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used new device layer for cv::gpu::sum

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pull/1540/head
Vladislav Vinogradov 11 years ago
parent 9fe92e2111
commit b705e0d886
6 changed files with 145 additions and 436 deletions
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  1. 414
      modules/cudaarithm/src/cuda/sum.cu
  2. 131
      modules/cudaarithm/src/reductions.cpp
  3. 2
      modules/cudev/CMakeLists.txt
  4. 9
      modules/cudev/include/opencv2/cudev/grid/detail/reduce.hpp
  5. 8
      modules/cudev/include/opencv2/cudev/grid/reduce.hpp
  6. 17
      modules/cudev/include/opencv2/cudev/util/vec_math.hpp

414
modules/cudaarithm/src/cuda/sum.cu
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@ -40,342 +40,164 @@
//
//M*/
#if !defined CUDA_DISABLER
#include "opencv2/opencv_modules.hpp"
#include "opencv2/core/cuda/common.hpp"
#include "opencv2/core/cuda/vec_traits.hpp"
#include "opencv2/core/cuda/vec_math.hpp"
#include "opencv2/core/cuda/functional.hpp"
#include "opencv2/core/cuda/reduce.hpp"
#include "opencv2/core/cuda/emulation.hpp"
#include "opencv2/core/cuda/utility.hpp"
#ifndef HAVE_OPENCV_CUDEV
#include "unroll_detail.hpp"
#error "opencv_cudev is required"
using namespace cv::cuda;
using namespace cv::cuda::device;
#else
namespace sum
{
__device__ unsigned int blocks_finished = 0;
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
template <typename R, int cn> struct AtomicAdd;
template <typename R> struct AtomicAdd<R, 1>
{
static __device__ void run(R* ptr, R val)
{
Emulation::glob::atomicAdd(ptr, val);
}
};
template <typename R> struct AtomicAdd<R, 2>
{
typedef typename TypeVec<R, 2>::vec_type val_type;
using namespace cv::cudev;
static __device__ void run(R* ptr, val_type val)
{
Emulation::glob::atomicAdd(ptr, val.x);
Emulation::glob::atomicAdd(ptr + 1, val.y);
}
};
template <typename R> struct AtomicAdd<R, 3>
{
typedef typename TypeVec<R, 3>::vec_type val_type;
static __device__ void run(R* ptr, val_type val)
{
Emulation::glob::atomicAdd(ptr, val.x);
Emulation::glob::atomicAdd(ptr + 1, val.y);
Emulation::glob::atomicAdd(ptr + 2, val.z);
}
};
template <typename R> struct AtomicAdd<R, 4>
namespace
{
template <typename T, typename R, int cn>
cv::Scalar sumImpl(const GpuMat& _src, const GpuMat& mask, GpuMat& _buf)
{
typedef typename TypeVec<R, 4>::vec_type val_type;
static __device__ void run(R* ptr, val_type val)
{
Emulation::glob::atomicAdd(ptr, val.x);
Emulation::glob::atomicAdd(ptr + 1, val.y);
Emulation::glob::atomicAdd(ptr + 2, val.z);
Emulation::glob::atomicAdd(ptr + 3, val.w);
}
};
typedef typename MakeVec<T, cn>::type src_type;
typedef typename MakeVec<R, cn>::type res_type;
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;
if (tid == 0)
{
result[bid] = sum;
__threadfence();
unsigned int ticket = ::atomicAdd(&blocks_finished, 1);
is_last = (ticket == gridDim.x * gridDim.y - 1);
}
__syncthreads();
if (is_last)
{
sum = tid < gridDim.x * gridDim.y ? result[tid] : VecTraits<result_type>::all(0);
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)
{
result[0] = sum;
blocks_finished = 0;
}
}
#endif
}
};
GpuMat_<src_type> src(_src);
GpuMat_<res_type> buf(_buf);
template <int BLOCK_SIZE, typename src_type, typename result_type, class Mask, class Op>
__global__ void kernel(const PtrStepSz<src_type> src, result_type* result, const Mask mask, 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;
__shared__ R smem[BLOCK_SIZE * cn];
if (mask.empty())
gridCalcSum(src, buf);
else
gridCalcSum(src, buf, globPtr<uchar>(mask));
const int x0 = blockIdx.x * blockDim.x * twidth + threadIdx.x;
const int y0 = blockIdx.y * blockDim.y * theight + threadIdx.y;
cv::Scalar_<R> res;
cv::Mat res_mat(buf.size(), buf.type(), res.val);
buf.download(res_mat);
const int tid = threadIdx.y * blockDim.x + threadIdx.x;
const int bid = blockIdx.y * gridDim.x + blockIdx.x;
return res;
}
result_type sum = VecTraits<result_type>::all(0);
template <typename T, typename R, int cn>
cv::Scalar sumAbsImpl(const GpuMat& _src, const GpuMat& mask, GpuMat& _buf)
{
typedef typename MakeVec<T, cn>::type src_type;
typedef typename MakeVec<R, cn>::type res_type;
for (int i = 0, y = y0; i < theight && y < src.rows; ++i, y += blockDim.y)
{
const src_type* ptr = src.ptr(y);
GpuMat_<src_type> src(_src);
GpuMat_<res_type> buf(_buf);
for (int j = 0, x = x0; j < twidth && x < src.cols; ++j, x += blockDim.x)
{
if (mask(y, x))
{
const src_type srcVal = ptr[x];
sum = sum + op(saturate_cast<result_type>(srcVal));
}
}
}
if (mask.empty())
gridCalcSum(abs_(cvt_<res_type>(src)), buf);
else
gridCalcSum(abs_(cvt_<res_type>(src)), buf, globPtr<uchar>(mask));
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>()));
cv::Scalar_<R> res;
cv::Mat res_mat(buf.size(), buf.type(), res.val);
buf.download(res_mat);
GlobalReduce<BLOCK_SIZE, R, cn>::run(sum, result, tid, bid, smem);
return res;
}
const int threads_x = 32;
const int threads_y = 8;
void getLaunchCfg(int cols, int rows, dim3& block, dim3& grid)
template <typename T, typename R, int cn>
cv::Scalar sumSqrImpl(const GpuMat& _src, const GpuMat& mask, GpuMat& _buf)
{
block = dim3(threads_x, threads_y);
typedef typename MakeVec<T, cn>::type src_type;
typedef typename MakeVec<R, cn>::type res_type;
grid = dim3(divUp(cols, block.x * block.y),
divUp(rows, block.y * block.x));
GpuMat_<src_type> src(_src);
GpuMat_<res_type> buf(_buf);
grid.x = ::min(grid.x, block.x);
grid.y = ::min(grid.y, block.y);
}
if (mask.empty())
gridCalcSum(sqr_(cvt_<res_type>(src)), buf);
else
gridCalcSum(sqr_(cvt_<res_type>(src)), buf, globPtr<uchar>(mask));
void getBufSize(int cols, int rows, int cn, int& bufcols, int& bufrows)
{
dim3 block, grid;
getLaunchCfg(cols, rows, block, grid);
cv::Scalar_<R> res;
cv::Mat res_mat(buf.size(), buf.type(), res.val);
buf.download(res_mat);
bufcols = grid.x * grid.y * sizeof(double) * cn;
bufrows = 1;
return res;
}
}
template <typename T, typename R, int cn, template <typename> class Op>
void caller(PtrStepSzb src_, void* buf_, double* out, PtrStepSzb mask)
cv::Scalar cv::cuda::sum(InputArray _src, InputArray _mask, GpuMat& buf)
{
typedef cv::Scalar (*func_t)(const GpuMat& _src, const GpuMat& mask, GpuMat& _buf);
static const func_t funcs[7][4] =
{
typedef typename TypeVec<T, cn>::vec_type src_type;
typedef typename TypeVec<R, cn>::vec_type result_type;
{sumImpl<uchar , uint , 1>, sumImpl<uchar , uint , 2>, sumImpl<uchar , uint , 3>, sumImpl<uchar , uint , 4>},
{sumImpl<schar , int , 1>, sumImpl<schar , int , 2>, sumImpl<schar , int , 3>, sumImpl<schar , int , 4>},
{sumImpl<ushort, uint , 1>, sumImpl<ushort, uint , 2>, sumImpl<ushort, uint , 3>, sumImpl<ushort, uint , 4>},
{sumImpl<short , int , 1>, sumImpl<short , int , 2>, sumImpl<short , int , 3>, sumImpl<short , int , 4>},
{sumImpl<int , int , 1>, sumImpl<int , int , 2>, sumImpl<int , int , 3>, sumImpl<int , int , 4>},
{sumImpl<float , float , 1>, sumImpl<float , float , 2>, sumImpl<float , float , 3>, sumImpl<float , float , 4>},
{sumImpl<double, double, 1>, sumImpl<double, double, 2>, sumImpl<double, double, 3>, sumImpl<double, double, 4>}
};
PtrStepSz<src_type> src(src_);
result_type* buf = (result_type*) buf_;
GpuMat src = _src.getGpuMat();
GpuMat mask = _mask.getGpuMat();
dim3 block, grid;
getLaunchCfg(src.cols, src.rows, block, grid);
CV_DbgAssert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size()) );
const int twidth = divUp(divUp(src.cols, grid.x), block.x);
const int theight = divUp(divUp(src.rows, grid.y), block.y);
const int res_depth = std::max(src.depth(), CV_32F);
cv::cuda::ensureSizeIsEnough(1, 1, CV_MAKE_TYPE(res_depth, src.channels()), buf);
Op<result_type> op;
const func_t func = funcs[src.depth()][src.channels() - 1];
if (mask.data)
kernel<threads_x * threads_y><<<grid, block>>>(src, buf, SingleMask(mask), op, twidth, theight);
else
kernel<threads_x * threads_y><<<grid, block>>>(src, buf, WithOutMask(), op, twidth, theight);
cudaSafeCall( cudaGetLastError() );
return func(src, mask, buf);
}
cudaSafeCall( cudaDeviceSynchronize() );
cv::Scalar cv::cuda::absSum(InputArray _src, InputArray _mask, GpuMat& buf)
{
typedef cv::Scalar (*func_t)(const GpuMat& _src, const GpuMat& mask, GpuMat& _buf);
static const func_t funcs[7][4] =
{
{sumAbsImpl<uchar , uint , 1>, sumAbsImpl<uchar , uint , 2>, sumAbsImpl<uchar , uint , 3>, sumAbsImpl<uchar , uint , 4>},
{sumAbsImpl<schar , int , 1>, sumAbsImpl<schar , int , 2>, sumAbsImpl<schar , int , 3>, sumAbsImpl<schar , int , 4>},
{sumAbsImpl<ushort, uint , 1>, sumAbsImpl<ushort, uint , 2>, sumAbsImpl<ushort, uint , 3>, sumAbsImpl<ushort, uint , 4>},
{sumAbsImpl<short , int , 1>, sumAbsImpl<short , int , 2>, sumAbsImpl<short , int , 3>, sumAbsImpl<short , int , 4>},
{sumAbsImpl<int , int , 1>, sumAbsImpl<int , int , 2>, sumAbsImpl<int , int , 3>, sumAbsImpl<int , int , 4>},
{sumAbsImpl<float , float , 1>, sumAbsImpl<float , float , 2>, sumAbsImpl<float , float , 3>, sumAbsImpl<float , float , 4>},
{sumAbsImpl<double, double, 1>, sumAbsImpl<double, double, 2>, sumAbsImpl<double, double, 3>, sumAbsImpl<double, double, 4>}
};
R result[4] = {0, 0, 0, 0};
cudaSafeCall( cudaMemcpy(&result, buf, sizeof(result_type), cudaMemcpyDeviceToHost) );
GpuMat src = _src.getGpuMat();
GpuMat mask = _mask.getGpuMat();
out[0] = result[0];
out[1] = result[1];
out[2] = result[2];
out[3] = result[3];
}
CV_DbgAssert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size()) );
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, PtrStepSzb mask)
{
typedef typename SumType<T>::R R;
caller<T, R, cn, identity>(src, buf, out, mask);
}
const int res_depth = std::max(src.depth(), CV_32F);
cv::cuda::ensureSizeIsEnough(1, 1, CV_MAKE_TYPE(res_depth, src.channels()), buf);
template void run<uchar, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<uchar, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<uchar, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<uchar, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<schar, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<schar, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<schar, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<schar, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<ushort, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<ushort, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<ushort, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<ushort, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<short, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<short, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<short, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<short, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<int, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<int, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<int, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<int, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<float, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<float, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<float, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<float, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<double, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<double, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<double, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void run<double, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template <typename T, int cn>
void runAbs(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask)
{
typedef typename SumType<T>::R R;
caller<T, R, cn, abs_func>(src, buf, out, mask);
}
const func_t func = funcs[src.depth()][src.channels() - 1];
template void runAbs<uchar, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<uchar, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<uchar, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<uchar, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<schar, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<schar, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<schar, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<schar, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<ushort, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<ushort, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<ushort, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<ushort, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<short, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<short, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<short, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<short, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<int, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<int, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<int, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<int, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<float, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<float, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<float, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<float, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<double, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<double, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<double, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runAbs<double, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template <typename T> struct Sqr : unary_function<T, T>
return func(src, mask, buf);
}
cv::Scalar cv::cuda::sqrSum(InputArray _src, InputArray _mask, GpuMat& buf)
{
typedef cv::Scalar (*func_t)(const GpuMat& _src, const GpuMat& mask, GpuMat& _buf);
static const func_t funcs[7][4] =
{
__device__ __forceinline__ T operator ()(T x) const
{
return x * x;
}
{sumSqrImpl<uchar , double, 1>, sumSqrImpl<uchar , double, 2>, sumSqrImpl<uchar , double, 3>, sumSqrImpl<uchar , double, 4>},
{sumSqrImpl<schar , double, 1>, sumSqrImpl<schar , double, 2>, sumSqrImpl<schar , double, 3>, sumSqrImpl<schar , double, 4>},
{sumSqrImpl<ushort, double, 1>, sumSqrImpl<ushort, double, 2>, sumSqrImpl<ushort, double, 3>, sumSqrImpl<ushort, double, 4>},
{sumSqrImpl<short , double, 1>, sumSqrImpl<short , double, 2>, sumSqrImpl<short , double, 3>, sumSqrImpl<short , double, 4>},
{sumSqrImpl<int , double, 1>, sumSqrImpl<int , double, 2>, sumSqrImpl<int , double, 3>, sumSqrImpl<int , double, 4>},
{sumSqrImpl<float , double, 1>, sumSqrImpl<float , double, 2>, sumSqrImpl<float , double, 3>, sumSqrImpl<float , double, 4>},
{sumSqrImpl<double, double, 1>, sumSqrImpl<double, double, 2>, sumSqrImpl<double, double, 3>, sumSqrImpl<double, double, 4>}
};
template <typename T, int cn>
void runSqr(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask)
{
caller<T, double, cn, Sqr>(src, buf, out, mask);
}
GpuMat src = _src.getGpuMat();
GpuMat mask = _mask.getGpuMat();
CV_DbgAssert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size()) );
const int res_depth = CV_64F;
cv::cuda::ensureSizeIsEnough(1, 1, CV_MAKE_TYPE(res_depth, src.channels()), buf);
const func_t func = funcs[src.depth()][src.channels() - 1];
template void runSqr<uchar, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<uchar, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<uchar, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<uchar, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<schar, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<schar, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<schar, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<schar, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<ushort, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<ushort, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<ushort, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<ushort, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<short, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<short, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<short, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<short, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<int, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<int, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<int, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<int, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<float, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<float, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<float, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<float, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<double, 1>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<double, 2>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<double, 3>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
template void runSqr<double, 4>(PtrStepSzb src, void* buf, double* out, PtrStepSzb mask);
return func(src, mask, buf);
}
#endif // CUDA_DISABLER
#endif

131
modules/cudaarithm/src/reductions.cpp
Unescape View File

@ -186,137 +186,6 @@ double cv::cuda::norm(InputArray _src1, InputArray _src2, GpuMat& buf, int normT
return retVal;
}
////////////////////////////////////////////////////////////////////////
// Sum
namespace sum
{
void getBufSize(int cols, int rows, int cn, int& bufcols, int& bufrows);
template <typename T, int cn>
void run(PtrStepSzb src, void* buf, double* sum, PtrStepSzb mask);
template <typename T, int cn>
void runAbs(PtrStepSzb src, void* buf, double* sum, PtrStepSzb mask);
template <typename T, int cn>
void runSqr(PtrStepSzb src, void* buf, double* sum, PtrStepSzb mask);
}
Scalar cv::cuda::sum(InputArray _src, InputArray _mask, GpuMat& buf)
{
GpuMat src = _src.getGpuMat();
GpuMat mask = _mask.getGpuMat();
typedef void (*func_t)(PtrStepSzb src, void* buf, double* sum, PtrStepSzb mask);
static const func_t funcs[7][5] =
{
{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( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size()) );
if (src.depth() == CV_64F)
{
if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(cv::Error::StsUnsupportedFormat, "The device doesn't support double");
}
Size buf_size;
::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));
const func_t func = funcs[src.depth()][src.channels()];
double result[4];
func(src, buf.data, result, mask);
return Scalar(result[0], result[1], result[2], result[3]);
}
Scalar cv::cuda::absSum(InputArray _src, InputArray _mask, GpuMat& buf)
{
GpuMat src = _src.getGpuMat();
GpuMat mask = _mask.getGpuMat();
typedef void (*func_t)(PtrStepSzb src, void* buf, double* sum, PtrStepSzb mask);
static const func_t funcs[7][5] =
{
{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>}
};
CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size()) );
if (src.depth() == CV_64F)
{
if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(cv::Error::StsUnsupportedFormat, "The device doesn't support double");
}
Size buf_size;
::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));
const func_t func = funcs[src.depth()][src.channels()];
double result[4];
func(src, buf.data, result, mask);
return Scalar(result[0], result[1], result[2], result[3]);
}
Scalar cv::cuda::sqrSum(InputArray _src, InputArray _mask, GpuMat& buf)
{
GpuMat src = _src.getGpuMat();
GpuMat mask = _mask.getGpuMat();
typedef void (*func_t)(PtrStepSzb src, void* buf, double* sum, PtrStepSzb mask);
static const func_t funcs[7][5] =
{
{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>}
};
CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size()) );
if (src.depth() == CV_64F)
{
if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(cv::Error::StsUnsupportedFormat, "The device doesn't support double");
}
Size buf_size;
::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));
const func_t func = funcs[src.depth()][src.channels()];
double result[4];
func(src, buf.data, result, mask);
return Scalar(result[0], result[1], result[2], result[3]);
}
////////////////////////////////////////////////////////////////////////
// minMax

2
modules/cudev/CMakeLists.txt
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@ -4,7 +4,7 @@ endif()
set(the_description "CUDA device layer")
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4189 /wd4505 -Wundef -Wmissing-declarations -Wunused-function -Wunused-variable)
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4189 /wd4505 -Wundef -Wmissing-declarations -Wunused-function -Wunused-variable -Wenum-compare)
ocv_add_module(cudev)

9
modules/cudev/include/opencv2/cudev/grid/detail/reduce.hpp
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@ -418,9 +418,7 @@ namespace grid_reduce_detail
const dim3 block(Policy::block_size_x, Policy::block_size_y);
const dim3 grid(divUp(cols, block.x * Policy::patch_size_x), divUp(rows, block.y * Policy::patch_size_y));
const int BLOCK_SIZE = Policy::block_size_x * Policy::block_size_y;
glob_reduce<Reductor, BLOCK_SIZE, Policy::patch_size_x, Policy::patch_size_y><<<grid, block, 0, stream>>>(src, result, mask, rows, cols);
glob_reduce<Reductor, Policy::block_size_x * Policy::block_size_y, Policy::patch_size_x, Policy::patch_size_y><<<grid, block, 0, stream>>>(src, result, mask, rows, cols);
CV_CUDEV_SAFE_CALL( cudaGetLastError() );
if (stream == 0)
@ -433,10 +431,9 @@ namespace grid_reduce_detail
__host__ void sum(const SrcPtr& src, ResType* result, const MaskPtr& mask, int rows, int cols, cudaStream_t stream)
{
typedef typename PtrTraits<SrcPtr>::value_type src_type;
const int cn = VecTraits<src_type>::cn;
typedef typename MakeVec<ResType, cn>::type work_type;
typedef typename VecTraits<ResType>::elem_type res_elem_type;
glob_reduce<SumReductor<src_type, work_type>, Policy>(src, result, mask, rows, cols, stream);
glob_reduce<SumReductor<src_type, ResType>, Policy>(src, (res_elem_type*) result, mask, rows, cols, stream);
}
template <class Policy, class SrcPtr, typename ResType, class MaskPtr>

8
modules/cudev/include/opencv2/cudev/grid/reduce.hpp
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@ -59,6 +59,10 @@ namespace cv { namespace cudev {
template <class Policy, class SrcPtr, typename ResType, class MaskPtr>
__host__ void gridCalcSum_(const SrcPtr& src, GpuMat_<ResType>& dst, const MaskPtr& mask, Stream& stream = Stream::Null())
{
typedef typename PtrTraits<SrcPtr>::value_type src_type;
CV_StaticAssert( VecTraits<src_type>::cn == VecTraits<ResType>::cn, "" );
dst.create(1, 1);
dst.setTo(0, stream);
@ -77,6 +81,10 @@ __host__ void gridCalcSum_(const SrcPtr& src, GpuMat_<ResType>& dst, const MaskP
template <class Policy, class SrcPtr, typename ResType>
__host__ void gridCalcSum_(const SrcPtr& src, GpuMat_<ResType>& dst, Stream& stream = Stream::Null())
{
typedef typename PtrTraits<SrcPtr>::value_type src_type;
CV_StaticAssert( VecTraits<src_type>::cn == VecTraits<ResType>::cn, "" );
dst.create(1, 1);
dst.setTo(0, stream);

17
modules/cudev/include/opencv2/cudev/util/vec_math.hpp
Unescape View File

@ -194,10 +194,23 @@ CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uint, uint)
return VecTraits<output_type ## 4>::make(func (a.x), func (a.y), func (a.z), func (a.w)); \
}
namespace vec_math_detail
{
__device__ __forceinline__ schar abs_(schar val)
{
return (schar) ::abs((int) val);
}
__device__ __forceinline__ short abs_(short val)
{
return (short) ::abs((int) val);
}
}
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uchar, uchar)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, char, char)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, vec_math_detail::abs_, char, char)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, ushort, ushort)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, short, short)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, vec_math_detail::abs_, short, short)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, int, int)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uint, uint)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabsf, float, float)

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