Project import generated by Copybara.

GitOrigin-RevId: 3864d8399e063cfcacc9e3f6e7ba653ea9045c52

BUILD

@@ -1,4 +1,4 @@
-# Copyright 2019 The MediaPipe Authors.
+# Copyright 2019-2020 The MediaPipe Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

README.md

@@ -127,7 +127,7 @@ run code search using
 *   [Discuss](https://groups.google.com/forum/#!forum/mediapipe) - General
     community discussion around MediaPipe
 
-## Alpha Disclaimer
+## Alpha disclaimer
 
 MediaPipe is currently in alpha at v0.7. We may be still making breaking API
 changes and expect to get to stable APIs by v1.0.

docs/conf.py

@@ -19,7 +19,7 @@
 author = 'Google LLC'
 
 # The full version, including alpha/beta/rc tags
-release = 'v0.5'
+release = 'v0.7.5'
 
 
 # -- General configuration ---------------------------------------------------

docs/index.md

@@ -127,7 +127,7 @@ run code search using
 *   [Discuss](https://groups.google.com/forum/#!forum/mediapipe) - General
     community discussion around MediaPipe
 
-## Alpha Disclaimer
+## Alpha disclaimer
 
 MediaPipe is currently in alpha at v0.7. We may be still making breaking API
 changes and expect to get to stable APIs by v1.0.

docs/index.rst

@@ -1,66 +1,3 @@
 MediaPipe
 =====================================
-`MediaPipe <http://github.com/google/mediapipe>`_ is a graph-based framework for
-building multimodal (video, audio, and sensor) applied machine learning pipelines.
-MediaPipe is cross-platform running on mobile devices, workstations and servers,
-and supports mobile GPU acceleration. With MediaPipe, an applied
-machine learning pipeline can be built as a graph of modular components,
-including, for instance, inference models and media processing functions. Sensory
-data such as audio and video streams enter the graph, and perceived descriptions
-such as object-localization and face-landmark streams exit the graph. An example
-graph that performs real-time hand tracking on mobile GPU is shown below.
-
-.. image:: images/mobile/hand_tracking_mobile.png
-   :width: 400
-   :alt: Example MediaPipe graph
-
-MediaPipe is designed for machine learning (ML) practitioners, including
-researchers, students, and software developers, who implement production-ready
-ML applications, publish code accompanying research work, and build technology
-prototypes. The main use case for MediaPipe is rapid prototyping of applied
-machine learning pipelines with inference models and other reusable components.
-MediaPipe also facilitates the deployment of machine learning technology into
-demos and applications on a wide variety of different hardware platforms
-(e.g., Android, iOS, workstations).
-
-APIs for MediaPipe
-    * Calculator API in C++
-    * Graph Construction API in ProtoBuf
-    * (Coming Soon) Graph Construction API in C++
-    * Graph Execution API in C++
-    * Graph Execution API in Java (Android)
-    * Graph Execution API in Objective-C (iOS)
-
-Alpha Disclaimer
-==================
-MediaPipe is currently in alpha for v0.6. We are still making breaking API changes and expect to get to stable API by v1.0. We recommend that you target a specific version of MediaPipe, and periodically bump to the latest release. That way you have control over when a breaking change affects you.
-
-User Documentation
-==================
-
-.. toctree::
-   :maxdepth: 3
-
-   getting_started/install
-   Examples <examples>
-   tools/visualizer
-   tools/tracing_and_profiling
-   tools/performance_benchmarking
-   getting_started/help
-   getting_started/faq
-   getting_started/troubleshooting
-   framework_concepts/framework_concepts
-   framework_concepts/calculators
-   framework_concepts/graphs
-   framework_concepts/packets
-   framework_concepts/synchronization
-   framework_concepts/gpu
-   license
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
-
+Please see https://docs.mediapipe.dev.