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457 lines
16 KiB
C++
457 lines
16 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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#include "test_chessboardgenerator.hpp"
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#include <limits>
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#include <numeric>
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using namespace std;
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using namespace cv;
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#define _L2_ERR
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void show_points( const Mat& gray, const Mat& u, const vector<Point2f>& v, Size pattern_size, bool was_found )
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{
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Mat rgb( gray.size(), CV_8U);
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merge(vector<Mat>(3, gray), rgb);
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for(size_t i = 0; i < v.size(); i++ )
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circle( rgb, v[i], 3, CV_RGB(255, 0, 0), CV_FILLED);
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if( !u.empty() )
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{
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const Point2f* u_data = u.ptr<Point2f>();
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size_t count = u.cols * u.rows;
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for(size_t i = 0; i < count; i++ )
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circle( rgb, u_data[i], 3, CV_RGB(0, 255, 0), CV_FILLED);
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}
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if (!v.empty())
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{
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Mat corners((int)v.size(), 1, CV_32FC2, (void*)&v[0]);
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drawChessboardCorners( rgb, pattern_size, corners, was_found );
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}
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//namedWindow( "test", 0 ); imshow( "test", rgb ); waitKey(0);
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}
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enum Pattern { CHESSBOARD, CIRCLES_GRID, ASYMMETRIC_CIRCLES_GRID };
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class CV_ChessboardDetectorTest : public cvtest::BaseTest
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{
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public:
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CV_ChessboardDetectorTest( Pattern pattern, int algorithmFlags = 0 );
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protected:
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void run(int);
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void run_batch(const string& filename);
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bool checkByGenerator();
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Pattern pattern;
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int algorithmFlags;
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};
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CV_ChessboardDetectorTest::CV_ChessboardDetectorTest( Pattern _pattern, int _algorithmFlags )
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{
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pattern = _pattern;
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algorithmFlags = _algorithmFlags;
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}
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double calcError(const vector<Point2f>& v, const Mat& u)
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{
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int count_exp = u.cols * u.rows;
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const Point2f* u_data = u.ptr<Point2f>();
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double err = numeric_limits<double>::max();
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for( int k = 0; k < 2; ++k )
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{
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double err1 = 0;
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for( int j = 0; j < count_exp; ++j )
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{
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int j1 = k == 0 ? j : count_exp - j - 1;
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double dx = fabs( v[j].x - u_data[j1].x );
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double dy = fabs( v[j].y - u_data[j1].y );
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#if defined(_L2_ERR)
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err1 += dx*dx + dy*dy;
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#else
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dx = MAX( dx, dy );
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if( dx > err1 )
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err1 = dx;
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#endif //_L2_ERR
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//printf("dx = %f\n", dx);
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}
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//printf("\n");
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err = min(err, err1);
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}
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#if defined(_L2_ERR)
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err = sqrt(err/count_exp);
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#endif //_L2_ERR
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return err;
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}
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const double rough_success_error_level = 2.5;
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const double precise_success_error_level = 2;
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/* ///////////////////// chess_corner_test ///////////////////////// */
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void CV_ChessboardDetectorTest::run( int /*start_from */)
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{
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/*if (!checkByGenerator())
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return;*/
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switch( pattern )
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{
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case CHESSBOARD:
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checkByGenerator();
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run_batch("chessboard_list.dat");
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run_batch("chessboard_list_subpixel.dat");
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break;
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case CIRCLES_GRID:
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run_batch("circles_list.dat");
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break;
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case ASYMMETRIC_CIRCLES_GRID:
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run_batch("acircles_list.dat");
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break;
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}
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}
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void CV_ChessboardDetectorTest::run_batch( const string& filename )
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{
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cvtest::TS& ts = *this->ts;
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ts.set_failed_test_info( cvtest::TS::OK );
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ts.printf(cvtest::TS::LOG, "\nRunning batch %s\n", filename.c_str());
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//#define WRITE_POINTS 1
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#ifndef WRITE_POINTS
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double max_rough_error = 0, max_precise_error = 0;
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#endif
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string folder;
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switch( pattern )
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{
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case CHESSBOARD:
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folder = string(ts.get_data_path()) + "cameracalibration/";
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break;
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case CIRCLES_GRID:
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folder = string(ts.get_data_path()) + "cameracalibration/circles/";
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break;
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case ASYMMETRIC_CIRCLES_GRID:
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folder = string(ts.get_data_path()) + "cameracalibration/asymmetric_circles/";
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break;
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}
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FileStorage fs( folder + filename, FileStorage::READ );
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FileNode board_list = fs["boards"];
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if( !fs.isOpened() || board_list.empty() || !board_list.isSeq() || board_list.size() % 2 != 0 )
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{
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ts.printf( cvtest::TS::LOG, "%s can not be readed or is not valid\n", (folder + filename).c_str() );
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ts.printf( cvtest::TS::LOG, "fs.isOpened=%d, board_list.empty=%d, board_list.isSeq=%d,board_list.size()%2=%d\n",
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fs.isOpened(), (int)board_list.empty(), board_list.isSeq(), board_list.size()%2);
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ts.set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
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return;
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}
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int progress = 0;
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int max_idx = board_list.node->data.seq->total/2;
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double sum_error = 0.0;
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int count = 0;
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for(int idx = 0; idx < max_idx; ++idx )
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{
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ts.update_context( this, idx, true );
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/* read the image */
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string img_file = board_list[idx * 2];
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Mat gray = imread( folder + img_file, 0);
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if( gray.empty() )
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{
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ts.printf( cvtest::TS::LOG, "one of chessboard images can't be read: %s\n", img_file.c_str() );
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ts.set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
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continue;
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}
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string filename = folder + (string)board_list[idx * 2 + 1];
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Mat expected;
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{
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CvMat *u = (CvMat*)cvLoad( filename.c_str() );
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if(!u )
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{
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ts.printf( cvtest::TS::LOG, "one of chessboard corner files can't be read: %s\n", filename.c_str() );
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ts.set_failed_test_info( cvtest::TS::FAIL_MISSING_TEST_DATA );
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continue;
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}
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expected = Mat(u, true);
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cvReleaseMat( &u );
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}
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size_t count_exp = static_cast<size_t>(expected.cols * expected.rows);
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Size pattern_size = expected.size();
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vector<Point2f> v;
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bool result = false;
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switch( pattern )
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{
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case CHESSBOARD:
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result = findChessboardCorners(gray, pattern_size, v, CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_NORMALIZE_IMAGE);
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break;
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case CIRCLES_GRID:
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result = findCirclesGrid(gray, pattern_size, v);
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break;
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case ASYMMETRIC_CIRCLES_GRID:
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result = findCirclesGrid(gray, pattern_size, v, CALIB_CB_ASYMMETRIC_GRID | algorithmFlags);
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break;
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}
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show_points( gray, Mat(), v, pattern_size, result );
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if( !result || v.size() != count_exp )
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{
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ts.printf( cvtest::TS::LOG, "chessboard is not found in %s\n", img_file.c_str() );
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ts.set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
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continue;
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}
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#ifndef WRITE_POINTS
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double err = calcError(v, expected);
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#if 0
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if( err > rough_success_error_level )
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{
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ts.printf( cvtest::TS::LOG, "bad accuracy of corner guesses\n" );
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ts.set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
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continue;
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}
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#endif
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max_rough_error = MAX( max_rough_error, err );
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#endif
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if( pattern == CHESSBOARD )
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cornerSubPix( gray, v, Size(5, 5), Size(-1,-1), TermCriteria(TermCriteria::EPS|TermCriteria::MAX_ITER, 30, 0.1));
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//find4QuadCornerSubpix(gray, v, Size(5, 5));
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show_points( gray, expected, v, pattern_size, result );
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#ifndef WRITE_POINTS
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// printf("called find4QuadCornerSubpix\n");
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err = calcError(v, expected);
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sum_error += err;
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count++;
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#if 1
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if( err > precise_success_error_level )
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{
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ts.printf( cvtest::TS::LOG, "Image %s: bad accuracy of adjusted corners %f\n", img_file.c_str(), err );
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ts.set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
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continue;
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}
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#endif
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ts.printf(cvtest::TS::LOG, "Error on %s is %f\n", img_file.c_str(), err);
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max_precise_error = MAX( max_precise_error, err );
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#else
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Mat mat_v(pattern_size, CV_32FC2, (void*)&v[0]);
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CvMat cvmat_v = mat_v;
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cvSave( filename.c_str(), &cvmat_v );
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#endif
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progress = update_progress( progress, idx, max_idx, 0 );
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}
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sum_error /= count;
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ts.printf(cvtest::TS::LOG, "Average error is %f\n", sum_error);
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}
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double calcErrorMinError(const Size& cornSz, const vector<Point2f>& corners_found, const vector<Point2f>& corners_generated)
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{
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Mat m1(cornSz, CV_32FC2, (Point2f*)&corners_generated[0]);
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Mat m2; flip(m1, m2, 0);
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Mat m3; flip(m1, m3, 1); m3 = m3.t(); flip(m3, m3, 1);
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Mat m4 = m1.t(); flip(m4, m4, 1);
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double min1 = min(calcError(corners_found, m1), calcError(corners_found, m2));
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double min2 = min(calcError(corners_found, m3), calcError(corners_found, m4));
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return min(min1, min2);
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}
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bool validateData(const ChessBoardGenerator& cbg, const Size& imgSz,
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const vector<Point2f>& corners_generated)
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{
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Size cornersSize = cbg.cornersSize();
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Mat_<Point2f> mat(cornersSize.height, cornersSize.width, (Point2f*)&corners_generated[0]);
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double minNeibDist = std::numeric_limits<double>::max();
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double tmp = 0;
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for(int i = 1; i < mat.rows - 2; ++i)
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for(int j = 1; j < mat.cols - 2; ++j)
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{
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const Point2f& cur = mat(i, j);
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tmp = norm( cur - mat(i + 1, j + 1) );
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if (tmp < minNeibDist)
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tmp = minNeibDist;
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tmp = norm( cur - mat(i - 1, j + 1 ) );
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if (tmp < minNeibDist)
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tmp = minNeibDist;
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tmp = norm( cur - mat(i + 1, j - 1) );
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if (tmp < minNeibDist)
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tmp = minNeibDist;
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tmp = norm( cur - mat(i - 1, j - 1) );
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if (tmp < minNeibDist)
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tmp = minNeibDist;
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}
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const double threshold = 0.25;
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double cbsize = (max(cornersSize.width, cornersSize.height) + 1) * minNeibDist;
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int imgsize = min(imgSz.height, imgSz.width);
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return imgsize * threshold < cbsize;
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}
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bool CV_ChessboardDetectorTest::checkByGenerator()
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{
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bool res = true;
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//theRNG() = 0x58e6e895b9913160;
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//cv::DefaultRngAuto dra;
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//theRNG() = *ts->get_rng();
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Mat bg(Size(800, 600), CV_8UC3, Scalar::all(255));
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randu(bg, Scalar::all(0), Scalar::all(255));
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GaussianBlur(bg, bg, Size(7,7), 3.0);
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Mat_<float> camMat(3, 3);
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camMat << 300.f, 0.f, bg.cols/2.f, 0, 300.f, bg.rows/2.f, 0.f, 0.f, 1.f;
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Mat_<float> distCoeffs(1, 5);
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distCoeffs << 1.2f, 0.2f, 0.f, 0.f, 0.f;
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const Size sizes[] = { Size(6, 6), Size(8, 6), Size(11, 12), Size(5, 4) };
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const size_t sizes_num = sizeof(sizes)/sizeof(sizes[0]);
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const int test_num = 16;
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int progress = 0;
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for(int i = 0; i < test_num; ++i)
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{
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progress = update_progress( progress, i, test_num, 0 );
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ChessBoardGenerator cbg(sizes[i % sizes_num]);
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vector<Point2f> corners_generated;
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Mat cb = cbg(bg, camMat, distCoeffs, corners_generated);
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if(!validateData(cbg, cb.size(), corners_generated))
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{
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ts->printf( cvtest::TS::LOG, "Chess board skipped - too small" );
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continue;
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}
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/*cb = cb * 0.8 + Scalar::all(30);
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GaussianBlur(cb, cb, Size(3, 3), 0.8); */
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//cv::addWeighted(cb, 0.8, bg, 0.2, 20, cb);
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//cv::namedWindow("CB"); cv::imshow("CB", cb); cv::waitKey();
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vector<Point2f> corners_found;
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int flags = i % 8; // need to check branches for all flags
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bool found = findChessboardCorners(cb, cbg.cornersSize(), corners_found, flags);
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if (!found)
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{
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ts->printf( cvtest::TS::LOG, "Chess board corners not found\n" );
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
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res = false;
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continue;
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}
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double err = calcErrorMinError(cbg.cornersSize(), corners_found, corners_generated);
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if( err > rough_success_error_level )
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{
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ts->printf( cvtest::TS::LOG, "bad accuracy of corner guesses" );
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
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res = false;
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continue;
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}
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}
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/* ***** negative ***** */
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{
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vector<Point2f> corners_found;
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bool found = findChessboardCorners(bg, Size(8, 7), corners_found);
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if (found)
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res = false;
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ChessBoardGenerator cbg(Size(8, 7));
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vector<Point2f> cg;
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Mat cb = cbg(bg, camMat, distCoeffs, cg);
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found = findChessboardCorners(cb, Size(3, 4), corners_found);
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if (found)
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res = false;
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Point2f c = std::accumulate(cg.begin(), cg.end(), Point2f(), plus<Point2f>()) * (1.f/cg.size());
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Mat_<double> aff(2, 3);
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aff << 1.0, 0.0, -(double)c.x, 0.0, 1.0, 0.0;
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Mat sh;
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warpAffine(cb, sh, aff, cb.size());
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found = findChessboardCorners(sh, cbg.cornersSize(), corners_found);
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if (found)
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res = false;
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vector< vector<Point> > cnts(1);
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vector<Point>& cnt = cnts[0];
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cnt.push_back(cg[ 0]); cnt.push_back(cg[0+2]);
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cnt.push_back(cg[7+0]); cnt.push_back(cg[7+2]);
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cv::drawContours(cb, cnts, -1, Scalar::all(128), CV_FILLED);
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found = findChessboardCorners(cb, cbg.cornersSize(), corners_found);
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if (found)
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res = false;
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cv::drawChessboardCorners(cb, cbg.cornersSize(), Mat(corners_found), found);
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}
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return res;
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}
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TEST(Calib3d_ChessboardDetector, accuracy) { CV_ChessboardDetectorTest test( CHESSBOARD ); test.safe_run(); }
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TEST(Calib3d_CirclesPatternDetector, accuracy) { CV_ChessboardDetectorTest test( CIRCLES_GRID ); test.safe_run(); }
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TEST(Calib3d_AsymmetricCirclesPatternDetector, accuracy) { CV_ChessboardDetectorTest test( ASYMMETRIC_CIRCLES_GRID ); test.safe_run(); }
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TEST(Calib3d_AsymmetricCirclesPatternDetectorWithClustering, accuracy) { CV_ChessboardDetectorTest test( ASYMMETRIC_CIRCLES_GRID, CALIB_CB_CLUSTERING ); test.safe_run(); }
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/* End of file. */
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