OpenVINO™ Model Server fork of MediaPipe.

This repository allows users to take advantage of OpenVINO™ in the Mediapipe framework. It includes inference calculators which can replace Tensorflow backend with OpenVINO™ Runtime. That way, you can expect more efficient execution and lower latency on CPU.

Check the included demos with pipeline examples or create your own graphs and execution flows.

List of changes introduced in this repository fork

added Dockerfile.openvino dockerfile that creates runtime and development environment. This is a build environment image not suitable for production use.
added Makefile file with build, test and demo targets for the ease of use.
modified build_desktop_examples.sh script to build new demos.
added calculators and calculators for OpenVINO™ inference in mediapipe graphs detailed description.
added calculators for serialization and inference tasks.
modified bazel targets to use OpenVINO™ inference calculators (the list of available demos is in the table below).
modified WORKSPACE file to add OpenVINO™ Model Server dependencies. Specifically target @ovms//src:ovms_lib as dependency from OpenVINO Model Server
modified graphs and bazel targets to use OpenVINO™ inference instead of TensorFlow inference.
added setup_ovms.py script to create models repository used in OpenVINO™ inference. The script needs to be executed to prepare specific directory structures with tflite models and config.json in the mediapipe/models/ovms.
modified setup_opecv.py to install 4.7.0 OpenCV version instead of previous 3.4.

OpenVINO™ demo	C++	Python	Original Google demo
Face Detection	✅	✅	Face Detection
Iris	✅		Iris
Pose	✅		Pose
Holistic	✅	✅	Holistic
Object Detection	✅	✅	Object Detection

Quick start guide

Check the quick start guide to with easy to follow instructions for building and running the example applications and graphs.

Development instructions

The developer guide includes the list of instructions and practices in developing your own application and graphs.

Live ML anywhere

MediaPipe offers cross-platform, customizable ML solutions for live and streaming media.


End-to-End acceleration: Built-in fast ML inference and processing accelerated even on common hardware	Build once, deploy anywhere: Unified solution works across Android, iOS, desktop/cloud, web and IoT

Ready-to-use solutions: Cutting-edge ML solutions demonstrating full power of the framework	Free and open source: Framework and solutions both under Apache 2.0, fully extensible and customizable