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
Properly check markers when none are provided. #25938
CharucoDetectorImpl::detectBoard finds temporary markers when none are provided but those are discarded when
charucoDetectorImpl::checkBoard is called.
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HAL for dot product added #25936
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videoio: fix cv::VideoWriter with FFmpeg encapsulation timestamps #25874
Fix https://github.com/opencv/opencv/issues/25873 by modifying `cv::VideoWriter` to use provided presentation indices (pts).
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dnn: optimize activations with v_exp #25881
Merge with https://github.com/opencv/opencv_extra/pull/1191.
This PR optimizes the following activations:
- [x] Swish
- [x] Mish
- [x] Elu
- [x] Celu
- [x] Selu
- [x] HardSwish
### Performance (Updated on 2024-07-18)
#### AmLogic A311D2 (ARM Cortex A73 + A53)
```
Geometric mean (ms)
Name of Test activations activations.patch activations.patch
vs
activations
(x-factor)
Celu::Layer_Elementwise::OCV/CPU 115.859 27.930 4.15
Elu::Layer_Elementwise::OCV/CPU 27.846 27.003 1.03
Gelu::Layer_Elementwise::OCV/CPU 0.657 0.602 1.09
HardSwish::Layer_Elementwise::OCV/CPU 31.885 6.781 4.70
Mish::Layer_Elementwise::OCV/CPU 35.729 32.089 1.11
Selu::Layer_Elementwise::OCV/CPU 61.955 27.850 2.22
Swish::Layer_Elementwise::OCV/CPU 30.819 26.688 1.15
```
#### Apple M1
```
Geometric mean (ms)
Name of Test activations activations.patch activations.patch
vs
activations
(x-factor)
Celu::Layer_Elementwise::OCV/CPU 16.184 2.118 7.64
Celu::Layer_Elementwise::OCV/CPU_FP16 16.280 2.123 7.67
Elu::Layer_Elementwise::OCV/CPU 9.123 1.878 4.86
Elu::Layer_Elementwise::OCV/CPU_FP16 9.085 1.897 4.79
Gelu::Layer_Elementwise::OCV/CPU 0.089 0.081 1.11
Gelu::Layer_Elementwise::OCV/CPU_FP16 0.086 0.074 1.17
HardSwish::Layer_Elementwise::OCV/CPU 1.560 1.555 1.00
HardSwish::Layer_Elementwise::OCV/CPU_FP16 1.536 1.523 1.01
Mish::Layer_Elementwise::OCV/CPU 6.077 2.476 2.45
Mish::Layer_Elementwise::OCV/CPU_FP16 5.990 2.496 2.40
Selu::Layer_Elementwise::OCV/CPU 11.351 1.976 5.74
Selu::Layer_Elementwise::OCV/CPU_FP16 11.533 1.985 5.81
Swish::Layer_Elementwise::OCV/CPU 4.687 1.890 2.48
Swish::Layer_Elementwise::OCV/CPU_FP16 4.715 1.873 2.52
```
#### Intel i7-12700K
```
Geometric mean (ms)
Name of Test activations activations.patch activations.patch
vs
activations
(x-factor)
Celu::Layer_Elementwise::OCV/CPU 17.106 3.560 4.81
Elu::Layer_Elementwise::OCV/CPU 5.064 3.478 1.46
Gelu::Layer_Elementwise::OCV/CPU 0.036 0.035 1.04
HardSwish::Layer_Elementwise::OCV/CPU 2.914 2.893 1.01
Mish::Layer_Elementwise::OCV/CPU 3.820 3.529 1.08
Selu::Layer_Elementwise::OCV/CPU 10.799 3.593 3.01
Swish::Layer_Elementwise::OCV/CPU 3.651 3.473 1.05
```
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Upgrade RISC-V Vector intrinsic and cleanup the obsolete RVV backend. #25883
This patch upgrade RISC-V Vector intrinsic from `v0.10` to `v0.12`/`v1.0`:
- Update cmake check and options;
- Upgrade RVV implement for Universal Intrinsic;
- Upgrade RVV optimized DNN kernel.
- Cleanup the obsolete RVV backend (`intrin_rvv.hpp`) and compatable header file.
With this patch, RVV backend require Clang 17+ or GCC 14+ (which means `__riscv_v_intrinsic >= 12000`, see https://godbolt.org/z/es7ncETE3)
This patch is test with Clang 17.0.6 (require extra `-DWITH_PNG=OFF` due to ICE), Clang 18.1.8 and GCC 14.1.0 on QEMU and k230 (with `--gtest_filter="*hal_*"`).
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Add a check for src == dst in ocl warpTransform #25898
As mentioned in #25853, when doing WarpAffine with Mat and UMat respectively, if you force the use of the in-place operation (so that src and dst are passed the same variables), Mat produces the correct results, but UMat produces unexpected results.
Obviously in-place operations are not possible with this transformation. When Mat performs the operation, if dst and src are the same variable, the function inherently makes a copy of src without telling the user.
74b50c7af0/modules/imgproc/src/imgwarp.cpp (L2831-L2834)
So I did the same check in UMat, but I'm not sure if it's appropriate, should we just do a copy operation without telling the user (even if the user thinks he's doing an in-place operation), or should we throw an exception to indicate that we shouldn't pass in two same variables here?
The possible reason for this problem is that there is a create function here, so it gives the developer the false impression that this create function has allocated new memory for dst, however it does not.
74b50c7af0/modules/imgproc/src/imgwarp.cpp (L2607-L2609)
Because by the time the check is done here, the function has returned back.
74b50c7af0/modules/core/src/umatrix.cpp (L668-L675)
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code clean #25931
Align code and remove redundant CMake code
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Support OpenGL GTK3 New API #25822Fixes#20001
GSoC2024 Project
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calib3d: fix Rodrigues CV_32F and CV_64F type mismatch in projectPoints #25824Fixes#25318
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Added flag to GaussianBlur for faster but not bit-exact implementation #25792
Rationale:
Current implementation of GaussianBlur is almost always bit-exact. It helps to get predictable results according platforms, but prohibits most of approximations and optimization tricks.
The patch converts `borderType` parameter to more generic `flags` and introduces `GAUSS_ALLOW_APPROXIMATIONS` flag to allow not bit-exact implementation. With the flag IPP and generic HAL implementation are called first. The flag naming and location is a subject for discussion.
Replaces https://github.com/opencv/opencv/pull/22073
Possibly related issue: https://github.com/opencv/opencv/issues/24135
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Mark cv::Mat(Mat&&) as noexcept #25899
This fixes https://github.com/opencv/opencv/issues/25065
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Handling I32/I64 data types in G-API ONNX back-end #25817
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Add a new function that approximates the polygon bounding a convex hull with a certain number of sides #25607
merge PR with <https://github.com/opencv/opencv_extra/pull/1179>
This PR is based on the paper [View Frustum Optimization To Maximize Object’s Image Area](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=1fbd43f3827fffeb76641a9c5ab5b625eb5a75ba).
# Problem
I needed to reduce the number of vertices of the convex hull so that the additional area was minimal, andall vertices of the original contour enter the new contour.
# Description
Initially in the contour of n vertices, at each stage we consider the intersection points of the lines formed by each adjacent edges. Each of these intersection points will form a triangle with vertices through which lines pass. Let's choose a triangle with the minimum area and merge the two vertices at the intersection point. We continue until there are more vertices than the specified number of sides of the approximated polygon.
# Complexity:
Using a std::priority_queue or std::set time complexity is **(O(n\*ln(n))**, memory **O(n)**,
n - number of vertices in convex hull.
count of sides - the number of points by which we must reduce.
## Comment
If epsilon_percentage more 0, algorithm can return more values than _side_.
Algorithm returns OutputArray. If OutputArray.type() equals 0, algorithm returns values with InputArray.type().
New test uses image which are not in opencv_extra, needs to be added.
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* added v_erf and implemented gelu acceleration via vectorization
* remove anonymous v_erf and use v_erf from intrin_math
* enable perf for ov and cuda backend
Enable checkerboard detection with a central / corner marker on a black tile #25808
This pull request closes the issue #25806.
The issue doesn't require any documentation - it's quite intuitive that the detection result shouldn't depend on the color of the marker's tile.
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core: add v_erf #25872
This patch adds v_erf, which is needed by https://github.com/opencv/opencv/pull/25147.
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Make sure all the lines of a JPEG are read #25864
In case of corrupted JPEG, imread would still return a JPEG of the proper size (as indicated by the header) but with some uninitialized values. I do not have a short reproducer I can add as a test as this was found by our fuzzers.
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imgproc: remove C-API usage from tests #25842
Final cleanup will be done in 5.x after regular merge.
Some tests have been reworked, some required only slight modifications.
Merge pull request #25861 from Abdurrahheem:ash/torch-attention-export-fix-4x
Support for Unflatten operation requred by Attention layer - 4.x #25861
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All test data and models for PR are located [#1190](https://github.com/opencv/opencv_extra/pull/1190)
This PR fixes issue reised when importing batched vanilla `Attention` layer from `PyTorch` via ONNX. Currently batched version of `Attention` layer in PyTorch [has unflatten operation inside](e3b3431c42/torch/nn/functional.py (L5500C17-L5500C31)). `unflatten` operation causes issue in `reshape` layer (see the Reshape_2 in the graph below) due to incorrect output of `slice` layer. This PR particularly fixes `slice` and `concat` layers to handle `unflatten` operation.
<img width="673" alt="image" src="https://github.com/opencv/opencv/assets/44877829/5b612b31-657a-47f1-83a4-0ac35a950abd">
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Fixed kotlin requirement in Android build.gradle #25856
Now OpenCV Android SDK doesn't always require kotlin plugin. Kotlin code is compiled only if the application uses kotlin plugin.
Fixes#24663
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python: attempts to fix 3d mat parsing problem for dnn #25810
Fixes https://github.com/opencv/opencv/issues/25762https://github.com/opencv/opencv/issues/23242
Relates https://github.com/opencv/opencv/issues/25763https://github.com/opencv/opencv/issues/19091
Although `cv.Mat` has already been introduced to workaround this problem, people do not know it and it kind of leads to confusion with `numpy.array`. This patch adds a "switch" to turn off the auto multichannel feature when the API is from cv::dnn::Net (more specifically, `setInput`) and the parameter is of type `Mat`. This patch only leads to changes of three places in `pyopencv_generated_types_content.h`:
```.diff
static PyObject* pyopencv_cv_dnn_dnn_Net_setInput(PyObject* self, PyObject* py_args, PyObject* kw)
{
...
- pyopencv_to_safe(pyobj_blob, blob, ArgInfo("blob", 0)) &&
+ pyopencv_to_safe(pyobj_blob, blob, ArgInfo("blob", 8)) &&
...
}
// I guess we also need to change this as one-channel blob is expected for param
static PyObject* pyopencv_cv_dnn_dnn_Net_setParam(PyObject* self, PyObject* py_args, PyObject* kw)
{
...
- pyopencv_to_safe(pyobj_blob, blob, ArgInfo("blob", 0)) )
+ pyopencv_to_safe(pyobj_blob, blob, ArgInfo("blob", 8)) )
...
- pyopencv_to_safe(pyobj_blob, blob, ArgInfo("blob", 0)) )
+ pyopencv_to_safe(pyobj_blob, blob, ArgInfo("blob", 8)) )
...
}
```
Others are unchanged, e.g. `dnn_SegmentationModel` and stuff like that.
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Add support for v_log (Natural Logarithm) #25781
This PR aims to implement `v_log(v_float16 x)`, `v_log(v_float32 x)` and `v_log(v_float64 x)`.
Merged after https://github.com/opencv/opencv/pull/24941
TODO:
- [x] double and half float precision
- [x] tests for them
- [x] doc to explain the implementation
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imgcodecs: Add rgb flag for imread and imdecode #25809
Try to `imread` images by RGB to save R-B swapping costs.
## How to use it?
```
img_rgb = cv2.imread("PATH", IMREAD_COLOR_RGB) # OpenCV decode the image by RGB format.
```
## TODO
- [x] Fix the broken code
- [x] Add imread rgb test
- [x] Speed test of rgb mode.
## Performance test
| file name | IMREAD_COLOR | IMREAD_COLOR_RGB |
| --------- | ------ | --------- |
| jpg01 | 284 ms | 277 ms |
| jpg02 | 376 ms | 366 ms |
| png01 | 62 ms | 60 ms |
| Png02 | 97 ms | 94 ms |
Test with [image_test.zip](https://github.com/user-attachments/files/15982949/image_test.zip)
```.cpp
string img_path = "/Users/mzh/work/data/image_test/png02.png";
int loop = 20;
TickMeter t;
double t0 = 10000;
for (int i = 0; i < loop; i++)
{
t.reset();
t.start();
img_bgr = imread(img_path, IMREAD_COLOR);
t.stop();
if (t.getTimeMilli() < t0) t0 = t.getTimeMilli();
}
std::cout<<"bgr time = "<<t0<<std::endl;
t0 = 10000;
for (int i = 0; i < loop; i++)
{
t.reset();
t.start();
img_rgb = imread(img_path, IMREAD_COLOR_RGB);
t.stop();
if (t.getTimeMilli() < t0) t0 = t.getTimeMilli();
}
std::cout<<"rgb time = "<<t0<<std::endl;
```
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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
- [ ] 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.
- [ ] The feature is well documented and sample code can be built with the project CMake
dnn: parallelize nary elementwise forward implementation & enable related conformance tests #25630
This PR introduces the following changes:
- [x] Parallelize binary forward impl
- [x] Parallelize ternary forward impl (Where)
- [x] Parallelize nary (Operator that can take >=1 operands)
- [x] Enable conformance tests if workable
## Performance
### i7-12700K, RAM 64GB, Ubuntu 22.04
```
Geometric mean (ms)
Name of Test opencv opencv opencv
perf perf perf
core.x64.0606 core.x64.0606 core.x64.0606
vs
opencv
perf
core.x64.0606
(x-factor)
NCHW_C_sum::Layer_NaryEltwise::OCV/CPU 16.116 11.161 1.44
NCHW_NCHW_add::Layer_NaryEltwise::OCV/CPU 17.469 11.446 1.53
NCHW_NCHW_div::Layer_NaryEltwise::OCV/CPU 17.531 11.469 1.53
NCHW_NCHW_equal::Layer_NaryEltwise::OCV/CPU 28.653 13.682 2.09
NCHW_NCHW_greater::Layer_NaryEltwise::OCV/CPU 21.899 13.422 1.63
NCHW_NCHW_less::Layer_NaryEltwise::OCV/CPU 21.738 13.185 1.65
NCHW_NCHW_max::Layer_NaryEltwise::OCV/CPU 16.172 11.473 1.41
NCHW_NCHW_mean::Layer_NaryEltwise::OCV/CPU 16.309 11.565 1.41
NCHW_NCHW_min::Layer_NaryEltwise::OCV/CPU 16.166 11.454 1.41
NCHW_NCHW_mul::Layer_NaryEltwise::OCV/CPU 16.157 11.443 1.41
NCHW_NCHW_pow::Layer_NaryEltwise::OCV/CPU 163.459 15.234 10.73
NCHW_NCHW_ref_div::Layer_NaryEltwise::OCV/CPU 10.880 10.868 1.00
NCHW_NCHW_ref_max::Layer_NaryEltwise::OCV/CPU 10.947 11.058 0.99
NCHW_NCHW_ref_min::Layer_NaryEltwise::OCV/CPU 10.948 10.910 1.00
NCHW_NCHW_ref_mul::Layer_NaryEltwise::OCV/CPU 10.874 10.871 1.00
NCHW_NCHW_ref_sum::Layer_NaryEltwise::OCV/CPU 10.971 10.920 1.00
NCHW_NCHW_sub::Layer_NaryEltwise::OCV/CPU 17.546 11.462 1.53
NCHW_NCHW_sum::Layer_NaryEltwise::OCV/CPU 16.175 11.475 1.41
NHWC_C::Layer_NaryEltwise::OCV/CPU 11.339 11.333 1.00
NHWC_H::Layer_NaryEltwise::OCV/CPU 16.154 11.102 1.46
```
### Apple M1, RAM 16GB, macOS 14.4.1
```
Geometric mean (ms)
Name of Test opencv opencv opencv
perf perf perf
core.m1.0606 core.m1.0606.patch core.m1.0606.patch
vs
opencv
perf
core.m1.0606
(x-factor)
NCHW_C_sum::Layer_NaryEltwise::OCV/CPU 28.418 3.768 7.54
NCHW_NCHW_add::Layer_NaryEltwise::OCV/CPU 6.942 5.679 1.22
NCHW_NCHW_div::Layer_NaryEltwise::OCV/CPU 5.822 5.653 1.03
NCHW_NCHW_equal::Layer_NaryEltwise::OCV/CPU 5.751 5.628 1.02
NCHW_NCHW_greater::Layer_NaryEltwise::OCV/CPU 5.797 5.599 1.04
NCHW_NCHW_less::Layer_NaryEltwise::OCV/CPU 7.272 5.578 1.30
NCHW_NCHW_max::Layer_NaryEltwise::OCV/CPU 5.777 5.562 1.04
NCHW_NCHW_mean::Layer_NaryEltwise::OCV/CPU 5.819 5.559 1.05
NCHW_NCHW_min::Layer_NaryEltwise::OCV/CPU 5.830 5.574 1.05
NCHW_NCHW_mul::Layer_NaryEltwise::OCV/CPU 5.759 5.567 1.03
NCHW_NCHW_pow::Layer_NaryEltwise::OCV/CPU 342.260 74.655 4.58
NCHW_NCHW_ref_div::Layer_NaryEltwise::OCV/CPU 8.338 8.280 1.01
NCHW_NCHW_ref_max::Layer_NaryEltwise::OCV/CPU 8.359 8.309 1.01
NCHW_NCHW_ref_min::Layer_NaryEltwise::OCV/CPU 8.412 8.295 1.01
NCHW_NCHW_ref_mul::Layer_NaryEltwise::OCV/CPU 8.380 8.297 1.01
NCHW_NCHW_ref_sum::Layer_NaryEltwise::OCV/CPU 8.356 8.323 1.00
NCHW_NCHW_sub::Layer_NaryEltwise::OCV/CPU 6.818 5.561 1.23
NCHW_NCHW_sum::Layer_NaryEltwise::OCV/CPU 5.805 5.570 1.04
NHWC_C::Layer_NaryEltwise::OCV/CPU 3.834 4.817 0.80
NHWC_H::Layer_NaryEltwise::OCV/CPU 28.402 3.771 7.53
```
### 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
- [ ] 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.
- [ ] The feature is well documented and sample code can be built with the project CMake
Add sample support of YOLOv9 and YOLOv10 in OpenCV #25794
This PR adds sample support of [`YOLOv9`](https://github.com/WongKinYiu/yolov9) and [`YOLOv10`](https://github.com/THU-MIG/yolov10/tree/main)) in OpenCV. Models for this test are located in this [PR](https://github.com/opencv/opencv_extra/pull/1186).
**Running YOLOv10 using OpenCV.**
1. In oder to run `YOLOv10` one needs to cut off postporcessing with dynamic shapes from torch and then convert it to ONNX. If someone is looking for ready solution, there is [this forked branch](https://github.com/Abdurrahheem/yolov10/tree/ash/opencv-export) from official YOLOv10. Particularty follow this proceduce.
```bash
git clone git@github.com:Abdurrahheem/yolov10.git
conda create -n yolov10 python=3.9
conda activate yolov10
pip install -r requirements.txt
python export_opencv.py --model=<model-name> --imgsz=<input-img-size>
```
By default `model="yolov10s"` and `imgsz=(480,640)`. This will generate file `yolov10s.onnx`, which can be use for inference in OpenCV
2. For inference part on OpenCV. one can use `yolo_detector.cpp` [sample](https://github.com/opencv/opencv/blob/4.x/samples/dnn/yolo_detector.cpp). If you have followed above exporting procedure, then you can use following command to run the model.
``` bash
build opencv from source
cd build
./bin/example_dnn_yolo_detector --model=<path-to-yolov10s.onnx-file> --yolo=yolov10 --width=640 --height=480 --input=<path-to-image> --scale=0.003921568627 --padvalue=114
```
If you do not specify `--input` argument, OpenCV will grab first camera that is avaliable on your platform.
For more deatils on how to run the `yolo_detector.cpp` file see this [guide](https://docs.opencv.org/4.x/da/d9d/tutorial_dnn_yolo.html#autotoc_md443)
**Running YOLOv9 using OpenCV**
1. Export model following [official guide](https://github.com/WongKinYiu/yolov9)of the YOLOv9 repository. Particularly you can do following for converting.
```bash
git clone https://github.com/WongKinYiu/yolov9.git
cd yolov9
conda create -n yolov9 python=3.9
conda activate yolov9
pip install -r requirements.txt
wget https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-t-converted.pt
python export.py --weights=./yolov9-t-converted.pt --include=onnx --img-size=(480,640)
```
This will generate <yolov9-t-converted.onnx> file.
2. Inference on OpenCV.
```bash
build opencv from source
cd build
./bin/example_dnn_yolo_detector --model=<path-to-yolov9-t-converted.onnx> --yolo=yolov9 --width=640 --height=480 --scale=0.003921568627 --padvalue=114 --path=<path-to-image>
```
### 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.
- [x] The feature is well documented and sample code can be built with the project CMake
Add support for v_exp (exponential) #24941
This PR aims to implement `v_exp(v_float16 x)`, `v_exp(v_float32 x)` and `v_exp(v_float64 x)`.
### 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
- [ ] 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.
- [ ] The feature is well documented and sample code can be built with the project CMake
Kumataro
Alexander Lyulkov
武士风度的牛
Vincent Rabaud
Alexander Smorkalov
Rostislav Vasilikhin
cudawarped
Yuantao Feng
HAN Liutong
_Ayaka
zihaomu
Yang Chao
Richard Barnes
Corentin Noël
Yoshiki Obinata
j3knk
Alexander Smorkalov
lamiayous
Suleyman TURKMEN
Aliaksei Urbanski
Maksim Shabunin
fengyuentau
Mironov Arseny
Sourav Kumar
Dmitry Yurov
Abduragim Shtanchaev
alexlyulkov
Wanli
kaingwade
Dietmar Schabus