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Commit 020624c4 authored by Vladislav Vinogradov's avatar Vladislav Vinogradov
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used new device layer for cv::gpu::minMaxLoc

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modules/cudaarithm/src/cuda/minmaxloc.cu
View file @ 020624c4
...@@ -40,197 +40,88 @@ ...@@ -40,197 +40,88 @@
// //
//M*/ //M*/
#if !defined CUDA_DISABLER #include "opencv2/opencv_modules.hpp"
#include "opencv2/core/cuda/common.hpp" #ifndef HAVE_OPENCV_CUDEV
#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"
using namespace cv::cuda; #error "opencv_cudev is required"
using namespace cv::cuda::device;
namespace minMaxLoc #else
{
// 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<unsigned char> { typedef int best_type; };
template <> struct MinMaxTypeTraits<signed char> { typedef int best_type; };
template <> struct MinMaxTypeTraits<unsigned short> { 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_pass_1(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;
__shared__ work_type sminval[BLOCK_SIZE];
__shared__ work_type smaxval[BLOCK_SIZE];
__shared__ unsigned int sminloc[BLOCK_SIZE];
__shared__ unsigned int smaxloc[BLOCK_SIZE];
const int x0 = blockIdx.x * blockDim.x * twidth + threadIdx.x; #include "opencv2/cudaarithm.hpp"
const int y0 = blockIdx.y * blockDim.y * theight + threadIdx.y; #include "opencv2/cudev.hpp"
const int tid = threadIdx.y * blockDim.x + threadIdx.x; using namespace cv::cudev;
const int bid = blockIdx.y * gridDim.x + blockIdx.x;
work_type mymin = numeric_limits<work_type>::max(); namespace
work_type mymax = -numeric_limits<work_type>::max(); {
unsigned int myminloc = 0; template <typename T>
unsigned int mymaxloc = 0; void minMaxLocImpl(const GpuMat& _src, const GpuMat& mask, GpuMat& _valBuf, GpuMat& _locBuf, double* minVal, double* maxVal, cv::Point* minLoc, cv::Point* maxLoc)
for (int i = 0, y = y0; i < theight && y < src.rows; ++i, y += blockDim.y)
{
const T* ptr = src.ptr(y);
for (int j = 0, x = x0; j < twidth && x < src.cols; ++j, x += blockDim.x)
{
if (mask(y, x))
{
const work_type srcVal = ptr[x];
if (srcVal < mymin)
{
mymin = srcVal;
myminloc = y * src.cols + x;
}
if (srcVal > mymax)
{
mymax = srcVal;
mymaxloc = y * src.cols + x;
}
}
}
}
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>()));
if (tid == 0)
{
minval[bid] = (T) mymin;
maxval[bid] = (T) mymax;
minloc[bid] = myminloc;
maxloc[bid] = mymaxloc;
}
}
template <int BLOCK_SIZE, typename T>
__global__ void kernel_pass_2(T* minval, T* maxval, unsigned int* minloc, unsigned int* maxloc, int count)
{ {
typedef typename MinMaxTypeTraits<T>::best_type work_type; typedef typename SelectIf<
TypesEquals<T, double>::value,
double,
typename SelectIf<TypesEquals<T, float>::value, float, int>::type
>::type work_type;
const GpuMat_<T>& src = (const GpuMat_<T>&) _src;
GpuMat_<work_type>& valBuf = (GpuMat_<work_type>&) _valBuf;
GpuMat_<int>& locBuf = (GpuMat_<int>&) _locBuf;
if (mask.empty())
gridMinMaxLoc(src, valBuf, locBuf);
else
gridMinMaxLoc(src, valBuf, locBuf, globPtr<uchar>(mask));
__shared__ work_type sminval[BLOCK_SIZE]; cv::Mat_<work_type> h_valBuf;
__shared__ work_type smaxval[BLOCK_SIZE]; cv::Mat_<int> h_locBuf;
__shared__ unsigned int sminloc[BLOCK_SIZE];
__shared__ unsigned int smaxloc[BLOCK_SIZE];
unsigned int idx = ::min(threadIdx.x, count - 1); valBuf.download(h_valBuf);
locBuf.download(h_locBuf);
work_type mymin = minval[idx]; if (minVal)
work_type mymax = maxval[idx]; *minVal = h_valBuf(0, 0);
unsigned int myminloc = minloc[idx];
unsigned int mymaxloc = maxloc[idx];
reduceKeyVal<BLOCK_SIZE>(smem_tuple(sminval, smaxval), thrust::tie(mymin, mymax), if (maxVal)
smem_tuple(sminloc, smaxloc), thrust::tie(myminloc, mymaxloc), *maxVal = h_valBuf(1, 0);
threadIdx.x,
thrust::make_tuple(less<work_type>(), greater<work_type>()));
if (threadIdx.x == 0) if (minLoc)
{ {
minval[0] = (T) mymin; const int idx = h_locBuf(0, 0);
maxval[0] = (T) mymax; *minLoc = cv::Point(idx % src.cols, idx / src.cols);
minloc[0] = myminloc;
maxloc[0] = mymaxloc;
} }
}
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);
grid = dim3(divUp(cols, block.x * block.y), if (maxLoc)
divUp(rows, block.y * block.x)); {
const int idx = h_locBuf(1, 0);
grid.x = ::min(grid.x, block.x); *maxLoc = cv::Point(idx % src.cols, idx / src.cols);
grid.y = ::min(grid.y, block.y); }
}
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);
// For values
b1cols = (int)(grid.x * grid.y * elem_size);
b1rows = 2;
// For locations
b2cols = grid.x * grid.y * sizeof(int);
b2rows = 2;
} }
}
template <typename T> void cv::cuda::minMaxLoc(InputArray _src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc, InputArray _mask, GpuMat& valBuf, GpuMat& locBuf)
void run(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, int* minloc, int* maxloc, PtrStepb valbuf, PtrStep<unsigned int> locbuf) {
typedef void (*func_t)(const GpuMat& _src, const GpuMat& mask, GpuMat& _valBuf, GpuMat& _locBuf, double* minVal, double* maxVal, cv::Point* minLoc, cv::Point* maxLoc);
static const func_t funcs[] =
{ {
dim3 block, grid; minMaxLocImpl<uchar>,
getLaunchCfg(src.cols, src.rows, block, grid); minMaxLocImpl<schar>,
minMaxLocImpl<ushort>,
const int twidth = divUp(divUp(src.cols, grid.x), block.x); minMaxLocImpl<short>,
const int theight = divUp(divUp(src.rows, grid.y), block.y); minMaxLocImpl<int>,
minMaxLocImpl<float>,
minMaxLocImpl<double>
};
T* minval_buf = (T*) valbuf.ptr(0); GpuMat src = _src.getGpuMat();
T* maxval_buf = (T*) valbuf.ptr(1); GpuMat mask = _mask.getGpuMat();
unsigned int* minloc_buf = locbuf.ptr(0);
unsigned int* maxloc_buf = locbuf.ptr(1);
if (mask.data)
kernel_pass_1<threads_x * threads_y><<<grid, block>>>((PtrStepSz<T>) src, SingleMask(mask), minval_buf, maxval_buf, minloc_buf, maxloc_buf, twidth, theight);
else
kernel_pass_1<threads_x * threads_y><<<grid, block>>>((PtrStepSz<T>) src, WithOutMask(), minval_buf, maxval_buf, minloc_buf, maxloc_buf, twidth, theight);
cudaSafeCall( cudaGetLastError() ); CV_Assert( src.channels() == 1 );
CV_DbgAssert( mask.empty() || (mask.size() == src.size() && mask.type() == CV_8U) );
kernel_pass_2<threads_x * threads_y><<<1, threads_x * threads_y>>>(minval_buf, maxval_buf, minloc_buf, maxloc_buf, grid.x * grid.y); const func_t func = funcs[src.depth()];
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<unsigned char >(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, int* minloc, int* maxloc, PtrStepb valbuf, PtrStep<unsigned int> locbuf); func(src, mask, valBuf, locBuf, minVal, maxVal, minLoc, maxLoc);
template void run<signed char >(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, int* minloc, int* maxloc, PtrStepb valbuf, PtrStep<unsigned int> locbuf);
template void run<unsigned short>(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);
} }
#endif // CUDA_DISABLER #endif
modules/cudaarithm/src/reductions.cpp
View file @ 020624c4
...@@ -186,56 +186,6 @@ double cv::cuda::norm(InputArray _src1, InputArray _src2, GpuMat& buf, int normT ...@@ -186,56 +186,6 @@ double cv::cuda::norm(InputArray _src1, InputArray _src2, GpuMat& buf, int normT
return retVal; return retVal;
} }
////////////////////////////////////////////////////////////////////////
// minMaxLoc
namespace minMaxLoc
{
void getBufSize(int cols, int rows, size_t elem_size, int& b1cols, int& b1rows, int& b2cols, int& b2rows);
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::cuda::minMaxLoc(InputArray _src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
InputArray _mask, GpuMat& valBuf, GpuMat& locBuf)
{
GpuMat src = _src.getGpuMat();
GpuMat mask = _mask.getGpuMat();
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[] =
{
::minMaxLoc::run<uchar>,
::minMaxLoc::run<schar>,
::minMaxLoc::run<ushort>,
::minMaxLoc::run<short>,
::minMaxLoc::run<int>,
::minMaxLoc::run<float>,
::minMaxLoc::run<double>
};
CV_Assert( src.channels() == 1 );
CV_Assert( mask.empty() || (mask.size() == src.size() && mask.type() == CV_8U) );
if (src.depth() == CV_64F)
{
if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(cv::Error::StsUnsupportedFormat, "The device doesn't support double");
}
Size valbuf_size, locbuf_size;
::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);
const func_t func = funcs[src.depth()];
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);
}
////////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////////
// countNonZero // countNonZero
......
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