libtiff upgrade to version 4.7.1 #27806
close https://github.com/opencv/opencv/issues/27784
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Skip ARM assembly file on QNX #27774
This fixes build failures on QNX caused by unsupported ARM NEON assembly in arm/filter_neon.S. The QNX environment does not handle this assembly source correctly, resulting in compilation errors.
Build and test instruction for QNX:
https://github.com/qnx-ports/build-files/blob/main/ports/opencv/README.md
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libpng upgrade to 1.6.45 and cICP metadata support for PNG imwrite #27741
### Pull Request Readiness Checklist
resolves#24185
libpng docs: https://www.w3.org/TR/png-3/#cICP-chunk
similar code from ffmpeg: a700f0f72d/libavcodec/pngenc.c (L452-L456)
So issue #24185 can be solved by replacing `cv.imwrite` in user's code to `cv.imwriteWithMetadata`:
```python
cv.imwriteWithMetadata("frame.png", frame, [cv.IMAGE_METADATA_CICP], np.array([[9, 18, 0, 1]], np.uint8))
```
```
$ exiftool /home/d.kurtaev/opencv_build/frames_pr/image_38.png
ExifTool Version Number : 12.76
File Name : image_38.png
Directory : /home/d.kurtaev/opencv_build/frames_pr
File Size : 3.8 MB
File Modification Date/Time : 2025:09:02 20:48:22+03:00
File Access Date/Time : 2025:09:02 20:48:22+03:00
File Inode Change Date/Time : 2025:09:02 20:48:22+03:00
File Permissions : -rw-r--r--
File Type : PNG
File Type Extension : png
MIME Type : image/png
Image Width : 1080
Image Height : 1920
Bit Depth : 8
Color Type : RGB
Compression : Deflate/Inflate
Filter : Adaptive
Interlace : Noninterlaced
Color Primaries : BT.2020, BT.2100
Transfer Characteristics : BT.2100 HLG, ARIB STD-B67
Matrix Coefficients : Identity matrix
Video Full Range Flag : 1
Image Size : 1080x1920
Megapixels : 2.1
```
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### Pull Request Readiness Checklist
resolves#16295
```
docker run --gpus 0 -v ~/opencv:/opencv -v ~/opencv_contrib:/opencv_contrib -it nvidia/cuda:12.8.1-cudnn-devel-ubuntu22.04
apt-get update && apt-get install -y cmake python3-dev python3-pip python3-venv &&
python3 -m venv .venv &&
source .venv/bin/activate &&
pip install -U pip &&
pip install -U numpy &&
pip install torch --index-url https://download.pytorch.org/whl/cu128 &&
cmake \
-DWITH_OPENCL=OFF \
-DCMAKE_BUILD_TYPE=Release \
-DBUILD_DOCS=OFF \
-DWITH_CUDA=ON \
-DOPENCV_DNN_CUDA=ON \
-DOPENCV_EXTRA_MODULES_PATH=/opencv_contrib/modules \
-DBUILD_LIST=ts,cudev,python3 \
-S /opencv -B /opencv_build &&
cmake --build /opencv_build -j16
export PYTHONPATH=/opencv_build/lib/python3/:$PYTHONPATH
```
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.
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libtiff upgrade to version 4.7.0 #27679
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Update FastCV lib hash for Linux and Android
Updated libs PR [opencv/opencv_3rdparty#101](https://github.com/opencv/opencv_3rdparty/pull/101)
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FastCV latest libs hash update #27403
Update hash for the fastcv libs for Linux
Updated libs PR: https://github.com/opencv/opencv_3rdparty/pull/97
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build: fix more warnings from recent gcc versions after #27337#27343
More fixings after https://github.com/opencv/opencv/pull/27337
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Update hash for the fastcv libs for both Linux and Android #27340
Replaces https://github.com/opencv/opencv/pull/27290
Updated libs PR: https://github.com/opencv/opencv_3rdparty/pull/95
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Extract all HALs from 3rdparty to dedicated folder. #27252
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FastCV gemm hal #27184
FastCV hal for gemm 32f
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HAL: implemented cv_hal_transpose in hal_rvv #27229
Checklists:
- [x] transpose2d_8u
- [x] transpose2d_16u
- [ ] ~transpose2d_8uC3~
- [x] transpose2d_32s
- [ ] ~transpose2d_16uC3~
- [x] transpose2d_32sC2
- [ ] ~transpose_32sC3~
- [ ] ~transpose_32sC4~
- [ ] ~transpose_32sC6~
- [ ] ~transpose_32sC8~
- [ ] ~inplace transpose~
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HAL: implemented cv_hal_dotProduct in hal_rvv #27201
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Optimize gaussian blur performance in FastCV HAL #27217
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Parallel_for in box Filter and support for 32f box filter in Fastcv hal #27182
Added parallel_for in box filter hal and support for 32f box filter
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Adding latest FastCV static libs
updated libs PR: [opencv/opencv_3rdparty/pull/94](https://github.com/opencv/opencv_3rdparty/pull/94)
### Pull Request Readiness Checklist
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HAL: implemented cv_hal_div* and cv_hal_recip* in hal_rvv #27175
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HAL: added copyToMask and implemented in hal_rvv #27162
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Add RISC-V HAL implementation for cv::resize #27160
This patch implements `cv_hal_resize` using native intrinsics, optimizing the performance of `cv::resize` for `CV_INTER_NEAREST/CV_INTER_NEAREST_EXACT/CV_INTER_LINEAR/CV_INTER_LINEAR_EXACT/CV_INTER_AREA` modes.
Tested on MUSE-PI (Spacemit X60) for both gcc 14.2 and clang 20.1.
```
$ ./opencv_test_imgproc --gtest_filter="*Resize*:*resize*"
$ ./opencv_perf_imgproc --gtest_filter="*Resize*:*resize*" --perf_min_samples=300 --perf_force_samples=300
```
View the full perf table here: [hal_rvv_resize.pdf](https://github.com/user-attachments/files/19480756/hal_rvv_resize.pdf)
### Pull Request Readiness Checklist
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Add RISC-V HAL implementation for cv::warp series #27119
This patch implements `cv_hal_remap`, `cv_hal_warpAffine` and `cv_hal_warpPerspective` using native intrinsics, optimizing the performance of `cv::remap/cv::warpAffine/cv::warpPerspective` for `CV_HAL_INTER_NEAREST/CV_HAL_INTER_LINEAR/CV_HAL_INTER_CUBIC/CV_HAL_INTER_LANCZOS4` modes.
Tested on MUSE-PI (Spacemit X60) for both gcc 14.2 and clang 20.0.
```
$ ./opencv_test_imgproc --gtest_filter="*Remap*:*Warp*"
$ ./opencv_perf_imgproc --gtest_filter="*Remap*:*remap*:*Warp*" --perf_min_samples=200 --perf_force_samples=200
```
View the full perf table here: [hal_rvv_warp.pdf](https://github.com/user-attachments/files/19403718/hal_rvv_warp.pdf)
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core: refactored normDiff in hal_rvv and extended with support of more data types #27115
Merge wtih https://github.com/opencv/opencv_extra/pull/1246.
### Pull Request Readiness Checklist
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Move IPP norm and normDiff to HAL #27128
Continues https://github.com/opencv/opencv/pull/26880
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Initial version of IPP-based HAL for x86 and x86_64 platforms #26880
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Fix RISC-V HAL solve/SVD and BGRtoLab #27046Closes#27044.
Also suppressed some warnings in other HAL.
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Add RISC-V HAL implementation for cv::blur series #27097
This patch implements `cv_hal_gaussianBlurBinomial`, `cv_hal_medianBlur`, `cv_hal_boxFilter` and `cv_hal_bilateralFilter` using native intrinsics, optimizing the performance of `cv::GaussianBlur/cv::medianBlur/cv::boxFilter/cv::bilateralFilter` for `3x3/5x5` kernels.
Tested on MUSE-PI (Spacemit X60) for both gcc 14.2 and clang 20.0.
```
$ ./opencv_test_imgproc --gtest_filter="*Filter*:*Blur*"
$ ./opencv_perf_imgproc --gtest_filter="*gauss*:*box*:*Bilateral*:*median*" --perf_min_samples=2000 --perf_force_samples=2000
```
View the full perf table here: [hal_rvv_blur.pdf](https://github.com/user-attachments/files/19335582/hal_rvv_blur.pdf)
### Pull Request Readiness Checklist
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Add RISC-V HAL implementation for cv::moments #27096
This patch implements `cv_hal_imageMoments` using native intrinsics, optimizing the performance of `cv::moments` for data types `CV_16U/CV_16S/CV_32F/CV_64F`.
Tested on MUSE-PI (Spacemit X60) for both gcc 14.2 and clang 20.0.
```
$ ./opencv_test_imgproc --gtest_filter="*Moments*"
$ ./opencv_perf_imgproc --gtest_filter="*Moments*" --perf_min_samples=1000 --perf_force_samples=1000
```
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core: improve norm of hal rvv #26991
Merge with https://github.com/opencv/opencv_extra/pull/1241
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Add RISC-V HAL implementation for cv::threshold and cv::adaptiveThreshold #27072
This patch implements `cv_hal_threshold_otsu` and `cv_hal_adaptiveThreshold` using native intrinsics, optimizing the performance of `cv::threshold(THRESH_OTSU)` and `cv::adaptiveThreshold`.
Since UI is as fast as HAL `cv_hal_rvv::threshold::threshold` so `cv_hal_threshold` is not redirected, but this part of HAL is keeped because `cv_hal_threshold_otsu` depends on it.
Tested on MUSE-PI (Spacemit X60) for both gcc 14.2 and clang 20.0.
```
$ ./opencv_test_imgproc --gtest_filter="*thresh*:*Thresh*"
$ ./opencv_perf_imgproc --gtest_filter="*otsu*:*adaptiveThreshold*" --perf_min_samples=1000 --perf_force_samples=1000
```
### Pull Request Readiness Checklist
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[HAL RVV] unify and impl polar_to_cart | add perf test #26999
### Summary
1. Implement through the existing `cv_hal_polarToCart32f` and `cv_hal_polarToCart64f` interfaces.
2. Add `polarToCart` performance tests
3. Make `cv::polarToCart` use CALL_HAL in the same way as `cv::cartToPolar`
4. To achieve the 3rd point, the original implementation was moved, and some modifications were made.
Tested through:
```sh
opencv_test_core --gtest_filter="*PolarToCart*:*Core_CartPolar_reverse*"
opencv_perf_core --gtest_filter="*PolarToCart*" --perf_min_samples=300 --perf_force_samples=300
```
### HAL performance test
***UPDATE***: Current implementation is no more depending on vlen.
**NOTE**: Due to the 4th point in the summary above, the `scalar` and `ui` test is based on the modified code of this PR. The impact of this patch on `scalar` and `ui` is evaluated in the next section, `Effect of Point 4`.
Vlen 256 (Muse Pi):
```
Name of Test scalar ui rvv ui rvv
vs vs
scalar scalar
(x-factor) (x-factor)
PolarToCart::PolarToCartFixture::(127x61, 32FC1) 0.315 0.110 0.034 2.85 9.34
PolarToCart::PolarToCartFixture::(127x61, 64FC1) 0.423 0.163 0.045 2.59 9.34
PolarToCart::PolarToCartFixture::(640x480, 32FC1) 13.695 4.325 1.278 3.17 10.71
PolarToCart::PolarToCartFixture::(640x480, 64FC1) 17.719 7.118 2.105 2.49 8.42
PolarToCart::PolarToCartFixture::(1280x720, 32FC1) 40.678 13.114 3.977 3.10 10.23
PolarToCart::PolarToCartFixture::(1280x720, 64FC1) 53.124 21.298 6.519 2.49 8.15
PolarToCart::PolarToCartFixture::(1920x1080, 32FC1) 95.158 29.465 8.894 3.23 10.70
PolarToCart::PolarToCartFixture::(1920x1080, 64FC1) 119.262 47.743 14.129 2.50 8.44
```
### Effect of Point 4
To make `cv::polarToCart` behave the same as `cv::cartToPolar`, the implementation detail of the former has been moved to the latter's location (from `mathfuncs.cpp` to `mathfuncs_core.simd.hpp`).
#### Reason for Changes:
This function works as follows:
$y = \text{mag} \times \sin(\text{angle})$ and $x = \text{mag} \times \cos(\text{angle})$. The original implementation first calculates the values of $\sin$ and $\cos$, storing the results in the output buffers $x$ and $y$, and then multiplies the result by $\text{mag}$.
However, when the function is used as an in-place operation (one of the output buffers is also an input buffer), the original implementation allocates an extra buffer to store the $\sin$ and $\cos$ values in case the $\text{mag}$ value gets overwritten. This extra buffer allocation prevents `cv::polarToCart` from functioning in the same way as `cv::cartToPolar`.
Therefore, the multiplication is now performed immediately without storing intermediate values. Since the original implementation also had AVX2 optimizations, I have applied the same optimizations to the AVX2 version of this implementation.
***UPDATE***: UI use v_sincos from #25892 now. The original implementation has AVX2 optimizations but is slower much than current UI so it's removed, and AVX2 perf test is below. Scalar implementation isn't changed because it's faster than using UI's method.
#### Test Result
`scalar` and `ui` test is done on Muse PI, and AVX2 test is done on Intel(R) Xeon(R) Gold 6140 CPU @ 2.30GHz.
`scalar` test:
```
Name of Test orig pr pr
vs
orig
(x-factor)
PolarToCart::PolarToCartFixture::(127x61, 32FC1) 0.333 0.294 1.13
PolarToCart::PolarToCartFixture::(127x61, 64FC1) 0.385 0.403 0.96
PolarToCart::PolarToCartFixture::(640x480, 32FC1) 14.749 12.343 1.19
PolarToCart::PolarToCartFixture::(640x480, 64FC1) 19.419 16.743 1.16
PolarToCart::PolarToCartFixture::(1280x720, 32FC1) 44.155 37.822 1.17
PolarToCart::PolarToCartFixture::(1280x720, 64FC1) 62.108 50.358 1.23
PolarToCart::PolarToCartFixture::(1920x1080, 32FC1) 99.011 85.769 1.15
PolarToCart::PolarToCartFixture::(1920x1080, 64FC1) 127.740 112.874 1.13
```
`ui` test:
```
Name of Test orig pr pr
vs
orig
(x-factor)
PolarToCart::PolarToCartFixture::(127x61, 32FC1) 0.306 0.110 2.77
PolarToCart::PolarToCartFixture::(127x61, 64FC1) 0.455 0.163 2.79
PolarToCart::PolarToCartFixture::(640x480, 32FC1) 13.381 4.325 3.09
PolarToCart::PolarToCartFixture::(640x480, 64FC1) 21.851 7.118 3.07
PolarToCart::PolarToCartFixture::(1280x720, 32FC1) 39.975 13.114 3.05
PolarToCart::PolarToCartFixture::(1280x720, 64FC1) 67.006 21.298 3.15
PolarToCart::PolarToCartFixture::(1920x1080, 32FC1) 90.362 29.465 3.07
PolarToCart::PolarToCartFixture::(1920x1080, 64FC1) 129.637 47.743 2.72
```
AVX2 test:
```
Name of Test orig pr pr
vs
orig
(x-factor)
PolarToCart::PolarToCartFixture::(127x61, 32FC1) 0.019 0.009 2.11
PolarToCart::PolarToCartFixture::(127x61, 64FC1) 0.022 0.013 1.74
PolarToCart::PolarToCartFixture::(640x480, 32FC1) 0.788 0.355 2.22
PolarToCart::PolarToCartFixture::(640x480, 64FC1) 1.102 0.618 1.78
PolarToCart::PolarToCartFixture::(1280x720, 32FC1) 2.383 1.042 2.29
PolarToCart::PolarToCartFixture::(1280x720, 64FC1) 3.758 2.316 1.62
PolarToCart::PolarToCartFixture::(1920x1080, 32FC1) 5.577 2.559 2.18
PolarToCart::PolarToCartFixture::(1920x1080, 64FC1) 9.710 6.424 1.51
```
A slight performance loss occurs because the check for whether $mag$ is nullptr is performed with every calculation, instead of being done once per batch. This is to reuse current `SinCos_32f` function.
### 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
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
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