Fixed issues identified by PVS Studio #28185
Partially fixes https://github.com/opencv/opencv/issues/28167
Paper: https://pvs-studio.com/en/blog/posts/cpp/1321/
Closed items: N2, N4, N5, N6, N7, N8, N10, N11, N13, N14.
To be continued...
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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.
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[calib3d] Add estimateTranslation2D() #27950
Merge with opencv_extra PR: opencv/opencv_extra#1286
### **Description**
This PR adds a new API, `cv::estimateTranslation2D()`, to the **calib3d** module.
It computes a **pure 2D translation** between two sets of corresponding points using robust methods (`RANSAC` and `LMedS`).
The function mirrors the interface and behavior of `estimateAffine2D()` and `estimateAffinePartial2D()`, but constrains the transformation to translation only.
This model is particularly useful for cases where the motion between images is purely translational, such as:
- Aerial stitching and planar mosaics.
- Image alignment in fixed-camera systems.
- Lightweight pipelines where affine or homography models are unnecessarily complex.
The implementation introduces a new internal class `Translation2DEstimatorCallback` and integrates seamlessly into OpenCV’s existing robust estimation framework (`PointSetRegistrator`).
---
### **Key Features**
- Implements `cv::estimateTranslation2D()` in the `calib3d` module.
- Supports robust methods **RANSAC** and **LMedS**.
- Adds accuracy and performance tests.
- Provides full **C++ and Python bindings**.
- Includes **Doxygen documentation** consistent with OpenCV’s standards.
- Verified correctness across noise, outlier, and datatype variations.
---
### **Testing & Verification**
**Unit Tests** (`modules/calib3d/`)
- **Minimal sample:**
`test1Point` validates that a single correspondence recovers the correct translation under both **RANSAC** and **LMedS** across 500 randomized trials.
- **Robustness to noise and outliers:**
`testNPoints` generates 100 correspondences, injects noise and outliers (≤40% for RANSAC, ≤50% for LMedS), and verifies that:
- Estimated **T** closely matches ground truth (`cvtest::norm(..., NORM_L2)`).
- Inlier mask consistency and correctness are maintained.
- **Datatype conversion:**
`testConversion` checks mixed input datatypes (integer → float) to ensure correct conversion and consistent results.
- **Input immutability:**
`dont_change_inputs` confirms that input arrays remain unchanged after function execution, mirroring affine behavior.
**Performance Tests** (`modules/calib3d/`)
- `EstimateTranslation2DPerf` benchmarks **RANSAC** and **LMedS** using:
- Point counts: 1000
- Confidence levels: 0.95
- Refinement iterations: 10, 0
These tests confirm **numerical stability**, **performance scaling**, and **consistency** across datatypes and noise levels.
---
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Fix typos #27338
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Add image dimension check to avoid StereoSGBM non-determinism #27305
Addresses #25828
Users noticed that StereoSGBM would occasionally give non-deterministic results for `.compute(imgL, imgR)`.
I and others traced the cause to out-of-bounds access that was not being caught when the input images were not wide enough for the input block size and number of disparities to StereoSGBM. The specific math and logic can be found in the above issue's discussion.
This PR adds a CV_Check to make sure images are wider than 1/2 of the block size + the max disparity the algorithm will search.
The check was only added to the regular `compute` method for StereoSGBM and not to the other modes, as I did not observe the non-deterministic behavior with the other compute modes like HH.
In addition, this PR adds a test case to Calib3d to make sure the check is being thrown in the problem case and that the results are deterministic in the good case.
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Specify DLS and UPnP mappings to EPnP in all places for solvePnP* tests #27185
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solvePnPRansac implementation for Fisheye camera model #26669
Related: https://github.com/opencv/opencv/pull/25028
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Support C++20 standard #26590
Close https://github.com/opencv/opencv/issues/26589
Related https://github.com/opencv/opencv_contrib/pull/3842
Related: https://github.com/opencv/opencv/issues/20269
- do not arithmetic enums and ( different enums or floating numeric)
- remove unused variable
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Enable checkerboard detection with a central / corner marker on a black tile #25808
This pull request closes the issue #25806.
The issue doesn't require any documentation - it's quite intuitive that the detection result shouldn't depend on the color of the marker's tile.
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Fix Homography computation. #25665
The bug was introduced in https://github.com/opencv/opencv/pull/25308
I am sorry I do not have a proper test.
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Fix handeye #24897
Fixes to the hand-eye calibration methods, from #24871.
The Tsai method is sensitive to poses separated by small rotations, so I filter those out.
The Horaud and Daniilidis methods use quaternions (and dual quaternions), where $q$ and $-q$ represent the same transform.
However, these methods depend on the gripper motion and camera motion having the same sign for the real part.
The fix was simply to multiply the (dual) quaternions by -1 if their real part is negative.
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Handle degenerate cases in RQDecomp3x3 #25050
The point of the Givens rotations here is to iteratively set the lower left matrix entries to zero. If an element is zero already, we don't need to do anything. This resolves#24330.
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Compensate edge length in ChessBoardDetector::generateQuads (attempt 2) #25090
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New attempt for #24833, which was reverted as #25036.
Locally I fixed `Calib3d_StereoCalibrate_CPP.regression` test by corners refinement using `cornerSubPix` function
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solvePnP implementation for Fisheye camera model #25028
Credits to Linfei Pan
Extracted from https://github.com/opencv/opencv/pull/24052
**Warning:** The patch changes Obj-C generator behaviour and adds "fisheye_" prefix for all ObjC functions from namespace.
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Co-authored-by: lpanaf <linpan@student.ethz.ch>
Co-authored-by: Vadim Levin <vadim.levin@xperience.ai>
Check Checkerboard Corners #24546
What I did was get you to pull out of findChessboardCorners cornres the whole part that "checks" and sorts the corners of the checkerboard if present.
The main reason for this is that findChessboardCorners is often very slow to find the corners and this depends in that the size the contrast etc of the checkerboards can be very different from each other and writing a function that works on all kinds of images is complicated.
So I find it very useful to have the ability to write your own code to process the image and then have a function that controls or orders the corners.
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Fix crash in ap3p #23607
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Update USAC #23078
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Resolves https://github.com/opencv/opencv/issues/23304
Fixes the incorrect pixel grid
Switches type to double to avoid precision loss as all callers use doubles
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Add `estimateSE2(...)`, `estimateSE3(...)`, `estimateSIM2(...)`, `estimateSIM3(...)` for estimating an geometric transformation with rotation and translation (with scaling for SIM) using USAC: as alternative for `estimateAffinePartial2D` and `estimateAffine3D`.
Modified test module.
Remove unused variables.
Remove initializer of unused variable.
Add interfaces to accept UsacParams() and corresponding test codes.
Revise test code.
PartialNd removed
Umeyama rewritten for code quality & speed
comments & minors
rise number of points
fix, and +30% faster!
only one number should be that big
remove USAC code, leave fix only
big number
Usage of imread(): magic number 0, unchecked result
* docs: rewrite 0/1 to IMREAD_GRAYSCALE/IMREAD_COLOR in imread()
* samples, apps: rewrite 0/1 to IMREAD_GRAYSCALE/IMREAD_COLOR in imread()
* tests: rewrite 0/1 to IMREAD_GRAYSCALE/IMREAD_COLOR in imread()
* doc/py_tutorials: check imread() result
Alexander Smorkalov
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