72 lines
1.9 KiB
C++
72 lines
1.9 KiB
C++
#include "perf_precomp.hpp"
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using namespace std;
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using namespace cv;
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using namespace perf;
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using std::tr1::make_tuple;
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using std::tr1::get;
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typedef perf::TestBaseWithParam<size_t> VectorLength;
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typedef std::tr1::tuple<int, int> MaxDim_MaxPoints_t;
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typedef perf::TestBaseWithParam<MaxDim_MaxPoints_t> MaxDim_MaxPoints;
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PERF_TEST_P(VectorLength, phase32f, testing::Values(128, 1000, 128*1024, 512*1024, 1024*1024))
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{
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size_t length = GetParam();
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vector<float> X(length);
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vector<float> Y(length);
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vector<float> angle(length);
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declare.in(X, Y, WARMUP_RNG).out(angle);
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TEST_CYCLE_N(200) cv::phase(X, Y, angle, true);
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SANITY_CHECK(angle, 5e-5);
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}
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PERF_TEST_P( MaxDim_MaxPoints, kmeans,
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testing::Combine( testing::Values( 16, 32, 64 ),
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testing::Values( 300, 400, 500) ) )
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{
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RNG& rng = theRNG();
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const int MAX_DIM = get<0>(GetParam());
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const int MAX_POINTS = get<1>(GetParam());
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const int attempts = 5;
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Mat labels, centers;
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int i, N = 0, N0 = 0, K = 0, dims = 0;
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dims = rng.uniform(1, MAX_DIM+1);
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N = rng.uniform(1, MAX_POINTS+1);
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N0 = rng.uniform(1, MAX(N/10, 2));
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K = rng.uniform(1, N+1);
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Mat data0(N0, dims, CV_32F);
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rng.fill(data0, RNG::UNIFORM, -1, 1);
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Mat data(N, dims, CV_32F);
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for( i = 0; i < N; i++ )
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data0.row(rng.uniform(0, N0)).copyTo(data.row(i));
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declare.in(data);
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TEST_CYCLE()
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{
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kmeans(data, K, labels, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 0),
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attempts, KMEANS_PP_CENTERS, centers);
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}
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Mat clusterPointsNumber = Mat::zeros(1, K, CV_32S);
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for( i = 0; i < labels.rows; i++ )
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{
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int clusterIdx = labels.at<int>(i);
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clusterPointsNumber.at<int>(clusterIdx)++;
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}
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Mat sortedClusterPointsNumber;
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cv::sort(clusterPointsNumber, sortedClusterPointsNumber, cv::SORT_EVERY_ROW + cv::SORT_ASCENDING);
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SANITY_CHECK(sortedClusterPointsNumber);
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}
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