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>
Update Android OpenCL sample #24715
Update Android OpenCL sample and tutorial text.
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Add support for Orbbec Gemini2 and Gemini2 XL camera #24666
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Android sample for VideoWriter #24592
This PR:
* adds an Android sample for video recording with MediaNDK and built-in MJPEG.
* adds a flag `--no_media_ndk` for `build_sdk.py` script to disable MediaNDK linkage.
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Android camera tutorial update #24692
This PR extends the OpenCV 4 Android tutorial by a simple camera app based on existing code.
This part was accidentally removed during the #24653 preparation, this PR restores it and aligns it to the latest Android Studio.
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- Use the same tools and plugins for SDK build and AAR build
- Added script to test Gradle-based samples against local maven repo
- Various local fixes and debug prints
Updated Android samples for modern Android studio. Added OpenCV from Maven support. #24473
Updated samples for recent Android studio:
- added namespace field that is required in build.gradle files
- replaced _switch_ by _if-else_ because it doesn't work with constants from resources
- added missed log library dependency in face-detection/jni/CMakeLists.txt
- use local.properties to define NDK location
Added support for OpenCV from Maven. Now you can choose 3 possible sources of OpenCV lib in settings.gradle: SDK path, local Maven repository, public Maven repository. (Creating Maven repository from SDK is added here #24456 )
There are differences in project configs for SDK and Maven versions:
- different dependencies in build.gradle
- different OpenCV library names in CMakeLists.txt
- SDK version requires OpenCV_DIR definition
Requires:
- https://github.com/opencv/ci-gha-workflow/pull/124
- https://github.com/opencv-infrastructure/opencv-gha-dockerfile/pull/26
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.
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- [] There is a reference to the original bug report and related work
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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.
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Remove torch (old torch7) from dnn in 5.x #24294
Merge with https://github.com/opencv/opencv_extra/pull/1097
Completely removed torch (old torch7) from dnn:
- removed modules/dnn/src/torch directory that contained torch7 model parser
- removed readNetFromTorch() and readTorchBlob() public functions
- removed torch7 references from comments and help texts
- replaced links to t7 models by links to similar onnx models in js_style_transfer turtorial (similar to https://github.com/opencv/opencv/pull/24245/files)
dnn: cleanup of halide backend for 5.x #24231
Merge with https://github.com/opencv/opencv_extra/pull/1092.
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* attempt to add 0d/1d mat support to OpenCV
* revised the patch; now 1D mat is treated as 1xN 2D mat rather than Nx1.
* a step towards 'green' tests
* another little step towards 'green' tests
* calib test failures seem to be fixed now
* more fixes _core & _dnn
* another step towards green ci; even 0D mat's (a.k.a. scalars) are now partly supported!
* * fixed strange bug in aruco/charuco detector, not sure why it did not work
* also fixed a few remaining failures (hopefully) in dnn & core
* disabled failing GAPI tests - too complex to dig into this compiler pipeline
* hopefully fixed java tests
* trying to fix some more tests
* quick followup fix
* continue to fix test failures and warnings
* quick followup fix
* trying to fix some more tests
* partly fixed support for 0D/scalar UMat's
* use updated parseReduce() from upstream
* trying to fix the remaining test failures
* fixed [ch]aruco tests in Python
* still trying to fix tests
* revert "fix" in dnn's CUDA tensor
* trying to fix dnn+CUDA test failures
* fixed 1D umat creation
* hopefully fixed remaining cuda test failures
* removed training whitespaces
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.
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