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opencv/modules/3d/test/test_undistort.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
namespace opencv_test { 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] = Size(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_GetOptimalNewCameraMatrixNoDistortionTest : public cvtest::ArrayTest
{
public:
CV_GetOptimalNewCameraMatrixNoDistortionTest();
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::Mat camera_mat;
cv::Mat distortion_coeffs;
cv::Mat new_camera_mat;
cv::Size img_size;
double alpha;
bool center_principal_point;
int matrix_type;
static const int MAX_X = 2000;
static const int MAX_Y = 2000;
};
CV_GetOptimalNewCameraMatrixNoDistortionTest::CV_GetOptimalNewCameraMatrixNoDistortionTest()
{
test_array[INPUT].push_back(NULL); // camera_mat
test_array[INPUT].push_back(NULL); // distortion_coeffs
test_array[OUTPUT].push_back(NULL); // new_camera_mat
test_array[REF_OUTPUT].push_back(NULL);
alpha = 0.0;
center_principal_point = false;
matrix_type = 0;
}
void CV_GetOptimalNewCameraMatrixNoDistortionTest::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[INPUT][1] = 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] = Size(3,3);
sizes[INPUT][1] = Size(1,4);
}
int CV_GetOptimalNewCameraMatrixNoDistortionTest::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();
alpha = cvtest::randReal(rng);
center_principal_point = ((cvtest::randInt(rng) % 2)!=0);
// Generate random camera matrix. Use floating point precision for source to avoid precision loss
img_size.width = cvtest::randInt(rng) % MAX_X + 1;
img_size.height = cvtest::randInt(rng) % MAX_Y + 1;
const float aspect_ratio = static_cast<float>(img_size.width) / img_size.height;
float cam_array[9] = {0,0,0,0,0,0,0,0,1};
cam_array[2] = static_cast<float>((img_size.width - 1)*0.5); // center
cam_array[5] = static_cast<float>((img_size.height - 1)*0.5); // center
cam_array[0] = static_cast<float>(MAX(img_size.width, img_size.height)/(0.9 - cvtest::randReal(rng)*0.6));
cam_array[4] = aspect_ratio*cam_array[0];
Mat& input_camera_mat = test_mat[INPUT][0];
cvtest::convert(Mat(3, 3, CV_32F, cam_array), input_camera_mat, input_camera_mat.type());
camera_mat = input_camera_mat;
// Generate zero distortion matrix
const Mat zero_dist_coeffs = Mat::zeros(1, 4, CV_32F);
Mat& input_dist_coeffs = test_mat[INPUT][1];
cvtest::convert(zero_dist_coeffs, input_dist_coeffs, input_dist_coeffs.type());
distortion_coeffs = input_dist_coeffs;
return code;
}
void CV_GetOptimalNewCameraMatrixNoDistortionTest::run_func()
{
new_camera_mat = cv::getOptimalNewCameraMatrix(camera_mat, distortion_coeffs, img_size, alpha, img_size, NULL, center_principal_point);
}
void CV_GetOptimalNewCameraMatrixNoDistortionTest::prepare_to_validation(int /*test_case_idx*/)
{
const Mat& src = test_mat[INPUT][0];
Mat& dst = test_mat[REF_OUTPUT][0];
cvtest::copy(src, dst);
Mat& output = test_mat[OUTPUT][0];
cvtest::convert(new_camera_mat, output, output.type());
}
//////////////////////////////////////////////////////////////////////////////////////////////////////
TEST(Calib3d_DefaultNewCameraMatrix, accuracy) { CV_DefaultNewCameraMatrixTest test; test.safe_run(); }
TEST(Calib3d_GetOptimalNewCameraMatrixNoDistortion, accuracy) { CV_GetOptimalNewCameraMatrixNoDistortionTest 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:
CV_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:
CV_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:
CV_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] = Size(3,3);
sizes[INPUT][2] = cvtest::randInt(rng)%2 ? Size(4,1) : Size(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] = Size(3,3);
sizes[INPUT][1] = cvtest::randInt(rng)%2 ? Size(4,1) : Size(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);
}
TEST(Calib3d_initInverseRectificationMap, regression_20165)
{
Size size_w_h(1280, 800);
Mat dst(size_w_h, CV_32FC2); // Reference for validation
Mat mapxy; // Output of initInverseRectificationMap()
// Camera Matrix
double k[9]={
1.5393951443032472e+03, 0., 6.7491727003047140e+02,
0., 1.5400748240626747e+03, 5.1226968329123963e+02,
0., 0., 1.
};
Mat _K(3, 3, CV_64F, k);
// Distortion
// double d[5]={0,0,0,0,0}; // Zero Distortion
double d[5]={ // Non-zero distortion
-3.4134571357400023e-03, 2.9733267766101856e-03, // K1, K2
3.6653586399031184e-03, -3.1960714017365702e-03, // P1, P2
0. // K3
};
Mat _d(1, 5, CV_64F, d);
// Rotation
//double R[9]={1., 0., 0., 0., 1., 0., 0., 0., 1.}; // Identity transform (none)
double R[9]={ // Random transform
9.6625486010428052e-01, 1.6055789378989216e-02, 2.5708706103628531e-01,
-8.0300261706161002e-03, 9.9944797497929860e-01, -3.2237617614807819e-02,
-2.5746274294459848e-01, 2.9085338870243265e-02, 9.6585039165403186e-01
};
Mat _R(3, 3, CV_64F, R);
// --- Validation --- //
initInverseRectificationMap(_K, _d, _R, _K, size_w_h, CV_32FC2, mapxy, noArray());
// Copy camera matrix
double fx, fy, cx, cy, ifx, ify, cxn, cyn;
fx = k[0]; fy = k[4]; cx = k[2]; cy = k[5];
// Copy new camera matrix
ifx = k[0]; ify = k[4]; cxn = k[2]; cyn = k[5];
// Distort Points
for( int v = 0; v < size_w_h.height; v++ )
{
for( int u = 0; u < size_w_h.width; u++ )
{
// Convert from image to pin-hole coordinates
double x = (u - cx)/fx;
double y = (v - cy)/fy;
// Undistort
double x2 = x*x, y2 = y*y;
double r2 = x2 + y2;
double cdist = 1./(1. + (d[0] + (d[1] + d[4]*r2)*r2)*r2); // (1. + (d[5] + (d[6] + d[7]*r2)*r2)*r2) == 1 as d[5-7]=0;
double x_ = (x - (d[2]*2.*x*y + d[3]*(r2 + 2.*x2)))*cdist;
double y_ = (y - (d[3]*2.*x*y + d[2]*(r2 + 2.*y2)))*cdist;
// Rectify
double X = R[0]*x_ + R[1]*y_ + R[2];
double Y = R[3]*x_ + R[4]*y_ + R[5];
double Z = R[6]*x_ + R[7]*y_ + R[8];
double x__ = X/Z;
double y__ = Y/Z;
// Convert from pin-hole to image coordinates
dst.at<Vec2f>(v, u) = Vec2f((float)(x__*ifx + cxn), (float)(y__*ify + cyn));
}
}
// Check Result
EXPECT_LE(cvtest::norm(dst, mapxy, NORM_INF), 2e-1);
}
}} // namespace
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