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opencv/modules/gpu/src/cuda/resize.cu

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/*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*/
#include "internal_shared.hpp"
#include "opencv2/gpu/device/border_interpolate.hpp"
#include "opencv2/gpu/device/vec_traits.hpp"
#include "opencv2/gpu/device/vec_math.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
#include "opencv2/gpu/device/filters.hpp"
# include <cfloat>
namespace cv { namespace gpu { namespace device
{
namespace imgproc
{
template <typename Ptr2D, typename T> __global__ void resize(const Ptr2D src, float fx, float fy, DevMem2D_<T> dst)
{
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x < dst.cols && y < dst.rows)
{
const float xcoo = x * fx;
const float ycoo = y * fy;
dst(y, x) = saturate_cast<T>(src(ycoo, xcoo));
}
}
template <typename Ptr2D, typename T> __global__ void resize_area(const Ptr2D src, float fx, float fy, DevMem2D_<T> dst)
{
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x < dst.cols && y < dst.rows)
{
dst(y, x) = saturate_cast<T>(src(y, x));
}
}
template <template <typename> class Filter, typename T> struct ResizeDispatcherStream
{
static void call(DevMem2D_<T> src, float fx, float fy, DevMem2D_<T> dst, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y));
BrdReplicate<T> brd(src.rows, src.cols);
BorderReader< PtrStep<T>, BrdReplicate<T> > brdSrc(src, brd);
Filter< BorderReader< PtrStep<T>, BrdReplicate<T> > > filteredSrc(brdSrc, fx, fy);
resize<<<grid, block, 0, stream>>>(filteredSrc, fx, fy, dst);
cudaSafeCall( cudaGetLastError() );
}
};
template <typename T> struct ResizeDispatcherStream<AreaFilter, T>
{
static void call(DevMem2D_<T> src, float fx, float fy, DevMem2D_<T> dst, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y));
BrdConstant<T> brd(src.rows, src.cols);
BorderReader< PtrStep<T>, BrdConstant<T> > brdSrc(src, brd);
AreaFilter< BorderReader< PtrStep<T>, BrdConstant<T> > > filteredSrc(brdSrc, fx, fy);
resize_area<<<grid, block, 0, stream>>>(filteredSrc, fx, fy, dst);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template <typename T> struct ResizeDispatcherStream<IntegerAreaFilter, T>
{
static void call(DevMem2D_<T> src, float fx, float fy, DevMem2D_<T> dst, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y));
BrdConstant<T> brd(src.rows, src.cols);
BorderReader< PtrStep<T>, BrdConstant<T> > brdSrc(src, brd);
IntegerAreaFilter< BorderReader< PtrStep<T>, BrdConstant<T> > > filteredSrc(brdSrc, fx, fy);
resize_area<<<grid, block, 0, stream>>>(filteredSrc, fx, fy, dst);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template <template <typename> class Filter, typename T> struct ResizeDispatcherNonStream
{
static void call(DevMem2D_<T> src, DevMem2D_<T> srcWhole, int xoff, int yoff, float fx, float fy, DevMem2D_<T> dst)
{
dim3 block(32, 8);
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y));
BrdReplicate<T> brd(src.rows, src.cols);
BorderReader< PtrStep<T>, BrdReplicate<T> > brdSrc(src, brd);
Filter< BorderReader< PtrStep<T>, BrdReplicate<T> > > filteredSrc(brdSrc);
resize<<<grid, block>>>(filteredSrc, fx, fy, dst);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
};
#define OPENCV_GPU_IMPLEMENT_RESIZE_TEX(type) \
texture< type , cudaTextureType2D> tex_resize_ ## type (0, cudaFilterModePoint, cudaAddressModeClamp); \
struct tex_resize_ ## type ## _reader \
{ \
typedef type elem_type; \
typedef int index_type; \
const int xoff; \
const int yoff; \
__host__ tex_resize_ ## type ## _reader(int xoff_, int yoff_) : xoff(xoff_), yoff(yoff_) {} \
__device__ __forceinline__ elem_type operator ()(index_type y, index_type x) const \
{ \
return tex2D(tex_resize_ ## type, x + xoff, y + yoff); \
} \
}; \
template <template <typename> class Filter> struct ResizeDispatcherNonStream<Filter, type > \
{ \
static void call(DevMem2D_< type > src, DevMem2D_< type > srcWhole, int xoff, int yoff, float fx, float fy, DevMem2D_< type > dst) \
{ \
dim3 block(32, 8); \
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y)); \
bindTexture(&tex_resize_ ## type, srcWhole); \
tex_resize_ ## type ## _reader texSrc(xoff, yoff); \
if (srcWhole.cols == src.cols && srcWhole.rows == src.rows) \
{ \
Filter<tex_resize_ ## type ## _reader> filteredSrc(texSrc); \
resize<<<grid, block>>>(filteredSrc, fx, fy, dst); \
} \
else \
{ \
BrdReplicate< type > brd(src.rows, src.cols); \
BorderReader<tex_resize_ ## type ## _reader, BrdReplicate< type > > brdSrc(texSrc, brd); \
Filter< BorderReader<tex_resize_ ## type ## _reader, BrdReplicate< type > > > filteredSrc(brdSrc); \
resize<<<grid, block>>>(filteredSrc, fx, fy, dst); \
} \
cudaSafeCall( cudaGetLastError() ); \
cudaSafeCall( cudaDeviceSynchronize() ); \
} \
};
OPENCV_GPU_IMPLEMENT_RESIZE_TEX(uchar)
OPENCV_GPU_IMPLEMENT_RESIZE_TEX(uchar4)
//OPENCV_GPU_IMPLEMENT_RESIZE_TEX(schar)
//OPENCV_GPU_IMPLEMENT_RESIZE_TEX(char4)
OPENCV_GPU_IMPLEMENT_RESIZE_TEX(ushort)
OPENCV_GPU_IMPLEMENT_RESIZE_TEX(ushort4)
OPENCV_GPU_IMPLEMENT_RESIZE_TEX(short)
OPENCV_GPU_IMPLEMENT_RESIZE_TEX(short4)
//OPENCV_GPU_IMPLEMENT_RESIZE_TEX(int)
//OPENCV_GPU_IMPLEMENT_RESIZE_TEX(int4)
OPENCV_GPU_IMPLEMENT_RESIZE_TEX(float)
OPENCV_GPU_IMPLEMENT_RESIZE_TEX(float4)
#undef OPENCV_GPU_IMPLEMENT_RESIZE_TEX
template <template <typename> class Filter, typename T> struct ResizeDispatcher
{
static void call(DevMem2D_<T> src, DevMem2D_<T> srcWhole, int xoff, int yoff, float fx, float fy, DevMem2D_<T> dst, cudaStream_t stream)
{
if (stream == 0)
ResizeDispatcherNonStream<Filter, T>::call(src, srcWhole, xoff, yoff, fx, fy, dst);
else
ResizeDispatcherStream<Filter, T>::call(src, fx, fy, dst, stream);
}
};
template <typename T> struct ResizeDispatcher<AreaFilter, T>
{
static void call(DevMem2D_<T> src, DevMem2D_<T> srcWhole, int xoff, int yoff, float fx, float fy, DevMem2D_<T> dst, cudaStream_t stream)
{
int iscale_x = round(fx);
int iscale_y = round(fy);
if( std::abs(fx - iscale_x) < FLT_MIN && std::abs(fy - iscale_y) < FLT_MIN)
ResizeDispatcherStream<IntegerAreaFilter, T>::call(src, fx, fy, dst, stream);
else
ResizeDispatcherStream<AreaFilter, T>::call(src, fx, fy, dst, stream);
}
};
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)
{
typedef void (*caller_t)(DevMem2D_<T> src, DevMem2D_<T> srcWhole, int xoff, int yoff, float fx, float fy, DevMem2D_<T> dst, cudaStream_t stream);
static const caller_t callers[4] =
{
ResizeDispatcher<PointFilter, T>::call,
ResizeDispatcher<LinearFilter, T>::call,
ResizeDispatcher<CubicFilter, T>::call,
ResizeDispatcher<AreaFilter, T>::call
};
// chenge to linear if area interpolation upscaling
if (interpolation == 3 && (fx <= 1.f || fy <= 1.f))
interpolation = 1;
callers[interpolation](static_cast< DevMem2D_<T> >(src), static_cast< DevMem2D_<T> >(srcWhole), xoff, yoff, fx, fy,
static_cast< DevMem2D_<T> >(dst), stream);
}
template void resize_gpu<uchar >(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
//template void resize_gpu<uchar2>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
template void resize_gpu<uchar3>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
template void resize_gpu<uchar4>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
//template void resize_gpu<schar>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
//template void resize_gpu<char2>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
//template void resize_gpu<char3>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
//template void resize_gpu<char4>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
template void resize_gpu<ushort >(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
//template void resize_gpu<ushort2>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
template void resize_gpu<ushort3>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
template void resize_gpu<ushort4>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
template void resize_gpu<short >(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
//template void resize_gpu<short2>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
template void resize_gpu<short3>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
template void resize_gpu<short4>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
//template void resize_gpu<int >(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
//template void resize_gpu<int2>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
//template void resize_gpu<int3>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
//template void resize_gpu<int4>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
template void resize_gpu<float >(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
//template void resize_gpu<float2>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
template void resize_gpu<float3>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
template void resize_gpu<float4>(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
template<typename T> struct scan_traits{};
template<> struct scan_traits<uchar>
{
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)
{
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 - y * thred_lines) * blockDim.x;
int x = input_stride + tid;
// store global data in shared memory
sbuf[tid] = src(y, x);
__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];
if (blockIdx.x == 0)
printf("%d~~~~~~~~ start_idx %d, end_idx %d, start %f, end %f\n",
tid, start_idx, end_idx, start, end);
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)
{
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) * blockDim.x;
if (!tid) printf("STRIDE : %d", global_stride);
int y = global_stride + tid;
// store global data in shared memory
sbuf[tid] = src(y, x);
__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];
if (blockIdx.x == 0)
printf("!!!!!!!!%d~~~~~~~~ start_idx %d, end_idx %d, start %f, end %f\n",
tid, start_idx, end_idx, start, end);
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);
const int warps = 4;
const int threads = 32 * warps;
int thred_lines = divUp(src.cols, threads);
int blocks = src.rows * thred_lines;
printf("device code executed for X coordinate with:\nsize %d warps %d, threads %d, thred_lines %d, blocks %d\n",
src.cols, warps, threads, thred_lines, blocks);
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);
thred_lines = divUp(src.rows, threads);
blocks = dst.cols * thred_lines;
printf("device code executed for Y coordinate with:\nsize %d warps %d, threads %d, thred_lines %d, blocks %d\n",
dst.rows, warps, threads, thred_lines, blocks);
resise_scan_fast_y<T, smem_type><<<blocks, threads, warps * 32 * sizeof(smem_type)>>>
(buffer, dst, iscale_x, iscale_y, thred_lines);
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