Project import generated by Copybara.

GitOrigin-RevId: f4b1fe3f15810450fb6539e733f6a260d3ee082c

.bazelrc

@@ -5,25 +5,28 @@ common --experimental_repo_remote_exec
 
 # Basic build settings
 build --jobs 128
-build --define='absl=1'
+build --define='absl=1'  # for gtest
 build --enable_platform_specific_config
 
+# Enable stack traces
+test --test_env="GTEST_INSTALL_FAILURE_SIGNAL_HANDLER=1"
+
 # Linux
-build:linux --cxxopt=-std=c++14
-build:linux --host_cxxopt=-std=c++14
+build:linux --cxxopt=-std=c++17
+build:linux --host_cxxopt=-std=c++17
 build:linux --copt=-w
 
 # windows
-build:windows --cxxopt=/std:c++14
-build:windows --host_cxxopt=/std:c++14
+build:windows --cxxopt=/std:c++17
+build:windows --host_cxxopt=/std:c++17
 build:windows --copt=/w
 # For using M_* math constants on Windows with MSVC.
 build:windows --copt=/D_USE_MATH_DEFINES
 build:windows --host_copt=/D_USE_MATH_DEFINES
 
 # macOS
-build:macos --cxxopt=-std=c++14
-build:macos --host_cxxopt=-std=c++14
+build:macos --cxxopt=-std=c++17
+build:macos --host_cxxopt=-std=c++17
 build:macos --copt=-w
 
 # Sets the default Apple platform to macOS.
@@ -83,3 +86,9 @@ build:ios_fat --watchos_cpus=armv7k
 build:darwin_x86_64 --apple_platform_type=macos
 build:darwin_x86_64 --macos_minimum_os=10.12
 build:darwin_x86_64 --cpu=darwin_x86_64
+
+# This bazelrc file is meant to be written by a setup script.
+try-import %workspace%/.configure.bazelrc
+
+# This bazelrc file can be used for user-specific custom build settings.
+try-import %workspace%/.user.bazelrc

.bazelversion

@@ -0,0 +1 @@
+3.7.2

.github/ISSUE_TEMPLATE/00-build-installation-issue.md

@@ -0,0 +1,27 @@
+---
+name: "Build/Installation Issue"
+about: Use this template for build/installation issues
+labels: type:build/install
+
+---
+<em>Please make sure that this is a build/installation issue and also refer to the [troubleshooting](https://google.github.io/mediapipe/getting_started/troubleshooting.html) documentation before raising any issues.</em>
+
+**System information** (Please provide as much relevant information as possible)
+- OS Platform and Distribution (e.g. Linux Ubuntu 16.04, Android 11, iOS 14.4):
+- Compiler version (e.g. gcc/g++ 8 /Apple clang version 12.0.0):
+- Programming Language and version ( e.g. C++ 14, Python 3.6, Java ):
+- Installed using virtualenv? pip? Conda? (if python):
+- [MediaPipe version](https://github.com/google/mediapipe/releases):
+- Bazel version:
+- XCode and Tulsi versions (if iOS):
+- Android SDK and NDK versions (if android):
+- Android [AAR](https://google.github.io/mediapipe/getting_started/android_archive_library.html) ( if android):
+- OpenCV version (if running on desktop):
+
+**Describe the problem**:
+
+
+**[Provide the exact sequence of commands / steps that you executed before running into the problem](https://google.github.io/mediapipe/getting_started/getting_started.html):**
+
+**Complete Logs:**
+Include Complete Log information or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached:

.github/ISSUE_TEMPLATE/10-solution-issue.md

@@ -0,0 +1,26 @@
+---
+name: "Solution Issue"
+about: Use this template for assistance with a specific mediapipe solution, such as "Pose" or "Iris", including inference model usage/training, solution-specific calculators, etc.
+labels: type:support
+
+---
+<em>Please make sure that this is a [solution](https://google.github.io/mediapipe/solutions/solutions.html) issue.<em>
+
+**System information** (Please provide as much relevant information as possible)
+- Have I written custom code (as opposed to using a stock example script provided in Mediapipe):
+- OS Platform and Distribution (e.g., Linux Ubuntu 16.04, Android 11, iOS 14.4):
+- [MediaPipe version](https://github.com/google/mediapipe/releases):
+- Bazel version:
+- Solution (e.g. FaceMesh, Pose, Holistic):
+- Programming Language and version ( e.g. C++, Python, Java):
+
+**Describe the expected behavior:**
+
+**Standalone code you may have used to try to get what you need :**
+
+If there is a problem, provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to Colab/repo link /any notebook:
+
+**Other info / Complete Logs :**
+Include any logs or source code that would be helpful to
+diagnose the problem. If including tracebacks, please include the full
+traceback. Large logs and files should be attached:

.github/ISSUE_TEMPLATE/20-documentation-issue.md

@@ -0,0 +1,51 @@
+---
+name: "Documentation Issue"
+about: Use this template for documentation related issues
+labels: type:docs
+
+---
+Thank you for submitting a MediaPipe documentation issue.
+The MediaPipe docs are open source! To get involved, read the documentation Contributor Guide
+## URL(s) with the issue:
+
+Please provide a link to the documentation entry, for example: https://github.com/google/mediapipe/blob/master/docs/solutions/face_mesh.md#models
+
+## Description of issue (what needs changing):
+
+Kinds of documentation problems:
+
+### Clear description
+
+For example, why should someone use this method? How is it useful?
+
+### Correct links
+
+Is the link to the source code correct?
+
+### Parameters defined
+Are all parameters defined and formatted correctly?
+
+### Returns defined
+
+Are return values defined?
+
+### Raises listed and defined
+
+Are the errors defined? For example,
+
+### Usage example
+
+Is there a usage example?
+
+See the API guide:
+on how to write testable usage examples.
+
+### Request visuals, if applicable
+
+Are there currently visuals? If not, will it clarify the content?
+
+### Submit a pull request?
+
+Are you planning to also submit a pull request to fix the issue? See the docs
+https://github.com/google/mediapipe/blob/master/CONTRIBUTING.md
+

.github/ISSUE_TEMPLATE/30-bug-issue.md

@@ -0,0 +1,32 @@
+---
+name: "Bug Issue"
+about: Use this template for reporting a bug
+labels: type:bug
+
+---
+<em>Please make sure that this is a bug and also refer to the [troubleshooting](https://google.github.io/mediapipe/getting_started/troubleshooting.html), FAQ documentation before raising any issues.</em>
+
+**System information** (Please provide as much relevant information as possible)
+
+- Have I written custom code (as opposed to using a stock example script provided in MediaPipe):
+- OS Platform and Distribution (e.g., Linux Ubuntu 16.04, Android 11, iOS 14.4):
+- Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
+- Browser and version (e.g. Google Chrome, Safari) if the issue happens on browser:
+- Programming Language and version ( e.g. C++, Python, Java):
+- [MediaPipe version](https://github.com/google/mediapipe/releases):
+- Bazel version (if compiling from source):
+- Solution ( e.g. FaceMesh, Pose, Holistic ):
+- Android Studio, NDK, SDK versions (if issue is related to building in Android environment):
+- Xcode & Tulsi version (if issue is related to building for iOS):
+
+**Describe the current behavior:**
+
+**Describe the expected behavior:**
+
+**Standalone code to reproduce the issue:**
+Provide a reproducible test case that is the bare minimum necessary to replicate the problem. If possible, please share a link to Colab/repo link /any notebook:
+
+**Other info / Complete Logs :**
+ Include any logs or source code that would be helpful to
+diagnose the problem. If including tracebacks, please include the full
+traceback. Large logs and files should be attached

.github/ISSUE_TEMPLATE/40-feature-request.md

@@ -0,0 +1,24 @@
+---
+name: "Feature Request"
+about: Use this template for raising a feature request
+labels: type:feature
+
+---
+<em>Please make sure that this is a feature request.</em>
+
+**System information** (Please provide as much relevant information as possible)
+
+- MediaPipe Solution (you are using):
+- Programming language : C++/typescript/Python/Objective C/Android Java
+- Are you willing to contribute it (Yes/No):
+
+
+**Describe the feature and the current behavior/state:**
+
+**Will this change the current api? How?**
+
+**Who will benefit with this feature?**
+
+**Please specify the use cases for this feature:**
+
+**Any Other info:**

.github/ISSUE_TEMPLATE/50-other-issues.md

@@ -0,0 +1,14 @@
+---
+name: "Other Issue"
+about: Use this template for any other non-support related issues.
+labels: type:others
+
+---
+This template is for miscellaneous issues not covered by the other issue categories
+
+For questions on how to work with MediaPipe, or support for problems that are not verified bugs in MediaPipe, please go to [StackOverflow](https://stackoverflow.com/questions/tagged/mediapipe) and [Slack](https://mediapipe.page.link/joinslack) communities.
+
+If you are reporting a vulnerability, please use the [dedicated reporting process](https://github.com/google/mediapipe/security).
+
+For high-level discussions about MediaPipe, please post to discuss@mediapipe.org, for questions about the development or internal workings of MediaPipe, or if you would like to know how to contribute to MediaPipe, please post to developers@mediapipe.org.
+

.github/bot_config.yml

@@ -0,0 +1,18 @@
+# Copyright 2021 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.
+# ============================================================================
+
+# A list of assignees
+assignees:
+   - sgowroji

.github/stale.yml

@@ -0,0 +1,34 @@
+# Copyright 2021 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.
+# ============================================================================
+#
+# This file was assembled from multiple pieces, whose use is documented
+# throughout. Please refer to the TensorFlow dockerfiles documentation
+# for more information.
+
+# Number of days of inactivity before an Issue or Pull Request becomes stale
+daysUntilStale: 7
+# Number of days of inactivity before a stale Issue or Pull Request is closed
+daysUntilClose: 7
+# Only issues or pull requests with all of these labels are checked if stale. Defaults to `[]` (disabled)
+onlyLabels:
+ - stat:awaiting response
+# Comment to post when marking as stale. Set to `false` to disable
+markComment: >
+  This issue has been automatically marked as stale because it has not had
+  recent activity. It will be closed if no further activity occurs. Thank you.
+# Comment to post when removing the stale label. Set to `false` to disable
+unmarkComment: false
+closeComment: >
+  Closing as stale. Please reopen if you'd like to work on this further.

.gitignore

@@ -2,3 +2,5 @@ bazel-*
 mediapipe/MediaPipe.xcodeproj
 mediapipe/MediaPipe.tulsiproj/*.tulsiconf-user
 mediapipe/provisioning_profile.mobileprovision
+.configure.bazelrc
+.user.bazelrc

Dockerfile

@@ -23,6 +23,7 @@ ENV DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get update && apt-get install -y --no-install-recommends \
         build-essential \
+        gcc-8 g++-8 \
         ca-certificates \
         curl \
         ffmpeg \
@@ -44,6 +45,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     apt-get clean && \
     rm -rf /var/lib/apt/lists/*
 
+RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 100 --slave /usr/bin/g++ g++ /usr/bin/g++-8
 RUN pip3 install --upgrade setuptools
 RUN pip3 install wheel
 RUN pip3 install future
@@ -54,7 +56,7 @@ RUN pip3 install tf_slim
 RUN ln -s /usr/bin/python3 /usr/bin/python
 
 # Install bazel
-ARG BAZEL_VERSION=3.4.1
+ARG BAZEL_VERSION=3.7.2
 RUN mkdir /bazel && \
     wget --no-check-certificate -O /bazel/installer.sh "https://github.com/bazelbuild/bazel/releases/download/${BAZEL_VERSION}/b\
 azel-${BAZEL_VERSION}-installer-linux-x86_64.sh" && \

MANIFEST.in

@@ -8,3 +8,13 @@ include README.md
 include requirements.txt
 
 recursive-include mediapipe/modules *.tflite *.txt *.binarypb
+exclude mediapipe/modules/face_detection/face_detection_full_range.tflite
+exclude mediapipe/modules/objectron/object_detection_3d_chair_1stage.tflite
+exclude mediapipe/modules/objectron/object_detection_3d_sneakers_1stage.tflite
+exclude mediapipe/modules/objectron/object_detection_3d_sneakers.tflite
+exclude mediapipe/modules/objectron/object_detection_3d_chair.tflite
+exclude mediapipe/modules/objectron/object_detection_3d_camera.tflite
+exclude mediapipe/modules/objectron/object_detection_3d_cup.tflite
+exclude mediapipe/modules/objectron/object_detection_ssd_mobilenetv2_oidv4_fp16.tflite
+exclude mediapipe/modules/pose_landmark/pose_landmark_lite.tflite
+exclude mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite

README.md

@@ -21,82 +21,56 @@ ML solutions for live and streaming media.
 
 ## ML solutions in MediaPipe
 
-Face Detection                                                                                                                 | Face Mesh                                                                                                       | Iris                                                                                                      | Hands                                                                                                      | Pose                                                                                                      | Hair Segmentation
-:----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :---------------:
-[![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](docs/images/mobile/hair_segmentation_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hair_segmentation)
+Face Detection                                                                                                                 | Face Mesh                                                                                                       | Iris                                                                                                      | Hands                                                                                                      | Pose                                                                                                      | Holistic
+:----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :------:
+[![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](docs/images/mobile/holistic_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/holistic)
 
-Object Detection                                                                                                                     | Box Tracking                                                                                                                | Instant Motion Tracking                                                                                                                               | Objectron                                                                                                             | KNIFT
-:----------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: | :---:
-[![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) | [![instant_motion_tracking](docs/images/mobile/instant_motion_tracking_android_small.gif)](https://google.github.io/mediapipe/solutions/instant_motion_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)
+Hair Segmentation                                                                                                                       | Object Detection                                                                                                                     | Box Tracking                                                                                                                | Instant Motion 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) | [![instant_motion_tracking](docs/images/mobile/instant_motion_tracking_android_small.gif)](https://google.github.io/mediapipe/solutions/instant_motion_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)                       | ✅       | ✅   | ✅       |        |     |
-[Instant Motion Tracking](https://google.github.io/mediapipe/solutions/instant_motion_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)                           |         |     | ✅       |        |     |
+[]()                                                                                      | [Android](https://google.github.io/mediapipe/getting_started/android) | [iOS](https://google.github.io/mediapipe/getting_started/ios) | [C++](https://google.github.io/mediapipe/getting_started/cpp) | [Python](https://google.github.io/mediapipe/getting_started/python) | [JS](https://google.github.io/mediapipe/getting_started/javascript) | [Coral](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/README.md)
+:---------------------------------------------------------------------------------------- | :-------------------------------------------------------------: | :-----------------------------------------------------: | :-----------------------------------------------------: | :-----------------------------------------------------------: | :-----------------------------------------------------------: | :--------------------------------------------------------------------:
+[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)                                       | ✅                                                               | ✅                                                       | ✅                                                       | ✅                                                             | ✅                                                             |
+[Holistic](https://google.github.io/mediapipe/solutions/holistic)                               | ✅                                                               | ✅                                                       | ✅                                                       | ✅                                                             | ✅                                                             |
+[Selfie Segmentation](https://google.github.io/mediapipe/solutions/selfie_segmentation)         | ✅                                                               | ✅                                                       | ✅                                                       | ✅                                                             | ✅                                                             |
+[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)                       | ✅                                                               | ✅                                                       | ✅                                                       |                                                               |                                                               |
+[Instant Motion Tracking](https://google.github.io/mediapipe/solutions/instant_motion_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)                           |                                                                 |                                                         | ✅                                                       |                                                               |                                                               |
 
 See also
 [MediaPipe Models and Model Cards](https://google.github.io/mediapipe/solutions/models)
 for ML models released in MediaPipe.
 
-## MediaPipe in Python
-
-MediaPipe Python package is available on
-[PyPI](https://pypi.org/project/mediapipe/), and can be installed simply by `pip
-install mediapipe` on Linux and macOS, as described in:
-
-*   [MediaPipe Face Mesh](../solutions/pose.md#python) and
-    [colab](https://mediapipe.page.link/face_mesh_py_colab)
-*   [MediaPipe Hands](../solutions/pose.md#python) and
-    [colab](https://mediapipe.page.link/hands_py_colab)
-*   [MediaPipe Pose](../solutions/pose.md#python) and
-    [colab](https://mediapipe.page.link/pose_py_colab)
-
-## 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
 
-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.
+To start using MediaPipe
+[solutions](https://google.github.io/mediapipe/solutions/solutions) with only a few
+lines code, see example code and demos in
+[MediaPipe in Python](https://google.github.io/mediapipe/getting_started/python) and
+[MediaPipe in JavaScript](https://google.github.io/mediapipe/getting_started/javascript).
+
+To use MediaPipe in C++, Android and iOS, which allow further customization of
+the [solutions](https://google.github.io/mediapipe/solutions/solutions) as well as
+building your own, learn how to
+[install](https://google.github.io/mediapipe/getting_started/install) MediaPipe and
+start building example applications in
+[C++](https://google.github.io/mediapipe/getting_started/cpp),
+[Android](https://google.github.io/mediapipe/getting_started/android) and
+[iOS](https://google.github.io/mediapipe/getting_started/ios).
 
 The source code is hosted in the
 [MediaPipe Github repository](https://github.com/google/mediapipe), and you can
@@ -105,6 +79,17 @@ run code search using
 
 ## Publications
 
+*   [Bringing artworks to life with AR](https://developers.googleblog.com/2021/07/bringing-artworks-to-life-with-ar.html)
+    in Google Developers Blog
+*   [Prosthesis control via Mirru App using MediaPipe hand tracking](https://developers.googleblog.com/2021/05/control-your-mirru-prosthesis-with-mediapipe-hand-tracking.html)
+    in Google Developers Blog
+*   [SignAll SDK: Sign language interface using MediaPipe is now available for
+    developers](https://developers.googleblog.com/2021/04/signall-sdk-sign-language-interface-using-mediapipe-now-available.html)
+    in Google Developers Blog
+*   [MediaPipe Holistic - Simultaneous Face, Hand and Pose Prediction, on Device](https://ai.googleblog.com/2020/12/mediapipe-holistic-simultaneous-face.html)
+    in Google AI Blog
+*   [Background Features in Google Meet, Powered by Web ML](https://ai.googleblog.com/2020/10/background-features-in-google-meet.html)
+    in Google AI Blog
 *   [MediaPipe 3D Face Transform](https://developers.googleblog.com/2020/09/mediapipe-3d-face-transform.html)
     in Google Developers Blog
 *   [Instant Motion Tracking With MediaPipe](https://developers.googleblog.com/2020/08/instant-motion-tracking-with-mediapipe.html)

WORKSPACE

@@ -2,22 +2,25 @@ workspace(name = "mediapipe")
 
 load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
 
-skylib_version = "0.9.0"
 http_archive(
     name = "bazel_skylib",
     type = "tar.gz",
-    url = "https://github.com/bazelbuild/bazel-skylib/releases/download/{}/bazel_skylib-{}.tar.gz".format (skylib_version, skylib_version),
-    sha256 = "1dde365491125a3db70731e25658dfdd3bc5dbdfd11b840b3e987ecf043c7ca0",
+    urls = [
+        "https://github.com/bazelbuild/bazel-skylib/releases/download/1.0.3/bazel-skylib-1.0.3.tar.gz",
+        "https://mirror.bazel.build/github.com/bazelbuild/bazel-skylib/releases/download/1.0.3/bazel-skylib-1.0.3.tar.gz",
+    ],
+    sha256 = "1c531376ac7e5a180e0237938a2536de0c54d93f5c278634818e0efc952dd56c",
 )
+load("@bazel_skylib//:workspace.bzl", "bazel_skylib_workspace")
+bazel_skylib_workspace()
 load("@bazel_skylib//lib:versions.bzl", "versions")
-versions.check(minimum_bazel_version = "3.4.0")
+versions.check(minimum_bazel_version = "3.7.2")
 
-
-# ABSL cpp library lts_2020_02_25
+# ABSL cpp library lts_2020_09_23
 http_archive(
     name = "com_google_absl",
     urls = [
-        "https://github.com/abseil/abseil-cpp/archive/20200225.tar.gz",
+        "https://github.com/abseil/abseil-cpp/archive/20200923.tar.gz",
     ],
     # Remove after https://github.com/abseil/abseil-cpp/issues/326 is solved.
     patches = [
@@ -26,20 +29,20 @@ http_archive(
     patch_args = [
         "-p1",
     ],
-    strip_prefix = "abseil-cpp-20200225",
-    sha256 = "728a813291bdec2aa46eab8356ace9f75ac2ed9dfe2df5ab603c4e6c09f1c353"
+    strip_prefix = "abseil-cpp-20200923",
+    sha256 = "b3744a4f7a249d5eaf2309daad597631ce77ea62e0fc6abffbab4b4c3dc0fc08"
 )
 
 http_archive(
     name = "rules_cc",
-    strip_prefix = "rules_cc-master",
-    urls = ["https://github.com/bazelbuild/rules_cc/archive/master.zip"],
+    strip_prefix = "rules_cc-main",
+    urls = ["https://github.com/bazelbuild/rules_cc/archive/main.zip"],
 )
 
 http_archive(
    name = "rules_foreign_cc",
-   strip_prefix = "rules_foreign_cc-master",
-   url = "https://github.com/bazelbuild/rules_foreign_cc/archive/master.zip",
+   strip_prefix = "rules_foreign_cc-0.1.0",
+   url = "https://github.com/bazelbuild/rules_foreign_cc/archive/0.1.0.zip",
 )
 
 load("@rules_foreign_cc//:workspace_definitions.bzl", "rules_foreign_cc_dependencies")
@@ -50,26 +53,19 @@ rules_foreign_cc_dependencies()
 all_content = """filegroup(name = "all", srcs = glob(["**"]), visibility = ["//visibility:public"])"""
 
 # GoogleTest/GoogleMock framework. Used by most unit-tests.
-# Last updated 2020-06-30.
+# Last updated 2021-07-02.
 http_archive(
     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",
+    urls = ["https://github.com/google/googletest/archive/4ec4cd23f486bf70efcc5d2caa40f24368f752e3.zip"],
+    strip_prefix = "googletest-4ec4cd23f486bf70efcc5d2caa40f24368f752e3",
+    sha256 = "de682ea824bfffba05b4e33b67431c247397d6175962534305136aa06f92e049",
 )
 
 # Google Benchmark library.
 http_archive(
     name = "com_google_benchmark",
-    urls = ["https://github.com/google/benchmark/archive/master.zip"],
-    strip_prefix = "benchmark-master",
+    urls = ["https://github.com/google/benchmark/archive/main.zip"],
+    strip_prefix = "benchmark-main",
     build_file = "@//third_party:benchmark.BUILD",
 )
 
@@ -99,7 +95,7 @@ http_archive(
         "https://github.com/google/glog/archive/0a2e5931bd5ff22fd3bf8999eb8ce776f159cda6.zip",
     ],
     patches = [
-        "@//third_party:com_github_glog_glog_9779e5ea6ef59562b030248947f787d1256132ae.diff"
+        "@//third_party:com_github_glog_glog_9779e5ea6ef59562b030248947f787d1256132ae.diff",
     ],
     patch_args = [
         "-p1",
@@ -117,7 +113,8 @@ http_archive(
 # libyuv
 http_archive(
     name = "libyuv",
-    urls = ["https://chromium.googlesource.com/libyuv/libyuv/+archive/refs/heads/master.tar.gz"],
+    # Error: operand type mismatch for `vbroadcastss' caused by commit 8a13626e42f7fdcf3a6acbb0316760ee54cda7d8.
+    urls = ["https://chromium.googlesource.com/libyuv/libyuv/+archive/2525698acba9bf9b701ba6b4d9584291a1f62257.tar.gz"],
     build_file = "@//third_party:libyuv.BUILD",
 )
 
@@ -160,25 +157,25 @@ http_archive(
 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",
+        "https://storage.googleapis.com/mirror.tensorflow.org/github.com/pybind/pybind11/archive/v2.7.1.tar.gz",
+        "https://github.com/pybind/pybind11/archive/v2.7.1.tar.gz",
     ],
-    sha256 = "1eed57bc6863190e35637290f97a20c81cfe4d9090ac0a24f3bbf08f265eb71d",
-    strip_prefix = "pybind11-2.4.3",
+    sha256 = "616d1c42e4cf14fa27b2a4ff759d7d7b33006fdc5ad8fd603bb2c22622f27020",
+    strip_prefix = "pybind11-2.7.1",
     build_file = "@pybind11_bazel//:pybind11.BUILD",
 )
 
 http_archive(
     name = "ceres_solver",
-    url = "https://github.com/ceres-solver/ceres-solver/archive/1.14.0.zip",
+    url = "https://github.com/ceres-solver/ceres-solver/archive/2.0.0.zip",
     patches = [
         "@//third_party:ceres_solver_compatibility_fixes.diff"
     ],
     patch_args = [
         "-p1",
     ],
-    strip_prefix = "ceres-solver-1.14.0",
-    sha256 = "5ba6d0db4e784621fda44a50c58bb23b0892684692f0c623e2063f9c19f192f1"
+    strip_prefix = "ceres-solver-2.0.0",
+    sha256 = "db12d37b4cebb26353ae5b7746c7985e00877baa8e7b12dc4d3a1512252fff3b"
 )
 
 http_archive(
@@ -238,6 +235,20 @@ http_archive(
     url = "https://github.com/opencv/opencv/releases/download/3.2.0/opencv-3.2.0-ios-framework.zip",
 )
 
+http_archive(
+    name = "stblib",
+    strip_prefix = "stb-b42009b3b9d4ca35bc703f5310eedc74f584be58",
+    sha256 = "13a99ad430e930907f5611325ec384168a958bf7610e63e60e2fd8e7b7379610",
+    urls = ["https://github.com/nothings/stb/archive/b42009b3b9d4ca35bc703f5310eedc74f584be58.tar.gz"],
+    build_file = "@//third_party:stblib.BUILD",
+    patches = [
+        "@//third_party:stb_image_impl.diff"
+    ],
+    patch_args = [
+        "-p1",
+    ],
+)
+
 # You may run setup_android.sh to install Android SDK and NDK.
 android_ndk_repository(
     name = "androidndk",
@@ -304,8 +315,8 @@ http_archive(
 
 # Maven dependencies.
 
-RULES_JVM_EXTERNAL_TAG = "3.2"
-RULES_JVM_EXTERNAL_SHA = "82262ff4223c5fda6fb7ff8bd63db8131b51b413d26eb49e3131037e79e324af"
+RULES_JVM_EXTERNAL_TAG = "4.0"
+RULES_JVM_EXTERNAL_SHA = "31701ad93dbfe544d597dbe62c9a1fdd76d81d8a9150c2bf1ecf928ecdf97169"
 
 http_archive(
     name = "rules_jvm_external",
@@ -318,10 +329,11 @@ load("@rules_jvm_external//:defs.bzl", "maven_install")
 
 # Important: there can only be one maven_install rule. Add new maven deps here.
 maven_install(
-    name = "maven",
     artifacts = [
         "androidx.concurrent:concurrent-futures:1.0.0-alpha03",
-        "androidx.lifecycle:lifecycle-common:2.2.0",
+        "androidx.lifecycle:lifecycle-common:2.3.1",
+        "androidx.activity:activity:1.2.2",
+        "androidx.fragment:fragment:1.3.4",
         "androidx.annotation:annotation:aar:1.1.0",
         "androidx.appcompat:appcompat:aar:1.1.0-rc01",
         "androidx.camera:camera-core:1.0.0-beta10",
@@ -334,19 +346,21 @@ maven_install(
         "androidx.test.espresso:espresso-core:3.1.1",
         "com.github.bumptech.glide:glide:4.11.0",
         "com.google.android.material:material:aar:1.0.0-rc01",
-        "com.google.code.findbugs:jsr305:3.0.2",
-        "com.google.flogger:flogger-system-backend:0.3.1",
-        "com.google.flogger:flogger:0.3.1",
+        "com.google.auto.value:auto-value:1.8.1",
+        "com.google.auto.value:auto-value-annotations:1.8.1",
+        "com.google.code.findbugs:jsr305:latest.release",
+        "com.google.flogger:flogger-system-backend:latest.release",
+        "com.google.flogger:flogger:latest.release",
         "com.google.guava:guava:27.0.1-android",
         "com.google.guava:listenablefuture:1.0",
         "junit:junit:4.12",
         "org.hamcrest:hamcrest-library:1.3",
     ],
     repositories = [
-        "https://jcenter.bintray.com",
         "https://maven.google.com",
         "https://dl.google.com/dl/android/maven2",
         "https://repo1.maven.org/maven2",
+        "https://jcenter.bintray.com",
     ],
     fetch_sources = True,
     version_conflict_policy = "pinned",
@@ -363,10 +377,10 @@ http_archive(
     ],
 )
 
-#Tensorflow repo should always go after the other external dependencies.
-# 2020-10-30
-_TENSORFLOW_GIT_COMMIT = "84384703c0d8b502e33ff6fd7eefd219dca5ff8e"
-_TENSORFLOW_SHA256= "23fb322fc15a20f7a7838d9a31f8b16f60700a494ea654311a0aa8621769df98"
+# Tensorflow repo should always go after the other external dependencies.
+# 2021-07-29
+_TENSORFLOW_GIT_COMMIT = "52a2905cbc21034766c08041933053178c5d10e3"
+_TENSORFLOW_SHA256 = "06d4691bcdb700f3275fa0971a1585221c2b9f3dffe867963be565a6643d7f56"
 http_archive(
     name = "org_tensorflow",
     urls = [
@@ -374,6 +388,7 @@ http_archive(
     ],
     patches = [
         "@//third_party:org_tensorflow_compatibility_fixes.diff",
+        "@//third_party:org_tensorflow_objc_cxx17.diff",
     ],
     patch_args = [
         "-p1",
@@ -382,5 +397,22 @@ http_archive(
     sha256 = _TENSORFLOW_SHA256,
 )
 
-load("@org_tensorflow//tensorflow:workspace.bzl", "tf_workspace")
-tf_workspace(tf_repo_name = "org_tensorflow")
+load("@org_tensorflow//tensorflow:workspace3.bzl", "tf_workspace3")
+tf_workspace3()
+load("@org_tensorflow//tensorflow:workspace2.bzl", "tf_workspace2")
+tf_workspace2()
+
+# Edge TPU
+http_archive(
+  name = "libedgetpu",
+  sha256 = "14d5527a943a25bc648c28a9961f954f70ba4d79c0a9ca5ae226e1831d72fe80",
+  strip_prefix = "libedgetpu-3164995622300286ef2bb14d7fdc2792dae045b7",
+  urls = [
+    "https://github.com/google-coral/libedgetpu/archive/3164995622300286ef2bb14d7fdc2792dae045b7.tar.gz"
+  ],
+)
+load("@libedgetpu//:workspace.bzl", "libedgetpu_dependencies")
+libedgetpu_dependencies()
+
+load("@coral_crosstool//:configure.bzl", "cc_crosstool")
+cc_crosstool(name = "crosstool")

build_android_examples.sh

@@ -89,33 +89,34 @@ for app in ${apps}; do
     fi
     target="${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
+    fi
+
     if [[ ${app_name} == "objectdetection3d" ]]; then
       categories=("shoe" "chair" "cup" "camera" "shoe_1stage" "chair_1stage")
-      for category in ${categories[@]}; do
+      for category in "${categories[@]}"; do
         apk="${out_dir}/${target_name}_${category}.apk"
         if [[ $install_only == false ]]; then
           bazel_flags_extended=("${bazel_flags[@]}")
           if [[ ${category} != "shoe" ]]; then
             bazel_flags_extended+=(--define ${category}=true)
           fi
-          echo "bazel ${bazel_flags_extended[@]}"
           bazel "${bazel_flags_extended[@]}"
           cp -f "${bin}" "${apk}"
         fi
         apks+=(${apk})
       done
     else
+      apk="${out_dir}/${target_name}.apk"
       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

build_desktop_examples.sh

@@ -17,15 +17,15 @@
 # Script to build/run all MediaPipe desktop example apps (with webcam input).
 #
 # To build and run all apps and store them in out_dir:
-#   $ ./build_ios_examples.sh -d out_dir
+#   $ ./build_desktop_examples.sh -d out_dir
 #   Omitting -d and the associated directory saves all generated apps in the
 #   current directory.
 # To build all apps and store them in out_dir:
-#   $ ./build_ios_examples.sh -d out_dir -b
+#   $ ./build_desktop_examples.sh -d out_dir -b
 #   Omitting -d and the associated directory saves all generated apps in the
 #   current directory.
 # To run all apps already stored in out_dir:
-#   $ ./build_ios_examples.sh -d out_dir -r
+#   $ ./build_desktop_examples.sh -d out_dir -r
 #   Omitting -d and the associated directory assumes all apps are in the current
 #   directory.
 
@@ -70,6 +70,7 @@ for app in ${apps}; do
     if [[ "${target_name}" == "autoflip" ||
           "${target_name}" == "hello_world" ||
           "${target_name}" == "media_sequence" ||
+          "${target_name}" == "object_detection_3d" ||
           "${target_name}" == "template_matching" ||
           "${target_name}" == "youtube8m" ]]; then
       continue
@@ -93,7 +94,10 @@ for app in ${apps}; do
       else
         graph_name="${target_name}/${target_name}"
       fi
-      if [[ ${target_name} == "iris_tracking" ||
+      if [[ ${target_name} == "holistic_tracking" ||
+            ${target_name} == "iris_tracking" ||
+            ${target_name} == "pose_tracking" ||
+            ${target_name} == "selfie_segmentation" ||
             ${target_name} == "upper_body_pose_tracking" ]]; then
         graph_suffix="cpu"
       else

docs/framework_concepts/calculators.md

@@ -67,26 +67,26 @@ class CalculatorBase {
 
   // The subclasses of CalculatorBase must implement GetContract.
   // ...
-  static ::MediaPipe::Status GetContract(CalculatorContract* cc);
+  static absl::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();
+  virtual absl::Status Open(CalculatorContext* cc) {
+    return absl::OkStatus();
   }
 
   // Processes the incoming inputs. May call the methods on cc to access
   // inputs and produce outputs.
   // ...
-  virtual ::MediaPipe::Status Process(CalculatorContext* cc) = 0;
+  virtual absl::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();
+  virtual absl::Status Close(CalculatorContext* cc) {
+    return absl::OkStatus();
   }
 
   ...
@@ -187,7 +187,7 @@ node {
 ```
 
 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:
+name and index number. In the function below input and output are identified:
 
 *   By index number: The combined input stream is identified simply by index
     `0`.
@@ -199,7 +199,7 @@ name and index number. In the function below input are output are identified:
 // c++ Code snippet describing the SomeAudioVideoCalculator GetContract() method
 class SomeAudioVideoCalculator : public CalculatorBase {
  public:
-  static ::mediapipe::Status GetContract(CalculatorContract* cc) {
+  static absl::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
@@ -209,13 +209,13 @@ class SomeAudioVideoCalculator : public CalculatorBase {
     cc->Outputs().Tag("VIDEO").Set<ImageFrame>();
     cc->Outputs().Get("AUDIO", 0).Set<Matrix>();
     cc->Outputs().Get("AUDIO", 1).Set<Matrix>();
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 ```
 
 ## Processing
 
-`Process()` called on a non-source node must return `::mediapipe::OkStatus()` to
+`Process()` called on a non-source node must return `absl::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
@@ -224,12 +224,12 @@ 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.
+as it returns `absl::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.
+`Close()` returns `absl::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
@@ -238,22 +238,80 @@ and does the calculations. When done it releases the pointer when adding it to
 the output stream.
 
 ```c++
-::util::Status MyCalculator::Process() {
+absl::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();
+  return absl::OkStatus();
 }
 ```
 
+## Calculator options
+
+Calculators accept processing parameters through (1) input stream packets (2)
+input side packets, and (3) calculator options. Calculator options, if
+specified, appear as literal values in the `node_options` field of the
+`CalculatorGraphConfiguration.Node` message.
+
+```
+  node {
+    calculator: "TfLiteInferenceCalculator"
+    input_stream: "TENSORS:main_model_input"
+    output_stream: "TENSORS:main_model_output"
+    node_options: {
+      [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
+        model_path: "mediapipe/models/detection_model.tflite"
+      }
+    }
+  }
+```
+
+The `node_options` field accepts the proto3 syntax.  Alternatively, calculator
+options can be specified in the `options` field using proto2 syntax.
+
+```
+  node {
+    calculator: "TfLiteInferenceCalculator"
+    input_stream: "TENSORS:main_model_input"
+    output_stream: "TENSORS:main_model_output"
+    node_options: {
+      [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
+        model_path: "mediapipe/models/detection_model.tflite"
+      }
+    }
+  }
+```
+
+Not all calculators accept calcuator options. In order to accept options, a
+calculator will normally define a new protobuf message type to represent its
+options, such as `PacketClonerCalculatorOptions`. The calculator will then
+read that protobuf message in its `CalculatorBase::Open` method, and possibly
+also in its `CalculatorBase::GetContract` function or its
+`CalculatorBase::Process` method. Normally, the new protobuf message type will
+be defined as a protobuf schema using a ".proto" file and a
+`mediapipe_proto_library()` build rule.
+
+```
+  mediapipe_proto_library(
+      name = "packet_cloner_calculator_proto",
+      srcs = ["packet_cloner_calculator.proto"],
+      visibility = ["//visibility:public"],
+      deps = [
+          "//mediapipe/framework:calculator_options_proto",
+          "//mediapipe/framework:calculator_proto",
+      ],
+  )
+```
+
+
 ## 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` 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
@@ -279,8 +337,8 @@ input streams:
     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
+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++
@@ -312,7 +370,7 @@ namespace mediapipe {
 //
 class PacketClonerCalculator : public CalculatorBase {
  public:
-  static ::mediapipe::Status GetContract(CalculatorContract* cc) {
+  static absl::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
@@ -322,10 +380,10 @@ class PacketClonerCalculator : public CalculatorBase {
       cc->Outputs().Index(i).SetSameAs(&cc->Inputs().Index(i));
     }
     cc->Inputs().Index(tick_signal_index).SetAny();
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
-  ::mediapipe::Status Open(CalculatorContext* cc) final {
+  absl::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.
@@ -336,10 +394,10 @@ class PacketClonerCalculator : public CalculatorBase {
         // the header for the input stream of index i
       }
     }
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
-  ::mediapipe::Status Process(CalculatorContext* cc) final {
+  absl::Status Process(CalculatorContext* cc) final {
     // Store input signals.
     for (int i = 0; i < tick_signal_index_; ++i) {
       if (!cc->Inputs().Index(i).Value().IsEmpty()) {
@@ -355,7 +413,6 @@ class PacketClonerCalculator : public CalculatorBase {
               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());
@@ -364,7 +421,7 @@ class PacketClonerCalculator : public CalculatorBase {
         }
       }
     }
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
  private:
@@ -382,7 +439,7 @@ 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.
+(PacketClonerCalculator) and 2 output streams.
 
 ```proto
 input_stream: "room_mic_signal"
@@ -402,6 +459,6 @@ node {
 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.* |
+![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.* |

docs/framework_concepts/framework_concepts.md

@@ -110,3 +110,12 @@ Other policies are also available, implemented using a separate kind of
 component known as an InputStreamHandler.
 
 See [Synchronization](synchronization.md) for more details.
+
+### Real-time streams
+
+MediaPipe calculator graphs are often used to process streams of video or audio
+frames for interactive applications. Normally, each Calculator runs as soon as
+all of its input packets for a given timestamp become available. Calculators
+used in real-time graphs need to define output timestamp bounds based on input
+timestamp bounds in order to allow downstream calculators to be scheduled
+promptly. See [Real-time Streams](realtime_streams.md) for details.

docs/framework_concepts/gpu.md

@@ -66,10 +66,10 @@ calculator derived from base class GlSimpleCalculator. The GPU calculator
 // See GlSimpleCalculator for inputs, outputs and input side packets.
 class LuminanceCalculator : public GlSimpleCalculator {
  public:
-  ::mediapipe::Status GlSetup() override;
-  ::mediapipe::Status GlRender(const GlTexture& src,
-                               const GlTexture& dst) override;
-  ::mediapipe::Status GlTeardown() override;
+  absl::Status GlSetup() override;
+  absl::Status GlRender(const GlTexture& src,
+                        const GlTexture& dst) override;
+  absl::Status GlTeardown() override;
 
  private:
   GLuint program_ = 0;
@@ -77,8 +77,8 @@ class LuminanceCalculator : public GlSimpleCalculator {
 };
 REGISTER_CALCULATOR(LuminanceCalculator);
 
-::mediapipe::Status LuminanceCalculator::GlRender(const GlTexture& src,
-                                                  const GlTexture& dst) {
+absl::Status LuminanceCalculator::GlRender(const GlTexture& src,
+                                           const GlTexture& dst) {
   static const GLfloat square_vertices[] = {
       -1.0f, -1.0f,  // bottom left
       1.0f,  -1.0f,  // bottom right
@@ -128,7 +128,7 @@ REGISTER_CALCULATOR(LuminanceCalculator);
   glDeleteVertexArrays(1, &vao);
   glDeleteBuffers(2, vbo);
 
-  return ::mediapipe::OkStatus();
+  return absl::OkStatus();
 }
 ```
 

docs/framework_concepts/graphs.md

@@ -83,12 +83,12 @@ Below is an example of how to create a subgraph named `TwoPassThroughSubgraph`.
     output_stream: "out3"
 
     node {
-        calculator: "PassThroughculator"
+        calculator: "PassThroughCalculator"
         input_stream: "out1"
         output_stream: "out2"
     }
     node {
-        calculator: "PassThroughculator"
+        calculator: "PassThroughCalculator"
         input_stream: "out2"
         output_stream: "out3"
     }
@@ -219,23 +219,23 @@ packet timestamps 0, 1, 2, 3, ...
 ```c++
 class UnitDelayCalculator : public Calculator {
  public:
-  static ::util::Status FillExpectations(
+  static absl::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();
+    return absl::OkStatus();
   }
 
-  ::util::Status Open() final {
+  absl::Status Open() final {
     Output()->Add(new int(0), Timestamp(0));
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
-  ::util::Status Process() final {
+  absl::Status Process() final {
     const Packet& packet = Input()->Value();
     Output()->AddPacket(packet.At(packet.Timestamp().NextAllowedInStream()));
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 };
 ```

docs/framework_concepts/packets.md

@@ -12,19 +12,30 @@ nav_order: 3
 {: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
+Calculators communicate by sending and receiving packets. Typically a single
+packet is sent along each input stream at each input timestamp. A packet can
+contain any kind of data, such as a single frame of video or a single integer
+detection count.
 
 ## Creating a packet
 
-Packets are generally created with `MediaPipe::Adopt()` (from packet.h).
+Packets are generally created with `mediapipe::MakePacket<T>()` or
+`mediapipe::Adopt()` (from packet.h).
 
 ```c++
-// Create some data.
-auto data = absl::make_unique<MyDataClass>("constructor_argument");
-// Create a packet to own the data.
-Packet p = Adopt(data.release());
+// Create a packet containing some new data.
+Packet p = MakePacket<MyDataClass>("constructor_argument");
 // Make a new packet with the same data and a different timestamp.
 Packet p2 = p.At(Timestamp::PostStream());
 ```
 
+or:
+
+```c++
+// Create some new data.
+auto data = absl::make_unique<MyDataClass>("constructor_argument");
+// Create a packet to own the data.
+Packet p = Adopt(data.release()).At(Timestamp::PostStream());
+```
+
 Data within a packet is accessed with `Packet::Get<T>()`

docs/framework_concepts/realtime_streams.md

@@ -0,0 +1,186 @@
+---
+layout: default
+title: Real-time Streams
+parent: Framework Concepts
+nav_order: 6
+---
+
+# Real-time Streams
+{: .no_toc }
+
+1. TOC
+{:toc}
+---
+
+## Real-time timestamps
+
+MediaPipe calculator graphs are often used to process streams of video or audio
+frames for interactive applications. The MediaPipe framework requires only that
+successive packets be assigned monotonically increasing timestamps. By
+convention, real-time calculators and graphs use the recording time or the
+presentation time of each frame as its timestamp, with each timestamp indicating
+the microseconds since `Jan/1/1970:00:00:00`. This allows packets from various
+sources to be processed in a globally consistent sequence.
+
+## Real-time scheduling
+
+Normally, each Calculator runs as soon as all of its input packets for a given
+timestamp become available. Normally, this happens when the calculator has
+finished processing the previous frame, and each of the calculators producing
+its inputs have finished processing the current frame. The MediaPipe scheduler
+invokes each calculator as soon as these conditions are met. See
+[Synchronization](synchronization.md) for more details.
+
+## Timestamp bounds
+
+When a calculator does not produce any output packets for a given timestamp, it
+can instead output a "timestamp bound" indicating that no packet will be
+produced for that timestamp. This indication is necessary to allow downstream
+calculators to run at that timestamp, even though no packet has arrived for
+certain streams for that timestamp. This is especially important for real-time
+graphs in interactive applications, where it is crucial that each calculator
+begin processing as soon as possible.
+
+Consider a graph like the following:
+
+```
+node {
+   calculator: "A"
+   input_stream: "alpha_in"
+   output_stream: "alpha"
+}
+node {
+   calculator: "B"
+   input_stream: "alpha"
+   input_stream: "foo"
+   output_stream: "beta"
+}
+```
+
+Suppose: at timestamp `T`, node `A` doesn't send a packet in its output stream
+`alpha`. Node `B` gets a packet in `foo` at timestamp `T` and is waiting for a
+packet in `alpha` at timestamp `T`. If `A` doesn't send `B` a timestamp bound
+update for `alpha`, `B` will keep waiting for a packet to arrive in `alpha`.
+Meanwhile, the packet queue of `foo` will accumulate packets at `T`, `T+1` and
+so on.
+
+To output a packet on a stream, a calculator uses the API functions
+`CalculatorContext::Outputs` and `OutputStream::Add`. To instead output a
+timestamp bound on a stream, a calculator can use the API functions
+`CalculatorContext::Outputs` and `CalculatorContext::SetNextTimestampBound`. The
+specified bound is the lowest allowable timestamp for the next packet on the
+specified output stream. When no packet is output, a calculator will typically
+do something like:
+
+```
+cc->Outputs().Tag("output_frame").SetNextTimestampBound(
+  cc->InputTimestamp().NextAllowedInStream());
+```
+
+The function `Timestamp::NextAllowedInStream` returns the successive timestamp.
+For example, `Timestamp(1).NextAllowedInStream() == Timestamp(2)`.
+
+## Propagating timestamp bounds
+
+Calculators that will be used in real-time graphs need to define output
+timestamp bounds based on input timestamp bounds in order to allow downstream
+calculators to be scheduled promptly. A common pattern is for calculators to
+output packets with the same timestamps as their input packets. In this case,
+simply outputting a packet on every call to `Calculator::Process` is sufficient
+to define output timestamp bounds.
+
+However, calculators are not required to follow this common pattern for output
+timestamps, they are only required to choose monotonically increasing output
+timestamps. As a result, certain calculators must calculate timestamp bounds
+explicitly. MediaPipe provides several tools for computing appropriate timestamp
+bound for each calculator.
+
+1\. **SetNextTimestampBound()** can be used to specify the timestamp bound, `t +
+1`, for an output stream.
+
+```
+cc->Outputs.Tag("OUT").SetNextTimestampBound(t.NextAllowedInStream());
+```
+
+Alternatively, an empty packet with timestamp `t` can be produced to specify the
+timestamp bound `t + 1`.
+
+```
+cc->Outputs.Tag("OUT").Add(Packet(), t);
+```
+
+The timestamp bound of an input stream is indicated by the packet or the empty
+packet on the input stream.
+
+```
+Timestamp bound = cc->Inputs().Tag("IN").Value().Timestamp();
+```
+
+2\. **TimestampOffset()** can be specified in order to automatically copy the
+timestamp bound from input streams to output streams.
+
+```
+cc->SetTimestampOffset(0);
+```
+
+This setting has the advantage of propagating timestamp bounds automatically,
+even when only timestamp bounds arrive and Calculator::Process is not invoked.
+
+3\. **ProcessTimestampBounds()** can be specified in order to invoke
+`Calculator::Process` for each new "settled timestamp", where the "settled
+timestamp" is the new highest timestamp below the current timestamp bounds.
+Without `ProcessTimestampBounds()`, `Calculator::Process` is invoked only with
+one or more arriving packets.
+
+```
+cc->SetProcessTimestampBounds(true);
+```
+
+This setting allows a calculator to perform its own timestamp bounds calculation
+and propagation, even when only input timestamps are updated. It can be used to
+replicate the effect of `TimestampOffset()`, but it can also be used to
+calculate a timestamp bound that takes into account additional factors.
+
+For example, in order to replicate `SetTimestampOffset(0)`, a calculator could
+do the following:
+
+```
+absl::Status Open(CalculatorContext* cc) {
+  cc->SetProcessTimestampBounds(true);
+}
+
+absl::Status Process(CalculatorContext* cc) {
+  cc->Outputs.Tag("OUT").SetNextTimestampBound(
+      cc->InputTimestamp().NextAllowedInStream());
+}
+```
+
+## Scheduling of Calculator::Open and Calculator::Close
+
+`Calculator::Open` is invoked when all required input side-packets have been
+produced. Input side-packets can be provided by the enclosing application or by
+"side-packet calculators" inside the graph. Side-packets can be specified from
+outside the graph using the API's `CalculatorGraph::Initialize` and
+`CalculatorGraph::StartRun`. Side packets can be specified by calculators within
+the graph using `CalculatorGraphConfig::OutputSidePackets` and
+`OutputSidePacket::Set`.
+
+Calculator::Close is invoked when all of the input streams have become `Done` by
+being closed or reaching timestamp bound `Timestamp::Done`.
+
+**Note:** If the graph finishes all pending calculator execution and becomes
+`Done`, before some streams become `Done`, then MediaPipe will invoke the
+remaining calls to `Calculator::Close`, so that every calculator can produce its
+final outputs.
+
+The use of `TimestampOffset` has some implications for `Calculator::Close`. A
+calculator specifying `SetTimestampOffset(0)` will by design signal that all of
+its output streams have reached `Timestamp::Done` when all of its input streams
+have reached `Timestamp::Done`, and therefore no further outputs are possible.
+This prevents such a calculator from emitting any packets during
+`Calculator::Close`. If a calculator needs to produce a summary packet during
+`Calculator::Close`, `Calculator::Process` must specify timestamp bounds such
+that at least one timestamp (such as `Timestamp::Max`) remains available during
+`Calculator::Close`. This means that such a calculator normally cannot rely upon
+`SetTimestampOffset(0)` and must instead specify timestamp bounds explicitly
+using `SetNextTimestampBounds()`.

docs/getting_started/android.md

@@ -0,0 +1,78 @@
+---
+layout: default
+title: MediaPipe on Android
+parent: Getting Started
+has_children: true
+has_toc: false
+nav_order: 1
+---
+
+# MediaPipe on Android
+{: .no_toc }
+
+1. TOC
+{:toc}
+---
+
+Please follow instructions below to build Android example apps in the supported
+MediaPipe [solutions](../solutions/solutions.md). To learn more about these
+example apps, start from [Hello World! on Android](./hello_world_android.md).
+
+To incorporate MediaPipe into Android Studio projects, see these
+[instructions](./android_solutions.md) to use the MediaPipe Android Solution
+APIs (currently in alpha) that are now available in
+[Google's Maven Repository](https://maven.google.com/web/index.html?#com.google.mediapipe).
+
+## Building Android example apps with Bazel
+
+### Prerequisite
+
+*   Install MediaPipe following these [instructions](./install.md).
+*   Setup Java Runtime.
+*   Setup Android SDK release 28.0.3 and above.
+*   Setup Android NDK version between 18 and 21.
+
+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.
+
+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
+    ```

docs/getting_started/android_archive_library.md

@@ -1,8 +1,9 @@
 ---
 layout: default
 title: MediaPipe Android Archive
-parent: Getting Started
-nav_order: 7
+parent: MediaPipe on Android
+grand_parent: Getting Started
+nav_order: 3
 ---
 
 # MediaPipe Android Archive
@@ -36,7 +37,7 @@ each project.
     load("//mediapipe/java/com/google/mediapipe:mediapipe_aar.bzl", "mediapipe_aar")
 
     mediapipe_aar(
-        name = "mp_face_detection_aar",
+        name = "mediapipe_face_detection",
         calculators = ["//mediapipe/graphs/face_detection:mobile_calculators"],
     )
     ```
@@ -44,25 +45,29 @@ each project.
 2.  Run the Bazel build command to generate the AAR.
 
     ```bash
-    bazel build -c opt --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --fat_apk_cpu=arm64-v8a,armeabi-v7a \
-        //path/to/the/aar/build/file:aar_name
+    bazel build -c opt --strip=ALWAYS \
+        --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
+        --fat_apk_cpu=arm64-v8a,armeabi-v7a \
+        //path/to/the/aar/build/file:aar_name.aar
     ```
 
-    For the face detection AAR target we made in the step 1, run:
+    For the face detection AAR target we made in step 1, run:
 
     ```bash
-    bazel build -c opt --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --fat_apk_cpu=arm64-v8a,armeabi-v7a \
-        //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mp_face_detection_aar
+    bazel build -c opt --strip=ALWAYS \
+        --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
+        --fat_apk_cpu=arm64-v8a,armeabi-v7a \
+        //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mediapipe_face_detection.aar
 
     # It should print:
-    # Target //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mp_face_detection_aar up-to-date:
-    # bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mp_face_detection_aar.aar
+    # Target //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mediapipe_face_detection.aar up-to-date:
+    # bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mediapipe_face_detection.aar
     ```
 
 3.  (Optional) Save the AAR to your preferred location.
 
     ```bash
-    cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mp_face_detection_aar.aar
+    cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mediapipe_face_detection.aar
     /absolute/path/to/your/preferred/location
     ```
 
@@ -73,7 +78,7 @@ each project.
 2.  Copy the AAR into app/libs.
 
     ```bash
-    cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mp_face_detection_aar.aar
+    cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mediapipe_face_detection.aar
     /path/to/your/app/libs/
     ```
 
@@ -85,36 +90,19 @@ each project.
     Build the MediaPipe binary graph and copy the assets into
     app/src/main/assets, e.g., for the face detection graph, you need to build
     and copy
-    [the binary graph](https://github.com/google/mediapipe/blob/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectiongpu/BUILD#L41),
-    [the tflite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/face_detection_front.tflite),
+    [the binary graph](https://github.com/google/mediapipe/blob/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectiongpu/BUILD#L41)
     and
-    [the label map](https://github.com/google/mediapipe/blob/master/mediapipe/models/face_detection_front_labelmap.txt).
+    [the face detection tflite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_short_range.tflite).
 
     ```bash
-    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/
+    bazel build -c opt mediapipe/graphs/face_detection:face_detection_mobile_gpu_binary_graph
+    cp bazel-bin/mediapipe/graphs/face_detection/face_detection_mobile_gpu.binarypb /path/to/your/app/src/main/assets/
+    cp mediapipe/modules/face_detection/face_detection_short_range.tflite /path/to/your/app/src/main/assets/
     ```
 
     ![Screenshot](../images/mobile/assets_location.png)
 
-4.  Make app/src/main/jniLibs and copy OpenCV JNI libraries into
-    app/src/main/jniLibs.
-
-    MediaPipe depends on OpenCV, you will need to copy the precompiled OpenCV so
-    files into app/src/main/jniLibs. You can download the official OpenCV
-    Android SDK from
-    [here](https://github.com/opencv/opencv/releases/download/3.4.3/opencv-3.4.3-android-sdk.zip)
-    and run:
-
-    ```bash
-    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)
-
-5.  Modify app/build.gradle to add MediaPipe dependencies and MediaPipe AAR.
+4.  Modify app/build.gradle to add MediaPipe dependencies and MediaPipe AAR.
 
     ```
     dependencies {
@@ -125,10 +113,9 @@ each project.
         androidTestImplementation 'androidx.test.ext:junit:1.1.0'
         androidTestImplementation 'androidx.test.espresso:espresso-core:3.1.1'
         // MediaPipe deps
-        implementation 'com.google.flogger:flogger:0.3.1'
-        implementation 'com.google.flogger:flogger-system-backend:0.3.1'
-        implementation 'com.google.code.findbugs:jsr305:3.0.2'
-        implementation 'com.google.guava:guava:27.0.1-android'
+        implementation 'com.google.flogger:flogger:latest.release'
+        implementation 'com.google.flogger:flogger-system-backend:latest.release'
+        implementation 'com.google.code.findbugs:jsr305:latest.release'
         implementation 'com.google.guava:guava:27.0.1-android'
         implementation 'com.google.protobuf:protobuf-java:3.11.4'
         // CameraX core library
@@ -136,10 +123,14 @@ each project.
         implementation "androidx.camera:camera-core:$camerax_version"
         implementation "androidx.camera:camera-camera2:$camerax_version"
         implementation "androidx.camera:camera-lifecycle:$camerax_version"
+        // AutoValue
+        def auto_value_version = "1.8.1"
+        implementation "com.google.auto.value:auto-value-annotations:$auto_value_version"
+        annotationProcessor "com.google.auto.value:auto-value:$auto_value_version"
     }
     ```
 
-6.  Follow our Android app examples to use MediaPipe in Android Studio for your
+5.  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

docs/getting_started/android_solutions.md

@@ -0,0 +1,79 @@
+---
+layout: default
+title: Android Solutions
+parent: MediaPipe on Android
+grand_parent: Getting Started
+nav_order: 2
+---
+
+# Android Solution APIs
+{: .no_toc }
+
+1. TOC
+{:toc}
+---
+
+Please follow instructions below to use the MediaPipe Solution APIs in Android
+Studio projects and build the Android example apps in the supported MediaPipe
+[solutions](../solutions/solutions.md).
+
+## Integrate MediaPipe Android Solutions in Android Studio
+
+MediaPipe Android Solution APIs (currently in alpha) are now available in
+[Google's Maven Repository](https://maven.google.com/web/index.html?#com.google.mediapipe).
+To incorporate MediaPipe Android Solutions into an Android Studio project, add
+the following into the project's Gradle dependencies:
+
+```
+dependencies {
+    // MediaPipe solution-core is the foundation of any MediaPipe solutions.
+    implementation 'com.google.mediapipe:solution-core:latest.release'
+    // Optional: MediaPipe Hands solution.
+    implementation 'com.google.mediapipe:hands:latest.release'
+    // Optional: MediaPipe FaceMesh solution.
+    implementation 'com.google.mediapipe:facemesh:latest.release'
+    // MediaPipe deps
+    implementation 'com.google.flogger:flogger:latest.release'
+    implementation 'com.google.flogger:flogger-system-backend:latest.release'
+    implementation 'com.google.guava:guava:27.0.1-android'
+    implementation 'com.google.protobuf:protobuf-java:3.11.4'
+    // CameraX core library
+    def camerax_version = "1.0.0-beta10"
+    implementation "androidx.camera:camera-core:$camerax_version"
+    implementation "androidx.camera:camera-camera2:$camerax_version"
+    implementation "androidx.camera:camera-lifecycle:$camerax_version"
+}
+```
+
+See the detailed solutions API usage examples for different use cases in the
+solution example apps'
+[source code](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/solutions).
+If the prebuilt maven packages are not sufficient, building the MediaPipe
+Android archive library locally by following these
+[instructions](./android_archive_library.md).
+
+## Build solution example apps in Android Studio
+
+1.  Open Android Studio Arctic Fox on Linux, macOS, or Windows.
+
+2.  Import mediapipe/examples/android/solutions directory into Android Studio.
+
+    ![Screenshot](../images/import_mp_android_studio_project.png)
+
+3.  For Windows users, run `create_win_symlinks.bat` as administrator to create
+    res directory symlinks.
+
+    ![Screenshot](../images/run_create_win_symlinks.png)
+
+4.  Select "File" -> "Sync Project with Gradle Files" to sync project.
+
+5.  Run solution example app in Android Studio.
+
+    ![Screenshot](../images/run_android_solution_app.png)
+
+6.  (Optional) Run solutions on CPU.
+
+    MediaPipe solution example apps run the pipeline and the model inference on
+    GPU by default. If needed, for example to run the apps on Android Emulator,
+    set the `RUN_ON_GPU` boolean variable to `false` in the app's
+    MainActivity.java to run the pipeline and the model inference on CPU.

docs/getting_started/building_examples.md

@@ -2,7 +2,7 @@
 layout: default
 title: Building MediaPipe Examples
 parent: Getting Started
-nav_order: 2
+nav_exclude: true
 ---
 
 # Building MediaPipe Examples
@@ -12,464 +12,22 @@ nav_order: 2
 {:toc}
 ---
 
-## Android
+### Android
 
-### Prerequisite
+Please see these [instructions](./android.md).
 
-*   Java Runtime.
-*   Android SDK release 28.0.3 and above.
-*   Android NDK r18b and above.
+### iOS
 
-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.
+Please see these [instructions](./ios.md).
 
-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.
+### Python
 
-```bash
-export ANDROID_HOME=<path to the Android SDK>
-export ANDROID_NDK_HOME=<path to the Android NDK>
-```
+Please see these [instructions](./python.md).
 
-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.
+### JavaScript
 
-Please verify all the necessary packages are installed.
+Please see these [instructions](./javascript.md).
 
-*   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
+### C++
 
-### 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
-
-MediaPipe Python package is available on
-[PyPI](https://pypi.org/project/mediapipe/), and can be installed simply by `pip
-install mediapipe` on Linux and macOS, as described in, for instance,
-[Python section in MediaPipe Pose](../solutions/pose.md#python) and in this
-[colab](https://mediapipe.page.link/pose_py_colab).
-
-Follow the steps below only if you have local changes and need to build the
-Python package from source. Otherwise, we strongly encourage our users to simply
-run `pip install mediapipe`, more convenient and much faster.
-
-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
-    ```
-
-3.  Activate a Python virtual environment.
-
-    ```bash
-    $ python3 -m venv mp_env && source mp_env/bin/activate
-    ```
-
-4.  In the virtual environment, go to the MediaPipe repo directory.
-
-5.  Install the required Python packages.
-
-    ```bash
-    (mp_env)mediapipe$ pip3 install -r requirements.txt
-    ```
-
-6.  Generate and install MediaPipe package.
-
-    ```bash
-    (mp_env)mediapipe$ python3 setup.py gen_protos
-    (mp_env)mediapipe$ python3 setup.py install --link-opencv
-    ```
+Please see these [instructions](./cpp.md).

docs/getting_started/cpp.md

@@ -0,0 +1,62 @@
+---
+layout: default
+title: MediaPipe in C++
+parent: Getting Started
+has_children: true
+has_toc: false
+nav_order: 5
+---
+
+# MediaPipe in C++
+{: .no_toc }
+
+1. TOC
+{:toc}
+---
+
+Please follow instructions below to build C++ command-line example apps in the
+supported MediaPipe [solutions](../solutions/solutions.md). To learn more about
+these example apps, start from [Hello World! in C++](./hello_world_cpp.md).
+
+## Building C++ command-line example apps
+
+### 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.

docs/getting_started/gpu_support.md

@@ -2,7 +2,7 @@
 layout: default
 title: GPU Support
 parent: Getting Started
-nav_order: 6
+nav_order: 7
 ---
 
 # GPU Support

docs/getting_started/hello_world_android.md

@@ -1,8 +1,9 @@
 ---
 layout: default
 title: Hello World! on Android
-parent: Getting Started
-nav_order: 3
+parent: MediaPipe on Android
+grand_parent: Getting Started
+nav_order: 1
 ---
 
 # Hello World! on Android
@@ -30,8 +31,8 @@ stream on an Android device.
 
 ## Setup
 
-1.  Install MediaPipe on your system, see [MediaPipe installation guide] for
-    details.
+1.  Install MediaPipe on your system, see
+    [MediaPipe installation guide](./install.md) for details.
 2.  Install Android Development SDK and Android NDK. See how to do so also in
     [MediaPipe installation guide].
 3.  Enable [developer options] on your Android device.
@@ -58,7 +59,7 @@ 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"
@@ -496,7 +497,7 @@ CameraHelper.CameraFacing cameraFacing =
     applicationInfo.metaData.getBoolean("cameraFacingFront", false)
         ? CameraHelper.CameraFacing.FRONT
         : CameraHelper.CameraFacing.BACK;
-cameraHelper.startCamera(this, cameraFacing, /*surfaceTexture=*/ null);
+cameraHelper.startCamera(this, cameraFacing, /*unusedSurfaceTexture=*/ null);
 ```
 
 At this point, the application should build successfully. However, when you run
@@ -769,7 +770,6 @@ If you ran into any issues, please see the full code of the tutorial
 [`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
 [`FrameProcessor`]:https://github.com/google/mediapipe/tree/master/mediapipe/java/com/google/mediapipe/components/FrameProcessor.java
-[MediaPipe installation guide]:./install.md
 [`PermissionHelper`]: https://github.com/google/mediapipe/tree/master/mediapipe/java/com/google/mediapipe/components/PermissionHelper.java
 [`SurfaceHolder.Callback`]:https://developer.android.com/reference/android/view/SurfaceHolder.Callback.html
 [`SurfaceView`]:https://developer.android.com/reference/android/view/SurfaceView

docs/getting_started/hello_world_cpp.md

@@ -1,11 +1,12 @@
 ---
 layout: default
-title: Hello World! on Desktop (C++)
-parent: Getting Started
-nav_order: 5
+title: Hello World! in C++
+parent: MediaPipe in C++
+grand_parent: Getting Started
+nav_order: 1
 ---
 
-# Hello World! on Desktop (C++)
+# Hello World! in C++
 {: .no_toc }
 
 1. TOC
@@ -43,7 +44,7 @@ nav_order: 5
     `PrintHelloWorld()` function, defined in a [`CalculatorGraphConfig`] proto.
 
     ```C++
-    ::mediapipe::Status PrintHelloWorld() {
+    absl::Status PrintHelloWorld() {
       // Configures a simple graph, which concatenates 2 PassThroughCalculators.
       CalculatorGraphConfig config = ParseTextProtoOrDie<CalculatorGraphConfig>(R"(
         input_stream: "in"

docs/getting_started/hello_world_ios.md

@@ -1,8 +1,9 @@
 ---
 layout: default
 title: Hello World! on iOS
-parent: Getting Started
-nav_order: 4
+parent: MediaPipe on iOS
+grand_parent: Getting Started
+nav_order: 1
 ---
 
 # Hello World! on iOS
@@ -30,8 +31,8 @@ stream on an iOS device.
 
 ## Setup
 
-1.  Install MediaPipe on your system, see [MediaPipe installation guide] for
-    details.
+1.  Install MediaPipe on your system, see
+    [MediaPipe installation guide](./install.md) for details.
 2.  Setup your iOS device for development.
 3.  Setup [Bazel] on your system to build and deploy the iOS app.
 
@@ -112,6 +113,10 @@ bazel to build the iOS application. The content of the
 5.  `Main.storyboard` and `Launch.storyboard`
 6.  `Assets.xcassets` directory.
 
+Note: In newer versions of Xcode, you may see additional files `SceneDelegate.h`
+and `SceneDelegate.m`. Make sure to copy them too and add them to the `BUILD`
+file mentioned below.
+
 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
@@ -193,8 +198,7 @@ bazel build -c opt --config=ios_arm64 mediapipe/examples/ios/helloworld:HelloWor
 
 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](./ios.md) iOS MediaPipe apps.
 
 Open the application on your device. Since it is empty, it should display a
 blank white screen.
@@ -247,6 +251,12 @@ We need to get frames from the `_cameraSource` into our application
 `MPPInputSourceDelegate`. So our application `ViewController` can be a delegate
 of `_cameraSource`.
 
+Update the interface definition of `ViewController` accordingly:
+
+```
+@interface ViewController () <MPPInputSourceDelegate>
+```
+
 To handle camera setup and process incoming frames, we should use a queue
 different from the main queue. Add the following to the implementation block of
 the `ViewController`:
@@ -288,6 +298,12 @@ utility called `MPPLayerRenderer` to display images on the screen. This utility
 can be used to display `CVPixelBufferRef` objects, which is the type of the
 images provided by `MPPCameraInputSource` to its delegates.
 
+In `ViewController.m`, add the following import line:
+
+```
+#import "mediapipe/objc/MPPLayerRenderer.h"
+```
+
 To display images of the screen, we need to add a new `UIView` object called
 `_liveView` to the `ViewController`.
 
@@ -411,6 +427,12 @@ Objective-C++.
 
 ### Use the graph in `ViewController`
 
+In `ViewController.m`, add the following import line:
+
+```
+#import "mediapipe/objc/MPPGraph.h"
+```
+
 Declare a static constant with the name of the graph, the input stream and the
 output stream:
 
@@ -492,6 +514,9 @@ in our app:
     if (![self.mediapipeGraph startWithError:&error]) {
       NSLog(@"Failed to start graph: %@", error);
     }
+    else if (![self.mediapipeGraph waitUntilIdleWithError:&error]) {
+      NSLog(@"Failed to complete graph initial run: %@", error);
+    }
 
     dispatch_async(_videoQueue, ^{
       [_cameraSource start];
@@ -500,8 +525,9 @@ 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 and wait
+until completion, 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
@@ -545,6 +571,12 @@ method to receive packets on this output stream and display them on the screen:
 }
 ```
 
+Update the interface definition of `ViewController` with `MPPGraphDelegate`:
+
+```
+@interface ViewController () <MPPGraphDelegate, MPPInputSourceDelegate>
+```
+
 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!
 
@@ -556,6 +588,5 @@ 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/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)
+[common]:https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/common
+[helloworld]:https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/helloworld

docs/getting_started/install.md

@@ -2,7 +2,7 @@
 layout: default
 title: Installation
 parent: Getting Started
-nav_order: 1
+nav_order: 6
 ---
 
 # Installation
@@ -23,139 +23,209 @@ 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`.
 
-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
 
-1.  Checkout MediaPipe repository.
+1.  Install Bazelisk.
+
+    Follow the official
+    [Bazel documentation](https://docs.bazel.build/versions/master/install-bazelisk.html)
+    to install Bazelisk.
+
+2.  Checkout MediaPipe repository.
 
     ```bash
+    $ cd $HOME
     $ git clone https://github.com/google/mediapipe.git
 
     # Change directory into MediaPipe root directory
     $ cd mediapipe
     ```
 
-2.  Install Bazel.
-
-    Follow the official
-    [Bazel documentation](https://docs.bazel.build/versions/master/install-ubuntu.html)
-    to install Bazel 3.4 or higher.
-
-    For Nvidia Jetson and Raspberry Pi devices with ARM Ubuntu, Bazel needs to
-    be built from source.
-
-    ```bash
-    # For Bazel 3.4.0
-    wget https://github.com/bazelbuild/bazel/releases/download/3.4.0/bazel-3.4.0-dist.zip
-    sudo apt-get install build-essential openjdk-8-jdk python zip unzip
-    unzip bazel-3.4.0-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
-    libraries. FFmpeg will be installed via libopencv-video-dev.
+    **Option 1**. Use package manager tool to install the pre-compiled OpenCV
+    libraries. FFmpeg will be installed via `libopencv-video-dev`.
 
-    Note: Debian 9 and Ubuntu 16.04 provide OpenCV 2.4.9. You may want to take
-    option 2 or 3 to install OpenCV 3 or above.
+    OS                   | OpenCV
+    -------------------- | ------
+    Debian 9 (stretch)   | 2.4
+    Debian 10 (buster)   | 3.2
+    Debian 11 (bullseye) | 4.5
+    Ubuntu 16.04 LTS     | 2.4
+    Ubuntu 18.04 LTS     | 3.2
+    Ubuntu 20.04 LTS     | 4.2
+    Ubuntu 20.04 LTS     | 4.2
+    Ubuntu 21.04         | 4.5
 
     ```bash
-    $ sudo apt-get install libopencv-core-dev libopencv-highgui-dev \
-                           libopencv-calib3d-dev libopencv-features2d-dev \
-                           libopencv-imgproc-dev libopencv-video-dev
+    $ sudo apt-get install -y \
+        libopencv-core-dev \
+        libopencv-highgui-dev \
+        libopencv-calib3d-dev \
+        libopencv-features2d-dev \
+        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:
+    MediaPipe's [`opencv_linux.BUILD`] and [`WORKSPACE`] are already configured
+    for OpenCV 2/3 and should work correctly on any architecture:
 
     ```bash
-    sed -i "s/x86_64-linux-gnu/aarch64-linux-gnu/g" third_party/opencv_linux.BUILD
+    # WORKSPACE
+    new_local_repository(
+      name = "linux_opencv",
+      build_file = "@//third_party:opencv_linux.BUILD",
+      path = "/usr",
+    )
+
+    # opencv_linux.BUILD for OpenCV 2/3 installed from Debian package
+    cc_library(
+      name = "opencv",
+      linkopts = [
+        "-l:libopencv_core.so",
+        "-l:libopencv_calib3d.so",
+        "-l:libopencv_features2d.so",
+        "-l:libopencv_highgui.so",
+        "-l:libopencv_imgcodecs.so",
+        "-l:libopencv_imgproc.so",
+        "-l:libopencv_video.so",
+        "-l:libopencv_videoio.so",
+      ],
+    )
     ```
 
-    Option 2. Run [`setup_opencv.sh`] to automatically build OpenCV from source
-    and modify MediaPipe's OpenCV config.
+    For OpenCV 4 you need to modify [`opencv_linux.BUILD`] taking into account
+    current architecture:
 
-    Option 3. Follow OpenCV's
+    ```bash
+    # WORKSPACE
+    new_local_repository(
+      name = "linux_opencv",
+      build_file = "@//third_party:opencv_linux.BUILD",
+      path = "/usr",
+    )
+
+    # opencv_linux.BUILD for OpenCV 4 installed from Debian package
+    cc_library(
+      name = "opencv",
+      hdrs = glob([
+        # Uncomment according to your multiarch value (gcc -print-multiarch):
+        #  "include/aarch64-linux-gnu/opencv4/opencv2/cvconfig.h",
+        #  "include/arm-linux-gnueabihf/opencv4/opencv2/cvconfig.h",
+        #  "include/x86_64-linux-gnu/opencv4/opencv2/cvconfig.h",
+        "include/opencv4/opencv2/**/*.h*",
+      ]),
+      includes = [
+        # Uncomment according to your multiarch value (gcc -print-multiarch):
+        #  "include/aarch64-linux-gnu/opencv4/",
+        #  "include/arm-linux-gnueabihf/opencv4/",
+        #  "include/x86_64-linux-gnu/opencv4/",
+        "include/opencv4/",
+      ],
+      linkopts = [
+        "-l:libopencv_core.so",
+        "-l:libopencv_calib3d.so",
+        "-l:libopencv_features2d.so",
+        "-l:libopencv_highgui.so",
+        "-l:libopencv_imgcodecs.so",
+        "-l:libopencv_imgproc.so",
+        "-l:libopencv_video.so",
+        "-l:libopencv_videoio.so",
+      ],
+    )
+    ```
+
+    **Option 2**. Run [`setup_opencv.sh`] to automatically build OpenCV from
+    source and modify MediaPipe's OpenCV config. This option will do all steps
+    defined in Option 3 automatically.
+
+    **Option 3**. Follow OpenCV's
     [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`], [`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:
+    You may need to modify [`WORKSPACE`] and [`opencv_linux.BUILD`] to point
+    MediaPipe to your own OpenCV libraries. Assume OpenCV would be installed to
+    `/usr/local/` which is recommended by default.
+
+    OpenCV 2/3 setup:
 
     ```bash
+    # WORKSPACE
     new_local_repository(
-        name = "linux_opencv",
-        build_file = "@//third_party:opencv_linux.BUILD",
-        path = "/usr/local",
+      name = "linux_opencv",
+      build_file = "@//third_party:opencv_linux.BUILD",
+      path = "/usr/local",
     )
 
+    # opencv_linux.BUILD for OpenCV 2/3 installed to /usr/local
+    cc_library(
+      name = "opencv",
+      linkopts = [
+        "-L/usr/local/lib",
+        "-l:libopencv_core.so",
+        "-l:libopencv_calib3d.so",
+        "-l:libopencv_features2d.so",
+        "-l:libopencv_highgui.so",
+        "-l:libopencv_imgcodecs.so",
+        "-l:libopencv_imgproc.so",
+        "-l:libopencv_video.so",
+        "-l:libopencv_videoio.so",
+      ],
+    )
+    ```
+
+    OpenCV 4 setup:
+
+    ```bash
+    # WORKSPACE
     new_local_repository(
-        name = "linux_ffmpeg",
-        build_file = "@//third_party:ffmpeg_linux.BUILD",
-        path = "/usr/local",
+      name = "linux_opencv",
+      build_file = "@//third_party:opencv_linux.BUILD",
+      path = "/usr/local",
     )
 
+    # opencv_linux.BUILD for OpenCV 4 installed to /usr/local
     cc_library(
-        name = "opencv",
-        srcs = glob(
-            [
-                "lib/libopencv_core.so",
-                "lib/libopencv_highgui.so",
-                "lib/libopencv_imgcodecs.so",
-                "lib/libopencv_imgproc.so",
-                "lib/libopencv_video.so",
-                "lib/libopencv_videoio.so",
-            ],
-        ),
-        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"],
+      name = "opencv",
+      hdrs = glob([
+        "include/opencv4/opencv2/**/*.h*",
+      ]),
+      includes = [
+        "include/opencv4/",
+      ],
+      linkopts = [
+        "-L/usr/local/lib",
+        "-l:libopencv_core.so",
+        "-l:libopencv_calib3d.so",
+        "-l:libopencv_features2d.so",
+        "-l:libopencv_highgui.so",
+        "-l:libopencv_imgcodecs.so",
+        "-l:libopencv_imgproc.so",
+        "-l:libopencv_video.so",
+        "-l:libopencv_videoio.so",
+      ],
+    )
+    ```
+
+    Current FFmpeg setup is defined in [`ffmpeg_linux.BUILD`] and should work
+    for any architecture:
+
+    ```bash
+    # WORKSPACE
+    new_local_repository(
+      name = "linux_ffmpeg",
+      build_file = "@//third_party:ffmpeg_linux.BUILD",
+      path = "/usr"
     )
 
+    # ffmpeg_linux.BUILD for FFmpeg installed from Debian package
     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"],
+      name = "libffmpeg",
+      linkopts = [
+        "-l:libavcodec.so",
+        "-l:libavformat.so",
+        "-l:libavutil.so",
+      ],
     )
     ```
 
@@ -174,7 +244,7 @@ apps, see these [instructions](./building_examples.md#ios).
     # when building GPU examples.
     ```
 
-5.  Run the [Hello World desktop example](./hello_world_desktop.md).
+5.  Run the [Hello World! in C++ example](./hello_world_cpp.md).
 
     ```bash
     $ export GLOG_logtostderr=1
@@ -208,7 +278,13 @@ build issues.
 
 **Disclaimer**: Running MediaPipe on CentOS is experimental.
 
-1.  Checkout MediaPipe repository.
+1.  Install Bazelisk.
+
+    Follow the official
+    [Bazel documentation](https://docs.bazel.build/versions/master/install-bazelisk.html)
+    to install Bazelisk.
+
+2.  Checkout MediaPipe repository.
 
     ```bash
     $ git clone https://github.com/google/mediapipe.git
@@ -217,12 +293,6 @@ build issues.
     $ cd mediapipe
     ```
 
-2.  Install Bazel.
-
-    Follow the official
-    [Bazel documentation](https://docs.bazel.build/versions/master/install-redhat.html)
-    to install Bazel 3.4 or higher.
-
 3.  Install OpenCV.
 
     Option 1. Use package manager tool to install the pre-compiled version.
@@ -304,7 +374,7 @@ build issues.
     )
     ```
 
-4.  Run the [Hello World desktop example](./hello_world_desktop.md).
+4.  Run the [Hello World! in C++ example](./hello_world_cpp.md).
 
     ```bash
     $ export GLOG_logtostderr=1
@@ -337,7 +407,13 @@ build issues.
     *   Install [Xcode](https://developer.apple.com/xcode/) and its Command Line
         Tools by `xcode-select --install`.
 
-2.  Checkout MediaPipe repository.
+2.  Install Bazelisk.
+
+    Follow the official
+    [Bazel documentation](https://docs.bazel.build/versions/master/install-bazelisk.html)
+    to install Bazelisk.
+
+3.  Checkout MediaPipe repository.
 
     ```bash
     $ git clone https://github.com/google/mediapipe.git
@@ -345,23 +421,10 @@ build issues.
     $ cd mediapipe
     ```
 
-3.  Install Bazel.
-
-    Option 1. Use package manager tool to install Bazel
-
-    ```bash
-    $ brew install bazel
-    # Run 'bazel version' to check version of bazel
-    ```
-
-    Option 2. Follow the official
-    [Bazel documentation](https://docs.bazel.build/versions/master/install-os-x.html#install-with-installer-mac-os-x)
-    to install Bazel 3.4 or higher.
-
 4.  Install OpenCV and FFmpeg.
 
     Option 1. Use HomeBrew package manager tool to install the pre-compiled
-    OpenCV 3.4.5 libraries. FFmpeg will be installed via OpenCV.
+    OpenCV 3 libraries. FFmpeg will be installed via OpenCV.
 
     ```bash
     $ brew install opencv@3
@@ -439,7 +502,7 @@ build issues.
     $ pip3 install --user six
     ```
 
-6.  Run the [Hello World desktop example](./hello_world_desktop.md).
+6.  Run the [Hello World! in C++ example](./hello_world_cpp.md).
 
     ```bash
     $ export GLOG_logtostderr=1
@@ -492,29 +555,36 @@ next section.
 
 4.  Install Visual C++ Build Tools 2019 and WinSDK
 
-    Go to https://visualstudio.microsoft.com/visual-cpp-build-tools, download
-    build tools, and install Microsoft Visual C++ 2019 Redistributable and
-    Microsoft Build Tools 2019.
+    Go to
+    [the VisualStudio website](https://visualstudio.microsoft.com/visual-cpp-build-tools),
+    download build tools, and install Microsoft Visual C++ 2019 Redistributable
+    and Microsoft Build Tools 2019.
 
     Download the WinSDK from
-    https://developer.microsoft.com/en-us/windows/downloads/windows-10-sdk/ and
-    install.
+    [the official MicroSoft website](https://developer.microsoft.com/en-us/windows/downloads/windows-10-sdk/)
+    and install.
 
-5.  Install Bazel and add the location of the Bazel executable to the `%PATH%`
-    environment variable.
+5.  Install Bazel or Bazelisk and add the location of the Bazel executable to
+    the `%PATH%` environment variable.
 
-    Follow the official
-    [Bazel documentation](https://docs.bazel.build/versions/master/install-windows.html)
-    to install Bazel 3.4 or higher.
+    Option 1. Follow
+    [the official Bazel documentation](https://docs.bazel.build/versions/master/install-windows.html)
+    to install Bazel 3.7.2 or higher.
 
-6.  Set Bazel variables.
+    Option 2. Follow the official
+    [Bazel documentation](https://docs.bazel.build/versions/master/install-bazelisk.html)
+    to install Bazelisk.
+
+6.  Set Bazel variables. Learn more details about
+    ["Build on Windows"](https://docs.bazel.build/versions/master/windows.html#build-c-with-msvc)
+    in the Bazel official documentation.
 
     ```
-    # Find the exact paths and version numbers from your local version.
+    # Please find the exact paths and version numbers from your local version.
     C:\> set BAZEL_VS=C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools
     C:\> set BAZEL_VC=C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC
-    C:\> set BAZEL_VC_FULL_VERSION=14.25.28610
-    C:\> set BAZEL_WINSDK_FULL_VERSION=10.1.18362.1
+    C:\> set BAZEL_VC_FULL_VERSION=<Your local VC version>
+    C:\> set BAZEL_WINSDK_FULL_VERSION=<Your local WinSDK version>
     ```
 
 7.  Checkout MediaPipe repository.
@@ -540,7 +610,7 @@ next section.
     )
     ```
 
-9.  Run the [Hello World desktop example](./hello_world_desktop.md).
+9.  Run the [Hello World! in C++ example](./hello_world_cpp.md).
 
     Note: For building MediaPipe on Windows, please add `--action_env
     PYTHON_BIN_PATH="C://path//to//python.exe"` to the build command.
@@ -601,19 +671,11 @@ cameras. Alternatively, you use a video file as input.
     username@DESKTOP-TMVLBJ1:~$ sudo apt-get update && sudo apt-get install -y build-essential git python zip adb openjdk-8-jdk
     ```
 
-5.  Install Bazel.
+5.  Install Bazelisk.
 
-    ```bash
-    username@DESKTOP-TMVLBJ1:~$ curl -sLO --retry 5 --retry-max-time 10 \
-    https://storage.googleapis.com/bazel/3.4.0/release/bazel-3.4.0-installer-linux-x86_64.sh && \
-    sudo mkdir -p /usr/local/bazel/3.4.0 && \
-    chmod 755 bazel-3.4.0-installer-linux-x86_64.sh && \
-    sudo ./bazel-3.4.0-installer-linux-x86_64.sh --prefix=/usr/local/bazel/3.4.0 && \
-    source /usr/local/bazel/3.4.0/lib/bazel/bin/bazel-complete.bash
-
-    username@DESKTOP-TMVLBJ1:~$ /usr/local/bazel/3.4.0/lib/bazel/bin/bazel version && \
-    alias bazel='/usr/local/bazel/3.4.0/lib/bazel/bin/bazel'
-    ```
+    Follow the official
+    [Bazel documentation](https://docs.bazel.build/versions/master/install-bazelisk.html)
+    to install Bazelisk.
 
 6.  Checkout MediaPipe repository.
 
@@ -673,7 +735,7 @@ cameras. Alternatively, you use a video file as input.
     )
     ```
 
-8.  Run the [Hello World desktop example](./hello_world_desktop.md).
+8.  Run the [Hello World! in C++ example](./hello_world_cpp.md).
 
     ```bash
     username@DESKTOP-TMVLBJ1:~/mediapipe$ export GLOG_logtostderr=1
@@ -729,12 +791,12 @@ This will use a Docker image that will isolate mediapipe's installation from the
     # Successfully tagged mediapipe:latest
     ```
 
-3.  Run the [Hello World desktop example](./hello_world_desktop.md).
+3.  Run the [Hello World! in C++ example](./hello_world_cpp.md).
 
     ```bash
     $ docker run -it --name mediapipe mediapipe:latest
 
-    root@bca08b91ff63:/mediapipe# GLOG_logtostderr=1 bazel run --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/hello_world:hello_world
+    root@bca08b91ff63:/mediapipe# GLOG_logtostderr=1 bazelisk run --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/hello_world:hello_world
 
     # Should print:
     # Hello World!
@@ -761,7 +823,7 @@ common build issues.
     root@bca08b91ff63:/mediapipe# bash ./setup_android_sdk_and_ndk.sh
 
     # Should print:
-    # Android NDK is now installed. Consider setting $ANDROID_NDK_HOME environment variable to be /root/Android/Sdk/ndk-bundle/android-ndk-r18b
+    # Android NDK is now installed. Consider setting $ANDROID_NDK_HOME environment variable to be /root/Android/Sdk/ndk-bundle/android-ndk-r19c
     # Set android_ndk_repository and android_sdk_repository in WORKSPACE
     # Done
 

docs/getting_started/ios.md

@@ -0,0 +1,222 @@
+---
+layout: default
+title: MediaPipe on iOS
+parent: Getting Started
+has_children: true
+has_toc: false
+nav_order: 2
+---
+
+# MediaPipe on iOS
+{: .no_toc }
+
+1. TOC
+{:toc}
+---
+
+Please follow instructions below to build iOS example apps in the supported
+MediaPipe [solutions](../solutions/solutions.md). To learn more about these
+example apps, start from, start from
+[Hello World! on iOS](./hello_world_ios.md).
+
+## Building iOS example apps
+
+### Prerequisite
+
+1.  Install MediaPipe following these [instructions](./install.md).
+
+2.  Install [Xcode](https://developer.apple.com/xcode/), then install the
+    Command Line Tools using:
+
+    ```bash
+    xcode-select --install
+    ```
+
+3.  Install [Bazel](https://bazel.build/).
+
+    We recommend using [Homebrew](https://brew.sh/) to get the latest version.
+
+4.  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
+    ```
+
+5.  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.

docs/getting_started/javascript.md

@@ -0,0 +1,102 @@
+---
+layout: default
+title: MediaPipe in JavaScript
+parent: Getting Started
+nav_order: 4
+---
+
+# MediaPipe in JavaScript
+{: .no_toc }
+
+1. TOC
+{:toc}
+---
+
+## Ready-to-use JavaScript Solutions
+
+MediaPipe currently offers the following solutions:
+
+Solution                    | NPM Package                             | Example
+--------------------------- | --------------------------------------- | -------
+[Face Mesh][F-pg]           | [@mediapipe/face_mesh][F-npm]           | [mediapipe.dev/demo/face_mesh][F-demo]
+[Face Detection][Fd-pg]     | [@mediapipe/face_detection][Fd-npm]     | [mediapipe.dev/demo/face_detection][Fd-demo]
+[Hands][H-pg]               | [@mediapipe/hands][H-npm]               | [mediapipe.dev/demo/hands][H-demo]
+[Holistic][Ho-pg]           | [@mediapipe/holistic][Ho-npm]           | [mediapipe.dev/demo/holistic][Ho-demo]
+[Objectron][Ob-pg]          | [@mediapipe/objectron][Ob-npm]          | [mediapipe.dev/demo/objectron][Ob-demo]
+[Pose][P-pg]                | [@mediapipe/pose][P-npm]                | [mediapipe.dev/demo/pose][P-demo]
+[Selfie Segmentation][S-pg] | [@mediapipe/selfie_segmentation][S-npm] | [mediapipe.dev/demo/selfie_segmentation][S-demo]
+
+Click on a solution link above for more information, including API and code
+snippets.
+
+### Supported plaforms:
+
+| Browser | Platform                | Notes                                  |
+| ------- | ----------------------- | -------------------------------------- |
+| Chrome  | Android / Windows / Mac | Pixel 4 and older unsupported. Fuschia |
+|         |                         | unsupported.                           |
+| Chrome  | iOS                     | Camera unavailable in Chrome on iOS.   |
+| Safari  | iPad/iPhone/Mac         | iOS and Safari on iPad / iPhone /      |
+|         |                         | MacBook                                |
+
+The quickest way to get acclimated is to look at the examples above. Each demo
+has a link to a [CodePen][codepen] so that you can edit the code and try it
+yourself. We have included a number of utility packages to help you get started:
+
+*   [@mediapipe/drawing_utils][draw-npm] - Utilities to draw landmarks and
+    connectors.
+*   [@mediapipe/camera_utils][cam-npm] - Utilities to operate the camera.
+*   [@mediapipe/control_utils][ctrl-npm] - Utilities to show sliders and FPS
+    widgets.
+
+Note: See these demos and more at [MediaPipe on CodePen][codepen]
+
+All of these solutions are staged in [NPM][npm]. You can install any package
+locally with `npm install`. Example:
+
+```
+npm install @mediapipe/holistic.
+```
+
+If you would rather not stage these locally, you can rely on a CDN (e.g.,
+[jsDelivr](https://www.jsdelivr.com/)). This will allow you to add scripts
+directly to your HTML:
+
+```
+<head>
+<script src="https://cdn.jsdelivr.net/npm/@mediapipe/[email protected]/drawing_utils.js" crossorigin="anonymous"></script>
+<script src="https://cdn.jsdelivr.net/npm/@mediapipe/[email protected]/holistic.js" crossorigin="anonymous"></script>
+</head>
+```
+
+Note: You can specify version numbers to both NPM and jsdelivr. They are
+structured as `<major>.<minor>.<build>`. To prevent breaking changes from
+affecting your work, restrict your request to a `<minor>` number. e.g.,
+`@mediapipe/[email protected]`.
+
+[Ho-pg]: ../solutions/holistic#javascript-solution-api
+[F-pg]: ../solutions/face_mesh#javascript-solution-api
+[Fd-pg]: ../solutions/face_detection#javascript-solution-api
+[H-pg]: ../solutions/hands#javascript-solution-api
+[Ob-pg]: ../solutions/objectron#javascript-solution-api
+[P-pg]: ../solutions/pose#javascript-solution-api
+[S-pg]: ../solutions/selfie_segmentation#javascript-solution-api
+[Ho-npm]: https://www.npmjs.com/package/@mediapipe/holistic
+[F-npm]: https://www.npmjs.com/package/@mediapipe/face_mesh
+[Fd-npm]: https://www.npmjs.com/package/@mediapipe/face_detection
+[H-npm]: https://www.npmjs.com/package/@mediapipe/hands
+[Ob-npm]: https://www.npmjs.com/package/@mediapipe/objectron
+[P-npm]: https://www.npmjs.com/package/@mediapipe/pose
+[S-npm]: https://www.npmjs.com/package/@mediapipe/selfie_segmentation
+[draw-npm]: https://www.npmjs.com/package/@mediapipe/drawing_utils
+[cam-npm]: https://www.npmjs.com/package/@mediapipe/camera_utils
+[ctrl-npm]: https://www.npmjs.com/package/@mediapipe/control_utils
+[Ho-demo]: https://mediapipe.dev/demo/holistic
+[F-demo]: https://mediapipe.dev/demo/face_mesh
+[Fd-demo]: https://mediapipe.dev/demo/face_detection
+[H-demo]: https://mediapipe.dev/demo/hands
+[Ob-demo]: https://mediapipe.dev/demo/objectron
+[P-demo]: https://mediapipe.dev/demo/pose
+[S-demo]: https://mediapipe.dev/demo/selfie_segmentation
+[npm]: https://www.npmjs.com/package/@mediapipe
+[codepen]: https://code.mediapipe.dev/codepen

docs/getting_started/python.md

@@ -0,0 +1,146 @@
+---
+layout: default
+title: MediaPipe in Python
+parent: Getting Started
+has_children: true
+has_toc: false
+nav_order: 3
+---
+
+# MediaPipe in Python
+{: .no_toc }
+
+1. TOC
+{:toc}
+---
+
+## Ready-to-use Python Solutions
+
+MediaPipe offers ready-to-use yet customizable Python solutions as a prebuilt
+Python package. MediaPipe Python package is available on
+[PyPI](https://pypi.org/project/mediapipe/) for Linux, macOS and Windows.
+
+You can, for instance, activate a Python virtual environment:
+
+```bash
+$ python3 -m venv mp_env && source mp_env/bin/activate
+```
+
+Install MediaPipe Python package and start Python interpreter:
+
+```bash
+(mp_env)$ pip install mediapipe
+(mp_env)$ python3
+```
+
+In Python interpreter, import the package and start using one of the solutions:
+
+```python
+import mediapipe as mp
+mp_face_mesh = mp.solutions.face_mesh
+```
+
+Tip: Use command `deactivate` to later exit the Python virtual environment.
+
+To learn more about configuration options and usage examples, please find
+details in each solution via the links below:
+
+*   [MediaPipe Face Detection](../solutions/face_detection#python-solution-api)
+*   [MediaPipe Face Mesh](../solutions/face_mesh#python-solution-api)
+*   [MediaPipe Hands](../solutions/hands#python-solution-api)
+*   [MediaPipe Holistic](../solutions/holistic#python-solution-api)
+*   [MediaPipe Objectron](../solutions/objectron#python-solution-api)
+*   [MediaPipe Pose](../solutions/pose#python-solution-api)
+*   [MediaPipe Selfie Segmentation](../solutions/selfie_segmentation#python-solution-api)
+
+## MediaPipe on Google Colab
+
+*   [MediaPipe Face Detection Colab](https://mediapipe.page.link/face_detection_py_colab)
+*   [MediaPipe Face Mesh Colab](https://mediapipe.page.link/face_mesh_py_colab)
+*   [MediaPipe Hands Colab](https://mediapipe.page.link/hands_py_colab)
+*   [MediaPipe Holistic Colab](https://mediapipe.page.link/holistic_py_colab)
+*   [MediaPipe Objectron Colab](https://mediapipe.page.link/objectron_py_colab)
+*   [MediaPipe Pose Colab](https://mediapipe.page.link/pose_py_colab)
+*   [MediaPipe Pose Classification Colab (Basic)](https://mediapipe.page.link/pose_classification_basic)
+*   [MediaPipe Pose Classification Colab (Extended)](https://mediapipe.page.link/pose_classification_extended)
+*   [MediaPipe Selfie Segmentation Colab](https://mediapipe.page.link/selfie_segmentation_py_colab)
+
+## MediaPipe Python Framework
+
+The ready-to-use solutions are built upon the MediaPipe Python framework, which
+can be used by advanced users to run their own MediaPipe graphs in Python.
+Please see [here](./python_framework.md) for more info.
+
+## Building MediaPipe Python Package
+
+Follow the steps below only if you have local changes and need to build the
+Python package from source. Otherwise, we strongly encourage our users to simply
+run `pip install mediapipe` to use the ready-to-use solutions, more convenient
+and much faster.
+
+MediaPipe PyPI currently doesn't provide aarch64 Python wheel
+files. For building and using MediaPipe Python on aarch64 Linux systems such as
+Nvidia Jetson and Raspberry Pi, please read
+[here](https://github.com/jiuqiant/mediapipe-python-aarch64).
+
+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.
+
+    Debian or Ubuntu:
+
+    ```bash
+    $ sudo apt install python3-dev
+    $ sudo apt install python3-venv
+    $ sudo apt install -y protobuf-compiler
+
+    # If you need to build opencv from source.
+    $ sudo apt install cmake
+    ```
+
+    macOS:
+
+    ```bash
+    $ brew install protobuf
+
+    # If you need to build opencv from source.
+    $ brew install cmake
+    ```
+
+    Windows:
+
+    Download the latest protoc win64 zip from
+    [the Protobuf GitHub repo](https://github.com/protocolbuffers/protobuf/releases),
+    unzip the file, and copy the protoc.exe executable to a preferred
+    location. Please ensure that location is added into the Path environment
+    variable.
+
+3.  Activate a Python virtual environment.
+
+    ```bash
+    $ python3 -m venv mp_env && source mp_env/bin/activate
+    ```
+
+4.  In the virtual environment, go to the MediaPipe repo directory.
+
+5.  Install the required Python packages.
+
+    ```bash
+    (mp_env)mediapipe$ pip3 install -r requirements.txt
+    ```
+
+6.  Generate and install MediaPipe package.
+
+    ```bash
+    (mp_env)mediapipe$ python3 setup.py gen_protos
+    (mp_env)mediapipe$ python3 setup.py install --link-opencv
+    ```
+
+    or
+
+    ```bash
+    (mp_env)mediapipe$ python3 setup.py gen_protos
+    (mp_env)mediapipe$ python3 setup.py bdist_wheel
+    ```

docs/getting_started/python_framework.md

@@ -0,0 +1,268 @@
+---
+layout: default
+title: MediaPipe Python Framework
+parent: MediaPipe in Python
+grand_parent: Getting Started
+nav_order: 1
+---
+
+# MediaPipe Python Framework
+{: .no_toc }
+
+1. TOC
+{:toc}
+---
+
+The MediaPipe Python framework grants direct access to the core components of
+the MediaPipe C++ framework such as Timestamp, Packet, and CalculatorGraph,
+whereas the
+[ready-to-use Python solutions](./python.md#ready-to-use-python-solutions) hide
+the technical details of the framework and simply return the readable model
+inference results back to the callers.
+
+MediaPipe framework sits on top of
+[the pybind11 library](https://pybind11.readthedocs.io/en/stable/index.html).
+The C++ core framework is exposed in Python via a C++/Python language binding.
+The content below assumes that the reader already has a basic understanding of
+the MediaPipe C++ framework. Otherwise, you can find useful information in
+[Framework Concepts](../framework_concepts/framework_concepts.md).
+
+### Packet
+
+The packet is the basic data flow unit in MediaPipe. A packet consists of a
+numeric timestamp and a shared pointer to an immutable payload. In Python, a
+MediaPipe packet can be created by calling one of the packet creator methods in
+the
+[`mp.packet_creator`](https://github.com/google/mediapipe/tree/master/mediapipe/python/pybind/packet_creator.cc)
+module. Correspondingly, the packet payload can be retrieved by using one of the
+packet getter methods in the
+[`mp.packet_getter`](https://github.com/google/mediapipe/tree/master/mediapipe/python/pybind/packet_getter.cc)
+module. Note that the packet payload becomes **immutable** after packet
+creation. Thus, the modification of the retrieved packet content doesn't affect
+the actual payload in the packet. MediaPipe framework Python API supports the
+most commonly used data types of MediaPipe (e.g., ImageFrame, Matrix, Protocol
+Buffers, and the primitive data types) in the core binding. The comprehensive
+table below shows the type mappings between the Python and the C++ data type
+along with the packet creator and the content getter method for each data type
+supported by the MediaPipe Python framework API.
+
+Python Data Type                     | C++ Data Type                 | Packet Creator                                                                                                                                               | Content Getter
+------------------------------------ | ----------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------
+bool                                 | bool                          | create_bool(True)                                                                                                                                            | get_bool(packet)
+int or np.intc                       | int_t                         | create_int(1)                                                                                                                                                | get_int(packet)
+int or np.int8                       | int8_t                        | create_int8(2**7-1)                                                                                                                                          | get_int(packet)
+int or np.int16                      | int16_t                       | create_int16(2**15-1)                                                                                                                                        | get_int(packet)
+int or np.int32                      | int32_t                       | create_int32(2**31-1)                                                                                                                                        | get_int(packet)
+int or np.int64                      | int64_t                       | create_int64(2**63-1)                                                                                                                                        | get_int(packet)
+int or np.uint8                      | uint8_t                       | create_uint8(2**8-1)                                                                                                                                         | get_uint(packet)
+int or np.uint16                     | uint16_t                      | create_uint16(2**16-1)                                                                                                                                       | get_uint(packet)
+int or np.uint32                     | uint32_t                      | create_uint32(2**32-1)                                                                                                                                       | get_uint(packet)
+int or np.uint64                     | uint64_t                      | create_uint64(2**64-1)                                                                                                                                       | get_uint(packet)
+float or np.float32                  | float                         | create_float(1.1)                                                                                                                                            | get_float(packet)
+float or np.double                   | double                        | create_double(1.1)                                                                                                                                           | get_float(packet)
+str (UTF-8)                          | std::string                   | create_string('abc')                                                                                                                                         | get_str(packet)
+bytes                                | std::string                   | create_string(b'\xd0\xd0\xd0')                                                                                                                               | get_bytes(packet)
+mp.Packet                            | mp::Packet                    | create_packet(p)                                                                                                                                             | get_packet(packet)
+List\[bool\]                         | std::vector\<bool\>           | create_bool_vector(\[True, False\])                                                                                                                          | get_bool_list(packet)
+List\[int\] or List\[np.intc\]       | int\[\]                       | create_int_array(\[1, 2, 3\])                                                                                                                                | get_int_list(packet, size=10)
+List\[int\] or List\[np.intc\]       | std::vector\<int\>            | create_int_vector(\[1, 2, 3\])                                                                                                                               | get_int_list(packet)
+List\[float\] or List\[np.float\]    | float\[\]                     | create_float_arrary(\[0.1, 0.2\])                                                                                                                            | get_float_list(packet, size=10)
+List\[float\] or List\[np.float\]    | std::vector\<float\>          | create_float_vector(\[0.1, 0.2\])                                                                                                                            | get_float_list(packet, size=10)
+List\[str\]                          | std::vector\<std::string\>    | create_string_vector(\['a'\])                                                                                                                                | get_str_list(packet)
+List\[mp.Packet\]                    | std::vector\<mp::Packet\>     | create_packet_vector(<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\[packet1, packet2\])                                                               | get_packet_list(p)
+Mapping\[str, Packet\]               | std::map<std::string, Packet> | create_string_to_packet_map(<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;{'a': packet1, 'b': packet2})                                                | get_str_to_packet_dict(packet)
+np.ndarray<br>(cv.mat and PIL.Image) | mp::ImageFrame                | create_image_frame(<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;format=ImageFormat.SRGB,<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;data=mat) | get_image_frame(packet)
+np.ndarray                           | mp::Matrix                    | create_matrix(data)                                                                                                                                          | get_matrix(packet)
+Google Proto Message                 | Google Proto Message          | create_proto(proto)                                                                                                                                          | get_proto(packet)
+List\[Proto\]                        | std::vector\<Proto\>          | n/a                                                                                                                                                          | get_proto_list(packet)
+
+It's not uncommon that users create custom C++ classes and and send those into
+the graphs and calculators. To allow the custom classes to be used in Python
+with MediaPipe, you may extend the Packet API for a new data type in the
+following steps:
+
+1.  Write the pybind11
+    [class binding code](https://pybind11.readthedocs.io/en/stable/advanced/classes.html)
+    or
+    [a custom type caster](https://pybind11.readthedocs.io/en/stable/advanced/cast/custom.html?highlight=custom%20type%20caster)
+    for the custom type in a cc file.
+
+    ```c++
+    #include "path/to/my_type/header/file.h"
+    #include "pybind11/pybind11.h"
+
+    namespace py = pybind11;
+
+    PYBIND11_MODULE(my_type_binding, m) {
+      // Write binding code or a custom type caster for MyType.
+      py::class_<MyType>(m, "MyType")
+          .def(py::init<>())
+          .def(...);
+    }
+    ```
+
+2.  Create a new packet creator and getter method of the custom type in a
+    separate cc file.
+
+    ```c++
+    #include "path/to/my_type/header/file.h"
+    #include "mediapipe/framework/packet.h"
+    #include "pybind11/pybind11.h"
+
+    namespace mediapipe {
+    namespace py = pybind11;
+
+    PYBIND11_MODULE(my_packet_methods, m) {
+      m.def(
+          "create_my_type",
+          [](const MyType& my_type) { return MakePacket<MyType>(my_type); });
+
+      m.def(
+          "get_my_type",
+          [](const Packet& packet) {
+            if(!packet.ValidateAsType<MyType>().ok()) {
+              PyErr_SetString(PyExc_ValueError, "Packet data type mismatch.");
+              return py::error_already_set();
+            }
+            return packet.Get<MyType>();
+          });
+    }  // namespace mediapipe
+    ```
+
+3.  Add two bazel build rules for the custom type binding and the new packet
+    methods in the BUILD file.
+
+    ```
+    load("@pybind11_bazel//:build_defs.bzl", "pybind_extension")
+
+    pybind_extension(
+        name = "my_type_binding",
+        srcs = ["my_type_binding.cc"],
+        deps = [":my_type"],
+    )
+
+    pybind_extension(
+        name = "my_packet_methods",
+        srcs = ["my_packet_methods.cc"],
+        deps = [
+            ":my_type",
+            "//mediapipe/framework:packet"
+        ],
+    )
+    ```
+
+4.  Build the pybind extension targets (with the suffix .so) by Bazel and move the generated dynamic libraries into one of the $LD_LIBRARY_PATH dirs.
+
+5.  Use the binding modules in Python.
+
+    ```python
+    import my_type_binding
+    import my_packet_methods
+
+    packet = my_packet_methods.create_my_type(my_type_binding.MyType())
+    my_type = my_packet_methods.get_my_type(packet)
+    ```
+
+### Timestamp
+
+Each packet contains a timestamp that is in units of microseconds. In Python,
+the Packet API provides a convenience method `packet.at()` to define the numeric
+timestamp of a packet. More generally, `packet.timestamp` is the packet class
+property for accessing the underlying timestamp. To convert an Unix epoch to a
+MediaPipe timestamp,
+[the Timestamp API](https://github.com/google/mediapipe/tree/master/mediapipe/python/pybind/timestamp.cc)
+offers a method `mp.Timestamp.from_seconds()` for this purpose.
+
+### ImageFrame
+
+ImageFrame is the container for storing an image or a video frame. Formats
+supported by ImageFrame are listed in
+[the ImageFormat enum](https://github.com/google/mediapipe/tree/master/mediapipe/python/pybind/image_frame.cc#l=170).
+Pixels are encoded row-major with interleaved color components, and ImageFrame
+supports uint8, uint16, and float as its data types. MediaPipe provides
+[an ImageFrame Python API](https://github.com/google/mediapipe/tree/master/mediapipe/python/pybind/image_frame.cc)
+to access the ImageFrame C++ class. In Python, the easiest way to retrieve the
+pixel data is to call `image_frame.numpy_view()` to get a numpy ndarray. Note
+that the returned numpy ndarray, a reference to the internal pixel data, is
+unwritable. If the callers need to modify the numpy ndarray, it's required to
+explicitly call a copy operation to obtain a copy. When MediaPipe takes a numpy
+ndarray to make an ImageFrame, it assumes that the data is stored contiguously.
+Correspondingly, the pixel data of an ImageFrame will be realigned to be
+contiguous when it's returned to the Python side.
+
+### Graph
+
+In MediaPipe, all processing takes places within the context of a
+CalculatorGraph.
+[The CalculatorGraph Python API](https://github.com/google/mediapipe/tree/master/mediapipe/python/pybind/calculator_graph.cc)
+is a direct binding to the C++ CalculatorGraph class. The major difference is
+the CalculatorGraph Python API raises a Python error instead of returning a
+non-OK Status when an error occurs. Therefore, as a Python user, you can handle
+the exceptions as you normally do. The life cycle of a CalculatorGraph contains
+three stages: initialization and setup, graph run, and graph shutdown.
+
+1.  Initialize a CalculatorGraph with a CalculatorGraphConfig protobuf or binary
+    protobuf file, and provide callback method(s) to observe the output
+    stream(s).
+
+    Option 1. Initialize a CalculatorGraph with a CalculatorGraphConfig protobuf
+    or its text representation, and observe the output stream(s):
+
+    ```python
+    import mediapipe as mp
+
+    config_text = """
+      input_stream: 'in_stream'
+      output_stream: 'out_stream'
+      node {
+        calculator: 'PassThroughCalculator'
+        input_stream: 'in_stream'
+        output_stream: 'out_stream'
+      }
+    """
+    graph = mp.CalculatorGraph(graph_config=config_text)
+    output_packets = []
+    graph.observe_output_stream(
+        'out_stream',
+        lambda stream_name, packet:
+            output_packets.append(mp.packet_getter.get_str(packet)))
+    ```
+
+    Option 2. Initialize a CalculatorGraph with with a binary protobuf file, and
+    observe the output stream(s).
+
+    ```python
+    import mediapipe as mp
+    # resources dependency
+
+    graph = mp.CalculatorGraph(
+        binary_graph=os.path.join(
+            resources.GetRunfilesDir(), 'path/to/your/graph.binarypb'))
+    graph.observe_output_stream(
+        'out_stream',
+        lambda stream_name, packet: print(f'Get {packet} from {stream_name}'))
+    ```
+
+2.  Start the graph run and feed packets into the graph.
+
+    ```python
+    graph.start_run()
+
+    graph.add_packet_to_input_stream(
+        'in_stream', mp.packet_creator.create_str('abc').at(0))
+
+    rgb_img = cv2.cvtColor(cv2.imread('/path/to/your/image.png'), cv2.COLOR_BGR2RGB)
+    graph.add_packet_to_input_stream(
+        'in_stream',
+        mp.packet_creator.create_image_frame(format=mp.ImageFormat.SRGB,
+                                             data=rgb_img).at(1))
+    ```
+
+3.  Close the graph after finish. You may restart the graph for another graph
+    run after the call to `close()`.
+
+    ```python
+    graph.close()
+    ```
+
+The Python script can be run by your local Python runtime.

docs/getting_started/troubleshooting.md

@@ -97,6 +97,49 @@ 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.
 
+## Python pip install failure
+
+The error message:
+
+```
+ERROR: Could not find a version that satisfies the requirement mediapipe
+ERROR: No matching distribution found for mediapipe
+```
+
+after running `pip install mediapipe` usually indicates that there is no qualified MediaPipe Python for your system.
+Please note that MediaPipe Python PyPI officially supports the **64-bit**
+version of Python 3.7 and above on the following OS:
+
+-   x86_64 Linux
+-   x86_64 macOS 10.15+
+-   amd64 Windows
+
+If the OS is currently supported and you still see this error, please make sure
+that both the Python and pip binary are for Python 3.7 and above. Otherwise,
+please consider building the MediaPipe Python package locally by following the
+instructions [here](python.md#building-mediapipe-python-package).
+
+## Python DLL load failure on Windows
+
+The error message:
+
+```
+ImportError: DLL load failed: The specified module could not be found
+```
+
+usually indicates that the local Windows system is missing Visual C++
+redistributable packages and/or Visual C++ runtime DLLs. This can be solved by
+either installing the official
+[vc_redist.x64.exe](https://support.microsoft.com/en-us/topic/the-latest-supported-visual-c-downloads-2647da03-1eea-4433-9aff-95f26a218cc0)
+or installing the "msvc-runtime" Python package by running
+
+```bash
+$ python -m pip install msvc-runtime
+```
+
+Please note that the "msvc-runtime" Python package is not released or maintained
+by Microsoft.
+
 ## Native method not found
 
 The error message:

docs/index.md

@@ -21,82 +21,56 @@ ML solutions for live and streaming media.
 
 ## ML solutions in MediaPipe
 
-Face Detection                                                                                                                 | Face Mesh                                                                                                       | Iris                                                                                                      | Hands                                                                                                      | Pose                                                                                                      | Hair Segmentation
-:----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :---------------:
-[![face_detection](images/mobile/face_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_detection) | [![face_mesh](images/mobile/face_mesh_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_mesh) | [![iris](images/mobile/iris_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/iris) | [![hand](images/mobile/hand_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hands) | [![pose](images/mobile/pose_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/pose) | [![hair_segmentation](images/mobile/hair_segmentation_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hair_segmentation)
+Face Detection                                                                                                                 | Face Mesh                                                                                                       | Iris                                                                                                      | Hands                                                                                                      | Pose                                                                                                      | Holistic
+:----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: | :------:
+[![face_detection](images/mobile/face_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_detection) | [![face_mesh](images/mobile/face_mesh_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/face_mesh) | [![iris](images/mobile/iris_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/iris) | [![hand](images/mobile/hand_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hands) | [![pose](images/mobile/pose_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/pose) | [![hair_segmentation](images/mobile/holistic_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/holistic)
 
-Object Detection                                                                                                                     | Box Tracking                                                                                                                | Instant Motion Tracking                                                                                                                               | Objectron                                                                                                             | KNIFT
-:----------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: | :---:
-[![object_detection](images/mobile/object_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/object_detection) | [![box_tracking](images/mobile/object_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/box_tracking) | [![instant_motion_tracking](images/mobile/instant_motion_tracking_android_small.gif)](https://google.github.io/mediapipe/solutions/instant_motion_tracking) | [![objectron](images/mobile/objectron_chair_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/objectron) | [![knift](images/mobile/template_matching_android_cpu_small.gif)](https://google.github.io/mediapipe/solutions/knift)
+Hair Segmentation                                                                                                                       | Object Detection                                                                                                                     | Box Tracking                                                                                                                | Instant Motion Tracking                                                                                                                               | Objectron                                                                                                             | KNIFT
+:-------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: | :---:
+[![hair_segmentation](images/mobile/hair_segmentation_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/hair_segmentation) | [![object_detection](images/mobile/object_detection_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/object_detection) | [![box_tracking](images/mobile/object_tracking_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/box_tracking) | [![instant_motion_tracking](images/mobile/instant_motion_tracking_android_small.gif)](https://google.github.io/mediapipe/solutions/instant_motion_tracking) | [![objectron](images/mobile/objectron_chair_android_gpu_small.gif)](https://google.github.io/mediapipe/solutions/objectron) | [![knift](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)                       | ✅       | ✅   | ✅       |        |     |
-[Instant Motion Tracking](https://google.github.io/mediapipe/solutions/instant_motion_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)                           |         |     | ✅       |        |     |
+[]()                                                                                      | [Android](https://google.github.io/mediapipe/getting_started/android) | [iOS](https://google.github.io/mediapipe/getting_started/ios) | [C++](https://google.github.io/mediapipe/getting_started/cpp) | [Python](https://google.github.io/mediapipe/getting_started/python) | [JS](https://google.github.io/mediapipe/getting_started/javascript) | [Coral](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/README.md)
+:---------------------------------------------------------------------------------------- | :-------------------------------------------------------------: | :-----------------------------------------------------: | :-----------------------------------------------------: | :-----------------------------------------------------------: | :-----------------------------------------------------------: | :--------------------------------------------------------------------:
+[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)                                       | ✅                                                               | ✅                                                       | ✅                                                       | ✅                                                             | ✅                                                             |
+[Holistic](https://google.github.io/mediapipe/solutions/holistic)                               | ✅                                                               | ✅                                                       | ✅                                                       | ✅                                                             | ✅                                                             |
+[Selfie Segmentation](https://google.github.io/mediapipe/solutions/selfie_segmentation)         | ✅                                                               | ✅                                                       | ✅                                                       | ✅                                                             | ✅                                                             |
+[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)                       | ✅                                                               | ✅                                                       | ✅                                                       |                                                               |                                                               |
+[Instant Motion Tracking](https://google.github.io/mediapipe/solutions/instant_motion_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)                           |                                                                 |                                                         | ✅                                                       |                                                               |                                                               |
 
 See also
 [MediaPipe Models and Model Cards](https://google.github.io/mediapipe/solutions/models)
 for ML models released in MediaPipe.
 
-## MediaPipe in Python
-
-MediaPipe Python package is available on
-[PyPI](https://pypi.org/project/mediapipe/), and can be installed simply by `pip
-install mediapipe` on Linux and macOS, as described in:
-
-*   [MediaPipe Face Mesh](../solutions/pose.md#python) and
-    [colab](https://mediapipe.page.link/face_mesh_py_colab)
-*   [MediaPipe Hands](../solutions/pose.md#python) and
-    [colab](https://mediapipe.page.link/hands_py_colab)
-*   [MediaPipe Pose](../solutions/pose.md#python) and
-    [colab](https://mediapipe.page.link/pose_py_colab)
-
-## 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](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
 
-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.
+To start using MediaPipe
+[solutions](https://google.github.io/mediapipe/solutions/solutions) with only a few
+lines code, see example code and demos in
+[MediaPipe in Python](https://google.github.io/mediapipe/getting_started/python) and
+[MediaPipe in JavaScript](https://google.github.io/mediapipe/getting_started/javascript).
+
+To use MediaPipe in C++, Android and iOS, which allow further customization of
+the [solutions](https://google.github.io/mediapipe/solutions/solutions) as well as
+building your own, learn how to
+[install](https://google.github.io/mediapipe/getting_started/install) MediaPipe and
+start building example applications in
+[C++](https://google.github.io/mediapipe/getting_started/cpp),
+[Android](https://google.github.io/mediapipe/getting_started/android) and
+[iOS](https://google.github.io/mediapipe/getting_started/ios).
 
 The source code is hosted in the
 [MediaPipe Github repository](https://github.com/google/mediapipe), and you can
@@ -105,6 +79,17 @@ run code search using
 
 ## Publications
 
+*   [Bringing artworks to life with AR](https://developers.googleblog.com/2021/07/bringing-artworks-to-life-with-ar.html)
+    in Google Developers Blog
+*   [Prosthesis control via Mirru App using MediaPipe hand tracking](https://developers.googleblog.com/2021/05/control-your-mirru-prosthesis-with-mediapipe-hand-tracking.html)
+    in Google Developers Blog
+*   [SignAll SDK: Sign language interface using MediaPipe is now available for
+    developers](https://developers.googleblog.com/2021/04/signall-sdk-sign-language-interface-using-mediapipe-now-available.html)
+    in Google Developers Blog
+*   [MediaPipe Holistic - Simultaneous Face, Hand and Pose Prediction, on Device](https://ai.googleblog.com/2020/12/mediapipe-holistic-simultaneous-face.html)
+    in Google AI Blog
+*   [Background Features in Google Meet, Powered by Web ML](https://ai.googleblog.com/2020/10/background-features-in-google-meet.html)
+    in Google AI Blog
 *   [MediaPipe 3D Face Transform](https://developers.googleblog.com/2020/09/mediapipe-3d-face-transform.html)
     in Google Developers Blog
 *   [Instant Motion Tracking With MediaPipe](https://developers.googleblog.com/2020/08/instant-motion-tracking-with-mediapipe.html)

docs/solutions/autoflip.md

@@ -2,14 +2,20 @@
 layout: default
 title: AutoFlip (Saliency-aware Video Cropping)
 parent: Solutions
-nav_order: 12
+nav_order: 14
 ---
 
 # AutoFlip: Saliency-aware Video Cropping
 {: .no_toc }
 
+<details close markdown="block">
+  <summary>
+    Table of contents
+  </summary>
+  {: .text-delta }
 1. TOC
 {:toc}
+</details>
 ---
 
 ## Overview

docs/solutions/box_tracking.md

@@ -2,14 +2,20 @@
 layout: default
 title: Box Tracking
 parent: Solutions
-nav_order: 8
+nav_order: 10
 ---
 
 # MediaPipe Box Tracking
 {: .no_toc }
 
+<details close markdown="block">
+  <summary>
+    Table of contents
+  </summary>
+  {: .text-delta }
 1. TOC
 {:toc}
+</details>
 ---
 
 ## Overview
@@ -105,9 +111,8 @@ new detections to remove obsolete or duplicated boxes.
 ## Example Apps
 
 Please first see general instructions for
-[Android](../getting_started/building_examples.md#android), [iOS](../getting_started/building_examples.md#ios)
-and [desktop](../getting_started/building_examples.md#desktop) on how to build MediaPipe
-examples.
+[Android](../getting_started/android.md), [iOS](../getting_started/ios.md) and
+[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
 
 Note: To visualize a graph, copy the graph and paste it into
 [MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how

docs/solutions/face_detection.md

@@ -8,8 +8,14 @@ nav_order: 1
 # MediaPipe Face Detection
 {: .no_toc }
 
+<details close markdown="block">
+  <summary>
+    Table of contents
+  </summary>
+  {: .text-delta }
 1. TOC
 {:toc}
+</details>
 ---
 
 ## Overview
@@ -33,12 +39,188 @@ section.
 
 ![face_detection_android_gpu.gif](../images/mobile/face_detection_android_gpu.gif)
 
+## Solution APIs
+
+### Configuration Options
+
+Naming style and availability may differ slightly across platforms/languages.
+
+#### model_selection
+
+An integer index `0` or `1`. Use `0` to select a short-range model that works
+best for faces within 2 meters from the camera, and `1` for a full-range model
+best for faces within 5 meters. For the full-range option, a sparse model is
+used for its improved inference speed. Please refer to the
+[model cards](./models.md#face_detection) for details. Default to `0` if not
+specified.
+
+#### min_detection_confidence
+
+Minimum confidence value (`[0.0, 1.0]`) from the face detection model for the
+detection to be considered successful. Default to `0.5`.
+
+### Output
+
+Naming style may differ slightly across platforms/languages.
+
+#### detections
+
+Collection of detected faces, where each face is represented as a detection
+proto message that contains a bounding box and 6 key points (right eye, left
+eye, nose tip, mouth center, right ear tragion, and left ear tragion). The
+bounding box is composed of `xmin` and `width` (both normalized to `[0.0, 1.0]`
+by the image width) and `ymin` and `height` (both normalized to `[0.0, 1.0]` by
+the image height). Each key point is composed of `x` and `y`, which are
+normalized to `[0.0, 1.0]` by the image width and height respectively.
+
+### Python Solution API
+
+Please first follow general [instructions](../getting_started/python.md) to
+install MediaPipe Python package, then learn more in the companion
+[Python Colab](#resources) and the usage example below.
+
+Supported configuration options:
+
+*   [model_selection](#model_selection)
+*   [min_detection_confidence](#min_detection_confidence)
+
+```python
+import cv2
+import mediapipe as mp
+mp_face_detection = mp.solutions.face_detection
+mp_drawing = mp.solutions.drawing_utils
+
+# For static images:
+IMAGE_FILES = []
+with mp_face_detection.FaceDetection(
+    model_selection=1, min_detection_confidence=0.5) as face_detection:
+  for idx, file in enumerate(IMAGE_FILES):
+    image = cv2.imread(file)
+    # Convert the BGR image to RGB and process it with MediaPipe Face Detection.
+    results = face_detection.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+    # Draw face detections of each face.
+    if not results.detections:
+      continue
+    annotated_image = image.copy()
+    for detection in results.detections:
+      print('Nose tip:')
+      print(mp_face_detection.get_key_point(
+          detection, mp_face_detection.FaceKeyPoint.NOSE_TIP))
+      mp_drawing.draw_detection(annotated_image, detection)
+    cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
+
+# For webcam input:
+cap = cv2.VideoCapture(0)
+with mp_face_detection.FaceDetection(
+    model_selection=0, min_detection_confidence=0.5) as face_detection:
+  while cap.isOpened():
+    success, image = cap.read()
+    if not success:
+      print("Ignoring empty camera frame.")
+      # If loading a video, use 'break' instead of 'continue'.
+      continue
+
+    # Flip the image horizontally for a later selfie-view display, and convert
+    # the BGR image to RGB.
+    image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
+    # To improve performance, optionally mark the image as not writeable to
+    # pass by reference.
+    image.flags.writeable = False
+    results = face_detection.process(image)
+
+    # Draw the face detection annotations on the image.
+    image.flags.writeable = True
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    if results.detections:
+      for detection in results.detections:
+        mp_drawing.draw_detection(image, detection)
+    cv2.imshow('MediaPipe Face Detection', image)
+    if cv2.waitKey(5) & 0xFF == 27:
+      break
+cap.release()
+```
+
+### JavaScript Solution API
+
+Please first see general [introduction](../getting_started/javascript.md) on
+MediaPipe in JavaScript, then learn more in the companion [web demo](#resources)
+and the following usage example.
+
+Supported configuration options:
+
+*   [modelSelection](#model_selection)
+*   [minDetectionConfidence](#min_detection_confidence)
+
+```html
+<!DOCTYPE html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/camera_utils/camera_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/control_utils/control_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/drawing_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/face_detection/face_detection.js" crossorigin="anonymous"></script>
+</head>
+
+<body>
+  <div class="container">
+    <video class="input_video"></video>
+    <canvas class="output_canvas" width="1280px" height="720px"></canvas>
+  </div>
+</body>
+</html>
+```
+
+```javascript
+<script type="module">
+const videoElement = document.getElementsByClassName('input_video')[0];
+const canvasElement = document.getElementsByClassName('output_canvas')[0];
+const canvasCtx = canvasElement.getContext('2d');
+
+function onResults(results) {
+  // Draw the overlays.
+  canvasCtx.save();
+  canvasCtx.clearRect(0, 0, canvasElement.width, canvasElement.height);
+  canvasCtx.drawImage(
+      results.image, 0, 0, canvasElement.width, canvasElement.height);
+  if (results.detections.length > 0) {
+    drawingUtils.drawRectangle(
+        canvasCtx, results.detections[0].boundingBox,
+        {color: 'blue', lineWidth: 4, fillColor: '#00000000'});
+    drawingUtils.drawLandmarks(canvasCtx, results.detections[0].landmarks, {
+      color: 'red',
+      radius: 5,
+    });
+  }
+  canvasCtx.restore();
+}
+
+const faceDetection = new FaceDetection({locateFile: (file) => {
+  return `https://cdn.jsdelivr.net/npm/@mediapipe/[email protected]/${file}`;
+}});
+faceDetection.setOptions({
+  modelSelection: 0
+  minDetectionConfidence: 0.5
+});
+faceDetection.onResults(onResults);
+
+const camera = new Camera(videoElement, {
+  onFrame: async () => {
+    await faceDetection.send({image: videoElement});
+  },
+  width: 1280,
+  height: 720
+});
+camera.start();
+</script>
+```
+
 ## Example Apps
 
 Please first see general instructions for
-[Android](../getting_started/building_examples.md#android), [iOS](../getting_started/building_examples.md#ios)
-and [desktop](../getting_started/building_examples.md#desktop) on how to build MediaPipe
-examples.
+[Android](../getting_started/android.md), [iOS](../getting_started/ios.md) and
+[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
 
 Note: To visualize a graph, copy the graph and paste it into
 [MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
@@ -85,10 +267,6 @@ same configuration as the GPU pipeline, runs entirely on CPU.
     *   Target:
         [`mediapipe/examples/desktop/face_detection:face_detection_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/face_detection/BUILD)
 
-### Web
-
-Please refer to [these instructions](../index.md#mediapipe-on-the-web).
-
 ### Coral
 
 Please refer to
@@ -103,3 +281,5 @@ to cross-compile and run MediaPipe examples on the
     ([presentation](https://docs.google.com/presentation/d/1YCtASfnYyZtH-41QvnW5iZxELFnf0MF-pPWSLGj8yjQ/present?slide=id.g5bc8aeffdd_1_0))
     ([poster](https://drive.google.com/file/d/1u6aB6wxDY7X2TmeUUKgFydulNtXkb3pu/view))
 *   [Models and model cards](./models.md#face_detection)
+*   [Web demo](https://code.mediapipe.dev/codepen/face_detection)
+*   [Python Colab](https://mediapipe.page.link/face_detection_py_colab)

docs/solutions/face_mesh.md

@@ -8,8 +8,14 @@ nav_order: 2
 # MediaPipe Face Mesh
 {: .no_toc }
 
+<details close markdown="block">
+  <summary>
+    Table of contents
+  </summary>
+  {: .text-delta }
 1. TOC
 {:toc}
+</details>
 ---
 
 ## Overview
@@ -63,7 +69,7 @@ and renders using a dedicated
 The
 [face landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/face_landmark_front_gpu.pbtxt)
 internally uses a
-[face_detection_subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_front_gpu.pbtxt)
+[face_detection_subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_short_range_gpu.pbtxt)
 from the
 [face detection module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection).
 
@@ -179,8 +185,8 @@ following steps are executed in the given order:
 The geometry pipeline is implemented as a MediaPipe
 [calculator](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/geometry_pipeline_calculator.cc).
 For your convenience, the face geometry pipeline calculator is bundled together
-with the face landmark module into a unified MediaPipe
-[subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/face_geometry_front_gpu.pbtxt).
+with corresponding metadata into a unified MediaPipe
+[subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/face_geometry_from_landmarks.pbtxt).
 The face geometry format is defined as a Protocol Buffer
 [message](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/protos/face_geometry.proto).
 
@@ -206,11 +212,433 @@ The effect renderer is implemented as a MediaPipe
 | :---------------------------------------------------------------------: |
 | *Fig 4. An example of face effects rendered by the Face Geometry Effect Renderer.* |
 
+## Solution APIs
+
+### Configuration Options
+
+Naming style and availability may differ slightly across platforms/languages.
+
+#### static_image_mode
+
+If set to `false`, the solution treats the input images as a video stream. It
+will try to detect faces in the first input images, and upon a successful
+detection further localizes the face landmarks. In subsequent images, once all
+[max_num_faces](#max_num_faces) faces are detected and the corresponding face
+landmarks are localized, it simply tracks those landmarks without invoking
+another detection until it loses track of any of the faces. This reduces latency
+and is ideal for processing video frames. If set to `true`, face detection runs
+on every input image, ideal for processing a batch of static, possibly
+unrelated, images. Default to `false`.
+
+#### max_num_faces
+
+Maximum number of faces to detect. Default to `1`.
+
+#### min_detection_confidence
+
+Minimum confidence value (`[0.0, 1.0]`) from the face detection model for the
+detection to be considered successful. Default to `0.5`.
+
+#### min_tracking_confidence
+
+Minimum confidence value (`[0.0, 1.0]`) from the landmark-tracking model for the
+face landmarks to be considered tracked successfully, or otherwise face
+detection will be invoked automatically on the next input image. Setting it to a
+higher value can increase robustness of the solution, at the expense of a higher
+latency. Ignored if [static_image_mode](#static_image_mode) is `true`, where
+face detection simply runs on every image. Default to `0.5`.
+
+### Output
+
+Naming style may differ slightly across platforms/languages.
+
+#### multi_face_landmarks
+
+Collection of detected/tracked faces, where each face is represented as a list
+of 468 face landmarks and each landmark is composed of `x`, `y` and `z`. `x` and
+`y` are normalized to `[0.0, 1.0]` by the image width and height respectively.
+`z` represents the landmark depth with the depth at center of the head being the
+origin, and the smaller the value the closer the landmark is to the camera. The
+magnitude of `z` uses roughly the same scale as `x`.
+
+### Python Solution API
+
+Please first follow general [instructions](../getting_started/python.md) to
+install MediaPipe Python package, then learn more in the companion
+[Python Colab](#resources) and the usage example below.
+
+Supported configuration options:
+
+*   [static_image_mode](#static_image_mode)
+*   [max_num_faces](#max_num_faces)
+*   [min_detection_confidence](#min_detection_confidence)
+*   [min_tracking_confidence](#min_tracking_confidence)
+
+```python
+import cv2
+import mediapipe as mp
+mp_drawing = mp.solutions.drawing_utils
+mp_drawing_styles = mp.solutions.drawing_styles
+mp_face_mesh = mp.solutions.face_mesh
+
+# For static images:
+IMAGE_FILES = []
+drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
+with mp_face_mesh.FaceMesh(
+    static_image_mode=True,
+    max_num_faces=1,
+    min_detection_confidence=0.5) as face_mesh:
+  for idx, file in enumerate(IMAGE_FILES):
+    image = cv2.imread(file)
+    # Convert the BGR image to RGB before processing.
+    results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+    # Print and draw face mesh landmarks on the image.
+    if not results.multi_face_landmarks:
+      continue
+    annotated_image = image.copy()
+    for face_landmarks in results.multi_face_landmarks:
+      print('face_landmarks:', face_landmarks)
+      mp_drawing.draw_landmarks(
+          image=annotated_image,
+          landmark_list=face_landmarks,
+          connections=mp_face_mesh.FACEMESH_TESSELATION,
+          landmark_drawing_spec=None,
+          connection_drawing_spec=mp_drawing_styles
+          .get_default_face_mesh_tesselation_style())
+      mp_drawing.draw_landmarks(
+          image=annotated_image,
+          landmark_list=face_landmarks,
+          connections=mp_face_mesh.FACEMESH_CONTOURS,
+          landmark_drawing_spec=None,
+          connection_drawing_spec=mp_drawing_styles
+          .get_default_face_mesh_contours_style())
+    cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
+
+# For webcam input:
+drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
+cap = cv2.VideoCapture(0)
+with mp_face_mesh.FaceMesh(
+    min_detection_confidence=0.5,
+    min_tracking_confidence=0.5) as face_mesh:
+  while cap.isOpened():
+    success, image = cap.read()
+    if not success:
+      print("Ignoring empty camera frame.")
+      # If loading a video, use 'break' instead of 'continue'.
+      continue
+
+    # Flip the image horizontally for a later selfie-view display, and convert
+    # the BGR image to RGB.
+    image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
+    # To improve performance, optionally mark the image as not writeable to
+    # pass by reference.
+    image.flags.writeable = False
+    results = face_mesh.process(image)
+
+    # Draw the face mesh annotations on the image.
+    image.flags.writeable = True
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    if results.multi_face_landmarks:
+      for face_landmarks in results.multi_face_landmarks:
+        mp_drawing.draw_landmarks(
+            image=image,
+            landmark_list=face_landmarks,
+            connections=mp_face_mesh.FACEMESH_TESSELATION,
+            landmark_drawing_spec=None,
+            connection_drawing_spec=mp_drawing_styles
+            .get_default_face_mesh_tesselation_style())
+        mp_drawing.draw_landmarks(
+            image=image,
+            landmark_list=face_landmarks,
+            connections=mp_face_mesh.FACEMESH_CONTOURS,
+            landmark_drawing_spec=None,
+            connection_drawing_spec=mp_drawing_styles
+            .get_default_face_mesh_contours_style())
+    cv2.imshow('MediaPipe FaceMesh', image)
+    if cv2.waitKey(5) & 0xFF == 27:
+      break
+cap.release()
+```
+
+### JavaScript Solution API
+
+Please first see general [introduction](../getting_started/javascript.md) on
+MediaPipe in JavaScript, then learn more in the companion [web demo](#resources)
+and the following usage example.
+
+Supported configuration options:
+
+*   [maxNumFaces](#max_num_faces)
+*   [minDetectionConfidence](#min_detection_confidence)
+*   [minTrackingConfidence](#min_tracking_confidence)
+
+```html
+<!DOCTYPE html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/camera_utils/camera_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/control_utils/control_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/drawing_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/face_mesh/face_mesh.js" crossorigin="anonymous"></script>
+</head>
+
+<body>
+  <div class="container">
+    <video class="input_video"></video>
+    <canvas class="output_canvas" width="1280px" height="720px"></canvas>
+  </div>
+</body>
+</html>
+```
+
+```javascript
+<script type="module">
+const videoElement = document.getElementsByClassName('input_video')[0];
+const canvasElement = document.getElementsByClassName('output_canvas')[0];
+const canvasCtx = canvasElement.getContext('2d');
+
+function onResults(results) {
+  canvasCtx.save();
+  canvasCtx.clearRect(0, 0, canvasElement.width, canvasElement.height);
+  canvasCtx.drawImage(
+      results.image, 0, 0, canvasElement.width, canvasElement.height);
+  if (results.multiFaceLandmarks) {
+    for (const landmarks of results.multiFaceLandmarks) {
+      drawConnectors(canvasCtx, landmarks, FACEMESH_TESSELATION,
+                     {color: '#C0C0C070', lineWidth: 1});
+      drawConnectors(canvasCtx, landmarks, FACEMESH_RIGHT_EYE, {color: '#FF3030'});
+      drawConnectors(canvasCtx, landmarks, FACEMESH_RIGHT_EYEBROW, {color: '#FF3030'});
+      drawConnectors(canvasCtx, landmarks, FACEMESH_LEFT_EYE, {color: '#30FF30'});
+      drawConnectors(canvasCtx, landmarks, FACEMESH_LEFT_EYEBROW, {color: '#30FF30'});
+      drawConnectors(canvasCtx, landmarks, FACEMESH_FACE_OVAL, {color: '#E0E0E0'});
+      drawConnectors(canvasCtx, landmarks, FACEMESH_LIPS, {color: '#E0E0E0'});
+    }
+  }
+  canvasCtx.restore();
+}
+
+const faceMesh = new FaceMesh({locateFile: (file) => {
+  return `https://cdn.jsdelivr.net/npm/@mediapipe/face_mesh/${file}`;
+}});
+faceMesh.setOptions({
+  maxNumFaces: 1,
+  minDetectionConfidence: 0.5,
+  minTrackingConfidence: 0.5
+});
+faceMesh.onResults(onResults);
+
+const camera = new Camera(videoElement, {
+  onFrame: async () => {
+    await faceMesh.send({image: videoElement});
+  },
+  width: 1280,
+  height: 720
+});
+camera.start();
+</script>
+```
+
+### Android Solution API
+
+Please first follow general
+[instructions](../getting_started/android_solutions.md#integrate-mediapipe-android-solutions-api)
+to add MediaPipe Gradle dependencies, then try the FaceMash solution API in the
+companion
+[example Android Studio project](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/solutions/facemesh)
+following
+[these instructions](../getting_started/android_solutions.md#build-solution-example-apps-in-android-studio)
+and learn more in the usage example below.
+
+Supported configuration options:
+
+*   [staticImageMode](#static_image_mode)
+*   [maxNumFaces](#max_num_faces)
+*   runOnGpu: Run the pipeline and the model inference on GPU or CPU.
+
+#### Camera Input
+
+```java
+// For camera input and result rendering with OpenGL.
+FaceMeshOptions faceMeshOptions =
+    FaceMeshOptions.builder()
+        .setMode(FaceMeshOptions.STREAMING_MODE)  // API soon to become
+        .setMaxNumFaces(1)                        // setStaticImageMode(false)
+        .setRunOnGpu(true).build();
+FaceMesh facemesh = new FaceMesh(this, faceMeshOptions);
+facemesh.setErrorListener(
+    (message, e) -> Log.e(TAG, "MediaPipe FaceMesh error:" + message));
+
+// Initializes a new CameraInput instance and connects it to MediaPipe FaceMesh.
+CameraInput cameraInput = new CameraInput(this);
+cameraInput.setNewFrameListener(
+    textureFrame -> facemesh.send(textureFrame));
+
+// Initializes a new GlSurfaceView with a ResultGlRenderer<FaceMeshResult> instance
+// that provides the interfaces to run user-defined OpenGL rendering code.
+// See mediapipe/examples/android/solutions/facemesh/src/main/java/com/google/mediapipe/examples/facemesh/FaceMeshResultGlRenderer.java
+// as an example.
+SolutionGlSurfaceView<FaceMeshResult> glSurfaceView =
+    new SolutionGlSurfaceView<>(
+        this, facemesh.getGlContext(), facemesh.getGlMajorVersion());
+glSurfaceView.setSolutionResultRenderer(new FaceMeshResultGlRenderer());
+glSurfaceView.setRenderInputImage(true);
+
+facemesh.setResultListener(
+    faceMeshResult -> {
+      NormalizedLandmark noseLandmark =
+          result.multiFaceLandmarks().get(0).getLandmarkList().get(1);
+      Log.i(
+          TAG,
+          String.format(
+              "MediaPipe FaceMesh nose normalized coordinates (value range: [0, 1]): x=%f, y=%f",
+              noseLandmark.getX(), noseLandmark.getY()));
+      // Request GL rendering.
+      glSurfaceView.setRenderData(faceMeshResult);
+      glSurfaceView.requestRender();
+    });
+
+// The runnable to start camera after the GLSurfaceView is attached.
+glSurfaceView.post(
+    () ->
+        cameraInput.start(
+            this,
+            facemesh.getGlContext(),
+            CameraInput.CameraFacing.FRONT,
+            glSurfaceView.getWidth(),
+            glSurfaceView.getHeight()));
+```
+
+#### Image Input
+
+```java
+// For reading images from gallery and drawing the output in an ImageView.
+FaceMeshOptions faceMeshOptions =
+    FaceMeshOptions.builder()
+        .setMode(FaceMeshOptions.STATIC_IMAGE_MODE)  // API soon to become
+        .setMaxNumFaces(1)                           // setStaticImageMode(true)
+        .setRunOnGpu(true).build();
+FaceMesh facemesh = new FaceMesh(this, faceMeshOptions);
+
+// Connects MediaPipe FaceMesh to the user-defined ImageView instance that allows
+// users to have the custom drawing of the output landmarks on it.
+// See mediapipe/examples/android/solutions/facemesh/src/main/java/com/google/mediapipe/examples/facemesh/FaceMeshResultImageView.java
+// as an example.
+FaceMeshResultImageView imageView = new FaceMeshResultImageView(this);
+facemesh.setResultListener(
+    faceMeshResult -> {
+      int width = faceMeshResult.inputBitmap().getWidth();
+      int height = faceMeshResult.inputBitmap().getHeight();
+      NormalizedLandmark noseLandmark =
+          result.multiFaceLandmarks().get(0).getLandmarkList().get(1);
+      Log.i(
+          TAG,
+          String.format(
+              "MediaPipe FaceMesh nose coordinates (pixel values): x=%f, y=%f",
+              noseLandmark.getX() * width, noseLandmark.getY() * height));
+      // Request canvas drawing.
+      imageView.setFaceMeshResult(faceMeshResult);
+      runOnUiThread(() -> imageView.update());
+    });
+facemesh.setErrorListener(
+    (message, e) -> Log.e(TAG, "MediaPipe FaceMesh error:" + message));
+
+// ActivityResultLauncher to get an image from the gallery as Bitmap.
+ActivityResultLauncher<Intent> imageGetter =
+    registerForActivityResult(
+        new ActivityResultContracts.StartActivityForResult(),
+        result -> {
+          Intent resultIntent = result.getData();
+          if (resultIntent != null && result.getResultCode() == RESULT_OK) {
+            Bitmap bitmap = null;
+            try {
+              bitmap =
+                  MediaStore.Images.Media.getBitmap(
+                      this.getContentResolver(), resultIntent.getData());
+            } catch (IOException e) {
+              Log.e(TAG, "Bitmap reading error:" + e);
+            }
+            if (bitmap != null) {
+              facemesh.send(bitmap);
+            }
+          }
+        });
+Intent gallery = new Intent(
+    Intent.ACTION_PICK, MediaStore.Images.Media.INTERNAL_CONTENT_URI);
+imageGetter.launch(gallery);
+```
+
+#### Video Input
+
+```java
+// For video input and result rendering with OpenGL.
+FaceMeshOptions faceMeshOptions =
+    FaceMeshOptions.builder()
+        .setMode(FaceMeshOptions.STREAMING_MODE)  // API soon to become
+        .setMaxNumFaces(1)                        // setStaticImageMode(false)
+        .setRunOnGpu(true).build();
+FaceMesh facemesh = new FaceMesh(this, faceMeshOptions);
+facemesh.setErrorListener(
+    (message, e) -> Log.e(TAG, "MediaPipe FaceMesh error:" + message));
+
+// Initializes a new VideoInput instance and connects it to MediaPipe FaceMesh.
+VideoInput videoInput = new VideoInput(this);
+videoInput.setNewFrameListener(
+    textureFrame -> facemesh.send(textureFrame));
+
+// Initializes a new GlSurfaceView with a ResultGlRenderer<FaceMeshResult> instance
+// that provides the interfaces to run user-defined OpenGL rendering code.
+// See mediapipe/examples/android/solutions/facemesh/src/main/java/com/google/mediapipe/examples/facemesh/FaceMeshResultGlRenderer.java
+// as an example.
+SolutionGlSurfaceView<FaceMeshResult> glSurfaceView =
+    new SolutionGlSurfaceView<>(
+        this, facemesh.getGlContext(), facemesh.getGlMajorVersion());
+glSurfaceView.setSolutionResultRenderer(new FaceMeshResultGlRenderer());
+glSurfaceView.setRenderInputImage(true);
+
+facemesh.setResultListener(
+    faceMeshResult -> {
+      NormalizedLandmark noseLandmark =
+          result.multiFaceLandmarks().get(0).getLandmarkList().get(1);
+      Log.i(
+          TAG,
+          String.format(
+              "MediaPipe FaceMesh nose normalized coordinates (value range: [0, 1]): x=%f, y=%f",
+              noseLandmark.getX(), noseLandmark.getY()));
+      // Request GL rendering.
+      glSurfaceView.setRenderData(faceMeshResult);
+      glSurfaceView.requestRender();
+    });
+
+ActivityResultLauncher<Intent> videoGetter =
+    registerForActivityResult(
+        new ActivityResultContracts.StartActivityForResult(),
+        result -> {
+          Intent resultIntent = result.getData();
+          if (resultIntent != null) {
+            if (result.getResultCode() == RESULT_OK) {
+              glSurfaceView.post(
+                  () ->
+                      videoInput.start(
+                          this,
+                          resultIntent.getData(),
+                          facemesh.getGlContext(),
+                          glSurfaceView.getWidth(),
+                          glSurfaceView.getHeight()));
+            }
+          }
+        });
+Intent gallery =
+    new Intent(Intent.ACTION_PICK, MediaStore.Video.Media.INTERNAL_CONTENT_URI);
+videoGetter.launch(gallery);
+```
+
 ## Example Apps
 
 Please first see general instructions for
-[Android](../getting_started/building_examples.md#android), [iOS](../getting_started/building_examples.md#ios) and
-[desktop](../getting_started/building_examples.md#desktop) on how to build MediaPipe examples.
+[Android](../getting_started/android.md), [iOS](../getting_started/ios.md) and
+[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
 
 Note: To visualize a graph, copy the graph and paste it into
 [MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
@@ -254,99 +682,6 @@ and for iOS modify `kNumFaces` in
 Tip: Maximum number of faces to detect/process is set to 1 by default. To change
 it, in the graph file modify the option of `ConstantSidePacketCalculator`.
 
-#### Python
-
-MediaPipe Python package is available on
-[PyPI](https://pypi.org/project/mediapipe/), and can be installed simply by `pip
-install mediapipe` on Linux and macOS, as described below and in this
-[colab](https://mediapipe.page.link/face_mesh_py_colab). If you do need to build
-the Python package from source, see
-[additional instructions](../getting_started/building_examples.md#python).
-
-Activate a Python virtual environment:
-
-```bash
-$ python3 -m venv mp_env && source mp_env/bin/activate
-```
-
-Install MediaPipe Python package:
-
-```bash
-(mp_env)$ pip install mediapipe
-```
-
-Run the following Python code:
-
-<!-- Do not change the example code below directly. Change the corresponding example in mediapipe/python/solutions/face_mesh.py and copy it over. -->
-
-```python
-import cv2
-import mediapipe as mp
-mp_drawing = mp.solutions.drawing_utils
-mp_face_mesh = mp.solutions.face_mesh
-
-# For static images:
-face_mesh = mp_face_mesh.FaceMesh(
-    static_image_mode=True,
-    max_num_faces=1,
-    min_detection_confidence=0.5)
-drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
-for idx, file in enumerate(file_list):
-  image = cv2.imread(file)
-  # Convert the BGR image to RGB before processing.
-  results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
-
-  # Print and draw face mesh landmarks on the image.
-  if not results.multi_face_landmarks:
-    continue
-  annotated_image = image.copy()
-  for face_landmarks in results.multi_face_landmarks:
-    print('face_landmarks:', face_landmarks)
-    mp_drawing.draw_landmarks(
-        image=annotated_image,
-        landmark_list=face_landmarks,
-        connections=mp_face_mesh.FACE_CONNECTIONS,
-        landmark_drawing_spec=drawing_spec,
-        connection_drawing_spec=drawing_spec)
-  cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', image)
-face_mesh.close()
-
-# For webcam input:
-face_mesh = mp_face_mesh.FaceMesh(
-    min_detection_confidence=0.5, min_tracking_confidence=0.5)
-drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
-cap = cv2.VideoCapture(0)
-while cap.isOpened():
-  success, image = cap.read()
-  if not success:
-    break
-
-  # Flip the image horizontally for a later selfie-view display, and convert
-  # the BGR image to RGB.
-  image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
-  # To improve performance, optionally mark the image as not writeable to
-  # pass by reference.
-  image.flags.writeable = False
-  results = face_mesh.process(image)
-
-  # Draw the face mesh annotations on the image.
-  image.flags.writeable = True
-  image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-  if results.multi_face_landmarks:
-    for face_landmarks in results.multi_face_landmarks:
-      mp_drawing.draw_landmarks(
-          image=image,
-          landmark_list=face_landmarks,
-          connections=mp_face_mesh.FACE_CONNECTIONS,
-          landmark_drawing_spec=drawing_spec,
-          connection_drawing_spec=drawing_spec)
-  cv2.imshow('MediaPipe FaceMesh', image)
-  if cv2.waitKey(5) & 0xFF == 27:
-    break
-face_mesh.close()
-cap.release()
-```
-
 ### Face Effect Example
 
 Face effect example showcases real-time mobile face effect application use case
@@ -379,3 +714,5 @@ only works for a single face. For visual reference, please refer to *Fig. 4*.
     [OBJ](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/data/canonical_face_model.obj),
     [UV visualization](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/data/canonical_face_model_uv_visualization.png)
 *   [Models and model cards](./models.md#face_mesh)
+*   [Web demo](https://code.mediapipe.dev/codepen/face_mesh)
+*   [Python Colab](https://mediapipe.page.link/face_mesh_py_colab)

docs/solutions/hair_segmentation.md

@@ -2,14 +2,20 @@
 layout: default
 title: Hair Segmentation
 parent: Solutions
-nav_order: 6
+nav_order: 8
 ---
 
 # MediaPipe Hair Segmentation
 {: .no_toc }
 
+<details close markdown="block">
+  <summary>
+    Table of contents
+  </summary>
+  {: .text-delta }
 1. TOC
 {:toc}
+</details>
 ---
 
 ![hair_segmentation_android_gpu_gif](../images/mobile/hair_segmentation_android_gpu.gif)
@@ -17,9 +23,8 @@ nav_order: 6
 ## Example Apps
 
 Please first see general instructions for
-[Android](../getting_started/building_examples.md#android), [iOS](../getting_started/building_examples.md#ios)
-and [desktop](../getting_started/building_examples.md#desktop) on how to build MediaPipe
-examples.
+[Android](../getting_started/android.md), [iOS](../getting_started/ios.md) and
+[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
 
 Note: To visualize a graph, copy the graph and paste it into
 [MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
@@ -46,7 +51,14 @@ to visualize its associated subgraphs, please see
 
 ### Web
 
-Please refer to [these instructions](../index.md#mediapipe-on-the-web).
+Use [this link](https://viz.mediapipe.dev/demo/hair_segmentation) 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. Please see
+[MediaPipe on the Web](https://developers.googleblog.com/2020/01/mediapipe-on-web.html)
+in Google Developers Blog for details.
+
+![visualizer_runner](../images/visualizer_runner.png)
 
 ## Resources
 

docs/solutions/hands.md

@@ -8,8 +8,14 @@ nav_order: 4
 # MediaPipe Hands
 {: .no_toc }
 
+<details close markdown="block">
+  <summary>
+    Table of contents
+  </summary>
+  {: .text-delta }
 1. TOC
 {:toc}
+</details>
 ---
 
 ## Overview
@@ -85,13 +91,14 @@ To detect initial hand locations, we designed a
 mobile real-time uses in a manner similar to the face detection model in
 [MediaPipe Face Mesh](./face_mesh.md). Detecting hands is a decidedly complex
 task: our
-[model](https://github.com/google/mediapipe/tree/master/mediapipe/models/palm_detection.tflite) has
-to work across a variety of hand sizes with a large scale span (~20x) relative
-to the image frame and be able to detect occluded and self-occluded hands.
-Whereas faces have high contrast patterns, e.g., in the eye and mouth region,
-the lack of such features in hands makes it comparatively difficult to detect
-them reliably from their visual features alone. Instead, providing additional
-context, like arm, body, or person features, aids accurate hand localization.
+[model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/palm_detection/palm_detection.tflite)
+has to work across a variety of hand sizes with a large scale span (~20x)
+relative to the image frame and be able to detect occluded and self-occluded
+hands. Whereas faces have high contrast patterns, e.g., in the eye and mouth
+region, the lack of such features in hands makes it comparatively difficult to
+detect them reliably from their visual features alone. Instead, providing
+additional context, like arm, body, or person features, aids accurate hand
+localization.
 
 Our method addresses the above challenges using different strategies. First, we
 train a palm detector instead of a hand detector, since estimating bounding
@@ -113,7 +120,7 @@ just 86.22%.
 ### Hand Landmark Model
 
 After the palm detection over the whole image our subsequent hand landmark
-[model](https://github.com/google/mediapipe/tree/master/mediapipe/models/hand_landmark.tflite)
+[model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark.tflite)
 performs precise keypoint localization of 21 3D hand-knuckle coordinates inside
 the detected hand regions via regression, that is direct coordinate prediction.
 The model learns a consistent internal hand pose representation and is robust
@@ -126,16 +133,440 @@ and provide additional supervision on the nature of hand geometry, we also
 render a high-quality synthetic hand model over various backgrounds and map it
 to the corresponding 3D coordinates.
 
-| ![hand_crops.png](../images/mobile/hand_crops.png)                          |
-| :-------------------------------------------------------------------------: |
-| *Fig 2. Top: Aligned hand crops passed to the tracking network with ground truth annotation. Bottom: Rendered synthetic hand images with ground truth annotation.* |
+![hand_landmarks.png](../images/mobile/hand_landmarks.png) |
+:--------------------------------------------------------: |
+*Fig 2. 21 hand landmarks.*                                |
+
+![hand_crops.png](../images/mobile/hand_crops.png)                          |
+:-------------------------------------------------------------------------: |
+*Fig 3. Top: Aligned hand crops passed to the tracking network with ground truth annotation. Bottom: Rendered synthetic hand images with ground truth annotation.* |
+
+## Solution APIs
+
+### Configuration Options
+
+Naming style and availability may differ slightly across platforms/languages.
+
+#### static_image_mode
+
+If set to `false`, the solution treats the input images as a video stream. It
+will try to detect hands in the first input images, and upon a successful
+detection further localizes the hand landmarks. In subsequent images, once all
+[max_num_hands](#max_num_hands) hands are detected and the corresponding hand
+landmarks are localized, it simply tracks those landmarks without invoking
+another detection until it loses track of any of the hands. This reduces latency
+and is ideal for processing video frames. If set to `true`, hand detection runs
+on every input image, ideal for processing a batch of static, possibly
+unrelated, images. Default to `false`.
+
+#### max_num_hands
+
+Maximum number of hands to detect. Default to `2`.
+
+#### min_detection_confidence
+
+Minimum confidence value (`[0.0, 1.0]`) from the hand detection model for the
+detection to be considered successful. Default to `0.5`.
+
+#### min_tracking_confidence:
+
+Minimum confidence value (`[0.0, 1.0]`) from the landmark-tracking model for the
+hand landmarks to be considered tracked successfully, or otherwise hand
+detection will be invoked automatically on the next input image. Setting it to a
+higher value can increase robustness of the solution, at the expense of a higher
+latency. Ignored if [static_image_mode](#static_image_mode) is `true`, where
+hand detection simply runs on every image. Default to `0.5`.
+
+### Output
+
+Naming style may differ slightly across platforms/languages.
+
+#### multi_hand_landmarks
+
+Collection of detected/tracked hands, where each hand is represented as a list
+of 21 hand landmarks and each landmark is composed of `x`, `y` and `z`. `x` and
+`y` are normalized to `[0.0, 1.0]` by the image width and height respectively.
+`z` represents the landmark depth with the depth at the wrist being the origin,
+and the smaller the value the closer the landmark is to the camera. The
+magnitude of `z` uses roughly the same scale as `x`.
+
+#### multi_handedness
+
+Collection of handedness of the detected/tracked hands (i.e. is it a left or
+right hand). Each hand is composed of `label` and `score`. `label` is a string
+of value either `"Left"` or `"Right"`. `score` is the estimated probability of
+the predicted handedness and is always greater than or equal to `0.5` (and the
+opposite handedness has an estimated probability of `1 - score`).
+
+Note that handedness is determined assuming the input image is mirrored, i.e.,
+taken with a front-facing/selfie camera with images flipped horizontally. If it
+is not the case, please swap the handedness output in the application.
+
+### Python Solution API
+
+Please first follow general [instructions](../getting_started/python.md) to
+install MediaPipe Python package, then learn more in the companion
+[Python Colab](#resources) and the usage example below.
+
+Supported configuration options:
+
+*   [static_image_mode](#static_image_mode)
+*   [max_num_hands](#max_num_hands)
+*   [min_detection_confidence](#min_detection_confidence)
+*   [min_tracking_confidence](#min_tracking_confidence)
+
+```python
+import cv2
+import mediapipe as mp
+mp_drawing = mp.solutions.drawing_utils
+mp_drawing_styles = mp.solutions.drawing_styles
+mp_hands = mp.solutions.hands
+
+# For static images:
+IMAGE_FILES = []
+with mp_hands.Hands(
+    static_image_mode=True,
+    max_num_hands=2,
+    min_detection_confidence=0.5) as hands:
+  for idx, file in enumerate(IMAGE_FILES):
+    # Read an image, flip it around y-axis for correct handedness output (see
+    # above).
+    image = cv2.flip(cv2.imread(file), 1)
+    # Convert the BGR image to RGB before processing.
+    results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+    # Print handedness and draw hand landmarks on the image.
+    print('Handedness:', results.multi_handedness)
+    if not results.multi_hand_landmarks:
+      continue
+    image_height, image_width, _ = image.shape
+    annotated_image = image.copy()
+    for hand_landmarks in results.multi_hand_landmarks:
+      print('hand_landmarks:', hand_landmarks)
+      print(
+          f'Index finger tip coordinates: (',
+          f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x * image_width}, '
+          f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y * image_height})'
+      )
+      mp_drawing.draw_landmarks(
+          annotated_image,
+          hand_landmarks,
+          mp_hands.HAND_CONNECTIONS,
+          mp_drawing_styles.get_default_hand_landmarks_style(),
+          mp_drawing_styles.get_default_hand_connections_style())
+    cv2.imwrite(
+        '/tmp/annotated_image' + str(idx) + '.png', cv2.flip(annotated_image, 1))
+
+# For webcam input:
+cap = cv2.VideoCapture(0)
+with mp_hands.Hands(
+    min_detection_confidence=0.5,
+    min_tracking_confidence=0.5) as hands:
+  while cap.isOpened():
+    success, image = cap.read()
+    if not success:
+      print("Ignoring empty camera frame.")
+      # If loading a video, use 'break' instead of 'continue'.
+      continue
+
+    # Flip the image horizontally for a later selfie-view display, and convert
+    # the BGR image to RGB.
+    image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
+    # To improve performance, optionally mark the image as not writeable to
+    # pass by reference.
+    image.flags.writeable = False
+    results = hands.process(image)
+
+    # Draw the hand annotations on the image.
+    image.flags.writeable = True
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    if results.multi_hand_landmarks:
+      for hand_landmarks in results.multi_hand_landmarks:
+        mp_drawing.draw_landmarks(
+            image,
+            hand_landmarks,
+            mp_hands.HAND_CONNECTIONS,
+            mp_drawing_styles.get_default_hand_landmarks_style(),
+            mp_drawing_styles.get_default_hand_connections_style())
+    cv2.imshow('MediaPipe Hands', image)
+    if cv2.waitKey(5) & 0xFF == 27:
+      break
+cap.release()
+```
+
+### JavaScript Solution API
+
+Please first see general [introduction](../getting_started/javascript.md) on
+MediaPipe in JavaScript, then learn more in the companion [web demo](#resources)
+and a [fun application], and the following usage example.
+
+Supported configuration options:
+
+*   [maxNumHands](#max_num_hands)
+*   [minDetectionConfidence](#min_detection_confidence)
+*   [minTrackingConfidence](#min_tracking_confidence)
+
+```html
+<!DOCTYPE html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/camera_utils/camera_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/control_utils/control_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/drawing_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/hands/hands.js" crossorigin="anonymous"></script>
+</head>
+
+<body>
+  <div class="container">
+    <video class="input_video"></video>
+    <canvas class="output_canvas" width="1280px" height="720px"></canvas>
+  </div>
+</body>
+</html>
+```
+
+```javascript
+<script type="module">
+const videoElement = document.getElementsByClassName('input_video')[0];
+const canvasElement = document.getElementsByClassName('output_canvas')[0];
+const canvasCtx = canvasElement.getContext('2d');
+
+function onResults(results) {
+  canvasCtx.save();
+  canvasCtx.clearRect(0, 0, canvasElement.width, canvasElement.height);
+  canvasCtx.drawImage(
+      results.image, 0, 0, canvasElement.width, canvasElement.height);
+  if (results.multiHandLandmarks) {
+    for (const landmarks of results.multiHandLandmarks) {
+      drawConnectors(canvasCtx, landmarks, HAND_CONNECTIONS,
+                     {color: '#00FF00', lineWidth: 5});
+      drawLandmarks(canvasCtx, landmarks, {color: '#FF0000', lineWidth: 2});
+    }
+  }
+  canvasCtx.restore();
+}
+
+const hands = new Hands({locateFile: (file) => {
+  return `https://cdn.jsdelivr.net/npm/@mediapipe/hands/${file}`;
+}});
+hands.setOptions({
+  maxNumHands: 2,
+  minDetectionConfidence: 0.5,
+  minTrackingConfidence: 0.5
+});
+hands.onResults(onResults);
+
+const camera = new Camera(videoElement, {
+  onFrame: async () => {
+    await hands.send({image: videoElement});
+  },
+  width: 1280,
+  height: 720
+});
+camera.start();
+</script>
+```
+
+### Android Solution API
+
+Please first follow general
+[instructions](../getting_started/android_solutions.md#integrate-mediapipe-android-solutions-api)
+to add MediaPipe Gradle dependencies, then try the Hands solution API in the
+companion
+[example Android Studio project](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/solutions/hands)
+following
+[these instructions](../getting_started/android_solutions.md#build-solution-example-apps-in-android-studio)
+and learn more in usage example below.
+
+Supported configuration options:
+
+*   [staticImageMode](#static_image_mode)
+*   [maxNumHands](#max_num_hands)
+*   runOnGpu: Run the pipeline and the model inference on GPU or CPU.
+
+#### Camera Input
+
+```java
+// For camera input and result rendering with OpenGL.
+HandsOptions handsOptions =
+    HandsOptions.builder()
+        .setMode(HandsOptions.STREAMING_MODE)  // API soon to become
+        .setMaxNumHands(1)                     // setStaticImageMode(false)
+        .setRunOnGpu(true).build();
+Hands hands = new Hands(this, handsOptions);
+hands.setErrorListener(
+    (message, e) -> Log.e(TAG, "MediaPipe Hands error:" + message));
+
+// Initializes a new CameraInput instance and connects it to MediaPipe Hands.
+CameraInput cameraInput = new CameraInput(this);
+cameraInput.setNewFrameListener(
+    textureFrame -> hands.send(textureFrame));
+
+// Initializes a new GlSurfaceView with a ResultGlRenderer<HandsResult> instance
+// that provides the interfaces to run user-defined OpenGL rendering code.
+// See mediapipe/examples/android/solutions/hands/src/main/java/com/google/mediapipe/examples/hands/HandsResultGlRenderer.java
+// as an example.
+SolutionGlSurfaceView<HandsResult> glSurfaceView =
+    new SolutionGlSurfaceView<>(
+        this, hands.getGlContext(), hands.getGlMajorVersion());
+glSurfaceView.setSolutionResultRenderer(new HandsResultGlRenderer());
+glSurfaceView.setRenderInputImage(true);
+
+hands.setResultListener(
+    handsResult -> {
+      NormalizedLandmark wristLandmark = Hands.getHandLandmark(
+          handsResult, 0, HandLandmark.WRIST);
+      Log.i(
+          TAG,
+          String.format(
+              "MediaPipe Hand wrist normalized coordinates (value range: [0, 1]): x=%f, y=%f",
+              wristLandmark.getX(), wristLandmark.getY()));
+      // Request GL rendering.
+      glSurfaceView.setRenderData(handsResult);
+      glSurfaceView.requestRender();
+    });
+
+// The runnable to start camera after the GLSurfaceView is attached.
+glSurfaceView.post(
+    () ->
+        cameraInput.start(
+            this,
+            hands.getGlContext(),
+            CameraInput.CameraFacing.FRONT,
+            glSurfaceView.getWidth(),
+            glSurfaceView.getHeight()));
+```
+
+#### Image Input
+
+```java
+// For reading images from gallery and drawing the output in an ImageView.
+HandsOptions handsOptions =
+    HandsOptions.builder()
+        .setMode(HandsOptions.STATIC_IMAGE_MODE)  // API soon to become
+        .setMaxNumHands(1)                        // setStaticImageMode(true)
+        .setRunOnGpu(true).build();
+Hands hands = new Hands(this, handsOptions);
+
+// Connects MediaPipe Hands to the user-defined ImageView instance that allows
+// users to have the custom drawing of the output landmarks on it.
+// See mediapipe/examples/android/solutions/hands/src/main/java/com/google/mediapipe/examples/hands/HandsResultImageView.java
+// as an example.
+HandsResultImageView imageView = new HandsResultImageView(this);
+hands.setResultListener(
+    handsResult -> {
+      int width = handsResult.inputBitmap().getWidth();
+      int height = handsResult.inputBitmap().getHeight();
+      NormalizedLandmark wristLandmark = Hands.getHandLandmark(
+          handsResult, 0, HandLandmark.WRIST);
+      Log.i(
+          TAG,
+          String.format(
+              "MediaPipe Hand wrist coordinates (pixel values): x=%f, y=%f",
+              wristLandmark.getX() * width, wristLandmark.getY() * height));
+      // Request canvas drawing.
+      imageView.setHandsResult(handsResult);
+      runOnUiThread(() -> imageView.update());
+    });
+hands.setErrorListener(
+    (message, e) -> Log.e(TAG, "MediaPipe Hands error:" + message));
+
+// ActivityResultLauncher to get an image from the gallery as Bitmap.
+ActivityResultLauncher<Intent> imageGetter =
+    registerForActivityResult(
+        new ActivityResultContracts.StartActivityForResult(),
+        result -> {
+          Intent resultIntent = result.getData();
+          if (resultIntent != null && result.getResultCode() == RESULT_OK) {
+            Bitmap bitmap = null;
+            try {
+              bitmap =
+                  MediaStore.Images.Media.getBitmap(
+                      this.getContentResolver(), resultIntent.getData());
+            } catch (IOException e) {
+              Log.e(TAG, "Bitmap reading error:" + e);
+            }
+            if (bitmap != null) {
+              hands.send(bitmap);
+            }
+          }
+        });
+Intent gallery = new Intent(
+    Intent.ACTION_PICK, MediaStore.Images.Media.INTERNAL_CONTENT_URI);
+imageGetter.launch(gallery);
+```
+
+#### Video Input
+
+```java
+// For video input and result rendering with OpenGL.
+HandsOptions handsOptions =
+    HandsOptions.builder()
+        .setMode(HandsOptions.STREAMING_MODE)  // API soon to become
+        .setMaxNumHands(1)                     // setStaticImageMode(false)
+        .setRunOnGpu(true).build();
+Hands hands = new Hands(this, handsOptions);
+hands.setErrorListener(
+    (message, e) -> Log.e(TAG, "MediaPipe Hands error:" + message));
+
+// Initializes a new VideoInput instance and connects it to MediaPipe Hands.
+VideoInput videoInput = new VideoInput(this);
+videoInput.setNewFrameListener(
+    textureFrame -> hands.send(textureFrame));
+
+// Initializes a new GlSurfaceView with a ResultGlRenderer<HandsResult> instance
+// that provides the interfaces to run user-defined OpenGL rendering code.
+// See mediapipe/examples/android/solutions/hands/src/main/java/com/google/mediapipe/examples/hands/HandsResultGlRenderer.java
+// as an example.
+SolutionGlSurfaceView<HandsResult> glSurfaceView =
+    new SolutionGlSurfaceView<>(
+        this, hands.getGlContext(), hands.getGlMajorVersion());
+glSurfaceView.setSolutionResultRenderer(new HandsResultGlRenderer());
+glSurfaceView.setRenderInputImage(true);
+
+hands.setResultListener(
+    handsResult -> {
+      NormalizedLandmark wristLandmark = Hands.getHandLandmark(
+          handsResult, 0, HandLandmark.WRIST);
+      Log.i(
+          TAG,
+          String.format(
+              "MediaPipe Hand wrist normalized coordinates (value range: [0, 1]): x=%f, y=%f",
+              wristLandmark.getX(), wristLandmark.getY()));
+      // Request GL rendering.
+      glSurfaceView.setRenderData(handsResult);
+      glSurfaceView.requestRender();
+    });
+
+ActivityResultLauncher<Intent> videoGetter =
+    registerForActivityResult(
+        new ActivityResultContracts.StartActivityForResult(),
+        result -> {
+          Intent resultIntent = result.getData();
+          if (resultIntent != null) {
+            if (result.getResultCode() == RESULT_OK) {
+              glSurfaceView.post(
+                  () ->
+                      videoInput.start(
+                          this,
+                          resultIntent.getData(),
+                          hands.getGlContext(),
+                          glSurfaceView.getWidth(),
+                          glSurfaceView.getHeight()));
+            }
+          }
+        });
+Intent gallery =
+    new Intent(Intent.ACTION_PICK, MediaStore.Video.Media.INTERNAL_CONTENT_URI);
+videoGetter.launch(gallery);
+```
 
 ## Example Apps
 
 Please first see general instructions for
-[Android](../getting_started/building_examples.md#android), [iOS](../getting_started/building_examples.md#ios)
-and [desktop](../getting_started/building_examples.md#desktop) on how to build MediaPipe
-examples.
+[Android](../getting_started/android.md), [iOS](../getting_started/ios.md) and
+[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
 
 Note: To visualize a graph, copy the graph and paste it into
 [MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
@@ -186,99 +617,6 @@ and for iOS modify `kNumHands` in
 Tip: Maximum number of hands to detect/process is set to 2 by default. To change
 it, in the graph file modify the option of `ConstantSidePacketCalculator`.
 
-### Python
-
-MediaPipe Python package is available on
-[PyPI](https://pypi.org/project/mediapipe/), and can be installed simply by `pip
-install mediapipe` on Linux and macOS, as described below and in this
-[colab](https://mediapipe.page.link/hands_py_colab). If you do need to build the
-Python package from source, see
-[additional instructions](../getting_started/building_examples.md#python).
-
-Activate a Python virtual environment:
-
-```bash
-$ python3 -m venv mp_env && source mp_env/bin/activate
-```
-
-Install MediaPipe Python package:
-
-```bash
-(mp_env)$ pip install mediapipe
-```
-
-Run the following Python code:
-
-<!-- Do not change the example code below directly. Change the corresponding example in mediapipe/python/solutions/hands.py and copy it over. -->
-
-```python
-import cv2
-import mediapipe as mp
-mp_drawing = mp.solutions.drawing_utils
-mp_hands = mp.solutions.hands
-
-# For static images:
-hands = mp_hands.Hands(
-    static_image_mode=True,
-    max_num_hands=2,
-    min_detection_confidence=0.7)
-for idx, file in enumerate(file_list):
-  # Read an image, flip it around y-axis for correct handedness output (see
-  # above).
-  image = cv2.flip(cv2.imread(file), 1)
-  # Convert the BGR image to RGB before processing.
-  results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
-
-  # Print handedness and draw hand landmarks on the image.
-  print('handedness:', results.multi_handedness)
-  if not results.multi_hand_landmarks:
-    continue
-  annotated_image = image.copy()
-  for hand_landmarks in results.multi_hand_landmarks:
-    print('hand_landmarks:', hand_landmarks)
-    mp_drawing.draw_landmarks(
-        annotated_image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
-  cv2.imwrite(
-      '/tmp/annotated_image' + str(idx) + '.png', cv2.flip(image, 1))
-hands.close()
-
-# For webcam input:
-hands = mp_hands.Hands(
-    min_detection_confidence=0.7, min_tracking_confidence=0.5)
-cap = cv2.VideoCapture(0)
-while cap.isOpened():
-  success, image = cap.read()
-  if not success:
-    break
-
-  # Flip the image horizontally for a later selfie-view display, and convert
-  # the BGR image to RGB.
-  image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
-  # To improve performance, optionally mark the image as not writeable to
-  # pass by reference.
-  image.flags.writeable = False
-  results = hands.process(image)
-
-  # Draw the hand annotations on the image.
-  image.flags.writeable = True
-  image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-  if results.multi_hand_landmarks:
-    for hand_landmarks in results.multi_hand_landmarks:
-      mp_drawing.draw_landmarks(
-          image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
-  cv2.imshow('MediaPipe Hands', image)
-  if cv2.waitKey(5) & 0xFF == 27:
-    break
-hands.close()
-cap.release()
-```
-
-Tip: Use command `deactivate` to exit the Python virtual environment.
-
-### Web
-
-Please refer to [these instructions](../index.md#mediapipe-on-the-web).
-
 ## Resources
 
 *   Google AI Blog:
@@ -289,3 +627,6 @@ Please refer to [these instructions](../index.md#mediapipe-on-the-web).
     [MediaPipe Hands: On-device Real-time Hand Tracking](https://arxiv.org/abs/2006.10214)
     ([presentation](https://www.youtube.com/watch?v=I-UOrvxxXEk))
 *   [Models and model cards](./models.md#hands)
+*   [Web demo](https://code.mediapipe.dev/codepen/hands)
+*   [Fun application](https://code.mediapipe.dev/codepen/defrost)
+*   [Python Colab](https://mediapipe.page.link/hands_py_colab)

docs/solutions/holistic.md

@@ -0,0 +1,436 @@
+---
+layout: default
+title: Holistic
+parent: Solutions
+nav_order: 6
+---
+
+# MediaPipe Holistic
+{: .no_toc }
+
+<details close markdown="block">
+  <summary>
+    Table of contents
+  </summary>
+  {: .text-delta }
+1. TOC
+{:toc}
+</details>
+---
+
+## Overview
+
+Live perception of simultaneous [human pose](./pose.md),
+[face landmarks](./face_mesh.md), and [hand tracking](./hands.md) in real-time
+on mobile devices can enable various modern life applications: fitness and sport
+analysis, gesture control and sign language recognition, augmented reality
+try-on and effects. MediaPipe already offers fast and accurate, yet separate,
+solutions for these tasks. Combining them all in real-time into a semantically
+consistent end-to-end solution is a uniquely difficult problem requiring
+simultaneous inference of multiple, dependent neural networks.
+
+![holistic_sports_and_gestures_example.gif](../images/mobile/holistic_sports_and_gestures_example.gif) |
+:----------------------------------------------------------------------------------------------------: |
+*Fig 1. Example of MediaPipe Holistic.*                                                                |
+
+## ML Pipeline
+
+The MediaPipe Holistic pipeline integrates separate models for
+[pose](./pose.md), [face](./face_mesh.md) and [hand](./hands.md) components,
+each of which are optimized for their particular domain. However, because of
+their different specializations, the input to one component is not well-suited
+for the others. The pose estimation model, for example, takes a lower, fixed
+resolution video frame (256x256) as input. But if one were to crop the hand and
+face regions from that image to pass to their respective models, the image
+resolution would be too low for accurate articulation. Therefore, we designed
+MediaPipe Holistic as a multi-stage pipeline, which treats the different regions
+using a region appropriate image resolution.
+
+First, we estimate the human pose (top of Fig 2) with [BlazePose](./pose.md)’s
+pose detector and subsequent landmark model. Then, using the inferred pose
+landmarks we derive three regions of interest (ROI) crops for each hand (2x) and
+the face, and employ a re-crop model to improve the ROI. We then crop the
+full-resolution input frame to these ROIs and apply task-specific face and hand
+models to estimate their corresponding landmarks. Finally, we merge all
+landmarks with those of the pose model to yield the full 540+ landmarks.
+
+![holistic_pipeline_example.jpg](../images/mobile/holistic_pipeline_example.jpg) |
+:------------------------------------------------------------------------------: |
+*Fig 2. MediaPipe Holistic Pipeline Overview.*                                   |
+
+To streamline the identification of ROIs for face and hands, we utilize a
+tracking approach similar to the one we use for standalone
+[face](./face_mesh.md) and [hand](./hands.md) pipelines. It assumes that the
+object doesn't move significantly between frames and uses estimation from the
+previous frame as a guide to the object region on the current one. However,
+during fast movements, the tracker can lose the target, which requires the
+detector to re-localize it in the image. MediaPipe Holistic uses
+[pose](./pose.md) prediction (on every frame) as an additional ROI prior to
+reduce the response time of the pipeline when reacting to fast movements. This
+also enables the model to retain semantic consistency across the body and its
+parts by preventing a mixup between left and right hands or body parts of one
+person in the frame with another.
+
+In addition, the resolution of the input frame to the pose model is low enough
+that the resulting ROIs for face and hands are still too inaccurate to guide the
+re-cropping of those regions, which require a precise input crop to remain
+lightweight. To close this accuracy gap we use lightweight face and hand re-crop
+models that play the role of
+[spatial transformers](https://arxiv.org/abs/1506.02025) and cost only ~10% of
+corresponding model's inference time.
+
+The pipeline is implemented as a MediaPipe
+[graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/holistic_tracking/holistic_tracking_gpu.pbtxt)
+that uses a
+[holistic landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/holistic_landmark/holistic_landmark_gpu.pbtxt)
+from the
+[holistic landmark module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/holistic_landmark)
+and renders using a dedicated
+[holistic renderer subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/holistic_tracking/holistic_tracking_to_render_data.pbtxt).
+The
+[holistic landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/holistic_landmark/holistic_landmark_gpu.pbtxt)
+internally uses a
+[pose landmark module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark)
+,
+[hand landmark module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark)
+and
+[face landmark module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/).
+Please check them for implementation details.
+
+Note: To visualize a graph, copy the graph and paste it into
+[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
+to visualize its associated subgraphs, please see
+[visualizer documentation](../tools/visualizer.md).
+
+## Models
+
+### Landmark Models
+
+MediaPipe Holistic utilizes the pose, face and hand landmark models in
+[MediaPipe Pose](./pose.md), [MediaPipe Face Mesh](./face_mesh.md) and
+[MediaPipe Hands](./hands.md) respectively to generate a total of 543 landmarks
+(33 pose landmarks, 468 face landmarks, and 21 hand landmarks per hand).
+
+### Hand Recrop Model
+
+For cases when the accuracy of the pose model is low enough that the resulting
+ROIs for hands are still too inaccurate we run the additional lightweight hand
+re-crop model that play the role of
+[spatial transformer](https://arxiv.org/abs/1506.02025) and cost only ~10% of
+hand model inference time.
+
+## Solution APIs
+
+### Cross-platform Configuration Options
+
+Naming style and availability may differ slightly across platforms/languages.
+
+#### static_image_mode
+
+If set to `false`, the solution treats the input images as a video stream. It
+will try to detect the most prominent person in the very first images, and upon
+a successful detection further localizes the pose and other landmarks. In
+subsequent images, it then simply tracks those landmarks without invoking
+another detection until it loses track, on reducing computation and latency. If
+set to `true`, person detection runs every input image, ideal for processing a
+batch of static, possibly unrelated, images. Default to `false`.
+
+#### model_complexity
+
+Complexity of the pose landmark model: `0`, `1` or `2`. Landmark accuracy as
+well as inference latency generally go up with the model complexity. Default to
+`1`.
+
+#### smooth_landmarks
+
+If set to `true`, the solution filters pose landmarks across different input
+images to reduce jitter, but ignored if [static_image_mode](#static_image_mode)
+is also set to `true`. Default to `true`.
+
+#### min_detection_confidence
+
+Minimum confidence value (`[0.0, 1.0]`) from the person-detection model for the
+detection to be considered successful. Default to `0.5`.
+
+#### min_tracking_confidence
+
+Minimum confidence value (`[0.0, 1.0]`) from the landmark-tracking model for the
+pose landmarks to be considered tracked successfully, or otherwise person
+detection will be invoked automatically on the next input image. Setting it to a
+higher value can increase robustness of the solution, at the expense of a higher
+latency. Ignored if [static_image_mode](#static_image_mode) is `true`, where
+person detection simply runs on every image. Default to `0.5`.
+
+### Output
+
+Naming style may differ slightly across platforms/languages.
+
+#### pose_landmarks
+
+A list of pose landmarks. Each landmark consists of the following:
+
+*   `x` and `y`: Landmark coordinates normalized to `[0.0, 1.0]` by the image
+    width and height respectively.
+*   `z`: Should be discarded as currently the model is not fully trained to
+    predict depth, but this is something on the roadmap.
+*   `visibility`: A value in `[0.0, 1.0]` indicating the likelihood of the
+    landmark being visible (present and not occluded) in the image.
+
+#### pose_world_landmarks
+
+Another list of pose landmarks in world coordinates. Each landmark consists of
+the following:
+
+*   `x`, `y` and `z`: Real-world 3D coordinates in meters with the origin at the
+    center between hips.
+*   `visibility`: Identical to that defined in the corresponding
+    [pose_landmarks](#pose_landmarks).
+
+#### face_landmarks
+
+A list of 468 face landmarks. Each landmark consists of `x`, `y` and `z`. `x`
+and `y` are normalized to `[0.0, 1.0]` by the image width and height
+respectively. `z` represents the landmark depth with the depth at center of the
+head being the origin, and the smaller the value the closer the landmark is to
+the camera. The magnitude of `z` uses roughly the same scale as `x`.
+
+#### left_hand_landmarks
+
+A list of 21 hand landmarks on the left hand. Each landmark consists of `x`, `y`
+and `z`. `x` and `y` are normalized to `[0.0, 1.0]` by the image width and
+height respectively. `z` represents the landmark depth with the depth at the
+wrist being the origin, and the smaller the value the closer the landmark is to
+the camera. The magnitude of `z` uses roughly the same scale as `x`.
+
+#### right_hand_landmarks
+
+A list of 21 hand landmarks on the right hand, in the same representation as
+[left_hand_landmarks](#left_hand_landmarks).
+
+### Python Solution API
+
+Please first follow general [instructions](../getting_started/python.md) to
+install MediaPipe Python package, then learn more in the companion
+[Python Colab](#resources) and the usage example below.
+
+Supported configuration options:
+
+*   [static_image_mode](#static_image_mode)
+*   [model_complexity](#model_complexity)
+*   [smooth_landmarks](#smooth_landmarks)
+*   [min_detection_confidence](#min_detection_confidence)
+*   [min_tracking_confidence](#min_tracking_confidence)
+
+```python
+import cv2
+import mediapipe as mp
+mp_drawing = mp.solutions.drawing_utils
+mp_drawing_styles = mp.solutions.drawing_styles
+mp_holistic = mp.solutions.holistic
+
+# For static images:
+IMAGE_FILES = []
+with mp_holistic.Holistic(
+    static_image_mode=True,
+    model_complexity=2) as holistic:
+  for idx, file in enumerate(IMAGE_FILES):
+    image = cv2.imread(file)
+    image_height, image_width, _ = image.shape
+    # Convert the BGR image to RGB before processing.
+    results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+    if results.pose_landmarks:
+      print(
+          f'Nose coordinates: ('
+          f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].x * image_width}, '
+          f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].y * image_height})'
+      )
+    # Draw pose, left and right hands, and face landmarks on the image.
+    annotated_image = image.copy()
+    mp_drawing.draw_landmarks(
+        annotated_image,
+        results.face_landmarks,
+        mp_holistic.FACEMESH_TESSELATION,
+        landmark_drawing_spec=None,
+        connection_drawing_spec=mp_drawing_styles
+        .get_default_face_mesh_tesselation_style())
+    mp_drawing.draw_landmarks(
+        annotated_image,
+        results.pose_landmarks,
+        mp_holistic.POSE_CONNECTIONS,
+        landmark_drawing_spec=mp_drawing_styles.
+        get_default_pose_landmarks_style())
+    cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
+    # Plot pose world landmarks.
+    mp_drawing.plot_landmarks(
+        results.pose_world_landmarks, mp_holistic.POSE_CONNECTIONS)
+
+# For webcam input:
+cap = cv2.VideoCapture(0)
+with mp_holistic.Holistic(
+    min_detection_confidence=0.5,
+    min_tracking_confidence=0.5) as holistic:
+  while cap.isOpened():
+    success, image = cap.read()
+    if not success:
+      print("Ignoring empty camera frame.")
+      # If loading a video, use 'break' instead of 'continue'.
+      continue
+
+    # Flip the image horizontally for a later selfie-view display, and convert
+    # the BGR image to RGB.
+    image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
+    # To improve performance, optionally mark the image as not writeable to
+    # pass by reference.
+    image.flags.writeable = False
+    results = holistic.process(image)
+
+    # Draw landmark annotation on the image.
+    image.flags.writeable = True
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    mp_drawing.draw_landmarks(
+        image,
+        results.face_landmarks,
+        mp_holistic.FACEMESH_CONTOURS,
+        landmark_drawing_spec=None,
+        connection_drawing_spec=mp_drawing_styles
+        .get_default_face_mesh_contours_style())
+    mp_drawing.draw_landmarks(
+        image,
+        results.pose_landmarks,
+        mp_holistic.POSE_CONNECTIONS,
+        landmark_drawing_spec=mp_drawing_styles
+        .get_default_pose_landmarks_style())
+    cv2.imshow('MediaPipe Holistic', image)
+    if cv2.waitKey(5) & 0xFF == 27:
+      break
+cap.release()
+```
+
+### JavaScript Solution API
+
+Please first see general [introduction](../getting_started/javascript.md) on
+MediaPipe in JavaScript, then learn more in the companion [web demo](#resources)
+and the following usage example.
+
+Supported configuration options:
+
+*   [modelComplexity](#model_complexity)
+*   [smoothLandmarks](#smooth_landmarks)
+*   [minDetectionConfidence](#min_detection_confidence)
+*   [minTrackingConfidence](#min_tracking_confidence)
+
+```html
+<!DOCTYPE html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/camera_utils/camera_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/control_utils/control_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/drawing_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/holistic/holistic.js" crossorigin="anonymous"></script>
+</head>
+
+<body>
+  <div class="container">
+    <video class="input_video"></video>
+    <canvas class="output_canvas" width="1280px" height="720px"></canvas>
+  </div>
+</body>
+</html>
+```
+
+```javascript
+<script type="module">
+const videoElement = document.getElementsByClassName('input_video')[0];
+const canvasElement = document.getElementsByClassName('output_canvas')[0];
+const canvasCtx = canvasElement.getContext('2d');
+
+function onResults(results) {
+  canvasCtx.save();
+  canvasCtx.clearRect(0, 0, canvasElement.width, canvasElement.height);
+  canvasCtx.drawImage(
+      results.image, 0, 0, canvasElement.width, canvasElement.height);
+  drawConnectors(canvasCtx, results.poseLandmarks, POSE_CONNECTIONS,
+                 {color: '#00FF00', lineWidth: 4});
+  drawLandmarks(canvasCtx, results.poseLandmarks,
+                {color: '#FF0000', lineWidth: 2});
+  drawConnectors(canvasCtx, results.faceLandmarks, FACEMESH_TESSELATION,
+                 {color: '#C0C0C070', lineWidth: 1});
+  drawConnectors(canvasCtx, results.leftHandLandmarks, HAND_CONNECTIONS,
+                 {color: '#CC0000', lineWidth: 5});
+  drawLandmarks(canvasCtx, results.leftHandLandmarks,
+                {color: '#00FF00', lineWidth: 2});
+  drawConnectors(canvasCtx, results.rightHandLandmarks, HAND_CONNECTIONS,
+                 {color: '#00CC00', lineWidth: 5});
+  drawLandmarks(canvasCtx, results.rightHandLandmarks,
+                {color: '#FF0000', lineWidth: 2});
+  canvasCtx.restore();
+}
+
+const holistic = new Holistic({locateFile: (file) => {
+  return `https://cdn.jsdelivr.net/npm/@mediapipe/holistic/${file}`;
+}});
+holistic.setOptions({
+  modelComplexity: 1,
+  smoothLandmarks: true,
+  minDetectionConfidence: 0.5,
+  minTrackingConfidence: 0.5
+});
+holistic.onResults(onResults);
+
+const camera = new Camera(videoElement, {
+  onFrame: async () => {
+    await holistic.send({image: videoElement});
+  },
+  width: 1280,
+  height: 720
+});
+camera.start();
+</script>
+```
+
+## Example Apps
+
+Please first see general instructions for
+[Android](../getting_started/android.md), [iOS](../getting_started/ios.md), and
+[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
+
+Note: To visualize a graph, copy the graph and paste it into
+[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
+to visualize its associated subgraphs, please see
+[visualizer documentation](../tools/visualizer.md).
+
+### Mobile
+
+*   Graph:
+    [`mediapipe/graphs/holistic_tracking/holistic_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/holistic_tracking/holistic_tracking_gpu.pbtxt)
+*   Android target:
+    [(or download prebuilt ARM64 APK)](https://drive.google.com/file/d/1o-Trp2GIRitA0OvmZWUQjVMa476xpfgK/view?usp=sharing)
+    [`mediapipe/examples/android/src/java/com/google/mediapipe/apps/holistictrackinggpu:holistictrackinggpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/holistictrackinggpu/BUILD)
+*   iOS target:
+    [`mediapipe/examples/ios/holistictrackinggpu:HolisticTrackingGpuApp`](http:/mediapipe/examples/ios/holistictrackinggpu/BUILD)
+
+### Desktop
+
+Please first see general instructions for [desktop](../getting_started/cpp.md)
+on how to build MediaPipe examples.
+
+*   Running on CPU
+    *   Graph:
+        [`mediapipe/graphs/holistic_tracking/holistic_tracking_cpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/holistic_tracking/holistic_tracking_cpu.pbtxt)
+    *   Target:
+        [`mediapipe/examples/desktop/holistic_tracking:holistic_tracking_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/holistic_tracking/BUILD)
+*   Running on GPU
+    *   Graph:
+        [`mediapipe/graphs/holistic_tracking/holistic_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/holistic_tracking/holistic_tracking_gpu.pbtxt)
+    *   Target:
+        [`mediapipe/examples/desktop/holistic_tracking:holistic_tracking_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/holistic_tracking/BUILD)
+
+## Resources
+
+*   Google AI Blog:
+    [MediaPipe Holistic - Simultaneous Face, Hand and Pose Prediction, on Device](https://ai.googleblog.com/2020/12/mediapipe-holistic-simultaneous-face.html)
+*   [Models and model cards](./models.md#holistic)
+*   [Web demo](https://code.mediapipe.dev/codepen/holistic)
+*   [Python Colab](https://mediapipe.page.link/holistic_py_colab)

docs/solutions/instant_motion_tracking.md

@@ -2,14 +2,20 @@
 layout: default
 title: Instant Motion Tracking
 parent: Solutions
-nav_order: 9
+nav_order: 11
 ---
 
 # MediaPipe Instant Motion Tracking
 {: .no_toc }
 
+<details close markdown="block">
+  <summary>
+    Table of contents
+  </summary>
+  {: .text-delta }
 1. TOC
 {:toc}
+</details>
 ---
 
 ## Overview
@@ -104,14 +110,32 @@ and connected camera.
 ## Example Apps
 
 Please first see general instructions for
-[Android](../getting_started/building_examples.md#android) on how to build
-MediaPipe examples.
+[Android](../getting_started/android.md) on how to build MediaPipe examples.
 
 * Graph: [mediapipe/graphs/instant_motion_tracking/instant_motion_tracking.pbtxt](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/instant_motion_tracking/instant_motion_tracking.pbtxt)
 
 * Android target (or download prebuilt [ARM64 APK](https://drive.google.com/file/d/1KnaBBoKpCHR73nOBJ4fL_YdWVTAcwe6L/view?usp=sharing)):
 [`mediapipe/examples/android/src/java/com/google/mediapipe/apps/instantmotiontracking:instantmotiontracking`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/instantmotiontracking/BUILD)
 
+* Assets rendered by the [GlAnimationOverlayCalculator](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.cc) must be preprocessed into an OpenGL-ready custom .uuu format. This can be done
+for user assets as follows:
+> First run
+>
+> ```shell
+> ./mediapipe/graphs/object_detection_3d/obj_parser/obj_cleanup.sh [INPUT_DIR] [INTERMEDIATE_OUTPUT_DIR]
+> ```
+> and then run
+>
+> ```build
+> bazel run -c opt mediapipe/graphs/object_detection_3d/obj_parser:ObjParser -- input_dir=[INTERMEDIATE_OUTPUT_DIR] output_dir=[OUTPUT_DIR]
+> ```
+> INPUT_DIR should be the folder with initial asset .obj files to be processed,
+> and OUTPUT_DIR is the folder where the processed asset .uuu file will be placed.
+>
+> Note: ObjParser combines all .obj files found in the given directory into a
+> single .uuu animation file, using the order given by sorting the filenames alphanumerically. Also the ObjParser directory inputs must be given as
+> absolute paths, not relative paths. See parser utility library at [`mediapipe/graphs/object_detection_3d/obj_parser/`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/obj_parser/) for more details.
+
 ## Resources
 
 *   Google Developers Blog:

docs/solutions/iris.md

@@ -8,8 +8,14 @@ nav_order: 3
 # MediaPipe Iris
 {: .no_toc }
 
+<details close markdown="block">
+  <summary>
+    Table of contents
+  </summary>
+  {: .text-delta }
 1. TOC
 {:toc}
+</details>
 ---
 
 ## Overview
@@ -63,7 +69,7 @@ and renders using a dedicated
 The
 [face landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/face_landmark_front_gpu.pbtxt)
 internally uses a
-[face detection subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_front_gpu.pbtxt)
+[face detection subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_short_range_gpu.pbtxt)
 from the
 [face detection module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection).
 
@@ -116,10 +122,8 @@ along with some simple geometric arguments. For more details please refer to our
 ## Example Apps
 
 Please first see general instructions for
-[Android](../getting_started/building_examples.md#android),
-[iOS](../getting_started/building_examples.md#ios) and
-[desktop](../getting_started/building_examples.md#desktop) on how to build
-MediaPipe examples.
+[Android](../getting_started/android.md), [iOS](../getting_started/ios.md) and
+[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
 
 Note: To visualize a graph, copy the graph and paste it into
 [MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
@@ -140,9 +144,8 @@ to visualize its associated subgraphs, please see
 
 #### Live Camera Input
 
-Please first see general instructions for
-[desktop](../getting_started/building_examples.md#desktop) on how to build
-MediaPipe examples.
+Please first see general instructions for [desktop](../getting_started/cpp.md)
+on how to build MediaPipe examples.
 
 *   Running on CPU
     *   Graph:
@@ -190,7 +193,17 @@ MediaPipe examples.
 
 ### Web
 
-Please refer to [these instructions](../index.md#mediapipe-on-the-web).
+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. Please see
+[MediaPipe on the Web](https://developers.googleblog.com/2020/01/mediapipe-on-web.html)
+in Google Developers Blog for details.
+
+![visualizer_runner](../images/visualizer_runner.png)
+
+*   [MediaPipe Iris](https://viz.mediapipe.dev/demo/iris_tracking)
+*   [MediaPipe Iris: Depth-from-Iris](https://viz.mediapipe.dev/demo/iris_depth)
 
 ## Resources
 

docs/solutions/knift.md

@@ -2,14 +2,20 @@
 layout: default
 title: KNIFT (Template-based Feature Matching)
 parent: Solutions
-nav_order: 11
+nav_order: 13
 ---
 
 # MediaPipe KNIFT
 {: .no_toc }
 
+<details close markdown="block">
+  <summary>
+    Table of contents
+  </summary>
+  {: .text-delta }
 1. TOC
 {:toc}
+</details>
 ---
 
 ## Overview
@@ -67,7 +73,7 @@ you'd like to use your own template images, see
 ![template_matching_mobile_template.jpg](../images/mobile/template_matching_mobile_template.jpg)
 
 Please first see general instructions for
-[Android](../getting_started/building_examples.md#android) on how to build MediaPipe examples.
+[Android](../getting_started/android.md) on how to build MediaPipe examples.
 
 Note: To visualize a graph, copy the graph and paste it into
 [MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how

docs/solutions/media_sequence.md

@@ -2,14 +2,20 @@
 layout: default
 title: Dataset Preparation with MediaSequence
 parent: Solutions
-nav_order: 13
+nav_order: 15
 ---
 
 # Dataset Preparation with MediaSequence
 {: .no_toc }
 
+<details close markdown="block">
+  <summary>
+    Table of contents
+  </summary>
+  {: .text-delta }
 1. TOC
 {:toc}
+</details>
 ---
 
 ## Overview

docs/solutions/models.md

@@ -14,12 +14,27 @@ nav_order: 30
 
 ### [Face Detection](https://google.github.io/mediapipe/solutions/face_detection)
 
-*   Face detection model for front-facing/selfie camera:
-    [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/face_detection_front.tflite),
-    [TFLite model quantized for EdgeTPU/Coral](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/models/face-detector-quantized_edgetpu.tflite)
-*   Face detection model for back-facing camera:
-    [TFLite model ](https://github.com/google/mediapipe/tree/master/mediapipe/models/face_detection_back.tflite)
-*   [Model card](https://mediapipe.page.link/blazeface-mc)
+*   Short-range model (best for faces within 2 meters from the camera):
+    [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_short_range.tflite),
+    [TFLite model quantized for EdgeTPU/Coral](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/models/face-detector-quantized_edgetpu.tflite),
+    [Model card](https://mediapipe.page.link/blazeface-mc)
+*   Full-range model (dense, best for faces within 5 meters from the camera):
+    [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_full_range.tflite),
+    [Model card](https://mediapipe.page.link/blazeface-back-mc)
+*   Full-range model (sparse, best for faces within 5 meters from the camera):
+    [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite),
+    [Model card](https://mediapipe.page.link/blazeface-back-sparse-mc)
+
+Full-range dense and sparse models have the same quality in terms of
+[F-score](https://en.wikipedia.org/wiki/F-score) however differ in underlying
+metrics. The dense model is slightly better in
+[Recall](https://en.wikipedia.org/wiki/Precision_and_recall) whereas the sparse
+model outperforms the dense one in
+[Precision](https://en.wikipedia.org/wiki/Precision_and_recall). Speed-wise
+sparse model is ~30% faster when executing on CPU via
+[XNNPACK](https://github.com/google/XNNPACK) whereas on GPU the models
+demonstrate comparable latencies. Depending on your application, you may prefer
+one over the other.
 
 ### [Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh)
 
@@ -41,17 +56,31 @@ nav_order: 30
     [TF.js model](https://tfhub.dev/mediapipe/handdetector/1)
 *   Hand landmark model:
     [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark.tflite),
+    [TFLite model (sparse)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_sparse.tflite),
     [TF.js model](https://tfhub.dev/mediapipe/handskeleton/1)
-*   [Model card](https://mediapipe.page.link/handmc)
+*   [Model card](https://mediapipe.page.link/handmc), [Model card (sparse)](https://mediapipe.page.link/handmc-sparse)
 
 ### [Pose](https://google.github.io/mediapipe/solutions/pose)
 
 *   Pose detection model:
     [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_detection/pose_detection.tflite)
-*   Upper-body pose landmark model:
-    [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body.tflite)
+*   Pose landmark model:
+    [TFLite model (lite)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_lite.tflite),
+    [TFLite model (full)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_full.tflite),
+    [TFLite model (heavy)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite)
 *   [Model card](https://mediapipe.page.link/blazepose-mc)
 
+### [Holistic](https://google.github.io/mediapipe/solutions/holistic)
+
+*   Hand recrop model:
+    [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/holistic_landmark/hand_recrop.tflite)
+
+### [Selfie Segmentation](https://google.github.io/mediapipe/solutions/selfie_segmentation)
+
+*   [TFLite model (general)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/selfie_segmentation/selfie_segmentation.tflite)
+*   [TFLite model (landscape)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/selfie_segmentation/selfie_segmentation_landscape.tflite)
+*   [Model card](https://mediapipe.page.link/selfiesegmentation-mc)
+
 ### [Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation)
 
 *   [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/hair_segmentation.tflite)
@@ -66,12 +95,12 @@ nav_order: 30
 
 ### [Objectron](https://google.github.io/mediapipe/solutions/objectron)
 
-*   [TFLite model for shoes](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_3d_sneakers.tflite)
-*   [TFLite model for chairs](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_3d_chair.tflite)
-*   [TFLite model for cameras](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_3d_camera.tflite)
-*   [TFLite model for cups](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_3d_cup.tflite)
-*   [Single-stage TFLite model for shoes](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_3d_sneakers_1stage.tflite)
-*   [Single-stage TFLite model for chairs](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_3d_chair_1stage.tflite)
+*   [TFLite model for shoes](https://github.com/google/mediapipe/tree/master/mediapipe/modules/objectron/object_detection_3d_sneakers.tflite)
+*   [TFLite model for chairs](https://github.com/google/mediapipe/tree/master/mediapipe/modules/objectron/object_detection_3d_chair.tflite)
+*   [TFLite model for cameras](https://github.com/google/mediapipe/tree/master/mediapipe/modules/objectron/object_detection_3d_camera.tflite)
+*   [TFLite model for cups](https://github.com/google/mediapipe/tree/master/mediapipe/modules/objectron/object_detection_3d_cup.tflite)
+*   [Single-stage TFLite model for shoes](https://github.com/google/mediapipe/tree/master/mediapipe/modules/objectron/object_detection_3d_sneakers_1stage.tflite)
+*   [Single-stage TFLite model for chairs](https://github.com/google/mediapipe/tree/master/mediapipe/modules/objectron/object_detection_3d_chair_1stage.tflite)
 *   [Model card](https://mediapipe.page.link/objectron-mc)
 
 ### [KNIFT](https://google.github.io/mediapipe/solutions/knift)

docs/solutions/object_detection.md

@@ -2,14 +2,20 @@
 layout: default
 title: Object Detection
 parent: Solutions
-nav_order: 7
+nav_order: 9
 ---
 
 # MediaPipe Object Detection
 {: .no_toc }
 
+<details close markdown="block">
+  <summary>
+    Table of contents
+  </summary>
+  {: .text-delta }
 1. TOC
 {:toc}
+</details>
 ---
 
 ![object_detection_android_gpu.gif](../images/mobile/object_detection_android_gpu.gif)
@@ -24,8 +30,8 @@ to visualize its associated subgraphs, please see
 ### Mobile
 
 Please first see general instructions for
-[Android](../getting_started/building_examples.md#android) and
-[iOS](../getting_started/building_examples.md#ios) on how to build MediaPipe examples.
+[Android](../getting_started/android.md) and [iOS](../getting_started/ios.md) on
+how to build MediaPipe examples.
 
 #### GPU Pipeline
 
@@ -56,8 +62,8 @@ same configuration as the GPU pipeline, runs entirely on CPU.
 
 #### Live Camera Input
 
-Please first see general instructions for
-[desktop](../getting_started/building_examples.md#desktop) on how to build MediaPipe examples.
+Please first see general instructions for [desktop](../getting_started/cpp.md)
+on how to build MediaPipe examples.
 
 *   Graph:
     [`mediapipe/graphs/object_detection/object_detection_desktop_live.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection/object_detection_desktop_live.pbtxt)

docs/solutions/objectron.md

@@ -2,25 +2,31 @@
 layout: default
 title: Objectron (3D Object Detection)
 parent: Solutions
-nav_order: 10
+nav_order: 12
 ---
 
 # MediaPipe Objectron
 {: .no_toc }
 
+<details close markdown="block">
+  <summary>
+    Table of contents
+  </summary>
+  {: .text-delta }
 1. TOC
 {:toc}
+</details>
 ---
 
 ## Overview
 
 MediaPipe Objectron is a mobile real-time 3D object detection solution for
 everyday objects. It detects objects in 2D images, and estimates their poses
-through a machine learning (ML) model, trained on a newly created 3D dataset.
+through a machine learning (ML) model, trained on the [Objectron dataset](https://github.com/google-research-datasets/Objectron).
 
 ![objectron_shoe_android_gpu.gif](../images/mobile/objectron_shoe_android_gpu.gif) | ![objectron_chair_android_gpu.gif](../images/mobile/objectron_chair_android_gpu.gif) | ![objectron_camera_android_gpu.gif](../images/mobile/objectron_camera_android_gpu.gif) | ![objectron_cup_android_gpu.gif](../images/mobile/objectron_cup_android_gpu.gif)
 :--------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------:
-*Fig 1(a). Objectron for Shoes.*                                                   | *Fig 1(b). Objectron for Chairs.*                                                    | *Fig 1(c). Objectron for Cameras.*                                                     | *Fig 1(d). Objectron for Cups.*
+*Fig 1a. Shoe Objectron*                                                           | *Fig 1b. Chair Objectron*                                                            | *Fig 1c. Camera Objectron*                                                             | *Fig 1d. Cup Objectron*
 
 Object detection is an extensively studied computer vision problem, but most of
 the research has focused on
@@ -106,7 +112,8 @@ detector does not need to run every frame.
 *Fig 5. Network architecture and post-processing for two-stage 3D object detection.*       |
 
 We can use any 2D object detector for the first stage. In this solution, we use
-[TensorFlow Object Detection](https://github.com/tensorflow/models/tree/master/research/object_detection).
+[TensorFlow Object Detection](https://github.com/tensorflow/models/tree/master/research/object_detection) trained
+with the [Open Images dataset](https://storage.googleapis.com/openimages/web/index.html).
 The second stage 3D bounding box predictor we released runs 83FPS on Adreno 650
 mobile GPU.
 
@@ -157,9 +164,9 @@ The Objectron 3D object detection and tracking pipeline is implemented as a
 MediaPipe
 [graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/object_occlusion_tracking_1stage.pbtxt),
 which internally uses a
-[detection subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/subgraphs/objectron_detection_gpu.pbtxt)
+[detection subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/objectron/objectron_detection_1stage_gpu.pbtxt)
 and a
-[tracking subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/subgraphs/objectron_tracking_gpu.pbtxt).
+[tracking subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/objectron/objectron_tracking_1stage_gpu.pbtxt).
 The detection subgraph performs ML inference only once every few frames to
 reduce computation load, and decodes the output tensor to a FrameAnnotation that
 contains nine keypoints: the 3D bounding box's center and its eight vertices.
@@ -176,20 +183,280 @@ tracking results, based on the area of overlap.
 
 We also released our [Objectron dataset](http://objectron.dev), with which we
 trained our 3D object detection models. The technical details of the Objectron
-dataset, including usage and tutorials, are available on the dataset website.
+dataset, including usage and tutorials, are available on
+the [dataset website](https://github.com/google-research-datasets/Objectron/).
+
+## Solution APIs
+
+### Cross-platform Configuration Options
+
+Naming style and availability may differ slightly across platforms/languages.
+
+#### static_image_mode
+
+If set to `false`, the solution treats the input images as a video stream. It
+will try to detect objects in the very first images, and upon successful
+detection further localizes the 3D bounding box landmarks. In subsequent images,
+once all [max_num_objects](#max_num_objects) objects are detected and the
+corresponding 3D bounding box landmarks are localized, it simply tracks those
+landmarks without invoking another detection until it loses track of any of the
+objects. This reduces latency and is ideal for processing video frames. If set
+to `true`, object detection runs every input image, ideal for processing a batch
+of static, possibly unrelated, images. Default to `false`.
+
+#### max_num_objects
+
+Maximum number of objects to detect. Default to `5`.
+
+#### min_detection_confidence
+
+Minimum confidence value (`[0.0, 1.0]`) from the object-detection model for the
+detection to be considered successful. Default to `0.5`.
+
+#### min_tracking_confidence
+
+Minimum confidence value (`[0.0, 1.0]`) from the landmark-tracking model for the
+3D bounding box landmarks to be considered tracked successfully, or otherwise
+object detection will be invoked automatically on the next input image. Setting
+it to a higher value can increase robustness of the solution, at the expense of
+a higher latency. Ignored if [static_image_mode](#static_image_mode) is `true`,
+where object detection simply runs on every image. Default to `0.99`.
+
+#### model_name
+
+Name of the model to use for predicting 3D bounding box landmarks. Currently
+supports `{'Shoe', 'Chair', 'Cup', 'Camera'}`. Default to `Shoe`.
+
+#### focal_length
+
+By default, camera focal length defined in [NDC space](#ndc-space), i.e., `(fx,
+fy)`. Default to `(1.0, 1.0)`. To specify focal length in
+[pixel space](#pixel-space) instead, i.e., `(fx_pixel, fy_pixel)`, users should
+provide [`image_size`](#image_size) = `(image_width, image_height)` to enable
+conversions inside the API. For further details about NDC and pixel space,
+please see [Coordinate Systems](#coordinate-systems).
+
+#### principal_point
+
+By default, camera principal point defined in [NDC space](#ndc-space), i.e.,
+`(px, py)`. Default to `(0.0, 0.0)`. To specify principal point in
+[pixel space](#pixel-space), i.e.,`(px_pixel, py_pixel)`, users should provide
+[`image_size`](#image_size) = `(image_width, image_height)` to enable
+conversions inside the API. For further details about NDC and pixel space,
+please see [Coordinate Systems](#coordinate-systems).
+
+#### image_size
+
+**Specify only when [`focal_length`](#focal_length) and
+[`principal_point`](#principal_point) are specified in pixel space.**
+
+Size of the input image, i.e., `(image_width, image_height)`.
+
+### Output
+
+<!-- Naming style may differ slightly across platforms/languages. -->
+
+#### detected_objects
+
+A list of detected 3D bounding box. Each 3D bounding box consists of the
+following:
+
+*   `landmarks_2d` : 2D landmarks of the object's 3D bounding box. The landmark
+    coordinates are normalized to `[0.0, 1.0]` by the image width and height
+    respectively.
+
+*   `landmarks_3d` : 3D landmarks of the object's 3D bounding box. The landmark
+    coordinates are represented in [camera coordinate](#camera-coordinate)
+    frame.
+
+*   `rotation` : rotation matrix from object coordinate frame to camera
+    coordinate frame.
+
+*   `translation` : translation vector from object coordinate frame to camera
+    coordinate frame.
+
+*   `scale` : relative scale of the object along `x`, `y` and `z` directions.
+
+## Python Solution API
+
+Please first follow general [instructions](../getting_started/python.md) to
+install MediaPipe Python package, then learn more in the companion
+[Python Colab](#resources) and the usage example below.
+
+Supported configuration options:
+
+*   [static_image_mode](#static_image_mode)
+*   [max_num_objects](#max_num_objects)
+*   [min_detection_confidence](#min_detection_confidence)
+*   [min_tracking_confidence](#min_tracking_confidence)
+*   [model_name](#model_name)
+*   [focal_length](#focal_length)
+*   [principal_point](#principal_point)
+*   [image_size](#image_size)
+
+```python
+import cv2
+import mediapipe as mp
+mp_drawing = mp.solutions.drawing_utils
+mp_objectron = mp.solutions.objectron
+
+# For static images:
+IMAGE_FILES = []
+with mp_objectron.Objectron(static_image_mode=True,
+                            max_num_objects=5,
+                            min_detection_confidence=0.5,
+                            model_name='Shoe') as objectron:
+  for idx, file in enumerate(IMAGE_FILES):
+    image = cv2.imread(file)
+    # Convert the BGR image to RGB and process it with MediaPipe Objectron.
+    results = objectron.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+    # Draw box landmarks.
+    if not results.detected_objects:
+      print(f'No box landmarks detected on {file}')
+      continue
+    print(f'Box landmarks of {file}:')
+    annotated_image = image.copy()
+    for detected_object in results.detected_objects:
+      mp_drawing.draw_landmarks(
+          annotated_image, detected_object.landmarks_2d, mp_objectron.BOX_CONNECTIONS)
+      mp_drawing.draw_axis(annotated_image, detected_object.rotation,
+                           detected_object.translation)
+      cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
+
+# For webcam input:
+cap = cv2.VideoCapture(0)
+with mp_objectron.Objectron(static_image_mode=False,
+                            max_num_objects=5,
+                            min_detection_confidence=0.5,
+                            min_tracking_confidence=0.99,
+                            model_name='Shoe') as objectron:
+  while cap.isOpened():
+    success, image = cap.read()
+    if not success:
+      print("Ignoring empty camera frame.")
+      # If loading a video, use 'break' instead of 'continue'.
+      continue
+
+    # Convert the BGR image to RGB.
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    # To improve performance, optionally mark the image as not writeable to
+    # pass by reference.
+    image.flags.writeable = False
+    results = objectron.process(image)
+
+    # Draw the box landmarks on the image.
+    image.flags.writeable = True
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    if results.detected_objects:
+        for detected_object in results.detected_objects:
+            mp_drawing.draw_landmarks(
+              image, detected_object.landmarks_2d, mp_objectron.BOX_CONNECTIONS)
+            mp_drawing.draw_axis(image, detected_object.rotation,
+                                 detected_object.translation)
+    cv2.imshow('MediaPipe Objectron', image)
+    if cv2.waitKey(5) & 0xFF == 27:
+      break
+cap.release()
+```
+
+## JavaScript Solution API
+
+Please first see general [introduction](../getting_started/javascript.md) on
+MediaPipe in JavaScript, then learn more in the companion [web demo](#resources)
+and the following usage example.
+
+Supported configuration options:
+
+*   [staticImageMode](#static_image_mode)
+*   [maxNumObjects](#max_num_objects)
+*   [minDetectionConfidence](#min_detection_confidence)
+*   [minTrackingConfidence](#min_tracking_confidence)
+*   [modelName](#model_name)
+*   [focalLength](#focal_length)
+*   [principalPoint](#principal_point)
+*   [imageSize](#image_size)
+
+```html
+<!DOCTYPE html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/camera_utils/camera_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/control_utils/control_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/control_utils_3d.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/drawing_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/objectron/objectron.js" crossorigin="anonymous"></script>
+</head>
+
+<body>
+  <div class="container">
+    <video class="input_video"></video>
+    <canvas class="output_canvas" width="1280px" height="720px"></canvas>
+  </div>
+</body>
+</html>
+```
+
+```javascript
+<script type="module">
+const videoElement = document.getElementsByClassName('input_video')[0];
+const canvasElement = document.getElementsByClassName('output_canvas')[0];
+const canvasCtx = canvasElement.getContext('2d');
+
+function onResults(results) {
+  canvasCtx.save();
+  canvasCtx.drawImage(
+      results.image, 0, 0, canvasElement.width, canvasElement.height);
+  if (!!results.objectDetections) {
+    for (const detectedObject of results.objectDetections) {
+      // Reformat keypoint information as landmarks, for easy drawing.
+      const landmarks: mpObjectron.Point2D[] =
+          detectedObject.keypoints.map(x => x.point2d);
+      // Draw bounding box.
+      drawingUtils.drawConnectors(canvasCtx, landmarks,
+          mpObjectron.BOX_CONNECTIONS, {color: '#FF0000'});
+      // Draw centroid.
+      drawingUtils.drawLandmarks(canvasCtx, [landmarks[0]], {color: '#FFFFFF'});
+    }
+  }
+  canvasCtx.restore();
+}
+
+const objectron = new Objectron({locateFile: (file) => {
+  return `https://cdn.jsdelivr.net/npm/@mediapipe/objectron/${file}`;
+}});
+objectron.setOptions({
+  modelName: 'Chair',
+  maxNumObjects: 3,
+});
+objectron.onResults(onResults);
+
+const camera = new Camera(videoElement, {
+  onFrame: async () => {
+    await objectron.send({image: videoElement});
+  },
+  width: 1280,
+  height: 720
+});
+camera.start();
+</script>
+```
 
 ## Example Apps
 
 Please first see general instructions for
-[Android](../getting_started/building_examples.md#android) and
-[iOS](../getting_started/building_examples.md#ios) on how to build MediaPipe examples.
+[Android](../getting_started/android.md), [iOS](../getting_started/ios.md), and
+[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
 
 Note: To visualize a graph, copy the graph and paste it into
 [MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
 to visualize its associated subgraphs, please see
 [visualizer documentation](../tools/visualizer.md).
 
-### Two-stage Objectron
+### Mobile
+
+#### Two-stage Objectron
 
 *   Graph:
     [`mediapipe/graphs/object_detection_3d/object_occlusion_tracking.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/object_occlusion_tracking.pbtxt)
@@ -227,7 +494,7 @@ to visualize its associated subgraphs, please see
 
 *   iOS target: Not available
 
-### Single-stage Objectron
+#### Single-stage Objectron
 
 *   Graph:
     [`mediapipe/graphs/object_detection_3d/object_occlusion_tracking_1stage.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/object_occlusion_tracking.pbtxt)
@@ -251,15 +518,145 @@ to visualize its associated subgraphs, please see
 
 *   iOS target: Not available
 
+#### Assets
+
+Example app bounding boxes are rendered with [GlAnimationOverlayCalculator](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.cc) using a parsing of the sequenced .obj file
+ format into a custom .uuu format. This can be done for user assets as follows:
+> First run
+>
+> ```shell
+> ./mediapipe/graphs/object_detection_3d/obj_parser/obj_cleanup.sh [INPUT_DIR] [INTERMEDIATE_OUTPUT_DIR]
+> ```
+> and then run
+>
+> ```bash
+> bazel run -c opt mediapipe/graphs/object_detection_3d/obj_parser:ObjParser -- input_dir=[INTERMEDIATE_OUTPUT_DIR] output_dir=[OUTPUT_DIR]
+> ```
+> INPUT_DIR should be the folder with initial asset .obj files to be processed,
+> and OUTPUT_DIR is the folder where the processed asset .uuu file will be placed.
+>
+> Note: ObjParser combines all .obj files found in the given directory into a
+> single .uuu animation file, using the order given by sorting the filenames alphanumerically. Also the ObjParser directory inputs must be given as
+> absolute paths, not relative paths. See parser utility library at [`mediapipe/graphs/object_detection_3d/obj_parser/`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection_3d/obj_parser/) for more details.
+
+
+### Desktop
+
+To build the application, run:
+
+```bash
+bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/object_detection_3d:objectron_cpu
+```
+
+To run the application, replace `<input video path>` and `<output video path>`
+in the command below with your own paths, and `<landmark model path>` and
+`<allowed labels>` with the following:
+
+Category | `<landmark model path>`                                                     | `<allowed labels>`
+:------- | :-------------------------------------------------------------------------- | :-----------------
+Shoe     | mediapipe/modules/objectron/object_detection_3d_sneakers.tflite | Footwear
+Chair    | mediapipe/modules/objectron/object_detection_3d_chair.tflite    | Chair
+Cup      | mediapipe/modules/objectron/object_detection_3d_cup.tflite      | Mug
+Camera   | mediapipe/modules/objectron/object_detection_3d_camera.tflite   | Camera
+
+```
+GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/object_detection_3d/objectron_cpu \
+  --calculator_graph_config_file=mediapipe/graphs/object_detection_3d/objectron_desktop_cpu.pbtxt \
+  --input_side_packets=input_video_path=<input video path>,output_video_path=<output video path>,box_landmark_model_path=<landmark model path>,allowed_labels=<allowed labels>
+```
+
+## Coordinate Systems
+
+### Object Coordinate
+
+Each object has its object coordinate frame. We use the below object coordinate
+definition, with `+x` pointing right, `+y` pointing up and `+z` pointing front,
+origin is at the center of the 3D bounding box.
+
+![box_coordinate.svg](../images/box_coordinate.svg)
+
+### Camera Coordinate
+
+A 3D object is parameterized by its `scale` and `rotation`, `translation` with
+regard to the camera coordinate frame. In this API we use the below camera
+coordinate definition, with `+x` pointing right, `+y` pointing up and `-z`
+pointing to the scene.
+
+![camera_coordinate.svg](../images/camera_coordinate.svg)
+
+To work with box landmarks, one can first derive landmark coordinates in object
+frame by scaling a origin centered unit box with `scale`, then transform to
+camera frame by applying `rotation` and `translation`:
+
+```
+landmarks_3d = rotation * scale * unit_box + translation
+```
+
+### NDC Space
+
+In this API we use
+[NDC(normalized device coordinates)](http://www.songho.ca/opengl/gl_projectionmatrix.html)
+as an intermediate space when projecting points from 3D to 2D. In NDC space,
+`x`, `y` are confined to `[-1, 1]`.
+
+![ndc_coordinate.svg](../images/ndc_coordinate.svg)
+
+By default the camera parameters `(fx, fy)` and `(px, py)` are defined in NDC
+space. Given `(X, Y, Z)` of 3D points in camera coordinate, one can project 3D
+points to NDC space as follows:
+
+```
+x_ndc = -fx * X / Z + px
+y_ndc = -fy * Y / Z + py
+z_ndc = 1 / Z
+```
+
+### Pixel Space
+
+In this API we set upper-left coner of an image as the origin of pixel
+coordinate. One can convert from NDC to pixel space as follows:
+
+```
+x_pixel = (1 + x_ndc) / 2.0 * image_width
+y_pixel = (1 - y_ndc) / 2.0 * image_height
+```
+
+Alternatively one can directly project from camera coordinate to pixel
+coordinate with camera parameters `(fx_pixel, fy_pixel)` and `(px_pixel,
+py_pixel)` defined in pixel space as follows:
+
+```
+x_pixel = -fx_pixel * X / Z + px_pixel
+y_pixel =  fy_pixel * Y / Z + py_pixel
+```
+
+Conversion of camera parameters from pixel space to NDC space:
+
+```
+fx = fx_pixel * 2.0 / image_width
+fy = fy_pixel * 2.0 / image_height
+```
+
+```
+px = -px_pixel * 2.0 / image_width  + 1.0
+py = -py_pixel * 2.0 / image_height + 1.0
+```
+
 ## Resources
 
 *   Google AI Blog:
-    [Announcing the Objectron Dataset](https://mediapipe.page.link/objectron_dataset_ai_blog)
+    [Announcing the Objectron Dataset](https://ai.googleblog.com/2020/11/announcing-objectron-dataset.html)
 *   Google AI 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)
+*   Paper: [Objectron: A Large Scale Dataset of Object-Centric Videos in the
+    Wild with Pose Annotations](https://arxiv.org/abs/2012.09988), to appear in
+    CVPR 2021
 *   Paper: [MobilePose: Real-Time Pose Estimation for Unseen Objects with Weak
     Shape Supervision](https://arxiv.org/abs/2003.03522)
 *   Paper:
     [Instant 3D Object Tracking with Applications in Augmented Reality](https://drive.google.com/open?id=1O_zHmlgXIzAdKljp20U_JUkEHOGG52R8)
-    ([presentation](https://www.youtube.com/watch?v=9ndF1AIo7h0))
+    ([presentation](https://www.youtube.com/watch?v=9ndF1AIo7h0)), Fourth
+    Workshop on Computer Vision for AR/VR, CVPR 2020
 *   [Models and model cards](./models.md#objectron)
+*   [Web demo](https://code.mediapipe.dev/codepen/objectron)
+*   [Python Colab](https://mediapipe.page.link/objectron_py_colab)

docs/solutions/pose.md

@@ -2,61 +2,71 @@
 layout: default
 title: Pose
 parent: Solutions
+has_children: true
+has_toc: false
 nav_order: 5
 ---
 
 # MediaPipe Pose
 {: .no_toc }
 
+<details close markdown="block">
+  <summary>
+    Table of contents
+  </summary>
+  {: .text-delta }
 1. TOC
 {:toc}
+</details>
 ---
 
 ## Overview
 
 Human pose estimation from video plays a critical role in various applications
-such as quantifying physical exercises, sign language recognition, and full-body
-gesture control. For example, it can form the basis for yoga, dance, and fitness
-applications. It can also enable the overlay of digital content and information
-on top of the physical world in augmented reality.
+such as [quantifying physical exercises](./pose_classification.md), sign
+language recognition, and full-body gesture control. For example, it can form
+the basis for yoga, dance, and fitness applications. It can also enable the
+overlay of digital content and information on top of the physical world in
+augmented reality.
 
-MediaPipe Pose is a ML solution for high-fidelity upper-body pose tracking,
-inferring 25 2D upper-body landmarks from RGB video frames utilizing our
+MediaPipe Pose is a ML solution for high-fidelity body pose tracking, inferring
+33 3D landmarks and background segmentation mask on the whole body from RGB
+video frames utilizing our
 [BlazePose](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
-research. Current state-of-the-art approaches rely primarily on powerful desktop
+research that also powers the
+[ML Kit Pose Detection API](https://developers.google.com/ml-kit/vision/pose-detection).
+Current state-of-the-art approaches rely primarily on powerful desktop
 environments for inference, whereas our method achieves real-time performance on
 most modern [mobile phones](#mobile), [desktops/laptops](#desktop), in
-[python](#python) and even on the [web](#web). A variant of MediaPipe Pose that
-performs full-body pose tracking on mobile phones will be included in an
-upcoming release of
-[ML Kit](https://developers.google.com/ml-kit/early-access/pose-detection).
+[python](#python-solution-api) and even on the [web](#javascript-solution-api).
 
-![pose_tracking_upper_body_example.gif](../images/mobile/pose_tracking_upper_body_example.gif) |
-:--------------------------------------------------------------------------------------------: |
-*Fig 1. Example of MediaPipe Pose for upper-body pose tracking.*                               |
+![pose_tracking_example.gif](../images/mobile/pose_tracking_example.gif) |
+:----------------------------------------------------------------------: |
+*Fig 1. Example of MediaPipe Pose for pose tracking.*                    |
 
 ## ML Pipeline
 
 The solution utilizes a two-step detector-tracker ML pipeline, proven to be
 effective in our [MediaPipe Hands](./hands.md) and
 [MediaPipe Face Mesh](./face_mesh.md) solutions. Using a detector, the pipeline
-first locates the pose region-of-interest (ROI) within the frame. The tracker
-subsequently predicts the pose landmarks within the ROI using the ROI-cropped
-frame as input. Note that for video use cases the detector is invoked only as
-needed, i.e., for the very first frame and when the tracker could no longer
-identify body pose presence in the previous frame. For other frames the pipeline
-simply derives the ROI from the previous frame’s pose landmarks.
+first locates the person/pose region-of-interest (ROI) within the frame. The
+tracker subsequently predicts the pose landmarks and segmentation mask within
+the ROI using the ROI-cropped frame as input. Note that for video use cases the
+detector is invoked only as needed, i.e., for the very first frame and when the
+tracker could no longer identify body pose presence in the previous frame. For
+other frames the pipeline simply derives the ROI from the previous frame’s pose
+landmarks.
 
 The pipeline is implemented as a MediaPipe
-[graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt)
+[graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt)
 that uses a
-[pose landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body_gpu.pbtxt)
+[pose landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_gpu.pbtxt)
 from the
 [pose landmark module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark)
 and renders using a dedicated
-[upper-body pose renderer subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/subgraphs/upper_body_pose_renderer_gpu.pbtxt).
+[pose renderer subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/subgraphs/pose_renderer_gpu.pbtxt).
 The
-[pose landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body_gpu.pbtxt)
+[pose landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_gpu.pbtxt)
 internally uses a
 [pose detection subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_detection/pose_detection_gpu.pbtxt)
 from the
@@ -67,9 +77,39 @@ Note: To visualize a graph, copy the graph and paste it into
 to visualize its associated subgraphs, please see
 [visualizer documentation](../tools/visualizer.md).
 
+## Pose Estimation Quality
+
+To evaluate the quality of our [models](./models.md#pose) against other
+well-performing publicly available solutions, we use three different validation
+datasets, representing different verticals: Yoga, Dance and HIIT. Each image
+contains only a single person located 2-4 meters from the camera. To be
+consistent with other solutions, we perform evaluation only for 17 keypoints
+from [COCO topology](https://cocodataset.org/#keypoints-2020).
+
+Method                                                                                                | Yoga <br/> [`mAP`] | Yoga <br/> [`[email protected]`] | Dance <br/> [`mAP`] | Dance <br/> [`[email protected]`] | HIIT <br/> [`mAP`] | HIIT <br/> [`[email protected]`]
+----------------------------------------------------------------------------------------------------- | -----------------: | ---------------------: | ------------------: | ----------------------: | -----------------: | ---------------------:
+BlazePose.Heavy                                                                                       | 68.1               | **96.4**               | 73.0                | **97.2**                | 74.0               | **97.5**
+BlazePose.Full                                                                                        | 62.6               | **95.5**               | 67.4                | **96.3**                | 68.0               | **95.7**
+BlazePose.Lite                                                                                        | 45.0               | **90.2**               | 53.6                | **92.5**                | 53.8               | **93.5**
+[AlphaPose.ResNet50](https://github.com/MVIG-SJTU/AlphaPose)                                          | 63.4               | **96.0**               | 57.8                | **95.5**                | 63.4               | **96.0**
+[Apple.Vision](https://developer.apple.com/documentation/vision/detecting_human_body_poses_in_images) | 32.8               | **82.7**               | 36.4                | **91.4**                | 44.5               | **88.6**
+
+![pose_tracking_pck_chart.png](../images/mobile/pose_tracking_pck_chart.png) |
+:--------------------------------------------------------------------------: |
+*Fig 2. Quality evaluation in [`[email protected]`].*                                  |
+
+We designed our models specifically for live perception use cases, so all of
+them work in real-time on the majority of modern devices.
+
+Method          | Latency <br/> Pixel 3 [TFLite GPU](https://www.tensorflow.org/lite/performance/gpu_advanced) | Latency <br/> MacBook Pro (15-inch 2017)
+--------------- | -------------------------------------------------------------------------------------------: | ---------------------------------------:
+BlazePose.Heavy | 53 ms                                                                                        | 38 ms
+BlazePose.Full  | 25 ms                                                                                        | 27 ms
+BlazePose.Lite  | 20 ms                                                                                        | 25 ms
+
 ## Models
 
-### Pose Detection Model (BlazePose Detector)
+### Person/pose Detection Model (BlazePose Detector)
 
 The detector is inspired by our own lightweight
 [BlazeFace](https://arxiv.org/abs/1907.05047) model, used in
@@ -83,31 +123,326 @@ hip midpoints.
 
 ![pose_tracking_detector_vitruvian_man.png](../images/mobile/pose_tracking_detector_vitruvian_man.png) |
 :----------------------------------------------------------------------------------------------------: |
-*Fig 2. Vitruvian man aligned via two virtual keypoints predicted by BlazePose detector in addition to the face bounding box.* |
+*Fig 3. Vitruvian man aligned via two virtual keypoints predicted by BlazePose detector in addition to the face bounding box.* |
 
-### Pose Landmark Model (BlazePose Tracker)
+### Pose Landmark Model (BlazePose GHUM 3D)
 
-The landmark model currently included in MediaPipe Pose predicts the location of
-25 upper-body landmarks (see figure below), each with `(x, y, z, visibility)`.
-Note that the `z` value should be discarded as the model is currently not fully
-trained to predict depth, but this is something we have on the roadmap. The
-model shares the same architecture as the full-body version that predicts 33
-landmarks, described in more detail in the
-[BlazePose Google AI Blog](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
-and in this [paper](https://arxiv.org/abs/2006.10204).
+The landmark model in MediaPipe Pose predicts the location of 33 pose landmarks
+(see figure below).
 
-![pose_tracking_upper_body_landmarks.png](../images/mobile/pose_tracking_upper_body_landmarks.png) |
-:------------------------------------------------------------------------------------------------: |
-*Fig 3. 25 upper-body pose landmarks.*                                                             |
+![pose_tracking_full_body_landmarks.png](../images/mobile/pose_tracking_full_body_landmarks.png) |
+:----------------------------------------------------------------------------------------------: |
+*Fig 4. 33 pose landmarks.*                                                                      |
+
+Optionally, MediaPipe Pose can predicts a full-body
+[segmentation mask](#segmentation_mask) represented as a two-class segmentation
+(human or background).
+
+Please find more detail in the
+[BlazePose Google AI Blog](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html),
+this [paper](https://arxiv.org/abs/2006.10204),
+[the model card](./models.md#pose) and the [Output](#output) section below.
+
+## Solution APIs
+
+### Cross-platform Configuration Options
+
+Naming style and availability may differ slightly across platforms/languages.
+
+#### static_image_mode
+
+If set to `false`, the solution treats the input images as a video stream. It
+will try to detect the most prominent person in the very first images, and upon
+a successful detection further localizes the pose landmarks. In subsequent
+images, it then simply tracks those landmarks without invoking another detection
+until it loses track, on reducing computation and latency. If set to `true`,
+person detection runs every input image, ideal for processing a batch of static,
+possibly unrelated, images. Default to `false`.
+
+#### model_complexity
+
+Complexity of the pose landmark model: `0`, `1` or `2`. Landmark accuracy as
+well as inference latency generally go up with the model complexity. Default to
+`1`.
+
+#### smooth_landmarks
+
+If set to `true`, the solution filters pose landmarks across different input
+images to reduce jitter, but ignored if [static_image_mode](#static_image_mode)
+is also set to `true`. Default to `true`.
+
+#### enable_segmentation
+
+If set to `true`, in addition to the pose landmarks the solution also generates
+the segmentation mask. Default to `false`.
+
+#### smooth_segmentation
+
+If set to `true`, the solution filters segmentation masks across different input
+images to reduce jitter. Ignored if [enable_segmentation](#enable_segmentation)
+is `false` or [static_image_mode](#static_image_mode) is `true`. Default to
+`true`.
+
+#### min_detection_confidence
+
+Minimum confidence value (`[0.0, 1.0]`) from the person-detection model for the
+detection to be considered successful. Default to `0.5`.
+
+#### min_tracking_confidence
+
+Minimum confidence value (`[0.0, 1.0]`) from the landmark-tracking model for the
+pose landmarks to be considered tracked successfully, or otherwise person
+detection will be invoked automatically on the next input image. Setting it to a
+higher value can increase robustness of the solution, at the expense of a higher
+latency. Ignored if [static_image_mode](#static_image_mode) is `true`, where
+person detection simply runs on every image. Default to `0.5`.
+
+### Output
+
+Naming style may differ slightly across platforms/languages.
+
+#### pose_landmarks
+
+A list of pose landmarks. Each landmark consists of the following:
+
+*   `x` and `y`: Landmark coordinates normalized to `[0.0, 1.0]` by the image
+    width and height respectively.
+*   `z`: Represents the landmark depth with the depth at the midpoint of hips
+    being the origin, and the smaller the value the closer the landmark is to
+    the camera. The magnitude of `z` uses roughly the same scale as `x`.
+*   `visibility`: A value in `[0.0, 1.0]` indicating the likelihood of the
+    landmark being visible (present and not occluded) in the image.
+
+#### pose_world_landmarks
+
+*Fig 5. Example of MediaPipe Pose real-world 3D coordinates.* |
+:-----------------------------------------------------------: |
+<video autoplay muted loop preload style="height: auto; width: 480px"><source src="../images/mobile/pose_world_landmarks.mp4" type="video/mp4"></video> |
+
+Another list of pose landmarks in world coordinates. Each landmark consists of
+the following:
+
+*   `x`, `y` and `z`: Real-world 3D coordinates in meters with the origin at the
+    center between hips.
+*   `visibility`: Identical to that defined in the corresponding
+    [pose_landmarks](#pose_landmarks).
+
+#### segmentation_mask
+
+The output segmentation mask, predicted only when
+[enable_segmentation](#enable_segmentation) is set to `true`. The mask has the
+same width and height as the input image, and contains values in `[0.0, 1.0]`
+where `1.0` and `0.0` indicate high certainty of a "human" and "background"
+pixel respectively. Please refer to the platform-specific usage examples below
+for usage details.
+
+*Fig 6. Example of MediaPipe Pose segmentation mask.* |
+:-----------------------------------------------------------: |
+<video autoplay muted loop preload style="height: auto; width: 480px"><source src="../images/mobile/pose_segmentation.mp4" type="video/mp4"></video> |
+
+### Python Solution API
+
+Please first follow general [instructions](../getting_started/python.md) to
+install MediaPipe Python package, then learn more in the companion
+[Python Colab](#resources) and the usage example below.
+
+Supported configuration options:
+
+*   [static_image_mode](#static_image_mode)
+*   [model_complexity](#model_complexity)
+*   [smooth_landmarks](#smooth_landmarks)
+*   [enable_segmentation](#enable_segmentation)
+*   [smooth_segmentation](#smooth_segmentation)
+*   [min_detection_confidence](#min_detection_confidence)
+*   [min_tracking_confidence](#min_tracking_confidence)
+
+```python
+import cv2
+import mediapipe as mp
+mp_drawing = mp.solutions.drawing_utils
+mp_drawing_styles = mp.solutions.drawing_styles
+mp_pose = mp.solutions.pose
+
+# For static images:
+IMAGE_FILES = []
+BG_COLOR = (192, 192, 192) # gray
+with mp_pose.Pose(
+    static_image_mode=True,
+    model_complexity=2,
+    enable_segmentation=True,
+    min_detection_confidence=0.5) as pose:
+  for idx, file in enumerate(IMAGE_FILES):
+    image = cv2.imread(file)
+    image_height, image_width, _ = image.shape
+    # Convert the BGR image to RGB before processing.
+    results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+    if not results.pose_landmarks:
+      continue
+    print(
+        f'Nose coordinates: ('
+        f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].x * image_width}, '
+        f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].y * image_height})'
+    )
+
+    annotated_image = image.copy()
+    # Draw segmentation on the image.
+    # To improve segmentation around boundaries, consider applying a joint
+    # bilateral filter to "results.segmentation_mask" with "image".
+    condition = np.stack((results.segmentation_mask,) * 3, axis=-1) > 0.1
+    bg_image = np.zeros(image.shape, dtype=np.uint8)
+    bg_image[:] = BG_COLOR
+    annotated_image = np.where(condition, annotated_image, bg_image)
+    # Draw pose landmarks on the image.
+    mp_drawing.draw_landmarks(
+        annotated_image,
+        results.pose_landmarks,
+        mp_pose.POSE_CONNECTIONS,
+        landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
+    cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
+    # Plot pose world landmarks.
+    mp_drawing.plot_landmarks(
+        results.pose_world_landmarks, mp_pose.POSE_CONNECTIONS)
+
+# For webcam input:
+cap = cv2.VideoCapture(0)
+with mp_pose.Pose(
+    min_detection_confidence=0.5,
+    min_tracking_confidence=0.5) as pose:
+  while cap.isOpened():
+    success, image = cap.read()
+    if not success:
+      print("Ignoring empty camera frame.")
+      # If loading a video, use 'break' instead of 'continue'.
+      continue
+
+    # Flip the image horizontally for a later selfie-view display, and convert
+    # the BGR image to RGB.
+    image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
+    # To improve performance, optionally mark the image as not writeable to
+    # pass by reference.
+    image.flags.writeable = False
+    results = pose.process(image)
+
+    # Draw the pose annotation on the image.
+    image.flags.writeable = True
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    mp_drawing.draw_landmarks(
+        image,
+        results.pose_landmarks,
+        mp_pose.POSE_CONNECTIONS,
+        landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
+    cv2.imshow('MediaPipe Pose', image)
+    if cv2.waitKey(5) & 0xFF == 27:
+      break
+cap.release()
+```
+
+### JavaScript Solution API
+
+Please first see general [introduction](../getting_started/javascript.md) on
+MediaPipe in JavaScript, then learn more in the companion [web demo](#resources)
+and the following usage example.
+
+Supported configuration options:
+
+*   [modelComplexity](#model_complexity)
+*   [smoothLandmarks](#smooth_landmarks)
+*   [enableSegmentation](#enable_segmentation)
+*   [smoothSegmentation](#smooth_segmentation)
+*   [minDetectionConfidence](#min_detection_confidence)
+*   [minTrackingConfidence](#min_tracking_confidence)
+
+```html
+<!DOCTYPE html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/camera_utils/camera_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/control_utils/control_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/control_utils_3d.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/drawing_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/pose/pose.js" crossorigin="anonymous"></script>
+</head>
+
+<body>
+  <div class="container">
+    <video class="input_video"></video>
+    <canvas class="output_canvas" width="1280px" height="720px"></canvas>
+    <div class="landmark-grid-container"></div>
+  </div>
+</body>
+</html>
+```
+
+```javascript
+<script type="module">
+const videoElement = document.getElementsByClassName('input_video')[0];
+const canvasElement = document.getElementsByClassName('output_canvas')[0];
+const canvasCtx = canvasElement.getContext('2d');
+const landmarkContainer = document.getElementsByClassName('landmark-grid-container')[0];
+const grid = new LandmarkGrid(landmarkContainer);
+
+function onResults(results) {
+  if (!results.poseLandmarks) {
+    grid.updateLandmarks([]);
+    return;
+  }
+
+  canvasCtx.save();
+  canvasCtx.clearRect(0, 0, canvasElement.width, canvasElement.height);
+  canvasCtx.drawImage(results.segmentationMask, 0, 0,
+                      canvasElement.width, canvasElement.height);
+
+  // Only overwrite existing pixels.
+  canvasCtx.globalCompositeOperation = 'source-in';
+  canvasCtx.fillStyle = '#00FF00';
+  canvasCtx.fillRect(0, 0, canvasElement.width, canvasElement.height);
+
+  // Only overwrite missing pixels.
+  canvasCtx.globalCompositeOperation = 'destination-atop';
+  canvasCtx.drawImage(
+      results.image, 0, 0, canvasElement.width, canvasElement.height);
+
+  canvasCtx.globalCompositeOperation = 'source-over';
+  drawConnectors(canvasCtx, results.poseLandmarks, POSE_CONNECTIONS,
+                 {color: '#00FF00', lineWidth: 4});
+  drawLandmarks(canvasCtx, results.poseLandmarks,
+                {color: '#FF0000', lineWidth: 2});
+  canvasCtx.restore();
+
+  grid.updateLandmarks(results.poseWorldLandmarks);
+}
+
+const pose = new Pose({locateFile: (file) => {
+  return `https://cdn.jsdelivr.net/npm/@mediapipe/pose/${file}`;
+}});
+pose.setOptions({
+  modelComplexity: 1,
+  smoothLandmarks: true,
+  enableSegmentation: true,
+  smoothSegmentation: true,
+  minDetectionConfidence: 0.5,
+  minTrackingConfidence: 0.5
+});
+pose.onResults(onResults);
+
+const camera = new Camera(videoElement, {
+  onFrame: async () => {
+    await pose.send({image: videoElement});
+  },
+  width: 1280,
+  height: 720
+});
+camera.start();
+</script>
+```
 
 ## Example Apps
 
 Please first see general instructions for
-[Android](../getting_started/building_examples.md#android),
-[iOS](../getting_started/building_examples.md#ios),
-[desktop](../getting_started/building_examples.md#desktop) and
-[Python](../getting_started/building_examples.md#python) on how to build
-MediaPipe examples.
+[Android](../getting_started/android.md), [iOS](../getting_started/ios.md), and
+[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
 
 Note: To visualize a graph, copy the graph and paste it into
 [MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
@@ -116,114 +451,33 @@ to visualize its associated subgraphs, please see
 
 ### Mobile
 
+#### Main Example
+
 *   Graph:
-    [`mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt)
+    [`mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt)
 *   Android target:
-    [(or download prebuilt ARM64 APK)](https://drive.google.com/file/d/1uKc6T7KSuA0Mlq2URi5YookHu0U3yoh_/view?usp=sharing)
-    [`mediapipe/examples/android/src/java/com/google/mediapipe/apps/upperbodyposetrackinggpu:upperbodyposetrackinggpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/upperbodyposetrackinggpu/BUILD)
+    [(or download prebuilt ARM64 APK)](https://drive.google.com/file/d/17GFIrqEJS6W8UHKXlYevTtSCLxN9pWlY/view?usp=sharing)
+    [`mediapipe/examples/android/src/java/com/google/mediapipe/apps/posetrackinggpu:posetrackinggpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/posetrackinggpu/BUILD)
 *   iOS target:
-    [`mediapipe/examples/ios/upperbodyposetrackinggpu:UpperBodyPoseTrackingGpuApp`](http:/mediapipe/examples/ios/upperbodyposetrackinggpu/BUILD)
+    [`mediapipe/examples/ios/posetrackinggpu:PoseTrackingGpuApp`](http:/mediapipe/examples/ios/posetrackinggpu/BUILD)
 
 ### Desktop
 
-Please first see general instructions for
-[desktop](../getting_started/building_examples.md#desktop) on how to build
-MediaPipe examples.
+Please first see general instructions for [desktop](../getting_started/cpp.md)
+on how to build MediaPipe examples.
+
+#### Main Example
 
 *   Running on CPU
     *   Graph:
-        [`mediapipe/graphs/pose_tracking/upper_body_pose_tracking_cpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_cpu.pbtxt)
+        [`mediapipe/graphs/pose_tracking/pose_tracking_cpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/pose_tracking_cpu.pbtxt)
     *   Target:
-        [`mediapipe/examples/desktop/upper_body_pose_tracking:upper_body_pose_tracking_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/upper_body_pose_tracking/BUILD)
+        [`mediapipe/examples/desktop/pose_tracking:pose_tracking_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/pose_tracking/BUILD)
 *   Running on GPU
     *   Graph:
-        [`mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt)
+        [`mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt)
     *   Target:
-        [`mediapipe/examples/desktop/upper_body_pose_tracking:upper_body_pose_tracking_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/upper_body_pose_tracking/BUILD)
-
-### Python
-
-MediaPipe Python package is available on
-[PyPI](https://pypi.org/project/mediapipe/), and can be installed simply by `pip
-install mediapipe` on Linux and macOS, as described below and in this
-[colab](https://mediapipe.page.link/pose_py_colab). If you do need to build the
-Python package from source, see
-[additional instructions](../getting_started/building_examples.md#python).
-
-Activate a Python virtual environment:
-
-```bash
-$ python3 -m venv mp_env && source mp_env/bin/activate
-```
-
-Install MediaPipe Python package:
-
-```bash
-(mp_env)$ pip install mediapipe
-```
-
-Run the following Python code:
-
-<!-- Do not change the example code below directly. Change the corresponding example in mediapipe/python/solutions/pose.py and copy it over. -->
-
-```python
-import cv2
-import mediapipe as mp
-mp_drawing = mp.solutions.drawing_utils
-mp_pose = mp.solutions.pose
-
-# For static images:
-pose = mp_pose.Pose(
-    static_image_mode=True, min_detection_confidence=0.5)
-for idx, file in enumerate(file_list):
-  image = cv2.imread(file)
-  # Convert the BGR image to RGB before processing.
-  results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
-
-  # Print and draw pose landmarks on the image.
-  print(
-      'nose landmark:',
-       results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE])
-  annotated_image = image.copy()
-  mp_drawing.draw_landmarks(
-      annotated_image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
-  cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', image)
-pose.close()
-
-# For webcam input:
-pose = mp_pose.Pose(
-    min_detection_confidence=0.5, min_tracking_confidence=0.5)
-cap = cv2.VideoCapture(0)
-while cap.isOpened():
-  success, image = cap.read()
-  if not success:
-    break
-
-  # Flip the image horizontally for a later selfie-view display, and convert
-  # the BGR image to RGB.
-  image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
-  # To improve performance, optionally mark the image as not writeable to
-  # pass by reference.
-  image.flags.writeable = False
-  results = pose.process(image)
-
-  # Draw the pose annotation on the image.
-  image.flags.writeable = True
-  image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-  mp_drawing.draw_landmarks(
-      image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
-  cv2.imshow('MediaPipe Pose', image)
-  if cv2.waitKey(5) & 0xFF == 27:
-    break
-pose.close()
-cap.release()
-```
-
-Tip: Use command `deactivate` to exit the Python virtual environment.
-
-### Web
-
-Please refer to [these instructions](../index.md#mediapipe-on-the-web).
+        [`mediapipe/examples/desktop/pose_tracking:pose_tracking_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/pose_tracking/BUILD)
 
 ## Resources
 
@@ -233,3 +487,8 @@ Please refer to [these instructions](../index.md#mediapipe-on-the-web).
     [BlazePose: On-device Real-time Body Pose Tracking](https://arxiv.org/abs/2006.10204)
     ([presentation](https://youtu.be/YPpUOTRn5tA))
 *   [Models and model cards](./models.md#pose)
+*   [Web demo](https://code.mediapipe.dev/codepen/pose)
+*   [Python Colab](https://mediapipe.page.link/pose_py_colab)
+
+[`mAP`]: https://cocodataset.org/#keypoints-eval
+[`[email protected]`]: https://github.com/cbsudux/Human-Pose-Estimation-101

docs/solutions/pose_classification.md

@@ -0,0 +1,145 @@
+---
+layout: default
+title: Pose Classification
+parent: Pose
+grand_parent: Solutions
+nav_order: 1
+---
+
+# Pose Classification
+{: .no_toc }
+
+<details close markdown="block">
+  <summary>
+    Table of contents
+  </summary>
+  {: .text-delta }
+1. TOC
+{:toc}
+</details>
+---
+
+## Overview
+
+One of the applications
+[BlazePose](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
+can enable is fitness. More specifically - pose classification and repetition
+counting. In this section we'll provide basic guidance on building a custom pose
+classifier with the help of [Colabs](#colabs) and wrap it in a simple fitness
+demo within
+[ML Kit quickstart app](https://developers.google.com/ml-kit/vision/pose-detection/classifying-poses#4_integrate_with_the_ml_kit_quickstart_app).
+Push-ups and squats are used for demonstration purposes as the most common
+exercises.
+
+![pose_classification_pushups_and_squats.gif](../images/mobile/pose_classification_pushups_and_squats.gif) |
+:--------------------------------------------------------------------------------------------------------: |
+*Fig 1. Pose classification and repetition counting with MediaPipe Pose.*                                  |
+
+We picked the
+[k-nearest neighbors algorithm](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)
+(k-NN) as the classifier. It's simple and easy to start with. The algorithm
+determines the object's class based on the closest samples in the training set.
+
+**To build it, one needs to:**
+
+1.  Collect image samples of the target exercises and run pose prediction on
+    them,
+2.  Convert obtained pose landmarks to a representation suitable for the k-NN
+    classifier and form a training set using these [Colabs](#colabs),
+3.  Perform the classification itself followed by repetition counting (e.g., in
+    the
+    [ML Kit quickstart app](https://developers.google.com/ml-kit/vision/pose-detection/classifying-poses#4_integrate_with_the_ml_kit_quickstart_app)).
+
+## Training Set
+
+To build a good classifier appropriate samples should be collected for the
+training set: about a few hundred samples for each terminal state of each
+exercise (e.g., "up" and "down" positions for push-ups). It's important that
+collected samples cover different camera angles, environment conditions, body
+shapes, and exercise variations.
+
+![pose_classification_pushups_un_and_down_samples.jpg](../images/mobile/pose_classification_pushups_un_and_down_samples.jpg) |
+:--------------------------------------------------------------------------------------------------------------------------: |
+*Fig 2. Two terminal states of push-ups.*                                                                                    |
+
+To transform samples into a k-NN classifier training set, both
+[`Pose Classification Colab (Basic)`] and
+[`Pose Classification Colab (Extended)`] could be used. They use the
+[Python Solution API](./pose.md#python-solution-api) to run the BlazePose models
+on given images and dump predicted pose landmarks to a CSV file. Additionally,
+the [`Pose Classification Colab (Extended)`] provides useful tools to find
+outliers (e.g., wrongly predicted poses) and underrepresented classes (e.g., not
+covering all camera angles) by classifying each sample against the entire
+training set. After that, you'll be able to test the classifier on an arbitrary
+video right in the Colab.
+
+## Classification
+
+Code of the classifier is available both in the
+[`Pose Classification Colab (Extended)`] and in the
+[ML Kit quickstart app](https://developers.google.com/ml-kit/vision/pose-detection/classifying-poses#4_integrate_with_the_ml_kit_quickstart_app).
+Please refer to them for details of the approach described below.
+
+The k-NN algorithm used for pose classification requires a feature vector
+representation of each sample and a metric to compute the distance between two
+such vectors to find the nearest pose samples to a target one.
+
+To convert pose landmarks to a feature vector, we use pairwise distances between
+predefined lists of pose joints, such as distances between wrist and shoulder,
+ankle and hip, and two wrists. Since the algorithm relies on distances, all
+poses are normalized to have the same torso size and vertical torso orientation
+before the conversion.
+
+![pose_classification_pairwise_distances.png](../images/mobile/pose_classification_pairwise_distances.png) |
+:--------------------------------------------------------------------------------------------------------: |
+*Fig 3. Main pairwise distances used for the pose feature vector.*                                         |
+
+To get a better classification result, k-NN search is invoked twice with
+different distance metrics:
+
+*   First, to filter out samples that are almost the same as the target one but
+    have only a few different values in the feature vector (which means
+    differently bent joints and thus other pose class), minimum per-coordinate
+    distance is used as distance metric,
+*   Then average per-coordinate distance is used to find the nearest pose
+    cluster among those from the first search.
+
+Finally, we apply
+[exponential moving average](https://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average)
+(EMA) smoothing to level any noise from pose prediction or classification. To do
+that, we search not only for the nearest pose cluster, but we calculate a
+probability for each of them and use it for smoothing over time.
+
+## Repetition Counting
+
+To count the repetitions, the algorithm monitors the probability of a target
+pose class. Let's take push-ups with its "up" and "down" terminal states:
+
+*   When the probability of the "down" pose class passes a certain threshold for
+    the first time, the algorithm marks that the "down" pose class is entered.
+*   Once the probability drops below the threshold, the algorithm marks that the
+    "down" pose class has been exited and increases the counter.
+
+To avoid cases when the probability fluctuates around the threshold (e.g., when
+the user pauses between "up" and "down" states) causing phantom counts, the
+threshold used to detect when the state is exited is actually slightly lower
+than the one used to detect when the state is entered. It creates an interval
+where the pose class and the counter can't be changed.
+
+## Future Work
+
+We are actively working on improving
+[BlazePose GHUM 3D](./pose.md#pose-landmark-model-blazepose-ghum-3d)'s Z
+prediction. It will allow us to use joint angles in the feature vectors, which
+are more natural and easier to configure (although distances can still be useful
+to detect touches between body parts) and to perform rotation normalization of
+poses and reduce the number of camera angles required for accurate k-NN
+classification.
+
+## Colabs
+
+*   [`Pose Classification Colab (Basic)`]
+*   [`Pose Classification Colab (Extended)`]
+
+[`Pose Classification Colab (Basic)`]: https://mediapipe.page.link/pose_classification_basic
+[`Pose Classification Colab (Extended)`]: https://mediapipe.page.link/pose_classification_extended

docs/solutions/selfie_segmentation.md

@@ -0,0 +1,290 @@
+---
+layout: default
+title: Selfie Segmentation
+parent: Solutions
+nav_order: 7
+---
+
+# MediaPipe Selfie Segmentation
+{: .no_toc }
+
+<details close markdown="block">
+  <summary>
+    Table of contents
+  </summary>
+  {: .text-delta }
+1. TOC
+{:toc}
+</details>
+---
+
+## Overview
+
+*Fig 1. Example of MediaPipe Selfie Segmentation.* |
+:------------------------------------------------: |
+<video autoplay muted loop preload style="height: auto; width: 480px"><source src="../images/selfie_segmentation_web.mp4" type="video/mp4"></video> |
+
+MediaPipe Selfie Segmentation segments the prominent humans in the scene. It can
+run in real-time on both smartphones and laptops. The intended use cases include
+selfie effects and video conferencing, where the person is close (< 2m) to the
+camera.
+
+## Models
+
+In this solution, we provide two models: general and landscape. Both models are
+based on
+[MobileNetV3](https://ai.googleblog.com/2019/11/introducing-next-generation-on-device.html),
+with modifications to make them more efficient. The general model operates on a
+256x256x3 (HWC) tensor, and outputs a 256x256x1 tensor representing the
+segmentation mask. The landscape model is similar to the general model, but
+operates on a 144x256x3 (HWC) tensor. It has fewer FLOPs than the general model,
+and therefore, runs faster. Note that MediaPipe Selfie Segmentation
+automatically resizes the input image to the desired tensor dimension before
+feeding it into the ML models.
+
+The general model is also powering
+[ML Kit](https://developers.google.com/ml-kit/vision/selfie-segmentation), and a
+variant of the landscape model is powering
+[Google Meet](https://ai.googleblog.com/2020/10/background-features-in-google-meet.html).
+Please find more detail about the models in the
+[model card](./models.md#selfie-segmentation).
+
+## ML Pipeline
+
+The pipeline is implemented as a MediaPipe
+[graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/selfie_segmentation/selfie_segmentation_gpu.pbtxt)
+that uses a
+[selfie segmentation subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/selfie_segmentation/selfie_segmentation_gpu.pbtxt)
+from the
+[selfie segmentation module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/selfie_segmentation).
+
+Note: To visualize a graph, copy the graph and paste it into
+[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
+to visualize its associated subgraphs, please see
+[visualizer documentation](../tools/visualizer.md).
+
+## Solution APIs
+
+### Cross-platform Configuration Options
+
+Naming style and availability may differ slightly across platforms/languages.
+
+#### model_selection
+
+An integer index `0` or `1`. Use `0` to select the general model, and `1` to
+select the landscape model (see details in [Models](#models)). Default to `0` if
+not specified.
+
+### Output
+
+Naming style may differ slightly across platforms/languages.
+
+#### segmentation_mask
+
+The output segmentation mask, which has the same dimension as the input image.
+
+### Python Solution API
+
+Please first follow general [instructions](../getting_started/python.md) to
+install MediaPipe Python package, then learn more in the companion
+[Python Colab](#resources) and the usage example below.
+
+Supported configuration options:
+
+*   [model_selection](#model_selection)
+
+```python
+import cv2
+import mediapipe as mp
+import numpy as np
+mp_drawing = mp.solutions.drawing_utils
+mp_selfie_segmentation = mp.solutions.selfie_segmentation
+
+# For static images:
+IMAGE_FILES = []
+BG_COLOR = (192, 192, 192) # gray
+MASK_COLOR = (255, 255, 255) # white
+with mp_selfie_segmentation.SelfieSegmentation(
+    model_selection=0) as selfie_segmentation:
+  for idx, file in enumerate(IMAGE_FILES):
+    image = cv2.imread(file)
+    image_height, image_width, _ = image.shape
+    # Convert the BGR image to RGB before processing.
+    results = selfie_segmentation.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+    # Draw selfie segmentation on the background image.
+    # To improve segmentation around boundaries, consider applying a joint
+    # bilateral filter to "results.segmentation_mask" with "image".
+    condition = np.stack((results.segmentation_mask,) * 3, axis=-1) > 0.1
+    # Generate solid color images for showing the output selfie segmentation mask.
+    fg_image = np.zeros(image.shape, dtype=np.uint8)
+    fg_image[:] = MASK_COLOR
+    bg_image = np.zeros(image.shape, dtype=np.uint8)
+    bg_image[:] = BG_COLOR
+    output_image = np.where(condition, fg_image, bg_image)
+    cv2.imwrite('/tmp/selfie_segmentation_output' + str(idx) + '.png', output_image)
+
+# For webcam input:
+BG_COLOR = (192, 192, 192) # gray
+cap = cv2.VideoCapture(0)
+with mp_selfie_segmentation.SelfieSegmentation(
+    model_selection=1) as selfie_segmentation:
+  bg_image = None
+  while cap.isOpened():
+    success, image = cap.read()
+    if not success:
+      print("Ignoring empty camera frame.")
+      # If loading a video, use 'break' instead of 'continue'.
+      continue
+
+    # Flip the image horizontally for a later selfie-view display, and convert
+    # the BGR image to RGB.
+    image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
+    # To improve performance, optionally mark the image as not writeable to
+    # pass by reference.
+    image.flags.writeable = False
+    results = selfie_segmentation.process(image)
+
+    image.flags.writeable = True
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+    # Draw selfie segmentation on the background image.
+    # To improve segmentation around boundaries, consider applying a joint
+    # bilateral filter to "results.segmentation_mask" with "image".
+    condition = np.stack(
+      (results.segmentation_mask,) * 3, axis=-1) > 0.1
+    # The background can be customized.
+    #   a) Load an image (with the same width and height of the input image) to
+    #      be the background, e.g., bg_image = cv2.imread('/path/to/image/file')
+    #   b) Blur the input image by applying image filtering, e.g.,
+    #      bg_image = cv2.GaussianBlur(image,(55,55),0)
+    if bg_image is None:
+      bg_image = np.zeros(image.shape, dtype=np.uint8)
+      bg_image[:] = BG_COLOR
+    output_image = np.where(condition, image, bg_image)
+
+    cv2.imshow('MediaPipe Selfie Segmentation', output_image)
+    if cv2.waitKey(5) & 0xFF == 27:
+      break
+cap.release()
+```
+
+### JavaScript Solution API
+
+Please first see general [introduction](../getting_started/javascript.md) on
+MediaPipe in JavaScript, then learn more in the companion [web demo](#resources)
+and the following usage example.
+
+Supported configuration options:
+
+*   [modelSelection](#model_selection)
+
+```html
+<!DOCTYPE html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/camera_utils/camera_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/control_utils/control_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/drawing_utils.js" crossorigin="anonymous"></script>
+  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/selfie_segmentation/selfie_segmentation.js" crossorigin="anonymous"></script>
+</head>
+
+<body>
+  <div class="container">
+    <video class="input_video"></video>
+    <canvas class="output_canvas" width="1280px" height="720px"></canvas>
+  </div>
+</body>
+</html>
+```
+
+```javascript
+<script type="module">
+const videoElement = document.getElementsByClassName('input_video')[0];
+const canvasElement = document.getElementsByClassName('output_canvas')[0];
+const canvasCtx = canvasElement.getContext('2d');
+
+function onResults(results) {
+  canvasCtx.save();
+  canvasCtx.clearRect(0, 0, canvasElement.width, canvasElement.height);
+  canvasCtx.drawImage(results.segmentationMask, 0, 0,
+                      canvasElement.width, canvasElement.height);
+
+  // Only overwrite existing pixels.
+  canvasCtx.globalCompositeOperation = 'source-in';
+  canvasCtx.fillStyle = '#00FF00';
+  canvasCtx.fillRect(0, 0, canvasElement.width, canvasElement.height);
+
+  // Only overwrite missing pixels.
+  canvasCtx.globalCompositeOperation = 'destination-atop';
+  canvasCtx.drawImage(
+      results.image, 0, 0, canvasElement.width, canvasElement.height);
+
+  canvasCtx.restore();
+}
+
+const selfieSegmentation = new SelfieSegmentation({locateFile: (file) => {
+  return `https://cdn.jsdelivr.net/npm/@mediapipe/selfie_segmentation/${file}`;
+}});
+selfieSegmentation.setOptions({
+  modelSelection: 1,
+});
+selfieSegmentation.onResults(onResults);
+
+const camera = new Camera(videoElement, {
+  onFrame: async () => {
+    await selfieSegmentation.send({image: videoElement});
+  },
+  width: 1280,
+  height: 720
+});
+camera.start();
+</script>
+```
+
+## Example Apps
+
+Please first see general instructions for
+[Android](../getting_started/android.md), [iOS](../getting_started/ios.md), and
+[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
+
+Note: To visualize a graph, copy the graph and paste it into
+[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
+to visualize its associated subgraphs, please see
+[visualizer documentation](../tools/visualizer.md).
+
+### Mobile
+
+*   Graph:
+    [`mediapipe/graphs/selfie_segmentation/selfie_segmentation_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/selfie_segmentation/selfie_segmentation_gpu.pbtxt)
+*   Android target:
+    [(or download prebuilt ARM64 APK)](https://drive.google.com/file/d/1DoeyGzMmWUsjfVgZfGGecrn7GKzYcEAo/view?usp=sharing)
+    [`mediapipe/examples/android/src/java/com/google/mediapipe/apps/selfiesegmentationgpu:selfiesegmentationgpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/selfiesegmentationgpu/BUILD)
+*   iOS target:
+    [`mediapipe/examples/ios/selfiesegmentationgpu:SelfieSegmentationGpuApp`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/selfiesegmentationgpu/BUILD)
+
+### Desktop
+
+Please first see general instructions for [desktop](../getting_started/cpp.md)
+on how to build MediaPipe examples.
+
+*   Running on CPU
+    *   Graph:
+        [`mediapipe/graphs/selfie_segmentation/selfie_segmentation_cpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/selfie_segmentation/selfie_segmentation_cpu.pbtxt)
+    *   Target:
+        [`mediapipe/examples/desktop/selfie_segmentation:selfie_segmentation_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/selfie_segmentation/BUILD)
+*   Running on GPU
+    *   Graph:
+        [`mediapipe/graphs/selfie_segmentation/selfie_segmentation_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/selfie_segmentation/selfie_segmentation_gpu.pbtxt)
+    *   Target:
+        [`mediapipe/examples/desktop/selfie_segmentation:selfie_segmentation_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/selfie_segmentation/BUILD)
+
+## Resources
+
+*   Google AI Blog:
+    [Background Features in Google Meet, Powered by Web ML](https://ai.googleblog.com/2020/10/background-features-in-google-meet.html)
+*   [ML Kit Selfie Segmentation API](https://developers.google.com/ml-kit/vision/selfie-segmentation)
+*   [Models and model cards](./models.md#selfie-segmentation)
+*   [Web demo](https://code.mediapipe.dev/codepen/selfie_segmentation)
+*   [Python Colab](https://mediapipe.page.link/selfie_segmentation_py_colab)

docs/solutions/solutions.md

@@ -13,25 +13,30 @@ has_toc: false
 {:toc}
 ---
 
+MediaPipe offers open source cross-platform, customizable ML solutions for live
+and streaming media.
+
 <!-- []() in the first cell is needed to preserve table formatting in GitHub Pages. -->
 <!-- Whenever this table is updated, paste a copy to ../external_index.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)                       | ✅       | ✅   | ✅       |        |     |
-[Instant Motion Tracking](https://google.github.io/mediapipe/solutions/instant_motion_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)                           |         |     | ✅       |        |     |
+[]()                                                                                      | [Android](https://google.github.io/mediapipe/getting_started/android) | [iOS](https://google.github.io/mediapipe/getting_started/ios) | [C++](https://google.github.io/mediapipe/getting_started/cpp) | [Python](https://google.github.io/mediapipe/getting_started/python) | [JS](https://google.github.io/mediapipe/getting_started/javascript) | [Coral](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/README.md)
+:---------------------------------------------------------------------------------------- | :-------------------------------------------------------------: | :-----------------------------------------------------: | :-----------------------------------------------------: | :-----------------------------------------------------------: | :-----------------------------------------------------------: | :--------------------------------------------------------------------:
+[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)                                       | ✅                                                               | ✅                                                       | ✅                                                       | ✅                                                             | ✅                                                             |
+[Holistic](https://google.github.io/mediapipe/solutions/holistic)                               | ✅                                                               | ✅                                                       | ✅                                                       | ✅                                                             | ✅                                                             |
+[Selfie Segmentation](https://google.github.io/mediapipe/solutions/selfie_segmentation)         | ✅                                                               | ✅                                                       | ✅                                                       | ✅                                                             | ✅                                                             |
+[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)                       | ✅                                                               | ✅                                                       | ✅                                                       |                                                               |                                                               |
+[Instant Motion Tracking](https://google.github.io/mediapipe/solutions/instant_motion_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)                           |                                                                 |                                                         | ✅                                                       |                                                               |                                                               |
 
 See also
 [MediaPipe Models and Model Cards](https://google.github.io/mediapipe/solutions/models)

docs/solutions/youtube_8m.md

@@ -2,14 +2,20 @@
 layout: default
 title: YouTube-8M Feature Extraction and Model Inference
 parent: Solutions
-nav_order: 14
+nav_order: 16
 ---
 
 # YouTube-8M Feature Extraction and Model Inference
 {: .no_toc }
 
+<details close markdown="block">
+  <summary>
+    Table of contents
+  </summary>
+  {: .text-delta }
 1. TOC
 {:toc}
+</details>
 ---
 
 MediaPipe is a useful and general framework for media processing that can assist

docs/tools/tracing_and_profiling.md

@@ -41,6 +41,7 @@ profiler_config {
   trace_enabled: true
   enable_profiler: true
   trace_log_interval_count: 200
+  trace_log_path: "/sdcard/Download/"
 }
 ```
 
@@ -64,7 +65,7 @@ MediaPipe will emit data into a pre-specified directory:
 
 *   On the desktop, this will be the `/tmp` directory.
 
-*   On Android, this will be the `/sdcard` directory.
+*   On Android, this will be the external storage directory (e.g., `/storage/emulated/0/`).
 
 *   On iOS, this can be reached through XCode. Select "Window/Devices and
     Simulators" and select the "Devices" tab.
@@ -103,7 +104,7 @@ we record ten intervals of half a second each. This can be overridden by adding
     *   Include the line below in your `AndroidManifest.xml` file.
 
         ```xml
-        <uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE" />
+        <uses-permission android:name="android.permission.MANAGE_EXTERNAL_STORAGE" />
         ```
 
     *   Grant the permission either upon first app launch, or by going into
@@ -130,8 +131,8 @@ we record ten intervals of half a second each. This can be overridden by adding
     events to a trace log files at:
 
     ```bash
-    /sdcard/mediapipe_trace_0.binarypb
-    /sdcard/mediapipe_trace_1.binarypb
+    /storage/emulated/0/Download/mediapipe_trace_0.binarypb
+    /storage/emulated/0/Download/mediapipe_trace_1.binarypb
     ```
 
     After every 5 sec, writing shifts to a successive trace log file, such that
@@ -139,10 +140,10 @@ we record ten intervals of half a second each. This can be overridden by adding
     trace files have been written to the device using adb shell.
 
     ```bash
-    adb shell "ls -la /sdcard/"
+    adb shell "ls -la /storage/emulated/0/Download"
     ```
 
-    On android, MediaPipe selects the external storage directory `/sdcard` for
+    On android, MediaPipe selects the external storage (e.g., `/storage/emulated/0/`) for
     trace logs. This directory can be overridden using the setting
     `trace_log_path`, like:
 
@@ -150,7 +151,7 @@ we record ten intervals of half a second each. This can be overridden by adding
     profiler_config {
       trace_enabled: true
       enable_profiler: true
-      trace_log_path: "/sdcard/profiles/"
+      trace_log_path: "/sdcard/Download/profiles/"
     }
     ```
 
@@ -161,7 +162,7 @@ we record ten intervals of half a second each. This can be overridden by adding
 
     ```bash
     # from your terminal
-    adb pull /sdcard/mediapipe_trace_0.binarypb
+    adb pull /storage/emulated/0/Download/mediapipe_trace_0.binarypb
     # if successful you should see something like
     # /sdcard/mediapipe_trace_0.binarypb: 1 file pulled. 0.1 MB/s (6766 bytes in 0.045s)
     ```

docs/tools/visualizer.md

@@ -37,7 +37,7 @@ The graph can be modified by adding and editing code in the Editor view.
     ![New Button](../images/upload_button.png)
 
 *   Pressing the "Upload" button will prompt the user to select a local PBTXT
-    file, which will everwrite the current code within the editor.
+    file, which will overwrite the current code within the editor.
 
 *   Alternatively, code can be pasted directly into the editor window.
 

mediapipe/MediaPipe.tulsiproj/Configs/MediaPipe.tulsigen

@@ -2,35 +2,41 @@
   "additionalFilePaths" : [
     "/BUILD",
     "mediapipe/BUILD",
-    "mediapipe/objc/BUILD",
-    "mediapipe/framework/BUILD",
-    "mediapipe/gpu/BUILD",
-    "mediapipe/objc/testing/app/BUILD",
     "mediapipe/examples/ios/common/BUILD",
-    "mediapipe/examples/ios/helloworld/BUILD",
     "mediapipe/examples/ios/facedetectioncpu/BUILD",
     "mediapipe/examples/ios/facedetectiongpu/BUILD",
     "mediapipe/examples/ios/faceeffect/BUILD",
     "mediapipe/examples/ios/facemeshgpu/BUILD",
     "mediapipe/examples/ios/handdetectiongpu/BUILD",
     "mediapipe/examples/ios/handtrackinggpu/BUILD",
+    "mediapipe/examples/ios/helloworld/BUILD",
+    "mediapipe/examples/ios/holistictrackinggpu/BUILD",
     "mediapipe/examples/ios/iristrackinggpu/BUILD",
     "mediapipe/examples/ios/objectdetectioncpu/BUILD",
     "mediapipe/examples/ios/objectdetectiongpu/BUILD",
-    "mediapipe/examples/ios/upperbodyposetrackinggpu/BUILD"
+    "mediapipe/examples/ios/objectdetectiontrackinggpu/BUILD",
+    "mediapipe/examples/ios/posetrackinggpu/BUILD",
+    "mediapipe/examples/ios/selfiesegmentationgpu/BUILD",
+    "mediapipe/framework/BUILD",
+    "mediapipe/gpu/BUILD",
+    "mediapipe/objc/BUILD",
+    "mediapipe/objc/testing/app/BUILD"
   ],
   "buildTargets" : [
-    "//mediapipe/examples/ios/helloworld:HelloWorldApp",
     "//mediapipe/examples/ios/facedetectioncpu:FaceDetectionCpuApp",
     "//mediapipe/examples/ios/facedetectiongpu:FaceDetectionGpuApp",
     "//mediapipe/examples/ios/faceeffect:FaceEffectApp",
     "//mediapipe/examples/ios/facemeshgpu:FaceMeshGpuApp",
     "//mediapipe/examples/ios/handdetectiongpu:HandDetectionGpuApp",
     "//mediapipe/examples/ios/handtrackinggpu:HandTrackingGpuApp",
+    "//mediapipe/examples/ios/helloworld:HelloWorldApp",
+    "//mediapipe/examples/ios/holistictrackinggpu:HolisticTrackingGpuApp",
     "//mediapipe/examples/ios/iristrackinggpu:IrisTrackingGpuApp",
     "//mediapipe/examples/ios/objectdetectioncpu:ObjectDetectionCpuApp",
     "//mediapipe/examples/ios/objectdetectiongpu:ObjectDetectionGpuApp",
-    "//mediapipe/examples/ios/upperbodyposetrackinggpu:UpperBodyPoseTrackingGpuApp",
+    "//mediapipe/examples/ios/objectdetectiontrackinggpu:ObjectDetectionTrackingGpuApp",
+    "//mediapipe/examples/ios/posetrackinggpu:PoseTrackingGpuApp",
+    "//mediapipe/examples/ios/selfiesegmentationgpu:SelfieSegmentationGpuApp",
     "//mediapipe/objc:mediapipe_framework_ios"
   ],
   "optionSet" : {
@@ -87,17 +93,19 @@
     "mediapipe/examples/ios",
     "mediapipe/examples/ios/common",
     "mediapipe/examples/ios/common/Base.lproj",
-    "mediapipe/examples/ios/helloworld",
     "mediapipe/examples/ios/facedetectioncpu",
     "mediapipe/examples/ios/facedetectiongpu",
     "mediapipe/examples/ios/faceeffect",
     "mediapipe/examples/ios/faceeffect/Base.lproj",
     "mediapipe/examples/ios/handdetectiongpu",
     "mediapipe/examples/ios/handtrackinggpu",
+    "mediapipe/examples/ios/helloworld",
+    "mediapipe/examples/ios/holistictrackinggpu",
     "mediapipe/examples/ios/iristrackinggpu",
     "mediapipe/examples/ios/objectdetectioncpu",
     "mediapipe/examples/ios/objectdetectiongpu",
-    "mediapipe/examples/ios/upperbodyposetrackinggpu",
+    "mediapipe/examples/ios/posetrackinggpu",
+    "mediapipe/examples/ios/selfiesegmentationgpu",
     "mediapipe/framework",
     "mediapipe/framework/deps",
     "mediapipe/framework/formats",
@@ -115,6 +123,7 @@
     "mediapipe/graphs/hand_tracking",
     "mediapipe/graphs/object_detection",
     "mediapipe/graphs/pose_tracking",
+    "mediapipe/graphs/selfie_segmentation",
     "mediapipe/models",
     "mediapipe/modules",
     "mediapipe/objc",

mediapipe/MediaPipe.tulsiproj/project.tulsiconf

@@ -9,7 +9,6 @@
   "packages" : [
     "",
     "mediapipe",
-    "mediapipe/objc",
     "mediapipe/examples/ios",
     "mediapipe/examples/ios/facedetectioncpu",
     "mediapipe/examples/ios/facedetectiongpu",
@@ -17,10 +16,14 @@
     "mediapipe/examples/ios/facemeshgpu",
     "mediapipe/examples/ios/handdetectiongpu",
     "mediapipe/examples/ios/handtrackinggpu",
+    "mediapipe/examples/ios/holistictrackinggpu",
     "mediapipe/examples/ios/iristrackinggpu",
     "mediapipe/examples/ios/objectdetectioncpu",
     "mediapipe/examples/ios/objectdetectiongpu",
-    "mediapipe/examples/ios/upperbodyposetrackinggpu"
+    "mediapipe/examples/ios/objectdetectiontrackinggpu",
+    "mediapipe/examples/ios/posetrackinggpu",
+    "mediapipe/examples/ios/selfiesegmentationgpu",
+    "mediapipe/objc"
   ],
   "projectName" : "Mediapipe",
   "workspaceRoot" : "../.."

mediapipe/calculators/audio/BUILD

@@ -1,4 +1,4 @@
-# Copyright 2019 The MediaPipe Authors.
+# Copyright 2019, 2021 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.
@@ -128,7 +128,7 @@ cc_library(
         "//mediapipe/framework/port:ret_check",
         "//mediapipe/util:time_series_util",
         "@com_google_absl//absl/strings",
-        "@eigen_archive//:eigen",
+        "@eigen_archive//:eigen3",
     ],
     alwayslink = 1,
 )
@@ -147,7 +147,7 @@ cc_library(
         "//mediapipe/util:time_series_util",
         "@com_google_absl//absl/strings",
         "@com_google_audio_tools//audio/dsp/mfcc",
-        "@eigen_archive//:eigen",
+        "@eigen_archive//:eigen3",
     ],
     alwayslink = 1,
 )
@@ -167,8 +167,8 @@ cc_library(
         "//mediapipe/util:time_series_util",
         "@com_google_absl//absl/strings",
         "@com_google_audio_tools//audio/dsp:resampler",
-        "@com_google_audio_tools//audio/dsp:resampler_rational_factor",
-        "@eigen_archive//:eigen",
+        "@com_google_audio_tools//audio/dsp:resampler_q",
+        "@eigen_archive//:eigen3",
     ],
     alwayslink = 1,
 )
@@ -208,7 +208,7 @@ cc_library(
         "@com_google_absl//absl/strings",
         "@com_google_audio_tools//audio/dsp:window_functions",
         "@com_google_audio_tools//audio/dsp/spectrogram",
-        "@eigen_archive//:eigen",
+        "@eigen_archive//:eigen3",
     ],
     alwayslink = 1,
 )
@@ -228,7 +228,7 @@ cc_library(
         "//mediapipe/framework/port:status",
         "//mediapipe/util:time_series_util",
         "@com_google_audio_tools//audio/dsp:window_functions",
-        "@eigen_archive//:eigen",
+        "@eigen_archive//:eigen3",
     ],
     alwayslink = 1,
 )
@@ -244,6 +244,7 @@ cc_test(
         "//mediapipe/framework/formats:time_series_header_cc_proto",
         "//mediapipe/framework/port:gtest_main",
         "//mediapipe/framework/port:parse_text_proto",
+        "@com_google_absl//absl/flags:flag",
     ],
 )
 
@@ -260,7 +261,7 @@ cc_test(
         "//mediapipe/framework/port:integral_types",
         "//mediapipe/framework/port:parse_text_proto",
         "//mediapipe/util:time_series_test_util",
-        "@eigen_archive//:eigen",
+        "@eigen_archive//:eigen3",
     ],
 )
 
@@ -275,7 +276,7 @@ cc_test(
         "//mediapipe/framework/port:gtest_main",
         "//mediapipe/framework/port:status",
         "//mediapipe/util:time_series_test_util",
-        "@eigen_archive//:eigen",
+        "@eigen_archive//:eigen3",
     ],
 )
 
@@ -295,7 +296,7 @@ cc_test(
         "//mediapipe/framework/port:status",
         "//mediapipe/util:time_series_test_util",
         "@com_google_audio_tools//audio/dsp:number_util",
-        "@eigen_archive//:eigen",
+        "@eigen_archive//:eigen3",
     ],
 )
 
@@ -313,7 +314,7 @@ cc_test(
         "//mediapipe/framework/port:integral_types",
         "//mediapipe/framework/port:status",
         "//mediapipe/util:time_series_test_util",
-        "@eigen_archive//:eigen",
+        "@eigen_archive//:eigen3",
     ],
 )
 
@@ -332,7 +333,7 @@ cc_test(
         "//mediapipe/framework/port:status",
         "//mediapipe/util:time_series_test_util",
         "@com_google_audio_tools//audio/dsp:window_functions",
-        "@eigen_archive//:eigen",
+        "@eigen_archive//:eigen3",
     ],
 )
 
@@ -351,6 +352,6 @@ cc_test(
         "//mediapipe/framework/tool:validate_type",
         "//mediapipe/util:time_series_test_util",
         "@com_google_audio_tools//audio/dsp:signal_vector_util",
-        "@eigen_archive//:eigen",
+        "@eigen_archive//:eigen3",
     ],
 )

mediapipe/calculators/audio/audio_decoder_calculator.cc

@@ -48,18 +48,17 @@ namespace mediapipe {
 // TODO: support decoding multiple streams.
 class AudioDecoderCalculator : public CalculatorBase {
  public:
-  static ::mediapipe::Status GetContract(CalculatorContract* cc);
+  static absl::Status GetContract(CalculatorContract* cc);
 
-  ::mediapipe::Status Open(CalculatorContext* cc) override;
-  ::mediapipe::Status Process(CalculatorContext* cc) override;
-  ::mediapipe::Status Close(CalculatorContext* cc) override;
+  absl::Status Open(CalculatorContext* cc) override;
+  absl::Status Process(CalculatorContext* cc) override;
+  absl::Status Close(CalculatorContext* cc) override;
 
  private:
   std::unique_ptr<AudioDecoder> decoder_;
 };
 
-::mediapipe::Status AudioDecoderCalculator::GetContract(
-    CalculatorContract* cc) {
+absl::Status AudioDecoderCalculator::GetContract(CalculatorContract* cc) {
   cc->InputSidePackets().Tag("INPUT_FILE_PATH").Set<std::string>();
   if (cc->InputSidePackets().HasTag("OPTIONS")) {
     cc->InputSidePackets().Tag("OPTIONS").Set<mediapipe::AudioDecoderOptions>();
@@ -68,10 +67,10 @@ class AudioDecoderCalculator : public CalculatorBase {
   if (cc->Outputs().HasTag("AUDIO_HEADER")) {
     cc->Outputs().Tag("AUDIO_HEADER").SetNone();
   }
-  return ::mediapipe::OkStatus();
+  return absl::OkStatus();
 }
 
-::mediapipe::Status AudioDecoderCalculator::Open(CalculatorContext* cc) {
+absl::Status AudioDecoderCalculator::Open(CalculatorContext* cc) {
   const std::string& input_file_path =
       cc->InputSidePackets().Tag("INPUT_FILE_PATH").Get<std::string>();
   const auto& decoder_options =
@@ -88,10 +87,10 @@ class AudioDecoderCalculator : public CalculatorBase {
     cc->Outputs().Tag("AUDIO_HEADER").SetHeader(Adopt(header.release()));
   }
   cc->Outputs().Tag("AUDIO_HEADER").Close();
-  return ::mediapipe::OkStatus();
+  return absl::OkStatus();
 }
 
-::mediapipe::Status AudioDecoderCalculator::Process(CalculatorContext* cc) {
+absl::Status AudioDecoderCalculator::Process(CalculatorContext* cc) {
   Packet data;
   int options_index = -1;
   auto status = decoder_->GetData(&options_index, &data);
@@ -101,7 +100,7 @@ class AudioDecoderCalculator : public CalculatorBase {
   return status;
 }
 
-::mediapipe::Status AudioDecoderCalculator::Close(CalculatorContext* cc) {
+absl::Status AudioDecoderCalculator::Close(CalculatorContext* cc) {
   return decoder_->Close();
 }
 

mediapipe/calculators/audio/audio_decoder_calculator_test.cc

@@ -12,6 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include "absl/flags/flag.h"
 #include "mediapipe/framework/calculator_runner.h"
 #include "mediapipe/framework/deps/file_path.h"
 #include "mediapipe/framework/formats/time_series_header.pb.h"
@@ -24,7 +25,7 @@ namespace mediapipe {
 
 TEST(AudioDecoderCalculatorTest, TestWAV) {
   CalculatorGraphConfig::Node node_config =
-      ParseTextProtoOrDie<CalculatorGraphConfig::Node>(R"(
+      ParseTextProtoOrDie<CalculatorGraphConfig::Node>(R"pb(
         calculator: "AudioDecoderCalculator"
         input_side_packet: "INPUT_FILE_PATH:input_file_path"
         output_stream: "AUDIO:audio"
@@ -33,7 +34,7 @@ TEST(AudioDecoderCalculatorTest, TestWAV) {
           [type.googleapis.com/mediapipe.AudioDecoderOptions]: {
             audio_stream { stream_index: 0 }
           }
-        })");
+        })pb");
   CalculatorRunner runner(node_config);
   runner.MutableSidePackets()->Tag("INPUT_FILE_PATH") = MakePacket<std::string>(
       file::JoinPath("./",
@@ -55,7 +56,7 @@ TEST(AudioDecoderCalculatorTest, TestWAV) {
 
 TEST(AudioDecoderCalculatorTest, Test48KWAV) {
   CalculatorGraphConfig::Node node_config =
-      ParseTextProtoOrDie<CalculatorGraphConfig::Node>(R"(
+      ParseTextProtoOrDie<CalculatorGraphConfig::Node>(R"pb(
         calculator: "AudioDecoderCalculator"
         input_side_packet: "INPUT_FILE_PATH:input_file_path"
         output_stream: "AUDIO:audio"
@@ -64,7 +65,7 @@ TEST(AudioDecoderCalculatorTest, Test48KWAV) {
           [type.googleapis.com/mediapipe.AudioDecoderOptions]: {
             audio_stream { stream_index: 0 }
           }
-        })");
+        })pb");
   CalculatorRunner runner(node_config);
   runner.MutableSidePackets()->Tag("INPUT_FILE_PATH") = MakePacket<std::string>(
       file::JoinPath("./",
@@ -86,7 +87,7 @@ TEST(AudioDecoderCalculatorTest, Test48KWAV) {
 
 TEST(AudioDecoderCalculatorTest, TestMP3) {
   CalculatorGraphConfig::Node node_config =
-      ParseTextProtoOrDie<CalculatorGraphConfig::Node>(R"(
+      ParseTextProtoOrDie<CalculatorGraphConfig::Node>(R"pb(
         calculator: "AudioDecoderCalculator"
         input_side_packet: "INPUT_FILE_PATH:input_file_path"
         output_stream: "AUDIO:audio"
@@ -95,7 +96,7 @@ TEST(AudioDecoderCalculatorTest, TestMP3) {
           [type.googleapis.com/mediapipe.AudioDecoderOptions]: {
             audio_stream { stream_index: 0 }
           }
-        })");
+        })pb");
   CalculatorRunner runner(node_config);
   runner.MutableSidePackets()->Tag("INPUT_FILE_PATH") = MakePacket<std::string>(
       file::JoinPath("./",
@@ -117,7 +118,7 @@ TEST(AudioDecoderCalculatorTest, TestMP3) {
 
 TEST(AudioDecoderCalculatorTest, TestAAC) {
   CalculatorGraphConfig::Node node_config =
-      ParseTextProtoOrDie<CalculatorGraphConfig::Node>(R"(
+      ParseTextProtoOrDie<CalculatorGraphConfig::Node>(R"pb(
         calculator: "AudioDecoderCalculator"
         input_side_packet: "INPUT_FILE_PATH:input_file_path"
         output_stream: "AUDIO:audio"
@@ -126,7 +127,7 @@ TEST(AudioDecoderCalculatorTest, TestAAC) {
           [type.googleapis.com/mediapipe.AudioDecoderOptions]: {
             audio_stream { stream_index: 0 }
           }
-        })");
+        })pb");
   CalculatorRunner runner(node_config);
   runner.MutableSidePackets()->Tag("INPUT_FILE_PATH") = MakePacket<std::string>(
       file::JoinPath("./",

mediapipe/calculators/audio/basic_time_series_calculators.cc

@@ -38,7 +38,7 @@ static bool SafeMultiply(int x, int y, int* result) {
 }
 }  // namespace
 
-::mediapipe::Status BasicTimeSeriesCalculatorBase::GetContract(
+absl::Status BasicTimeSeriesCalculatorBase::GetContract(
     CalculatorContract* cc) {
   cc->Inputs().Index(0).Set<Matrix>(
       // Input stream with TimeSeriesHeader.
@@ -46,10 +46,10 @@ static bool SafeMultiply(int x, int y, int* result) {
   cc->Outputs().Index(0).Set<Matrix>(
       // Output stream with TimeSeriesHeader.
   );
-  return ::mediapipe::OkStatus();
+  return absl::OkStatus();
 }
 
-::mediapipe::Status BasicTimeSeriesCalculatorBase::Open(CalculatorContext* cc) {
+absl::Status BasicTimeSeriesCalculatorBase::Open(CalculatorContext* cc) {
   TimeSeriesHeader input_header;
   MP_RETURN_IF_ERROR(time_series_util::FillTimeSeriesHeaderIfValid(
       cc->Inputs().Index(0).Header(), &input_header));
@@ -57,11 +57,13 @@ static bool SafeMultiply(int x, int y, int* result) {
   auto output_header = new TimeSeriesHeader(input_header);
   MP_RETURN_IF_ERROR(MutateHeader(output_header));
   cc->Outputs().Index(0).SetHeader(Adopt(output_header));
-  return ::mediapipe::OkStatus();
+
+  cc->SetOffset(0);
+
+  return absl::OkStatus();
 }
 
-::mediapipe::Status BasicTimeSeriesCalculatorBase::Process(
-    CalculatorContext* cc) {
+absl::Status BasicTimeSeriesCalculatorBase::Process(CalculatorContext* cc) {
   const Matrix& input = cc->Inputs().Index(0).Get<Matrix>();
   MP_RETURN_IF_ERROR(time_series_util::IsMatrixShapeConsistentWithHeader(
       input, cc->Inputs().Index(0).Header().Get<TimeSeriesHeader>()));
@@ -71,12 +73,12 @@ static bool SafeMultiply(int x, int y, int* result) {
       *output, cc->Outputs().Index(0).Header().Get<TimeSeriesHeader>()));
 
   cc->Outputs().Index(0).Add(output.release(), cc->InputTimestamp());
-  return ::mediapipe::OkStatus();
+  return absl::OkStatus();
 }
 
-::mediapipe::Status BasicTimeSeriesCalculatorBase::MutateHeader(
+absl::Status BasicTimeSeriesCalculatorBase::MutateHeader(
     TimeSeriesHeader* output_header) {
-  return ::mediapipe::OkStatus();
+  return absl::OkStatus();
 }
 
 // Calculator to sum an input time series across channels.  This is
@@ -86,9 +88,9 @@ static bool SafeMultiply(int x, int y, int* result) {
 class SumTimeSeriesAcrossChannelsCalculator
     : public BasicTimeSeriesCalculatorBase {
  protected:
-  ::mediapipe::Status MutateHeader(TimeSeriesHeader* output_header) final {
+  absl::Status MutateHeader(TimeSeriesHeader* output_header) final {
     output_header->set_num_channels(1);
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
   Matrix ProcessMatrix(const Matrix& input_matrix) final {
@@ -104,9 +106,9 @@ REGISTER_CALCULATOR(SumTimeSeriesAcrossChannelsCalculator);
 class AverageTimeSeriesAcrossChannelsCalculator
     : public BasicTimeSeriesCalculatorBase {
  protected:
-  ::mediapipe::Status MutateHeader(TimeSeriesHeader* output_header) final {
+  absl::Status MutateHeader(TimeSeriesHeader* output_header) final {
     output_header->set_num_channels(1);
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
   Matrix ProcessMatrix(const Matrix& input_matrix) final {
@@ -122,7 +124,7 @@ REGISTER_CALCULATOR(AverageTimeSeriesAcrossChannelsCalculator);
 // Options proto: None.
 class SummarySaiToPitchogramCalculator : public BasicTimeSeriesCalculatorBase {
  protected:
-  ::mediapipe::Status MutateHeader(TimeSeriesHeader* output_header) final {
+  absl::Status MutateHeader(TimeSeriesHeader* output_header) final {
     if (output_header->num_channels() != 1) {
       return tool::StatusInvalid(
           absl::StrCat("Expected single-channel input, got ",
@@ -131,7 +133,7 @@ class SummarySaiToPitchogramCalculator : public BasicTimeSeriesCalculatorBase {
     output_header->set_num_channels(output_header->num_samples());
     output_header->set_num_samples(1);
     output_header->set_sample_rate(output_header->packet_rate());
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
   Matrix ProcessMatrix(const Matrix& input_matrix) final {
@@ -160,7 +162,7 @@ REGISTER_CALCULATOR(ReverseChannelOrderCalculator);
 // Options proto: None.
 class FlattenPacketCalculator : public BasicTimeSeriesCalculatorBase {
  protected:
-  ::mediapipe::Status MutateHeader(TimeSeriesHeader* output_header) final {
+  absl::Status MutateHeader(TimeSeriesHeader* output_header) final {
     const int num_input_channels = output_header->num_channels();
     const int num_input_samples = output_header->num_samples();
     RET_CHECK(num_input_channels >= 0)
@@ -174,7 +176,7 @@ class FlattenPacketCalculator : public BasicTimeSeriesCalculatorBase {
     output_header->set_num_channels(output_num_channels);
     output_header->set_num_samples(1);
     output_header->set_sample_rate(output_header->packet_rate());
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
   Matrix ProcessMatrix(const Matrix& input_matrix) final {
@@ -253,10 +255,10 @@ REGISTER_CALCULATOR(DivideByMeanAcrossChannelsCalculator);
 // Options proto: None.
 class MeanCalculator : public BasicTimeSeriesCalculatorBase {
  protected:
-  ::mediapipe::Status MutateHeader(TimeSeriesHeader* output_header) final {
+  absl::Status MutateHeader(TimeSeriesHeader* output_header) final {
     output_header->set_num_samples(1);
     output_header->set_sample_rate(output_header->packet_rate());
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
   Matrix ProcessMatrix(const Matrix& input_matrix) final {
@@ -272,10 +274,10 @@ REGISTER_CALCULATOR(MeanCalculator);
 // Options proto: None.
 class StandardDeviationCalculator : public BasicTimeSeriesCalculatorBase {
  protected:
-  ::mediapipe::Status MutateHeader(TimeSeriesHeader* output_header) final {
+  absl::Status MutateHeader(TimeSeriesHeader* output_header) final {
     output_header->set_num_samples(1);
     output_header->set_sample_rate(output_header->packet_rate());
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
   Matrix ProcessMatrix(const Matrix& input_matrix) final {
@@ -293,9 +295,9 @@ REGISTER_CALCULATOR(StandardDeviationCalculator);
 // Options proto: None.
 class CovarianceCalculator : public BasicTimeSeriesCalculatorBase {
  protected:
-  ::mediapipe::Status MutateHeader(TimeSeriesHeader* output_header) final {
+  absl::Status MutateHeader(TimeSeriesHeader* output_header) final {
     output_header->set_num_samples(output_header->num_channels());
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
   Matrix ProcessMatrix(const Matrix& input_matrix) final {
@@ -313,9 +315,9 @@ REGISTER_CALCULATOR(CovarianceCalculator);
 // Options proto: None.
 class L2NormCalculator : public BasicTimeSeriesCalculatorBase {
  protected:
-  ::mediapipe::Status MutateHeader(TimeSeriesHeader* output_header) final {
+  absl::Status MutateHeader(TimeSeriesHeader* output_header) final {
     output_header->set_num_channels(1);
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
   Matrix ProcessMatrix(const Matrix& input_matrix) final {
@@ -385,12 +387,12 @@ REGISTER_CALCULATOR(ElementwiseSquareCalculator);
 // Options proto: None.
 class FirstHalfSlicerCalculator : public BasicTimeSeriesCalculatorBase {
  protected:
-  ::mediapipe::Status MutateHeader(TimeSeriesHeader* output_header) final {
+  absl::Status MutateHeader(TimeSeriesHeader* output_header) final {
     const int num_input_samples = output_header->num_samples();
     RET_CHECK(num_input_samples >= 0)
         << "FirstHalfSlicerCalculator: num_input_samples < 0";
     output_header->set_num_samples(num_input_samples / 2);
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
   Matrix ProcessMatrix(const Matrix& input_matrix) final {

mediapipe/calculators/audio/basic_time_series_calculators.h

@@ -28,16 +28,16 @@ namespace mediapipe {
 
 class BasicTimeSeriesCalculatorBase : public CalculatorBase {
  public:
-  static ::mediapipe::Status GetContract(CalculatorContract* cc);
-  ::mediapipe::Status Open(CalculatorContext* cc) override;
-  ::mediapipe::Status Process(CalculatorContext* cc) override;
+  static absl::Status GetContract(CalculatorContract* cc);
+  absl::Status Open(CalculatorContext* cc) final;
+  absl::Status Process(CalculatorContext* cc) final;
 
  protected:
   // Open() calls this method to mutate the output stream header.  The input
   // to this function will contain a copy of the input stream header, so
   // subclasses that do not need to mutate the header do not need to override
   // it.
-  virtual ::mediapipe::Status MutateHeader(TimeSeriesHeader* output_header);
+  virtual absl::Status MutateHeader(TimeSeriesHeader* output_header);
 
   // Process() calls this method on each packet to compute the output matrix.
   virtual Matrix ProcessMatrix(const Matrix& input_matrix) = 0;

mediapipe/calculators/audio/mfcc_mel_calculators.cc

@@ -66,7 +66,7 @@ std::string PortableDebugString(const TimeSeriesHeader& header) {
 // rows corresponding to the new feature space).
 class FramewiseTransformCalculatorBase : public CalculatorBase {
  public:
-  static ::mediapipe::Status GetContract(CalculatorContract* cc) {
+  static absl::Status GetContract(CalculatorContract* cc) {
     cc->Inputs().Index(0).Set<Matrix>(
         // Sequence of Matrices, each column describing a particular time frame,
         // each row a feature dimension, with TimeSeriesHeader.
@@ -75,11 +75,11 @@ class FramewiseTransformCalculatorBase : public CalculatorBase {
         // Sequence of Matrices, each column describing a particular time frame,
         // each row a feature dimension, with TimeSeriesHeader.
     );
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
-  ::mediapipe::Status Open(CalculatorContext* cc) override;
-  ::mediapipe::Status Process(CalculatorContext* cc) override;
+  absl::Status Open(CalculatorContext* cc) final;
+  absl::Status Process(CalculatorContext* cc) final;
 
   int num_output_channels(void) { return num_output_channels_; }
 
@@ -90,8 +90,8 @@ class FramewiseTransformCalculatorBase : public CalculatorBase {
  private:
   // Takes header and options, and sets up state including calling
   // set_num_output_channels() on the base object.
-  virtual ::mediapipe::Status ConfigureTransform(const TimeSeriesHeader& header,
-                                                 CalculatorContext* cc) = 0;
+  virtual absl::Status ConfigureTransform(const TimeSeriesHeader& header,
+                                          CalculatorContext* cc) = 0;
 
   // Takes a vector<double> corresponding to an input frame, and
   // perform the specific transformation to produce an output frame.
@@ -102,23 +102,23 @@ class FramewiseTransformCalculatorBase : public CalculatorBase {
   int num_output_channels_;
 };
 
-::mediapipe::Status FramewiseTransformCalculatorBase::Open(
-    CalculatorContext* cc) {
+absl::Status FramewiseTransformCalculatorBase::Open(CalculatorContext* cc) {
   TimeSeriesHeader input_header;
   MP_RETURN_IF_ERROR(time_series_util::FillTimeSeriesHeaderIfValid(
       cc->Inputs().Index(0).Header(), &input_header));
 
-  ::mediapipe::Status status = ConfigureTransform(input_header, cc);
+  absl::Status status = ConfigureTransform(input_header, cc);
 
   auto output_header = new TimeSeriesHeader(input_header);
   output_header->set_num_channels(num_output_channels_);
   cc->Outputs().Index(0).SetHeader(Adopt(output_header));
 
+  cc->SetOffset(0);
+
   return status;
 }
 
-::mediapipe::Status FramewiseTransformCalculatorBase::Process(
-    CalculatorContext* cc) {
+absl::Status FramewiseTransformCalculatorBase::Process(CalculatorContext* cc) {
   const Matrix& input = cc->Inputs().Index(0).Get<Matrix>();
   const int num_frames = input.cols();
   std::unique_ptr<Matrix> output(new Matrix(num_output_channels_, num_frames));
@@ -145,7 +145,7 @@ class FramewiseTransformCalculatorBase : public CalculatorBase {
   }
   cc->Outputs().Index(0).Add(output.release(), cc->InputTimestamp());
 
-  return ::mediapipe::OkStatus();
+  return absl::OkStatus();
 }
 
 // Calculator wrapper around the dsp/mfcc/mfcc.cc routine.
@@ -170,13 +170,13 @@ class FramewiseTransformCalculatorBase : public CalculatorBase {
 // }
 class MfccCalculator : public FramewiseTransformCalculatorBase {
  public:
-  static ::mediapipe::Status GetContract(CalculatorContract* cc) {
+  static absl::Status GetContract(CalculatorContract* cc) {
     return FramewiseTransformCalculatorBase::GetContract(cc);
   }
 
  private:
-  ::mediapipe::Status ConfigureTransform(const TimeSeriesHeader& header,
-                                         CalculatorContext* cc) override {
+  absl::Status ConfigureTransform(const TimeSeriesHeader& header,
+                                  CalculatorContext* cc) override {
     MfccCalculatorOptions mfcc_options = cc->Options<MfccCalculatorOptions>();
     mfcc_.reset(new audio_dsp::Mfcc());
     int input_length = header.num_channels();
@@ -194,7 +194,7 @@ class MfccCalculator : public FramewiseTransformCalculatorBase {
     // audio_dsp::MelFilterBank needs to know this to
     // correctly interpret the spectrogram bins.
     if (!header.has_audio_sample_rate()) {
-      return ::mediapipe::InvalidArgumentError(
+      return absl::InvalidArgumentError(
           absl::StrCat("No audio_sample_rate in input TimeSeriesHeader ",
                        PortableDebugString(header)));
     }
@@ -203,10 +203,10 @@ class MfccCalculator : public FramewiseTransformCalculatorBase {
         mfcc_->Initialize(input_length, header.audio_sample_rate());
 
     if (initialized) {
-      return ::mediapipe::OkStatus();
+      return absl::OkStatus();
     } else {
-      return ::mediapipe::Status(mediapipe::StatusCode::kInternal,
-                                 "Mfcc::Initialize returned uninitialized");
+      return absl::Status(absl::StatusCode::kInternal,
+                          "Mfcc::Initialize returned uninitialized");
     }
   }
 
@@ -228,13 +228,13 @@ REGISTER_CALCULATOR(MfccCalculator);
 // if you ask for too many channels.
 class MelSpectrumCalculator : public FramewiseTransformCalculatorBase {
  public:
-  static ::mediapipe::Status GetContract(CalculatorContract* cc) {
+  static absl::Status GetContract(CalculatorContract* cc) {
     return FramewiseTransformCalculatorBase::GetContract(cc);
   }
 
  private:
-  ::mediapipe::Status ConfigureTransform(const TimeSeriesHeader& header,
-                                         CalculatorContext* cc) override {
+  absl::Status ConfigureTransform(const TimeSeriesHeader& header,
+                                  CalculatorContext* cc) override {
     MelSpectrumCalculatorOptions mel_spectrum_options =
         cc->Options<MelSpectrumCalculatorOptions>();
     mel_filterbank_.reset(new audio_dsp::MelFilterbank());
@@ -245,7 +245,7 @@ class MelSpectrumCalculator : public FramewiseTransformCalculatorBase {
     // audio_dsp::MelFilterBank needs to know this to
     // correctly interpret the spectrogram bins.
     if (!header.has_audio_sample_rate()) {
-      return ::mediapipe::InvalidArgumentError(
+      return absl::InvalidArgumentError(
           absl::StrCat("No audio_sample_rate in input TimeSeriesHeader ",
                        PortableDebugString(header)));
     }
@@ -255,10 +255,10 @@ class MelSpectrumCalculator : public FramewiseTransformCalculatorBase {
         mel_spectrum_options.max_frequency_hertz());
 
     if (initialized) {
-      return ::mediapipe::OkStatus();
+      return absl::OkStatus();
     } else {
-      return ::mediapipe::Status(mediapipe::StatusCode::kInternal,
-                                 "mfcc::Initialize returned uninitialized");
+      return absl::Status(absl::StatusCode::kInternal,
+                          "mfcc::Initialize returned uninitialized");
     }
   }
 

mediapipe/calculators/audio/mfcc_mel_calculators_test.cc

@@ -84,7 +84,7 @@ class FramewiseTransformCalculatorTest
     num_samples_per_packet_ = GenerateRandomNonnegInputStream(kNumPackets);
   }
 
-  ::mediapipe::Status Run() { return this->RunGraph(); }
+  absl::Status Run() { return this->RunGraph(); }
 
   void CheckResults(int expected_num_channels) {
     const auto& output_header =

mediapipe/calculators/audio/rational_factor_resample_calculator.cc

@@ -1,4 +1,4 @@
-// Copyright 2019 The MediaPipe Authors.
+// Copyright 2019, 2021 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.
@@ -16,22 +16,18 @@
 
 #include "mediapipe/calculators/audio/rational_factor_resample_calculator.h"
 
-#include "audio/dsp/resampler_rational_factor.h"
+#include "audio/dsp/resampler_q.h"
 
-using audio_dsp::DefaultResamplingKernel;
-using audio_dsp::RationalFactorResampler;
 using audio_dsp::Resampler;
 
 namespace mediapipe {
-::mediapipe::Status RationalFactorResampleCalculator::Process(
-    CalculatorContext* cc) {
+absl::Status RationalFactorResampleCalculator::Process(CalculatorContext* cc) {
   return ProcessInternal(cc->Inputs().Index(0).Get<Matrix>(), false, cc);
 }
 
-::mediapipe::Status RationalFactorResampleCalculator::Close(
-    CalculatorContext* cc) {
+absl::Status RationalFactorResampleCalculator::Close(CalculatorContext* cc) {
   if (initial_timestamp_ == Timestamp::Unstarted()) {
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
   Matrix empty_input_frame(num_channels_, 0);
   return ProcessInternal(empty_input_frame, true, cc);
@@ -40,11 +36,8 @@ namespace mediapipe {
 namespace {
 void CopyChannelToVector(const Matrix& matrix, int channel,
                          std::vector<float>* vec) {
-  vec->clear();
-  vec->reserve(matrix.cols());
-  for (int sample = 0; sample < matrix.cols(); ++sample) {
-    vec->push_back(matrix(channel, sample));
-  }
+  vec->resize(matrix.cols());
+  Eigen::Map<Eigen::ArrayXf>(vec->data(), vec->size()) = matrix.row(channel);
 }
 
 void CopyVectorToChannel(const std::vector<float>& vec, Matrix* matrix,
@@ -53,17 +46,14 @@ void CopyVectorToChannel(const std::vector<float>& vec, Matrix* matrix,
     matrix->resize(matrix->rows(), vec.size());
   } else {
     CHECK_EQ(vec.size(), matrix->cols());
-    CHECK_LT(channel, matrix->rows());
-  }
-  for (int sample = 0; sample < matrix->cols(); ++sample) {
-    (*matrix)(channel, sample) = vec[sample];
   }
+  CHECK_LT(channel, matrix->rows());
+  matrix->row(channel) =
+      Eigen::Map<const Eigen::ArrayXf>(vec.data(), vec.size());
 }
-
 }  // namespace
 
-::mediapipe::Status RationalFactorResampleCalculator::Open(
-    CalculatorContext* cc) {
+absl::Status RationalFactorResampleCalculator::Open(CalculatorContext* cc) {
   RationalFactorResampleCalculatorOptions resample_options =
       cc->Options<RationalFactorResampleCalculatorOptions>();
 
@@ -88,7 +78,7 @@ void CopyVectorToChannel(const std::vector<float>& vec, Matrix* matrix,
                                resample_options);
       if (!r) {
         LOG(ERROR) << "Failed to initialize resampler.";
-        return ::mediapipe::UnknownError("Failed to initialize resampler.");
+        return absl::UnknownError("Failed to initialize resampler.");
       }
     }
   }
@@ -106,10 +96,10 @@ void CopyVectorToChannel(const std::vector<float>& vec, Matrix* matrix,
   initial_timestamp_ = Timestamp::Unstarted();
   check_inconsistent_timestamps_ =
       resample_options.check_inconsistent_timestamps();
-  return ::mediapipe::OkStatus();
+  return absl::OkStatus();
 }
 
-::mediapipe::Status RationalFactorResampleCalculator::ProcessInternal(
+absl::Status RationalFactorResampleCalculator::ProcessInternal(
     const Matrix& input_frame, bool should_flush, CalculatorContext* cc) {
   if (initial_timestamp_ == Timestamp::Unstarted()) {
     initial_timestamp_ = cc->InputTimestamp();
@@ -131,7 +121,7 @@ void CopyVectorToChannel(const std::vector<float>& vec, Matrix* matrix,
     *output_frame = input_frame;
   } else {
     if (!Resample(input_frame, output_frame.get(), should_flush)) {
-      return ::mediapipe::UnknownError("Resample() failed.");
+      return absl::UnknownError("Resample() failed.");
     }
   }
   cumulative_output_samples_ += output_frame->cols();
@@ -139,7 +129,7 @@ void CopyVectorToChannel(const std::vector<float>& vec, Matrix* matrix,
   if (output_frame->cols() > 0) {
     cc->Outputs().Index(0).Add(output_frame.release(), output_timestamp);
   }
-  return ::mediapipe::OkStatus();
+  return absl::OkStatus();
 }
 
 bool RationalFactorResampleCalculator::Resample(const Matrix& input_frame,
@@ -167,25 +157,28 @@ RationalFactorResampleCalculator::ResamplerFromOptions(
   std::unique_ptr<Resampler<float>> resampler;
   const auto& rational_factor_options =
       options.resampler_rational_factor_options();
-  std::unique_ptr<DefaultResamplingKernel> kernel;
+  audio_dsp::QResamplerParams params;
   if (rational_factor_options.has_radius() &&
       rational_factor_options.has_cutoff() &&
       rational_factor_options.has_kaiser_beta()) {
-    kernel = absl::make_unique<DefaultResamplingKernel>(
-        source_sample_rate, target_sample_rate,
-        rational_factor_options.radius(), rational_factor_options.cutoff(),
-        rational_factor_options.kaiser_beta());
-  } else {
-    kernel = absl::make_unique<DefaultResamplingKernel>(source_sample_rate,
-                                                        target_sample_rate);
+    // Convert RationalFactorResampler kernel parameters to QResampler
+    // settings.
+    params.filter_radius_factor =
+        rational_factor_options.radius() *
+        std::min(1.0, target_sample_rate / source_sample_rate);
+    params.cutoff_proportion = 2 * rational_factor_options.cutoff() /
+                               std::min(source_sample_rate, target_sample_rate);
+    params.kaiser_beta = rational_factor_options.kaiser_beta();
   }
-
   // Set large enough so that the resampling factor between common sample
   // rates (e.g. 8kHz, 16kHz, 22.05kHz, 32kHz, 44.1kHz, 48kHz) is exact, and
   // that any factor is represented with error less than 0.025%.
-  const int kMaxDenominator = 2000;
-  resampler = absl::make_unique<RationalFactorResampler<float>>(
-      *kernel, kMaxDenominator);
+  params.max_denominator = 2000;
+
+  // NOTE: QResampler supports multichannel resampling, so the code might be
+  // simplified using a single instance rather than one per channel.
+  resampler = absl::make_unique<audio_dsp::QResampler<float>>(
+      source_sample_rate, target_sample_rate, /*num_channels=*/1, params);
   if (resampler != nullptr && !resampler->Valid()) {
     resampler = std::unique_ptr<Resampler<float>>();
   }

mediapipe/calculators/audio/rational_factor_resample_calculator.h

@@ -1,4 +1,4 @@
-// Copyright 2019 The MediaPipe Authors.
+// Copyright 2019, 2021 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.
@@ -36,28 +36,31 @@ namespace mediapipe {
 // stream's sampling rate is specified by target_sample_rate in the
 // RationalFactorResampleCalculatorOptions.  The output time series may have
 // a varying number of samples per frame.
+//
+// NOTE: This calculator uses QResampler, despite the name, which supersedes
+// RationalFactorResampler.
 class RationalFactorResampleCalculator : public CalculatorBase {
  public:
   struct TestAccess;
 
-  static ::mediapipe::Status GetContract(CalculatorContract* cc) {
+  static absl::Status GetContract(CalculatorContract* cc) {
     cc->Inputs().Index(0).Set<Matrix>(
         // Single input stream with TimeSeriesHeader.
     );
     cc->Outputs().Index(0).Set<Matrix>(
         // Resampled stream with TimeSeriesHeader.
     );
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
   // Returns FAIL if the input stream header is invalid or if the
   // resampler cannot be initialized.
-  ::mediapipe::Status Open(CalculatorContext* cc) override;
+  absl::Status Open(CalculatorContext* cc) override;
   // Resamples a packet of TimeSeries data.  Returns FAIL if the
   // resampler state becomes inconsistent.
-  ::mediapipe::Status Process(CalculatorContext* cc) override;
+  absl::Status Process(CalculatorContext* cc) override;
   // Flushes any remaining state.  Returns FAIL if the resampler state
   // becomes inconsistent.
-  ::mediapipe::Status Close(CalculatorContext* cc) override;
+  absl::Status Close(CalculatorContext* cc) override;
 
  protected:
   typedef audio_dsp::Resampler<float> ResamplerType;
@@ -72,8 +75,8 @@ class RationalFactorResampleCalculator : public CalculatorBase {
   // Does Timestamp bookkeeping and resampling common to Process() and
   // Close().  Returns FAIL if the resampler state becomes
   // inconsistent.
-  ::mediapipe::Status ProcessInternal(const Matrix& input_frame,
-                                      bool should_flush, CalculatorContext* cc);
+  absl::Status ProcessInternal(const Matrix& input_frame, bool should_flush,
+                               CalculatorContext* cc);
 
   // Uses the internal resampler_ objects to actually resample each
   // row of the input TimeSeries.  Returns false if the resampler

mediapipe/calculators/audio/rational_factor_resample_calculator.proto

@@ -1,4 +1,4 @@
-// Copyright 2019 The MediaPipe Authors.
+// Copyright 2019, 2021 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.
@@ -18,6 +18,8 @@ package mediapipe;
 
 import "mediapipe/framework/calculator.proto";
 
+// NOTE: This calculator uses QResampler, despite the name, which supersedes
+// RationalFactorResampler.
 message RationalFactorResampleCalculatorOptions {
   extend CalculatorOptions {
     optional RationalFactorResampleCalculatorOptions ext = 259760074;
@@ -27,8 +29,7 @@ message RationalFactorResampleCalculatorOptions {
   // stream.  Required.  Must be greater than 0.
   optional double target_sample_rate = 1;
 
-  // Parameters for initializing the RationalFactorResampler. See
-  // RationalFactorResampler for more details.
+  // Parameters for initializing QResampler. See QResampler for more details.
   message ResamplerRationalFactorOptions {
     // Kernel radius in units of input samples.
     optional double radius = 1;

mediapipe/calculators/audio/rational_factor_resample_calculator_test.cc

@@ -80,7 +80,7 @@ class RationalFactorResampleCalculatorTest
   }
 
   // Initializes and runs the test graph.
-  ::mediapipe::Status Run(double output_sample_rate) {
+  absl::Status Run(double output_sample_rate) {
     options_.set_target_sample_rate(output_sample_rate);
     InitializeGraph();
 
@@ -120,7 +120,6 @@ class RationalFactorResampleCalculatorTest
 
     // The exact number of expected samples may vary based on the implementation
     // of the resampler since the exact value is not an integer.
-    // TODO: Reduce this offset to + 1 once cl/185829520 is submitted.
     const double expected_num_output_samples = num_input_samples_ * factor;
     EXPECT_LE(ceil(expected_num_output_samples), num_output_samples);
     EXPECT_GE(ceil(expected_num_output_samples) + 11, num_output_samples);

mediapipe/calculators/audio/spectrogram_calculator.cc

@@ -66,7 +66,7 @@ namespace mediapipe {
 // analysis frame will advance from its predecessor by the same time step.
 class SpectrogramCalculator : public CalculatorBase {
  public:
-  static ::mediapipe::Status GetContract(CalculatorContract* cc) {
+  static absl::Status GetContract(CalculatorContract* cc) {
     cc->Inputs().Index(0).Set<Matrix>(
         // Input stream with TimeSeriesHeader.
     );
@@ -96,26 +96,34 @@ class SpectrogramCalculator : public CalculatorBase {
         );
       }
     }
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
   // Returns FAIL if the input stream header is invalid.
-  ::mediapipe::Status Open(CalculatorContext* cc) override;
+  absl::Status Open(CalculatorContext* cc) override;
 
   // Outputs at most one packet consisting of a single Matrix with one or
   // more columns containing the spectral values from as many input frames
   // as are completed by the input samples.  Always returns OK.
-  ::mediapipe::Status Process(CalculatorContext* cc) override;
+  absl::Status Process(CalculatorContext* cc) override;
 
   // Performs zero-padding and processing of any remaining samples
   // if pad_final_packet is set.
   // Returns OK.
-  ::mediapipe::Status Close(CalculatorContext* cc) override;
+  absl::Status Close(CalculatorContext* cc) override;
 
  private:
   Timestamp CurrentOutputTimestamp(CalculatorContext* cc) {
     if (use_local_timestamp_) {
-      return cc->InputTimestamp();
+      const Timestamp now = cc->InputTimestamp();
+      if (now == Timestamp::Done()) {
+        // During Close the timestamp is not available, send an estimate.
+        return last_local_output_timestamp_ +
+               round(last_completed_frames_ * frame_step_samples() *
+                     Timestamp::kTimestampUnitsPerSecond / input_sample_rate_);
+      }
+      last_local_output_timestamp_ = now;
+      return now;
     }
     return CumulativeOutputTimestamp();
   }
@@ -138,17 +146,20 @@ class SpectrogramCalculator : public CalculatorBase {
   // Convert the output of the spectrogram object into a Matrix (or an
   // Eigen::MatrixXcf if complex-valued output is requested) and pass to
   // MediaPipe output.
-  ::mediapipe::Status ProcessVector(const Matrix& input_stream,
-                                    CalculatorContext* cc);
+  absl::Status ProcessVector(const Matrix& input_stream, CalculatorContext* cc);
 
   // Templated function to process either real- or complex-output spectrogram.
   template <class OutputMatrixType>
-  ::mediapipe::Status ProcessVectorToOutput(
+  absl::Status ProcessVectorToOutput(
       const Matrix& input_stream,
       const OutputMatrixType postprocess_output_fn(const OutputMatrixType&),
       CalculatorContext* cc);
 
+  // Use the MediaPipe timestamp instead of the estimated one. Useful when the
+  // data is intermittent.
   bool use_local_timestamp_;
+  Timestamp last_local_output_timestamp_;
+
   double input_sample_rate_;
   bool pad_final_packet_;
   int frame_duration_samples_;
@@ -157,6 +168,9 @@ class SpectrogramCalculator : public CalculatorBase {
   int64 cumulative_input_samples_;
   // How many frames we've emitted, used for calculating output time stamps.
   int64 cumulative_completed_frames_;
+  // How many frames were emitted last, used for estimating the timestamp on
+  // Close when use_local_timestamp_ is true;
+  int64 last_completed_frames_;
   Timestamp initial_input_timestamp_;
   int num_input_channels_;
   // How many frequency bins we emit (=N_FFT/2 + 1).
@@ -177,7 +191,7 @@ REGISTER_CALCULATOR(SpectrogramCalculator);
 // Factor to convert ln(magnitude_squared) to deciBels = 10.0/ln(10.0).
 const float SpectrogramCalculator::kLnPowerToDb = 4.342944819032518;
 
-::mediapipe::Status SpectrogramCalculator::Open(CalculatorContext* cc) {
+absl::Status SpectrogramCalculator::Open(CalculatorContext* cc) {
   SpectrogramCalculatorOptions spectrogram_options =
       cc->Options<SpectrogramCalculatorOptions>();
 
@@ -271,11 +285,20 @@ const float SpectrogramCalculator::kLnPowerToDb = 4.342944819032518;
         Adopt(multichannel_output_header.release()));
   }
   cumulative_completed_frames_ = 0;
+  last_completed_frames_ = 0;
   initial_input_timestamp_ = Timestamp::Unstarted();
-  return ::mediapipe::OkStatus();
+  if (use_local_timestamp_) {
+    // Inform the framework that the calculator will output packets at the same
+    // timestamps as input packets to enable packet queueing optimizations. The
+    // final packet (emitted from Close()) does not follow this rule but it's
+    // sufficient that its timestamp is strictly greater than the timestamp of
+    // the previous packet.
+    cc->SetOffset(0);
+  }
+  return absl::OkStatus();
 }
 
-::mediapipe::Status SpectrogramCalculator::Process(CalculatorContext* cc) {
+absl::Status SpectrogramCalculator::Process(CalculatorContext* cc) {
   if (initial_input_timestamp_ == Timestamp::Unstarted()) {
     initial_input_timestamp_ = cc->InputTimestamp();
   }
@@ -291,7 +314,7 @@ const float SpectrogramCalculator::kLnPowerToDb = 4.342944819032518;
 }
 
 template <class OutputMatrixType>
-::mediapipe::Status SpectrogramCalculator::ProcessVectorToOutput(
+absl::Status SpectrogramCalculator::ProcessVectorToOutput(
     const Matrix& input_stream,
     const OutputMatrixType postprocess_output_fn(const OutputMatrixType&),
     CalculatorContext* cc) {
@@ -311,8 +334,8 @@ template <class OutputMatrixType>
 
     if (!spectrogram_generators_[channel]->ComputeSpectrogram(
             input_vector, &output_vectors)) {
-      return ::mediapipe::Status(mediapipe::StatusCode::kInternal,
-                                 "Spectrogram returned failure");
+      return absl::Status(absl::StatusCode::kInternal,
+                          "Spectrogram returned failure");
     }
     if (channel == 0) {
       // Record the number of time frames we expect from each channel.
@@ -354,12 +377,19 @@ template <class OutputMatrixType>
           CurrentOutputTimestamp(cc));
     }
     cumulative_completed_frames_ += output_vectors.size();
+    last_completed_frames_ = output_vectors.size();
+    if (!use_local_timestamp_) {
+      // In non-local timestamp mode the timestamp of the next packet will be
+      // equal to CumulativeOutputTimestamp(). Inform the framework about this
+      // fact to enable packet queueing optimizations.
+      cc->Outputs().Index(0).SetNextTimestampBound(CumulativeOutputTimestamp());
+    }
   }
-  return ::mediapipe::OkStatus();
+  return absl::OkStatus();
 }
 
-::mediapipe::Status SpectrogramCalculator::ProcessVector(
-    const Matrix& input_stream, CalculatorContext* cc) {
+absl::Status SpectrogramCalculator::ProcessVector(const Matrix& input_stream,
+                                                  CalculatorContext* cc) {
   switch (output_type_) {
     // These blocks deliberately ignore clang-format to preserve the
     // "silhouette" of the different cases.
@@ -394,13 +424,13 @@ template <class OutputMatrixType>
     }
     // clang-format on
     default: {
-      return ::mediapipe::Status(mediapipe::StatusCode::kInvalidArgument,
-                                 "Unrecognized spectrogram output type.");
+      return absl::Status(absl::StatusCode::kInvalidArgument,
+                          "Unrecognized spectrogram output type.");
     }
   }
 }
 
-::mediapipe::Status SpectrogramCalculator::Close(CalculatorContext* cc) {
+absl::Status SpectrogramCalculator::Close(CalculatorContext* cc) {
   if (cumulative_input_samples_ > 0 && pad_final_packet_) {
     // We can flush any remaining samples by sending frame_step_samples - 1
     // zeros to the Process method, and letting it do its thing,
@@ -416,7 +446,7 @@ template <class OutputMatrixType>
         Matrix::Zero(num_input_channels_, required_padding_samples), cc);
   }
 
-  return ::mediapipe::OkStatus();
+  return absl::OkStatus();
 }
 
 }  // namespace mediapipe

mediapipe/calculators/audio/spectrogram_calculator_test.cc

@@ -50,7 +50,7 @@ class SpectrogramCalculatorTest
   }
 
   // Initializes and runs the test graph.
-  ::mediapipe::Status Run() {
+  absl::Status Run() {
     // Now that options are set, we can set up some internal constants.
     frame_duration_samples_ =
         round(options_.frame_duration_seconds() * input_sample_rate_);

mediapipe/calculators/audio/stabilized_log_calculator.cc

@@ -41,17 +41,17 @@ namespace mediapipe {
 // }
 class StabilizedLogCalculator : public CalculatorBase {
  public:
-  static ::mediapipe::Status GetContract(CalculatorContract* cc) {
+  static absl::Status GetContract(CalculatorContract* cc) {
     cc->Inputs().Index(0).Set<Matrix>(
         // Input stream with TimeSeriesHeader.
     );
     cc->Outputs().Index(0).Set<Matrix>(
         // Output stabilized log stream with TimeSeriesHeader.
     );
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
-  ::mediapipe::Status Open(CalculatorContext* cc) override {
+  absl::Status Open(CalculatorContext* cc) override {
     StabilizedLogCalculatorOptions stabilized_log_calculator_options =
         cc->Options<StabilizedLogCalculatorOptions>();
 
@@ -70,23 +70,23 @@ class StabilizedLogCalculator : public CalculatorBase {
       cc->Outputs().Index(0).SetHeader(
           Adopt(new TimeSeriesHeader(input_header)));
     }
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
-  ::mediapipe::Status Process(CalculatorContext* cc) override {
+  absl::Status Process(CalculatorContext* cc) override {
     auto input_matrix = cc->Inputs().Index(0).Get<Matrix>();
     if (input_matrix.array().isNaN().any()) {
-      return ::mediapipe::InvalidArgumentError("NaN input to log operation.");
+      return absl::InvalidArgumentError("NaN input to log operation.");
     }
     if (check_nonnegativity_) {
       if (input_matrix.minCoeff() < 0.0) {
-        return ::mediapipe::OutOfRangeError("Negative input to log operation.");
+        return absl::OutOfRangeError("Negative input to log operation.");
       }
     }
     std::unique_ptr<Matrix> output_frame(new Matrix(
         output_scale_ * (input_matrix.array() + stabilizer_).log().matrix()));
     cc->Outputs().Index(0).Add(output_frame.release(), cc->InputTimestamp());
-    return ::mediapipe::OkStatus();
+    return absl::OkStatus();
   }
 
  private: