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

Merged the trunk r8575:8583 (INTER_AREA interpolation for GPU resize)

Browse Source
pull/13383/head
Andrey Kamaev 13 years ago
parent ab20da0f53
commit 73c152abc4
10 changed files with 1019 additions and 481 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. 1141
      modules/gpu/perf/perf_imgproc.cpp
  2. 27
      modules/gpu/perf/perf_utility.hpp
  3. 72
      modules/gpu/src/cuda/resize.cu
  4. 111
      modules/gpu/src/opencv2/gpu/device/filters.hpp
  5. 4
      modules/gpu/src/opencv2/gpu/device/vec_traits.hpp
  6. 5
      modules/gpu/src/resize.cpp
  7. 48
      modules/gpu/test/test_resize.cpp
  8. 2
      modules/gpu/test/utility.hpp
  9. 47
      modules/imgproc/src/imgwarp.cpp
  10. 43
      modules/imgproc/test/test_imgwarp.cpp

1141
modules/gpu/perf/perf_imgproc.cpp
View File

File diff suppressed because it is too large Load Diff

27
modules/gpu/perf/perf_utility.hpp
Unescape View File

@ -3,15 +3,16 @@
void fill(cv::Mat& m, double a, double b);
using perf::MatType;
using perf::MatDepth;
enum {HORIZONTAL_AXIS = 0, VERTICAL_AXIS = 1, BOTH_AXIS = -1};
CV_ENUM(MorphOp, cv::MORPH_ERODE, cv::MORPH_DILATE)
CV_ENUM(BorderMode, cv::BORDER_REFLECT101, cv::BORDER_REPLICATE, cv::BORDER_CONSTANT, cv::BORDER_REFLECT, cv::BORDER_WRAP)
CV_ENUM(FlipCode, HORIZONTAL_AXIS, VERTICAL_AXIS, BOTH_AXIS)
CV_ENUM(Interpolation, cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_CUBIC)
CV_ENUM(MatchMethod, cv::TM_SQDIFF, cv::TM_SQDIFF_NORMED, cv::TM_CCORR, cv::TM_CCORR_NORMED, cv::TM_CCOEFF, cv::TM_CCOEFF_NORMED)
CV_ENUM(NormType, cv::NORM_INF, cv::NORM_L1, cv::NORM_L2)
CV_ENUM(AlphaOp, cv::gpu::ALPHA_OVER, cv::gpu::ALPHA_IN, cv::gpu::ALPHA_OUT, cv::gpu::ALPHA_ATOP, cv::gpu::ALPHA_XOR, cv::gpu::ALPHA_PLUS, cv::gpu::ALPHA_OVER_PREMUL, cv::gpu::ALPHA_IN_PREMUL, cv::gpu::ALPHA_OUT_PREMUL, cv::gpu::ALPHA_ATOP_PREMUL, cv::gpu::ALPHA_XOR_PREMUL, cv::gpu::ALPHA_PLUS_PREMUL, cv::gpu::ALPHA_PREMUL)
CV_ENUM(Interpolation, cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_CUBIC, cv::INTER_AREA)
CV_ENUM(NormType, cv::NORM_INF, cv::NORM_L1, cv::NORM_L2, cv::NORM_HAMMING)
struct CvtColorInfo
{
@ -24,6 +25,22 @@ struct CvtColorInfo
void PrintTo(const CvtColorInfo& info, std::ostream* os);
#define IMPLEMENT_PARAM_CLASS(name, type) \
class name \
{ \
public: \
name ( type arg = type ()) : val_(arg) {} \
operator type () const {return val_;} \
private: \
type val_; \
}; \
inline void PrintTo( name param, std::ostream* os) \
{ \
*os << #name << " = " << testing::PrintToString(static_cast< type >(param)); \
}
IMPLEMENT_PARAM_CLASS(Channels, int)
namespace cv { namespace gpu
{
void PrintTo(const cv::gpu::DeviceInfo& info, std::ostream* os);
@ -55,8 +72,6 @@ namespace cv { namespace gpu
cv::Mat readImage(const std::string& fileName, int flags = cv::IMREAD_COLOR);
bool supportFeature(const cv::gpu::DeviceInfo& info, cv::gpu::FeatureSet feature);
const std::vector<cv::gpu::DeviceInfo>& devices();
std::vector<cv::gpu::DeviceInfo> devices(cv::gpu::FeatureSet feature);

72
modules/gpu/src/cuda/resize.cu
Unescape View File

@ -46,6 +46,7 @@
#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
{
@ -65,6 +66,17 @@ namespace cv { namespace gpu { namespace device
}
}
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)
@ -74,13 +86,47 @@ namespace cv { namespace gpu { namespace device
BrdReplicate<T> brd(src.rows, src.cols);
BorderReader< PtrStep<T>, BrdReplicate<T> > brdSrc(src, brd);
Filter< BorderReader< PtrStep<T>, BrdReplicate<T> > > filteredSrc(brdSrc);
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)
@ -169,15 +215,35 @@ namespace cv { namespace gpu { namespace device
}
};
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[3] =
static const caller_t callers[4] =
{
ResizeDispatcher<PointFilter, T>::call, ResizeDispatcher<LinearFilter, T>::call, ResizeDispatcher<CubicFilter, T>::call
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);

111
modules/gpu/src/opencv2/gpu/device/filters.hpp
Unescape View File

@ -55,7 +55,7 @@ namespace cv { namespace gpu { namespace device
typedef typename Ptr2D::elem_type elem_type;
typedef float index_type;
explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_) : src(src_) {}
explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) : src(src_) {}
__device__ __forceinline__ elem_type operator ()(float y, float x) const
{
@ -70,7 +70,7 @@ namespace cv { namespace gpu { namespace device
typedef typename Ptr2D::elem_type elem_type;
typedef float index_type;
explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_) : src(src_) {}
explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) : src(src_) {}
__device__ __forceinline__ elem_type operator ()(float y, float x) const
{
@ -107,7 +107,7 @@ namespace cv { namespace gpu { namespace device
typedef float index_type;
typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
explicit __host__ __device__ __forceinline__ CubicFilter(const Ptr2D& src_) : src(src_) {}
explicit __host__ __device__ __forceinline__ CubicFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f) : src(src_) {}
static __device__ __forceinline__ float bicubicCoeff(float x_)
{
@ -154,6 +154,111 @@ namespace cv { namespace gpu { namespace device
const Ptr2D src;
};
// for integer scaling
template <typename Ptr2D> struct IntegerAreaFilter
{
typedef typename Ptr2D::elem_type elem_type;
typedef float index_type;
explicit __host__ __device__ __forceinline__ IntegerAreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_)
: src(src_), scale_x(scale_x_), scale_y(scale_y_), scale(1.f / (scale_x * scale_y)) {}
__device__ __forceinline__ elem_type operator ()(float y, float x) const
{
float fsx1 = x * scale_x;
float fsx2 = fsx1 + scale_x;
int sx1 = __float2int_ru(fsx1);
int sx2 = __float2int_rd(fsx2);
float fsy1 = y * scale_y;
float fsy2 = fsy1 + scale_y;
int sy1 = __float2int_ru(fsy1);
int sy2 = __float2int_rd(fsy2);
typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
work_type out = VecTraits<work_type>::all(0.f);
for(int dy = sy1; dy < sy2; ++dy)
for(int dx = sx1; dx < sx2; ++dx)
{
out = out + src(dy, dx) * scale;
}
return saturate_cast<elem_type>(out);
}
const Ptr2D src;
float scale_x, scale_y ,scale;
};
template <typename Ptr2D> struct AreaFilter
{
typedef typename Ptr2D::elem_type elem_type;
typedef float index_type;
explicit __host__ __device__ __forceinline__ AreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_)
: src(src_), scale_x(scale_x_), scale_y(scale_y_){}
__device__ __forceinline__ elem_type operator ()(float y, float x) const
{
float fsx1 = x * scale_x;
float fsx2 = fsx1 + scale_x;
int sx1 = __float2int_ru(fsx1);
int sx2 = __float2int_rd(fsx2);
float fsy1 = y * scale_y;
float fsy2 = fsy1 + scale_y;
int sy1 = __float2int_ru(fsy1);
int sy2 = __float2int_rd(fsy2);
float scale = 1.f / (fminf(scale_x, src.width - fsx1) * fminf(scale_y, src.height - fsy1));
typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
work_type out = VecTraits<work_type>::all(0.f);
for (int dy = sy1; dy < sy2; ++dy)
{
for (int dx = sx1; dx < sx2; ++dx)
out = out + src(dy, dx) * scale;
if (sx1 > fsx1)
out = out + src(dy, (sx1 -1) ) * ((sx1 - fsx1) * scale);
if (sx2 < fsx2)
out = out + src(dy, sx2) * ((fsx2 -sx2) * scale);
}
if (sy1 > fsy1)
for (int dx = sx1; dx < sx2; ++dx)
out = out + src( (sy1 - 1) , dx) * ((sy1 -fsy1) * scale);
if (sy2 < fsy2)
for (int dx = sx1; dx < sx2; ++dx)
out = out + src(sy2, dx) * ((fsy2 -sy2) * scale);
if ((sy1 > fsy1) && (sx1 > fsx1))
out = out + src( (sy1 - 1) , (sx1 - 1)) * ((sy1 -fsy1) * (sx1 -fsx1) * scale);
if ((sy1 > fsy1) && (sx2 < fsx2))
out = out + src( (sy1 - 1) , sx2) * ((sy1 -fsy1) * (fsx2 -sx2) * scale);
if ((sy2 < fsy2) && (sx2 < fsx2))
out = out + src(sy2, sx2) * ((fsy2 -sy2) * (fsx2 -sx2) * scale);
if ((sy2 < fsy2) && (sx1 > fsx1))
out = out + src(sy2, (sx1 - 1)) * ((fsy2 -sy2) * (sx1 -fsx1) * scale);
return saturate_cast<elem_type>(out);
}
const Ptr2D src;
float scale_x, scale_y;
int width, haight;
};
}}} // namespace cv { namespace gpu { namespace device
#endif // __OPENCV_GPU_FILTERS_HPP__

4
modules/gpu/src/opencv2/gpu/device/vec_traits.hpp
Unescape View File

@ -221,7 +221,7 @@ namespace cv { namespace gpu { namespace device
template<> struct VecTraits<char>
{
typedef char elem_type;
typedef char elem_type;
enum {cn=1};
static __device__ __host__ __forceinline__ char all(char v) {return v;}
static __device__ __host__ __forceinline__ char make(char x) {return x;}
@ -229,7 +229,7 @@ namespace cv { namespace gpu { namespace device
};
template<> struct VecTraits<schar>
{
typedef schar elem_type;
typedef schar elem_type;
enum {cn=1};
static __device__ __host__ __forceinline__ schar all(schar v) {return v;}
static __device__ __host__ __forceinline__ schar make(schar x) {return x;}

5
modules/gpu/src/resize.cpp
Unescape View File

@ -61,7 +61,8 @@ namespace cv { namespace gpu { namespace device
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);
CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC);
CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR
|| interpolation == INTER_CUBIC || interpolation == INTER_AREA);
CV_Assert(!(dsize == Size()) || (fx > 0 && fy > 0));
if (dsize == Size())
@ -90,7 +91,7 @@ void cv::gpu::resize(const GpuMat& src, GpuMat& dst, Size dsize, double fx, doub
src.locateROI(wholeSize, ofs);
bool useNpp = (src.type() == CV_8UC1 || src.type() == CV_8UC4);
useNpp = useNpp && (interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || src.type() == CV_8UC4);
useNpp = useNpp && (interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || (src.type() == CV_8UC4 && interpolation != INTER_AREA));
if (useNpp)
{

48
modules/gpu/test/test_resize.cpp
Unescape View File

@ -48,7 +48,8 @@
namespace
{
template <typename T, template <typename> class Interpolator> void resizeImpl(const cv::Mat& src, cv::Mat& dst, double fx, double fy)
template <typename T, template <typename> class Interpolator>
void resizeImpl(const cv::Mat& src, cv::Mat& dst, double fx, double fy)
{
const int cn = src.channels();
@ -156,6 +157,51 @@ INSTANTIATE_TEST_CASE_P(GPU_ImgProc, Resize, testing::Combine(
testing::Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)),
WHOLE_SUBMAT));
/////////////////
PARAM_TEST_CASE(ResizeArea, cv::gpu::DeviceInfo, cv::Size, MatType, double, Interpolation, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
double coeff;
int interpolation;
int type;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
coeff = GET_PARAM(3);
interpolation = GET_PARAM(4);
useRoi = GET_PARAM(5);
cv::gpu::setDevice(devInfo.deviceID());
}
};
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::Mat dst_cpu;
cv::resize(src, dst_cpu, cv::Size(), coeff, coeff, interpolation);
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(Interpolation(cv::INTER_AREA)),
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////
// Test NPP

2
modules/gpu/test/utility.hpp
Unescape View File

@ -277,7 +277,7 @@ IMPLEMENT_PARAM_CLASS(Channels, int)
CV_ENUM(NormCode, cv::NORM_INF, cv::NORM_L1, cv::NORM_L2, cv::NORM_TYPE_MASK, cv::NORM_RELATIVE, cv::NORM_MINMAX)
CV_ENUM(Interpolation, cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_CUBIC)
CV_ENUM(Interpolation, cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_CUBIC, cv::INTER_AREA)
CV_ENUM(BorderType, cv::BORDER_REFLECT101, cv::BORDER_REPLICATE, cv::BORDER_CONSTANT, cv::BORDER_REFLECT, cv::BORDER_WRAP)
#define ALL_BORDER_TYPES testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_CONSTANT), BorderType(cv::BORDER_REFLECT), BorderType(cv::BORDER_WRAP))

47
modules/imgproc/src/imgwarp.cpp
Unescape View File

@ -878,8 +878,8 @@ struct VResizeLinear
VecOp vecOp;
int x = vecOp((const uchar**)src, (uchar*)dst, (const uchar*)beta, width);
#if CV_ENABLE_UNROLLED
for( ; x <= width - 4; x += 4 )
#if CV_ENABLE_UNROLLED
for( ; x <= width - 4; x += 4 )
{
WT t0, t1;
t0 = S0[x]*b0 + S1[x]*b1;
@ -1035,7 +1035,7 @@ struct VResizeLanczos4
CastOp castOp;
VecOp vecOp;
int k, x = vecOp((const uchar**)src, (uchar*)dst, (const uchar*)beta, width);
#if CV_ENABLE_UNROLLED
#if CV_ENABLE_UNROLLED
for( ; x <= width - 4; x += 4 )
{
WT b = beta[0];
@ -1130,7 +1130,7 @@ static void resizeGeneric_( const Mat& src, Mat& dst,
if( k0 < ksize )
hresize( srows + k0, rows + k0, ksize - k0, xofs, alpha,
ssize.width, dsize.width, cn, xmin, xmax );
vresize( (const WT**)rows, (T*)(dst.data + dst.step*dy), beta, dsize.width );
vresize( (const WT**)rows, (T*)(dst.data + dst.step*dy), beta, dsize.width );
}
}
@ -1163,8 +1163,8 @@ static void resizeAreaFast_( const Mat& src, Mat& dst, const int* ofs, const int
{
const T* S = (const T*)(src.data + src.step*sy0) + xofs[dx];
WT sum = 0;
k=0;
#if CV_ENABLE_UNROLLED
k=0;
#if CV_ENABLE_UNROLLED
for( ; k <= area - 4; k += 4 )
sum += S[ofs[k]] + S[ofs[k+1]] + S[ofs[k+2]] + S[ofs[k+3]];
#endif
@ -1272,15 +1272,18 @@ static void resizeArea_( const Mat& src, Mat& dst, const DecimateAlpha* xofs, in
WT beta1 = 1 - beta;
T* D = (T*)(dst.data + dst.step*cur_dy);
if( fabs(beta) < 1e-3 )
{
if(cur_dy >= dsize.height) return;
for( dx = 0; dx < dsize.width; dx++ )
{
D[dx] = saturate_cast<T>(sum[dx] + buf[dx]);
D[dx] = saturate_cast<T>((sum[dx] + buf[dx]) / min(scale_y, src.rows - cur_dy * scale_y));
sum[dx] = buf[dx] = 0;
}
}
else
for( dx = 0; dx < dsize.width; dx++ )
{
D[dx] = saturate_cast<T>(sum[dx] + buf[dx]*beta1);
D[dx] = saturate_cast<T>((sum[dx] + buf[dx]* beta1)/ min(scale_y, src.rows - cur_dy*scale_y));
sum[dx] = buf[dx]*beta;
buf[dx] = 0;
}
@ -1329,11 +1332,11 @@ void cv::resize( InputArray _src, OutputArray _dst, Size dsize,
resizeGeneric_<
HResizeLinear<uchar, int, short,
INTER_RESIZE_COEF_SCALE,
HResizeLinearVec_8u32s>,
HResizeLinearVec_8u32s>,
VResizeLinear<uchar, int, short,
FixedPtCast<int, uchar, INTER_RESIZE_COEF_BITS*2>,
VResizeLinearVec_32s8u> >,
0,
VResizeLinearVec_32s8u> >,
0,
resizeGeneric_<
HResizeLinear<ushort, float, float, 1,
HResizeLinearVec_16u32f>,
@ -1344,7 +1347,7 @@ void cv::resize( InputArray _src, OutputArray _dst, Size dsize,
HResizeLinearVec_16s32f>,
VResizeLinear<short, float, float, Cast<float, short>,
VResizeLinearVec_32f16s> >,
0,
0,
resizeGeneric_<
HResizeLinear<float, float, float, 1,
HResizeLinearVec_32f>,
@ -1374,7 +1377,7 @@ void cv::resize( InputArray _src, OutputArray _dst, Size dsize,
HResizeCubic<short, float, float>,
VResizeCubic<short, float, float, Cast<float, short>,
VResizeCubicVec_32f16s> >,
0,
0,
resizeGeneric_<
HResizeCubic<float, float, float>,
VResizeCubic<float, float, float, Cast<float, float>,
@ -1396,10 +1399,10 @@ void cv::resize( InputArray _src, OutputArray _dst, Size dsize,
resizeGeneric_<HResizeLanczos4<ushort, float, float>,
VResizeLanczos4<ushort, float, float, Cast<float, ushort>,
VResizeNoVec> >,
resizeGeneric_<HResizeLanczos4<short, float, float>,
resizeGeneric_<HResizeLanczos4<short, float, float>,
VResizeLanczos4<short, float, float, Cast<float, short>,
VResizeNoVec> >,
0,
0,
resizeGeneric_<HResizeLanczos4<float, float, float>,
VResizeLanczos4<float, float, float, Cast<float, float>,
VResizeNoVec> >,
@ -1412,8 +1415,8 @@ void cv::resize( InputArray _src, OutputArray _dst, Size dsize,
static ResizeAreaFastFunc areafast_tab[] =
{
resizeAreaFast_<uchar, int>, 0,
resizeAreaFast_<ushort, float>,
resizeAreaFast_<short, float>,
resizeAreaFast_<ushort, float>,
resizeAreaFast_<short, float>,
0,
resizeAreaFast_<float, float>,
resizeAreaFast_<double, double>,
@ -1498,7 +1501,6 @@ void cv::resize( InputArray _src, OutputArray _dst, Size dsize,
AutoBuffer<DecimateAlpha> _xofs(ssize.width*2);
DecimateAlpha* xofs = _xofs;
double scale = 1.f/(scale_x*scale_y);
for( dx = 0, k = 0; dx < dsize.width; dx++ )
{
@ -1512,7 +1514,7 @@ void cv::resize( InputArray _src, OutputArray _dst, Size dsize,
assert( k < ssize.width*2 );
xofs[k].di = dx*cn;
xofs[k].si = (sx1-1)*cn;
xofs[k++].alpha = (float)((sx1 - fsx1)*scale);
xofs[k++].alpha = (float)((sx1 - fsx1) / min(scale_x, src.cols - fsx1));
}
for( sx = sx1; sx < sx2; sx++ )
@ -1520,7 +1522,7 @@ void cv::resize( InputArray _src, OutputArray _dst, Size dsize,
assert( k < ssize.width*2 );
xofs[k].di = dx*cn;
xofs[k].si = sx*cn;
xofs[k++].alpha = (float)scale;
xofs[k++].alpha = 1.f / min(scale_x, src.cols - fsx1);
}
if( fsx2 - sx2 > 1e-3 )
@ -1528,10 +1530,9 @@ void cv::resize( InputArray _src, OutputArray _dst, Size dsize,
assert( k < ssize.width*2 );
xofs[k].di = dx*cn;
xofs[k].si = sx2*cn;
xofs[k++].alpha = (float)((fsx2 - sx2)*scale);
xofs[k++].alpha = (float)(min(fsx2 - sx2, 1.) / min(scale_x, src.cols - fsx1));
}
}
func( src, dst, xofs, k ,scale_y);
return;
}
@ -3480,7 +3481,7 @@ void cvLinearPolar( const CvArr* srcarr, CvArr* dstarr,
if( !CV_ARE_TYPES_EQ( src, dst ))
CV_Error( CV_StsUnmatchedFormats, "" );
ssize.width = src->cols;
ssize.width = src->cols;
ssize.height = src->rows;
dsize.width = dst->cols;
dsize.height = dst->rows;

43
modules/imgproc/test/test_imgwarp.cpp
Unescape View File

@ -1462,6 +1462,49 @@ TEST(Imgproc_fitLine_Mat_3dC1, regression)
ASSERT_EQ(line2.size(), (size_t)6);
}
TEST(Imgproc_resize_area, regression)
{
static ushort input_data[16 * 16] = {
90, 94, 80, 3, 231, 2, 186, 245, 188, 165, 10, 19, 201, 169, 8, 228,
86, 5, 203, 120, 136, 185, 24, 94, 81, 150, 163, 137, 88, 105, 132, 132,
236, 48, 250, 218, 19, 52, 54, 221, 159, 112, 45, 11, 152, 153, 112, 134,
78, 133, 136, 83, 65, 76, 82, 250, 9, 235, 148, 26, 236, 179, 200, 50,
99, 51, 103, 142, 201, 65, 176, 33, 49, 226, 177, 109, 46, 21, 67, 130,
54, 125, 107, 154, 145, 51, 199, 189, 161, 142, 231, 240, 139, 162, 240, 22,
231, 86, 79, 106, 92, 47, 146, 156, 36, 207, 71, 33, 2, 244, 221, 71,
44, 127, 71, 177, 75, 126, 68, 119, 200, 129, 191, 251, 6, 236, 247, 6,
133, 175, 56, 239, 147, 221, 243, 154, 242, 82, 106, 99, 77, 158, 60, 229,
2, 42, 24, 174, 27, 198, 14, 204, 246, 251, 141, 31, 114, 163, 29, 147,
121, 53, 74, 31, 147, 189, 42, 98, 202, 17, 228, 123, 209, 40, 77, 49,
112, 203, 30, 12, 205, 25, 19, 106, 145, 185, 163, 201, 237, 223, 247, 38,
33, 105, 243, 117, 92, 179, 204, 248, 160, 90, 73, 126, 2, 41, 213, 204,
6, 124, 195, 201, 230, 187, 210, 167, 48, 79, 123, 159, 145, 218, 105, 209,
240, 152, 136, 235, 235, 164, 157, 9, 152, 38, 27, 209, 120, 77, 238, 196,
240, 233, 10, 241, 90, 67, 12, 79, 0, 43, 58, 27, 83, 199, 190, 182};
static ushort expected_data[5 * 5] = {
120, 100, 151, 101, 130,
106, 115, 141, 130, 127,
91, 136, 170, 114, 140,
104, 122, 131, 147, 133,
161, 163, 70, 107, 182
};
cv::Mat src(16, 16, CV_16UC1, input_data);
cv::Mat actual;
cv::Mat expected(5,5,CV_16UC1, expected_data);
cv::resize(src, actual, cv::Size(), 0.3, 0.3, INTER_AREA);
ASSERT_EQ(actual.type(), expected.type());
ASSERT_EQ(actual.size(), expected.size());
Mat diff;
absdiff(actual, expected, diff);
Mat one_channel_diff = diff.reshape(1);
ASSERT_EQ(norm(one_channel_diff, cv::NORM_INF),0);
}
//////////////////////////////////////////////////////////////////////////
TEST(Imgproc_Resize, accuracy) { CV_ResizeTest test; test.safe_run(); }

Write Preview
Loading…
Cancel
Save