Open Source Computer Vision Library https://opencv.org/
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#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, 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();
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:
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:
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;
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, 0);
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;
Mat expected;
{
FileStorage fs1(_filename, FileStorage::READ);
fs1["corners"] >> expected;
fs1["isFound"] >> doesContatinChessboard;
fs1.release();
}
size_t count_exp = static_cast<size_t>(expected.cols * expected.rows);
Size pattern_size = expected.size();
vector<Point2f> v;
bool result = false;
switch( pattern )
{
case CHESSBOARD:
result = findChessboardCorners(gray, pattern_size, v, CALIB_CB_ADAPTIVE_THRESH | CALIB_CB_NORMALIZE_IMAGE);
break;
case CIRCLES_GRID:
result = findCirclesGrid(gray, pattern_size, v);
break;
case ASYMMETRIC_CIRCLES_GRID:
result = findCirclesGrid(gray, pattern_size, v, CALIB_CB_ASYMMETRIC_GRID | algorithmFlags);
break;
}
if( result ^ 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 );
return;
}
if( result )
{
#ifndef WRITE_POINTS
double err = calcError(v, expected);
max_rough_error = MAX( max_rough_error, err );
#endif
if( pattern == CHESSBOARD )
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::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(7,7), 3.0);
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)
{
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 = findChessboardCorners(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);
if( err > rough_success_error_level )
{
ts->printf( cvtest::TS::LOG, "bad accuracy of corner guesses" );
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
res = false;
return res;
}
}
/* ***** negative ***** */
{
vector<Point2f> corners_found;
bool found = findChessboardCorners(bg, Size(8, 7), corners_found);
if (found)
res = false;
ChessBoardGenerator cbg(Size(8, 7));
vector<Point2f> cg;
Mat cb = cbg(bg, camMat, distCoeffs, cg);
found = findChessboardCorners(cb, Size(3, 4), corners_found);
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 = findChessboardCorners(sh, cbg.cornersSize(), corners_found);
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 = findChessboardCorners(cb, cbg.cornersSize(), corners_found);
if (found)
res = false;
cv::drawChessboardCorners(cb, cbg.cornersSize(), Mat(corners_found), found);
}
return res;
}
TEST(Calib3d_ChessboardDetector, accuracy) { CV_ChessboardDetectorTest test( CHESSBOARD ); 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();
}
}
}
}} // namespace
/* End of file. */