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#include "perf_precomp.hpp"
using namespace std;
using namespace cv;
using namespace perf;
namespace {
typedef perf::TestBaseWithParam<size_t> VectorLength;
PERF_TEST_P(VectorLength, phase32f, testing::Values(128, 1000, 128*1024, 512*1024, 1024*1024))
{
size_t length = GetParam();
vector<float> X(length);
vector<float> Y(length);
vector<float> angle(length);
declare.in(X, Y, WARMUP_RNG).out(angle);
TEST_CYCLE_N(200) cv::phase(X, Y, angle, true);
SANITY_CHECK(angle, 5e-5);
}
PERF_TEST_P(VectorLength, phase64f, testing::Values(128, 1000, 128*1024, 512*1024, 1024*1024))
{
size_t length = GetParam();
vector<double> X(length);
vector<double> Y(length);
vector<double> angle(length);
declare.in(X, Y, WARMUP_RNG).out(angle);
TEST_CYCLE_N(200) cv::phase(X, Y, angle, true);
SANITY_CHECK(angle, 5e-5);
}
typedef perf::TestBaseWithParam< testing::tuple<int, int, int> > KMeans;
PERF_TEST_P_(KMeans, single_iter)
{
RNG& rng = theRNG();
const int K = testing::get<0>(GetParam());
const int dims = testing::get<1>(GetParam());
const int N = testing::get<2>(GetParam());
const int attempts = 5;
Mat data(N, dims, CV_32F);
rng.fill(data, RNG::UNIFORM, -0.1, 0.1);
const int N0 = K;
Mat data0(N0, dims, CV_32F);
rng.fill(data0, RNG::UNIFORM, -1, 1);
for (int i = 0; i < N; i++)
{
int base = rng.uniform(0, N0);
cv::add(data0.row(base), data.row(i), data.row(i));
}
declare.in(data);
Mat labels, centers;
TEST_CYCLE()
{
kmeans(data, K, labels, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 1, 0),
attempts, KMEANS_PP_CENTERS, centers);
}
SANITY_CHECK_NOTHING();
}
PERF_TEST_P_(KMeans, good)
{
RNG& rng = theRNG();
const int K = testing::get<0>(GetParam());
const int dims = testing::get<1>(GetParam());
const int N = testing::get<2>(GetParam());
const int attempts = 5;
Mat data(N, dims, CV_32F);
rng.fill(data, RNG::UNIFORM, -0.1, 0.1);
const int N0 = K;
Mat data0(N0, dims, CV_32F);
rng.fill(data0, RNG::UNIFORM, -1, 1);
for (int i = 0; i < N; i++)
{
int base = rng.uniform(0, N0);
cv::add(data0.row(base), data.row(i), data.row(i));
}
declare.in(data);
Mat labels, centers;
TEST_CYCLE()
{
kmeans(data, K, labels, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 0),
attempts, KMEANS_PP_CENTERS, centers);
}
SANITY_CHECK_NOTHING();
}
PERF_TEST_P_(KMeans, with_duplicates)
{
RNG& rng = theRNG();
const int K = testing::get<0>(GetParam());
const int dims = testing::get<1>(GetParam());
const int N = testing::get<2>(GetParam());
const int attempts = 5;
Mat data(N, dims, CV_32F, Scalar::all(0));
const int N0 = std::max(2, K * 2 / 3);
Mat data0(N0, dims, CV_32F);
rng.fill(data0, RNG::UNIFORM, -1, 1);
for (int i = 0; i < N; i++)
{
int base = rng.uniform(0, N0);
data0.row(base).copyTo(data.row(i));
}
declare.in(data);
Mat labels, centers;
TEST_CYCLE()
{
kmeans(data, K, labels, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 0),
attempts, KMEANS_PP_CENTERS, centers);
}
SANITY_CHECK_NOTHING();
}
INSTANTIATE_TEST_CASE_P(/*nothing*/ , KMeans,
testing::Values(
// K clusters, dims, N points
testing::make_tuple(2, 3, 100000),
testing::make_tuple(4, 3, 500),
testing::make_tuple(4, 3, 1000),
testing::make_tuple(4, 3, 10000),
testing::make_tuple(8, 3, 1000),
testing::make_tuple(8, 16, 1000),
testing::make_tuple(8, 64, 1000),
testing::make_tuple(16, 16, 1000),
testing::make_tuple(16, 32, 1000),
testing::make_tuple(32, 16, 1000),
testing::make_tuple(32, 32, 1000),
testing::make_tuple(100, 2, 1000)
)
);
}