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opencv/modules/calib3d/test/test_undistort.cpp
Maksim Shabunin b379969c63 Test: avoid duplicated test cases
2019-12-19 14:38:59 +03:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
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#include "test_precomp.hpp"
#include "opencv2/imgproc/imgproc_c.h"
namespace opencv_test { namespace {
class CV_DefaultNewCameraMatrixTest : public cvtest::ArrayTest
{
public:
CV_DefaultNewCameraMatrixTest();
protected:
int prepare_test_case (int test_case_idx);
void prepare_to_validation( 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_func();
private:
cv::Size img_size;
cv::Mat camera_mat;
cv::Mat new_camera_mat;
int matrix_type;
bool center_principal_point;
static const int MAX_X = 2048;
static const int MAX_Y = 2048;
//static const int MAX_VAL = 10000;
};
CV_DefaultNewCameraMatrixTest::CV_DefaultNewCameraMatrixTest()
{
test_array[INPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
matrix_type = 0;
center_principal_point = false;
}
void CV_DefaultNewCameraMatrixTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
cvtest::ArrayTest::get_test_array_types_and_sizes(test_case_idx,sizes,types);
RNG& rng = ts->get_rng();
matrix_type = types[INPUT][0] = types[OUTPUT][0]= types[REF_OUTPUT][0] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
sizes[INPUT][0] = sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = cvSize(3,3);
}
int CV_DefaultNewCameraMatrixTest::prepare_test_case(int test_case_idx)
{
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
if (code <= 0)
return code;
RNG& rng = ts->get_rng();
img_size.width = cvtest::randInt(rng) % MAX_X + 1;
img_size.height = cvtest::randInt(rng) % MAX_Y + 1;
center_principal_point = ((cvtest::randInt(rng) % 2)!=0);
// Generating camera_mat matrix
double sz = MAX(img_size.width, img_size.height);
double aspect_ratio = cvtest::randReal(rng)*0.6 + 0.7;
double a[9] = {0,0,0,0,0,0,0,0,1};
Mat _a(3,3,CV_64F,a);
a[2] = (img_size.width - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[5] = (img_size.height - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[0] = sz/(0.9 - cvtest::randReal(rng)*0.6);
a[4] = aspect_ratio*a[0];
Mat& _a0 = test_mat[INPUT][0];
cvtest::convert(_a, _a0, _a0.type());
camera_mat = _a0;
return code;
}
void CV_DefaultNewCameraMatrixTest::run_func()
{
new_camera_mat = cv::getDefaultNewCameraMatrix(camera_mat,img_size,center_principal_point);
}
void CV_DefaultNewCameraMatrixTest::prepare_to_validation( int /*test_case_idx*/ )
{
const Mat& src = test_mat[INPUT][0];
Mat& dst = test_mat[REF_OUTPUT][0];
Mat& test_output = test_mat[OUTPUT][0];
Mat& output = new_camera_mat;
cvtest::convert( output, test_output, test_output.type() );
if (!center_principal_point)
{
cvtest::copy(src, dst);
}
else
{
double a[9] = {0,0,0,0,0,0,0,0,1};
Mat _a(3,3,CV_64F,a);
if (matrix_type == CV_64F)
{
a[0] = src.at<double>(0,0);
a[4] = src.at<double>(1,1);
}
else
{
a[0] = src.at<float>(0,0);
a[4] = src.at<float>(1,1);
}
a[2] = (img_size.width - 1)*0.5;
a[5] = (img_size.height - 1)*0.5;
cvtest::convert( _a, dst, dst.type() );
}
}
//---------
class CV_UndistortPointsTest : public cvtest::ArrayTest
{
public:
CV_UndistortPointsTest();
protected:
int prepare_test_case (int test_case_idx);
void prepare_to_validation( int test_case_idx );
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
double get_success_error_level( int test_case_idx, int i, int j );
void run_func();
void distortPoints(const CvMat* _src, CvMat* _dst, const CvMat* _cameraMatrix,
const CvMat* _distCoeffs, const CvMat* matR, const CvMat* matP);
private:
bool useDstMat;
static const int N_POINTS = 10;
static const int MAX_X = 2048;
static const int MAX_Y = 2048;
bool zero_new_cam;
bool zero_distortion;
bool zero_R;
cv::Size img_size;
cv::Mat dst_points_mat;
cv::Mat camera_mat;
cv::Mat R;
cv::Mat P;
cv::Mat distortion_coeffs;
cv::Mat src_points;
std::vector<cv::Point2f> dst_points;
};
CV_UndistortPointsTest::CV_UndistortPointsTest()
{
test_array[INPUT].push_back(NULL); // points matrix
test_array[INPUT].push_back(NULL); // camera matrix
test_array[INPUT].push_back(NULL); // distortion coeffs
test_array[INPUT].push_back(NULL); // R matrix
test_array[INPUT].push_back(NULL); // P matrix
test_array[OUTPUT].push_back(NULL); // distorted dst points
test_array[TEMP].push_back(NULL); // dst points
test_array[REF_OUTPUT].push_back(NULL);
useDstMat = false;
zero_new_cam = zero_distortion = zero_R = false;
}
void CV_UndistortPointsTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
cvtest::ArrayTest::get_test_array_types_and_sizes(test_case_idx,sizes,types);
RNG& rng = ts->get_rng();
//rng.next();
types[INPUT][0] = types[OUTPUT][0] = types[REF_OUTPUT][0] = types[TEMP][0]= CV_32FC2;
types[INPUT][1] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][2] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][3] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][4] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
sizes[INPUT][0] = sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = sizes[TEMP][0]= cvtest::randInt(rng)%2 ? cvSize(1,N_POINTS) : cvSize(N_POINTS,1);
sizes[INPUT][1] = sizes[INPUT][3] = cvSize(3,3);
sizes[INPUT][4] = cvtest::randInt(rng)%2 ? cvSize(3,3) : cvSize(4,3);
if (cvtest::randInt(rng)%2)
{
if (cvtest::randInt(rng)%2)
{
sizes[INPUT][2] = cvSize(1,4);
}
else
{
sizes[INPUT][2] = cvSize(1,5);
}
}
else
{
if (cvtest::randInt(rng)%2)
{
sizes[INPUT][2] = cvSize(4,1);
}
else
{
sizes[INPUT][2] = cvSize(5,1);
}
}
}
int CV_UndistortPointsTest::prepare_test_case(int test_case_idx)
{
RNG& rng = ts->get_rng();
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
if (code <= 0)
return code;
useDstMat = (cvtest::randInt(rng) % 2) == 0;
img_size.width = cvtest::randInt(rng) % MAX_X + 1;
img_size.height = cvtest::randInt(rng) % MAX_Y + 1;
int dist_size = test_mat[INPUT][2].cols > test_mat[INPUT][2].rows ? test_mat[INPUT][2].cols : test_mat[INPUT][2].rows;
double cam[9] = {0,0,0,0,0,0,0,0,1};
vector<double> dist(dist_size);
vector<double> proj(test_mat[INPUT][4].cols * test_mat[INPUT][4].rows);
vector<Point2d> points(N_POINTS);
Mat _camera(3,3,CV_64F,cam);
Mat _distort(test_mat[INPUT][2].rows,test_mat[INPUT][2].cols,CV_64F,&dist[0]);
Mat _proj(test_mat[INPUT][4].size(), CV_64F, &proj[0]);
Mat _points(test_mat[INPUT][0].size(), CV_64FC2, &points[0]);
_proj = Scalar::all(0);
//Generating points
for( int i = 0; i < N_POINTS; i++ )
{
points[i].x = cvtest::randReal(rng)*img_size.width;
points[i].y = cvtest::randReal(rng)*img_size.height;
}
//Generating camera matrix
double sz = MAX(img_size.width,img_size.height);
double aspect_ratio = cvtest::randReal(rng)*0.6 + 0.7;
cam[2] = (img_size.width - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
cam[5] = (img_size.height - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
cam[0] = sz/(0.9 - cvtest::randReal(rng)*0.6);
cam[4] = aspect_ratio*cam[0];
//Generating distortion coeffs
dist[0] = cvtest::randReal(rng)*0.06 - 0.03;
dist[1] = cvtest::randReal(rng)*0.06 - 0.03;
if( dist[0]*dist[1] > 0 )
dist[1] = -dist[1];
if( cvtest::randInt(rng)%4 != 0 )
{
dist[2] = cvtest::randReal(rng)*0.004 - 0.002;
dist[3] = cvtest::randReal(rng)*0.004 - 0.002;
if (dist_size > 4)
dist[4] = cvtest::randReal(rng)*0.004 - 0.002;
}
else
{
dist[2] = dist[3] = 0;
if (dist_size > 4)
dist[4] = 0;
}
//Generating P matrix (projection)
if( test_mat[INPUT][4].cols != 4 )
{
proj[8] = 1;
if (cvtest::randInt(rng)%2 == 0) // use identity new camera matrix
{
proj[0] = 1;
proj[4] = 1;
}
else
{
proj[0] = cam[0] + (cvtest::randReal(rng) - (double)0.5)*0.2*cam[0]; //10%
proj[4] = cam[4] + (cvtest::randReal(rng) - (double)0.5)*0.2*cam[4]; //10%
proj[2] = cam[2] + (cvtest::randReal(rng) - (double)0.5)*0.3*img_size.width; //15%
proj[5] = cam[5] + (cvtest::randReal(rng) - (double)0.5)*0.3*img_size.height; //15%
}
}
else
{
proj[10] = 1;
proj[0] = cam[0] + (cvtest::randReal(rng) - (double)0.5)*0.2*cam[0]; //10%
proj[5] = cam[4] + (cvtest::randReal(rng) - (double)0.5)*0.2*cam[4]; //10%
proj[2] = cam[2] + (cvtest::randReal(rng) - (double)0.5)*0.3*img_size.width; //15%
proj[6] = cam[5] + (cvtest::randReal(rng) - (double)0.5)*0.3*img_size.height; //15%
proj[3] = (img_size.height + img_size.width - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
proj[7] = (img_size.height + img_size.width - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
proj[11] = (img_size.height + img_size.width - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
}
//Generating R matrix
Mat _rot(3,3,CV_64F);
Mat rotation(1,3,CV_64F);
rotation.at<double>(0) = CV_PI*(cvtest::randReal(rng) - (double)0.5); // phi
rotation.at<double>(1) = CV_PI*(cvtest::randReal(rng) - (double)0.5); // ksi
rotation.at<double>(2) = CV_PI*(cvtest::randReal(rng) - (double)0.5); //khi
cvtest::Rodrigues(rotation, _rot);
//copying data
//src_points = &_points;
_points.convertTo(test_mat[INPUT][0], test_mat[INPUT][0].type());
_camera.convertTo(test_mat[INPUT][1], test_mat[INPUT][1].type());
_distort.convertTo(test_mat[INPUT][2], test_mat[INPUT][2].type());
_rot.convertTo(test_mat[INPUT][3], test_mat[INPUT][3].type());
_proj.convertTo(test_mat[INPUT][4], test_mat[INPUT][4].type());
zero_distortion = (cvtest::randInt(rng)%2) == 0 ? false : true;
zero_new_cam = (cvtest::randInt(rng)%2) == 0 ? false : true;
zero_R = (cvtest::randInt(rng)%2) == 0 ? false : true;
_points.convertTo(src_points, CV_32F);
camera_mat = test_mat[INPUT][1];
distortion_coeffs = test_mat[INPUT][2];
R = test_mat[INPUT][3];
P = test_mat[INPUT][4];
return code;
}
void CV_UndistortPointsTest::prepare_to_validation(int /*test_case_idx*/)
{
int dist_size = test_mat[INPUT][2].cols > test_mat[INPUT][2].rows ? test_mat[INPUT][2].cols : test_mat[INPUT][2].rows;
double cam[9] = {0,0,0,0,0,0,0,0,1};
double rot[9] = {1,0,0,0,1,0,0,0,1};
double* dist = new double[dist_size ];
double* proj = new double[test_mat[INPUT][4].cols * test_mat[INPUT][4].rows];
double* points = new double[N_POINTS*2];
double* r_points = new double[N_POINTS*2];
//Run reference calculations
CvMat ref_points= cvMat(test_mat[INPUT][0].rows,test_mat[INPUT][0].cols,CV_64FC2,r_points);
CvMat _camera = cvMat(3,3,CV_64F,cam);
CvMat _rot = cvMat(3,3,CV_64F,rot);
CvMat _distort = cvMat(test_mat[INPUT][2].rows,test_mat[INPUT][2].cols,CV_64F,dist);
CvMat _proj = cvMat(test_mat[INPUT][4].rows,test_mat[INPUT][4].cols,CV_64F,proj);
CvMat _points= cvMat(test_mat[TEMP][0].rows,test_mat[TEMP][0].cols,CV_64FC2,points);
Mat __camera = cvarrToMat(&_camera);
Mat __distort = cvarrToMat(&_distort);
Mat __rot = cvarrToMat(&_rot);
Mat __proj = cvarrToMat(&_proj);
Mat __points = cvarrToMat(&_points);
Mat _ref_points = cvarrToMat(&ref_points);
cvtest::convert(test_mat[INPUT][1], __camera, __camera.type());
cvtest::convert(test_mat[INPUT][2], __distort, __distort.type());
cvtest::convert(test_mat[INPUT][3], __rot, __rot.type());
cvtest::convert(test_mat[INPUT][4], __proj, __proj.type());
if (useDstMat)
{
CvMat temp = cvMat(dst_points_mat);
for (int i=0;i<N_POINTS*2;i++)
{
points[i] = temp.data.fl[i];
}
}
else
{
for (int i=0;i<N_POINTS;i++)
{
points[2*i] = dst_points[i].x;
points[2*i+1] = dst_points[i].y;
}
}
CvMat* input2 = zero_distortion ? 0 : &_distort;
CvMat* input3 = zero_R ? 0 : &_rot;
CvMat* input4 = zero_new_cam ? 0 : &_proj;
distortPoints(&_points,&ref_points,&_camera,input2,input3,input4);
Mat& dst = test_mat[REF_OUTPUT][0];
cvtest::convert(_ref_points, dst, dst.type());
cvtest::copy(test_mat[INPUT][0], test_mat[OUTPUT][0]);
delete[] dist;
delete[] proj;
delete[] points;
delete[] r_points;
}
void CV_UndistortPointsTest::run_func()
{
cv::Mat input2,input3,input4;
input2 = zero_distortion ? cv::Mat() : cv::Mat(test_mat[INPUT][2]);
input3 = zero_R ? cv::Mat() : cv::Mat(test_mat[INPUT][3]);
input4 = zero_new_cam ? cv::Mat() : cv::Mat(test_mat[INPUT][4]);
if (useDstMat)
{
//cv::undistortPoints(src_points,dst_points_mat,camera_mat,distortion_coeffs,R,P);
cv::undistortPoints(src_points,dst_points_mat,camera_mat,input2,input3,input4);
}
else
{
//cv::undistortPoints(src_points,dst_points,camera_mat,distortion_coeffs,R,P);
cv::undistortPoints(src_points,dst_points,camera_mat,input2,input3,input4);
}
}
void CV_UndistortPointsTest::distortPoints(const CvMat* _src, CvMat* _dst, const CvMat* _cameraMatrix,
const CvMat* _distCoeffs,
const CvMat* matR, const CvMat* matP)
{
double a[9];
CvMat* __P;
if ((!matP)||(matP->cols == 3))
__P = cvCreateMat(3,3,CV_64F);
else
__P = cvCreateMat(3,4,CV_64F);
if (matP)
{
cvtest::convert(cvarrToMat(matP), cvarrToMat(__P), -1);
}
else
{
cvZero(__P);
__P->data.db[0] = 1;
__P->data.db[4] = 1;
__P->data.db[8] = 1;
}
CvMat* __R = cvCreateMat(3,3,CV_64F);
if (matR)
{
cvCopy(matR,__R);
}
else
{
cvZero(__R);
__R->data.db[0] = 1;
__R->data.db[4] = 1;
__R->data.db[8] = 1;
}
for (int i=0;i<N_POINTS;i++)
{
int movement = __P->cols > 3 ? 1 : 0;
double x = (_src->data.db[2*i]-__P->data.db[2])/__P->data.db[0];
double y = (_src->data.db[2*i+1]-__P->data.db[5+movement])/__P->data.db[4+movement];
CvMat inverse = cvMat(3,3,CV_64F,a);
cvInvert(__R,&inverse);
double w1 = x*inverse.data.db[6]+y*inverse.data.db[7]+inverse.data.db[8];
double _x = (x*inverse.data.db[0]+y*inverse.data.db[1]+inverse.data.db[2])/w1;
double _y = (x*inverse.data.db[3]+y*inverse.data.db[4]+inverse.data.db[5])/w1;
//Distortions
double __x = _x;
double __y = _y;
if (_distCoeffs)
{
double r2 = _x*_x+_y*_y;
__x = _x*(1+_distCoeffs->data.db[0]*r2+_distCoeffs->data.db[1]*r2*r2)+
2*_distCoeffs->data.db[2]*_x*_y+_distCoeffs->data.db[3]*(r2+2*_x*_x);
__y = _y*(1+_distCoeffs->data.db[0]*r2+_distCoeffs->data.db[1]*r2*r2)+
2*_distCoeffs->data.db[3]*_x*_y+_distCoeffs->data.db[2]*(r2+2*_y*_y);
if ((_distCoeffs->cols > 4) || (_distCoeffs->rows > 4))
{
__x+=_x*_distCoeffs->data.db[4]*r2*r2*r2;
__y+=_y*_distCoeffs->data.db[4]*r2*r2*r2;
}
}
_dst->data.db[2*i] = __x*_cameraMatrix->data.db[0]+_cameraMatrix->data.db[2];
_dst->data.db[2*i+1] = __y*_cameraMatrix->data.db[4]+_cameraMatrix->data.db[5];
}
cvReleaseMat(&__R);
cvReleaseMat(&__P);
}
double CV_UndistortPointsTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return 5e-2;
}
//------------------------------------------------------
class CV_InitUndistortRectifyMapTest : public cvtest::ArrayTest
{
public:
CV_InitUndistortRectifyMapTest();
protected:
int prepare_test_case (int test_case_idx);
void prepare_to_validation( int test_case_idx );
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
double get_success_error_level( int test_case_idx, int i, int j );
void run_func();
private:
static const int MAX_X = 1024;
static const int MAX_Y = 1024;
bool zero_new_cam;
bool zero_distortion;
bool zero_R;
cv::Size img_size;
int map_type;
};
CV_InitUndistortRectifyMapTest::CV_InitUndistortRectifyMapTest()
{
test_array[INPUT].push_back(NULL); // camera matrix
test_array[INPUT].push_back(NULL); // distortion coeffs
test_array[INPUT].push_back(NULL); // R matrix
test_array[INPUT].push_back(NULL); // new camera matrix
test_array[OUTPUT].push_back(NULL); // distorted mapx
test_array[OUTPUT].push_back(NULL); // distorted mapy
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
zero_distortion = zero_new_cam = zero_R = false;
map_type = 0;
}
void CV_InitUndistortRectifyMapTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
cvtest::ArrayTest::get_test_array_types_and_sizes(test_case_idx,sizes,types);
RNG& rng = ts->get_rng();
//rng.next();
map_type = CV_32F;
types[OUTPUT][0] = types[OUTPUT][1] = types[REF_OUTPUT][0] = types[REF_OUTPUT][1] = map_type;
img_size.width = cvtest::randInt(rng) % MAX_X + 1;
img_size.height = cvtest::randInt(rng) % MAX_Y + 1;
types[INPUT][0] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][1] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][2] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][3] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
sizes[OUTPUT][0] = sizes[OUTPUT][1] = sizes[REF_OUTPUT][0] = sizes[REF_OUTPUT][1] = img_size;
sizes[INPUT][0] = sizes[INPUT][2] = sizes[INPUT][3] = cvSize(3,3);
Size dsize;
if (cvtest::randInt(rng)%2)
{
if (cvtest::randInt(rng)%2)
{
dsize = Size(1,4);
}
else
{
dsize = Size(1,5);
}
}
else
{
if (cvtest::randInt(rng)%2)
{
dsize = Size(4,1);
}
else
{
dsize = Size(5,1);
}
}
sizes[INPUT][1] = dsize;
}
int CV_InitUndistortRectifyMapTest::prepare_test_case(int test_case_idx)
{
RNG& rng = ts->get_rng();
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
if (code <= 0)
return code;
int dist_size = test_mat[INPUT][1].cols > test_mat[INPUT][1].rows ? test_mat[INPUT][1].cols : test_mat[INPUT][1].rows;
double cam[9] = {0,0,0,0,0,0,0,0,1};
vector<double> dist(dist_size);
vector<double> new_cam(test_mat[INPUT][3].cols * test_mat[INPUT][3].rows);
Mat _camera(3,3,CV_64F,cam);
Mat _distort(test_mat[INPUT][1].size(),CV_64F,&dist[0]);
Mat _new_cam(test_mat[INPUT][3].size(),CV_64F,&new_cam[0]);
//Generating camera matrix
double sz = MAX(img_size.width,img_size.height);
double aspect_ratio = cvtest::randReal(rng)*0.6 + 0.7;
cam[2] = (img_size.width - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
cam[5] = (img_size.height - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
cam[0] = sz/(0.9 - cvtest::randReal(rng)*0.6);
cam[4] = aspect_ratio*cam[0];
//Generating distortion coeffs
dist[0] = cvtest::randReal(rng)*0.06 - 0.03;
dist[1] = cvtest::randReal(rng)*0.06 - 0.03;
if( dist[0]*dist[1] > 0 )
dist[1] = -dist[1];
if( cvtest::randInt(rng)%4 != 0 )
{
dist[2] = cvtest::randReal(rng)*0.004 - 0.002;
dist[3] = cvtest::randReal(rng)*0.004 - 0.002;
if (dist_size > 4)
dist[4] = cvtest::randReal(rng)*0.004 - 0.002;
}
else
{
dist[2] = dist[3] = 0;
if (dist_size > 4)
dist[4] = 0;
}
//Generating new camera matrix
_new_cam = Scalar::all(0);
new_cam[8] = 1;
//new_cam[0] = cam[0];
//new_cam[4] = cam[4];
//new_cam[2] = cam[2];
//new_cam[5] = cam[5];
new_cam[0] = cam[0] + (cvtest::randReal(rng) - (double)0.5)*0.2*cam[0]; //10%
new_cam[4] = cam[4] + (cvtest::randReal(rng) - (double)0.5)*0.2*cam[4]; //10%
new_cam[2] = cam[2] + (cvtest::randReal(rng) - (double)0.5)*0.3*img_size.width; //15%
new_cam[5] = cam[5] + (cvtest::randReal(rng) - (double)0.5)*0.3*img_size.height; //15%
//Generating R matrix
Mat _rot(3,3,CV_64F);
Mat rotation(1,3,CV_64F);
rotation.at<double>(0) = CV_PI/8*(cvtest::randReal(rng) - (double)0.5); // phi
rotation.at<double>(1) = CV_PI/8*(cvtest::randReal(rng) - (double)0.5); // ksi
rotation.at<double>(2) = CV_PI/3*(cvtest::randReal(rng) - (double)0.5); //khi
cvtest::Rodrigues(rotation, _rot);
//cvSetIdentity(_rot);
//copying data
cvtest::convert( _camera, test_mat[INPUT][0], test_mat[INPUT][0].type());
cvtest::convert( _distort, test_mat[INPUT][1], test_mat[INPUT][1].type());
cvtest::convert( _rot, test_mat[INPUT][2], test_mat[INPUT][2].type());
cvtest::convert( _new_cam, test_mat[INPUT][3], test_mat[INPUT][3].type());
zero_distortion = (cvtest::randInt(rng)%2) == 0 ? false : true;
zero_new_cam = (cvtest::randInt(rng)%2) == 0 ? false : true;
zero_R = (cvtest::randInt(rng)%2) == 0 ? false : true;
return code;
}
void CV_InitUndistortRectifyMapTest::prepare_to_validation(int/* test_case_idx*/)
{
cvtest::initUndistortMap(test_mat[INPUT][0],
zero_distortion ? cv::Mat() : test_mat[INPUT][1],
zero_R ? cv::Mat() : test_mat[INPUT][2],
zero_new_cam ? test_mat[INPUT][0] : test_mat[INPUT][3],
img_size, test_mat[REF_OUTPUT][0], test_mat[REF_OUTPUT][1],
test_mat[REF_OUTPUT][0].type());
}
void CV_InitUndistortRectifyMapTest::run_func()
{
cv::Mat camera_mat = test_mat[INPUT][0];
cv::Mat dist = zero_distortion ? cv::Mat() : test_mat[INPUT][1];
cv::Mat R = zero_R ? cv::Mat() : test_mat[INPUT][2];
cv::Mat new_cam = zero_new_cam ? cv::Mat() : test_mat[INPUT][3];
cv::Mat& mapx = test_mat[OUTPUT][0], &mapy = test_mat[OUTPUT][1];
cv::initUndistortRectifyMap(camera_mat,dist,R,new_cam,img_size,map_type,mapx,mapy);
}
double CV_InitUndistortRectifyMapTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return 8;
}
//////////////////////////////////////////////////////////////////////////////////////////////////////
TEST(Calib3d_DefaultNewCameraMatrix, accuracy) { CV_DefaultNewCameraMatrixTest test; test.safe_run(); }
TEST(Calib3d_UndistortPoints, accuracy) { CV_UndistortPointsTest test; test.safe_run(); }
TEST(Calib3d_InitUndistortRectifyMap, accuracy) { CV_InitUndistortRectifyMapTest test; test.safe_run(); }
////////////////////////////// undistort /////////////////////////////////
static void test_remap( const Mat& src, Mat& dst, const Mat& mapx, const Mat& mapy,
Mat* mask=0, int interpolation=CV_INTER_LINEAR )
{
int x, y, k;
int drows = dst.rows, dcols = dst.cols;
int srows = src.rows, scols = src.cols;
const uchar* sptr0 = src.ptr();
int depth = src.depth(), cn = src.channels();
int elem_size = (int)src.elemSize();
int step = (int)(src.step / CV_ELEM_SIZE(depth));
int delta;
if( interpolation != CV_INTER_CUBIC )
{
delta = 0;
scols -= 1; srows -= 1;
}
else
{
delta = 1;
scols = MAX(scols - 3, 0);
srows = MAX(srows - 3, 0);
}
int scols1 = MAX(scols - 2, 0);
int srows1 = MAX(srows - 2, 0);
if( mask )
*mask = Scalar::all(0);
for( y = 0; y < drows; y++ )
{
uchar* dptr = dst.ptr(y);
const float* mx = mapx.ptr<float>(y);
const float* my = mapy.ptr<float>(y);
uchar* m = mask ? mask->ptr(y) : 0;
for( x = 0; x < dcols; x++, dptr += elem_size )
{
float xs = mx[x];
float ys = my[x];
int ixs = cvFloor(xs);
int iys = cvFloor(ys);
if( (unsigned)(ixs - delta - 1) >= (unsigned)scols1 ||
(unsigned)(iys - delta - 1) >= (unsigned)srows1 )
{
if( m )
m[x] = 1;
if( (unsigned)(ixs - delta) >= (unsigned)scols ||
(unsigned)(iys - delta) >= (unsigned)srows )
continue;
}
xs -= ixs;
ys -= iys;
switch( depth )
{
case CV_8U:
{
const uchar* sptr = sptr0 + iys*step + ixs*cn;
for( k = 0; k < cn; k++ )
{
float v00 = sptr[k];
float v01 = sptr[cn + k];
float v10 = sptr[step + k];
float v11 = sptr[step + cn + k];
v00 = v00 + xs*(v01 - v00);
v10 = v10 + xs*(v11 - v10);
v00 = v00 + ys*(v10 - v00);
dptr[k] = (uchar)cvRound(v00);
}
}
break;
case CV_16U:
{
const ushort* sptr = (const ushort*)sptr0 + iys*step + ixs*cn;
for( k = 0; k < cn; k++ )
{
float v00 = sptr[k];
float v01 = sptr[cn + k];
float v10 = sptr[step + k];
float v11 = sptr[step + cn + k];
v00 = v00 + xs*(v01 - v00);
v10 = v10 + xs*(v11 - v10);
v00 = v00 + ys*(v10 - v00);
((ushort*)dptr)[k] = (ushort)cvRound(v00);
}
}
break;
case CV_32F:
{
const float* sptr = (const float*)sptr0 + iys*step + ixs*cn;
for( k = 0; k < cn; k++ )
{
float v00 = sptr[k];
float v01 = sptr[cn + k];
float v10 = sptr[step + k];
float v11 = sptr[step + cn + k];
v00 = v00 + xs*(v01 - v00);
v10 = v10 + xs*(v11 - v10);
v00 = v00 + ys*(v10 - v00);
((float*)dptr)[k] = (float)v00;
}
}
break;
default:
assert(0);
}
}
}
}
class CV_ImgWarpBaseTest : public cvtest::ArrayTest
{
public:
CV_ImgWarpBaseTest( bool warp_matrix );
protected:
int read_params( const cv::FileStorage& fs );
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 get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
int interpolation;
int max_interpolation;
double spatial_scale_zoom, spatial_scale_decimate;
};
CV_ImgWarpBaseTest::CV_ImgWarpBaseTest( bool warp_matrix )
{
test_array[INPUT].push_back(NULL);
if( warp_matrix )
test_array[INPUT].push_back(NULL);
test_array[INPUT_OUTPUT].push_back(NULL);
test_array[REF_INPUT_OUTPUT].push_back(NULL);
max_interpolation = 5;
interpolation = 0;
element_wise_relative_error = false;
spatial_scale_zoom = 0.01;
spatial_scale_decimate = 0.005;
}
int CV_ImgWarpBaseTest::read_params( const cv::FileStorage& fs )
{
int code = cvtest::ArrayTest::read_params( fs );
return code;
}
void CV_ImgWarpBaseTest::get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high )
{
cvtest::ArrayTest::get_minmax_bounds( i, j, type, low, high );
if( CV_MAT_DEPTH(type) == CV_32F )
{
low = Scalar::all(-10.);
high = Scalar::all(10);
}
}
void CV_ImgWarpBaseTest::get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
int depth = cvtest::randInt(rng) % 3;
int cn = cvtest::randInt(rng) % 3 + 1;
cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
depth = depth == 0 ? CV_8U : depth == 1 ? CV_16U : CV_32F;
cn += cn == 2;
types[INPUT][0] = types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(depth, cn);
if( test_array[INPUT].size() > 1 )
types[INPUT][1] = cvtest::randInt(rng) & 1 ? CV_32FC1 : CV_64FC1;
interpolation = cvtest::randInt(rng) % max_interpolation;
}
void CV_ImgWarpBaseTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
if( i != INPUT || j != 0 )
cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr );
}
int CV_ImgWarpBaseTest::prepare_test_case( int test_case_idx )
{
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
Mat& img = test_mat[INPUT][0];
int i, j, cols = img.cols;
int type = img.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
double scale = depth == CV_16U ? 1000. : 255.*0.5;
double space_scale = spatial_scale_decimate;
vector<float> buffer(img.cols*cn);
if( code <= 0 )
return code;
if( test_mat[INPUT_OUTPUT][0].cols >= img.cols &&
test_mat[INPUT_OUTPUT][0].rows >= img.rows )
space_scale = spatial_scale_zoom;
for( i = 0; i < img.rows; i++ )
{
uchar* ptr = img.ptr(i);
switch( cn )
{
case 1:
for( j = 0; j < cols; j++ )
buffer[j] = (float)((sin((i+1)*space_scale)*sin((j+1)*space_scale)+1.)*scale);
break;
case 2:
for( j = 0; j < cols; j++ )
{
buffer[j*2] = (float)((sin((i+1)*space_scale)+1.)*scale);
buffer[j*2+1] = (float)((sin((i+j)*space_scale)+1.)*scale);
}
break;
case 3:
for( j = 0; j < cols; j++ )
{
buffer[j*3] = (float)((sin((i+1)*space_scale)+1.)*scale);
buffer[j*3+1] = (float)((sin(j*space_scale)+1.)*scale);
buffer[j*3+2] = (float)((sin((i+j)*space_scale)+1.)*scale);
}
break;
case 4:
for( j = 0; j < cols; j++ )
{
buffer[j*4] = (float)((sin((i+1)*space_scale)+1.)*scale);
buffer[j*4+1] = (float)((sin(j*space_scale)+1.)*scale);
buffer[j*4+2] = (float)((sin((i+j)*space_scale)+1.)*scale);
buffer[j*4+3] = (float)((sin((i-j)*space_scale)+1.)*scale);
}
break;
default:
assert(0);
}
/*switch( depth )
{
case CV_8U:
for( j = 0; j < cols*cn; j++ )
ptr[j] = (uchar)cvRound(buffer[j]);
break;
case CV_16U:
for( j = 0; j < cols*cn; j++ )
((ushort*)ptr)[j] = (ushort)cvRound(buffer[j]);
break;
case CV_32F:
for( j = 0; j < cols*cn; j++ )
((float*)ptr)[j] = (float)buffer[j];
break;
default:
assert(0);
}*/
cv::Mat src(1, cols*cn, CV_32F, &buffer[0]);
cv::Mat dst(1, cols*cn, depth, ptr);
src.convertTo(dst, dst.type());
}
return code;
}
class CV_UndistortTest : public CV_ImgWarpBaseTest
{
public:
CV_UndistortTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void run_func();
int prepare_test_case( int test_case_idx );
void prepare_to_validation( int /*test_case_idx*/ );
double get_success_error_level( int test_case_idx, int i, int j );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
private:
cv::Mat input0;
cv::Mat input1;
cv::Mat input2;
cv::Mat input_new_cam;
cv::Mat input_output;
bool zero_new_cam;
bool zero_distortion;
};
CV_UndistortTest::CV_UndistortTest() : CV_ImgWarpBaseTest( false )
{
//spatial_scale_zoom = spatial_scale_decimate;
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
spatial_scale_decimate = spatial_scale_zoom;
}
void CV_UndistortTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
int type = types[INPUT][0];
type = CV_MAKETYPE( CV_8U, CV_MAT_CN(type) );
types[INPUT][0] = types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = type;
types[INPUT][1] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][2] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
sizes[INPUT][1] = cvSize(3,3);
sizes[INPUT][2] = cvtest::randInt(rng)%2 ? cvSize(4,1) : cvSize(1,4);
types[INPUT][3] = types[INPUT][1];
sizes[INPUT][3] = sizes[INPUT][1];
interpolation = CV_INTER_LINEAR;
}
void CV_UndistortTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
if( i != INPUT )
CV_ImgWarpBaseTest::fill_array( test_case_idx, i, j, arr );
}
void CV_UndistortTest::run_func()
{
if (zero_distortion)
{
cv::undistort(input0,input_output,input1,cv::Mat());
}
else
{
cv::undistort(input0,input_output,input1,input2);
}
}
double CV_UndistortTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
int depth = test_mat[INPUT][0].depth();
return depth == CV_8U ? 16 : depth == CV_16U ? 1024 : 5e-2;
}
int CV_UndistortTest::prepare_test_case( int test_case_idx )
{
RNG& rng = ts->get_rng();
int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx );
const Mat& src = test_mat[INPUT][0];
double k[4], a[9] = {0,0,0,0,0,0,0,0,1};
double new_cam[9] = {0,0,0,0,0,0,0,0,1};
double sz = MAX(src.rows, src.cols);
Mat& _new_cam0 = test_mat[INPUT][3];
Mat _new_cam(test_mat[INPUT][3].rows,test_mat[INPUT][3].cols,CV_64F,new_cam);
Mat& _a0 = test_mat[INPUT][1];
Mat _a(3,3,CV_64F,a);
Mat& _k0 = test_mat[INPUT][2];
Mat _k(_k0.rows,_k0.cols, CV_MAKETYPE(CV_64F,_k0.channels()),k);
if( code <= 0 )
return code;
double aspect_ratio = cvtest::randReal(rng)*0.6 + 0.7;
a[2] = (src.cols - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[5] = (src.rows - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[0] = sz/(0.9 - cvtest::randReal(rng)*0.6);
a[4] = aspect_ratio*a[0];
k[0] = cvtest::randReal(rng)*0.06 - 0.03;
k[1] = cvtest::randReal(rng)*0.06 - 0.03;
if( k[0]*k[1] > 0 )
k[1] = -k[1];
if( cvtest::randInt(rng)%4 != 0 )
{
k[2] = cvtest::randReal(rng)*0.004 - 0.002;
k[3] = cvtest::randReal(rng)*0.004 - 0.002;
}
else
k[2] = k[3] = 0;
new_cam[0] = a[0] + (cvtest::randReal(rng) - (double)0.5)*0.2*a[0]; //10%
new_cam[4] = a[4] + (cvtest::randReal(rng) - (double)0.5)*0.2*a[4]; //10%
new_cam[2] = a[2] + (cvtest::randReal(rng) - (double)0.5)*0.3*test_mat[INPUT][0].rows; //15%
new_cam[5] = a[5] + (cvtest::randReal(rng) - (double)0.5)*0.3*test_mat[INPUT][0].cols; //15%
_a.convertTo(_a0, _a0.depth());
zero_distortion = (cvtest::randInt(rng)%2) == 0 ? false : true;
_k.convertTo(_k0, _k0.depth());
zero_new_cam = (cvtest::randInt(rng)%2) == 0 ? false : true;
_new_cam.convertTo(_new_cam0, _new_cam0.depth());
//Testing C++ code
//useCPlus = ((cvtest::randInt(rng) % 2)!=0);
input0 = test_mat[INPUT][0];
input1 = test_mat[INPUT][1];
input2 = test_mat[INPUT][2];
input_new_cam = test_mat[INPUT][3];
return code;
}
void CV_UndistortTest::prepare_to_validation( int /*test_case_idx*/ )
{
Mat& output = test_mat[INPUT_OUTPUT][0];
input_output.convertTo(output, output.type());
Mat& src = test_mat[INPUT][0];
Mat& dst = test_mat[REF_INPUT_OUTPUT][0];
Mat& dst0 = test_mat[INPUT_OUTPUT][0];
Mat mapx, mapy;
cvtest::initUndistortMap( test_mat[INPUT][1], test_mat[INPUT][2],
Mat(), Mat(), dst.size(), mapx, mapy, CV_32F );
Mat mask( dst.size(), CV_8U );
test_remap( src, dst, mapx, mapy, &mask, interpolation );
dst.setTo(Scalar::all(0), mask);
dst0.setTo(Scalar::all(0), mask);
}
class CV_UndistortMapTest : public cvtest::ArrayTest
{
public:
CV_UndistortMapTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void run_func();
int prepare_test_case( int test_case_idx );
void prepare_to_validation( int /*test_case_idx*/ );
double get_success_error_level( int test_case_idx, int i, int j );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
private:
bool dualChannel;
};
CV_UndistortMapTest::CV_UndistortMapTest()
{
test_array[INPUT].push_back(NULL);
test_array[INPUT].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;
}
void CV_UndistortMapTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
int depth = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
Size sz = sizes[OUTPUT][0];
types[INPUT][0] = types[INPUT][1] = depth;
dualChannel = cvtest::randInt(rng)%2 == 0;
types[OUTPUT][0] = types[OUTPUT][1] =
types[REF_OUTPUT][0] = types[REF_OUTPUT][1] = dualChannel ? CV_32FC2 : CV_32F;
sizes[INPUT][0] = cvSize(3,3);
sizes[INPUT][1] = cvtest::randInt(rng)%2 ? cvSize(4,1) : cvSize(1,4);
sz.width = MAX(sz.width,16);
sz.height = MAX(sz.height,16);
sizes[OUTPUT][0] = sizes[OUTPUT][1] =
sizes[REF_OUTPUT][0] = sizes[REF_OUTPUT][1] = sz;
}
void CV_UndistortMapTest::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 );
}
void CV_UndistortMapTest::run_func()
{
cv::Mat a = test_mat[INPUT][0], k = test_mat[INPUT][1];
cv::Mat &mapx = test_mat[OUTPUT][0], &mapy = !dualChannel ? test_mat[OUTPUT][1] : mapx;
cv::Size mapsz = test_mat[OUTPUT][0].size();
cv::initUndistortRectifyMap(a, k, cv::Mat(), a,
mapsz, dualChannel ? CV_32FC2 : CV_32FC1,
mapx, !dualChannel ? cv::_InputOutputArray(mapy) : cv::noArray());
}
double CV_UndistortMapTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return 1e-3;
}
int CV_UndistortMapTest::prepare_test_case( int test_case_idx )
{
RNG& rng = ts->get_rng();
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
const Mat& mapx = test_mat[OUTPUT][0];
double k[4], a[9] = {0,0,0,0,0,0,0,0,1};
double sz = MAX(mapx.rows, mapx.cols);
Mat& _a0 = test_mat[INPUT][0], &_k0 = test_mat[INPUT][1];
Mat _a(3,3,CV_64F,a);
Mat _k(_k0.rows,_k0.cols, CV_MAKETYPE(CV_64F,_k0.channels()),k);
if( code <= 0 )
return code;
double aspect_ratio = cvtest::randReal(rng)*0.6 + 0.7;
a[2] = (mapx.cols - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[5] = (mapx.rows - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[0] = sz/(0.9 - cvtest::randReal(rng)*0.6);
a[4] = aspect_ratio*a[0];
k[0] = cvtest::randReal(rng)*0.06 - 0.03;
k[1] = cvtest::randReal(rng)*0.06 - 0.03;
if( k[0]*k[1] > 0 )
k[1] = -k[1];
k[2] = cvtest::randReal(rng)*0.004 - 0.002;
k[3] = cvtest::randReal(rng)*0.004 - 0.002;
_a.convertTo(_a0, _a0.depth());
_k.convertTo(_k0, _k0.depth());
if (dualChannel)
{
test_mat[REF_OUTPUT][1] = Scalar::all(0);
test_mat[OUTPUT][1] = Scalar::all(0);
}
return code;
}
void CV_UndistortMapTest::prepare_to_validation( int )
{
Mat mapx, mapy;
cvtest::initUndistortMap( test_mat[INPUT][0], test_mat[INPUT][1], Mat(), Mat(),
test_mat[REF_OUTPUT][0].size(), mapx, mapy, CV_32F );
if( !dualChannel )
{
mapx.copyTo(test_mat[REF_OUTPUT][0]);
mapy.copyTo(test_mat[REF_OUTPUT][1]);
}
else
{
Mat p[2] = {mapx, mapy};
cv::merge(p, 2, test_mat[REF_OUTPUT][0]);
}
}
TEST(Calib3d_UndistortImgproc, accuracy) { CV_UndistortTest test; test.safe_run(); }
TEST(Calib3d_InitUndistortMap, accuracy) { CV_UndistortMapTest test; test.safe_run(); }
TEST(Calib3d_UndistortPoints, inputShape)
{
//https://github.com/opencv/opencv/issues/14423
Matx33d cameraMatrix = Matx33d::eye();
{
//2xN 1-channel
Mat imagePoints(2, 3, CV_32FC1);
imagePoints.at<float>(0,0) = 320; imagePoints.at<float>(1,0) = 240;
imagePoints.at<float>(0,1) = 0; imagePoints.at<float>(1,1) = 240;
imagePoints.at<float>(0,2) = 320; imagePoints.at<float>(1,2) = 0;
vector<Point2f> normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(static_cast<int>(normalized.size()), imagePoints.cols);
for (int i = 0; i < static_cast<int>(normalized.size()); i++) {
EXPECT_NEAR(normalized[i].x, imagePoints.at<float>(0,i), std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized[i].y, imagePoints.at<float>(1,i), std::numeric_limits<float>::epsilon());
}
}
{
//Nx2 1-channel
Mat imagePoints(3, 2, CV_32FC1);
imagePoints.at<float>(0,0) = 320; imagePoints.at<float>(0,1) = 240;
imagePoints.at<float>(1,0) = 0; imagePoints.at<float>(1,1) = 240;
imagePoints.at<float>(2,0) = 320; imagePoints.at<float>(2,1) = 0;
vector<Point2f> normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(static_cast<int>(normalized.size()), imagePoints.rows);
for (int i = 0; i < static_cast<int>(normalized.size()); i++) {
EXPECT_NEAR(normalized[i].x, imagePoints.at<float>(i,0), std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized[i].y, imagePoints.at<float>(i,1), std::numeric_limits<float>::epsilon());
}
}
{
//1xN 2-channel
Mat imagePoints(1, 3, CV_32FC2);
imagePoints.at<Vec2f>(0,0) = Vec2f(320, 240);
imagePoints.at<Vec2f>(0,1) = Vec2f(0, 240);
imagePoints.at<Vec2f>(0,2) = Vec2f(320, 0);
vector<Point2f> normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(static_cast<int>(normalized.size()), imagePoints.cols);
for (int i = 0; i < static_cast<int>(normalized.size()); i++) {
EXPECT_NEAR(normalized[i].x, imagePoints.at<Vec2f>(0,i)(0), std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized[i].y, imagePoints.at<Vec2f>(0,i)(1), std::numeric_limits<float>::epsilon());
}
}
{
//Nx1 2-channel
Mat imagePoints(3, 1, CV_32FC2);
imagePoints.at<Vec2f>(0,0) = Vec2f(320, 240);
imagePoints.at<Vec2f>(1,0) = Vec2f(0, 240);
imagePoints.at<Vec2f>(2,0) = Vec2f(320, 0);
vector<Point2f> normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(static_cast<int>(normalized.size()), imagePoints.rows);
for (int i = 0; i < static_cast<int>(normalized.size()); i++) {
EXPECT_NEAR(normalized[i].x, imagePoints.at<Vec2f>(i,0)(0), std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized[i].y, imagePoints.at<Vec2f>(i,0)(1), std::numeric_limits<float>::epsilon());
}
}
{
//vector<Point2f>
vector<Point2f> imagePoints;
imagePoints.push_back(Point2f(320, 240));
imagePoints.push_back(Point2f(0, 240));
imagePoints.push_back(Point2f(320, 0));
vector<Point2f> normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(normalized.size(), imagePoints.size());
for (int i = 0; i < static_cast<int>(normalized.size()); i++) {
EXPECT_NEAR(normalized[i].x, imagePoints[i].x, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized[i].y, imagePoints[i].y, std::numeric_limits<float>::epsilon());
}
}
{
//vector<Point2d>
vector<Point2d> imagePoints;
imagePoints.push_back(Point2d(320, 240));
imagePoints.push_back(Point2d(0, 240));
imagePoints.push_back(Point2d(320, 0));
vector<Point2d> normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(normalized.size(), imagePoints.size());
for (int i = 0; i < static_cast<int>(normalized.size()); i++) {
EXPECT_NEAR(normalized[i].x, imagePoints[i].x, std::numeric_limits<double>::epsilon());
EXPECT_NEAR(normalized[i].y, imagePoints[i].y, std::numeric_limits<double>::epsilon());
}
}
}
TEST(Calib3d_UndistortPoints, outputShape)
{
Matx33d cameraMatrix = Matx33d::eye();
{
vector<Point2f> imagePoints;
imagePoints.push_back(Point2f(320, 240));
imagePoints.push_back(Point2f(0, 240));
imagePoints.push_back(Point2f(320, 0));
//Mat --> will be Nx1 2-channel
Mat normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(static_cast<int>(imagePoints.size()), normalized.rows);
for (int i = 0; i < normalized.rows; i++) {
EXPECT_NEAR(normalized.at<Vec2f>(i,0)(0), imagePoints[i].x, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized.at<Vec2f>(i,0)(1), imagePoints[i].y, std::numeric_limits<float>::epsilon());
}
}
{
vector<Point2f> imagePoints;
imagePoints.push_back(Point2f(320, 240));
imagePoints.push_back(Point2f(0, 240));
imagePoints.push_back(Point2f(320, 0));
//Nx1 2-channel
Mat normalized(static_cast<int>(imagePoints.size()), 1, CV_32FC2);
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(static_cast<int>(imagePoints.size()), normalized.rows);
for (int i = 0; i < normalized.rows; i++) {
EXPECT_NEAR(normalized.at<Vec2f>(i,0)(0), imagePoints[i].x, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized.at<Vec2f>(i,0)(1), imagePoints[i].y, std::numeric_limits<float>::epsilon());
}
}
{
vector<Point2f> imagePoints;
imagePoints.push_back(Point2f(320, 240));
imagePoints.push_back(Point2f(0, 240));
imagePoints.push_back(Point2f(320, 0));
//1xN 2-channel
Mat normalized(1, static_cast<int>(imagePoints.size()), CV_32FC2);
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(static_cast<int>(imagePoints.size()), normalized.cols);
for (int i = 0; i < normalized.rows; i++) {
EXPECT_NEAR(normalized.at<Vec2f>(0,i)(0), imagePoints[i].x, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized.at<Vec2f>(0,i)(1), imagePoints[i].y, std::numeric_limits<float>::epsilon());
}
}
{
vector<Point2f> imagePoints;
imagePoints.push_back(Point2f(320, 240));
imagePoints.push_back(Point2f(0, 240));
imagePoints.push_back(Point2f(320, 0));
//vector<Point2f>
vector<Point2f> normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(imagePoints.size(), normalized.size());
for (int i = 0; i < static_cast<int>(normalized.size()); i++) {
EXPECT_NEAR(normalized[i].x, imagePoints[i].x, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized[i].y, imagePoints[i].y, std::numeric_limits<float>::epsilon());
}
}
{
vector<Point2d> imagePoints;
imagePoints.push_back(Point2d(320, 240));
imagePoints.push_back(Point2d(0, 240));
imagePoints.push_back(Point2d(320, 0));
//vector<Point2d>
vector<Point2d> normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(imagePoints.size(), normalized.size());
for (int i = 0; i < static_cast<int>(normalized.size()); i++) {
EXPECT_NEAR(normalized[i].x, imagePoints[i].x, std::numeric_limits<double>::epsilon());
EXPECT_NEAR(normalized[i].y, imagePoints[i].y, std::numeric_limits<double>::epsilon());
}
}
}
TEST(Imgproc_undistort, regression_15286)
{
double kmat_data[9] = { 3217, 0, 1592, 0, 3217, 1201, 0, 0, 1 };
Mat kmat(3, 3, CV_64F, kmat_data);
double dist_coeff_data[5] = { 0.04, -0.4, -0.01, 0.04, 0.7 };
Mat dist_coeffs(5, 1, CV_64F, dist_coeff_data);
Mat img = Mat::zeros(512, 512, CV_8UC1);
img.at<uchar>(128, 128) = 255;
img.at<uchar>(128, 384) = 255;
img.at<uchar>(384, 384) = 255;
img.at<uchar>(384, 128) = 255;
Mat ref = Mat::zeros(512, 512, CV_8UC1);
ref.at<uchar>(Point(24, 98)) = 78;
ref.at<uchar>(Point(24, 99)) = 114;
ref.at<uchar>(Point(25, 98)) = 36;
ref.at<uchar>(Point(25, 99)) = 60;
ref.at<uchar>(Point(27, 361)) = 6;
ref.at<uchar>(Point(28, 361)) = 188;
ref.at<uchar>(Point(28, 362)) = 49;
ref.at<uchar>(Point(29, 361)) = 44;
ref.at<uchar>(Point(29, 362)) = 16;
ref.at<uchar>(Point(317, 366)) = 134;
ref.at<uchar>(Point(317, 367)) = 78;
ref.at<uchar>(Point(318, 366)) = 40;
ref.at<uchar>(Point(318, 367)) = 29;
ref.at<uchar>(Point(310, 104)) = 106;
ref.at<uchar>(Point(310, 105)) = 30;
ref.at<uchar>(Point(311, 104)) = 112;
ref.at<uchar>(Point(311, 105)) = 38;
Mat img_undist;
undistort(img, img_undist, kmat, dist_coeffs);
ASSERT_EQ(0.0, cvtest::norm(img_undist, ref, cv::NORM_INF));
}
TEST(Calib3d_initUndistortRectifyMap, regression_14467)
{
Size size_w_h(512 + 3, 512);
Matx33f k(
6200, 0, size_w_h.width / 2.0f,
0, 6200, size_w_h.height / 2.0f,
0, 0, 1
);
Mat mesh_uv(size_w_h, CV_32FC2);
for (int i = 0; i < size_w_h.height; i++)
{
for (int j = 0; j < size_w_h.width; j++)
{
mesh_uv.at<Vec2f>(i, j) = Vec2f((float)j, (float)i);
}
}
Matx<double, 1, 14> d(
0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0,
0.09, 0.0
);
Mat mapxy, dst;
initUndistortRectifyMap(k, d, noArray(), k, size_w_h, CV_32FC2, mapxy, noArray());
undistortPoints(mapxy.reshape(2, (int)mapxy.total()), dst, k, d, noArray(), k);
dst = dst.reshape(2, mapxy.rows);
EXPECT_LE(cvtest::norm(dst, mesh_uv, NORM_INF), 1e-3);
}
}} // namespace
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