Project import generated by Copybara.

GitOrigin-RevId: 5cd50ad1abca6bd5d52832975bb60e6c0fc60a27
2020-08-13 04:32:11 -04:00 · 2020-08-13 02:18:32 -04:00 · 2020-08-13 01:32:08 -04:00 · 2020-08-05 22:14:25 -04:00 · 2020-08-05 20:27:31 -04:00 · 2020-07-29 21:18:36 -04:00
734 changed files with 28363 additions and 14884 deletions
@@ -1,8 +1,4 @@
-mediapipe/provisioning_profile.mobileprovision
-bazel-bin
-bazel-genfiles
-bazel-mediapipe-ioss
-bazel-out
-bazel-testlogs
+bazel-*
 mediapipe/MediaPipe.xcodeproj
 mediapipe/MediaPipe.tulsiproj/*.tulsiconf-user
+mediapipe/provisioning_profile.mobileprovision
@@ -12,6 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-licenses(["notice"])  # Apache 2.0
+licenses(["notice"])

 exports_files(["LICENSE"])
@@ -1,15 +1,17 @@
 # Contributing guidelines

-## Pull Request Checklist
+## What type of pull request do we accept into MediaPipe repository?
+
+* Bug fixes
+* Documentation fixes
+
+For new feature additions (e.g., new graphs and calculators), we are currently not planning to accept new feature pull requests into the MediaPipe repository. Instead, we like to get contributors to create their own repositories of the new feature and list it at [Awesome MediaPipe](https://mediapipe.org). This will allow contributors to more quickly get their code out to the community.

 Before sending your pull requests, make sure you followed this list.

 - Read [contributing guidelines](CONTRIBUTING.md).
 - Read [Code of Conduct](CODE_OF_CONDUCT.md).
 - Ensure you have signed the [Contributor License Agreement (CLA)](https://cla.developers.google.com/).
- Check if my changes are consistent with the [guidelines](https://github.com/google/mediapipe/blob/master/CONTRIBUTING.md#general-guidelines-and-philosophy-for-contribution).
- Changes are consistent with the [Coding Style](https://github.com/google/mediapipe/blob/master/CONTRIBUTING.md#c-coding-style).
- Run [Unit Tests](https://github.com/google/mediapipe/blob/master/CONTRIBUTING.md#running-unit-tests).

 ## How to become a contributor and submit your own code

@@ -28,100 +30,7 @@ Follow either of the two links above to access the appropriate CLA and instructi

 ### Contributing code

-If you have improvements to MediaPipe, send us your pull requests! For those
+If you have bug fixes and documentation fixes to MediaPipe, send us your pull requests! For those
 just getting started, GitHub has a [howto](https://help.github.com/articles/using-pull-requests/).

-MediaPipe team members will be assigned to review your pull requests. Once the
-pull requests are approved and pass continuous integration checks, a MediaPipe
-team member will apply `ready to pull` label to your change. This means we are
-working on getting your pull request submitted to our internal repository. After
-the change has been submitted internally, your pull request will be merged
-automatically on GitHub.
-
-If you want to contribute but you're not sure where to start, take a look at the
-[issues with the "contributions welcome" label](https://github.com/google/mediapipe/labels/stat%3Acontributions%20welcome).
-These are issues that we believe are particularly well suited for outside
-contributions, often because we probably won't get to them right now. If you
-decide to start on an issue, leave a comment so that other people know that
-you're working on it. If you want to help out, but not alone, use the issue
-comment thread to coordinate.
-
-### Contribution guidelines and standards
-
-Before sending your pull request for
-[review](https://github.com/google/mediapipe/pulls),
-make sure your changes are consistent with the guidelines and follow the
-MediaPipe coding style.
-
-#### General guidelines and philosophy for contribution
-
-*   Include unit tests when you contribute new features, as they help to a)
-    prove that your code works correctly, and b) guard against future breaking
-    changes to lower the maintenance cost.
-*   Bug fixes also generally require unit tests, because the presence of bugs
-    usually indicates insufficient test coverage.
-*   Keep API compatibility in mind when you change code in MediaPipe framework
-    e.g., code in
-    [mediapipe/framework](https://github.com/google/mediapipe/tree/master/mediapipe/framework)
-    and
-    [mediapipe/calculators](https://github.com/google/mediapipe/tree/master/mediapipe/calculators).
-    Once MediaPipe has reached version 1 and we will not make
-    non-backward-compatible API changes without a major release. Reviewers of
-    your pull request will comment on any API compatibility issues.
-*   When you contribute a new feature to MediaPipe, the maintenance burden is
-    (by default) transferred to the MediaPipe team. This means that benefit of
-    the contribution must be compared against the cost of maintaining the
-    feature.
-*   Full new features (e.g., a new op implementing a cutting-edge algorithm)
-    typically will live in
-    [mediapipe/addons](https://github.com/google/mediapipe/addons) to get some
-    airtime before decision is made regarding whether they are to be migrated to
-    the core.
-
-#### License
-
-Include a license at the top of new files.
-
-* [C/C++ license example](https://github.com/google/mediapipe/blob/master/mediapipe/framework/calculator_base.cc#L1)
-* [Java license example](https://github.com/google/mediapipe/blob/master/mediapipe/java/com/google/mediapipe/components/CameraHelper.java)
-
-Bazel BUILD files also need to include a license section, e.g.,
-[BUILD example](https://github.com/google/mediapipe/blob/master/mediapipe/framework/BUILD#L61).
-
-#### C++ coding style
-
-Changes to MediaPipe C++ code should conform to
-[Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).
-
-Use `clang-tidy` to check your C/C++ changes. To install `clang-tidy` on ubuntu:16.04, do:
-
-```bash
-apt-get install -y clang-tidy
-```
-
-You can check a C/C++ file by doing:
-
-
-```bash
-clang-format <my_cc_file> --style=google > /tmp/my_cc_file.cc
-diff <my_cc_file> /tmp/my_cc_file.cc
-```
-
-#### Coding style for other languages
-
-* [Google Java Style Guide](https://google.github.io/styleguide/javaguide.html)
-* [Google JavaScript Style Guide](https://google.github.io/styleguide/jsguide.html)
-* [Google Shell Style Guide](https://google.github.io/styleguide/shell.xml)
-* [Google Objective-C Style Guide](https://google.github.io/styleguide/objcguide.html)
-
-#### Running sanity check
-
-If you have Docker installed on your system, you can perform a sanity check on
-your changes by running the command:
-
-```bash
-mediapipe/tools/ci_build/ci_build.sh CPU mediapipe/tools/ci_build/ci_sanity.sh
-```
-
-This will catch most license, Python coding style and BUILD file issues that
-may exist in your changes.
+MediaPipe team members will be assigned to review your pull requests. Once the bug/documentation fixes are verified, a MediaPipe team member will acknowledge your contribution in the pull request comments, manually merge the fixes into our internal codebase upstream, and apply the `to be closed` label to the pull request. These fixes will later be pushed to GitHub in the next release, and a MediaPipe team member will then close the pull request.
@@ -0,0 +1,6 @@
+global-exclude .git*
+global-exclude *_test.py
+
+recursive-include mediapipe/models *.tflite *.txt
+recursive-include mediapipe/modules *.tflite *.txt
+recursive-include mediapipe/graphs *.binarypb
@@ -1,84 +1,150 @@
-![MediaPipe](mediapipe/docs/images/mediapipe_small.png?raw=true "MediaPipe logo")
-=======================================================================
+---
+layout: default
+title: Home
+nav_order: 1
+---

-[MediaPipe](http://mediapipe.dev) is the simplest way for researchers and developers to build world-class ML solutions and applications for mobile, edge, cloud and the web.
+![MediaPipe](docs/images/mediapipe_small.png)

-## ML Solutions in MediaPipe
+--------------------------------------------------------------------------------

-* [Face Detection](mediapipe/docs/face_detection_mobile_gpu.md) [(web demo)](https://viz.mediapipe.dev/runner/demos/face_detection/face_detection.html)
-* [Face Mesh](mediapipe/docs/face_mesh_mobile_gpu.md)
-* [Hand Detection](mediapipe/docs/hand_detection_mobile_gpu.md)
-* [Hand Tracking](mediapipe/docs/hand_tracking_mobile_gpu.md) [(web demo)](https://viz.mediapipe.dev/runner/demos/hand_tracking/hand_tracking.html)
-* [Multi-hand Tracking](mediapipe/docs/multi_hand_tracking_mobile_gpu.md)
-* [Hair Segmentation](mediapipe/docs/hair_segmentation_mobile_gpu.md) [(web demo)](https://viz.mediapipe.dev/runner/demos/hair_segmentation/hair_segmentation.html)
-* [Object Detection](mediapipe/docs/object_detection_mobile_gpu.md)
-* [Object Detection and Tracking](mediapipe/docs/object_tracking_mobile_gpu.md)
-* [Objectron: 3D Object Detection and Tracking](mediapipe/docs/objectron_mobile_gpu.md)
-* [AutoFlip: Intelligent Video Reframing](mediapipe/docs/autoflip.md)
-* [KNIFT: Template Matching with Neural Image Features](mediapipe/docs/template_matching_mobile_cpu.md)
+## Cross-platform ML solutions made simple

-![face_detection](mediapipe/docs/images/mobile/face_detection_android_gpu_small.gif)
-![face_mesh](mediapipe/docs/images/mobile/face_mesh_android_gpu_small.gif)
-![hand_tracking](mediapipe/docs/images/mobile/hand_tracking_android_gpu_small.gif)
-![multi-hand_tracking](mediapipe/docs/images/mobile/multi_hand_tracking_3d_android_gpu_small.gif)
-![hair_segmentation](mediapipe/docs/images/mobile/hair_segmentation_android_gpu_small.gif)
-![object_detection](mediapipe/docs/images/mobile/object_detection_android_gpu_small.gif)
-![object_tracking](mediapipe/docs/images/mobile/object_tracking_android_gpu_small.gif)
-![objectron_shoes](mediapipe/docs/images/mobile/objectron_shoe_android_gpu_small.gif)
-![objectron_chair](mediapipe/docs/images/mobile/objectron_chair_android_gpu_small.gif)
-![template_matching](mediapipe/docs/images/mobile/template_matching_android_cpu_small.gif)
+[MediaPipe](https://google.github.io/mediapipe/) is the simplest way for researchers
+and developers to build world-class ML solutions and applications for mobile,
+desktop/cloud, web and IoT devices.

-## Installation
-Follow these [instructions](mediapipe/docs/install.md).
+![accelerated.png](docs/images/accelerated_small.png)                                                               | ![cross_platform.png](docs/images/cross_platform_small.png)
+:------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------:
+***End-to-End acceleration***: *built-in fast ML inference and processing accelerated even on common hardware* | ***Build one, deploy anywhere***: *Unified solution works across Android, iOS, desktop/cloud, web and IoT*
+![ready_to_use.png](docs/images/ready_to_use_small.png)                                                             | ![open_source.png](docs/images/open_source_small.png)
+***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*
+
+## ML solutions in MediaPipe
+
+Face Detection                                                                                                                 | Face Mesh                                                                                                       | Iris 🆕                                                                                                    | Hands                                                                                                      | Pose 🆕
+:----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: | :----:
+[![face_detection](docs/images/mobile/face_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_detection) | [![face_mesh](docs/images/mobile/face_mesh_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_mesh) | [![iris](docs/images/mobile/iris_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/iris) | [![hand](docs/images/mobile/hand_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hands) | [![pose](docs/images/mobile/pose_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/pose)
+
+Hair Segmentation                                                                                                                       | Object Detection                                                                                                                     | Box Tracking                                                                                                                | Objectron                                                                                                             | KNIFT
+:-------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: | :---:
+[![hair_segmentation](docs/images/mobile/hair_segmentation_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hair_segmentation) | [![object_detection](docs/images/mobile/object_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/object_detection) | [![box_tracking](docs/images/mobile/object_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/box_tracking) | [![objectron](docs/images/mobile/objectron_chair_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/objectron) | [![knift](docs/images/mobile/template_matching_android_cpu_small.gif)](https://google.github.io/mediapipe/solutions/knift)
+
+<!-- []() in the first cell is needed to preserve table formatting in GitHub Pages. -->
+<!-- Whenever this table is updated, paste a copy to solutions/solutions.md. -->
+
+[]()                                                                          | Android | iOS | Desktop | Python | Web | Coral
+:---------------------------------------------------------------------------- | :-----: | :-: | :-----: | :----: | :-: | :---:
+[Face Detection](https://google.github.io/mediapipe/solutions/face_detection)       | ✅       | ✅   | ✅       |        | ✅   | ✅
+[Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh)                 | ✅       | ✅   | ✅       |        |     |
+[Iris](https://google.github.io/mediapipe/solutions/iris) 🆕                         | ✅       | ✅   | ✅       |        | ✅   |
+[Hands](https://google.github.io/mediapipe/solutions/hands)                         | ✅       | ✅   | ✅       |        | ✅   |
+[Pose](https://google.github.io/mediapipe/solutions/pose) 🆕                         | ✅       | ✅   | ✅       | ✅      | ✅   |
+[Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation) | ✅       |     | ✅       |        | ✅   |
+[Object Detection](https://google.github.io/mediapipe/solutions/object_detection)   | ✅       | ✅   | ✅       |        |     | ✅
+[Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking)           | ✅       | ✅   | ✅       |        |     |
+[Objectron](https://google.github.io/mediapipe/solutions/objectron)                 | ✅       |     |         |        |     |
+[KNIFT](https://google.github.io/mediapipe/solutions/knift)                         | ✅       |     |         |        |     |
+[AutoFlip](https://google.github.io/mediapipe/solutions/autoflip)                   |         |     | ✅       |        |     |
+[MediaSequence](https://google.github.io/mediapipe/solutions/media_sequence)        |         |     | ✅       |        |     |
+[YouTube 8M](https://google.github.io/mediapipe/solutions/youtube_8m)               |         |     | ✅       |        |     |
+
+## MediaPipe on the Web
+
+MediaPipe on the Web is an effort to run the same ML solutions built for mobile
+and desktop also in web browsers. The official API is under construction, but
+the core technology has been proven effective. Please see
+[MediaPipe on the Web](https://developers.googleblog.com/2020/01/mediapipe-on-web.html)
+in Google Developers Blog for details.
+
+You can use the following links to load a demo in the MediaPipe Visualizer, and
+over there click the "Runner" icon in the top bar like shown below. The demos
+use your webcam video as input, which is processed all locally in real-time and
+never leaves your device.
+
+![visualizer_runner](docs/images/visualizer_runner.png)
+
+*   [MediaPipe Face Detection](https://viz.mediapipe.dev/demo/face_detection)
+*   [MediaPipe Iris](https://viz.mediapipe.dev/demo/iris_tracking)
+*   [MediaPipe Iris: Depth-from-Iris](https://viz.mediapipe.dev/demo/iris_depth)
+*   [MediaPipe Hands](https://viz.mediapipe.dev/demo/hand_tracking)
+*   [MediaPipe Hands (palm/hand detection only)](https://viz.mediapipe.dev/demo/hand_detection)
+*   [MediaPipe Pose](https://viz.mediapipe.dev/demo/pose_tracking)
+*   [MediaPipe Hair Segmentation](https://viz.mediapipe.dev/demo/hair_segmentation)

 ## Getting started
-See mobile, desktop, web and Google Coral [examples](mediapipe/docs/examples.md).

-## Documentation
-[MediaPipe Read-the-Docs](https://mediapipe.readthedocs.io/) or [docs.mediapipe.dev](https://docs.mediapipe.dev)
+Learn how to [install](https://google.github.io/mediapipe/getting_started/install)
+MediaPipe and
+[build example applications](https://google.github.io/mediapipe/getting_started/building_examples),
+and start exploring our ready-to-use
+[solutions](https://google.github.io/mediapipe/solutions/solutions) that you can
+further extend and customize.

-Check out the [Examples page](https://mediapipe.readthedocs.io/en/latest/examples.html) for tutorials on how to use MediaPipe. [Concepts page](https://mediapipe.readthedocs.io/en/latest/concepts.html) for basic definitions
-
-## Visualizing MediaPipe graphs
-A web-based visualizer is hosted on [viz.mediapipe.dev](https://viz.mediapipe.dev/). Please also see instructions [here](mediapipe/docs/visualizer.md).
-
-## Google Open Source Code search
-Search MediaPipe Github repository using [Google Open Source code search](https://t.co/LSZnbMUUnT?amp=1)
-
-## Videos
-*  [YouTube Channel](https://www.youtube.com/channel/UCObqmpuSMx-usADtL_qdMAw)
+The source code is hosted in the
+[MediaPipe Github repository](https://github.com/google/mediapipe), and you can
+run code search using
+[Google Open Source Code Search](https://cs.opensource.google/mediapipe/mediapipe).

 ## Publications
-* [MediaPipe KNIFT: Template-based Feature Matching](https://mediapipe.page.link/knift-blog)
-* [Alfred Camera: Smart camera features using MediaPipe](https://developers.googleblog.com/2020/03/alfred-camera-smart-camera-features-using-mediapipe.html)
-* [MediaPipe Objectron: Real-time 3D Object Detection on Mobile Devices](https://mediapipe.page.link/objectron-aiblog)
-* [AutoFlip: An Open Source Framework for Intelligent Video Reframing](https://mediapipe.page.link/autoflip)
-* [Google Developer Blog: MediaPipe on the Web](https://mediapipe.page.link/webdevblog)
-* [Google Developer Blog: Object Detection and Tracking using MediaPipe](https://mediapipe.page.link/objecttrackingblog)
-* [On-Device, Real-Time Hand Tracking with MediaPipe](https://ai.googleblog.com/2019/08/on-device-real-time-hand-tracking-with.html)
-* [MediaPipe: A Framework for Building Perception Pipelines](https://arxiv.org/abs/1906.08172)
+
+*   [BlazePose - On-device Real-time Body Pose Tracking](https://mediapipe.page.link/blazepose-blog)
+    in Google AI Blog
+*   [MediaPipe Iris: Real-time Eye Tracking and Depth Estimation](https://ai.googleblog.com/2020/08/mediapipe-iris-real-time-iris-tracking.html)
+    in Google AI Blog
+*   [MediaPipe KNIFT: Template-based feature matching](https://developers.googleblog.com/2020/04/mediapipe-knift-template-based-feature-matching.html)
+    in Google Developers Blog
+*   [Alfred Camera: Smart camera features using MediaPipe](https://developers.googleblog.com/2020/03/alfred-camera-smart-camera-features-using-mediapipe.html)
+    in Google Developers Blog
+*   [Real-Time 3D Object Detection on Mobile Devices with MediaPipe](https://ai.googleblog.com/2020/03/real-time-3d-object-detection-on-mobile.html)
+    in Google AI Blog
+*   [AutoFlip: An Open Source Framework for Intelligent Video Reframing](https://ai.googleblog.com/2020/02/autoflip-open-source-framework-for.html)
+    in Google AI Blog
+*   [MediaPipe on the Web](https://developers.googleblog.com/2020/01/mediapipe-on-web.html)
+    in Google Developers Blog
+*   [Object Detection and Tracking using MediaPipe](https://developers.googleblog.com/2019/12/object-detection-and-tracking-using-mediapipe.html)
+    in Google Developers Blog
+*   [On-Device, Real-Time Hand Tracking with MediaPipe](https://ai.googleblog.com/2019/08/on-device-real-time-hand-tracking-with.html)
+    in Google AI Blog
+*   [MediaPipe: A Framework for Building Perception Pipelines](https://arxiv.org/abs/1906.08172)
+
+## Videos
+
+*   [YouTube Channel](https://www.youtube.com/c/MediaPipe)

 ## Events
-* [MediaPipe Seattle Meetup, Google Building Waterside, 13 Feb 2020](https://mediapipe.page.link/seattle2020)
-* [AI Nextcon 2020, 12-16 Feb 2020, Seattle](http://aisea20.xnextcon.com/)
-* [MediaPipe Madrid Meetup, 16 Dec 2019](https://www.meetup.com/Madrid-AI-Developers-Group/events/266329088/)
-* [MediaPipe London Meetup, Google 123 Building, 12 Dec 2019](https://www.meetup.com/London-AI-Tech-Talk/events/266329038)
-* [ML Conference, Berlin, 11 Dec 2019](https://mlconference.ai/machine-learning-advanced-development/mediapipe-building-real-time-cross-platform-mobile-web-edge-desktop-video-audio-ml-pipelines/)
-* [MediaPipe Berlin Meetup, Google Berlin, 11 Dec 2019](https://www.meetup.com/Berlin-AI-Tech-Talk/events/266328794/)
-* [The 3rd Workshop on YouTube-8M Large Scale Video Understanding Workshop](https://research.google.com/youtube8m/workshop2019/index.html) Seoul, Korea ICCV 2019
-* [AI DevWorld 2019](https://aidevworld.com) on Oct 10 in San Jose, California
-* [Google Industry Workshop at ICIP 2019](http://2019.ieeeicip.org/?action=page4&id=14#Google) [Presentation](https://docs.google.com/presentation/d/e/2PACX-1vRIBBbO_LO9v2YmvbHHEt1cwyqH6EjDxiILjuT0foXy1E7g6uyh4CesB2DkkEwlRDO9_lWfuKMZx98T/pub?start=false&loop=false&delayms=3000&slide=id.g556cc1a659_0_5) on Sept 24 in Taipei, Taiwan
-* [Open sourced at CVPR 2019](https://sites.google.com/corp/view/perception-cv4arvr/mediapipe) on June 17~20 in Long Beach, CA
+
+*   [MediaPipe Seattle Meetup, Google Building Waterside, 13 Feb 2020](https://mediapipe.page.link/seattle2020)
+*   [AI Nextcon 2020, 12-16 Feb 2020, Seattle](http://aisea20.xnextcon.com/)
+*   [MediaPipe Madrid Meetup, 16 Dec 2019](https://www.meetup.com/Madrid-AI-Developers-Group/events/266329088/)
+*   [MediaPipe London Meetup, Google 123 Building, 12 Dec 2019](https://www.meetup.com/London-AI-Tech-Talk/events/266329038)
+*   [ML Conference, Berlin, 11 Dec 2019](https://mlconference.ai/machine-learning-advanced-development/mediapipe-building-real-time-cross-platform-mobile-web-edge-desktop-video-audio-ml-pipelines/)
+*   [MediaPipe Berlin Meetup, Google Berlin, 11 Dec 2019](https://www.meetup.com/Berlin-AI-Tech-Talk/events/266328794/)
+*   [The 3rd Workshop on YouTube-8M Large Scale Video Understanding Workshop,
+    Seoul, Korea ICCV
+    2019](https://research.google.com/youtube8m/workshop2019/index.html)
+*   [AI DevWorld 2019, 10 Oct 2019, San Jose, CA](https://aidevworld.com)
+*   [Google Industry Workshop at ICIP 2019, 24 Sept 2019, Taipei, Taiwan](http://2019.ieeeicip.org/?action=page4&id=14#Google)
+    ([presentation](https://docs.google.com/presentation/d/e/2PACX-1vRIBBbO_LO9v2YmvbHHEt1cwyqH6EjDxiILjuT0foXy1E7g6uyh4CesB2DkkEwlRDO9_lWfuKMZx98T/pub?start=false&loop=false&delayms=3000&slide=id.g556cc1a659_0_5))
+*   [Open sourced at CVPR 2019, 17~20 June, Long Beach, CA](https://sites.google.com/corp/view/perception-cv4arvr/mediapipe)

 ## Community
-*  [Awesome MediaPipe: curation of code related to MediaPipe](https://mediapipe.org)
-*  [Slack community for MediaPipe users](https://mediapipe.slack.com)
-*  [Discuss](https://groups.google.com/forum/#!forum/mediapipe) - General community discussion around MediaPipe

-## Alpha Disclaimer
-MediaPipe is currently in alpha for v0.7. We are still making breaking API changes and expect to get to stable API by v1.0.
+*   [Awesome MediaPipe](https://mediapipe.org) - A curated list of awesome
+    MediaPipe related frameworks, libraries and software
+*   [Slack community](https://mediapipe.page.link/joinslack) for MediaPipe users
+*   [Discuss](https://groups.google.com/forum/#!forum/mediapipe) - General
+    community discussion around MediaPipe
+
+## 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.

 ## Contributing
-We welcome contributions. Please follow these [guidelines](./CONTRIBUTING.md).

-We use GitHub issues for tracking requests and bugs. Please post questions to the MediaPipe Stack Overflow with a 'mediapipe' tag.
+We welcome contributions. Please follow these
+[guidelines](https://github.com/google/mediapipe/blob/master/CONTRIBUTING.md).
+
+We use GitHub issues for tracking requests and bugs. Please post questions to
+the MediaPipe Stack Overflow with a `mediapipe` tag.
@@ -37,10 +37,19 @@ http_archive(
 )

 # GoogleTest/GoogleMock framework. Used by most unit-tests.
+# Last updated 2020-06-30.
 http_archive(
-     name = "com_google_googletest",
-     urls = ["https://github.com/google/googletest/archive/master.zip"],
-     strip_prefix = "googletest-master",
+    name = "com_google_googletest",
+    urls = ["https://github.com/google/googletest/archive/aee0f9d9b5b87796ee8a0ab26b7587ec30e8858e.zip"],
+    patches = [
+        # fix for https://github.com/google/googletest/issues/2817
+        "@//third_party:com_google_googletest_9d580ea80592189e6d44fa35bcf9cdea8bf620d6.diff"
+    ],
+    patch_args = [
+        "-p1",
+    ],
+    strip_prefix = "googletest-aee0f9d9b5b87796ee8a0ab26b7587ec30e8858e",
+    sha256 = "04a1751f94244307cebe695a69cc945f9387a80b0ef1af21394a490697c5c895",
 )

 # Google Benchmark library.
@@ -128,6 +137,25 @@ http_archive(
    urls = ["https://github.com/google/multichannel-audio-tools/archive/master.zip"],
 )

+# 2020-07-09
+http_archive(
+    name = "pybind11_bazel",
+    strip_prefix = "pybind11_bazel-203508e14aab7309892a1c5f7dd05debda22d9a5",
+    urls = ["https://github.com/pybind/pybind11_bazel/archive/203508e14aab7309892a1c5f7dd05debda22d9a5.zip"],
+    sha256 = "75922da3a1bdb417d820398eb03d4e9bd067c4905a4246d35a44c01d62154d91",
+)
+
+http_archive(
+    name = "pybind11",
+    urls = [
+        "https://storage.googleapis.com/mirror.tensorflow.org/github.com/pybind/pybind11/archive/v2.4.3.tar.gz",
+        "https://github.com/pybind/pybind11/archive/v2.4.3.tar.gz",
+    ],
+    sha256 = "1eed57bc6863190e35637290f97a20c81cfe4d9090ac0a24f3bbf08f265eb71d",
+    strip_prefix = "pybind11-2.4.3",
+    build_file = "@pybind11_bazel//:pybind11.BUILD",
+)
+
 http_archive(
    name = "ceres_solver",
    url = "https://github.com/ceres-solver/ceres-solver/archive/1.14.0.zip",
@@ -0,0 +1,140 @@
+#!/bin/bash
+# Copyright 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.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =========================================================================
+#
+# Script to build all MediaPipe Android example apps.
+#
+# To build all apps and store them in out_dir, and install them:
+#   $ ./build_android_examples.sh -d out_dir
+#   Omitting -d and the associated directory saves all generated APKs in the
+#   current directory.
+#   $ ./build_android_examples.sh -d out_dir --nostrip
+#   Same as above except that the symnbols are not stripped.
+#
+# To install the apps already stored in out_dir (after building them with the
+# usages above):
+#   $ ./build_android_examples.sh -d out_dir -i
+#   Omitting -d and the associated directory assumes the apps are in the
+#   current directory.
+
+set -e
+
+function switch_to_opencv_3() {
+  echo "Switching to OpenCV 3"
+  sed -i -e 's:4.0.1/opencv-4.0.1:3.4.3/opencv-3.4.3:g' WORKSPACE
+  sed -i -e 's:libopencv_java4:libopencv_java3:g' third_party/opencv_android.BUILD
+}
+
+function switch_to_opencv_4() {
+  echo "Switching to OpenCV 4"
+  sed -i -e 's:3.4.3/opencv-3.4.3:4.0.1/opencv-4.0.1:g' WORKSPACE
+  sed -i -e 's:libopencv_java3:libopencv_java4:g' third_party/opencv_android.BUILD
+}
+
+out_dir="."
+strip=true
+install_only=false
+app_dir="mediapipe/examples/android/src/java/com/google/mediapipe/apps"
+bin_dir="bazel-bin"
+declare -a default_bazel_flags=(build -c opt --config=android_arm64)
+
+while [[ -n $1 ]]; do
+  case $1 in
+    -d)
+      shift
+      out_dir=$1
+      ;;
+    --nostrip)
+      strip=false
+      ;;
+    -i)
+      install_only=true
+      ;;
+    *)
+      echo "Unsupported input argument $1."
+      exit 1
+      ;;
+  esac
+  shift
+done
+
+echo "app_dir: $app_dir"
+echo "out_dir: $out_dir"
+echo "strip: $strip"
+
+declare -a apks=()
+declare -a bazel_flags
+switch_to_opencv_3
+
+apps="${app_dir}/*"
+for app in ${apps}; do
+  if [[ -d "${app}" ]]; then
+    app_name=${app##*/}
+    if [[ ${app_name} == "basic" ]]; then
+      target_name="helloworld"
+    else
+      target_name=${app_name}
+    fi
+    target="${app}:${target_name}"
+    bin="${bin_dir}/${app}/${target_name}.apk"
+    apk="${out_dir}/${target_name}.apk"
+
+    echo "=== Target: ${target}"
+
+    if [[ $install_only == false ]]; then
+      bazel_flags=("${default_bazel_flags[@]}")
+      bazel_flags+=(${target})
+      if [[ $strip == true ]]; then
+        bazel_flags+=(--linkopt=-s)
+      fi
+
+      if [[ ${app_name} == "templatematchingcpu" ]]; then
+        switch_to_opencv_4
+      fi
+      bazel "${bazel_flags[@]}"
+      cp -f "${bin}" "${apk}"
+      if [[ ${app_name} == "templatematchingcpu" ]]; then
+        switch_to_opencv_3
+      fi
+    fi
+
+    if [[ ${app_name} == "objectdetection3d" ]]; then
+      orig_apk=${apk}
+      apk="${out_dir}/${target_name}_shoes.apk"
+      cp -f "${orig_apk}" "${apk}"
+      apks+=(${apk})
+
+      apk="${out_dir}/${target_name}_chairs.apk"
+      if [[ $install_only == false ]]; then
+        bazel_flags+=(--define chair=true)
+        bazel "${bazel_flags[@]}"
+        cp -f "${bin}" "${apk}"
+      fi
+    fi
+
+    apks+=(${apk})
+  fi
+done
+
+echo
+echo "Connect your device via adb to install the apps."
+read -p "Press 'a' to abort, or press any other key to continue ..." -n 1 -r
+echo
+if [[ ! $REPLY =~ ^[Aa]$ ]]; then
+  for apk in "${apks[@]}"; do
+    echo "=== Installing $apk"
+    adb install -r "${apk}"
+  done
+fi
@@ -0,0 +1,77 @@
+#!/bin/bash
+# Copyright 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.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =========================================================================
+#
+# Script to build all MediaPipe iOS example apps.
+#
+# To build all apps and store them in out_dir:
+#   $ ./build_ios_examples.sh -d out_dir
+#   Omitting -d and the associated directory saves all generated IPAs in the
+#   current directory.
+#   $ ./build_ios_examples.sh -d out_dir --nostrip
+#   Same as above except that the symnbols are not stripped.
+
+set -e
+
+out_dir="."
+strip=true
+app_dir="mediapipe/examples/ios"
+bin_dir="bazel-bin"
+declare -a default_bazel_flags=(build -c opt --config=ios_arm64)
+
+while [[ -n $1 ]]; do
+  case $1 in
+    -d)
+      shift
+      out_dir=$1
+      ;;
+    --nostrip)
+      strip=false
+      ;;
+    *)
+      echo "Unsupported input argument $1."
+      exit 1
+      ;;
+  esac
+  shift
+done
+
+echo "app_dir: $app_dir"
+echo "out_dir: $out_dir"
+echo "strip: $strip"
+
+declare -a bazel_flags
+
+apps="${app_dir}/*"
+for app in ${apps}; do
+  if [[ -d "${app}" ]]; then
+    target_name=${app##*/}
+    if [[ "${target_name}" == "common" ]]; then
+      continue
+    fi
+    target="${app}:${target_name}"
+
+    echo "=== Target: ${target}"
+
+    bazel_flags=("${default_bazel_flags[@]}")
+    bazel_flags+=(${target})
+    if [[ $strip == true ]]; then
+      bazel_flags+=(--linkopt=-s)
+    fi
+
+    bazel "${bazel_flags[@]}"
+    cp -f "${bin_dir}/${app}/"*".ipa" "${out_dir}"
+  fi
+done
@@ -0,0 +1,29 @@
+# Configuration for GitHub Pages
+
+remote_theme: pmarsceill/just-the-docs
+
+# Set a path/url to a logo that will be displayed instead of the title
+logo: "images/logo_horizontal_color.png"
+
+# Enable or disable the site search
+search_enabled: true
+
+# Set the search token separator for hyphenated-word search:
+search_tokenizer_separator: /[\s/]+/
+
+# Enable or disable heading anchors
+heading_anchors: true
+
+# Aux links for the upper right navigation
+aux_links:
+  "MediaPipe on GitHub":
+    - "//github.com/google/mediapipe"
+
+# Footer content appears at the bottom of every page's main content
+footer_content: "&copy; 2020 GOOGLE LLC | <a href=\"https://policies.google.com/privacy\">PRIVACY POLICY</a> | <a href=\"https://policies.google.com/terms\">TERMS OF SERVICE</a>"
+
+# Color scheme currently only supports "dark" or nil (default)
+color_scheme: nil
+
+# Google Analytics Tracking (optional)
+ga_tracking: UA-140696581-2
@@ -19,7 +19,7 @@ project = 'MediaPipe'
 author = 'Google LLC'

 # The full version, including alpha/beta/rc tags
-release = 'v0.5'
+release = 'v0.7.5'


 # -- General configuration ---------------------------------------------------
@@ -0,0 +1,407 @@
+---
+layout: default
+title: Calculators
+parent: Framework Concepts
+nav_order: 1
+---
+
+# Calculators
+{: .no_toc }
+
+1. TOC
+{:toc}
+---
+
+Each calculator is a node of a graph. We describe how to create a new
+calculator, how to initialize a calculator, how to perform its calculations,
+input and output streams, timestamps, and options. Each node in the graph is
+implemented as a `Calculator`. The bulk of graph execution happens inside its
+calculators. A calculator may receive zero or more input streams and/or side
+packets and produces zero or more output streams and/or side packets.
+
+## CalculatorBase
+
+A calculator is created by defining a new sub-class of the
+[`CalculatorBase`](https://github.com/google/mediapipe/tree/master/mediapipe/framework/calculator_base.cc)
+class, implementing a number of methods, and registering the new sub-class with
+Mediapipe. At a minimum, a new calculator must implement the below four methods
+
+*   `GetContract()`
+    *   Calculator authors can specify the expected types of inputs and outputs
+        of a calculator in GetContract(). When a graph is initialized, the
+        framework calls a static method to verify if the packet types of the
+        connected inputs and outputs match the information in this
+        specification.
+*   `Open()`
+    *   After a graph starts, the framework calls `Open()`. The input side
+        packets are available to the calculator at this point. `Open()`
+        interprets the node configuration operations (see [Graphs](graphs.md))
+        and prepares the calculator's per-graph-run state. This function may
+        also write packets to calculator outputs. An error during `Open()` can
+        terminate the graph run.
+*   `Process()`
+    *   For a calculator with inputs, the framework calls `Process()` repeatedly
+        whenever at least one input stream has a packet available. The framework
+        by default guarantees that all inputs have the same timestamp (see
+        [Synchronization](synchronization.md) for more information). Multiple
+        `Process()` calls can be invoked simultaneously when parallel execution
+        is enabled. If an error occurs during `Process()`, the framework calls
+        `Close()` and the graph run terminates.
+*   `Close()`
+    *   After all calls to `Process()` finish or when all input streams close,
+        the framework calls `Close()`. This function is always called if
+        `Open()` was called and succeeded and even if the graph run terminated
+        because of an error. No inputs are available via any input streams
+        during `Close()`, but it still has access to input side packets and
+        therefore may write outputs. After `Close()` returns, the calculator
+        should be considered a dead node. The calculator object is destroyed as
+        soon as the graph finishes running.
+
+The following are code snippets from
+[CalculatorBase.h](https://github.com/google/mediapipe/tree/master/mediapipe/framework/calculator_base.h).
+
+```c++
+class CalculatorBase {
+ public:
+  ...
+
+  // The subclasses of CalculatorBase must implement GetContract.
+  // ...
+  static ::MediaPipe::Status GetContract(CalculatorContract* cc);
+
+  // Open is called before any Process() calls, on a freshly constructed
+  // calculator.  Subclasses may override this method to perform necessary
+  // setup, and possibly output Packets and/or set output streams' headers.
+  // ...
+  virtual ::MediaPipe::Status Open(CalculatorContext* cc) {
+    return ::MediaPipe::OkStatus();
+  }
+
+  // Processes the incoming inputs. May call the methods on cc to access
+  // inputs and produce outputs.
+  // ...
+  virtual ::MediaPipe::Status Process(CalculatorContext* cc) = 0;
+
+  // Is called if Open() was called and succeeded.  Is called either
+  // immediately after processing is complete or after a graph run has ended
+  // (if an error occurred in the graph).  ...
+  virtual ::MediaPipe::Status Close(CalculatorContext* cc) {
+    return ::MediaPipe::OkStatus();
+  }
+
+  ...
+};
+```
+
+## Life of a calculator
+
+During initialization of a MediaPipe graph, the framework calls a
+`GetContract()` static method to determine what kinds of packets are expected.
+
+The framework constructs and destroys the entire calculator for each graph run
+(e.g. once per video or once per image). Expensive or large objects that remain
+constant across graph runs should be supplied as input side packets so the
+calculations are not repeated on subsequent runs.
+
+After initialization, for each run of the graph, the following sequence occurs:
+
+*   `Open()`
+*   `Process()` (repeatedly)
+*   `Close()`
+
+The framework calls `Open()` to initialize the calculator. `Open()` should
+interpret any options and set up the calculator's per-graph-run state. `Open()`
+may obtain input side packets and write packets to calculator outputs. If
+appropriate, it should call `SetOffset()` to reduce potential packet buffering
+of input streams.
+
+If an error occurs during `Open()` or `Process()` (as indicated by one of them
+returning a non-`Ok` status), the graph run is terminated with no further calls
+to the calculator's methods, and the calculator is destroyed.
+
+For a calculator with inputs, the framework calls `Process()` whenever at least
+one input has a packet available. The framework guarantees that inputs all have
+the same timestamp, that timestamps increase with each call to `Process()` and
+that all packets are delivered. As a consequence, some inputs may not have any
+packets when `Process()` is called. An input whose packet is missing appears to
+produce an empty packet (with no timestamp).
+
+The framework calls `Close()` after all calls to `Process()`. All inputs will
+have been exhausted, but `Close()` has access to input side packets and may
+write outputs. After Close returns, the calculator is destroyed.
+
+Calculators with no inputs are referred to as sources. A source calculator
+continues to have `Process()` called as long as it returns an `Ok` status. A
+source calculator indicates that it is exhausted by returning a stop status
+(i.e. MediaPipe::tool::StatusStop).
+
+## Identifying inputs and outputs
+
+The public interface to a calculator consists of a set of input streams and
+output streams. In a CalculatorGraphConfiguration, the outputs from some
+calculators are connected to the inputs of other calculators using named
+streams. Stream names are normally lowercase, while input and output tags are
+normally UPPERCASE. In the example below, the output with tag name `VIDEO` is
+connected to the input with tag name `VIDEO_IN` using the stream named
+`video_stream`.
+
+```proto
+# Graph describing calculator SomeAudioVideoCalculator
+node {
+  calculator: "SomeAudioVideoCalculator"
+  input_stream: "INPUT:combined_input"
+  output_stream: "VIDEO:video_stream"
+}
+node {
+  calculator: "SomeVideoCalculator"
+  input_stream: "VIDEO_IN:video_stream"
+  output_stream: "VIDEO_OUT:processed_video"
+}
+```
+
+Input and output streams can be identified by index number, by tag name, or by a
+combination of tag name and index number. You can see some examples of input and
+output identifiers in the example below. `SomeAudioVideoCalculator` identifies
+its video output by tag and its audio outputs by the combination of tag and
+index. The input with tag `VIDEO` is connected to the stream named
+`video_stream`. The outputs with tag `AUDIO` and indices `0` and `1` are
+connected to the streams named `audio_left` and `audio_right`.
+`SomeAudioCalculator` identifies its audio inputs by index only (no tag needed).
+
+```proto
+# Graph describing calculator SomeAudioVideoCalculator
+node {
+  calculator: "SomeAudioVideoCalculator"
+  input_stream: "combined_input"
+  output_stream: "VIDEO:video_stream"
+  output_stream: "AUDIO:0:audio_left"
+  output_stream: "AUDIO:1:audio_right"
+}
+
+node {
+  calculator: "SomeAudioCalculator"
+  input_stream: "audio_left"
+  input_stream: "audio_right"
+  output_stream: "audio_energy"
+}
+```
+
+In the calculator implementation, inputs and outputs are also identified by tag
+name and index number. In the function below input are output are identified:
+
+*   By index number: The combined input stream is identified simply by index
+    `0`.
+*   By tag name: The video output stream is identified by tag name "VIDEO".
+*   By tag name and index number: The output audio streams are identified by the
+    combination of the tag name `AUDIO` and the index numbers `0` and `1`.
+
+```c++
+// c++ Code snippet describing the SomeAudioVideoCalculator GetContract() method
+class SomeAudioVideoCalculator : public CalculatorBase {
+ public:
+  static ::mediapipe::Status GetContract(CalculatorContract* cc) {
+    cc->Inputs().Index(0).SetAny();
+    // SetAny() is used to specify that whatever the type of the
+    // stream is, it's acceptable.  This does not mean that any
+    // packet is acceptable.  Packets in the stream still have a
+    // particular type.  SetAny() has the same effect as explicitly
+    // setting the type to be the stream's type.
+    cc->Outputs().Tag("VIDEO").Set<ImageFrame>();
+    cc->Outputs().Get("AUDIO", 0).Set<Matrix>();
+    cc->Outputs().Get("AUDIO", 1).Set<Matrix>();
+    return ::mediapipe::OkStatus();
+  }
+```
+
+## Processing
+
+`Process()` called on a non-source node must return `::mediapipe::OkStatus()` to
+indicate that all went well, or any other status code to signal an error
+
+If a non-source calculator returns `tool::StatusStop()`, then this signals the
+graph is being cancelled early. In this case, all source calculators and graph
+input streams will be closed (and remaining Packets will propagate through the
+graph).
+
+A source node in a graph will continue to have `Process()` called on it as long
+as it returns `::mediapipe::OkStatus(`). To indicate that there is no more data
+to be generated return `tool::StatusStop()`. Any other status indicates an error
+has occurred.
+
+`Close()` returns `::mediapipe::OkStatus()` to indicate success. Any other
+status indicates a failure.
+
+Here is the basic `Process()` function. It uses the `Input()` method (which can
+be used only if the calculator has a single input) to request its input data. It
+then uses `std::unique_ptr` to allocate the memory needed for the output packet,
+and does the calculations. When done it releases the pointer when adding it to
+the output stream.
+
+```c++
+::util::Status MyCalculator::Process() {
+  const Matrix& input = Input()->Get<Matrix>();
+  std::unique_ptr<Matrix> output(new Matrix(input.rows(), input.cols()));
+  // do your magic here....
+  //    output->row(n) =  ...
+  Output()->Add(output.release(), InputTimestamp());
+  return ::mediapipe::OkStatus();
+}
+```
+
+## Example calculator
+
+This section discusses the implementation of `PacketClonerCalculator`, which
+does a relatively simple job, and is used in many calculator graphs.
+`PacketClonerCalculator` simply produces a copy of its most recent input
+packets on demand.
+
+`PacketClonerCalculator` is useful when the timestamps of arriving data packets
+are not aligned perfectly. Suppose we have a room with a microphone, light
+sensor and a video camera that is collecting sensory data. Each of the sensors
+operates independently and collects data intermittently. Suppose that the output
+of each sensor is:
+
+*   microphone = loudness in decibels of sound in the room (Integer)
+*   light sensor = brightness of room (Integer)
+*   video camera = RGB image frame of room (ImageFrame)
+
+Our simple perception pipeline is designed to process sensory data from these 3
+sensors such that at any time when we have image frame data from the camera that
+is synchronized with the last collected microphone loudness data and light
+sensor brightness data. To do this with MediaPipe, our perception pipeline has 3
+input streams:
+
+*   room_mic_signal - Each packet of data in this input stream is integer data
+    representing how loud audio is in a room with timestamp.
+*   room_lightening_sensor - Each packet of data in this input stream is integer
+    data representing how bright is the room illuminated with timestamp.
+*   room_video_tick_signal - Each packet of data in this input stream is
+    imageframe of video data representing video collected from camera in the
+    room with timestamp.
+
+Below is the implementation of the `PacketClonerCalculator`.  You can see
+the `GetContract()`, `Open()`, and `Process()` methods as well as the instance
+variable `current_` which holds the most recent input packets.
+
+```c++
+// This takes packets from N+1 streams, A_1, A_2, ..., A_N, B.
+// For every packet that appears in B, outputs the most recent packet from each
+// of the A_i on a separate stream.
+
+#include <vector>
+
+#include "absl/strings/str_cat.h"
+#include "mediapipe/framework/calculator_framework.h"
+
+namespace mediapipe {
+
+// For every packet received on the last stream, output the latest packet
+// obtained on all other streams. Therefore, if the last stream outputs at a
+// higher rate than the others, this effectively clones the packets from the
+// other streams to match the last.
+//
+// Example config:
+// node {
+//   calculator: "PacketClonerCalculator"
+//   input_stream: "first_base_signal"
+//   input_stream: "second_base_signal"
+//   input_stream: "tick_signal"
+//   output_stream: "cloned_first_base_signal"
+//   output_stream: "cloned_second_base_signal"
+// }
+//
+class PacketClonerCalculator : public CalculatorBase {
+ public:
+  static ::mediapipe::Status GetContract(CalculatorContract* cc) {
+    const int tick_signal_index = cc->Inputs().NumEntries() - 1;
+    // cc->Inputs().NumEntries() returns the number of input streams
+    // for the PacketClonerCalculator
+    for (int i = 0; i < tick_signal_index; ++i) {
+      cc->Inputs().Index(i).SetAny();
+      // cc->Inputs().Index(i) returns the input stream pointer by index
+      cc->Outputs().Index(i).SetSameAs(&cc->Inputs().Index(i));
+    }
+    cc->Inputs().Index(tick_signal_index).SetAny();
+    return ::mediapipe::OkStatus();
+  }
+
+  ::mediapipe::Status Open(CalculatorContext* cc) final {
+    tick_signal_index_ = cc->Inputs().NumEntries() - 1;
+    current_.resize(tick_signal_index_);
+    // Pass along the header for each stream if present.
+    for (int i = 0; i < tick_signal_index_; ++i) {
+      if (!cc->Inputs().Index(i).Header().IsEmpty()) {
+        cc->Outputs().Index(i).SetHeader(cc->Inputs().Index(i).Header());
+        // Sets the output stream of index i header to be the same as
+        // the header for the input stream of index i
+      }
+    }
+    return ::mediapipe::OkStatus();
+  }
+
+  ::mediapipe::Status Process(CalculatorContext* cc) final {
+    // Store input signals.
+    for (int i = 0; i < tick_signal_index_; ++i) {
+      if (!cc->Inputs().Index(i).Value().IsEmpty()) {
+        current_[i] = cc->Inputs().Index(i).Value();
+      }
+    }
+
+    // Output if the tick signal is non-empty.
+    if (!cc->Inputs().Index(tick_signal_index_).Value().IsEmpty()) {
+      for (int i = 0; i < tick_signal_index_; ++i) {
+        if (!current_[i].IsEmpty()) {
+          cc->Outputs().Index(i).AddPacket(
+              current_[i].At(cc->InputTimestamp()));
+          // Add a packet to output stream of index i a packet from inputstream i
+          // with timestamp common to all present inputs
+          //
+        } else {
+          cc->Outputs().Index(i).SetNextTimestampBound(
+              cc->InputTimestamp().NextAllowedInStream());
+          // if current_[i], 1 packet buffer for input stream i is empty, we will set
+          // next allowed timestamp for input stream i to be current timestamp + 1
+        }
+      }
+    }
+    return ::mediapipe::OkStatus();
+  }
+
+ private:
+  std::vector<Packet> current_;
+  int tick_signal_index_;
+};
+
+REGISTER_CALCULATOR(PacketClonerCalculator);
+}  // namespace mediapipe
+```
+
+Typically, a calculator has only a .cc file. No .h is required, because
+mediapipe uses registration to make calculators known to it. After you have
+defined your calculator class, register it with a macro invocation
+REGISTER_CALCULATOR(calculator_class_name).
+
+Below is a trivial MediaPipe graph that has 3 input streams, 1 node
+(PacketClonerCalculator) and 3 output streams.
+
+```proto
+input_stream: "room_mic_signal"
+input_stream: "room_lighting_sensor"
+input_stream: "room_video_tick_signal"
+
+node {
+   calculator: "PacketClonerCalculator"
+   input_stream: "room_mic_signal"
+   input_stream: "room_lighting_sensor"
+   input_stream: "room_video_tick_signal"
+   output_stream: "cloned_room_mic_signal"
+   output_stream: "cloned_lighting_sensor"
+ }
+```
+
+The diagram below shows how the `PacketClonerCalculator` defines its output
+packets (bottom) based on its series of input packets (top).
+
+| ![Graph using PacketClonerCalculator](../images/packet_cloner_calculator.png) |
+| :---------------------------------------------------------------------------: |
+| *Each time it receives a packet on its TICK input stream, the PacketClonerCalculator outputs the most recent packet from each of its input streams. The sequence of output packets (bottom) is determined by the sequence of input packets (top) and their timestamps. The timestamps are shown along the right side of the diagram.* |
@@ -1,24 +1,42 @@
-# MediaPipe Concepts
+---
+layout: default
+title: Framework Concepts
+nav_order: 5
+has_children: true
+has_toc: false
+---
+
+# Framework Concepts
+{: .no_toc }
+
+1. TOC
+{:toc}
+---

 ## The basics

 ### Packet

-The basic data flow unit. A packet consists of a numeric timestamp and a shared pointer to an **immutable** payload. The payload can be of any C++ type, and the payload's type is also referred to as the type of the packet. Packets are value classes and can be copied cheaply. Each copy shares ownership of the payload, with reference-counting semantics. Each copy has its own timestamp. [Details](packets.md).
+The basic data flow unit. A packet consists of a numeric timestamp and a shared
+pointer to an **immutable** payload. The payload can be of any C++ type, and the
+payload's type is also referred to as the type of the packet. Packets are value
+classes and can be copied cheaply. Each copy shares ownership of the payload,
+with reference-counting semantics. Each copy has its own timestamp. See also
+[Packet](packets.md).

 ### Graph

 MediaPipe processing takes place inside a graph, which defines packet flow paths
 between **nodes**. A graph can have any number of inputs and outputs, and data
-flow can branch and merge. Generally data flows forward, but
-[backward loops](cycles.md) are possible.
+flow can branch and merge. Generally data flows forward, but backward loops are
+possible. See [Graphs](graphs.md) for details.

 ### Nodes

 Nodes produce and/or consume packets, and they are where the bulk of the graph’s
 work takes place. They are also known as “calculators”, for historical reasons.
-Each node’s interface defines a number of input and output **ports**, identified by
-a tag and/or an index.
+Each node’s interface defines a number of input and output **ports**, identified
+by a tag and/or an index. See [Calculators](calculators.md) for details.

 ### Streams

@@ -34,21 +52,23 @@ whereas a stream represents a flow of data that changes over time.
 ### Packet Ports

 A port has an associated type; packets transiting through the port must be of
-that type. An output stream port can be connected to any number of
-input stream ports of the same type; each consumer receives a separate copy of
-the output packets, and has its own queue, so it can consume them at its own
-pace. Similarly, a side packet output port can be connected to as many side
-packet input ports as desired.
+that type. An output stream port can be connected to any number of input stream
+ports of the same type; each consumer receives a separate copy of the output
+packets, and has its own queue, so it can consume them at its own pace.
+Similarly, a side packet output port can be connected to as many side packet
+input ports as desired.

 A port can be required, meaning that a connection must be made for the graph to
 be valid, or optional, meaning it may remain unconnected.

-Note: even if a stream connection is required, the stream may not carry a packet for all timestamps.
+Note: even if a stream connection is required, the stream may not carry a packet
+for all timestamps.

 ## Input and output

 Data flow can originate from **source nodes**, which have no input streams and
-produce packets spontaneously (e.g. by reading from a file); or from **graph input streams**, which let an application feed packets into a graph.
+produce packets spontaneously (e.g. by reading from a file); or from **graph
+input streams**, which let an application feed packets into a graph.

 Similarly, there are **sink nodes** that receive data and write it to various
 destinations (e.g. a file, a memory buffer, etc.), and an application can also
@@ -78,15 +98,15 @@ processed data.

 ### Input policies

-The default input policy is deterministic collation of packets by timestamp. A node receives
-all inputs for the same timestamp at the same time, in an invocation of its
-Process method; and successive input sets are received in their timestamp order. This can
-require delaying the processing of some packets until a packet with the same
-timestamp is received on all input streams, or until it can be guaranteed that a
-packet with that timestamp will not be arriving on the streams that have not
-received it.
+The default input policy is deterministic collation of packets by timestamp. A
+node receives all inputs for the same timestamp at the same time, in an
+invocation of its Process method; and successive input sets are received in
+their timestamp order. This can require delaying the processing of some packets
+until a packet with the same timestamp is received on all input streams, or
+until it can be guaranteed that a packet with that timestamp will not be
+arriving on the streams that have not received it.

 Other policies are also available, implemented using a separate kind of
 component known as an InputStreamHandler.

-See [scheduling](scheduling_sync.md) for more details.
+See [Synchronization](synchronization.md) for more details.
@@ -1,14 +1,18 @@
-## Running on GPUs
+---
+layout: default
+title: GPU
+parent: Framework Concepts
+nav_order: 5
+---

-   [Overview](#overview)
-   [OpenGL ES Support](#opengl-es-support)
-   [Disable OpenGL ES Support](#disable-opengl-es-support)
-   [OpenGL ES Setup on Linux Desktop](#opengl-es-setup-on-linux-desktop)
-   [TensorFlow CUDA Support and Setup on Linux Desktop](#tensorflow-cuda-support-and-setup-on-linux-desktop)
-   [Life of a GPU Calculator](#life-of-a-gpu-calculator)
-   [GpuBuffer to ImageFrame Converters](#gpubuffer-to-imageframe-converters)
+# GPU
+{: .no_toc }

-### Overview
+1. TOC
+{:toc}
+---
+
+## Overview

 MediaPipe supports calculator nodes for GPU compute and rendering, and allows combining multiple GPU nodes, as well as mixing them with CPU based calculator nodes. There exist several GPU APIs on mobile platforms (eg, OpenGL ES, Metal and Vulkan). MediaPipe does not attempt to offer a single cross-API GPU abstraction. Individual nodes can be written using different APIs, allowing them to take advantage of platform specific features when needed.

@@ -25,7 +29,7 @@ Below are the design principles for GPU support in MediaPipe
   * Because different platforms may require different techniques for best performance, the API should allow flexibility in the way things are implemented behind the scenes.
   * A calculator should be allowed maximum flexibility in using the GPU for all or part of its operation, combining it with the CPU if necessary.

-### OpenGL ES Support
+## OpenGL ES Support

 MediaPipe supports OpenGL ES up to version 3.2 on Android/Linux and up to ES 3.0
 on iOS. In addition, MediaPipe also supports Metal on iOS.
@@ -50,172 +54,7 @@ some Android devices. Therefore, our approach is to have one dedicated thread
 per context. Each thread issues GL commands, building up a serial command queue
 on its context, which is then executed by the GPU asynchronously.

-### Disable OpenGL ES Support
-
-By default, building MediaPipe (with no special bazel flags) attempts to compile
-and link against OpenGL ES (and for iOS also Metal) libraries.
-
-On platforms where OpenGL ES is not available (see also
-[OpenGL ES Setup on Linux Desktop](#opengl-es-setup-on-linux-desktop)), you
-should disable OpenGL ES support with:
-
-```
-$ bazel build --define MEDIAPIPE_DISABLE_GPU=1 <my-target>
-```
-
-Note: On Android and iOS, OpenGL ES is required by MediaPipe framework and the
-support should never be disabled.
-
-### OpenGL ES Setup on Linux Desktop
-
-On Linux desktop with video cards that support OpenGL ES 3.1+, MediaPipe can run
-GPU compute and rendering and perform TFLite inference on GPU.
-
-To check if your Linux desktop GPU can run MediaPipe with OpenGL ES:
-
-```bash
-$ sudo apt-get install mesa-common-dev libegl1-mesa-dev libgles2-mesa-dev
-$ sudo apt-get install mesa-utils
-$ glxinfo | grep -i opengl
-```
-
-For example, it may print:
-
-```bash
-$ glxinfo | grep -i opengl
-...
-OpenGL ES profile version string: OpenGL ES 3.2 NVIDIA 430.50
-OpenGL ES profile shading language version string: OpenGL ES GLSL ES 3.20
-OpenGL ES profile extensions:
-```
-
-*Notice the ES 3.20 text above.*
-
-You need to see ES 3.1 or greater printed in order to perform TFLite inference
-on GPU in MediaPipe. With this setup, build with:
-
-```
-$ bazel build --copt -DMESA_EGL_NO_X11_HEADERS --copt -DEGL_NO_X11 <my-target>
-```
-
-If only ES 3.0 or below is supported, you can still build MediaPipe targets that
-don't require TFLite inference on GPU with:
-
-```
-$ bazel build --copt -DMESA_EGL_NO_X11_HEADERS --copt -DEGL_NO_X11 --copt -DMEDIAPIPE_DISABLE_GL_COMPUTE <my-target>
-```
-
-Note: MEDIAPIPE_DISABLE_GL_COMPUTE is already defined automatically on all Apple
-systems (Apple doesn't support OpenGL ES 3.1+).
-
-### TensorFlow CUDA Support and Setup on Linux Desktop
-
-MediaPipe framework doesn't require CUDA for GPU compute and rendering. However,
-MediaPipe can work with TensorFlow to perform GPU inference on video cards that
-support CUDA.
-
-To enable TensorFlow GPU inference with MediaPipe, the first step is to follow
-the
-[TensorFlow GPU documentation](https://www.tensorflow.org/install/gpu#software_requirements)
-to install the required NVIDIA software on your Linux desktop.
-
-After installation, update `$PATH` and `$LD_LIBRARY_PATH` and run `ldconfig`
-with:
-
-```
-$ export PATH=/usr/local/cuda-10.1/bin${PATH:+:${PATH}}
-$ export LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64,/usr/local/cuda-10.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
-$ sudo ldconfig
-```
-
-It's recommended to verify the installation of CUPTI, CUDA, CuDNN, and NVCC:
-
-```
-$ ls /usr/local/cuda/extras/CUPTI
-/lib64
-libcupti.so       libcupti.so.10.1.208  libnvperf_host.so        libnvperf_target.so
-libcupti.so.10.1  libcupti_static.a     libnvperf_host_static.a
-
-$ ls /usr/local/cuda-10.1
-LICENSE  bin  extras   lib64      libnvvp           nvml  samples  src      tools
-README   doc  include  libnsight  nsightee_plugins  nvvm  share    targets  version.txt
-
-$ nvcc -V
-nvcc: NVIDIA (R) Cuda compiler driver
-Copyright (c) 2005-2019 NVIDIA Corporation
-Built on Sun_Jul_28_19:07:16_PDT_2019
-Cuda compilation tools, release 10.1, V10.1.243
-
-$ ls /usr/lib/x86_64-linux-gnu/ | grep libcudnn.so
-libcudnn.so
-libcudnn.so.7
-libcudnn.so.7.6.4
-```
-
-Setting `$TF_CUDA_PATHS` is the way to declare where the CUDA library is. Note
-that the following code snippet also adds `/usr/lib/x86_64-linux-gnu` and
-`/usr/include` into `$TF_CUDA_PATHS` for cudablas and libcudnn.
-
-```
-$ export TF_CUDA_PATHS=/usr/local/cuda-10.1,/usr/lib/x86_64-linux-gnu,/usr/include
-```
-
-To make MediaPipe get TensorFlow's CUDA settings, find TensorFlow's
-[.bazelrc](https://github.com/tensorflow/tensorflow/blob/master/.bazelrc) and
-copy the `build:using_cuda` and `build:cuda` section into MediaPipe's .bazelrc
-file. For example, as of April 23, 2020, TensorFlow's CUDA setting is the
-following:
-
-```
-# This config refers to building with CUDA available. It does not necessarily
-# mean that we build CUDA op kernels.
-build:using_cuda --define=using_cuda=true
-build:using_cuda --action_env TF_NEED_CUDA=1
-build:using_cuda --crosstool_top=@local_config_cuda//crosstool:toolchain
-
-# This config refers to building CUDA op kernels with nvcc.
-build:cuda --config=using_cuda
-build:cuda --define=using_cuda_nvcc=true
-```
-
-Finally, build MediaPipe with TensorFlow GPU with two more flags `--config=cuda`
-and `--spawn_strategy=local`. For example:
-
-```
-$ bazel build -c opt --config=cuda --spawn_strategy=local \
-    --define no_aws_support=true --copt -DMESA_EGL_NO_X11_HEADERS \
-    mediapipe/examples/desktop/object_detection:object_detection_tensorflow
-```
-
-While the binary is running, it prints out the GPU device info:
-
-```
-I external/org_tensorflow/tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
-I external/org_tensorflow/tensorflow/core/common_runtime/gpu/gpu_device.cc:1544] Found device 0 with properties: pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5 coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s
-I external/org_tensorflow/tensorflow/core/common_runtime/gpu/gpu_device.cc:1686] Adding visible gpu devices: 0
-```
-
-You can monitor the GPU usage to verify whether the GPU is used for model
-inference.
-
-```
-$ nvidia-smi --query-gpu=utilization.gpu --format=csv --loop=1
-
-0 %
-0 %
-4 %
-5 %
-83 %
-21 %
-22 %
-27 %
-29 %
-100 %
-0 %
-0%
-```
-
-### Life of a GPU Calculator
+## Life of a GPU Calculator

 This section presents the basic structure of the Process method of a GPU
 calculator derived from base class GlSimpleCalculator. The GPU calculator
@@ -302,7 +141,7 @@ choices for MediaPipe GPU support:
   * Data that needs to be shared between all GPU-based calculators is provided as a external input that is implemented as a graph service and is managed by the `GlCalculatorHelper` class.
   * The combination of calculator-specific helpers and a shared graph service allows us great flexibility in managing the GPU resource: we can have a separate context per calculator, share a single context, share a lock or other synchronization primitives, etc. -- and all of this is managed by the helper and hidden from the individual calculators.

-### GpuBuffer to ImageFrame Converters
+## GpuBuffer to ImageFrame Converters

 We provide two calculators called `GpuBufferToImageFrameCalculator` and `ImageFrameToGpuBufferCalculator`. These calculators convert between `ImageFrame` and `GpuBuffer`, allowing the construction of graphs that combine GPU and CPU calculators. They are supported on both iOS and Android

@@ -310,6 +149,15 @@ When possible, these calculators use platform-specific functionality to share da

 The below diagram shows the data flow in a mobile application that captures video from the camera, runs it through a MediaPipe graph, and renders the output on the screen in real time. The dashed line indicates which parts are inside the MediaPipe graph proper. This application runs a Canny edge-detection filter on the CPU using OpenCV, and overlays it on top of the original video using the GPU.

-| ![How GPU calculators interact](images/gpu_example_graph.png) |
-|:--:|
-| *Video frames from the camera are fed into the graph as `GpuBuffer` packets. The input stream is accessed by two calculators in parallel. `GpuBufferToImageFrameCalculator` converts the buffer into an `ImageFrame`, which is then sent through a grayscale converter and a canny filter (both based on OpenCV and running on the CPU), whose output is then converted into a `GpuBuffer` again. A multi-input GPU calculator, GlOverlayCalculator, takes as input both the original `GpuBuffer` and the one coming out of the edge detector, and overlays them using a shader. The output is then sent back to the application using a callback calculator, and the application renders the image to the screen using OpenGL.* |
+![How GPU calculators interact](../images/gpu_example_graph.png)
+
+Video frames from the camera are fed into the graph as `GpuBuffer` packets. The
+input stream is accessed by two calculators in parallel.
+`GpuBufferToImageFrameCalculator` converts the buffer into an `ImageFrame`,
+which is then sent through a grayscale converter and a canny filter (both based
+on OpenCV and running on the CPU), whose output is then converted into a
+`GpuBuffer` again. A multi-input GPU calculator, GlOverlayCalculator, takes as
+input both the original `GpuBuffer` and the one coming out of the edge detector,
+and overlays them using a shader. The output is then sent back to the
+application using a callback calculator, and the application renders the image
+to the screen using OpenGL.
@@ -0,0 +1,271 @@
+---
+layout: default
+title: Graphs
+parent: Framework Concepts
+nav_order: 2
+---
+
+# Graphs
+{: .no_toc }
+
+1. TOC
+{:toc}
+---
+
+## GraphConfig
+
+A `GraphConfig` is a specification that describes the topology and functionality
+of a MediaPipe graph. In the specification, a node in the graph represents an
+instance of a particular calculator. All the necessary configurations of the
+node, such its type, inputs and outputs must be described in the specification.
+Description of the node can also include several optional fields, such as
+node-specific options, input policy and executor, discussed in
+[Synchronization](synchronization.md).
+
+`GraphConfig` has several other fields to configure the global graph-level
+settings, eg, graph executor configs, number of threads, and maximum queue size
+of input streams. Several graph-level settings are useful for tuning the
+performance of the graph on different platforms (eg, desktop v.s. mobile). For
+instance, on mobile, attaching a heavy model-inference calculator to a separate
+executor can improve the performance of a real-time application since this
+enables thread locality.
+
+Below is a trivial `GraphConfig` example where we have series of passthrough
+calculators :
+
+```proto
+# This graph named main_pass_throughcals_nosubgraph.pbtxt contains 4
+# passthrough calculators.
+input_stream: "in"
+node {
+    calculator: "PassThroughCalculator"
+    input_stream: "in"
+    output_stream: "out1"
+}
+node {
+    calculator: "PassThroughCalculator"
+    input_stream: "out1"
+    output_stream: "out2"
+}
+node {
+    calculator: "PassThroughCalculator"
+    input_stream: "out2"
+    output_stream: "out3"
+}
+node {
+    calculator: "PassThroughCalculator"
+    input_stream: "out3"
+    output_stream: "out4"
+}
+```
+
+## Subgraph
+
+To modularize a `CalculatorGraphConfig` into sub-modules and assist with re-use
+of perception solutions, a MediaPipe graph can be defined as a `Subgraph`. The
+public interface of a subgraph consists of a set of input and output streams
+similar to a calculator's public interface. The subgraph can then be included in
+an `CalculatorGraphConfig` as if it were a calculator. When a MediaPipe graph is
+loaded from a `CalculatorGraphConfig`, each subgraph node is replaced by the
+corresponding graph of calculators. As a result, the semantics and performance
+of the subgraph is identical to the corresponding graph of calculators.
+
+Below is an example of how to create a subgraph named `TwoPassThroughSubgraph`.
+
+1.  Defining the subgraph.
+
+    ```proto
+    # This subgraph is defined in two_pass_through_subgraph.pbtxt
+    # and is registered as "TwoPassThroughSubgraph"
+
+    type: "TwoPassThroughSubgraph"
+    input_stream: "out1"
+    output_stream: "out3"
+
+    node {
+        calculator: "PassThroughculator"
+        input_stream: "out1"
+        output_stream: "out2"
+    }
+    node {
+        calculator: "PassThroughculator"
+        input_stream: "out2"
+        output_stream: "out3"
+    }
+    ```
+
+    The public interface to the subgraph consists of:
+
+    *   Graph input streams
+    *   Graph output streams
+    *   Graph input side packets
+    *   Graph output side packets
+
+2.  Register the subgraph using BUILD rule `mediapipe_simple_subgraph`. The
+    parameter `register_as` defines the component name for the new subgraph.
+
+    ```proto
+    # Small section of BUILD file for registering the "TwoPassThroughSubgraph"
+    # subgraph for use by main graph main_pass_throughcals.pbtxt
+
+    mediapipe_simple_subgraph(
+        name = "twopassthrough_subgraph",
+        graph = "twopassthrough_subgraph.pbtxt",
+        register_as = "TwoPassThroughSubgraph",
+        deps = [
+                "//mediapipe/calculators/core:pass_through_calculator",
+                "//mediapipe/framework:calculator_graph",
+        ],
+    )
+    ```
+
+3.  Use the subgraph in the main graph.
+
+    ```proto
+    # This main graph is defined in main_pass_throughcals.pbtxt
+    # using subgraph called "TwoPassThroughSubgraph"
+
+    input_stream: "in"
+    node {
+        calculator: "PassThroughCalculator"
+        input_stream: "in"
+        output_stream: "out1"
+    }
+    node {
+        calculator: "TwoPassThroughSubgraph"
+        input_stream: "out1"
+        output_stream: "out3"
+    }
+    node {
+        calculator: "PassThroughCalculator"
+        input_stream: "out3"
+        output_stream: "out4"
+    }
+    ```
+
+## Cycles
+
+<!-- TODO: add discussion of PreviousLoopbackCalculator -->
+
+By default, MediaPipe requires calculator graphs to be acyclic and treats cycles
+in a graph as errors. If a graph is intended to have cycles, the cycles need to
+be annotated in the graph config. This page describes how to do that.
+
+NOTE: The current approach is experimental and subject to change. We welcome
+your feedback.
+
+Please use the `CalculatorGraphTest.Cycle` unit test in
+`mediapipe/framework/calculator_graph_test.cc` as sample code. Shown
+below is the cyclic graph in the test. The `sum` output of the adder is the sum
+of the integers generated by the integer source calculator.
+
+![a cyclic graph that adds a stream of integers](../images/cyclic_integer_sum_graph.svg "A cyclic graph")
+
+This simple graph illustrates all the issues in supporting cyclic graphs.
+
+### Back Edge Annotation
+
+We require that an edge in each cycle be annotated as a back edge. This allows
+MediaPipe’s topological sort to work, after removing all the back edges.
+
+There are usually multiple ways to select the back edges. Which edges are marked
+as back edges affects which nodes are considered as upstream and which nodes are
+considered as downstream, which in turn affects the priorities MediaPipe assigns
+to the nodes.
+
+For example, the `CalculatorGraphTest.Cycle` test marks the `old_sum` edge as a
+back edge, so the Delay node is considered as a downstream node of the adder
+node and is given a higher priority. Alternatively, we could mark the `sum`
+input to the delay node as the back edge, in which case the delay node would be
+considered as an upstream node of the adder node and is given a lower priority.
+
+### Initial Packet
+
+For the adder calculator to be runnable when the first integer from the integer
+source arrives, we need an initial packet, with value 0 and with the same
+timestamp, on the `old_sum` input stream to the adder. This initial packet
+should be output by the delay calculator in the `Open()` method.
+
+### Delay in a Loop
+
+Each loop should incur a delay to align the previous `sum` output with the next
+integer input. This is also done by the delay node. So the delay node needs to
+know the following about the timestamps of the integer source calculator:
+
+*   The timestamp of the first output.
+
+*   The timestamp delta between successive outputs.
+
+We plan to add an alternative scheduling policy that only cares about packet
+ordering and ignores packet timestamps, which will eliminate this inconvenience.
+
+### Early Termination of a Calculator When One Input Stream is Done
+
+By default, MediaPipe calls the `Close()` method of a non-source calculator when
+all of its input streams are done. In the example graph, we want to stop the
+adder node as soon as the integer source is done. This is accomplished by
+configuring the adder node with an alternative input stream handler,
+`EarlyCloseInputStreamHandler`.
+
+### Relevant Source Code
+
+#### Delay Calculator
+
+Note the code in `Open()` that outputs the initial packet and the code in
+`Process()` that adds a (unit) delay to input packets. As noted above, this
+delay node assumes that its output stream is used alongside an input stream with
+packet timestamps 0, 1, 2, 3, ...
+
+```c++
+class UnitDelayCalculator : public Calculator {
+ public:
+  static ::util::Status FillExpectations(
+      const CalculatorOptions& extendable_options, PacketTypeSet* inputs,
+      PacketTypeSet* outputs, PacketTypeSet* input_side_packets) {
+    inputs->Index(0)->Set<int>("An integer.");
+    outputs->Index(0)->Set<int>("The input delayed by one time unit.");
+    return ::mediapipe::OkStatus();
+  }
+
+  ::util::Status Open() final {
+    Output()->Add(new int(0), Timestamp(0));
+    return ::mediapipe::OkStatus();
+  }
+
+  ::util::Status Process() final {
+    const Packet& packet = Input()->Value();
+    Output()->AddPacket(packet.At(packet.Timestamp().NextAllowedInStream()));
+    return ::mediapipe::OkStatus();
+  }
+};
+```
+
+#### Graph Config
+
+Note the `back_edge` annotation and the alternative `input_stream_handler`.
+
+```proto
+node {
+  calculator: 'GlobalCountSourceCalculator'
+  input_side_packet: 'global_counter'
+  output_stream: 'integers'
+}
+node {
+  calculator: 'IntAdderCalculator'
+  input_stream: 'integers'
+  input_stream: 'old_sum'
+  input_stream_info: {
+    tag_index: ':1'  # 'old_sum'
+    back_edge: true
+  }
+  output_stream: 'sum'
+  input_stream_handler {
+    input_stream_handler: 'EarlyCloseInputStreamHandler'
+  }
+}
+node {
+  calculator: 'UnitDelayCalculator'
+  input_stream: 'sum'
+  output_stream: 'old_sum'
+}
+```
@@ -1,15 +1,26 @@
-### Packets
+---
+layout: default
+title: Packets
+parent: Framework Concepts
+nav_order: 3
+---

- [Creating a packet](#creating-a-packet)
+# Packets
+{: .no_toc }
+
+1. TOC
+{:toc}
+---

 Each calculator is a node of of a graph. We describe how to create a new calculator, how to initialize a calculator, how to perform its calculations, input and output streams, timestamps, and options

-#### Creating a packet
+## Creating a packet
+
 Packets are generally created with `MediaPipe::Adopt()` (from packet.h).

 ```c++
 // Create some data.
-auto data = gtl::MakeUnique<MyDataClass>("constructor_argument");
+auto data = absl::make_unique<MyDataClass>("constructor_argument");
 // Create a packet to own the data.
 Packet p = Adopt(data.release());
 // Make a new packet with the same data and a different timestamp.
@@ -1,4 +1,16 @@
-# Framework Architecture
+---
+layout: default
+title: Synchronization
+parent: Framework Concepts
+nav_order: 4
+---
+
+# Synchronization
+{: .no_toc }
+
+1. TOC
+{:toc}
+---

 ## Scheduling mechanics

@@ -1,4 +1,16 @@
-## MediaPipe Android Archive Library
+---
+layout: default
+title: MediaPipe Android Archive
+parent: Getting Started
+nav_order: 7
+---
+
+# MediaPipe Android Archive
+{: .no_toc }
+
+1. TOC
+{:toc}
+---

 ***Experimental Only***

@@ -9,15 +21,15 @@ target to generate a custom AAR file for their own projects. This is necessary
 in order to include specific resources such as MediaPipe calculators needed for
 each project.

-### Steps to build a MediaPipe AAR
+## Steps to build a MediaPipe AAR

 1.  Create a mediapipe_aar() target.

    In the MediaPipe directory, create a new mediapipe_aar() target in a BUILD
    file. You need to figure out what calculators are used in the graph and
    provide the calculator dependencies to the mediapipe_aar(). For example, to
-    build an AAR for [face detection gpu](./face_detection_mobile_gpu.md), you
-    can put the following code into
+    build an AAR for [MediaPipe Face Detection](../solutions/face_detection.md),
+    you can put the following code into
    mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/BUILD.

    ```
@@ -54,7 +66,7 @@ each project.
    /absolute/path/to/your/preferred/location
    ```

-### Steps to use a MediaPipe AAR in Android Studio with Gradle
+## Steps to use a MediaPipe AAR in Android Studio with Gradle

 1.  Start Android Studio and go to your project.

@@ -65,7 +77,7 @@ each project.
    /path/to/your/app/libs/
    ```

-    ![Screenshot](images/mobile/aar_location.png)
+    ![Screenshot](../images/mobile/aar_location.png)

 3.  Make app/src/main/assets and copy assets (graph, model, and etc) into
    app/src/main/assets.
@@ -79,13 +91,13 @@ each project.
    [the label map](https://github.com/google/mediapipe/blob/master/mediapipe/models/face_detection_front_labelmap.txt).

    ```bash
-    bazel build -c opt mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectiongpu:binary_graph
-    cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectiongpu/facedetectiongpu.binarypb /path/to/your/app/src/main/assets/
+    bazel build -c opt mediapipe/mediapipe/graphs/face_detection:mobile_gpu_binary_graph
+    cp bazel-bin/mediapipe/graphs/face_detection/mobile_gpu.binarypb /path/to/your/app/src/main/assets/
    cp mediapipe/models/face_detection_front.tflite /path/to/your/app/src/main/assets/
    cp mediapipe/models/face_detection_front_labelmap.txt /path/to/your/app/src/main/assets/
    ```

-    ![Screenshot](images/mobile/assets_location.png)
+    ![Screenshot](../images/mobile/assets_location.png)

 4.  Make app/src/main/jniLibs and copy OpenCV JNI libraries into
    app/src/main/jniLibs.
@@ -100,7 +112,7 @@ each project.
    cp -R ~/Downloads/OpenCV-android-sdk/sdk/native/libs/arm* /path/to/your/app/src/main/jniLibs/
    ```

-    ![Screenshot](images/mobile/android_studio_opencv_location.png)
+    ![Screenshot](../images/mobile/android_studio_opencv_location.png)

 5.  Modify app/build.gradle to add MediaPipe dependencies and MediaPipe AAR.

@@ -118,7 +130,7 @@ each project.
        implementation 'com.google.code.findbugs:jsr305:3.0.2'
        implementation 'com.google.guava:guava:27.0.1-android'
        implementation 'com.google.guava:guava:27.0.1-android'
-        implementation 'com.google.protobuf:protobuf-java:3.11.4''
+        implementation 'com.google.protobuf:protobuf-java:3.11.4'
        // CameraX core library
        def camerax_version = "1.0.0-alpha06"
        implementation "androidx.camera:camera-core:$camerax_version"
@@ -127,6 +139,8 @@ each project.
    ```

 6.  Follow our Android app examples to use MediaPipe in Android Studio for your
-    use case. If you are looking for an example, a face detection
-    example can be found
-    [here](https://github.com/jiuqiant/mediapipe_face_detection_aar_example) and a multi-hand tracking example can be found [here](https://github.com/jiuqiant/mediapipe_multi_hands_tracking_aar_example).
+    use case. If you are looking for an example, a face detection example can be
+    found
+    [here](https://github.com/jiuqiant/mediapipe_face_detection_aar_example) and
+    a multi-hand tracking example can be found
+    [here](https://github.com/jiuqiant/mediapipe_multi_hands_tracking_aar_example).
@@ -0,0 +1,494 @@
+---
+layout: default
+title: Building MediaPipe Examples
+parent: Getting Started
+nav_order: 2
+---
+
+# Building MediaPipe Examples
+{: .no_toc }
+
+1. TOC
+{:toc}
+---
+
+## Android
+
+### Prerequisite
+
+*   Java Runtime.
+*   Android SDK release 28.0.3 and above.
+*   Android NDK r18b and above.
+
+MediaPipe recommends setting up Android SDK and NDK via Android Studio (and see
+below for Android Studio setup). However, if you prefer using MediaPipe without
+Android Studio, please run
+[`setup_android_sdk_and_ndk.sh`](https://github.com/google/mediapipe/blob/master/setup_android_sdk_and_ndk.sh)
+to download and setup Android SDK and NDK before building any Android example
+apps.
+
+If Android SDK and NDK are already installed (e.g., by Android Studio), set
+$ANDROID_HOME and $ANDROID_NDK_HOME to point to the installed SDK and NDK.
+
+```bash
+export ANDROID_HOME=<path to the Android SDK>
+export ANDROID_NDK_HOME=<path to the Android NDK>
+```
+
+In order to use MediaPipe on earlier Android versions, MediaPipe needs to switch
+to a lower Android API level. You can achieve this by specifying `api_level =
+$YOUR_INTENDED_API_LEVEL` in android_ndk_repository() and/or
+android_sdk_repository() in the
+[`WORKSPACE`](https://github.com/google/mediapipe/blob/master/WORKSPACE) file.
+
+Please verify all the necessary packages are installed.
+
+*   Android SDK Platform API Level 28 or 29
+*   Android SDK Build-Tools 28 or 29
+*   Android SDK Platform-Tools 28 or 29
+*   Android SDK Tools 26.1.1
+*   Android NDK 17c or above
+
+### Option 1: Build with Bazel in Command Line
+
+Tip: You can run this
+[script](https://github.com/google/mediapipe/blob/master/build_android_examples.sh)
+to build (and install) all MediaPipe Android example apps.
+
+1.  To build an Android example app, build against the corresponding
+    `android_binary` build target. For instance, for
+    [MediaPipe Hands](../solutions/hands.md) the target is `handtrackinggpu` in
+    the
+    [BUILD](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu/BUILD)
+    file:
+
+    Note: To reduce the binary size, consider appending `--linkopt="-s"` to the
+    command below to strip symbols.
+
+    ```bash
+    bazel build -c opt --config=android_arm64 mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu:handtrackinggpu
+    ```
+
+2.  Install it on a device with:
+
+    ```bash
+    adb install bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu/handtrackinggpu.apk
+    ```
+
+### Option 2: Build with Bazel in Android Studio
+
+The MediaPipe project can be imported into Android Studio using the Bazel
+plugins. This allows the MediaPipe examples to be built and modified in Android
+Studio.
+
+To incorporate MediaPipe into an existing Android Studio project, see these
+[instructions](./android_archive_library.md) that use Android Archive (AAR) and
+Gradle.
+
+The steps below use Android Studio 3.5 to build and install a MediaPipe example
+app:
+
+1.  Install and launch Android Studio 3.5.
+
+2.  Select `Configure` -> `SDK Manager` -> `SDK Platforms`.
+
+    *   Verify that Android SDK Platform API Level 28 or 29 is installed.
+    *   Take note of the Android SDK Location, e.g.,
+        `/usr/local/home/Android/Sdk`.
+
+3.  Select `Configure` -> `SDK Manager` -> `SDK Tools`.
+
+    *   Verify that Android SDK Build-Tools 28 or 29 is installed.
+    *   Verify that Android SDK Platform-Tools 28 or 29 is installed.
+    *   Verify that Android SDK Tools 26.1.1 is installed.
+    *   Verify that Android NDK 17c or above is installed.
+    *   Take note of the Android NDK Location, e.g.,
+        `/usr/local/home/Android/Sdk/ndk-bundle` or
+        `/usr/local/home/Android/Sdk/ndk/20.0.5594570`.
+
+4.  Set environment variables `$ANDROID_HOME` and `$ANDROID_NDK_HOME` to point
+    to the installed SDK and NDK.
+
+    ```bash
+    export ANDROID_HOME=/usr/local/home/Android/Sdk
+
+    # If the NDK libraries are installed by a previous version of Android Studio, do
+    export ANDROID_NDK_HOME=/usr/local/home/Android/Sdk/ndk-bundle
+    # If the NDK libraries are installed by Android Studio 3.5, do
+    export ANDROID_NDK_HOME=/usr/local/home/Android/Sdk/ndk/<version number>
+    ```
+
+5.  Select `Configure` -> `Plugins` to install `Bazel`.
+
+6.  On Linux, select `File` -> `Settings` -> `Bazel settings`. On macos, select
+    `Android Studio` -> `Preferences` -> `Bazel settings`. Then, modify `Bazel
+    binary location` to be the same as the output of `$ which bazel`.
+
+7.  Select `Import Bazel Project`.
+
+    *   Select `Workspace`: `/path/to/mediapipe` and select `Next`.
+    *   Select `Generate from BUILD file`: `/path/to/mediapipe/BUILD` and select
+        `Next`.
+    *   Modify `Project View` to be the following and select `Finish`.
+
+    ```
+    directories:
+      # read project settings, e.g., .bazelrc
+      .
+      -mediapipe/objc
+      -mediapipe/examples/ios
+
+    targets:
+      //mediapipe/examples/android/...:all
+      //mediapipe/java/...:all
+
+    android_sdk_platform: android-29
+
+    sync_flags:
+      --host_crosstool_top=@bazel_tools//tools/cpp:toolchain
+    ```
+
+8.  Select `Bazel` -> `Sync` -> `Sync project with Build files`.
+
+    Note: Even after doing step 4, if you still see the error: `"no such package
+    '@androidsdk//': Either the path attribute of android_sdk_repository or the
+    ANDROID_HOME environment variable must be set."`, please modify the
+    [`WORKSPACE`](https://github.com/google/mediapipe/blob/master/WORKSPACE)
+    file to point to your SDK and NDK library locations, as below:
+
+    ```
+    android_sdk_repository(
+        name = "androidsdk",
+        path = "/path/to/android/sdk"
+    )
+
+    android_ndk_repository(
+        name = "androidndk",
+        path = "/path/to/android/ndk"
+    )
+    ```
+
+9.  Connect an Android device to the workstation.
+
+10. Select `Run...` -> `Edit Configurations...`.
+
+    *   Select `Templates` -> `Bazel Command`.
+    *   Enter Target Expression:
+        `//mediapipe/examples/android/src/java/com/google/mediapipe/apps/handtrackinggpu:handtrackinggpu`
+    *   Enter Bazel command: `mobile-install`.
+    *   Enter Bazel flags: `-c opt --config=android_arm64`.
+    *   Press the `[+]` button to add the new configuration.
+    *   Select `Run` to run the example app on the connected Android device.
+
+## iOS
+
+### Prerequisite
+
+1.  Install [Xcode](https://developer.apple.com/xcode/), then install the
+    Command Line Tools using:
+
+    ```bash
+    xcode-select --install
+    ```
+
+2.  Install [Bazel](https://bazel.build/).
+
+    We recommend using [Homebrew](https://brew.sh/) to get the latest version.
+
+3.  Set Python 3.7 as the default Python version and install the Python "six"
+    library. This is needed for TensorFlow.
+
+    ```bash
+    pip3 install --user six
+    ```
+
+4.  Clone the MediaPipe repository.
+
+    ```bash
+    git clone https://github.com/google/mediapipe.git
+    ```
+
+### Set up a bundle ID prefix
+
+All iOS apps must have a bundle ID, and you must have a provisioning profile
+that lets you install an app with that ID onto your phone. To avoid clashes
+between different MediaPipe users, you need to configure a unique prefix for the
+bundle IDs of our iOS demo apps.
+
+If you have a custom provisioning profile, see
+[Custom provisioning](#custom-provisioning) below.
+
+Otherwise, run this command to generate a unique prefix:
+
+```bash
+python3 mediapipe/examples/ios/link_local_profiles.py
+```
+
+### Create an Xcode project
+
+This allows you to edit and debug one of the example apps in Xcode. It also
+allows you to make use of automatic provisioning (see later section).
+
+1.  We will use a tool called [Tulsi](https://tulsi.bazel.build/) for generating
+    Xcode projects from Bazel build configurations.
+
+    ```bash
+    # cd out of the mediapipe directory, then:
+    git clone https://github.com/bazelbuild/tulsi.git
+    cd tulsi
+    # remove Xcode version from Tulsi's .bazelrc (see http://github.com/bazelbuild/tulsi#building-and-installing):
+    sed -i .orig '/xcode_version/d' .bazelrc
+    # build and run Tulsi:
+    sh build_and_run.sh
+    ```
+
+    This will install `Tulsi.app` inside the `Applications` directory in your
+    home directory.
+
+2.  Open `mediapipe/Mediapipe.tulsiproj` using the Tulsi app.
+
+    Tip: If Tulsi displays an error saying "Bazel could not be found", press the
+    "Bazel..." button in the Packages tab and select the `bazel` executable in
+    your homebrew `/bin/` directory.
+
+3.  Select the MediaPipe config in the Configs tab, then press the Generate
+    button below. You will be asked for a location to save the Xcode project.
+    Once the project is generated, it will be opened in Xcode.
+
+    If you get an error about bundle IDs, see the
+    [previous section](#set-up-a-bundle-id-prefix).
+
+### Set up provisioning
+
+To install applications on an iOS device, you need a provisioning profile. There
+are two options:
+
+1.  Automatic provisioning. This allows you to build and install an app to your
+    personal device. The provisining profile is managed by Xcode, and has to be
+    updated often (it is valid for about a week).
+
+2.  Custom provisioning. This uses a provisioning profile associated with an
+    Apple developer account. These profiles have a longer validity period and
+    can target multiple devices, but you need a paid developer account with
+    Apple to obtain one.
+
+#### Automatic provisioning
+
+1.  Create an Xcode project for MediaPipe, as discussed
+    [earlier](#create-an-xcode-project).
+
+2.  In the project navigator in the left sidebar, select the "Mediapipe"
+    project.
+
+3.  Select one of the application targets, e.g. HandTrackingGpuApp.
+
+4.  Select the "Signing & Capabilities" tab.
+
+5.  Check "Automatically manage signing", and confirm the dialog box.
+
+6.  Select "_Your Name_ (Personal Team)" in the Team pop-up menu.
+
+7.  This set-up needs to be done once for each application you want to install.
+    Repeat steps 3-6 as needed.
+
+This generates provisioning profiles for each app you have selected. Now we need
+to tell Bazel to use them. We have provided a script to make this easier.
+
+1.  In the terminal, to the `mediapipe` directory where you cloned the
+    repository.
+
+2.  Run this command:
+
+    ```bash
+    python3 mediapipe/examples/ios/link_local_profiles.py
+    ```
+
+This will find and link the provisioning profile for all applications for which
+you have enabled automatic provisioning in Xcode.
+
+Note: once a profile expires, Xcode will generate a new one; you must then run
+this script again to link the updated profiles.
+
+#### Custom provisioning
+
+1.  Obtain a provisioning profile from Apple.
+
+Tip: You can use this command to see the provisioning profiles you have
+previously downloaded using Xcode: `open ~/Library/MobileDevice/"Provisioning
+Profiles"`. If there are none, generate and download a profile on
+[Apple's developer site](https://developer.apple.com/account/resources/).
+
+1.  Symlink or copy your provisioning profile to
+    `mediapipe/mediapipe/provisioning_profile.mobileprovision`.
+
+    ```bash
+    cd mediapipe
+    ln -s ~/Downloads/MyProvisioningProfile.mobileprovision mediapipe/provisioning_profile.mobileprovision
+    ```
+
+Note: if you had previously set up automatic provisioning, you should remove the
+`provisioning_profile.mobileprovision` symlink in each example's directory,
+since it will take precedence over the common one. You can also overwrite it
+with you own profile if you need a different profile for different apps.
+
+1.  Open `mediapipe/examples/ios/bundle_id.bzl`, and change the
+    `BUNDLE_ID_PREFIX` to a prefix associated with your provisioning profile.
+
+### Build and run an app using Xcode
+
+1.  Create the Xcode project, and make sure you have set up either automatic or
+    custom provisioning.
+
+2.  You can now select any of the MediaPipe demos in the target menu, and build
+    and run them as normal.
+
+Note: When you ask Xcode to run an app, by default it will use the Debug
+configuration. Some of our demos are computationally heavy; you may want to use
+the Release configuration for better performance.
+
+Tip: To switch build configuration in Xcode, click on the target menu, choose
+"Edit Scheme...", select the Run action, and switch the Build Configuration from
+Debug to Release. Note that this is set independently for each target.
+
+Tip: On the device, in Settings > General > Device Management, make sure the
+developer (yourself) is trusted.
+
+### Build an app using the command line
+
+1.  Make sure you have set up either automatic or custom provisioning.
+
+2.  Using [MediaPipe Hands](../solutions/hands.md) for example, run:
+
+    ```bash
+    bazel build -c opt --config=ios_arm64 mediapipe/examples/ios/handtrackinggpu:HandTrackingGpuApp
+    ```
+
+    You may see a permission request from `codesign` in order to sign the app.
+
+    Tip: If you are using custom provisioning, you can run this
+    [script](https://github.com/google/mediapipe/blob/master/build_ios_examples.sh)
+    to build all MediaPipe iOS example apps.
+
+3.  In Xcode, open the `Devices and Simulators` window (command-shift-2).
+
+4.  Make sure your device is connected. You will see a list of installed apps.
+    Press the "+" button under the list, and select the `.ipa` file built by
+    Bazel.
+
+5.  You can now run the app on your device.
+
+Tip: On the device, in Settings > General > Device Management, make sure the
+developer (yourself) is trusted.
+
+## Desktop
+
+### Option 1: Running on CPU
+
+1.  To build, for example, [MediaPipe Hands](../solutions/hands.md), run:
+
+    ```bash
+    bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu
+    ```
+
+2.  To run the application:
+
+    ```bash
+    GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/hand_tracking/hand_tracking_cpu \
+      --calculator_graph_config_file=mediapipe/graphs/hand_tracking/hand_tracking_desktop_live.pbtxt
+    ```
+
+    This will open up your webcam as long as it is connected and on. Any errors
+    is likely due to your webcam being not accessible.
+
+### Option 2: Running on GPU
+
+Note: This currently works only on Linux, and please first follow
+[OpenGL ES Setup on Linux Desktop](./gpu_support.md#opengl-es-setup-on-linux-desktop).
+
+1.  To build, for example, [MediaPipe Hands](../solutions/hands.md), run:
+
+    ```bash
+    bazel build -c opt --copt -DMESA_EGL_NO_X11_HEADERS --copt -DEGL_NO_X11 \
+      mediapipe/examples/desktop/hand_tracking:hand_tracking_gpu
+    ```
+
+2.  To run the application:
+
+    ```bash
+    GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/hand_tracking/hand_tracking_gpu \
+      --calculator_graph_config_file=mediapipe/graphs/hand_tracking/hand_tracking_mobile.pbtxt
+    ```
+
+    This will open up your webcam as long as it is connected and on. Any errors
+    is likely due to your webcam being not accessible, or GPU drivers not setup
+    properly.
+
+## Python
+
+### Prerequisite
+
+1.  Make sure that Bazel and OpenCV are correctly installed and configured for
+    MediaPipe. Please see [Installation](./install.md) for how to setup Bazel
+    and OpenCV for MediaPipe on Linux and macOS.
+
+2.  Install the following dependencies.
+
+    ```bash
+    # Debian or Ubuntu
+    $ sudo apt install python3-dev
+    $ sudo apt install python3-venv
+    $ sudo apt install -y protobuf-compiler
+    ```
+
+    ```bash
+    # macOS
+    $ brew install protobuf
+    ```
+
+### Set up Python virtual environment.
+
+1.  Activate a Python virtual environment.
+
+    ```bash
+    $ python3 -m venv mp_env && source mp_env/bin/activate
+    ```
+
+2.  In the virtual environment, go to the MediaPipe repo directory.
+
+3.  Install the required Python packages.
+
+    ```bash
+    (mp_env)mediapipe$ pip3 install -r requirements.txt
+    ```
+
+4.  Generate and install MediaPipe package.
+
+    ```bash
+    (mp_env)mediapipe$ python3 setup.py gen_protos
+    (mp_env)mediapipe$ python3 setup.py install
+    ```
+
+### Run in Python interpreter
+
+Make sure you are not in the MediaPipe repo directory.
+
+Using [MediaPipe Pose](../solutions/pose.md) as an example:
+
+```bash
+(mp_env)$ python3
+>>> import mediapipe as mp
+>>> pose_tracker = mp.examples.UpperBodyPoseTracker()
+
+# For image input
+>>> pose_landmarks, _ = pose_tracker.run(input_file='/path/to/input/file', output_file='/path/to/output/file')
+>>> pose_landmarks, annotated_image = pose_tracker.run(input_file='/path/to/file')
+
+# For live camera input
+# (Press Esc within the output image window to stop the run or let it self terminate after 30 seconds.)
+>>> pose_tracker.run_live()
+
+# Close the tracker.
+>>> pose_tracker.close()
+```
+
+Tip: Use command `deactivate` to exit the Python virtual environment.
@@ -1,12 +1,16 @@
-## Questions and Answers
+---
+layout: default
+title: FAQ
+parent: Getting Started
+nav_order: 9
+---

-   [How to convert ImageFrames and GpuBuffers](#how-to-convert-imageframes-and-gpubuffers)
-   [How to visualize perceived results](#how-to-visualize-perception-results)
-   [How to run calculators in parallel](#how-to-run-calculators-in-parallel)
-   [Output timestamps when using ImmediateInputStreamHandler](#output-timestamps-when-using-immediateinputstreamhandler)
-   [How to change settings at runtime](#how-to-change-settings-at-runtime)
-   [How to process real-time input streams](#how-to-process-real-time-input-streams)
-   [Can I run MediaPipe on MS Windows?](#can-i-run-mediapipe-on-ms-windows)
+# FAQ
+{: .no_toc }
+
+1. TOC
+{:toc}
+---

 ### How to convert ImageFrames and GpuBuffers

@@ -0,0 +1,13 @@
+---
+layout: default
+title: Getting Started
+nav_order: 2
+has_children: true
+---
+
+# Getting Started
+{: .no_toc }
+
+1. TOC
+{:toc}
+---
@@ -0,0 +1,186 @@
+---
+layout: default
+title: GPU Support
+parent: Getting Started
+nav_order: 6
+---
+
+# GPU Support
+{: .no_toc }
+
+1. TOC
+{:toc}
+---
+
+## OpenGL ES Support
+
+MediaPipe supports OpenGL ES up to version 3.2 on Android/Linux and up to ES 3.0
+on iOS. In addition, MediaPipe also supports Metal on iOS.
+
+OpenGL ES 3.1 or greater is required (on Android/Linux systems) for running
+machine learning inference calculators and graphs.
+
+## Disable OpenGL ES Support
+
+By default, building MediaPipe (with no special bazel flags) attempts to compile
+and link against OpenGL ES (and for iOS also Metal) libraries.
+
+On platforms where OpenGL ES is not available (see also
+[OpenGL ES Setup on Linux Desktop](#opengl-es-setup-on-linux-desktop)), you
+should disable OpenGL ES support with:
+
+```
+$ bazel build --define MEDIAPIPE_DISABLE_GPU=1 <my-target>
+```
+
+Note: On Android and iOS, OpenGL ES is required by MediaPipe framework and the
+support should never be disabled.
+
+## OpenGL ES Setup on Linux Desktop
+
+On Linux desktop with video cards that support OpenGL ES 3.1+, MediaPipe can run
+GPU compute and rendering and perform TFLite inference on GPU.
+
+To check if your Linux desktop GPU can run MediaPipe with OpenGL ES:
+
+```bash
+$ sudo apt-get install mesa-common-dev libegl1-mesa-dev libgles2-mesa-dev
+$ sudo apt-get install mesa-utils
+$ glxinfo | grep -i opengl
+```
+
+For example, it may print:
+
+```bash
+$ glxinfo | grep -i opengl
+...
+OpenGL ES profile version string: OpenGL ES 3.2 NVIDIA 430.50
+OpenGL ES profile shading language version string: OpenGL ES GLSL ES 3.20
+OpenGL ES profile extensions:
+```
+
+*Notice the ES 3.20 text above.*
+
+You need to see ES 3.1 or greater printed in order to perform TFLite inference
+on GPU in MediaPipe. With this setup, build with:
+
+```
+$ bazel build --copt -DMESA_EGL_NO_X11_HEADERS --copt -DEGL_NO_X11 <my-target>
+```
+
+If only ES 3.0 or below is supported, you can still build MediaPipe targets that
+don't require TFLite inference on GPU with:
+
+```
+$ bazel build --copt -DMESA_EGL_NO_X11_HEADERS --copt -DEGL_NO_X11 --copt -DMEDIAPIPE_DISABLE_GL_COMPUTE <my-target>
+```
+
+Note: MEDIAPIPE_DISABLE_GL_COMPUTE is already defined automatically on all Apple
+systems (Apple doesn't support OpenGL ES 3.1+).
+
+## TensorFlow CUDA Support and Setup on Linux Desktop
+
+MediaPipe framework doesn't require CUDA for GPU compute and rendering. However,
+MediaPipe can work with TensorFlow to perform GPU inference on video cards that
+support CUDA.
+
+To enable TensorFlow GPU inference with MediaPipe, the first step is to follow
+the
+[TensorFlow GPU documentation](https://www.tensorflow.org/install/gpu#software_requirements)
+to install the required NVIDIA software on your Linux desktop.
+
+After installation, update `$PATH` and `$LD_LIBRARY_PATH` and run `ldconfig`
+with:
+
+```
+$ export PATH=/usr/local/cuda-10.1/bin${PATH:+:${PATH}}
+$ export LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64,/usr/local/cuda-10.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
+$ sudo ldconfig
+```
+
+It's recommended to verify the installation of CUPTI, CUDA, CuDNN, and NVCC:
+
+```
+$ ls /usr/local/cuda/extras/CUPTI
+/lib64
+libcupti.so       libcupti.so.10.1.208  libnvperf_host.so        libnvperf_target.so
+libcupti.so.10.1  libcupti_static.a     libnvperf_host_static.a
+
+$ ls /usr/local/cuda-10.1
+LICENSE  bin  extras   lib64      libnvvp           nvml  samples  src      tools
+README   doc  include  libnsight  nsightee_plugins  nvvm  share    targets  version.txt
+
+$ nvcc -V
+nvcc: NVIDIA (R) Cuda compiler driver
+Copyright (c) 2005-2019 NVIDIA Corporation
+Built on Sun_Jul_28_19:07:16_PDT_2019
+Cuda compilation tools, release 10.1, V10.1.243
+
+$ ls /usr/lib/x86_64-linux-gnu/ | grep libcudnn.so
+libcudnn.so
+libcudnn.so.7
+libcudnn.so.7.6.4
+```
+
+Setting `$TF_CUDA_PATHS` is the way to declare where the CUDA library is. Note
+that the following code snippet also adds `/usr/lib/x86_64-linux-gnu` and
+`/usr/include` into `$TF_CUDA_PATHS` for cudablas and libcudnn.
+
+```
+$ export TF_CUDA_PATHS=/usr/local/cuda-10.1,/usr/lib/x86_64-linux-gnu,/usr/include
+```
+
+To make MediaPipe get TensorFlow's CUDA settings, find TensorFlow's
+[.bazelrc](https://github.com/tensorflow/tensorflow/blob/master/.bazelrc) and
+copy the `build:using_cuda` and `build:cuda` section into MediaPipe's .bazelrc
+file. For example, as of April 23, 2020, TensorFlow's CUDA setting is the
+following:
+
+```
+# This config refers to building with CUDA available. It does not necessarily
+# mean that we build CUDA op kernels.
+build:using_cuda --define=using_cuda=true
+build:using_cuda --action_env TF_NEED_CUDA=1
+build:using_cuda --crosstool_top=@local_config_cuda//crosstool:toolchain
+
+# This config refers to building CUDA op kernels with nvcc.
+build:cuda --config=using_cuda
+build:cuda --define=using_cuda_nvcc=true
+```
+
+Finally, build MediaPipe with TensorFlow GPU with two more flags `--config=cuda`
+and `--spawn_strategy=local`. For example:
+
+```
+$ bazel build -c opt --config=cuda --spawn_strategy=local \
+    --define no_aws_support=true --copt -DMESA_EGL_NO_X11_HEADERS \
+    mediapipe/examples/desktop/object_detection:object_detection_tensorflow
+```
+
+While the binary is running, it prints out the GPU device info:
+
+```
+I external/org_tensorflow/tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
+I external/org_tensorflow/tensorflow/core/common_runtime/gpu/gpu_device.cc:1544] Found device 0 with properties: pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5 coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s
+I external/org_tensorflow/tensorflow/core/common_runtime/gpu/gpu_device.cc:1686] Adding visible gpu devices: 0
+```
+
+You can monitor the GPU usage to verify whether the GPU is used for model
+inference.
+
+```
+$ nvidia-smi --query-gpu=utilization.gpu --format=csv --loop=1
+
+0 %
+0 %
+4 %
+5 %
+83 %
+21 %
+22 %
+27 %
+29 %
+100 %
+0 %
+0%
+```
@@ -1,4 +1,16 @@
-# Hello World! in MediaPipe on Android
+---
+layout: default
+title: Hello World! on Android
+parent: Getting Started
+nav_order: 3
+---
+
+# Hello World! on Android
+{: .no_toc }
+
+1. TOC
+{:toc}
+---

 ## Introduction

@@ -14,7 +26,7 @@ graph on Android.
 A simple camera app for real-time Sobel edge detection applied to a live video
 stream on an Android device.

-![edge_detection_android_gpu_gif](images/mobile/edge_detection_android_gpu.gif)
+![edge_detection_android_gpu_gif](../images/mobile/edge_detection_android_gpu.gif)

 ## Setup

@@ -31,8 +43,8 @@ We will be using the following graph, [`edge_detection_mobile_gpu.pbtxt`]:

 ```
 # MediaPipe graph that performs GPU Sobel edge detection on a live video stream.
-# Used in the examples
-# mediapipe/examples/android/src/java/com/mediapipe/apps/basic.
+# Used in the examples in
+# mediapipe/examples/android/src/java/com/mediapipe/apps/basic and
 # mediapipe/examples/ios/edgedetectiongpu.

 # Images coming into and out of the graph.
@@ -56,7 +68,7 @@ node: {

 A visualization of the graph is shown below:

-![edge_detection_mobile_gpu](images/mobile/edge_detection_mobile_gpu.png)
+![edge_detection_mobile_gpu](../images/mobile/edge_detection_mobile_gpu.png)

 This graph has a single input stream named `input_video` for all incoming frames
 that will be provided by your device's camera.
@@ -247,7 +259,7 @@ adb install bazel-bin/$APPLICATION_PATH/helloworld.apk
 Open the application on your device. It should display a screen with the text
 `Hello World!`.

-![bazel_hello_world_android](images/mobile/bazel_hello_world_android.png)
+![bazel_hello_world_android](../images/mobile/bazel_hello_world_android.png)

 ## Using the camera via `CameraX`

@@ -364,7 +376,7 @@ Add the following line in the `$APPLICATION_PATH/res/values/strings.xml` file:
 When the user doesn't grant camera permission, the screen will now look like
 this:

-![missing_camera_permission_android](images/mobile/missing_camera_permission_android.png)
+![missing_camera_permission_android](../images/mobile/missing_camera_permission_android.png)

 Now, we will add the [`SurfaceTexture`] and [`SurfaceView`] objects to
 `MainActivity`:
@@ -740,7 +752,7 @@ And that's it! You should now be able to successfully build and run the
 application on the device and see Sobel edge detection running on a live camera
 feed! Congrats!

-![edge_detection_android_gpu_gif](images/mobile/edge_detection_android_gpu.gif)
+![edge_detection_android_gpu_gif](../images/mobile/edge_detection_android_gpu.gif)

 If you ran into any issues, please see the full code of the tutorial
 [here](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/basic).
@@ -752,7 +764,7 @@ If you ran into any issues, please see the full code of the tutorial
 [CameraX]:https://developer.android.com/training/camerax
 [`CameraXPreviewHelper`]:https://github.com/google/mediapipe/tree/master/mediapipe/java/com/google/mediapipe/components/CameraXPreviewHelper.java
 [developer options]:https://developer.android.com/studio/debug/dev-options
-[`edge_detection_mobile_gpu.pbtxt`]:https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection/object_detection_mobile_gpu.pbtxt
+[`edge_detection_mobile_gpu.pbtxt`]:https://github.com/google/mediapipe/tree/master/mediapipe/graphs/edge_detection/edge_detection_mobile_gpu.pbtxt
 [`EglManager`]:https://github.com/google/mediapipe/tree/master/mediapipe/java/com/google/mediapipe/glutil/EglManager.java
 [`ExternalTextureConverter`]:https://github.com/google/mediapipe/tree/master/mediapipe/java/com/google/mediapipe/components/ExternalTextureConverter.java
 [`FrameLayout`]:https://developer.android.com/reference/android/widget/FrameLayout
@@ -1,4 +1,16 @@
-## Hello World for C++
+---
+layout: default
+title: Hello World! on Desktop (C++)
+parent: Getting Started
+nav_order: 5
+---
+
+# Hello World! on Desktop (C++)
+{: .no_toc }
+
+1. TOC
+{:toc}
+---

 1.  Ensure you have a working version of MediaPipe. See
    [installation instructions](./install.md).
@@ -6,7 +18,7 @@
 2.  To run the [`hello world`] example:

    ```bash
-    $ git clone https://github.com/google/mediapipe/mediapipe.git
+    $ git clone https://github.com/google/mediapipe.git
    $ cd mediapipe

    $ export GLOG_logtostderr=1
@@ -52,7 +64,7 @@
    You can visualize this graph using
    [MediaPipe Visualizer](https://viz.mediapipe.dev) by pasting the
    CalculatorGraphConfig content below into the visualizer. See
-    [here](./visualizer.md) for help on the visualizer.
+    [here](../tools/visualizer.md) for help on the visualizer.

    ```bash
        input_stream: "in"
@@ -72,7 +84,7 @@
    This graph consists of 1 graph input stream (`in`) and 1 graph output stream
    (`out`), and 2 [`PassThroughCalculator`]s connected serially.

-    ![hello_world graph](./images/hello_world.png)
+    ![hello_world graph](../images/hello_world.png)

 4.  Before running the graph, an `OutputStreamPoller` object is connected to the
    output stream in order to later retrieve the graph output, and a graph run
@@ -80,10 +92,10 @@

    ```c++
    CalculatorGraph graph;
-    RETURN_IF_ERROR(graph.Initialize(config));
-    ASSIGN_OR_RETURN(OutputStreamPoller poller,
-                     graph.AddOutputStreamPoller("out"));
-    RETURN_IF_ERROR(graph.StartRun({}));
+    MP_RETURN_IF_ERROR(graph.Initialize(config));
+    MP_ASSIGN_OR_RETURN(OutputStreamPoller poller,
+                        graph.AddOutputStreamPoller("out"));
+    MP_RETURN_IF_ERROR(graph.StartRun({}));
    ```

 5.  The example then creates 10 packets (each packet contains a string "Hello
@@ -93,9 +105,10 @@

    ```c++
    for (int i = 0; i < 10; ++i) {
-      RETURN_IF_ERROR(graph.AddPacketToInputStream("in", MakePacket<std::string>("Hello World!").At(Timestamp(i))));
+      MP_RETURN_IF_ERROR(graph.AddPacketToInputStream("in",
+                         MakePacket<std::string>("Hello World!").At(Timestamp(i))));
    }
-    RETURN_IF_ERROR(graph.CloseInputStream("in"));
+    MP_RETURN_IF_ERROR(graph.CloseInputStream("in"));
    ```

 6.  Through the `OutputStreamPoller` object the example then retrieves all 10
@@ -1,4 +1,16 @@
-# Hello World! in MediaPipe on iOS
+---
+layout: default
+title: Hello World! on iOS
+parent: Getting Started
+nav_order: 4
+---
+
+# Hello World! on iOS
+{: .no_toc }
+
+1. TOC
+{:toc}
+---

 ## Introduction

@@ -6,15 +18,15 @@ This codelab uses MediaPipe on an iOS device.

 ### What you will learn

-How to develop an iOS application that uses MediaPipe and run a MediaPipe
-graph on iOS.
+How to develop an iOS application that uses MediaPipe and run a MediaPipe graph
+on iOS.

 ### What you will build

 A simple camera app for real-time Sobel edge detection applied to a live video
 stream on an iOS device.

-![edge_detection_ios_gpu_gif](images/mobile/edge_detection_ios_gpu.gif)
+![edge_detection_ios_gpu_gif](../images/mobile/edge_detection_ios_gpu.gif)

 ## Setup

@@ -30,8 +42,8 @@ We will be using the following graph, [`edge_detection_mobile_gpu.pbtxt`]:
 ```
 # MediaPipe graph that performs GPU Sobel edge detection on a live video stream.
 # Used in the examples
-# mediapipe/examples/android/src/java/com/mediapipe/apps/edgedetectiongpu.
-# mediapipe/examples/ios/edgedetectiongpu.
+# mediapipe/examples/android/src/java/com/google/mediapipe/apps/basic:helloworld
+# and mediapipe/examples/ios/helloworld.

 # Images coming into and out of the graph.
 input_stream: "input_video"
@@ -44,7 +56,7 @@ node: {
  output_stream: "luma_video"
 }

-# Applies the Sobel filter to luminance images sotred in RGB format.
+# Applies the Sobel filter to luminance images stored in RGB format.
 node: {
  calculator: "SobelEdgesCalculator"
  input_stream: "luma_video"
@@ -54,7 +66,7 @@ node: {

 A visualization of the graph is shown below:

-![edge_detection_mobile_gpu](images/mobile/edge_detection_mobile_gpu.png)
+![edge_detection_mobile_gpu](../images/mobile/edge_detection_mobile_gpu.png)

 This graph has a single input stream named `input_video` for all incoming frames
 that will be provided by your device's camera.
@@ -77,21 +89,21 @@ to build it.

 First, create an XCode project via File > New > Single View App.

-Set the product name to "EdgeDetectionGpu", and use an appropriate organization
+Set the product name to "HelloWorld", and use an appropriate organization
 identifier, such as `com.google.mediapipe`. The organization identifier
 alongwith the product name will be the `bundle_id` for the application, such as
-`com.google.mediapipe.EdgeDetectionGpu`.
+`com.google.mediapipe.HelloWorld`.

 Set the language to Objective-C.

 Save the project to an appropriate location. Let's call this
 `$PROJECT_TEMPLATE_LOC`. So your project will be in the
-`$PROJECT_TEMPLATE_LOC/EdgeDetectionGpu` directory. This directory will contain
-another directory named `EdgeDetectionGpu` and an `EdgeDetectionGpu.xcodeproj` file.
+`$PROJECT_TEMPLATE_LOC/HelloWorld` directory. This directory will contain
+another directory named `HelloWorld` and an `HelloWorld.xcodeproj` file.

-The `EdgeDetectionGpu.xcodeproj` will not be useful for this tutorial, as we will
-use bazel to build the iOS application. The content of the
-`$PROJECT_TEMPLATE_LOC/EdgeDetectionGpu/EdgeDetectionGpu` directory is listed below:
+The `HelloWorld.xcodeproj` will not be useful for this tutorial, as we will use
+bazel to build the iOS application. The content of the
+`$PROJECT_TEMPLATE_LOC/HelloWorld/HelloWorld` directory is listed below:

 1.  `AppDelegate.h` and `AppDelegate.m`
 2.  `ViewController.h` and `ViewController.m`
@@ -100,10 +112,10 @@ use bazel to build the iOS application. The content of the
 5.  `Main.storyboard` and `Launch.storyboard`
 6.  `Assets.xcassets` directory.

-Copy these files to a directory named `EdgeDetectionGpu` to a location that can
-access the MediaPipe source code. For example, the source code of the
-application that we will build in this tutorial is located in
-`mediapipe/examples/ios/EdgeDetectionGpu`. We will refer to this path as the
+Copy these files to a directory named `HelloWorld` to a location that can access
+the MediaPipe source code. For example, the source code of the application that
+we will build in this tutorial is located in
+`mediapipe/examples/ios/HelloWorld`. We will refer to this path as the
 `$APPLICATION_PATH` throughout the codelab.

 Note: MediaPipe provides Objective-C bindings for iOS. The edge detection
@@ -122,8 +134,8 @@ load(
 )

 ios_application(
-    name = "EdgeDetectionGpuApp",
-    bundle_id = "com.google.mediapipe.EdgeDetectionGpu",
+    name = "HelloWorldApp",
+    bundle_id = "com.google.mediapipe.HelloWorld",
    families = [
        "iphone",
        "ipad",
@@ -131,11 +143,11 @@ ios_application(
    infoplists = ["Info.plist"],
    minimum_os_version = MIN_IOS_VERSION,
    provisioning_profile = "//mediapipe/examples/ios:developer_provisioning_profile",
-    deps = [":EdgeDetectionGpuAppLibrary"],
+    deps = [":HelloWorldAppLibrary"],
 )

 objc_library(
-    name = "EdgeDetectionGpuAppLibrary",
+    name = "HelloWorldAppLibrary",
    srcs = [
        "AppDelegate.m",
        "ViewController.m",
@@ -160,9 +172,8 @@ The `objc_library` rule adds dependencies for the `AppDelegate` and
 `ViewController` classes, `main.m` and the application storyboards. The
 templated app depends only on the `UIKit` SDK.

-The `ios_application` rule uses the `EdgeDetectionGpuAppLibrary` Objective-C
-library generated to build an iOS application for installation on your iOS
-device.
+The `ios_application` rule uses the `HelloWorldAppLibrary` Objective-C library
+generated to build an iOS application for installation on your iOS device.

 Note: You need to point to your own iOS developer provisioning profile to be
 able to run the application on your iOS device.
@@ -170,21 +181,20 @@ able to run the application on your iOS device.
 To build the app, use the following command in a terminal:

 ```
-bazel build -c opt --config=ios_arm64 <$APPLICATION_PATH>:EdgeDetectionGpuApp'
+bazel build -c opt --config=ios_arm64 <$APPLICATION_PATH>:HelloWorldApp'
 ```

-For example, to build the `EdgeDetectionGpuApp` application in
-`mediapipe/examples/ios/edgedetectiongpu`, use the following
-command:
+For example, to build the `HelloWorldApp` application in
+`mediapipe/examples/ios/helloworld`, use the following command:

 ```
-bazel build -c opt --config=ios_arm64 mediapipe/examples/ios/edgedetectiongpu:EdgeDetectionGpuApp
+bazel build -c opt --config=ios_arm64 mediapipe/examples/ios/helloworld:HelloWorldApp
 ```

 Then, go back to XCode, open Window > Devices and Simulators, select your
 device, and add the `.ipa` file generated by the command above to your device.
-Here is the document on [setting up and compiling](./building_examples.md#ios) iOS
-MediaPipe apps.
+Here is the document on [setting up and compiling](./building_examples.md#ios)
+iOS MediaPipe apps.

 Open the application on your device. Since it is empty, it should display a
 blank white screen.
@@ -490,8 +500,8 @@ in our app:
 }];
 ```

-Note: It is important to start the graph before starting the camera, so that
-the graph is ready to process frames as soon as the camera starts sending them.
+Note: It is important to start the graph before starting the camera, so that the
+graph is ready to process frames as soon as the camera starts sending them.

 Earlier, when we received frames from the camera in the `processVideoFrame`
 function, we displayed them in the `_liveView` using the `_renderer`. Now, we
@@ -538,11 +548,14 @@ method to receive packets on this output stream and display them on the screen:
 And that is all! Build and run the app on your iOS device. You should see the
 results of running the edge detection graph on a live video feed. Congrats!

-![edge_detection_ios_gpu_gif](images/mobile/edge_detection_ios_gpu.gif)
+![edge_detection_ios_gpu_gif](../images/mobile/edge_detection_ios_gpu.gif)

-If you ran into any issues, please see the full code of the tutorial
-[here](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/edgedetectiongpu).
+Please note that the iOS examples now use a [common] template app. The code in
+this tutorial is used in the [common] template app. The [helloworld] app has the
+appropriate `BUILD` file dependencies for the edge detection graph.

 [Bazel]:https://bazel.build/
-[`edge_detection_mobile_gpu.pbtxt`]:https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection/object_detection_mobile_gpu.pbtxt
+[`edge_detection_mobile_gpu.pbtxt`]:https://github.com/google/mediapipe/tree/master/mediapipe/graphs/edge_detection/edge_detection_mobile_gpu.pbtxt
 [MediaPipe installation guide]:./install.md
+[common]:(https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/common)
+[helloworld]:(https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/helloworld)
@@ -1,17 +1,24 @@
-## Getting Help
+---
+layout: default
+title: Getting Help
+parent: Getting Started
+nav_order: 8
+---

-   [Technical questions](#technical-questions)
-   [Bugs and feature requests](#bugs-and-feature-requests)
+# Getting Help
+{: .no_toc }

-Below are the various ways to get help:
+1. TOC
+{:toc}
+---

-### Technical questions
+## Technical questions

 For help with technical or algorithmic questions, visit
 [Stack Overflow](https://stackoverflow.com/questions/tagged/mediapipe) to find
 answers and support from the MediaPipe community.

-### Bugs and feature requests
+## Bugs and feature requests

 To report bugs or make feature requests,
 [file an issue on GitHub](https://github.com/google/mediapipe/issues).
@@ -1,4 +1,16 @@
-## Installing MediaPipe
+---
+layout: default
+title: Installation
+parent: Getting Started
+nav_order: 1
+---
+
+# Installation
+{: .no_toc }
+
+1. TOC
+{:toc}
+---

 Note: To interoperate with OpenCV, OpenCV 3.x and above are preferred. OpenCV
 2.x currently works but interoperability support may be deprecated in the
@@ -11,22 +23,11 @@ Note: To make Mediapipe work with TensorFlow, please set Python 3.7 as the
 default Python version and install the Python "six" library by running `pip3
 install --user six`.

-Choose your operating system:
+Note: To build and run Android example apps, see these
+[instructions](./building_examples.md#android). To build and run iOS example
+apps, see these [instructions](./building_examples.md#ios).

-   [Installing on Debian and Ubuntu](#installing-on-debian-and-ubuntu)
-   [Installing on CentOS](#installing-on-centos)
-   [Installing on macOS](#installing-on-macos)
-   [Installing on Windows](#installing-on-windows)
-   [Installing on Windows Subsystem for Linux (WSL)](#installing-on-windows-subsystem-for-linux-wsl)
-   [Installing using Docker](#installing-using-docker)
-
-To build and run Android example apps, see these
-[instuctions](./building_examples.md#android).
-
-To build and run iOS example apps, see these
-[instuctions](./building_examples.md#ios).
-
-### Installing on Debian and Ubuntu
+## Installing on Debian and Ubuntu

 1.  Checkout MediaPipe repository.

@@ -43,6 +44,18 @@ To build and run iOS example apps, see these
    [Bazel documentation](https://docs.bazel.build/versions/master/install-ubuntu.html)
    to install Bazel 2.0 or higher.

+    For Nvidia Jetson and Raspberry Pi devices with ARM Ubuntu, Bazel needs to
+    be built from source.
+
+    ```bash
+    # For Bazel 3.0.0
+    wget https://github.com/bazelbuild/bazel/releases/download/3.0.0/bazel-3.0.0-dist.zip
+    sudo apt-get install build-essential openjdk-8-jdk python zip unzip
+    unzip bazel-3.0.0-dist.zip
+    env EXTRA_BAZEL_ARGS="--host_javabase=@local_jdk//:jdk" bash ./compile.sh
+    sudo cp output/bazel /usr/local/bin/
+    ```
+
 3.  Install OpenCV and FFmpeg.

    Option 1. Use package manager tool to install the pre-compiled OpenCV
@@ -57,6 +70,20 @@ To build and run iOS example apps, see these
                           libopencv-imgproc-dev libopencv-video-dev
    ```

+    Debian 9 and Ubuntu 18.04 install the packages in
+    `/usr/lib/x86_64-linux-gnu`. MediaPipe's [`opencv_linux.BUILD`] and
+    [`ffmpeg_linux.BUILD`] are configured for this library path. Ubuntu 20.04
+    may install the OpenCV and FFmpeg packages in `/usr/local`, Please follow
+    the option 3 below to modify the [`WORKSPACE`], [`opencv_linux.BUILD`] and
+    [`ffmpeg_linux.BUILD`] files accordingly.
+
+    Moreover, for Nvidia Jetson and Raspberry Pi devices with ARM Ubuntu, the
+    library path needs to be modified like the following:
+
+    ```bash
+    sed -i "s/x86_64-linux-gnu/aarch64-linux-gnu/g" third_party/opencv_linux.BUILD
+    ```
+
    Option 2. Run [`setup_opencv.sh`] to automatically build OpenCV from source
    and modify MediaPipe's OpenCV config.

@@ -64,11 +91,13 @@ To build and run iOS example apps, see these
    [documentation](https://docs.opencv.org/3.4.6/d7/d9f/tutorial_linux_install.html)
    to manually build OpenCV from source code.

-    Note: You may need to modify [`WORKSPACE`] and [`opencv_linux.BUILD`] to
-    point MediaPipe to your own OpenCV libraries, e.g., if OpenCV 4 is installed
-    in "/usr/local/", you need to update the "linux_opencv" new_local_repository
-    rule in [`WORKSPACE`] and "opencv" cc_library rule in [`opencv_linux.BUILD`]
-    like the following:
+    Note: You may need to modify [`WORKSPACE`], [`opencv_linux.BUILD`] and
+    [`ffmpeg_linux.BUILD`] to point MediaPipe to your own OpenCV and FFmpeg
+    libraries. For example if OpenCV and FFmpeg are both manually installed in
+    "/usr/local/", you will need to update: (1) the "linux_opencv" and
+    "linux_ffmpeg" new_local_repository rules in [`WORKSPACE`], (2) the "opencv"
+    cc_library rule in [`opencv_linux.BUILD`], and (3) the "libffmpeg"
+    cc_library rule in [`ffmpeg_linux.BUILD`]. These 3 changes are shown below:

    ```bash
    new_local_repository(
@@ -77,6 +106,12 @@ To build and run iOS example apps, see these
        path = "/usr/local",
    )

+    new_local_repository(
+        name = "linux_ffmpeg",
+        build_file = "@//third_party:ffmpeg_linux.BUILD",
+        path = "/usr/local",
+    )
+
    cc_library(
        name = "opencv",
        srcs = glob(
@@ -89,8 +124,36 @@ To build and run iOS example apps, see these
                "lib/libopencv_videoio.so",
            ],
        ),
-        hdrs = glob(["include/opencv4/**/*.h*"]),
-        includes = ["include/opencv4/"],
+        hdrs = glob([
+            # For OpenCV 3.x
+            "include/opencv2/**/*.h*",
+            # For OpenCV 4.x
+            # "include/opencv4/opencv2/**/*.h*",
+        ]),
+        includes = [
+            # For OpenCV 3.x
+            "include/",
+            # For OpenCV 4.x
+            # "include/opencv4/",
+        ],
+        linkstatic = 1,
+        visibility = ["//visibility:public"],
+    )
+
+    cc_library(
+        name = "libffmpeg",
+        srcs = glob(
+            [
+                "lib/libav*.so",
+            ],
+        ),
+        hdrs = glob(["include/libav*/*.h"]),
+        includes = ["include"],
+        linkopts = [
+            "-lavcodec",
+            "-lavformat",
+            "-lavutil",
+        ],
        linkstatic = 1,
        visibility = ["//visibility:public"],
    )
@@ -137,7 +200,13 @@ To build and run iOS example apps, see these
    # Hello World!
    ```

-### Installing on CentOS
+If you run into a build error, please read
+[Troubleshooting](./troubleshooting.md) to find the solutions of several common
+build issues.
+
+## Installing on CentOS
+
+**Disclaimer**: Running MediaPipe on CentOS is experimental.

 1.  Checkout MediaPipe repository.

@@ -167,11 +236,13 @@ To build and run iOS example apps, see these

    Option 2. Build OpenCV from source code.

-    Note: You may need to modify [`WORKSPACE`] and [`opencv_linux.BUILD`] to
-    point MediaPipe to your own OpenCV libraries, e.g., if OpenCV 4 is installed
-    in "/usr/local/", you need to update the "linux_opencv" new_local_repository
-    rule in [`WORKSPACE`] and "opencv" cc_library rule in [`opencv_linux.BUILD`]
-    like the following:
+    Note: You may need to modify [`WORKSPACE`], [`opencv_linux.BUILD`] and
+    [`ffmpeg_linux.BUILD`] to point MediaPipe to your own OpenCV and FFmpeg
+    libraries. For example if OpenCV and FFmpeg are both manually installed in
+    "/usr/local/", you will need to update: (1) the "linux_opencv" and
+    "linux_ffmpeg" new_local_repository rules in [`WORKSPACE`], (2) the "opencv"
+    cc_library rule in [`opencv_linux.BUILD`], and (3) the "libffmpeg"
+    cc_library rule in [`ffmpeg_linux.BUILD`]. These 3 changes are shown below:

    ```bash
    new_local_repository(
@@ -180,6 +251,12 @@ To build and run iOS example apps, see these
        path = "/usr/local",
    )

+    new_local_repository(
+        name = "linux_ffmpeg",
+        build_file = "@//third_party:ffmpeg_linux.BUILD",
+        path = "/usr/local",
+    )
+
    cc_library(
        name = "opencv",
        srcs = glob(
@@ -192,8 +269,36 @@ To build and run iOS example apps, see these
                "lib/libopencv_videoio.so",
            ],
        ),
-        hdrs = glob(["include/opencv4/**/*.h*"]),
-        includes = ["include/opencv4/"],
+        hdrs = glob([
+            # For OpenCV 3.x
+            "include/opencv2/**/*.h*",
+            # For OpenCV 4.x
+            # "include/opencv4/opencv2/**/*.h*",
+        ]),
+        includes = [
+            # For OpenCV 3.x
+            "include/",
+            # For OpenCV 4.x
+            # "include/opencv4/",
+        ],
+        linkstatic = 1,
+        visibility = ["//visibility:public"],
+    )
+
+    cc_library(
+        name = "libffmpeg",
+        srcs = glob(
+            [
+                "lib/libav*.so",
+            ],
+        ),
+        hdrs = glob(["include/libav*/*.h"]),
+        includes = ["include"],
+        linkopts = [
+            "-lavcodec",
+            "-lavformat",
+            "-lavutil",
+        ],
        linkstatic = 1,
        visibility = ["//visibility:public"],
    )
@@ -220,7 +325,11 @@ To build and run iOS example apps, see these
    # Hello World!
    ```

-### Installing on macOS
+If you run into a build error, please read
+[Troubleshooting](./troubleshooting.md) to find the solutions of several common
+build issues.
+
+## Installing on macOS

 1.  Prework:

@@ -352,7 +461,11 @@ To build and run iOS example apps, see these
    # Hello World!
    ```

-### Installing on Windows
+If you run into a build error, please read
+[Troubleshooting](./troubleshooting.md) to find the solutions of several common
+build issues.
+
+## Installing on Windows

 **Disclaimer**: Running MediaPipe on Windows is experimental.

@@ -431,13 +544,13 @@ next section.
 9.  Run the [Hello World desktop example](./hello_world_desktop.md).

    Note: For building MediaPipe on Windows, please add `--action_env
-    PYTHON_BIN_PATH="C:/path/to/python.exe"` to the build command.
+    PYTHON_BIN_PATH="C://path//to//python.exe"` to the build command.
    Alternatively, you can follow
    [issue 724](https://github.com/google/mediapipe/issues/724) to fix the
    python configuration manually.

    ```
-    C:\Users\Username\mediapipe_repo>bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 --action_env PYTHON_BIN_PATH="C:/python_36/python.exe" mediapipe/examples/desktop/hello_world
+    C:\Users\Username\mediapipe_repo>bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 --action_env PYTHON_BIN_PATH="C://python_36//python.exe" mediapipe/examples/desktop/hello_world

    C:\Users\Username\mediapipe_repo>set GLOG_logtostderr=1

@@ -457,7 +570,11 @@ next section.

    ```

-### Installing on Windows Subsystem for Linux (WSL)
+If you run into a build error, please read
+[Troubleshooting](./troubleshooting.md) to find the solutions of several common
+build issues.
+
+## Installing on Windows Subsystem for Linux (WSL)

 Note: The pre-built OpenCV packages don't support cameras in WSL. Unless you
 [compile](https://funvision.blogspot.com/2019/12/opencv-web-camera-and-video-streams-in.html)
@@ -490,14 +607,14 @@ cameras. Alternatively, you use a video file as input.

    ```bash
    username@DESKTOP-TMVLBJ1:~$ curl -sLO --retry 5 --retry-max-time 10 \
-    https://storage.googleapis.com/bazel/2.0.0/release/bazel-2.0.0-installer-linux-x86_64.sh && \
-    sudo mkdir -p /usr/local/bazel/2.0.0 && \
-    chmod 755 bazel-2.0.0-installer-linux-x86_64.sh && \
-    sudo ./bazel-2.0.0-installer-linux-x86_64.sh --prefix=/usr/local/bazel/2.0.0 && \
-    source /usr/local/bazel/2.0.0/lib/bazel/bin/bazel-complete.bash
+    https://storage.googleapis.com/bazel/3.0.0/release/bazel-3.0.0-installer-linux-x86_64.sh && \
+    sudo mkdir -p /usr/local/bazel/3.0.0 && \
+    chmod 755 bazel-3.0.0-installer-linux-x86_64.sh && \
+    sudo ./bazel-3.0.0-installer-linux-x86_64.sh --prefix=/usr/local/bazel/3.0.0 && \
+    source /usr/local/bazel/3.0.0/lib/bazel/bin/bazel-complete.bash

-    username@DESKTOP-TMVLBJ1:~$ /usr/local/bazel/2.0.0/lib/bazel/bin/bazel version && \
-    alias bazel='/usr/local/bazel/2.0.0/lib/bazel/bin/bazel'
+    username@DESKTOP-TMVLBJ1:~$ /usr/local/bazel/3.0.0/lib/bazel/bin/bazel version && \
+    alias bazel='/usr/local/bazel/3.0.0/lib/bazel/bin/bazel'
    ```

 6.  Checkout MediaPipe repository.
@@ -580,7 +697,11 @@ cameras. Alternatively, you use a video file as input.
    # Hello World!
    ```

-### Installing using Docker
+If you run into a build error, please
+read [Troubleshooting](./troubleshooting.md) to find the solutions of several
+common build issues.
+
+## Installing using Docker

 This will use a Docker image that will isolate mediapipe's installation from the rest of the system.

@@ -630,7 +751,11 @@ This will use a Docker image that will isolate mediapipe's installation from the
    # Hello World!
    ```

-4.  Build Mediapipe [Android demos](./examples.md).
+If you run into a build error, please
+read [Troubleshooting](./troubleshooting.md) to find the solutions of several
+common build issues.
+
+4.  Build a MediaPipe Android example.

    ```bash
    $ docker run -it --name mediapipe mediapipe:latest
@@ -667,8 +792,9 @@ This will use a Docker image that will isolate mediapipe's installation from the
    docker run -i -t mediapipe:latest
    ``` -->

-[`WORKSPACE`]: https://github.com/google/mediapipe/tree/master/WORKSPACE
+[`WORKSPACE`]: https://github.com/google/mediapipe/blob/master/WORKSPACE
 [`opencv_linux.BUILD`]: https://github.com/google/mediapipe/tree/master/third_party/opencv_linux.BUILD
+[`ffmpeg_linux.BUILD`]:https://github.com/google/mediapipe/tree/master/third_party/ffmpeg_linux.BUILD
 [`opencv_macos.BUILD`]: https://github.com/google/mediapipe/tree/master/third_party/opencv_macos.BUILD
 [`ffmpeg_macos.BUILD`]:https://github.com/google/mediapipe/tree/master/third_party/ffmpeg_macos.BUILD
-[`setup_opencv.sh`]: https://github.com/google/mediapipe/tree/master/setup_opencv.sh
+[`setup_opencv.sh`]: https://github.com/google/mediapipe/blob/master/setup_opencv.sh
@@ -1,12 +1,101 @@
-# Troubleshooting
+---
+layout: default
+title: Troubleshooting
+parent: Getting Started
+nav_order: 10
+---

-   [Native method not found](#native-method-not-found)
-   [No registered calculator found](#no-registered-calculator-found)
-   [Out Of Memory error](#out-of-memory-error)
-   [Graph hangs](#graph-hangs)
-   [Calculator is scheduled infrequently](#calculator-is-scheduled-infrequently)
-   [Output timing is uneven](#output-timing-is-uneven)
-   [CalculatorGraph lags behind inputs](#calculatorgraph-lags-behind-inputs)
+# Troubleshooting
+{: .no_toc }
+
+1. TOC
+{:toc}
+---
+
+## Missing Python binary path
+
+The error message:
+
+```
+ERROR: An error occurred during the fetch of repository 'local_execution_config_python':
+  Traceback (most recent call last):
+       File "/sandbox_path/external/org_tensorflow/third_party/py/python_configure.bzl", line 208
+               get_python_bin(repository_ctx)
+    ...
+Repository command failed
+```
+
+usually indicates that Bazel fails to find the local Python binary. To solve
+this issue, please first find where the python binary is and then add
+`--action_env PYTHON_BIN_PATH=<path to python binary>` to the Bazel command. For
+example, you can switch to use the system default python3 binary by the
+following command:
+
+```
+bazel build -c opt \
+  --define MEDIAPIPE_DISABLE_GPU=1 \
+  --action_env PYTHON_BIN_PATH=$(which python3) \
+  mediapipe/examples/desktop/hello_world
+```
+
+## Missing necessary Python packages
+
+The error message:
+
+```
+ImportError: No module named numpy
+Is numpy installed?
+```
+
+usually indicates that certain Python packages are not installed. Please run
+`pip install` or `pip3 install` depending on your Python binary version to
+install those packages.
+
+## Fail to fetch remote dependency repositories
+
+The error message:
+
+```
+ERROR: An error occurred during the fetch of repository 'org_tensorflow':
+   java.io.IOException: Error downloading [https://mirror.bazel.build/github.com/tensorflow/tensorflow/archive/77e9ffb9b2bfb1a4f7056e62d84039626923e328.tar.gz, https://github.com/tensorflow/tensorflow/archive/77e9ffb9b2bfb1a4f7056e62d84039626923e328.tar.gz] to /sandbox_path/external/org_tensorflow/77e9ffb9b2bfb1a4f7056e62d84039626923e328.tar.gz: Tried to reconnect at offset 9,944,151 but server didn't support it
+
+or
+
+WARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/bazelbuild/rules_swift/releases/download/0.12.1/rules_swift.0.12.1.tar.gz failed: class java.net.ConnectException Connection timed out (Connection timed out)
+```
+
+usually indicates that Bazel fails to download necessary dependency repositories
+that MediaPipe needs. MedaiPipe has several dependency repositories that are
+hosted by Google sites. In some regions, you may need to set up a network proxy
+or use a VPN to access those resources. You may also need to append
+`--host_jvm_args "-DsocksProxyHost=<ip address> -DsocksProxyPort=<port number>"`
+to the Bazel command. See
+[this GitHub issue](https://github.com/google/mediapipe/issues/581#issuecomment-610356857)
+for more details.
+
+If you believe that it's not a network issue, another possibility is that some
+resources could be temporarily unavailable, please run `bazel clean --expunge`
+and retry it later. If it's still not working, please file a GitHub issue with
+the detailed error message.
+
+## Incorrect MediaPipe OpenCV config
+
+The error message:
+
+```
+error: undefined reference to 'cv::String::deallocate()'
+error: undefined reference to 'cv::String::allocate(unsigned long)'
+error: undefined reference to 'cv::VideoCapture::VideoCapture(cv::String const&)'
+...
+error: undefined reference to 'cv::putText(cv::InputOutputArray const&, cv::String const&, cv::Point, int, double, cv::Scalar, int, int, bool)'
+```
+
+usually indicates that OpenCV is not properly configured for MediaPipe. Please
+take a look at the "Install OpenCV and FFmpeg" sections in
+[Installation](./install.md) to see how to modify MediaPipe's WORKSPACE and
+linux_opencv/macos_opencv/windows_opencv.BUILD files for your local opencv
+libraries. [This GitHub issue](https://github.com/google/mediapipe/issues/666)
+may also help.

 ## Native method not found

@@ -55,7 +144,7 @@ running MediaPipe graph. This can occur for a number of reasons, such as:

 For problem (1), it may be necessary to drop some old packets in older to
 process the more recent packets. For some hints, see:
-[How to process realtime input streams](how_to_questions.md).
+[`How to process realtime input streams`].

 For problem (2), it could be that one input stream is lacking packets for some
 reason. A device or a calculator may be misconfigured or may produce packets
@@ -63,7 +152,7 @@ only sporadically. This can cause downstream calculators to wait for many
 packets that will never arrive, which in turn causes packets to accumulate on
 some of their input streams. MediaPipe addresses this sort of problem using
 "timestamp bounds". For some hints see:
-[How to process realtime input streams](how_to_questions.md).
+[`How to process realtime input streams`].

 The MediaPipe setting [`CalculatorGraphConfig::max_queue_size`] limits the
 number of packets enqueued on any input stream by throttling inputs to the
@@ -122,14 +211,14 @@ each packet as early as possible. Normally the lowest possible latency is the
 total time required by each calculator along a "critical path" of successive
 calculators. The latency of the a MediaPipe graph could be worse than the ideal
 due to delays introduced to display frames a even intervals as described in
-[Output timing is uneven](troubleshooting.md?cl=252235797#output-timing-is-uneven).
+[Output timing is uneven](#output-timing-is-uneven).

 If some of the calculators in the graph cannot keep pace with the realtime input
 streams, then latency will continue to increase, and it becomes necessary to
 drop some input packets. The recommended technique is to use the MediaPipe
 calculators designed specifically for this purpose such as
 [`FlowLimiterCalculator`] as described in
-[How to process realtime input streams](how_to_questions.md).
+[`How to process realtime input streams`].

 [`CalculatorGraphConfig`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/calculator.proto
 [`CalculatorGraphConfig::max_queue_size`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/calculator.proto
@@ -142,3 +231,4 @@ calculators designed specifically for this purpose such as
 [`Timestamp::Done`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/timestamp.h
 [`CalculatorBase::Close`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/calculator_base.h
 [`FlowLimiterCalculator`]: https://github.com/google/mediapipe/tree/master/mediapipe/calculators/core/flow_limiter_calculator.cc
+[`How to process realtime input streams`]: faq.md#how-to-process-realtime-input-streams
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
MediaPipe Teamandchuoling	73f4475c17	Project import generated by Copybara. GitOrigin-RevId: 5cd50ad1abca6bd5d52832975bb60e6c0fc60a27	2020-08-13 04:32:11 -04:00
MediaPipe Teamandchuoling	f57ff46845	Project import generated by Copybara. GitOrigin-RevId: 933c6691064eac6ce3f2df6908611aefbed1d194	2020-08-13 02:18:32 -04:00
MediaPipe Teamandchuoling	d7c287c4e9	Project import generated by Copybara. GitOrigin-RevId: 9295f8ea2339edb71073695ed4fb3fded2f48c60	2020-08-13 01:32:08 -04:00
MediaPipe Teamandchuoling	6b0ab0e012	Project import generated by Copybara. GitOrigin-RevId: e9b30181bd6a75481835643d6f48c1c05730ff87	2020-08-05 22:14:25 -04:00
MediaPipe Teamandchuoling	2f86a459b6	Project import generated by Copybara. GitOrigin-RevId: 5b23708185311ae39a8605b0c2eff721e7b4939f	2020-08-05 20:27:31 -04:00
MediaPipe Teamandchuoling	bdfdaef305	Project import generated by Copybara. GitOrigin-RevId: b2062656e5b3d33264e28ed0cbca31c4b93fe1bf	2020-07-29 21:18:36 -04:00
MediaPipe Teamandjqtang	e9fbe868e5	Project import generated by Copybara. GitOrigin-RevId: e3a43e4e5e519cd14df7095749059e2613bdcf76	2020-07-08 18:24:46 -07:00
MediaPipe Teamandchuoling	67bd8a2bf0	Project import generated by Copybara. GitOrigin-RevId: d38dc934bcd08e03061c37d26d36da216456d10d	2020-06-12 15:44:38 -04:00
MediaPipe Teamandchuoling	59ee17c1f3	Project import generated by Copybara. GitOrigin-RevId: 3864d8399e063cfcacc9e3f6e7ba653ea9045c52	2020-06-09 18:35:58 -04:00
MediaPipe Teamandjqtang	b09cc090d4	Project import generated by Copybara. GitOrigin-RevId: 4143ead7dd769b489c313bb9e8856002736f5ecd	2020-06-08 10:30:49 -07:00
MediaPipe Teamandchuoling	cd2b69d58c	Project import generated by Copybara. GitOrigin-RevId: f72a0f86c2c2acdb1920973c718a9e26ed3ec4b6	2020-06-08 12:08:33 -04:00