opencv/modules/3d/perf/perf_affine2d.cpp
Vadim Pisarevsky d6c699c014 calib3d module in opencv is split into 3 modules: 3d, calib and stereo.
stereo module in opencv_contrib is renamed to xstereo
2020-12-01 23:42:15 +03:00

166 lines
5.3 KiB
C++

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#include "perf_precomp.hpp"
#include <algorithm>
#include <functional>
namespace opencv_test
{
using namespace perf;
CV_ENUM(Method, RANSAC, LMEDS)
typedef tuple<int, double, Method, size_t> AffineParams;
typedef TestBaseWithParam<AffineParams> EstimateAffine;
#define ESTIMATE_PARAMS Combine(Values(100000, 5000, 100), Values(0.99, 0.95, 0.9), Method::all(), Values(10, 0))
static float rngIn(float from, float to) { return from + (to-from) * (float)theRNG(); }
static Mat rngPartialAffMat() {
double theta = rngIn(0, (float)CV_PI*2.f);
double scale = rngIn(0, 3);
double tx = rngIn(-2, 2);
double ty = rngIn(-2, 2);
double aff[2*3] = { std::cos(theta) * scale, -std::sin(theta) * scale, tx,
std::sin(theta) * scale, std::cos(theta) * scale, ty };
return Mat(2, 3, CV_64F, aff).clone();
}
PERF_TEST_P( EstimateAffine, EstimateAffine2D, ESTIMATE_PARAMS )
{
AffineParams params = GetParam();
const int n = get<0>(params);
const double confidence = get<1>(params);
const int method = get<2>(params);
const size_t refining = get<3>(params);
Mat aff(2, 3, CV_64F);
cv::randu(aff, -2., 2.);
// LMEDS can't handle more than 50% outliers (by design)
int m;
if (method == LMEDS)
m = 3*n/5;
else
m = 2*n/5;
const float shift_outl = 15.f;
const float noise_level = 20.f;
Mat fpts(1, n, CV_32FC2);
Mat tpts(1, n, CV_32FC2);
randu(fpts, 0., 100.);
transform(fpts, tpts, aff);
/* adding noise to some points */
Mat outliers = tpts.colRange(m, n);
outliers.reshape(1) += shift_outl;
Mat noise (outliers.size(), outliers.type());
randu(noise, 0., noise_level);
outliers += noise;
Mat aff_est;
vector<uchar> inliers (n);
warmup(inliers, WARMUP_WRITE);
warmup(fpts, WARMUP_READ);
warmup(tpts, WARMUP_READ);
TEST_CYCLE()
{
aff_est = estimateAffine2D(fpts, tpts, inliers, method, 3, 2000, confidence, refining);
}
// we already have accuracy tests
SANITY_CHECK_NOTHING();
}
PERF_TEST_P( EstimateAffine, EstimateAffinePartial2D, ESTIMATE_PARAMS )
{
AffineParams params = GetParam();
const int n = get<0>(params);
const double confidence = get<1>(params);
const int method = get<2>(params);
const size_t refining = get<3>(params);
Mat aff = rngPartialAffMat();
int m;
// LMEDS can't handle more than 50% outliers (by design)
if (method == LMEDS)
m = 3*n/5;
else
m = 2*n/5;
const float shift_outl = 15.f; const float noise_level = 20.f;
Mat fpts(1, n, CV_32FC2);
Mat tpts(1, n, CV_32FC2);
randu(fpts, 0., 100.);
transform(fpts, tpts, aff);
/* adding noise*/
Mat outliers = tpts.colRange(m, n);
outliers.reshape(1) += shift_outl;
Mat noise (outliers.size(), outliers.type());
randu(noise, 0., noise_level);
outliers += noise;
Mat aff_est;
vector<uchar> inliers (n);
warmup(inliers, WARMUP_WRITE);
warmup(fpts, WARMUP_READ);
warmup(tpts, WARMUP_READ);
TEST_CYCLE()
{
aff_est = estimateAffinePartial2D(fpts, tpts, inliers, method, 3, 2000, confidence, refining);
}
// we already have accuracy tests
SANITY_CHECK_NOTHING();
}
} // namespace opencv_test