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提交 42ced17c 编写于 作者: V Vladislav Vinogradov
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fixed some warnings under win64

上级 767ac9aa
隐藏空白更改
内联 并排
Showing with 191 addition and 171 deletion
modules/gpu/src/arithm.cpp
浏览文件 @ 42ced17c
......@@ -82,7 +82,8 @@ void cv::gpu::transpose(const GpuMat& src, GpuMat& dst, Stream& s)
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiTranspose_8u_C1R(src.ptr<Npp8u>(), src.step, dst.ptr<Npp8u>(), dst.step, sz) );
nppSafeCall( nppiTranspose_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz) );
}
else if (src.elemSize() == 4)
{
......@@ -92,8 +93,8 @@ void cv::gpu::transpose(const GpuMat& src, GpuMat& dst, Stream& s)
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiStTranspose_32u_C1R(const_cast<Ncv32u*>(src.ptr<Ncv32u>()), src.step,
dst.ptr<Ncv32u>(), dst.step, sz) );
nppSafeCall( nppiStTranspose_32u_C1R(const_cast<Ncv32u*>(src.ptr<Ncv32u>()), static_cast<int>(src.step),
dst.ptr<Ncv32u>(), static_cast<int>(dst.step), sz) );
}
else // if (src.elemSize() == 8)
{
......@@ -103,8 +104,8 @@ void cv::gpu::transpose(const GpuMat& src, GpuMat& dst, Stream& s)
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiStTranspose_64u_C1R(const_cast<Ncv64u*>(src.ptr<Ncv64u>()), src.step,
dst.ptr<Ncv64u>(), dst.step, sz) );
nppSafeCall( nppiStTranspose_64u_C1R(const_cast<Ncv64u*>(src.ptr<Ncv64u>()), static_cast<int>(src.step),
dst.ptr<Ncv64u>(), static_cast<int>(dst.step), sz) );
}
if (stream == 0)
......@@ -130,14 +131,14 @@ void cv::gpu::flip(const GpuMat& src, GpuMat& dst, int flipCode, Stream& s)
if (src.type() == CV_8UC1)
{
nppSafeCall( nppiMirror_8u_C1R(src.ptr<Npp8u>(), src.step,
dst.ptr<Npp8u>(), dst.step, sz,
nppSafeCall( nppiMirror_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz,
(flipCode == 0 ? NPP_HORIZONTAL_AXIS : (flipCode > 0 ? NPP_VERTICAL_AXIS : NPP_BOTH_AXIS))) );
}
else
{
nppSafeCall( nppiMirror_8u_C4R(src.ptr<Npp8u>(), src.step,
dst.ptr<Npp8u>(), dst.step, sz,
nppSafeCall( nppiMirror_8u_C4R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz,
(flipCode == 0 ? NPP_HORIZONTAL_AXIS : (flipCode > 0 ? NPP_VERTICAL_AXIS : NPP_BOTH_AXIS))) );
}
......@@ -187,7 +188,8 @@ void cv::gpu::LUT(const GpuMat& src, const Mat& lut, GpuMat& dst, Stream& s)
if (src.type() == CV_8UC1)
{
nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), src.step, dst.ptr<Npp8u>(), dst.step, sz, nppLut.ptr<Npp32s>(), lvls.pLevels, 256) );
nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, nppLut.ptr<Npp32s>(), lvls.pLevels, 256) );
}
else
{
......@@ -202,7 +204,8 @@ void cv::gpu::LUT(const GpuMat& src, const Mat& lut, GpuMat& dst, Stream& s)
pValues3[1] = nppLut3[1].ptr<Npp32s>();
pValues3[2] = nppLut3[2].ptr<Npp32s>();
}
nppSafeCall( nppiLUT_Linear_8u_C3R(src.ptr<Npp8u>(), src.step, dst.ptr<Npp8u>(), dst.step, sz, pValues3, lvls.pLevels3, lvls.nValues3) );
nppSafeCall( nppiLUT_Linear_8u_C3R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, pValues3, lvls.pLevels3, lvls.nValues3) );
}
if (stream == 0)
......@@ -226,7 +229,7 @@ void cv::gpu::exp(const GpuMat& src, GpuMat& dst, Stream& s)
NppStreamHandler h(stream);
nppSafeCall( nppiExp_32f_C1R(src.ptr<Npp32f>(), src.step, dst.ptr<Npp32f>(), dst.step, sz) );
nppSafeCall( nppiExp_32f_C1R(src.ptr<Npp32f>(), static_cast<int>(src.step), dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -249,7 +252,7 @@ void cv::gpu::log(const GpuMat& src, GpuMat& dst, Stream& s)
NppStreamHandler h(stream);
nppSafeCall( nppiLn_32f_C1R(src.ptr<Npp32f>(), src.step, dst.ptr<Npp32f>(), dst.step, sz) );
nppSafeCall( nppiLn_32f_C1R(src.ptr<Npp32f>(), static_cast<int>(src.step), dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -274,7 +277,7 @@ namespace
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<Npp32fc>(), src.step, dst.ptr<Npp32f>(), dst.step, sz) );
nppSafeCall( func(src.ptr<Npp32fc>(), static_cast<int>(src.step), dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......
modules/gpu/src/brute_force_matcher.cpp
浏览文件 @ 42ced17c
......@@ -265,7 +265,7 @@ void cv::gpu::BruteForceMatcher_GPU_base::makeGpuCollection(GpuMat& trainCollect
if (masks.empty())
{
Mat trainCollectionCPU(1, trainDescCollection.size(), CV_8UC(sizeof(DevMem2D)));
Mat trainCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(DevMem2D)));
for (size_t i = 0; i < trainDescCollection.size(); ++i)
{
......@@ -280,8 +280,8 @@ void cv::gpu::BruteForceMatcher_GPU_base::makeGpuCollection(GpuMat& trainCollect
{
CV_Assert(masks.size() == trainDescCollection.size());
Mat trainCollectionCPU(1, trainDescCollection.size(), CV_8UC(sizeof(DevMem2D)));
Mat maskCollectionCPU(1, trainDescCollection.size(), CV_8UC(sizeof(PtrStep)));
Mat trainCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(DevMem2D)));
Mat maskCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(PtrStep)));
for (size_t i = 0; i < trainDescCollection.size(); ++i)
{
......
modules/gpu/src/cascadeclassifier.cpp
浏览文件 @ 42ced17c
......@@ -87,7 +87,7 @@ struct cv::gpu::CascadeClassifier_GPU::CascadeClassifierImpl
src_seg.begin = src_beg;
src_seg.size = src.step * src.rows;
NCVMatrixReuse<Ncv8u> d_src(src_seg, devProp.textureAlignment, src.cols, src.rows, src.step, true);
NCVMatrixReuse<Ncv8u> d_src(src_seg, static_cast<int>(devProp.textureAlignment), src.cols, src.rows, static_cast<int>(src.step), true);
ncvAssertReturn(d_src.isMemReused(), NCV_ALLOCATOR_BAD_REUSE);
CV_Assert(objects.rows == 1);
......@@ -141,8 +141,8 @@ private:
ncvAssertCUDAReturn(cudaGetDeviceProperties(&devProp, devId), NCV_CUDA_ERROR);
// Load the classifier from file (assuming its size is about 1 mb) using a simple allocator
gpuCascadeAllocator = new NCVMemNativeAllocator(NCVMemoryTypeDevice, devProp.textureAlignment);
cpuCascadeAllocator = new NCVMemNativeAllocator(NCVMemoryTypeHostPinned, devProp.textureAlignment);
gpuCascadeAllocator = new NCVMemNativeAllocator(NCVMemoryTypeDevice, static_cast<int>(devProp.textureAlignment));
cpuCascadeAllocator = new NCVMemNativeAllocator(NCVMemoryTypeHostPinned, static_cast<int>(devProp.textureAlignment));
ncvAssertPrintReturn(gpuCascadeAllocator->isInitialized(), "Error creating cascade GPU allocator", NCV_CUDA_ERROR);
ncvAssertPrintReturn(cpuCascadeAllocator->isInitialized(), "Error creating cascade CPU allocator", NCV_CUDA_ERROR);
......@@ -189,8 +189,8 @@ private:
}
// Calculate memory requirements and create real allocators
NCVMemStackAllocator gpuCounter(devProp.textureAlignment);
NCVMemStackAllocator cpuCounter(devProp.textureAlignment);
NCVMemStackAllocator gpuCounter(static_cast<int>(devProp.textureAlignment));
NCVMemStackAllocator cpuCounter(static_cast<int>(devProp.textureAlignment));
ncvAssertPrintReturn(gpuCounter.isInitialized(), "Error creating GPU memory counter", NCV_CUDA_ERROR);
ncvAssertPrintReturn(cpuCounter.isInitialized(), "Error creating CPU memory counter", NCV_CUDA_ERROR);
......@@ -214,8 +214,8 @@ private:
ncvAssertReturnNcvStat(ncvStat);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
gpuAllocator = new NCVMemStackAllocator(NCVMemoryTypeDevice, gpuCounter.maxSize(), devProp.textureAlignment);
cpuAllocator = new NCVMemStackAllocator(NCVMemoryTypeHostPinned, cpuCounter.maxSize(), devProp.textureAlignment);
gpuAllocator = new NCVMemStackAllocator(NCVMemoryTypeDevice, gpuCounter.maxSize(), static_cast<int>(devProp.textureAlignment));
cpuAllocator = new NCVMemStackAllocator(NCVMemoryTypeHostPinned, cpuCounter.maxSize(), static_cast<int>(devProp.textureAlignment));
ncvAssertPrintReturn(gpuAllocator->isInitialized(), "Error creating GPU memory allocator", NCV_CUDA_ERROR);
ncvAssertPrintReturn(cpuAllocator->isInitialized(), "Error creating CPU memory allocator", NCV_CUDA_ERROR);
......@@ -372,7 +372,7 @@ NCVStatus loadFromXML(const std::string &filename,
for(int s = 0; s < stagesCound; ++s) // by stages
{
HaarStage64 curStage;
curStage.setStartClassifierRootNodeOffset(haarClassifierNodes.size());
curStage.setStartClassifierRootNodeOffset(static_cast<Ncv32u>(haarClassifierNodes.size()));
curStage.setStageThreshold(oldCascade->stage_classifier[s].threshold);
......@@ -452,7 +452,7 @@ NCVStatus loadFromXML(const std::string &filename,
HaarFeatureDescriptor32 tmpFeatureDesc;
ncvStat = tmpFeatureDesc.create(haar.bNeedsTiltedII, bIsLeftNodeLeaf, bIsRightNodeLeaf,
featureId, haarFeatures.size() - featureId);
featureId, static_cast<Ncv32u>(haarFeatures.size()) - featureId);
ncvAssertReturn(NCV_SUCCESS == ncvStat, ncvStat);
curNode.setFeatureDesc(tmpFeatureDesc);
......@@ -478,13 +478,13 @@ NCVStatus loadFromXML(const std::string &filename,
}
//fill in cascade stats
haar.NumStages = haarStages.size();
haar.NumClassifierRootNodes = haarClassifierNodes.size();
haar.NumClassifierTotalNodes = haar.NumClassifierRootNodes + h_TmpClassifierNotRootNodes.size();
haar.NumFeatures = haarFeatures.size();
haar.NumStages = static_cast<Ncv32u>(haarStages.size());
haar.NumClassifierRootNodes = static_cast<Ncv32u>(haarClassifierNodes.size());
haar.NumClassifierTotalNodes = static_cast<Ncv32u>(haar.NumClassifierRootNodes + h_TmpClassifierNotRootNodes.size());
haar.NumFeatures = static_cast<Ncv32u>(haarFeatures.size());
//merge root and leaf nodes in one classifiers array
Ncv32u offsetRoot = haarClassifierNodes.size();
Ncv32u offsetRoot = static_cast<Ncv32u>(haarClassifierNodes.size());
for (Ncv32u i=0; i<haarClassifierNodes.size(); i++)
{
HaarFeatureDescriptor32 featureDesc = haarClassifierNodes[i].getFeatureDesc();
......
modules/gpu/src/cuda/element_operations.cu
浏览文件 @ 42ced17c
......@@ -171,10 +171,10 @@ namespace cv { namespace gpu { namespace mathfunc
}
void bitwiseNotCaller(int rows, int cols, int elem_size1, int cn,
void bitwiseNotCaller(int rows, int cols, size_t elem_size1, int cn,
const PtrStep src, PtrStep dst, cudaStream_t stream)
{
bitwiseUnOp<UN_OP_NOT>(rows, cols * elem_size1 * cn, src, dst, stream);
bitwiseUnOp<UN_OP_NOT>(rows, static_cast<int>(cols * elem_size1 * cn), src, dst, stream);
}
......@@ -296,10 +296,10 @@ namespace cv { namespace gpu { namespace mathfunc
}
void bitwiseOrCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1,
void bitwiseOrCaller(int rows, int cols, size_t elem_size1, int cn, const PtrStep src1,
const PtrStep src2, PtrStep dst, cudaStream_t stream)
{
bitwiseBinOp<BIN_OP_OR>(rows, cols * elem_size1 * cn, src1, src2, dst, stream);
bitwiseBinOp<BIN_OP_OR>(rows, static_cast<int>(cols * elem_size1 * cn), src1, src2, dst, stream);
}
......@@ -315,10 +315,10 @@ namespace cv { namespace gpu { namespace mathfunc
template void bitwiseMaskOrCaller<uint>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
void bitwiseAndCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1,
void bitwiseAndCaller(int rows, int cols, size_t elem_size1, int cn, const PtrStep src1,
const PtrStep src2, PtrStep dst, cudaStream_t stream)
{
bitwiseBinOp<BIN_OP_AND>(rows, cols * elem_size1 * cn, src1, src2, dst, stream);
bitwiseBinOp<BIN_OP_AND>(rows, static_cast<int>(cols * elem_size1 * cn), src1, src2, dst, stream);
}
......@@ -334,10 +334,10 @@ namespace cv { namespace gpu { namespace mathfunc
template void bitwiseMaskAndCaller<uint>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
void bitwiseXorCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1,
void bitwiseXorCaller(int rows, int cols, size_t elem_size1, int cn, const PtrStep src1,
const PtrStep src2, PtrStep dst, cudaStream_t stream)
{
bitwiseBinOp<BIN_OP_XOR>(rows, cols * elem_size1 * cn, src1, src2, dst, stream);
bitwiseBinOp<BIN_OP_XOR>(rows, static_cast<int>(cols * elem_size1 * cn), src1, src2, dst, stream);
}
......
modules/gpu/src/cuda/hist.cu
浏览文件 @ 42ced17c
......@@ -176,7 +176,7 @@ namespace cv { namespace gpu { namespace histograms
histogram256<<<PARTIAL_HISTOGRAM256_COUNT, HISTOGRAM256_THREADBLOCK_SIZE, 0, stream>>>(
DevMem2D_<uint>(src),
buf,
src.rows * src.step / sizeof(uint),
static_cast<uint>(src.rows * src.step / sizeof(uint)),
src.cols);
cudaSafeCall( cudaGetLastError() );
......
modules/gpu/src/cuda/imgproc.cu
浏览文件 @ 42ced17c
......@@ -161,7 +161,7 @@ namespace cv { namespace gpu { namespace imgproc
texture<uchar4, 2> tex_meanshift;
__device__ short2 do_mean_shift(int x0, int y0, unsigned char* out,
int out_step, int cols, int rows,
size_t out_step, int cols, int rows,
int sp, int sr, int maxIter, float eps)
{
int isr2 = sr*sr;
......@@ -225,7 +225,7 @@ namespace cv { namespace gpu { namespace imgproc
return make_short2((short)x0, (short)y0);
}
extern "C" __global__ void meanshift_kernel( unsigned char* out, int out_step, int cols, int rows,
extern "C" __global__ void meanshift_kernel( unsigned char* out, size_t out_step, int cols, int rows,
int sp, int sr, int maxIter, float eps )
{
int x0 = blockIdx.x * blockDim.x + threadIdx.x;
......@@ -235,8 +235,8 @@ namespace cv { namespace gpu { namespace imgproc
do_mean_shift(x0, y0, out, out_step, cols, rows, sp, sr, maxIter, eps);
}
extern "C" __global__ void meanshiftproc_kernel( unsigned char* outr, int outrstep,
unsigned char* outsp, int outspstep,
extern "C" __global__ void meanshiftproc_kernel( unsigned char* outr, size_t outrstep,
unsigned char* outsp, size_t outspstep,
int cols, int rows,
int sp, int sr, int maxIter, float eps )
{
......
modules/gpu/src/cuda/matrix_operations.cu
浏览文件 @ 42ced17c
......@@ -62,7 +62,7 @@ namespace cv { namespace gpu { namespace matrix_operations {
///////////////////////////////////////////////////////////////////////////
template<typename T>
__global__ void copy_to_with_mask(T * mat_src, T * mat_dst, const unsigned char * mask, int cols, int rows, int step_mat, int step_mask, int channels)
__global__ void copy_to_with_mask(T * mat_src, T * mat_dst, const unsigned char * mask, int cols, int rows, size_t step_mat, size_t step_mask, int channels)
{
size_t x = blockIdx.x * blockDim.x + threadIdx.x;
size_t y = blockIdx.y * blockDim.y + threadIdx.y;
......@@ -162,7 +162,7 @@ namespace cv { namespace gpu { namespace matrix_operations {
}
template<typename T>
__global__ void set_to_without_mask(T * mat, int cols, int rows, int step, int channels)
__global__ void set_to_without_mask(T * mat, int cols, int rows, size_t step, int channels)
{
size_t x = blockIdx.x * blockDim.x + threadIdx.x;
size_t y = blockIdx.y * blockDim.y + threadIdx.y;
......@@ -175,7 +175,7 @@ namespace cv { namespace gpu { namespace matrix_operations {
}
template<typename T>
__global__ void set_to_with_mask(T * mat, const unsigned char * mask, int cols, int rows, int step, int channels, int step_mask)
__global__ void set_to_with_mask(T * mat, const unsigned char * mask, int cols, int rows, size_t step, int channels, size_t step_mask)
{
size_t x = blockIdx.x * blockDim.x + threadIdx.x;
size_t y = blockIdx.y * blockDim.y + threadIdx.y;
......
modules/gpu/src/cuda/split_merge.cu
浏览文件 @ 42ced17c
......@@ -276,7 +276,7 @@ namespace cv { namespace gpu { namespace split_merge {
extern "C" void merge_caller(const DevMem2D* src, DevMem2D& dst,
int total_channels, int elem_size,
int total_channels, size_t elem_size,
const cudaStream_t& stream)
{
static MergeFunction merge_func_tbl[] =
......@@ -286,7 +286,7 @@ namespace cv { namespace gpu { namespace split_merge {
mergeC4_<char>, mergeC4_<short>, mergeC4_<int>, 0, mergeC4_<double>,
};
int merge_func_id = (total_channels - 2) * 5 + (elem_size >> 1);
size_t merge_func_id = (total_channels - 2) * 5 + (elem_size >> 1);
MergeFunction merge_func = merge_func_tbl[merge_func_id];
if (merge_func == 0)
......@@ -485,7 +485,7 @@ namespace cv { namespace gpu { namespace split_merge {
extern "C" void split_caller(const DevMem2D& src, DevMem2D* dst,
int num_channels, int elem_size1,
int num_channels, size_t elem_size1,
const cudaStream_t& stream)
{
static SplitFunction split_func_tbl[] =
......@@ -495,7 +495,7 @@ namespace cv { namespace gpu { namespace split_merge {
splitC4_<char>, splitC4_<short>, splitC4_<int>, 0, splitC4_<double>,
};
int split_func_id = (num_channels - 2) * 5 + (elem_size1 >> 1);
size_t split_func_id = (num_channels - 2) * 5 + (elem_size1 >> 1);
SplitFunction split_func = split_func_tbl[split_func_id];
if (split_func == 0)
......
modules/gpu/src/element_operations.cpp
浏览文件 @ 42ced17c
......@@ -98,16 +98,20 @@ namespace
switch (src1.type())
{
case CV_8UC1:
nppSafeCall( npp_func_8uc1(src1.ptr<Npp8u>(), src1.step, src2.ptr<Npp8u>(), src2.step, dst.ptr<Npp8u>(), dst.step, sz, 0) );
nppSafeCall( npp_func_8uc1(src1.ptr<Npp8u>(), static_cast<int>(src1.step), src2.ptr<Npp8u>(), static_cast<int>(src2.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, 0) );
break;
case CV_8UC4:
nppSafeCall( npp_func_8uc4(src1.ptr<Npp8u>(), src1.step, src2.ptr<Npp8u>(), src2.step, dst.ptr<Npp8u>(), dst.step, sz, 0) );
nppSafeCall( npp_func_8uc4(src1.ptr<Npp8u>(), static_cast<int>(src1.step), src2.ptr<Npp8u>(), static_cast<int>(src2.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, 0) );
break;
case CV_32SC1:
nppSafeCall( npp_func_32sc1(src1.ptr<Npp32s>(), src1.step, src2.ptr<Npp32s>(), src2.step, dst.ptr<Npp32s>(), dst.step, sz) );
nppSafeCall( npp_func_32sc1(src1.ptr<Npp32s>(), static_cast<int>(src1.step), src2.ptr<Npp32s>(), static_cast<int>(src2.step),
dst.ptr<Npp32s>(), static_cast<int>(dst.step), sz) );
break;
case CV_32FC1:
nppSafeCall( npp_func_32fc1(src1.ptr<Npp32f>(), src1.step, src2.ptr<Npp32f>(), src2.step, dst.ptr<Npp32f>(), dst.step, sz) );
nppSafeCall( npp_func_32fc1(src1.ptr<Npp32f>(), static_cast<int>(src1.step), src2.ptr<Npp32f>(), static_cast<int>(src2.step),
dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz) );
break;
default:
CV_Assert(!"Unsupported source type");
......@@ -141,7 +145,7 @@ namespace
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<Npp32f>(), src.step, (Npp32f)sc[0], dst.ptr<Npp32f>(), dst.step, sz) );
nppSafeCall( func(src.ptr<Npp32f>(), static_cast<int>(src.step), (Npp32f)sc[0], dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -163,7 +167,7 @@ namespace
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<Npp32fc>(), src.step, nValue, dst.ptr<Npp32fc>(), dst.step, sz) );
nppSafeCall( func(src.ptr<Npp32fc>(), static_cast<int>(src.step), nValue, dst.ptr<Npp32fc>(), static_cast<int>(dst.step), sz) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -238,7 +242,7 @@ void cv::gpu::multiply(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stream&
NppStreamHandler h(cudaStream);
nppSafeCall( nppiMulC_32f_C1R(src.ptr<Npp32f>(), src.step, (Npp32f)sc[0], dst.ptr<Npp32f>(), dst.step, sz) );
nppSafeCall( nppiMulC_32f_C1R(src.ptr<Npp32f>(), static_cast<int>(src.step), (Npp32f)sc[0], dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz) );
if (cudaStream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -258,7 +262,7 @@ void cv::gpu::divide(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stream& s
NppStreamHandler h(cudaStream);
nppSafeCall( nppiDivC_32f_C1R(src.ptr<Npp32f>(), src.step, (Npp32f)sc[0], dst.ptr<Npp32f>(), dst.step, sz) );
nppSafeCall( nppiDivC_32f_C1R(src.ptr<Npp32f>(), static_cast<int>(src.step), (Npp32f)sc[0], dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz) );
if (cudaStream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -287,16 +291,20 @@ void cv::gpu::absdiff(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Strea
switch (src1.type())
{
case CV_8UC1:
nppSafeCall( nppiAbsDiff_8u_C1R(src1.ptr<Npp8u>(), src1.step, src2.ptr<Npp8u>(), src2.step, dst.ptr<Npp8u>(), dst.step, sz) );
nppSafeCall( nppiAbsDiff_8u_C1R(src1.ptr<Npp8u>(), static_cast<int>(src1.step), src2.ptr<Npp8u>(), static_cast<int>(src2.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz) );
break;
case CV_8UC4:
nppSafeCall( nppiAbsDiff_8u_C4R(src1.ptr<Npp8u>(), src1.step, src2.ptr<Npp8u>(), src2.step, dst.ptr<Npp8u>(), dst.step, sz) );
nppSafeCall( nppiAbsDiff_8u_C4R(src1.ptr<Npp8u>(), static_cast<int>(src1.step), src2.ptr<Npp8u>(), static_cast<int>(src2.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz) );
break;
case CV_32SC1:
nppSafeCall( nppiAbsDiff_32s_C1R(src1.ptr<Npp32s>(), src1.step, src2.ptr<Npp32s>(), src2.step, dst.ptr<Npp32s>(), dst.step, sz) );
nppSafeCall( nppiAbsDiff_32s_C1R(src1.ptr<Npp32s>(), static_cast<int>(src1.step), src2.ptr<Npp32s>(), static_cast<int>(src2.step),
dst.ptr<Npp32s>(), static_cast<int>(dst.step), sz) );
break;
case CV_32FC1:
nppSafeCall( nppiAbsDiff_32f_C1R(src1.ptr<Npp32f>(), src1.step, src2.ptr<Npp32f>(), src2.step, dst.ptr<Npp32f>(), dst.step, sz) );
nppSafeCall( nppiAbsDiff_32f_C1R(src1.ptr<Npp32f>(), static_cast<int>(src1.step), src2.ptr<Npp32f>(), static_cast<int>(src2.step),
dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz) );
break;
default:
CV_Assert(!"Unsupported source type");
......@@ -320,7 +328,7 @@ void cv::gpu::absdiff(const GpuMat& src1, const Scalar& src2, GpuMat& dst, Strea
NppStreamHandler h(stream);
nppSafeCall( nppiAbsDiffC_32f_C1R(src1.ptr<Npp32f>(), src1.step, dst.ptr<Npp32f>(), dst.step, sz, (Npp32f)src2[0]) );
nppSafeCall( nppiAbsDiffC_32f_C1R(src1.ptr<Npp32f>(), static_cast<int>(src1.step), dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz, (Npp32f)src2[0]) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -358,9 +366,9 @@ void cv::gpu::compare(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, int c
{
NppStreamHandler h(stream);
nppSafeCall( nppiCompare_8u_C4R(src1.ptr<Npp8u>(), src1.step,
src2.ptr<Npp8u>(), src2.step,
dst.ptr<Npp8u>(), dst.step, sz, nppCmpOp[cmpop]) );
nppSafeCall( nppiCompare_8u_C4R(src1.ptr<Npp8u>(), static_cast<int>(src1.step),
src2.ptr<Npp8u>(), static_cast<int>(src2.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, nppCmpOp[cmpop]) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -376,9 +384,9 @@ void cv::gpu::compare(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, int c
{
NppStreamHandler h(stream);
nppSafeCall( nppiCompare_32f_C1R(src1.ptr<Npp32f>(), src1.step,
src2.ptr<Npp32f>(), src2.step,
dst.ptr<Npp8u>(), dst.step, sz, nppCmpOp[cmpop]) );
nppSafeCall( nppiCompare_32f_C1R(src1.ptr<Npp32f>(), static_cast<int>(src1.step),
src2.ptr<Npp32f>(), static_cast<int>(src2.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, nppCmpOp[cmpop]) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -396,7 +404,7 @@ void cv::gpu::compare(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, int c
namespace cv { namespace gpu { namespace mathfunc
{
void bitwiseNotCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src, PtrStep dst, cudaStream_t stream);
void bitwiseNotCaller(int rows, int cols, size_t elem_size1, int cn, const PtrStep src, PtrStep dst, cudaStream_t stream);
template <typename T>
void bitwiseMaskNotCaller(int rows, int cols, int cn, const PtrStep src, const PtrStep mask, PtrStep dst, cudaStream_t stream);
......@@ -450,17 +458,17 @@ void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask, St
namespace cv { namespace gpu { namespace mathfunc
{
void bitwiseOrCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream);
void bitwiseOrCaller(int rows, int cols, size_t elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream);
template <typename T>
void bitwiseMaskOrCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream);
void bitwiseAndCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream);
void bitwiseAndCaller(int rows, int cols, size_t elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream);
template <typename T>
void bitwiseMaskAndCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream);
void bitwiseXorCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream);
void bitwiseXorCaller(int rows, int cols, size_t elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream);
template <typename T>
void bitwiseMaskXorCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream);
......@@ -732,8 +740,8 @@ double cv::gpu::threshold(const GpuMat& src, GpuMat& dst, double thresh, double
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiThreshold_32f_C1R(src.ptr<Npp32f>(), src.step,
dst.ptr<Npp32f>(), dst.step, sz, static_cast<Npp32f>(thresh), NPP_CMP_GREATER) );
nppSafeCall( nppiThreshold_32f_C1R(src.ptr<Npp32f>(), static_cast<int>(src.step),
dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz, static_cast<Npp32f>(thresh), NPP_CMP_GREATER) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......
modules/gpu/src/error.cpp
浏览文件 @ 42ced17c
......@@ -110,7 +110,7 @@ namespace
error_entry( NPP_ODD_ROI_WARNING )
};
int error_num = sizeof(npp_errors)/sizeof(npp_errors[0]);
const size_t error_num = sizeof(npp_errors) / sizeof(npp_errors[0]);
struct Searcher
{
......@@ -161,7 +161,7 @@ namespace cv
{
const string getNppErrorString( int err )
{
int idx = std::find_if(npp_errors, npp_errors + error_num, Searcher(err)) - npp_errors;
size_t idx = std::find_if(npp_errors, npp_errors + error_num, Searcher(err)) - npp_errors;
const string& msg = (idx != error_num) ? npp_errors[idx].str : string("Unknown error code");
std::stringstream interpreter;
......
modules/gpu/src/filtering.cpp
浏览文件 @ 42ced17c
......@@ -253,7 +253,8 @@ namespace
NppStreamHandler h(stream);
nppSafeCall( nppiSumWindowRow_8u32f_C1R(src.ptr<Npp8u>(), src.step, dst.ptr<Npp32f>(), dst.step, sz, ksize, anchor) );
nppSafeCall( nppiSumWindowRow_8u32f_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz, ksize, anchor) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -287,7 +288,8 @@ namespace
NppStreamHandler h(stream);
nppSafeCall( nppiSumWindowColumn_8u32f_C1R(src.ptr<Npp8u>(), src.step, dst.ptr<Npp32f>(), dst.step, sz, ksize, anchor) );
nppSafeCall( nppiSumWindowColumn_8u32f_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz, ksize, anchor) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -333,7 +335,8 @@ namespace
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<Npp8u>(), src.step, dst.ptr<Npp8u>(), dst.step, sz, oKernelSize, oAnchor) );
nppSafeCall( func(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, oKernelSize, oAnchor) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -401,7 +404,8 @@ namespace
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<Npp8u>(), src.step, dst.ptr<Npp8u>(), dst.step, sz, kernel.ptr<Npp8u>(), oKernelSize, oAnchor) );
nppSafeCall( func(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, kernel.ptr<Npp8u>(), oKernelSize, oAnchor) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -584,7 +588,7 @@ namespace
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<Npp8u>(), src.step, dst.ptr<Npp8u>(), dst.step, sz,
nppSafeCall( func(src.ptr<Npp8u>(), static_cast<int>(src.step), dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz,
kernel.ptr<Npp32s>(), oKernelSize, oAnchor, nDivisor) );
if (stream == 0)
......@@ -666,7 +670,8 @@ namespace
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<Npp8u>(), src.step, dst.ptr<Npp8u>(), dst.step, sz, kernel.ptr<Npp32s>(), ksize, anchor, nDivisor) );
nppSafeCall( func(src.ptr<Npp8u>(), static_cast<int>(src.step), dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz,
kernel.ptr<Npp32s>(), ksize, anchor, nDivisor) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -780,7 +785,8 @@ namespace
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<Npp8u>(), src.step, dst.ptr<Npp8u>(), dst.step, sz, kernel.ptr<Npp32s>(), ksize, anchor, nDivisor) );
nppSafeCall( func(src.ptr<Npp8u>(), static_cast<int>(src.step), dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz,
kernel.ptr<Npp32s>(), ksize, anchor, nDivisor) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -1040,7 +1046,7 @@ namespace
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<Npp8u>(), src.step, dst.ptr<Npp8u>(), dst.step, sz, oKernelSize, oAnchor) );
nppSafeCall( func(src.ptr<Npp8u>(), static_cast<int>(src.step), dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, oKernelSize, oAnchor) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......
modules/gpu/src/graphcuts.cpp
浏览文件 @ 42ced17c
......@@ -78,7 +78,7 @@ void cv::gpu::graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTrans
NppStreamHandler h(stream);
nppSafeCall( nppiGraphcut_32s8u(terminals.ptr<Npp32s>(), leftTransp.ptr<Npp32s>(), rightTransp.ptr<Npp32s>(), top.ptr<Npp32s>(), bottom.ptr<Npp32s>(),
terminals.step, leftTransp.step, sznpp, labels.ptr<Npp8u>(), labels.step, buf.ptr<Npp8u>()) );
static_cast<int>(terminals.step), static_cast<int>(leftTransp.step), sznpp, labels.ptr<Npp8u>(), static_cast<int>(labels.step), buf.ptr<Npp8u>()) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......
modules/gpu/src/hog.cpp
浏览文件 @ 42ced17c
......@@ -218,7 +218,7 @@ void cv::gpu::HOGDescriptor::computeBlockHistograms(const GpuMat& img)
Size blocks_per_img = numPartsWithin(img.size(), block_size, block_stride);
// block_hists.create(1, block_hist_size * blocks_per_img.area(), CV_32F);
block_hists = getBuffer(1, block_hist_size * blocks_per_img.area(), CV_32F, block_hists_buf);
block_hists = getBuffer(1, static_cast<int>(block_hist_size * blocks_per_img.area()), CV_32F, block_hists_buf);
hog::compute_hists(nbins, block_stride.width, block_stride.height, img.rows, img.cols,
grad, qangle, (float)getWinSigma(), block_hists.ptr<float>());
......@@ -234,11 +234,11 @@ void cv::gpu::HOGDescriptor::getDescriptors(const GpuMat& img, Size win_stride,
computeBlockHistograms(img);
const int block_hist_size = getBlockHistogramSize();
const size_t block_hist_size = getBlockHistogramSize();
Size blocks_per_win = numPartsWithin(win_size, block_size, block_stride);
Size wins_per_img = numPartsWithin(img.size(), win_size, win_stride);
descriptors.create(wins_per_img.area(), blocks_per_win.area() * block_hist_size, CV_32F);
descriptors.create(wins_per_img.area(), static_cast<int>(blocks_per_win.area() * block_hist_size), CV_32F);
switch (descr_format)
{
......
modules/gpu/src/imgproc_gpu.cpp
浏览文件 @ 42ced17c
......@@ -287,13 +287,13 @@ void cv::gpu::resize(const GpuMat& src, GpuMat& dst, Size dsize, double fx, doub
if (src.type() == CV_8UC1)
{
nppSafeCall( nppiResize_8u_C1R(src.ptr<Npp8u>(), srcsz, src.step, srcrect,
dst.ptr<Npp8u>(), dst.step, dstsz, fx, fy, npp_inter[interpolation]) );
nppSafeCall( nppiResize_8u_C1R(src.ptr<Npp8u>(), srcsz, static_cast<int>(src.step), srcrect,
dst.ptr<Npp8u>(), static_cast<int>(dst.step), dstsz, fx, fy, npp_inter[interpolation]) );
}
else
{
nppSafeCall( nppiResize_8u_C4R(src.ptr<Npp8u>(), srcsz, src.step, srcrect,
dst.ptr<Npp8u>(), dst.step, dstsz, fx, fy, npp_inter[interpolation]) );
nppSafeCall( nppiResize_8u_C4R(src.ptr<Npp8u>(), srcsz, static_cast<int>(src.step), srcrect,
dst.ptr<Npp8u>(), static_cast<int>(dst.step), dstsz, fx, fy, npp_inter[interpolation]) );
}
if (stream == 0)
......@@ -325,30 +325,30 @@ void cv::gpu::copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bottom
case CV_8UC1:
{
Npp8u nVal = static_cast<Npp8u>(value[0]);
nppSafeCall( nppiCopyConstBorder_8u_C1R(src.ptr<Npp8u>(), src.step, srcsz,
dst.ptr<Npp8u>(), dst.step, dstsz, top, left, nVal) );
nppSafeCall( nppiCopyConstBorder_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step), srcsz,
dst.ptr<Npp8u>(), static_cast<int>(dst.step), dstsz, top, left, nVal) );
break;
}
case CV_8UC4:
{
Npp8u nVal[] = {static_cast<Npp8u>(value[0]), static_cast<Npp8u>(value[1]), static_cast<Npp8u>(value[2]), static_cast<Npp8u>(value[3])};
nppSafeCall( nppiCopyConstBorder_8u_C4R(src.ptr<Npp8u>(), src.step, srcsz,
dst.ptr<Npp8u>(), dst.step, dstsz, top, left, nVal) );
nppSafeCall( nppiCopyConstBorder_8u_C4R(src.ptr<Npp8u>(), static_cast<int>(src.step), srcsz,
dst.ptr<Npp8u>(), static_cast<int>(dst.step), dstsz, top, left, nVal) );
break;
}
case CV_32SC1:
{
Npp32s nVal = static_cast<Npp32s>(value[0]);
nppSafeCall( nppiCopyConstBorder_32s_C1R(src.ptr<Npp32s>(), src.step, srcsz,
dst.ptr<Npp32s>(), dst.step, dstsz, top, left, nVal) );
nppSafeCall( nppiCopyConstBorder_32s_C1R(src.ptr<Npp32s>(), static_cast<int>(src.step), srcsz,
dst.ptr<Npp32s>(), static_cast<int>(dst.step), dstsz, top, left, nVal) );
break;
}
case CV_32FC1:
{
Npp32f val = static_cast<Npp32f>(value[0]);
Npp32s nVal = *(reinterpret_cast<Npp32s*>(&val));
nppSafeCall( nppiCopyConstBorder_32s_C1R(src.ptr<Npp32s>(), src.step, srcsz,
dst.ptr<Npp32s>(), dst.step, dstsz, top, left, nVal) );
nppSafeCall( nppiCopyConstBorder_32s_C1R(src.ptr<Npp32s>(), static_cast<int>(src.step), srcsz,
dst.ptr<Npp32s>(), static_cast<int>(dst.step), dstsz, top, left, nVal) );
break;
}
default:
......@@ -409,20 +409,20 @@ namespace
switch (src.depth())
{
case CV_8U:
nppSafeCall( npp_warp_8u[src.channels()][warpInd](src.ptr<Npp8u>(), srcsz, src.step, srcroi,
dst.ptr<Npp8u>(), dst.step, dstroi, coeffs, npp_inter[interpolation]) );
nppSafeCall( npp_warp_8u[src.channels()][warpInd](src.ptr<Npp8u>(), srcsz, static_cast<int>(src.step), srcroi,
dst.ptr<Npp8u>(), static_cast<int>(dst.step), dstroi, coeffs, npp_inter[interpolation]) );
break;
case CV_16U:
nppSafeCall( npp_warp_16u[src.channels()][warpInd](src.ptr<Npp16u>(), srcsz, src.step, srcroi,
dst.ptr<Npp16u>(), dst.step, dstroi, coeffs, npp_inter[interpolation]) );
nppSafeCall( npp_warp_16u[src.channels()][warpInd](src.ptr<Npp16u>(), srcsz, static_cast<int>(src.step), srcroi,
dst.ptr<Npp16u>(), static_cast<int>(dst.step), dstroi, coeffs, npp_inter[interpolation]) );
break;
case CV_32S:
nppSafeCall( npp_warp_32s[src.channels()][warpInd](src.ptr<Npp32s>(), srcsz, src.step, srcroi,
dst.ptr<Npp32s>(), dst.step, dstroi, coeffs, npp_inter[interpolation]) );
nppSafeCall( npp_warp_32s[src.channels()][warpInd](src.ptr<Npp32s>(), srcsz, static_cast<int>(src.step), srcroi,
dst.ptr<Npp32s>(), static_cast<int>(dst.step), dstroi, coeffs, npp_inter[interpolation]) );
break;
case CV_32F:
nppSafeCall( npp_warp_32f[src.channels()][warpInd](src.ptr<Npp32f>(), srcsz, src.step, srcroi,
dst.ptr<Npp32f>(), dst.step, dstroi, coeffs, npp_inter[interpolation]) );
nppSafeCall( npp_warp_32f[src.channels()][warpInd](src.ptr<Npp32f>(), srcsz, static_cast<int>(src.step), srcroi,
dst.ptr<Npp32f>(), static_cast<int>(dst.step), dstroi, coeffs, npp_inter[interpolation]) );
break;
default:
CV_Assert(!"Unsupported source type");
......@@ -541,7 +541,8 @@ void cv::gpu::buildWarpPlaneMaps(Size src_size, Rect dst_roi, const Mat& R, doub
map_x.create(dst_roi.size(), CV_32F);
map_y.create(dst_roi.size(), CV_32F);
imgproc::buildWarpPlaneMaps(dst_roi.tl().x, dst_roi.tl().y, map_x, map_y, R.ptr<float>(), Rinv.ptr<float>(),
f, s, dist, 0.5f*src_size.width, 0.5f*src_size.height, StreamAccessor::getStream(stream));
static_cast<float>(f), static_cast<float>(s), static_cast<float>(dist),
0.5f*src_size.width, 0.5f*src_size.height, StreamAccessor::getStream(stream));
}
//////////////////////////////////////////////////////////////////////////////
......@@ -564,7 +565,8 @@ void cv::gpu::buildWarpCylindricalMaps(Size src_size, Rect dst_roi, const Mat& R
map_x.create(dst_roi.size(), CV_32F);
map_y.create(dst_roi.size(), CV_32F);
imgproc::buildWarpCylindricalMaps(dst_roi.tl().x, dst_roi.tl().y, map_x, map_y, R.ptr<float>(), Rinv.ptr<float>(),
f, s, 0.5f*src_size.width, 0.5f*src_size.height, StreamAccessor::getStream(stream));
static_cast<float>(f), static_cast<float>(s), 0.5f*src_size.width, 0.5f*src_size.height,
StreamAccessor::getStream(stream));
}
......@@ -588,7 +590,8 @@ void cv::gpu::buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat& R,
map_x.create(dst_roi.size(), CV_32F);
map_y.create(dst_roi.size(), CV_32F);
imgproc::buildWarpSphericalMaps(dst_roi.tl().x, dst_roi.tl().y, map_x, map_y, R.ptr<float>(), Rinv.ptr<float>(),
f, s, 0.5f*src_size.width, 0.5f*src_size.height, StreamAccessor::getStream(stream));
static_cast<float>(f), static_cast<float>(s), 0.5f*src_size.width, 0.5f*src_size.height,
StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
......@@ -621,13 +624,13 @@ void cv::gpu::rotate(const GpuMat& src, GpuMat& dst, Size dsize, double angle, d
if (src.type() == CV_8UC1)
{
nppSafeCall( nppiRotate_8u_C1R(src.ptr<Npp8u>(), srcsz, src.step, srcroi,
dst.ptr<Npp8u>(), dst.step, dstroi, angle, xShift, yShift, npp_inter[interpolation]) );
nppSafeCall( nppiRotate_8u_C1R(src.ptr<Npp8u>(), srcsz, static_cast<int>(src.step), srcroi,
dst.ptr<Npp8u>(), static_cast<int>(dst.step), dstroi, angle, xShift, yShift, npp_inter[interpolation]) );
}
else
{
nppSafeCall( nppiRotate_8u_C4R(src.ptr<Npp8u>(), srcsz, src.step, srcroi,
dst.ptr<Npp8u>(), dst.step, dstroi, angle, xShift, yShift, npp_inter[interpolation]) );
nppSafeCall( nppiRotate_8u_C4R(src.ptr<Npp8u>(), srcsz, static_cast<int>(src.step), srcroi,
dst.ptr<Npp8u>(), static_cast<int>(dst.step), dstroi, angle, xShift, yShift, npp_inter[interpolation]) );
}
if (stream == 0)
......@@ -664,8 +667,8 @@ void cv::gpu::integralBuffered(const GpuMat& src, GpuMat& sum, GpuMat& buffer, S
NppStStreamHandler h(stream);
nppSafeCall( nppiStIntegral_8u32u_C1R(const_cast<Ncv8u*>(src.ptr<Ncv8u>()), src.step,
sum.ptr<Ncv32u>(), sum.step, roiSize, buffer.ptr<Ncv8u>(), bufSize, prop) );
nppSafeCall( nppiStIntegral_8u32u_C1R(const_cast<Ncv8u*>(src.ptr<Ncv8u>()), static_cast<int>(src.step),
sum.ptr<Ncv32u>(), static_cast<int>(sum.step), roiSize, buffer.ptr<Ncv8u>(), bufSize, prop) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -688,8 +691,8 @@ void cv::gpu::integral(const GpuMat& src, GpuMat& sum, GpuMat& sqsum, Stream& s)
NppStreamHandler h(stream);
nppSafeCall( nppiSqrIntegral_8u32s32f_C1R(const_cast<Npp8u*>(src.ptr<Npp8u>()), src.step, sum.ptr<Npp32s>(),
sum.step, sqsum.ptr<Npp32f>(), sqsum.step, sz, 0, 0.0f, height) );
nppSafeCall( nppiSqrIntegral_8u32s32f_C1R(const_cast<Npp8u*>(src.ptr<Npp8u>()), static_cast<int>(src.step),
sum.ptr<Npp32s>(), static_cast<int>(sum.step), sqsum.ptr<Npp32f>(), static_cast<int>(sqsum.step), sz, 0, 0.0f, height) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -718,8 +721,8 @@ void cv::gpu::sqrIntegral(const GpuMat& src, GpuMat& sqsum, Stream& s)
NppStStreamHandler h(stream);
sqsum.create(src.rows + 1, src.cols + 1, CV_64F);
nppSafeCall(nppiStSqrIntegral_8u64u_C1R(const_cast<Ncv8u*>(src.ptr<Ncv8u>(0)), src.step,
sqsum.ptr<Ncv64u>(0), sqsum.step, roiSize, buf.ptr<Ncv8u>(0), bufSize, prop));
nppSafeCall(nppiStSqrIntegral_8u64u_C1R(const_cast<Ncv8u*>(src.ptr<Ncv8u>(0)), static_cast<int>(src.step),
sqsum.ptr<Ncv64u>(0), static_cast<int>(sqsum.step), roiSize, buf.ptr<Ncv8u>(0), bufSize, prop));
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -761,8 +764,8 @@ void cv::gpu::rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, cons
NppStreamHandler h(stream);
nppSafeCall( nppiRectStdDev_32s32f_C1R(src.ptr<Npp32s>(), src.step, sqr.ptr<Npp32f>(), sqr.step,
dst.ptr<Npp32f>(), dst.step, sz, nppRect) );
nppSafeCall( nppiRectStdDev_32s32f_C1R(src.ptr<Npp32s>(), static_cast<int>(src.step), sqr.ptr<Npp32f>(), static_cast<int>(sqr.step),
dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz, nppRect) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -820,7 +823,7 @@ namespace
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<src_t>(), src.step, sz, hist.ptr<Npp32s>(), levels,
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), sz, hist.ptr<Npp32s>(), levels,
lowerLevel, upperLevel, buffer.ptr<Npp8u>()) );
if (stream == 0)
......@@ -854,7 +857,7 @@ namespace
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<src_t>(), src.step, sz, pHist, levels, lowerLevel, upperLevel, buffer.ptr<Npp8u>()) );
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), sz, pHist, levels, lowerLevel, upperLevel, buffer.ptr<Npp8u>()) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -923,7 +926,7 @@ namespace
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<src_t>(), src.step, sz, hist.ptr<Npp32s>(), levels.ptr<level_t>(), levels.cols, buffer.ptr<Npp8u>()) );
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), sz, hist.ptr<Npp32s>(), levels.ptr<level_t>(), levels.cols, buffer.ptr<Npp8u>()) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -964,7 +967,7 @@ namespace
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<src_t>(), src.step, sz, pHist, pLevels, nLevels, buffer.ptr<Npp8u>()) );
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), sz, pHist, pLevels, nLevels, buffer.ptr<Npp8u>()) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -1103,7 +1106,7 @@ void cv::gpu::equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat&
int intBufSize;
nppSafeCall( nppsIntegralGetBufferSize_32s(256, &intBufSize) );
int bufSize = std::max(256 * 240 * sizeof(int), intBufSize + 256 * sizeof(int));
int bufSize = static_cast<int>(std::max(256 * 240 * sizeof(int), intBufSize + 256 * sizeof(int)));
ensureSizeIsEnough(1, bufSize, CV_8UC1, buf);
......
modules/gpu/src/matrix_operations.cpp
浏览文件 @ 42ced17c
......@@ -177,7 +177,7 @@ namespace
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func(src.ptr<src_t>(), src.step, dst.ptr<dst_t>(), dst.step, sz) );
nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), dst.ptr<dst_t>(), static_cast<int>(dst.step), sz) );
cudaSafeCall( cudaDeviceSynchronize() );
}
......@@ -191,7 +191,7 @@ namespace
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func(src.ptr<Npp32f>(), src.step, dst.ptr<dst_t>(), dst.step, sz, NPP_RND_NEAR) );
nppSafeCall( func(src.ptr<Npp32f>(), static_cast<int>(src.step), dst.ptr<dst_t>(), static_cast<int>(dst.step), sz, NPP_RND_NEAR) );
cudaSafeCall( cudaDeviceSynchronize() );
}
......@@ -347,7 +347,7 @@ namespace
sz.width = src.cols;
sz.height = src.rows;
Scalar_<src_t> nppS = s;
nppSafeCall( func(nppS.val, src.ptr<src_t>(), src.step, sz) );
nppSafeCall( func(nppS.val, src.ptr<src_t>(), static_cast<int>(src.step), sz) );
cudaSafeCall( cudaDeviceSynchronize() );
}
......@@ -362,7 +362,7 @@ namespace
sz.width = src.cols;
sz.height = src.rows;
Scalar_<src_t> nppS = s;
nppSafeCall( func(nppS[0], src.ptr<src_t>(), src.step, sz) );
nppSafeCall( func(nppS[0], src.ptr<src_t>(), static_cast<int>(src.step), sz) );
cudaSafeCall( cudaDeviceSynchronize() );
}
......@@ -398,7 +398,7 @@ namespace
sz.width = src.cols;
sz.height = src.rows;
Scalar_<src_t> nppS = s;
nppSafeCall( func(nppS.val, src.ptr<src_t>(), src.step, sz, mask.ptr<Npp8u>(), mask.step) );
nppSafeCall( func(nppS.val, src.ptr<src_t>(), static_cast<int>(src.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) );
cudaSafeCall( cudaDeviceSynchronize() );
}
......@@ -413,7 +413,7 @@ namespace
sz.width = src.cols;
sz.height = src.rows;
Scalar_<src_t> nppS = s;
nppSafeCall( func(nppS[0], src.ptr<src_t>(), src.step, sz, mask.ptr<Npp8u>(), mask.step) );
nppSafeCall( func(nppS[0], src.ptr<src_t>(), static_cast<int>(src.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) );
cudaSafeCall( cudaDeviceSynchronize() );
}
......
modules/gpu/src/matrix_reductions.cpp
浏览文件 @ 42ced17c
......@@ -116,7 +116,7 @@ void cv::gpu::meanStdDev(const GpuMat& src, Scalar& mean, Scalar& stddev)
DeviceBuffer dbuf(2);
nppSafeCall( nppiMean_StdDev_8u_C1R(src.ptr<Npp8u>(), src.step, sz, dbuf, (double*)dbuf + 1) );
nppSafeCall( nppiMean_StdDev_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step), sz, dbuf, (double*)dbuf + 1) );
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -177,7 +177,7 @@ double cv::gpu::norm(const GpuMat& src1, const GpuMat& src2, int normType)
DeviceBuffer dbuf;
nppSafeCall( npp_norm_diff_func[funcIdx](src1.ptr<Npp8u>(), src1.step, src2.ptr<Npp8u>(), src2.step, sz, dbuf) );
nppSafeCall( npp_norm_diff_func[funcIdx](src1.ptr<Npp8u>(), static_cast<int>(src1.step), src2.ptr<Npp8u>(), static_cast<int>(src2.step), sz, dbuf) );
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -409,7 +409,7 @@ void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal, const Gp
double maxVal_; if (!maxVal) maxVal = &maxVal_;
Size buf_size;
getBufSizeRequired(src.cols, src.rows, src.elemSize(), buf_size.width, buf_size.height);
getBufSizeRequired(src.cols, src.rows, static_cast<int>(src.elemSize()), buf_size.width, buf_size.height);
ensureSizeIsEnough(buf_size, CV_8U, buf);
if (mask.empty())
......@@ -510,7 +510,7 @@ void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point
int maxLoc_[2];
Size valbuf_size, locbuf_size;
getBufSizeRequired(src.cols, src.rows, src.elemSize(), valbuf_size.width,
getBufSizeRequired(src.cols, src.rows, static_cast<int>(src.elemSize()), valbuf_size.width,
valbuf_size.height, locbuf_size.width, locbuf_size.height);
ensureSizeIsEnough(valbuf_size, CV_8U, valBuf);
ensureSizeIsEnough(locbuf_size, CV_8U, locBuf);
......
modules/gpu/src/nvidia/NCVHaarObjectDetection.cu
浏览文件 @ 42ced17c
......@@ -1096,7 +1096,7 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
NCVVectorReuse<Ncv32u> d_vecPixelMask(d_pixelMask.getSegment(), anchorsRoi.height * d_pixelMask.stride());
ncvAssertReturn(d_vecPixelMask.isMemReused(), NCV_ALLOCATOR_BAD_REUSE);
NCVVectorAlloc<Ncv32u> d_vecPixelMaskTmp(gpuAllocator, d_vecPixelMask.length());
NCVVectorAlloc<Ncv32u> d_vecPixelMaskTmp(gpuAllocator, static_cast<Ncv32u>(d_vecPixelMask.length()));
ncvAssertReturn(d_vecPixelMaskTmp.isMemAllocated(), NCV_ALLOCATOR_BAD_ALLOC);
NCVVectorAlloc<Ncv32u> hp_pool32u(cpuAllocator, 2);
......@@ -1120,7 +1120,7 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
NCVVector<Ncv32u> *d_ptrNowTmp = &d_vecPixelMaskTmp;
Ncv32u szNppCompactTmpBuf;
nppsStCompactGetSize_32u(d_vecPixelMask.length(), &szNppCompactTmpBuf, devProp);
nppsStCompactGetSize_32u(static_cast<Ncv32u>(d_vecPixelMask.length()), &szNppCompactTmpBuf, devProp);
if (bDoAtomicCompaction)
{
szNppCompactTmpBuf = 0;
......@@ -1206,7 +1206,7 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
gridInit, blockInit, cuStream,
d_ptrNowData->ptr(),
d_ptrNowTmp->ptr(),
d_vecPixelMask.length(), d_pixelMask.stride(),
static_cast<Ncv32u>(d_vecPixelMask.length()), d_pixelMask.stride(),
anchorsRoi, pixelStep);
ncvAssertCUDAReturn(cudaGetLastError(), NCV_CUDA_ERROR);
......@@ -1221,7 +1221,7 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
else
{
NCVStatus nppSt;
nppSt = nppsStCompact_32u(d_ptrNowTmp->ptr(), d_vecPixelMask.length(),
nppSt = nppsStCompact_32u(d_ptrNowTmp->ptr(), static_cast<Ncv32u>(d_vecPixelMask.length()),
d_ptrNowData->ptr(), hp_numDet, OBJDET_MASK_ELEMENT_INVALID_32U,
d_tmpBufCompact.ptr(), szNppCompactTmpBuf, devProp);
ncvAssertReturn(nppSt == NPPST_SUCCESS, NCV_NPP_ERROR);
......@@ -1276,7 +1276,7 @@ NCVStatus ncvApplyHaarClassifierCascade_device(NCVMatrix<Ncv32u> &d_integralImag
else
{
NCVStatus nppSt;
nppSt = nppsStCompact_32u(d_ptrNowData->ptr(), d_vecPixelMask.length(),
nppSt = nppsStCompact_32u(d_ptrNowData->ptr(), static_cast<Ncv32u>(d_vecPixelMask.length()),
d_ptrNowTmp->ptr(), hp_numDet, OBJDET_MASK_ELEMENT_INVALID_32U,
d_tmpBufCompact.ptr(), szNppCompactTmpBuf, devProp);
ncvAssertReturnNcvStat(nppSt);
......@@ -1783,7 +1783,7 @@ NCVStatus ncvDetectObjectsMultiScale_device(NCVMatrix<Ncv8u> &d_srcImg,
detectionsOnThisScale,
d_hypothesesIntermediate,
dstNumRects,
d_hypothesesIntermediate.length(),
static_cast<Ncv32u>(d_hypothesesIntermediate.length()),
haar.ClassifierSize.width,
haar.ClassifierSize.height,
(Ncv32f)scale,
......@@ -1880,7 +1880,7 @@ NCVStatus ncvDetectObjectsMultiScale_device(NCVMatrix<Ncv8u> &d_srcImg,
if (dstNumRects > d_dstRects.length())
{
ncvRetCode = NCV_WARNING_HAAR_DETECTIONS_VECTOR_OVERFLOW;
dstNumRects = d_dstRects.length();
dstNumRects = static_cast<Ncv32u>(d_dstRects.length());
}
if (dstNumRects != 0)
......
modules/gpu/src/nvidia/NPP_staging/NPP_staging.cu
浏览文件 @ 42ced17c
......@@ -457,7 +457,7 @@ NCVStatus nppiStIntegralGetSize_8u32u(NcvSize32u roiSize, Ncv32u *pBufsize, cuda
ncvAssertReturn(pBufsize != NULL, NPPST_NULL_POINTER_ERROR);
ncvAssertReturn(roiSize.width > 0 && roiSize.height > 0, NPPST_INVALID_ROI);
NCVMemStackAllocator gpuCounter(devProp.textureAlignment);
NCVMemStackAllocator gpuCounter(static_cast<Ncv32u>(devProp.textureAlignment));
ncvAssertReturn(gpuCounter.isInitialized(), NPPST_MEM_INTERNAL_ERROR);
NCVStatus ncvStat = ncvIntegralImage_device((Ncv8u*)NULL, roiSize.width,
......@@ -475,7 +475,7 @@ NCVStatus nppiStIntegralGetSize_32f32f(NcvSize32u roiSize, Ncv32u *pBufsize, cud
ncvAssertReturn(pBufsize != NULL, NPPST_NULL_POINTER_ERROR);
ncvAssertReturn(roiSize.width > 0 && roiSize.height > 0, NPPST_INVALID_ROI);
NCVMemStackAllocator gpuCounter(devProp.textureAlignment);
NCVMemStackAllocator gpuCounter(static_cast<Ncv32u>(devProp.textureAlignment));
ncvAssertReturn(gpuCounter.isInitialized(), NPPST_MEM_INTERNAL_ERROR);
NCVStatus ncvStat = ncvIntegralImage_device((Ncv32f*)NULL, roiSize.width * sizeof(Ncv32f),
......@@ -493,7 +493,7 @@ NCVStatus nppiStSqrIntegralGetSize_8u64u(NcvSize32u roiSize, Ncv32u *pBufsize, c
ncvAssertReturn(pBufsize != NULL, NPPST_NULL_POINTER_ERROR);
ncvAssertReturn(roiSize.width > 0 && roiSize.height > 0, NPPST_INVALID_ROI);
NCVMemStackAllocator gpuCounter(devProp.textureAlignment);
NCVMemStackAllocator gpuCounter(static_cast<Ncv32u>(devProp.textureAlignment));
ncvAssertReturn(gpuCounter.isInitialized(), NPPST_MEM_INTERNAL_ERROR);
NCVStatus ncvStat = ncvSquaredIntegralImage_device(NULL, roiSize.width,
......@@ -511,7 +511,7 @@ NCVStatus nppiStIntegral_8u32u_C1R(Ncv8u *d_src, Ncv32u srcStep,
NcvSize32u roiSize, Ncv8u *pBuffer,
Ncv32u bufSize, cudaDeviceProp &devProp)
{
NCVMemStackAllocator gpuAllocator(NCVMemoryTypeDevice, bufSize, devProp.textureAlignment, pBuffer);
NCVMemStackAllocator gpuAllocator(NCVMemoryTypeDevice, bufSize, static_cast<Ncv32u>(devProp.textureAlignment), pBuffer);
ncvAssertReturn(gpuAllocator.isInitialized(), NPPST_MEM_INTERNAL_ERROR);
NCVStatus ncvStat = ncvIntegralImage_device(d_src, srcStep, d_dst, dstStep, roiSize, gpuAllocator);
......@@ -526,7 +526,7 @@ NCVStatus nppiStIntegral_32f32f_C1R(Ncv32f *d_src, Ncv32u srcStep,
NcvSize32u roiSize, Ncv8u *pBuffer,
Ncv32u bufSize, cudaDeviceProp &devProp)
{
NCVMemStackAllocator gpuAllocator(NCVMemoryTypeDevice, bufSize, devProp.textureAlignment, pBuffer);
NCVMemStackAllocator gpuAllocator(NCVMemoryTypeDevice, bufSize, static_cast<Ncv32u>(devProp.textureAlignment), pBuffer);
ncvAssertReturn(gpuAllocator.isInitialized(), NPPST_MEM_INTERNAL_ERROR);
NCVStatus ncvStat = ncvIntegralImage_device(d_src, srcStep, d_dst, dstStep, roiSize, gpuAllocator);
......@@ -541,7 +541,7 @@ NCVStatus nppiStSqrIntegral_8u64u_C1R(Ncv8u *d_src, Ncv32u srcStep,
NcvSize32u roiSize, Ncv8u *pBuffer,
Ncv32u bufSize, cudaDeviceProp &devProp)
{
NCVMemStackAllocator gpuAllocator(NCVMemoryTypeDevice, bufSize, devProp.textureAlignment, pBuffer);
NCVMemStackAllocator gpuAllocator(NCVMemoryTypeDevice, bufSize, static_cast<Ncv32u>(devProp.textureAlignment), pBuffer);
ncvAssertReturn(gpuAllocator.isInitialized(), NPPST_MEM_INTERNAL_ERROR);
NCVStatus ncvStat = ncvSquaredIntegralImage_device(d_src, srcStep, d_dst, dstStep, roiSize, gpuAllocator);
......@@ -1506,7 +1506,7 @@ NCVStatus nppsStCompactGetSize_32u(Ncv32u srcLen, Ncv32u *pBufsize, cudaDevicePr
return NPPST_SUCCESS;
}
NCVMemStackAllocator gpuCounter(devProp.textureAlignment);
NCVMemStackAllocator gpuCounter(static_cast<Ncv32u>(devProp.textureAlignment));
ncvAssertReturn(gpuCounter.isInitialized(), NPPST_MEM_INTERNAL_ERROR);
NCVStatus ncvStat = compactVector_32u_device(NULL, srcLen, NULL, NULL, 0xC001C0DE,
......@@ -1535,7 +1535,7 @@ NCVStatus nppsStCompact_32u(Ncv32u *d_src, Ncv32u srcLen,
Ncv32u elemRemove, Ncv8u *pBuffer,
Ncv32u bufSize, cudaDeviceProp &devProp)
{
NCVMemStackAllocator gpuAllocator(NCVMemoryTypeDevice, bufSize, devProp.textureAlignment, pBuffer);
NCVMemStackAllocator gpuAllocator(NCVMemoryTypeDevice, bufSize, static_cast<Ncv32u>(devProp.textureAlignment), pBuffer);
ncvAssertReturn(gpuAllocator.isInitialized(), NPPST_MEM_INTERNAL_ERROR);
NCVStatus ncvStat = compactVector_32u_device(d_src, srcLen, d_dst, p_dstLen, elemRemove,
......
modules/gpu/src/nvidia/core/NCV.cu
浏览文件 @ 42ced17c
......@@ -355,7 +355,7 @@ NCVStatus NCVMemStackAllocator::alloc(NCVMemSegment &seg, size_t size)
seg.clear();
ncvAssertReturn(isInitialized(), NCV_ALLOCATOR_BAD_ALLOC);
size = alignUp(size, this->_alignment);
size = alignUp(static_cast<Ncv32u>(size), this->_alignment);
this->currentSize += size;
this->_maxSize = std::max(this->_maxSize, this->currentSize);
......@@ -464,7 +464,7 @@ NCVStatus NCVMemNativeAllocator::alloc(NCVMemSegment &seg, size_t size)
break;
}
this->currentSize += alignUp(size, this->_alignment);
this->currentSize += alignUp(static_cast<Ncv32u>(size), this->_alignment);
this->_maxSize = std::max(this->_maxSize, this->currentSize);
seg.begin.memtype = this->_memType;
......@@ -480,8 +480,8 @@ NCVStatus NCVMemNativeAllocator::dealloc(NCVMemSegment &seg)
ncvAssertReturn(seg.begin.memtype == this->_memType, NCV_ALLOCATOR_BAD_DEALLOC);
ncvAssertReturn(seg.begin.ptr != NULL, NCV_ALLOCATOR_BAD_DEALLOC);
ncvAssertReturn(currentSize >= alignUp(seg.size, this->_alignment), NCV_ALLOCATOR_BAD_DEALLOC);
currentSize -= alignUp(seg.size, this->_alignment);
ncvAssertReturn(currentSize >= alignUp(static_cast<Ncv32u>(seg.size), this->_alignment), NCV_ALLOCATOR_BAD_DEALLOC);
currentSize -= alignUp(static_cast<Ncv32u>(seg.size), this->_alignment);
switch (this->_memType)
{
......
modules/gpu/src/opencv2/gpu/device/border_interpolate.hpp
浏览文件 @ 42ced17c
......@@ -92,7 +92,7 @@ namespace cv { namespace gpu { namespace device
template <typename D> struct BrdColReflect101 : BrdReflect101
{
BrdColReflect101(int len, int step): BrdReflect101(len), step(step) {}
BrdColReflect101(int len, size_t step): BrdReflect101(len), step(step) {}
template <typename T> __device__ __forceinline__ D at_low(int i, const T* data) const
{
......@@ -104,7 +104,7 @@ namespace cv { namespace gpu { namespace device
return saturate_cast<D>(*(const D*)((const char*)data + idx_high(i)*step));
}
int step;
size_t step;
};
struct BrdReplicate
......@@ -152,7 +152,7 @@ namespace cv { namespace gpu { namespace device
template <typename D> struct BrdColReplicate : BrdReplicate
{
BrdColReplicate(int len, int step): BrdReplicate(len), step(step) {}
BrdColReplicate(int len, size_t step): BrdReplicate(len), step(step) {}
template <typename T> __device__ __forceinline__ D at_low(int i, const T* data) const
{
......@@ -164,7 +164,7 @@ namespace cv { namespace gpu { namespace device
return saturate_cast<D>(*(const D*)((const char*)data + idx_high(i)*step));
}
int step;
size_t step;
};
template <typename D> struct BrdRowConstant
......@@ -192,7 +192,7 @@ namespace cv { namespace gpu { namespace device
template <typename D> struct BrdColConstant
{
BrdColConstant(int len_, int step_, const D& val_ = VecTraits<D>::all(0)): len(len_), step(step_), val(val_) {}
BrdColConstant(int len_, size_t step_, const D& val_ = VecTraits<D>::all(0)): len(len_), step(step_), val(val_) {}
template <typename T> __device__ __forceinline__ D at_low(int i, const T* data) const
{
......@@ -210,7 +210,7 @@ namespace cv { namespace gpu { namespace device
}
int len;
int step;
size_t step;
D val;
};
......
modules/gpu/src/split_merge.cpp
浏览文件 @ 42ced17c
......@@ -56,11 +56,11 @@ void cv::gpu::split(const GpuMat& /*src*/, vector<GpuMat>& /*dst*/, Stream& /*st
namespace cv { namespace gpu { namespace split_merge
{
extern "C" void merge_caller(const DevMem2D* src, DevMem2D& dst,
int total_channels, int elem_size,
int total_channels, size_t elem_size,
const cudaStream_t& stream);
extern "C" void split_caller(const DevMem2D& src, DevMem2D* dst,
int num_channels, int elem_size1,
int num_channels, size_t elem_size1,
const cudaStream_t& stream);
void merge(const GpuMat* src, size_t n, GpuMat& dst, const cudaStream_t& stream)
......
modules/gpu/src/stereocsbp.cpp
浏览文件 @ 42ced17c
......@@ -167,7 +167,7 @@ static void csbp_operator(StereoConstantSpaceBP& rthis, GpuMat u[2], GpuMat d[2]
nr_plane_pyr[0] = rthis.nr_plane;
const int n = 64;
step_pyr[0] = alignSize(cols * sizeof(T), n) / sizeof(T);
step_pyr[0] = static_cast<int>(alignSize(cols * sizeof(T), n) / sizeof(T));
for (int i = 1; i < levels; i++)
{
cols_pyr[i] = (cols_pyr[i-1] + 1) / 2;
......@@ -175,7 +175,7 @@ static void csbp_operator(StereoConstantSpaceBP& rthis, GpuMat u[2], GpuMat d[2]
nr_plane_pyr[i] = nr_plane_pyr[i-1] * 2;
step_pyr[i] = alignSize(cols_pyr[i] * sizeof(T), n) / sizeof(T);
step_pyr[i] = static_cast<int>(alignSize(cols_pyr[i] * sizeof(T), n) / sizeof(T));
}
Size msg_size(step_pyr[0], rows * nr_plane_pyr[0]);
......@@ -197,7 +197,7 @@ static void csbp_operator(StereoConstantSpaceBP& rthis, GpuMat u[2], GpuMat d[2]
data_cost.create(data_cost_size, DataType<T>::type);
data_cost_selected.create(msg_size, DataType<T>::type);
step_pyr[0] = data_cost.step / sizeof(T);
step_pyr[0] = static_cast<int>(data_cost.step / sizeof(T));
Size temp_size = data_cost_size;
if (data_cost_size.width * data_cost_size.height < step_pyr[levels - 1] * rows_pyr[levels - 1] * rthis.ndisp)
......
modules/gpu/src/surf.cpp
浏览文件 @ 42ced17c
......@@ -260,7 +260,7 @@ void cv::gpu::SURF_GPU::uploadKeypoints(const vector<KeyPoint>& keypoints, GpuMa
keypointsGPU.release();
else
{
Mat keypointsCPU(SURF_GPU::SF_FEATURE_STRIDE, keypoints.size(), CV_32FC1);
Mat keypointsCPU(SURF_GPU::SF_FEATURE_STRIDE, static_cast<int>(keypoints.size()), CV_32FC1);
float* kp_x = keypointsCPU.ptr<float>(SURF_GPU::SF_X);
float* kp_y = keypointsCPU.ptr<float>(SURF_GPU::SF_Y);
......
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