estimator.cpp
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// 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.
#include "../precomp.hpp"
#include "../usac.hpp"
namespace cv { namespace usac {
class HomographyEstimatorImpl : public HomographyEstimator {
private:
const Ptr<MinimalSolver> min_solver;
const Ptr<NonMinimalSolver> non_min_solver;
const Ptr<Degeneracy> degeneracy;
public:
HomographyEstimatorImpl (const Ptr<MinimalSolver> &min_solver_,
const Ptr<NonMinimalSolver> &non_min_solver_, const Ptr<Degeneracy> °eneracy_) :
min_solver (min_solver_), non_min_solver (non_min_solver_), degeneracy (degeneracy_) {}
inline int estimateModels (const std::vector<int> &sample, std::vector<Mat> &models) const override {
if (! degeneracy->isSampleGood(sample)) return 0;
return min_solver->estimate (sample, models);
}
int estimateModelNonMinimalSample(const std::vector<int> &sample, int sample_size,
std::vector<Mat> &models, const std::vector<double> &weights) const override {
return non_min_solver->estimate (sample, sample_size, models, weights);
};
int getMaxNumSolutions () const override {
return min_solver->getMaxNumberOfSolutions();
}
int getMaxNumSolutionsNonMinimal () const override {
return non_min_solver->getMaxNumberOfSolutions();
}
int getMinimalSampleSize () const override {
return min_solver->getSampleSize();
}
int getNonMinimalSampleSize () const override {
return non_min_solver->getMinimumRequiredSampleSize();
}
Ptr<Estimator> clone() const override {
return makePtr<HomographyEstimatorImpl>(min_solver->clone(), non_min_solver->clone(),
degeneracy->clone(0 /*we don't need state here*/));
}
};
Ptr<HomographyEstimator> HomographyEstimator::create (const Ptr<MinimalSolver> &min_solver_,
const Ptr<NonMinimalSolver> &non_min_solver_, const Ptr<Degeneracy> °eneracy_) {
return makePtr<HomographyEstimatorImpl>(min_solver_, non_min_solver_, degeneracy_);
}
/////////////////////////////////////////////////////////////////////////
class FundamentalEstimatorImpl : public FundamentalEstimator {
private:
const Ptr<MinimalSolver> min_solver;
const Ptr<NonMinimalSolver> non_min_solver;
const Ptr<Degeneracy> degeneracy;
public:
FundamentalEstimatorImpl (const Ptr<MinimalSolver> &min_solver_,
const Ptr<NonMinimalSolver> &non_min_solver_, const Ptr<Degeneracy> °eneracy_) :
min_solver (min_solver_), non_min_solver (non_min_solver_), degeneracy (degeneracy_) {}
inline int
estimateModels(const std::vector<int> &sample, std::vector<Mat> &models) const override {
std::vector<Mat> F;
const int models_count = min_solver->estimate(sample, F);
int valid_models_count = 0;
for (int i = 0; i < models_count; i++)
if (degeneracy->isModelValid(F[i], sample))
models[valid_models_count++] = F[i];
return valid_models_count;
}
int estimateModelNonMinimalSample(const std::vector<int> &sample, int sample_size,
std::vector<Mat> &models, const std::vector<double> &weights) const override {
return non_min_solver->estimate(sample, sample_size, models, weights);
}
int getMaxNumSolutions () const override {
return min_solver->getMaxNumberOfSolutions();
}
int getMinimalSampleSize () const override {
return min_solver->getSampleSize();
}
int getNonMinimalSampleSize () const override {
return non_min_solver->getMinimumRequiredSampleSize();
}
int getMaxNumSolutionsNonMinimal () const override {
return non_min_solver->getMaxNumberOfSolutions();
}
Ptr<Estimator> clone() const override {
return makePtr<FundamentalEstimatorImpl>(min_solver->clone(), non_min_solver->clone(),
degeneracy->clone(0));
}
};
Ptr<FundamentalEstimator> FundamentalEstimator::create (const Ptr<MinimalSolver> &min_solver_,
const Ptr<NonMinimalSolver> &non_min_solver_, const Ptr<Degeneracy> °eneracy_) {
return makePtr<FundamentalEstimatorImpl>(min_solver_, non_min_solver_, degeneracy_);
}
/////////////////////////////////////////////////////////////////////////
class EssentialEstimatorImpl : public EssentialEstimator {
private:
const Ptr<MinimalSolver> min_solver;
const Ptr<NonMinimalSolver> non_min_solver;
const Ptr<Degeneracy> degeneracy;
public:
explicit EssentialEstimatorImpl (const Ptr<MinimalSolver> &min_solver_,
const Ptr<NonMinimalSolver> &non_min_solver_, const Ptr<Degeneracy> °eneracy_) :
min_solver (min_solver_), non_min_solver (non_min_solver_), degeneracy (degeneracy_) {}
inline int
estimateModels(const std::vector<int> &sample, std::vector<Mat> &models) const override {
std::vector<Mat> E;
const int models_count = min_solver->estimate(sample, E);
int valid_models_count = 0;
for (int i = 0; i < models_count; i++)
if (degeneracy->isModelValid (E[i], sample))
models[valid_models_count++] = E[i];
return valid_models_count;
}
int estimateModelNonMinimalSample(const std::vector<int> &sample, int sample_size,
std::vector<Mat> &models, const std::vector<double> &weights) const override {
return non_min_solver->estimate(sample, sample_size, models, weights);
};
int getMaxNumSolutions () const override {
return min_solver->getMaxNumberOfSolutions();
}
int getMinimalSampleSize () const override {
return min_solver->getSampleSize();
}
int getNonMinimalSampleSize () const override {
return non_min_solver->getMinimumRequiredSampleSize();
}
int getMaxNumSolutionsNonMinimal () const override {
return non_min_solver->getMaxNumberOfSolutions();
}
Ptr<Estimator> clone() const override {
return makePtr<EssentialEstimatorImpl>(min_solver->clone(), non_min_solver->clone(),
degeneracy->clone(0));
}
};
Ptr<EssentialEstimator> EssentialEstimator::create (const Ptr<MinimalSolver> &min_solver_,
const Ptr<NonMinimalSolver> &non_min_solver_, const Ptr<Degeneracy> °eneracy_) {
return makePtr<EssentialEstimatorImpl>(min_solver_, non_min_solver_, degeneracy_);
}
/////////////////////////////////////////////////////////////////////////
class AffineEstimatorImpl : public AffineEstimator {
private:
const Ptr<MinimalSolver> min_solver;
const Ptr<NonMinimalSolver> non_min_solver;
public:
explicit AffineEstimatorImpl (const Ptr<MinimalSolver> &min_solver_,
const Ptr<NonMinimalSolver> &non_min_solver_) :
min_solver (min_solver_), non_min_solver (non_min_solver_) {}
int estimateModels(const std::vector<int> &sample, std::vector<Mat> &models) const override {
return min_solver->estimate(sample, models);
}
int estimateModelNonMinimalSample (const std::vector<int> &sample, int sample_size,
std::vector<Mat> &models, const std::vector<double> &weights) const override {
return non_min_solver->estimate(sample, sample_size, models, weights);
}
int getMinimalSampleSize() const override {
return min_solver->getSampleSize(); // 3 points required
}
int getNonMinimalSampleSize() const override {
return non_min_solver->getMinimumRequiredSampleSize();
}
int getMaxNumSolutions () const override {
return min_solver->getMaxNumberOfSolutions();
}
int getMaxNumSolutionsNonMinimal () const override {
return non_min_solver->getMaxNumberOfSolutions();
}
Ptr<Estimator> clone() const override {
return makePtr<AffineEstimatorImpl>(min_solver->clone(), non_min_solver->clone());
}
};
Ptr<AffineEstimator> AffineEstimator::create (const Ptr<MinimalSolver> &min_solver_,
const Ptr<NonMinimalSolver> &non_min_solver_) {
return makePtr<AffineEstimatorImpl>(min_solver_, non_min_solver_);
}
/////////////////////////////////////////////////////////////////////////
class PnPEstimatorImpl : public PnPEstimator {
private:
const Ptr<MinimalSolver> min_solver;
const Ptr<NonMinimalSolver> non_min_solver;
public:
explicit PnPEstimatorImpl (const Ptr<MinimalSolver> &min_solver_,
const Ptr<NonMinimalSolver> &non_min_solver_) :
min_solver(min_solver_), non_min_solver(non_min_solver_) {}
int estimateModels (const std::vector<int> &sample, std::vector<Mat> &models) const override {
return min_solver->estimate(sample, models);
}
int estimateModelNonMinimalSample (const std::vector<int> &sample, int sample_size,
std::vector<Mat> &models, const std::vector<double> &weights) const override {
return non_min_solver->estimate(sample, sample_size, models, weights);
}
int getMinimalSampleSize() const override {
return min_solver->getSampleSize();
}
int getNonMinimalSampleSize() const override {
return non_min_solver->getMinimumRequiredSampleSize();
}
int getMaxNumSolutions () const override {
return min_solver->getMaxNumberOfSolutions();
}
int getMaxNumSolutionsNonMinimal () const override {
return non_min_solver->getMaxNumberOfSolutions();
}
Ptr<Estimator> clone() const override {
return makePtr<PnPEstimatorImpl>(min_solver->clone(), non_min_solver->clone());
}
};
Ptr<PnPEstimator> PnPEstimator::create (const Ptr<MinimalSolver> &min_solver_,
const Ptr<NonMinimalSolver> &non_min_solver_) {
return makePtr<PnPEstimatorImpl>(min_solver_, non_min_solver_);
}
///////////////////////////////////////////// ERROR /////////////////////////////////////////
// Symmetric Reprojection Error
class ReprojectionErrorSymmetricImpl : public ReprojectionErrorSymmetric {
private:
const Mat * points_mat;
const float * const points;
float m11, m12, m13, m21, m22, m23, m31, m32, m33;
float minv11, minv12, minv13, minv21, minv22, minv23, minv31, minv32, minv33;
std::vector<float> errors;
public:
explicit ReprojectionErrorSymmetricImpl (const Mat &points_)
: points_mat(&points_), points ((float *) points_.data)
, m11(0), m12(0), m13(0), m21(0), m22(0), m23(0), m31(0), m32(0), m33(0)
, minv11(0), minv12(0), minv13(0), minv21(0), minv22(0), minv23(0), minv31(0), minv32(0), minv33(0)
, errors(points_.rows)
{
CV_DbgAssert(points);
}
inline void setModelParameters(const Mat& model) override
{
CV_Assert(!model.empty());
CV_CheckTypeEQ(model.depth(), CV_64F, "");
const auto * const m = (double *) model.data;
m11=static_cast<float>(m[0]); m12=static_cast<float>(m[1]); m13=static_cast<float>(m[2]);
m21=static_cast<float>(m[3]); m22=static_cast<float>(m[4]); m23=static_cast<float>(m[5]);
m31=static_cast<float>(m[6]); m32=static_cast<float>(m[7]); m33=static_cast<float>(m[8]);
const Mat model_inv = model.inv();
CV_CheckTypeEQ(model_inv.depth(), CV_64F, "");
const auto * const minv = (double *) model_inv.data;
minv11=(float)minv[0]; minv12=(float)minv[1]; minv13=(float)minv[2];
minv21=(float)minv[3]; minv22=(float)minv[4]; minv23=(float)minv[5];
minv31=(float)minv[6]; minv32=(float)minv[7]; minv33=(float)minv[8];
}
inline float getError (int point_idx) const override {
const int smpl = 4*point_idx;
const float x1=points[smpl], y1=points[smpl+1], x2=points[smpl+2], y2=points[smpl+3];
const float est_z2 = 1 / (m31 * x1 + m32 * y1 + m33),
dx2 = x2 - (m11 * x1 + m12 * y1 + m13) * est_z2,
dy2 = y2 - (m21 * x1 + m22 * y1 + m23) * est_z2;
const float est_z1 = 1 / (minv31 * x2 + minv32 * y2 + minv33),
dx1 = x1 - (minv11 * x2 + minv12 * y2 + minv13) * est_z1,
dy1 = y1 - (minv21 * x2 + minv22 * y2 + minv23) * est_z1;
return (dx2 * dx2 + dy2 * dy2 + dx1 * dx1 + dy1 * dy1) * .5f;
}
const std::vector<float> &getErrors (const Mat &model) override {
setModelParameters(model);
for (int point_idx = 0; point_idx < points_mat->rows; point_idx++) {
const int smpl = 4*point_idx;
const float x1=points[smpl], y1=points[smpl+1], x2=points[smpl+2], y2=points[smpl+3];
const float est_z2 = 1 / (m31 * x1 + m32 * y1 + m33),
dx2 = x2 - (m11 * x1 + m12 * y1 + m13) * est_z2,
dy2 = y2 - (m21 * x1 + m22 * y1 + m23) * est_z2;
const float est_z1 = 1 / (minv31 * x2 + minv32 * y2 + minv33),
dx1 = x1 - (minv11 * x2 + minv12 * y2 + minv13) * est_z1,
dy1 = y1 - (minv21 * x2 + minv22 * y2 + minv23) * est_z1;
errors[point_idx] = (dx2 * dx2 + dy2 * dy2 + dx1 * dx1 + dy1 * dy1) * .5f;
}
return errors;
}
Ptr<Error> clone () const override {
return makePtr<ReprojectionErrorSymmetricImpl>(*points_mat);
}
};
Ptr<ReprojectionErrorSymmetric>
ReprojectionErrorSymmetric::create(const Mat &points) {
return makePtr<ReprojectionErrorSymmetricImpl>(points);
}
// Forward Reprojection Error
class ReprojectionErrorForwardImpl : public ReprojectionErrorForward {
private:
const Mat * points_mat;
const float * const points;
float m11, m12, m13, m21, m22, m23, m31, m32, m33;
std::vector<float> errors;
public:
explicit ReprojectionErrorForwardImpl (const Mat &points_)
: points_mat(&points_), points ((float *)points_.data)
, m11(0), m12(0), m13(0), m21(0), m22(0), m23(0), m31(0), m32(0), m33(0)
, errors(points_.rows)
{
CV_DbgAssert(points);
}
inline void setModelParameters(const Mat& model) override
{
CV_Assert(!model.empty());
CV_CheckTypeEQ(model.depth(), CV_64F, "");
const auto * const m = (double *) model.data;
m11=static_cast<float>(m[0]); m12=static_cast<float>(m[1]); m13=static_cast<float>(m[2]);
m21=static_cast<float>(m[3]); m22=static_cast<float>(m[4]); m23=static_cast<float>(m[5]);
m31=static_cast<float>(m[6]); m32=static_cast<float>(m[7]); m33=static_cast<float>(m[8]);
}
inline float getError (int point_idx) const override {
const int smpl = 4*point_idx;
const float x1 = points[smpl], y1 = points[smpl+1], x2 = points[smpl+2], y2 = points[smpl+3];
const float est_z2 = 1 / (m31 * x1 + m32 * y1 + m33),
dx2 = x2 - (m11 * x1 + m12 * y1 + m13) * est_z2,
dy2 = y2 - (m21 * x1 + m22 * y1 + m23) * est_z2;
return dx2 * dx2 + dy2 * dy2;
}
const std::vector<float> &getErrors (const Mat &model) override {
setModelParameters(model);
for (int point_idx = 0; point_idx < points_mat->rows; point_idx++) {
const int smpl = 4*point_idx;
const float x1=points[smpl], y1=points[smpl+1], x2=points[smpl+2], y2=points[smpl+3];
const float est_z2 = 1 / (m31 * x1 + m32 * y1 + m33),
dx2 = x2 - (m11 * x1 + m12 * y1 + m13) * est_z2,
dy2 = y2 - (m21 * x1 + m22 * y1 + m23) * est_z2;
errors[point_idx] = dx2 * dx2 + dy2 * dy2;
}
return errors;
}
Ptr<Error> clone () const override {
return makePtr<ReprojectionErrorForwardImpl>(*points_mat);
}
};
Ptr<ReprojectionErrorForward>
ReprojectionErrorForward::create(const Mat &points) {
return makePtr<ReprojectionErrorForwardImpl>(points);
}
class SampsonErrorImpl : public SampsonError {
private:
const Mat * points_mat;
const float * const points;
float m11, m12, m13, m21, m22, m23, m31, m32, m33;
std::vector<float> errors;
public:
explicit SampsonErrorImpl (const Mat &points_)
: points_mat(&points_), points ((float *) points_.data)
, m11(0), m12(0), m13(0), m21(0), m22(0), m23(0), m31(0), m32(0), m33(0)
, errors(points_.rows)
{
CV_DbgAssert(points);
}
inline void setModelParameters(const Mat& model) override
{
CV_Assert(!model.empty());
CV_CheckTypeEQ(model.depth(), CV_64F, "");
const auto * const m = (double *) model.data;
m11=static_cast<float>(m[0]); m12=static_cast<float>(m[1]); m13=static_cast<float>(m[2]);
m21=static_cast<float>(m[3]); m22=static_cast<float>(m[4]); m23=static_cast<float>(m[5]);
m31=static_cast<float>(m[6]); m32=static_cast<float>(m[7]); m33=static_cast<float>(m[8]);
}
/*
* (pt2^t * F * pt1)^2)
* Sampson error = ------------------------------------------------------------------------
* (((F⋅pt1)(0))^2 + ((F⋅pt1)(1))^2 + ((F^t⋅pt2)(0))^2 + ((F^t⋅pt2)(1))^2)
*
* [ x2 y2 1 ] * [ F(1,1) F(1,2) F(1,3) ] [ x1 ]
* [ F(2,1) F(2,2) F(2,3) ] * [ y1 ]
* [ F(3,1) F(3,2) F(3,3) ] [ 1 ]
*
*/
inline float getError (int point_idx) const override {
const int smpl = 4*point_idx;
const float x1=points[smpl], y1=points[smpl+1], x2=points[smpl+2], y2=points[smpl+3];
const float F_pt1_x = m11 * x1 + m12 * y1 + m13,
F_pt1_y = m21 * x1 + m22 * y1 + m23;
const float pt2_F_x = x2 * m11 + y2 * m21 + m31,
pt2_F_y = x2 * m12 + y2 * m22 + m32;
const float pt2_F_pt1 = x2 * F_pt1_x + y2 * F_pt1_y + m31 * x1 + m32 * y1 + m33;
return pt2_F_pt1 * pt2_F_pt1 / (F_pt1_x * F_pt1_x + F_pt1_y * F_pt1_y +
pt2_F_x * pt2_F_x + pt2_F_y * pt2_F_y);
}
const std::vector<float> &getErrors (const Mat &model) override {
setModelParameters(model);
for (int point_idx = 0; point_idx < points_mat->rows; point_idx++) {
const int smpl = 4*point_idx;
const float x1=points[smpl], y1=points[smpl+1], x2=points[smpl+2], y2=points[smpl+3];
const float F_pt1_x = m11 * x1 + m12 * y1 + m13,
F_pt1_y = m21 * x1 + m22 * y1 + m23;
const float pt2_F_x = x2 * m11 + y2 * m21 + m31,
pt2_F_y = x2 * m12 + y2 * m22 + m32;
const float pt2_F_pt1 = x2 * F_pt1_x + y2 * F_pt1_y + m31 * x1 + m32 * y1 + m33;
errors[point_idx] = pt2_F_pt1 * pt2_F_pt1 / (F_pt1_x * F_pt1_x + F_pt1_y * F_pt1_y +
pt2_F_x * pt2_F_x + pt2_F_y * pt2_F_y);
}
return errors;
}
Ptr<Error> clone () const override {
return makePtr<SampsonErrorImpl>(*points_mat);
}
};
Ptr<SampsonError>
SampsonError::create(const Mat &points) {
return makePtr<SampsonErrorImpl>(points);
}
class SymmetricGeometricDistanceImpl : public SymmetricGeometricDistance {
private:
const Mat * points_mat;
const float * const points;
float m11, m12, m13, m21, m22, m23, m31, m32, m33;
std::vector<float> errors;
public:
explicit SymmetricGeometricDistanceImpl (const Mat &points_)
: points_mat(&points_), points ((float *) points_.data)
, m11(0), m12(0), m13(0), m21(0), m22(0), m23(0), m31(0), m32(0), m33(0)
, errors(points_.rows)
{
CV_DbgAssert(points);
}
inline void setModelParameters(const Mat& model) override
{
CV_Assert(!model.empty());
CV_CheckTypeEQ(model.depth(), CV_64F, "");
const auto * const m = (double *) model.data;
m11=static_cast<float>(m[0]); m12=static_cast<float>(m[1]); m13=static_cast<float>(m[2]);
m21=static_cast<float>(m[3]); m22=static_cast<float>(m[4]); m23=static_cast<float>(m[5]);
m31=static_cast<float>(m[6]); m32=static_cast<float>(m[7]); m33=static_cast<float>(m[8]);
}
inline float getError (int point_idx) const override {
const int smpl = 4*point_idx;
const float x1=points[smpl], y1=points[smpl+1], x2=points[smpl+2], y2=points[smpl+3];
// pt2^T * E, line 1 = [l1 l2]
const float l1 = x2 * m11 + y2 * m21 + m31,
l2 = x2 * m12 + y2 * m22 + m32;
// E * pt1, line 2 = [t1 t2]
const float t1 = m11 * x1 + m12 * y1 + m13,
t2 = m21 * x1 + m22 * y1 + m23;
float p2Ep1 = l1 * x1 + l2 * y1 + x2 * m13 + y2 * m23 + m33;
p2Ep1 *= p2Ep1;
return p2Ep1 / (l1 * l1 + l2 * l2) // distance from pt1 to line 1
+
p2Ep1 / (t1 * t1 + t2 * t2); // distance from pt2 to line 2
}
const std::vector<float> &getErrors (const Mat &model) override {
setModelParameters(model);
for (int point_idx = 0; point_idx < points_mat->rows; point_idx++) {
const int smpl = 4*point_idx;
const float x1=points[smpl], y1=points[smpl+1], x2=points[smpl+2], y2=points[smpl+3];
const float l1 = x2 * m11 + y2 * m21 + m31, t1 = m11 * x1 + m12 * y1 + m13,
l2 = x2 * m12 + y2 * m22 + m32, t2 = m21 * x1 + m22 * y1 + m23;
float p2Ep1 = l1 * x1 + l2 * y1 + x2 * m13 + y2 * m23 + m33;
p2Ep1 *= p2Ep1;
errors[point_idx] = p2Ep1 / (l1 * l1 + l2 * l2) + p2Ep1 / (t1 * t1 + t2 * t2);
}
return errors;
}
Ptr<Error> clone () const override {
return makePtr<SymmetricGeometricDistanceImpl>(*points_mat);
}
};
Ptr<SymmetricGeometricDistance>
SymmetricGeometricDistance::create(const Mat &points) {
return makePtr<SymmetricGeometricDistanceImpl>(points);
}
class ReprojectionErrorPmatrixImpl : public ReprojectionErrorPmatrix {
private:
const Mat * points_mat;
const float * const points;
float p11, p12, p13, p14, p21, p22, p23, p24, p31, p32, p33, p34;
std::vector<float> errors;
public:
explicit ReprojectionErrorPmatrixImpl (const Mat &points_)
: points_mat(&points_), points ((float *) points_.data)
, p11(0), p12(0), p13(0), p14(0), p21(0), p22(0), p23(0), p24(0), p31(0), p32(0), p33(0), p34(0)
, errors(points_.rows)
{
CV_DbgAssert(points);
}
inline void setModelParameters (const Mat& model) override
{
CV_Assert(!model.empty());
CV_CheckTypeEQ(model.depth(), CV_64F, "");
const auto * const p = (double *) model.data;
p11 = (float)p[0]; p12 = (float)p[1]; p13 = (float)p[2]; p14 = (float)p[3];
p21 = (float)p[4]; p22 = (float)p[5]; p23 = (float)p[6]; p24 = (float)p[7];
p31 = (float)p[8]; p32 = (float)p[9]; p33 = (float)p[10]; p34 = (float)p[11];
}
inline float getError (int point_idx) const override {
const int smpl = 5*point_idx;
const float u = points[smpl ], v = points[smpl+1],
x = points[smpl+2], y = points[smpl+3], z = points[smpl+4];
const float depth = 1 / (p31 * x + p32 * y + p33 * z + p34);
const float du = u - (p11 * x + p12 * y + p13 * z + p14) * depth;
const float dv = v - (p21 * x + p22 * y + p23 * z + p24) * depth;
return du * du + dv * dv;
}
const std::vector<float> &getErrors (const Mat &model) override {
setModelParameters(model);
for (int point_idx = 0; point_idx < points_mat->rows; point_idx++) {
const int smpl = 5*point_idx;
const float u = points[smpl ], v = points[smpl+1],
x = points[smpl+2], y = points[smpl+3], z = points[smpl+4];
const float depth = 1 / (p31 * x + p32 * y + p33 * z + p34);
const float du = u - (p11 * x + p12 * y + p13 * z + p14) * depth;
const float dv = v - (p21 * x + p22 * y + p23 * z + p24) * depth;
errors[point_idx] = du * du + dv * dv;
}
return errors;
}
Ptr<Error> clone () const override {
return makePtr<ReprojectionErrorPmatrixImpl>(*points_mat);
}
};
Ptr<ReprojectionErrorPmatrix> ReprojectionErrorPmatrix::create(const Mat &points) {
return makePtr<ReprojectionErrorPmatrixImpl>(points);
}
///////////////////////////////////////////////////////////////////////////////////////////////////
// Computes forward reprojection error for affine transformation.
class ReprojectionDistanceAffineImpl : public ReprojectionErrorAffine {
private:
/*
* m11 m12 m13
* m21 m22 m23
* 0 0 1
*/
const Mat * points_mat;
const float * const points;
float m11, m12, m13, m21, m22, m23;
std::vector<float> errors;
public:
explicit ReprojectionDistanceAffineImpl (const Mat &points_)
: points_mat(&points_), points ((float *) points_.data)
, m11(0), m12(0), m13(0), m21(0), m22(0), m23(0)
, errors(points_.rows)
{
CV_DbgAssert(points);
}
inline void setModelParameters(const Mat& model) override
{
CV_Assert(!model.empty());
CV_CheckTypeEQ(model.depth(), CV_64F, "");
const auto * const m = (double *) model.data;
m11 = (float)m[0]; m12 = (float)m[1]; m13 = (float)m[2];
m21 = (float)m[3]; m22 = (float)m[4]; m23 = (float)m[5];
}
inline float getError (int point_idx) const override {
const int smpl = 4*point_idx;
const float x1=points[smpl], y1=points[smpl+1], x2=points[smpl+2], y2=points[smpl+3];
const float dx2 = x2 - (m11 * x1 + m12 * y1 + m13), dy2 = y2 - (m21 * x1 + m22 * y1 + m23);
return dx2 * dx2 + dy2 * dy2;
}
const std::vector<float> &getErrors (const Mat &model) override {
setModelParameters(model);
for (int point_idx = 0; point_idx < points_mat->rows; point_idx++) {
const int smpl = 4*point_idx;
const float x1=points[smpl], y1=points[smpl+1], x2=points[smpl+2], y2=points[smpl+3];
const float dx2 = x2 - (m11 * x1 + m12 * y1 + m13), dy2 = y2 - (m21 * x1 + m22 * y1 + m23);
errors[point_idx] = dx2 * dx2 + dy2 * dy2;
}
return errors;
}
Ptr<Error> clone () const override {
return makePtr<ReprojectionDistanceAffineImpl>(*points_mat);
}
};
Ptr<ReprojectionErrorAffine>
ReprojectionErrorAffine::create(const Mat &points) {
return makePtr<ReprojectionDistanceAffineImpl>(points);
}
////////////////////////////////////// NORMALIZING TRANSFORMATION /////////////////////////
class NormTransformImpl : public NormTransform {
private:
const float * const points;
public:
explicit NormTransformImpl (const Mat &points_) : points((float*)points_.data) {}
// Compute normalized points and transformation matrices.
void getNormTransformation (Mat& norm_points, const std::vector<int> &sample,
int sample_size, Matx33d &T1, Matx33d &T2) const override {
double mean_pts1_x = 0, mean_pts1_y = 0, mean_pts2_x = 0, mean_pts2_y = 0;
// find average of each coordinate of points.
int smpl;
for (int i = 0; i < sample_size; i++) {
smpl = 4 * sample[i];
mean_pts1_x += points[smpl ];
mean_pts1_y += points[smpl + 1];
mean_pts2_x += points[smpl + 2];
mean_pts2_y += points[smpl + 3];
}
mean_pts1_x /= sample_size; mean_pts1_y /= sample_size;
mean_pts2_x /= sample_size; mean_pts2_y /= sample_size;
double avg_dist1 = 0, avg_dist2 = 0, x1_m, y1_m, x2_m, y2_m;
for (int i = 0; i < sample_size; i++) {
smpl = 4 * sample[i];
/*
* Compute a similarity transform T that takes points xi
* to a new set of points x̃i such that the centroid of
* the points x̃i is the coordinate origin and their
* average distance from the origin is √2
*
* sqrt(x̃*x̃ + ỹ*ỹ) = sqrt(2)
* ax*ax + by*by = 2
*/
x1_m = points[smpl ] - mean_pts1_x;
y1_m = points[smpl + 1] - mean_pts1_y;
x2_m = points[smpl + 2] - mean_pts2_x;
y2_m = points[smpl + 3] - mean_pts2_y;
avg_dist1 += sqrt (x1_m * x1_m + y1_m * y1_m);
avg_dist2 += sqrt (x2_m * x2_m + y2_m * y2_m);
}
// scale
avg_dist1 = M_SQRT2 / (avg_dist1 / sample_size);
avg_dist2 = M_SQRT2 / (avg_dist2 / sample_size);
const double transl_x1 = -mean_pts1_x * avg_dist1, transl_y1 = -mean_pts1_y * avg_dist1;
const double transl_x2 = -mean_pts2_x * avg_dist2, transl_y2 = -mean_pts2_y * avg_dist2;
// transformation matrices
T1 = Matx33d (avg_dist1, 0, transl_x1,0, avg_dist1, transl_y1,0, 0, 1);
T2 = Matx33d (avg_dist2, 0, transl_x2,0, avg_dist2, transl_y2,0, 0, 1);
norm_points = Mat_<float>(sample_size, 4); // normalized points Nx4 matrix
auto * norm_points_ptr = (float *) norm_points.data;
// Normalize points: Npts = T*pts 3x3 * 3xN
const float avg_dist1f = (float)avg_dist1, avg_dist2f = (float)avg_dist2;
const float transl_x1f = (float)transl_x1, transl_y1f = (float)transl_y1;
const float transl_x2f = (float)transl_x2, transl_y2f = (float)transl_y2;
for (int i = 0; i < sample_size; i++) {
smpl = 4 * sample[i];
(*norm_points_ptr++) = avg_dist1f * points[smpl ] + transl_x1f;
(*norm_points_ptr++) = avg_dist1f * points[smpl + 1] + transl_y1f;
(*norm_points_ptr++) = avg_dist2f * points[smpl + 2] + transl_x2f;
(*norm_points_ptr++) = avg_dist2f * points[smpl + 3] + transl_y2f;
}
}
};
Ptr<NormTransform> NormTransform::create (const Mat &points) {
return makePtr<NormTransformImpl>(points);
}
}}