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Completed findHomography test (source data has type CV_32FC2). In plans add the same checking for vector <Point2f>.

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Alexander Reshetnikov 13 years ago
parent c377804ee8
commit b5034ac33f
1 changed files with 837 additions and 35 deletions
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  1. 872
      modules/calib3d/test/test_homography.cpp

872
modules/calib3d/test/test_homography.cpp
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@ -1,41 +1,563 @@
#include "test_precomp.hpp"
#include <time.h>
#define CALIB3D_HOMOGRAPHY_ERROR_MATRIX_SIZE 1
#define CALIB3D_HOMOGRAPHY_ERROR_MATRIX_DIFF 2
#define CALIB3D_HOMOGRAPHY_ERROR_REPROJ_DIFF 3
#define CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK 4
#define CALIB3D_HOMOGRAPHY_ERROR_RANSAC_DIFF 5
#define MESSAGE_MATRIX_SIZE "Homography matrix must have 3*3 sizes."
#define MESSAGE_MATRIX_DIFF "Accuracy of homography transformation matrix less than required."
#define MESSAGE_REPROJ_DIFF_1 "Reprojection error for current pair of points more than required."
#define MESSAGE_REPROJ_DIFF_2 "Reprojection error is not optimal."
#define MESSAGE_RANSAC_MASK_1 "Sizes of inliers/outliers mask are incorrect."
#define MESSAGE_RANSAC_MASK_2 "Mask mustn't have any outliers."
#define MESSAGE_RANSAC_MASK_3 "Mask of inliers/outliers is incorrect."
#define MESSAGE_RANSAC_MASK_4 "Inlier in original mask shouldn't be outlier in found mask."
#define MESSAGE_RANSAC_DIFF "Reprojection error for current pair of points more than required."
#define MAX_COUNT_OF_POINTS 500
#define COUNT_NORM_TYPES 3
#define METHODS_COUNT 3
size_t NORM_TYPE[COUNT_NORM_TYPES] = {cv::NORM_L1, cv::NORM_L2, cv::NORM_INF};
size_t METHOD[METHODS_COUNT] = {0, CV_RANSAC, CV_LMEDS};
using namespace cv;
using namespace std;
class CV_HomographyTest: public cvtest::BaseTest
class CV_HomographyTest: public cvtest::ArrayTest
{
public:
CV_HomographyTest();
~CV_HomographyTest();
protected:
int read_params( CvFileStorage* fs );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
int prepare_test_case( int test_case_idx );
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void run (int);
bool check_matrix (const Mat& H);
bool check_transform (const Mat& src, const Mat& dst, const Mat& H);
void run (int);
void prepare_to_validation( int test_case_idx );
protected:
int method;
int image_size;
int square_size;
double reproj_threshold;
double sigma;
bool test_cpp;
double get_success_error_level( int test_case_idx, int i, int j );
void test_projectPoints(Mat& src_2d, Mat& dst_2d, const Mat& H, RNG* rng, double sigma); // checking for quality of perpective transformation
private:
float max_diff;
void check_matrix_size(const cv::Mat& H);
void check_matrix_diff(const cv::Mat& original, const cv::Mat& found, const int norm_type = NORM_L2);
float max_diff, max_2diff;
bool check_matrix_size(const cv::Mat& H);
bool check_matrix_diff(const cv::Mat& original, const cv::Mat& found, const int norm_type, double &diff);
// bool check_reproj_error(const cv::Mat& src_3d, const cv::Mat& dst_3d, const int norm_type = NORM_L2);
bool check_ransac_mask_1(const Mat& src, const Mat& mask);
bool check_ransac_mask_2(const Mat& original_mask, const Mat& found_mask);
void check_transform_quality(cv::InputArray src_points, cv::InputArray dst_poits, const cv::Mat& H, const int norm_type = NORM_L2);
void check_transform_quality(const cv::InputArray src_points, const vector <cv::Point2f> dst_points, const cv::Mat& H, const int norm_type = NORM_L2);
void check_transform_quality(const vector <cv::Point2f> src_points, const cv::InputArray dst_points, const cv::Mat& H, const int norm_type = NORM_L2);
void check_transform_quality(const vector <cv::Point2f> src_points, const vector <cv::Point2f> dst_points, const cv::Mat& H, const int norm_type = NORM_L2);
};
CV_HomographyTest::CV_HomographyTest(): max_diff(1e-5) {}
/* void CV_HomographyTest::run_func () {} */
CV_HomographyTest::CV_HomographyTest() : max_diff(1e-2), max_2diff(2e-2)
{
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[TEMP].push_back(NULL);
test_array[TEMP].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
element_wise_relative_error = false;
method = 0;
image_size = 1e+2;
reproj_threshold = 3.0;
sigma = 0.01;
test_cpp = false;
}
CV_HomographyTest::~CV_HomographyTest() {}
void CV_HomographyTest::check_matrix_size(const cv::Mat& H)
void CV_HomographyTest::get_test_array_types_and_sizes( int /*test_case_idx*/, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
CV_Assert ( H.rows == 3 && H.cols == 3);
RNG& rng = ts->get_rng();
int pt_depth = CV_32F;
double pt_count_exp = cvtest::randReal(rng)*6 + 1;
int pt_count = cvRound(exp(pt_count_exp));
/* dims = cvtest::randInt(rng) % 2 + 2;
method = 1 << (cvtest::randInt(rng) % 4);
if( method == CV_FM_7POINT )
pt_count = 7;
else
{
pt_count = MAX( pt_count, 8 + (method == CV_FM_8POINT) );
if( pt_count >= 8 && cvtest::randInt(rng) % 2 )
method |= CV_FM_8POINT;
} */
types[INPUT][0] = CV_MAKETYPE(pt_depth, 2);
types[INPUT][1] = types[INPUT][0];
types[OUTPUT][0] = CV_MAKETYPE(pt_depth, 1);
/* if( cvtest::randInt(rng) % 2 )
sizes[INPUT][0] = cvSize(pt_count, dims);
else
{
sizes[INPUT][0] = cvSize(dims, pt_count);
if( cvtest::randInt(rng) % 2 )
{
types[INPUT][0] = CV_MAKETYPE(pt_depth, dims);
if( cvtest::randInt(rng) % 2 )
sizes[INPUT][0] = cvSize(pt_count, 1);
else
sizes[INPUT][0] = cvSize(1, pt_count);
}
}
sizes[INPUT][1] = sizes[INPUT][0];
types[INPUT][1] = types[INPUT][0];
sizes[INPUT][2] = cvSize(pt_count, 1 );
types[INPUT][2] = CV_64FC3;
sizes[INPUT][3] = cvSize(4,3);
types[INPUT][3] = CV_64FC1;
sizes[INPUT][4] = sizes[INPUT][5] = cvSize(3,3);
types[INPUT][4] = types[INPUT][5] = CV_MAKETYPE(CV_64F, 1);
sizes[TEMP][0] = cvSize(3,3);
types[TEMP][0] = CV_64FC1;
sizes[TEMP][1] = cvSize(pt_count,1);
types[TEMP][1] = CV_8UC1;
sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = cvSize(3,1);
types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_64FC1;
sizes[OUTPUT][1] = sizes[REF_OUTPUT][1] = cvSize(pt_count,1);
types[OUTPUT][1] = types[REF_OUTPUT][1] = CV_8UC1;
test_cpp = (cvtest::randInt(rng) & 256) == 0;
*/
}
int CV_HomographyTest::read_params(CvFileStorage *fs)
{
int code = cvtest::ArrayTest::read_params(fs);
return code;
}
double CV_HomographyTest::get_success_error_level(int test_case_idx, int i, int j)
{
return max_diff;
}
void CV_HomographyTest::check_matrix_diff(const cv::Mat& original, const cv::Mat& found, const int norm_type)
void CV_HomographyTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
double diff = cv::norm(original, found, norm_type);
CV_Assert ( diff <= max_diff );
double t[9]={0};
RNG& rng = ts->get_rng();
if ( i != INPUT )
{
cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr );
return;
}
switch( j )
{
case 0:
case 1:
return; // fill them later in prepare_test_case
case 2:
{
double* p = arr.ptr<double>();
for( i = 0; i < arr.cols*3; i += 3 )
{
/* p[i] = cvtest::randReal(rng)*square_size;
p[i+1] = cvtest::randReal(rng)*square_size;
p[i+2] = cvtest::randReal(rng)*square_size + square_size; */
}
}
break;
case 3:
{
double r[3];
Mat rot_vec( 3, 1, CV_64F, r );
Mat rot_mat( 3, 3, CV_64F, t, 4*sizeof(t[0]) );
r[0] = cvtest::randReal(rng)*CV_PI*2;
r[1] = cvtest::randReal(rng)*CV_PI*2;
r[2] = cvtest::randReal(rng)*CV_PI*2;
cvtest::Rodrigues( rot_vec, rot_mat );
/* t[3] = cvtest::randReal(rng)*square_size;
t[7] = cvtest::randReal(rng)*square_size;
t[11] = cvtest::randReal(rng)*square_size; */
Mat( 3, 4, CV_64F, t ).convertTo(arr, arr.type());
}
break;
case 4:
case 5:
{
/* t[0] = t[4] = cvtest::randReal(rng)*(max_f - min_f) + min_f;
t[2] = (img_size*0.5 + cvtest::randReal(rng)*4. - 2.)*t[0];
t[5] = (img_size*0.5 + cvtest::randReal(rng)*4. - 2.)*t[4];
t[8] = 1.0f;
Mat( 3, 3, CV_64F, t ).convertTo( arr, arr.type() ); */
break;
}
}
}
int CV_HomographyTest::prepare_test_case(int test_case_idx)
{
int code = cvtest::ArrayTest::prepare_test_case(test_case_idx);
if (code > 0)
{
Mat& src = test_mat[INPUT][0];
RNG& rng = ts->get_rng();
float Hdata[] = { sqrt(2.0f)/2, -sqrt(2.0f)/2, 0.0f,
sqrt(2.0f)/2, sqrt(2.0f)/2, 0.0f,
0.0f, 0.0f, 1.0f };
Mat H( 3, 3, CV_32F, Hdata );
cv::Mat dst(1, src.cols, CV_32FC2);
int k;
for( k = 0; k < 2; k++ )
{
const Mat& H = test_mat[OUTPUT][0];
Mat& dst = test_mat[INPUT][k == 0 ? 1 : 2];
for (int i = 0; i < src.cols; ++i)
{
float *s = src.ptr<float>()+2*i;
float *d = dst.ptr<float>()+2*i;
d[0] = Hdata[0]*s[0] + Hdata[1]*s[1] + Hdata[2];
d[1] = Hdata[3]*s[0] + Hdata[4]*s[1] + Hdata[5];
}
test_projectPoints( src, dst, H, &rng, sigma );
}
}
return code;
}
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 )
{
if (count != dst.rows) ; // CV_Error should be here
CV_Assert( count == dst.rows );
ddims = dst.channels()*dst.cols;
dstep1 = (int)(dst.step/sizeof(double));
dstep2 = 1;
}
else
{
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());
}
void CV_HomographyTest::test_projectPoints( Mat& src_2d, Mat& dst, const Mat& H, RNG* rng, double sigma )
{
if (!src_2d.isContinuous())
{
CV_Error(-1, "");
return;
}
cv::Mat src_3d(1, src_2d.cols, CV_32FC3);
for (int i = 0; i < src_2d.cols; ++i)
{
float *c_3d = src_3d.ptr<float>()+3*i;
float *c_2d = src_2d.ptr<float>()+2*i;
c_3d[0] = c_2d[0]; c_3d[1] = c_2d[1]; c_3d[2] = 1.0f;
}
cv::Mat dst_3d; gemm(H, src_3d, 1, Mat(), 0, dst_3d);
int i, count = src_2d.cols;
Mat noise;
if ( rng )
{
if( sigma == 0 ) rng = 0;
else
{
noise.create( 1, count, CV_32FC2 );
rng->fill(noise, RNG::NORMAL, Scalar::all(0), Scalar::all(sigma) );
}
}
cv::Mat dst_2d(1, count, CV_32FC2);
for (size_t i = 0; i < count; ++i)
{
float *c_3d = dst_3d.ptr<float>()+3*i;
float *c_2d = dst_2d.ptr<float>()+2*i;
c_2d[0] = c_3d[0]/c_3d[2];
c_2d[1] = c_3d[1]/c_3d[2];
}
Mat temp( 1, count, CV_32FC2 );
for( i = 0; i < count; i++ )
{
const double* M = src_2d.ptr<double>() + 2*i;
double* m = temp.ptr<double>() + 2*i;
double X = M[0], Y = M[1], Z = M[2];
double u = H.at<float>(0, 0)*X + H.at<float>(0, 1)*Y + H.at<float>(0, 2);
double v = H.at<float>(1, 0)*X + H.at<float>(1, 1)*Y + H.at<float>(1, 2);
double s = H.at<float>(2, 0)*X + H.at<float>(2, 1)*Y + H.at<float>(2, 2);
if( !noise.empty() )
{
u += noise.at<Point2f>(i).x*s;
v += noise.at<Point2f>(i).y*s;
}
m[0] = u;
m[1] = v;
m[2] = s;
}
test_convertHomogeneous( dst_2d, dst );
}
void CV_HomographyTest::prepare_to_validation(int test_case_idx)
{
const Mat& H = test_mat[INPUT][3];
const Mat& A1 = test_mat[INPUT][4];
const Mat& A2 = test_mat[INPUT][5];
double h0[9], h[9];
Mat H0(3, 3, CV_32FC1, h0);
Mat invA1, invA2, T;
cv::invert(A1, invA1, CV_SVD);
cv::invert(A2, invA2, CV_SVD);
double tx = H.at<double>(0, 2);
double ty = H.at<double>(1, 2);
double tz = H.at<double>(2, 2);
// double _t_x[] = { 0, -tz, ty, tz, 0, -tx, -ty, tx, 0 };
// F = (A2^-T)*[t]_x*R*(A1^-1)
/* cv::gemm( invA2, Mat( 3, 3, CV_64F, _t_x ), 1, Mat(), 0, T, CV_GEMM_A_T );
cv::gemm( R, invA1, 1, Mat(), 0, invA2 );
cv::gemm( T, invA2, 1, Mat(), 0, F0 ); */
H0 *= 1./h0[8];
uchar* status = test_mat[TEMP][1].data;
double err_level = get_success_error_level( test_case_idx, OUTPUT, 1 );
uchar* mtfm1 = test_mat[REF_OUTPUT][1].data;
uchar* mtfm2 = test_mat[OUTPUT][1].data;
double* f_prop1 = (double*)test_mat[REF_OUTPUT][0].data;
double* f_prop2 = (double*)test_mat[OUTPUT][0].data;
int i, pt_count = test_mat[INPUT][2].cols;
Mat p1( 1, pt_count, CV_64FC2 );
Mat p2( 1, pt_count, CV_64FC2 );
test_convertHomogeneous( test_mat[INPUT][0], p1 );
test_convertHomogeneous( test_mat[INPUT][1], p2 );
cvtest::convert(test_mat[TEMP][0], H0, H.type());
if( method <= CV_FM_8POINT )
memset( status, 1, pt_count );
for( i = 0; i < pt_count; i++ )
{
double x1 = p1.at<Point2f>(i).x;
double y1 = p1.at<Point2f>(i).y;
double x2 = p2.at<Point2f>(i).x;
double y2 = p2.at<Point2f>(i).y;
double n1 = 1./sqrt(x1*x1 + y1*y1 + 1);
double n2 = 1./sqrt(x2*x2 + y2*y2 + 1);
double t0 = fabs(h0[0]*x2*x1 + h0[1]*x2*y1 + h0[2]*x2 +
h0[3]*y2*x1 + h0[4]*y2*y1 + h0[5]*y2 +
h0[6]*x1 + h0[7]*y1 + h0[8])*n1*n2;
double t = fabs(h[0]*x2*x1 + h[1]*x2*y1 + h[2]*x2 +
h[3]*y2*x1 + h[4]*y2*y1 + h[5]*y2 +
h[6]*x1 + h[7]*y1 + h[8])*n1*n2;
mtfm1[i] = 1;
mtfm2[i] = !status[i] || t0 > err_level || t < err_level;
}
f_prop1[0] = 1;
f_prop1[1] = 1;
f_prop1[2] = 0;
// f_prop2[0] = f_result != 0;
f_prop2[1] = h[8];
f_prop2[2] = cv::determinant( H );
}
bool CV_HomographyTest::check_matrix_size(const cv::Mat& H)
{
return (H.rows == 3) && (H.cols == 3);
}
bool CV_HomographyTest::check_matrix_diff(const cv::Mat& original, const cv::Mat& found, const int norm_type, double &diff)
{
diff = cv::norm(original, found, norm_type);
return diff <= max_diff;
}
bool CV_HomographyTest::check_ransac_mask_1(const Mat& src, const Mat& mask)
{
if (!(mask.cols == 1) && (mask.rows == src.cols))
{
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_1);
return false;
}
if (countNonZero(mask) < mask.rows)
{
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_2);
return false;
}
return true;
}
bool CV_HomographyTest::check_ransac_mask_2(const Mat& original_mask, const Mat& found_mask)
{
if (!(found_mask.cols == 1) && (found_mask.rows == original_mask.rows))
{
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_1);
return false;
}
}
void CV_HomographyTest::check_transform_quality(cv::InputArray src_points, cv::InputArray dst_points, const cv::Mat& H, const int norm_type)
@ -55,34 +577,314 @@ void CV_HomographyTest::check_transform_quality(cv::InputArray src_points, cv::I
void CV_HomographyTest::run(int)
{
// test data without outliers
cv::Vec3f n_src(1.0f, 1.0f, 1.0f), n_dst(1.0f, -1.0f, 0.0f);
const float d_src = 1.0f, d_dst = 0.0f;
const int n_points = 100;
for (size_t N = 4; N <= MAX_COUNT_OF_POINTS; ++N)
{
RNG& rng = ts->get_rng();
float P[2*n_points], Q[2*n_points];
float *src_data = new float [2*N];
for (size_t i = 0; i < 2*n_points; i += 2)
{
float u1 = cv::randu<float>(), v1 = cv::randu<float>();
float w1 = 1.0f/(d_src - n_src[0]*u1 - n_src[1]*v1);
P[i] = u1*w1; P[i+1] = v1*w1;
for (int i = 0; i < N; ++i)
{
src_data[2*i] = (float)cvtest::randReal(rng)*image_size;
src_data[2*i+1] = (float)cvtest::randReal(rng)*image_size;
}
float u2 = cv::randu<float>(), v2 = cv::randu<float>();
float w2 = 1.0f/(d_src - n_src[0]*u1 - n_src[1]*v1);
Q[i] = u2*w2; Q[i+1] = v2*w2;
}
cv::Mat src_mat_2f(1, N, CV_32FC2, src_data),
src_mat_2d(2, N, CV_32F, src_data),
src_mat_3d(3, N, CV_32F);
cv::Mat dst_mat_2f, dst_mat_2d, dst_mat_3d;
cv::Mat src(n_points, 1, CV_32FC2, P);
cv::Mat dst(n_points, 1, CV_32FC2, Q);
cv::Mat H = cv::findHomography(src, dst);
check_matrix_size(H);
for (size_t i = 0; i < N; ++i)
{
float *tmp = src_mat_2d.ptr<float>()+2*i;
src_mat_3d.at<float>(0, i) = tmp[0];
src_mat_3d.at<float>(1, i) = tmp[1];
src_mat_3d.at<float>(2, i) = 1.0f;
}
double fi = cvtest::randReal(rng)*2*CV_PI;
double t_x = cvtest::randReal(rng)*sqrt(image_size*1.0),
t_y = cvtest::randReal(rng)*sqrt(image_size*1.0);
double Hdata[9] = { cos(fi), -sin(fi), t_x,
sin(fi), cos(fi), t_y,
0.0f, 0.0f, 1.0f };
cv::Mat H_64(3, 3, CV_64F, Hdata), H_32;
H_64.convertTo(H_32, CV_32F);
dst_mat_3d = H_32*src_mat_3d;
dst_mat_2d.create(2, N, CV_32F); dst_mat_2f.create(1, N, CV_32FC2);
for (size_t i = 0; i < N; ++i)
{
float *tmp_2f = dst_mat_2f.ptr<float>()+2*i;
tmp_2f[0] = dst_mat_2d.at<float>(0, i) = dst_mat_3d.at<float>(0, i) /= dst_mat_3d.at<float>(2, i);
tmp_2f[1] = dst_mat_2d.at<float>(1, i) = dst_mat_3d.at<float>(1, i) /= dst_mat_3d.at<float>(2, i);
}
for (size_t i = 0; i < METHODS_COUNT; ++i)
{
method = METHOD[i];
switch (method)
{
case 0:
case CV_LMEDS:
{
Mat H_res_64 = cv::findHomography(src_mat_2f, dst_mat_2f, method);
if (!check_matrix_size(H_res_64))
{
cout << endl; cout << "Checking for homography matrix sizes..." << endl; cout << endl;
cout << "Count of points: " << N << endl; cout << endl;
cout << "Homography matrix:" << endl; cout << endl;
cout << H_res_64 << endl; cout << endl;
cout << "Number of rows: " << H_res_64.rows << " Number of cols: " << H_res_64.cols << endl; cout << endl;
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_SIZE, MESSAGE_MATRIX_SIZE);
return;
}
double diff;
for (size_t j = 0; j < COUNT_NORM_TYPES; ++j)
if (!check_matrix_diff(H_64, H_res_64, NORM_TYPE[j], diff))
{
cout << endl; cout << "Checking for accuracy of homography matrix computing..." << endl; cout << endl;
cout << "Count of points: " << N << endl; cout << endl;
cout << "Original matrix:" << endl; cout << endl;
cout << H_64 << endl; cout << endl;
cout << "Found matrix:" << endl; cout << endl;
cout << H_res_64 << endl; cout << endl;
cout << "Norm type using in criteria: "; if (NORM_TYPE[j] == 1) cout << "INF"; else if (NORM_TYPE[j] == 2) cout << "L1"; else cout << "L2"; cout << endl;
cout << "Difference between matrix: " << diff << endl;
cout << "Maximum allowed difference: " << max_diff << endl; cout << endl;
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_DIFF, MESSAGE_MATRIX_DIFF);
return;
}
continue;
}
case CV_RANSAC:
{
cv::Mat mask; double diff;
Mat H_res_64 = cv::findHomography(src_mat_2f, dst_mat_2f, CV_RANSAC, reproj_threshold, mask);
if (!check_matrix_size(H_res_64))
{
cout << endl; cout << "Checking for homography matrix sizes..." << endl; cout << endl;
cout << "Count of points: " << N << endl; cout << endl;
cout << "Homography matrix:" << endl; cout << endl;
cout << H_res_64 << endl; cout << endl;
cout << "Number of rows: " << H_res_64.rows << " Number of cols: " << H_res_64.cols << endl; cout << endl;
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_SIZE, MESSAGE_MATRIX_SIZE);
return;
}
for (size_t j = 0; j < COUNT_NORM_TYPES; ++j)
if (!check_matrix_diff(H_64, H_res_64, NORM_TYPE[j], diff))
{
cout << endl; cout << "Checking for accuracy of homography matrix computing..." << endl; cout << endl;
cout << "Count of points: " << N << endl; cout << endl;
cout << "Original matrix:" << endl; cout << endl;
cout << H_64 << endl; cout << endl;
cout << "Found matrix:" << endl; cout << endl;
cout << H_res_64 << endl; cout << endl;
cout << "Norm type using in criteria: "; if (NORM_TYPE[j] == 1) cout << "INF"; else if (NORM_TYPE[j] == 2) cout << "L1"; else cout << "L2"; cout << endl;
cout << "Difference between matrix: " << diff << endl;
cout << "Maximum allowed difference: " << max_diff << endl; cout << endl;
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_DIFF, MESSAGE_MATRIX_DIFF);
return;
}
if (!check_ransac_mask_1(src_mat_2f, mask)) return;
continue;
}
default: continue;
}
}
Mat noise_2f(1, N, CV_32FC2);
rng.fill(noise_2f, RNG::NORMAL, Scalar::all(0), Scalar::all(sigma));
// check_transform_quality(src, dst, H, NORM_L1);
cv::Mat mask(N, 1, CV_8UC1);
// check_matrix_diff(_H, H, NORM_L1);
for (int i = 0; i < N; ++i)
{
float *a = noise_2f.ptr<float>()+2*i, *_2f = dst_mat_2f.ptr<float>()+2*i;
_2f[0] /* = dst_mat_2d.at<float>(0, i) = dst_mat_3d.at<float>(0, i) */ += a[0];
_2f[1] /* = dst_mat_2d.at<float>(1, i) = dst_mat_3d.at<float>(1, i) */ += a[1];
mask.at<uchar>(i, 0) = sqrt(a[0]*a[0]+a[1]*a[1]) > reproj_threshold ? 0 : 1;
}
for (size_t i = 0; i < METHODS_COUNT; ++i)
{
method = METHOD[i];
switch (method)
{
case 0:
case CV_LMEDS:
{
Mat H_res_64 = cv::findHomography(src_mat_2f, dst_mat_2f, mask);
if (!check_matrix_size(H_res_64))
{
cout << endl; cout << "Checking for homography matrix sizes..." << endl; cout << endl;
cout << "Count of points: " << N << endl; cout << endl;
cout << "Homography matrix:" << endl; cout << endl;
cout << H_res_64 << endl; cout << endl;
cout << "Number of rows: " << H_res_64.rows << " Number of cols: " << H_res_64.cols << endl; cout << endl;
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_SIZE, MESSAGE_MATRIX_SIZE);
return;
}
Mat H_res_32; H_res_64.convertTo(H_res_32, CV_32F);
cv::Mat dst_res_3d(3, N, CV_32F), noise_2d(2, N, CV_32F);
for (size_t k = 0; k < N; ++k)
{
Mat tmp_mat_3d = H_res_32*src_mat_3d.col(k);
dst_res_3d.at<float>(0, k) = tmp_mat_3d.at<float>(0, 0) /= tmp_mat_3d.at<float>(2, 0);
dst_res_3d.at<float>(1, k) = tmp_mat_3d.at<float>(1, 0) /= tmp_mat_3d.at<float>(2, 0);
dst_res_3d.at<float>(2, k) = tmp_mat_3d.at<float>(2, 0) = 1.0f;
float *a = noise_2f.ptr<float>()+2*k;
noise_2d.at<float>(0, k) = a[0]; noise_2d.at<float>(1, k) = a[1];
for (size_t j = 0; j < METHODS_COUNT; ++j)
if (cv::norm(tmp_mat_3d, dst_mat_3d.col(k), NORM_TYPE[j]) - cv::norm(noise_2d.col(k), NORM_TYPE[j]) > max_2diff)
{
cout << endl; cout << "Checking for accuracy of reprojection error computing..." << endl; cout << endl;
cout << "Method: "; if (method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl;
cout << "Sigma of normal noise: " << sigma << endl;
cout << "Count of points: " << N << endl;
cout << "Number of point: " << k << endl;
cout << "Norm type using in criteria: "; if (NORM_TYPE[j] == 1) cout << "INF"; else if (NORM_TYPE[j] == 2) cout << "L1"; else cout << "L2"; cout << endl;
cout << "Difference with noise of point: " << cv::norm(tmp_mat_3d, dst_mat_3d.col(k), NORM_TYPE[j]) - cv::norm(noise_2d.col(k), NORM_TYPE[j]) << endl;
cout << "Maxumum allowed difference: " << max_2diff << endl; cout << endl;
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_REPROJ_DIFF, MESSAGE_REPROJ_DIFF_1);
return;
}
}
Mat tmp_mat_3d = H_res_32*src_mat_3d;
for (size_t j = 0; j < METHODS_COUNT; ++j)
if (cv::norm(dst_res_3d, dst_mat_3d, NORM_TYPE[j]) - cv::norm(noise_2d, NORM_TYPE[j]) > max_diff)
{
cout << endl; cout << "Checking for accuracy of reprojection error computing..." << endl; cout << endl;
cout << "Method: "; if (method == 0) cout << 0 << endl; else cout << "CV_LMEDS" << endl;
cout << "Sigma of normal noise: " << sigma << endl;
cout << "Count of points: " << N << endl;
cout << "Norm type using in criteria: "; if (NORM_TYPE[j] == 1) cout << "INF"; else if (NORM_TYPE[j] == 2) cout << "L1"; else cout << "L2"; cout << endl;
cout << "Difference with noise of points: " << cv::norm(dst_res_3d, dst_mat_3d, NORM_TYPE[j]) - cv::norm(noise_2d, NORM_TYPE[j]) << endl;
cout << "Maxumum allowed difference: " << max_diff << endl; cout << endl;
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_REPROJ_DIFF, MESSAGE_REPROJ_DIFF_2);
return;
}
continue;
}
case CV_RANSAC:
{
cv::Mat mask_res;
Mat H_res_64 = cv::findHomography(src_mat_2f, dst_mat_2f, CV_RANSAC, reproj_threshold, mask_res);
if (!check_matrix_size(H_res_64))
{
cout << endl; cout << "Checking for homography matrix sizes..." << endl; cout << endl;
cout << "Count of points: " << N << endl; cout << endl;
cout << "Homography matrix:" << endl; cout << endl;
cout << H_res_64 << endl; cout << endl;
cout << "Number of rows: " << H_res_64.rows << " Number of cols: " << H_res_64.cols << endl; cout << endl;
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_MATRIX_SIZE, MESSAGE_MATRIX_SIZE);
return;
}
if (!check_ransac_mask_2(mask, mask_res)) return;
cv::Mat H_res_32; H_res_64.convertTo(H_res_32, CV_32F);
cv::Mat dst_res_3d = H_res_32*src_mat_3d;
for (size_t k = 0; k < N; ++k)
{
dst_res_3d.at<float>(0, k) /= dst_res_3d.at<float>(2, k);
dst_res_3d.at<float>(1, k) /= dst_res_3d.at<float>(2, k);
dst_res_3d.at<float>(2, k) = 1.0f;
float *p = dst_mat_2f.ptr<float>()+2*k;
dst_mat_3d.at<float>(0, k) = p[0];
dst_mat_3d.at<float>(1, k) = p[1];
double diff = cv::norm(dst_res_3d.col(k), dst_mat_3d.col(k), NORM_L2);
if (mask_res.at<bool>(k, 0) != (diff <= reproj_threshold))
{
cout << endl; cout << "Checking for inliers/outliers mask..." << endl; cout << endl;
cout << "Count of points: " << N << " " << endl;
cout << "Number of point: " << k << " " << endl;
cout << "Reprojection error for this point: " << diff << " " << endl;
cout << "Reprojection error threshold: " << reproj_threshold << " " << endl;
cout << "Value of found mask: "<< mask_res.at<bool>(k, 0) << endl; cout << endl;
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_3);
return;
}
if (mask.at<bool>(k, 0) && !mask_res.at<bool>(k, 0))
{
cout << endl; cout << "Checking for inliers/outliers mask..." << endl; cout << endl;
cout << "Count of points: " << N << " " << endl;
cout << "Number of point: " << k << " " << endl;
cout << "Reprojection error for this point: " << diff << " " << endl;
cout << "Reprojection error threshold: " << reproj_threshold << " " << endl;
cout << "Value of original mask: "<< mask.at<bool>(k, 0) << " Value of found mask: " << mask_res.at<bool>(k, 0) << endl; cout << endl;
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_MASK, MESSAGE_RANSAC_MASK_4);
return;
}
if (mask_res.at<bool>(k, 0))
{
float *a = noise_2f.ptr<float>()+2*k;
dst_mat_3d.at<float>(0, k) -= a[0];
dst_mat_3d.at<float>(1, k) -= a[1];
cv::Mat noise_2d(2, 1, CV_32F);
noise_2d.at<float>(0, 0) = a[0]; noise_2d.at<float>(1, 0) = a[1];
for (size_t j = 0; j < METHODS_COUNT; ++j)
{
diff = cv::norm(dst_res_3d.col(k), dst_mat_3d.col(k), NORM_TYPE[j]);
if (diff - cv::norm(noise_2d, NORM_TYPE[j]) > max_2diff)
{
cout << endl; cout << "Checking for reprojection error of inlier..." << endl; cout << endl;
cout << "Count of points: " << N << " " << endl;
cout << "Number of point: " << k << " " << endl;
cout << "Reprojection error for this point: " << diff << " " << endl;
CV_Error(CALIB3D_HOMOGRAPHY_ERROR_RANSAC_DIFF, MESSAGE_RANSAC_DIFF);
return;
}
}
}
}
// Checking of reprojection error for any points pair.
continue;
}
default: continue;
}
}
}
}
TEST(Core_Homography, complex_test) { CV_HomographyTest test; test.safe_run(); }
TEST(Calib3d_Homography, complex_test) { CV_HomographyTest test; test.safe_run(); }
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