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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Libjpeg-turbo update to version 3.0.3 #25623
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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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Fixed CMake Missing variable is: CMAKE_ASM_COMPILE_OBJECT in PNG build #25631
Error message with `-DBUILD_PNG=ON` on ARM64:
```
-- Configuring done
CMake Error: Error required internal CMake variable not set, cmake may not be built correctly.
Missing variable is:
CMAKE_ASM_COMPILE_OBJECT
-- Generating done
CMake Generate step failed. Build files cannot be regenerated correctly.
```
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KleidiCV HAL update to version 0.1.0. #25618
Original integration PR: https://github.com/opencv/opencv/pull/25443
Force the library for testing with CI
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3rdparty: update libpng 1.6.43 #25580
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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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Element-wise test for 1D #25116
This PR introduces 1D parametrized test for element wise layer. The means that the tests covers following layer:
`Clip`, `ReLU6`, `ReLU`,
`GeLU`, `GeluApprox`, `TanH`,
`Swish`, `Mish`, `Sigmoid`,
`ELULayer`, `Abs`, `BNLL`,
`Ceil`, `Floor`, `LogLayer`,
`Round`, `Sqrt`, `Acos`,
`Acosh`, `Asin`, `Asinh`,
`Atan`, `Atanh`, `Cos`,
`Sin`, `Sinh`, `Tan`, `Erf`,
`Reciprocal`, `Cosh`, `HardSwish`,
`Softplus`, `Softsign`, `Celu`,
`HardSigmid`, `Selu`, `ThresholdedRelu`,
`Power`, `Exp`, `Sign`, `Shrink`,
`ChannelsPReLU`
Not sure if this is best way to implement this test.
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Add support for scalar and matrix multiplication in einsum #25595
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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:
![actual_screenshot_08 05 2024](https://github.com/opencv/opencv/assets/3304494/d0ae1d64-ddad-46bb-8acc-6f696874f71b)
a
New behavior (also expected by the test) - all contours are drawn:
![expected_screenshot_08 05 2024](https://github.com/opencv/opencv/assets/3304494/57ccd980-9dde-4006-90ee-19d6ce76912a)
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.
![image](https://github.com/opencv/opencv/assets/17219438/0644bed1-da71-4019-8466-88390698e4df)
## 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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