Rotation estimator base class. It takes features of all images, pairwise matches between all images and estimates rotations of all cameras.
Note
The coordinate system origin is implementation-dependent, but you can always normalize the rotations in respect to the first camera, for instance.
class CV_EXPORTS Estimator
{
public:
virtual ~Estimator() {}
void operator ()(const std::vector<ImageFeatures> &features, const std::vector<MatchesInfo> &pairwise_matches,
std::vector<CameraParams> &cameras)
{ estimate(features, pairwise_matches, cameras); }
protected:
virtual void estimate(const std::vector<ImageFeatures> &features, const std::vector<MatchesInfo> &pairwise_matches,
std::vector<CameraParams> &cameras) = 0;
};
Estimates camera parameters.
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This method must implement camera parameters estimation logic in order to make the wrapper detail::Estimator::operator() work.
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Homography based rotation estimator.
class CV_EXPORTS HomographyBasedEstimator : public Estimator
{
public:
HomographyBasedEstimator(bool is_focals_estimated = false)
: is_focals_estimated_(is_focals_estimated) {}
private:
/* hidden */
};
Base class for all camera parameters refinement methods.
class CV_EXPORTS BundleAdjusterBase : public Estimator
{
public:
const Mat refinementMask() const { return refinement_mask_.clone(); }
void setRefinementMask(const Mat &mask)
{
CV_Assert(mask.type() == CV_8U && mask.size() == Size(3, 3));
refinement_mask_ = mask.clone();
}
double confThresh() const { return conf_thresh_; }
void setConfThresh(double conf_thresh) { conf_thresh_ = conf_thresh; }
CvTermCriteria termCriteria() { return term_criteria_; }
void setTermCriteria(const CvTermCriteria& term_criteria) { term_criteria_ = term_criteria; }
protected:
BundleAdjusterBase(int num_params_per_cam, int num_errs_per_measurement)
: num_params_per_cam_(num_params_per_cam),
num_errs_per_measurement_(num_errs_per_measurement)
{
setRefinementMask(Mat::ones(3, 3, CV_8U));
setConfThresh(1.);
setTermCriteria(cvTermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 1000, DBL_EPSILON));
}
// Runs bundle adjustment
virtual void estimate(const std::vector<ImageFeatures> &features,
const std::vector<MatchesInfo> &pairwise_matches,
std::vector<CameraParams> &cameras);
virtual void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) = 0;
virtual void obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const = 0;
virtual void calcError(Mat &err) = 0;
virtual void calcJacobian(Mat &jac) = 0;
// 3x3 8U mask, where 0 means don't refine respective parameter, != 0 means refine
Mat refinement_mask_;
int num_images_;
int total_num_matches_;
int num_params_per_cam_;
int num_errs_per_measurement_;
const ImageFeatures *features_;
const MatchesInfo *pairwise_matches_;
// Threshold to filter out poorly matched image pairs
double conf_thresh_;
//Levenberg–Marquardt algorithm termination criteria
CvTermCriteria term_criteria_;
// Camera parameters matrix (CV_64F)
Mat cam_params_;
// Connected images pairs
std::vector<std::pair<int,int> > edges_;
};
See also
Construct a bundle adjuster base instance.
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Sets initial camera parameter to refine.
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Calculates error vector.
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Calculates the cost function jacobian.
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Gets the refined camera parameters.
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Implementation of the camera parameters refinement algorithm which minimizes sum of the reprojection error squares.
class CV_EXPORTS BundleAdjusterReproj : public BundleAdjusterBase
{
public:
BundleAdjusterReproj() : BundleAdjusterBase(7, 2) {}
private:
/* hidden */
};
See also
Implementation of the camera parameters refinement algorithm which minimizes sum of the distances between the rays passing through the camera center and a feature.
class CV_EXPORTS BundleAdjusterRay : public BundleAdjusterBase
{
public:
BundleAdjusterRay() : BundleAdjusterBase(4, 3) {}
private:
/* hidden */
};
See also
Wave correction kind.
Tries to make panorama more horizontal (or vertical).
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