Implemented fast median filter for CUDA using Wavelet Matrix, a constant-time, HDR-compatible method #3627
I replaced the existing CUDA implementation of the histogram-based median filter with an implementation of a new wavelet matrix-based median filter algorithm, which I presented at SIGGRAPH Asia 2022.
This paper won the Best Paper Award in the journal track of technical papers (ACM Transactions on Graphics).
This new algorithm, like the histogram method, has the property that the window radius does not affect the computation time, and is several times faster than the histogram method. Furthermore, while the histogram method does not support HDR and only supports 8U images, this new algorithm supports HDR and also supports 16U and 32F images.
I (the author) have published the implementation on my personal GitHub and made some modifications for OpenCV to make it accessible from OpenCV. I used the CUB library, which is part of the standard toolkit since CUDA 11.0. Therefore, depending on the CUDA_VERSION, the code is written to use the new algorithm for versions 11.0 and above, and the existing histogram method for versions 10 and below.
Regarding the old histogram-based code, the CPU version of the median filter supports 16U and 32F for window sizes up to 5, but it seems that the histogram CUDA version of the median filter does not. Also, the number of channels supported is different: the CPU version supports 1, 3, and 4 channels, while the CUDA version supports only 1 channel. In addition, for the CUDA version of the histogram method, pixels at the edges of the image, i.e. where the window is insufficient, were set to zero. For example, if the window size is 7, the width of the 3 pixels at the top, bottom, left, and right were not calculated correctly. When checking the tests, it was found that they compared with the CPU version by cropping the edges with rect, and also the cropping area was too wide, with 8 pixels cropped from the top, bottom, left, and right when the window size was 7.
In this PR, I first corrected the rect range for the tests so that both the old histogram method and the new wavelet matrix method can pass. Also, the CUDA version now supports 16U, 32F, and multi-channel formats such as 3 and 4 channels. In addition, while the CPU version only supports window sizes up to 5 for HDR, the new CUDA Wavelet Matrix method supports sizes of 7 and above. Additionally, I have added new tests for 16U, 32F, and multi-channel formats, specifically 3 and 4 channels.
Paper’s project page: [Constant Time Median Filter using 2D Wavelet Matrix | Interactive Graphics & Engineering Lab](https://cgenglab.github.io/en/publication/sigga22_wmatrix_median/)
My implementation (as author): [GitHub - TumoiYorozu/WMatrixMedian](https://github.com/TumoiYorozu/WMatrixMedian)
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Fixed Wredundant-move produced by GCC 13.2 (Ubuntu 24.04). #3734
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ovis: force camera extent update to get correct bbox #3735
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---
fixes#3732
Camera spatial velocity computation through interaction matrix #3641
### Feature
The code computed camera spatial velocity given two images, pixels depth, camera matrix and the timestamp between the images. This is enabled by, so called, interaction matrix (usually utilized in visual servoing applications) relating pixel plane velocities to the camera spatial velocity in 3D i.e., twist - velocity and angular rate of the camera. The inverse problem can be solved by sampling pixel & their velocities to solve least-squares for twist. The relationship can be seen below in the picture.
The code does not include a proper example and is not tested but if there is interest I could contribute more appealing example and use case for camera velocity computation. However, I attach below a dummy example with random data simply to make sure that it's running as is. I have used this before in aiding UAV localization and thought someone else might benefit from it being integrated into `opencv`.
```c++
#include "opencv2/rgbd/twist.hpp"
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
int main()
{
using namespace cv::rgbd;
Twist t;
// create two random image
cv::Mat im0(480, 640, CV_8UC1);
cv::Mat im1(480, 640, CV_8UC1);
cv::Mat depths0(480, 640, CV_32F);
cv::Mat K = cv::Mat::eye(3, 3, CV_32F);
cv::theRNG().state = time(0);
cv::randn(im0, 0, 255);
cv::randn(im1, 0, 255);
cv::randn(depths0, 0, 100);
cv::Vec6d twist = t.compute(im0, im1, depths0, K, 0.1);
return 0;
}
```
References
1. Chaumette, François, and Seth Hutchinson. "Visual servo control. I. Basic approaches." IEEE Robotics & Automation Magazine 13.4 (2006): 82-90.
2. https://robotacademy.net.au/lesson/image-based-visual-servoing/
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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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Wang Qiang
Alexander Smorkalov
Vincent Rabaud
TumoiYorozu
Alexander Smorkalov
Pavel Rojtberg
sdy623
catree
ernie
Maksim Shabunin
AleksandrPanov
roger8
Suleyman TURKMEN
cudawarped
Alexander Alekhin
Kumataro
Alex
Mengqing Cao
Yuantao Feng
Evgeny Latkin