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Commit e7f6c4b7 authored by Marina Kolpakova's avatar Marina Kolpakova
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scan operations are moved in separate header

parent 8748cbc2
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Showing with 230 additions and 487 deletions
modules/gpu/include/opencv2/gpu/gpu.hpp
View file @ e7f6c4b7
......@@ -629,10 +629,6 @@ CV_EXPORTS double threshold(const GpuMat& src, GpuMat& dst, double thresh, doubl
//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_AREA
CV_EXPORTS void resize(const GpuMat& src, GpuMat& dst, Size dsize, double fx=0, double fy=0, int interpolation = INTER_LINEAR, Stream& stream = Stream::Null());
//! resizes the image
//! Supports INTER_AREA
CV_EXPORTS void resize(const GpuMat& src, GpuMat& dst, GpuMat& buffer, Size dsize, double fx=0, double fy=0, int interpolation = INTER_AREA, Stream& stream = Stream::Null());
//! warps the image using affine transformation
//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
CV_EXPORTS void warpAffine(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags = INTER_LINEAR,
......
modules/gpu/perf/perf_imgproc.cpp
View file @ e7f6c4b7
......@@ -118,10 +118,10 @@ GPU_PERF_TEST(ResizeArea, cv::gpu::DeviceInfo, cv::Size, MatType, Scale)
INSTANTIATE_TEST_CASE_P(ImgProc, ResizeArea, testing::Combine(
ALL_DEVICES,
testing::Values(perf::sz1080p, cv::Size(4096, 2048)),
testing::Values(MatType(CV_8UC1)/*, MatType(CV_8UC3), MatType(CV_8UC4),
testing::Values(perf::sz1080p/*, cv::Size(4096, 2048)*/),
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_8UC4),
MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4),
MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)*/),
MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
testing::Values(Scale(0.2),Scale(0.1),Scale(0.05))));
//////////////////////////////////////////////////////////////////////
......
modules/gpu/src/cuda/element_operations.cu
View file @ e7f6c4b7
......@@ -1253,7 +1253,7 @@ namespace cv { namespace gpu { namespace device
{
const T val;
__host__ explicit CompareScalar(T val) : val(val) {}
__host__ explicit CompareScalar(T val_) : val(val_) {}
__device__ __forceinline__ uchar operator()(T src) const
{
......@@ -1266,7 +1266,7 @@ namespace cv { namespace gpu { namespace device
{
const TYPE_VEC(T, 2) val;
__host__ explicit CompareScalar(TYPE_VEC(T, 2) val) : val(val) {}
__host__ explicit CompareScalar(TYPE_VEC(T, 2) val_) : val(val_) {}
__device__ __forceinline__ TYPE_VEC(uchar, 2) operator()(const TYPE_VEC(T, 2) & src) const
{
......@@ -1281,7 +1281,7 @@ namespace cv { namespace gpu { namespace device
{
const TYPE_VEC(T, 3) val;
__host__ explicit CompareScalar(TYPE_VEC(T, 3) val) : val(val) {}
__host__ explicit CompareScalar(TYPE_VEC(T, 3) val_) : val(val_) {}
__device__ __forceinline__ TYPE_VEC(uchar, 3) operator()(const TYPE_VEC(T, 3) & src) const
{
......@@ -1297,7 +1297,7 @@ namespace cv { namespace gpu { namespace device
{
const TYPE_VEC(T, 4) val;
__host__ explicit CompareScalar(TYPE_VEC(T, 4) val) : val(val) {}
__host__ explicit CompareScalar(TYPE_VEC(T, 4) val_) : val(val_) {}
__device__ __forceinline__ TYPE_VEC(uchar, 4) operator()(const TYPE_VEC(T, 4) & src) const
{
......
modules/gpu/src/cuda/matrix_reductions.cu
View file @ e7f6c4b7
......@@ -72,7 +72,7 @@ namespace cv { namespace gpu { namespace device
struct Mask8U
{
explicit Mask8U(PtrStepb mask): mask(mask) {}
explicit Mask8U(PtrStepb mask_): mask(mask_) {}
__device__ __forceinline__ bool operator()(int y, int x) const
{
......
modules/gpu/src/cuda/resize.cu
View file @ e7f6c4b7
......@@ -46,7 +46,8 @@
#include "opencv2/gpu/device/vec_math.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
#include "opencv2/gpu/device/filters.hpp"
# include <cfloat>
#include <cfloat>
#include <opencv2/gpu/device/scan.hpp>
namespace cv { namespace gpu { namespace device
{
......@@ -285,367 +286,5 @@ namespace cv { namespace gpu { namespace device
typedef float scan_line_type;
};
// template <typename T>
// __global__ void resize_area_scan(const DevMem2D_<T> src, DevMem2D_<T> dst, int fx, int fy, DevMem2D_<T> buffer)
// {
// typedef typename scan_traits<T>::scan_line_type W;
// extern __shared__ W line[];
// const int x = threadIdx.x;
// const int y = blockIdx.x;
// if (y >= src.rows) return;
// int offset = 1;
// line[2 * x + 0] = src(y, 2 * x + 0);
// line[2 * x + 1] = src(y, 2 * x + 1);
// __syncthreads();//???
// // reduction
// for (int d = blockDim.x; d > 0; d >>= 1)
// {
// __syncthreads();
// if (x < d)
// {
// int ai = 2 * x * offset -1 + 1 * offset;
// int bi = 2 * x * offset -1 + 2 * offset;
// line[bi] += line[ai];
// }
// offset *= 2;
// }
// __syncthreads();
// // convolution
// if (x == 0) { line[(blockDim.x << 1) - 1] = 0; printf("offset: %d!!!!!!!!!!!!!\n", fx);}
// for (int d = 1; d < (blockDim.x << 1); d *= 2)
// {
// offset >>= 1;
// __syncthreads();
// if (x < d)
// {
// int ai = offset * 2 * x + 1 * offset - 1;
// int bi = offset * 2 * x + 2 * offset - 1;
// W t = line[ai];
// line[ai] = line[bi];
// line[bi] += t;
// }
// }
// __syncthreads();
// // calculate sum
// int start = 0;
// int out_idx = 0;
// int end = start + fx;
// while (start < (blockDim.x << 1) && end < (blockDim.x << 1))
// {
// buffer(y, out_idx) = saturate_cast<T>((line[end] - line[start]) / fx);
// start = end;
// end = start + fx;
// out_idx++;
// }
// }
template <typename T>
__device__ void scan_y(DevMem2D_<typename scan_traits<T>::scan_line_type> buffer,int fx, int fy, DevMem2D_<T> dst,
typename scan_traits<T>::scan_line_type* line, int g_base)
{
typedef typename scan_traits<T>::scan_line_type W;
const int y = threadIdx.x;
const int x = blockIdx.x;
float scale = 1.f / (fx * fy);
if (x >= buffer.cols) return;
int offset = 1;
line[2 * y + 0] = buffer((g_base * fy) + 2 * y + 1, x);
if (y != (blockDim.x -1) )
line[2 * y + 1] = buffer((g_base * fy) + 2 * y + 2, x);
else
line[2 * y + 1] = 0;
__syncthreads();
// reduction
for (int d = blockDim.x; d > 0; d >>= 1)
{
__syncthreads();
if (y < d)
{
int ai = 2 * y * offset -1 + 1 * offset;
int bi = 2 * y * offset -1 + 2 * offset;
line[bi] += line[ai];
}
offset *= 2;
}
__syncthreads();
// convolution
if (y == 0) line[(blockDim.x << 1) - 1] = (W)buffer(0, x);
for (int d = 1; d < (blockDim.x << 1); d *= 2)
{
offset >>= 1;
__syncthreads();
if (y < d)
{
int ai = offset * 2 * y + 1 * offset - 1;
int bi = offset * 2 * y + 2 * offset - 1;
W t = line[ai];
line[ai] = line[bi];
line[bi] += t;
}
}
__syncthreads();
if (y < dst.rows)
{
W start = (y == 0)? (W)0:line[y * fy -1];
W end = line[y * fy + fy - 1];
dst(g_base + y ,x) = saturate_cast<T>((end - start) * scale);
}
}
template <typename T>
__device__ void scan_x(const DevMem2D_<T> src, int fx, int fy, DevMem2D_<typename scan_traits<T>::scan_line_type> buffer,
typename scan_traits<T>::scan_line_type* line, int g_base)
{
typedef typename scan_traits<T>::scan_line_type W;
const int x = threadIdx.x;
const int y = blockIdx.x;
float scale = 1.f / (fx * fy);
if (y >= src.rows) return;
int offset = 1;
line[2 * x + 0] = (W)src(y, (g_base * fx) + 2 * x + 1);
if (x != (blockDim.x -1) )
line[2 * x + 1] = (W)src(y, (g_base * fx) + 2 * x + 2);
else
line[2 * x + 1] = 0;
__syncthreads();
// reduction
for (int d = blockDim.x; d > 0; d >>= 1)
{
__syncthreads();
if (x < d)
{
int ai = 2 * x * offset -1 + 1 * offset;
int bi = 2 * x * offset -1 + 2 * offset;
line[bi] += line[ai];
}
offset *= 2;
}
__syncthreads();
// convolution
if (x == 0) line[(blockDim.x << 1) - 1] = (W)src(y, 0);
for (int d = 1; d < (blockDim.x << 1); d *= 2)
{
offset >>= 1;
__syncthreads();
if (x < d)
{
int ai = offset * 2 * x + 1 * offset - 1;
int bi = offset * 2 * x + 2 * offset - 1;
W t = line[ai];
line[ai] = line[bi];
line[bi] += t;
}
}
__syncthreads();
if (x < buffer.cols)
{
W start = (x == 0)? (W)0:line[x * fx -1];
W end = line[x * fx + fx - 1];
buffer(y, g_base + x) =(end - start);
}
}
enum ScanKind { exclusive, inclusive } ;
template <ScanKind Kind , class T>
__device__ __forceinline__ T scan_warp ( volatile T *ptr , const unsigned int idx = threadIdx.x )
{
const unsigned int lane = idx & 31;
if ( lane >= 1) ptr [idx ] = ptr [idx - 1] + ptr [idx];
if ( lane >= 2) ptr [idx ] = ptr [idx - 2] + ptr [idx];
if ( lane >= 4) ptr [idx ] = ptr [idx - 4] + ptr [idx];
if ( lane >= 8) ptr [idx ] = ptr [idx - 8] + ptr [idx];
if ( lane >= 16) ptr [idx ] = ptr [idx - 16] + ptr [idx];
if( Kind == inclusive )
return ptr [idx ];
else
return (lane > 0) ? ptr [idx - 1] : 0;
}
template <ScanKind Kind , class T>
__device__ __forceinline__ T scan_block( volatile T *ptr)
{
const unsigned int idx = threadIdx.x;
const unsigned int lane = idx & 31;
const unsigned int warp = idx >> 5;
T val = scan_warp <Kind>( ptr , idx );
__syncthreads ();
if( lane == 31 )
ptr [ warp ] = ptr [idx ];
__syncthreads ();
if( warp == 0 )
scan_warp<inclusive>( ptr , idx );
__syncthreads ();
if ( warp > 0)
val = ptr [warp -1] + val;
__syncthreads ();
ptr[idx] = val;
__syncthreads ();
return val ;
}
template<typename T, typename W>
__global__ void resise_scan_fast_x(const DevMem2D_<T> src, DevMem2D_<W> dst, int fx, int fy, int thred_lines, int stride)
{
extern __shared__ W sbuf[];
const unsigned int tid = threadIdx. x;
// load line-block on shared memory
int y = blockIdx.x / thred_lines;
int input_stride = (blockIdx.x % thred_lines) * stride;
int x = input_stride + tid;
// store global data in shared memory
if (x < src.cols && y < src.rows)
sbuf[tid] = src(y, x);
else
sbuf[tid] = 0;
__syncthreads();
scan_block<inclusive, W>(sbuf);
float scale = __fdividef(1.f, fx);
int out_stride = input_stride / fx;
int count = blockDim.x / fx;
if (tid < count)
{
int start_idx = (tid == 0)? 0 : tid * fx - 1;
int end_idx = tid * fx + fx - 1;
W start = (tid == 0)? (W)0:sbuf[start_idx];
W end = sbuf[end_idx];
dst(y, out_stride + tid) = (end - start);
}
}
template<typename T, typename W>
__global__ void resise_scan_fast_y(const DevMem2D_<W> src, DevMem2D_<T> dst, int fx, int fy, int thred_lines, int stride)
{
extern __shared__ W sbuf[];
const unsigned int tid = threadIdx. x;
// load line-block on shared memory
int x = blockIdx.x / thred_lines;
int global_stride = (blockIdx.x % thred_lines) * stride;
int y = global_stride + tid;
// store global data in shared memory
if (x < src.cols && y < src.rows)
sbuf[tid] = src(y, x);
else
sbuf[tid] = 0;
__syncthreads();
scan_block<inclusive, W>(sbuf);
float scale = __fdividef(1.f, fx * fy);
int out_stride = global_stride / fx;
int count = blockDim.x / fx;
if (tid < count)
{
int start_idx = (tid == 0)? 0 : tid * fx - 1;
int end_idx = tid * fx + fx - 1;
W start = (tid == 0)? (W)0:sbuf[start_idx];
W end = sbuf[end_idx];
dst(out_stride + tid, x) = saturate_cast<T>((end - start) * scale);
}
}
template <typename T>
void resize_area_gpu(const DevMem2Db src, DevMem2Db dst,float fx, float fy,
int interpolation, DevMem2Df buffer, cudaStream_t stream)
{
(void)interpolation;
int iscale_x = round(fx);
int iscale_y = round(fy);
int warps = 4;
const int threads = 32 * warps;
int input_stride = threads / iscale_x;
int thred_lines = divUp(src.cols, input_stride * iscale_x);
int blocks = src.rows * thred_lines;
typedef typename scan_traits<T>::scan_line_type smem_type;
resise_scan_fast_x<T, smem_type><<<blocks, threads, warps * 32 * sizeof(smem_type)>>>
(src, buffer, iscale_x, iscale_y, thred_lines, input_stride * iscale_x);
input_stride = threads / iscale_y;
thred_lines = divUp(src.rows, input_stride * iscale_y);
blocks = dst.cols * thred_lines;
resise_scan_fast_y<T, smem_type><<<blocks, threads, warps * 32 * sizeof(smem_type)>>>
(buffer, dst, iscale_x, iscale_y, thred_lines, input_stride * iscale_y);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
template void resize_area_gpu<uchar>(DevMem2Db src, DevMem2Db dst, float fx, float fy, int interpolation, DevMem2Df buffer, cudaStream_t stream);
} // namespace imgproc
}}} // namespace cv { namespace gpu { namespace device
modules/gpu/src/cuda/split_merge.cu
View file @ e7f6c4b7
......@@ -228,9 +228,9 @@ namespace cv { namespace gpu { namespace device
template <typename T>
static void mergeC2_(const DevMem2Db* src, DevMem2Db& dst, const cudaStream_t& stream)
{
dim3 blockDim(32, 8);
dim3 gridDim(divUp(dst.cols, blockDim.x), divUp(dst.rows, blockDim.y));
mergeC2_<T><<<gridDim, blockDim, 0, stream>>>(
dim3 block(32, 8);
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y));
mergeC2_<T><<<grid, block, 0, stream>>>(
src[0].data, src[0].step,
src[1].data, src[1].step,
dst.rows, dst.cols, dst.data, dst.step);
......@@ -244,9 +244,9 @@ namespace cv { namespace gpu { namespace device
template <typename T>
static void mergeC3_(const DevMem2Db* src, DevMem2Db& dst, const cudaStream_t& stream)
{
dim3 blockDim(32, 8);
dim3 gridDim(divUp(dst.cols, blockDim.x), divUp(dst.rows, blockDim.y));
mergeC3_<T><<<gridDim, blockDim, 0, stream>>>(
dim3 block(32, 8);
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y));
mergeC3_<T><<<grid, block, 0, stream>>>(
src[0].data, src[0].step,
src[1].data, src[1].step,
src[2].data, src[2].step,
......@@ -261,9 +261,9 @@ namespace cv { namespace gpu { namespace device
template <typename T>
static void mergeC4_(const DevMem2Db* src, DevMem2Db& dst, const cudaStream_t& stream)
{
dim3 blockDim(32, 8);
dim3 gridDim(divUp(dst.cols, blockDim.x), divUp(dst.rows, blockDim.y));
mergeC4_<T><<<gridDim, blockDim, 0, stream>>>(
dim3 block(32, 8);
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y));
mergeC4_<T><<<grid, block, 0, stream>>>(
src[0].data, src[0].step,
src[1].data, src[1].step,
src[2].data, src[2].step,
......@@ -437,9 +437,9 @@ namespace cv { namespace gpu { namespace device
template <typename T>
static void splitC2_(const DevMem2Db& src, DevMem2Db* dst, const cudaStream_t& stream)
{
dim3 blockDim(32, 8);
dim3 gridDim(divUp(src.cols, blockDim.x), divUp(src.rows, blockDim.y));
splitC2_<T><<<gridDim, blockDim, 0, stream>>>(
dim3 block(32, 8);
dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y));
splitC2_<T><<<grid, block, 0, stream>>>(
src.data, src.step, src.rows, src.cols,
dst[0].data, dst[0].step,
dst[1].data, dst[1].step);
......@@ -453,9 +453,9 @@ namespace cv { namespace gpu { namespace device
template <typename T>
static void splitC3_(const DevMem2Db& src, DevMem2Db* dst, const cudaStream_t& stream)
{
dim3 blockDim(32, 8);
dim3 gridDim(divUp(src.cols, blockDim.x), divUp(src.rows, blockDim.y));
splitC3_<T><<<gridDim, blockDim, 0, stream>>>(
dim3 block(32, 8);
dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y));
splitC3_<T><<<grid, block, 0, stream>>>(
src.data, src.step, src.rows, src.cols,
dst[0].data, dst[0].step,
dst[1].data, dst[1].step,
......@@ -470,9 +470,9 @@ namespace cv { namespace gpu { namespace device
template <typename T>
static void splitC4_(const DevMem2Db& src, DevMem2Db* dst, const cudaStream_t& stream)
{
dim3 blockDim(32, 8);
dim3 gridDim(divUp(src.cols, blockDim.x), divUp(src.rows, blockDim.y));
splitC4_<T><<<gridDim, blockDim, 0, stream>>>(
dim3 block(32, 8);
dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y));
splitC4_<T><<<grid, block, 0, stream>>>(
src.data, src.step, src.rows, src.cols,
dst[0].data, dst[0].step,
dst[1].data, dst[1].step,
......
modules/gpu/src/nvidia/NCVBroxOpticalFlow.cu
View file @ e7f6c4b7
......@@ -1121,18 +1121,18 @@ NCVStatus NCVBroxOpticalFlow(const NCVBroxOpticalFlowDescriptor desc,
dim3 p_blocks(iDivUp(nw, 32), iDivUp(nh, 8));
dim3 p_threads(32, 8);
NcvSize32u srcSize (kLevelWidth, kLevelHeight);
NcvSize32u inner_srcSize (kLevelWidth, kLevelHeight);
NcvSize32u dstSize (nw, nh);
NcvRect32u srcROI (0, 0, kLevelWidth, kLevelHeight);
NcvRect32u dstROI (0, 0, nw, nh);
ncvAssertReturnNcvStat( nppiStResize_32f_C1R (ptrU->ptr(), srcSize, kLevelStride * sizeof (float), srcROI,
ncvAssertReturnNcvStat( nppiStResize_32f_C1R (ptrU->ptr(), inner_srcSize, kLevelStride * sizeof (float), srcROI,
ptrUNew->ptr(), dstSize, ns * sizeof (float), dstROI, 1.0f/scale_factor, 1.0f/scale_factor, nppStBicubic) );
ScaleVector(ptrUNew->ptr(), ptrUNew->ptr(), 1.0f/scale_factor, ns * nh, stream);
ncvAssertCUDALastErrorReturn(NCV_CUDA_ERROR);
ncvAssertReturnNcvStat( nppiStResize_32f_C1R (ptrV->ptr(), srcSize, kLevelStride * sizeof (float), srcROI,
ncvAssertReturnNcvStat( nppiStResize_32f_C1R (ptrV->ptr(), inner_srcSize, kLevelStride * sizeof (float), srcROI,
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);
......
modules/gpu/src/nvidia/core/NCV.cu
View file @ e7f6c4b7
......@@ -252,7 +252,7 @@ NCVStatus memSegCopyHelper2D(void *dst, Ncv32u dstPitch, NCVMemoryType dstType,
//===================================================================
NCVMemStackAllocator::NCVMemStackAllocator(Ncv32u alignment)
NCVMemStackAllocator::NCVMemStackAllocator(Ncv32u alignment_)
:
currentSize(0),
_maxSize(0),
......@@ -260,23 +260,23 @@ NCVMemStackAllocator::NCVMemStackAllocator(Ncv32u alignment)
begin(NULL),
end(NULL),
_memType(NCVMemoryTypeNone),
_alignment(alignment),
_alignment(alignment_),
bReusesMemory(false)
{
NcvBool bProperAlignment = (alignment & (alignment-1)) == 0;
NcvBool bProperAlignment = (alignment_ & (alignment_ - 1)) == 0;
ncvAssertPrintCheck(bProperAlignment, "NCVMemStackAllocator ctor:: alignment not power of 2");
}
NCVMemStackAllocator::NCVMemStackAllocator(NCVMemoryType memT, size_t capacity, Ncv32u alignment, void *reusePtr)
NCVMemStackAllocator::NCVMemStackAllocator(NCVMemoryType memT, size_t capacity, Ncv32u alignment_, void *reusePtr)
:
currentSize(0),
_maxSize(0),
allocBegin(NULL),
_memType(memT),
_alignment(alignment)
_alignment(alignment_)
{
NcvBool bProperAlignment = (alignment & (alignment-1)) == 0;
NcvBool bProperAlignment = (alignment_ & (alignment_ - 1)) == 0;
ncvAssertPrintCheck(bProperAlignment, "NCVMemStackAllocator ctor:: _alignment not power of 2");
ncvAssertPrintCheck(memT != NCVMemoryTypeNone, "NCVMemStackAllocator ctor:: Incorrect allocator type");
......@@ -425,12 +425,12 @@ size_t NCVMemStackAllocator::maxSize(void) const
//===================================================================
NCVMemNativeAllocator::NCVMemNativeAllocator(NCVMemoryType memT, Ncv32u alignment)
NCVMemNativeAllocator::NCVMemNativeAllocator(NCVMemoryType memT, Ncv32u alignment_)
:
currentSize(0),
_maxSize(0),
_memType(memT),
_alignment(alignment)
_alignment(alignment_)
{
ncvAssertPrintReturn(memT != NCVMemoryTypeNone, "NCVMemNativeAllocator ctor:: counting not permitted for this allocator type", );
}
......
modules/gpu/src/opencv2/gpu/device/functional.hpp
View file @ e7f6c4b7
......@@ -416,6 +416,8 @@ namespace cv { namespace gpu { namespace device
{
return src1 * src1 + src2 * src2;
}
__device__ __forceinline__ hypot_sqr_func(const hypot_sqr_func& other) : binary_function<T, T, float>(){}
__device__ __forceinline__ hypot_sqr_func() : binary_function<T, T, float>(){}
};
// Saturate Cast Functor
......@@ -438,6 +440,7 @@ namespace cv { namespace gpu { namespace device
{
return (src > thresh) * maxVal;
}
__device__ __forceinline__ thresh_binary_func(const thresh_binary_func& other)
: unary_function<T, T>(), thresh(other.thresh), maxVal(other.maxVal){}
......@@ -455,6 +458,7 @@ namespace cv { namespace gpu { namespace device
{
return (src <= thresh) * maxVal;
}
__device__ __forceinline__ thresh_binary_inv_func(const thresh_binary_inv_func& other)
: unary_function<T, T>(), thresh(other.thresh), maxVal(other.maxVal){}
......@@ -523,8 +527,12 @@ namespace cv { namespace gpu { namespace device
return !pred(x);
}
__device__ __forceinline__ unary_negate(const unary_negate& other) : unary_function<typename Predicate::argument_type, bool>(){}
__device__ __forceinline__ unary_negate() : unary_function<typename Predicate::argument_type, bool>(){}
const Predicate pred;
};
template <typename Predicate> __host__ __device__ __forceinline__ unary_negate<Predicate> not1(const Predicate& pred)
{
return unary_negate<Predicate>(pred);
......@@ -534,13 +542,20 @@ namespace cv { namespace gpu { namespace device
{
explicit __host__ __device__ __forceinline__ binary_negate(const Predicate& p) : pred(p) {}
__device__ __forceinline__ bool operator()(typename TypeTraits<typename Predicate::first_argument_type>::ParameterType x, typename TypeTraits<typename Predicate::second_argument_type>::ParameterType y) const
__device__ __forceinline__ bool operator()(typename TypeTraits<typename Predicate::first_argument_type>::ParameterType x,
typename TypeTraits<typename Predicate::second_argument_type>::ParameterType y) const
{
return !pred(x,y);
}
__device__ __forceinline__ binary_negate(const binary_negate& other)
: binary_function<typename Predicate::first_argument_type, typename Predicate::second_argument_type, bool>(){}
__device__ __forceinline__ binary_negate() :
binary_function<typename Predicate::first_argument_type, typename Predicate::second_argument_type, bool>(){}
const Predicate pred;
};
template <typename BinaryPredicate> __host__ __device__ __forceinline__ binary_negate<BinaryPredicate> not2(const BinaryPredicate& pred)
{
return binary_negate<BinaryPredicate>(pred);
......@@ -555,9 +570,13 @@ namespace cv { namespace gpu { namespace device
return op(arg1, a);
}
__device__ __forceinline__ binder1st(const binder1st& other) :
unary_function<typename Op::second_argument_type, typename Op::result_type>(){}
const Op op;
const typename Op::first_argument_type arg1;
};
template <typename Op, typename T> __host__ __device__ __forceinline__ binder1st<Op> bind1st(const Op& op, const T& x)
{
return binder1st<Op>(op, typename Op::first_argument_type(x));
......@@ -572,16 +591,19 @@ namespace cv { namespace gpu { namespace device
return op(a, arg2);
}
__device__ __forceinline__ binder2nd(const binder2nd& other) :
unary_function<typename Op::first_argument_type, typename Op::result_type>(), op(other.op), arg2(other.arg2){}
const Op op;
const typename Op::second_argument_type arg2;
};
template <typename Op, typename T> __host__ __device__ __forceinline__ binder2nd<Op> bind2nd(const Op& op, const T& x)
{
return binder2nd<Op>(op, typename Op::second_argument_type(x));
}
// Functor Traits
template <typename F> struct IsUnaryFunction
{
typedef char Yes;
......
modules/gpu/src/opencv2/gpu/device/scan.hpp 0 → 100644
View file @ e7f6c4b7
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// 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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_GPU_SCAN_HPP__
#define __OPENCV_GPU_SCAN_HPP__
enum ScanKind { EXCLUSIVE = 0, INCLUSIVE = 1 };
template <ScanKind Kind, typename T, typename F> struct WarpScan
{
__device__ __forceinline__ WarpScan() {}
__device__ __forceinline__ WarpScan(const WarpScan& other) { (void)other; }
__device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx)
{
const unsigned int lane = idx & 31;
F op;
if ( lane >= 1) ptr [idx ] = op(ptr [idx - 1], ptr [idx]);
if ( lane >= 2) ptr [idx ] = op(ptr [idx - 2], ptr [idx]);
if ( lane >= 4) ptr [idx ] = op(ptr [idx - 4], ptr [idx]);
if ( lane >= 8) ptr [idx ] = op(ptr [idx - 8], ptr [idx]);
if ( lane >= 16) ptr [idx ] = op(ptr [idx - 16], ptr [idx]);
if( Kind == INCLUSIVE )
return ptr [idx];
else
return (lane > 0) ? ptr [idx - 1] : 0;
}
__device__ __forceinline__ unsigned int index(const unsigned int tid)
{
return tid;
}
__device__ __forceinline__ void init(volatile T *ptr){}
static const int warp_offset = 0;
typedef WarpScan<INCLUSIVE, T, F> merge;
};
template <ScanKind Kind , typename T, typename F> struct WarpScanNoComp
{
__device__ __forceinline__ WarpScanNoComp() {}
__device__ __forceinline__ WarpScanNoComp(const WarpScanNoComp& other) { (void)other; }
__device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx)
{
const unsigned int lane = threadIdx.x & 31;
F op;
ptr [idx ] = op(ptr [idx - 1], ptr [idx]);
ptr [idx ] = op(ptr [idx - 2], ptr [idx]);
ptr [idx ] = op(ptr [idx - 4], ptr [idx]);
ptr [idx ] = op(ptr [idx - 8], ptr [idx]);
ptr [idx ] = op(ptr [idx - 16], ptr [idx]);
if( Kind == INCLUSIVE )
return ptr [idx];
else
return (lane > 0) ? ptr [idx - 1] : 0;
}
__device__ __forceinline__ unsigned int index(const unsigned int tid)
{
return (tid >> warp_log) * warp_smem_stride + 16 + (tid & warp_mask);
}
__device__ __forceinline__ void init(volatile T *ptr)
{
ptr[threadIdx.x] = 0;
}
static const int warp_smem_stride = 32 + 16 + 1;
static const int warp_offset = 16;
static const int warp_log = 5;
static const int warp_mask = 31;
typedef WarpScanNoComp<INCLUSIVE, T, F> merge;
};
template <ScanKind Kind , typename T, typename Sc, typename F> struct BlockScan
{
__device__ __forceinline__ BlockScan() {}
__device__ __forceinline__ BlockScan(const BlockScan& other) { (void)other; }
__device__ __forceinline__ T operator()(volatile T *ptr)
{
const unsigned int tid = threadIdx.x;
const unsigned int lane = tid & warp_mask;
const unsigned int warp = tid >> warp_log;
Sc scan;
typename Sc::merge merge_scan;
const unsigned int idx = scan.index(tid);
T val = scan(ptr, idx);
__syncthreads ();
if( warp == 0)
scan.init(ptr);
__syncthreads ();
if( lane == 31 )
ptr [scan.warp_offset + warp ] = (Kind == INCLUSIVE) ? val : ptr [idx];
__syncthreads ();
if( warp == 0 )
merge_scan(ptr, idx);
__syncthreads();
if ( warp > 0)
val = ptr [scan.warp_offset + warp - 1] + val;
__syncthreads ();
ptr[idx] = val;
__syncthreads ();
return val ;
}
static const int warp_log = 5;
static const int warp_mask = 31;
};
#endif
\ No newline at end of file
modules/gpu/src/resize.cpp
View file @ e7f6c4b7
......@@ -80,51 +80,9 @@ namespace cv { namespace gpu { namespace device
template <typename T>
void resize_gpu(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy,
DevMem2Db dst, int interpolation, cudaStream_t stream);
template <typename T>
void resize_area_gpu(const DevMem2Db src, DevMem2Db dst,float fx, float fy,
int interpolation, DevMem2Df buffer, cudaStream_t stream);
}
}}}
void cv::gpu::resize(const GpuMat& src, GpuMat& dst, GpuMat& buffer, Size dsize, double fx, double fy,
int interpolation, Stream& s)
{
CV_Assert(src.depth() <= CV_32F && src.channels() <= 4);
CV_Assert(interpolation == INTER_AREA);
CV_Assert( (fx < 1.0) && (fy < 1.0));
CV_Assert(!(dsize == Size()) || (fx > 0 && fy > 0));
CV_Assert(src.cols >= 128 && src.rows >= 128);
CV_Assert((fx - 128.0) <= 0 && (fy - 128.0) <= 0);
if (dsize == Size())
dsize = Size(saturate_cast<int>(src.cols * fx), saturate_cast<int>(src.rows * fy));
else
{
fx = static_cast<double>(dsize.width) / src.cols;
fy = static_cast<double>(dsize.height) / src.rows;
}
fx = static_cast<float>(1.0 / fx);
fy = static_cast<float>(1.0 / fy);
dst.create(dsize, src.type());
buffer.create(cv::Size(dsize.width, src.rows), CV_32FC1);
if (dsize == src.size())
{
if (s)
s.enqueueCopy(src, dst);
else
src.copyTo(dst);
return;
}
cudaStream_t stream = StreamAccessor::getStream(s);
cv::gpu::device::imgproc::resize_area_gpu<uchar>(src, dst, fx, fy, interpolation, buffer, stream);
}
void cv::gpu::resize(const GpuMat& src, GpuMat& dst, Size dsize, double fx, double fy, int interpolation, Stream& s)
{
CV_Assert(src.depth() <= CV_32F && src.channels() <= 4);
......
modules/gpu/test/test_resize.cpp
View file @ e7f6c4b7
......@@ -182,45 +182,6 @@ PARAM_TEST_CASE(ResizeArea, cv::gpu::DeviceInfo, cv::Size, MatType, double, Inte
}
};
TEST_P(ResizeArea, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::gpu::GpuMat dst = createMat(cv::Size(cv::saturate_cast<int>(src.cols * coeff), cv::saturate_cast<int>(src.rows * coeff)), type, useRoi);
cv::gpu::GpuMat buffer = createMat(cv::Size(dst.cols, src.rows), CV_32FC1);
cv::gpu::resize(loadMat(src, useRoi), dst, buffer, cv::Size(), coeff, coeff, interpolation);
cv::Mat dst_cpu;
cv::resize(src, dst_cpu, cv::Size(), coeff, coeff, interpolation);
cv::Mat gpu_buff;
buffer.download(gpu_buff);
cv::Mat gpu;
dst.download(gpu);
// std::cout // << src
// // << std::endl << std::endl
// // << gpu_buff
// // << std::endl << std::endl
// << gpu
// << std::endl << std::endl
// << dst_cpu<< std::endl;
EXPECT_MAT_NEAR(dst_cpu, dst, src.depth() == CV_32F ? 1e-2 : 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, ResizeArea, testing::Combine(
ALL_DEVICES,
testing::Values(cv::Size(640, 480)),//DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1)/*MatType(CV_8UC3), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)*/),
testing::Values(0.05, 0.1),
testing::Values(Interpolation(cv::INTER_AREA)),
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////
// Test NPP
......
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