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fixes for gpu module:

Browse Source 
- fixed printCudaDeviceInfo for new CC
- fixed some compilation errors and warnings
- removed unset command from CMake script
- removed unused std imports
This commit is contained in:
Vladislav Vinogradov
2013-01-23 13:59:14 +04:00
parent b7e6b5af1b
commit ae6266e101
32 changed files with 232 additions and 144 deletions
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14
modules/core/include/opencv2/core/cuda_devptrs.hpp
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@@ -177,6 +177,20 @@ namespace cv
//#undef __CV_GPU_DEPR_BEFORE__
//#undef __CV_GPU_DEPR_AFTER__
namespace device
{
using cv::gpu::PtrSz;
using cv::gpu::PtrStep;
using cv::gpu::PtrStepSz;
using cv::gpu::PtrStepSzb;
using cv::gpu::PtrStepSzf;
using cv::gpu::PtrStepSzi;
using cv::gpu::PtrStepb;
using cv::gpu::PtrStepf;
using cv::gpu::PtrStepi;
}
}
}

42
modules/core/src/gpumat.cpp
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@@ -315,18 +315,6 @@ 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
@@ -335,7 +323,7 @@ namespace
int Cores;
} SMtoCores;
SMtoCores gpuArchCoresPerSM[] = { { 0x10, 8 }, { 0x11, 8 }, { 0x12, 8 }, { 0x13, 8 }, { 0x20, 32 }, { 0x21, 48 }, {0x30, 192}, { -1, -1 } };
SMtoCores gpuArchCoresPerSM[] = { { 0x10, 8 }, { 0x11, 8 }, { 0x12, 8 }, { 0x13, 8 }, { 0x20, 32 }, { 0x21, 48 }, {0x30, 192}, {0x35, 192}, { -1, -1 } };
int index = 0;
while (gpuArchCoresPerSM[index].SM != -1)
@@ -344,7 +332,7 @@ namespace
return gpuArchCoresPerSM[index].Cores;
index++;
}
printf("MapSMtoCores undefined SMversion %d.%d!\n", major, minor);
return -1;
}
}
@@ -382,22 +370,13 @@ void cv::gpu::printCudaDeviceInfo(int device)
printf(" CUDA Driver Version / Runtime Version %d.%d / %d.%d\n", driverVersion/1000, driverVersion%100, runtimeVersion/1000, runtimeVersion%100);
printf(" CUDA Capability Major/Minor version number: %d.%d\n", prop.major, prop.minor);
printf(" Total amount of global memory: %.0f MBytes (%llu bytes)\n", (float)prop.totalGlobalMem/1048576.0f, (unsigned long long) prop.totalGlobalMem);
printf(" (%2d) Multiprocessors x (%2d) CUDA Cores/MP: %d CUDA Cores\n",
prop.multiProcessorCount, convertSMVer2Cores(prop.major, prop.minor),
convertSMVer2Cores(prop.major, prop.minor) * prop.multiProcessorCount);
int cores = convertSMVer2Cores(prop.major, prop.minor);
if (cores > 0)
printf(" (%2d) Multiprocessors x (%2d) CUDA Cores/MP: %d CUDA Cores\n", prop.multiProcessorCount, cores, cores * 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]);
@@ -457,7 +436,12 @@ void cv::gpu::printShortCudaDeviceInfo(int device)
const char *arch_str = prop.major < 2 ? " (not Fermi)" : "";
printf("Device %d: \"%s\" %.0fMb", dev, prop.name, (float)prop.totalGlobalMem/1048576.0f);
printf(", sm_%d%d%s, %d cores", prop.major, prop.minor, arch_str, convertSMVer2Cores(prop.major, prop.minor) * prop.multiProcessorCount);
printf(", sm_%d%d%s", prop.major, prop.minor, arch_str);
int cores = convertSMVer2Cores(prop.major, prop.minor);
if (cores > 0)
printf(", %d cores", cores * prop.multiProcessorCount);
printf(", Driver/Runtime ver.%d.%d/%d.%d\n", driverVersion/1000, driverVersion%100, runtimeVersion/1000, runtimeVersion%100);
}
fflush(stdout);

2
modules/gpu/CMakeLists.txt
View File

@@ -52,13 +52,11 @@ if (HAVE_CUDA)
set(cuda_link_libs ${CUDA_LIBRARIES} ${CUDA_npp_LIBRARY})
if(NOT APPLE)
unset(CUDA_nvcuvid_LIBRARY CACHE)
find_cuda_helper_libs(nvcuvid)
set(cuda_link_libs ${cuda_link_libs} ${CUDA_nvcuvid_LIBRARY})
endif()
if(WIN32)
unset(CUDA_nvcuvenc_LIBRARY CACHE)
find_cuda_helper_libs(nvcuvenc)
set(cuda_link_libs ${cuda_link_libs} ${CUDA_nvcuvenc_LIBRARY})
endif()

2
modules/gpu/doc/matrix_reductions.rst
View File

@@ -185,7 +185,7 @@ Reduces a matrix to a vector.
* **CV_REDUCE_MIN** The output is the minimum (column/row-wise) of all rows/columns of the matrix.
:param dtype: When it is negative, the destination vector will have the same type as the source matrix. Otherwise, its type will be ``CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), mtx.channels())`` .
The function ``reduce`` reduces the matrix to a vector by treating the matrix rows/columns as a set of 1D vectors and performing the specified operation on the vectors until a single row/column is obtained. For example, the function can be used to compute horizontal and vertical projections of a raster image. In case of ``CV_REDUCE_SUM`` and ``CV_REDUCE_AVG`` , the output may have a larger element bit-depth to preserve accuracy. And multi-channel arrays are also supported in these two reduction modes.
.. seealso:: :ocv:func:`reduce`

0
modules/gpu/src/opencv2/gpu/device/block.hpp → modules/gpu/include/opencv2/gpu/device/block.hpp
View File

12
modules/gpu/include/opencv2/gpu/device/common.hpp
View File

@@ -85,8 +85,6 @@ static inline void ___cudaSafeCall(cudaError_t err, const char *file, const int
cv::gpu::error(cudaGetErrorString(err), file, line, func);
}
#ifdef __CUDACC__
namespace cv { namespace gpu
{
__host__ __device__ __forceinline__ int divUp(int total, int grain)
@@ -96,19 +94,25 @@ namespace cv { namespace gpu
namespace device
{
using cv::gpu::divUp;
#ifdef __CUDACC__
typedef unsigned char uchar;
typedef unsigned short ushort;
typedef signed char schar;
typedef unsigned int uint;
#ifdef _WIN32
typedef unsigned int uint;
#endif
template<class T> inline void bindTexture(const textureReference* tex, const PtrStepSz<T>& img)
{
cudaChannelFormatDesc desc = cudaCreateChannelDesc<T>();
cudaSafeCall( cudaBindTexture2D(0, tex, img.ptr(), &desc, img.cols, img.rows, img.step) );
}
#endif // __CUDACC__
}
}}
#endif // __CUDACC__
#endif // __OPENCV_GPU_COMMON_HPP__

1
modules/gpu/include/opencv2/gpu/device/emulation.hpp
View File

@@ -44,7 +44,6 @@
#define OPENCV_GPU_EMULATION_HPP_
#include "warp_reduce.hpp"
#include <stdio.h>
namespace cv { namespace gpu { namespace device
{

123
modules/gpu/include/opencv2/gpu/device/functional.hpp
View File

@@ -302,18 +302,18 @@ namespace cv { namespace gpu { namespace device
template <> struct name<type> : binary_function<type, type, type> \
{ \
__device__ __forceinline__ type operator()(type lhs, type rhs) const {return op(lhs, rhs);} \
__device__ __forceinline__ name(const name& other):binary_function<type, type, type>(){}\
__device__ __forceinline__ name():binary_function<type, type, type>(){}\
__device__ __forceinline__ name() {}\
__device__ __forceinline__ name(const name&) {}\
};
template <typename T> struct maximum : binary_function<T, T, T>
{
__device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType lhs, typename TypeTraits<T>::ParameterType rhs) const
{
return lhs < rhs ? rhs : lhs;
return max(lhs, rhs);
}
__device__ __forceinline__ maximum(const maximum& other):binary_function<T, T, T>(){}
__device__ __forceinline__ maximum():binary_function<T, T, T>(){}
__device__ __forceinline__ maximum() {}
__device__ __forceinline__ maximum(const maximum&) {}
};
OPENCV_GPU_IMPLEMENT_MINMAX(maximum, uchar, ::max)
@@ -330,10 +330,10 @@ namespace cv { namespace gpu { namespace device
{
__device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType lhs, typename TypeTraits<T>::ParameterType rhs) const
{
return lhs < rhs ? lhs : rhs;
return min(lhs, rhs);
}
__device__ __forceinline__ minimum(const minimum& other):binary_function<T, T, T>(){}
__device__ __forceinline__ minimum():binary_function<T, T, T>(){}
__device__ __forceinline__ minimum() {}
__device__ __forceinline__ minimum(const minimum&) {}
};
OPENCV_GPU_IMPLEMENT_MINMAX(minimum, uchar, ::min)
@@ -350,6 +350,108 @@ namespace cv { namespace gpu { namespace device
// Math functions
///bound=========================================
template <typename T> struct abs_func : unary_function<T, T>
{
__device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType x) const
{
return abs(x);
}
__device__ __forceinline__ abs_func() {}
__device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<unsigned char> : unary_function<unsigned char, unsigned char>
{
__device__ __forceinline__ unsigned char operator ()(unsigned char x) const
{
return x;
}
__device__ __forceinline__ abs_func() {}
__device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<signed char> : unary_function<signed char, signed char>
{
__device__ __forceinline__ signed char operator ()(signed char x) const
{
return ::abs((int)x);
}
__device__ __forceinline__ abs_func() {}
__device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<char> : unary_function<char, char>
{
__device__ __forceinline__ char operator ()(char x) const
{
return ::abs((int)x);
}
__device__ __forceinline__ abs_func() {}
__device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<unsigned short> : unary_function<unsigned short, unsigned short>
{
__device__ __forceinline__ unsigned short operator ()(unsigned short x) const
{
return x;
}
__device__ __forceinline__ abs_func() {}
__device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<short> : unary_function<short, short>
{
__device__ __forceinline__ short operator ()(short x) const
{
return ::abs((int)x);
}
__device__ __forceinline__ abs_func() {}
__device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<unsigned int> : unary_function<unsigned int, unsigned int>
{
__device__ __forceinline__ unsigned int operator ()(unsigned int x) const
{
return x;
}
__device__ __forceinline__ abs_func() {}
__device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<int> : unary_function<int, int>
{
__device__ __forceinline__ int operator ()(int x) const
{
return ::abs(x);
}
__device__ __forceinline__ abs_func() {}
__device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<float> : unary_function<float, float>
{
__device__ __forceinline__ float operator ()(float x) const
{
return ::fabsf(x);
}
__device__ __forceinline__ abs_func() {}
__device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<double> : unary_function<double, double>
{
__device__ __forceinline__ double operator ()(double x) const
{
return ::fabs(x);
}
__device__ __forceinline__ abs_func() {}
__device__ __forceinline__ abs_func(const abs_func&) {}
};
#define OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(name, func) \
template <typename T> struct name ## _func : unary_function<T, float> \
{ \
@@ -357,6 +459,8 @@ namespace cv { namespace gpu { namespace device
{ \
return func ## f(v); \
} \
__device__ __forceinline__ name ## _func() {} \
__device__ __forceinline__ name ## _func(const name ## _func&) {} \
}; \
template <> struct name ## _func<double> : unary_function<double, double> \
{ \
@@ -364,6 +468,8 @@ namespace cv { namespace gpu { namespace device
{ \
return func(v); \
} \
__device__ __forceinline__ name ## _func() {} \
__device__ __forceinline__ name ## _func(const name ## _func&) {} \
};
#define OPENCV_GPU_IMPLEMENT_BIN_FUNCTOR(name, func) \
@@ -382,7 +488,6 @@ namespace cv { namespace gpu { namespace device
} \
};
OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(fabs, ::fabs)
OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(sqrt, ::sqrt)
OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(exp, ::exp)
OPENCV_GPU_IMPLEMENT_UN_FUNCTOR(exp2, ::exp2)

4
modules/gpu/include/opencv2/gpu/device/vec_math.hpp
View File

@@ -280,7 +280,7 @@ namespace cv { namespace gpu { namespace device
OPENCV_GPU_IMPLEMENT_VEC_UNOP (type, operator ! , logical_not) \
OPENCV_GPU_IMPLEMENT_VEC_BINOP(type, max, maximum) \
OPENCV_GPU_IMPLEMENT_VEC_BINOP(type, min, minimum) \
OPENCV_GPU_IMPLEMENT_VEC_UNOP(type, fabs, fabs_func) \
OPENCV_GPU_IMPLEMENT_VEC_UNOP(type, abs, abs_func) \
OPENCV_GPU_IMPLEMENT_VEC_UNOP(type, sqrt, sqrt_func) \
OPENCV_GPU_IMPLEMENT_VEC_UNOP(type, exp, exp_func) \
OPENCV_GPU_IMPLEMENT_VEC_UNOP(type, exp2, exp2_func) \
@@ -327,4 +327,4 @@ namespace cv { namespace gpu { namespace device
#undef OPENCV_GPU_IMPLEMENT_VEC_INT_OP
}}} // namespace cv { namespace gpu { namespace device
#endif // __OPENCV_GPU_VECMATH_HPP__
#endif // __OPENCV_GPU_VECMATH_HPP__

58
modules/gpu/src/brute_force_matcher.cpp
View File

@@ -268,14 +268,14 @@ void cv::gpu::BruteForceMatcher_GPU_base::matchConvert(const Mat& trainIdx, cons
const float* distance_ptr = distance.ptr<float>();
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx, ++trainIdx_ptr, ++distance_ptr)
{
int _trainIdx = *trainIdx_ptr;
int train_idx = *trainIdx_ptr;
if (_trainIdx == -1)
if (train_idx == -1)
continue;
float _distance = *distance_ptr;
float distance_local = *distance_ptr;
DMatch m(queryIdx, _trainIdx, 0, _distance);
DMatch m(queryIdx, train_idx, 0, distance_local);
matches.push_back(m);
}
@@ -413,16 +413,16 @@ void cv::gpu::BruteForceMatcher_GPU_base::matchConvert(const Mat& trainIdx, cons
const float* distance_ptr = distance.ptr<float>();
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
{
int trainIdx = *trainIdx_ptr;
int _trainIdx = *trainIdx_ptr;
if (trainIdx == -1)
if (_trainIdx == -1)
continue;
int imgIdx = *imgIdx_ptr;
int _imgIdx = *imgIdx_ptr;
float distance = *distance_ptr;
float _distance = *distance_ptr;
DMatch m(queryIdx, trainIdx, imgIdx, distance);
DMatch m(queryIdx, _trainIdx, _imgIdx, _distance);
matches.push_back(m);
}
@@ -548,13 +548,13 @@ void cv::gpu::BruteForceMatcher_GPU_base::knnMatchConvert(const Mat& trainIdx, c
for (int i = 0; i < k; ++i, ++trainIdx_ptr, ++distance_ptr)
{
int trainIdx = *trainIdx_ptr;
int _trainIdx = *trainIdx_ptr;
if (trainIdx != -1)
if (_trainIdx != -1)
{
float distance = *distance_ptr;
float _distance = *distance_ptr;
DMatch m(queryIdx, trainIdx, 0, distance);
DMatch m(queryIdx, _trainIdx, 0, _distance);
curMatches.push_back(m);
}
@@ -667,15 +667,15 @@ void cv::gpu::BruteForceMatcher_GPU_base::knnMatch2Convert(const Mat& trainIdx,
for (int i = 0; i < 2; ++i, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
{
int trainIdx = *trainIdx_ptr;
int _trainIdx = *trainIdx_ptr;
if (trainIdx != -1)
if (_trainIdx != -1)
{
int imgIdx = *imgIdx_ptr;
int _imgIdx = *imgIdx_ptr;
float distance = *distance_ptr;
float _distance = *distance_ptr;
DMatch m(queryIdx, trainIdx, imgIdx, distance);
DMatch m(queryIdx, _trainIdx, _imgIdx, _distance);
curMatches.push_back(m);
}
@@ -852,25 +852,25 @@ void cv::gpu::BruteForceMatcher_GPU_base::radiusMatchConvert(const Mat& trainIdx
const int* trainIdx_ptr = trainIdx.ptr<int>(queryIdx);
const float* distance_ptr = distance.ptr<float>(queryIdx);
const int nMatches = std::min(nMatches_ptr[queryIdx], trainIdx.cols);
const int nMatched = std::min(nMatches_ptr[queryIdx], trainIdx.cols);
if (nMatches == 0)
if (nMatched == 0)
{
if (!compactResult)
matches.push_back(vector<DMatch>());
continue;
}
matches.push_back(vector<DMatch>(nMatches));
matches.push_back(vector<DMatch>(nMatched));
vector<DMatch>& curMatches = matches.back();
for (int i = 0; i < nMatches; ++i, ++trainIdx_ptr, ++distance_ptr)
for (int i = 0; i < nMatched; ++i, ++trainIdx_ptr, ++distance_ptr)
{
int trainIdx = *trainIdx_ptr;
int _trainIdx = *trainIdx_ptr;
float distance = *distance_ptr;
float _distance = *distance_ptr;
DMatch m(queryIdx, trainIdx, 0, distance);
DMatch m(queryIdx, _trainIdx, 0, _distance);
curMatches[i] = m;
}
@@ -990,9 +990,9 @@ void cv::gpu::BruteForceMatcher_GPU_base::radiusMatchConvert(const Mat& trainIdx
const int* imgIdx_ptr = imgIdx.ptr<int>(queryIdx);
const float* distance_ptr = distance.ptr<float>(queryIdx);
const int nMatches = std::min(nMatches_ptr[queryIdx], trainIdx.cols);
const int nMatched = std::min(nMatches_ptr[queryIdx], trainIdx.cols);
if (nMatches == 0)
if (nMatched == 0)
{
if (!compactResult)
matches.push_back(vector<DMatch>());
@@ -1001,9 +1001,9 @@ void cv::gpu::BruteForceMatcher_GPU_base::radiusMatchConvert(const Mat& trainIdx
matches.push_back(vector<DMatch>());
vector<DMatch>& curMatches = matches.back();
curMatches.reserve(nMatches);
curMatches.reserve(nMatched);
for (int i = 0; i < nMatches; ++i, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
for (int i = 0; i < nMatched; ++i, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
{
int _trainIdx = *trainIdx_ptr;
int _imgIdx = *imgIdx_ptr;

2
modules/gpu/src/cascadeclassifier.cpp
View File

@@ -622,7 +622,7 @@ private:
}
// copy data structures on gpu
stage_mat.upload(cv::Mat(1, stages.size() * sizeof(Stage), CV_8UC1, (uchar*)&(stages[0]) ));
stage_mat.upload(cv::Mat(1, (int) (stages.size() * sizeof(Stage)), CV_8UC1, (uchar*)&(stages[0]) ));
trees_mat.upload(cv::Mat(cl_trees).reshape(1,1));
nodes_mat.upload(cv::Mat(cl_nodes).reshape(1,1));
leaves_mat.upload(cv::Mat(cl_leaves).reshape(1,1));

3
modules/gpu/src/cuda/ccomponetns.cu
View File

@@ -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));
@@ -529,4 +530,4 @@ namespace cv { namespace gpu { namespace device
}
} } }
#endif /* CUDA_DISABLER */
#endif /* CUDA_DISABLER */

3
modules/gpu/src/cuda/gftt.cu
View File

@@ -47,6 +47,7 @@
#if !defined CUDA_DISABLER
#include <thrust/device_ptr.h>
#include <thrust/sort.h>
#include "opencv2/gpu/device/common.hpp"
@@ -148,4 +149,4 @@ namespace cv { namespace gpu { namespace device
}}}
#endif /* CUDA_DISABLER */
#endif /* CUDA_DISABLER */

4
modules/gpu/src/cuda/hough.cu
View File

@@ -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"
@@ -1509,4 +1511,4 @@ namespace cv { namespace gpu { namespace device
}}}
#endif /* CUDA_DISABLER */
#endif /* CUDA_DISABLER */

2
modules/gpu/src/cuda/lbp.cu
View File

@@ -295,7 +295,7 @@ namespace cv { namespace gpu { namespace device
int grid = divUp(workAmount, block);
cudaFuncSetCacheConfig(lbp_cascade, cudaFuncCachePreferL1);
Cascade cascade((Stage*)mstages.ptr(), nstages, (ClNode*)mnodes.ptr(), mleaves.ptr(), msubsets.ptr(), (uchar4*)mfeatures.ptr(), subsetSize);
lbp_cascade<<<grid, block>>>(cascade, frameW, frameH, windowW, windowH, initialScale, factor, workAmount, integral.ptr(), integral.step / sizeof(int), objects, classified);
lbp_cascade<<<grid, block>>>(cascade, frameW, frameH, windowW, windowH, initialScale, factor, workAmount, integral.ptr(), (int)integral.step / sizeof(int), objects, classified);
}
}
}}}

8
modules/gpu/src/cuda/mathfunc.cu
View File

@@ -76,7 +76,7 @@ namespace cv { namespace gpu { namespace device
static __device__ __forceinline__ void calc(int x, int y, float x_data, float y_data, float* dst, size_t dst_step, float scale)
{
float angle = ::atan2f(y_data, x_data);
angle += (angle < 0) * 2.0 * CV_PI;
angle += (angle < 0) * 2.0f * CV_PI_F;
dst[y * dst_step + x] = scale * angle;
}
};
@@ -140,7 +140,7 @@ namespace cv { namespace gpu { namespace device
grid.x = divUp(x.cols, threads.x);
grid.y = divUp(x.rows, threads.y);
const float scale = angleInDegrees ? (float)(180.0f / CV_PI) : 1.f;
const float scale = angleInDegrees ? (180.0f / CV_PI_F) : 1.f;
cartToPolar<Mag, Angle><<<grid, threads, 0, stream>>>(
x.data, x.step/x.elemSize(), y.data, y.step/y.elemSize(),
@@ -190,7 +190,7 @@ namespace cv { namespace gpu { namespace device
grid.x = divUp(mag.cols, threads.x);
grid.y = divUp(mag.rows, threads.y);
const float scale = angleInDegrees ? (float)(CV_PI / 180.0f) : 1.0f;
const float scale = angleInDegrees ? (CV_PI_F / 180.0f) : 1.0f;
polarToCart<Mag><<<grid, threads, 0, stream>>>(mag.data, mag.step/mag.elemSize(),
angle.data, angle.step/angle.elemSize(), scale, x.data, x.step/x.elemSize(), y.data, y.step/y.elemSize(), mag.cols, mag.rows);
@@ -214,4 +214,4 @@ namespace cv { namespace gpu { namespace device
} // namespace mathfunc
}}} // namespace cv { namespace gpu { namespace device
#endif /* CUDA_DISABLER */
#endif /* CUDA_DISABLER */

22
modules/gpu/src/cuda/optical_flow.cu
View File

@@ -164,40 +164,40 @@ namespace cv { namespace gpu { namespace device
r = ::fmin(r, 2.5f);
v[1].x = arrow_x + r * ::cosf(theta - CV_PI / 2.0f);
v[1].y = arrow_y + r * ::sinf(theta - CV_PI / 2.0f);
v[1].x = arrow_x + r * ::cosf(theta - CV_PI_F / 2.0f);
v[1].y = arrow_y + r * ::sinf(theta - CV_PI_F / 2.0f);
v[4].x = arrow_x + r * ::cosf(theta + CV_PI / 2.0f);
v[4].y = arrow_y + r * ::sinf(theta + CV_PI / 2.0f);
v[4].x = arrow_x + r * ::cosf(theta + CV_PI_F / 2.0f);
v[4].y = arrow_y + r * ::sinf(theta + CV_PI_F / 2.0f);
int indx = (y * u_avg.cols + x) * NUM_VERTS_PER_ARROW * 3;
color_data[indx] = (theta - CV_PI) / CV_PI * 180.0f;
color_data[indx] = (theta - CV_PI_F) / CV_PI_F * 180.0f;
vertex_data[indx++] = v[0].x * xscale;
vertex_data[indx++] = v[0].y * yscale;
vertex_data[indx++] = v[0].z;
color_data[indx] = (theta - CV_PI) / CV_PI * 180.0f;
color_data[indx] = (theta - CV_PI_F) / CV_PI_F * 180.0f;
vertex_data[indx++] = v[1].x * xscale;
vertex_data[indx++] = v[1].y * yscale;
vertex_data[indx++] = v[1].z;
color_data[indx] = (theta - CV_PI) / CV_PI * 180.0f;
color_data[indx] = (theta - CV_PI_F) / CV_PI_F * 180.0f;
vertex_data[indx++] = v[2].x * xscale;
vertex_data[indx++] = v[2].y * yscale;
vertex_data[indx++] = v[2].z;
color_data[indx] = (theta - CV_PI) / CV_PI * 180.0f;
color_data[indx] = (theta - CV_PI_F) / CV_PI_F * 180.0f;
vertex_data[indx++] = v[3].x * xscale;
vertex_data[indx++] = v[3].y * yscale;
vertex_data[indx++] = v[3].z;
color_data[indx] = (theta - CV_PI) / CV_PI * 180.0f;
color_data[indx] = (theta - CV_PI_F) / CV_PI_F * 180.0f;
vertex_data[indx++] = v[4].x * xscale;
vertex_data[indx++] = v[4].y * yscale;
vertex_data[indx++] = v[4].z;
color_data[indx] = (theta - CV_PI) / CV_PI * 180.0f;
color_data[indx] = (theta - CV_PI_F) / CV_PI_F * 180.0f;
vertex_data[indx++] = v[5].x * xscale;
vertex_data[indx++] = v[5].y * yscale;
vertex_data[indx++] = v[5].z;
@@ -217,4 +217,4 @@ namespace cv { namespace gpu { namespace device
}
}}}
#endif /* CUDA_DISABLER */
#endif /* CUDA_DISABLER */

3
modules/gpu/src/cuda/optical_flow_farneback.cu
View File

@@ -42,7 +42,6 @@
#if !defined CUDA_DISABLER
#include <stdio.h>
#include "internal_shared.hpp"
#include "opencv2/gpu/device/common.hpp"
#include "opencv2/gpu/device/border_interpolate.hpp"
@@ -57,8 +56,6 @@
#define BORDER_SIZE 5
#define MAX_KSIZE_HALF 100
using namespace std;
namespace cv { namespace gpu { namespace device { namespace optflow_farneback
{
__constant__ float c_g[8];

4
modules/gpu/src/cuda/pyrlk.cu
View File

@@ -267,7 +267,7 @@ namespace cv { namespace gpu { namespace device
}
__device__ __forceinline__ float4 abs_(const float4& a)
{
return fabs(a);
return abs(a);
}
template <int cn, int PATCH_X, int PATCH_Y, bool calcErr>
@@ -681,4 +681,4 @@ namespace cv { namespace gpu { namespace device
}
}}}
#endif /* CUDA_DISABLER */
#endif /* CUDA_DISABLER */

2
modules/gpu/src/cuda/split_merge.cu
View File

@@ -508,4 +508,4 @@ namespace cv { namespace gpu { namespace device
}}} // namespace cv { namespace gpu { namespace device
#endif /* CUDA_DISABLER */
#endif /* CUDA_DISABLER */

2
modules/gpu/src/cuda/stereobp.cu
View File

@@ -454,7 +454,7 @@ namespace cv { namespace gpu { namespace device
grid.x = divUp(cols, threads.x << 1);
grid.y = divUp(rows, threads.y);
int elem_step = u.step/sizeof(T);
int elem_step = (int)(u.step / sizeof(T));
for(int t = 0; t < iters; ++t)
{

4
modules/gpu/src/cuda/surf.cu
View File

@@ -638,7 +638,7 @@ namespace cv { namespace gpu { namespace device
kp_dir *= 180.0f / CV_PI_F;
kp_dir = 360.0f - kp_dir;
if (abs(kp_dir - 360.f) < FLT_EPSILON)
if (::fabsf(kp_dir - 360.f) < FLT_EPSILON)
kp_dir = 0.f;
featureDir[blockIdx.x] = kp_dir;
@@ -1003,4 +1003,4 @@ namespace cv { namespace gpu { namespace device
}}} // namespace cv { namespace gpu { namespace device
#endif /* CUDA_DISABLER */
#endif /* CUDA_DISABLER */

2
modules/gpu/src/cuda/texture_binder.hpp
View File

@@ -85,7 +85,7 @@ namespace cv
namespace device
{
using pcl::gpu::TextureBinder;
using cv::gpu::TextureBinder;
}
}

6
modules/gpu/src/fast.cpp
View File

@@ -125,9 +125,6 @@ int cv::gpu::FAST_GPU::calcKeyPointsLocation(const GpuMat& img, const GpuMat& ma
CV_Assert(img.type() == CV_8UC1);
CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.size() == img.size()));
if (!TargetArchs::builtWith(GLOBAL_ATOMICS) || !DeviceInfo().supports(GLOBAL_ATOMICS))
CV_Error(CV_StsNotImplemented, "The device doesn't support global atomics");
int maxKeypoints = static_cast<int>(keypointsRatio * img.size().area());
ensureSizeIsEnough(1, maxKeypoints, CV_16SC2, kpLoc_);
@@ -148,9 +145,6 @@ int cv::gpu::FAST_GPU::getKeyPoints(GpuMat& keypoints)
{
using namespace cv::gpu::device::fast;
if (!TargetArchs::builtWith(GLOBAL_ATOMICS) || !DeviceInfo().supports(GLOBAL_ATOMICS))
CV_Error(CV_StsNotImplemented, "The device doesn't support global atomics");
if (count_ == 0)
return 0;

3
modules/gpu/src/gftt.cpp
View File

@@ -68,9 +68,6 @@ void cv::gpu::GoodFeaturesToTrackDetector_GPU::operator ()(const GpuMat& image,
CV_Assert(qualityLevel > 0 && minDistance >= 0 && maxCorners >= 0);
CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()));
if (!TargetArchs::builtWith(GLOBAL_ATOMICS) || !DeviceInfo().supports(GLOBAL_ATOMICS))
CV_Error(CV_StsNotImplemented, "The device doesn't support global atomics");
ensureSizeIsEnough(image.size(), CV_32F, eig_);
if (useHarrisDetector)

8
modules/gpu/src/nvidia/core/NCV.cu
View File

@@ -45,8 +45,6 @@
#include <vector>
#include "NCV.hpp"
using namespace std;
//==============================================================================
//
@@ -55,16 +53,16 @@ using namespace std;
//==============================================================================
static void stdDebugOutput(const string &msg)
static void stdDebugOutput(const std::string &msg)
{
cout << msg;
std::cout << msg;
}
static NCVDebugOutputHandler *debugOutputHandler = stdDebugOutput;
void ncvDebugOutput(const string &msg)
void ncvDebugOutput(const std::string &msg)
{
debugOutputHandler(msg);
}

4
modules/gpu/src/nvidia/core/NCV.hpp
View File

@@ -288,7 +288,7 @@ NCV_EXPORTS void ncvSetDebugOutputHandler(NCVDebugOutputHandler* func);
do \
{ \
cudaError_t res = cudacall; \
ncvAssertPrintReturn(cudaSuccess==res, "cudaError_t=" << res, errCode); \
ncvAssertPrintReturn(cudaSuccess==res, "cudaError_t=" << (int)res, errCode); \
} while (0)
@@ -296,7 +296,7 @@ NCV_EXPORTS void ncvSetDebugOutputHandler(NCVDebugOutputHandler* func);
do \
{ \
cudaError_t res = cudaGetLastError(); \
ncvAssertPrintReturn(cudaSuccess==res, "cudaError_t=" << res, errCode); \
ncvAssertPrintReturn(cudaSuccess==res, "cudaError_t=" << (int)res, errCode); \
} while (0)

8
samples/gpu/cascadeclassifier_nvidia_api.cpp
View File

@@ -30,7 +30,7 @@ const Size2i preferredVideoFrameSize(640, 480);
const string wndTitle = "NVIDIA Computer Vision :: Haar Classifiers Cascade";
void matPrint(Mat &img, int lineOffsY, Scalar fontColor, const string &ss)
static void matPrint(Mat &img, int lineOffsY, Scalar fontColor, const string &ss)
{
int fontFace = FONT_HERSHEY_DUPLEX;
double fontScale = 0.8;
@@ -45,7 +45,7 @@ void matPrint(Mat &img, int lineOffsY, Scalar fontColor, const string &ss)
}
void displayState(Mat &canvas, bool bHelp, bool bGpu, bool bLargestFace, bool bFilter, double fps)
static void displayState(Mat &canvas, bool bHelp, bool bGpu, bool bLargestFace, bool bFilter, double fps)
{
Scalar fontColorRed = CV_RGB(255,0,0);
Scalar fontColorNV = CV_RGB(118,185,0);
@@ -74,7 +74,7 @@ void displayState(Mat &canvas, bool bHelp, bool bGpu, bool bLargestFace, bool bF
}
NCVStatus process(Mat *srcdst,
static NCVStatus process(Mat *srcdst,
Ncv32u width, Ncv32u height,
NcvBool bFilterRects, NcvBool bLargestFace,
HaarClassifierCascadeDescriptor &haar,
@@ -281,7 +281,7 @@ int main(int argc, const char** argv)
//==============================================================================
namedWindow(wndTitle, 1);
Mat gray, frameDisp;
Mat frameDisp;
do
{

8
samples/gpu/driver_api_multi.cpp
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)
int main()
{
if (argc > 1)
{
cout << "CUDA driver API sample\n";
return -1;
}
int num_devices = getCudaEnabledDeviceCount();
if (num_devices < 2)
{

2
samples/gpu/driver_api_stereo_multi.cpp
View File

@@ -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";
}

16
samples/gpu/opticalflow_nvidia_api.cpp
View File

@@ -59,7 +59,7 @@ public:
class RgbToR
{
public:
float operator ()(unsigned char b, unsigned char g, unsigned char r)
float operator ()(unsigned char /*b*/, unsigned char /*g*/, unsigned char r)
{
return static_cast<float>(r)/255.0f;
}
@@ -69,7 +69,7 @@ public:
class RgbToG
{
public:
float operator ()(unsigned char b, unsigned char g, unsigned char r)
float operator ()(unsigned char /*b*/, unsigned char g, unsigned char /*r*/)
{
return static_cast<float>(g)/255.0f;
}
@@ -78,7 +78,7 @@ public:
class RgbToB
{
public:
float operator ()(unsigned char b, unsigned char g, unsigned char r)
float operator ()(unsigned char b, unsigned char /*g*/, unsigned char /*r*/)
{
return static_cast<float>(b)/255.0f;
}
@@ -135,7 +135,7 @@ NCVStatus CopyData(const IplImage *image, const NCVMatrixAlloc<Ncv32f> &dst)
return NCV_SUCCESS;
}
NCVStatus LoadImages (const char *frame0Name,
static NCVStatus LoadImages (const char *frame0Name,
const char *frame1Name,
int &width,
int &height,
@@ -186,7 +186,7 @@ inline T MapValue (T x, T a, T b, T c, T d)
return c + (d - c) * (x - a) / (b - a);
}
NCVStatus ShowFlow (NCVMatrixAlloc<Ncv32f> &u, NCVMatrixAlloc<Ncv32f> &v, const char *name)
static NCVStatus ShowFlow (NCVMatrixAlloc<Ncv32f> &u, NCVMatrixAlloc<Ncv32f> &v, const char *name)
{
IplImage *flowField;
@@ -246,7 +246,7 @@ NCVStatus ShowFlow (NCVMatrixAlloc<Ncv32f> &u, NCVMatrixAlloc<Ncv32f> &v, const
return NCV_SUCCESS;
}
IplImage *CreateImage (NCVMatrixAlloc<Ncv32f> &h_r, NCVMatrixAlloc<Ncv32f> &h_g, NCVMatrixAlloc<Ncv32f> &h_b)
static IplImage *CreateImage (NCVMatrixAlloc<Ncv32f> &h_r, NCVMatrixAlloc<Ncv32f> &h_g, NCVMatrixAlloc<Ncv32f> &h_b)
{
CvSize imageSize = cvSize (h_r.width (), h_r.height ());
IplImage *image = cvCreateImage (imageSize, IPL_DEPTH_8U, 4);
@@ -270,7 +270,7 @@ IplImage *CreateImage (NCVMatrixAlloc<Ncv32f> &h_r, NCVMatrixAlloc<Ncv32f> &h_g,
return image;
}
void PrintHelp ()
static void PrintHelp ()
{
std::cout << "Usage help:\n";
std::cout << std::setiosflags(std::ios::left);
@@ -286,7 +286,7 @@ void PrintHelp ()
std::cout << "\t" << std::setw(15) << PARAM_HELP << " - display this help message\n";
}
int ProcessCommandLine(int argc, char **argv,
static int ProcessCommandLine(int argc, char **argv,
Ncv32f &timeStep,
char *&frame0Name,
char *&frame1Name,

2
samples/gpu/stereo_multi.cpp
View File

@@ -47,7 +47,7 @@ GpuMat d_result[2];
// CPU result
Mat result;
void printHelp()
static void printHelp()
{
std::cout << "Usage: stereo_multi_gpu --left <image> --right <image>\n";
}