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Graph API

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Dmitry Matveev edited this page May 16, 2023 · 52 revisions

What is it?

  • OpenCV 4.0 introduced an experimental Graph API module (see opencv/modules/gapi). This is a new API which allows to enable offload and optimizations for image processing / CV algorithms on pipeline level.

  • The idea behind G-API is to declare image processing task in form of expressions and then submit it for execution – using a number of available backends. At the moment, there’s reference “CPU” (OpenCV-based), "GPU" (also OpenCV T-API-based), and experimental “Fluid’ backends available, with other backends coming up next.

  • G-API is an uncommon OpenCV module since it acts as a framework: it provides means for declaring operations, building graphs of operations, and finally implementing the operations for a particular backend. G-API model enforces separation between interfaces and implementations, so once an algorithm is expressed in G-API terms, it can be scaled/ported/offloaded to a new platform easily.

Quick tour of G-API Backends

  • G-API CPU (OpenCV) backend implements G-API standard functions using OpenCV itself (core/imgproc modules) and acts as a quick prototyping/porting/testing backend. If you have an image processing algorithm composed of OpenCV-like functions already, you would be able to switch quickly to G-API by using this backend.

  • G-API Fluid backend implements a cache-efficient execution model and allows to save memory dramatically – e.g. a 1.5GB image processing pipeline fits into 750KB memory footprint with G-API/Fluid. G-API comes with a number of operations implemented for Fluid backend, so one can switch OpenCV/Fluid operations within a graph easily and even mix both in the same graph.

  • G-API OpenCL (also known as GPU) backend implements the majority of available functions and allows to run OpenCL kernels on available OpenCL-programmable devices. At the moment, GPU backend is based on OpenCV Transparent API; in future versions it will be extended to support integration of arbitrary OpenCL kernels (and likely be renamed to "OpenCL backend").

  • G-API OpenVINO backend brings OpenVINO inference support in G-API. The OpenVINO backend is the most function-complete G-API inference backend at the moment, utilizing advanced features as OpenVINO-based preprocessing, remote memory and context support, and more.

  • G-API ONNX backend implements ONNX models inference operations on input data and outputs the results. At the moment, ONNX backend is based on ONNX Runtime C/C++ API, build & run instruction can be found here.

Note you can find more backends in the G-API's source directory. Some of those are used implicitly, and some are experimental and provide specific functionality.

Building G-API

G-API is built with OpenCV by default, however some features may require additional options or dependencies enabled. Refer to the following instructions for details:

Testing G-API

By default, the OpenCV G-API comes with its own test suite (opencv_test_gapi). Note that extra (external) G-API modules may introduce their own test suites. G-API tests are built and run in a regular way:

Requirements

Some G-API tests require test data to be available. This data is taken from the opencv_extra repo. You have to set the OPENCV_TEST_DATA_PATH environment variable to avoid failed tests (due to absence of test data):

export OPENCV_TEST_DATA_PATH=/Linux/path/to/opencv_extra/testdata

or

SET OPENCV_TEST_DATA_PATH=\Windows\path\to\opencv_extra\testdata

Some tests require external test data to be available. This is test data not included in opencv_extra. ONNX models are an example. The absence of this data doesn't break the tests. Tests are skipped without external test data. See the respective subpages to find more details.

Linux

$ make -j4 opencv_test_gapi
$ bin/opencv_test_gapi

Windows

$ cmake --build . --target opencv_test_gapi --config Release -- /maxcpucount:4
$ bin\Release\opencv_test_gapi.exe

Materials

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