mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2024-11-26 04:00:30 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity
302250f241
opencv/modules/ocl/perf/perf_imgproc.cpp
Roman Donchenko bae85660da Merge remote-tracking branch 'origin/2.4' 
Pull requests:
	#943 from jet47:cuda-5.5-support
	#944 from jet47:cmake-2.8.11-cuda-fix
	#912 from SpecLad:contributing
	#934 from SpecLad:parallel-for
	#931 from jet47:gpu-test-fixes
	#932 from bitwangyaoyao:2.4_fixBFM
	#918 from bitwangyaoyao:2.4_samples
	#924 from pengx17:2.4_arithm_fix
	#925 from pengx17:2.4_canny_tmp_fix
	#927 from bitwangyaoyao:2.4_perf
	#930 from pengx17:2.4_haar_ext
	#928 from apavlenko:bugfix_3027
	#920 from asmorkalov:android_move
	#910 from pengx17:2.4_oclgfft
	#913 from janm399:2.4
	#916 from bitwangyaoyao:2.4_fixPyrLK
	#919 from abidrahmank:2.4
	#923 from pengx17:2.4_macfix

Conflicts:
	modules/calib3d/src/stereobm.cpp
	modules/features2d/src/detectors.cpp
	modules/gpu/src/error.cpp
	modules/gpu/src/precomp.hpp
	modules/imgproc/src/distransform.cpp
	modules/imgproc/src/morph.cpp
	modules/ocl/include/opencv2/ocl/ocl.hpp
	modules/ocl/perf/perf_color.cpp
	modules/ocl/perf/perf_imgproc.cpp
	modules/ocl/perf/perf_match_template.cpp
	modules/ocl/perf/precomp.cpp
	modules/ocl/perf/precomp.hpp
	modules/ocl/src/arithm.cpp
	modules/ocl/src/canny.cpp
	modules/ocl/src/filtering.cpp
	modules/ocl/src/haar.cpp
	modules/ocl/src/hog.cpp
	modules/ocl/src/imgproc.cpp
	modules/ocl/src/opencl/haarobjectdetect.cl
	modules/ocl/src/pyrlk.cpp
	modules/video/src/bgfg_gaussmix2.cpp
	modules/video/src/lkpyramid.cpp
	platforms/linux/scripts/cmake_arm_gnueabi_hardfp.sh
	platforms/linux/scripts/cmake_arm_gnueabi_softfp.sh
	platforms/scripts/ABI_compat_generator.py
	samples/ocl/facedetect.cpp
2013-06-05 15:42:07 +04:00

1108 lines
31 KiB
C++
Raw Blame History

/*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
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
///////////// equalizeHist ////////////////////////
PERFTEST(equalizeHist)
{
Mat src, dst, ocl_dst;
int all_type[] = {CV_8UC1};
std::string type_name[] = {"CV_8UC1"};
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
{
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
{
SUBTEST << size << 'x' << size << "; " << type_name[j] ;
gen(src, size, size, all_type[j], 0, 256);
equalizeHist(src, dst);
CPU_ON;
equalizeHist(src, dst);
CPU_OFF;
ocl::oclMat d_src(src);
ocl::oclMat d_dst;
ocl::oclMat d_hist;
ocl::oclMat d_buf;
WARMUP_ON;
ocl::equalizeHist(d_src, d_dst);
WARMUP_OFF;
GPU_ON;
ocl::equalizeHist(d_src, d_dst);
GPU_OFF;
GPU_FULL_ON;
d_src.upload(src);
ocl::equalizeHist(d_src, d_dst);
d_dst.download(ocl_dst);
GPU_FULL_OFF;
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.1);
}
}
}
/////////// CopyMakeBorder //////////////////////
PERFTEST(CopyMakeBorder)
{
Mat src, dst, ocl_dst;
ocl::oclMat d_dst;
int bordertype = BORDER_CONSTANT;
int all_type[] = {CV_8UC1, CV_8UC4};
std::string type_name[] = {"CV_8UC1", "CV_8UC4"};
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
{
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
{
SUBTEST << size << 'x' << size << "; " << type_name[j] ;
gen(src, size, size, all_type[j], 0, 256);
copyMakeBorder(src, dst, 7, 5, 5, 7, bordertype, cv::Scalar(1.0));
CPU_ON;
copyMakeBorder(src, dst, 7, 5, 5, 7, bordertype, cv::Scalar(1.0));
CPU_OFF;
ocl::oclMat d_src(src);
WARMUP_ON;
ocl::copyMakeBorder(d_src, d_dst, 7, 5, 5, 7, bordertype, cv::Scalar(1.0));
WARMUP_OFF;
GPU_ON;
ocl::copyMakeBorder(d_src, d_dst, 7, 5, 5, 7, bordertype, cv::Scalar(1.0));
GPU_OFF;
GPU_FULL_ON;
d_src.upload(src);
ocl::copyMakeBorder(d_src, d_dst, 7, 5, 5, 7, bordertype, cv::Scalar(1.0));
d_dst.download(ocl_dst);
GPU_FULL_OFF;
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 0.0);
}
}
}
///////////// cornerMinEigenVal ////////////////////////
PERFTEST(cornerMinEigenVal)
{
Mat src, dst, ocl_dst;
ocl::oclMat d_dst;
int blockSize = 7, apertureSize = 1 + 2 * (rand() % 4);
int borderType = BORDER_REFLECT;
int all_type[] = {CV_8UC1, CV_32FC1};
std::string type_name[] = {"CV_8UC1", "CV_32FC1"};
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
{
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
{
SUBTEST << size << 'x' << size << "; " << type_name[j] ;
gen(src, size, size, all_type[j], 0, 256);
cornerMinEigenVal(src, dst, blockSize, apertureSize, borderType);
CPU_ON;
cornerMinEigenVal(src, dst, blockSize, apertureSize, borderType);
CPU_OFF;
ocl::oclMat d_src(src);
WARMUP_ON;
ocl::cornerMinEigenVal(d_src, d_dst, blockSize, apertureSize, borderType);
WARMUP_OFF;
GPU_ON;
ocl::cornerMinEigenVal(d_src, d_dst, blockSize, apertureSize, borderType);
GPU_OFF;
GPU_FULL_ON;
d_src.upload(src);
ocl::cornerMinEigenVal(d_src, d_dst, blockSize, apertureSize, borderType);
d_dst.download(ocl_dst);
GPU_FULL_OFF;
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
}
}
}
///////////// cornerHarris ////////////////////////
PERFTEST(cornerHarris)
{
Mat src, dst, ocl_dst;
ocl::oclMat d_src, d_dst;
int all_type[] = {CV_8UC1, CV_32FC1};
std::string type_name[] = {"CV_8UC1", "CV_32FC1"};
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
{
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
{
SUBTEST << size << 'x' << size << "; " << type_name[j] << " ; BORDER_REFLECT";
gen(src, size, size, all_type[j], 0, 1);
cornerHarris(src, dst, 5, 7, 0.1, BORDER_REFLECT);
CPU_ON;
cornerHarris(src, dst, 5, 7, 0.1, BORDER_REFLECT);
CPU_OFF;
d_src.upload(src);
WARMUP_ON;
ocl::cornerHarris(d_src, d_dst, 5, 7, 0.1, BORDER_REFLECT);
WARMUP_OFF;
GPU_ON;
ocl::cornerHarris(d_src, d_dst, 5, 7, 0.1, BORDER_REFLECT);
GPU_OFF;
GPU_FULL_ON;
d_src.upload(src);
ocl::cornerHarris(d_src, d_dst, 5, 7, 0.1, BORDER_REFLECT);
d_dst.download(ocl_dst);
GPU_FULL_OFF;
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
}
}
}
///////////// integral ////////////////////////
PERFTEST(integral)
{
Mat src, sum, ocl_sum;
ocl::oclMat d_src, d_sum, d_buf;
int all_type[] = {CV_8UC1};
std::string type_name[] = {"CV_8UC1"};
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
{
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
{
SUBTEST << size << 'x' << size << "; " << type_name[j] ;
gen(src, size, size, all_type[j], 0, 256);
integral(src, sum);
CPU_ON;
integral(src, sum);
CPU_OFF;
d_src.upload(src);
WARMUP_ON;
ocl::integral(d_src, d_sum);
WARMUP_OFF;
GPU_ON;
ocl::integral(d_src, d_sum);
GPU_OFF;
GPU_FULL_ON;
d_src.upload(src);
ocl::integral(d_src, d_sum);
d_sum.download(ocl_sum);
GPU_FULL_OFF;
if(sum.type() == ocl_sum.type()) //we won't test accuracy when cpu function overlow
TestSystem::instance().ExpectedMatNear(sum, ocl_sum, 0.0);
}
}
}
///////////// WarpAffine ////////////////////////
PERFTEST(WarpAffine)
{
Mat src, dst, ocl_dst;
ocl::oclMat d_src, d_dst;
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);
int interpolation = INTER_NEAREST;
int all_type[] = {CV_8UC1, CV_8UC4};
std::string type_name[] = {"CV_8UC1", "CV_8UC4"};
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
{
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
{
SUBTEST << size << 'x' << size << "; " << type_name[j] ;
gen(src, size, size, all_type[j], 0, 256);
gen(dst, size, size, all_type[j], 0, 256);
Size size1 = Size(size, size);
warpAffine(src, dst, M, size1, interpolation);
CPU_ON;
warpAffine(src, dst, M, size1, interpolation);
CPU_OFF;
d_src.upload(src);
WARMUP_ON;
ocl::warpAffine(d_src, d_dst, M, size1, interpolation);
WARMUP_OFF;
GPU_ON;
ocl::warpAffine(d_src, d_dst, M, size1, interpolation);
GPU_OFF;
GPU_FULL_ON;
d_src.upload(src);
ocl::warpAffine(d_src, d_dst, M, size1, interpolation);
d_dst.download(ocl_dst);
GPU_FULL_OFF;
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
}
}
}
///////////// WarpPerspective ////////////////////////
PERFTEST(WarpPerspective)
{
Mat src, dst, ocl_dst;
ocl::oclMat d_src, d_dst;
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);
int interpolation = INTER_LINEAR;
int all_type[] = {CV_8UC1, CV_8UC4};
std::string type_name[] = {"CV_8UC1", "CV_8UC4"};
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
{
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
{
SUBTEST << size << 'x' << size << "; " << type_name[j] ;
gen(src, size, size, all_type[j], 0, 256);
gen(dst, size, size, all_type[j], 0, 256);
Size size1 = Size(size, size);
warpPerspective(src, dst, M, size1, interpolation);
CPU_ON;
warpPerspective(src, dst, M, size1, interpolation);
CPU_OFF;
d_src.upload(src);
WARMUP_ON;
ocl::warpPerspective(d_src, d_dst, M, size1, interpolation);
WARMUP_OFF;
GPU_ON;
ocl::warpPerspective(d_src, d_dst, M, size1, interpolation);
GPU_OFF;
GPU_FULL_ON;
d_src.upload(src);
ocl::warpPerspective(d_src, d_dst, M, size1, interpolation);
d_dst.download(ocl_dst);
GPU_FULL_OFF;
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
}
}
}
///////////// resize ////////////////////////
PERFTEST(resize)
{
Mat src, dst, ocl_dst;
ocl::oclMat d_src, d_dst;
int all_type[] = {CV_8UC1, CV_8UC4};
std::string type_name[] = {"CV_8UC1", "CV_8UC4"};
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
{
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
{
SUBTEST << size << 'x' << size << "; " << type_name[j] << " ; up";
gen(src, size, size, all_type[j], 0, 256);
resize(src, dst, Size(), 2.0, 2.0);
CPU_ON;
resize(src, dst, Size(), 2.0, 2.0);
CPU_OFF;
d_src.upload(src);
WARMUP_ON;
ocl::resize(d_src, d_dst, Size(), 2.0, 2.0);
WARMUP_OFF;
GPU_ON;
ocl::resize(d_src, d_dst, Size(), 2.0, 2.0);
GPU_OFF;
GPU_FULL_ON;
d_src.upload(src);
ocl::resize(d_src, d_dst, Size(), 2.0, 2.0);
d_dst.download(ocl_dst);
GPU_FULL_OFF;
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
}
}
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
{
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
{
SUBTEST << size << 'x' << size << "; " << type_name[j] << " ; down";
gen(src, size, size, all_type[j], 0, 256);
resize(src, dst, Size(), 0.5, 0.5);
CPU_ON;
resize(src, dst, Size(), 0.5, 0.5);
CPU_OFF;
d_src.upload(src);
WARMUP_ON;
ocl::resize(d_src, d_dst, Size(), 0.5, 0.5);
WARMUP_OFF;
GPU_ON;
ocl::resize(d_src, d_dst, Size(), 0.5, 0.5);
GPU_OFF;
GPU_FULL_ON;
d_src.upload(src);
ocl::resize(d_src, d_dst, Size(), 0.5, 0.5);
d_dst.download(ocl_dst);
GPU_FULL_OFF;
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
}
}
}
///////////// threshold////////////////////////
PERFTEST(threshold)
{
Mat src, dst, ocl_dst;
ocl::oclMat d_src, d_dst;
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
{
SUBTEST << size << 'x' << size << "; 8UC1; THRESH_BINARY";
gen(src, size, size, CV_8U, 0, 100);
threshold(src, dst, 50.0, 0.0, THRESH_BINARY);
CPU_ON;
threshold(src, dst, 50.0, 0.0, THRESH_BINARY);
CPU_OFF;
d_src.upload(src);
WARMUP_ON;
ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_BINARY);
WARMUP_OFF;
GPU_ON;
ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_BINARY);
GPU_OFF;
GPU_FULL_ON;
d_src.upload(src);
ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_BINARY);
d_dst.download(ocl_dst);
GPU_FULL_OFF;
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
}
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
{
SUBTEST << size << 'x' << size << "; 32FC1; THRESH_TRUNC [NPP]";
gen(src, size, size, CV_32FC1, 0, 100);
threshold(src, dst, 50.0, 0.0, THRESH_TRUNC);
CPU_ON;
threshold(src, dst, 50.0, 0.0, THRESH_TRUNC);
CPU_OFF;
d_src.upload(src);
WARMUP_ON;
ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_TRUNC);
WARMUP_OFF;
GPU_ON;
ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_TRUNC);
GPU_OFF;
GPU_FULL_ON;
d_src.upload(src);
ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_TRUNC);
d_dst.download(ocl_dst);
GPU_FULL_OFF;
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
}
}
///////////// meanShiftFiltering////////////////////////
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;
}
void meanShiftFiltering_(const Mat &src_roi, Mat &dst_roi, int sp, int sr, cv::TermCriteria crit);
void meanShiftFiltering_(const Mat &src_roi, Mat &dst_roi, int sp, int sr, cv::TermCriteria crit)
{
if( src_roi.empty() )
CV_Error( Error::StsBadArg, "The input image is empty" );
if( src_roi.depth() != CV_8U || src_roi.channels() != 4 )
CV_Error( Error::StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported" );
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);
}
}
}
PERFTEST(meanShiftFiltering)
{
int sp = 5, sr = 6;
Mat src, dst, ocl_dst;
ocl::oclMat d_src, d_dst;
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
{
SUBTEST << size << 'x' << size << "; 8UC3 vs 8UC4";
gen(src, size, size, CV_8UC4, Scalar::all(0), Scalar::all(256));
//gen(dst, size, size, CV_8UC4, Scalar::all(0), Scalar::all(256));
dst = src;
dst.setTo(0);
cv::TermCriteria crit(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 5, 1);
meanShiftFiltering_(src, dst, sp, sr, crit);
CPU_ON;
meanShiftFiltering_(src, dst, sp, sr, crit);
CPU_OFF;
d_src.upload(src);
WARMUP_ON;
ocl::meanShiftFiltering(d_src, d_dst, sp, sr, crit);
WARMUP_OFF;
GPU_ON;
ocl::meanShiftFiltering(d_src, d_dst, sp, sr);
GPU_OFF;
GPU_FULL_ON;
d_src.upload(src);
ocl::meanShiftFiltering(d_src, d_dst, sp, sr);
d_dst.download(ocl_dst);
GPU_FULL_OFF;
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 0.0);
}
}
///////////// meanShiftProc////////////////////////
#if 0
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;
}
#endif
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(Error::StsBadArg, "The input image is empty");
}
if (src_roi.depth() != CV_8U || src_roi.channels() != 4)
{
CV_Error(Error::StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported");
}
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);
}
}
}
PERFTEST(meanShiftProc)
{
Mat src;
vector<Mat> dst(2), ocl_dst(2);
ocl::oclMat d_src, d_dst, d_dstCoor;
TermCriteria crit(TermCriteria::COUNT + TermCriteria::EPS, 5, 1);
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
{
SUBTEST << size << 'x' << size << "; 8UC4 and CV_16SC2 ";
gen(src, size, size, CV_8UC4, Scalar::all(0), Scalar::all(256));
gen(dst[0], size, size, CV_8UC4, Scalar::all(0), Scalar::all(256));
gen(dst[1], size, size, CV_16SC2, Scalar::all(0), Scalar::all(256));
meanShiftProc_(src, dst[0], dst[1], 5, 6, crit);
CPU_ON;
meanShiftProc_(src, dst[0], dst[1], 5, 6, crit);
CPU_OFF;
d_src.upload(src);
WARMUP_ON;
ocl::meanShiftProc(d_src, d_dst, d_dstCoor, 5, 6, crit);
WARMUP_OFF;
GPU_ON;
ocl::meanShiftProc(d_src, d_dst, d_dstCoor, 5, 6, crit);
GPU_OFF;
GPU_FULL_ON;
d_src.upload(src);
ocl::meanShiftProc(d_src, d_dst, d_dstCoor, 5, 6, crit);
d_dst.download(ocl_dst[0]);
d_dstCoor.download(ocl_dst[1]);
GPU_FULL_OFF;
vector<double> eps(2, 0.);
TestSystem::instance().ExpectMatsNear(dst, ocl_dst, eps);
}
}
///////////// remap////////////////////////
PERFTEST(remap)
{
Mat src, dst, xmap, ymap, ocl_dst;
ocl::oclMat d_src, d_dst, d_xmap, d_ymap;
int all_type[] = {CV_8UC1, CV_8UC4};
std::string type_name[] = {"CV_8UC1", "CV_8UC4"};
int interpolation = INTER_LINEAR;
int borderMode = BORDER_CONSTANT;
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
{
for (size_t t = 0; t < sizeof(all_type) / sizeof(int); t++)
{
SUBTEST << size << 'x' << size << "; src " << type_name[t] << "; map CV_32FC1";
gen(src, size, size, all_type[t], 0, 256);
xmap.create(size, size, CV_32FC1);
dst.create(size, size, CV_32FC1);
ymap.create(size, size, CV_32FC1);
for (int i = 0; i < size; ++i)
{
float *xmap_row = xmap.ptr<float>(i);
float *ymap_row = ymap.ptr<float>(i);
for (int j = 0; j < size; ++j)
{
xmap_row[j] = (j - size * 0.5f) * 0.75f + size * 0.5f;
ymap_row[j] = (i - size * 0.5f) * 0.75f + size * 0.5f;
}
}
remap(src, dst, xmap, ymap, interpolation, borderMode);
CPU_ON;
remap(src, dst, xmap, ymap, interpolation, borderMode);
CPU_OFF;
d_src.upload(src);
d_dst.upload(dst);
d_xmap.upload(xmap);
d_ymap.upload(ymap);
WARMUP_ON;
ocl::remap(d_src, d_dst, d_xmap, d_ymap, interpolation, borderMode);
WARMUP_OFF;
GPU_ON;
ocl::remap(d_src, d_dst, d_xmap, d_ymap, interpolation, borderMode);
GPU_OFF;
GPU_FULL_ON;
d_src.upload(src);
ocl::remap(d_src, d_dst, d_xmap, d_ymap, interpolation, borderMode);
d_dst.download(ocl_dst);
GPU_FULL_OFF;
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 2.0);
}
}
}
Reference in New Issue View Git Blame Copy Permalink