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