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2c7591c57f
Depends on https://github.com/opencv/opencv/pull/26316 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [ ] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
80 lines
2.5 KiB
C++
80 lines
2.5 KiB
C++
/*
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* Copyright (c) 2024 Qualcomm Innovation Center, Inc. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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*/
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#include "perf_precomp.hpp"
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namespace opencv_test {
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typedef std::tuple<int /* nPts */, int /*nDims*/, int /*nClusters*/> ClusterEuclideanPerfParams;
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typedef perf::TestBaseWithParam<ClusterEuclideanPerfParams> ClusterEuclideanPerfTest;
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PERF_TEST_P(ClusterEuclideanPerfTest, run,
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::testing::Combine(::testing::Values(100, 1000, 10000), // nPts
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::testing::Values(2, 10, 32), // nDims
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::testing::Values(5, 10, 16)) // nClusters
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)
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{
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auto p = GetParam();
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int nPts = std::get<0>(p);
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int nDims = std::get<1>(p);
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int nClusters = std::get<2>(p);
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Mat points(nPts, nDims, CV_8U);
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Mat clusterCenters(nClusters, nDims, CV_32F);
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Mat trueMeans(nClusters, nDims, CV_32F);
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Mat stddevs(nClusters, nDims, CV_32F);
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std::vector<int> trueClusterSizes(nClusters, 0);
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std::vector<int> trueClusterBindings(nPts, 0);
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std::vector<float> trueSumDists(nClusters, 0);
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cv::RNG& rng = cv::theRNG();
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for (int i = 0; i < nClusters; i++)
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{
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Mat mean(1, nDims, CV_64F), stdev(1, nDims, CV_64F);
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rng.fill(mean, cv::RNG::UNIFORM, 0, 256);
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rng.fill(stdev, cv::RNG::UNIFORM, 5.f, 16);
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int lo = i * nPts / nClusters;
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int hi = (i + 1) * nPts / nClusters;
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for (int d = 0; d < nDims; d++)
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{
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rng.fill(points.col(d).rowRange(lo, hi), cv::RNG::NORMAL,
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mean.at<double>(d), stdev.at<double>(d));
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}
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float sd = 0;
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for (int j = lo; j < hi; j++)
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{
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Mat pts64f;
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points.row(j).convertTo(pts64f, CV_64F);
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sd += cv::norm(mean, pts64f, NORM_L2);
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trueClusterBindings.at(j) = i;
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trueClusterSizes.at(i)++;
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}
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trueSumDists.at(i) = sd;
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// let's shift initial cluster center a bit
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Mat(mean + stdev * 0.5).copyTo(clusterCenters.row(i));
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mean.copyTo(trueMeans.row(i));
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stdev.copyTo(stddevs.row(i));
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}
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while(next())
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{
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Mat newClusterCenters;
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std::vector<int> clusterSizes, clusterBindings;
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std::vector<float> clusterSumDists;
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startTimer();
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cv::fastcv::clusterEuclidean(points, clusterCenters, newClusterCenters, clusterSizes, clusterBindings, clusterSumDists);
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stopTimer();
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}
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SANITY_CHECK_NOTHING();
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}
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} // namespace
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