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added bicubic interpolation to gpu::remap

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This commit is contained in:
Vladislav Vinogradov 2011-09-06 07:01:23 +00:00
parent 415978b1c9
commit 84022779a1
9 changed files with 385 additions and 262 deletions
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2
modules/gpu/src/cuda/brute_force_matcher.cu
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@ -42,7 +42,7 @@
#include "internal_shared.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/utility.hpp"
#include "opencv2/gpu/device/vec_distance.hpp"
using namespace cv::gpu;
using namespace cv::gpu::device;

11
modules/gpu/src/cuda/imgproc.cu
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@ -45,7 +45,7 @@
#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/utility.hpp"
#include "opencv2/gpu/device/filters.hpp"
using namespace cv::gpu;
using namespace cv::gpu::device;
@ -186,7 +186,7 @@ namespace cv { namespace gpu { namespace imgproc
{
typedef void (*caller_t)(const DevMem2D_<T>& src, const DevMem2Df& xmap, const DevMem2Df& ymap, const DevMem2D_<T>& dst, const float* borderValue, cudaStream_t stream);
static const caller_t callers[2][5] =
static const caller_t callers[3][5] =
{
{
RemapDispatcher<PointFilter, BrdReflect101, T>::call,
@ -201,6 +201,13 @@ namespace cv { namespace gpu { namespace imgproc
RemapDispatcher<LinearFilter, BrdConstant, T>::call,
RemapDispatcher<LinearFilter, BrdReflect, T>::call,
RemapDispatcher<LinearFilter, BrdWrap, T>::call
},
{
RemapDispatcher<CubicFilter, BrdReflect101, T>::call,
RemapDispatcher<CubicFilter, BrdReplicate, T>::call,
RemapDispatcher<CubicFilter, BrdConstant, T>::call,
RemapDispatcher<CubicFilter, BrdReflect, T>::call,
RemapDispatcher<CubicFilter, BrdWrap, T>::call
}
};

1
modules/gpu/src/cuda/surf.cu
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@ -50,6 +50,7 @@
#include "opencv2/gpu/device/saturate_cast.hpp"
#include "opencv2/gpu/device/utility.hpp"
#include "opencv2/gpu/device/functional.hpp"
#include "opencv2/gpu/device/filters.hpp"
using namespace cv::gpu;
using namespace cv::gpu::device;

2
modules/gpu/src/imgproc.cpp
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@ -131,7 +131,7 @@ void cv::gpu::remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const Gp
CV_Assert(src.depth() <= CV_32F && src.channels() <= 4);
CV_Assert(xmap.type() == CV_32F && ymap.type() == CV_32F && xmap.size() == ymap.size());
CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR);
CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC);
CV_Assert(borderMode == BORDER_REFLECT101 || borderMode == BORDER_REPLICATE || borderMode == BORDER_CONSTANT || borderMode == BORDER_REFLECT || borderMode == BORDER_WRAP);
int gpuBorderType;

135
modules/gpu/src/opencv2/gpu/device/filters.hpp Normal file
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@ -0,0 +1,135 @@
/*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_FILTERS_HPP__
#define __OPENCV_GPU_FILTERS_HPP__
#include "saturate_cast.hpp"
#include "vec_traits.hpp"
#include "vec_math.hpp"
namespace cv { namespace gpu { namespace device
{
template <typename Ptr2D> struct PointFilter
{
typedef typename Ptr2D::elem_type elem_type;
typedef float index_type;
explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_) : src(src_) {}
__device__ __forceinline__ elem_type operator ()(float y, float x) const
{
return src(__float2int_rn(y), __float2int_rn(x));
}
const Ptr2D src;
};
template <typename Ptr2D> struct LinearFilter
{
typedef typename Ptr2D::elem_type elem_type;
typedef float index_type;
explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_) : src(src_) {}
__device__ __forceinline__ elem_type operator ()(float y, float x) const
{
typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
work_type out = VecTraits<work_type>::all(0);
const int x1 = __float2int_rd(x);
const int y1 = __float2int_rd(y);
const int x2 = x1 + 1;
const int y2 = y1 + 1;
elem_type src_reg = src(y1, x1);
out = out + src_reg * ((x2 - x) * (y2 - y));
src_reg = src(y1, x2);
out = out + src_reg * ((x - x1) * (y2 - y));
src_reg = src(y2, x1);
out = out + src_reg * ((x2 - x) * (y - y1));
src_reg = src(y2, x2);
out = out + src_reg * ((x - x1) * (y - y1));
return saturate_cast<elem_type>(out);
}
const Ptr2D src;
};
template <typename Ptr2D> struct CubicFilter
{
typedef typename Ptr2D::elem_type elem_type;
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_) {}
static __device__ __forceinline__ work_type cubicInterpolate(const work_type& p0, const work_type& p1, const work_type& p2, const work_type& p3, float x)
{
return p1 + 0.5f * x * (p2 - p0 + x * (2.0f * p0 - 5.0f * p1 + 4.0f * p2 - p3 + x * (3.0f * (p1 - p2) + p3 - p0)));
}
__device__ elem_type operator ()(float y, float x) const
{
const int xi = __float2int_rn(x);
const int yi = __float2int_rn(y);
work_type arr[4];
arr[0] = cubicInterpolate(saturate_cast<work_type>(src(yi - 1, xi - 1)), saturate_cast<work_type>(src(yi - 1, xi)), saturate_cast<work_type>(src(yi - 1, xi + 1)), saturate_cast<work_type>(src(yi - 1, xi + 2)), x - xi);
arr[1] = cubicInterpolate(saturate_cast<work_type>(src(yi , xi - 1)), saturate_cast<work_type>(src(yi , xi)), saturate_cast<work_type>(src(yi , xi + 1)), saturate_cast<work_type>(src(yi , xi + 2)), x - xi);
arr[2] = cubicInterpolate(saturate_cast<work_type>(src(yi + 1, xi - 1)), saturate_cast<work_type>(src(yi + 1, xi)), saturate_cast<work_type>(src(yi + 1, xi + 1)), saturate_cast<work_type>(src(yi + 1, xi + 2)), x - xi);
arr[3] = cubicInterpolate(saturate_cast<work_type>(src(yi + 2, xi - 1)), saturate_cast<work_type>(src(yi + 2, xi)), saturate_cast<work_type>(src(yi + 2, xi + 1)), saturate_cast<work_type>(src(yi + 2, xi + 2)), x - xi);
return saturate_cast<elem_type>(cubicInterpolate(arr[0], arr[1], arr[2], arr[3], y - yi));
}
const Ptr2D src;
};
}}}
#endif // __OPENCV_GPU_FILTERS_HPP__

228
modules/gpu/src/opencv2/gpu/device/utility.hpp
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@ -135,180 +135,6 @@ namespace cv { namespace gpu { namespace device
StaticAssert<n >= 8 && n <= 512>::check();
detail::PredValReductionDispatcher<n <= 64>::reduce<n>(myData, myVal, sdata, sval, tid, pred);
}
///////////////////////////////////////////////////////////////////////////////
// Vector Distance
template <typename T> struct L1Dist
{
typedef int value_type;
typedef int result_type;
__device__ __forceinline__ L1Dist() : mySum(0) {}
__device__ __forceinline__ void reduceIter(int val1, int val2)
{
mySum = __sad(val1, val2, mySum);
}
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
{
reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile int>());
}
__device__ __forceinline__ operator int() const
{
return mySum;
}
int mySum;
};
template <> struct L1Dist<float>
{
typedef float value_type;
typedef float result_type;
__device__ __forceinline__ L1Dist() : mySum(0.0f) {}
__device__ __forceinline__ void reduceIter(float val1, float val2)
{
mySum += ::fabs(val1 - val2);
}
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
{
reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile float>());
}
__device__ __forceinline__ operator float() const
{
return mySum;
}
float mySum;
};
struct L2Dist
{
typedef float value_type;
typedef float result_type;
__device__ __forceinline__ L2Dist() : mySum(0.0f) {}
__device__ __forceinline__ void reduceIter(float val1, float val2)
{
float reg = val1 - val2;
mySum += reg * reg;
}
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
{
reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile float>());
}
__device__ __forceinline__ operator float() const
{
return sqrtf(mySum);
}
float mySum;
};
struct HammingDist
{
typedef int value_type;
typedef int result_type;
__device__ __forceinline__ HammingDist() : mySum(0) {}
__device__ __forceinline__ void reduceIter(int val1, int val2)
{
mySum += __popc(val1 ^ val2);
}
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
{
reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile int>());
}
__device__ __forceinline__ operator int() const
{
return mySum;
}
int mySum;
};
// calc distance between two vectors in global memory
template <int THREAD_DIM, typename Dist, typename T1, typename T2>
__device__ void calcVecDiffGlobal(const T1* vec1, const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid)
{
for (int i = tid; i < len; i += THREAD_DIM)
{
T1 val1;
ForceGlob<T1>::Load(vec1, i, val1);
T2 val2;
ForceGlob<T2>::Load(vec2, i, val2);
dist.reduceIter(val1, val2);
}
dist.reduceAll<THREAD_DIM>(smem, tid);
}
// calc distance between two vectors, first vector is cached in register or shared memory, second vector is in global memory
template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename Dist, typename T1, typename T2>
__device__ __forceinline__ void calcVecDiffCached(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, typename Dist::result_type* smem, int tid)
{
detail::VecDiffCachedCalculator<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>::calc(vecCached, vecGlob, len, dist, tid);
dist.reduceAll<THREAD_DIM>(smem, tid);
}
// calc distance between two vectors in global memory
template <int THREAD_DIM, typename T1> struct VecDiffGlobal
{
explicit __device__ __forceinline__ VecDiffGlobal(const T1* vec1_, int = 0, void* = 0, int = 0, int = 0)
{
vec1 = vec1_;
}
template <typename T2, typename Dist>
__device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
{
calcVecDiffGlobal<THREAD_DIM>(vec1, vec2, len, dist, smem, tid);
}
const T1* vec1;
};
// calc distance between two vectors, first vector is cached in register memory, second vector is in global memory
template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename U> struct VecDiffCachedRegister
{
template <typename T1> __device__ __forceinline__ VecDiffCachedRegister(const T1* vec1, int len, U* smem, int glob_tid, int tid)
{
if (glob_tid < len)
smem[glob_tid] = vec1[glob_tid];
__syncthreads();
U* vec1ValsPtr = vec1Vals;
#pragma unroll
for (int i = tid; i < MAX_LEN; i += THREAD_DIM)
*vec1ValsPtr++ = smem[i];
__syncthreads();
}
template <typename T2, typename Dist>
__device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
{
calcVecDiffCached<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>(vec1Vals, vec2, len, dist, smem, tid);
}
U vec1Vals[MAX_LEN / THREAD_DIM];
};
///////////////////////////////////////////////////////////////////////////////
// Solve linear system
@ -363,60 +189,6 @@ namespace cv { namespace gpu { namespace device
return false;
}
///////////////////////////////////////////////////////////////////////////////
// Filters
template <typename Ptr2D> struct PointFilter
{
typedef typename Ptr2D::elem_type elem_type;
typedef float index_type;
explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_) : src(src_) {}
__device__ __forceinline__ elem_type operator ()(float y, float x) const
{
return src(__float2int_rn(y), __float2int_rn(x));
}
const Ptr2D src;
};
template <typename Ptr2D> struct LinearFilter
{
typedef typename Ptr2D::elem_type elem_type;
typedef float index_type;
explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_) : src(src_) {}
__device__ __forceinline__ elem_type operator ()(float y, float x) const
{
typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
work_type out = VecTraits<work_type>::all(0);
const int x1 = __float2int_rd(x);
const int y1 = __float2int_rd(y);
const int x2 = x1 + 1;
const int y2 = y1 + 1;
elem_type src_reg = src(y1, x1);
out = out + src_reg * ((x2 - x) * (y2 - y));
src_reg = src(y1, x2);
out = out + src_reg * ((x - x1) * (y2 - y));
src_reg = src(y2, x1);
out = out + src_reg * ((x2 - x) * (y - y1));
src_reg = src(y2, x2);
out = out + src_reg * ((x - x1) * (y - y1));
return saturate_cast<elem_type>(out);
}
const Ptr2D src;
};
}}}
#endif // __OPENCV_GPU_UTILITY_HPP__

223
modules/gpu/src/opencv2/gpu/device/vec_distance.hpp Normal file
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@ -0,0 +1,223 @@
/*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_VEC_DISTANCE_HPP__
#define __OPENCV_GPU_VEC_DISTANCE_HPP__
#include "utility.hpp"
namespace cv { namespace gpu { namespace device
{
template <typename T> struct L1Dist
{
typedef int value_type;
typedef int result_type;
__device__ __forceinline__ L1Dist() : mySum(0) {}
__device__ __forceinline__ void reduceIter(int val1, int val2)
{
mySum = __sad(val1, val2, mySum);
}
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
{
reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile int>());
}
__device__ __forceinline__ operator int() const
{
return mySum;
}
int mySum;
};
template <> struct L1Dist<float>
{
typedef float value_type;
typedef float result_type;
__device__ __forceinline__ L1Dist() : mySum(0.0f) {}
__device__ __forceinline__ void reduceIter(float val1, float val2)
{
mySum += ::fabs(val1 - val2);
}
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
{
reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile float>());
}
__device__ __forceinline__ operator float() const
{
return mySum;
}
float mySum;
};
struct L2Dist
{
typedef float value_type;
typedef float result_type;
__device__ __forceinline__ L2Dist() : mySum(0.0f) {}
__device__ __forceinline__ void reduceIter(float val1, float val2)
{
float reg = val1 - val2;
mySum += reg * reg;
}
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
{
reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile float>());
}
__device__ __forceinline__ operator float() const
{
return sqrtf(mySum);
}
float mySum;
};
struct HammingDist
{
typedef int value_type;
typedef int result_type;
__device__ __forceinline__ HammingDist() : mySum(0) {}
__device__ __forceinline__ void reduceIter(int val1, int val2)
{
mySum += __popc(val1 ^ val2);
}
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
{
reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile int>());
}
__device__ __forceinline__ operator int() const
{
return mySum;
}
int mySum;
};
// calc distance between two vectors in global memory
template <int THREAD_DIM, typename Dist, typename T1, typename T2>
__device__ void calcVecDiffGlobal(const T1* vec1, const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid)
{
for (int i = tid; i < len; i += THREAD_DIM)
{
T1 val1;
ForceGlob<T1>::Load(vec1, i, val1);
T2 val2;
ForceGlob<T2>::Load(vec2, i, val2);
dist.reduceIter(val1, val2);
}
dist.reduceAll<THREAD_DIM>(smem, tid);
}
// calc distance between two vectors, first vector is cached in register or shared memory, second vector is in global memory
template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename Dist, typename T1, typename T2>
__device__ __forceinline__ void calcVecDiffCached(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, typename Dist::result_type* smem, int tid)
{
detail::VecDiffCachedCalculator<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>::calc(vecCached, vecGlob, len, dist, tid);
dist.reduceAll<THREAD_DIM>(smem, tid);
}
// calc distance between two vectors in global memory
template <int THREAD_DIM, typename T1> struct VecDiffGlobal
{
explicit __device__ __forceinline__ VecDiffGlobal(const T1* vec1_, int = 0, void* = 0, int = 0, int = 0)
{
vec1 = vec1_;
}
template <typename T2, typename Dist>
__device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
{
calcVecDiffGlobal<THREAD_DIM>(vec1, vec2, len, dist, smem, tid);
}
const T1* vec1;
};
// calc distance between two vectors, first vector is cached in register memory, second vector is in global memory
template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename U> struct VecDiffCachedRegister
{
template <typename T1> __device__ __forceinline__ VecDiffCachedRegister(const T1* vec1, int len, U* smem, int glob_tid, int tid)
{
if (glob_tid < len)
smem[glob_tid] = vec1[glob_tid];
__syncthreads();
U* vec1ValsPtr = vec1Vals;
#pragma unroll
for (int i = tid; i < MAX_LEN; i += THREAD_DIM)
*vec1ValsPtr++ = smem[i];
__syncthreads();
}
template <typename T2, typename Dist>
__device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
{
calcVecDiffCached<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>(vec1Vals, vec2, len, dist, smem, tid);
}
U vec1Vals[MAX_LEN / THREAD_DIM];
};
}}}
#endif // __OPENCV_GPU_VEC_DISTANCE_HPP__

26
modules/gpu/test/test_imgproc.cpp
View File

@ -210,20 +210,8 @@ struct Remap : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int,
src = cvtest::randomMat(rng, size, type, 0.0, 256.0, false);
xmap.create(size, CV_32FC1);
ymap.create(size, CV_32FC1);
for (int y = 0; y < src.rows; ++y)
{
float* xmap_row = xmap.ptr<float>(y);
float* ymap_row = ymap.ptr<float>(y);
for (int x = 0; x < src.cols; ++x)
{
xmap_row[x] = src.cols - 1 - x + 10;
ymap_row[x] = src.rows - 1 - y + 10;
}
}
xmap = cvtest::randomMat(rng, size, CV_32FC1, -20.0, src.cols + 20, false);
ymap = cvtest::randomMat(rng, size, CV_32FC1, -20.0, src.rows + 20, false);
cv::remap(src, dst_gold, xmap, ymap, interpolation, borderType);
}
@ -253,13 +241,7 @@ TEST_P(Remap, Accuracy)
gpuRes.download(dst);
);
if (dst_gold.depth() == CV_32F)
{
dst_gold.convertTo(dst_gold, CV_8U);
dst.convertTo(dst, CV_8U);
}
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5);
EXPECT_MAT_SIMILAR(dst_gold, dst, 1e-1);
}
INSTANTIATE_TEST_CASE_P
@ -272,7 +254,7 @@ INSTANTIATE_TEST_CASE_P
CV_8UC1, CV_8UC3, CV_8UC4,
CV_32FC1, CV_32FC3, CV_32FC4
),
testing::Values((int)cv::INTER_NEAREST, (int)cv::INTER_LINEAR),
testing::Values((int)cv::INTER_NEAREST, (int)cv::INTER_LINEAR, (int)cv::INTER_CUBIC),
testing::Values((int)cv::BORDER_REFLECT101, (int)cv::BORDER_REPLICATE, (int)cv::BORDER_CONSTANT, (int)cv::BORDER_REFLECT, (int)cv::BORDER_WRAP)
)
);

19
samples/gpu/performance/tests.cpp
View File

@ -78,6 +78,9 @@ TEST(remap)
{
Mat src, dst, xmap, ymap;
gpu::GpuMat d_src, d_dst, d_xmap, d_ymap;
int interpolation = INTER_LINEAR;
int borderMode = BORDER_CONSTANT;
for (int size = 1000; size <= 4000; size *= 2)
{
@ -101,7 +104,7 @@ TEST(remap)
dst.create(xmap.size(), src.type());
CPU_ON;
remap(src, dst, xmap, ymap, INTER_LINEAR, BORDER_REPLICATE);
remap(src, dst, xmap, ymap, interpolation, borderMode);
CPU_OFF;
d_src = src;
@ -110,7 +113,7 @@ TEST(remap)
d_dst.create(d_xmap.size(), d_src.type());
GPU_ON;
gpu::remap(d_src, d_dst, d_xmap, d_ymap, INTER_LINEAR, BORDER_REPLICATE);
gpu::remap(d_src, d_dst, d_xmap, d_ymap, interpolation, borderMode);
GPU_OFF;
}
@ -136,7 +139,7 @@ TEST(remap)
dst.create(xmap.size(), src.type());
CPU_ON;
remap(src, dst, xmap, ymap, INTER_LINEAR, BORDER_REPLICATE);
remap(src, dst, xmap, ymap, interpolation, borderMode);
CPU_OFF;
d_src = src;
@ -145,7 +148,7 @@ TEST(remap)
d_dst.create(d_xmap.size(), d_src.type());
GPU_ON;
gpu::remap(d_src, d_dst, d_xmap, d_ymap, INTER_LINEAR, BORDER_REPLICATE);
gpu::remap(d_src, d_dst, d_xmap, d_ymap, interpolation, borderMode);
GPU_OFF;
}
@ -171,7 +174,7 @@ TEST(remap)
dst.create(xmap.size(), src.type());
CPU_ON;
remap(src, dst, xmap, ymap, INTER_LINEAR, BORDER_REPLICATE);
remap(src, dst, xmap, ymap, interpolation, borderMode);
CPU_OFF;
d_src = src;
@ -180,7 +183,7 @@ TEST(remap)
d_dst.create(d_xmap.size(), d_src.type());
GPU_ON;
gpu::remap(d_src, d_dst, d_xmap, d_ymap, INTER_LINEAR, BORDER_REPLICATE);
gpu::remap(d_src, d_dst, d_xmap, d_ymap, interpolation, borderMode);
GPU_OFF;
}
@ -206,7 +209,7 @@ TEST(remap)
dst.create(xmap.size(), src.type());
CPU_ON;
remap(src, dst, xmap, ymap, INTER_LINEAR, BORDER_REPLICATE);
remap(src, dst, xmap, ymap, interpolation, borderMode);
CPU_OFF;
d_src = src;
@ -215,7 +218,7 @@ TEST(remap)
d_dst.create(d_xmap.size(), d_src.type());
GPU_ON;
gpu::remap(d_src, d_dst, d_xmap, d_ymap, INTER_LINEAR, BORDER_REPLICATE);
gpu::remap(d_src, d_dst, d_xmap, d_ymap, interpolation, borderMode);
GPU_OFF;
}
}