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cameracv/libs/opencv/modules/calib3d/test/test_cameracalibration.cpp
Danyok 4e24d82c36 initial commit
2023-05-18 21:39:43 +03:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
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// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// 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 Intel Corporation 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.
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// 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
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//M*/
#include "test_precomp.hpp"
#include "opencv2/calib3d/calib3d_c.h"
namespace opencv_test { namespace {
#if 0
class CV_ProjectPointsTest : public cvtest::ArrayTest
{
public:
CV_ProjectPointsTest();
protected:
int read_params( const cv::FileStorage& fs );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
int prepare_test_case( int test_case_idx );
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
double get_success_error_level( int test_case_idx, int i, int j );
void run_func();
void prepare_to_validation( int );
bool calc_jacobians;
};
CV_ProjectPointsTest::CV_ProjectPointsTest()
: cvtest::ArrayTest( "3d-ProjectPoints", "cvProjectPoints2", "" )
{
test_array[INPUT].push_back(NULL); // rotation vector
test_array[OUTPUT].push_back(NULL); // rotation matrix
test_array[OUTPUT].push_back(NULL); // jacobian (J)
test_array[OUTPUT].push_back(NULL); // rotation vector (backward transform result)
test_array[OUTPUT].push_back(NULL); // inverse transform jacobian (J1)
test_array[OUTPUT].push_back(NULL); // J*J1 (or J1*J) == I(3x3)
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
element_wise_relative_error = false;
calc_jacobians = false;
}
int CV_ProjectPointsTest::read_params( const cv::FileStorage& fs )
{
int code = cvtest::ArrayTest::read_params( fs );
return code;
}
void CV_ProjectPointsTest::get_test_array_types_and_sizes(
int /*test_case_idx*/, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
int depth = cvtest::randInt(rng) % 2 == 0 ? CV_32F : CV_64F;
int i, code;
code = cvtest::randInt(rng) % 3;
types[INPUT][0] = CV_MAKETYPE(depth, 1);
if( code == 0 )
{
sizes[INPUT][0] = cvSize(1,1);
types[INPUT][0] = CV_MAKETYPE(depth, 3);
}
else if( code == 1 )
sizes[INPUT][0] = cvSize(3,1);
else
sizes[INPUT][0] = cvSize(1,3);
sizes[OUTPUT][0] = cvSize(3, 3);
types[OUTPUT][0] = CV_MAKETYPE(depth, 1);
types[OUTPUT][1] = CV_MAKETYPE(depth, 1);
if( cvtest::randInt(rng) % 2 )
sizes[OUTPUT][1] = cvSize(3,9);
else
sizes[OUTPUT][1] = cvSize(9,3);
types[OUTPUT][2] = types[INPUT][0];
sizes[OUTPUT][2] = sizes[INPUT][0];
types[OUTPUT][3] = types[OUTPUT][1];
sizes[OUTPUT][3] = cvSize(sizes[OUTPUT][1].height, sizes[OUTPUT][1].width);
types[OUTPUT][4] = types[OUTPUT][1];
sizes[OUTPUT][4] = cvSize(3,3);
calc_jacobians = 1;//cvtest::randInt(rng) % 3 != 0;
if( !calc_jacobians )
sizes[OUTPUT][1] = sizes[OUTPUT][3] = sizes[OUTPUT][4] = cvSize(0,0);
for( i = 0; i < 5; i++ )
{
types[REF_OUTPUT][i] = types[OUTPUT][i];
sizes[REF_OUTPUT][i] = sizes[OUTPUT][i];
}
}
double CV_ProjectPointsTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int j )
{
return j == 4 ? 1e-2 : 1e-2;
}
void CV_ProjectPointsTest::fill_array( int /*test_case_idx*/, int /*i*/, int /*j*/, CvMat* arr )
{
double r[3], theta0, theta1, f;
CvMat _r = cvMat( arr->rows, arr->cols, CV_MAKETYPE(CV_64F,CV_MAT_CN(arr->type)), r );
RNG& rng = ts->get_rng();
r[0] = cvtest::randReal(rng)*CV_PI*2;
r[1] = cvtest::randReal(rng)*CV_PI*2;
r[2] = cvtest::randReal(rng)*CV_PI*2;
theta0 = sqrt(r[0]*r[0] + r[1]*r[1] + r[2]*r[2]);
theta1 = fmod(theta0, CV_PI*2);
if( theta1 > CV_PI )
theta1 = -(CV_PI*2 - theta1);
f = theta1/(theta0 ? theta0 : 1);
r[0] *= f;
r[1] *= f;
r[2] *= f;
cvTsConvert( &_r, arr );
}
int CV_ProjectPointsTest::prepare_test_case( int test_case_idx )
{
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
return code;
}
void CV_ProjectPointsTest::run_func()
{
CvMat *v2m_jac = 0, *m2v_jac = 0;
if( calc_jacobians )
{
v2m_jac = &test_mat[OUTPUT][1];
m2v_jac = &test_mat[OUTPUT][3];
}
cvProjectPoints2( &test_mat[INPUT][0], &test_mat[OUTPUT][0], v2m_jac );
cvProjectPoints2( &test_mat[OUTPUT][0], &test_mat[OUTPUT][2], m2v_jac );
}
void CV_ProjectPointsTest::prepare_to_validation( int /*test_case_idx*/ )
{
const CvMat* vec = &test_mat[INPUT][0];
CvMat* m = &test_mat[REF_OUTPUT][0];
CvMat* vec2 = &test_mat[REF_OUTPUT][2];
CvMat* v2m_jac = 0, *m2v_jac = 0;
double theta0, theta1;
if( calc_jacobians )
{
v2m_jac = &test_mat[REF_OUTPUT][1];
m2v_jac = &test_mat[REF_OUTPUT][3];
}
cvTsProjectPoints( vec, m, v2m_jac );
cvTsProjectPoints( m, vec2, m2v_jac );
cvTsCopy( vec, vec2 );
theta0 = cvtest::norm( cvarrtomat(vec2), 0, CV_L2 );
theta1 = fmod( theta0, CV_PI*2 );
if( theta1 > CV_PI )
theta1 = -(CV_PI*2 - theta1);
cvScale( vec2, vec2, theta1/(theta0 ? theta0 : 1) );
if( calc_jacobians )
{
//cvInvert( v2m_jac, m2v_jac, CV_SVD );
if( cvtest::norm(cvarrtomat(&test_mat[OUTPUT][3]), 0, CV_C) < 1000 )
{
cvTsGEMM( &test_mat[OUTPUT][1], &test_mat[OUTPUT][3],
1, 0, 0, &test_mat[OUTPUT][4],
v2m_jac->rows == 3 ? 0 : CV_GEMM_A_T + CV_GEMM_B_T );
}
else
{
cvTsSetIdentity( &test_mat[OUTPUT][4], cvScalarAll(1.) );
cvTsCopy( &test_mat[REF_OUTPUT][2], &test_mat[OUTPUT][2] );
}
cvTsSetIdentity( &test_mat[REF_OUTPUT][4], cvScalarAll(1.) );
}
}
CV_ProjectPointsTest ProjectPoints_test;
#endif
// --------------------------------- CV_CameraCalibrationTest --------------------------------------------
typedef Matx33d RotMat;
class CV_CameraCalibrationTest : public cvtest::BaseTest
{
public:
CV_CameraCalibrationTest();
~CV_CameraCalibrationTest();
void clear();
protected:
int compare(double* val, double* refVal, int len,
double eps, const char* paramName);
virtual void calibrate(Size imageSize,
const std::vector<std::vector<Point2d> >& imagePoints,
const std::vector<std::vector<Point3d> >& objectPoints,
int iFixedPoint, Mat& distortionCoeffs, Mat& cameraMatrix, std::vector<Vec3d>& translationVectors,
std::vector<RotMat>& rotationMatrices, std::vector<Point3d>& newObjPoints,
std::vector<double>& stdDevs, std::vector<double>& perViewErrors,
int flags ) = 0;
virtual void project( const std::vector<Point3d>& objectPoints,
const RotMat& rotationMatrix, const Vec3d& translationVector,
const Mat& cameraMatrix, const Mat& distortion,
std::vector<Point2d>& imagePoints ) = 0;
void run(int);
};
CV_CameraCalibrationTest::CV_CameraCalibrationTest()
{
}
CV_CameraCalibrationTest::~CV_CameraCalibrationTest()
{
clear();
}
void CV_CameraCalibrationTest::clear()
{
cvtest::BaseTest::clear();
}
int CV_CameraCalibrationTest::compare(double* val, double* ref_val, int len,
double eps, const char* param_name )
{
return cvtest::cmpEps2_64f( ts, val, ref_val, len, eps, param_name );
}
void CV_CameraCalibrationTest::run( int start_from )
{
int code = cvtest::TS::OK;
cv::String filepath;
cv::String filename;
std::vector<std::vector<Point2d> > imagePoints;
std::vector<std::vector<Point3d> > objectPoints;
std::vector<std::vector<Point2d> > reprojectPoints;
std::vector<Vec3d> transVects;
std::vector<RotMat> rotMatrs;
std::vector<Point3d> newObjPoints;
std::vector<double> stdDevs;
std::vector<double> perViewErrors;
std::vector<Vec3d> goodTransVects;
std::vector<RotMat> goodRotMatrs;
std::vector<Point3d> goodObjPoints;
std::vector<double> goodPerViewErrors;
std::vector<double> goodStdDevs;
Mat cameraMatrix;
Mat distortion = Mat::zeros(1, 5, CV_64F);
Mat goodDistortion = Mat::zeros(1, 5, CV_64F);
FILE* file = 0;
FILE* datafile = 0;
int i,j;
int currImage;
int currPoint;
char i_dat_file[100];
int progress = 0;
int values_read = -1;
filepath = cv::format("%scv/cameracalibration/", ts->get_data_path().c_str() );
filename = cv::format("%sdatafiles.txt", filepath.c_str() );
datafile = fopen( filename.c_str(), "r" );
if( datafile == 0 )
{
ts->printf( cvtest::TS::LOG, "Could not open file with list of test files: %s\n", filename.c_str() );
code = cvtest::TS::FAIL_MISSING_TEST_DATA;
ts->set_failed_test_info( code );
return;
}
int numTests = 0;
values_read = fscanf(datafile,"%d",&numTests);
CV_Assert(values_read == 1);
for( int currTest = start_from; currTest < numTests; currTest++ )
{
values_read = fscanf(datafile,"%s",i_dat_file);
CV_Assert(values_read == 1);
filename = cv::format("%s%s", filepath.c_str(), i_dat_file);
file = fopen(filename.c_str(),"r");
ts->update_context( this, currTest, true );
if( file == 0 )
{
ts->printf( cvtest::TS::LOG,
"Can't open current test file: %s\n",filename.c_str());
if( numTests == 1 )
{
code = cvtest::TS::FAIL_MISSING_TEST_DATA;
break;
}
continue; // if there is more than one test, just skip the test
}
Size imageSize;
values_read = fscanf(file,"%d %d\n",&(imageSize.width),&(imageSize.height));
CV_Assert(values_read == 2);
if( imageSize.width <= 0 || imageSize.height <= 0 )
{
ts->printf( cvtest::TS::LOG, "Image size in test file is incorrect\n" );
code = cvtest::TS::FAIL_INVALID_TEST_DATA;
break;
}
/* Read etalon size */
Size etalonSize;
values_read = fscanf(file,"%d %d\n",&(etalonSize.width),&(etalonSize.height));
CV_Assert(values_read == 2);
if( etalonSize.width <= 0 || etalonSize.height <= 0 )
{
ts->printf( cvtest::TS::LOG, "Pattern size in test file is incorrect\n" );
code = cvtest::TS::FAIL_INVALID_TEST_DATA;
break;
}
int numPoints = etalonSize.width * etalonSize.height;
/* Read number of images */
int numImages = 0;
values_read = fscanf(file,"%d\n",&numImages);
CV_Assert(values_read == 1);
if( numImages <=0 )
{
ts->printf( cvtest::TS::LOG, "Number of images in test file is incorrect\n");
code = cvtest::TS::FAIL_INVALID_TEST_DATA;
break;
}
/* Read calibration flags */
int calibFlags = 0;
values_read = fscanf(file,"%d\n",&calibFlags);
CV_Assert(values_read == 1);
/* Read index of the fixed point */
int iFixedPoint;
values_read = fscanf(file,"%d\n",&iFixedPoint);
CV_Assert(values_read == 1);
/* Need to allocate memory */
imagePoints.resize(numImages);
objectPoints.resize(numImages);
reprojectPoints.resize(numImages);
for( currImage = 0; currImage < numImages; currImage++ )
{
imagePoints[currImage].resize(numPoints);
objectPoints[currImage].resize(numPoints);
reprojectPoints[currImage].resize(numPoints);
}
transVects.resize(numImages);
rotMatrs.resize(numImages);
newObjPoints.resize(numPoints);
stdDevs.resize(CALIB_NINTRINSIC + 6*numImages + 3*numPoints);
perViewErrors.resize(numImages);
goodTransVects.resize(numImages);
goodRotMatrs.resize(numImages);
goodObjPoints.resize(numPoints);
goodPerViewErrors.resize(numImages);
int nstddev = CALIB_NINTRINSIC + 6*numImages + 3*numPoints;
goodStdDevs.resize(nstddev);
for( currImage = 0; currImage < numImages; currImage++ )
{
for( currPoint = 0; currPoint < numPoints; currPoint++ )
{
double x,y,z;
values_read = fscanf(file,"%lf %lf %lf\n",&x,&y,&z);
CV_Assert(values_read == 3);
objectPoints[currImage][currPoint].x = x;
objectPoints[currImage][currPoint].y = y;
objectPoints[currImage][currPoint].z = z;
}
}
/* Read image points */
for( currImage = 0; currImage < numImages; currImage++ )
{
for( currPoint = 0; currPoint < numPoints; currPoint++ )
{
double x,y;
values_read = fscanf(file,"%lf %lf\n",&x,&y);
CV_Assert(values_read == 2);
imagePoints[currImage][currPoint].x = x;
imagePoints[currImage][currPoint].y = y;
}
}
/* Read good data computed before */
/* Focal lengths */
double goodFcx,goodFcy;
values_read = fscanf(file,"%lf %lf",&goodFcx,&goodFcy);
CV_Assert(values_read == 2);
/* Principal points */
double goodCx,goodCy;
values_read = fscanf(file,"%lf %lf",&goodCx,&goodCy);
CV_Assert(values_read == 2);
/* Read distortion */
for( i = 0; i < 4; i++ )
{
values_read = fscanf(file,"%lf",&goodDistortion.at<double>(i)); CV_Assert(values_read == 1);
}
/* Read good Rot matrices */
for( currImage = 0; currImage < numImages; currImage++ )
{
for( i = 0; i < 3; i++ )
for( j = 0; j < 3; j++ )
{
values_read = fscanf(file, "%lf", &goodRotMatrs[currImage].val[i*3+j]);
CV_Assert(values_read == 1);
}
}
/* Read good Trans vectors */
for( currImage = 0; currImage < numImages; currImage++ )
{
for( i = 0; i < 3; i++ )
{
values_read = fscanf(file, "%lf", &goodTransVects[currImage].val[i]);
CV_Assert(values_read == 1);
}
}
bool releaseObject = iFixedPoint > 0 && iFixedPoint < numPoints - 1;
/* Read good refined 3D object points */
if( releaseObject )
{
for( i = 0; i < numPoints; i++ )
{
for( j = 0; j < 3; j++ )
{
values_read = fscanf(file, "%lf", &goodObjPoints[i].x + j);
CV_Assert(values_read == 1);
}
}
}
/* Read good stdDeviations */
for (i = 0; i < CALIB_NINTRINSIC + numImages*6; i++)
{
values_read = fscanf(file, "%lf", &goodStdDevs[i]);
CV_Assert(values_read == 1);
}
for( ; i < nstddev; i++ )
{
if( releaseObject )
{
values_read = fscanf(file, "%lf", &goodStdDevs[i]);
CV_Assert(values_read == 1);
}
else
goodStdDevs[i] = 0.0;
}
cameraMatrix = Mat::zeros(3, 3, CV_64F);
cameraMatrix.at<double>(0, 0) = cameraMatrix.at<double>(1, 1) = 807.;
cameraMatrix.at<double>(0, 2) = (imageSize.width - 1)*0.5;
cameraMatrix.at<double>(1, 2) = (imageSize.height - 1)*0.5;
cameraMatrix.at<double>(2, 2) = 1.;
/* Now we can calibrate camera */
calibrate( imageSize,
imagePoints,
objectPoints,
iFixedPoint,
distortion,
cameraMatrix,
transVects,
rotMatrs,
newObjPoints,
stdDevs,
perViewErrors,
calibFlags );
/* ---- Reproject points to the image ---- */
for( currImage = 0; currImage < numImages; currImage++ )
{
if( releaseObject )
{
objectPoints[currImage] = newObjPoints;
}
project( objectPoints[currImage],
rotMatrs[currImage],
transVects[currImage],
cameraMatrix,
distortion,
reprojectPoints[currImage]);
}
/* ----- Compute reprojection error ----- */
double dx,dy;
double rx,ry;
double meanDx,meanDy;
double maxDx = 0.0;
double maxDy = 0.0;
meanDx = 0;
meanDy = 0;
for( currImage = 0; currImage < numImages; currImage++ )
{
double imageMeanDx = 0;
double imageMeanDy = 0;
for( currPoint = 0; currPoint < etalonSize.width * etalonSize.height; currPoint++ )
{
rx = reprojectPoints[currImage][currPoint].x;
ry = reprojectPoints[currImage][currPoint].y;
dx = rx - imagePoints[currImage][currPoint].x;
dy = ry - imagePoints[currImage][currPoint].y;
meanDx += dx;
meanDy += dy;
imageMeanDx += dx*dx;
imageMeanDy += dy*dy;
dx = fabs(dx);
dy = fabs(dy);
if( dx > maxDx )
maxDx = dx;
if( dy > maxDy )
maxDy = dy;
}
goodPerViewErrors[currImage] = sqrt( (imageMeanDx + imageMeanDy) /
(etalonSize.width * etalonSize.height));
//only for c-version of test (it does not provides evaluation of perViewErrors
//and returns zeros)
if(perViewErrors[currImage] == 0.0)
perViewErrors[currImage] = goodPerViewErrors[currImage];
}
meanDx /= numImages * etalonSize.width * etalonSize.height;
meanDy /= numImages * etalonSize.width * etalonSize.height;
/* ========= Compare parameters ========= */
CV_Assert(cameraMatrix.type() == CV_64F && cameraMatrix.size() == Size(3, 3));
CV_Assert(distortion.type() == CV_64F);
Size dsz = distortion.size();
CV_Assert(dsz == Size(4, 1) || dsz == Size(1, 4) || dsz == Size(5, 1) || dsz == Size(1, 5));
/*std::cout << "cameraMatrix: " << cameraMatrix << "\n";
std::cout << "curr distCoeffs: " << distortion << "\n";
std::cout << "good distCoeffs: " << goodDistortion << "\n";*/
/* ----- Compare focal lengths ----- */
code = compare(&cameraMatrix.at<double>(0, 0), &goodFcx, 1, 0.1, "fx");
if( code < 0 )
break;
code = compare(&cameraMatrix.at<double>(1, 1),&goodFcy, 1, 0.1, "fy");
if( code < 0 )
break;
/* ----- Compare principal points ----- */
code = compare(&cameraMatrix.at<double>(0,2), &goodCx, 1, 0.1, "cx");
if( code < 0 )
break;
code = compare(&cameraMatrix.at<double>(1,2), &goodCy, 1, 0.1, "cy");
if( code < 0 )
break;
/* ----- Compare distortion ----- */
code = compare(&distortion.at<double>(0), &goodDistortion.at<double>(0), 4, 0.1, "[k1,k2,p1,p2]");
if( code < 0 )
break;
/* ----- Compare rot matrixs ----- */
CV_Assert(rotMatrs.size() == (size_t)numImages);
CV_Assert(transVects.size() == (size_t)numImages);
//code = compare(rotMatrs[0].val, goodRotMatrs[0].val, 9*numImages, 0.05, "rotation matrices");
for( i = 0; i < numImages; i++ )
{
if( cv::norm(rotMatrs[i], goodRotMatrs[i], NORM_INF) > 0.05 )
{
printf("rot mats for frame #%d are very different\n", i);
std::cout << "curr:\n" << rotMatrs[i] << std::endl;
std::cout << "good:\n" << goodRotMatrs[i] << std::endl;
code = TS::FAIL_BAD_ACCURACY;
break;
}
}
if( code < 0 )
break;
/* ----- Compare rot matrixs ----- */
code = compare(transVects[0].val, goodTransVects[0].val, 3*numImages, 0.1, "translation vectors");
if( code < 0 )
break;
/* ----- Compare refined 3D object points ----- */
if( releaseObject )
{
code = compare(&newObjPoints[0].x, &goodObjPoints[0].x, 3*numPoints, 0.1, "refined 3D object points");
if( code < 0 )
break;
}
/* ----- Compare per view re-projection errors ----- */
CV_Assert(perViewErrors.size() == (size_t)numImages);
code = compare(&perViewErrors[0], &goodPerViewErrors[0], numImages, 1.1, "per view errors vector");
if( code < 0 )
break;
/* ----- Compare standard deviations of parameters ----- */
if( stdDevs.size() < (size_t)nstddev )
stdDevs.resize(nstddev);
for ( i = 0; i < nstddev; i++)
{
if(stdDevs[i] == 0.0)
stdDevs[i] = goodStdDevs[i];
}
code = compare(&stdDevs[0], &goodStdDevs[0], nstddev, .5,
"stdDevs vector");
if( code < 0 )
break;
/*if( maxDx > 1.0 )
{
ts->printf( cvtest::TS::LOG,
"Error in reprojection maxDx=%f > 1.0\n",maxDx);
code = cvtest::TS::FAIL_BAD_ACCURACY; break;
}
if( maxDy > 1.0 )
{
ts->printf( cvtest::TS::LOG,
"Error in reprojection maxDy=%f > 1.0\n",maxDy);
code = cvtest::TS::FAIL_BAD_ACCURACY; break;
}*/
progress = update_progress( progress, currTest, numTests, 0 );
fclose(file);
file = 0;
}
if( file )
fclose(file);
if( datafile )
fclose(datafile);
if( code < 0 )
ts->set_failed_test_info( code );
}
// --------------------------------- CV_CameraCalibrationTest_CPP --------------------------------------------
class CV_CameraCalibrationTest_CPP : public CV_CameraCalibrationTest
{
public:
CV_CameraCalibrationTest_CPP(){}
protected:
virtual void calibrate(Size imageSize,
const std::vector<std::vector<Point2d> >& imagePoints,
const std::vector<std::vector<Point3d> >& objectPoints,
int iFixedPoint, Mat& distortionCoeffs, Mat& cameraMatrix, std::vector<Vec3d>& translationVectors,
std::vector<RotMat>& rotationMatrices, std::vector<Point3d>& newObjPoints,
std::vector<double>& stdDevs, std::vector<double>& perViewErrors,
int flags );
virtual void project( const std::vector<Point3d>& objectPoints,
const RotMat& rotationMatrix, const Vec3d& translationVector,
const Mat& cameraMatrix, const Mat& distortion,
std::vector<Point2d>& imagePoints );
};
void CV_CameraCalibrationTest_CPP::calibrate(Size imageSize,
const std::vector<std::vector<Point2d> >& _imagePoints,
const std::vector<std::vector<Point3d> >& _objectPoints,
int iFixedPoint, Mat& _distCoeffs, Mat& _cameraMatrix, std::vector<Vec3d>& translationVectors,
std::vector<RotMat>& rotationMatrices, std::vector<Point3d>& newObjPoints,
std::vector<double>& stdDevs, std::vector<double>& perViewErrors,
int flags )
{
int pointCount = (int)_imagePoints[0].size();
size_t i, imageCount = _imagePoints.size();
vector<vector<Point3f> > objectPoints( imageCount );
vector<vector<Point2f> > imagePoints( imageCount );
Mat cameraMatrix, distCoeffs(1,4,CV_64F,Scalar::all(0));
vector<Mat> rvecs, tvecs;
Mat newObjMat;
Mat stdDevsMatInt, stdDevsMatExt;
Mat stdDevsMatObj;
Mat perViewErrorsMat;
for( i = 0; i < imageCount; i++ )
{
Mat(_imagePoints[i]).convertTo(imagePoints[i], CV_32F);
Mat(_objectPoints[i]).convertTo(objectPoints[i], CV_32F);
}
size_t nstddev0 = CV_CALIB_NINTRINSIC + imageCount*6, nstddev1 = nstddev0 + _imagePoints[0].size()*3;
for( i = nstddev0; i < nstddev1; i++ )
{
stdDevs[i] = 0.0;
}
calibrateCameraRO( objectPoints,
imagePoints,
imageSize,
iFixedPoint,
cameraMatrix,
distCoeffs,
rvecs,
tvecs,
newObjMat,
stdDevsMatInt,
stdDevsMatExt,
stdDevsMatObj,
perViewErrorsMat,
flags );
bool releaseObject = iFixedPoint > 0 && iFixedPoint < pointCount - 1;
if( releaseObject )
{
newObjMat.convertTo( newObjPoints, CV_64F );
}
Mat stdDevMats[] = {stdDevsMatInt, stdDevsMatExt, stdDevsMatObj}, stdDevsMat;
vconcat(stdDevMats, releaseObject ? 3 : 2, stdDevsMat);
stdDevsMat.convertTo(stdDevs, CV_64F);
perViewErrorsMat.convertTo(perViewErrors, CV_64F);
cameraMatrix.convertTo(_cameraMatrix, CV_64F);
distCoeffs.convertTo(_distCoeffs, CV_64F);
for( i = 0; i < imageCount; i++ )
{
Mat r9;
cvtest::Rodrigues( rvecs[i], r9 );
cv::transpose(r9, r9);
r9.convertTo(rotationMatrices[i], CV_64F);
tvecs[i].convertTo(translationVectors[i], CV_64F);
}
}
void CV_CameraCalibrationTest_CPP::project( const std::vector<Point3d>& objectPoints,
const RotMat& rotationMatrix, const Vec3d& translationVector,
const Mat& cameraMatrix, const Mat& distortion,
std::vector<Point2d>& imagePoints )
{
projectPoints(objectPoints, rotationMatrix, translationVector, cameraMatrix, distortion, imagePoints );
/*Mat objectPoints( pointCount, 3, CV_64FC1, _objectPoints );
Mat rmat( 3, 3, CV_64FC1, rotationMatrix ),
rvec( 1, 3, CV_64FC1 ),
tvec( 1, 3, CV_64FC1, translationVector );
Mat cameraMatrix( 3, 3, CV_64FC1, _cameraMatrix );
Mat distCoeffs( 1, 4, CV_64FC1, distortion );
vector<Point2f> imagePoints;
cvtest::Rodrigues( rmat, rvec );
objectPoints.convertTo( objectPoints, CV_32FC1 );
projectPoints( objectPoints, rvec, tvec,
cameraMatrix, distCoeffs, imagePoints );
vector<Point2f>::const_iterator it = imagePoints.begin();
for( int i = 0; it != imagePoints.end(); ++it, i++ )
{
_imagePoints[i] = cvPoint2D64f( it->x, it->y );
}*/
}
//----------------------------------------- CV_CalibrationMatrixValuesTest --------------------------------
class CV_CalibrationMatrixValuesTest : public cvtest::BaseTest
{
public:
CV_CalibrationMatrixValuesTest() {}
protected:
void run(int);
virtual void calibMatrixValues( const Mat& cameraMatrix, Size imageSize,
double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength,
Point2d& principalPoint, double& aspectRatio ) = 0;
};
void CV_CalibrationMatrixValuesTest::run(int)
{
int code = cvtest::TS::OK;
const double fcMinVal = 1e-5;
const double fcMaxVal = 1000;
const double apertureMaxVal = 0.01;
RNG rng = ts->get_rng();
double fx, fy, cx, cy, nx, ny;
Mat cameraMatrix( 3, 3, CV_64FC1 );
cameraMatrix.setTo( Scalar(0) );
fx = cameraMatrix.at<double>(0,0) = rng.uniform( fcMinVal, fcMaxVal );
fy = cameraMatrix.at<double>(1,1) = rng.uniform( fcMinVal, fcMaxVal );
cx = cameraMatrix.at<double>(0,2) = rng.uniform( fcMinVal, fcMaxVal );
cy = cameraMatrix.at<double>(1,2) = rng.uniform( fcMinVal, fcMaxVal );
cameraMatrix.at<double>(2,2) = 1;
Size imageSize( 600, 400 );
double apertureWidth = (double)rng * apertureMaxVal,
apertureHeight = (double)rng * apertureMaxVal;
double fovx, fovy, focalLength, aspectRatio,
goodFovx, goodFovy, goodFocalLength, goodAspectRatio;
Point2d principalPoint, goodPrincipalPoint;
calibMatrixValues( cameraMatrix, imageSize, apertureWidth, apertureHeight,
fovx, fovy, focalLength, principalPoint, aspectRatio );
// calculate calibration matrix values
goodAspectRatio = fy / fx;
if( apertureWidth != 0.0 && apertureHeight != 0.0 )
{
nx = imageSize.width / apertureWidth;
ny = imageSize.height / apertureHeight;
}
else
{
nx = 1.0;
ny = goodAspectRatio;
}
goodFovx = (atan2(cx, fx) + atan2(imageSize.width - cx, fx)) * 180.0 / CV_PI;
goodFovy = (atan2(cy, fy) + atan2(imageSize.height - cy, fy)) * 180.0 / CV_PI;
goodFocalLength = fx / nx;
goodPrincipalPoint.x = cx / nx;
goodPrincipalPoint.y = cy / ny;
// check results
if( fabs(fovx - goodFovx) > FLT_EPSILON )
{
ts->printf( cvtest::TS::LOG, "bad fovx (real=%f, good = %f\n", fovx, goodFovx );
code = cvtest::TS::FAIL_BAD_ACCURACY;
goto _exit_;
}
if( fabs(fovy - goodFovy) > FLT_EPSILON )
{
ts->printf( cvtest::TS::LOG, "bad fovy (real=%f, good = %f\n", fovy, goodFovy );
code = cvtest::TS::FAIL_BAD_ACCURACY;
goto _exit_;
}
if( fabs(focalLength - goodFocalLength) > FLT_EPSILON )
{
ts->printf( cvtest::TS::LOG, "bad focalLength (real=%f, good = %f\n", focalLength, goodFocalLength );
code = cvtest::TS::FAIL_BAD_ACCURACY;
goto _exit_;
}
if( fabs(aspectRatio - goodAspectRatio) > FLT_EPSILON )
{
ts->printf( cvtest::TS::LOG, "bad aspectRatio (real=%f, good = %f\n", aspectRatio, goodAspectRatio );
code = cvtest::TS::FAIL_BAD_ACCURACY;
goto _exit_;
}
if( cv::norm(principalPoint - goodPrincipalPoint) > FLT_EPSILON ) // Point2d
{
ts->printf( cvtest::TS::LOG, "bad principalPoint\n" );
code = cvtest::TS::FAIL_BAD_ACCURACY;
goto _exit_;
}
_exit_:
RNG& _rng = ts->get_rng();
_rng = rng;
ts->set_failed_test_info( code );
}
//----------------------------------------- CV_CalibrationMatrixValuesTest_CPP --------------------------------
class CV_CalibrationMatrixValuesTest_CPP : public CV_CalibrationMatrixValuesTest
{
public:
CV_CalibrationMatrixValuesTest_CPP() {}
protected:
virtual void calibMatrixValues( const Mat& cameraMatrix, Size imageSize,
double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength,
Point2d& principalPoint, double& aspectRatio );
};
void CV_CalibrationMatrixValuesTest_CPP::calibMatrixValues( const Mat& cameraMatrix, Size imageSize,
double apertureWidth, double apertureHeight,
double& fovx, double& fovy, double& focalLength,
Point2d& principalPoint, double& aspectRatio )
{
calibrationMatrixValues( cameraMatrix, imageSize, apertureWidth, apertureHeight,
fovx, fovy, focalLength, principalPoint, aspectRatio );
}
//----------------------------------------- CV_ProjectPointsTest --------------------------------
void calcdfdx( const vector<vector<Point2f> >& leftF, const vector<vector<Point2f> >& rightF, double eps, Mat& dfdx )
{
const int fdim = 2;
CV_Assert( !leftF.empty() && !rightF.empty() && !leftF[0].empty() && !rightF[0].empty() );
CV_Assert( leftF[0].size() == rightF[0].size() );
CV_Assert( fabs(eps) > std::numeric_limits<double>::epsilon() );
int fcount = (int)leftF[0].size(), xdim = (int)leftF.size();
dfdx.create( fcount*fdim, xdim, CV_64FC1 );
vector<vector<Point2f> >::const_iterator arrLeftIt = leftF.begin();
vector<vector<Point2f> >::const_iterator arrRightIt = rightF.begin();
for( int xi = 0; xi < xdim; xi++, ++arrLeftIt, ++arrRightIt )
{
CV_Assert( (int)arrLeftIt->size() == fcount );
CV_Assert( (int)arrRightIt->size() == fcount );
vector<Point2f>::const_iterator lIt = arrLeftIt->begin();
vector<Point2f>::const_iterator rIt = arrRightIt->begin();
for( int fi = 0; fi < dfdx.rows; fi+=fdim, ++lIt, ++rIt )
{
dfdx.at<double>(fi, xi ) = 0.5 * ((double)(rIt->x - lIt->x)) / eps;
dfdx.at<double>(fi+1, xi ) = 0.5 * ((double)(rIt->y - lIt->y)) / eps;
}
}
}
class CV_ProjectPointsTest : public cvtest::BaseTest
{
public:
CV_ProjectPointsTest() {}
protected:
void run(int);
virtual void project( const Mat& objectPoints,
const Mat& rvec, const Mat& tvec,
const Mat& cameraMatrix,
const Mat& distCoeffs,
vector<Point2f>& imagePoints,
Mat& dpdrot, Mat& dpdt, Mat& dpdf,
Mat& dpdc, Mat& dpddist,
double aspectRatio=0 ) = 0;
};
void CV_ProjectPointsTest::run(int)
{
//typedef float matType;
int code = cvtest::TS::OK;
const int pointCount = 100;
const float zMinVal = 10.0f, zMaxVal = 100.0f,
rMinVal = -0.3f, rMaxVal = 0.3f,
tMinVal = -2.0f, tMaxVal = 2.0f;
const float imgPointErr = 1e-3f,
dEps = 1e-3f;
double err;
Size imgSize( 600, 800 );
Mat_<float> objPoints( pointCount, 3), rvec( 1, 3), rmat, tvec( 1, 3 ), cameraMatrix( 3, 3 ), distCoeffs( 1, 4 ),
leftRvec, rightRvec, leftTvec, rightTvec, leftCameraMatrix, rightCameraMatrix, leftDistCoeffs, rightDistCoeffs;
RNG rng = ts->get_rng();
// generate data
cameraMatrix << 300.f, 0.f, imgSize.width/2.f,
0.f, 300.f, imgSize.height/2.f,
0.f, 0.f, 1.f;
distCoeffs << 0.1, 0.01, 0.001, 0.001;
rvec(0,0) = rng.uniform( rMinVal, rMaxVal );
rvec(0,1) = rng.uniform( rMinVal, rMaxVal );
rvec(0,2) = rng.uniform( rMinVal, rMaxVal );
rmat = cv::Mat_<float>::zeros(3, 3);
cvtest::Rodrigues( rvec, rmat );
tvec(0,0) = rng.uniform( tMinVal, tMaxVal );
tvec(0,1) = rng.uniform( tMinVal, tMaxVal );
tvec(0,2) = rng.uniform( tMinVal, tMaxVal );
for( int y = 0; y < objPoints.rows; y++ )
{
Mat point(1, 3, CV_32FC1, objPoints.ptr(y) );
float z = rng.uniform( zMinVal, zMaxVal );
point.at<float>(0,2) = z;
point.at<float>(0,0) = (rng.uniform(2.f,(float)(imgSize.width-2)) - cameraMatrix(0,2)) / cameraMatrix(0,0) * z;
point.at<float>(0,1) = (rng.uniform(2.f,(float)(imgSize.height-2)) - cameraMatrix(1,2)) / cameraMatrix(1,1) * z;
point = (point - tvec) * rmat;
}
vector<Point2f> imgPoints;
vector<vector<Point2f> > leftImgPoints;
vector<vector<Point2f> > rightImgPoints;
Mat dpdrot, dpdt, dpdf, dpdc, dpddist,
valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist;
project( objPoints, rvec, tvec, cameraMatrix, distCoeffs,
imgPoints, dpdrot, dpdt, dpdf, dpdc, dpddist, 0 );
// calculate and check image points
CV_Assert( (int)imgPoints.size() == pointCount );
vector<Point2f>::const_iterator it = imgPoints.begin();
for( int i = 0; i < pointCount; i++, ++it )
{
Point3d p( objPoints(i,0), objPoints(i,1), objPoints(i,2) );
double z = p.x*rmat(2,0) + p.y*rmat(2,1) + p.z*rmat(2,2) + tvec(0,2),
x = (p.x*rmat(0,0) + p.y*rmat(0,1) + p.z*rmat(0,2) + tvec(0,0)) / z,
y = (p.x*rmat(1,0) + p.y*rmat(1,1) + p.z*rmat(1,2) + tvec(0,1)) / z,
r2 = x*x + y*y,
r4 = r2*r2;
Point2f validImgPoint;
double a1 = 2*x*y,
a2 = r2 + 2*x*x,
a3 = r2 + 2*y*y,
cdist = 1+distCoeffs(0,0)*r2+distCoeffs(0,1)*r4;
validImgPoint.x = static_cast<float>((double)cameraMatrix(0,0)*(x*cdist + (double)distCoeffs(0,2)*a1 + (double)distCoeffs(0,3)*a2)
+ (double)cameraMatrix(0,2));
validImgPoint.y = static_cast<float>((double)cameraMatrix(1,1)*(y*cdist + (double)distCoeffs(0,2)*a3 + distCoeffs(0,3)*a1)
+ (double)cameraMatrix(1,2));
if( fabs(it->x - validImgPoint.x) > imgPointErr ||
fabs(it->y - validImgPoint.y) > imgPointErr )
{
ts->printf( cvtest::TS::LOG, "bad image point\n" );
code = cvtest::TS::FAIL_BAD_ACCURACY;
goto _exit_;
}
}
// check derivatives
// 1. rotation
leftImgPoints.resize(3);
rightImgPoints.resize(3);
for( int i = 0; i < 3; i++ )
{
rvec.copyTo( leftRvec ); leftRvec(0,i) -= dEps;
project( objPoints, leftRvec, tvec, cameraMatrix, distCoeffs,
leftImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
rvec.copyTo( rightRvec ); rightRvec(0,i) += dEps;
project( objPoints, rightRvec, tvec, cameraMatrix, distCoeffs,
rightImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
}
calcdfdx( leftImgPoints, rightImgPoints, dEps, valDpdrot );
err = cvtest::norm( dpdrot, valDpdrot, NORM_INF );
if( err > 3 )
{
ts->printf( cvtest::TS::LOG, "bad dpdrot: too big difference = %g\n", err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
// 2. translation
for( int i = 0; i < 3; i++ )
{
tvec.copyTo( leftTvec ); leftTvec(0,i) -= dEps;
project( objPoints, rvec, leftTvec, cameraMatrix, distCoeffs,
leftImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
tvec.copyTo( rightTvec ); rightTvec(0,i) += dEps;
project( objPoints, rvec, rightTvec, cameraMatrix, distCoeffs,
rightImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
}
calcdfdx( leftImgPoints, rightImgPoints, dEps, valDpdt );
if( cvtest::norm( dpdt, valDpdt, NORM_INF ) > 0.2 )
{
ts->printf( cvtest::TS::LOG, "bad dpdtvec\n" );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
// 3. camera matrix
// 3.1. focus
leftImgPoints.resize(2);
rightImgPoints.resize(2);
cameraMatrix.copyTo( leftCameraMatrix ); leftCameraMatrix(0,0) -= dEps;
project( objPoints, rvec, tvec, leftCameraMatrix, distCoeffs,
leftImgPoints[0], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
cameraMatrix.copyTo( leftCameraMatrix ); leftCameraMatrix(1,1) -= dEps;
project( objPoints, rvec, tvec, leftCameraMatrix, distCoeffs,
leftImgPoints[1], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
cameraMatrix.copyTo( rightCameraMatrix ); rightCameraMatrix(0,0) += dEps;
project( objPoints, rvec, tvec, rightCameraMatrix, distCoeffs,
rightImgPoints[0], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
cameraMatrix.copyTo( rightCameraMatrix ); rightCameraMatrix(1,1) += dEps;
project( objPoints, rvec, tvec, rightCameraMatrix, distCoeffs,
rightImgPoints[1], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
calcdfdx( leftImgPoints, rightImgPoints, dEps, valDpdf );
if ( cvtest::norm( dpdf, valDpdf, NORM_L2 ) > 0.2 )
{
ts->printf( cvtest::TS::LOG, "bad dpdf\n" );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
// 3.2. principal point
leftImgPoints.resize(2);
rightImgPoints.resize(2);
cameraMatrix.copyTo( leftCameraMatrix ); leftCameraMatrix(0,2) -= dEps;
project( objPoints, rvec, tvec, leftCameraMatrix, distCoeffs,
leftImgPoints[0], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
cameraMatrix.copyTo( leftCameraMatrix ); leftCameraMatrix(1,2) -= dEps;
project( objPoints, rvec, tvec, leftCameraMatrix, distCoeffs,
leftImgPoints[1], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
cameraMatrix.copyTo( rightCameraMatrix ); rightCameraMatrix(0,2) += dEps;
project( objPoints, rvec, tvec, rightCameraMatrix, distCoeffs,
rightImgPoints[0], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
cameraMatrix.copyTo( rightCameraMatrix ); rightCameraMatrix(1,2) += dEps;
project( objPoints, rvec, tvec, rightCameraMatrix, distCoeffs,
rightImgPoints[1], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
calcdfdx( leftImgPoints, rightImgPoints, dEps, valDpdc );
if ( cvtest::norm( dpdc, valDpdc, NORM_L2 ) > 0.2 )
{
ts->printf( cvtest::TS::LOG, "bad dpdc\n" );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
// 4. distortion
leftImgPoints.resize(distCoeffs.cols);
rightImgPoints.resize(distCoeffs.cols);
for( int i = 0; i < distCoeffs.cols; i++ )
{
distCoeffs.copyTo( leftDistCoeffs ); leftDistCoeffs(0,i) -= dEps;
project( objPoints, rvec, tvec, cameraMatrix, leftDistCoeffs,
leftImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
distCoeffs.copyTo( rightDistCoeffs ); rightDistCoeffs(0,i) += dEps;
project( objPoints, rvec, tvec, cameraMatrix, rightDistCoeffs,
rightImgPoints[i], valDpdrot, valDpdt, valDpdf, valDpdc, valDpddist, 0 );
}
calcdfdx( leftImgPoints, rightImgPoints, dEps, valDpddist );
if( cvtest::norm( dpddist, valDpddist, NORM_L2 ) > 0.3 )
{
ts->printf( cvtest::TS::LOG, "bad dpddist\n" );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
_exit_:
RNG& _rng = ts->get_rng();
_rng = rng;
ts->set_failed_test_info( code );
}
//----------------------------------------- CV_ProjectPointsTest_CPP --------------------------------
class CV_ProjectPointsTest_CPP : public CV_ProjectPointsTest
{
public:
CV_ProjectPointsTest_CPP() {}
protected:
virtual void project( const Mat& objectPoints,
const Mat& rvec, const Mat& tvec,
const Mat& cameraMatrix,
const Mat& distCoeffs,
vector<Point2f>& imagePoints,
Mat& dpdrot, Mat& dpdt, Mat& dpdf,
Mat& dpdc, Mat& dpddist,
double aspectRatio=0 );
};
void CV_ProjectPointsTest_CPP::project( const Mat& objectPoints, const Mat& rvec, const Mat& tvec,
const Mat& cameraMatrix, const Mat& distCoeffs, vector<Point2f>& imagePoints,
Mat& dpdrot, Mat& dpdt, Mat& dpdf, Mat& dpdc, Mat& dpddist, double aspectRatio)
{
Mat J;
projectPoints( objectPoints, rvec, tvec, cameraMatrix, distCoeffs, imagePoints, J, aspectRatio);
J.colRange(0, 3).copyTo(dpdrot);
J.colRange(3, 6).copyTo(dpdt);
J.colRange(6, 8).copyTo(dpdf);
J.colRange(8, 10).copyTo(dpdc);
J.colRange(10, J.cols).copyTo(dpddist);
}
///////////////////////////////// Stereo Calibration /////////////////////////////////////
class CV_StereoCalibrationTest : public cvtest::BaseTest
{
public:
CV_StereoCalibrationTest();
~CV_StereoCalibrationTest();
void clear();
protected:
bool checkPandROI( int test_case_idx,
const Mat& M, const Mat& D, const Mat& R,
const Mat& P, Size imgsize, Rect roi );
// covers of tested functions
virtual double calibrateStereoCamera( const vector<vector<Point3f> >& objectPoints,
const vector<vector<Point2f> >& imagePoints1,
const vector<vector<Point2f> >& imagePoints2,
Mat& cameraMatrix1, Mat& distCoeffs1,
Mat& cameraMatrix2, Mat& distCoeffs2,
Size imageSize, Mat& R, Mat& T,
Mat& E, Mat& F, TermCriteria criteria, int flags ) = 0;
virtual void rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1,
const Mat& cameraMatrix2, const Mat& distCoeffs2,
Size imageSize, const Mat& R, const Mat& T,
Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q,
double alpha, Size newImageSize,
Rect* validPixROI1, Rect* validPixROI2, int flags ) = 0;
virtual bool rectifyUncalibrated( const Mat& points1,
const Mat& points2, const Mat& F, Size imgSize,
Mat& H1, Mat& H2, double threshold=5 ) = 0;
virtual void triangulate( const Mat& P1, const Mat& P2,
const Mat &points1, const Mat &points2,
Mat &points4D ) = 0;
virtual void correct( const Mat& F,
const Mat &points1, const Mat &points2,
Mat &newPoints1, Mat &newPoints2 ) = 0;
void run(int);
};
CV_StereoCalibrationTest::CV_StereoCalibrationTest()
{
}
CV_StereoCalibrationTest::~CV_StereoCalibrationTest()
{
clear();
}
void CV_StereoCalibrationTest::clear()
{
cvtest::BaseTest::clear();
}
bool CV_StereoCalibrationTest::checkPandROI( int test_case_idx, const Mat& M, const Mat& D, const Mat& R,
const Mat& P, Size imgsize, Rect roi )
{
const double eps = 0.05;
const int N = 21;
int x, y, k;
vector<Point2f> pts, upts;
// step 1. check that all the original points belong to the destination image
for( y = 0; y < N; y++ )
for( x = 0; x < N; x++ )
pts.push_back(Point2f((float)x*imgsize.width/(N-1), (float)y*imgsize.height/(N-1)));
undistortPoints(pts, upts, M, D, R, P );
for( k = 0; k < N*N; k++ )
if( upts[k].x < -imgsize.width*eps || upts[k].x > imgsize.width*(1+eps) ||
upts[k].y < -imgsize.height*eps || upts[k].y > imgsize.height*(1+eps) )
{
ts->printf(cvtest::TS::LOG, "Test #%d. The point (%g, %g) was mapped to (%g, %g) which is out of image\n",
test_case_idx, pts[k].x, pts[k].y, upts[k].x, upts[k].y);
return false;
}
// step 2. check that all the points inside ROI belong to the original source image
Mat temp(imgsize, CV_8U), utemp, map1, map2;
temp = Scalar::all(1);
initUndistortRectifyMap(M, D, R, P, imgsize, CV_16SC2, map1, map2);
remap(temp, utemp, map1, map2, INTER_LINEAR);
if(roi.x < 0 || roi.y < 0 || roi.x + roi.width > imgsize.width || roi.y + roi.height > imgsize.height)
{
ts->printf(cvtest::TS::LOG, "Test #%d. The ROI=(%d, %d, %d, %d) is outside of the imge rectangle\n",
test_case_idx, roi.x, roi.y, roi.width, roi.height);
return false;
}
double s = sum(utemp(roi))[0];
if( s > roi.area() || roi.area() - s > roi.area()*(1-eps) )
{
ts->printf(cvtest::TS::LOG, "Test #%d. The ratio of black pixels inside the valid ROI (~%g%%) is too large\n",
test_case_idx, s*100./roi.area());
return false;
}
return true;
}
void CV_StereoCalibrationTest::run( int )
{
const int ntests = 1;
const double maxReprojErr = 2;
const double maxScanlineDistErr_c = 3;
const double maxScanlineDistErr_uc = 4;
FILE* f = 0;
for(int testcase = 1; testcase <= ntests; testcase++)
{
cv::String filepath;
char buf[1000];
filepath = cv::format("%scv/stereo/case%d/stereo_calib.txt", ts->get_data_path().c_str(), testcase );
f = fopen(filepath.c_str(), "rt");
Size patternSize;
vector<string> imglist;
if( !f || !fgets(buf, sizeof(buf)-3, f) || sscanf(buf, "%d%d", &patternSize.width, &patternSize.height) != 2 )
{
ts->printf( cvtest::TS::LOG, "The file %s can not be opened or has invalid content\n", filepath.c_str() );
ts->set_failed_test_info( f ? cvtest::TS::FAIL_INVALID_TEST_DATA : cvtest::TS::FAIL_MISSING_TEST_DATA );
if (f)
fclose(f);
return;
}
for(;;)
{
if( !fgets( buf, sizeof(buf)-3, f ))
break;
size_t len = strlen(buf);
while( len > 0 && isspace(buf[len-1]))
buf[--len] = '\0';
if( buf[0] == '#')
continue;
filepath = cv::format("%scv/stereo/case%d/%s", ts->get_data_path().c_str(), testcase, buf );
imglist.push_back(string(filepath));
}
fclose(f);
if( imglist.size() == 0 || imglist.size() % 2 != 0 )
{
ts->printf( cvtest::TS::LOG, "The number of images is 0 or an odd number in the case #%d\n", testcase );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
int nframes = (int)(imglist.size()/2);
int npoints = patternSize.width*patternSize.height;
vector<vector<Point3f> > objpt(nframes);
vector<vector<Point2f> > imgpt1(nframes);
vector<vector<Point2f> > imgpt2(nframes);
Size imgsize;
int total = 0;
for( int i = 0; i < nframes; i++ )
{
Mat left = imread(imglist[i*2]);
Mat right = imread(imglist[i*2+1]);
if(left.empty() || right.empty())
{
ts->printf( cvtest::TS::LOG, "Can not load images %s and %s, testcase %d\n",
imglist[i*2].c_str(), imglist[i*2+1].c_str(), testcase );
ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
return;
}
imgsize = left.size();
bool found1 = findChessboardCorners(left, patternSize, imgpt1[i]);
bool found2 = findChessboardCorners(right, patternSize, imgpt2[i]);
if(!found1 || !found2)
{
ts->printf( cvtest::TS::LOG, "The function could not detect boards (%d x %d) on the images %s and %s, testcase %d\n",
patternSize.width, patternSize.height,
imglist[i*2].c_str(), imglist[i*2+1].c_str(), testcase );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
total += (int)imgpt1[i].size();
for( int j = 0; j < npoints; j++ )
objpt[i].push_back(Point3f((float)(j%patternSize.width), (float)(j/patternSize.width), 0.f));
}
// rectify (calibrated)
Mat M1 = Mat::eye(3,3,CV_64F), M2 = Mat::eye(3,3,CV_64F), D1(5,1,CV_64F), D2(5,1,CV_64F), R, T, E, F;
M1.at<double>(0,2) = M2.at<double>(0,2)=(imgsize.width-1)*0.5;
M1.at<double>(1,2) = M2.at<double>(1,2)=(imgsize.height-1)*0.5;
D1 = Scalar::all(0);
D2 = Scalar::all(0);
double err = calibrateStereoCamera(objpt, imgpt1, imgpt2, M1, D1, M2, D2, imgsize, R, T, E, F,
TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 30, 1e-6),
CV_CALIB_SAME_FOCAL_LENGTH
//+ CV_CALIB_FIX_ASPECT_RATIO
+ CV_CALIB_FIX_PRINCIPAL_POINT
+ CV_CALIB_ZERO_TANGENT_DIST
+ CV_CALIB_FIX_K3
+ CV_CALIB_FIX_K4 + CV_CALIB_FIX_K5 //+ CV_CALIB_FIX_K6
);
err /= nframes*npoints;
if( err > maxReprojErr )
{
ts->printf( cvtest::TS::LOG, "The average reprojection error is too big (=%g), testcase %d\n", err, testcase);
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
Mat R1, R2, P1, P2, Q;
Rect roi1, roi2;
rectify(M1, D1, M2, D2, imgsize, R, T, R1, R2, P1, P2, Q, 1, imgsize, &roi1, &roi2, 0);
Mat eye33 = Mat::eye(3,3,CV_64F);
Mat R1t = R1.t(), R2t = R2.t();
if( cvtest::norm(R1t*R1 - eye33, NORM_L2) > 0.01 ||
cvtest::norm(R2t*R2 - eye33, NORM_L2) > 0.01 ||
abs(determinant(F)) > 0.01)
{
ts->printf( cvtest::TS::LOG, "The computed (by rectify) R1 and R2 are not orthogonal,"
"or the computed (by calibrate) F is not singular, testcase %d\n", testcase);
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
if(!checkPandROI(testcase, M1, D1, R1, P1, imgsize, roi1))
{
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
if(!checkPandROI(testcase, M2, D2, R2, P2, imgsize, roi2))
{
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
//check that Tx after rectification is equal to distance between cameras
double tx = fabs(P2.at<double>(0, 3) / P2.at<double>(0, 0));
if (fabs(tx - cvtest::norm(T, NORM_L2)) > 1e-5)
{
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
//check that Q reprojects points before the camera
double testPoint[4] = {0.0, 0.0, 100.0, 1.0};
Mat reprojectedTestPoint = Q * Mat_<double>(4, 1, testPoint);
CV_Assert(reprojectedTestPoint.type() == CV_64FC1);
if( reprojectedTestPoint.at<double>(2) / reprojectedTestPoint.at<double>(3) < 0 )
{
ts->printf( cvtest::TS::LOG, "A point after rectification is reprojected behind the camera, testcase %d\n", testcase);
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
//check that Q reprojects the same points as reconstructed by triangulation
const float minCoord = -300.0f;
const float maxCoord = 300.0f;
const float minDisparity = 0.1f;
const float maxDisparity = 60.0f;
const int pointsCount = 500;
const float requiredAccuracy = 1e-3f;
const float allowToFail = 0.2f; // 20%
RNG& rng = ts->get_rng();
Mat projectedPoints_1(2, pointsCount, CV_32FC1);
Mat projectedPoints_2(2, pointsCount, CV_32FC1);
Mat disparities(1, pointsCount, CV_32FC1);
rng.fill(projectedPoints_1, RNG::UNIFORM, minCoord, maxCoord);
rng.fill(disparities, RNG::UNIFORM, minDisparity, maxDisparity);
projectedPoints_2.row(0) = projectedPoints_1.row(0) - disparities;
Mat ys_2 = projectedPoints_2.row(1);
projectedPoints_1.row(1).copyTo(ys_2);
Mat points4d;
triangulate(P1, P2, projectedPoints_1, projectedPoints_2, points4d);
Mat homogeneousPoints4d = points4d.t();
const int dimension = 4;
homogeneousPoints4d = homogeneousPoints4d.reshape(dimension);
Mat triangulatedPoints;
convertPointsFromHomogeneous(homogeneousPoints4d, triangulatedPoints);
Mat sparsePoints;
sparsePoints.push_back(projectedPoints_1);
sparsePoints.push_back(disparities);
sparsePoints = sparsePoints.t();
sparsePoints = sparsePoints.reshape(3);
Mat reprojectedPoints;
perspectiveTransform(sparsePoints, reprojectedPoints, Q);
Mat diff;
absdiff(triangulatedPoints, reprojectedPoints, diff);
Mat mask = diff > requiredAccuracy;
mask = mask.reshape(1);
mask = mask.col(0) | mask.col(1) | mask.col(2);
int numFailed = countNonZero(mask);
#if 0
std::cout << "numFailed=" << numFailed << std::endl;
for (int i = 0; i < triangulatedPoints.rows; i++)
{
if (mask.at<uchar>(i))
{
// failed points usually have 'w'~0 (points4d[3])
std::cout << "i=" << i << " triangulatePoints=" << triangulatedPoints.row(i) << " reprojectedPoints=" << reprojectedPoints.row(i) << std::endl <<
" points4d=" << points4d.col(i).t() << " projectedPoints_1=" << projectedPoints_1.col(i).t() << " disparities=" << disparities.col(i).t() << std::endl;
}
}
#endif
if (numFailed >= allowToFail * pointsCount)
{
ts->printf( cvtest::TS::LOG, "Points reprojected with a matrix Q and points reconstructed by triangulation are different (tolerance=%g, failed=%d), testcase %d\n",
requiredAccuracy, numFailed, testcase);
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
//check correctMatches
const float constraintAccuracy = 1e-5f;
Mat newPoints1, newPoints2;
Mat points1 = projectedPoints_1.t();
points1 = points1.reshape(2, 1);
Mat points2 = projectedPoints_2.t();
points2 = points2.reshape(2, 1);
correctMatches(F, points1, points2, newPoints1, newPoints2);
Mat newHomogeneousPoints1, newHomogeneousPoints2;
convertPointsToHomogeneous(newPoints1, newHomogeneousPoints1);
convertPointsToHomogeneous(newPoints2, newHomogeneousPoints2);
newHomogeneousPoints1 = newHomogeneousPoints1.reshape(1);
newHomogeneousPoints2 = newHomogeneousPoints2.reshape(1);
Mat typedF;
F.convertTo(typedF, newHomogeneousPoints1.type());
for (int i = 0; i < newHomogeneousPoints1.rows; ++i)
{
Mat error = newHomogeneousPoints2.row(i) * typedF * newHomogeneousPoints1.row(i).t();
CV_Assert(error.rows == 1 && error.cols == 1);
if (cvtest::norm(error, NORM_L2) > constraintAccuracy)
{
ts->printf( cvtest::TS::LOG, "Epipolar constraint is violated after correctMatches, testcase %d\n", testcase);
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
}
}
// rectifyUncalibrated
CV_Assert( imgpt1.size() == imgpt2.size() );
Mat _imgpt1( total, 1, CV_32FC2 ), _imgpt2( total, 1, CV_32FC2 );
vector<vector<Point2f> >::const_iterator iit1 = imgpt1.begin();
vector<vector<Point2f> >::const_iterator iit2 = imgpt2.begin();
for( int pi = 0; iit1 != imgpt1.end(); ++iit1, ++iit2 )
{
vector<Point2f>::const_iterator pit1 = iit1->begin();
vector<Point2f>::const_iterator pit2 = iit2->begin();
CV_Assert( iit1->size() == iit2->size() );
for( ; pit1 != iit1->end(); ++pit1, ++pit2, pi++ )
{
_imgpt1.at<Point2f>(pi,0) = Point2f( pit1->x, pit1->y );
_imgpt2.at<Point2f>(pi,0) = Point2f( pit2->x, pit2->y );
}
}
Mat _M1, _M2, _D1, _D2;
vector<Mat> _R1, _R2, _T1, _T2;
calibrateCamera( objpt, imgpt1, imgsize, _M1, _D1, _R1, _T1, 0 );
calibrateCamera( objpt, imgpt2, imgsize, _M2, _D2, _R2, _T2, 0 );
undistortPoints( _imgpt1, _imgpt1, _M1, _D1, Mat(), _M1 );
undistortPoints( _imgpt2, _imgpt2, _M2, _D2, Mat(), _M2 );
Mat matF, _H1, _H2;
matF = findFundamentalMat( _imgpt1, _imgpt2 );
rectifyUncalibrated( _imgpt1, _imgpt2, matF, imgsize, _H1, _H2 );
Mat rectifPoints1, rectifPoints2;
perspectiveTransform( _imgpt1, rectifPoints1, _H1 );
perspectiveTransform( _imgpt2, rectifPoints2, _H2 );
bool verticalStereo = abs(P2.at<double>(0,3)) < abs(P2.at<double>(1,3));
double maxDiff_c = 0, maxDiff_uc = 0;
for( int i = 0, k = 0; i < nframes; i++ )
{
vector<Point2f> temp[2];
undistortPoints(imgpt1[i], temp[0], M1, D1, R1, P1);
undistortPoints(imgpt2[i], temp[1], M2, D2, R2, P2);
for( int j = 0; j < npoints; j++, k++ )
{
double diff_c = verticalStereo ? abs(temp[0][j].x - temp[1][j].x) : abs(temp[0][j].y - temp[1][j].y);
Point2f d = rectifPoints1.at<Point2f>(k,0) - rectifPoints2.at<Point2f>(k,0);
double diff_uc = verticalStereo ? abs(d.x) : abs(d.y);
maxDiff_c = max(maxDiff_c, diff_c);
maxDiff_uc = max(maxDiff_uc, diff_uc);
if( maxDiff_c > maxScanlineDistErr_c )
{
ts->printf( cvtest::TS::LOG, "The distance between %s coordinates is too big(=%g) (used calibrated stereo), testcase %d\n",
verticalStereo ? "x" : "y", diff_c, testcase);
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
if( maxDiff_uc > maxScanlineDistErr_uc )
{
ts->printf( cvtest::TS::LOG, "The distance between %s coordinates is too big(=%g) (used uncalibrated stereo), testcase %d\n",
verticalStereo ? "x" : "y", diff_uc, testcase);
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
}
}
ts->printf( cvtest::TS::LOG, "Testcase %d. Max distance (calibrated) =%g\n"
"Max distance (uncalibrated) =%g\n", testcase, maxDiff_c, maxDiff_uc );
}
}
//-------------------------------- CV_StereoCalibrationTest_CPP ------------------------------
class CV_StereoCalibrationTest_CPP : public CV_StereoCalibrationTest
{
public:
CV_StereoCalibrationTest_CPP() {}
protected:
virtual double calibrateStereoCamera( const vector<vector<Point3f> >& objectPoints,
const vector<vector<Point2f> >& imagePoints1,
const vector<vector<Point2f> >& imagePoints2,
Mat& cameraMatrix1, Mat& distCoeffs1,
Mat& cameraMatrix2, Mat& distCoeffs2,
Size imageSize, Mat& R, Mat& T,
Mat& E, Mat& F, TermCriteria criteria, int flags );
virtual void rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1,
const Mat& cameraMatrix2, const Mat& distCoeffs2,
Size imageSize, const Mat& R, const Mat& T,
Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q,
double alpha, Size newImageSize,
Rect* validPixROI1, Rect* validPixROI2, int flags );
virtual bool rectifyUncalibrated( const Mat& points1,
const Mat& points2, const Mat& F, Size imgSize,
Mat& H1, Mat& H2, double threshold=5 );
virtual void triangulate( const Mat& P1, const Mat& P2,
const Mat &points1, const Mat &points2,
Mat &points4D );
virtual void correct( const Mat& F,
const Mat &points1, const Mat &points2,
Mat &newPoints1, Mat &newPoints2 );
};
double CV_StereoCalibrationTest_CPP::calibrateStereoCamera( const vector<vector<Point3f> >& objectPoints,
const vector<vector<Point2f> >& imagePoints1,
const vector<vector<Point2f> >& imagePoints2,
Mat& cameraMatrix1, Mat& distCoeffs1,
Mat& cameraMatrix2, Mat& distCoeffs2,
Size imageSize, Mat& R, Mat& T,
Mat& E, Mat& F, TermCriteria criteria, int flags )
{
return stereoCalibrate( objectPoints, imagePoints1, imagePoints2,
cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2,
imageSize, R, T, E, F, flags, criteria );
}
void CV_StereoCalibrationTest_CPP::rectify( const Mat& cameraMatrix1, const Mat& distCoeffs1,
const Mat& cameraMatrix2, const Mat& distCoeffs2,
Size imageSize, const Mat& R, const Mat& T,
Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q,
double alpha, Size newImageSize,
Rect* validPixROI1, Rect* validPixROI2, int flags )
{
stereoRectify( cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2,
imageSize, R, T, R1, R2, P1, P2, Q, flags, alpha, newImageSize,validPixROI1, validPixROI2 );
}
bool CV_StereoCalibrationTest_CPP::rectifyUncalibrated( const Mat& points1,
const Mat& points2, const Mat& F, Size imgSize, Mat& H1, Mat& H2, double threshold )
{
return stereoRectifyUncalibrated( points1, points2, F, imgSize, H1, H2, threshold );
}
void CV_StereoCalibrationTest_CPP::triangulate( const Mat& P1, const Mat& P2,
const Mat &points1, const Mat &points2,
Mat &points4D )
{
triangulatePoints(P1, P2, points1, points2, points4D);
}
void CV_StereoCalibrationTest_CPP::correct( const Mat& F,
const Mat &points1, const Mat &points2,
Mat &newPoints1, Mat &newPoints2 )
{
correctMatches(F, points1, points2, newPoints1, newPoints2);
}
///////////////////////////////////////////////////////////////////////////////////////////////////
TEST(Calib3d_CalibrateCamera_CPP, regression) { CV_CameraCalibrationTest_CPP test; test.safe_run(); }
TEST(Calib3d_CalibrationMatrixValues_CPP, accuracy) { CV_CalibrationMatrixValuesTest_CPP test; test.safe_run(); }
TEST(Calib3d_ProjectPoints_CPP, regression) { CV_ProjectPointsTest_CPP test; test.safe_run(); }
TEST(Calib3d_ProjectPoints_CPP, inputShape)
{
Matx31d rvec = Matx31d::zeros();
Matx31d tvec(0, 0, 1);
Matx33d cameraMatrix = Matx33d::eye();
const float L = 0.1f;
{
//3xN 1-channel
Mat objectPoints = (Mat_<float>(3, 2) << -L, L,
L, L,
0, 0);
vector<Point2f> imagePoints;
projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
EXPECT_EQ(objectPoints.cols, static_cast<int>(imagePoints.size()));
EXPECT_NEAR(imagePoints[0].x, -L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[0].y, L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[1].x, L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[1].y, L, std::numeric_limits<float>::epsilon());
}
{
//Nx2 1-channel
Mat objectPoints = (Mat_<float>(2, 3) << -L, L, 0,
L, L, 0);
vector<Point2f> imagePoints;
projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
EXPECT_EQ(objectPoints.rows, static_cast<int>(imagePoints.size()));
EXPECT_NEAR(imagePoints[0].x, -L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[0].y, L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[1].x, L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[1].y, L, std::numeric_limits<float>::epsilon());
}
{
//1xN 3-channel
Mat objectPoints(1, 2, CV_32FC3);
objectPoints.at<Vec3f>(0,0) = Vec3f(-L, L, 0);
objectPoints.at<Vec3f>(0,1) = Vec3f(L, L, 0);
vector<Point2f> imagePoints;
projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
EXPECT_EQ(objectPoints.cols, static_cast<int>(imagePoints.size()));
EXPECT_NEAR(imagePoints[0].x, -L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[0].y, L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[1].x, L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[1].y, L, std::numeric_limits<float>::epsilon());
}
{
//Nx1 3-channel
Mat objectPoints(2, 1, CV_32FC3);
objectPoints.at<Vec3f>(0,0) = Vec3f(-L, L, 0);
objectPoints.at<Vec3f>(1,0) = Vec3f(L, L, 0);
vector<Point2f> imagePoints;
projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
EXPECT_EQ(objectPoints.rows, static_cast<int>(imagePoints.size()));
EXPECT_NEAR(imagePoints[0].x, -L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[0].y, L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[1].x, L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[1].y, L, std::numeric_limits<float>::epsilon());
}
{
//vector<Point3f>
vector<Point3f> objectPoints;
objectPoints.push_back(Point3f(-L, L, 0));
objectPoints.push_back(Point3f(L, L, 0));
vector<Point2f> imagePoints;
projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
EXPECT_EQ(objectPoints.size(), imagePoints.size());
EXPECT_NEAR(imagePoints[0].x, -L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[0].y, L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[1].x, L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[1].y, L, std::numeric_limits<float>::epsilon());
}
{
//vector<Point3d>
vector<Point3d> objectPoints;
objectPoints.push_back(Point3d(-L, L, 0));
objectPoints.push_back(Point3d(L, L, 0));
vector<Point2d> imagePoints;
projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
EXPECT_EQ(objectPoints.size(), imagePoints.size());
EXPECT_NEAR(imagePoints[0].x, -L, std::numeric_limits<double>::epsilon());
EXPECT_NEAR(imagePoints[0].y, L, std::numeric_limits<double>::epsilon());
EXPECT_NEAR(imagePoints[1].x, L, std::numeric_limits<double>::epsilon());
EXPECT_NEAR(imagePoints[1].y, L, std::numeric_limits<double>::epsilon());
}
}
TEST(Calib3d_ProjectPoints_CPP, outputShape)
{
Matx31d rvec = Matx31d::zeros();
Matx31d tvec(0, 0, 1);
Matx33d cameraMatrix = Matx33d::eye();
const float L = 0.1f;
{
vector<Point3f> objectPoints;
objectPoints.push_back(Point3f(-L, L, 0));
objectPoints.push_back(Point3f( L, L, 0));
objectPoints.push_back(Point3f( L, -L, 0));
//Mat --> will be Nx1 2-channel
Mat imagePoints;
projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
EXPECT_EQ(static_cast<int>(objectPoints.size()), imagePoints.rows);
EXPECT_NEAR(imagePoints.at<Vec2f>(0,0)(0), -L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints.at<Vec2f>(0,0)(1), L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints.at<Vec2f>(1,0)(0), L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints.at<Vec2f>(1,0)(1), L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints.at<Vec2f>(2,0)(0), L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints.at<Vec2f>(2,0)(1), -L, std::numeric_limits<float>::epsilon());
}
{
vector<Point3f> objectPoints;
objectPoints.push_back(Point3f(-L, L, 0));
objectPoints.push_back(Point3f( L, L, 0));
objectPoints.push_back(Point3f( L, -L, 0));
//Nx1 2-channel
Mat imagePoints(3,1,CV_32FC2);
projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
EXPECT_EQ(static_cast<int>(objectPoints.size()), imagePoints.rows);
EXPECT_NEAR(imagePoints.at<Vec2f>(0,0)(0), -L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints.at<Vec2f>(0,0)(1), L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints.at<Vec2f>(1,0)(0), L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints.at<Vec2f>(1,0)(1), L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints.at<Vec2f>(2,0)(0), L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints.at<Vec2f>(2,0)(1), -L, std::numeric_limits<float>::epsilon());
}
{
vector<Point3f> objectPoints;
objectPoints.push_back(Point3f(-L, L, 0));
objectPoints.push_back(Point3f( L, L, 0));
objectPoints.push_back(Point3f( L, -L, 0));
//1xN 2-channel
Mat imagePoints(1,3,CV_32FC2);
projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
EXPECT_EQ(static_cast<int>(objectPoints.size()), imagePoints.cols);
EXPECT_NEAR(imagePoints.at<Vec2f>(0,0)(0), -L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints.at<Vec2f>(0,0)(1), L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints.at<Vec2f>(0,1)(0), L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints.at<Vec2f>(0,1)(1), L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints.at<Vec2f>(0,2)(0), L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints.at<Vec2f>(0,2)(1), -L, std::numeric_limits<float>::epsilon());
}
{
vector<Point3f> objectPoints;
objectPoints.push_back(Point3f(-L, L, 0));
objectPoints.push_back(Point3f(L, L, 0));
//vector<Point2f>
vector<Point2f> imagePoints;
projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
EXPECT_EQ(objectPoints.size(), imagePoints.size());
EXPECT_NEAR(imagePoints[0].x, -L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[0].y, L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[1].x, L, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(imagePoints[1].y, L, std::numeric_limits<float>::epsilon());
}
{
vector<Point3d> objectPoints;
objectPoints.push_back(Point3d(-L, L, 0));
objectPoints.push_back(Point3d(L, L, 0));
//vector<Point2d>
vector<Point2d> imagePoints;
projectPoints(objectPoints, rvec, tvec, cameraMatrix, noArray(), imagePoints);
EXPECT_EQ(objectPoints.size(), imagePoints.size());
EXPECT_NEAR(imagePoints[0].x, -L, std::numeric_limits<double>::epsilon());
EXPECT_NEAR(imagePoints[0].y, L, std::numeric_limits<double>::epsilon());
EXPECT_NEAR(imagePoints[1].x, L, std::numeric_limits<double>::epsilon());
EXPECT_NEAR(imagePoints[1].y, L, std::numeric_limits<double>::epsilon());
}
}
TEST(Calib3d_StereoCalibrate_CPP, regression) { CV_StereoCalibrationTest_CPP test; test.safe_run(); }
TEST(Calib3d_StereoCalibrate_CPP, extended)
{
cvtest::TS* ts = cvtest::TS::ptr();
String filepath = cv::format("%scv/stereo/case%d/", ts->get_data_path().c_str(), 1 );
Mat left = imread(filepath+"left01.png");
Mat right = imread(filepath+"right01.png");
if(left.empty() || right.empty())
{
ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
return;
}
vector<vector<Point2f> > imgpt1(1), imgpt2(1);
vector<vector<Point3f> > objpt(1);
Size patternSize(9, 6), imageSize(640, 480);
bool found1 = findChessboardCorners(left, patternSize, imgpt1[0]);
bool found2 = findChessboardCorners(right, patternSize, imgpt2[0]);
if(!found1 || !found2)
{
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
for( int j = 0; j < patternSize.width*patternSize.height; j++ )
objpt[0].push_back(Point3f((float)(j%patternSize.width), (float)(j/patternSize.width), 0.f));
Mat K1, K2, c1, c2, R, T, E, F, err;
int flags = 0;
double res0 = stereoCalibrate( objpt, imgpt1, imgpt2,
K1, c1, K2, c2,
imageSize, R, T, E, F, err, flags);
flags = CALIB_USE_EXTRINSIC_GUESS;
double res1 = stereoCalibrate( objpt, imgpt1, imgpt2,
K1, c1, K2, c2,
imageSize, R, T, E, F, err, flags);
EXPECT_LE(res1, res0);
EXPECT_TRUE(err.total() == 2);
}
TEST(Calib3d_StereoCalibrate, regression_10791)
{
const Matx33d M1(
853.1387981631528, 0, 704.154907802121,
0, 853.6445089162528, 520.3600712930319,
0, 0, 1
);
const Matx33d M2(
848.6090216909176, 0, 701.6162856852185,
0, 849.7040162357157, 509.1864036137,
0, 0, 1
);
const Matx<double, 14, 1> D1(-6.463598629567206, 79.00104930508179, -0.0001006144444464403, -0.0005437499822299972,
12.56900616588467, -6.056719942752855, 76.3842481414836, 45.57460250612659,
0, 0, 0, 0, 0, 0);
const Matx<double, 14, 1> D2(0.6123436439798265, -0.4671756923224087, -0.0001261947899033442, -0.000597334584036978,
-0.05660119809538371, 1.037075740629769, -0.3076042835831711, -0.2502169324283623,
0, 0, 0, 0, 0, 0);
const Matx33d R(
0.9999926627018476, -0.0001095586963765905, 0.003829169539302921,
0.0001021735876758584, 0.9999981346680941, 0.0019287874145156,
-0.003829373712065528, -0.001928382022437616, 0.9999908085776333
);
const Matx31d T(-58.9161771697128, -0.01581306249996402, -0.8492960216760961);
const Size imageSize(1280, 960);
Mat R1, R2, P1, P2, Q;
Rect roi1, roi2;
stereoRectify(M1, D1, M2, D2, imageSize, R, T,
R1, R2, P1, P2, Q,
CALIB_ZERO_DISPARITY, 1, imageSize, &roi1, &roi2);
EXPECT_GE(roi1.area(), 400*300) << roi1;
EXPECT_GE(roi2.area(), 400*300) << roi2;
}
TEST(Calib3d_StereoCalibrate, regression_11131)
{
const Matx33d M1(
1457.572438721727, 0, 1212.945694211622,
0, 1457.522226502963, 1007.32058848921,
0, 0, 1
);
const Matx33d M2(
1460.868570835972, 0, 1215.024068023046,
0, 1460.791367088, 1011.107202932225,
0, 0, 1
);
const Matx<double, 5, 1> D1(0, 0, 0, 0, 0);
const Matx<double, 5, 1> D2(0, 0, 0, 0, 0);
const Matx33d R(
0.9985404059825475, 0.02963547172078553, -0.04515303352041626,
-0.03103795276460111, 0.9990471552537432, -0.03068268351343364,
0.04420071389006859, 0.03203935697372317, 0.9985087763742083
);
const Matx31d T(0.9995500167379527, 0.0116311595111068, 0.02764923448462666);
const Size imageSize(2456, 2058);
Mat R1, R2, P1, P2, Q;
Rect roi1, roi2;
stereoRectify(M1, D1, M2, D2, imageSize, R, T,
R1, R2, P1, P2, Q,
CALIB_ZERO_DISPARITY, 1, imageSize, &roi1, &roi2);
EXPECT_GT(P1.at<double>(0, 0), 0);
EXPECT_GT(P2.at<double>(0, 0), 0);
EXPECT_GT(R1.at<double>(0, 0), 0);
EXPECT_GT(R2.at<double>(0, 0), 0);
EXPECT_GE(roi1.area(), 400*300) << roi1;
EXPECT_GE(roi2.area(), 400*300) << roi2;
}
TEST(Calib3d_Triangulate, accuracy)
{
// the testcase from http://code.opencv.org/issues/4334
{
double P1data[] = { 250, 0, 200, 0, 0, 250, 150, 0, 0, 0, 1, 0 };
double P2data[] = { 250, 0, 200, -250, 0, 250, 150, 0, 0, 0, 1, 0 };
Mat P1(3, 4, CV_64F, P1data), P2(3, 4, CV_64F, P2data);
float x1data[] = { 200.f, 0.f };
float x2data[] = { 170.f, 1.f };
float Xdata[] = { 0.f, -5.f, 25/3.f };
Mat x1(2, 1, CV_32F, x1data);
Mat x2(2, 1, CV_32F, x2data);
Mat res0(1, 3, CV_32F, Xdata);
Mat res_, res;
triangulatePoints(P1, P2, x1, x2, res_);
cv::transpose(res_, res_); // TODO cvtest (transpose doesn't support inplace)
convertPointsFromHomogeneous(res_, res);
res = res.reshape(1, 1);
cout << "[1]:" << endl;
cout << "\tres0: " << res0 << endl;
cout << "\tres: " << res << endl;
ASSERT_LE(cvtest::norm(res, res0, NORM_INF), 1e-1);
}
// another testcase http://code.opencv.org/issues/3461
{
Matx33d K1(6137.147949, 0.000000, 644.974609,
0.000000, 6137.147949, 573.442749,
0.000000, 0.000000, 1.000000);
Matx33d K2(6137.147949, 0.000000, 644.674438,
0.000000, 6137.147949, 573.079834,
0.000000, 0.000000, 1.000000);
Matx34d RT1(1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, 0);
Matx34d RT2(0.998297, 0.0064108, -0.0579766, 143.614334,
-0.0065818, 0.999975, -0.00275888, -5.160085,
0.0579574, 0.00313577, 0.998314, 96.066109);
Matx34d P1 = K1*RT1;
Matx34d P2 = K2*RT2;
float x1data[] = { 438.f, 19.f };
float x2data[] = { 452.363600f, 16.452225f };
float Xdata[] = { -81.049530f, -215.702804f, 2401.645449f };
Mat x1(2, 1, CV_32F, x1data);
Mat x2(2, 1, CV_32F, x2data);
Mat res0(1, 3, CV_32F, Xdata);
Mat res_, res;
triangulatePoints(P1, P2, x1, x2, res_);
cv::transpose(res_, res_); // TODO cvtest (transpose doesn't support inplace)
convertPointsFromHomogeneous(res_, res);
res = res.reshape(1, 1);
cout << "[2]:" << endl;
cout << "\tres0: " << res0 << endl;
cout << "\tres: " << res << endl;
ASSERT_LE(cvtest::norm(res, res0, NORM_INF), 2);
}
}
///////////////////////////////////////////////////////////////////////////////////////////////////
TEST(CV_RecoverPoseTest, regression_15341)
{
// initialize test data
const int invalid_point_count = 2;
const float _points1_[] = {
1537.7f, 166.8f,
1599.1f, 179.6f,
1288.0f, 207.5f,
1507.1f, 193.2f,
1742.7f, 210.0f,
1041.6f, 271.7f,
1591.8f, 247.2f,
1524.0f, 261.3f,
1330.3f, 285.0f,
1403.1f, 284.0f,
1506.6f, 342.9f,
1502.8f, 347.3f,
1344.9f, 364.9f,
0.0f, 0.0f // last point is initial invalid
};
const float _points2_[] = {
1533.4f, 532.9f,
1596.6f, 552.4f,
1277.0f, 556.4f,
1502.1f, 557.6f,
1744.4f, 601.3f,
1023.0f, 612.6f,
1589.2f, 621.6f,
1519.4f, 629.0f,
1320.3f, 637.3f,
1395.2f, 642.2f,
1501.5f, 710.3f,
1497.6f, 714.2f,
1335.1f, 719.61f,
1000.0f, 1000.0f // last point is initial invalid
};
vector<Point2f> _points1; Mat(14, 1, CV_32FC2, (void*)_points1_).copyTo(_points1);
vector<Point2f> _points2; Mat(14, 1, CV_32FC2, (void*)_points2_).copyTo(_points2);
const int point_count = (int) _points1.size();
CV_Assert(point_count == (int) _points2.size());
// camera matrix with both focal lengths = 1, and principal point = (0, 0)
const Mat cameraMatrix = Mat::eye(3, 3, CV_64F);
// camera matrix with focal lengths 0.5 and 0.6 respectively and principal point = (100, 200)
double cameraMatrix2Data[] = { 0.5, 0, 100,
0, 0.6, 200,
0, 0, 1 };
const Mat cameraMatrix2( 3, 3, CV_64F, cameraMatrix2Data );
// zero and nonzero distortion coefficients
double nonZeroDistCoeffsData[] = { 0.01, 0.0001, 0, 0, 1e-04, 0.2, 0.02, 0.0002 }; // k1, k2, p1, p2, k3, k4, k5, k6
vector<Mat> distCoeffsList = {Mat::zeros(1, 5, CV_64F), Mat{1, 8, CV_64F, nonZeroDistCoeffsData}};
const auto &zeroDistCoeffs = distCoeffsList[0];
int Inliers = 0;
const int ntests = 3;
for (int testcase = 1; testcase <= ntests; ++testcase)
{
if (testcase == 1) // testcase with vector input data
{
// init temporary test data
vector<unsigned char> mask(point_count);
vector<Point2f> points1(_points1);
vector<Point2f> points2(_points2);
// Estimation of fundamental matrix using the RANSAC algorithm
Mat E, E2, R, t;
// Check pose when camera matrices are different.
for (const auto &distCoeffs: distCoeffsList)
{
E = findEssentialMat(points1, points2, cameraMatrix, distCoeffs, cameraMatrix2, distCoeffs, RANSAC, 0.999, 1.0, mask);
recoverPose(points1, points2, cameraMatrix, distCoeffs, cameraMatrix2, distCoeffs, E2, R, t, RANSAC, 0.999, 1.0, mask);
EXPECT_LT(cv::norm(E, E2, NORM_INF), 1e-4) <<
"Two big difference between the same essential matrices computed using different functions with different cameras, testcase " << testcase;
EXPECT_EQ(0, (int)mask[13]) << "Detecting outliers in function failed with different cameras, testcase " << testcase;
}
// Check pose when camera matrices are the same.
E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
E2 = findEssentialMat(points1, points2, cameraMatrix, zeroDistCoeffs, cameraMatrix, zeroDistCoeffs, RANSAC, 0.999, 1.0, mask);
EXPECT_LT(cv::norm(E, E2, NORM_INF), 1e-4) <<
"Two big difference between the same essential matrices computed using different functions with same cameras, testcase " << testcase;
EXPECT_EQ(0, (int)mask[13]) << "Detecting outliers in function findEssentialMat failed with same cameras, testcase " << testcase;
points2[12] = Point2f(0.0f, 0.0f); // provoke another outlier detection for recover Pose
Inliers = recoverPose(E, points1, points2, cameraMatrix, R, t, mask);
EXPECT_EQ(0, (int)mask[12]) << "Detecting outliers in function failed with same cameras, testcase " << testcase;
}
else // testcase with mat input data
{
Mat points1(_points1, true);
Mat points2(_points2, true);
Mat mask;
if (testcase == 2)
{
// init temporary testdata
mask = Mat::zeros(point_count, 1, CV_8UC1);
}
else // testcase == 3 - with transposed mask
{
mask = Mat::zeros(1, point_count, CV_8UC1);
}
// Estimation of fundamental matrix using the RANSAC algorithm
Mat E, E2, R, t;
// Check pose when camera matrices are different.
for (const auto &distCoeffs: distCoeffsList)
{
E = findEssentialMat(points1, points2, cameraMatrix, distCoeffs, cameraMatrix2, distCoeffs, RANSAC, 0.999, 1.0, mask);
recoverPose(points1, points2, cameraMatrix, distCoeffs, cameraMatrix2, distCoeffs, E2, R, t, RANSAC, 0.999, 1.0, mask);
EXPECT_LT(cv::norm(E, E2, NORM_INF), 1e-4) <<
"Two big difference between the same essential matrices computed using different functions with different cameras, testcase " << testcase;
EXPECT_EQ(0, (int)mask.at<unsigned char>(13)) << "Detecting outliers in function failed with different cameras, testcase " << testcase;
}
// Check pose when camera matrices are the same.
E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
E2 = findEssentialMat(points1, points2, cameraMatrix, zeroDistCoeffs, cameraMatrix, zeroDistCoeffs, RANSAC, 0.999, 1.0, mask);
EXPECT_LT(cv::norm(E, E2, NORM_INF), 1e-4) <<
"Two big difference between the same essential matrices computed using different functions with same cameras, testcase " << testcase;
EXPECT_EQ(0, (int)mask.at<unsigned char>(13)) << "Detecting outliers in function findEssentialMat failed with same cameras, testcase " << testcase;
points2.at<Point2f>(12) = Point2f(0.0f, 0.0f); // provoke an outlier detection
Inliers = recoverPose(E, points1, points2, cameraMatrix, R, t, mask);
EXPECT_EQ(0, (int)mask.at<unsigned char>(12)) << "Detecting outliers in function failed with same cameras, testcase " << testcase;
}
EXPECT_EQ(Inliers, point_count - invalid_point_count) <<
"Number of inliers differs from expected number of inliers, testcase " << testcase;
}
}
}} // namespace
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