Project import generated by Copybara.

GitOrigin-RevId: bb059a0721c92e8154d33ce8057b3915a25b3d7d

.bazelrc

@@ -58,6 +58,7 @@ build:android_arm64 --fat_apk_cpu=arm64-v8a
 
 # iOS configs.
 build:ios --apple_platform_type=ios
+build:ios --copt=-fno-aligned-allocation
 
 build:ios_i386 --config=ios
 build:ios_i386 --cpu=ios_i386

.bazelversion

@@ -1 +1 @@
-3.7.2
+4.2.1

.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.

CONTRIBUTING.md

@@ -5,7 +5,7 @@
 * Bug fixes
 * Documentation fixes
 
-For new feature additions (e.g., new graphs and calculators), we are currently not planning to accept new feature pull requests into the MediaPipe repository. Instead, we like to get contributors to create their own repositories of the new feature and list it at [Awesome MediaPipe](https://mediapipe.org). This will allow contributors to more quickly get their code out to the community.
+For new feature additions (e.g., new graphs and calculators), we are currently not planning to accept new feature pull requests into the MediaPipe repository. Instead, we like to get contributors to create their own repositories of the new feature and list it at [Awesome MediaPipe](https://mediapipe.page.link/awesome-mediapipe). This will allow contributors to more quickly get their code out to the community.
 
 Before sending your pull requests, make sure you followed this list.
 

Dockerfile

@@ -56,7 +56,7 @@ RUN pip3 install tf_slim
 RUN ln -s /usr/bin/python3 /usr/bin/python
 
 # Install bazel
-ARG BAZEL_VERSION=3.7.2
+ARG BAZEL_VERSION=4.2.1
 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

@@ -8,6 +8,7 @@ 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

README.md

@@ -40,11 +40,12 @@ Hair Segmentation
 [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)                             | ✅                                                               |                                                        | ✅                                                         | ✅                                                             |                                                               |
+[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)                    |                                                                 |                                                         | ✅                                                       |                                                               |                                                               |
@@ -54,46 +55,22 @@ See also
 [MediaPipe Models and Model Cards](https://google.github.io/mediapipe/solutions/models)
 for ML models released in MediaPipe.
 
-## MediaPipe in Python
-
-MediaPipe offers customizable Python solutions as a prebuilt Python package on
-[PyPI](https://pypi.org/project/mediapipe/), which can be installed simply with
-`pip install mediapipe`. It also provides tools for users to build their own
-solutions. Please see
-[MediaPipe in Python](https://google.github.io/mediapipe/getting_started/python)
-for more info.
-
-## 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
@@ -102,6 +79,13 @@ 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)

WORKSPACE

@@ -16,11 +16,11 @@ bazel_skylib_workspace()
 load("@bazel_skylib//lib:versions.bzl", "versions")
 versions.check(minimum_bazel_version = "3.7.2")
 
-# ABSL cpp library lts_2020_09_23
+# ABSL cpp library lts_2021_03_24, patch 2.
 http_archive(
     name = "com_google_absl",
     urls = [
-        "https://github.com/abseil/abseil-cpp/archive/20200923.tar.gz",
+        "https://github.com/abseil/abseil-cpp/archive/refs/tags/20210324.2.tar.gz",
     ],
     # Remove after https://github.com/abseil/abseil-cpp/issues/326 is solved.
     patches = [
@@ -29,14 +29,14 @@ http_archive(
     patch_args = [
         "-p1",
     ],
-    strip_prefix = "abseil-cpp-20200923",
-    sha256 = "b3744a4f7a249d5eaf2309daad597631ce77ea62e0fc6abffbab4b4c3dc0fc08"
+    strip_prefix = "abseil-cpp-20210324.2",
+    sha256 = "59b862f50e710277f8ede96f083a5bb8d7c9595376146838b9580be90374ee1f"
 )
 
 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(
@@ -53,26 +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",
 )
 
@@ -129,16 +122,16 @@ http_archive(
 # ...but the Java download is currently broken, so we use the "source" download.
 http_archive(
     name = "com_google_protobuf_javalite",
-    sha256 = "a79d19dcdf9139fa4b81206e318e33d245c4c9da1ffed21c87288ed4380426f9",
-    strip_prefix = "protobuf-3.11.4",
-    urls = ["https://github.com/protocolbuffers/protobuf/archive/v3.11.4.tar.gz"],
+    sha256 = "87407cd28e7a9c95d9f61a098a53cf031109d451a7763e7dd1253abf8b4df422",
+    strip_prefix = "protobuf-3.19.1",
+    urls = ["https://github.com/protocolbuffers/protobuf/archive/v3.19.1.tar.gz"],
 )
 
 http_archive(
     name = "com_google_protobuf",
-    sha256 = "a79d19dcdf9139fa4b81206e318e33d245c4c9da1ffed21c87288ed4380426f9",
-    strip_prefix = "protobuf-3.11.4",
-    urls = ["https://github.com/protocolbuffers/protobuf/archive/v3.11.4.tar.gz"],
+    sha256 = "87407cd28e7a9c95d9f61a098a53cf031109d451a7763e7dd1253abf8b4df422",
+    strip_prefix = "protobuf-3.19.1",
+    urls = ["https://github.com/protocolbuffers/protobuf/archive/v3.19.1.tar.gz"],
     patches = [
         "@//third_party:com_google_protobuf_fixes.diff"
     ],
@@ -161,28 +154,29 @@ http_archive(
     sha256 = "75922da3a1bdb417d820398eb03d4e9bd067c4905a4246d35a44c01d62154d91",
 )
 
+# Point to the commit that deprecates the usage of Eigen::MappedSparseMatrix.
 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://github.com/pybind/pybind11/archive/70a58c577eaf067748c2ec31bfd0b0a614cffba6.zip",
     ],
-    sha256 = "1eed57bc6863190e35637290f97a20c81cfe4d9090ac0a24f3bbf08f265eb71d",
-    strip_prefix = "pybind11-2.4.3",
+    sha256 = "b971842fab1b5b8f3815a2302331782b7d137fef0e06502422bc4bc360f4956c",
+    strip_prefix = "pybind11-70a58c577eaf067748c2ec31bfd0b0a614cffba6",
     build_file = "@pybind11_bazel//:pybind11.BUILD",
 )
 
+# Point to the commit that deprecates the usage of Eigen::MappedSparseMatrix.
 http_archive(
     name = "ceres_solver",
-    url = "https://github.com/ceres-solver/ceres-solver/archive/2.0.0.zip",
+    url = "https://github.com/ceres-solver/ceres-solver/archive/123fba61cf2611a3c8bddc9d91416db26b10b558.zip",
     patches = [
         "@//third_party:ceres_solver_compatibility_fixes.diff"
     ],
     patch_args = [
         "-p1",
     ],
-    strip_prefix = "ceres-solver-2.0.0",
-    sha256 = "db12d37b4cebb26353ae5b7746c7985e00877baa8e7b12dc4d3a1512252fff3b"
+    strip_prefix = "ceres-solver-123fba61cf2611a3c8bddc9d91416db26b10b558",
+    sha256 = "8b7b16ceb363420e0fd499576daf73fa338adb0b1449f58bea7862766baa1ac7"
 )
 
 http_archive(
@@ -242,21 +236,26 @@ http_archive(
     url = "https://github.com/opencv/opencv/releases/download/3.2.0/opencv-3.2.0-ios-framework.zip",
 )
 
-# You may run setup_android.sh to install Android SDK and NDK.
-android_ndk_repository(
-    name = "androidndk",
-)
-
-android_sdk_repository(
-    name = "androidsdk",
+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",
+    ],
 )
 
 # iOS basic build deps.
 
 http_archive(
     name = "build_bazel_rules_apple",
-    sha256 = "7a7afdd4869bb201c9352eed2daf37294d42b093579b70423490c1b4d4f6ce42",
-    url = "https://github.com/bazelbuild/rules_apple/releases/download/0.19.0/rules_apple.0.19.0.tar.gz",
+    sha256 = "77e8bf6fda706f420a55874ae6ee4df0c9d95da6c7838228b26910fc82eea5a2",
+    url = "https://github.com/bazelbuild/rules_apple/releases/download/0.32.0/rules_apple.0.32.0.tar.gz",
     patches = [
         # Bypass checking ios unit test runner when building MP ios applications.
         "@//third_party:build_bazel_rules_apple_bypass_test_runner_check.diff"
@@ -282,10 +281,9 @@ swift_rules_dependencies()
 
 http_archive(
     name = "build_bazel_apple_support",
-    sha256 = "122ebf7fe7d1c8e938af6aeaee0efe788a3a2449ece5a8d6a428cb18d6f88033",
+    sha256 = "741366f79d900c11e11d8efd6cc6c66a31bfb2451178b58e0b5edc6f1db17b35",
     urls = [
-        "https://storage.googleapis.com/mirror.tensorflow.org/github.com/bazelbuild/apple_support/releases/download/0.7.1/apple_support.0.7.1.tar.gz",
-        "https://github.com/bazelbuild/apple_support/releases/download/0.7.1/apple_support.0.7.1.tar.gz",
+        "https://github.com/bazelbuild/apple_support/releases/download/0.10.0/apple_support.0.10.0.tar.gz"
     ],
 )
 
@@ -324,7 +322,10 @@ load("@rules_jvm_external//:defs.bzl", "maven_install")
 maven_install(
     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.exifinterface:exifinterface:1.3.3",
+        "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",
@@ -337,11 +338,14 @@ 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.auto.value:auto-value:1.6.4",
-        "com.google.auto.value:auto-value-annotations:1.6.4",
-        "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.android.datatransport:transport-api:3.0.0",
+        "com.google.android.datatransport:transport-backend-cct:3.1.0",
+        "com.google.android.datatransport:transport-runtime:3.1.0",
+        "com.google.flogger:flogger-system-backend:0.6",
+        "com.google.flogger:flogger:0.6",
         "com.google.guava:guava:27.0.1-android",
         "com.google.guava:listenablefuture:1.0",
         "junit:junit:4.12",
@@ -369,9 +373,9 @@ http_archive(
 )
 
 # Tensorflow repo should always go after the other external dependencies.
-# 2021-04-30
-_TENSORFLOW_GIT_COMMIT = "5bd3c57ef184543d22e34e36cff9d9bea608e06d"
-_TENSORFLOW_SHA256= "9a45862834221aafacf6fb275f92b3876bc89443cbecc51be93f13839a6609f0"
+# 2021-12-02
+_TENSORFLOW_GIT_COMMIT = "18a1dc0ba806dc023808531f0373d9ec068e64bf"
+_TENSORFLOW_SHA256 = "85b90416f7a11339327777bccd634de00ca0de2cf334f5f0727edcb11ff9289a"
 http_archive(
     name = "org_tensorflow",
     urls = [
@@ -380,6 +384,8 @@ http_archive(
     patches = [
         "@//third_party:org_tensorflow_compatibility_fixes.diff",
         "@//third_party:org_tensorflow_objc_cxx17.diff",
+        # Diff is generated with a script, don't update it manually.
+        "@//third_party:org_tensorflow_custom_ops.diff",
     ],
     patch_args = [
         "-p1",
@@ -392,3 +398,18 @@ 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_desktop_examples.sh

@@ -97,6 +97,7 @@ for app in ${apps}; do
       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

@@ -248,12 +248,70 @@ absl::Status MyCalculator::Process() {
 }
 ```
 
+## 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++
@@ -401,6 +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

@@ -111,11 +111,11 @@ component known as an InputStreamHandler.
 
 See [Synchronization](synchronization.md) for more details.
 
-### Realtime data streams
+### 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 realtime graphs need to define output timestamp bounds based on input
+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 [Realtime data streams](realtime.md) for details.
+promptly. See [Real-time Streams](realtime_streams.md) for details.

docs/framework_concepts/realtime_streams.md

@@ -1,29 +1,28 @@
 ---
 layout: default
-title: Processing real-time data streams
+title: Real-time Streams
+parent: Framework Concepts
 nav_order: 6
-has_children: true
-has_toc: false
 ---
 
-# Processing real-time data streams
+# Real-time Streams
 {: .no_toc }
 
 1. TOC
 {:toc}
 ---
 
-## Realtime timestamps
+## 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, realtime calculators and graphs use the recording time or the
+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.
 
-## Realtime scheduling
+## 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
@@ -38,7 +37,7 @@ 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 realtime
+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.
 
@@ -83,12 +82,12 @@ For example, `Timestamp(1).NextAllowedInStream() == Timestamp(2)`.
 
 ## Propagating timestamp bounds
 
-Calculators that will be used in realtime 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.
+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

docs/getting_started/android.md

@@ -16,19 +16,21 @@ nav_order: 1
 
 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 an existing Android Studio project, see these
-[instructions](./android_archive_library.md) that use Android Archive (AAR) and
-Gradle.
+example apps, start from [Hello World! on Android](./hello_world_android.md).
 
-## Building Android example apps
+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.
+*   Setup Android SDK release 30.0.0 and above.
+*   Setup Android NDK version 18 and above.
 
 MediaPipe recommends setting up Android SDK and NDK via Android Studio (and see
 below for Android Studio setup). However, if you prefer using MediaPipe without
@@ -45,22 +47,21 @@ export ANDROID_HOME=<path to the Android SDK>
 export ANDROID_NDK_HOME=<path to the Android NDK>
 ```
 
+and add android_ndk_repository() and android_sdk_repository() rules into the
+[`WORKSPACE`](https://github.com/google/mediapipe/blob/master/WORKSPACE) file as
+the following:
+
+```bash
+$ echo "android_sdk_repository(name = \"androidsdk\")" >> WORKSPACE
+$ echo "android_ndk_repository(name = \"androidndk\")" >> WORKSPACE
+```
+
 In order to use MediaPipe on earlier Android versions, MediaPipe needs to switch
 to a lower Android API level. You can achieve this by specifying `api_level =
 $YOUR_INTENDED_API_LEVEL` in android_ndk_repository() and/or
 android_sdk_repository() in the
 [`WORKSPACE`](https://github.com/google/mediapipe/blob/master/WORKSPACE) file.
 
-Please verify all the necessary packages are installed.
-
-*   Android SDK Platform API Level 28 or 29
-*   Android SDK Build-Tools 28 or 29
-*   Android SDK Platform-Tools 28 or 29
-*   Android SDK Tools 26.1.1
-*   Android NDK 19c or above
-
-### Option 1: Build with Bazel in Command Line
-
 Tip: You can run this
 [script](https://github.com/google/mediapipe/blob/master/build_android_examples.sh)
 to build (and install) all MediaPipe Android example apps.
@@ -84,108 +85,3 @@ to build (and install) all MediaPipe Android example apps.
     ```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 19c 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.

docs/getting_started/android_archive_library.md

@@ -3,7 +3,7 @@ layout: default
 title: MediaPipe Android Archive
 parent: MediaPipe on Android
 grand_parent: Getting Started
-nav_order: 2
+nav_order: 3
 ---
 
 # MediaPipe Android Archive
@@ -92,12 +92,12 @@ each project.
     and copy
     [the binary graph](https://github.com/google/mediapipe/blob/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectiongpu/BUILD#L41)
     and
-    [the face detection tflite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_front.tflite).
+    [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/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_front.tflite /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)
@@ -113,19 +113,18 @@ 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.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.guava:guava:27.0.1-android'
-        implementation 'com.google.protobuf:protobuf-java:3.11.4'
+        implementation 'com.google.protobuf:protobuf-javalite:3.19.1'
         // 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:$camerax_version"
         // AutoValue
-        def auto_value_version = "1.6.4"
+        def auto_value_version = "1.8.1"
         implementation "com.google.auto.value:auto-value-annotations:$auto_value_version"
         annotationProcessor "com.google.auto.value:auto-value:$auto_value_version"
     }

docs/getting_started/android_solutions.md

@@ -0,0 +1,131 @@
+---
+layout: default
+title: MediaPipe Android Solutions
+parent: MediaPipe on Android
+grand_parent: Getting Started
+nav_order: 2
+---
+
+# MediaPipe Android Solutions
+{: .no_toc }
+
+1. TOC
+{:toc}
+---
+
+MediaPipe Android Solution APIs (currently in alpha) are available in:
+
+*   [MediaPipe Face Detection](../solutions/face_detection#android-solution-api)
+*   [MediaPipe Face Mesh](../solutions/face_mesh#android-solution-api)
+*   [MediaPipe Hands](../solutions/hands#android-solution-api)
+
+## Incorporation in Android Studio
+
+Prebuilt packages of Android Solution APIs can be found in
+[Google's Maven Repository](https://maven.google.com/web/index.html?#com.google.mediapipe).
+To incorporate them 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 Face Detection Solution.
+    implementation 'com.google.mediapipe:facedetection:latest.release'
+    // Optional: MediaPipe Face Mesh Solution.
+    implementation 'com.google.mediapipe:facemesh:latest.release'
+    // Optional: MediaPipe Hands Solution.
+    implementation 'com.google.mediapipe:hands:latest.release'
+}
+```
+
+If you need further customization, instead of using the prebuilt maven packages
+consider building a MediaPipe Android Archive library locally from source by
+following these [instructions](./android_archive_library.md).
+
+## Building solution example apps
+
+Detailed usage examples of the Android Solution APIs can be found in the
+[source code](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/solutions)
+of the solution example apps.
+
+To build these apps:
+
+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 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 model inference on CPU.
+
+## MediaPipe Solution APIs Terms of Service
+
+Last modified: November 12, 2021
+
+Use of MediaPipe Solution APIs is subject to the
+[Google APIs Terms of Service](https://developers.google.com/terms),
+[Google API Services User Data Policy](https://developers.google.com/terms/api-services-user-data-policy),
+and the terms below. Please check back from time to time as these terms and
+policies are occasionally updated.
+
+**Privacy**
+
+When you use MediaPipe Solution APIs, processing of the input data (e.g. images,
+video, text) fully happens on-device, and **MediaPipe does not send that input
+data to Google servers**. As a result, you can use our APIs for processing data
+that should not leave the device.
+
+MediaPipe Android Solution APIs will contact Google servers from time to time in
+order to receive things like bug fixes, updated models, and hardware accelerator
+compatibility information. MediaPipe Android Solution APIs also send metrics
+about the performance and utilization of the APIs in your app to Google. Google
+uses this metrics data to measure performance, API usage, debug, maintain and
+improve the APIs, and detect misuse or abuse, as further described in our
+[Privacy Policy](https://policies.google.com/privacy).
+
+**You are responsible for obtaining informed consent from your app users about
+Google’s processing of MediaPipe metrics data as required by applicable law.**
+
+Data we collect may include the following, across all MediaPipe Android Solution
+APIs:
+
+-   Device information (such as manufacturer, model, OS version and build) and
+    available ML hardware accelerators (GPU and DSP). Used for diagnostics and
+    usage analytics.
+
+-   App identification information (package name / bundle id, app version). Used
+    for diagnostics and usage analytics.
+
+-   API configuration (such as image format, resolution, and MediaPipe version
+    used). Used for diagnostics and usage analytics.
+
+-   Event type (such as initialize, download model, update, run, and detection).
+    Used for diagnostics and usage analytics.
+
+-   Error codes. Used for diagnostics.
+
+-   Performance metrics. Used for diagnostics.
+
+-   Per-installation identifiers that do not uniquely identify a user or
+    physical device. Used for operation of remote configuration and usage
+    analytics.
+
+-   Network request sender IP addresses. Used for remote configuration
+    diagnostics. Collected IP addresses are retained temporarily.

docs/getting_started/faq.md

@@ -103,7 +103,7 @@ monotonically increasing timestamps. By convention, realtime calculators and
 graphs use the recording time or the presentation time as the timestamp for each
 packet, with each timestamp representing microseconds since
 `Jan/1/1970:00:00:00`. This allows packets from various sources to be processed
-in a gloablly consistent order.
+in a globally consistent order.
 
 Normally for offline processing, every input packet is processed and processing
 continues as long as necessary. For online processing, it is often necessary to

docs/getting_started/hello_world_android.md

@@ -31,8 +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.
@@ -770,7 +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_ios.md

@@ -31,8 +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.
 
@@ -113,6 +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
@@ -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:
 
@@ -549,6 +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!
 
@@ -560,6 +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

@@ -43,104 +43,189 @@ install --user six`.
 
 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",
+      ],
     )
     ```
 
@@ -484,7 +569,7 @@ next section.
 
     Option 1. Follow
     [the official Bazel documentation](https://docs.bazel.build/versions/master/install-windows.html)
-    to install Bazel 3.7.2 or higher.
+    to install Bazel 4.2.1 or higher.
 
     Option 2. Follow the official
     [Bazel documentation](https://docs.bazel.build/versions/master/install-bazelisk.html)
@@ -572,7 +657,7 @@ cameras. Alternatively, you use a video file as input.
 
     Note: Windows' and WSL’s adb versions must be the same version, e.g., if WSL
     has ADB 1.0.39, you need to download the corresponding Windows ADB from
-    [here](https://dl.google.com/android/repository/platform-tools_r26.0.1-windows.zip).
+    [here](https://dl.google.com/android/repository/platform-tools_r30.0.3-windows.zip).
 
 3.  Launch WSL.
 
@@ -711,7 +796,7 @@ This will use a Docker image that will isolate mediapipe's installation from the
     ```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 bazel run --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/hello_world
 
     # Should print:
     # Hello World!

docs/getting_started/javascript.md

@@ -16,17 +16,29 @@ nav_order: 4
 
 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]
-[Pose][P-pg]      | [@mediapipe/pose][P-npm]      | [mediapipe.dev/demo/pose][P-demo]
+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:
@@ -66,29 +78,25 @@ affecting your work, restrict your request to a `<minor>` number. e.g.,
 [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-jsd]: https://www.jsdelivr.com/package/npm/@mediapipe/holistic
-[F-jsd]: https://www.jsdelivr.com/package/npm/@mediapipe/face_mesh
-[Fd-jsd]: https://www.jsdelivr.com/package/npm/@mediapipe/face_detection
-[H-jsd]: https://www.jsdelivr.com/package/npm/@mediapipe/hands
-[P-jsd]: https://www.jsdelivr.com/package/npm/@mediapipe/pose
-[Ho-pen]: https://code.mediapipe.dev/codepen/holistic
-[F-pen]: https://code.mediapipe.dev/codepen/face_mesh
-[Fd-pen]: https://code.mediapipe.dev/codepen/face_detection
-[H-pen]: https://code.mediapipe.dev/codepen/hands
-[P-pen]: https://code.mediapipe.dev/codepen/pose
 [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

@@ -51,6 +51,7 @@ details in each solution via the links below:
 *   [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
 
@@ -62,6 +63,7 @@ details in each solution via the links below:
 *   [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
 

docs/getting_started/python_framework.md

@@ -74,7 +74,7 @@ Mapping\[str, Packet\]               | std::map<std::string, Packet> | create_st
 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\>          | create_proto_vector(proto_list)                                                                                                                              | get_proto_list(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
@@ -249,12 +249,12 @@ three stages: initialization and setup, graph run, and graph shutdown.
     graph.start_run()
 
     graph.add_packet_to_input_stream(
-        'in_stream', mp.packet_creator.create_str('abc').at(0))
+        'in_stream', mp.packet_creator.create_string('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,
+        mp.packet_creator.create_image_frame(image_format=mp.ImageFormat.SRGB,
                                              data=rgb_img).at(1))
     ```
 

docs/getting_started/troubleshooting.md

@@ -108,14 +108,14 @@ 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:
+version of Python 3.7 to 3.10 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,
+that both the Python and pip binary are for Python 3.7 to 3.10. Otherwise,
 please consider building the MediaPipe Python package locally by following the
 instructions [here](python.md#building-mediapipe-python-package).
 

docs/index.md

@@ -40,11 +40,12 @@ Hair Segmentation
 [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)                             | ✅                                                               |                                                        | ✅                                                         | ✅                                                             |                                                               |
+[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)                    |                                                                 |                                                         | ✅                                                       |                                                               |                                                               |
@@ -54,46 +55,22 @@ See also
 [MediaPipe Models and Model Cards](https://google.github.io/mediapipe/solutions/models)
 for ML models released in MediaPipe.
 
-## MediaPipe in Python
-
-MediaPipe offers customizable Python solutions as a prebuilt Python package on
-[PyPI](https://pypi.org/project/mediapipe/), which can be installed simply with
-`pip install mediapipe`. It also provides tools for users to build their own
-solutions. Please see
-[MediaPipe in Python](https://google.github.io/mediapipe/getting_started/python)
-for more info.
-
-## 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
@@ -102,6 +79,13 @@ 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)

docs/solutions/autoflip.md

@@ -2,7 +2,7 @@
 layout: default
 title: AutoFlip (Saliency-aware Video Cropping)
 parent: Solutions
-nav_order: 13
+nav_order: 14
 ---
 
 # AutoFlip: Saliency-aware Video Cropping

docs/solutions/box_tracking.md

@@ -2,7 +2,7 @@
 layout: default
 title: Box Tracking
 parent: Solutions
-nav_order: 9
+nav_order: 10
 ---
 
 # MediaPipe Box Tracking

docs/solutions/face_detection.md

@@ -45,6 +45,15 @@ section.
 
 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
@@ -68,10 +77,11 @@ normalized to `[0.0, 1.0]` by the image width and height respectively.
 
 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 following usage example.
+[Python Colab](#resources) and the usage example below.
 
 Supported configuration options:
 
+*   [model_selection](#model_selection)
 *   [min_detection_confidence](#min_detection_confidence)
 
 ```python
@@ -81,9 +91,10 @@ mp_face_detection = mp.solutions.face_detection
 mp_drawing = mp.solutions.drawing_utils
 
 # For static images:
+IMAGE_FILES = []
 with mp_face_detection.FaceDetection(
-    min_detection_confidence=0.5) as face_detection:
-  for idx, file in enumerate(file_list):
+    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))
@@ -102,7 +113,7 @@ with mp_face_detection.FaceDetection(
 # For webcam input:
 cap = cv2.VideoCapture(0)
 with mp_face_detection.FaceDetection(
-    min_detection_confidence=0.5) as face_detection:
+    model_selection=0, min_detection_confidence=0.5) as face_detection:
   while cap.isOpened():
     success, image = cap.read()
     if not success:
@@ -110,12 +121,10 @@ with mp_face_detection.FaceDetection(
       # 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
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     results = face_detection.process(image)
 
     # Draw the face detection annotations on the image.
@@ -124,7 +133,8 @@ with mp_face_detection.FaceDetection(
     if results.detections:
       for detection in results.detections:
         mp_drawing.draw_detection(image, detection)
-    cv2.imshow('MediaPipe Face Detection', image)
+    # Flip the image horizontally for a selfie-view display.
+    cv2.imshow('MediaPipe Face Detection', cv2.flip(image, 1))
     if cv2.waitKey(5) & 0xFF == 27:
       break
 cap.release()
@@ -138,6 +148,7 @@ and the following usage example.
 
 Supported configuration options:
 
+*   [modelSelection](#model_selection)
 *   [minDetectionConfidence](#min_detection_confidence)
 
 ```html
@@ -188,6 +199,7 @@ 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);
@@ -203,6 +215,214 @@ camera.start();
 </script>
 ```
 
+### Android Solution API
+
+Please first follow general
+[instructions](../getting_started/android_solutions.md) to add MediaPipe Gradle
+dependencies and try the Android Solution API in the companion
+[example Android Studio project](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/solutions/facedetection),
+and learn more in the usage example below.
+
+Supported configuration options:
+
+*   [staticImageMode](#static_image_mode)
+*   [modelSelection](#model_selection)
+
+#### Camera Input
+
+```java
+// For camera input and result rendering with OpenGL.
+FaceDetectionOptions faceDetectionOptions =
+    FaceDetectionOptions.builder()
+        .setStaticImageMode(false)
+        .setModelSelection(0).build();
+FaceDetection faceDetection = new FaceDetection(this, faceDetectionOptions);
+faceDetection.setErrorListener(
+    (message, e) -> Log.e(TAG, "MediaPipe Face Detection error:" + message));
+
+// Initializes a new CameraInput instance and connects it to MediaPipe Face Detection Solution.
+CameraInput cameraInput = new CameraInput(this);
+cameraInput.setNewFrameListener(
+    textureFrame -> faceDetection.send(textureFrame));
+
+// Initializes a new GlSurfaceView with a ResultGlRenderer<FaceDetectionResult> instance
+// that provides the interfaces to run user-defined OpenGL rendering code.
+// See mediapipe/examples/android/solutions/facedetection/src/main/java/com/google/mediapipe/examples/facedetection/FaceDetectionResultGlRenderer.java
+// as an example.
+SolutionGlSurfaceView<FaceDetectionResult> glSurfaceView =
+    new SolutionGlSurfaceView<>(
+        this, faceDetection.getGlContext(), faceDetection.getGlMajorVersion());
+glSurfaceView.setSolutionResultRenderer(new FaceDetectionResultGlRenderer());
+glSurfaceView.setRenderInputImage(true);
+faceDetection.setResultListener(
+    faceDetectionResult -> {
+      if (faceDetectionResult.multiFaceDetections().isEmpty()) {
+        return;
+      }
+      RelativeKeypoint noseTip =
+          faceDetectionResult
+              .multiFaceDetections()
+              .get(0)
+              .getLocationData()
+              .getRelativeKeypoints(FaceKeypoint.NOSE_TIP);
+      Log.i(
+          TAG,
+          String.format(
+              "MediaPipe Face Detection nose tip normalized coordinates (value range: [0, 1]): x=%f, y=%f",
+              noseTip.getX(), noseTip.getY()));
+      // Request GL rendering.
+      glSurfaceView.setRenderData(faceDetectionResult);
+      glSurfaceView.requestRender();
+    });
+
+// The runnable to start camera after the GLSurfaceView is attached.
+glSurfaceView.post(
+    () ->
+        cameraInput.start(
+            this,
+            faceDetection.getGlContext(),
+            CameraInput.CameraFacing.FRONT,
+            glSurfaceView.getWidth(),
+            glSurfaceView.getHeight()));
+```
+
+#### Image Input
+
+```java
+// For reading images from gallery and drawing the output in an ImageView.
+FaceDetectionOptions faceDetectionOptions =
+    FaceDetectionOptions.builder()
+        .setStaticImageMode(true)
+        .setModelSelection(0).build();
+FaceDetection faceDetection = new FaceDetection(this, faceDetectionOptions);
+
+// Connects MediaPipe Face Detection Solution 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/facedetection/src/main/java/com/google/mediapipe/examples/facedetection/FaceDetectionResultImageView.java
+// as an example.
+FaceDetectionResultImageView imageView = new FaceDetectionResultImageView(this);
+faceDetection.setResultListener(
+    faceDetectionResult -> {
+      if (faceDetectionResult.multiFaceDetections().isEmpty()) {
+        return;
+      }
+      int width = faceDetectionResult.inputBitmap().getWidth();
+      int height = faceDetectionResult.inputBitmap().getHeight();
+      RelativeKeypoint noseTip =
+          faceDetectionResult
+              .multiFaceDetections()
+              .get(0)
+              .getLocationData()
+              .getRelativeKeypoints(FaceKeypoint.NOSE_TIP);
+      Log.i(
+          TAG,
+          String.format(
+              "MediaPipe Face Detection nose tip coordinates (pixel values): x=%f, y=%f",
+              noseTip.getX() * width, noseTip.getY() * height));
+      // Request canvas drawing.
+      imageView.setFaceDetectionResult(faceDetectionResult);
+      runOnUiThread(() -> imageView.update());
+    });
+faceDetection.setErrorListener(
+    (message, e) -> Log.e(TAG, "MediaPipe Face Detection 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());
+              // Please also rotate the Bitmap based on its orientation.
+            } catch (IOException e) {
+              Log.e(TAG, "Bitmap reading error:" + e);
+            }
+            if (bitmap != null) {
+              faceDetection.send(bitmap);
+            }
+          }
+        });
+Intent pickImageIntent = new Intent(Intent.ACTION_PICK);
+pickImageIntent.setDataAndType(MediaStore.Images.Media.INTERNAL_CONTENT_URI, "image/*");
+imageGetter.launch(pickImageIntent);
+```
+
+#### Video Input
+
+```java
+// For video input and result rendering with OpenGL.
+FaceDetectionOptions faceDetectionOptions =
+    FaceDetectionOptions.builder()
+        .setStaticImageMode(false)
+        .setModelSelection(0).build();
+FaceDetection faceDetection = new FaceDetection(this, faceDetectionOptions);
+faceDetection.setErrorListener(
+    (message, e) -> Log.e(TAG, "MediaPipe Face Detection error:" + message));
+
+// Initializes a new VideoInput instance and connects it to MediaPipe Face Detection Solution.
+VideoInput videoInput = new VideoInput(this);
+videoInput.setNewFrameListener(
+    textureFrame -> faceDetection.send(textureFrame));
+
+// Initializes a new GlSurfaceView with a ResultGlRenderer<FaceDetectionResult> instance
+// that provides the interfaces to run user-defined OpenGL rendering code.
+// See mediapipe/examples/android/solutions/facedetection/src/main/java/com/google/mediapipe/examples/facedetection/FaceDetectionResultGlRenderer.java
+// as an example.
+SolutionGlSurfaceView<FaceDetectionResult> glSurfaceView =
+    new SolutionGlSurfaceView<>(
+        this, faceDetection.getGlContext(), faceDetection.getGlMajorVersion());
+glSurfaceView.setSolutionResultRenderer(new FaceDetectionResultGlRenderer());
+glSurfaceView.setRenderInputImage(true);
+
+faceDetection.setResultListener(
+    faceDetectionResult -> {
+      if (faceDetectionResult.multiFaceDetections().isEmpty()) {
+        return;
+      }
+      RelativeKeypoint noseTip =
+          faceDetectionResult
+              .multiFaceDetections()
+              .get(0)
+              .getLocationData()
+              .getRelativeKeypoints(FaceKeypoint.NOSE_TIP);
+      Log.i(
+          TAG,
+          String.format(
+              "MediaPipe Face Detection nose tip normalized coordinates (value range: [0, 1]): x=%f, y=%f",
+              noseTip.getX(), noseTip.getY()));
+      // Request GL rendering.
+      glSurfaceView.setRenderData(faceDetectionResult);
+      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(),
+                          faceDetection.getGlContext(),
+                          glSurfaceView.getWidth(),
+                          glSurfaceView.getHeight()));
+            }
+          }
+        });
+Intent pickVideoIntent = new Intent(Intent.ACTION_PICK);
+pickVideoIntent.setDataAndType(MediaStore.Video.Media.INTERNAL_CONTENT_URI, "video/*");
+videoGetter.launch(pickVideoIntent);
+```
+
 ## Example Apps
 
 Please first see general instructions for
@@ -254,10 +474,6 @@ 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

docs/solutions/face_mesh.md

@@ -69,7 +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).
 
@@ -111,6 +111,23 @@ You can find more information about the face landmark model in this
 :------------------------------------------------------------------------: |
 *Fig 2. Face landmarks: the red box indicates the cropped area as input to the landmark model, the red dots represent the 468 landmarks in 3D, and the green lines connecting landmarks illustrate the contours around the eyes, eyebrows, lips and the entire face.* |
 
+#### Attention Mesh Model
+
+In addition to the [Face Landmark Model](#face-landmark-model) we provide
+another model that applies
+[attention](https://en.wikipedia.org/wiki/Attention_(machine_learning)) to
+semantically meaningful face regions, and therefore predicting landmarks more
+accurately around lips, eyes and irises, at the expense of more compute. It
+enables applications like AR makeup and AR puppeteering.
+
+The attention mesh model can be selected in the Solution APIs via the
+[refine_landmarks](#refine_landmarks) option. You can also find more information
+about the model in this [paper](https://arxiv.org/abs/2006.10962).
+
+![attention_mesh_architecture.png](../images/attention_mesh_architecture.png) |
+:---------------------------------------------------------------------------: |
+*Fig 3. Attention Mesh: Overview of model architecture.*                      |
+
 ## Face Geometry Module
 
 The [Face Landmark Model](#face-landmark-model) performs a single-camera face landmark
@@ -145,8 +162,8 @@ be set freely, however for better results it is advised to set them as close to
 the *real physical camera parameters* as possible.
 
 ![face_geometry_metric_3d_space.gif](../images/face_geometry_metric_3d_space.gif) |
-:----------------------------------------------------------------------------: |
-*Fig 3. A visualization of multiple key elements in the Metric 3D space.*      |
+:-------------------------------------------------------------------------------: |
+*Fig 4. A visualization of multiple key elements in the Metric 3D space.*         |
 
 #### Canonical Face Model
 
@@ -210,7 +227,7 @@ The effect renderer is implemented as a MediaPipe
 
 | ![face_geometry_renderer.gif](../images/face_geometry_renderer.gif)     |
 | :---------------------------------------------------------------------: |
-| *Fig 4. An example of face effects rendered by the Face Geometry Effect Renderer.* |
+| *Fig 5. An example of face effects rendered by the Face Geometry Effect Renderer.* |
 
 ## Solution APIs
 
@@ -234,6 +251,12 @@ unrelated, images. Default to `false`.
 
 Maximum number of faces to detect. Default to `1`.
 
+#### refine_landmarks
+
+Whether to further refine the landmark coordinates around the eyes and lips, and
+output additional landmarks around the irises by applying the
+[Attention Mesh Model](#attention-mesh-model). Default to `false`.
+
 #### min_detection_confidence
 
 Minimum confidence value (`[0.0, 1.0]`) from the face detection model for the
@@ -265,12 +288,13 @@ magnitude of `z` uses roughly the same scale as `x`.
 
 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 following usage example.
+[Python Colab](#resources) and the usage example below.
 
 Supported configuration options:
 
 *   [static_image_mode](#static_image_mode)
 *   [max_num_faces](#max_num_faces)
+*   [refine_landmarks](#refine_landmarks)
 *   [min_detection_confidence](#min_detection_confidence)
 *   [min_tracking_confidence](#min_tracking_confidence)
 
@@ -278,15 +302,18 @@ Supported configuration options:
 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,
+    refine_landmarks=True,
     min_detection_confidence=0.5) as face_mesh:
-  for idx, file in enumerate(file_list):
+  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))
@@ -300,15 +327,32 @@ with mp_face_mesh.FaceMesh(
       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)
+          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())
+      mp_drawing.draw_landmarks(
+          image=annotated_image,
+          landmark_list=face_landmarks,
+          connections=mp_face_mesh.FACEMESH_IRISES,
+          landmark_drawing_spec=None,
+          connection_drawing_spec=mp_drawing_styles
+          .get_default_face_mesh_iris_connections_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(
+    max_num_faces=1,
+    refine_landmarks=True,
     min_detection_confidence=0.5,
     min_tracking_confidence=0.5) as face_mesh:
   while cap.isOpened():
@@ -318,12 +362,10 @@ with mp_face_mesh.FaceMesh(
       # 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
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     results = face_mesh.process(image)
 
     # Draw the face mesh annotations on the image.
@@ -334,10 +376,26 @@ with mp_face_mesh.FaceMesh(
         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)
+            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())
+        mp_drawing.draw_landmarks(
+            image=image,
+            landmark_list=face_landmarks,
+            connections=mp_face_mesh.FACEMESH_IRISES,
+            landmark_drawing_spec=None,
+            connection_drawing_spec=mp_drawing_styles
+            .get_default_face_mesh_iris_connections_style())
+    # Flip the image horizontally for a selfie-view display.
+    cv2.imshow('MediaPipe Face Mesh', cv2.flip(image, 1))
     if cv2.waitKey(5) & 0xFF == 27:
       break
 cap.release()
@@ -352,6 +410,7 @@ and the following usage example.
 Supported configuration options:
 
 *   [maxNumFaces](#max_num_faces)
+*   [refineLandmarks](#refine_landmarks)
 *   [minDetectionConfidence](#min_detection_confidence)
 *   [minTrackingConfidence](#min_tracking_confidence)
 
@@ -392,8 +451,10 @@ function onResults(results) {
                      {color: '#C0C0C070', lineWidth: 1});
       drawConnectors(canvasCtx, landmarks, FACEMESH_RIGHT_EYE, {color: '#FF3030'});
       drawConnectors(canvasCtx, landmarks, FACEMESH_RIGHT_EYEBROW, {color: '#FF3030'});
+      drawConnectors(canvasCtx, landmarks, FACEMESH_RIGHT_IRIS, {color: '#FF3030'});
       drawConnectors(canvasCtx, landmarks, FACEMESH_LEFT_EYE, {color: '#30FF30'});
       drawConnectors(canvasCtx, landmarks, FACEMESH_LEFT_EYEBROW, {color: '#30FF30'});
+      drawConnectors(canvasCtx, landmarks, FACEMESH_LEFT_IRIS, {color: '#30FF30'});
       drawConnectors(canvasCtx, landmarks, FACEMESH_FACE_OVAL, {color: '#E0E0E0'});
       drawConnectors(canvasCtx, landmarks, FACEMESH_LIPS, {color: '#E0E0E0'});
     }
@@ -406,6 +467,7 @@ const faceMesh = new FaceMesh({locateFile: (file) => {
 }});
 faceMesh.setOptions({
   maxNumFaces: 1,
+  refineLandmarks: true,
   minDetectionConfidence: 0.5,
   minTrackingConfidence: 0.5
 });
@@ -422,6 +484,202 @@ camera.start();
 </script>
 ```
 
+### Android Solution API
+
+Please first follow general
+[instructions](../getting_started/android_solutions.md) to add MediaPipe Gradle
+dependencies and try the Android Solution API in the companion
+[example Android Studio project](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/solutions/facemesh),
+and learn more in the usage example below.
+
+Supported configuration options:
+
+*   [staticImageMode](#static_image_mode)
+*   [maxNumFaces](#max_num_faces)
+*   [refineLandmarks](#refine_landmarks)
+*   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()
+        .setStaticImageMode(false)
+        .setRefineLandmarks(true)
+        .setMaxNumFaces(1)
+        .setRunOnGpu(true).build();
+FaceMesh faceMesh = new FaceMesh(this, faceMeshOptions);
+faceMesh.setErrorListener(
+    (message, e) -> Log.e(TAG, "MediaPipe Face Mesh error:" + message));
+
+// Initializes a new CameraInput instance and connects it to MediaPipe Face Mesh Solution.
+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 Face Mesh 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()
+        .setStaticImageMode(true)
+        .setRefineLandmarks(true)
+        .setMaxNumFaces(1)
+        .setRunOnGpu(true).build();
+FaceMesh faceMesh = new FaceMesh(this, faceMeshOptions);
+
+// Connects MediaPipe Face Mesh Solution 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 Face Mesh 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 Face Mesh 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());
+              // Please also rotate the Bitmap based on its orientation.
+            } catch (IOException e) {
+              Log.e(TAG, "Bitmap reading error:" + e);
+            }
+            if (bitmap != null) {
+              faceMesh.send(bitmap);
+            }
+          }
+        });
+Intent pickImageIntent = new Intent(Intent.ACTION_PICK);
+pickImageIntent.setDataAndType(MediaStore.Images.Media.INTERNAL_CONTENT_URI, "image/*");
+imageGetter.launch(pickImageIntent);
+```
+
+#### Video Input
+
+```java
+// For video input and result rendering with OpenGL.
+FaceMeshOptions faceMeshOptions =
+    FaceMeshOptions.builder()
+        .setStaticImageMode(false)
+        .setRefineLandmarks(true)
+        .setMaxNumFaces(1)
+        .setRunOnGpu(true).build();
+FaceMesh faceMesh = new FaceMesh(this, faceMeshOptions);
+faceMesh.setErrorListener(
+    (message, e) -> Log.e(TAG, "MediaPipe Face Mesh error:" + message));
+
+// Initializes a new VideoInput instance and connects it to MediaPipe Face Mesh Solution.
+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 Face Mesh 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 pickVideoIntent = new Intent(Intent.ACTION_PICK);
+pickVideoIntent.setDataAndType(MediaStore.Video.Media.INTERNAL_CONTENT_URI, "video/*");
+videoGetter.launch(pickVideoIntent);
+```
+
 ## Example Apps
 
 Please first see general instructions for

docs/solutions/hair_segmentation.md

@@ -2,7 +2,7 @@
 layout: default
 title: Hair Segmentation
 parent: Solutions
-nav_order: 7
+nav_order: 8
 ---
 
 # MediaPipe Hair Segmentation
@@ -51,7 +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

@@ -91,8 +91,10 @@ 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/modules/palm_detection/palm_detection.tflite)
-has to work across a variety of hand sizes with a large scale span (~20x)
+[lite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/palm_detection/palm_detection_lite.tflite)
+and
+[full model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/palm_detection/palm_detection_full.tflite)
+have 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
@@ -120,7 +122,7 @@ 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/modules/hand_landmark/hand_landmark.tflite)
+[model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_full.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
@@ -163,6 +165,11 @@ unrelated, images. Default to `false`.
 
 Maximum number of hands to detect. Default to `2`.
 
+#### model_complexity
+
+Complexity of the hand landmark model: `0` or `1`. Landmark accuracy as well as
+inference latency generally go up with the model complexity. Default to `1`.
+
 #### min_detection_confidence
 
 Minimum confidence value (`[0.0, 1.0]`) from the hand detection model for the
@@ -190,6 +197,13 @@ of 21 hand landmarks and each landmark is composed of `x`, `y` and `z`. `x` and
 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_hand_world_landmarks
+
+Collection of detected/tracked hands, where each hand is represented as a list
+of 21 hand landmarks in world coordinates. Each landmark is composed of `x`, `y`
+and `z`: real-world 3D coordinates in meters with the origin at the hand's
+approximate geometric center.
+
 #### multi_handedness
 
 Collection of handedness of the detected/tracked hands (i.e. is it a left or
@@ -206,12 +220,13 @@ is not the case, please swap the handedness output in the application.
 
 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 following usage example.
+[Python Colab](#resources) and the usage example below.
 
 Supported configuration options:
 
 *   [static_image_mode](#static_image_mode)
 *   [max_num_hands](#max_num_hands)
+*   [model_complexity](#model_complexity)
 *   [min_detection_confidence](#min_detection_confidence)
 *   [min_tracking_confidence](#min_tracking_confidence)
 
@@ -219,14 +234,16 @@ Supported configuration options:
 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(file_list):
+  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)
@@ -247,13 +264,24 @@ with mp_hands.Hands(
           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)
+          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))
+    # Draw hand world landmarks.
+    if not results.multi_hand_world_landmarks:
+      continue
+    for hand_world_landmarks in results.multi_hand_world_landmarks:
+      mp_drawing.plot_landmarks(
+        hand_world_landmarks, mp_hands.HAND_CONNECTIONS, azimuth=5)
 
 # For webcam input:
 cap = cv2.VideoCapture(0)
 with mp_hands.Hands(
+    model_complexity=0,
     min_detection_confidence=0.5,
     min_tracking_confidence=0.5) as hands:
   while cap.isOpened():
@@ -263,12 +291,10 @@ with mp_hands.Hands(
       # 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
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     results = hands.process(image)
 
     # Draw the hand annotations on the image.
@@ -277,8 +303,13 @@ with mp_hands.Hands(
     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)
+            image,
+            hand_landmarks,
+            mp_hands.HAND_CONNECTIONS,
+            mp_drawing_styles.get_default_hand_landmarks_style(),
+            mp_drawing_styles.get_default_hand_connections_style())
+    # Flip the image horizontally for a selfie-view display.
+    cv2.imshow('MediaPipe Hands', cv2.flip(image, 1))
     if cv2.waitKey(5) & 0xFF == 27:
       break
 cap.release()
@@ -293,6 +324,7 @@ and a [fun application], and the following usage example.
 Supported configuration options:
 
 *   [maxNumHands](#max_num_hands)
+*   [modelComplexity](#model_complexity)
 *   [minDetectionConfidence](#min_detection_confidence)
 *   [minTrackingConfidence](#min_tracking_confidence)
 
@@ -342,6 +374,7 @@ const hands = new Hands({locateFile: (file) => {
 }});
 hands.setOptions({
   maxNumHands: 2,
+  modelComplexity: 1,
   minDetectionConfidence: 0.5,
   minTrackingConfidence: 0.5
 });
@@ -358,6 +391,207 @@ camera.start();
 </script>
 ```
 
+### Android Solution API
+
+Please first follow general
+[instructions](../getting_started/android_solutions.md) to add MediaPipe Gradle
+dependencies and try the Android Solution API in the companion
+[example Android Studio project](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/solutions/hands),
+and learn more in the 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()
+        .setStaticImageMode(false)
+        .setMaxNumHands(2)
+        .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 Solution.
+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 -> {
+      if (result.multiHandLandmarks().isEmpty()) {
+        return;
+      }
+      NormalizedLandmark wristLandmark =
+          handsResult.multiHandLandmarks().get(0).getLandmarkList().get(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()
+        .setStaticImageMode(true)
+        .setMaxNumHands(2)
+        .setRunOnGpu(true).build();
+Hands hands = new Hands(this, handsOptions);
+
+// Connects MediaPipe Hands Solution 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 -> {
+      if (result.multiHandLandmarks().isEmpty()) {
+        return;
+      }
+      int width = handsResult.inputBitmap().getWidth();
+      int height = handsResult.inputBitmap().getHeight();
+      NormalizedLandmark wristLandmark =
+          handsResult.multiHandLandmarks().get(0).getLandmarkList().get(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());
+              // Please also rotate the Bitmap based on its orientation.
+            } catch (IOException e) {
+              Log.e(TAG, "Bitmap reading error:" + e);
+            }
+            if (bitmap != null) {
+              hands.send(bitmap);
+            }
+          }
+        });
+Intent pickImageIntent = new Intent(Intent.ACTION_PICK);
+pickImageIntent.setDataAndType(MediaStore.Images.Media.INTERNAL_CONTENT_URI, "image/*");
+imageGetter.launch(pickImageIntent);
+```
+
+#### Video Input
+
+```java
+// For video input and result rendering with OpenGL.
+HandsOptions handsOptions =
+    HandsOptions.builder()
+        .setStaticImageMode(false)
+        .setMaxNumHands(2)
+        .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 Solution.
+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 -> {
+      if (result.multiHandLandmarks().isEmpty()) {
+        return;
+      }
+      NormalizedLandmark wristLandmark =
+          handsResult.multiHandLandmarks().get(0).getLandmarkList().get(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 pickVideoIntent = new Intent(Intent.ACTION_PICK);
+pickVideoIntent.setDataAndType(MediaStore.Video.Media.INTERNAL_CONTENT_URI, "video/*");
+videoGetter.launch(pickVideoIntent);
+```
+
 ## Example Apps
 
 Please first see general instructions for

docs/solutions/holistic.md

@@ -147,6 +147,23 @@ 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, face and hand 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`.
+
+#### refine_face_landmarks
+
+Whether to further refine the landmark coordinates around the eyes and lips, and
+output additional landmarks around the irises. Default to `false`.
+
 #### min_detection_confidence
 
 Minimum confidence value (`[0.0, 1.0]`) from the person-detection model for the
@@ -176,6 +193,16 @@ A list of pose landmarks. Each landmark consists of the following:
 *   `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`
@@ -197,17 +224,29 @@ the camera. The magnitude of `z` uses roughly the same scale as `x`.
 A list of 21 hand landmarks on the right hand, in the same representation as
 [left_hand_landmarks](#left_hand_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.
+
 ### 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 following usage example.
+[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)
+*   [refine_face_landmarks](#refine_face_landmarks)
 *   [min_detection_confidence](#min_detection_confidence)
 *   [min_tracking_confidence](#min_tracking_confidence)
 
@@ -215,13 +254,17 @@ Supported configuration options:
 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(file_list):
+    model_complexity=2,
+    enable_segmentation=True,
+    refine_face_landmarks=True) 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.
@@ -233,17 +276,33 @@ with mp_holistic.Holistic(
           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()
+    # 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, left and right hands, and face landmarks on the image.
     mp_drawing.draw_landmarks(
-        annotated_image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS)
+        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.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
-    mp_drawing.draw_landmarks(
-        annotated_image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
-    mp_drawing.draw_landmarks(
-        annotated_image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
+        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)
@@ -257,26 +316,30 @@ with mp_holistic.Holistic(
       # 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
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     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.FACE_CONNECTIONS)
+        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.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
-    mp_drawing.draw_landmarks(
-        image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
-    mp_drawing.draw_landmarks(
-        image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
-    cv2.imshow('MediaPipe Holistic', image)
+        image,
+        results.pose_landmarks,
+        mp_holistic.POSE_CONNECTIONS,
+        landmark_drawing_spec=mp_drawing_styles
+        .get_default_pose_landmarks_style())
+    # Flip the image horizontally for a selfie-view display.
+    cv2.imshow('MediaPipe Holistic', cv2.flip(image, 1))
     if cv2.waitKey(5) & 0xFF == 27:
       break
 cap.release()
@@ -292,6 +355,9 @@ Supported configuration options:
 
 *   [modelComplexity](#model_complexity)
 *   [smoothLandmarks](#smooth_landmarks)
+*   [enableSegmentation](#enable_segmentation)
+*   [smoothSegmentation](#smooth_segmentation)
+*   [refineFaceLandmarks](#refineFaceLandmarks)
 *   [minDetectionConfidence](#min_detection_confidence)
 *   [minTrackingConfidence](#min_tracking_confidence)
 
@@ -324,8 +390,20 @@ 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.globalCompositeOperation = 'source-over';
   drawConnectors(canvasCtx, results.poseLandmarks, POSE_CONNECTIONS,
                  {color: '#00FF00', lineWidth: 4});
   drawLandmarks(canvasCtx, results.poseLandmarks,
@@ -349,6 +427,9 @@ const holistic = new Holistic({locateFile: (file) => {
 holistic.setOptions({
   modelComplexity: 1,
   smoothLandmarks: true,
+  enableSegmentation: true,
+  smoothSegmentation: true,
+  refineFaceLandmarks: true,
   minDetectionConfidence: 0.5,
   minTrackingConfidence: 0.5
 });

docs/solutions/instant_motion_tracking.md

@@ -2,7 +2,7 @@
 layout: default
 title: Instant Motion Tracking
 parent: Solutions
-nav_order: 10
+nav_order: 11
 ---
 
 # MediaPipe Instant Motion Tracking

docs/solutions/iris.md

@@ -69,7 +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).
 
@@ -193,7 +193,17 @@ on how to build 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

@@ -2,7 +2,7 @@
 layout: default
 title: KNIFT (Template-based Feature Matching)
 parent: Solutions
-nav_order: 12
+nav_order: 13
 ---
 
 # MediaPipe KNIFT

docs/solutions/media_sequence.md

@@ -2,7 +2,7 @@
 layout: default
 title: Dataset Preparation with MediaSequence
 parent: Solutions
-nav_order: 14
+nav_order: 15
 ---
 
 # Dataset Preparation with MediaSequence

docs/solutions/models.md

@@ -14,19 +14,37 @@ 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/modules/face_detection/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/modules/face_detection/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)
 
 *   Face landmark model:
     [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/face_landmark.tflite),
     [TF.js model](https://tfhub.dev/mediapipe/facemesh/1)
-*   [Model card](https://mediapipe.page.link/facemesh-mc)
+*   Face landmark model w/ attention (aka Attention Mesh):
+    [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/face_landmark_with_attention.tflite)
+*   [Model card](https://mediapipe.page.link/facemesh-mc),
+    [Model card (w/ attention)](https://mediapipe.page.link/attentionmesh-mc)
 
 ### [Iris](https://google.github.io/mediapipe/solutions/iris)
 
@@ -37,13 +55,14 @@ nav_order: 30
 ### [Hands](https://google.github.io/mediapipe/solutions/hands)
 
 *   Palm detection model:
-    [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/palm_detection/palm_detection.tflite),
+    [TFLite model (lite)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/palm_detection/palm_detection_lite.tflite),
+    [TFLite model (full)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/palm_detection/palm_detection_full.tflite),
     [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),
+    [TFLite model (lite)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_lite.tflite),
+    [TFLite model (full)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_full.tflite),
     [TF.js model](https://tfhub.dev/mediapipe/handskeleton/1)
-*   [Model card](https://mediapipe.page.link/handmc), [Model card (sparse)](https://mediapipe.page.link/handmc-sparse)
+*   [Model card](https://mediapipe.page.link/handmc)
 
 ### [Pose](https://google.github.io/mediapipe/solutions/pose)
 
@@ -60,6 +79,12 @@ nav_order: 30
 *   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)

docs/solutions/object_detection.md

@@ -2,7 +2,7 @@
 layout: default
 title: Object Detection
 parent: Solutions
-nav_order: 8
+nav_order: 9
 ---
 
 # MediaPipe Object Detection

docs/solutions/objectron.md

@@ -2,7 +2,7 @@
 layout: default
 title: Objectron (3D Object Detection)
 parent: Solutions
-nav_order: 11
+nav_order: 12
 ---
 
 # MediaPipe Objectron
@@ -224,29 +224,33 @@ 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'}`.
+Name of the model to use for predicting 3D bounding box landmarks. Currently
+supports `{'Shoe', 'Chair', 'Cup', 'Camera'}`. Default to `Shoe`.
 
 #### focal_length
 
-Camera focal length `(fx, fy)`, by default is defined in
-[NDC space](#ndc-space). To use focal length `(fx_pixel, fy_pixel)` in
-[pixel space](#pixel-space), users should provide `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).
+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
 
-Camera principal point `(px, py)`, by default is defined in
-[NDC space](#ndc-space). To use principal point `(px_pixel, py_pixel)` in
-[pixel space](#pixel-space), users should provide `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).
+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
 
-(**Optional**) size `(image_width, image_height)` of the input image, **ONLY**
-needed when use `focal_length` and `principal_point` in pixel space.
+**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
 
@@ -277,7 +281,7 @@ following:
 
 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 following usage example.
+[Python Colab](#resources) and the usage example below.
 
 Supported configuration options:
 
@@ -297,11 +301,12 @@ 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(file_list):
+  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))
@@ -333,11 +338,10 @@ with mp_objectron.Objectron(static_image_mode=False,
       # 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
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     results = objectron.process(image)
 
     # Draw the box landmarks on the image.
@@ -349,12 +353,96 @@ with mp_objectron.Objectron(static_image_mode=False,
               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)
+    # Flip the image horizontally for a selfie-view display.
+    cv2.imshow('MediaPipe Objectron', cv2.flip(image, 1))
     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
@@ -441,7 +529,7 @@ Example app bounding boxes are rendered with [GlAnimationOverlayCalculator](http
 > ```
 > and then run
 >
-> ```build
+> ```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,
@@ -560,11 +648,15 @@ py = -py_pixel * 2.0 / image_height + 1.0
     [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: [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)), Fourth Workshop on Computer Vision for AR/VR, CVPR 2020
+    ([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

@@ -30,7 +30,8 @@ overlay of digital content and information on top of the physical world in
 augmented reality.
 
 MediaPipe Pose is a ML solution for high-fidelity body pose tracking, inferring
-33 3D landmarks on the whole body from RGB video frames utilizing our
+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 that also powers the
 [ML Kit Pose Detection API](https://developers.google.com/ml-kit/vision/pose-detection).
@@ -49,11 +50,11 @@ 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 person/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
+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
@@ -87,11 +88,11 @@ 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**
+BlazePose GHUM Heavy                                                                                  | 68.1               | **96.4**               | 73.0                | **97.2**                | 74.0               | **97.5**
+BlazePose GHUM Full                                                                                   | 62.6               | **95.5**               | 67.4                | **96.3**                | 68.0               | **95.7**
+BlazePose GHUM 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) |
 :--------------------------------------------------------------------------: |
@@ -100,11 +101,11 @@ BlazePose.Lite
 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
+Method               | Latency <br/> Pixel 3 [TFLite GPU](https://www.tensorflow.org/lite/performance/gpu_advanced) | Latency <br/> MacBook Pro (15-inch 2017)
+-------------------- | -------------------------------------------------------------------------------------------: | ---------------------------------------:
+BlazePose GHUM Heavy | 53 ms                                                                                        | 38 ms
+BlazePose GHUM Full  | 25 ms                                                                                        | 27 ms
+BlazePose GHUM Lite  | 20 ms                                                                                        | 25 ms
 
 ## Models
 
@@ -124,21 +125,24 @@ hip midpoints.
 :----------------------------------------------------------------------------------------------------: |
 *Fig 3. Vitruvian man aligned via two virtual keypoints predicted by BlazePose detector in addition to the face bounding box.* |
 
-### Pose Landmark Model (BlazePose GHUM 3D)
+### Pose Landmark Model (BlazePose [GHUM](https://github.com/google-research/google-research/tree/master/ghum) 3D)
 
 The landmark model in MediaPipe Pose predicts the location of 33 pose landmarks
 (see figure below).
 
-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) and
-[the model card](./models.md#pose), and the attributes in each landmark
-[below](#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
@@ -167,6 +171,18 @@ 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
@@ -187,28 +203,56 @@ Naming style may differ slightly across platforms/languages.
 
 #### pose_landmarks
 
-A list of pose landmarks. Each lanmark consists of the following:
+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 following usage example.
+[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)
 
@@ -216,14 +260,18 @@ Supported configuration options:
 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(file_list):
+  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.
@@ -233,14 +281,28 @@ with mp_pose.Pose(
       continue
     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})'
+        f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].x * image_width}, '
+        f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].y * image_height})'
     )
-    # Draw pose landmarks on the image.
+
     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)
+        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)
@@ -254,20 +316,22 @@ with mp_pose.Pose(
       # 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
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     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)
+        image,
+        results.pose_landmarks,
+        mp_pose.POSE_CONNECTIONS,
+        landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
+    # Flip the image horizontally for a selfie-view display.
+    cv2.imshow('MediaPipe Pose', cv2.flip(image, 1))
     if cv2.waitKey(5) & 0xFF == 27:
       break
 cap.release()
@@ -283,6 +347,8 @@ Supported configuration options:
 
 *   [modelComplexity](#model_complexity)
 *   [smoothLandmarks](#smooth_landmarks)
+*   [enableSegmentation](#enable_segmentation)
+*   [smoothSegmentation](#smooth_segmentation)
 *   [minDetectionConfidence](#min_detection_confidence)
 *   [minTrackingConfidence](#min_tracking_confidence)
 
@@ -293,6 +359,7 @@ Supported configuration options:
   <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>
@@ -301,6 +368,7 @@ Supported configuration options:
   <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>
@@ -311,17 +379,38 @@ Supported configuration options:
 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) => {
@@ -330,6 +419,8 @@ const pose = new Pose({locateFile: (file) => {
 pose.setOptions({
   modelComplexity: 1,
   smoothLandmarks: true,
+  enableSegmentation: true,
+  smoothSegmentation: true,
   minDetectionConfidence: 0.5,
   minTrackingConfidence: 0.5
 });
@@ -395,8 +486,9 @@ on how to build MediaPipe examples.
     [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)
+*   [GHUM & GHUML: Generative 3D Human Shape and Articulated Pose Models](https://github.com/google-research/google-research/tree/master/ghum)
 *   [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
+[`[email protected]`]: https://github.com/cbsudux/Human-Pose-Estimation-101

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

@@ -13,6 +13,9 @@ 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. -->
 
@@ -24,11 +27,12 @@ has_toc: false
 [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)                             | ✅                                                               |                                                         | ✅                                                        | ✅                                                             |                                                               |
+[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)                    |                                                                 |                                                         | ✅                                                       |                                                               |                                                               |

docs/solutions/youtube_8m.md

@@ -2,7 +2,7 @@
 layout: default
 title: YouTube-8M Feature Extraction and Model Inference
 parent: Solutions
-nav_order: 15
+nav_order: 16
 ---
 
 # YouTube-8M Feature Extraction and Model Inference

mediapipe/MediaPipe.tulsiproj/Configs/MediaPipe.tulsigen

@@ -16,6 +16,7 @@
     "mediapipe/examples/ios/objectdetectiongpu/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",
@@ -35,6 +36,7 @@
     "//mediapipe/examples/ios/objectdetectiongpu:ObjectDetectionGpuApp",
     "//mediapipe/examples/ios/objectdetectiontrackinggpu:ObjectDetectionTrackingGpuApp",
     "//mediapipe/examples/ios/posetrackinggpu:PoseTrackingGpuApp",
+    "//mediapipe/examples/ios/selfiesegmentationgpu:SelfieSegmentationGpuApp",
     "//mediapipe/objc:mediapipe_framework_ios"
   ],
   "optionSet" : {
@@ -103,6 +105,7 @@
     "mediapipe/examples/ios/objectdetectioncpu",
     "mediapipe/examples/ios/objectdetectiongpu",
     "mediapipe/examples/ios/posetrackinggpu",
+    "mediapipe/examples/ios/selfiesegmentationgpu",
     "mediapipe/framework",
     "mediapipe/framework/deps",
     "mediapipe/framework/formats",
@@ -120,6 +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

@@ -22,6 +22,7 @@
     "mediapipe/examples/ios/objectdetectiongpu",
     "mediapipe/examples/ios/objectdetectiontrackinggpu",
     "mediapipe/examples/ios/posetrackinggpu",
+    "mediapipe/examples/ios/selfiesegmentationgpu",
     "mediapipe/objc"
   ],
   "projectName" : "Mediapipe",

mediapipe/calculators/core/BUILD

@@ -140,6 +140,16 @@ mediapipe_proto_library(
     ],
 )
 
+mediapipe_proto_library(
+    name = "graph_profile_calculator_proto",
+    srcs = ["graph_profile_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_options_proto",
+        "//mediapipe/framework:calculator_proto",
+    ],
+)
+
 cc_library(
     name = "add_header_calculator",
     srcs = ["add_header_calculator.cc"],
@@ -419,6 +429,23 @@ cc_library(
     alwayslink = 1,
 )
 
+cc_test(
+    name = "make_pair_calculator_test",
+    size = "small",
+    srcs = ["make_pair_calculator_test.cc"],
+    deps = [
+        ":make_pair_calculator",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework:calculator_runner",
+        "//mediapipe/framework:timestamp",
+        "//mediapipe/framework/port:gtest_main",
+        "//mediapipe/framework/port:status",
+        "//mediapipe/framework/tool:validate_type",
+        "//mediapipe/util:packet_test_util",
+        "//mediapipe/util:time_series_test_util",
+    ],
+)
+
 cc_library(
     name = "matrix_multiply_calculator",
     srcs = ["matrix_multiply_calculator.cc"],
@@ -504,9 +531,13 @@ cc_test(
         ":split_vector_calculator",
         "//mediapipe/framework:calculator_framework",
         "//mediapipe/framework:calculator_runner",
+        "//mediapipe/framework/api2:node",
+        "//mediapipe/framework/api2:port",
         "//mediapipe/framework/port:gtest_main",
         "//mediapipe/framework/port:parse_text_proto",
         "//mediapipe/framework/port:status",
+        "@com_google_absl//absl/status",
+        "@com_google_absl//absl/types:optional",
     ],
 )
 
@@ -933,8 +964,8 @@ cc_test(
 )
 
 cc_library(
-    name = "split_normalized_landmark_list_calculator",
-    srcs = ["split_normalized_landmark_list_calculator.cc"],
+    name = "split_landmarks_calculator",
+    srcs = ["split_landmarks_calculator.cc"],
     visibility = ["//visibility:public"],
     deps = [
         ":split_vector_calculator_cc_proto",
@@ -948,10 +979,10 @@ cc_library(
 )
 
 cc_test(
-    name = "split_normalized_landmark_list_calculator_test",
-    srcs = ["split_normalized_landmark_list_calculator_test.cc"],
+    name = "split_landmarks_calculator_test",
+    srcs = ["split_landmarks_calculator_test.cc"],
     deps = [
-        ":split_normalized_landmark_list_calculator",
+        ":split_landmarks_calculator",
         ":split_vector_calculator_cc_proto",
         "//mediapipe/framework:calculator_framework",
         "//mediapipe/framework:calculator_runner",
@@ -1183,3 +1214,44 @@ cc_test(
         "@com_google_absl//absl/strings",
     ],
 )
+
+cc_library(
+    name = "graph_profile_calculator",
+    srcs = ["graph_profile_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":graph_profile_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework:calculator_profile_cc_proto",
+        "//mediapipe/framework/api2:node",
+        "//mediapipe/framework/api2:packet",
+        "//mediapipe/framework/api2:port",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+    ],
+    alwayslink = 1,
+)
+
+cc_test(
+    name = "graph_profile_calculator_test",
+    srcs = ["graph_profile_calculator_test.cc"],
+    deps = [
+        ":graph_profile_calculator",
+        "//mediapipe/framework:calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework:calculator_profile_cc_proto",
+        "//mediapipe/framework:test_calculators",
+        "//mediapipe/framework/deps:clock",
+        "//mediapipe/framework/port:core_proto",
+        "//mediapipe/framework/port:gtest_main",
+        "//mediapipe/framework/port:integral_types",
+        "//mediapipe/framework/port:logging",
+        "//mediapipe/framework/port:parse_text_proto",
+        "//mediapipe/framework/port:threadpool",
+        "//mediapipe/framework/tool:simulation_clock_executor",
+        "//mediapipe/framework/tool:sink",
+        "@com_google_absl//absl/status",
+        "@com_google_absl//absl/strings",
+        "@com_google_absl//absl/time",
+    ],
+)

mediapipe/calculators/core/add_header_calculator_test.cc

@@ -24,6 +24,9 @@
 
 namespace mediapipe {
 
+constexpr char kDataTag[] = "DATA";
+constexpr char kHeaderTag[] = "HEADER";
+
 class AddHeaderCalculatorTest : public ::testing::Test {};
 
 TEST_F(AddHeaderCalculatorTest, HeaderStream) {
@@ -36,11 +39,11 @@ TEST_F(AddHeaderCalculatorTest, HeaderStream) {
   CalculatorRunner runner(node);
 
   // Set header and add 5 packets.
-  runner.MutableInputs()->Tag("HEADER").header =
+  runner.MutableInputs()->Tag(kHeaderTag).header =
       Adopt(new std::string("my_header"));
   for (int i = 0; i < 5; ++i) {
     Packet packet = Adopt(new int(i)).At(Timestamp(i * 1000));
-    runner.MutableInputs()->Tag("DATA").packets.push_back(packet);
+    runner.MutableInputs()->Tag(kDataTag).packets.push_back(packet);
   }
 
   // Run calculator.
@@ -85,13 +88,14 @@ TEST_F(AddHeaderCalculatorTest, NoPacketsOnHeaderStream) {
   CalculatorRunner runner(node);
 
   // Set header and add 5 packets.
-  runner.MutableInputs()->Tag("HEADER").header =
+  runner.MutableInputs()->Tag(kHeaderTag).header =
       Adopt(new std::string("my_header"));
-  runner.MutableInputs()->Tag("HEADER").packets.push_back(
-      Adopt(new std::string("not allowed")));
+  runner.MutableInputs()
+      ->Tag(kHeaderTag)
+      .packets.push_back(Adopt(new std::string("not allowed")));
   for (int i = 0; i < 5; ++i) {
     Packet packet = Adopt(new int(i)).At(Timestamp(i * 1000));
-    runner.MutableInputs()->Tag("DATA").packets.push_back(packet);
+    runner.MutableInputs()->Tag(kDataTag).packets.push_back(packet);
   }
 
   // Run calculator.
@@ -108,11 +112,11 @@ TEST_F(AddHeaderCalculatorTest, InputSidePacket) {
   CalculatorRunner runner(node);
 
   // Set header and add 5 packets.
-  runner.MutableSidePackets()->Tag("HEADER") =
+  runner.MutableSidePackets()->Tag(kHeaderTag) =
       Adopt(new std::string("my_header"));
   for (int i = 0; i < 5; ++i) {
     Packet packet = Adopt(new int(i)).At(Timestamp(i * 1000));
-    runner.MutableInputs()->Tag("DATA").packets.push_back(packet);
+    runner.MutableInputs()->Tag(kDataTag).packets.push_back(packet);
   }
 
   // Run calculator.
@@ -143,13 +147,13 @@ TEST_F(AddHeaderCalculatorTest, UsingBothSideInputAndStream) {
   CalculatorRunner runner(node);
 
   // Set both headers and add 5 packets.
-  runner.MutableSidePackets()->Tag("HEADER") =
+  runner.MutableSidePackets()->Tag(kHeaderTag) =
       Adopt(new std::string("my_header"));
-  runner.MutableSidePackets()->Tag("HEADER") =
+  runner.MutableSidePackets()->Tag(kHeaderTag) =
       Adopt(new std::string("my_header"));
   for (int i = 0; i < 5; ++i) {
     Packet packet = Adopt(new int(i)).At(Timestamp(i * 1000));
-    runner.MutableInputs()->Tag("DATA").packets.push_back(packet);
+    runner.MutableInputs()->Tag(kDataTag).packets.push_back(packet);
   }
 
   // Run should fail because header can only be provided one way.

mediapipe/calculators/core/begin_loop_calculator.cc

@@ -42,4 +42,13 @@ REGISTER_CALCULATOR(BeginLoopDetectionCalculator);
 typedef BeginLoopCalculator<std::vector<Matrix>> BeginLoopMatrixCalculator;
 REGISTER_CALCULATOR(BeginLoopMatrixCalculator);
 
+// A calculator to process std::vector<std::vector<Matrix>>.
+typedef BeginLoopCalculator<std::vector<std::vector<Matrix>>>
+    BeginLoopMatrixVectorCalculator;
+REGISTER_CALCULATOR(BeginLoopMatrixVectorCalculator);
+
+// A calculator to process std::vector<uint64_t>.
+typedef BeginLoopCalculator<std::vector<uint64_t>> BeginLoopUint64tCalculator;
+REGISTER_CALCULATOR(BeginLoopUint64tCalculator);
+
 }  // namespace mediapipe

mediapipe/calculators/core/counting_source_calculator.cc

@@ -19,6 +19,13 @@
 
 namespace mediapipe {
 
+constexpr char kIncrementTag[] = "INCREMENT";
+constexpr char kInitialValueTag[] = "INITIAL_VALUE";
+constexpr char kBatchSizeTag[] = "BATCH_SIZE";
+constexpr char kErrorCountTag[] = "ERROR_COUNT";
+constexpr char kMaxCountTag[] = "MAX_COUNT";
+constexpr char kErrorOnOpenTag[] = "ERROR_ON_OPEN";
+
 // Source calculator that produces MAX_COUNT*BATCH_SIZE int packets of
 // sequential numbers from INITIAL_VALUE (default 0) with a common
 // difference of INCREMENT (default 1) between successive numbers (with
@@ -33,53 +40,53 @@ class CountingSourceCalculator : public CalculatorBase {
   static absl::Status GetContract(CalculatorContract* cc) {
     cc->Outputs().Index(0).Set<int>();
 
-    if (cc->InputSidePackets().HasTag("ERROR_ON_OPEN")) {
-      cc->InputSidePackets().Tag("ERROR_ON_OPEN").Set<bool>();
+    if (cc->InputSidePackets().HasTag(kErrorOnOpenTag)) {
+      cc->InputSidePackets().Tag(kErrorOnOpenTag).Set<bool>();
     }
 
-    RET_CHECK(cc->InputSidePackets().HasTag("MAX_COUNT") ||
-              cc->InputSidePackets().HasTag("ERROR_COUNT"));
-    if (cc->InputSidePackets().HasTag("MAX_COUNT")) {
-      cc->InputSidePackets().Tag("MAX_COUNT").Set<int>();
+    RET_CHECK(cc->InputSidePackets().HasTag(kMaxCountTag) ||
+              cc->InputSidePackets().HasTag(kErrorCountTag));
+    if (cc->InputSidePackets().HasTag(kMaxCountTag)) {
+      cc->InputSidePackets().Tag(kMaxCountTag).Set<int>();
     }
-    if (cc->InputSidePackets().HasTag("ERROR_COUNT")) {
-      cc->InputSidePackets().Tag("ERROR_COUNT").Set<int>();
+    if (cc->InputSidePackets().HasTag(kErrorCountTag)) {
+      cc->InputSidePackets().Tag(kErrorCountTag).Set<int>();
     }
 
-    if (cc->InputSidePackets().HasTag("BATCH_SIZE")) {
-      cc->InputSidePackets().Tag("BATCH_SIZE").Set<int>();
+    if (cc->InputSidePackets().HasTag(kBatchSizeTag)) {
+      cc->InputSidePackets().Tag(kBatchSizeTag).Set<int>();
     }
-    if (cc->InputSidePackets().HasTag("INITIAL_VALUE")) {
-      cc->InputSidePackets().Tag("INITIAL_VALUE").Set<int>();
+    if (cc->InputSidePackets().HasTag(kInitialValueTag)) {
+      cc->InputSidePackets().Tag(kInitialValueTag).Set<int>();
     }
-    if (cc->InputSidePackets().HasTag("INCREMENT")) {
-      cc->InputSidePackets().Tag("INCREMENT").Set<int>();
+    if (cc->InputSidePackets().HasTag(kIncrementTag)) {
+      cc->InputSidePackets().Tag(kIncrementTag).Set<int>();
     }
     return absl::OkStatus();
   }
 
   absl::Status Open(CalculatorContext* cc) override {
-    if (cc->InputSidePackets().HasTag("ERROR_ON_OPEN") &&
-        cc->InputSidePackets().Tag("ERROR_ON_OPEN").Get<bool>()) {
+    if (cc->InputSidePackets().HasTag(kErrorOnOpenTag) &&
+        cc->InputSidePackets().Tag(kErrorOnOpenTag).Get<bool>()) {
       return absl::NotFoundError("expected error");
     }
-    if (cc->InputSidePackets().HasTag("ERROR_COUNT")) {
-      error_count_ = cc->InputSidePackets().Tag("ERROR_COUNT").Get<int>();
+    if (cc->InputSidePackets().HasTag(kErrorCountTag)) {
+      error_count_ = cc->InputSidePackets().Tag(kErrorCountTag).Get<int>();
       RET_CHECK_LE(0, error_count_);
     }
-    if (cc->InputSidePackets().HasTag("MAX_COUNT")) {
-      max_count_ = cc->InputSidePackets().Tag("MAX_COUNT").Get<int>();
+    if (cc->InputSidePackets().HasTag(kMaxCountTag)) {
+      max_count_ = cc->InputSidePackets().Tag(kMaxCountTag).Get<int>();
       RET_CHECK_LE(0, max_count_);
     }
-    if (cc->InputSidePackets().HasTag("BATCH_SIZE")) {
-      batch_size_ = cc->InputSidePackets().Tag("BATCH_SIZE").Get<int>();
+    if (cc->InputSidePackets().HasTag(kBatchSizeTag)) {
+      batch_size_ = cc->InputSidePackets().Tag(kBatchSizeTag).Get<int>();
       RET_CHECK_LT(0, batch_size_);
     }
-    if (cc->InputSidePackets().HasTag("INITIAL_VALUE")) {
-      counter_ = cc->InputSidePackets().Tag("INITIAL_VALUE").Get<int>();
+    if (cc->InputSidePackets().HasTag(kInitialValueTag)) {
+      counter_ = cc->InputSidePackets().Tag(kInitialValueTag).Get<int>();
     }
-    if (cc->InputSidePackets().HasTag("INCREMENT")) {
-      increment_ = cc->InputSidePackets().Tag("INCREMENT").Get<int>();
+    if (cc->InputSidePackets().HasTag(kIncrementTag)) {
+      increment_ = cc->InputSidePackets().Tag(kIncrementTag).Get<int>();
       RET_CHECK_LT(0, increment_);
     }
     RET_CHECK(error_count_ >= 0 || max_count_ >= 0);

mediapipe/calculators/core/dequantize_byte_array_calculator.cc

@@ -35,11 +35,14 @@
 //   }
 namespace mediapipe {
 
+constexpr char kFloatVectorTag[] = "FLOAT_VECTOR";
+constexpr char kEncodedTag[] = "ENCODED";
+
 class DequantizeByteArrayCalculator : public CalculatorBase {
  public:
   static absl::Status GetContract(CalculatorContract* cc) {
-    cc->Inputs().Tag("ENCODED").Set<std::string>();
-    cc->Outputs().Tag("FLOAT_VECTOR").Set<std::vector<float>>();
+    cc->Inputs().Tag(kEncodedTag).Set<std::string>();
+    cc->Outputs().Tag(kFloatVectorTag).Set<std::vector<float>>();
     return absl::OkStatus();
   }
 
@@ -66,7 +69,7 @@ class DequantizeByteArrayCalculator : public CalculatorBase {
 
   absl::Status Process(CalculatorContext* cc) final {
     const std::string& encoded =
-        cc->Inputs().Tag("ENCODED").Value().Get<std::string>();
+        cc->Inputs().Tag(kEncodedTag).Value().Get<std::string>();
     std::vector<float> float_vector;
     float_vector.reserve(encoded.length());
     for (int i = 0; i < encoded.length(); ++i) {
@@ -74,7 +77,7 @@ class DequantizeByteArrayCalculator : public CalculatorBase {
           static_cast<unsigned char>(encoded.at(i)) * scalar_ + bias_);
     }
     cc->Outputs()
-        .Tag("FLOAT_VECTOR")
+        .Tag(kFloatVectorTag)
         .AddPacket(MakePacket<std::vector<float>>(float_vector)
                        .At(cc->InputTimestamp()));
     return absl::OkStatus();

mediapipe/calculators/core/dequantize_byte_array_calculator_test.cc

@@ -25,6 +25,9 @@
 
 namespace mediapipe {
 
+constexpr char kFloatVectorTag[] = "FLOAT_VECTOR";
+constexpr char kEncodedTag[] = "ENCODED";
+
 TEST(QuantizeFloatVectorCalculatorTest, WrongConfig) {
   CalculatorGraphConfig::Node node_config =
       ParseTextProtoOrDie<CalculatorGraphConfig::Node>(R"pb(
@@ -39,8 +42,10 @@ TEST(QuantizeFloatVectorCalculatorTest, WrongConfig) {
       )pb");
   CalculatorRunner runner(node_config);
   std::string empty_string;
-  runner.MutableInputs()->Tag("ENCODED").packets.push_back(
-      MakePacket<std::string>(empty_string).At(Timestamp(0)));
+  runner.MutableInputs()
+      ->Tag(kEncodedTag)
+      .packets.push_back(
+          MakePacket<std::string>(empty_string).At(Timestamp(0)));
   auto status = runner.Run();
   EXPECT_FALSE(status.ok());
   EXPECT_THAT(
@@ -64,8 +69,10 @@ TEST(QuantizeFloatVectorCalculatorTest, WrongConfig2) {
       )pb");
   CalculatorRunner runner(node_config);
   std::string empty_string;
-  runner.MutableInputs()->Tag("ENCODED").packets.push_back(
-      MakePacket<std::string>(empty_string).At(Timestamp(0)));
+  runner.MutableInputs()
+      ->Tag(kEncodedTag)
+      .packets.push_back(
+          MakePacket<std::string>(empty_string).At(Timestamp(0)));
   auto status = runner.Run();
   EXPECT_FALSE(status.ok());
   EXPECT_THAT(
@@ -89,8 +96,10 @@ TEST(QuantizeFloatVectorCalculatorTest, WrongConfig3) {
       )pb");
   CalculatorRunner runner(node_config);
   std::string empty_string;
-  runner.MutableInputs()->Tag("ENCODED").packets.push_back(
-      MakePacket<std::string>(empty_string).At(Timestamp(0)));
+  runner.MutableInputs()
+      ->Tag(kEncodedTag)
+      .packets.push_back(
+          MakePacket<std::string>(empty_string).At(Timestamp(0)));
   auto status = runner.Run();
   EXPECT_FALSE(status.ok());
   EXPECT_THAT(
@@ -114,14 +123,16 @@ TEST(DequantizeByteArrayCalculatorTest, TestDequantization) {
       )pb");
   CalculatorRunner runner(node_config);
   unsigned char input[4] = {0x7F, 0xFF, 0x00, 0x01};
-  runner.MutableInputs()->Tag("ENCODED").packets.push_back(
-      MakePacket<std::string>(
-          std::string(reinterpret_cast<char const*>(input), 4))
-          .At(Timestamp(0)));
+  runner.MutableInputs()
+      ->Tag(kEncodedTag)
+      .packets.push_back(
+          MakePacket<std::string>(
+              std::string(reinterpret_cast<char const*>(input), 4))
+              .At(Timestamp(0)));
   auto status = runner.Run();
   MP_ASSERT_OK(runner.Run());
   const std::vector<Packet>& outputs =
-      runner.Outputs().Tag("FLOAT_VECTOR").packets;
+      runner.Outputs().Tag(kFloatVectorTag).packets;
   EXPECT_EQ(1, outputs.size());
   const std::vector<float>& result = outputs[0].Get<std::vector<float>>();
   ASSERT_FALSE(result.empty());

mediapipe/calculators/core/end_loop_calculator.cc

@@ -28,6 +28,10 @@ typedef EndLoopCalculator<std::vector<::mediapipe::NormalizedRect>>
     EndLoopNormalizedRectCalculator;
 REGISTER_CALCULATOR(EndLoopNormalizedRectCalculator);
 
+typedef EndLoopCalculator<std::vector<::mediapipe::LandmarkList>>
+    EndLoopLandmarkListVectorCalculator;
+REGISTER_CALCULATOR(EndLoopLandmarkListVectorCalculator);
+
 typedef EndLoopCalculator<std::vector<::mediapipe::NormalizedLandmarkList>>
     EndLoopNormalizedLandmarkListVectorCalculator;
 REGISTER_CALCULATOR(EndLoopNormalizedLandmarkListVectorCalculator);

mediapipe/calculators/core/flow_limiter_calculator.cc

@@ -24,6 +24,11 @@
 
 namespace mediapipe {
 
+constexpr char kFinishedTag[] = "FINISHED";
+constexpr char kAllowTag[] = "ALLOW";
+constexpr char kMaxInFlightTag[] = "MAX_IN_FLIGHT";
+constexpr char kOptionsTag[] = "OPTIONS";
+
 // FlowLimiterCalculator is used to limit the number of frames in flight
 // by dropping input frames when necessary.
 //
@@ -69,16 +74,19 @@ class FlowLimiterCalculator : public CalculatorBase {
  public:
   static absl::Status GetContract(CalculatorContract* cc) {
     auto& side_inputs = cc->InputSidePackets();
-    side_inputs.Tag("OPTIONS").Set<FlowLimiterCalculatorOptions>().Optional();
-    cc->Inputs().Tag("OPTIONS").Set<FlowLimiterCalculatorOptions>().Optional();
+    side_inputs.Tag(kOptionsTag).Set<FlowLimiterCalculatorOptions>().Optional();
+    cc->Inputs()
+        .Tag(kOptionsTag)
+        .Set<FlowLimiterCalculatorOptions>()
+        .Optional();
     RET_CHECK_GE(cc->Inputs().NumEntries(""), 1);
     for (int i = 0; i < cc->Inputs().NumEntries(""); ++i) {
       cc->Inputs().Get("", i).SetAny();
       cc->Outputs().Get("", i).SetSameAs(&(cc->Inputs().Get("", i)));
     }
     cc->Inputs().Get("FINISHED", 0).SetAny();
-    cc->InputSidePackets().Tag("MAX_IN_FLIGHT").Set<int>().Optional();
-    cc->Outputs().Tag("ALLOW").Set<bool>().Optional();
+    cc->InputSidePackets().Tag(kMaxInFlightTag).Set<int>().Optional();
+    cc->Outputs().Tag(kAllowTag).Set<bool>().Optional();
     cc->SetInputStreamHandler("ImmediateInputStreamHandler");
     cc->SetProcessTimestampBounds(true);
     return absl::OkStatus();
@@ -87,9 +95,9 @@ class FlowLimiterCalculator : public CalculatorBase {
   absl::Status Open(CalculatorContext* cc) final {
     options_ = cc->Options<FlowLimiterCalculatorOptions>();
     options_ = tool::RetrieveOptions(options_, cc->InputSidePackets());
-    if (cc->InputSidePackets().HasTag("MAX_IN_FLIGHT")) {
+    if (cc->InputSidePackets().HasTag(kMaxInFlightTag)) {
       options_.set_max_in_flight(
-          cc->InputSidePackets().Tag("MAX_IN_FLIGHT").Get<int>());
+          cc->InputSidePackets().Tag(kMaxInFlightTag).Get<int>());
     }
     input_queues_.resize(cc->Inputs().NumEntries(""));
     RET_CHECK_OK(CopyInputHeadersToOutputs(cc->Inputs(), &(cc->Outputs())));
@@ -104,8 +112,8 @@ class FlowLimiterCalculator : public CalculatorBase {
 
   // Outputs a packet indicating whether a frame was sent or dropped.
   void SendAllow(bool allow, Timestamp ts, CalculatorContext* cc) {
-    if (cc->Outputs().HasTag("ALLOW")) {
-      cc->Outputs().Tag("ALLOW").AddPacket(MakePacket<bool>(allow).At(ts));
+    if (cc->Outputs().HasTag(kAllowTag)) {
+      cc->Outputs().Tag(kAllowTag).AddPacket(MakePacket<bool>(allow).At(ts));
     }
   }
 
@@ -155,7 +163,7 @@ class FlowLimiterCalculator : public CalculatorBase {
     options_ = tool::RetrieveOptions(options_, cc->Inputs());
 
     // Process the FINISHED input stream.
-    Packet finished_packet = cc->Inputs().Tag("FINISHED").Value();
+    Packet finished_packet = cc->Inputs().Tag(kFinishedTag).Value();
     if (finished_packet.Timestamp() == cc->InputTimestamp()) {
       while (!frames_in_flight_.empty() &&
              frames_in_flight_.front() <= finished_packet.Timestamp()) {
@@ -210,8 +218,8 @@ class FlowLimiterCalculator : public CalculatorBase {
       Timestamp bound =
           cc->Inputs().Get("", 0).Value().Timestamp().NextAllowedInStream();
       SetNextTimestampBound(bound, &cc->Outputs().Get("", 0));
-      if (cc->Outputs().HasTag("ALLOW")) {
-        SetNextTimestampBound(bound, &cc->Outputs().Tag("ALLOW"));
+      if (cc->Outputs().HasTag(kAllowTag)) {
+        SetNextTimestampBound(bound, &cc->Outputs().Tag(kAllowTag));
       }
     }
 

mediapipe/calculators/core/flow_limiter_calculator.proto

@@ -30,7 +30,7 @@ message FlowLimiterCalculatorOptions {
   optional int32 max_in_flight = 1 [default = 1];
 
   // The maximum number of frames queued waiting for processing.
-  // The default value limits to 1 frame awaiting processing.
+  // The default value limits to 0 frames awaiting processing.
   optional int32 max_in_queue = 2 [default = 0];
 
   // The maximum time in microseconds to wait for a frame to finish processing.

mediapipe/calculators/core/flow_limiter_calculator_test.cc

@@ -36,6 +36,13 @@
 namespace mediapipe {
 
 namespace {
+
+constexpr char kDropTimestampsTag[] = "DROP_TIMESTAMPS";
+constexpr char kClockTag[] = "CLOCK";
+constexpr char kWarmupTimeTag[] = "WARMUP_TIME";
+constexpr char kSleepTimeTag[] = "SLEEP_TIME";
+constexpr char kPacketTag[] = "PACKET";
+
 // A simple Semaphore for synchronizing test threads.
 class AtomicSemaphore {
  public:
@@ -204,17 +211,17 @@ TEST_F(FlowLimiterCalculatorSemaphoreTest, FramesDropped) {
 class SleepCalculator : public CalculatorBase {
  public:
   static absl::Status GetContract(CalculatorContract* cc) {
-    cc->Inputs().Tag("PACKET").SetAny();
-    cc->Outputs().Tag("PACKET").SetSameAs(&cc->Inputs().Tag("PACKET"));
-    cc->InputSidePackets().Tag("SLEEP_TIME").Set<int64>();
-    cc->InputSidePackets().Tag("WARMUP_TIME").Set<int64>();
-    cc->InputSidePackets().Tag("CLOCK").Set<mediapipe::Clock*>();
+    cc->Inputs().Tag(kPacketTag).SetAny();
+    cc->Outputs().Tag(kPacketTag).SetSameAs(&cc->Inputs().Tag(kPacketTag));
+    cc->InputSidePackets().Tag(kSleepTimeTag).Set<int64>();
+    cc->InputSidePackets().Tag(kWarmupTimeTag).Set<int64>();
+    cc->InputSidePackets().Tag(kClockTag).Set<mediapipe::Clock*>();
     cc->SetTimestampOffset(0);
     return absl::OkStatus();
   }
 
   absl::Status Open(CalculatorContext* cc) final {
-    clock_ = cc->InputSidePackets().Tag("CLOCK").Get<mediapipe::Clock*>();
+    clock_ = cc->InputSidePackets().Tag(kClockTag).Get<mediapipe::Clock*>();
     return absl::OkStatus();
   }
 
@@ -222,10 +229,12 @@ class SleepCalculator : public CalculatorBase {
     ++packet_count;
     absl::Duration sleep_time = absl::Microseconds(
         packet_count == 1
-            ? cc->InputSidePackets().Tag("WARMUP_TIME").Get<int64>()
-            : cc->InputSidePackets().Tag("SLEEP_TIME").Get<int64>());
+            ? cc->InputSidePackets().Tag(kWarmupTimeTag).Get<int64>()
+            : cc->InputSidePackets().Tag(kSleepTimeTag).Get<int64>());
     clock_->Sleep(sleep_time);
-    cc->Outputs().Tag("PACKET").AddPacket(cc->Inputs().Tag("PACKET").Value());
+    cc->Outputs()
+        .Tag(kPacketTag)
+        .AddPacket(cc->Inputs().Tag(kPacketTag).Value());
     return absl::OkStatus();
   }
 
@@ -240,24 +249,27 @@ REGISTER_CALCULATOR(SleepCalculator);
 class DropCalculator : public CalculatorBase {
  public:
   static absl::Status GetContract(CalculatorContract* cc) {
-    cc->Inputs().Tag("PACKET").SetAny();
-    cc->Outputs().Tag("PACKET").SetSameAs(&cc->Inputs().Tag("PACKET"));
-    cc->InputSidePackets().Tag("DROP_TIMESTAMPS").Set<bool>();
+    cc->Inputs().Tag(kPacketTag).SetAny();
+    cc->Outputs().Tag(kPacketTag).SetSameAs(&cc->Inputs().Tag(kPacketTag));
+    cc->InputSidePackets().Tag(kDropTimestampsTag).Set<bool>();
     cc->SetProcessTimestampBounds(true);
     return absl::OkStatus();
   }
 
   absl::Status Process(CalculatorContext* cc) final {
-    if (!cc->Inputs().Tag("PACKET").Value().IsEmpty()) {
+    if (!cc->Inputs().Tag(kPacketTag).Value().IsEmpty()) {
       ++packet_count;
     }
     bool drop = (packet_count == 3);
-    if (!drop && !cc->Inputs().Tag("PACKET").Value().IsEmpty()) {
-      cc->Outputs().Tag("PACKET").AddPacket(cc->Inputs().Tag("PACKET").Value());
+    if (!drop && !cc->Inputs().Tag(kPacketTag).Value().IsEmpty()) {
+      cc->Outputs()
+          .Tag(kPacketTag)
+          .AddPacket(cc->Inputs().Tag(kPacketTag).Value());
     }
-    if (!drop || !cc->InputSidePackets().Tag("DROP_TIMESTAMPS").Get<bool>()) {
-      cc->Outputs().Tag("PACKET").SetNextTimestampBound(
-          cc->InputTimestamp().NextAllowedInStream());
+    if (!drop || !cc->InputSidePackets().Tag(kDropTimestampsTag).Get<bool>()) {
+      cc->Outputs()
+          .Tag(kPacketTag)
+          .SetNextTimestampBound(cc->InputTimestamp().NextAllowedInStream());
     }
     return absl::OkStatus();
   }

mediapipe/calculators/core/gate_calculator.cc

@@ -21,6 +21,11 @@
 namespace mediapipe {
 
 namespace {
+
+constexpr char kStateChangeTag[] = "STATE_CHANGE";
+constexpr char kDisallowTag[] = "DISALLOW";
+constexpr char kAllowTag[] = "ALLOW";
+
 enum GateState {
   GATE_UNINITIALIZED,
   GATE_ALLOW,
@@ -59,8 +64,9 @@ std::string ToString(GateState state) {
 // ALLOW or DISALLOW can also be specified as an input side packet. The rules
 // for evaluation remain the same as above.
 //
-// ALLOW/DISALLOW inputs must be specified either using input stream or
-// via input side packet but not both.
+// ALLOW/DISALLOW inputs must be specified either using input stream or via
+// input side packet but not both. If neither is specified, the behavior is then
+// determined by the "allow" field in the calculator options.
 //
 // Intended to be used with the default input stream handler, which synchronizes
 // all data input streams with the ALLOW/DISALLOW control input stream.
@@ -83,30 +89,33 @@ class GateCalculator : public CalculatorBase {
   GateCalculator() {}
 
   static absl::Status CheckAndInitAllowDisallowInputs(CalculatorContract* cc) {
-    bool input_via_side_packet = cc->InputSidePackets().HasTag("ALLOW") ||
-                                 cc->InputSidePackets().HasTag("DISALLOW");
+    bool input_via_side_packet = cc->InputSidePackets().HasTag(kAllowTag) ||
+                                 cc->InputSidePackets().HasTag(kDisallowTag);
     bool input_via_stream =
-        cc->Inputs().HasTag("ALLOW") || cc->Inputs().HasTag("DISALLOW");
-    // Only one of input_side_packet or input_stream may specify ALLOW/DISALLOW
-    // input.
-    RET_CHECK(input_via_side_packet ^ input_via_stream);
+        cc->Inputs().HasTag(kAllowTag) || cc->Inputs().HasTag(kDisallowTag);
 
+    // Only one of input_side_packet or input_stream may specify
+    // ALLOW/DISALLOW input.
     if (input_via_side_packet) {
-      RET_CHECK(cc->InputSidePackets().HasTag("ALLOW") ^
-                cc->InputSidePackets().HasTag("DISALLOW"));
+      RET_CHECK(!input_via_stream);
+      RET_CHECK(cc->InputSidePackets().HasTag(kAllowTag) ^
+                cc->InputSidePackets().HasTag(kDisallowTag));
 
-      if (cc->InputSidePackets().HasTag("ALLOW")) {
-        cc->InputSidePackets().Tag("ALLOW").Set<bool>();
+      if (cc->InputSidePackets().HasTag(kAllowTag)) {
+        cc->InputSidePackets().Tag(kAllowTag).Set<bool>().Optional();
       } else {
-        cc->InputSidePackets().Tag("DISALLOW").Set<bool>();
+        cc->InputSidePackets().Tag(kDisallowTag).Set<bool>().Optional();
       }
-    } else {
-      RET_CHECK(cc->Inputs().HasTag("ALLOW") ^ cc->Inputs().HasTag("DISALLOW"));
+    }
+    if (input_via_stream) {
+      RET_CHECK(!input_via_side_packet);
+      RET_CHECK(cc->Inputs().HasTag(kAllowTag) ^
+                cc->Inputs().HasTag(kDisallowTag));
 
-      if (cc->Inputs().HasTag("ALLOW")) {
-        cc->Inputs().Tag("ALLOW").Set<bool>();
+      if (cc->Inputs().HasTag(kAllowTag)) {
+        cc->Inputs().Tag(kAllowTag).Set<bool>();
       } else {
-        cc->Inputs().Tag("DISALLOW").Set<bool>();
+        cc->Inputs().Tag(kDisallowTag).Set<bool>();
       }
     }
     return absl::OkStatus();
@@ -125,23 +134,22 @@ class GateCalculator : public CalculatorBase {
       cc->Outputs().Get("", i).SetSameAs(&cc->Inputs().Get("", i));
     }
 
-    if (cc->Outputs().HasTag("STATE_CHANGE")) {
-      cc->Outputs().Tag("STATE_CHANGE").Set<bool>();
+    if (cc->Outputs().HasTag(kStateChangeTag)) {
+      cc->Outputs().Tag(kStateChangeTag).Set<bool>();
     }
 
     return absl::OkStatus();
   }
 
   absl::Status Open(CalculatorContext* cc) final {
-    use_side_packet_for_allow_disallow_ = false;
-    if (cc->InputSidePackets().HasTag("ALLOW")) {
+    if (cc->InputSidePackets().HasTag(kAllowTag)) {
       use_side_packet_for_allow_disallow_ = true;
       allow_by_side_packet_decision_ =
-          cc->InputSidePackets().Tag("ALLOW").Get<bool>();
-    } else if (cc->InputSidePackets().HasTag("DISALLOW")) {
+          cc->InputSidePackets().Tag(kAllowTag).Get<bool>();
+    } else if (cc->InputSidePackets().HasTag(kDisallowTag)) {
       use_side_packet_for_allow_disallow_ = true;
       allow_by_side_packet_decision_ =
-          !cc->InputSidePackets().Tag("DISALLOW").Get<bool>();
+          !cc->InputSidePackets().Tag(kDisallowTag).Get<bool>();
     }
 
     cc->SetOffset(TimestampDiff(0));
@@ -152,26 +160,34 @@ class GateCalculator : public CalculatorBase {
     const auto& options = cc->Options<::mediapipe::GateCalculatorOptions>();
     empty_packets_as_allow_ = options.empty_packets_as_allow();
 
+    if (!use_side_packet_for_allow_disallow_ &&
+        !cc->Inputs().HasTag(kAllowTag) && !cc->Inputs().HasTag(kDisallowTag)) {
+      use_option_for_allow_disallow_ = true;
+      allow_by_option_decision_ = options.allow();
+    }
+
     return absl::OkStatus();
   }
 
   absl::Status Process(CalculatorContext* cc) final {
     bool allow = empty_packets_as_allow_;
-    if (use_side_packet_for_allow_disallow_) {
+    if (use_option_for_allow_disallow_) {
+      allow = allow_by_option_decision_;
+    } else if (use_side_packet_for_allow_disallow_) {
       allow = allow_by_side_packet_decision_;
     } else {
-      if (cc->Inputs().HasTag("ALLOW") &&
-          !cc->Inputs().Tag("ALLOW").IsEmpty()) {
-        allow = cc->Inputs().Tag("ALLOW").Get<bool>();
+      if (cc->Inputs().HasTag(kAllowTag) &&
+          !cc->Inputs().Tag(kAllowTag).IsEmpty()) {
+        allow = cc->Inputs().Tag(kAllowTag).Get<bool>();
       }
-      if (cc->Inputs().HasTag("DISALLOW") &&
-          !cc->Inputs().Tag("DISALLOW").IsEmpty()) {
-        allow = !cc->Inputs().Tag("DISALLOW").Get<bool>();
+      if (cc->Inputs().HasTag(kDisallowTag) &&
+          !cc->Inputs().Tag(kDisallowTag).IsEmpty()) {
+        allow = !cc->Inputs().Tag(kDisallowTag).Get<bool>();
       }
     }
     const GateState new_gate_state = allow ? GATE_ALLOW : GATE_DISALLOW;
 
-    if (cc->Outputs().HasTag("STATE_CHANGE")) {
+    if (cc->Outputs().HasTag(kStateChangeTag)) {
       if (last_gate_state_ != GATE_UNINITIALIZED &&
           last_gate_state_ != new_gate_state) {
         VLOG(2) << "State transition in " << cc->NodeName() << " @ "
@@ -179,7 +195,7 @@ class GateCalculator : public CalculatorBase {
                 << ToString(last_gate_state_) << " to "
                 << ToString(new_gate_state);
         cc->Outputs()
-            .Tag("STATE_CHANGE")
+            .Tag(kStateChangeTag)
             .AddPacket(MakePacket<bool>(allow).At(cc->InputTimestamp()));
       }
     }
@@ -211,8 +227,10 @@ class GateCalculator : public CalculatorBase {
   GateState last_gate_state_ = GATE_UNINITIALIZED;
   int num_data_streams_;
   bool empty_packets_as_allow_;
-  bool use_side_packet_for_allow_disallow_;
+  bool use_side_packet_for_allow_disallow_ = false;
   bool allow_by_side_packet_decision_;
+  bool use_option_for_allow_disallow_ = false;
+  bool allow_by_option_decision_;
 };
 REGISTER_CALCULATOR(GateCalculator);
 

mediapipe/calculators/core/gate_calculator.proto

@@ -29,4 +29,8 @@ message GateCalculatorOptions {
   // disallowing the corresponding packets in the data input streams. Setting
   // this option to true inverts that, allowing the data packets to go through.
   optional bool empty_packets_as_allow = 1;
+
+  // Whether to allow or disallow the input streams to pass when no
+  // ALLOW/DISALLOW input or side input is specified.
+  optional bool allow = 2 [default = false];
 }

mediapipe/calculators/core/gate_calculator_test.cc

@@ -22,6 +22,9 @@ namespace mediapipe {
 
 namespace {
 
+constexpr char kDisallowTag[] = "DISALLOW";
+constexpr char kAllowTag[] = "ALLOW";
+
 class GateCalculatorTest : public ::testing::Test {
  protected:
   // Helper to run a graph and return status.
@@ -110,6 +113,68 @@ TEST_F(GateCalculatorTest, InvalidInputs) {
   )")));
 }
 
+TEST_F(GateCalculatorTest, AllowByALLOWOptionToTrue) {
+  SetRunner(R"(
+        calculator: "GateCalculator"
+        input_stream: "test_input"
+        output_stream: "test_output"
+        options: {
+          [mediapipe.GateCalculatorOptions.ext] {
+            allow: true
+          }
+        }
+  )");
+
+  constexpr int64 kTimestampValue0 = 42;
+  RunTimeStep(kTimestampValue0, true);
+  constexpr int64 kTimestampValue1 = 43;
+  RunTimeStep(kTimestampValue1, false);
+
+  const std::vector<Packet>& output = runner()->Outputs().Get("", 0).packets;
+  ASSERT_EQ(2, output.size());
+  EXPECT_EQ(kTimestampValue0, output[0].Timestamp().Value());
+  EXPECT_EQ(kTimestampValue1, output[1].Timestamp().Value());
+  EXPECT_EQ(true, output[0].Get<bool>());
+  EXPECT_EQ(false, output[1].Get<bool>());
+}
+
+TEST_F(GateCalculatorTest, DisallowByALLOWOptionSetToFalse) {
+  SetRunner(R"(
+        calculator: "GateCalculator"
+        input_stream: "test_input"
+        output_stream: "test_output"
+        options: {
+          [mediapipe.GateCalculatorOptions.ext] {
+            allow: false
+          }
+        }
+  )");
+
+  constexpr int64 kTimestampValue0 = 42;
+  RunTimeStep(kTimestampValue0, true);
+  constexpr int64 kTimestampValue1 = 43;
+  RunTimeStep(kTimestampValue1, false);
+
+  const std::vector<Packet>& output = runner()->Outputs().Get("", 0).packets;
+  ASSERT_EQ(0, output.size());
+}
+
+TEST_F(GateCalculatorTest, DisallowByALLOWOptionNotSet) {
+  SetRunner(R"(
+        calculator: "GateCalculator"
+        input_stream: "test_input"
+        output_stream: "test_output"
+  )");
+
+  constexpr int64 kTimestampValue0 = 42;
+  RunTimeStep(kTimestampValue0, true);
+  constexpr int64 kTimestampValue1 = 43;
+  RunTimeStep(kTimestampValue1, false);
+
+  const std::vector<Packet>& output = runner()->Outputs().Get("", 0).packets;
+  ASSERT_EQ(0, output.size());
+}
+
 TEST_F(GateCalculatorTest, AllowByALLOWSidePacketSetToTrue) {
   SetRunner(R"(
         calculator: "GateCalculator"
@@ -117,7 +182,7 @@ TEST_F(GateCalculatorTest, AllowByALLOWSidePacketSetToTrue) {
         input_stream: "test_input"
         output_stream: "test_output"
   )");
-  runner()->MutableSidePackets()->Tag("ALLOW") = Adopt(new bool(true));
+  runner()->MutableSidePackets()->Tag(kAllowTag) = Adopt(new bool(true));
 
   constexpr int64 kTimestampValue0 = 42;
   RunTimeStep(kTimestampValue0, true);
@@ -139,7 +204,7 @@ TEST_F(GateCalculatorTest, AllowByDisallowSidePacketSetToFalse) {
         input_stream: "test_input"
         output_stream: "test_output"
   )");
-  runner()->MutableSidePackets()->Tag("DISALLOW") = Adopt(new bool(false));
+  runner()->MutableSidePackets()->Tag(kDisallowTag) = Adopt(new bool(false));
 
   constexpr int64 kTimestampValue0 = 42;
   RunTimeStep(kTimestampValue0, true);
@@ -161,7 +226,7 @@ TEST_F(GateCalculatorTest, DisallowByALLOWSidePacketSetToFalse) {
         input_stream: "test_input"
         output_stream: "test_output"
   )");
-  runner()->MutableSidePackets()->Tag("ALLOW") = Adopt(new bool(false));
+  runner()->MutableSidePackets()->Tag(kAllowTag) = Adopt(new bool(false));
 
   constexpr int64 kTimestampValue0 = 42;
   RunTimeStep(kTimestampValue0, true);
@@ -179,7 +244,7 @@ TEST_F(GateCalculatorTest, DisallowByDISALLOWSidePacketSetToTrue) {
         input_stream: "test_input"
         output_stream: "test_output"
   )");
-  runner()->MutableSidePackets()->Tag("DISALLOW") = Adopt(new bool(true));
+  runner()->MutableSidePackets()->Tag(kDisallowTag) = Adopt(new bool(true));
 
   constexpr int64 kTimestampValue0 = 42;
   RunTimeStep(kTimestampValue0, true);

mediapipe/calculators/core/graph_profile_calculator.cc

@@ -0,0 +1,70 @@
+// Copyright 2019 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.
+
+#include <memory>
+
+#include "mediapipe/calculators/core/graph_profile_calculator.pb.h"
+#include "mediapipe/framework/api2/node.h"
+#include "mediapipe/framework/api2/packet.h"
+#include "mediapipe/framework/api2/port.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/calculator_profile.pb.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+
+namespace mediapipe {
+namespace api2 {
+
+// This calculator periodically copies the GraphProfile from
+// mediapipe::GraphProfiler::CaptureProfile to the "PROFILE" output stream.
+//
+// Example config:
+// node {
+//   calculator: "GraphProfileCalculator"
+//   output_stream: "FRAME:any_frame"
+//   output_stream: "PROFILE:graph_profile"
+// }
+//
+class GraphProfileCalculator : public Node {
+ public:
+  static constexpr Input<AnyType>::Multiple kFrameIn{"FRAME"};
+  static constexpr Output<GraphProfile> kProfileOut{"PROFILE"};
+
+  MEDIAPIPE_NODE_CONTRACT(kFrameIn, kProfileOut);
+
+  static absl::Status UpdateContract(CalculatorContract* cc) {
+    return absl::OkStatus();
+  }
+
+  absl::Status Process(CalculatorContext* cc) final {
+    auto options = cc->Options<::mediapipe::GraphProfileCalculatorOptions>();
+
+    if (prev_profile_ts_ == Timestamp::Unset() ||
+        cc->InputTimestamp() - prev_profile_ts_ >= options.profile_interval()) {
+      prev_profile_ts_ = cc->InputTimestamp();
+      GraphProfile result;
+      MP_RETURN_IF_ERROR(cc->GetProfilingContext()->CaptureProfile(&result));
+      kProfileOut(cc).Send(result);
+    }
+    return absl::OkStatus();
+  }
+
+ private:
+  Timestamp prev_profile_ts_;
+};
+
+MEDIAPIPE_REGISTER_NODE(GraphProfileCalculator);
+
+}  // namespace api2
+}  // namespace mediapipe

mediapipe/calculators/core/graph_profile_calculator.proto

@@ -0,0 +1,30 @@
+// Copyright 2019 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.
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator.proto";
+
+option objc_class_prefix = "MediaPipe";
+
+message GraphProfileCalculatorOptions {
+  extend mediapipe.CalculatorOptions {
+    optional GraphProfileCalculatorOptions ext = 367481815;
+  }
+
+  // The interval in microseconds between successive reported GraphProfiles.
+  optional int64 profile_interval = 1 [default = 1000000];
+}

mediapipe/calculators/core/graph_profile_calculator_test.cc

@@ -0,0 +1,210 @@
+// Copyright 2019 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.
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "absl/status/status.h"
+#include "absl/strings/str_cat.h"
+#include "absl/time/time.h"
+#include "mediapipe/framework/calculator.pb.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/calculator_profile.pb.h"
+#include "mediapipe/framework/deps/clock.h"
+#include "mediapipe/framework/port/gmock.h"
+#include "mediapipe/framework/port/gtest.h"
+#include "mediapipe/framework/port/integral_types.h"
+#include "mediapipe/framework/port/logging.h"
+#include "mediapipe/framework/port/parse_text_proto.h"
+#include "mediapipe/framework/port/proto_ns.h"
+#include "mediapipe/framework/port/status_matchers.h"
+#include "mediapipe/framework/port/threadpool.h"
+#include "mediapipe/framework/tool/simulation_clock_executor.h"
+
+// Tests for GraphProfileCalculator.
+using testing::ElementsAre;
+
+namespace mediapipe {
+namespace {
+
+constexpr char kClockTag[] = "CLOCK";
+
+using mediapipe::Clock;
+
+// A Calculator with a fixed Process call latency.
+class SleepCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc) {
+    cc->InputSidePackets().Tag(kClockTag).Set<std::shared_ptr<Clock>>();
+    cc->Inputs().Index(0).SetAny();
+    cc->Outputs().Index(0).SetSameAs(&cc->Inputs().Index(0));
+    cc->SetTimestampOffset(TimestampDiff(0));
+    return absl::OkStatus();
+  }
+  absl::Status Open(CalculatorContext* cc) final {
+    clock_ =
+        cc->InputSidePackets().Tag(kClockTag).Get<std::shared_ptr<Clock>>();
+    return absl::OkStatus();
+  }
+
+  absl::Status Process(CalculatorContext* cc) final {
+    clock_->Sleep(absl::Milliseconds(5));
+    cc->Outputs().Index(0).AddPacket(cc->Inputs().Index(0).Value());
+    return absl::OkStatus();
+  }
+  std::shared_ptr<::mediapipe::Clock> clock_ = nullptr;
+};
+REGISTER_CALCULATOR(SleepCalculator);
+
+// Tests showing GraphProfileCalculator reporting GraphProfile output packets.
+class GraphProfileCalculatorTest : public ::testing::Test {
+ protected:
+  void SetUpProfileGraph() {
+    ASSERT_TRUE(proto_ns::TextFormat::ParseFromString(R"(
+        input_stream: "input_packets_0"
+        node {
+          calculator: 'SleepCalculator'
+          input_side_packet: 'CLOCK:sync_clock'
+          input_stream: 'input_packets_0'
+          output_stream: 'output_packets_1'
+        }
+        node {
+          calculator: "GraphProfileCalculator"
+          options: {
+            [mediapipe.GraphProfileCalculatorOptions.ext]: {
+              profile_interval: 25000
+            }
+          }
+          input_stream: "FRAME:output_packets_1"
+          output_stream: "PROFILE:output_packets_0"
+        }
+    )",
+                                                      &graph_config_));
+  }
+
+  static Packet PacketAt(int64 ts) {
+    return Adopt(new int64(999)).At(Timestamp(ts));
+  }
+  static Packet None() { return Packet().At(Timestamp::OneOverPostStream()); }
+  static bool IsNone(const Packet& packet) {
+    return packet.Timestamp() == Timestamp::OneOverPostStream();
+  }
+  // Return the values of the timestamps of a vector of Packets.
+  static std::vector<int64> TimestampValues(
+      const std::vector<Packet>& packets) {
+    std::vector<int64> result;
+    for (const Packet& p : packets) {
+      result.push_back(p.Timestamp().Value());
+    }
+    return result;
+  }
+
+  // Runs a CalculatorGraph with a series of packet sets.
+  // Returns a vector of packets from each graph output stream.
+  void RunGraph(const std::vector<std::vector<Packet>>& input_sets,
+                std::vector<Packet>* output_packets) {
+    // Register output packet observers.
+    tool::AddVectorSink("output_packets_0", &graph_config_, output_packets);
+
+    // Start running the graph.
+    std::shared_ptr<SimulationClockExecutor> executor(
+        new SimulationClockExecutor(3 /*num_threads*/));
+    CalculatorGraph graph;
+    MP_ASSERT_OK(graph.SetExecutor("", executor));
+    graph.profiler()->SetClock(executor->GetClock());
+    MP_ASSERT_OK(graph.Initialize(graph_config_));
+    executor->GetClock()->ThreadStart();
+    MP_ASSERT_OK(graph.StartRun({
+        {"sync_clock",
+         Adopt(new std::shared_ptr<::mediapipe::Clock>(executor->GetClock()))},
+    }));
+
+    // Send each packet to the graph in the specified order.
+    for (int t = 0; t < input_sets.size(); t++) {
+      const std::vector<Packet>& input_set = input_sets[t];
+      for (int i = 0; i < input_set.size(); i++) {
+        const Packet& packet = input_set[i];
+        if (!IsNone(packet)) {
+          MP_EXPECT_OK(graph.AddPacketToInputStream(
+              absl::StrCat("input_packets_", i), packet));
+        }
+        executor->GetClock()->Sleep(absl::Milliseconds(10));
+      }
+    }
+    MP_ASSERT_OK(graph.CloseAllInputStreams());
+    executor->GetClock()->Sleep(absl::Milliseconds(100));
+    executor->GetClock()->ThreadFinish();
+    MP_ASSERT_OK(graph.WaitUntilDone());
+  }
+
+  CalculatorGraphConfig graph_config_;
+};
+
+TEST_F(GraphProfileCalculatorTest, GraphProfile) {
+  SetUpProfileGraph();
+  auto profiler_config = graph_config_.mutable_profiler_config();
+  profiler_config->set_enable_profiler(true);
+  profiler_config->set_trace_enabled(false);
+  profiler_config->set_trace_log_disabled(true);
+  profiler_config->set_enable_stream_latency(true);
+  profiler_config->set_calculator_filter(".*Calculator");
+
+  // Run the graph with a series of packet sets.
+  std::vector<std::vector<Packet>> input_sets = {
+      {PacketAt(10000)},  //
+      {PacketAt(20000)},  //
+      {PacketAt(30000)},  //
+      {PacketAt(40000)},
+  };
+  std::vector<Packet> output_packets;
+  RunGraph(input_sets, &output_packets);
+
+  // Validate the output packets.
+  EXPECT_THAT(TimestampValues(output_packets),  //
+              ElementsAre(10000, 40000));
+
+  GraphProfile expected_profile =
+      mediapipe::ParseTextProtoOrDie<GraphProfile>(R"pb(
+        calculator_profiles {
+          name: "GraphProfileCalculator"
+          open_runtime: 0
+          process_runtime { total: 0 count: 3 }
+          process_input_latency { total: 15000 count: 3 }
+          process_output_latency { total: 15000 count: 3 }
+          input_stream_profiles {
+            name: "output_packets_1"
+            back_edge: false
+            latency { total: 0 count: 3 }
+          }
+        }
+        calculator_profiles {
+          name: "SleepCalculator"
+          open_runtime: 0
+          process_runtime { total: 15000 count: 3 }
+          process_input_latency { total: 0 count: 3 }
+          process_output_latency { total: 15000 count: 3 }
+          input_stream_profiles {
+            name: "input_packets_0"
+            back_edge: false
+            latency { total: 0 count: 3 }
+          }
+        })pb");
+
+  EXPECT_THAT(output_packets[1].Get<GraphProfile>(),
+              mediapipe::EqualsProto(expected_profile));
+}
+
+}  // namespace
+}  // namespace mediapipe

mediapipe/calculators/core/make_pair_calculator_test.cc

@@ -0,0 +1,70 @@
+// 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.
+
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/calculator_runner.h"
+#include "mediapipe/framework/port/canonical_errors.h"
+#include "mediapipe/framework/port/gmock.h"
+#include "mediapipe/framework/port/gtest.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/framework/port/status_matchers.h"
+#include "mediapipe/framework/timestamp.h"
+#include "mediapipe/framework/tool/validate_type.h"
+#include "mediapipe/util/packet_test_util.h"
+#include "mediapipe/util/time_series_test_util.h"
+
+namespace mediapipe {
+
+class MakePairCalculatorTest
+    : public mediapipe::TimeSeriesCalculatorTest<mediapipe::NoOptions> {
+ protected:
+  void SetUp() override {
+    calculator_name_ = "MakePairCalculator";
+    num_input_streams_ = 2;
+  }
+};
+
+TEST_F(MakePairCalculatorTest, ProducesExpectedPairs) {
+  InitializeGraph();
+  AppendInputPacket(new std::string("first packet"), Timestamp(1),
+                    /* input_index= */ 0);
+  AppendInputPacket(new std::string("second packet"), Timestamp(5),
+                    /* input_index= */ 0);
+  AppendInputPacket(new int(10), Timestamp(1), /* input_index= */ 1);
+  AppendInputPacket(new int(20), Timestamp(5), /* input_index= */ 1);
+
+  MP_ASSERT_OK(RunGraph());
+
+  EXPECT_THAT(
+      output().packets,
+      ::testing::ElementsAre(
+          mediapipe::PacketContainsTimestampAndPayload<
+              std::pair<Packet, Packet>>(
+              Timestamp(1),
+              ::testing::Pair(
+                  mediapipe::PacketContainsTimestampAndPayload<std::string>(
+                      Timestamp(1), std::string("first packet")),
+                  mediapipe::PacketContainsTimestampAndPayload<int>(
+                      Timestamp(1), 10))),
+          mediapipe::PacketContainsTimestampAndPayload<
+              std::pair<Packet, Packet>>(
+              Timestamp(5),
+              ::testing::Pair(
+                  mediapipe::PacketContainsTimestampAndPayload<std::string>(
+                      Timestamp(5), std::string("second packet")),
+                  mediapipe::PacketContainsTimestampAndPayload<int>(
+                      Timestamp(5), 20)))));
+}
+
+}  // namespace mediapipe

mediapipe/calculators/core/matrix_subtract_calculator_test.cc

@@ -29,6 +29,9 @@
 namespace mediapipe {
 namespace {
 
+constexpr char kMinuendTag[] = "MINUEND";
+constexpr char kSubtrahendTag[] = "SUBTRAHEND";
+
 // A 3x4 Matrix of random integers in [0,1000).
 const char kMatrixText[] =
     "rows: 3\n"
@@ -104,12 +107,13 @@ TEST(MatrixSubtractCalculatorTest, SubtractFromInput) {
   CalculatorRunner runner(node_config);
   Matrix* side_matrix = new Matrix();
   MatrixFromTextProto(kMatrixText, side_matrix);
-  runner.MutableSidePackets()->Tag("SUBTRAHEND") = Adopt(side_matrix);
+  runner.MutableSidePackets()->Tag(kSubtrahendTag) = Adopt(side_matrix);
 
   Matrix* input_matrix = new Matrix();
   MatrixFromTextProto(kMatrixText2, input_matrix);
-  runner.MutableInputs()->Tag("MINUEND").packets.push_back(
-      Adopt(input_matrix).At(Timestamp(0)));
+  runner.MutableInputs()
+      ->Tag(kMinuendTag)
+      .packets.push_back(Adopt(input_matrix).At(Timestamp(0)));
 
   MP_ASSERT_OK(runner.Run());
   EXPECT_EQ(1, runner.Outputs().Index(0).packets.size());
@@ -133,12 +137,12 @@ TEST(MatrixSubtractCalculatorTest, SubtractFromSideMatrix) {
   CalculatorRunner runner(node_config);
   Matrix* side_matrix = new Matrix();
   MatrixFromTextProto(kMatrixText, side_matrix);
-  runner.MutableSidePackets()->Tag("MINUEND") = Adopt(side_matrix);
+  runner.MutableSidePackets()->Tag(kMinuendTag) = Adopt(side_matrix);
 
   Matrix* input_matrix = new Matrix();
   MatrixFromTextProto(kMatrixText2, input_matrix);
   runner.MutableInputs()
-      ->Tag("SUBTRAHEND")
+      ->Tag(kSubtrahendTag)
       .packets.push_back(Adopt(input_matrix).At(Timestamp(0)));
 
   MP_ASSERT_OK(runner.Run());

mediapipe/calculators/core/mux_calculator_test.cc

@@ -14,7 +14,11 @@
 
 #include <memory>
 
+#include "absl/status/status.h"
+#include "absl/types/optional.h"
 #include "mediapipe/calculators/core/split_vector_calculator.h"
+#include "mediapipe/framework/api2/node.h"
+#include "mediapipe/framework/api2/port.h"
 #include "mediapipe/framework/calculator_framework.h"
 #include "mediapipe/framework/calculator_runner.h"
 #include "mediapipe/framework/port/gtest.h"
@@ -301,4 +305,99 @@ TEST(MuxCalculatorTest, DiscardSkippedInputs_MuxInputStreamHandler) {
 }
 
 }  // namespace
+
+class PassThroughAndTsBoundUpdateNode : public mediapipe::api2::Node {
+ public:
+  static constexpr mediapipe::api2::Input<int> kInValue{"VALUE"};
+  static constexpr mediapipe::api2::Output<int> kOutValue{"VALUE"};
+  static constexpr mediapipe::api2::Output<int> kOutTsBoundUpdate{
+      "TS_BOUND_UPDATE"};
+  MEDIAPIPE_NODE_CONTRACT(kInValue, kOutValue, kOutTsBoundUpdate);
+
+  absl::Status Process(CalculatorContext* cc) override {
+    kOutValue(cc).Send(kInValue(cc));
+    kOutTsBoundUpdate(cc).SetNextTimestampBound(
+        cc->InputTimestamp().NextAllowedInStream());
+    return absl::OkStatus();
+  }
+};
+MEDIAPIPE_REGISTER_NODE(PassThroughAndTsBoundUpdateNode);
+
+class ToOptionalNode : public mediapipe::api2::Node {
+ public:
+  static constexpr mediapipe::api2::Input<int> kTick{"TICK"};
+  static constexpr mediapipe::api2::Input<int> kInValue{"VALUE"};
+  static constexpr mediapipe::api2::Output<absl::optional<int>> kOutValue{
+      "OUTPUT"};
+  MEDIAPIPE_NODE_CONTRACT(kTick, kInValue, kOutValue);
+
+  absl::Status Process(CalculatorContext* cc) override {
+    if (kInValue(cc).IsEmpty()) {
+      kOutValue(cc).Send(absl::nullopt);
+    } else {
+      kOutValue(cc).Send({kInValue(cc).Get()});
+    }
+    return absl::OkStatus();
+  }
+};
+MEDIAPIPE_REGISTER_NODE(ToOptionalNode);
+
+namespace {
+
+TEST(MuxCalculatorTest, HandleTimestampBoundUpdates) {
+  CalculatorGraphConfig config =
+      mediapipe::ParseTextProtoOrDie<CalculatorGraphConfig>(
+          R"pb(
+            input_stream: "select"
+            node {
+              calculator: "PassThroughAndTsBoundUpdateNode"
+              input_stream: "VALUE:select"
+              output_stream: "VALUE:select_ps"
+              output_stream: "TS_BOUND_UPDATE:ts_bound_update"
+            }
+            node {
+              calculator: "MuxCalculator"
+              input_stream: "INPUT:0:select_ps"
+              input_stream: "INPUT:1:ts_bound_update"
+              input_stream: "SELECT:select"
+              output_stream: "OUTPUT:select_or_ts_bound_update"
+            }
+            node {
+              calculator: "ToOptionalNode"
+              input_stream: "TICK:select"
+              input_stream: "VALUE:select_or_ts_bound_update"
+              output_stream: "OUTPUT:output"
+            }
+          )pb");
+  std::vector<Packet> output_packets;
+  tool::AddVectorSink("output", &config, &output_packets);
+
+  CalculatorGraph graph;
+  MP_ASSERT_OK(graph.Initialize(config));
+  MP_ASSERT_OK(graph.StartRun({}));
+
+  auto send_value_fn = [&](int value, Timestamp ts) -> absl::Status {
+    MP_RETURN_IF_ERROR(
+        graph.AddPacketToInputStream("select", MakePacket<int>(value).At(ts)));
+    return graph.WaitUntilIdle();
+  };
+
+  MP_ASSERT_OK(send_value_fn(0, Timestamp(1)));
+  ASSERT_EQ(output_packets.size(), 1);
+  EXPECT_EQ(output_packets[0].Get<absl::optional<int>>(), 0);
+
+  MP_ASSERT_OK(send_value_fn(1, Timestamp(2)));
+  ASSERT_EQ(output_packets.size(), 2);
+  EXPECT_EQ(output_packets[1].Get<absl::optional<int>>(), absl::nullopt);
+
+  MP_ASSERT_OK(send_value_fn(0, Timestamp(3)));
+  ASSERT_EQ(output_packets.size(), 3);
+  EXPECT_EQ(output_packets[2].Get<absl::optional<int>>(), 0);
+
+  MP_ASSERT_OK(graph.CloseAllInputStreams());
+  MP_ASSERT_OK(graph.WaitUntilDone());
+}
+
+}  // namespace
+
 }  // namespace mediapipe

mediapipe/calculators/core/packet_cloner_calculator.cc

@@ -60,7 +60,10 @@ class PacketClonerCalculator : public CalculatorBase {
     const auto calculator_options =
         cc->Options<mediapipe::PacketClonerCalculatorOptions>();
     output_only_when_all_inputs_received_ =
-        calculator_options.output_only_when_all_inputs_received();
+        calculator_options.output_only_when_all_inputs_received() ||
+        calculator_options.output_packets_only_when_all_inputs_received();
+    output_empty_packets_before_all_inputs_received_ =
+        calculator_options.output_packets_only_when_all_inputs_received();
 
     // Parse input streams.
     tick_signal_index_ = cc->Inputs().NumEntries() - 1;
@@ -88,6 +91,9 @@ class PacketClonerCalculator : public CalculatorBase {
         // Return if one of the input is null.
         for (int i = 0; i < tick_signal_index_; ++i) {
           if (current_[i].IsEmpty()) {
+            if (output_empty_packets_before_all_inputs_received_) {
+              SetAllNextTimestampBounds(cc);
+            }
             return absl::OkStatus();
           }
         }
@@ -107,9 +113,17 @@ class PacketClonerCalculator : public CalculatorBase {
   }
 
  private:
+  void SetAllNextTimestampBounds(CalculatorContext* cc) {
+    for (int j = 0; j < tick_signal_index_; ++j) {
+      cc->Outputs().Index(j).SetNextTimestampBound(
+          cc->InputTimestamp().NextAllowedInStream());
+    }
+  }
+
   std::vector<Packet> current_;
   int tick_signal_index_;
   bool output_only_when_all_inputs_received_;
+  bool output_empty_packets_before_all_inputs_received_;
 };
 
 REGISTER_CALCULATOR(PacketClonerCalculator);

mediapipe/calculators/core/packet_cloner_calculator.proto

@@ -28,4 +28,9 @@ message PacketClonerCalculatorOptions {
   // When true, this calculator will drop received TICK packets if any input
   // stream hasn't received a packet yet.
   optional bool output_only_when_all_inputs_received = 1 [default = false];
+
+  // Similar with above, but also transmit empty packet for all streams before
+  // all inputs are received.
+  optional bool output_packets_only_when_all_inputs_received = 2
+      [default = false];
 }

mediapipe/calculators/core/packet_presence_calculator.cc

@@ -17,6 +17,9 @@
 
 namespace mediapipe {
 
+constexpr char kPresenceTag[] = "PRESENCE";
+constexpr char kPacketTag[] = "PACKET";
+
 // For each non empty input packet, emits a single output packet containing a
 // boolean value "true", "false" in response to empty packets (a.k.a. timestamp
 // bound updates) This can be used to "flag" the presence of an arbitrary packet
@@ -58,8 +61,8 @@ namespace mediapipe {
 class PacketPresenceCalculator : public CalculatorBase {
  public:
   static absl::Status GetContract(CalculatorContract* cc) {
-    cc->Inputs().Tag("PACKET").SetAny();
-    cc->Outputs().Tag("PRESENCE").Set<bool>();
+    cc->Inputs().Tag(kPacketTag).SetAny();
+    cc->Outputs().Tag(kPresenceTag).Set<bool>();
     // Process() function is invoked in response to input stream timestamp
     // bound updates.
     cc->SetProcessTimestampBounds(true);
@@ -73,8 +76,8 @@ class PacketPresenceCalculator : public CalculatorBase {
 
   absl::Status Process(CalculatorContext* cc) final {
     cc->Outputs()
-        .Tag("PRESENCE")
-        .AddPacket(MakePacket<bool>(!cc->Inputs().Tag("PACKET").IsEmpty())
+        .Tag(kPresenceTag)
+        .AddPacket(MakePacket<bool>(!cc->Inputs().Tag(kPacketTag).IsEmpty())
                        .At(cc->InputTimestamp()));
     return absl::OkStatus();
   }

mediapipe/calculators/core/packet_resampler_calculator.cc

@@ -39,6 +39,11 @@ namespace mediapipe {
 
 REGISTER_CALCULATOR(PacketResamplerCalculator);
 namespace {
+
+constexpr char kSeedTag[] = "SEED";
+constexpr char kVideoHeaderTag[] = "VIDEO_HEADER";
+constexpr char kOptionsTag[] = "OPTIONS";
+
 // Returns a TimestampDiff (assuming microseconds) corresponding to the
 // given time in seconds.
 TimestampDiff TimestampDiffFromSeconds(double seconds) {
@@ -50,16 +55,16 @@ TimestampDiff TimestampDiffFromSeconds(double seconds) {
 absl::Status PacketResamplerCalculator::GetContract(CalculatorContract* cc) {
   const auto& resampler_options =
       cc->Options<PacketResamplerCalculatorOptions>();
-  if (cc->InputSidePackets().HasTag("OPTIONS")) {
-    cc->InputSidePackets().Tag("OPTIONS").Set<CalculatorOptions>();
+  if (cc->InputSidePackets().HasTag(kOptionsTag)) {
+    cc->InputSidePackets().Tag(kOptionsTag).Set<CalculatorOptions>();
   }
   CollectionItemId input_data_id = cc->Inputs().GetId("DATA", 0);
   if (!input_data_id.IsValid()) {
     input_data_id = cc->Inputs().GetId("", 0);
   }
   cc->Inputs().Get(input_data_id).SetAny();
-  if (cc->Inputs().HasTag("VIDEO_HEADER")) {
-    cc->Inputs().Tag("VIDEO_HEADER").Set<VideoHeader>();
+  if (cc->Inputs().HasTag(kVideoHeaderTag)) {
+    cc->Inputs().Tag(kVideoHeaderTag).Set<VideoHeader>();
   }
 
   CollectionItemId output_data_id = cc->Outputs().GetId("DATA", 0);
@@ -67,15 +72,15 @@ absl::Status PacketResamplerCalculator::GetContract(CalculatorContract* cc) {
     output_data_id = cc->Outputs().GetId("", 0);
   }
   cc->Outputs().Get(output_data_id).SetSameAs(&cc->Inputs().Get(input_data_id));
-  if (cc->Outputs().HasTag("VIDEO_HEADER")) {
-    cc->Outputs().Tag("VIDEO_HEADER").Set<VideoHeader>();
+  if (cc->Outputs().HasTag(kVideoHeaderTag)) {
+    cc->Outputs().Tag(kVideoHeaderTag).Set<VideoHeader>();
   }
 
   if (resampler_options.jitter() != 0.0) {
     RET_CHECK_GT(resampler_options.jitter(), 0.0);
     RET_CHECK_LE(resampler_options.jitter(), 1.0);
-    RET_CHECK(cc->InputSidePackets().HasTag("SEED"));
-    cc->InputSidePackets().Tag("SEED").Set<std::string>();
+    RET_CHECK(cc->InputSidePackets().HasTag(kSeedTag));
+    cc->InputSidePackets().Tag(kSeedTag).Set<std::string>();
   }
   return absl::OkStatus();
 }
@@ -143,9 +148,9 @@ absl::Status PacketResamplerCalculator::Open(CalculatorContext* cc) {
 
 absl::Status PacketResamplerCalculator::Process(CalculatorContext* cc) {
   if (cc->InputTimestamp() == Timestamp::PreStream() &&
-      cc->Inputs().UsesTags() && cc->Inputs().HasTag("VIDEO_HEADER") &&
-      !cc->Inputs().Tag("VIDEO_HEADER").IsEmpty()) {
-    video_header_ = cc->Inputs().Tag("VIDEO_HEADER").Get<VideoHeader>();
+      cc->Inputs().UsesTags() && cc->Inputs().HasTag(kVideoHeaderTag) &&
+      !cc->Inputs().Tag(kVideoHeaderTag).IsEmpty()) {
+    video_header_ = cc->Inputs().Tag(kVideoHeaderTag).Get<VideoHeader>();
     video_header_.frame_rate = frame_rate_;
     if (cc->Inputs().Get(input_data_id_).IsEmpty()) {
       return absl::OkStatus();
@@ -234,7 +239,7 @@ absl::Status LegacyJitterWithReflectionStrategy::Open(CalculatorContext* cc) {
                     "ignored, because we are adding jitter.";
   }
 
-  const auto& seed = cc->InputSidePackets().Tag("SEED").Get<std::string>();
+  const auto& seed = cc->InputSidePackets().Tag(kSeedTag).Get<std::string>();
   random_ = CreateSecureRandom(seed);
   if (random_ == nullptr) {
     return absl::InvalidArgumentError(
@@ -357,7 +362,7 @@ absl::Status ReproducibleJitterWithReflectionStrategy::Open(
                     "ignored, because we are adding jitter.";
   }
 
-  const auto& seed = cc->InputSidePackets().Tag("SEED").Get<std::string>();
+  const auto& seed = cc->InputSidePackets().Tag(kSeedTag).Get<std::string>();
   random_ = CreateSecureRandom(seed);
   if (random_ == nullptr) {
     return absl::InvalidArgumentError(
@@ -504,7 +509,7 @@ absl::Status JitterWithoutReflectionStrategy::Open(CalculatorContext* cc) {
                     "ignored, because we are adding jitter.";
   }
 
-  const auto& seed = cc->InputSidePackets().Tag("SEED").Get<std::string>();
+  const auto& seed = cc->InputSidePackets().Tag(kSeedTag).Get<std::string>();
   random_ = CreateSecureRandom(seed);
   if (random_ == nullptr) {
     return absl::InvalidArgumentError(
@@ -635,9 +640,9 @@ absl::Status NoJitterStrategy::Process(CalculatorContext* cc) {
           base_timestamp_ +
           TimestampDiffFromSeconds(first_index / calculator_->frame_rate_);
     }
-    if (cc->Outputs().UsesTags() && cc->Outputs().HasTag("VIDEO_HEADER")) {
+    if (cc->Outputs().UsesTags() && cc->Outputs().HasTag(kVideoHeaderTag)) {
       cc->Outputs()
-          .Tag("VIDEO_HEADER")
+          .Tag(kVideoHeaderTag)
           .Add(new VideoHeader(calculator_->video_header_),
                Timestamp::PreStream());
     }

mediapipe/calculators/core/packet_resampler_calculator_test.cc

@@ -32,6 +32,12 @@ namespace mediapipe {
 
 using ::testing::ElementsAre;
 namespace {
+
+constexpr char kOptionsTag[] = "OPTIONS";
+constexpr char kSeedTag[] = "SEED";
+constexpr char kVideoHeaderTag[] = "VIDEO_HEADER";
+constexpr char kDataTag[] = "DATA";
+
 // A simple version of CalculatorRunner with built-in convenience
 // methods for setting inputs from a vector and checking outputs
 // against expected outputs (both timestamps and contents).
@@ -464,7 +470,7 @@ TEST(PacketResamplerCalculatorTest, SetVideoHeader) {
   )pb"));
 
   for (const int64 ts : {0, 5000, 10010, 15001, 19990}) {
-    runner.MutableInputs()->Tag("DATA").packets.push_back(
+    runner.MutableInputs()->Tag(kDataTag).packets.push_back(
         Adopt(new std::string(absl::StrCat("Frame #", ts))).At(Timestamp(ts)));
   }
   VideoHeader video_header_in;
@@ -474,16 +480,16 @@ TEST(PacketResamplerCalculatorTest, SetVideoHeader) {
   video_header_in.duration = 1.0;
   video_header_in.format = ImageFormat::SRGB;
   runner.MutableInputs()
-      ->Tag("VIDEO_HEADER")
+      ->Tag(kVideoHeaderTag)
       .packets.push_back(
           Adopt(new VideoHeader(video_header_in)).At(Timestamp::PreStream()));
   MP_ASSERT_OK(runner.Run());
 
-  ASSERT_EQ(1, runner.Outputs().Tag("VIDEO_HEADER").packets.size());
+  ASSERT_EQ(1, runner.Outputs().Tag(kVideoHeaderTag).packets.size());
   EXPECT_EQ(Timestamp::PreStream(),
-            runner.Outputs().Tag("VIDEO_HEADER").packets[0].Timestamp());
+            runner.Outputs().Tag(kVideoHeaderTag).packets[0].Timestamp());
   const VideoHeader& video_header_out =
-      runner.Outputs().Tag("VIDEO_HEADER").packets[0].Get<VideoHeader>();
+      runner.Outputs().Tag(kVideoHeaderTag).packets[0].Get<VideoHeader>();
   EXPECT_EQ(video_header_in.width, video_header_out.width);
   EXPECT_EQ(video_header_in.height, video_header_out.height);
   EXPECT_DOUBLE_EQ(50.0, video_header_out.frame_rate);
@@ -725,7 +731,7 @@ TEST(PacketResamplerCalculatorTest, OptionsSidePacket) {
               [mediapipe.PacketResamplerCalculatorOptions.ext] {
                 frame_rate: 30
               })pb"));
-    runner.MutableSidePackets()->Tag("OPTIONS") = Adopt(options);
+    runner.MutableSidePackets()->Tag(kOptionsTag) = Adopt(options);
     runner.SetInput({-222, 15000, 32000, 49999, 150000});
     MP_ASSERT_OK(runner.Run());
     EXPECT_EQ(6, runner.Outputs().Index(0).packets.size());
@@ -740,7 +746,7 @@ TEST(PacketResamplerCalculatorTest, OptionsSidePacket) {
             frame_rate: 30
             base_timestamp: 0
           })pb"));
-    runner.MutableSidePackets()->Tag("OPTIONS") = Adopt(options);
+    runner.MutableSidePackets()->Tag(kOptionsTag) = Adopt(options);
 
     runner.SetInput({-222, 15000, 32000, 49999, 150000});
     MP_ASSERT_OK(runner.Run());

mediapipe/calculators/core/packet_thinner_calculator.cc

@@ -217,6 +217,7 @@ absl::Status PacketThinnerCalculator::Open(CalculatorContext* cc) {
       header->format = video_header.format;
       header->width = video_header.width;
       header->height = video_header.height;
+      header->duration = video_header.duration;
       header->frame_rate = new_frame_rate;
       cc->Outputs().Index(0).SetHeader(Adopt(header.release()));
     } else {

mediapipe/calculators/core/packet_thinner_calculator_test.cc

@@ -29,6 +29,8 @@
 namespace mediapipe {
 namespace {
 
+constexpr char kPeriodTag[] = "PERIOD";
+
 // A simple version of CalculatorRunner with built-in convenience methods for
 // setting inputs from a vector and checking outputs against a vector of
 // expected outputs.
@@ -121,7 +123,7 @@ TEST(PacketThinnerCalculatorTest, ASyncUniformStreamThinningTestBySidePacket) {
 
   SimpleRunner runner(node);
   runner.SetInput({2, 4, 6, 8, 10, 12, 14});
-  runner.MutableSidePackets()->Tag("PERIOD") = MakePacket<int64>(5);
+  runner.MutableSidePackets()->Tag(kPeriodTag) = MakePacket<int64>(5);
   MP_ASSERT_OK(runner.Run());
 
   const std::vector<int64> expected_timestamps = {2, 8, 14};
@@ -160,7 +162,7 @@ TEST(PacketThinnerCalculatorTest, SyncUniformStreamThinningTestBySidePacket1) {
 
   SimpleRunner runner(node);
   runner.SetInput({2, 4, 6, 8, 10, 12, 14});
-  runner.MutableSidePackets()->Tag("PERIOD") = MakePacket<int64>(5);
+  runner.MutableSidePackets()->Tag(kPeriodTag) = MakePacket<int64>(5);
   MP_ASSERT_OK(runner.Run());
 
   const std::vector<int64> expected_timestamps = {2, 6, 10, 14};

mediapipe/calculators/core/previous_loopback_calculator_test.cc

@@ -39,6 +39,8 @@ using ::testing::Pair;
 using ::testing::Value;
 namespace {
 
+constexpr char kDisallowTag[] = "DISALLOW";
+
 // Returns the timestamp values for a vector of Packets.
 // TODO: puth this kind of test util in a common place.
 std::vector<int64> TimestampValues(const std::vector<Packet>& packets) {
@@ -702,14 +704,14 @@ class DroppingGateCalculator : public CalculatorBase {
  public:
   static absl::Status GetContract(CalculatorContract* cc) {
     cc->Inputs().Index(0).SetAny();
-    cc->Inputs().Tag("DISALLOW").Set<bool>();
+    cc->Inputs().Tag(kDisallowTag).Set<bool>();
     cc->Outputs().Index(0).SetSameAs(&cc->Inputs().Index(0));
     return absl::OkStatus();
   }
 
   absl::Status Process(CalculatorContext* cc) final {
     if (!cc->Inputs().Index(0).IsEmpty() &&
-        !cc->Inputs().Tag("DISALLOW").Get<bool>()) {
+        !cc->Inputs().Tag(kDisallowTag).Get<bool>()) {
       cc->Outputs().Index(0).AddPacket(cc->Inputs().Index(0).Value());
     }
     return absl::OkStatus();

mediapipe/calculators/core/quantize_float_vector_calculator.cc

@@ -41,11 +41,14 @@
 //   }
 namespace mediapipe {
 
+constexpr char kEncodedTag[] = "ENCODED";
+constexpr char kFloatVectorTag[] = "FLOAT_VECTOR";
+
 class QuantizeFloatVectorCalculator : public CalculatorBase {
  public:
   static absl::Status GetContract(CalculatorContract* cc) {
-    cc->Inputs().Tag("FLOAT_VECTOR").Set<std::vector<float>>();
-    cc->Outputs().Tag("ENCODED").Set<std::string>();
+    cc->Inputs().Tag(kFloatVectorTag).Set<std::vector<float>>();
+    cc->Outputs().Tag(kEncodedTag).Set<std::string>();
     return absl::OkStatus();
   }
 
@@ -70,7 +73,7 @@ class QuantizeFloatVectorCalculator : public CalculatorBase {
 
   absl::Status Process(CalculatorContext* cc) final {
     const std::vector<float>& float_vector =
-        cc->Inputs().Tag("FLOAT_VECTOR").Value().Get<std::vector<float>>();
+        cc->Inputs().Tag(kFloatVectorTag).Value().Get<std::vector<float>>();
     int feature_size = float_vector.size();
     std::string encoded_features;
     encoded_features.reserve(feature_size);
@@ -86,8 +89,10 @@ class QuantizeFloatVectorCalculator : public CalculatorBase {
           (old_value - min_quantized_value_) * (255.0 / range_));
       encoded_features += encoded;
     }
-    cc->Outputs().Tag("ENCODED").AddPacket(
-        MakePacket<std::string>(encoded_features).At(cc->InputTimestamp()));
+    cc->Outputs()
+        .Tag(kEncodedTag)
+        .AddPacket(
+            MakePacket<std::string>(encoded_features).At(cc->InputTimestamp()));
     return absl::OkStatus();
   }
 

mediapipe/calculators/core/quantize_float_vector_calculator_test.cc

@@ -25,6 +25,9 @@
 
 namespace mediapipe {
 
+constexpr char kEncodedTag[] = "ENCODED";
+constexpr char kFloatVectorTag[] = "FLOAT_VECTOR";
+
 TEST(QuantizeFloatVectorCalculatorTest, WrongConfig) {
   CalculatorGraphConfig::Node node_config =
       ParseTextProtoOrDie<CalculatorGraphConfig::Node>(R"pb(
@@ -40,7 +43,7 @@ TEST(QuantizeFloatVectorCalculatorTest, WrongConfig) {
   CalculatorRunner runner(node_config);
   std::vector<float> empty_vector;
   runner.MutableInputs()
-      ->Tag("FLOAT_VECTOR")
+      ->Tag(kFloatVectorTag)
       .packets.push_back(
           MakePacket<std::vector<float>>(empty_vector).At(Timestamp(0)));
   auto status = runner.Run();
@@ -67,7 +70,7 @@ TEST(QuantizeFloatVectorCalculatorTest, WrongConfig2) {
   CalculatorRunner runner(node_config);
   std::vector<float> empty_vector;
   runner.MutableInputs()
-      ->Tag("FLOAT_VECTOR")
+      ->Tag(kFloatVectorTag)
       .packets.push_back(
           MakePacket<std::vector<float>>(empty_vector).At(Timestamp(0)));
   auto status = runner.Run();
@@ -94,7 +97,7 @@ TEST(QuantizeFloatVectorCalculatorTest, WrongConfig3) {
   CalculatorRunner runner(node_config);
   std::vector<float> empty_vector;
   runner.MutableInputs()
-      ->Tag("FLOAT_VECTOR")
+      ->Tag(kFloatVectorTag)
       .packets.push_back(
           MakePacket<std::vector<float>>(empty_vector).At(Timestamp(0)));
   auto status = runner.Run();
@@ -121,11 +124,12 @@ TEST(QuantizeFloatVectorCalculatorTest, TestEmptyVector) {
   CalculatorRunner runner(node_config);
   std::vector<float> empty_vector;
   runner.MutableInputs()
-      ->Tag("FLOAT_VECTOR")
+      ->Tag(kFloatVectorTag)
       .packets.push_back(
           MakePacket<std::vector<float>>(empty_vector).At(Timestamp(0)));
   MP_ASSERT_OK(runner.Run());
-  const std::vector<Packet>& outputs = runner.Outputs().Tag("ENCODED").packets;
+  const std::vector<Packet>& outputs =
+      runner.Outputs().Tag(kEncodedTag).packets;
   EXPECT_EQ(1, outputs.size());
   EXPECT_TRUE(outputs[0].Get<std::string>().empty());
   EXPECT_EQ(Timestamp(0), outputs[0].Timestamp());
@@ -147,11 +151,12 @@ TEST(QuantizeFloatVectorCalculatorTest, TestNonEmptyVector) {
   CalculatorRunner runner(node_config);
   std::vector<float> vector = {0.0f, -64.0f, 64.0f, -32.0f, 32.0f};
   runner.MutableInputs()
-      ->Tag("FLOAT_VECTOR")
+      ->Tag(kFloatVectorTag)
       .packets.push_back(
           MakePacket<std::vector<float>>(vector).At(Timestamp(0)));
   MP_ASSERT_OK(runner.Run());
-  const std::vector<Packet>& outputs = runner.Outputs().Tag("ENCODED").packets;
+  const std::vector<Packet>& outputs =
+      runner.Outputs().Tag(kEncodedTag).packets;
   EXPECT_EQ(1, outputs.size());
   const std::string& result = outputs[0].Get<std::string>();
   ASSERT_FALSE(result.empty());
@@ -185,11 +190,12 @@ TEST(QuantizeFloatVectorCalculatorTest, TestSaturation) {
   CalculatorRunner runner(node_config);
   std::vector<float> vector = {-65.0f, 65.0f};
   runner.MutableInputs()
-      ->Tag("FLOAT_VECTOR")
+      ->Tag(kFloatVectorTag)
       .packets.push_back(
           MakePacket<std::vector<float>>(vector).At(Timestamp(0)));
   MP_ASSERT_OK(runner.Run());
-  const std::vector<Packet>& outputs = runner.Outputs().Tag("ENCODED").packets;
+  const std::vector<Packet>& outputs =
+      runner.Outputs().Tag(kEncodedTag).packets;
   EXPECT_EQ(1, outputs.size());
   const std::string& result = outputs[0].Get<std::string>();
   ASSERT_FALSE(result.empty());

mediapipe/calculators/core/real_time_flow_limiter_calculator.cc

@@ -23,6 +23,9 @@
 
 namespace mediapipe {
 
+constexpr char kAllowTag[] = "ALLOW";
+constexpr char kMaxInFlightTag[] = "MAX_IN_FLIGHT";
+
 // RealTimeFlowLimiterCalculator is used to limit the number of pipelined
 // processing operations in a section of the graph.
 //
@@ -86,11 +89,11 @@ class RealTimeFlowLimiterCalculator : public CalculatorBase {
       cc->Outputs().Get("", i).SetSameAs(&(cc->Inputs().Get("", i)));
     }
     cc->Inputs().Get("FINISHED", 0).SetAny();
-    if (cc->InputSidePackets().HasTag("MAX_IN_FLIGHT")) {
-      cc->InputSidePackets().Tag("MAX_IN_FLIGHT").Set<int>();
+    if (cc->InputSidePackets().HasTag(kMaxInFlightTag)) {
+      cc->InputSidePackets().Tag(kMaxInFlightTag).Set<int>();
     }
-    if (cc->Outputs().HasTag("ALLOW")) {
-      cc->Outputs().Tag("ALLOW").Set<bool>();
+    if (cc->Outputs().HasTag(kAllowTag)) {
+      cc->Outputs().Tag(kAllowTag).Set<bool>();
     }
 
     cc->SetInputStreamHandler("ImmediateInputStreamHandler");
@@ -101,8 +104,8 @@ class RealTimeFlowLimiterCalculator : public CalculatorBase {
   absl::Status Open(CalculatorContext* cc) final {
     finished_id_ = cc->Inputs().GetId("FINISHED", 0);
     max_in_flight_ = 1;
-    if (cc->InputSidePackets().HasTag("MAX_IN_FLIGHT")) {
-      max_in_flight_ = cc->InputSidePackets().Tag("MAX_IN_FLIGHT").Get<int>();
+    if (cc->InputSidePackets().HasTag(kMaxInFlightTag)) {
+      max_in_flight_ = cc->InputSidePackets().Tag(kMaxInFlightTag).Get<int>();
     }
     RET_CHECK_GE(max_in_flight_, 1);
     num_in_flight_ = 0;

mediapipe/calculators/core/real_time_flow_limiter_calculator_test.cc

@@ -33,6 +33,9 @@
 namespace mediapipe {
 
 namespace {
+
+constexpr char kFinishedTag[] = "FINISHED";
+
 // A simple Semaphore for synchronizing test threads.
 class AtomicSemaphore {
  public:
@@ -112,7 +115,7 @@ TEST(RealTimeFlowLimiterCalculator, BasicTest) {
     Timestamp timestamp =
         Timestamp((i + 1) * Timestamp::kTimestampUnitsPerSecond);
     runner.MutableInputs()
-        ->Tag("FINISHED")
+        ->Tag(kFinishedTag)
         .packets.push_back(MakePacket<bool>(true).At(timestamp));
   }
 

mediapipe/calculators/core/sequence_shift_calculator_test.cc

@@ -22,6 +22,8 @@ namespace mediapipe {
 
 namespace {
 
+constexpr char kPacketOffsetTag[] = "PACKET_OFFSET";
+
 // Adds packets containing integers equal to their original timestamp.
 void AddPackets(CalculatorRunner* runner) {
   for (int i = 0; i < 10; ++i) {
@@ -111,7 +113,7 @@ TEST(SequenceShiftCalculatorTest, SidePacketOffset) {
 
   CalculatorRunner runner(node);
   AddPackets(&runner);
-  runner.MutableSidePackets()->Tag("PACKET_OFFSET") = Adopt(new int(-2));
+  runner.MutableSidePackets()->Tag(kPacketOffsetTag) = Adopt(new int(-2));
   MP_ASSERT_OK(runner.Run());
   const std::vector<Packet>& input_packets =
       runner.MutableInputs()->Index(0).packets;

mediapipe/calculators/core/split_landmarks_calculator.cc

@@ -12,8 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#ifndef MEDIAPIPE_CALCULATORS_CORE_SPLIT_NORMALIZED_LANDMARK_LIST_CALCULATOR_H_  // NOLINT
-#define MEDIAPIPE_CALCULATORS_CORE_SPLIT_NORMALIZED_LANDMARK_LIST_CALCULATOR_H_  // NOLINT
+#ifndef MEDIAPIPE_CALCULATORS_CORE_SPLIT_LANDMARKS_CALCULATOR_H_  // NOLINT
+#define MEDIAPIPE_CALCULATORS_CORE_SPLIT_LANDMARKS_CALCULATOR_H_  // NOLINT
 
 #include "mediapipe/calculators/core/split_vector_calculator.pb.h"
 #include "mediapipe/framework/calculator_framework.h"
@@ -24,29 +24,30 @@
 
 namespace mediapipe {
 
-// Splits an input packet with NormalizedLandmarkList into
-// multiple NormalizedLandmarkList output packets using the [begin, end) ranges
+// Splits an input packet with LandmarkListType into
+// multiple LandmarkListType output packets using the [begin, end) ranges
 // specified in SplitVectorCalculatorOptions. If the option "element_only" is
 // set to true, all ranges should be of size 1 and all outputs will be elements
-// of type NormalizedLandmark. If "element_only" is false, ranges can be
-// non-zero in size and all outputs will be of type NormalizedLandmarkList.
+// of type LandmarkType. If "element_only" is false, ranges can be
+// non-zero in size and all outputs will be of type LandmarkListType.
 // If the option "combine_outputs" is set to true, only one output stream can be
 // specified and all ranges of elements will be combined into one
-// NormalizedLandmarkList.
-class SplitNormalizedLandmarkListCalculator : public CalculatorBase {
+// LandmarkListType.
+template <typename LandmarkType, typename LandmarkListType>
+class SplitLandmarksCalculator : public CalculatorBase {
  public:
   static absl::Status GetContract(CalculatorContract* cc) {
     RET_CHECK(cc->Inputs().NumEntries() == 1);
     RET_CHECK(cc->Outputs().NumEntries() != 0);
 
-    cc->Inputs().Index(0).Set<NormalizedLandmarkList>();
+    cc->Inputs().Index(0).Set<LandmarkListType>();
 
     const auto& options =
         cc->Options<::mediapipe::SplitVectorCalculatorOptions>();
 
     if (options.combine_outputs()) {
       RET_CHECK_EQ(cc->Outputs().NumEntries(), 1);
-      cc->Outputs().Index(0).Set<NormalizedLandmarkList>();
+      cc->Outputs().Index(0).Set<LandmarkListType>();
       for (int i = 0; i < options.ranges_size() - 1; ++i) {
         for (int j = i + 1; j < options.ranges_size(); ++j) {
           const auto& range_0 = options.ranges(i);
@@ -81,9 +82,9 @@ class SplitNormalizedLandmarkListCalculator : public CalculatorBase {
             return absl::InvalidArgumentError(
                 "Since element_only is true, all ranges should be of size 1.");
           }
-          cc->Outputs().Index(i).Set<NormalizedLandmark>();
+          cc->Outputs().Index(i).Set<LandmarkType>();
         } else {
-          cc->Outputs().Index(i).Set<NormalizedLandmarkList>();
+          cc->Outputs().Index(i).Set<LandmarkListType>();
         }
       }
     }
@@ -110,40 +111,39 @@ class SplitNormalizedLandmarkListCalculator : public CalculatorBase {
   }
 
   absl::Status Process(CalculatorContext* cc) override {
-    const NormalizedLandmarkList& input =
-        cc->Inputs().Index(0).Get<NormalizedLandmarkList>();
+    const LandmarkListType& input =
+        cc->Inputs().Index(0).Get<LandmarkListType>();
     RET_CHECK_GE(input.landmark_size(), max_range_end_)
         << "Max range end " << max_range_end_ << " exceeds landmarks size "
         << input.landmark_size();
 
     if (combine_outputs_) {
-      NormalizedLandmarkList output;
+      LandmarkListType output;
       for (int i = 0; i < ranges_.size(); ++i) {
         for (int j = ranges_[i].first; j < ranges_[i].second; ++j) {
-          const NormalizedLandmark& input_landmark = input.landmark(j);
+          const LandmarkType& input_landmark = input.landmark(j);
           *output.add_landmark() = input_landmark;
         }
       }
       RET_CHECK_EQ(output.landmark_size(), total_elements_);
       cc->Outputs().Index(0).AddPacket(
-          MakePacket<NormalizedLandmarkList>(output).At(cc->InputTimestamp()));
+          MakePacket<LandmarkListType>(output).At(cc->InputTimestamp()));
     } else {
       if (element_only_) {
         for (int i = 0; i < ranges_.size(); ++i) {
           cc->Outputs().Index(i).AddPacket(
-              MakePacket<NormalizedLandmark>(input.landmark(ranges_[i].first))
+              MakePacket<LandmarkType>(input.landmark(ranges_[i].first))
                   .At(cc->InputTimestamp()));
         }
       } else {
         for (int i = 0; i < ranges_.size(); ++i) {
-          NormalizedLandmarkList output;
+          LandmarkListType output;
           for (int j = ranges_[i].first; j < ranges_[i].second; ++j) {
-            const NormalizedLandmark& input_landmark = input.landmark(j);
+            const LandmarkType& input_landmark = input.landmark(j);
             *output.add_landmark() = input_landmark;
           }
           cc->Outputs().Index(i).AddPacket(
-              MakePacket<NormalizedLandmarkList>(output).At(
-                  cc->InputTimestamp()));
+              MakePacket<LandmarkListType>(output).At(cc->InputTimestamp()));
         }
       }
     }
@@ -159,9 +159,15 @@ class SplitNormalizedLandmarkListCalculator : public CalculatorBase {
   bool combine_outputs_ = false;
 };
 
+typedef SplitLandmarksCalculator<NormalizedLandmark, NormalizedLandmarkList>
+    SplitNormalizedLandmarkListCalculator;
 REGISTER_CALCULATOR(SplitNormalizedLandmarkListCalculator);
 
+typedef SplitLandmarksCalculator<Landmark, LandmarkList>
+    SplitLandmarkListCalculator;
+REGISTER_CALCULATOR(SplitLandmarkListCalculator);
+
 }  // namespace mediapipe
 
 // NOLINTNEXTLINE
-#endif  // MEDIAPIPE_CALCULATORS_CORE_SPLIT_NORMALIZED_LANDMARK_LIST_CALCULATOR_H_
+#endif  // MEDIAPIPE_CALCULATORS_CORE_SPLIT_LANDMARKS_CALCULATOR_H_

mediapipe/calculators/core/split_vector_calculator.cc

@@ -80,4 +80,7 @@ typedef SplitVectorCalculator<mediapipe::ClassificationList, false>
     SplitClassificationListVectorCalculator;
 REGISTER_CALCULATOR(SplitClassificationListVectorCalculator);
 
+typedef SplitVectorCalculator<uint64_t, false> SplitUint64tVectorCalculator;
+REGISTER_CALCULATOR(SplitUint64tVectorCalculator);
+
 }  // namespace mediapipe

mediapipe/calculators/image/BUILD

@@ -80,6 +80,16 @@ mediapipe_proto_library(
     ],
 )
 
+mediapipe_proto_library(
+    name = "segmentation_smoothing_calculator_proto",
+    srcs = ["segmentation_smoothing_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_options_proto",
+        "//mediapipe/framework:calculator_proto",
+    ],
+)
+
 cc_library(
     name = "color_convert_calculator",
     srcs = ["color_convert_calculator.cc"],
@@ -405,6 +415,36 @@ cc_library(
     alwayslink = 1,
 )
 
+mediapipe_proto_library(
+    name = "image_clone_calculator_proto",
+    srcs = ["image_clone_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_options_proto",
+        "//mediapipe/framework:calculator_proto",
+    ],
+)
+
+cc_library(
+    name = "image_clone_calculator",
+    srcs = ["image_clone_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":image_clone_calculator_cc_proto",
+        "//mediapipe/framework/api2:node",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/formats:image",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+    ] + select({
+        "//mediapipe/gpu:disable_gpu": [],
+        "//conditions:default": [
+            "//mediapipe/gpu:gl_calculator_helper",
+        ],
+    }),
+    alwayslink = 1,
+)
+
 cc_library(
     name = "image_properties_calculator",
     srcs = ["image_properties_calculator.cc"],
@@ -572,3 +612,187 @@ cc_test(
         "//mediapipe/framework/port:parse_text_proto",
     ],
 )
+
+cc_library(
+    name = "segmentation_smoothing_calculator",
+    srcs = ["segmentation_smoothing_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":segmentation_smoothing_calculator_cc_proto",
+        "//mediapipe/framework:calculator_options_cc_proto",
+        "//mediapipe/framework/formats:image_format_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/formats:image_frame",
+        "//mediapipe/framework/formats:image_frame_opencv",
+        "//mediapipe/framework/formats:image",
+        "//mediapipe/framework/formats:image_opencv",
+        "//mediapipe/framework/port:logging",
+        "//mediapipe/framework/port:opencv_core",
+        "//mediapipe/framework/port:status",
+        "//mediapipe/framework/port:vector",
+    ] + select({
+        "//mediapipe/gpu:disable_gpu": [],
+        "//conditions:default": [
+            "//mediapipe/gpu:gl_calculator_helper",
+            "//mediapipe/gpu:gl_simple_shaders",
+            "//mediapipe/gpu:gl_quad_renderer",
+            "//mediapipe/gpu:shader_util",
+        ],
+    }),
+    alwayslink = 1,
+)
+
+cc_test(
+    name = "segmentation_smoothing_calculator_test",
+    srcs = ["segmentation_smoothing_calculator_test.cc"],
+    deps = [
+        ":image_clone_calculator",
+        ":image_clone_calculator_cc_proto",
+        ":segmentation_smoothing_calculator",
+        ":segmentation_smoothing_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework:calculator_runner",
+        "//mediapipe/framework/deps:file_path",
+        "//mediapipe/framework/formats:image_frame",
+        "//mediapipe/framework/formats:image_opencv",
+        "//mediapipe/framework/port:gtest_main",
+        "//mediapipe/framework/port:opencv_imgcodecs",
+        "//mediapipe/framework/port:opencv_imgproc",
+        "//mediapipe/framework/port:parse_text_proto",
+    ],
+)
+
+cc_library(
+    name = "affine_transformation",
+    hdrs = ["affine_transformation.h"],
+    deps = ["@com_google_absl//absl/status:statusor"],
+)
+
+cc_library(
+    name = "affine_transformation_runner_gl",
+    srcs = ["affine_transformation_runner_gl.cc"],
+    hdrs = ["affine_transformation_runner_gl.h"],
+    deps = [
+        ":affine_transformation",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/gpu:gl_calculator_helper",
+        "//mediapipe/gpu:gl_simple_shaders",
+        "//mediapipe/gpu:gpu_buffer",
+        "//mediapipe/gpu:gpu_origin_cc_proto",
+        "//mediapipe/gpu:shader_util",
+        "@com_google_absl//absl/memory",
+        "@com_google_absl//absl/status",
+        "@com_google_absl//absl/status:statusor",
+        "@eigen_archive//:eigen3",
+    ],
+)
+
+cc_library(
+    name = "affine_transformation_runner_opencv",
+    srcs = ["affine_transformation_runner_opencv.cc"],
+    hdrs = ["affine_transformation_runner_opencv.h"],
+    deps = [
+        ":affine_transformation",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/formats:image_frame",
+        "//mediapipe/framework/formats:image_frame_opencv",
+        "//mediapipe/framework/port:opencv_core",
+        "//mediapipe/framework/port:opencv_imgproc",
+        "//mediapipe/framework/port:ret_check",
+        "@com_google_absl//absl/memory",
+        "@com_google_absl//absl/status:statusor",
+        "@eigen_archive//:eigen3",
+    ],
+)
+
+mediapipe_proto_library(
+    name = "warp_affine_calculator_proto",
+    srcs = ["warp_affine_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_options_proto",
+        "//mediapipe/framework:calculator_proto",
+        "//mediapipe/gpu:gpu_origin_proto",
+    ],
+)
+
+cc_library(
+    name = "warp_affine_calculator",
+    srcs = ["warp_affine_calculator.cc"],
+    hdrs = ["warp_affine_calculator.h"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":affine_transformation",
+        ":affine_transformation_runner_opencv",
+        ":warp_affine_calculator_cc_proto",
+        "@com_google_absl//absl/status",
+        "@com_google_absl//absl/status:statusor",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/api2:node",
+        "//mediapipe/framework/api2:port",
+        "//mediapipe/framework/formats:image",
+        "//mediapipe/framework/formats:image_frame",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+    ] + select({
+        "//mediapipe/gpu:disable_gpu": [],
+        "//conditions:default": [
+            "//mediapipe/gpu:gl_calculator_helper",
+            "//mediapipe/gpu:gpu_buffer",
+            ":affine_transformation_runner_gl",
+        ],
+    }),
+    alwayslink = 1,
+)
+
+cc_test(
+    name = "warp_affine_calculator_test",
+    srcs = ["warp_affine_calculator_test.cc"],
+    data = [
+        "//mediapipe/calculators/tensor:testdata/image_to_tensor/input.jpg",
+        "//mediapipe/calculators/tensor:testdata/image_to_tensor/large_sub_rect.png",
+        "//mediapipe/calculators/tensor:testdata/image_to_tensor/large_sub_rect_border_zero.png",
+        "//mediapipe/calculators/tensor:testdata/image_to_tensor/large_sub_rect_keep_aspect.png",
+        "//mediapipe/calculators/tensor:testdata/image_to_tensor/large_sub_rect_keep_aspect_border_zero.png",
+        "//mediapipe/calculators/tensor:testdata/image_to_tensor/large_sub_rect_keep_aspect_with_rotation.png",
+        "//mediapipe/calculators/tensor:testdata/image_to_tensor/large_sub_rect_keep_aspect_with_rotation_border_zero.png",
+        "//mediapipe/calculators/tensor:testdata/image_to_tensor/medium_sub_rect_keep_aspect.png",
+        "//mediapipe/calculators/tensor:testdata/image_to_tensor/medium_sub_rect_keep_aspect_border_zero.png",
+        "//mediapipe/calculators/tensor:testdata/image_to_tensor/medium_sub_rect_keep_aspect_with_rotation.png",
+        "//mediapipe/calculators/tensor:testdata/image_to_tensor/medium_sub_rect_keep_aspect_with_rotation_border_zero.png",
+        "//mediapipe/calculators/tensor:testdata/image_to_tensor/medium_sub_rect_with_rotation.png",
+        "//mediapipe/calculators/tensor:testdata/image_to_tensor/medium_sub_rect_with_rotation_border_zero.png",
+        "//mediapipe/calculators/tensor:testdata/image_to_tensor/noop_except_range.png",
+    ],
+    tags = ["desktop_only_test"],
+    deps = [
+        ":affine_transformation",
+        ":warp_affine_calculator",
+        "//mediapipe/calculators/image:image_transformation_calculator",
+        "//mediapipe/calculators/tensor:image_to_tensor_converter",
+        "//mediapipe/calculators/tensor:image_to_tensor_utils",
+        "//mediapipe/calculators/util:from_image_calculator",
+        "//mediapipe/calculators/util:to_image_calculator",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework:calculator_runner",
+        "//mediapipe/framework/deps:file_path",
+        "//mediapipe/framework/formats:image",
+        "//mediapipe/framework/formats:image_format_cc_proto",
+        "//mediapipe/framework/formats:image_frame",
+        "//mediapipe/framework/formats:image_frame_opencv",
+        "//mediapipe/framework/formats:rect_cc_proto",
+        "//mediapipe/framework/formats:tensor",
+        "//mediapipe/framework/port:gtest_main",
+        "//mediapipe/framework/port:integral_types",
+        "//mediapipe/framework/port:opencv_core",
+        "//mediapipe/framework/port:opencv_imgcodecs",
+        "//mediapipe/framework/port:opencv_imgproc",
+        "//mediapipe/framework/port:parse_text_proto",
+        "//mediapipe/gpu:gpu_buffer_to_image_frame_calculator",
+        "//mediapipe/gpu:image_frame_to_gpu_buffer_calculator",
+        "@com_google_absl//absl/flags:flag",
+        "@com_google_absl//absl/memory",
+        "@com_google_absl//absl/strings",
+    ],
+)

mediapipe/calculators/image/affine_transformation.h

@@ -0,0 +1,55 @@
+// 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.
+
+#ifndef MEDIAPIPE_CALCULATORS_IMAGE_AFFINE_TRANSFORMATION_H_
+#define MEDIAPIPE_CALCULATORS_IMAGE_AFFINE_TRANSFORMATION_H_
+
+#include <array>
+
+#include "absl/status/statusor.h"
+
+namespace mediapipe {
+
+class AffineTransformation {
+ public:
+  // Pixel extrapolation method.
+  // When converting image to tensor it may happen that tensor needs to read
+  // pixels outside image boundaries. Border mode helps to specify how such
+  // pixels will be calculated.
+  enum class BorderMode { kZero, kReplicate };
+
+  struct Size {
+    int width;
+    int height;
+  };
+
+  template <typename InputT, typename OutputT>
+  class Runner {
+   public:
+    virtual ~Runner() = default;
+
+    // Transforms input into output using @matrix as following:
+    //   output(x, y) = input(matrix[0] * x + matrix[1] * y + matrix[3],
+    //                        matrix[4] * x + matrix[5] * y + matrix[7])
+    // where x and y ranges are defined by @output_size.
+    virtual absl::StatusOr<OutputT> Run(const InputT& input,
+                                        const std::array<float, 16>& matrix,
+                                        const Size& output_size,
+                                        BorderMode border_mode) = 0;
+  };
+};
+
+}  // namespace mediapipe
+
+#endif  // MEDIAPIPE_CALCULATORS_IMAGE_AFFINE_TRANSFORMATION_H_

mediapipe/calculators/image/affine_transformation_runner_gl.cc

@@ -0,0 +1,354 @@
+// 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.
+
+#include "mediapipe/calculators/image/affine_transformation_runner_gl.h"
+
+#include <memory>
+#include <optional>
+
+#include "Eigen/Core"
+#include "Eigen/Geometry"
+#include "Eigen/LU"
+#include "absl/memory/memory.h"
+#include "absl/status/status.h"
+#include "absl/status/statusor.h"
+#include "mediapipe/calculators/image/affine_transformation.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/gpu/gl_calculator_helper.h"
+#include "mediapipe/gpu/gl_simple_shaders.h"
+#include "mediapipe/gpu/gpu_buffer.h"
+#include "mediapipe/gpu/gpu_origin.pb.h"
+#include "mediapipe/gpu/shader_util.h"
+
+namespace mediapipe {
+
+namespace {
+
+using mediapipe::GlCalculatorHelper;
+using mediapipe::GlhCreateProgram;
+using mediapipe::GlTexture;
+using mediapipe::GpuBuffer;
+using mediapipe::GpuOrigin;
+
+bool IsMatrixVerticalFlipNeeded(GpuOrigin::Mode gpu_origin) {
+  switch (gpu_origin) {
+    case GpuOrigin::DEFAULT:
+    case GpuOrigin::CONVENTIONAL:
+#ifdef __APPLE__
+      return false;
+#else
+      return true;
+#endif  //  __APPLE__
+    case GpuOrigin::TOP_LEFT:
+      return false;
+  }
+}
+
+#ifdef __APPLE__
+#define GL_CLAMP_TO_BORDER_MAY_BE_SUPPORTED 0
+#else
+#define GL_CLAMP_TO_BORDER_MAY_BE_SUPPORTED 1
+#endif  //  __APPLE__
+
+bool IsGlClampToBorderSupported(const mediapipe::GlContext& gl_context) {
+  return gl_context.gl_major_version() > 3 ||
+         (gl_context.gl_major_version() == 3 &&
+          gl_context.gl_minor_version() >= 2);
+}
+
+constexpr int kAttribVertex = 0;
+constexpr int kAttribTexturePosition = 1;
+constexpr int kNumAttributes = 2;
+
+class GlTextureWarpAffineRunner
+    : public AffineTransformation::Runner<GpuBuffer,
+                                          std::unique_ptr<GpuBuffer>> {
+ public:
+  GlTextureWarpAffineRunner(std::shared_ptr<GlCalculatorHelper> gl_helper,
+                            GpuOrigin::Mode gpu_origin)
+      : gl_helper_(gl_helper), gpu_origin_(gpu_origin) {}
+  absl::Status Init() {
+    return gl_helper_->RunInGlContext([this]() -> absl::Status {
+      const GLint attr_location[kNumAttributes] = {
+          kAttribVertex,
+          kAttribTexturePosition,
+      };
+      const GLchar* attr_name[kNumAttributes] = {
+          "position",
+          "texture_coordinate",
+      };
+
+      constexpr GLchar kVertShader[] = R"(
+            in vec4 position;
+            in mediump vec4 texture_coordinate;
+            out mediump vec2 sample_coordinate;
+            uniform mat4 transform_matrix;
+
+            void main() {
+              gl_Position = position;
+              vec4 tc = transform_matrix * texture_coordinate;
+              sample_coordinate = tc.xy;
+            }
+          )";
+
+      constexpr GLchar kFragShader[] = R"(
+            DEFAULT_PRECISION(mediump, float)
+            in vec2 sample_coordinate;
+            uniform sampler2D input_texture;
+
+          #ifdef GL_ES
+            #define fragColor gl_FragColor
+          #else
+            out vec4 fragColor;
+          #endif  // defined(GL_ES);
+
+            void main() {
+              vec4 color = texture2D(input_texture, sample_coordinate);
+          #ifdef CUSTOM_ZERO_BORDER_MODE
+              float out_of_bounds =
+                  float(sample_coordinate.x < 0.0 || sample_coordinate.x > 1.0 ||
+                        sample_coordinate.y < 0.0 || sample_coordinate.y > 1.0);
+              color = mix(color, vec4(0.0, 0.0, 0.0, 0.0), out_of_bounds);
+          #endif  // defined(CUSTOM_ZERO_BORDER_MODE)
+              fragColor = color;
+            }
+          )";
+
+      // Create program and set parameters.
+      auto create_fn = [&](const std::string& vs,
+                           const std::string& fs) -> absl::StatusOr<Program> {
+        GLuint program = 0;
+        GlhCreateProgram(vs.c_str(), fs.c_str(), kNumAttributes, &attr_name[0],
+                         attr_location, &program);
+
+        RET_CHECK(program) << "Problem initializing warp affine program.";
+        glUseProgram(program);
+        glUniform1i(glGetUniformLocation(program, "input_texture"), 1);
+        GLint matrix_id = glGetUniformLocation(program, "transform_matrix");
+        return Program{.id = program, .matrix_id = matrix_id};
+      };
+
+      const std::string vert_src =
+          absl::StrCat(mediapipe::kMediaPipeVertexShaderPreamble, kVertShader);
+
+      const std::string frag_src = absl::StrCat(
+          mediapipe::kMediaPipeFragmentShaderPreamble, kFragShader);
+
+      ASSIGN_OR_RETURN(program_, create_fn(vert_src, frag_src));
+
+      auto create_custom_zero_fn = [&]() -> absl::StatusOr<Program> {
+        std::string custom_zero_border_mode_def = R"(
+          #define CUSTOM_ZERO_BORDER_MODE
+        )";
+        const std::string frag_custom_zero_src =
+            absl::StrCat(mediapipe::kMediaPipeFragmentShaderPreamble,
+                         custom_zero_border_mode_def, kFragShader);
+        return create_fn(vert_src, frag_custom_zero_src);
+      };
+#if GL_CLAMP_TO_BORDER_MAY_BE_SUPPORTED
+      if (!IsGlClampToBorderSupported(gl_helper_->GetGlContext())) {
+        ASSIGN_OR_RETURN(program_custom_zero_, create_custom_zero_fn());
+      }
+#else
+      ASSIGN_OR_RETURN(program_custom_zero_, create_custom_zero_fn());
+#endif  // GL_CLAMP_TO_BORDER_MAY_BE_SUPPORTED
+
+      glGenFramebuffers(1, &framebuffer_);
+
+      // vertex storage
+      glGenBuffers(2, vbo_);
+      glGenVertexArrays(1, &vao_);
+
+      // vbo 0
+      glBindBuffer(GL_ARRAY_BUFFER, vbo_[0]);
+      glBufferData(GL_ARRAY_BUFFER, sizeof(mediapipe::kBasicSquareVertices),
+                   mediapipe::kBasicSquareVertices, GL_STATIC_DRAW);
+
+      // vbo 1
+      glBindBuffer(GL_ARRAY_BUFFER, vbo_[1]);
+      glBufferData(GL_ARRAY_BUFFER, sizeof(mediapipe::kBasicTextureVertices),
+                   mediapipe::kBasicTextureVertices, GL_STATIC_DRAW);
+
+      glBindBuffer(GL_ARRAY_BUFFER, 0);
+
+      return absl::OkStatus();
+    });
+  }
+
+  absl::StatusOr<std::unique_ptr<GpuBuffer>> Run(
+      const GpuBuffer& input, const std::array<float, 16>& matrix,
+      const AffineTransformation::Size& size,
+      AffineTransformation::BorderMode border_mode) override {
+    std::unique_ptr<GpuBuffer> gpu_buffer;
+    MP_RETURN_IF_ERROR(
+        gl_helper_->RunInGlContext([this, &input, &matrix, &size, &border_mode,
+                                    &gpu_buffer]() -> absl::Status {
+          auto input_texture = gl_helper_->CreateSourceTexture(input);
+          auto output_texture = gl_helper_->CreateDestinationTexture(
+              size.width, size.height, input.format());
+
+          MP_RETURN_IF_ERROR(
+              RunInternal(input_texture, matrix, border_mode, &output_texture));
+          gpu_buffer = output_texture.GetFrame<GpuBuffer>();
+          return absl::OkStatus();
+        }));
+
+    return gpu_buffer;
+  }
+
+  absl::Status RunInternal(const GlTexture& texture,
+                           const std::array<float, 16>& matrix,
+                           AffineTransformation::BorderMode border_mode,
+                           GlTexture* output) {
+    glDisable(GL_DEPTH_TEST);
+    glBindFramebuffer(GL_FRAMEBUFFER, framebuffer_);
+    glViewport(0, 0, output->width(), output->height());
+
+    glActiveTexture(GL_TEXTURE0);
+    glBindTexture(GL_TEXTURE_2D, output->name());
+    glFramebufferTexture2D(GL_FRAMEBUFFER, GL_COLOR_ATTACHMENT0, GL_TEXTURE_2D,
+                           output->name(), 0);
+
+    glActiveTexture(GL_TEXTURE1);
+    glBindTexture(texture.target(), texture.name());
+
+    // a) Filtering.
+    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR);
+    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR);
+
+    // b) Clamping.
+    std::optional<Program> program = program_;
+    switch (border_mode) {
+      case AffineTransformation::BorderMode::kReplicate: {
+        glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE);
+        glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_CLAMP_TO_EDGE);
+        break;
+      }
+      case AffineTransformation::BorderMode::kZero: {
+#if GL_CLAMP_TO_BORDER_MAY_BE_SUPPORTED
+        if (program_custom_zero_) {
+          program = program_custom_zero_;
+        } else {
+          glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_BORDER);
+          glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_CLAMP_TO_BORDER);
+          glTexParameterfv(GL_TEXTURE_2D, GL_TEXTURE_BORDER_COLOR,
+                           std::array<float, 4>{0.0f, 0.0f, 0.0f, 0.0f}.data());
+        }
+#else
+        RET_CHECK(program_custom_zero_)
+            << "Program must have been initialized.";
+        program = program_custom_zero_;
+#endif  // GL_CLAMP_TO_BORDER_MAY_BE_SUPPORTED
+        break;
+      }
+    }
+    glUseProgram(program->id);
+
+    Eigen::Matrix<float, 4, 4, Eigen::RowMajor> eigen_mat(matrix.data());
+    if (IsMatrixVerticalFlipNeeded(gpu_origin_)) {
+      // @matrix describes affine transformation in terms of TOP LEFT origin, so
+      // in some cases/on some platforms an extra flipping should be done before
+      // and after.
+      const Eigen::Matrix<float, 4, 4, Eigen::RowMajor> flip_y(
+          {{1.0f, 0.0f, 0.0f, 0.0f},
+           {0.0f, -1.0f, 0.0f, 1.0f},
+           {0.0f, 0.0f, 1.0f, 0.0f},
+           {0.0f, 0.0f, 0.0f, 1.0f}});
+      eigen_mat = flip_y * eigen_mat * flip_y;
+    }
+
+    // If GL context is ES2, then GL_FALSE must be used for 'transpose'
+    // GLboolean in glUniformMatrix4fv, or else INVALID_VALUE error is reported.
+    // Hence, transposing the matrix and always passing transposed.
+    eigen_mat.transposeInPlace();
+    glUniformMatrix4fv(program->matrix_id, 1, GL_FALSE, eigen_mat.data());
+
+    // vao
+    glBindVertexArray(vao_);
+
+    // vbo 0
+    glBindBuffer(GL_ARRAY_BUFFER, vbo_[0]);
+    glEnableVertexAttribArray(kAttribVertex);
+    glVertexAttribPointer(kAttribVertex, 2, GL_FLOAT, 0, 0, nullptr);
+
+    // vbo 1
+    glBindBuffer(GL_ARRAY_BUFFER, vbo_[1]);
+    glEnableVertexAttribArray(kAttribTexturePosition);
+    glVertexAttribPointer(kAttribTexturePosition, 2, GL_FLOAT, 0, 0, nullptr);
+
+    // draw
+    glDrawArrays(GL_TRIANGLE_STRIP, 0, 4);
+
+    // Resetting to MediaPipe texture param defaults.
+    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR);
+    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR);
+    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE);
+    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_CLAMP_TO_EDGE);
+
+    glDisableVertexAttribArray(kAttribVertex);
+    glDisableVertexAttribArray(kAttribTexturePosition);
+    glBindBuffer(GL_ARRAY_BUFFER, 0);
+    glBindVertexArray(0);
+
+    glActiveTexture(GL_TEXTURE1);
+    glBindTexture(GL_TEXTURE_2D, 0);
+    glActiveTexture(GL_TEXTURE0);
+    glBindTexture(GL_TEXTURE_2D, 0);
+
+    return absl::OkStatus();
+  }
+
+  ~GlTextureWarpAffineRunner() override {
+    gl_helper_->RunInGlContext([this]() {
+      // Release OpenGL resources.
+      if (framebuffer_ != 0) glDeleteFramebuffers(1, &framebuffer_);
+      if (program_.id != 0) glDeleteProgram(program_.id);
+      if (program_custom_zero_ && program_custom_zero_->id != 0) {
+        glDeleteProgram(program_custom_zero_->id);
+      }
+      if (vao_ != 0) glDeleteVertexArrays(1, &vao_);
+      glDeleteBuffers(2, vbo_);
+    });
+  }
+
+ private:
+  struct Program {
+    GLuint id;
+    GLint matrix_id;
+  };
+  std::shared_ptr<GlCalculatorHelper> gl_helper_;
+  GpuOrigin::Mode gpu_origin_;
+  GLuint vao_ = 0;
+  GLuint vbo_[2] = {0, 0};
+  Program program_;
+  std::optional<Program> program_custom_zero_;
+  GLuint framebuffer_ = 0;
+};
+
+#undef GL_CLAMP_TO_BORDER_MAY_BE_SUPPORTED
+
+}  // namespace
+
+absl::StatusOr<std::unique_ptr<
+    AffineTransformation::Runner<GpuBuffer, std::unique_ptr<GpuBuffer>>>>
+CreateAffineTransformationGlRunner(
+    std::shared_ptr<GlCalculatorHelper> gl_helper, GpuOrigin::Mode gpu_origin) {
+  auto runner =
+      absl::make_unique<GlTextureWarpAffineRunner>(gl_helper, gpu_origin);
+  MP_RETURN_IF_ERROR(runner->Init());
+  return runner;
+}
+
+}  // namespace mediapipe

mediapipe/calculators/image/affine_transformation_runner_gl.h

@@ -0,0 +1,36 @@
+// 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.
+
+#ifndef MEDIAPIPE_CALCULATORS_IMAGE_AFFINE_TRANSFORMATION_RUNNER_GL_H_
+#define MEDIAPIPE_CALCULATORS_IMAGE_AFFINE_TRANSFORMATION_RUNNER_GL_H_
+
+#include <memory>
+
+#include "absl/status/statusor.h"
+#include "mediapipe/calculators/image/affine_transformation.h"
+#include "mediapipe/gpu/gl_calculator_helper.h"
+#include "mediapipe/gpu/gpu_buffer.h"
+#include "mediapipe/gpu/gpu_origin.pb.h"
+
+namespace mediapipe {
+
+absl::StatusOr<std::unique_ptr<AffineTransformation::Runner<
+    mediapipe::GpuBuffer, std::unique_ptr<mediapipe::GpuBuffer>>>>
+CreateAffineTransformationGlRunner(
+    std::shared_ptr<mediapipe::GlCalculatorHelper> gl_helper,
+    mediapipe::GpuOrigin::Mode gpu_origin);
+
+}  // namespace mediapipe
+
+#endif  // MEDIAPIPE_CALCULATORS_IMAGE_AFFINE_TRANSFORMATION_RUNNER_GL_H_

mediapipe/calculators/image/affine_transformation_runner_opencv.cc

@@ -0,0 +1,160 @@
+// 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.
+
+#include "mediapipe/calculators/image/affine_transformation_runner_opencv.h"
+
+#include <memory>
+
+#include "absl/memory/memory.h"
+#include "absl/status/statusor.h"
+#include "mediapipe/calculators/image/affine_transformation.h"
+#include "mediapipe/framework/formats/image_frame.h"
+#include "mediapipe/framework/formats/image_frame_opencv.h"
+#include "mediapipe/framework/port/opencv_core_inc.h"
+#include "mediapipe/framework/port/opencv_imgproc_inc.h"
+#include "mediapipe/framework/port/ret_check.h"
+
+namespace mediapipe {
+
+namespace {
+
+cv::BorderTypes GetBorderModeForOpenCv(
+    AffineTransformation::BorderMode border_mode) {
+  switch (border_mode) {
+    case AffineTransformation::BorderMode::kZero:
+      return cv::BORDER_CONSTANT;
+    case AffineTransformation::BorderMode::kReplicate:
+      return cv::BORDER_REPLICATE;
+  }
+}
+
+class OpenCvRunner
+    : public AffineTransformation::Runner<ImageFrame, ImageFrame> {
+ public:
+  absl::StatusOr<ImageFrame> Run(
+      const ImageFrame& input, const std::array<float, 16>& matrix,
+      const AffineTransformation::Size& size,
+      AffineTransformation::BorderMode border_mode) override {
+    // OpenCV warpAffine works in absolute coordinates, so the transfom (which
+    // accepts and produces relative coordinates) should be adjusted to first
+    // normalize coordinates and then scale them.
+    // clang-format off
+    cv::Matx44f normalize_dst_coordinate({
+      1.0f / size.width, 0.0f,               0.0f, 0.0f,
+      0.0f,              1.0f / size.height, 0.0f, 0.0f,
+      0.0f,              0.0f,               1.0f, 0.0f,
+      0.0f,              0.0f,               0.0f, 1.0f});
+    cv::Matx44f scale_src_coordinate({
+      1.0f * input.Width(), 0.0f,                  0.0f, 0.0f,
+      0.0f,                 1.0f * input.Height(), 0.0f, 0.0f,
+      0.0f,                 0.0f,                  1.0f, 0.0f,
+      0.0f,                 0.0f,                  0.0f, 1.0f});
+    // clang-format on
+    cv::Matx44f adjust_dst_coordinate;
+    cv::Matx44f adjust_src_coordinate;
+    // TODO: update to always use accurate implementation.
+    constexpr bool kOpenCvCompatibility = true;
+    if (kOpenCvCompatibility) {
+      adjust_dst_coordinate = normalize_dst_coordinate;
+      adjust_src_coordinate = scale_src_coordinate;
+    } else {
+      // To do an accurate affine image transformation and make "on-cpu" and
+      // "on-gpu" calculations aligned - extra offset is required to select
+      // correct pixels.
+      //
+      // Each destination pixel corresponds to some pixels region from source
+      // image.(In case of downscaling there can be more than one pixel.) The
+      // offset for x and y is calculated in the way, so pixel in the middle of
+      // the region is selected.
+      //
+      // For simplicity sake, let's consider downscaling from 100x50 to 10x10
+      // without a rotation:
+      // 1. Each destination pixel corresponds to 10x5 region
+      //    X range: [0, .. , 9]
+      //    Y range: [0, .. , 4]
+      // 2. Considering we have __discrete__ pixels, the center of the region is
+      //    between (4, 2) and (5, 2) pixels, let's assume it's a "pixel"
+      //    (4.5, 2).
+      // 3. When using the above as an offset for every pixel select while
+      //    downscaling, resulting pixels are:
+      //      (4.5, 2), (14.5, 2), .. , (94.5, 2)
+      //      (4.5, 7), (14.5, 7), .. , (94.5, 7)
+      //      ..
+      //      (4.5, 47), (14.5, 47), .., (94.5, 47)
+      //    instead of:
+      //      (0, 0), (10, 0), .. , (90, 0)
+      //      (0, 5), (10, 7), .. , (90, 5)
+      //      ..
+      //      (0, 45), (10, 45), .., (90, 45)
+      //    The latter looks shifted.
+      //
+      // Offsets are needed, so that __discrete__ pixel at (0, 0) corresponds to
+      // the same pixel as would __non discrete__ pixel at (0.5, 0.5). Hence,
+      // transformation matrix should shift coordinates by (0.5, 0.5) as the
+      // very first step.
+      //
+      // Due to the above shift, transformed coordinates would be valid for
+      // float coordinates where pixel (0, 0) spans [0.0, 1.0) x [0.0, 1.0).
+      // T0 make it valid for __discrete__ pixels, transformation matrix should
+      // shift coordinate by (-0.5f, -0.5f) as the very last step. (E.g. if we
+      // get (0.5f, 0.5f), then it's (0, 0) __discrete__ pixel.)
+      // clang-format off
+      cv::Matx44f shift_dst({1.0f, 0.0f, 0.0f, 0.5f,
+                              0.0f, 1.0f, 0.0f, 0.5f,
+                              0.0f, 0.0f, 1.0f, 0.0f,
+                              0.0f, 0.0f, 0.0f, 1.0f});
+      cv::Matx44f shift_src({1.0f, 0.0f, 0.0f, -0.5f,
+                              0.0f, 1.0f, 0.0f, -0.5f,
+                              0.0f, 0.0f, 1.0f,  0.0f,
+                              0.0f, 0.0f, 0.0f,  1.0f});
+      // clang-format on
+      adjust_dst_coordinate = normalize_dst_coordinate * shift_dst;
+      adjust_src_coordinate = shift_src * scale_src_coordinate;
+    }
+
+    cv::Matx44f transform(matrix.data());
+    cv::Matx44f transform_absolute =
+        adjust_src_coordinate * transform * adjust_dst_coordinate;
+
+    cv::Mat in_mat = formats::MatView(&input);
+
+    cv::Mat cv_affine_transform(2, 3, CV_32F);
+    cv_affine_transform.at<float>(0, 0) = transform_absolute.val[0];
+    cv_affine_transform.at<float>(0, 1) = transform_absolute.val[1];
+    cv_affine_transform.at<float>(0, 2) = transform_absolute.val[3];
+    cv_affine_transform.at<float>(1, 0) = transform_absolute.val[4];
+    cv_affine_transform.at<float>(1, 1) = transform_absolute.val[5];
+    cv_affine_transform.at<float>(1, 2) = transform_absolute.val[7];
+
+    ImageFrame out_image(input.Format(), size.width, size.height);
+    cv::Mat out_mat = formats::MatView(&out_image);
+
+    cv::warpAffine(in_mat, out_mat, cv_affine_transform,
+                   cv::Size(out_mat.cols, out_mat.rows),
+                   /*flags=*/cv::INTER_LINEAR | cv::WARP_INVERSE_MAP,
+                   GetBorderModeForOpenCv(border_mode));
+
+    return out_image;
+  }
+};
+
+}  // namespace
+
+absl::StatusOr<
+    std::unique_ptr<AffineTransformation::Runner<ImageFrame, ImageFrame>>>
+CreateAffineTransformationOpenCvRunner() {
+  return absl::make_unique<OpenCvRunner>();
+}
+
+}  // namespace mediapipe

mediapipe/calculators/image/affine_transformation_runner_opencv.h

@@ -0,0 +1,32 @@
+// 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.
+
+#ifndef MEDIAPIPE_CALCULATORS_IMAGE_AFFINE_TRANSFORMATION_RUNNER_OPENCV_H_
+#define MEDIAPIPE_CALCULATORS_IMAGE_AFFINE_TRANSFORMATION_RUNNER_OPENCV_H_
+
+#include <memory>
+
+#include "absl/status/statusor.h"
+#include "mediapipe/calculators/image/affine_transformation.h"
+#include "mediapipe/framework/formats/image_frame.h"
+
+namespace mediapipe {
+
+absl::StatusOr<
+    std::unique_ptr<AffineTransformation::Runner<ImageFrame, ImageFrame>>>
+CreateAffineTransformationOpenCvRunner();
+
+}  // namespace mediapipe
+
+#endif  // MEDIAPIPE_CALCULATORS_IMAGE_AFFINE_TRANSFORMATION_RUNNER_OPENCV_H_