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259 lines
9.2 KiB
C++
259 lines
9.2 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 "opencv2/imgproc/imgproc_c.h"
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#include "test_chessboardgenerator.hpp"
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namespace opencv_test { namespace {
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class CV_ChessboardSubpixelTest : public cvtest::BaseTest
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{
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public:
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CV_ChessboardSubpixelTest();
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protected:
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Mat intrinsic_matrix_;
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Mat distortion_coeffs_;
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Size image_size_;
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void run(int);
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void generateIntrinsicParams();
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};
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int calcDistance(const vector<Point2f>& set1, const vector<Point2f>& set2, double& mean_dist)
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{
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if(set1.size() != set2.size())
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{
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return 0;
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}
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std::vector<int> indices;
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double sum_dist = 0.0;
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for(size_t i = 0; i < set1.size(); i++)
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{
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double min_dist = std::numeric_limits<double>::max();
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int min_idx = -1;
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for(int j = 0; j < (int)set2.size(); j++)
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{
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double dist = cv::norm(set1[i] - set2[j]); // TODO cvtest
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if(dist < min_dist)
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{
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min_idx = j;
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min_dist = dist;
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}
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}
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// check validity of min_idx
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if(min_idx == -1)
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{
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return 0;
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}
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std::vector<int>::iterator it = std::find(indices.begin(), indices.end(), min_idx);
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if(it != indices.end())
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{
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// there are two points in set1 corresponding to the same point in set2
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return 0;
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}
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indices.push_back(min_idx);
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// printf("dist %d = %f\n", (int)i, min_dist);
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sum_dist += min_dist*min_dist;
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}
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mean_dist = sqrt(sum_dist/set1.size());
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// printf("sum_dist = %f, set1.size() = %d, mean_dist = %f\n", sum_dist, (int)set1.size(), mean_dist);
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return 1;
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}
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CV_ChessboardSubpixelTest::CV_ChessboardSubpixelTest() :
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intrinsic_matrix_(Size(3, 3), CV_64FC1), distortion_coeffs_(Size(1, 4), CV_64FC1),
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image_size_(640, 480)
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{
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}
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/* ///////////////////// chess_corner_test ///////////////////////// */
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void CV_ChessboardSubpixelTest::run( int )
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{
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int code = cvtest::TS::OK;
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int progress = 0;
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RNG& rng = ts->get_rng();
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const int runs_count = 20;
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const int max_pattern_size = 8;
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const int min_pattern_size = 5;
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Mat bg(image_size_, CV_8UC1);
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bg = Scalar(0);
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double sum_dist = 0.0;
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int count = 0;
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for(int i = 0; i < runs_count; i++)
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{
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const int pattern_width = min_pattern_size + cvtest::randInt(rng) % (max_pattern_size - min_pattern_size);
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const int pattern_height = min_pattern_size + cvtest::randInt(rng) % (max_pattern_size - min_pattern_size);
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Size pattern_size;
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if(pattern_width > pattern_height)
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{
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pattern_size = Size(pattern_height, pattern_width);
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}
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else
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{
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pattern_size = Size(pattern_width, pattern_height);
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}
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ChessBoardGenerator gen_chessboard(Size(pattern_size.width + 1, pattern_size.height + 1));
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// generates intrinsic camera and distortion matrices
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generateIntrinsicParams();
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vector<Point2f> corners;
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Mat chessboard_image = gen_chessboard(bg, intrinsic_matrix_, distortion_coeffs_, corners);
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vector<Point2f> test_corners;
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bool result = findChessboardCorners(chessboard_image, pattern_size, test_corners, 15);
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if (!result && cvtest::debugLevel > 0)
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{
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ts->printf(cvtest::TS::LOG, "Warning: chessboard was not detected! Writing image to test.png\n");
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ts->printf(cvtest::TS::LOG, "Size = %d, %d\n", pattern_size.width, pattern_size.height);
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ts->printf(cvtest::TS::LOG, "Intrinsic params: fx = %f, fy = %f, cx = %f, cy = %f\n",
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intrinsic_matrix_.at<double>(0, 0), intrinsic_matrix_.at<double>(1, 1),
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intrinsic_matrix_.at<double>(0, 2), intrinsic_matrix_.at<double>(1, 2));
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ts->printf(cvtest::TS::LOG, "Distortion matrix: %f, %f, %f, %f, %f\n",
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distortion_coeffs_.at<double>(0, 0), distortion_coeffs_.at<double>(0, 1),
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distortion_coeffs_.at<double>(0, 2), distortion_coeffs_.at<double>(0, 3),
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distortion_coeffs_.at<double>(0, 4));
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imwrite("test.png", chessboard_image);
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}
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if (!result)
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{
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continue;
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}
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double dist1 = 0.0;
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int ret = calcDistance(corners, test_corners, dist1);
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if(ret == 0)
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{
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ts->printf(cvtest::TS::LOG, "findChessboardCorners returns invalid corner coordinates!\n");
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code = cvtest::TS::FAIL_INVALID_OUTPUT;
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break;
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}
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cornerSubPix(chessboard_image, test_corners,
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Size(3, 3), Size(1, 1), TermCriteria(TermCriteria::EPS|TermCriteria::MAX_ITER, 300, 0.1));
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find4QuadCornerSubpix(chessboard_image, test_corners, Size(5, 5));
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double dist2 = 0.0;
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ret = calcDistance(corners, test_corners, dist2);
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if(ret == 0)
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{
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ts->printf(cvtest::TS::LOG, "findCornerSubpix returns invalid corner coordinates!\n");
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code = cvtest::TS::FAIL_INVALID_OUTPUT;
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break;
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}
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ts->printf(cvtest::TS::LOG, "Error after findChessboardCorners: %f, after findCornerSubPix: %f\n",
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dist1, dist2);
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sum_dist += dist2;
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count++;
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const double max_reduce_factor = 0.8;
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if(dist1 < dist2*max_reduce_factor)
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{
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ts->printf(cvtest::TS::LOG, "findCornerSubPix increases average error!\n");
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code = cvtest::TS::FAIL_INVALID_OUTPUT;
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break;
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}
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progress = update_progress( progress, i-1, runs_count, 0 );
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}
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ASSERT_NE(0, count);
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sum_dist /= count;
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ts->printf(cvtest::TS::LOG, "Average error after findCornerSubpix: %f\n", sum_dist);
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if( code < 0 )
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ts->set_failed_test_info( code );
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}
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void CV_ChessboardSubpixelTest::generateIntrinsicParams()
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{
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RNG& rng = ts->get_rng();
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const double max_focus_length = 1000.0;
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const double max_focus_diff = 5.0;
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double fx = cvtest::randReal(rng)*max_focus_length;
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double fy = fx + cvtest::randReal(rng)*max_focus_diff;
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double cx = image_size_.width/2;
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double cy = image_size_.height/2;
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double k1 = 0.5*cvtest::randReal(rng);
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double k2 = 0.05*cvtest::randReal(rng);
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double p1 = 0.05*cvtest::randReal(rng);
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double p2 = 0.05*cvtest::randReal(rng);
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double k3 = 0.0;
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intrinsic_matrix_ = (Mat_<double>(3, 3) << fx, 0.0, cx, 0.0, fy, cy, 0.0, 0.0, 1.0);
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distortion_coeffs_ = (Mat_<double>(1, 5) << k1, k2, p1, p2, k3);
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}
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TEST(Calib3d_ChessboardSubPixDetector, accuracy) { CV_ChessboardSubpixelTest test; test.safe_run(); }
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TEST(Calib3d_CornerSubPix, regression_7204)
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{
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cv::Mat image(cv::Size(70, 38), CV_8UC1, cv::Scalar::all(0));
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image(cv::Rect(65, 26, 5, 5)).setTo(cv::Scalar::all(255));
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image(cv::Rect(55, 31, 8, 1)).setTo(cv::Scalar::all(255));
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image(cv::Rect(56, 35, 14, 2)).setTo(cv::Scalar::all(255));
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image(cv::Rect(66, 24, 4, 2)).setTo(cv::Scalar::all(255));
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image.at<uchar>(24, 69) = 0;
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std::vector<cv::Point2f> corners;
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corners.push_back(cv::Point2f(65, 30));
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cv::cornerSubPix(image, corners, cv::Size(3, 3), cv::Size(-1, -1),
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cv::TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
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}
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}} // namespace
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/* End of file. */
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