TEST_F(fisheyeTest, Homography)
{
const int n_images = 1;
std::vector<std::vector<cv::Point2d> > imagePoints(n_images);
std::vector<std::vector<cv::Point3d> > objectPoints(n_images);
const std::string folder =combine(datasets_repository_path, "calib-3_stereo_from_JY");
cv::FileStorage fs_left(combine(folder, "left.xml"), cv::FileStorage::READ);
CV_Assert(fs_left.isOpened());
for(int i = 0; i < n_images; ++i)
fs_left[cv::format("image_%d", i )] >> imagePoints[i];
fs_left.release();
cv::FileStorage fs_object(combine(folder, "object.xml"), cv::FileStorage::READ);
CV_Assert(fs_object.isOpened());
for(int i = 0; i < n_images; ++i)
fs_object[cv::format("image_%d", i )] >> objectPoints[i];
fs_object.release();
cv::internal::IntrinsicParams param;
param.Init(cv::Vec2d(cv::max(imageSize.width, imageSize.height) / CV_PI, cv::max(imageSize.width, imageSize.height) / CV_PI),
cv::Vec2d(imageSize.width / 2.0 - 0.5, imageSize.height / 2.0 - 0.5));
cv::Mat _imagePoints (imagePoints[0]);
cv::Mat _objectPoints(objectPoints[0]);
cv::Mat imagePointsNormalized = NormalizePixels(_imagePoints, param).reshape(1).t();
_objectPoints = _objectPoints.reshape(1).t();
cv::Mat objectPointsMean, covObjectPoints;
int Np = imagePointsNormalized.cols;
cv::calcCovarMatrix(_objectPoints, covObjectPoints, objectPointsMean, cv::COVAR_NORMAL | cv::COVAR_COLS);
cv::SVD svd(covObjectPoints);
cv::Mat theR(svd.vt);
if (cv::norm(theR(cv::Rect(2, 0, 1, 2))) < 1e-6)
theR = cv::Mat::eye(3,3, CV_64FC1);
if (cv::determinant(theR) < 0)
theR = -theR;
cv::Mat theT = -theR * objectPointsMean;
cv::Mat X_new = theR * _objectPoints + theT * cv::Mat::ones(1, Np, CV_64FC1);
cv::Mat H = cv::internal::ComputeHomography(imagePointsNormalized, X_new.rowRange(0, 2));
cv::Mat M = cv::Mat::ones(3, X_new.cols, CV_64FC1);
X_new.rowRange(0, 2).copyTo(M.rowRange(0, 2));
cv::Mat mrep = H * M;
cv::divide(mrep, cv::Mat::ones(3,1, CV_64FC1) * mrep.row(2).clone(), mrep);
cv::Mat merr = (mrep.rowRange(0, 2) - imagePointsNormalized).t();
cv::Vec2d std_err;
cv::meanStdDev(merr.reshape(2), cv::noArray(), std_err);
std_err *= sqrt((double)merr.reshape(2).total() / (merr.reshape(2).total() - 1));
cv::Vec2d correct_std_err(0.00516740156010384, 0.00644205331553901);
EXPECT_MAT_NEAR(std_err, correct_std_err, 1e-12);
}
// On copy apply for type image!
bool histogramRGBL::apply(const image& src,dvector& dest) const {
if (src.empty()) {
dest.clear();
setStatusString("input channel empty");
return false;
}
const parameters& param = getParameters();
int theMin(0),theMax(255);
const int lastIdx = param.cells-1;
const float m = float(lastIdx)/(theMax-theMin);
int y,r,g,b,l;
int idx;
int entries;
vector<rgbPixel>::const_iterator it,eit;
dest.resize(4*param.cells,0.0,false,true); // initialize with 0
dvector theR(param.cells,0.0);
dvector theG(param.cells,0.0);
dvector theB(param.cells,0.0);
dvector theL(param.cells,0.0);
entries = 0;
// if b too small, it's possible to calculate everything faster...
// check if the ignore value
if (param.considerAllData) {
for (y=0;y<src.rows();++y) {
const vector<rgbPixel>& vct = src.getRow(y);
for (it=vct.begin(),eit=vct.end();it!=eit;++it) {
r = (*it).getRed();
g = (*it).getGreen();
b = (*it).getBlue();
l = (min(r,g,b)+max(r,g,b))/2;
idx = static_cast<int>(r*m);
theR.at(idx)++;
idx = static_cast<int>(g*m);
theG.at(idx)++;
idx = static_cast<int>(b*m);
theB.at(idx)++;
idx = static_cast<int>(l*m);
theL.at(idx)++;
entries++;
}
}
} else {
for (y=0;y<src.rows();++y) {
const vector<rgbPixel>& vct = src.getRow(y);
for (it=vct.begin(),eit=vct.end();it!=eit;++it) {
if ((*it) != param.ignoreValue) {
r = (*it).getRed();
g = (*it).getGreen();
b = (*it).getBlue();
l = (min(r,g,b)+max(r,g,b))/2;
idx = static_cast<int>(r*m);
theR.at(idx)++;
idx = static_cast<int>(g*m);
theG.at(idx)++;
idx = static_cast<int>(b*m);
theB.at(idx)++;
idx = static_cast<int>(l*m);
theL.at(idx)++;
entries++;
}
}
}
}
if (param.smooth) {
convolution convolver;
convolution::parameters cpar;
cpar.boundaryType = lti::Mirror;
cpar.setKernel(param.kernel);
convolver.setParameters(cpar);
matrix<double> tmp;
tmp.useExternData(4,param.cells,&dest.at(0));
convolver.apply(theR,tmp.getRow(0));
convolver.apply(theG,tmp.getRow(1));
convolver.apply(theB,tmp.getRow(2));
convolver.apply(theL,tmp.getRow(3));
} else {
dest.fill(theR,0);
dest.fill(theG,param.cells);
dest.fill(theB,2*param.cells);
dest.fill(theL,3*param.cells);
}
if (param.normalize) {
if (entries > 0) {
dest.divide(entries);
}
}
return true;
};
