generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Merge pull request #3670 from obilaniu:master

Browse Source
This commit is contained in:
Vadim Pisarevsky 2015-03-25 10:10:04 +00:00
commit 0545aeb11b
5 changed files with 3061 additions and 34 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
modules/calib3d/include/opencv2/calib3d.hpp
View File

@ -184,7 +184,8 @@ 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,
@ -265,8 +266,9 @@ a vector\<Point2f\> .
- **0** - a regular method using all the points
- **RANSAC** - RANSAC-based robust method
- **LMEDS** - Least-Median robust method
- **RHO** - PROSAC-based robust method
@param ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier
(used in the RANSAC method only). That is, if
(used in the RANSAC and RHO methods only). That is, if
\f[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} * \texttt{srcPoints} _i) \| > \texttt{ransacReprojThreshold}\f]
then the point \f$i\f$ is considered an outlier. If srcPoints and dstPoints are measured in pixels,
it usually makes sense to set this parameter somewhere in the range of 1 to 10.
@ -289,7 +291,7 @@ pairs to compute an initial homography estimate with a simple least-squares sche
However, if not all of the point pairs ( \f$srcPoints_i\f$, \f$dstPoints_i\f$ ) fit the rigid perspective
transformation (that is, there are some outliers), this initial estimate will be poor. In this case,
you can use one of the two robust methods. Both methods, RANSAC and LMeDS , try many different
you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different
random subsets of the corresponding point pairs (of four pairs each), estimate the homography matrix
using this subset and a simple least-square algorithm, and then compute the quality/goodness of the
computed homography (which is the number of inliers for RANSAC or the median re-projection error for
@ -300,7 +302,7 @@ Regardless of the method, robust or not, the computed homography matrix is refin
inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the
re-projection error even more.
The method RANSAC can handle practically any ratio of outliers but it needs a threshold to
The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to
distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the
noise is rather small, use the default method (method=0).

84
modules/calib3d/src/fundam.cpp
View File

@ -41,6 +41,7 @@
//M*/
#include "precomp.hpp"
#include "rho.h"
#include <iostream>
namespace cv
@ -259,6 +260,85 @@ 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;
bool result;
double beta = 0.35;/* 0.35 is a value that often works. */
/* Create temporary output matrix (RHO outputs a single-precision H only). */
Mat tmpH = Mat(3, 3, CV_32FC1);
/* Create output mask. */
tempMask = Mat(npoints, 1, CV_8U);
/**
* Make use of the RHO estimator API.
*
* This is where the math happens. A homography estimation context is
* initialized, used, then finalized.
*/
Ptr<RHO_HEST> p = rhoInit();
/**
* Optional. Ideally, the context would survive across calls to
* findHomography(), but no clean way appears to exit to do so. The price
* to pay is marginally more computational work than strictly needed.
*/
rhoEnsureCapacity(p, npoints, beta);
/**
* The critical call. All parameters are heavily documented in rhorefc.h.
*
* Currently, NR (Non-Randomness criterion) and Final Refinement (with
* internal, optimized Levenberg-Marquardt method) are enabled. However,
* while refinement seems to correctly smooth jitter most of the time, when
* refinement fails it tends to make the estimate visually very much worse.
* It may be necessary to remove the refinement flags in a future commit if
* this behaviour is too problematic.
*/
result = !!rhoHest(p,
(const float*)src.data,
(const float*)dst.data,
(char*) tempMask.data,
(unsigned) npoints,
(float) ransacReprojThreshold,
(unsigned) maxIters,
(unsigned) maxIters,
confidence,
4U,
beta,
RHO_FLAG_ENABLE_NR | RHO_FLAG_ENABLE_FINAL_REFINEMENT,
NULL,
(float*)tmpH.data);
/* Convert float homography to double precision. */
tmpH.convertTo(_H, CV_64FC1);
/* Maps non-zero mask elems to 1, for the sake of the testcase. */
for(int k=0;k<npoints;k++){
tempMask.data[k] = !!tempMask.data[k];
}
tempMask.copyTo(_tempMask);
return result;
}
}
cv::Mat cv::findHomography( InputArray _points1, InputArray _points2,
int method, double ransacReprojThreshold, OutputArray _mask,
const int maxIters, const double confidence)
@ -303,10 +383,12 @@ 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 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)
{
compressElems( src.ptr<Point2f>(), tempMask.ptr<uchar>(), 1, npoints );
npoints = compressElems( dst.ptr<Point2f>(), tempMask.ptr<uchar>(), 1, npoints );

2673
modules/calib3d/src/rho.cpp Normal file
View File

File diff suppressed because it is too large Load Diff

268
modules/calib3d/src/rho.h Normal file
View File

@ -0,0 +1,268 @@
/*
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 __OPENCV_RHO_H__
#define __OPENCV_RHO_H__
/* Includes */
#include <opencv2/core.hpp>
#include <stdint.h>
/* Defines */
/* 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
/* Namespace cv */
namespace cv{
/* Data structures */
/**
* Homography Estimation context.
*/
struct RHO_HEST;
typedef struct RHO_HEST RHO_HEST;
/* Functions */
/**
* Initialize the estimator context, by allocating the aligned buffers
* internally needed.
*
* @return A pointer to the context if successful; NULL if an error occured.
*/
Ptr<RHO_HEST> rhoInit(void);
/**
* 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 unsuccessful; non-zero otherwise.
*/
int rhoEnsureCapacity(Ptr<RHO_HEST> p, unsigned N, double beta);
/**
* Seeds the internal PRNG with the given seed.
*
* Although it is not required to call this function, since context
* initialization seeds itself with entropy from rand(), this function allows
* reproducible results by using a specified seed.
*
* @param [in] p The estimator context whose PRNG is to be seeded.
* @param [in] seed The 64-bit integer seed.
*/
void rhoSeed(Ptr<RHO_HEST> p, uint64_t seed);
/**
* 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 rhoHest(Ptr<RHO_HEST> p, /* Homography estimation context. */
const float* src, /* Source points */
const float* dst, /* Destination points */
char* 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. */
/* End Namespace cv */
}
#endif

60
modules/calib3d/test/test_homography.cpp
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;
@ -94,12 +94,12 @@ private:
void print_information_1(int j, int N, int method, const Mat& H);
void print_information_2(int j, int N, int method, const Mat& H, const Mat& H_res, int k, double diff);
void print_information_3(int j, int N, const Mat& mask);
void print_information_3(int method, int j, int N, const Mat& mask);
void print_information_4(int method, int j, int N, int k, int l, double diff);
void print_information_5(int method, int j, int N, int l, double diff);
void print_information_6(int j, int N, int k, double diff, bool value);
void print_information_7(int j, int N, int k, double diff, bool original_value, bool found_value);
void print_information_8(int j, int N, int k, int l, double diff);
void print_information_6(int method, int j, int N, int k, double diff, bool value);
void print_information_7(int method, int j, int N, int k, double diff, bool original_value, bool found_value);
void print_information_8(int method, int j, int N, int k, int l, double diff);
};
CV_HomographyTest::CV_HomographyTest() : max_diff(1e-2f), max_2diff(2e-2f)
@ -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;
@ -166,13 +166,13 @@ void CV_HomographyTest::print_information_2(int j, int N, int _method, const Mat
cout << "Maximum allowed difference: " << max_diff << endl; cout << endl;
}
void CV_HomographyTest::print_information_3(int j, int N, const Mat& mask)
void CV_HomographyTest::print_information_3(int _method, int j, int N, const Mat& mask)
{
cout << endl; cout << "Checking for inliers/outliers mask..." << endl; cout << endl;
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: RANSAC" << endl;
cout << "Method: "; if (_method == RANSAC) cout << "RANSAC" << endl; else if (_method == cv::RHO) cout << "RHO" << endl; else cout << _method << endl;
cout << "Found mask:" << endl; cout << endl;
cout << mask << endl; cout << endl;
cout << "Number of rows: " << mask.rows << " Number of cols: " << mask.cols << endl; cout << endl;
@ -205,10 +205,10 @@ void CV_HomographyTest::print_information_5(int _method, int j, int N, int l, do
cout << "Maxumum allowed difference: " << max_diff << endl; cout << endl;
}
void CV_HomographyTest::print_information_6(int j, int N, int k, double diff, bool value)
void CV_HomographyTest::print_information_6(int _method, int j, int N, int k, double diff, bool value)
{
cout << endl; cout << "Checking for inliers/outliers mask..." << endl; cout << endl;
cout << "Method: RANSAC" << endl;
cout << "Method: "; if (_method == RANSAC) cout << "RANSAC" << endl; else if (_method == cv::RHO) cout << "RHO" << endl; else cout << _method << endl;
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;
@ -218,10 +218,10 @@ void CV_HomographyTest::print_information_6(int j, int N, int k, double diff, bo
cout << "Value of found mask: "<< value << endl; cout << endl;
}
void CV_HomographyTest::print_information_7(int j, int N, int k, double diff, bool original_value, bool found_value)
void CV_HomographyTest::print_information_7(int _method, int j, int N, int k, double diff, bool original_value, bool found_value)
{
cout << endl; cout << "Checking for inliers/outliers mask..." << endl; cout << endl;
cout << "Method: RANSAC" << endl;
cout << "Method: "; if (_method == RANSAC) cout << "RANSAC" << endl; else if (_method == cv::RHO) cout << "RHO" << endl; else cout << _method << endl;
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;
@ -231,10 +231,10 @@ void CV_HomographyTest::print_information_7(int j, int N, int k, double diff, bo
cout << "Value of original mask: "<< original_value << " Value of found mask: " << found_value << endl; cout << endl;
}
void CV_HomographyTest::print_information_8(int j, int N, int k, int l, double diff)
void CV_HomographyTest::print_information_8(int _method, int j, int N, int k, int l, double diff)
{
cout << endl; cout << "Checking for reprojection error of inlier..." << endl; cout << endl;
cout << "Method: RANSAC" << endl;
cout << "Method: "; if (_method == RANSAC) cout << "RANSAC" << endl; else if (_method == cv::RHO) cout << "RHO" << endl; else cout << _method << endl;
cout << "Sigma of normal noise: " << sigma << endl;
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;
@ -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)
{
@ -370,7 +371,7 @@ void CV_HomographyTest::run(int)
if (code)
{
print_information_3(j, N, mask[j]);
print_information_3(method, j, N, mask[j]);
switch (code)
{
@ -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)
{
@ -488,7 +490,7 @@ void CV_HomographyTest::run(int)
if (code)
{
print_information_3(j, N, mask_res[j]);
print_information_3(method, j, N, mask_res[j]);
switch (code)
{
@ -520,14 +522,14 @@ void CV_HomographyTest::run(int)
if (mask_res[j].at<bool>(k, 0) != (diff <= reproj_threshold))
{
print_information_6(j, N, k, diff, mask_res[j].at<bool>(k, 0));
print_information_6(method, j, N, k, diff, mask_res[j].at<bool>(k, 0));
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_4);
return;
}
if (mask.at<bool>(k, 0) && !mask_res[j].at<bool>(k, 0))
{
print_information_7(j, N, k, diff, mask.at<bool>(k, 0), mask_res[j].at<bool>(k, 0));
print_information_7(method, j, N, k, diff, mask.at<bool>(k, 0), mask_res[j].at<bool>(k, 0));
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_5);
return;
}
@ -547,7 +549,7 @@ void CV_HomographyTest::run(int)
if (diff - cv::norm(noise_2d, NORM_TYPE[l]) > max_2diff)
{
print_information_8(j, N, k, l, diff - cv::norm(noise_2d, NORM_TYPE[l]));
print_information_8(method, j, N, k, l, diff - cv::norm(noise_2d, NORM_TYPE[l]));
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_DIFF, MESSAGE_RANSAC_DIFF);
return;
}