Solve Linking error for Hello World iOS example

PiperOrigin-RevId: 515466634
Update ImageFrameToGpuBufferCalculator to use api2 and GpuBuffer conversions
2023-03-09 16:02:07 -08:00 · 2023-03-09 12:21:00 -08:00 · 2023-03-09 11:27:11 -08:00 · 2023-03-09 10:30:49 -08:00 · 2023-03-09 09:54:19 -08:00 · 2023-03-09 09:49:23 -08:00
866 changed files with 70077 additions and 4225 deletions
@@ -1,27 +0,0 @@
 ---
 name: "Build/Installation Issue"
 about: Use this template for build/installation issues
 labels: type:build/install
 ---
 <em>Please make sure that this is a build/installation issue and also refer to the [troubleshooting](https://google.github.io/mediapipe/getting_started/troubleshooting.html) documentation before raising any issues.</em>
 **System information** (Please provide as much relevant information as possible)
 - OS Platform and Distribution (e.g. Linux Ubuntu 16.04, Android 11, iOS 14.4):
 - Compiler version (e.g. gcc/g++ 8 /Apple clang version 12.0.0):
 - Programming Language and version ( e.g. C++ 14, Python 3.6, Java ):
 - Installed using virtualenv? pip? Conda? (if python):
 - [MediaPipe version](https://github.com/google/mediapipe/releases):
 - Bazel version:
 - XCode and Tulsi versions (if iOS):
 - Android SDK and NDK versions (if android):
 - Android [AAR](https://google.github.io/mediapipe/getting_started/android_archive_library.html) ( if android):
 - OpenCV version (if running on desktop):
 **Describe the problem**:
 **[Provide the exact sequence of commands / steps that you executed before running into the problem](https://google.github.io/mediapipe/getting_started/getting_started.html):**
 **Complete Logs:**
 Include Complete Log information or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached:
@@ -1,26 +0,0 @@
 ---
 name: "Solution Issue"
 about: Use this template for assistance with a specific mediapipe solution, such as "Pose" or "Iris", including inference model usage/training, solution-specific calculators, etc.
 labels: type:support
 ---
 <em>Please make sure that this is a [solution](https://google.github.io/mediapipe/solutions/solutions.html) issue.<em>
 **System information** (Please provide as much relevant information as possible)
 - Have I written custom code (as opposed to using a stock example script provided in Mediapipe):
 - OS Platform and Distribution (e.g., Linux Ubuntu 16.04, Android 11, iOS 14.4):
 - [MediaPipe version](https://github.com/google/mediapipe/releases):
 - Bazel version:
 - Solution (e.g. FaceMesh, Pose, Holistic):
 - Programming Language and version ( e.g. C++, Python, Java):
 **Describe the expected behavior:**
 **Standalone code you may have used to try to get what you need :**
 If there is a problem, provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to Colab/repo link /any notebook:
 **Other info / Complete Logs :**
 Include any logs or source code that would be helpful to
 diagnose the problem. If including tracebacks, please include the full
 traceback. Large logs and files should be attached:
@@ -1,25 +0,0 @@
 ---
 name: "Tasks Issue"
 about: Use this template for assistance with using MediaPipe Tasks to deploy on-device ML solutions (e.g. gesture recognition etc.) on supported platforms.
 labels: type:support
 ---
 <em>Please make sure that this is a [Tasks](https://developers.google.com/mediapipe/solutions) issue.<em>
 **System information** (Please provide as much relevant information as possible)
 - Have I written custom code (as opposed to using a stock example script provided in MediaPipe):
 - OS Platform and Distribution (e.g., Linux Ubuntu 16.04, Android 11, iOS 14.4):
 - MediaPipe Tasks SDK version:
 - Task name (e.g. Object detection, Gesture recognition etc.):
 - Programming Language and version ( e.g. C++, Python, Java):
 **Describe the expected behavior:**
 **Standalone code you may have used to try to get what you need :**
 If there is a problem, provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to Colab, GitHub repo link or anything that we can use to reproduce the problem:
 **Other info / Complete Logs :**
 Include any logs or source code that would be helpful to
 diagnose the problem. If including tracebacks, please include the full
 traceback. Large logs and files should be attached:
@@ -1,25 +0,0 @@
 ---
 name: "Model Maker Issue"
 about: Use this template for assistance with using MediaPipe Model Maker to create custom on-device ML solutions.
 labels: type:support
 ---
 <em>Please make sure that this is a [Model Maker](https://developers.google.com/mediapipe/solutions) issue.<em>
 **System information** (Please provide as much relevant information as possible)
 - Have I written custom code (as opposed to using a stock example script provided in MediaPipe):
 - OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
 - Python version (e.g. 3.8):
 - [MediaPipe Model Maker version](https://pypi.org/project/mediapipe-model-maker/):
 - Task name (e.g. Image classification, Gesture recognition etc.):
 **Describe the expected behavior:**
 **Standalone code you may have used to try to get what you need :**
 If there is a problem, provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to Colab, GitHub repo link or anything that we can use to reproduce the problem:
 **Other info / Complete Logs :**
 Include any logs or source code that would be helpful to
 diagnose the problem. If including tracebacks, please include the full
 traceback. Large logs and files should be attached:
@@ -1,51 +0,0 @@
 ---
 name: "Documentation Issue"
 about: Use this template for documentation related issues
 labels: type:docs
 ---
 Thank you for submitting a MediaPipe documentation issue.
 The MediaPipe docs are open source! To get involved, read the documentation Contributor Guide
 ## URL(s) with the issue:
 Please provide a link to the documentation entry, for example: https://github.com/google/mediapipe/blob/master/docs/solutions/face_mesh.md#models
 ## Description of issue (what needs changing):
 Kinds of documentation problems:
 ### Clear description
 For example, why should someone use this method? How is it useful?
 ### Correct links
 Is the link to the source code correct?
 ### Parameters defined
 Are all parameters defined and formatted correctly?
 ### Returns defined
 Are return values defined?
 ### Raises listed and defined
 Are the errors defined? For example,
 ### Usage example
 Is there a usage example?
 See the API guide:
 on how to write testable usage examples.
 ### Request visuals, if applicable
 Are there currently visuals? If not, will it clarify the content?
 ### Submit a pull request?
 Are you planning to also submit a pull request to fix the issue? See the docs
 https://github.com/google/mediapipe/blob/master/CONTRIBUTING.md
@@ -1,32 +0,0 @@
 ---
 name: "Bug Issue"
 about: Use this template for reporting a bug
 labels: type:bug
 ---
 <em>Please make sure that this is a bug and also refer to the [troubleshooting](https://google.github.io/mediapipe/getting_started/troubleshooting.html), FAQ documentation before raising any issues.</em>
 **System information** (Please provide as much relevant information as possible)
 - Have I written custom code (as opposed to using a stock example script provided in MediaPipe):
 - OS Platform and Distribution (e.g., Linux Ubuntu 16.04, Android 11, iOS 14.4):
 - Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
 - Browser and version (e.g. Google Chrome, Safari) if the issue happens on browser:
 - Programming Language and version ( e.g. C++, Python, Java):
 - [MediaPipe version](https://github.com/google/mediapipe/releases):
 - Bazel version (if compiling from source):
 - Solution ( e.g. FaceMesh, Pose, Holistic ):
 - Android Studio, NDK, SDK versions (if issue is related to building in Android environment):
 - Xcode & Tulsi version (if issue is related to building for iOS):
 **Describe the current behavior:**
 **Describe the expected behavior:**
 **Standalone code to reproduce the issue:**
 Provide a reproducible test case that is the bare minimum necessary to replicate the problem. If possible, please share a link to Colab/repo link /any notebook:
 **Other info / Complete Logs :**
 Include any logs or source code that would be helpful to
 diagnose the problem. If including tracebacks, please include the full
 traceback. Large logs and files should be attached
@@ -1,24 +0,0 @@
 ---
 name: "Feature Request"
 about: Use this template for raising a feature request
 labels: type:feature
 ---
 <em>Please make sure that this is a feature request.</em>
 **System information** (Please provide as much relevant information as possible)
 - MediaPipe Solution (you are using):
 - Programming language : C++/typescript/Python/Objective C/Android Java
 - Are you willing to contribute it (Yes/No):
 **Describe the feature and the current behavior/state:**
 **Will this change the current api? How?**
 **Who will benefit with this feature?**
 **Please specify the use cases for this feature:**
 **Any Other info:**
@@ -0,0 +1,73 @@
 name: Documentation issue
 description: Use this template for documentation related issues. If this doesn’t look right, choose a different type.
 labels: 'type:doc-bug'
 body:
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      value: Thank you for submitting a MediaPipe documentation issue. The MediaPipe docs are open source! To get involved, read the documentation Contributor Guide
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@@ -0,0 +1,80 @@
 name: Solution(Legacy) Issue
 description: Use this template for assistance with a specific Mediapipe solution (google.github.io/mediapipe/solutions) such as "Pose", including inference model usage/training, solution-specific calculators etc.
 labels: 'type:support'
 body:
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@@ -0,0 +1,112 @@
 name: Bug Issues
 description: Use this template for reporting a bug. If this doesn’t look right, choose a different type.
 labels: 'type:bug'
 body:
  - type: markdown
    id: link
    attributes:
      value: 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.
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@@ -0,0 +1,109 @@
 name: Build/Install Issue
 description: Use this template to report build/install issue
 labels: 'type:build/install'
 body:
  - type: markdown
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    attributes:
      value: 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.
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      render: shell
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@@ -0,0 +1,64 @@
 name: Feature Request Issues
 description: Use this template for raising a feature request. If this doesn’t look right, choose a different type.
 labels: 'type:feature'
 body:
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@@ -0,0 +1,73 @@
 name: Model Maker Issues
 description: Use this template for assistance with using MediaPipe Model Maker (developers.google.com/mediapipe/solutions) to create custom on-device ML solutions.
 labels: 'type:modelmaker'
 body:
  - type: markdown
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@@ -0,0 +1,63 @@
 name: Studio Issues
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@@ -0,0 +1,72 @@
 name: Task Issue
 description: Use this template for assistance with using MediaPipe Tasks (developers.google.com/mediapipe/solutions) to deploy on-device ML solutions (e.g. gesture recognition etc.) on supported platforms
 labels: 'type:task'
 body:
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      label: OS Platform and Distribution
      placeholder: e.g. Linux Ubuntu 16.04, Android 11, iOS 14.4
    validations:
      required: true
  - type: input
    id: task-sdk-version
    attributes:
      label: MediaPipe Tasks SDK version
    validations:
      required: false
  - type: input
    id: taskname
    attributes:
      label: Task name (e.g. Image classification, Gesture recognition etc.)
    validations:
       required: true
  - type: input
    id: programminglang
    attributes:
      label: Programming Language and version (e.g. C++, Python, Java)
    validations:
      required: true
  - type: textarea
    id: current_model
    attributes:
      label: Describe the actual behavior
      render: shell
    validations:
      required: true
  - type: textarea
    id: expected_model
    attributes:
      label: Describe the expected behaviour
      render: shell
    validations:
      required: true
  - type: textarea
    id: what-happened_model
    attributes:
      label: Standalone code/steps you may have used to try to get what you need
      description: If there is a problem, provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to Colab, GitHub repo link or anything that we can use to reproduce the problem
      render: shell
    validations:
       required: true
  - type: textarea
    id: other_info
    attributes:
      label: Other info / Complete Logs
      description: Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached
      render: shell
    validations:
      required: false
@@ -19,6 +19,17 @@ ML solutions for live and streaming media.
 ![ready_to_use.png](https://mediapipe.dev/images/ready_to_use_small.png)                                                             | ![open_source.png](https://mediapipe.dev/images/open_source_small.png)
 ***Ready-to-use solutions***: *Cutting-edge ML solutions demonstrating full power of the framework*            | ***Free and open source***: *Framework and solutions both under Apache 2.0, fully extensible and customizable*
 ----
 **Attention:** *Thanks for your interest in MediaPipe! We are moving to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation
 site for MediaPipe starting April 3, 2023.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ## ML solutions in MediaPipe
 Face Detection                                                                                                                 | Face Mesh                                                                                                       | Iris                                                                                                      | Hands                                                                                                      | Pose                                                                                                      | Holistic
@@ -10,33 +10,31 @@ bind(
 http_archive(
    name = "bazel_skylib",
-    type = "tar.gz",
+    sha256 = "74d544d96f4a5bb630d465ca8bbcfe231e3594e5aae57e1edbf17a6eb3ca2506",
    urls = [
-        "https://github.com/bazelbuild/bazel-skylib/releases/download/1.0.3/bazel-skylib-1.0.3.tar.gz",
+        "https://storage.googleapis.com/mirror.tensorflow.org/github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz",
-        "https://mirror.bazel.build/github.com/bazelbuild/bazel-skylib/releases/download/1.0.3/bazel-skylib-1.0.3.tar.gz",
+        "https://github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz",
    ],
    sha256 = "1c531376ac7e5a180e0237938a2536de0c54d93f5c278634818e0efc952dd56c",
 )
 load("@bazel_skylib//:workspace.bzl", "bazel_skylib_workspace")
 bazel_skylib_workspace()
 load("@bazel_skylib//lib:versions.bzl", "versions")
 versions.check(minimum_bazel_version = "3.7.2")
-# ABSL cpp library lts_2021_03_24, patch 2.
+# ABSL cpp library lts_2023_01_25.
 http_archive(
    name = "com_google_absl",
    urls = [
-        "https://github.com/abseil/abseil-cpp/archive/refs/tags/20220623.1.tar.gz",
+        "https://github.com/abseil/abseil-cpp/archive/refs/tags/20230125.0.tar.gz",
    ],
    # Remove after https://github.com/abseil/abseil-cpp/issues/326 is solved.
    patches = [
-        "@//third_party:com_google_absl_f863b622fe13612433fdf43f76547d5edda0c93001.diff"
+        "@//third_party:com_google_absl_windows_patch.diff"
    ],
    patch_args = [
        "-p1",
    ],
-    strip_prefix = "abseil-cpp-20220623.1",
+    strip_prefix = "abseil-cpp-20230125.0",
-    sha256 = "91ac87d30cc6d79f9ab974c51874a704de9c2647c40f6932597329a282217ba8"
+    sha256 = "3ea49a7d97421b88a8c48a0de16c16048e17725c7ec0f1d3ea2683a2a75adc21"
 )
 http_archive(
@@ -148,13 +146,14 @@ http_archive(
    ],
 )
-load("//third_party/flatbuffers:workspace.bzl", flatbuffers = "repo")
+load("@//third_party/flatbuffers:workspace.bzl", flatbuffers = "repo")
 flatbuffers()
 http_archive(
    name = "com_google_audio_tools",
-    strip_prefix = "multichannel-audio-tools-master",
+    strip_prefix = "multichannel-audio-tools-1f6b1319f13282eda6ff1317be13de67f4723860",
-    urls = ["https://github.com/google/multichannel-audio-tools/archive/master.zip"],
+    urls = ["https://github.com/google/multichannel-audio-tools/archive/1f6b1319f13282eda6ff1317be13de67f4723860.zip"],
    sha256 = "fe346e1aee4f5069c4cbccb88706a9a2b2b4cf98aeb91ec1319be77e07dd7435",
    repo_mapping = {"@com_github_glog_glog" : "@com_github_glog_glog_no_gflags"},
 )
@@ -174,7 +173,7 @@ http_archive(
        "https://github.com/google/sentencepiece/archive/1.0.0.zip",
    ],
    patches = [
-        "//third_party:com_google_sentencepiece_no_gflag_no_gtest.diff",
+        "@//third_party:com_google_sentencepiece_no_gflag_no_gtest.diff",
    ],
    patch_args = ["-p1"],
    repo_mapping = {"@com_google_glog" : "@com_github_glog_glog_no_gflags"},
@@ -188,8 +187,8 @@ http_archive(
        "https://github.com/tensorflow/text/archive/v2.2.0.zip",
    ],
    patches = [
-        "//third_party:tensorflow_text_remove_tf_deps.diff",
+        "@//third_party:tensorflow_text_remove_tf_deps.diff",
-        "//third_party:tensorflow_text_a0f49e63.diff",
+        "@//third_party:tensorflow_text_a0f49e63.diff",
    ],
    patch_args = ["-p1"],
    repo_mapping = {"@com_google_re2": "@com_googlesource_code_re2"},
@@ -320,12 +319,30 @@ http_archive(
    ],
 )
-# iOS basic build deps.
+# Load Zlib before initializing TensorFlow and the iOS build rules to guarantee
 # that the target @zlib//:mini_zlib is available
 http_archive(
    name = "zlib",
    build_file = "@//third_party:zlib.BUILD",
    sha256 = "c3e5e9fdd5004dcb542feda5ee4f0ff0744628baf8ed2dd5d66f8ca1197cb1a1",
    strip_prefix = "zlib-1.2.11",
    urls = [
        "http://mirror.bazel.build/zlib.net/fossils/zlib-1.2.11.tar.gz",
        "http://zlib.net/fossils/zlib-1.2.11.tar.gz",  # 2017-01-15
    ],
    patches = [
        "@//third_party:zlib.diff",
    ],
    patch_args = [
        "-p1",
    ],
 )
 # iOS basic build deps.
 http_archive(
    name = "build_bazel_rules_apple",
-    sha256 = "77e8bf6fda706f420a55874ae6ee4df0c9d95da6c7838228b26910fc82eea5a2",
+    sha256 = "f94e6dddf74739ef5cb30f000e13a2a613f6ebfa5e63588305a71fce8a8a9911",
-    url = "https://github.com/bazelbuild/rules_apple/releases/download/0.32.0/rules_apple.0.32.0.tar.gz",
+    url = "https://github.com/bazelbuild/rules_apple/releases/download/1.1.3/rules_apple.1.1.3.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"
@@ -339,29 +356,24 @@ load(
    "@build_bazel_rules_apple//apple:repositories.bzl",
    "apple_rules_dependencies",
 )
 apple_rules_dependencies()
 load(
    "@build_bazel_rules_swift//swift:repositories.bzl",
    "swift_rules_dependencies",
 )
 swift_rules_dependencies()
-http_archive(
+load(
-    name = "build_bazel_apple_support",
+    "@build_bazel_rules_swift//swift:extras.bzl",
-    sha256 = "741366f79d900c11e11d8efd6cc6c66a31bfb2451178b58e0b5edc6f1db17b35",
+    "swift_rules_extra_dependencies",
    urls = [
        "https://github.com/bazelbuild/apple_support/releases/download/0.10.0/apple_support.0.10.0.tar.gz"
    ],
 )
 swift_rules_extra_dependencies()
 load(
    "@build_bazel_apple_support//lib:repositories.bzl",
    "apple_support_dependencies",
 )
 apple_support_dependencies()
 # More iOS deps.
@@ -442,29 +454,10 @@ http_archive(
    ],
 )
 # Load Zlib before initializing TensorFlow to guarantee that the target
 # @zlib//:mini_zlib is available
 http_archive(
    name = "zlib",
    build_file = "//third_party:zlib.BUILD",
    sha256 = "c3e5e9fdd5004dcb542feda5ee4f0ff0744628baf8ed2dd5d66f8ca1197cb1a1",
    strip_prefix = "zlib-1.2.11",
    urls = [
        "http://mirror.bazel.build/zlib.net/fossils/zlib-1.2.11.tar.gz",
        "http://zlib.net/fossils/zlib-1.2.11.tar.gz",  # 2017-01-15
    ],
    patches = [
        "@//third_party:zlib.diff",
    ],
    patch_args = [
        "-p1",
    ],
 )
 # TensorFlow repo should always go after the other external dependencies.
-# TF on 2022-08-10.
+# TF on 2023-03-08.
-_TENSORFLOW_GIT_COMMIT = "af1d5bc4fbb66d9e6cc1cf89503014a99233583b"
+_TENSORFLOW_GIT_COMMIT = "24f7ee636d62e1f8d8330357f8bbd65956dfb84d"
-_TENSORFLOW_SHA256 = "f85a5443264fc58a12d136ca6a30774b5bc25ceaf7d114d97f252351b3c3a2cb"
+_TENSORFLOW_SHA256 = "7f8a96dd99215c0cdc77230d3dbce43e60102b64a89203ad04aa09b0a187a4bd"
 http_archive(
    name = "org_tensorflow",
    urls = [
@@ -518,8 +511,8 @@ load("@build_bazel_rules_nodejs//:index.bzl", "node_repositories", "yarn_install
 node_repositories()
 yarn_install(
    name = "npm",
-    package_json = "//:package.json",
+    package_json = "@//:package.json",
-    yarn_lock = "//:yarn.lock",
+    yarn_lock = "@//:yarn.lock",
 )
 # Protobuf for Node dependencies
@@ -545,8 +538,48 @@ load("@rules_proto//proto:repositories.bzl", "rules_proto_dependencies", "rules_
 rules_proto_dependencies()
 rules_proto_toolchains()
-load("//third_party:external_files.bzl", "external_files")
+load("@//third_party:external_files.bzl", "external_files")
 external_files()
-load("//third_party:wasm_files.bzl", "wasm_files")
+load("@//third_party:wasm_files.bzl", "wasm_files")
 wasm_files()
 # Halide
 new_local_repository(
    name = "halide",
    build_file = "@//third_party/halide:BUILD.bazel",
    path = "third_party/halide"
 )
 http_archive(
    name = "linux_halide",
    sha256 = "f62b2914823d6e33d18693f5b74484f274523bf5402ce51988e24393d123b375",
    strip_prefix = "Halide-15.0.0-x86-64-linux",
    urls = ["https://github.com/halide/Halide/releases/download/v15.0.0/Halide-15.0.0-x86-64-linux-d7651f4b32f9dbd764f243134001f7554378d62d.tar.gz"],
    build_file = "@//third_party:halide.BUILD",
 )
 http_archive(
    name = "macos_x86_64_halide",
    sha256 = "3d832aed942080ea89aa832462c68fbb906f3055c440b7b6d35093d7c52f6aab",
    strip_prefix = "Halide-15.0.0-x86-64-osx",
    urls = ["https://github.com/halide/Halide/releases/download/v15.0.0/Halide-15.0.0-x86-64-osx-d7651f4b32f9dbd764f243134001f7554378d62d.tar.gz"],
    build_file = "@//third_party:halide.BUILD",
 )
 http_archive(
    name = "macos_arm_64_halide",
    sha256 = "b1fad3c9810122b187303d7031d9e35fb43761f345d18cc4492c00ed5877f641",
    strip_prefix = "Halide-15.0.0-arm-64-osx",
    urls = ["https://github.com/halide/Halide/releases/download/v15.0.0/Halide-15.0.0-arm-64-osx-d7651f4b32f9dbd764f243134001f7554378d62d.tar.gz"],
    build_file = "@//third_party:halide.BUILD",
 )
 http_archive(
    name = "windows_halide",
    sha256 = "5acf6fe161dd375856a2b43f4bb0a32815ba958b0585ee312c44e008aa7b0b64",
    strip_prefix = "Halide-15.0.0-x86-64-windows",
    urls = ["https://github.com/halide/Halide/releases/download/v15.0.0/Halide-15.0.0-x86-64-windows-d7651f4b32f9dbd764f243134001f7554378d62d.zip"],
    build_file = "@//third_party:halide.BUILD",
 )
@@ -4,12 +4,10 @@ py_binary(
    name = "build_py_api_docs",
    srcs = ["build_py_api_docs.py"],
    deps = [
        "//mediapipe",
        "//third_party/py/absl:app",
        "//third_party/py/absl/flags",
-        "//third_party/py/tensorflow_docs",
+        "//third_party/py/mediapipe",
        "//third_party/py/tensorflow_docs/api_generator:generate_lib",
        "//third_party/py/tensorflow_docs/api_generator:public_api",
    ],
 )
@@ -0,0 +1,13 @@
 <html lang="en">
 <head>
    <meta charset="utf-8"/>
    <meta http-equiv="refresh" content="0;url={{ page.target }}"/>
    <link rel="canonical" href="{{ page.target }}"/>
    <title>Redirecting</title>
 </head>
 <body>
    <p>This page now lives on https://developers.google.com/mediapipe/. If you aren't automatically
      redirected, follow this
    <a href="{{ page.target }}">link</a>.</p>
 </body>
 </html>
@@ -44,14 +44,14 @@ _OUTPUT_DIR = flags.DEFINE_string(
 _URL_PREFIX = flags.DEFINE_string(
    'code_url_prefix',
-    'https://github.com/google/mediapipe/tree/master/mediapipe',
+    'https://github.com/google/mediapipe/blob/master/mediapipe',
    'The url prefix for links to code.')
 _SEARCH_HINTS = flags.DEFINE_bool(
    'search_hints', True,
    'Include metadata search hints in the generated files')
-_SITE_PATH = flags.DEFINE_string('site_path', '/mediapipe/api_docs/python',
+_SITE_PATH = flags.DEFINE_string('site_path', '/mediapipe/api/solutions/python',
                                 'Path prefix in the _toc.yaml')
@@ -0,0 +1,697 @@
 ---
 layout: default
 title: Building Graphs in C++
 parent: Graphs
 nav_order: 1
 ---
 # Building Graphs in C++
 {: .no_toc }
 1. TOC
 {:toc}
 ---
 C++ graph builder is a powerful tool for:
 *   Building complex graphs
 *   Parametrizing graphs (e.g. setting a delegate on `InferenceCalculator`,
    enabling/disabling parts of the graph)
 *   Deduplicating graphs (e.g. instead of CPU and GPU dedicated graphs in pbtxt
    you can have a single code that constructs required graphs, sharing as much
    as possible)
 *   Supporting optional graph inputs/outputs
 *   Customizing graphs per platform
 ## Basic Usage
 Let's see how C++ graph builder can be used for a simple graph:
 ```proto
 # Graph inputs.
 input_stream: "input_tensors"
 input_side_packet: "model"
 # Graph outputs.
 output_stream: "output_tensors"
 node {
  calculator: "InferenceCalculator"
  input_stream: "TENSORS:input_tensors"
  input_side_packet: "MODEL:model"
  output_stream: "TENSORS:output_tensors"
  node_options: {
    [type.googleapis.com/mediapipe.InferenceCalculatorOptions] {
      # Requesting GPU delegate.
      delegate { gpu {} }
    }
  }
 }
 ```
 Function to build the above `CalculatorGraphConfig` may look like:
 ```c++
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Graph inputs.
  Stream<std::vector<Tensor>> input_tensors =
      graph.In(0).SetName("input_tensors").Cast<std::vector<Tensor>>();
  SidePacket<TfLiteModelPtr> model =
      graph.SideIn(0).SetName("model").Cast<TfLiteModelPtr>();
  auto& inference_node = graph.AddNode("InferenceCalculator");
  auto& inference_opts =
      inference_node.GetOptions<InferenceCalculatorOptions>();
  // Requesting GPU delegate.
  inference_opts.mutable_delegate()->mutable_gpu();
  input_tensors.ConnectTo(inference_node.In("TENSORS"));
  model.ConnectTo(inference_node.SideIn("MODEL"));
  Stream<std::vector<Tensor>> output_tensors =
      inference_node.Out("TENSORS").Cast<std::vector<Tensor>>();
  // Graph outputs.
  output_tensors.SetName("output_tensors").ConnectTo(graph.Out(0));
  // Get `CalculatorGraphConfig` to pass it into `CalculatorGraph`
  return graph.GetConfig();
 }
 ```
 Short summary:
 *   Use `Graph::In/SideIn` to get graph inputs as `Stream/SidePacket`
 *   Use `Node::Out/SideOut` to get node outputs as `Stream/SidePacket`
 *   Use `Stream/SidePacket::ConnectTo` to connect streams and side packets to
    node inputs (`Node::In/SideIn`) and graph outputs (`Graph::Out/SideOut`)
    *   There's a "shortcut" operator `>>` that you can use instead of
        `ConnectTo` function (E.g. `x >> node.In("IN")`).
 *   `Stream/SidePacket::Cast` is used to cast stream or side packet of `AnyType`
    (E.g. `Stream<AnyType> in = graph.In(0);`) to a particular type
    *   Using actual types instead of `AnyType` sets you on a better path for
        unleashing graph builder capabilities and improving your graphs
        readability.
 ## Advanced Usage
 ### Utility Functions
 Let's extract inference construction code into a dedicated utility function to
 help for readability and code reuse:
 ```c++
 // Updates graph to run inference.
 Stream<std::vector<Tensor>> RunInference(
    Stream<std::vector<Tensor>> tensors, SidePacket<TfLiteModelPtr> model,
    const InferenceCalculatorOptions::Delegate& delegate, Graph& graph) {
  auto& inference_node = graph.AddNode("InferenceCalculator");
  auto& inference_opts =
      inference_node.GetOptions<InferenceCalculatorOptions>();
  *inference_opts.mutable_delegate() = delegate;
  tensors.ConnectTo(inference_node.In("TENSORS"));
  model.ConnectTo(inference_node.SideIn("MODEL"));
  return inference_node.Out("TENSORS").Cast<std::vector<Tensor>>();
 }
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Graph inputs.
  Stream<std::vector<Tensor>> input_tensors =
      graph.In(0).SetName("input_tensors").Cast<std::vector<Tensor>>();
  SidePacket<TfLiteModelPtr> model =
      graph.SideIn(0).SetName("model").Cast<TfLiteModelPtr>();
  InferenceCalculatorOptions::Delegate delegate;
  delegate.mutable_gpu();
  Stream<std::vector<Tensor>> output_tensors =
      RunInference(input_tensors, model, delegate, graph);
  // Graph outputs.
  output_tensors.SetName("output_tensors").ConnectTo(graph.Out(0));
  return graph.GetConfig();
 }
 ```
 As a result, `RunInference` provides a clear interface stating what are the
 inputs/outputs and their types.
 It can be easily reused, e.g. it's only a few lines if you want to run an extra
 model inference:
 ```c++
  // Run first inference.
  Stream<std::vector<Tensor>> output_tensors =
      RunInference(input_tensors, model, delegate, graph);
  // Run second inference on the output of the first one.
  Stream<std::vector<Tensor>> extra_output_tensors =
      RunInference(output_tensors, extra_model, delegate, graph);
 ```
 And you don't need to duplicate names and tags (`InferenceCalculator`,
 `TENSORS`, `MODEL`) or introduce dedicated constants here and there - those
 details are localized to `RunInference` function.
 Tip: extracting `RunInference` and similar functions to dedicated modules (e.g.
 inference.h/cc which depends on the inference calculator) enables reuse in
 graphs construction code and helps automatically pull in calculator dependencies
 (e.g. no need to manually add `:inference_calculator` dep, just let your IDE
 include `inference.h` and build cleaner pull in corresponding dependency).
 ### Utility Classes
 And surely, it's not only about functions, in some cases it's beneficial to
 introduce utility classes which can help making your graph construction code
 more readable and less error prone.
 MediaPipe offers `PassThroughCalculator` calculator, which is simply passing
 through its inputs:
 ```
 input_stream: "float_value"
 input_stream: "int_value"
 input_stream: "bool_value"
 output_stream: "passed_float_value"
 output_stream: "passed_int_value"
 output_stream: "passed_bool_value"
 node {
  calculator: "PassThroughCalculator"
  input_stream: "float_value"
  input_stream: "int_value"
  input_stream: "bool_value"
  # The order must be the same as for inputs (or you can use explicit indexes)
  output_stream: "passed_float_value"
  output_stream: "passed_int_value"
  output_stream: "passed_bool_value"
 }
 ```
 Let's see the straightforward C++ construction code to create the above graph:
 ```c++
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Graph inputs.
  Stream<float> float_value = graph.In(0).SetName("float_value").Cast<float>();
  Stream<int> int_value = graph.In(1).SetName("int_value").Cast<int>();
  Stream<bool> bool_value = graph.In(2).SetName("bool_value").Cast<bool>();
  auto& pass_node = graph.AddNode("PassThroughCalculator");
  float_value.ConnectTo(pass_node.In("")[0]);
  int_value.ConnectTo(pass_node.In("")[1]);
  bool_value.ConnectTo(pass_node.In("")[2]);
  Stream<float> passed_float_value = pass_node.Out("")[0].Cast<float>();
  Stream<int> passed_int_value = pass_node.Out("")[1].Cast<int>();
  Stream<bool> passed_bool_value = pass_node.Out("")[2].Cast<bool>();
  // Graph outputs.
  passed_float_value.SetName("passed_float_value").ConnectTo(graph.Out(0));
  passed_int_value.SetName("passed_int_value").ConnectTo(graph.Out(1));
  passed_bool_value.SetName("passed_bool_value").ConnectTo(graph.Out(2));
  // Get `CalculatorGraphConfig` to pass it into `CalculatorGraph`
  return graph.GetConfig();
 }
 ```
 While `pbtxt` representation maybe error prone (when we have many inputs to pass
 through), C++ code looks even worse: repeated empty tags and `Cast` calls. Let's
 see how we can do better by introducing a `PassThroughNodeBuilder`:
 ```c++
 class PassThroughNodeBuilder {
 public:
  explicit PassThroughNodeBuilder(Graph& graph)
      : node_(graph.AddNode("PassThroughCalculator")) {}
  template <typename T>
  Stream<T> PassThrough(Stream<T> stream) {
    stream.ConnectTo(node_.In(index_));
    return node_.Out(index_++).Cast<T>();
  }
 private:
  int index_ = 0;
  GenericNode& node_;
 };
 ```
 And now graph construction code can look like:
 ```c++
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Graph inputs.
  Stream<float> float_value = graph.In(0).SetName("float_value").Cast<float>();
  Stream<int> int_value = graph.In(1).SetName("int_value").Cast<int>();
  Stream<bool> bool_value = graph.In(2).SetName("bool_value").Cast<bool>();
  PassThroughNodeBuilder pass_node_builder(graph);
  Stream<float> passed_float_value = pass_node_builder.PassThrough(float_value);
  Stream<int> passed_int_value = pass_node_builder.PassThrough(int_value);
  Stream<bool> passed_bool_value = pass_node_builder.PassThrough(bool_value);
  // Graph outputs.
  passed_float_value.SetName("passed_float_value").ConnectTo(graph.Out(0));
  passed_int_value.SetName("passed_int_value").ConnectTo(graph.Out(1));
  passed_bool_value.SetName("passed_bool_value").ConnectTo(graph.Out(2));
  // Get `CalculatorGraphConfig` to pass it into `CalculatorGraph`
  return graph.GetConfig();
 }
 ```
 Now you can't have incorrect order or index in your pass through construction
 code and save some typing by guessing the type for `Cast` from the `PassThrough`
 input.
 Tip: the same as for the `RunInference` function, extracting
 `PassThroughNodeBuilder` and similar utility classes into dedicated modules
 enables reuse in graph construction code and helps to automatically pull in the
 corresponding calculator dependencies.
 ## Dos and Don'ts
 ### Define graph inputs at the very beginning if possible
 ```c++ {.bad}
 Stream<D> RunSomething(Stream<A> a, Stream<B> b, Graph& graph) {
  Stream<C> c = graph.In(2).SetName("c").Cast<C>();  // Bad.
  // ...
 }
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  Stream<A> a = graph.In(0).SetName("a").Cast<A>();
  // 10/100/N lines of code.
  Stream<B> b = graph.In(1).SetName("b").Cast<B>()  // Bad.
  Stream<D> d = RunSomething(a, b, graph);
  // ...
  return graph.GetConfig();
 }
 ```
 In the above code:
 *   It can be hard to guess how many inputs you have in the graph.
 *   Can be error prone overall and hard to maintain in future (e.g. is it a
    correct index? name? what if some inputs are removed or made optional?
    etc.).
 *   `RunSomething` reuse is limited because other graphs may have different
    inputs
 Instead, define your graph inputs at the very beginning of your graph builder:
 ```c++ {.good}
 Stream<D> RunSomething(Stream<A> a, Stream<B> b, Stream<C> c, Graph& graph) {
  // ...
 }
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Inputs.
  Stream<A> a = graph.In(0).SetName("a").Cast<A>();
  Stream<B> b = graph.In(1).SetName("b").Cast<B>();
  Stream<C> c = graph.In(2).SetName("c").Cast<C>();
  // 10/100/N lines of code.
  Stream<D> d = RunSomething(a, b, c, graph);
  // ...
  return graph.GetConfig();
 }
 ```
 Use `std::optional` if you have an input stream or side packet that is not
 always defined and put it at the very beginning:
 ```c++ {.good}
 std::optional<Stream<A>> a;
 if (needs_a) {
  a = graph.In(0).SetName(a).Cast<A>();
 }
 ```
 Note: of course, there can be exceptions - for example, there can be a use case
 where calling `RunSomething1(..., graph)`, ..., `RunSomethingN(..., graph)` is
 **intended to add new inputs**, so afterwards you can iterate over them and feed
 only added inputs into the graph. However, in any case, try to make it easy for
 readers to find out what graph inputs it has or may have.
 ### Define graph outputs at the very end
 ```c++ {.bad}
 void RunSomething(Stream<Input> input, Graph& graph) {
  // ...
  node.Out("OUTPUT_F")
      .SetName("output_f").ConnectTo(graph.Out(2));  // Bad.
 }
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // 10/100/N lines of code.
  node.Out("OUTPUT_D")
      .SetName("output_d").ConnectTo(graph.Out(0));  // Bad.
  // 10/100/N lines of code.
  node.Out("OUTPUT_E")
      .SetName("output_e").ConnectTo(graph.Out(1));  // Bad.
  // 10/100/N lines of code.
  RunSomething(input, graph);
  // ...
  return graph.GetConfig();
 }
 ```
 In the above code:
 *   It can be hard to guess how many outputs you have in the graph.
 *   Can be error prone overall and hard to maintain in future (e.g. is it a
    correct index? name? what if some outpus are removed or made optional?
    etc.).
 *   `RunSomething` reuse is limited as other graphs may have different outputs
 Instead, define your graph outputs at the very end of your graph builder:
 ```c++ {.good}
 Stream<F> RunSomething(Stream<Input> input, Graph& graph) {
  // ...
  return node.Out("OUTPUT_F").Cast<F>();
 }
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // 10/100/N lines of code.
  Stream<D> d = node.Out("OUTPUT_D").Cast<D>();
  // 10/100/N lines of code.
  Stream<E> e = node.Out("OUTPUT_E").Cast<E>();
  // 10/100/N lines of code.
  Stream<F> f = RunSomething(input, graph);
  // ...
  // Outputs.
  d.SetName("output_d").ConnectTo(graph.Out(0));
  e.SetName("output_e").ConnectTo(graph.Out(1));
  f.SetName("output_f").ConnectTo(graph.Out(2));
  return graph.GetConfig();
 }
 ```
 ### Keep nodes decoupled from each other
 In MediaPipe, packet streams and side packets are as meaningful as processing
 nodes. And any node input requirements and output products are expressed clearly
 and independently in terms of the streams and side packets it consumes and
 produces.
 ```c++ {.bad}
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Inputs.
  Stream<A> a = graph.In(0).Cast<A>();
  auto& node1 = graph.AddNode("Calculator1");
  a.ConnectTo(node1.In("INPUT"));
  auto& node2 = graph.AddNode("Calculator2");
  node1.Out("OUTPUT").ConnectTo(node2.In("INPUT"));  // Bad.
  auto& node3 = graph.AddNode("Calculator3");
  node1.Out("OUTPUT").ConnectTo(node3.In("INPUT_B"));  // Bad.
  node2.Out("OUTPUT").ConnectTo(node3.In("INPUT_C"));  // Bad.
  auto& node4 = graph.AddNode("Calculator4");
  node1.Out("OUTPUT").ConnectTo(node4.In("INPUT_B"));  // Bad.
  node2.Out("OUTPUT").ConnectTo(node4.In("INPUT_C"));  // Bad.
  node3.Out("OUTPUT").ConnectTo(node4.In("INPUT_D"));  // Bad.
  // Outputs.
  node1.Out("OUTPUT").SetName("b").ConnectTo(graph.Out(0));  // Bad.
  node2.Out("OUTPUT").SetName("c").ConnectTo(graph.Out(1));  // Bad.
  node3.Out("OUTPUT").SetName("d").ConnectTo(graph.Out(2));  // Bad.
  node4.Out("OUTPUT").SetName("e").ConnectTo(graph.Out(3));  // Bad.
  return graph.GetConfig();
 }
 ```
 In the above code:
 *   Nodes are coupled to each other, e.g. `node4` knows where its inputs are
    coming from (`node1`, `node2`, `node3`) and it complicates refactoring,
    maintenance and code reuse
    *   Such usage pattern is a downgrade from proto representation, where nodes
        are decoupled by default.
 *   `node#.Out("OUTPUT")` calls are duplicated and readability suffers as you
    could use cleaner names instead and also provide an actual type.
 So, to fix the above issues you can write the following graph construction code:
 ```c++ {.good}
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Inputs.
  Stream<A> a = graph.In(0).Cast<A>();
  // `node1` usage is limited to 3 lines below.
  auto& node1 = graph.AddNode("Calculator1");
  a.ConnectTo(node1.In("INPUT"));
  Stream<B> b = node1.Out("OUTPUT").Cast<B>();
  // `node2` usage is limited to 3 lines below.
  auto& node2 = graph.AddNode("Calculator2");
  b.ConnectTo(node2.In("INPUT"));
  Stream<C> c = node2.Out("OUTPUT").Cast<C>();
  // `node3` usage is limited to 4 lines below.
  auto& node3 = graph.AddNode("Calculator3");
  b.ConnectTo(node3.In("INPUT_B"));
  c.ConnectTo(node3.In("INPUT_C"));
  Stream<D> d = node3.Out("OUTPUT").Cast<D>();
  // `node4` usage is limited to 5 lines below.
  auto& node4 = graph.AddNode("Calculator4");
  b.ConnectTo(node4.In("INPUT_B"));
  c.ConnectTo(node4.In("INPUT_C"));
  d.ConnectTo(node4.In("INPUT_D"));
  Stream<E> e = node4.Out("OUTPUT").Cast<E>();
  // Outputs.
  b.SetName("b").ConnectTo(graph.Out(0));
  c.SetName("c").ConnectTo(graph.Out(1));
  d.SetName("d").ConnectTo(graph.Out(2));
  e.SetName("e").ConnectTo(graph.Out(3));
  return graph.GetConfig();
 }
 ```
 Now, if needed, you can easily remove `node1` and make `b` a graph input and no
 updates are needed to `node2`, `node3`, `node4` (same as in proto representation
 by the way), because they are decoupled from each other.
 Overall, the above code replicates the proto graph more closely:
 ```proto
 input_stream: "a"
 node {
  calculator: "Calculator1"
  input_stream: "INPUT:a"
  output_stream: "OUTPUT:b"
 }
 node {
  calculator: "Calculator2"
  input_stream: "INPUT:b"
  output_stream: "OUTPUT:C"
 }
 node {
  calculator: "Calculator3"
  input_stream: "INPUT_B:b"
  input_stream: "INPUT_C:c"
  output_stream: "OUTPUT:d"
 }
 node {
  calculator: "Calculator4"
  input_stream: "INPUT_B:b"
  input_stream: "INPUT_C:c"
  input_stream: "INPUT_D:d"
  output_stream: "OUTPUT:e"
 }
 output_stream: "b"
 output_stream: "c"
 output_stream: "d"
 output_stream: "e"
 ```
 On top of that, now you can extract utility functions for further reuse in other graphs:
 ```c++ {.good}
 Stream<B> RunCalculator1(Stream<A> a, Graph& graph) {
  auto& node = graph.AddNode("Calculator1");
  a.ConnectTo(node.In("INPUT"));
  return node.Out("OUTPUT").Cast<B>();
 }
 Stream<C> RunCalculator2(Stream<B> b, Graph& graph) {
  auto& node = graph.AddNode("Calculator2");
  b.ConnectTo(node.In("INPUT"));
  return node.Out("OUTPUT").Cast<C>();
 }
 Stream<D> RunCalculator3(Stream<B> b, Stream<C> c, Graph& graph) {
  auto& node = graph.AddNode("Calculator3");
  b.ConnectTo(node.In("INPUT_B"));
  c.ConnectTo(node.In("INPUT_C"));
  return node.Out("OUTPUT").Cast<D>();
 }
 Stream<E> RunCalculator4(Stream<B> b, Stream<C> c, Stream<D> d, Graph& graph) {
  auto& node = graph.AddNode("Calculator4");
  b.ConnectTo(node.In("INPUT_B"));
  c.ConnectTo(node.In("INPUT_C"));
  d.ConnectTo(node.In("INPUT_D"));
  return node.Out("OUTPUT").Cast<E>();
 }
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Inputs.
  Stream<A> a = graph.In(0).Cast<A>();
  Stream<B> b = RunCalculator1(a, graph);
  Stream<C> c = RunCalculator2(b, graph);
  Stream<D> d = RunCalculator3(b, c, graph);
  Stream<E> e = RunCalculator4(b, c, d, graph);
  // Outputs.
  b.SetName("b").ConnectTo(graph.Out(0));
  c.SetName("c").ConnectTo(graph.Out(1));
  d.SetName("d").ConnectTo(graph.Out(2));
  e.SetName("e").ConnectTo(graph.Out(3));
  return graph.GetConfig();
 }
 ```
 ### Separate nodes for better readability
 ```c++ {.bad}
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Inputs.
  Stream<A> a = graph.In(0).Cast<A>();
  auto& node1 = graph.AddNode("Calculator1");
  a.ConnectTo(node1.In("INPUT"));
  Stream<B> b = node1.Out("OUTPUT").Cast<B>();
  auto& node2 = graph.AddNode("Calculator2");
  b.ConnectTo(node2.In("INPUT"));
  Stream<C> c = node2.Out("OUTPUT").Cast<C>();
  auto& node3 = graph.AddNode("Calculator3");
  b.ConnectTo(node3.In("INPUT_B"));
  c.ConnectTo(node3.In("INPUT_C"));
  Stream<D> d = node3.Out("OUTPUT").Cast<D>();
  auto& node4 = graph.AddNode("Calculator4");
  b.ConnectTo(node4.In("INPUT_B"));
  c.ConnectTo(node4.In("INPUT_C"));
  d.ConnectTo(node4.In("INPUT_D"));
  Stream<E> e = node4.Out("OUTPUT").Cast<E>();
  // Outputs.
  b.SetName("b").ConnectTo(graph.Out(0));
  c.SetName("c").ConnectTo(graph.Out(1));
  d.SetName("d").ConnectTo(graph.Out(2));
  e.SetName("e").ConnectTo(graph.Out(3));
  return graph.GetConfig();
 }
 ```
 In the above code, it can be hard to grasp the idea where each node begins and
 ends. To improve this and help your code readers, you can simply have blank
 lines before and after each node:
 ```c++ {.good}
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Inputs.
  Stream<A> a = graph.In(0).Cast<A>();
  auto& node1 = graph.AddNode("Calculator1");
  a.ConnectTo(node1.In("INPUT"));
  Stream<B> b = node1.Out("OUTPUT").Cast<B>();
  auto& node2 = graph.AddNode("Calculator2");
  b.ConnectTo(node2.In("INPUT"));
  Stream<C> c = node2.Out("OUTPUT").Cast<C>();
  auto& node3 = graph.AddNode("Calculator3");
  b.ConnectTo(node3.In("INPUT_B"));
  c.ConnectTo(node3.In("INPUT_C"));
  Stream<D> d = node3.Out("OUTPUT").Cast<D>();
  auto& node4 = graph.AddNode("Calculator4");
  b.ConnectTo(node4.In("INPUT_B"));
  c.ConnectTo(node4.In("INPUT_C"));
  d.ConnectTo(node4.In("INPUT_D"));
  Stream<E> e = node4.Out("OUTPUT").Cast<E>();
  // Outputs.
  b.SetName("b").ConnectTo(graph.Out(0));
  c.SetName("c").ConnectTo(graph.Out(1));
  d.SetName("d").ConnectTo(graph.Out(2));
  e.SetName("e").ConnectTo(graph.Out(3));
  return graph.GetConfig();
 }
 ```
 Also, the above representation matches `CalculatorGraphConfig` proto
 representation better.
 If you extract nodes into utility functions, they are scoped within functions
 already and it's clear where they begin and end, so it's completely fine to
 have:
 ```c++ {.good}
 CalculatorGraphConfig BuildGraph() {
  Graph graph;
  // Inputs.
  Stream<A> a = graph.In(0).Cast<A>();
  Stream<B> b = RunCalculator1(a, graph);
  Stream<C> c = RunCalculator2(b, graph);
  Stream<D> d = RunCalculator3(b, c, graph);
  Stream<E> e = RunCalculator4(b, c, d, graph);
  // Outputs.
  b.SetName("b").ConnectTo(graph.Out(0));
  c.SetName("c").ConnectTo(graph.Out(1));
  d.SetName("d").ConnectTo(graph.Out(2));
  e.SetName("e").ConnectTo(graph.Out(3));
  return graph.GetConfig();
 }
 ```
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/framework_concepts/calculators
 title: Calculators
 parent: Framework Concepts
 nav_order: 1
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/framework_concepts/overview
 title: Framework Concepts
 nav_order: 5
 has_children: true
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/framework_concepts/gpu
 title: GPU
 parent: Framework Concepts
 nav_order: 5
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/framework_concepts/graphs
 title: Graphs
 parent: Framework Concepts
 nav_order: 2
@@ -12,31 +13,31 @@ nav_order: 2
 {:toc}
 ---
-## GraphConfig
+## Graph
-A `GraphConfig` is a specification that describes the topology and functionality
+A `CalculatorGraphConfig` proto specifies the topology and functionality of a
-of a MediaPipe graph. In the specification, a node in the graph represents an
+MediaPipe graph. Each `node` in the graph represents a particular calculator or
-instance of a particular calculator. All the necessary configurations of the
+subgraph, and specifies necessary configurations, such as registered
-node, such its type, inputs and outputs must be described in the specification.
+calculator/subgraph type, inputs, outputs and optional fields, such as
 Description of the node can also include several optional fields, such as
 node-specific options, input policy and executor, discussed in
 [Synchronization](synchronization.md).
-`GraphConfig` has several other fields to configure the global graph-level
+`CalculatorGraphConfig` has several other fields to configure global graph-level
-settings, eg, graph executor configs, number of threads, and maximum queue size
+settings, e.g. graph executor configs, number of threads, and maximum queue size
 of input streams. Several graph-level settings are useful for tuning the
-performance of the graph on different platforms (eg, desktop v.s. mobile). For
+performance of the graph on different platforms (e.g., desktop v.s. mobile). For
 instance, on mobile, attaching a heavy model-inference calculator to a separate
 executor can improve the performance of a real-time application since this
 enables thread locality.
-Below is a trivial `GraphConfig` example where we have series of passthrough
+Below is a trivial `CalculatorGraphConfig` example where we have series of
-calculators :
+passthrough calculators :
 ```proto
 # This graph named main_pass_throughcals_nosubgraph.pbtxt contains 4
 # passthrough calculators.
 input_stream: "in"
 output_stream: "out"
 node {
    calculator: "PassThroughCalculator"
    input_stream: "in"
@@ -55,17 +56,46 @@ node {
 node {
    calculator: "PassThroughCalculator"
    input_stream: "out3"
-    output_stream: "out4"
+    output_stream: "out"
 }
 ```
 MediaPipe offers an alternative `C++` representation for complex graphs (e.g. ML pipelines, handling model metadata, optional nodes, etc.). The above graph may look like:
 ```c++
 CalculatorGraphConfig BuildGraphConfig() {
  Graph graph;
  // Graph inputs
  Stream<AnyType> in = graph.In(0).SetName("in");
  auto pass_through_fn = [](Stream<AnyType> in,
                            Graph& graph) -> Stream<AnyType> {
    auto& node = graph.AddNode("PassThroughCalculator");
    in.ConnectTo(node.In(0));
    return node.Out(0);
  };
  Stream<AnyType> out1 = pass_through_fn(in, graph);
  Stream<AnyType> out2 = pass_through_fn(out1, graph);
  Stream<AnyType> out3 = pass_through_fn(out2, graph);
  Stream<AnyType> out4 = pass_through_fn(out3, graph);
  // Graph outputs
  out4.SetName("out").ConnectTo(graph.Out(0));
  return graph.GetConfig();
 }
 ```
 See more details in [Building Graphs in C++](building_graphs_cpp.md)
 ## Subgraph
 To modularize a `CalculatorGraphConfig` into sub-modules and assist with re-use
 of perception solutions, a MediaPipe graph can be defined as a `Subgraph`. The
 public interface of a subgraph consists of a set of input and output streams
 similar to a calculator's public interface. The subgraph can then be included in
-an `CalculatorGraphConfig` as if it were a calculator. When a MediaPipe graph is
+a `CalculatorGraphConfig` as if it were a calculator. When a MediaPipe graph is
 loaded from a `CalculatorGraphConfig`, each subgraph node is replaced by the
 corresponding graph of calculators. As a result, the semantics and performance
 of the subgraph is identical to the corresponding graph of calculators.
@@ -151,7 +181,7 @@ protobuf specified for a MediaPipe calculator. These "graph options" can be
 specified where a graph is invoked, and used to populate calculator options and
 subgraph options within the graph.
-In a CalculatorGraphConfig, graph options can be specified for a subgraph
+In a `CalculatorGraphConfig`, graph options can be specified for a subgraph
 exactly like calculator options, as shown below:
 ```
@@ -178,7 +208,7 @@ node {
 }
 ```
-In a CalculatorGraphConfig, graph options can be accepted and used to populate
+In a `CalculatorGraphConfig`, graph options can be accepted and used to populate
 calculator options, as shown below:
 ```
@@ -188,7 +218,7 @@ graph_options: {
 node: {
  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:multi_backend_image"
+  input_stream: "IMAGE:image"
  node_options: {
    [type.googleapis.com/mediapipe.ImageToTensorCalculatorOptions] {
        keep_aspect_ratio: true
@@ -247,9 +277,9 @@ NOTE: The current approach is experimental and subject to change. We welcome
 your feedback.
 Please use the `CalculatorGraphTest.Cycle` unit test in
-`mediapipe/framework/calculator_graph_test.cc` as sample code. Shown
+`mediapipe/framework/calculator_graph_test.cc` as sample code. Shown below is
-below is the cyclic graph in the test. The `sum` output of the adder is the sum
+the cyclic graph in the test. The `sum` output of the adder is the sum of the
-of the integers generated by the integer source calculator.
+integers generated by the integer source calculator.
 ![a cyclic graph that adds a stream of integers](https://mediapipe.dev/images/cyclic_integer_sum_graph.svg "A cyclic graph")
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/framework_concepts/packets
 title: Packets
 parent: Framework Concepts
 nav_order: 3
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/framework_concepts/realtime_streams
 title: Real-time Streams
 parent: Framework Concepts
 nav_order: 6
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/framework_concepts/synchronization
 title: Synchronization
 parent: Framework Concepts
 nav_order: 4
@@ -112,14 +113,14 @@ Warning: On the other hand, it is not guaranteed that an input packet will
 always be available for all streams.
 To explain how it works, we need to introduce the definition of a settled
-timestamp. We say that a timestamp in a stream is *settled* if it lower than the
+timestamp. We say that a timestamp in a stream is *settled* if it is lower than
-timestamp bound. In other words, a timestamp is settled for a stream once the
+the timestamp bound. In other words, a timestamp is settled for a stream once
-state of the input at that timestamp is irrevocably known: either there is a
+the state of the input at that timestamp is irrevocably known: either there is a
 packet, or there is the certainty that a packet with that timestamp will not
 arrive.
 Note: For this reason, MediaPipe also allows a stream producer to explicitly
-advance the timestamp bound farther that what the last packet implies, i.e. to
+advance the timestamp bound farther than what the last packet implies, i.e. to
 provide a tighter bound. This can allow the downstream nodes to settle their
 inputs sooner.
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/android
 title: MediaPipe on Android
 parent: Getting Started
 has_children: true
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/android_archive_library
 title: MediaPipe Android Archive
 parent: MediaPipe on Android
 grand_parent: Getting Started
@@ -13,6 +13,17 @@ nav_order: 2
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We are moving to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation
 site for MediaPipe starting April 3, 2023. This content will not be moved to
 the new site, but will remain available in the source code repository on an
 as-is basis.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 MediaPipe Android Solution APIs (currently in alpha) are available in:
 *   [MediaPipe Face Detection](../solutions/face_detection#android-solution-api)
@@ -12,6 +12,17 @@ nav_exclude: true
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We are moving to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation
 site for MediaPipe starting April 3, 2023. This content will not be moved to
 the new site, but will remain available in the source code repository on an
 as-is basis.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ### Android
 Please see these [instructions](./android.md).
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/cpp
 title: MediaPipe in C++
 parent: Getting Started
 has_children: true
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/faq
 title: FAQ
 parent: Getting Started
 nav_order: 9
@@ -59,7 +60,7 @@ The second approach allows up to [`max_in_flight`] invocations of the
 packets from [`CalculatorBase::Process`] are automatically ordered by timestamp
 before they are passed along to downstream calculators.
-With either aproach, you must be aware that the calculator running in parallel
+With either approach, you must be aware that the calculator running in parallel
 cannot maintain internal state in the same way as a normal sequential
 calculator.
@@ -11,3 +11,14 @@ has_children: true
 1. TOC
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We are moving to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation
 site for MediaPipe starting April 3, 2023. This content will not be moved to
 the new site, but will remain available in the source code repository on an
 as-is basis.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/gpu_support
 title: GPU Support
 parent: Getting Started
 nav_order: 7
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/hello_world_android
 title: Hello World! on Android
 parent: MediaPipe on Android
 grand_parent: Getting Started
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/hello_world_cpp
 title: Hello World! in C++
 parent: MediaPipe in C++
 grand_parent: Getting Started
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/hello_world_ios
 title: Hello World! on iOS
 parent: MediaPipe on iOS
 grand_parent: Getting Started
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/help
 title: Getting Help
 parent: Getting Started
 nav_order: 8
@@ -37,8 +38,8 @@ If you open a GitHub issue, here is our policy:
 - **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**:
 - **Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device**:
 - **Bazel version**:
- **Android Studio, NDK, SDK versions (if issue is related to building in mobile dev enviroment)**:
+- **Android Studio, NDK, SDK versions (if issue is related to building in mobile dev environment)**:
- **Xcode & Tulsi version (if issue is related to building in mobile dev enviroment)**:
+- **Xcode & Tulsi version (if issue is related to building in mobile dev environment)**:
 - **Exact steps to reproduce**:
 ### Describe the problem
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/install
 title: Installation
 parent: Getting Started
 nav_order: 6
@@ -35,7 +36,7 @@ install --user six`.
    ```bash
    $ cd $HOME
-    $ git clone https://github.com/google/mediapipe.git
+    $ git clone --depth 1 https://github.com/google/mediapipe.git
    # Change directory into MediaPipe root directory
    $ cd mediapipe
@@ -67,6 +68,13 @@ install --user six`.
        libopencv-video-dev
    ```
    **Note**. On Debian 11/Ubuntu 21.04 where OpenCV 4.5 is installed with
    `libopencv-video-dev`, `libopencv-contrib-dev` should also be installed.
    ```bash
    $ sudo apt-get install -y libopencv-contrib-dev
    ```
    MediaPipe's [`opencv_linux.BUILD`] and [`WORKSPACE`] are already configured
    for OpenCV 2/3 and should work correctly on any architecture:
@@ -287,7 +295,7 @@ build issues.
 2.  Checkout MediaPipe repository.
    ```bash
-    $ git clone https://github.com/google/mediapipe.git
+    $ git clone --depth 1 https://github.com/google/mediapipe.git
    # Change directory into MediaPipe root directory
    $ cd mediapipe
@@ -416,7 +424,7 @@ build issues.
 3.  Checkout MediaPipe repository.
    ```bash
-    $ git clone https://github.com/google/mediapipe.git
+    $ git clone --depth 1 https://github.com/google/mediapipe.git
    $ cd mediapipe
    ```
@@ -590,7 +598,7 @@ next section.
 7.  Checkout MediaPipe repository.
    ```
-    C:\Users\Username\mediapipe_repo> git clone https://github.com/google/mediapipe.git
+    C:\Users\Username\mediapipe_repo> git clone --depth 1 https://github.com/google/mediapipe.git
    # Change directory into MediaPipe root directory
    C:\Users\Username\mediapipe_repo> cd mediapipe
@@ -651,7 +659,7 @@ cameras. Alternatively, you use a video file as input.
 1.  Follow the
    [instruction](https://docs.microsoft.com/en-us/windows/wsl/install-win10) to
-    install Windows Sysystem for Linux (Ubuntu).
+    install Windows Subsystem for Linux (Ubuntu).
 2.  Install Windows ADB and start the ADB server in Windows.
@@ -680,7 +688,7 @@ cameras. Alternatively, you use a video file as input.
 6.  Checkout MediaPipe repository.
    ```bash
-    username@DESKTOP-TMVLBJ1:~$ git clone https://github.com/google/mediapipe.git
+    username@DESKTOP-TMVLBJ1:~$ git clone --depth 1 https://github.com/google/mediapipe.git
    username@DESKTOP-TMVLBJ1:~$ cd mediapipe
    ```
@@ -771,7 +779,7 @@ This will use a Docker image that will isolate mediapipe's installation from the
 2.  Build a docker image with tag "mediapipe".
    ```bash
-    $ git clone https://github.com/google/mediapipe.git
+    $ git clone --depth 1 https://github.com/google/mediapipe.git
    $ cd mediapipe
    $ docker build --tag=mediapipe .
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/ios
 title: MediaPipe on iOS
 parent: Getting Started
 has_children: true
@@ -91,6 +92,10 @@ allows you to make use of automatic provisioning (see later section).
    This will install `Tulsi.app` inside the `Applications` directory in your
    home directory.
    **Note**: Please ensure the `xcode_version` in the
    [`build_and_run.sh`](https://github.com/bazelbuild/tulsi/blob/b1d0108e6a93dbe8ab01529b2c607b6b651f0759/build_and_run.sh#L26)
    file in tulsi repo is the same version as installed in your system.
 2.  Open `mediapipe/Mediapipe.tulsiproj` using the Tulsi app.
    Tip: If Tulsi displays an error saying "Bazel could not be found", press the
@@ -12,6 +12,17 @@ nav_order: 4
 {:toc}
 ---
 **Attention:** *Thanks for your interest in MediaPipe! We are moving to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation
 site for MediaPipe starting April 3, 2023. This content will not be moved to
 the new site, but will remain available in the source code repository on an
 as-is basis.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ## Ready-to-use JavaScript Solutions
 MediaPipe currently offers the following solutions:
@@ -29,11 +40,11 @@ Solution                    | NPM Package                             | Example
 Click on a solution link above for more information, including API and code
 snippets.
-### Supported plaforms:
+### Supported platforms:
 | Browser | Platform                | Notes                                  |
 | ------- | ----------------------- | -------------------------------------- |
-| Chrome  | Android / Windows / Mac | Pixel 4 and older unsupported. Fuschia |
+| Chrome  | Android / Windows / Mac | Pixel 4 and older unsupported. Fuchsia |
 |         |                         | unsupported.                           |
 | Chrome  | iOS                     | Camera unavailable in Chrome on iOS.   |
 | Safari  | iPad/iPhone/Mac         | iOS and Safari on iPad / iPhone /      |
@@ -1,6 +1,6 @@
 ---
-layout: default
+layout: forward
-title: MediaPipe Python Framework
+target: https://developers.google.com/mediapipe/framework/getting_started/python_framework
 parent: MediaPipe in Python
 grand_parent: Getting Started
 nav_order: 1
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/getting_started/troubleshooting
 title: Troubleshooting
 parent: Getting Started
 nav_order: 10
@@ -65,7 +66,7 @@ WARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/gith
 ```
 usually indicates that Bazel fails to download necessary dependency repositories
-that MediaPipe needs. MedaiPipe has several dependency repositories that are
+that MediaPipe needs. MediaPipe has several dependency repositories that are
 hosted by Google sites. In some regions, you may need to set up a network proxy
 or use a VPN to access those resources. You may also need to append
 `--host_jvm_args "-DsocksProxyHost=<ip address> -DsocksProxyPort=<port number>"`
@@ -19,6 +19,17 @@ ML solutions for live and streaming media.
 ![ready_to_use.png](https://mediapipe.dev/images/ready_to_use_small.png)                                                             | ![open_source.png](https://mediapipe.dev/images/open_source_small.png)
 ***Ready-to-use solutions***: *Cutting-edge ML solutions demonstrating full power of the framework*            | ***Free and open source***: *Framework and solutions both under Apache 2.0, fully extensible and customizable*
 ----
 **Attention:** *Thanks for your interest in MediaPipe! We are moving to
 [https://developers.google.com/mediapipe](https://developers.google.com/mediapipe)
 as the primary developer documentation
 site for MediaPipe starting April 3, 2023.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ## ML solutions in MediaPipe
 Face Detection                                                                                                                 | Face Mesh                                                                                                       | Iris                                                                                                      | Hands                                                                                                      | Pose                                                                                                      | Holistic
@@ -18,6 +18,16 @@ nav_order: 14
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 We have ended support for this MediaPipe Legacy Solution as of March 1, 2023.
 For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ## Overview
 AutoFlip is an automatic video cropping pipeline built on top of MediaPipe. This
@@ -18,6 +18,16 @@ nav_order: 10
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 We have ended support for this MediaPipe Legacy Solution as of March 1, 2023.
 For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ## Overview
 MediaPipe Box Tracking has been powering real-time tracking in
@@ -18,6 +18,16 @@ nav_order: 1
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution is planned to be upgraded to a new MediaPipe
 Solution. For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ## Overview
 MediaPipe Face Detection is an ultrafast face detection solution that comes with
@@ -54,6 +64,25 @@ used for its improved inference speed. Please refer to the
 [model cards](./models.md#face_detection) for details. Default to `0` if not
 specified.
 Note: Not available for JavaScript (use "model" instead).
 #### model
 A string value to indicate which model should be used. Use "short" to
 select a short-range model that works best for faces within 2 meters from the
 camera, and "full" 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 for details. Default to empty string.
 Note: Valid only for JavaScript solution.
 #### selfie_mode
 A boolean value to indicate whether to flip the images/video frames
 horizontally or not. Default to `false`.
 Note: Valid only for JavaScript solution.
 #### min_detection_confidence
 Minimum confidence value (`[0.0, 1.0]`) from the face detection model for the
@@ -146,9 +175,9 @@ 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:
+Supported face detection options:
-
+*   [selfieMode](#selfie_mode)
-*   [modelSelection](#model_selection)
+*   [model](#model)
 *   [minDetectionConfidence](#min_detection_confidence)
 ```html
@@ -176,6 +205,7 @@ Supported configuration options:
 const videoElement = document.getElementsByClassName('input_video')[0];
 const canvasElement = document.getElementsByClassName('output_canvas')[0];
 const canvasCtx = canvasElement.getContext('2d');
 const drawingUtils = window;
 function onResults(results) {
  // Draw the overlays.
@@ -199,7 +229,7 @@ const faceDetection = new FaceDetection({locateFile: (file) => {
  return `https://cdn.jsdelivr.net/npm/@mediapipe/[email protected]/${file}`;
 }});
 faceDetection.setOptions({
-  modelSelection: 0,
+  model: 'short',
  minDetectionConfidence: 0.5
 });
 faceDetection.onResults(onResults);
@@ -18,6 +18,16 @@ nav_order: 2
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution is planned to be upgraded to a new MediaPipe
 Solution. For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ## Overview
 MediaPipe Face Mesh is a solution that estimates 468 3D face landmarks in
@@ -133,7 +143,7 @@ about the model in this [paper](https://arxiv.org/abs/2006.10962).
 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
 normalized screen coordinates, while the Z coordinate is relative and is scaled
-as the X coodinate under the
+as the X coordinate under the
 [weak perspective projection camera model](https://en.wikipedia.org/wiki/3D_projection#Weak_perspective_projection).
 This format is well-suited for some applications, however it does not directly
 enable the full spectrum of augmented reality (AR) features like aligning a
@@ -18,6 +18,16 @@ nav_order: 8
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution is planned to be upgraded to a new MediaPipe
 Solution. For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ![hair_segmentation_android_gpu_gif](https://mediapipe.dev/images/mobile/hair_segmentation_android_gpu.gif)
 ## Example Apps
@@ -18,6 +18,16 @@ nav_order: 4
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution is planned to be upgraded to a new MediaPipe
 Solution. For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ## Overview
 The ability to perceive the shape and motion of hands can be a vital component
@@ -18,6 +18,16 @@ nav_order: 6
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution is planned to be upgraded to a new MediaPipe
 Solution. For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ## Overview
 Live perception of simultaneous [human pose](./pose.md),
@@ -259,6 +269,7 @@ mp_holistic = mp.solutions.holistic
 # For static images:
 IMAGE_FILES = []
 BG_COLOR = (192, 192, 192) # gray
 with mp_holistic.Holistic(
    static_image_mode=True,
    model_complexity=2,
@@ -18,6 +18,16 @@ nav_order: 11
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 We have ended support for this MediaPipe Legacy Solution as of March 1, 2023.
 For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ## Overview
 Augmented Reality (AR) technology creates fun, engaging, and immersive user
@@ -18,6 +18,16 @@ nav_order: 3
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution is planned to be upgraded to a new MediaPipe
 Solution. For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ## Overview
 A wide range of real-world applications, including computational photography
@@ -38,7 +48,7 @@ camera, in real-time, without the need for specialized hardware. Through use of
 iris landmarks, the solution is also able to determine the metric distance
 between the subject and the camera with relative error less than 10%. Note that
 iris tracking does not infer the location at which people are looking, nor does
-it provide any form of identity recognition. With the cross-platfrom capability
+it provide any form of identity recognition. With the cross-platform capability
 of the MediaPipe framework, MediaPipe Iris can run on most modern
 [mobile phones](#mobile), [desktops/laptops](#desktop) and even on the
 [web](#web).
@@ -99,7 +109,7 @@ You can also find more details in this
 ### Iris Landmark Model
 The iris model takes an image patch of the eye region and estimates both the eye
-landmarks (along the eyelid) and iris landmarks (along ths iris contour). You
+landmarks (along the eyelid) and iris landmarks (along this iris contour). You
 can find more details in this [paper](https://arxiv.org/abs/2006.11341).
 ![iris_tracking_eye_and_iris_landmarks.png](https://mediapipe.dev/images/mobile/iris_tracking_eye_and_iris_landmarks.png) |
@@ -18,6 +18,16 @@ nav_order: 13
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 We have ended support for this MediaPipe Legacy Solution as of March 1, 2023.
 For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ## Overview
 MediaPipe KNIFT is a template-based feature matching solution using KNIFT
@@ -18,6 +18,16 @@ nav_order: 15
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 We have ended support for this MediaPipe Legacy Solution as of March 1, 2023.
 For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ## Overview
 MediaPipe is a useful and general framework for media processing that can
@@ -85,7 +95,7 @@ process new data sets, in the documentation of
    MediaSequence uses SequenceExamples as the format of both inputs and
    outputs. Annotations are encoded as inputs in a SequenceExample of metadata
-    that defines the labels and the path to the cooresponding video file. This
+    that defines the labels and the path to the corresponding video file. This
    metadata is passed as input to the C++ `media_sequence_demo` binary, and the
    output is a SequenceExample filled with images and annotations ready for
    model training.
@@ -12,6 +12,20 @@ nav_order: 30
 {:toc}
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 We have ended support for
 [these MediaPipe Legacy Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 as of March 1, 2023. All other
 [MediaPipe Legacy Solutions will be upgraded](https://developers.google.com/mediapipe/solutions/guide#legacy)
 to a new MediaPipe Solution. The code repository and prebuilt binaries for all
 MediaPipe Legacy Solutions will continue to be provided on an as-is basis.
 We encourage you to check out the new MediaPipe Solutions at:
 [https://developers.google.com/mediapipe/solutions](https://developers.google.com/mediapipe/solutions)*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ### [Face Detection](https://google.github.io/mediapipe/solutions/face_detection)
 *   Short-range model (best for faces within 2 meters from the camera):
@@ -94,8 +108,6 @@ one over the other.
 *   [TFLite model](https://storage.googleapis.com/mediapipe-assets/ssdlite_object_detection.tflite)
 *   [TFLite model quantized for EdgeTPU/Coral](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/models/object-detector-quantized_edgetpu.tflite)
 *   [TensorFlow model](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_saved_model)
 *   [Model information](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_saved_model/README.md)
 ### [Objectron](https://google.github.io/mediapipe/solutions/objectron)
@@ -18,6 +18,16 @@ nav_order: 9
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution is planned to be upgraded to a new MediaPipe
 Solution. For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ![object_detection_android_gpu.gif](https://mediapipe.dev/images/mobile/object_detection_android_gpu.gif)
 ## Example Apps
@@ -18,6 +18,16 @@ nav_order: 12
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 We have ended support for this MediaPipe Legacy Solution as of March 1, 2023.
 For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ## Overview
 MediaPipe Objectron is a mobile real-time 3D object detection solution for
@@ -170,7 +180,7 @@ and a
 The detection subgraph performs ML inference only once every few frames to
 reduce computation load, and decodes the output tensor to a FrameAnnotation that
 contains nine keypoints: the 3D bounding box's center and its eight vertices.
-The tracking subgraph runs every frame, using the box traker in
+The tracking subgraph runs every frame, using the box tracker in
 [MediaPipe Box Tracking](./box_tracking.md) to track the 2D box tightly
 enclosing the projection of the 3D bounding box, and lifts the tracked 2D
 keypoints to 3D with
@@ -613,7 +623,7 @@ z_ndc = 1 / Z
 ### Pixel Space
-In this API we set upper-left coner of an image as the origin of pixel
+In this API we set upper-left corner of an image as the origin of pixel
 coordinate. One can convert from NDC to pixel space as follows:
 ```
@@ -20,6 +20,16 @@ nav_order: 5
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution is planned to be upgraded to a new MediaPipe
 Solution. For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ## Overview
 Human pose estimation from video plays a critical role in various applications
@@ -19,6 +19,16 @@ nav_order: 1
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution is planned to be upgraded to a new MediaPipe
 Solution. For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ## Overview
 One of the applications
@@ -18,6 +18,16 @@ nav_order: 7
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 As of March 1, 2023, this solution is planned to be upgraded to a new MediaPipe
 Solution. For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 ## Overview
 *Fig 1. Example of MediaPipe Selfie Segmentation.* |
@@ -13,6 +13,22 @@ has_toc: false
 {:toc}
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions. We have
 ended support for
 [these MediaPipe Legacy Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 as of March 1, 2023. All other
 [MediaPipe Legacy Solutions will be upgraded](https://developers.google.com/mediapipe/solutions/guide#legacy)
 to a new MediaPipe Solution. The
 [code repository](https://github.com/google/mediapipe/tree/master/mediapipe)
 and prebuilt binaries for all MediaPipe Legacy Solutions will continue to
 be provided on an as-is basis. We encourage you to check out the new MediaPipe
 Solutions at:
 [https://developers.google.com/mediapipe/solutions](https://developers.google.com/mediapipe/solutions)*
 *This notice and web page will be removed on June 1, 2023.*
 ----
 MediaPipe offers open source cross-platform, customizable ML solutions for live
 and streaming media.
@@ -18,6 +18,16 @@ nav_order: 16
 </details>
 ---
 **Attention:** *Thank you for your interest in MediaPipe Solutions.
 We have ended support for this MediaPipe Legacy Solution as of March 1, 2023.
 For more information, see the new
 [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/guide#legacy)
 site.*
 *This notice and web page will be removed on April 3, 2023.*
 ----
 MediaPipe is a useful and general framework for media processing that can assist
 with research, development, and deployment of ML models. This example focuses on
 model development by demonstrating how to prepare training data and do model
@@ -1,5 +1,6 @@
 ---
-layout: default
+layout: forward
 target: https://developers.google.com/mediapipe/framework/tools/visualizer
 title: Visualizer
 parent: Tools
 nav_order: 1
@@ -12,12 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 load("//mediapipe/framework/port:build_config.bzl", "mediapipe_cc_proto_library")
 licenses(["notice"])
 package(default_visibility = ["//visibility:private"])
 load("//mediapipe/framework/port:build_config.bzl", "mediapipe_cc_proto_library")
 proto_library(
    name = "mfcc_mel_calculators_proto",
    srcs = ["mfcc_mel_calculators.proto"],
@@ -280,6 +280,13 @@ absl::Status SpectrogramCalculator::Open(CalculatorContext* cc) {
      audio_dsp::HammingWindow().GetPeriodicSamples(frame_duration_samples_,
                                                    &window);
      break;
    case SpectrogramCalculatorOptions::SQRT_HANN: {
      audio_dsp::HannWindow().GetPeriodicSamples(frame_duration_samples_,
                                                 &window);
      absl::c_transform(window, window.begin(),
                        [](double x) { return std::sqrt(x); });
      break;
    }
  }
  // Propagate settings down to the actual Spectrogram object.
@@ -68,6 +68,7 @@ message SpectrogramCalculatorOptions {
    HANN = 0;
    HAMMING = 1;
    COSINE = 2;
    SQRT_HANN = 4;
  }
  optional WindowType window_type = 6 [default = HANN];
@@ -13,12 +13,21 @@
 # limitations under the License.
 #
 load("@bazel_skylib//lib:selects.bzl", "selects")
 load("//mediapipe/framework/port:build_config.bzl", "mediapipe_proto_library")
 licenses(["notice"])
 package(default_visibility = ["//visibility:public"])
 selects.config_setting_group(
    name = "ios_or_disable_gpu",
    match_any = [
        "//mediapipe/gpu:disable_gpu",
        "//mediapipe:ios",
    ],
 )
 mediapipe_proto_library(
    name = "concatenate_vector_calculator_proto",
    srcs = ["concatenate_vector_calculator.proto"],
@@ -108,6 +117,7 @@ mediapipe_proto_library(
        "//mediapipe/framework:calculator_proto",
        "//mediapipe/framework/formats:classification_proto",
        "//mediapipe/framework/formats:landmark_proto",
        "//mediapipe/framework/formats:time_series_header_proto",
    ],
 )
@@ -188,6 +198,7 @@ cc_library(
        "//mediapipe/framework/formats:landmark_cc_proto",
        "//mediapipe/framework/formats:matrix",
        "//mediapipe/framework/formats:rect_cc_proto",
        "//mediapipe/framework/formats:tensor",
        "//mediapipe/framework/port:integral_types",
        "//mediapipe/framework/port:ret_check",
        "//mediapipe/framework/port:status",
@@ -210,6 +221,7 @@ cc_library(
        "//mediapipe/framework/formats:landmark_cc_proto",
        "//mediapipe/framework/formats:matrix",
        "//mediapipe/framework/formats:rect_cc_proto",
        "//mediapipe/framework/formats:tensor",
        "//mediapipe/framework/port:integral_types",
        "//mediapipe/framework/port:ret_check",
        "//mediapipe/framework/port:status",
@@ -226,10 +238,12 @@ cc_test(
        ":begin_loop_calculator",
        ":end_loop_calculator",
        ":gate_calculator",
        ":pass_through_calculator",
        "//mediapipe/framework:calculator_context",
        "//mediapipe/framework:calculator_contract",
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework:packet",
        "//mediapipe/framework/formats:tensor",
        "//mediapipe/framework/port:gtest_main",
        "//mediapipe/framework/port:integral_types",
        "//mediapipe/framework/port:parse_text_proto",
@@ -567,7 +581,7 @@ cc_library(
    name = "packet_thinner_calculator",
    srcs = ["packet_thinner_calculator.cc"],
    deps = [
-        "//mediapipe/calculators/core:packet_thinner_calculator_cc_proto",
+        ":packet_thinner_calculator_cc_proto",
        "//mediapipe/framework:calculator_context",
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework/formats:video_stream_header",
@@ -584,7 +598,7 @@ cc_test(
    srcs = ["packet_thinner_calculator_test.cc"],
    deps = [
        ":packet_thinner_calculator",
-        "//mediapipe/calculators/core:packet_thinner_calculator_cc_proto",
+        ":packet_thinner_calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework:calculator_runner",
        "//mediapipe/framework/formats:video_stream_header",
@@ -762,7 +776,7 @@ cc_library(
    srcs = ["packet_resampler_calculator.cc"],
    hdrs = ["packet_resampler_calculator.h"],
    deps = [
-        "//mediapipe/calculators/core:packet_resampler_calculator_cc_proto",
+        ":packet_resampler_calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework:collection_item_id",
        "//mediapipe/framework/deps:mathutil",
@@ -786,7 +800,7 @@ cc_test(
    ],
    deps = [
        ":packet_resampler_calculator",
-        "//mediapipe/calculators/core:packet_resampler_calculator_cc_proto",
+        ":packet_resampler_calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework:calculator_runner",
        "//mediapipe/framework/formats:video_stream_header",
@@ -852,10 +866,10 @@ cc_test(
    name = "flow_limiter_calculator_test",
    srcs = ["flow_limiter_calculator_test.cc"],
    deps = [
        ":counting_source_calculator",
        ":flow_limiter_calculator",
        ":flow_limiter_calculator_cc_proto",
-        "//mediapipe/calculators/core:counting_source_calculator",
+        ":pass_through_calculator",
        "//mediapipe/calculators/core:pass_through_calculator",
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework:calculator_runner",
        "//mediapipe/framework:test_calculators",
@@ -899,8 +913,7 @@ cc_library(
        "@org_tensorflow//tensorflow/lite:framework",
        "@org_tensorflow//tensorflow/lite/kernels:builtin_ops",
    ] + select({
-        "//mediapipe/gpu:disable_gpu": [],
+        ":ios_or_disable_gpu": [],
        "//mediapipe:ios": [],
        "//conditions:default": [
            "@org_tensorflow//tensorflow/lite/delegates/gpu/gl:gl_buffer",
        ],
@@ -1150,6 +1163,7 @@ cc_library(
        "//mediapipe/framework:collection_item_id",
        "//mediapipe/framework/formats:classification_cc_proto",
        "//mediapipe/framework/formats:landmark_cc_proto",
        "//mediapipe/framework/formats:time_series_header_cc_proto",
        "//mediapipe/framework/port:integral_types",
        "//mediapipe/framework/port:ret_check",
        "//mediapipe/framework/port:status",
@@ -1302,7 +1316,7 @@ cc_test(
    srcs = ["packet_sequencer_calculator_test.cc"],
    deps = [
        ":packet_sequencer_calculator",
-        "//mediapipe/calculators/core:pass_through_calculator",
+        ":pass_through_calculator",
        "//mediapipe/framework:calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework:subgraph",
@@ -1321,8 +1335,10 @@ cc_library(
    hdrs = ["merge_to_vector_calculator.h"],
    deps = [
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework:packet",
        "//mediapipe/framework/api2:node",
        "//mediapipe/framework/api2:port",
        "//mediapipe/framework/formats:detection_cc_proto",
        "//mediapipe/framework/formats:image",
        "@com_google_absl//absl/status",
    ],
@@ -20,6 +20,7 @@
 #include "mediapipe/calculators/core/end_loop_calculator.h"
 #include "mediapipe/framework/calculator_contract.h"
 #include "mediapipe/framework/calculator_framework.h"
 #include "mediapipe/framework/formats/tensor.h"
 #include "mediapipe/framework/packet.h"
 #include "mediapipe/framework/port/gmock.h"
 #include "mediapipe/framework/port/gtest.h"
@@ -444,5 +445,67 @@ TEST_F(BeginEndLoopCalculatorGraphWithClonedInputsTest, MultipleVectors) {
                  PacketOfIntsEq(input_timestamp2, std::vector<int>{6, 9})));
 }
 absl::Status InitBeginEndTensorLoopTestGraph(
    CalculatorGraph& graph, std::vector<Packet>& output_packets) {
  auto graph_config = ParseTextProtoOrDie<CalculatorGraphConfig>(
      R"pb(
        num_threads: 4
        input_stream: "tensors"
        node {
          calculator: "BeginLoopTensorCalculator"
          input_stream: "ITERABLE:tensors"
          output_stream: "ITEM:tensor"
          output_stream: "BATCH_END:timestamp"
        }
        node {
          calculator: "PassThroughCalculator"
          input_stream: "tensor"
          output_stream: "passed_tensor"
        }
        node {
          calculator: "EndLoopTensorCalculator"
          input_stream: "ITEM:passed_tensor"
          input_stream: "BATCH_END:timestamp"
          output_stream: "ITERABLE:output_tensors"
        }
      )pb");
  tool::AddVectorSink("output_tensors", &graph_config, &output_packets);
  MP_RETURN_IF_ERROR(graph.Initialize(graph_config));
  return graph.StartRun({});
 }
 TEST(BeginEndTensorLoopCalculatorGraphTest, SingleNonEmptyVector) {
  // Initialize the graph.
  CalculatorGraph graph;
  std::vector<Packet> output_packets;
  MP_ASSERT_OK(InitBeginEndTensorLoopTestGraph(graph, output_packets));
  // Prepare the inputs and run.
  Timestamp input_timestamp = Timestamp(0);
  std::vector<mediapipe::Tensor> tensors;
  for (int i = 0; i < 4; i++) {
    tensors.emplace_back(Tensor::ElementType::kFloat32,
                         Tensor::Shape{4, 3, 2, 1});
  }
  Packet vector_packet =
      MakePacket<std::vector<mediapipe::Tensor>>(std::move(tensors));
  MP_ASSERT_OK(graph.AddPacketToInputStream(
      "tensors", std::move(vector_packet).At(input_timestamp)));
  MP_ASSERT_OK(graph.WaitUntilIdle());
  // Verify the output packet.
  EXPECT_EQ(output_packets.size(), 1);
  const std::vector<Tensor>& output_tensors =
      output_packets[0].Get<std::vector<Tensor>>();
  EXPECT_EQ(output_tensors.size(), 4);
  for (int i = 0; i < output_tensors.size(); i++) {
    EXPECT_THAT(output_tensors[i].shape().dims,
                testing::ElementsAre(4, 3, 2, 1));
  }
  MP_ASSERT_OK(graph.CloseAllPacketSources());
  MP_ASSERT_OK(graph.WaitUntilDone());
 }
 }  // namespace
 }  // namespace mediapipe
@@ -20,6 +20,7 @@
 #include "mediapipe/framework/formats/landmark.pb.h"
 #include "mediapipe/framework/formats/matrix.h"
 #include "mediapipe/framework/formats/rect.pb.h"
 #include "mediapipe/framework/formats/tensor.h"
 namespace mediapipe {
@@ -55,4 +56,8 @@ REGISTER_CALCULATOR(BeginLoopMatrixVectorCalculator);
 typedef BeginLoopCalculator<std::vector<uint64_t>> BeginLoopUint64tCalculator;
 REGISTER_CALCULATOR(BeginLoopUint64tCalculator);
 // A calculator to process std::vector<mediapipe::Tensor>.
 typedef BeginLoopCalculator<std::vector<Tensor>> BeginLoopTensorCalculator;
 REGISTER_CALCULATOR(BeginLoopTensorCalculator);
 }  // namespace mediapipe
@@ -15,7 +15,6 @@
 #ifndef MEDIAPIPE_CALCULATORS_CORE_BEGIN_LOOP_CALCULATOR_H_
 #define MEDIAPIPE_CALCULATORS_CORE_BEGIN_LOOP_CALCULATOR_H_
 #include "absl/memory/memory.h"
 #include "mediapipe/framework/calculator_context.h"
 #include "mediapipe/framework/calculator_contract.h"
 #include "mediapipe/framework/calculator_framework.h"
@@ -24,6 +23,7 @@
 #include "mediapipe/framework/port/integral_types.h"
 #include "mediapipe/framework/port/ret_check.h"
 #include "mediapipe/framework/port/status.h"
 #include "mediapipe/framework/port/status_macros.h"
 namespace mediapipe {
@@ -49,7 +49,7 @@ namespace mediapipe {
 //   calculator:    "EndLoopWithOutputCalculator"
 //   input_stream:  "ITEM:output_of_loop_body"     # ItemU     @loop_internal_ts
 //   input_stream:  "BATCH_END:ext_ts"             # Timestamp @loop_internal_ts
-//   output_stream: "OUTPUT:aggregated_result"     # IterableU @ext_ts
+//   output_stream: "ITERABLE:aggregated_result"   # IterableU @ext_ts
 // }
 //
 // Input streams tagged with "CLONE" are cloned to the corresponding output
@@ -112,13 +112,38 @@ class BeginLoopCalculator : public CalculatorBase {
  absl::Status Process(CalculatorContext* cc) final {
    Timestamp last_timestamp = loop_internal_timestamp_;
    if (!cc->Inputs().Tag("ITERABLE").IsEmpty()) {
-      const IterableT& collection =
+      // Try to consume the ITERABLE packet if possible to obtain the ownership
-          cc->Inputs().Tag("ITERABLE").template Get<IterableT>();
+      // and emit the item packets by moving them.
-      for (const auto& item : collection) {
+      // If the ITERABLE packet is not consumable, then try to copy each item
-        cc->Outputs().Tag("ITEM").AddPacket(
+      // instead. If the ITEM type is not copy constructible, an error will be
-            MakePacket<ItemT>(item).At(loop_internal_timestamp_));
+      // returned.
-        ForwardClonePackets(cc, loop_internal_timestamp_);
+      auto iterable_ptr_or =
-        ++loop_internal_timestamp_;
+          cc->Inputs().Tag("ITERABLE").Value().Consume<IterableT>();
      if (iterable_ptr_or.ok()) {
        for (auto& item : *iterable_ptr_or.value()) {
          Packet item_packet = MakePacket<ItemT>(std::move(item));
          cc->Outputs().Tag("ITEM").AddPacket(
              item_packet.At(loop_internal_timestamp_));
          ForwardClonePackets(cc, loop_internal_timestamp_);
          ++loop_internal_timestamp_;
        }
      } else {
        if constexpr (std::is_copy_constructible<ItemT>()) {
          const IterableT& collection =
              cc->Inputs().Tag("ITERABLE").template Get<IterableT>();
          for (const auto& item : collection) {
            cc->Outputs().Tag("ITEM").AddPacket(
                MakePacket<ItemT>(item).At(loop_internal_timestamp_));
            ForwardClonePackets(cc, loop_internal_timestamp_);
            ++loop_internal_timestamp_;
          }
        } else {
          return absl::InternalError(
              "The element type is not copiable. Consider making the "
              "BeginLoopCalculator the sole owner of the input packet so that "
              "the "
              "items can be consumed and moved.");
        }
      }
    }
@@ -138,7 +163,6 @@ class BeginLoopCalculator : public CalculatorBase {
        .Tag("BATCH_END")
        .AddPacket(MakePacket<Timestamp>(cc->InputTimestamp())
                       .At(Timestamp(loop_internal_timestamp_ - 1)));
    return absl::OkStatus();
  }
@@ -19,6 +19,7 @@
 #include "mediapipe/framework/collection_item_id.h"
 #include "mediapipe/framework/formats/classification.pb.h"
 #include "mediapipe/framework/formats/landmark.pb.h"
 #include "mediapipe/framework/formats/time_series_header.pb.h"
 #include "mediapipe/framework/port/canonical_errors.h"
 #include "mediapipe/framework/port/integral_types.h"
 #include "mediapipe/framework/port/ret_check.h"
@@ -84,6 +85,8 @@ class ConstantSidePacketCalculator : public CalculatorBase {
        packet.Set<LandmarkList>();
      } else if (packet_options.has_double_value()) {
        packet.Set<double>();
      } else if (packet_options.has_time_series_header_value()) {
        packet.Set<TimeSeriesHeader>();
      } else {
        return absl::InvalidArgumentError(
            "None of supported values were specified in options.");
@@ -118,6 +121,9 @@ class ConstantSidePacketCalculator : public CalculatorBase {
            MakePacket<LandmarkList>(packet_options.landmark_list_value()));
      } else if (packet_options.has_double_value()) {
        packet.Set(MakePacket<double>(packet_options.double_value()));
      } else if (packet_options.has_time_series_header_value()) {
        packet.Set(MakePacket<TimeSeriesHeader>(
            packet_options.time_series_header_value()));
      } else {
        return absl::InvalidArgumentError(
            "None of supported values were specified in options.");
@@ -19,6 +19,7 @@ package mediapipe;
 import "mediapipe/framework/calculator.proto";
 import "mediapipe/framework/formats/classification.proto";
 import "mediapipe/framework/formats/landmark.proto";
 import "mediapipe/framework/formats/time_series_header.proto";
 message ConstantSidePacketCalculatorOptions {
  extend CalculatorOptions {
@@ -35,6 +36,7 @@ message ConstantSidePacketCalculatorOptions {
      ClassificationList classification_list_value = 6;
      LandmarkList landmark_list_value = 7;
      double double_value = 9;
      TimeSeriesHeader time_series_header_value = 10;
    }
  }
@@ -21,6 +21,7 @@
 #include "mediapipe/framework/formats/landmark.pb.h"
 #include "mediapipe/framework/formats/matrix.h"
 #include "mediapipe/framework/formats/rect.pb.h"
 #include "mediapipe/framework/formats/tensor.h"
 #include "mediapipe/util/render_data.pb.h"
 #include "tensorflow/lite/interpreter.h"
@@ -52,8 +53,9 @@ typedef EndLoopCalculator<std::vector<::mediapipe::ClassificationList>>
    EndLoopClassificationListCalculator;
 REGISTER_CALCULATOR(EndLoopClassificationListCalculator);
-typedef EndLoopCalculator<std::vector<TfLiteTensor>> EndLoopTensorCalculator;
+typedef EndLoopCalculator<std::vector<TfLiteTensor>>
-REGISTER_CALCULATOR(EndLoopTensorCalculator);
+    EndLoopTfLiteTensorCalculator;
 REGISTER_CALCULATOR(EndLoopTfLiteTensorCalculator);
 typedef EndLoopCalculator<std::vector<::mediapipe::Detection>>
    EndLoopDetectionCalculator;
@@ -62,4 +64,7 @@ REGISTER_CALCULATOR(EndLoopDetectionCalculator);
 typedef EndLoopCalculator<std::vector<Matrix>> EndLoopMatrixCalculator;
 REGISTER_CALCULATOR(EndLoopMatrixCalculator);
 typedef EndLoopCalculator<std::vector<Tensor>> EndLoopTensorCalculator;
 REGISTER_CALCULATOR(EndLoopTensorCalculator);
 }  // namespace mediapipe
@@ -15,6 +15,8 @@
 #ifndef MEDIAPIPE_CALCULATORS_CORE_END_LOOP_CALCULATOR_H_
 #define MEDIAPIPE_CALCULATORS_CORE_END_LOOP_CALCULATOR_H_
 #include <type_traits>
 #include "mediapipe/framework/calculator_context.h"
 #include "mediapipe/framework/calculator_contract.h"
 #include "mediapipe/framework/calculator_framework.h"
@@ -75,8 +77,23 @@ class EndLoopCalculator : public CalculatorBase {
      if (!input_stream_collection_) {
        input_stream_collection_.reset(new IterableT);
      }
-      input_stream_collection_->push_back(
+      // Try to consume the item and move it into the collection. If the items
-          cc->Inputs().Tag("ITEM").template Get<ItemT>());
+      // are not consumable, then try to copy them instead. If the items are
      // not copiable, then an error will be returned.
      auto item_ptr_or = cc->Inputs().Tag("ITEM").Value().Consume<ItemT>();
      if (item_ptr_or.ok()) {
        input_stream_collection_->push_back(std::move(*item_ptr_or.value()));
      } else {
        if constexpr (std::is_copy_constructible_v<ItemT>) {
          input_stream_collection_->push_back(
              cc->Inputs().Tag("ITEM").template Get<ItemT>());
        } else {
          return absl::InternalError(
              "The item type is not copiable. Consider making the "
              "EndLoopCalculator the sole owner of the input packets so that "
              "it can be moved instead of copying.");
        }
      }
    }
    if (!cc->Inputs().Tag("BATCH_END").Value().IsEmpty()) {  // flush signal
@@ -35,7 +35,6 @@ message FlowLimiterCalculatorOptions {
  optional int32 max_in_queue = 2 [default = 0];
  // The maximum time in microseconds to wait for a frame to finish processing.
-  // The default value stops waiting after 1 sec.
+  // The default value 0 specifies no timeout.
-  // The value 0 specifies no timeout.
+  optional int64 in_flight_timeout = 3 [default = 0];
  optional int64 in_flight_timeout = 3 [default = 1000000];
 }
@@ -47,7 +47,7 @@ namespace api2 {
 //     calculator: "Get{SpecificType}VectorItemCalculator"
 //     input_stream: "VECTOR:vector"
 //     input_stream: "INDEX:index"
-//     input_stream: "ITEM:item"
+//     output_stream: "ITEM:item"
 //     options {
 //       [mediapipe.GetVectorItemCalculatorOptions.ext] {
 //         item_index: 5
@@ -15,6 +15,7 @@ limitations under the License.
 #include "mediapipe/calculators/core/merge_to_vector_calculator.h"
 #include "mediapipe/framework/formats/detection.pb.h"
 #include "mediapipe/framework/formats/image.h"
 namespace mediapipe {
@@ -27,5 +28,9 @@ typedef MergeToVectorCalculator<mediapipe::GpuBuffer>
    MergeGpuBuffersToVectorCalculator;
 MEDIAPIPE_REGISTER_NODE(MergeGpuBuffersToVectorCalculator);
 typedef MergeToVectorCalculator<mediapipe::Detection>
    MergeDetectionsToVectorCalculator;
 MEDIAPIPE_REGISTER_NODE(MergeDetectionsToVectorCalculator);
 }  // namespace api2
 }  // namespace mediapipe
@@ -48,7 +48,6 @@ class MergeToVectorCalculator : public Node {
  }
  absl::Status Process(CalculatorContext* cc) {
    const int input_num = kIn(cc).Count();
    std::vector<T> output_vector;
    for (auto it = kIn(cc).begin(); it != kIn(cc).end(); it++) {
      const auto& elem = *it;
@@ -76,7 +76,11 @@ constexpr char kMaxInFlightTag[] = "MAX_IN_FLIGHT";
 //   }
 //   output_stream: "gated_frames"
 // }
-class RealTimeFlowLimiterCalculator : public CalculatorBase {
+//
 // Please use FlowLimiterCalculator, which replaces this calculator and
 // defines a few additional configuration options.
 class ABSL_DEPRECATED("Use FlowLimiterCalculator instead.")
    RealTimeFlowLimiterCalculator : public CalculatorBase {
 public:
  static absl::Status GetContract(CalculatorContract* cc) {
    int num_data_streams = cc->Inputs().NumEntries("");
@@ -66,12 +66,16 @@ class SequenceShiftCalculator : public Node {
  // The number of packets or timestamps we need to store to output packet[i] at
  // the timestamp of packet[i + packet_offset]; equal to abs(packet_offset).
  int cache_size_;
  bool emit_empty_packets_before_first_packet_ = false;
 };
 MEDIAPIPE_REGISTER_NODE(SequenceShiftCalculator);
 absl::Status SequenceShiftCalculator::Open(CalculatorContext* cc) {
  packet_offset_ = kOffset(cc).GetOr(
      cc->Options<mediapipe::SequenceShiftCalculatorOptions>().packet_offset());
  emit_empty_packets_before_first_packet_ =
      cc->Options<mediapipe::SequenceShiftCalculatorOptions>()
          .emit_empty_packets_before_first_packet();
  cache_size_ = abs(packet_offset_);
  // An offset of zero is a no-op, but someone might still request it.
  if (packet_offset_ == 0) {
@@ -96,6 +100,8 @@ void SequenceShiftCalculator::ProcessPositiveOffset(CalculatorContext* cc) {
    // Ready to output oldest packet with current timestamp.
    kOut(cc).Send(packet_cache_.front().At(cc->InputTimestamp()));
    packet_cache_.pop_front();
  } else if (emit_empty_packets_before_first_packet_) {
    LOG(FATAL) << "Not supported yet";
  }
  // Store current packet for later output.
  packet_cache_.push_back(kIn(cc).packet());
@@ -23,4 +23,8 @@ message SequenceShiftCalculatorOptions {
    optional SequenceShiftCalculatorOptions ext = 107633927;
  }
  optional int32 packet_offset = 1 [default = -1];
  // Emits empty packets before the first delayed packet is emitted. Takes
  // effect only when packet offset is set to positive.
  optional bool emit_empty_packets_before_first_packet = 2 [default = false];
 }
@@ -301,12 +301,18 @@ cc_test(
        ":image_cropping_calculator",
        ":image_cropping_calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework:calculator_runner",
        "//mediapipe/framework/formats:image_frame_opencv",
        "//mediapipe/framework/formats:rect_cc_proto",
        "//mediapipe/framework/port:gtest_main",
        "//mediapipe/framework/port:opencv_core",
        "//mediapipe/framework/port:opencv_imgproc",
        "//mediapipe/framework/port:parse_text_proto",
        "//mediapipe/framework/port:status",
        "//mediapipe/framework/tool:tag_map",
        "//mediapipe/framework/tool:tag_map_helper",
        "//mediapipe/gpu:gpu_buffer_to_image_frame_calculator",
        "//mediapipe/gpu:image_frame_to_gpu_buffer_calculator",
    ],
 )
@@ -378,8 +384,8 @@ cc_library(
    name = "scale_image_calculator",
    srcs = ["scale_image_calculator.cc"],
    deps = [
        ":scale_image_calculator_cc_proto",
        ":scale_image_utils",
        "//mediapipe/calculators/image:scale_image_calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
        "//mediapipe/framework/formats:image_format_cc_proto",
        "//mediapipe/framework/formats:image_frame",
@@ -747,8 +753,8 @@ cc_test(
    tags = ["desktop_only_test"],
    deps = [
        ":affine_transformation",
        ":image_transformation_calculator",
        ":warp_affine_calculator",
        "//mediapipe/calculators/image:image_transformation_calculator",
        "//mediapipe/calculators/tensor:image_to_tensor_converter",
        "//mediapipe/calculators/tensor:image_to_tensor_utils",
        "//mediapipe/calculators/util:from_image_calculator",
@@ -92,8 +92,8 @@ class GlTextureWarpAffineRunner
      constexpr GLchar kVertShader[] = R"(
            in vec4 position;
-            in mediump vec4 texture_coordinate;
+            in highp vec4 texture_coordinate;
-            out mediump vec2 sample_coordinate;
+            out highp vec2 sample_coordinate;
            uniform mat4 transform_matrix;
            void main() {
@@ -104,7 +104,7 @@ class GlTextureWarpAffineRunner
          )";
      constexpr GLchar kFragShader[] = R"(
-            DEFAULT_PRECISION(mediump, float)
+            DEFAULT_PRECISION(highp, float)
            in vec2 sample_coordinate;
            uniform sampler2D input_texture;
@@ -38,6 +38,7 @@ void SetColorChannel(int channel, uint8 value, cv::Mat* mat) {
 constexpr char kRgbaInTag[] = "RGBA_IN";
 constexpr char kRgbInTag[] = "RGB_IN";
 constexpr char kBgrInTag[] = "BGR_IN";
 constexpr char kBgraInTag[] = "BGRA_IN";
 constexpr char kGrayInTag[] = "GRAY_IN";
 constexpr char kRgbaOutTag[] = "RGBA_OUT";
@@ -57,6 +58,7 @@ constexpr char kGrayOutTag[] = "GRAY_OUT";
 //   RGB  -> RGBA
 //   RGBA -> BGRA
 //   BGRA -> RGBA
 //   BGR  -> RGB
 //
 // This calculator only supports a single input stream and output stream at a
 // time. If more than one input stream or output stream is present, the
@@ -69,6 +71,7 @@ constexpr char kGrayOutTag[] = "GRAY_OUT";
 //   RGB_IN:        The input video stream (ImageFrame, SRGB).
 //   BGRA_IN:       The input video stream (ImageFrame, SBGRA).
 //   GRAY_IN:       The input video stream (ImageFrame, GRAY8).
 //   BGR_IN:        The input video stream (ImageFrame, SBGR).
 //
 // Output streams:
 //   RGBA_OUT:      The output video stream (ImageFrame, SRGBA).
@@ -122,6 +125,10 @@ absl::Status ColorConvertCalculator::GetContract(CalculatorContract* cc) {
    cc->Inputs().Tag(kBgraInTag).Set<ImageFrame>();
  }
  if (cc->Inputs().HasTag(kBgrInTag)) {
    cc->Inputs().Tag(kBgrInTag).Set<ImageFrame>();
  }
  if (cc->Outputs().HasTag(kRgbOutTag)) {
    cc->Outputs().Tag(kRgbOutTag).Set<ImageFrame>();
  }
@@ -194,6 +201,11 @@ absl::Status ColorConvertCalculator::Process(CalculatorContext* cc) {
    return ConvertAndOutput(kRgbaInTag, kBgraOutTag, ImageFormat::SBGRA,
                            cv::COLOR_RGBA2BGRA, cc);
  }
  // BGR -> RGB
  if (cc->Inputs().HasTag(kBgrInTag) && cc->Outputs().HasTag(kRgbOutTag)) {
    return ConvertAndOutput(kBgrInTag, kRgbOutTag, ImageFormat::SRGB,
                            cv::COLOR_BGR2RGB, cc);
  }
  return mediapipe::InvalidArgumentErrorBuilder(MEDIAPIPE_LOC)
         << "Unsupported image format conversion.";
@@ -37,7 +37,8 @@ enum { ATTRIB_VERTEX, ATTRIB_TEXTURE_POSITION, NUM_ATTRIBUTES };
 namespace mediapipe {
 namespace {
-
+using ::mediapipe::NormalizedRect;
 using ::mediapipe::Rect;
 #if !MEDIAPIPE_DISABLE_GPU
 #endif  // !MEDIAPIPE_DISABLE_GPU
@@ -222,8 +223,8 @@ absl::Status ImageCroppingCalculator::RenderCpu(CalculatorContext* cc) {
  cv::Mat input_mat = formats::MatView(&input_img);
  RectSpec specs = GetCropSpecs(cc, input_img.Width(), input_img.Height());
-  int target_width = specs.width, target_height = specs.height,
+  int target_width = specs.width, target_height = specs.height;
-      rect_center_x = specs.center_x, rect_center_y = specs.center_y;
+  float rect_center_x = specs.center_x, rect_center_y = specs.center_y;
  float rotation = specs.rotation;
  // Get border mode and value for OpenCV.
@@ -243,19 +244,29 @@ absl::Status ImageCroppingCalculator::RenderCpu(CalculatorContext* cc) {
  output_width *= scale;
  output_height *= scale;
-  float dst_corners[8] = {0,
+  float dst_corners[8] = {
-                          output_height - 1,
+      0, output_height, 0, 0, output_width, 0, output_width, output_height};
-                          0,
+  const cv::Mat dst_points = cv::Mat(4, 2, CV_32F, dst_corners);
-                          0,
+  // The projection matrix is computed using the corners of rects, not the
-                          output_width - 1,
+  // centers of corner pixels
-                          0,
+  const cv::Mat projection_matrix =
                          output_width - 1,
                          output_height - 1};
  cv::Mat dst_points = cv::Mat(4, 2, CV_32F, dst_corners);
  cv::Mat projection_matrix =
      cv::getPerspectiveTransform(src_points, dst_points);
  // The projection matrix need to be adjusted because `cv::warpPerspective` is
  // based on integer centers.
  // clang-format off
  double shift_src_vec[9] = {1.0, 0.0, 0.5,
                             0.0, 1.0, 0.5,
                             0.0, 0.0, 1.0};
  double shift_dst_vec[9] = {1.0, 0.0, -0.5,
                             0.0, 1.0, -0.5,
                             0.0, 0.0,  1.0};
  // clang-format on
  const cv::Mat shift_src = cv::Mat(3, 3, CV_64F, shift_src_vec);
  const cv::Mat shift_dst = cv::Mat(3, 3, CV_64F, shift_dst_vec);
  const cv::Mat adjusted_projection_matrix =
      shift_dst * projection_matrix * shift_src;
  cv::Mat cropped_image;
-  cv::warpPerspective(input_mat, cropped_image, projection_matrix,
+  cv::warpPerspective(input_mat, cropped_image, adjusted_projection_matrix,
                      cv::Size(output_width, output_height),
                      /* flags = */ 0,
                      /* borderMode = */ border_mode);
@@ -415,8 +426,8 @@ void ImageCroppingCalculator::GetOutputDimensions(CalculatorContext* cc,
                                                  int* dst_width,
                                                  int* dst_height) {
  RectSpec specs = GetCropSpecs(cc, src_width, src_height);
-  int crop_width = specs.width, crop_height = specs.height,
+  int crop_width = specs.width, crop_height = specs.height;
-      x_center = specs.center_x, y_center = specs.center_y;
+  float x_center = specs.center_x, y_center = specs.center_y;
  float rotation = specs.rotation;
  const float half_width = crop_width / 2.0f;
@@ -465,8 +476,8 @@ RectSpec ImageCroppingCalculator::GetCropSpecs(const CalculatorContext* cc,
  int crop_width = src_width;
  int crop_height = src_height;
  // Get the center of cropping box. Default is the at the center.
-  int x_center = src_width / 2;
+  float x_center = src_width / 2.0f;
-  int y_center = src_height / 2;
+  float y_center = src_height / 2.0f;
  // Get the rotation of the cropping box.
  float rotation = 0.0f;
  // Get the normalized width and height if specified by the inputs or options.
@@ -493,8 +504,8 @@ RectSpec ImageCroppingCalculator::GetCropSpecs(const CalculatorContext* cc,
    if (norm_rect.width() > 0.0 && norm_rect.height() > 0.0) {
      normalized_width = norm_rect.width();
      normalized_height = norm_rect.height();
-      x_center = std::round(norm_rect.x_center() * src_width);
+      x_center = norm_rect.x_center() * src_width;
-      y_center = std::round(norm_rect.y_center() * src_height);
+      y_center = norm_rect.y_center() * src_height;
      rotation = norm_rect.rotation();
    }
  } else if (cc->Inputs().HasTag(kWidthTag) &&
@@ -520,14 +531,30 @@ RectSpec ImageCroppingCalculator::GetCropSpecs(const CalculatorContext* cc,
  // present from the inputs.
  if (!cc->Inputs().HasTag(kRectTag) && !cc->Inputs().HasTag(kNormRectTag)) {
    if (options.has_norm_center_x() && options.has_norm_center_y()) {
-      x_center = std::round(options.norm_center_x() * src_width);
+      x_center = options.norm_center_x() * src_width;
-      y_center = std::round(options.norm_center_y() * src_height);
+      y_center = options.norm_center_y() * src_height;
    }
    if (options.has_rotation()) {
      rotation = options.rotation();
    }
  }
  if (rotation == 0.0f) {
    // Adjust the center to the closest integer when the crop size is
    // even-number and to the closest half-integer when the crop size is
    // odd-number.
    if (crop_width % 2 == 0) {
      x_center = std::round(x_center);
    } else {
      x_center = std::round(x_center + 0.5f) - 0.5f;
    }
    if (crop_height % 2 == 0) {
      y_center = std::round(y_center);
    } else {
      y_center = std::round(y_center + 0.5f) - 0.5f;
    }
  }
  return {crop_width, crop_height, x_center, y_center, rotation};
 }
@@ -42,8 +42,8 @@ namespace mediapipe {
 struct RectSpec {
  int width;
  int height;
-  int center_x;
+  float center_x;
-  int center_y;
+  float center_y;
  float rotation;
  bool operator==(const RectSpec& rect) const {
@@ -19,8 +19,12 @@
 #include "mediapipe/calculators/image/image_cropping_calculator.pb.h"
 #include "mediapipe/framework/calculator_framework.h"
 #include "mediapipe/framework/calculator_runner.h"
 #include "mediapipe/framework/formats/image_frame_opencv.h"
 #include "mediapipe/framework/formats/rect.pb.h"
 #include "mediapipe/framework/port/gtest.h"
 #include "mediapipe/framework/port/opencv_core_inc.h"
 #include "mediapipe/framework/port/opencv_imgproc_inc.h"
 #include "mediapipe/framework/port/parse_text_proto.h"
 #include "mediapipe/framework/port/status_matchers.h"
 #include "mediapipe/framework/tool/tag_map.h"
@@ -37,6 +41,166 @@ constexpr char kRectTag[] = "RECT";
 constexpr char kHeightTag[] = "HEIGHT";
 constexpr char kWidthTag[] = "WIDTH";
 std::unique_ptr<mediapipe::ImageFrame> GetInputFrame(int width, int height,
                                                     int channel) {
  const int total_size = width * height * channel;
  auto image_format = channel == 4 ? mediapipe::ImageFormat::SRGBA
                                   : mediapipe::ImageFormat::SRGB;
  auto input_frame = std::make_unique<mediapipe::ImageFrame>(
      image_format, width, height, /*alignment_boundary =*/1);
  for (int i = 0; i < total_size; ++i) {
    input_frame->MutablePixelData()[i] = i % 256;
  }
  return input_frame;
 }
 // Test identity function, where cropping size is same as input size
 TEST(ImageCroppingCalculatorTest, IdentityFunctionCropWithOriginalSize) {
  auto calculator_node =
      ParseTextProtoOrDie<mediapipe::CalculatorGraphConfig::Node>(
          absl::Substitute(
              R"pb(
                calculator: "ImageCroppingCalculator"
                input_stream: "IMAGE:input_frames"
                output_stream: "IMAGE:cropped_output_frames"
                options: {
                  [mediapipe.ImageCroppingCalculatorOptions.ext] {
                    width: $0
                    height: $1
                  }
                }
              )pb",
              input_width, input_height));
  mediapipe::CalculatorRunner runner(calculator_node);
  // Input frame.
  const auto input_frame = GetInputFrame(input_width, input_height, 3);
  auto input_frame_packet =
      mediapipe::MakePacket<mediapipe::ImageFrame>(std::move(*input_frame));
  runner.MutableInputs()->Tag("IMAGE").packets.push_back(
      input_frame_packet.At(mediapipe::Timestamp(1)));
  MP_ASSERT_OK(runner.Run());
  const auto& outputs = runner.Outputs();
  EXPECT_EQ(outputs.NumEntries(), 1);
  const auto& output_image =
      outputs.Tag("IMAGE").packets[0].Get<mediapipe::ImageFrame>();
  const auto expected_output = GetInputFrame(input_width, input_height, 3);
  cv::Mat output_mat = formats::MatView(&output_image);
  cv::Mat expected_mat = formats::MatView(expected_output.get());
  double max_diff = cv::norm(expected_mat, output_mat, cv::NORM_INF);
  EXPECT_EQ(max_diff, 0);
 }  // TEST
 // Test identity function, where cropping size is same as input size.
 // When an image has an odd number for its size, its center falls on a
 // fractional pixel. As a result, the values for center_x and center_y need to
 // be of type float.
 TEST(ImageCroppingCalculatorTest, IdentityFunctionCropWithOddSize) {
  const int input_width = 99;
  const int input_height = 99;
  auto calculator_node =
      ParseTextProtoOrDie<mediapipe::CalculatorGraphConfig::Node>(
          absl::Substitute(
              R"pb(
                calculator: "ImageCroppingCalculator"
                input_stream: "IMAGE:input_frames"
                output_stream: "IMAGE:cropped_output_frames"
                options: {
                  [mediapipe.ImageCroppingCalculatorOptions.ext] {
                    width: $0
                    height: $1
                  }
                }
              )pb",
              input_width, input_height));
  mediapipe::CalculatorRunner runner(calculator_node);
  // Input frame.
  const auto input_frame = GetInputFrame(input_width, input_height, 3);
  auto input_frame_packet =
      mediapipe::MakePacket<mediapipe::ImageFrame>(std::move(*input_frame));
  runner.MutableInputs()->Tag("IMAGE").packets.push_back(
      input_frame_packet.At(mediapipe::Timestamp(1)));
  MP_ASSERT_OK(runner.Run());
  const auto& outputs = runner.Outputs();
  EXPECT_EQ(outputs.NumEntries(), 1);
  const auto& output_image =
      outputs.Tag("IMAGE").packets[0].Get<mediapipe::ImageFrame>();
  const auto expected_output = GetInputFrame(input_width, input_height, 3);
  cv::Mat output_mat = formats::MatView(&output_image);
  cv::Mat expected_mat = formats::MatView(expected_output.get());
  double max_diff = cv::norm(expected_mat, output_mat, cv::NORM_INF);
  EXPECT_EQ(max_diff, 0);
 }  // TEST
 // Test identity function on GPU, where cropping size is same as input size.
 TEST(ImageCroppingCalculatorTest, IdentityFunctionCropWithOriginalSizeGPU) {
  mediapipe::CalculatorGraphConfig config =
      ParseTextProtoOrDie<mediapipe::CalculatorGraphConfig>(absl::Substitute(
          R"pb(
            input_stream: "input_frames"
            node {
              calculator: "ImageFrameToGpuBufferCalculator"
              input_stream: "input_frames"
              output_stream: "input_frames_gpu"
            }
            node {
              calculator: "ImageCroppingCalculator"
              input_stream: "IMAGE_GPU:input_frames_gpu"
              output_stream: "IMAGE_GPU:cropped_output_frames_gpu"
              options: {
                [mediapipe.ImageCroppingCalculatorOptions.ext] {
                  width: $0
                  height: $1
                }
              }
            }
            node {
              calculator: "GpuBufferToImageFrameCalculator"
              input_stream: "cropped_output_frames_gpu"
              output_stream: "cropped_output_frames"
            }
          )pb",
          input_width, input_height));
  std::vector<Packet> output_packets;
  tool::AddVectorSink("cropped_output_frames", &config, &output_packets);
  CalculatorGraph graph;
  MP_ASSERT_OK(graph.Initialize(config));
  // Input frame.
  const auto input_frame = GetInputFrame(input_width, input_height, 4);
  auto input_frame_packet =
      mediapipe::MakePacket<mediapipe::ImageFrame>(std::move(*input_frame));
  MP_ASSERT_OK(graph.StartRun({}));
  MP_ASSERT_OK(graph.AddPacketToInputStream(
      "input_frames", input_frame_packet.At(mediapipe::Timestamp(1))));
  MP_ASSERT_OK(graph.WaitUntilIdle());
  // Get and process results.
  const ImageFrame& output_image = output_packets[0].Get<ImageFrame>();
  std::cout << output_image.Width();
  const auto expected_output = GetInputFrame(input_width, input_height, 4);
  cv::Mat output_mat = formats::MatView(&output_image);
  cv::Mat expected_mat = formats::MatView(expected_output.get());
  double max_diff = cv::norm(expected_mat, output_mat, cv::NORM_INF);
  EXPECT_EQ(max_diff, 0);
 }  // TEST
 // Test normal case, where norm_width and norm_height in options are set.
 TEST(ImageCroppingCalculatorTest, GetCroppingDimensionsNormal) {
  auto calculator_node =
@@ -195,11 +359,11 @@ TEST(ImageCroppingCalculatorTest, RedundantSpecWithInputStream) {
  auto cc = absl::make_unique<CalculatorContext>(
      calculator_state.get(), inputTags, tool::CreateTagMap({}).value());
  auto& inputs = cc->Inputs();
-  mediapipe::Rect rect = ParseTextProtoOrDie<mediapipe::Rect>(
+  Rect rect = ParseTextProtoOrDie<Rect>(
      R"pb(
        width: 1 height: 1 x_center: 40 y_center: 40 rotation: 0.5
      )pb");
-  inputs.Tag(kRectTag).Value() = MakePacket<mediapipe::Rect>(rect);
+  inputs.Tag(kRectTag).Value() = MakePacket<Rect>(rect);
  RectSpec expectRect = {
      .width = 1,
      .height = 1,
@@ -207,6 +207,7 @@ class ImageTransformationCalculator : public CalculatorBase {
  bool flip_vertically_ = false;
  bool use_gpu_ = false;
  cv::Scalar padding_color_;
 #if !MEDIAPIPE_DISABLE_GPU
  GlCalculatorHelper gpu_helper_;
  std::unique_ptr<QuadRenderer> rgb_renderer_;
@@ -338,6 +339,9 @@ absl::Status ImageTransformationCalculator::Open(CalculatorContext* cc) {
  }
  scale_mode_ = ParseScaleMode(options_.scale_mode(), DEFAULT_SCALE_MODE);
  padding_color_ = cv::Scalar(options_.padding_color().red(),
                              options_.padding_color().green(),
                              options_.padding_color().blue());
  if (use_gpu_) {
 #if !MEDIAPIPE_DISABLE_GPU
@@ -480,7 +484,8 @@ absl::Status ImageTransformationCalculator::RenderCpu(CalculatorContext* cc) {
        cv::copyMakeBorder(intermediate_mat, scaled_mat, top, bottom, left,
                           right,
                           options_.constant_padding() ? cv::BORDER_CONSTANT
-                                                       : cv::BORDER_REPLICATE);
+                                                       : cv::BORDER_REPLICATE,
                           padding_color_);
      } else {
        cv::resize(input_mat, scaled_mat, cv::Size(target_width, target_height),
                   0, 0, scale_flag);
@@ -20,11 +20,21 @@ import "mediapipe/calculators/image/rotation_mode.proto";
 import "mediapipe/framework/calculator.proto";
 import "mediapipe/gpu/scale_mode.proto";
 option java_package = "com.google.mediapipe.calculator.proto";
 option java_outer_classname = "ImageTransformationCalculatorOptionsProto";
 message ImageTransformationCalculatorOptions {
  extend CalculatorOptions {
    optional ImageTransformationCalculatorOptions ext = 251952830;
  }
  // RGB values in range of 0 - 255
  message Color {
    optional int32 red = 1 [default = 0];
    optional int32 green = 2 [default = 0];
    optional int32 blue = 3 [default = 0];
  }
  // Output dimensions. Set to 0 if they should be the same as the input.
  optional int32 output_width = 1 [default = 0];
  optional int32 output_height = 2 [default = 0];
@@ -40,4 +50,8 @@ message ImageTransformationCalculatorOptions {
  // Default is to use BORDER_CONSTANT. If set to false, it will use
  // BORDER_REPLICATE instead.
  optional bool constant_padding = 7 [default = true];
  // The color for the padding. This option is only used when the scale mode is
  // FIT. Default is black. This is for CPU only.
  optional Color padding_color = 8;
 }
@@ -142,6 +142,9 @@ absl::Status FindOutputDimensions(int input_width,             //
                                      static_cast<double>(input_height));
    try_width = (try_width / 2) * 2;
    try_height = (try_height / 2) * 2;
    // The output width/height should be greater than 0.
    try_width = std::max(try_width, 1);
    try_height = std::max(try_height, 1);
    if (target_height <= 0 || try_height <= target_height) {
      // The resulting height based on the target width and aspect ratio
@@ -160,6 +163,9 @@ absl::Status FindOutputDimensions(int input_width,             //
                                     static_cast<double>(input_width));
    try_width = (try_width / 2) * 2;
    try_height = (try_height / 2) * 2;
    // The output width/height should be greater than 0.
    try_width = std::max(try_width, 1);
    try_height = std::max(try_height, 1);
    if (target_width <= 0 || try_width <= target_width) {
      // The resulting width based on the target width and aspect ratio
@@ -124,6 +124,16 @@ TEST(ScaleImageUtilsTest, FindOutputDimensionsPreserveRatio) {
                                    &output_width, &output_height));
  EXPECT_EQ(151, output_width);
  EXPECT_EQ(101, output_height);
  // Scale to height 1.
  MP_ASSERT_OK(FindOutputDimensions(10000, 10, 100, 0, 0, true, 2,
                                    &output_width, &output_height));
  EXPECT_EQ(100, output_width);
  EXPECT_EQ(1, output_height);
  // Scale to width 1.
  MP_ASSERT_OK(FindOutputDimensions(10, 10000, 0, 100, 0, true, 2,
                                    &output_width, &output_height));
  EXPECT_EQ(1, output_width);
  EXPECT_EQ(100, output_height);
 }
 // Tests scaling without keeping the aspect ratio fixed.
@@ -47,10 +47,11 @@ constexpr int kNumChannelsRGBA = 4;
 enum { ATTRIB_VERTEX, ATTRIB_TEXTURE_POSITION, NUM_ATTRIBUTES };
-// Combines an RGB cv::Mat and a single-channel alpha cv::Mat of the same
+// Combines an RGB cv::Mat and an alpha cv::Mat of the same dimensions into an
-// dimensions into an RGBA cv::Mat. Alpha may be read as uint8 or as another
+// RGBA cv::Mat. Alpha may be read as uint8 or as another numeric type; in the
-// numeric type; in the latter case, it is upscaled to values between 0 and 255
+// latter case, it is upscaled to values between 0 and 255 from an assumed input
-// from an assumed input range of [0, 1). RGB and RGBA Mat's must be uchar.
+// range of [0, 1). Only the first channel of Alpha is used. Input & output Mat
 // must be uchar.
 template <typename AlphaType>
 absl::Status MergeRGBA8Image(const cv::Mat input_mat, const cv::Mat& alpha_mat,
                             cv::Mat& output_mat) {
@@ -71,9 +72,9 @@ absl::Status MergeRGBA8Image(const cv::Mat input_mat, const cv::Mat& alpha_mat,
      out_ptr[out_idx + 1] = in_ptr[in_idx + 1];
      out_ptr[out_idx + 2] = in_ptr[in_idx + 2];
      if constexpr (std::is_same<AlphaType, uchar>::value) {
-        out_ptr[out_idx + 3] = alpha_ptr[alpha_idx + 0];
+        out_ptr[out_idx + 3] = alpha_ptr[alpha_idx + 0];  // channel 0 of mask
      } else {
-        const AlphaType alpha = alpha_ptr[alpha_idx + 0];
+        const AlphaType alpha = alpha_ptr[alpha_idx + 0];  // channel 0 of mask
        out_ptr[out_idx + 3] = static_cast<uchar>(round(alpha * 255.0f));
      }
    }
@@ -291,8 +292,8 @@ absl::Status SetAlphaCalculator::RenderCpu(CalculatorContext* cc) {
    const auto& alpha_mask = cc->Inputs().Tag(kInputAlphaTag).Get<ImageFrame>();
    cv::Mat alpha_mat = mediapipe::formats::MatView(&alpha_mask);
-    const bool alpha_is_float = alpha_mat.type() == CV_32FC1;
+    const bool alpha_is_float = CV_MAT_DEPTH(alpha_mat.type()) == CV_32F;
-    RET_CHECK(alpha_is_float || alpha_mat.type() == CV_8UC1);
+    RET_CHECK(alpha_is_float || CV_MAT_DEPTH(alpha_mat.type()) == CV_8U);
    if (alpha_is_float) {
      MP_RETURN_IF_ERROR(
@@ -12,10 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 licenses(["notice"])
 load("//mediapipe/framework/port:build_config.bzl", "mediapipe_cc_proto_library")
 licenses(["notice"])
 package(default_visibility = ["//visibility:private"])
 proto_library(
@@ -21,10 +21,12 @@ load(
 )
 load("//mediapipe/framework:mediapipe_cc_test.bzl", "mediapipe_cc_test")
 load("//mediapipe/framework:encode_binary_proto.bzl", "encode_binary_proto")
 load("@org_tensorflow//tensorflow/lite/core/shims:cc_library_with_tflite.bzl", "cc_library_with_tflite")
 load("//mediapipe/framework:more_selects.bzl", "more_selects")
 licenses(["notice"])
-package(default_visibility = ["//visibility:private"])
+package(default_visibility = ["//visibility:public"])
 exports_files(
    glob(["testdata/image_to_tensor/*"]),
@@ -34,6 +36,27 @@ exports_files(
    ],
 )
 config_setting(
    name = "disable_gpu",
    define_values = {
        "MEDIAPIPE_DISABLE_GPU": "1",
    },
    visibility = ["//visibility:public"],
 )
 more_selects.config_setting_negation(
    name = "not_disable_gpu",
    negate = ":disable_gpu",
 )
 selects.config_setting_group(
    name = "platform_ios_with_gpu",
    match_all = [
        ":not_disable_gpu",
        "//mediapipe:ios",
    ],
 )
 selects.config_setting_group(
    name = "compute_shader_unavailable",
    match_any = [
@@ -44,9 +67,6 @@ selects.config_setting_group(
 mediapipe_proto_library(
    name = "audio_to_tensor_calculator_proto",
    srcs = ["audio_to_tensor_calculator.proto"],
    visibility = [
        "//mediapipe/framework:mediapipe_internal",
    ],
    deps = [
        "//mediapipe/framework:calculator_options_proto",
        "//mediapipe/framework:calculator_proto",
@@ -56,17 +76,6 @@ mediapipe_proto_library(
 cc_library(
    name = "audio_to_tensor_calculator",
    srcs = ["audio_to_tensor_calculator.cc"],
    copts = select({
        # b/215212850
        "//mediapipe:apple": [
            "-x objective-c++",
            "-fobjc-arc",
        ],
        "//conditions:default": [],
    }),
    visibility = [
        "//mediapipe/framework:mediapipe_internal",
    ],
    deps = [
        ":audio_to_tensor_calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
@@ -113,9 +122,6 @@ cc_test(
 mediapipe_proto_library(
    name = "tensors_to_audio_calculator_proto",
    srcs = ["tensors_to_audio_calculator.proto"],
    visibility = [
        "//mediapipe/framework:mediapipe_internal",
    ],
    deps = [
        "//mediapipe/framework:calculator_options_proto",
        "//mediapipe/framework:calculator_proto",
@@ -125,9 +131,6 @@ mediapipe_proto_library(
 cc_library(
    name = "tensors_to_audio_calculator",
    srcs = ["tensors_to_audio_calculator.cc"],
    visibility = [
        "//mediapipe/framework:mediapipe_internal",
    ],
    deps = [
        ":tensors_to_audio_calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
@@ -164,9 +167,6 @@ cc_test(
 mediapipe_proto_library(
    name = "feedback_tensors_calculator_proto",
    srcs = ["feedback_tensors_calculator.proto"],
    visibility = [
        "//mediapipe/framework:mediapipe_internal",
    ],
    deps = [
        "//mediapipe/framework:calculator_options_proto",
        "//mediapipe/framework:calculator_proto",
@@ -176,17 +176,6 @@ mediapipe_proto_library(
 cc_library(
    name = "feedback_tensors_calculator",
    srcs = ["feedback_tensors_calculator.cc"],
    copts = select({
        # b/215212850
        "//mediapipe:apple": [
            "-x objective-c++",
            "-fobjc-arc",
        ],
        "//conditions:default": [],
    }),
    visibility = [
        "//mediapipe/framework:mediapipe_internal",
    ],
    deps = [
        ":feedback_tensors_calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
@@ -216,9 +205,6 @@ cc_test(
 mediapipe_proto_library(
    name = "bert_preprocessor_calculator_proto",
    srcs = ["bert_preprocessor_calculator.proto"],
    visibility = [
        "//mediapipe/framework:mediapipe_internal",
    ],
    deps = [
        "//mediapipe/framework:calculator_options_proto",
        "//mediapipe/framework:calculator_proto",
@@ -228,9 +214,6 @@ mediapipe_proto_library(
 cc_library(
    name = "bert_preprocessor_calculator",
    srcs = ["bert_preprocessor_calculator.cc"],
    visibility = [
        "//mediapipe/framework:mediapipe_internal",
    ],
    deps = [
        ":bert_preprocessor_calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
@@ -274,9 +257,6 @@ cc_test(
 mediapipe_proto_library(
    name = "regex_preprocessor_calculator_proto",
    srcs = ["regex_preprocessor_calculator.proto"],
    visibility = [
        "//mediapipe/framework:mediapipe_internal",
    ],
    deps = [
        "//mediapipe/framework:calculator_options_proto",
        "//mediapipe/framework:calculator_proto",
@@ -286,9 +266,6 @@ mediapipe_proto_library(
 cc_library(
    name = "regex_preprocessor_calculator",
    srcs = ["regex_preprocessor_calculator.cc"],
    visibility = [
        "//mediapipe/framework:mediapipe_internal",
    ],
    deps = [
        ":regex_preprocessor_calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
@@ -330,9 +307,6 @@ cc_test(
 cc_library(
    name = "text_to_tensor_calculator",
    srcs = ["text_to_tensor_calculator.cc"],
    visibility = [
        "//mediapipe/framework:mediapipe_internal",
    ],
    deps = [
        "//mediapipe/framework:calculator_context",
        "//mediapipe/framework:calculator_framework",
@@ -366,9 +340,6 @@ cc_test(
 cc_library(
    name = "universal_sentence_encoder_preprocessor_calculator",
    srcs = ["universal_sentence_encoder_preprocessor_calculator.cc"],
    visibility = [
        "//mediapipe/framework:mediapipe_internal",
    ],
    deps = [
        "//mediapipe/framework:calculator_context",
        "//mediapipe/framework:calculator_framework",
@@ -408,7 +379,6 @@ cc_test(
 mediapipe_proto_library(
    name = "inference_calculator_proto",
    srcs = ["inference_calculator.proto"],
    visibility = ["//visibility:public"],
    deps = [
        "//mediapipe/framework:calculator_options_proto",
        "//mediapipe/framework:calculator_proto",
@@ -423,19 +393,15 @@ mediapipe_proto_library(
 # size concerns), depend on those implementations directly, and do not depend on
 # :inference_calculator.
 # In all cases, use "InferenceCalulator" in your graphs.
-cc_library(
+cc_library_with_tflite(
    name = "inference_calculator_interface",
    srcs = ["inference_calculator.cc"],
    hdrs = ["inference_calculator.h"],
-    copts = select({
+    tflite_deps = [
-        # TODO: fix tensor.h not to require this, if possible
+        "//mediapipe/util/tflite:tflite_model_loader",
-        "//mediapipe:apple": [
+        "@org_tensorflow//tensorflow/lite/core/shims:framework_stable",
-            "-x objective-c++",
+        "@org_tensorflow//tensorflow/lite/core/shims:builtin_ops",
-            "-fobjc-arc",  # enable reference-counting
+    ],
        ],
        "//conditions:default": [],
    }),
    visibility = ["//visibility:public"],
    deps = [
        ":inference_calculator_cc_proto",
        ":inference_calculator_options_lib",
@@ -446,12 +412,9 @@ cc_library(
        "//mediapipe/framework/port:ret_check",
        "//mediapipe/framework/stream_handler:fixed_size_input_stream_handler",
        "//mediapipe/framework/tool:subgraph_expansion",
        "//mediapipe/util/tflite:tflite_model_loader",
        "@com_google_absl//absl/memory",
        "@com_google_absl//absl/strings",
        "@org_tensorflow//tensorflow/lite:framework",
        "@org_tensorflow//tensorflow/lite/core/api:op_resolver",
        "@org_tensorflow//tensorflow/lite/kernels:builtin_ops",
    ],
    alwayslink = 1,
 )
@@ -460,7 +423,6 @@ cc_library(
    name = "inference_calculator_gl",
    srcs = ["inference_calculator_gl.cc"],
    tags = ["nomac"],  # config problem with cpuinfo via TF
    visibility = ["//visibility:public"],
    deps = [
        ":inference_calculator_cc_proto",
        ":inference_calculator_interface",
@@ -478,7 +440,6 @@ cc_library(
    name = "inference_calculator_gl_advanced",
    srcs = ["inference_calculator_gl_advanced.cc"],
    tags = ["nomac"],
    visibility = ["//visibility:public"],
    deps = [
        ":inference_calculator_interface",
        "@com_google_absl//absl/memory",
@@ -509,9 +470,9 @@ cc_library(
        "-framework MetalKit",
    ],
    tags = ["ios"],
    visibility = ["//visibility:public"],
    deps = [
        "inference_calculator_interface",
        "//mediapipe/framework/formats:tensor",
        "//mediapipe/gpu:MPPMetalHelper",
        "//mediapipe/gpu:MPPMetalUtil",
        "//mediapipe/gpu:gpu_buffer",
@@ -530,15 +491,6 @@ cc_library(
 cc_library(
    name = "inference_runner",
    hdrs = ["inference_runner.h"],
    copts = select({
        # TODO: fix tensor.h not to require this, if possible
        "//mediapipe:apple": [
            "-x objective-c++",
            "-fobjc-arc",  # enable reference-counting
        ],
        "//conditions:default": [],
    }),
    visibility = ["//visibility:public"],
    deps = [
        "//mediapipe/framework:calculator_context",
        "//mediapipe/framework/formats:tensor",
@@ -546,31 +498,33 @@ cc_library(
    ],
 )
-cc_library(
+cc_library_with_tflite(
    name = "tflite_delegate_ptr",
    hdrs = ["tflite_delegate_ptr.h"],
    tflite_deps = [
        "@org_tensorflow//tensorflow/lite/core/shims:c_api_types",
    ],
 )
 cc_library_with_tflite(
    name = "inference_interpreter_delegate_runner",
    srcs = ["inference_interpreter_delegate_runner.cc"],
    hdrs = ["inference_interpreter_delegate_runner.h"],
-    copts = select({
+    tflite_deps = [
-        # TODO: fix tensor.h not to require this, if possible
+        ":tflite_delegate_ptr",
-        "//mediapipe:apple": [
+        "//mediapipe/util/tflite:tflite_model_loader",
-            "-x objective-c++",
+        "@org_tensorflow//tensorflow/lite/core/shims:c_api_types",
-            "-fobjc-arc",  # enable reference-counting
+        "@org_tensorflow//tensorflow/lite/core/shims:framework_stable",
-        ],
+    ],
        "//conditions:default": [],
    }),
    visibility = ["//visibility:public"],
    deps = [
        ":inference_runner",
        "//mediapipe/framework:mediapipe_profiling",
        "//mediapipe/framework/api2:packet",
        "//mediapipe/framework/formats:tensor",
        "//mediapipe/framework/port:ret_check",
        "//mediapipe/util/tflite:tflite_model_loader",
        "@com_google_absl//absl/status",
        "@com_google_absl//absl/status:statusor",
        "@org_tensorflow//tensorflow/lite:framework_stable",
        "@org_tensorflow//tensorflow/lite:string_util",
        "@org_tensorflow//tensorflow/lite/c:c_api_types",
        "@org_tensorflow//tensorflow/lite/core/api:op_resolver",
    ],
 )
@@ -580,15 +534,6 @@ cc_library(
    srcs = [
        "inference_calculator_cpu.cc",
    ],
    copts = select({
        # TODO: fix tensor.h not to require this, if possible
        "//mediapipe:apple": [
            "-x objective-c++",
            "-fobjc-arc",  # enable reference-counting
        ],
        "//conditions:default": [],
    }),
    visibility = ["//visibility:public"],
    deps = [
        ":inference_calculator_interface",
        ":inference_calculator_utils",
@@ -597,9 +542,9 @@ cc_library(
        "@com_google_absl//absl/memory",
        "@com_google_absl//absl/status",
        "@com_google_absl//absl/status:statusor",
        "@org_tensorflow//tensorflow/lite/core/shims:c_api_types",
        "@org_tensorflow//tensorflow/lite/core/shims:framework_stable",
        "@org_tensorflow//tensorflow/lite/delegates/xnnpack:xnnpack_delegate",
        "@org_tensorflow//tensorflow/lite:framework_stable",
        "@org_tensorflow//tensorflow/lite/c:c_api_types",
    ] + select({
        "//conditions:default": [],
        "//mediapipe:android": ["@org_tensorflow//tensorflow/lite/delegates/nnapi:nnapi_delegate"],
@@ -627,15 +572,6 @@ cc_library(
    srcs = [
        "inference_calculator_xnnpack.cc",
    ],
    copts = select({
        # TODO: fix tensor.h not to require this, if possible
        "//mediapipe:apple": [
            "-x objective-c++",
            "-fobjc-arc",  # enable reference-counting
        ],
        "//conditions:default": [],
    }),
    visibility = ["//visibility:public"],
    deps = [
        ":inference_calculator_interface",
        ":inference_calculator_utils",
@@ -651,7 +587,6 @@ cc_library(
 cc_library(
    name = "inference_calculator_gl_if_compute_shader_available",
    visibility = ["//visibility:public"],
    deps = selects.with_or({
        ":compute_shader_unavailable": [],
        "//conditions:default": [
@@ -667,13 +602,12 @@ cc_library(
 # inference_calculator_interface.
 cc_library(
    name = "inference_calculator",
    visibility = ["//visibility:public"],
    deps = [
        ":inference_calculator_interface",
        ":inference_calculator_cpu",
    ] + select({
        "//conditions:default": [":inference_calculator_gl_if_compute_shader_available"],
-        "//mediapipe:ios": [":inference_calculator_metal"],
+        ":platform_ios_with_gpu": [":inference_calculator_metal"],
    }),
    alwayslink = 1,
 )
@@ -681,7 +615,6 @@ cc_library(
 mediapipe_proto_library(
    name = "tensor_converter_calculator_proto",
    srcs = ["tensor_converter_calculator.proto"],
    visibility = ["//visibility:public"],
    deps = [
        "//mediapipe/framework:calculator_options_proto",
        "//mediapipe/framework:calculator_proto",
@@ -706,7 +639,6 @@ cc_library(
        ],
        "//conditions:default": [],
    }),
    visibility = ["//visibility:public"],
    deps = [
        ":tensor_converter_calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
@@ -725,6 +657,7 @@ cc_library(
 cc_library(
    name = "tensor_converter_calculator_gpu_deps",
    visibility = ["//visibility:private"],
    deps = select({
        "//mediapipe:android": [
            "//mediapipe/gpu:gl_calculator_helper",
@@ -769,7 +702,6 @@ cc_test(
 mediapipe_proto_library(
    name = "tensors_to_detections_calculator_proto",
    srcs = ["tensors_to_detections_calculator.proto"],
    visibility = ["//visibility:public"],
    deps = [
        "//mediapipe/framework:calculator_options_proto",
        "//mediapipe/framework:calculator_proto",
@@ -794,7 +726,6 @@ cc_library(
        ],
        "//conditions:default": [],
    }),
    visibility = ["//visibility:public"],
    deps = [
        ":tensors_to_detections_calculator_cc_proto",
        "//mediapipe/framework/formats:detection_cc_proto",
@@ -817,6 +748,7 @@ cc_library(
 cc_library(
    name = "tensors_to_detections_calculator_gpu_deps",
    visibility = ["//visibility:private"],
    deps = select({
        "//mediapipe:ios": [
            "//mediapipe/gpu:MPPMetalUtil",
@@ -832,7 +764,6 @@ cc_library(
 mediapipe_proto_library(
    name = "tensors_to_landmarks_calculator_proto",
    srcs = ["tensors_to_landmarks_calculator.proto"],
    visibility = ["//visibility:public"],
    deps = [
        "//mediapipe/framework:calculator_options_proto",
        "//mediapipe/framework:calculator_proto",
@@ -849,7 +780,6 @@ cc_library(
        ],
        "//conditions:default": [],
    }),
    visibility = ["//visibility:public"],
    deps = [
        ":tensors_to_landmarks_calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
@@ -864,7 +794,6 @@ cc_library(
 mediapipe_proto_library(
    name = "landmarks_to_tensor_calculator_proto",
    srcs = ["landmarks_to_tensor_calculator.proto"],
    visibility = ["//visibility:public"],
    deps = [
        "//mediapipe/framework:calculator_options_proto",
        "//mediapipe/framework:calculator_proto",
@@ -882,7 +811,6 @@ cc_library(
        ],
        "//conditions:default": [],
    }),
    visibility = ["//visibility:public"],
    deps = [
        ":landmarks_to_tensor_calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
@@ -915,7 +843,6 @@ cc_test(
 mediapipe_proto_library(
    name = "tensors_to_floats_calculator_proto",
    srcs = ["tensors_to_floats_calculator.proto"],
    visibility = ["//visibility:public"],
    deps = [
        "//mediapipe/framework:calculator_options_proto",
        "//mediapipe/framework:calculator_proto",
@@ -932,7 +859,6 @@ cc_library(
        ],
        "//conditions:default": [],
    }),
    visibility = ["//visibility:public"],
    deps = [
        ":tensors_to_floats_calculator_cc_proto",
        "//mediapipe/framework:calculator_framework",
@@ -970,7 +896,6 @@ cc_library(
        ],
        "//conditions:default": [],
    }),
    visibility = ["//visibility:public"],
    deps = [
        ":tensors_to_classification_calculator_cc_proto",
        "@com_google_absl//absl/container:node_hash_map",
@@ -1001,7 +926,6 @@ cc_library(
 mediapipe_proto_library(
    name = "tensors_to_classification_calculator_proto",
    srcs = ["tensors_to_classification_calculator.proto"],
    visibility = ["//visibility:public"],
    deps = [
        "//mediapipe/framework:calculator_options_proto",
        "//mediapipe/framework:calculator_proto",
@@ -1039,7 +963,6 @@ cc_library(
        "//conditions:default": [],
    }),
    features = ["-layering_check"],  # allow depending on image_to_tensor_calculator_gpu_deps
    visibility = ["//visibility:public"],
    deps = [
        ":image_to_tensor_calculator_cc_proto",
        ":image_to_tensor_converter",
@@ -1068,6 +991,7 @@ cc_library(
 cc_library(
    name = "image_to_tensor_calculator_gpu_deps",
    visibility = ["//visibility:private"],
    deps = selects.with_or({
        "//mediapipe:android": [
            ":image_to_tensor_converter_gl_buffer",
@@ -1091,7 +1015,6 @@ cc_library(
 mediapipe_proto_library(
    name = "image_to_tensor_calculator_proto",
    srcs = ["image_to_tensor_calculator.proto"],
    visibility = ["//visibility:public"],
    deps = [
        "//mediapipe/framework:calculator_options_proto",
        "//mediapipe/framework:calculator_proto",
@@ -1141,6 +1064,7 @@ cc_test(
        "@com_google_absl//absl/flags:flag",
        "@com_google_absl//absl/memory",
        "@com_google_absl//absl/strings",
        "@com_google_absl//absl/strings:str_format",
    ],
 )
@@ -1154,7 +1078,6 @@ cc_library(
        ],
        "//conditions:default": [],
    }),
    visibility = ["//visibility:public"],
    deps = [
        ":image_to_tensor_utils",
        "//mediapipe/framework/formats:image",
@@ -1174,7 +1097,6 @@ cc_library(
        ],
        "//conditions:default": [],
    }),
    visibility = ["//visibility:public"],
    deps = [
        ":image_to_tensor_converter",
        ":image_to_tensor_utils",
@@ -1194,6 +1116,7 @@ cc_library(
    name = "image_to_tensor_converter_gl_buffer",
    srcs = ["image_to_tensor_converter_gl_buffer.cc"],
    hdrs = ["image_to_tensor_converter_gl_buffer.h"],
    visibility = ["//visibility:private"],
    deps = ["//mediapipe/framework:port"] + selects.with_or({
        "//mediapipe:apple": [],
        "//conditions:default": [
@@ -1227,6 +1150,7 @@ cc_library(
    name = "image_to_tensor_converter_gl_texture",
    srcs = ["image_to_tensor_converter_gl_texture.cc"],
    hdrs = ["image_to_tensor_converter_gl_texture.h"],
    visibility = ["//visibility:private"],
    deps = ["//mediapipe/framework:port"] + select({
        "//mediapipe/gpu:disable_gpu": [],
        "//conditions:default": [
@@ -1251,6 +1175,7 @@ cc_library(
    name = "image_to_tensor_converter_gl_utils",
    srcs = ["image_to_tensor_converter_gl_utils.cc"],
    hdrs = ["image_to_tensor_converter_gl_utils.h"],
    visibility = ["//visibility:private"],
    deps = ["//mediapipe/framework:port"] + select({
        "//mediapipe/gpu:disable_gpu": [],
        "//conditions:default": [
@@ -1280,6 +1205,7 @@ cc_library(
        ],
        "//conditions:default": [],
    }),
    visibility = ["//visibility:private"],
    deps = ["//mediapipe/framework:port"] + select({
        "//mediapipe:apple": [
            ":image_to_tensor_converter",
@@ -1311,7 +1237,6 @@ cc_library(
        ],
        "//conditions:default": [],
    }),
    visibility = ["//visibility:public"],
    deps = [
        ":image_to_tensor_calculator_cc_proto",
        "@com_google_absl//absl/status",
@@ -1354,7 +1279,6 @@ selects.config_setting_group(
 mediapipe_proto_library(
    name = "tensors_to_segmentation_calculator_proto",
    srcs = ["tensors_to_segmentation_calculator.proto"],
    visibility = ["//visibility:public"],
    deps = [
        "//mediapipe/framework:calculator_options_proto",
        "//mediapipe/framework:calculator_proto",
@@ -1372,7 +1296,6 @@ cc_library(
        ],
        "//conditions:default": [],
    }),
    visibility = ["//visibility:public"],
    deps = [
        ":tensors_to_segmentation_calculator_cc_proto",
        "@com_google_absl//absl/strings:str_format",
@@ -1430,7 +1353,6 @@ cc_library(
        ],
        "//conditions:default": [],
    }),
    visibility = ["//visibility:public"],
    deps = [
        "//mediapipe/framework:calculator_context",
        "//mediapipe/framework:calculator_framework",
@@ -203,6 +203,7 @@ class AudioToTensorCalculator : public Node {
  std::unique_ptr<audio_dsp::QResampler<float>> resampler_;
  Matrix sample_buffer_;
  int processed_buffer_cols_ = 0;
  double gain_ = 1.0;
  // The internal state of the FFT library.
  PFFFT_Setup* fft_state_ = nullptr;
@@ -278,7 +279,9 @@ absl::Status AudioToTensorCalculator::Open(CalculatorContext* cc) {
  padding_samples_after_ = options.padding_samples_after();
  dft_tensor_format_ = options.dft_tensor_format();
  flush_mode_ = options.flush_mode();
-
+  if (options.has_volume_gain_db()) {
    gain_ = pow(10, options.volume_gain_db() / 20.0);
  }
  RET_CHECK(kAudioSampleRateIn(cc).IsConnected() ^
            !kAudioIn(cc).Header().IsEmpty())
      << "Must either specify the time series header of the \"AUDIO\" stream "
@@ -344,6 +347,10 @@ absl::Status AudioToTensorCalculator::Process(CalculatorContext* cc) {
  const Matrix& input = channels_match ? input_frame
                                       // Mono mixdown.
                                       : input_frame.colwise().mean();
  if (gain_ != 1.0) {
    return stream_mode_ ? ProcessStreamingData(cc, input * gain_)
                        : ProcessNonStreamingData(cc, input * gain_);
  }
  return stream_mode_ ? ProcessStreamingData(cc, input)
                      : ProcessNonStreamingData(cc, input);
 }
@@ -81,4 +81,8 @@ message AudioToTensorCalculatorOptions {
    WITH_DC_AND_NYQUIST = 3;
  }
  optional DftTensorFormat dft_tensor_format = 11 [default = WITH_NYQUIST];
  // The volume gain, measured in dB.
  // Scale the input audio amplitude by 10^(volume_gain_db/20).
  optional double volume_gain_db = 12;
 }
@@ -195,8 +195,9 @@ class ImageToTensorCalculator : public Node {
 #endif  // MEDIAPIPE_DISABLE_GPU
    RotatedRect roi = GetRoi(image->width(), image->height(), norm_rect);
-    ASSIGN_OR_RETURN(auto padding, PadRoi(options_.output_tensor_width(),
+    const int tensor_width = params_.output_width.value_or(image->width());
-                                          options_.output_tensor_height(),
+    const int tensor_height = params_.output_height.value_or(image->height());
    ASSIGN_OR_RETURN(auto padding, PadRoi(tensor_width, tensor_height,
                                          options_.keep_aspect_ratio(), &roi));
    if (kOutLetterboxPadding(cc).IsConnected()) {
      kOutLetterboxPadding(cc).Send(padding);
@@ -214,9 +215,8 @@ class ImageToTensorCalculator : public Node {
    Tensor::ElementType output_tensor_type =
        GetOutputTensorType(image->UsesGpu(), params_);
-    Tensor tensor(output_tensor_type,
+    Tensor tensor(output_tensor_type, {1, tensor_height, tensor_width,
-                  {1, params_.output_height, params_.output_width,
+                                       GetNumOutputChannels(*image)});
                   GetNumOutputChannels(*image)});
    MP_RETURN_IF_ERROR((image->UsesGpu() ? gpu_converter_ : cpu_converter_)
                           ->Convert(*image, roi, params_.range_min,
                                     params_.range_max,
--- a/Show More
+++ b/Show More