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opencv/modules/ocl/perf/perf_imgproc.cpp

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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) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Fangfang Bai, fangfang@multicorewareinc.com
// Jin Ma, jin@multicorewareinc.com
//
// 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 oclMaterials 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
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//
//M*/
#include "perf_precomp.hpp"
using namespace perf;
using std::tr1::tuple;
using std::tr1::get;
///////////// equalizeHist ////////////////////////
typedef TestBaseWithParam<Size> equalizeHistFixture;
PERF_TEST_P(equalizeHistFixture, equalizeHist, OCL_TYPICAL_MAT_SIZES)
{
// getting params
const Size srcSize = GetParam();
const string impl = getSelectedImpl();
// creating src data
Mat src(srcSize, CV_8UC1), dst(srcSize, CV_8UC1);
declare.in(src, WARMUP_RNG).out(dst);
// select implementation
if (impl == "ocl")
{
ocl::oclMat oclSrc(src), oclDst(srcSize, src.type());
TEST_CYCLE() cv::ocl::equalizeHist(oclSrc, oclDst);
oclDst.download(dst);
SANITY_CHECK(dst, 1 + DBL_EPSILON);
}
else if (impl == "plain")
{
TEST_CYCLE() cv::equalizeHist(src, dst);
SANITY_CHECK(dst, 1 + DBL_EPSILON);
}
#ifdef HAVE_OPENCV_GPU
else if (impl == "gpu")
CV_TEST_FAIL_NO_IMPL();
#endif
else
CV_TEST_FAIL_NO_IMPL();
}
/////////// CopyMakeBorder //////////////////////
CV_ENUM(CopyMakeBorderMatType, CV_8UC1, CV_8UC4)
typedef tuple<Size, CopyMakeBorderMatType> CopyMakeBorderParams;
typedef TestBaseWithParam<CopyMakeBorderParams> CopyMakeBorderFixture;
PERF_TEST_P(CopyMakeBorderFixture, CopyMakeBorder,
::testing::Combine(OCL_TYPICAL_MAT_SIZES,
CopyMakeBorderMatType::all()))
{
// getting params
CopyMakeBorderParams params = GetParam();
const Size srcSize = get<0>(params);
const int type = get<1>(params), borderType = BORDER_CONSTANT;
const string impl = getSelectedImpl();
// creating src data
Mat src(srcSize, type), dst;
const Size dstSize = srcSize + Size(12, 12);
dst.create(dstSize, type);
declare.in(src, WARMUP_RNG).out(dst);
// select implementation
if (impl == "ocl")
{
ocl::oclMat oclSrc(src), oclDst(dstSize, type);
TEST_CYCLE() cv::ocl::copyMakeBorder(oclSrc, oclDst, 7, 5, 5, 7, borderType, cv::Scalar(1.0));
oclDst.download(dst);
SANITY_CHECK(dst);
}
else if (impl == "plain")
{
TEST_CYCLE() cv::copyMakeBorder(src, dst, 7, 5, 5, 7, borderType, cv::Scalar(1.0));
SANITY_CHECK(dst);
}
#ifdef HAVE_OPENCV_GPU
else if (impl == "gpu")
CV_TEST_FAIL_NO_IMPL();
#endif
else
CV_TEST_FAIL_NO_IMPL();
}
///////////// cornerMinEigenVal ////////////////////////
CV_ENUM(cornerMinEigenValMatType, CV_8UC1, CV_32FC1)
typedef tuple<Size, cornerMinEigenValMatType> cornerMinEigenValParams;
typedef TestBaseWithParam<cornerMinEigenValParams> cornerMinEigenValFixture;
PERF_TEST_P(cornerMinEigenValFixture, cornerMinEigenVal,
::testing::Combine(OCL_TYPICAL_MAT_SIZES,
cornerMinEigenValMatType::all()))
{
// getting params
cornerMinEigenValParams params = GetParam();
const Size srcSize = get<0>(params);
const int type = get<1>(params), borderType = BORDER_REFLECT;
const int blockSize = 7, apertureSize = 1 + 2 * 3;
const string impl = getSelectedImpl();
// creating src data
Mat src(srcSize, type), dst(srcSize, CV_32FC1);
declare.in(src, WARMUP_RNG).out(dst)
.time(srcSize == OCL_SIZE_4000 ? 20 : srcSize == OCL_SIZE_2000 ? 5 : 3);
const int depth = CV_MAT_DEPTH(type);
const ERROR_TYPE errorType = depth == CV_8U ? ERROR_ABSOLUTE : ERROR_RELATIVE;
// select implementation
if (impl == "ocl")
{
ocl::oclMat oclSrc(src), oclDst(srcSize, CV_32FC1);
TEST_CYCLE() cv::ocl::cornerMinEigenVal(oclSrc, oclDst, blockSize, apertureSize, borderType);
oclDst.download(dst);
SANITY_CHECK(dst, 1e-6, errorType);
}
else if (impl == "plain")
{
TEST_CYCLE() cv::cornerMinEigenVal(src, dst, blockSize, apertureSize, borderType);
SANITY_CHECK(dst, 1e-6, errorType);
}
#ifdef HAVE_OPENCV_GPU
else if (impl == "gpu")
CV_TEST_FAIL_NO_IMPL();
#endif
else
CV_TEST_FAIL_NO_IMPL();
}
///////////// cornerHarris ////////////////////////
typedef cornerMinEigenValMatType cornerHarrisMatType;
typedef tuple<Size, cornerHarrisMatType> cornerHarrisParams;
typedef TestBaseWithParam<cornerHarrisParams> cornerHarrisFixture;
PERF_TEST_P(cornerHarrisFixture, cornerHarris,
::testing::Combine(OCL_TYPICAL_MAT_SIZES,
cornerHarrisMatType::all()))
{
// getting params
cornerHarrisParams params = GetParam();
const Size srcSize = get<0>(params);
const int type = get<1>(params), borderType = BORDER_REFLECT;
const string impl = getSelectedImpl();
// creating src data
Mat src(srcSize, type), dst(srcSize, CV_32FC1);
randu(src, 0, 1);
declare.in(src).out(dst)
.time(srcSize == OCL_SIZE_4000 ? 20 : srcSize == OCL_SIZE_2000 ? 5 : 3);
// select implementation
if (impl == "ocl")
{
ocl::oclMat oclSrc(src), oclDst(srcSize, CV_32FC1);
TEST_CYCLE() cv::ocl::cornerHarris(oclSrc, oclDst, 5, 7, 0.1, borderType);
oclDst.download(dst);
SANITY_CHECK(dst, 3e-5);
}
else if (impl == "plain")
{
TEST_CYCLE() cv::cornerHarris(src, dst, 5, 7, 0.1, borderType);
SANITY_CHECK(dst, 3e-5);
}
#ifdef HAVE_OPENCV_GPU
else if (impl == "gpu")
CV_TEST_FAIL_NO_IMPL();
#endif
else
CV_TEST_FAIL_NO_IMPL();
}
///////////// integral ////////////////////////
typedef TestBaseWithParam<Size> integralFixture;
PERF_TEST_P(integralFixture, DISABLED_integral, OCL_TYPICAL_MAT_SIZES)
{
// getting params
const Size srcSize = GetParam();
const string impl = getSelectedImpl();
// creating src data
Mat src(srcSize, CV_8UC1), dst;
declare.in(src, WARMUP_RNG);
// select implementation
if (impl == "ocl")
{
ocl::oclMat oclSrc(src), oclDst;
TEST_CYCLE() cv::ocl::integral(oclSrc, oclDst);
oclDst.download(dst);
SANITY_CHECK(dst);
}
else if (impl == "plain")
{
TEST_CYCLE() cv::integral(src, dst);
SANITY_CHECK(dst);
}
#ifdef HAVE_OPENCV_GPU
else if (impl == "gpu")
CV_TEST_FAIL_NO_IMPL();
#endif
else
CV_TEST_FAIL_NO_IMPL();
}
///////////// WarpAffine ////////////////////////
typedef CopyMakeBorderMatType WarpAffineMatType;
typedef tuple<Size, WarpAffineMatType> WarpAffineParams;
typedef TestBaseWithParam<WarpAffineParams> WarpAffineFixture;
PERF_TEST_P(WarpAffineFixture, WarpAffine,
::testing::Combine(OCL_TYPICAL_MAT_SIZES,
WarpAffineMatType::all()))
{
static const double coeffs[2][3] =
{
{ cos(CV_PI / 6), -sin(CV_PI / 6), 100.0 },
{ sin(CV_PI / 6), cos(CV_PI / 6), -100.0 }
};
Mat M(2, 3, CV_64F, (void *)coeffs);
const int interpolation = INTER_NEAREST;
// getting params
WarpAffineParams params = GetParam();
const Size srcSize = get<0>(params);
const int type = get<1>(params);
const string impl = getSelectedImpl();
// creating src data
Mat src(srcSize, type), dst(srcSize, type);
declare.in(src, WARMUP_RNG).out(dst);
// select implementation
if (impl == "ocl")
{
ocl::oclMat oclSrc(src), oclDst(srcSize, type);
TEST_CYCLE() cv::ocl::warpAffine(oclSrc, oclDst, M, srcSize, interpolation);
oclDst.download(dst);
SANITY_CHECK(dst);
}
else if (impl == "plain")
{
TEST_CYCLE() cv::warpAffine(src, dst, M, srcSize, interpolation);
SANITY_CHECK(dst);
}
#ifdef HAVE_OPENCV_GPU
else if (impl == "gpu")
CV_TEST_FAIL_NO_IMPL();
#endif
else
CV_TEST_FAIL_NO_IMPL();
}
///////////// WarpPerspective ////////////////////////
typedef CopyMakeBorderMatType WarpPerspectiveMatType;
typedef tuple<Size, WarpPerspectiveMatType> WarpPerspectiveParams;
typedef TestBaseWithParam<WarpPerspectiveParams> WarpPerspectiveFixture;
PERF_TEST_P(WarpPerspectiveFixture, WarpPerspective,
::testing::Combine(OCL_TYPICAL_MAT_SIZES,
WarpPerspectiveMatType::all()))
{
static const double coeffs[3][3] =
{
{cos(CV_PI / 6), -sin(CV_PI / 6), 100.0},
{sin(CV_PI / 6), cos(CV_PI / 6), -100.0},
{0.0, 0.0, 1.0}
};
Mat M(3, 3, CV_64F, (void *)coeffs);
const int interpolation = INTER_LINEAR;
// getting params
WarpPerspectiveParams params = GetParam();
const Size srcSize = get<0>(params);
const int type = get<1>(params);
const string impl = getSelectedImpl();
// creating src data
Mat src(srcSize, type), dst(srcSize, type);
declare.in(src, WARMUP_RNG).out(dst)
.time(srcSize == OCL_SIZE_4000 ? 18 : srcSize == OCL_SIZE_2000 ? 5 : 2);
// select implementation
if (impl == "ocl")
{
ocl::oclMat oclSrc(src), oclDst(srcSize, type);
TEST_CYCLE() cv::ocl::warpPerspective(oclSrc, oclDst, M, srcSize, interpolation);
oclDst.download(dst);
SANITY_CHECK(dst);
}
else if (impl == "plain")
{
TEST_CYCLE() cv::warpPerspective(src, dst, M, srcSize, interpolation);
SANITY_CHECK(dst);
}
#ifdef HAVE_OPENCV_GPU
else if (impl == "gpu")
CV_TEST_FAIL_NO_IMPL();
#endif
else
CV_TEST_FAIL_NO_IMPL();
}
///////////// resize ////////////////////////
CV_ENUM(resizeInterType, INTER_NEAREST, INTER_LINEAR)
typedef CopyMakeBorderMatType resizeMatType;
typedef tuple<Size, resizeMatType, resizeInterType, double> resizeParams;
typedef TestBaseWithParam<resizeParams> resizeFixture;
PERF_TEST_P(resizeFixture, resize,
::testing::Combine(OCL_TYPICAL_MAT_SIZES,
resizeMatType::all(),
resizeInterType::all(),
::testing::Values(0.5, 2.0)))
{
// getting params
resizeParams params = GetParam();
const Size srcSize = get<0>(params);
const int type = get<1>(params), interType = get<2>(params);
double scale = get<3>(params);
const string impl = getSelectedImpl();
// creating src data
Mat src(srcSize, type), dst;
const Size dstSize(cvRound(srcSize.width * scale), cvRound(srcSize.height * scale));
dst.create(dstSize, type);
declare.in(src, WARMUP_RNG).out(dst);
if (interType == INTER_LINEAR && type == CV_8UC4 && OCL_SIZE_4000 == srcSize)
declare.time(11);
// select implementation
if (impl == "ocl")
{
ocl::oclMat oclSrc(src), oclDst(dstSize, type);
TEST_CYCLE() cv::ocl::resize(oclSrc, oclDst, Size(), scale, scale, interType);
oclDst.download(dst);
SANITY_CHECK(dst, 1 + DBL_EPSILON);
}
else if (impl == "plain")
{
TEST_CYCLE() cv::resize(src, dst, Size(), scale, scale, interType);
SANITY_CHECK(dst, 1 + DBL_EPSILON);
}
#ifdef HAVE_OPENCV_GPU
else if (impl == "gpu")
CV_TEST_FAIL_NO_IMPL();
#endif
else
CV_TEST_FAIL_NO_IMPL();
}
///////////// threshold////////////////////////
CV_ENUM(ThreshType, THRESH_BINARY, THRESH_TRUNC)
typedef tuple<Size, ThreshType> ThreshParams;
typedef TestBaseWithParam<ThreshParams> ThreshFixture;
PERF_TEST_P(ThreshFixture, threshold,
::testing::Combine(OCL_TYPICAL_MAT_SIZES,
ThreshType::all()))
{
// getting params
ThreshParams params = GetParam();
const Size srcSize = get<0>(params);
const int threshType = get<1>(params);
const string impl = getSelectedImpl();
// creating src data
Mat src(srcSize, CV_8U), dst(srcSize, CV_8U);
randu(src, 0, 100);
declare.in(src).out(dst);
// select implementation
if (impl == "ocl")
{
ocl::oclMat oclSrc(src), oclDst(srcSize, CV_8U);
TEST_CYCLE() cv::ocl::threshold(oclSrc, oclDst, 50.0, 0.0, threshType);
oclDst.download(dst);
SANITY_CHECK(dst);
}
else if (impl == "plain")
{
TEST_CYCLE() cv::threshold(src, dst, 50.0, 0.0, threshType);
SANITY_CHECK(dst);
}
#ifdef HAVE_OPENCV_GPU
else if (impl == "gpu")
CV_TEST_FAIL_NO_IMPL();
#endif
else
CV_TEST_FAIL_NO_IMPL();
}
///////////// meanShiftFiltering////////////////////////
typedef struct
{
short x;
short y;
} COOR;
static COOR do_meanShift(int x0, int y0, uchar *sptr, uchar *dptr, int sstep, cv::Size size, int sp, int sr, int maxIter, float eps, int *tab)
{
int isr2 = sr * sr;
int c0, c1, c2, c3;
int iter;
uchar *ptr = NULL;
uchar *pstart = NULL;
int revx = 0, revy = 0;
c0 = sptr[0];
c1 = sptr[1];
c2 = sptr[2];
c3 = sptr[3];
// iterate meanshift procedure
for(iter = 0; iter < maxIter; iter++ )
{
int count = 0;
int s0 = 0, s1 = 0, s2 = 0, sx = 0, sy = 0;
//mean shift: process pixels in window (p-sigmaSp)x(p+sigmaSp)
int minx = x0 - sp;
int miny = y0 - sp;
int maxx = x0 + sp;
int maxy = y0 + sp;
//deal with the image boundary
if(minx < 0) minx = 0;
if(miny < 0) miny = 0;
if(maxx >= size.width) maxx = size.width - 1;
if(maxy >= size.height) maxy = size.height - 1;
if(iter == 0)
{
pstart = sptr;
}
else
{
pstart = pstart + revy * sstep + (revx << 2); //point to the new position
}
ptr = pstart;
ptr = ptr + (miny - y0) * sstep + ((minx - x0) << 2); //point to the start in the row
for( int y = miny; y <= maxy; y++, ptr += sstep - ((maxx - minx + 1) << 2))
{
int rowCount = 0;
int x = minx;
#if CV_ENABLE_UNROLLED
for( ; x + 4 <= maxx; x += 4, ptr += 16)
{
int t0, t1, t2;
t0 = ptr[0], t1 = ptr[1], t2 = ptr[2];
if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2)
{
s0 += t0;
s1 += t1;
s2 += t2;
sx += x;
rowCount++;
}
t0 = ptr[4], t1 = ptr[5], t2 = ptr[6];
if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2)
{
s0 += t0;
s1 += t1;
s2 += t2;
sx += x + 1;
rowCount++;
}
t0 = ptr[8], t1 = ptr[9], t2 = ptr[10];
if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2)
{
s0 += t0;
s1 += t1;
s2 += t2;
sx += x + 2;
rowCount++;
}
t0 = ptr[12], t1 = ptr[13], t2 = ptr[14];
if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2)
{
s0 += t0;
s1 += t1;
s2 += t2;
sx += x + 3;
rowCount++;
}
}
#endif
for(; x <= maxx; x++, ptr += 4)
{
int t0 = ptr[0], t1 = ptr[1], t2 = ptr[2];
if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2)
{
s0 += t0;
s1 += t1;
s2 += t2;
sx += x;
rowCount++;
}
}
if(rowCount == 0)
continue;
count += rowCount;
sy += y * rowCount;
}
if( count == 0 )
break;
int x1 = sx / count;
int y1 = sy / count;
s0 = s0 / count;
s1 = s1 / count;
s2 = s2 / count;
bool stopFlag = (x0 == x1 && y0 == y1) || (abs(x1 - x0) + abs(y1 - y0) +
tab[s0 - c0 + 255] + tab[s1 - c1 + 255] + tab[s2 - c2 + 255] <= eps);
//revise the pointer corresponding to the new (y0,x0)
revx = x1 - x0;
revy = y1 - y0;
x0 = x1;
y0 = y1;
c0 = s0;
c1 = s1;
c2 = s2;
if( stopFlag )
break;
} //for iter
dptr[0] = (uchar)c0;
dptr[1] = (uchar)c1;
dptr[2] = (uchar)c2;
dptr[3] = (uchar)c3;
COOR coor;
coor.x = static_cast<short>(x0);
coor.y = static_cast<short>(y0);
return coor;
}
static void meanShiftFiltering_(const Mat &src_roi, Mat &dst_roi, int sp, int sr, cv::TermCriteria crit)
{
if( src_roi.empty() )
CV_Error( CV_StsBadArg, "The input image is empty" );
if( src_roi.depth() != CV_8U || src_roi.channels() != 4 )
CV_Error( CV_StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported" );
dst_roi.create(src_roi.size(), src_roi.type());
CV_Assert( (src_roi.cols == dst_roi.cols) && (src_roi.rows == dst_roi.rows) );
CV_Assert( !(dst_roi.step & 0x3) );
if( !(crit.type & cv::TermCriteria::MAX_ITER) )
crit.maxCount = 5;
int maxIter = std::min(std::max(crit.maxCount, 1), 100);
float eps;
if( !(crit.type & cv::TermCriteria::EPS) )
eps = 1.f;
eps = (float)std::max(crit.epsilon, 0.0);
int tab[512];
for(int i = 0; i < 512; i++)
tab[i] = (i - 255) * (i - 255);
uchar *sptr = src_roi.data;
uchar *dptr = dst_roi.data;
int sstep = (int)src_roi.step;
int dstep = (int)dst_roi.step;
cv::Size size = src_roi.size();
for(int i = 0; i < size.height; i++, sptr += sstep - (size.width << 2),
dptr += dstep - (size.width << 2))
{
for(int j = 0; j < size.width; j++, sptr += 4, dptr += 4)
{
do_meanShift(j, i, sptr, dptr, sstep, size, sp, sr, maxIter, eps, tab);
}
}
}
typedef TestBaseWithParam<Size> meanShiftFilteringFixture;
PERF_TEST_P(meanShiftFilteringFixture, meanShiftFiltering,
OCL_TYPICAL_MAT_SIZES)
{
const Size srcSize = GetParam();
const int sp = 5, sr = 6;
const string impl = getSelectedImpl();
cv::TermCriteria crit(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 5, 1);
Mat src(srcSize, CV_8UC4), dst(srcSize, CV_8UC4);
declare.in(src, WARMUP_RNG).out(dst)
.time(srcSize == OCL_SIZE_4000 ?
56 : srcSize == OCL_SIZE_2000 ? 15 : 3.8);
if (impl == "plain")
{
TEST_CYCLE() meanShiftFiltering_(src, dst, sp, sr, crit);
SANITY_CHECK(dst);
}
else if (impl == "ocl")
{
ocl::oclMat oclSrc(src), oclDst(srcSize, CV_8UC4);
TEST_CYCLE() ocl::meanShiftFiltering(oclSrc, oclDst, sp, sr, crit);
oclDst.download(dst);
SANITY_CHECK(dst);
}
#ifdef HAVE_OPENCV_GPU
else if (impl == "gpu")
CV_TEST_FAIL_NO_IMPL();
#endif
else
CV_TEST_FAIL_NO_IMPL();
}
static void meanShiftProc_(const Mat &src_roi, Mat &dst_roi, Mat &dstCoor_roi, int sp, int sr, cv::TermCriteria crit)
{
if (src_roi.empty())
{
CV_Error(CV_StsBadArg, "The input image is empty");
}
if (src_roi.depth() != CV_8U || src_roi.channels() != 4)
{
CV_Error(CV_StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported");
}
dst_roi.create(src_roi.size(), src_roi.type());
dstCoor_roi.create(src_roi.size(), CV_16SC2);
CV_Assert((src_roi.cols == dst_roi.cols) && (src_roi.rows == dst_roi.rows) &&
(src_roi.cols == dstCoor_roi.cols) && (src_roi.rows == dstCoor_roi.rows));
CV_Assert(!(dstCoor_roi.step & 0x3));
if (!(crit.type & cv::TermCriteria::MAX_ITER))
{
crit.maxCount = 5;
}
int maxIter = std::min(std::max(crit.maxCount, 1), 100);
float eps;
if (!(crit.type & cv::TermCriteria::EPS))
{
eps = 1.f;
}
eps = (float)std::max(crit.epsilon, 0.0);
int tab[512];
for (int i = 0; i < 512; i++)
{
tab[i] = (i - 255) * (i - 255);
}
uchar *sptr = src_roi.data;
uchar *dptr = dst_roi.data;
short *dCoorptr = (short *)dstCoor_roi.data;
int sstep = (int)src_roi.step;
int dstep = (int)dst_roi.step;
int dCoorstep = (int)dstCoor_roi.step >> 1;
cv::Size size = src_roi.size();
for (int i = 0; i < size.height; i++, sptr += sstep - (size.width << 2),
dptr += dstep - (size.width << 2), dCoorptr += dCoorstep - (size.width << 1))
{
for (int j = 0; j < size.width; j++, sptr += 4, dptr += 4, dCoorptr += 2)
{
*((COOR *)dCoorptr) = do_meanShift(j, i, sptr, dptr, sstep, size, sp, sr, maxIter, eps, tab);
}
}
}
typedef TestBaseWithParam<Size> meanShiftProcFixture;
PERF_TEST_P(meanShiftProcFixture, meanShiftProc,
OCL_TYPICAL_MAT_SIZES)
{
const Size srcSize = GetParam();
const string impl = getSelectedImpl();
TermCriteria crit(TermCriteria::COUNT + TermCriteria::EPS, 5, 1);
Mat src(srcSize, CV_8UC4), dst1(srcSize, CV_8UC4),
dst2(srcSize, CV_16SC2);
declare.in(src, WARMUP_RNG).out(dst1, dst2)
.time(srcSize == OCL_SIZE_4000 ?
56 : srcSize == OCL_SIZE_2000 ? 15 : 3.8);;
if (impl == "plain")
{
TEST_CYCLE() meanShiftProc_(src, dst1, dst2, 5, 6, crit);
SANITY_CHECK(dst1);
SANITY_CHECK(dst2);
}
else if (impl == "ocl")
{
ocl::oclMat oclSrc(src), oclDst1(srcSize, CV_8UC4),
oclDst2(srcSize, CV_16SC2);
TEST_CYCLE() ocl::meanShiftProc(oclSrc, oclDst1, oclDst2, 5, 6, crit);
oclDst1.download(dst1);
oclDst2.download(dst2);
SANITY_CHECK(dst1);
SANITY_CHECK(dst2);
}
#ifdef HAVE_OPENCV_GPU
else if (impl == "gpu")
CV_TEST_FAIL_NO_IMPL();
#endif
else
CV_TEST_FAIL_NO_IMPL();
}
///////////// remap////////////////////////
CV_ENUM(RemapInterType, INTER_NEAREST, INTER_LINEAR)
typedef CopyMakeBorderMatType remapMatType;
typedef tuple<Size, remapMatType, RemapInterType> remapParams;
typedef TestBaseWithParam<remapParams> remapFixture;
PERF_TEST_P(remapFixture, remap,
::testing::Combine(OCL_TYPICAL_MAT_SIZES,
remapMatType::all(),
RemapInterType::all()))
{
// getting params
remapParams params = GetParam();
const Size srcSize = get<0>(params);
const int type = get<1>(params), interpolation = get<2>(params);
const string impl = getSelectedImpl();
// creating src data
Mat src(srcSize, type), dst(srcSize, type);
declare.in(src, WARMUP_RNG).out(dst);
if (srcSize == OCL_SIZE_4000 && interpolation == INTER_LINEAR)
declare.time(9);
Mat xmap, ymap;
xmap.create(srcSize, CV_32FC1);
ymap.create(srcSize, CV_32FC1);
for (int i = 0; i < srcSize.height; ++i)
{
float * const xmap_row = xmap.ptr<float>(i);
float * const ymap_row = ymap.ptr<float>(i);
for (int j = 0; j < srcSize.width; ++j)
{
xmap_row[j] = (j - srcSize.width * 0.5f) * 0.75f + srcSize.width * 0.5f;
ymap_row[j] = (i - srcSize.height * 0.5f) * 0.75f + srcSize.height * 0.5f;
}
}
const int borderMode = BORDER_CONSTANT;
// select implementation
if (impl == "ocl")
{
ocl::oclMat oclSrc(src), oclDst(srcSize, type);
ocl::oclMat oclXMap(xmap), oclYMap(ymap);
TEST_CYCLE() cv::ocl::remap(oclSrc, oclDst, oclXMap, oclYMap, interpolation, borderMode);
oclDst.download(dst);
SANITY_CHECK(dst, 1 + DBL_EPSILON);
}
else if (impl == "plain")
{
TEST_CYCLE() cv::remap(src, dst, xmap, ymap, interpolation, borderMode);
SANITY_CHECK(dst, 1 + DBL_EPSILON);
}
#ifdef HAVE_OPENCV_GPU
else if (impl == "gpu")
CV_TEST_FAIL_NO_IMPL();
#endif
else
CV_TEST_FAIL_NO_IMPL();
}
///////////// CLAHE ////////////////////////
typedef TestBaseWithParam<Size> CLAHEFixture;
PERF_TEST_P(CLAHEFixture, CLAHE, OCL_TYPICAL_MAT_SIZES)
{
// getting params
const Size srcSize = GetParam();
const string impl = getSelectedImpl();
// creating src data
Mat src(srcSize, CV_8UC1), dst;
const double clipLimit = 40.0;
declare.in(src, WARMUP_RNG);
if (srcSize == OCL_SIZE_4000)
declare.time(11);
// select implementation
if (impl == "ocl")
{
ocl::oclMat oclSrc(src), oclDst;
cv::Ptr<cv::CLAHE> oclClahe = cv::ocl::createCLAHE(clipLimit);
TEST_CYCLE() oclClahe->apply(oclSrc, oclDst);
oclDst.download(dst);
SANITY_CHECK(dst);
}
else if (impl == "plain")
{
cv::Ptr<cv::CLAHE> clahe = cv::createCLAHE(clipLimit);
TEST_CYCLE() clahe->apply(src, dst);
SANITY_CHECK(dst);
}
#ifdef HAVE_OPENCV_GPU
else if (impl == "gpu")
CV_TEST_FAIL_NO_IMPL();
#endif
else
CV_TEST_FAIL_NO_IMPL();
}
///////////// columnSum////////////////////////
typedef TestBaseWithParam<Size> columnSumFixture;
static void columnSumPerfTest(const Mat & src, Mat & dst)
{
for (int j = 0; j < src.cols; j++)
dst.at<float>(0, j) = src.at<float>(0, j);
for (int i = 1; i < src.rows; ++i)
for (int j = 0; j < src.cols; ++j)
dst.at<float>(i, j) = dst.at<float>(i - 1 , j) + src.at<float>(i , j);
}
PERF_TEST_P(columnSumFixture, columnSum, OCL_TYPICAL_MAT_SIZES)
{
// getting params
const Size srcSize = GetParam();
const string impl = getSelectedImpl();
// creating src data
Mat src(srcSize, CV_32FC1), dst(srcSize, CV_32FC1);
declare.in(src, WARMUP_RNG).out(dst);
if (srcSize == OCL_SIZE_4000)
declare.time(5);
// select implementation
if (impl == "ocl")
{
ocl::oclMat oclSrc(src), oclDst(srcSize, CV_32FC1);
TEST_CYCLE() cv::ocl::columnSum(oclSrc, oclDst);
oclDst.download(dst);
SANITY_CHECK(dst);
}
else if (impl == "plain")
{
TEST_CYCLE() columnSumPerfTest(src, dst);
SANITY_CHECK(dst);
}
#ifdef HAVE_OPENCV_GPU
else if (impl == "gpu")
CV_TEST_FAIL_NO_IMPL();
#endif
else
CV_TEST_FAIL_NO_IMPL();
}
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