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scan based area interpolation for naive cases

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pull/2/head
Marina Kolpakova 13 years ago
parent 7cccc93bdf
commit 8c6dc17a9f
3 changed files with 243 additions and 19 deletions
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  1. 229
      modules/gpu/src/cuda/resize.cu
  2. 6
      modules/gpu/src/resize.cpp
  3. 27
      modules/gpu/test/test_resize.cpp

229
modules/gpu/src/cuda/resize.cu
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@ -282,27 +282,232 @@ namespace cv { namespace gpu { namespace device
template<> struct scan_traits<uchar>
{
typedef int scan_line_type;
typedef float scan_line_type;
};
template <typename Ptr2D, typename T>
__global__ void resize_area_scan(const Ptr2D src, int fx, int fy, DevMem2D_<T> dst, DevMem2D_<T> buffer)
// 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);
}
}
template <typename T>
__global__ void resize_area_scan_x(const DevMem2D_<T> src, DevMem2D_<T> dst, int fx, int fy, DevMem2D_<typename scan_traits<T>::scan_line_type> buffer)
{
typedef typename scan_traits<T>::scan_line_type W;
extern __shared__ W line[];
scan_x(src,fx,fy, buffer,line, 0);
}
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
template <typename T>
__global__ void resize_area_scan_y(const DevMem2D_<T> src, DevMem2D_<T> dst, int fx, int fy, DevMem2D_<typename scan_traits<T>::scan_line_type> buffer)
{
typedef typename scan_traits<T>::scan_line_type W;
extern __shared__ W line[];
scan_y(buffer,fx, fy, dst, line, 0);
}
template <typename T> struct InterAreaDispatcherStream
{
static void call(DevMem2D_<T> src, int fx, int fy, DevMem2D_<T> dst, DevMem2D_<T> buffer, cudaStream_t stream)
static void call(const DevMem2D_<T> src, int fx, int fy, DevMem2D_<T> dst, DevMem2D_<typename scan_traits<T>::scan_line_type> buffer, cudaStream_t stream)
{
dim3 block(256, 1);
dim3 grid(divUp(dst.cols, block.x), 1);
resize_area_scan_x<T><<<src.rows, (src.cols >> 1), src.cols * sizeof(typename scan_traits<T>::scan_line_type) >>>(src, dst, fx, fy, buffer);
resize_area_scan<<<grid, block, 256 * 2 * sizeof(typename scan_traits<T>::scan_line_type) >>>(src, fx, fy, dst, buffer);
resize_area_scan_y<T><<<dst.cols, (src.rows >> 1), src.rows * sizeof(typename scan_traits<T>::scan_line_type) >>>(src, dst, fx, fy, buffer);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
@ -311,8 +516,8 @@ namespace cv { namespace gpu { namespace device
};
template <typename T>
void resize_area_gpu(DevMem2Db src, DevMem2Db dst,float fx, float fy,
int interpolation, DevMem2Db buffer, cudaStream_t stream)
void resize_area_gpu(const DevMem2Db src, DevMem2Db dst,float fx, float fy,
int interpolation, DevMem2Df buffer, cudaStream_t stream)
{
(void)interpolation;
@ -322,7 +527,7 @@ namespace cv { namespace gpu { namespace device
InterAreaDispatcherStream<T>::call(src, iscale_x, iscale_y, dst, buffer, stream);
}
template void resize_area_gpu<uchar>(DevMem2Db src, DevMem2Db dst, float fx, float fy, int interpolation, DevMem2Db buffer, cudaStream_t stream);
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

6
modules/gpu/src/resize.cpp
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@ -82,8 +82,8 @@ namespace cv { namespace gpu { namespace device
DevMem2Db dst, int interpolation, cudaStream_t stream);
template <typename T>
void resize_area_gpu(DevMem2Db src, DevMem2Db dst,float fx, float fy,
int interpolation, DevMem2Db buffer, cudaStream_t stream);
void resize_area_gpu(const DevMem2Db src, DevMem2Db dst,float fx, float fy,
int interpolation, DevMem2Df buffer, cudaStream_t stream);
}
}}}
@ -107,7 +107,7 @@ void cv::gpu::resize(const GpuMat& src, GpuMat& dst, Size dsize, GpuMat& buffer,
fy = static_cast<float>(1.0 / fy);
dst.create(dsize, src.type());
buffer.create(cv::Size(dsize.width, src.rows), src.type());
buffer.create(cv::Size(dsize.width, src.rows), CV_32FC1);
if (dsize == src.size())
{

27
modules/gpu/test/test_resize.cpp
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@ -40,6 +40,7 @@
//M*/
#include "precomp.hpp"
#include <iostream>
#ifdef HAVE_CUDA
@ -186,19 +187,37 @@ 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::resize(loadMat(src, useRoi), dst, cv::Size(), coeff, coeff, interpolation);
cv::gpu::GpuMat buffer = createMat(cv::Size(dst.cols, src.rows), CV_32FC1);
cv::gpu::resize(loadMat(src, useRoi), dst, cv::Size(), buffer, 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,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC3), MatType(CV_16UC1), MatType(CV_16UC3), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC3), MatType(CV_32FC4)),
testing::Values(0.3, 0.5),
testing::Values(cv::Size(512, 256)),//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(0.5),
testing::Values(Interpolation(cv::INTER_AREA)),
WHOLE_SUBMAT));

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