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opencv/modules/calib/test/test_chesscorners.cpp
Alexander Smorkalov c739117a7c Merge branch 4.x
2024-01-19 17:32:22 +03:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
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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.
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//
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// 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,
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// 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.
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// (including, but not limited to, procurement of substitute goods or services;
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//M*/
#include "test_precomp.hpp"
#include "test_chessboardgenerator.hpp"
#include <functional>
namespace opencv_test { namespace {
#define _L2_ERR
//#define DEBUG_CHESSBOARD
#ifdef DEBUG_CHESSBOARD
void show_points( const Mat& gray, const Mat& expected, const vector<Point2f>& actual, bool was_found )
{
Mat rgb( gray.size(), CV_8U);
merge(vector<Mat>(3, gray), rgb);
for(size_t i = 0; i < actual.size(); i++ )
circle( rgb, actual[i], 5, Scalar(0, 0, 200), 1, LINE_AA);
if( !expected.empty() )
{
const Point2f* u_data = expected.ptr<Point2f>();
size_t count = expected.cols * expected.rows;
for(size_t i = 0; i < count; i++ )
circle(rgb, u_data[i], 4, Scalar(0, 240, 0), 1, LINE_AA);
}
putText(rgb, was_found ? "FOUND !!!" : "NOT FOUND", Point(5, 20), FONT_HERSHEY_PLAIN, 1, Scalar(0, 240, 0));
imshow( "test", rgb ); while ((uchar)waitKey(0) != 'q') {};
}
#else
#define show_points(...)
#endif
enum Pattern { CHESSBOARD, CHESSBOARD_SB, CHESSBOARD_PLAIN, CIRCLES_GRID, ASYMMETRIC_CIRCLES_GRID};
class CV_ChessboardDetectorTest : public cvtest::BaseTest
{
public:
CV_ChessboardDetectorTest( Pattern pattern, int algorithmFlags = 0 );
protected:
void run(int);
void run_batch(const string& filename);
bool checkByGenerator();
bool checkByGeneratorHighAccuracy();
// wraps calls based on the given pattern
bool findChessboardCornersWrapper(InputArray image, Size patternSize, OutputArray corners,int flags);
Pattern pattern;
int algorithmFlags;
};
CV_ChessboardDetectorTest::CV_ChessboardDetectorTest( Pattern _pattern, int _algorithmFlags )
{
pattern = _pattern;
algorithmFlags = _algorithmFlags;
}
double calcError(const vector<Point2f>& v, const Mat& u)
{
int count_exp = u.cols * u.rows;
const Point2f* u_data = u.ptr<Point2f>();
double err = std::numeric_limits<double>::max();
for( int k = 0; k < 2; ++k )
{
double err1 = 0;
for( int j = 0; j < count_exp; ++j )
{
int j1 = k == 0 ? j : count_exp - j - 1;
double dx = fabs( v[j].x - u_data[j1].x );
double dy = fabs( v[j].y - u_data[j1].y );
#if defined(_L2_ERR)
err1 += dx*dx + dy*dy;
#else
dx = MAX( dx, dy );
if( dx > err1 )
err1 = dx;
#endif //_L2_ERR
//printf("dx = %f\n", dx);
}
//printf("\n");
err = min(err, err1);
}
#if defined(_L2_ERR)
err = sqrt(err/count_exp);
#endif //_L2_ERR
return err;
}
const double rough_success_error_level = 2.5;
const double precise_success_error_level = 2;
/* ///////////////////// chess_corner_test ///////////////////////// */
void CV_ChessboardDetectorTest::run( int /*start_from */)
{
ts->set_failed_test_info( cvtest::TS::OK );
/*if (!checkByGenerator())
return;*/
switch( pattern )
{
case CHESSBOARD_SB:
checkByGeneratorHighAccuracy(); // not supported by CHESSBOARD
/* fallthrough */
case CHESSBOARD_PLAIN:
checkByGenerator();
if (ts->get_err_code() != cvtest::TS::OK)
{
break;
}
run_batch("negative_list.dat");
if (ts->get_err_code() != cvtest::TS::OK)
{
break;
}
run_batch("chessboard_list.dat");
if (ts->get_err_code() != cvtest::TS::OK)
{
break;
}
break;
case CHESSBOARD:
checkByGenerator();
if (ts->get_err_code() != cvtest::TS::OK)
{
break;
}
run_batch("negative_list.dat");
if (ts->get_err_code() != cvtest::TS::OK)
{
break;
}
run_batch("chessboard_list.dat");
if (ts->get_err_code() != cvtest::TS::OK)
{
break;
}
run_batch("chessboard_list_subpixel.dat");
break;
case CIRCLES_GRID:
run_batch("circles_list.dat");
break;
case ASYMMETRIC_CIRCLES_GRID:
run_batch("acircles_list.dat");
break;
}
}
void CV_ChessboardDetectorTest::run_batch( const string& filename )
{
ts->printf(cvtest::TS::LOG, "\nRunning batch %s\n", filename.c_str());
//#define WRITE_POINTS 1
#ifndef WRITE_POINTS
double max_rough_error = 0, max_precise_error = 0;
#endif
string folder;
switch( pattern )
{
case CHESSBOARD:
case CHESSBOARD_SB:
case CHESSBOARD_PLAIN:
folder = string(ts->get_data_path()) + "cv/cameracalibration/";
break;
case CIRCLES_GRID:
folder = string(ts->get_data_path()) + "cv/cameracalibration/circles/";
break;
case ASYMMETRIC_CIRCLES_GRID:
folder = string(ts->get_data_path()) + "cv/cameracalibration/asymmetric_circles/";
break;
}
FileStorage fs( folder + filename, FileStorage::READ );
FileNode board_list = fs["boards"];
if( !fs.isOpened() || board_list.empty() || !board_list.isSeq() || board_list.size() % 2 != 0 )
{
ts->printf( cvtest::TS::LOG, "%s can not be read or is not valid\n", (folder + filename).c_str() );
ts->printf( cvtest::TS::LOG, "fs.isOpened=%d, board_list.empty=%d, board_list.isSeq=%d,board_list.size()%2=%d\n",
fs.isOpened(), (int)board_list.empty(), board_list.isSeq(), board_list.size()%2);
ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
return;
}
int progress = 0;
int max_idx = (int)board_list.size()/2;
if(filename.compare("chessboard_list.dat") == 0 && pattern == CHESSBOARD_PLAIN)
max_idx = 7;
double sum_error = 0.0;
int count = 0;
for(int idx = 0; idx < max_idx; ++idx )
{
ts->update_context( this, idx, true );
/* read the image */
String img_file = board_list[idx * 2];
Mat gray = imread( folder + img_file, IMREAD_GRAYSCALE);
if( gray.empty() )
{
ts->printf( cvtest::TS::LOG, "one of chessboard images can't be read: %s\n", img_file.c_str() );
ts->set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
return;
}
String _filename = folder + (String)board_list[idx * 2 + 1];
bool doesContatinChessboard;
float sharpness;
Mat expected;
{
FileStorage fs1(_filename, FileStorage::READ);
fs1["corners"] >> expected;
fs1["isFound"] >> doesContatinChessboard;
fs1["sharpness"] >> sharpness ;
fs1.release();
}
size_t count_exp = static_cast<size_t>(expected.cols * expected.rows);
Size pattern_size = expected.size();
Mat ori;
vector<Point2f> v;
int flags = 0;
switch( pattern )
{
case CHESSBOARD:
flags = CALIB_CB_ADAPTIVE_THRESH | CALIB_CB_NORMALIZE_IMAGE;
break;
case CHESSBOARD_PLAIN: {
flags = CALIB_CB_PLAIN;
ori = gray.clone();
int min_size = cvRound((gray.cols * gray.rows * 0.05) / ((pattern_size.width+1) * (pattern_size.height+1)));
if(min_size%2==0) min_size += 1;
adaptiveThreshold(gray, gray, 255, ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY, min_size, 0);
dilate(gray, gray, Mat(), Point(-1, -1), 1);
break;
}
case CIRCLES_GRID:
case CHESSBOARD_SB:
case ASYMMETRIC_CIRCLES_GRID:
default:
flags = 0;
}
bool result = findChessboardCornersWrapper(gray, pattern_size,v,flags);
if(result && pattern == CHESSBOARD_PLAIN) {
gray = ori;
cornerSubPix(gray, v, Size(6,6), Size(-1,-1), TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 30, 0.1));
}
if(result && sharpness && (pattern == CHESSBOARD_SB || pattern == CHESSBOARD || pattern == CHESSBOARD_PLAIN))
{
Scalar s= estimateChessboardSharpness(gray,pattern_size,v);
if(fabs(s[0] - sharpness) > 0.1)
{
ts->printf(cvtest::TS::LOG, "chessboard image has a wrong sharpness in %s. Expected %f but measured %f\n", img_file.c_str(),sharpness,s[0]);
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
show_points( gray, expected, v, result );
return;
}
}
if(result ^ doesContatinChessboard || (doesContatinChessboard && v.size() != count_exp))
{
ts->printf( cvtest::TS::LOG, "chessboard is detected incorrectly in %s\n", img_file.c_str() );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
show_points( gray, expected, v, result );
return;
}
if( result )
{
#ifndef WRITE_POINTS
double err = calcError(v, expected);
max_rough_error = MAX( max_rough_error, err );
#endif
if( pattern == CHESSBOARD || pattern == CHESSBOARD_PLAIN )
cornerSubPix( gray, v, Size(5, 5), Size(-1,-1), TermCriteria(TermCriteria::EPS|TermCriteria::MAX_ITER, 30, 0.1));
//find4QuadCornerSubpix(gray, v, Size(5, 5));
show_points( gray, expected, v, result );
#ifndef WRITE_POINTS
// printf("called find4QuadCornerSubpix\n");
err = calcError(v, expected);
sum_error += err;
count++;
if( err > precise_success_error_level )
{
ts->printf( cvtest::TS::LOG, "Image %s: bad accuracy of adjusted corners %f\n", img_file.c_str(), err );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return;
}
ts->printf(cvtest::TS::LOG, "Error on %s is %f\n", img_file.c_str(), err);
max_precise_error = MAX( max_precise_error, err );
#endif
}
else
{
show_points( gray, Mat(), v, result );
}
#ifdef WRITE_POINTS
Mat mat_v(pattern_size, CV_32FC2, (void*)&v[0]);
FileStorage fs(_filename, FileStorage::WRITE);
fs << "isFound" << result;
fs << "corners" << mat_v;
fs.release();
#endif
progress = update_progress( progress, idx, max_idx, 0 );
}
if (count != 0)
sum_error /= count;
ts->printf(cvtest::TS::LOG, "Average error is %f (%d patterns have been found)\n", sum_error, count);
}
double calcErrorMinError(const Size& cornSz, const vector<Point2f>& corners_found, const vector<Point2f>& corners_generated)
{
Mat m1(cornSz, CV_32FC2, (Point2f*)&corners_generated[0]);
Mat m2; flip(m1, m2, 0);
Mat m3; flip(m1, m3, 1); m3 = m3.t(); flip(m3, m3, 1);
Mat m4 = m1.t(); flip(m4, m4, 1);
double min1 = min(calcError(corners_found, m1), calcError(corners_found, m2));
double min2 = min(calcError(corners_found, m3), calcError(corners_found, m4));
return min(min1, min2);
}
bool validateData(const ChessBoardGenerator& cbg, const Size& imgSz,
const vector<Point2f>& corners_generated)
{
Size cornersSize = cbg.cornersSize();
Mat_<Point2f> mat(cornersSize.height, cornersSize.width, (Point2f*)&corners_generated[0]);
double minNeibDist = std::numeric_limits<double>::max();
double tmp = 0;
for(int i = 1; i < mat.rows - 2; ++i)
for(int j = 1; j < mat.cols - 2; ++j)
{
const Point2f& cur = mat(i, j);
tmp = cv::norm(cur - mat(i + 1, j + 1)); // TODO cvtest
if (tmp < minNeibDist)
minNeibDist = tmp;
tmp = cv::norm(cur - mat(i - 1, j + 1)); // TODO cvtest
if (tmp < minNeibDist)
minNeibDist = tmp;
tmp = cv::norm(cur - mat(i + 1, j - 1)); // TODO cvtest
if (tmp < minNeibDist)
minNeibDist = tmp;
tmp = cv::norm(cur - mat(i - 1, j - 1)); // TODO cvtest
if (tmp < minNeibDist)
minNeibDist = tmp;
}
const double threshold = 0.25;
double cbsize = (max(cornersSize.width, cornersSize.height) + 1) * minNeibDist;
int imgsize = min(imgSz.height, imgSz.width);
return imgsize * threshold < cbsize;
}
bool CV_ChessboardDetectorTest::findChessboardCornersWrapper(InputArray image, Size patternSize, OutputArray corners,int flags)
{
switch(pattern)
{
case CHESSBOARD:
case CHESSBOARD_PLAIN:
return findChessboardCorners(image,patternSize,corners,flags);
case CHESSBOARD_SB:
// check default settings until flags have been specified
return findChessboardCornersSB(image,patternSize,corners,0);
case ASYMMETRIC_CIRCLES_GRID:
flags |= CALIB_CB_ASYMMETRIC_GRID | algorithmFlags;
return findCirclesGrid(image, patternSize,corners,flags);
case CIRCLES_GRID:
flags |= CALIB_CB_SYMMETRIC_GRID;
return findCirclesGrid(image, patternSize,corners,flags);
default:
ts->printf( cvtest::TS::LOG, "Internal Error: unsupported chessboard pattern" );
ts->set_failed_test_info( cvtest::TS::FAIL_GENERIC);
}
return false;
}
bool CV_ChessboardDetectorTest::checkByGenerator()
{
bool res = true;
//theRNG() = 0x58e6e895b9913160;
//cv::DefaultRngAuto dra;
//theRNG() = *ts->get_rng();
Mat bg(Size(800, 600), CV_8UC3, Scalar::all(255));
randu(bg, Scalar::all(0), Scalar::all(255));
GaussianBlur(bg, bg, Size(5, 5), 0.0);
Mat_<float> camMat(3, 3);
camMat << 300.f, 0.f, bg.cols/2.f, 0, 300.f, bg.rows/2.f, 0.f, 0.f, 1.f;
Mat_<float> distCoeffs(1, 5);
distCoeffs << 1.2f, 0.2f, 0.f, 0.f, 0.f;
const Size sizes[] = { Size(6, 6), Size(8, 6), Size(11, 12), Size(5, 4) };
const size_t sizes_num = sizeof(sizes)/sizeof(sizes[0]);
const int test_num = 16;
int progress = 0;
for(int i = 0; i < test_num; ++i)
{
SCOPED_TRACE(cv::format("test_num=%d", test_num));
progress = update_progress( progress, i, test_num, 0 );
ChessBoardGenerator cbg(sizes[i % sizes_num]);
vector<Point2f> corners_generated;
Mat cb = cbg(bg, camMat, distCoeffs, corners_generated);
if(!validateData(cbg, cb.size(), corners_generated))
{
ts->printf( cvtest::TS::LOG, "Chess board skipped - too small" );
continue;
}
/*cb = cb * 0.8 + Scalar::all(30);
GaussianBlur(cb, cb, Size(3, 3), 0.8); */
//cv::addWeighted(cb, 0.8, bg, 0.2, 20, cb);
//cv::namedWindow("CB"); cv::imshow("CB", cb); cv::waitKey();
vector<Point2f> corners_found;
int flags = i % 8; // need to check branches for all flags
bool found = findChessboardCornersWrapper(cb, cbg.cornersSize(), corners_found, flags);
if (!found)
{
ts->printf( cvtest::TS::LOG, "Chess board corners not found\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
res = false;
return res;
}
double err = calcErrorMinError(cbg.cornersSize(), corners_found, corners_generated);
EXPECT_LE(err, rough_success_error_level) << "bad accuracy of corner guesses";
#if 0
if (err >= rough_success_error_level)
{
imshow("cb", cb);
Mat cb_corners = cb.clone();
cv::drawChessboardCorners(cb_corners, cbg.cornersSize(), Mat(corners_found), found);
imshow("corners", cb_corners);
waitKey(0);
}
#endif
}
/* ***** negative ***** */
{
vector<Point2f> corners_found;
bool found = findChessboardCornersWrapper(bg, Size(8, 7), corners_found,0);
if (found)
res = false;
ChessBoardGenerator cbg(Size(8, 7));
vector<Point2f> cg;
Mat cb = cbg(bg, camMat, distCoeffs, cg);
found = findChessboardCornersWrapper(cb, Size(3, 4), corners_found,0);
if (found)
res = false;
Point2f c = std::accumulate(cg.begin(), cg.end(), Point2f(), std::plus<Point2f>()) * (1.f/cg.size());
Mat_<double> aff(2, 3);
aff << 1.0, 0.0, -(double)c.x, 0.0, 1.0, 0.0;
Mat sh;
warpAffine(cb, sh, aff, cb.size());
found = findChessboardCornersWrapper(sh, cbg.cornersSize(), corners_found,0);
if (found)
res = false;
vector< vector<Point> > cnts(1);
vector<Point>& cnt = cnts[0];
cnt.push_back(cg[ 0]); cnt.push_back(cg[0+2]);
cnt.push_back(cg[7+0]); cnt.push_back(cg[7+2]);
cv::drawContours(cb, cnts, -1, Scalar::all(128), FILLED);
found = findChessboardCornersWrapper(cb, cbg.cornersSize(), corners_found,0);
if (found)
res = false;
cv::drawChessboardCorners(cb, cbg.cornersSize(), Mat(corners_found), found);
}
return res;
}
// generates artificial checkerboards using warpPerspective which supports
// subpixel rendering. The transformation is found by transferring corners to
// the camera image using a virtual plane.
bool CV_ChessboardDetectorTest::checkByGeneratorHighAccuracy()
{
// draw 2D pattern
cv::Size pattern_size(6,5);
int cell_size = 80;
bool bwhite = true;
cv::Mat image = cv::Mat::ones((pattern_size.height+3)*cell_size,(pattern_size.width+3)*cell_size,CV_8UC1)*255;
cv::Mat pimage = image(Rect(cell_size,cell_size,(pattern_size.width+1)*cell_size,(pattern_size.height+1)*cell_size));
pimage = 0;
for(int row=0;row<=pattern_size.height;++row)
{
int y = int(cell_size*row+0.5F);
bool bwhite2 = bwhite;
for(int col=0;col<=pattern_size.width;++col)
{
if(bwhite2)
{
int x = int(cell_size*col+0.5F);
pimage(cv::Rect(x,y,cell_size,cell_size)) = 255;
}
bwhite2 = !bwhite2;
}
bwhite = !bwhite;
}
// generate 2d points
std::vector<Point2f> pts1,pts2,pts1_all,pts2_all;
std::vector<Point3f> pts3d;
for(int row=0;row<pattern_size.height;++row)
{
int y = int(cell_size*(row+2));
for(int col=0;col<pattern_size.width;++col)
{
int x = int(cell_size*(col+2));
pts1_all.push_back(cv::Point2f(x-0.5F,y-0.5F));
}
}
// back project chessboard corners to a virtual plane
double fx = 500;
double fy = 500;
cv::Point2f center(250,250);
double fxi = 1.0/fx;
double fyi = 1.0/fy;
for(auto &&pt : pts1_all)
{
// calc camera ray
cv::Vec3f ray(float((pt.x-center.x)*fxi),float((pt.y-center.y)*fyi),1.0F);
ray /= cv::norm(ray);
// intersect ray with virtual plane
cv::Scalar plane(0,0,1,-1);
cv::Vec3f n(float(plane(0)),float(plane(1)),float(plane(2)));
cv::Point3f p0(0,0,0);
cv::Point3f l0(0,0,0); // camera center in world coordinates
p0.z = float(-plane(3)/plane(2));
double val1 = ray.dot(n);
if(val1 == 0)
{
ts->printf( cvtest::TS::LOG, "Internal Error: ray and plane are parallel" );
ts->set_failed_test_info( cvtest::TS::FAIL_GENERIC);
return false;
}
pts3d.push_back(Point3f(ray/val1*cv::Vec3f((p0-l0)).dot(n))+l0);
}
// generate multiple rotations
for(int i=15;i<90;i=i+15)
{
// project 3d points to new camera
Vec3f rvec(0.0F,0.05F,float(float(i)/180.0*CV_PI));
Vec3f tvec(0,0,0);
cv::Mat k = (cv::Mat_<double>(3,3) << fx/2,0,center.x*2, 0,fy/2,center.y, 0,0,1);
cv::projectPoints(pts3d,rvec,tvec,k,cv::Mat(),pts2_all);
// get perspective transform using four correspondences and wrap original image
pts1.clear();
pts2.clear();
pts1.push_back(pts1_all[0]);
pts1.push_back(pts1_all[pattern_size.width-1]);
pts1.push_back(pts1_all[pattern_size.width*pattern_size.height-1]);
pts1.push_back(pts1_all[pattern_size.width*(pattern_size.height-1)]);
pts2.push_back(pts2_all[0]);
pts2.push_back(pts2_all[pattern_size.width-1]);
pts2.push_back(pts2_all[pattern_size.width*pattern_size.height-1]);
pts2.push_back(pts2_all[pattern_size.width*(pattern_size.height-1)]);
Mat m2 = getPerspectiveTransform(pts1,pts2);
Mat out(image.size(),image.type());
warpPerspective(image,out,m2,out.size());
// find checkerboard
vector<Point2f> corners_found;
bool found = findChessboardCornersWrapper(out,pattern_size,corners_found,0);
if (!found)
{
ts->printf( cvtest::TS::LOG, "Chess board corners not found\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return false;
}
double err = calcErrorMinError(pattern_size,corners_found,pts2_all);
if(err > 0.08)
{
ts->printf( cvtest::TS::LOG, "bad accuracy of corner guesses" );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
return false;
}
//cv::cvtColor(out,out,cv::COLOR_GRAY2BGR);
//cv::drawChessboardCorners(out,pattern_size,corners_found,true);
//cv::imshow("img",out);
//cv::waitKey(-1);
}
return true;
}
TEST(Calib3d_ChessboardDetector, accuracy) { CV_ChessboardDetectorTest test( CHESSBOARD ); test.safe_run(); }
TEST(Calib3d_ChessboardDetector2, accuracy) { CV_ChessboardDetectorTest test( CHESSBOARD_SB ); test.safe_run(); }
TEST(Calib3d_ChessboardDetector3, accuracy) { CV_ChessboardDetectorTest test( CHESSBOARD_PLAIN ); test.safe_run(); }
TEST(Calib3d_CirclesPatternDetector, accuracy) { CV_ChessboardDetectorTest test( CIRCLES_GRID ); test.safe_run(); }
TEST(Calib3d_AsymmetricCirclesPatternDetector, accuracy) { CV_ChessboardDetectorTest test( ASYMMETRIC_CIRCLES_GRID ); test.safe_run(); }
#ifdef HAVE_OPENCV_FLANN
TEST(Calib3d_AsymmetricCirclesPatternDetectorWithClustering, accuracy) { CV_ChessboardDetectorTest test( ASYMMETRIC_CIRCLES_GRID, CALIB_CB_CLUSTERING ); test.safe_run(); }
#endif
TEST(Calib3d_CirclesPatternDetectorWithClustering, accuracy)
{
cv::String dataDir = string(TS::ptr()->get_data_path()) + "cv/cameracalibration/circles/";
cv::Mat expected;
FileStorage fs(dataDir + "circles_corners15.dat", FileStorage::READ);
fs["corners"] >> expected;
fs.release();
cv::Mat image = cv::imread(dataDir + "circles15.png");
std::vector<Point2f> centers;
cv::findCirclesGrid(image, Size(10, 8), centers, CALIB_CB_SYMMETRIC_GRID | CALIB_CB_CLUSTERING);
ASSERT_EQ(expected.total(), centers.size());
double error = calcError(centers, expected);
ASSERT_LE(error, precise_success_error_level);
}
TEST(Calib3d_AsymmetricCirclesPatternDetector, regression_18713)
{
float pts_[][2] = {
{ 166.5, 107 }, { 146, 236 }, { 147, 92 }, { 184, 162 }, { 150, 185.5 },
{ 215, 105 }, { 270.5, 186 }, { 159, 142 }, { 6, 205.5 }, { 32, 148.5 },
{ 126, 163.5 }, { 181, 208.5 }, { 240.5, 62 }, { 84.5, 76.5 }, { 190, 120.5 },
{ 10, 189 }, { 266, 104 }, { 307.5, 207.5 }, { 97, 184 }, { 116.5, 210 },
{ 114, 139 }, { 84.5, 233 }, { 269.5, 139 }, { 136, 126.5 }, { 120, 107.5 },
{ 129.5, 65.5 }, { 212.5, 140.5 }, { 204.5, 60.5 }, { 207.5, 241 }, { 61.5, 94.5 },
{ 186.5, 61.5 }, { 220, 63 }, { 239, 120.5 }, { 212, 186 }, { 284, 87.5 },
{ 62, 114.5 }, { 283, 61.5 }, { 238.5, 88.5 }, { 243, 159 }, { 245, 208 },
{ 298.5, 158.5 }, { 57, 129 }, { 156.5, 63.5 }, { 192, 90.5 }, { 281, 235.5 },
{ 172, 62.5 }, { 291.5, 119.5 }, { 90, 127 }, { 68.5, 166.5 }, { 108.5, 83.5 },
{ 22, 176 }
};
Mat candidates(51, 1, CV_32FC2, (void*)pts_);
Size patternSize(4, 9);
std::vector< Point2f > result;
bool res = false;
// issue reports about hangs
EXPECT_NO_THROW(res = findCirclesGrid(candidates, patternSize, result, CALIB_CB_ASYMMETRIC_GRID, Ptr<FeatureDetector>()/*blobDetector=NULL*/));
EXPECT_FALSE(res);
if (cvtest::debugLevel > 0)
{
std::cout << Mat(candidates) << std::endl;
std::cout << Mat(result) << std::endl;
Mat img(Size(400, 300), CV_8UC3, Scalar::all(0));
std::vector< Point2f > centers;
candidates.copyTo(centers);
for (size_t i = 0; i < centers.size(); i++)
{
const Point2f& pt = centers[i];
//printf("{ %g, %g }, \n", pt.x, pt.y);
circle(img, pt, 5, Scalar(0, 255, 0));
}
for (size_t i = 0; i < result.size(); i++)
{
const Point2f& pt = result[i];
circle(img, pt, 10, Scalar(0, 0, 255));
}
imwrite("test_18713.png", img);
if (cvtest::debugLevel >= 10)
{
imshow("result", img);
waitKey();
}
}
}
TEST(Calib3d_AsymmetricCirclesPatternDetector, regression_19498)
{
float pts_[121][2] = {
{ 84.7462f, 404.504f }, { 49.1586f, 404.092f }, { 12.3362f, 403.434f }, { 102.542f, 386.214f }, { 67.6042f, 385.475f },
{ 31.4982f, 384.569f }, { 141.231f, 377.856f }, { 332.834f, 370.745f }, { 85.7663f, 367.261f }, { 50.346f, 366.051f },
{ 13.7726f, 364.663f }, { 371.746f, 362.011f }, { 68.8543f, 347.883f }, { 32.9334f, 346.263f }, { 331.926f, 343.291f },
{ 351.535f, 338.112f }, { 51.7951f, 328.247f }, { 15.4613f, 326.095f }, { 311.719f, 319.578f }, { 330.947f, 313.708f },
{ 256.706f, 307.584f }, { 34.6834f, 308.167f }, { 291.085f, 295.429f }, { 17.4316f, 287.824f }, { 252.928f, 277.92f },
{ 270.19f, 270.93f }, { 288.473f, 263.484f }, { 216.401f, 260.94f }, { 232.195f, 253.656f }, { 266.757f, 237.708f },
{ 211.323f, 229.005f }, { 227.592f, 220.498f }, { 154.749f, 188.52f }, { 222.52f, 184.906f }, { 133.85f, 163.968f },
{ 200.024f, 158.05f }, { 147.485f, 153.643f }, { 161.967f, 142.633f }, { 177.396f, 131.059f }, { 125.909f, 128.116f },
{ 139.817f, 116.333f }, { 91.8639f, 114.454f }, { 104.343f, 102.542f }, { 117.635f, 89.9116f }, { 70.9465f, 89.4619f },
{ 82.8524f, 76.7862f }, { 131.738f, 76.4741f }, { 95.5012f, 63.3351f }, { 109.034f, 49.0424f }, { 314.886f, 374.711f },
{ 351.735f, 366.489f }, { 279.113f, 357.05f }, { 313.371f, 348.131f }, { 260.123f, 335.271f }, { 276.346f, 330.325f },
{ 293.588f, 325.133f }, { 240.86f, 313.143f }, { 273.436f, 301.667f }, { 206.762f, 296.574f }, { 309.877f, 288.796f },
{ 187.46f, 274.319f }, { 201.521f, 267.804f }, { 248.973f, 245.918f }, { 181.644f, 244.655f }, { 196.025f, 237.045f },
{ 148.41f, 229.131f }, { 161.604f, 221.215f }, { 175.455f, 212.873f }, { 244.748f, 211.459f }, { 128.661f, 206.109f },
{ 190.217f, 204.108f }, { 141.346f, 197.568f }, { 205.876f, 194.781f }, { 168.937f, 178.948f }, { 121.006f, 173.714f },
{ 183.998f, 168.806f }, { 88.9095f, 159.731f }, { 100.559f, 149.867f }, { 58.553f, 146.47f }, { 112.849f, 139.302f },
{ 80.0968f, 125.74f }, { 39.24f, 123.671f }, { 154.582f, 103.85f }, { 59.7699f, 101.49f }, { 266.334f, 385.387f },
{ 234.053f, 368.718f }, { 263.347f, 361.184f }, { 244.763f, 339.958f }, { 198.16f, 328.214f }, { 211.675f, 323.407f },
{ 225.905f, 318.426f }, { 192.98f, 302.119f }, { 221.267f, 290.693f }, { 161.437f, 286.46f }, { 236.656f, 284.476f },
{ 168.023f, 251.799f }, { 105.385f, 221.988f }, { 116.724f, 214.25f }, { 97.2959f, 191.81f }, { 108.89f, 183.05f },
{ 77.9896f, 169.242f }, { 48.6763f, 156.088f }, { 68.9635f, 136.415f }, { 29.8484f, 133.886f }, { 49.1966f, 112.826f },
{ 113.059f, 29.003f }, { 251.698f, 388.562f }, { 281.689f, 381.929f }, { 297.875f, 378.518f }, { 248.376f, 365.025f },
{ 295.791f, 352.763f }, { 216.176f, 348.586f }, { 230.143f, 344.443f }, { 179.89f, 307.457f }, { 174.083f, 280.51f },
{ 142.867f, 265.085f }, { 155.127f, 258.692f }, { 124.187f, 243.661f }, { 136.01f, 236.553f }, { 86.4651f, 200.13f },
{ 67.5711f, 178.221f }
};
Mat candidates(121, 1, CV_32FC2, (void*)pts_);
Size patternSize(13, 8);
std::vector< Point2f > result;
bool res = false;
EXPECT_NO_THROW(res = findCirclesGrid(candidates, patternSize, result, CALIB_CB_SYMMETRIC_GRID, Ptr<FeatureDetector>()/*blobDetector=NULL*/));
EXPECT_FALSE(res);
}
}} // namespace
/* End of file. */
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