37c93d54a5
PiperOrigin-RevId: 255714511 |
||
---|---|---|
mediapipe | ||
third_party | ||
.bazelrc | ||
.dockerignore | ||
BUILD | ||
CONTRIBUTING.md | ||
Dockerfile | ||
LICENSE | ||
README.md | ||
setup_android_sdk_and_ndk.sh | ||
setup_opencv.sh | ||
WORKSPACE |
MediaPipe is a framework for building multimodal (eg. video, audio, any time series data) applied ML pipelines. With MediaPipe, a perception pipeline can be built as a graph of modular components, including, for instance, inference models (e.g., TensorFlow, TFLite) and media processing functions. http://mediapipe.dev
Installation
Follow these instructions.
Getting started
See mobile and desktop examples.
Documentation
MediaPipe Read-the-Docs or docs.mediapipe.dev
Check out the Examples page for tutorials on how to use MediaPipe. Concepts page for basic definitions
Visualizing MediaPipe graphs
A web-based visualizer is hosted on viz.mediapipe.dev. Please also see instructions here.
Community forum
- discuss - General community discussion around MediaPipe
Publications
Events
Open sourced at CVPR 2019 on June 17~20 in Long Beach, CA
Alpha Disclaimer
MediaPipe is currently in alpha for v0.5. We are still making breaking API changes and expect to get to stable API by v1.0.
Contributing
We welcome contributions. Please follow these guidelines.
We use GitHub issues for tracking requests and bugs. Please post questions to the MediaPipe Stack Overflow with a 'mediapipe' tag.