Update Gao P3P with Ding P3P #27736
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---
The current Gao P3P implementation does not cover all the degenerate cases, **see last line** in: 6d889ee74c/modules/calib3d/src/p3p.cpp (L211-L221)
See also:
- https://github.com/opencv/opencv/issues/4854
---
<details>
<summary>OBSOLETE</summary>
To fix this, the USAC P3P from OpenCV 5 is used instead: 7e6da007cd/modules/3d/src/usac/pnp_solver.cpp (L282)
---
## Some results
### Old P3P vs new
In the following video, I have tried to highlight the viewpoints which cause issues:
https://github.com/user-attachments/assets/97bec6a6-4043-4509-b50e-a9856d6423bd
| | Old P3P | New P3P |
| -------- | ------- | ------- |
| Mean (ms) | 0.045701 | 0.024816 |
| Median (ms) | 0.025146 | 0.023193 |
| Std (ms) | 0.028953 | 0.006124 |
### New P3P vs AP3P
https://github.com/user-attachments/assets/eaeb21dc-3ffd-4b6c-9902-4352f824aa45
The AP3 method is superior both in term of accuracy and computation time:
| | New P3P | AP3P |
| -------- | ------- | ------- |
| Mean (ms) | 0.043750 | 0.023442 |
| Median (ms) | 0.023193 | 0.021484 |
| Std (ms) | 0.039920 | 0.005265 |
### New P3P vs AP3P (range test)
https://github.com/user-attachments/assets/572e7b7a-2966-4bed-8e0c-b93d863987dc
The implemented P3P method does not work well when the tag is small, at long range.
| | New P3P | AP3P |
| -------- | ------- | ------- |
| Mean (ms) | 0.031351 | 0.025189 |
| Median (ms) | 0.022217 | 0.020996 |
| Std (ms) | 0.024920 | 0.009633 |
---
- I have tried to simplify the P3P code, hope I did not break the implementation code
- calculations are performed using double type for simplicity.
- code such as the following are redundant and no more needed and should be replaced by `cv::Rodrigues`:
6d889ee74c/modules/calib3d/src/usac/pnp_solver.cpp (L395)
</details>
Fix invalid memory access in USAC #27865
### Pull Request Readiness Checklist
Fix#27863
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Moved pattern generator to apps and rewrote tutorial #27833
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Fixed bugs in orthogonalization; simplified column vectors copying #27437
This PR mirrors to OpenCV a bug fix addressed by commit [a03d34b](a03d34b641) in SQPnP
It also fixes bugs in the orthogonalization introduced during the porting to OpenCV and simplifies column vectors copying, eliminating double loops.
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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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Optimize undistort points #26988
Skips unnecessary rotation with identity matrix if no R or P mats are given.
---------
Co-authored-by: Daniel <daniel@mail.de>
Fix Logical defect in FilterSpecklesImpl #26996
Fixes : #24963
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solvePnPRansac implementation for Fisheye camera model #26669
Related: https://github.com/opencv/opencv/pull/25028
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Bug fix for #25546 - Updating inliers for homography estimation #26742Fixes#25546
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Switch calibration.cpp to C++ #26490
The CvLevMarq code has to be kept in order to keep the same accuracy (the C++ solver is not as good).
There are two ways to review this PR: by comparing to the old code, or by checking what is different from the 5.x version (which is the first commit).
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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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The new C++ code is copy/pasted from OpenCV5:
- functions initIntrinsicParams2D, subMatrix (the first 160 lines)
- function prepareDistCoeffs
- the different asserts
Not all the API/code is ported to C++ yet to ease the review.
Backport C++ stereo/stereo_geom.cpp:5.x to calib3d/stereo_geom.cpp:4.x #26437
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Mirror most recent changes from https://github.com/terzakig/sqpnp/pull/24
- rank revealing QR in nullspace computation
- sqrt-free Cholesky (i.e., L*D*Lt) in the SQP solution
- replaced divisions with multiplications by inverses
- simplified checks in computeRowAndNullspace()
- removed unnecessary negations
- broke some dependency chains with parentheses
- minor other changes
Split Javascript white-list to support contrib modules #25986
Single whitelist converted to several per-module json files. They are concatenated automatically and can be overriden by user config.
Related to https://github.com/opencv/opencv/pull/25656
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Increase neighbors search radius for corners in ChessBoardDetector:findQuadNeighbors #26014
I didn't do everything right the way I wanted at #25991. I forgot that `edge_len` is edge **squared** length as well as `thresh_scale` is threshold for **squared** scale. So, I wanted to increase scale by `sqrt(2)` times (idea is to use quad diagonal instead of quad side) and therefore `thresh_scale` should be equal to `sqrt(2)^2 = 2`.
And refactor variables names to explicitly indicate that they are squared, so that no one else falls into this trap
I tested this PR with benchmark
```
python3 objdetect_benchmark.py --configuration=generate_run --board_x=7 --path=res_chessboard --synthetic_object=chessboard
```
PR increases detected chessboards number by `1/2%`:
```
cell_img_size = 100 (default)
before
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.941667 13560 14400 0.596726
Total detected time: 136.68963200000007 sec
after
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.952083 13710 14400 0.595984
Total detected time: 136.55770600000014 sec
----------------------------------------------------------------------------------------------------------------------------------------------
cell_img_size = 10
before
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.579167 8340 14400 4.198448
Total detected time: 2.535998999999999 sec
after
category detected chessboard total detected chessboard total chessboard average detected error
all 0.591389 8516 14400 4.155250
Total detected time: 2.700832999999997 sec
```
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Improve corners matching in ChessBoardDetector::NeighborsFinder::findCornerNeighbor #25991
### Pull Request Readiness Checklist
Idea was mentioned in `Section III-B. New Heuristic for Quadrangle Linking` of `Rufli, Martin & Scaramuzza, Davide & Siegwart, Roland. (2008). Automatic Detection of Checkerboards on Blurred and Distorted Images. 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS. 3121-3126. 10.1109/IROS.2008.4650703` (https://rpg.ifi.uzh.ch/docs/IROS08_scaramuzza_b.pdf):
```
* For each candidate pair, focus on the quadrangles they belong to and draw two straight lines passing through the midsections of the respective quadrangle edges (see Fig. 6).
* If the candidate corner and the source corner are on the same side of every of the four straight lines drawn this way (this corresponds to the yellow shaded area in Fig. 6), then the corners are successfully matched.
```
By improving corners matching, we can increase the search radius (`thresh_scale`).
I tested this PR with benchmark
```
python3 objdetect_benchmark.py --configuration=generate_run --board_x=7 --path=res_chessboard --synthetic_object=chessboard
```
PR increases detected chessboards number by `3/7%`:
```
cell_img_size = 100 (default)
before
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.910417 13110 14400 0.599746
Total detected time: 147.50906700000002 sec
after
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.941667 13560 14400 0.596726
Total detected time: 136.68963200000007 sec
----------------------------------------------------------------------------------------------------------------------------------------------
cell_img_size = 10
before
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.539792 7773 14400 4.208237
Total detected time: 2.668964 sec
after
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.579167 8340 14400 4.198448
Total detected time: 2.535998999999999 sec
```
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Patch to opencv_extra has the same branch name.
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Search in two directions when try to add new quad in addOuterQuad #25807
In ChessBoardDetector::addOuterQuad, previous code try to connect new quad with inner quad, if possible, but only search for one direction. I have made three test images, one is normal(a.jpg), one lossed an outer quad(b.jpg), and then i flipped it vertically(c.jpg). Only last one fails. I fixed it by check two directions and row/col.
Here is the test code and images:
```
Mat img;
vector<Point2f> corners;
auto size = cv::Size(6, 6);
img = imread("D:/tmp/a.jpg", 0);
std::cout<<cv::findChessboardCorners(img, size, corners)<<"\n";
std::cout << corners.size() << "\n";
img = imread("D:/tmp/b.jpg", 0);
std::cout<<cv::findChessboardCorners(img, size, corners)<<"\n";
std::cout << corners.size() << "\n";
img = imread("D:/tmp/c.jpg", 0);
std::cout<<cv::findChessboardCorners(img, size, corners)<<"\n";
std::cout << corners.size() << "\n";
```
a
b
c
