Add sample for GPT2 inference #25868
### Pull Request Readiness Checklist
This PR adds sample for inferencing GPT-2 model. More specificly implementation of GPT-2 from [this repository](https://github.com/karpathy/build-nanogpt). Currently inference in OpenCV is only possible to do with fixed window size due to not supported dynamic shapes.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Patch to opencv_extra has the same branch name.
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Support OpenGL GTK3 New API #25822Fixes#20001
GSoC2024 Project
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Move API focused C++ samples to snippets #25252
Clean Samples #25006
This PR removes 39 outdated C++ samples from the project, as part of an effort to keep the codebase clean and focused on current best practices.
Add videocapture_depth.cpp sample #25410
The PR is to combine the examples `videocapture_openni.cpp`, `videocapture_realsense.cpp` and `videocapture_obsensor.cpp` into `videocapture_depth.cpp`.
Tested cameras and OS using this sample are listed below:
| | Windows 10 | Ubuntu 22.04 | Mac M1 14.3 |
|------------------------|--------------|--------------|---------------|
| Orbbec Gemini 2 Series | ✓ | ✓ | ✓ |
| RealSense D435, D455 | ✓ | ✓ | ✗ |
| Kinect, XtionPRO | - | - | - |
Note:
- OpenNI based cameras (Kinect, XtionPRO) are not tested as I don't have them.
- RealSense D435 and D455 don't work on Mac with OpenCV.
[BUG FIX] Segmentation sample u2netp model results #25756
PR resloves #25753 related to incorrect output from u2netp model in segmentation sample
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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>
```
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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`.
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Add yolov8l.onnx to samples #25775
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Hello, I noticed that the /samples/dnn/models.yml said it should be used for all yolov8 models, but the YOLOv8l is not included in the file, so I added it to the file, thanks.
video: fix vittrack in the case where crop size grows until out-of-memory when the input is black #25771
Fixes https://github.com/opencv/opencv/issues/25760
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Improved segmentation sample #25559#25006
This pull request replaces caffe models with onnx for the dnn segmentation sample in cpp and python
fcnresnet-50 and fcnresnet-101 has been replaced
u2netp (foreground-background) segmentation onnx model has been added [U2NET](https://github.com/xuebinqin/U-2-Net)
### Pull Request Readiness Checklist
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Change opencv_face_detector related tests and samples from caffe to onnx #25463
Part of https://github.com/opencv/opencv/issues/25314
This PR aims to change the tests related to opencv_face_detector from caffe framework to onnx. Tests in `test_int8_layer.cpp` and `test_caffe_importer.cpp` will be removed in https://github.com/opencv/opencv/pull/25323
### Pull Request Readiness Checklist
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Remove goturn caffe model #25503
**Merged with:** https://github.com/opencv/opencv_extra/pull/1174
**Merged with:** https://github.com/opencv/opencv_contrib/pull/3729
Part of https://github.com/opencv/opencv/issues/25314
This PR aims to remove goturn tracking model because Caffe importer will be remove in 5.0
The GOTURN model will take **388 MB** of traffic for each download if converted to onnx. If the user wants to use the tracking method, we can recommend they use Vit or dasimRPN.
### Pull Request Readiness Checklist
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Replaced caffe model with onnx for colorization sample #25433#25006
Improved sample for colorization with onnx model in cpp and python. Added a demo image in data folder for testing
### Pull Request Readiness Checklist
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Improving the drawing cpp sample to draw shapes based on user input #25415
Relates to #25006
The updated samples allows user to draw random shapes by using hot keys.
### Pull Request Readiness Checklist
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Move Charuco/Calib tutorials and samples to main repo #25378
Merge with https://github.com/opencv/opencv_contrib/pull/3708
Move Charuco/Calib tutorials and samples to main repo:
- [x] update/fix charuco_detection.markdown and samples
- [x] update/fix charuco_diamond_detection.markdown and samples
- [x] update/fix aruco_calibration.markdown and samples
- [x] update/fix aruco_faq.markdown
- [x] move tutorials, samples and tests to main repo
- [x] remove old tutorials, samples and tests from contrib
### Pull Request Readiness Checklist
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Fix mesh loading for texture coordinates and face indices #25382
### This PR changes
* Texture coordinates were stored incorrectly (3-channel array is read as if there were 2 channels), fixed
* Faces were pushed back to the output array instead of indexed writing which produced a lot of empty faces, fixed
* A set of ground truth tests were added to cover these issues
* `std::vector<cv::Mat>` support added for `saveMesh()` which is required for Python bindings
* More command line args were added to rasterization test data generator
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Geometry C++ sample combining other shape detection samples #25304
Clean Samples #25006
This PR removes adds a new cpp sample (geometry) which combines different methods of finding and drawing shapes in an image. It makes separate samples for convexHull, fitellipse, minAreaRect, minAreaCircle redudant. Shapes can be changed using hotkeys after running the program
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Added and tested yolov8m model. #25357
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Below is evidence of the test:
Orbbec Camera supports MacOS，Gemini2 and Gemini2L support Y16 format #24877
note:
1.Gemini2 and Gemini2L must use the latest firmware -- https://github.com/orbbec/OrbbecFirmware;
2.Administrator privileges are necessary to run on MacOS.
Added and tested yolov8s and yolov8n model #25176
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Below is evidence of the test:
Documentation transition to fresh Doxygen #25042
* current Doxygen version is 1.10, but we will use 1.9.8 for now due to issue with snippets (https://github.com/doxygen/doxygen/pull/10584)
* Doxyfile adapted to new version
* MathJax updated to 3.x
* `@relates` instructions removed temporarily due to issue in Doxygen (to avoid warnings)
* refactored matx.hpp - extracted matx.inl.hpp
* opencv_contrib - https://github.com/opencv/opencv_contrib/pull/3638
Added and tested yolov8x model #25095
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Move Aruco tutorials and samples to main repo #23018
merge with https://github.com/opencv/opencv_contrib/pull/3401
merge with https://github.com/opencv/opencv_extra/pull/1143
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---------
Co-authored-by: AleksandrPanov <alexander.panov@xperience.ai>
Co-authored-by: Alexander Smorkalov <alexander.smorkalov@xperience.ai>
Triangle rasterization function #24459#24065 reopened since the previous one was automatically closed after rebase
Connected PR with ground truth data: [#1113@extra](https://github.com/opencv/opencv_extra/pull/1113)
### Pull Request Readiness Checklist
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PLY mesh support #24961
**Warning:** The PR changes exising API.
Fixes#24960
Connected PR: [#1145@extra](https://github.com/opencv/opencv_extra/pull/1145)
### Changes
* Adds faces loading from and saving to PLY files
* Fixes incorrect PLY loading (see issue)
* Adds per-vertex color loading / saving
### Pull Request Readiness Checklist
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Documentation for Yolo usage in Opencv #24898
This PR introduces documentation for the usage of yolo detection model family in open CV. This is not to be merge before #24691, as the sample will need to be changed.
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Raft support added in this sample code #24913
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fix: https://github.com/opencv/opencv/issues/24424 Update DNN Optical Flow sample with RAFT model
I implemented both RAFT and FlowNet v2 leaving it to the user which one he wants to use to estimate the optical flow.
Co-authored-by: Uday Sharma <uday@192.168.1.35>
Removed all pre-C++11 code, workarounds, and branches #23736
This removes a bunch of pre-C++11 workrarounds that are no longer necessary as C++11 is now required.
It is a nice clean up and simplification.
* No longer unconditionally #include <array> in cvdef.h, include explicitly where needed
* Removed deprecated CV_NODISCARD, already unused in the codebase
* Removed some pre-C++11 workarounds, and simplified some backwards compat defines
* Removed CV_CXX_STD_ARRAY
* Removed CV_CXX_MOVE_SEMANTICS and CV_CXX_MOVE
* Removed all tests of CV_CXX11, now assume it's always true. This allowed removing a lot of dead code.
* Updated some documentation consequently.
* Removed all tests of CV_CXX11, now assume it's always true
* Fixed links.
---------
Co-authored-by: Maksim Shabunin <maksim.shabunin@gmail.com>
Co-authored-by: Alexander Smorkalov <alexander.smorkalov@xperience.ai>
