/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved. // Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // @Authors // Fangfang Bai, fangfang@multicorewareinc.com // Jin Ma, jin@multicorewareinc.com // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors as is and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "perf_precomp.hpp" using namespace perf; using std::tr1::tuple; using std::tr1::get; ///////////// equalizeHist //////////////////////// typedef TestBaseWithParam equalizeHistFixture; PERF_TEST_P(equalizeHistFixture, equalizeHist, OCL_TYPICAL_MAT_SIZES) { const Size srcSize = GetParam(); const double eps = 1 + DBL_EPSILON; Mat src(srcSize, CV_8UC1), dst(srcSize, CV_8UC1); declare.in(src, WARMUP_RNG).out(dst); if (RUN_OCL_IMPL) { ocl::oclMat oclSrc(src), oclDst(srcSize, src.type()); OCL_TEST_CYCLE() cv::ocl::equalizeHist(oclSrc, oclDst); oclDst.download(dst); SANITY_CHECK(dst, eps); } else if (RUN_PLAIN_IMPL) { TEST_CYCLE() cv::equalizeHist(src, dst); SANITY_CHECK(dst, eps); } else OCL_PERF_ELSE } /////////// CopyMakeBorder ////////////////////// CV_ENUM(Border, BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT, BORDER_WRAP, BORDER_REFLECT_101) typedef tuple CopyMakeBorderParamType; typedef TestBaseWithParam CopyMakeBorderFixture; PERF_TEST_P(CopyMakeBorderFixture, CopyMakeBorder, ::testing::Combine(OCL_TYPICAL_MAT_SIZES, OCL_PERF_ENUM(CV_8UC1, CV_8UC4), Border::all())) { const CopyMakeBorderParamType params = GetParam(); const Size srcSize = get<0>(params); const int type = get<1>(params), borderType = get<2>(params); Mat src(srcSize, type), dst; const Size dstSize = srcSize + Size(12, 12); dst.create(dstSize, type); declare.in(src, WARMUP_RNG).out(dst); if (RUN_OCL_IMPL) { ocl::oclMat oclSrc(src), oclDst(dstSize, type); OCL_TEST_CYCLE() cv::ocl::copyMakeBorder(oclSrc, oclDst, 7, 5, 5, 7, borderType, cv::Scalar(1.0)); oclDst.download(dst); SANITY_CHECK(dst); } else if (RUN_PLAIN_IMPL) { TEST_CYCLE() cv::copyMakeBorder(src, dst, 7, 5, 5, 7, borderType, cv::Scalar(1.0)); SANITY_CHECK(dst); } else OCL_PERF_ELSE } ///////////// cornerMinEigenVal //////////////////////// typedef Size_MatType cornerMinEigenValFixture; PERF_TEST_P(cornerMinEigenValFixture, cornerMinEigenVal, ::testing::Combine(OCL_TYPICAL_MAT_SIZES, OCL_PERF_ENUM(CV_8UC1, CV_32FC1))) { const Size_MatType_t params = GetParam(); const Size srcSize = get<0>(params); const int type = get<1>(params), borderType = BORDER_REFLECT; const int blockSize = 7, apertureSize = 1 + 2 * 3; Mat src(srcSize, type), dst(srcSize, CV_32FC1); declare.in(src, WARMUP_RNG).out(dst) .time(srcSize == OCL_SIZE_4000 ? 20 : srcSize == OCL_SIZE_2000 ? 5 : 3); const int depth = CV_MAT_DEPTH(type); const ERROR_TYPE errorType = depth == CV_8U ? ERROR_ABSOLUTE : ERROR_RELATIVE; if (RUN_OCL_IMPL) { ocl::oclMat oclSrc(src), oclDst(srcSize, CV_32FC1); OCL_TEST_CYCLE() cv::ocl::cornerMinEigenVal(oclSrc, oclDst, blockSize, apertureSize, borderType); oclDst.download(dst); SANITY_CHECK(dst, 1e-6, errorType); } else if (RUN_PLAIN_IMPL) { TEST_CYCLE() cv::cornerMinEigenVal(src, dst, blockSize, apertureSize, borderType); SANITY_CHECK(dst, 1e-6, errorType); } else OCL_PERF_ELSE } ///////////// cornerHarris //////////////////////// typedef Size_MatType cornerHarrisFixture; PERF_TEST_P(cornerHarrisFixture, cornerHarris, ::testing::Combine(OCL_TYPICAL_MAT_SIZES, OCL_PERF_ENUM(CV_8UC1, CV_32FC1))) { const Size_MatType_t params = GetParam(); const Size srcSize = get<0>(params); const int type = get<1>(params), borderType = BORDER_REFLECT; Mat src(srcSize, type), dst(srcSize, CV_32FC1); randu(src, 0, 1); declare.in(src).out(dst) .time(srcSize == OCL_SIZE_4000 ? 20 : srcSize == OCL_SIZE_2000 ? 5 : 3); if (RUN_OCL_IMPL) { ocl::oclMat oclSrc(src), oclDst(srcSize, CV_32FC1); OCL_TEST_CYCLE() cv::ocl::cornerHarris(oclSrc, oclDst, 5, 7, 0.1, borderType); oclDst.download(dst); SANITY_CHECK(dst, 3e-5); } else if (RUN_PLAIN_IMPL) { TEST_CYCLE() cv::cornerHarris(src, dst, 5, 7, 0.1, borderType); SANITY_CHECK(dst, 3e-5); } else OCL_PERF_ELSE } ///////////// integral //////////////////////// typedef TestBaseWithParam integralFixture; PERF_TEST_P(integralFixture, integral, OCL_TYPICAL_MAT_SIZES) { const Size srcSize = GetParam(); Mat src(srcSize, CV_8UC1), dst; declare.in(src, WARMUP_RNG); if (RUN_OCL_IMPL) { ocl::oclMat oclSrc(src), oclDst; OCL_TEST_CYCLE() cv::ocl::integral(oclSrc, oclDst); oclDst.download(dst); SANITY_CHECK(dst); } else if (RUN_PLAIN_IMPL) { TEST_CYCLE() cv::integral(src, dst); SANITY_CHECK(dst); } else OCL_PERF_ELSE } ///////////// WarpAffine //////////////////////// typedef Size_MatType WarpAffineFixture; PERF_TEST_P(WarpAffineFixture, WarpAffine, ::testing::Combine(OCL_TYPICAL_MAT_SIZES, OCL_PERF_ENUM(CV_8UC1, CV_8UC4))) { static const double coeffs[2][3] = { { cos(CV_PI / 6), -sin(CV_PI / 6), 100.0 }, { sin(CV_PI / 6), cos(CV_PI / 6), -100.0 } }; Mat M(2, 3, CV_64F, (void *)coeffs); const int interpolation = INTER_NEAREST; const Size_MatType_t params = GetParam(); const Size srcSize = get<0>(params); const int type = get<1>(params); Mat src(srcSize, type), dst(srcSize, type); declare.in(src, WARMUP_RNG).out(dst); if (RUN_OCL_IMPL) { ocl::oclMat oclSrc(src), oclDst(srcSize, type); OCL_TEST_CYCLE() cv::ocl::warpAffine(oclSrc, oclDst, M, srcSize, interpolation); oclDst.download(dst); SANITY_CHECK(dst); } else if (RUN_PLAIN_IMPL) { TEST_CYCLE() cv::warpAffine(src, dst, M, srcSize, interpolation); SANITY_CHECK(dst); } else OCL_PERF_ELSE } ///////////// WarpPerspective //////////////////////// typedef Size_MatType WarpPerspectiveFixture; PERF_TEST_P(WarpPerspectiveFixture, WarpPerspective, ::testing::Combine(OCL_TYPICAL_MAT_SIZES, OCL_PERF_ENUM(CV_8UC1, CV_8UC4))) { static const double coeffs[3][3] = { {cos(CV_PI / 6), -sin(CV_PI / 6), 100.0}, {sin(CV_PI / 6), cos(CV_PI / 6), -100.0}, {0.0, 0.0, 1.0} }; Mat M(3, 3, CV_64F, (void *)coeffs); const int interpolation = INTER_LINEAR; const Size_MatType_t params = GetParam(); const Size srcSize = get<0>(params); const int type = get<1>(params); Mat src(srcSize, type), dst(srcSize, type); declare.in(src, WARMUP_RNG).out(dst) .time(srcSize == OCL_SIZE_4000 ? 18 : srcSize == OCL_SIZE_2000 ? 5 : 2); if (RUN_OCL_IMPL) { ocl::oclMat oclSrc(src), oclDst(srcSize, type); OCL_TEST_CYCLE() cv::ocl::warpPerspective(oclSrc, oclDst, M, srcSize, interpolation); oclDst.download(dst); SANITY_CHECK(dst); } else if (RUN_PLAIN_IMPL) { TEST_CYCLE() cv::warpPerspective(src, dst, M, srcSize, interpolation); SANITY_CHECK(dst); } else OCL_PERF_ELSE } ///////////// resize //////////////////////// CV_ENUM(resizeInterType, INTER_NEAREST, INTER_LINEAR) typedef tuple resizeParams; typedef TestBaseWithParam resizeFixture; PERF_TEST_P(resizeFixture, resize, ::testing::Combine(OCL_TYPICAL_MAT_SIZES, OCL_PERF_ENUM(CV_8UC1, CV_8UC4), resizeInterType::all(), ::testing::Values(0.5, 2.0))) { const resizeParams params = GetParam(); const Size srcSize = get<0>(params); const int type = get<1>(params), interType = get<2>(params); double scale = get<3>(params); Mat src(srcSize, type), dst; const Size dstSize(cvRound(srcSize.width * scale), cvRound(srcSize.height * scale)); dst.create(dstSize, type); declare.in(src, WARMUP_RNG).out(dst); if (interType == INTER_LINEAR && type == CV_8UC4 && OCL_SIZE_4000 == srcSize) declare.time(11); if (RUN_OCL_IMPL) { ocl::oclMat oclSrc(src), oclDst(dstSize, type); OCL_TEST_CYCLE() cv::ocl::resize(oclSrc, oclDst, Size(), scale, scale, interType); oclDst.download(dst); SANITY_CHECK(dst, 1 + DBL_EPSILON); } else if (RUN_PLAIN_IMPL) { TEST_CYCLE() cv::resize(src, dst, Size(), scale, scale, interType); SANITY_CHECK(dst, 1 + DBL_EPSILON); } else OCL_PERF_ELSE } ///////////// threshold//////////////////////// CV_ENUM(ThreshType, THRESH_BINARY, THRESH_TOZERO_INV) typedef tuple ThreshParams; typedef TestBaseWithParam ThreshFixture; PERF_TEST_P(ThreshFixture, threshold, ::testing::Combine(OCL_TYPICAL_MAT_SIZES, OCL_PERF_ENUM(CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC4, CV_32FC1), ThreshType::all())) { const ThreshParams params = GetParam(); const Size srcSize = get<0>(params); const int srcType = get<1>(params); const int threshType = get<2>(params); const double maxValue = 220.0, threshold = 50; Mat src(srcSize, srcType), dst(srcSize, srcType); randu(src, 0, 100); declare.in(src).out(dst); if (RUN_OCL_IMPL) { ocl::oclMat oclSrc(src), oclDst(srcSize, CV_8U); OCL_TEST_CYCLE() cv::ocl::threshold(oclSrc, oclDst, threshold, maxValue, threshType); oclDst.download(dst); SANITY_CHECK(dst); } else if (RUN_PLAIN_IMPL) { TEST_CYCLE() cv::threshold(src, dst, threshold, maxValue, threshType); SANITY_CHECK(dst); } else OCL_PERF_ELSE } ///////////// meanShiftFiltering//////////////////////// typedef struct _COOR { short x; short y; } COOR; static COOR do_meanShift(int x0, int y0, uchar *sptr, uchar *dptr, int sstep, cv::Size size, int sp, int sr, int maxIter, float eps, int *tab) { int isr2 = sr * sr; int c0, c1, c2, c3; int iter; uchar *ptr = NULL; uchar *pstart = NULL; int revx = 0, revy = 0; c0 = sptr[0]; c1 = sptr[1]; c2 = sptr[2]; c3 = sptr[3]; // iterate meanshift procedure for(iter = 0; iter < maxIter; iter++ ) { int count = 0; int s0 = 0, s1 = 0, s2 = 0, sx = 0, sy = 0; //mean shift: process pixels in window (p-sigmaSp)x(p+sigmaSp) int minx = x0 - sp; int miny = y0 - sp; int maxx = x0 + sp; int maxy = y0 + sp; //deal with the image boundary if(minx < 0) minx = 0; if(miny < 0) miny = 0; if(maxx >= size.width) maxx = size.width - 1; if(maxy >= size.height) maxy = size.height - 1; if(iter == 0) { pstart = sptr; } else { pstart = pstart + revy * sstep + (revx << 2); //point to the new position } ptr = pstart; ptr = ptr + (miny - y0) * sstep + ((minx - x0) << 2); //point to the start in the row for( int y = miny; y <= maxy; y++, ptr += sstep - ((maxx - minx + 1) << 2)) { int rowCount = 0; int x = minx; #if CV_ENABLE_UNROLLED for( ; x + 4 <= maxx; x += 4, ptr += 16) { int t0, t1, t2; t0 = ptr[0], t1 = ptr[1], t2 = ptr[2]; if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) { s0 += t0; s1 += t1; s2 += t2; sx += x; rowCount++; } t0 = ptr[4], t1 = ptr[5], t2 = ptr[6]; if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) { s0 += t0; s1 += t1; s2 += t2; sx += x + 1; rowCount++; } t0 = ptr[8], t1 = ptr[9], t2 = ptr[10]; if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) { s0 += t0; s1 += t1; s2 += t2; sx += x + 2; rowCount++; } t0 = ptr[12], t1 = ptr[13], t2 = ptr[14]; if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) { s0 += t0; s1 += t1; s2 += t2; sx += x + 3; rowCount++; } } #endif for(; x <= maxx; x++, ptr += 4) { int t0 = ptr[0], t1 = ptr[1], t2 = ptr[2]; if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) { s0 += t0; s1 += t1; s2 += t2; sx += x; rowCount++; } } if(rowCount == 0) continue; count += rowCount; sy += y * rowCount; } if( count == 0 ) break; int x1 = sx / count; int y1 = sy / count; s0 = s0 / count; s1 = s1 / count; s2 = s2 / count; bool stopFlag = (x0 == x1 && y0 == y1) || (abs(x1 - x0) + abs(y1 - y0) + tab[s0 - c0 + 255] + tab[s1 - c1 + 255] + tab[s2 - c2 + 255] <= eps); //revise the pointer corresponding to the new (y0,x0) revx = x1 - x0; revy = y1 - y0; x0 = x1; y0 = y1; c0 = s0; c1 = s1; c2 = s2; if( stopFlag ) break; } //for iter dptr[0] = (uchar)c0; dptr[1] = (uchar)c1; dptr[2] = (uchar)c2; dptr[3] = (uchar)c3; COOR coor; coor.x = static_cast(x0); coor.y = static_cast(y0); return coor; } static void meanShiftFiltering_(const Mat &src_roi, Mat &dst_roi, int sp, int sr, cv::TermCriteria crit) { if( src_roi.empty() ) CV_Error( Error::StsBadArg, "The input image is empty" ); if( src_roi.depth() != CV_8U || src_roi.channels() != 4 ) CV_Error( Error::StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported" ); dst_roi.create(src_roi.size(), src_roi.type()); CV_Assert( (src_roi.cols == dst_roi.cols) && (src_roi.rows == dst_roi.rows) ); CV_Assert( !(dst_roi.step & 0x3) ); if( !(crit.type & cv::TermCriteria::MAX_ITER) ) crit.maxCount = 5; int maxIter = std::min(std::max(crit.maxCount, 1), 100); float eps; if( !(crit.type & cv::TermCriteria::EPS) ) eps = 1.f; eps = (float)std::max(crit.epsilon, 0.0); int tab[512]; for(int i = 0; i < 512; i++) tab[i] = (i - 255) * (i - 255); uchar *sptr = src_roi.data; uchar *dptr = dst_roi.data; int sstep = (int)src_roi.step; int dstep = (int)dst_roi.step; cv::Size size = src_roi.size(); for(int i = 0; i < size.height; i++, sptr += sstep - (size.width << 2), dptr += dstep - (size.width << 2)) { for(int j = 0; j < size.width; j++, sptr += 4, dptr += 4) { do_meanShift(j, i, sptr, dptr, sstep, size, sp, sr, maxIter, eps, tab); } } } typedef TestBaseWithParam meanShiftFilteringFixture; PERF_TEST_P(meanShiftFilteringFixture, meanShiftFiltering, OCL_TYPICAL_MAT_SIZES) { const Size srcSize = GetParam(); const int sp = 5, sr = 6; cv::TermCriteria crit(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 5, 1); Mat src(srcSize, CV_8UC4), dst(srcSize, CV_8UC4); declare.in(src, WARMUP_RNG).out(dst) .time(srcSize == OCL_SIZE_4000 ? 56 : srcSize == OCL_SIZE_2000 ? 15 : 3.8); if (RUN_PLAIN_IMPL) { TEST_CYCLE() meanShiftFiltering_(src, dst, sp, sr, crit); SANITY_CHECK(dst); } else if (RUN_OCL_IMPL) { ocl::oclMat oclSrc(src), oclDst(srcSize, CV_8UC4); OCL_TEST_CYCLE() ocl::meanShiftFiltering(oclSrc, oclDst, sp, sr, crit); oclDst.download(dst); SANITY_CHECK(dst); } else OCL_PERF_ELSE } static void meanShiftProc_(const Mat &src_roi, Mat &dst_roi, Mat &dstCoor_roi, int sp, int sr, cv::TermCriteria crit) { if (src_roi.empty()) { CV_Error(Error::StsBadArg, "The input image is empty"); } if (src_roi.depth() != CV_8U || src_roi.channels() != 4) { CV_Error(Error::StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported"); } dst_roi.create(src_roi.size(), src_roi.type()); dstCoor_roi.create(src_roi.size(), CV_16SC2); CV_Assert((src_roi.cols == dst_roi.cols) && (src_roi.rows == dst_roi.rows) && (src_roi.cols == dstCoor_roi.cols) && (src_roi.rows == dstCoor_roi.rows)); CV_Assert(!(dstCoor_roi.step & 0x3)); if (!(crit.type & cv::TermCriteria::MAX_ITER)) { crit.maxCount = 5; } int maxIter = std::min(std::max(crit.maxCount, 1), 100); float eps; if (!(crit.type & cv::TermCriteria::EPS)) { eps = 1.f; } eps = (float)std::max(crit.epsilon, 0.0); int tab[512]; for (int i = 0; i < 512; i++) { tab[i] = (i - 255) * (i - 255); } uchar *sptr = src_roi.data; uchar *dptr = dst_roi.data; short *dCoorptr = (short *)dstCoor_roi.data; int sstep = (int)src_roi.step; int dstep = (int)dst_roi.step; int dCoorstep = (int)dstCoor_roi.step >> 1; cv::Size size = src_roi.size(); for (int i = 0; i < size.height; i++, sptr += sstep - (size.width << 2), dptr += dstep - (size.width << 2), dCoorptr += dCoorstep - (size.width << 1)) { for (int j = 0; j < size.width; j++, sptr += 4, dptr += 4, dCoorptr += 2) { *((COOR *)dCoorptr) = do_meanShift(j, i, sptr, dptr, sstep, size, sp, sr, maxIter, eps, tab); } } } typedef TestBaseWithParam meanShiftProcFixture; PERF_TEST_P(meanShiftProcFixture, meanShiftProc, OCL_TYPICAL_MAT_SIZES) { const Size srcSize = GetParam(); TermCriteria crit(TermCriteria::COUNT + TermCriteria::EPS, 5, 1); Mat src(srcSize, CV_8UC4), dst1(srcSize, CV_8UC4), dst2(srcSize, CV_16SC2); declare.in(src, WARMUP_RNG).out(dst1, dst2) .time(srcSize == OCL_SIZE_4000 ? 56 : srcSize == OCL_SIZE_2000 ? 15 : 3.8);; if (RUN_PLAIN_IMPL) { TEST_CYCLE() meanShiftProc_(src, dst1, dst2, 5, 6, crit); SANITY_CHECK(dst1); SANITY_CHECK(dst2); } else if (RUN_OCL_IMPL) { ocl::oclMat oclSrc(src), oclDst1(srcSize, CV_8UC4), oclDst2(srcSize, CV_16SC2); OCL_TEST_CYCLE() ocl::meanShiftProc(oclSrc, oclDst1, oclDst2, 5, 6, crit); oclDst1.download(dst1); oclDst2.download(dst2); SANITY_CHECK(dst1); SANITY_CHECK(dst2); } else OCL_PERF_ELSE } ///////////// remap//////////////////////// CV_ENUM(RemapInterType, INTER_NEAREST, INTER_LINEAR) typedef tuple remapParams; typedef TestBaseWithParam remapFixture; PERF_TEST_P(remapFixture, remap, ::testing::Combine(OCL_TYPICAL_MAT_SIZES, OCL_PERF_ENUM(CV_8UC1, CV_8UC4), RemapInterType::all())) { const remapParams params = GetParam(); const Size srcSize = get<0>(params); const int type = get<1>(params), interpolation = get<2>(params); Mat src(srcSize, type), dst(srcSize, type); declare.in(src, WARMUP_RNG).out(dst); if (srcSize == OCL_SIZE_4000 && interpolation == INTER_LINEAR) declare.time(9); Mat xmap, ymap; xmap.create(srcSize, CV_32FC1); ymap.create(srcSize, CV_32FC1); for (int i = 0; i < srcSize.height; ++i) { float * const xmap_row = xmap.ptr(i); float * const ymap_row = ymap.ptr(i); for (int j = 0; j < srcSize.width; ++j) { xmap_row[j] = (j - srcSize.width * 0.5f) * 0.75f + srcSize.width * 0.5f; ymap_row[j] = (i - srcSize.height * 0.5f) * 0.75f + srcSize.height * 0.5f; } } const int borderMode = BORDER_CONSTANT; if (RUN_OCL_IMPL) { ocl::oclMat oclSrc(src), oclDst(srcSize, type); ocl::oclMat oclXMap(xmap), oclYMap(ymap); OCL_TEST_CYCLE() cv::ocl::remap(oclSrc, oclDst, oclXMap, oclYMap, interpolation, borderMode); oclDst.download(dst); SANITY_CHECK(dst, 1 + DBL_EPSILON); } else if (RUN_PLAIN_IMPL) { TEST_CYCLE() cv::remap(src, dst, xmap, ymap, interpolation, borderMode); SANITY_CHECK(dst, 1 + DBL_EPSILON); } else OCL_PERF_ELSE } ///////////// CLAHE //////////////////////// typedef TestBaseWithParam CLAHEFixture; PERF_TEST_P(CLAHEFixture, CLAHE, OCL_TYPICAL_MAT_SIZES) { const Size srcSize = GetParam(); const string impl = getSelectedImpl(); Mat src(srcSize, CV_8UC1), dst; const double clipLimit = 40.0; declare.in(src, WARMUP_RNG); if (srcSize == OCL_SIZE_4000) declare.time(11); if (RUN_OCL_IMPL) { ocl::oclMat oclSrc(src), oclDst; cv::Ptr oclClahe = cv::ocl::createCLAHE(clipLimit); OCL_TEST_CYCLE() oclClahe->apply(oclSrc, oclDst); oclDst.download(dst); SANITY_CHECK(dst); } else if (RUN_PLAIN_IMPL) { cv::Ptr clahe = cv::createCLAHE(clipLimit); TEST_CYCLE() clahe->apply(src, dst); SANITY_CHECK(dst); } else OCL_PERF_ELSE } ///////////// columnSum//////////////////////// typedef TestBaseWithParam columnSumFixture; static void columnSumPerfTest(const Mat & src, Mat & dst) { for (int j = 0; j < src.cols; j++) dst.at(0, j) = src.at(0, j); for (int i = 1; i < src.rows; ++i) for (int j = 0; j < src.cols; ++j) dst.at(i, j) = dst.at(i - 1 , j) + src.at(i , j); } PERF_TEST_P(columnSumFixture, columnSum, OCL_TYPICAL_MAT_SIZES) { const Size srcSize = GetParam(); Mat src(srcSize, CV_32FC1), dst(srcSize, CV_32FC1); declare.in(src, WARMUP_RNG).out(dst); if (srcSize == OCL_SIZE_4000) declare.time(5); if (RUN_OCL_IMPL) { ocl::oclMat oclSrc(src), oclDst(srcSize, CV_32FC1); OCL_TEST_CYCLE() cv::ocl::columnSum(oclSrc, oclDst); oclDst.download(dst); SANITY_CHECK(dst); } else if (RUN_PLAIN_IMPL) { TEST_CYCLE() columnSumPerfTest(src, dst); SANITY_CHECK(dst); } else OCL_PERF_ELSE } //////////////////////////////distanceToCenters//////////////////////////////////////////////// CV_ENUM(DistType, NORM_L1, NORM_L2SQR) typedef tuple distanceToCentersParameters; typedef TestBaseWithParam distanceToCentersFixture; static void distanceToCentersPerfTest(Mat& src, Mat& centers, Mat& dists, Mat& labels, int distType) { Mat batch_dists; cv::batchDistance(src, centers, batch_dists, CV_32FC1, noArray(), distType); std::vector dists_v; std::vector labels_v; for (int i = 0; i < batch_dists.rows; i++) { Mat r = batch_dists.row(i); double mVal; Point mLoc; minMaxLoc(r, &mVal, NULL, &mLoc, NULL); dists_v.push_back(static_cast(mVal)); labels_v.push_back(mLoc.x); } Mat(dists_v).copyTo(dists); Mat(labels_v).copyTo(labels); } PERF_TEST_P(distanceToCentersFixture, distanceToCenters, ::testing::Combine(::testing::Values(cv::Size(256,256), cv::Size(512,512)), DistType::all()) ) { Size size = get<0>(GetParam()); int distType = get<1>(GetParam()); Mat src(size, CV_32FC1), centers(size, CV_32FC1); Mat dists(src.rows, 1, CV_32FC1), labels(src.rows, 1, CV_32SC1); declare.in(src, centers, WARMUP_RNG).out(dists, labels); if (RUN_OCL_IMPL) { ocl::oclMat ocl_src(src), ocl_centers(centers); OCL_TEST_CYCLE() ocl::distanceToCenters(ocl_src, ocl_centers, dists, labels, distType); SANITY_CHECK(dists, 1e-6, ERROR_RELATIVE); SANITY_CHECK(labels); } else if (RUN_PLAIN_IMPL) { TEST_CYCLE() distanceToCentersPerfTest(src, centers, dists, labels, distType); SANITY_CHECK(dists, 1e-6, ERROR_RELATIVE); SANITY_CHECK(labels); } else OCL_PERF_ELSE }