Add additional image processing operators for Ascend NPU by utilizing DVPP #3608
The user base for [Ascend NPU](https://www.hiascend.com/en/) and programming with CANN is increasing rapidly, with a growing number of users joining each day. To facilitate the use of these users, this PR provides more support for Ascend backend operators. All operators this PR offers are using use DVPP as the computational unit. Digital Vision Pre-Processing (DVPP) is an image processing unit built into the Ascend AI processor. Its main functions include image and video encoding/decoding, as well as image cropping and scaling.
The high-frequency operators with NPU as the backend and basic data structure AscendMat has been provided in #3552, while it still lacks many image processing operators. Moreover, only two interpolation algorithms for the resize operator are supported in #3552. In this PR, the bilinear interpolation algorithm and nearest neighbour interpolation algorithm are implemented for the resize operator, as well as the Ascend implementation of the copyMakeBorder operator.
In addition, the serialization of image processing operations is widely used in the preprocessing and post-processing stages of computer vision deep learning methods. Therefore, providing integrated operators is very meaningful for improving the convenience of use for OpenCV and deep learning crossover users. For example, torchvision also provides similar operators: [RESIZED_CROP](https://pytorch.org/vision/stable/generated/torchvision.transforms.functional.resized_crop.html?highlight=resizedcrop).
Thus, this PR also provides two serialization processing operators: cropResize and cropResizeMakeBorder.
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Mcc add perf tests improve performance #3699
Added perf tests to mcc module.
Also these optimizations have been added:
- added `parallel_for_` to `performThreshold()`
- removed `toL`/`fromL` and added `dst` to avoid copy data
- added `parallel_for_` to `elementWise()` ("batch" optimization improves performance of Windows version, Linux without changes).
Configuration:
Ryzen 5950X, 2x16 GB 3000 MHz DDR4
OS: Windows 10, Ubuntu 20.04.5 LTS
Performance results in milliseconds:
| OS and alg version | process, ms | infer, ms |
| -------------------- | ----- | ------ |
| win_default | 63.09 | 457.57 |
| win_optimized_without_batch | 48.69 | 111.78 |
| win_optimized_batch | 48.42 | 47.28 |
| linux_default | 50.88 | 300.7 |
| linux_optimized_batch| 36.06 | 41.62 |
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Use proper C++ types. #3703
This is necessary to get https://github.com/opencv/opencv/pull/25248 working.
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Add to mcc detect and infer test #3647
merge with https://github.com/opencv/opencv_extra/pull/1153
Added a full pipeline tests:
1. detector->process(img, (TYPECHART)0, 1, true);
2. ColorCorrectionModel model(src, COLORCHECKER_Macbeth); model.run();
3. calibratedImage = model.infer(calibratedImage)*255.;
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Fixes #25081:Latch input corruption fix#3646
Fixes https://github.com/opencv/opencv/issues/25081
As mentioned by WennPaper making it `grayImage = image.clone();` will make a deep copy. It prevents the `InputArray` from being modified.
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---
Co-authored-by: Dhanwanth1803 <dhanwanthvarala@gmail,com>
Co-authored-by: Dmitry Kurtaev <dmitry.kurtaev@gmail.com>
Move Aruco tutorials and samples to main repo #3401
merge with https://github.com/opencv/opencv/pull/23018
merge with merge with https://github.com/opencv/opencv_extra/pull/1143
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updated readme for several opencv_contrib modules (done by Gary)
Sorry for not putting it through pull requests, but I was citing it in GSoC idea projects and wanted the Google Reviewers to see something visually more compelling.
Fix a bug in knnMatchConvert when a feature couldn't be matched #3632
After I started using a mask with `knnMatchAsync`, I found that the result from `knnMatchConvert` would be clipped at random.
Investigating the issue, I found that `knnMatchAsync` will initialize all `trainIdx` to `-1`, which will be overwritten by the CUDA kernel. A mask can be used to prevent certain features from being matched and this will prevent the CUDA kernel from setting the match distance. `knnMatchConvert` is not properly incrementing the pointers when `trainIdx == -1`, so an unmatched feature will get it stuck at `if (trainIdx == -1)`. Eventually the outer for-loop finishes and returns a vector with the matches up until the first missing match distance.
My solution is to increment the counters the same way as a successful iteration would.
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cudaimgproc: update for CUDA 8.0 and fix out of bounds memory error in cuda::moments #3618
Fix https://github.com/opencv/opencv_contrib/issues/3612 and address out of bounds memory error when not calculating all image moments.
### Pull Request Readiness Checklist
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Alexander Smorkalov
Suleyman TURKMEN
Alexander Alekhin
Kumataro
Alex
Mengqing Cao
Alexander Smorkalov
Vincent Rabaud
Alexander Panov
Yuantao Feng
Evgeny Latkin
Maksim Shabunin
刘佩其
Dhanwanth1803
Sergei Shutov
MengqingCao
harshitnagar22
Gary Bradski
Vadim Pisarevsky
Troels Ynddal
zanaviska
cudawarped