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opencv/modules/core/perf/perf_math.cpp at master · pythonmjs/opencv · GitHub
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#
include
"
perf_precomp.hpp
"
namespace
opencv_test
{
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
)
)
);
}
}
//
namespace
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