Open Source Computer Vision Library https://opencv.org/
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#include "test_precomp.hpp"
#include <string>
#include <limits>
using namespace cv;
using namespace std;
template<class T> double thres() { return 1.0; }
template<> double thres<float>() { return 1e-5; }
class CV_ReprojectImageTo3DTest : public cvtest::BaseTest
{
public:
CV_ReprojectImageTo3DTest() {}
~CV_ReprojectImageTo3DTest() {}
protected:
void run(int)
{
ts->set_failed_test_info(cvtest::TS::OK);
int progress = 0;
int caseId = 0;
progress = update_progress( progress, 1, 14, 0 );
runCase<float, float>(++caseId, -100.f, 100.f);
progress = update_progress( progress, 2, 14, 0 );
runCase<int, float>(++caseId, -100, 100);
progress = update_progress( progress, 3, 14, 0 );
runCase<short, float>(++caseId, -100, 100);
progress = update_progress( progress, 4, 14, 0 );
runCase<unsigned char, float>(++caseId, 10, 100);
progress = update_progress( progress, 5, 14, 0 );
runCase<float, int>(++caseId, -100.f, 100.f);
progress = update_progress( progress, 6, 14, 0 );
runCase<int, int>(++caseId, -100, 100);
progress = update_progress( progress, 7, 14, 0 );
runCase<short, int>(++caseId, -100, 100);
progress = update_progress( progress, 8, 14, 0 );
runCase<unsigned char, int>(++caseId, 10, 100);
progress = update_progress( progress, 10, 14, 0 );
runCase<float, short>(++caseId, -100.f, 100.f);
progress = update_progress( progress, 11, 14, 0 );
runCase<int, short>(++caseId, -100, 100);
progress = update_progress( progress, 12, 14, 0 );
runCase<short, short>(++caseId, -100, 100);
progress = update_progress( progress, 13, 14, 0 );
runCase<unsigned char, short>(++caseId, 10, 100);
progress = update_progress( progress, 14, 14, 0 );
}
template<class U, class V> double error(const Vec<U, 3>& v1, const Vec<V, 3>& v2) const
{
double tmp, sum = 0;
double nsum = 0;
for(int i = 0; i < 3; ++i)
{
tmp = v1[i];
nsum += tmp * tmp;
tmp = tmp - v2[i];
sum += tmp * tmp;
}
return sqrt(sum)/(sqrt(nsum)+1.);
}
template<class InT, class OutT> void runCase(int caseId, InT min, InT max)
{
typedef Vec<OutT, 3> out3d_t;
bool handleMissingValues = (unsigned)theRNG() % 2 == 0;
Mat_<InT> disp(Size(320, 240));
randu(disp, Scalar(min), Scalar(max));
if (handleMissingValues)
disp(disp.rows/2, disp.cols/2) = min - 1;
Mat_<double> Q(4, 4);
randu(Q, Scalar(-5), Scalar(5));
Mat_<out3d_t> _3dImg(disp.size());
CvMat cvdisp = disp; CvMat cv_3dImg = _3dImg; CvMat cvQ = Q;
cvReprojectImageTo3D( &cvdisp, &cv_3dImg, &cvQ, handleMissingValues );
if (numeric_limits<OutT>::max() == numeric_limits<float>::max())
reprojectImageTo3D(disp, _3dImg, Q, handleMissingValues);
for(int y = 0; y < disp.rows; ++y)
for(int x = 0; x < disp.cols; ++x)
{
InT d = disp(y, x);
double from[4] = {
static_cast<double>(x),
static_cast<double>(y),
static_cast<double>(d),
1.0,
};
Mat_<double> res = Q * Mat_<double>(4, 1, from);
res /= res(3, 0);
out3d_t pixel_exp = *(Vec3d*)res.data;
out3d_t pixel_out = _3dImg(y, x);
const int largeZValue = 10000; /* see documentation */
if (handleMissingValues && y == disp.rows/2 && x == disp.cols/2)
{
if (pixel_out[2] == largeZValue)
continue;
ts->printf(cvtest::TS::LOG, "Missing values are handled improperly\n");
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
else
{
double err = error(pixel_out, pixel_exp), t = thres<OutT>();
if ( err > t )
{
ts->printf(cvtest::TS::LOG, "case %d. too big error at (%d, %d): %g vs expected %g: res = (%g, %g, %g, w=%g) vs pixel_out = (%g, %g, %g)\n",
caseId, x, y, err, t, res(0,0), res(1,0), res(2,0), res(3,0),
(double)pixel_out[0], (double)pixel_out[1], (double)pixel_out[2]);
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
}
}
}
};
TEST(Calib3d_ReprojectImageTo3D, accuracy) { CV_ReprojectImageTo3DTest test; test.safe_run(); }