vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

2026-08-22 00:11:13 +08:00
commit 12022378a3
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+/*
+ * Copyright (c) 2024 Qualcomm Innovation Center, Inc. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+*/
+
+#include "perf_precomp.hpp"
+
+namespace opencv_test {
+
+typedef std::tuple<int /* nPts */, int /*nDims*/, int /*nClusters*/> ClusterEuclideanPerfParams;
+typedef perf::TestBaseWithParam<ClusterEuclideanPerfParams> ClusterEuclideanPerfTest;
+
+PERF_TEST_P(ClusterEuclideanPerfTest, run,
+            ::testing::Combine(::testing::Values(100, 1000, 10000), // nPts
+                               ::testing::Values(2, 10, 32),        // nDims
+                               ::testing::Values(5, 10, 16))        // nClusters
+           )
+{
+    auto p = GetParam();
+    int nPts      = std::get<0>(p);
+    int nDims     = std::get<1>(p);
+    int nClusters = std::get<2>(p);
+
+    Mat points(nPts, nDims, CV_8U);
+    Mat clusterCenters(nClusters, nDims, CV_32F);
+
+    Mat trueMeans(nClusters, nDims, CV_32F);
+    Mat stddevs(nClusters, nDims, CV_32F);
+    std::vector<int> trueClusterSizes(nClusters, 0);
+    std::vector<int> trueClusterBindings(nPts, 0);
+    std::vector<float> trueSumDists(nClusters, 0);
+
+    cv::RNG& rng = cv::theRNG();
+    for (int i = 0; i < nClusters; i++)
+    {
+        Mat mean(1, nDims, CV_64F), stdev(1, nDims, CV_64F);
+        rng.fill(mean,  cv::RNG::UNIFORM, 0, 256);
+        rng.fill(stdev, cv::RNG::UNIFORM, 5.f, 16);
+        int lo =    i    * nPts / nClusters;
+        int hi = (i + 1) * nPts / nClusters;
+
+        for (int d = 0; d < nDims; d++)
+        {
+            rng.fill(points.col(d).rowRange(lo, hi), cv::RNG::NORMAL,
+                     mean.at<double>(d), stdev.at<double>(d));
+        }
+
+        float sd = 0;
+        for (int j = lo; j < hi; j++)
+        {
+            Mat pts64f;
+            points.row(j).convertTo(pts64f, CV_64F);
+            sd += cv::norm(mean, pts64f, NORM_L2);
+            trueClusterBindings.at(j) = i;
+            trueClusterSizes.at(i)++;
+        }
+        trueSumDists.at(i) = sd;
+
+        // let's shift initial cluster center a bit
+        Mat(mean + stdev * 0.5).copyTo(clusterCenters.row(i));
+
+        mean.copyTo(trueMeans.row(i));
+        stdev.copyTo(stddevs.row(i));
+    }
+
+    while(next())
+    {
+        Mat newClusterCenters;
+        std::vector<int> clusterSizes, clusterBindings;
+        std::vector<float> clusterSumDists;
+        startTimer();
+        cv::fastcv::clusterEuclidean(points, clusterCenters, newClusterCenters, clusterSizes, clusterBindings, clusterSumDists);
+        stopTimer();
+    }
+
+    SANITY_CHECK_NOTHING();
+}
+
+} // namespace