Project import generated by Copybara.

GitOrigin-RevId: 0f2489d226f1e2a5d718a8b9efe5e8198ba4ab3b
2022-06-29 18:45:02 +00:00 · 2022-06-28 12:11:05 +00:00 · 2022-05-06 17:05:30 -07:00 · 2022-05-05 19:57:20 +00:00 · 2022-03-22 17:48:17 -07:00 · 2022-03-21 12:12:39 -07:00
918 changed files with 47142 additions and 8604 deletions
@@ -32,6 +32,9 @@ build:macos --copt=-w
 # Sets the default Apple platform to macOS.
 build --apple_platform_type=macos
 # Compile ObjC++ files with C++17
 build --per_file_copt=.*\.mm\$@-std=c++17
 # Allow debugging with XCODE
 build --apple_generate_dsym
@@ -58,6 +61,7 @@ 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
@@ -87,6 +91,10 @@ build:darwin_x86_64 --apple_platform_type=macos
 build:darwin_x86_64 --macos_minimum_os=10.12
 build:darwin_x86_64 --cpu=darwin_x86_64
 build:darwin_arm64 --apple_platform_type=macos
 build:darwin_arm64 --macos_minimum_os=10.16
 build:darwin_arm64 --cpu=darwin_arm64
 # This bazelrc file is meant to be written by a setup script.
 try-import %workspace%/.configure.bazelrc
@@ -1 +1 @@
-3.7.2
+5.2.0
@@ -1,3 +1,9 @@
 ---
 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)
@@ -1,3 +1,9 @@
 ---
 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)
@@ -1,3 +1,9 @@
 ---
 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:
@@ -1,3 +1,9 @@
 ---
 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)
@@ -1,3 +1,9 @@
 ---
 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)
@@ -1,8 +1,12 @@
 ---
 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.
@@ -15,4 +15,4 @@
 # A list of assignees
 assignees:
-   - sgowroji
+   - sureshdagooglecom
@@ -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.
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
-FROM ubuntu:18.04
+FROM ubuntu:20.04
 MAINTAINER <[email protected]>
@@ -42,6 +42,8 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
        software-properties-common && \
    add-apt-repository -y ppa:openjdk-r/ppa && \
    apt-get update && apt-get install -y openjdk-8-jdk && \
    apt-get install -y mesa-common-dev libegl1-mesa-dev libgles2-mesa-dev && \
    apt-get install -y mesa-utils && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*
@@ -50,13 +52,13 @@ RUN pip3 install --upgrade setuptools
 RUN pip3 install wheel
 RUN pip3 install future
 RUN pip3 install six==1.14.0
-RUN pip3 install tensorflow==1.14.0
+RUN pip3 install tensorflow==2.2.0
 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=5.2.0
 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" && \
@@ -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
@@ -45,7 +45,7 @@ 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)                    |                                                                 |                                                         | ✅                                                       |                                                               |                                                               |
@@ -55,46 +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)
+To start using MediaPipe
-MediaPipe and
+[solutions](https://google.github.io/mediapipe/solutions/solutions) with only a few
-[build example applications](https://google.github.io/mediapipe/getting_started/building_examples),
+lines code, see example code and demos in
-and start exploring our ready-to-use
+[MediaPipe in Python](https://google.github.io/mediapipe/getting_started/python) and
-[solutions](https://google.github.io/mediapipe/solutions/solutions) that you can
+[MediaPipe in JavaScript](https://google.github.io/mediapipe/getting_started/javascript).
-further extend and customize.
+
 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
@@ -103,6 +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)
@@ -153,8 +136,8 @@ run code search using
 ## Community
-*   [Awesome MediaPipe](https://mediapipe.org) - A curated list of awesome
+*   [Awesome MediaPipe](https://mediapipe.page.link/awesome-mediapipe) - A
-    MediaPipe related frameworks, libraries and software
+    curated list of awesome MediaPipe related frameworks, libraries and software
 *   [Slack community](https://mediapipe.page.link/joinslack) for MediaPipe users
 *   [Discuss](https://groups.google.com/forum/#!forum/mediapipe) - General
    community discussion around MediaPipe
@@ -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 +29,15 @@ http_archive(
    patch_args = [
        "-p1",
    ],
-    strip_prefix = "abseil-cpp-20200923",
+    strip_prefix = "abseil-cpp-20210324.2",
-    sha256 = "b3744a4f7a249d5eaf2309daad597631ce77ea62e0fc6abffbab4b4c3dc0fc08"
+    sha256 = "59b862f50e710277f8ede96f083a5bb8d7c9595376146838b9580be90374ee1f"
 )
 http_archive(
    name = "rules_cc",
-    strip_prefix = "rules_cc-master",
+    strip_prefix = "rules_cc-2f8c04c04462ab83c545ab14c0da68c3b4c96191",
-    urls = ["https://github.com/bazelbuild/rules_cc/archive/master.zip"],
+# The commit can be updated if the build passes. Last updated 6/23/22.
    urls = ["https://github.com/bazelbuild/rules_cc/archive/2f8c04c04462ab83c545ab14c0da68c3b4c96191.zip"],
 )
 http_archive(
@@ -53,26 +54,20 @@ 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"],
+    urls = ["https://github.com/google/googletest/archive/4ec4cd23f486bf70efcc5d2caa40f24368f752e3.zip"],
-    patches = [
+    strip_prefix = "googletest-4ec4cd23f486bf70efcc5d2caa40f24368f752e3",
-        # fix for https://github.com/google/googletest/issues/2817
+    sha256 = "de682ea824bfffba05b4e33b67431c247397d6175962534305136aa06f92e049",
        "@//third_party:com_google_googletest_9d580ea80592189e6d44fa35bcf9cdea8bf620d6.diff"
    ],
    patch_args = [
        "-p1",
    ],
    strip_prefix = "googletest-aee0f9d9b5b87796ee8a0ab26b7587ec30e8858e",
    sha256 = "04a1751f94244307cebe695a69cc945f9387a80b0ef1af21394a490697c5c895",
 )
-# Google Benchmark library.
+# Google Benchmark library v1.6.1 released on 2022-01-10.
 http_archive(
    name = "com_google_benchmark",
-    urls = ["https://github.com/google/benchmark/archive/main.zip"],
+    urls = ["https://github.com/google/benchmark/archive/refs/tags/v1.6.1.tar.gz"],
-    strip_prefix = "benchmark-main",
+    strip_prefix = "benchmark-1.6.1",
    sha256 = "6132883bc8c9b0df5375b16ab520fac1a85dc9e4cf5be59480448ece74b278d4",
    build_file = "@//third_party:benchmark.BUILD",
 )
@@ -129,16 +124,16 @@ http_archive(
 # ...but the Java download is currently broken, so we use the "source" download.
 http_archive(
    name = "com_google_protobuf_javalite",
-    sha256 = "a79d19dcdf9139fa4b81206e318e33d245c4c9da1ffed21c87288ed4380426f9",
+    sha256 = "87407cd28e7a9c95d9f61a098a53cf031109d451a7763e7dd1253abf8b4df422",
-    strip_prefix = "protobuf-3.11.4",
+    strip_prefix = "protobuf-3.19.1",
-    urls = ["https://github.com/protocolbuffers/protobuf/archive/v3.11.4.tar.gz"],
+    urls = ["https://github.com/protocolbuffers/protobuf/archive/v3.19.1.tar.gz"],
 )
 http_archive(
    name = "com_google_protobuf",
-    sha256 = "a79d19dcdf9139fa4b81206e318e33d245c4c9da1ffed21c87288ed4380426f9",
+    sha256 = "87407cd28e7a9c95d9f61a098a53cf031109d451a7763e7dd1253abf8b4df422",
-    strip_prefix = "protobuf-3.11.4",
+    strip_prefix = "protobuf-3.19.1",
-    urls = ["https://github.com/protocolbuffers/protobuf/archive/v3.11.4.tar.gz"],
+    urls = ["https://github.com/protocolbuffers/protobuf/archive/v3.19.1.tar.gz"],
    patches = [
        "@//third_party:com_google_protobuf_fixes.diff"
    ],
@@ -161,28 +156,29 @@ 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/70a58c577eaf067748c2ec31bfd0b0a614cffba6.zip",
        "https://github.com/pybind/pybind11/archive/v2.4.3.tar.gz",
    ],
-    sha256 = "1eed57bc6863190e35637290f97a20c81cfe4d9090ac0a24f3bbf08f265eb71d",
+    sha256 = "b971842fab1b5b8f3815a2302331782b7d137fef0e06502422bc4bc360f4956c",
-    strip_prefix = "pybind11-2.4.3",
+    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",
+    strip_prefix = "ceres-solver-123fba61cf2611a3c8bddc9d91416db26b10b558",
-    sha256 = "db12d37b4cebb26353ae5b7746c7985e00877baa8e7b12dc4d3a1512252fff3b"
+    sha256 = "8b7b16ceb363420e0fd499576daf73fa338adb0b1449f58bea7862766baa1ac7"
 )
 http_archive(
@@ -207,7 +203,10 @@ new_local_repository(
 new_local_repository(
    name = "macos_opencv",
    build_file = "@//third_party:opencv_macos.BUILD",
-    path = "/usr/local/opt/opencv@3",
+    # For local MacOS builds, the path should point to an opencv@3 installation.
    # If you edit the path here, you will also need to update the corresponding
    # prefix in "opencv_macos.BUILD".
    path = "/usr/local",
 )
 new_local_repository(
@@ -242,21 +241,26 @@ 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.
+http_archive(
-android_ndk_repository(
+    name = "stblib",
-    name = "androidndk",
+    strip_prefix = "stb-b42009b3b9d4ca35bc703f5310eedc74f584be58",
-)
+    sha256 = "13a99ad430e930907f5611325ec384168a958bf7610e63e60e2fd8e7b7379610",
-
+    urls = ["https://github.com/nothings/stb/archive/b42009b3b9d4ca35bc703f5310eedc74f584be58.tar.gz"],
-android_sdk_repository(
+    build_file = "@//third_party:stblib.BUILD",
-    name = "androidsdk",
+    patches = [
        "@//third_party:stb_image_impl.diff"
    ],
    patch_args = [
        "-p1",
    ],
 )
 # iOS basic build deps.
 http_archive(
    name = "build_bazel_rules_apple",
-    sha256 = "7a7afdd4869bb201c9352eed2daf37294d42b093579b70423490c1b4d4f6ce42",
+    sha256 = "77e8bf6fda706f420a55874ae6ee4df0c9d95da6c7838228b26910fc82eea5a2",
-    url = "https://github.com/bazelbuild/rules_apple/releases/download/0.19.0/rules_apple.0.19.0.tar.gz",
+    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 +286,9 @@ 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/0.10.0/apple_support.0.10.0.tar.gz"
        "https://github.com/bazelbuild/apple_support/releases/download/0.7.1/apple_support.0.7.1.tar.gz",
    ],
 )
@@ -324,7 +327,10 @@ 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 +343,14 @@ 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:1.8.1",
-        "com.google.auto.value:auto-value-annotations:1.6.4",
+        "com.google.auto.value:auto-value-annotations:1.8.1",
-        "com.google.code.findbugs:jsr305:3.0.2",
+        "com.google.code.findbugs:jsr305:latest.release",
-        "com.google.flogger:flogger-system-backend:0.3.1",
+        "com.google.android.datatransport:transport-api:3.0.0",
-        "com.google.flogger:flogger:0.3.1",
+        "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 +378,9 @@ http_archive(
 )
 # Tensorflow repo should always go after the other external dependencies.
-# 2021-05-27
+# 2022-02-15
-_TENSORFLOW_GIT_COMMIT = "d6bfcdb0926173dbb7aa02ceba5aae6250b8aaa6"
+_TENSORFLOW_GIT_COMMIT = "a3419acc751dfc19caf4d34a1594e1f76810ec58"
-_TENSORFLOW_SHA256 = "ec40e1462239d8783d02f76a43412c8f80bac71ea20e41e1b7729b990aad6923"
+_TENSORFLOW_SHA256 = "b95b2a83632d4055742ae1a2dcc96b45da6c12a339462dbc76c8bca505308e3a"
 http_archive(
    name = "org_tensorflow",
    urls = [
@@ -379,7 +388,8 @@ 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 +402,18 @@ 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")
@@ -109,7 +109,7 @@ for app in ${apps}; do
          if [[ ${category} != "shoe" ]]; then
            bazel_flags_extended+=(--define ${category}=true)
          fi
-          bazel "${bazel_flags_extended[@]}"
+          bazelisk "${bazel_flags_extended[@]}"
          cp -f "${bin}" "${apk}"
        fi
        apks+=(${apk})
@@ -120,7 +120,7 @@ for app in ${apps}; do
        if [[ ${app_name} == "templatematchingcpu" ]]; then
          switch_to_opencv_4
        fi
-        bazel "${bazel_flags[@]}"
+        bazelisk "${bazel_flags[@]}"
        cp -f "${bin}" "${apk}"
        if [[ ${app_name} == "templatematchingcpu" ]]; then
          switch_to_opencv_3
@@ -83,7 +83,7 @@ for app in ${apps}; do
      bazel_flags=("${default_bazel_flags[@]}")
      bazel_flags+=(${target})
-      bazel "${bazel_flags[@]}"
+      bazelisk "${bazel_flags[@]}"
      cp -f "${bin_dir}/${app}/"*"_cpu" "${out_dir}"
    fi
    if [[ $build_only == false ]]; then
@@ -71,7 +71,7 @@ for app in ${apps}; do
      bazel_flags+=(--linkopt=-s)
    fi
-    bazel "${bazel_flags[@]}"
+    bazelisk "${bazel_flags[@]}"
    cp -f "${bin_dir}/${app}/"*".ipa" "${out_dir}"
  fi
 done
@@ -262,7 +262,7 @@ specified, appear as literal values in the `node_options` field of the
    output_stream: "TENSORS:main_model_output"
    node_options: {
      [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
-        model_path: "mediapipe/models/active_speaker_detection/audio_visual_model.tflite"
+        model_path: "mediapipe/models/detection_model.tflite"
      }
    }
  }
@@ -272,14 +272,13 @@ The `node_options` field accepts the proto3 syntax.  Alternatively, calculator
 options can be specified in the `options` field using proto2 syntax.
 ```
-  node: {
+  node {
-    calculator: "IntervalFilterCalculator"
+    calculator: "TfLiteInferenceCalculator"
    input_stream: "TENSORS:main_model_input"
    output_stream: "TENSORS:main_model_output"
    node_options: {
-      [type.googleapis.com/mediapipe.IntervalFilterCalculatorOptions] {
+      [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
-        intervals {
+        model_path: "mediapipe/models/detection_model.tflite"
          start_us: 20000
          end_us: 40000
        }
      }
    }
  }
@@ -287,13 +286,26 @@ options can be specified in the `options` field using proto2 syntax.
 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 `IntervalFilterCalculatorOptions`. The calculator will then
+options, such as `PacketClonerCalculatorOptions`. The calculator will then
 read that protobuf message in its `CalculatorBase::Open` method, and possibly
-also in the `CalculatorBase::GetContract` function or its
+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
@@ -169,7 +169,7 @@ behavior depending on resource constraints.
 [`CalculatorBase`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/calculator_base.h
 [`DefaultInputStreamHandler`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/stream_handler/default_input_stream_handler.h
-[`SyncSetInputStreamHandler`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/stream_handler/sync_set_input_stream_handler.h
+[`SyncSetInputStreamHandler`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/stream_handler/sync_set_input_stream_handler.cc
-[`ImmediateInputStreamHandler`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/stream_handler/immediate_input_stream_handler.h
+[`ImmediateInputStreamHandler`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/stream_handler/immediate_input_stream_handler.cc
 [`CalculatorGraphConfig::max_queue_size`]: https://github.com/google/mediapipe/tree/master/mediapipe/framework/calculator.proto
 [`FlowLimiterCalculator`]: https://github.com/google/mediapipe/tree/master/mediapipe/calculators/core/flow_limiter_calculator.cc
@@ -16,18 +16,20 @@ 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
+example apps, start from [Hello World! on Android](./hello_world_android.md).
 incorporate MediaPipe into an existing Android Studio project, see these
 [instructions](./android_archive_library.md) that use Android Archive (AAR) and
 Gradle.
-## 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 SDK release 30.0.0 and above.
 *   Setup Android NDK version between 18 and 21.
 MediaPipe recommends setting up Android SDK and NDK via Android Studio (and see
@@ -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\", api_level=21)" >> 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.
@@ -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
@@ -48,6 +48,16 @@ each project.
    bazel build -c opt --strip=ALWAYS \
        --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
        --fat_apk_cpu=arm64-v8a,armeabi-v7a \
        --legacy_whole_archive=0 \
        --features=-legacy_whole_archive \
        --copt=-fvisibility=hidden \
        --copt=-ffunction-sections \
        --copt=-fdata-sections \
        --copt=-fstack-protector \
        --copt=-Oz \
        --copt=-fomit-frame-pointer \
        --copt=-DABSL_MIN_LOG_LEVEL=2 \
        --linkopt=-Wl,--gc-sections,--strip-all \
        //path/to/the/aar/build/file:aar_name.aar
    ```
@@ -57,6 +67,16 @@ each project.
    bazel build -c opt --strip=ALWAYS \
        --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
        --fat_apk_cpu=arm64-v8a,armeabi-v7a \
        --legacy_whole_archive=0 \
        --features=-legacy_whole_archive \
        --copt=-fvisibility=hidden \
        --copt=-ffunction-sections \
        --copt=-fdata-sections \
        --copt=-fstack-protector \
        --copt=-Oz \
        --copt=-fomit-frame-pointer \
        --copt=-DABSL_MIN_LOG_LEVEL=2 \
        --linkopt=-Wl,--gc-sections,--strip-all \
        //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mediapipe_face_detection.aar
    # It should print:
@@ -92,12 +112,12 @@ 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 +133,18 @@ 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:latest.release'
-        implementation 'com.google.flogger:flogger-system-backend:0.3.1'
+        implementation 'com.google.flogger:flogger-system-backend:latest.release'
-        implementation 'com.google.code.findbugs:jsr305:3.0.2'
+        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-javalite:3.19.1'
        implementation 'com.google.protobuf:protobuf-java:3.11.4'
        // CameraX core library
        def camerax_version = "1.0.0-beta10"
        implementation "androidx.camera:camera-core:$camerax_version"
        implementation "androidx.camera:camera-camera2:$camerax_version"
        implementation "androidx.camera:camera-lifecycle:$camerax_version"
        // AutoValue
-        def auto_value_version = "1.6.4"
+        def auto_value_version = "1.8.1"
        implementation "com.google.auto.value:auto-value-annotations:$auto_value_version"
        annotationProcessor "com.google.auto.value:auto-value:$auto_value_version"
    }
@@ -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.
@@ -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
@@ -59,6 +59,21 @@ OpenGL ES profile shading language version string: OpenGL ES GLSL ES 3.20
 OpenGL ES profile extensions:
 ```
 If you have connected to your computer through SSH and find when you probe for
 GPU information you see the output:
 ```bash
 glxinfo | grep -i opengl
 Error: unable to open display
 ```
 Try re-establishing your SSH connection with the `-X` option and try again. For
 example:
 ```bash
 ssh -X <user>@<host>
 ```
 *Notice the ES 3.20 text above.*
 You need to see ES 3.1 or greater printed in order to perform TFLite inference
@@ -31,8 +31,8 @@ stream on an Android device.
 ## Setup
-1.  Install MediaPipe on your system, see [MediaPipe installation guide] for
+1.  Install MediaPipe on your system, see
-    details.
+    [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
@@ -31,8 +31,8 @@ stream on an iOS device.
 ## Setup
-1.  Install MediaPipe on your system, see [MediaPipe installation guide] for
+1.  Install MediaPipe on your system, see
-    details.
+    [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
@@ -127,7 +131,7 @@ Create a `BUILD` file in the `$APPLICATION_PATH` and add the following build
 rules:
 ```
-MIN_IOS_VERSION = "10.0"
+MIN_IOS_VERSION = "11.0"
 load(
    "@build_bazel_rules_apple//apple:ios.bzl",
@@ -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
-[common]:(https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/common)
+[helloworld]:https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/helloworld
 [helloworld]:(https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/helloworld)
@@ -43,104 +43,189 @@ install --user six`.
 3.  Install OpenCV and FFmpeg.
-    Option 1. Use package manager tool to install the pre-compiled OpenCV
+    **Option 1**. Use package manager tool to install the pre-compiled OpenCV
-    libraries. FFmpeg will be installed via libopencv-video-dev.
+    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
+    OS                   | OpenCV
-    option 2 or 3 to install OpenCV 3 or above.
+    -------------------- | ------
    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 \
+    $ sudo apt-get install -y \
-                           libopencv-calib3d-dev libopencv-features2d-dev \
+        libopencv-core-dev \
-                           libopencv-imgproc-dev libopencv-video-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
+    MediaPipe's [`opencv_linux.BUILD`] and [`WORKSPACE`] are already configured
-    `/usr/lib/x86_64-linux-gnu`. MediaPipe's [`opencv_linux.BUILD`] and
+    for OpenCV 2/3 and should work correctly on any architecture:
    [`ffmpeg_linux.BUILD`] are configured for this library path. Ubuntu 20.04
    may install the OpenCV and FFmpeg packages in `/usr/local`, Please follow
    the option 3 below to modify the [`WORKSPACE`], [`opencv_linux.BUILD`] and
    [`ffmpeg_linux.BUILD`] files accordingly.
    Moreover, for Nvidia Jetson and Raspberry Pi devices with ARM Ubuntu, the
    library path needs to be modified like the following:
    ```bash
-    sed -i "s/x86_64-linux-gnu/aarch64-linux-gnu/g" third_party/opencv_linux.BUILD
+    # 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
+    For OpenCV 4 you need to modify [`opencv_linux.BUILD`] taking into account
-    and modify MediaPipe's OpenCV config.
+    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
+    You may need to modify [`WORKSPACE`] and [`opencv_linux.BUILD`] to point
-    [`ffmpeg_linux.BUILD`] to point MediaPipe to your own OpenCV and FFmpeg
+    MediaPipe to your own OpenCV libraries. Assume OpenCV would be installed to
-    libraries. For example if OpenCV and FFmpeg are both manually installed in
+    `/usr/local/` which is recommended by default.
-    "/usr/local/", you will need to update: (1) the "linux_opencv" and
+
-    "linux_ffmpeg" new_local_repository rules in [`WORKSPACE`], (2) the "opencv"
+    OpenCV 2/3 setup:
    cc_library rule in [`opencv_linux.BUILD`], and (3) the "libffmpeg"
    cc_library rule in [`ffmpeg_linux.BUILD`]. These 3 changes are shown below:
    ```bash
    # WORKSPACE
    new_local_repository(
-        name = "linux_opencv",
+      name = "linux_opencv",
-        build_file = "@//third_party:opencv_linux.BUILD",
+      build_file = "@//third_party:opencv_linux.BUILD",
-        path = "/usr/local",
+      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",
+      name = "linux_opencv",
-        build_file = "@//third_party:ffmpeg_linux.BUILD",
+      build_file = "@//third_party:opencv_linux.BUILD",
-        path = "/usr/local",
+      path = "/usr/local",
    )
    # opencv_linux.BUILD for OpenCV 4 installed to /usr/local
    cc_library(
-        name = "opencv",
+      name = "opencv",
-        srcs = glob(
+      hdrs = glob([
-            [
+        "include/opencv4/opencv2/**/*.h*",
-                "lib/libopencv_core.so",
+      ]),
-                "lib/libopencv_highgui.so",
+      includes = [
-                "lib/libopencv_imgcodecs.so",
+        "include/opencv4/",
-                "lib/libopencv_imgproc.so",
+      ],
-                "lib/libopencv_video.so",
+      linkopts = [
-                "lib/libopencv_videoio.so",
+        "-L/usr/local/lib",
-            ],
+        "-l:libopencv_core.so",
-        ),
+        "-l:libopencv_calib3d.so",
-        hdrs = glob([
+        "-l:libopencv_features2d.so",
-            # For OpenCV 3.x
+        "-l:libopencv_highgui.so",
-            "include/opencv2/**/*.h*",
+        "-l:libopencv_imgcodecs.so",
-            # For OpenCV 4.x
+        "-l:libopencv_imgproc.so",
-            # "include/opencv4/opencv2/**/*.h*",
+        "-l:libopencv_video.so",
-        ]),
+        "-l:libopencv_videoio.so",
-        includes = [
+      ],
-            # For OpenCV 3.x
+    )
-            "include/",
+    ```
-            # For OpenCV 4.x
+
-            # "include/opencv4/",
+    Current FFmpeg setup is defined in [`ffmpeg_linux.BUILD`] and should work
-        ],
+    for any architecture:
-        linkstatic = 1,
+
-        visibility = ["//visibility:public"],
+    ```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",
+      name = "libffmpeg",
-        srcs = glob(
+      linkopts = [
-            [
+        "-l:libavcodec.so",
-                "lib/libav*.so",
+        "-l:libavformat.so",
-            ],
+        "-l:libavutil.so",
-        ),
+      ],
        hdrs = glob(["include/libav*/*.h"]),
        includes = ["include"],
        linkopts = [
            "-lavcodec",
            "-lavformat",
            "-lavutil",
        ],
        linkstatic = 1,
        visibility = ["//visibility:public"],
    )
    ```
@@ -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 5.2.0 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!
@@ -32,9 +32,14 @@ example apps, start from, start from
    xcode-select --install
    ```
-3.  Install [Bazel](https://bazel.build/).
+3.  Install [Bazelisk](https://github.com/bazelbuild/bazelisk)
 .
-    We recommend using [Homebrew](https://brew.sh/) to get the latest version.
+    We recommend using [Homebrew](https://brew.sh/) to get the latest versions.
    ```bash
    brew install bazelisk
    ```
 4.  Set Python 3.7 as the default Python version and install the Python "six"
    library. This is needed for TensorFlow.
@@ -187,6 +192,9 @@ Note: When you ask Xcode to run an app, by default it will use the Debug
 configuration. Some of our demos are computationally heavy; you may want to use
 the Release configuration for better performance.
 Note: Due to an imcoptibility caused by one of our dependencies, MediaPipe
 cannot be used for apps running on the iPhone Simulator on Apple Silicon (M1).
 Tip: To switch build configuration in Xcode, click on the target menu, choose
 "Edit Scheme...", select the Run action, and switch the Build Configuration from
 Debug to Release. Note that this is set independently for each target.
@@ -22,12 +22,23 @@ Solution                    | NPM Package                             | Example
 [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:
@@ -67,33 +78,24 @@ 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
 [P-jsd]: https://www.jsdelivr.com/package/npm/@mediapipe/selfie_segmentation
 [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
 [S-pen]: https://code.mediapipe.dev/codepen/selfie_segmentation
 [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
@@ -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
@@ -126,6 +126,7 @@ following steps:
            }
            return packet.Get<MyType>();
          });
    }
    }  // namespace mediapipe
    ```
@@ -249,12 +250,12 @@ 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))
    ```
@@ -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).
@@ -45,7 +45,7 @@ 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)                    |                                                                 |                                                         | ✅                                                       |                                                               |                                                               |
@@ -55,46 +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)
+To start using MediaPipe
-MediaPipe and
+[solutions](https://google.github.io/mediapipe/solutions/solutions) with only a few
-[build example applications](https://google.github.io/mediapipe/getting_started/building_examples),
+lines code, see example code and demos in
-and start exploring our ready-to-use
+[MediaPipe in Python](https://google.github.io/mediapipe/getting_started/python) and
-[solutions](https://google.github.io/mediapipe/solutions/solutions) that you can
+[MediaPipe in JavaScript](https://google.github.io/mediapipe/getting_started/javascript).
-further extend and customize.
+
 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
@@ -103,6 +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)
@@ -153,8 +136,8 @@ run code search using
 ## Community
-*   [Awesome MediaPipe](https://mediapipe.org) - A curated list of awesome
+*   [Awesome MediaPipe](https://mediapipe.page.link/awesome-mediapipe) - A
-    MediaPipe related frameworks, libraries and software
+    curated list of awesome MediaPipe related frameworks, libraries and software
 *   [Slack community](https://mediapipe.page.link/joinslack) for MediaPipe users
 *   [Discuss](https://groups.google.com/forum/#!forum/mediapipe) - General
    community discussion around MediaPipe
@@ -26,7 +26,7 @@ MediaPipe Face Detection is an ultrafast face detection solution that comes with
 face detector tailored for mobile GPU inference. The detector's super-realtime
 performance enables it to be applied to any live viewfinder experience that
 requires an accurate facial region of interest as an input for other
-task-specific models, such as 3D facial keypoint or geometry estimation (e.g.,
+task-specific models, such as 3D facial keypoint estimation (e.g.,
 [MediaPipe Face Mesh](./face_mesh.md)), facial features or expression
 classification, and face region segmentation. BlazeFace uses a lightweight
 feature extraction network inspired by, but distinct from
@@ -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
@@ -72,6 +81,7 @@ install MediaPipe Python package, then learn more in the companion
 Supported configuration options:
 *   [model_selection](#model_selection)
 *   [min_detection_confidence](#min_detection_confidence)
 ```python
@@ -83,7 +93,7 @@ mp_drawing = mp.solutions.drawing_utils
 # For static images:
 IMAGE_FILES = []
 with mp_face_detection.FaceDetection(
-    min_detection_confidence=0.5) as face_detection:
+    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.
@@ -103,7 +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:
@@ -111,12 +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.
@@ -125,7 +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()
@@ -139,6 +148,7 @@ and the following usage example.
 Supported configuration options:
 *   [modelSelection](#model_selection)
 *   [minDetectionConfidence](#min_detection_confidence)
 ```html
@@ -189,6 +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);
@@ -204,6 +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
@@ -255,10 +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
@@ -20,34 +20,34 @@ nav_order: 2
 ## Overview
-MediaPipe Face Mesh is a face geometry solution that estimates 468 3D face
+MediaPipe Face Mesh is a solution that estimates 468 3D face landmarks in
-landmarks in real-time even on mobile devices. It employs machine learning (ML)
+real-time even on mobile devices. It employs machine learning (ML) to infer the
-to infer the 3D surface geometry, requiring only a single camera input without
+3D facial surface, requiring only a single camera input without the need for a
-the need for a dedicated depth sensor. Utilizing lightweight model architectures
+dedicated depth sensor. Utilizing lightweight model architectures together with
-together with GPU acceleration throughout the pipeline, the solution delivers
+GPU acceleration throughout the pipeline, the solution delivers real-time
-real-time performance critical for live experiences.
+performance critical for live experiences.
-Additionally, the solution is bundled with the Face Geometry module that bridges
+Additionally, the solution is bundled with the Face Transform module that
-the gap between the face landmark estimation and useful real-time augmented
+bridges the gap between the face landmark estimation and useful real-time
-reality (AR) applications. It establishes a metric 3D space and uses the face
+augmented reality (AR) applications. It establishes a metric 3D space and uses
-landmark screen positions to estimate face geometry within that space. The face
+the face landmark screen positions to estimate a face transform within that
-geometry data consists of common 3D geometry primitives, including a face pose
+space. The face transform data consists of common 3D primitives, including a
-transformation matrix and a triangular face mesh. Under the hood, a lightweight
+face pose transformation matrix and a triangular face mesh. Under the hood, a
-statistical analysis method called
+lightweight statistical analysis method called
 [Procrustes Analysis](https://en.wikipedia.org/wiki/Procrustes_analysis) is
 employed to drive a robust, performant and portable logic. The analysis runs on
 CPU and has a minimal speed/memory footprint on top of the ML model inference.
 ![face_mesh_ar_effects.gif](../images/face_mesh_ar_effects.gif) |
 :-------------------------------------------------------------: |
-*Fig 1. AR effects utilizing facial surface geometry.*          |
+*Fig 1. AR effects utilizing the 3D facial surface.*            |
 ## ML Pipeline
 Our ML pipeline consists of two real-time deep neural network models that work
 together: A detector that operates on the full image and computes face locations
 and a 3D face landmark model that operates on those locations and predicts the
-approximate surface geometry via regression. Having the face accurately cropped
+approximate 3D surface via regression. Having the face accurately cropped
 drastically reduces the need for common data augmentations like affine
 transformations consisting of rotations, translation and scale changes. Instead
 it allows the network to dedicate most of its capacity towards coordinate
@@ -55,8 +55,8 @@ prediction accuracy. In addition, in our pipeline the crops can also be
 generated based on the face landmarks identified in the previous frame, and only
 when the landmark model could no longer identify face presence is the face
 detector invoked to relocalize the face. This strategy is similar to that
-employed in our [MediaPipe Hands](./hands.md) solution, which uses a palm detector
+employed in our [MediaPipe Hands](./hands.md) solution, which uses a palm
-together with a hand landmark model.
+detector together with a hand landmark model.
 The pipeline is implemented as a MediaPipe
 [graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/face_mesh/face_mesh_mobile.pbtxt)
@@ -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).
@@ -111,7 +111,24 @@ 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.* |
-## Face Geometry Module
+#### 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 Transform Module
 The [Face Landmark Model](#face-landmark-model) performs a single-camera face landmark
 detection in the screen coordinate space: the X- and Y- coordinates are
@@ -123,7 +140,7 @@ enable the full spectrum of augmented reality (AR) features like aligning a
 virtual 3D object with a detected face.
 The
-[Face Geometry module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry)
+[Face Transform module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry)
 moves away from the screen coordinate space towards a metric 3D space and
 provides necessary primitives to handle a detected face as a regular 3D object.
 By design, you'll be able to use a perspective camera to project the final 3D
@@ -134,7 +151,7 @@ landmark positions are not changed.
 #### Metric 3D Space
-The **Metric 3D space** established within the Face Geometry module is a
+The **Metric 3D space** established within the Face Transform module is a
 right-handed orthonormal metric 3D coordinate space. Within the space, there is
 a **virtual perspective camera** located at the space origin and pointed in the
 negative direction of the Z-axis. In the current pipeline, it is assumed that
@@ -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
@@ -167,11 +184,11 @@ functions:
 ### Components
-#### Geometry Pipeline
+#### Transform Pipeline
-The **Geometry Pipeline** is a key component, which is responsible for
+The **Transform Pipeline** is a key component, which is responsible for
-estimating face geometry objects within the Metric 3D space. On each frame, the
+estimating the face transform objects within the Metric 3D space. On each frame,
-following steps are executed in the given order:
+the following steps are executed in the given order:
 -   Face landmark screen coordinates are converted into the Metric 3D space
    coordinates;
@@ -182,12 +199,12 @@ following steps are executed in the given order:
    positions (XYZ), while both the vertex texture coordinates (UV) and the
    triangular topology are inherited from the canonical face model.
-The geometry pipeline is implemented as a MediaPipe
+The transform pipeline is implemented as a MediaPipe
 [calculator](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/geometry_pipeline_calculator.cc).
-For your convenience, the face geometry pipeline calculator is bundled together
+For your convenience, this calculator is bundled together with corresponding
-with corresponding metadata into a unified MediaPipe
+metadata into a unified MediaPipe
 [subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/face_geometry_from_landmarks.pbtxt).
-The face geometry format is defined as a Protocol Buffer
+The face transform format is defined as a Protocol Buffer
 [message](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/protos/face_geometry.proto).
 #### Effect Renderer
@@ -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 Transform 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
@@ -271,6 +294,7 @@ 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)
@@ -278,6 +302,7 @@ 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:
@@ -286,6 +311,7 @@ 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(IMAGE_FILES):
    image = cv2.imread(file)
@@ -301,15 +327,32 @@ with mp_face_mesh.FaceMesh(
      mp_drawing.draw_landmarks(
          image=annotated_image,
          landmark_list=face_landmarks,
-          connections=mp_face_mesh.FACE_CONNECTIONS,
+          connections=mp_face_mesh.FACEMESH_TESSELATION,
-          landmark_drawing_spec=drawing_spec,
+          landmark_drawing_spec=None,
-          connection_drawing_spec=drawing_spec)
+          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():
@@ -319,12 +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.
@@ -335,10 +376,26 @@ with mp_face_mesh.FaceMesh(
        mp_drawing.draw_landmarks(
            image=image,
            landmark_list=face_landmarks,
-            connections=mp_face_mesh.FACE_CONNECTIONS,
+            connections=mp_face_mesh.FACEMESH_TESSELATION,
-            landmark_drawing_spec=drawing_spec,
+            landmark_drawing_spec=None,
-            connection_drawing_spec=drawing_spec)
+            connection_drawing_spec=mp_drawing_styles
-    cv2.imshow('MediaPipe FaceMesh', image)
+            .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()
@@ -353,6 +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)
@@ -393,8 +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'});
    }
@@ -407,6 +467,7 @@ const faceMesh = new FaceMesh({locateFile: (file) => {
 }});
 faceMesh.setOptions({
  maxNumFaces: 1,
  refineLandmarks: true,
  minDetectionConfidence: 0.5,
  minTrackingConfidence: 0.5
 });
@@ -423,6 +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
@@ -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
@@ -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)
+[lite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/palm_detection/palm_detection_lite.tflite)
-has to work across a variety of hand sizes with a large scale span (~20x)
+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
@@ -212,6 +226,7 @@ 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)
@@ -219,6 +234,7 @@ 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:
@@ -248,13 +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():
@@ -264,12 +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.
@@ -278,8 +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)
+            image,
-    cv2.imshow('MediaPipe Hands', 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()
@@ -294,6 +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)
@@ -343,6 +374,7 @@ const hands = new Hands({locateFile: (file) => {
 }});
 hands.setOptions({
  maxNumHands: 2,
  modelComplexity: 1,
  minDetectionConfidence: 0.5,
  minTrackingConfidence: 0.5
 });
@@ -359,6 +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
@@ -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`
@@ -197,6 +224,15 @@ 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
@@ -208,6 +244,9 @@ 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 +254,16 @@ 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:
+    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
@@ -234,17 +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)
+        annotated_image,
-    mp_drawing.draw_landmarks(
+        results.pose_landmarks,
-        annotated_image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
+        mp_holistic.POSE_CONNECTIONS,
-    mp_drawing.draw_landmarks(
+        landmark_drawing_spec=mp_drawing_styles.
-        annotated_image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
+        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)
@@ -258,26 +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)
+        image,
-    mp_drawing.draw_landmarks(
+        results.pose_landmarks,
-        image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
+        mp_holistic.POSE_CONNECTIONS,
-    mp_drawing.draw_landmarks(
+        landmark_drawing_spec=mp_drawing_styles
-        image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
+        .get_default_pose_landmarks_style())
-    cv2.imshow('MediaPipe Holistic', image)
+    # 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()
@@ -293,6 +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)
@@ -325,8 +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,
@@ -350,6 +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
 });
@@ -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
@@ -14,24 +14,37 @@ nav_order: 30
 ### [Face Detection](https://google.github.io/mediapipe/solutions/face_detection)
-*   Face detection model for front-facing/selfie camera:
+*   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_front.tflite),
+    [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)
-*   Face detection model for back-facing camera:
+*   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_back.tflite),
+    [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)
-*   Face detection model for back-facing camera (sparse):
+*   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_back_sparse.tflite),
+    [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)
@@ -42,13 +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 (lite)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_lite.tflite),
-    [TFLite model (sparse)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_sparse.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)
@@ -116,7 +116,7 @@ on how to build MediaPipe examples.
    Note: The following runs TensorFlow inference on CPU. If you would like to
    run inference on GPU (Linux only), please follow
-    [TensorFlow CUDA Support and Setup on Linux Desktop](gpu.md#tensorflow-cuda-support-and-setup-on-linux-desktop)
+    [TensorFlow CUDA Support and Setup on Linux Desktop](../getting_started/gpu_support.md#tensorflow-cuda-support-and-setup-on-linux-desktop)
    instead.
    To build the TensorFlow CPU inference example on desktop, run:
@@ -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
+Name of the model to use for predicting 3D bounding box landmarks. Currently
-`{'Shoe', 'Chair', 'Cup', 'Camera'}`.
+supports `{'Shoe', 'Chair', 'Cup', 'Camera'}`. Default to `Shoe`.
 #### focal_length
-Camera focal length `(fx, fy)`, by default is defined in
+By default, camera focal length defined in [NDC space](#ndc-space), i.e., `(fx,
-[NDC space](#ndc-space). To use focal length `(fx_pixel, fy_pixel)` in
+fy)`. Default to `(1.0, 1.0)`. To specify focal length in
-[pixel space](#pixel-space), users should provide `image_size` = `(image_width,
+[pixel space](#pixel-space) instead, i.e., `(fx_pixel, fy_pixel)`, users should
-image_height)` to enable conversions inside the API. For further details about
+provide [`image_size`](#image_size) = `(image_width, image_height)` to enable
-NDC and pixel space, please see [Coordinate Systems](#coordinate-systems).
+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
+By default, camera principal point defined in [NDC space](#ndc-space), i.e.,
-[NDC space](#ndc-space). To use principal point `(px_pixel, py_pixel)` in
+`(px, py)`. Default to `(0.0, 0.0)`. To specify principal point in
-[pixel space](#pixel-space), users should provide `image_size` = `(image_width,
+[pixel space](#pixel-space), i.e.,`(px_pixel, py_pixel)`, users should provide
-image_height)` to enable conversions inside the API. For further details about
+[`image_size`](#image_size) = `(image_width, image_height)` to enable
-NDC and pixel space, please see [Coordinate Systems](#coordinate-systems).
+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**
+**Specify only when [`focal_length`](#focal_length) and
-needed when use `focal_length` and `principal_point` in pixel space.
+[`principal_point`](#principal_point) are specified in pixel space.**
 Size of the input image, i.e., `(image_width, image_height)`.
 ### Output
@@ -334,11 +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.
@@ -350,12 +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/control_utils_3d/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
@@ -442,7 +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,
@@ -561,11 +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)
@@ -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
+tracker subsequently predicts the pose landmarks and segmentation mask within
-ROI-cropped frame as input. Note that for video use cases the detector is
+the ROI using the ROI-cropped frame as input. Note that for video use cases the
-invoked only as needed, i.e., for the very first frame and when the tracker
+detector is invoked only as needed, i.e., for the very first frame and when the
-could no longer identify body pose presence in the previous frame. For other
+tracker could no longer identify body pose presence in the previous frame. For
-frames the pipeline simply derives the ROI from the previous frame’s pose
+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 GHUM 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 GHUM 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**
+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**
+[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**
+[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)
+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 GHUM Heavy | 53 ms                                                                                        | 38 ms
-BlazePose.Full  | 25 ms                                                                                        | 27 ms
+BlazePose GHUM Full  | 25 ms                                                                                        | 27 ms
-BlazePose.Lite  | 20 ms                                                                                        | 25 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
@@ -194,10 +210,36 @@ A list of pose landmarks. Each landmark consists of the following:
 *   `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
@@ -209,6 +251,8 @@ 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)
@@ -216,13 +260,16 @@ 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(IMAGE_FILES):
    image = cv2.imread(file)
@@ -234,14 +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_pose.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].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)
@@ -255,20 +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)
+        image,
-    cv2.imshow('MediaPipe Pose', 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()
@@ -284,6 +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)
@@ -294,6 +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/control_utils_3d/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>
@@ -302,6 +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>
@@ -312,17 +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) => {
@@ -331,6 +419,8 @@ const pose = new Pose({locateFile: (file) => {
 pose.setOptions({
  modelComplexity: 1,
  smoothLandmarks: true,
  enableSegmentation: true,
  smoothSegmentation: true,
  minDetectionConfidence: 0.5,
  minTrackingConfidence: 0.5
 });
@@ -396,6 +486,7 @@ 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)
@@ -96,6 +96,7 @@ Supported configuration options:
 ```python
 import cv2
 import mediapipe as mp
 import numpy as np
 mp_drawing = mp.solutions.drawing_utils
 mp_selfie_segmentation = mp.solutions.selfie_segmentation
@@ -261,7 +262,7 @@ to visualize its associated subgraphs, please see
    [(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`](http:/mediapipe/examples/ios/selfiesegmentationgpu/BUILD)
+    [`mediapipe/examples/ios/selfiesegmentationgpu:SelfieSegmentationGpuApp`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/selfiesegmentationgpu/BUILD)
 ### Desktop
@@ -284,6 +285,6 @@ on how to build MediaPipe examples.
 *   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)
+*   [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)
@@ -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. -->
@@ -29,7 +32,7 @@ has_toc: false
 [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)                    |                                                                 |                                                         | ✅                                                       |                                                               |                                                               |
@@ -258,13 +258,14 @@ Many of the following settings are advanced and not recommended for general
 usage. Consult [Enabling tracing and profiling](#enabling-tracing-and-profiling)
 for a friendlier introduction.
-histogram_interval_size_usec :Specifies the size of the runtimes histogram
+histogram_interval_size_usec
-intervals (in microseconds) to generate the histogram of the Process() time. The
+:   Specifies the size of the runtimes histogram intervals (in microseconds) to
-last interval extends to +inf. If not specified, the interval is 1000000 usec =
+    generate the histogram of the `Process()` time. The last interval extends to
-1 sec.
+    +inf. If not specified, the interval is 1000000 usec = 1 sec.
-num_histogram_intervals :Specifies the number of intervals to generate the
+num_histogram_intervals
-histogram of the `Process()` runtime. If not specified, one interval is used.
+:   Specifies the number of intervals to generate the histogram of the
    `Process()` runtime. If not specified, one interval is used.
 enable_profiler
 :   If true, the profiler starts profiling when graph is initialized.
@@ -288,7 +289,7 @@ trace_event_types_disabled
 trace_log_path
 :   The output directory and base-name prefix for trace log files. Log files are
-    written to: StrCat(trace_log_path, index, "`.binarypb`")
+    written to: `StrCat(trace_log_path, index, ".binarypb")`
 trace_log_count
 :   The number of trace log files retained. The trace log files are named
@@ -310,8 +311,8 @@ trace_log_instant_events
 trace_log_interval_count
 :   The number of trace log intervals per file. The total log duration is:
-    `trace_log_interval_usec * trace_log_file_count * trace_log_interval_count`.
+    `trace_log_interval_usec * trace_log_count * trace_log_interval_count`. The
-    The default value specifies 10 intervals per file.
+    default value specifies 10 intervals per file.
 trace_log_disabled
 :   An option to turn ON/OFF writing trace files to disk. Saving trace files to
@@ -75,6 +75,7 @@ alias(
    actual = select({
        ":macos_i386": ":macos_i386",
        ":macos_x86_64": ":macos_x86_64",
        ":macos_arm64": ":macos_arm64",
        "//conditions:default": ":macos_i386",  # Arbitrarily chosen from above.
    }),
    visibility = ["//visibility:public"],
@@ -119,6 +120,15 @@ config_setting(
    visibility = ["//visibility:public"],
 )
 config_setting(
    name = "macos_arm64",
    values = {
        "apple_platform_type": "macos",
        "cpu": "darwin_arm64",
    },
    visibility = ["//visibility:public"],
 )
 [
    config_setting(
        name = arch,
@@ -244,6 +244,7 @@ cc_test(
        "//mediapipe/framework/formats:time_series_header_cc_proto",
        "//mediapipe/framework/port:gtest_main",
        "//mediapipe/framework/port:parse_text_proto",
        "//mediapipe/framework/tool:test_util",
        "@com_google_absl//absl/flags:flag",
    ],
 )
@@ -20,8 +20,12 @@
 #include "mediapipe/framework/port/gtest.h"
 #include "mediapipe/framework/port/parse_text_proto.h"
 #include "mediapipe/framework/port/status_matchers.h"
 #include "mediapipe/framework/tool/test_util.h"
 namespace mediapipe {
 namespace {
 constexpr char kTestPackageRoot[] = "mediapipe/calculators/audio";
 TEST(AudioDecoderCalculatorTest, TestWAV) {
  CalculatorGraphConfig::Node node_config =
@@ -37,9 +41,8 @@ TEST(AudioDecoderCalculatorTest, TestWAV) {
        })pb");
  CalculatorRunner runner(node_config);
  runner.MutableSidePackets()->Tag("INPUT_FILE_PATH") = MakePacket<std::string>(
-      file::JoinPath("./",
+      file::JoinPath(GetTestDataDir(kTestPackageRoot),
-                     "/mediapipe/calculators/audio/"
+                     "sine_wave_1k_44100_mono_2_sec_wav.audio"));
                     "testdata/sine_wave_1k_44100_mono_2_sec_wav.audio"));
  MP_ASSERT_OK(runner.Run());
  MP_EXPECT_OK(runner.Outputs()
                   .Tag("AUDIO_HEADER")
@@ -68,9 +71,8 @@ TEST(AudioDecoderCalculatorTest, Test48KWAV) {
        })pb");
  CalculatorRunner runner(node_config);
  runner.MutableSidePackets()->Tag("INPUT_FILE_PATH") = MakePacket<std::string>(
-      file::JoinPath("./",
+      file::JoinPath(GetTestDataDir(kTestPackageRoot),
-                     "/mediapipe/calculators/audio/"
+                     "sine_wave_1k_48000_stereo_2_sec_wav.audio"));
                     "testdata/sine_wave_1k_48000_stereo_2_sec_wav.audio"));
  MP_ASSERT_OK(runner.Run());
  MP_EXPECT_OK(runner.Outputs()
                   .Tag("AUDIO_HEADER")
@@ -99,9 +101,8 @@ TEST(AudioDecoderCalculatorTest, TestMP3) {
        })pb");
  CalculatorRunner runner(node_config);
  runner.MutableSidePackets()->Tag("INPUT_FILE_PATH") = MakePacket<std::string>(
-      file::JoinPath("./",
+      file::JoinPath(GetTestDataDir(kTestPackageRoot),
-                     "/mediapipe/calculators/audio/"
+                     "sine_wave_1k_44100_stereo_2_sec_mp3.audio"));
                     "testdata/sine_wave_1k_44100_stereo_2_sec_mp3.audio"));
  MP_ASSERT_OK(runner.Run());
  MP_EXPECT_OK(runner.Outputs()
                   .Tag("AUDIO_HEADER")
@@ -130,9 +131,8 @@ TEST(AudioDecoderCalculatorTest, TestAAC) {
        })pb");
  CalculatorRunner runner(node_config);
  runner.MutableSidePackets()->Tag("INPUT_FILE_PATH") = MakePacket<std::string>(
-      file::JoinPath("./",
+      file::JoinPath(GetTestDataDir(kTestPackageRoot),
-                     "/mediapipe/calculators/audio/"
+                     "sine_wave_1k_44100_stereo_2_sec_aac.audio"));
                     "testdata/sine_wave_1k_44100_stereo_2_sec_aac.audio"));
  MP_ASSERT_OK(runner.Run());
  MP_EXPECT_OK(runner.Outputs()
                   .Tag("AUDIO_HEADER")
@@ -147,4 +147,5 @@ TEST(AudioDecoderCalculatorTest, TestAAC) {
              std::ceil(44100.0 * 2 / 1024));
 }
 }  // namespace
 }  // namespace mediapipe
@@ -20,24 +20,22 @@
 #include <memory>
 #include <string>
 #include "Eigen/Core"
 #include "absl/strings/string_view.h"
 #include "audio/dsp/spectrogram/spectrogram.h"
 #include "audio/dsp/window_functions.h"
 #include "mediapipe/calculators/audio/spectrogram_calculator.pb.h"
 #include "mediapipe/framework/calculator_framework.h"
 #include "mediapipe/framework/formats/matrix.h"
 #include "mediapipe/framework/formats/time_series_header.pb.h"
 #include "mediapipe/framework/port/core_proto_inc.h"
 #include "mediapipe/framework/port/integral_types.h"
 #include "mediapipe/framework/port/logging.h"
 #include "mediapipe/framework/port/ret_check.h"
 #include "mediapipe/framework/port/source_location.h"
 #include "mediapipe/framework/port/status_builder.h"
 #include "mediapipe/util/time_series_util.h"
 namespace mediapipe {
 namespace {
 constexpr char kFrameDurationTag[] = "FRAME_DURATION";
 constexpr char kFrameOverlapTag[] = "FRAME_OVERLAP";
 }  // namespace
 // MediaPipe Calculator for computing the "spectrogram" (short-time Fourier
 // transform squared-magnitude, by default) of a multichannel input
 // time series, including optionally overlapping frames.  Options are
@@ -46,11 +44,14 @@ namespace mediapipe {
 //
 // Result is a MatrixData record (for single channel input and when the
 // allow_multichannel_input flag is false), or a vector of MatrixData records,
-// one for each channel (when the allow_multichannel_input flag is set). The
+// one for each channel (when the allow_multichannel_input flag is set). Each
-// rows of each spectrogram matrix correspond to the n_fft/2+1 unique complex
+// waveform frame is converted to frequency by a fast Fourier transform whose
-// values, or squared/linear/dB magnitudes, depending on the output_type option.
+// size, n_fft, is the smallest power of two large enough to enclose the frame
-// Each input packet will result in zero or one output packets, each containing
+// length of round(frame_duration_seconds * sample_rate).The rows of each
-// one Matrix for each channel of the input, where each Matrix has one or more
+// spectrogram matrix(result) correspond to the n_fft/2+1 unique complex values,
 // or squared/linear/dB magnitudes, depending on the output_type option. Each
 // input packet will result in zero or one output packets, each containing one
 // Matrix for each channel of the input, where each Matrix has one or more
 // columns of spectral values, one for each complete frame of input samples. If
 // the input packet contains too few samples to trigger a new output frame, no
 // output packet is generated (since zero-length packets are not legal since
@@ -71,6 +72,22 @@ class SpectrogramCalculator : public CalculatorBase {
        // Input stream with TimeSeriesHeader.
    );
    if (cc->InputSidePackets().HasTag(kFrameDurationTag)) {
      cc->InputSidePackets()
          .Tag(kFrameDurationTag)
          .Set<double>(
              // Optional side packet for frame_duration_seconds if provided.
          );
    }
    if (cc->InputSidePackets().HasTag(kFrameOverlapTag)) {
      cc->InputSidePackets()
          .Tag(kFrameOverlapTag)
          .Set<double>(
              // Optional side packet for frame_overlap_seconds if provided.
          );
    }
    SpectrogramCalculatorOptions spectrogram_options =
        cc->Options<SpectrogramCalculatorOptions>();
    if (!spectrogram_options.allow_multichannel_input()) {
@@ -184,27 +201,47 @@ class SpectrogramCalculator : public CalculatorBase {
  // Fixed scale factor applied to output values (regardless of type).
  double output_scale_;
-  static const float kLnPowerToDb;
+  static const float kLnSquaredMagnitudeToDb;
 };
 REGISTER_CALCULATOR(SpectrogramCalculator);
-// Factor to convert ln(magnitude_squared) to deciBels = 10.0/ln(10.0).
+// DECIBELS = 20*log10(LINEAR_MAGNITUDE) = 10*Log10(SQUARED_MAGNITUDE)
-const float SpectrogramCalculator::kLnPowerToDb = 4.342944819032518;
+// =10/ln(10)*ln(SQUARED_MAGNITUDE).
 // Factor to convert ln(SQUARED_MAGNITUDE) to deciBels = 10.0/ln(10.0).
 const float SpectrogramCalculator::kLnSquaredMagnitudeToDb = 4.342944819032518;
 absl::Status SpectrogramCalculator::Open(CalculatorContext* cc) {
  SpectrogramCalculatorOptions spectrogram_options =
      cc->Options<SpectrogramCalculatorOptions>();
  // Provide frame_duration_seconds and frame_overlap_seconds either from static
  // options, or dynamically from a side packet, the side packet one will
  // override the options one if provided.
  double frame_duration_seconds = 0;
  double frame_overlap_seconds = 0;
  if (cc->InputSidePackets().HasTag(kFrameDurationTag)) {
    frame_duration_seconds =
        cc->InputSidePackets().Tag(kFrameDurationTag).Get<double>();
  } else {
    frame_duration_seconds = spectrogram_options.frame_duration_seconds();
  }
  if (cc->InputSidePackets().HasTag(kFrameOverlapTag)) {
    frame_overlap_seconds =
        cc->InputSidePackets().Tag(kFrameOverlapTag).Get<double>();
  } else {
    frame_overlap_seconds = spectrogram_options.frame_overlap_seconds();
  }
  use_local_timestamp_ = spectrogram_options.use_local_timestamp();
-  if (spectrogram_options.frame_duration_seconds() <= 0.0) {
+  if (frame_duration_seconds <= 0.0) {
    // TODO: return an error.
  }
-  if (spectrogram_options.frame_overlap_seconds() >=
+  if (frame_overlap_seconds >= frame_duration_seconds) {
      spectrogram_options.frame_duration_seconds()) {
    // TODO: return an error.
  }
-  if (spectrogram_options.frame_overlap_seconds() < 0.0) {
+  if (frame_overlap_seconds < 0.0) {
    // TODO: return an error.
  }
@@ -220,10 +257,8 @@ absl::Status SpectrogramCalculator::Open(CalculatorContext* cc) {
    // TODO: return an error.
  }
-  frame_duration_samples_ =
+  frame_duration_samples_ = round(frame_duration_seconds * input_sample_rate_);
-      round(spectrogram_options.frame_duration_seconds() * input_sample_rate_);
+  frame_overlap_samples_ = round(frame_overlap_seconds * input_sample_rate_);
  frame_overlap_samples_ =
      round(spectrogram_options.frame_overlap_seconds() * input_sample_rate_);
  pad_final_packet_ = spectrogram_options.pad_final_packet();
  output_type_ = spectrogram_options.output_type();
@@ -419,7 +454,7 @@ absl::Status SpectrogramCalculator::ProcessVector(const Matrix& input_stream,
      return ProcessVectorToOutput(
          input_stream,
          +[](const Matrix& col) -> const Matrix {
-            return kLnPowerToDb * col.array().log().matrix();
+            return kLnSquaredMagnitudeToDb * col.array().log().matrix();
          }, cc);
    }
    // clang-format on
@@ -32,7 +32,11 @@ message SpectrogramCalculatorOptions {
  // Duration of overlap between adjacent windows.
  // Hence, frame_rate = 1/(frame_duration_seconds - frame_overlap_seconds).
-  // Required that 0 <= frame_overlap_seconds <  frame_duration_seconds.
+  // Note the frame_rate here is not the MediaPipe packet rate, the frame here
  // means each Fourier transform analysis waveform frame, the output MediaPipe
  // packet rate will the the same as input, if frame rate is lower than input
  // packet rate, will result in intermittent empty output packets. Required
  // that 0 <= frame_overlap_seconds <  frame_duration_seconds.
  optional double frame_overlap_seconds = 2 [default = 0.0];
  // Whether to pad the final packet with zeros.  If true, guarantees that
@@ -42,6 +46,11 @@ message SpectrogramCalculatorOptions {
  // Output value type can be squared-magnitude, linear-magnitude,
  // deciBels (dB, = 20*log10(linear_magnitude)), or std::complex.
  // Their relationship:
  // COMPLEX c = Re + Im*i;
  // SQUARED_MAGNITUDE = Re^2 + Im^2;
  // LINEAR_MAGNITUDE = sqrt(SQUARED_MAGNITUDE);
  // DECIBELS = 20*log10(LINEAR_MAGNITUDE) = 10*log10(SQUARED_MAGNITUDE);
  enum OutputType {
    SQUARED_MAGNITUDE = 0;
    LINEAR_MAGNITUDE = 1;
@@ -117,6 +117,7 @@ mediapipe_proto_library(
        "//mediapipe/framework:calculator_options_proto",
        "//mediapipe/framework:calculator_proto",
        "//mediapipe/framework/formats:classification_proto",
        "//mediapipe/framework/formats:landmark_proto",
    ],
 )
@@ -140,6 +141,16 @@ 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"],
@@ -203,6 +214,7 @@ cc_library(
        "//mediapipe/framework:collection_item_id",
        "//mediapipe/framework:packet",
        "//mediapipe/framework/formats:classification_cc_proto",
        "//mediapipe/framework/formats:detection_cc_proto",
        "//mediapipe/framework/formats:landmark_cc_proto",
        "//mediapipe/framework/formats:rect_cc_proto",
        "//mediapipe/framework/port:integral_types",
@@ -299,8 +311,8 @@ cc_library(
 )
 cc_library(
-    name = "concatenate_normalized_landmark_list_calculator",
+    name = "concatenate_proto_list_calculator",
-    srcs = ["concatenate_normalized_landmark_list_calculator.cc"],
+    srcs = ["concatenate_proto_list_calculator.cc"],
    visibility = ["//visibility:public"],
    deps = [
        ":concatenate_vector_calculator_cc_proto",
@@ -314,10 +326,10 @@ cc_library(
 )
 cc_test(
-    name = "concatenate_normalized_landmark_list_calculator_test",
+    name = "concatenate_proto_list_calculator_test",
-    srcs = ["concatenate_normalized_landmark_list_calculator_test.cc"],
+    srcs = ["concatenate_proto_list_calculator_test.cc"],
    deps = [
-        ":concatenate_normalized_landmark_list_calculator",
+        ":concatenate_proto_list_calculator",
        ":concatenate_vector_calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework:calculator_runner",
@@ -419,6 +431,23 @@ 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 +533,13 @@ 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",
    ],
 )
@@ -524,6 +557,22 @@ cc_library(
    alwayslink = 1,
 )
 cc_test(
    name = "packet_cloner_calculator_test",
    srcs = ["packet_cloner_calculator_test.cc"],
    deps = [
        ":packet_cloner_calculator",
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework:timestamp",
        "//mediapipe/framework/port:gtest_main",
        "//mediapipe/framework/port:parse_text_proto",
        "//mediapipe/framework/port:status",
        "//mediapipe/framework/stream_handler:immediate_input_stream_handler",
        "//mediapipe/framework/tool:sink",
        "@com_google_absl//absl/strings",
    ],
 )
 cc_library(
    name = "packet_inner_join_calculator",
    srcs = ["packet_inner_join_calculator.cc"],
@@ -933,8 +982,8 @@ cc_test(
 )
 cc_library(
-    name = "split_normalized_landmark_list_calculator",
+    name = "split_proto_list_calculator",
-    srcs = ["split_normalized_landmark_list_calculator.cc"],
+    srcs = ["split_proto_list_calculator.cc"],
    visibility = ["//visibility:public"],
    deps = [
        ":split_vector_calculator_cc_proto",
@@ -948,10 +997,10 @@ cc_library(
 )
 cc_test(
-    name = "split_normalized_landmark_list_calculator_test",
+    name = "split_proto_list_calculator_test",
-    srcs = ["split_normalized_landmark_list_calculator_test.cc"],
+    srcs = ["split_proto_list_calculator_test.cc"],
    deps = [
-        ":split_normalized_landmark_list_calculator",
+        ":split_proto_list_calculator",
        ":split_vector_calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework:calculator_runner",
@@ -1164,6 +1213,7 @@ cc_library(
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework:collection_item_id",
        "//mediapipe/framework/formats:classification_cc_proto",
        "//mediapipe/framework/formats:landmark_cc_proto",
        "//mediapipe/framework/port:integral_types",
        "//mediapipe/framework/port:ret_check",
        "//mediapipe/framework/port:status",
@@ -1183,3 +1233,77 @@ 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",
    ],
 )
 cc_library(
    name = "get_vector_item_calculator",
    srcs = ["get_vector_item_calculator.cc"],
    hdrs = ["get_vector_item_calculator.h"],
    visibility = ["//visibility:public"],
    deps = [
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework:packet",
        "//mediapipe/framework/api2:node",
        "//mediapipe/framework/api2:port",
        "//mediapipe/framework/formats:classification_cc_proto",
        "//mediapipe/framework/formats:landmark_cc_proto",
        "//mediapipe/framework/port:ret_check",
        "//mediapipe/framework/port:status",
    ],
    alwayslink = 1,
 )
 cc_library(
    name = "vector_size_calculator",
    srcs = ["vector_size_calculator.cc"],
    hdrs = ["vector_size_calculator.h"],
    visibility = ["//visibility:public"],
    deps = [
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework/api2:node",
        "//mediapipe/framework/formats:classification_cc_proto",
        "//mediapipe/framework/formats:landmark_cc_proto",
        "//mediapipe/framework/port:status",
    ],
    alwayslink = 1,
 )
@@ -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(
+  runner.MutableInputs()
-      Adopt(new std::string("not allowed")));
+      ->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.
@@ -28,6 +28,10 @@ typedef BeginLoopCalculator<std::vector<::mediapipe::NormalizedLandmarkList>>
    BeginLoopNormalizedLandmarkListVectorCalculator;
 REGISTER_CALCULATOR(BeginLoopNormalizedLandmarkListVectorCalculator);
 // A calculator to process std::vector<int>.
 typedef BeginLoopCalculator<std::vector<int>> BeginLoopIntCalculator;
 REGISTER_CALCULATOR(BeginLoopIntCalculator);
 // A calculator to process std::vector<NormalizedRect>.
 typedef BeginLoopCalculator<std::vector<::mediapipe::NormalizedRect>>
    BeginLoopNormalizedRectCalculator;
@@ -42,4 +46,13 @@ 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
@@ -1,79 +0,0 @@
 // 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.
 #ifndef MEDIAPIPE_CALCULATORS_CORE_CONCATENATE_NORMALIZED_LIST_CALCULATOR_H_  // NOLINT
 #define MEDIAPIPE_CALCULATORS_CORE_CONCATENATE_NORMALIZED_LIST_CALCULATOR_H_  // NOLINT
 #include "mediapipe/calculators/core/concatenate_vector_calculator.pb.h"
 #include "mediapipe/framework/api2/node.h"
 #include "mediapipe/framework/calculator_framework.h"
 #include "mediapipe/framework/formats/landmark.pb.h"
 #include "mediapipe/framework/port/canonical_errors.h"
 #include "mediapipe/framework/port/ret_check.h"
 #include "mediapipe/framework/port/status.h"
 namespace mediapipe {
 namespace api2 {
 // Concatenates several NormalizedLandmarkList protos following stream index
 // order. This class assumes that every input stream contains a
 // NormalizedLandmarkList proto object.
 class ConcatenateNormalizedLandmarkListCalculator : public Node {
 public:
  static constexpr Input<NormalizedLandmarkList>::Multiple kIn{""};
  static constexpr Output<NormalizedLandmarkList> kOut{""};
  MEDIAPIPE_NODE_CONTRACT(kIn, kOut);
  static absl::Status UpdateContract(CalculatorContract* cc) {
    RET_CHECK_GE(kIn(cc).Count(), 1);
    return absl::OkStatus();
  }
  absl::Status Open(CalculatorContext* cc) override {
    only_emit_if_all_present_ =
        cc->Options<::mediapipe::ConcatenateVectorCalculatorOptions>()
            .only_emit_if_all_present();
    return absl::OkStatus();
  }
  absl::Status Process(CalculatorContext* cc) override {
    if (only_emit_if_all_present_) {
      for (const auto& input : kIn(cc)) {
        if (input.IsEmpty()) return absl::OkStatus();
      }
    }
    NormalizedLandmarkList output;
    for (const auto& input : kIn(cc)) {
      if (input.IsEmpty()) continue;
      const NormalizedLandmarkList& list = *input;
      for (int j = 0; j < list.landmark_size(); ++j) {
        *output.add_landmark() = list.landmark(j);
      }
    }
    kOut(cc).Send(std::move(output));
    return absl::OkStatus();
  }
 private:
  bool only_emit_if_all_present_;
 };
 MEDIAPIPE_REGISTER_NODE(ConcatenateNormalizedLandmarkListCalculator);
 }  // namespace api2
 }  // namespace mediapipe
 // NOLINTNEXTLINE
 #endif  // MEDIAPIPE_CALCULATORS_CORE_CONCATENATE_NORMALIZED_LIST_CALCULATOR_H_
@@ -0,0 +1,118 @@
 // 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.
 #ifndef MEDIAPIPE_CALCULATORS_CORE_CONCATENATE_PROTO_LIST_CALCULATOR_H_  // NOLINT
 #define MEDIAPIPE_CALCULATORS_CORE_CONCATENATE_PROTO_LIST_CALCULATOR_H_  // NOLINT
 #include "mediapipe/calculators/core/concatenate_vector_calculator.pb.h"
 #include "mediapipe/framework/api2/node.h"
 #include "mediapipe/framework/calculator_framework.h"
 #include "mediapipe/framework/formats/landmark.pb.h"
 #include "mediapipe/framework/port/canonical_errors.h"
 #include "mediapipe/framework/port/ret_check.h"
 #include "mediapipe/framework/port/status.h"
 namespace mediapipe {
 namespace api2 {
 // Concatenate several input packets of ListType with a repeated field of
 // ItemType into a single output packet of ListType following stream index
 // order.
 template <typename ItemType, typename ListType>
 class ConcatenateListsCalculator : public Node {
 public:
  static constexpr typename Input<ListType>::Multiple kIn{""};
  static constexpr Output<ListType> kOut{""};
  MEDIAPIPE_NODE_CONTRACT(kIn, kOut);
  static absl::Status UpdateContract(CalculatorContract* cc) {
    RET_CHECK_GE(kIn(cc).Count(), 1);
    return absl::OkStatus();
  }
  absl::Status Open(CalculatorContext* cc) override {
    only_emit_if_all_present_ =
        cc->Options<::mediapipe::ConcatenateVectorCalculatorOptions>()
            .only_emit_if_all_present();
    return absl::OkStatus();
  }
  absl::Status Process(CalculatorContext* cc) override {
    if (only_emit_if_all_present_) {
      for (const auto& input : kIn(cc)) {
        if (input.IsEmpty()) return absl::OkStatus();
      }
    }
    ListType output;
    for (const auto& input : kIn(cc)) {
      if (input.IsEmpty()) continue;
      const ListType& list = *input;
      for (int j = 0; j < ListSize(list); ++j) {
        *AddItem(output) = GetItem(list, j);
      }
    }
    kOut(cc).Send(std::move(output));
    return absl::OkStatus();
  }
 protected:
  virtual int ListSize(const ListType& list) const = 0;
  virtual const ItemType GetItem(const ListType& list, int idx) const = 0;
  virtual ItemType* AddItem(ListType& list) const = 0;
 private:
  bool only_emit_if_all_present_;
 };
 // TODO: Move calculators to separate *.cc files
 class ConcatenateNormalizedLandmarkListCalculator
    : public ConcatenateListsCalculator<NormalizedLandmark,
                                        NormalizedLandmarkList> {
 protected:
  int ListSize(const NormalizedLandmarkList& list) const override {
    return list.landmark_size();
  }
  const NormalizedLandmark GetItem(const NormalizedLandmarkList& list,
                                   int idx) const override {
    return list.landmark(idx);
  }
  NormalizedLandmark* AddItem(NormalizedLandmarkList& list) const override {
    return list.add_landmark();
  }
 };
 MEDIAPIPE_REGISTER_NODE(ConcatenateNormalizedLandmarkListCalculator);
 class ConcatenateLandmarkListCalculator
    : public ConcatenateListsCalculator<Landmark, LandmarkList> {
 protected:
  int ListSize(const LandmarkList& list) const override {
    return list.landmark_size();
  }
  const Landmark GetItem(const LandmarkList& list, int idx) const override {
    return list.landmark(idx);
  }
  Landmark* AddItem(LandmarkList& list) const override {
    return list.add_landmark();
  }
 };
 MEDIAPIPE_REGISTER_NODE(ConcatenateLandmarkListCalculator);
 }  // namespace api2
 }  // namespace mediapipe
 // NOLINTNEXTLINE
 #endif  // MEDIAPIPE_CALCULATORS_CORE_CONCATENATE_PROTO_LIST_CALCULATOR_H_
@@ -73,8 +73,17 @@ typedef ConcatenateVectorCalculator<::mediapipe::NormalizedLandmark>
    ConcatenateLandmarkVectorCalculator;
 MEDIAPIPE_REGISTER_NODE(ConcatenateLandmarkVectorCalculator);
 typedef ConcatenateVectorCalculator<::mediapipe::LandmarkList>
    ConcatenateLandmarkListVectorCalculator;
 MEDIAPIPE_REGISTER_NODE(ConcatenateLandmarkListVectorCalculator);
 typedef ConcatenateVectorCalculator<::mediapipe::NormalizedLandmarkList>
-    ConcatenateLandmarListVectorCalculator;
+    ConcatenateNormalizedLandmarkListVectorCalculator;
 MEDIAPIPE_REGISTER_NODE(ConcatenateNormalizedLandmarkListVectorCalculator);
 // For backwards compatibility, keep the version with the typo.
 using ConcatenateLandmarListVectorCalculator =
    ConcatenateNormalizedLandmarkListVectorCalculator;
 MEDIAPIPE_REGISTER_NODE(ConcatenateLandmarListVectorCalculator);
 typedef ConcatenateVectorCalculator<mediapipe::ClassificationList>
@@ -18,6 +18,7 @@
 #include "mediapipe/framework/calculator_framework.h"
 #include "mediapipe/framework/collection_item_id.h"
 #include "mediapipe/framework/formats/classification.pb.h"
 #include "mediapipe/framework/formats/landmark.pb.h"
 #include "mediapipe/framework/port/canonical_errors.h"
 #include "mediapipe/framework/port/integral_types.h"
 #include "mediapipe/framework/port/ret_check.h"
@@ -79,6 +80,10 @@ class ConstantSidePacketCalculator : public CalculatorBase {
        packet.Set<uint64>();
      } else if (packet_options.has_classification_list_value()) {
        packet.Set<ClassificationList>();
      } else if (packet_options.has_landmark_list_value()) {
        packet.Set<LandmarkList>();
      } else if (packet_options.has_double_value()) {
        packet.Set<double>();
      } else {
        return absl::InvalidArgumentError(
            "None of supported values were specified in options.");
@@ -108,6 +113,11 @@ class ConstantSidePacketCalculator : public CalculatorBase {
      } else if (packet_options.has_classification_list_value()) {
        packet.Set(MakePacket<ClassificationList>(
            packet_options.classification_list_value()));
      } else if (packet_options.has_landmark_list_value()) {
        packet.Set(
            MakePacket<LandmarkList>(packet_options.landmark_list_value()));
      } else if (packet_options.has_double_value()) {
        packet.Set(MakePacket<double>(packet_options.double_value()));
      } else {
        return absl::InvalidArgumentError(
            "None of supported values were specified in options.");
@@ -18,6 +18,7 @@ package mediapipe;
 import "mediapipe/framework/calculator.proto";
 import "mediapipe/framework/formats/classification.proto";
 import "mediapipe/framework/formats/landmark.proto";
 option objc_class_prefix = "MediaPipe";
@@ -34,6 +35,8 @@ message ConstantSidePacketCalculatorOptions {
      string string_value = 4;
      uint64 uint64_value = 5;
      ClassificationList classification_list_value = 6;
      LandmarkList landmark_list_value = 7;
      double double_value = 9;
    }
  }
@@ -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")) {
+    if (cc->InputSidePackets().HasTag(kErrorOnOpenTag)) {
-      cc->InputSidePackets().Tag("ERROR_ON_OPEN").Set<bool>();
+      cc->InputSidePackets().Tag(kErrorOnOpenTag).Set<bool>();
    }
-    RET_CHECK(cc->InputSidePackets().HasTag("MAX_COUNT") ||
+    RET_CHECK(cc->InputSidePackets().HasTag(kMaxCountTag) ||
-              cc->InputSidePackets().HasTag("ERROR_COUNT"));
+              cc->InputSidePackets().HasTag(kErrorCountTag));
-    if (cc->InputSidePackets().HasTag("MAX_COUNT")) {
+    if (cc->InputSidePackets().HasTag(kMaxCountTag)) {
-      cc->InputSidePackets().Tag("MAX_COUNT").Set<int>();
+      cc->InputSidePackets().Tag(kMaxCountTag).Set<int>();
    }
-    if (cc->InputSidePackets().HasTag("ERROR_COUNT")) {
+    if (cc->InputSidePackets().HasTag(kErrorCountTag)) {
-      cc->InputSidePackets().Tag("ERROR_COUNT").Set<int>();
+      cc->InputSidePackets().Tag(kErrorCountTag).Set<int>();
    }
-    if (cc->InputSidePackets().HasTag("BATCH_SIZE")) {
+    if (cc->InputSidePackets().HasTag(kBatchSizeTag)) {
-      cc->InputSidePackets().Tag("BATCH_SIZE").Set<int>();
+      cc->InputSidePackets().Tag(kBatchSizeTag).Set<int>();
    }
-    if (cc->InputSidePackets().HasTag("INITIAL_VALUE")) {
+    if (cc->InputSidePackets().HasTag(kInitialValueTag)) {
-      cc->InputSidePackets().Tag("INITIAL_VALUE").Set<int>();
+      cc->InputSidePackets().Tag(kInitialValueTag).Set<int>();
    }
-    if (cc->InputSidePackets().HasTag("INCREMENT")) {
+    if (cc->InputSidePackets().HasTag(kIncrementTag)) {
-      cc->InputSidePackets().Tag("INCREMENT").Set<int>();
+      cc->InputSidePackets().Tag(kIncrementTag).Set<int>();
    }
    return absl::OkStatus();
  }
  absl::Status Open(CalculatorContext* cc) override {
-    if (cc->InputSidePackets().HasTag("ERROR_ON_OPEN") &&
+    if (cc->InputSidePackets().HasTag(kErrorOnOpenTag) &&
-        cc->InputSidePackets().Tag("ERROR_ON_OPEN").Get<bool>()) {
+        cc->InputSidePackets().Tag(kErrorOnOpenTag).Get<bool>()) {
      return absl::NotFoundError("expected error");
    }
-    if (cc->InputSidePackets().HasTag("ERROR_COUNT")) {
+    if (cc->InputSidePackets().HasTag(kErrorCountTag)) {
-      error_count_ = cc->InputSidePackets().Tag("ERROR_COUNT").Get<int>();
+      error_count_ = cc->InputSidePackets().Tag(kErrorCountTag).Get<int>();
      RET_CHECK_LE(0, error_count_);
    }
-    if (cc->InputSidePackets().HasTag("MAX_COUNT")) {
+    if (cc->InputSidePackets().HasTag(kMaxCountTag)) {
-      max_count_ = cc->InputSidePackets().Tag("MAX_COUNT").Get<int>();
+      max_count_ = cc->InputSidePackets().Tag(kMaxCountTag).Get<int>();
      RET_CHECK_LE(0, max_count_);
    }
-    if (cc->InputSidePackets().HasTag("BATCH_SIZE")) {
+    if (cc->InputSidePackets().HasTag(kBatchSizeTag)) {
-      batch_size_ = cc->InputSidePackets().Tag("BATCH_SIZE").Get<int>();
+      batch_size_ = cc->InputSidePackets().Tag(kBatchSizeTag).Get<int>();
      RET_CHECK_LT(0, batch_size_);
    }
-    if (cc->InputSidePackets().HasTag("INITIAL_VALUE")) {
+    if (cc->InputSidePackets().HasTag(kInitialValueTag)) {
-      counter_ = cc->InputSidePackets().Tag("INITIAL_VALUE").Get<int>();
+      counter_ = cc->InputSidePackets().Tag(kInitialValueTag).Get<int>();
    }
-    if (cc->InputSidePackets().HasTag("INCREMENT")) {
+    if (cc->InputSidePackets().HasTag(kIncrementTag)) {
-      increment_ = cc->InputSidePackets().Tag("INCREMENT").Get<int>();
+      increment_ = cc->InputSidePackets().Tag(kIncrementTag).Get<int>();
      RET_CHECK_LT(0, increment_);
    }
    RET_CHECK(error_count_ >= 0 || max_count_ >= 0);
@@ -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->Inputs().Tag(kEncodedTag).Set<std::string>();
-    cc->Outputs().Tag("FLOAT_VECTOR").Set<std::vector<float>>();
+    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();
@@ -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(
+  runner.MutableInputs()
-      MakePacket<std::string>(empty_string).At(Timestamp(0)));
+      ->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(
+  runner.MutableInputs()
-      MakePacket<std::string>(empty_string).At(Timestamp(0)));
+      ->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(
+  runner.MutableInputs()
-      MakePacket<std::string>(empty_string).At(Timestamp(0)));
+      ->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(
+  runner.MutableInputs()
-      MakePacket<std::string>(
+      ->Tag(kEncodedTag)
-          std::string(reinterpret_cast<char const*>(input), 4))
+      .packets.push_back(
-          .At(Timestamp(0)));
+          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());
@@ -17,6 +17,7 @@
 #include <vector>
 #include "mediapipe/framework/formats/classification.pb.h"
 #include "mediapipe/framework/formats/detection.pb.h"
 #include "mediapipe/framework/formats/landmark.pb.h"
 #include "mediapipe/framework/formats/rect.pb.h"
 #include "mediapipe/util/render_data.pb.h"
@@ -28,6 +29,10 @@ 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);
@@ -46,4 +51,8 @@ REGISTER_CALCULATOR(EndLoopClassificationListCalculator);
 typedef EndLoopCalculator<std::vector<TfLiteTensor>> EndLoopTensorCalculator;
 REGISTER_CALCULATOR(EndLoopTensorCalculator);
 typedef EndLoopCalculator<std::vector<::mediapipe::Detection>>
    EndLoopDetectionCalculator;
 REGISTER_CALCULATOR(EndLoopDetectionCalculator);
 }  // namespace mediapipe
@@ -24,11 +24,16 @@
 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.
 //
-// The input stream "FINISH" is used to signal the FlowLimiterCalculator
+// The input stream "FINISHED" is used to signal the FlowLimiterCalculator
-// when a frame is finished processing.  Either a non-empty "FINISH" packet
+// when a frame is finished processing.  Either a non-empty "FINISHED" packet
 // or a timestamp bound should be received for each processed frame.
 //
 // The combination of `max_in_flight: 1` and `max_in_queue: 1` generally gives
@@ -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();
+    side_inputs.Tag(kOptionsTag).Set<FlowLimiterCalculatorOptions>().Optional();
-    cc->Inputs().Tag("OPTIONS").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->InputSidePackets().Tag(kMaxInFlightTag).Set<int>().Optional();
-    cc->Outputs().Tag("ALLOW").Set<bool>().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")) {
+    if (cc->Outputs().HasTag(kAllowTag)) {
-      cc->Outputs().Tag("ALLOW").AddPacket(MakePacket<bool>(allow).At(ts));
+      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")) {
+      if (cc->Outputs().HasTag(kAllowTag)) {
-        SetNextTimestampBound(bound, &cc->Outputs().Tag("ALLOW"));
+        SetNextTimestampBound(bound, &cc->Outputs().Tag(kAllowTag));
      }
    }
@@ -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.
@@ -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->Inputs().Tag(kPacketTag).SetAny();
-    cc->Outputs().Tag("PACKET").SetSameAs(&cc->Inputs().Tag("PACKET"));
+    cc->Outputs().Tag(kPacketTag).SetSameAs(&cc->Inputs().Tag(kPacketTag));
-    cc->InputSidePackets().Tag("SLEEP_TIME").Set<int64>();
+    cc->InputSidePackets().Tag(kSleepTimeTag).Set<int64>();
-    cc->InputSidePackets().Tag("WARMUP_TIME").Set<int64>();
+    cc->InputSidePackets().Tag(kWarmupTimeTag).Set<int64>();
-    cc->InputSidePackets().Tag("CLOCK").Set<mediapipe::Clock*>();
+    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(kWarmupTimeTag).Get<int64>()
-            : cc->InputSidePackets().Tag("SLEEP_TIME").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->Inputs().Tag(kPacketTag).SetAny();
-    cc->Outputs().Tag("PACKET").SetSameAs(&cc->Inputs().Tag("PACKET"));
+    cc->Outputs().Tag(kPacketTag).SetSameAs(&cc->Inputs().Tag(kPacketTag));
-    cc->InputSidePackets().Tag("DROP_TIMESTAMPS").Set<bool>();
+    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()) {
+    if (!drop && !cc->Inputs().Tag(kPacketTag).Value().IsEmpty()) {
-      cc->Outputs().Tag("PACKET").AddPacket(cc->Inputs().Tag("PACKET").Value());
+      cc->Outputs()
          .Tag(kPacketTag)
          .AddPacket(cc->Inputs().Tag(kPacketTag).Value());
    }
-    if (!drop || !cc->InputSidePackets().Tag("DROP_TIMESTAMPS").Get<bool>()) {
+    if (!drop || !cc->InputSidePackets().Tag(kDropTimestampsTag).Get<bool>()) {
-      cc->Outputs().Tag("PACKET").SetNextTimestampBound(
+      cc->Outputs()
-          cc->InputTimestamp().NextAllowedInStream());
+          .Tag(kPacketTag)
          .SetNextTimestampBound(cc->InputTimestamp().NextAllowedInStream());
    }
    return absl::OkStatus();
  }
@@ -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
+// ALLOW/DISALLOW inputs must be specified either using input stream or via
-// via input side packet but not both.
+// 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") ||
+    bool input_via_side_packet = cc->InputSidePackets().HasTag(kAllowTag) ||
-                                 cc->InputSidePackets().HasTag("DISALLOW");
+                                 cc->InputSidePackets().HasTag(kDisallowTag);
    bool input_via_stream =
-        cc->Inputs().HasTag("ALLOW") || cc->Inputs().HasTag("DISALLOW");
+        cc->Inputs().HasTag(kAllowTag) || cc->Inputs().HasTag(kDisallowTag);
    // Only one of input_side_packet or input_stream may specify ALLOW/DISALLOW
    // input.
    RET_CHECK(input_via_side_packet ^ input_via_stream);
    // 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") ^
+      RET_CHECK(!input_via_stream);
-                cc->InputSidePackets().HasTag("DISALLOW"));
+      RET_CHECK(cc->InputSidePackets().HasTag(kAllowTag) ^
                cc->InputSidePackets().HasTag(kDisallowTag));
-      if (cc->InputSidePackets().HasTag("ALLOW")) {
+      if (cc->InputSidePackets().HasTag(kAllowTag)) {
-        cc->InputSidePackets().Tag("ALLOW").Set<bool>();
+        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")) {
+      if (cc->Inputs().HasTag(kAllowTag)) {
-        cc->Inputs().Tag("ALLOW").Set<bool>();
+        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")) {
+    if (cc->Outputs().HasTag(kStateChangeTag)) {
-      cc->Outputs().Tag("STATE_CHANGE").Set<bool>();
+      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(kAllowTag)) {
    if (cc->InputSidePackets().HasTag("ALLOW")) {
      use_side_packet_for_allow_disallow_ = true;
      allow_by_side_packet_decision_ =
-          cc->InputSidePackets().Tag("ALLOW").Get<bool>();
+          cc->InputSidePackets().Tag(kAllowTag).Get<bool>();
-    } else if (cc->InputSidePackets().HasTag("DISALLOW")) {
+    } 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") &&
+      if (cc->Inputs().HasTag(kAllowTag) &&
-          !cc->Inputs().Tag("ALLOW").IsEmpty()) {
+          !cc->Inputs().Tag(kAllowTag).IsEmpty()) {
-        allow = cc->Inputs().Tag("ALLOW").Get<bool>();
+        allow = cc->Inputs().Tag(kAllowTag).Get<bool>();
      }
-      if (cc->Inputs().HasTag("DISALLOW") &&
+      if (cc->Inputs().HasTag(kDisallowTag) &&
-          !cc->Inputs().Tag("DISALLOW").IsEmpty()) {
+          !cc->Inputs().Tag(kDisallowTag).IsEmpty()) {
-        allow = !cc->Inputs().Tag("DISALLOW").Get<bool>();
+        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);
@@ -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];
 }
@@ -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);
@@ -0,0 +1,32 @@
 // Copyright 2022 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/core/get_vector_item_calculator.h"
 #include "mediapipe/framework/formats/classification.pb.h"
 #include "mediapipe/framework/formats/landmark.pb.h"
 namespace mediapipe {
 namespace api2 {
 using GetLandmarkListVectorItemCalculator =
    GetVectorItemCalculator<mediapipe::LandmarkList>;
 REGISTER_CALCULATOR(GetLandmarkListVectorItemCalculator);
 using GetClassificationListVectorItemCalculator =
    GetVectorItemCalculator<mediapipe::ClassificationList>;
 REGISTER_CALCULATOR(GetClassificationListVectorItemCalculator);
 }  // namespace api2
 }  // namespace mediapipe
@@ -0,0 +1,77 @@
 // 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.
 #ifndef MEDIAPIPE_CALCULATORS_CORE_GET_VECTOR_ITEM_CALCULATOR_H_
 #define MEDIAPIPE_CALCULATORS_CORE_GET_VECTOR_ITEM_CALCULATOR_H_
 #include <optional>
 #include "mediapipe/framework/api2/node.h"
 #include "mediapipe/framework/api2/port.h"
 #include "mediapipe/framework/calculator_framework.h"
 #include "mediapipe/framework/packet.h"
 #include "mediapipe/framework/port/ret_check.h"
 #include "mediapipe/framework/port/status.h"
 namespace mediapipe {
 namespace api2 {
 // A calcutlator to return an item from the vector by its index.
 //
 // Inputs:
 //   VECTOR - std::vector<T>
 //     Vector to take an item from.
 //   INDEX - int
 //     Index of the item to return.
 //
 // Outputs:
 //   ITEM - T
 //     Item from the vector at given index.
 //
 // Example config:
 //   node {
 //     calculator: "Get{SpecificType}VectorItemCalculator"
 //     input_stream: "VECTOR:vector"
 //     input_stream: "INDEX:index"
 //     input_stream: "ITEM:item"
 //   }
 //
 template <typename T>
 class GetVectorItemCalculator : public Node {
 public:
  static constexpr Input<std::vector<T>> kIn{"VECTOR"};
  static constexpr Input<int> kIdx{"INDEX"};
  static constexpr Output<T> kOut{"ITEM"};
  MEDIAPIPE_NODE_CONTRACT(kIn, kIdx, kOut);
  absl::Status Process(CalculatorContext* cc) final {
    if (kIn(cc).IsEmpty() || kIdx(cc).IsEmpty()) {
      return absl::OkStatus();
    }
    const std::vector<T>& items = kIn(cc).Get();
    const int idx = kIdx(cc).Get();
    RET_CHECK_LT(idx, items.size());
    kOut(cc).Send(items[idx]);
    return absl::OkStatus();
  }
 };
 }  // namespace api2
 }  // namespace mediapipe
 #endif  // MEDIAPIPE_CALCULATORS_CORE_GET_VECTOR_ITEM_CALCULATOR_H_
@@ -0,0 +1,78 @@
 // 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.
 //
 // Similarly to the log files saved by GraphProfiler::WriteProfile when trace
 // logging is enabled, the first captured profile contains the full
 // canonicalized graph config and, if tracing is enabled, calculator names in
 // graph traces. Subsequent profiles omit this information.
 //
 // 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>();
    bool first_profile = prev_profile_ts_ == Timestamp::Unset();
    if (first_profile ||
        cc->InputTimestamp() - prev_profile_ts_ >= options.profile_interval()) {
      prev_profile_ts_ = cc->InputTimestamp();
      GraphProfile result;
      MP_RETURN_IF_ERROR(cc->GetProfilingContext()->CaptureProfile(
          &result, first_profile ? PopulateGraphConfig::kFull
                                 : PopulateGraphConfig::kNo));
      kProfileOut(cc).Send(result);
    }
    return absl::OkStatus();
  }
 private:
  Timestamp prev_profile_ts_;
 };
 MEDIAPIPE_REGISTER_NODE(GraphProfileCalculator);
 }  // namespace api2
 }  // namespace mediapipe
@@ -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];
 }
@@ -0,0 +1,212 @@
 // 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");
  ASSERT_EQ(output_packets.size(), 2);
  EXPECT_TRUE(output_packets[0].Get<GraphProfile>().has_config());
  EXPECT_THAT(output_packets[1].Get<GraphProfile>(),
              mediapipe::EqualsProto(expected_profile));
 }
 }  // namespace
 }  // namespace mediapipe
@@ -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
@@ -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(
+  runner.MutableInputs()
-      Adopt(input_matrix).At(Timestamp(0)));
+      ->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());
@@ -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
@@ -16,9 +16,10 @@
 // For every packet that appears in B, outputs the most recent packet from each
 // of the A_i on a separate stream.
 #include <string_view>
 #include <vector>
-#include "absl/strings/str_cat.h"
+#include "absl/strings/string_view.h"
 #include "mediapipe/calculators/core/packet_cloner_calculator.pb.h"
 #include "mediapipe/framework/calculator_framework.h"
@@ -34,7 +35,18 @@ namespace mediapipe {
 //   calculator: "PacketClonerCalculator"
 //   input_stream: "first_base_signal"
 //   input_stream: "second_base_signal"
-//   input_stream: "tick_signal"
+//   input_stream: "tick_signal"  # or input_stream: "TICK:tick_signal"
 //   output_stream: "cloned_first_base_signal"
 //   output_stream: "cloned_second_base_signal"
 // }
 //
 // Or you can use "TICK" tag and put corresponding input stream at any location,
 // for example at the very beginning:
 // node {
 //   calculator: "PacketClonerCalculator"
 //   input_stream: "TICK:tick_signal"
 //   input_stream: "first_base_signal"
 //   input_stream: "second_base_signal"
 //   output_stream: "cloned_first_base_signal"
 //   output_stream: "cloned_second_base_signal"
 // }
@@ -46,12 +58,13 @@ namespace mediapipe {
 class PacketClonerCalculator : public CalculatorBase {
 public:
  static absl::Status GetContract(CalculatorContract* cc) {
-    const int tick_signal_index = cc->Inputs().NumEntries() - 1;
+    const Ids ids = GetIds(*cc);
-    for (int i = 0; i < tick_signal_index; ++i) {
+    for (const auto& in_out : ids.inputs_outputs) {
-      cc->Inputs().Index(i).SetAny();
+      auto& input = cc->Inputs().Get(in_out.in);
-      cc->Outputs().Index(i).SetSameAs(&cc->Inputs().Index(i));
+      input.SetAny();
      cc->Outputs().Get(in_out.out).SetSameAs(&input);
    }
-    cc->Inputs().Index(tick_signal_index).SetAny();
+    cc->Inputs().Get(ids.tick_id).SetAny();
    return absl::OkStatus();
  }
@@ -60,15 +73,20 @@ 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();
    // Prepare input and output ids.
    ids_ = GetIds(*cc);
    current_.resize(ids_.inputs_outputs.size());
    // Parse input streams.
    tick_signal_index_ = cc->Inputs().NumEntries() - 1;
    current_.resize(tick_signal_index_);
    // Pass along the header for each stream if present.
-    for (int i = 0; i < tick_signal_index_; ++i) {
+    for (const auto& in_out : ids_.inputs_outputs) {
-      if (!cc->Inputs().Index(i).Header().IsEmpty()) {
+      auto& input = cc->Inputs().Get(in_out.in);
-        cc->Outputs().Index(i).SetHeader(cc->Inputs().Index(i).Header());
+      if (!input.Header().IsEmpty()) {
        cc->Outputs().Get(in_out.out).SetHeader(input.Header());
      }
    }
    return absl::OkStatus();
@@ -76,29 +94,33 @@ class PacketClonerCalculator : public CalculatorBase {
  absl::Status Process(CalculatorContext* cc) final {
    // Store input signals.
-    for (int i = 0; i < tick_signal_index_; ++i) {
+    for (int i = 0; i < ids_.inputs_outputs.size(); ++i) {
-      if (!cc->Inputs().Index(i).Value().IsEmpty()) {
+      const auto& input = cc->Inputs().Get(ids_.inputs_outputs[i].in);
-        current_[i] = cc->Inputs().Index(i).Value();
+      if (!input.IsEmpty()) {
        current_[i] = input.Value();
      }
    }
    // Output according to the TICK signal.
-    if (!cc->Inputs().Index(tick_signal_index_).Value().IsEmpty()) {
+    if (!cc->Inputs().Get(ids_.tick_id).IsEmpty()) {
      if (output_only_when_all_inputs_received_) {
        // Return if one of the input is null.
-        for (int i = 0; i < tick_signal_index_; ++i) {
+        for (int i = 0; i < ids_.inputs_outputs.size(); ++i) {
          if (current_[i].IsEmpty()) {
            if (output_empty_packets_before_all_inputs_received_) {
              SetAllNextTimestampBounds(cc);
            }
            return absl::OkStatus();
          }
        }
      }
      // Output each stream.
-      for (int i = 0; i < tick_signal_index_; ++i) {
+      for (int i = 0; i < ids_.inputs_outputs.size(); ++i) {
        auto& output = cc->Outputs().Get(ids_.inputs_outputs[i].out);
        if (!current_[i].IsEmpty()) {
-          cc->Outputs().Index(i).AddPacket(
+          output.AddPacket(current_[i].At(cc->InputTimestamp()));
              current_[i].At(cc->InputTimestamp()));
        } else {
-          cc->Outputs().Index(i).SetNextTimestampBound(
+          output.SetNextTimestampBound(
              cc->InputTimestamp().NextAllowedInStream());
        }
      }
@@ -107,9 +129,46 @@ class PacketClonerCalculator : public CalculatorBase {
  }
 private:
  struct Ids {
    struct InputOutput {
      CollectionItemId in;
      CollectionItemId out;
    };
    CollectionItemId tick_id;
    std::vector<InputOutput> inputs_outputs;
  };
  template <typename CC>
  static Ids GetIds(CC& cc) {
    Ids ids;
    static constexpr absl::string_view kEmptyTag = "";
    int num_inputs_to_clone = cc.Inputs().NumEntries(kEmptyTag);
    static constexpr absl::string_view kTickTag = "TICK";
    if (cc.Inputs().HasTag(kTickTag)) {
      ids.tick_id = cc.Inputs().GetId(kTickTag, 0);
    } else {
      --num_inputs_to_clone;
      ids.tick_id = cc.Inputs().GetId(kEmptyTag, num_inputs_to_clone);
    }
    for (int i = 0; i < num_inputs_to_clone; ++i) {
      ids.inputs_outputs.push_back({.in = cc.Inputs().GetId(kEmptyTag, i),
                                    .out = cc.Outputs().GetId(kEmptyTag, i)});
    }
    return ids;
  }
  void SetAllNextTimestampBounds(CalculatorContext* cc) {
    for (const auto& in_out : ids_.inputs_outputs) {
      cc->Outputs()
          .Get(in_out.out)
          .SetNextTimestampBound(cc->InputTimestamp().NextAllowedInStream());
    }
  }
  std::vector<Packet> current_;
-  int tick_signal_index_;
+  Ids ids_;
  bool output_only_when_all_inputs_received_;
  bool output_empty_packets_before_all_inputs_received_;
 };
 REGISTER_CALCULATOR(PacketClonerCalculator);
@@ -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];
 }
@@ -0,0 +1,349 @@
 // Copyright 2022 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 <algorithm>
 #include <functional>
 #include <memory>
 #include <string>
 #include <vector>
 #include "absl/strings/str_cat.h"
 #include "mediapipe/framework/calculator_framework.h"
 #include "mediapipe/framework/port/gmock.h"
 #include "mediapipe/framework/port/gtest.h"
 #include "mediapipe/framework/port/parse_text_proto.h"
 #include "mediapipe/framework/port/status.h"
 #include "mediapipe/framework/port/status_matchers.h"
 #include "mediapipe/framework/timestamp.h"
 #include "mediapipe/framework/tool/sink.h"
 namespace mediapipe {
 namespace {
 using ::testing::ElementsAre;
 using ::testing::Eq;
 using ::testing::Value;
 MATCHER_P2(IntPacket, value, ts, "") {
  return Value(arg.template Get<int>(), Eq(value)) &&
         Value(arg.Timestamp(), Eq(Timestamp(ts)));
 }
 MATCHER_P2(FloatPacket, value, ts, "") {
  return Value(arg.template Get<float>(), Eq(value)) &&
         Value(arg.Timestamp(), Eq(Timestamp(ts)));
 }
 template <typename T>
 absl::Status SendPacket(const std::string& input_name, T value, int ts,
                        CalculatorGraph& graph) {
  return graph.AddPacketToInputStream(input_name,
                                      MakePacket<T>(value).At(Timestamp(ts)));
 }
 struct Params {
  bool use_tick_tag = false;
 };
 class PacketClonerCalculatorTest : public testing::TestWithParam<Params> {};
 TEST_P(PacketClonerCalculatorTest, ClonesSingleInputSameTimestamps) {
  CalculatorGraphConfig graph_config =
      ParseTextProtoOrDie<CalculatorGraphConfig>([&]() {
        if (GetParam().use_tick_tag) {
          return R"pb(
            input_stream: 'in1'
            input_stream: 'tick'
            node {
              calculator: 'PacketClonerCalculator'
              input_stream: 'in1'
              input_stream: 'TICK:tick'
              output_stream: 'out1'
            })pb";
        }
        return R"pb(
          input_stream: 'in1'
          input_stream: 'tick'
          node {
            calculator: 'PacketClonerCalculator'
            input_stream: 'in1'
            input_stream: 'tick'
            output_stream: 'out1'
          })pb";
      }());
  std::vector<Packet> out1;
  tool::AddVectorSink("out1", &graph_config, &out1);
  CalculatorGraph graph;
  MP_ASSERT_OK(graph.Initialize(graph_config, {}));
  MP_ASSERT_OK(graph.StartRun({}));
  MP_ASSERT_OK(SendPacket("in1", 1, /*ts=*/10000, graph));
  MP_ASSERT_OK(SendPacket("tick", 1000, /*ts=*/10000, graph));
  MP_ASSERT_OK(graph.WaitUntilIdle());
  EXPECT_THAT(out1, ElementsAre(IntPacket(1, 10000)));
 }
 TEST_P(PacketClonerCalculatorTest, ClonesSingleInputEarlierTimestamps) {
  CalculatorGraphConfig graph_config =
      ParseTextProtoOrDie<CalculatorGraphConfig>([&]() {
        if (GetParam().use_tick_tag) {
          return R"pb(
            input_stream: 'in1'
            input_stream: 'tick'
            node {
              calculator: 'PacketClonerCalculator'
              input_stream: 'in1'
              input_stream: 'TICK:tick'
              output_stream: 'out1'
            })pb";
        }
        return R"pb(
          input_stream: 'in1'
          input_stream: 'tick'
          node {
            calculator: 'PacketClonerCalculator'
            input_stream: 'in1'
            input_stream: 'tick'
            output_stream: 'out1'
          })pb";
      }());
  std::vector<Packet> out1;
  tool::AddVectorSink("out1", &graph_config, &out1);
  CalculatorGraph graph;
  MP_ASSERT_OK(graph.Initialize(graph_config, {}));
  MP_ASSERT_OK(graph.StartRun({}));
  // PacketClonerCalculator is non-ImmediateInputStreamHandler
  // PacketClonerCalculator waits for "in1" to arrive for ts=5000
  MP_ASSERT_OK(SendPacket("in1", 1, /*ts=*/5000, graph));
  // Newer tick at ts=10000, should NOT trigger output for ts=5000
  // PacketClonerCalculator waits for "in1" to arrive for ts=10000
  MP_ASSERT_OK(SendPacket("tick", 1000, /*ts=*/10000, graph));
  MP_ASSERT_OK(SendPacket("tick", 1001, /*ts=*/10001, graph));
  MP_ASSERT_OK(SendPacket("tick", 1002, /*ts=*/10002, graph));
  // Newer "in1" at ts=15000, should trigger output for ts=10000
  MP_ASSERT_OK(SendPacket("in1", 2, /*ts=*/15000, graph));
  MP_ASSERT_OK(graph.WaitUntilIdle());
  EXPECT_THAT(out1, ElementsAre(IntPacket(1, 10000), IntPacket(1, 10001),
                                IntPacket(1, 10002)));
 }
 TEST_P(PacketClonerCalculatorTest, ClonesFiveInputs) {
  CalculatorGraphConfig graph_config =
      ParseTextProtoOrDie<CalculatorGraphConfig>([&]() {
        if (GetParam().use_tick_tag) {
          return R"pb(
            input_stream: 'in1'
            input_stream: 'in2'
            input_stream: 'in3'
            input_stream: 'in4'
            input_stream: 'in5'
            input_stream: 'tick'
            node {
              calculator: 'PacketClonerCalculator'
              input_stream: 'in1'
              input_stream: 'in2'
              input_stream: 'in3'
              input_stream: 'in4'
              input_stream: 'in5'
              output_stream: 'out1'
              output_stream: 'out2'
              output_stream: 'out3'
              input_stream: 'TICK:tick'  # arbitrary location
              output_stream: 'out4'
              output_stream: 'out5'
            }
          )pb";
        }
        return R"pb(
          input_stream: 'in1'
          input_stream: 'in2'
          input_stream: 'in3'
          input_stream: 'in4'
          input_stream: 'in5'
          input_stream: 'tick'
          node {
            calculator: 'PacketClonerCalculator'
            input_stream: 'in1'
            input_stream: 'in2'
            input_stream: 'in3'
            input_stream: 'in4'
            input_stream: 'in5'
            input_stream: 'tick'
            output_stream: 'out1'
            output_stream: 'out2'
            output_stream: 'out3'
            output_stream: 'out4'
            output_stream: 'out5'
          }
        )pb";
      }());
  constexpr int kNumToClone = 5;
  std::array<std::vector<Packet>, kNumToClone> outs;
  for (int i = 0; i < kNumToClone; ++i) {
    tool::AddVectorSink(absl::StrCat("out", i + 1), &graph_config, &outs[i]);
  }
  CalculatorGraph graph;
  MP_ASSERT_OK(graph.Initialize(graph_config, {}));
  MP_ASSERT_OK(graph.StartRun({}));
  MP_ASSERT_OK(SendPacket("in1", 10, /*ts=*/10000, graph));
  MP_ASSERT_OK(SendPacket("in2", 20.0f, /*ts=*/10000, graph));
  MP_ASSERT_OK(SendPacket("in3", 30, /*ts=*/10000, graph));
  MP_ASSERT_OK(SendPacket("in4", 40.0f, /*ts=*/10000, graph));
  MP_ASSERT_OK(SendPacket("in5", 50, /*ts=*/10000, graph));
  MP_ASSERT_OK(SendPacket("tick", 1000, /*ts=*/10000, graph));
  // Below "tick" packets won't trigger output, until newer inputs are sent,
  // because inputs are missing and ImmediateInputStreamHandler is not
  // configured.
  MP_ASSERT_OK(SendPacket("tick", 1001, /*ts=*/10001, graph));
  MP_ASSERT_OK(SendPacket("tick", 1002, /*ts=*/10002, graph));
  MP_ASSERT_OK(graph.WaitUntilIdle());
  EXPECT_THAT(outs, ElementsAre(ElementsAre(IntPacket(10, 10000)),
                                ElementsAre(FloatPacket(20.0f, 10000)),
                                ElementsAre(IntPacket(30, 10000)),
                                ElementsAre(FloatPacket(40.0f, 10000)),
                                ElementsAre(IntPacket(50, 10000))));
  MP_ASSERT_OK(SendPacket("in1", 100, /*ts=*/20000, graph));
  MP_ASSERT_OK(SendPacket("in2", 200.0f, /*ts=*/20000, graph));
  MP_ASSERT_OK(SendPacket("in3", 300, /*ts=*/20000, graph));
  MP_ASSERT_OK(SendPacket("in4", 400.0f, /*ts=*/20000, graph));
  MP_ASSERT_OK(SendPacket("in5", 500, /*ts=*/20000, graph));
  MP_ASSERT_OK(SendPacket("tick", 2000, /*ts=*/20000, graph));
  // Below "tick" packets won't trigger output, because inputs are missing and
  // ImmediateInputStreamHandler is not configured.
  MP_ASSERT_OK(SendPacket("tick", 2001, /*ts=*/20001, graph));
  MP_ASSERT_OK(SendPacket("tick", 2002, /*ts=*/20002, graph));
  MP_ASSERT_OK(graph.WaitUntilIdle());
  EXPECT_THAT(
      outs,
      ElementsAre(
          ElementsAre(IntPacket(10, 10000), IntPacket(10, 10001),
                      IntPacket(10, 10002), IntPacket(100, 20000)),
          ElementsAre(FloatPacket(20.0f, 10000), FloatPacket(20.0f, 10001),
                      FloatPacket(20.0f, 10002), FloatPacket(200.0f, 20000)),
          ElementsAre(IntPacket(30, 10000), IntPacket(30, 10001),
                      IntPacket(30, 10002), IntPacket(300, 20000)),
          ElementsAre(FloatPacket(40.0f, 10000), FloatPacket(40.0f, 10001),
                      FloatPacket(40.0f, 10002), FloatPacket(400.0f, 20000)),
          ElementsAre(IntPacket(50, 10000), IntPacket(50, 10001),
                      IntPacket(50, 10002), IntPacket(500, 20000))));
 }
 TEST_P(PacketClonerCalculatorTest,
       ClonesTwoInputsWithImmediateInputStreamHandler) {
  CalculatorGraphConfig graph_config =
      ParseTextProtoOrDie<CalculatorGraphConfig>([&]() {
        if (GetParam().use_tick_tag) {
          return R"pb(
            input_stream: 'in1'
            input_stream: 'in2'
            input_stream: 'tick'
            node {
              calculator: 'PacketClonerCalculator'
              input_stream: 'TICK:tick'
              input_stream: 'in1'
              input_stream: 'in2'
              output_stream: 'out1'
              output_stream: 'out2'
              input_stream_handler {
                input_stream_handler: "ImmediateInputStreamHandler"
              }
            })pb";
        }
        return R"pb(
          input_stream: 'in1'
          input_stream: 'in2'
          input_stream: 'tick'
          node {
            calculator: 'PacketClonerCalculator'
            input_stream: 'in1'
            input_stream: 'in2'
            input_stream: 'tick'
            output_stream: 'out1'
            output_stream: 'out2'
            input_stream_handler {
              input_stream_handler: "ImmediateInputStreamHandler"
            }
          })pb";
      }());
  constexpr int kNumToClone = 2;
  std::array<std::vector<Packet>, kNumToClone> outs;
  for (int i = 0; i < kNumToClone; ++i) {
    tool::AddVectorSink(absl::StrCat("out", i + 1), &graph_config, &outs[i]);
  }
  CalculatorGraph graph;
  MP_ASSERT_OK(graph.Initialize(graph_config, {}));
  MP_ASSERT_OK(graph.StartRun({}));
  // No packets to clone.
  MP_ASSERT_OK(SendPacket("tick", 0, /*ts=*/0, graph));
  MP_ASSERT_OK(graph.WaitUntilIdle());
  // Cloning current packets.
  MP_ASSERT_OK(SendPacket("in1", 1, /*ts=*/10000, graph));
  MP_ASSERT_OK(SendPacket("in2", 10.0f, /*ts=*/10000, graph));
  MP_ASSERT_OK(SendPacket("tick", 1000, /*ts=*/10000, graph));
  MP_ASSERT_OK(graph.WaitUntilIdle());
  // Cloning past packets.
  MP_ASSERT_OK(SendPacket("tick", 1500, /*ts=*/15000, graph));
  MP_ASSERT_OK(graph.WaitUntilIdle());
  // Cloning past packets.
  MP_ASSERT_OK(SendPacket("in1", 2, /*ts=*/10001, graph));
  MP_ASSERT_OK(SendPacket("in2", 20.0f, /*ts=*/10001, graph));
  MP_ASSERT_OK(SendPacket("tick", 2000, /*ts=*/20000, graph));
  MP_ASSERT_OK(graph.WaitUntilIdle());
  // Cloning future packets.
  MP_ASSERT_OK(SendPacket("in1", 3, /*ts=*/30000, graph));
  MP_ASSERT_OK(SendPacket("in2", 30.0f, /*ts=*/30000, graph));
  // Waiting to ensure newer packets (ts=30000) to clone would get into the
  // cloner before tick (ts=25000) does.
  MP_ASSERT_OK(graph.WaitUntilIdle());
  MP_ASSERT_OK(SendPacket("tick", 3000, /*ts=*/25000, graph));
  MP_ASSERT_OK(graph.WaitUntilIdle());
  // Cloning packets having different timestamps.
  MP_ASSERT_OK(SendPacket("in1", 4, /*ts=*/38000, graph));
  MP_ASSERT_OK(SendPacket("in2", 40.0f, /*ts=*/39000, graph));
  MP_ASSERT_OK(SendPacket("tick", 4000, /*ts=*/40000, graph));
  MP_ASSERT_OK(graph.WaitUntilIdle());
  EXPECT_THAT(
      outs,
      ElementsAre(
          ElementsAre(IntPacket(1, 10000), IntPacket(1, 15000),
                      IntPacket(2, 20000), IntPacket(3, 25000),
                      IntPacket(4, 40000)),
          ElementsAre(FloatPacket(10.0f, 10000), FloatPacket(10.0f, 15000),
                      FloatPacket(20.0f, 20000), FloatPacket(30.0f, 25000),
                      FloatPacket(40.0f, 40000))));
 }
 INSTANTIATE_TEST_SUITE_P(PacketClonerCalculator, PacketClonerCalculatorTest,
                         testing::ValuesIn({Params{.use_tick_tag = false},
                                            Params{.use_tick_tag = true}}));
 }  // anonymous namespace
 }  // namespace mediapipe
@@ -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->Inputs().Tag(kPacketTag).SetAny();
-    cc->Outputs().Tag("PRESENCE").Set<bool>();
+    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")
+        .Tag(kPresenceTag)
-        .AddPacket(MakePacket<bool>(!cc->Inputs().Tag("PACKET").IsEmpty())
+        .AddPacket(MakePacket<bool>(!cc->Inputs().Tag(kPacketTag).IsEmpty())
                       .At(cc->InputTimestamp()));
    return absl::OkStatus();
  }
@@ -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")) {
+  if (cc->InputSidePackets().HasTag(kOptionsTag)) {
-    cc->InputSidePackets().Tag("OPTIONS").Set<CalculatorOptions>();
+    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")) {
+  if (cc->Inputs().HasTag(kVideoHeaderTag)) {
-    cc->Inputs().Tag("VIDEO_HEADER").Set<VideoHeader>();
+    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")) {
+  if (cc->Outputs().HasTag(kVideoHeaderTag)) {
-    cc->Outputs().Tag("VIDEO_HEADER").Set<VideoHeader>();
+    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"));
+    RET_CHECK(cc->InputSidePackets().HasTag(kSeedTag));
-    cc->InputSidePackets().Tag("SEED").Set<std::string>();
+    cc->InputSidePackets().Tag(kSeedTag).Set<std::string>();
  }
  return absl::OkStatus();
 }
@@ -143,18 +148,16 @@ 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().UsesTags() && cc->Inputs().HasTag(kVideoHeaderTag) &&
-      !cc->Inputs().Tag("VIDEO_HEADER").IsEmpty()) {
+      !cc->Inputs().Tag(kVideoHeaderTag).IsEmpty()) {
-    video_header_ = cc->Inputs().Tag("VIDEO_HEADER").Get<VideoHeader>();
+    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();
    }
  }
-  if (absl::Status status = strategy_->Process(cc); !status.ok()) {
+  MP_RETURN_IF_ERROR(strategy_->Process(cc));
    return status;  // Avoid MP_RETURN_IF_ERROR macro for external release.
  }
  last_packet_ = cc->Inputs().Get(input_data_id_).Value();
@@ -234,7 +237,7 @@ 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 +360,7 @@ 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 +507,7 @@ 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 +638,9 @@ 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());
    }
@@ -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());
@@ -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 {
@@ -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};
@@ -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();
@@ -23,8 +23,8 @@
 #include "mediapipe/framework/port/canonical_errors.h"
 #include "mediapipe/framework/port/status.h"
-// Quantizes a vector of floats to a std::string so that each float becomes a
+// Quantizes a vector of floats to a string so that each float becomes a byte
-// byte in the [0, 255] range. Any value above max_quantized_value or below
+// in the [0, 255] range. Any value above max_quantized_value or below
 // min_quantized_value will be saturated to '/xFF' or '/0'.
 //
 // Example config:
@@ -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->Inputs().Tag(kFloatVectorTag).Set<std::vector<float>>();
-    cc->Outputs().Tag("ENCODED").Set<std::string>();
+    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(
+    cc->Outputs()
-        MakePacket<std::string>(encoded_features).At(cc->InputTimestamp()));
+        .Tag(kEncodedTag)
        .AddPacket(
            MakePacket<std::string>(encoded_features).At(cc->InputTimestamp()));
    return absl::OkStatus();
  }
@@ -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());
@@ -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")) {
+    if (cc->InputSidePackets().HasTag(kMaxInFlightTag)) {
-      cc->InputSidePackets().Tag("MAX_IN_FLIGHT").Set<int>();
+      cc->InputSidePackets().Tag(kMaxInFlightTag).Set<int>();
    }
-    if (cc->Outputs().HasTag("ALLOW")) {
+    if (cc->Outputs().HasTag(kAllowTag)) {
-      cc->Outputs().Tag("ALLOW").Set<bool>();
+      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")) {
+    if (cc->InputSidePackets().HasTag(kMaxInFlightTag)) {
-      max_in_flight_ = cc->InputSidePackets().Tag("MAX_IN_FLIGHT").Get<int>();
+      max_in_flight_ = cc->InputSidePackets().Tag(kMaxInFlightTag).Get<int>();
    }
    RET_CHECK_GE(max_in_flight_, 1);
    num_in_flight_ = 0;
@@ -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));
  }
@@ -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;
@@ -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
+#ifndef MEDIAPIPE_CALCULATORS_CORE_SPLIT_PROTO_LIST_CALCULATOR_H_  // NOLINT
-#define MEDIAPIPE_CALCULATORS_CORE_SPLIT_NORMALIZED_LANDMARK_LIST_CALCULATOR_H_  // NOLINT
+#define MEDIAPIPE_CALCULATORS_CORE_SPLIT_PROTO_LIST_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
+// Splits an input packet of ListType with a repeated field of ItemType
-// multiple NormalizedLandmarkList output packets using the [begin, end) ranges
+// into multiple ListType 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
+// of type ItemType. If "element_only" is false, ranges can be
-// non-zero in size and all outputs will be of type NormalizedLandmarkList.
+// non-zero in size and all outputs will be of type ListType.
 // 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.
+// ListType.
-class SplitNormalizedLandmarkListCalculator : public CalculatorBase {
+template <typename ItemType, typename ListType>
 class SplitListsCalculator : 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<ListType>();
    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<ListType>();
      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<ItemType>();
        } else {
-          cc->Outputs().Index(i).Set<NormalizedLandmarkList>();
+          cc->Outputs().Index(i).Set<ListType>();
        }
      }
    }
@@ -110,40 +111,38 @@ class SplitNormalizedLandmarkListCalculator : public CalculatorBase {
  }
  absl::Status Process(CalculatorContext* cc) override {
-    const NormalizedLandmarkList& input =
+    const ListType& input = cc->Inputs().Index(0).Get<ListType>();
-        cc->Inputs().Index(0).Get<NormalizedLandmarkList>();
+    RET_CHECK_GE(ListSize(input), max_range_end_)
-    RET_CHECK_GE(input.landmark_size(), max_range_end_)
+        << "Max range end " << max_range_end_ << " exceeds list size "
-        << "Max range end " << max_range_end_ << " exceeds landmarks size "
+        << ListSize(input);
        << input.landmark_size();
    if (combine_outputs_) {
-      NormalizedLandmarkList output;
+      ListType 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 ItemType& input_item = GetItem(input, j);
-          *output.add_landmark() = input_landmark;
+          *AddItem(output) = input_item;
        }
      }
-      RET_CHECK_EQ(output.landmark_size(), total_elements_);
+      RET_CHECK_EQ(ListSize(output), total_elements_);
      cc->Outputs().Index(0).AddPacket(
-          MakePacket<NormalizedLandmarkList>(output).At(cc->InputTimestamp()));
+          MakePacket<ListType>(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<ItemType>(GetItem(input, ranges_[i].first))
                  .At(cc->InputTimestamp()));
        }
      } else {
        for (int i = 0; i < ranges_.size(); ++i) {
-          NormalizedLandmarkList output;
+          ListType output;
          for (int j = ranges_[i].first; j < ranges_[i].second; ++j) {
-            const NormalizedLandmark& input_landmark = input.landmark(j);
+            const ItemType& input_item = GetItem(input, j);
-            *output.add_landmark() = input_landmark;
+            *AddItem(output) = input_item;
          }
          cc->Outputs().Index(i).AddPacket(
-              MakePacket<NormalizedLandmarkList>(output).At(
+              MakePacket<ListType>(output).At(cc->InputTimestamp()));
                  cc->InputTimestamp()));
        }
      }
    }
@@ -151,6 +150,11 @@ class SplitNormalizedLandmarkListCalculator : public CalculatorBase {
    return absl::OkStatus();
  }
 protected:
  virtual int ListSize(const ListType& list) const = 0;
  virtual const ItemType GetItem(const ListType& list, int idx) const = 0;
  virtual ItemType* AddItem(ListType& list) const = 0;
 private:
  std::vector<std::pair<int32, int32>> ranges_;
  int32 max_range_end_ = -1;
@@ -159,9 +163,40 @@ class SplitNormalizedLandmarkListCalculator : public CalculatorBase {
  bool combine_outputs_ = false;
 };
 // TODO: Move calculators to separate *.cc files
 class SplitNormalizedLandmarkListCalculator
    : public SplitListsCalculator<NormalizedLandmark, NormalizedLandmarkList> {
 protected:
  int ListSize(const NormalizedLandmarkList& list) const override {
    return list.landmark_size();
  }
  const NormalizedLandmark GetItem(const NormalizedLandmarkList& list,
                                   int idx) const override {
    return list.landmark(idx);
  }
  NormalizedLandmark* AddItem(NormalizedLandmarkList& list) const override {
    return list.add_landmark();
  }
 };
 REGISTER_CALCULATOR(SplitNormalizedLandmarkListCalculator);
 class SplitLandmarkListCalculator
    : public SplitListsCalculator<Landmark, LandmarkList> {
 protected:
  int ListSize(const LandmarkList& list) const override {
    return list.landmark_size();
  }
  const Landmark GetItem(const LandmarkList& list, int idx) const override {
    return list.landmark(idx);
  }
  Landmark* AddItem(LandmarkList& list) const override {
    return list.add_landmark();
  }
 };
 REGISTER_CALCULATOR(SplitLandmarkListCalculator);
 }  // namespace mediapipe
 // NOLINTNEXTLINE
-#endif  // MEDIAPIPE_CALCULATORS_CORE_SPLIT_NORMALIZED_LANDMARK_LIST_CALCULATOR_H_
+#endif  // MEDIAPIPE_CALCULATORS_CORE_SPLIT_PROTO_LIST_CALCULATOR_H_
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
MediaPipe TeamandSebastian Schmidt	63e679d99c	Project import generated by Copybara. GitOrigin-RevId: 0f2489d226f1e2a5d718a8b9efe5e8198ba4ab3b	2022-06-29 18:45:02 +00:00
MediaPipe TeamandSebastian Schmidt	c688862570	Project import generated by Copybara. GitOrigin-RevId: 6e5aa035cd1f6a9333962df5d3ab97a05bd5744e	2022-06-28 12:11:05 +00:00
MediaPipe Teamandjqtang	4a20e9909d	Project import generated by Copybara. GitOrigin-RevId: b66251317fbebfbb8e1f2ddc64ea5da84bceb7e5	2022-05-06 17:05:30 -07:00
MediaPipe Teamandschmidt-sebastian	7fb37c80e8	Project import generated by Copybara. GitOrigin-RevId: 19a829ffd755edb43e54d20c0e7b9348512d5108	2022-05-05 19:57:20 +00:00
MediaPipe Teamandjqtang	c6c80c3745	Project import generated by Copybara. GitOrigin-RevId: 17113e259b160929c49262e7aa78ac22d228f9fc	2022-03-22 17:48:17 -07:00
MediaPipe Teamandjqtang	cc6a2f7af6	Project import generated by Copybara. GitOrigin-RevId: 73d686c40057684f8bfaca285368bf1813f9fc26	2022-03-21 12:12:39 -07:00
MediaPipe Teamandjqtang	e6c19885c6	Project import generated by Copybara. GitOrigin-RevId: bb059a0721c92e8154d33ce8057b3915a25b3d7d	2021-12-13 15:56:02 -08:00
MediaPipe Teamandchuoling	cf101e62a9	Project import generated by Copybara. GitOrigin-RevId: 7e1d382a1788ebd8412c5626581b4c4cf2fe75ea	2021-11-16 14:32:04 -05:00
MediaPipe Teamandchuoling	f4e7f6cc48	Project import generated by Copybara. GitOrigin-RevId: 412b20ea6bc8e49ba5b50798a6114ad6173ff073	2021-11-04 01:53:16 -04:00
MediaPipe Teamandjqtang	d4bb35fe5a	Project import generated by Copybara. GitOrigin-RevId: d4a11282d20fe4d2e137f9032cf349750030dcb9	2021-11-03 17:27:30 -07:00
MediaPipe Teamandchuoling	1faeaae7e5	Project import generated by Copybara. GitOrigin-RevId: bbbbcb4f5174dea33525729ede47c770069157cd	2021-10-18 17:00:29 -04:00
MediaPipe Teamandjqtang	33d683c671	Project import generated by Copybara. GitOrigin-RevId: 373e3ac1e5839befd95bf7d73ceff3c5f1171969	2021-10-06 14:27:49 -07:00
MediaPipe Teamandchuoling	137e1cc763	Project import generated by Copybara. GitOrigin-RevId: 283c1a295de0a53e47d7a94996bda0c52dcfd677	2021-09-13 21:35:51 -04:00
MediaPipe Teamandjqtang	6abec128ed	Project import generated by Copybara. GitOrigin-RevId: f4b1fe3f15810450fb6539e733f6a260d3ee082c	2021-09-01 18:15:31 -07:00
MediaPipe Teamandjqtang	710fb3de58	Project import generated by Copybara. GitOrigin-RevId: 1610e588e497817fae2d9a458093ab6a370e2972	2021-08-18 17:45:46 -07:00
MediaPipe Teamandchuoling	b899d17f18	Project import generated by Copybara. GitOrigin-RevId: 8e1da4611d93ccb7d9674713157d43be0348d98f	2021-07-27 22:36:23 -04:00
MediaPipe Teamandchuoling	50c92c6623	Project import generated by Copybara. GitOrigin-RevId: 27c70b5fe62ab71189d358ca122ee4b19c817a8f	2021-07-27 19:36:32 -04:00
MediaPipe Teamandjqtang	374f5e2e7e	Project import generated by Copybara. GitOrigin-RevId: 65b427572550bd9c5bc5f053eeea0f44340d5673	2021-06-28 10:17:10 -07:00
MediaPipe Teamandchuoling	139237092f	Project import generated by Copybara. GitOrigin-RevId: 33adfdf31f3a5cbf9edc07ee1ea583e95080bdc5	2021-06-24 17:55:26 -04:00
MediaPipe Teamandchuoling	b544a314b3	Project import generated by Copybara. GitOrigin-RevId: ec25bf2e416c3689477e82946fb69de2e53b9161	2021-06-10 01:38:18 -04:00