bool solvePnPRansac( InputArray objectPoints, InputArray imagePoints,
InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec,
bool /*useExtrinsicGuess*/, int max_iters, float thr, double conf,
- OutputArray mask, int method) {
+ OutputArray inliers, int method) {
Ptr<Model> params;
setParameters(method, params, cameraMatrix.empty() ? EstimationMethod ::P6P : EstimationMethod ::P3P,
- thr, max_iters, conf, mask.needed());
+ thr, max_iters, conf, inliers.needed());
Ptr<RansacOutput> ransac_output;
if (run(params, imagePoints, objectPoints, params->getRandomGeneratorState(),
ransac_output, cameraMatrix, noArray(), distCoeffs, noArray())) {
- saveMask(mask, ransac_output->getInliersMask());
+ if (inliers.needed()) {
+ const auto &inliers_mask = ransac_output->getInliersMask();
+ Mat inliers_;
+ for (int i = 0; i < (int)inliers_mask.size(); i++)
+ if (inliers_mask[i])
+ inliers_.push_back(i);
+ inliers_.copyTo(inliers);
+ }
const Mat &model = ransac_output->getModel();
model.col(0).copyTo(rvec);
model.col(1).copyTo(tvec);
return true;
}
- if (mask.needed()){
- mask.create(std::max(objectPoints.getMat().rows, objectPoints.getMat().cols), 1, CV_8U);
- mask.setTo(Scalar::all(0));
- }
return false;
}
log(1 - pow(inl_ratio, 3 /* sample size */));
for (auto flag : flags) {
+ std::vector<int> inliers;
cv::Mat rvec, tvec, mask, R, P;
CV_Assert(cv::solvePnPRansac(obj_pts, img_pts, K1, cv::noArray(), rvec, tvec,
- false, (int)max_iters, (float)thr, conf, mask, flag));
+ false, (int)max_iters, (float)thr, conf, inliers, flag));
cv::Rodrigues(rvec, R);
cv::hconcat(K1 * R, K1 * tvec, P);
+ mask.create(pts_size, 1, CV_8U);
+ mask.setTo(Scalar::all(0));
+ for (auto inl : inliers)
+ mask.at<uchar>(inl) = true;
checkInliersMask(TestSolver ::PnP, inl_size, thr, img_pts, obj_pts, P, mask);
}
}
// CV_Error(cv::Error::StsError, "Essential matrix estimation failed!");
}
+ std::vector<int> inliers(pts_size);
// P3P
inl_size = generatePoints(rng, pts1, pts2, K1, K2, false, pts_size, TestSolver::PnP,
getInlierRatio(usac_params.maxIterations, 3, usac_params.confidence), 0.01, gt_inliers);
- CV_Assert(cv::solvePnPRansac(pts2, pts1, K1, dist_coeff, rvec, tvec, mask, usac_params));
+ CV_Assert(cv::solvePnPRansac(pts2, pts1, K1, dist_coeff, rvec, tvec, inliers, usac_params));
cv::Rodrigues(rvec, R); cv::hconcat(K1 * R, K1 * tvec, model);
+ mask.create(pts_size, 1, CV_8U);
+ mask.setTo(Scalar::all(0));
+ for (auto inl : inliers)
+ mask.at<uchar>(inl) = true;
checkInliersMask(TestSolver::PnP, inl_size, usac_params.threshold, pts1, pts2, model, mask);
// P6P
inl_size = generatePoints(rng, pts1, pts2, K1, K2, false, pts_size, TestSolver::PnP,
getInlierRatio(usac_params.maxIterations, 6, usac_params.confidence), 0.1, gt_inliers);
cv::Mat K_est;
- CV_Assert(cv::solvePnPRansac(pts2, pts1, K_est, dist_coeff, rvec, tvec, mask, usac_params));
+ CV_Assert(cv::solvePnPRansac(pts2, pts1, K_est, dist_coeff, rvec, tvec, inliers, usac_params));
cv::Rodrigues(rvec, R); cv::hconcat(K_est * R, K_est * tvec, model);
+ mask.setTo(Scalar::all(0));
+ for (auto inl : inliers)
+ mask.at<uchar>(inl) = true;
checkInliersMask(TestSolver::PnP, inl_size, usac_params.threshold, pts1, pts2, model, mask);
// Affine2D