Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

fixed gpu::filter2D

Browse Source
pull/13383/head
Vladislav Vinogradov 13 years ago
parent fd31234122
commit 82d619a34d
4 changed files with 167 additions and 69 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 7
      modules/gpu/include/opencv2/gpu/gpu.hpp
  2. 103
      modules/gpu/src/cuda/imgproc.cu
  3. 106
      modules/gpu/src/filtering.cpp
  4. 20
      modules/gpu/test/test_filters.cpp

7
modules/gpu/include/opencv2/gpu/gpu.hpp
Unescape View File

@ -284,12 +284,11 @@ CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, co
//! returns 2D filter with the specified kernel
//! supports CV_8UC1 and CV_8UC4 types
CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, const Size& ksize,
Point anchor = Point(-1, -1));
CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
//! returns the non-separable linear filter engine
CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel,
const Point& anchor = Point(-1,-1));
Point anchor = Point(-1,-1), int borderType = BORDER_DEFAULT);
//! returns the primitive row filter with the specified kernel.
//! supports only CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC2, CV_32SC1, CV_32FC1 source type.
@ -367,7 +366,7 @@ CV_EXPORTS void morphologyEx(const GpuMat& src, GpuMat& dst, int op, const Mat&
Point anchor = Point(-1, -1), int iterations = 1, Stream& stream = Stream::Null());
//! applies non-separable 2D linear filter to the image
CV_EXPORTS void filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernel, Point anchor=Point(-1,-1), Stream& stream = Stream::Null());
CV_EXPORTS void filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernel, Point anchor=Point(-1,-1), int borderType = BORDER_DEFAULT, Stream& stream = Stream::Null());
//! applies separable 2D linear filter to the image
CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY,

103
modules/gpu/src/cuda/imgproc.cu
Unescape View File

@ -900,49 +900,100 @@ namespace cv { namespace gpu { namespace device
__constant__ float c_filter2DKernel[FILTER2D_MAX_KERNEL_SIZE * FILTER2D_MAX_KERNEL_SIZE];
texture<float, cudaTextureType2D, cudaReadModeElementType> filter2DTex(0, cudaFilterModePoint, cudaAddressModeClamp);
__global__ void filter2D(int ofsX, int ofsY, PtrStepf dst, const int kWidth, const int kHeight, const int anchorX, const int anchorY, const BrdReflect101<float> brd)
template <class SrcT, typename D>
__global__ void filter2D(const SrcT src, DevMem2D_<D> dst, const int kWidth, const int kHeight, const int anchorX, const int anchorY)
{
typedef typename TypeVec<float, VecTraits<D>::cn>::vec_type sum_t;
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x > brd.last_col || y > brd.last_row)
if (x >= dst.cols || y >= dst.rows)
return;
float res = 0;
sum_t res = VecTraits<sum_t>::all(0);
int kInd = 0;
for (int i = 0; i < kHeight; ++i)
{
for (int j = 0; j < kWidth; ++j)
{
const int srcX = ofsX + brd.idx_col(x - anchorX + j);
const int srcY = ofsY + brd.idx_row(y - anchorY + i);
res += tex2D(filter2DTex, srcX, srcY) * c_filter2DKernel[kInd++];
}
res = res + src(y - anchorY + i, x - anchorX + j) * c_filter2DKernel[kInd++];
}
dst.ptr(y)[x] = res;
}
dst(y, x) = saturate_cast<D>(res);
}
template <typename T, typename D, template <typename> class Brd> struct Filter2DCaller;
#define IMPLEMENT_FILTER2D_TEX_READER(type) \
texture< type , cudaTextureType2D, cudaReadModeElementType> tex_filter2D_ ## type (0, cudaFilterModePoint, cudaAddressModeClamp); \
struct tex_filter2D_ ## type ## _reader \
{ \
typedef type elem_type; \
typedef int index_type; \
const int xoff; \
const int yoff; \
tex_filter2D_ ## 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_filter2D_ ## type , x + xoff, y + yoff); \
} \
}; \
template <typename D, template <typename> class Brd> struct Filter2DCaller< type , D, Brd> \
{ \
static void call(const DevMem2D_< type > srcWhole, int xoff, int yoff, DevMem2D_<D> dst, \
int kWidth, int kHeight, int anchorX, int anchorY, const float* borderValue, cudaStream_t stream) \
{ \
typedef typename TypeVec<float, VecTraits< type >::cn>::vec_type work_type; \
dim3 block(16, 16); \
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y)); \
bindTexture(&tex_filter2D_ ## type , srcWhole); \
tex_filter2D_ ## type ##_reader texSrc(xoff, yoff); \
Brd<work_type> brd(dst.rows, dst.cols, VecTraits<work_type>::make(borderValue)); \
BorderReader< tex_filter2D_ ## type ##_reader, Brd<work_type> > brdSrc(texSrc, brd); \
filter2D<<<grid, block, 0, stream>>>(brdSrc, dst, kWidth, kHeight, anchorX, anchorY); \
cudaSafeCall( cudaGetLastError() ); \
if (stream == 0) \
cudaSafeCall( cudaDeviceSynchronize() ); \
} \
};
IMPLEMENT_FILTER2D_TEX_READER(uchar);
IMPLEMENT_FILTER2D_TEX_READER(uchar4);
IMPLEMENT_FILTER2D_TEX_READER(ushort);
IMPLEMENT_FILTER2D_TEX_READER(ushort4);
IMPLEMENT_FILTER2D_TEX_READER(float);
IMPLEMENT_FILTER2D_TEX_READER(float4);
#undef IMPLEMENT_FILTER2D_TEX_READER
void filter2D_gpu(DevMem2Df src, int ofsX, int ofsY, DevMem2Df dst, int kWidth, int kHeight, int anchorX, int anchorY, float* kernel, cudaStream_t stream)
template <typename T, typename D>
void filter2D_gpu(DevMem2Db srcWhole, int ofsX, int ofsY, DevMem2Db dst,
int kWidth, int kHeight, int anchorX, int anchorY, const float* kernel,
int borderMode, const float* borderValue, cudaStream_t stream)
{
cudaSafeCall(cudaMemcpyToSymbol(c_filter2DKernel, kernel, kWidth * kHeight * sizeof(float), 0, cudaMemcpyDeviceToDevice) );
const dim3 block(16, 16);
const dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y));
bindTexture(&filter2DTex, src);
BrdReflect101<float> brd(dst.rows, dst.cols);
typedef void (*func_t)(const DevMem2D_<T> srcWhole, int xoff, int yoff, DevMem2D_<D> dst, int kWidth, int kHeight, int anchorX, int anchorY, const float* borderValue, cudaStream_t stream);
static const func_t funcs[] =
{
Filter2DCaller<T, D, BrdReflect101>::call,
Filter2DCaller<T, D, BrdReplicate>::call,
Filter2DCaller<T, D, BrdConstant>::call,
Filter2DCaller<T, D, BrdReflect>::call,
Filter2DCaller<T, D, BrdWrap>::call
};
filter2D<<<grid, block, 0, stream>>>(ofsX, ofsY, dst, kWidth, kHeight, anchorX, anchorY, brd);
cudaSafeCall(cudaGetLastError());
cudaSafeCall(cudaMemcpyToSymbol(c_filter2DKernel, kernel, kWidth * kHeight * sizeof(float), 0, cudaMemcpyDeviceToDevice) );
if (stream == 0)
cudaSafeCall(cudaDeviceSynchronize());
funcs[borderMode](static_cast< DevMem2D_<T> >(srcWhole), ofsX, ofsY, static_cast< DevMem2D_<D> >(dst), kWidth, kHeight, anchorX, anchorY, borderValue, stream);
}
template void filter2D_gpu<uchar, uchar>(DevMem2Db srcWhole, int ofsX, int ofsY, DevMem2Db dst, int kWidth, int kHeight, int anchorX, int anchorY, const float* kernel, int borderMode, const float* borderValue, cudaStream_t stream);
template void filter2D_gpu<uchar4, uchar4>(DevMem2Db srcWhole, int ofsX, int ofsY, DevMem2Db dst, int kWidth, int kHeight, int anchorX, int anchorY, const float* kernel, int borderMode, const float* borderValue, cudaStream_t stream);
template void filter2D_gpu<ushort, ushort>(DevMem2Db srcWhole, int ofsX, int ofsY, DevMem2Db dst, int kWidth, int kHeight, int anchorX, int anchorY, const float* kernel, int borderMode, const float* borderValue, cudaStream_t stream);
template void filter2D_gpu<ushort4, ushort4>(DevMem2Db srcWhole, int ofsX, int ofsY, DevMem2Db dst, int kWidth, int kHeight, int anchorX, int anchorY, const float* kernel, int borderMode, const float* borderValue, cudaStream_t stream);
template void filter2D_gpu<float, float>(DevMem2Db srcWhole, int ofsX, int ofsY, DevMem2Db dst, int kWidth, int kHeight, int anchorX, int anchorY, const float* kernel, int borderMode, const float* borderValue, cudaStream_t stream);
template void filter2D_gpu<float4, float4>(DevMem2Db srcWhole, int ofsX, int ofsY, DevMem2Db dst, int kWidth, int kHeight, int anchorX, int anchorY, const float* kernel, int borderMode, const float* borderValue, cudaStream_t stream);
} // namespace imgproc
}}} // namespace cv { namespace gpu { namespace device {

106
modules/gpu/src/filtering.cpp
Unescape View File

@ -58,8 +58,8 @@ Ptr<FilterEngine_GPU> cv::gpu::createBoxFilter_GPU(int, int, const Size&, const
Ptr<BaseFilter_GPU> cv::gpu::getMorphologyFilter_GPU(int, int, const Mat&, const Size&, Point) { throw_nogpu(); return Ptr<BaseFilter_GPU>(0); }
Ptr<FilterEngine_GPU> cv::gpu::createMorphologyFilter_GPU(int, int, const Mat&, const Point&, int) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<FilterEngine_GPU> cv::gpu::createMorphologyFilter_GPU(int, int, const Mat&, GpuMat&, const Point&, int) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<BaseFilter_GPU> cv::gpu::getLinearFilter_GPU(int, int, const Mat&, const Size&, Point) { throw_nogpu(); return Ptr<BaseFilter_GPU>(0); }
Ptr<FilterEngine_GPU> cv::gpu::createLinearFilter_GPU(int, int, const Mat&, const Point&) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<BaseFilter_GPU> cv::gpu::getLinearFilter_GPU(int, int, const Mat&, Point, int) { throw_nogpu(); return Ptr<BaseFilter_GPU>(0); }
Ptr<FilterEngine_GPU> cv::gpu::createLinearFilter_GPU(int, int, const Mat&, Point, int) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
Ptr<BaseRowFilter_GPU> cv::gpu::getLinearRowFilter_GPU(int, int, const Mat&, int, int) { throw_nogpu(); return Ptr<BaseRowFilter_GPU>(0); }
Ptr<BaseColumnFilter_GPU> cv::gpu::getLinearColumnFilter_GPU(int, int, const Mat&, int, int) { throw_nogpu(); return Ptr<BaseColumnFilter_GPU>(0); }
Ptr<FilterEngine_GPU> cv::gpu::createSeparableLinearFilter_GPU(int, int, const Mat&, const Mat&, const Point&, int, int) { throw_nogpu(); return Ptr<FilterEngine_GPU>(0); }
@ -78,7 +78,7 @@ void cv::gpu::dilate(const GpuMat&, GpuMat&, const Mat&, Point, int) { throw_nog
void cv::gpu::dilate(const GpuMat&, GpuMat&, const Mat&, GpuMat&, Point, int, Stream&) { throw_nogpu(); }
void cv::gpu::morphologyEx(const GpuMat&, GpuMat&, int, const Mat&, Point, int) { throw_nogpu(); }
void cv::gpu::morphologyEx(const GpuMat&, GpuMat&, int, const Mat&, GpuMat&, GpuMat&, Point, int, Stream&) { throw_nogpu(); }
void cv::gpu::filter2D(const GpuMat&, GpuMat&, int, const Mat&, Point, Stream&) { throw_nogpu(); }
void cv::gpu::filter2D(const GpuMat&, GpuMat&, int, const Mat&, Point, int, Stream&) { throw_nogpu(); }
void cv::gpu::sepFilter2D(const GpuMat&, GpuMat&, int, const Mat&, const Mat&, Point, int, int) { throw_nogpu(); }
void cv::gpu::sepFilter2D(const GpuMat&, GpuMat&, int, const Mat&, const Mat&, GpuMat&, Point, int, int, Stream&) { throw_nogpu(); }
void cv::gpu::Sobel(const GpuMat&, GpuMat&, int, int, int, int, double, int, int) { throw_nogpu(); }
@ -663,7 +663,10 @@ namespace cv { namespace gpu { namespace device
{
namespace imgproc
{
void filter2D_gpu(DevMem2Df src, int ofsX, int ofsY, DevMem2Df dst, int kWidth, int kHeight, int anchorX, int anchorY, float* kernel, cudaStream_t stream);
template <typename T, typename D>
void filter2D_gpu(DevMem2Db srcWhole, int ofsX, int ofsY, DevMem2Db dst,
int kWidth, int kHeight, int anchorX, int anchorY, const float* kernel,
int borderMode, const float* borderValue, cudaStream_t stream);
}
}}}
@ -705,10 +708,16 @@ namespace
nppFilter2D_t func;
};
struct GpuLinearFilter : public BaseFilter_GPU
typedef void (*gpuFilter2D_t)(DevMem2Db srcWhole, int ofsX, int ofsY, DevMem2Db dst,
int kWidth, int kHeight, int anchorX, int anchorY, const float* kernel,
int borderMode, const float* borderValue, cudaStream_t stream);
struct GpuFilter2D : public BaseFilter_GPU
{
GpuLinearFilter(Size ksize_, Point anchor_, const GpuMat& kernel_) :
BaseFilter_GPU(ksize_, anchor_), kernel(kernel_) {}
GpuFilter2D(Size ksize_, Point anchor_, gpuFilter2D_t func_, const GpuMat& kernel_, int brd_type_) :
BaseFilter_GPU(ksize_, anchor_), func(func_), kernel(kernel_), brd_type(brd_type_)
{
}
virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null())
{
@ -719,30 +728,32 @@ namespace
src.locateROI(wholeSize, ofs);
GpuMat srcWhole(wholeSize, src.type(), src.datastart);
filter2D_gpu(srcWhole, ofs.x, ofs.y, dst, ksize.width, ksize.height, anchor.x, anchor.y, kernel.ptr<float>(), StreamAccessor::getStream(stream));
static const Scalar_<float> zero = Scalar_<float>::all(0.0f);
func(srcWhole, ofs.x, ofs.y, dst, ksize.width, ksize.height, anchor.x, anchor.y, kernel.ptr<float>(), brd_type, zero.val, StreamAccessor::getStream(stream));
}
gpuFilter2D_t func;
GpuMat kernel;
int brd_type;
};
}
Ptr<BaseFilter_GPU> cv::gpu::getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, const Size& ksize, Point anchor)
Ptr<BaseFilter_GPU> cv::gpu::getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, Point anchor, int brd_type)
{
CV_Assert(srcType == CV_8UC1 || srcType == CV_8UC4 || srcType == CV_32FC1);
CV_Assert(dstType == srcType);
using namespace cv::gpu::device::imgproc;
if (srcType == CV_32FC1)
{
CV_Assert(ksize.width * ksize.height <= 16 * 16);
int sdepth = CV_MAT_DEPTH(srcType);
int scn = CV_MAT_CN(srcType);
GpuMat gpu_krnl;
normalizeKernel(kernel, gpu_krnl, CV_32F);
CV_Assert(sdepth == CV_8U || sdepth == CV_16U || sdepth == CV_32F);
CV_Assert(scn == 1 || scn == 4);
CV_Assert(dstType == srcType);
CV_Assert(brd_type == BORDER_REFLECT101 || brd_type == BORDER_REPLICATE || brd_type == BORDER_CONSTANT || brd_type == BORDER_REFLECT || brd_type == BORDER_WRAP);
normalizeAnchor(anchor, ksize);
Size ksize = kernel.size();
return Ptr<BaseFilter_GPU>(new GpuLinearFilter(ksize, anchor, gpu_krnl));
}
else
#if 0
if ((srcType == CV_8UC1 || srcType == CV_8UC4) && brd_type == BORDER_CONSTANT)
{
static const nppFilter2D_t cppFilter2D_callers[] = {0, nppiFilter_8u_C1R, 0, 0, nppiFilter_8u_C4R};
@ -754,27 +765,64 @@ Ptr<BaseFilter_GPU> cv::gpu::getLinearFilter_GPU(int srcType, int dstType, const
return Ptr<BaseFilter_GPU>(new NPPLinearFilter(ksize, anchor, gpu_krnl, nDivisor, cppFilter2D_callers[CV_MAT_CN(srcType)]));
}
#endif
CV_Assert(ksize.width * ksize.height <= 16 * 16);
int gpuBorderType;
CV_Assert( tryConvertToGpuBorderType(brd_type, gpuBorderType) );
GpuMat gpu_krnl;
normalizeKernel(kernel, gpu_krnl, CV_32F);
normalizeAnchor(anchor, ksize);
gpuFilter2D_t func = 0;
switch (srcType)
{
case CV_8UC1:
func = filter2D_gpu<uchar, uchar>;
break;
case CV_8UC4:
func = filter2D_gpu<uchar4, uchar4>;
break;
case CV_16UC1:
func = filter2D_gpu<ushort, ushort>;
break;
case CV_16UC4:
func = filter2D_gpu<ushort4, ushort4>;
break;
case CV_32FC1:
func = filter2D_gpu<float, float>;
break;
case CV_32FC4:
func = filter2D_gpu<float4, float4>;
break;
}
return Ptr<BaseFilter_GPU>(new GpuFilter2D(ksize, anchor, func, gpu_krnl, gpuBorderType));
}
Ptr<FilterEngine_GPU> cv::gpu::createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, const Point& anchor)
Ptr<FilterEngine_GPU> cv::gpu::createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, Point anchor, int borderType)
{
Size ksize = kernel.size();
Ptr<BaseFilter_GPU> linearFilter = getLinearFilter_GPU(srcType, dstType, kernel, ksize, anchor);
Ptr<BaseFilter_GPU> linearFilter = getLinearFilter_GPU(srcType, dstType, kernel, anchor, borderType);
return createFilter2D_GPU(linearFilter, srcType, dstType);
}
void cv::gpu::filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernel, Point anchor, Stream& stream)
void cv::gpu::filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernel, Point anchor, int borderType, Stream& stream)
{
if( ddepth < 0 )
if (ddepth < 0)
ddepth = src.depth();
dst.create(src.size(), CV_MAKETYPE(ddepth, src.channels()));
int dst_type = CV_MAKE_TYPE(ddepth, src.channels());
Ptr<FilterEngine_GPU> f = createLinearFilter_GPU(src.type(), dst.type(), kernel, anchor);
Ptr<FilterEngine_GPU> f = createLinearFilter_GPU(src.type(), dst_type, kernel, anchor, borderType);
f->apply(src, dst, src.type() == CV_32FC1 ? Rect(0, 0, src.cols, src.rows) : Rect(0, 0, -1, -1), stream);
dst.create(src.size(), dst_type);
f->apply(src, dst, Rect(0, 0, src.cols, src.rows), stream);
}
////////////////////////////////////////////////////////////////////////////////////////////////////

20
modules/gpu/test/test_filters.cpp
Unescape View File

@ -505,13 +505,14 @@ INSTANTIATE_TEST_CASE_P(GPU_Filter, MorphEx, testing::Combine(
/////////////////////////////////////////////////////////////////////////////////////////////////
// Filter2D
PARAM_TEST_CASE(Filter2D, cv::gpu::DeviceInfo, cv::Size, MatType, KSize, Anchor, UseRoi)
PARAM_TEST_CASE(Filter2D, cv::gpu::DeviceInfo, cv::Size, MatType, KSize, Anchor, BorderType, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
cv::Size ksize;
cv::Point anchor;
int borderType;
bool useRoi;
cv::Mat img;
@ -524,7 +525,8 @@ PARAM_TEST_CASE(Filter2D, cv::gpu::DeviceInfo, cv::Size, MatType, KSize, Anchor,
type = GET_PARAM(2);
ksize = GET_PARAM(3);
anchor = GET_PARAM(4);
useRoi = GET_PARAM(5);
borderType = GET_PARAM(5);
useRoi = GET_PARAM(6);
cv::gpu::setDevice(devInfo.deviceID());
}
@ -533,26 +535,24 @@ PARAM_TEST_CASE(Filter2D, cv::gpu::DeviceInfo, cv::Size, MatType, KSize, Anchor,
TEST_P(Filter2D, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::Mat kernel = cv::Mat::ones(ksize.height, ksize.width, CV_32FC1);
cv::Mat kernel = randomMat(cv::Size(ksize.width, ksize.height), CV_32FC1, 0.0, 1.0);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
cv::gpu::filter2D(loadMat(src, useRoi), dst, -1, kernel, anchor);
cv::gpu::filter2D(loadMat(src, useRoi), dst, -1, kernel, anchor, borderType);
cv::Mat dst_gold;
cv::filter2D(src, dst_gold, -1, kernel, anchor, 0, type == CV_32FC1 ? cv::BORDER_DEFAULT : cv::BORDER_CONSTANT);
cv::filter2D(src, dst_gold, -1, kernel, anchor, 0, borderType);
if (type == CV_32FC1)
EXPECT_MAT_NEAR(dst_gold, dst, 1e-1);
else
EXPECT_MAT_NEAR(getInnerROI(dst_gold, ksize), getInnerROI(dst, ksize), 0.0);
EXPECT_MAT_NEAR(dst_gold, dst, CV_MAT_DEPTH(type) == CV_32F ? 1e-1 : 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_Filter, Filter2D, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4), MatType(CV_32FC1)),
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4), MatType(CV_16UC1), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC4)),
testing::Values(KSize(cv::Size(3, 3)), KSize(cv::Size(5, 5)), KSize(cv::Size(7, 7)), KSize(cv::Size(11, 11)), KSize(cv::Size(13, 13)), KSize(cv::Size(15, 15))),
testing::Values(Anchor(cv::Point(-1, -1)), Anchor(cv::Point(0, 0)), Anchor(cv::Point(2, 2))),
testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_CONSTANT), BorderType(cv::BORDER_REFLECT)),
WHOLE_SUBMAT));
} // namespace

Write Preview
Loading…
Cancel
Save