469 lines
16 KiB
C++
469 lines
16 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// 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.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#include "test_precomp.hpp"
|
|
#include "test_chessboardgenerator.hpp"
|
|
|
|
#include <limits>
|
|
#include <numeric>
|
|
|
|
using namespace std;
|
|
using namespace cv;
|
|
|
|
#define _L2_ERR
|
|
|
|
void show_points( const Mat& gray, const Mat& u, const vector<Point2f>& v, Size pattern_size, bool was_found )
|
|
{
|
|
Mat rgb( gray.size(), CV_8U);
|
|
merge(vector<Mat>(3, gray), rgb);
|
|
|
|
for(size_t i = 0; i < v.size(); i++ )
|
|
circle( rgb, v[i], 3, CV_RGB(255, 0, 0), CV_FILLED);
|
|
|
|
if( !u.empty() )
|
|
{
|
|
const Point2f* u_data = u.ptr<Point2f>();
|
|
size_t count = u.cols * u.rows;
|
|
for(size_t i = 0; i < count; i++ )
|
|
circle( rgb, u_data[i], 3, CV_RGB(0, 255, 0), CV_FILLED);
|
|
}
|
|
if (!v.empty())
|
|
{
|
|
Mat corners((int)v.size(), 1, CV_32FC2, (void*)&v[0]);
|
|
drawChessboardCorners( rgb, pattern_size, corners, was_found );
|
|
}
|
|
//namedWindow( "test", 0 ); imshow( "test", rgb ); waitKey(0);
|
|
}
|
|
|
|
|
|
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 = 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 */)
|
|
{
|
|
cvtest::TS& ts = *this->ts;
|
|
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("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 )
|
|
{
|
|
cvtest::TS& ts = *this->ts;
|
|
|
|
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()) + "cameracalibration/";
|
|
break;
|
|
case CIRCLES_GRID:
|
|
folder = string(ts.get_data_path()) + "cameracalibration/circles/";
|
|
break;
|
|
case ASYMMETRIC_CIRCLES_GRID:
|
|
folder = string(ts.get_data_path()) + "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 readed 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 = board_list.node->data.seq->total/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];
|
|
Mat expected;
|
|
{
|
|
CvMat *u = (CvMat*)cvLoad( filename.c_str() );
|
|
if(!u )
|
|
{
|
|
ts.printf( cvtest::TS::LOG, "one of chessboard corner files can't be read: %s\n", filename.c_str() );
|
|
ts.set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
|
|
continue;
|
|
}
|
|
expected = Mat(u, true);
|
|
cvReleaseMat( &u );
|
|
}
|
|
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, CV_CALIB_CB_ADAPTIVE_THRESH | CV_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;
|
|
}
|
|
show_points( gray, Mat(), v, pattern_size, result );
|
|
if( !result || v.size() != count_exp )
|
|
{
|
|
ts.printf( cvtest::TS::LOG, "chessboard is not found in %s\n", img_file.c_str() );
|
|
ts.set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
|
return;
|
|
}
|
|
|
|
#ifndef WRITE_POINTS
|
|
double err = calcError(v, expected);
|
|
#if 0
|
|
if( err > rough_success_error_level )
|
|
{
|
|
ts.printf( cvtest::TS::LOG, "bad accuracy of corner guesses\n" );
|
|
ts.set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
|
continue;
|
|
}
|
|
#endif
|
|
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, pattern_size, result );
|
|
|
|
#ifndef WRITE_POINTS
|
|
// printf("called find4QuadCornerSubpix\n");
|
|
err = calcError(v, expected);
|
|
sum_error += err;
|
|
count++;
|
|
#if 1
|
|
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;
|
|
}
|
|
#endif
|
|
ts.printf(cvtest::TS::LOG, "Error on %s is %f\n", img_file.c_str(), err);
|
|
max_precise_error = MAX( max_precise_error, err );
|
|
#else
|
|
Mat mat_v(pattern_size, CV_32FC2, (void*)&v[0]);
|
|
CvMat cvmat_v = mat_v;
|
|
cvSave( filename.c_str(), &cvmat_v );
|
|
#endif
|
|
progress = update_progress( progress, idx, max_idx, 0 );
|
|
}
|
|
|
|
sum_error /= count;
|
|
ts.printf(cvtest::TS::LOG, "Average error is %f\n", sum_error);
|
|
}
|
|
|
|
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 = norm( cur - mat(i + 1, j + 1) );
|
|
if (tmp < minNeibDist)
|
|
tmp = minNeibDist;
|
|
|
|
tmp = norm( cur - mat(i - 1, j + 1 ) );
|
|
if (tmp < minNeibDist)
|
|
tmp = minNeibDist;
|
|
|
|
tmp = norm( cur - mat(i + 1, j - 1) );
|
|
if (tmp < minNeibDist)
|
|
tmp = minNeibDist;
|
|
|
|
tmp = norm( cur - mat(i - 1, j - 1) );
|
|
if (tmp < minNeibDist)
|
|
tmp = minNeibDist;
|
|
}
|
|
|
|
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(), 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), CV_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(); }
|
|
TEST(Calib3d_AsymmetricCirclesPatternDetectorWithClustering, accuracy) { CV_ChessboardDetectorTest test( ASYMMETRIC_CIRCLES_GRID, CALIB_CB_CLUSTERING ); test.safe_run(); }
|
|
|
|
/* End of file. */
|