1757 lines
52 KiB
C++
1757 lines
52 KiB
C++
/*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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// 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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//
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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 oclMaterials 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 "precomp.hpp"
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#ifdef HAVE_OPENCL
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using namespace cvtest;
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using namespace testing;
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using namespace std;
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MatType nulltype = -1;
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#define ONE_TYPE(type) testing::ValuesIn(typeVector(type))
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#define NULL_TYPE testing::ValuesIn(typeVector(nulltype))
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vector<MatType> typeVector(MatType type)
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{
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vector<MatType> v;
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v.push_back(type);
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return v;
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}
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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, cv::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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double icount = 1.0 / count;
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int x1 = cvFloor(sx * icount);
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int y1 = cvFloor(sy * icount);
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s0 = cvFloor(s0 * icount);
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s1 = cvFloor(s1 * icount);
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s2 = cvFloor(s2 * icount);
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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 = x0;
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coor.y = 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, cv::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 & cv::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 & cv::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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cv::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, cv::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 & cv::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 & cv::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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cv::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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PARAM_TEST_CASE(ImgprocTestBase, MatType, MatType, MatType, MatType, MatType, bool)
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{
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int type1, type2, type3, type4, type5;
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cv::Scalar val;
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// set up roi
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int roicols;
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int roirows;
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int src1x;
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int src1y;
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int src2x;
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int src2y;
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int dstx;
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int dsty;
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int dst1x;
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int dst1y;
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int maskx;
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int masky;
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//mat
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cv::Mat mat1;
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cv::Mat mat2;
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cv::Mat mask;
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cv::Mat dst;
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cv::Mat dst1; //bak, for two outputs
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//mat with roi
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cv::Mat mat1_roi;
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cv::Mat mat2_roi;
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cv::Mat mask_roi;
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cv::Mat dst_roi;
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cv::Mat dst1_roi; //bak
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//std::vector<cv::ocl::Info> oclinfo;
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//ocl mat
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cv::ocl::oclMat clmat1;
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cv::ocl::oclMat clmat2;
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cv::ocl::oclMat clmask;
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cv::ocl::oclMat cldst;
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cv::ocl::oclMat cldst1; //bak
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//ocl mat with roi
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cv::ocl::oclMat clmat1_roi;
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cv::ocl::oclMat clmat2_roi;
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cv::ocl::oclMat clmask_roi;
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cv::ocl::oclMat cldst_roi;
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cv::ocl::oclMat cldst1_roi;
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virtual void SetUp()
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{
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type1 = GET_PARAM(0);
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type2 = GET_PARAM(1);
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type3 = GET_PARAM(2);
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type4 = GET_PARAM(3);
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type5 = GET_PARAM(4);
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cv::RNG &rng = TS::ptr()->get_rng();
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cv::Size size(MWIDTH, MHEIGHT);
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double min = 1, max = 20;
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//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
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//CV_Assert(devnums > 0);
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////if you want to use undefault device, set it here
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////setDevice(oclinfo[0]);
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if(type1 != nulltype)
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{
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mat1 = randomMat(rng, size, type1, min, max, false);
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clmat1 = mat1;
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}
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if(type2 != nulltype)
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{
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mat2 = randomMat(rng, size, type2, min, max, false);
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clmat2 = mat2;
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}
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if(type3 != nulltype)
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{
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dst = randomMat(rng, size, type3, min, max, false);
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cldst = dst;
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}
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if(type4 != nulltype)
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{
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dst1 = randomMat(rng, size, type4, min, max, false);
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cldst1 = dst1;
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}
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if(type5 != nulltype)
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{
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mask = randomMat(rng, size, CV_8UC1, 0, 2, false);
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cv::threshold(mask, mask, 0.5, 255., type5);
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clmask = mask;
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}
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val = cv::Scalar(rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0));
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}
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void random_roi()
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{
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#ifdef RANDOMROI
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//randomize ROI
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cv::RNG &rng = TS::ptr()->get_rng();
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roicols = rng.uniform(1, mat1.cols);
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roirows = rng.uniform(1, mat1.rows);
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src1x = rng.uniform(0, mat1.cols - roicols);
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src1y = rng.uniform(0, mat1.rows - roirows);
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src2x = rng.uniform(0, mat2.cols - roicols);
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src2y = rng.uniform(0, mat2.rows - roirows);
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dstx = rng.uniform(0, dst.cols - roicols);
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dsty = rng.uniform(0, dst.rows - roirows);
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dst1x = rng.uniform(0, dst1.cols - roicols);
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dst1y = rng.uniform(0, dst1.rows - roirows);
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maskx = rng.uniform(0, mask.cols - roicols);
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masky = rng.uniform(0, mask.rows - roirows);
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#else
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roicols = mat1.cols;
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roirows = mat1.rows;
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src1x = 0;
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src1y = 0;
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src2x = 0;
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src2y = 0;
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dstx = 0;
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dsty = 0;
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dst1x = 0;
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dst1y = 0;
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maskx = 0;
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masky = 0;
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#endif
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if(type1 != nulltype)
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{
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mat1_roi = mat1(Rect(src1x, src1y, roicols, roirows));
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clmat1_roi = clmat1(Rect(src1x, src1y, roicols, roirows));
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}
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if(type2 != nulltype)
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{
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mat2_roi = mat2(Rect(src2x, src2y, roicols, roirows));
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clmat2_roi = clmat2(Rect(src2x, src2y, roicols, roirows));
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}
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if(type3 != nulltype)
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{
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dst_roi = dst(Rect(dstx, dsty, roicols, roirows));
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cldst_roi = cldst(Rect(dstx, dsty, roicols, roirows));
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}
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if(type4 != nulltype)
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{
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dst1_roi = dst1(Rect(dst1x, dst1y, roicols, roirows));
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cldst1_roi = cldst1(Rect(dst1x, dst1y, roicols, roirows));
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}
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if(type5 != nulltype)
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{
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mask_roi = mask(Rect(maskx, masky, roicols, roirows));
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clmask_roi = clmask(Rect(maskx, masky, roicols, roirows));
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}
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}
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};
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////////////////////////////////equalizeHist//////////////////////////////////////////
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struct equalizeHist : ImgprocTestBase {};
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TEST_P(equalizeHist, Mat)
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{
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if (mat1.type() != CV_8UC1 || mat1.type() != dst.type())
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{
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cout << "Unsupported type" << endl;
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EXPECT_DOUBLE_EQ(0.0, 0.0);
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}
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else
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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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cv::equalizeHist(mat1_roi, dst_roi);
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cv::ocl::equalizeHist(clmat1_roi, cldst_roi);
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cv::Mat cpu_cldst;
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|
cldst.download(cpu_cldst);
|
|
char sss[1024];
|
|
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,dst1x=%d,dst1y=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, dst1x, dst1y, maskx, masky, src2x, src2y);
|
|
EXPECT_MAT_NEAR(dst, cpu_cldst, 1.1, sss);
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
////////////////////////////////bilateralFilter////////////////////////////////////////////
|
|
|
|
struct bilateralFilter : ImgprocTestBase {};
|
|
|
|
TEST_P(bilateralFilter, Mat)
|
|
{
|
|
double sigmacolor = 50.0;
|
|
int radius = 9;
|
|
int d = 2 * radius + 1;
|
|
double sigmaspace = 20.0;
|
|
int bordertype[] = {cv::BORDER_CONSTANT, cv::BORDER_REPLICATE,cv::BORDER_REFLECT,cv::BORDER_WRAP,cv::BORDER_REFLECT_101};
|
|
const char* borderstr[]={"BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT","BORDER_WRAP","BORDER_REFLECT_101"};
|
|
|
|
if (mat1.type() != CV_8UC1 || mat1.type() != dst.type())
|
|
{
|
|
cout << "Unsupported type" << endl;
|
|
EXPECT_DOUBLE_EQ(0.0, 0.0);
|
|
}
|
|
else
|
|
{
|
|
for(int i = 0; i < sizeof(bordertype) / sizeof(int); i++)
|
|
for(int j = 0; j < LOOP_TIMES; j++)
|
|
{
|
|
random_roi();
|
|
#ifdef RANDOMROI
|
|
if(((bordertype[i]!=cv::BORDER_CONSTANT) && (bordertype[i]!=cv::BORDER_REPLICATE))&&(mat1_roi.cols<=radius) || (mat1_roi.cols<=radius) || (mat1_roi.rows <= radius) || (mat1_roi.rows <= radius))
|
|
{
|
|
continue;
|
|
}
|
|
if((dstx>=radius) && (dsty >= radius) && (dstx+cldst_roi.cols+radius <=cldst_roi.wholecols) && (dsty+cldst_roi.rows+radius <= cldst_roi.wholerows))
|
|
{
|
|
dst_roi.adjustROI(radius, radius, radius, radius);
|
|
cldst_roi.adjustROI(radius, radius, radius, radius);
|
|
}
|
|
else
|
|
{
|
|
continue;
|
|
}
|
|
#endif
|
|
cv::bilateralFilter(mat1_roi, dst_roi, d, sigmacolor, sigmaspace, bordertype[i]|cv::BORDER_ISOLATED);
|
|
cv::ocl::bilateralFilter(clmat1_roi, cldst_roi, d, sigmacolor, sigmaspace, bordertype[i]|cv::BORDER_ISOLATED);
|
|
|
|
cv::Mat cpu_cldst;
|
|
#ifndef RANDOMROI
|
|
cldst_roi.download(cpu_cldst);
|
|
#else
|
|
cldst.download(cpu_cldst);
|
|
#endif
|
|
|
|
char sss[1024];
|
|
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,radius=%d,boredertype=%s", roicols, roirows, src1x, src1y, dstx, dsty, radius, borderstr[i]);
|
|
|
|
#ifndef RANDOMROI
|
|
EXPECT_MAT_NEAR(dst_roi, cpu_cldst, 0.0, sss);
|
|
#else
|
|
//for(int i=0;i<dst_roi.rows;i++)
|
|
//{
|
|
// for(int j=0;j<dst_roi.cols;j++)
|
|
// {
|
|
// cout<< (int)dst_roi.at<uchar>(i,j)<<" "<< (int)cpu_cldst.at<uchar>(i,j)<<" ";
|
|
// }
|
|
// cout<<endl;
|
|
//}
|
|
EXPECT_MAT_NEAR(dst, cpu_cldst, 0.0, sss);
|
|
#endif
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
|
|
////////////////////////////////copyMakeBorder////////////////////////////////////////////
|
|
|
|
struct CopyMakeBorder : ImgprocTestBase {};
|
|
|
|
TEST_P(CopyMakeBorder, Mat)
|
|
{
|
|
int bordertype[] = {cv::BORDER_CONSTANT, cv::BORDER_REPLICATE,cv::BORDER_REFLECT,cv::BORDER_WRAP,cv::BORDER_REFLECT_101};
|
|
const char* borderstr[]={"BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT","BORDER_WRAP","BORDER_REFLECT_101"};
|
|
cv::RNG &rng = TS::ptr()->get_rng();
|
|
int top = rng.uniform(0, 10);
|
|
int bottom = rng.uniform(0, 10);
|
|
int left = rng.uniform(0, 10);
|
|
int right = rng.uniform(0, 10);
|
|
if (mat1.type() != dst.type())
|
|
{
|
|
cout << "Unsupported type" << endl;
|
|
EXPECT_DOUBLE_EQ(0.0, 0.0);
|
|
}
|
|
else
|
|
{
|
|
for(int i = 0; i < sizeof(bordertype) / sizeof(int); i++)
|
|
for(int j = 0; j < LOOP_TIMES; j++)
|
|
{
|
|
random_roi();
|
|
#ifdef RANDOMROI
|
|
if(((bordertype[i]!=cv::BORDER_CONSTANT) && (bordertype[i]!=cv::BORDER_REPLICATE))&&(mat1_roi.cols<=left) || (mat1_roi.cols<=right) || (mat1_roi.rows <= top) || (mat1_roi.rows <= bottom))
|
|
{
|
|
continue;
|
|
}
|
|
if((dstx>=left) && (dsty >= top) && (dstx+cldst_roi.cols+right <=cldst_roi.wholecols) && (dsty+cldst_roi.rows+bottom <= cldst_roi.wholerows))
|
|
{
|
|
dst_roi.adjustROI(top, bottom, left, right);
|
|
cldst_roi.adjustROI(top, bottom, left, right);
|
|
}
|
|
else
|
|
{
|
|
continue;
|
|
}
|
|
#endif
|
|
cv::copyMakeBorder(mat1_roi, dst_roi, top, bottom, left, right, bordertype[i]| cv::BORDER_ISOLATED, cv::Scalar(1.0));
|
|
cv::ocl::copyMakeBorder(clmat1_roi, cldst_roi, top, bottom, left, right, bordertype[i]| cv::BORDER_ISOLATED, cv::Scalar(1.0));
|
|
|
|
cv::Mat cpu_cldst;
|
|
#ifndef RANDOMROI
|
|
cldst_roi.download(cpu_cldst);
|
|
#else
|
|
cldst.download(cpu_cldst);
|
|
#endif
|
|
char sss[1024];
|
|
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,dst1x=%d,dst1y=%d,top=%d,bottom=%d,left=%d,right=%d, bordertype=%s", roicols, roirows, src1x, src1y, dstx, dsty, dst1x, dst1y, top, bottom, left, right,borderstr[i]);
|
|
#ifndef RANDOMROI
|
|
EXPECT_MAT_NEAR(dst_roi, cpu_cldst, 0.0, sss);
|
|
#else
|
|
//for(int i=0;i<dst.rows;i++)
|
|
//{
|
|
//for(int j=0;j<dst.cols;j++)
|
|
//{
|
|
// cout<< (int)dst.at<uchar>(i,j)<<" ";
|
|
//}
|
|
//cout<<endl;
|
|
//}
|
|
EXPECT_MAT_NEAR(dst, cpu_cldst, 0.0, sss);
|
|
#endif
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
|
|
////////////////////////////////cornerMinEigenVal//////////////////////////////////////////
|
|
|
|
struct cornerMinEigenVal : ImgprocTestBase {};
|
|
|
|
TEST_P(cornerMinEigenVal, Mat)
|
|
{
|
|
for(int j = 0; j < LOOP_TIMES; j++)
|
|
{
|
|
|
|
random_roi();
|
|
int blockSize = 3, apertureSize = 3;//1 + 2 * (rand() % 4);
|
|
//int borderType = cv::BORDER_CONSTANT;
|
|
//int borderType = cv::BORDER_REPLICATE;
|
|
int borderType = cv::BORDER_REFLECT;
|
|
cv::cornerMinEigenVal(mat1_roi, dst_roi, blockSize, apertureSize, borderType);
|
|
cv::ocl::cornerMinEigenVal(clmat1_roi, cldst_roi, blockSize, apertureSize, borderType);
|
|
|
|
|
|
cv::Mat cpu_cldst;
|
|
cldst.download(cpu_cldst);
|
|
|
|
char sss[1024];
|
|
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,dst1x=%d,dst1y=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, dst1x, dst1y, maskx, masky, src2x, src2y);
|
|
EXPECT_MAT_NEAR(dst, cpu_cldst, 1, sss);
|
|
}
|
|
}
|
|
|
|
|
|
|
|
////////////////////////////////cornerHarris//////////////////////////////////////////
|
|
|
|
struct cornerHarris : ImgprocTestBase {};
|
|
|
|
TEST_P(cornerHarris, Mat)
|
|
{
|
|
for(int j = 0; j < LOOP_TIMES; j++)
|
|
{
|
|
|
|
random_roi();
|
|
int blockSize = 3, apertureSize = 3; //1 + 2 * (rand() % 4);
|
|
double k = 2;
|
|
//int borderType = cv::BORDER_CONSTANT;
|
|
//int borderType = cv::BORDER_REPLICATE;
|
|
int borderType = cv::BORDER_REFLECT;
|
|
cv::cornerHarris(mat1_roi, dst_roi, blockSize, apertureSize, k, borderType);
|
|
cv::ocl::cornerHarris(clmat1_roi, cldst_roi, blockSize, apertureSize, k, borderType);
|
|
cv::Mat cpu_cldst;
|
|
cldst.download(cpu_cldst);
|
|
|
|
char sss[1024];
|
|
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,dst1x=%d,dst1y=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, dst1x, dst1y, maskx, masky, src2x, src2y);
|
|
|
|
EXPECT_MAT_NEAR(dst, cpu_cldst, 1, sss);
|
|
}
|
|
}
|
|
|
|
|
|
////////////////////////////////integral/////////////////////////////////////////////////
|
|
|
|
struct integral : ImgprocTestBase {};
|
|
|
|
TEST_P(integral, Mat)
|
|
{
|
|
for(int j = 0; j < LOOP_TIMES; j++)
|
|
{
|
|
random_roi();
|
|
|
|
cv::ocl::integral(clmat1_roi, cldst_roi, cldst1_roi);
|
|
cv::integral(mat1_roi, dst_roi, dst1_roi);
|
|
|
|
cv::Mat cpu_cldst, cpu_cldst1;
|
|
cldst.download(cpu_cldst);
|
|
cldst1.download(cpu_cldst1);
|
|
char sss[1024];
|
|
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,dst1x=%d,dst1y=%d,maskx=%d,masky=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, dst1x, dst1y, maskx, masky, src2x, src2y);
|
|
|
|
EXPECT_MAT_NEAR(dst, cpu_cldst, 0.0, sss);
|
|
EXPECT_MAT_NEAR(dst1, cpu_cldst1, 0.0, sss);
|
|
}
|
|
}
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// warpAffine & warpPerspective
|
|
|
|
PARAM_TEST_CASE(WarpTestBase, MatType, int)
|
|
{
|
|
int type;
|
|
cv::Size size;
|
|
int interpolation;
|
|
|
|
//src mat
|
|
cv::Mat mat1;
|
|
cv::Mat dst;
|
|
|
|
// set up roi
|
|
int src_roicols;
|
|
int src_roirows;
|
|
int dst_roicols;
|
|
int dst_roirows;
|
|
int src1x;
|
|
int src1y;
|
|
int dstx;
|
|
int dsty;
|
|
|
|
|
|
//src mat with roi
|
|
cv::Mat mat1_roi;
|
|
cv::Mat dst_roi;
|
|
//std::vector<cv::ocl::Info> oclinfo;
|
|
//ocl dst mat for testing
|
|
cv::ocl::oclMat gdst_whole;
|
|
|
|
//ocl mat with roi
|
|
cv::ocl::oclMat gmat1;
|
|
cv::ocl::oclMat gdst;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
type = GET_PARAM(0);
|
|
//dsize = GET_PARAM(1);
|
|
interpolation = GET_PARAM(1);
|
|
|
|
cv::RNG &rng = TS::ptr()->get_rng();
|
|
size = cv::Size(MWIDTH, MHEIGHT);
|
|
|
|
mat1 = randomMat(rng, size, type, 5, 16, false);
|
|
dst = randomMat(rng, size, type, 5, 16, false);
|
|
|
|
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
|
|
//CV_Assert(devnums > 0);
|
|
////if you want to use undefault device, set it here
|
|
////setDevice(oclinfo[0]);
|
|
}
|
|
|
|
void random_roi()
|
|
{
|
|
#ifdef RANDOMROI
|
|
//randomize ROI
|
|
cv::RNG &rng = TS::ptr()->get_rng();
|
|
src_roicols = rng.uniform(1, mat1.cols);
|
|
src_roirows = rng.uniform(1, mat1.rows);
|
|
dst_roicols = rng.uniform(1, dst.cols);
|
|
dst_roirows = rng.uniform(1, dst.rows);
|
|
src1x = rng.uniform(0, mat1.cols - src_roicols);
|
|
src1y = rng.uniform(0, mat1.rows - src_roirows);
|
|
dstx = rng.uniform(0, dst.cols - dst_roicols);
|
|
dsty = rng.uniform(0, dst.rows - dst_roirows);
|
|
#else
|
|
src_roicols = mat1.cols;
|
|
src_roirows = mat1.rows;
|
|
dst_roicols = dst.cols;
|
|
dst_roirows = dst.rows;
|
|
src1x = 0;
|
|
src1y = 0;
|
|
dstx = 0;
|
|
dsty = 0;
|
|
#endif
|
|
|
|
|
|
mat1_roi = mat1(Rect(src1x, src1y, src_roicols, src_roirows));
|
|
dst_roi = dst(Rect(dstx, dsty, dst_roicols, dst_roirows));
|
|
|
|
gdst_whole = dst;
|
|
gdst = gdst_whole(Rect(dstx, dsty, dst_roicols, dst_roirows));
|
|
|
|
|
|
gmat1 = mat1_roi;
|
|
}
|
|
|
|
};
|
|
|
|
/////warpAffine
|
|
|
|
struct WarpAffine : WarpTestBase {};
|
|
|
|
TEST_P(WarpAffine, Mat)
|
|
{
|
|
static const double coeffs[2][3] =
|
|
{
|
|
{cos(3.14 / 6), -sin(3.14 / 6), 100.0},
|
|
{sin(3.14 / 6), cos(3.14 / 6), -100.0}
|
|
};
|
|
Mat M(2, 3, CV_64F, (void *)coeffs);
|
|
|
|
for(int j = 0; j < LOOP_TIMES; j++)
|
|
{
|
|
random_roi();
|
|
|
|
cv::warpAffine(mat1_roi, dst_roi, M, size, interpolation);
|
|
cv::ocl::warpAffine(gmat1, gdst, M, size, interpolation);
|
|
|
|
cv::Mat cpu_dst;
|
|
gdst_whole.download(cpu_dst);
|
|
char sss[1024];
|
|
sprintf(sss, "src_roicols=%d,src_roirows=%d,dst_roicols=%d,dst_roirows=%d,src1x =%d,src1y=%d,dstx=%d,dsty=%d", src_roicols, src_roirows, dst_roicols, dst_roirows, src1x, src1y, dstx, dsty);
|
|
|
|
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0, sss);
|
|
}
|
|
|
|
}
|
|
|
|
|
|
// warpPerspective
|
|
|
|
struct WarpPerspective : WarpTestBase {};
|
|
|
|
TEST_P(WarpPerspective, Mat)
|
|
{
|
|
static const double coeffs[3][3] =
|
|
{
|
|
{cos(3.14 / 6), -sin(3.14 / 6), 100.0},
|
|
{sin(3.14 / 6), cos(3.14 / 6), -100.0},
|
|
{0.0, 0.0, 1.0}
|
|
};
|
|
Mat M(3, 3, CV_64F, (void *)coeffs);
|
|
|
|
for(int j = 0; j < LOOP_TIMES; j++)
|
|
{
|
|
random_roi();
|
|
|
|
cv::warpPerspective(mat1_roi, dst_roi, M, size, interpolation);
|
|
cv::ocl::warpPerspective(gmat1, gdst, M, size, interpolation);
|
|
|
|
cv::Mat cpu_dst;
|
|
gdst_whole.download(cpu_dst);
|
|
char sss[1024];
|
|
sprintf(sss, "src_roicols=%d,src_roirows=%d,dst_roicols=%d,dst_roirows=%d,src1x =%d,src1y=%d,dstx=%d,dsty=%d", src_roicols, src_roirows, dst_roicols, dst_roirows, src1x, src1y, dstx, dsty);
|
|
|
|
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0, sss);
|
|
}
|
|
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// remap
|
|
//////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
PARAM_TEST_CASE(Remap, MatType, MatType, MatType, int, int)
|
|
{
|
|
int srcType;
|
|
int map1Type;
|
|
int map2Type;
|
|
cv::Scalar val;
|
|
|
|
int interpolation;
|
|
int bordertype;
|
|
|
|
cv::Mat src;
|
|
cv::Mat dst;
|
|
cv::Mat map1;
|
|
cv::Mat map2;
|
|
|
|
//std::vector<cv::ocl::Info> oclinfo;
|
|
|
|
int src_roicols;
|
|
int src_roirows;
|
|
int dst_roicols;
|
|
int dst_roirows;
|
|
int map1_roicols;
|
|
int map1_roirows;
|
|
int map2_roicols;
|
|
int map2_roirows;
|
|
int srcx;
|
|
int srcy;
|
|
int dstx;
|
|
int dsty;
|
|
int map1x;
|
|
int map1y;
|
|
int map2x;
|
|
int map2y;
|
|
|
|
cv::Mat src_roi;
|
|
cv::Mat dst_roi;
|
|
cv::Mat map1_roi;
|
|
cv::Mat map2_roi;
|
|
|
|
//ocl mat for testing
|
|
cv::ocl::oclMat gdst;
|
|
|
|
//ocl mat with roi
|
|
cv::ocl::oclMat gsrc_roi;
|
|
cv::ocl::oclMat gdst_roi;
|
|
cv::ocl::oclMat gmap1_roi;
|
|
cv::ocl::oclMat gmap2_roi;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
srcType = GET_PARAM(0);
|
|
map1Type = GET_PARAM(1);
|
|
map2Type = GET_PARAM(2);
|
|
interpolation = GET_PARAM(3);
|
|
bordertype = GET_PARAM(4);
|
|
|
|
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
|
|
//CV_Assert(devnums > 0);
|
|
|
|
cv::RNG& rng = TS::ptr()->get_rng();
|
|
cv::Size srcSize = cv::Size(MWIDTH, MHEIGHT);
|
|
cv::Size dstSize = cv::Size(MWIDTH, MHEIGHT);
|
|
cv::Size map1Size = cv::Size(MWIDTH, MHEIGHT);
|
|
double min = 5, max = 16;
|
|
|
|
if(srcType != nulltype)
|
|
{
|
|
src = randomMat(rng, srcSize, srcType, min, max, false);
|
|
}
|
|
if((map1Type == CV_16SC2 && map2Type == nulltype) || (map1Type == CV_32FC2 && map2Type == nulltype))
|
|
{
|
|
map1 = randomMat(rng, map1Size, map1Type, min, max, false);
|
|
}
|
|
else if (map1Type == CV_32FC1 && map2Type == CV_32FC1)
|
|
{
|
|
map1 = randomMat(rng, map1Size, map1Type, min, max, false);
|
|
map2 = randomMat(rng, map1Size, map1Type, min, max, false);
|
|
}
|
|
|
|
else
|
|
{
|
|
cout<<"The wrong input type"<<endl;
|
|
return;
|
|
}
|
|
|
|
dst = randomMat(rng, map1Size, srcType, min, max, false);
|
|
switch (src.channels())
|
|
{
|
|
case 1:
|
|
val = cv::Scalar(rng.uniform(0.0, 10.0), 0, 0, 0);
|
|
break;
|
|
case 2:
|
|
val = cv::Scalar(rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0), 0, 0);
|
|
break;
|
|
case 3:
|
|
val = cv::Scalar(rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0), 0);
|
|
break;
|
|
case 4:
|
|
val = cv::Scalar(rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0), rng.uniform(0.0, 10.0));
|
|
break;
|
|
}
|
|
|
|
}
|
|
void random_roi()
|
|
{
|
|
cv::RNG& rng = TS::ptr()->get_rng();
|
|
|
|
dst_roicols = rng.uniform(1, dst.cols);
|
|
dst_roirows = rng.uniform(1, dst.rows);
|
|
|
|
src_roicols = rng.uniform(1, src.cols);
|
|
src_roirows = rng.uniform(1, src.rows);
|
|
|
|
|
|
srcx = rng.uniform(0, src.cols - src_roicols);
|
|
srcy = rng.uniform(0, src.rows - src_roirows);
|
|
dstx = rng.uniform(0, dst.cols - dst_roicols);
|
|
dsty = rng.uniform(0, dst.rows - dst_roirows);
|
|
map1_roicols = dst_roicols;
|
|
map1_roirows = dst_roirows;
|
|
map2_roicols = dst_roicols;
|
|
map2_roirows = dst_roirows;
|
|
map1x = dstx;
|
|
map1y = dsty;
|
|
map2x = dstx;
|
|
map2y = dsty;
|
|
|
|
if((map1Type == CV_16SC2 && map2Type == nulltype) || (map1Type == CV_32FC2 && map2Type == nulltype))
|
|
{
|
|
map1_roi = map1(Rect(map1x,map1y,map1_roicols,map1_roirows));
|
|
gmap1_roi = map1_roi;
|
|
}
|
|
|
|
else if (map1Type == CV_32FC1 && map2Type == CV_32FC1)
|
|
{
|
|
map1_roi = map1(Rect(map1x,map1y,map1_roicols,map1_roirows));
|
|
gmap1_roi = map1_roi;
|
|
map2_roi = map2(Rect(map2x,map2y,map2_roicols,map2_roirows));
|
|
gmap2_roi = map2_roi;
|
|
}
|
|
src_roi = src(Rect(srcx,srcy,src_roicols,src_roirows));
|
|
dst_roi = dst(Rect(dstx, dsty, dst_roicols, dst_roirows));
|
|
gsrc_roi = src_roi;
|
|
gdst = dst;
|
|
gdst_roi = gdst(Rect(dstx, dsty, dst_roicols, dst_roirows));
|
|
}
|
|
};
|
|
|
|
TEST_P(Remap, Mat)
|
|
{
|
|
if((interpolation == 1 && map1Type == CV_16SC2) ||(map1Type == CV_32FC1 && map2Type == nulltype) || (map1Type == CV_16SC2 && map2Type == CV_32FC1) || (map1Type == CV_32FC2 && map2Type == CV_32FC1))
|
|
{
|
|
cout << "Don't support the dataType" << endl;
|
|
return;
|
|
}
|
|
int bordertype[] = {cv::BORDER_CONSTANT,cv::BORDER_REPLICATE/*,BORDER_REFLECT,BORDER_WRAP,BORDER_REFLECT_101*/};
|
|
const char* borderstr[]={"BORDER_CONSTANT", "BORDER_REPLICATE"/*, "BORDER_REFLECT","BORDER_WRAP","BORDER_REFLECT_101"*/};
|
|
// for(int i = 0; i < sizeof(bordertype)/sizeof(int); i++)
|
|
for(int j=0; j<100; j++)
|
|
{
|
|
random_roi();
|
|
cv::remap(src_roi, dst_roi, map1_roi, map2_roi, interpolation, bordertype[0], val);
|
|
cv::ocl::remap(gsrc_roi, gdst_roi, gmap1_roi, gmap2_roi, interpolation, bordertype[0], val);
|
|
cv::Mat cpu_dst;
|
|
gdst.download(cpu_dst);
|
|
|
|
char sss[1024];
|
|
sprintf(sss, "src_roicols=%d,src_roirows=%d,dst_roicols=%d,dst_roirows=%d,src1x =%d,src1y=%d,dstx=%d,dsty=%d", src_roicols, src_roirows, dst_roicols, dst_roirows, srcx, srcy, dstx, dsty);
|
|
|
|
|
|
if(interpolation == 0)
|
|
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0, sss);
|
|
EXPECT_MAT_NEAR(dst, cpu_dst, 2.0, sss);
|
|
|
|
}
|
|
}
|
|
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// resize
|
|
|
|
PARAM_TEST_CASE(Resize, MatType, cv::Size, double, double, int)
|
|
{
|
|
int type;
|
|
cv::Size dsize;
|
|
double fx, fy;
|
|
int interpolation;
|
|
|
|
//src mat
|
|
cv::Mat mat1;
|
|
cv::Mat dst;
|
|
|
|
// set up roi
|
|
int src_roicols;
|
|
int src_roirows;
|
|
int dst_roicols;
|
|
int dst_roirows;
|
|
int src1x;
|
|
int src1y;
|
|
int dstx;
|
|
int dsty;
|
|
|
|
//std::vector<cv::ocl::Info> oclinfo;
|
|
//src mat with roi
|
|
cv::Mat mat1_roi;
|
|
cv::Mat dst_roi;
|
|
|
|
//ocl dst mat for testing
|
|
cv::ocl::oclMat gdst_whole;
|
|
|
|
//ocl mat with roi
|
|
cv::ocl::oclMat gmat1;
|
|
cv::ocl::oclMat gdst;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
type = GET_PARAM(0);
|
|
dsize = GET_PARAM(1);
|
|
fx = GET_PARAM(2);
|
|
fy = GET_PARAM(3);
|
|
interpolation = GET_PARAM(4);
|
|
|
|
cv::RNG &rng = TS::ptr()->get_rng();
|
|
|
|
cv::Size size(MWIDTH, MHEIGHT);
|
|
|
|
if(dsize == cv::Size() && !(fx > 0 && fy > 0))
|
|
{
|
|
cout << "invalid dsize and fx fy" << endl;
|
|
return;
|
|
}
|
|
|
|
if(dsize == cv::Size())
|
|
{
|
|
dsize.width = (int)(size.width * fx);
|
|
dsize.height = (int)(size.height * fy);
|
|
}
|
|
|
|
mat1 = randomMat(rng, size, type, 5, 16, false);
|
|
dst = randomMat(rng, dsize, type, 5, 16, false);
|
|
|
|
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
|
|
//CV_Assert(devnums > 0);
|
|
////if you want to use undefault device, set it here
|
|
////setDevice(oclinfo[0]);
|
|
}
|
|
|
|
void random_roi()
|
|
{
|
|
#ifdef RANDOMROI
|
|
//randomize ROI
|
|
cv::RNG &rng = TS::ptr()->get_rng();
|
|
src_roicols = rng.uniform(1, mat1.cols);
|
|
src_roirows = rng.uniform(1, mat1.rows);
|
|
dst_roicols = (int)(src_roicols*fx);
|
|
dst_roirows = (int)(src_roirows*fy);
|
|
src1x = rng.uniform(0, mat1.cols - src_roicols);
|
|
src1y = rng.uniform(0, mat1.rows - src_roirows);
|
|
dstx = rng.uniform(0, dst.cols - dst_roicols);
|
|
dsty = rng.uniform(0, dst.rows - dst_roirows);
|
|
#else
|
|
src_roicols = mat1.cols;
|
|
src_roirows = mat1.rows;
|
|
dst_roicols = dst.cols;
|
|
dst_roirows = dst.rows;
|
|
src1x = 0;
|
|
src1y = 0;
|
|
dstx = 0;
|
|
dsty = 0;
|
|
#endif
|
|
dsize.width = dst_roicols;
|
|
dsize.height = dst_roirows;
|
|
mat1_roi = mat1(Rect(src1x, src1y, src_roicols, src_roirows));
|
|
dst_roi = dst(Rect(dstx, dsty, dst_roicols, dst_roirows));
|
|
|
|
gdst_whole = dst;
|
|
gdst = gdst_whole(Rect(dstx, dsty, dst_roicols, dst_roirows));
|
|
|
|
dsize.width = (int)(mat1_roi.size().width * fx);
|
|
dsize.height = (int)(mat1_roi.size().height * fy);
|
|
|
|
gmat1 = mat1_roi;
|
|
}
|
|
|
|
};
|
|
|
|
TEST_P(Resize, Mat)
|
|
{
|
|
for(int j = 0; j < LOOP_TIMES; j++)
|
|
{
|
|
random_roi();
|
|
|
|
// cv::resize(mat1_roi, dst_roi, dsize, fx, fy, interpolation);
|
|
// cv::ocl::resize(gmat1, gdst, dsize, fx, fy, interpolation);
|
|
if(dst_roicols<1||dst_roirows<1) continue;
|
|
cv::resize(mat1_roi, dst_roi, dsize, fx, fy, interpolation);
|
|
cv::ocl::resize(gmat1, gdst, dsize, fx, fy, interpolation);
|
|
|
|
cv::Mat cpu_dst;
|
|
gdst_whole.download(cpu_dst);
|
|
char sss[1024];
|
|
sprintf(sss, "src_roicols=%d,src_roirows=%d,dst_roicols=%d,dst_roirows=%d,src1x =%d,src1y=%d,dstx=%d,dsty=%d", src_roicols, src_roirows, dst_roicols, dst_roirows, src1x, src1y, dstx, dsty);
|
|
|
|
EXPECT_MAT_NEAR(dst, cpu_dst, 1.0, sss);
|
|
}
|
|
|
|
}
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
//threshold
|
|
|
|
PARAM_TEST_CASE(Threshold, MatType, ThreshOp)
|
|
{
|
|
int type;
|
|
int threshOp;
|
|
|
|
//src mat
|
|
cv::Mat mat1;
|
|
cv::Mat dst;
|
|
|
|
// set up roi
|
|
int roicols;
|
|
int roirows;
|
|
int src1x;
|
|
int src1y;
|
|
int dstx;
|
|
int dsty;
|
|
|
|
//src mat with roi
|
|
cv::Mat mat1_roi;
|
|
cv::Mat dst_roi;
|
|
//std::vector<cv::ocl::Info> oclinfo;
|
|
//ocl dst mat for testing
|
|
cv::ocl::oclMat gdst_whole;
|
|
|
|
//ocl mat with roi
|
|
cv::ocl::oclMat gmat1;
|
|
cv::ocl::oclMat gdst;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
type = GET_PARAM(0);
|
|
threshOp = GET_PARAM(1);
|
|
|
|
cv::RNG &rng = TS::ptr()->get_rng();
|
|
cv::Size size(MWIDTH, MHEIGHT);
|
|
|
|
mat1 = randomMat(rng, size, type, 5, 16, false);
|
|
dst = randomMat(rng, size, type, 5, 16, false);
|
|
|
|
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
|
|
//CV_Assert(devnums > 0);
|
|
////if you want to use undefault device, set it here
|
|
////setDevice(oclinfo[0]);
|
|
}
|
|
|
|
void random_roi()
|
|
{
|
|
#ifdef RANDOMROI
|
|
//randomize ROI
|
|
cv::RNG &rng = TS::ptr()->get_rng();
|
|
roicols = rng.uniform(1, mat1.cols);
|
|
roirows = rng.uniform(1, mat1.rows);
|
|
src1x = rng.uniform(0, mat1.cols - roicols);
|
|
src1y = rng.uniform(0, mat1.rows - roirows);
|
|
dstx = rng.uniform(0, dst.cols - roicols);
|
|
dsty = rng.uniform(0, dst.rows - roirows);
|
|
#else
|
|
roicols = mat1.cols;
|
|
roirows = mat1.rows;
|
|
src1x = 0;
|
|
src1y = 0;
|
|
dstx = 0;
|
|
dsty = 0;
|
|
#endif
|
|
|
|
mat1_roi = mat1(Rect(src1x, src1y, roicols, roirows));
|
|
dst_roi = dst(Rect(dstx, dsty, roicols, roirows));
|
|
|
|
gdst_whole = dst;
|
|
gdst = gdst_whole(Rect(dstx, dsty, roicols, roirows));
|
|
|
|
|
|
gmat1 = mat1_roi;
|
|
}
|
|
|
|
};
|
|
|
|
TEST_P(Threshold, Mat)
|
|
{
|
|
for(int j = 0; j < LOOP_TIMES; j++)
|
|
{
|
|
random_roi();
|
|
double maxVal = randomDouble(20.0, 127.0);
|
|
double thresh = randomDouble(0.0, maxVal);
|
|
|
|
cv::threshold(mat1_roi, dst_roi, thresh, maxVal, threshOp);
|
|
cv::ocl::threshold(gmat1, gdst, thresh, maxVal, threshOp);
|
|
|
|
cv::Mat cpu_dst;
|
|
gdst_whole.download(cpu_dst);
|
|
|
|
//EXPECT_MAT_NEAR(dst, cpu_dst, 1e-5)
|
|
char sss[1024];
|
|
sprintf(sss, "roicols=%d,roirows=%d,src1x =%d,src1y=%d,dstx=%d,dsty=%d", roicols, roirows, src1x , src1y, dstx, dsty);
|
|
|
|
EXPECT_MAT_NEAR(dst, cpu_dst, 1, sss);
|
|
}
|
|
|
|
}
|
|
|
|
PARAM_TEST_CASE(meanShiftTestBase, MatType, MatType, int, int, cv::TermCriteria)
|
|
{
|
|
int type, typeCoor;
|
|
int sp, sr;
|
|
cv::TermCriteria crit;
|
|
//src mat
|
|
cv::Mat src;
|
|
cv::Mat dst;
|
|
cv::Mat dstCoor;
|
|
|
|
//set up roi
|
|
int roicols;
|
|
int roirows;
|
|
int srcx;
|
|
int srcy;
|
|
int dstx;
|
|
int dsty;
|
|
|
|
//src mat with roi
|
|
cv::Mat src_roi;
|
|
cv::Mat dst_roi;
|
|
cv::Mat dstCoor_roi;
|
|
|
|
//ocl dst mat
|
|
cv::ocl::oclMat gdst;
|
|
cv::ocl::oclMat gdstCoor;
|
|
|
|
//std::vector<cv::ocl::Info> oclinfo;
|
|
//ocl mat with roi
|
|
cv::ocl::oclMat gsrc_roi;
|
|
cv::ocl::oclMat gdst_roi;
|
|
cv::ocl::oclMat 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);
|
|
|
|
cv::RNG &rng = TS::ptr()->get_rng();
|
|
|
|
// MWIDTH=256, MHEIGHT=256. defined in utility.hpp
|
|
cv::Size size = cv::Size(MWIDTH, MHEIGHT);
|
|
|
|
src = randomMat(rng, size, type, 5, 16, false);
|
|
dst = randomMat(rng, size, type, 5, 16, false);
|
|
dstCoor = randomMat(rng, size, typeCoor, 5, 16, false);
|
|
|
|
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
|
|
//CV_Assert(devnums > 0);
|
|
////if you want to use undefault device, set it here
|
|
////setDevice(oclinfo[0]);
|
|
}
|
|
|
|
void random_roi()
|
|
{
|
|
#ifdef RANDOMROI
|
|
cv::RNG &rng = TS::ptr()->get_rng();
|
|
|
|
//randomize ROI
|
|
roicols = rng.uniform(1, src.cols);
|
|
roirows = rng.uniform(1, src.rows);
|
|
srcx = rng.uniform(0, src.cols - roicols);
|
|
srcy = rng.uniform(0, src.rows - roirows);
|
|
dstx = rng.uniform(0, dst.cols - roicols);
|
|
dsty = rng.uniform(0, dst.rows - roirows);
|
|
#else
|
|
roicols = src.cols;
|
|
roirows = src.rows;
|
|
srcx = 0;
|
|
srcy = 0;
|
|
dstx = 0;
|
|
dsty = 0;
|
|
#endif
|
|
src_roi = src(Rect(srcx, srcy, roicols, roirows));
|
|
dst_roi = dst(Rect(dstx, dsty, roicols, roirows));
|
|
dstCoor_roi = dstCoor(Rect(dstx, dsty, roicols, roirows));
|
|
|
|
gdst = dst;
|
|
gdstCoor = dstCoor;
|
|
|
|
gsrc_roi = src_roi;
|
|
gdst_roi = gdst(Rect(dstx, dsty, roicols, roirows)); //gdst_roi
|
|
gdstCoor_roi = gdstCoor(Rect(dstx, dsty, roicols, roirows));
|
|
}
|
|
};
|
|
|
|
/////////////////////////meanShiftFiltering/////////////////////////////
|
|
struct meanShiftFiltering : meanShiftTestBase {};
|
|
|
|
TEST_P(meanShiftFiltering, Mat)
|
|
{
|
|
|
|
for(int j = 0; j < LOOP_TIMES; j++)
|
|
{
|
|
random_roi();
|
|
|
|
cv::Mat cpu_gdst;
|
|
gdst.download(cpu_gdst);
|
|
|
|
meanShiftFiltering_(src_roi, dst_roi, sp, sr, crit);
|
|
cv::ocl::meanShiftFiltering(gsrc_roi, gdst_roi, sp, sr, crit);
|
|
|
|
gdst.download(cpu_gdst);
|
|
|
|
char sss[1024];
|
|
sprintf(sss, "roicols=%d,roirows=%d,srcx=%d,srcy=%d,dstx=%d,dsty=%d\n", roicols, roirows, srcx, srcy, dstx, dsty);
|
|
EXPECT_MAT_NEAR(dst, cpu_gdst, 0.0, sss);
|
|
|
|
}
|
|
}
|
|
|
|
///////////////////////////meanShiftProc//////////////////////////////////
|
|
struct meanShiftProc : meanShiftTestBase {};
|
|
|
|
TEST_P(meanShiftProc, Mat)
|
|
{
|
|
|
|
for(int j = 0; j < LOOP_TIMES; j++)
|
|
{
|
|
random_roi();
|
|
|
|
cv::Mat cpu_gdst;
|
|
cv::Mat cpu_gdstCoor;
|
|
|
|
meanShiftProc_(src_roi, dst_roi, dstCoor_roi, sp, sr, crit);
|
|
cv::ocl::meanShiftProc(gsrc_roi, gdst_roi, gdstCoor_roi, sp, sr, crit);
|
|
|
|
gdst.download(cpu_gdst);
|
|
gdstCoor.download(cpu_gdstCoor);
|
|
|
|
char sss[1024];
|
|
sprintf(sss, "roicols=%d,roirows=%d,srcx=%d,srcy=%d,dstx=%d,dsty=%d\n", roicols, roirows, srcx, srcy, dstx, dsty);
|
|
EXPECT_MAT_NEAR(dst, cpu_gdst, 0.0, sss);
|
|
EXPECT_MAT_NEAR(dstCoor, cpu_gdstCoor, 0.0, sss);
|
|
}
|
|
}
|
|
|
|
///////////////////////////////////////////////////////////////////////////////////////
|
|
//hist
|
|
void calcHistGold(const cv::Mat& src, cv::Mat& hist)
|
|
{
|
|
hist.create(1, 256, CV_32SC1);
|
|
hist.setTo(cv::Scalar::all(0));
|
|
|
|
int* hist_row = hist.ptr<int>();
|
|
for (int y = 0; y < src.rows; ++y)
|
|
{
|
|
const uchar* src_row = src.ptr(y);
|
|
|
|
for (int x = 0; x < src.cols; ++x)
|
|
++hist_row[src_row[x]];
|
|
}
|
|
}
|
|
|
|
PARAM_TEST_CASE(histTestBase, MatType, MatType)
|
|
{
|
|
int type_src;
|
|
|
|
//src mat
|
|
cv::Mat src;
|
|
cv::Mat dst_hist;
|
|
//set up roi
|
|
int roicols;
|
|
int roirows;
|
|
int srcx;
|
|
int srcy;
|
|
//src mat with roi
|
|
cv::Mat src_roi;
|
|
//ocl dst mat, dst_hist and gdst_hist don't have roi
|
|
cv::ocl::oclMat gdst_hist;
|
|
//ocl mat with roi
|
|
cv::ocl::oclMat gsrc_roi;
|
|
// std::vector<cv::ocl::Info> oclinfo;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
type_src = GET_PARAM(0);
|
|
|
|
cv::RNG &rng = TS::ptr()->get_rng();
|
|
cv::Size size = cv::Size(MWIDTH, MHEIGHT);
|
|
|
|
src = randomMat(rng, size, type_src, 0, 256, false);
|
|
|
|
// int devnums = getDevice(oclinfo);
|
|
// CV_Assert(devnums > 0);
|
|
//if you want to use undefault device, set it here
|
|
//setDevice(oclinfo[0]);
|
|
}
|
|
|
|
void random_roi()
|
|
{
|
|
#ifdef RANDOMROI
|
|
cv::RNG &rng = TS::ptr()->get_rng();
|
|
|
|
//randomize ROI
|
|
roicols = rng.uniform(1, src.cols);
|
|
roirows = rng.uniform(1, src.rows);
|
|
srcx = rng.uniform(0, src.cols - roicols);
|
|
srcy = rng.uniform(0, src.rows - roirows);
|
|
#else
|
|
roicols = src.cols;
|
|
roirows = src.rows;
|
|
srcx = 0;
|
|
srcy = 0;
|
|
#endif
|
|
src_roi = src(Rect(srcx, srcy, roicols, roirows));
|
|
|
|
gsrc_roi = src_roi;
|
|
}
|
|
};
|
|
///////////////////////////calcHist///////////////////////////////////////
|
|
struct calcHist : histTestBase {};
|
|
|
|
TEST_P(calcHist, Mat)
|
|
{
|
|
for(int j = 0; j < LOOP_TIMES; j++)
|
|
{
|
|
random_roi();
|
|
|
|
cv::Mat cpu_hist;
|
|
|
|
calcHistGold(src_roi, dst_hist);
|
|
cv::ocl::calcHist(gsrc_roi, gdst_hist);
|
|
|
|
gdst_hist.download(cpu_hist);
|
|
|
|
char sss[1024];
|
|
sprintf(sss, "roicols=%d,roirows=%d,srcx=%d,srcy=%d\n", roicols, roirows, srcx, srcy);
|
|
EXPECT_MAT_NEAR(dst_hist, cpu_hist, 0.0, sss);
|
|
}
|
|
}
|
|
|
|
///////////////////////////Convolve//////////////////////////////////
|
|
PARAM_TEST_CASE(ConvolveTestBase, MatType, bool)
|
|
{
|
|
int type;
|
|
//src mat
|
|
cv::Mat mat1;
|
|
cv::Mat mat2;
|
|
cv::Mat dst;
|
|
cv::Mat dst1; //bak, for two outputs
|
|
// set up roi
|
|
int roicols;
|
|
int roirows;
|
|
int src1x;
|
|
int src1y;
|
|
int src2x;
|
|
int src2y;
|
|
int dstx;
|
|
int dsty;
|
|
//src mat with roi
|
|
cv::Mat mat1_roi;
|
|
cv::Mat mat2_roi;
|
|
cv::Mat dst_roi;
|
|
cv::Mat dst1_roi; //bak
|
|
//ocl dst mat for testing
|
|
cv::ocl::oclMat gdst_whole;
|
|
cv::ocl::oclMat gdst1_whole; //bak
|
|
//ocl mat with roi
|
|
cv::ocl::oclMat gmat1;
|
|
cv::ocl::oclMat gmat2;
|
|
cv::ocl::oclMat gdst;
|
|
cv::ocl::oclMat gdst1; //bak
|
|
virtual void SetUp()
|
|
{
|
|
type = GET_PARAM(0);
|
|
|
|
cv::RNG &rng = TS::ptr()->get_rng();
|
|
|
|
cv::Size size(MWIDTH, MHEIGHT);
|
|
|
|
mat1 = randomMat(rng, size, type, 5, 16, false);
|
|
mat2 = randomMat(rng, size, type, 5, 16, false);
|
|
dst = randomMat(rng, size, type, 5, 16, false);
|
|
dst1 = randomMat(rng, size, type, 5, 16, false);
|
|
}
|
|
void random_roi()
|
|
{
|
|
cv::RNG &rng = TS::ptr()->get_rng();
|
|
|
|
#ifdef RANDOMROI
|
|
//randomize ROI
|
|
roicols = rng.uniform(1, mat1.cols);
|
|
roirows = rng.uniform(1, mat1.rows);
|
|
src1x = rng.uniform(0, mat1.cols - roicols);
|
|
src1y = rng.uniform(0, mat1.rows - roirows);
|
|
dstx = rng.uniform(0, dst.cols - roicols);
|
|
dsty = rng.uniform(0, dst.rows - roirows);
|
|
#else
|
|
roicols = mat1.cols;
|
|
roirows = mat1.rows;
|
|
src1x = 0;
|
|
src1y = 0;
|
|
dstx = 0;
|
|
dsty = 0;
|
|
#endif
|
|
src2x = rng.uniform(0, mat2.cols - roicols);
|
|
src2y = rng.uniform(0, mat2.rows - roirows);
|
|
mat1_roi = mat1(Rect(src1x, src1y, roicols, roirows));
|
|
mat2_roi = mat2(Rect(src2x, src2y, roicols, roirows));
|
|
dst_roi = dst(Rect(dstx, dsty, roicols, roirows));
|
|
dst1_roi = dst1(Rect(dstx, dsty, roicols, roirows));
|
|
|
|
gdst_whole = dst;
|
|
gdst = gdst_whole(Rect(dstx, dsty, roicols, roirows));
|
|
|
|
gdst1_whole = dst1;
|
|
gdst1 = gdst1_whole(Rect(dstx, dsty, roicols, roirows));
|
|
|
|
gmat1 = mat1_roi;
|
|
gmat2 = mat2_roi;
|
|
//end
|
|
}
|
|
|
|
};
|
|
struct Convolve : ConvolveTestBase {};
|
|
|
|
void conv2( cv::Mat x, cv::Mat y, cv::Mat z)
|
|
{
|
|
int N1 = x.rows;
|
|
int M1 = x.cols;
|
|
int N2 = y.rows;
|
|
int M2 = y.cols;
|
|
|
|
int i,j;
|
|
int m,n;
|
|
|
|
|
|
float *kerneldata = (float *)(x.data);
|
|
float *srcdata = (float *)(y.data);
|
|
float *dstdata = (float *)(z.data);
|
|
|
|
for(i=0;i<N2;i++)
|
|
for(j=0;j<M2;j++)
|
|
{
|
|
float temp =0;
|
|
for(m=0;m<N1;m++)
|
|
for(n=0;n<M1;n++)
|
|
{
|
|
int r, c;
|
|
r = min(max((i-N1/2+m), 0), N2-1);
|
|
c = min(max((j-M1/2+n), 0), M2-1);
|
|
temp += kerneldata[m*(x.step>>2)+n]*srcdata[r*(y.step>>2)+c];
|
|
}
|
|
dstdata[i*(z.step >> 2)+j]=temp;
|
|
}
|
|
}
|
|
TEST_P(Convolve, Mat)
|
|
{
|
|
if(mat1.type()!=CV_32FC1)
|
|
{
|
|
cout<<"\tUnsupported type\t\n";
|
|
}
|
|
for(int j=0;j<LOOP_TIMES;j++)
|
|
{
|
|
random_roi();
|
|
cv::ocl::oclMat temp1;
|
|
cv::Mat kernel_cpu= mat2(Rect(0,0,7,7));
|
|
temp1 = kernel_cpu;
|
|
|
|
conv2(kernel_cpu,mat1_roi,dst_roi);
|
|
cv::ocl::convolve(gmat1,temp1,gdst);
|
|
|
|
cv::Mat cpu_dst;
|
|
gdst_whole.download(cpu_dst);
|
|
|
|
char sss[1024];
|
|
sprintf(sss, "roicols=%d,roirows=%d,src1x=%d,src1y=%d,dstx=%d,dsty=%d,src2x=%d,src2y=%d", roicols, roirows, src1x, src1y, dstx, dsty, src2x, src2y);
|
|
|
|
EXPECT_MAT_NEAR(dst, cpu_dst, 1e-1, sss);
|
|
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(ImgprocTestBase, equalizeHist, Combine(
|
|
ONE_TYPE(CV_8UC1),
|
|
NULL_TYPE,
|
|
ONE_TYPE(CV_8UC1),
|
|
NULL_TYPE,
|
|
NULL_TYPE,
|
|
Values(false))); // Values(false) is the reserved parameter
|
|
|
|
//INSTANTIATE_TEST_CASE_P(ImgprocTestBase, bilateralFilter, Combine(
|
|
// ONE_TYPE(CV_8UC1),
|
|
// NULL_TYPE,
|
|
// ONE_TYPE(CV_8UC1),
|
|
// NULL_TYPE,
|
|
// NULL_TYPE,
|
|
// Values(false))); // Values(false) is the reserved parameter
|
|
|
|
|
|
INSTANTIATE_TEST_CASE_P(ImgprocTestBase, CopyMakeBorder, Combine(
|
|
Values(CV_8UC1, CV_8UC4,CV_32SC1, CV_32SC4,CV_32FC1, CV_32FC4),
|
|
NULL_TYPE,
|
|
Values(CV_8UC1,CV_8UC4,CV_32SC1, CV_32SC4,CV_32FC1, CV_32FC4),
|
|
NULL_TYPE,
|
|
NULL_TYPE,
|
|
Values(false))); // Values(false) is the reserved parameter
|
|
|
|
INSTANTIATE_TEST_CASE_P(ImgprocTestBase, cornerMinEigenVal, Combine(
|
|
Values(CV_8UC1,CV_32FC1),
|
|
NULL_TYPE,
|
|
ONE_TYPE(CV_32FC1),
|
|
NULL_TYPE,
|
|
NULL_TYPE,
|
|
Values(false))); // Values(false) is the reserved parameter
|
|
|
|
INSTANTIATE_TEST_CASE_P(ImgprocTestBase, cornerHarris, Combine(
|
|
Values(CV_8UC1,CV_32FC1),
|
|
NULL_TYPE,
|
|
ONE_TYPE(CV_32FC1),
|
|
NULL_TYPE,
|
|
NULL_TYPE,
|
|
Values(false))); // Values(false) is the reserved parameter
|
|
|
|
|
|
INSTANTIATE_TEST_CASE_P(ImgprocTestBase, integral, Combine(
|
|
ONE_TYPE(CV_8UC1),
|
|
NULL_TYPE,
|
|
ONE_TYPE(CV_32SC1),
|
|
ONE_TYPE(CV_32FC1),
|
|
NULL_TYPE,
|
|
Values(false))); // Values(false) is the reserved parameter
|
|
|
|
INSTANTIATE_TEST_CASE_P(Imgproc, WarpAffine, Combine(
|
|
Values(CV_8UC1, CV_8UC3,CV_8UC4, CV_32FC1, CV_32FC4),
|
|
Values((MatType)cv::INTER_NEAREST, (MatType)cv::INTER_LINEAR,
|
|
(MatType)cv::INTER_CUBIC, (MatType)(cv::INTER_NEAREST | cv::WARP_INVERSE_MAP),
|
|
(MatType)(cv::INTER_LINEAR | cv::WARP_INVERSE_MAP), (MatType)(cv::INTER_CUBIC | cv::WARP_INVERSE_MAP))));
|
|
|
|
|
|
INSTANTIATE_TEST_CASE_P(Imgproc, WarpPerspective, Combine
|
|
(Values(CV_8UC1, CV_8UC3,CV_8UC4, CV_32FC1, CV_32FC4),
|
|
Values((MatType)cv::INTER_NEAREST, (MatType)cv::INTER_LINEAR,
|
|
(MatType)cv::INTER_CUBIC, (MatType)(cv::INTER_NEAREST | cv::WARP_INVERSE_MAP),
|
|
(MatType)(cv::INTER_LINEAR | cv::WARP_INVERSE_MAP), (MatType)(cv::INTER_CUBIC | cv::WARP_INVERSE_MAP))));
|
|
|
|
|
|
INSTANTIATE_TEST_CASE_P(Imgproc, Resize, Combine(
|
|
Values(CV_8UC1, CV_8UC3,CV_8UC4, CV_32FC1, CV_32FC4), Values(cv::Size()),
|
|
Values(0.5, 1.5, 2), Values(0.5, 1.5, 2), Values((MatType)cv::INTER_NEAREST, (MatType)cv::INTER_LINEAR)));
|
|
|
|
|
|
INSTANTIATE_TEST_CASE_P(Imgproc, Threshold, Combine(
|
|
Values(CV_8UC1, CV_32FC1), Values(ThreshOp(cv::THRESH_BINARY),
|
|
ThreshOp(cv::THRESH_BINARY_INV), ThreshOp(cv::THRESH_TRUNC),
|
|
ThreshOp(cv::THRESH_TOZERO), ThreshOp(cv::THRESH_TOZERO_INV))));
|
|
|
|
|
|
INSTANTIATE_TEST_CASE_P(Imgproc, meanShiftFiltering, Combine(
|
|
ONE_TYPE(CV_8UC4),
|
|
ONE_TYPE(CV_16SC2),
|
|
Values(5),
|
|
Values(6),
|
|
Values(cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 5, 1))
|
|
));
|
|
|
|
|
|
INSTANTIATE_TEST_CASE_P(Imgproc, meanShiftProc, Combine(
|
|
ONE_TYPE(CV_8UC4),
|
|
ONE_TYPE(CV_16SC2),
|
|
Values(5),
|
|
Values(6),
|
|
Values(cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 5, 1))
|
|
));
|
|
|
|
INSTANTIATE_TEST_CASE_P(Imgproc, Remap, Combine(
|
|
Values(CV_8UC1, CV_8UC3,CV_8UC4, CV_32FC1, CV_32FC4),
|
|
Values(CV_32FC1, CV_16SC2, CV_32FC2),Values(-1,CV_32FC1),
|
|
Values((int)cv::INTER_NEAREST, (int)cv::INTER_LINEAR),
|
|
Values((int)cv::BORDER_CONSTANT)));
|
|
|
|
|
|
INSTANTIATE_TEST_CASE_P(histTestBase, calcHist, Combine(
|
|
ONE_TYPE(CV_8UC1),
|
|
ONE_TYPE(CV_32SC1) //no use
|
|
));
|
|
|
|
INSTANTIATE_TEST_CASE_P(ConvolveTestBase, Convolve, Combine(
|
|
Values(CV_32FC1, CV_32FC1),
|
|
Values(false))); // Values(false) is the reserved parameter
|
|
#endif // HAVE_OPENCL
|