|
|
|
/*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, Institute Of Software Chinese Academy Of Science, all rights reserved.
|
|
|
|
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
|
|
|
|
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
|
|
|
|
// Third party copyrights are property of their respective owners.
|
|
|
|
//
|
|
|
|
// @Authors
|
|
|
|
// Niko Li, newlife20080214@gmail.com
|
|
|
|
// Jia Haipeng, jiahaipeng95@gmail.com
|
|
|
|
// Shengen Yan, yanshengen@gmail.com
|
|
|
|
// Jiang Liyuan, lyuan001.good@163.com
|
|
|
|
// Rock Li, Rock.Li@amd.com
|
|
|
|
// Wu Zailong, bullet@yeah.net
|
|
|
|
// Xu Pang, pangxu010@163.com
|
|
|
|
// Sen Liu, swjtuls1987@126.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 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*/
|
|
|
|
|
|
|
|
#include "test_precomp.hpp"
|
|
|
|
|
|
|
|
#ifdef HAVE_OPENCL
|
|
|
|
|
|
|
|
using namespace testing;
|
|
|
|
using namespace std;
|
|
|
|
using namespace cv;
|
|
|
|
|
|
|
|
typedef struct
|
|
|
|
{
|
|
|
|
short x;
|
|
|
|
short y;
|
|
|
|
} COOR;
|
|
|
|
|
|
|
|
COOR do_meanShift(int x0, int y0, uchar *sptr, uchar *dptr, int sstep, 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 = (short)x0;
|
|
|
|
coor.y = (short)y0;
|
|
|
|
return coor;
|
|
|
|
}
|
|
|
|
|
|
|
|
void meanShiftFiltering_(const Mat &src_roi, Mat &dst_roi, int sp, int sr, 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" );
|
|
|
|
|
|
|
|
CV_Assert( (src_roi.cols == dst_roi.cols) && (src_roi.rows == dst_roi.rows) );
|
|
|
|
CV_Assert( !(dst_roi.step & 0x3) );
|
|
|
|
|
|
|
|
if( !(crit.type & TermCriteria::MAX_ITER) )
|
|
|
|
crit.maxCount = 5;
|
|
|
|
int maxIter = std::min(std::max(crit.maxCount, 1), 100);
|
|
|
|
float eps;
|
|
|
|
if( !(crit.type & 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;
|
|
|
|
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);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
void meanShiftProc_(const Mat &src_roi, Mat &dst_roi, Mat &dstCoor_roi, int sp, int sr, 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" );
|
|
|
|
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 & TermCriteria::MAX_ITER) )
|
|
|
|
crit.maxCount = 5;
|
|
|
|
int maxIter = std::min(std::max(crit.maxCount, 1), 100);
|
|
|
|
float eps;
|
|
|
|
if( !(crit.type & 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;
|
|
|
|
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);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
//////////////////////////////// meanShift //////////////////////////////////////////
|
|
|
|
|
|
|
|
PARAM_TEST_CASE(meanShiftTestBase, MatType, MatType, int, int, TermCriteria, bool)
|
|
|
|
{
|
|
|
|
int type, typeCoor;
|
|
|
|
int sp, sr;
|
|
|
|
TermCriteria crit;
|
|
|
|
bool useRoi;
|
|
|
|
|
|
|
|
// src mat
|
|
|
|
Mat src, src_roi;
|
|
|
|
Mat dst, dst_roi;
|
|
|
|
Mat dstCoor, dstCoor_roi;
|
|
|
|
|
|
|
|
// ocl dst mat
|
|
|
|
ocl::oclMat gsrc, gsrc_roi;
|
|
|
|
ocl::oclMat gdst, gdst_roi;
|
|
|
|
ocl::oclMat gdstCoor, gdstCoor_roi;
|
|
|
|
|
|
|
|
virtual void SetUp()
|
|
|
|
{
|
|
|
|
type = GET_PARAM(0);
|
|
|
|
typeCoor = GET_PARAM(1);
|
|
|
|
sp = GET_PARAM(2);
|
|
|
|
sr = GET_PARAM(3);
|
|
|
|
crit = GET_PARAM(4);
|
|
|
|
useRoi = GET_PARAM(5);
|
|
|
|
}
|
|
|
|
|
|
|
|
void random_roi()
|
|
|
|
{
|
|
|
|
Size roiSize = randomSize(1, MAX_VALUE);
|
|
|
|
Border srcBorder = randomBorder(0, useRoi ? MAX_VALUE : 0);
|
|
|
|
randomSubMat(src, src_roi, roiSize, srcBorder, type, 5, 256);
|
|
|
|
generateOclMat(gsrc, gsrc_roi, src, roiSize, srcBorder);
|
|
|
|
|
|
|
|
Border dstBorder = randomBorder(0, useRoi ? MAX_VALUE : 0);
|
|
|
|
randomSubMat(dst, dst_roi, roiSize, dstBorder, type, 5, 256);
|
|
|
|
generateOclMat(gdst, gdst_roi, dst, roiSize, dstBorder);
|
|
|
|
|
|
|
|
randomSubMat(dstCoor, dstCoor_roi, roiSize, dstBorder, typeCoor, 5, 256);
|
|
|
|
generateOclMat(gdstCoor, gdstCoor_roi, dstCoor, roiSize, dstBorder);
|
|
|
|
}
|
|
|
|
|
|
|
|
void Near(double threshold = 0.0)
|
|
|
|
{
|
|
|
|
Mat whole, roi;
|
|
|
|
gdst.download(whole);
|
|
|
|
gdst_roi.download(roi);
|
|
|
|
|
|
|
|
EXPECT_MAT_NEAR(dst, whole, threshold);
|
|
|
|
EXPECT_MAT_NEAR(dst_roi, roi, threshold);
|
|
|
|
}
|
|
|
|
|
|
|
|
void Near1(double threshold = 0.0)
|
|
|
|
{
|
|
|
|
Mat whole, roi;
|
|
|
|
gdstCoor.download(whole);
|
|
|
|
gdstCoor_roi.download(roi);
|
|
|
|
|
|
|
|
EXPECT_MAT_NEAR(dstCoor, whole, threshold);
|
|
|
|
EXPECT_MAT_NEAR(dstCoor_roi, roi, threshold);
|
|
|
|
}
|
|
|
|
};
|
|
|
|
|
|
|
|
/////////////////////////meanShiftFiltering/////////////////////////////
|
|
|
|
|
|
|
|
typedef meanShiftTestBase meanShiftFiltering;
|
|
|
|
|
|
|
|
OCL_TEST_P(meanShiftFiltering, Mat)
|
|
|
|
{
|
|
|
|
for (int j = 0; j < LOOP_TIMES; j++)
|
|
|
|
{
|
|
|
|
random_roi();
|
|
|
|
|
|
|
|
meanShiftFiltering_(src_roi, dst_roi, sp, sr, crit);
|
|
|
|
ocl::meanShiftFiltering(gsrc_roi, gdst_roi, sp, sr, crit);
|
|
|
|
|
|
|
|
Near();
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
///////////////////////////meanShiftProc//////////////////////////////////
|
|
|
|
|
|
|
|
typedef meanShiftTestBase meanShiftProc;
|
|
|
|
|
|
|
|
OCL_TEST_P(meanShiftProc, Mat)
|
|
|
|
{
|
|
|
|
for (int j = 0; j < LOOP_TIMES; j++)
|
|
|
|
{
|
|
|
|
random_roi();
|
|
|
|
|
|
|
|
meanShiftProc_(src_roi, dst_roi, dstCoor_roi, sp, sr, crit);
|
|
|
|
ocl::meanShiftProc(gsrc_roi, gdst_roi, gdstCoor_roi, sp, sr, crit);
|
|
|
|
|
|
|
|
Near();
|
|
|
|
Near1();
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
|
|
|
INSTANTIATE_TEST_CASE_P(Imgproc, meanShiftFiltering, Combine(
|
|
|
|
Values((MatType)CV_8UC4),
|
|
|
|
Values((MatType)CV_16SC2),
|
|
|
|
Values(5),
|
|
|
|
Values(6),
|
|
|
|
Values(TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 5, 1)),
|
|
|
|
Bool()
|
|
|
|
));
|
|
|
|
|
|
|
|
INSTANTIATE_TEST_CASE_P(Imgproc, meanShiftProc, Combine(
|
|
|
|
Values((MatType)CV_8UC4),
|
|
|
|
Values((MatType)CV_16SC2),
|
|
|
|
Values(5),
|
|
|
|
Values(6),
|
|
|
|
Values(TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 5, 1)),
|
|
|
|
Bool()
|
|
|
|
));
|
|
|
|
|
|
|
|
#endif // HAVE_OPENCL
|