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
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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
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Mark cv::Mat(Mat&&) as noexcept #25899
This fixes https://github.com/opencv/opencv/issues/25065
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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.
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Explicitly prefer legacy GL in cmake on Linux? #22836
Pertaining Issue: #22835
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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.
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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.
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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
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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">
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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
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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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