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Using G API with MS ONNX Runtime
Dmitry Matveev edited this page Apr 28, 2023
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Build and install the ONNX RT (currently tested with v1.14.1):
$ git clone --recursive https://github.com/microsoft/onnxruntime.git $ cd onnxruntime $ git checkout v1.14.1 $ git submodule update --init $ ./build.sh --config Release --build_shared_lib --parallel \ $ --cmake_extra_defines CMAKE_INSTALL_PREFIX=/path-to-install-dir $ cd build/Linux/Release $ make install
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Then specify extra options to OpenCV CMake:
$ cmake /path-to-opencv -DWITH_ONNX=ON -DORT_INSTALL_DIR=/path-to-ort-install-dir
When you build G-API with ONNX Runtime support, tests for inference are enabled and require OPENCV_GAPI_ONNX_MODEL_PATH to be set:
$ export OPENCV_TEST_DATA_PATH=/path-to-opencv-extra/testdata
$ export OPENCV_GAPI_ONNX_MODEL_PATH=/path-to-onnx-models/and models are downloaded using the commands:
$ git clone --recursive https://github.com/onnx/models.git
$ cd models
$ git lfs pull --include=path-to-desired-onnx-model --exclude=""The following models are known to be required as for OpenCV 4.7:
git lfs pull --include vision/body_analysis/emotion_ferplus/model/emotion-ferplus-8.onnx --exclude=""
git lfs pull --include vision/classification/squeezenet/model/squeezenet1.0-9.onnx --exclude=""
git lfs pull --include vision/object_detection_segmentation/faster-rcnn/model/FasterRCNN-10.onnx --exclude=""
git lfs pull --include vision/object_detection_segmentation/ssd-mobilenetv1/model/ssd_mobilenet_v1_10.onnx --exclude=""
git lfs pull --include vision/object_detection_segmentation/tiny-yolov2/model/tinyyolov2-8.onnx --exclude=""
git lfs pull --include vision/object_detection_segmentation/yolov3/model/yolov3-10.onnx --exclude=""© Copyright 2019-2025, OpenCV team
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