added Aruco MIP dictionaries #23785
added Aruco MIP dictionaries: DICT_ARUCO_MIP_16h3, DICT_ARUCO_MIP_25h7, DICT_ARUCO_MIP_36h12 from [Aruco.js](https://github.com/damianofalcioni/js-aruco2), converted in opencv format using https://github.com/damianofalcioni/js-aruco2/blob/master/src/dictionaries/utils/dic2opencv.js
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G-API: Expose explicit preprocessing for IE Backend #23786
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G-API: Refine Semantic Segmentation Demo #23766
### Overview
* Supported demo working with camera id (e.g `--input=0`)
* Supported 3d output segmentation models (e.g `deeplabv3`)
* Supported `desync` execution
* Supported higher camera resolution
* Changed the color map to pascal voc (https://cloud.githubusercontent.com/assets/4503207/17803328/1006ca80-65f6-11e6-9ff6-36b7ef5b9ac6.png)
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Although acceptible to Intel CPUs, it's still undefined behaviour according to the C++ standard.
It can be replaced with memcpy, which makes the code simpler, and it generates the same assembly code with gcc and clang with -O2 (verified with godbolt).
Also expanded the test to include other little endian CPUs by testing for __LITTLE_ENDIAN__.
Add AVIF support through libavif. #23596
This is to fix https://github.com/opencv/opencv/issues/19271
Extra: https://github.com/opencv/opencv_extra/pull/1069
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DNN: fix bug for X86 Winograd #23763
Address https://github.com/opencv/opencv/issues/23760
The patch aims to add a runtime check for X86 platform without AVX(2).
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Assertion Fix in Split Layer #23746
### Pull Request Readiness Checklist
This PR fixes issue mentioned in [#23663](https://github.com/opencv/opencv/issues/23663)
Merge with https://github.com/opencv/opencv_extra/pull/1067
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imgproc: add contour values check to IntelligentScissorsMB tests
Preparation for the #21959 changes as per @asmorkalov's https://github.com/opencv/opencv/pull/21959#issuecomment-1560511500 suggestion.
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[G-API] Implement OpenVINO 2.0 backend #23595
### Pull Request Readiness Checklist
Implemented basic functionality for `OpenVINO` 2.0 G-API backend.
#### Overview
- [x] Implement `Infer` kernel with some of essential configurable parameters + IR/Blob models format support.
- [ ] Implement the rest of kernels: `InferList`, `InferROI`, `Infer2` + other configurable params (e.g reshape)
- [x] Asyncrhonous execution support
- [ ] Remote context support
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GAPI Fluid SIMD:Add support of new several types for the Merge3
- Support of the new several types was added.
- Fixes for the Split/Merge and ConvertTo issues.
G-API: Integration branch for ONNX & Python-related changes #23597
# Changes overview
## 1. Expose ONNX backend's Normalization and Mean-value parameters in Python
* Since Python G-API bindings rely on `Generic` infer to express Inference, the `Generic` specialization of `onnx::Params` was extended with new methods to control normalization (`/255`) and mean-value; these methods were exposed in the Python bindings
* Found some questionable parts in the existing API which I'd like to review/discuss (see comments)
UPD:
1. Thanks to @TolyaTalamanov normalization inconsistencies have been identified with `squeezenet1.0-9` ONNX model itself; tests using these model were updated to DISABLE normalization and NOT using mean/value.
2. Questionable parts were removed and tests still pass.
### Details (taken from @TolyaTalamanov's comment):
`squeezenet1.0.*onnx` - doesn't require scaling to [0,1] and mean/std because the weights of the first convolution already scaled. ONNX documentation is broken. So the correct approach to use this models is:
1. ONNX: apply preprocessing from the documentation: https://github.com/onnx/models/blob/main/vision/classification/imagenet_preprocess.py#L8-L44 but without normalization step:
```
# DON'T DO IT:
# mean_vec = np.array([0.485, 0.456, 0.406])
# stddev_vec = np.array([0.229, 0.224, 0.225])
# norm_img_data = np.zeros(img_data.shape).astype('float32')
# for i in range(img_data.shape[0]):
# norm_img_data[i,:,:] = (img_data[i,:,:]/255 - mean_vec[i]) / stddev_vec[i]
# # add batch channel
# norm_img_data = norm_img_data.reshape(1, 3, 224, 224).astype('float32')
# return norm_img_data
# INSTEAD
return img_data.reshape(1, 3, 224, 224)
```
2. G-API: Convert image from BGR to RGB and then pass to `apply` as-is with configuring parameters:
```
net = cv.gapi.onnx.params('squeezenet', model_filename)
net.cfgNormalize('data_0', False)
```
**Note**: Results might be difference because `G-API` doesn't apply central crop but just do resize to model resolution.
---
`squeezenet1.1.*onnx` - requires scaling to [0,1] and mean/std - onnx documentation is correct.
1. ONNX: apply preprocessing from the documentation: https://github.com/onnx/models/blob/main/vision/classification/imagenet_preprocess.py#L8-L44
2. G-API: Convert image from BGR to RGB and then pass to `apply` as-is with configuring parameters:
```
net = cv.gapi.onnx.params('squeezenet', model_filename)
net.cfgNormalize('data_0', True) // default
net.cfgMeanStd('data_0', [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
```
**Note**: Results might be difference because `G-API` doesn't apply central crop but just do resize to model resolution.
## 2. Expose Fluid & kernel package-related functionality in Python
* `cv::gapi::combine()`
* `cv::GKernelPackage::size()` (mainly for testing purposes)
* `cv::gapi::imgproc::fluid::kernels()`
Added a test for the above.
## 3. Fixed issues with Python stateful kernel handling
Fixed error message when `outMeta()` of custom python operation fails.
## 4. Fixed various issues in Python tests
1. `test_gapi_streaming.py` - fixed behavior of Desync test to avoid sporadic issues
2. `test_gapi_infer_onnx.py` - fixed model lookup (it was still using the ONNX Zoo layout but was NOT using the proper env var we use to point to one).
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better accuracy for _rotatedRectangleIntersection() (proposal for #23546) #23690
_rotatedRectangleIntersection() can be (statically) customized to use double instead of float for better accuracy
this is a proposal for experimentation around #23546
for better accuracy, _rotatedRectangleIntersection() could use double. It will still return cv::Point2f list for backward compatibility, but the inner computations are controlled by a typedef
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imgproc: add basic IntelligentScissorsMB performance test #23698
Adding basic performance test that can be used before and after the #21959 changes etc. as per @asmorkalov's https://github.com/opencv/opencv/pull/21959#issuecomment-1565240926 comment.
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Pointer arithmetic overflow is always undefined, whether signed or unsigned.
It warned here:
`Addition of unsigned offset to 0x00017fd31b97 overflowed to 0x00017fd30c97`
Convert the offset to a signed number, so that we can offset either forward or backwards.
In my own use of OpenCV at least, this is the only case of pointer arithmetic overflow.
Import and export np.float16 in Python #23691
### Pull Request Readiness Checklist
* Also, fixes `cv::norm` with `NORM_INF` and `CV_16F`
resolves https://github.com/opencv/opencv/issues/23687
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Python typing stub generation #20370
Add stub generation to `gen2.py`, addressing #14590.
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Python bindings for CV_8UC(n) and other types macros #23679
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resolves https://github.com/opencv/opencv/issues/23628#issuecomment-1562468327
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Support ONNX operator QLinearSoftmax in dnn #23655
Resolves https://github.com/opencv/opencv/issues/23636.
Merge with https://github.com/opencv/opencv_extra/pull/1064.
This PR maps the QLinearSoftmax (from com.microsoft domain) to SoftmaxInt8 in dnn along with some speed optimization.
Todo:
- [x] support QLinearSoftmax with opset = 13
- [x] add model and test data for QLinearSoftmax with opset = 13
- [x] ensure all models have dims >= 3.
- [x] add the script to generate model and test data
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CV_MAKETYPE Python binding #23674
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/23628
```python
import cv2 as cv
t = cv.CV_MAKETYPE(cv.CV_32F, 4)
```
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Fix truncated sentenced in boxPoints documentation #22975#23662Resolves#22975
Completed the sentence as per the suggestion given in the issue #22975
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QRCodeDetector: don't floodFill with outside-of-image seedPoint #23612Fixes#21532.
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/build/build_cuda/3p/opencv/linux-x64/ubuntu22.04/Debug/modules/dnn/src/layers/cpu_kernels/convolution.cpp: In function 'void cv::dnn::packData8(char*&, float*&, int&, int&, int&, const int*, int, int, int)':
/build/build_cuda/3p/opencv/linux-x64/ubuntu22.04/Debug/modules/dnn/src/layers/cpu_kernels/convolution.cpp:448:43: error: 'CONV_NR' was not declared in this scope; did you mean 'CONV_3D'?
448 | vx_store(inpbufC_FP32 + k*CONV_NR, vx_load(inptrInC + k1));
| ^~~~~~~
| CONV_3D
Fix even input dimensions for INTER_NEAREST_EXACT #23634
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/22204
related: https://github.com/opencv/opencv/issues/9096#issuecomment-1551306017
/cc @Yosshi999
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LSTM ONNX Layout Attribute Support #23614
### Explanation
This PR contains necessary changes to support `layout` attribute. This attributes is present in [ONNX](https://github.com/onnx/onnx/blob/main/docs/Operators.md#lstm) and [Torch](https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html#lstm) (in touch it is name as `batch_first=True`) libraries. When `layout = 1` input to LSTM layer is expected to have batch dimension first -> `[batch_size, sequence_length, features]` vs `layout = 0` - default `[sequence_length, batch_size, features]`
### Test Data
Test data and data generator for PR located here [#1063](https://github.com/opencv/opencv_extra/pull/1063)
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videoio/FFmpeg: increased packet read attempt limit, allow configuring it
resolves#9455
related #3225
* Use different counters for wrong packets recieved by demuxer and errors from decoder
* Allow modifying these counters via environment variables `OPENCV_FFMPEG_READ_ATTEMPTS`/`OPENCV_FFMPEG_DECODE_ATTEMPTS`
* Added logging when reading breaks at one of error limits
Notes:
* I've been able to reproduce original issue with a video file with 14 total streams (video + audio + subtitles), at some point in the video only packets from the last stream are being sent by the demuxer, thus exceeding our limit. For my specific video total number of packets from wrong stream was about 2700. I've chosen 4096 as default value.
* Default limit of decoding attempts is quite low, because I'm not sure in which cases it can be exceeded (network stream?). I tried to read 8k video from the disk, but it did not cause break at decode point.
Build DNN without Protobuf
DNN module can be built without Protobuf for Darknet, TFLite, OpenVINO, Torch (not PyTorch) models.
```
cmake \
-DCMAKE_BUILD_TYPE=Release \
-DBUILD_LIST=dnn \
-DWITH_PROTOBUF=OFF \
-DWITH_OPENCL=OFF
7.1M lib/libopencv_dnn.so.4.7.0
```
```
cmake \
-DCMAKE_BUILD_TYPE=Release \
-DBUILD_LIST=dnn \
-DWITH_OPENCL=OFF
3.9M lib/libopencv_dnn.so.4.7.0
```
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Improve document of cv::RotatedRect for #23335#23342fix#23335
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don't ignore documentation for cv::format in doxygen #23555
Issue https://github.com/opencv/opencv/issues/23553
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AGP 8.0 build.gradle namespace and aidl buildFeature requirement added #23447
Hello,
Android Gradle Plugin version 8.0 is asking for namespace. This is become mandatory and after I update my AGP to 8.0, I got this error
```
Namespace not specified. Please specify a namespace in the module's build.gradle file like so:
android {
namespace 'com.example.namespace'
}
If the package attribute is specified in the source AndroidManifest.xml, it can be migrated automatically to the namespace value in the build.gradle file using the AGP Upgrade Assistant; please refer to https://developer.android.com/studio/build/agp-upgrade-assistant for more information.
```
This change fix this future releases. However I am not sure how opencv wants to user namespace I used "org.opencv" if there is a different namespace please let me know so I can changed that too. Also should I add namepsace into "opencv/modules/java/android_sdk/android_gradle_lib/build.gradle" here ?
### Sources
Android developer link: https://developer.android.com/studio/preview/features#namespace-dsl
Issue Tracker Google: https://issuetracker.google.com/issues/191813691?pli=1#comment19
### Pull Request Readiness Checklist
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Import and inference INT8 quantized TFLite model #23409
### Pull Request Readiness Checklist
* Support quantized TFLite models
* Enable fused activations (FP32, INT8)
**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1048
on the image, green boxes are from TFLite and red boxes from OpenCV
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Fix ONNX parser for single-layer LSTM hidden and cell states #23475
### Fix ONNX parser for single-layer LSTM hidden and cell states
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This PR addresses #21118 [issue](https://github.com/opencv/opencv/issues/21118). The problem is that the ONNX parser is unable to read the hidden state and cell state for single-layer LSTMs. This PR fixes the issue by updating the parser to correctly read hidden and cell states.
DNN: Add New API blobFromImageParam #22750
The purpose of this PR:
1. Add new API `blobFromImageParam` to extend `blobFromImage` API. It can support the different data layout (NCHW or NHWC), and letter_box.
2. ~~`blobFromImage` can output `CV_16F`~~
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dnn: Support more operators in CANN backend #23401
This PR adds the support of following layers:
- [x] Sub
- [x] PRelu
- [x] DeConv
- [x] Also warn users if backend is switched back to default if some of the layers are not supported.
- [ ] [Dropped] LSTM: some hacks (adding layers) were introduced which makes it even harder to build the graph for CANN backend.
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Added LSTM and GRU tests for various batch and input length sizes #23501
Added tests with various sequence length and batch sizes
Test data: https://github.com/opencv/opencv_extra/pull/1057
### Pull Request Readiness Checklist
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* Replaced most remaining sprintf with snprintf
* Deprecated encodeFormat and introduced new method that takes the buffer length
* Also increased buffer size at call sites to be a little bigger, in case int is 64 bit
Added explicit cast to unsigned before doing the left shift.
This was caught by UBSan which reported things like:
drawing.cpp:361:22: runtime error: left shift of negative value -26214
drawing.cpp:383:22: runtime error: left shift of negative value -78642
Fix python bindings for setCharucoParameters #23436
setCharucoParameters fails in python
Fixes: https://github.com/opencv/opencv/issues/23440
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Add scrollWheel to Cocoa #23394
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Add notes for the output format of FaceDetectorYN.detect()
Resolves https://github.com/opencv/opencv/pull/23020#issuecomment-1499010015
### Pull Request Readiness Checklist
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imgcodecs: tiff: Support to encode for CV_32S with compression params
Fix https://github.com/opencv/opencv/issues/23416
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Fix identifying initializers in ONNX graph simplification #23296
Fixes https://github.com/opencv/opencv/issues/23295
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**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1050
For 32 bits per pixel with 3 or 4 channel destination images, apply scale factor and mask to parse BMP files correctly
closes#23445
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This makes LineSegmentDetector deterministic by using stable_sort for ordering points by norm. Without this change the region growing in LSD is non-determinstic and thus the returned lines are changing between invocations.
This is a replacement for https://github.com/opencv/opencv/pull/23370
Propagate inputs info for ONNX and TFLite models
### Pull Request Readiness Checklist
Needed for generic applications such as benchmarking pipelines. So OpenCV can tell about the default input shapes specified in the models.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Support VideoCapture CAP_PROP_AUTO_WB and CV_CAP_PROP_WHITE_BALANCE_BLUE_U for DShow
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https://github.com/opencv/opencv/issues/19621https://github.com/opencv/opencv/issues/21408
### Before apply this pull request console output.
before AWB setting
CAP_PROP_WHITE_BALANCE_BLUE_U: 2000
CAP_PROP_AUTO_WB: -1
after AWB disable setting
CAP_PROP_WHITE_BALANCE_BLUE_U: 2000
CAP_PROP_AUTO_WB: -1
after AWB enable setting
CAP_PROP_WHITE_BALANCE_BLUE_U: 2000
CAP_PROP_AUTO_WB: -1
after Manual WB(and Disable AWB) setting
CAP_PROP_WHITE_BALANCE_BLUE_U: 2000
CAP_PROP_AUTO_WB: -1
### After apply this pull request console output.
before AWB setting
CAP_PROP_WHITE_BALANCE_BLUE_U: 2000
CAP_PROP_AUTO_WB: 0
after AWB disable setting
CAP_PROP_WHITE_BALANCE_BLUE_U: 4000
CAP_PROP_AUTO_WB: 0
after AWB enable setting
CAP_PROP_WHITE_BALANCE_BLUE_U: 4000
CAP_PROP_AUTO_WB: 1
after Manual WB(and Disable AWB) setting
CAP_PROP_WHITE_BALANCE_BLUE_U: 2000
CAP_PROP_AUTO_WB: 0
### Test Code
[OpenCvVideoCapTest.zip](https://github.com/opencv/opencv/files/10825399/OpenCvVideoCapTest.zip)
Added argument to print notice in `roiSelector.cpp`
Related Issue : https://github.com/opencv/opencv/issues/23175
I've added a printNotice argument to `selectROI` (and it's overload) and `selectROIs` functions.
I've also updated the function declarations in `highgui.hpp`.
Tested by building locally.
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Fixed potential memory leak in flann
Issue #22426
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4 failed tests in open_test_dnn listed below:
* Test_Caffe_layers.Conv_Elu/0, where GetParam() = OCV/CPU
* Test_ONNX_layers.ConvResizePool1d/0, where GetParam() = OCV/CPU
* Test_TensorFlow_layers.tf_reshape_nhwc/0, where GetParam() = OCV/CPU
* Test_Torch_layers.net_inception_block/0, where GetParam() = OCV/CPU
In winofunc_AtXA_8x8_f32 and winofunc_BtXB_8x8_f32
implementation, incorrect input parameters cause tests failure.
Add four new different variables for the last four input parameters of
v_transpose4x4 to fix bugs, and update related comments.
Signed-off-by: tingbo.liao <tingbo.liao@starfivetech.com>
Related issue: https://github.com/opencv/opencv_zoo/issues/136
Features added:
- Support operators with multiple output: ONNX Split.
- Support Slice without steps.
Bugs fixed:
- Wrong settings in ClipByValue (Relu6).
- Wrong calculation of pads in convolution layer (It is wrong generally but only fixed specifically for CANN for now).
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Fixed strict type in slerp and spline; Fixed nlerp usage condition
Fixes#23293
The PR is fixing the issue described in [Issue #23293 ](https://github.com/opencv/opencv/issues/23293)
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Resolves https://github.com/opencv/opencv/issues/23304
Fixes the incorrect pixel grid
Switches type to double to avoid precision loss as all callers use doubles
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In case of huge (and probably invalid) input, make sure we do not
rely only on the while loops for truncation.
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Make the GTK+3 API the default one by wrapping the missing GTK+2 functions in defines
Make sure to always guard with GTK_VERSION2 or GTK_VERSION3 to allow future addition
of Gtk4
* different interpolation by double image
* fixing scaling mapping
* fixing a test
* added an option to enable previous interpolation
* added doxygen entries for the new parameter
* ASSERT_TRUE -> ASSERT_EQ
* changed log message when using old upscale mode
Fix misaligned-pointer-use in intrin_sse.hpp
* Fix misaligned-pointer-use in intrin_sse.hpp
* Use _mm_loadu_si32() instead of memcpy()
* Use CV_DECL_ALIGNED instead of _mm_loadu_si32()
Fix rect_nfa (lsd)
* Fix missing log_gamma in nfa()
Comparing the nfa function with the function in the binomial_nfa repository (https://github.com/rafael-grompone-von-gioi/binomial_nfa/blob/main/C99/log_binomial_nfa.c#L152), the first log_gamma call is missing.
* Fix rect_nfa pixel index
* Replace std::rotate
* Rename tmp to v_tmp
* Replace auto and std::min_element
* Change slope equality check to int
* Fix left limit check
dnn: add layer normalization for vision transformers
* add layer norm onnx parser, impl and tests
* add onnx graph simplifier for layer norm expanded
* handle the case when constants are of type Initializer
* add test case for layer norm expanded with initializers
* use CV_Assert & CV_CheckType in place of CV_Assert_N; use forward_fallback for OCL_FP16
* use const ref / ref in parameters of invoker::run; extract inner const if from nested loop; use size_t in place of ull
* template hasBias
* remove trailing whitespace
* use pointer parameter with null check; move normSize division & mean_square division outside of loop; use std::max to ensure positive value before std::sqrt
* refactor implementation, optimize parallel_for
* disable layer norm expanded
* remove the removal of layer norm optional outputs
Omit the first check of the double-checked locking pattern in
recordException() in parallel.cpp when CV_THREAD_SANITIZER is defined.
This should only slow recordException() down when the thread sanitizer
is used, and avoids the TSAN data race warning.
Adding HEVC/H265 FourCC support to MSMF video writer
* Adding HEVC/H265 fourcc to MSMF video writer
Adding HEVC/H265 fourcc to MSMF video writer. I have verified it with my own video input stream, and it works well on my workstation.
* Update video io testing
* Adding macro fence to get rid of compiler error
H265/HEVC encoder is only available in Windows or later. https://learn.microsoft.com/en-us/windows/win32/medfound/h-265---hevc-video-encoder
* Update test_video_io.cpp
Backport of #22992 to 3.4
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Add `estimateSE2(...)`, `estimateSE3(...)`, `estimateSIM2(...)`, `estimateSIM3(...)` for estimating an geometric transformation with rotation and translation (with scaling for SIM) using USAC: as alternative for `estimateAffinePartial2D` and `estimateAffine3D`.
Modified test module.
Remove unused variables.
Remove initializer of unused variable.
Add interfaces to accept UsacParams() and corresponding test codes.
Revise test code.
PartialNd removed
Umeyama rewritten for code quality & speed
comments & minors
rise number of points
fix, and +30% faster!
only one number should be that big
remove USAC code, leave fix only
big number
* fix openmp include and link issue on macos
* turn off have_openmp if OpenMP_CXX_INCLUDE_DIRS is empty
* test commit
* use condition HAVE_OPENMP and OpenMP_CXX_LIBRARIES for linking
* remove trailing whitespace
* remove notes
* update conditions
* use OpenMP_CXX_LIBRARIES for linking
Fix broken paper link for fastNlMeansDenoising
* Fix broken link
* Move citation to `opencv.bib`
* Cite researchgate reference
* Correct citation label
* Use semantic scholar BibTex
Use reinterpret instead of c-style casting for GCC
Co-authored-by: Xu Zhang <xu.zhang@hexintek.com>
Co-authored-by: Maksim Shabunin <maksim.shabunin@gmail.com>
Usage of imread(): magic number 0, unchecked result
* docs: rewrite 0/1 to IMREAD_GRAYSCALE/IMREAD_COLOR in imread()
* samples, apps: rewrite 0/1 to IMREAD_GRAYSCALE/IMREAD_COLOR in imread()
* tests: rewrite 0/1 to IMREAD_GRAYSCALE/IMREAD_COLOR in imread()
* doc/py_tutorials: check imread() result
merge with https://github.com/opencv/opencv_contrib/pull/3394
move Charuco API from contrib to main repo:
- add CharucoDetector:
```
CharucoDetector::detectBoard(InputArray image, InputOutputArrayOfArrays markerCorners, InputOutputArray markerIds,
OutputArray charucoCorners, OutputArray charucoIds) const // detect charucoCorners and/or markerCorners
CharucoDetector::detectDiamonds(InputArray image, InputOutputArrayOfArrays _markerCorners,
InputOutputArrayOfArrays _markerIds, OutputArrayOfArrays _diamondCorners,
OutputArray _diamondIds) const
```
- add `matchImagePoints()` for `CharucoBoard`
- remove contrib aruco dependencies from interactive-calibration tool
- move almost all aruco tests to objdetect
### Pull Request Readiness Checklist
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Switch to new OpenVINO API after 2022.1 release
* Pass Layer_Test_Convolution_DLDT.Accuracy/0 test
* Pass test Test_Caffe_layers.Softmax
* Failed 136 tests
* Fix Concat. Failed 120 tests
* Custom nGraph ops. 19 failed tests
* Set and get properties from Core
* Read model from buffer
* Change MaxPooling layer output names. Restore reshape
* Cosmetic changes
* Cosmetic changes
* Override getOutputsInfo
* Fixes for OpenVINO < 2022.1
* Async inference for 2021.4 and less
* Compile model with config
* Fix serialize for 2022.1
* Asynchronous inference with 2022.1
* Handle 1d outputs
* Work with model with dynamic output shape
* Fixes with 1d output for old API
* Control outputs by nGraph function for all OpenVINO versions
* Refer inputs in PrePostProcessor by indices
* Fix cycled dependency between InfEngineNgraphNode and InfEngineNgraphNet.
Add InferRequest callback only for async inference. Do not capture InferRequest object.
* Fix tests thresholds
* Fix HETERO:GPU,CPU plugin issues with unsupported layer
This change replaces references to a number of deprecated NumPy
type aliases (np.bool, np.int, np.float, np.complex, np.object,
np.str) with their recommended replacement (bool, int, float,
complex, object, str).
Those types were deprecated in 1.20 and are removed in 1.24,
cf https://github.com/numpy/numpy/pull/22607.
Parallelize implementation of HDR MergeMertens.
* Parallelize MergeMertens.
* Added performance tests for HDR.
* Ran clang-format.
* Optimizations.
* Fix data path for Windows.
* Remove compiiation warning on Windows.
* Remove clang-format for existing file.
* Addressing reviewer comments.
* Ensure correct summation order.
* Add test for determinism.
* Move result pyramid into sync struct.
* Reuse sync for first loop as well.
* Use OpenCV's threading primitives.
* Remove cout.
**Merge with contrib**: https://github.com/opencv/opencv_contrib/pull/3003
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- [ ] The feature is well documented and sample code can be built with the project CMake
* cann backend impl v1
* cann backend impl v2: use opencv parsers to build models for cann
* adjust fc according to the new transA and transB
* put cann net in cann backend node and reuse forwardLayer
* use fork() to create a child process and compile cann model
* remove legacy code
* remove debug code
* fall bcak to CPU backend if there is one layer not supoorted by CANN backend
* fix netInput forward
G-API: replace GAPI_Assert() with 'false' and '0' to GAPI_Error()
* gapi: GAPI_Error() macro
* gapi: replace GAPI_Assert() with 'false' and '0' to GAPI_Error()
* build: eliminate 'unreachable code' after CV_Error() (MSVC 2015)
* build: eliminate 'unreachable code' warning for MSVS 2015/2017
- observed in constructors stubs with throwing exception
Megre together with https://github.com/opencv/opencv_contrib/pull/3325
1. Move aruco_detector, aruco_board, aruco_dictionary, aruco_utils to objdetect
1.1 add virtual Board::draw(), virtual ~Board()
1.2 move `testCharucoCornersCollinear` to Board classes (and rename to `checkCharucoCornersCollinear`)
1.3 add wrappers to keep the old api working
3. Reduce inludes
4. Fix java tests (add objdetect import)
5. Refactoring
### 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
```
**WIP**
force_builders=linux,win64,docs,Linux x64 Debug,Custom
Xbuild_contrib:Docs=OFF
build_image:Custom=ubuntu:22.04
build_worker:Custom=linux-1
```
videoio: add Orbbec Gemini 2 and Astra 2 camera support
### Test Result
| OS | Compiler | Camera | Result |
|-----|-----------|---------|--------|
|Windows11| (VS2022)MSVC17.3|Orbbec Gemini 2|Pass|
|Windows11| (VS2022)MSVC17.3|Orbbec Astra 2|Pass|
|Ubuntu22.04|GCC9.2|Orbbec Gemini 2|Pass|
|Ubuntu22.04|GCC9.2|Orbbec Astra 2|Pass|
### Pull Request Readiness Checklist
- [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] The feature is well documented and sample code can be built with the project CMake
Address https://github.com/opencv/opencv/issues/22868
Used the same defaults as it's done for FFmpeg
### 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
```
force_builders=Custom
build_image:Custom=gstreamer:16.04
buildworker:Custom=linux-1
```
Add Python bindings for VideoCapture::waitAny #21826
### 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
DNN: reduce the memory used in convolution layer
* reduce the memory in winograd and disabel the test when usage memory is larger than 2gb.
* remove VERY_LOG tag
[teset data in opencv_extra](https://github.com/opencv/opencv_extra/pull/1016)
NanoTrack is an extremely lightweight and fast object-tracking model.
The total size is **1.1 MB**.
And the FPS on M1 chip is **150**, on Raspberry Pi 4 is about **30**. (Float32 CPU only)
With this model, many users can run object tracking on the edge device.
The author of NanoTrack is @HonglinChu.
The original repo is https://github.com/HonglinChu/NanoTrack.
### 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.
- [ ] The feature is well documented and sample code can be built with the project CMake
The current implementation overwrites the result rotation and translation in every iteration.
If SOLVEPNP_ITERATIVE was run as a refinement it will start from the incorrect initial
transformation thus degrading the final outcome.
The old documentation implies that the call is only valid for the next parallel region and must be called again if addtional regions should be affected as well.
Modify the SIMD loop in color_hsv.
* Modify the SIMD loops in color_hsv.
* Add FP supporting in bit logic.
* Add temporary compatibility code.
* Use max_nlanes instead of vlanes for array declaration.
* Use "CV_SIMD || CV_SIMD_SCALABLE".
* Revert the modify of the Universal Intrinsic API
* Fix warnings.
* Use v_select instead of bits manipulation.
Minor refactoring
Partially address review comments
Move DX-related stuff from the sample to a default source
Simplify the default OneVPL config
Address minor review comments
Add class for the default VPL source
WIP: Add initial stub for tests with description
Removing default vpl source and minor refactoring
Refactor default files
Fix build and application crash
Address review comments
Add test on VPL + OCL interaction compared to CPU behavior
Fix test
Introduce libavdevice to make v4l2 available to the ffmpeg backend
* introduce libavdevice to make v4l2 available to the ffmpeg backend
* downgrade the min required libavdevice version to 53.2.0
* make libavdevice optional
* create OCV_OPTION OPENCV_FFMPEG_ENABLE_LIBAVDEVICE and add definition through ocv_add_external_target
* move OCV_OPTION 'OPENCV_FFMPEG_ENABLE_LIBAVDEVICE' to detect_ffmpeg.cmake
OpenEXR encoder: add capability to set the DWA compression level
* OpenEXR encoder: add capability to set the DWA compression level from outside
* Do not try to call `header.dwaCompressionLevel()` if OpenEXR is not version 3 or later
* Minor cleanup
DNN: let Quant and Dequant of ONNX_importer support the Constant input.
* let Quant and Dequant support the Constant input.
* fix negative value of axis.
Setting CAP_PROP_AUTO_EXPOSURE on VideoCapture with backend DSHOW does not change anything. Now with this implementation the property can be used with value 1 for availability.
added blob contours to blob detector
* added blob contours
* Fixed Java regression test after new parameter addition to SimpleBlobDetector.
* Added stub implementation of SimpleBlobDetector::getBlobContours to presume source API compatibility.
* cmake: Fix DirectX detection in mingw
The pragma comment directive is valid for MSVC only. So, the DirectX detection
fails in mingw. The failure is fixed by adding the required linking library
(here d3d11) in the try_compile() function in OpenCVDetectDirectX.cmake file.
Also add a message if the first DirectX check fails.
* gapi: Fix compilation with mingw
These changes remove MSVC specific pragma directive. The compilation fails at
linking time due to absence of proper linking library. The required libraries
are added in corresponding CMakeLists.txt file.
* samples: Fix compilation with mingw
These changes remove MSVC specific pragma directive. The compilation fails at
linking time due to absence of proper linking library. The required libraries
are added in corresponding CMakeLists.txt file.
