Below instructions are prepared so that you can experiment and develop your own applications with custom graphs taking advantage of OpenVINO™ Model Serving inference.
- run the
make docker_buildcommand to prepare the development and runtime environment
- start the
docker run -it mediapipe_ovms:latest bash- modify the contents of global config_holistic.json or specific object_detection config.json to change the models and graphs that are loaded for your example application
- make sure the new pbtxt and model files are present in the mediapipe/models/ovms/ directory during the application execution and are setup according to the config json file paths. Currently the existing demos setup is prepared automatically with the command.
python setup_ovms.py --get_models'- modify or create new mediapipe example target with the
//mediapipe/graphs/object_detection:desktop_ovms_calculatorsand
@ovms//src:ovms_lib dependencies similar to object_detection_ovms target.
- build your application graph file based on existing examples or object_detection making sure that OpenVINOModelServerSessionCalculator and OpenVINOInferenceCalculator are properly placed in the inference pipeline of your graph.
- build your new application using the bazel command inside the mediapipe_ovms container, for example in object_detection case it is:
build -c opt --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/object_detection:object_detection_openvino'- execute your application with the specific input parameters, for example in object_detection case those are:
'bazel-bin/mediapipe/examples/desktop/object_detection/object_detection_openvino --calculator_graph_config_file mediapipe/graphs/object_detection/object_detection_desktop_openvino_graph.pbtxt --input_side_packets "input_video_path=/mediapipe/mediapipe/examples/desktop/object_detection/test_video.mp4,output_video_path=/mediapipe/tested_video.mp4"'- in object_detection example the input video will get all the objects detected by the model and return the tested_video.mp4 output.