232 lines
8.5 KiB
C++
232 lines
8.5 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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#if 0
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#include "_modelest.h"
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using namespace std;
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using namespace cv;
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class BareModelEstimator : public CvModelEstimator2
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{
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public:
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BareModelEstimator(int modelPoints, CvSize modelSize, int maxBasicSolutions);
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virtual int runKernel( const CvMat*, const CvMat*, CvMat* );
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virtual void computeReprojError( const CvMat*, const CvMat*,
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const CvMat*, CvMat* );
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bool checkSubsetPublic( const CvMat* ms1, int count, bool checkPartialSubset );
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};
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BareModelEstimator::BareModelEstimator(int _modelPoints, CvSize _modelSize, int _maxBasicSolutions)
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:CvModelEstimator2(_modelPoints, _modelSize, _maxBasicSolutions)
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{
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}
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int BareModelEstimator::runKernel( const CvMat*, const CvMat*, CvMat* )
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{
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return 0;
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}
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void BareModelEstimator::computeReprojError( const CvMat*, const CvMat*,
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const CvMat*, CvMat* )
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{
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}
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bool BareModelEstimator::checkSubsetPublic( const CvMat* ms1, int count, bool checkPartialSubset )
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{
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checkPartialSubsets = checkPartialSubset;
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return checkSubset(ms1, count);
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}
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class CV_ModelEstimator2_Test : public cvtest::ArrayTest
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{
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public:
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CV_ModelEstimator2_Test();
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protected:
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void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
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void fill_array( int test_case_idx, int i, int j, Mat& arr );
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double get_success_error_level( int test_case_idx, int i, int j );
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void run_func();
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void prepare_to_validation( int test_case_idx );
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bool checkPartialSubsets;
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int usedPointsCount;
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bool checkSubsetResult;
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int generalPositionsCount;
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int maxPointsCount;
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};
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CV_ModelEstimator2_Test::CV_ModelEstimator2_Test()
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{
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generalPositionsCount = get_test_case_count() / 2;
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maxPointsCount = 100;
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test_array[INPUT].push_back(NULL);
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test_array[OUTPUT].push_back(NULL);
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test_array[REF_OUTPUT].push_back(NULL);
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}
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void CV_ModelEstimator2_Test::get_test_array_types_and_sizes( int /*test_case_idx*/,
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vector<vector<Size> > &sizes, vector<vector<int> > &types )
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{
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RNG &rng = ts->get_rng();
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checkPartialSubsets = (cvtest::randInt(rng) % 2 == 0);
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int pointsCount = cvtest::randInt(rng) % maxPointsCount;
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usedPointsCount = pointsCount == 0 ? 0 : cvtest::randInt(rng) % pointsCount;
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sizes[INPUT][0] = cvSize(1, pointsCount);
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types[INPUT][0] = CV_64FC2;
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sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = cvSize(1, 1);
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types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_8UC1;
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}
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void CV_ModelEstimator2_Test::fill_array( int test_case_idx, int i, int j, Mat& arr )
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{
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if( i != INPUT )
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{
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cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr );
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return;
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}
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if (test_case_idx < generalPositionsCount)
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{
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//generate points in a general position (i.e. no three points can lie on the same line.)
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bool isGeneralPosition;
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do
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{
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ArrayTest::fill_array(test_case_idx, i, j, arr);
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//a simple check that the position is general:
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// for each line check that all other points don't belong to it
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isGeneralPosition = true;
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for (int startPointIndex = 0; startPointIndex < usedPointsCount && isGeneralPosition; startPointIndex++)
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{
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for (int endPointIndex = startPointIndex + 1; endPointIndex < usedPointsCount && isGeneralPosition; endPointIndex++)
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{
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for (int testPointIndex = 0; testPointIndex < usedPointsCount && isGeneralPosition; testPointIndex++)
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{
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if (testPointIndex == startPointIndex || testPointIndex == endPointIndex)
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{
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continue;
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}
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CV_Assert(arr.type() == CV_64FC2);
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Point2d tangentVector_1 = arr.at<Point2d>(endPointIndex) - arr.at<Point2d>(startPointIndex);
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Point2d tangentVector_2 = arr.at<Point2d>(testPointIndex) - arr.at<Point2d>(startPointIndex);
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const float eps = 1e-4f;
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//TODO: perhaps it is better to normalize the cross product by norms of the tangent vectors
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if (fabs(tangentVector_1.cross(tangentVector_2)) < eps)
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{
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isGeneralPosition = false;
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}
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}
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}
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}
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}
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while(!isGeneralPosition);
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}
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else
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{
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//create points in a degenerate position (there are at least 3 points belonging to the same line)
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ArrayTest::fill_array(test_case_idx, i, j, arr);
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if (usedPointsCount <= 2)
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{
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return;
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}
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RNG &rng = ts->get_rng();
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int startPointIndex, endPointIndex, modifiedPointIndex;
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do
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{
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startPointIndex = cvtest::randInt(rng) % usedPointsCount;
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endPointIndex = cvtest::randInt(rng) % usedPointsCount;
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modifiedPointIndex = checkPartialSubsets ? usedPointsCount - 1 : cvtest::randInt(rng) % usedPointsCount;
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}
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while (startPointIndex == endPointIndex || startPointIndex == modifiedPointIndex || endPointIndex == modifiedPointIndex);
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double startWeight = cvtest::randReal(rng);
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CV_Assert(arr.type() == CV_64FC2);
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arr.at<Point2d>(modifiedPointIndex) = startWeight * arr.at<Point2d>(startPointIndex) + (1.0 - startWeight) * arr.at<Point2d>(endPointIndex);
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}
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}
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double CV_ModelEstimator2_Test::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
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{
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return 0;
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}
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void CV_ModelEstimator2_Test::prepare_to_validation( int test_case_idx )
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{
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test_mat[OUTPUT][0].at<uchar>(0) = checkSubsetResult;
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test_mat[REF_OUTPUT][0].at<uchar>(0) = test_case_idx < generalPositionsCount || usedPointsCount <= 2;
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}
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void CV_ModelEstimator2_Test::run_func()
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{
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//make the input continuous
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Mat input = test_mat[INPUT][0].clone();
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CvMat _input = input;
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RNG &rng = ts->get_rng();
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int modelPoints = cvtest::randInt(rng);
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CvSize modelSize = cvSize(2, modelPoints);
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int maxBasicSolutions = cvtest::randInt(rng);
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BareModelEstimator modelEstimator(modelPoints, modelSize, maxBasicSolutions);
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checkSubsetResult = modelEstimator.checkSubsetPublic(&_input, usedPointsCount, checkPartialSubsets);
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}
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TEST(Calib3d_ModelEstimator2, accuracy) { CV_ModelEstimator2_Test test; test.safe_run(); }
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#endif
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