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
### Pull Request Readiness Checklist
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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
### Pull Request Readiness Checklist
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Mark cv::Mat(Mat&&) as noexcept #25899
This fixes https://github.com/opencv/opencv/issues/25065
### Pull Request Readiness Checklist
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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.
### Pull Request Readiness Checklist
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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.
### Pull Request Readiness Checklist
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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.
### Pull Request Readiness Checklist
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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
### 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
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Patch to opencv_extra has the same branch name.
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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
### Pull Request Readiness Checklist
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- [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
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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
### Pull Request Readiness Checklist
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- [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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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
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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
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Patch to opencv_extra has the same branch name.
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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.
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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>
```
### 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.
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Fill mean and stdDev tails with zeros for HAL branch in meanStdDev #25789
as it's done for other branches.
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Highgui backend on top of Framebuffer #25661
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Environment variables used:
OPENCV_UI_BACKEND - you need to add the value “FB”
OPENCV_UI_PRIORITY_FB - requires priority indication
OPENCV_HIGHGUI_FB_MODE={FB|XVFB|EMU} - mode of using Framebuffer (default "FB")
- FB - Linux Framebuffer
- XVFB - virtual Framebuffer
- EMU - emulation (images are not displayed)
OPENCV_HIGHGUI_FB_DEVICE (FRAMEBUFFER) - path to the Framebuffer file (default "/dev/fb0").
Examples of using:
sudo OPENCV_UI_BACKEND=FB ./opencv_test_highgui
sudo OPENCV_UI_PRIORITY_FB=1111 ./opencv_test_highgui
OPENCV_UI_BACKEND=FB OPENCV_HIGHGUI_FB_MODE=EMU ./opencv_test_highgui
sudo OPENCV_UI_BACKEND=FB OPENCV_HIGHGUI_FB_MODE=FB ./opencv_test_highgui
export DISPLAY=:99
Xvfb $DISPLAY -screen 0 1024x768x24 -fbdir /tmp/ -f /tmp/user.xvfb.auth&
sudo -u sipeed XAUTHORITY=/tmp/user.xvfb.auth x11vnc -display $DISPLAY -listen localhost&
DISPLAY=:0 gvncviewer localhost&
FRAMEBUFFER=/tmp/Xvfb_screen0 OPENCV_UI_BACKEND=FB OPENCV_HIGHGUI_FB_MODE=XVFB ./opencv_test_highgui
dnn: add DepthToSpace and SpaceToDepth #25779
We are working on updating WeChat QRCode module. One of the new models is a fully convolutional model and hence it should be able to run with different input shapes. However, it has an operator `DepthToSpace`, which is parsed as a subgraph of `Reshape -> Permute -> Reshape` with a fixed shape getting during parsing. The subgraph itself is not a problem, but the true problem is the subgraph with a fixed input and output shape regardless input changes. This does not allow the model to run with different input shapes.
Solution is to add a dedicated layer for DepthtoSpace and SpaceToDepth.
Backend support:
- [x] CPU
- [x] CUDA
- [x] OpenCL
- [x] OpenVINO
- [x] CANN
- [x] TIMVX
- ~Vulkan~ (missing fundamental tools, like permutation and reshape)
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Extending G-API onnx::Params to pass arbitrary session options #25791
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Add missing cv2eigen overload #25751Fixes#16606
Add overloads to cv2eigen to handle eigen matrices of type
Eigen::Matrix<Tp_, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>
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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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Use onRuntimeInitialized with OpenCV.js Node tests #25757
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tests: https://github.com/opencv/ci-gha-workflow/pull/174
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Accuracy tests for equalizeHist() added #25759
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Fix OpenCV.js tests #25732
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* Firefox tests passed
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Relates to #24603
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Tests added for mixed type arithmetic operations #25671
### Changes
* added accuracy tests for mixed type arithmetic operations
_Note: div-by-zero values are removed from checking since the result is implementation-defined in common case_
* added perf tests for the same cases
* fixed a typo in `getMulExtTab()` function that lead to dead code
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Suppress build warnings for GCC14 #25686Close#25674
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Fix Homography computation. #25665
The bug was introduced in https://github.com/opencv/opencv/pull/25308
I am sorry I do not have a proper test.
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Support Global_Pool_2D ops in .tflite model #25613
### Pull Request Readiness Checklist
**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1180
This PR adds support for `GlobalAveragePooling2D` and `GlobalMaxPool2D` on the TFlite backend. When the k`eep_dims` option is enabled, the output is a 2D tensor, necessitating the inclusion of an additional flatten layer. Additionally, the names of these layers have been updated to match the output tensor names generated by `generate.py` from the opencv_extra repository.
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Reverted contour approximation behavior #25680
Related issue #25663 - revert new function behavior despite it returning different result than the old one (reverts PR #25672).
Also added Coverity issue fix.
Port G-API ONNXRT backend into V2 API #25662
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Slice layer parser fix to support empty input case #25660
This PR fixes Slice Layer's parser to handle empty input cases (cases with initializer)
It fixed the issue rased in #24838
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Refactor DNN module to build with cudnn 9 #25412
A lot of APIs that are currently being used in the dnn module have been removed in cudnn 9. They were deprecated in 8.
This PR updates said code accordingly to the newer API.
Some key notes:
1) This is my first PR. I am new to openCV.
2) `opencv_test_core` tests pass
3) On a 3080, cuda 12.4(should be irrelevant since I didn't build the `opencv_modules`, gcc 11.4, WSL 2.
4) For brevity I will avoid including macro code that will allow for older versions of cudnn to build.
I was unable to get the tests working for `opencv_test_dnn` and `opencv_perf_dnn`. The errors I get are of the following:
```
OpenCV tests: Can't find required data file: dnn/onnx/conformance/node/test_reduce_prod_default_axes_keepdims_example/model.onnx in function 'findData'
" thrown in the test body.
```
So before I spend more time investigating I was hoping to get a maintainer to point me in the right direction here. I would like to run these tests and confirm things are working as intended. I may have missed some details.
### Pull Request Readiness Checklist
relevant issue
(https://github.com/opencv/opencv/issues/24983
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imgcodecs: support IMWRITE_JPEG_LUMA/CHROMA_QUALITY with internal libjpeg-turbo #25647Close#25646
- increase JPEG_LIB_VERSION for internal libjpeg-turbo from 62 to 70
- add log when using IMWRITE_JPEG_LUMA/CHROMA_QUALITY with JPEG_LIB_VERSION<70
- add document IMWRITE_JPEG_LUMA/CHROMA_QUALITY requests JPEG_LIB_VERSION >= 70
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YUV codes for cvtColor: descriptions added #25616
This PR contains descriptions for various RGB <-> YUV color conversion codes as well as detailed comments in the source code.
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Fix handeye #24897
Fixes to the hand-eye calibration methods, from #24871.
The Tsai method is sensitive to poses separated by small rotations, so I filter those out.
The Horaud and Daniilidis methods use quaternions (and dual quaternions), where $q$ and $-q$ represent the same transform.
However, these methods depend on the gripper motion and camera motion having the same sign for the real part.
The fix was simply to multiply the (dual) quaternions by -1 if their real part is negative.
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Tests for cvSmooth -> tests for boxFilter #25634fixes#25448
### Motivation
The obsolete function `cvSmooth` has two modes in which it calls `cv::boxFilter()` inside with and without normalization.
This function is covered by tests exactly for that modes.
This means that by replacing `cvSmooth` call by `cv::boxFilter()` we will leave the coverage untouched (but more obvious) and remove that obsolete function from tests.
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Tests for cv::rotate() added #25633fixes#25449
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core: try to solve warnings caused by Apple's new LAPACK interface #24804
Resolves https://github.com/opencv/opencv/issues/24660
Apple's BLAS documentation: https://developer.apple.com/documentation/accelerate/blas?language=objc
New interface since macOS >= 13.3, iOS >= 16.4.
Todo:
- [x] Detect macOS version.
- [x] ~Detect iOS versions (major and minor version).~ No calling of Accelerate New LAPACK on iOS.
- [x] Solve calling `cblas_cgemm` and `cblas_zgemm`.
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Fix v_round and enable unit tests for scalable universal intrinsic 64F type. #25586
This may be a legacy issue from the previous PR #24325. I don't quite remember why the float 64 part of the unit test was not enabled at that time.
Whatever, this patch enables the unit tests for scalable 64F type , and makes the necessary modifications to the RVV backend to make the tests pass.
This patch is compiled by GCC 14 and LLVM 17 &18, and tested on QEMU and k230.
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HAL mul8x8to16 added #25506Fixes#25034
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HAL for projectPoints() added #25511
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Feature barcode detector parameters #24903
Attempt to solve #24902 without changing the default detector behaviour.
Megre with extra: https://github.com/opencv/opencv_extra/pull/1150
**Introduces new parameters and methods to `cv::barcode::BarcodeDetector`**.
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imgproc: C-API cleanup, drawContours refactor #25564
Changes:
* moved several macros from types_c.h to cvdef.h (assuming we will continue using them)
* removed some cases of C-API usage in _imgproc_ module (`CV_TERMCRIT_*` and `CV_CMP_*`)
* refactored `drawContours` to use C++ API instead of calling `cvDrawContours` + test for filled contours with holes (case with non-filled contours is simpler and is covered in some other tests)
#### Note:
There is one case where old drawContours behavior doesn't match the new one - when `contourIdx == -1` (means "draw all contours") and `maxLevel == 0` (means draw only selected contours, but not what is inside).
From the docs:
> **contourIdx** Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
> **maxLevel** Maximal level for drawn contours. If it is 0, only the specified contour is drawn. If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
Old behavior - only one first contour is drawn:
a
New behavior (also expected by the test) - all contours are drawn:
Current net exporter `dump` and `dumpToFile` exports the network structure (and its params) to a .dot file which works with `graphviz`. This is hard to use and not friendly to new user. What's worse, the produced picture is not looking pretty.
dnn: better net exporter that works with netron #25582
This PR introduces new exporter `dumpToPbtxt` and uses this new exporter by default with environment variable `OPENCV_DNN_NETWORK_DUMP`. It mimics the string output of a onnx model but modified with dnn-specific changes, see below for an example.
## Usage
Call `cv::dnn::Net::dumpToPbtxt`:
```cpp
TEST(DumpNet, dumpToPbtxt) {
std::string path = "/path/to/model.onnx";
auto net = readNet(path);
Mat input(std::vector<int>{1, 3, 640, 480}, CV_32F);
net.setInput(input);
net.dumpToPbtxt("yunet.pbtxt");
}
```
Set `export OPENCV_DNN_NETWORK_DUMP=1`
```cpp
TEST(DumpNet, env) {
std::string path = "/path/to/model.onnx";
auto net = readNet(path);
Mat input(std::vector<int>{1, 3, 640, 480}, CV_32F);
net.setInput(input);
net.forward();
}
```
---
Note:
- `pbtxt` is registered as one of the ONNX model suffix in netron. So you can see `module: ai.onnx` and such in the model.
- We can get the string output of an ONNX model with the following script
```python
import onnx
net = onnx.load("/path/to/model.onnx")
net_str = str(net)
file = open("/path/to/model.pbtxt", "w")
file.write(net_str)
file.close()
```
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Check range for type-dependant function tables #25598
Address https://github.com/opencv/opencv/issues/24703
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Support Transpose op in TFlite #25297
**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1168
The purpose of this PR is to introduce support for the Transpose op in TFlite format and to add a shape comparison between the output tensors and the references. In some occasional cases, the shape of the output tensor is `[1,4,1,1]`, while the shape of the reference tensor is `[1,4]`. Consequently, the norm check incorrectly reports that the test has passed, as the residual is zero.
Below is a Python script for generating testing data. The generated data can be integrated into the repo `opencv_extra`.
```python
import numpy as np
import tensorflow as tf
PREFIX_TFL = '/path/to/opencv_extra/testdata/dnn/tflite/'
def generator(input_tensor, model, saved_name):
# convert keras model to .tflite format
converter = tf.lite.TFLiteConverter.from_keras_model(model)
#converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.optimizations = [None]
tflite_model = converter.convert()
with open(f'{PREFIX_TFL}/{saved_name}.tflite', 'wb') as f:
f.write(tflite_model)
# save the input tensor to .npy
if input_tensor.ndim == 4:
opencv_tensor = np.transpose(input_tensor, (0,3,1,2))
else:
opencv_tensor = input_tensor
opencv_tensor = np.copy(opencv_tensor, order='C').astype(np.float32)
np.save(f'{PREFIX_TFL}/{saved_name}_inp.npy', opencv_tensor)
# generate output tenosr and save it to .npy
mat_out = model(input_tensor).numpy()
mat_out = np.copy(mat_out, order='C').astype(np.float32)
if mat_out.ndim == 4:
mat_out = np.transpose(mat_out, (0,3,1,2))
interpreter = tf.lite.Interpreter(model_content=tflite_model)
out_name = interpreter.get_output_details()[0]['name']
np.save(f'{PREFIX_TFL}/{saved_name}_out_{out_name}.npy', mat_out)
def build_transpose():
model_name = "keras_permute"
mat_in = np.array([[[1,2,3], [4,5,6]]], dtype=np.float32)
model = tf.keras.Sequential()
model.add(tf.keras.Input(shape=(2,3)))
model.add(tf.keras.layers.Permute((2,1)))
model.summary()
generator(mat_in, model, model_name)
if __name__ == '__main__':
build_transpose()
```
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Remove dnn::layer::allocate in doc #25591
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highgui: wayland: expand image width if title bar cannot be shown
Close#25560
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Fixed OpenVINO gemm layer #25518
Fixed OpenVINO gemm layer
The problem was that our layer didn't properly handle all the possible gemm options in OpenVINO mode
Fixes#25472
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highgui: wayland: fix to pass highgui test #25551Close#25550
- optimize Mat to XRGB8888 conversion with OpenCV functions
- extend to support CV_8S/16U/16S/32F/64F
- extend to support 1/4 channels
- fix to update value timing
- initilize slider_ value if value is not nullptr.
- Update user-ptr value and call on_change() function if cv_wl_trackbar::draw() is not called.
- Update usage of WAYLAND/XDG macro to avoid reference undefined macro.
- Update documents
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Merge pull request #25565 from savuor/rv/hal_eq_hist
HAL for equalizeHist() added #25565fixes#25530
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Transform offset to indeces for MatND in minMaxIdx HAL #25563
Address comments in https://github.com/opencv/opencv/pull/25553
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Merge pull request #25554 from savuor:rv/hal_lut
HAL for LUT added #25554
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Fix HAL interface for hal_ni_minMaxIdx #25553
Fixes https://github.com/opencv/opencv/issues/25540
The original implementation call HAL with the same parameters independently from amount of channels. The patch uses HAL correctly for the case cn > 1.
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HAL added for Otsu threshold #25509fixes#25393
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videoio: obsensor: remove OB_EXT_CMD10 to suppress warning #25523Close#25522
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It is said "see above" because calcBackProject is listed under calcHist function in source file, while it is listed before due to the lexicographic ordering.
highgui: wayland: show "NO" status if dependency is missing #25496Close#25495
- [doc] Add document to enable Wayland highgui-backend in ubuntu 24.04.
- [build] Show "NO" status instead of version if dependency library is missing.
- [build] Fix to find Wayland EGL.
- [fix] Add some callback stub functions to suppress build warning.
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HAL for Hamming norm added #25491fixes#25474
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Perf tests for SVD and solve() created #25450fixes#25336
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Fix documentation for findEssentialMat to reflect how it actually works. #25488
Documentation for findEssentialMat() incorrectly states that the method uses the same cameraMatrix for both lists of points even though there are two cameraMatrix and distCoeffs.
Checked the code and it does the right thing i.e. uses cameraMatrix1, distCoeffs1 for points1 and cameraMatrix2, distCoeffs2 for points2.
Updated the documentation for the method to clarify what it does. The code itself is not changed.
G-API OV backend requires cv::MediaFrame #24938
### Pull Request Readiness Checklist
**Background_subtraction demo G-API issue. Update:**
Porting to API20 resulted in an error (both for CPU and NPU):
```
[ERROR] OpenCV(4.9.0-dev) /home/runner/work/open_model_zoo/open_model_zoo/cache/opencv/modules/gapi/src/backends/ov/govbackend.cpp:813: error: (-215: assertion not done ) cv::util::holds_alternative<cv::GMatDesc>(input_meta) in function 'cfgPreProcessing'
```
Adding cv::MediaFrame support to govbackend resulted in the following (tested with CPU):
<img width="941" alt="image" src="https://github.com/opencv/opencv/assets/52502732/3a003d61-bda7-4b1e-9117-3410cda1ba32">
### TODO
- [ ] **As part of the review process [this comment](https://github.com/opencv/opencv/pull/24938#discussion_r1487694043) was addressed which make it impossible to run the demo. I will bring those changes back in a separate PR [support `PartialShape`]**
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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imgproc: refactor EMD to reduce C-API usage #25469
- added more tests for EMD
- refactored to remove CvArr
- used BufferArea for memory allocations
- renamed functions and variables and formatted the code
- kept legacy functions intact in separate header
Calibrate hand eye datatype fix#25423
Fix for issue https://github.com/opencv/opencv/issues/25421.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Add cv::currentUIFramework #25354
issue https://github.com/opencv/opencv/issues/25329
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Remove unnecessary FIXIT section in grfmt_tiff.cpp #25447
No int64/uint64 is used in the code anymore.
grfmt_tiff.hpp includes the tiff.h header inside of the tiff_dummy_namespace declaration. One implication of this is that all namespaced declarations made in tiff.h become qualified with tiff_dummy_namespace::.
Because tiff.h includes standard library headers, the std namespace declarations are converted to tiff_dummy_namespace::std declarations.
Subsequently, grfmt_tiff.hpp declares using namespace tiff_dummy_namespace;.
This can lead to an ambiguity error during the resolution of the std namespace.
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Fix race condition in InternalFFMpegRegister initialization. #25419
initLogger_ does not check if the logger has been initizalized before and it might initialize it several times from different threads, racing with other threads that are logging.
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Handle top and left border masked pixels correctly in inpaint method #25402Fixes#25389
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Fixed ONNX range layer #25414
Partially address https://github.com/opencv/opencv/issues/25363
Fixed ONNX range layer. It should support any input type.
Added tests (extra [PR](https://github.com/opencv/opencv_extra/pull/1170))
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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
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Rename remaining float16_t for future proof #25387
Resolves comment: https://github.com/opencv/opencv/pull/25217#discussion_r1547733187.
`std::float16_t` and `std::bfloat16_t` are introduced since c++23: https://en.cppreference.com/w/cpp/types/floating-point.
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core: persistence: output reals as human-friendly expression. #25351Close#25073
Related https://github.com/opencv/opencv/pull/25087
This patch is need to merge same time with https://github.com/opencv/opencv_contrib/pull/3714
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imgproc: fix unaligned memory access in filters and Gaussian blur #25364
* filter/SIMD: removed parts which casted 8u pointers to int causing unaligned memory access on RISC-V platform.
* GaussianBlur/fixed_point: replaced casts from s16 to u32 with union operations
Performance comparison:
- [x] check performance on x86_64 - (4 threads, `-DCPU_BASELINE=AVX2`, GCC 11.4, Ubuntu 22) - [report_imgproc_x86_64.ods](https://github.com/opencv/opencv/files/14904702/report_x86_64.ods)
- [x] check performance on AArch64 - (4 cores of RK3588, GCC 11.4 aarch64, Raspbian) - [report_imgproc_aarch64.ods](https://github.com/opencv/opencv/files/14908437/report_aarch64.ods)
Note: for some reason my performance results are quite unstable, unaffected functions show speedups and slowdowns in many cases. Filter2D and GaussianBlur seem to be OK.
Slightly related PR: https://github.com/opencv/ci-gha-workflow/pull/165
Reworked findContours to reduce C-API usage #25146
What is done:
* rewritten `findContours` and `icvApproximateChainTC89` using C++ data structures
* extracted LINK_RUNS mode to separate new public functions - `findContoursLinkRuns` (it uses completely different algorithm)
* ~added new public `cv::approximateChainTC89`~ - **❌ decided to hide it**
* enabled chain code output (method = 0, no public enum value for this in C++ yet)
* kept old function as `findContours_old` (exported, but not exposed to user)
* added more tests for findContours (`test_contours_new.cpp`), some tests compare results of old function with new one. Following tests have been added:
* contours of random rectangle
* contours of many small (1-2px) blobs
* contours of random noise
* backport of old accuracy test
* separate test for LINK RUNS variant
What is left to be done (can be done now or later):
* improve tests:
* some tests have limited verification (e.g. only verify contour sizes)
* perhaps reference data can be collected and stored
* maybe more test variants can be added (?)
* add enum value for chain code output and a method of returning starting points (e.g. first 8 elements of returned `vector<uchar>` can represent 2 int point coordinates)
* add documentation for new functions - **✔️ DONE**
* check and improve performance (my experiment showed 0.7x-1.1x some time ago)
* remove old functions completely (?)
* change contour return order (BFS) or allow to select it (?)
* return result tree as-is (?) (new data structures should be exposed, bindings should adapt)
core: doc: add note for countNonZero, hasNonZero and findNonZero #25356Close#25345
### 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
