mirror of
https://github.com/opencv/opencv.git
synced 2026-07-29 23:33:05 +04:00
Merge pull request #29175 from asmorkalov:as/geometry2
Geometry module #29175 OpenCV Contrib: https://github.com/opencv/opencv_contrib/pull/4129 CI changes: https://github.com/opencv/ci-gha-workflow/pull/313 Continues - https://github.com/opencv/opencv/pull/28804 - https://github.com/opencv/opencv/pull/29101 - https://github.com/opencv/opencv/pull/29108 - https://github.com/opencv/opencv/pull/28810 Todo for followup PRs: - [x] Rename doxygen groups - [x] Fix JS modules layout and whitelists - [ ] Sort tutorials code/snippets ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Alexander Smorkalov
committed by
GitHub
GitHub
parent
14a475aa0b
commit
59218f9edd
@@ -0,0 +1,432 @@
|
||||
/*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"
|
||||
|
||||
namespace opencv_test {
|
||||
|
||||
static void test_convertHomogeneous( const Mat& _src, Mat& _dst )
|
||||
{
|
||||
Mat src = _src, dst = _dst;
|
||||
int i, count, sdims, ddims;
|
||||
int sstep1, sstep2, dstep1, dstep2;
|
||||
|
||||
if( src.depth() != CV_64F )
|
||||
_src.convertTo(src, CV_64F);
|
||||
|
||||
if( dst.depth() != CV_64F )
|
||||
dst.create(dst.size(), CV_MAKETYPE(CV_64F, _dst.channels()));
|
||||
|
||||
if( src.rows > src.cols )
|
||||
{
|
||||
count = src.rows;
|
||||
sdims = src.channels()*src.cols;
|
||||
sstep1 = (int)(src.step/sizeof(double));
|
||||
sstep2 = 1;
|
||||
}
|
||||
else
|
||||
{
|
||||
count = src.cols;
|
||||
sdims = src.channels()*src.rows;
|
||||
if( src.rows == 1 )
|
||||
{
|
||||
sstep1 = sdims;
|
||||
sstep2 = 1;
|
||||
}
|
||||
else
|
||||
{
|
||||
sstep1 = 1;
|
||||
sstep2 = (int)(src.step/sizeof(double));
|
||||
}
|
||||
}
|
||||
|
||||
if( dst.rows > dst.cols )
|
||||
{
|
||||
CV_Assert( count == dst.rows );
|
||||
ddims = dst.channels()*dst.cols;
|
||||
dstep1 = (int)(dst.step/sizeof(double));
|
||||
dstep2 = 1;
|
||||
}
|
||||
else
|
||||
{
|
||||
CV_Assert( count == dst.cols );
|
||||
ddims = dst.channels()*dst.rows;
|
||||
if( dst.rows == 1 )
|
||||
{
|
||||
dstep1 = ddims;
|
||||
dstep2 = 1;
|
||||
}
|
||||
else
|
||||
{
|
||||
dstep1 = 1;
|
||||
dstep2 = (int)(dst.step/sizeof(double));
|
||||
}
|
||||
}
|
||||
|
||||
double* s = src.ptr<double>();
|
||||
double* d = dst.ptr<double>();
|
||||
|
||||
if( sdims <= ddims )
|
||||
{
|
||||
int wstep = dstep2*(ddims - 1);
|
||||
|
||||
for( i = 0; i < count; i++, s += sstep1, d += dstep1 )
|
||||
{
|
||||
double x = s[0];
|
||||
double y = s[sstep2];
|
||||
|
||||
d[wstep] = 1;
|
||||
d[0] = x;
|
||||
d[dstep2] = y;
|
||||
|
||||
if( sdims >= 3 )
|
||||
{
|
||||
d[dstep2*2] = s[sstep2*2];
|
||||
if( sdims == 4 )
|
||||
d[dstep2*3] = s[sstep2*3];
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
int wstep = sstep2*(sdims - 1);
|
||||
|
||||
for( i = 0; i < count; i++, s += sstep1, d += dstep1 )
|
||||
{
|
||||
double w = s[wstep];
|
||||
double x = s[0];
|
||||
double y = s[sstep2];
|
||||
|
||||
w = w ? 1./w : 1;
|
||||
|
||||
d[0] = x*w;
|
||||
d[dstep2] = y*w;
|
||||
|
||||
if( ddims == 3 )
|
||||
d[dstep2*2] = s[sstep2*2]*w;
|
||||
}
|
||||
}
|
||||
|
||||
if( dst.data != _dst.data )
|
||||
dst.convertTo(_dst, _dst.depth());
|
||||
}
|
||||
|
||||
namespace {
|
||||
|
||||
/********************************** convert homogeneous *********************************/
|
||||
|
||||
class CV_ConvertHomogeneousTest : public cvtest::ArrayTest
|
||||
{
|
||||
public:
|
||||
CV_ConvertHomogeneousTest();
|
||||
|
||||
protected:
|
||||
int read_params( const cv::FileStorage& fs );
|
||||
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 );
|
||||
|
||||
int dims1, dims2;
|
||||
int pt_count;
|
||||
};
|
||||
|
||||
|
||||
CV_ConvertHomogeneousTest::CV_ConvertHomogeneousTest()
|
||||
{
|
||||
test_array[INPUT].push_back(NULL);
|
||||
test_array[OUTPUT].push_back(NULL);
|
||||
test_array[REF_OUTPUT].push_back(NULL);
|
||||
element_wise_relative_error = false;
|
||||
|
||||
pt_count = dims1 = dims2 = 0;
|
||||
}
|
||||
|
||||
|
||||
int CV_ConvertHomogeneousTest::read_params( const cv::FileStorage& fs )
|
||||
{
|
||||
int code = cvtest::ArrayTest::read_params( fs );
|
||||
return code;
|
||||
}
|
||||
|
||||
|
||||
void CV_ConvertHomogeneousTest::get_test_array_types_and_sizes( int /*test_case_idx*/,
|
||||
vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
||||
{
|
||||
RNG& rng = ts->get_rng();
|
||||
int pt_depth1 = cvtest::randInt(rng) % 2 == 0 ? CV_32F : CV_64F;
|
||||
int pt_depth2 = pt_depth1;//cvtest::randInt(rng) % 2 == 0 ? CV_32F : CV_64F;
|
||||
double pt_count_exp = cvtest::randReal(rng)*6 + 1;
|
||||
int t;
|
||||
|
||||
pt_count = cvRound(exp(pt_count_exp));
|
||||
pt_count = MAX( pt_count, 5 );
|
||||
|
||||
dims1 = 2 + (cvtest::randInt(rng) % 2);
|
||||
dims2 = dims1 + 1;
|
||||
|
||||
if( cvtest::randInt(rng) % 2 )
|
||||
CV_SWAP( dims1, dims2, t );
|
||||
|
||||
types[INPUT][0] = CV_MAKETYPE(pt_depth1, 1);
|
||||
|
||||
sizes[INPUT][0] = Size(dims1, pt_count);
|
||||
if( cvtest::randInt(rng) % 2 )
|
||||
{
|
||||
types[INPUT][0] = CV_MAKETYPE(pt_depth1, dims1);
|
||||
if( cvtest::randInt(rng) % 2 )
|
||||
sizes[INPUT][0] = Size(pt_count, 1);
|
||||
else
|
||||
sizes[INPUT][0] = Size(1, pt_count);
|
||||
}
|
||||
|
||||
types[OUTPUT][0] = CV_MAKETYPE(pt_depth2, dims2);
|
||||
sizes[OUTPUT][0] = Size(1, pt_count);
|
||||
|
||||
types[REF_OUTPUT][0] = types[OUTPUT][0];
|
||||
sizes[REF_OUTPUT][0] = sizes[OUTPUT][0];
|
||||
}
|
||||
|
||||
|
||||
double CV_ConvertHomogeneousTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
|
||||
{
|
||||
return 1e-5;
|
||||
}
|
||||
|
||||
|
||||
void CV_ConvertHomogeneousTest::fill_array( int /*test_case_idx*/, int /*i*/, int /*j*/, Mat& arr )
|
||||
{
|
||||
Mat temp( 1, pt_count, CV_MAKETYPE(CV_64FC1,dims1) );
|
||||
RNG& rng = ts->get_rng();
|
||||
Scalar low = Scalar::all(0), high = Scalar::all(10);
|
||||
|
||||
if( dims1 > dims2 )
|
||||
low.val[dims1-1] = 1.;
|
||||
|
||||
cvtest::randUni( rng, temp, low, high );
|
||||
test_convertHomogeneous( temp, arr );
|
||||
}
|
||||
|
||||
|
||||
void CV_ConvertHomogeneousTest::run_func()
|
||||
{
|
||||
cv::Mat _input = test_mat[INPUT][0], &_output = test_mat[OUTPUT][0];
|
||||
if( dims1 > dims2 )
|
||||
cv::convertPointsFromHomogeneous(_input, _output);
|
||||
else
|
||||
cv::convertPointsToHomogeneous(_input, _output);
|
||||
}
|
||||
|
||||
|
||||
void CV_ConvertHomogeneousTest::prepare_to_validation( int /*test_case_idx*/ )
|
||||
{
|
||||
test_convertHomogeneous( test_mat[INPUT][0], test_mat[REF_OUTPUT][0] );
|
||||
}
|
||||
|
||||
|
||||
/************************** compute corresponding epipolar lines ************************/
|
||||
|
||||
class CV_ComputeEpilinesTest : public cvtest::ArrayTest
|
||||
{
|
||||
public:
|
||||
CV_ComputeEpilinesTest();
|
||||
|
||||
protected:
|
||||
int read_params( const cv::FileStorage& fs );
|
||||
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 );
|
||||
|
||||
int which_image;
|
||||
int dims;
|
||||
int pt_count;
|
||||
};
|
||||
|
||||
|
||||
CV_ComputeEpilinesTest::CV_ComputeEpilinesTest()
|
||||
{
|
||||
test_array[INPUT].push_back(NULL);
|
||||
test_array[INPUT].push_back(NULL);
|
||||
test_array[OUTPUT].push_back(NULL);
|
||||
test_array[REF_OUTPUT].push_back(NULL);
|
||||
element_wise_relative_error = false;
|
||||
|
||||
pt_count = dims = which_image = 0;
|
||||
}
|
||||
|
||||
|
||||
int CV_ComputeEpilinesTest::read_params( const cv::FileStorage& fs )
|
||||
{
|
||||
int code = cvtest::ArrayTest::read_params( fs );
|
||||
return code;
|
||||
}
|
||||
|
||||
|
||||
void CV_ComputeEpilinesTest::get_test_array_types_and_sizes( int /*test_case_idx*/,
|
||||
vector<vector<Size> >& sizes, vector<vector<int> >& types )
|
||||
{
|
||||
RNG& rng = ts->get_rng();
|
||||
int fm_depth = cvtest::randInt(rng) % 2 == 0 ? CV_32F : CV_64F;
|
||||
int pt_depth = cvtest::randInt(rng) % 2 == 0 ? CV_32F : CV_64F;
|
||||
int ln_depth = pt_depth;
|
||||
double pt_count_exp = cvtest::randReal(rng)*6;
|
||||
|
||||
which_image = 1 + (cvtest::randInt(rng) % 2);
|
||||
|
||||
pt_count = cvRound(exp(pt_count_exp));
|
||||
pt_count = MAX( pt_count, 1 );
|
||||
bool few_points = pt_count < 5;
|
||||
|
||||
dims = 2 + (cvtest::randInt(rng) % 2);
|
||||
|
||||
types[INPUT][0] = CV_MAKETYPE(pt_depth, 1);
|
||||
|
||||
sizes[INPUT][0] = Size(dims, pt_count);
|
||||
if( cvtest::randInt(rng) % 2 || few_points )
|
||||
{
|
||||
types[INPUT][0] = CV_MAKETYPE(pt_depth, dims);
|
||||
if( cvtest::randInt(rng) % 2 )
|
||||
sizes[INPUT][0] = Size(pt_count, 1);
|
||||
else
|
||||
sizes[INPUT][0] = Size(1, pt_count);
|
||||
}
|
||||
|
||||
types[INPUT][1] = CV_MAKETYPE(fm_depth, 1);
|
||||
sizes[INPUT][1] = Size(3, 3);
|
||||
|
||||
types[OUTPUT][0] = CV_MAKETYPE(ln_depth, 3);
|
||||
sizes[OUTPUT][0] = Size(1, pt_count);
|
||||
|
||||
types[REF_OUTPUT][0] = types[OUTPUT][0];
|
||||
sizes[REF_OUTPUT][0] = sizes[OUTPUT][0];
|
||||
}
|
||||
|
||||
|
||||
double CV_ComputeEpilinesTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
|
||||
{
|
||||
return 1e-5;
|
||||
}
|
||||
|
||||
|
||||
void CV_ComputeEpilinesTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
|
||||
{
|
||||
RNG& rng = ts->get_rng();
|
||||
|
||||
if( i == INPUT && j == 0 )
|
||||
{
|
||||
Mat temp( 1, pt_count, CV_MAKETYPE(CV_64FC1,dims) );
|
||||
cvtest::randUni( rng, temp, Scalar(0,0,1), Scalar::all(10) );
|
||||
test_convertHomogeneous( temp, arr );
|
||||
}
|
||||
else if( i == INPUT && j == 1 )
|
||||
cvtest::randUni( rng, arr, Scalar::all(0), Scalar::all(10) );
|
||||
else
|
||||
cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr );
|
||||
}
|
||||
|
||||
|
||||
void CV_ComputeEpilinesTest::run_func()
|
||||
{
|
||||
cv::Mat _points = test_mat[INPUT][0], _F = test_mat[INPUT][1], &_lines = test_mat[OUTPUT][0];
|
||||
cv::computeCorrespondEpilines( _points, which_image, _F, _lines );
|
||||
}
|
||||
|
||||
|
||||
void CV_ComputeEpilinesTest::prepare_to_validation( int /*test_case_idx*/ )
|
||||
{
|
||||
Mat pt( 1, pt_count, CV_MAKETYPE(CV_64F, 3) );
|
||||
Mat lines( 1, pt_count, CV_MAKETYPE(CV_64F, 3) );
|
||||
double f[9];
|
||||
Mat F( 3, 3, CV_64F, f );
|
||||
|
||||
test_convertHomogeneous( test_mat[INPUT][0], pt );
|
||||
test_mat[INPUT][1].convertTo(F, CV_64F);
|
||||
if( which_image == 2 )
|
||||
cv::transpose( F, F );
|
||||
|
||||
for( int i = 0; i < pt_count; i++ )
|
||||
{
|
||||
double* p = pt.ptr<double>() + i*3;
|
||||
double* l = lines.ptr<double>() + i*3;
|
||||
double t0 = f[0]*p[0] + f[1]*p[1] + f[2]*p[2];
|
||||
double t1 = f[3]*p[0] + f[4]*p[1] + f[5]*p[2];
|
||||
double t2 = f[6]*p[0] + f[7]*p[1] + f[8]*p[2];
|
||||
double d = sqrt(t0*t0 + t1*t1);
|
||||
d = d ? 1./d : 1.;
|
||||
l[0] = t0*d; l[1] = t1*d; l[2] = t2*d;
|
||||
}
|
||||
|
||||
test_convertHomogeneous( lines, test_mat[REF_OUTPUT][0] );
|
||||
}
|
||||
|
||||
TEST(Calib3d_ConvertHomogeneoous, accuracy) { CV_ConvertHomogeneousTest test; test.safe_run(); }
|
||||
TEST(Calib3d_ComputeEpilines, accuracy) { CV_ComputeEpilinesTest test; test.safe_run(); }
|
||||
|
||||
TEST(Calib3d_FindFundamentalMat, correctMatches)
|
||||
{
|
||||
double fdata[] = {0, 0, 0, 0, 0, -1, 0, 1, 0};
|
||||
double p1data[] = {200, 0, 1};
|
||||
double p2data[] = {170, 0, 1};
|
||||
|
||||
Mat F(3, 3, CV_64F, fdata);
|
||||
Mat p1(1, 1, CV_64FC2, p1data);
|
||||
Mat p2(1, 1, CV_64FC2, p2data);
|
||||
Mat np1, np2;
|
||||
|
||||
correctMatches(F, p1, p2, np1, np2);
|
||||
|
||||
cout << np1 << endl;
|
||||
cout << np2 << endl;
|
||||
}
|
||||
|
||||
TEST(Calib3d_FindFundamentalMat, Crash)
|
||||
{
|
||||
vector<Point2f> m1 = {{245, 128},{284, 226},{140, 60},{133, 127},{71, 218},{152, 138},{181, 106}};
|
||||
vector<Point2f> m2 = m1;
|
||||
vector<uchar> mask;
|
||||
findFundamentalMat(m1, m2, mask, FM_LMEDS, 1, 0.99);
|
||||
}
|
||||
|
||||
}} // namespace
|
||||
/* End of file. */
|
||||
Reference in New Issue
Block a user