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fixed build for CARMA platform

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pull/258/merge
Vladislav Vinogradov 13 years ago
parent 889674ef43
commit da93a1dab9
27 changed files with 543 additions and 594 deletions
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  1. 1
      cmake/OpenCVDetectCUDA.cmake
  2. 1
      modules/core/CMakeLists.txt
  3. 26
      modules/core/src/gpumat.cpp
  4. 9
      modules/core/src/opengl_interop.cpp
  5. 2
      modules/gpu/app/nv_perf_test/CMakeLists.txt
  6. 1
      modules/gpu/include/opencv2/gpu/device/common.hpp
  7. 50
      modules/gpu/src/cuda/canny.cu
  8. 1
      modules/gpu/src/cuda/ccomponetns.cu
  9. 335
      modules/gpu/src/cuda/column_filter.h
  10. 136
      modules/gpu/src/cuda/element_operations.cu
  11. 1
      modules/gpu/src/cuda/gftt.cu
  12. 11
      modules/gpu/src/cuda/global_motion.cu
  13. 13
      modules/gpu/src/cuda/hist.cu
  14. 4
      modules/gpu/src/cuda/hog.cu
  15. 2
      modules/gpu/src/cuda/hough.cu
  16. 15
      modules/gpu/src/cuda/matrix_reductions.cu
  17. 5
      modules/gpu/src/cuda/optflowbm.cu
  18. 1
      modules/gpu/src/cuda/orb.cu
  19. 37
      modules/gpu/src/cuda/pyrlk.cu
  20. 335
      modules/gpu/src/cuda/row_filter.h
  21. 36
      modules/gpu/src/cuda/tvl1flow.cu
  22. 2
      modules/gpu/src/imgproc.cpp
  23. 12
      modules/gpu/test/nvidia/TestHaarCascadeApplication.cpp
  24. 8
      samples/gpu/driver_api_multi.cpp
  25. 2
      samples/gpu/driver_api_stereo_multi.cpp
  26. 3
      samples/gpu/softcascade.cpp

1
cmake/OpenCVDetectCUDA.cmake
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@ -92,7 +92,6 @@ if(CUDA_FOUND)
mark_as_advanced(CUDA_BUILD_CUBIN CUDA_BUILD_EMULATION CUDA_VERBOSE_BUILD CUDA_SDK_ROOT_DIR)
unset(CUDA_npp_LIBRARY CACHE)
find_cuda_helper_libs(npp)
macro(ocv_cuda_compile VAR)

1
modules/core/CMakeLists.txt
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@ -10,7 +10,6 @@ if(HAVE_CUDA)
file(GLOB lib_cuda "src/cuda/*.cu")
ocv_cuda_compile(cuda_objs ${lib_cuda})
set(cuda_link_libs ${CUDA_LIBRARIES} ${CUDA_npp_LIBRARY})
else()
set(lib_cuda "")

26
modules/core/src/gpumat.cpp
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@ -45,8 +45,7 @@
#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
@ -394,18 +393,6 @@ void cv::gpu::DeviceInfo::queryMemory(size_t& free_memory, size_t& total_memory)
namespace
{
template <class T> void getCudaAttribute(T *attribute, CUdevice_attribute device_attribute, int device)
{
*attribute = T();
//CUresult error = CUDA_SUCCESS;// = cuDeviceGetAttribute( attribute, device_attribute, device ); why link erros under ubuntu??
CUresult error = cuDeviceGetAttribute( attribute, device_attribute, device );
if( CUDA_SUCCESS == error )
return;
printf("Driver API error = %04d\n", error);
cv::gpu::error("driver API error", __FILE__, __LINE__);
}
int convertSMVer2Cores(int major, int minor)
{
// Defines for GPU Architecture types (using the SM version to determine the # of cores per SM
@ -466,17 +453,6 @@ void cv::gpu::printCudaDeviceInfo(int device)
convertSMVer2Cores(prop.major, prop.minor) * prop.multiProcessorCount);
printf(" GPU Clock Speed: %.2f GHz\n", prop.clockRate * 1e-6f);
// This is not available in the CUDA Runtime API, so we make the necessary calls the driver API to support this for output
int memoryClock, memBusWidth, L2CacheSize;
getCudaAttribute<int>( &memoryClock, CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE, dev );
getCudaAttribute<int>( &memBusWidth, CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH, dev );
getCudaAttribute<int>( &L2CacheSize, CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE, dev );
printf(" Memory Clock rate: %.2f Mhz\n", memoryClock * 1e-3f);
printf(" Memory Bus Width: %d-bit\n", memBusWidth);
if (L2CacheSize)
printf(" L2 Cache Size: %d bytes\n", L2CacheSize);
printf(" Max Texture Dimension Size (x,y,z) 1D=(%d), 2D=(%d,%d), 3D=(%d,%d,%d)\n",
prop.maxTexture1D, prop.maxTexture2D[0], prop.maxTexture2D[1],
prop.maxTexture3D[0], prop.maxTexture3D[1], prop.maxTexture3D[2]);

9
modules/core/src/opengl_interop.cpp
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@ -44,12 +44,14 @@
#include "opencv2/core/opengl_interop.hpp"
#include "opencv2/core/gpumat.hpp"
#ifdef HAVE_OPENGL
#include "gl_core_3_1.hpp"
#ifdef HAVE_CUDA
#include <cuda_runtime.h>
#include <cuda_gl_interop.h>
#endif
#endif
using namespace std;
using namespace cv;
@ -61,7 +63,6 @@ namespace
void throw_nogl() { CV_Error(CV_OpenGlNotSupported, "The library is compiled without OpenGL support"); }
#else
void throw_nogl() { CV_Error(CV_OpenGlApiCallError, "OpenGL context doesn't exist"); }
#endif
#ifndef HAVE_CUDA
void throw_nocuda() { CV_Error(CV_GpuNotSupported, "The library is compiled without GPU support"); }
@ -80,6 +81,7 @@ namespace
cv::gpu::error(cudaGetErrorString(err), file, line, func);
}
#endif
#endif
}
bool cv::checkGlError(const char* file, const int line, const char* func)
@ -139,12 +141,17 @@ namespace
void cv::gpu::setGlDevice(int device)
{
#ifndef HAVE_OPENGL
(void) device;
throw_nogl();
#else
#if !defined(HAVE_CUDA) || defined(CUDA_DISABLER)
(void) device;
throw_nocuda();
#else
cudaSafeCall( cudaGLSetGLDevice(device) );
#endif
#endif
}
////////////////////////////////////////////////////////////////////////

2
modules/gpu/app/nv_perf_test/CMakeLists.txt
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@ -1,4 +1,4 @@
cmake_minimum_required(VERSION 2.8.6)
cmake_minimum_required(VERSION 2.8.3)
project(nv_perf_test)

1
modules/gpu/include/opencv2/gpu/device/common.hpp
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@ -100,7 +100,6 @@ namespace cv { namespace gpu
typedef unsigned char uchar;
typedef unsigned short ushort;
typedef signed char schar;
typedef unsigned int uint;
template<class T> inline void bindTexture(const textureReference* tex, const PtrStepSz<T>& img)
{

50
modules/gpu/src/cuda/canny.cu
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@ -52,7 +52,7 @@
using namespace cv::gpu;
using namespace cv::gpu::device;
namespace
namespace canny
{
struct L1 : binary_function<int, int, float>
{
@ -78,17 +78,17 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <> struct TransformFunctorTraits<L1> : DefaultTransformFunctorTraits<L1>
template <> struct TransformFunctorTraits<canny::L1> : DefaultTransformFunctorTraits<canny::L1>
{
enum { smart_shift = 4 };
};
template <> struct TransformFunctorTraits<L2> : DefaultTransformFunctorTraits<L2>
template <> struct TransformFunctorTraits<canny::L2> : DefaultTransformFunctorTraits<canny::L2>
{
enum { smart_shift = 4 };
};
}}}
namespace
namespace canny
{
texture<uchar, cudaTextureType2D, cudaReadModeElementType> tex_src(false, cudaFilterModePoint, cudaAddressModeClamp);
struct SrcTex
@ -104,7 +104,7 @@ namespace
};
template <class Norm> __global__
void calcMagnitude(const SrcTex src, PtrStepi dx, PtrStepi dy, PtrStepSzf mag, const Norm norm)
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;
@ -120,10 +120,7 @@ namespace
mag(y, x) = norm(dxVal, dyVal);
}
}
namespace canny
{
void calcMagnitude(PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, bool L2Grad)
{
const dim3 block(16, 16);
@ -135,12 +132,12 @@ namespace canny
if (L2Grad)
{
L2 norm;
::calcMagnitude<<<grid, block>>>(src, dx, dy, mag, norm);
calcMagnitudeKernel<<<grid, block>>>(src, dx, dy, mag, norm);
}
else
{
L1 norm;
::calcMagnitude<<<grid, block>>>(src, dx, dy, mag, norm);
calcMagnitudeKernel<<<grid, block>>>(src, dx, dy, mag, norm);
}
cudaSafeCall( cudaGetLastError() );
@ -165,11 +162,11 @@ namespace canny
//////////////////////////////////////////////////////////////////////////////////////////
namespace
namespace canny
{
texture<float, cudaTextureType2D, cudaReadModeElementType> tex_mag(false, cudaFilterModePoint, cudaAddressModeClamp);
__global__ void calcMap(const PtrStepSzi dx, const PtrStepi dy, PtrStepi map, const float low_thresh, const float high_thresh)
__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);
@ -220,10 +217,7 @@ namespace
map(y, x) = edge_type;
}
}
namespace canny
{
void calcMap(PtrStepSzi dx, PtrStepSzi dy, PtrStepSzf mag, PtrStepSzi map, float low_thresh, float high_thresh)
{
const dim3 block(16, 16);
@ -231,7 +225,7 @@ namespace canny
bindTexture(&tex_mag, mag);
::calcMap<<<grid, block>>>(dx, dy, map, low_thresh, high_thresh);
calcMapKernel<<<grid, block>>>(dx, dy, map, low_thresh, high_thresh);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
@ -240,11 +234,11 @@ namespace canny
//////////////////////////////////////////////////////////////////////////////////////////
namespace
namespace canny
{
__device__ int counter = 0;
__global__ void edgesHysteresisLocal(PtrStepSzi map, ushort2* st)
__global__ void edgesHysteresisLocalKernel(PtrStepSzi map, ushort2* st)
{
__shared__ volatile int smem[18][18];
@ -325,10 +319,7 @@ namespace
st[ind] = make_ushort2(x, y);
}
}
}
namespace canny
{
void edgesHysteresisLocal(PtrStepSzi map, ushort2* st1)
{
void* counter_ptr;
@ -339,7 +330,7 @@ namespace canny
const dim3 block(16, 16);
const dim3 grid(divUp(map.cols, block.x), divUp(map.rows, block.y));
::edgesHysteresisLocal<<<grid, block>>>(map, st1);
edgesHysteresisLocalKernel<<<grid, block>>>(map, st1);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
@ -348,12 +339,12 @@ namespace canny
//////////////////////////////////////////////////////////////////////////////////////////
namespace
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};
__global__ void edgesHysteresisGlobal(PtrStepSzi map, ushort2* st1, ushort2* st2, const int count)
__global__ void edgesHysteresisGlobalKernel(PtrStepSzi map, ushort2* st1, ushort2* st2, const int count)
{
const int stack_size = 512;
@ -439,14 +430,11 @@ namespace
st2[ind + i] = s_st[i];
}
}
}
namespace canny
{
void edgesHysteresisGlobal(PtrStepSzi map, ushort2* st1, ushort2* st2)
{
void* counter_ptr;
cudaSafeCall( cudaGetSymbolAddress(&counter_ptr, ::counter) );
cudaSafeCall( cudaGetSymbolAddress(&counter_ptr, canny::counter) );
int count;
cudaSafeCall( cudaMemcpy(&count, counter_ptr, sizeof(int), cudaMemcpyDeviceToHost) );
@ -458,7 +446,7 @@ namespace canny
const dim3 block(128);
const dim3 grid(::min(count, 65535u), divUp(count, 65535), 1);
::edgesHysteresisGlobal<<<grid, block>>>(map, st1, st2, count);
edgesHysteresisGlobalKernel<<<grid, block>>>(map, st1, st2, count);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
@ -472,7 +460,7 @@ namespace canny
//////////////////////////////////////////////////////////////////////////////////////////
namespace
namespace canny
{
struct GetEdges : unary_function<int, uchar>
{
@ -488,7 +476,7 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <> struct TransformFunctorTraits<GetEdges> : DefaultTransformFunctorTraits<GetEdges>
template <> struct TransformFunctorTraits<canny::GetEdges> : DefaultTransformFunctorTraits<canny::GetEdges>
{
enum { smart_shift = 4 };
};

1
modules/gpu/src/cuda/ccomponetns.cu
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@ -497,6 +497,7 @@ namespace cv { namespace gpu { namespace device
void labelComponents(const PtrStepSzb& edges, PtrStepSzi comps, int flags, cudaStream_t stream)
{
(void) flags;
dim3 block(CTA_SIZE_X, CTA_SIZE_Y);
dim3 grid(divUp(edges.cols, TILE_COLS), divUp(edges.rows, TILE_ROWS));

335
modules/gpu/src/cuda/column_filter.h
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@ -49,20 +49,12 @@
using namespace cv::gpu;
using namespace cv::gpu::device;
namespace
namespace column_filter
{
#define MAX_KERNEL_SIZE 32
__constant__ float c_kernel[MAX_KERNEL_SIZE];
void loadKernel(const float* kernel, int ksize, cudaStream_t stream)
{
if (stream == 0)
cudaSafeCall( cudaMemcpyToSymbol(c_kernel, kernel, ksize * sizeof(float), 0, cudaMemcpyDeviceToDevice) );
else
cudaSafeCall( cudaMemcpyToSymbolAsync(c_kernel, kernel, ksize * sizeof(float), 0, cudaMemcpyDeviceToDevice, stream) );
}
template <int KSIZE, typename T, typename D, typename B>
__global__ void linearColumnFilter(const PtrStepSz<T> src, PtrStep<D> dst, const int anchor, const B brd)
{
@ -196,182 +188,185 @@ namespace filter
{
{
0,
::caller< 1, T, D, BrdColReflect101>,
::caller< 2, T, D, BrdColReflect101>,
::caller< 3, T, D, BrdColReflect101>,
::caller< 4, T, D, BrdColReflect101>,
::caller< 5, T, D, BrdColReflect101>,
::caller< 6, T, D, BrdColReflect101>,
::caller< 7, T, D, BrdColReflect101>,
::caller< 8, T, D, BrdColReflect101>,
::caller< 9, T, D, BrdColReflect101>,
::caller<10, T, D, BrdColReflect101>,
::caller<11, T, D, BrdColReflect101>,
::caller<12, T, D, BrdColReflect101>,
::caller<13, T, D, BrdColReflect101>,
::caller<14, T, D, BrdColReflect101>,
::caller<15, T, D, BrdColReflect101>,
::caller<16, T, D, BrdColReflect101>,
::caller<17, T, D, BrdColReflect101>,
::caller<18, T, D, BrdColReflect101>,
::caller<19, T, D, BrdColReflect101>,
::caller<20, T, D, BrdColReflect101>,
::caller<21, T, D, BrdColReflect101>,
::caller<22, T, D, BrdColReflect101>,
::caller<23, T, D, BrdColReflect101>,
::caller<24, T, D, BrdColReflect101>,
::caller<25, T, D, BrdColReflect101>,
::caller<26, T, D, BrdColReflect101>,
::caller<27, T, D, BrdColReflect101>,
::caller<28, T, D, BrdColReflect101>,
::caller<29, T, D, BrdColReflect101>,
::caller<30, T, D, BrdColReflect101>,
::caller<31, T, D, BrdColReflect101>,
::caller<32, T, D, BrdColReflect101>
column_filter::caller< 1, T, D, BrdColReflect101>,
column_filter::caller< 2, T, D, BrdColReflect101>,
column_filter::caller< 3, T, D, BrdColReflect101>,
column_filter::caller< 4, T, D, BrdColReflect101>,
column_filter::caller< 5, T, D, BrdColReflect101>,
column_filter::caller< 6, T, D, BrdColReflect101>,
column_filter::caller< 7, T, D, BrdColReflect101>,
column_filter::caller< 8, T, D, BrdColReflect101>,
column_filter::caller< 9, T, D, BrdColReflect101>,
column_filter::caller<10, T, D, BrdColReflect101>,
column_filter::caller<11, T, D, BrdColReflect101>,
column_filter::caller<12, T, D, BrdColReflect101>,
column_filter::caller<13, T, D, BrdColReflect101>,
column_filter::caller<14, T, D, BrdColReflect101>,
column_filter::caller<15, T, D, BrdColReflect101>,
column_filter::caller<16, T, D, BrdColReflect101>,
column_filter::caller<17, T, D, BrdColReflect101>,
column_filter::caller<18, T, D, BrdColReflect101>,
column_filter::caller<19, T, D, BrdColReflect101>,
column_filter::caller<20, T, D, BrdColReflect101>,
column_filter::caller<21, T, D, BrdColReflect101>,
column_filter::caller<22, T, D, BrdColReflect101>,
column_filter::caller<23, T, D, BrdColReflect101>,
column_filter::caller<24, T, D, BrdColReflect101>,
column_filter::caller<25, T, D, BrdColReflect101>,
column_filter::caller<26, T, D, BrdColReflect101>,
column_filter::caller<27, T, D, BrdColReflect101>,
column_filter::caller<28, T, D, BrdColReflect101>,
column_filter::caller<29, T, D, BrdColReflect101>,
column_filter::caller<30, T, D, BrdColReflect101>,
column_filter::caller<31, T, D, BrdColReflect101>,
column_filter::caller<32, T, D, BrdColReflect101>
},
{
0,
::caller< 1, T, D, BrdColReplicate>,
::caller< 2, T, D, BrdColReplicate>,
::caller< 3, T, D, BrdColReplicate>,
::caller< 4, T, D, BrdColReplicate>,
::caller< 5, T, D, BrdColReplicate>,
::caller< 6, T, D, BrdColReplicate>,
::caller< 7, T, D, BrdColReplicate>,
::caller< 8, T, D, BrdColReplicate>,
::caller< 9, T, D, BrdColReplicate>,
::caller<10, T, D, BrdColReplicate>,
::caller<11, T, D, BrdColReplicate>,
::caller<12, T, D, BrdColReplicate>,
::caller<13, T, D, BrdColReplicate>,
::caller<14, T, D, BrdColReplicate>,
::caller<15, T, D, BrdColReplicate>,
::caller<16, T, D, BrdColReplicate>,
::caller<17, T, D, BrdColReplicate>,
::caller<18, T, D, BrdColReplicate>,
::caller<19, T, D, BrdColReplicate>,
::caller<20, T, D, BrdColReplicate>,
::caller<21, T, D, BrdColReplicate>,
::caller<22, T, D, BrdColReplicate>,
::caller<23, T, D, BrdColReplicate>,
::caller<24, T, D, BrdColReplicate>,
::caller<25, T, D, BrdColReplicate>,
::caller<26, T, D, BrdColReplicate>,
::caller<27, T, D, BrdColReplicate>,
::caller<28, T, D, BrdColReplicate>,
::caller<29, T, D, BrdColReplicate>,
::caller<30, T, D, BrdColReplicate>,
::caller<31, T, D, BrdColReplicate>,
::caller<32, T, D, BrdColReplicate>
column_filter::caller< 1, T, D, BrdColReplicate>,
column_filter::caller< 2, T, D, BrdColReplicate>,
column_filter::caller< 3, T, D, BrdColReplicate>,
column_filter::caller< 4, T, D, BrdColReplicate>,
column_filter::caller< 5, T, D, BrdColReplicate>,
column_filter::caller< 6, T, D, BrdColReplicate>,
column_filter::caller< 7, T, D, BrdColReplicate>,
column_filter::caller< 8, T, D, BrdColReplicate>,
column_filter::caller< 9, T, D, BrdColReplicate>,
column_filter::caller<10, T, D, BrdColReplicate>,
column_filter::caller<11, T, D, BrdColReplicate>,
column_filter::caller<12, T, D, BrdColReplicate>,
column_filter::caller<13, T, D, BrdColReplicate>,
column_filter::caller<14, T, D, BrdColReplicate>,
column_filter::caller<15, T, D, BrdColReplicate>,
column_filter::caller<16, T, D, BrdColReplicate>,
column_filter::caller<17, T, D, BrdColReplicate>,
column_filter::caller<18, T, D, BrdColReplicate>,
column_filter::caller<19, T, D, BrdColReplicate>,
column_filter::caller<20, T, D, BrdColReplicate>,
column_filter::caller<21, T, D, BrdColReplicate>,
column_filter::caller<22, T, D, BrdColReplicate>,
column_filter::caller<23, T, D, BrdColReplicate>,
column_filter::caller<24, T, D, BrdColReplicate>,
column_filter::caller<25, T, D, BrdColReplicate>,
column_filter::caller<26, T, D, BrdColReplicate>,
column_filter::caller<27, T, D, BrdColReplicate>,
column_filter::caller<28, T, D, BrdColReplicate>,
column_filter::caller<29, T, D, BrdColReplicate>,
column_filter::caller<30, T, D, BrdColReplicate>,
column_filter::caller<31, T, D, BrdColReplicate>,
column_filter::caller<32, T, D, BrdColReplicate>
},
{
0,
::caller< 1, T, D, BrdColConstant>,
::caller< 2, T, D, BrdColConstant>,
::caller< 3, T, D, BrdColConstant>,
::caller< 4, T, D, BrdColConstant>,
::caller< 5, T, D, BrdColConstant>,
::caller< 6, T, D, BrdColConstant>,
::caller< 7, T, D, BrdColConstant>,
::caller< 8, T, D, BrdColConstant>,
::caller< 9, T, D, BrdColConstant>,
::caller<10, T, D, BrdColConstant>,
::caller<11, T, D, BrdColConstant>,
::caller<12, T, D, BrdColConstant>,
::caller<13, T, D, BrdColConstant>,
::caller<14, T, D, BrdColConstant>,
::caller<15, T, D, BrdColConstant>,
::caller<16, T, D, BrdColConstant>,
::caller<17, T, D, BrdColConstant>,
::caller<18, T, D, BrdColConstant>,
::caller<19, T, D, BrdColConstant>,
::caller<20, T, D, BrdColConstant>,
::caller<21, T, D, BrdColConstant>,
::caller<22, T, D, BrdColConstant>,
::caller<23, T, D, BrdColConstant>,
::caller<24, T, D, BrdColConstant>,
::caller<25, T, D, BrdColConstant>,
::caller<26, T, D, BrdColConstant>,
::caller<27, T, D, BrdColConstant>,
::caller<28, T, D, BrdColConstant>,
::caller<29, T, D, BrdColConstant>,
::caller<30, T, D, BrdColConstant>,
::caller<31, T, D, BrdColConstant>,
::caller<32, T, D, BrdColConstant>
column_filter::caller< 1, T, D, BrdColConstant>,
column_filter::caller< 2, T, D, BrdColConstant>,
column_filter::caller< 3, T, D, BrdColConstant>,
column_filter::caller< 4, T, D, BrdColConstant>,
column_filter::caller< 5, T, D, BrdColConstant>,
column_filter::caller< 6, T, D, BrdColConstant>,
column_filter::caller< 7, T, D, BrdColConstant>,
column_filter::caller< 8, T, D, BrdColConstant>,
column_filter::caller< 9, T, D, BrdColConstant>,
column_filter::caller<10, T, D, BrdColConstant>,
column_filter::caller<11, T, D, BrdColConstant>,
column_filter::caller<12, T, D, BrdColConstant>,
column_filter::caller<13, T, D, BrdColConstant>,
column_filter::caller<14, T, D, BrdColConstant>,
column_filter::caller<15, T, D, BrdColConstant>,
column_filter::caller<16, T, D, BrdColConstant>,
column_filter::caller<17, T, D, BrdColConstant>,
column_filter::caller<18, T, D, BrdColConstant>,
column_filter::caller<19, T, D, BrdColConstant>,
column_filter::caller<20, T, D, BrdColConstant>,
column_filter::caller<21, T, D, BrdColConstant>,
column_filter::caller<22, T, D, BrdColConstant>,
column_filter::caller<23, T, D, BrdColConstant>,
column_filter::caller<24, T, D, BrdColConstant>,
column_filter::caller<25, T, D, BrdColConstant>,
column_filter::caller<26, T, D, BrdColConstant>,
column_filter::caller<27, T, D, BrdColConstant>,
column_filter::caller<28, T, D, BrdColConstant>,
column_filter::caller<29, T, D, BrdColConstant>,
column_filter::caller<30, T, D, BrdColConstant>,
column_filter::caller<31, T, D, BrdColConstant>,
column_filter::caller<32, T, D, BrdColConstant>
},
{
0,
::caller< 1, T, D, BrdColReflect>,
::caller< 2, T, D, BrdColReflect>,
::caller< 3, T, D, BrdColReflect>,
::caller< 4, T, D, BrdColReflect>,
::caller< 5, T, D, BrdColReflect>,
::caller< 6, T, D, BrdColReflect>,
::caller< 7, T, D, BrdColReflect>,
::caller< 8, T, D, BrdColReflect>,
::caller< 9, T, D, BrdColReflect>,
::caller<10, T, D, BrdColReflect>,
::caller<11, T, D, BrdColReflect>,
::caller<12, T, D, BrdColReflect>,
::caller<13, T, D, BrdColReflect>,
::caller<14, T, D, BrdColReflect>,
::caller<15, T, D, BrdColReflect>,
::caller<16, T, D, BrdColReflect>,
::caller<17, T, D, BrdColReflect>,
::caller<18, T, D, BrdColReflect>,
::caller<19, T, D, BrdColReflect>,
::caller<20, T, D, BrdColReflect>,
::caller<21, T, D, BrdColReflect>,
::caller<22, T, D, BrdColReflect>,
::caller<23, T, D, BrdColReflect>,
::caller<24, T, D, BrdColReflect>,
::caller<25, T, D, BrdColReflect>,
::caller<26, T, D, BrdColReflect>,
::caller<27, T, D, BrdColReflect>,
::caller<28, T, D, BrdColReflect>,
::caller<29, T, D, BrdColReflect>,
::caller<30, T, D, BrdColReflect>,
::caller<31, T, D, BrdColReflect>,
::caller<32, T, D, BrdColReflect>
column_filter::caller< 1, T, D, BrdColReflect>,
column_filter::caller< 2, T, D, BrdColReflect>,
column_filter::caller< 3, T, D, BrdColReflect>,
column_filter::caller< 4, T, D, BrdColReflect>,
column_filter::caller< 5, T, D, BrdColReflect>,
column_filter::caller< 6, T, D, BrdColReflect>,
column_filter::caller< 7, T, D, BrdColReflect>,
column_filter::caller< 8, T, D, BrdColReflect>,
column_filter::caller< 9, T, D, BrdColReflect>,
column_filter::caller<10, T, D, BrdColReflect>,
column_filter::caller<11, T, D, BrdColReflect>,
column_filter::caller<12, T, D, BrdColReflect>,
column_filter::caller<13, T, D, BrdColReflect>,
column_filter::caller<14, T, D, BrdColReflect>,
column_filter::caller<15, T, D, BrdColReflect>,
column_filter::caller<16, T, D, BrdColReflect>,
column_filter::caller<17, T, D, BrdColReflect>,
column_filter::caller<18, T, D, BrdColReflect>,
column_filter::caller<19, T, D, BrdColReflect>,
column_filter::caller<20, T, D, BrdColReflect>,
column_filter::caller<21, T, D, BrdColReflect>,
column_filter::caller<22, T, D, BrdColReflect>,
column_filter::caller<23, T, D, BrdColReflect>,
column_filter::caller<24, T, D, BrdColReflect>,
column_filter::caller<25, T, D, BrdColReflect>,
column_filter::caller<26, T, D, BrdColReflect>,
column_filter::caller<27, T, D, BrdColReflect>,
column_filter::caller<28, T, D, BrdColReflect>,
column_filter::caller<29, T, D, BrdColReflect>,
column_filter::caller<30, T, D, BrdColReflect>,
column_filter::caller<31, T, D, BrdColReflect>,
column_filter::caller<32, T, D, BrdColReflect>
},
{
0,
::caller< 1, T, D, BrdColWrap>,
::caller< 2, T, D, BrdColWrap>,
::caller< 3, T, D, BrdColWrap>,
::caller< 4, T, D, BrdColWrap>,
::caller< 5, T, D, BrdColWrap>,
::caller< 6, T, D, BrdColWrap>,
::caller< 7, T, D, BrdColWrap>,
::caller< 8, T, D, BrdColWrap>,
::caller< 9, T, D, BrdColWrap>,
::caller<10, T, D, BrdColWrap>,
::caller<11, T, D, BrdColWrap>,
::caller<12, T, D, BrdColWrap>,
::caller<13, T, D, BrdColWrap>,
::caller<14, T, D, BrdColWrap>,
::caller<15, T, D, BrdColWrap>,
::caller<16, T, D, BrdColWrap>,
::caller<17, T, D, BrdColWrap>,
::caller<18, T, D, BrdColWrap>,
::caller<19, T, D, BrdColWrap>,
::caller<20, T, D, BrdColWrap>,
::caller<21, T, D, BrdColWrap>,
::caller<22, T, D, BrdColWrap>,
::caller<23, T, D, BrdColWrap>,
::caller<24, T, D, BrdColWrap>,
::caller<25, T, D, BrdColWrap>,
::caller<26, T, D, BrdColWrap>,
::caller<27, T, D, BrdColWrap>,
::caller<28, T, D, BrdColWrap>,
::caller<29, T, D, BrdColWrap>,
::caller<30, T, D, BrdColWrap>,
::caller<31, T, D, BrdColWrap>,
::caller<32, T, D, BrdColWrap>
column_filter::caller< 1, T, D, BrdColWrap>,
column_filter::caller< 2, T, D, BrdColWrap>,
column_filter::caller< 3, T, D, BrdColWrap>,
column_filter::caller< 4, T, D, BrdColWrap>,
column_filter::caller< 5, T, D, BrdColWrap>,
column_filter::caller< 6, T, D, BrdColWrap>,
column_filter::caller< 7, T, D, BrdColWrap>,
column_filter::caller< 8, T, D, BrdColWrap>,
column_filter::caller< 9, T, D, BrdColWrap>,
column_filter::caller<10, T, D, BrdColWrap>,
column_filter::caller<11, T, D, BrdColWrap>,
column_filter::caller<12, T, D, BrdColWrap>,
column_filter::caller<13, T, D, BrdColWrap>,
column_filter::caller<14, T, D, BrdColWrap>,
column_filter::caller<15, T, D, BrdColWrap>,
column_filter::caller<16, T, D, BrdColWrap>,
column_filter::caller<17, T, D, BrdColWrap>,
column_filter::caller<18, T, D, BrdColWrap>,
column_filter::caller<19, T, D, BrdColWrap>,
column_filter::caller<20, T, D, BrdColWrap>,
column_filter::caller<21, T, D, BrdColWrap>,
column_filter::caller<22, T, D, BrdColWrap>,
column_filter::caller<23, T, D, BrdColWrap>,
column_filter::caller<24, T, D, BrdColWrap>,
column_filter::caller<25, T, D, BrdColWrap>,
column_filter::caller<26, T, D, BrdColWrap>,
column_filter::caller<27, T, D, BrdColWrap>,
column_filter::caller<28, T, D, BrdColWrap>,
column_filter::caller<29, T, D, BrdColWrap>,
column_filter::caller<30, T, D, BrdColWrap>,
column_filter::caller<31, T, D, BrdColWrap>,
column_filter::caller<32, T, D, BrdColWrap>
}
};
::loadKernel(kernel, ksize, stream);
if (stream == 0)
cudaSafeCall( cudaMemcpyToSymbol(column_filter::c_kernel, kernel, ksize * sizeof(float), 0, cudaMemcpyDeviceToDevice) );
else
cudaSafeCall( cudaMemcpyToSymbolAsync(column_filter::c_kernel, kernel, ksize * sizeof(float), 0, cudaMemcpyDeviceToDevice, stream) );
callers[brd_type][ksize]((PtrStepSz<T>)src, (PtrStepSz<D>)dst, anchor, cc, stream);
}

136
modules/gpu/src/cuda/element_operations.cu
Unescape View File

@ -52,7 +52,7 @@
using namespace cv::gpu;
using namespace cv::gpu::device;
namespace
namespace arithm
{
template <size_t src_size, size_t dst_size> struct ArithmFuncTraits
{
@ -152,7 +152,7 @@ namespace
//////////////////////////////////////////////////////////////////////////
// addMat
namespace
namespace arithm
{
template <typename T, typename D> struct VAdd4;
template <> struct VAdd4<uint, uint> : binary_function<uint, uint, uint>
@ -336,19 +336,19 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <typename T, typename D> struct TransformFunctorTraits< VAdd4<T, D> > : ArithmFuncTraits<sizeof(T), sizeof(D)>
template <typename T, typename D> struct TransformFunctorTraits< arithm::VAdd4<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
////////////////////////////////////
template <typename T, typename D> struct TransformFunctorTraits< VAdd2<T, D> > : ArithmFuncTraits<sizeof(T), sizeof(D)>
template <typename T, typename D> struct TransformFunctorTraits< arithm::VAdd2<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
////////////////////////////////////
template <typename T, typename D> struct TransformFunctorTraits< AddMat<T, D> > : ArithmFuncTraits<sizeof(T), sizeof(D)>
template <typename T, typename D> struct TransformFunctorTraits< arithm::AddMat<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
}}}
@ -446,7 +446,7 @@ namespace arithm
//////////////////////////////////////////////////////////////////////////
// addScalar
namespace
namespace arithm
{
template <typename T, typename S, typename D> struct AddScalar : unary_function<T, D>
{
@ -463,7 +463,7 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <typename T, typename S, typename D> struct TransformFunctorTraits< AddScalar<T, S, D> > : ArithmFuncTraits<sizeof(T), sizeof(D)>
template <typename T, typename S, typename D> struct TransformFunctorTraits< arithm::AddScalar<T, S, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
}}}
@ -541,7 +541,7 @@ namespace arithm
//////////////////////////////////////////////////////////////////////////
// subMat
namespace
namespace arithm
{
template <typename T, typename D> struct VSub4;
template <> struct VSub4<uint, uint> : binary_function<uint, uint, uint>
@ -725,19 +725,19 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <typename T, typename D> struct TransformFunctorTraits< VSub4<T, D> > : ArithmFuncTraits<sizeof(T), sizeof(D)>
template <typename T, typename D> struct TransformFunctorTraits< arithm::VSub4<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
////////////////////////////////////
template <typename T, typename D> struct TransformFunctorTraits< VSub2<T, D> > : ArithmFuncTraits<sizeof(T), sizeof(D)>
template <typename T, typename D> struct TransformFunctorTraits< arithm::VSub2<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
////////////////////////////////////
template <typename T, typename D> struct TransformFunctorTraits< SubMat<T, D> > : ArithmFuncTraits<sizeof(T), sizeof(D)>
template <typename T, typename D> struct TransformFunctorTraits< arithm::SubMat<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
}}}
@ -908,7 +908,7 @@ namespace arithm
//////////////////////////////////////////////////////////////////////////
// mulMat
namespace
namespace arithm
{
struct Mul_8uc4_32f : binary_function<uint, float, uint>
{
@ -966,15 +966,15 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <> struct TransformFunctorTraits<Mul_8uc4_32f> : ArithmFuncTraits<sizeof(uint), sizeof(uint)>
template <> struct TransformFunctorTraits<arithm::Mul_8uc4_32f> : arithm::ArithmFuncTraits<sizeof(uint), sizeof(uint)>
{
};
template <typename T, typename D> struct TransformFunctorTraits< Mul<T, D> > : ArithmFuncTraits<sizeof(T), sizeof(D)>
template <typename T, typename D> struct TransformFunctorTraits< arithm::Mul<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
template <typename T, typename S, typename D> struct TransformFunctorTraits< MulScale<T, S, D> > : ArithmFuncTraits<sizeof(T), sizeof(D)>
template <typename T, typename S, typename D> struct TransformFunctorTraits< arithm::MulScale<T, S, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
}}}
@ -1066,7 +1066,7 @@ namespace arithm
//////////////////////////////////////////////////////////////////////////
// mulScalar
namespace
namespace arithm
{
template <typename T, typename S, typename D> struct MulScalar : unary_function<T, D>
{
@ -1083,7 +1083,7 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <typename T, typename S, typename D> struct TransformFunctorTraits< MulScalar<T, S, D> > : ArithmFuncTraits<sizeof(T), sizeof(D)>
template <typename T, typename S, typename D> struct TransformFunctorTraits< arithm::MulScalar<T, S, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
}}}
@ -1157,7 +1157,7 @@ namespace arithm
//////////////////////////////////////////////////////////////////////////
// divMat
namespace
namespace arithm
{
struct Div_8uc4_32f : binary_function<uint, float, uint>
{
@ -1234,15 +1234,15 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <> struct TransformFunctorTraits<Div_8uc4_32f> : ArithmFuncTraits<sizeof(uint), sizeof(uint)>
template <> struct TransformFunctorTraits<arithm::Div_8uc4_32f> : arithm::ArithmFuncTraits<sizeof(uint), sizeof(uint)>
{
};
template <typename T, typename D> struct TransformFunctorTraits< Div<T, D> > : ArithmFuncTraits<sizeof(T), sizeof(D)>
template <typename T, typename D> struct TransformFunctorTraits< arithm::Div<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
template <typename T, typename S, typename D> struct TransformFunctorTraits< DivScale<T, S, D> > : ArithmFuncTraits<sizeof(T), sizeof(D)>
template <typename T, typename S, typename D> struct TransformFunctorTraits< arithm::DivScale<T, S, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
}}}
@ -1403,7 +1403,7 @@ namespace arithm
//////////////////////////////////////////////////////////////////////////
// divInv
namespace
namespace arithm
{
template <typename T, typename S, typename D> struct DivInv : unary_function<T, D>
{
@ -1420,7 +1420,7 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <typename T, typename S, typename D> struct TransformFunctorTraits< DivInv<T, S, D> > : ArithmFuncTraits<sizeof(T), sizeof(D)>
template <typename T, typename S, typename D> struct TransformFunctorTraits< arithm::DivInv<T, S, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
}}}
@ -1494,7 +1494,7 @@ namespace arithm
//////////////////////////////////////////////////////////////////////////
// absDiffMat
namespace
namespace arithm
{
template <typename T, typename D> struct VAbsDiff4;
template <> struct VAbsDiff4<uint, uint> : binary_function<uint, uint, uint>
@ -1611,19 +1611,19 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <typename T, typename D> struct TransformFunctorTraits< VAbsDiff4<T, D> > : ArithmFuncTraits<sizeof(T), sizeof(D)>
template <typename T, typename D> struct TransformFunctorTraits< arithm::VAbsDiff4<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
////////////////////////////////////
template <typename T, typename D> struct TransformFunctorTraits< VAbsDiff2<T, D> > : ArithmFuncTraits<sizeof(T), sizeof(D)>
template <typename T, typename D> struct TransformFunctorTraits< arithm::VAbsDiff2<T, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
////////////////////////////////////
template <typename T> struct TransformFunctorTraits< AbsDiffMat<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< arithm::AbsDiffMat<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
}}}
@ -1666,7 +1666,7 @@ namespace arithm
//////////////////////////////////////////////////////////////////////////
// absDiffScalar
namespace
namespace arithm
{
template <typename T, typename S> struct AbsDiffScalar : unary_function<T, T>
{
@ -1684,7 +1684,7 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <typename T, typename S> struct TransformFunctorTraits< AbsDiffScalar<T, S> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T, typename S> struct TransformFunctorTraits< arithm::AbsDiffScalar<T, S> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
}}}
@ -1713,7 +1713,7 @@ namespace arithm
namespace cv { namespace gpu { namespace device
{
template <typename T> struct TransformFunctorTraits< abs_func<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< abs_func<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
}}}
@ -1738,7 +1738,7 @@ namespace arithm
//////////////////////////////////////////////////////////////////////////
// sqrMat
namespace
namespace arithm
{
template <typename T> struct Sqr : unary_function<T, T>
{
@ -1754,7 +1754,7 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <typename T> struct TransformFunctorTraits< Sqr<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< arithm::Sqr<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
}}}
@ -1781,7 +1781,7 @@ namespace arithm
namespace cv { namespace gpu { namespace device
{
template <typename T> struct TransformFunctorTraits< sqrt_func<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< sqrt_func<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
}}}
@ -1808,7 +1808,7 @@ namespace arithm
namespace cv { namespace gpu { namespace device
{
template <typename T> struct TransformFunctorTraits< log_func<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< log_func<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
}}}
@ -1833,7 +1833,7 @@ namespace arithm
//////////////////////////////////////////////////////////////////////////
// expMat
namespace
namespace arithm
{
template <typename T> struct Exp : unary_function<T, T>
{
@ -1850,7 +1850,7 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <typename T> struct TransformFunctorTraits< Exp<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< arithm::Exp<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
}}}
@ -1875,7 +1875,7 @@ namespace arithm
//////////////////////////////////////////////////////////////////////////////////////
// cmpMat
namespace
namespace arithm
{
template <class Op, typename T>
struct Cmp : binary_function<T, T, uchar>
@ -1890,7 +1890,7 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <class Op, typename T> struct TransformFunctorTraits< Cmp<Op, T> > : ArithmFuncTraits<sizeof(T), sizeof(uchar)>
template <class Op, typename T> struct TransformFunctorTraits< arithm::Cmp<Op, T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(uchar)>
{
};
}}}
@ -1957,7 +1957,7 @@ namespace arithm
//////////////////////////////////////////////////////////////////////////////////////
// cmpScalar
namespace
namespace arithm
{
#define TYPE_VEC(type, cn) typename TypeVec<type, cn>::vec_type
@ -2020,7 +2020,7 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <class Op, typename T> struct TransformFunctorTraits< CmpScalar<Op, T, 1> > : ArithmFuncTraits<sizeof(T), sizeof(uchar)>
template <class Op, typename T> struct TransformFunctorTraits< arithm::CmpScalar<Op, T, 1> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(uchar)>
{
};
}}}
@ -2179,19 +2179,19 @@ namespace arithm
namespace cv { namespace gpu { namespace device
{
template <typename T> struct TransformFunctorTraits< bit_not<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< bit_not<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
template <typename T> struct TransformFunctorTraits< bit_and<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< bit_and<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
template <typename T> struct TransformFunctorTraits< bit_or<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< bit_or<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
template <typename T> struct TransformFunctorTraits< bit_xor<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< bit_xor<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
}}}
@ -2252,15 +2252,15 @@ namespace arithm
namespace cv { namespace gpu { namespace device
{
template <typename T> struct TransformFunctorTraits< binder2nd< bit_and<T> > > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< binder2nd< bit_and<T> > > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
template <typename T> struct TransformFunctorTraits< binder2nd< bit_or<T> > > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< binder2nd< bit_or<T> > > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
template <typename T> struct TransformFunctorTraits< binder2nd< bit_xor<T> > > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< binder2nd< bit_xor<T> > > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
}}}
@ -2298,7 +2298,7 @@ namespace arithm
//////////////////////////////////////////////////////////////////////////
// min
namespace
namespace arithm
{
template <typename T> struct VMin4;
template <> struct VMin4<uint> : binary_function<uint, uint, uint>
@ -2389,23 +2389,23 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <typename T> struct TransformFunctorTraits< VMin4<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< arithm::VMin4<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
////////////////////////////////////
template <typename T> struct TransformFunctorTraits< VMin2<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< arithm::VMin2<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
////////////////////////////////////
template <typename T> struct TransformFunctorTraits< minimum<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< minimum<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
template <typename T> struct TransformFunctorTraits< binder2nd< minimum<T> > > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< binder2nd< minimum<T> > > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
}}}
@ -2458,7 +2458,7 @@ namespace arithm
//////////////////////////////////////////////////////////////////////////
// max
namespace
namespace arithm
{
template <typename T> struct VMax4;
template <> struct VMax4<uint> : binary_function<uint, uint, uint>
@ -2549,23 +2549,23 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <typename T> struct TransformFunctorTraits< VMax4<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< arithm::VMax4<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
////////////////////////////////////
template <typename T> struct TransformFunctorTraits< VMax2<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< arithm::VMax2<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
////////////////////////////////////
template <typename T> struct TransformFunctorTraits< maximum<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< maximum<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
template <typename T> struct TransformFunctorTraits< binder2nd< maximum<T> > > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< binder2nd< maximum<T> > > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
}}}
@ -2620,23 +2620,23 @@ namespace arithm
namespace cv { namespace gpu { namespace device
{
template <typename T> struct TransformFunctorTraits< thresh_binary_func<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
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> > : ArithmFuncTraits<sizeof(T), sizeof(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> > : ArithmFuncTraits<sizeof(T), sizeof(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> > : ArithmFuncTraits<sizeof(T), sizeof(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> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< thresh_to_zero_inv_func<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
}}}
@ -2679,7 +2679,7 @@ namespace arithm
//////////////////////////////////////////////////////////////////////////
// pow
namespace
namespace arithm
{
template<typename T, bool Signed = numeric_limits<T>::is_signed> struct PowOp : unary_function<T, T>
{
@ -2734,7 +2734,7 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <typename T> struct TransformFunctorTraits< PowOp<T> > : ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T> struct TransformFunctorTraits< arithm::PowOp<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
}}}
@ -2759,7 +2759,7 @@ namespace arithm
//////////////////////////////////////////////////////////////////////////
// addWeighted
namespace
namespace arithm
{
template <typename T> struct UseDouble_
{
@ -2809,14 +2809,14 @@ namespace
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< AddWeighted<T1, T2, D> >
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> >
{
};
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> : ArithmFuncTraits<src_size, dst_size>
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>
{
};
template <typename T1, typename T2, typename D> struct TransformFunctorTraits< AddWeighted<T1, T2, D> > : AddWeightedTraits<T1, T2, D, sizeof(T1), sizeof(T2), sizeof(D)>
template <typename T1, typename T2, typename D> struct TransformFunctorTraits< arithm::AddWeighted<T1, T2, D> > : AddWeightedTraits<T1, T2, D, sizeof(T1), sizeof(T2), sizeof(D)>
{
};
}}}

1
modules/gpu/src/cuda/gftt.cu
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@ -47,6 +47,7 @@
#if !defined CUDA_DISABLER
#include <thrust/device_ptr.h>
#include <thrust/sort.h>
#include "opencv2/gpu/device/common.hpp"

11
modules/gpu/src/cuda/global_motion.cu
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@ -43,12 +43,11 @@
#if !defined CUDA_DISABLER
#include "thrust/device_ptr.h"
#include "thrust/remove.h"
#include "thrust/functional.h"
#include "internal_shared.hpp"
#include <thrust/device_ptr.h>
#include <thrust/remove.h>
#include <thrust/functional.h>
using namespace thrust;
#include "internal_shared.hpp"
namespace cv { namespace gpu { namespace device { namespace globmotion {
@ -64,7 +63,7 @@ int compactPoints(int N, float *points0, float *points1, const uchar *mask)
return 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));
}

13
modules/gpu/src/cuda/hist.cu
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@ -51,9 +51,9 @@
using namespace cv::gpu;
using namespace cv::gpu::device;
namespace
namespace hist
{
__global__ void histogram256(const uchar* src, int cols, int rows, size_t step, int* hist)
__global__ void histogram256Kernel(const uchar* src, int cols, int rows, size_t step, int* hist)
{
__shared__ int shist[256];
@ -94,16 +94,13 @@ namespace
if (histVal > 0)
::atomicAdd(hist + tid, histVal);
}
}
namespace hist
{
void histogram256(PtrStepSzb src, int* hist, cudaStream_t stream)
{
const dim3 block(32, 8);
const dim3 grid(divUp(src.rows, block.y));
::histogram256<<<grid, block, 0, stream>>>(src.data, src.cols, src.rows, src.step, hist);
histogram256Kernel<<<grid, block, 0, stream>>>(src.data, src.cols, src.rows, src.step, hist);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
@ -113,7 +110,7 @@ namespace hist
/////////////////////////////////////////////////////////////////////////
namespace
namespace hist
{
__constant__ int c_lut[256];
@ -133,7 +130,7 @@ namespace
namespace cv { namespace gpu { namespace device
{
template <> struct TransformFunctorTraits<EqualizeHist> : DefaultTransformFunctorTraits<EqualizeHist>
template <> struct TransformFunctorTraits<hist::EqualizeHist> : DefaultTransformFunctorTraits<hist::EqualizeHist>
{
enum { smart_shift = 4 };
};

4
modules/gpu/src/cuda/hog.cu
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@ -244,7 +244,8 @@ namespace cv { namespace gpu { namespace device
return smem[0];
#endif
}
else
{
#if __CUDA_ARCH__ >= 300
if (threadIdx.x == 0)
smem[0] = sum;
@ -254,6 +255,7 @@ namespace cv { namespace gpu { namespace device
return smem[0];
}
}
template <int nthreads, // Number of threads which process one block historgam

2
modules/gpu/src/cuda/hough.cu
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@ -42,7 +42,9 @@
#if !defined CUDA_DISABLER
#include <thrust/device_ptr.h>
#include <thrust/sort.h>
#include "opencv2/gpu/device/common.hpp"
#include "opencv2/gpu/device/emulation.hpp"
#include "opencv2/gpu/device/vec_math.hpp"

15
modules/gpu/src/cuda/matrix_reductions.cu
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@ -55,7 +55,7 @@
using namespace cv::gpu;
using namespace cv::gpu::device;
namespace
namespace detail
{
template <int cn> struct Unroll;
template <> struct Unroll<1>
@ -218,7 +218,7 @@ namespace sum
{
sum = tid < gridDim.x * gridDim.y ? result[tid] : VecTraits<result_type>::all(0);
device::reduce<BLOCK_SIZE>(Unroll<cn>::template smem_tuple<BLOCK_SIZE>(smem), Unroll<cn>::tie(sum), tid, Unroll<cn>::op(plus<R>()));
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)
{
@ -254,7 +254,7 @@ namespace sum
{
sum = tid < gridDim.x * gridDim.y ? result[tid] : VecTraits<result_type>::all(0);
device::reduce<BLOCK_SIZE>(Unroll<cn>::template smem_tuple<BLOCK_SIZE>(smem), Unroll<cn>::tie(sum), tid, Unroll<cn>::op(plus<double>()));
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)
{
@ -294,7 +294,7 @@ namespace sum
}
}
device::reduce<BLOCK_SIZE>(Unroll<cn>::template smem_tuple<BLOCK_SIZE>(smem), Unroll<cn>::tie(sum), tid, Unroll<cn>::op(plus<R>()));
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>()));
GlobalReduce<BLOCK_SIZE, R, cn>::run(sum, result, tid, bid, smem);
}
@ -918,13 +918,11 @@ namespace countNonZero
__global__ void kernel(const PtrStepSz<T> src, unsigned int* count, const int twidth, const int theight)
{
__shared__ unsigned int scount[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;
const int tid = threadIdx.y * blockDim.x + threadIdx.x;
const int bid = blockIdx.y * gridDim.x + blockIdx.x;
unsigned int mycount = 0;
@ -946,6 +944,9 @@ namespace countNonZero
if (tid == 0)
::atomicAdd(count, mycount);
#else
__shared__ bool is_last;
const int bid = blockIdx.y * gridDim.x + blockIdx.x;
if (tid == 0)
{
count[bid] = mycount;
@ -1244,7 +1245,7 @@ namespace reduce
for (int x = threadIdx.x; x < src.cols; x += BLOCK_SIZE)
myVal = op(myVal, saturate_cast<work_type>(srcRow[x]));
device::reduce<BLOCK_SIZE>(Unroll<cn>::template smem_tuple<BLOCK_SIZE>(smem), Unroll<cn>::tie(myVal), threadIdx.x, Unroll<cn>::op(op));
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 == 0)
dst[y] = saturate_cast<dst_type>(op.result(myVal, src.cols));

5
modules/gpu/src/cuda/optflowbm.cu
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@ -48,7 +48,7 @@
using namespace cv::gpu;
using namespace cv::gpu::device;
namespace
namespace optflowbm
{
texture<uchar, cudaTextureType2D, cudaReadModeElementType> tex_prev(false, cudaFilterModePoint, cudaAddressModeClamp);
texture<uchar, cudaTextureType2D, cudaReadModeElementType> tex_curr(false, cudaFilterModePoint, cudaAddressModeClamp);
@ -145,10 +145,7 @@ namespace
velx(i, j) = static_cast<float>(sumx) / countMin;
vely(i, j) = static_cast<float>(sumy) / countMin;
}
}
namespace optflowbm
{
void calc(PtrStepSzb prev, PtrStepSzb curr, PtrStepSzf velx, PtrStepSzf vely, int2 blockSize, int2 shiftSize, bool usePrevious,
int maxX, int maxY, int acceptLevel, int escapeLevel, const short2* ss, int ssCount, cudaStream_t stream)
{

1
modules/gpu/src/cuda/orb.cu
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@ -47,6 +47,7 @@
#if !defined CUDA_DISABLER
#include <thrust/device_ptr.h>
#include <thrust/sort.h>
#include "opencv2/gpu/device/common.hpp"

37
modules/gpu/src/cuda/pyrlk.cu
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@ -57,7 +57,7 @@
using namespace cv::gpu;
using namespace cv::gpu::device;
namespace
namespace pyrlk
{
__constant__ int c_winSize_x;
__constant__ int c_winSize_y;
@ -123,7 +123,7 @@ namespace
}
template <int cn, int PATCH_X, int PATCH_Y, bool calcErr>
__global__ void sparse(const float2* prevPts, float2* nextPts, uchar* status, float* err, const int level, const int rows, const int cols)
__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;
@ -321,9 +321,9 @@ namespace
dim3 grid(ptcount);
if (level == 0 && err)
sparse<cn, PATCH_X, PATCH_Y, true><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
sparseKernel<cn, PATCH_X, PATCH_Y, true><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
else
sparse<cn, PATCH_X, PATCH_Y, false><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
sparseKernel<cn, PATCH_X, PATCH_Y, false><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
cudaSafeCall( cudaGetLastError() );
@ -332,7 +332,7 @@ namespace
}
template <bool calcErr>
__global__ void dense(PtrStepf u, PtrStepf v, const PtrStepf prevU, const PtrStepf prevV, PtrStepf err, const int rows, const int cols)
__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[];
@ -476,10 +476,7 @@ namespace
err(y, x) = static_cast<float>(errval) / (c_winSize_x * c_winSize_y);
}
}
}
namespace pyrlk
{
void loadConstants(int2 winSize, int iters)
{
cudaSafeCall( cudaMemcpyToSymbol(c_winSize_x, &winSize.x, sizeof(int)) );
@ -500,11 +497,11 @@ namespace pyrlk
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>}
{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>}
};
bindTexture(&tex_If, I);
@ -522,11 +519,11 @@ namespace pyrlk
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>}
{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>}
};
bindTexture(&tex_If4, I);
@ -551,12 +548,12 @@ namespace pyrlk
if (err.data)
{
::dense<true><<<grid, block, smem_size, stream>>>(u, v, prevU, prevV, err, I.rows, I.cols);
denseKernel<true><<<grid, block, smem_size, stream>>>(u, v, prevU, prevV, err, I.rows, I.cols);
cudaSafeCall( cudaGetLastError() );
}
else
{
::dense<false><<<grid, block, smem_size, stream>>>(u, v, prevU, prevV, PtrStepf(), I.rows, I.cols);
denseKernel<false><<<grid, block, smem_size, stream>>>(u, v, prevU, prevV, PtrStepf(), I.rows, I.cols);
cudaSafeCall( cudaGetLastError() );
}

335
modules/gpu/src/cuda/row_filter.h
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@ -49,20 +49,12 @@
using namespace cv::gpu;
using namespace cv::gpu::device;
namespace
namespace row_filter
{
#define MAX_KERNEL_SIZE 32
__constant__ float c_kernel[MAX_KERNEL_SIZE];
void loadKernel(const float* kernel, int ksize, cudaStream_t stream)
{
if (stream == 0)
cudaSafeCall( cudaMemcpyToSymbol(c_kernel, kernel, ksize * sizeof(float), 0, cudaMemcpyDeviceToDevice) );
else
cudaSafeCall( cudaMemcpyToSymbolAsync(c_kernel, kernel, ksize * sizeof(float), 0, cudaMemcpyDeviceToDevice, stream) );
}
template <int KSIZE, typename T, typename D, typename B>
__global__ void linearRowFilter(const PtrStepSz<T> src, PtrStep<D> dst, const int anchor, const B brd)
{
@ -195,182 +187,185 @@ namespace filter
{
{
0,
::caller< 1, T, D, BrdRowReflect101>,
::caller< 2, T, D, BrdRowReflect101>,
::caller< 3, T, D, BrdRowReflect101>,
::caller< 4, T, D, BrdRowReflect101>,
::caller< 5, T, D, BrdRowReflect101>,
::caller< 6, T, D, BrdRowReflect101>,
::caller< 7, T, D, BrdRowReflect101>,
::caller< 8, T, D, BrdRowReflect101>,
::caller< 9, T, D, BrdRowReflect101>,
::caller<10, T, D, BrdRowReflect101>,
::caller<11, T, D, BrdRowReflect101>,
::caller<12, T, D, BrdRowReflect101>,
::caller<13, T, D, BrdRowReflect101>,
::caller<14, T, D, BrdRowReflect101>,
::caller<15, T, D, BrdRowReflect101>,
::caller<16, T, D, BrdRowReflect101>,
::caller<17, T, D, BrdRowReflect101>,
::caller<18, T, D, BrdRowReflect101>,
::caller<19, T, D, BrdRowReflect101>,
::caller<20, T, D, BrdRowReflect101>,
::caller<21, T, D, BrdRowReflect101>,
::caller<22, T, D, BrdRowReflect101>,
::caller<23, T, D, BrdRowReflect101>,
::caller<24, T, D, BrdRowReflect101>,
::caller<25, T, D, BrdRowReflect101>,
::caller<26, T, D, BrdRowReflect101>,
::caller<27, T, D, BrdRowReflect101>,
::caller<28, T, D, BrdRowReflect101>,
::caller<29, T, D, BrdRowReflect101>,
::caller<30, T, D, BrdRowReflect101>,
::caller<31, T, D, BrdRowReflect101>,
::caller<32, T, D, BrdRowReflect101>
row_filter::caller< 1, T, D, BrdRowReflect101>,
row_filter::caller< 2, T, D, BrdRowReflect101>,
row_filter::caller< 3, T, D, BrdRowReflect101>,
row_filter::caller< 4, T, D, BrdRowReflect101>,
row_filter::caller< 5, T, D, BrdRowReflect101>,
row_filter::caller< 6, T, D, BrdRowReflect101>,
row_filter::caller< 7, T, D, BrdRowReflect101>,
row_filter::caller< 8, T, D, BrdRowReflect101>,
row_filter::caller< 9, T, D, BrdRowReflect101>,
row_filter::caller<10, T, D, BrdRowReflect101>,
row_filter::caller<11, T, D, BrdRowReflect101>,
row_filter::caller<12, T, D, BrdRowReflect101>,
row_filter::caller<13, T, D, BrdRowReflect101>,
row_filter::caller<14, T, D, BrdRowReflect101>,
row_filter::caller<15, T, D, BrdRowReflect101>,
row_filter::caller<16, T, D, BrdRowReflect101>,
row_filter::caller<17, T, D, BrdRowReflect101>,
row_filter::caller<18, T, D, BrdRowReflect101>,
row_filter::caller<19, T, D, BrdRowReflect101>,
row_filter::caller<20, T, D, BrdRowReflect101>,
row_filter::caller<21, T, D, BrdRowReflect101>,
row_filter::caller<22, T, D, BrdRowReflect101>,
row_filter::caller<23, T, D, BrdRowReflect101>,
row_filter::caller<24, T, D, BrdRowReflect101>,
row_filter::caller<25, T, D, BrdRowReflect101>,
row_filter::caller<26, T, D, BrdRowReflect101>,
row_filter::caller<27, T, D, BrdRowReflect101>,
row_filter::caller<28, T, D, BrdRowReflect101>,
row_filter::caller<29, T, D, BrdRowReflect101>,
row_filter::caller<30, T, D, BrdRowReflect101>,
row_filter::caller<31, T, D, BrdRowReflect101>,
row_filter::caller<32, T, D, BrdRowReflect101>
},
{
0,
::caller< 1, T, D, BrdRowReplicate>,
::caller< 2, T, D, BrdRowReplicate>,
::caller< 3, T, D, BrdRowReplicate>,
::caller< 4, T, D, BrdRowReplicate>,
::caller< 5, T, D, BrdRowReplicate>,
::caller< 6, T, D, BrdRowReplicate>,
::caller< 7, T, D, BrdRowReplicate>,
::caller< 8, T, D, BrdRowReplicate>,
::caller< 9, T, D, BrdRowReplicate>,
::caller<10, T, D, BrdRowReplicate>,
::caller<11, T, D, BrdRowReplicate>,
::caller<12, T, D, BrdRowReplicate>,
::caller<13, T, D, BrdRowReplicate>,
::caller<14, T, D, BrdRowReplicate>,
::caller<15, T, D, BrdRowReplicate>,
::caller<16, T, D, BrdRowReplicate>,
::caller<17, T, D, BrdRowReplicate>,
::caller<18, T, D, BrdRowReplicate>,
::caller<19, T, D, BrdRowReplicate>,
::caller<20, T, D, BrdRowReplicate>,
::caller<21, T, D, BrdRowReplicate>,
::caller<22, T, D, BrdRowReplicate>,
::caller<23, T, D, BrdRowReplicate>,
::caller<24, T, D, BrdRowReplicate>,
::caller<25, T, D, BrdRowReplicate>,
::caller<26, T, D, BrdRowReplicate>,
::caller<27, T, D, BrdRowReplicate>,
::caller<28, T, D, BrdRowReplicate>,
::caller<29, T, D, BrdRowReplicate>,
::caller<30, T, D, BrdRowReplicate>,
::caller<31, T, D, BrdRowReplicate>,
::caller<32, T, D, BrdRowReplicate>
row_filter::caller< 1, T, D, BrdRowReplicate>,
row_filter::caller< 2, T, D, BrdRowReplicate>,
row_filter::caller< 3, T, D, BrdRowReplicate>,
row_filter::caller< 4, T, D, BrdRowReplicate>,
row_filter::caller< 5, T, D, BrdRowReplicate>,
row_filter::caller< 6, T, D, BrdRowReplicate>,
row_filter::caller< 7, T, D, BrdRowReplicate>,
row_filter::caller< 8, T, D, BrdRowReplicate>,
row_filter::caller< 9, T, D, BrdRowReplicate>,
row_filter::caller<10, T, D, BrdRowReplicate>,
row_filter::caller<11, T, D, BrdRowReplicate>,
row_filter::caller<12, T, D, BrdRowReplicate>,
row_filter::caller<13, T, D, BrdRowReplicate>,
row_filter::caller<14, T, D, BrdRowReplicate>,
row_filter::caller<15, T, D, BrdRowReplicate>,
row_filter::caller<16, T, D, BrdRowReplicate>,
row_filter::caller<17, T, D, BrdRowReplicate>,
row_filter::caller<18, T, D, BrdRowReplicate>,
row_filter::caller<19, T, D, BrdRowReplicate>,
row_filter::caller<20, T, D, BrdRowReplicate>,
row_filter::caller<21, T, D, BrdRowReplicate>,
row_filter::caller<22, T, D, BrdRowReplicate>,
row_filter::caller<23, T, D, BrdRowReplicate>,
row_filter::caller<24, T, D, BrdRowReplicate>,
row_filter::caller<25, T, D, BrdRowReplicate>,
row_filter::caller<26, T, D, BrdRowReplicate>,
row_filter::caller<27, T, D, BrdRowReplicate>,
row_filter::caller<28, T, D, BrdRowReplicate>,
row_filter::caller<29, T, D, BrdRowReplicate>,
row_filter::caller<30, T, D, BrdRowReplicate>,
row_filter::caller<31, T, D, BrdRowReplicate>,
row_filter::caller<32, T, D, BrdRowReplicate>
},
{
0,
::caller< 1, T, D, BrdRowConstant>,
::caller< 2, T, D, BrdRowConstant>,
::caller< 3, T, D, BrdRowConstant>,
::caller< 4, T, D, BrdRowConstant>,
::caller< 5, T, D, BrdRowConstant>,
::caller< 6, T, D, BrdRowConstant>,
::caller< 7, T, D, BrdRowConstant>,
::caller< 8, T, D, BrdRowConstant>,
::caller< 9, T, D, BrdRowConstant>,
::caller<10, T, D, BrdRowConstant>,
::caller<11, T, D, BrdRowConstant>,
::caller<12, T, D, BrdRowConstant>,
::caller<13, T, D, BrdRowConstant>,
::caller<14, T, D, BrdRowConstant>,
::caller<15, T, D, BrdRowConstant>,
::caller<16, T, D, BrdRowConstant>,
::caller<17, T, D, BrdRowConstant>,
::caller<18, T, D, BrdRowConstant>,
::caller<19, T, D, BrdRowConstant>,
::caller<20, T, D, BrdRowConstant>,
::caller<21, T, D, BrdRowConstant>,
::caller<22, T, D, BrdRowConstant>,
::caller<23, T, D, BrdRowConstant>,
::caller<24, T, D, BrdRowConstant>,
::caller<25, T, D, BrdRowConstant>,
::caller<26, T, D, BrdRowConstant>,
::caller<27, T, D, BrdRowConstant>,
::caller<28, T, D, BrdRowConstant>,
::caller<29, T, D, BrdRowConstant>,
::caller<30, T, D, BrdRowConstant>,
::caller<31, T, D, BrdRowConstant>,
::caller<32, T, D, BrdRowConstant>
row_filter::caller< 1, T, D, BrdRowConstant>,
row_filter::caller< 2, T, D, BrdRowConstant>,
row_filter::caller< 3, T, D, BrdRowConstant>,
row_filter::caller< 4, T, D, BrdRowConstant>,
row_filter::caller< 5, T, D, BrdRowConstant>,
row_filter::caller< 6, T, D, BrdRowConstant>,
row_filter::caller< 7, T, D, BrdRowConstant>,
row_filter::caller< 8, T, D, BrdRowConstant>,
row_filter::caller< 9, T, D, BrdRowConstant>,
row_filter::caller<10, T, D, BrdRowConstant>,
row_filter::caller<11, T, D, BrdRowConstant>,
row_filter::caller<12, T, D, BrdRowConstant>,
row_filter::caller<13, T, D, BrdRowConstant>,
row_filter::caller<14, T, D, BrdRowConstant>,
row_filter::caller<15, T, D, BrdRowConstant>,
row_filter::caller<16, T, D, BrdRowConstant>,
row_filter::caller<17, T, D, BrdRowConstant>,
row_filter::caller<18, T, D, BrdRowConstant>,
row_filter::caller<19, T, D, BrdRowConstant>,
row_filter::caller<20, T, D, BrdRowConstant>,
row_filter::caller<21, T, D, BrdRowConstant>,
row_filter::caller<22, T, D, BrdRowConstant>,
row_filter::caller<23, T, D, BrdRowConstant>,
row_filter::caller<24, T, D, BrdRowConstant>,
row_filter::caller<25, T, D, BrdRowConstant>,
row_filter::caller<26, T, D, BrdRowConstant>,
row_filter::caller<27, T, D, BrdRowConstant>,
row_filter::caller<28, T, D, BrdRowConstant>,
row_filter::caller<29, T, D, BrdRowConstant>,
row_filter::caller<30, T, D, BrdRowConstant>,
row_filter::caller<31, T, D, BrdRowConstant>,
row_filter::caller<32, T, D, BrdRowConstant>
},
{
0,
::caller< 1, T, D, BrdRowReflect>,
::caller< 2, T, D, BrdRowReflect>,
::caller< 3, T, D, BrdRowReflect>,
::caller< 4, T, D, BrdRowReflect>,
::caller< 5, T, D, BrdRowReflect>,
::caller< 6, T, D, BrdRowReflect>,
::caller< 7, T, D, BrdRowReflect>,
::caller< 8, T, D, BrdRowReflect>,
::caller< 9, T, D, BrdRowReflect>,
::caller<10, T, D, BrdRowReflect>,
::caller<11, T, D, BrdRowReflect>,
::caller<12, T, D, BrdRowReflect>,
::caller<13, T, D, BrdRowReflect>,
::caller<14, T, D, BrdRowReflect>,
::caller<15, T, D, BrdRowReflect>,
::caller<16, T, D, BrdRowReflect>,
::caller<17, T, D, BrdRowReflect>,
::caller<18, T, D, BrdRowReflect>,
::caller<19, T, D, BrdRowReflect>,
::caller<20, T, D, BrdRowReflect>,
::caller<21, T, D, BrdRowReflect>,
::caller<22, T, D, BrdRowReflect>,
::caller<23, T, D, BrdRowReflect>,
::caller<24, T, D, BrdRowReflect>,
::caller<25, T, D, BrdRowReflect>,
::caller<26, T, D, BrdRowReflect>,
::caller<27, T, D, BrdRowReflect>,
::caller<28, T, D, BrdRowReflect>,
::caller<29, T, D, BrdRowReflect>,
::caller<30, T, D, BrdRowReflect>,
::caller<31, T, D, BrdRowReflect>,
::caller<32, T, D, BrdRowReflect>
row_filter::caller< 1, T, D, BrdRowReflect>,
row_filter::caller< 2, T, D, BrdRowReflect>,
row_filter::caller< 3, T, D, BrdRowReflect>,
row_filter::caller< 4, T, D, BrdRowReflect>,
row_filter::caller< 5, T, D, BrdRowReflect>,
row_filter::caller< 6, T, D, BrdRowReflect>,
row_filter::caller< 7, T, D, BrdRowReflect>,
row_filter::caller< 8, T, D, BrdRowReflect>,
row_filter::caller< 9, T, D, BrdRowReflect>,
row_filter::caller<10, T, D, BrdRowReflect>,
row_filter::caller<11, T, D, BrdRowReflect>,
row_filter::caller<12, T, D, BrdRowReflect>,
row_filter::caller<13, T, D, BrdRowReflect>,
row_filter::caller<14, T, D, BrdRowReflect>,
row_filter::caller<15, T, D, BrdRowReflect>,
row_filter::caller<16, T, D, BrdRowReflect>,
row_filter::caller<17, T, D, BrdRowReflect>,
row_filter::caller<18, T, D, BrdRowReflect>,
row_filter::caller<19, T, D, BrdRowReflect>,
row_filter::caller<20, T, D, BrdRowReflect>,
row_filter::caller<21, T, D, BrdRowReflect>,
row_filter::caller<22, T, D, BrdRowReflect>,
row_filter::caller<23, T, D, BrdRowReflect>,
row_filter::caller<24, T, D, BrdRowReflect>,
row_filter::caller<25, T, D, BrdRowReflect>,
row_filter::caller<26, T, D, BrdRowReflect>,
row_filter::caller<27, T, D, BrdRowReflect>,
row_filter::caller<28, T, D, BrdRowReflect>,
row_filter::caller<29, T, D, BrdRowReflect>,
row_filter::caller<30, T, D, BrdRowReflect>,
row_filter::caller<31, T, D, BrdRowReflect>,
row_filter::caller<32, T, D, BrdRowReflect>
},
{
0,
::caller< 1, T, D, BrdRowWrap>,
::caller< 2, T, D, BrdRowWrap>,
::caller< 3, T, D, BrdRowWrap>,
::caller< 4, T, D, BrdRowWrap>,
::caller< 5, T, D, BrdRowWrap>,
::caller< 6, T, D, BrdRowWrap>,
::caller< 7, T, D, BrdRowWrap>,
::caller< 8, T, D, BrdRowWrap>,
::caller< 9, T, D, BrdRowWrap>,
::caller<10, T, D, BrdRowWrap>,
::caller<11, T, D, BrdRowWrap>,
::caller<12, T, D, BrdRowWrap>,
::caller<13, T, D, BrdRowWrap>,
::caller<14, T, D, BrdRowWrap>,
::caller<15, T, D, BrdRowWrap>,
::caller<16, T, D, BrdRowWrap>,
::caller<17, T, D, BrdRowWrap>,
::caller<18, T, D, BrdRowWrap>,
::caller<19, T, D, BrdRowWrap>,
::caller<20, T, D, BrdRowWrap>,
::caller<21, T, D, BrdRowWrap>,
::caller<22, T, D, BrdRowWrap>,
::caller<23, T, D, BrdRowWrap>,
::caller<24, T, D, BrdRowWrap>,
::caller<25, T, D, BrdRowWrap>,
::caller<26, T, D, BrdRowWrap>,
::caller<27, T, D, BrdRowWrap>,
::caller<28, T, D, BrdRowWrap>,
::caller<29, T, D, BrdRowWrap>,
::caller<30, T, D, BrdRowWrap>,
::caller<31, T, D, BrdRowWrap>,
::caller<32, T, D, BrdRowWrap>
row_filter::caller< 1, T, D, BrdRowWrap>,
row_filter::caller< 2, T, D, BrdRowWrap>,
row_filter::caller< 3, T, D, BrdRowWrap>,
row_filter::caller< 4, T, D, BrdRowWrap>,
row_filter::caller< 5, T, D, BrdRowWrap>,
row_filter::caller< 6, T, D, BrdRowWrap>,
row_filter::caller< 7, T, D, BrdRowWrap>,
row_filter::caller< 8, T, D, BrdRowWrap>,
row_filter::caller< 9, T, D, BrdRowWrap>,
row_filter::caller<10, T, D, BrdRowWrap>,
row_filter::caller<11, T, D, BrdRowWrap>,
row_filter::caller<12, T, D, BrdRowWrap>,
row_filter::caller<13, T, D, BrdRowWrap>,
row_filter::caller<14, T, D, BrdRowWrap>,
row_filter::caller<15, T, D, BrdRowWrap>,
row_filter::caller<16, T, D, BrdRowWrap>,
row_filter::caller<17, T, D, BrdRowWrap>,
row_filter::caller<18, T, D, BrdRowWrap>,
row_filter::caller<19, T, D, BrdRowWrap>,
row_filter::caller<20, T, D, BrdRowWrap>,
row_filter::caller<21, T, D, BrdRowWrap>,
row_filter::caller<22, T, D, BrdRowWrap>,
row_filter::caller<23, T, D, BrdRowWrap>,
row_filter::caller<24, T, D, BrdRowWrap>,
row_filter::caller<25, T, D, BrdRowWrap>,
row_filter::caller<26, T, D, BrdRowWrap>,
row_filter::caller<27, T, D, BrdRowWrap>,
row_filter::caller<28, T, D, BrdRowWrap>,
row_filter::caller<29, T, D, BrdRowWrap>,
row_filter::caller<30, T, D, BrdRowWrap>,
row_filter::caller<31, T, D, BrdRowWrap>,
row_filter::caller<32, T, D, BrdRowWrap>
}
};
loadKernel(kernel, ksize, stream);
if (stream == 0)
cudaSafeCall( cudaMemcpyToSymbol(row_filter::c_kernel, kernel, ksize * sizeof(float), 0, cudaMemcpyDeviceToDevice) );
else
cudaSafeCall( cudaMemcpyToSymbolAsync(row_filter::c_kernel, kernel, ksize * sizeof(float), 0, cudaMemcpyDeviceToDevice, stream) );
callers[brd_type][ksize]((PtrStepSz<T>)src, (PtrStepSz<D>)dst, anchor, cc, stream);
}

36
modules/gpu/src/cuda/tvl1flow.cu
Unescape View File

@ -52,9 +52,9 @@ using namespace cv::gpu::device;
////////////////////////////////////////////////////////////
// centeredGradient
namespace
namespace tvl1flow
{
__global__ void centeredGradient(const PtrStepSzf src, PtrStepf dx, PtrStepf dy)
__global__ void centeredGradientKernel(const PtrStepSzf src, PtrStepf dx, PtrStepf dy)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
@ -65,16 +65,13 @@ namespace
dx(y, x) = 0.5f * (src(y, ::min(x + 1, src.cols - 1)) - src(y, ::max(x - 1, 0)));
dy(y, x) = 0.5f * (src(::min(y + 1, src.rows - 1), x) - src(::max(y - 1, 0), x));
}
}
namespace tvl1flow
{
void centeredGradient(PtrStepSzf src, PtrStepSzf dx, PtrStepSzf dy)
{
const dim3 block(32, 8);
const dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y));
::centeredGradient<<<grid, block>>>(src, dx, dy);
centeredGradientKernel<<<grid, block>>>(src, dx, dy);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
@ -84,7 +81,7 @@ namespace tvl1flow
////////////////////////////////////////////////////////////
// warpBackward
namespace
namespace tvl1flow
{
static __device__ __forceinline__ float bicubicCoeff(float x_)
{
@ -107,7 +104,7 @@ namespace
texture<float, cudaTextureType2D, cudaReadModeElementType> tex_I1x(false, cudaFilterModePoint, cudaAddressModeClamp);
texture<float, cudaTextureType2D, cudaReadModeElementType> tex_I1y(false, cudaFilterModePoint, cudaAddressModeClamp);
__global__ void warpBackward(const PtrStepSzf I0, const PtrStepf u1, const PtrStepf u2, PtrStepf I1w, PtrStepf I1wx, PtrStepf I1wy, PtrStepf grad, PtrStepf rho)
__global__ void warpBackwardKernel(const PtrStepSzf I0, const PtrStepf u1, const PtrStepf u2, PtrStepf I1w, PtrStepf I1wx, PtrStepf I1wy, PtrStepf grad, PtrStepf rho)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
@ -166,10 +163,7 @@ namespace
const float I0Val = I0(y, x);
rho(y, x) = I1wVal - I1wxVal * u1Val - I1wyVal * u2Val - I0Val;
}
}
namespace tvl1flow
{
void warpBackward(PtrStepSzf I0, PtrStepSzf I1, PtrStepSzf I1x, PtrStepSzf I1y, PtrStepSzf u1, PtrStepSzf u2, PtrStepSzf I1w, PtrStepSzf I1wx, PtrStepSzf I1wy, PtrStepSzf grad, PtrStepSzf rho)
{
const dim3 block(32, 8);
@ -179,7 +173,7 @@ namespace tvl1flow
bindTexture(&tex_I1x, I1x);
bindTexture(&tex_I1y, I1y);
::warpBackward<<<grid, block>>>(I0, u1, u2, I1w, I1wx, I1wy, grad, rho);
warpBackwardKernel<<<grid, block>>>(I0, u1, u2, I1w, I1wx, I1wy, grad, rho);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
@ -189,7 +183,7 @@ namespace tvl1flow
////////////////////////////////////////////////////////////
// estimateU
namespace
namespace tvl1flow
{
__device__ float divergence(const PtrStepf& v1, const PtrStepf& v2, int y, int x)
{
@ -213,7 +207,7 @@ namespace
}
}
__global__ void estimateU(const PtrStepSzf I1wx, const PtrStepf I1wy,
__global__ void estimateUKernel(const PtrStepSzf I1wx, const PtrStepf I1wy,
const PtrStepf grad, const PtrStepf rho_c,
const PtrStepf p11, const PtrStepf p12, const PtrStepf p21, const PtrStepf p22,
PtrStepf u1, PtrStepf u2, PtrStepf error,
@ -275,10 +269,7 @@ namespace
const float n2 = (u2OldVal - u2NewVal) * (u2OldVal - u2NewVal);
error(y, x) = n1 + n2;
}
}
namespace tvl1flow
{
void estimateU(PtrStepSzf I1wx, PtrStepSzf I1wy,
PtrStepSzf grad, PtrStepSzf rho_c,
PtrStepSzf p11, PtrStepSzf p12, PtrStepSzf p21, PtrStepSzf p22,
@ -288,7 +279,7 @@ namespace tvl1flow
const dim3 block(32, 8);
const dim3 grid(divUp(I1wx.cols, block.x), divUp(I1wx.rows, block.y));
::estimateU<<<grid, block>>>(I1wx, I1wy, grad, rho_c, p11, p12, p21, p22, u1, u2, error, l_t, theta);
estimateUKernel<<<grid, block>>>(I1wx, I1wy, grad, rho_c, p11, p12, p21, p22, u1, u2, error, l_t, theta);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
@ -298,9 +289,9 @@ namespace tvl1flow
////////////////////////////////////////////////////////////
// estimateDualVariables
namespace
namespace tvl1flow
{
__global__ void estimateDualVariables(const PtrStepSzf u1, const PtrStepf u2, PtrStepf p11, PtrStepf p12, PtrStepf p21, PtrStepf p22, const float taut)
__global__ void estimateDualVariablesKernel(const PtrStepSzf u1, const PtrStepf u2, PtrStepf p11, PtrStepf p12, PtrStepf p21, PtrStepf p22, const float taut)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
@ -325,16 +316,13 @@ namespace
p21(y, x) = (p21(y, x) + taut * u2x) / ng2;
p22(y, x) = (p22(y, x) + taut * u2y) / ng2;
}
}
namespace tvl1flow
{
void estimateDualVariables(PtrStepSzf u1, PtrStepSzf u2, PtrStepSzf p11, PtrStepSzf p12, PtrStepSzf p21, PtrStepSzf p22, float taut)
{
const dim3 block(32, 8);
const dim3 grid(divUp(u1.cols, block.x), divUp(u1.rows, block.y));
::estimateDualVariables<<<grid, block>>>(u1, u2, p11, p12, p21, p22, taut);
estimateDualVariablesKernel<<<grid, block>>>(u1, u2, p11, p12, p21, p22, taut);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );

2
modules/gpu/src/imgproc.cpp
Unescape View File

@ -551,7 +551,7 @@ void cv::gpu::integralBuffered(const GpuMat& src, GpuMat& sum, GpuMat& buffer, S
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))
&& 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);

12
modules/gpu/test/nvidia/TestHaarCascadeApplication.cpp
Unescape View File

@ -210,6 +210,18 @@ bool TestHaarCascadeApplication::process()
#if defined(__GNUC__)
//http://www.christian-seiler.de/projekte/fpmath/
#ifndef _FPU_EXTENDED
#define _FPU_EXTENDED 0
#endif
#ifndef _FPU_DOUBLE
#define _FPU_DOUBLE 0
#endif
#ifndef _FPU_SINGLE
#define _FPU_SINGLE 0
#endif
fpu_control_t fpu_oldcw, fpu_cw;
_FPU_GETCW(fpu_oldcw); // store old cw
fpu_cw = (fpu_oldcw & ~_FPU_EXTENDED & ~_FPU_DOUBLE & ~_FPU_SINGLE) | _FPU_SINGLE;

8
samples/gpu/driver_api_multi.cpp
Unescape View File

@ -54,14 +54,8 @@ inline void safeCall_(int code, const char* expr, const char* file, int line)
// Each GPU is associated with its own context
CUcontext contexts[2];
int main(int argc, char **argv)
{
if (argc > 1)
int main()
{
cout << "CUDA driver API sample\n";
return -1;
}
int num_devices = getCudaEnabledDeviceCount();
if (num_devices < 2)
{

2
samples/gpu/driver_api_stereo_multi.cpp
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@ -76,7 +76,7 @@ GpuMat d_result[2];
// CPU result
Mat result;
void printHelp()
static void printHelp()
{
std::cout << "Usage: driver_api_stereo_multi_gpu --left <left_image> --right <right_image>\n";
}

3
samples/gpu/softcascade.cpp
Unescape View File

@ -76,8 +76,7 @@ int main(int argc, char** argv)
cv::gpu::GpuMat dframe(frame), roi(frame.rows, frame.cols, CV_8UC1), trois;
roi.setTo(cv::Scalar::all(1));
cascade.genRoi(roi, trois);
cascade.detect(dframe, trois, objects);
cascade.detect(dframe, roi, objects);
cv::Mat dt(objects);
typedef cv::gpu::SCascade::Detection Detection;

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