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Open Source Computer Vision Library
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408 lines
13 KiB
408 lines
13 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved. |
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// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved. |
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// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// @Authors |
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// Niko Li, newlife20080214@gmail.com |
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// Jia Haipeng, jiahaipeng95@gmail.com |
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// Shengen Yan, yanshengen@gmail.com |
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// Jiang Liyuan, lyuan001.good@163.com |
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// Rock Li, Rock.Li@amd.com |
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// Wu Zailong, bullet@yeah.net |
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// Xu Pang, pangxu010@163.com |
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// Sen Liu, swjtuls1987@126.com |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "test_precomp.hpp" |
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#ifdef HAVE_OPENCL |
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using namespace testing; |
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using namespace std; |
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using namespace cv; |
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typedef struct |
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{ |
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short x; |
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short y; |
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} COOR; |
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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) |
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{ |
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int isr2 = sr * sr; |
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int c0, c1, c2, c3; |
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int iter; |
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uchar *ptr = NULL; |
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uchar *pstart = NULL; |
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int revx = 0, revy = 0; |
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c0 = sptr[0]; |
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c1 = sptr[1]; |
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c2 = sptr[2]; |
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c3 = sptr[3]; |
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// iterate meanshift procedure |
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for(iter = 0; iter < maxIter; iter++ ) |
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{ |
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int count = 0; |
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int s0 = 0, s1 = 0, s2 = 0, sx = 0, sy = 0; |
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//mean shift: process pixels in window (p-sigmaSp)x(p+sigmaSp) |
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int minx = x0 - sp; |
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int miny = y0 - sp; |
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int maxx = x0 + sp; |
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int maxy = y0 + sp; |
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//deal with the image boundary |
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if(minx < 0) minx = 0; |
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if(miny < 0) miny = 0; |
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if(maxx >= size.width) maxx = size.width - 1; |
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if(maxy >= size.height) maxy = size.height - 1; |
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if(iter == 0) |
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{ |
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pstart = sptr; |
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} |
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else |
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{ |
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pstart = pstart + revy * sstep + (revx << 2); //point to the new position |
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} |
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ptr = pstart; |
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ptr = ptr + (miny - y0) * sstep + ((minx - x0) << 2); //point to the start in the row |
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for( int y = miny; y <= maxy; y++, ptr += sstep - ((maxx - minx + 1) << 2)) |
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{ |
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int rowCount = 0; |
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int x = minx; |
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#if CV_ENABLE_UNROLLED |
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for( ; x + 4 <= maxx; x += 4, ptr += 16) |
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{ |
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int t0, t1, t2; |
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t0 = ptr[0], t1 = ptr[1], t2 = ptr[2]; |
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if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) |
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{ |
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s0 += t0; |
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s1 += t1; |
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s2 += t2; |
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sx += x; |
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rowCount++; |
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} |
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t0 = ptr[4], t1 = ptr[5], t2 = ptr[6]; |
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if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) |
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{ |
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s0 += t0; |
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s1 += t1; |
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s2 += t2; |
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sx += x + 1; |
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rowCount++; |
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} |
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t0 = ptr[8], t1 = ptr[9], t2 = ptr[10]; |
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if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) |
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{ |
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s0 += t0; |
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s1 += t1; |
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s2 += t2; |
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sx += x + 2; |
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rowCount++; |
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} |
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t0 = ptr[12], t1 = ptr[13], t2 = ptr[14]; |
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if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) |
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{ |
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s0 += t0; |
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s1 += t1; |
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s2 += t2; |
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sx += x + 3; |
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rowCount++; |
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} |
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} |
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#endif |
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for(; x <= maxx; x++, ptr += 4) |
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{ |
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int t0 = ptr[0], t1 = ptr[1], t2 = ptr[2]; |
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if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) |
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{ |
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s0 += t0; |
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s1 += t1; |
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s2 += t2; |
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sx += x; |
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rowCount++; |
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} |
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} |
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if(rowCount == 0) |
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continue; |
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count += rowCount; |
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sy += y * rowCount; |
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} |
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if( count == 0 ) |
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break; |
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int x1 = sx / count; |
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int y1 = sy / count; |
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s0 = s0 / count; |
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s1 = s1 / count; |
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s2 = s2 / count; |
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bool stopFlag = (x0 == x1 && y0 == y1) || (abs(x1 - x0) + abs(y1 - y0) + |
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tab[s0 - c0 + 255] + tab[s1 - c1 + 255] + tab[s2 - c2 + 255] <= eps); |
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//revise the pointer corresponding to the new (y0,x0) |
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revx = x1 - x0; |
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revy = y1 - y0; |
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x0 = x1; |
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y0 = y1; |
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c0 = s0; |
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c1 = s1; |
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c2 = s2; |
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if( stopFlag ) |
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break; |
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} //for iter |
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dptr[0] = (uchar)c0; |
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dptr[1] = (uchar)c1; |
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dptr[2] = (uchar)c2; |
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dptr[3] = (uchar)c3; |
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COOR coor; |
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coor.x = (short)x0; |
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coor.y = (short)y0; |
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return coor; |
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} |
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void meanShiftFiltering_(const Mat &src_roi, Mat &dst_roi, int sp, int sr, TermCriteria crit) |
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{ |
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if( src_roi.empty() ) |
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CV_Error( CV_StsBadArg, "The input image is empty" ); |
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if( src_roi.depth() != CV_8U || src_roi.channels() != 4 ) |
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CV_Error( CV_StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported" ); |
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CV_Assert( (src_roi.cols == dst_roi.cols) && (src_roi.rows == dst_roi.rows) ); |
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CV_Assert( !(dst_roi.step & 0x3) ); |
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if( !(crit.type & TermCriteria::MAX_ITER) ) |
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crit.maxCount = 5; |
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int maxIter = std::min(std::max(crit.maxCount, 1), 100); |
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float eps; |
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if( !(crit.type & TermCriteria::EPS) ) |
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eps = 1.f; |
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eps = (float)std::max(crit.epsilon, 0.0); |
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int tab[512]; |
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for(int i = 0; i < 512; i++) |
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tab[i] = (i - 255) * (i - 255); |
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uchar *sptr = src_roi.data; |
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uchar *dptr = dst_roi.data; |
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int sstep = (int)src_roi.step; |
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int dstep = (int)dst_roi.step; |
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Size size = src_roi.size(); |
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for(int i = 0; i < size.height; i++, sptr += sstep - (size.width << 2), |
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dptr += dstep - (size.width << 2)) |
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{ |
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for(int j = 0; j < size.width; j++, sptr += 4, dptr += 4) |
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{ |
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do_meanShift(j, i, sptr, dptr, sstep, size, sp, sr, maxIter, eps, tab); |
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} |
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} |
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} |
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void meanShiftProc_(const Mat &src_roi, Mat &dst_roi, Mat &dstCoor_roi, int sp, int sr, TermCriteria crit) |
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{ |
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if( src_roi.empty() ) |
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CV_Error( CV_StsBadArg, "The input image is empty" ); |
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if( src_roi.depth() != CV_8U || src_roi.channels() != 4 ) |
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CV_Error( CV_StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported" ); |
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CV_Assert( (src_roi.cols == dst_roi.cols) && (src_roi.rows == dst_roi.rows) && |
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(src_roi.cols == dstCoor_roi.cols) && (src_roi.rows == dstCoor_roi.rows)); |
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CV_Assert( !(dstCoor_roi.step & 0x3) ); |
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if( !(crit.type & TermCriteria::MAX_ITER) ) |
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crit.maxCount = 5; |
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int maxIter = std::min(std::max(crit.maxCount, 1), 100); |
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float eps; |
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if( !(crit.type & TermCriteria::EPS) ) |
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eps = 1.f; |
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eps = (float)std::max(crit.epsilon, 0.0); |
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int tab[512]; |
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for(int i = 0; i < 512; i++) |
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tab[i] = (i - 255) * (i - 255); |
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uchar *sptr = src_roi.data; |
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uchar *dptr = dst_roi.data; |
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short *dCoorptr = (short *)dstCoor_roi.data; |
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int sstep = (int)src_roi.step; |
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int dstep = (int)dst_roi.step; |
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int dCoorstep = (int)dstCoor_roi.step >> 1; |
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Size size = src_roi.size(); |
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for(int i = 0; i < size.height; i++, sptr += sstep - (size.width << 2), |
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dptr += dstep - (size.width << 2), dCoorptr += dCoorstep - (size.width << 1)) |
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{ |
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for(int j = 0; j < size.width; j++, sptr += 4, dptr += 4, dCoorptr += 2) |
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{ |
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*((COOR *)dCoorptr) = do_meanShift(j, i, sptr, dptr, sstep, size, sp, sr, maxIter, eps, tab); |
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} |
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} |
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} |
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//////////////////////////////// meanShift ////////////////////////////////////////// |
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PARAM_TEST_CASE(meanShiftTestBase, MatType, MatType, int, int, TermCriteria, bool) |
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{ |
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int type, typeCoor; |
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int sp, sr; |
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TermCriteria crit; |
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bool useRoi; |
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// src mat |
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Mat src, src_roi; |
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Mat dst, dst_roi; |
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Mat dstCoor, dstCoor_roi; |
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// ocl dst mat |
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ocl::oclMat gsrc, gsrc_roi; |
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ocl::oclMat gdst, gdst_roi; |
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ocl::oclMat gdstCoor, gdstCoor_roi; |
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virtual void SetUp() |
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{ |
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type = GET_PARAM(0); |
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typeCoor = GET_PARAM(1); |
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sp = GET_PARAM(2); |
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sr = GET_PARAM(3); |
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crit = GET_PARAM(4); |
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useRoi = GET_PARAM(5); |
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} |
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void random_roi() |
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{ |
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Size roiSize = randomSize(1, MAX_VALUE); |
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Border srcBorder = randomBorder(0, useRoi ? MAX_VALUE : 0); |
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randomSubMat(src, src_roi, roiSize, srcBorder, type, 5, 256); |
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generateOclMat(gsrc, gsrc_roi, src, roiSize, srcBorder); |
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Border dstBorder = randomBorder(0, useRoi ? MAX_VALUE : 0); |
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randomSubMat(dst, dst_roi, roiSize, dstBorder, type, 5, 256); |
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generateOclMat(gdst, gdst_roi, dst, roiSize, dstBorder); |
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randomSubMat(dstCoor, dstCoor_roi, roiSize, dstBorder, typeCoor, 5, 256); |
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generateOclMat(gdstCoor, gdstCoor_roi, dstCoor, roiSize, dstBorder); |
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} |
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void Near(double threshold = 0.0) |
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{ |
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Mat whole, roi; |
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gdst.download(whole); |
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gdst_roi.download(roi); |
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EXPECT_MAT_NEAR(dst, whole, threshold); |
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EXPECT_MAT_NEAR(dst_roi, roi, threshold); |
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} |
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void Near1(double threshold = 0.0) |
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{ |
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Mat whole, roi; |
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gdstCoor.download(whole); |
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gdstCoor_roi.download(roi); |
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EXPECT_MAT_NEAR(dstCoor, whole, threshold); |
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EXPECT_MAT_NEAR(dstCoor_roi, roi, threshold); |
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} |
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}; |
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/////////////////////////meanShiftFiltering///////////////////////////// |
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typedef meanShiftTestBase meanShiftFiltering; |
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OCL_TEST_P(meanShiftFiltering, Mat) |
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{ |
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for (int j = 0; j < LOOP_TIMES; j++) |
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{ |
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random_roi(); |
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meanShiftFiltering_(src_roi, dst_roi, sp, sr, crit); |
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ocl::meanShiftFiltering(gsrc_roi, gdst_roi, sp, sr, crit); |
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Near(); |
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} |
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} |
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///////////////////////////meanShiftProc////////////////////////////////// |
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typedef meanShiftTestBase meanShiftProc; |
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OCL_TEST_P(meanShiftProc, Mat) |
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{ |
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for (int j = 0; j < LOOP_TIMES; j++) |
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{ |
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random_roi(); |
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meanShiftProc_(src_roi, dst_roi, dstCoor_roi, sp, sr, crit); |
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ocl::meanShiftProc(gsrc_roi, gdst_roi, gdstCoor_roi, sp, sr, crit); |
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Near(); |
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Near1(); |
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} |
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} |
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///////////////////////////////////////////////////////////////////////////////////// |
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INSTANTIATE_TEST_CASE_P(Imgproc, meanShiftFiltering, Combine( |
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Values((MatType)CV_8UC4), |
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Values((MatType)CV_16SC2), |
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Values(5), |
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Values(6), |
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Values(TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 5, 1)), |
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Bool() |
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)); |
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INSTANTIATE_TEST_CASE_P(Imgproc, meanShiftProc, Combine( |
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Values((MatType)CV_8UC4), |
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Values((MatType)CV_16SC2), |
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Values(5), |
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Values(6), |
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Values(TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 5, 1)), |
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Bool() |
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)); |
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#endif // HAVE_OPENCL
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