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`]**
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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.
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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
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[BugFix] dnn (ONNX): Foce dropping constant inputs in parseClip if they are shared #25319
Resolves https://github.com/opencv/opencv/issues/25278
Merge with https://github.com/opencv/opencv_extra/pull/1165
In Gold-YOLO ,`Div` has a constant input `B=6` which is then parsed into a `Const` layer in the ONNX importer, but `Clip` also has the shared constant input `max=6` which is already a `Const` layer and then connected to `Elementwise` layer. This should not happen because in the `forward()` of `Elementwise` layer, the legacy code goes through and apply activation to each input. More details on https://github.com/opencv/opencv/issues/25278#issuecomment-2032199630.
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Ownership check in TFLite importer #25312
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resolves https://github.com/opencv/opencv/issues/25310
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Optimize int8 layers in DNN modules by using RISC-V Vector intrinsic. #25230
This patch optimize 3 functions in the int8 layer by using RVV Native Intrinsic.
This patch was tested on QEMU using VLEN=128 and VLEN=256 on `./bin/opencv_test_dnn --gtest_filter="*Int8*"`;
On the real device (k230, VLEN=128), `EfficientDet_int8` in `opencv_perf_dnn` showed a performance improvement of 1.46x.
| Name of Test | Original | optimized | Speed-up |
| ------------------------------------------ | -------- | ---------- | -------- |
| EfficientDet_int8::DNNTestNetwork::OCV/CPU | 2843.467 | 1947.013 | 1.46 |
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imgcodecs: jpeg: re-support to read CMYK Jpeg #25280Close#25274
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1163
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