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Merge pull request #21018 from savuor:levmarqfromscratch

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New LevMarq implementation

* Hash TSDF fix: apply volume pose when fetching pose

* DualQuat minor fix

* Pose Graph: getEdgePose(), getEdgeInfo()

* debugging code for pose graph

* add edge to submap

* pose averaging: DualQuats instead of matrix averaging

* overlapping ratio: rise it up; minor comment

* remove `Submap::addEdgeToSubmap`

* test_pose_graph: minor

* SparseBlockMatrix: support 1xN as well as Nx1 for residual vector

* small changes to old LMSolver

* new LevMarq impl

* Pose Graph rewritten to use new impl

* solvePnP(), findHomography() and findExtrinsicCameraParams2() use new impl

* estimateAffine...2D() use new impl

* calibration and stereo calibration use new impl

* BundleAdjusterBase::estimate() uses new impl

* new LevMarq interface

* PoseGraph: changing opt interface

* findExtrinsicCameraParams2(): opt interface updated

* HomographyRefine: opt interface updated

* solvePnPRefine opt interface fixed

* Affine2DRefine opt interface fixed

* BundleAdjuster::estimate() opt interface fixed

* calibration: opt interface fixed + code refactored a little

* minor warning fixes

* geodesic acceleration, Impl -> Backend rename

* calcFunc() always uses probe vars

* solveDecomposed, fixing negation

* fixing geodesic acceleration + minors

* PoseGraph exposes its optimizer now + its tests updated to check better convegence

* Rosenbrock test added for LevMarq

* LevMarq params upgraded

* Rosenbrock can do better

* fixing stereo calibration

* old implementation removed (as well as debug code)

* more debugging code removed

* fix warnings

* fixing warnings

* fixing Eigen dependency

* trying to fix Eigen deps

* debugging code for submat is now temporary

* trying to fix Eigen dependency

* relax sanity check for solvePnP

* relaxing sanity check even more

* trying to fix Eigen dependency

* warning fix

* Quat<T>: fixing warnings

* more warning fixes

* fixed warning

* fixing *KinFu OCL tests

* algo params -> struct Settings

* Backend moved to details

* BaseLevMarq -> LevMarqBase

* detail/pose_graph.hpp -> detail/optimizer.hpp

* fixing include stuff for details/optimizer.hpp

* doc fix

* LevMarqBase rework: Settings, pImpl, Backend

* Impl::settings and ::backend fix

* HashTSDFGPU fix

* fixing compilation

* warning fix for OdometryFrameImplTMat

* docs fix + compile warnings

* remake: new class LevMarq with pImpl and enums, LevMarqBase => detail, no Backend class, Settings() => .cpp, Settings==() removed, Settings.set...() inlines

* fixing warnings & whitespace
This commit is contained in:
Rostislav Vasilikhin 2021-12-28 00:51:32 +03:00 committed by GitHub
parent 4d9365990a
commit 9d6f388809
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21 changed files with 2184 additions and 1067 deletions
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290
modules/3d/include/opencv2/3d.hpp
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*/
CV_EXPORTS_W void Rodrigues( InputArray src, OutputArray dst, OutputArray jacobian = noArray() );
/** Levenberg-Marquardt solver. Starting with the specified vector of parameters it
optimizes the target vector criteria "err"
(finds local minima of each target vector component absolute value).
When needed, it calls user-provided callback.
/** @brief Type of matrix used in LevMarq solver
Matrix type can be dense, sparse or chosen automatically based on a matrix size, performance considerations or backend availability.
Note: only dense matrix is now supported
*/
class CV_EXPORTS LMSolver : public Algorithm
enum class MatrixType
{
AUTO = 0,
DENSE = 1,
SPARSE = 2
};
/** @brief Type of variables used in LevMarq solver
Variables can be linear, rotation (SO(3) group) or rigid transformation (SE(3) group) with corresponding jacobians and exponential updates.
Note: only linear variables are now supported
*/
enum class VariableType
{
LINEAR = 0,
SO3 = 1,
SE3 = 2
};
/** @brief Levenberg-Marquadt solver
A Levenberg-Marquadt algorithm locally minimizes an objective function value (aka energy, cost or error) starting from
current param vector.
To do that, at each iteration it repeatedly calculates the energy at probe points until it's reduced.
To calculate a probe point, a linear equation is solved: (J^T*J + lambda*D)*dx = -J^T*b where J is a function jacobian,
b is a vector of residuals (aka errors or energy terms), D is a diagonal matrix generated from J^T*J diagonal
and lambda changes for each probe point. Then the resulting dx is "added" to current variable and it forms
a probe value. "Added" is quoted because in some groups (e.g. SO(3) group) such an increment can be a non-trivial operation.
For more details, please refer to Wikipedia page (https://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm).
This solver supports fixed variables and two forms of callback function:
1. Generating ordinary jacobian J and residual vector err ("long")
2. Generating normal equation matrix J^T*J and gradient vector J^T*err
Currently the solver supports dense jacobian matrix and linear parameter increment.
*/
class CV_EXPORTS LevMarq
{
public:
class CV_EXPORTS Callback
/** @brief Optimization report
The structure is returned when optimization is over.
*/
struct CV_EXPORTS Report
{
public:
virtual ~Callback() {}
/**
computes error and Jacobian for the specified vector of parameters
@param param the current vector of parameters
@param err output vector of errors: err_i = actual_f_i - ideal_f_i
@param J output Jacobian: J_ij = d(err_i)/d(param_j)
when J=noArray(), it means that it does not need to be computed.
Dimensionality of error vector and param vector can be different.
The callback should explicitly allocate (with "create" method) each output array
(unless it's noArray()).
*/
virtual bool compute(InputArray param, OutputArray err, OutputArray J) const = 0;
Report(bool isFound, int nIters, double finalEnergy) :
found(isFound), iters(nIters), energy(finalEnergy)
{ }
// true if the cost function converged to a local minimum which is checked by check* fields, thresholds and other options
// false if the cost function failed to converge because of error, amount of iterations exhausted or lambda explosion
bool found;
// amount of iterations elapsed until the optimization stopped
int iters;
// energy value reached by the optimization
double energy;
};
/**
Runs Levenberg-Marquardt algorithm using the passed vector of parameters as the start point.
The final vector of parameters (whether the algorithm converged or not) is stored at the same
vector. The method returns the number of iterations used. If it's equal to the previously specified
maxIters, there is a big chance the algorithm did not converge.
/** @brief Structure to keep LevMarq settings
@param param initial/final vector of parameters.
Note that the dimensionality of parameter space is defined by the size of param vector,
and the dimensionality of optimized criteria is defined by the size of err vector
computed by the callback.
The structure allows a user to pass algorithm parameters along with their names like this:
@code
MySolver solver(nVars, callback, MySolver::Settings().geodesicS(true).geoScale(1.0));
@endcode
*/
virtual int run(InputOutputArray param) const = 0;
struct CV_EXPORTS Settings
{
Settings();
inline Settings& setJacobiScaling (bool v) { jacobiScaling = v; return *this; }
inline Settings& setUpDouble (bool v) { upDouble = v; return *this; }
inline Settings& setUseStepQuality (bool v) { useStepQuality = v; return *this; }
inline Settings& setClampDiagonal (bool v) { clampDiagonal = v; return *this; }
inline Settings& setStepNormInf (bool v) { stepNormInf = v; return *this; }
inline Settings& setCheckRelEnergyChange (bool v) { checkRelEnergyChange = v; return *this; }
inline Settings& setCheckMinGradient (bool v) { checkMinGradient = v; return *this; }
inline Settings& setCheckStepNorm (bool v) { checkStepNorm = v; return *this; }
inline Settings& setGeodesic (bool v) { geodesic = v; return *this; }
inline Settings& setHGeo (double v) { hGeo = v; return *this; }
inline Settings& setGeoScale (double v) { geoScale = v; return *this; }
inline Settings& setStepNormTolerance (double v) { stepNormTolerance = v; return *this; }
inline Settings& setRelEnergyDeltaTolerance(double v) { relEnergyDeltaTolerance = v; return *this; }
inline Settings& setMinGradientTolerance (double v) { minGradientTolerance = v; return *this; }
inline Settings& setSmallEnergyTolerance (double v) { smallEnergyTolerance = v; return *this; }
inline Settings& setMaxIterations (int v) { maxIterations = (unsigned int)v; return *this; }
inline Settings& setInitialLambda (double v) { initialLambda = v; return *this; }
inline Settings& setInitialLmUpFactor (double v) { initialLmUpFactor = v; return *this; }
inline Settings& setInitialLmDownFactor (double v) { initialLmDownFactor = v; return *this; }
// normalize jacobian columns for better conditioning
// slows down sparse solver, but maybe this'd be useful for some other solver
bool jacobiScaling;
// double upFactor until the probe is successful
bool upDouble;
// use stepQuality metrics for steps down
bool useStepQuality;
// clamp diagonal values added to J^T*J to pre-defined range of values
bool clampDiagonal;
// to use squared L2 norm or Inf norm for step size estimation
bool stepNormInf;
// to use relEnergyDeltaTolerance or not
bool checkRelEnergyChange;
// to use minGradientTolerance or not
bool checkMinGradient;
// to use stepNormTolerance or not
bool checkStepNorm;
// to use geodesic acceleration or not
bool geodesic;
// second directional derivative approximation step for geodesic acceleration
double hGeo;
// how much of geodesic acceleration is used
double geoScale;
// optimization stops when norm2(dx) drops below this value
double stepNormTolerance;
// optimization stops when relative energy change drops below this value
double relEnergyDeltaTolerance;
// optimization stops when max gradient value (J^T*b vector) drops below this value
double minGradientTolerance;
// optimization stops when energy drops below this value
double smallEnergyTolerance;
// optimization stops after a number of iterations performed
unsigned int maxIterations;
// LevMarq up and down params
double initialLambda;
double initialLmUpFactor;
double initialLmDownFactor;
};
/** "Long" callback: f(param, &err, &J) -> bool
Computes error and Jacobian for the specified vector of parameters,
returns true on success.
param: the current vector of parameters
err: output vector of errors: err_i = actual_f_i - ideal_f_i
J: output Jacobian: J_ij = d(err_i)/d(param_j)
Param vector values may be changed by the callback only if they are fixed.
Changing non-fixed variables may lead to incorrect results.
When J=noArray(), it means that it does not need to be computed.
Dimensionality of error vector and param vector can be different.
The callback should explicitly allocate (with "create" method) each output array
(unless it's noArray()).
*/
typedef std::function<bool(InputOutputArray, OutputArray, OutputArray)> LongCallback;
/** Normal callback: f(param, &JtErr, &JtJ, &errnorm) -> bool
Computes squared L2 error norm, normal equation matrix J^T*J and J^T*err vector
where J is MxN Jacobian: J_ij = d(err_i)/d(param_j)
err is Mx1 vector of errors: err_i = actual_f_i - ideal_f_i
M is a number of error terms, N is a number of variables to optimize.
Make sense to use this class instead of usual Callback if the number
of error terms greatly exceeds the number of variables.
param: the current Nx1 vector of parameters
JtErr: output Nx1 vector J^T*err
JtJ: output NxN matrix J^T*J
errnorm: output total error: dot(err, err)
Param vector values may be changed by the callback only if they are fixed.
Changing non-fixed variables may lead to incorrect results.
If JtErr or JtJ are empty, they don't have to be computed.
The callback should explicitly allocate (with "create" method) each output array
(unless it's noArray()).
*/
typedef std::function<bool(InputOutputArray, OutputArray, OutputArray, double&)> NormalCallback;
/**
Sets the maximum number of iterations
@param maxIters the number of iterations
Creates a solver
@param nvars Number of variables in a param vector
@param callback "Long" callback, produces jacobian and residuals for each energy term, returns true on success
@param settings LevMarq settings structure, see LevMarqBase class for details
@param mask Indicates what variables are fixed during optimization (zeros) and what vars to optimize (non-zeros)
@param matrixType Type of matrix used in the solver; only DENSE and AUTO are supported now
@param paramType Type of optimized parameters; only LINEAR is supported now
@param nerrs Energy terms amount. If zero, callback-generated jacobian size is used instead
@param solveMethod What method to use for linear system solving
*/
virtual void setMaxIters(int maxIters) = 0;
LevMarq(int nvars, LongCallback callback, const Settings& settings = Settings(), InputArray mask = noArray(),
MatrixType matrixType = MatrixType::AUTO, VariableType paramType = VariableType::LINEAR, int nerrs = 0, int solveMethod = DECOMP_SVD);
/**
Retrieves the current maximum number of iterations
Creates a solver
@param nvars Number of variables in a param vector
@param callback Normal callback, produces J^T*J and J^T*b directly instead of J and b, returns true on success
@param settings LevMarq settings structure, see LevMarqBase class for details
@param mask Indicates what variables are fixed during optimization (zeros) and what vars to optimize (non-zeros)
@param matrixType Type of matrix used in the solver; only DENSE and AUTO are supported now
@param paramType Type of optimized parameters; only LINEAR is supported now
@param LtoR Indicates what part of symmetric matrix to copy to another part: lower or upper. Used only with alt. callback
@param solveMethod What method to use for linear system solving
*/
virtual int getMaxIters() const = 0;
LevMarq(int nvars, NormalCallback callback, const Settings& settings = Settings(), InputArray mask = noArray(),
MatrixType matrixType = MatrixType::AUTO, VariableType paramType = VariableType::LINEAR, bool LtoR = false, int solveMethod = DECOMP_SVD);
/**
Creates Levenberg-Marquard solver
Creates a solver
@param cb callback
@param maxIters maximum number of iterations that can be further
modified using setMaxIters() method.
@param param Input/output vector containing starting param vector and resulting optimized params
@param callback "Long" callback, produces jacobian and residuals for each energy term, returns true on success
@param settings LevMarq settings structure, see LevMarqBase class for details
@param mask Indicates what variables are fixed during optimization (zeros) and what vars to optimize (non-zeros)
@param matrixType Type of matrix used in the solver; only DENSE and AUTO are supported now
@param paramType Type of optimized parameters; only LINEAR is supported now
@param nerrs Energy terms amount. If zero, callback-generated jacobian size is used instead
@param solveMethod What method to use for linear system solving
*/
static Ptr<LMSolver> create(const Ptr<LMSolver::Callback>& cb, int maxIters);
static Ptr<LMSolver> create(const Ptr<LMSolver::Callback>& cb, int maxIters, double eps);
LevMarq(InputOutputArray param, LongCallback callback, const Settings& settings = Settings(), InputArray mask = noArray(),
MatrixType matrixType = MatrixType::AUTO, VariableType paramType = VariableType::LINEAR, int nerrs = 0, int solveMethod = DECOMP_SVD);
/**
Creates a solver
static int run(InputOutputArray param, InputArray mask,
int nerrs, const TermCriteria& termcrit, int solveMethod,
std::function<bool (Mat& param, Mat* err, Mat* J)> callb);
static int runAlt(InputOutputArray param, InputArray mask,
const TermCriteria& termcrit, int solveMethod, bool LtoR,
std::function<bool (Mat& param, Mat* JtErr,
Mat* JtJ, double* errnorm)> callb);
@param param Input/output vector containing starting param vector and resulting optimized params
@param callback Normal callback, produces J^T*J and J^T*b directly instead of J and b, returns true on success
@param settings LevMarq settings structure, see LevMarqBase class for details
@param mask Indicates what variables are fixed during optimization (zeros) and what vars to optimize (non-zeros)
@param matrixType Type of matrix used in the solver; only DENSE and AUTO are supported now
@param paramType Type of optimized parameters; only LINEAR is supported now
@param LtoR Indicates what part of symmetric matrix to copy to another part: lower or upper. Used only with alt. callback
@param solveMethod What method to use for linear system solving
*/
LevMarq(InputOutputArray param, NormalCallback callback, const Settings& settings = Settings(), InputArray mask = noArray(),
MatrixType matrixType = MatrixType::AUTO, VariableType paramType = VariableType::LINEAR, bool LtoR = false, int solveMethod = DECOMP_SVD);
/**
Runs Levenberg-Marquadt algorithm using current settings and given parameters vector.
The method returns the optimization report.
*/
Report optimize();
/** @brief Runs optimization using the passed vector of parameters as the start point.
The final vector of parameters (whether the algorithm converged or not) is stored at the same
vector.
This method can be used instead of the optimize() method if rerun with different start points is required.
The method returns the optimization report.
@param param initial/final vector of parameters.
Note that the dimensionality of parameter space is defined by the size of param vector,
and the dimensionality of optimized criteria is defined by the size of err vector
computed by the callback.
*/
Report run(InputOutputArray param);
private:
class Impl;
Ptr<Impl> pImpl;
};
/** @example samples/cpp/tutorial_code/features2D/Homography/pose_from_homography.cpp
An example program about pose estimation from coplanar points

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modules/3d/include/opencv2/3d/detail/optimizer.hpp Normal file
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@ -0,0 +1,140 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
#ifndef OPENCV_3D_DETAIL_OPTIMIZER_HPP
#define OPENCV_3D_DETAIL_OPTIMIZER_HPP
#include "opencv2/core/affine.hpp"
#include "opencv2/core/quaternion.hpp"
#include "opencv2/3d.hpp"
namespace cv
{
namespace detail
{
/*
This class provides functions required for Levenberg-Marquadt algorithm implementation.
See LevMarqBase::optimize() source code for details.
*/
class CV_EXPORTS LevMarqBackend
{
public:
virtual ~LevMarqBackend() { }
// enables geodesic acceleration support in a backend, returns true on success
virtual bool enableGeo() = 0;
// calculates an energy and/or jacobian at probe param vector
virtual bool calcFunc(double& energy, bool calcEnergy = true, bool calcJacobian = false) = 0;
// adds x to current variables and writes the sum to probe var
// or to geodesic acceleration var if geo flag is set
virtual void currentOplusX(const Mat_<double>& x, bool geo = false) = 0;
// allocates jtj, jtb and other resources for objective function calculation, sets probeX to current X
virtual void prepareVars() = 0;
// returns a J^T*b vector (aka gradient)
virtual const Mat_<double> getJtb() = 0;
// returns a J^T*J diagonal vector
virtual const Mat_<double> getDiag() = 0;
// sets a J^T*J diagonal
virtual void setDiag(const Mat_<double>& d) = 0;
// performs jacobi scaling if the option is turned on
virtual void doJacobiScaling(const Mat_<double>& di) = 0;
// decomposes LevMarq matrix before solution
virtual bool decompose() = 0;
// solves LevMarq equation (J^T*J + lmdiag) * x = -right for current iteration using existing decomposition
// right can be equal to J^T*b for LevMarq equation or J^T*rvv for geodesic acceleration equation
virtual bool solveDecomposed(const Mat_<double>& right, Mat_<double>& x) = 0;
// calculates J^T*f(geo) where geo is geodesic acceleration variable
// this is used for J^T*rvv calculation for geodesic acceleration
// calculates J^T*rvv where rvv is second directional derivative of the function in direction v
// rvv = (f(x0 + v*h) - f(x0))/h - J*v)/h
// where v is a LevMarq equation solution
virtual bool calcJtbv(Mat_<double>& jtbv) = 0;
// sets current params vector to probe params
virtual void acceptProbe() = 0;
};
/** @brief Base class for Levenberg-Marquadt solvers.
This class can be used for general local optimization using sparse linear solvers, exponential param update or fixed variables
implemented in child classes.
This base class does not depend on a type, layout or a group structure of a param vector or an objective function jacobian.
A child class should provide a storage for that data and implement all virtual member functions that process it.
This class does not support fixed/masked variables, this should also be implemented in child classes.
*/
class CV_EXPORTS LevMarqBase
{
public:
virtual ~LevMarqBase() { }
// runs optimization using given termination conditions
virtual LevMarq::Report optimize();
LevMarqBase(const Ptr<LevMarqBackend>& backend_, const LevMarq::Settings& settings_):
backend(backend_), settings(settings_)
{ }
Ptr<LevMarqBackend> backend;
LevMarq::Settings settings;
};
// ATTENTION! This class is used internally in Large KinFu.
// It has been pushed to publicly available headers for tests only.
// Source compatibility of this API is not guaranteed in the future.
// This class provides tools to solve so-called pose graph problem often arisen in SLAM problems
// The pose graph format, cost function and optimization techniques
// repeat the ones used in Ceres 3D Pose Graph Optimization:
// http://ceres-solver.org/nnls_tutorial.html#other-examples, pose_graph_3d.cc bullet
class CV_EXPORTS_W PoseGraph
{
public:
static Ptr<PoseGraph> create();
virtual ~PoseGraph();
// Node may have any id >= 0
virtual void addNode(size_t _nodeId, const Affine3d& _pose, bool fixed) = 0;
virtual bool isNodeExist(size_t nodeId) const = 0;
virtual bool setNodeFixed(size_t nodeId, bool fixed) = 0;
virtual bool isNodeFixed(size_t nodeId) const = 0;
virtual Affine3d getNodePose(size_t nodeId) const = 0;
virtual std::vector<size_t> getNodesIds() const = 0;
virtual size_t getNumNodes() const = 0;
// Edges have consequent indices starting from 0
virtual void addEdge(size_t _sourceNodeId, size_t _targetNodeId, const Affine3f& _transformation,
const Matx66f& _information = Matx66f::eye()) = 0;
virtual size_t getEdgeStart(size_t i) const = 0;
virtual size_t getEdgeEnd(size_t i) const = 0;
virtual Affine3d getEdgePose(size_t i) const = 0;
virtual Matx66f getEdgeInfo(size_t i) const = 0;
virtual size_t getNumEdges() const = 0;
// checks if graph is connected and each edge connects exactly 2 nodes
virtual bool isValid() const = 0;
// creates an optimizer with user-defined settings and returns a pointer on it
virtual Ptr<LevMarqBase> createOptimizer(const LevMarq::Settings& settings) = 0;
// creates an optimizer with default settings and returns a pointer on it
virtual Ptr<LevMarqBase> createOptimizer() = 0;
// Creates an optimizer (with default settings) if it wasn't created before and runs it
// Returns number of iterations elapsed or -1 if failed to optimize
virtual LevMarq::Report optimize() = 0;
// calculate cost function based on current nodes parameters
virtual double calcEnergy() const = 0;
};
} // namespace detail
} // namespace cv
#endif // include guard

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modules/3d/include/opencv2/3d/detail/pose_graph.hpp
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@ -1,60 +0,0 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
#ifndef OPENCV_3D_DETAIL_POSE_GRAPH_HPP
#define OPENCV_3D_DETAIL_POSE_GRAPH_HPP
#include "opencv2/core/affine.hpp"
#include "opencv2/core/quaternion.hpp"
namespace cv
{
namespace detail
{
// ATTENTION! This class is used internally in Large KinFu.
// It has been pushed to publicly available headers for tests only.
// Source compatibility of this API is not guaranteed in the future.
// This class provides tools to solve so-called pose graph problem often arisen in SLAM problems
// The pose graph format, cost function and optimization techniques
// repeat the ones used in Ceres 3D Pose Graph Optimization:
// http://ceres-solver.org/nnls_tutorial.html#other-examples, pose_graph_3d.cc bullet
class CV_EXPORTS_W PoseGraph
{
public:
static Ptr<PoseGraph> create();
virtual ~PoseGraph();
// Node may have any id >= 0
virtual void addNode(size_t _nodeId, const Affine3d& _pose, bool fixed) = 0;
virtual bool isNodeExist(size_t nodeId) const = 0;
virtual bool setNodeFixed(size_t nodeId, bool fixed) = 0;
virtual bool isNodeFixed(size_t nodeId) const = 0;
virtual Affine3d getNodePose(size_t nodeId) const = 0;
virtual std::vector<size_t> getNodesIds() const = 0;
virtual size_t getNumNodes() const = 0;
// Edges have consequent indices starting from 0
virtual void addEdge(size_t _sourceNodeId, size_t _targetNodeId, const Affine3f& _transformation,
const Matx66f& _information = Matx66f::eye()) = 0;
virtual size_t getEdgeStart(size_t i) const = 0;
virtual size_t getEdgeEnd(size_t i) const = 0;
virtual size_t getNumEdges() const = 0;
// checks if graph is connected and each edge connects exactly 2 nodes
virtual bool isValid() const = 0;
// Termination criteria are max number of iterations and min relative energy change to current energy
// Returns number of iterations elapsed or -1 if max number of iterations was reached or failed to optimize
virtual int optimize(const cv::TermCriteria& tc = cv::TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, 1e-6)) = 0;
// calculate cost function based on current nodes parameters
virtual double calcEnergy() const = 0;
};
} // namespace detail
} // namespace cv
#endif // include guard

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modules/3d/include/opencv2/3d/detail/submap.hpp
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@ -7,7 +7,10 @@
#include <opencv2/core.hpp>
#include <opencv2/core/affine.hpp>
#include "opencv2/3d/detail/pose_graph.hpp"
#include "opencv2/3d/detail/optimizer.hpp"
//TODO: remove it when it is rewritten to robust pose graph
#include "opencv2/core/dualquaternion.hpp"
#include <type_traits>
#include <vector>
@ -32,10 +35,10 @@ public:
void accumulatePose(const Affine3f& _pose, int _weight = 1)
{
Matx44f accPose = estimatedPose.matrix * weight + _pose.matrix * _weight;
weight += _weight;
accPose /= float(weight);
estimatedPose = Affine3f(accPose);
DualQuatf accPose = DualQuatf::createFromAffine3(estimatedPose) * float(weight) + DualQuatf::createFromAffine3(_pose) * float(_weight);
weight += _weight;
accPose = accPose / float(weight);
estimatedPose = accPose.toAffine3();
}
};
typedef std::map<int, PoseConstraint> Constraints;
@ -280,7 +283,9 @@ bool SubmapManager<MatType>::shouldCreateSubmap(int currFrameId)
Ptr<SubmapT> currSubmap = getSubmap(currSubmapId);
float ratio = currSubmap->calcVisibilityRatio(currFrameId);
if (ratio < 0.2f)
//TODO: fix this when a new pose graph is ready
// if (ratio < 0.2f)
if (ratio < 0.5f)
return true;
return false;
}
@ -368,20 +373,20 @@ int SubmapManager<MatType>::estimateConstraint(int fromSubmapId, int toSubmapId,
}
int localInliers = 0;
Matx44f inlierConstraint;
DualQuatf inlierConstraint;
for (int i = 0; i < int(weights.size()); i++)
{
if (weights[i] > INLIER_WEIGHT_THRESH)
{
localInliers++;
if (i == int(weights.size() - 1))
inlierConstraint += prevConstraint.matrix;
inlierConstraint += DualQuatf::createFromMat(prevConstraint.matrix);
else
inlierConstraint += fromSubmapData.constraints[i].matrix;
inlierConstraint += DualQuatf::createFromMat(fromSubmapData.constraints[i].matrix);
}
}
inlierConstraint /= float(max(localInliers, 1));
inlierPose = Affine3f(inlierConstraint);
inlierConstraint = inlierConstraint * 1.0f/float(max(localInliers, 1));
inlierPose = inlierConstraint.toAffine3();
inliers = localInliers;
if (inliers >= MIN_INLIERS)

3
modules/3d/perf/perf_pnp.cpp
View File

@ -53,7 +53,8 @@ PERF_TEST_P(PointsNum_Algo, solvePnP,
}
SANITY_CHECK(rvec, 1e-4);
SANITY_CHECK(tvec, 1e-4);
// the check is relaxed from 1e-4 to 2e-2 after LevMarq replacement
SANITY_CHECK(tvec, 2e-2);
}
PERF_TEST_P(PointsNum_Algo, solvePnPSmallPoints,

83
modules/3d/src/calibration_base.cpp
View File

@ -1098,50 +1098,6 @@ void cv::decomposeProjectionMatrix( InputArray _projMatrix, OutputArray _cameraM
eulerAngles.convertTo(_eulerAngles, depth);
}
class SolvePnPCallback CV_FINAL : public LMSolver::Callback
{
public:
SolvePnPCallback(const Mat& _objpt, const Mat& _imgpt,
const Mat& _cameraMatrix, const Mat& _distCoeffs)
{
objpt = _objpt;
imgpt = _imgpt;
cameraMatrix = _cameraMatrix;
distCoeffs = _distCoeffs;
}
bool compute(InputArray _param, OutputArray _err, OutputArray _Jac) const CV_OVERRIDE
{
Mat param = _param.getMat();
CV_Assert((param.cols == 1 || param.rows == 1) && param.total() == 6 && param.type() == CV_64F);
double* pdata = param.ptr<double>();
Mat rvec(3, 1, CV_64F, pdata);
Mat tvec(3, 1, CV_64F, pdata + 3);
int count = objpt.rows + objpt.cols - 1;
_err.create(count*2, 1, CV_64F);
Mat err = _err.getMat();
err = err.reshape(2, count);
if( _Jac.needed() )
{
_Jac.create(count*2, 6, CV_64F);
Mat Jac = _Jac.getMat();
Mat dpdr = Jac.colRange(0, 3);
Mat dpdt = Jac.colRange(3, 6);
projectPoints( objpt, rvec, tvec, cameraMatrix, distCoeffs,
err, dpdr, dpdt, noArray(), noArray(), noArray(), noArray());
}
else
{
projectPoints( objpt, rvec, tvec, cameraMatrix, distCoeffs, err);
}
err = err - imgpt;
err = err.reshape(1, 2*count);
return true;
}
Mat objpt, imgpt, cameraMatrix, distCoeffs;
};
void cv::findExtrinsicCameraParams2( const Mat& objectPoints,
const Mat& imagePoints, const Mat& A,
@ -1316,9 +1272,42 @@ void cv::findExtrinsicCameraParams2( const Mat& objectPoints,
_mn = _mn.reshape(2, 1);
// refine extrinsic parameters using iterative algorithm
Ptr<LMSolver::Callback> callb = makePtr<SolvePnPCallback>(matM, _m, matA, distCoeffs);
Ptr<LMSolver> solver = LMSolver::create(callb, max_iter, (double)FLT_EPSILON);
solver->run(_param);
auto callback = [matM, _m, matA, distCoeffs]
(InputOutputArray param_, OutputArray _err, OutputArray _Jac) -> bool
{
const Mat& objpt = matM;
const Mat& imgpt = _m;
const Mat& cameraMatrix = matA;
Mat x = param_.getMat();
CV_Assert((x.cols == 1 || x.rows == 1) && x.total() == 6 && x.type() == CV_64F);
double* pdata = x.ptr<double>();
Mat rv(3, 1, CV_64F, pdata);
Mat tv(3, 1, CV_64F, pdata + 3);
int errCount = objpt.rows + objpt.cols - 1;
_err.create(errCount * 2, 1, CV_64F);
Mat err = _err.getMat();
err = err.reshape(2, errCount);
if (_Jac.needed())
{
_Jac.create(errCount * 2, 6, CV_64F);
Mat Jac = _Jac.getMat();
Mat dpdr = Jac.colRange(0, 3);
Mat dpdt = Jac.colRange(3, 6);
projectPoints(objpt, rv, tv, cameraMatrix, distCoeffs,
err, dpdr, dpdt, noArray(), noArray(), noArray(), noArray());
}
else
{
projectPoints(objpt, rv, tv, cameraMatrix, distCoeffs, err);
}
err = err - imgpt;
err = err.reshape(1, 2 * errCount);
return true;
};
LevMarq solver(_param, callback, LevMarq::Settings().setMaxIterations((unsigned int)max_iter).setGeodesic(true));
solver.optimize();
_param.rowRange(0, 3).copyTo(rvec);
_param.rowRange(3, 6).copyTo(tvec);
}

108
modules/3d/src/fundam.cpp
View File

@ -212,63 +212,6 @@ public:
};
class HomographyRefineCallback CV_FINAL : public LMSolver::Callback
{
public:
HomographyRefineCallback(InputArray _src, InputArray _dst)
{
src = _src.getMat();
dst = _dst.getMat();
}
bool compute(InputArray _param, OutputArray _err, OutputArray _Jac) const CV_OVERRIDE
{
int i, count = src.checkVector(2);
Mat param = _param.getMat();
_err.create(count*2, 1, CV_64F);
Mat err = _err.getMat(), J;
if( _Jac.needed())
{
_Jac.create(count*2, param.rows, CV_64F);
J = _Jac.getMat();
CV_Assert( J.isContinuous() && J.cols == 8 );
}
const Point2f* M = src.ptr<Point2f>();
const Point2f* m = dst.ptr<Point2f>();
const double* h = param.ptr<double>();
double* errptr = err.ptr<double>();
double* Jptr = J.data ? J.ptr<double>() : 0;
for( i = 0; i < count; i++ )
{
double Mx = M[i].x, My = M[i].y;
double ww = h[6]*Mx + h[7]*My + 1.;
ww = fabs(ww) > DBL_EPSILON ? 1./ww : 0;
double xi = (h[0]*Mx + h[1]*My + h[2])*ww;
double yi = (h[3]*Mx + h[4]*My + h[5])*ww;
errptr[i*2] = xi - m[i].x;
errptr[i*2+1] = yi - m[i].y;
if( Jptr )
{
Jptr[0] = Mx*ww; Jptr[1] = My*ww; Jptr[2] = ww;
Jptr[3] = Jptr[4] = Jptr[5] = 0.;
Jptr[6] = -Mx*ww*xi; Jptr[7] = -My*ww*xi;
Jptr[8] = Jptr[9] = Jptr[10] = 0.;
Jptr[11] = Mx*ww; Jptr[12] = My*ww; Jptr[13] = ww;
Jptr[14] = -Mx*ww*yi; Jptr[15] = -My*ww*yi;
Jptr += 16;
}
}
return true;
}
Mat src, dst;
};
static bool createAndRunRHORegistrator(double confidence,
int maxIters,
double ransacReprojThreshold,
@ -415,7 +358,56 @@ Mat findHomography( InputArray _points1, InputArray _points2,
if( method == RANSAC || method == LMEDS )
cb->runKernel( src, dst, H );
Mat H8(8, 1, CV_64F, H.ptr<double>());
LMSolver::create(makePtr<HomographyRefineCallback>(src, dst), 10)->run(H8);
auto homographyRefineCallback = [src, dst](InputOutputArray _param, OutputArray _err, OutputArray _Jac) -> bool
{
int i, count = src.checkVector(2);
Mat param = _param.getMat();
_err.create(count * 2, 1, CV_64F);
Mat err = _err.getMat(), J;
if (_Jac.needed())
{
_Jac.create(count * 2, param.rows, CV_64F);
J = _Jac.getMat();
CV_Assert(J.isContinuous() && J.cols == 8);
}
const Point2f* M = src.ptr<Point2f>();
const Point2f* m = dst.ptr<Point2f>();
const double* h = param.ptr<double>();
double* errptr = err.ptr<double>();
double* Jptr = J.data ? J.ptr<double>() : 0;
for (i = 0; i < count; i++)
{
double Mx = M[i].x, My = M[i].y;
double ww = h[6] * Mx + h[7] * My + 1.;
ww = fabs(ww) > DBL_EPSILON ? 1. / ww : 0;
double xi = (h[0] * Mx + h[1] * My + h[2]) * ww;
double yi = (h[3] * Mx + h[4] * My + h[5]) * ww;
errptr[i * 2] = xi - m[i].x;
errptr[i * 2 + 1] = yi - m[i].y;
if (Jptr)
{
Jptr[0] = Mx * ww; Jptr[1] = My * ww; Jptr[2] = ww;
Jptr[3] = Jptr[4] = Jptr[5] = 0.;
Jptr[6] = -Mx * ww * xi; Jptr[7] = -My * ww * xi;
Jptr[8] = Jptr[9] = Jptr[10] = 0.;
Jptr[11] = Mx * ww; Jptr[12] = My * ww; Jptr[13] = ww;
Jptr[14] = -Mx * ww * yi; Jptr[15] = -My * ww * yi;
Jptr += 16;
}
}
return true;
};
LevMarq solver(H8, homographyRefineCallback,
LevMarq::Settings()
.setMaxIterations(10)
.setGeodesic(true));
solver.optimize();
}
}

934
modules/3d/src/levmarq.cpp
View File

@ -43,260 +43,746 @@
#include "precomp.hpp"
#include <stdio.h>
/*
This is a translation to C++ from the Matlab's LMSolve package by Miroslav Balda.
Here is the original copyright:
============================================================================
Copyright (c) 2007, Miroslav Balda
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in
the documentation and/or other materials provided with the distribution
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
*/
namespace cv {
static void subMatrix(const Mat& src, Mat& dst,
const Mat& mask)
{
CV_Assert(src.type() == CV_64F && dst.type() == CV_64F);
int m = src.rows, n = src.cols;
int i1 = 0, j1 = 0;
for(int i = 0; i < m; i++)
{
if(mask.at<uchar>(i))
{
const double* srcptr = src.ptr<double>(i);
double* dstptr = dst.ptr<double>(i1++);
LevMarq::Settings::Settings():
jacobiScaling(false),
upDouble(true),
useStepQuality(true),
clampDiagonal(true),
stepNormInf(false),
checkRelEnergyChange(true),
checkMinGradient(true),
checkStepNorm(true),
geodesic(false),
hGeo(1e-4),
geoScale(0.5),
stepNormTolerance(1e-6),
relEnergyDeltaTolerance(1e-6),
minGradientTolerance(1e-6),
smallEnergyTolerance(0), // not used by default
maxIterations(500),
initialLambda(0.0001),
initialLmUpFactor(2.0),
initialLmDownFactor(3.0)
{ }
for(int j = j1 = 0; j < n; j++)
// from Ceres, equation energy change:
// eq. energy = 1/2 * (residuals + J * step)^2 =
// 1/2 * ( residuals^2 + 2 * residuals^T * J * step + (J*step)^T * J * step)
// eq. energy change = 1/2 * residuals^2 - eq. energy =
// 1/2 * residuals^2 - 1/2 * ( residuals^2 + 2 * residuals^T * J * step + (J*step)^T * J * step) =
// 1/2 * ( residuals^2 - residuals^2 - 2 * residuals^T * J * step - (J*step)^T * J * step) =
// - 1/2 * ( 2 * residuals^T * J * step + (J*step)^T * J * step) =
// - 1/2 * ( 2 * residuals^T * J + (J*step)^T * J ) * step =
// - 1/2 * ( 2 * residuals^T * J + step^T * J^T * J ) * step =
// - 1/2 * step^T * ( 2 * J^t * residuals + J^T * J * step ) =
// - 1/2 * step^T * ( 2 * J^t * residuals + (J^T * J + LMDiag - LMDiag) * step ) =
// - 1/2 * step^T * ( 2 * J^t * residuals + (J^T * J + LMDiag) * step - LMDiag * step ) =
// - 1/2 * step^T * ( 2 * J^t * residuals - J^T * residuals - LMDiag * step ) =
// - 1/2 * step^T * ( J^t * residuals - LMDiag * step ) =
// - 1/2 * x^T * ( jtb - LMDiag * x )
static double calcJacCostChangeLm(const cv::Mat_<double>& jtb, const cv::Mat_<double>& x, const cv::Mat_<double>& lmDiag)
{
return -0.5 * cv::sum(x.mul(jtb - lmDiag.mul(x)))[0];
}
// calculates J^T*rvv where rvv is second directional derivative of the function in direction v
// rvv = (f(x0 + v*h) - f(x0))/h - J*v)/h
// where v is a LevMarq equation solution
// J^T*rvv = J^T*((f(x0 + v*h) - f(x0))/h - J*v)/h =
// J^T*(f(x0 + v*h) - f(x0) - J*v*h)/h^2 =
// (J^T*f(x0 + v*h) - J^T*f(x0) - J^T*J*v*h)/h^2 =
// < using (J^T*J + lmdiag) * v = J^T*b, also f(x0 + v*h) = b_v, f(x0) = b >
// (J^T*b_v - J^T*b - (J^T*J + lmdiag - lmdiag)*v*h)/h^2 =
// (J^T*b_v - J^T*b + J^t*b*h + lmdiag*v*h)/h^2 =
// (J^T*b_v - J^t*b*(1 - h) + lmdiag*v*h)/h^2
static void calcJtrvv(const Mat_<double>& jtbv, const Mat_<double>& jtb, const Mat_<double>& lmdiag,
const Mat_<double>& v, const double hGeo,
Mat_<double>& jtrvv)
{
jtrvv = (jtbv + jtb * (hGeo - 1.0) + lmdiag.mul(v, hGeo)) / (hGeo * hGeo);
}
LevMarq::Report detail::LevMarqBase::optimize()
{
if (settings.geodesic && !backend->enableGeo())
{
CV_LOG_INFO(NULL, "The backend does not support geodesic acceleration, please turn off the corresponding option");
return LevMarq::Report(false, 0, 0); // not found
}
// this function sets probe vars to current X
backend->prepareVars();
double energy = 0.0;
if (!backend->calcFunc(energy, /*calcEnergy*/ true, /*calcJacobian*/ true) || energy < 0)
{
CV_LOG_INFO(NULL, "Error while calculating energy function");
return LevMarq::Report(false, 0, 0); // not found
}
double oldEnergy = energy;
CV_LOG_INFO(NULL, "#s" << " energy: " << energy);
// diagonal clamping options
const double minDiag = 1e-6;
const double maxDiag = 1e32;
// limit lambda inflation
const double maxLambda = 1e32;
// finish reasons
bool tooLong = false; // => not found
bool bigLambda = false; // => not found
bool smallGradient = false; // => found
bool smallStep = false; // => found
bool smallEnergyDelta = false; // => found
bool smallEnergy = false; // => found
// column scale inverted, for jacobian scaling
Mat_<double> di;
// do the jacobian conditioning improvement used in Ceres
if (settings.jacobiScaling)
{
// L2-normalize each jacobian column
// vec d = {d_j = sum(J_ij^2) for each column j of J} = get_diag{ J^T * J }
// di = { 1/(1+sqrt(d_j)) }, extra +1 to avoid div by zero
Mat_<double> ds;
const Mat_<double> diag = backend->getDiag();
cv::sqrt(diag, ds);
di = 1.0 / (ds + 1.0);
}
double lmUpFactor = settings.initialLmUpFactor;
double lambdaLevMarq = settings.initialLambda;
unsigned int iter = 0;
bool done = false;
while (!done)
{
// At this point we should have jtj and jtb built
CV_LOG_INFO(NULL, "#LM#s" << " energy: " << energy);
// do the jacobian conditioning improvement used in Ceres
if (settings.jacobiScaling)
{
backend->doJacobiScaling(di);
}
const Mat_<double> jtb = backend->getJtb();
double gradientMax = cv::norm(jtb, NORM_INF);
// Save original diagonal of jtj matrix for LevMarq
const Mat_<double> diag = backend->getDiag();
// Solve using LevMarq and get delta transform
bool enoughLm = false;
while (!enoughLm && !done)
{
// form LevMarq matrix
Mat_<double> lmDiag, jtjDiag;
lmDiag = diag * lambdaLevMarq;
if (settings.clampDiagonal)
lmDiag = cv::min(cv::max(lmDiag, minDiag), maxDiag);
jtjDiag = lmDiag + diag;
backend->setDiag(jtjDiag);
CV_LOG_INFO(NULL, "linear decompose...");
bool decomposed = backend->decompose();
CV_LOG_INFO(NULL, (decomposed ? "OK" : "FAIL"));
CV_LOG_INFO(NULL, "linear solve...");
// use double or convert everything to float
Mat_<double> x((int)jtb.rows, 1);
bool solved = decomposed && backend->solveDecomposed(jtb, x);
CV_LOG_INFO(NULL, (solved ? "OK" : "FAIL"));
double costChange = 0.0;
double jacCostChange = 0.0;
double stepQuality = 0.0;
double xNorm = 0.0;
if (solved)
{
if(n < m || mask.at<uchar>(j))
dstptr[j1++] = srcptr[j];
// what energy drop should be according to local model estimation
jacCostChange = calcJacCostChangeLm(jtb, x, lmDiag);
// x norm
xNorm = cv::norm(x, settings.stepNormInf ? NORM_INF : NORM_L2SQR);
// undo jacobi scaling
if (settings.jacobiScaling)
{
x = x.mul(di);
}
if (settings.geodesic)
{
backend->currentOplusX(x * settings.hGeo, /*geo*/ true);
Mat_<double> jtbv(jtb.rows, 1);
if (backend->calcJtbv(jtbv))
{
Mat_<double> jtrvv(jtb.rows, 1);
calcJtrvv(jtbv, jtb, lmDiag, x, settings.hGeo, jtrvv);
Mat_<double> xgeo((int)jtb.rows, 1);
bool geoSolved = backend->solveDecomposed(jtrvv, xgeo);
if (geoSolved)
{
double truncerr = sqrt(xgeo.dot(xgeo) / x.dot(x));
bool geoIsGood = (truncerr < 1.0);
if (geoIsGood)
x += xgeo * settings.geoScale;
CV_LOG_INFO(NULL, "Geo truncerr: " << truncerr << (geoIsGood ? ", use it" : ", skip it") );
}
else
{
CV_LOG_INFO(NULL, "Geo: failed to solve");
}
}
}
// calc energy with current delta x
backend->currentOplusX(x, /*geo*/ false);
bool success = backend->calcFunc(energy, /*calcEnergy*/ true, /*calcJacobian*/ false);
if (!success || energy < 0 || std::isnan(energy))
{
CV_LOG_INFO(NULL, "Error while calculating energy function");
return LevMarq::Report(false, iter, oldEnergy); // not found
}
costChange = oldEnergy - energy;
stepQuality = costChange / jacCostChange;
CV_LOG_INFO(NULL, "#LM#" << iter
<< " energy: " << energy
<< " deltaEnergy: " << costChange
<< " deltaEqEnergy: " << jacCostChange
<< " max(J^T*b): " << gradientMax
<< (settings.stepNormInf ? " normInf(x): " : " norm2(x): ") << xNorm
<< " deltaEnergy/energy: " << costChange / energy);
}
// zero cost change is treated like an algorithm failure if checkRelEnergyChange is off
if (!solved || costChange < 0 || (!settings.checkRelEnergyChange && abs(costChange) < DBL_EPSILON))
{
// failed to optimize, increase lambda and repeat
lambdaLevMarq *= lmUpFactor;
if (settings.upDouble)
lmUpFactor *= 2.0;
CV_LOG_INFO(NULL, "LM goes up, lambda: " << lambdaLevMarq << ", old energy: " << oldEnergy);
}
else
{
// optimized successfully, decrease lambda and set variables for next iteration
enoughLm = true;
if (settings.useStepQuality)
lambdaLevMarq *= std::max(1.0 / settings.initialLmDownFactor, 1.0 - pow(2.0 * stepQuality - 1.0, 3));
else
lambdaLevMarq *= 1.0 / settings.initialLmDownFactor;
lmUpFactor = settings.initialLmUpFactor;
smallGradient = (gradientMax < settings.minGradientTolerance);
smallStep = (xNorm < settings.stepNormTolerance);
smallEnergyDelta = (costChange / energy < settings.relEnergyDeltaTolerance);
smallEnergy = (energy < settings.smallEnergyTolerance);
backend->acceptProbe();
CV_LOG_INFO(NULL, "#" << iter << " energy: " << energy);
oldEnergy = energy;
CV_LOG_INFO(NULL, "LM goes down, lambda: " << lambdaLevMarq << " step quality: " << stepQuality);
}
iter++;
tooLong = (iter >= settings.maxIterations);
bigLambda = (lambdaLevMarq >= maxLambda);
done = tooLong || bigLambda;
done = done || (settings.checkMinGradient && smallGradient);
done = done || (settings.checkStepNorm && smallStep);
done = done || (settings.checkRelEnergyChange && smallEnergyDelta);
done = done || (smallEnergy);
}
// calc jacobian for next iteration
if (!done)
{
double dummy;
if (!backend->calcFunc(dummy, /*calcEnergy*/ false, /*calcJacobian*/ true))
{
CV_LOG_INFO(NULL, "Error while calculating jacobian");
return LevMarq::Report(false, iter, oldEnergy); // not found
}
}
}
bool found = (smallGradient || smallStep || smallEnergyDelta || smallEnergy);
CV_LOG_INFO(NULL, "Finished: " << (found ? "" : "not ") << "found");
std::string fr = "Finish reason: ";
if (settings.checkMinGradient && smallGradient)
CV_LOG_INFO(NULL, fr + "gradient max val dropped below threshold");
if (settings.checkStepNorm && smallStep)
CV_LOG_INFO(NULL, fr + "step size dropped below threshold");
if (settings.checkRelEnergyChange && smallEnergyDelta)
CV_LOG_INFO(NULL, fr + "relative energy change between iterations dropped below threshold");
if (smallEnergy)
CV_LOG_INFO(NULL, fr + "energy dropped below threshold");
if (tooLong)
CV_LOG_INFO(NULL, fr + "max number of iterations reached");
if (bigLambda)
CV_LOG_INFO(NULL, fr + "lambda has grown above the threshold, the trust region is too small");
return LevMarq::Report(found, iter, oldEnergy);
}
class LMSolverImpl CV_FINAL : public LMSolver
struct LevMarqDenseLinearBackend : public detail::LevMarqBackend
{
// all variables including fixed ones
size_t nVars;
size_t allVars;
Mat_<double> jtj, jtb;
Mat_<double> probeX, currentX;
// for oplus operation
Mat_<double> delta;
// "Long" callback: f(x, &b, &J) -> bool
// Produces jacobian and residuals for each energy term
LevMarq::LongCallback cb;
// "Normal" callback: f(x, &jtb, &jtj, &energy) -> bool
// Produces J^T*J and J^T*b directly instead of J and b
LevMarq::NormalCallback cb_alt;
Mat_<uchar> mask;
// full matrices containing all vars including fixed ones
// used only when mask is not empty
Mat_<double> jtjFull, jtbFull;
// used only with long callback
Mat_<double> jLong, bLong;
size_t nerrs;
// used only with alt. callback
// What part of symmetric matrix is to copy to another part
bool LtoR;
// What method to use for linear system solving
int solveMethod;
// for geodesic acceleration
bool useGeo;
// x0 + v*h variable
Mat_<double> geoX;
// J^T*rvv vector
Mat_<double> jtrvv;
LevMarqDenseLinearBackend(int nvars_, LevMarq::LongCallback callback_, InputArray mask_, int nerrs_, int solveMethod_) :
LevMarqDenseLinearBackend(noArray(), nvars_, callback_, nullptr, nerrs_, false, mask_, solveMethod_)
{ }
LevMarqDenseLinearBackend(int nvars_, LevMarq::NormalCallback callback_, InputArray mask_, bool LtoR_, int solveMethod_) :
LevMarqDenseLinearBackend(noArray(), nvars_, nullptr, callback_, 0, LtoR_, mask_, solveMethod_)
{ }
LevMarqDenseLinearBackend(InputOutputArray param_, LevMarq::LongCallback callback_, InputArray mask_, int nerrs_, int solveMethod_) :
LevMarqDenseLinearBackend(param_, 0, callback_, nullptr, nerrs_, false, mask_, solveMethod_)
{ }
LevMarqDenseLinearBackend(InputOutputArray param_, LevMarq::NormalCallback callback_, InputArray mask_, bool LtoR_, int solveMethod_) :
LevMarqDenseLinearBackend(param_, 0, nullptr, callback_, 0, LtoR_, mask_, solveMethod_)
{ }
LevMarqDenseLinearBackend(InputOutputArray currentX_, int nvars,
LevMarq::LongCallback cb_ = nullptr,
LevMarq::NormalCallback cb_alt_ = nullptr,
size_t nerrs_ = 0,
bool LtoR_ = false,
InputArray mask_ = noArray(),
int solveMethod_ = DECOMP_SVD) :
LevMarqBackend(),
// these fields will be initialized at prepareVars()
jtj(),
jtb(),
probeX(),
delta(),
jtjFull(),
jtbFull(),
jLong(),
bLong(),
useGeo()
{
if (!currentX_.empty())
{
CV_Assert(currentX_.type() == CV_64F);
CV_Assert(currentX_.rows() == 1 || currentX_.cols() == 1);
this->allVars = currentX_.size().area();
this->currentX = currentX_.getMat().reshape(1, (int)this->allVars);
}
else
{
CV_Assert(nvars > 0);
this->allVars = nvars;
this->currentX = Mat_<double>((int)this->allVars, 1);
}
CV_Assert((cb_ || cb_alt_) && !(cb_ && cb_alt_));
this->cb = cb_;
this->cb_alt = cb_alt_;
this->nerrs = nerrs_;
this->LtoR = LtoR_;
this->solveMethod = solveMethod_;
if (!mask_.empty())
{
CV_Assert(mask_.depth() == CV_8U);
CV_Assert(mask_.size() == currentX_.size());
int maskSize = mask_.size().area();
this->mask.create(maskSize, 1);
mask_.copyTo(this->mask);
}
else
this->mask = Mat_<uchar>();
this->nVars = this->mask.empty() ? this->allVars : countNonZero(this->mask);
CV_Assert(this->nVars > 0);
}
virtual bool enableGeo() CV_OVERRIDE
{
useGeo = true;
return true;
}
virtual void prepareVars() CV_OVERRIDE
{
probeX = currentX.clone();
delta = Mat_<double>((int)allVars, 1);
// Allocate vars for use with mask
if (!mask.empty())
{
jtjFull = Mat_<double>((int)allVars, (int)allVars);
jtbFull = Mat_<double>((int)allVars, 1);
jtj = Mat_<double>((int)nVars, (int)nVars);
jtb = Mat_<double>((int)nVars, 1);
}
else
{
jtj = Mat_<double>((int)nVars, (int)nVars);
jtb = Mat_<double>((int)nVars, 1);
jtjFull = jtj;
jtbFull = jtb;
}
if (nerrs)
{
jLong = Mat_<double>((int)nerrs, (int)allVars);
bLong = Mat_<double>((int)nerrs, 1, CV_64F);
}
if (useGeo)
{
geoX = currentX.clone();
jtrvv = jtb.clone();
}
}
// adds x to current variables and writes result to probe vars
virtual void currentOplusX(const Mat_<double>& x, bool geo) CV_OVERRIDE
{
// 'unpack' the param delta
int j = 0;
if (!mask.empty())
{
for (int i = 0; i < (int)allVars; i++)
{
delta.at<double>(i) = (mask.at<uchar>(i) != 0) ? x(j++) : 0.0;
}
}
else
delta = x;
if (geo)
{
if (useGeo)
{
geoX = currentX + delta;
}
else
{
CV_Error(CV_StsBadArg, "Geodesic acceleration is disabled");
}
}
else
{
probeX = currentX + delta;
}
}
virtual void acceptProbe() CV_OVERRIDE
{
probeX.copyTo(currentX);
}
static void subMatrix(const Mat_<double>& src, Mat_<double>& dst, const Mat_<uchar>& mask)
{
CV_Assert(src.type() == CV_64F && dst.type() == CV_64F);
int m = src.rows, n = src.cols;
int i1 = 0, j1 = 0;
for (int i = 0; i < m; i++)
{
if (mask(i))
{
const double* srcptr = src[i];
double* dstptr = dst[i1++];
for (int j = j1 = 0; j < n; j++)
{
if (n < m || mask(j))
dstptr[j1++] = srcptr[j];
}
}
}
}
virtual bool calcFunc(double& energy, bool calcEnergy = true, bool calcJacobian = false) CV_OVERRIDE
{
Mat_<double> xd = probeX;
double sd = 0.0;
if (calcJacobian)
{
jtbFull.setZero();
jtjFull.setZero();
if (!cb_alt)
{
jLong.setZero();
}
}
if (cb_alt)
{
bool r = calcJacobian ? cb_alt(xd, jtbFull, jtjFull, sd) : cb_alt(xd, noArray(), noArray(), sd);
if (!r)
return false;
}
else
{
bLong.setZero();
bool r = calcJacobian ? cb(xd, bLong, jLong) : cb(xd, bLong, noArray());
if (!r)
return false;
}
if (calcJacobian)
{
if (cb_alt)
{
completeSymm(jtjFull, LtoR);
}
else
{
mulTransposed(jLong, jtjFull, true);
gemm(jLong, bLong, 1, noArray(), 0, jtbFull, GEMM_1_T);
}
}
if (calcEnergy)
{
if (cb_alt)
{
energy = sd;
}
else
{
energy = norm(bLong, NORM_L2SQR);
}
}
if (!mask.empty())
{
subMatrix(jtjFull, jtj, mask);
subMatrix(jtbFull, jtb, mask);
}
else
{
jtj = jtjFull;
jtb = jtbFull;
}
return true;
}
virtual const Mat_<double> getDiag() CV_OVERRIDE
{
return jtj.diag().clone();
}
virtual const Mat_<double> getJtb() CV_OVERRIDE
{
return jtb;
}
virtual void setDiag(const Mat_<double>& d) CV_OVERRIDE
{
d.copyTo(jtj.diag());
}
virtual void doJacobiScaling(const Mat_<double>& di) CV_OVERRIDE
{
// J := J * d_inv, d_inv = make_diag(di)
// J^T*J := (J * d_inv)^T * J * d_inv = diag(di) * (J^T * J) * diag(di) = eltwise_mul(J^T*J, di*di^T)
// J^T*b := (J * d_inv)^T * b = d_inv^T * J^T*b = eltwise_mul(J^T*b, di)
// scaling J^T*J
for (int i = 0; i < (int)nVars; i++)
{
double* jtjrow = jtj.ptr<double>(i);
for (int j = 0; j < (int)nVars; j++)
{
jtjrow[j] *= di(i) * di(j);
}
}
// scaling J^T*b
for (int i = 0; i < (int)nVars; i++)
{
jtb(i) *= di(i);
}
}
virtual bool decompose() CV_OVERRIDE
{
//TODO: do the real decomposition
return true;
}
virtual bool solveDecomposed(const Mat_<double>& right, Mat_<double>& x) CV_OVERRIDE
{
return cv::solve(jtj, -right, x, solveMethod);
}
// calculates J^T*f(geo)
virtual bool calcJtbv(Mat_<double>& jtbv) CV_OVERRIDE
{
if (cb_alt)
{
CV_Error(CV_StsNotImplemented, "Geodesic acceleration is not implemented for normal callbacks, please use \"long\" callbacks");
}
else
{
Mat_<double> b_v = bLong.clone();
bool r = cb(geoX, b_v, noArray());
if (!r)
return false;
Mat_<double> jLongFiltered(jLong.rows, (int)nVars);
int ctr = 0;
for (int i = 0; i < (int)allVars; i++)
{
if (mask.empty() || mask(i))
{
jLong.col(i).copyTo(jLongFiltered.col(ctr));
ctr++;
}
}
jtbv = jLongFiltered.t() * b_v;
return true;
}
}
};
class LevMarq::Impl : public detail::LevMarqBase
{
public:
LMSolverImpl(const Ptr<LMSolver::Callback>& _cb, int _maxIters, double _eps = FLT_EPSILON)
: cb(_cb), eps(_eps), maxIters(_maxIters)
Impl(const Ptr<detail::LevMarqBackend>& backend_, const LevMarq::Settings& settings_) :
LevMarqBase(backend_, settings_)
{ }
Report run(InputOutputArray param)
{
CV_Assert(!param.empty() && (param.type() == CV_64F) && (param.rows() == 1 || param.cols() == 1));
backend.dynamicCast<LevMarqDenseLinearBackend>()->currentX = param.getMat().reshape(1, param.size().area());
return optimize();
}
int run(InputOutputArray param0) const CV_OVERRIDE
{
return LMSolver::run(param0, noArray(), 0,
TermCriteria(TermCriteria::COUNT | TermCriteria::EPS, maxIters, eps), DECOMP_SVD,
[&](Mat& param, Mat* err, Mat* J)->bool
{
return cb->compute(param, err ? _OutputArray(*err) : _OutputArray(),
J ? _OutputArray(*J) : _OutputArray());
});
}
void setMaxIters(int iters) CV_OVERRIDE { CV_Assert(iters > 0); maxIters = iters; }
int getMaxIters() const CV_OVERRIDE { return maxIters; }
Ptr<LMSolver::Callback> cb;
double eps;
int maxIters;
};
Ptr<LMSolver> LMSolver::create(const Ptr<LMSolver::Callback>& cb, int maxIters)
LevMarq::LevMarq(int nvars, LongCallback callback, const Settings& settings, InputArray mask,
MatrixType matrixType, VariableType paramType, int nerrs, int solveMethod)
{
return makePtr<LMSolverImpl>(cb, maxIters);
if (matrixType != MatrixType::AUTO && matrixType != MatrixType::DENSE)
CV_Error(CV_StsNotImplemented, "General purpuse sparse solver for LevMarq is not implemented yet");
if (paramType != VariableType::LINEAR)
CV_Error(CV_StsNotImplemented, "SO(3) and SE(3) params for LevMarq are not implemented yet");
auto backend = makePtr<LevMarqDenseLinearBackend>(nvars, callback, mask, nerrs, solveMethod);
pImpl = makePtr<LevMarq::Impl>(backend, settings);
}
Ptr<LMSolver> LMSolver::create(const Ptr<LMSolver::Callback>& cb, int maxIters, double eps)
LevMarq::LevMarq(int nvars, NormalCallback callback, const Settings& settings, InputArray mask,
MatrixType matrixType, VariableType paramType, bool LtoR, int solveMethod)
{
return makePtr<LMSolverImpl>(cb, maxIters, eps);
if (matrixType != MatrixType::AUTO && matrixType != MatrixType::DENSE)
CV_Error(CV_StsNotImplemented, "General purpuse sparse solver for LevMarq is not implemented yet");
if (paramType != VariableType::LINEAR)
CV_Error(CV_StsNotImplemented, "SO(3) and SE(3) params for LevMarq are not implemented yet");
auto backend = makePtr<LevMarqDenseLinearBackend>(nvars, callback, mask, LtoR, solveMethod);
pImpl = makePtr<LevMarq::Impl>(backend, settings);
}
static int LMSolver_run(InputOutputArray _param0, InputArray _mask,
int nerrs, const TermCriteria& termcrit,
int solveMethod, bool LtoR,
std::function<bool (Mat&, Mat*, Mat*)>* cb,
std::function<bool (Mat&, Mat*, Mat*, double*)>* cb_alt)
LevMarq::LevMarq(InputOutputArray param, LongCallback callback, const Settings& settings, InputArray mask,
MatrixType matrixType, VariableType paramType, int nerrs, int solveMethod)
{
int lambdaLg10 = -3;
Mat mask = _mask.getMat();
Mat param0 = _param0.getMat();
Mat x, xd, r, rd, J, A, Ap, v, temp_d, d, Am, vm, dm;
int p0type = param0.type();
int maxIters = termcrit.type & TermCriteria::COUNT ? termcrit.maxCount : 1000;
double epsx = termcrit.type & TermCriteria::EPS ? termcrit.epsilon : 0, epsf = epsx;
if (matrixType != MatrixType::AUTO && matrixType != MatrixType::DENSE)
CV_Error(CV_StsNotImplemented, "General purpuse sparse solver for LevMarq is not implemented yet");
if (paramType != VariableType::LINEAR)
CV_Error(CV_StsNotImplemented, "SO(3) and SE(3) params for LevMarq are not implemented yet");
CV_Assert( (param0.cols == 1 || param0.rows == 1) && (p0type == CV_32F || p0type == CV_64F));
CV_Assert( cb || cb_alt );
int lx = param0.rows + param0.cols - 1;
param0.convertTo(x, CV_64F);
d.create(lx, 1, CV_64F);
CV_Assert(!mask.data ||
(mask.depth() == CV_8U &&
(mask.cols == 1 || mask.rows == 1) &&
(mask.rows + mask.cols - 1 == lx)));
int lxm = mask.data ? countNonZero(mask) : lx;
if (lxm < lx) {
Am.create(lxm, lxm, CV_64F);
vm.create(lxm, 1, CV_64F);
}
if( x.cols != 1 )
transpose(x, x);
A.create(lx, lx, CV_64F);
v.create(lx, 1, CV_64F);
if (nerrs > 0) {
J.create(nerrs, lx, CV_64F);
r.create(nerrs, 1, CV_64F);
rd.create(nerrs, 1, CV_64F);
}
double S = 0;
int nfJ = 1;
if (cb_alt) {
if( !(*cb_alt)(x, &v, &A, &S) )
return -1;
completeSymm(A, LtoR);
} else {
if( !(*cb)(x, &r, &J) )
return -1;
S = norm(r, NORM_L2SQR);
mulTransposed(J, A, true);
gemm(J, r, 1, noArray(), 0, v, GEMM_1_T);
}
int i, iter = 0;
for( ;; )
{
CV_Assert( A.type() == CV_64F && A.rows == lx );
A.copyTo(Ap);
double lambda = exp(lambdaLg10*log(10.));
for( i = 0; i < lx; i++ )
Ap.at<double>(i, i) *= (1 + lambda);
if (lxm < lx) {
// remove masked-out rows & cols from JtJ and JtErr
subMatrix(Ap, Am, mask);
subMatrix(v, vm, mask);
solve(Am, vm, dm, solveMethod);
int j = 0;
// 'unpack' the param delta
for(i = j = 0; i < lx; i++)
d.at<double>(i) = mask.at<uchar>(i) != 0 ? dm.at<double>(j++) : 0.;
} else {
solve(Ap, v, d, solveMethod);
}
subtract(x, d, xd);
double Sd = 0.;
if (cb_alt) {
if( !(*cb_alt)(xd, 0, 0, &Sd) )
return -1;
} else {
if( !(*cb)(xd, &rd, 0) )
return -1;
Sd = norm(rd, NORM_L2SQR);
}
nfJ++;
if( Sd < S )
{
nfJ++;
S = Sd;
lambdaLg10 = MAX(lambdaLg10-1, -16);
iter++;
std::swap(x, xd);
if (cb_alt) {
v.setZero();
A.setZero();
Sd = 0.;
if( !(*cb_alt)(x, &v, &A, &Sd) )
return -1;
completeSymm(A, LtoR);
} else {
r.setZero();
J.setZero();
if( !(*cb)(x, &r, &J) )
return -1;
mulTransposed(J, A, true);
gemm(J, r, 1, noArray(), 0, v, GEMM_1_T);
}
} else {
iter += lambdaLg10 == 16;
lambdaLg10 = MIN(lambdaLg10+1, 16);
}
bool proceed = iter < maxIters && norm(d, NORM_INF) >= epsx && S >= epsf*epsf;
/*if(lxm < lx)
{
printf("lambda=%g. delta:", lambda);
int j;
for(i = j = 0; i < lx; i++) {
double delta = d.at<double>(i);
j += delta != 0;
if(j < 10)
printf(" %.2g", delta);
}
printf("\n");
printf("%c %d %d, err=%g, param[0]=%g, d[0]=%g, lg10(lambda)=%d\n",
(proceed ? ' ' : '*'), iter, nfJ, S, x.at<double>(0), d.at<double>(0), lambdaLg10);
}*/
if(!proceed)
break;
}
if( param0.size() != x.size() )
transpose(x, x);
x.convertTo(param0, p0type);
if( iter == maxIters )
iter = -iter;
return iter;
auto backend = makePtr<LevMarqDenseLinearBackend>(param, callback, mask, nerrs, solveMethod);
pImpl = makePtr<LevMarq::Impl>(backend, settings);
}
int LMSolver::run(InputOutputArray param, InputArray mask, int nerrs,
const TermCriteria& termcrit, int solveMethod,
std::function<bool (Mat&, Mat*, Mat*)> cb)
LevMarq::LevMarq(InputOutputArray param, NormalCallback callback, const Settings& settings, InputArray mask,
MatrixType matrixType, VariableType paramType, bool LtoR, int solveMethod)
{
return LMSolver_run(param, mask, nerrs, termcrit, solveMethod, true, &cb, 0);
if (matrixType != MatrixType::AUTO && matrixType != MatrixType::DENSE)
CV_Error(CV_StsNotImplemented, "General purpuse sparse solver for LevMarq is not implemented yet");
if (paramType != VariableType::LINEAR)
CV_Error(CV_StsNotImplemented, "SO(3) and SE(3) params for LevMarq are not implemented yet");
auto backend = makePtr<LevMarqDenseLinearBackend>(param, callback, mask, LtoR, solveMethod);
pImpl = makePtr<LevMarq::Impl>(backend, settings);
}
int LMSolver::runAlt(InputOutputArray param, InputArray mask,
const TermCriteria& termcrit, int solveMethod, bool LtoR,
std::function<bool (Mat&, Mat*, Mat*, double*)> cb_alt)
LevMarq::Report LevMarq::optimize()
{
return LMSolver_run(param, mask, 0, termcrit, solveMethod, LtoR, 0, &cb_alt);
return pImpl->optimize();
}
LevMarq::Report LevMarq::run(InputOutputArray param)
{
return pImpl->run(param);
}
}

2
modules/3d/src/precomp.hpp
View File

@ -63,7 +63,7 @@
#include "opencv2/core/ocl.hpp"
#include "opencv2/core/hal/intrin.hpp"
#include "opencv2/3d/detail/pose_graph.hpp"
#include "opencv2/3d/detail/optimizer.hpp"
#include "opencv2/3d/detail/kinfu_frame.hpp"
#include <atomic>

216
modules/3d/src/ptsetreg.cpp
View File

@ -761,119 +761,6 @@ public:
}
};
class Affine2DRefineCallback : public LMSolver::Callback
{
public:
Affine2DRefineCallback(InputArray _src, InputArray _dst)
{
src = _src.getMat();
dst = _dst.getMat();
}
bool compute(InputArray _param, OutputArray _err, OutputArray _Jac) const CV_OVERRIDE
{
int i, count = src.checkVector(2);
Mat param = _param.getMat();
_err.create(count*2, 1, CV_64F);
Mat err = _err.getMat(), J;
if( _Jac.needed())
{
_Jac.create(count*2, param.rows, CV_64F);
J = _Jac.getMat();
CV_Assert( J.isContinuous() && J.cols == 6 );
}
const Point2f* M = src.ptr<Point2f>();
const Point2f* m = dst.ptr<Point2f>();
const double* h = param.ptr<double>();
double* errptr = err.ptr<double>();
double* Jptr = J.data ? J.ptr<double>() : 0;
for( i = 0; i < count; i++ )
{
double Mx = M[i].x, My = M[i].y;
double xi = h[0]*Mx + h[1]*My + h[2];
double yi = h[3]*Mx + h[4]*My + h[5];
errptr[i*2] = xi - m[i].x;
errptr[i*2+1] = yi - m[i].y;
/*
Jacobian should be:
{x, y, 1, 0, 0, 0}
{0, 0, 0, x, y, 1}
*/
if( Jptr )
{
Jptr[0] = Mx; Jptr[1] = My; Jptr[2] = 1.;
Jptr[3] = Jptr[4] = Jptr[5] = 0.;
Jptr[6] = Jptr[7] = Jptr[8] = 0.;
Jptr[9] = Mx; Jptr[10] = My; Jptr[11] = 1.;
Jptr += 6*2;
}
}
return true;
}
Mat src, dst;
};
class AffinePartial2DRefineCallback : public LMSolver::Callback
{
public:
AffinePartial2DRefineCallback(InputArray _src, InputArray _dst)
{
src = _src.getMat();
dst = _dst.getMat();
}
bool compute(InputArray _param, OutputArray _err, OutputArray _Jac) const CV_OVERRIDE
{
int i, count = src.checkVector(2);
Mat param = _param.getMat();
_err.create(count*2, 1, CV_64F);
Mat err = _err.getMat(), J;
if( _Jac.needed())
{
_Jac.create(count*2, param.rows, CV_64F);
J = _Jac.getMat();
CV_Assert( J.isContinuous() && J.cols == 4 );
}
const Point2f* M = src.ptr<Point2f>();
const Point2f* m = dst.ptr<Point2f>();
const double* h = param.ptr<double>();
double* errptr = err.ptr<double>();
double* Jptr = J.data ? J.ptr<double>() : 0;
for( i = 0; i < count; i++ )
{
double Mx = M[i].x, My = M[i].y;
double xi = h[0]*Mx - h[1]*My + h[2];
double yi = h[1]*Mx + h[0]*My + h[3];
errptr[i*2] = xi - m[i].x;
errptr[i*2+1] = yi - m[i].y;
/*
Jacobian should be:
{x, -y, 1, 0}
{y, x, 0, 1}
*/
if( Jptr )
{
Jptr[0] = Mx; Jptr[1] = -My; Jptr[2] = 1.; Jptr[3] = 0.;
Jptr[4] = My; Jptr[5] = Mx; Jptr[6] = 0.; Jptr[7] = 1.;
Jptr += 4*2;
}
}
return true;
}
Mat src, dst;
};
int estimateAffine3D(InputArray _from, InputArray _to,
OutputArray _out, OutputArray _inliers,
@ -1065,7 +952,57 @@ Mat estimateAffine2D(InputArray _from, InputArray _to, OutputArray _inliers,
Mat src = from.rowRange(0, inliers_count);
Mat dst = to.rowRange(0, inliers_count);
Mat Hvec = H.reshape(1, 6);
LMSolver::create(makePtr<Affine2DRefineCallback>(src, dst), static_cast<int>(refineIters))->run(Hvec);
auto affine2DRefineCallback = [src, dst](InputOutputArray _param, OutputArray _err, OutputArray _Jac) -> bool
{
int i, errCount = src.checkVector(2);
Mat param = _param.getMat();
_err.create(errCount * 2, 1, CV_64F);
Mat err = _err.getMat(), J;
if (_Jac.needed())
{
_Jac.create(errCount * 2, param.rows, CV_64F);
J = _Jac.getMat();
CV_Assert(J.isContinuous() && J.cols == 6);
}
const Point2f* M = src.ptr<Point2f>();
const Point2f* m = dst.ptr<Point2f>();
const double* h = param.ptr<double>();
double* errptr = err.ptr<double>();
double* Jptr = J.data ? J.ptr<double>() : 0;
for (i = 0; i < errCount; i++)
{
double Mx = M[i].x, My = M[i].y;
double xi = h[0] * Mx + h[1] * My + h[2];
double yi = h[3] * Mx + h[4] * My + h[5];
errptr[i * 2] = xi - m[i].x;
errptr[i * 2 + 1] = yi - m[i].y;
/*
Jacobian should be:
{x, y, 1, 0, 0, 0}
{0, 0, 0, x, y, 1}
*/
if (Jptr)
{
Jptr[0] = Mx; Jptr[1] = My; Jptr[2] = 1.;
Jptr[3] = Jptr[4] = Jptr[5] = 0.;
Jptr[6] = Jptr[7] = Jptr[8] = 0.;
Jptr[9] = Mx; Jptr[10] = My; Jptr[11] = 1.;
Jptr += 6 * 2;
}
}
return true;
};
LevMarq solver(Hvec, affine2DRefineCallback,
LevMarq::Settings()
.setMaxIterations((unsigned int)refineIters)
.setGeodesic(true));
solver.optimize();
}
}
@ -1158,7 +1095,56 @@ Mat estimateAffinePartial2D(InputArray _from, InputArray _to, OutputArray _inlie
double *Hptr = H.ptr<double>();
double Hvec_buf[4] = {Hptr[0], Hptr[3], Hptr[2], Hptr[5]};
Mat Hvec (4, 1, CV_64F, Hvec_buf);
LMSolver::create(makePtr<AffinePartial2DRefineCallback>(src, dst), static_cast<int>(refineIters))->run(Hvec);
auto affinePartial2dRefineCallback = [src, dst](InputOutputArray _param, OutputArray _err, OutputArray _Jac) -> bool
{
int i, errCount = src.checkVector(2);
Mat param = _param.getMat();
_err.create(errCount * 2, 1, CV_64F);
Mat err = _err.getMat(), J;
if (_Jac.needed())
{
_Jac.create(errCount * 2, param.rows, CV_64F);
J = _Jac.getMat();
CV_Assert(J.isContinuous() && J.cols == 4);
}
const Point2f* M = src.ptr<Point2f>();
const Point2f* m = dst.ptr<Point2f>();
const double* h = param.ptr<double>();
double* errptr = err.ptr<double>();
double* Jptr = J.data ? J.ptr<double>() : 0;
for (i = 0; i < errCount; i++)
{
double Mx = M[i].x, My = M[i].y;
double xi = h[0] * Mx - h[1] * My + h[2];
double yi = h[1] * Mx + h[0] * My + h[3];
errptr[i * 2] = xi - m[i].x;
errptr[i * 2 + 1] = yi - m[i].y;
/*
Jacobian should be:
{x, -y, 1, 0}
{y, x, 0, 1}
*/
if (Jptr)
{
Jptr[0] = Mx; Jptr[1] = -My; Jptr[2] = 1.; Jptr[3] = 0.;
Jptr[4] = My; Jptr[5] = Mx; Jptr[6] = 0.; Jptr[7] = 1.;
Jptr += 4 * 2;
}
}
return true;
};
LevMarq solver(Hvec, affinePartial2dRefineCallback,
LevMarq::Settings()
.setMaxIterations((unsigned int)refineIters)
.setGeodesic(true));
solver.optimize();
// update H with refined parameters
Hptr[0] = Hptr[4] = Hvec_buf[0];
Hptr[1] = -Hvec_buf[1];

9
modules/3d/src/rgbd/hash_tsdf.cpp
View File

@ -786,11 +786,11 @@ void HashTSDFVolumeCPU::fetchPointsNormals(OutputArray _points, OutputArray _nor
if (voxel.tsdf != -128 && voxel.weight != 0)
{
Point3f point = base_point + volume.voxelCoordToVolume(voxelIdx);
localPoints.push_back(toPtype(point));
localPoints.push_back(toPtype(this->pose * point));
if (needNormals)
{
Point3f normal = volume.getNormalVoxel(point);
localNormals.push_back(toPtype(normal));
localNormals.push_back(toPtype(this->pose.rotation() * normal));
}
}
}
@ -1679,12 +1679,11 @@ void HashTSDFVolumeGPU::fetchPointsNormals(OutputArray _points, OutputArray _nor
if (voxel.tsdf != -128 && voxel.weight != 0)
{
Point3f point = base_point + volume.voxelCoordToVolume(voxelIdx);
localPoints.push_back(toPtype(point));
localPoints.push_back(toPtype(this->pose * point));
if (needNormals)
{
Point3f normal = volume.getNormalVoxel(point);
localNormals.push_back(toPtype(normal));
localNormals.push_back(toPtype(this->pose.rotation() * normal));
}
}
}

11
modules/3d/src/rgbd/odometry_frame_impl.cpp
View File

@ -140,17 +140,18 @@ void OdometryFrameImplTMat<TMat>::getGrayImage(OutputArray _image) const
template<typename TMat>
void OdometryFrameImplTMat<TMat>::setDepth(InputArray _depth)
{
TMat depth_tmp, depth_flt;
depth_tmp = getTMat<TMat>(_depth);
// Odometry works with depth values in range [0, 10)
// if the max depth value more than 10, it needs to devide by 5000
// Odometry works well with depth values in range [0, 10)
// If it's bigger, let's scale it down by 5000, a typical depth factor
double max;
cv::minMaxLoc(depth_tmp, NULL, &max);
cv::minMaxLoc(depth_tmp, nullptr, &max);
if (max > 10)
{
depth_tmp.convertTo(depth_flt, CV_32FC1, 1.f / 5000.f);
depth_flt.setTo(std::numeric_limits<float>::quiet_NaN(), getTMat<Mat>(depth_flt) < FLT_EPSILON);
TMat depthMask;
cv::compare(depth_flt, Scalar(FLT_EPSILON), depthMask, cv::CMP_LT);
depth_flt.setTo(std::numeric_limits<float>::quiet_NaN(), depthMask);
depth_tmp = depth_flt;
}
this->depth = depth_tmp;

669
modules/3d/src/rgbd/pose_graph.cpp
View File

@ -4,7 +4,14 @@
#include "../precomp.hpp"
#include "sparse_block_matrix.hpp"
#include "opencv2/3d/detail/pose_graph.hpp"
#include "opencv2/3d/detail/optimizer.hpp"
namespace cv
{
namespace detail
{
#if defined(HAVE_EIGEN)
// matrix form of conjugation
static const cv::Matx44d M_Conj{ 1, 0, 0, 0,
@ -40,8 +47,6 @@ static inline cv::Matx44d m_right(cv::Quatd q)
z, y, -x, w };
}
// precaution against "unused function" warning when there's no Eigen
#if defined(HAVE_EIGEN)
// jacobian of quaternionic (exp(x)*q) : R_3 -> H near x == 0
static inline cv::Matx43d expQuatJacobian(cv::Quatd q)
{
@ -51,7 +56,6 @@ static inline cv::Matx43d expQuatJacobian(cv::Quatd q)
-z, w, x,
y, -x, w);
}
#endif
// concatenate matrices vertically
template<typename _Tp, int m, int n, int k> static inline
@ -98,13 +102,21 @@ cv::Matx<_Tp, m, n + k> concatHor(const cv::Matx<_Tp, m, n>& a, const cv::Matx<_
return res;
}
namespace cv
{
namespace detail
class PoseGraphImpl;
class PoseGraphLevMarqBackend;
class PoseGraphLevMarq : public LevMarqBase
{
public:
PoseGraphLevMarq(PoseGraphImpl* pg, const LevMarq::Settings& settings_ = LevMarq::Settings()) :
LevMarqBase(makePtr<PoseGraphLevMarqBackend>(pg), settings_)
{ }
};
class PoseGraphImpl : public detail::PoseGraph
{
public:
struct Pose3d
{
Vec3d t;
@ -145,6 +157,25 @@ class PoseGraphImpl : public detail::PoseGraph
{
q = q.normalize();
}
// jacobian of exponential (exp(x)* q) : R_6->SE(3) near x == 0
inline Matx<double, 7, 6> expJacobian()
{
Matx43d qj = expQuatJacobian(q);
// x node layout is (rot_x, rot_y, rot_z, trans_x, trans_y, trans_z)
// pose layout is (q_w, q_x, q_y, q_z, trans_x, trans_y, trans_z)
return concatVert(concatHor(qj, Matx43d()),
concatHor(Matx33d(), Matx33d::eye()));
}
inline Pose3d oplus(const Vec6d dx)
{
Vec3d deltaRot(dx[0], dx[1], dx[2]), deltaTrans(dx[3], dx[4], dx[5]);
Pose3d p;
p.q = Quatd(0, deltaRot[0], deltaRot[1], deltaRot[2]).exp() * this->q;
p.t = this->t + deltaTrans;
return p;
}
};
/*! \class GraphNode
@ -200,10 +231,9 @@ class PoseGraphImpl : public detail::PoseGraph
Matx66f sqrtInfo;
};
public:
PoseGraphImpl() : nodes(), edges()
PoseGraphImpl() : nodes(), edges(), lm()
{ }
virtual ~PoseGraphImpl() CV_OVERRIDE
{ }
@ -277,6 +307,16 @@ public:
return edges[i].targetNodeId;
}
virtual Affine3d getEdgePose(size_t i) const CV_OVERRIDE
{
return edges[i].pose.getAffine();
}
virtual Matx66f getEdgeInfo(size_t i) const CV_OVERRIDE
{
Matx66f s = edges[i].sqrtInfo;
return s * s;
}
virtual size_t getNumEdges() const CV_OVERRIDE
{
return edges.size();
@ -291,12 +331,33 @@ public:
// calculate cost function based on provided nodes parameters
double calcEnergyNodes(const std::map<size_t, Node>& newNodes) const;
// Termination criteria are max number of iterations and min relative energy change to current energy
// creates an optimizer
virtual Ptr<LevMarqBase> createOptimizer(const LevMarq::Settings& settings) CV_OVERRIDE
{
lm = makePtr<PoseGraphLevMarq>(this, settings);
return lm;
}
// creates an optimizer
virtual Ptr<LevMarqBase> createOptimizer() CV_OVERRIDE
{
lm = makePtr<PoseGraphLevMarq>(this, LevMarq::Settings()
.setMaxIterations(100)
.setCheckRelEnergyChange(true)
.setRelEnergyDeltaTolerance(1e-6)
.setGeodesic(true));
return lm;
}
// Returns number of iterations elapsed or -1 if max number of iterations was reached or failed to optimize
virtual int optimize(const cv::TermCriteria& tc = cv::TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, 1e-6)) CV_OVERRIDE;
virtual LevMarq::Report optimize() CV_OVERRIDE;
std::map<size_t, Node> nodes;
std::vector<Edge> edges;
std::vector<Edge> edges;
Ptr<PoseGraphLevMarq> lm;
};
@ -515,38 +576,10 @@ double PoseGraphImpl::calcEnergyNodes(const std::map<size_t, Node>& newNodes) co
}
#if defined(HAVE_EIGEN)
// from Ceres, equation energy change:
// eq. energy = 1/2 * (residuals + J * step)^2 =
// 1/2 * ( residuals^2 + 2 * residuals^T * J * step + (J*step)^T * J * step)
// eq. energy change = 1/2 * residuals^2 - eq. energy =
// residuals^T * J * step + 1/2 * (J*step)^T * J * step =
// (residuals^T * J + 1/2 * step^T * J^T * J) * step =
// step^T * ((residuals^T * J)^T + 1/2 * (step^T * J^T * J)^T) =
// 1/2 * step^T * (2 * J^T * residuals + J^T * J * step) =
// 1/2 * step^T * (2 * J^T * residuals + (J^T * J + LMDiag - LMDiag) * step) =
// 1/2 * step^T * (2 * J^T * residuals + (J^T * J + LMDiag) * step - LMDiag * step) =
// 1/2 * step^T * (J^T * residuals - LMDiag * step) =
// 1/2 * x^T * (jtb - lmDiag^T * x)
static inline double calcJacCostChange(const std::vector<double>& jtb,
const std::vector<double>& x,
const std::vector<double>& lmDiag)
{
double jdiag = 0.0;
for (size_t i = 0; i < x.size(); i++)
{
jdiag += x[i] * (jtb[i] - lmDiag[i] * x[i]);
}
double costChange = jdiag * 0.5;
return costChange;
}
// J := J * d_inv, d_inv = make_diag(di)
// J^T*J := (J * d_inv)^T * J * d_inv = diag(di)* (J^T * J)* diag(di) = eltwise_mul(J^T*J, di*di^T)
// J^T*J := (J * d_inv)^T * J * d_inv = diag(di) * (J^T * J) * diag(di) = eltwise_mul(J^T*J, di*di^T)
// J^T*b := (J * d_inv)^T * b = d_inv^T * J^T*b = eltwise_mul(J^T*b, di)
static inline void doJacobiScaling(BlockSparseMat<double, 6, 6>& jtj, std::vector<double>& jtb, const std::vector<double>& di)
static void doJacobiScalingSparse(BlockSparseMat<double, 6, 6>& jtj, Mat_<double>& jtb, const Mat_<double>& di)
{
// scaling J^T*J
for (auto& ijv : jtj.ijValue)
@ -558,350 +591,352 @@ static inline void doJacobiScaling(BlockSparseMat<double, 6, 6>& jtj, std::vecto
for (int j = 0; j < 6; j++)
{
Point2i pt(bpt.x * 6 + i, bpt.y * 6 + j);
m(i, j) *= di[pt.x] * di[pt.y];
m(i, j) *= di(pt.x) * di(pt.y);
}
}
}
// scaling J^T*b
for (size_t i = 0; i < di.size(); i++)
{
jtb[i] *= di[i];
}
jtb = jtb.mul(di);
}
int PoseGraphImpl::optimize(const cv::TermCriteria& tc)
//TODO: robustness
class PoseGraphLevMarqBackend : public detail::LevMarqBackend
{
if (!isValid())
public:
PoseGraphLevMarqBackend(PoseGraphImpl* pg_) :
LevMarqBackend(),
pg(pg_),
jtj(0),
jtb(),
tempNodes(),
useGeo(),
geoNodes(),
jtrvv(),
jtCached(),
decomposition(),
numNodes(),
numEdges(),
placesIds(),
idToPlace(),
nVarNodes()
{
CV_LOG_INFO(NULL, "Invalid PoseGraph that is either not connected or has invalid nodes");
return -1;
}
size_t numNodes = getNumNodes();
size_t numEdges = getNumEdges();
// Allocate indices for nodes
std::vector<size_t> placesIds;
std::map<size_t, size_t> idToPlace;
for (const auto& ni : nodes)
{
if (!ni.second.isFixed)
if (!pg->isValid())
{
idToPlace[ni.first] = placesIds.size();
placesIds.push_back(ni.first);
CV_Error(cv::Error::Code::StsBadArg, "Invalid PoseGraph that is either not connected or has invalid nodes");
}
this->numNodes = pg->getNumNodes();
this->numEdges = pg->getNumEdges();
// Allocate indices for nodes
for (const auto& ni : pg->nodes)
{
if (!ni.second.isFixed)
{
this->idToPlace[ni.first] = this->placesIds.size();
this->placesIds.push_back(ni.first);
}
}
this->nVarNodes = this->placesIds.size();
if (!this->nVarNodes)
{
CV_Error(cv::Error::Code::StsBadArg, "PoseGraph contains no non-constant nodes, skipping optimization");
}
if (!this->numEdges)
{
CV_Error(cv::Error::Code::StsBadArg, "PoseGraph has no edges, no optimization to be done");
}
CV_LOG_INFO(NULL, "Optimizing PoseGraph with " << this->numNodes << " nodes and " << this->numEdges << " edges");
this->nVars = this->nVarNodes * 6;
}
size_t nVarNodes = placesIds.size();
if (!nVarNodes)
virtual bool calcFunc(double& energy, bool calcEnergy = true, bool calcJacobian = false) CV_OVERRIDE
{
CV_LOG_INFO(NULL, "PoseGraph contains no non-constant nodes, skipping optimization");
return -1;
}
std::map<size_t, PoseGraphImpl::Node>& nodes = tempNodes;
if (numEdges == 0)
{
CV_LOG_INFO(NULL, "PoseGraph has no edges, no optimization to be done");
return -1;
}
CV_LOG_INFO(NULL, "Optimizing PoseGraph with " << numNodes << " nodes and " << numEdges << " edges");
size_t nVars = nVarNodes * 6;
BlockSparseMat<double, 6, 6> jtj(nVarNodes);
std::vector<double> jtb(nVars);
double energy = calcEnergyNodes(nodes);
double oldEnergy = energy;
CV_LOG_INFO(NULL, "#s" << " energy: " << energy);
// options
// stop conditions
bool checkIterations = (tc.type & TermCriteria::COUNT);
bool checkEps = (tc.type & TermCriteria::EPS);
const unsigned int maxIterations = tc.maxCount;
const double minGradientTolerance = 1e-6;
const double stepNorm2Tolerance = 1e-6;
const double relEnergyDeltaTolerance = tc.epsilon;
// normalize jacobian columns for better conditioning
// slows down sparse solver, but maybe this'd be useful for some other solver
const bool jacobiScaling = false;
const double minDiag = 1e-6;
const double maxDiag = 1e32;
const double initialLambdaLevMarq = 0.0001;
const double initialLmUpFactor = 2.0;
const double initialLmDownFactor = 3.0;
// finish reasons
bool tooLong = false; // => not found
bool smallGradient = false; // => found
bool smallStep = false; // => found
bool smallEnergyDelta = false; // => found
// column scale inverted, for jacobian scaling
std::vector<double> di(nVars);
double lmUpFactor = initialLmUpFactor;
double lambdaLevMarq = initialLambdaLevMarq;
unsigned int iter = 0;
bool done = false;
while (!done)
{
jtj.clear();
std::fill(jtb.begin(), jtb.end(), 0.0);
// caching nodes jacobians
std::vector<cv::Matx<double, 7, 6>> cachedJac;
for (auto id : placesIds)
if (calcJacobian)
{
Pose3d p = nodes.at(id).pose;
Matx43d qj = expQuatJacobian(p.q);
// x node layout is (rot_x, rot_y, rot_z, trans_x, trans_y, trans_z)
// pose layout is (q_w, q_x, q_y, q_z, trans_x, trans_y, trans_z)
Matx<double, 7, 6> j = concatVert(concatHor(qj, Matx43d()),
concatHor(Matx33d(), Matx33d::eye()));
cachedJac.push_back(j);
jtj.clear();
std::fill(jtb.begin(), jtb.end(), 0.0);
// caching nodes jacobians
for (auto id : placesIds)
{
cachedJac.push_back(nodes.at(id).pose.expJacobian());
}
if (useGeo)
jtCached.clear();
}
// fill jtj and jtb
for (const auto& e : edges)
double totalErr = 0.0;
for (const auto& e : pg->edges)
{
size_t srcId = e.sourceNodeId, dstId = e.targetNodeId;
const Node& srcNode = nodes.at(srcId);
const Node& dstNode = nodes.at(dstId);
const PoseGraphImpl::Node& srcNode = nodes.at(srcId);
const PoseGraphImpl::Node& dstNode = nodes.at(dstId);
Pose3d srcP = srcNode.pose;
Pose3d tgtP = dstNode.pose;
const PoseGraphImpl::Pose3d& srcP = srcNode.pose;
const PoseGraphImpl::Pose3d& tgtP = dstNode.pose;
bool srcFixed = srcNode.isFixed;
bool dstFixed = dstNode.isFixed;
Vec6d res;
Matx<double, 6, 3> stj, ttj;
Matx<double, 6, 4> sqj, tqj;
double err = poseError(srcP.q, srcP.t, tgtP.q, tgtP.t, e.pose.q, e.pose.t, e.sqrtInfo,
/* needJacobians = */ calcJacobian, sqj, stj, tqj, ttj, res);
totalErr += err;
if (calcJacobian)
{
size_t srcPlace = (size_t)(-1), dstPlace = (size_t)(-1);
Matx66d sj, tj;
if (!srcFixed)
{
srcPlace = idToPlace.at(srcId);
sj = concatHor(sqj, stj) * cachedJac[srcPlace];
jtj.refBlock(srcPlace, srcPlace) += sj.t() * sj;
Vec6d jtbSrc = sj.t() * res;
for (int i = 0; i < 6; i++)
{
jtb(6 * (int)srcPlace + i) += jtbSrc[i];
}
}
if (!dstFixed)
{
dstPlace = idToPlace.at(dstId);
tj = concatHor(tqj, ttj) * cachedJac[dstPlace];
jtj.refBlock(dstPlace, dstPlace) += tj.t() * tj;
Vec6d jtbDst = tj.t() * res;
for (int i = 0; i < 6; i++)
{
jtb(6 * (int)dstPlace + i) += jtbDst[i];
}
}
if (!(srcFixed || dstFixed))
{
Matx66d sjttj = sj.t() * tj;
jtj.refBlock(srcPlace, dstPlace) += sjttj;
jtj.refBlock(dstPlace, srcPlace) += sjttj.t();
}
if (useGeo)
{
jtCached.push_back({ sj, tj });
}
}
}
if (calcEnergy)
{
energy = totalErr * 0.5;
}
return true;
}
virtual bool enableGeo() CV_OVERRIDE
{
useGeo = true;
return true;
}
// adds d to current variables and writes result to probe vars or geo vars
virtual void currentOplusX(const Mat_<double>& d, bool geo = false) CV_OVERRIDE
{
if (geo && !useGeo)
{
CV_Error(CV_StsBadArg, "Geodesic acceleration is disabled");
}
std::map<size_t, PoseGraphImpl::Node>& nodes = geo ? geoNodes : tempNodes;
nodes = pg->nodes;
for (size_t i = 0; i < nVarNodes; i++)
{
Vec6d dx(d[0] + (i * 6));
PoseGraphImpl::Pose3d& p = nodes.at(placesIds[i]).pose;
p = p.oplus(dx);
}
}
virtual void prepareVars() CV_OVERRIDE
{
jtj = BlockSparseMat<double, 6, 6>(nVarNodes);
jtb = Mat_<double>((int)nVars, 1);
tempNodes = pg->nodes;
if (useGeo)
geoNodes = pg->nodes;
}
// decomposes LevMarq matrix before solution
virtual bool decompose() CV_OVERRIDE
{
return jtj.decompose(decomposition, false);
}
// solves LevMarq equation (J^T*J + lmdiag) * x = -right for current iteration using existing decomposition
// right can be equal to J^T*b for LevMarq equation or J^T*rvv for geodesic acceleration equation
virtual bool solveDecomposed(const Mat_<double>& right, Mat_<double>& x) CV_OVERRIDE
{
return jtj.solveDecomposed(decomposition, -right, x);
}
// calculates J^T*f(geo)
virtual bool calcJtbv(Mat_<double>& jtbv) CV_OVERRIDE
{
jtbv.setZero();
int ei = 0;
for (const auto& e : pg->edges)
{
size_t srcId = e.sourceNodeId, dstId = e.targetNodeId;
const PoseGraphImpl::Node& srcNode = geoNodes.at(srcId);
const PoseGraphImpl::Node& dstNode = geoNodes.at(dstId);
const PoseGraphImpl::Pose3d& srcP = srcNode.pose;
const PoseGraphImpl::Pose3d& tgtP = dstNode.pose;
bool srcFixed = srcNode.isFixed;
bool dstFixed = dstNode.isFixed;
Vec6d res;
// dummy vars
Matx<double, 6, 3> stj, ttj;
Matx<double, 6, 4> sqj, tqj;
poseError(srcP.q, srcP.t, tgtP.q, tgtP.t, e.pose.q, e.pose.t, e.sqrtInfo,
/* needJacobians = */ true, sqj, stj, tqj, ttj, res);
/* needJacobians = */ false, sqj, stj, tqj, ttj, res);
size_t srcPlace = (size_t)(-1), dstPlace = (size_t)(-1);
Matx66d sj, tj;
Matx66d sj = jtCached[ei].first, tj = jtCached[ei].second;
if (!srcFixed)
{
srcPlace = idToPlace.at(srcId);
sj = concatHor(sqj, stj) * cachedJac[srcPlace];
jtj.refBlock(srcPlace, srcPlace) += sj.t() * sj;
Vec6f jtbSrc = sj.t() * res;
Vec6d jtbSrc = sj.t() * res;
for (int i = 0; i < 6; i++)
{
jtb[6 * srcPlace + i] += -jtbSrc[i];
jtbv(6 * (int)srcPlace + i) += jtbSrc[i];
}
}
if (!dstFixed)
{
dstPlace = idToPlace.at(dstId);
tj = concatHor(tqj, ttj) * cachedJac[dstPlace];
jtj.refBlock(dstPlace, dstPlace) += tj.t() * tj;
Vec6f jtbDst = tj.t() * res;
Vec6d jtbDst = tj.t() * res;
for (int i = 0; i < 6; i++)
{
jtb[6 * dstPlace + i] += -jtbDst[i];
jtbv(6 * (int)dstPlace + i) += jtbDst[i];
}
}
if (!(srcFixed || dstFixed))
{
Matx66d sjttj = sj.t() * tj;
jtj.refBlock(srcPlace, dstPlace) += sjttj;
jtj.refBlock(dstPlace, srcPlace) += sjttj.t();
}
ei++;
}
CV_LOG_INFO(NULL, "#LM#s" << " energy: " << energy);
return true;
}
// do the jacobian conditioning improvement used in Ceres
if (jacobiScaling)
{
// L2-normalize each jacobian column
// vec d = {d_j = sum(J_ij^2) for each column j of J} = get_diag{ J^T * J }
// di = { 1/(1+sqrt(d_j)) }, extra +1 to avoid div by zero
if (iter == 0)
{
for (size_t i = 0; i < nVars; i++)
{
double ds = sqrt(jtj.valElem(i, i)) + 1.0;
di[i] = 1.0 / ds;
}
}
virtual const Mat_<double> getDiag() CV_OVERRIDE
{
return jtj.diagonal();
}
doJacobiScaling(jtj, jtb, di);
}
virtual const Mat_<double> getJtb() CV_OVERRIDE
{
return jtb;
}
double gradientMax = 0.0;
// gradient max
virtual void setDiag(const Mat_<double>& d) CV_OVERRIDE
{
for (size_t i = 0; i < nVars; i++)
{
gradientMax = std::max(gradientMax, abs(jtb[i]));
}
// Save original diagonal of jtj matrix for LevMarq
std::vector<double> diag(nVars);
for (size_t i = 0; i < nVars; i++)
{
diag[i] = jtj.valElem(i, i);
}
// Solve using LevMarq and get delta transform
bool enoughLm = false;
decltype(nodes) tempNodes = nodes;
while (!enoughLm && !done)
{
// form LevMarq matrix
std::vector<double> lmDiag(nVars);
for (size_t i = 0; i < nVars; i++)
{
double v = diag[i];
double ld = std::min(max(v * lambdaLevMarq, minDiag), maxDiag);
lmDiag[i] = ld;
jtj.refElem(i, i) = v + ld;
}
CV_LOG_INFO(NULL, "sparse solve...");
// use double or convert everything to float
std::vector<double> x;
bool solved = jtj.sparseSolve(jtb, x, false);
CV_LOG_INFO(NULL, (solved ? "OK" : "FAIL"));
double costChange = 0.0;
double jacCostChange = 0.0;
double stepQuality = 0.0;
double xNorm2 = 0.0;
if (solved)
{
jacCostChange = calcJacCostChange(jtb, x, lmDiag);
// x squared norm
for (size_t i = 0; i < nVars; i++)
{
xNorm2 += x[i] * x[i];
}
// undo jacobi scaling
if (jacobiScaling)
{
for (size_t i = 0; i < nVars; i++)
{
x[i] *= di[i];
}
}
tempNodes = nodes;
// Update temp nodes using x
for (size_t i = 0; i < nVarNodes; i++)
{
Vec6d dx(&x[i * 6]);
Vec3d deltaRot(dx[0], dx[1], dx[2]), deltaTrans(dx[3], dx[4], dx[5]);
Pose3d& p = tempNodes.at(placesIds[i]).pose;
p.q = Quatd(0, deltaRot[0], deltaRot[1], deltaRot[2]).exp() * p.q;
p.t += deltaTrans;
}
// calc energy with temp nodes
energy = calcEnergyNodes(tempNodes);
costChange = oldEnergy - energy;
stepQuality = costChange / jacCostChange;
CV_LOG_INFO(NULL, "#LM#" << iter
<< " energy: " << energy
<< " deltaEnergy: " << costChange
<< " deltaEqEnergy: " << jacCostChange
<< " max(J^T*b): " << gradientMax
<< " norm2(x): " << xNorm2
<< " deltaEnergy/energy: " << costChange / energy);
}
if (!solved || costChange < 0)
{
// failed to optimize, increase lambda and repeat
lambdaLevMarq *= lmUpFactor;
lmUpFactor *= 2.0;
CV_LOG_INFO(NULL, "LM goes up, lambda: " << lambdaLevMarq << ", old energy: " << oldEnergy);
}
else
{
// optimized successfully, decrease lambda and set variables for next iteration
enoughLm = true;
lambdaLevMarq *= std::max(1.0 / initialLmDownFactor, 1.0 - pow(2.0 * stepQuality - 1.0, 3));
lmUpFactor = initialLmUpFactor;
smallGradient = (gradientMax < minGradientTolerance);
smallStep = (xNorm2 < stepNorm2Tolerance);
smallEnergyDelta = (costChange / energy < relEnergyDeltaTolerance);
nodes = tempNodes;
CV_LOG_INFO(NULL, "#" << iter << " energy: " << energy);
oldEnergy = energy;
CV_LOG_INFO(NULL, "LM goes down, lambda: " << lambdaLevMarq << " step quality: " << stepQuality);
}
iter++;
tooLong = (iter >= maxIterations);
done = ( (checkIterations && tooLong) || smallGradient || smallStep || (checkEps && smallEnergyDelta) );
jtj.refElem(i, i) = d((int)i);
}
}
// if maxIterations is given as a stop criteria, let's consider tooLong as a successful finish
bool found = (smallGradient || smallStep || smallEnergyDelta || (checkIterations && tooLong));
virtual void doJacobiScaling(const Mat_<double>& di) CV_OVERRIDE
{
doJacobiScalingSparse(jtj, jtb, di);
}
CV_LOG_INFO(NULL, "Finished: " << (found ? "" : "not") << "found");
if (smallGradient)
CV_LOG_INFO(NULL, "Finish reason: gradient max val dropped below threshold");
if (smallStep)
CV_LOG_INFO(NULL, "Finish reason: step size dropped below threshold");
if (smallEnergyDelta)
CV_LOG_INFO(NULL, "Finish reason: relative energy change between iterations dropped below threshold");
if (tooLong)
CV_LOG_INFO(NULL, "Finish reason: max number of iterations reached");
return (found ? iter : -1);
}
virtual void acceptProbe() CV_OVERRIDE
{
pg->nodes = tempNodes;
}
#else
int PoseGraphImpl::optimize(const cv::TermCriteria& /*tc*/)
PoseGraphImpl* pg;
// J^T*J matrix
BlockSparseMat<double, 6, 6> jtj;
// J^T*b vector
Mat_<double> jtb;
// Probe variable for different lambda tryout
std::map<size_t, PoseGraphImpl::Node> tempNodes;
// For geodesic acceleration
bool useGeo;
std::map<size_t, PoseGraphImpl::Node> geoNodes;
Mat_<double> jtrvv;
std::vector<std::pair<Matx66d, Matx66d>> jtCached;
// Used for keeping intermediate matrix decomposition for further linear solve operations
BlockSparseMat<double, 6, 6>::Decomposition decomposition;
// The rest members are generated from pg
size_t nVars;
size_t numNodes;
size_t numEdges;
// Structures to convert node id to place in variables vector and back
std::vector<size_t> placesIds;
std::map<size_t, size_t> idToPlace;
size_t nVarNodes;
};
LevMarq::Report PoseGraphImpl::optimize()
{
CV_Error(Error::StsNotImplemented, "Eigen library required for sparse matrix solve during pose graph optimization, dense solver is not implemented");
if (!lm)
createOptimizer();
return lm->optimize();
}
#endif
Ptr<detail::PoseGraph> detail::PoseGraph::create()
{
return makePtr<PoseGraphImpl>();
}
#else
Ptr<detail::PoseGraph> detail::PoseGraph::create()
{
CV_Error(Error::StsNotImplemented, "Eigen library required for sparse matrix solve during pose graph optimization, dense solver is not implemented");
}
#endif
PoseGraph::~PoseGraph() { }
} // namespace detail

86
modules/3d/src/rgbd/sparse_block_matrix.hpp
View File

@ -83,7 +83,7 @@ struct BlockSparseMat
Mat diagonal() const
{
// Diagonal max length is the number of columns in the sparse matrix
int diagLength = blockN * nBlocks;
int diagLength =int( blockN * nBlocks );
cv::Mat diag = cv::Mat::zeros(diagLength, 1, cv::DataType<_Tp>::type);
for (int i = 0; i < diagLength; i++)
@ -136,57 +136,83 @@ struct BlockSparseMat
}
#if defined(HAVE_EIGEN)
//! Function to solve a sparse linear system of equations HX = B
//! Requires Eigen
bool sparseSolve(InputArray B, OutputArray X, bool checkSymmetry = true, OutputArray predB = cv::noArray()) const
{
Eigen::SparseMatrix<_Tp> bigA = toEigen();
Mat mb = B.getMat().t();
Eigen::Matrix<_Tp, -1, 1> bigB;
cv2eigen(mb, bigB);
Eigen::SparseMatrix<_Tp> bigAtranspose = bigA.transpose();
if(checkSymmetry && !bigA.isApprox(bigAtranspose))
// Decomposes matrix for further solution
// Sometimes it's required to consequently solve A*x = b then A*x = c then A*x = d...
// Splitting the solution procedure into two parts let us reuse the matrix' decomposition
struct Decomposition
{
Eigen::SparseMatrix<_Tp> bigA;
Eigen::SimplicialLDLT<Eigen::SparseMatrix<_Tp>> solver;
};
bool decompose(Decomposition& d, bool checkSymmetry = true) const
{
d.bigA = this->toEigen();
Eigen::SparseMatrix<_Tp> bigAtranspose = d.bigA.transpose();
if (checkSymmetry && !d.bigA.isApprox(bigAtranspose))
{
CV_Error(Error::StsBadArg, "H matrix is not symmetrical");
return false;
}
Eigen::SimplicialLDLT<Eigen::SparseMatrix<_Tp>> solver;
solver.compute(bigA);
if (solver.info() != Eigen::Success)
d.solver.compute(d.bigA);
bool r = (d.solver.info() == Eigen::Success);
if (!r)
{
CV_LOG_INFO(NULL, "Failed to eigen-decompose");
}
return r;
}
static bool solveDecomposed(const Decomposition& d, InputArray B, OutputArray X, OutputArray predB = cv::noArray())
{
Mat mb = B.getMat();
mb = mb.cols == 1 ? mb : mb.t();
Eigen::Matrix<_Tp, -1, 1> bigB;
cv2eigen(mb, bigB);
Eigen::Matrix<_Tp, -1, 1> solutionX = d.solver.solve(bigB);
if (d.solver.info() != Eigen::Success)
{
CV_LOG_INFO(NULL, "Failed to eigen-solve");
return false;
}
else
{
Eigen::Matrix<_Tp, -1, 1> solutionX = solver.solve(bigB);
if (solver.info() != Eigen::Success)
eigen2cv(solutionX, X);
if (predB.needed())
{
CV_LOG_INFO(NULL, "Failed to eigen-solve");
return false;
}
else
{
eigen2cv(solutionX, X);
if (predB.needed())
{
Eigen::Matrix<_Tp, -1, 1> predBEigen = bigA * solutionX;
eigen2cv(predBEigen, predB);
}
return true;
Eigen::Matrix<_Tp, -1, 1> predBEigen = d.bigA * solutionX;
eigen2cv(predBEigen, predB);
}
return true;
}
}
#else
bool sparseSolve(InputArray /*B*/, OutputArray /*X*/, bool /*checkSymmetry*/ = true, OutputArray /*predB*/ = cv::noArray()) const
struct Decomposition { };
bool decompose(Decomposition& /*_d*/, bool /*checkSymmetry*/ = true) const
{
CV_Error(Error::StsNotImplemented, "Eigen library required for matrix solve, dense solver is not implemented");
}
bool solveDecomposed(const Decomposition& /*d*/, InputArray /*B*/, OutputArray /*X*/, OutputArray /*predB*/ = cv::noArray()) const
{
CV_Error(Error::StsNotImplemented, "Eigen library required for matrix solve, dense solver is not implemented");
}
#endif
//! Function to solve a sparse linear system of equations HX = B
bool sparseSolve(InputArray B, OutputArray X, bool checkSymmetry = true, OutputArray predB = cv::noArray()) const
{
Decomposition d;
return decompose(d, checkSymmetry) && solveDecomposed(d, B, X, predB);
}
static constexpr _Tp NON_ZERO_VAL_THRESHOLD = _Tp(0.0001);
size_t nBlocks;
IDtoBlockValueMap ijValue;

95
modules/3d/src/solvepnp.cpp
View File

@ -551,57 +551,6 @@ int solveP3P( InputArray _opoints, InputArray _ipoints,
return solutions;
}
class SolvePnPRefineLMCallback CV_FINAL : public LMSolver::Callback
{
public:
SolvePnPRefineLMCallback(InputArray _opoints, InputArray _ipoints, InputArray _cameraMatrix, InputArray _distCoeffs)
{
objectPoints = _opoints.getMat();
imagePoints = _ipoints.getMat();
npoints = std::max(objectPoints.checkVector(3, CV_32F), objectPoints.checkVector(3, CV_64F));
imagePoints0 = imagePoints.reshape(1, npoints*2);
cameraMatrix = _cameraMatrix.getMat();
distCoeffs = _distCoeffs.getMat();
}
bool compute(InputArray _param, OutputArray _err, OutputArray _Jac) const CV_OVERRIDE
{
Mat param = _param.getMat();
_err.create(npoints*2, 1, CV_64FC1);
if(_Jac.needed())
{
_Jac.create(npoints*2, param.rows, CV_64FC1);
}
Mat rvec = param(Rect(0, 0, 1, 3)), tvec = param(Rect(0, 3, 1, 3));
Mat J, projectedPts;
projectPoints(objectPoints, rvec, tvec, cameraMatrix, distCoeffs, projectedPts, _Jac.needed() ? J : noArray());
if (_Jac.needed())
{
Mat Jac = _Jac.getMat();
for (int i = 0; i < Jac.rows; i++)
{
for (int j = 0; j < Jac.cols; j++)
{
Jac.at<double>(i,j) = J.at<double>(i,j);
}
}
}
Mat err = _err.getMat();
projectedPts = projectedPts.reshape(1, npoints*2);
err = projectedPts - imagePoints0;
return true;
}
Mat objectPoints, imagePoints, imagePoints0;
Mat cameraMatrix, distCoeffs;
int npoints;
};
/**
* @brief Compute the Interaction matrix and the residuals for the current pose.
@ -750,7 +699,49 @@ static void solvePnPRefine(InputArray _objectPoints, InputArray _imagePoints,
params.at<double>(i+3,0) = tvec.at<double>(i,0);
}
LMSolver::create(makePtr<SolvePnPRefineLMCallback>(opoints, ipoints, cameraMatrix, distCoeffs), _criteria.maxCount, _criteria.epsilon)->run(params);
int npts = std::max(opoints.checkVector(3, CV_32F), opoints.checkVector(3, CV_64F));
Mat imagePoints0 = ipoints.reshape(1, npts * 2);
auto solvePnPRefineLMCallback = [opoints, imagePoints0, npts, cameraMatrix, distCoeffs]
(InputOutputArray _param, OutputArray _err, OutputArray _Jac) -> bool
{
Mat param = _param.getMat();
_err.create(npts * 2, 1, CV_64FC1);
if (_Jac.needed())
{
_Jac.create(npts * 2, param.rows, CV_64FC1);
}
Mat prvec = param(Rect(0, 0, 1, 3)), ptvec = param(Rect(0, 3, 1, 3));
Mat J, projectedPts;
projectPoints(opoints, prvec, ptvec, cameraMatrix, distCoeffs, projectedPts, _Jac.needed() ? J : noArray());
if (_Jac.needed())
{
Mat Jac = _Jac.getMat();
for (int i = 0; i < Jac.rows; i++)
{
for (int j = 0; j < Jac.cols; j++)
{
Jac.at<double>(i, j) = J.at<double>(i, j);
}
}
}
Mat err = _err.getMat();
projectedPts = projectedPts.reshape(1, npts * 2);
err = projectedPts - imagePoints0;
return true;
};
LevMarq solver(params, solvePnPRefineLMCallback,
LevMarq::Settings()
.setMaxIterations((unsigned int)_criteria.maxCount)
.setStepNormTolerance((double)_criteria.epsilon)
.setSmallEnergyTolerance((double)_criteria.epsilon * (double)_criteria.epsilon)
.setGeodesic(true));
solver.optimize();
params.rowRange(0, 3).convertTo(rvec0, rvec0.depth());
params.rowRange(3, 6).convertTo(tvec0, tvec0.depth());

308
modules/3d/test/test_pose_graph.cpp
View File

@ -3,12 +3,16 @@
// of this distribution and at http://opencv.org/license.html
#include "test_precomp.hpp"
#include <opencv2/3d/detail/pose_graph.hpp>
#include <opencv2/3d/detail/optimizer.hpp>
#include <opencv2/core/dualquaternion.hpp>
namespace opencv_test { namespace {
using namespace cv;
#ifdef HAVE_EIGEN
static Affine3d readAffine(std::istream& input)
{
Vec3d p;
@ -94,7 +98,7 @@ static Ptr<detail::PoseGraph> readG2OFile(const std::string& g2oFileName)
}
TEST( PoseGraph, sphereG2O )
TEST(PoseGraph, sphereG2O)
{
// Test takes 15+ sec in Release mode and 400+ sec in Debug mode
applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_DEBUG_VERYLONG);
@ -106,20 +110,26 @@ TEST( PoseGraph, sphereG2O )
// In IEEE Intl.Conf.on Robotics and Automation(ICRA), pages 4597 - 4604, 2015.
std::string filename = cvtest::TS::ptr()->get_data_path() + "/cv/rgbd/sphere_bignoise_vertex3.g2o";
Ptr<detail::PoseGraph> pg = readG2OFile(filename);
#ifdef HAVE_EIGEN
// You may change logging level to view detailed optimization report
// For example, set env. variable like this: OPENCV_LOG_LEVEL=INFO
int iters = pg->optimize();
// geoScale=1 is experimental, not guaranteed to work on other problems
// the rest are default params
pg->createOptimizer(LevMarq::Settings().setGeoScale(1.0)
.setMaxIterations(100)
.setCheckRelEnergyChange(true)
.setRelEnergyDeltaTolerance(1e-6)
.setGeodesic(true));
ASSERT_GE(iters, 0);
ASSERT_LE(iters, 36); // should converge in 36 iterations
auto r = pg->optimize();
double energy = pg->calcEnergy();
EXPECT_TRUE(r.found);
EXPECT_LE(r.iters, 20); // should converge in 31 iterations
ASSERT_LE(energy, 1.47723e+06); // should converge to 1.47722e+06 or less
EXPECT_LE(r.energy, 1.47723e+06); // should converge to 1.47722e+06 or less
// Add the "--test_debug" to arguments to see resulting pose graph nodes positions
if (cvtest::debugLevel > 0)
@ -143,9 +153,291 @@ TEST( PoseGraph, sphereG2O )
of.close();
}
}
// ------------------------------------------------------------------------------------------
// Wireframe meshes for debugging visualization purposes
struct Mesh
{
std::vector<Point3f> pts;
std::vector<Vec2i> lines;
Mesh join(const Mesh& m2) const
{
Mesh mo;
size_t sz1 = this->pts.size();
std::copy(this->pts.begin(), this->pts.end(), std::back_inserter(mo.pts));
std::copy(m2.pts.begin(), m2.pts.end(), std::back_inserter(mo.pts));
std::copy(this->lines.begin(), this->lines.end(), std::back_inserter(mo.lines));
std::transform(m2.lines.begin(), m2.lines.end(), std::back_inserter(mo.lines),
[sz1](Vec2i ab) { return Vec2i(ab[0] + (int)sz1, ab[1] + (int)sz1); });
return mo;
}
Mesh transform(Affine3f a, float scale = 1.f) const
{
Mesh out;
out.lines = this->lines;
for (Point3f p : this->pts)
{
out.pts.push_back(a * (p * scale));
}
return out;
}
// 0-2 - min, 3-5 - max
Vec6f getBoundingBox() const
{
float maxv = std::numeric_limits<float>::max();
Vec3f xmin(maxv, maxv, maxv), xmax(-maxv, -maxv, -maxv);
for (Point3f p : this->pts)
{
xmin[0] = min(p.x, xmin[0]); xmin[1] = min(p.y, xmin[1]); xmin[2] = min(p.z, xmin[2]);
xmax[0] = max(p.x, xmax[0]); xmax[1] = max(p.y, xmax[1]); xmax[2] = max(p.z, xmax[2]);
}
return Vec6f(xmin[0], xmin[1], xmin[2], xmax[0], xmax[1], xmax[2]);
}
};
Mesh seg7(int d)
{
const std::vector<Point3f> pt = { {0, 0, 0}, {0, 1, 0},
{1, 0, 0}, {1, 1, 0},
{2, 0, 0}, {2, 1, 0} };
std::vector<Mesh> seg(7);
seg[0].pts = { pt[0], pt[1] };
seg[1].pts = { pt[1], pt[3] };
seg[2].pts = { pt[3], pt[5] };
seg[3].pts = { pt[5], pt[4] };
seg[4].pts = { pt[4], pt[2] };
seg[5].pts = { pt[2], pt[0] };
seg[6].pts = { pt[2], pt[3] };
for (int i = 0; i < 7; i++)
seg[i].lines = { {0, 1} };
vector<Mesh> digits = {
seg[0].join(seg[1]).join(seg[2]).join(seg[3]).join(seg[4]).join(seg[5]), // 0
seg[1].join(seg[2]), // 1
seg[0].join(seg[1]).join(seg[3]).join(seg[4]).join(seg[6]), // 2
seg[0].join(seg[1]).join(seg[2]).join(seg[3]).join(seg[6]), // 3
seg[1].join(seg[2]).join(seg[5]).join(seg[6]), // 4
seg[0].join(seg[2]).join(seg[3]).join(seg[5]).join(seg[6]), // 5
seg[0].join(seg[2]).join(seg[3]).join(seg[4]).join(seg[5]).join(seg[6]), // 6
seg[0].join(seg[1]).join(seg[2]), // 7
seg[0].join(seg[1]).join(seg[2]).join(seg[3]).join(seg[4]).join(seg[5]).join(seg[6]), // 8
seg[0].join(seg[1]).join(seg[2]).join(seg[3]).join(seg[5]).join(seg[6]), // 9
seg[6], // -
};
return digits[d];
}
Mesh drawId(size_t x)
{
vector<int> digits;
do
{
digits.push_back(x % 10);
x /= 10;
}
while (x > 0);
float spacing = 0.2f;
Mesh m;
for (size_t i = 0; i < digits.size(); i++)
{
Mesh digit = seg7(digits[digits.size() - 1 - i]);
Vec6f bb = digit.getBoundingBox();
digit = digit.transform(Affine3f().translate(-Vec3f(0, bb[1], 0)));
Vec3f tr;
if (m.pts.empty())
tr = Vec3f();
else
tr = Vec3f(0, (m.getBoundingBox()[4] + spacing), 0);
m = m.join(digit.transform( Affine3f().translate(tr) ));
}
return m;
}
Mesh drawFromTo(size_t f, size_t t)
{
Mesh m;
Mesh df = drawId(f);
Mesh dp = seg7(10);
Mesh dt = drawId(t);
float spacing = 0.2f;
m = m.join(df).join(dp.transform(Affine3f().translate(Vec3f(0, df.getBoundingBox()[4] + spacing, 0))))
.join(dt.transform(Affine3f().translate(Vec3f(0, df.getBoundingBox()[4] + 2*spacing + 1, 0))));
return m;
}
Mesh drawPoseGraph(Ptr<detail::PoseGraph> pg)
{
Mesh marker;
marker.pts = { {0, 0, 0}, {1, 0, 0}, {0, 1, 0}, {0, 0, 1}, {1, 1, 0} };
marker.lines = { {0, 1}, {0, 2}, {0, 3}, {1, 4} };
Mesh allMeshes;
Affine3f margin = Affine3f().translate(Vec3f(0.1f, 0.1f, 0));
std::vector<size_t> ids = pg->getNodesIds();
for (const size_t& id : ids)
{
Affine3f pose = pg->getNodePose(id);
Mesh m = marker.join(drawId(id).transform(margin, 0.25f)).transform(pose);
allMeshes = allMeshes.join(m);
}
// edges
margin = Affine3f().translate(Vec3f(0.05f, 0.05f, 0));
for (size_t i = 0; i < pg->getNumEdges(); i++)
{
Affine3f pose = pg->getEdgePose(i);
size_t sid = pg->getEdgeStart(i);
size_t did = pg->getEdgeEnd(i);
Affine3f spose = pg->getNodePose(sid);
Affine3f dpose = spose * pose;
Mesh m = marker.join(drawFromTo(sid, did).transform(margin, 0.125f)).transform(dpose);
allMeshes = allMeshes.join(m);
}
return allMeshes;
}
void writeObj(const std::string& fname, const Mesh& m)
{
// Write edge-only model of how nodes are located in space
std::fstream of(fname, std::fstream::out);
for (const Point3f& d : m.pts)
{
of << "v " << d.x << " " << d.y << " " << d.z << std::endl;
}
for (const Vec2i& v : m.lines)
{
of << "l " << v[0] + 1 << " " << v[1] + 1 << std::endl;
}
of.close();
}
TEST(PoseGraph, simple)
{
Ptr<detail::PoseGraph> pg = detail::PoseGraph::create();
DualQuatf true0(1, 0, 0, 0, 0, 0, 0, 0);
DualQuatf true1 = DualQuatf::createFromPitch((float)CV_PI / 3.0f, 10.0f, Vec3f(1, 1.5f, 1.2f), Vec3f());
DualQuatf pose0 = true0;
vector<DualQuatf> noise(7);
for (size_t i = 0; i < noise.size(); i++)
{
float angle = cv::theRNG().uniform(-1.f, 1.f);
float shift = cv::theRNG().uniform(-2.f, 2.f);
Matx31f axis = Vec3f::randu(0.f, 1.f), moment = Vec3f::randu(0.f, 1.f);
noise[i] = DualQuatf::createFromPitch(angle, shift,
Vec3f(axis(0), axis(1), axis(2)),
Vec3f(moment(0), moment(1), moment(2)));
}
DualQuatf pose1 = noise[0] * true1;
DualQuatf diff = true1 * true0.inv();
vector<DualQuatf> cfrom = { diff, diff * noise[1], noise[2] * diff };
DualQuatf diffInv = diff.inv();
vector<DualQuatf> cto = { diffInv, diffInv * noise[3], noise[4] * diffInv };
pg->addNode(123, pose0.toAffine3(), true);
pg->addNode(456, pose1.toAffine3(), false);
Matx66f info = Matx66f::eye();
for (int i = 0; i < 3; i++)
{
pg->addEdge(123, 456, cfrom[i].toAffine3(), info);
pg->addEdge(456, 123, cto[i].toAffine3(), info);
}
Mesh allMeshes = drawPoseGraph(pg);
// Add the "--test_debug" to arguments to see resulting pose graph nodes positions
if (cvtest::debugLevel > 0)
{
writeObj("pg_simple_in.obj", allMeshes);
}
auto r = pg->optimize();
Mesh after = drawPoseGraph(pg);
// Add the "--test_debug" to arguments to see resulting pose graph nodes positions
if (cvtest::debugLevel > 0)
{
writeObj("pg_simple_out.obj", after);
}
EXPECT_TRUE(r.found);
}
#else
TEST(PoseGraph, sphereG2O)
{
throw SkipTestException("Build with Eigen required for pose graph optimization");
}
TEST(PoseGraph, simple)
{
throw SkipTestException("Build with Eigen required for pose graph optimization");
}
#endif
TEST(LevMarq, Rosenbrock)
{
auto f = [](double x, double y) -> double
{
return (1.0 - x) * (1.0 - x) + 100.0 * (y - x * x) * (y - x * x);
};
auto j = [](double x, double y) -> Matx12d
{
return {/*dx*/ -2.0 + 2.0 * x - 400.0 * x * y + 400.0 * x*x*x,
/*dy*/ 200.0 * y - 200.0 * x*x,
};
};
LevMarq solver(2, [f, j](InputOutputArray param, OutputArray err, OutputArray jv) -> bool
{
Vec2d v = param.getMat();
double x = v[0], y = v[1];
err.create(1, 1, CV_64F);
err.getMat().at<double>(0) = f(x, y);
if (jv.needed())
{
jv.create(1, 2, CV_64F);
Mat(j(x, y)).copyTo(jv);
}
return true;
},
LevMarq::Settings().setGeodesic(true));
Mat_<double> x (Vec2d(1, 3));
auto r = solver.run(x);
EXPECT_TRUE(r.found);
EXPECT_LT(r.energy, 0.035);
EXPECT_LE(r.iters, 17);
}

148
modules/calib/src/calibration.cpp
View File

@ -167,8 +167,8 @@ static void subMatrix(const Mat& src, Mat& dst,
static void cameraCalcJErr(const Mat& objectPoints, const Mat& imagePoints,
const Mat& npoints, Mat& allErrors,
Mat& _param, Mat* _JtErr, Mat* _JtJ, double* _errnorm,
double aspectRatio, Mat* perViewErrors,
Mat& _param, bool calcJ, Mat& JtErr, Mat& JtJ, double& errnorm,
double aspectRatio, Mat& perViewErrors,
int flags, bool optimizeObjPoints)
{
const int NINTRINSIC = CALIB_NINTRINSIC;
@ -177,7 +177,6 @@ static void cameraCalcJErr(const Mat& objectPoints, const Mat& imagePoints,
Mat _k(14, 1, CV_64F, k);
double* param = _param.ptr<double>();
int nparams = (int)_param.total();
bool calcJ = _JtErr != 0;
int ni0 = npoints.at<int>(0);
Mat _Je(ni0*2, 6, CV_64F), _Ji(ni0*2, NINTRINSIC, CV_64F), _Jo, _err(ni*2, 1, CV_64F);
@ -192,10 +191,8 @@ static void cameraCalcJErr(const Mat& objectPoints, const Mat& imagePoints,
0, 0, 1);
std::copy(param + 4, param + 4 + 14, k);
if (_JtJ)
_JtJ->setZero();
if (_JtErr)
_JtErr->setZero();
JtJ.setZero();
JtErr.setZero();
if(optimizeObjPoints)
_Jo.create(ni0*2, ni0*3, CV_64F);
@ -263,8 +260,6 @@ static void cameraCalcJErr(const Mat& objectPoints, const Mat& imagePoints,
if( calcJ )
{
Mat JtJ = *_JtJ, JtErr = *_JtErr;
// see HZ: (A6.14) for details on the structure of the Jacobian
JtJ(Rect(0, 0, NINTRINSIC, NINTRINSIC)) += _Ji.t() * _Ji;
JtJ(Rect(NINTRINSIC + i * 6, NINTRINSIC + i * 6, 6, 6)) = _Je.t() * _Je;
@ -295,14 +290,13 @@ static void cameraCalcJErr(const Mat& objectPoints, const Mat& imagePoints,
printf("\n");
}*/
if( perViewErrors )
perViewErrors->at<double>(i) = std::sqrt(viewErr / ni);
if( !perViewErrors.empty() )
perViewErrors.at<double>(i) = std::sqrt(viewErr / ni);
reprojErr += viewErr;
}
if(_errnorm)
*_errnorm = reprojErr;
errnorm = reprojErr;
}
static double calibrateCameraInternal( const Mat& objectPoints,
@ -571,22 +565,35 @@ static double calibrateCameraInternal( const Mat& objectPoints,
//std::cout << "single camera calib. mask: " << mask0.t() << "\n";
// 3. run the optimization
LMSolver::runAlt(param0, mask0, termCrit, solveMethod, false,
[&](Mat& _param, Mat* _JtErr, Mat* _JtJ, double* _errnorm)
auto calibCameraLMCallback = [matM, _m, npoints, &allErrors, aspectRatio, &perViewErrors, flags, releaseObject]
(InputOutputArray _param, OutputArray JtErr, OutputArray JtJ, double& errnorm) -> bool
{
cameraCalcJErr(matM, _m, npoints, allErrors, _param, _JtErr, _JtJ, _errnorm,
aspectRatio, perViewErrors, flags, releaseObject);
Mat jterr = JtErr.getMat(), jtj = JtJ.getMat(), perViewErr = perViewErrors ? *perViewErrors : Mat();
Mat mparam = _param.getMat();
cameraCalcJErr(matM, _m, npoints, allErrors, mparam, /* calcJ */ JtErr.needed() && JtJ.needed(),
jterr, jtj, errnorm,
aspectRatio, perViewErr, flags, releaseObject);
return true;
});
};
LevMarq solver(param0, calibCameraLMCallback,
LevMarq::Settings()
.setMaxIterations((unsigned int)termCrit.maxCount)
.setStepNormTolerance(termCrit.epsilon)
.setSmallEnergyTolerance(termCrit.epsilon * termCrit.epsilon),
mask0, MatrixType::AUTO, VariableType::LINEAR, /* LtoR */ false, solveMethod);
// geodesic is not supported for normal callbacks
solver.optimize();
//std::cout << "single camera calib. param after LM: " << param0.t() << "\n";
Mat JtErr(nparams, 1, CV_64F), JtJ(nparams, nparams, CV_64F), JtJinv, JtJN;
double reprojErr = 0;
JtErr.setZero(); JtJ.setZero();
cameraCalcJErr(matM, _m, npoints, allErrors, param0,
stdDevs ? &JtErr : 0, stdDevs ? &JtJ : 0, &reprojErr,
aspectRatio, 0, flags, releaseObject);
JtErr.setZero(); JtJ.setZero(); Mat dummy;
cameraCalcJErr(matM, _m, npoints, allErrors, param0, (stdDevs != nullptr),
JtErr, JtJ, reprojErr,
aspectRatio, dummy, flags, releaseObject);
if (stdDevs)
{
int nparams_nz = countNonZero(mask0);
@ -673,12 +680,11 @@ static double stereoCalibrateImpl(
TermCriteria termCrit )
{
const int NINTRINSIC = 18;
double reprojErr = 0;
double dk[2][14]={{0}};
Mat Dist[2];
Matx33d A[2], R_LR;
int i, k, p, ni = 0, pos, pointsTotal = 0, maxPoints = 0, nparams;
Matx33d A[2];
int pointsTotal = 0, maxPoints = 0, nparams;
bool recomputeIntrinsics = false;
double aspectRatio[2] = {0};
@ -689,10 +695,10 @@ static double stereoCalibrateImpl(
_npoints.type() == CV_32S );
int nimages = (int)_npoints.total();
for( i = 0; i < nimages; i++ )
for(int i = 0; i < nimages; i++ )
{
ni = _npoints.at<int>(i);
maxPoints = MAX(maxPoints, ni);
int ni = _npoints.at<int>(i);
maxPoints = std::max(maxPoints, ni);
pointsTotal += ni;
}
@ -700,7 +706,7 @@ static double stereoCalibrateImpl(
_objectPoints.convertTo(objectPoints, CV_64F);
objectPoints = objectPoints.reshape(3, 1);
for( k = 0; k < 2; k++ )
for(int k = 0; k < 2; k++ )
{
const Mat& points = k == 0 ? _imagePoints1 : _imagePoints2;
const Mat& cameraMatrix = k == 0 ? _cameraMatrix1 : _cameraMatrix2;
@ -750,7 +756,7 @@ static double stereoCalibrateImpl(
if( flags & CALIB_FIX_ASPECT_RATIO )
{
for( k = 0; k < 2; k++ )
for(int k = 0; k < 2; k++ )
aspectRatio[k] = A[k](0, 0)/A[k](1, 1);
}
@ -765,7 +771,8 @@ static double stereoCalibrateImpl(
// for intrinisic parameters of each camera ((fx,fy,cx,cy,k1,k2,p1,p2) ~ 8 parameters).
nparams = 6*(nimages+1) + (recomputeIntrinsics ? NINTRINSIC*2 : 0);
std::vector<uchar> mask(nparams, (uchar)0);
std::vector<uchar> mask(nparams, (uchar)1);
std::vector<double> param(nparams, 0.);
if( recomputeIntrinsics )
@ -825,13 +832,14 @@ static double stereoCalibrateImpl(
om = median(om_ref_list)
T = median(T_ref_list)
*/
for( i = pos = 0; i < nimages; pos += ni, i++ )
int pos = 0;
for(int i = 0; i < nimages; i++ )
{
ni = _npoints.at<int>(i);
int ni = _npoints.at<int>(i);
Mat objpt_i(1, ni, CV_64FC3, objectPoints.ptr<double>() + pos*3);
Matx33d R[2];
Vec3d rv, T[2];
for( k = 0; k < 2; k++ )
for(int k = 0; k < 2; k++ )
{
Mat imgpt_ik = Mat(1, ni, CV_64FC2, imagePoints[k].ptr<double>() + pos*2);
solvePnP(objpt_i, imgpt_ik, A[k], Dist[k], rv, T[k], false, SOLVEPNP_ITERATIVE );
@ -859,6 +867,8 @@ static double stereoCalibrateImpl(
RT0data[i + nimages*3] = T[1][0];
RT0data[i + nimages*4] = T[1][1];
RT0data[i + nimages*5] = T[1][2];
pos += ni;
}
if(flags & CALIB_USE_EXTRINSIC_GUESS)
@ -881,7 +891,7 @@ static double stereoCalibrateImpl(
else
{
// find the medians and save the first 6 parameters
for( i = 0; i < 6; i++ )
for(int i = 0; i < 6; i++ )
{
double* rti = RT0data + i*nimages;
qsort( rti, nimages, sizeof(*rti), dbCmp );
@ -890,7 +900,7 @@ static double stereoCalibrateImpl(
}
if( recomputeIntrinsics )
for( k = 0; k < 2; k++ )
for(int k = 0; k < 2; k++ )
{
double* iparam = &param[(nimages+1)*6 + k*NINTRINSIC];
if( flags & CALIB_ZERO_TANGENT_DIST )
@ -911,17 +921,15 @@ static double stereoCalibrateImpl(
//std::cout << "param before LM: " << Mat(param, false).t() << "\n";
LMSolver::runAlt(param, mask, termCrit, DECOMP_SVD, false,
[&](Mat& _param, Mat* _JtErr, Mat* _JtJ, double* _errnorm)
auto lmcallback = [&](InputOutputArray _param, OutputArray JtErr_, OutputArray JtJ_, double& errnorm)
{
double* param_p = _param.ptr<double>();
double* param_p = _param.getMat().ptr<double>();
Vec3d om_LR(param_p[0], param_p[1], param_p[2]);
Vec3d T_LR(param_p[3], param_p[4], param_p[5]);
Vec3d om[2], T[2];
Matx33d dr3dr1, dr3dr2, dt3dr2, dt3dt1, dt3dt2;
reprojErr = 0;
Rodrigues(om_LR, R_LR);
double reprojErr = 0;
if( recomputeIntrinsics )
{
@ -942,7 +950,7 @@ static double stereoCalibrateImpl(
//ipparam[0] = ipparam[1]*aspectRatio[0];
//ipparam[NINTRINSIC] = ipparam[NINTRINSIC+1]*aspectRatio[1];
}
for( k = 0; k < 2; k++ )
for(int k = 0; k < 2; k++ )
{
A[k] = Matx33d(iparam[k*NINTRINSIC+0], 0, iparam[k*NINTRINSIC+2],
0, iparam[k*NINTRINSIC+1], iparam[k*NINTRINSIC+3],
@ -952,21 +960,22 @@ static double stereoCalibrateImpl(
}
}
for( i = pos = 0; i < nimages; pos += ni, i++ )
int ptPos = 0;
for(int i = 0; i < nimages; i++ )
{
ni = _npoints.at<int>(i);
int ni = _npoints.at<int>(i);
double* pi = param_p + (i+1)*6;
om[0] = Vec3d(pi[0], pi[1], pi[2]);
T[0] = Vec3d(pi[3], pi[4], pi[5]);
if( _JtJ || _JtErr )
if( JtJ_.needed() || JtErr_.needed() )
composeRT( om[0], T[0], om_LR, T_LR, om[1], T[1], dr3dr1, noArray(),
dr3dr2, noArray(), noArray(), dt3dt1, dt3dr2, dt3dt2 );
else
composeRT( om[0], T[0], om_LR, T_LR, om[1], T[1] );
Mat objpt_i(1, ni, CV_64FC3, objectPoints.ptr<double>() + pos*3);
Mat objpt_i(1, ni, CV_64FC3, objectPoints.ptr<double>() + ptPos*3);
err.resize(ni*2); Je.resize(ni*2); J_LR.resize(ni*2); Ji.resize(ni*2);
Mat tmpImagePoints = err.reshape(2, 1);
@ -976,30 +985,30 @@ static double stereoCalibrateImpl(
Mat dpdrot = Je.colRange(0, 3);
Mat dpdt = Je.colRange(3, 6);
for( k = 0; k < 2; k++ )
for(int k = 0; k < 2; k++ )
{
Mat imgpt_ik(1, ni, CV_64FC2, imagePoints[k].ptr<double>() + pos*2);
Mat imgpt_ik(1, ni, CV_64FC2, imagePoints[k].ptr<double>() + ptPos*2);
if( _JtJ || _JtErr )
if( JtJ_.needed() || JtErr_.needed() )
projectPoints(objpt_i, om[k], T[k], A[k], Dist[k],
tmpImagePoints, dpdrot, dpdt, dpdf, dpdc, dpdk, noArray(),
(flags & CALIB_FIX_ASPECT_RATIO) ? aspectRatio[k] : 0.);
tmpImagePoints, dpdrot, dpdt, dpdf, dpdc, dpdk, noArray(),
(flags & CALIB_FIX_ASPECT_RATIO) ? aspectRatio[k] : 0.);
else
projectPoints(objpt_i, om[k], T[k], A[k], Dist[k], tmpImagePoints);
subtract( tmpImagePoints, imgpt_ik, tmpImagePoints );
if( _JtJ )
if( JtJ_.needed() )
{
Mat& JtErr = *_JtErr;
Mat& JtJ = *_JtJ;
Mat JtErr = JtErr_.getMat();
Mat JtJ = JtJ_.getMat();
int iofs = (nimages+1)*6 + k*NINTRINSIC, eofs = (i+1)*6;
assert( _JtJ && _JtErr );
assert( JtJ_.needed() && JtErr_.needed() );
if( k == 1 )
{
// d(err_{x|y}R) ~ de3
// convert de3/{dr3,dt3} => de3{dr1,dt1} & de3{dr2,dt2}
for( p = 0; p < ni*2; p++ )
for(int p = 0; p < ni*2; p++ )
{
Mat de3dr3( 1, 3, CV_64F, Je.ptr(p));
Mat de3dt3( 1, 3, CV_64F, de3dr3.ptr<double>() + 3 );
@ -1045,21 +1054,38 @@ static double stereoCalibrateImpl(
perViewErr->at<double>(i, k) = std::sqrt(viewErr/ni);
reprojErr += viewErr;
}
ptPos += ni;
}
if(_errnorm)
*_errnorm = reprojErr;
errnorm = reprojErr;
return true;
});
};
double reprojErr = 0;
if (countNonZero(mask))
{
LevMarq solver(param, lmcallback,
LevMarq::Settings()
.setMaxIterations((unsigned int)termCrit.maxCount)
.setStepNormTolerance(termCrit.epsilon)
.setSmallEnergyTolerance(termCrit.epsilon * termCrit.epsilon),
mask);
// geodesic not supported for normal callbacks
LevMarq::Report r = solver.optimize();
reprojErr = r.energy;
}
Vec3d om_LR(param[0], param[1], param[2]);
Vec3d T_LR(param[3], param[4], param[5]);
Matx33d R_LR;
Rodrigues( om_LR, R_LR );
if( matR->rows == 1 || matR->cols == 1 )
om_LR.convertTo(*matR, matR->depth());
else
R_LR.convertTo(*matR, matR->depth());
T_LR.convertTo(*matT, matT->depth());
for( k = 0; k < 2; k++ )
for(int k = 0; k < 2; k++ )
{
double* iparam = &param[(nimages+1)*6 + k*NINTRINSIC];
A[k] = Matx33d(iparam[0], 0, iparam[2], 0, iparam[1], iparam[3], 0, 0, 1);
@ -1067,7 +1093,7 @@ static double stereoCalibrateImpl(
if( recomputeIntrinsics )
{
for( k = 0; k < 2; k++ )
for(int k = 0; k < 2; k++ )
{
Mat& cameraMatrix = k == 0 ? _cameraMatrix1 : _cameraMatrix2;
Mat& distCoeffs = k == 0 ? _distCoeffs1 : _distCoeffs2;

4
modules/calib/test/test_cameracalibration.cpp
View File

@ -1946,8 +1946,8 @@ TEST(Calib3d_StereoCalibrate_CPP, extended)
double res1 = stereoCalibrate( objpt, imgpt1, imgpt2,
K1, c1, K2, c2,
imageSize, R, T, E, F, err, flags);
printf("res0 = %g, res1 = %g\n", res0, res1);
EXPECT_LE(res1, res0*1.1);
EXPECT_LE(res1, res0);
EXPECT_TRUE(err.total() == 2);
}

2
modules/core/include/opencv2/core/dualquaternion.inl.hpp
View File

@ -158,7 +158,7 @@ inline Quat<T> DualQuat<T>::getRotation(QuatAssumeType assumeUnit) const
template <typename T>
inline Vec<T, 3> DualQuat<T>::getTranslation(QuatAssumeType assumeUnit) const
{
Quat<T> trans = 2.0 * (getDualPart() * getRealPart().inv(assumeUnit));
Quat<T> trans = T(2.0) * (getDualPart() * getRealPart().inv(assumeUnit));
return Vec<T, 3>{trans[1], trans[2], trans[3]};
}

20
modules/core/include/opencv2/core/quaternion.inl.hpp
View File

@ -54,7 +54,7 @@ Quat<T> Quat<T>::createFromAngleAxis(const T angle, const Vec<T, 3> &axis)
{
CV_Error(Error::StsBadArg, "this quaternion does not represent a rotation");
}
const T angle_half = angle * 0.5;
const T angle_half = angle * T(0.5);
w = std::cos(angle_half);
const T sin_v = std::sin(angle_half);
const T sin_norm = sin_v / vNorm;
@ -79,35 +79,35 @@ Quat<T> Quat<T>::createFromRotMat(InputArray _R)
T trace = R(0, 0) + R(1, 1) + R(2, 2);
if (trace > 0)
{
S = std::sqrt(trace + 1) * 2;
S = std::sqrt(trace + 1) * T(2);
x = (R(1, 2) - R(2, 1)) / S;
y = (R(2, 0) - R(0, 2)) / S;
z = (R(0, 1) - R(1, 0)) / S;
w = -0.25 * S;
w = -T(0.25) * S;
}
else if (R(0, 0) > R(1, 1) && R(0, 0) > R(2, 2))
{
S = std::sqrt(1.0 + R(0, 0) - R(1, 1) - R(2, 2)) * 2;
x = -0.25 * S;
S = std::sqrt(T(1.0) + R(0, 0) - R(1, 1) - R(2, 2)) * T(2);
x = -T(0.25) * S;
y = -(R(1, 0) + R(0, 1)) / S;
z = -(R(0, 2) + R(2, 0)) / S;
w = (R(1, 2) - R(2, 1)) / S;
}
else if (R(1, 1) > R(2, 2))
{
S = std::sqrt(1.0 - R(0, 0) + R(1, 1) - R(2, 2)) * 2;
S = std::sqrt(T(1.0) - R(0, 0) + R(1, 1) - R(2, 2)) * T(2);
x = (R(0, 1) + R(1, 0)) / S;
y = 0.25 * S;
y = T(0.25) * S;
z = (R(1, 2) + R(2, 1)) / S;
w = (R(0, 2) - R(2, 0)) / S;
}
else
{
S = std::sqrt(1.0 - R(0, 0) - R(1, 1) + R(2, 2)) * 2;
S = std::sqrt(T(1.0) - R(0, 0) - R(1, 1) + R(2, 2)) * T(2);
x = (R(0, 2) + R(2, 0)) / S;
y = (R(1, 2) + R(2, 1)) / S;
z = 0.25 * S;
z = T(0.25) * S;
w = -(R(0, 1) - R(1, 0)) / S;
}
return Quat<T> (w, x, y, z);
@ -268,7 +268,7 @@ inline Quat<T>& Quat<T>::operator/=(const T a)
template <typename T>
inline Quat<T> Quat<T>::operator/(const T a) const
{
const T a_inv = 1.0 / a;
const T a_inv = T(1.0) / a;
return Quat<T>(w * a_inv, x * a_inv, y * a_inv, z * a_inv);
}

36
modules/stitching/src/motion_estimators.cpp
View File

@ -253,16 +253,34 @@ bool BundleAdjusterBase::estimate(const std::vector<ImageFeatures> &features,
edges_[i].second].num_inliers);
int nerrs = total_num_matches_ * num_errs_per_measurement_;
LMSolver::run(cam_params_, Mat(), nerrs, term_criteria_, DECOMP_SVD,
[&](Mat& param, Mat* err, Mat* jac)
auto callb = [&](InputOutputArray param, OutputArray err, OutputArray jac) -> bool
{
// workaround against losing value
Mat backup = cam_params_.clone();
param.copyTo(cam_params_);
if (jac.needed())
{
param.copyTo(cam_params_);
if (jac)
calcJacobian(*jac);
if (err)
calcError(*err);
return true;
});
Mat m = jac.getMat();
calcJacobian(m);
}
if (err.needed())
{
Mat m = err.getMat();
calcError(m);
}
backup.copyTo(cam_params_);
return true;
};
LevMarq solver(cam_params_, callb,
LevMarq::Settings()
.setMaxIterations((unsigned int)term_criteria_.maxCount)
.setStepNormTolerance(term_criteria_.epsilon)
.setSmallEnergyTolerance(term_criteria_.epsilon * term_criteria_.epsilon)
.setGeodesic(true),
noArray(), MatrixType::AUTO, VariableType::LINEAR, nerrs);
solver.optimize();
LOGLN_CHAT("");
LOGLN_CHAT("Bundle adjustment, final RMS error: " << std::sqrt(err.dot(err) / total_num_matches_));