Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
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#include "opencv2/core/types.hpp"
#include "test_precomp.hpp"
#include "test_chessboardgenerator.hpp"
namespace opencv_test { namespace {
class CV_ChessboardSubpixelTest : public cvtest::BaseTest
{
public:
CV_ChessboardSubpixelTest();
protected:
Mat intrinsic_matrix_;
Mat distortion_coeffs_;
Size image_size_;
void run(int);
void generateIntrinsicParams();
};
int calcDistance(const vector<Point2f>& set1, const vector<Point2f>& set2, double& mean_dist)
{
if(set1.size() != set2.size())
{
return 0;
}
std::vector<int> indices;
double sum_dist = 0.0;
for(size_t i = 0; i < set1.size(); i++)
{
double min_dist = std::numeric_limits<double>::max();
int min_idx = -1;
for(int j = 0; j < (int)set2.size(); j++)
{
double dist = cv::norm(set1[i] - set2[j]); // TODO cvtest
if(dist < min_dist)
{
min_idx = j;
min_dist = dist;
}
}
// check validity of min_idx
if(min_idx == -1)
{
return 0;
}
std::vector<int>::iterator it = std::find(indices.begin(), indices.end(), min_idx);
if(it != indices.end())
{
// there are two points in set1 corresponding to the same point in set2
return 0;
}
indices.push_back(min_idx);
// printf("dist %d = %f\n", (int)i, min_dist);
sum_dist += min_dist*min_dist;
}
mean_dist = sqrt(sum_dist/set1.size());
// printf("sum_dist = %f, set1.size() = %d, mean_dist = %f\n", sum_dist, (int)set1.size(), mean_dist);
return 1;
}
CV_ChessboardSubpixelTest::CV_ChessboardSubpixelTest() :
intrinsic_matrix_(Size(3, 3), CV_64FC1), distortion_coeffs_(Size(1, 4), CV_64FC1),
image_size_(640, 480)
{
}
/* ///////////////////// chess_corner_test ///////////////////////// */
void CV_ChessboardSubpixelTest::run( int )
{
int code = cvtest::TS::OK;
int progress = 0;
RNG& rng = ts->get_rng();
const int runs_count = 20;
const int max_pattern_size = 8;
const int min_pattern_size = 5;
Mat bg(image_size_, CV_8UC1);
bg = Scalar(0);
double sum_dist = 0.0;
int count = 0;
for(int i = 0; i < runs_count; i++)
{
const int pattern_width = min_pattern_size + cvtest::randInt(rng) % (max_pattern_size - min_pattern_size);
const int pattern_height = min_pattern_size + cvtest::randInt(rng) % (max_pattern_size - min_pattern_size);
Size pattern_size;
if(pattern_width > pattern_height)
{
pattern_size = Size(pattern_height, pattern_width);
}
else
{
pattern_size = Size(pattern_width, pattern_height);
}
ChessBoardGenerator gen_chessboard(Size(pattern_size.width + 1, pattern_size.height + 1));
// generates intrinsic camera and distortion matrices
generateIntrinsicParams();
vector<Point2f> corners;
Mat chessboard_image = gen_chessboard(bg, intrinsic_matrix_, distortion_coeffs_, corners);
vector<Point2f> test_corners;
bool result = findChessboardCorners(chessboard_image, pattern_size, test_corners, 15);
if (!result && cvtest::debugLevel > 0)
{
ts->printf(cvtest::TS::LOG, "Warning: chessboard was not detected! Writing image to test.png\n");
ts->printf(cvtest::TS::LOG, "Size = %d, %d\n", pattern_size.width, pattern_size.height);
ts->printf(cvtest::TS::LOG, "Intrinsic params: fx = %f, fy = %f, cx = %f, cy = %f\n",
intrinsic_matrix_.at<double>(0, 0), intrinsic_matrix_.at<double>(1, 1),
intrinsic_matrix_.at<double>(0, 2), intrinsic_matrix_.at<double>(1, 2));
ts->printf(cvtest::TS::LOG, "Distortion matrix: %f, %f, %f, %f, %f\n",
distortion_coeffs_.at<double>(0, 0), distortion_coeffs_.at<double>(0, 1),
distortion_coeffs_.at<double>(0, 2), distortion_coeffs_.at<double>(0, 3),
distortion_coeffs_.at<double>(0, 4));
imwrite("test.png", chessboard_image);
}
if (!result)
{
continue;
}
double dist1 = 0.0;
int ret = calcDistance(corners, test_corners, dist1);
if(ret == 0)
{
ts->printf(cvtest::TS::LOG, "findChessboardCorners returns invalid corner coordinates!\n");
code = cvtest::TS::FAIL_INVALID_OUTPUT;
break;
}
cornerSubPix(chessboard_image, test_corners,
Size(3, 3), Size(1, 1), TermCriteria(TermCriteria::EPS|TermCriteria::MAX_ITER, 300, 0.1));
find4QuadCornerSubpix(chessboard_image, test_corners, Size(5, 5));
double dist2 = 0.0;
ret = calcDistance(corners, test_corners, dist2);
if(ret == 0)
{
ts->printf(cvtest::TS::LOG, "findCornerSubpix returns invalid corner coordinates!\n");
code = cvtest::TS::FAIL_INVALID_OUTPUT;
break;
}
ts->printf(cvtest::TS::LOG, "Error after findChessboardCorners: %f, after findCornerSubPix: %f\n",
dist1, dist2);
sum_dist += dist2;
count++;
const double max_reduce_factor = 0.8;
if(dist1 < dist2*max_reduce_factor)
{
ts->printf(cvtest::TS::LOG, "findCornerSubPix increases average error!\n");
code = cvtest::TS::FAIL_INVALID_OUTPUT;
break;
}
progress = update_progress( progress, i-1, runs_count, 0 );
}
ASSERT_NE(0, count);
sum_dist /= count;
ts->printf(cvtest::TS::LOG, "Average error after findCornerSubpix: %f\n", sum_dist);
if( code < 0 )
ts->set_failed_test_info( code );
}
void CV_ChessboardSubpixelTest::generateIntrinsicParams()
{
RNG& rng = ts->get_rng();
const double max_focus_length = 1000.0;
const double max_focus_diff = 5.0;
double fx = cvtest::randReal(rng)*max_focus_length;
double fy = fx + cvtest::randReal(rng)*max_focus_diff;
double cx = image_size_.width/2;
double cy = image_size_.height/2;
double k1 = 0.5*cvtest::randReal(rng);
double k2 = 0.05*cvtest::randReal(rng);
double p1 = 0.05*cvtest::randReal(rng);
double p2 = 0.05*cvtest::randReal(rng);
double k3 = 0.0;
intrinsic_matrix_ = (Mat_<double>(3, 3) << fx, 0.0, cx, 0.0, fy, cy, 0.0, 0.0, 1.0);
distortion_coeffs_ = (Mat_<double>(1, 5) << k1, k2, p1, p2, k3);
}
TEST(Calib3d_ChessboardSubPixDetector, accuracy) { CV_ChessboardSubpixelTest test; test.safe_run(); }
TEST(Calib3d_CornerSubPix, regression_7204)
{
cv::Mat image(cv::Size(70, 38), CV_8UC1, cv::Scalar::all(0));
image(cv::Rect(65, 26, 5, 5)).setTo(cv::Scalar::all(255));
image(cv::Rect(55, 31, 8, 1)).setTo(cv::Scalar::all(255));
image(cv::Rect(56, 35, 14, 2)).setTo(cv::Scalar::all(255));
image(cv::Rect(66, 24, 4, 2)).setTo(cv::Scalar::all(255));
image.at<uchar>(24, 69) = 0;
std::vector<cv::Point2f> corners;
corners.push_back(cv::Point2f(65, 30));
cv::cornerSubPix(image, corners, cv::Size(3, 3), cv::Size(-1, -1),
cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::COUNT, 30, 0.1));
}
}} // namespace
/* End of file. */