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opencv/modules/calib3d/test/test_undistort_points.cpp
Adrian Kretz c0364e4e31 Merge pull request #27993 from akretz:undistortPoints_convergence
Undistort points convergence #27993

I have looked into the `undistortPoints()` problem of issue #27916 and have found a solution. The problem is, as @Linhuihang has correctly pointed out, that the fixed-point iterations do not converge. Here are the functions which are optimized for the undistortion problem:

$$
\begin{aligned}
  r^2  &= x'^2 + y'^2 \\
  f_1(x') &= \frac{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}{1 + k_1 r^2 + k_2 r^4 + k_3 r^6} (x'' - 2p_1 x' y' - p_2(r^2 + 2 x'^2) - s_1 r^2 + s_2 r^4) = x' \\
  f_2(y') &=  \frac{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}{1 + k_1 r^2 + k_2 r^4 + k_3 r^6} (y'' - p_1 (r^2 + 2 y'^2) - 2 p_2 x' y' - s_3 r^2 - s_4 r^4) = y'
\end{aligned}
$$

where $x', y'$ are the undistorted points we want to compute and and $x'', y''$ are the given distorted points. This problem is solved using fixed-point iterations like

$$
  x'_{k+1} = f_1(x'_k),\quad
  y'_{k+1} = f_2(y'_k)
$$

I guess the issue here is that the distortion function does not necessarily satisfy the [Banach fixed-point theorem](https://en.wikipedia.org/wiki/Banach_fixed-point_theorem), i.e. the slope of the function can be too large. This can be seen in @Linhuihang's comment https://github.com/opencv/opencv/issues/27916#issuecomment-3417883642 - the point series jumps around and doesn't converge.

A common solution is to instead do damped fixed-point iterations, so that the updates are "more smooth".

$$
  x'_{k+1} = (1 - \alpha) x'_k + \alpha f_1(x'_k),\quad
  y'_{k+1} = (1 - \alpha) y'_k + \alpha f_2(y'_k)
$$

I have implemented a simple logic which starts with $\alpha = 1$ (so just like it is now) and reduces $\alpha$ whenever the optimization error would increase. This seems reasonable to me: the initial logic is to do normal fixed-point iterations and to gradually become "more damped" when we notice that we don't converge. Perhaps there is a better way to ensure convergence, but this is the most straightforward modification to the current code that I have found.

This problem is not due to the $\tau_x, \tau_y$ parameters; it also occurs when they are zero. In fact, the fixed-point iterations are done when the tilt correction of $\tau_x, \tau_y$ has already been applied. I have added a test to reproduce the problem. This PR fixes #27916.


### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2025-11-21 10:52:02 +03:00

258 lines
9.3 KiB
C++

// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include <opencv2/ts/cuda_test.hpp> // EXPECT_MAT_NEAR
#include "opencv2/core/types.hpp"
#include "test_precomp.hpp"
namespace opencv_test { namespace {
class UndistortPointsTest : public ::testing::Test
{
protected:
void generate3DPointCloud(vector<Point3f>& points, Point3f pmin = Point3f(-1,
-1, 5), Point3f pmax = Point3f(1, 1, 10));
void generateCameraMatrix(Mat& cameraMatrix);
void generateDistCoeffs(Mat& distCoeffs, int count);
cv::Mat generateRotationVector();
std::vector<cv::Point2d> distortPoints(const cv::Mat &cameraMatrix, const cv::Mat &dist, const std::vector<cv::Point2d> &points);
double thresh = 1.0e-2;
};
void UndistortPointsTest::generate3DPointCloud(vector<Point3f>& points, Point3f pmin, Point3f pmax)
{
RNG rng_Point = cv::theRNG(); // fix the seed to use "fixed" input 3D points
for (size_t i = 0; i < points.size(); i++)
{
float _x = rng_Point.uniform(pmin.x, pmax.x);
float _y = rng_Point.uniform(pmin.y, pmax.y);
float _z = rng_Point.uniform(pmin.z, pmax.z);
points[i] = Point3f(_x, _y, _z);
}
}
void UndistortPointsTest::generateCameraMatrix(Mat& cameraMatrix)
{
const double fcMinVal = 1e-3;
const double fcMaxVal = 100;
cameraMatrix.create(3, 3, CV_64FC1);
cameraMatrix.setTo(Scalar(0));
cameraMatrix.at<double>(0,0) = theRNG().uniform(fcMinVal, fcMaxVal);
cameraMatrix.at<double>(1,1) = theRNG().uniform(fcMinVal, fcMaxVal);
cameraMatrix.at<double>(0,2) = theRNG().uniform(fcMinVal, fcMaxVal);
cameraMatrix.at<double>(1,2) = theRNG().uniform(fcMinVal, fcMaxVal);
cameraMatrix.at<double>(2,2) = 1;
}
void UndistortPointsTest::generateDistCoeffs(Mat& distCoeffs, int count)
{
distCoeffs = Mat::zeros(count, 1, CV_64FC1);
for (int i = 0; i < count; i++)
distCoeffs.at<double>(i,0) = theRNG().uniform(-0.1, 0.1);
}
cv::Mat UndistortPointsTest::generateRotationVector()
{
Mat rvec(1, 3, CV_64F);
theRNG().fill(rvec, RNG::UNIFORM, -0.2, 0.2);
return rvec;
}
std::vector<cv::Point2d> UndistortPointsTest::distortPoints(const cv::Mat &cameraMatrix, const cv::Mat &dist, const std::vector<cv::Point2d> &points)
{
CV_Assert(cameraMatrix.rows == 3 && cameraMatrix.cols == 3);
CV_Assert(cameraMatrix.type() == CV_64F);
CV_Assert(dist.rows * dist.cols == 12);
CV_Assert(dist.type() == CV_64F);
double *k = reinterpret_cast<double *>(dist.data);
double fx = cameraMatrix.at<double>(0, 0);
double fy = cameraMatrix.at<double>(1, 1);
double cx = cameraMatrix.at<double>(0, 2);
double cy = cameraMatrix.at<double>(1, 2);
std::vector<cv::Point2d> distortedPoints;
distortedPoints.reserve(points.size());
for (const cv::Point2d p : points) {
double x = (p.x - cx) / fx;
double y = (p.y - cy) / fy;
double r2 = x*x + y*y;
double cdist = (1 + ((k[4]*r2 + k[1])*r2 + k[0])*r2)/(1 + ((k[7]*r2 + k[6])*r2 + k[5])*r2);
CV_Assert(cdist >= 0);
double deltaX = 2*k[2]*x*y + k[3]*(r2 + 2*x*x)+ k[8]*r2+k[9]*r2*r2;
double deltaY = k[2]*(r2 + 2*y*y) + 2*k[3]*x*y+ k[10]*r2+k[11]*r2*r2;
distortedPoints.push_back(cv::Point2d((x * cdist + deltaX) * fx + cx, (y * cdist + deltaY) * fy + cy));
}
return distortedPoints;
}
TEST_F(UndistortPointsTest, accuracy)
{
Mat intrinsics, distCoeffs;
generateCameraMatrix(intrinsics);
vector<Point3f> points(500);
generate3DPointCloud(points);
Mat rvec = generateRotationVector();
Mat R;
cv::Rodrigues(rvec, R);
int modelMembersCount[] = {4,5,8};
for (int idx = 0; idx < 3; idx++)
{
generateDistCoeffs(distCoeffs, modelMembersCount[idx]);
/* Project points with distortion */
vector<Point2f> projectedPoints;
projectPoints(Mat(points), Mat::zeros(3,1,CV_64FC1),
Mat::zeros(3,1,CV_64FC1), intrinsics,
distCoeffs, projectedPoints);
/* Project points without distortion */
vector<Point2f> realUndistortedPoints;
projectPoints(Mat(points), rvec,
Mat::zeros(3,1,CV_64FC1), intrinsics,
Mat::zeros(4,1,CV_64FC1), realUndistortedPoints);
/* Undistort points */
Mat undistortedPoints;
undistortPoints(Mat(projectedPoints), undistortedPoints, intrinsics, distCoeffs, R, intrinsics);
EXPECT_MAT_NEAR(realUndistortedPoints, undistortedPoints.t(), thresh);
}
}
TEST_F(UndistortPointsTest, undistortImagePointsAccuracy)
{
Mat intrinsics, distCoeffs;
generateCameraMatrix(intrinsics);
vector<Point3f> points(500);
generate3DPointCloud(points);
int modelMembersCount[] = {4,5,8};
for (int idx = 0; idx < 3; idx++)
{
generateDistCoeffs(distCoeffs, modelMembersCount[idx]);
/* Project points with distortion */
vector<Point2f> projectedPoints;
projectPoints(Mat(points), Mat::zeros(3,1,CV_64FC1),
Mat::zeros(3,1,CV_64FC1), intrinsics,
distCoeffs, projectedPoints);
/* Project points without distortion */
vector<Point2f> realUndistortedPoints;
projectPoints(Mat(points), Mat::zeros(3, 1, CV_64FC1),
Mat::zeros(3,1,CV_64FC1), intrinsics,
Mat::zeros(4,1,CV_64FC1), realUndistortedPoints);
/* Undistort points */
Mat undistortedPoints;
TermCriteria termCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, thresh / 2);
undistortImagePoints(Mat(projectedPoints), undistortedPoints, intrinsics, distCoeffs,
termCriteria);
EXPECT_MAT_NEAR(realUndistortedPoints, undistortedPoints.t(), thresh);
}
}
TEST_F(UndistortPointsTest, stop_criteria)
{
Mat cameraMatrix = (Mat_<double>(3,3,CV_64F) << 857.48296979, 0, 968.06224829,
0, 876.71824265, 556.37145899,
0, 0, 1);
Mat distCoeffs = (Mat_<double>(5,1,CV_64F) <<
-2.57614020e-01, 8.77086999e-02, -2.56970803e-04, -5.93390389e-04, -1.52194091e-02);
Point2d pt_distorted(theRNG().uniform(0.0, 1920.0), theRNG().uniform(0.0, 1080.0));
std::vector<Point2d> pt_distorted_vec;
pt_distorted_vec.push_back(pt_distorted);
const double maxError = 1e-6;
TermCriteria criteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 100, maxError);
std::vector<Point2d> pt_undist_vec;
Mat rVec = Mat(Matx31d(0.1, -0.2, 0.2));
Mat R;
cv::Rodrigues(rVec, R);
undistortPoints(pt_distorted_vec, pt_undist_vec, cameraMatrix, distCoeffs, R, noArray(), criteria);
std::vector<Point3d> pt_undist_vec_homogeneous;
pt_undist_vec_homogeneous.emplace_back(pt_undist_vec[0].x, pt_undist_vec[0].y, 1.0 );
std::vector<Point2d> pt_redistorted_vec;
projectPoints(pt_undist_vec_homogeneous, -rVec,
Mat::zeros(3,1,CV_64F), cameraMatrix, distCoeffs, pt_redistorted_vec);
const double obtainedError = sqrt( pow(pt_distorted.x - pt_redistorted_vec[0].x, 2) + pow(pt_distorted.y - pt_redistorted_vec[0].y, 2) );
ASSERT_LE(obtainedError, maxError);
}
TEST_F(UndistortPointsTest, regression_14583)
{
const int col = 720;
// const int row = 540;
float camera_matrix_value[] = {
437.8995f, 0.0f, 342.9241f,
0.0f, 438.8216f, 273.7163f,
0.0f, 0.0f, 1.0f
};
cv::Mat camera_interior(3, 3, CV_32F, camera_matrix_value);
float camera_distort_value[] = {-0.34329f, 0.11431f, 0.0f, 0.0f, -0.017375f};
cv::Mat camera_distort(1, 5, CV_32F, camera_distort_value);
float distort_points_value[] = {col, 0.};
cv::Mat distort_pt(1, 1, CV_32FC2, distort_points_value);
cv::Mat undistort_pt;
cv::undistortPoints(distort_pt, undistort_pt, camera_interior,
camera_distort, cv::Mat(), camera_interior);
EXPECT_NEAR(distort_pt.at<Vec2f>(0)[0], undistort_pt.at<Vec2f>(0)[0], col / 2)
<< "distort point: " << distort_pt << std::endl
<< "undistort point: " << undistort_pt;
}
TEST_F(UndistortPointsTest, regression_27916)
{
cv::Mat K = (cv::Mat_<double>(3, 3) <<
1570.8956145992222, 0., 744.87337646727406, 0.,
1570.3494207432338, 575.55087456337526, 0., 0., 1.);
cv::Mat dist = (cv::Mat_<double>(1, 12) <<
-2.8247717583453804, -0.80078070764368037,
-0.014595359484103326, 0.0018820998949700702, 1.9827795585249783,
-2.7306773773930897, -1.217725820479524, 2.4052243546080136,
-0.0020670359760441713, 3.4660880793174063e-05,
0.014100351510458799, -3.0935329736207612e-05);
const cv::TermCriteria termCriteria(TermCriteria::MAX_ITER | TermCriteria::EPS, 100, thresh / 2);
std::vector<cv::Point2d> distortedPoints, distortedPoints2;
std::vector<cv::Point2d> undistortedPoints;
for (int i = 0; i < 50; i++)
{
for (int j = 0; j < 50; j++)
{
distortedPoints.push_back(cv::Point2d(i, j));
}
}
cv::undistortPoints(distortedPoints, undistortedPoints, K, dist, cv::noArray(), K, termCriteria);
distortedPoints2 = distortPoints(K, dist, undistortedPoints);
EXPECT_MAT_NEAR(distortedPoints2, distortedPoints, thresh);
}
}} // namespace