Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

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

Browse Source
pull/13383/head
Alexander Reshetnikov 13 years ago
parent c377804ee8
commit b5034ac33f
1 changed files with 837 additions and 35 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 862
      modules/calib3d/test/test_homography.cpp

862
modules/calib3d/test/test_homography.cpp
Unescape View File

@ -1,41 +1,563 @@
#include "test_precomp.hpp" #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 cv;
using namespace std; using namespace std;
class CV_HomographyTest: public cvtest::BaseTest class CV_HomographyTest: public cvtest::ArrayTest
{ {
public: public:
CV_HomographyTest(); CV_HomographyTest();
~CV_HomographyTest(); ~CV_HomographyTest();
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 prepare_to_validation( int test_case_idx );
protected: protected:
void run (int); 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: private:
float max_diff; float max_diff, max_2diff;
void check_matrix_size(const cv::Mat& H); bool 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); 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(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 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 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); 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() {} 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 )
{
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::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
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)
{ {
CV_Assert ( H.rows == 3 && H.cols == 3); 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];
} }
void CV_HomographyTest::check_matrix_diff(const cv::Mat& original, const cv::Mat& found, const int norm_type) 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() )
{ {
double diff = cv::norm(original, found, norm_type); u += noise.at<Point2f>(i).x*s;
CV_Assert ( diff <= max_diff ); 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) 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) void CV_HomographyTest::run(int)
{ {
// test data without outliers for (size_t N = 4; N <= MAX_COUNT_OF_POINTS; ++N)
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; RNG& rng = ts->get_rng();
const int n_points = 100;
float *src_data = new float [2*N];
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;
}
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;
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;
}
float P[2*n_points], Q[2*n_points]; default: continue;
}
}
Mat noise_2f(1, N, CV_32FC2);
rng.fill(noise_2f, RNG::NORMAL, Scalar::all(0), Scalar::all(sigma));
for (size_t i = 0; i < 2*n_points; i += 2) cv::Mat mask(N, 1, CV_8UC1);
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:
{ {
float u1 = cv::randu<float>(), v1 = cv::randu<float>(); Mat H_res_64 = cv::findHomography(src_mat_2f, dst_mat_2f, mask);
float w1 = 1.0f/(d_src - n_src[0]*u1 - n_src[1]*v1);
P[i] = u1*w1; P[i+1] = v1*w1;
float u2 = cv::randu<float>(), v2 = cv::randu<float>(); if (!check_matrix_size(H_res_64))
float w2 = 1.0f/(d_src - n_src[0]*u1 - n_src[1]*v1); {
Q[i] = u2*w2; Q[i+1] = v2*w2; 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;
} }
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); Mat tmp_mat_3d = H_res_32*src_mat_3d;
check_matrix_size(H); 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;
}
// check_transform_quality(src, dst, H, NORM_L1); 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);
// check_matrix_diff(_H, H, NORM_L1); 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(); }
Write Preview
Loading…
Cancel
Save