Commit Graph

34675 Commits

Author SHA1 Message Date
Mironov Arseny
b964943517
Merge pull request #25607 from Fest1veNapkin:imgproc_approx_bounding_poly
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.

![image](https://github.com/Fest1veNapkin/opencv/assets/98156294/efac35f6-b8f0-46ec-91e4-60800432620c)

![image](https://github.com/Fest1veNapkin/opencv/assets/98156294/2292d9d7-1c10-49c9-8489-23221b4b28f7)

# 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.
![image](https://github.com/Fest1veNapkin/opencv/assets/98156294/b87b21c4-112e-450d-a776-2a120048ca30)

# 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.
![image](https://github.com/Fest1veNapkin/opencv/assets/98156294/31ad5562-a67d-4e3c-bdc2-29f8b52caf88)

## 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.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [ ] I agree to contribute to the project under Apache 2 License.
- [ ] 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.
- [ ] The feature is well documented and sample code can be built with the project CMake
2024-07-09 17:11:23 +03:00
Alexander Smorkalov
8d935e2184
Merge pull request #25885 from Sourav6971:4.x
Update imgcodecs.hpp
2024-07-09 10:00:20 +03:00
Sourav Kumar
e962395565
Update imgcodecs.hpp 2024-07-09 06:53:16 +05:30
Yuantao Feng
e3858cc5a3
Merge pull request #25147 from fengyuentau:dnn/elementwise_layers/speedup
* 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
2024-07-08 14:24:36 +03:00
Dmitry Yurov
31b308f882
Merge pull request #25808 from DmitryYurov:bug-25806-checkerboard-marker-black-tile
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.

### 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
2024-07-08 12:36:56 +03:00
Alexander Smorkalov
a54de7d55e
Merge pull request #25877 from Kumataro:fix25875
calib3d: doc: enable line breaks in formulas
2024-07-08 08:37:13 +03:00
Kumataro
0b5b40179c calib3d: doc: enable line breaks in formulas 2024-07-07 07:15:28 +09:00
Amir Hassan
d7a237aefc
Merge pull request #22836 from kallaballa:opengl_cmake_warning_linux
Explicitly prefer legacy GL in cmake on Linux? #22836

Pertaining Issue: #22835

### 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
- [ ] 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
2024-07-05 16:39:01 +03:00
Yuantao Feng
d30b9450c1
Merge pull request #25872 from fengyuentau:core/v_erf
core: add v_erf #25872

This patch adds v_erf, which is needed by https://github.com/opencv/opencv/pull/25147.

### 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
2024-07-05 15:46:01 +03:00
Suleyman TURKMEN
cdd8395f68 add getFrameCount() 2024-07-05 14:43:30 +03:00
Alexander Smorkalov
88b28ee2a0
Merge pull request #25844 from dietmar:dont_rely_on_nb_frames
Don't rely on nb_frames to be correct
2024-07-05 11:23:20 +03:00
Vincent Rabaud
dfbd18e9aa
Merge pull request #25864 from vrabaud:legacy
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.

### 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
2024-07-05 08:53:28 +03:00
Maksim Shabunin
94b7a2d320
Merge pull request #25842 from mshabunin:cpp-imgproc-test-4.x
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.
2024-07-04 16:29:08 +03:00
Abduragim Shtanchaev
efbc9f0b66
Merge pull request #25861 from Abdurrahheem:ash/torch-attention-export-fix-4x
Merge pull request #25861 from Abdurrahheem:ash/torch-attention-export-fix-4x

Support for Unflatten operation requred by Attention layer - 4.x #25861

### Pull Request Readiness Checklist

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">


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
2024-07-04 16:25:31 +03:00
Alexander Smorkalov
9f5139b575
Merge pull request #25865 from asmorkalov:as/gtk_2_opengl_fix
Hacked gtkglext search in cmake
2024-07-04 16:20:51 +03:00
alexlyulkov
20e72b0b30
Merge pull request #25856 from alexlyulkov:al/android-optional-kotlin
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

### 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
- [ ] 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
2024-07-04 13:26:37 +03:00
Yuantao Feng
5510718381
Merge pull request #25810 from fengyuentau:python/fix_parsing_3d_mat_in_dnn
python: attempts to fix 3d mat parsing problem for dnn #25810

Fixes https://github.com/opencv/opencv/issues/25762 https://github.com/opencv/opencv/issues/23242
Relates https://github.com/opencv/opencv/issues/25763 https://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.

### 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
2024-07-04 08:33:20 +03:00
Alexander Smorkalov
e644ab0b40
Merge pull request #25858 from asmorkalov:as/kleidicv_on
Enable KleidiCV for Android aarch64 by default.
2024-07-04 08:30:08 +03:00
Alexander Smorkalov
e28c6eb3b6 Fixed gtkglext search in cmake. 2024-07-03 19:22:06 +03:00
Alexander Smorkalov
99c0a96a2f Enable KleidiCV for Android aarch64 by default. 2024-07-03 15:17:51 +03:00
Alexander Smorkalov
53f51275e4
Merge pull request #25857 from asmorkalov:as/yolov9_harrowing_conversion
Fixed narrowing conversion warning with MSVC compiler.
2024-07-03 12:42:29 +03:00
Alexander Smorkalov
25fb55601b Fixed narrowing conversion warning with MSVC compiler. 2024-07-03 12:10:31 +03:00
Alexander Smorkalov
aac8bc877a
Merge pull request #25799 from asmorkalov:as/HAL_GaussianBlurBinomial_Carotene
Use Carotene implementation of TEGRA_GaussianBlurBinomial 3x3 and 5x5 on ARM
2024-07-03 11:24:36 +03:00
Wanli
bef6c110a4
Merge pull request #25781 from WanliZhong:v_log
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

### 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
2024-07-03 10:59:44 +03:00
zihaomu
934e6899f8
Merge pull request #25809 from zihaomu:imread_rgb_flag
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;
``` 
### 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
2024-07-03 10:58:25 +03:00
Yuantao Feng
a7fd9446cf
Merge pull request #25630 from fengyuentau:nary-multi-thread
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
2024-07-03 10:09:05 +03:00
Abduragim Shtanchaev
a8d1373919
Merge pull request #25794 from Abdurrahheem:ash/yolov10-support
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
2024-07-02 18:26:34 +03:00
Alexander Smorkalov
939cb58cd6
Merge pull request #25845 from kaingwade:orbbecsdk_mac_off
Set using Orbbec SDK on MacOS OFF by default.
2024-07-02 14:02:42 +03:00
Alexander Smorkalov
2799c74d50 Use Carotene implementation of TEGRA_GaussianBlurBinomial 3x3 and 5x5 on ARM. 2024-07-02 12:50:09 +03:00
Wanli
6e1864e3fc
Merge pull request #24941 from WanliZhong:v_exp
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
2024-07-02 12:32:49 +03:00
kaingwade
883faf8871 Set using Orbbec SDK on MacOS OFF by default. 2024-07-02 17:23:20 +08:00
Alexander Smorkalov
75339a5528
Merge pull request #25800 from xndcn:patch-2
photo: doc: Fix window range for fastNlMeansDenoisingMulti
2024-07-02 10:26:56 +03:00
Alexander Smorkalov
35d9e8ef5c
Merge pull request #25841 from asmorkalov:as/cudnn9_version_check
Fixed CuDNN runtime version check for CuDNN 9+.
2024-07-02 10:05:04 +03:00
Dietmar Schabus
fd5efabdd9 Don't rely on nb_frames to be correct 2024-07-02 06:45:05 +02:00
Alexander Smorkalov
3d74d646d8 Fixed CuDNN runtime version check for CuDNN 9+. 2024-07-01 17:33:24 +03:00
WU Jia
39a7b3d186
Merge pull request #25813 from kaingwade:orbbec-astra
Update the tutorial of using Orbbec Astra cameras #25813

This PR is the backport of Orbbec OpenNI-based Astra camera related changes from #25410 to the 4.x branch, which includes updating the tutorial of Orbbec Astra cameras, renaming `orbbec_astra.cpp`.

### 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.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-07-01 10:55:35 +03:00
Alexander Smorkalov
930af15f47
Merge pull request #25834 from thiru31:4.x
Fix: compilation Issue on ARM64 (msys2 clangarm)
2024-07-01 10:34:36 +03:00
Alexander Smorkalov
34ed88d7fb
Merge pull request #25836 from dan-masek:fix_win32_topmost_toggle
Fix #25833: The correct way to disable top-most state is with HWND_NOTOPMOST, not HWND_TOP.
2024-07-01 10:30:23 +03:00
Alexander Smorkalov
25609ef5fd
Merge pull request #25838 from tyler92:fix-fd-leak
Fix file descriptor leak in HDR decoder
2024-07-01 10:11:27 +03:00
Alexander Smorkalov
996ed0a2eb
Merge pull request #25829 from asmorkalov:as/force_legal_requirements
Force contributors to define Apache 2.0 license for the new PRs.
2024-07-01 09:51:07 +03:00
Thirumalai Nagalingam
ce1d840adf Fix: compilation Issue on ARM64 (msys2 clangarm)
Added a definition for M_PI in the code to resolve a compilation error encountered when building OpenCV on the MSYS2 environment. The M_PI constant was not defined, causing the compilation to fail.
2024-06-30 18:58:56 +00:00
Mikhail Khachayants
bbf65a166e Fix file descriptor leak in HDR decoder 2024-06-30 18:43:04 +03:00
Dan Mašek
1e5407a9ba Fix #25833: The correct way to disable top-most state is with HWND_NOTOPMOST, not HWND_TOP. 2024-06-29 21:39:49 +02:00
Alexander Smorkalov
b7c9221b80 Force contributors to define Apache 2.0 license for the new PRs. 2024-06-28 16:54:14 +03:00
Alexander Smorkalov
be00247ca0
Merge pull request #25820 from asmorkalov:as/HAL_non_strict_equalizeHist
Relax equalizeHist test for some HAL implementations
2024-06-28 16:51:15 +03:00
Alexander Smorkalov
855f6cfdd4
Merge pull request #25827 from asmorkalov:as/exclude_ios_platform_header
Exclude cap_ios.h from installation where it's not needed
2024-06-28 14:54:15 +03:00
Alexander Smorkalov
310169490a Exclude cap_ios.h from installation where it's not needed. 2024-06-28 14:11:25 +03:00
Alexander Smorkalov
aab7239f38
Merge pull request #25826 from asmorkalov:as/clean_download_models_deps
Drop redundant dependency from download_models.py
2024-06-28 14:01:29 +03:00
Alexander Smorkalov
4500eb937f Drop redundant dependency from download_models.py 2024-06-28 10:45:52 +03:00
Maxim Milashchenko
786726719f
Merge pull request #25793 from MaximMilashchenko:hal_rvv
Fixed build error hal_rvv_071 #25793

Fixed bug with enabling vector header when vector extension is disabled (RVV=OFF) in hal_rvv_071
2024-06-28 09:00:16 +03:00