Added and tested yolov8m model. #25357
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Added and tested yolov8s and yolov8n model #25176
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Added and tested yolov8x model #25095
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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>
Using cv2 dnn interface to run yolov8 model #24396
This is a sample code for using opencv dnn interface to run ultralytics yolov8 model for object detection.
### Pull Request Readiness Checklist
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Add weights yolov3 in models.yml #24496
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I don't know if this action is necessary, or the previous PR scale for the brach master.
Thanks.
Added PyTorch fcnresnet101 segmentation conversion cases #24397
We write a sample code about transforming Pytorch fcnresnet101 to ONNX running on OpenCV.
The input source image was shooted by ourself.
### Pull Request Readiness Checklist
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- [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
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Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
VIT track(gsoc realtime object tracking model) #24201
Vit tracker(vision transformer tracker) is a much better model for real-time object tracking. Vit tracker can achieve speeds exceeding nanotrack by 20% in single-threaded mode with ARM chip, and the advantage becomes even more pronounced in multi-threaded mode. In addition, on the dataset, vit tracker demonstrates better performance compared to nanotrack. Moreover, vit trackerprovides confidence values during the tracking process, which can be used to determine if the tracking is currently lost.
opencv_zoo: https://github.com/opencv/opencv_zoo/pull/194
opencv_extra: [https://github.com/opencv/opencv_extra/pull/1088](https://github.com/opencv/opencv_extra/pull/1088)
# Performance comparison is as follows:
NOTE: The speed below is tested by **onnxruntime** because opencv has poor support for the transformer architecture for now.
ONNX speed test on ARM platform(apple M2)(ms):
| thread nums | 1| 2| 3| 4|
|--------|--------|--------|--------|--------|
| nanotrack| 5.25| 4.86| 4.72| 4.49|
| vit tracker| 4.18| 2.41| 1.97| **1.46 (3X)**|
ONNX speed test on x86 platform(intel i3 10105)(ms):
| thread nums | 1| 2| 3| 4|
|--------|--------|--------|--------|--------|
| nanotrack|3.20|2.75|2.46|2.55|
| vit tracker|3.84|2.37|2.10|2.01|
opencv speed test on x86 platform(intel i3 10105)(ms):
| thread nums | 1| 2| 3| 4|
|--------|--------|--------|--------|--------|
| vit tracker|31.3|31.4|31.4|31.4|
preformance test on lasot dataset(AUC is the most important data. Higher AUC means better tracker):
|LASOT | AUC| P| Pnorm|
|--------|--------|--------|--------|
| nanotrack| 46.8| 45.0| 43.3|
| vit tracker| 48.6| 44.8| 54.7|
[https://youtu.be/MJiPnu1ZQRI](https://youtu.be/MJiPnu1ZQRI)
In target tracking tasks, the score is an important indicator that can indicate whether the current target is lost. In the video, vit tracker can track the target and display the current score in the upper left corner of the video. When the target is lost, the score drops significantly. While nanotrack will only return 0.9 score in any situation, so that we cannot determine whether the target is lost.
### Pull Request Readiness Checklist
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- [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
- [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
[teset data in opencv_extra](https://github.com/opencv/opencv_extra/pull/1016)
NanoTrack is an extremely lightweight and fast object-tracking model.
The total size is **1.1 MB**.
And the FPS on M1 chip is **150**, on Raspberry Pi 4 is about **30**. (Float32 CPU only)
With this model, many users can run object tracking on the edge device.
The author of NanoTrack is @HonglinChu.
The original repo is https://github.com/HonglinChu/NanoTrack.
### 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
there is a recent change, how `std::vector<int>` is wrapped in python,
it used to be a 2d array (requirig that weird `[0]` indexing), now it is only 1d
Avoid `SyntaxWarning` on Python >= 3.8
```
>>> "convolutional" == "convolutional"
True
>>> "convolutional" is "convolutional"
<stdin>:1: SyntaxWarning: "is" with a literal. Did you mean "=="?
True
```
Related to #21121
[GSoC] OpenCV.js: Accelerate OpenCV.js DNN via WebNN
* Add WebNN backend for OpenCV DNN Module
Update dnn.cpp
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update the skeleton
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Solved the problems of released variables.
Fixed the bugs in op_webnn.cpp
Implement op_webnn
Implement Relu by WebNN API
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Implement ReLU6
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Implement poolingLayer by WebNN API and add more detailed logs
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* Fix the build issue
* Implement BatchNorm Layer by WebNN API
* Update convolution_layer.cpp
This is a temporary file for Conv2d layer implementation
* Integrate some general functions into op_webnn.cpp&hpp
* Update const_layer.cpp
* Update convolution_layer.cpp
Still have some bugs that should be fixed.
* Update conv2d layer and fc layer
still have some problems to be fixed.
* update constLayer, conv layer, fc layer
There are still some bugs to be fixed.
* Fix the build issue
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* Update conv2d layer, fully connected layer and const layer
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* Add OpenCV.js DNN module WebNN Backend (both using webnn-polyfill and electron)
* Delete bib19450.aux
* Add WebNN backend for OpenCV DNN Module
Update dnn.cpp
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Update dnn.cpp
Add WebNN head files into OpenCV 3rd partiy files
Create webnn.hpp
update cmake
Complete README and add OpenCVDetectWebNN.cmake file
add webnn.cpp
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Can successfully compile the codes for creating a MLContext
Update webnn.cpp
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update README.md
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update op_webnn
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update op_webnn.cpp & hpp
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update the skeleton
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Update op_webnn.cpp
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update op_webnn
update op_webnn
Solved the problems of released variables.
Fixed the bugs in op_webnn.cpp
Implement op_webnn
Implement Relu by WebNN API
Update dnn.cpp for better test
Update elementwise_layers.cpp
Implement ReLU6
Update elementwise_layers.cpp
Implement SoftMax using WebNN API
Implement Reshape by WebNN API
Implement PermuteLayer by WebNN API
Implement PoolingLayer using WebNN API
Update pooling_layer.cpp
Update pooling_layer.cpp
Update pooling_layer.cpp
Update pooling_layer.cpp
Update pooling_layer.cpp
Update pooling_layer.cpp
Implement poolingLayer by WebNN API and add more detailed logs
Update dnn.cpp
Update dnn.cpp
Remove redundant codes and add more logs for poolingLayer
Add more logs in the pooling layer implementation
Fix the indent issue and resolve the compiling issue
Fix the build problems
Fix the build issue
FIx the build issue
Update dnn.cpp
Update dnn.cpp
* Fix the build issue
* Implement BatchNorm Layer by WebNN API
* Update convolution_layer.cpp
This is a temporary file for Conv2d layer implementation
* Integrate some general functions into op_webnn.cpp&hpp
* Update const_layer.cpp
* Update convolution_layer.cpp
Still have some bugs that should be fixed.
* Update conv2d layer and fc layer
still have some problems to be fixed.
* update constLayer, conv layer, fc layer
There are still some bugs to be fixed.
* Update conv2d layer, fully connected layer and const layer
* Update convolution_layer.cpp
* Add OpenCV.js DNN module WebNN Backend (both using webnn-polyfill and electron)
* Update dnn.cpp
* Fix Error in dnn.cpp
* Resolve duplication in conditions in convolution_layer.cpp
* Fixed the issues in the comments
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* Update tutorial
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* Offer more accurate perf test on native
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* Modify per tests for better results
* Use more latest version of Electron
* Support latest WebNN Clamp op
* Add definition of HAVE_WEBNN macro
* Support group convolution
* Implement Scale_layer using WebNN
* Add Softmax option for native classification example
* Fix comments
* Fix comments
Add DNN-based face detection and face recognition into modules/objdetect
* Add DNN-based face detector impl and interface
* Add a sample for DNN-based face detector
* add recog
* add notes
* move samples from samples/cpp to samples/dnn
* add documentation for dnn_face
* add set/get methods for input size, nms & score threshold and topk
* remove the DNN prefix from the face detector and face recognizer
* remove default values in the constructor of impl
* regenerate priors after setting input size
* two filenames for readnet
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* fix a compile error for python interface; add python examples for face detection and recognition
* Major changes for Vadim's comments:
* Replace class name FaceDetector with FaceDetectorYN in related failes
* Declare local mat before loop in modules/objdetect/src/face_detect.cpp
* Make input image and save flag optional in samples/dnn/face_detect(.cpp, .py)
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* correct file paths for regression test
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* update face_recog
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* Fix warnings and errors for the default CI reports:
* Remove trailing white spaces and extra new lines.
* Fix convertion warnings for windows and iOS.
* Add braces around initialization of subobjects.
* Fix warnings and errors for the default CI systems:
* Add prefix 'FR_' for each value name in enum DisType to solve the
redefinition error for iOS compilation; Modify other code accordingly
* Add bookmark '#tutorial_dnn_face' to solve warnings from doxygen
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* update FaceRecognizerSF
* Fix the error for CI to find ONNX models correctly
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* add backend & target options for initializing face recognizer
* add checkeq for checking input size and preset size
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* changes in response to alalek's comments:
* fix typos in samples/dnn/face_match.py
* import numpy before importing cv2
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* add time suffix to YuNet filename in test
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* Added PaddlePaddle classification model conversion case
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Add DaSiamRPN tracker sample of c++ version
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* samples(dasiamrpn_tracker.cpp): apply clang-format
- exclude "keys" variable
* samples(dasiamrpn_tracker.cpp): coding style and UX fixes
Wanli
Alessandro de Oliveira Faria (A.K.A.CABELO)
Abduragim Shtanchaev
uday
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