>,
+ mulScalar<signed char, float, signed char>,
+ mulScalar<signed char, float, unsigned short>,
+ mulScalar<signed char, float, short>,
+ mulScalar<signed char, float, int>,
+ mulScalar<signed char, float, float>,
+ mulScalar<signed char, double, double>
+ },
+ {
+ 0 /*mulScalar<unsigned short, float, unsigned char>*/,
+ 0 /*mulScalar<unsigned short, float, signed char>*/,
+ mulScalar<unsigned short, float, unsigned short>,
+ mulScalar<unsigned short, float, short>,
+ mulScalar<unsigned short, float, int>,
+ mulScalar<unsigned short, float, float>,
+ mulScalar<unsigned short, double, double>
+ },
+ {
+ 0 /*mulScalar<short, float, unsigned char>*/,
+ 0 /*mulScalar<short, float, signed char>*/,
+ mulScalar<short, float, unsigned short>,
+ mulScalar<short, float, short>,
+ mulScalar<short, float, int>,
+ mulScalar<short, float, float>,
+ mulScalar<short, double, double>
+ },
+ {
+ 0 /*mulScalar<int, float, unsigned char>*/,
+ 0 /*mulScalar<int, float, signed char>*/,
+ 0 /*mulScalar<int, float, unsigned short>*/,
+ 0 /*mulScalar<int, float, short>*/,
+ mulScalar<int, float, int>,
+ mulScalar<int, float, float>,
+ mulScalar<int, double, double>
+ },
+ {
+ 0 /*mulScalar<float, float, unsigned char>*/,
+ 0 /*mulScalar<float, float, signed char>*/,
+ 0 /*mulScalar<float, float, unsigned short>*/,
+ 0 /*mulScalar<float, float, short>*/,
+ 0 /*mulScalar<float, float, int>*/,
+ mulScalar<float, float, float>,
+ mulScalar<float, double, double>
+ },
+ {
+ 0 /*mulScalar<double, double, unsigned char>*/,
+ 0 /*mulScalar<double, double, signed char>*/,
+ 0 /*mulScalar<double, double, unsigned short>*/,
+ 0 /*mulScalar<double, double, short>*/,
+ 0 /*mulScalar<double, double, int>*/,
+ 0 /*mulScalar<double, double, float>*/,
+ mulScalar<double, double, double>
+ }
};
typedef void (*npp_func_t)(const PtrStepSzb src, Scalar sc, PtrStepb dst, cudaStream_t stream);
if (dtype < 0)
dtype = src.depth();
- CV_Assert(src.depth() <= CV_64F && CV_MAT_DEPTH(dtype) <= CV_64F);
- CV_Assert(src.channels() <= 4);
+ const int sdepth = src.depth();
+ const int ddepth = CV_MAT_DEPTH(dtype);
+ const int cn = src.channels();
- if (src.depth() == CV_64F || CV_MAT_DEPTH(dtype) == CV_64F)
+ CV_Assert( sdepth <= CV_64F && ddepth <= CV_64F );
+ CV_Assert( cn <= 4 );
+
+ if (sdepth == CV_64F || ddepth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
- dst.create(src.size(), CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()));
+ dst.create(src.size(), CV_MAKE_TYPE(ddepth, cn));
cudaStream_t stream = StreamAccessor::getStream(s);
- if (dst.type() == src.type() && scale == 1 && (src.depth() == CV_32F || isIntScalar(sc)))
- {
- const npp_func_t npp_func = npp_funcs[src.depth()][src.channels() - 1];
+ const Scalar nsc(sc.val[0] * scale, sc.val[1] * scale, sc.val[2] * scale, sc.val[3] * scale);
- if (npp_func)
- {
- npp_func(src, sc, dst, stream);
- return;
- }
+ const npp_func_t npp_func = npp_funcs[sdepth][cn - 1];
+ if (ddepth == sdepth && cn > 1 && npp_func != 0)
+ {
+ npp_func(src, nsc, dst, stream);
+ return;
}
- CV_Assert(src.channels() == 1);
+ CV_Assert( cn == 1 );
- const func_t func = funcs[src.depth()][dst.depth()];
+ const func_t func = funcs[sdepth][ddepth];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of source and destination types");
- func(src, sc.val[0], dst, scale, stream);
+ func(src, nsc.val[0], dst, stream);
}
////////////////////////////////////////////////////////////////////////
// divide
-namespace cv { namespace gpu { namespace device
+namespace arithm
{
- void divide_gpu(const PtrStepSz<uchar4>& src1, const PtrStepSzf& src2, const PtrStepSz<uchar4>& dst, cudaStream_t stream);
- void divide_gpu(const PtrStepSz<short4>& src1, const PtrStepSzf& src2, const PtrStepSz<short4>& dst, cudaStream_t stream);
-
- template <typename T, typename D>
- void divide_gpu(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
+ void divMat_8uc4_32f(PtrStepSz<unsigned int> src1, PtrStepSzf src2, PtrStepSz<unsigned int> dst, cudaStream_t stream);
- template <typename T, typename D>
- void divide_gpu(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
+ void divMat_16sc4_32f(PtrStepSz<short4> src1, PtrStepSzf src2, PtrStepSz<short4> dst, cudaStream_t stream);
- template <typename T, typename D>
- void divide_gpu(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
-}}}
+ template <typename T, typename S, typename D>
+ void divMat(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
+}
void cv::gpu::divide(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, double scale, int dtype, Stream& s)
{
- using namespace cv::gpu::device;
+ using namespace arithm;
cudaStream_t stream = StreamAccessor::getStream(s);
if (src1.type() == CV_8UC4 && src2.type() == CV_32FC1)
{
- CV_Assert(src1.size() == src2.size());
+ CV_Assert( src1.size() == src2.size() );
dst.create(src1.size(), src1.type());
- divide_gpu(static_cast<PtrStepSz<uchar4> >(src1), static_cast<PtrStepSzf>(src2), static_cast<PtrStepSz<uchar4> >(dst), stream);
+ divMat_8uc4_32f(src1, src2, dst, stream);
}
else if (src1.type() == CV_16SC4 && src2.type() == CV_32FC1)
{
- CV_Assert(src1.size() == src2.size());
+ CV_Assert( src1.size() == src2.size() );
dst.create(src1.size(), src1.type());
- divide_gpu(static_cast<PtrStepSz<short4> >(src1), static_cast<PtrStepSzf>(src2), static_cast<PtrStepSz<short4> >(dst), stream);
+ divMat_16sc4_32f(src1, src2, dst, stream);
}
else
{
- typedef void (*func_t)(const PtrStepSzb& src1, const PtrStepSzb& src2, const PtrStepSzb& dst, double scale, cudaStream_t stream);
+ typedef void (*func_t)(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, double scale, cudaStream_t stream);
static const func_t funcs[7][7] =
{
- {divide_gpu<unsigned char, unsigned char> , 0 /*divide_gpu<unsigned char, signed char>*/ , divide_gpu<unsigned char, unsigned short> , divide_gpu<unsigned char, short> , divide_gpu<unsigned char, int> , divide_gpu<unsigned char, float> , divide_gpu<unsigned char, double> },
- {0 /*divide_gpu<signed char, unsigned char>*/ , 0 /*divide_gpu<signed char, signed char>*/ , 0 /*divide_gpu<signed char, unsigned short>*/, 0 /*divide_gpu<signed char, short>*/ , 0 /*divide_gpu<signed char, int>*/, 0 /*divide_gpu<signed char, float>*/, 0 /*divide_gpu<signed char, double>*/},
- {0 /*divide_gpu<unsigned short, unsigned char>*/, 0 /*divide_gpu<unsigned short, signed char>*/, divide_gpu<unsigned short, unsigned short> , 0 /*divide_gpu<unsigned short, short>*/, divide_gpu<unsigned short, int> , divide_gpu<unsigned short, float> , divide_gpu<unsigned short, double> },
- {0 /*divide_gpu<short, unsigned char>*/ , 0 /*divide_gpu<short, signed char>*/ , 0 /*divide_gpu<short, unsigned short>*/ , divide_gpu<short, short> , divide_gpu<short, int> , divide_gpu<short, float> , divide_gpu<short, double> },
- {0 /*divide_gpu<int, unsigned char>*/ , 0 /*divide_gpu<int, signed char>*/ , 0 /*divide_gpu<int, unsigned short>*/ , 0 /*divide_gpu<int, short>*/ , divide_gpu<int, int> , divide_gpu<int, float> , divide_gpu<int, double> },
- {0 /*divide_gpu<float, unsigned char>*/ , 0 /*divide_gpu<float, signed char>*/ , 0 /*divide_gpu<float, unsigned short>*/ , 0 /*divide_gpu<float, short>*/ , 0 /*divide_gpu<float, int>*/ , divide_gpu<float, float> , divide_gpu<float, double> },
- {0 /*divide_gpu<double, unsigned char>*/ , 0 /*divide_gpu<double, signed char>*/ , 0 /*divide_gpu<double, unsigned short>*/ , 0 /*divide_gpu<double, short>*/ , 0 /*divide_gpu<double, int>*/ , 0 /*divide_gpu<double, float>*/ , divide_gpu<double, double> }
- };
-
- typedef void (*npp_func_t)(const PtrStepSzb src1, const PtrStepb src2, PtrStepb dst, cudaStream_t stream);
- static const npp_func_t npp_funcs[6] =
- {
- NppArithm<CV_8U , nppiDiv_8u_C1RSfs >::call,
- 0,
- NppArithm<CV_16U, nppiDiv_16u_C1RSfs>::call,
- NppArithm<CV_16S, nppiDiv_16s_C1RSfs>::call,
- NppArithm<CV_32S, nppiDiv_32s_C1RSfs>::call,
- NppArithm<CV_32F, nppiDiv_32f_C1R >::call
+ {
+ divMat<unsigned char, float, unsigned char>,
+ divMat<unsigned char, float, signed char>,
+ divMat<unsigned char, float, unsigned short>,
+ divMat<unsigned char, float, short>,
+ divMat<unsigned char, float, int>,
+ divMat<unsigned char, float, float>,
+ divMat<unsigned char, double, double>
+ },
+ {
+ divMat<signed char, float, unsigned char>,
+ divMat<signed char, float, signed char>,
+ divMat<signed char, float, unsigned short>,
+ divMat<signed char, float, short>,
+ divMat<signed char, float, int>,
+ divMat<signed char, float, float>,
+ divMat<signed char, double, double>
+ },
+ {
+ 0 /*divMat<unsigned short, float, unsigned char>*/,
+ 0 /*divMat<unsigned short, float, signed char>*/,
+ divMat<unsigned short, float, unsigned short>,
+ divMat<unsigned short, float, short>,
+ divMat<unsigned short, float, int>,
+ divMat<unsigned short, float, float>,
+ divMat<unsigned short, double, double>
+ },
+ {
+ 0 /*divMat<short, float, unsigned char>*/,
+ 0 /*divMat<short, float, signed char>*/,
+ divMat<short, float, unsigned short>,
+ divMat<short, float, short>,
+ divMat<short, float, int>,
+ divMat<short, float, float>,
+ divMat<short, double, double>
+ },
+ {
+ 0 /*divMat<int, float, unsigned char>*/,
+ 0 /*divMat<int, float, signed char>*/,
+ 0 /*divMat<int, float, unsigned short>*/,
+ 0 /*divMat<int, float, short>*/,
+ divMat<int, float, int>,
+ divMat<int, float, float>,
+ divMat<int, double, double>
+ },
+ {
+ 0 /*divMat<float, float, unsigned char>*/,
+ 0 /*divMat<float, float, signed char>*/,
+ 0 /*divMat<float, float, unsigned short>*/,
+ 0 /*divMat<float, float, short>*/,
+ 0 /*divMat<float, float, int>*/,
+ divMat<float, float, float>,
+ divMat<float, double, double>
+ },
+ {
+ 0 /*divMat<double, double, unsigned char>*/,
+ 0 /*divMat<double, double, signed char>*/,
+ 0 /*divMat<double, double, unsigned short>*/,
+ 0 /*divMat<double, double, short>*/,
+ 0 /*divMat<double, double, int>*/,
+ 0 /*divMat<double, double, float>*/,
+ divMat<double, double, double>
+ }
};
if (dtype < 0)
dtype = src1.depth();
- CV_Assert(src1.depth() <= CV_64F && CV_MAT_DEPTH(dtype) <= CV_64F);
- CV_Assert(src1.type() == src2.type() && src1.size() == src2.size());
+ const int sdepth = src1.depth();
+ const int ddepth = CV_MAT_DEPTH(dtype);
+ const int cn = src1.channels();
+
+ CV_Assert( sdepth <= CV_64F && ddepth <= CV_64F );
+ CV_Assert( src2.type() == src1.type() && src2.size() == src1.size() );
- if (src1.depth() == CV_64F || CV_MAT_DEPTH(dtype) == CV_64F)
+ if (sdepth == CV_64F || ddepth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
- dst.create(src1.size(), CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src1.channels()));
+ dst.create(src1.size(), CV_MAKE_TYPE(ddepth, cn));
- if (scale == 1 && dst.type() == src1.type() && src1.depth() <= CV_32F)
- {
- npp_funcs[src1.depth()](src2.reshape(1), src1.reshape(1), dst.reshape(1), stream);
- return;
- }
+ PtrStepSzb src1_(src1.rows, src1.cols * cn, src1.data, src1.step);
+ PtrStepSzb src2_(src1.rows, src1.cols * cn, src2.data, src2.step);
+ PtrStepSzb dst_(src1.rows, src1.cols * cn, dst.data, dst.step);
- const func_t func = funcs[src1.depth()][dst.depth()];
+ const func_t func = funcs[sdepth][ddepth];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of source and destination types");
- func(src1.reshape(1), src2.reshape(1), dst.reshape(1), scale, stream);
+ func(src1_, src2_, dst_, scale, stream);
}
}
+namespace arithm
+{
+ template <typename T, typename S, typename D>
+ void divScalar(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+}
+
void cv::gpu::divide(const GpuMat& src, const Scalar& sc, GpuMat& dst, double scale, int dtype, Stream& s)
{
- using namespace cv::gpu::device;
+ using namespace arithm;
- typedef void (*func_t)(const PtrStepSzb& src1, double val, const PtrStepSzb& dst, double scale, cudaStream_t stream);
+ typedef void (*func_t)(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[7][7] =
{
- {divide_gpu<unsigned char, unsigned char> , 0 /*divide_gpu<unsigned char, signed char>*/ , divide_gpu<unsigned char, unsigned short> , divide_gpu<unsigned char, short> , divide_gpu<unsigned char, int> , divide_gpu<unsigned char, float> , divide_gpu<unsigned char, double> },
- {0 /*divide_gpu<signed char, unsigned char>*/ , 0 /*divide_gpu<signed char, signed char>*/ , 0 /*divide_gpu<signed char, unsigned short>*/, 0 /*divide_gpu<signed char, short>*/ , 0 /*divide_gpu<signed char, int>*/, 0 /*divide_gpu<signed char, float>*/, 0 /*divide_gpu<signed char, double>*/},
- {0 /*divide_gpu<unsigned short, unsigned char>*/, 0 /*divide_gpu<unsigned short, signed char>*/, divide_gpu<unsigned short, unsigned short> , 0 /*divide_gpu<unsigned short, short>*/, divide_gpu<unsigned short, int> , divide_gpu<unsigned short, float> , divide_gpu<unsigned short, double> },
- {0 /*divide_gpu<short, unsigned char>*/ , 0 /*divide_gpu<short, signed char>*/ , 0 /*divide_gpu<short, unsigned short>*/ , divide_gpu<short, short> , divide_gpu<short, int> , divide_gpu<short, float> , divide_gpu<short, double> },
- {0 /*divide_gpu<int, unsigned char>*/ , 0 /*divide_gpu<int, signed char>*/ , 0 /*divide_gpu<int, unsigned short>*/ , 0 /*divide_gpu<int, short>*/ , divide_gpu<int, int> , divide_gpu<int, float> , divide_gpu<int, double> },
- {0 /*divide_gpu<float, unsigned char>*/ , 0 /*divide_gpu<float, signed char>*/ , 0 /*divide_gpu<float, unsigned short>*/ , 0 /*divide_gpu<float, short>*/ , 0 /*divide_gpu<float, int>*/ , divide_gpu<float, float> , divide_gpu<float, double> },
- {0 /*divide_gpu<double, unsigned char>*/ , 0 /*divide_gpu<double, signed char>*/ , 0 /*divide_gpu<double, unsigned short>*/ , 0 /*divide_gpu<double, short>*/ , 0 /*divide_gpu<double, int>*/ , 0 /*divide_gpu<double, float>*/ , divide_gpu<double, double> }
+ {
+ divScalar<unsigned char, float, unsigned char>,
+ divScalar<unsigned char, float, signed char>,
+ divScalar<unsigned char, float, unsigned short>,
+ divScalar<unsigned char, float, short>,
+ divScalar<unsigned char, float, int>,
+ divScalar<unsigned char, float, float>,
+ divScalar<unsigned char, double, double>
+ },
+ {
+ divScalar<signed char, float, unsigned char>,
+ divScalar<signed char, float, signed char>,
+ divScalar<signed char, float, unsigned short>,
+ divScalar<signed char, float, short>,
+ divScalar<signed char, float, int>,
+ divScalar<signed char, float, float>,
+ divScalar<signed char, double, double>
+ },
+ {
+ 0 /*divScalar<unsigned short, float, unsigned char>*/,
+ 0 /*divScalar<unsigned short, float, signed char>*/,
+ divScalar<unsigned short, float, unsigned short>,
+ divScalar<unsigned short, float, short>,
+ divScalar<unsigned short, float, int>,
+ divScalar<unsigned short, float, float>,
+ divScalar<unsigned short, double, double>
+ },
+ {
+ 0 /*divScalar<short, float, unsigned char>*/,
+ 0 /*divScalar<short, float, signed char>*/,
+ divScalar<short, float, unsigned short>,
+ divScalar<short, float, short>,
+ divScalar<short, float, int>,
+ divScalar<short, float, float>,
+ divScalar<short, double, double>
+ },
+ {
+ 0 /*divScalar<int, float, unsigned char>*/,
+ 0 /*divScalar<int, float, signed char>*/,
+ 0 /*divScalar<int, float, unsigned short>*/,
+ 0 /*divScalar<int, float, short>*/,
+ divScalar<int, float, int>,
+ divScalar<int, float, float>,
+ divScalar<int, double, double>
+ },
+ {
+ 0 /*divScalar<float, float, unsigned char>*/,
+ 0 /*divScalar<float, float, signed char>*/,
+ 0 /*divScalar<float, float, unsigned short>*/,
+ 0 /*divScalar<float, float, short>*/,
+ 0 /*divScalar<float, float, int>*/,
+ divScalar<float, float, float>,
+ divScalar<float, double, double>
+ },
+ {
+ 0 /*divScalar<double, double, unsigned char>*/,
+ 0 /*divScalar<double, double, signed char>*/,
+ 0 /*divScalar<double, double, unsigned short>*/,
+ 0 /*divScalar<double, double, short>*/,
+ 0 /*divScalar<double, double, int>*/,
+ 0 /*divScalar<double, double, float>*/,
+ divScalar<double, double, double>
+ }
};
typedef void (*npp_func_t)(const PtrStepSzb src, Scalar sc, PtrStepb dst, cudaStream_t stream);
if (dtype < 0)
dtype = src.depth();
- CV_Assert(src.depth() <= CV_64F && CV_MAT_DEPTH(dtype) <= CV_64F);
- CV_Assert(src.channels() <= 4);
+ const int sdepth = src.depth();
+ const int ddepth = CV_MAT_DEPTH(dtype);
+ const int cn = src.channels();
+
+ CV_Assert( sdepth <= CV_64F && ddepth <= CV_64F );
+ CV_Assert( cn <= 4 );
- if (src.depth() == CV_64F || CV_MAT_DEPTH(dtype) == CV_64F)
+ if (sdepth == CV_64F || ddepth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
- dst.create(src.size(), CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()));
+ dst.create(src.size(), CV_MAKE_TYPE(ddepth, cn));
cudaStream_t stream = StreamAccessor::getStream(s);
- if (dst.type() == src.type() && scale == 1 && (src.depth() == CV_32F || isIntScalar(sc)))
- {
- const npp_func_t npp_func = npp_funcs[src.depth()][src.channels() - 1];
+ const Scalar nsc(sc.val[0] / scale, sc.val[1] / scale, sc.val[2] / scale, sc.val[3] / scale);
- if (npp_func)
- {
- npp_func(src, sc, dst, stream);
- return;
- }
+ const npp_func_t npp_func = npp_funcs[sdepth][cn - 1];
+ if (ddepth == sdepth && cn > 1 && npp_func != 0)
+ {
+ npp_func(src, nsc, dst, stream);
+ return;
}
- CV_Assert(src.channels() == 1);
+ CV_Assert( cn == 1 );
- const func_t func = funcs[src.depth()][dst.depth()];
+ const func_t func = funcs[sdepth][ddepth];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of source and destination types");
- func(src, sc.val[0], dst, scale, stream);
+ func(src, nsc.val[0], dst, stream);
+}
+
+namespace arithm
+{
+ template <typename T, typename S, typename D>
+ void divInv(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
}
void cv::gpu::divide(double scale, const GpuMat& src, GpuMat& dst, int dtype, Stream& s)
{
- using namespace cv::gpu::device;
+ using namespace arithm;
- typedef void (*func_t)(double scalar, const PtrStepSzb& src2, const PtrStepSzb& dst, cudaStream_t stream);
+ typedef void (*func_t)(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[7][7] =
{
- {divide_gpu<unsigned char, unsigned char> , 0 /*divide_gpu<unsigned char, signed char>*/ , divide_gpu<unsigned char, unsigned short> , divide_gpu<unsigned char, short> , divide_gpu<unsigned char, int> , divide_gpu<unsigned char, float> , divide_gpu<unsigned char, double> },
- {0 /*divide_gpu<signed char, unsigned char>*/ , 0 /*divide_gpu<signed char, signed char>*/ , 0 /*divide_gpu<signed char, unsigned short>*/, 0 /*divide_gpu<signed char, short>*/ , 0 /*divide_gpu<signed char, int>*/, 0 /*divide_gpu<signed char, float>*/, 0 /*divide_gpu<signed char, double>*/},
- {0 /*divide_gpu<unsigned short, unsigned char>*/, 0 /*divide_gpu<unsigned short, signed char>*/, divide_gpu<unsigned short, unsigned short> , 0 /*divide_gpu<unsigned short, short>*/, divide_gpu<unsigned short, int> , divide_gpu<unsigned short, float> , divide_gpu<unsigned short, double> },
- {0 /*divide_gpu<short, unsigned char>*/ , 0 /*divide_gpu<short, signed char>*/ , 0 /*divide_gpu<short, unsigned short>*/ , divide_gpu<short, short> , divide_gpu<short, int> , divide_gpu<short, float> , divide_gpu<short, double> },
- {0 /*divide_gpu<int, unsigned char>*/ , 0 /*divide_gpu<int, signed char>*/ , 0 /*divide_gpu<int, unsigned short>*/ , 0 /*divide_gpu<int, short>*/ , divide_gpu<int, int> , divide_gpu<int, float> , divide_gpu<int, double> },
- {0 /*divide_gpu<float, unsigned char>*/ , 0 /*divide_gpu<float, signed char>*/ , 0 /*divide_gpu<float, unsigned short>*/ , 0 /*divide_gpu<float, short>*/ , 0 /*divide_gpu<float, int>*/ , divide_gpu<float, float> , divide_gpu<float, double> },
- {0 /*divide_gpu<double, unsigned char>*/ , 0 /*divide_gpu<double, signed char>*/ , 0 /*divide_gpu<double, unsigned short>*/ , 0 /*divide_gpu<double, short>*/ , 0 /*divide_gpu<double, int>*/ , 0 /*divide_gpu<double, float>*/ , divide_gpu<double, double> }
+ {
+ divInv<unsigned char, float, unsigned char>,
+ divInv<unsigned char, float, signed char>,
+ divInv<unsigned char, float, unsigned short>,
+ divInv<unsigned char, float, short>,
+ divInv<unsigned char, float, int>,
+ divInv<unsigned char, float, float>,
+ divInv<unsigned char, double, double>
+ },
+ {
+ divInv<signed char, float, unsigned char>,
+ divInv<signed char, float, signed char>,
+ divInv<signed char, float, unsigned short>,
+ divInv<signed char, float, short>,
+ divInv<signed char, float, int>,
+ divInv<signed char, float, float>,
+ divInv<signed char, double, double>
+ },
+ {
+ 0 /*divInv<unsigned short, float, unsigned char>*/,
+ 0 /*divInv<unsigned short, float, signed char>*/,
+ divInv<unsigned short, float, unsigned short>,
+ divInv<unsigned short, float, short>,
+ divInv<unsigned short, float, int>,
+ divInv<unsigned short, float, float>,
+ divInv<unsigned short, double, double>
+ },
+ {
+ 0 /*divInv<short, float, unsigned char>*/,
+ 0 /*divInv<short, float, signed char>*/,
+ divInv<short, float, unsigned short>,
+ divInv<short, float, short>,
+ divInv<short, float, int>,
+ divInv<short, float, float>,
+ divInv<short, double, double>
+ },
+ {
+ 0 /*divInv<int, float, unsigned char>*/,
+ 0 /*divInv<int, float, signed char>*/,
+ 0 /*divInv<int, float, unsigned short>*/,
+ 0 /*divInv<int, float, short>*/,
+ divInv<int, float, int>,
+ divInv<int, float, float>,
+ divInv<int, double, double>
+ },
+ {
+ 0 /*divInv<float, float, unsigned char>*/,
+ 0 /*divInv<float, float, signed char>*/,
+ 0 /*divInv<float, float, unsigned short>*/,
+ 0 /*divInv<float, float, short>*/,
+ 0 /*divInv<float, float, int>*/,
+ divInv<float, float, float>,
+ divInv<float, double, double>
+ },
+ {
+ 0 /*divInv<double, double, unsigned char>*/,
+ 0 /*divInv<double, double, signed char>*/,
+ 0 /*divInv<double, double, unsigned short>*/,
+ 0 /*divInv<double, double, short>*/,
+ 0 /*divInv<double, double, int>*/,
+ 0 /*divInv<double, double, float>*/,
+ divInv<double, double, double>
+ }
};
if (dtype < 0)
dtype = src.depth();
- CV_Assert(src.depth() <= CV_64F && CV_MAT_DEPTH(dtype) <= CV_64F);
- CV_Assert(src.channels() == 1);
+ const int sdepth = src.depth();
+ const int ddepth = CV_MAT_DEPTH(dtype);
+ const int cn = src.channels();
- if (src.depth() == CV_64F || CV_MAT_DEPTH(dtype) == CV_64F)
+ CV_Assert( sdepth <= CV_64F && ddepth <= CV_64F );
+ CV_Assert( cn == 1 );
+
+ if (sdepth == CV_64F || ddepth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
- dst.create(src.size(), CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()));
+ dst.create(src.size(), CV_MAKE_TYPE(ddepth, cn));
cudaStream_t stream = StreamAccessor::getStream(s);
- const func_t func = funcs[src.depth()][dst.depth()];
+ const func_t func = funcs[sdepth][ddepth];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of source and destination types");
- func(scale, src, dst, stream);
+ func(src, scale, dst, stream);
}
//////////////////////////////////////////////////////////////////////////////
// absdiff
-namespace cv { namespace gpu { namespace device
+namespace arithm
{
template <typename T>
- void absdiff_gpu(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ void vabsDiff4(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
template <typename T>
- void absdiff_gpu(const PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
-}}}
+ void vabsDiff2(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
-namespace
+ template <typename T>
+ void absDiffMat(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+}
+
+void cv::gpu::absdiff(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& s)
{
- template <int DEPTH> struct NppAbsDiffFunc
- {
- typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
+ using namespace arithm;
- typedef NppStatus (*func_t)(const npp_t* src1, int src1_step, const npp_t* src2, int src2_step, npp_t* dst, int dst_step, NppiSize sz);
+ typedef void (*func_t)(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ static const func_t funcs[] =
+ {
+ absDiffMat<unsigned char>,
+ absDiffMat<signed char>,
+ absDiffMat<unsigned short>,
+ absDiffMat<short>,
+ absDiffMat<int>,
+ absDiffMat<float>,
+ absDiffMat<double>
};
-
- template <int DEPTH, typename NppAbsDiffFunc<DEPTH>::func_t func> struct NppAbsDiff
+ static const func_t vfuncs4[] =
{
- typedef typename NppAbsDiffFunc<DEPTH>::npp_t npp_t;
-
- static void call(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream)
- {
- NppStreamHandler h(stream);
-
- NppiSize sz;
- sz.width = src1.cols;
- sz.height = src1.rows;
+ vabsDiff4<unsigned int>,
+ vabsDiff4<int>,
+ 0,
+ 0
+ };
+ static const func_t vfuncs2[] =
+ {
+ 0,
+ 0,
+ vabsDiff2<unsigned int>,
+ vabsDiff2<int>
+ };
- nppSafeCall( func((const npp_t*)src1.data, static_cast<int>(src1.step), (const npp_t*)src2.data, static_cast<int>(src2.step),
- (npp_t*)dst.data, static_cast<int>(dst.step), sz) );
+ const int depth = src1.depth();
+ const int cn = src1.channels();
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
- }
- };
+ CV_Assert( depth <= CV_64F );
+ CV_Assert( src2.type() == src1.type() && src2.size() == src1.size() );
- template <int DEPTH> struct NppAbsDiffCFunc
+ if (depth == CV_64F)
{
- typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
- typedef npp_t scalar_t;
+ if (!deviceSupports(NATIVE_DOUBLE))
+ CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
+ }
- typedef NppStatus (*func_t)(const npp_t* pSrc1, int nSrc1Step, npp_t* pDst, int nDstStep, NppiSize oSizeROI, npp_t nConstant);
- };
- template <> struct NppAbsDiffCFunc<CV_16U>
- {
- typedef NppTypeTraits<CV_16U>::npp_t npp_t;
- typedef Npp32u scalar_t;
+ dst.create(src1.size(), src1.type());
-#if (CUDA_VERSION <= 4020)
- typedef NppStatus (*func_t)(const Npp16u* pSrc1, int nSrc1Step, Npp16u* pDst, int nDstStep, NppiSize oSizeROI, Npp32u nConstant);
-#else
- typedef NppStatus (*func_t)(const Npp16u * pSrc1, int nSrc1Step, Npp16u * pDst, int nDstStep, NppiSize oSizeROI, Npp16u nConstant);
-#endif
- };
+ cudaStream_t stream = StreamAccessor::getStream(s);
- template <int DEPTH, typename NppAbsDiffCFunc<DEPTH>::func_t func> struct NppAbsDiffC
+ PtrStepSzb src1_(src1.rows, src1.cols * cn, src1.data, src1.step);
+ PtrStepSzb src2_(src1.rows, src1.cols * cn, src2.data, src2.step);
+ PtrStepSzb dst_(src1.rows, src1.cols * cn, dst.data, dst.step);
+
+ if (depth < CV_32S)
{
- typedef typename NppAbsDiffCFunc<DEPTH>::npp_t npp_t;
- typedef typename NppAbsDiffCFunc<DEPTH>::scalar_t scalar_t;
+ const intptr_t src1ptr = reinterpret_cast<intptr_t>(src1_.data);
+ const intptr_t src2ptr = reinterpret_cast<intptr_t>(src2_.data);
+ const intptr_t dstptr = reinterpret_cast<intptr_t>(dst_.data);
- static void call(const PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream)
- {
- NppStreamHandler h(stream);
+ const bool isAllAligned = (src1ptr & 31) == 0 && (src2ptr & 31) == 0 && (dstptr & 31) == 0;
- NppiSize sz;
- sz.width = src1.cols;
- sz.height = src1.rows;
+ if (deviceSupports(FEATURE_SET_COMPUTE_20) && isAllAligned)
+ {
+ const func_t vfunc4 = vfuncs4[depth];
+ const func_t vfunc2 = vfuncs2[depth];
- nppSafeCall( func((const npp_t*)src1.data, static_cast<int>(src1.step),
- (npp_t*)dst.data, static_cast<int>(dst.step), sz, static_cast<scalar_t>(val)) );
+ if (vfunc4 != 0 && (src1_.cols & 3) == 0)
+ {
+ const int vcols = src1_.cols >> 2;
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
- }
- };
-}
+ vfunc4(PtrStepSzb(src1_.rows, vcols, src1_.data, src1_.step),
+ PtrStepSzb(src1_.rows, vcols, src2_.data, src2_.step),
+ PtrStepSzb(src1_.rows, vcols, dst_.data, dst_.step),
+ stream);
-void cv::gpu::absdiff(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
-{
- using namespace cv::gpu::device;
+ return;
+ }
- typedef void (*func_t)(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- static const func_t funcs[] =
- {
- NppAbsDiff<CV_8U, nppiAbsDiff_8u_C1R>::call,
- absdiff_gpu<signed char>,
- NppAbsDiff<CV_16U, nppiAbsDiff_16u_C1R>::call,
- absdiff_gpu<short>,
- absdiff_gpu<int>,
- NppAbsDiff<CV_32F, nppiAbsDiff_32f_C1R>::call,
- absdiff_gpu<double>
- };
+ if (vfunc2 != 0 && (src1_.cols & 1) == 0)
+ {
+ const int vcols = src1_.cols >> 1;
- CV_Assert(src1.depth() <= CV_64F);
- CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
+ vfunc2(PtrStepSzb(src1_.rows, vcols, src1_.data, src1_.step),
+ PtrStepSzb(src1_.rows, vcols, src2_.data, src2_.step),
+ PtrStepSzb(src1_.rows, vcols, dst_.data, dst_.step),
+ stream);
- if (src1.depth() == CV_64F)
- {
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
- CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
+ return;
+ }
+ }
}
- dst.create(src1.size(), src1.type());
+ const func_t func = funcs[depth];
+
+ if (!func)
+ CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of source and destination types");
+
+ func(src1_, src2_, dst_, stream);
+}
- funcs[src1.depth()](src1.reshape(1), src2.reshape(1), dst.reshape(1), StreamAccessor::getStream(stream));
+namespace arithm
+{
+ template <typename T, typename S>
+ void absDiffScalar(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
}
void cv::gpu::absdiff(const GpuMat& src1, const Scalar& src2, GpuMat& dst, Stream& stream)
{
- using namespace cv::gpu::device;
+ using namespace arithm;
- typedef void (*func_t)(const PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
+ typedef void (*func_t)(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[] =
{
- NppAbsDiffC<CV_8U, nppiAbsDiffC_8u_C1R>::call,
- absdiff_gpu<signed char>,
- NppAbsDiffC<CV_16U, nppiAbsDiffC_16u_C1R>::call,
- absdiff_gpu<short>,
- absdiff_gpu<int>,
- NppAbsDiffC<CV_32F, nppiAbsDiffC_32f_C1R>::call,
- absdiff_gpu<double>
+ absDiffScalar<unsigned char, float>,
+ absDiffScalar<signed char, float>,
+ absDiffScalar<unsigned short, float>,
+ absDiffScalar<short, float>,
+ absDiffScalar<int, float>,
+ absDiffScalar<float, float>,
+ absDiffScalar<double, double>
};
- CV_Assert(src1.depth() <= CV_64F);
- CV_Assert(src1.channels() == 1);
+ const int depth = src1.depth();
+
+ CV_Assert( depth <= CV_64F );
+ CV_Assert( src1.channels() == 1 );
- if (src1.depth() == CV_64F)
+ if (depth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
dst.create(src1.size(), src1.type());
- funcs[src1.depth()](src1, src2.val[0], dst, StreamAccessor::getStream(stream));
+ funcs[depth](src1, src2.val[0], dst, StreamAccessor::getStream(stream));
}
//////////////////////////////////////////////////////////////////////////////
// abs
-void cv::gpu::abs(const GpuMat& src, GpuMat& dst, Stream& s)
+namespace arithm
{
- CV_Assert(src.depth() == CV_16S || src.depth() == CV_32F);
-
- dst.create(src.size(), src.type());
-
- cudaStream_t stream = StreamAccessor::getStream(s);
-
- NppStreamHandler h(stream);
-
- NppiSize oSizeROI;
- oSizeROI.width = src.cols * src.channels();
- oSizeROI.height = src.rows;
-
- bool aligned = isAligned(src.data, 16) && isAligned(dst.data, 16);
-
- if (src.depth() == CV_16S)
- {
- if (aligned && oSizeROI.width % 4 == 0)
- {
- oSizeROI.width /= 4;
- nppSafeCall( nppiAbs_16s_C4R(src.ptr<Npp16s>(), static_cast<int>(src.step), dst.ptr<Npp16s>(), static_cast<int>(dst.step), oSizeROI) );
- }
- else
- {
- nppSafeCall( nppiAbs_16s_C1R(src.ptr<Npp16s>(), static_cast<int>(src.step), dst.ptr<Npp16s>(), static_cast<int>(dst.step), oSizeROI) );
- }
- }
- else
- {
- if (aligned && oSizeROI.width % 4 == 0)
- {
- oSizeROI.width /= 4;
- nppSafeCall( nppiAbs_32f_C4R(src.ptr<Npp32f>(), static_cast<int>(src.step), dst.ptr<Npp32f>(), static_cast<int>(dst.step), oSizeROI) );
- }
- else
- {
- nppSafeCall( nppiAbs_32f_C1R(src.ptr<Npp32f>(), static_cast<int>(src.step), dst.ptr<Npp32f>(), static_cast<int>(dst.step), oSizeROI) );
- }
- }
-
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
+ template <typename T>
+ void absMat(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
}
-//////////////////////////////////////////////////////////////////////////////
-// sqr
-
-namespace
+void cv::gpu::abs(const GpuMat& src, GpuMat& dst, Stream& stream)
{
- template <int DEPTH> struct NppSqrFunc
- {
- typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
+ using namespace arithm;
- typedef NppStatus (*func_t)(const npp_t* pSrc, int nSrcStep, npp_t* pDst, int nDstStep, NppiSize oSizeROI, int nScaleFactor);
- };
- template <> struct NppSqrFunc<CV_32F>
+ typedef void (*func_t)(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+ static const func_t funcs[] =
{
- typedef NppTypeTraits<CV_32F>::npp_t npp_t;
-
- typedef NppStatus (*func_t)(const npp_t* pSrc, int nSrcStep, npp_t* pDst, int nDstStep, NppiSize oSizeROI);
+ absMat<unsigned char>,
+ absMat<signed char>,
+ absMat<unsigned short>,
+ absMat<short>,
+ absMat<int>,
+ absMat<float>,
+ absMat<double>
};
- template <int DEPTH, typename NppSqrFunc<DEPTH>::func_t func, typename NppSqrFunc<DEPTH>::func_t func_c4> struct NppSqr
- {
- typedef typename NppSqrFunc<DEPTH>::npp_t npp_t;
-
- static void call(const GpuMat& src, GpuMat& dst, cudaStream_t stream)
- {
- NppStreamHandler h(stream);
+ const int depth = src.depth();
- NppiSize oSizeROI;
- oSizeROI.width = src.cols * src.channels();
- oSizeROI.height = src.rows;
+ CV_Assert( depth <= CV_64F );
+ CV_Assert( src.channels() == 1 );
- bool aligned = isAligned(src.data, 16) && isAligned(dst.data, 16);
-
- if (aligned && oSizeROI.width % 4 == 0)
- {
- oSizeROI.width /= 4;
- nppSafeCall( func_c4(src.ptr<npp_t>(), static_cast<int>(src.step), dst.ptr<npp_t>(), static_cast<int>(dst.step), oSizeROI, 0) );
- }
- else
- {
- nppSafeCall( func(src.ptr<npp_t>(), static_cast<int>(src.step), dst.ptr<npp_t>(), static_cast<int>(dst.step), oSizeROI, 0) );
- }
-
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
- }
- };
- template <typename NppSqrFunc<CV_32F>::func_t func, typename NppSqrFunc<CV_32F>::func_t func_c4> struct NppSqr<CV_32F, func, func_c4>
+ if (depth == CV_64F)
{
- typedef NppSqrFunc<CV_32F>::npp_t npp_t;
-
- static void call(const GpuMat& src, GpuMat& dst, cudaStream_t stream)
- {
- NppStreamHandler h(stream);
+ if (!deviceSupports(NATIVE_DOUBLE))
+ CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
+ }
- NppiSize oSizeROI;
- oSizeROI.width = src.cols * src.channels();
- oSizeROI.height = src.rows;
+ dst.create(src.size(), src.type());
- bool aligned = isAligned(src.data, 16) && isAligned(dst.data, 16);
+ funcs[depth](src, dst, StreamAccessor::getStream(stream));
+}
- if (aligned && oSizeROI.width % 4 == 0)
- {
- oSizeROI.width /= 4;
- nppSafeCall( func_c4(src.ptr<npp_t>(), static_cast<int>(src.step), dst.ptr<npp_t>(), static_cast<int>(dst.step), oSizeROI) );
- }
- else
- {
- nppSafeCall( func(src.ptr<npp_t>(), static_cast<int>(src.step), dst.ptr<npp_t>(), static_cast<int>(dst.step), oSizeROI) );
- }
+//////////////////////////////////////////////////////////////////////////////
+// sqr
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
- }
- };
+namespace arithm
+{
+ template <typename T>
+ void sqrMat(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
}
void cv::gpu::sqr(const GpuMat& src, GpuMat& dst, Stream& stream)
{
- typedef void (*func_t)(const GpuMat& src, GpuMat& dst, cudaStream_t stream);
+ using namespace arithm;
+ typedef void (*func_t)(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[] =
{
- NppSqr<CV_8U, nppiSqr_8u_C1RSfs, nppiSqr_8u_C4RSfs>::call,
- 0,
- NppSqr<CV_16U, nppiSqr_16u_C1RSfs, nppiSqr_16u_C4RSfs>::call,
- NppSqr<CV_16S, nppiSqr_16s_C1RSfs, nppiSqr_16s_C4RSfs>::call,
- 0,
- NppSqr<CV_32F, nppiSqr_32f_C1R, nppiSqr_32f_C4R>::call
+ sqrMat<unsigned char>,
+ sqrMat<signed char>,
+ sqrMat<unsigned short>,
+ sqrMat<short>,
+ sqrMat<int>,
+ sqrMat<float>,
+ sqrMat<double>
};
- CV_Assert(src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_16S || src.depth() == CV_32F);
+ const int depth = src.depth();
+
+ CV_Assert( depth <= CV_64F );
+ CV_Assert( src.channels() == 1 );
+
+ if (depth == CV_64F)
+ {
+ if (!deviceSupports(NATIVE_DOUBLE))
+ CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
+ }
dst.create(src.size(), src.type());
- funcs[src.depth()](src, dst, StreamAccessor::getStream(stream));
+ funcs[depth](src, dst, StreamAccessor::getStream(stream));
}
//////////////////////////////////////////////////////////////////////////////
// sqrt
-namespace
-{
- template <int DEPTH> struct NppOneSourceFunc
- {
- typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
-
- typedef NppStatus (*func_t)(const npp_t* pSrc, int nSrcStep, npp_t* pDst, int nDstStep, NppiSize oSizeROI, int nScaleFactor);
- };
- template <> struct NppOneSourceFunc<CV_32F>
- {
- typedef NppTypeTraits<CV_32F>::npp_t npp_t;
-
- typedef NppStatus (*func_t)(const npp_t* pSrc, int nSrcStep, npp_t* pDst, int nDstStep, NppiSize oSizeROI);
- };
-
- template <int DEPTH, typename NppOneSourceFunc<DEPTH>::func_t func> struct NppOneSource
- {
- typedef typename NppOneSourceFunc<DEPTH>::npp_t npp_t;
-
- static void call(const GpuMat& src, GpuMat& dst, cudaStream_t stream)
- {
- NppStreamHandler h(stream);
-
- NppiSize oSizeROI;
- oSizeROI.width = src.cols * src.channels();
- oSizeROI.height = src.rows;
-
- nppSafeCall( func(src.ptr<npp_t>(), static_cast<int>(src.step), dst.ptr<npp_t>(), static_cast<int>(dst.step), oSizeROI, 0) );
-
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
- }
- };
- template <typename NppOneSourceFunc<CV_32F>::func_t func> struct NppOneSource<CV_32F, func>
- {
- typedef NppOneSourceFunc<CV_32F>::npp_t npp_t;
-
- static void call(const GpuMat& src, GpuMat& dst, cudaStream_t stream)
- {
- NppStreamHandler h(stream);
-
- NppiSize oSizeROI;
- oSizeROI.width = src.cols * src.channels();
- oSizeROI.height = src.rows;
-
- nppSafeCall( func(src.ptr<npp_t>(), static_cast<int>(src.step), dst.ptr<npp_t>(), static_cast<int>(dst.step), oSizeROI) );
-
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
- }
- };
+namespace arithm
+{
+ template <typename T>
+ void sqrtMat(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
}
void cv::gpu::sqrt(const GpuMat& src, GpuMat& dst, Stream& stream)
{
- typedef void (*func_t)(const GpuMat& src, GpuMat& dst, cudaStream_t stream);
+ using namespace arithm;
+ typedef void (*func_t)(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[] =
{
- NppOneSource<CV_8U, nppiSqrt_8u_C1RSfs>::call,
- 0,
- NppOneSource<CV_16U, nppiSqrt_16u_C1RSfs>::call,
- NppOneSource<CV_16S, nppiSqrt_16s_C1RSfs>::call,
- 0,
- NppOneSource<CV_32F, nppiSqrt_32f_C1R>::call
+ sqrtMat<unsigned char>,
+ sqrtMat<signed char>,
+ sqrtMat<unsigned short>,
+ sqrtMat<short>,
+ sqrtMat<int>,
+ sqrtMat<float>,
+ sqrtMat<double>
};
- CV_Assert(src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_16S || src.depth() == CV_32F);
+ const int depth = src.depth();
+
+ CV_Assert( depth <= CV_64F );
+ CV_Assert( src.channels() == 1 );
+
+ if (depth == CV_64F)
+ {
+ if (!deviceSupports(NATIVE_DOUBLE))
+ CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
+ }
dst.create(src.size(), src.type());
- funcs[src.depth()](src, dst, StreamAccessor::getStream(stream));
+ funcs[depth](src, dst, StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
// log
+namespace arithm
+{
+ template <typename T>
+ void logMat(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+}
+
void cv::gpu::log(const GpuMat& src, GpuMat& dst, Stream& stream)
{
- typedef void (*func_t)(const GpuMat& src, GpuMat& dst, cudaStream_t stream);
+ using namespace arithm;
+ typedef void (*func_t)(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[] =
{
- NppOneSource<CV_8U, nppiLn_8u_C1RSfs>::call,
- 0,
- NppOneSource<CV_16U, nppiLn_16u_C1RSfs>::call,
- NppOneSource<CV_16S, nppiLn_16s_C1RSfs>::call,
- 0,
- NppOneSource<CV_32F, nppiLn_32f_C1R>::call
+ logMat<unsigned char>,
+ logMat<signed char>,
+ logMat<unsigned short>,
+ logMat<short>,
+ logMat<int>,
+ logMat<float>,
+ logMat<double>
};
- CV_Assert(src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_16S || src.depth() == CV_32F);
+ const int depth = src.depth();
+
+ CV_Assert( depth <= CV_64F );
+ CV_Assert( src.channels() == 1 );
+
+ if (depth == CV_64F)
+ {
+ if (!deviceSupports(NATIVE_DOUBLE))
+ CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
+ }
dst.create(src.size(), src.type());
- funcs[src.depth()](src, dst, StreamAccessor::getStream(stream));
+ funcs[depth](src, dst, StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
// exp
+namespace arithm
+{
+ template <typename T>
+ void expMat(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
+}
+
void cv::gpu::exp(const GpuMat& src, GpuMat& dst, Stream& stream)
{
- typedef void (*func_t)(const GpuMat& src, GpuMat& dst, cudaStream_t stream);
+ using namespace arithm;
+ typedef void (*func_t)(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[] =
{
- NppOneSource<CV_8U, nppiExp_8u_C1RSfs>::call,
- 0,
- NppOneSource<CV_16U, nppiExp_16u_C1RSfs>::call,
- NppOneSource<CV_16S, nppiExp_16s_C1RSfs>::call,
- 0,
- NppOneSource<CV_32F, nppiExp_32f_C1R>::call
+ expMat<unsigned char>,
+ expMat<signed char>,
+ expMat<unsigned short>,
+ expMat<short>,
+ expMat<int>,
+ expMat<float>,
+ expMat<double>
};
- CV_Assert(src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_16S || src.depth() == CV_32F);
+ const int depth = src.depth();
+
+ CV_Assert( depth <= CV_64F );
+ CV_Assert( src.channels() == 1 );
+
+ if (depth == CV_64F)
+ {
+ if (!deviceSupports(NATIVE_DOUBLE))
+ CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
+ }
dst.create(src.size(), src.type());
- funcs[src.depth()](src, dst, StreamAccessor::getStream(stream));
+ funcs[depth](src, dst, StreamAccessor::getStream(stream));
}
//////////////////////////////////////////////////////////////////////////////
-// Comparison of two matrixes
+// compare
-namespace cv { namespace gpu { namespace device
+namespace arithm
{
- template <typename T> void compare_eq(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template <typename T> void compare_ne(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template <typename T> void compare_lt(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template <typename T> void compare_le(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
-
- template <typename T> void compare_eq(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template <typename T> void compare_ne(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template <typename T> void compare_lt(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template <typename T> void compare_le(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template <typename T> void compare_gt(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
- template <typename T> void compare_ge(PtrStepSzb src, int cn, double val[4], PtrStepSzb dst, cudaStream_t stream);
-}}}
-
-void cv::gpu::compare(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, int cmpop, Stream& stream)
+ template <typename T> void cmpMatEq(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template <typename T> void cmpMatNe(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template <typename T> void cmpMatLt(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template <typename T> void cmpMatLe(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+}
+
+void cv::gpu::compare(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, int cmpop, Stream& s)
{
- using namespace cv::gpu::device;
+ using namespace arithm;
typedef void (*func_t)(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[7][4] =
{
- {compare_eq<unsigned char> , compare_ne<unsigned char> , compare_lt<unsigned char> , compare_le<unsigned char> },
- {compare_eq<signed char> , compare_ne<signed char> , compare_lt<signed char> , compare_le<signed char> },
- {compare_eq<unsigned short>, compare_ne<unsigned short>, compare_lt<unsigned short>, compare_le<unsigned short>},
- {compare_eq<short> , compare_ne<short> , compare_lt<short> , compare_le<short> },
- {compare_eq<int> , compare_ne<int> , compare_lt<int> , compare_le<int> },
- {compare_eq<float> , compare_ne<float> , compare_lt<float> , compare_le<float> },
- {compare_eq<double> , compare_ne<double> , compare_lt<double> , compare_le<double> }
+ {cmpMatEq<unsigned char> , cmpMatNe<unsigned char> , cmpMatLt<unsigned char> , cmpMatLe<unsigned char> },
+ {cmpMatEq<signed char> , cmpMatNe<signed char> , cmpMatLt<signed char> , cmpMatLe<signed char> },
+ {cmpMatEq<unsigned short>, cmpMatNe<unsigned short>, cmpMatLt<unsigned short>, cmpMatLe<unsigned short>},
+ {cmpMatEq<short> , cmpMatNe<short> , cmpMatLt<short> , cmpMatLe<short> },
+ {cmpMatEq<int> , cmpMatNe<int> , cmpMatLt<int> , cmpMatLe<int> },
+ {cmpMatEq<float> , cmpMatNe<float> , cmpMatLt<float> , cmpMatLe<float> },
+ {cmpMatEq<double> , cmpMatNe<double> , cmpMatLt<double> , cmpMatLe<double> }
};
- CV_Assert(src1.depth() <= CV_64F);
- CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
- CV_Assert(cmpop >= CMP_EQ && cmpop <= CMP_NE);
+ const int depth = src1.depth();
+ const int cn = src1.channels();
+
+ CV_Assert( depth <= CV_64F );
+ CV_Assert( src2.size() == src1.size() && src2.type() == src1.type() );
+ CV_Assert( cmpop >= CMP_EQ && cmpop <= CMP_NE );
- if (src1.depth() == CV_64F)
+ if (depth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
+ dst.create(src1.size(), CV_MAKE_TYPE(CV_8U, cn));
+
+ cudaStream_t stream = StreamAccessor::getStream(s);
+
static const int codes[] =
{
0, 2, 3, 2, 3, 1
&src2, &src1, &src1, &src2, &src2, &src2
};
- dst.create(src1.size(), CV_MAKE_TYPE(CV_8U, src1.channels()));
+ const int code = codes[cmpop];
+ PtrStepSzb src1_(src1.rows, src1.cols * cn, psrc1[cmpop]->data, psrc1[cmpop]->step);
+ PtrStepSzb src2_(src1.rows, src1.cols * cn, psrc2[cmpop]->data, psrc2[cmpop]->step);
+ PtrStepSzb dst_(src1.rows, src1.cols * cn, dst.data, dst.step);
- funcs[src1.depth()][codes[cmpop]](psrc1[cmpop]->reshape(1), psrc2[cmpop]->reshape(1), dst.reshape(1), StreamAccessor::getStream(stream));
-}
+ const func_t func = funcs[depth][code];
-namespace
-{
- template <typename T>
- void castScalar(Scalar& sc)
- {
- sc.val[0] = saturate_cast<T>(sc.val[0]);
- sc.val[1] = saturate_cast<T>(sc.val[1]);
- sc.val[2] = saturate_cast<T>(sc.val[2]);
- sc.val[3] = saturate_cast<T>(sc.val[3]);
- }
+ func(src1_, src2_, dst_, stream);
}
//////////////////////////////////////////////////////////////////////////////
// Unary bitwise logical operations
-namespace cv { namespace gpu { namespace device
+namespace arithm
{
- void bitwiseNotCaller(int rows, int cols, size_t elem_size1, int cn, const PtrStepb src, PtrStepb dst, cudaStream_t stream);
-
- template <typename T>
- void bitwiseMaskNotCaller(int rows, int cols, int cn, const PtrStepb src, const PtrStepb mask, PtrStepb dst, cudaStream_t stream);
-}}}
+ template <typename T> void bitMatNot(PtrStepSzb src, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+}
-namespace
+void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask, Stream& s)
{
- void bitwiseNotCaller(const GpuMat& src, GpuMat& dst, cudaStream_t stream)
- {
- dst.create(src.size(), src.type());
+ using namespace arithm;
- cv::gpu::device::bitwiseNotCaller(src.rows, src.cols, src.elemSize1(), dst.channels(), src, dst, stream);
- }
-
- void bitwiseNotCaller(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
- {
- using namespace cv::gpu::device;
+ const int depth = src.depth();
- typedef void (*func_t)(int, int, int, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
- static func_t funcs[] =
- {
- bitwiseMaskNotCaller<unsigned char>, bitwiseMaskNotCaller<unsigned char>,
- bitwiseMaskNotCaller<unsigned short>, bitwiseMaskNotCaller<unsigned short>,
- bitwiseMaskNotCaller<unsigned int>, bitwiseMaskNotCaller<unsigned int>,
- bitwiseMaskNotCaller<unsigned int>
- };
+ CV_Assert( depth <= CV_64F );
+ CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size()) );
- CV_Assert(src.depth() <= CV_64F);
- CV_Assert(mask.type() == CV_8U && mask.size() == src.size());
+ dst.create(src.size(), src.type());
- dst.create(src.size(), src.type());
+ cudaStream_t stream = StreamAccessor::getStream(s);
- const func_t func = funcs[src.depth()];
+ const int bcols = (int) (src.cols * src.elemSize());
- int cn = src.depth() != CV_64F ? src.channels() : src.channels() * (sizeof(double) / sizeof(unsigned int));
+ if ((bcols & 3) == 0)
+ {
+ const int vcols = bcols >> 2;
- func(src.rows, src.cols, cn, src, mask, dst, stream);
+ bitMatNot<unsigned int>(
+ PtrStepSzb(src.rows, vcols, src.data, src.step),
+ PtrStepSzb(src.rows, vcols, dst.data, dst.step),
+ mask, stream);
}
-}
+ else if ((bcols & 1) == 0)
+ {
+ const int vcols = bcols >> 1;
-void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask, Stream& stream)
-{
- if (mask.empty())
- bitwiseNotCaller(src, dst, StreamAccessor::getStream(stream));
+ bitMatNot<unsigned short>(
+ PtrStepSzb(src.rows, vcols, src.data, src.step),
+ PtrStepSzb(src.rows, vcols, dst.data, dst.step),
+ mask, stream);
+ }
else
- bitwiseNotCaller(src, dst, mask, StreamAccessor::getStream(stream));
+ {
+ bitMatNot<unsigned short>(
+ PtrStepSzb(src.rows, bcols, src.data, src.step),
+ PtrStepSzb(src.rows, bcols, dst.data, dst.step),
+ mask, stream);
+ }
}
//////////////////////////////////////////////////////////////////////////////
// Binary bitwise logical operations
-namespace cv { namespace gpu { namespace device
+namespace arithm
{
- void bitwiseOrCaller(int rows, int cols, size_t elem_size1, int cn, const PtrStepb src1, const PtrStepb src2, PtrStepb dst, cudaStream_t stream);
+ template <typename T> void bitMatAnd(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template <typename T> void bitMatOr(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+ template <typename T> void bitMatXor(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
+}
- template <typename T>
- void bitwiseMaskOrCaller(int rows, int cols, int cn, const PtrStepb src1, const PtrStepb src2, const PtrStepb mask, PtrStepb dst, cudaStream_t stream);
+void cv::gpu::bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, Stream& s)
+{
+ using namespace arithm;
- void bitwiseAndCaller(int rows, int cols, size_t elem_size1, int cn, const PtrStepb src1, const PtrStepb src2, PtrStepb dst, cudaStream_t stream);
+ const int depth = src1.depth();
- template <typename T>
- void bitwiseMaskAndCaller(int rows, int cols, int cn, const PtrStepb src1, const PtrStepb src2, const PtrStepb mask, PtrStepb dst, cudaStream_t stream);
+ CV_Assert( depth <= CV_64F );
+ CV_Assert( src2.size() == src1.size() && src2.type() == src1.type() );
+ CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src1.size()) );
- void bitwiseXorCaller(int rows, int cols, size_t elem_size1, int cn, const PtrStepb src1, const PtrStepb src2, PtrStepb dst, cudaStream_t stream);
+ dst.create(src1.size(), src1.type());
- template <typename T>
- void bitwiseMaskXorCaller(int rows, int cols, int cn, const PtrStepb src1, const PtrStepb src2, const PtrStepb mask, PtrStepb dst, cudaStream_t stream);
-}}}
+ cudaStream_t stream = StreamAccessor::getStream(s);
-namespace
-{
- void bitwiseOrCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream)
- {
- CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
+ const int bcols = (int) (src1.cols * src1.elemSize());
- dst.create(src1.size(), src1.type());
+ if ((bcols & 3) == 0)
+ {
+ const int vcols = bcols >> 2;
- cv::gpu::device::bitwiseOrCaller(dst.rows, dst.cols, dst.elemSize1(), dst.channels(), src1, src2, dst, stream);
+ bitMatAnd<unsigned int>(
+ PtrStepSzb(src1.rows, vcols, src1.data, src1.step),
+ PtrStepSzb(src1.rows, vcols, src2.data, src2.step),
+ PtrStepSzb(src1.rows, vcols, dst.data, dst.step),
+ mask, stream);
}
+ else if ((bcols & 1) == 0)
+ {
+ const int vcols = bcols >> 1;
- void bitwiseOrCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
+ bitMatAnd<unsigned int>(
+ PtrStepSzb(src1.rows, vcols, src1.data, src1.step),
+ PtrStepSzb(src1.rows, vcols, src2.data, src2.step),
+ PtrStepSzb(src1.rows, vcols, dst.data, dst.step),
+ mask, stream);
+ }
+ else
{
- using namespace cv::gpu::device;
- typedef void (*func_t)(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
- static func_t funcs[] =
- {
- bitwiseMaskOrCaller<unsigned char>, bitwiseMaskOrCaller<unsigned char>,
- bitwiseMaskOrCaller<unsigned short>, bitwiseMaskOrCaller<unsigned short>,
- bitwiseMaskOrCaller<unsigned int>, bitwiseMaskOrCaller<unsigned int>,
- bitwiseMaskOrCaller<unsigned int>
- };
+ bitMatAnd<unsigned int>(
+ PtrStepSzb(src1.rows, bcols, src1.data, src1.step),
+ PtrStepSzb(src1.rows, bcols, src2.data, src2.step),
+ PtrStepSzb(src1.rows, bcols, dst.data, dst.step),
+ mask, stream);
+ }
+}
- CV_Assert(src1.depth() <= CV_64F);
- CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
- CV_Assert(mask.type() == CV_8U && mask.size() == src1.size());
+void cv::gpu::bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, Stream& s)
+{
+ using namespace arithm;
- dst.create(src1.size(), src1.type());
+ const int depth = src1.depth();
- const func_t func = funcs[src1.depth()];
+ CV_Assert( depth <= CV_64F );
+ CV_Assert( src2.size() == src1.size() && src2.type() == src1.type() );
+ CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src1.size()) );
- int cn = dst.depth() != CV_64F ? dst.channels() : dst.channels() * (sizeof(double) / sizeof(unsigned int));
+ dst.create(src1.size(), src1.type());
- func(dst.rows, dst.cols, cn, src1, src2, mask, dst, stream);
- }
+ cudaStream_t stream = StreamAccessor::getStream(s);
+ const int bcols = (int) (src1.cols * src1.elemSize());
- void bitwiseAndCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream)
+ if ((bcols & 3) == 0)
{
- CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
-
- dst.create(src1.size(), src1.type());
+ const int vcols = bcols >> 2;
- cv::gpu::device::bitwiseAndCaller(dst.rows, dst.cols, dst.elemSize1(), dst.channels(), src1, src2, dst, stream);
+ bitMatOr<unsigned int>(
+ PtrStepSzb(src1.rows, vcols, src1.data, src1.step),
+ PtrStepSzb(src1.rows, vcols, src2.data, src2.step),
+ PtrStepSzb(src1.rows, vcols, dst.data, dst.step),
+ mask, stream);
}
+ else if ((bcols & 1) == 0)
+ {
+ const int vcols = bcols >> 1;
- void bitwiseAndCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
+ bitMatOr<unsigned int>(
+ PtrStepSzb(src1.rows, vcols, src1.data, src1.step),
+ PtrStepSzb(src1.rows, vcols, src2.data, src2.step),
+ PtrStepSzb(src1.rows, vcols, dst.data, dst.step),
+ mask, stream);
+ }
+ else
{
- using namespace cv::gpu::device;
- typedef void (*func_t)(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
- static func_t funcs[] =
- {
- bitwiseMaskAndCaller<unsigned char>, bitwiseMaskAndCaller<unsigned char>,
- bitwiseMaskAndCaller<unsigned short>, bitwiseMaskAndCaller<unsigned short>,
- bitwiseMaskAndCaller<unsigned int>, bitwiseMaskAndCaller<unsigned int>,
- bitwiseMaskAndCaller<unsigned int>
- };
+ bitMatOr<unsigned int>(
+ PtrStepSzb(src1.rows, bcols, src1.data, src1.step),
+ PtrStepSzb(src1.rows, bcols, src2.data, src2.step),
+ PtrStepSzb(src1.rows, bcols, dst.data, dst.step),
+ mask, stream);
+ }
+}
- CV_Assert(src1.depth() <= CV_64F);
- CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
- CV_Assert(mask.type() == CV_8U && mask.size() == src1.size());
+void cv::gpu::bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, Stream& s)
+{
+ using namespace arithm;
- dst.create(src1.size(), src1.type());
+ const int depth = src1.depth();
- const func_t func = funcs[src1.depth()];
+ CV_Assert( depth <= CV_64F );
+ CV_Assert( src2.size() == src1.size() && src2.type() == src1.type() );
+ CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src1.size()) );
- int cn = dst.depth() != CV_64F ? dst.channels() : dst.channels() * (sizeof(double) / sizeof(unsigned int));
+ dst.create(src1.size(), src1.type());
- func(dst.rows, dst.cols, cn, src1, src2, mask, dst, stream);
- }
+ cudaStream_t stream = StreamAccessor::getStream(s);
+ const int bcols = (int) (src1.cols * src1.elemSize());
- void bitwiseXorCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream)
+ if ((bcols & 3) == 0)
{
- CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
+ const int vcols = bcols >> 2;
- dst.create(src1.size(), src1.type());
-
- cv::gpu::device::bitwiseXorCaller(dst.rows, dst.cols, dst.elemSize1(), dst.channels(), src1, src2, dst, stream);
+ bitMatXor<unsigned int>(
+ PtrStepSzb(src1.rows, vcols, src1.data, src1.step),
+ PtrStepSzb(src1.rows, vcols, src2.data, src2.step),
+ PtrStepSzb(src1.rows, vcols, dst.data, dst.step),
+ mask, stream);
}
-
- void bitwiseXorCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
+ else if ((bcols & 1) == 0)
{
- using namespace cv::gpu::device;
-
- typedef void (*func_t)(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
- static func_t funcs[] =
- {
- bitwiseMaskXorCaller<unsigned char>, bitwiseMaskXorCaller<unsigned char>,
- bitwiseMaskXorCaller<unsigned short>, bitwiseMaskXorCaller<unsigned short>,
- bitwiseMaskXorCaller<unsigned int>, bitwiseMaskXorCaller<unsigned int>,
- bitwiseMaskXorCaller<unsigned int>
- };
-
- CV_Assert(src1.depth() <= CV_64F);
- CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
- CV_Assert(mask.type() == CV_8U && mask.size() == src1.size());
-
- dst.create(src1.size(), src1.type());
-
- const func_t func = funcs[src1.depth()];
+ const int vcols = bcols >> 1;
- int cn = dst.depth() != CV_64F ? dst.channels() : dst.channels() * (sizeof(double) / sizeof(unsigned int));
-
- func(dst.rows, dst.cols, cn, src1, src2, mask, dst, stream);
+ bitMatXor<unsigned int>(
+ PtrStepSzb(src1.rows, vcols, src1.data, src1.step),
+ PtrStepSzb(src1.rows, vcols, src2.data, src2.step),
+ PtrStepSzb(src1.rows, vcols, dst.data, dst.step),
+ mask, stream);
}
-}
-
-void cv::gpu::bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, Stream& stream)
-{
- if (mask.empty())
- bitwiseOrCaller(src1, src2, dst, StreamAccessor::getStream(stream));
else
- bitwiseOrCaller(src1, src2, dst, mask, StreamAccessor::getStream(stream));
-}
+ {
-void cv::gpu::bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, Stream& stream)
-{
- if (mask.empty())
- bitwiseAndCaller(src1, src2, dst, StreamAccessor::getStream(stream));
- else
- bitwiseAndCaller(src1, src2, dst, mask, StreamAccessor::getStream(stream));
+ bitMatXor<unsigned int>(
+ PtrStepSzb(src1.rows, bcols, src1.data, src1.step),
+ PtrStepSzb(src1.rows, bcols, src2.data, src2.step),
+ PtrStepSzb(src1.rows, bcols, dst.data, dst.step),
+ mask, stream);
+ }
}
-void cv::gpu::bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, Stream& stream)
+//////////////////////////////////////////////////////////////////////////////
+// Binary bitwise logical operations with scalars
+
+namespace arithm
{
- if (mask.empty())
- bitwiseXorCaller(src1, src2, dst, StreamAccessor::getStream(stream));
- else
- bitwiseXorCaller(src1, src2, dst, mask, StreamAccessor::getStream(stream));
+ template <typename T> void bitScalarAnd(PtrStepSzb src1, unsigned int src2, PtrStepSzb dst, cudaStream_t stream);
+ template <typename T> void bitScalarOr(PtrStepSzb src1, unsigned int src2, PtrStepSzb dst, cudaStream_t stream);
+ template <typename T> void bitScalarXor(PtrStepSzb src1, unsigned int src2, PtrStepSzb dst, cudaStream_t stream);
}
namespace
{
+ typedef void (*bit_scalar_func_t)(PtrStepSzb src1, unsigned int src2, PtrStepSzb dst, cudaStream_t stream);
+
+ template <bit_scalar_func_t func> struct BitScalar
+ {
+ static void call(const GpuMat& src, Scalar sc, GpuMat& dst, cudaStream_t stream)
+ {
+ func(src, static_cast<unsigned int>(sc.val[0]), dst, stream);
+ }
+ };
+
+ template <bit_scalar_func_t func> struct BitScalar4
+ {
+ static void call(const GpuMat& src, Scalar sc, GpuMat& dst, cudaStream_t stream)
+ {
+ Scalar_<unsigned int> isc = sc;
+
+ unsigned int packedVal = 0;
+
+ packedVal |= (isc.val[0] & 0xffff);
+ packedVal |= (isc.val[1] & 0xffff) << 8;
+ packedVal |= (isc.val[2] & 0xffff) << 16;
+ packedVal |= (isc.val[3] & 0xffff) << 24;
+
+ func(src, packedVal, dst, stream);
+ }
+ };
+
template <int DEPTH, int cn> struct NppBitwiseCFunc
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
};
}
-void cv::gpu::bitwise_or(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stream& stream)
+void cv::gpu::bitwise_and(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stream& stream)
{
+ using namespace arithm;
+
typedef void (*func_t)(const GpuMat& src, Scalar sc, GpuMat& dst, cudaStream_t stream);
static const func_t funcs[5][4] =
{
- {NppBitwiseC<CV_8U , 1, nppiOrC_8u_C1R >::call, 0, NppBitwiseC<CV_8U , 3, nppiOrC_8u_C3R >::call, NppBitwiseC<CV_8U , 4, nppiOrC_8u_C4R >::call},
+ {BitScalar< bitScalarAnd<unsigned char> >::call , 0, NppBitwiseC<CV_8U , 3, nppiAndC_8u_C3R >::call, BitScalar4< bitScalarAnd<unsigned int> >::call},
{0,0,0,0},
- {NppBitwiseC<CV_16U, 1, nppiOrC_16u_C1R>::call, 0, NppBitwiseC<CV_16U, 3, nppiOrC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiOrC_16u_C4R>::call},
+ {BitScalar< bitScalarAnd<unsigned short> >::call, 0, NppBitwiseC<CV_16U, 3, nppiAndC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiAndC_16u_C4R>::call},
{0,0,0,0},
- {NppBitwiseC<CV_32S, 1, nppiOrC_32s_C1R>::call, 0, NppBitwiseC<CV_32S, 3, nppiOrC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiOrC_32s_C4R>::call}
+ {BitScalar< bitScalarAnd<unsigned int> >::call , 0, NppBitwiseC<CV_32S, 3, nppiAndC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiAndC_32s_C4R>::call}
};
- CV_Assert(src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32S);
- CV_Assert(src.channels() == 1 || src.channels() == 3 || src.channels() == 4);
+ const int depth = src.depth();
+ const int cn = src.channels();
+
+ CV_Assert( depth == CV_8U || depth == CV_16U || depth == CV_32S );
+ CV_Assert( cn == 1 || cn == 3 || cn == 4 );
dst.create(src.size(), src.type());
- funcs[src.depth()][src.channels() - 1](src, sc, dst, StreamAccessor::getStream(stream));
+ funcs[depth][cn - 1](src, sc, dst, StreamAccessor::getStream(stream));
}
-void cv::gpu::bitwise_and(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stream& stream)
+void cv::gpu::bitwise_or(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stream& stream)
{
+ using namespace arithm;
+
typedef void (*func_t)(const GpuMat& src, Scalar sc, GpuMat& dst, cudaStream_t stream);
static const func_t funcs[5][4] =
{
- {NppBitwiseC<CV_8U , 1, nppiAndC_8u_C1R >::call, 0, NppBitwiseC<CV_8U , 3, nppiAndC_8u_C3R >::call, NppBitwiseC<CV_8U , 4, nppiAndC_8u_C4R >::call},
+ {BitScalar< bitScalarOr<unsigned char> >::call , 0, NppBitwiseC<CV_8U , 3, nppiOrC_8u_C3R >::call, BitScalar4< bitScalarOr<unsigned int> >::call},
{0,0,0,0},
- {NppBitwiseC<CV_16U, 1, nppiAndC_16u_C1R>::call, 0, NppBitwiseC<CV_16U, 3, nppiAndC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiAndC_16u_C4R>::call},
+ {BitScalar< bitScalarOr<unsigned short> >::call, 0, NppBitwiseC<CV_16U, 3, nppiOrC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiOrC_16u_C4R>::call},
{0,0,0,0},
- {NppBitwiseC<CV_32S, 1, nppiAndC_32s_C1R>::call, 0, NppBitwiseC<CV_32S, 3, nppiAndC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiAndC_32s_C4R>::call}
+ {BitScalar< bitScalarOr<unsigned int> >::call , 0, NppBitwiseC<CV_32S, 3, nppiOrC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiOrC_32s_C4R>::call}
};
- CV_Assert(src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32S);
- CV_Assert(src.channels() == 1 || src.channels() == 3 || src.channels() == 4);
+ const int depth = src.depth();
+ const int cn = src.channels();
+
+ CV_Assert( depth == CV_8U || depth == CV_16U || depth == CV_32S );
+ CV_Assert( cn == 1 || cn == 3 || cn == 4 );
dst.create(src.size(), src.type());
- funcs[src.depth()][src.channels() - 1](src, sc, dst, StreamAccessor::getStream(stream));
+ funcs[depth][cn - 1](src, sc, dst, StreamAccessor::getStream(stream));
}
void cv::gpu::bitwise_xor(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stream& stream)
{
+ using namespace arithm;
+
typedef void (*func_t)(const GpuMat& src, Scalar sc, GpuMat& dst, cudaStream_t stream);
static const func_t funcs[5][4] =
{
- {NppBitwiseC<CV_8U , 1, nppiXorC_8u_C1R >::call, 0, NppBitwiseC<CV_8U , 3, nppiXorC_8u_C3R >::call, NppBitwiseC<CV_8U , 4, nppiXorC_8u_C4R >::call},
+ {BitScalar< bitScalarXor<unsigned char> >::call , 0, NppBitwiseC<CV_8U , 3, nppiXorC_8u_C3R >::call, BitScalar4< bitScalarXor<unsigned int> >::call},
{0,0,0,0},
- {NppBitwiseC<CV_16U, 1, nppiXorC_16u_C1R>::call, 0, NppBitwiseC<CV_16U, 3, nppiXorC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiXorC_16u_C4R>::call},
+ {BitScalar< bitScalarXor<unsigned short> >::call, 0, NppBitwiseC<CV_16U, 3, nppiXorC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiXorC_16u_C4R>::call},
{0,0,0,0},
- {NppBitwiseC<CV_32S, 1, nppiXorC_32s_C1R>::call, 0, NppBitwiseC<CV_32S, 3, nppiXorC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiXorC_32s_C4R>::call}
+ {BitScalar< bitScalarXor<unsigned int> >::call , 0, NppBitwiseC<CV_32S, 3, nppiXorC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiXorC_32s_C4R>::call}
};
- CV_Assert(src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32S);
- CV_Assert(src.channels() == 1 || src.channels() == 3 || src.channels() == 4);
+ const int depth = src.depth();
+ const int cn = src.channels();
+
+ CV_Assert( depth == CV_8U || depth == CV_16U || depth == CV_32S );
+ CV_Assert( cn == 1 || cn == 3 || cn == 4 );
dst.create(src.size(), src.type());
- funcs[src.depth()][src.channels() - 1](src, sc, dst, StreamAccessor::getStream(stream));
+ funcs[depth][cn - 1](src, sc, dst, StreamAccessor::getStream(stream));
}
//////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////
// Minimum and maximum operations
-namespace cv { namespace gpu { namespace device
+namespace arithm
{
- template <typename T> void min_gpu(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
- template <typename T> void max_gpu(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
-
- template <typename T> void min_gpu(const PtrStepSzb src, T val, PtrStepSzb dst, cudaStream_t stream);
- template <typename T> void max_gpu(const PtrStepSzb src, T val, PtrStepSzb dst, cudaStream_t stream);
-}}}
+ template <typename T> void vmin4(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template <typename T> void vmin2(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template <typename T> void minMat(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template <typename T> void minScalar(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+
+ template <typename T> void vmax4(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template <typename T> void vmax2(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template <typename T> void maxMat(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ template <typename T> void maxScalar(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+}
-void cv::gpu::min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
+void cv::gpu::min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& s)
{
- using namespace cv::gpu::device;
+ using namespace arithm;
- typedef void (*func_t)(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ typedef void (*func_t)(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[] =
{
- min_gpu<unsigned char>,
- min_gpu<signed char>,
- min_gpu<unsigned short>,
- min_gpu<short>,
- min_gpu<int>,
- min_gpu<float>,
- min_gpu<double>
+ minMat<unsigned char>,
+ minMat<signed char>,
+ minMat<unsigned short>,
+ minMat<short>,
+ minMat<int>,
+ minMat<float>,
+ minMat<double>
+ };
+ static const func_t vfuncs4[] =
+ {
+ vmin4<unsigned int>,
+ vmin4<int>,
+ 0,
+ 0
+ };
+ static const func_t vfuncs2[] =
+ {
+ 0,
+ 0,
+ vmin2<unsigned int>,
+ vmin2<int>
};
- CV_Assert(src1.depth() <= CV_64F);
- CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
+ const int depth = src1.depth();
+ const int cn = src1.channels();
+
+ CV_Assert( depth <= CV_64F );
+ CV_Assert( src2.type() == src1.type() && src2.size() == src1.size() );
- if (src1.depth() == CV_64F)
+ if (depth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
dst.create(src1.size(), src1.type());
- funcs[src1.depth()](src1.reshape(1), src2.reshape(1), dst.reshape(1), StreamAccessor::getStream(stream));
+ cudaStream_t stream = StreamAccessor::getStream(s);
+
+ PtrStepSzb src1_(src1.rows, src1.cols * cn, src1.data, src1.step);
+ PtrStepSzb src2_(src1.rows, src1.cols * cn, src2.data, src2.step);
+ PtrStepSzb dst_(src1.rows, src1.cols * cn, dst.data, dst.step);
+
+ if (depth < CV_32S)
+ {
+ const intptr_t src1ptr = reinterpret_cast<intptr_t>(src1_.data);
+ const intptr_t src2ptr = reinterpret_cast<intptr_t>(src2_.data);
+ const intptr_t dstptr = reinterpret_cast<intptr_t>(dst_.data);
+
+ const bool isAllAligned = (src1ptr & 31) == 0 && (src2ptr & 31) == 0 && (dstptr & 31) == 0;
+
+ if (deviceSupports(FEATURE_SET_COMPUTE_20) && isAllAligned)
+ {
+ const func_t vfunc4 = vfuncs4[depth];
+ const func_t vfunc2 = vfuncs2[depth];
+
+ if (vfunc4 != 0 && (src1_.cols & 3) == 0)
+ {
+ const int vcols = src1_.cols >> 2;
+
+ vfunc4(PtrStepSzb(src1_.rows, vcols, src1_.data, src1_.step),
+ PtrStepSzb(src1_.rows, vcols, src2_.data, src2_.step),
+ PtrStepSzb(src1_.rows, vcols, dst_.data, dst_.step),
+ stream);
+
+ return;
+ }
+
+ if (vfunc2 != 0 && (src1_.cols & 1) == 0)
+ {
+ const int vcols = src1_.cols >> 1;
+
+ vfunc2(PtrStepSzb(src1_.rows, vcols, src1_.data, src1_.step),
+ PtrStepSzb(src1_.rows, vcols, src2_.data, src2_.step),
+ PtrStepSzb(src1_.rows, vcols, dst_.data, dst_.step),
+ stream);
+
+ return;
+ }
+ }
+ }
+
+ const func_t func = funcs[depth];
+
+ if (!func)
+ CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of source and destination types");
+
+ func(src1_, src2_, dst_, stream);
}
-void cv::gpu::max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
+void cv::gpu::max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& s)
{
- using namespace cv::gpu::device;
+ using namespace arithm;
- typedef void (*func_t)(const PtrStepSzb src1, const PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
+ typedef void (*func_t)(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[] =
{
- max_gpu<unsigned char>,
- max_gpu<signed char>,
- max_gpu<unsigned short>,
- max_gpu<short>,
- max_gpu<int>,
- max_gpu<float>,
- max_gpu<double>
+ maxMat<unsigned char>,
+ maxMat<signed char>,
+ maxMat<unsigned short>,
+ maxMat<short>,
+ maxMat<int>,
+ maxMat<float>,
+ maxMat<double>
+ };
+ static const func_t vfuncs4[] =
+ {
+ vmax4<unsigned int>,
+ vmax4<int>,
+ 0,
+ 0
+ };
+ static const func_t vfuncs2[] =
+ {
+ 0,
+ 0,
+ vmax2<unsigned int>,
+ vmax2<int>
};
- CV_Assert(src1.depth() <= CV_64F);
- CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
+ const int depth = src1.depth();
+ const int cn = src1.channels();
- if (src1.depth() == CV_64F)
+ CV_Assert( depth <= CV_64F );
+ CV_Assert( src2.type() == src1.type() && src2.size() == src1.size() );
+
+ if (depth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
dst.create(src1.size(), src1.type());
- funcs[src1.depth()](src1.reshape(1), src2.reshape(1), dst.reshape(1), StreamAccessor::getStream(stream));
-}
+ cudaStream_t stream = StreamAccessor::getStream(s);
-namespace
-{
- template <typename T> void minScalar(const PtrStepSzb src, double val, PtrStepSzb dst, cudaStream_t stream)
+ PtrStepSzb src1_(src1.rows, src1.cols * cn, src1.data, src1.step);
+ PtrStepSzb src2_(src1.rows, src1.cols * cn, src2.data, src2.step);
+ PtrStepSzb dst_(src1.rows, src1.cols * cn, dst.data, dst.step);
+
+ if (depth < CV_32S)
{
- cv::gpu::device::min_gpu(src, saturate_cast<T>(val), dst, stream);
+ const intptr_t src1ptr = reinterpret_cast<intptr_t>(src1_.data);
+ const intptr_t src2ptr = reinterpret_cast<intptr_t>(src2_.data);
+ const intptr_t dstptr = reinterpret_cast<intptr_t>(dst_.data);
+
+ const bool isAllAligned = (src1ptr & 31) == 0 && (src2ptr & 31) == 0 && (dstptr & 31) == 0;
+
+ if (deviceSupports(FEATURE_SET_COMPUTE_20) && isAllAligned)
+ {
+ const func_t vfunc4 = vfuncs4[depth];
+ const func_t vfunc2 = vfuncs2[depth];
+
+ if (vfunc4 != 0 && (src1_.cols & 3) == 0)
+ {
+ const int vcols = src1_.cols >> 2;
+
+ vfunc4(PtrStepSzb(src1_.rows, vcols, src1_.data, src1_.step),
+ PtrStepSzb(src1_.rows, vcols, src2_.data, src2_.step),
+ PtrStepSzb(src1_.rows, vcols, dst_.data, dst_.step),
+ stream);
+
+ return;
+ }
+
+ if (vfunc2 != 0 && (src1_.cols & 1) == 0)
+ {
+ const int vcols = src1_.cols >> 1;
+
+ vfunc2(PtrStepSzb(src1_.rows, vcols, src1_.data, src1_.step),
+ PtrStepSzb(src1_.rows, vcols, src2_.data, src2_.step),
+ PtrStepSzb(src1_.rows, vcols, dst_.data, dst_.step),
+ stream);
+
+ return;
+ }
+ }
}
- template <typename T> void maxScalar(const PtrStepSzb src, double val, PtrStepSzb dst, cudaStream_t stream)
+ const func_t func = funcs[depth];
+
+ if (!func)
+ CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of source and destination types");
+
+ func(src1_, src2_, dst_, stream);
+}
+
+namespace
+{
+ template <typename T> double castScalar(double val)
{
- cv::gpu::device::max_gpu(src, saturate_cast<T>(val), dst, stream);
+ return saturate_cast<T>(val);
}
}
void cv::gpu::min(const GpuMat& src, double val, GpuMat& dst, Stream& stream)
{
- typedef void (*func_t)(const PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ using namespace arithm;
+
+ typedef void (*func_t)(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[] =
{
minScalar<unsigned char>,
minScalar<double>
};
- CV_Assert(src.depth() <= CV_64F);
- CV_Assert(src.channels() == 1);
+ typedef double (*cast_func_t)(double sc);
+ static const cast_func_t cast_func[] =
+ {
+ castScalar<unsigned char>, castScalar<signed char>, castScalar<unsigned short>, castScalar<short>, castScalar<int>, castScalar<float>, castScalar<double>
+ };
+
+ const int depth = src.depth();
- if (src.depth() == CV_64F)
+ CV_Assert( depth <= CV_64F );
+ CV_Assert( src.channels() == 1 );
+
+ if (depth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
dst.create(src.size(), src.type());
- funcs[src.depth()](src, val, dst, StreamAccessor::getStream(stream));
+ funcs[depth](src, cast_func[depth](val), dst, StreamAccessor::getStream(stream));
}
void cv::gpu::max(const GpuMat& src, double val, GpuMat& dst, Stream& stream)
{
- typedef void (*func_t)(const PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
+ using namespace arithm;
+
+ typedef void (*func_t)(PtrStepSzb src1, double src2, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[] =
{
maxScalar<unsigned char>,
maxScalar<double>
};
- CV_Assert(src.depth() <= CV_64F);
- CV_Assert(src.channels() == 1);
+ typedef double (*cast_func_t)(double sc);
+ static const cast_func_t cast_func[] =
+ {
+ castScalar<unsigned char>, castScalar<signed char>, castScalar<unsigned short>, castScalar<short>, castScalar<int>, castScalar<float>, castScalar<double>
+ };
+
+ const int depth = src.depth();
+
+ CV_Assert( depth <= CV_64F );
+ CV_Assert( src.channels() == 1 );
- if (src.depth() == CV_64F)
+ if (depth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
dst.create(src.size(), src.type());
- funcs[src.depth()](src, val, dst, StreamAccessor::getStream(stream));
+ funcs[depth](src, cast_func[depth](val), dst, StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
// threshold
-namespace cv { namespace gpu { namespace device
+namespace arithm
{
template <typename T>
- void threshold_gpu(const PtrStepSzb& src, const PtrStepSzb& dst, T thresh, T maxVal, int type, cudaStream_t stream);
-}}}
-
-namespace
-{
- template <typename T> void threshold_caller(const GpuMat& src, GpuMat& dst, double thresh, double maxVal, int type, cudaStream_t stream)
- {
- cv::gpu::device::threshold_gpu<T>(src, dst, saturate_cast<T>(thresh), saturate_cast<T>(maxVal), type, stream);
- }
+ void threshold(PtrStepSzb src, PtrStepSzb dst, double thresh, double maxVal, int type, cudaStream_t stream);
}
double cv::gpu::threshold(const GpuMat& src, GpuMat& dst, double thresh, double maxVal, int type, Stream& s)
{
- CV_Assert(src.channels() == 1 && src.depth() <= CV_64F);
- CV_Assert(type <= THRESH_TOZERO_INV);
+ const int depth = src.depth();
+
+ CV_Assert( src.channels() == 1 && depth <= CV_64F );
+ CV_Assert( type <= THRESH_TOZERO_INV );
- if (src.depth() == CV_64F)
+ if (depth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
}
else
{
- typedef void (*func_t)(const GpuMat& src, GpuMat& dst, double thresh, double maxVal, int type, cudaStream_t stream);
+ typedef void (*func_t)(PtrStepSzb src, PtrStepSzb dst, double thresh, double maxVal, int type, cudaStream_t stream);
static const func_t funcs[] =
{
- threshold_caller<unsigned char>, threshold_caller<signed char>,
- threshold_caller<unsigned short>, threshold_caller<short>,
- threshold_caller<int>, threshold_caller<float>, threshold_caller<double>
+ arithm::threshold<unsigned char>,
+ arithm::threshold<signed char>,
+ arithm::threshold<unsigned short>,
+ arithm::threshold<short>,
+ arithm::threshold<int>,
+ arithm::threshold<float>,
+ arithm::threshold<double>
};
- if (src.depth() != CV_32F && src.depth() != CV_64F)
+ if (depth != CV_32F && depth != CV_64F)
{
thresh = cvFloor(thresh);
maxVal = cvRound(maxVal);
}
- funcs[src.depth()](src, dst, thresh, maxVal, type, stream);
+ funcs[depth](src, dst, thresh, maxVal, type, stream);
}
return thresh;
////////////////////////////////////////////////////////////////////////
// pow
-namespace cv { namespace gpu { namespace device
+namespace arithm
{
- template<typename T> void pow_caller(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream);
-}}}
+ template<typename T> void pow(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream);
+}
void cv::gpu::pow(const GpuMat& src, double power, GpuMat& dst, Stream& stream)
{
- using namespace cv::gpu::device;
-
typedef void (*func_t)(PtrStepSzb src, double power, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[] =
{
- pow_caller<unsigned char>, pow_caller<signed char>,
- pow_caller<unsigned short>, pow_caller<short>,
- pow_caller<int>, pow_caller<float>, pow_caller<double>
+ arithm::pow<unsigned char>,
+ arithm::pow<signed char>,
+ arithm::pow<unsigned short>,
+ arithm::pow<short>,
+ arithm::pow<int>,
+ arithm::pow<float>,
+ arithm::pow<double>
};
- CV_Assert(src.depth() <= CV_64F);
+ const int depth = src.depth();
+ const int cn = src.channels();
- if (src.depth() == CV_64F)
+ CV_Assert(depth <= CV_64F);
+
+ if (depth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
dst.create(src.size(), src.type());
- funcs[src.depth()](src.reshape(1), power, dst.reshape(1), StreamAccessor::getStream(stream));
+ PtrStepSzb src_(src.rows, src.cols * cn, src.data, src.step);
+ PtrStepSzb dst_(src.rows, src.cols * cn, dst.data, dst.step);
+
+ funcs[depth](src_, power, dst_, StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
NppAlphaComp<CV_32F, nppiAlphaComp_32f_AC4R>::call
};
- CV_Assert(img1.type() == CV_8UC4 || img1.type() == CV_16UC4 || img1.type() == CV_32SC4 || img1.type() == CV_32FC4);
- CV_Assert(img1.size() == img2.size() && img1.type() == img2.type());
+ CV_Assert( img1.type() == CV_8UC4 || img1.type() == CV_16UC4 || img1.type() == CV_32SC4 || img1.type() == CV_32FC4 );
+ CV_Assert( img1.size() == img2.size() && img1.type() == img2.type() );
dst.create(img1.size(), img1.type());
////////////////////////////////////////////////////////////////////////
// addWeighted
-namespace cv { namespace gpu { namespace device
+namespace arithm
{
template <typename T1, typename T2, typename D>
- void addWeighted_gpu(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
-}}}
+ void addWeighted(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
+}
-void cv::gpu::addWeighted(const GpuMat& src1, double alpha, const GpuMat& src2, double beta, double gamma, GpuMat& dst, int dtype, Stream& stream)
+void cv::gpu::addWeighted(const GpuMat& src1, double alpha, const GpuMat& src2, double beta, double gamma, GpuMat& dst, int ddepth, Stream& stream)
{
- using namespace cv::gpu::device;
-
- typedef void (*func_t)(const PtrStepSzb& src1, double alpha, const PtrStepSzb& src2, double beta, double gamma, const PtrStepSzb& dst, cudaStream_t stream);
-
+ typedef void (*func_t)(PtrStepSzb src1, double alpha, PtrStepSzb src2, double beta, double gamma, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[7][7][7] =
{
{
{
- addWeighted_gpu<unsigned char, unsigned char, unsigned char >,
- addWeighted_gpu<unsigned char, unsigned char, signed char >,
- addWeighted_gpu<unsigned char, unsigned char, unsigned short>,
- addWeighted_gpu<unsigned char, unsigned char, short >,
- addWeighted_gpu<unsigned char, unsigned char, int >,
- addWeighted_gpu<unsigned char, unsigned char, float >,
- addWeighted_gpu<unsigned char, unsigned char, double>
+ arithm::addWeighted<unsigned char, unsigned char, unsigned char >,
+ arithm::addWeighted<unsigned char, unsigned char, signed char >,
+ arithm::addWeighted<unsigned char, unsigned char, unsigned short>,
+ arithm::addWeighted<unsigned char, unsigned char, short >,
+ arithm::addWeighted<unsigned char, unsigned char, int >,
+ arithm::addWeighted<unsigned char, unsigned char, float >,
+ arithm::addWeighted<unsigned char, unsigned char, double>
},
{
- addWeighted_gpu<unsigned char, signed char, unsigned char >,
- addWeighted_gpu<unsigned char, signed char, signed char >,
- addWeighted_gpu<unsigned char, signed char, unsigned short>,
- addWeighted_gpu<unsigned char, signed char, short >,
- addWeighted_gpu<unsigned char, signed char, int >,
- addWeighted_gpu<unsigned char, signed char, float >,
- addWeighted_gpu<unsigned char, signed char, double>
+ arithm::addWeighted<unsigned char, signed char, unsigned char >,
+ arithm::addWeighted<unsigned char, signed char, signed char >,
+ arithm::addWeighted<unsigned char, signed char, unsigned short>,
+ arithm::addWeighted<unsigned char, signed char, short >,
+ arithm::addWeighted<unsigned char, signed char, int >,
+ arithm::addWeighted<unsigned char, signed char, float >,
+ arithm::addWeighted<unsigned char, signed char, double>
},
{
- addWeighted_gpu<unsigned char, unsigned short, unsigned char >,
- addWeighted_gpu<unsigned char, unsigned short, signed char >,
- addWeighted_gpu<unsigned char, unsigned short, unsigned short>,
- addWeighted_gpu<unsigned char, unsigned short, short >,
- addWeighted_gpu<unsigned char, unsigned short, int >,
- addWeighted_gpu<unsigned char, unsigned short, float >,
- addWeighted_gpu<unsigned char, unsigned short, double>
+ arithm::addWeighted<unsigned char, unsigned short, unsigned char >,
+ arithm::addWeighted<unsigned char, unsigned short, signed char >,
+ arithm::addWeighted<unsigned char, unsigned short, unsigned short>,
+ arithm::addWeighted<unsigned char, unsigned short, short >,
+ arithm::addWeighted<unsigned char, unsigned short, int >,
+ arithm::addWeighted<unsigned char, unsigned short, float >,
+ arithm::addWeighted<unsigned char, unsigned short, double>
},
{
- addWeighted_gpu<unsigned char, short, unsigned char >,
- addWeighted_gpu<unsigned char, short, signed char >,
- addWeighted_gpu<unsigned char, short, unsigned short>,
- addWeighted_gpu<unsigned char, short, short >,
- addWeighted_gpu<unsigned char, short, int >,
- addWeighted_gpu<unsigned char, short, float >,
- addWeighted_gpu<unsigned char, short, double>
+ arithm::addWeighted<unsigned char, short, unsigned char >,
+ arithm::addWeighted<unsigned char, short, signed char >,
+ arithm::addWeighted<unsigned char, short, unsigned short>,
+ arithm::addWeighted<unsigned char, short, short >,
+ arithm::addWeighted<unsigned char, short, int >,
+ arithm::addWeighted<unsigned char, short, float >,
+ arithm::addWeighted<unsigned char, short, double>
},
{
- addWeighted_gpu<unsigned char, int, unsigned char >,
- addWeighted_gpu<unsigned char, int, signed char >,
- addWeighted_gpu<unsigned char, int, unsigned short>,
- addWeighted_gpu<unsigned char, int, short >,
- addWeighted_gpu<unsigned char, int, int >,
- addWeighted_gpu<unsigned char, int, float >,
- addWeighted_gpu<unsigned char, int, double>
+ arithm::addWeighted<unsigned char, int, unsigned char >,
+ arithm::addWeighted<unsigned char, int, signed char >,
+ arithm::addWeighted<unsigned char, int, unsigned short>,
+ arithm::addWeighted<unsigned char, int, short >,
+ arithm::addWeighted<unsigned char, int, int >,
+ arithm::addWeighted<unsigned char, int, float >,
+ arithm::addWeighted<unsigned char, int, double>
},
{
- addWeighted_gpu<unsigned char, float, unsigned char >,
- addWeighted_gpu<unsigned char, float, signed char >,
- addWeighted_gpu<unsigned char, float, unsigned short>,
- addWeighted_gpu<unsigned char, float, short >,
- addWeighted_gpu<unsigned char, float, int >,
- addWeighted_gpu<unsigned char, float, float >,
- addWeighted_gpu<unsigned char, float, double>
+ arithm::addWeighted<unsigned char, float, unsigned char >,
+ arithm::addWeighted<unsigned char, float, signed char >,
+ arithm::addWeighted<unsigned char, float, unsigned short>,
+ arithm::addWeighted<unsigned char, float, short >,
+ arithm::addWeighted<unsigned char, float, int >,
+ arithm::addWeighted<unsigned char, float, float >,
+ arithm::addWeighted<unsigned char, float, double>
},
{
- addWeighted_gpu<unsigned char, double, unsigned char >,
- addWeighted_gpu<unsigned char, double, signed char >,
- addWeighted_gpu<unsigned char, double, unsigned short>,
- addWeighted_gpu<unsigned char, double, short >,
- addWeighted_gpu<unsigned char, double, int >,
- addWeighted_gpu<unsigned char, double, float >,
- addWeighted_gpu<unsigned char, double, double>
+ arithm::addWeighted<unsigned char, double, unsigned char >,
+ arithm::addWeighted<unsigned char, double, signed char >,
+ arithm::addWeighted<unsigned char, double, unsigned short>,
+ arithm::addWeighted<unsigned char, double, short >,
+ arithm::addWeighted<unsigned char, double, int >,
+ arithm::addWeighted<unsigned char, double, float >,
+ arithm::addWeighted<unsigned char, double, double>
}
},
{
{
- 0/*addWeighted_gpu<signed char, unsigned char, unsigned char >*/,
- 0/*addWeighted_gpu<signed char, unsigned char, signed char >*/,
- 0/*addWeighted_gpu<signed char, unsigned char, unsigned short>*/,
- 0/*addWeighted_gpu<signed char, unsigned char, short >*/,
- 0/*addWeighted_gpu<signed char, unsigned char, int >*/,
- 0/*addWeighted_gpu<signed char, unsigned char, float >*/,
- 0/*addWeighted_gpu<signed char, unsigned char, double>*/
+ 0/*arithm::addWeighted<signed char, unsigned char, unsigned char >*/,
+ 0/*arithm::addWeighted<signed char, unsigned char, signed char >*/,
+ 0/*arithm::addWeighted<signed char, unsigned char, unsigned short>*/,
+ 0/*arithm::addWeighted<signed char, unsigned char, short >*/,
+ 0/*arithm::addWeighted<signed char, unsigned char, int >*/,
+ 0/*arithm::addWeighted<signed char, unsigned char, float >*/,
+ 0/*arithm::addWeighted<signed char, unsigned char, double>*/
},
{
- addWeighted_gpu<signed char, signed char, unsigned char >,
- addWeighted_gpu<signed char, signed char, signed char >,
- addWeighted_gpu<signed char, signed char, unsigned short>,
- addWeighted_gpu<signed char, signed char, short >,
- addWeighted_gpu<signed char, signed char, int >,
- addWeighted_gpu<signed char, signed char, float >,
- addWeighted_gpu<signed char, signed char, double>
+ arithm::addWeighted<signed char, signed char, unsigned char >,
+ arithm::addWeighted<signed char, signed char, signed char >,
+ arithm::addWeighted<signed char, signed char, unsigned short>,
+ arithm::addWeighted<signed char, signed char, short >,
+ arithm::addWeighted<signed char, signed char, int >,
+ arithm::addWeighted<signed char, signed char, float >,
+ arithm::addWeighted<signed char, signed char, double>
},
{
- addWeighted_gpu<signed char, unsigned short, unsigned char >,
- addWeighted_gpu<signed char, unsigned short, signed char >,
- addWeighted_gpu<signed char, unsigned short, unsigned short>,
- addWeighted_gpu<signed char, unsigned short, short >,
- addWeighted_gpu<signed char, unsigned short, int >,
- addWeighted_gpu<signed char, unsigned short, float >,
- addWeighted_gpu<signed char, unsigned short, double>
+ arithm::addWeighted<signed char, unsigned short, unsigned char >,
+ arithm::addWeighted<signed char, unsigned short, signed char >,
+ arithm::addWeighted<signed char, unsigned short, unsigned short>,
+ arithm::addWeighted<signed char, unsigned short, short >,
+ arithm::addWeighted<signed char, unsigned short, int >,
+ arithm::addWeighted<signed char, unsigned short, float >,
+ arithm::addWeighted<signed char, unsigned short, double>
},
{
- addWeighted_gpu<signed char, short, unsigned char >,
- addWeighted_gpu<signed char, short, signed char >,
- addWeighted_gpu<signed char, short, unsigned short>,
- addWeighted_gpu<signed char, short, short >,
- addWeighted_gpu<signed char, short, int >,
- addWeighted_gpu<signed char, short, float >,
- addWeighted_gpu<signed char, short, double>
+ arithm::addWeighted<signed char, short, unsigned char >,
+ arithm::addWeighted<signed char, short, signed char >,
+ arithm::addWeighted<signed char, short, unsigned short>,
+ arithm::addWeighted<signed char, short, short >,
+ arithm::addWeighted<signed char, short, int >,
+ arithm::addWeighted<signed char, short, float >,
+ arithm::addWeighted<signed char, short, double>
},
{
- addWeighted_gpu<signed char, int, unsigned char >,
- addWeighted_gpu<signed char, int, signed char >,
- addWeighted_gpu<signed char, int, unsigned short>,
- addWeighted_gpu<signed char, int, short >,
- addWeighted_gpu<signed char, int, int >,
- addWeighted_gpu<signed char, int, float >,
- addWeighted_gpu<signed char, int, double>
+ arithm::addWeighted<signed char, int, unsigned char >,
+ arithm::addWeighted<signed char, int, signed char >,
+ arithm::addWeighted<signed char, int, unsigned short>,
+ arithm::addWeighted<signed char, int, short >,
+ arithm::addWeighted<signed char, int, int >,
+ arithm::addWeighted<signed char, int, float >,
+ arithm::addWeighted<signed char, int, double>
},
{
- addWeighted_gpu<signed char, float, unsigned char >,
- addWeighted_gpu<signed char, float, signed char >,
- addWeighted_gpu<signed char, float, unsigned short>,
- addWeighted_gpu<signed char, float, short >,
- addWeighted_gpu<signed char, float, int >,
- addWeighted_gpu<signed char, float, float >,
- addWeighted_gpu<signed char, float, double>
+ arithm::addWeighted<signed char, float, unsigned char >,
+ arithm::addWeighted<signed char, float, signed char >,
+ arithm::addWeighted<signed char, float, unsigned short>,
+ arithm::addWeighted<signed char, float, short >,
+ arithm::addWeighted<signed char, float, int >,
+ arithm::addWeighted<signed char, float, float >,
+ arithm::addWeighted<signed char, float, double>
},
{
- addWeighted_gpu<signed char, double, unsigned char >,
- addWeighted_gpu<signed char, double, signed char >,
- addWeighted_gpu<signed char, double, unsigned short>,
- addWeighted_gpu<signed char, double, short >,
- addWeighted_gpu<signed char, double, int >,
- addWeighted_gpu<signed char, double, float >,
- addWeighted_gpu<signed char, double, double>
+ arithm::addWeighted<signed char, double, unsigned char >,
+ arithm::addWeighted<signed char, double, signed char >,
+ arithm::addWeighted<signed char, double, unsigned short>,
+ arithm::addWeighted<signed char, double, short >,
+ arithm::addWeighted<signed char, double, int >,
+ arithm::addWeighted<signed char, double, float >,
+ arithm::addWeighted<signed char, double, double>
}
},
{
{
- 0/*addWeighted_gpu<unsigned short, unsigned char, unsigned char >*/,
- 0/*addWeighted_gpu<unsigned short, unsigned char, signed char >*/,
- 0/*addWeighted_gpu<unsigned short, unsigned char, unsigned short>*/,
- 0/*addWeighted_gpu<unsigned short, unsigned char, short >*/,
- 0/*addWeighted_gpu<unsigned short, unsigned char, int >*/,
- 0/*addWeighted_gpu<unsigned short, unsigned char, float >*/,
- 0/*addWeighted_gpu<unsigned short, unsigned char, double>*/
+ 0/*arithm::addWeighted<unsigned short, unsigned char, unsigned char >*/,
+ 0/*arithm::addWeighted<unsigned short, unsigned char, signed char >*/,
+ 0/*arithm::addWeighted<unsigned short, unsigned char, unsigned short>*/,
+ 0/*arithm::addWeighted<unsigned short, unsigned char, short >*/,
+ 0/*arithm::addWeighted<unsigned short, unsigned char, int >*/,
+ 0/*arithm::addWeighted<unsigned short, unsigned char, float >*/,
+ 0/*arithm::addWeighted<unsigned short, unsigned char, double>*/
},
{
- 0/*addWeighted_gpu<unsigned short, signed char, unsigned char >*/,
- 0/*addWeighted_gpu<unsigned short, signed char, signed char >*/,
- 0/*addWeighted_gpu<unsigned short, signed char, unsigned short>*/,
- 0/*addWeighted_gpu<unsigned short, signed char, short >*/,
- 0/*addWeighted_gpu<unsigned short, signed char, int >*/,
- 0/*addWeighted_gpu<unsigned short, signed char, float >*/,
- 0/*addWeighted_gpu<unsigned short, signed char, double>*/
+ 0/*arithm::addWeighted<unsigned short, signed char, unsigned char >*/,
+ 0/*arithm::addWeighted<unsigned short, signed char, signed char >*/,
+ 0/*arithm::addWeighted<unsigned short, signed char, unsigned short>*/,
+ 0/*arithm::addWeighted<unsigned short, signed char, short >*/,
+ 0/*arithm::addWeighted<unsigned short, signed char, int >*/,
+ 0/*arithm::addWeighted<unsigned short, signed char, float >*/,
+ 0/*arithm::addWeighted<unsigned short, signed char, double>*/
},
{
- addWeighted_gpu<unsigned short, unsigned short, unsigned char >,
- addWeighted_gpu<unsigned short, unsigned short, signed char >,
- addWeighted_gpu<unsigned short, unsigned short, unsigned short>,
- addWeighted_gpu<unsigned short, unsigned short, short >,
- addWeighted_gpu<unsigned short, unsigned short, int >,
- addWeighted_gpu<unsigned short, unsigned short, float >,
- addWeighted_gpu<unsigned short, unsigned short, double>
+ arithm::addWeighted<unsigned short, unsigned short, unsigned char >,
+ arithm::addWeighted<unsigned short, unsigned short, signed char >,
+ arithm::addWeighted<unsigned short, unsigned short, unsigned short>,
+ arithm::addWeighted<unsigned short, unsigned short, short >,
+ arithm::addWeighted<unsigned short, unsigned short, int >,
+ arithm::addWeighted<unsigned short, unsigned short, float >,
+ arithm::addWeighted<unsigned short, unsigned short, double>
},
{
- addWeighted_gpu<unsigned short, short, unsigned char >,
- addWeighted_gpu<unsigned short, short, signed char >,
- addWeighted_gpu<unsigned short, short, unsigned short>,
- addWeighted_gpu<unsigned short, short, short >,
- addWeighted_gpu<unsigned short, short, int >,
- addWeighted_gpu<unsigned short, short, float >,
- addWeighted_gpu<unsigned short, short, double>
+ arithm::addWeighted<unsigned short, short, unsigned char >,
+ arithm::addWeighted<unsigned short, short, signed char >,
+ arithm::addWeighted<unsigned short, short, unsigned short>,
+ arithm::addWeighted<unsigned short, short, short >,
+ arithm::addWeighted<unsigned short, short, int >,
+ arithm::addWeighted<unsigned short, short, float >,
+ arithm::addWeighted<unsigned short, short, double>
},
{
- addWeighted_gpu<unsigned short, int, unsigned char >,
- addWeighted_gpu<unsigned short, int, signed char >,
- addWeighted_gpu<unsigned short, int, unsigned short>,
- addWeighted_gpu<unsigned short, int, short >,
- addWeighted_gpu<unsigned short, int, int >,
- addWeighted_gpu<unsigned short, int, float >,
- addWeighted_gpu<unsigned short, int, double>
+ arithm::addWeighted<unsigned short, int, unsigned char >,
+ arithm::addWeighted<unsigned short, int, signed char >,
+ arithm::addWeighted<unsigned short, int, unsigned short>,
+ arithm::addWeighted<unsigned short, int, short >,
+ arithm::addWeighted<unsigned short, int, int >,
+ arithm::addWeighted<unsigned short, int, float >,
+ arithm::addWeighted<unsigned short, int, double>
},
{
- addWeighted_gpu<unsigned short, float, unsigned char >,
- addWeighted_gpu<unsigned short, float, signed char >,
- addWeighted_gpu<unsigned short, float, unsigned short>,
- addWeighted_gpu<unsigned short, float, short >,
- addWeighted_gpu<unsigned short, float, int >,
- addWeighted_gpu<unsigned short, float, float >,
- addWeighted_gpu<unsigned short, float, double>
+ arithm::addWeighted<unsigned short, float, unsigned char >,
+ arithm::addWeighted<unsigned short, float, signed char >,
+ arithm::addWeighted<unsigned short, float, unsigned short>,
+ arithm::addWeighted<unsigned short, float, short >,
+ arithm::addWeighted<unsigned short, float, int >,
+ arithm::addWeighted<unsigned short, float, float >,
+ arithm::addWeighted<unsigned short, float, double>
},
{
- addWeighted_gpu<unsigned short, double, unsigned char >,
- addWeighted_gpu<unsigned short, double, signed char >,
- addWeighted_gpu<unsigned short, double, unsigned short>,
- addWeighted_gpu<unsigned short, double, short >,
- addWeighted_gpu<unsigned short, double, int >,
- addWeighted_gpu<unsigned short, double, float >,
- addWeighted_gpu<unsigned short, double, double>
+ arithm::addWeighted<unsigned short, double, unsigned char >,
+ arithm::addWeighted<unsigned short, double, signed char >,
+ arithm::addWeighted<unsigned short, double, unsigned short>,
+ arithm::addWeighted<unsigned short, double, short >,
+ arithm::addWeighted<unsigned short, double, int >,
+ arithm::addWeighted<unsigned short, double, float >,
+ arithm::addWeighted<unsigned short, double, double>
}
},
{
{
- 0/*addWeighted_gpu<short, unsigned char, unsigned char >*/,
- 0/*addWeighted_gpu<short, unsigned char, signed char >*/,
- 0/*addWeighted_gpu<short, unsigned char, unsigned short>*/,
- 0/*addWeighted_gpu<short, unsigned char, short >*/,
- 0/*addWeighted_gpu<short, unsigned char, int >*/,
- 0/*addWeighted_gpu<short, unsigned char, float >*/,
- 0/*addWeighted_gpu<short, unsigned char, double>*/
+ 0/*arithm::addWeighted<short, unsigned char, unsigned char >*/,
+ 0/*arithm::addWeighted<short, unsigned char, signed char >*/,
+ 0/*arithm::addWeighted<short, unsigned char, unsigned short>*/,
+ 0/*arithm::addWeighted<short, unsigned char, short >*/,
+ 0/*arithm::addWeighted<short, unsigned char, int >*/,
+ 0/*arithm::addWeighted<short, unsigned char, float >*/,
+ 0/*arithm::addWeighted<short, unsigned char, double>*/
},
{
- 0/*addWeighted_gpu<short, signed char, unsigned char >*/,
- 0/*addWeighted_gpu<short, signed char, signed char >*/,
- 0/*addWeighted_gpu<short, signed char, unsigned short>*/,
- 0/*addWeighted_gpu<short, signed char, short >*/,
- 0/*addWeighted_gpu<short, signed char, int >*/,
- 0/*addWeighted_gpu<short, signed char, float >*/,
- 0/*addWeighted_gpu<short, signed char, double>*/
+ 0/*arithm::addWeighted<short, signed char, unsigned char >*/,
+ 0/*arithm::addWeighted<short, signed char, signed char >*/,
+ 0/*arithm::addWeighted<short, signed char, unsigned short>*/,
+ 0/*arithm::addWeighted<short, signed char, short >*/,
+ 0/*arithm::addWeighted<short, signed char, int >*/,
+ 0/*arithm::addWeighted<short, signed char, float >*/,
+ 0/*arithm::addWeighted<short, signed char, double>*/
},
{
- 0/*addWeighted_gpu<short, unsigned short, unsigned char >*/,
- 0/*addWeighted_gpu<short, unsigned short, signed char >*/,
- 0/*addWeighted_gpu<short, unsigned short, unsigned short>*/,
- 0/*addWeighted_gpu<short, unsigned short, short >*/,
- 0/*addWeighted_gpu<short, unsigned short, int >*/,
- 0/*addWeighted_gpu<short, unsigned short, float >*/,
- 0/*addWeighted_gpu<short, unsigned short, double>*/
+ 0/*arithm::addWeighted<short, unsigned short, unsigned char >*/,
+ 0/*arithm::addWeighted<short, unsigned short, signed char >*/,
+ 0/*arithm::addWeighted<short, unsigned short, unsigned short>*/,
+ 0/*arithm::addWeighted<short, unsigned short, short >*/,
+ 0/*arithm::addWeighted<short, unsigned short, int >*/,
+ 0/*arithm::addWeighted<short, unsigned short, float >*/,
+ 0/*arithm::addWeighted<short, unsigned short, double>*/
},
{
- addWeighted_gpu<short, short, unsigned char >,
- addWeighted_gpu<short, short, signed char >,
- addWeighted_gpu<short, short, unsigned short>,
- addWeighted_gpu<short, short, short >,
- addWeighted_gpu<short, short, int >,
- addWeighted_gpu<short, short, float >,
- addWeighted_gpu<short, short, double>
+ arithm::addWeighted<short, short, unsigned char >,
+ arithm::addWeighted<short, short, signed char >,
+ arithm::addWeighted<short, short, unsigned short>,
+ arithm::addWeighted<short, short, short >,
+ arithm::addWeighted<short, short, int >,
+ arithm::addWeighted<short, short, float >,
+ arithm::addWeighted<short, short, double>
},
{
- addWeighted_gpu<short, int, unsigned char >,
- addWeighted_gpu<short, int, signed char >,
- addWeighted_gpu<short, int, unsigned short>,
- addWeighted_gpu<short, int, short >,
- addWeighted_gpu<short, int, int >,
- addWeighted_gpu<short, int, float >,
- addWeighted_gpu<short, int, double>
+ arithm::addWeighted<short, int, unsigned char >,
+ arithm::addWeighted<short, int, signed char >,
+ arithm::addWeighted<short, int, unsigned short>,
+ arithm::addWeighted<short, int, short >,
+ arithm::addWeighted<short, int, int >,
+ arithm::addWeighted<short, int, float >,
+ arithm::addWeighted<short, int, double>
},
{
- addWeighted_gpu<short, float, unsigned char >,
- addWeighted_gpu<short, float, signed char >,
- addWeighted_gpu<short, float, unsigned short>,
- addWeighted_gpu<short, float, short >,
- addWeighted_gpu<short, float, int >,
- addWeighted_gpu<short, float, float >,
- addWeighted_gpu<short, float, double>
+ arithm::addWeighted<short, float, unsigned char >,
+ arithm::addWeighted<short, float, signed char >,
+ arithm::addWeighted<short, float, unsigned short>,
+ arithm::addWeighted<short, float, short >,
+ arithm::addWeighted<short, float, int >,
+ arithm::addWeighted<short, float, float >,
+ arithm::addWeighted<short, float, double>
},
{
- addWeighted_gpu<short, double, unsigned char >,
- addWeighted_gpu<short, double, signed char >,
- addWeighted_gpu<short, double, unsigned short>,
- addWeighted_gpu<short, double, short >,
- addWeighted_gpu<short, double, int >,
- addWeighted_gpu<short, double, float >,
- addWeighted_gpu<short, double, double>
+ arithm::addWeighted<short, double, unsigned char >,
+ arithm::addWeighted<short, double, signed char >,
+ arithm::addWeighted<short, double, unsigned short>,
+ arithm::addWeighted<short, double, short >,
+ arithm::addWeighted<short, double, int >,
+ arithm::addWeighted<short, double, float >,
+ arithm::addWeighted<short, double, double>
}
},
{
{
- 0/*addWeighted_gpu<int, unsigned char, unsigned char >*/,
- 0/*addWeighted_gpu<int, unsigned char, signed char >*/,
- 0/*addWeighted_gpu<int, unsigned char, unsigned short>*/,
- 0/*addWeighted_gpu<int, unsigned char, short >*/,
- 0/*addWeighted_gpu<int, unsigned char, int >*/,
- 0/*addWeighted_gpu<int, unsigned char, float >*/,
- 0/*addWeighted_gpu<int, unsigned char, double>*/
+ 0/*arithm::addWeighted<int, unsigned char, unsigned char >*/,
+ 0/*arithm::addWeighted<int, unsigned char, signed char >*/,
+ 0/*arithm::addWeighted<int, unsigned char, unsigned short>*/,
+ 0/*arithm::addWeighted<int, unsigned char, short >*/,
+ 0/*arithm::addWeighted<int, unsigned char, int >*/,
+ 0/*arithm::addWeighted<int, unsigned char, float >*/,
+ 0/*arithm::addWeighted<int, unsigned char, double>*/
},
{
- 0/*addWeighted_gpu<int, signed char, unsigned char >*/,
- 0/*addWeighted_gpu<int, signed char, signed char >*/,
- 0/*addWeighted_gpu<int, signed char, unsigned short>*/,
- 0/*addWeighted_gpu<int, signed char, short >*/,
- 0/*addWeighted_gpu<int, signed char, int >*/,
- 0/*addWeighted_gpu<int, signed char, float >*/,
- 0/*addWeighted_gpu<int, signed char, double>*/
+ 0/*arithm::addWeighted<int, signed char, unsigned char >*/,
+ 0/*arithm::addWeighted<int, signed char, signed char >*/,
+ 0/*arithm::addWeighted<int, signed char, unsigned short>*/,
+ 0/*arithm::addWeighted<int, signed char, short >*/,
+ 0/*arithm::addWeighted<int, signed char, int >*/,
+ 0/*arithm::addWeighted<int, signed char, float >*/,
+ 0/*arithm::addWeighted<int, signed char, double>*/
},
{
- 0/*addWeighted_gpu<int, unsigned short, unsigned char >*/,
- 0/*addWeighted_gpu<int, unsigned short, signed char >*/,
- 0/*addWeighted_gpu<int, unsigned short, unsigned short>*/,
- 0/*addWeighted_gpu<int, unsigned short, short >*/,
- 0/*addWeighted_gpu<int, unsigned short, int >*/,
- 0/*addWeighted_gpu<int, unsigned short, float >*/,
- 0/*addWeighted_gpu<int, unsigned short, double>*/
+ 0/*arithm::addWeighted<int, unsigned short, unsigned char >*/,
+ 0/*arithm::addWeighted<int, unsigned short, signed char >*/,
+ 0/*arithm::addWeighted<int, unsigned short, unsigned short>*/,
+ 0/*arithm::addWeighted<int, unsigned short, short >*/,
+ 0/*arithm::addWeighted<int, unsigned short, int >*/,
+ 0/*arithm::addWeighted<int, unsigned short, float >*/,
+ 0/*arithm::addWeighted<int, unsigned short, double>*/
},
{
- 0/*addWeighted_gpu<int, short, unsigned char >*/,
- 0/*addWeighted_gpu<int, short, signed char >*/,
- 0/*addWeighted_gpu<int, short, unsigned short>*/,
- 0/*addWeighted_gpu<int, short, short >*/,
- 0/*addWeighted_gpu<int, short, int >*/,
- 0/*addWeighted_gpu<int, short, float >*/,
- 0/*addWeighted_gpu<int, short, double>*/
+ 0/*arithm::addWeighted<int, short, unsigned char >*/,
+ 0/*arithm::addWeighted<int, short, signed char >*/,
+ 0/*arithm::addWeighted<int, short, unsigned short>*/,
+ 0/*arithm::addWeighted<int, short, short >*/,
+ 0/*arithm::addWeighted<int, short, int >*/,
+ 0/*arithm::addWeighted<int, short, float >*/,
+ 0/*arithm::addWeighted<int, short, double>*/
},
{
- addWeighted_gpu<int, int, unsigned char >,
- addWeighted_gpu<int, int, signed char >,
- addWeighted_gpu<int, int, unsigned short>,
- addWeighted_gpu<int, int, short >,
- addWeighted_gpu<int, int, int >,
- addWeighted_gpu<int, int, float >,
- addWeighted_gpu<int, int, double>
+ arithm::addWeighted<int, int, unsigned char >,
+ arithm::addWeighted<int, int, signed char >,
+ arithm::addWeighted<int, int, unsigned short>,
+ arithm::addWeighted<int, int, short >,
+ arithm::addWeighted<int, int, int >,
+ arithm::addWeighted<int, int, float >,
+ arithm::addWeighted<int, int, double>
},
{
- addWeighted_gpu<int, float, unsigned char >,
- addWeighted_gpu<int, float, signed char >,
- addWeighted_gpu<int, float, unsigned short>,
- addWeighted_gpu<int, float, short >,
- addWeighted_gpu<int, float, int >,
- addWeighted_gpu<int, float, float >,
- addWeighted_gpu<int, float, double>
+ arithm::addWeighted<int, float, unsigned char >,
+ arithm::addWeighted<int, float, signed char >,
+ arithm::addWeighted<int, float, unsigned short>,
+ arithm::addWeighted<int, float, short >,
+ arithm::addWeighted<int, float, int >,
+ arithm::addWeighted<int, float, float >,
+ arithm::addWeighted<int, float, double>
},
{
- addWeighted_gpu<int, double, unsigned char >,
- addWeighted_gpu<int, double, signed char >,
- addWeighted_gpu<int, double, unsigned short>,
- addWeighted_gpu<int, double, short >,
- addWeighted_gpu<int, double, int >,
- addWeighted_gpu<int, double, float >,
- addWeighted_gpu<int, double, double>
+ arithm::addWeighted<int, double, unsigned char >,
+ arithm::addWeighted<int, double, signed char >,
+ arithm::addWeighted<int, double, unsigned short>,
+ arithm::addWeighted<int, double, short >,
+ arithm::addWeighted<int, double, int >,
+ arithm::addWeighted<int, double, float >,
+ arithm::addWeighted<int, double, double>
}
},
{
{
- 0/*addWeighted_gpu<float, unsigned char, unsigned char >*/,
- 0/*addWeighted_gpu<float, unsigned char, signed char >*/,
- 0/*addWeighted_gpu<float, unsigned char, unsigned short>*/,
- 0/*addWeighted_gpu<float, unsigned char, short >*/,
- 0/*addWeighted_gpu<float, unsigned char, int >*/,
- 0/*addWeighted_gpu<float, unsigned char, float >*/,
- 0/*addWeighted_gpu<float, unsigned char, double>*/
+ 0/*arithm::addWeighted<float, unsigned char, unsigned char >*/,
+ 0/*arithm::addWeighted<float, unsigned char, signed char >*/,
+ 0/*arithm::addWeighted<float, unsigned char, unsigned short>*/,
+ 0/*arithm::addWeighted<float, unsigned char, short >*/,
+ 0/*arithm::addWeighted<float, unsigned char, int >*/,
+ 0/*arithm::addWeighted<float, unsigned char, float >*/,
+ 0/*arithm::addWeighted<float, unsigned char, double>*/
},
{
- 0/*addWeighted_gpu<float, signed char, unsigned char >*/,
- 0/*addWeighted_gpu<float, signed char, signed char >*/,
- 0/*addWeighted_gpu<float, signed char, unsigned short>*/,
- 0/*addWeighted_gpu<float, signed char, short >*/,
- 0/*addWeighted_gpu<float, signed char, int >*/,
- 0/*addWeighted_gpu<float, signed char, float >*/,
- 0/*addWeighted_gpu<float, signed char, double>*/
+ 0/*arithm::addWeighted<float, signed char, unsigned char >*/,
+ 0/*arithm::addWeighted<float, signed char, signed char >*/,
+ 0/*arithm::addWeighted<float, signed char, unsigned short>*/,
+ 0/*arithm::addWeighted<float, signed char, short >*/,
+ 0/*arithm::addWeighted<float, signed char, int >*/,
+ 0/*arithm::addWeighted<float, signed char, float >*/,
+ 0/*arithm::addWeighted<float, signed char, double>*/
},
{
- 0/*addWeighted_gpu<float, unsigned short, unsigned char >*/,
- 0/*addWeighted_gpu<float, unsigned short, signed char >*/,
- 0/*addWeighted_gpu<float, unsigned short, unsigned short>*/,
- 0/*addWeighted_gpu<float, unsigned short, short >*/,
- 0/*addWeighted_gpu<float, unsigned short, int >*/,
- 0/*addWeighted_gpu<float, unsigned short, float >*/,
- 0/*addWeighted_gpu<float, unsigned short, double>*/
+ 0/*arithm::addWeighted<float, unsigned short, unsigned char >*/,
+ 0/*arithm::addWeighted<float, unsigned short, signed char >*/,
+ 0/*arithm::addWeighted<float, unsigned short, unsigned short>*/,
+ 0/*arithm::addWeighted<float, unsigned short, short >*/,
+ 0/*arithm::addWeighted<float, unsigned short, int >*/,
+ 0/*arithm::addWeighted<float, unsigned short, float >*/,
+ 0/*arithm::addWeighted<float, unsigned short, double>*/
},
{
- 0/*addWeighted_gpu<float, short, unsigned char >*/,
- 0/*addWeighted_gpu<float, short, signed char >*/,
- 0/*addWeighted_gpu<float, short, unsigned short>*/,
- 0/*addWeighted_gpu<float, short, short >*/,
- 0/*addWeighted_gpu<float, short, int >*/,
- 0/*addWeighted_gpu<float, short, float >*/,
- 0/*addWeighted_gpu<float, short, double>*/
+ 0/*arithm::addWeighted<float, short, unsigned char >*/,
+ 0/*arithm::addWeighted<float, short, signed char >*/,
+ 0/*arithm::addWeighted<float, short, unsigned short>*/,
+ 0/*arithm::addWeighted<float, short, short >*/,
+ 0/*arithm::addWeighted<float, short, int >*/,
+ 0/*arithm::addWeighted<float, short, float >*/,
+ 0/*arithm::addWeighted<float, short, double>*/
},
{
- 0/*addWeighted_gpu<float, int, unsigned char >*/,
- 0/*addWeighted_gpu<float, int, signed char >*/,
- 0/*addWeighted_gpu<float, int, unsigned short>*/,
- 0/*addWeighted_gpu<float, int, short >*/,
- 0/*addWeighted_gpu<float, int, int >*/,
- 0/*addWeighted_gpu<float, int, float >*/,
- 0/*addWeighted_gpu<float, int, double>*/
+ 0/*arithm::addWeighted<float, int, unsigned char >*/,
+ 0/*arithm::addWeighted<float, int, signed char >*/,
+ 0/*arithm::addWeighted<float, int, unsigned short>*/,
+ 0/*arithm::addWeighted<float, int, short >*/,
+ 0/*arithm::addWeighted<float, int, int >*/,
+ 0/*arithm::addWeighted<float, int, float >*/,
+ 0/*arithm::addWeighted<float, int, double>*/
},
{
- addWeighted_gpu<float, float, unsigned char >,
- addWeighted_gpu<float, float, signed char >,
- addWeighted_gpu<float, float, unsigned short>,
- addWeighted_gpu<float, float, short >,
- addWeighted_gpu<float, float, int >,
- addWeighted_gpu<float, float, float >,
- addWeighted_gpu<float, float, double>
+ arithm::addWeighted<float, float, unsigned char >,
+ arithm::addWeighted<float, float, signed char >,
+ arithm::addWeighted<float, float, unsigned short>,
+ arithm::addWeighted<float, float, short >,
+ arithm::addWeighted<float, float, int >,
+ arithm::addWeighted<float, float, float >,
+ arithm::addWeighted<float, float, double>
},
{
- addWeighted_gpu<float, double, unsigned char >,
- addWeighted_gpu<float, double, signed char >,
- addWeighted_gpu<float, double, unsigned short>,
- addWeighted_gpu<float, double, short >,
- addWeighted_gpu<float, double, int >,
- addWeighted_gpu<float, double, float >,
- addWeighted_gpu<float, double, double>
+ arithm::addWeighted<float, double, unsigned char >,
+ arithm::addWeighted<float, double, signed char >,
+ arithm::addWeighted<float, double, unsigned short>,
+ arithm::addWeighted<float, double, short >,
+ arithm::addWeighted<float, double, int >,
+ arithm::addWeighted<float, double, float >,
+ arithm::addWeighted<float, double, double>
}
},
{
{
- 0/*addWeighted_gpu<double, unsigned char, unsigned char >*/,
- 0/*addWeighted_gpu<double, unsigned char, signed char >*/,
- 0/*addWeighted_gpu<double, unsigned char, unsigned short>*/,
- 0/*addWeighted_gpu<double, unsigned char, short >*/,
- 0/*addWeighted_gpu<double, unsigned char, int >*/,
- 0/*addWeighted_gpu<double, unsigned char, float >*/,
- 0/*addWeighted_gpu<double, unsigned char, double>*/
+ 0/*arithm::addWeighted<double, unsigned char, unsigned char >*/,
+ 0/*arithm::addWeighted<double, unsigned char, signed char >*/,
+ 0/*arithm::addWeighted<double, unsigned char, unsigned short>*/,
+ 0/*arithm::addWeighted<double, unsigned char, short >*/,
+ 0/*arithm::addWeighted<double, unsigned char, int >*/,
+ 0/*arithm::addWeighted<double, unsigned char, float >*/,
+ 0/*arithm::addWeighted<double, unsigned char, double>*/
},
{
- 0/*addWeighted_gpu<double, signed char, unsigned char >*/,
- 0/*addWeighted_gpu<double, signed char, signed char >*/,
- 0/*addWeighted_gpu<double, signed char, unsigned short>*/,
- 0/*addWeighted_gpu<double, signed char, short >*/,
- 0/*addWeighted_gpu<double, signed char, int >*/,
- 0/*addWeighted_gpu<double, signed char, float >*/,
- 0/*addWeighted_gpu<double, signed char, double>*/
+ 0/*arithm::addWeighted<double, signed char, unsigned char >*/,
+ 0/*arithm::addWeighted<double, signed char, signed char >*/,
+ 0/*arithm::addWeighted<double, signed char, unsigned short>*/,
+ 0/*arithm::addWeighted<double, signed char, short >*/,
+ 0/*arithm::addWeighted<double, signed char, int >*/,
+ 0/*arithm::addWeighted<double, signed char, float >*/,
+ 0/*arithm::addWeighted<double, signed char, double>*/
},
{
- 0/*addWeighted_gpu<double, unsigned short, unsigned char >*/,
- 0/*addWeighted_gpu<double, unsigned short, signed char >*/,
- 0/*addWeighted_gpu<double, unsigned short, unsigned short>*/,
- 0/*addWeighted_gpu<double, unsigned short, short >*/,
- 0/*addWeighted_gpu<double, unsigned short, int >*/,
- 0/*addWeighted_gpu<double, unsigned short, float >*/,
- 0/*addWeighted_gpu<double, unsigned short, double>*/
+ 0/*arithm::addWeighted<double, unsigned short, unsigned char >*/,
+ 0/*arithm::addWeighted<double, unsigned short, signed char >*/,
+ 0/*arithm::addWeighted<double, unsigned short, unsigned short>*/,
+ 0/*arithm::addWeighted<double, unsigned short, short >*/,
+ 0/*arithm::addWeighted<double, unsigned short, int >*/,
+ 0/*arithm::addWeighted<double, unsigned short, float >*/,
+ 0/*arithm::addWeighted<double, unsigned short, double>*/
},
{
- 0/*addWeighted_gpu<double, short, unsigned char >*/,
- 0/*addWeighted_gpu<double, short, signed char >*/,
- 0/*addWeighted_gpu<double, short, unsigned short>*/,
- 0/*addWeighted_gpu<double, short, short >*/,
- 0/*addWeighted_gpu<double, short, int >*/,
- 0/*addWeighted_gpu<double, short, float >*/,
- 0/*addWeighted_gpu<double, short, double>*/
+ 0/*arithm::addWeighted<double, short, unsigned char >*/,
+ 0/*arithm::addWeighted<double, short, signed char >*/,
+ 0/*arithm::addWeighted<double, short, unsigned short>*/,
+ 0/*arithm::addWeighted<double, short, short >*/,
+ 0/*arithm::addWeighted<double, short, int >*/,
+ 0/*arithm::addWeighted<double, short, float >*/,
+ 0/*arithm::addWeighted<double, short, double>*/
},
{
- 0/*addWeighted_gpu<double, int, unsigned char >*/,
- 0/*addWeighted_gpu<double, int, signed char >*/,
- 0/*addWeighted_gpu<double, int, unsigned short>*/,
- 0/*addWeighted_gpu<double, int, short >*/,
- 0/*addWeighted_gpu<double, int, int >*/,
- 0/*addWeighted_gpu<double, int, float >*/,
- 0/*addWeighted_gpu<double, int, double>*/
+ 0/*arithm::addWeighted<double, int, unsigned char >*/,
+ 0/*arithm::addWeighted<double, int, signed char >*/,
+ 0/*arithm::addWeighted<double, int, unsigned short>*/,
+ 0/*arithm::addWeighted<double, int, short >*/,
+ 0/*arithm::addWeighted<double, int, int >*/,
+ 0/*arithm::addWeighted<double, int, float >*/,
+ 0/*arithm::addWeighted<double, int, double>*/
},
{
- 0/*addWeighted_gpu<double, float, unsigned char >*/,
- 0/*addWeighted_gpu<double, float, signed char >*/,
- 0/*addWeighted_gpu<double, float, unsigned short>*/,
- 0/*addWeighted_gpu<double, float, short >*/,
- 0/*addWeighted_gpu<double, float, int >*/,
- 0/*addWeighted_gpu<double, float, float >*/,
- 0/*addWeighted_gpu<double, float, double>*/
+ 0/*arithm::addWeighted<double, float, unsigned char >*/,
+ 0/*arithm::addWeighted<double, float, signed char >*/,
+ 0/*arithm::addWeighted<double, float, unsigned short>*/,
+ 0/*arithm::addWeighted<double, float, short >*/,
+ 0/*arithm::addWeighted<double, float, int >*/,
+ 0/*arithm::addWeighted<double, float, float >*/,
+ 0/*arithm::addWeighted<double, float, double>*/
},
{
- addWeighted_gpu<double, double, unsigned char >,
- addWeighted_gpu<double, double, signed char >,
- addWeighted_gpu<double, double, unsigned short>,
- addWeighted_gpu<double, double, short >,
- addWeighted_gpu<double, double, int >,
- addWeighted_gpu<double, double, float >,
- addWeighted_gpu<double, double, double>
+ arithm::addWeighted<double, double, unsigned char >,
+ arithm::addWeighted<double, double, signed char >,
+ arithm::addWeighted<double, double, unsigned short>,
+ arithm::addWeighted<double, double, short >,
+ arithm::addWeighted<double, double, int >,
+ arithm::addWeighted<double, double, float >,
+ arithm::addWeighted<double, double, double>
}
}
};
- CV_Assert(src1.size() == src2.size());
- CV_Assert(src1.type() == src2.type() || (dtype >= 0 && src1.channels() == src2.channels()));
-
- dtype = dtype >= 0 ? CV_MAKETYPE(dtype, src1.channels()) : src1.type();
+ int sdepth1 = src1.depth();
+ int sdepth2 = src2.depth();
+ ddepth = ddepth >= 0 ? CV_MAT_DEPTH(ddepth) : std::max(sdepth1, sdepth2);
+ const int cn = src1.channels();
- CV_Assert(src1.depth() <= CV_64F && src2.depth() <= CV_64F && CV_MAT_DEPTH(dtype) <= CV_64F);
+ CV_Assert( src2.size() == src1.size() && src2.channels() == cn );
+ CV_Assert( sdepth1 <= CV_64F && sdepth2 <= CV_64F && ddepth <= CV_64F );
- if (src1.depth() == CV_64F || src2.depth() == CV_64F || CV_MAT_DEPTH(dtype) == CV_64F)
+ if (sdepth1 == CV_64F || sdepth2 == CV_64F || ddepth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
- dst.create(src1.size(), dtype);
+ dst.create(src1.size(), CV_MAKE_TYPE(ddepth, cn));
- const GpuMat* psrc1 = &src1;
- const GpuMat* psrc2 = &src2;
+ PtrStepSzb src1_(src1.rows, src1.cols * cn, src1.data, src1.step);
+ PtrStepSzb src2_(src1.rows, src1.cols * cn, src2.data, src2.step);
+ PtrStepSzb dst_(src1.rows, src1.cols * cn, dst.data, dst.step);
- if (src1.depth() > src2.depth())
+ if (sdepth1 > sdepth2)
{
- std::swap(psrc1, psrc2);
+ std::swap(src1_.data, src2_.data);
+ std::swap(src1_.step, src2_.step);
std::swap(alpha, beta);
+ std::swap(sdepth1, sdepth2);
}
- const func_t func = funcs[psrc1->depth()][psrc2->depth()][dst.depth()];
+ const func_t func = funcs[sdepth1][sdepth2][ddepth];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of source and destination types");
- func(psrc1->reshape(1), alpha, psrc2->reshape(1), beta, gamma, dst.reshape(1), StreamAccessor::getStream(stream));
+ func(src1_, alpha, src2_, beta, gamma, dst_, StreamAccessor::getStream(stream));
}
#endif
{
void calcDiffHistogram(const cv::gpu::GpuMat& prevFrame, const cv::gpu::GpuMat& curFrame, cv::gpu::GpuMat& hist, cv::gpu::GpuMat& histBuf)
{
- typedef void (*func_t)(cv::gpu::PtrStepSzb prevFrame, cv::gpu::PtrStepSzb curFrame, unsigned int* hist0, unsigned int* hist1, unsigned int* hist2, unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2, int cc, cudaStream_t stream);
+ typedef void (*func_t)(cv::gpu::PtrStepSzb prevFrame, cv::gpu::PtrStepSzb curFrame, unsigned int* hist0, unsigned int* hist1, unsigned int* hist2, unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2, bool cc20, cudaStream_t stream);
static const func_t funcs[4][4] =
{
{0,0,0,0},
hist.create(3, 256, CV_32SC1);
histBuf.create(3, bgfg::PARTIAL_HISTOGRAM_COUNT * bgfg::HISTOGRAM_BIN_COUNT, CV_32SC1);
- cv::gpu::DeviceInfo devInfo;
- int cc = devInfo.majorVersion() * 10 + devInfo.minorVersion();
-
funcs[prevFrame.channels() - 1][curFrame.channels() - 1](
prevFrame, curFrame,
hist.ptr<unsigned int>(0), hist.ptr<unsigned int>(1), hist.ptr<unsigned int>(2),
histBuf.ptr<unsigned int>(0), histBuf.ptr<unsigned int>(1), histBuf.ptr<unsigned int>(2),
- cc, 0);
+ cv::gpu::deviceSupports(cv::gpu::FEATURE_SET_COMPUTE_20), 0);
}
void calcRelativeVariance(unsigned int hist[3 * 256], double relativeVariance[3][bgfg::HISTOGRAM_BIN_COUNT])
size_t total = all_contours.size();
- _contours.create(total, 1, 0, -1, true);
+ _contours.create((int) total, 1, 0, -1, true);
cv::SeqIterator<CvSeq*> it = all_contours.begin();
for (size_t i = 0; i < total; ++i, ++it)
{
CvSeq* c = *it;
((CvContour*)c)->color = (int)i;
- _contours.create((int)c->total, 1, CV_32SC2, i, true);
- cv::Mat ci = _contours.getMat(i);
+ _contours.create((int)c->total, 1, CV_32SC2, (int)i, true);
+ cv::Mat ci = _contours.getMat((int)i);
CV_Assert( ci.isContinuous() );
cvCvtSeqToArray(c, ci.data);
}
ensureSizeIsEnough(1, maxCircles, CV_32FC3, circles);
- DeviceInfo devInfo;
const int circlesCount = circlesAccumRadius_gpu(centers, centersCount, srcPoints, pointsCount, circles.ptr<float3>(), maxCircles,
- dp, minRadius, maxRadius, votesThreshold, devInfo.supports(FEATURE_SET_COMPUTE_20));
+ dp, minRadius, maxRadius, votesThreshold, deviceSupports(FEATURE_SET_COMPUTE_20));
if (circlesCount > 0)
circles.cols = circlesCount;
const func_t func = funcs[dx.depth()];
CV_Assert(func != 0);
- edgePointList.cols = edgePointList.step / sizeof(int);
+ edgePointList.cols = (int) (edgePointList.step / sizeof(int));
ensureSizeIsEnough(2, edges.size().area(), CV_32SC1, edgePointList);
edgePointList.cols = func(edges, dx, dy, edgePointList.ptr<unsigned int>(0), edgePointList.ptr<float>(1));
cudaStream_t stream = StreamAccessor::getStream(s);
- DeviceInfo info;
cv::Size whole;
cv::Point offset;
src.locateROI(whole, offset);
- if (info.supports(WARP_SHUFFLE_FUNCTIONS) && src.cols <= 2048
- && offset.x % 16 == 0 && ((src.cols + 63) / 64) * 64 <= (src.step - offset.x))
+ if (deviceSupports(WARP_SHUFFLE_FUNCTIONS) && src.cols <= 2048
+ && offset.x % 16 == 0 && ((src.cols + 63) / 64) * 64 <= (static_cast<int>(src.step) - offset.x))
{
ensureSizeIsEnough(((src.rows + 7) / 8) * 8, ((src.cols + 63) / 64) * 64, CV_32SC1, buffer);
hist_callers[src.depth()](src, hist, levels, buf, StreamAccessor::getStream(stream));
}
-namespace cv { namespace gpu { namespace device
-{
- namespace hist
- {
- void histogram256_gpu(PtrStepSzb src, int* hist, unsigned int* buf, cudaStream_t stream);
-
- const int PARTIAL_HISTOGRAM256_COUNT = 240;
- const int HISTOGRAM256_BIN_COUNT = 256;
-
- void equalizeHist_gpu(PtrStepSzb src, PtrStepSzb dst, const int* lut, cudaStream_t stream);
- }
-}}}
-
-void cv::gpu::calcHist(const GpuMat& src, GpuMat& hist, Stream& stream)
+namespace hist
{
- GpuMat buf;
- calcHist(src, hist, buf, stream);
+ void histogram256(PtrStepSzb src, int* hist, cudaStream_t stream);
+ void equalizeHist(PtrStepSzb src, PtrStepSzb dst, const int* lut, cudaStream_t stream);
}
-void cv::gpu::calcHist(const GpuMat& src, GpuMat& hist, GpuMat& buf, Stream& stream)
+void cv::gpu::calcHist(const GpuMat& src, GpuMat& hist, Stream& stream)
{
- using namespace ::cv::gpu::device::hist;
-
CV_Assert(src.type() == CV_8UC1);
hist.create(1, 256, CV_32SC1);
+ hist.setTo(Scalar::all(0));
- ensureSizeIsEnough(1, PARTIAL_HISTOGRAM256_COUNT * HISTOGRAM256_BIN_COUNT, CV_32SC1, buf);
+ hist::histogram256(src, hist.ptr<int>(), StreamAccessor::getStream(stream));
+}
- histogram256_gpu(src, hist.ptr<int>(), buf.ptr<unsigned int>(), StreamAccessor::getStream(stream));
+void cv::gpu::calcHist(const GpuMat& src, GpuMat& hist, GpuMat& buf, Stream& stream)
+{
+ (void) buf;
+ calcHist(src, hist, stream);
}
void cv::gpu::equalizeHist(const GpuMat& src, GpuMat& dst, Stream& stream)
void cv::gpu::equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat& buf, Stream& s)
{
- using namespace ::cv::gpu::device::hist;
-
CV_Assert(src.type() == CV_8UC1);
dst.create(src.size(), src.type());
int intBufSize;
nppSafeCall( nppsIntegralGetBufferSize_32s(256, &intBufSize) );
- int bufSize = static_cast<int>(std::max(256 * 240 * sizeof(int), intBufSize + 256 * sizeof(int)));
-
- ensureSizeIsEnough(1, bufSize, CV_8UC1, buf);
+ ensureSizeIsEnough(1, intBufSize + 256 * sizeof(int), CV_8UC1, buf);
- GpuMat histBuf(1, 256 * 240, CV_32SC1, buf.ptr());
GpuMat intBuf(1, intBufSize, CV_8UC1, buf.ptr());
GpuMat lut(1, 256, CV_32S, buf.ptr() + intBufSize);
- calcHist(src, hist, histBuf, s);
+ calcHist(src, hist, s);
cudaStream_t stream = StreamAccessor::getStream(s);
nppSafeCall( nppsIntegral_32s(hist.ptr<Npp32s>(), lut.ptr<Npp32s>(), 256, intBuf.ptr<Npp8u>()) );
- if (stream == 0)
- cudaSafeCall( cudaDeviceSynchronize() );
-
- equalizeHist_gpu(src, dst, lut.ptr<int>(), stream);
+ hist::equalizeHist(src, dst, lut.ptr<int>(), stream);
}
////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////
// Canny
-cv::gpu::CannyBuf::CannyBuf(const GpuMat& dx_, const GpuMat& dy_) : dx(dx_), dy(dy_)
+cv::gpu::CannyBuf::CannyBuf(const GpuMat& dx_, const GpuMat& dy_)
{
- CV_Assert(dx_.type() == CV_32SC1 && dy_.type() == CV_32SC1 && dx_.size() == dy_.size());
-
- create(dx_.size(), -1);
+ (void) dx_;
+ (void) dy_;
}
void cv::gpu::CannyBuf::create(const Size& image_size, int apperture_size)
{
- ensureSizeIsEnough(image_size, CV_32SC1, dx);
- ensureSizeIsEnough(image_size, CV_32SC1, dy);
-
- if (apperture_size == 3)
- {
- ensureSizeIsEnough(image_size, CV_32SC1, dx_buf);
- ensureSizeIsEnough(image_size, CV_32SC1, dy_buf);
- }
- else if(apperture_size > 0)
+ if (apperture_size > 0)
{
- if (!filterDX)
+ ensureSizeIsEnough(image_size, CV_32SC1, dx);
+ ensureSizeIsEnough(image_size, CV_32SC1, dy);
+
+ if (apperture_size != 3)
+ {
filterDX = createDerivFilter_GPU(CV_8UC1, CV_32S, 1, 0, apperture_size, BORDER_REPLICATE);
- if (!filterDY)
filterDY = createDerivFilter_GPU(CV_8UC1, CV_32S, 0, 1, apperture_size, BORDER_REPLICATE);
+ }
}
- ensureSizeIsEnough(image_size.height + 2, image_size.width + 2, CV_32FC1, edgeBuf);
+ ensureSizeIsEnough(image_size, CV_32FC1, edgeBuf);
+ ensureSizeIsEnough(image_size, CV_32SC1, dx_buf);
- ensureSizeIsEnough(1, image_size.width * image_size.height, CV_16UC2, trackBuf1);
- ensureSizeIsEnough(1, image_size.width * image_size.height, CV_16UC2, trackBuf2);
+ ensureSizeIsEnough(1, image_size.area(), CV_16UC2, trackBuf1);
+ ensureSizeIsEnough(1, image_size.area(), CV_16UC2, trackBuf2);
}
void cv::gpu::CannyBuf::release()
trackBuf2.release();
}
-namespace cv { namespace gpu { namespace device
+namespace canny
{
- namespace canny
- {
- void calcSobelRowPass_gpu(PtrStepb src, PtrStepi dx_buf, PtrStepi dy_buf, int rows, int cols);
-
- void calcMagnitude_gpu(PtrStepi dx_buf, PtrStepi dy_buf, PtrStepi dx, PtrStepi dy, PtrStepf mag, int rows, int cols, bool L2Grad);
- void calcMagnitude_gpu(PtrStepi dx, PtrStepi dy, PtrStepf mag, int rows, int cols, bool L2Grad);
+ void calcMagnitude(PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, bool L2Grad);
+ void calcMagnitude(PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, bool L2Grad);
- void calcMap_gpu(PtrStepi dx, PtrStepi dy, PtrStepf mag, PtrStepi map, int rows, int cols, float low_thresh, float high_thresh);
+ void calcMap(PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, PtrStepSzi map, float low_thresh, float high_thresh);
- void edgesHysteresisLocal_gpu(PtrStepi map, ushort2* st1, int rows, int cols);
+ void edgesHysteresisLocal(PtrStepSzi map, ushort2* st1);
- void edgesHysteresisGlobal_gpu(PtrStepi map, ushort2* st1, ushort2* st2, int rows, int cols);
+ void edgesHysteresisGlobal(PtrStepSzi map, ushort2* st1, ushort2* st2);
- void getEdges_gpu(PtrStepi map, PtrStepb dst, int rows, int cols);
- }
-}}}
+ void getEdges(PtrStepSzi map, PtrStepSzb dst);
+}
namespace
{
- void CannyCaller(CannyBuf& buf, GpuMat& dst, float low_thresh, float high_thresh)
+ void CannyCaller(const GpuMat& dx, const GpuMat& dy, CannyBuf& buf, GpuMat& dst, float low_thresh, float high_thresh)
{
- using namespace ::cv::gpu::device::canny;
+ using namespace canny;
- calcMap_gpu(buf.dx, buf.dy, buf.edgeBuf, buf.edgeBuf, dst.rows, dst.cols, low_thresh, high_thresh);
+ calcMap(dx, dy, buf.edgeBuf, buf.dx_buf, low_thresh, high_thresh);
- edgesHysteresisLocal_gpu(buf.edgeBuf, buf.trackBuf1.ptr<ushort2>(), dst.rows, dst.cols);
+ edgesHysteresisLocal(buf.dx_buf, buf.trackBuf1.ptr<ushort2>());
- edgesHysteresisGlobal_gpu(buf.edgeBuf, buf.trackBuf1.ptr<ushort2>(), buf.trackBuf2.ptr<ushort2>(), dst.rows, dst.cols);
+ edgesHysteresisGlobal(buf.dx_buf, buf.trackBuf1.ptr<ushort2>(), buf.trackBuf2.ptr<ushort2>());
- getEdges_gpu(buf.edgeBuf, dst, dst.rows, dst.cols);
+ getEdges(buf.dx_buf, dst);
}
}
void cv::gpu::Canny(const GpuMat& src, GpuMat& dst, double low_thresh, double high_thresh, int apperture_size, bool L2gradient)
{
- CannyBuf buf(src.size(), apperture_size);
+ CannyBuf buf;
Canny(src, buf, dst, low_thresh, high_thresh, apperture_size, L2gradient);
}
void cv::gpu::Canny(const GpuMat& src, CannyBuf& buf, GpuMat& dst, double low_thresh, double high_thresh, int apperture_size, bool L2gradient)
{
- using namespace ::cv::gpu::device::canny;
+ using namespace canny;
CV_Assert(src.type() == CV_8UC1);
- if (!TargetArchs::builtWith(SHARED_ATOMICS) || !DeviceInfo().supports(SHARED_ATOMICS))
+ if (!deviceSupports(SHARED_ATOMICS))
CV_Error(CV_StsNotImplemented, "The device doesn't support shared atomics");
if( low_thresh > high_thresh )
std::swap( low_thresh, high_thresh);
dst.create(src.size(), CV_8U);
- dst.setTo(Scalar::all(0));
-
buf.create(src.size(), apperture_size);
- buf.edgeBuf.setTo(Scalar::all(0));
if (apperture_size == 3)
{
- calcSobelRowPass_gpu(src, buf.dx_buf, buf.dy_buf, src.rows, src.cols);
+ Size wholeSize;
+ Point ofs;
+ src.locateROI(wholeSize, ofs);
+ GpuMat srcWhole(wholeSize, src.type(), src.datastart, src.step);
- calcMagnitude_gpu(buf.dx_buf, buf.dy_buf, buf.dx, buf.dy, buf.edgeBuf, src.rows, src.cols, L2gradient);
+ calcMagnitude(srcWhole, ofs.x, ofs.y, buf.dx, buf.dy, buf.edgeBuf, L2gradient);
}
else
{
buf.filterDX->apply(src, buf.dx, Rect(0, 0, src.cols, src.rows));
buf.filterDY->apply(src, buf.dy, Rect(0, 0, src.cols, src.rows));
- calcMagnitude_gpu(buf.dx, buf.dy, buf.edgeBuf, src.rows, src.cols, L2gradient);
+ calcMagnitude(buf.dx, buf.dy, buf.edgeBuf, L2gradient);
}
- CannyCaller(buf, dst, static_cast<float>(low_thresh), static_cast<float>(high_thresh));
+ CannyCaller(buf.dx, buf.dy, buf, dst, static_cast<float>(low_thresh), static_cast<float>(high_thresh));
}
void cv::gpu::Canny(const GpuMat& dx, const GpuMat& dy, GpuMat& dst, double low_thresh, double high_thresh, bool L2gradient)
{
- CannyBuf buf(dx, dy);
+ CannyBuf buf;
Canny(dx, dy, buf, dst, low_thresh, high_thresh, L2gradient);
}
void cv::gpu::Canny(const GpuMat& dx, const GpuMat& dy, CannyBuf& buf, GpuMat& dst, double low_thresh, double high_thresh, bool L2gradient)
{
- using namespace ::cv::gpu::device::canny;
+ using namespace canny;
CV_Assert(TargetArchs::builtWith(SHARED_ATOMICS) && DeviceInfo().supports(SHARED_ATOMICS));
CV_Assert(dx.type() == CV_32SC1 && dy.type() == CV_32SC1 && dx.size() == dy.size());
std::swap( low_thresh, high_thresh);
dst.create(dx.size(), CV_8U);
- dst.setTo(Scalar::all(0));
-
- buf.dx = dx; buf.dy = dy;
buf.create(dx.size(), -1);
- buf.edgeBuf.setTo(Scalar::all(0));
- calcMagnitude_gpu(dx, dy, buf.edgeBuf, dx.rows, dx.cols, L2gradient);
+ calcMagnitude(dx, dy, buf.edgeBuf, L2gradient);
- CannyCaller(buf, dst, static_cast<float>(low_thresh), static_cast<float>(high_thresh));
+ CannyCaller(dx, dy, buf, dst, static_cast<float>(low_thresh), static_cast<float>(high_thresh));
}
#endif /* !defined (HAVE_CUDA) */
-
-
{
CV_Assert(src.type() == CV_8UC1);
- if (!TargetArchs::builtWith(FEATURE_SET_COMPUTE_13) || !DeviceInfo().supports(FEATURE_SET_COMPUTE_13))
+ if (!deviceSupports(FEATURE_SET_COMPUTE_13))
CV_Error(CV_StsNotImplemented, "Not sufficient compute capebility");
NppiSize sz;
////////////////////////////////////////////////////////////////////////
// Sum
-namespace cv { namespace gpu { namespace device
+namespace sum
{
- namespace matrix_reductions
- {
- namespace sum
- {
- template <typename T>
- void sumCaller(const PtrStepSzb src, PtrStepb buf, double* sum, int cn);
-
- template <typename T>
- void sumMultipassCaller(const PtrStepSzb src, PtrStepb buf, double* sum, int cn);
-
- template <typename T>
- void absSumCaller(const PtrStepSzb src, PtrStepb buf, double* sum, int cn);
+ void getBufSize(int cols, int rows, int cn, int& bufcols, int& bufrows);
- template <typename T>
- void absSumMultipassCaller(const PtrStepSzb src, PtrStepb buf, double* sum, int cn);
+ template <typename T, int cn>
+ void run(PtrStepSzb src, void* buf, double* sum);
- template <typename T>
- void sqrSumCaller(const PtrStepSzb src, PtrStepb buf, double* sum, int cn);
+ template <typename T, int cn>
+ void runAbs(PtrStepSzb src, void* buf, double* sum);
- template <typename T>
- void sqrSumMultipassCaller(const PtrStepSzb src, PtrStepb buf, double* sum, int cn);
-
- void getBufSizeRequired(int cols, int rows, int cn, int& bufcols, int& bufrows);
- }
- }
-}}}
+ template <typename T, int cn>
+ void runSqr(PtrStepSzb src, void* buf, double* sum);
+}
Scalar cv::gpu::sum(const GpuMat& src)
{
return sum(src, buf);
}
-
Scalar cv::gpu::sum(const GpuMat& src, GpuMat& buf)
{
- using namespace cv::gpu::device::matrix_reductions::sum;
-
- typedef void (*Caller)(const PtrStepSzb, PtrStepb, double*, int);
-
- static Caller multipass_callers[] =
+ typedef void (*func_t)(PtrStepSzb src, void* buf, double* sum);
+ static const func_t funcs[7][5] =
{
- sumMultipassCaller<unsigned char>, sumMultipassCaller<char>,
- sumMultipassCaller<unsigned short>, sumMultipassCaller<short>,
- sumMultipassCaller<int>, sumMultipassCaller<float>
- };
-
- static Caller singlepass_callers[] = {
- sumCaller<unsigned char>, sumCaller<char>,
- sumCaller<unsigned short>, sumCaller<short>,
- sumCaller<int>, sumCaller<float>
+ {0, ::sum::run<uchar , 1>, ::sum::run<uchar , 2>, ::sum::run<uchar , 3>, ::sum::run<uchar , 4>},
+ {0, ::sum::run<schar , 1>, ::sum::run<schar , 2>, ::sum::run<schar , 3>, ::sum::run<schar , 4>},
+ {0, ::sum::run<ushort, 1>, ::sum::run<ushort, 2>, ::sum::run<ushort, 3>, ::sum::run<ushort, 4>},
+ {0, ::sum::run<short , 1>, ::sum::run<short , 2>, ::sum::run<short , 3>, ::sum::run<short , 4>},
+ {0, ::sum::run<int , 1>, ::sum::run<int , 2>, ::sum::run<int , 3>, ::sum::run<int , 4>},
+ {0, ::sum::run<float , 1>, ::sum::run<float , 2>, ::sum::run<float , 3>, ::sum::run<float , 4>},
+ {0, ::sum::run<double, 1>, ::sum::run<double, 2>, ::sum::run<double, 3>, ::sum::run<double, 4>}
};
- CV_Assert(src.depth() <= CV_32F);
+ if (src.depth() == CV_64F)
+ {
+ if (!deviceSupports(NATIVE_DOUBLE))
+ CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
+ }
Size buf_size;
- getBufSizeRequired(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
+ ::sum::getBufSize(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
ensureSizeIsEnough(buf_size, CV_8U, buf);
+ buf.setTo(Scalar::all(0));
- Caller* callers = multipass_callers;
- if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
- callers = singlepass_callers;
-
- Caller caller = callers[src.depth()];
+ const func_t func = funcs[src.depth()][src.channels()];
double result[4];
- caller(src, buf, result, src.channels());
+ func(src, buf.data, result);
+
return Scalar(result[0], result[1], result[2], result[3]);
}
-
Scalar cv::gpu::absSum(const GpuMat& src)
{
GpuMat buf;
return absSum(src, buf);
}
-
Scalar cv::gpu::absSum(const GpuMat& src, GpuMat& buf)
{
- using namespace cv::gpu::device::matrix_reductions::sum;
-
- typedef void (*Caller)(const PtrStepSzb, PtrStepb, double*, int);
-
- static Caller multipass_callers[] =
+ typedef void (*func_t)(PtrStepSzb src, void* buf, double* sum);
+ static const func_t funcs[7][5] =
{
- absSumMultipassCaller<unsigned char>, absSumMultipassCaller<char>,
- absSumMultipassCaller<unsigned short>, absSumMultipassCaller<short>,
- absSumMultipassCaller<int>, absSumMultipassCaller<float>
+ {0, ::sum::runAbs<uchar , 1>, ::sum::runAbs<uchar , 2>, ::sum::runAbs<uchar , 3>, ::sum::runAbs<uchar , 4>},
+ {0, ::sum::runAbs<schar , 1>, ::sum::runAbs<schar , 2>, ::sum::runAbs<schar , 3>, ::sum::runAbs<schar , 4>},
+ {0, ::sum::runAbs<ushort, 1>, ::sum::runAbs<ushort, 2>, ::sum::runAbs<ushort, 3>, ::sum::runAbs<ushort, 4>},
+ {0, ::sum::runAbs<short , 1>, ::sum::runAbs<short , 2>, ::sum::runAbs<short , 3>, ::sum::runAbs<short , 4>},
+ {0, ::sum::runAbs<int , 1>, ::sum::runAbs<int , 2>, ::sum::runAbs<int , 3>, ::sum::runAbs<int , 4>},
+ {0, ::sum::runAbs<float , 1>, ::sum::runAbs<float , 2>, ::sum::runAbs<float , 3>, ::sum::runAbs<float , 4>},
+ {0, ::sum::runAbs<double, 1>, ::sum::runAbs<double, 2>, ::sum::runAbs<double, 3>, ::sum::runAbs<double, 4>}
};
- static Caller singlepass_callers[] =
+ if (src.depth() == CV_64F)
{
- absSumCaller<unsigned char>, absSumCaller<char>,
- absSumCaller<unsigned short>, absSumCaller<short>,
- absSumCaller<int>, absSumCaller<float>
- };
-
- CV_Assert(src.depth() <= CV_32F);
+ if (!deviceSupports(NATIVE_DOUBLE))
+ CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
+ }
Size buf_size;
- getBufSizeRequired(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
+ ::sum::getBufSize(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
ensureSizeIsEnough(buf_size, CV_8U, buf);
+ buf.setTo(Scalar::all(0));
- Caller* callers = multipass_callers;
- if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
- callers = singlepass_callers;
-
- Caller caller = callers[src.depth()];
+ const func_t func = funcs[src.depth()][src.channels()];
double result[4];
- caller(src, buf, result, src.channels());
+ func(src, buf.data, result);
+
return Scalar(result[0], result[1], result[2], result[3]);
}
-
Scalar cv::gpu::sqrSum(const GpuMat& src)
{
GpuMat buf;
return sqrSum(src, buf);
}
-
Scalar cv::gpu::sqrSum(const GpuMat& src, GpuMat& buf)
{
- using namespace cv::gpu::device::matrix_reductions::sum;
-
- typedef void (*Caller)(const PtrStepSzb, PtrStepb, double*, int);
-
- static Caller multipass_callers[] =
+ typedef void (*func_t)(PtrStepSzb src, void* buf, double* sum);
+ static const func_t funcs[7][5] =
{
- sqrSumMultipassCaller<unsigned char>, sqrSumMultipassCaller<char>,
- sqrSumMultipassCaller<unsigned short>, sqrSumMultipassCaller<short>,
- sqrSumMultipassCaller<int>, sqrSumMultipassCaller<float>
+ {0, ::sum::runSqr<uchar , 1>, ::sum::runSqr<uchar , 2>, ::sum::runSqr<uchar , 3>, ::sum::runSqr<uchar , 4>},
+ {0, ::sum::runSqr<schar , 1>, ::sum::runSqr<schar , 2>, ::sum::runSqr<schar , 3>, ::sum::runSqr<schar , 4>},
+ {0, ::sum::runSqr<ushort, 1>, ::sum::runSqr<ushort, 2>, ::sum::runSqr<ushort, 3>, ::sum::runSqr<ushort, 4>},
+ {0, ::sum::runSqr<short , 1>, ::sum::runSqr<short , 2>, ::sum::runSqr<short , 3>, ::sum::runSqr<short , 4>},
+ {0, ::sum::runSqr<int , 1>, ::sum::runSqr<int , 2>, ::sum::runSqr<int , 3>, ::sum::runSqr<int , 4>},
+ {0, ::sum::runSqr<float , 1>, ::sum::runSqr<float , 2>, ::sum::runSqr<float , 3>, ::sum::runSqr<float , 4>},
+ {0, ::sum::runSqr<double, 1>, ::sum::runSqr<double, 2>, ::sum::runSqr<double, 3>, ::sum::runSqr<double, 4>}
};
- static Caller singlepass_callers[7] =
+ if (src.depth() == CV_64F)
{
- sqrSumCaller<unsigned char>, sqrSumCaller<char>,
- sqrSumCaller<unsigned short>, sqrSumCaller<short>,
- sqrSumCaller<int>, sqrSumCaller<float>
- };
-
- CV_Assert(src.depth() <= CV_32F);
-
- Caller* callers = multipass_callers;
- if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
- callers = singlepass_callers;
+ if (!deviceSupports(NATIVE_DOUBLE))
+ CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
+ }
Size buf_size;
- getBufSizeRequired(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
+ ::sum::getBufSize(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
ensureSizeIsEnough(buf_size, CV_8U, buf);
+ buf.setTo(Scalar::all(0));
- Caller caller = callers[src.depth()];
+ const func_t func = funcs[src.depth()][src.channels()];
double result[4];
- caller(src, buf, result, src.channels());
+ func(src, buf.data, result);
+
return Scalar(result[0], result[1], result[2], result[3]);
}
////////////////////////////////////////////////////////////////////////
-// Find min or max
+// minMax
-namespace cv { namespace gpu { namespace device
+namespace minMax
{
- namespace matrix_reductions
- {
- namespace minmax
- {
- void getBufSizeRequired(int cols, int rows, int elem_size, int& bufcols, int& bufrows);
-
- template <typename T>
- void minMaxCaller(const PtrStepSzb src, double* minval, double* maxval, PtrStepb buf);
-
- template <typename T>
- void minMaxMaskCaller(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
-
- template <typename T>
- void minMaxMultipassCaller(const PtrStepSzb src, double* minval, double* maxval, PtrStepb buf);
-
- template <typename T>
- void minMaxMaskMultipassCaller(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
- }
- }
-}}}
+ void getBufSize(int cols, int rows, int& bufcols, int& bufrows);
+ template <typename T>
+ void run(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
+}
void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask)
{
minMax(src, minVal, maxVal, mask, buf);
}
-
void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask, GpuMat& buf)
{
- using namespace ::cv::gpu::device::matrix_reductions::minmax;
-
- typedef void (*Caller)(const PtrStepSzb, double*, double*, PtrStepb);
- typedef void (*MaskedCaller)(const PtrStepSzb, const PtrStepb, double*, double*, PtrStepb);
-
- static Caller multipass_callers[] =
+ typedef void (*func_t)(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
+ static const func_t funcs[] =
{
- minMaxMultipassCaller<unsigned char>, minMaxMultipassCaller<char>,
- minMaxMultipassCaller<unsigned short>, minMaxMultipassCaller<short>,
- minMaxMultipassCaller<int>, minMaxMultipassCaller<float>, 0
+ ::minMax::run<uchar>,
+ ::minMax::run<schar>,
+ ::minMax::run<ushort>,
+ ::minMax::run<short>,
+ ::minMax::run<int>,
+ ::minMax::run<float>,
+ ::minMax::run<double>
};
- static Caller singlepass_callers[] =
- {
- minMaxCaller<unsigned char>, minMaxCaller<char>,
- minMaxCaller<unsigned short>, minMaxCaller<short>,
- minMaxCaller<int>, minMaxCaller<float>, minMaxCaller<double>
- };
-
- static MaskedCaller masked_multipass_callers[] =
- {
- minMaxMaskMultipassCaller<unsigned char>, minMaxMaskMultipassCaller<char>,
- minMaxMaskMultipassCaller<unsigned short>, minMaxMaskMultipassCaller<short>,
- minMaxMaskMultipassCaller<int>, minMaxMaskMultipassCaller<float>, 0
- };
-
- static MaskedCaller masked_singlepass_callers[] =
- {
- minMaxMaskCaller<unsigned char>, minMaxMaskCaller<char>,
- minMaxMaskCaller<unsigned short>, minMaxMaskCaller<short>,
- minMaxMaskCaller<int>, minMaxMaskCaller<float>, minMaxMaskCaller<double>
- };
-
- CV_Assert(src.depth() <= CV_64F);
- CV_Assert(src.channels() == 1);
- CV_Assert(mask.empty() || (mask.type() == CV_8U && src.size() == mask.size()));
+ CV_Assert( src.channels() == 1 );
+ CV_Assert( mask.empty() || (mask.size() == src.size() && mask.type() == CV_8U) );
if (src.depth() == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
- double minVal_; if (!minVal) minVal = &minVal_;
- double maxVal_; if (!maxVal) maxVal = &maxVal_;
-
Size buf_size;
- getBufSizeRequired(src.cols, src.rows, static_cast<int>(src.elemSize()), buf_size.width, buf_size.height);
+ ::minMax::getBufSize(src.cols, src.rows, buf_size.width, buf_size.height);
ensureSizeIsEnough(buf_size, CV_8U, buf);
- if (mask.empty())
- {
- Caller* callers = multipass_callers;
- if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
- callers = singlepass_callers;
-
- Caller caller = callers[src.type()];
- CV_Assert(caller != 0);
- caller(src, minVal, maxVal, buf);
- }
- else
- {
- MaskedCaller* callers = masked_multipass_callers;
- if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
- callers = masked_singlepass_callers;
+ const func_t func = funcs[src.depth()];
- MaskedCaller caller = callers[src.type()];
- CV_Assert(caller != 0);
- caller(src, mask, minVal, maxVal, buf);
- }
+ double temp1, temp2;
+ func(src, mask, minVal ? minVal : &temp1, maxVal ? maxVal : &temp2, buf);
}
-
////////////////////////////////////////////////////////////////////////
-// Locate min and max
+// minMaxLoc
-namespace cv { namespace gpu { namespace device
+namespace minMaxLoc
{
- namespace matrix_reductions
- {
- namespace minmaxloc
- {
- void getBufSizeRequired(int cols, int rows, int elem_size, int& b1cols,
- int& b1rows, int& b2cols, int& b2rows);
-
- template <typename T>
- void minMaxLocCaller(const PtrStepSzb src, double* minval, double* maxval,
- int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf);
-
- template <typename T>
- void minMaxLocMaskCaller(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval,
- int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf);
-
- template <typename T>
- void minMaxLocMultipassCaller(const PtrStepSzb src, double* minval, double* maxval,
- int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf);
+ void getBufSize(int cols, int rows, size_t elem_size, int& b1cols, int& b1rows, int& b2cols, int& b2rows);
- template <typename T>
- void minMaxLocMaskMultipassCaller(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval,
- int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf);
- }
- }
-}}}
+ template <typename T>
+ void run(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, int* minloc, int* maxloc, PtrStepb valbuf, PtrStep<unsigned int> locbuf);
+}
void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc, const GpuMat& mask)
{
void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
const GpuMat& mask, GpuMat& valBuf, GpuMat& locBuf)
{
- using namespace ::cv::gpu::device::matrix_reductions::minmaxloc;
-
- typedef void (*Caller)(const PtrStepSzb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
- typedef void (*MaskedCaller)(const PtrStepSzb, const PtrStepb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
-
- static Caller multipass_callers[] =
- {
- minMaxLocMultipassCaller<unsigned char>, minMaxLocMultipassCaller<char>,
- minMaxLocMultipassCaller<unsigned short>, minMaxLocMultipassCaller<short>,
- minMaxLocMultipassCaller<int>, minMaxLocMultipassCaller<float>, 0
- };
-
- static Caller singlepass_callers[] =
+ typedef void (*func_t)(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, int* minloc, int* maxloc, PtrStepb valbuf, PtrStep<unsigned int> locbuf);
+ static const func_t funcs[] =
{
- minMaxLocCaller<unsigned char>, minMaxLocCaller<char>,
- minMaxLocCaller<unsigned short>, minMaxLocCaller<short>,
- minMaxLocCaller<int>, minMaxLocCaller<float>, minMaxLocCaller<double>
+ ::minMaxLoc::run<uchar>,
+ ::minMaxLoc::run<schar>,
+ ::minMaxLoc::run<ushort>,
+ ::minMaxLoc::run<short>,
+ ::minMaxLoc::run<int>,
+ ::minMaxLoc::run<float>,
+ ::minMaxLoc::run<double>
};
- static MaskedCaller masked_multipass_callers[] =
- {
- minMaxLocMaskMultipassCaller<unsigned char>, minMaxLocMaskMultipassCaller<char>,
- minMaxLocMaskMultipassCaller<unsigned short>, minMaxLocMaskMultipassCaller<short>,
- minMaxLocMaskMultipassCaller<int>, minMaxLocMaskMultipassCaller<float>, 0
- };
-
- static MaskedCaller masked_singlepass_callers[] =
- {
- minMaxLocMaskCaller<unsigned char>, minMaxLocMaskCaller<char>,
- minMaxLocMaskCaller<unsigned short>, minMaxLocMaskCaller<short>,
- minMaxLocMaskCaller<int>, minMaxLocMaskCaller<float>, minMaxLocMaskCaller<double>
- };
-
- CV_Assert(src.depth() <= CV_64F);
- CV_Assert(src.channels() == 1);
- CV_Assert(mask.empty() || (mask.type() == CV_8U && src.size() == mask.size()));
+ CV_Assert( src.channels() == 1 );
+ CV_Assert( mask.empty() || (mask.size() == src.size() && mask.type() == CV_8U) );
if (src.depth() == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
- double minVal_; if (!minVal) minVal = &minVal_;
- double maxVal_; if (!maxVal) maxVal = &maxVal_;
- int minLoc_[2];
- int maxLoc_[2];
-
Size valbuf_size, locbuf_size;
- getBufSizeRequired(src.cols, src.rows, static_cast<int>(src.elemSize()), valbuf_size.width,
- valbuf_size.height, locbuf_size.width, locbuf_size.height);
+ ::minMaxLoc::getBufSize(src.cols, src.rows, src.elemSize(), valbuf_size.width, valbuf_size.height, locbuf_size.width, locbuf_size.height);
ensureSizeIsEnough(valbuf_size, CV_8U, valBuf);
ensureSizeIsEnough(locbuf_size, CV_8U, locBuf);
- if (mask.empty())
- {
- Caller* callers = multipass_callers;
- if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
- callers = singlepass_callers;
-
- Caller caller = callers[src.type()];
- CV_Assert(caller != 0);
- caller(src, minVal, maxVal, minLoc_, maxLoc_, valBuf, locBuf);
- }
- else
- {
- MaskedCaller* callers = masked_multipass_callers;
- if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
- callers = masked_singlepass_callers;
-
- MaskedCaller caller = callers[src.type()];
- CV_Assert(caller != 0);
- caller(src, mask, minVal, maxVal, minLoc_, maxLoc_, valBuf, locBuf);
- }
+ const func_t func = funcs[src.depth()];
- if (minLoc) { minLoc->x = minLoc_[0]; minLoc->y = minLoc_[1]; }
- if (maxLoc) { maxLoc->x = maxLoc_[0]; maxLoc->y = maxLoc_[1]; }
+ double temp1, temp2;
+ Point temp3, temp4;
+ func(src, mask, minVal ? minVal : &temp1, maxVal ? maxVal : &temp2, minLoc ? &minLoc->x : &temp3.x, maxLoc ? &maxLoc->x : &temp4.x, valBuf, locBuf);
}
//////////////////////////////////////////////////////////////////////////////
-// Count non-zero elements
+// countNonZero
-namespace cv { namespace gpu { namespace device
+namespace countNonZero
{
- namespace matrix_reductions
- {
- namespace countnonzero
- {
- void getBufSizeRequired(int cols, int rows, int& bufcols, int& bufrows);
+ void getBufSize(int cols, int rows, int& bufcols, int& bufrows);
- template <typename T>
- int countNonZeroCaller(const PtrStepSzb src, PtrStepb buf);
-
- template <typename T>
- int countNonZeroMultipassCaller(const PtrStepSzb src, PtrStepb buf);
- }
- }
-}}}
+ template <typename T>
+ int run(const PtrStepSzb src, PtrStep<unsigned int> buf);
+}
int cv::gpu::countNonZero(const GpuMat& src)
{
return countNonZero(src, buf);
}
-
int cv::gpu::countNonZero(const GpuMat& src, GpuMat& buf)
{
- using namespace ::cv::gpu::device::matrix_reductions::countnonzero;
-
- typedef int (*Caller)(const PtrStepSzb src, PtrStepb buf);
-
- static Caller multipass_callers[7] =
+ typedef int (*func_t)(const PtrStepSzb src, PtrStep<unsigned int> buf);
+ static const func_t funcs[] =
{
- countNonZeroMultipassCaller<unsigned char>, countNonZeroMultipassCaller<char>,
- countNonZeroMultipassCaller<unsigned short>, countNonZeroMultipassCaller<short>,
- countNonZeroMultipassCaller<int>, countNonZeroMultipassCaller<float>, 0
+ ::countNonZero::run<uchar>,
+ ::countNonZero::run<schar>,
+ ::countNonZero::run<ushort>,
+ ::countNonZero::run<short>,
+ ::countNonZero::run<int>,
+ ::countNonZero::run<float>,
+ ::countNonZero::run<double>
};
- static Caller singlepass_callers[7] =
- {
- countNonZeroCaller<unsigned char>, countNonZeroCaller<char>,
- countNonZeroCaller<unsigned short>, countNonZeroCaller<short>,
- countNonZeroCaller<int>, countNonZeroCaller<float>, countNonZeroCaller<double> };
-
- CV_Assert(src.depth() <= CV_64F);
CV_Assert(src.channels() == 1);
if (src.depth() == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
Size buf_size;
- getBufSizeRequired(src.cols, src.rows, buf_size.width, buf_size.height);
+ ::countNonZero::getBufSize(src.cols, src.rows, buf_size.width, buf_size.height);
ensureSizeIsEnough(buf_size, CV_8U, buf);
- Caller* callers = multipass_callers;
- if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
- callers = singlepass_callers;
+ const func_t func = funcs[src.depth()];
- Caller caller = callers[src.type()];
- CV_Assert(caller != 0);
- return caller(src, buf);
+ return func(src, buf);
}
//////////////////////////////////////////////////////////////////////////////
// reduce
-namespace cv { namespace gpu { namespace device
+namespace reduce
{
- namespace matrix_reductions
- {
- template <typename T, typename S, typename D> void reduceRows_gpu(const PtrStepSzb& src, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
- template <typename T, typename S, typename D> void reduceCols_gpu(const PtrStepSzb& src, int cn, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
- }
-}}}
+ template <typename T, typename S, typename D>
+ void rows(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
+
+ template <typename T, typename S, typename D>
+ void cols(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
+}
void cv::gpu::reduce(const GpuMat& src, GpuMat& dst, int dim, int reduceOp, int dtype, Stream& stream)
{
- using namespace ::cv::gpu::device::matrix_reductions;
-
- CV_Assert(src.depth() <= CV_32F && src.channels() <= 4 && dtype <= CV_32F);
- CV_Assert(dim == 0 || dim == 1);
- CV_Assert(reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG || reduceOp == CV_REDUCE_MAX || reduceOp == CV_REDUCE_MIN);
+ CV_Assert( src.channels() <= 4 );
+ CV_Assert( dim == 0 || dim == 1 );
+ CV_Assert( reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG || reduceOp == CV_REDUCE_MAX || reduceOp == CV_REDUCE_MIN );
if (dtype < 0)
dtype = src.depth();
- dst.create(1, dim == 0 ? src.cols : src.rows, CV_MAKETYPE(dtype, src.channels()));
+ dst.create(1, dim == 0 ? src.cols : src.rows, CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()));
if (dim == 0)
{
- typedef void (*caller_t)(const PtrStepSzb& src, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
-
- static const caller_t callers[6][6] =
+ typedef void (*func_t)(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
+ static const func_t funcs[7][7] =
{
{
- reduceRows_gpu<unsigned char, int, unsigned char>,
- 0/*reduceRows_gpu<unsigned char, int, signed char>*/,
- 0/*reduceRows_gpu<unsigned char, int, unsigned short>*/,
- 0/*reduceRows_gpu<unsigned char, int, short>*/,
- reduceRows_gpu<unsigned char, int, int>,
- reduceRows_gpu<unsigned char, int, float>
+ ::reduce::rows<unsigned char, int, unsigned char>,
+ 0/*::reduce::rows<unsigned char, int, signed char>*/,
+ 0/*::reduce::rows<unsigned char, int, unsigned short>*/,
+ 0/*::reduce::rows<unsigned char, int, short>*/,
+ ::reduce::rows<unsigned char, int, int>,
+ ::reduce::rows<unsigned char, float, float>,
+ ::reduce::rows<unsigned char, double, double>
+ },
+ {
+ 0/*::reduce::rows<signed char, int, unsigned char>*/,
+ 0/*::reduce::rows<signed char, int, signed char>*/,
+ 0/*::reduce::rows<signed char, int, unsigned short>*/,
+ 0/*::reduce::rows<signed char, int, short>*/,
+ 0/*::reduce::rows<signed char, int, int>*/,
+ 0/*::reduce::rows<signed char, float, float>*/,
+ 0/*::reduce::rows<signed char, double, double>*/
},
{
- 0/*reduceRows_gpu<signed char, int, unsigned char>*/,
- 0/*reduceRows_gpu<signed char, int, signed char>*/,
- 0/*reduceRows_gpu<signed char, int, unsigned short>*/,
- 0/*reduceRows_gpu<signed char, int, short>*/,
- 0/*reduceRows_gpu<signed char, int, int>*/,
- 0/*reduceRows_gpu<signed char, int, float>*/
+ 0/*::reduce::rows<unsigned short, int, unsigned char>*/,
+ 0/*::reduce::rows<unsigned short, int, signed char>*/,
+ ::reduce::rows<unsigned short, int, unsigned short>,
+ 0/*::reduce::rows<unsigned short, int, short>*/,
+ ::reduce::rows<unsigned short, int, int>,
+ ::reduce::rows<unsigned short, float, float>,
+ ::reduce::rows<unsigned short, double, double>
},
{
- 0/*reduceRows_gpu<unsigned short, int, unsigned char>*/,
- 0/*reduceRows_gpu<unsigned short, int, signed char>*/,
- reduceRows_gpu<unsigned short, int, unsigned short>,
- 0/*reduceRows_gpu<unsigned short, int, short>*/,
- reduceRows_gpu<unsigned short, int, int>,
- reduceRows_gpu<unsigned short, int, float>
+ 0/*::reduce::rows<short, int, unsigned char>*/,
+ 0/*::reduce::rows<short, int, signed char>*/,
+ 0/*::reduce::rows<short, int, unsigned short>*/,
+ ::reduce::rows<short, int, short>,
+ ::reduce::rows<short, int, int>,
+ ::reduce::rows<short, float, float>,
+ ::reduce::rows<short, double, double>
},
{
- 0/*reduceRows_gpu<short, int, unsigned char>*/,
- 0/*reduceRows_gpu<short, int, signed char>*/,
- 0/*reduceRows_gpu<short, int, unsigned short>*/,
- reduceRows_gpu<short, int, short>,
- reduceRows_gpu<short, int, int>,
- reduceRows_gpu<short, int, float>
+ 0/*::reduce::rows<int, int, unsigned char>*/,
+ 0/*::reduce::rows<int, int, signed char>*/,
+ 0/*::reduce::rows<int, int, unsigned short>*/,
+ 0/*::reduce::rows<int, int, short>*/,
+ ::reduce::rows<int, int, int>,
+ ::reduce::rows<int, float, float>,
+ ::reduce::rows<int, double, double>
},
{
- 0/*reduceRows_gpu<int, int, unsigned char>*/,
- 0/*reduceRows_gpu<int, int, signed char>*/,
- 0/*reduceRows_gpu<int, int, unsigned short>*/,
- 0/*reduceRows_gpu<int, int, short>*/,
- reduceRows_gpu<int, int, int>,
- reduceRows_gpu<int, int, float>
+ 0/*::reduce::rows<float, float, unsigned char>*/,
+ 0/*::reduce::rows<float, float, signed char>*/,
+ 0/*::reduce::rows<float, float, unsigned short>*/,
+ 0/*::reduce::rows<float, float, short>*/,
+ 0/*::reduce::rows<float, float, int>*/,
+ ::reduce::rows<float, float, float>,
+ ::reduce::rows<float, double, double>
},
{
- 0/*reduceRows_gpu<float, float, unsigned char>*/,
- 0/*reduceRows_gpu<float, float, signed char>*/,
- 0/*reduceRows_gpu<float, float, unsigned short>*/,
- 0/*reduceRows_gpu<float, float, short>*/,
- 0/*reduceRows_gpu<float, float, int>*/,
- reduceRows_gpu<float, float, float>
+ 0/*::reduce::rows<double, double, unsigned char>*/,
+ 0/*::reduce::rows<double, double, signed char>*/,
+ 0/*::reduce::rows<double, double, unsigned short>*/,
+ 0/*::reduce::rows<double, double, short>*/,
+ 0/*::reduce::rows<double, double, int>*/,
+ 0/*::reduce::rows<double, double, float>*/,
+ ::reduce::rows<double, double, double>
}
};
- const caller_t func = callers[src.depth()][dst.depth()];
+ const func_t func = funcs[src.depth()][dst.depth()];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of input and output array formats");
- func(src.reshape(1), dst.reshape(1), reduceOp, StreamAccessor::getStream(stream));
+ func(src.reshape(1), dst.data, reduceOp, StreamAccessor::getStream(stream));
}
else
{
- typedef void (*caller_t)(const PtrStepSzb& src, int cn, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
-
- static const caller_t callers[6][6] =
+ typedef void (*func_t)(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
+ static const func_t funcs[7][7] =
{
{
- reduceCols_gpu<unsigned char, int, unsigned char>,
- 0/*reduceCols_gpu<unsigned char, int, signed char>*/,
- 0/*reduceCols_gpu<unsigned char, int, unsigned short>*/,
- 0/*reduceCols_gpu<unsigned char, int, short>*/,
- reduceCols_gpu<unsigned char, int, int>,
- reduceCols_gpu<unsigned char, int, float>
+ ::reduce::cols<unsigned char, int, unsigned char>,
+ 0/*::reduce::cols<unsigned char, int, signed char>*/,
+ 0/*::reduce::cols<unsigned char, int, unsigned short>*/,
+ 0/*::reduce::cols<unsigned char, int, short>*/,
+ ::reduce::cols<unsigned char, int, int>,
+ ::reduce::cols<unsigned char, float, float>,
+ ::reduce::cols<unsigned char, double, double>
+ },
+ {
+ 0/*::reduce::cols<signed char, int, unsigned char>*/,
+ 0/*::reduce::cols<signed char, int, signed char>*/,
+ 0/*::reduce::cols<signed char, int, unsigned short>*/,
+ 0/*::reduce::cols<signed char, int, short>*/,
+ 0/*::reduce::cols<signed char, int, int>*/,
+ 0/*::reduce::cols<signed char, float, float>*/,
+ 0/*::reduce::cols<signed char, double, double>*/
},
{
- 0/*reduceCols_gpu<signed char, int, unsigned char>*/,
- 0/*reduceCols_gpu<signed char, int, signed char>*/,
- 0/*reduceCols_gpu<signed char, int, unsigned short>*/,
- 0/*reduceCols_gpu<signed char, int, short>*/,
- 0/*reduceCols_gpu<signed char, int, int>*/,
- 0/*reduceCols_gpu<signed char, int, float>*/
+ 0/*::reduce::cols<unsigned short, int, unsigned char>*/,
+ 0/*::reduce::cols<unsigned short, int, signed char>*/,
+ ::reduce::cols<unsigned short, int, unsigned short>,
+ 0/*::reduce::cols<unsigned short, int, short>*/,
+ ::reduce::cols<unsigned short, int, int>,
+ ::reduce::cols<unsigned short, float, float>,
+ ::reduce::cols<unsigned short, double, double>
},
{
- 0/*reduceCols_gpu<unsigned short, int, unsigned char>*/,
- 0/*reduceCols_gpu<unsigned short, int, signed char>*/,
- reduceCols_gpu<unsigned short, int, unsigned short>,
- 0/*reduceCols_gpu<unsigned short, int, short>*/,
- reduceCols_gpu<unsigned short, int, int>,
- reduceCols_gpu<unsigned short, int, float>
+ 0/*::reduce::cols<short, int, unsigned char>*/,
+ 0/*::reduce::cols<short, int, signed char>*/,
+ 0/*::reduce::cols<short, int, unsigned short>*/,
+ ::reduce::cols<short, int, short>,
+ ::reduce::cols<short, int, int>,
+ ::reduce::cols<short, float, float>,
+ ::reduce::cols<short, double, double>
},
{
- 0/*reduceCols_gpu<short, int, unsigned char>*/,
- 0/*reduceCols_gpu<short, int, signed char>*/,
- 0/*reduceCols_gpu<short, int, unsigned short>*/,
- reduceCols_gpu<short, int, short>,
- reduceCols_gpu<short, int, int>,
- reduceCols_gpu<short, int, float>
+ 0/*::reduce::cols<int, int, unsigned char>*/,
+ 0/*::reduce::cols<int, int, signed char>*/,
+ 0/*::reduce::cols<int, int, unsigned short>*/,
+ 0/*::reduce::cols<int, int, short>*/,
+ ::reduce::cols<int, int, int>,
+ ::reduce::cols<int, float, float>,
+ ::reduce::cols<int, double, double>
},
{
- 0/*reduceCols_gpu<int, int, unsigned char>*/,
- 0/*reduceCols_gpu<int, int, signed char>*/,
- 0/*reduceCols_gpu<int, int, unsigned short>*/,
- 0/*reduceCols_gpu<int, int, short>*/,
- reduceCols_gpu<int, int, int>,
- reduceCols_gpu<int, int, float>
+ 0/*::reduce::cols<float, float, unsigned char>*/,
+ 0/*::reduce::cols<float, float, signed char>*/,
+ 0/*::reduce::cols<float, float, unsigned short>*/,
+ 0/*::reduce::cols<float, float, short>*/,
+ 0/*::reduce::cols<float, float, int>*/,
+ ::reduce::cols<float, float, float>,
+ ::reduce::cols<float, double, double>
},
{
- 0/*reduceCols_gpu<float, unsigned char>*/,
- 0/*reduceCols_gpu<float, signed char>*/,
- 0/*reduceCols_gpu<float, unsigned short>*/,
- 0/*reduceCols_gpu<float, short>*/,
- 0/*reduceCols_gpu<float, int>*/,
- reduceCols_gpu<float, float, float>
+ 0/*::reduce::cols<double, double, unsigned char>*/,
+ 0/*::reduce::cols<double, double, signed char>*/,
+ 0/*::reduce::cols<double, double, unsigned short>*/,
+ 0/*::reduce::cols<double, double, short>*/,
+ 0/*::reduce::cols<double, double, int>*/,
+ 0/*::reduce::cols<double, double, float>*/,
+ ::reduce::cols<double, double, double>
}
};
- const caller_t func = callers[src.depth()][dst.depth()];
+ const func_t func = funcs[src.depth()][dst.depth()];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of input and output array formats");
- func(src, src.channels(), dst, reduceOp, StreamAccessor::getStream(stream));
+ func(src, dst.data, src.channels(), reduceOp, StreamAccessor::getStream(stream));
}
}
#include "NPP_staging/NPP_staging.hpp"
#include "NCVRuntimeTemplates.hpp"
#include "NCVHaarObjectDetection.hpp"
+#include "opencv2/gpu/device/warp.hpp"
+#include "opencv2/gpu/device/warp_shuffle.hpp"
//==============================================================================
//assuming size <= WARP_SIZE and size is power of 2
__device__ Ncv32u warpScanInclusive(Ncv32u idata, volatile Ncv32u *s_Data)
{
+#if __CUDA_ARCH__ >= 300
+ const unsigned int laneId = cv::gpu::device::Warp::laneId();
+
+ // scan on shuffl functions
+ #pragma unroll
+ for (int i = 1; i <= (K_WARP_SIZE / 2); i *= 2)
+ {
+ const Ncv32u n = cv::gpu::device::shfl_up(idata, i);
+ if (laneId >= i)
+ idata += n;
+ }
+
+ return idata;
+#else
Ncv32u pos = 2 * threadIdx.x - (threadIdx.x & (K_WARP_SIZE - 1));
s_Data[pos] = 0;
pos += K_WARP_SIZE;
s_Data[pos] += s_Data[pos - 16];
return s_Data[pos];
+#endif
}
__device__ __forceinline__ Ncv32u warpScanExclusive(Ncv32u idata, volatile Ncv32u *s_Data)
return NCV_SUCCESS;
}
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+#endif /* CUDA_DISABLER */
#include <vector>
#include <cuda_runtime.h>
#include "NPP_staging.hpp"
+#include "opencv2/gpu/device/warp.hpp"
+#include "opencv2/gpu/device/warp_shuffle.hpp"
texture<Ncv8u, 1, cudaReadModeElementType> tex8u;
template <class T>
inline __device__ T warpScanInclusive(T idata, volatile T *s_Data)
{
+#if __CUDA_ARCH__ >= 300
+ const unsigned int laneId = cv::gpu::device::Warp::laneId();
+
+ // scan on shuffl functions
+ #pragma unroll
+ for (int i = 1; i <= (K_WARP_SIZE / 2); i *= 2)
+ {
+ const T n = cv::gpu::device::shfl_up(idata, i);
+ if (laneId >= i)
+ idata += n;
+ }
+
+ return idata;
+#else
+ Ncv32u pos = 2 * threadIdx.x - (threadIdx.x & (K_WARP_SIZE - 1));
+ s_Data[pos] = 0;
+ pos += K_WARP_SIZE;
+ s_Data[pos] = idata;
+
+ s_Data[pos] += s_Data[pos - 1];
+ s_Data[pos] += s_Data[pos - 2];
+ s_Data[pos] += s_Data[pos - 4];
+ s_Data[pos] += s_Data[pos - 8];
+ s_Data[pos] += s_Data[pos - 16];
+
+ return s_Data[pos];
+#endif
+}
+inline __device__ Ncv64u warpScanInclusive(Ncv64u idata, volatile Ncv64u *s_Data)
+{
Ncv32u pos = 2 * threadIdx.x - (threadIdx.x & (K_WARP_SIZE - 1));
s_Data[pos] = 0;
pos += K_WARP_SIZE;
return status;
}
-#endif /* CUDA_DISABLER */
\ No newline at end of file
+#endif /* CUDA_DISABLER */
const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat &flowy,
GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[])
{
- if (!isDeviceArch11_)
+ if (deviceSupports(FEATURE_SET_COMPUTE_12))
device::optflow_farneback::boxFilter5Gpu(M, blockSize/2, bufM, S(streams[0]));
else
device::optflow_farneback::boxFilter5Gpu_CC11(M, blockSize/2, bufM, S(streams[0]));
const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat& flowy,
GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[])
{
- if (!isDeviceArch11_)
+ if (deviceSupports(FEATURE_SET_COMPUTE_12))
device::optflow_farneback::gaussianBlur5Gpu(
M, blockSize/2, bufM, BORDER_REPLICATE_GPU, S(streams[0]));
else
void cv::gpu::FarnebackOpticalFlow::operator ()(
const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s)
{
- CV_Assert(frame0.type() == CV_8U && frame1.type() == CV_8U);
+ CV_Assert(frame0.channels() == 1 && frame1.channels() == 1);
CV_Assert(frame0.size() == frame1.size());
CV_Assert(polyN == 5 || polyN == 7);
CV_Assert(!fastPyramids || std::abs(pyrScale - 0.5) < 1e-6);
#else /* !defined (HAVE_CUDA) */
-namespace cv { namespace gpu { namespace device
+namespace pyrlk
{
- namespace pyrlk
- {
- void loadConstants(int2 winSize, int iters);
+ void loadConstants(int2 winSize, int iters);
- void lkSparse1_gpu(PtrStepSzf I, PtrStepSzf J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
- int level, dim3 block, dim3 patch, cudaStream_t stream = 0);
- void lkSparse4_gpu(PtrStepSz<float4> I, PtrStepSz<float4> J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
- int level, dim3 block, dim3 patch, cudaStream_t stream = 0);
+ void sparse1(PtrStepSzf I, PtrStepSzf J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
+ int level, dim3 block, dim3 patch, cudaStream_t stream = 0);
+ void sparse4(PtrStepSz<float4> I, PtrStepSz<float4> J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
+ int level, dim3 block, dim3 patch, cudaStream_t stream = 0);
- void lkDense_gpu(PtrStepSzb I, PtrStepSzf J, PtrStepSzf u, PtrStepSzf v, PtrStepSzf prevU, PtrStepSzf prevV,
- PtrStepSzf err, int2 winSize, cudaStream_t stream = 0);
- }
-}}}
+ void dense(PtrStepSzb I, PtrStepSzf J, PtrStepSzf u, PtrStepSzf v, PtrStepSzf prevU, PtrStepSzf prevV,
+ PtrStepSzf err, int2 winSize, cudaStream_t stream = 0);
+}
namespace
{
- void calcPatchSize(cv::Size winSize, dim3& block, dim3& patch, bool isDeviceArch11)
+ void calcPatchSize(cv::Size winSize, dim3& block, dim3& patch)
{
if (winSize.width > 32 && winSize.width > 2 * winSize.height)
{
- block.x = isDeviceArch11 ? 16 : 32;
+ block.x = deviceSupports(FEATURE_SET_COMPUTE_12) ? 32 : 16;
block.y = 8;
}
else
{
block.x = 16;
- block.y = isDeviceArch11 ? 8 : 16;
+ block.y = deviceSupports(FEATURE_SET_COMPUTE_12) ? 16 : 8;
}
patch.x = (winSize.width + block.x - 1) / block.x;
void cv::gpu::PyrLKOpticalFlow::sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts, GpuMat& status, GpuMat* err)
{
- using namespace cv::gpu::device::pyrlk;
-
if (prevPts.empty())
{
nextPts.release();
}
dim3 block, patch;
- calcPatchSize(winSize, block, patch, isDeviceArch11_);
+ calcPatchSize(winSize, block, patch);
- CV_Assert(prevImg.type() == CV_8UC1 || prevImg.type() == CV_8UC3 || prevImg.type() == CV_8UC4);
+ CV_Assert(prevImg.channels() == 1 || prevImg.channels() == 3 || prevImg.channels() == 4);
CV_Assert(prevImg.size() == nextImg.size() && prevImg.type() == nextImg.type());
CV_Assert(maxLevel >= 0);
CV_Assert(winSize.width > 2 && winSize.height > 2);
pyrDown(nextPyr_[level - 1], nextPyr_[level]);
}
- loadConstants(make_int2(winSize.width, winSize.height), iters);
+ pyrlk::loadConstants(make_int2(winSize.width, winSize.height), iters);
for (int level = maxLevel; level >= 0; level--)
{
if (cn == 1)
{
- lkSparse1_gpu(prevPyr_[level], nextPyr_[level],
+ pyrlk::sparse1(prevPyr_[level], nextPyr_[level],
prevPts.ptr<float2>(), nextPts.ptr<float2>(), status.ptr(), level == 0 && err ? err->ptr<float>() : 0, prevPts.cols,
level, block, patch);
}
else
{
- lkSparse4_gpu(prevPyr_[level], nextPyr_[level],
+ pyrlk::sparse4(prevPyr_[level], nextPyr_[level],
prevPts.ptr<float2>(), nextPts.ptr<float2>(), status.ptr(), level == 0 && err ? err->ptr<float>() : 0, prevPts.cols,
level, block, patch);
}
void cv::gpu::PyrLKOpticalFlow::dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err)
{
- using namespace cv::gpu::device::pyrlk;
-
CV_Assert(prevImg.type() == CV_8UC1);
CV_Assert(prevImg.size() == nextImg.size() && prevImg.type() == nextImg.type());
CV_Assert(maxLevel >= 0);
vPyr_[1].setTo(Scalar::all(0));
int2 winSize2i = make_int2(winSize.width, winSize.height);
- loadConstants(winSize2i, iters);
+ pyrlk::loadConstants(winSize2i, iters);
PtrStepSzf derr = err ? *err : PtrStepSzf();
{
int idx2 = (idx + 1) & 1;
- lkDense_gpu(prevPyr_[level], nextPyr_[level], uPyr_[idx], vPyr_[idx], uPyr_[idx2], vPyr_[idx2],
+ pyrlk::dense(prevPyr_[level], nextPyr_[level], uPyr_[idx], vPyr_[idx], uPyr_[idx2], vPyr_[idx2],
level == 0 ? derr : PtrStepSzf(), winSize2i);
if (level > 0)
{
template <typename T>
void remap_gpu(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst,
- int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, int cc);
+ int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
}
}}}
using namespace cv::gpu::device::imgproc;
typedef void (*func_t)(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation,
- int borderMode, const float* borderValue, cudaStream_t stream, int cc);
+ int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
static const func_t funcs[6][4] =
{
Scalar_<float> borderValueFloat;
borderValueFloat = borderValue;
- DeviceInfo info;
- int cc = info.majorVersion() * 10 + info.minorVersion();
-
Size wholeSize;
Point ofs;
src.locateROI(wholeSize, ofs);
func(src, PtrStepSzb(wholeSize.height, wholeSize.width, src.datastart, src.step), ofs.x, ofs.y, xmap, ymap,
- dst, interpolation, gpuBorderType, borderValueFloat.val, StreamAccessor::getStream(stream), cc);
+ dst, interpolation, gpuBorderType, borderValueFloat.val, StreamAccessor::getStream(stream), deviceSupports(FEATURE_SET_COMPUTE_20));
}
#endif // HAVE_CUDA
if (depth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
if (depth == CV_64F)
{
- if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
+ if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
void icvCalcOrientation_gpu(const float* featureX, const float* featureY, const float* featureSize, float* featureDir, int nFeatures);
- void compute_descriptors_gpu(const PtrStepSzf& descriptors,
- const float* featureX, const float* featureY, const float* featureSize, const float* featureDir, int nFeatures);
+ void compute_descriptors_gpu(PtrStepSz<float4> descriptors, const float* featureX, const float* featureY, const float* featureSize, const float* featureDir, int nFeatures);
}
}}}
CV_Assert(mask.empty() || (mask.size() == img.size() && mask.type() == CV_8UC1));
CV_Assert(surf_.nOctaves > 0 && surf_.nOctaveLayers > 0);
- if (!TargetArchs::builtWith(GLOBAL_ATOMICS) || !DeviceInfo().supports(GLOBAL_ATOMICS))
- CV_Error(CV_StsNotImplemented, "The device doesn't support global atomics");
-
const int min_size = calcSize(surf_.nOctaves - 1, 0);
CV_Assert(img_rows - min_size >= 0);
CV_Assert(img_cols - min_size >= 0);
template <typename T>
void warpAffine_gpu(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation,
- int borderMode, const float* borderValue, cudaStream_t stream, int cc);
+ int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
void buildWarpPerspectiveMaps_gpu(float coeffs[3 * 3], PtrStepSzf xmap, PtrStepSzf ymap, cudaStream_t stream);
template <typename T>
void warpPerspective_gpu(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation,
- int borderMode, const float* borderValue, cudaStream_t stream, int cc);
+ int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
}
}}}
{
typedef typename NppWarpFunc<DEPTH>::npp_t npp_t;
- static void call(const cv::gpu::GpuMat& src, cv::Size wholeSize, cv::Point ofs, cv::gpu::GpuMat& dst,
- double coeffs[][3], cv::Size dsize, int interpolation, cudaStream_t stream)
+ static void call(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, double coeffs[][3], int interpolation, cudaStream_t stream)
{
static const int npp_inter[] = {NPPI_INTER_NN, NPPI_INTER_LINEAR, NPPI_INTER_CUBIC};
- dst.create(dsize, src.type());
- dst.setTo(cv::Scalar::all(0));
-
NppiSize srcsz;
- srcsz.height = wholeSize.height;
- srcsz.width = wholeSize.width;
+ srcsz.height = src.rows;
+ srcsz.width = src.cols;
NppiRect srcroi;
- srcroi.x = ofs.x;
- srcroi.y = ofs.y;
+ srcroi.x = 0;
+ srcroi.y = 0;
srcroi.height = src.rows;
srcroi.width = src.cols;
NppiRect dstroi;
- dstroi.x = dstroi.y = 0;
+ dstroi.x = 0;
+ dstroi.y = 0;
dstroi.height = dst.rows;
dstroi.width = dst.cols;
cv::gpu::NppStreamHandler h(stream);
- nppSafeCall( func((npp_t*)src.datastart, srcsz, static_cast<int>(src.step), srcroi,
- dst.ptr<npp_t>(), static_cast<int>(dst.step), dstroi, coeffs, npp_inter[interpolation]) );
+ nppSafeCall( func(src.ptr<npp_t>(), srcsz, static_cast<int>(src.step), srcroi,
+ dst.ptr<npp_t>(), static_cast<int>(dst.step), dstroi,
+ coeffs, npp_inter[interpolation]) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC);
CV_Assert(borderMode == BORDER_REFLECT101 || borderMode == BORDER_REPLICATE || borderMode == BORDER_CONSTANT || borderMode == BORDER_REFLECT || borderMode == BORDER_WRAP);
+ dst.create(dsize, src.type());
+
Size wholeSize;
Point ofs;
src.locateROI(wholeSize, ofs);
}
};
- bool useNpp = borderMode == BORDER_CONSTANT;
- useNpp = useNpp && useNppTab[src.depth()][src.channels() - 1][interpolation];
+ bool useNpp = borderMode == BORDER_CONSTANT && ofs.x == 0 && ofs.y == 0 && useNppTab[src.depth()][src.channels() - 1][interpolation];
#ifdef linux
// NPP bug on float data
useNpp = useNpp && src.depth() != CV_32F;
if (useNpp)
{
- typedef void (*func_t)(const cv::gpu::GpuMat& src, cv::Size wholeSize, cv::Point ofs, cv::gpu::GpuMat& dst, double coeffs[][3], cv::Size dsize, int flags, cudaStream_t stream);
+ typedef void (*func_t)(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, double coeffs[][3], int flags, cudaStream_t stream);
static const func_t funcs[2][6][4] =
{
}
};
+ dst.setTo(borderValue);
+
double coeffs[2][3];
Mat coeffsMat(2, 3, CV_64F, (void*)coeffs);
M.convertTo(coeffsMat, coeffsMat.type());
const func_t func = funcs[(flags & WARP_INVERSE_MAP) != 0][src.depth()][src.channels() - 1];
CV_Assert(func != 0);
- func(src, wholeSize, ofs, dst, coeffs, dsize, interpolation, StreamAccessor::getStream(s));
+ func(src, dst, coeffs, interpolation, StreamAccessor::getStream(s));
}
else
{
using namespace cv::gpu::device::imgproc;
typedef void (*func_t)(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation,
- int borderMode, const float* borderValue, cudaStream_t stream, int cc);
+ int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
static const func_t funcs[6][4] =
{
int gpuBorderType;
CV_Assert(tryConvertToGpuBorderType(borderMode, gpuBorderType));
- dst.create(dsize, src.type());
-
float coeffs[2 * 3];
Mat coeffsMat(2, 3, CV_32F, (void*)coeffs);
Scalar_<float> borderValueFloat;
borderValueFloat = borderValue;
- DeviceInfo info;
- int cc = info.majorVersion() * 10 + info.minorVersion();
-
func(src, PtrStepSzb(wholeSize.height, wholeSize.width, src.datastart, src.step), ofs.x, ofs.y, coeffs,
- dst, interpolation, gpuBorderType, borderValueFloat.val, StreamAccessor::getStream(s), cc);
+ dst, interpolation, gpuBorderType, borderValueFloat.val, StreamAccessor::getStream(s), deviceSupports(FEATURE_SET_COMPUTE_20));
}
}
CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC);
CV_Assert(borderMode == BORDER_REFLECT101 || borderMode == BORDER_REPLICATE || borderMode == BORDER_CONSTANT || borderMode == BORDER_REFLECT || borderMode == BORDER_WRAP);
+ dst.create(dsize, src.type());
+
Size wholeSize;
Point ofs;
src.locateROI(wholeSize, ofs);
}
};
- bool useNpp = borderMode == BORDER_CONSTANT;
- useNpp = useNpp && useNppTab[src.depth()][src.channels() - 1][interpolation];
+ bool useNpp = borderMode == BORDER_CONSTANT && ofs.x == 0 && ofs.y == 0 && useNppTab[src.depth()][src.channels() - 1][interpolation];
#ifdef linux
// NPP bug on float data
useNpp = useNpp && src.depth() != CV_32F;
if (useNpp)
{
- typedef void (*func_t)(const cv::gpu::GpuMat& src, cv::Size wholeSize, cv::Point ofs, cv::gpu::GpuMat& dst, double coeffs[][3], cv::Size dsize, int flags, cudaStream_t stream);
+ typedef void (*func_t)(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, double coeffs[][3], int flags, cudaStream_t stream);
static const func_t funcs[2][6][4] =
{
}
};
+ dst.setTo(borderValue);
+
double coeffs[3][3];
Mat coeffsMat(3, 3, CV_64F, (void*)coeffs);
M.convertTo(coeffsMat, coeffsMat.type());
const func_t func = funcs[(flags & WARP_INVERSE_MAP) != 0][src.depth()][src.channels() - 1];
CV_Assert(func != 0);
- func(src, wholeSize, ofs, dst, coeffs, dsize, interpolation, StreamAccessor::getStream(s));
+ func(src, dst, coeffs, interpolation, StreamAccessor::getStream(s));
}
else
{
using namespace cv::gpu::device::imgproc;
typedef void (*func_t)(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation,
- int borderMode, const float* borderValue, cudaStream_t stream, int cc);
+ int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
static const func_t funcs[6][4] =
{
int gpuBorderType;
CV_Assert(tryConvertToGpuBorderType(borderMode, gpuBorderType));
- dst.create(dsize, src.type());
-
float coeffs[3 * 3];
Mat coeffsMat(3, 3, CV_32F, (void*)coeffs);
Scalar_<float> borderValueFloat;
borderValueFloat = borderValue;
- DeviceInfo info;
- int cc = info.majorVersion() * 10 + info.minorVersion();
-
func(src, PtrStepSzb(wholeSize.height, wholeSize.width, src.datastart, src.step), ofs.x, ofs.y, coeffs,
- dst, interpolation, gpuBorderType, borderValueFloat.val, StreamAccessor::getStream(s), cc);
+ dst, interpolation, gpuBorderType, borderValueFloat.val, StreamAccessor::getStream(s), deviceSupports(FEATURE_SET_COMPUTE_20));
}
}
projects / profile / ivi / opencv.git / commitdiff