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fixes for the newly added gcc warning keys

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pull/2/head
Marina Kolpakova 13 years ago
parent f6ef504ef0
commit b065c7a296
20 changed files with 113 additions and 67 deletions
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  1. 1
      modules/gpu/CMakeLists.txt
  2. 2
      modules/gpu/src/arithm.cpp
  3. 3
      modules/gpu/src/cuda/bf_knnmatch.cu
  4. 2
      modules/gpu/src/cuda/bf_match.cu
  5. 2
      modules/gpu/src/cuda/bf_radius_match.cu
  6. 2
      modules/gpu/src/cuda/hog.cu
  7. 3
      modules/gpu/src/cuda/remap.cu
  8. 7
      modules/gpu/src/cuda/resize.cu
  9. 4
      modules/gpu/src/cuda/warp.cu
  10. 10
      modules/gpu/src/nvidia/NCVBroxOpticalFlow.cu
  11. 3
      modules/gpu/src/nvidia/NCVHaarObjectDetection.cu
  12. 1
      modules/gpu/src/nvidia/core/NCV.cu
  13. 22
      modules/gpu/src/nvidia/core/NCV.hpp
  14. 1
      modules/gpu/src/nvidia/core/NCVRuntimeTemplates.hpp
  15. 22
      modules/gpu/src/opencv2/gpu/device/filters.hpp
  16. 6
      modules/gpu/src/opencv2/gpu/device/functional.hpp
  17. 34
      modules/gpu/test/nvidia/TestHypothesesFilter.cpp
  18. 24
      modules/gpu/test/nvidia/TestResize.cpp
  19. 5
      modules/gpu/test/nvidia/main_nvidia.cpp
  20. 26
      modules/gpu/test/test_nvidia.cpp

1
modules/gpu/CMakeLists.txt
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@ -30,6 +30,7 @@ if (HAVE_CUDA)
source_group("Src\\NVidia" FILES ${ncv_files})
ocv_include_directories("src/nvidia" "src/nvidia/core" "src/nvidia/NPP_staging" ${CUDA_INCLUDE_DIRS})
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef -Wmissing-declarations /wd4211 /wd4201 /wd4100 /wd4505 /wd4408)
string(REPLACE "-Wsign-promo" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
#set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-keep")
#set (CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler;/EHsc-;")

2
modules/gpu/src/arithm.cpp
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@ -68,7 +68,7 @@ void cv::gpu::polarToCart(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, bool,
void cv::gpu::gemm(const GpuMat& src1, const GpuMat& src2, double alpha, const GpuMat& src3, double beta, GpuMat& dst, int flags, Stream& stream)
{
#ifndef HAVE_CUBLAS
(void)src1; (void)src2; (void)alpha; (void)src3; (void)beta; (void)dst; (void)flags; (void)stream;
CV_Error(CV_StsNotImplemented, "The library was build without CUBLAS");
#else

3
modules/gpu/src/cuda/bf_knnmatch.cu
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@ -748,6 +748,7 @@ namespace cv { namespace gpu { namespace device
const DevMem2Db& trainIdx, const DevMem2Db& distance,
int cc, cudaStream_t stream)
{
(void)cc;
if (query.cols <= 64)
{
matchUnrolledCached<16, 64, Dist>(query, train, mask, static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<float2> > (distance), stream);
@ -779,6 +780,7 @@ namespace cv { namespace gpu { namespace device
const DevMem2Db& trainIdx, const DevMem2Db& imgIdx, const DevMem2Db& distance,
int cc, cudaStream_t stream)
{
(void)cc;
if (query.cols <= 64)
{
matchUnrolledCached<16, 64, Dist>(query, trains, n, mask, static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<int2> >(imgIdx), static_cast< DevMem2D_<float2> > (distance), stream);
@ -943,6 +945,7 @@ namespace cv { namespace gpu { namespace device
const DevMem2Df& allDist,
int cc, cudaStream_t stream)
{
(void)cc;
if (query.cols <= 64)
{
calcDistanceUnrolled<16, 64, Dist>(query, train, mask, allDist, stream);

2
modules/gpu/src/cuda/bf_match.cu
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@ -567,6 +567,7 @@ namespace cv { namespace gpu { namespace device
const DevMem2Di& trainIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream)
{
(void)cc;
if (query.cols <= 64)
{
matchUnrolledCached<16, 64, Dist>(query, train, mask, trainIdx, distance, stream);
@ -598,6 +599,7 @@ namespace cv { namespace gpu { namespace device
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream)
{
(void)cc;
if (query.cols <= 64)
{
matchUnrolledCached<16, 64, Dist>(query, trains, n, mask, trainIdx, imgIdx, distance, stream);

2
modules/gpu/src/cuda/bf_radius_match.cu
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@ -281,6 +281,7 @@ namespace cv { namespace gpu { namespace device
const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream)
{
(void)cc;
if (query.cols <= 64)
{
matchUnrolled<16, 64, Dist>(query, train, maxDistance, mask, trainIdx, distance, nMatches, stream);
@ -312,6 +313,7 @@ namespace cv { namespace gpu { namespace device
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream)
{
(void)cc;
if (query.cols <= 64)
{
matchUnrolled<16, 64, Dist>(query, trains, n, maxDistance, masks, trainIdx, imgIdx, distance, nMatches, stream);

2
modules/gpu/src/cuda/hog.cu
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@ -619,6 +619,7 @@ namespace cv { namespace gpu { namespace device
void compute_gradients_8UC4(int nbins, int height, int width, const DevMem2Db& img,
float angle_scale, DevMem2Df grad, DevMem2Db qangle, bool correct_gamma)
{
(void)nbins;
const int nthreads = 256;
dim3 bdim(nthreads, 1);
@ -691,6 +692,7 @@ namespace cv { namespace gpu { namespace device
void compute_gradients_8UC1(int nbins, int height, int width, const DevMem2Db& img,
float angle_scale, DevMem2Df grad, DevMem2Db qangle, bool correct_gamma)
{
(void)nbins;
const int nthreads = 256;
dim3 bdim(nthreads, 1);

3
modules/gpu/src/cuda/remap.cu
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@ -87,6 +87,9 @@ namespace cv { namespace gpu { namespace device
{
static void call(DevMem2D_<T> src, DevMem2D_<T> srcWhole, int xoff, int yoff, DevMem2Df mapx, DevMem2Df mapy, DevMem2D_<T> dst, const float* borderValue, int)
{
(void)srcWhole;
(void)xoff;
(void)yoff;
typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type work_type;
dim3 block(32, 8);

7
modules/gpu/src/cuda/resize.cu
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@ -131,6 +131,10 @@ namespace cv { namespace gpu { namespace device
{
static void call(DevMem2D_<T> src, DevMem2D_<T> srcWhole, int xoff, int yoff, float fx, float fy, DevMem2D_<T> dst)
{
(void)srcWhole;
(void)xoff;
(void)yoff;
dim3 block(32, 8);
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y));
@ -219,6 +223,9 @@ namespace cv { namespace gpu { namespace device
{
static void call(DevMem2D_<T> src, DevMem2D_<T> srcWhole, int xoff, int yoff, float fx, float fy, DevMem2D_<T> dst, cudaStream_t stream)
{
(void)srcWhole;
(void)xoff;
(void)yoff;
int iscale_x = round(fx);
int iscale_y = round(fy);

4
modules/gpu/src/cuda/warp.cu
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@ -158,6 +158,10 @@ namespace cv { namespace gpu { namespace device
{
static void call(DevMem2D_<T> src, DevMem2D_<T> srcWhole, int xoff, int yoff, DevMem2D_<T> dst, const float* borderValue, int)
{
(void)xoff;
(void)yoff;
(void)srcWhole;
typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type work_type;
dim3 block(32, 8);

10
modules/gpu/src/nvidia/NCVBroxOpticalFlow.cu
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@ -1136,7 +1136,7 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
ptrVNew->ptr(), dstSize, ns * sizeof (float), dstROI, 1.0f/scale_factor, 1.0f/scale_factor, nppStBicubic) );
ScaleVector(ptrVNew->ptr(), ptrVNew->ptr(), 1.0f/scale_factor, ns * nh, stream);
ncvAssertCUDALastErrorReturn(NCV_CUDA_ERROR);
ncvAssertCUDALastErrorReturn((int)NCV_CUDA_ERROR);
cv::gpu::device::swap<FloatVector*>(ptrU, ptrUNew);
cv::gpu::device::swap<FloatVector*>(ptrV, ptrVNew);
@ -1145,17 +1145,17 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
}
// end of warping iterations
ncvAssertCUDAReturn(cudaStreamSynchronize(stream), NCV_CUDA_ERROR);
ncvAssertCUDAReturn(cudaStreamSynchronize(stream), (int)NCV_CUDA_ERROR);
ncvAssertCUDAReturn( cudaMemcpy2DAsync
(uOut.ptr(), uOut.pitch(), ptrU->ptr(),
kSourcePitch, kSourceWidth*sizeof(float), kSourceHeight, cudaMemcpyDeviceToDevice, stream), NCV_CUDA_ERROR );
kSourcePitch, kSourceWidth*sizeof(float), kSourceHeight, cudaMemcpyDeviceToDevice, stream), (int)NCV_CUDA_ERROR );
ncvAssertCUDAReturn( cudaMemcpy2DAsync
(vOut.ptr(), vOut.pitch(), ptrV->ptr(),
kSourcePitch, kSourceWidth*sizeof(float), kSourceHeight, cudaMemcpyDeviceToDevice, stream), NCV_CUDA_ERROR );
kSourcePitch, kSourceWidth*sizeof(float), kSourceHeight, cudaMemcpyDeviceToDevice, stream), (int)NCV_CUDA_ERROR );
ncvAssertCUDAReturn(cudaStreamSynchronize(stream), NCV_CUDA_ERROR);
ncvAssertCUDAReturn(cudaStreamSynchronize(stream), (int)NCV_CUDA_ERROR);
}
return NCV_SUCCESS;

3
modules/gpu/src/nvidia/NCVHaarObjectDetection.cu
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@ -687,6 +687,7 @@ struct applyHaarClassifierAnchorParallelFunctor
template<class TList>
void call(TList tl)
{
(void)tl;
applyHaarClassifierAnchorParallel <
Loki::TL::TypeAt<TList, 0>::Result::value,
Loki::TL::TypeAt<TList, 1>::Result::value,
@ -796,6 +797,7 @@ struct applyHaarClassifierClassifierParallelFunctor
template<class TList>
void call(TList tl)
{
(void)tl;
applyHaarClassifierClassifierParallel <
Loki::TL::TypeAt<TList, 0>::Result::value,
Loki::TL::TypeAt<TList, 1>::Result::value,
@ -876,6 +878,7 @@ struct initializeMaskVectorFunctor
template<class TList>
void call(TList tl)
{
(void)tl;
initializeMaskVector <
Loki::TL::TypeAt<TList, 0>::Result::value,
Loki::TL::TypeAt<TList, 1>::Result::value >

1
modules/gpu/src/nvidia/core/NCV.cu
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@ -854,6 +854,7 @@ static NCVStatus drawRectsWrapperDevice(T *d_dst,
T color,
cudaStream_t cuStream)
{
(void)cuStream;
ncvAssertReturn(d_dst != NULL && d_rects != NULL, NCV_NULL_PTR);
ncvAssertReturn(dstWidth > 0 && dstHeight > 0, NCV_DIMENSIONS_INVALID);
ncvAssertReturn(dstStride >= dstWidth, NCV_INVALID_STEP);

22
modules/gpu/src/nvidia/core/NCV.hpp
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@ -1,7 +1,7 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
@ -461,7 +461,7 @@ public:
virtual NcvBool isInitialized(void) const = 0;
virtual NcvBool isCounting(void) const = 0;
virtual NCVMemoryType memType(void) const = 0;
virtual Ncv32u alignment(void) const = 0;
virtual size_t maxSize(void) const = 0;
@ -585,11 +585,11 @@ public:
}
else
{
ncvAssertReturn(dst._length * sizeof(T) >= howMuch &&
ncvAssertReturn(dst._length * sizeof(T) >= howMuch &&
this->_length * sizeof(T) >= howMuch &&
howMuch > 0, NCV_MEM_COPY_ERROR);
}
ncvAssertReturn((this->_ptr != NULL || this->_memtype == NCVMemoryTypeNone) &&
ncvAssertReturn((this->_ptr != NULL || this->_memtype == NCVMemoryTypeNone) &&
(dst._ptr != NULL || dst._memtype == NCVMemoryTypeNone), NCV_NULL_PTR);
NCVStatus ncvStat = NCV_SUCCESS;
@ -766,18 +766,18 @@ public:
}
else
{
ncvAssertReturn(dst._pitch * dst._height >= howMuch &&
ncvAssertReturn(dst._pitch * dst._height >= howMuch &&
this->_pitch * this->_height >= howMuch &&
howMuch > 0, NCV_MEM_COPY_ERROR);
}
ncvAssertReturn((this->_ptr != NULL || this->_memtype == NCVMemoryTypeNone) &&
ncvAssertReturn((this->_ptr != NULL || this->_memtype == NCVMemoryTypeNone) &&
(dst._ptr != NULL || dst._memtype == NCVMemoryTypeNone), NCV_NULL_PTR);
NCVStatus ncvStat = NCV_SUCCESS;
if (this->_memtype != NCVMemoryTypeNone)
{
ncvStat = memSegCopyHelper(dst._ptr, dst._memtype,
this->_ptr, this->_memtype,
ncvStat = memSegCopyHelper(dst._ptr, dst._memtype,
this->_ptr, this->_memtype,
howMuch, cuStream);
}
@ -788,7 +788,7 @@ public:
{
ncvAssertReturn(this->width() >= roi.width && this->height() >= roi.height &&
dst.width() >= roi.width && dst.height() >= roi.height, NCV_MEM_COPY_ERROR);
ncvAssertReturn((this->_ptr != NULL || this->_memtype == NCVMemoryTypeNone) &&
ncvAssertReturn((this->_ptr != NULL || this->_memtype == NCVMemoryTypeNone) &&
(dst._ptr != NULL || dst._memtype == NCVMemoryTypeNone), NCV_NULL_PTR);
NCVStatus ncvStat = NCV_SUCCESS;
@ -802,7 +802,7 @@ public:
return ncvStat;
}
T &at(Ncv32u x, Ncv32u y) const
T& at(Ncv32u x, Ncv32u y) const
{
NcvBool bOutRange = (x >= this->_width || y >= this->_height);
ncvAssertPrintCheck(!bOutRange, "Error addressing matrix at [" << x << ", " << y << "]");

1
modules/gpu/src/nvidia/core/NCVRuntimeTemplates.hpp
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@ -211,6 +211,7 @@ namespace NCVRuntimeTemplateBool
static void call(Func &functor, std::vector<int> &templateParams)
{
(void)templateParams;
functor.call(TList());
}
};

22
modules/gpu/src/opencv2/gpu/device/filters.hpp
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@ -55,7 +55,12 @@ namespace cv { namespace gpu { namespace device
typedef typename Ptr2D::elem_type elem_type;
typedef float index_type;
explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) : src(src_) {}
explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f)
: src(src_)
{
(void)fx;
(void)fy;
}
__device__ __forceinline__ elem_type operator ()(float y, float x) const
{
@ -70,8 +75,12 @@ namespace cv { namespace gpu { namespace device
typedef typename Ptr2D::elem_type elem_type;
typedef float index_type;
explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) : src(src_) {}
explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f)
: src(src_)
{
(void)fx;
(void)fy;
}
__device__ __forceinline__ elem_type operator ()(float y, float x) const
{
typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
@ -107,7 +116,12 @@ namespace cv { namespace gpu { namespace device
typedef float index_type;
typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
explicit __host__ __device__ __forceinline__ CubicFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) : src(src_) {}
explicit __host__ __device__ __forceinline__ CubicFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f)
: src(src_)
{
(void)fx;
(void)fy;
}
static __device__ __forceinline__ float bicubicCoeff(float x_)
{

6
modules/gpu/src/opencv2/gpu/device/functional.hpp
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@ -470,7 +470,7 @@ namespace cv { namespace gpu { namespace device
template <typename T> struct thresh_trunc_func : unary_function<T, T>
{
explicit __host__ __device__ __forceinline__ thresh_trunc_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {}
explicit __host__ __device__ __forceinline__ thresh_trunc_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;}
__device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
{
@ -487,7 +487,7 @@ namespace cv { namespace gpu { namespace device
template <typename T> struct thresh_to_zero_func : unary_function<T, T>
{
explicit __host__ __device__ __forceinline__ thresh_to_zero_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {}
explicit __host__ __device__ __forceinline__ thresh_to_zero_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;}
__device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
{
@ -503,7 +503,7 @@ namespace cv { namespace gpu { namespace device
template <typename T> struct thresh_to_zero_inv_func : unary_function<T, T>
{
explicit __host__ __device__ __forceinline__ thresh_to_zero_inv_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {}
explicit __host__ __device__ __forceinline__ thresh_to_zero_inv_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;}
__device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
{

34
modules/gpu/test/nvidia/TestHypothesesFilter.cpp
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@ -1,11 +1,11 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
@ -13,14 +13,14 @@
#include "NCVHaarObjectDetection.hpp"
TestHypothesesFilter::TestHypothesesFilter(std::string testName, NCVTestSourceProvider<Ncv32u> &src,
Ncv32u numDstRects, Ncv32u minNeighbors, Ncv32f eps)
TestHypothesesFilter::TestHypothesesFilter(std::string testName, NCVTestSourceProvider<Ncv32u> &src_,
Ncv32u numDstRects_, Ncv32u minNeighbors_, Ncv32f eps_)
:
NCVTestProvider(testName),
src(src),
numDstRects(numDstRects),
minNeighbors(minNeighbors),
eps(eps)
src(src_),
numDstRects(numDstRects_),
minNeighbors(minNeighbors_),
eps(eps_)
{
}
@ -94,11 +94,11 @@ bool TestHypothesesFilter::process()
for (Ncv32u j=0; j<numNeighbors; j++)
{
randVal = (1.0 * h_random32u.ptr()[randCnt++]) / 0xFFFFFFFF; randCnt = randCnt % h_random32u.length();
h_vecSrc.ptr()[srcSlotSize * i + j].x =
h_vecSrc.ptr()[srcSlotSize * i + j].x =
h_vecDst_groundTruth.ptr()[i].x +
(Ncv32s)(h_vecDst_groundTruth.ptr()[i].width * this->eps * (randVal - 0.5));
randVal = (1.0 * h_random32u.ptr()[randCnt++]) / 0xFFFFFFFF; randCnt = randCnt % h_random32u.length();
h_vecSrc.ptr()[srcSlotSize * i + j].y =
h_vecSrc.ptr()[srcSlotSize * i + j].y =
h_vecDst_groundTruth.ptr()[i].y +
(Ncv32s)(h_vecDst_groundTruth.ptr()[i].height * this->eps * (randVal - 0.5));
h_vecSrc.ptr()[srcSlotSize * i + j].width = h_vecDst_groundTruth.ptr()[i].width;
@ -109,11 +109,11 @@ bool TestHypothesesFilter::process()
for (Ncv32u j=numNeighbors; j<srcSlotSize; j++)
{
randVal = (1.0 * h_random32u.ptr()[randCnt++]) / 0xFFFFFFFF; randCnt = randCnt % h_random32u.length();
h_vecSrc.ptr()[srcSlotSize * i + j].x =
h_vecSrc.ptr()[srcSlotSize * i + j].x =
this->canvasWidth + h_vecDst_groundTruth.ptr()[i].x +
(Ncv32s)(h_vecDst_groundTruth.ptr()[i].width * this->eps * (randVal - 0.5));
randVal = (1.0 * h_random32u.ptr()[randCnt++]) / 0xFFFFFFFF; randCnt = randCnt % h_random32u.length();
h_vecSrc.ptr()[srcSlotSize * i + j].y =
h_vecSrc.ptr()[srcSlotSize * i + j].y =
this->canvasHeight + h_vecDst_groundTruth.ptr()[i].y +
(Ncv32s)(h_vecDst_groundTruth.ptr()[i].height * this->eps * (randVal - 0.5));
h_vecSrc.ptr()[srcSlotSize * i + j].width = h_vecDst_groundTruth.ptr()[i].width;
@ -124,8 +124,8 @@ bool TestHypothesesFilter::process()
//shuffle
for (Ncv32u i=0; i<this->numDstRects*srcSlotSize-1; i++)
{
Ncv32u randVal = h_random32u.ptr()[randCnt++]; randCnt = randCnt % h_random32u.length();
Ncv32u secondSwap = randVal % (this->numDstRects*srcSlotSize-1 - i);
Ncv32u randValLocal = h_random32u.ptr()[randCnt++]; randCnt = randCnt % h_random32u.length();
Ncv32u secondSwap = randValLocal % (this->numDstRects*srcSlotSize-1 - i);
NcvRect32u tmp = h_vecSrc.ptr()[i + secondSwap];
h_vecSrc.ptr()[i + secondSwap] = h_vecSrc.ptr()[i];
h_vecSrc.ptr()[i] = tmp;

24
modules/gpu/test/nvidia/TestResize.cpp
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@ -1,11 +1,11 @@
/*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* NVIDIA Corporation and its licensors retain all intellectual
* property and proprietary rights in and to this software and
* related documentation and any modifications thereto.
* Any use, reproduction, disclosure, or distribution of this
* software and related documentation without an express license
* agreement from NVIDIA Corporation is strictly prohibited.
*/
@ -15,15 +15,15 @@
template <class T>
TestResize<T>::TestResize(std::string testName, NCVTestSourceProvider<T> &src,
Ncv32u width, Ncv32u height, Ncv32u scaleFactor, NcvBool bTextureCache)
TestResize<T>::TestResize(std::string testName, NCVTestSourceProvider<T> &src_,
Ncv32u width_, Ncv32u height_, Ncv32u scaleFactor_, NcvBool bTextureCache_)
:
NCVTestProvider(testName),
src(src),
width(width),
height(height),
scaleFactor(scaleFactor),
bTextureCache(bTextureCache)
src(src_),
width(width_),
height(height_),
scaleFactor(scaleFactor_),
bTextureCache(bTextureCache_)
{
}

5
modules/gpu/test/nvidia/main_nvidia.cpp
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@ -248,6 +248,7 @@ void generateHaarLoaderTests(NCVAutoTestLister &testLister)
void generateHaarApplicationTests(NCVAutoTestLister &testLister, NCVTestSourceProvider<Ncv8u> &src,
Ncv32u maxWidth, Ncv32u maxHeight)
{
(void)maxHeight;
for (Ncv32u i=20; i<512; i+=11)
{
for (Ncv32u j=20; j<128; j+=5)
@ -268,11 +269,12 @@ void generateHaarApplicationTests(NCVAutoTestLister &testLister, NCVTestSourcePr
static void devNullOutput(const std::string& msg)
{
(void)msg;
}
bool nvidia_NPPST_Integral_Image(const std::string& test_data_path, OutputLevel outputLevel)
{
path = test_data_path;
path = test_data_path.c_str();
ncvSetDebugOutputHandler(devNullOutput);
NCVAutoTestLister testListerII("NPPST Integral Image", outputLevel);
@ -374,6 +376,7 @@ bool nvidia_NCV_Vector_Operations(const std::string& test_data_path, OutputLevel
generateVectorTests(testListerVectorOperations, testSrcRandom_32u, 4096*4096);
return testListerVectorOperations.invoke();
}
bool nvidia_NCV_Haar_Cascade_Loader(const std::string& test_data_path, OutputLevel outputLevel)

26
modules/gpu/test/test_nvidia.cpp
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@ -58,15 +58,15 @@ struct NVidiaTest : TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
std::string path;
std::string _path;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
path = std::string(TS::ptr()->get_data_path()) + "haarcascade/";
_path = TS::ptr()->get_data_path().c_str();
_path = _path + "haarcascade/";
}
};
@ -84,63 +84,63 @@ OutputLevel nvidiaTestOutputLevel = OutputLevelCompact;
TEST_P(NPPST, SquaredIntegral)
{
bool res = nvidia_NPPST_Squared_Integral_Image(path, nvidiaTestOutputLevel);
bool res = nvidia_NPPST_Squared_Integral_Image(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NPPST, RectStdDev)
{
bool res = nvidia_NPPST_RectStdDev(path, nvidiaTestOutputLevel);
bool res = nvidia_NPPST_RectStdDev(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NPPST, Resize)
{
bool res = nvidia_NPPST_Resize(path, nvidiaTestOutputLevel);
bool res = nvidia_NPPST_Resize(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NPPST, VectorOperations)
{
bool res = nvidia_NPPST_Vector_Operations(path, nvidiaTestOutputLevel);
bool res = nvidia_NPPST_Vector_Operations(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NPPST, Transpose)
{
bool res = nvidia_NPPST_Transpose(path, nvidiaTestOutputLevel);
bool res = nvidia_NPPST_Transpose(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NCV, VectorOperations)
{
bool res = nvidia_NCV_Vector_Operations(path, nvidiaTestOutputLevel);
bool res = nvidia_NCV_Vector_Operations(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NCV, HaarCascadeLoader)
{
bool res = nvidia_NCV_Haar_Cascade_Loader(path, nvidiaTestOutputLevel);
bool res = nvidia_NCV_Haar_Cascade_Loader(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NCV, HaarCascadeApplication)
{
bool res = nvidia_NCV_Haar_Cascade_Application(path, nvidiaTestOutputLevel);
bool res = nvidia_NCV_Haar_Cascade_Application(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
TEST_P(NCV, HypothesesFiltration)
{
bool res = nvidia_NCV_Hypotheses_Filtration(path, nvidiaTestOutputLevel);
bool res = nvidia_NCV_Hypotheses_Filtration(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}
@ -148,7 +148,7 @@ TEST_P(NCV, HypothesesFiltration)
TEST_P(NCV, Visualization)
{
// this functionality doesn't used in gpu module
bool res = nvidia_NCV_Visualization(path, nvidiaTestOutputLevel);
bool res = nvidia_NCV_Visualization(_path, nvidiaTestOutputLevel);
ASSERT_TRUE(res);
}

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