G-API: Add support to set workload type dynamically in both OpenVINO and ONNX OVEP #27460
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Fixes#28095
Problem:
- Stateful kernels dereference null state pointer on line 446 in gcpukernel.hpp
- jinboson confirmed state_ptr is null on x86 Ubuntu (7 hours ago)
- Causes crash with std::shared_ptr assertion on LoongArch64 and strict platforms
Solution (addressing Copilot review feedback from PR #28096):
1. Added null pointer check using CV_Error instead of CV_Assert:
- CV_Assert with && 'message' doesn't display the message correctly
- CV_Error properly reports cv::Error::StsNullPtr with clear message
2. Fixed test kernels to properly initialize state using std::make_shared:
- GOCVStInvalidResize: Initialize state in setup()
- GOCVCountStateSetups: Initialize state before incrementing counter
- Used std::make_shared<int>() for modern C++ best practice
Impact:
- Prevents crashes on platforms with strict null pointer checking
- Provides actionable error message for developers
- Fixes StatefulKernel.StateInitOnceInRegularMode and InvalidReallocatingKernel tests
G-API: Implement cfgClampOutputs option to OpenVINO Params #27600
Added the option `cfgClampOutputs` to control where output clamping is performed for OpenVINO models. When enabled, output values are clamped in the PrePostProcessor stage instead of by the device or plugin. This provides a consistent and standardized clamping method across devices, helping to maintain accuracy regardless of device-specific clamping behavior.
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G-API: Implement cfgEnsureNamedTensors option to OpenVINO Params #27549
Added the option cfgEnsureNamedTensors to be applied on OpenVINO models with nameless tensors. If a tensor doesn't have a name, it sets a default one. The default name is created using OpenVINO's standard `make_default_tensor_name` . `make_default_tensor_name` had to be rewritten because it is only available in OpenVINO's dev_api.
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Implemented:
- A C++ proxy class PythonCustomStreamSource that implements the
IStreamSource interface. This class acts as a bridge between
G-API’s internal streaming engine and user-defined Python
objects. Internally, it stores a reference to a Python object
(PyObject*) and is responsible for: calling the Python object’s
pull() method to retrieve the next frame, calling the descr_of()
method to obtain the frame format description, acquiring and
releasing the Python GIL as needed, converting the returned
numpy.ndarray into cv::Mat, and handling any exceptions or
conversion errors with proper diagnostics.
- A Python-facing factory function, cv.gapi.wip.make_py_src(),
which takes a Python object as an argument and wraps it into a
cv::Ptr<IStreamSource>. Internally, this function constructs a
PythonCustomStreamSource instance and passes the Python object to
it. This design allows Python users to define any class that
implements two methods: pull() and descr_of(). No subclassing or
special decorators are required on the Python side. The user
simply needs to implement the expected interface.
Co-authored-by: Leonor Francisco <leonor.francisco@tecnico.ulisboa.pt>
G-API: Introduce level optimization flag for ONNXRT backend #26293
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Extending G-API onnx::Params to pass arbitrary session options #25791
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Port G-API ONNXRT backend into V2 API #25662
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G-API: A quick value-initialization support GMat #25055
This PR enables `GMat` objects to be value-initialized in the same way as it was done for `GScalar`s (and, possibly, other types).
- Added some helper methods in backends to distinguish if a certain G-type value initialization is supported or not;
- Added tests, including negative.
Where it is needed:
- Further extension of the OVCV backend (#24379 - will be refreshed soon);
- Further experiments with DNN module;
- Further experiments with "G-API behind UMat" sort of aggregation.
In the current form, PR can be reviewed & merged (@TolyaTalamanov please have a look)
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Documentation transition to fresh Doxygen #25042
* current Doxygen version is 1.10, but we will use 1.9.8 for now due to issue with snippets (https://github.com/doxygen/doxygen/pull/10584)
* Doxyfile adapted to new version
* MathJax updated to 3.x
* `@relates` instructions removed temporarily due to issue in Doxygen (to avoid warnings)
* refactored matx.hpp - extracted matx.inl.hpp
* opencv_contrib - https://github.com/opencv/opencv_contrib/pull/3638
G-API: Implement concurrent executor #24845
## Overview
This PR introduces the new G-API executor called `GThreadedExecutor` which can be selected when the `GComputation` is compiled in `serial` mode (a.k.a `GComputation::compile(...)`)
### ThreadPool
`cv::gapi::own::ThreadPool` has been introduced in order to abstract usage of threads in `GThreadedExecutor`.
`ThreadPool` is implemented by using `own::concurrent_bounded_queue`
`ThreadPool` has only as single method `schedule` that will push task into the queue for the further execution.
The **important** notice is that if `Task` executed in `ThreadPool` throws exception - this is `UB`.
### GThreadedExecutor
The `GThreadedExecutor` is mostly copy-paste of `GExecutor`, should we extend `GExecutor` instead?
#### Implementation details
1. Build the dependency graph for `Island` nodes.
2. Store the tasks that don't have dependencies into separate `vector` in order to run them first.
3. at the `GThreadedExecutor::run()` schedule the tasks that don't have dependencies that will schedule their dependents and wait for the completion.
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G-API: Support CoreML Execution Providers for ONNXRT Backend #24068
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G-API: Implement inference only mode for OV backend #24584
### Changes overview
Introduced `cv::gapi::wip::ov::benchmark_mode{}` compile argument which if enabled force `OpenVINO` backend to run only inference without populating input and copying back output tensors.
This mode is only relevant for measuring the performance of pure inference without data transfers. Similar approach is using on OpenVINO side in `benchmark_app`: https://github.com/openvinotoolkit/openvino/blob/master/samples/cpp/benchmark_app/benchmark_app.hpp#L134-L139
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G-API: Advanced device selection for ONNX DirectML Execution Provider #24060
### Overview
Extend `cv::gapi::onnx::ep::DirectML` to accept `adapter name` as `ctor` parameter in order to select execution device by `name`.
E.g:
```
pp.cfgAddExecutionProvider(cv::gapi::onnx::ep::DirectML("Intel Graphics"));
```
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G-API: Introduce a Queue Source #24178
- Added a new IStreamSource class: in fact, a wrapper over a concurrent queue;
- Added minimal example on how it can be used;
- Extended IStreamSource with optional "halt" interface to break the blocking calls in the emitter threads when required to stop.
- Introduced a QueueInput class which allows to pass the whole graph's input vector at once. In fact it is a thin wrapper atop of individual Queue Sources.
There is a hidden trap found with our type system as described in https://github.com/orgs/g-api-org/discussions/2
While it works even in this form, it should be addressed somewhere in the 5.0 timeframe.
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G-API: Support CUDA & TensoRT Execution Providers for ONNXRT Backend #24059
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G-API: Support DirectML Execution Provider for ONNXRT Backend #24045
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G-API: Support OpenVINO Execution Provider for ONNXRT Backend #24024
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G-API: Fix incorrect OpaqueKind for Kernel outputs #23843
### Pull Request Readiness Checklist
#### Overview
The PR is going to fix several problems:
1. Major: `GKernel` doesn't hold `kind` for its outputs. Since `GModelBuilder` traverse graph from outputs to inputs once it reaches any output of the operation it will use its `kind` to create `Data` meta for all operation outputs. Since it essential for `python` to know `GTypeInfo` (which is `shape` and `kind`) it will be confused.
Consider this operation:
```
@cv.gapi.op('custom.square_mean', in_types=[cv.GArray.Int], out_types=[cv.GOpaque.Float, cv.GArray.Int])
class GSquareMean:
@staticmethod
def outMeta(desc):
return cv.empty_gopaque_desc(), cv.empty_array_desc()
```
Even though `GOpaque` is `Float`, corresponding metadata might have `Int` kind because it might be taken from `cv.GArray.Int`
so it will be a problem if one of the outputs of these operation is graph output because python will cast it to the wrong type based on `Data` meta.
2. Minor: Some of the OpenVINO `IR`'s doesn't any layout information for input. It's usually true only for `IRv10` but since `OpenVINO 2.0` need this information to correctly configure resize we need to put default layout if there no such assigned in `ov::Model`.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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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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[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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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).
### Pull Request Readiness Checklist
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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
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
Murat Raimbekov
Igraine
Alexander Smorkalov
Anastasiya Pronina
fcmiron
Alexander Smorkalov
Ghazi-raad
Kavyansh Tyagi
Andrei Tamas
Rita Melo
shyama7004
Septimiu Neaga
Ujjayant Kadian
Dmitry Matveev
Maksim Shabunin
Anatoliy Talamanov
Sean McBride
Dmitry Matveev
Alexander Alekhin
Alexey Smirnov
