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