Fix distransform to work with large images #22798
This attempts to fix the following bug which was caused by storing squares of large integers into 32-bit floating point variables:
https://github.com/opencv/opencv/issues/22732
### 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.
- [ ] The feature is well documented and sample code can be built with the project CMake
This makes LineSegmentDetector deterministic by using stable_sort for ordering points by norm. Without this change the region growing in LSD is non-determinstic and thus the returned lines are changing between invocations.
This is a replacement for https://github.com/opencv/opencv/pull/23370
In case of huge (and probably invalid) input, make sure we do not
rely only on the while loops for truncation.
### 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
Fix rect_nfa (lsd)
* Fix missing log_gamma in nfa()
Comparing the nfa function with the function in the binomial_nfa repository (https://github.com/rafael-grompone-von-gioi/binomial_nfa/blob/main/C99/log_binomial_nfa.c#L152), the first log_gamma call is missing.
* Fix rect_nfa pixel index
* Replace std::rotate
* Rename tmp to v_tmp
* Replace auto and std::min_element
* Change slope equality check to int
* Fix left limit check
In some situations the last value was missing from the discrete theta
values. Now, the last value is chosen such that it is close to the
user-provided maximum theta, while the distance to pi remains always
at least theta_step/2. This should avoid duplicate detections.
A better way would probably be to use max_theta as is and adjust the
resolution (theta_step) instead, such that the discretization would
always be uniform (in a circular sense) when full angle range is used.
Fixed out-of-bounds read in parallel version of ippGaussianBlur()
* Fixed out-of-memory read in parallel version of ippGaussianBlur()
* Fixed check
* Revert changes in CMakeLists.txt
Fixed threshold(THRESH_TOZERO) at imgproc(IPP)
* Fixed#16085: imgproc(IPP): wrong result from threshold(THRESH_TOZERO)
* 1. Added test cases with float where all bits of mantissa equal 1, min and max float as inputs
2. Used nextafterf instead of cast to hex
* Used float value in test instead of hex and casts
* Changed input value in test
When computing:
t1 = (bayer[1] + bayer[bayer_step] + bayer[bayer_step+2] + bayer[bayer_step*2+1])*G2Y;
there is a T (unsigned short or char) multiplied by an int which can overflow.
Then again, it is stored to t1 which is unsigned so the overflow disappears.
Keeping all unsigned is safer.
* Fix integer overflow in cv::Luv2RGBinteger::process.
For LL=49, uu=205, vv=23, we end up with x=7373056 and y=458
which overflows y*x.
* imgproc(test): adjust test parameters to cover SIMD code
In case of very small negative h (e.g. -1e-40), with the current implementation,
you will go through the first condition and end up with h = 6.f, and will miss
the second condition.
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