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[RHO] Initial commit of RHO algorithm for rapid homography estimation.

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Implements the RHO algorithm as presented in

Paper: Bilaniuk, Olexa, Hamid Bazargani, and Robert Laganiere. "Fast
Target Recognition on Mobile Devices: Revisiting Gaussian Elimination
for the Estimation of Planar Homographies." In Computer Vision and
Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on, pp.
119-125. IEEE, 2014.

- Complete, heavily documented reference C implementation, as well as
temporarily disabled dirty SSE2 port.
- Enabled tests for RHO in test_homography; Currently these fail
presumably due to too-stringent accuracy requirements.
- Refinement and final refinement are not yet functional; Do not pass
their corresponding flags to RHO.
pull/3670/head
Olexa Bilaniuk 10 years ago
parent 32348604e3
commit 68e59d6154
7 changed files with 4329 additions and 15 deletions
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  1. 3
      modules/calib3d/include/opencv2/calib3d.hpp
  2. 77
      modules/calib3d/src/fundam.cpp
  3. 2042
      modules/calib3d/src/rhorefc.cpp
  4. 368
      modules/calib3d/src/rhorefc.h
  5. 1497
      modules/calib3d/src/rhosse2.cpp
  6. 331
      modules/calib3d/src/rhosse2.h
  7. 26
      modules/calib3d/test/test_homography.cpp

3
modules/calib3d/include/opencv2/calib3d.hpp
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@ -53,7 +53,8 @@ namespace cv
//! type of the robust estimation algorithm
enum { LMEDS = 4, //!< least-median algorithm
RANSAC = 8 //!< RANSAC algorithm
RANSAC = 8, //!< RANSAC algorithm
RHO = 16 //!< RHO algorithm
};
enum { SOLVEPNP_ITERATIVE = 0,

77
modules/calib3d/src/fundam.cpp
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@ -41,6 +41,10 @@
//M*/
#include "precomp.hpp"
#include "rhorefc.h"
#if CV_SSE2
#include "rhosse2.h"
#endif
#include <iostream>
namespace cv
@ -273,6 +277,73 @@ public:
}
namespace cv{
static bool createAndRunRHORegistrator(double confidence, int maxIters, double ransacReprojThreshold, int npoints, InputArray _src, InputArray _dst, OutputArray _H, OutputArray _tempMask){
Mat src = _src.getMat();
Mat dst = _dst.getMat();
Mat tempMask = _tempMask.getMat();
bool result;
/* Run RHO. Needs cleanup or separate function to invoke. */
Mat tmpH = Mat(3, 3, CV_32FC1);
tempMask = Mat(npoints, 1, CV_8U);
double beta = 0.35;/* 0.35 is a value that often works. */
#if CV_SSE2 && 0
if(useOptimized()){
RHO_HEST_SSE2 p;
rhoSSE2Init(&p);
rhoSSE2EnsureCapacity(&p, npoints, beta);
result = !!rhoSSE2(&p,
(const float*)src.data,
(const float*)dst.data,
(char*) tempMask.data,
npoints,
ransacReprojThreshold,
maxIters,
maxIters,
confidence,
4,
beta,
RHO_FLAG_ENABLE_NR,
NULL,
(float*)tmpH.data);
rhoSSE2Fini(&p);
}else
#endif
{
RHO_HEST_REFC p;
rhoRefCInit(&p);
rhoRefCEnsureCapacity(&p, npoints, beta);
result = !!rhoRefC(&p,
(const float*)src.data,
(const float*)dst.data,
(char*) tempMask.data,
npoints,
ransacReprojThreshold,
maxIters,
maxIters,
confidence,
4,
beta,
RHO_FLAG_ENABLE_NR,
NULL,
(float*)tmpH.data);
rhoRefCFini(&p);
}
tmpH.convertTo(_H, CV_64FC1);
/* Maps non-zero maks elems to 1, for the sake of the testcase. */
for(int k=0;k<npoints;k++){
tempMask.data[k] = !!tempMask.data[k];
}
return result;
}
}
cv::Mat cv::findHomography( InputArray _points1, InputArray _points2,
int method, double ransacReprojThreshold, OutputArray _mask,
const int maxIters, const double confidence)
@ -317,10 +388,12 @@ cv::Mat cv::findHomography( InputArray _points1, InputArray _points2,
result = createRANSACPointSetRegistrator(cb, 4, ransacReprojThreshold, confidence, maxIters)->run(src, dst, H, tempMask);
else if( method == LMEDS )
result = createLMeDSPointSetRegistrator(cb, 4, confidence, maxIters)->run(src, dst, H, tempMask);
else
else if( method == RHO ){
result = createAndRunRHORegistrator(confidence, maxIters, ransacReprojThreshold, npoints, src, dst, H, tempMask);
}else
CV_Error(Error::StsBadArg, "Unknown estimation method");
if( result && npoints > 4 )
if( result && npoints > 4 && method != RHO)
{
compressPoints( src.ptr<Point2f>(), tempMask.ptr<uchar>(), 1, npoints );
npoints = compressPoints( dst.ptr<Point2f>(), tempMask.ptr<uchar>(), 1, npoints );

2042
modules/calib3d/src/rhorefc.cpp
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368
modules/calib3d/src/rhorefc.h
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@ -0,0 +1,368 @@
/*
IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
By downloading, copying, installing or using the software you agree to this license.
If you do not agree to this license, do not download, install,
copy or use the software.
BSD 3-Clause License
Copyright (C) 2014, Olexa Bilaniuk, Hamid Bazargani & Robert Laganiere, all rights reserved.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
* Redistribution's of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
* Redistribution's 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.
* The name of the copyright holders may not be used to endorse or promote products
derived from this software without specific prior written permission.
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 Intel Corporation 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.
*/
/**
* Bilaniuk, Olexa, Hamid Bazargani, and Robert Laganiere. "Fast Target
* Recognition on Mobile Devices: Revisiting Gaussian Elimination for the
* Estimation of Planar Homographies." In Computer Vision and Pattern
* Recognition Workshops (CVPRW), 2014 IEEE Conference on, pp. 119-125.
* IEEE, 2014.
*/
/* Include Guards */
#ifndef __RHOREFC_H__
#define __RHOREFC_H__
/* Includes */
/* Defines */
#ifdef __cplusplus
/* C++ does not have the restrict keyword. */
#ifdef restrict
#undef restrict
#endif
#define restrict
#else
/* C99 and over has the restrict keyword. */
#if !defined(__STDC_VERSION__) || __STDC_VERSION__ < 199901L
#define restrict
#endif
#endif
/* Flags */
#ifndef RHO_FLAG_NONE
#define RHO_FLAG_NONE (0U<<0)
#endif
#ifndef RHO_FLAG_ENABLE_NR
#define RHO_FLAG_ENABLE_NR (1U<<0)
#endif
#ifndef RHO_FLAG_ENABLE_REFINEMENT
#define RHO_FLAG_ENABLE_REFINEMENT (1U<<1)
#endif
#ifndef RHO_FLAG_ENABLE_FINAL_REFINEMENT
#define RHO_FLAG_ENABLE_FINAL_REFINEMENT (1U<<2)
#endif
/* Data structures */
/**
* Homography Estimation context.
*/
typedef struct{
/**
* Virtual Arguments.
*
* Exactly the same as at function call, except:
* - minInl is enforced to be >= 4.
*/
struct{
const float* restrict src;
const float* restrict dst;
char* restrict inl;
unsigned N;
float maxD;
unsigned maxI;
unsigned rConvg;
double cfd;
unsigned minInl;
double beta;
unsigned flags;
const float* guessH;
float* finalH;
} arg;
/* PROSAC Control */
struct{
unsigned i; /* Iteration Number */
unsigned phNum; /* Phase Number */
unsigned phEndI; /* Phase End Iteration */
double phEndFpI; /* Phase floating-point End Iteration */
unsigned phMax; /* Termination phase number */
unsigned phNumInl; /* Number of inliers for termination phase */
unsigned numModels; /* Number of models tested */
unsigned* restrict smpl; /* Sample of match indexes */
} ctrl;
/* Current model being tested */
struct{
float* restrict pkdPts; /* Packed points */
float* restrict H; /* Homography */
char* restrict inl; /* Mask of inliers */
unsigned numInl; /* Number of inliers */
} curr;
/* Best model (so far) */
struct{
float* restrict H; /* Homography */
char* restrict inl; /* Mask of inliers */
unsigned numInl; /* Number of inliers */
} best;
/* Non-randomness criterion */
struct{
unsigned* restrict tbl; /* Non-Randomness: Table */
unsigned size; /* Non-Randomness: Size */
double beta; /* Non-Randomness: Beta */
} nr;
/* SPRT Evaluator */
struct{
double t_M; /* t_M */
double m_S; /* m_S */
double epsilon; /* Epsilon */
double delta; /* delta */
double A; /* SPRT Threshold */
unsigned Ntested; /* Number of points tested */
unsigned Ntestedtotal; /* Number of points tested in total */
int good; /* Good/bad flag */
double lambdaAccept; /* Accept multiplier */
double lambdaReject; /* Reject multiplier */
} eval;
/* Levenberg-Marquardt Refinement */
struct{
float* ws; /* Levenberg-Marqhard Workspace */
float (* restrict JtJ)[8]; /* JtJ matrix */
float (* restrict tmp1)[8]; /* Temporary 1 */
float (* restrict tmp2)[8]; /* Temporary 2 */
float* restrict Jte; /* Jte vector */
} lm;
} RHO_HEST_REFC;
/* Extern C */
#ifdef __cplusplus
namespace cv{
/* extern "C" { */
#endif
/* Functions */
/**
* Initialize the estimator context, by allocating the aligned buffers
* internally needed.
*
* @param [in/out] p The uninitialized estimator context to initialize.
* @return 0 if successful; non-zero if an error occured.
*/
int rhoRefCInit(RHO_HEST_REFC* p);
/**
* Ensure that the estimator context's internal table for non-randomness
* criterion is at least of the given size, and uses the given beta. The table
* should be larger than the maximum number of matches fed into the estimator.
*
* A value of N of 0 requests deallocation of the table.
*
* @param [in] p The initialized estimator context
* @param [in] N If 0, deallocate internal table. If > 0, ensure that the
* internal table is of at least this size, reallocating if
* necessary.
* @param [in] beta The beta-factor to use within the table.
* @return 0 if successful; non-zero if an error occured.
*/
int rhoRefCEnsureCapacity(RHO_HEST_REFC* p, unsigned N, double beta);
/**
* Finalize the estimator context, by freeing the aligned buffers used
* internally.
*
* @param [in] p The initialized estimator context to finalize.
*/
void rhoRefCFini(RHO_HEST_REFC* p);
/**
* Estimates the homography using the given context, matches and parameters to
* PROSAC.
*
* The given context must have been initialized.
*
* The matches are provided as two arrays of N single-precision, floating-point
* (x,y) points. Points with corresponding offsets in the two arrays constitute
* a match. The homography estimation attempts to find the 3x3 matrix H which
* best maps the homogeneous-coordinate points in the source array to their
* corresponding homogeneous-coordinate points in the destination array.
*
* Note: At least 4 matches must be provided (N >= 4).
* Note: A point in either array takes up 2 floats. The first of two stores
* the x-coordinate and the second of the two stores the y-coordinate.
* Thus, the arrays resemble this in memory:
*
* src = [x0, y0, x1, y1, x2, y2, x3, y3, x4, y4, ...]
* Matches: | | | | |
* dst = [x0, y0, x1, y1, x2, y2, x3, y3, x4, y4, ...]
* Note: The matches are expected to be provided sorted by quality, or at
* least not be worse-than-random in ordering.
*
* A pointer to the base of an array of N bytes can be provided. It serves as
* an output mask to indicate whether the corresponding match is an inlier to
* the returned homography, if any. A zero indicates an outlier; A non-zero
* value indicates an inlier.
*
* The PROSAC estimator requires a few parameters of its own. These are:
*
* - The maximum distance that a source point projected onto the destination
* plane can be from its putative match and still be considered an
* inlier. Must be non-negative.
* A sane default is 3.0.
* - The maximum number of PROSAC iterations. This corresponds to the
* largest number of samples that will be drawn and tested.
* A sane default is 2000.
* - The RANSAC convergence parameter. This corresponds to the number of
* iterations after which PROSAC will start sampling like RANSAC.
* A sane default is 2000.
* - The confidence threshold. This corresponds to the probability of
* finding a correct solution. Must be bounded by [0, 1].
* A sane default is 0.995.
* - The minimum number of inliers acceptable. Only a solution with at
* least this many inliers will be returned. The minimum is 4.
* A sane default is 10% of N.
* - The beta-parameter for the non-randomness termination criterion.
* Ignored if non-randomness criterion disabled, otherwise must be
* bounded by (0, 1).
* A sane default is 0.35.
* - Optional flags to control the estimation. Available flags are:
* HEST_FLAG_NONE:
* No special processing.
* HEST_FLAG_ENABLE_NR:
* Enable non-randomness criterion. If set, the beta parameter
* must also be set.
* HEST_FLAG_ENABLE_REFINEMENT:
* Enable refinement of each new best model, as they are found.
* HEST_FLAG_ENABLE_FINAL_REFINEMENT:
* Enable one final refinement of the best model found before
* returning it.
*
* The PROSAC estimator optionally accepts an extrinsic initial guess of H.
*
* The PROSAC estimator outputs a final estimate of H provided it was able to
* find one with a minimum of supporting inliers. If it was not, it outputs
* the all-zero matrix.
*
* The extrinsic guess at and final estimate of H are both in the same form:
* A 3x3 single-precision floating-point matrix with step 3. Thus, it is a
* 9-element array of floats, with the elements as follows:
*
* [ H00, H01, H02,
* H10, H11, H12,
* H20, H21, 1.0 ]
*
* Notice that the homography is normalized to H22 = 1.0.
*
* The function returns the number of inliers if it was able to find a
* homography with at least the minimum required support, and 0 if it was not.
*
*
* @param [in/out] p The context to use for homography estimation. Must
* be already initialized. Cannot be NULL.
* @param [in] src The pointer to the source points of the matches.
* Must be aligned to 4 bytes. Cannot be NULL.
* @param [in] dst The pointer to the destination points of the matches.
* Must be aligned to 4 bytes. Cannot be NULL.
* @param [out] inl The pointer to the output mask of inlier matches.
* Must be aligned to 4 bytes. May be NULL.
* @param [in] N The number of matches. Minimum 4.
* @param [in] maxD The maximum distance. Minimum 0.
* @param [in] maxI The maximum number of PROSAC iterations.
* @param [in] rConvg The RANSAC convergence parameter.
* @param [in] cfd The required confidence in the solution.
* @param [in] minInl The minimum required number of inliers. Minimum 4.
* @param [in] beta The beta-parameter for the non-randomness criterion.
* @param [in] flags A union of flags to fine-tune the estimation.
* @param [in] guessH An extrinsic guess at the solution H, or NULL if
* none provided.
* @param [out] finalH The final estimation of H, or the zero matrix if
* the minimum number of inliers was not met.
* Cannot be NULL.
* @return The number of inliers if the minimum number of
* inliers for acceptance was reached; 0 otherwise.
*/
unsigned rhoRefC(RHO_HEST_REFC* restrict p, /* Homography estimation context. */
const float* restrict src, /* Source points */
const float* restrict dst, /* Destination points */
char* restrict inl, /* Inlier mask */
unsigned N, /* = src.length = dst.length = inl.length */
float maxD, /* 3.0 */
unsigned maxI, /* 2000 */
unsigned rConvg, /* 2000 */
double cfd, /* 0.995 */
unsigned minInl, /* 4 */
double beta, /* 0.35 */
unsigned flags, /* 0 */
const float* guessH, /* Extrinsic guess, NULL if none provided */
float* finalH); /* Final result. */
/* Extern C */
#ifdef __cplusplus
/* } *//* End extern "C" */
}
#endif
#endif

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modules/calib3d/src/rhosse2.cpp
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modules/calib3d/src/rhosse2.h
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/*
IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
By downloading, copying, installing or using the software you agree to this license.
If you do not agree to this license, do not download, install,
copy or use the software.
BSD 3-Clause License
Copyright (C) 2014, Olexa Bilaniuk, Hamid Bazargani & Robert Laganiere, all rights reserved.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
* Redistribution's of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
* Redistribution's 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.
* The name of the copyright holders may not be used to endorse or promote products
derived from this software without specific prior written permission.
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 Intel Corporation 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.
*/
/**
* Bilaniuk, Olexa, Hamid Bazargani, and Robert Laganiere. "Fast Target
* Recognition on Mobile Devices: Revisiting Gaussian Elimination for the
* Estimation of Planar Homographies." In Computer Vision and Pattern
* Recognition Workshops (CVPRW), 2014 IEEE Conference on, pp. 119-125.
* IEEE, 2014.
*/
/* Include Guards */
#ifndef __RHOSSE2_H__
#define __RHOSSE2_H__
/* Includes */
/* Defines */
#ifdef __cplusplus
/* C++ does not have the restrict keyword. */
#ifdef restrict
#undef restrict
#endif
#define restrict
#else
/* C99 and over has the restrict keyword. */
#if !defined(__STDC_VERSION__) || __STDC_VERSION__ < 199901L
#define restrict
#endif
#endif
/* Flags */
#ifndef RHO_FLAG_NONE
#define RHO_FLAG_NONE (0U<<0)
#endif
#ifndef RHO_FLAG_ENABLE_NR
#define RHO_FLAG_ENABLE_NR (1U<<0)
#endif
#ifndef RHO_FLAG_ENABLE_REFINEMENT
#define RHO_FLAG_ENABLE_REFINEMENT (1U<<1)
#endif
#ifndef RHO_FLAG_ENABLE_FINAL_REFINEMENT
#define RHO_FLAG_ENABLE_FINAL_REFINEMENT (1U<<2)
#endif
/* Data structures */
/**
* Homography Estimation context.
*/
typedef struct{
/* Virtual Arguments */
const float* restrict src;
const float* restrict dst;
int allocBestInl;
char* restrict bestInl;
unsigned N;
float maxD;
unsigned maxI;
unsigned rConvg;
double cfd;
unsigned minInl;
double beta;
unsigned flags;
const float* guessH;
float* finalH;
/* PROSAC */
unsigned i; /* Iteration Number */
unsigned phNum; /* Phase Number */
unsigned phEndI; /* Phase End Iteration */
double phEndFpI; /* Phase floating-point End Iteration */
unsigned phMax; /* Termination phase number */
unsigned phNumInl; /* Number of inliers for termination phase */
unsigned bestNumInl; /* Best number of inliers */
unsigned numInl; /* Current number of inliers */
unsigned numModels; /* Number of models tested */
unsigned* restrict smpl; /* Sample */
float* restrict H; /* Current homography */
float* restrict bestH; /* Best homography */
float* restrict pkdPts; /* Packed points */
char* restrict inl; /* Inliers to current model */
unsigned* restrict nrTBL; /* Non-Randomness: Table */
unsigned nrSize; /* Non-Randomness: Size */
double nrBeta; /* Non-Randomness: Beta */
/* SPRT */
double t_M; /* t_M */
double m_S; /* m_S */
double epsilon; /* Epsilon */
double delta; /* delta */
double A; /* SPRT Threshold */
unsigned Ntested; /* Number of points tested */
unsigned Ntestedtotal; /* Number of points tested in total */
int good; /* Good/bad flag */
double lambdaAccept; /* Accept multiplier */
double lambdaReject; /* Reject multiplier */
double lambdaTBL[16]; /* Multiplier LUT */
} RHO_HEST_SSE2;
/* Extern C */
#ifdef __cplusplus
namespace cv{
//extern "C" {
#endif
/* Functions */
/**
* Initialize the estimator context, by allocating the aligned buffers
* internally needed.
*
* @param [in/out] p The uninitialized estimator context to initialize.
* @return 0 if successful; non-zero if an error occured.
*/
int rhoSSE2Init(RHO_HEST_SSE2* p);
/**
* Ensure that the estimator context's internal table for non-randomness
* criterion is at least of the given size, and uses the given beta. The table
* should be larger than the maximum number of matches fed into the estimator.
*
* A value of N of 0 requests deallocation of the table.
*
* @param [in] p The initialized estimator context
* @param [in] N If 0, deallocate internal table. If > 0, ensure that the
* internal table is of at least this size, reallocating if
* necessary.
* @param [in] beta The beta-factor to use within the table.
* @return 0 if successful; non-zero if an error occured.
*/
int rhoSSE2EnsureCapacity(RHO_HEST_SSE2* p, unsigned N, double beta);
/**
* Finalize the estimator context, by freeing the aligned buffers used
* internally.
*
* @param [in] p The initialized estimator context to finalize.
*/
void rhoSSE2Fini(RHO_HEST_SSE2* p);
/**
* Estimates the homography using the given context, matches and parameters to
* PROSAC.
*
* The given context must have been initialized.
*
* The matches are provided as two arrays of N single-precision, floating-point
* (x,y) points. Points with corresponding offsets in the two arrays constitute
* a match. The homography estimation attempts to find the 3x3 matrix H which
* best maps the homogeneous-coordinate points in the source array to their
* corresponding homogeneous-coordinate points in the destination array.
*
* Note: At least 4 matches must be provided (N >= 4).
* Note: A point in either array takes up 2 floats. The first of two stores
* the x-coordinate and the second of the two stores the y-coordinate.
* Thus, the arrays resemble this in memory:
*
* src = [x0, y0, x1, y1, x2, y2, x3, y3, x4, y4, ...]
* Matches: | | | | |
* dst = [x0, y0, x1, y1, x2, y2, x3, y3, x4, y4, ...]
* Note: The matches are expected to be provided sorted by quality, or at
* least not be worse-than-random in ordering.
*
* A pointer to the base of an array of N bytes can be provided. It serves as
* an output mask to indicate whether the corresponding match is an inlier to
* the returned homography, if any. A zero indicates an outlier; A non-zero
* value indicates an inlier.
*
* The PROSAC estimator requires a few parameters of its own. These are:
*
* - The maximum distance that a source point projected onto the destination
* plane can be from its putative match and still be considered an
* inlier. Must be non-negative.
* A sane default is 3.0.
* - The maximum number of PROSAC iterations. This corresponds to the
* largest number of samples that will be drawn and tested.
* A sane default is 2000.
* - The RANSAC convergence parameter. This corresponds to the number of
* iterations after which PROSAC will start sampling like RANSAC.
* A sane default is 2000.
* - The confidence threshold. This corresponds to the probability of
* finding a correct solution. Must be bounded by [0, 1].
* A sane default is 0.995.
* - The minimum number of inliers acceptable. Only a solution with at
* least this many inliers will be returned. The minimum is 4.
* A sane default is 10% of N.
* - The beta-parameter for the non-randomness termination criterion.
* Ignored if non-randomness criterion disabled, otherwise must be
* bounded by (0, 1).
* A sane default is 0.35.
* - Optional flags to control the estimation. Available flags are:
* HEST_FLAG_NONE:
* No special processing.
* HEST_FLAG_ENABLE_NR:
* Enable non-randomness criterion. If set, the beta parameter
* must also be set.
* HEST_FLAG_ENABLE_REFINEMENT:
* Enable refinement of each new best model, as they are found.
* HEST_FLAG_ENABLE_FINAL_REFINEMENT:
* Enable one final refinement of the best model found before
* returning it.
*
* The PROSAC estimator additionally accepts an extrinsic initial guess of H,
* and outputs a final estimate at H provided it was able to find one with a
* minimum of supporting inliers. If it was not, it outputs the all-zero
* matrix.
*
* The extrinsic guess at and final estimate of H are both in the same form:
* A 3x3 single-precision floating-point matrix with step 4. Thus, it is a
* 12-element array of floats, with the elements as follows:
*
* [ H00, H01, H02, <pad>,
* H10, H11, H12, <pad>,
* H20, H21, H22, <pad> ]
*
* The function returns the number of inliers if it was able to find a
* homography with at least the minimum required support, and 0 if it was not.
*
*
* @param [in/out] p The context to use for homography estimation. Must
* be already initialized. Cannot be NULL.
* @param [in] src The pointer to the source points of the matches.
* Must be aligned to 16 bytes. Cannot be NULL.
* @param [in] dst The pointer to the destination points of the matches.
* Must be aligned to 16 bytes. Cannot be NULL.
* @param [out] inl The pointer to the output mask of inlier matches.
* Must be aligned to 16 bytes. May be NULL.
* @param [in] N The number of matches.
* @param [in] maxD The maximum distance.
* @param [in] maxI The maximum number of PROSAC iterations.
* @param [in] rConvg The RANSAC convergence parameter.
* @param [in] cfd The required confidence in the solution.
* @param [in] minInl The minimum required number of inliers.
* @param [in] beta The beta-parameter for the non-randomness criterion.
* @param [in] flags A union of flags to control the estimation.
* @param [in] guessH An extrinsic guess at the solution H, or NULL if
* none provided.
* @param [out] finalH The final estimation of H, or the zero matrix if
* the minimum number of inliers was not met.
* Cannot be NULL.
* @return The number of inliers if the minimum number of
* inliers for acceptance was reached; 0 otherwise.
*/
unsigned rhoSSE2(RHO_HEST_SSE2* restrict p, /* Homography estimation context. */
const float* restrict src, /* Source points */
const float* restrict dst, /* Destination points */
char* restrict bestInl, /* Inlier mask */
unsigned N, /* = src.length = dst.length = inl.length */
float maxD, /* 3.0 */
unsigned maxI, /* 2000 */
unsigned rConvg, /* 2000 */
double cfd, /* 0.995 */
unsigned minInl, /* 4 */
double beta, /* 0.35 */
unsigned flags, /* 0 */
const float* guessH, /* Extrinsic guess, NULL if none provided */
float* finalH); /* Final result. */
/* Extern C */
#ifdef __cplusplus
//}
}
#endif
#endif

26
modules/calib3d/test/test_homography.cpp
Unescape View File

@ -62,10 +62,10 @@
#define MAX_COUNT_OF_POINTS 303
#define COUNT_NORM_TYPES 3
#define METHODS_COUNT 3
#define METHODS_COUNT 4
int NORM_TYPE[COUNT_NORM_TYPES] = {cv::NORM_L1, cv::NORM_L2, cv::NORM_INF};
int METHOD[METHODS_COUNT] = {0, cv::RANSAC, cv::LMEDS};
int METHOD[METHODS_COUNT] = {0, cv::RANSAC, cv::LMEDS, cv::RHO};
using namespace cv;
using namespace std;
@ -144,7 +144,7 @@ void CV_HomographyTest::print_information_1(int j, int N, int _method, const Mat
cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
cout << "Count of points: " << N << endl; cout << endl;
cout << "Method: "; if (_method == 0) cout << 0; else if (_method == 8) cout << "RANSAC"; else cout << "LMEDS"; cout << endl;
cout << "Method: "; if (_method == 0) cout << 0; else if (_method == 8) cout << "RANSAC"; else if (_method == cv::RHO) cout << "RHO"; else cout << "LMEDS"; cout << endl;
cout << "Homography matrix:" << endl; cout << endl;
cout << H << endl; cout << endl;
cout << "Number of rows: " << H.rows << " Number of cols: " << H.cols << endl; cout << endl;
@ -156,7 +156,7 @@ void CV_HomographyTest::print_information_2(int j, int N, int _method, const Mat
cout << "Type of srcPoints: "; if ((j>-1) && (j<2)) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>";
cout << " Type of dstPoints: "; if (j % 2 == 0) cout << "Mat of CV_32FC2"; else cout << "vector <Point2f>"; cout << endl;
cout << "Count of points: " << N << endl; cout << endl;
cout << "Method: "; if (_method == 0) cout << 0; else if (_method == 8) cout << "RANSAC"; else cout << "LMEDS"; cout << endl;
cout << "Method: "; if (_method == 0) cout << 0; else if (_method == 8) cout << "RANSAC"; else if (_method == cv::RHO) cout << "RHO"; else cout << "LMEDS"; cout << endl;
cout << "Original matrix:" << endl; cout << endl;
cout << H << endl; cout << endl;
cout << "Found matrix:" << endl; cout << endl;
@ -339,14 +339,15 @@ void CV_HomographyTest::run(int)
continue;
}
case cv::RHO:
case RANSAC:
{
cv::Mat mask [4]; double diff;
Mat H_res_64 [4] = { cv::findHomography(src_mat_2f, dst_mat_2f, RANSAC, reproj_threshold, mask[0]),
cv::findHomography(src_mat_2f, dst_vec, RANSAC, reproj_threshold, mask[1]),
cv::findHomography(src_vec, dst_mat_2f, RANSAC, reproj_threshold, mask[2]),
cv::findHomography(src_vec, dst_vec, RANSAC, reproj_threshold, mask[3]) };
Mat H_res_64 [4] = { cv::findHomography(src_mat_2f, dst_mat_2f, method, reproj_threshold, mask[0]),
cv::findHomography(src_mat_2f, dst_vec, method, reproj_threshold, mask[1]),
cv::findHomography(src_vec, dst_mat_2f, method, reproj_threshold, mask[2]),
cv::findHomography(src_vec, dst_vec, method, reproj_threshold, mask[3]) };
for (int j = 0; j < 4; ++j)
{
@ -466,14 +467,15 @@ void CV_HomographyTest::run(int)
continue;
}
case cv::RHO:
case RANSAC:
{
cv::Mat mask_res [4];
Mat H_res_64 [4] = { cv::findHomography(src_mat_2f, dst_mat_2f, RANSAC, reproj_threshold, mask_res[0]),
cv::findHomography(src_mat_2f, dst_vec, RANSAC, reproj_threshold, mask_res[1]),
cv::findHomography(src_vec, dst_mat_2f, RANSAC, reproj_threshold, mask_res[2]),
cv::findHomography(src_vec, dst_vec, RANSAC, reproj_threshold, mask_res[3]) };
Mat H_res_64 [4] = { cv::findHomography(src_mat_2f, dst_mat_2f, method, reproj_threshold, mask_res[0]),
cv::findHomography(src_mat_2f, dst_vec, method, reproj_threshold, mask_res[1]),
cv::findHomography(src_vec, dst_mat_2f, method, reproj_threshold, mask_res[2]),
cv::findHomography(src_vec, dst_vec, method, reproj_threshold, mask_res[3]) };
for (int j = 0; j < 4; ++j)
{

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