//
//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/limits.hpp"
-#include "opencv2/core/cuda/utility.hpp"
+#ifndef HAVE_OPENCV_CUDEV
-using namespace cv::cuda;
-using namespace cv::cuda::device;
+#error "opencv_cudev is required"
-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)
- {
- #if __CUDA_ARCH__ >= 200
- if (tid == 0)
- {
- Emulation::glob::atomicMin(minval, mymin);
- Emulation::glob::atomicMax(maxval, mymax);
- }
- #else
- __shared__ bool is_last;
-
- if (tid == 0)
- {
- minval[bid] = mymin;
- maxval[bid] = mymax;
-
- __threadfence();
-
- unsigned int ticket = ::atomicAdd(&blocks_finished, 1);
- is_last = (ticket == gridDim.x * gridDim.y - 1);
- }
-
- __syncthreads();
-
- if (is_last)
- {
- int idx = ::min(tid, gridDim.x * gridDim.y - 1);
-
- mymin = minval[idx];
- mymax = maxval[idx];
-
- 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] = mymin;
- maxval[0] = mymax;
-
- blocks_finished = 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];
-
- const int x0 = blockIdx.x * blockDim.x * twidth + threadIdx.x;
- const int y0 = blockIdx.y * blockDim.y * theight + threadIdx.y;
-
- 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();
-
- const minimum<R> minOp;
- const maximum<R> maxOp;
-
- for (int i = 0, y = y0; i < theight && y < src.rows; ++i, y += blockDim.y)
- {
- const T* ptr = src.ptr(y);
+#else
- for (int j = 0, x = x0; j < twidth && x < src.cols; ++j, x += blockDim.x)
- {
- if (mask(y, x))
- {
- const R srcVal = ptr[x];
+#include "opencv2/cudaarithm.hpp"
+#include "opencv2/cudev.hpp"
- mymin = minOp(mymin, srcVal);
- mymax = maxOp(mymax, srcVal);
- }
- }
- }
+using namespace cv::cudev;
- device::reduce<BLOCK_SIZE>(smem_tuple(sminval, smaxval), thrust::tie(mymin, mymax), tid, thrust::make_tuple(minOp, maxOp));
-
- GlobalReduce<BLOCK_SIZE, R>::run(mymin, mymax, minval, maxval, tid, bid, sminval, smaxval);
- }
-
- const int threads_x = 32;
- const int threads_y = 8;
-
- void getLaunchCfg(int cols, int rows, dim3& block, dim3& grid)
+namespace
+{
+ template <typename T>
+ void minMaxImpl(const GpuMat& _src, const GpuMat& mask, GpuMat& _buf, double* minVal, double* maxVal)
{
- block = dim3(threads_x, threads_y);
-
- grid = dim3(divUp(cols, block.x * block.y),
- divUp(rows, block.y * block.x));
+ typedef typename SelectIf<
+ TypesEquals<T, double>::value,
+ double,
+ typename SelectIf<TypesEquals<T, float>::value, float, int>::type
+ >::type work_type;
- grid.x = ::min(grid.x, block.x);
- grid.y = ::min(grid.y, block.y);
- }
+ GpuMat_<T> src(_src);
+ GpuMat_<work_type> buf(_buf);
- void getBufSize(int cols, int rows, int& bufcols, int& bufrows)
- {
- dim3 block, grid;
- getLaunchCfg(cols, rows, block, grid);
+ if (mask.empty())
+ gridFindMinMaxVal(src, buf);
+ else
+ gridFindMinMaxVal(src, buf, globPtr<uchar>(mask));
- bufcols = grid.x * grid.y * sizeof(double);
- bufrows = 2;
- }
+ work_type data[2];
+ buf.download(cv::Mat(1, 2, buf.type(), data));
- __global__ void setDefaultKernel(int* minval_buf, int* maxval_buf)
- {
- *minval_buf = numeric_limits<int>::max();
- *maxval_buf = numeric_limits<int>::min();
- }
- __global__ void setDefaultKernel(float* minval_buf, float* maxval_buf)
- {
- *minval_buf = numeric_limits<float>::max();
- *maxval_buf = -numeric_limits<float>::max();
- }
- __global__ void setDefaultKernel(double* minval_buf, double* maxval_buf)
- {
- *minval_buf = numeric_limits<double>::max();
- *maxval_buf = -numeric_limits<double>::max();
- }
+ if (minVal)
+ *minVal = data[0];
- template <typename R>
- void setDefault(R* minval_buf, R* maxval_buf)
- {
- setDefaultKernel<<<1, 1>>>(minval_buf, maxval_buf);
+ if (maxVal)
+ *maxVal = data[1];
}
+}
- template <typename T>
- void run(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf)
+void cv::cuda::minMax(InputArray _src, double* minVal, double* maxVal, InputArray _mask, GpuMat& buf)
+{
+ typedef void (*func_t)(const GpuMat& _src, const GpuMat& mask, GpuMat& _buf, double* minVal, double* maxVal);
+ static const func_t funcs[] =
{
- typedef typename MinMaxTypeTraits<T>::best_type R;
-
- 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);
-
- R* minval_buf = (R*) buf.ptr(0);
- R* maxval_buf = (R*) buf.ptr(1);
+ minMaxImpl<uchar>,
+ minMaxImpl<schar>,
+ minMaxImpl<ushort>,
+ minMaxImpl<short>,
+ minMaxImpl<int>,
+ minMaxImpl<float>,
+ minMaxImpl<double>
+ };
- setDefault(minval_buf, maxval_buf);
+ GpuMat src = _src.getGpuMat();
+ GpuMat mask = _mask.getGpuMat();
- 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);
+ CV_Assert( src.channels() == 1 );
+ CV_DbgAssert( mask.empty() || (mask.size() == src.size() && mask.type() == CV_8U) );
- cudaSafeCall( cudaGetLastError() );
+ const int depth = src.depth();
- cudaSafeCall( cudaDeviceSynchronize() );
+ const int work_type = depth == CV_64F ? CV_64F : depth == CV_32F ? CV_32F : CV_32S;
+ ensureSizeIsEnough(1, 2, work_type, buf);
- R minval_, maxval_;
- cudaSafeCall( cudaMemcpy(&minval_, minval_buf, sizeof(R), cudaMemcpyDeviceToHost) );
- cudaSafeCall( cudaMemcpy(&maxval_, maxval_buf, sizeof(R), cudaMemcpyDeviceToHost) );
- *minval = minval_;
- *maxval = maxval_;
- }
+ const func_t func = funcs[src.depth()];
- 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);
+ func(src, mask, buf, minVal, maxVal);
}
-#endif // CUDA_DISABLER
+#endif
__host__ void minVal(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;
- glob_reduce<MinMaxReductor<minop<work_type>, src_type, work_type>, Policy>(src, result, mask, rows, cols, stream);
+ glob_reduce<MinMaxReductor<minop<ResType>, src_type, ResType>, Policy>(src, result, mask, rows, cols, stream);
}
template <class Policy, class SrcPtr, typename ResType, class MaskPtr>
__host__ void maxVal(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;
- glob_reduce<MinMaxReductor<maxop<work_type>, src_type, work_type>, Policy>(src, result, mask, rows, cols, stream);
+ glob_reduce<MinMaxReductor<maxop<ResType>, src_type, ResType>, Policy>(src, result, mask, rows, cols, stream);
}
template <class Policy, class SrcPtr, typename ResType, class MaskPtr>
__host__ void minMaxVal(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;
- glob_reduce<MinMaxReductor<both, src_type, work_type>, Policy>(src, result, mask, rows, cols, stream);
+ glob_reduce<MinMaxReductor<both, src_type, ResType>, Policy>(src, result, mask, rows, cols, stream);
}
}
projects / platform / upstream / opencv.git / commitdiff